Medicine, Faculty of
Obstetrics and Gynaecology, Department of
DSpace
UBCV
Bone, Jeffrey
Doctor of Philosophy - PhD
University of British Columbia
Background: High pre-pregnancy body-mass-index (BMI) is an important risk factor for adverse maternal and perinatal outcomes. However, there are knowledge gaps regarding the association between high BMI and adverse birth outcomes, including the interrelationship between BMI and other risk factors for adverse pregnancy outcomes and how the risk associated with BMI may vary across gestational age. Methods: I used population-based data on live births and stillbirths from British Columbia, Canada (British Columbia Perinatal Database Registry, 2000-2022) and the United States (National Center for Health Statistics, 2016-2017). I examined whether earlier delivery explained the association between obesity and perinatal death. Possible heterogeneity in the association between BMI and adverse maternal and perinatal outcomes in women with chronic hypertension and diabetes mellitus, was quantified both overall and by gestational week. Further analyzed was the relationship between obesity and twinning, and adverse pregnancy outcomes in twin pregnancies. Results: The study population in British Columbia included live births and stillbirths to 524,845 women who delivered from 2000 to 2022. The prevalence of pre-pregnancy obesity among these women was 13%. Preterm delivery explained 63% of the association between obesity and the higher rates of perinatal death. Additionally, increasing pre-pregnancy BMI was associated with higher rates of twinning, which was partially explained by increased use of invitro fertilization. However, in contrast to singleton pregnancies, pre-pregnancy obesity was not associated with adverse pregnancy outcomes in twin pregnancies. Studies utilizing US data included 7,576,417 pregnancies, with a prevalence of pre-pregnancy obesity of 26.4%. Comorbid chronic hypertension or pre-pregnancy diabetes modified the relationship between obesity and adverse perinatal outcomes: obese women without these chronic conditions had a higher relative risk of adverse perinatal outcomes (compared with non-obese women), whereas this association was not present among obese women with chronic conditions. This effect modification was most evident between 26-35 weeks’ gestation. Conclusion: The strength of the relationship between pre-pregnancy BMI and adverse outcomes is attenuated in pregnancies complicated by other risk factors, such as chronic hypertension and diabetes, and twin pregnancy, and increases approaching term gestation. The pathophysiological underpinnings of these variations are likely complex and warrant further investigation.
Statistical modeling of the relationship between pre-pregnancy body-mass-index and maternal and perinatal outcomes by Jeffrey Bone MSc., Statistics, The University of British Columbia, 2016 BSc Honours., Mathematics and Statistics, The University of Victoria, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Women+ and Children’s Health Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2024 © Jeffrey Bone, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Statistical modeling of the relationship between pre-pregnancy body-mass-index and maternal and perinatal outcomes submitted by Jeffrey Bone in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Women+ and Children’s Health Sciences Examining Committee: Sarka Lisonkova, Associate Professor, Department of Obstetrics and Gynecology, UBC Supervisor KS Joseph, Professor, Department of Obstetrics and Gynecology, UBC Supervisory Committee Member Laura Magee, Professor, School of Life Course Sciences, King’s College London Supervisory Committee Member Geoffrey Cundiff, Professor, Department of Obstetrics and Gynecology, UBC University Examiner Jason Sutherland, Professor, School of Population and Public Health, UBC University Examiner Additional Supervisory Committee Members: Chantal Mayer, Clinical Associate Professor, Department of Obstetrics and Gynecology, UBC Supervisory Committee Member iii Abstract Background: High pre-pregnancy body-mass-index (BMI) is an important risk factor for adverse maternal and perinatal outcomes. However, there are knowledge gaps regarding the association between high BMI and adverse birth outcomes, including the interrelationship between BMI and other risk factors for adverse pregnancy outcomes and how the risk associated with BMI may vary across gestational age. Methods: I used population-based data on live births and stillbirths from British Columbia, Canada (British Columbia Perinatal Database Registry, 2000-2022) and the United States (National Center for Health Statistics, 2016-2017). I examined whether earlier delivery explained the association between obesity and perinatal death. Possible heterogeneity in the association between BMI and adverse maternal and perinatal outcomes in women with chronic hypertension and diabetes mellitus, was quantified both overall and by gestational week. Further analyzed was the relationship between obesity and twinning, and adverse pregnancy outcomes in twin pregnancies. Results: The study population in British Columbia included live births and stillbirths to 524,845 women who delivered from 2000 to 2022. The prevalence of pre-pregnancy obesity among these women was 13%. Preterm delivery explained 63% of the association between obesity and the higher rates of perinatal death. Additionally, increasing pre-pregnancy BMI was associated with higher rates of twinning, which was partially explained by increased use of invitro fertilization. However, in contrast to singleton pregnancies, pre-pregnancy obesity was not associated with iv adverse pregnancy outcomes in twin pregnancies. Studies utilizing US data included 7,576,417 pregnancies, with a prevalence of pre-pregnancy obesity of 26.4%. Comorbid chronic hypertension or pre-pregnancy diabetes modified the relationship between obesity and adverse perinatal outcomes: obese women without these chronic conditions had a higher relative risk of adverse perinatal outcomes (compared with non-obese women), whereas this association was not present among obese women with chronic conditions. This effect modification was most evident between 26-35 weeks’ gestation. Conclusion: The strength of the relationship between pre-pregnancy BMI and adverse outcomes is attenuated in pregnancies complicated by other risk factors, such as chronic hypertension and diabetes, and twin pregnancy, and increases approaching term gestation. The pathophysiological underpinnings of these variations are likely complex and warrant further investigation. v Lay Summary Obesity rates have increased steadily in recent decades, and obesity now represents one of the most common pregnancy risk factors for poor outcomes, for mothers and newborns. The understanding of why, and in whom, pre-pregnancy obesity is of most concern allows health care providers and patients to make more informed decisions about maternity care during pregnancy. This thesis explores the relationship between obesity and adverse pregnancy outcomes, such as death of unborn babies, death of babies soon after their birth, and severe illnesses in both mothers and babies. The studies carried out were based on all births in British Columbia (2000-2022) and the United States (2016-2017). The findings confirmed that obese pregnant women have higher rates of adverse outcomes, and that most of the risk of perinatal death is due to earlier delivery. However, in twin pregnancies and in pregnancies of women with high blood pressure or diabetes prior to pregnancy, obesity did not confer additional risk. These findings have important implications for patient counseling, patient care and maternity care guidelines. vi Preface My thesis presents the results of a series of studies that I identified and designed in consultation with my supervisor, Dr. Sarka Lisonkova, and the thesis committee. I identified the primary research questions, was the main person with access to data, selected analytical approaches, and wrote the first drafts of all manuscripts related to this work. For studies within the thesis that utilized data from the British Columbia Perinatal Database Registry, written ethics approval was obtained from the University of British Columbia - Children’s and Women’s Hospital and Health Centre of British Columbia Research Ethics Board (H18-03154). For studies that utilized data from the United States, ethics approval was not required as the data were publicly available. A version of Chapter 4 has been published (Bone JN, Joseph KS, Mayer C, Platt R, Lisonkova S (2022) The association between pre-pregnancy body mass index and perinatal death and the role of gestational age at delivery. PLoS ONE. 17(3): e0264565. I was responsible for analyzing the data, writing the manuscript and interpreting the results. Dr. Lisonkova helped design the study, and all co-authors contributed to the interpretation of results and manuscript writing. I was responsible for all manuscript revisions and editing during the peer-review process. A version of Chapter 5 been published (Bone JN, Joseph KS, Magee LA, Muraca GM, Razaz N, Mayer C, Lisonkova S. Pre-pregnancy body mass index and adverse perinatal outcomes in the presence of other maternal risk factors. AJOG Glob Rep. 2023 Feb 8;3(2):100175. I was responsible for analyzing the data, writing the manuscript and interpreting the results. Dr. vii Lisonkova helped design the study, and all co-authors contributed to the interpretation of results and manuscript writing. I was responsible for all manuscript revisions and editing during the peer-review process. A version of Chapter 6 has been published (Bone JN, Joseph KS, John S, Lisonkova S. A flexible approach to modelling stillbirths using the foetuses at risk approach. Paediatr Perinat Epidemiol. 2023 Aug;37(6):547-554. I was responsible for the study design, analyzing the data, writing the manuscript and interpreting the results. All co-authors contributed to the interpretation of results and manuscript writing. I was responsible for all manuscript revisions and editing during the peer-review process. A version of Chapter 7 has been accepted to publication in the American Journal of Obstetrics and Gynecology in November 2024 and is currently in-press. I was responsible for analyzing the data, writing the manuscript and interpreting the results. Dr. Lisonkova helped design the study, and all co-authors contributed to the interpretation of results and manuscript writing. I was responsible for all manuscript revisions and editing during the peer-review process. A version of Chapter 8 has been published (Bone JN, Joseph KS, Magee LA, Wang LQ, John S, Bedaiwy MA, Mayer C, Lisonkova S. Obesity, Twin Pregnancy, and the Role of Assisted Reproductive Technology. JAMA Netw Open. 2024;7(1):e2350934. I was responsible for analyzing the data, writing the manuscript and interpreting the results. Dr. Lisonkova helped design the study and all co-authors contributed to the interpretation of results and manuscript viii writing. I was responsible for all manuscript revisions and editing during the peer-review process. A version of Chapter 9 has been published (Bone JN, Joseph KS, Magee LA, Wang LQ, Mayer C, Lisonkova S Pre-pregnancy body mass index and adverse maternal and perinatal outcomes in twins: A population retrospective cohort study. Int J Obes (2023). I was responsible for analyzing the data, writing the manuscript and interpreting the results. Dr. Sarka Lisonkova helped design the study and all co-authors contributed to the interpretation of results and manuscript writing. I was responsible for all manuscript revisions and editing during the peer-review process. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ................................................................................................................................xx List of Figures ........................................................................................................................... xxvi List of Abbreviations .................................................................................................................xxx Acknowledgements ................................................................................................................. xxxii Dedication ............................................................................................................................... xxxiv Chapter 1: Introduction ................................................................................................................1 1.1 Overview ......................................................................................................................... 1 1.2 Body-mass-index and obesity ......................................................................................... 3 1.2.1 Definition of obesity ............................................................................................... 3 1.2.2 Trends in Canada and the United States ................................................................. 3 1.2.3 Obesity in Pregnancy .............................................................................................. 4 1.2.4 Self-reported BMI ................................................................................................... 5 1.2.5 Limitations of BMI ................................................................................................. 5 1.3 Comorbid conditions associated with high BMI ............................................................ 6 1.4 Twin pregnancy .............................................................................................................. 8 1.5 Data sources .................................................................................................................... 9 1.5.1 British Columbia Perinatal Database Registry ....................................................... 9 x 1.6.1....................................................................................................................................... 10 1.6 Research objectives ....................................................................................................... 11 1.7 Thesis overview ............................................................................................................ 12 1.8 Ethics and disclaimer .................................................................................................... 14 Chapter 2: Review of pre-pregnancy obesity as a risk factor for adverse maternal and perinatal outcomes .......................................................................................................................15 2.1 Overview ....................................................................................................................... 15 2.2 Biological and genetic effects of obesity in pregnancy ................................................ 15 2.3 Pre-pregnancy obesity and adverse maternal outcomes ............................................... 17 2.3.1 Gestational hypertension, pre-eclampsia, and gestational diabetes ...................... 17 2.3.2 Caesarean delivery ................................................................................................ 18 2.3.3 Severe maternal morbidity .................................................................................... 20 2.4 Pre-pregnancy obesity and adverse perinatal outcomes ............................................... 21 2.4.1 Stillbirth ................................................................................................................ 21 2.4.2 Preterm birth ......................................................................................................... 23 2.4.3 Neonatal death ...................................................................................................... 24 2.4.4 Neonatal morbidity ............................................................................................... 25 2.5 Summary ....................................................................................................................... 27 Chapter 3: Epidemiological and statistical considerations in observational studies in perinatal research. .......................................................................................................................28 3.1 Introduction ................................................................................................................... 28 3.2 Confounding ................................................................................................................. 28 3.2.1 Definition of a confounder .................................................................................... 28 xi 3.2.2 Considerations for confounder selection .............................................................. 29 3.2.3 Statistical approaches to confounder control ........................................................ 32 3.2.3.1 Outcome modeling ............................................................................................ 33 3.2.3.2 Exposure modeling ........................................................................................... 33 3.2.3.3 Doubly robust methods ..................................................................................... 34 3.2.4 Sensitivity analyses for unmeasured confounding ................................................ 34 3.3 Interaction ..................................................................................................................... 35 3.3.1 Effect modification vs interaction ......................................................................... 36 3.3.2 Multiplicative vs additive interaction ................................................................... 37 3.3.3 Causal interpretation of interaction ....................................................................... 39 3.4 Mediation ...................................................................................................................... 40 3.4.1 Direct and indirect effects ..................................................................................... 40 3.4.2 Controlling for confounding in mediation analysis .............................................. 41 3.5 Population-at-risk and challenges of using administrative data in perinatal epidemiology ............................................................................................................................. 42 3.5.1 Population of interest ............................................................................................ 42 3.5.2 Left truncation ....................................................................................................... 43 3.5.3 Fetuses-at-risk ....................................................................................................... 44 3.6 Tables ............................................................................................................................ 46 3.7 Figures........................................................................................................................... 48 Chapter 4: The association between pre-pregnancy body mass index and perinatal death and the role of gestational age at delivery .................................................................................51 4.1 Introduction ................................................................................................................... 51 xii 4.2 Methods......................................................................................................................... 52 4.2.1 Data source and study population ......................................................................... 52 4.2.2 Exposure, outcome and confounders .................................................................... 53 4.2.3 Statistical analyses ................................................................................................ 54 4.2.4 Mediation analysis ................................................................................................ 54 4.2.5 Sensitivity analyses ............................................................................................... 56 4.3 Results ........................................................................................................................... 57 4.3.1 Study population ................................................................................................... 57 4.3.2 Main outcomes ...................................................................................................... 57 4.3.3 Mediation analyses ................................................................................................ 58 4.3.4 Sensitivity analyses ............................................................................................... 58 4.4 Discussion ..................................................................................................................... 60 4.5 Conclusion .................................................................................................................... 65 4.6 Tables ............................................................................................................................ 66 4.7 Figures........................................................................................................................... 69 Chapter 5: Pre-pregnancy body mass index and adverse perinatal outcomes in the presence of other maternal risk factors .....................................................................................................70 5.1 Introduction ................................................................................................................... 70 5.2 Methods......................................................................................................................... 71 5.2.1 Study population ................................................................................................... 71 5.2.2 BMI, concomitant risk factors and covariates ...................................................... 71 5.2.3 Outcomes .............................................................................................................. 72 5.2.4 Statistical analyses ................................................................................................ 72 xiii 5.3 Results ........................................................................................................................... 74 5.3.1 Study population ................................................................................................... 74 5.3.2 Outcome rates ....................................................................................................... 75 5.3.3 Adjusted results ..................................................................................................... 75 5.3.4 Secondary outcomes ............................................................................................. 77 5.4 Discussion ..................................................................................................................... 77 5.4.1 Principal findings .................................................................................................. 77 5.4.2 Research in the context of what is known ............................................................ 78 5.4.3 Clinical and research implications ........................................................................ 81 5.4.4 Strengths and limitations ....................................................................................... 81 5.5 Conclusion .................................................................................................................... 82 5.6 Tables ............................................................................................................................ 83 5.7 Figures........................................................................................................................... 88 Chapter 6: A flexible approach to modeling stillbirth using the fetuses at risk approach ...90 6.1 Background ................................................................................................................... 90 6.2 Methods......................................................................................................................... 91 6.2.1 Cox proportional hazard models ........................................................................... 91 6.2.2 Piecewise Exponential Additive Mixed Models ................................................... 92 6.2.3 Data source and study population ......................................................................... 93 6.2.4 Statistical methods ................................................................................................ 94 6.2.5 Missing data .......................................................................................................... 95 6.2.6 Ethics approval ...................................................................................................... 96 6.3 Results ........................................................................................................................... 96 xiv 6.4 Comment ....................................................................................................................... 98 6.4.1 Principle findings .................................................................................................. 98 6.4.2 Strengths of the study ............................................................................................ 98 6.4.3 Limitations of the study ........................................................................................ 99 6.4.4 Interpretation ......................................................................................................... 99 6.5 Conclusion .................................................................................................................. 102 6.6 Tables .......................................................................................................................... 104 6.7 Figures......................................................................................................................... 105 Chapter 7: Relationship between pre-pregnancy body-mass-index and gestational age-specific risks of stillbirth in women with and without chronic hypertension and diabetes mellitus ........................................................................................................................................108 7.1 Introduction ................................................................................................................. 108 7.2 Methods....................................................................................................................... 109 7.2.1 Data source and inclusion, exclusion criteria ..................................................... 109 7.2.2 Exposure, outcomes and other covariates ........................................................... 110 7.2.3 Statistical analysis ............................................................................................... 110 7.2.4 Sensitivity analysis.............................................................................................. 112 7.3 Results ......................................................................................................................... 112 7.3.1 Study population ................................................................................................. 112 7.3.2 Gestational age specific rates .............................................................................. 114 7.3.3 Model results ....................................................................................................... 114 7.4 Discussion ................................................................................................................... 116 7.4.1 Principle findings ................................................................................................ 116 xv 7.4.2 Results in the context of what is known ............................................................. 117 7.4.3 Clinical and research implications ...................................................................... 117 7.4.4 Strengths and limitations ..................................................................................... 119 7.5 Conclusion .................................................................................................................. 120 7.6 Tables .......................................................................................................................... 121 7.7 Figures......................................................................................................................... 124 Chapter 8: Obesity, twin pregnancy and the role of reproductive technology ....................127 8.1 Introduction ................................................................................................................. 127 8.2 Methods....................................................................................................................... 128 8.2.1 Data source and study population ....................................................................... 128 8.2.2 Exposure, mediator, and outcome ....................................................................... 129 8.2.3 Statistical analyses .............................................................................................. 130 8.2.4 Sensitivity analyses ............................................................................................. 131 8.2.5 Ethics approval .................................................................................................... 131 8.3 Results ......................................................................................................................... 131 8.3.1 Sensitivity analyses ............................................................................................. 133 8.4 Discussion ................................................................................................................... 134 8.4.1 Summary of findings ........................................................................................... 134 8.4.2 Comparison with the literature ............................................................................ 135 8.4.3 Strengths and limitations ..................................................................................... 137 8.5 Conclusion .................................................................................................................. 138 8.6 Tables .......................................................................................................................... 139 8.7 Figures......................................................................................................................... 142 xvi Chapter 9: Pre-pregnancy body-mass-index and adverse maternal and perinatal outcomes in twins: a population retrospective cohort study ...................................................................143 9.1 Introduction ................................................................................................................. 143 9.2 Materials and methods ................................................................................................ 144 9.2.1 Data source and study population ....................................................................... 144 9.2.2 Outcome measures and potential confounders ................................................... 145 9.2.3 Statistical analyses .............................................................................................. 146 9.2.4 Sensitivity analyses ............................................................................................. 147 9.3 Results ......................................................................................................................... 148 9.4 Discussion ................................................................................................................... 151 9.4.1 Principal findings ................................................................................................ 151 9.4.2 Results in the context of prior literature ............................................................. 152 9.4.3 Clinical and research implications ...................................................................... 153 9.4.4 Strengths and limitations ..................................................................................... 155 9.5 Conclusion .................................................................................................................. 156 9.6 Tables .......................................................................................................................... 157 9.7 Figures......................................................................................................................... 161 Chapter 10: Discussion ..............................................................................................................162 10.1 Summary of findings ................................................................................................... 162 10.1.1 The role of gestational age in the relationship between pre-pregnancy body-mass-index and perinatal death (Chapter 4) ................................................................................. 162 10.1.2 Modification of the relationship between pre-pregnancy body-mass-index and perinatal outcomes (Chapter 5) ........................................................................................... 163 xvii 10.1.3 Modeling stillbirth risk from a fetuses-at-risk perspective in women with obesity and chronic conditions (Chapter 6 & 7): ............................................................................. 164 10.1.4 Pre-pregnancy body-mass-index and adverse birth outcomes in twin pregnancies (Chapter 8 & 9) ................................................................................................................... 165 10.2 Significance and contribution ..................................................................................... 167 10.2.1 Differential risks by pre-pregnancy BMI and obstetric management ................. 167 10.2.2 Methodological insights ...................................................................................... 169 10.3 Strengths and limitations ............................................................................................. 170 10.4 Application of findings ............................................................................................... 173 10.4.1 Applications to public health and clinical practice ............................................. 173 10.4.2 Applications to perinatal epidemiology .............................................................. 173 10.5 Future directions ......................................................................................................... 174 10.5.1 Generalizability of results ................................................................................... 174 10.5.2 Prospective studies .............................................................................................. 175 10.5.3 Extended fetuses-at-risk model for infant mortality ........................................... 177 10.5.4 Multiple mediator analyses ................................................................................. 179 10.5.5 Bias analysis ........................................................................................................ 180 10.6 Summary ..................................................................................................................... 182 10.7 Figures......................................................................................................................... 184 Bibliography ...............................................................................................................................185 Appendix A: Details of estimation of interaction ....................................................................219 A.1 Estimation of multiplicative interaction ........................................................................... 219 A.2 Estimation of additive interaction .................................................................................... 221 xviii Appendix B: Formal definitions for causal mediation analysis .............................................224 Appendix C: Bias analysis for relationship between obesity and adverse perinatal outcomes in twin pregnancy .......................................................................................................................229 C.1 Introduction ...................................................................................................................... 229 C.2 Methods ............................................................................................................................ 229 C.3 Results .............................................................................................................................. 234 C.4 Summary .......................................................................................................................... 235 C.5 Tables ............................................................................................................................... 236 C.6 Figures .............................................................................................................................. 238 Appendix D: Comparison of methods for estimating causal risk ratios: a simulation study and sample data analysis of obesity in pregnancy ..................................................................241 D.1 Introduction ...................................................................................................................... 241 D.2 Methods ............................................................................................................................ 242 D.3 Results .............................................................................................................................. 244 D.4 Summary .......................................................................................................................... 246 D.5 Tables ............................................................................................................................... 247 D.6 Figures .............................................................................................................................. 250 Appendix E: Supplementary data to Chapter 4 .....................................................................251 E.1 Tables ............................................................................................................................... 251 E.2 Figures .............................................................................................................................. 254 Appendix F: Supplementary data to Chapter 5 ......................................................................255 F.1 Tables ................................................................................................................................ 255 Appendix G: Supplementary data to Chapter 6 .....................................................................262 xix G.1 Tables ............................................................................................................................... 262 G.2 Figures .............................................................................................................................. 263 Appendix H: Supplementary data to Chapter 7 .....................................................................264 H.1 Tables ............................................................................................................................... 264 H.2 Figures .............................................................................................................................. 267 Appendix I: Supplementary data to Chapter 8 .......................................................................269 I.1 Statistical details of sensitivity analyses ............................................................................ 269 I.2 Tables ................................................................................................................................. 272 I.3 Figures ............................................................................................................................... 276 Appendix J: Supplementary data to Chapter 9 ......................................................................279 J.1 Tables ................................................................................................................................ 279 J.2 Figures ............................................................................................................................... 287 xx List of Tables Table 1: Example of hypothetical data from a study for the relationship between obesity and stillbirth with confounding by parity (nulliparous vs parous). ..................................................... 46 Table 2: Example of multiplicative and additive interactions. Data represent the proportion of stillbirths. ...................................................................................................................................... 47 Table 3: Hypothetical gestational age specific risk of stillbirth under a birth-based and fetuses-at-risk approach. ................................................................................................................................ 47 Table 4: Demographic characteristics, pre-pregnancy, and pregnancy morbidity of women by pre-pregnancy BMI category. ....................................................................................................... 66 Table 5: Perinatal mortality by pre-pregnancy BMI category. ..................................................... 67 Table 6: Mediation analyses of BMI on perinatal death through gestational age at delivery. ..... 67 Table 7: Demographics and obstetric characteristics among women with singleton birth by pre-pregnancy BMI category, USA, 2016-2017. ................................................................................ 83 Table 8: Rates of the composite outcome (perinatal mortality and/or severe neonatal morbidity) per 1000 women with singleton birth by pre-pregnancy BMI, stratified by maternal age, parity, chronic hypertension and pre-pregnancy diabetes, USA, 2016-2017. .......................................... 85 Table 9: Strata specific adjusted odds ratios (aOR) for the association between body mass index and the composite perinatal outcome including stillbirth, neonatal death and severe neonatal morbidity, singleton births in the USA, 2016-2017. ..................................................................... 86 Table 10: Stillbirth rates and hazard ratios for body-mass-index (BMI) categories obtained from Cox and PAMM models, United States, 2016-17. ...................................................................... 104 Table 11: Pre-pregnancy demographics and comorbidities, stratified by pre-pregnancy body mass index. .................................................................................................................................. 121 xxi Table 12: Rates of stillbirth by pre-pregnancy BMI, chronic hypertension, and pre-pregnancy diabetes mellitus. ......................................................................................................................... 123 Table 13: Demographic characteristics of women by pre-pregnancy BMI category, British Columbia, Canada, 2008-2020. .................................................................................................. 139 Table 14: Rates, rate ratios (RR) and adjusted rate ratios (aRR) for use of ART by pre-pregnancy BMI category. ............................................................................................................................. 139 Table 15: Twin delivery rates by pre-pregnancy BMI category and use of assisted reproductive technology (ART). ...................................................................................................................... 140 Table 16: Adjusted associations between pre-pregnancy BMI category and twin delivery, and mediation analysis by ART conception. ..................................................................................... 141 Table 17: Demographic, obstetric history, and current pregnancy characteristics by body-mass-index in twin pregnancies. .......................................................................................................... 157 Table 18: Severe maternal morbidity, perinatal mortality, and severe neonatal morbidity by pre-pregnancy body-mass-index in twin pregnancies (rates expressed per 10,000 pregnancies for severe maternal morbidity, per 10,000 livebirths for neonatal death and serious neonatal morbidity and per 10,000 births for stillbirth and perinatal mortality). ...................................... 158 Table 19: Adjusted risk ratios and risk differences for maternal and perinatal outcomes by pre-pregnancy body-mass-index in twin pregnancies. Estimates are from GEE Poisson models pooled from 50 imputed data sets. .............................................................................................. 160 Table 20: Selected simulation results of possible impact of chorionicity on estimate association between pre-pregnancy obesity and perinatal death in twin pregnancies. All simulations were based on a true risk ratio of 1.50 and in cohorts of size 10,000. Data are arranged from highest to lowest bias. .................................................................................................................................. 236 xxii Table 21: Selected simulation results of possible impact of left truncation on estimate association between pre-pregnancy obesity and perinatal death in twin pregnancies. All simulations were based on a true risk ratio of 1.50 and in cohorts of size 10,000. Data are arranged from highest to lowest bias. .................................................................................................................................. 236 Table 22: Summary statistics of estimated risk ratios and percent bias. All data are median and interquartile range from 1000 simulated data sets. ..................................................................... 247 Table 23: Confidence interval coverage and confidence interval width across five simulated scenarios. ..................................................................................................................................... 248 Table 24: Adjusted risk ratios comparing women with pre-pregnancy obesity to women without obesity in British Columbia Canada under varying estimation methods. ................................... 249 Table 25: ICD-10 codes definitions of relevant variables. ......................................................... 251 Table 26: The amount of unmeasured confounding (as a risk ratio) between mediator and outcome needed to explain away total, direct and indirect effects. Estimates for underweight estimates are not presented as the total effects were not significant. The e-value is also not estimated when the effect is not significant. ............................................................................... 251 Table 27: Comparison of women with and without complete BMI data .................................... 252 Table 28: Mediation analyses of BMI on perinatal death through gestational age at delivery after multiple imputation (n =10) of missing BMI values. ................................................................. 253 Table 29: Mediation analyses for stillbirth and neonatal death. ................................................. 253 Table 30: Rates of the components of the composite outcome (perinatal mortality and/or severe neonatal morbidity) per 1000 women (stillbirth) or 1000 livebirths (other outcomes) with singleton birth by pre-pregnancy BMI, stratified by maternal age, parity, chronic hypertension and pre-pregnancy diabetes, USA, 2016-2017. .......................................................................... 255 xxiii Table 31: Odds ratios expressing the joint association between pre-pregnancy BMI and parity, chronic hypertension and pre-pregnancy diabetes and the composite perinatal outcome singleton births, USA, 2016-2017. ............................................................................................................. 256 Table 32: Strata-specific adjusted odds ratios for the association between pre-pregnancy BMI and each of the components of the composite perinatal outcome and quantification of the effect modification on the multiplicative and additive scale, singleton births, USA, 2016-2017. ....... 257 Table 33: Summary of missing data overall for included pregnancies and amongst fetal deaths. Data are presented as n (%). Those with missing outcome or BMI (N = 1605) were excluded (N = 30,737). .................................................................................................................................... 261 Table 34: Example of counting process-style data needed for PAMM modeling where cut points are set every 5 gestational weeks. The first patient has a livebirth between 35-40 weeks and the second a stillbirth at 31 weeks. Gestational age categories can be used in more granular intervals, as in the primary analyses. .......................................................................................................... 262 Table 35: Comparison of risk factors (covariates) for stillbirth from multivariable Cox and PAMM models assuming proportional hazards. ......................................................................... 262 Table 36: Effective degrees of freedom for nonlinear terms for pre-pregnancy BMI, PAMM models. ........................................................................................................................................ 263 Table 37: Example gestational age-specific unadjusted and adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth in women with and without chronic hypertension. ............................................................................................................................... 264 Table 38: Example gestational age-specific unadjusted and adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth in women with and without pre-pregnancy diabetes. ...................................................................................................................................... 265 xxiv Table 39: Example gestational age-specific unadjusted and adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth in women with and without both chronic hypertension and pre-pregnancy diabetes. .................................................................................. 266 Table 40: Example gestational age-specific adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth,; sensitivity analysis including complete cases. ..... 266 Table 41: Comparison of women with and without information on pre-pregnancy BMI. ......... 272 Table 42: Rate of twin pregnancies of same and opposite sex by BMI group. .......................... 273 Table 43: Complete-case analysis. .............................................................................................. 273 Table 44: Results of mediation analysis based on imputation assuming 4 missing data scenarios...................................................................................................................................................... 274 Table 45 : Results of sensitivity analysis for body-mass-index misclassification compared to primary results (complete cases). ................................................................................................ 275 Table 46: Results of sensitivity analysis for body-mass-index misclassification compared to primary results (complete cases). ................................................................................................ 276 Table 47: Severe maternal morbidity (SMM) definitions. .......................................................... 279 Table 48: Serious neonatal morbidity definitions. ...................................................................... 283 Table 49: Definitions of maternal chronic conditions. ............................................................... 284 Table 50: Outcome rates for women with and without a pre-pregnancy BMI measurement. Data are N (%). .................................................................................................................................... 284 Table 51: Sensitivity analysis comparing the results for complete cases and using Targeted Maximum Likelihood Estimation with and without imputation. Risk ratios are compared to women with normal pre-pregnancy BMI. ................................................................................... 285 xxv Table 52: Outcome rates for maternal and perinatal outcomes by pre-pregnancy body-mass-index in twin pregnancies including both same sex and opposite sex twins. All data are N (rate per 10,000 pregnancies or livebirths). ............................................................................................... 286 Table 53: Maternal and perinatal outcomes by pre-pregnancy body-mass-index in twin pregnancies with (sensitivity) analyses restricted to opposite sex twins. ................................... 286 xxvi List of Figures Figure 1: Directed Acyclic Graphical examples of confounding scenarios. Figures are modified from ‘VanderWeele TJ. Principles of confounder selection’. A represents the exposure, Y the outcome, C a measured confounder, U an unmeasured confounder, and L a measured pre-exposure covariate. ....................................................................................................................... 48 Figure 2: Directed acyclic graphs illustrating mediation analysis without (A) and with (B) confounding. A represents the exposure, M the mediator, Y the outcome and C1, C2, and C3 measured confounders. ................................................................................................................. 49 Figure 3: Selection and attrition processes during the course of human reproduction and gestation, taken from Snowden et al, The curse of the perinatal epidemiologist: inferring causation amidst selection, Current Epidemiological Reports, 2018. .......................................... 50 Figure 4: Directed acrylic graph illustrating left truncation bias (collider bias) by conditioning on ongoing pregnancies at 20 weeks’ gestation. ................................................................................ 50 Figure 5: Gestational age specific perinatal death rates per 10,000 fetuses at risk by BMI class. Data are smoothed with three week moving averages. ................................................................. 69 Figure 6: Adjusted risk curves of the composite perinatal outcome including stillbirth, neonatal death and severe neonatal morbidity by pre-pregnancy BMI (continuous), stratified by maternal age category; singleton births in the USA, 2016-2017. Rates were adjusted for race/ethnicity, education, type of health care insurance, smoking status during pregnancy, and prior neonatal death or stillbirth, chronic hypertension, pre-pregnancy diabetes mellitus, and nulliparity. ........ 88 Figure 7: Adjusted risk curves (95% confidence intervals) of the composite perinatal outcome including stillbirth, neonatal death and severe neonatal morbidity by pre-pregnancy BMI (continuous), stratified by parity (A), chronic hypertension (B), and pre-pregnancy diabetes xxvii mellitus (C); singleton births, USA, 2016-2017. Risks were adjusted for maternal age, race/ethnicity, maternal education, type of health care insurance, smoking status during pregnancy, and prior neonatal death or stillbirth, and the other effect modifiers. ........................ 89 Figure 8: Gestational age-specific effects of body-mass-index (BMI) modeled as a continuous variable on stillbirth: Comparison between a Cox model with a time-varying linear effect of BMI modeled with a natural spline, and a Piecewise Exponential Additive Mixed Model with a linear term for BMI. .............................................................................................................................. 105 Figure 9: Results from Piecewise Exponential Additive Mixed Model allowing for a non-linear effect of body-mass-index (BMI) on stillbirth varying nonlinearly across gestational age, United States, 2016-17. The left panel shows gestational age-specific log hazard ratios for fixed BMI, while the right panel shows BMI-specific hazard ratios for fixed gestational age. All log hazard ratios are relative to a BMI of 20 kg/m2. .................................................................................... 106 Figure 10: Gestational age-specific effect for the number of average cigarettes smoked per day on stillbirth risk. Cigarettes per day was modeled as a time-varying covariate using a Piecewise Exponential Additive Mixed Model. Dotted line represents the estimated time constant log hazard ratio. ................................................................................................................................. 107 Figure 11: Prospective risk of stillbirth by pre-pregnancy BMI in women with and without chronic hypertension or diabetes by gestational week. Denominators for gestational age specific risks are all fetuses at risk at the given gestational week. ........................................................... 124 Figure 12: Log-hazard ratios (vs a pre-pregnancy BMI of 20 kg/m2) for the gestational age specific risk of stillbirth in women with increasing BMI in those with (left panel) and without (right panel) chronic hypertension. Curves are adjusted for maternal age, race, education, parity, pre-pregnancy smoking status and diabetes mellitus. ................................................................. 125 xxviii Figure 13: Adjusted log-hazard ratios (vs a pre-pregnancy BMI of 20 kg/m2) for the gestational age-specific risk of stillbirth by BMI category in women with (left panel) and without (right panel) pre-pregnancy diabetes mellitus; adjusted for maternal age, race, education, parity, smoking during pregnancy and chronic hypertension. ............................................................... 126 Figure 14: Association between pre-pregnancy BMI and rate of twin delivery. ........................ 142 Figure 15: Risk of adverse outcomes by continuous pre-pregnancy body mass index in twins per 10,000 pregnancies (severe maternal morbidity), per 10,000 total births (stillbirth, perinatal death, and the composite adverse perinatal outcome), and per 10,000 live births (neonatal death and serious neonatal morbidity). ................................................................................................. 161 Figure 16: Example directed acyclic graph illustrating collider stratification bias occurring from conditioning on (lack of) miscarriage. ........................................................................................ 184 Figure 17: Simplified possible structures that would lead to bias in the obesity -> perinatal death relationship in twin pregnancies due to (A) general unmeasured confounder, (B) bias by differential chorionicity, (C) differential left truncation due to miscarriage. ............................. 238 Figure 18: Estimated (biased) risk ratios under various scenarios of strength of association between an unmeasured confounder of pre-pregnancy obesity and chronicity based on simulated cohorts of size 10,000. True risk ratio between obesity and perinatal death is set to be 1.50. ... 239 Figure 19: Estimated (biased) risk ratios under various scenarios for differential early pregnancy loss between obese and normal BMI twin pregnancies. True risk ratio between obesity and perinatal death is set to be 1.50. .................................................................................................. 240 Figure 20: Distributions of estimated risk ratios across eight simulation scenarios. .................. 250 Figure 21: Directed acyclic graph representing assumed causal structure for causal mediation analysis. (C and L represent two sets of confounders: C confounders between the exposure and xxix the outcomes; L confounders between the mediator and the outcome). Mediation analyses in the text assume that all C and L are measured and adjusted for, and for the identification of natural direct and indirect effects that BMI does not affect any of the gestational age and perinatal death confounders (L). When this last assumption is violated, ‘interventional effects’ may still be estimated. .................................................................................................................................... 254 Figure 22: Distribution of cigarettes smoked per day by trimester amongst women who smoked prior to pregnancy (left) and amongst those who smoked throughout pregnancy (right). ......... 263 Figure 23: Weekly cumulative incidence rate of birth by gestational week in women with and without chronic hypertension. ..................................................................................................... 267 Figure 24: Weekly cumulative incidence rate of birth by gestational week in women with and without pre-pregnancy diabetes mellitus. ................................................................................... 268 Figure 25: Directed acyclic graph representing mediation model. ............................................. 276 Figure 26: Trends in twin rates amongst ART and non-ART conceptions during the study period...................................................................................................................................................... 277 Figure 27: Standardized mean difference of covariates between women with and without missing BMI among women with a twin pregnancy. ............................................................................... 287 xxx List of Abbreviations ACOG American College of Obstetricians and Gyneacologists AHR/aHR Adjusted hazard ratio AKI Acute kidney injury AMA American Medical Association AOR/aOR Adjusted odds ratio ARR/aRR Adjusted risk/rate ratio ART Assisted reproductive technology BC British Columbia BCPDR British Columbia Perinatal Database Registry BMI Body-mass-index CCI Canadian Classification of Interventions CDC Center for Disease Control and Prevention CDE Controlled direct effect CI Confidence interval C-section Caesarian section dBP Diastolic blood pressure DAG Directed Acyclic Graph DIC Disseminated intravascular coagulation DM Diabetes mellitus EDF Effective degrees of freedom FAR Fetuses at risk GA Gestational age GAM Generalized additive model GDM Gestational diabetes mellitus GEE Generalized estimating equations HELLP Hemolysis, elevated liver enzymes and low platelet count HR Hazard ratio ICU Intensive care unit IDE Natural indirect effect IUGR Intrauterine growth restriction IVF In vitro fertilization LGA Large for gestational age NCHS National Center for Health Statistics NDE Natural Direct effect NICU Neonatal intensive care unit OR Odds ratio PAF Population attributable fraction PAMM Piecewise additive mixed model PCOS Polycystic ovary syndrome PS Propensity score RCOG Royal College of Obstetricians RD Risk difference RERI Relative excess risk due to interaction xxxi RR Risk/rate ratio sBP Systolic blood pressure SGA Small for gestational age SMM Severe maternal morbidity SOGC Society of Obstetricians and Gynaecologists Canada TMLE Targeted Maximum Likelihood Estimation USA United States of America WHO World Health Organization xxxii Acknowledgements First, I am most thankful to my amazing research supervisor, Dr. Sarka Lisonkova, who has been incredibly supportive, and incredibly flexible in having me as a student. Likewise, I am thankful to my supervisory committee of Prof. Laura A Magee, Prof. K.S. Joseph, and Dr. Chantal Mayer, who have been supportive and engaged throughout my PhD studies. I also want to thank Dr. Gillian Hanley for chairing my committee meetings, and Prof. Joel Singer for his engagement during my studies and comprehensive exam. I wish to thank the University of British Columbia for their generous funding of my PhD through a four-year PhD scholarship. I am also indebted to the B.C. Children’s Research Institute (BCCHR) leadership, particularly Dr. Quynh Doan, for allowing me to work on my PhD thesis part-time while leading the BCCHR Biostatistical Division. I would not have been able to accomplish this work without their support and flexibility. I also want to thank my family: my wife, Dr. Maria Lesperance, who has always encouraged and supported me to pursue this degree and brings a smile to my face every day, and my son James, who has taught me more in the last year than any degree could (while simultaneously slowing down the completion of this thesis!). Finally, I want to acknowledge that the University of British Columbia and BC Children's Hospital Research Institute operates on the ancestral and unceded territory of the Coast Salish peoples — xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and Səl̓ílwətaʔ/Selilwitulh xxxiii (Tsleil-Waututh) Nations. Further, this acknowledgement, gratitude, and respect extends to all of the First Nations communities on whose territories the University and the Research Institute builds relationships and operates in British Columbia. xxxiv Dedication I dedicate this thesis to my Dad, who I think would be proud of this undertaking, and the level of detail. 1 Chapter 1: Introduction 1.1 Overview The global prevalence of obesity among adults has been increasing steadily in recent decades, in high- and low- resource settings (1,2). What underlies these increases is multifaceted, and include both changes in diet, more sedentary lifestyle, and increases in childhood obesity. Numerous national and international public health strategies have been developed to combat the rise, but the burden remains high. Women of reproductive age have not been spared from this increase, and obesity in pregnancy has become one of the most common and potentially preventable risk factors for adverse pregnancy outcomes (3,4). The pregnancy risks associated with obesity affect both the mother and the fetus/newborn (5–8). Women with obesity are more likely to experience miscarriage or stillbirth (7,9), and complications such as gestational diabetes and pre-eclampsia (10,11), and emergency caesarean delivery (12,13). Their infants have increased rates of neonatal death or severe morbidity (8). Obesity-associated risks have led to national societies and colleges of obstetricians and gynecologists, including the Society of Obstetricians and Gynaecologists of Canada (SOGC), the American College of Obstetricians and Gyneacologists (ACOG), and the Royal College of Obstetricians and Gynaecologists, UK (RCOG), to develop specific evidence-based guidelines for maternity care for women with pre-pregnancy obesity (14,15). However, knowledge gaps remain about the pathways by which obesity increases the risk of adverse outcomes, the possible 2 interactive effects between pre-pregnancy obesity and other pre-pregnancy risk factors, and the optimal timing of delivery for women with obesity. Due to the relatively low rates of serious adverse pregnancy outcomes in high-income settings such as Canada and the United States, large population data are required to answer research questions about these outcomes. Such data, including birth registries and detailed birth and death Vital Statistics data, have the advantage of providing large and representative samples, and consistency with respect to standardized methods of data collection (16,17). On the other hand, large population data may lack the level of clinical detail found in other data sources (e.g., study-specific data abstracted from hospital charts). Perinatal epidemiologists, statisticians, and clinicians are challenged by the need for large data on the one hand, and detailed clinical information on the other. Collaborative efforts that combine expertise from these various disciplines is required to enhance our understanding of adverse pregnancy outcome among women with obesity and improve their maternity care. The objective of this thesis is to enhance understanding of the epidemiology of obesity in pregnancy and to improve our knowledge of the relationship between pre-pregnancy obesity and adverse maternal and perinatal outcomes. To achieve these goals, I conducted a series of related studies using two primary population datasets: 1) the British Columbia Perinatal Database Registry, and 2) data on all births in the United States in 2016-2017, obtained from the live birth and fetal death files of the National Center for Health Statistics (NCHS), CDC, USA. 3 1.2 Body-mass-index and obesity A high BMI can be problematic for several reasons. It often indicates excess body weight, increasing the risk of various health problems such as heart disease, high blood pressure, and type 2 diabetes (18–20). Carrying extra weight puts additional stress on the joints, increasing the risk of joint pain, osteoarthritis, and mobility limitations (21,22). This can significantly impact an individual's quality of life and limit their physical activities (22,23). A high BMI can compromise respiratory function, making breathing more difficult and increasing the susceptibility to conditions like sleep apnea and asthma. Obesity can have a negative impact on mental health, contributing to low self-esteem, body image issues, and an increased risk of depression and anxiety (24,25). 1.2.1 Definition of obesity Body-mass-index (BMI), calculated as an individual’s weight in kilograms (kg) divided by their height in metres squared (m2), is often used to identify underweight, overweight and obese individuals. In clinical and public health research, these categories are defined by the World Health Organization (WHO) as follows: underweight (BMI < 18.5 kg/m2), normal (18.5 kg/m2 £ BMI < 25 kg/m2), overweight (25 kg/m2 £ BMI < 30 kg/m2) and obesity (BMI ≥ 30 kg/m2) 1.2.2 Trends in Canada and the United States 4 The global prevalence of obesity continues to rise, with the WHO estimating that 13% of adults worldwide are effected, and projections suggesting that by 2030, approximately 1 billion people will be living with obesity (26,27). The associated global health costs could exceed $4 trillion by 2035 (28). In Canada, obesity rates have been steadily increasing over the past few decades (29). According to data from Statistics Canada's Canadian Community Health Survey, the prevalence of obesity among Canadian adults aged 18 years and older rose from 22.2% in 2005 to 27.2% in 2018 (29). This increasing trend was consistent across geographical regions, age groups, males and females. In the United States, the prevalence of obesity is significantly higher, with an estimated 41.8% of adults classified as obese in 2020 (30). As in Canada, the trend has been steadily increasing (31) with some projections estimating that over half of adults will be obese by 2050 (32). These trends align with the global rise in obesity (2), reflecting the complex interplay of factors such as sedentary lifestyles, unhealthy diets, and environmental influences. 1.2.3 Obesity in Pregnancy As in the general population, obesity among women of reproductive age has also been increasing (4). In the United States, about half of all pregnant women were either overweight or obese in 2019 (3), while in Canada the prevalence is around 35% (33). Obesity in pregnancy is associated with numerous adverse maternal and perinatal outcomes, including gestational diabetes mellitus, gestational hypertension, pre-eclampsia, emergency 5 caesarean delivery, severe maternal morbidity, preterm birth, stillbirth, neonatal death, and severe neonatal morbidity (6,7,9,34–40). Further discussion of these associations is provided in Chapter 2. 1.2.4 Self-reported BMI While the gold standard for measuring body weight involves trained professionals using calibrated scales, many studies rely on self-reported data for weight and height. This is especially common in studies that use administrative data, including those focusing on pregnancy outcomes. Validation studies comparing self-reported weight and height with pre-pregnancy measurements have found a tendency for underreporting of weight, particularly among overweight and obese individuals, with a recent systematic review showing underreporting ranging from 1.0 to 2.4 kg (41). When BMI is categorized according to WHO guidelines, this underreporting can lead to misclassification. Validation studies have found that w agreement between self-reported and measured BMI is approximately 90% (42–44); however, the degree of underreporting and subsequent lower agreement is more pronounced among women who are overweight and obese (42,43). This differential can lead to bias, and the need for error adjustment or bias analyses in studies using self-reported BMI (44). 1.2.5 Limitations of BMI Although BMI is one of the most used health indicators of health status in adults, including among pregnant women, it has notable limitations. Firstly, high BMI can stem from diverse 6 causes including genetics, lifestyle, nutrition, and several medical conditions (e.g., hormonal disturbances in women with polycystic ovary syndrome). This has led to debates about what is meant by the ‘effect’ of high BMI or obesity (45–47) and whether it is possible to assess causal effects of obesity. As there are many possible causes of high BMI, there are many possible strategies to reduce obesity (e.g., exercise, diet, pharmacological treatment), and each of these interventions may lead to different changes in obesity-related adverse outcomes. Secondly, although BMI has been shown to be a good index of body size, which is useful both at the individual and population level, it does not always identify individuals with excess body fat, because BMI does not distinguish between weight from muscle and weight from fat (48,49). Finally, BMI categories were developed historically using data on white, male adult populations, and thus the standard cut-offs for BMI categories may not be informative for individuals of different sex/gender, age and race/ethnicity (50,51). Consequently, recent guidance from the American Medical Association advices against relying solely on BMI for individual patient assessment (52,53). 1.3 Comorbid conditions associated with high BMI Given the association between higher BMI and various other health complications, women with obesity often have accompanying comorbid conditions (19,20). Chronic hypertension and diabetes mellitus, in particular, are prevalent among women with high BMI and are established risk factors for adverse maternal and perinatal outcomes. 7 In pregnancy, chronic hypertension is characterized by elevated blood pressure prior to conception or before 20 weeks’ gestation (when blood pressure is usually falling), and thus independent of pregnancy. It complicates 1-2% of pregnancies, its prevalence steadily increasing over time, as it is more frequent in women with higher BMI (19,54–56). Women with chronic hypertension are at risk of developing superimposed pre-eclampsia and other adverse maternal and perinatal outcomes (54,55). Pre-pregnancy diabetes mellitus, particularly Type 2 diabetes, is more prevalent in individuals with high BMI (20,57–59). Type 2 diabetes is caused by the body developing resistance to insulin, and/or not enough insulin being produced by the body to lower blood sugar, while Type 1 diabetes is an autoimmune disease that damages the pancreas and resulting in little or no production of insulin. Elevated BMI is associated with increased insulin resistance and diabetes, particularly when coupled with inadequate insulin secretion (57,60). Both Type 1 and Type 2 diabetes during pregnancy are associated with adverse maternal and perinatal health outcomes (61,62). Despite the higher prevalence of chronic hypertension and diabetes prior to pregnancy in women with obesity, limited evidence exists on how these factors interact with obesity to influence pregnancy outcomes (63,64). In subsequent chapters, this thesis explores the impact of high pre-pregnancy BMI in conjunction with chronic hypertension or pre-pregnancy diabetes on perinatal outcomes both overall and at various periods of gestation. 8 1.4 Twin pregnancy A twin pregnancy occurs when a woman conceives two embryos instead of one during a single pregnancy. Twins can be either monozygotic (identical) or dizygotic (fraternal), depending on the formation of the embryos (65). Monozygotic twins result from the spitting of a single fertilized egg (zygote) into two separate embryos, while dizygotic twins occur when a woman releases two eggs during ovulation, and each egg is fertilized by a different sperm. The occurrence of twins varies among populations and is influenced by factors such as maternal age, and race/ethnicity (66,67). Fertility treatments, such as in-vitro fertilization (IVF) or ovulation-stimulating medications, can also increase the likelihood of having twins (68,69). Limited evidence also suggests a link between higher BMI and higher rates of twin pregnancy (70,71). The relationship between BMI and the rate of twins is assessed in detail in Chapter 8. Compared to singleton pregnancies, twin pregnancies pose higher risks for both the mother and baby (72–74). Maternal complications, such as gestational diabetes, hypertensive disorders and severe maternal morbidity, are more common in twin pregnancy (75). Fetal/newborn complications include stillbirth, neonatal death, preterm birth and low birth weight (72,73). However, there is limited evidence for whether obesity exerts a weaker or stronger effect in such pregnancies, and how (compared with singleton pregnancies). This question is addressed in more detail in Chapter 9. 9 1.5 Data sources 1.5.1 British Columbia Perinatal Database Registry British Columbia (BC) is a province in Canada with a population of approximately 5 million with access to a publicly funded universal health care for all citizens. Perinatal Services BC collects information on >99% of births including details about maternal demographics, behavioral factors, pregnancy complications, and fetal and neonatal health (76). The BC Perinatal Database Registry (BCPDR) compiles data abstracted from maternal and neonatal medical records, or midwives’ notes for home births Also, this repository includes clinical data on transfers and rehospitalization for 42 days postpartum for mothers and 28 days after birth for infants. The BCPDR include births since April 1st, 2000, and the database reached full provincial coverage in fiscal year 2001/02 (i.e., April 1, 2001-March 31, 2002). The BCPDR encompasses nearly 300 pre-pregnancy, obstetric and neonatal data elements, including information on relevant pre-pregnancy risk factors, complications during pregnancy, delivery details, and a wide array of maternal and perinatal birth outcomes. Information also includes up to 25 medical diagnoses, coded using the International Classification of Diseases and Related Health Problems, 10th Revision, Canadian version (ICD-10-CA), and up to 20 medical/surgical procedures coded using Canadian Classification of Interventions (CCI) codes related to maternal and newborn hospitalization or home birth, and subsequent hospitalization (up to 42 days postpartum for mothers, and 28 days after birth for infants). Previous validation 10 studies using re-abstraction have found high sensitivity and specificity of the collected data regarding major pregnancy events and risk factors (77). BCPDR information on pre-pregnancy BMI is based on self-reported height and weight, which have inherent limitations, as outlined above. Additionally, information on height or weight is missing in a sizable number of records (~25%), with a higher proportion of missing data in earlier years. Challenges posed by both potential underreporting and missing values are addressed using statistical methods in subsequent chapters. 1.6.1 United States’ National Center for Health Statistics: live birth and fetal death data The United States’ Centers for Disease Control and Prevention (CDC) collects selected data from live birth, infant death, and fetal death certificates (78,79). De-identified dataset are publicly available, including a linkage between live birth and infant death files (deaths within the first year after birth; cohort-linked files) (80). Collected information includes maternal demographic and behavioral characteristics, obstetric history, pregnancy complications, and birth outcomes. Similar to the BCPDR, pre-pregnancy BMI is self-reported, and records with missing data on BMI are relatively few. While the BCPDR includes more granular clinical information, an advantage of the NCHS data is the very large size, enabling detailed analyses of rare outcomes in multiple strata of women, including those with specific characteristics. In studies utilizing the NCHS data, I analyzed records on all births during a two-year period, from January 1, 2016, to December 31, 2017, 11 which were the most recent linked live births-infant death data available at the time of data analysis. 1.6 Research objectives The main research objectives and hypotheses of this thesis are as follows: 1. Question: To what extent do higher rates of preterm birth and earlier fetal death explain the association between pre-pregnancy obesity and perinatal death? Hypothesis: Part of the association between obesity and perinatal death can be explained by preterm birth (neonatal death) and earlier fetal demise (stillbirth). 2. Question: Does the presence of concomitant pre-pregnancy risk factors affect the association between pre-pregnancy obesity and adverse perinatal outcomes? Hypothesis: The relationship between obesity and adverse perinatal outcomes such as stillbirth, neonatal death and neonatal morbidity differs depending on the presence of other pre-pregnancy risk factors. 3. Question: Does the association between pre-pregnancy obesity and gestational age-specific risk of stillbirth differ depending on other concomitant pre-pregnancy risk factors? 12 Hypothesis: Obesity has varying effects on risk of stillbirth at different gestational ages and this relationship differs depending on the presence of other pre-pregnancy risk factors. 4. Question: Is there an association between maternal obesity, twin pregnancy and subsequent maternal and fetal health outcomes. Hypothesis: Pre-pregnancy obesity is associated with a higher rate of twinning, which affects maternal and neonatal morbidity in these pregnancies. 1.7 Thesis overview The remainder of the thesis is outlined as follows. Chapter 2 provides a review of the foremost literature on obesity and adverse maternal and perinatal outcomes, as well as current evidence on why such associations exist. Chapter 3 provides a review of core epidemiological and biostatistical concepts central to perinatal epidemiology and the issues addressed in this thesis. The core chapters (4-9) the population-based data sources mentioned above to explore the relationships between obesity and adverse outcomes as they relate to the above objectives. Specifically, Chapter 4 uses causal mediation analysis to assess the extent to which earlier gestational age at delivery and preterm birth mediate the relationship between pre-pregnancy obesity and perinatal death using population-based data from British Columbia, Canada from 13 2000 to 2017. In Chapter 5, I assessed the relationship between maternal obesity and perinatal death and severe morbidity in women with and without other common risk factors including nulliparity, chronic hypertension, pre-pregnancy diabetes mellitus and advanced maternal age in all births in the US in 2016-2017. Chapter 6 introduces a novel method for the estimation of gestational age-specific risks of stillbirth using Piecewise Additive Mixed Models. In Chapter 7, I applied these models to the same data from Chapter 5 to determine how chronic hypertension and pre-pregnancy diabetes mellitus alter the gestational age-specific risk of stillbirth in women with obesity. Chapters 8 and 9 focus on obesity and twin pregnancy. In Chapter 8, I demonstrate that more twins are born to women with higher BMI and assess how much of this is explained by the increased use of ART. Chapter 9 assesses whether in these twin pregnancies, there remains an association between pre-pregnancy obesity and maternal and perinatal outcomes as nearly all prior evidence has been generated from singleton births. Chapter 10 provides a discussion based on the findings from the studies carried out, and an outline for future directions for research on these topics. Finally, I provide statistical details in the appendices, including mathematical details on mediation analysis and interaction, applied examples and simulations comparing various approaches to measuring exposure effects (such as those of BMI), as well as simulations assessing possible explanations (e.g., bias) for the surprising findings from Chapter 9. 14 1.8 Ethics and disclaimer For studies using data from the BC Perinatal Database Registry have obtained written ethics approval from the University of British Columbia - Children’s and Women’s Hospital and Health Centre of British Columbia Research Ethics Board (H18-03154). Ethics approval was not required for studies using US data from the NCHS, as these data were de-identified and publicly available. Regarding data accessed through Population Data BC (BC data): Access to data requested for research projects through the Data Steward(s) or their designated service providers and provided by the Data Steward(s) were subject to approval from BC data custodians. However, all inferences, opinions, and conclusions drawn in this thesis are those of the author(s), and do not reflect the opinions or policies of the Data Steward(s). The term ‘woman/women’ has been used throughout the text in reference to pregnancy. This terminology is not meant to be exclusionary, and I recognize that some pregnant people may identify as women and some may not (81). The results of this thesis have implications for the care of all pregnant women and people. 15 Chapter 2: Review of pre-pregnancy obesity as a risk factor for adverse maternal and perinatal outcomes 2.1 Overview High body-mass-index (BMI) prior to pregnancy is a consistently documented risk factor for a variety of maternal and perinatal adverse outcomes as well as additional complications that are associated with these outcomes. With the documented rise in obesity globally, high BMI has become one of the most preventable risk factors in pregnancy. Below I review the biology of obesity, and its relationship with several key maternal and perinatal adverse outcomes. 2.2 Biological and genetic effects of obesity in pregnancy There are several biological processes by which obesity affects the human body overall, and in pregnancy. Obesity is often accompanied by a constant state of low-grade inflammation due to increased levels of pro-inflammatory cytokines (82,83). This inflammation can manifest in both adipose tissue, which acts both as passive energy storage and actively for secretion of various hormones and cytokines (84). Obesity can lead to changes and remodeling of the adipose tissue, often leading to changes in cellular composition and function (85,86). Further, inflammation may go beyond the adipose tissue, and become systemic, contributing to dyslipidemia or insulin resistance (82,87–90). Pregnancy can exacerbate this systemic inflammation, which increases risk of complications such as gestational diabetes (see below) (34,91). Further, increased 16 inflammatory markers in pregnancies with obesity can cross the placenta, potentially altering fetal growth and development (such as through decreased nutrition transfer) (92,93). Obesity is also associated with metabolic changes such as increased insulin resistance or alteration in glucose and lipid regulation (90,94). Insulin plays a key role in maintaining adequate blood glucose levels and insulin resistance occurs when cells become less responsive to the effects of insulin, leading to elevated blood glucose levels (95). In pregnancy, insulin resistance increases to help ensure adequate glucose levels for the fetus; however, in pregnancies complicated by obesity, the combination of increases due to obesity and increases due to pregnancy may become too high, which can lead to gestational diabetes, fetal macrosomia and other complications. Similarly, obesity can disrupt glucose and lipids, which also rise during normal pregnancy (94). Epigenetic modification has also been linked to obesity. Epigenetic modification occurs when different genes are turned on or off, which impacts which traits are expressed (96). A full discussion of epigenetics and obesity is beyond the scope of this thesis; however, obesity has been linked with changes in DNA methylation, and histone modification (97,98). Alteration in expressed genes can lead to changes in insulin resistance, inflammation, and appetite regulation and can lead to transgenerational effects where these changes are passed to the offspring (97). Further, interaction between obesity and risk factors for obesity such as diet and stress may result in increased epigenetic modification (99). 17 2.3 Pre-pregnancy obesity and adverse maternal outcomes 2.3.1 Gestational hypertension, pre-eclampsia, and gestational diabetes Pre-pregnancy obesity has been consistently associated with pregnancy complications. The two most common are forms of pregnancy hypertension, namely gestational hypertension and pre-eclampsia, and gestational diabetes mellitus (GDM). Gestational hypertension is defined as a systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg arising for the first time at ≥ 20 weeks’ gestation in the absence of proteinuria (100,101). High blood pressure prior 20 weeks’ gestation is classified as chronic hypertension (101). According to ACOG, gestational hypertension becomes pre-eclampsia when it is accompanied by one or more of proteinuria, or evidence of maternal end-organ involvement, defined as acute kidney injury (AKI), liver dysfunction, neurological features, hemolysis, or thrombocytopenia (101). Pre-eclampsia complicates 2-4% of pregnancies globally and is associated with higher rates of severe maternal morbidity, maternal and perinatal death and neonatal morbidity (102–104). Obesity has been consistently associated with gestational hypertension and pre-eclampsia across various study populations (11,35,105–108). However the pathophysiology and relationship between obesity and pre-eclampsia is not completely understood. One possibility is the obesity-related increase in metabolic dysfunction and a basal inflammatory state (106,107). Another possibility is the increased inflammation and triglycerides, which has been shown to partly mediate the effect between obesity and mild pre-eclampsia (106,109). Recent meta-analyses 18 have found associations between pre-eclampsia and various obesity-related biomarkers (e.g., leptin and adiponectin) (107). However, more data are needed to fully elucidate this relationship. Obesity is also an important risk factor for GDM, one of the most common metabolic disorders in pregnancy (110,111). GDM is defined as glucose intolerance that is first detected during pregnancy. Obese women have a 2-3 times higher risk of GDM compared with women of normal BMI (10,34,91). While GDM poses a lesser threat to health pregnancy than does pre-existing diabetes, GDM heightens the risk of adverse pregnancy outcomes, including pre-eclampsia, preterm birth, and neonatal morbidity, as well as the longer-term risk of Type 2 diabetes (91,112). The association between GDM and perinatal mortality remains contentious (62,112,113), although some of this controversy relates to biased analyses (114). The higher GDM rates in obese women likely stem from a combination of insulin resistance and systemic inflammation (34,91). In obese women, insulin resistance is exacerbated during pregnancy, potentially leading to GDM. An increases in inflammatory biomarkers, including C-reactive protein, is common in obese women (compared with women of normal BMI) (34). Nevertheless, further research is needed to further elucidate our understanding of the mechanisms behind the association. 2.3.2 Caesarean delivery Delivery by caesarean section is performed for numerous indications, including prior cesarean delivery, placenta previa, placental abruption, and acute fetal complications (e.g., fetal distress, umbilical cord prolapse), etc. (115). Caesarean deliveries can be planned prior to labour onset, 19 for maternal or fetal indications (e.g., breech presentation, previous caesarian delivery, twin pregnancy, and placenta previa), with national guidelines differing on specific indications (116). In some settings, women can opt for an elective cesarean delivery (i.e., by choice), although this is not common practice in Canada (117,118). Maternal obesity, particularly with a BMI ≥40 kg/m2, is a risk factor for both planned and emergency cesarean delivery (12,13,115,119). Pooled effects from existing meta-analyses show an 1.53, 2.26 and 3.38 increase in the odds of cesarean delivery among overweight, obese, and morbidly-obese women (compared with those of normal BMI) (13). Potential underlying mechanisms for this association include the presence of pelvic fat obstructing birth, development of gestational hypertension and pre-eclampsia, and/or suspected macrosomia (12,13). Women with high BMI are also more likely to undergo labour induction, largely due to higher rates of pregnancy complications. If labour induction fails, cesarean delivery follows. The relatively high rates of induction and planned cesarean delivery among women with very high BMI have prompted several studies comparing birth outcomes between those with a planned cesarean delivery and those attempting a vaginal birth, to resolve the controversy about the best practice (120–123); however, uncertainty remains regarding optimal mode of delivery for these women (120–123). Composite maternal morbidity (including operative complications, postpartum hemorrhage and infection) and composite adverse neonatal outcome (including intrapartum stillbirth, NICU admission and severe morbidity) rates have been found to be similar between groups, although higher infection rates have been observed in the induction groups, while higher rates for operative complications and wound-associated morbidity has been found 20 in the planed cesarean delivery groups (120). Women with a failed labor induction and subsequent emergency cesarean delivery were found consistently to have the highest rate of complications (120–123). For example, a meta-analysis of this group with a BMI ≥40 kg/m2 found that 70% of women with needing emergency caesarian delivery had postpartum hemorrhage, 4.4% had wound infection, and 6.7% required hospital stays > 4 days (121). Thus, in obese women, there is heterogeneity in the risk of adverse birth outcomes following labour induction (e.g., differs between those who deliver vaginally successfully and those who need emergency cesarean delivery). This highlights the need for well-performing prediction models that will identify who will succeed in a trial of labour, among obese women, and particularly those with a BMI ≥ 40 kg/m2 (121). A randomized controlled trial comparing the two approaches would offer valuable insight, although achieving sufficient enrollment and protocol adherence may present challenges (121). 2.3.3 Severe maternal morbidity Severe maternal morbidity (SMM) is typically defined as a composite set of outcomes that include conditions that are potentially life-threatening or that lead to serious sequelae, or conditions requiring intensive care unit admission or prolonged hospitalization. The Canadian Perinatal Surveillance System (CPSS) has identified a list of such conditions and their associated ICD-9 and ICD-10 codes (124,125). These conditions include: acute cardiac conditions, acute hepatic failure, acute renal failure, need for assisted ventilation, cerebrovascular morbidity, embolism, shock or disseminated intravascular coagulation (DIC), hysterectomy, surgical complications, severe pre-eclampsia/eclampsia/HELLP syndrome, maternal sepsis, severe hemorrhage, and uterine rupture (among others) (125). 21 The largest study of the association between high BMI and SMM was conducted using retrospective data from Washington State, USA, 2004-2013 including nearly 1 million pregnancies (6). The authors found relatively low rates of SMM among all BMI classes (143 – 202 per 10,000 women), and a dose response relationship between increasing BMI and rates of SMM, albeit with relatively small effect sizes. Morbidities with the strongest association with increased BMI were acute renal failure, cardiac morbidity, respiratory morbidity, eclampsia, and ICU admission. Both antepartum and postpartum hemorrhage had inverse associations with increasing BMI. Two other smaller studies also examined the association between high BMI and SMM; the first was a case-control study (2013), and the second a retrospective cohort study (2015), and neither found an association between obesity and SMM (126,127). 2.4 Pre-pregnancy obesity and adverse perinatal outcomes 2.4.1 Stillbirth In Canada, stillbirth is defined as the death of a fetus at ≥ 20 weeks’ gestation or ≥ 500 grams (birth weight) that occurs prior to its complete expulsion of extraction from the mother (128). The United States uses a similar definition based on death of a fetus at ≥ 20 weeks’ gestation (129). Due to variation in data collection practice and coding, for international comparisons, the ICD definition of late fetal deaths is sometimes used, corresponding to a threshold of 28 weeks’ gestation (130). 22 Stillbirth represents a serious public health concern. As of 2020, there were approximately 3000 stillbirths per year in Canada; 21,000 per year in the US, and 2 million stillbirth post 28 weeks’ gestation globally (130–132). Women with obesity consistently experience nearly double the rate of stillbirth compared with women of normal BMI; and this associations has been consistent in a variety of settings (7,38,133). The BMI-related differences are even more pronounced at late term gestation (41 weeks) and post-term gestation (42 weeks’ gestation or later) (9,134). One large population-based study from the US showed that compared with women with normal BMI, the hazard ratio for stillbirth in women with class III obesity increased from 1.41 at 30-33 weeks’ to 3.30 at 40-42 weeks’ gestation; similar relative increases were seen for women with class I and II obesity (9). Reasons behind the association between high BMI and stillbirth are still not completely understood. As discussed in the preceding section, women with obesity are at higher risk of pregnancy complications (e.g., gestational hypertension, pre-eclampsia, and GDM), which are also risk factors for stillbirth (135,136). Some studies have suggested that chronic inflammation, sleep apnea, and metabolic and vascular abnormalities in obese women are possible explanations (134,137,138). An additional consideration is the challenge of effective fetal monitoring and ultrasonographic assessment in women with obesity, especially those with very-high BMI. Understanding the gestational age specific risks of stillbirth by risk factor groups (e.g., BMI) is important for determining the optimal timing of delivery to minimize risk. However, in most 23 available data, only the time of diagnosis stillbirth is available (i.e., the delivery) and not the actual time of fetal death. Typically, the difference between these times is assumed to be small but significant misclassification of the actual gestational week at which fetal death occurs could bias analyses of gestational age specific risk of stillbirth. 2.4.2 Preterm birth Preterm birth is defined as birth prior to 37 weeks’ gestation. Depending on gestational age, preterm birth categories are as follows: extremely preterm birth (< 28 weeks), very preterm (28-32 weeks) and moderate to late preterm (33-36 weeks) (139,140). Late preterm birth is typically defined as birth at 34-36 weeks’ gestation. Preterm birth can be further categorized based on etiology, as spontaneous (related to preterm onset of labour and preterm premature rupture of membranes), and medically-indicated (i.e., iatrogenic; due to maternal or fetal risk). Preterm birth occurs in approximately 10% of pregnancies, and is associated with neonatal morbidity and mortality, as well as short-term and long-term health outcomes in children (140–142). Maternal obesity is associated with both spontaneous and clinician-initiated preterm birth, including extremely, very, and moderate to late preterm birth (39,143–146). Spontaneous preterm birth is typically characterized by increased levels of inflammatory cytokines, which are heightened in obese women (147). This can cause early cervical ripening and weakening of membranes, leading to subsequent preterm birth (148). Other possible explanations for the link between obesity and spontaneous preterm birth include increased glucose levels, and increased chorioamnionitis in women with obesity (39,147,149). Clinician-initiated preterm delivery is carried out to address increased health risks to the mother, fetus, or both. This is often due to 24 gestational complications such as severe fetal growth restriction or pre-eclampsia. These complications mediate a significant proportion of the association between obesity and preterm delivery (145). 2.4.3 Neonatal death Neonatal death is defined as a death of a liveborn infant prior within 28 days after birth (150,151). An estimated 2.4 million neonatal deaths occurred globally in 2019, representing a nearly 50% decrease in such deaths since 1990 (152). Neonatal death accounts for nearly half of mortality of children under 5 years of age globally, and neonates in low and middle income countries are disproportionately affected (152). Many studies including those on geographically diverse populations have shown an association between pre-pregnancy obesity and neonatal death, reporting between 50% and up to a 3-fold higher odds of neonatal death among women with obesity (5,38,133,153–156). The reasons behind this increased risk are complex and have not been completely elucidated. Obese women have higher rates of preterm birth, both spontaneous and clinician-initiated (39), and preterm (particularly very preterm) bith is one of the main determinants of neonatal death (157). Organ systems of preterm infants are not fully developed and immature. Thus, very preterm babies have difficulty adapting to conditions after birth, and suffer from increased morbidity, especially respiratory morbidity that requires respiratory support (158,159). Reasons for clinician- initiated preterm birth include serious pregnancy complications, for instance, severe preeclampsia/eclampsia, HELLP syndrome and GDM (together with other complications) (160–162) . These complication are uncommon, albeit more frequent among women with obesity 25 (163,164) . The most common explanation of reasons behind the increased risk of spontaneous preterm birth in women with obesity is inflammatory up-regulation and elevated levels of inflammatory proteins in obese women, which can cause early cervical ripening resulting in preterm birth (39). However, the exact pathophysiology is not completely understood. Obstetric management of pregnancies with complications at very preterm gestation and worsening in-utero conditions, involves a trade-off between an expedited physician-initiated delivery to decrease risk of fetal death and maternal complications, versus expectant management to decrease the risk of prematurity-related neonatal death (165,166). However, maternal life-threatening complications invariably require an immediate delivery. There are several other possible explanations for the increase in neonatal death among women with obesity. These include obesity-related fetal macrosomia (which can lead to severe birth injury and neonatal mortality (167,168), and congenital anomalies (that are associated with neonatal death) (169). 2.4.4 Neonatal morbidity Neonatal morbidity refers to the occurrence of health problems, illnesses, or complications in newborns during the first 28 after birth (170). This period is crucial for infant development and is particularly affected by in-utero conditions prior to delivery (e.g., hypoxia), labour and delivery complications (e.g., prolonged labour, shoulder dystocia), and post-delivery environmental factors (e.g., infection). Examples of neonatal morbidity include respiratory distress syndrome, infections, and birth injuries. These morbidities can range from relatively mild conditions (e.g., mild hyperglycemia, hyperbilirubinemia), to serious and potentially life-threatening morbidity (e.g., sepsis, necrotizing enterocolitis, seizures, intraventricular hemorrhage grade 3-4), 26 retinopathy of prematurity, and bronchopulmonary dysplasia). While admission to NICU is typically considered a marker of serious neonatal morbidity, NICU admission is also affected by the availability of NICU beds and other factors; for instance, a short NICU stay may occur for a relatively milder condition for the purposes of observation (171,172). Low Apgar score (< 4) at 5 or 10 minutes is a marker of initial neonatal health and includes components for: (1) color of the infant, (2) heart rate, (3) reflexes, (4) muscle tone and (5) respiration (173). High maternal BMI has been consistently associated with neonatal morbidity, including respiratory distress syndrome and birth trauma, with higher proportions of infants with a low 5-minute Apgar score, and NICU admission (153,170,174–178). There are several possible reasons behind this association. First, some of these morbidities (e.g., birth trauma) are associated with to macrosomia, which is more common in women with obesity (179). Macrosomic infants face larger difficulties during delivery and are at higher risk of birth injury related morbidity and intrapartum hypoxia due to prolonged labour, shoulder dystocia and operative vaginal delivery. Second, women with obesity have higher rates of gestational diabetes (10,34,91), which is associated with infant’s hypoglycemia (low blood sugar) and related morbidities such as seizures, respiratory distress, and other complications (180,181). Third, as discussed above, women with obesity have higher rates of preterm birth. Preterm infants are at a higher risk of severe neonatal morbidity and are more likely to be admitted to the NICU (133,182,183). Other explanations include the direct effect of maternal high pro-inflammatory state associated with obesity, and higher rates of hypertensive disorders in women with obesity which are associated with increased complications for the fetus and infant (102–104). 27 2.5 Summary Addressing the link between obesity and maternal and infant health requires a multi-pronged approach, including pre-conception care, early identification of risk factors other than obesity, targeted monitoring during pregnancy, and timely interventions to mitigate pregnancy complications. Deeper understanding of the causal pathways by which obesity leads to adverse outcomes is needed to develop effective preventive interventions, particularly with respect to the complex causes of obesity and associated comorbidity. Prenatal care, lifestyle modifications, and appropriate obstetric management play crucial roles in optimizing pregnancy care and improving outcomes for both mothers and infants. 28 Chapter 3: Epidemiological and statistical considerations in observational studies in perinatal research. 3.1 Introduction There are several important considerations in the design and analysis of observational studies in epidemiology, including understanding: limitations of the data source, the population at risk, measured and unmeasured confounding variables, and potential causal mechanisms, such as interaction and mediation. In the context of perinatal epidemiology, many of these issues are further complicated by the presence of potentially competing considerations for the mother and fetus/infant, as well as limitations of data from available pregnancy cohorts. 3.2 Confounding 3.2.1 Definition of a confounder Confounding is of primary concern in nearly all observational studies, and occurs when a variable or set of variables is associated with both the exposure and the outcome of interest, either directly or through some indirect path. However, the confounder cannot lie on the causal pathway between the exposure and outcome (184–190). For example, it is reasonable to think that the prevalence of obesity is higher in women who are parous (vs. nulliparous), and it is well-known that nulliparity is associated with an elevated risk of pregnancy complication, including stillbirth. Therefore, in a study examining the association between pre-pregnancy obesity and 29 stillbirth, it is important to control for parity. This can be illustrated by a hypothetical example of 10,000 women in Table 1. From this table we can compare women with and without pre-pregnancy obesity and calculate the following risk ratios for stillbirth, stratified by parity (nulliparous vs parous women): 𝑅𝑅!"" = 2.0, 𝑅𝑅#$""%& = 2.5,𝑅𝑅&!'($) = 2.6 The strata-specific risk ratios differ from the overall risk ratio. Further, we can compute a weighted average of the nulliparous and parous risk ratios using the Mantel-Haenszel estimator (191): 𝑅𝑅*+ = 60(40 + 2460)/4000 + 26(14 + 3486)/600040(60 + 1440)/5000 + 14(26 + 2474)/6000 = 2.53 Since this ‘adjusted’ risk ratio (for parity, 2.53) differs from the overall risk ratio (2.0), we have evidence of confounding by parity. 3.2.2 Considerations for confounder selection The epidemiological literature has numerous, and sometimes conflicting, suggestions for how to select potential confounders for multivariable (i.e., confounder-adjusted) analyses (187,192–195). For example, some authors suggest using subject matter expertise, while others rely on data-driven methods. Regardless of the approach, proper selection begins with assumptions surrounding the relationships between candidate confounders, the exposure and the outcome 30 (196). This is usually done based on existing literature, biological plausibility and expert input, which can be summarized and displayed using diagrams such as Directed Acyclic Graphs (DAGs). DAGs are path diagrams with nodes representing variables and arrows usually representing assumed causal relationships (196,197). First, it is important that any confounder-adjusted analysis excludes variables that are caused by the exposure and cause the outcome. Variables satisfying this criterion are referred to as mediators (on the causal pathway between the exposure and outcome). When mediators are present the overall association between the exposure and outcome (total effect) can be broken down into an effect through the mediator (indirect effect) and one not through the mediator (direct effect). When the study interest is in measuring the total effect of the exposure on the outcome (incorrect) adjustment for a mediator will remove the indirect effect from the resulting estimate (187,198) and underestimate the overall effect of the exposure on the outcome. After exclusion of possible mediators from the set of candidate confounders, there are several possible approaches for selecting variables to include in the confounder set. If all ‘common causes’ of the exposure and the outcome can be identified and measured, then adjusting for these variables is always sufficient to remove confounding (see the DAG in Figure 1A, where C is a common cause) (199). This can be restrictive in some cases if such variables are hard to measure, or unavailable. Therefore, perhaps the most common approach to attempt to control for confounding is to attempt to adjust for all (not mediator) variables that are associated with both the exposure and the outcome (199). In the above example, parity is associated with both obesity and stillbirth, and as demonstrated, and lack of adjustment confounded the risk ratio between 31 obesity and stillbirth. A second example is given in Figure 1B, where controlling for C would result in an unbiased estimate of A on Y, even though we do not have information about U, as including C blocks the path from the exposure A to the outcome Y through U (A->U->C->Y). A second option for choosing how to attempt to adjust for confounding is to adjust for all pre-exposure risk factors for either the exposure or the outcome (192,199). This approach has the advantage of requiring less knowledge of common causes, but in some situations may actually create bias (189,193,200). In Figure 1C controlling for L actually creates a spurious association between A and Y through the path A->L->U->Y (199). Furthermore, there is some evidence that adjusting for a cause of exposure that has no relationship with the outcome (known as an instrumental variable) may increase bias when there are additional unmeasured confounders (201,202). A third approach to confounder selection is based on statistical criteria, although these are not sufficient independent of subject matter knowledge. The two historical approaches to data-driven confounder control are a) stepwise regression (203) and b) change-in-estimate analysis (197,204,205). In stepwise regression, one starts with either a model with no confounders (forward) or with all candidate confounders (backwards) and enters/removes covariates one at a time, discarding or retaining covariates based on their (conditional) significance or improvement in some measure of model fit (such as AIC). On the other hand, change-in-estimate approaches determine the inclusion of a covariate when the model including that covariate changes the exposure effect by a pre-specified threshold (e.g., 10%). Unfortunately, each approach is flawed in that they a) invalidate the conventional interpretation of the final model’s inferential statistics 32 due to invalid standard errors (for stepwise regression) (206–208), b) may require large sample sizes when the number of potential confounders is large, c) change-in-estimate approaches may actually select variables that create bias (such as L in Figure 1C) (189,193), and d) result in changes in estimates due to statistical properties of the effect estimate (e.g., the non-collapsibility of the odds ratio) rather than actual change in exposure effect (209–211). The first issue (invalid standard errors) can be overcome by resampling approaches such as sample splitting, cross-validation or bootstrapping (207,212). More modern approaches to statistical confounder selection, such as Targeted Maximum Likelihood Estimation, overcome some of the issues above by using built-in validation approaches and leveraging machine learning algorithms for flexible modeling; however, even these approaches are incomplete without valid assumptions based on the subject matter expertise (213,214). 3.2.3 Statistical approaches to confounder control Several statistical approaches exist to control a-priori for a selected set of confounders, based on subject matter knowledge. The simplest approach is to stratify by the confounder (as in the example of obesity and stillbirth above) and compute an adjusted effect measure as a weighted average across the strata of the confounder. Unfortunately, in analyses including many potential confounders or confounders that are continuous variables, these techniques fail due to sparse data within combinations of strata, even in large data sets. Therefore, more sophisticated modeling approaches are needed, which can be largely categorized into three approaches: 1) outcome modeling, 2) exposure modeling and 3) doubly robust methods. 33 3.2.3.1 Outcome modeling Modeling the outcome via an appropriate generalized linear model is the most common, and long-established approach to confounder control. For example, for a binary outcome, if the target estimand is the odds ratio for the exposed vs unexposed then a logistic regression model including covariates for each confounder is often used and the exponent of the coefficient for exposure is taken as the estimate of effect. Similar regression models can be used for other types of outcomes and estimands. More recently, G-computation, which contrasts the average predicted outcome assuming the whole cohort was exposed vs the whole cohort was not exposed has, been proposed as an alternative to regression and is preferable for situations with time-varying confounding (215–217). 3.2.3.2 Exposure modeling The second option is to create a model for the exposure rather than the outcome. These models are often called ‘propensity score’ models, where the propensity score is defined as the probability of exposure conditional on included confounders (216,218). Propensity scores can be used to adjust for confounding in various ways. For instance, they can be used to ‘re-weight’ the sample (e.g., inverse probability weighting), match units (e.g., propensity score matching), or stratify, such as by quintiles of the propensity score (216,219). Such techniques are useful when the analyst has a better conceptual understanding of the relationships between the confounders and the exposure than between the confounders and the outcome. 34 3.2.3.3 Doubly robust methods Doubly robust methods combine both an outcome and exposure model and allow for unbiased estimation of the effect if either of the two models are correct (216,220,221). These include approaches such as doubly robust inverse probability weighting, and Targeted Maximum Likelihood Estimation (213,214,222,223). Despite this attractive property of doubly robust models, some evidence shows that bias may be higher, or confidence interval coverage worse, in situations where both models are incorrect (which is likely in practice) (221,224,225). These problems occur especially in situations where machine learning models are included in the estimation of the propensity score or outcome models (221,224,225). A practical solution, and the best practice, is to compare results under different modeling assumptions to assess the overall robustness of the results. 3.2.4 Sensitivity analyses for unmeasured confounding Even in observational studies with prospective data collection on all known confounders, unknown (unmeasured) confounding can impact results. This issue is of great importance in studies using administrative data or data with missing information on potentially important confounding factors (e.g., socio-economic status). To address potential unmeasured confounding, several methods have been proposed to assess the sensitivity of results to unmeasured confounding (226–230). The simplest method involves computation of an E-value, which represents the minimum strength of association on the risk ratio scale that an unmeasured variable would have to have with the exposure and outcome in order to explain away an observed effect (226). Calculation of the E-value requires no 35 assumptions and can provide a simple summary of the robustness of a specific association between exposure and outcome. However, E-values have attracted some controversy for a potential lack of plausibility and for a tendency towards a null-bias due to a smaller E-value, which may result in ‘explaining away’ (due to presumed residual confounding) true effects (231–233). Other approaches such as quantitative bias analysis and simulation can be used to more thoroughly investigate robustness of findings to unmeasured confounding when knowledge of the unmeasured confounder is available or under situations deemed plausible by the investigator (230,234,235). 3.3 Interaction The association between exposure and outcome can differ depending on the presence or absence of an additional exposure. Such situations are described as an ‘interaction’ between the two exposures of interest (198,236). Uncovering such interactions is important as it allows for better individual level risk estimates which can guide clinical decision making as the presence of an interaction by an additional risk factor may lead to a clinician treating two patients with the same risk factor differently. Understanding interaction is also beneficial from a public health perspective, as it can help address in which groups (potentially limited) resources should be directed to have the highest societal benefit. Further, in extreme cases, it may be that in some subgroups an exposure is harmful, while in others it is preventative. In these cases, understanding the interaction is essential to providing the most appropriate response (198,237). 36 3.3.1 Effect modification vs interaction In the preceding section, interaction was defined as differential exposure effect in the presence or absence of an additional exposure. In this scenario, the causal effect of both exposures is of interest (as well as their joint effect). In contrast, when a study is focused on a single exposure effect, but this exposure effect may be differential in specific subgroups, the identifying characteristic(s) of these subgroups may be considered effect modifiers (198,238,239). That is, when considering effects of possible intervention(s) interaction is defined in terms of two (or more) joint interventions, while effect modification is when the effect of one intervention varies between two (or more) subgroups, for instance, male vs female (238). Often the same form of statistical model may be used to assess either interaction or effect modification. For example, for a binary outcome, a logistic model with a product term between the two exposures of interest (or exposure and effect modifier) can be used (237). The differences lie in 1) the question of interest and biological understanding and 2) the presentation of results. When presenting interaction results it is recommended to include the 1) the joint effects of both exposures, 2) the effect of each individual exposure within levels of the other exposure and iii) measures of both multiplicative and additive interaction (see below). Conversely when effect modification is of interest it suffices to present 1) the joint effects of the exposure and effect modifier and 2) the exposure effects within levels of the effect modifier (240). If one or more variables is continuous, then graphical presentation of results is preferable. 37 3.3.2 Multiplicative vs additive interaction There are two possible scales on which interaction (or effect modification) may be present: multiplicative or additive. The assessment of scale will depend on the primary research question, but it is best practice to present both (198,240). Multiplicative interaction refers to the situation in which the combined (multiplicative) effect of two exposures differs from the product of the two individual exposure effects. Similarly, additive interaction occurs when the sum of the two exposure effects differs from their combined (additive) effect. There are advantages and disadvantages to both measures. Multiplicative interaction is generally easier to assess statistically (e.g., by including a product term in a generalized linear model such as logistic regression), and it tends to be ‘less heterogenous’ across populations, although, the latter is a subject of debate (241). As the multiplicative interaction reflects a relative scale, it may be more suitable to assess the exposures from a etiological perspective (198,237). Assuming causal associations, additive interaction corresponds to the absolute reductions in risk in various subgroups, and is generally viewed as preferable for public health decision making such as when considering preventive intervention in one or more subgroups (237,242). Multiplicative interaction is more routinely reported likely due to the computational ease when using standard statistical software, while additive interaction requires additional computational steps (198,243,244). To demonstrate both types of interaction, three hypothetical studies are provided in Table 2, examining possible interaction between obesity and nulliparity, and their effects on stillbirth. 38 The three studies correspond to 1) multiplicative interaction only, 2) additive interaction only, and 3) both multiplicative and additive interaction. The measure of multiplicative interaction is calculated as the ratio of relative risk (RR) for obesity and nulliparity combined divided by RR for obesity multiplied by RR for nulliparity, i.e., the joint association of nulliparity and obesity divided by the marginal risk ratios for each independently. Similarly, the measure of additive interaction is calculated as the risk difference (RD) associated with obesity and nulliparity combined minus the RD for obesity minus the RD for nulliparity. Non-obese parous women are the reference for all RRs and RDs. For example, in study 1 we can calculate: 𝑅𝑅 (,-)-,#$""%& = /./1//./23 = 4.0, 𝑅𝑅 (,-)-,&!'($) = /./43/./23 = 3.0, 𝑅𝑅 #(#5(,-)-,#$""%& = /./3/./23 = 2.0 So that the measure of multiplicative interaction 6./7./×2./ = 0.66 < 1.0, which indicates negative multiplicative interaction. Similarly, the risk differences are: 𝑅𝐷 (,-)-,#$""%& = 0.075, 𝑅𝐷 (,-)-,&!'($) = 0.05, 𝑅𝐷 #(#5(,-)-,#$""%& = 0.025 And therefore, the measure of additive interaction = 0.075 − 0.05 − 0.025 = 0 which implies no additive interaction. Similar calculations yield the values for examples of studies 2 and 3. 39 When both multiplicative and additive interaction are present, this suggests both some biological basis for the differential effects, and that treatment of specific subgroups may be of benefit. Statistical details of modeling interaction are covered in Appendix A. 3.3.3 Causal interpretation of interaction In the preceding section additive and multiplicative interaction was demonstrated regarding obesity and parity by examining the joint vs strata specific risk ratios and differences. To interpret all such effects causally, control must be made for all confounding variables of each exposure. In the above, one would therefore have to control for all confounders of obesity and stillbirth and of parity and stillbirth through, for example a multivariable regression model. When this level of control for confounding is possible the result is a ‘causal interaction’ (198,240). However, suppose the interest is in one exposure (e.g., obesity) and whether its effect is heterogenous across strata defined by the other factor (parity), i.e., effect modification. In this case, causal interpretation of the strata-specific effects of the primary exposure requires only no unmeasured confounding for the primary exposure and outcome (e.g., obesity and stillbirth). This would be useful in the case for evaluating the utility of subgroup specific interventions on the primary exposure but does not give information on whether the effect modification is due to the other factor, or to some other possibly related variable (198,240). 40 This latter scenario is considered in Chapter 5, where primary concern is subgroup specific effects of BMI on adverse perinatal outcomes, and not ‘causal interaction’. 3.4 Mediation Many studies focus on factors that are on the causal pathways between an exposure and outcome; these ‘intermediate’ variable(s) are mediators (see Figure 2) (198). Mediation analysis can be used to clarify the causal pathway in which exposure affects outcome, and may help to develop targeted interventions (e.g., on the mediator) to modify the effects of exposure. 3.4.1 Direct and indirect effects When a mediator is present, it is possible to decompose the total effect of exposure into a direct effect (not through the mediator) and an indirect effect (through the mediator). In Figure 2 (A) the direct effect is the path A -> Y, while the indirect effect is the path A -> M -> Y. There are multiple formulations of direct and indirect effects, some of which depend on the presence of interaction between exposure and mediator (in addition to mediation, see below). The first of these formulations is the ‘controlled direct effect’ (CDE), which represents the effect of the exposure on the outcome assuming the mediator is fixed for the whole study population, while the exposure is varied (245,246). This controlled direct effect is often directly related to possible effects of interventions surrounding exposure. The CDE does not have a corresponding indirect effect (246). 41 The second class of mediation effects are known as ‘natural’ direct and indirect effects (198,247). The natural direct effect is the effect of exposure on the outcome assuming the mediator would take the value it would’ve taken if unexposed. Similarly, the natural indirect effect contrasts the scenario when the mediator takes the value it would normally take under exposure vs the situation where it does not. These are ‘counterfactual’ comparisons where the exposure or mediator takes hypothetical values that are counter to what is observed in the data. Natural effects have been argued to be preferable to the CDE for understanding the etiology (248,249) but been criticized for relying on what are known as ‘cross world assumptions’ (250). Finally, in the presence of interaction between the mediator and exposure, the natural effects can be further broken down into ‘pure’ and ‘total’ effects (251). See Appendix B for a formal mathematical discussion. 3.4.2 Controlling for confounding in mediation analysis Recent progress in properly defining mediation effects has determined that in order to estimate unbiased mediation effects that are causal, the following four assumptions have to be met (see also Figure (B)) (198,246): 1. There is no unmeasured confounding between the exposure and outcome. 2. There is no unmeasured confounding between the exposure and mediator. 3. There is no unmeasured confounding between the mediator and outcome. 4. No confounders of the mediator-outcome relationship are caused by the exposure. 42 The first two conditions are sufficient to estimate the CDE, but all four are required to estimate natural direct and indirect causal effects. In particular, the fourth condition is difficult to verify (as it is known as a ‘cross-world’ assumption) and will often not be met (250). In such scenarios it is possible to still estimate ‘stochastic’ or ‘interventional’ mediation effects (252). 3.5 Population-at-risk and challenges of using administrative data in perinatal epidemiology 3.5.1 Population of interest One of the challenges of cohort studies in perinatal epidemiology is that depending on the exposure(s) of interest, the true population of interest is not always observed, which can happen for two main reasons: 1) not all women who want to conceive actually conceive, and 2) some pregnancies terminate at early gestation and these data are often unavailable for (as typically, data are collected on births at 20 weeks’ gestation and later). The latter issue can lead to left truncation bias (253). When studying a pre-pregnancy exposure and pregnancy outcome, ideally data on all women in the population of interest who are trying to conceive should be available, as the exposure may be linked to both successful conception and the outcome of interest. If available data include only data on identified pregnancies (which will be the case for most studies) and women who have or do not have exposure are more or less likely to conceive and reach a point of pregnancy where identification is possible (i.e., avoid a miscarriage prior to this identification), then the resulting cohort is subject to a possible selection bias. This is illustrated in Figure 3, from Snowden et al (253). 43 3.5.2 Left truncation Most administrative databases of births only include ongoing pregnancies at 20 weeks of gestation or later. This is because stillbirths, which are defined only after 20 weeks' gestation are typically registered in vital statistic databases, but pregnancies ending prior to this point are not. Therefore, miscarriages, early terminations of pregnancy or women who are unable to conceive are excluded. Depending on the research question, the selection process of cohorts requires that subjects must: 1) conceive, 2) reach the point of identification (either via reported pregnancy test or ultrasound), and 3) reach viability (20 weeks). This may create biases (253,254). One example of such bias would be the effect of smoking on pre-eclampsia. Women who smoke have been shown in a variety of observational studies to have lower rates of pre-eclampsia, which is contrary to the adverse association that smoking is known to have on most maternal and fetal outcomes. There is some biological plausibility to this association based on mediation by carbon monoxide (255); however, a selection bias due to left-truncation may also be partially responsible. For example, if women who smoked were less likely to reach 20 weeks’ gestation (prior to a pre-eclampsia diagnosis) and the smokers who ‘remained’ pregnant till 20 weeks’ gestation were less likely to have pre-eclampsia than those who did not remain pregnant, then, from the causal perspective, the association between smoking and pre-eclampsia would be ‘biased’ (256,257). This scenario illustrates a form of collider-stratification bias, which has also been observed in other perinatal phenomena, for instance, as the so called ‘birthweight paradox’, and also in studies conditioning of livebirths (200,258). If an exposure under study is also related to the ability to conceive, then similar concerns relate to the exclusion of women who did not conceive from observational 44 studies of perinatal outcomes. Figure 4 illustrates collider stratification bias in the context of left truncation, where conditioning on ongoing pregnancies in the presence of an unmeasured variable U that is associated with both early pregnancy loss and the outcome opens a ‘back-door’ path between the exposure A and the outcome Y. 3.5.3 Fetuses-at-risk A particularly important risk measure in perinatal epidemiology and obstetric medicine is the gestational age-specific rates of adverse maternal and perinatal outcomes, including stillbirth. Two analytical approaches to calculating GA-specific rates can be used: the birth-based and the fetuses-at-risk (FAR)-based approach. In the former, the population at risk (denominator) includes all babies born at a particular gestational week (birth-based approach), while the latter includes all in-utero fetuses at a particular gestational week as the population at risk of the adverse outcome (fetuses-at-risk approach). The birth-based approach has been the traditionally used to assess gestational age-specific risks, which has often led to paradoxical findings, such as a phenomenon or paradox of intersecting mortality curves. This occurs as the mortality curves of pregnancies with and without some exposure intersect between 20- and 43-weeks’ gestation, suggesting that the exposure may be protective at some gestational ages and harmful at others. This is paradoxical in cases where the exposure is thought or assumed to be harmful. Examples of this intersection have been demonstrated for smoker’s vs non-smokers, twin vs singletons and White vs Black pregnancies, amongst others (259–261). The FAR approach, first proposed by Yudkin et al., has subsequently been shown to resolve paradoxes of intersecting mortality curves (262,263). 45 The two analytical approaches answer different questions and lead to potentially different conclusions. In an example of stillbirth as an outcome of interest, the birth-based approach (i.e., using the total number of livebirths at a given gestational week as the denominator) corresponds to the question: “given that a birth occurs at X weeks, what is the probability the baby is stillborn?”. On the other hand, the FAR approach (using all in-utero fetuses at risk at the given gestational week as a denominator) corresponds to the question: “what is the risk of fetal death and delivery at X weeks’ gestation?”. The first question is conditional on birth and is more useful for predictive purposes while the latter relates to the incidence of stillbirth and is more useful for questions related to etiology and preventive measure (e.g., determining optimal timing of delivery). Table 3 shows an example of calculations based on the two approaches. The main difference between a birth-based and FAR approaches is in the way gestational age is regarded; in a birth-based approach, gestational age is regarded as a determinant of the outcome (a covariate in a statistical model), while in the FAR approach gestational age is used as the time-axis (survival time in a statistical model). The latter approach lends itself to estimate the cumulative (over a given gestational week) incidence of stillbirth, or incidence density if daily delivery data is available (264,265). The use of gestational age as survival time can also be extended to other outcomes beyond stillbirth (265,266). Under this framework, multivariable modeling can be conducted using typical survival models such as Cox regression (265,267,268). In Chapter 6, I explored a class of flexible generalized additive models known as PAMMs (piecewise additive mixed models) for modeling FAR and compare the results to those obtained from Cox regression models in a cohort of pregnancies in the United States. 46 Another attractive property of the FAR approach and the use of gestational age as survival time (and conception as time zero) is the ability to extend the model to include deaths that occur after birth up to some set time point. For example, from this perspective an infant born at 270 days post conception and dying at 280 days post conception is equivalent to a stillbirth at 280 days. In a conventional analysis the former would not be counted as a ‘perinatal death’ as it occurs > 7 days postpartum. Considering these two deaths equivalent, a survival model based on the FAR provides an answer questions such as “if a pregnant woman is exposed to exposure, E, at a given gestational week, does this increase or decrease the risk of death for the fetus/ or infant (up to some fixed time point)?” (268). A survival model with censoring at the end of the fixed time point or for any type of death (stillbirth, neonatal or post-neonatal) can be used to answer this question. Further, birth can be included as a time-varying covariate in order to assess how much of the exposure’s effect is mediated through gestational age at delivery, although the exact implementation and interpretation of this model has been debated (268–270). 3.6 Tables Table 1: Example of hypothetical data from a study for the relationship between obesity and stillbirth with confounding by parity (nulliparous vs parous). All women Obese Stillbirth No stillbirth Total Yes 80 3920 4000 No 60 5940 6000 Amongst nulliparous women Stillbirth No stillbirth Total Yes 60 1440 1500 No 40 2460 2500 Stillbirth No stillbirth Amongst parous women Total Yes 26 2474 2500 47 No 14 3486 3500 Table 2: Example of multiplicative and additive interactions. Data represent the proportion of stillbirths. Study Obese Proportion of stillbirths in nulliparous women Proportion of stillbirths in parous women Measure of multiplicative interaction (ratio of relative risks) Multiplicative interaction Measure of additive interaction (difference of risk differences) Additive interaction 1 Yes 0.10 0.075 0.66 Y 0.00 N No 0.05 0.025 2 Yes 0.10 0.05 1.0 N 0.025 Y No 0.05 0.025 3 Yes 0.05 0.025 2.0 Y 0.025 Y No 0.025 0.025 Table 3: Hypothetical gestational age specific risk of stillbirth under a birth-based and fetuses-at-risk approach. Gestational age Number of fetuses delivered at gestational week or later (FAR) Number of livebirths Number of stillbirths Stillbirth rate (birth-based) Stillbirth rate (FAR) 30 100000 100 100 100/200 = 0.50 100/100000= 0.001 31 99800 120 80 80/200 = 0.40 80/99800= 0.0008 32 99600 300 100 100/400 = 0.25 100/99600 = 0.001 48 3.7 Figures Figure 1: Directed Acyclic Graphical examples of confounding scenarios. Figures are modified from ‘VanderWeele TJ. Principles of confounder selection’. A represents the exposure, Y the outcome, C a measured confounder, U an unmeasured confounder, and L a measured pre-exposure covariate. (A) ACY(B)AUCY(C)AULY1 49 Figure 2: Directed acyclic graphs illustrating mediation analysis without (A) and with (B) confounding. A represents the exposure, M the mediator, Y the outcome and C1, C2, and C3 measured confounders. (A) AMY (B)AC1 C2C3MY1 50 Figure 3: Selection and attrition processes during the course of human reproduction and gestation, taken from Snowden et al, The curse of the perinatal epidemiologist: inferring causation amidst selection, Current Epidemiological Reports, 2018. Figure 4: Directed acrylic graph illustrating left truncation bias (collider bias) by conditioning on ongoing pregnancies at 20 weeks’ gestation. AUSurvival to 20 weeks’gestationY1 51 Chapter 4: The association between pre-pregnancy body mass index and perinatal death and the role of gestational age at delivery 4.1 Introduction Obesity has increased dramatically in the past decades and become a growing public health concern (271). These increases have also been observed amongst women of reproductive age (272). In the USA, 26% of pregnant women were overweight and 25% were obese in 2014. High pre-pregnancy body mass index (BMI) is associated with many adverse maternal and perinatal outcomes (273–278). For example, overweight women have a 36% higher risk of stillbirth than those of normal weight, and the risk is two-fold higher among obese women (273,279). Similar associations have been observed for neonatal and infant death (274,276). Obesity has thus become one of the most important preventable risk factors for adverse pregnancy outcomes. While some obesity-related causes of fetal death are known, the exact pathophysiology behind the effects of obesity on perinatal death are not completely understood (280,281). High pre-pregnancy BMI is strongly associated with both spontaneous and clinician-initiated preterm birth, especially at extremely preterm gestation (278,282,283). Among overweight and obese women, spontaneous preterm delivery or fetal compromise leading to clinician-initiated preterm delivery may be partly responsible for the increased risks of perinatal death. Better understanding of possible pathways between higher BMI and perinatal death, such as an increased frequency of pregnancy complications at early gestation (leading to stillbirth or preterm birth and neonatal death), may help elucidate pathological mechanisms underlying the increased risk and provide 52 information for specific preventive recommendations for perinatal outcomes that clinicians can undertake. The direct effect of obesity (i.e., the effect not arising through earlier gestation at delivery) is difficult to discern. As gestational age-related vulnerability at the time of in-utero death/delivery is on the causal pathway between obesity and perinatal death, the traditional analyses which include gestational age as a covariate in a multivariable regression model can yield spurious results (284). Modern methods to assess mediation, (285–288) however, can help quantify the fraction of the association between high BMI and perinatal mortality that is mediated through gestational age at birth. Mediation analysis with specific assumptions can partition the total effect of high BMI into the natural direct effect on perinatal death, and the mediated (or natural indirect) effect through gestational age at birth (289). The primary aim of our study was to quantify the proportion of the association between pre-pregnancy BMI and perinatal death that is mediated through differences in gestational age at birth. 4.2 Methods 4.2.1 Data source and study population The study included women with a singleton live birth or stillbirth in British Columbia between April 1, 2004, and March 31, 2017. The cohort was obtained from the British Columbia Perinatal Database Registry (BCPDR) (290) which contains individual-level data on >99% of all live 53 births and stillbirths in British Columbia. The registry captures nearly 300 pre-pregnancy, obstetric and neonatal data elements abstracted by trained personnel from the hospital charts and from delivery records of midwives attending home births. The information included up to 25 diagnostic fields (coded using the International Classification of Diseases and Related Health Problems, Canadian version, ICD-10-CA) and 20 procedure fields (coded using Canadian Classification of Interventions) related to the hospitalization or home birth for the mother and the infant. Additionally, the BCPDR includes data on hospital readmissions for mothers and infants within 42 and 28 days, respectively. Prior validation studies have found high sensitivity and specificity of collected data regarding major pregnancy events and risk factors compared with medical charts (77). Births at <20 weeks’ gestation and late pregnancy terminations were excluded. Birth records with missing information on BMI or relevant confounders were excluded from the primary analyses (Figure S1). 4.2.2 Exposure, outcome and confounders Self-reported pre-pregnancy weight and height were used to calculate BMI, categorized as follows: underweight (BMI <18.5 m/kg2), normal (18.5m/kg2 £ BMI < 25m/kg2), overweight (25m/kg2 < BMI < 30m/kg2) and obese (BMI ³30 m/kg2). The primary outcome was perinatal death, defined as either a stillbirth or neonatal death before hospital discharge. Gestational age at delivery, in weeks, was determined from a standard algorithm incorporating early ultrasound (before 20 weeks), last menstrual period, newborn clinical exam, and chart-documented gestational age (290). Information on maternal and pregnancy characteristics included: maternal 54 age, parity, chronic hypertension, assisted conception, self-reported use of drugs, alcohol or cigarettes during pregnancy, asthma, pre-pregnancy diabetes, chronic maternal conditions, year of delivery, sex of fetus, congenital anomalies and prior stillbirth, low birth weight infant, or caesarean delivery. Suspected intrauterine growth restriction (IUGR) was identified by prenatal ultrasound. Chronic conditions included cardiovascular diseases, chronic renal and hepatic conditions, and systemic lupus erythematosus (Table 19). 4.2.3 Statistical analyses All demographic and obstetric history data were summarized by BMI category. Perinatal death and stillbirth rates were expressed per 10,000 total births, while neonatal death rates were expressed per 10,000 live births. Gestational age-specific perinatal death rates were expressed per 10,000 fetuses-at-risk (291). 4.2.4 Mediation analysis We conducted a mediation analysis under a potential outcomes framework (292) to determine the proportion of the association between BMI and perinatal death mediated by gestational age at stillbirth or live birth. This analysis aimed to quantify how much of the total association between BMI and the outcome was direct and how much was mediated through differences in the distribution of gestational age at birth. The natural direct effect represents the influence of pre-pregnancy BMI on perinatal death assuming no difference in gestational age at delivery between women with normal vs high pre-pregnancy BMI. Conversely, the natural indirect effect, i.e., the mediated effect, measures the impact that higher BMI has on earlier gestation at delivery, and thus perinatal death. Namely, the indirect effect contrasts the expected perinatal death rate 55 assuming that timing of all deliveries in women with high BMI was independent (i.e. their gestational age at delivery was what it would be if they had normal BMI) versus the perinatal death rate observed among women with high BMI (given their actual gestational age at delivery, formal definitions in Appendix B). Standard errors were based on the robust ‘sandwich’ estimator (293). Unadjusted and adjusted mediation analyses were performed, with the latter including chronic hypertension, smoking, substance/alcohol use, prior stillbirth, prior preterm birth, parity, maternal age, year of birth, chronic diseases, and asthma as covariates. These confounders were thought to be associated with pre-pregnancy BMI and perinatal death (i.e., the exposure and the outcome), or with gestational age at delivery and perinatal death (i.e., the mediator and the outcome; Figure 16) (294). We did not have information about timing of chronic hypertension onset and because it may (for some women) be on the causal pathway between obesity and perinatal death, we performed the same analyses without adjustment for chronic hypertension. We found no indication of interaction between BMI and gestational age at delivery, and therefore mediation models did not include this term (295). Causal interpretations of these mediation analyses require strong assumptions. Firstly, that there are no unmeasured confounders of the relationships between pre-pregnancy BMI and gestational age, pre-pregnancy BMI and perinatal death, and gestational age and perinatal death. Secondly, that none of the confounders of the mediator/outcome relationship are caused by the exposure (292,296,297). In this case, this assumes that there are no additional mediators between increased BMI and perinatal death that also effect gestational age at delivery. 56 4.2.5 Sensitivity analyses To assess the robustness of our results, we conducted sensitivity analyses to determine the amount of unmeasured confounding (E-value) in the association between the gestational age at delivery and perinatal death required to explain away the estimated natural direct and indirect effects (298,299). To assess the impact of women missing BMI on our results we conducted multiple imputation by chained equations (10 datasets). In each imputed data set the natural direct and indirect effects and proportion mediated were estimated, then pooled using Rubin’s rules (300). Additionally, pre-pregnancy diabetes and congenital anomalies were considered as possible exposure induced mediator-outcome confounders in sensitivity analyses. In this last analyses, natural effects as defined above were not identifiable, but analogous ‘interventional’ effects were (see Appendix B for details) (301–303). Finally, we repeated our primary analysis for each of stillbirth, antepartum stillbirth and neonatal death separately to determine if the relationship observed for the primary outcome differed. Due to small numbers, we were unable to conduct this analysis for intrapartum stillbirth. All analyses were carried out using R version 3.5.3 (304), with the medflex (305) package used for mediation analyses. Written ethics approval was obtained from the University of British Columbia - Children’s and Women’s Hospital and Health Centre of British Columbia Research Ethics Board (H18-03154). 57 All data were fully anonymized prior to data access or analyses. Data were from administrative registry and individual participant consent was not required. 4.3 Results 4.3.1 Study population A total of 547,401 singleton live births and stillbirths occurred in British Columbia between April 1, 2004, and March 31, 2017 (excluding late terminations). After exclusion of women with births prior to 20 weeks’ gestation and those with unknown gestation, the study population included 546,675 (99.9%) women. Pre-pregnancy BMI was missing in 153,855 women (28.1%; Figure S1), and the main analyses included 392,820 women. Of these, 50,352 (12.8%) were classified as obese, 81,065 (20.6%) overweight, 237,726 (60.6%) normal weight and 23,677 (6.0%) underweight. Obese women were more likely to be multiparous, smoke during pregnancy, have chronic and gestational hypertension or diabetes, and a prior caesarean delivery as compared with women with normal BMI (Table 4). Gestational age differences were also evident: the preterm birth rate was 7.3% among women with a normal BMI, and 8.1% and 9.7%, among overweight and obese women, respectively. 4.3.2 Main outcomes Women who were underweight, overweight and obese had higher rates of stillbirth, neonatal death and perinatal death (Table 5). In all BMI categories, the majority of perinatal deaths were antepartum stillbirths. The rates of antepartum stillbirth were similar between women who were underweight prior to pregnancy and those with normal BMI. Gestational age-specific perinatal 58 death rates (per 10,000 fetuses-at-risk) increased with gestational age in women in all BMI categories and were generally higher in women with non-normal BMI (Figure 5). There was no strong evidence of an association between low BMI (underweight) and perinatal mortality (OR = 1.16, 95% CI: 0.96, 1.40). However, there was a positive unadjusted association between overweight and obese and perinatal death (OR=1.24, 95% CI 1.10, 1.39 and OR=1.61, 95% CI: 1.41, 1.83, respectively) and this association was only slightly attenuated after adjustment for confounders (Table 6). 4.3.3 Mediation analyses Mediation analyses showed that the indirect effects of BMI on perinatal death through gestational age at delivery were larger than the direct effects (Table 6). Half of the association between overweight BMI and perinatal death was mediated through gestational age at delivery (50.4%). The proportion of the association between obesity and perinatal death mediated by gestational age at delivery was 63.1% (natural direct effect AOR = 1.18, 95% CI: 1.05, 1.32; vs. natural indirect effect = 1.32, 95% CI: 1.23, 1.42). Estimates of the total, direct and indirect effects were similar between unadjusted and adjusted results. Further analyses adjusting for pre-pregnancy diabetes and congenital anomalies and those excluding chronic hypertension from adjustment set of covariates did not impact the estimates. 4.3.4 Sensitivity analyses Sensitivity analyses indicated that to explain away the statistical significance of the indirect effect of obesity on perinatal death by unmeasured confounding, such a confounder(s) 59 (associated with gestational age at delivery and perinatal death) would have to increase the rate of perinatal death and early gestational age at delivery by at least 75%. Similarly, such an unmeasured confounder would have to increase theses rates by 28% to explain away the direct effect. For overweight women unmeasured confounding would have to increase the rate of perinatal death and early gestational age at delivery by 25% to explain away the indirect effect (Table 20). There were no substantial differences in demographic characteristics between women with complete vs missing information about pre-pregnancy BMI, with the exception of year of delivery (higher proportion of missing BMI in earlier years), nulliparity, and previous caesarean delivery (Table 21). Those with missing BMI had higher rates of perinatal death (99.5 per 10,000 vs 46.6 per 10,000 total births among those without missing information). Further, a higher proportion of the perinatal deaths among women with missing BMI were stillbirths (71% vs 64%). The results using multiple imputation for missing BMI were similar to the complete-case analyses, albeit with a lower proportion of the total effect mediated (Table 22). Compared to the primary analyses for perinatal death, analyses restricted to stillbirth led to slightly higher odds ratios overall, while analyses for neonatal death were attenuated. Direct effects were higher, and mediation were lower for stillbirth than for neonatal death, where the total effect was entirely indirect (Table 23). Despite this, confidence intervals for all estimates were consistent with the primary analyses (Table 23). 60 4.4 Discussion Our population-based study shows that high pre-pregnancy BMI is positively associated with perinatal death: overweight women had a 22% (95% CI 8-37%) higher adjusted perinatal death rate compared with women with a normal BMI, while obese women had a 55% (95% CI 36-77%) higher adjusted perinatal death rate. Overweight and obese women delivered at a lower mean gestational age, and mediation analyses showed that 63% of the association between pre-pregnancy obesity and perinatal death was mediated by such gestational age differences. Elevated rates of perinatal death among overweight and obese women have been described previously. Our findings of 55% increased risk of perinatal death in obese women is similar to previous reports (278,306). However, some studies have found more than 2-fold elevated risk associated with pre-pregnancy obesity, (274,307,308) which could reflect differences in study populations. Also noteworthy, differences in the proportion of obese women in our population (12.8%) was lower than in other high-income countries, (274,306) and in the Canadian population as a whole (309). Our study showed that the stillbirth rate and the proportion of stillbirths among perinatal deaths increased with increasing pre-pregnancy BMI. This could reflect clinical challenges in antenatal fetal monitoring in women with high pre-pregnancy BMI (310). The mediating effect of gestational age at delivery likely applies to both components of perinatal death, viz., stillbirth and neonatal death. Overweight and obese women have increased rates of medically-indicated delivery at preterm gestation, and increased rates of spontaneous preterm delivery, especially at extremely preterm gestation (<28 weeks) compared with women with 61 normal BMI (283). Since preterm infants have immature organ systems, such births experience higher rates of neonatal death; the association between pre-pregnancy BMI and neonatal death is therefore likely to be, in large part, mediated through such early delivery. The mediating effect of gestational age on stillbirth is less clear though it is known that some causes of fetal death, such as infection, are significantly more frequent at early gestation (311). It is noteworthy that the indirect effect of pre-pregnancy obesity is substantial, and its magnitude (63.1%) is larger than the indirect effect of pre-pregnancy overweight (50.4%); stillbirths constitute a large proportion of perinatal deaths, and the stillbirth proportion is slightly larger among obese women compared with overweight women. This suggests that pregnancy complications at early gestation, which increase the risk of antepartum stillbirth, play an important role in mediating the association between high pre-pregnancy BMI and perinatal death. Most previous studies have only reported the total effects of high BMI on perinatal death, which obscures the mechanisms underlying the effects of pre-pregnancy BMI. One study (308) that estimated the direct effect of obesity by adjusting for gestational age in multivariable analysis, showed a stronger adjusted association between obesity and perinatal death and implied negative confounding by preterm delivery. Such paradoxical results can occur when variables in the causal pathway between exposure and outcome are included in causal models (284) in the presence of confounders between these intermediate variables and the outcome. We treated gestational age as a mediator in analyses, which partitions these possible effects explicitly; however, only under the strong causal assumptions outlined above can this completely avoid possible collider stratification bias, 62 Our estimated mediated effect provides insight into the possible mechanism by which adverse perinatal outcomes are disproportionately observed in overweight and obese women. Maternal complications of pregnancy, e.g., gestational hypertension and preeclampsia, are more frequent in overweight and obese women (311). Higher rates of pregnancy complications at preterm gestation result in higher rates of preterm live birth and stillbirth thereby contributing to the indirect effect of obesity. Conversely, a fraction of stillbirths that occur among normal, overweight, and obese women have similar gestational age distributions, although they occur at higher rates among overweight and obese women. These stillbirths are responsible for the direct effect of obesity on perinatal death. Causes of death that underlie such direct effects likely include placental disease (e.g. uteroplacental insufficiency, placenta previa), fetal structural and genetic anomalies, and umbilical cord abnormalities (280). Other conditions such as severe preeclampsia, contribute to both direct and indirect effects of obesity by leading to higher rates of stillbirth at any gestational age and to higher rates of clinician-initiated preterm delivery (sometimes resulting in neonatal death). Some maternal and fetal complications of pregnancy can be represented through complex causal pathways between high pre-pregnancy BMI and perinatal death and pose a challenge to evaluating the assumptions required for mediation analysis. Previous studies have indicated that the increased rate of preterm birth in obese women is in large part due to an increase in iatrogenic deliveries (282,283). The indirect effect of BMI on perinatal death is thus related to clinical management and optimal of timing of delivery in these women (312). While a physician-indicated preterm delivery may prevent fetal death, it may 63 increase the risk of neonatal death. This issue poses major challenges not only for health care practitioners but also for research methodologists (313). Physician-indicated delivery is a competing risk (or a competing outcome) for stillbirth. For this reason, we used a composite of stillbirth and neonatal (perinatal death). However, further methodological advances may be needed to accurately disentangle the pathways through which these competing risks are realized and therefore inform interventions. Such approaches are likely to require detailed information on clinical decision making, such as the choice to deliver a high risk pregnancy, and the accurate capture of the timing of relevant events. The causal interpretation of these results requires an assumption of no unmeasured confounding throughout, and the absence of any confounder of gestational age at delivery and perinatal death that are effected by BMI (295). The comprehensiveness of our data set makes it unlikely that clinical confounders important to our studied relationships were excluded. However, lack of socioeconomic data may represent a source of unmeasured confounding. To assess this, we performed sensitivity analyses to assess the impact of such unmeasured confounders and found that our results were reasonably robust. On the other hand, it is likely that there are confounders between gestational age at delivery and perinatal death are affected by pre-pregnancy BMI. In this case, if these exposure induced mediator-outcome confounders are adjusted for, one can still identify ‘interventional’ direct and indirect effects, which correspond to changing the distribution of the mediator in the exposed/unexposed rather than changing individual’s mediator values (see Appendix B for a detailed discussion) (301–303). We included two such possible confounders in a sensitivity analysis (pre-pregnancy diabetes and congenital anomalies) and found little difference in estimates. Despite this, additional BMI induced confounders of gestational age and 64 perinatal death may exist and could impact our findings. The validity of these types of assumptions is uncertain in most observational studies, and particularly those with complex causal pathways such as ours. Furthermore, there has been considerable debate about the relevance of ‘effects’ of exposures such as pre-pregnancy BMI as various conditions, such as sedentary lifestyle, high calorie food intake and medical or genetic conditions can lead to high BMI and all may have different effects on outcomes (in such situations, more general effects corresponding to reducing BMI by a ‘random intervention’ can be identified, see Appendix B for details) (314–318). Despite this, BMI remains an important clinical indicator of risk in pregnancy, and exploration of the reasons for this increased risk remain of value, even without directly linked interventions. In this study, we examined the relationship of obesity and perinatal death with respect to the ‘average’ population prevalence of various causes of high BMI (316). This limits the interpretation of our findings to a more explanatory nature in that the results cannot be linked to the possible effect of a specific obesity related intervention. Strengths of our study include the large population-based nature of our data and comprehensive information on possible confounders. Our study also has some limitations. First, the direct and indirect effects estimated require strong assumptions to be considered causal as discussed above. In particular, it is possible that there are common risk factors of both early delivery and perinatal death that are also affected by obesity. It would be of interest in future work to include such variables in models accounting for multiple mediators to assess their joint mediated effect (319). Second, as a mediator, we used gestational age at stillbirth (i.e., gestational age at delivery of a 65 dead fetus) which occurs after the actual in-utero fetal death. This is important because from the temporal point of view, gestational age at stillbirth cannot be a mediating factor between pre-pregnancy BMI and in-utero fetal death. Instead, gestational age at stillbirth approximates the time of fetal death and the intra-uterine environment leading to fetal demise. While gestational age at fetal death may have been preferable in this context, the difference between gestational age at fetal death and at stillbirth is usually a matter of hour and days (rarely a week or more), especially among singleton pregnancies. Hence, this limitation is unlikely to have significantly impacted our results. Thirdly, the information about pre-pregnancy BMI (weight and height) was self-reported, possibly leading to some misclassification, and missing in 28% of women, possibly leading to a selection bias. As for the latter, sensitivity analyses using multiple imputation for missing BMI yielded results similar with primary analyses. Further, we did not adjust for correlation between subsequent pregnancies of the same woman during the study period, as the information was not available. 4.5 Conclusion Pre-pregnancy obesity carries increased risk of perinatal mortality. Our mediation analysis results suggest that earlier gestational age at delivery may be implicated in many of these obesity-related perinatal deaths. Timely obstetric intervention coupled with access to neonatal intensive care earlier in gestation may further mitigate the risk of neonatal death among infants in these women. To better inform the pregnancy management in obese women, further studies should continue to disentangle the causal pathways under which obesity increases the risk of perinatal death, including, for example gestational diabetes and other obesity-related pregnancy complications. 66 4.6 Tables Table 4: Demographic characteristics, pre-pregnancy, and pregnancy morbidity of women by pre-pregnancy BMI category. Demographics Underweight N = 23,677 Number (%) Normal BMI N = 237,726 Number (%) Overweight N = 81,065 Number (%) Obese N = 50,352 Number (%) Maternal age (years) <20 1013 (4.3) 5595 (2.4) 1549 (1.9) 742 (1.5) 20-34 18799 (79.4) 175690 (73.9) 59852 (73.8) 38277 (76.0) 35-39 3226 (13.6) 46396 (19.5) 15912 (19.6) 9369 (18.6) ≥40 639 (2.7) 10045 (4.2) 3752 (4.6) 1964 (3.9) Nulliparous 13509 (57.1) 121285 (51.0) 36087 (44.5) 20564 (40.8) Year of delivery 2004-2006 4944 (20.9) 47161 (19.8) 15110 (18.6) 8682 (17.2) 2007-2009 5188 (21.9) 51187 (21.5) 17455 (21.5) 10485 (20.8) 2010-2012 5364 (22.7) 55878 (23.5) 19325 (23.8) 12066 (24.0) 2013-2014 3827 (16.2) 39065 (16.4) 13353 (16.5) 8654 (17.2) 2015-2017 4354 (18.4) 44435 (18.7) 15822 (19.5) 10465 (20.8) Pre-pregnancy conditions Prior stillbirth 96 (0.4) 1396 (0.6) 675 (0.8) 539 (1.1) Prior low birth weight infant 477 (2.0) 3710 (1.6) 1460 (1.8) 975 (1.9) Prior caesarean section 2041 (8.6) 28576 (12.0) 13626 (16.8) 10604 (21.1) Chronic disease 50 (0.2) 556 (0.2) 158 (0.2) 102 (0.2) Asthma 126 (0.5) 1529 (0.6) 639 (0.8) 478 (0.9) IVF** 284/15139 (1.9) 3802/156081 (2.4) 1422/54676 (2.6) 834/34967 (2.4) Pre-pregnancy hypertension 35 (0.1) 748 (0.3) 706 (0.9) 1220 (2.4) Pre-pregnancy diabetes mellitus 480 (2.0) 4915 (2.1) 2100 (2.6) 1748 (3.5) Pregnancy complications Gestational diabetes 1782 (7.5) 22117 (9.3) 12696 (15.7) 12375 (24.6) Gestational hypertension 1323 (5.6) 15162 (6.4) 8483 (10.5) 8202 (16.3) Placental previa 158 (0.7) 1666 (0.7) 564 (0.7) 276 (0.5) Placental disorders (excl. previa) 168 (0.7) 1758 (0.7) 566 (0.7) 324 (0.6) Preeclampsia 64 (0.3) 978 (0.4) 616 (0.8) 763 (1.5) Smoking during pregnancy* 2224 (9.4) 16044 (6.7) 7102 (8.8) 5965 (11.8) Alcohol use during pregnancy* 196 (0.8) 2093 (0.9) 828 (1.0) 635 (1.3) Drug use during pregnancy* 957 (4.0) 6503 (2.7) 2556 (3.2) 1925 (3.8) 67 Suspected IUGR*** 998 (4.2) 4785 (2.0) 1166 (1.4) 740 (1.5) Fetal/infant characteristics Gestational age at delivery (weeks) 20-27 93 (0.4) 757 (0.3) 331 (0.4) 279 (0.6) 28-33 247 (1.0) 1833 (0.8) 758 (0.9) 574 (1.1) 34-36 1692 (7.1) 14797 (6.2) 5512 (6.8) 4004 (8.0) 37-38 7803 (33.0) 72000 (30.3) 25166 (31.0) 17037 (33.8) 39-41 13583 (57.4) 145533 (61.2) 48302 (59.6) 27831 (55.3) ³42 259 (1.1) 2806 (1.2) 996 (1.2) 627 (1.2) Male fetus 11970 (50.6) 122220 (51.4) 41489 (51.2) 25849 (51.3) Congenital anomaly (any)**** 51 (0.2) 618 (0.3) 216 (0.3) 150 (0.3) *Self-reported **In-vitro-fertilization, information available only in pregnancies delivered in 2008/2009 onwards *** Intrauterine growth restriction suspected on antenatal ultrasound (i.e., prior to delivery; antenatal ultrasound does not detect all growth-restricted fetuses that are born small-for-gestational age) **** Only among live births, diagnosed at birth Table 5: Perinatal mortality by pre-pregnancy BMI category. Underweight N = 23,677 Normal BMI N = 237,726 Overweight N = 81,065 Obese N = 50,352 Stillbirth (n, per 10,000 total births) 64 (27.0) 632 (26.5) 270 (33.3) 225 (44.7) Stillbirth timing Intrapartum (n, %) 6 (9.4%) 48 (7.6%) 26 (9.6%) 19 (8.4%) Antepartum (n, %) 51 (79.7%) 520 (82.3%) 224 (83.0%) 182 (80.9%) Unknown (n, %) 7 (10.9%) 64 (10.1%) 20 (7.4%) 24 (10.7%) Neonatal death (n, per 10,000 live births) 49 (20.8) 346 (14.6%) 142 (17.6) 106 (21.2) Perinatal death (n, per 10,000 total births) 113 (47.7) 978 (41.1) 412 (50.8) 331 (65.7) Stillbirth as a proportion of perinatal death 56.6% 64.6% 65.6% 68.0% Table 6: Mediation analyses of BMI on perinatal death through gestational age at delivery. BMI Category Underweight Normal BMI Overweight Obese Unadjusted Total effect (OR) 1.16 (0.96, 1.40) Ref 1.24 (1.10, 1.39) 1.61 (1.41, 1.83) 68 Natural direct 1.03 (0.87, 1.21) Ref 1.09 (0.99, 1.20) 1.14 (1.02, 1.28) Natural indirect 1.13 (1.03, 1.24) Ref 1.13 (1.07, 1.20) 1.41 (1.31, 1.51) Proportion mediated (indirect effect) - - 59.7% 72.0% Adjusted* Total effect (AOR-2) 1.17 (0.96, 1.41) Ref 1.22 (1.08, 1.37) 1.55 (1.36, 1.77) Natural direct 1.01 (0.86, 1.19) Ref 1.10 (1.00, 1.22) 1.18 (1.05, 1.32) Natural indirect 1.15 (1.05, 1.26) Ref 1.11 (1.04, 1.17) 1.32 (1.23, 1.42) Proportion mediated (indirect effect) - - 50.4% 63.1% Adjusted** Total effect (AOR-1) 1.17 (0.96,1.41) Ref 1.22 (1.09,1.37) 1.57 (1.38,1.80) Natural direct*** 1.01 (0.86,1.20) Ref 1.10 (1.00,1.21) 1.16 (1.03,1.30) Natural indirect*** 1.15 (1.04,1.26) Ref 1.11 (1.05,1.18) 1.36 (1.26,1.46) Proportion mediated (indirect effect) - - 53.2% 67.4% Adjusted*** Total effect (AOR-3) 1.17 (0.96,1.41) Ref 1.22 (1.09,1.37) 1.55 (1.36,1.77) Natural direct**** 1.01 (0.86,1.19) Ref 1.11 (1.00,1.22) 1.18 (1.05,1.33) Natural indirect**** 1.15 (1.05,1.27) Ref 1.10 (1.04,1.17) 1.31 (1.22,1.41) Proportion mediated (indirect effect) - - 49.0% 61.6% * adjusted for chronic hypertension, smoking, substance/alcohol use, prior stillbirth, prior preterm birth, parity, maternal age, year of birth, chronic diseases, asthma ** adjusted for all above except chronic hypertension *** adjusted for all above and pre-pregnancy diabetes and congenital anomalies **** interpreted as ‘interventional effects, see Appendix A.2. 69 4.7 Figures Figure 5: Gestational age specific perinatal death rates per 10,000 fetuses at risk by BMI class. Data are smoothed with three week moving averages. 70 Chapter 5: Pre-pregnancy body mass index and adverse perinatal outcomes in the presence of other maternal risk factors 5.1 Introduction Obesity is an important risk factor for adverse birth outcomes and its prevalence among pregnant women has been increasing in recent decades (4). In the United States, about half of all pregnant women were either overweight or obese in 2019 (1,320). Overweight and obese women have higher rates of caesarean delivery and pregnancy complications including gestational diabetes, gestational hypertension, pre-eclampsia and severe maternal morbidity (6,34–37). The rates of stillbirth also increase with increasing pre-pregnancy body mass index (BMI), especially at late and post-term gestation (9,137). High BMI is often accompanied by chronic conditions including chronic hypertension, pre-pregnancy diabetes mellitus, and asthma (321). These comorbidities also constitute risk factors for adverse perinatal outcomes, together with other maternal characteristics, including nulliparity and advanced maternal age (322,323). While most studies have considered these factors as confounders or excluded them from data analyses, their possible role as modifiers of the relationship between BMI and adverse perinatal outcomes has been understudied (5,6,9,324). The goal of this population-based study was to assess whether the association between pre-pregnancy BMI and adverse perinatal outcomes is modified by maternal age, parity, chronic hypertension, and pre-pregnancy diabetes mellitus. 71 5.2 Methods 5.2.1 Study population The study included all women with a singleton live birth or stillbirth at ≥20 weeks’ gestation in the United States in 2016-2017 with data from live birth certificates, fetal death certificates, and linked infant death certificates, all obtained from the National Center for Health Statistics (NCHS). These datasets included information about maternal demographic and clinical characteristics, pregnancy complications, and birth outcomes. We excluded births with missing information on pre-pregnancy BMI or gestational age, and live births with missing information on indicators of severe neonatal morbidity (see below). 5.2.2 BMI, concomitant risk factors and covariates Self-reported information on pre-pregnancy weight and height was used to calculate pre-pregnancy BMI and categorized as: underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2£ BMI < 25 kg/m2), overweight (25 kg/m2£ BMI < 30 kg/m2), obesity class I (30 kg/m2£ BMI < 35 kg/m2), class II (35 kg/m2£ BMI < 40 kg/m2) and class III (BMI ≥ 40 kg/m2) (325). Four risk factors were examined as potential effect modifiers of the maternal pre-pregnancy BMI-and adverse perinatal outcome relationship: maternal age (continuous), parity (nulliparous vs. parous), chronic hypertension (yes/no), and pre-pregnancy diabetes mellitus (yes/no). Parous was defined as having had a prior live or stillbirth at ≥ 20 weeks’ gestation. Our hypothesis was that the relationship between pre-pregnancy BMI and outcomes may differ within strata of these four variables. 72 Additional pre-pregnancy characteristics included as potential confounders of the BMI-adverse perinatal outcome relationship and adjusted for in multivariable analyses were maternal race/ethnicity, level of maternal education (less than high school vs. high school or higher education), type of health care insurance, smoking during pregnancy, prior caesarian section, and prior neonatal death or stillbirth. The categories of self-reported race/ethnicity were based on prior literature and the terminology used in the U.S. National Vital Statistics Reports (326–328). 5.2.3 Outcomes The primary outcome was a composite of stillbirth, neonatal death (death of a live-born infant at ≤ 28 days), and severe neonatal morbidity. The latter was defined as need for assisted ventilation for at least 6 hours, neonatal seizures, and/or Apgar score <4 at 5 minutes. Secondary outcomes included each component of the composite outcome. 5.2.4 Statistical analyses Descriptive statistics were used to summarize women across pre-pregnancy BMI categories. Stillbirth, neonatal death and severe neonatal morbidity rates were calculated per 1000 total births (stillbirth) or per 1000 live births (death or morbidity). The association between pre-pregnancy BMI and the composite perinatal outcome was assessed using logistic regression models. Models were fit with and without adjustment for the aforementioned confounding variables and results expressed as odds ratios (OR) and 95% confidence intervals (CI). To assess effect modification by maternal age, nulliparity, chronic 73 hypertension and pre-pregnancy diabetes mellitus, interaction terms between BMI group and each of these variables were included in the models. Results, including interactions, are presented according to the relevant epidemiological guidelines; (240) analyses of effect modification by parity, hypertension and pre-pregnancy diabetes included estimates of the association between BMI and the composite perinatal outcome within strata of each modifier, and as the joint effects with BMI. Besides the multiplicative interactions between the BMI category and additional risk factors above, we further assessed interactions on the additive scale, which indicates the absolute change in outcome rates depending on the subgroup (240). Multiplicative interactions were expressed as the ratios of adjusted odds ratios (aORs), and additive interactions by the Relative Excess Risk due to Interaction (RERI) (234). On the multiplicative scale, a ratio of odds ratios > 1 and < 1 indicates positive and negative interaction, respectively. Similarly, on the additive scale, RERI values > 0 and < 0 indicate positive and negative additive interaction, respectively. We computed p-values for effect modifications via the likelihood ratio test (LRT) comparing models with and without the interaction terms. We further analyzed maternal BMI as a continuous variable and estimated ‘dose-response’ curves for the composite perinatal outcome, stratified by the four effect modifiers. All dose-response curves were constructed for average values of the remaining covariates. Curves were fit using restricted cubic splines with 5 knots, placed throughout the BMI distribution. 74 The same analyses were performed for each component of the composite perinatal outcome. There were few missing values for the variables included in the analysis (< 5% of records), and therefore a complete case analysis was performed. All analyses were conducted using R statistical software version 4.0.3 (329). 5.3 Results 5.3.1 Study population Overall, 7,576,417 singleton births were included in the study population, after the exclusion of 1605 (< 0.1%) record due to missing BMI or gestational age, and 30,737 (0.4%) records due to incomplete outcome information. The final cohort included 7,536,579 women, with 254,225 (3.5%) underweight 3,220,432 (43.9%), normal weight, 1,918,480 (26.1%) overweight, 1,062,177 (14.4%) obesity class I; 516,693 (7.0%) obesity class II; and 365,357 (5.0%) obesity class III. Obese women, compared with those of normal weight, were less likely to be nulliparous, have higher than a bachelor’s degree or private health care insurance and more likely to be non-Hispanic Black or Hispanic, have chronic hypertension, pre-pregnancy diabetes, or prior pregnancy complications (Table 7). In contrast, underweight women, compared with those of normal weight, were younger, more likely to be nulliparous, be of Asian/Pacific Islander ethnicity, have lower education levels and smoke, and less likely to have private health care insurance (Table 7). 75 5.3.2 Outcome rates The stillbirth rate was 4.9 per 1000 total births and the neonatal death rate was 2.8 per 1000 live births (Table 24). Severe neonatal morbidity was more common than mortality and constituted 89.0% of the composite perinatal outcome, with a large proportion of infants needing ventilation ≥6 hours (82.2% of morbidity). Rates of the composite perinatal outcome were 20.9 per 1,000 total births in underweight women, 18.5 in normal BMI women, 21.3 in overweight women and 25.6, 30.2 and 36.7 per 1,000 total births in women with obesity class I, II and III, respectively (Table 8). Older (vs. younger) women, nulliparous (vs. parous) women, and those with chronic hypertension or pre-pregnancy diabetes (vs. those without) had higher rates of the composite outcome. These increases were most substantial in women with chronic hypertension or pre-pregnancy diabetes (Table 8). 5.3.3 Adjusted results There was a positive association between higher-than-normal pre-pregnancy BMI and odds of composite perinatal outcome (Table 9). The odds of the composite perinatal outcome were highest in women in the obesity class III (aOR = 1.52, 95% CI 1.49 to 1.55). Underweight women had slightly higher adjusted odds of the composite perinatal outcome compared with women of normal BMI (aOR = 1.07, 95% CI 1.04 to 1.10). Maternal age, parity, chronic hypertension, and pre-pregnancy diabetes mellitus modified the BMI and composite perinatal outcome association on both the multiplicative and additive scales (Table 9). Nulliparous women had a sharper increase in composite outcome with increasing BMI compared to parous women (e.g., aOR=1.77, 95%, CI 1.73-1.83 vs. aOR=1.35, 95% CI 1.32- 76 1.39, respectively, in women with obesity class III; Table 9). The rate of the composite perinatal outcome did not increase with increasing BMI among women with chronic hypertension and those with pre-pregnancy diabetes, while this association was clearly observed in women without these chronic conditions (Table 9). For example, among women with chronic hypertension, the aORs were 1.01 (95% CI 0.93, 1.10) for women with obesity class I, aOR 0.99 (95% CI 0.91, 1.08) for obesity II, and 0.98 (95% CI 0.91, 1.06) for obesity class III, compared with women with normal BMI, while the corresponding aORs among women without chronic hypertension were 1.26 (95% CI 1.24-1.28), 1.41 (95% CI 1.39-1.44), and 1.58 (95% CI 1.55-1.62), respectively. Similar associations were observed among women with pre-pregnancy diabetes (Table 9). Despite this, joint associations between obesity and chronic hypertension or pre-pregnancy diabetes remained high (Table 25). For example, the aOR for obesity class III with pre-pregnancy diabetes compared with normal BMI with no diabetes was 3.45 (95% CI 3.23, 3.70). Analyses of maternal age and BMI showed that rates of the composite perinatal outcome started to increase at lower values of BMI as maternal age increased (Figure 6). In underweight women, there was less evidence of effect modification other than for parity, where odds of the composite perinatal outcome were slightly lower in nulliparous women and 21% higher in parous women (Table 9). Dose-response curves for the association between continuous BMI and the composite perinatal outcome also differing relationships by parity, and the presence of chronic hypertension and pre-pregnancy diabetes mellitus (Figure 7). 77 5.3.4 Secondary outcomes The results were similar the components of the composite perinatal outcome, but with wider confidence intervals due to smaller numbers (Table 24 and 26). Overall, women with class III obesity had 1.70-fold higher odds of stillbirth, 1.55-fold higher odds of neonatal death, and 1.50-fold higher odds of severe neonatal morbidity compared with women with a normal BMI (Table 26). Parity modified maternal BMI association with stillbirth and severe neonatal morbidity to a similar degree as with the primary composite perinatal outcome and more strongly with regard to maternal BMI-neonatal death association. Among women with chronic hypertension and those with pre-pregnancy diabetes, the association between BMI and severe neonatal morbidity was similar to the BMI-composite perinatal outcome association (i.e., no increase in odds with increasing BMI), although stillbirth and neonatal death rates increased with increasing BMI but were much smaller than increases observed among women without these conditions. Underweight women had higher odds of neonatal death and severe neonatal morbidity, and this was more evident among parous women (Table 26). 5.4 Discussion 5.4.1 Principal findings In this population-based cohort study of singleton births in 2016-2017 in the US we found that the effect of pre-pregnancy BMI on adverse perinatal outcomes was modified by maternal age, parity, chronic hypertension and pre-pregnancy diabetes mellitus. Overall, increasing maternal BMI was associated with increasing rates of adverse perinatal outcomes. While this association was evident among younger and older women, nulliparous and parous women and among 78 women without chronic hypertension and pre-pregnancy diabetes, there was no relationship between BMI and adverse perinatal outcomes in women with chronic hypertension or pre-pregnancy diabetes. The joint effects of older maternal age and higher BMI on adverse perinatal outcomes were larger than the expected independent effects of these two risk factors. Similarly, the effects of nulliparity and higher BMI interacted so that the joint effects were larger than the combined independent effects of each. Underweight women had an elevated risk of adverse perinatal outcomes, specifically neonatal mortality and severe neonatal morbidity and this association was stronger among parous women. 5.4.2 Research in the context of what is known Previous studies have shown that elevated pre-pregnancy BMI is associated with adverse perinatal outcomes including stillbirth, neonatal death and severe neonatal morbidity (7,8,138,330). A recent meta-analysis of the effects of obesity on perinatal outcomes also showed increased risk of low birth weight, large-for-gestational age infant, macrosomia, and admission to neonatal intensive care unit (8). While our findings of increased stillbirth and neonatal mortality in overweight and obese women are comparable to previous studies, (5,9,137) we have also shown that these associations are stronger in nulliparous women and absent among women with chronic hypertension and those with pre-pregnancy diabetes. The reasons for increased perinatal risks in women with higher BMI are not completely understood. Studies on stillbirth have suggested that chronic inflammation, sleep apnea, or metabolic and vascular abnormalities in obese women are possible explanations (137,331,332). A meta-analysis of North American, European and Australian studies suggests that nearly 25% 79 of pregnancy complications including gestational hypertension, preeclampsia, gestational diabetes, preterm birth, large-for-gestational-age infant can be attributed to overweight/obese mothers (40,333), and that these complication often lead to adverse birth outcomes. The fetuses of obese women may be at increased risk of stillbirth due to macrosomia (8,334) and/or placental dysfunction (137). Nevertheless, macrosomia is a recognized risk factor for complicated labor and delivery, meconium aspiration and neonatal morbidity among infants of obese women born at term gestation (179,335). Most prior studies of pre-pregnancy BMI adjusted for maternal age, nulliparity, chronic hypertension and pre-pregnancy diabetes as confounders, but did not assess these factors as possible effect modifiers. When effect modification is present, such adjustment can lead to under- or over-estimated associations (236,240). Our study shows modification of the effects of BMI on adverse perinatal outcomes by several factors and confirms that ignoring this phenomenon will lead to an underestimation of the risk of adverse perinatal outcome, especially among older, nulliparous women who are obese, and overestimates of risk among obese women with chronic hypertension and pre-pregnancy diabetes. Only a few studies have examined possible interactions between other risk factors and pre-pregnancy BMI. A multi-hospital Swiss study of 324,664 live births found that obesity was associated with macrosomia, prolonged labour and birth injury, while an increased risk of preterm birth, neonatal hypoglycemia, and neonatal death was associated predominantly with comorbid conditions including pre-pregnancy diabetes and hypertension. However, this study focused on population attributable fractions and had only a 7% prevalence of obesity (63). 80 Congruent with our results, a study from Finland found varying associations between pre-pregnancy BMI and LGA and preterm birth depending on the presence or absence of insulin-controlled diabetes, type 2 diabetes and GDM (336). The largest increase in risk of LGA and preterm birth with increasing BMI was observed in women with insulin-controlled diabetes, however, the risk of preterm birth differed depending on comorbidity. With increasing BMI, preterm birth declined among women with insulin-controlled diabetes, was stable in women with type 2 diabetes without insulin and increased among women with neither pre-pregnancy diabetes nor GDM. Effect modification of the association between pre-pregnancy BMI and preterm birth by maternal age has been described in a prior study, showing that while younger women had lower rates of preterm birth with increasing BMI, this relationship was reversed among older women (146). Our study showed that among younger women (vs older), the increase in the rates of the composite perinatal outcome started at relatively higher BMI levels. Our results also showed that associations were reversed among underweight women compared with those with higher BMIs. For example, underweight nulliparous women had a lower risk than underweight parous women. Our findings regarding underweight women with chronic hypertension or pre-pregnancy diabetes were less conclusive, due to the relatively small number of women in these strata. 81 5.4.3 Clinical and research implications These prior studies together with our results suggest that the relationship between BMI, comorbid conditions and adverse birth outcomes is complex and that the pathophysiological pathways to perinatal mortality and morbidity differ between women with comorbid conditions (no or weak association with BMI) and women who are otherwise relatively healthy (strong association with BMI). It’s also possible that in women without chronic conditions, the effect of BMI is mediated by increased risk of gestational diabetes and gestational hypertension, causal pathways that are not present in women with chronic hypertension and pre-pregnancy diabetes, respectively. Nevertheless, prognostically, the overall risks of adverse perinatal outcomes in obese women with chronic conditions remain high. 5.4.4 Strengths and limitations Our study is strengthened by leveraging population-level data with minimal missing values to assess rare adverse pregnancy outcomes within various subgroups of women, including those with obesity class I, II and III, with sufficient statistical power to provide precise estimates. Our study also has some limitations. First, we lacked data on the temporal sequence between obesity and chronic hypertension or pre-pregnancy diabetes, as well as the severity of these conditions. It’s possible that these two chronic conditions are both effect modifiers and mediators (i.e., caused by obesity) in the associations between obesity and adverse pregnancy outcomes. Second, there has been debate about the causal relationship between pre-pregnancy BMI and adverse pregnancy outcomes (45), given that there are many causes of high BMI (e.g., lifestyle, metabolic disorders) that may result in varying biological effects. This issue is important when 82 assessing effect modification because the effects of high BMI resulting from different underlying causes may be modified differently. Nevertheless, our results provide information on the average risk of adverse perinatal outcomes associated with pre-pregnancy BMI arising from the combination of antecedent contributors to low or high BMI among parturient women. Future prospective studies with information on cause of abnormal BMI may disentangle these pathways further. Third, we did not have information about potential confounders including socio-economic status (SES), substance/medication use, maternal social support, and comorbidities other than chronic hypertension and pre-pregnancy diabetes. Fourth, we did not have sufficient data to assess adverse maternal outcomes (e.g., severe maternal morbidity). Finally, similar to other observational studies, the data may have measurement errors which could lead to non-differential misclassification and bias towards the null. 5.5 Conclusion Pregnancies of obese women heterogeneous in their risk of adverse perinatal outcomes. Other risk factors including nulliparity, chronic hypertension, and pre-pregnancy diabetes affect the risks of adverse perinatal outcomes and modify the associations with pre-pregnancy BMI. The lack of association between pre-pregnancy BMI and adverse perinatal outcomes among women with chronic hypertension and pre-pregnancy diabetes should be interpreted cautiously from a prognostic perspective, as their overall risks remain disproportionately high compared with women without these chronic conditions, irrespective of BMI. A deeper understanding of the variations in the effects of maternal BMI is needed to uncover possible pathways to adverse pregnancy outcomes. 83 5.6 Tables Table 7: Demographics and obstetric characteristics among women with singleton birth by pre-pregnancy BMI category, USA, 2016-2017. Underweight (N = 254,225) Normal (N = 3,220,432) Overweight (N = 1,918,480) Obese I (N = 1,062,177) Obese II (N = 516,693) Obese III (N = 365,357) Maternal age (years) <20 28500 (11.2) 198977 (6.2) 90784 (4.7) 44116 (4.2) 18408 (3.6) 9778 (2.7) 20-24 74202 (29.2) 641299 (19.9) 374767 (19.5) 219713 (20.7) 108417 (21) 74020 (20.3) 25-29 71235 (28.0) 912894 (28.3) 562315 (29.3) 320185 (30.1) 161634 (31.3) 118308 (32.4) 30-34 53971 (21.2) 934042 (29) 545295 (28.4) 289062 (27.2) 138928 (26.9) 100480 (27.5) 35-39 22166 (8.7) 442172 (13.7) 279973 (14.6) 152467 (14.4) 72517 (14) 51738 (14.2) ≥40 4151 (1.6) 91048 (2.8) 65346 (3.4) 36634 (3.4) 16789 (3.2) 11033 (3.0) Nulliparous 122307 (48.2) 1373066 (42.7) 694631 (36.3) 350475 (33.1) 168300 (32.7) 119075 (32.7) Birth year 2016 130791 (51.5) 1644977 (51.1) 964863 (50.3) 528887 (49.8) 256021 (49.5) 179930 (49.2) 2017 123434 (48.5) 1575455 (48.9) 953617 (49.7) 533290 (50.2) 260672 (50.5) 185427 (50.8) Race/ethnicity Asian/Pacific Islander 38791 (15.3) 313957 (9.7) 116472 (6.1) 39486 (3.7) 11704 (2.3) 5077 (1.4) Non-Hispanic Black 34912 (13.7) 369731 (11.5) 292694 (15.3) 195879 (18.4) 107164 (20.7) 92403 (25.3) Hispanic 44690 (17.6) 651176 (20.2) 518128 (27.0) 297841 (28.0) 130189 (25.2) 78229 (21.4) Alaska Indian/American Native 1640 (0.6) 22277 (0.7) 18319 (1.0) 13379 (1.3) 7245 (1.4) 4854 (1.3) Non-Hispanic White 134192 (52.8) 1863291 (57.9) 972867 (50.7) 515592 (48.5) 260391 (50.4) 184794 (50.6) Education level Lower than high school 43842 (17.2) 391129 (12.1) 274058 (14.3) 156476 (14.7) 69374 (13.4) 45531 (12.5) High school 74195 (29.2) 720209 (22.4) 482255 (25.1) 300297 (28.3) 154991 (30.0) 115475 (31.6) College 106829 (42.0) 1579458 (49.0) 933409 (48.7) 512844 (48.3) 254907 (49.3) 182142 (49.9) Higher than Bachelors 26023 (10.2) 491178 (15.3) 205639 (10.7) 80605 (7.6) 32535 (6.3) 19083 (5.2) Unknown 3336 (1.3) 38458 (1.2) 23119 (1.2) 11955 (1.1) 4886 (0.9) 3126 (0.9) Health Care Insurance Medicaid 127091 (50.0) 1202089 (37.3) 822549 (42.9) 507810 (47.8) 257970 (49.9) 195606 (53.5) Private 101688 (40.0) 1718543 (53.4) 920754 (48.0) 467265 (44.0) 222467 (43.1) 147930 (40.5) Self-pay 13922 (5.5) 147329 (4.6) 76077 (4.0) 34887 (3.3) 12719 (2.5) 6326 (1.7) Other 9002 (3.5) 122638 (3.8) 79517 (4.1) 40369 (3.8) 17319 (3.4) 10553 (2.9) 84 Unknown 2522 (1.0) 29833 (0.9) 19583 (1.0) 11846 (1.1) 6218 (1.2) 4942 (1.4) Smoking during pregnancy 30331 (11.9) 209683 (6.5) 123851 (6.5) 79043 (7.4) 42587 (8.2) 32129 (8.8) Pre-pregnancy diabetes mellitus 656 (0.3) 12291 (0.4) 15068 (0.8) 14741 (1.4) 10882 (2.1) 12051 (3.3) Chronic hypertension 1048 (0.4) 21357 (0.7) 27955 (1.5) 28317 (2.7) 22484 (4.4) 29901 (8.2) Prior cesarean delivery 22737 (8.9) 382326 (11.9) 307681 (16.0) 204904 (19.3) 113930 (22.0) 96142 (26.3) Prior deceased infant 2517 (1.0) 31298 (1.0) 24724 (1.3) 16269 (1.5) 8688 (1.7) 6854 (1.9) Prior pregnancy termination 56830 (22.4) 791069 (24.7) 521174 (27.3) 306484 (29.0) 153208 (29.9) 110443 (30.5) 85 Table 8: Rates of the composite outcome (perinatal mortality and/or severe neonatal morbidity) per 1000 women with singleton birth by pre-pregnancy BMI, stratified by maternal age, parity, chronic hypertension and pre-pregnancy diabetes, USA, 2016-2017. Underweight (N = 254,225) Normal BMI (N = 3,220,432) Overweight (N = 1,918,480) Obese I (N = 1,062,177) Obese II (N = 516,693) Obese III (N = 365,357) Perinatal mortality and/or severe neonatal morbidity Number (rate per 1000 total births) 5317 (20.9) 59730 (18.5) 40988 (21.4) 27267 (25.7) 15611 (30.2) 13414 (36.7) Outcome rates stratified by maternal age (years) <20 778 (27.3) 5195 (26.1) 2465 (27.2) 1243 (28.2) 556 (30.2) 372 (38.0) 20-24 1707 (23.0) 13492 (21.0) 8180 (21.8) 5346 (24.3) 3060 (28.2) 2403 (32.5) 25-29 1382 (19.4) 16160 (17.7) 11000 (19.6) 7615 (23.8) 4437 (27.5) 3902 (33.0) 30-34 967 (17.9) 14891 (15.9) 10784 (19.8) 7176 (24.8) 4112 (29.6) 3823 (38/0) 35-39 380 (17.1) 7894 (17.9) 6446 (23.0) 4506 (29.6) 2656 (36.6) 2292 (44.3) ≥40 103 (24.8) 2098 (23.0) 2113 (32.3) 1381 (37.7) 790 (47.1) 622 (56.4) Outcome rates stratified by parity Nulliparous 2513 (205.5) 27982 (203.8) 17124 (246.5) 11002 (313.9) 6379 (379) 5458 (458.4) Parous 2751 (209.6) 31252 (169.9) 23441 (192.3) 15979 (225.4) 9077 (261.6) 7839 (319.6) Outcome rates stratified by chronic hypertension Chronic hypertension 79 (753.8) 1275 (597) 1710 (611.7) 1901 (671.3) 1572 (699.2) 2172 (726.4) No chronic hypertension 5261 (20.7) 58838 (18.3) 39756 (20.9) 26038 (24.9) 14616 (28.9) 12198 (34.5) Outcome rates stratified by pre-pregnancy diabetes mellitus Diabetes 56 (85.4) 892 (72.6) 1232 (81.8) 1229 (83.4) 995 (91.4) 1216 (100.9) No diabetes 5261 (20.7) 58838 (18.3) 39756 (20.9) 26038 (24.9) 14616 (28.9) 12198 (34.5) 86 Table 9: Strata specific adjusted odds ratios (aOR) for the association between body mass index and the composite perinatal outcome including stillbirth, neonatal death and severe neonatal morbidity, singleton births in the USA, 2016-2017. Underweight (N = 254,225) Normal (N = 3,220,432) Overweight (N = 1,918,480) Obese I (N = 1,062,177) Obese II (N = 516,693) Obese III (N = 365,357) p-value* Overall unadjusted 1.13 (1.10, 1.16) 1.00 (ref) 1.16 (1.14, 1.17) 1.39 (1.37, 1.41) 1.65 (1.62, 1.68) 2.02 (1.98, 2.06) - Overall adjusted 1.07 (1.04, 1.10) 1.00 (ref) 1.10 (1.08, 1.11) 1.25 (1.23, 1.26) 1.39 (1.36, 1.42) 1.52 (1.49, 1.55) - Stratified by parity Nulliparous 0.96 (0.92, 1.00) 1.00 (ref) 1.16 (1.14, 1.18) 1.41 (1.38, 1.45) 1.62 (1.57, 1.67) 1.77 (1.73, 1.83) < 0.001 Parous 1.21 (1.16, 1.26) 1.00 (ref) 1.04 (1.02, 1.06) 1.14 (1.12, 1.17) 1.24 (1.21, 1.28) 1.35 (1.32, 1.39) Multiplicative interaction (ratio of aORs) 0.79 (0.75, 0.84) 1.00 (ref) 1.11 (1.08, 1.14) 1.24 (1.20, 1.28) 1.30 (1.25, 1.36) 1.31 (1.26, 1.37) - Additive interaction (RERI) -0.27 (-0.35, -0.19) 0.00 (ref) 0.20 (0.16, 0.24) 0.48 (0.43, 0.53) 0.69 (0.62, 0.77) 0.81 (0.72, 0.90) - Stratified by chronic hypertension 87 Chronic hypertension 1.23 (0.94, 1.59) 1.00 (ref) 0.97 (0.89, 1.05) 1.01 (0.93, 1.10) 0.99 (0.91, 1.08) 0.98 (0.91, 1.06) < 0.001 No chronic hypertension 1.07 (1.04, 1.10) 1.00 (ref) 1.10 (1.08, 1.11) 1.26 (1.24, 1.28) 1.41 (1.39, 1.44) 1.58 (1.55, 1.62) Multiplicative interaction (ratio of aORs) 1.15 (0.88, 1.49) 1.00 (ref) 0.88 (0.81, 0.96) 0.81 (0.74, 0.87) 0.70 (0.64, 0.77) 0.62 (0.57, 0.67) - Additive interaction (RERI) 0.57 (-0.32, 1.45) 0.00 (ref) -0.19 (-0.42, 0.04) -0.22 (-0.45, 0.01) -0.43 (-0.67, -0.20) -0.63 (-0.86, -0.41) - Stratified by pre-pregnancy diabetes mellitus Diabetes 1.05 (0.76, 1.45) 1.00 (ref) 1.02 (0.92, 1.12) 0.95 (0.86, 1.05) 0.94 (0.85, 1.05) 0.95 (0.86, 1.05) < 0.001 No Diabetes 1.07 (1.04, 1.10) 1.00 (ref) 1.10 (1.08, 1.11) 1.26 (1.24, 1.28) 1.41 (1.38, 1.44) 1.55 (1.52, 1.59) Multiplicative interaction (ratio of aORs) 0.98 (0.71, 1.35) 1.00 (ref) 0.93 (0.84, 1.03) 0.75 (0.68, 0.83) 0.67 (0.60, 0.74) 0.61 (0.55, 0.68) - Additive interaction (RERI) 0.11 (-1.11, 1.33) 0.00 (ref) -0.03 (-0.39, 0.33) -0.44 (-0.80, -0.09) -0.62 (-0.99, -0.24) -0.73 (-1.09, -0.37) - *Computed from likelihood ratio test comparing models with and without interaction terms for possible effect modifiers Severe neonatal morbidity includes Apgar score < 4 at 5 minutes, neonatal seizure and/or assisted ventilation for ≥6 hours. RERI denotes Relative Excess of Risk due to Interaction; aORs were adjusted for race, education, type of health care insurance, smoking status during pregnancy, and prior neonatal death or stillbirth as well as other effect modifiers. 88 5.7 Figures Figure 6: Adjusted risk curves of the composite perinatal outcome including stillbirth, neonatal death and severe neonatal morbidity by pre-pregnancy BMI (continuous), stratified by maternal age category; singleton births in the USA, 2016-2017. Rates were adjusted for race/ethnicity, education, type of health care insurance, smoking status during pregnancy, and prior neonatal death or stillbirth, chronic hypertension, pre-pregnancy diabetes mellitus, and nulliparity. 89 Figure 7: Adjusted risk curves (95% confidence intervals) of the composite perinatal outcome including stillbirth, neonatal death and severe neonatal morbidity by pre-pregnancy BMI (continuous), stratified by parity (A), chronic hypertension (B), and pre-pregnancy diabetes mellitus (C); singleton births, USA, 2016-2017. Risks were adjusted for maternal age, race/ethnicity, maternal education, type of health care insurance, smoking status during pregnancy, and prior neonatal death or stillbirth, and the other effect modifiers. 90 Chapter 6: A flexible approach to modeling stillbirth using the fetuses at risk approach 6.1 Background The fetuses-at-risk (FAR) approach for quantifying gestational age-specific rates of stillbirth (and other perinatal phenomena) has become widely accepted in recent years (262,337,269,264,268,265). Gestational age is treated as survival time, and this results in the disentanglement of several paradoxical associations which characterize the birth-based formulation (265,268). With the FAR perspective enabling estimation of stillbirth incidence across pregnancy, the Cox proportional hazard model is increasingly used for the multivariable assessment of relationships between various determinants and stillbirth (9,269,338). Although the Cox model has many extensions and is ubiquitous in survival analysis (267), recent advances in generalized additive modeling (GAMs) (339) have yielded flexible models which may be better suited for modeling complex perinatal situations. Gestational age-specific relationships, especially those that vary over time, represent one such situation likely to benefit from these GAMs. This study aimed to demonstrate how Piecewise Exponential Additive Mixed Models (PAMMs), a class of GAMs, could be used to model complex gestational age-specific risks (340). Empirical quantification of the gestational age-specific relationship between both pre-pregnancy body-mass-index (BMI) and cigarette smoking during gestation and stillbirth was used to illustrate the use of PAMMs. 91 6.2 Methods 6.2.1 Cox proportional hazard models The most common approach to analyzing time-to-event data is to use a Cox proportional hazards model for the log hazard rate, 𝜆(𝑡): log;𝜆(𝑡)< = log;𝜆/(𝑡)< + 𝛽1𝑋1 + 𝛽2𝑋2 +⋯𝛽#𝑋# where 𝜆/(𝑡) is the ‘baseline hazard’, 𝑋% are the variables of interest, and 𝛽% are the regression coefficients (267). One of the advantages of the Cox model is that it is semiparametric, and therefore can be fit without specifying the form of 𝜆/(𝑡). A key assumption of the Cox model is that the hazards are assumed to be proportional, i.e., that the ratio of the survival curves is constant over time. When a covariate’s effect changes over the time axis, then this assumption will not be met, and the above model has to be extended to include time-dependent coefficients of the form 𝛽%(𝑡) (341). This differs from a time-dependent covariate whose value changes over time. In practice, time-dependent coefficients can be fit by including interaction terms between covariates and a (specified) function of time (341). In the context of FAR modeling, using such time-dependent coefficients in the Cox model is appropriate whenever there is expected/observed variation in the effect of a variable on stillbirth (or other outcomes) by gestational age. 92 6.2.2 Piecewise Exponential Additive Mixed Models Methodological advances related to GAMs (339,342,343) have allowed relationships between independent and dependent variables to be modeled through smooth functions (whose form is empirically based) (339). In survival analysis, PAMMs combine the well-known Poisson representation of piece-wise exponential models with GAMs (267) (which is equivalent to a Cox model in situations with no outcome event ties with regard to survival time). The Poisson representation requires data to be in the ‘counting-process’ form, wherein each subject has the number of rows equal to the number of time intervals (gestational weeks in this context) that they were at risk. PAMMs for the log-hazard rate at time 𝑡 can be written: log;𝜆(𝑡)< = 𝑓/(𝑡) + 𝑓1;𝑥1, 𝑡9< + 𝑓2;𝑥2, 𝑡9< + ⋯𝑓#(𝑥#, 𝑡9) for all 𝑡 within a given time interval (𝜃951, 𝜃9], where 𝜃9 is the 𝑗:;cut-point in the ‘counting-process’ format of the data, and 𝑡9 = 𝜃9 is constant over the interval (𝜃951, 𝜃9]. Note that other choices for 𝑡9 are also possible (such as the midpoint of the interval). See Table 28 for example of the data format. The functions 𝑓%;𝑥% , 𝑡9< represent the possibly nonlinear effect of covariate 𝑥% or a time-varying effect of covariate 𝑥% over time 𝑡9, or a combination of these. When 𝑓%;𝑥% , 𝑡9< = 𝛽%𝑥%, the above formulation will approximate a Cox proportional hazard model; the only difference is the estimation of 𝑓/(𝑡), the baseline hazard, which is not estimated directly in a Cox model. 93 As in most GAMs, the functions 𝑓% are typically specified as splines, where the degree of flexibility of the splines is estimated through penalization (339). The advantage is that the form of the relationship between the hazard function and covariate over time does not need to be pre-specified. PAMMs can therefore handle general cases more naturally than a Cox model, for example in situations where the effect of a variate varies nonlinearly over time (340). 6.2.3 Data source and study population The study was based on all live births and stillbirths at 20 to 43 weeks’ gestation in the United States in 2016-2017. Data were obtained from live births and fetal deaths files from the National Center for Health Statistics. The data include information on maternal age, pre-pregnancy BMI, parity, race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian/Pacific Islander, and Alaskan Indian/American Native), gestational age at delivery (in weeks), an average number of cigarettes smoked prior to pregnancy, and during the first, second and third semester of pregnancy. Stillbirth was defined as the birth of a dead fetus at or after 20 weeks’ gestation (excluding pregnancy terminations). The primary exposure used to illustrate PAMMs was pre-pregnancy BMI, analyzed as a categorical variable using the following categories (kg/m2): underweight (BMI < 18.5), normal BMI (18.5 - 24.9 kg/m2), overweight (25.0 - 29.9), obesity class I (30.0 - 34.9), class II (35.0 – 39.9) and class III (≥ 40 kg/m2), and also as a continuous variable. As pre-pregnancy BMI is a time-fixed covariate (prior to pregnancy), we conducted a second analysis examining the association between the average number of cigarettes smoked per day and stillbirth to demonstrate PAMM models with a time-varying covariate (with 94 trimester-specific values). The second analysis was restricted to women who self-reported as pre-pregnancy smokers. 6.2.4 Statistical methods We conducted a series of analyses to demonstrate similarities and differences between the estimates obtained from Cox models and PAMMs. First, under a proportional hazards assumption, we compared the hazard ratios (HR) and 95% confidence intervals (CI) for BMI and stillbirth from both models. Index categories of BMI were modeled with normal BMI as the reference category, and a linear effect of continuous BMI was also modeled. Second, we repeated these analyses adjusting for maternal age, parity (nulliparous vs parous), and race to compare adjusted hazard ratios from both models in a multivariable setting. Third, we allowed for a violation of the proportional hazards assumption by treating continuous BMI as a covariate with a time (i.e., gestational age)-varying effect. In the Cox models, we used a natural spline with 3 degrees of freedom for the shape of the time-varying function. In both models, we assumed that BMI had a linear association with stillbirth. Fourth, we demonstrated the flexibility of PAMMs by including a tensor product between the splines for BMI and gestational age to allow for the effect of BMI on stillbirth to vary nonlinearly over gestational age. In all PAMM models above, we split the data at 20 weeks, 24 weeks and each gestational week between 28 and 41 weeks. Results were summarized by their time-varying coefficient values, and time-varying HRs. For PAMM models, we reported the effective degrees of freedom (EDFs) for the time-varying coefficients, which are a measure of the degree of nonlinearity (higher EDF implies a more 'wiggly’ curve). 95 We conducted a second set of analyses to demonstrate similarities and differences between Cox and PAMM models when a time-varying exposure is of primary interest. For this analysis, we assessed the relationship between maternal smoking (average number of cigarettes smoked per day) in each trimester and stillbirth. We first compared results assuming a linear time-constant effect of the number of cigarettes smoked and then relaxed this assumption in the PAMM model to estimate gestational age-specific effects of the number of cigarettes smoked, and also allowing for a possible nonlinear effect of smoking. All analyses were conducted in R statistical software version 4.0.3 using the survival and pammtools packages (344,345). Sample analyses and data are available at https://figshare.com/s/6270e1d63e4332f75021. 6.2.5 Missing data A small number of women were missing pre-pregnancy BMI (N = 201,992, 2.7%) and were excluded from the primary analyses. Similarly, there were a total of 42,400 (0.5%) women who were missing information about cigarette smoking (39,868 before pregnancy and 2,532 during pregnancy) and were excluded from our secondary analysis. We did not perform imputation as the goal of paper was to demonstrate the use of Cox and PAMM models and the amount of missing data was very low. 96 6.2.6 Ethics approval All analyses were performed on publicly accessible de-identified data and thus did not require ethics approval. 6.3 Results The study population included 7,567,316 women who had a singleton live birth and 42,739 women who had a stillbirth (5.6 per 1,000 total births). Of these, 255,448 (3.5%), 3,233,787 (43.9%), 1,925,414 (26.1%), 1,065,804 (14.5%), 518,416 (7.0%) and 366,455 (5.0%) women were underweight, normal, overweight, obese class I, II and III, respectively. Rates of stillbirth increased with increasing pre-pregnancy BMI, and stillbirth rates increased with increasing gestation in all BMI categories. The gestational age-constant hazard ratios for the index BMI categories (compared with normal BMI) increased with increasing BMI in both models. For example, using the Cox model, the HR (95% CI) for obesity class I, II and III were 1.57, 95% CI = (1.53, 1.62), 1.84, 95% CI = (1.77, 1.91), and 2.22, 95% CI = (2.13, 2.31), respectively. When BMI was treated continuously, there was a 19% increase in the hazard per 5-unit increase in BMI (HR = 1.19, 95% CI = 1.18 to 1.20). The PAMMs analysis yielded essentially identical HRs (Table 10). The association with BMI did not change with adjustment in a multivariable model, and hazard ratios for all variables were nearly identical between Cox and PAMM models (Table 29). In models which allowed for nonlinear gestational age-varying effects with a linear BMI effect, the Cox and PAMM models yielded similar results. Both models showed an increasing effect of 97 BMI with increasing gestational age, with PAMMs showing slightly higher HRs at earlier gestation, and Cox models showing slightly higher HRs at later gestation. For example, the HRs (95% CI) for women with a BMI of 30 kg/m2 vs 20 kg/m2 at 32 weeks’ gestation were 1.33, 95% CI = (1.30, 1.37) and 1.30, 95% CI = (1.26, 1.35) when using the PAMM and Cox models, respectively; at 40 weeks’ gestation, the HRs were 1.65, , 95% CI = (1.57, 1.73) and 1.71, , 95% CI = (1.63, 1.78) and for the PAMM and Cox models, respectively. There was some evidence of nonlinearity across gestational age in both models (EDF = 5.15 for the PAMM model; Figure 8, Table 30). Figure 9 shows the PAMM estimated effects of BMI, which varied nonlinearly with time (P value <0.001 for the tensor product between splines and BMI, Table 30). The rate of stillbirth in women with a high BMI (e.g., 40 kg/m2) increased more rapidly as they approached term gestation compared with women of normal BMI (Figure 9, left panel). At 40 weeks’ gestation, the relationship between BMI and stillbirth was linear, whereas at earlier gestational ages (e.g., 32 and 36 weeks), the relationship tended to plateau beyond a BMI of 35 kg/m2 (Figure 9, right panel). Among 693,020 women who reported smoking, 521,529 (75.3%) smoked at some point during pregnancy. Of those, 513,615 (74.1%) smoked during the first semester, 440,193 (63.5%) during the second and 415,783 (60.0%) smoked in the third trimester. The median of the ‘average number of cigarettes per day’ declined amongst smokers from pre-pregnancy to the third trimester (Figure 18). Overall, 3,813 stillbirths (7.3 per 1000 total births) occurred among women who continued smoking at any point during pregnancy, and 870 stillbirths (5.0 per 1000 98 total births) occurred among those who quit smoking. Every 5 additional cigarettes smoked per day on average was associated with an increase in stillbirth, and the results were similar between Cox and PAMM models (HR = 1.09, 95% CI = 1.08 to 1.11 in both models). When allowing this effect to vary across gestational age, there was an approximately linear increase in stillbirth risk with increasing gestational age (Figure 10). 6.4 Comment 6.4.1 Principle findings Cox regression is commonly used to quantify the independent associations between risk factors and perinatal phenomena such as stillbirth. We used data on pre-pregnancy BMI, cigarette smoking during pregnancy and stillbirth to demonstrate that PAMMs provide a more flexible analytic method for assessing the time-varying effects of risk factors over gestation. Specifically, PAMMs were able to more naturally accommodate complex non-linear effects that varied across gestational age. 6.4.2 Strengths of the study The strength of this study includes the large size of the dataset that we used to compare methods for fetuses-at-risk analyses, and which provided ample statistical power to study rare outcomes such as stillbirth. We were able to demonstrate the analysis of time-varying covariates thanks to the trimester-specific data on cigarettes smoked during pregnancy which was collected through live birth and fetal death certificates. 99 6.4.3 Limitations of the study Our study also has some limitations. First, we showed some examples of multivariable analyses by including some confounders in our models. Our aim was to illustrate these analyses, rather than provide a complete investigation of the associations between pre-pregnancy BMI and cigarette smoking and stillbirth (which would have to include evaluation of potential bias, confounding and mediation analyses). Second, our time-varying exposure example used data on the average number of cigarettes smoked. This information was available for each trimester and could be subject to some misclassification if women changed their habits within a trimester. We assumed that the number of cigarettes smoked was consistent throughout the trimester(s), however, this limited our ability to detect any additional nonlinearity of the association. Third, our comparison between models was restricted to one dataset, and there may be differences between model results or properties in other contexts. Finally, we did not examine alternative models that also accommodate non-proportional hazards, such as accelerated failure time, or parametric survival models (346,347). 6.4.4 Interpretation Pre-pregnancy BMI is a well-known risk factor for stillbirth (7,133), and our analyses showed a dose-response relationship between increasing BMI and stillbirth, which varied according to gestational duration. Our findings are congruent with a previous study which used a Cox model (stratified by gestational age) to show the time-varying effect of obesity (9). This previous study used stepwise functions for modeling the time-varying hazard ratio across gestation and treated BMI categorically. On the other hand, we used PAMMs to allow for greater flexibility, treating 100 both pre-pregnancy BMI and gestational week as continuous variables and modeling their nonlinear effects. We found sharply rising increases in the risk of stillbirth in women with high BMI (e.g., ≥40 kg/m2) compared with women with a normal BMI from around 34 weeks of gestation. Further, by modeling nonlinear effects across gestational age, we showed that the dose-response relationship between BMI and stillbirth has different patterns at different gestational ages. For example, the estimated relationship between BMI and stillbirth begins to flatten out for higher BMIs at earlier gestations (e.g., 28-32 weeks). The reasons behind the association between high BMI and stillbirth are still not completely understood. Women with obesity are at higher risk of pregnancy complications such as pre-eclampsia and GDM (35,91,106), which are strong risk factors for stillbirth. Late-onset pre-eclampsia is more common than early-onset (106) and, therefore may partly explain some of the increases in risk of stillbirth at later gestation in women with high BMI. Increased growth velocity among fetuses of women with obesity leading to early development of uteroplacental insufficiency has also been proposed as a possible explanation (9). Similar to high BMI, smoking during the gestational period has been consistently shown to be associated with stillbirth, for instance, a meta-analysis of 34 studies showed an estimated 47% increase in odds of stillbirth associated with smoking during pregnancy (348). We analyzed trimester-specific data about average cigarettes smoked per day to examine time-varying effects of number of cigarettes smoked and to estimate gestational-age-specific risks. We found that the number of cigarettes smoked per day later in pregnancy was more strongly associated with stillbirth than in earlier gestation. 101 The primary advantage of PAMMs compared with Cox models is that they estimate potentially complex time-varying and time-dependent relationships without requiring assumptions about the form of these relationships, or significant pre-processing of data (340). This is of potential use in modeling perinatal phenomena, where both time-invariant and time-dependent risk factors often have complex relationships with adverse outcomes that vary depending on the gestational period. The appealing nature of the Cox model is that it is semi-parametric and does not require estimation of the baseline hazard function. The lack of specification of the shape of the baseline hazard function is a strength of the Cox model in that it requires fewer assumptions about the data, but this is also a weakness as it makes flexible modeling of time-dependent effects more challenging. In contrast, PAMMs estimate the baseline hazard function directly using flexible penalized regression splines. In analyses where we assumed that hazards were proportional, the two modeling approaches yielded identical results in both univariate and multivariable analyses, indicating that the estimation of the baseline hazard in PAMMs made little practical difference. This is consistent with the findings of a previous simulation study, which compared the two methods when the proportional hazards assumption was true (349). Since PAMMs use penalization, there is less risk of overfitting than other parametric models, and based on the above agreement between models, minimal downside to estimating the baseline hazard. This penalized estimation allows one to include a diverse set of effects in the model, such as those demonstrated above, without making strong assumptions about the data generating mechanism. 102 Further, time-varying covariates can be easily incorporated in PAMMs, as the data structure required for fitting these models needs less pre-processing than in Cox models (340). We illustrated this by modelling the average number of cigarettes smoked per day, which varied by trimester of pregnancy. Cox and PAMM models agreed when the hazard ratio was assumed constant over gestation, but PAMM models were able to easily incorporate gestational age-specific effects of this time-varying covariate. In studies examining more granular time-varying data (e.g., biomarker changes) in each subject, PAMMs would be able to estimate and assess potentially complex gestational-age-specific relationships. Both Cox models and PAMMs can be further extended to accommodate other commonly encountered issues in perinatal research such as left-truncation, competing risks, or cumulative effects of in-utero exposures (not shown in this study). As with all models, the Cox model and PAMMs can be used to simplify complex biological relationships and to provide estimates of the strength of specific associations. Despite these observed differences between models in the applied examples above, simulation studies (particularly of gestational age specific risks) with known input values would allow for further comparisons between modeling approaches. In particular, these studies would be useful for assessing possible bias and coverage properties of confidence intervals, and could easily be expanded to include other survival analysis techniques. 6.5 Conclusion 103 PAMMs provide a useful alternative to the Cox model for the survival analysis modeling of perinatal phenomena such as stillbirth by providing increased flexibility when proportional hazards assumptions are violated, and time-dependent or time-varying effects are present. 104 6.6 Tables Table 10: Stillbirth rates and hazard ratios for body-mass-index (BMI) categories obtained from Cox and PAMM models, United States, 2016-17. BMI Group Total births Stillbirths (per 1000 total births) Cox hazard ratio (95% CI) PAMM hazard ratio (95% CI) Underweight 255,448 1,033 (4.0) 1.07 (1.00, 1.14) 1.07 (1.00, 1.14) Normal 3,233,787 12,471 (3.9) Reference Reference Overweight 1,925,414 9,495 (4.9) 1.28 (1.25, 1.32) 1.28 (1.25, 1.32) Obese I 1,065,804 6,377 (6.0) 1.57 (1.53, 1.62) 1.57 (1.52, 1.62) Obese II 518,416 3,594 (6.9) 1.84 (1.77, 1.91) 1.83 (1.77, 1.90) Obese III 366,455 3,031 (8.3) 2.22 (2.13, 2.31) 2.21 (2.13, 2.30) Continuous BMI (per kg/m2 5-unit linear increase) - - 1.19 (1.18, 1.20) 1.19 (1.18, 1.20) 95% CI denotes 95% confidence intervals, PAMM denotes Piecewise Exponential Additive Mixed Models. 105 6.7 Figures Figure 8: Gestational age-specific effects of body-mass-index (BMI) modeled as a continuous variable on stillbirth: Comparison between a Cox model with a time-varying linear effect of BMI modeled with a natural spline, and a Piecewise Exponential Additive Mixed Model with a linear term for BMI. 0.000.020.040.0630.0 32.5 35.0 37.5 40.0Gestational age (wks)Time varying coefficentModel Cox model with spline for time−varying effect PAMM 106 Figure 9: Results from Piecewise Exponential Additive Mixed Model allowing for a non-linear effect of body-mass-index (BMI) on stillbirth varying nonlinearly across gestational age, United States, 2016-17. The left panel shows gestational age-specific log hazard ratios for fixed BMI, while the right panel shows BMI-specific hazard ratios for fixed gestational age. All log hazard ratios are relative to a BMI of 20 kg/m2. 0.000.250.500.751.001.2530 33 36 39Gestational age (weeks)Log hazard ratio relative to a BMI of 20 (95% CI)BMI 25 30 35 400.00.51.01.520 25 30 35 40 45Pre−pregnancy BMILog hazard ratio relative to a BMI of 20 (95% CI)Gestational age 28 32 36 40 107 Figure 10: Gestational age-specific effect for the number of average cigarettes smoked per day on stillbirth risk. Cigarettes per day was modeled as a time-varying covariate using a Piecewise Exponential Additive Mixed Model. Dotted line represents the estimated time constant log hazard ratio. −0.10.00.120 25 30 35 40Gestational age (wks)Log−hazard ratio for 5 (per−day) increase in cigarettes smoked 108 Chapter 7: Relationship between pre-pregnancy body-mass-index and gestational age-specific risks of stillbirth in women with and without chronic hypertension and diabetes mellitus 7.1 Introduction Obesity is an important risk factor for adverse birth outcomes and its prevalence among pregnant women has been steadily increasing in recent decades (4). In the United States, for example, about half of all pregnant women were either overweight or obese in 2019 (1,320). The rates of stillbirth increase with increasing pre-pregnancy body-mass-index (BMI), especially at late-term and post-term gestation (9,137). High BMI is also associated with co-morbidities including chronic hypertension, diabetes mellitus type 2, and other chronic conditions, which are themselves independent risk factors for stillbirth (321). These observations have led to discussions about early delivery of women with high pre-pregnancy BMI to prevent stillbirth (9,331,350,351). However, more data on gestational age specific risk is needed to provide evidence-based timing of delivery in this population. The Canadian Society of Obstetrics and Gynaecology (SOGC) guidelines recommend delivery at 39-40 weeks’ gestation for women with extreme obesity (BMI ≥ 40 kg/m2) (15); while the American College of Obstetricians and Gynaecologists (ACOG) and the Royal College of Obstetricians and Gynaecologists (RCOG) do not have a specific recommendation with respect to gestational age at delivery for obese women (14,352). SOGC also recommends early delivery at 38-40 weeks for women with pre-existing or gestational diabetes mellitus (61), and the same 109 recommendation has been made for women with chronic hypertension (353). Early delivery at term gestation has been recommended by ACOG for women with chronic hypertension, while RCOG leaves the timing of delivery dependent on signs of preeclampsia and other complications (354). Prior studies have shown an overall increase in the risk of stillbirth in women with various chronic conditions; however, less evidence is available about gestational age-specific risks of adverse birth outcomes among overweight and obese women with concurrent co-morbidity, which is increasingly common (355). Such information could help to create specific guidelines for obstetric intervention in these women. We aimed to assess the relationship between pre-pregnancy BMI and gestational age-specific risk of stillbirth and how this relationship is modified by chronic hypertension and diabetes mellitus. 7.2 Methods 7.2.1 Data source and inclusion, exclusion criteria We carried out a retrospective cohort study including all women with a singleton live birth or stillbirth at ≥20 weeks’ gestation in the United States from 2016 to 2017, which was the period with the most recent data at the time of analysis. Data were obtained from live birth certificates, fetal death certificates, and cohort-linked infant death certificates from the National Center for Health Statistics (NCHS). These datasets included information about maternal demographic and clinical characteristics, pregnancy complications, and birth outcomes. We 110 excluded live births and fetal deaths with missing information on pre-pregnancy BMI or gestational age, and those with gestational age < 20 weeks or ≥ 43 weeks. 7.2.2 Exposure, outcomes and other covariates The primary outcome was stillbirth, defined as the delivery following fetal death in utero or during delivery at ≥20 weeks’ gestation. Our primary exposure of interest was pre-pregnancy BMI. For descriptive analyses we stratified stillbirth rates by BMI categories as follows: underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 £ BMI < 25 kg/m2), overweight (25 kg/m2 £ BMI < 30 kg/m2), obesity class I (30 kg/m2 £ BMI < 35 kg/m2), class II (35 kg/m2 £ BMI < 40 kg/m2), and class III (BMI ≥ 40 kg/m2). We considered two subgroups of interest for assessing gestational age-specific risks: women with chronic hypertension and women with pre-pregnancy diabetes mellitus. Information on chronic hypertension and pre-pregnancy diabetes was obtained from birth certificates, as extracted from medical charts. Potential confounders included maternal education (less than high school vs. high school or higher education), parity (nulliparous vs. parous), maternal age (continuous), maternal smoking status during pregnancy, and maternal race/ethnicity. The NCHS data included information about self-reported race/ethnicity; the categories of race/ethnicity were based on prior literature and the terminology used in the U.S. National Vital Statistics Reports (326,327). 7.2.3 Statistical analysis We used medians and interquartile ranges for continuous variables and counts and percentages for categorical variables to summarize women’s characteristics by BMI categories. Rates of stillbirth per 1000 total births were calculated for women in each BMI category, stratified by 111 presence of chronic hypertension and pre-pregnancy diabetes. We quantified the gestational age-specific incidence rates of stillbirth in each of these strata using the fetuses-at-risk (FAR) approach, whereby the denominator for stillbirth rates at each gestational week included all in-utero fetuses at that week (i.e., all undelivered women at each week of gestational age); we plotted the locally estimated scatter plot (LOESS)- smoothed 1-week cumulative risk of stillbirth at each week of gestation (265,356). We used Piecewise Additive Mixed Models (PAMMs) (340,357), a flexible type of survival analysis model, to assess the relationship between maternal pre-pregnancy BMI and gestational age-specific risk of stillbirth in women with and without chronic hypertension and pre-pregnancy diabetes. Compared with conventional survival analysis models (e.g. Cox regression), PAMMs can easily incorporate various types of effects, including 1) nonlinearity between an exposure and an outcome, 2) nonlinear time-varying effects (i.e., nonproportional hazards), and 3) a combination of both these types of effects (340). These models are based on recent methodological advances in generalized additive models, which use penalization to avoid overfitting, thus allowing the analyst to model diverse types of effects without any strong assumptions about the form of the underlying associations (342). Using PAMMs, we relaxed the assumption that the relationship between increasing BMI and stillbirth is linear and let this association vary across gestational age. BMI was used as a continuous variable modeled with a penalized spline, with its effect allowed to vary non-linearly over gestational age. We fit models stratified by presence of chronic hypertension and diabetes mellitus, both unadjusted and adjusted for the potential confounders listed above. In analyses stratified by chronic hypertension, we adjusted for diabetes mellitus, and vice versa. Finally, we conducted the same 112 analysis on participants who had both chronic hypertension and pre-pregnancy diabetes. Results were summarized as gestational age-specific adjusted hazard ratios (aHR) for women with higher or lower BMI vs. women with a BMI of 20 kg/m2, starting at 20 weeks' gestation. 7.2.4 Sensitivity analysis A small percentage of women with stillbirth had missing information about chronic hypertension or diabetes (< 5%). In our primary analyses, we assumed that these chronic conditions were not present in these women. In a sensitivity analysis, we refit the same models restricted to women with complete information on chronic hypertension and diabetes. All analyses were conducted using R statistical software version 4.0.3, with PAMMs fit using pammtools (345). 7.3 Results 7.3.1 Study population The study population included 7,365,797 women, of which 255,464 (3.5%) were underweight prior to pregnancy, 3,233,710 (43.9%) had normal BMI, 1,925,510 (26.1%) were overweight, and 1,065,958 (14.5%), 518,543 (7.0%), and 366,612 (5.0%) had obesity class I, II and III, respectively. Compared with normal weight women, obese women were more likely to be parous, of non-Hispanic Black, native American or Hispanic race/ethnicity, and to have lower education. They were also more likely to smoke during pregnancy and, amongst livebirths, had higher rates of health care insurance provided by Medicaid. Further, those with elevated BMI 113 had higher rates of chronic hypertension and pre-pregnancy diabetes mellitus and were more likely to deliver at earlier gestation (Table 11). There were 12,469 stillbirths among women who had normal BMI (3.86 per 1000 total births). The number of stillbirths (rate per 1000 total births) among women who were underweight, overweight, and obese class I, II and III were: 1033 (4.04), 9495 (4.93), 6374 (5.98), 3594 (6.83) and 3028 (8.26), respectively. Rates were 3-fold and 4-fold higher in women with chronic hypertension (14.2 per 1000 total births, 3726 total stillbirths) and pre-pregnancy diabetes (20.7 per 1000 total births, 2728 total stillbirths) vs. those without (4.7 per 1000 total births, 33,136 total stillbirths). A total of 10,671 women had both chronic hypertension and pre-pregnancy diabetes, of these pregnancies, 370 ended in stillbirth (34.7 per 1000). In women without chronic hypertension, those with class III obesity had 2-fold higher rates than those with normal BMI (7.6 vs 3.8 per 1000 total births; Table 12). In women with chronic hypertension, the overall stillbirth rates were higher in all BMI groups; however, the relative increase in women with obesity was lower. For example, in those with class III obesity the stillbirth rate was approximately 50% higher than in those with normal BMI (16.0 vs 11.9 per 1000 total births). A similar relative increase in stillbirth rates among women with class III obesity compared to those with normal BMI was observed in women pre-pregnancy diabetes (23.8 vs 15.8 per 1000 total births). Underweight women with chronic hypertension or pre-pregnancy diabetes had higher rates of stillbirth than women with these chronic conditions and normal BMI (Table 12). 114 7.3.2 Gestational age specific rates The risk of stillbirth was high at early gestations and approaching term (Figure 11). In women without chronic hypertension and without pre-pregnancy diabetes the BMI-related differences in stillbirth rates remained consistent across the gestational period (higher rate with higher above-normal BMI) with the largest differences at term gestation. Underweight women without comorbidity had a slightly higher risk of stillbirth compared with women of normal BMI across gestation. Different patterns were observed among women with chronic hypertension and those with pre-pregnancy diabetes. In both of these groups, the gestational age-specific rates of stillbirth were substantially higher than in the groups without, and the BMI-related differences in stillbirth incidence rates were less pronounced at 26-35 weeks’ gestation where the rate in women with obesity was lower than those with normal BMI. After approximately 35 weeks, the BMI-specific differences in stillbirth rates more closely resembled those of women without chronic conditions. Women with chronic hypertension and those with pre-pregnancy diabetes delivered at earlier gestation than women without these conditions and almost all delivered before 40 weeks' gestation (Figure 23 and 24). This was consistent across all BMI categories. 7.3.3 Model results In both the unadjusted and adjusted analyses, the association between BMI and stillbirth varied across gestational age (p < 0.001 for time-varying effects). 115 Unadjusted gestational age-specific hazard ratios expressing the association between BMI and stillbirth differed between women with and without chronic hypertension. Adjustment for confounders slightly attenuated the magnitude of the hazard ratios, but the differences between those with and without chronic hypertension remained (Table 37). At 26-35 weeks’ gestations, above-normal BMI was associated with increased risk of stillbirth in women without chronic hypertension (e.g., at 34 weeks’ gestation, aHR comparing women with a BMI of 40kg/m2 vs 20 kg/m2 was 1.48, 95% CI = 1.37 to 1.59); however, this association was reversed in women with hypertension (aHR = 0.86, 95% CI: 0.69 to 1.07; Figure 12, Table 37). At term gestation, these differences between women with and without chronic hypertension were no longer present (e.g., at 40 weeks’ gestation, aHR comparing women with a BMI of 40kg/m2 vs 20 kg/m2 was 2.42, 95% CI: 1.51 to 4.27 in those without chronic hypertension; versus aHR = 2.54, 95% CI: 2.16 to 2.71, in women with chronic hypertension; Table 37). Similarly, unadjusted and unadjusted hazard ratios expressing the association between BMI and stillbirth differed between women with and without pre-pregnancy diabetes (Table 38). Approximately at 26-35 weeks' gestation, obesity was associated with increased risk of stillbirth in women without pre-pregnancy diabetes (e.g., at 34 weeks’ gestation, aHR comparing women with a BMI of 40kg/m2 vs 20 kg/m2 was 1.41, 95% CI: 1.31 to 1.53); while this association was not present in women with pre-pregnancy diabetes (e.g., at 34 weeks’ gestation, aHR for women with a BMI of 40kg/m2 vs 20 kg/m2 was 0.96, 95% CI: 0.79 to 1.19). At term gestation, the differences between those with and without pre-pregnancy diabetes were still present (e.g., at 40 weeks’ gestation, aHR comparing women with a BMI of 40kg/m2 vs 20 kg/m2 was 2.32, 95% 116 CI: 2.07 to 2.60, in those without pre-pregnancy diabetes; versus aHR = 1.41, 95% CI: 0.96 to 2.09, in women with pre-pregnancy diabetes; Figure 13, Table 38). Among women with both chronic hypertension and gestational diabetes, gestational age specific results showed no relationship between higher BMI and stillbirth at any gestation, although confidence intervals were wider than in the primary analyses (Table 39). Sensitivity analyses excluding women with unknown hypertension and pre-pregnancy diabetes (3.9% of stillbirths, < 0.1% of livebirths) did not change these results (Table 40). 7.4 Discussion 7.4.1 Principle findings In this population-based study, we found that the relationship between pre-pregnancy BMI and stillbirth differs across gestational age, and the relative risk of stillbirth associated with BMI increased with gestation, especially at term. However, this pattern differed in women with chronic hypertension and those with pre-pregnancy diabetes mellitus at 26-35 weeks’ gestations, showing no association or inverse association between BMI and stillbirth. Despite these variations in gestation age-specific risks, women with high pre-pregnancy BMI and comorbidity and women with low BMI and chronic hypertension had the highest rates of stillbirth at all gestations. 117 7.4.2 Results in the context of what is known The etiology of stillbirth in women with elevated BMI is still not completely understood. Presence of chronic inflammation, sleep apnea, or metabolic and vascular abnormalities in obese women are possible explanations (137,331,332). A meta-analysis of North American, European and Australian data suggests that nearly 25% of pregnancy complications including gestational hypertension, preeclampsia, gestational diabetes, preterm birth, and large-for-gestational-age infant can be attributable to high BMI (40,333), and most of these conditions increase risk of stillbirth. For instance, placental dysfunction associated with preeclampsia is proposed as one of the main causes of fetal death (137). Fetal macrosomia, which is more common in women with obesity, also increases the risk of intrapartum stillbirth (8,334). Several previous studies have reported a relationship between pre-pregnancy BMI and elevated rates of stillbirth (330,7,138,8), and some reported an increase in gestational age-specific rates of stillbirth at late-term and post-term gestational age in obese women (9,137). Our study corroborates these reports. In addition, our findings that the increase in gestational age-specific risks of stillbirth with increasing BMI is not observed in women with chronic hypertension and those with pre-pregnancy diabetes at 26-35 weeks’ gestation is novel, and shows that in the presence of these conditions, high BMI has a different association with stillbirth. 7.4.3 Clinical and research implications The SOGC recommends 39 weeks’ gestation as an optimal timing of delivery for morbidly obese women (BMI >40 kg/m2). (15) This recommendation is supported by some studies (358), while others have tentatively suggested earlier weeks as optimal delivery intervals (9,331,358,359). We 118 show that among women without hypertension and without pre-pregnancy diabetes, the risk of stillbirth increases by increasing BMI, and also with gestation in a dose-response fashion from 31 weeks onwards. Women with chronic hypertension and those with pre-pregnancy diabetes have much higher rates of stillbirth at all gestational weeks and in all BMI categories compared with women who were obese but without these chronic conditions prior to pregnancy. Among women with these chronic conditions, the risk of stillbirth increases with gestational age, but the association with above-normal BMI was observed only after 35 weeks’ gestation. Delivery at early term gestation may reduce stillbirths among these women, especially in those with elevated BMI. We have shown previously that BMI-specific risks of stillbirth, neonatal death and severe neonatal morbidity are modified by other risk factors including chronic hypertension, diabetes, nulliparity and advanced maternal age (360). We found, similar to our current study, that the relative impact of high BMI in women with chronic hypertension or diabetes on risk of stillbirth is lower than in women without these chronic conditions(360). Our current findings demonstrates that for stillbirth, this is largely due to a null or inverse association between BMI and stillbirth at 26-35 weeks’ gestation. The reason behind such gestational age-specific association is puzzling. One possible explanation is an increased fetal surveillance or a lower threshold for obstetrical interventions in these high-risk groups during this specific gestational period. The interventions may include labour induction to prevent severe maternal morbidity when pregnancy complications occur. These findings can be also due to idiocrasies of our data, thus should be further corroborated in other studies. 119 It is also noteworthy that underweight women with chronic hypertension had the highest cumulative risk of stillbirth. This observation is surprising; however, it is based on a relatively low number of women (1012 women; 17 stillbirths). In our previous study, we also found that among women with chronic hypertension the adjusted odds of stillbirth were highest in underweight women; however, this estimate had wide confidence intervals (aOR = 1.53, 95% CI = 0.52 to 4.77). Even though these findings are similar, they should be interpreted with caution and verified in other studies. 7.4.4 Strengths and limitations The strength of our study includes large population-based data with detailed information on demographic and behavioural factors that can affect pregnancy outcomes. Specifically, we had sufficient number of stillbirths in relatively small strata to be able to estimate gestational age-specific relative risks. Further, PAMMs models provided an advantage of highly flexible modelling of gestational age-specific risk, which helped to uncover complex patterns (e.g., changes in risk at 26 and 35 weeks) that would have been more difficult to detect using traditional modelling methods. Our study also has several limitations. First, we lacked information on the exact time of fetal death in-utero, and we used gestational age at delivery as a proxy for fetal death, which is common when using population data. That said, there is no evidence to suggest differential misclassification of fetal death timing across BMI strata and by presence of a chronic condition (e.g., between women with and without chronic hypertension). Second, residual confounding can potentially influence our results. We were not able to adjust for socio-economic status or health 120 insurance status (as these data were not included on fetal death certificates) and other behavioral factors (e.g., alcohol and drug use). Such unmeasured confounding could potentially lead to bias in the overall associations between higher BMI and stillbirth in each strata but would have to also have gestational-age varying effects to impact the different gestational age-specific patterns in women with and without chronic conditions that we observed. Third, we were unable to separate Type 1 vs Type 2 pre-pregnancy diabetes, which may potentially have showed different gestational age specific patterns. Fourth, the fetal death files do not include data on fetal abnormalities, limiting our ability to conduct subgroup analyses excluding these cases. Finally, our results with respect to hazard ratios at late term and post-term may underestimate the true risk associated with high BMI at term, as women with high BMI and chronic conditions who show signs of complications at early term gestation are typically delivered earlier to prevent severe maternal morbidity and stillbirth. 7.5 Conclusion Women with elevated pre-pregnancy BMI have higher risk of stillbirth, especially at late term and post-term gestations. The relationship between BMI and stillbirth is modified in the presence of chronic hypertension or diabetes mellitus at 26-35 weeks’ gestations, with lower or similar risk of stillbirth associated with increasing BMI. Nevertheless, women with chronic comorbidity and elevated BMI have higher absolute risk of stillbirth at all gestations. Our results suggest that in obese women, optimal timing of delivery may differ depending on the presence of other risk factors such as chronic hypertension and pre-pregnancy diabetes. 121 7.6 Tables Table 11: Pre-pregnancy demographics and comorbidities, stratified by pre-pregnancy body mass index. Normal (N=3,233,710) Underweight (N=255,464) Overweight (N=1,925,510) Obese I (N=1,065,958) Obese II (N=518,543) Obese III (N=366,612) Maternal age (years) <20 199637 (6.2%) 28612 (11.2%) 91131 (4.73%) 44261 (4.15%) 18463 (3.6%) 9824 (2.7%) 20-24 643803 (19.9%) 74577 (29.2%) 376030 (19.5%) 220411 (20.7%) 108751 (21.0%) 74263 (20.3%) 25-29 916594 (28.3%) 71544 (28.0%) 564326 (29.3%) 321340 (30.1%) 162202 (31.3%) 118688 (32.4%) 30-34 937950 (29.0%) 54242 (21.2%) 547320 (28.4%) 290079 (27.2%) 139449 (26.9%) 100822 (27.5%) 35-39 444233 (13.7%) 22298 (8.7%) 281073 (14.6%) 153079 (14.4%) 72823 (14.0%) 51931 (14.2%) >40 91493 (2.8%) 4191 (1.6%) 65630 (3.4%) 36788 (3.5%) 16855 (3.3%) 11084 (3.0%) Nulliparous 1377130 (42.6%) 122722 (48.0%) 696651 (36.2%) 351573 (33.0%) 168845 (32.6%) 119524 (32.6%) Maternal race Non-Hispanic White 1871581 (57.9%) 134951 (52.8%) 976587 (50.7%) 517381 (48.5%) 261295 (50.4%) 185376 (50.6%) Non-Hispanic Black 371580 (11.5%) 35093 (13.7%) 294024 (15.3%) 196802 (18.5%) 107680 (20.8%) 92802 (25.3%) American Indian/Alaskan Native 22405 (0.7%) 1651 (0.7%) 18393 (1.0%) 13434 (1.3%) 7257 (1.4%) 4863 (1.3%) Asian/Pacific Islander 314932 (9.7%) 38907 (15.2%) 116867 (6.1%) 39614 (3.7%) 11746 (2.3%) 5102 (1.4%) Hispanic 653212 (20.2%) 44862 (17.6%) 519639 (27.0%) 298727 (28.0%) 130565 (25.2%) 78469 (21.4%) Maternal education Less than high school 393115 (12.2%) 44110 (17.3%) 275372 (14.3%) 157192 (14.7%) 69667 (13.4%) 45691 (12.5%) High school 723255 (22.4%) 74533 (29.2%) 484054 (25.1%) 301343 (28.3%) 155584 (30.0%) 115862 (31.6%) College 1585655 (49.0%) 107327 (42.0%) 936522 (48.6%) 514523 (48.3%) 255722 (49.3%) 182736 (49.8%) Higher 492950 (15.2%) 26123 (10.2%) 206268 (10.7%) 80865 (7.6%) 32640 (6.3%) 19161 (5.2%) Unknown 38735 (1.2%) 3371 (1.3%) 23294 (1.2%) 12035 (1.1%) 4930 (1.0%) 3162 (0.9%) Smoking during pregnancy No 3010307 (93.1%) 223732 (87.6%) 1792950 (93.1%) 981896 (92.1%) 473420 (91.3%) 332592 (90.7%) Yes 211004 (6.5%) 30538 (12.0%) 124501 (6.5%) 79352 (7.4%) 42743 (8.2%) 32241 (8.8%) Unknown 12399 (0.4%) 1194 (0.5%) 8059 (0.4%) 4710 (0.4%) 2380 (0.5%) 1779 (0.5%) Insurance status* Medicaid 1206456 (37.3%) 127646 (50.0%) 825277 (42.9%) 509449 (47.8%) 258814 (49.9%) 196232 (53.5%) Private 1723428 (53.3%) 102003 (39.9%) 923251 (47.9%) 468555 (44.0%) 223136 (43.0%) 148381 (40.5%) Self pay 149778 (4.6%) 14163 (5.5%) 77033 (4.0%) 35289 (3.3%) 12865 (2.5%) 6392 (1.7%) 122 Other 123510 (3.8%) 9069 (3.6%) 79991 (4.2%) 40635 (3.8%) 17423 (3.4%) 10623 (2.9%) Unknown 30548 (0.95%) 2583 (1.01%) 19958 (0.9%) 12030 (1.1%) 6305 (1.2%) 4984 (1.4%) Diabetes mellitus No 3218488 (99.5%) 254548 (99.6%) 1908662 (99.1%) 1050198 (98.5%) 507139 (97.8%) 354205 (96.6%) Yes 12336 (0.4%) 659 (0.3%) 15118 (0.8%) 14782 (1.4%) 10922 (2.1%) 12097 (3.3%) Unknown 2886 (0.1%) 257 (0.1%) 1730 (0.1%) 978 (0.1%) 482 (0.1%) 310 (0.1%) Chronic hypertension No 3209394 (99.2%) 254155 (99.5%) 1895733 (98.5%) 1036574 (97.2%) 495509 (95.6%) 336326 (91.7%) Yes 21430 (0.7%) 1052 (0.4%) 28047 (1.5%) 28406 (2.7%) 22552 (4.4%) 29976 (8.2%) Unknown 2886 (0.1%) 257 (0.1%) 1730 (0.1%) 978 (0.1%) 482 (0.1%) 310 (0.1%) Gestational age at delivery (weeks) 20-25 13014 (0.4%) 1236 (0.5%) 9841 (0.5%) 7129 (0.7%) 4195 (0.8%) 3445 (0.9%) 26-28 9758 (0.3%) 1061 (0.4%) 6758 (0.4%) 4797 (0.4%) 2630 (0.5%) 2125 (0.6%) 29-32 26614 (0.8%) 3016 (1.2%) 17605 (0.9%) 11593 (1.1%) 6320 (1.2%) 5069 (1.4%) 33-37 190907 (5.9%) 20381 (8.0%) 119999 (6.2%) 74037 (7.0%) 39246 (7.6%) 31794 (8.7%) 37-40 2755747 (85.2%) 215835 (84.5%) 1635355 (84.9%) 900065 (84.4%) 435924 (84.1%) 306579 (83.6%) >40 237670 (7.4%) 13935 (5.5%) 135952 (7.1%) 68337 (6.4%) 30228 (5.8%) 17600 (4.8%) *Information about insurance status is not available for stillbirths 123 Table 12: Rates of stillbirth by pre-pregnancy BMI, chronic hypertension, and pre-pregnancy diabetes mellitus. BMI group No chronic hypertension Chronic hypertension N births N stillbirths (rate per 1000 births) N births N stillbirths (rate per 1000 births) Overall 7234334 34130 (4.7) 131463 1863 (14.2) Normal 3212280 12213 (3.8) 21430 256 (11.9) Underweight 254412 1016 (4.0) 1052 17 (16.2) Overweight 1897463 9125 (4.8) 28047 370 (13.2) Obese I 1037552 5975 (5.8) 28406 399 (14.0) Obese II 495991 3254 (6.6) 22552 340 (15.1) Obese III 336636 2547 (7.6) 29976 481 (16.0) BMI group No pre-pregnancy diabetes Pre-pregnancy diabetes N births N stillbirths (rate per 1000 births) N births N stillbirths (rate per 1000 births) Overall 7299883 34629 (4.7) 65914 1364 (20.7) Normal 3221374 12280 (3.8) 12336 189 (15.3) Underweight 254805 1019 (4.0) 659 14 (21.2) Overweight 1910392 9191 (4.8) 15118 304 (20.1) Obese I 1051176 6074 (5.8) 14782 300 (20.3) Obese II 507621 3325 (6.6) 10922 269 (24.6) Obese III 354515 2740 (7.7) 12097 288 (23.8) 124 7.7 Figures Figure 11: Prospective risk of stillbirth by pre-pregnancy BMI in women with and without chronic hypertension or diabetes by gestational week. Denominators for gestational age specific risks are all fetuses at risk at the given gestational week. 125 Figure 12: Log-hazard ratios (vs a pre-pregnancy BMI of 20 kg/m2) for the gestational age specific risk of stillbirth in women with increasing BMI in those with (left panel) and without (right panel) chronic hypertension. Curves are adjusted for maternal age, race, education, parity, pre-pregnancy smoking status and diabetes mellitus. 13520 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42Gestational age (weeks)Hazard ratio relative to a BMI of 20 (95% CI) (log scale)12320 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42Gestational age (weeks)BMI 25 30 35 40 126 Figure 13: Adjusted log-hazard ratios (vs a pre-pregnancy BMI of 20 kg/m2) for the gestational age-specific risk of stillbirth by BMI category in women with (left panel) and without (right panel) pre-pregnancy diabetes mellitus; adjusted for maternal age, race, education, parity, smoking during pregnancy and chronic hypertension. 0.81.02.020 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42Gestational age (weeks)Hazard ratio relative to a BMI of 20 (95% CI) (log−scale)12320 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42Gestational age (weeks)BMI 25 30 35 40 127 Chapter 8: Obesity, twin pregnancy and the role of reproductive technology 8.1 Introduction The global rate of twin deliveries has increased from 9.1 to 12.0 per 1000 deliveries between 1980-85 and 2010-15 (361). Most of this increase was due to increases in dizygotic twinning, as the rate of monozygotic twins has been broadly consistent worldwide and stable over time (362,363). Temporal increases in twin deliveries represent a clinical and public health concern, as twin pregnancy is associated with elevated risks of maternal and fetal/infant complications (72,73,75). A primary reason for the secular increase in twinning rates has been an increased use of assisted reproductive technology (ART) (66,68,69,362,364). In the United States, ART contributed to an estimated 10.6% of multiples (twins, triplets, etc.), and 16.8% of ART conceived infants in 2019 were multiples (365,366). Twins conceived by ART are mostly dizygotic, although there is some evidence that monozygotic twinning is also increased by ART (367). Another contributor to the increased rate of dizygotic twinning is a temporal increase in maternal age, as advanced maternal age is associated with a 25%-30% increased risk of twin pregnancies (65,66,368). Additional risk factors for twinning include higher parity (65,369,370), tall maternal stature (70,370), smoking (371), and non-White race (67,372). The proportion of women who are overweight and obese has increased in most countries, affecting 30-50% of women of reproductive age (3,4,373,374). This change is concerning as obesity is one of the most common and potentially modifiable risk factors for adverse pregnancy 128 outcomes (6,9,39,105). Previous studies from the US (1959-1966) and Denmark (1998-2001) have shown an association between obesity and twinning rates, particularly with regard to dizygotic twins (70,71). Although the mechanism underlying this association is unclear, it is possible that the higher rates of twins among obese women are linked to higher rates of ART, as overweight and obese women are more likely to have subfertility compared with those of normal BMI (375,376). The objective of this study was to examine the association between high pre-pregnancy BMI and twin delivery in a contemporary cohort of Canadian women, and to assess the extent to which this association is mediated by ART use. 8.2 Methods 8.2.1 Data source and study population This was a retrospective cohort study of all women who delivered a singleton or twin live or stillbirth at ≥ 20 weeks’ gestation in British Columbia, Canada, between April 2008 and March 2020. Women who delivered at <20 weeks’ gestation, those with missing information on gestational age and plurality and those with pregnancy termination were excluded. Data were obtained from the British Columbia Perinatal Database Registry (BCPDR), which contains individual level data on >99% of all deliveries in British Columbia since 2000, including home deliveries (76). All analyses were based on women, and the delivery (singleton or twin) constituted the unit of analysis (irrespective of whether the delivery resulted in one live birth, two live births, two stillbirths or one live birth and one stillbirth). Validation studies of BCPDR 129 data, based on re-abstraction of medical charts, have showed high accuracy of the collected data (377). The BCPDR includes information about maternal demographic characteristics, pre-pregnancy BMI, chronic medical conditions and pregnancy morbidity, as well as details about delivery and newborn hospitalizations. 8.2.2 Exposure, mediator, and outcome The primary exposure was pre-pregnancy BMI, based on self-reported height and pre-pregnancy weight and categorized as follows (in kg/m2): underweight (<18.5), normal BMI (18.5 to 24.9), overweight (25.0 to 29.9) and obese class I (30 to 34.9), class II (35 to 39.9) and class III (≥40). The primary outcome was twin delivery. To further explore the possible association between pre-pregnancy BMI and twin delivery, we conducted a mediation analysis with ART conception as the mediator. ART was defined as the use of IVF to conceive; this information was based on maternal recall and was abstracted from medical charts to BCPDR. Mediation analyses allow for the disentangling of possible pathways by which an exposure causes an outcome, thereby enabling an answer to the question, ‘What would the rate of twins be in obese women if they used ART at the same rate as women of normal BMI?’. To estimate unbiased associations, mediation analyses require adjustment for all confounders of all pairs of the exposure, mediator and outcome relationships. Confounders were identified from previous studies on twinning and included maternal age, parity, smoking during pregnancy (self-reported), and maternal height (see Figure 25 for the assumed relationships between these variables). We found evidence of a reduction in the rate of twin deliveries among ART pregnancies during our study period (Figure 26). Therefore, we included fiscal year as a confounder. 130 8.2.3 Statistical analyses Participant characteristics were summarized across pre-pregnancy BMI categories. Robust Poisson regression was used to estimate unadjusted and adjusted (for above confounders) rate ratios for the use of ART by BMI group. Rates of twin delivery were calculated per 1000 deliveries and the rates were compared between BMI categories, collectively and stratified by ART conception. We further summarized twin delivery rate by continuous BMI descriptively using a univariate logistic regression model with a natural spline with 5 knots at the quintiles of pre-pregnancy BMI distribution. Mediation analyses used the regression-based methods of Valeri and VanderWeele to estimate the total association of pre-pregnancy BMI on twin delivery and to decompose this into the indirect effect through ART conception and the remaining direct effect not mediated through ART (198,378). Log-binomial regression models were used to estimate total effects, natural direct and indirect effects (on the rate ratio scale), and the proportion mediated by ART. Standard errors were computed using the Delta method and models included an interaction between BMI and ART (198,251). We used the rate ratios for the total associations to calculate population attributable fractions (PAF) to estimate proportion of twin births attributable to pre-pregnancy overweight and obesity. Confidence intervals for PAFs were calculated using simulation (379). We computed E-values for the sensitivity of our results to unmeasured confounding (e.g., by genetic factors or race/ethnicity). E-values represent the minimum strength of the association (on the rate ratio 131 scale) that an unmeasured confounder would have to have with both pre-pregnancy BMI and twin delivery in order to render the observed association null (226). Multiple imputation was used for missing values for BMI in all above analyses. Results were pooled from the 20 multiply imputed data sets using Rubin’s rules (380). 8.2.4 Sensitivity analyses We conducted several sensitivity analyses, including restriction to opposite sex twins (as a proxy for chorionicity), complete case analysis, deterministic imputation, adjustment for BMI misclassification, and possible left-truncation bias. Further details and rationale of all sensitivity analyses are provided in Appendix I. Analyses were conducted using R statistical software version 4.3.0 (329), with mediation analysis performed using CMAverse (381). This study was conducted in accordance with STROBE guidelines. 8.2.5 Ethics approval Ethics approval was obtained from the University of British Columbia - Children’s and Women’s Hospital and Health Centre of British Columbia Research Ethics Board (H18-03154). 8.3 Results A total of 524,845 deliveries at ≥20 weeks’ gestation occurred in British Columbia from 2008 to 2020. Women who delivered had a median age of 31 years, about 50% were nulliparous, and < 132 10% smoked during pregnancy. A total of 392,046 (74.7%) women had complete data on pre-pregnancy BMI; of these, 22,396 (5.7%) women were underweight, 231,583 (59.1%) had normal BMI, 83,887 (21.4%) were overweight, and 33,363 (8.5%), 13,308 (3.4%) and 7609 (1.9%) were obese class I, II and III, respectively. Maternal age and height were similar between all BMI groups, except for underweight women who were relatively younger. Women with obesity were more likely to have higher parity, while underweight and obese women were more likely to smoke during pregnancy than those with normal BMI (Table 13). Women with missing BMI were less likely to be nulliparous and had a slightly higher rate of twin delivery (Table 41). The frequency of ART conception (per 1000 deliveries) was lowest amongst underweight (22.1), and class III obese women (20.4), and was similar in other groups (rates between 30 and 32); however, after adjustment for confounders, compared to women with normal BMI, women who were overweight, obese class I and obese class II had 9%, 23% and 26% higher rates of ART use, respectively. These results were similar in complete case analysis and after multiple imputation analyses (Table 14). The overall rate of twin delivery was 15.8 per 1000 deliveries. Compared with women with normal BMI (14.4 per 1000 deliveries), overweight and obese women class I and II had moderately higher rates of twin delivery (16.0, 16.0 and 16.7 per 1000 deliveries, respectively), and underweight women had lower rates (11.3 per 1000 deliveries; Table 15, Figure 14). Women with class III obesity had the highest rates of twin delivery (18.9 per 1000). Table 15 shows these 133 rates stratified by use of ART with similar results to those above, with the exception of class III obese women who conceived by ART, among whom the rates of twin birth were similar to other groups. Overall, ART use was associated with a nearly 12-fold increased rate of twinning (adjusted rate ratio [aRR] = 11.80, 95% CI 11.10 to 12.54). The adjusted rate of twin delivery increased with increasing BMI; women with obesity class I, II and III had 16%, 17% and 41%, higher rates of twin delivery than women with normal BMI, while underweight women had a lower rate (aRR=0.84, 95% CI 0.74 to 0.95, Table 3). Mediation analysis showed that an increasing proportion of the association between higher BMI and twinning was mediated by increased use of ART: 11% (95% CI = 3% to 18%) mediated in overweight women; 23% (95% CI = 7% to 39%) in women with class I obesity; and 27% (95% CI = 0% to 54%) in women with class II obesity. However, the trend was reversed in women with class III obesity with no evidence of mediation by ART (Table 16). Overall, 4% of twin births were attributable to above-normal pre-pregnancy BMI (overweight and obesity; PAF=4.0%; 95% CI = 1.9% to 6.0%). The association between obesity and twin birth was relatively sensitive to unmeasured confounding with all E-values <2.20 (Table 16). 8.3.1 Sensitivity analyses About two-thirds of twin deliveries included same sex twins (Table 42). Among pregnancies with opposite sex twins, the rate of ART use was 37% compared with 23% in pregnancies with same sex twins. Sensitivity analyses restricted to opposite sex twin births showed that the overall association between obesity and twin birth was stronger than in all twin births. The results of mediation analysis were slightly attenuated compared to the primary results (Table 16). 134 Complete-case analyses yielded nearly identical results to analyses using multiple imputation (Table 43). In scenarios examining worst-case biases for the proportion mediated we found large variation in the role of ART, varying from near null to >50% mediation for the association between BMI and twinning. In these scenarios, and the two corresponding to worst case biases for the total association, the dose-response relationship between higher BMI and higher rates of twinning was maintained (Table 44). The association between higher BMI and twinning was robust to possible measurement bias corresponding to under-reporting of BMI; however, such correction for measurement error attenuated the proportion mediated by ART (Table 45). Compared with the primary results, the quantitative bias analysis, assuming moderately different rates of pregnancy loss before 20 weeks’ gestation between women with normal BMI and obesity, yielded slightly stronger associations between high BMI and twinning (e.g., for class III obesity, bias-adjusted RR = 1.46; Table 46). 8.4 Discussion 8.4.1 Summary of findings In this population-based cohort study, we found a relatively small excess of twin deliveries in women who were overweight and obese prior to pregnancy, compared with women with normal BMI. Only 4% of twin deliveries among overweight and obese women were attributable to elevated BMI. Women with high BMI had a higher adjusted frequency of ART conception. ART accounted for about a quarter of the association between BMI and twinning in women with BMI between 30.0 and 39.9 kg/m2; however, in women with BMI ≥40 kg/m2, who had the highest 135 rates of twin birth, ART did not play any mediating role. We found a stronger association between BMI and opposite-sex twin delivery, but the proportion mediated by ART conception was smaller. 8.4.2 Comparison with the literature Understanding the potential causes of twin pregnancy and delivery is essential from both clinical and public health perspectives, as women with twin pregnancy are more likely to experience complications including severe maternal morbidity, preterm birth and stillbirth (72,73,75). Two large studies have shown elevated twinning rates in women with increased BMI (70,71). A cohort study from Denmark found that obese women had 44% higher odds of twin birth and 62% higher odds of opposite-sex twins (70). The second study examined twin pregnancies in the US in 1959-1966, before the widespread use of ART, and showed a two-fold increase in the twin rate in dizygotic twins among women with higher BMI, but no relationship between BMI and the rate of monozygotic twins (71). While our study also found a relationship between obesity and twin birth, the associations were weaker compared with these prior studies, which may be due to differences in study periods and prevalence of ART use. Our assessment of ART conception as a potential mediator in the association between obesity and twin delivery is novel. Obesity is a known risk factor for subfertility and infertility (375,376,382,383), leading to increased use of IVF and other fertility treatments among women who are overweight or obese (371). In accordance with prior studies, we observed a modest increase in the frequency of ART conception ending in childbirth in overweight and obese women; however, this increased use of ART mediated only a small proportion of the association 136 between obesity and twin delivery. Another possible reason for increased dizygotic twinning in women with high BMI is an increased prevalence of polycystic ovary syndrome (PCOS), which is associated with use of fertility drugs (384) that increase the risk of multifetal pregnancy (385). There are other possible factors that may contribute to the results found in our study. First, some women may undergo a selective reduction in twins, which is typically carried out around 10 weeks gestation (386). It’s possible that selective reduction occurs at higher rates in obese women who conceived by ART who may have other comorbidities that would jeopardize the continuation of twin pregnancy to the live birth of both twins. This could explain the lower rates of twin deliveries observed in women with obesity class I vs those with normal BMI. Second, in some settings, women with a very-high BMI are not eligible for publicly funded ART or are advised not to pursue ART due to a lower perceived success rate (387–389). Such restrictions have been disputed (382), and pooled data from several studies suggest that obesity alone is not sufficient to withhold treatment (383). ART practices in British Columbia do not follow explicit restrictions, but IVF access for women with a BMI ≥40 kg/m2 is restricted to hospital-based programs, as opposed to outpatient fertility clinics. It is, therefore, possible that some obese women are advised against pursuing ART until their BMI improves. This could explain our differential findings in women with class III obesity. 137 8.4.3 Strengths and limitations The strengths of this study include large population-level data including all births in British Columbia, Canada, minimizing possible selection bias. The medical chart ascertainment of ART conception was consistent throughout the study period. The study also has several limitations. First, pre-pregnancy body-mass-index had a sizeable proportion of missing data. Multiple imputation results showed little difference from the complete-case analyses, indicating that our results are valid under a missing at random assumption. We further investigated the validity of our findings under several non-missing-at-random scenarios. In each of these scenarios, the association between high BMI and twinning was maintained, but the degree of mediation by ART ranged from 0 to >50%. Second, we lacked information on chorionicity. The analysis of opposite-sex twins, which we used as a proxy to restrict to dichorionic twins, was congruent with the findings from previous studies of the association between obesity and dichorionic twinning (70). Third, we did not have data on pregnancies ending prior to 20 weeks’ gestation (miscarriages), and thus could not assess the true incidence of twin pregnancy. Twin pregnancy and obesity are both known risk factors for miscarriage in spontaneous and ART-conceived pregnancies (390,391), which could lead to a left-truncation bias. We performed quantitative bias analysis to address this bias assuming moderate differences in the rates of miscarriage by BMI groups and found similar results to our primary analyses. However, if the degree of differential pregnancy loss was higher than we assumed, our findings may be biased. Fourth, several studies show that self-reported BMI tends to underreport overweight and obesity (42,44,392). Sensitivity analysis adjusting for such misclassification showed similar results to our primary analysis, suggesting that our results are 138 robust in this respect. Fifth, the association between BMI and twin delivery in our study may be susceptible to unmeasured confounding, as evidenced by our relatively low estimated E-values. We did not have information about race/ethnicity, which is a possible confounder. Further, the use of BMI as a measure of adiposity-related body composition in different populations has been questioned, and therefore, further research related to our findings should be conducted in diverse populations and stratified by race/ethnicity. We were also unable to link subsequent deliveries to the same women over the study period. Finally, women with class III obesity may have a higher rate of fertility drug use, which may explain the elevated rate of twin birth in this group. 8.5 Conclusion In this cohort study of 524,845 women, we found overweight and obese women have higher rates of twin delivery; however, the proportion of twins that is attributed to elevated pre-pregnancy BMI was relatively low. Women with higher BMI used ART at higher rates than those with normal BMI, and this partially mediated the relationship between high pre-pregnancy BMI and twin delivery in those who were overweight or had class I or II obesity. However, we did not find any evidence of mediation by ART in women with class III obesity, who had the highest rates of twin delivery. 139 8.6 Tables Table 13: Demographic characteristics of women by pre-pregnancy BMI category, British Columbia, Canada, 2008-2020. Underweight (N = 22396) Normal (N = 231583) Overweight (N = 83887) Obese I (N = 33363) Obese II (N = 13308) Obese III (N = 7609) No. (%) with data Maternal age, median [IQR] 30.1 [26.3;33.7] 31.6 [28.1;35.0] 31.6 [28.0;35.1] 31.3 [27.6;34.9] 31.1 [27.4;34.7] 31.4 [27.8;34.9] < 20 779 (3.5) 4443 (1.9) 1276 (1.5) 779 (3.48) 4443 (1.9) 1276 (1.5) 20-24 3239 (14.5) 21908 (9.5) 8467 (10.1) 3239 (14.5) 21908 (9.5) 8467 (10.1) 25-34 14394 (64.3) 146819 (63.4) 52146 (62.2) 14394 (64.3) 146819 (63.4) 52146 (62.2) 35-39 3314 (14.8) 48101 (20.8) 17769 (21.2) 3314 (14.8) 48101 (20.8) 17769 (21.2) ≥40 670 (2.99) 10312 (4.5) 4229 (5.0) 670 (2.99) 10312 (4.5) 4229 (5.04) Parity Nulliparous 12554 (56.1) 117770 (50.9) 37211 (44.4) 12554 (56.1) 117770 (50.9) 37211 (44.4) 1 7199 (32.1) 81015 (35.0) 30935 (36.9) 7199 (32.1) 81015 (35.0) 30935 (36.9) ≥2 2642 (11.8) 32780 (14.2) 15728 (18.8) 2642 (11.8) 32780 (14.2) 15728 (18.8) Maternal height, median [IQR] 165 [160;170] 165 [160;170] 164 [160;169] 165 [160;170] 165 [160;170] 164 [160;169] < 165 10631 (47.5) 114967 (49.6) 42239 (50.4) 10631 (47.5) 114967 (49.6) 42239 (50.4) 165-168 5852 (26.1) 55105 (23.8) 19807 (23.6) 5852 (26.1) 55105 (23.8) 19807 (23.6) 169-172 2507 (11.2) 27675 (12.0) 10043 (12.0) 2507 (11.2) 27675 (12.0) 10043 (12.0) ≥173 3406 (15.2) 33836 (14.6) 11798 (14.1) 3406 (15.2) 33836 (14.6) 11798 (14.1) Smoking Yes 1727 (7.7) 12954 (5.6) 6159 (7.3) 1727 (7.7) 12954 (5.6) 6159 (7.3) No 20669 (92.3) 218629 (94.4) 77728 (92.7) 20669 (92.3) 218629 (94.4) 77728 (92.7) IQR = interquartile range. Table 14: Rates, rate ratios (RR) and adjusted rate ratios (aRR) for use of ART by pre-pregnancy BMI category. 140 Complete case analysis Multiple imputationb Pre-pregnancy BMI Total births N ART births (per 1000) RR (95% CI) aRR (95% CI)a RR (95% CI) aRR (95% CI)a Underweight 22396 495 (22.1) 0.75 (0.69, 0.82) 0.92 (0.84, 1.00) 0.80 (0.73, 0.88) 0.93 (0.85, 1.02) Normal 231583 6823 (29.5) 1.00 (reference) 1.00 (reference) 1.00 (reference) 1.00 (reference) Overweight 83887 2570 (30.6) 1.04 (0.99, 1.09) 1.09 (1.04, 1.14) 1.04 (0.99, 1.08) 1.09 (1.05, 1.14) Obese I 33263 1054 (31.7) 1.08 (1.01, 1.15) 1.25 (1.18, 1.33) 1.07 (1.00, 1.14) 1.23 (1.16, 1.31) Obese II 13308 426 (32.0) 1.09 (0.99, 1.20) 1.30 (1.18, 1.43) 1.07 (0.97, 1.18) 1.26 (1.15, 1.38) Obese III 7609 155 (20.4) 0.69 (0.59, 0.81) 0.82 (0.71, 0.96) (0.77 (0.65, 0.90) 0.91 (0.78, 1.06) a. Adjusted for maternal height, age, smoking status, parity and fiscal year. b. Results pooled from 20 imputed datasets. Table 15: Twin delivery rates by pre-pregnancy BMI category and use of assisted reproductive technology (ART). Pre-pregnancy BMI Overall ART Non-ART Total births N twin births (per 1000) Total births N twin births (per 1000) Total births N twin births (per 1000) Underweight 22396 253 (11.3) 495 65 (131.3) 21901 188 (8.58) Normal 231583 3323 (14.4) 6823 977 (143.2) 224760 2346 (10.44) Overweight 83887 1340 (16.0) 2570 363 (141.3) 81317 977 (12.01) Obese I 33263 531 (16.0) 1054 134 (127.1) 32209 397 (12.33) Obese II 13308 222 (16.7) 426 63 (147.9) 12882 159 (12.34) Obese III 7609 144 (18.9) 155 23 (148.4) 7454 121 (16.23) 141 Table 16: Adjusted associations between pre-pregnancy BMI category and twin delivery, and mediation analysis by ART conception. Pre-pregnancy BMI Total effect aRR (95 CI) Natural direct effect aRR (95 CI) Natural indirect effect aRR (95 CI) Proportion mediated % (95 CI) E-value for total effect All twins Underweight 0.84 (0.74, 0.95) 0.85 (0.75, 0.96) 0.99 (0.98, 1.00) 6 (-2, 13) 1.67 Normal 1.00 (ref) 1.00 (ref) 1.00 (ref) - - Overweight 1.14 (1.07, 1.21) 1.12 (1.05, 1.19) 1.01 (1.01, 1.02) 11 (3, 18) 1.54 Obese I 1.16 (1.06, 1.27) 1.12 (1.03, 1.23) 1.03 (1.02, 1.04) 23 (7, 39) 1.59 Obese II 1.17 (1.02, 1.34) 1.13 (0.98, 1.29) 1.04 (1.02, 1.06) 27 (0, 54) 1.61 Obese III 1.41 (1.19, 1.66) 1.44 (1.22, 1.69) 0.98 (0.96, 1.00) -7 (-15, 0) 2.17 Opposite sex twins only Underweight 0.73 (0.58, 0.91) 0.74 (0.69, 0.93) 0.98 (0.96, 1.01) 5 (-3, 13) 2.08 Normal 1.00 (ref) 1.00 (ref) 1.00 (ref) - - Overweight 1.22 (1.09, 1.35) 1.19 (1.07, 1.33) 1.02 (1.01, 1.03) 11 (3, 19) 1.73 Obese I 1.33 (1.15, 1.54) 1.27 (1.10, 1.48) 1.05 (1.03, 1.07) 18 (7, 30) 1.99 Obese II 1.47 (1.19, 1.81) 1.39 (1.13, 1.72) 1.05 (1.02, 1.08) 16 (5, 27) 2.30 Obese III 1.64 (1.26, 2.13) 1.68 (1.29, 2.18) 0.97 (0.95, 1.00) -7 (-15, 2) 2.67 Effects are estimated from mediation analyses pooled across 20 multiply imputed data sets. E-values represent strength of unmeasured confounder (on rate ratio scale) needed to bring the point estimate for total effects to 1.0. aRR = adjusted risk ratio for maternal height, age, smoking status, parity and fiscal year. 142 8.7 Figures Figure 14: Association between pre-pregnancy BMI and rate of twin delivery. 510152020 30 40Pre−pregnancy body−mass−index (kg/m2)Number of twin pregnancies per 1000 deliveries 143 Chapter 9: Pre-pregnancy body-mass-index and adverse maternal and perinatal outcomes in twins: a population retrospective cohort study 9.1 Introduction Pre-pregnancy obesity is an important risk factor for adverse birth outcomes, and its prevalence among pregnant women has been steadily increasing in recent decades.(4) For example, in the United States, recent studies show that pre-pregnancy obesity is prevalent in 29% of women as of 2019, an increase from 26% in 2016 (320). Similar trends have been observed predominantly in other high-income settings, but also in developing countries (4,373,374). High pre-pregnancy body-mass-index (BMI) is associated with an increased risk of adverse maternal and perinatal outcomes. In singleton pregnancies, depending on the level of obesity, women with obesity (vs. those of normal BMI) are at 20-40% increased risk of severe maternal morbidity (SMM) (6) and at 50% increased risk of stillbirth and neonatal death (133) (7). Multiple pregnancy is also a recognized risk factor for adverse pregnancy outcomes, compared with singleton pregnancy. Women with twin (vs. singleton) pregnancies experience up to four-fold higher rates of maternal complications, stillbirth rates that are five-fold (in dichorionic twins) to thirteen-fold higher (monochorionic twins), (72,75) and similarly elevated rates of neonatal death and serious neonatal morbidity (72,73). Though there is ample evidence for higher pregnancy risks associated with obesity in singleton pregnancies, these pre-pregnancy BMI-specific risks are understudied in twin pregnancies. 144 Previous studies in twin pregnancies have demonstrated an enhanced risk of obesity with regard to pre-eclampsia, gestational diabetes, and preterm birth (72,74,393). Relevant studies examining specific SMM conditions are scarce, possibly due to the need for large sample size to assess rare outcomes. Extrapolating from the known effects of BMI on adverse pregnancy outcomes in singletons, the highest rates of perinatal mortality would be expected in obese women with twins. However, some prior evidence suggests that the effect of BMI on adverse perinatal outcomes is modified in twins, and that the association between pre-pregnancy BMI and adverse perinatal outcomes including stillbirth, neonatal death and serious neonatal morbidity in twin pregnancies may be the opposite of what might be expected (74). The objective of this study was to examine the associations between pre-pregnancy BMI and SMM, stillbirth, neonatal death, and severe neonatal morbidity in women with twin pregnancies and their infants. 9.2 Materials and methods 9.2.1 Data source and study population We carried out a retrospective cohort study of all twin births at ≥ 20 weeks’ gestation in British Columbia, Canada, between 2000 and 2017. Pregnancies at <20 weeks’ gestation and those with missing information about pre-pregnancy BMI were excluded. Data were obtained from the British Columbia Perinatal Database Registry (BCPDR), which contains individual level data on >99% of births in British Columbia since 2000, including home births (394). Validation studies of BCPDR data against medical charts showed high accuracy of collected data (377). 145 The BCPDR includes information about maternal demographic characteristics, pre-pregnancy morbidity, BMI, and pregnancy morbidity, as well as details about delivery and newborn hospitalizations with up to 25 diagnoses related to each hospitalization for both mom and baby. The diagnoses are coded using International Classification of Diseases (Canadian modification version 10, ICD-10-CA), included are also up to 20 medical procedure codes (coded using the Canadian Classification of Interventions, CCI) for both the mother and newborn. In addition, the database contains information about hospital readmissions for mothers and babies for up to 42 and 28 days, respectively. The primary exposure was pre-pregnancy BMI, defined based on self-reported height and pre-pregnancy weight and expressed as kg/m2, and was categorized as underweight (<18.5), normal BMI (18.5 to 24.9), overweight (25.0 to 29.9) and obese (≥30). 9.2.2 Outcome measures and potential confounders We examined associations between pre-pregnancy BMI and both SMM and adverse perinatal outcomes. SMM was defined as a composite outcome that included conditions that are potentially life-threatening or leading to serious sequelae, conditions requiring intensive care unit admission or prolonged hospitalization. A list of such conditions was previously developed by the Canadian Perinatal Surveillance System, (125) and included: acute cardiac, hepatic, or renal failure, need for assisted ventilation, cerebrovascular morbidity, embolism, shock or disseminated intravascular coagulation (DIC), hysterectomy, surgical complications, severe pre-eclampsia (or eclampsia), maternal sepsis, severe hemorrhage or uterine rupture, and maternal 146 death. The adverse composite perinatal outcome included perinatal death (stillbirth or neonatal death), and severe neonatal morbidity including intraventricular hemorrhage (grades 3 and 4), periventricular leukomalacia, necrotizing enterocolitis, sepsis, and severe birth trauma. (Tables 47 and 48 show the ICD-10 codes and details for SMM and severe neonatal morbidity, respectively). We adjusted for variables identified previously as potential confounders in singleton pregnancies (197) including maternal age, parity, self-reported smoking, alcohol or drug use during pregnancy, chronic hypertension, pre-pregnancy diabetes mellitus, other chronic pre-pregnancy conditions (e.g., cardiac, hepatic, renal conditions; Table 49) and prior stillbirth or neonatal death. For perinatal outcomes, additional confounders included congenital anomalies and infant sex. 9.2.3 Statistical analyses Composite maternal outcomes were calculated per 10,000 pregnancies, fetal outcomes per 10,000 total births, and neonatal outcomes per 10,000 live births. For each BMI group, we first computed unadjusted and adjusted rate ratios (compared with normal BMI group), with Poisson regression models based on the method of Generalized Estimating Equations (GEE) to account for clustered responses for twin pregnancies. Second, we examined the dose-response relationship between pre-pregnancy BMI and adverse birth outcomes by analyzing BMI as a continuous variable, using a restricted cubic spline with five knots at the quintiles of the BMI distribution, again using GEE models to account for clustering. Results are presented as risk curves for the main adverse outcomes across the BMI spectrum. All GEE models were assumed 147 to have an exchangeable correlation structure. Finally, we estimated Population Attributable Fractions (PAF) from the adjusted rate ratios that were > 1.0. Confidence intervals for PAFs were derived using Monte Carlo simulation with 1,000,000 iterations (379). Given a high percentage of missing data on BMI (29.6%) we conducted these analyses after applying multiple imputation by chained equations using 50 imputed data sets. Rate ratios and estimated marginal risk curves were pooled across data sets using Rubin’s rules. 9.2.4 Sensitivity analyses We conducted several sensitivity analyses. First, we conducted a complete case analysis using the above Poisson regression models. Second, we applied Targeted Maximum Likelihood Estimation (TMLE) to the complete cases (395). TMLE is a doubly-robust estimation method in that it combines a propensity score model for the exposure, and another model for the outcome, thereby offering opportunities to adjust for possible confounding of both the exposure and the outcome. For both exposure and outcome models, we used an ensemble of traditional regression models and machine learning algorithms (i.e., ‘stacking’) to accommodate possible interactions and nonlinearities. These methods were: mean value regression, logistic regression (both main effects and all interactions), adaptive spline regression, generalized additive models, and random forest. For perinatal outcomes, we adjusted the standard errors for clustering within pregnancies, using previously published methods (396). Third, we included the cases with missing BMI data in the TMLE routine under a missing-at-random assumption using previously published methods that model the missing mechanism directly (220). Fourth, all primary analyses were repeated 148 after restricting the study population to opposite-sex twins, in order to evaluate potential confounding by chorionicity. Finally, to assess the possible impact of further unmeasured confounding, we computed an E-value for how strong an unmeasured confounder would need to be associated with both obesity and an adverse perinatal outcome in order for the true rate ratio for twins to be 1.50 (226). This value was the estimated rate ratio for obesity and perinatal death in a previously published cohort of singletons from the same population (397). All analyses were conducted using R statistical software and TMLE models were fit with the drtmle package (220). 9.3 Results Of 724,920 births in British Columbia between 2000 and 2017, there were 22,066 twin births (3.0%) resulting from 11,033 deliveries. Among women with twins, 3263 (29.6%) had missing pre-pregnancy BMI. Of those with complete data, 1016 women (13.1%) were obese, 1704 (21.9%) were overweight, 4682 (60.3%) had normal BMI, and 368 (4.7%) were underweight. Demographic risk factors were similar between those with and without missing BMI (Figure 27). On average, women with twin pregnancy were 31 to 32 years of age (Table 17). A higher percent of women who were overweight or obese had pre-pregnancy hypertension, diabetes, prior caesarean delivery, and prior stillbirth or neonatal death. They were also more likely to be multiparous, smoke or use substances during pregnancy. Assisted reproductive technologies 149 (ART) – initiated conception was similar across BMI categories. Suspected intrauterine growth restriction (IUGR, based on ultrasound examination), anemia, and preterm birth at <32 weeks were more common among underweight women and these rates declined with increasing BMI. In contrast, rates of gestational hypertension, gestational diabetes, and caesarean delivery were increased in women with higher BMI. Large for gestational age infants were more frequent among obese women. SMM occurred in 222 pregnancies, at a rate of 285.7 per 10,000 twin pregnancies. SMM rates were highest among women with normal BMI (320.4 per 10,000 pregnancies; Table 18). The most common SMM was severe pre-eclampsia and/or eclampsia, which comprised over a third of the composite SMM outcome. Other relatively frequent morbidities included severe hemorrhage (requiring transfusion), surgical complications, sepsis, and peripartum hysterectomy (Table 18). The perinatal composite outcome occurred in 877 fetuses/infants (5.6%), most commonly among underweight women (Table 18). Serious neonatal morbidity accounted for more than half of the composite outcome, largely due to bronchopulmonary dysplasia, severe respiratory distress syndrome (with mechanical ventilation for >3 days) and neonatal sepsis. Stillbirth rates were similar across BMI categories. After multiple imputation, unadjusted and adjusted analyses showed no clear association between pre-pregnancy BMI category and SMM (Table 19). However, the adjusted rate ratio (vs. normal BMI) was <1.0 for women who were overweight (aRR 0.89, 95% CI = 0.66 to 1.21) and 150 women with obesity (aRR 0.85, 95% CI = 0.57 to 1.27), and underweight women (aRR = 0.96, 95% 0.57 to 1.63), although confidence intervals were wide. Rate ratios for the perinatal composite comparing women who were overweight or obese versus women of normal BMI had confidence intervals that were centered narrowly around 1.0, indicating weak or no associations. E-value analysis estimated that an unmeasured confounder would have to increase risk of obesity and the perinatal composite by at least two-fold (E-value = 2.12) to explain away the difference in the point estimate for obesity (aRR = 1.08) and a RR of 1.50. Underweight women had elevated rates of the composite adverse perinatal outcome (aRR 1.34, 95% CI = 1.00 to 1.80), including all its components except stillbirth (Table 19). Dose-response curves between BMI as a continuous variable (kg/m2) and adverse pregnancy outcomes were largely flat between BMIs of 20 kg/m2 and 40 kg/m2. In underweight women, the risk of neonatal death and serious neonatal morbidity was similar to the higher risks noted in the categorized analyses above. There was an indication that neonatal morbidity was elevated among women with BMI >40 kg/m2, but associations at the extremes of high BMIs had very wide confidence intervals due to a small number of women (Figure 15). Maternal and perinatal outcome rates for women missing BMI were slightly higher than in those with complete BMI (Table 50). Sensitivity analyses on complete cases gave similar results to above for overweight and obese women, while for underweight women perinatal rate ratios increased slightly, other than for stillbirth. Likewise, rate ratios using TMLE (both with and without imputation) instead of Poisson regression gave similar results for overweight and obese 151 women. However, estimates for underweight women varied considerably with estimates being more extreme under TMLE (Table 51). Additionally, when restricting our analyses to opposite sex twins, we found that the rates of adverse outcome were lower for all perinatal outcomes, while results for SMM were similar compared with main analyses (Table 52). In the subgroup of opposite sex twins, adjusted associations were slightly attenuated when contrasting women with obesity vs. women with normal BMI, while the results for underweight and overweight women were similar. Analyses of opposite sex twins yielded similar aRRs, however, the confidence intervals were generally wider due to a reduced study size (Table 53). 9.4 Discussion 9.4.1 Principal findings This population-based cohort study of twin pregnancies showed that the rates of SMM, perinatal death and neonatal morbidity were similar or lower in women who were overweight or obese prior to pregnancy compared with women who had normal BMI. The narrow confidence intervals around the adjusted rate ratios suggest that large differences in maternal and fetal/infant risks associated with high maternal pre-pregnancy BMI are unlikely for twin gestations. This finding was consistent with the continuous BMI analysis (dose-response curves including at BMIs above 35 kg/m2). In contrast, rates of SMM, perinatal death and neonatal morbidity were similar or higher in underweight women compared with women of normal BMI. Underweight 152 women had higher rates of composite neonatal death or severe neonatal morbidity and especially high rates of neonatal death. 9.4.2 Results in the context of prior literature Prior literature assessing pre-pregnancy BMI-specific rates of severe adverse maternal and perinatal outcomes in twin pregnancies is limited. A population-based study from Ontario, Canada, reported inconclusive results for stillbirth and neonatal death and similar results to this study for composite neonatal morbidity outcome (e.g., for obesity class III, aRR = 1.05, 95% CI = 0.31 to 3.65, aRR = 0.72, 0.09 to 5.61, aRR = 1.02, 95% CI = 0.91 to 1.14, respectively) (74). In this same study, the rates of a composite maternal outcome of pre-eclampsia, gestational diabetes and preterm birth at <32 weeks were higher in women with obesity compared with women of normal BMI (aRR = 1.74, 95% CI = 1.37 to 2.20 for obesity class III).(74) Increased risk of GDM has been consistently reported in women with elevated BMI and twin pregnancy (393,398–401). Some studies have also shown an increased risk of pre-eclampsia in women with obesity, (398–400) however, these findings have not been consistent (402). Our results suggest that although obese women with twins may have higher rates of gestational hypertension and GDM than those with normal BMI, this does not lead to higher rates of severe pre-eclampsia, eclampsia, and SMM. With regards to perinatal outcomes, a large US-based study found approximately 50% higher rates of stillbirth in (vs. normal BMI) women with obesity with twins, (403) while others found no association between pre-pregnancy BMI and a broader composite of adverse perinatal outcomes, including stillbirth and neonatal death (74). Our study confirms the absence of 153 elevated risks of adverse perinatal outcomes among obese women. This is in contrast with the consistently reported strong association between obesity and adverse perinatal outcomes in singleton pregnancies (9,38,404,405). In particular, previously published data on a cohort of singletons from a similar time period from the BCPDR showed a 50% increase in risk in stillbirth and neonatal death for women with obesity compared with those of normal BMI. (397) 9.4.3 Clinical and research implications The absence of increased rates of adverse birth outcomes in women with twin pregnancy who have high BMI is somewhat unexpected given the strong nature of this relationship among singleton pregnancies. Several possible explanations could underlie our findings. Firstly, women with obesity are at higher risk of infertility, and infertility treatments typically results in dichorionic twins which have fewer adverse pregnancy outcomes compared with monochorionic twins (406) (138,407). However, we observed no difference in use of ART across BMI groups, and our sensitivity analysis restricted to opposite sex twins (which are only di-chorionic) showed similar result to our primary analysis. Secondly, women with obesity have larger fetuses on average and lower rates of small-for-gestational-age (SGA) infants, which can lead to a reduction in SGA-related adverse perinatal outcomes. This effect of obesity may be more evident in twins which have higher baseline rates of SGA (compared with singletons). Thirdly, it is possible that maternity care providers perceive women with obesity with twins to be at high risk due to these two major risk factors and opt for heightened fetal surveillance and timely intervention when a sign of a complication occurs. Finally, a selection bias could potentially contribute to the unexpected findings. Our cohort included only those women who continued to be pregnant at 20 weeks’ gestation, and women who miscarried were not included in our study. If women who 154 were overweight or obese with twin pregnancies who had higher propensity for adverse perinatal outcomes had also higher rates of miscarriage, it is possible that twin fetuses with higher survival potential reached 20 weeks’ gestation and were included in our study. This would lead to a survival bias and a similar hypothesis has been suggested for other puzzling findings in perinatal epidemiology (such as the lower rate of pre-eclampsia among women who smoke) (256,408). Additionally, as with any observational cohort, unmeasured confounders (such as nutrition, race/ethnicity and socioeconomic status) may also lead to spurious associations or the lack thereof (216). However, such factors would have to have differential effects in twin vs. singleton pregnancies to explain our findings. All of these possible explanations may also play a role in other similarly paradoxical associations, for instance, a lack of association between advanced maternal age and adverse perinatal outcomes in twins: while advanced maternal age is a strong risk factor for perinatal death in singletons, such an association has not been observed in twins (409). Young maternal age has been consistently associated with the highest maternal age-specific rates of adverse birth outcomes in twins, (409) similar to low pre-pregnancy BMI in our study. The most concerning finding in our study relates to high rates of neonatal death (2.7%) and serious neonatal morbidity (5.5%) in underweight women with twin pregnancies. These findings are consistent with results from previous studies (74,410) and possible causes of both low BMI and adverse outcomes include poor nutrition (itself associated with anemia, preterm delivery and low birthweight (411) (412,413)), anorexia nervosa and substance use (414). We did not have specific information about maternal nutrition, however, we observed higher rates of anemia and smoking and alcohol/drug use in underweight women compared to women with normal or higher 155 BMI. Future studies are needed to validate these findings and explore causal pathways between low BMI and adverse birth outcomes to guide targeted interventions. 9.4.4 Strengths and limitations Our study has several strengths including the detailed population level data on pregnancy characteristics and outcomes. Prior literature describing rates of specific maternal and neonatal morbidities in twin pregnancies is sparse, especially in relation to pre-pregnancy BMI. We applied advanced modeling techniques to flexibly model the relationship between BMI and adverse outcomes, which provided two opportunities (i.e., exposure and outcome models) to optimize adjustment for confounding. Our study has also several limitations. First, information about pre-pregnancy BMI was missing in 29% of women. Although we employed multiple imputation methods, did not observe large differences vs a sensitivity analysis of complete cases, and did not find large differences in measured covariates between those with and without measured BMI the possibility of residual bias remains. Second, we had a small number of events for underweight women and found discrepancies between our regression and TMLE analyses for some of the resulting rate ratio estimates. The possible reasons for such discrepancy may be due to more flexible confounding control when using TMLE, or due to increased volatility of estimates as has been previously observed in similar studies (415). In either case, results for underweight women need to be interpreted cautiously. Third, we were unable to completely stratify the study population by chorionicity, which may have confounded or modified the effects of BMI on adverse perinatal outcomes. However, we partially addressed this issue by analyses restricted to opposite-sex 156 twins. Fourth, height and weight information were self-reported, and documented in medical charts during pre-pregnancy or early pregnancy physician visits. Behavioural factors including smoking, alcohol/substance use were also self-reported, and thus subject to recall bias. This likely led to some non-differential misclassification between BMI classes and possibly biased the results towards null. Fifth, due to a relatively smaller number of women with very high BMI, we were unable to stratify obese women into classes I, II and III. However, when we examined BMI continuously, we did not find increased risk in those with very high BMI (>35kg/m2), although the confidence intervals were wide at that extreme BMI range. We were also unable to examine associations with maternal death and specific types of SMMs because of low numbers. Finally, as with any observational study, unmeasured confounders (e.g., nutritional status and race/ethnicity) may have influenced our results. Such unmeasured confounders would need to at least double the risk of obesity and adverse perinatal outcomes to explain the difference between our estimates and those previously published for singletons. 9.5 Conclusion In this population-based cohort study, we have not found any evidence that in twin pregnancies, overweight and obese women had an increased risk of SMM, and adverse perinatal outcome compared with women with normal BMI. Underweight women had elevated risks of preterm birth, neonatal death and neonatal morbidity. The association between pre-pregnancy BMI and adverse perinatal outcomes thus appears to be different in twin compared with singleton pregnancies. These findings have implications for pre-pregnancy counselling and pregnancy care, especially among underweight women with twin 157 pregnancy. Future research is needed to elucidate reasons behind the unexpected differences in adverse perinatal outcomes between singleton and twin pregnancies in women with high pre-pregnancy BMI. 9.6 Tables Table 17: Demographic, obstetric history, and current pregnancy characteristics by body-mass-index in twin pregnancies. Underweight (N = 368) Normal (N = 4682) Overweight (N = 1704) Obese (N = 1016) Maternal age in years (median, IQR) 31.3 [27.6, 35.1] 32.9 [29.0, 36.4] 32.7 [29.3, 36.3] 32.5 [29.0, 35.6] < 25 42 (11.4) 417 (8.9) 153 (9.0) 95 (9.4) 25-34 230 (62.5) 2592 (55.4) 989 (58.0) 613 (60.3) ≥35 96 (26.1) 1673 (35.7) 562 (33.0) 308 (30.3) Chronic conditions Chronic hypertension < 5 (< 1.4) 62 (1.3) 36 (2.1) 50 (4.9) Chronic diabetes mellitus < 5 (< 1.4) 12 (0.3) 10 (0.6) 27 (2.7) Other chronic conditions* 8 (2.2) 65 (1.4) 22 (1.3) 14 (1.4) Obstetric history Prior caesarean section 32 (8.7) 494 (10.6) 252 (14.8) 208 (20.5) Prior stillbirth < 5 (< 1.4) 30 (0.6) 19 (1.1) 13 (1.3) Prior neonatal death < 5 (< 1.4) 14 (0.3) 12 (0.7) 5 (0.5) Current pregnancy Nulliparous 210 (57.1) 2565 (54.8) 844 (49.5) 440 (43.3) Smoking 40 (10.9) 303 (6.5) 145 (8.5) 113 (11.1) Substance use 10 (2.7) 86 (1.8) 48 (2.8) 42 (4.1) Alcohol use < 5 (< 1.4) 28 (0.6) 15 (0.9) 14 (1.4) Assisted reproductive technology 58 (15.8) 794 (17.0) 283 (16.6) 150 (14.8) Gestational hypertension 24 (6.5) 346 (7.4) 201 (11.8) 179 (17.6) Gestational diabetes 14 (3.8) 228 (4.9) 116 (6.8) 109 (10.7) Intrauterine growth restriction** 95 (25.8) 775 (16.6) 248 (14.6) 116 (11.4) Morbidly adherent placenta 3 (0.8) 9 (0.2) 7 (0.4) < 5 (<4.9) Placenta previa 6 (1.6) 48 (1.0) 14 (0.8) 5 (0.5) Anemia*** 108 (29.3) 1084 (23.2) 374 (21.9) 197 (19.4) Caeasarean delivery 233 (63.3) 3023 (64.6) 1163 (68.2) 738 (72.6) Labour induction 81 (22.0) 1268 (27.1) 445 (26.1) 228 (22.4) Gestational age at delivery 36.0 [34.0, 37.0] 36.0 [34.0, 37.0] 36.0 [34.0, 37.0] 36.0 [34.0, 37.0] < 32 50 (13.6) 491 (10.5) 164 (9.6) 108 (10.6) 32-33 38 (10.3) 409 (8.7) 169 (9.9) 93 (9.2) 34-36 173 (47.0) 2003 (42.8) 788 (46.2) 466 (45.9) 37-39 105 (28.5) 1724 (36.9) 569 (33.4) 341 (33.6) ≥40 2 (0.5) 50 (1.1) 14 (0.8) 8 (0.8) Birth weight (g)**** 2319 [1884, 2606] 2482 [2115, 2802] 2516 [2148, 2845] 2590 [2170, 2954] 158 < 800 38 (5.2) 229 (2.5) 84 (2.5) 66 (3.3) 800-1499 61 (8.3) 498 (5.3) 152 (4.5) 88 (4.3) 1500-2499 394 (53.5) 4066 (43.5) 1413 (41.6) 744 (36.7) ≥2500 243 (33.0) 4553 (48.7) 1751 (51.5) 1131 (55.7) Small for gestational age***** 208 (77.6) 1869 (40.0) 541 (31.7) 278 (27.3) Large for gestational age***** 12 (4.4) 273 (5.8) 112 (6.6) 144 (14.2) * Includes HIV, asthma, chronic cardiac, renal and hepatic conditions, systemic lupus erythematous, sickle cell disease and thalassemia. ** Diagnosed by ultrasound during pregnancy *** Anemia was defined as hemoglobin <12 g/dL (Perinatal Database Registry, abstracted from the medical charts) **** Per infant, not per pregnancy *****Based on Canadian growth reference standard. Table 18: Severe maternal morbidity, perinatal mortality, and severe neonatal morbidity by pre-pregnancy body-mass-index in twin pregnancies (rates expressed per 10,000 pregnancies for severe maternal morbidity, per 10,000 livebirths for neonatal death and serious neonatal morbidity and per 10,000 births for stillbirth and perinatal mortality). Maternal outcomes: Underweight (N = 368) Normal (N = 4682) Overweight (N = 1704) Obese (N = 1016) SMM 10 (271.7) 150 (320.4) 46 (270.0) 26 (255.9) Severe pre-eclampsia, eclampsia <5 (< 135.8) 56 (119.6) 28 (164.3) 10 (98.4) Severe hemorrhage <5 (< 135.8) 21 (44.9) 7 (41.1) < 5 (49.2) Surgical complications <5 (< 135.8) 20 (42.7) 5 (29.3) < 5 (49.2) Sepsis <5 (< 135.8) 16 (34.2) < 5 (< 29.3) 5 (49.2) Hysterectomy <5 (< 135.8) 16 (34.2) < 5 (< 29.3) < 5 (49.2) Acute renal failure 0 (0) 10 (21.4) < 5 (< 29.3) < 5 (49.2) Acute cardiac condition <5 (< 135.8) < 5 (< 10.7) < 5 (< 29.3) < 5 (49.2) Assisted ventilation 0 (0) 7 (15.0) < 5 (< 29.3) < 5 (49.2) Embolism, shock, DIC* 0 (0) 10 (21.4) < 5 (< 29.3) 0 (0) Acute hepatic failure 0 (0) < 5 (< 10.7) 0 (0) 0 (0) Cerebrovascular morbidity 0 (0) 0 (0) 0 (0) < 5 (49.2) Severe uterine rupture 0 (0) 0 (0) 0 (0) 0 (0) Other 0 (0) 7 (15.0) < 5 (< 29.3) < 5 (49.2) Fetal/infant outcomes: Underweight (N = 736) Normal (N = 9364) Overweight (N = 3408) Obese (N = 2032) Perinatal composite 64 (869.6) 514 (549.1) 184 (539.9) 115 (565.9) Perinatal death 28 (380.4) 221 (236.0) 83 (243.5) 54 (265.8) Stillbirth 8 (108.7) 125 (133.5) 38 (111.5) 25 (123.0) Neonatal death 20 (274.7) 96 (103.9) 45 (133.5) 29 (144.5) Neonatal morbidity 40 (549.5) 309 (334.5) 108 (320.5) 68 (338.8) Bronchopulmonary dysplasia 22 (302.2) 106 (114.7) 43 (127.6) 23 (114.6) Convulsions of newborn < 5 (< 138.9) 18 (19.5) 5 (14.8) 8 (39.9) Intracranial haemorrhage 0 (0) 7 (7.6) < 5 (< 14.8) < 5 (<24.9) Intraventricular hemorrhage (grade 3,4) 5 (68.7) 18 (19.5) 8 (23.7) < 5 (<24.9) Necrotizing enterocolitis < 5 (<68.7) 49 (53) 9 (26.7) 13 (64.8) Periventricular leukomalacia < 5 (<68.7) 9 (9.7) 6 (17.8) < 5 (<24.9) Respiratory distress syndrome with ventilation** 26 (357.1) 154 (166.7) 28 (139.5) 118 (185.1) 159 Retinopathy of prematurity 0 (0) 0 (0) 0 (0) 0 (0) Severe birth trauma 0 (0) 11 (11.9) 6 (17.8) < 5 (<24.9) Sepsis 13 (178.6) 117 (126.6) 46 (136.5) 18 (89.7) SMM (severe maternal morbidity/mortality) *DIC denotes disseminates intravascular coagulation **Only in infants who had mechanical ventilation for >3 day 160 Table 19: Adjusted risk ratios and risk differences for maternal and perinatal outcomes by pre-pregnancy body-mass-index in twin pregnancies. Estimates are from GEE Poisson models pooled from 50 imputed data sets. Unadjusted risk ratio (95% CI) Adjusted risk ratio (95% CI) Population attributable fraction (95% CI) Under-weight Normal Over-weight Obese Under-weight Normal Over-weight Obese Under-weight Normal Over-weight Obese Severe maternal morbidity/mortality 0.96 (0.57, 1.63) 1.00 (ref) 0.87 (0.64, 1.17) 0.84 (0.57, 1.23) 1.01 (0.59, 1.71) 1.00 (ref) 0.89 (0.66, 1.21) 0.85 (0.57, 1.27) 0.1 (-3.9, 3.9) - - - Perinatal composite 1.34 (1.00, 1.80) 1.00 (ref) 1.03 (0.86, 1.25) 1.07 (0.84, 1.36) 1.30 (0.97, 1.74) 1.00 (ref) 1.04 (0.86, 1.26) 1.08 (0.85, 1.38) 2.0 (-0.6, 4.6) - 1.0 (-4.0, 5.7) 1.1 (-2.5, 4.6) Perinatal death 1.42 (0.93, 2.18) 1.00 (ref) 1.12 (0.85, 1.48) 1.17 (0.84, 1.63) 1.38 (0.90, 2.11) 1.00 (ref) 1.12 (0.85, 1.47) 1.17 (0.84, 1.63) 2.6 (-1.5, 6.5) - 2.9 (-4.5, 9.7) 2.3 (-3.0, 7.2) Stillbirth 1.08 (0.57, 2.05) 1.00 (ref) 1.02 (0.69, 1.48) 1.07 (0.68, 1.69) 1.05 (0.55, 2.00) 1.00 (ref) 1.02 (0.70, 1.49) 1.08 (0.68, 1.71) 0.4 (-4.8, 5.2) - 0.5 (-9.5, 9.6) 1.1 (-5.9, 7.6) Neonatal death 1.86 (1.08, 3.19) 1.00 (ref) 1.25 (0.83, 1.88) 1.28 (0.81, 2.04) 1.79 (1.04, 3.08) 1.00 (ref) 1.24 (0.82, 1.88) 1.29 (0.81, 2.05) 5.2 (-1.3, 11.3) - 5.6 (-6.6, 16.4) 3.8 (-4.2, 11.2) Neonatal morbidity 1.33 (0.90, 1.95) 1.00 (ref) 0.98 (0.76, 1.26) 1.02 (0.76, 1.39) 1.30 (0.88, 1.91) 1.00 (ref) 1.00 (0.77, 1.28) 1.05 (0.77, 1.43) 2.0 (-1.4, 5.4) - - 0.7 (-3.8, 5.0) 161 9.7 Figures Figure 15: Risk of adverse outcomes by continuous pre-pregnancy body mass index in twins per 10,000 pregnancies (severe maternal morbidity), per 10,000 total births (stillbirth, perinatal death, and the composite adverse perinatal outcome), and per 10,000 live births (neonatal death and serious neonatal morbidity). Stillbirth Neonatal death Neonatal morbiditySevere maternal morbidity Perinatal composite Perinatal death20 30 40 20 30 40 20 30 4020 30 40 20 30 40 20 30 40025050075010001250025050075010001250025050075010001250025050075010001250025050075010001250025050075010001250Pre−pregnancy body−mass−indexAdjusted risk (per 10,000) 162 Chapter 10: Discussion 10.1 Summary of findings The work presented in this thesis provides a comprehensive population-level assessment of the relationship between pre-pregnancy BMI and serious maternal morbidity and adverse perinatal outcomes. This topic was explored using various modeling approaches to elucidate how and in whom high maternal BMI leads to suboptimal birth outcomes. My results are described in the preceding chapters consisting of the following six studies: (1) a causal mediation analysis examining the role of early delivery in the relationship between maternal BMI and perinatal death; (2) an assessment of the modification of the effect of maternal BMI on adverse perinatal outcomes by other common medical risk factors; (3) a modeling study demonstrating the use of Piecewise Additive Mixed Models (PAMMs) for gestational age specific risk of stillbirth from a fetuses-at-risk perspective; (4) an application of these models to determine how the association between maternal BMI and stillbirth varies across gestational age in women with chronic conditions; (5) an assessment of the relationship between high BMI and rates of twin pregnancy, and the possible role of Assisted Reproductive Technology (ART); and (6) an analysis of the relationship between maternal BMI and adverse outcomes in twin pregnancy. 10.1.1 The role of gestational age in the relationship between pre-pregnancy body-mass-index and perinatal death (Chapter 4) My study of the association between pre-pregnancy BMI and perinatal death demonstrated that the association of these adverse outcomes and obesity is partly explained by early fetal demise 163 (stillbirth) or early delivery (neonatal death). Specifically, fetal demise or preterm gestational age at delivery accounted for 63.1% of the observed association between maternal obesity and perinatal death. Analyses independently assessing stillbirth and neonatal death yielded similar results. Prior studies of pre-pregnancy BMI and perinatal death have focused on estimation of the total effect, rather than attempting to decompose this association into the direct and indirect components (7,72,133). In many previous studies, gestational age (or preterm birth in context of neonatal death) has been adjusted for in multivariable analyses, leading to potentially serious biases (156). My study represents a new step towards disentangling the complex causal relationship between obesity and perinatal death. 10.1.2 Modification of the relationship between pre-pregnancy body-mass-index and perinatal outcomes (Chapter 5) In this study, I showed that the association between BMI and adverse perinatal outcomes is not uniform across pregnancies; rather, it varies depending on the presence of other risk factors. The relative impact of obesity on adverse perinatal outcomes is diminished in pregnancies with concurrent serious risk factors, such as chronic hypertension or diabetes mellitus. Furthermore, the effect of obesity on adverse perinatal outcomes is modified in nulliparous and mature women (both higher). These findings were consistent on both additive and multiplicative scales of effect modification. 164 The most striking finding from this analysis was the absence of any dose-response relationship between high pre-pregnancy BMI and stillbirth, neonatal death or severe neonatal morbidity in women with chronic hypertension or diabetes mellitus. Although these groups had the highest absolute risk of adverse outcomes, even morbid obesity (BMI ≥ 40kg/m2) was not associated with additional risk. For example, in women with chronic hypertension, the adjusted odds ratio for stillbirth in women with BMI ≥ 40kg/m2 vs normal BMI was 0.98 (95% CI = 0.91 to 1.06), showing an absence of any increased risk and reasonably narrow 95% CI. While many studies have demonstrated the relationship between pre-pregnancy obesity and adverse birth outcomes, few have examined subgroup effects. Two studies, one from Switzerland and one from Finland found evidence of effect modification by chronic conditions, however, these studies were limited in the type of outcomes studied and the relatively low prevalence of obesity compared with the United States (63,336). To date, my study represents the largest and most in-depth assessment of variation in the association between pre-pregnancy BMI and perinatal outcomes across different subgroups of pregnant women. 10.1.3 Modeling stillbirth risk from a fetuses-at-risk perspective in women with obesity and chronic conditions (Chapter 6 & 7): Given the surprising absence of the association between pre-pregnancy obesity and adverse perinatal outcomes in women with chronic hypertension and pre-pregnancy diabetes mellitus, I decided to focus on stillbirth and assess whether this unexpected absence of the association is consistent across gestational weeks from 22 weeks’ gestation onward. 165 To accomplish this objective, I unitized PAMMs, a relatively new class of survival models, to flexibly model the gestational age-specific relationship between pre-pregnancy BMI and stillbirth. Chapter 6 demonstrates the increased utility of this approach compared to Cox proportional hazards model and its extensions, which have been the typical analytical choice for modeling adverse pregnancy outcomes under the fetuses-at-risk paradigm (265). Applying these models revealed additional nuances to the overall null association between pre-pregnancy BMI and stillbirth in women with chronic conditions. At about ≥36 weeks’ gestation, above-normal BMI confers a higher risk of stillbirth in women with chronic conditions (albeit to a lesser extent than in women without chronic conditions), this dose-response relationship is reversed early in the third trimester (at 26-35 weeks’ gestation), when high BMI is associated with a lower risk of stillbirth, possibly due to increased fetal monitoring and obstetrical management. Two prior studies had demonstrated an increase in stillbirth risk in obese women at late-term gestation (9,134), but neither examined whether this effect was modified by other risk factors, which has important implications for developing clinical guidelines for the optimal timing of delivery of birth in these women. 10.1.4 Pre-pregnancy body-mass-index and adverse birth outcomes in twin pregnancies (Chapter 8 & 9) In the last two studies of this thesis, I examined in depth, the relationship between pre-pregnancy BMI and twin pregnancy proceeding to at least 20 weeks' gestation, resulting in birth. First, I corroborated previous findings of two historical cohorts where the rate of twin pregnancy ending in delivery increased with increasing BMI. Also, I demonstrated that among women with BMI 166 between 30 and 40 kg/m2 (but not those with a BMI ≥ 40 kg/m2), this increased rate of twin delivery was partly attributable to a higher use of ART. Second, I assessed the relationship between pre-pregnancy BMI and adverse maternal and perinatal outcomes in twin pregnancies and showed a lack of association between obesity and adverse birth outcomes. This contrasts with the strong association between pre-pregnancy BMI and adverse maternal and perinatal outcomes observed in singleton pregnancies (shown, for instance, in Chapter 4). Prior studies from the United States (1959-1966) and Denmark (1998-2001) have shown associations between elevated pre-pregnancy BMI and higher rates of twin pregnancy deliveries (70,71); however, both studies were conducted when use of ART (a possible mediator, which is strongly associated with twin and higher-order pregnancy (367)) was either unavailable, or used infrequently. My findings in Chapter 8 corroborated the earlier reports, albeit with somewhat attenuated results, but further demonstrated that the higher ART use in women with above-normal BMI was not sufficient to explain the elevated rates of twin delivery in women with a pre-pregnancy BMI of 40 kg/m2 or more. A recent study from Finland showed a similar relationship between high BMI and twin delivery, but did not assess the role of ART (416). The lack of a relationship between obesity and severe maternal morbidity or adverse perinatal outcomes in twin pregnancies among with obesity has not been previously demonstrated. One prior study from Ontario showed an association between obesity and less severe maternal morbidity (e.g., gestational diabetes and pre-eclampsia) in twin pregnancies (74); however, my findings suggest that this does not translate to higher rates of severe maternal outcomes. 167 Existing evidence with respect to adverse perinatal outcomes is also limited. In contrasts with our findings, a study from the United States found higher rates of stillbirth in obese women with twins compared to those with normal pre-pregnancy BMI (417). The aforementioned study from Ontario (76) also examined severe perinatal outcomes in twin pregnancies, but its results were largely uninformative due to very wide confidence intervals. Therefore, my findings should be interpreted cautiously, and more data from other settings is needed to confirm my findings. 10.2 Significance and contribution 10.2.1 Differential risks by pre-pregnancy BMI and obstetric management Clinical practice guidelines and shared patient-clinician decision-making require timely and accurate assessment of the risk of pregnancy complications. These risks are unlikely to be uniform across subgroups of pregnant women, and personalized approaches to care necessitate a detailed understanding of all risk factors. While pre-pregnancy obesity is a well-established risk factor for adverse pregnancy outcomes, my thesis has demonstrated that the relative increase in risk varies in specific groups of women (i.e., those without chronic hypertension or pre-pregnancy diabetes mellitus) and across gestational age (i.e., in early third trimester vs term). My research demonstrated that in patients with chronic conditions prior to pregnancy (i.e., hypertension and diabetes), obesity did not confer any additional risk of adverse perinatal outcomes, although the overall risk remained relatively high. Therefore, appropriate management (or ideally prevention) of these chronic conditions prior to conception may lead to a larger reduction in adverse pregnancy outcomes compared with a reduction in BMI alone, although 168 reducing BMI offers many other benefits and help in the management of chronic conditions. Furthermore, the refinement of clinical practice guidelines and risk assessment for women with obesity or these chronic conditions may benefit from the nuance discussed in the prior chapters. Likewise, if validated in other populations, the variation in gestational age-specific relative risk of stillbirth demonstrated in Chapter 7 has implications for shared patient-clinician decision-making regarding pre-term induction of labour to prevent pre-term stillbirth, especially in pregnancies complicated by high BMI and coexisting chronic conditions that show evidence of fetal compromise. Similar to women with chronic conditions, women with twin pregnancy are at elevated risk of pregnancy complications and their prenatal care and obstetric management should follow specific guidelines (73). My analysis of the effects of obesity in twin pregnancy suggests that, unlike in singletons, maternity care guidelines may consider obesity as a weaker (or even null) risk factor in twin pregnancies, while low BMI (underweight) should be considered a potentially stronger risk factor for stillbirth. These findings should also influence the patient counselling, as twin pregnancy has unique challenges compared with the singleton pregnancy, and information about additional risks (e.g., with respect to obesity) should only be considered when supported by evidence. Furthermore, twin pregnancy requires additional resources from both a patient perspective and a healthcare/health economic perspective. It is therefore important to understand not only risk factors for adverse outcomes in twin pregnancy, but risk factors for twinning itself. Together with existing evidence (70,71), my study demonstrated higher rates of twinning in women with 169 obesity - and partial explanation related to use of ART - providing valuable information for counseling women with high BMI and fertility problems. My results also provide important information for further studies of the pathophysiology and reasons behind the association between high pre-pregnancy BMI and twinning. Moreover, investigating the differential survival of twin fetuses before 20 weeks of gestation could help clarify the potential impact of varying survival rates in pregnancies affected by maternal chronic conditions. In summary, my findings quantify the increased risks of adverse pregnancy outcomes among women with obesity, and how the effects of obesity are modified by other risk factors, such as chronic hypertension, diabetes mellitus, or twin pregnancy. The impact of pre-pregnancy obesity is more pronounced in women without these coexisting risk factors. As maternity care providers increasingly embrace a personalized approach, it is critical to understand variations in risk of pregnancy complications according to individual circumstances. 10.2.2 Methodological insights My studies of perinatal data, as described in the preceding chapters, have benefitted significantly from my application of modern statistical methods that I applied to perinatal data. Furthermore, I employed novel statistical approaches to investigate gestational age-specific risks, an area that has not been extensively explored, providing new insights into modelling within the fetuses-at-risk paradigm (265). In Chapters 4 and 8, I utilized causal mediation analysis – an analytical framework and set of analytic techniques that has been largely developed in the last decade (198) but has only recently 170 been applied beyond methodological articles in perinatal epidemiology (145,418). Additionally, across the included studies, I have thoroughly examined sources of bias arising from the population selection, misclassification, missing data, and unmeasured confounding, adhering to the current best-practice recommendations in epidemiology and biostatistics (226,230). Therefore, these studies represent some of the most methodologically comprehensive investigations into the effects of pre-pregnancy BMI on pregnancy outcomes and serve as a foundation for further methodological advancements. As perinatal epidemiology and medical research increasingly embrace ‘big data’, it becomes imperative for methodologists to employ modern tools capable of extracting maximal information from vast data sources and for enhancing classical data analytical techniques. Chapter 6 introduces the PAMMs as a novel method for estimating gestational age-specific risk of stillbirth, representing a significant advancement over traditional approaches such as the Cox model, as PAMMs allows the relationships between variables to change over time. Notably, PAMMs have not been previously applied in perinatal epidemiology, and this thesis demonstrates its potential in leveraging statistical advancements for understanding complex, nonlinear patterns, such as gestational age-specific risk estimation. 10.3 Strengths and limitations The primary strengths of this thesis lie in its utilization of large-scale population-level databases, the application of modern statistical techniques, and the comprehensive sensitivity analyses conducted. Many of the outcomes studied in this thesis are rare and can only be assessed with statistical precision using large datasets such as those presented in this thesis. I applied several 171 advanced methods for statistical analysis (e.g., causal mediation analysis, Targeted Maximum Likelihood Estimation, Piecewise Additive Mixed Models) that have not been extensively used in perinatal epidemiology, enabling a deeper understanding of the role of obesity in the risk of adverse pregnancy outcomes (198,213,340). Additionally, throughout this thesis, I implemented a variety of sensitivity analyses to address potential biases. These included routine approaches (e.g., multiple imputation) as well as less commonly used methods in studies of pre-pregnancy BMI (e.g., correction for misclassification). The studies in this thesis have several limitations. First, as with most administrative datasets, the data sources used lacked information on potentially important covariates, such as socioeconomic status or race/ethnicity (in BCPDR data). For studies examining twin pregnancy, I did not have information on chorionicity, which would have provided further insights into the observed associations (409,419). Second, both datasets used in this thesis included pregnancy outcomes in only for pregnancies that reached 20 weeks' gestation. This left-truncation introduces the possibility of collider-stratification bias, discussed further below (189). Third, self-reported BMI typically underestimates overweight and obesity, potentially leading to misclassification bias in the results (42). I addressed this potential bias in Chapter 8 and found minimal changes in my findings. Similarly, most covariates in my analyses were identified using ICD-10 codes, which are subject to misclassification as the validity of these codes (i.e., sensitivity and specificity) may not always be optimal. Fourth, the link between subsequent pregnancies was not available in the BCPDR and in the NCHS data, which may have resulted in overly optimistic confidence intervals. However, most estimates had relatively high precision, would not be expected to qualitatively change my findings. Moreover, I assume that repeat pregnancies were relatively 172 infrequent in my studies using the US data, as they included only a two-year study period. However, data on linked-repeated pregnancies would potentially allow for assessing the timing of the development of obesity and the role of possible mediators (e.g., chronic hypertension). This would enable a deeper exploration of the timeline and relationships between these risk factors. Fifth, the mediation analyses in Chapters 4 and 8 examined only single mediators and assumed the absence of mediator-outcome confounders affected by BMI (420,421). In reality, such assumptions are unlikely to hold true, and more complex models or sensitivity analyses are required to interpret results from a truly causal perspective (418,420,422). Also, there are also important limitations regarding the use of BMI as a measure of obesity. The American Medical Association (AMA) has cautioned against the use of BMI (independent of other measures of adiposity) at the individual level (52,53). This caution is primarily because BMI and BMI thresholds for obesity have been derived from data predominantly involving Caucasian populations, often with a greater proportion of men than women (48). The populations studied in my thesis were exclusively women and included ethnically diverse groups. Thus the WHO-defined BMI thresholds may not apply uniformly to all women at the individual level (51,423). For example, it has been previously demonstrated that lower thresholds may be more appropriate to define overweight and obesity in Chinese or South Asian populations, prompting changes in guidelines by the United Kingdom’s National Institute for Health and Care Excellence to accommodate these differences. (424) 173 10.4 Application of findings 10.4.1 Applications to public health and clinical practice My studies assessing the effects of pre-pregnancy BMI and its effect modification by patient characteristics, concomitant risk factors, and twin pregnancy provide insights into possible pathways by which high BMI affects pregnancy outcomes. As clinical care continues to adopt personalized medicine, the development of clinical guidelines and recommendations will need to account for increasing heterogeneity in risk of adverse outcomes (425,426). My findings provide population-based evidence that can help inform the development of such guidelines and ultimately improve patient care and outcomes. Assessing gestational age-specific risks associated with BMI and the role of earlier delivery in the risk perinatal death further helps clinicians and patients make informed decisions about the optimal timing of delivery depending on the patient’s BMI and concurrent risk factors (427). From a public health perspective, my findings help illuminate which groups of women may benefit most from pre-pregnancy weight loss (e.g., otherwise healthy, nulliparous women) to minimize pregnancy risk. However, the latter assertion should be interpreted cautiously, as my thesis used observational data which are not sufficient to establish definitive causal links. The underlying causes of high BMI may vary among different subgroups of women, which needs to be acknowledged when considering preventive strategies. 10.4.2 Applications to perinatal epidemiology Understanding causal associations, effect modification, and heterogeneity in risk across time (gestational age) are all essential prerequisites for population health research. The two large 174 population-based cohorts examined in my thesis demonstrate these concepts concerning the effects of pre-pregnancy BMI and provide an evidence base for the improved assessment of pre-pregnancy obesity in future epidemiological studies. This deeper insight into mediation, subgroup effects, and gestational age-specific risks in women with obesity will facilitate improved design of future epidemiological and interventional studies related to pre-pregnancy BMI. For example, in studies assessing the optimal timing of delivery in different pre-pregnancy BMI groups of women, it is crucial to stratify by additional key indicators, including, for instance, chronic medical conditions. 10.5 Future directions 10.5.1 Generalizability of results The results presented in this thesis were population-based, i.e., derived from nearly all women with a live birth or stillbirth in 1) British Columbia, Canada; and 2) the United States within specific time periods. The results provide population-based estimates of the relationship between pre-pregnancy BMI and adverse pregnancy outcomes; however, they may not be applicable to different populations that are dissimilar in demographic, cultural and socio-economic characteristics and where baseline risk of adverse outcomes may be higher. Several findings presented in my thesis are novel, and some are unexpected. Confirmation (replication or refutation) of these findings in diverse populations may provide further insights (e.g., biological or situational) into these relationships. For example, it would be interesting to assess whether chronic medical conditions such as pre-pregnancy hypertension modify the effect of elevated BMI on perinatal outcomes (as shown in Chapter 5) in countries with lower rates of 175 obesity and a higher proportion of pregnant women who are underweight (4). Similarly, most estimates of gestational age-specific risks have been derived from studies in high-income settings. While recent studies have assessed gestational age-specific stillbirth risk using a fetuses-at-risk approach in a global context, studies including data from low- and middle-income countries have primarily relied on aggregate comparisons and have not examined the associations with BMI. In Chapter 8, I found that ART explained a portion, though not the majority, of the association between obesity and twin pregnancy in a cohort of women from British Columbia. However, in other settings where ART use is substantially lower, it would be of interest to explore whether women with obesity still have similarly high twinning rates. 10.5.2 Prospective studies While retrospective population data provide large numbers to study rare adverse pregnancy outcomes, they often lack sufficient clinical details to assess the severity of adverse outcomes, temporal sequence of events, genetic risk factors, and socioeconomic and behavioral factors to fully understand these associations. This limitation is particularly relevant for complex exposures such as pre-pregnancy BMI. Future studies would benefit from collecting prospective data with more detailed information on BMI and other potential confounders such as lifestyle and nutrition. Pre-pregnancy obesity is a condition with multiple possible causes, which may have different relationships with pregnancy outcomes. For example, obesity may be caused by genetic factors, 176 sedentary lifestyle (e.g., lack of exercise), diet, hormonal imbalances (e.g., polycystic ovary syndrome) or a combination of these components. Information on these factors was not available, thus I was unable to include these factors in the analysis. Detailed data collection at the population level is currently unrealistic; however, even small to moderate sized representative cohorts with detailed pre-pregnancy and pregnancy information about these conditions would be valuable. With consent, such data could be linked to population databases, allowing for validation studies and assessment of how much these factors impact associations between pre-pregnancy obesity and adverse outcomes. Future areas of research may also include multiple ways to measure visceral and overall body fat (428). BMI has been historically the most common and convenient way to approximate adiposity, thus the BMI information is routinely measured in clinical practice and collected in administrative data. However, BMI is not an optimal measure of adiposity (53) (52), and several other measures can be used instead of or in conjunction with BMI, including measurements of visceral fat, body adiposity index, body composition, relative fat mass, waist circumference, and genetic/metabolic markers (429). As none of these measures are routinely collected, prospective data collection of some or all of these measures in non-pregnant and pregnant women would be valuable for both assessing their correlation with BMI and determining if these measures have stronger or weaker associations with adverse outcomes compared with BMI. Together with race/ethnicity data, this information would help identify optimal measures of adiposity-related risk and potential variations across racial/ethnic groups, which, as discussed earlier, may have different thresholds for identifying adiposity-related risks. 177 Lastly, Vital Statistics data and birth registries typically include only pregnancies that reached 20 weeks' gestation. Since obesity is associated with ability to conceive (375,376) and miscarriages (390,430–432), the restriction to pregnancies of 20 weeks gestation or more in the study population may introduce selection (left-truncation) bias (254). Miscarriage (as well as the inability to conceive) is a competing risk for all the outcomes studied in my thesis and the majority of other population-based studies of associations between pre-pregnancy risk factors and birth outcomes. Prospective studies that initiate enrolment at the earliest identification of pregnancy, ideally prior to conception, would provide valuable insight in quantifying biases stemming from restricting the study population to ongoing pregnancies at 20 weeks' gestation. 10.5.3 Extended fetuses-at-risk model for infant mortality The fetuses-at-risk (FAR) approach applied in Chapters 6 and 7, has traditionally focused on modeling gestational age-specific risk of stillbirth (264). While this focus is valuable for identifying possible causal associations between pre-pregnancy and pregnancy exposures and stillbirth, and for determining the optimal timing of delivery to prevent stillbirth, it does not account for neonatal deaths occurring after delivery. In women with pregnancy complications that worsen the in-utero condition of the fetus, the optimal obstetric management may include induction of labour or cesarean delivery to prevent stillbirth. Since neonatal deaths occurring after such deliveries would not be included as outcomes in a typical fetuses-at-risk analysis, the findings focusing on stillbirth may obscure the association between an in-utero exposure and the risk of death of the baby shortly after delivery (neonatal death). Perinatal death, which includes stillbirth and early neonatal death, may share the same in-utero etiology and is ultimately the most critical outcome from clinical and patient perspectives. 178 To alleviate this issue, some have suggested extending the FAR approach to include both in-utero and post-delivery deaths, thereby drawing an (analytical) equivalence between a stillbirth at 35 weeks’ gestation and a baby born at 34 weeks who dies one week later (i.e., at corrected gestational age of 35 weeks) (269,433). This approach has previously been demonstrated by contrasting gestational age-specific perinatal mortality between white and black women and between smoker’s vs non-smokers using an extension of the Cox regression model. However, such extended FAR approach has not gained widespread acceptance in the perinatal epidemiology literature thus far (269). Given the unexpected results in Chapter 6 showing that higher BMI had an inverse association with gestational age-specific risk of stillbirth early in the third trimester in women with chronic hypertension or pre-pregnancy diabetes, it would be of future interest to expand these analyses to include neonatal deaths. In such analyses, ‘birth’ could be entered into the Cox model as a time-varying covariate to evaluate the extent to which the association between BMI and perinatal mortality is mediated by delivery (265,269), and how this effect varies across gestational age. The latter analysis would complement my findings from the mediation analyses in Chapter 4 and contribute to the emerging trend in perinatal epidemiology: conducting analyses of gestational age-specific risks using advanced methodologies in mediation analysis that enable the decomposition of time-dependent effects (252,434). 179 10.5.4 Multiple mediator analyses In Chapters 4 and 8, I used causal mediation analysis to better understand the relationship between pre-pregnancy obesity and perinatal death, and obesity and twin pregnancy, respectively. In both analyses, I considered only a single mediator (preterm birth, and use of ART, respectively) and decomposed the results into natural direct and indirect effects (or interventional effects). In both analyses, there are likely additional mediators that could partially explain the associations between the exposure (BMI) and the outcome (perinatal death or twin birth). For example, in the context of obesity and perinatal death, pre-eclampsia or gestational diabetes are additional possible mediators (10,107). When more than one mediator is present and the mediators are related, the analysis will yield biased causal estimates of the natural direct and indirect effects (421). There are three possible solutions to this problem: 1) interpret results as ‘interventional’ effects, which represent population level rather than individual level counterfactuals (see Appendix B for further detail) (422); 2) conduct sensitivity analyses to attempt to quantify the direction and magnitude of bias (435); and 3) use statistical approaches that accommodate multiple mediators (see below) (420,436). Approaches to multiple mediator analyses are a focus of an ongoing methodological research that has recently suggested several approaches; (420,436,437) for instance, inverse probability weighting or marginal structural models, which allow for assessment of combined indirect effects through sequential mediators (436), while expanded versions of natural effects models (as used in Chapter 4) allow for more complex decomposition of effects (420). The latter have been recently used in perinatal epidemiology studies, but only to a limited extend due to the lack of available of-the-shelf software (418). 180 Applying these additional methods using additional detailed data in my mediation analyses would provide further insight into the etiology of pre-pregnancy obesity and its association with pregnancy outcomes. It would also be of methodological interest to compare different techniques for multiple mediation using real-life data, as most literature to date has focused on theory and simulation studies, as opposed to applied comparisons. 10.5.5 Bias analysis Some findings in my thesis differed from the original hypotheses. I applied a variety of sensitivity analyses in all studies to assess and mitigate possible biases arising from missing data, choice of analytical method, and control for confounders. In some studies, I explored the role of left-truncation or collider-stratification bias, which can occur when the study population includes only pregnancies at 20 weeks' gestation or later. Collider stratification bias can occur when 1) data analysis conditions on a common effect of both the exposure and outcome (a ‘collider’) either by adjustment in a statistical model or by estimating strata-specific effects within each stratum of the collider; or 2) a study population sample for analysis is comprised of a single stratum of a collider (189,200). It is known that obesity increases the risk of early pregnancy loss (i.e., miscarriage) and therefore collider stratification bias will occur when there is a common unmeasured cause of pregnancy loss prior to 20 weeks' gestation (miscarriage) and the outcome under study (e.g., stillbirth). Figure 16 demonstrates this bias, using the example of an unmeasured genetic factor that increases risk of 181 both miscarriage and stillbirth. When conditioning (i.e., stratifying) on ongoing pregnancies at 20 weeks' gestation, the estimated causal association between obesity and stillbirth may be biased. In Chapter 9, I showed that obesity did not have a strong association with increased maternal and perinatal death in twin pregnancies, which is in contrast with the strong association that exists in singleton pregnancies (documented in Chapter 4). This lack of association between high BMI and perinatal death in twins, which has also been previously demonstrated in a retrospective population cohort study from Ontario (74), and could be potentially explained by collider stratification bias. If an unmeasured collider exists that is associated with miscarriage and perinatal death and this collider is either a) only present (or more often present) in twin pregnancies (such as shared in-utero environment), or b) has a stronger association with miscarriage and/or perinatal death in twin pregnancies, then this differential effect of the collider may explain (in part or in whole) singleton-twin differences in the association between obesity and perinatal death. Similarly, the effect modification by chronic conditions in Chapters 5 & 6, could be due in part to collider-stratification bias, which can create the appearance of effect modification when unmeasured confounders of the possible modifier and outcome are not accounted for (189,200). Future studies could assess such bias and the magnitude that would be required to resolve the differing risk ratios for obesity between twin and singletons and in those with and without chronic conditions. These aims could be accomplished by performing a quantitative bias analysis or a simulation study where the causal structures include assumed collider-stratification bias (438). 182 10.6 Summary This thesis examined two broad areas of study related to pre-pregnancy BMI: 1) differential effects of BMI on adverse outcomes in high-risk pregnancies, and 2) possible causal pathways between elevated pre-pregnancy BMI and adverse pregnancy outcomes. Simultaneously, my thesis explores several methodological areas including mediation analysis, effect modification, and modeling using the fetuses-at-risk approach. I applied mediation analysis to examine why obese women have higher rates of perinatal death, and further demonstrated that the effect of BMI is heterogenous in women with various baseline characteristics and across gestational age. In particular, chronic hypertension and pre-pregnancy diabetes were studied in depth as effect modifiers overall and across gestation by using novel methodology. I used modeling techniques outlined in Chapter 6 to analyze gestational age-specific effects of BMI in these subgroups and found unexpected results that need to be further verified and analyzed in further detail. The final two research chapters (8 and 9) of my thesis examined the relationship between BMI and twin pregnancy. I showed that women with elevated BMI have higher rates of twins, and that this can be only partially explained by more frequent used of ART. Also, I showed that although these women have a higher frequency of twins, these twins do not have worse birth outcomes than twins born to women with normal BMI. Overall, my research highlights the critical importance of detailed assessment of important pre-pregnancy exposures such as BMI and other risk factors, as prognosis for adverse outcomes varies among specific subgroups of women. Future studies should confirm some of the findings of this thesis and continue to disentangle the causal pathways between pre-pregnancy BMI and adverse birth outcomes. Pregnant women, maternity care providers, and public health administrators can use my findings to improve pre-conception 183 counselling, recommendations for clinical care guidelines, and shared patient-health care provider decision making process about reproductive and maternity care. 184 10.7 Figures Figure 16: Example directed acyclic graph illustrating collider stratification bias occurring from conditioning on (lack of) miscarriage. ObesityGenetic factor (unmeasured)Miscarriage Stillbirth1 185 Bibliography 1. Hales CM. Prevalence of Obesity and Severe Obesity Among Adults: United States, 2017–2018. 2020;(360):8. 2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet Lond Engl. 2014 Aug 30;384(9945):766–81. 3. Driscoll AK, Gregory ECW. 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The example in Table 2 yields simple calculations to determine the presence or absence of both types of interaction; however, in practice the assessment of interactions usually is the result of a statistical model as various confounders or nuisance variables are controlled for. The two subsequent sections discuss examples of how both multiplicative and additive interaction can be assessed in the scenario of multivariable modeling. A.1 Estimation of multiplicative interaction Multiplicative interaction is the most commonly estimated form of interaction, due largely to the ease with which it can be assessed in commonly used statistical models for epidemiological data. Take for example the standard logistic regression model for the risk of an outcome, 𝑌, given by 𝑝(𝑥1, 𝑥2, 𝑧), where 𝑋1 and 𝑋2 (and their interaction) are co-exposures of interest, both assumed to be binary, and Z is a vector of additional covariates. We then have: 𝑙𝑜𝑔𝑖𝑡;𝑝(𝑥1, 𝑥2, 𝑧)< = 𝛼 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽7𝑋1𝑋2 + 𝜸𝒁 Then for a fixed set of covariate values 𝒁 = 𝒛, due to the inclusion of an interaction term, the fitted coefficients for𝑋1 and 𝑋2 correspond to the log-odds ratios for 𝑋1 = 1 and 𝑋2 = 1$ in the absence of the other exposure. For example, for 𝛽1P we have 220 𝛽1P = 𝑙𝑜𝑔𝑖𝑡(𝑥1 = 1, 𝑥2 = 0, 𝒛) − 𝑙𝑜𝑔𝑖𝑡(𝑥1 = 0, 𝑥2 = 0, 𝒛) Or taking the exponent: 𝑒<!= = 𝑂𝑑𝑑𝑠(𝑥1 = 1, 𝑥2 = 0, 𝒛)𝑂𝑑𝑑𝑠(𝑥1 = 0, 𝑥2 = 0, 𝒛) Similarly, 𝑒<"= = 𝑂𝑑𝑑𝑠(𝑥1 = 0, 𝑥2 = 1, 𝒛)𝑂𝑑𝑑𝑠(𝑥1 = 0, 𝑥2 = 0, 𝒛) These two estimates provide strata-specific estimates of the effect between 𝑋1 and 𝑋2 and 𝑌. The individual coefficient for 𝛽7 is the difference on the logit scale between these two values and is interpreted on the exponential scale as a 'ratio of odds ratios', a measure of multiplicative interaction. Additionally, the strata specifics odds ratios in the presence of the other exposure are given by: 𝑒<!= ? <#= = 𝑂𝑑𝑑𝑠(𝑥1 = 1, 𝑥2 = 1, 𝒛)𝑂𝑑𝑑𝑠(𝑥1 = 1, 𝑥2 = 0, 𝒛) Similarly, 𝑒<"= ? <#= = 𝑂𝑑𝑑𝑠(𝑥1 = 1, 𝑥2 = 1, 𝒛)𝑂𝑑𝑑𝑠(𝑥1 = 1, 𝑥2 = 0, 𝒛) To determine the joint effect of both exposures on the logit scale, we can take the sum: 𝛽1P + 𝛽2P + 𝛽7P or for the odds ratio 𝑒<!= ? <"= ? <#= . 221 Modern guidance for presenting interactions encourage presentation of both the strata specific and joint associations from the above models. Confidence intervals for combinations of coefficients can be computed via the delta-method and a 'significance' test for the interaction term can either be based on the Wald test for 𝛽7 in the above model, or on a likelihood ratio test contrasting the above saturated model in 𝑋1 and 𝑋2 with a model excluding the interaction. The above interpretations are similar when continuous variables are involved, but the exception that the odds ratios are for one-unit increase in said variables. Similar logic to above yield similar estimates on the rate ratio when using Poisson models, or on the hazard ratio scale for time-to-event outcomes modeled using Cox proportional hazards models (or other multiplicative survival models). A.2 Estimation of additive interaction In contrast to the above, estimation of additive interaction can be more complex. A common approach in the literature is to assess additive interaction via a surrogate measure derived from a multiplicative model. The most common of these is Relative Excess Risk due to Interaction (RERI), which can be written (when the odds ratio estimates the risk ratio) as: 𝑅𝐸𝑅𝐼 = 𝑂𝑅@!A1,@"A1 − 𝑂𝑅@!A1,@"A/ − 𝑂𝑅@!A/,@"A1 + 1 and estimated as: 𝑅𝐸𝑅𝐼 = 𝑒<!= ? <"= ? <#= − 𝑒<!= − 𝑒<" = + 1 222 The above has the advantage of being easily computed from the standard logistic model for the risk 𝑝; however, the RERI (and other indices such as the synergy index and attributable proportion due to interaction, which are not discussed here) estimate the additivity of the relative risks, which is not always equivalent to additivity on the risk difference scale (see Greenland S. Additive risk versus additive relative risk models.). An alternative, analogous approach to the logistic regression in the preceding section would be to fit a model that is additive for the risk, 𝑝: 𝑝(𝑥1, 𝑥2, 𝑧) = 𝛼 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽7𝑋1𝑋2 + 𝜸𝒁 Similarly to the logistic regression example, the coefficients 𝛽1 and 𝛽2 from this additive model represent strata specific risk differences, while the coefficient 𝛽7 is the difference in risk differences. However, in practice this model suffers from (a) lack of convergence when many variables are included, or when continuous variables are used, and (b) may provide impossible (fitted) risk estimates outside [0,1]. In the case of count or time-to-event outcomes the additive Poisson model or Aalen additive hazards models circumvent these issues and provide direct alternatives to multiplicative models to assess additive interaction. A final strategy for estimating additive interactions directly on the additive scale is to use the predicted probabilities from the logistic model via back-transformation of the logit function. This approach has the advantage of overcoming issues with the linear risk model above, and unlike the RERI, being estimated on the additive scale of most interest. A complexity of this approach is that the covariates Z are controlled for on the logit scale (i.e., the interpretation of the 223 coefficients 𝛽1,𝛽2, and 𝛽7 are independent of values of Z) but are not controlled for on the predicted probability scales. That is, the predicted probabilities 𝑝(𝑥1, 𝑥2, 𝑧) will depend on the specific values 𝒁 = 𝒛. This complexity can be handled in one of three ways depending on the specific research question: 1. Compute several estimates of additive interaction conditional on the values 𝒁 = 𝒛. 2. Compute measures of additive interaction for the 'average' participant in the sample, i.e., 𝒁 = 𝒛𝒂𝒗𝒈. 3. Standardize over the sample by first computing predicted probabilities for each individual in the dataset, and then average over groups of interest (combinations of 𝑋1 and 𝑋2). The primary difference between (2) and (3) is when the averaging over \textbf{Z} is done. In (2) the covariates are first averaged, and one estimate is then given, while in (3) the averaging occurs after the estimation of the predicted risks. 224 Appendix B: Formal definitions for causal mediation analysis Chapters, 4 and 8 involved the decomposition of an association into a natural direct, and natural indirect effect. In Chapter 4, I used natural effect models, while in Chapter 8 I used combinations of regression coefficients from the mediator and outcome models (247,378). In the below, I discuss the mathematical details of causal mediation analysis, and of these two methods. Let A, M, C and Y represent the exposure, mediator, confounders and outcome, respectively. The causal mediation effects can be defined as contrast of potential outcomes. Let Ma be the value that the mediator, possibly contrary to fact, be under exposure A = a, and let Ya,m me the value of the outcome, again possibly contract to fact, when A = a and M = m. Then, assuming no exposure-mediator interaction we can define the natural direct effect as the contrast in Y between exposed vs not exposed assuming the mediator takes the value it would’ve in the non-exposed, conditional on C. Likewise, we define the natural indirect effect as the contrast in Y in the exposed group, when the mediator takes the value it naturally would, vs what it would it take in the unexposed group, conditional on C. Defining on the risk ratio scale, we have 1. Pure natural direct effect: 2. Total natural indirect effect: In the absence of an exposure-mediator interaction, the above effects are constant regardless of the value of a, i.e., we have: 225 1. Pure natural direct effect: 2. Total natural indirect effect: If there is interaction between the exposure and mediator, then the latter two equalities do not hold, and we have 4 total effects (ordered as above): i) the pure natural direct effect, ii) the total natural direct effect, iii) the total natural indirect effect and iv) the pure natural direct effect. Causal interpretation of these natural effects requires four assumptions: 1. No unmeasured confounding between A and Y 2. No unmeasured confounding between M and Y 3. No unmeasured confounding between A and M 4. No confounder of M and Y is affected by A As discussed in the main text of Chapter 4, there is often some confounder, L of the mediator and outcome that is also affected by exposure. For example, it is possible that a variable such as pre-pregnancy diabetes is a confounder of the gestational age and perinatal death and is caused in part by high BMI for the analysis in Chapter 4. In this case, Vanderweele & Vansteelandt (421) have shown that we can still estimate ‘interventional’ versions of the direct and indirect effects listed above, which correspond to setting the mediator to some (counterfactual) value drawn from the distribution of those with 226 opposite exposure status to the observed. That is, we now have effects based on population rather than individual counterfactuals. In our example from Chapter 4, this corresponds to shifting the gestational age at delivery distribution of obese women to match the gestational age delivery distribution of those of normal BMI. Formally, assuming the above notation and letting M ∼ fa|c, (i.e. the distribution of M amongst those with A = a, conditional on C = c) and Ga|c be a random draw from f, then: 1. Interventional direct effect: 2. Interventional indirect effect: These effects can be identified and unbiased assuming that L is adjusted for. Our second set of adjusted odds ratios in Chapter 4 correspond to these effects where L = {pre-pregnancy diabetes, congenital anomalies}. Our analyses were broadly consistent after adjustment for L, but as mentioned, there are additional potential variables in L that could affect our results. There are several possible modelling approaches to estimate the above quantities including: natural effect models, combinations of regression coefficients from outcome and mediator models, weighting based approaches, marginal structural models, etc. In the main text we used natural effect models in Chapter 4 and the regression-based approach in Chapter 8. 227 Based on the above assumptions, the natural effect model can estimate causal mediation effects via the following steps (247). 1. Fit an appropriate adjusted regression model to Y to estimate the conditional expectation: E(Y |A,M,C). 2. Expand the original data set to include (for each individual) all counterfactual exposure statuses as a separate variable. 3. Impute the outcome for those expanded rows from step 2. using the outcome model from step 1. 4. Refit the outcome model in step 1. on the imputed data set, including coefficients for each exposure status. 5. The coefficients from 4. represent the natural direct and indirect values. For example, the natural effects model for a binary outcome in step 4, could be logit(Y ) = β0+β1a+β1a∗+β3C, where β1 represents the natural direct effect, and β2, the indirect effect. Standard errors can be estimated robustly or via the bootstrap. Interactions between mediator and exposure can be accommodated by including an interaction between A and M in step 1, as well as include an interaction between a and a∗ in step 4. An alternative to the above is to use the regression-based approach of Valeri and Vanderweele . This approach can be essentially described in the following (general) steps (378): 1. Fit an appropriate model to the mediator variable M including A and all relevant confounders C. 2. Fit an appropriate model to the outcome variable Y including M, A and all relevant confounders C. 3. Using previously derived algebraic formulas, estimate the relevant direct and indirect effects using the coefficients from the above two models. 228 Description of all formula are beyond the scope of this text. Exposure-mediator interaction can be accommodated in step 2, with formulas in 3 adjusted accordingly. 229 Appendix C: Bias analysis for relationship between obesity and adverse perinatal outcomes in twin pregnancy C.1 Introduction The objective of this appendix was to assess whether to conduct a quantitative bias analysis to explore the possibility that the lack of association between obesity and perinatal outcomes in twin pregnancies observed in Chapter 9 and in Ram et al was due to selection bias via differential pregnancy loss prior to 20 weeks, or by unmeasured confounders such as chorioncity. C.2 Methods To address whether the lack of observed association between pre-pregnancy obesity and perinatal death in twin pregnancies could be explained by possible unmeasured confounding or by a selection bias via left truncation of administrative pregnancy cohorts, we conducted a quantitative bias analysis via Monte Carlo simulation study using estimates from Chapter 9 and previously published in Ram et al to inform our simulation parameters. We consider two primary potential sources of bias for the observed near null association between obesity and perinatal death or severe morbidity in twin pregnancies. We used directed acyclic graphs (DAGs) to illustrate (simplified versions) of these potential biases. First, we consider possible unmeasured confounding under two possible scenarios. Figure 17a and 17b describes these two possibilities. The first scenario is a general unmeasured confounder 230 of the obesity/perinatal death relationship. To account for the differences with singleton births such a confounder would need to have at least one of i) different prevalence in twin pregnancies or ii) differential effect on obesity, perinatal death or both in twins than in singletons. In the second scenario for unmeasured confounding, we consider the possibility of differences in chorionicity between obese and normal weight women under the assumed (simplified) causal structure of Figure 1b. Monochorionic twins have lower chance of survival than dichorionic twins (72,73) and therefore differences in the chorionicity distributions between normal and obese women could lead to bias in estimated the obesity – perinatal death associations via an open backdoor bath obesity -> U -> chorionicity -> perinatal death. Neither published study had information on chorionicity and therefore were unable to explore this possibility directly. The second source of bias we consider is a selection bias through differential left-truncation by obesity (Figure 17c). Both studies used administrative data capturing live and stillbirth after 20 weeks’ gestation, and therefore do not have information on early fetal loss (miscarriage), a competing event for perinatal death. Under the assumption of an unmeasured (or unadjusted) variable associated with both early fetal loss and perinatal death, if early fetal loss occurred more frequently in obese twin pregnancies compared with normal BMI, then the estimated association between obesity and perinatal death would be biased. That is, obese women with twin pregnancies that survive past 20 weeks may be healthier on average than those from women with normal BMI. In this scenario we assume the more general case of collider stratification bias where miscarriage is treated as a competing-risk for stillbirth and therefore include the arrow from miscarriage to stillbirth. 231 Simulation study and quantitative bias analysis To examine the extent to which these possible biases could affect the observed associations we conducted a simulation study under a range of plausible scenarios. Scenario 1: confounding by chorionicity To assess the possible impact of chorionicity, we assumed the following data generating mechanism: 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝑃𝑒𝑟𝑖𝑛𝑎𝑡𝑎𝑙 𝑑𝑒𝑎𝑡ℎ = 1)< = 𝛽/ + 𝛽1𝑀𝑜𝑛𝑜𝑐ℎ𝑜𝑟𝑖𝑜𝑛𝑖𝑐 + 𝛽2𝑂𝑏𝑒𝑠𝑒 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝑂𝑏𝑒𝑠𝑒 = 1)< = 𝛾/ + 𝛾1𝑈 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝑀𝑜𝑛𝑜𝑐ℎ𝑜𝑟𝑖𝑜𝑛𝑖𝑐 = 1)< = 𝛼/ + 𝛼1𝑈 𝑈 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑖(𝑝) Where, 𝑈 is an unmeasured confounder that is associated with obesity (with strength 𝛾1) and chorionicity (with strength 𝛼1), and prevalence 𝑝. In all simulations we assumed a prevalence of obesity of 15% (𝛾/), a rate (amongst twin pregnancies) of monochorionic twins of 70% (𝛼/), and 232 a perinatal death rate of 2%, which was consistent with the studies under consideration and existing published data. Based on existing literature, we assumed that 𝛽1 = 𝑙𝑜𝑔(3.0) so that monochorionic twins had three-fold higher risk of perinatal death compared with dichorionic twins (72,73). Further, we assumed 𝛽2 = 𝑙𝑜𝑔(1.5), to be consistent with the lower end of the estimated association between BMI and perinatal death in singleton pregnancies in the published cohorts and other existing literature (74,133,439). The inputs for 𝑝, 𝛾1, 𝛼1 were allowed to vary across a grid of plausible values representing low, moderate, and extreme confounding. Specifically, we took all combinations of 𝑝 = (0.2, 0.4, 0.6, 0.8), 𝛾1 = (log(1.5), log(2.25), log(3.0), log(3.75), log(4.5), log(5.25), log(6.0)) and 𝛼1 = (log(0.2) , log(0.6) , log(1.0) , log(1.4) , log(1.8)). We then estimated 𝛽2 both adjusted and unadjusted for 𝑈 to assess possible bias due to unmeasured chorionicity. Scenario 2: differential left-truncation To assess the possible impact of differential left truncation between twin pregnancies of obese and normal BMI women we assumed the following data generating mechanism: 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝑃𝑒𝑟𝑖𝑛𝑎𝑡𝑎𝑙 𝑑𝑒𝑎𝑡ℎ = 1)< = 𝛽/ + 𝛽1𝑈 + 𝛽2𝑂𝑏𝑒𝑠𝑒 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝑀𝑖𝑠𝑐𝑎𝑟𝑟𝑖𝑎𝑔𝑒 = 1)< = 𝛾/ + 𝛾1𝑈 + 𝛾2𝑂𝑏𝑒𝑠𝑒 233 𝑈 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑖(𝑝) Where 𝑈 is an unmeasured variable that is associated with both miscarriage (with strength 𝛾1) and perinatal death (with strength 𝛽1) in twin pregnancies. For example, such a variable could be a chronic condition (e.g., hypertension) or a nutrition associated biomarker, although in any case the effect would need be more pronounced in twins than in singletons to account for the differential associations previously observed. As in scenario 1, 𝛽/, 𝛾/, and 𝑝 denote the baseline risk of perinatal death, miscarriage, 𝑈, respectively. We assumed fixed baseline risks of perinatal death (2%), miscarriage (5%) and obesity (15%), and allowed the prevalence, 𝑝 to be any of (0.2, 0.4, 0.6, 0.8), As above, we fixed 𝛽2 at log(1.50) and allowed 𝛽1 𝛾1, 𝛾2 to vary across a grid of values comprised of all combinations of included: 𝛽1 and 𝛾1, = (log(2.0), log(6.0), log(10.0)) and 𝛾2 = (log(1.5), log(3.0), log(4.5), log(6.0)). Statistical analysis To assess the minimum strength a general unmeasured confounder would have to be associated with both obesity and perinatal death to shift the published point estimates to 1.50, we calculated E-values. For the other two scenarios above, the simulations were carried out on data sets of size 10,000 and 50,000 across varying parameters (see above), discarding any combinations of parameters that were impossible (i.e., leading to probability of risk ≥ 100% in some stratum). To minimize 234 error, for each of the above scenarios we simulated 1000 data sets. To assess the bias, we contrasted the ‘correct’ risk ratio (1.50) with the observed risk ratio under confounding or left truncation graphically. We also calculated the average bias per scenario by 1) averaging the differences between true and biased risk ratios and 2) computing the difference between the adjusted and unadjusted risk ratios. Finally, we computed the % of simulations that yielded an unadjusted risk ratio < 1.20, as an approximation of the likelihood of observing a result as extreme as those seen in the two previously published cohorts. All simulations were conducted using R statistical software version 4.0.3. C.3 Results Both reported studies provided data on a perinatal composite outcome of death and severe morbidity, stillbirth, and neonatal death, while only 1 reported on perinatal death. For the perinatal composite outcomes, both studies had risk ratios near 1.00 (1.02 (0.97, 1.07) and 1.08 (0.85, 1.38). In both studies, adjusted risk ratios for perinatal death, stillbirth and neonatal death were no more than 1.29. E-values for a general unmeasured confounder of obesity and these outcomes that would be needed to increase these risk ratios to 1.50 were close to 2.0 in all cases (Table 20). In our first simulation scenario to examine potential confounding through chorionicity, we found that all unadjusted (for U) estimates were biased. Bias increased as the risk ratio between obesity and U increased, and as the risk ratio between monochorionicity and U decreased. Prevalence of U had a lesser effect, but bias was minimized for scenarios where U had high or low prevalence and maximized for situations with prevalence near 50%. None of the simulated examples yielded 235 point estimates equal to those observed in the 3 cohorts (minimum risk ratio = 1.29) (Figure 18). When adjusting for U, there was minimal bias observed across all scenarios (Table 20). In our second simulation to assess differential early pregnancy loss we found more varied results. In cases where U had very strong associations with both miscarriage (RR = 10.0) and perinatal death (RR ≥ 6.0) we found that this was sufficient to explain away differences in the associations found in twins when obesity was assumed to increase risk of miscarriage by at least 3-fold, regardless of the assumed prevalence of U (red and green lines in Figure 19). In all scenarios, the strength of association for unmeasured confounder on miscarriage was more influential than its association on perinatal death. For example, in all scenarios where this risk ratio was 2.0 (pink, yellow and brown lines in Figure 3) the bias was minimal. In all scenarios where obesity was assumed to increase risk of miscarriage by only 50% the bias in the risk ratio was minimal regardless of other parameters. When including all pregnancies in analyses, there was minimal bias observed across all scenarios (Table 21). C.4 Summary Depending on the outcome, two independent studies have now documented a lack of, or much weaker association between obesity and perinatal outcomes in twin pregnancies compared with singleton pregnancies, where obesity has consistently been shown to be associated with worse outcomes. In this study, we have demonstrated that this difference is unlikely to be entirely due to bias resulting from unmeasured confounding, particularly by chorionicity, or by differential early pregnancy loss in obese vs normal weight women. 236 C.5 Tables Table 20: Selected simulation results of possible impact of chorionicity on estimate association between pre-pregnancy obesity and perinatal death in twin pregnancies. All simulations were based on a true risk ratio of 1.50 and in cohorts of size 10,000. Data are arranged from highest to lowest bias. Prevalence of U Risk ratio between U and obesity Risk ratio between U and monochorionic twins Mean estimated risk ratio with no adjustment for U Mean estimated risk ratio with adjustment for U Mean % Bias compared to true risk ratio of 1.50 Mean % Bias compared to adjusted risk ratio % of simulations with unadjusted risk ratio < 1.20 0.55 6 0.2 1.30 1.52 13.57 13.29 27.4 0.85 6 0.2 1.35 1.54 9.77 10.7 23.3 0.4 3 0.2 1.40 1.51 6.51 6.85 12.9 0.7 3 0.2 1.42 1.51 5.07 5.74 10 0.4 2.25 0.2 1.43 1.51 4.35 4.96 9.7 0.7 4.5 0.6 1.45 1.52 3.12 4.04 6.0 0.7 3 0.6 1.47 1.51 2.27 2.72 4.9 0.85 2.25 0.2 1.48 1.51 1.57 2.36 5.0 0.85 3 0.6 1.49 1.51 0.60 1.49 3.9 0.85 2.25 0.6 1.50 1.52 0.08 1.08 3.6 Table 21: Selected simulation results of possible impact of left truncation on estimate association between pre-pregnancy obesity and perinatal death in twin pregnancies. All simulations were based on a true risk ratio of 1.50 and in cohorts of size 10,000. Data are arranged from highest to lowest bias. Prevalence of U Risk ratio for U on miscarriage Risk ratio for U on perinatal death Risk ratio for obesity on miscarriage Mean estimated risk ratio in surviving pregnancies Mean estimated risk ratio in all pregnancies Mean % Bias compared to true risk ratio of 1.50 Mean % Bias compared to risk ratio in all pregnancies % of simulations with surviving pregnancy risk ratio < 1.20 0.8 10 10 6 0.19 1.50 87.50 87.53 100 0.8 10 6 4.5 0.78 1.50 47.86 47.9 99.7 0.8 10 2 4.5 1.12 1.51 25.59 26.04 61.6 0.6 6 2 6 1.28 1.51 14.89 15.72 39.5 237 0.2 6 6 3 1.36 1.51 9.20 9.98 18.2 0.2 2 6 6 1.43 1.50 4.98 4.86 10.5 0.4 6 10 1.5 1.46 1.50 2.83 2.80 0.7 0.2 2 6 3 1.48 1.50 1.57 1.74 5.1 0.8 2 10 3 1.49 1.50 0.84 0.92 0.0 0.6 2 10 1.5 1.50 1.50 0.11 0.36 0.0 238 C.6 Figures Figure 17: Simplified possible structures that would lead to bias in the obesity -> perinatal death relationship in twin pregnancies due to (A) general unmeasured confounder, (B) bias by differential chorionicity, (C) differential left truncation due to miscarriage. (A)Pre-pregnancy obesity Perinatal deathU(B)Pre-pregnancy obesity Chorionicity Perinatal deathU(C)Pre-pregnancy obesity Miscarriage Perinatal deathU1 239 Figure 18: Estimated (biased) risk ratios under various scenarios of strength of association between an unmeasured confounder of pre-pregnancy obesity and chronicity based on simulated cohorts of size 10,000. True risk ratio between obesity and perinatal death is set to be 1.50. Prevalence of U = 0.55 Prevalence of U = 0.7 Prevalence of U = 0.85Prevalence of U = 0.1 Prevalence of U = 0.25 Prevalence of U = 0.40.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.61.301.351.401.451.501.301.351.401.451.50Risk ratio between unmeasured confounder and monochorionicityEstimated risk ratio for obesity on perinatal deathRisk ratio between unmeasured confounder and obesity 1.52.2533.754.55.256 240 Figure 19: Estimated (biased) risk ratios under various scenarios for differential early pregnancy loss between obese and normal BMI twin pregnancies. True risk ratio between obesity and perinatal death is set to be 1.50. Prevalence of U = 0.6 Prevalence of U = 0.8Prevalence of U = 0.2 Prevalence of U = 0.42 3 4 5 6 2 3 4 5 60.40.81.21.60.40.81.21.6Risk ratio for obesity on miscarriageEstimated risk ratio for obesity on perinatal deathStrength of unmeasured confoundingRR U−misc = 10, RR U−PD = 10 RR U−misc = 10, RR U−PD = 2 RR U−misc = 10, RR U−PD = 6RR U−misc = 2, RR U−PD = 10 RR U−misc = 2, RR U−PD = 2 RR U−misc = 2, RR U−PD = 6RR U−misc = 6, RR U−PD = 10 RR U−misc = 6, RR U−PD = 2 RR U−misc = 6, RR U−PD = 6 241 Appendix D: Comparison of methods for estimating causal risk ratios: a simulation study and sample data analysis of obesity in pregnancy D.1 Introduction There has been a significant and steady increase in the biostatistical and epidemiological literature concerning methods for estimation of causal effects. Such modeling approaches focus on either modeling the outcome, modeling the propensity score (the probability of exposure given confounders), or combining both outcome and propensity score modeling to yield a ‘doubly-robust’ estimator. Examples of outcome models include techniques such as standard regression adjustment, or G-computation, while propensity score modeling includes PS matching, and inverse probability weighting (IPW). Recently, there has been suggestion that using flexible machine learning algorithms in the estimation of one or both of the outcome or PS models, such as with Targeted Maximum Likelihood Estimation (TMLE) may yield improved performance over methods based on standard generalized linear models. Most of these methods are available to varying degrees in many statistical software programs. Below we compare several common estimation techniques used in causal inference across a range of plausible simulation scenarios, and in an actual applied data set of pregnancies in British Columbia, Canada. 242 D.2 Methods In the following, we compare a series of methods for estimating the causal risk ratio between a binary exposure and a binary outcome via both simulation and a sample analysis on a large cohort of pregnancies from British Columbia, Canada. We compare the following four techniques: 1. Robust Poisson regression 2. G-computation 3. Inverse probability weighting 4. Targeted Maximum Likelihood Estimation (TMLE) We simulated data under eight scenarios under the following data generation mechanisms: 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝑌 = 1)< = 𝛽/ + 𝛽1𝐴+ 𝛽2𝐶1 + 𝛽7𝐶2 + 𝛽6𝐶1𝐶2 + 𝛽3𝐶22 + 𝛽E𝑈 𝑙𝑜𝑔;𝑃𝑟𝑜𝑏(𝐴 = 1)< = 𝛾/ + 𝛾1𝐶1 + 𝛾2𝐶2 + 𝛾7𝐶1𝐶2 + 𝛾6𝐶22 + 𝛾3𝑈 𝐶1 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑖(𝑝1) 𝐶2 ~ 𝑈𝑛𝑖𝑓𝑜𝑟𝑚[0,1] 𝑈 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑖(𝑝$) Where 𝑌 is a binary outcome, 𝐴 a binary exposure, 𝐶1, 𝐶2 are measured confounders, and 𝑈 is a sometimes (see below) unmeasured confounder. We set the baseline prevalence (in the absence 243 of confounders) of the exposure to 10% and of the outcome to 5%. Further parameter details are available in Table S1. We conducted simulations under eight scenarios: 1. No unmeasured confounders: data were generated assuming that 𝑝$ = 0 and that each of the outcome and propensity score models were correctly specified. 2. Weak unmeasured confounding: assumed a weak association between 𝑈 and 𝐴 and between 𝑈 and 𝑌 with 𝑝$ = 0.20. 3. Strong unmeasured confounding: assumed a strong association between 𝑈 and 𝐴 and between 𝑈 and 𝑌 with 𝑝$ = 0.20. 4. Correctly specified PS model, but incorrectly specified outcome model: data were generated assuming a moderate amount of confounding by 𝑈. 𝑈 was adjusted for in the PS models (IPW and TMLE) but not in the outcome models (Poisson, G-comp, and TMLE). 5. Correctly specified outcome model, but incorrectly specified PS model: data were generated assuming a moderate amount of confounding by 𝑈. 𝑈 was adjusted for in the outcome models (Poisson, G-comp, and TMLE) but not in the PS models (IPW and TMLE). 6. Excluded interaction term: data were generated assuming an interaction between 𝐶1 and 𝐶2, but no models included this interaction term. 7. Excluded quadratic term: data were generated assuming a quadratic effect for 𝐶2, but no models included this quadratic term. 8. Excluded interaction and quadratic term: data were generated assuming an interaction between 𝐶1 and 𝐶2, and a quadratic effect for 𝐶2, but no models included either of these terms. No interactions or quadratic terms were included in simulation scenarios 1-5 (i.e., 𝛽6 = 𝛽3 = 𝛾7 = 𝛾6 = 0). Sample summary statistics from each dataset are available in Table S2. 244 For each scenario we compared the distribution of estimated risk ratios, the bias from the true exposure effect (calculated as a %), and the coverage probability and width of the 95% confidence intervals. All simulations were conducted on hypothetical data sets of size 10,000 and were repeated 1,000 times. Further, we used data on all singleton pregnancies from 2008-2020 in British Columbia, Canada to compare these methods on an actual data set. Our analysis sought to estimate the adjusted association between maternal pre-pregnancy obesity (defined as a body-mass-index ≥30 kg/m2), and stillbirth (defined as fetal death in utero at ≥20 weeks). We applied each of the above models to this dataset, adjusting for the nulliparity, maternal age, maternal height, prior stillbirth, alcohol or substance use, pre-pregnancy diabetes and pre-pregnancy hypertension. All analyses were conducted using R statistical software version 4.0.4 using the following packages: geepack, marginaleffects, WeightIt, and drtmle, tmle. D.3 Results In the first simulation scenario with correctly specified models all models performed well with minimal bias in the estimated risk ratio with only G-computation having any observed bias (median of 1.6% overestimation of the risk ratio). Results were very similar between with a weak unmeasured confounder. All models resulted in very little bias (< 3% for all methods) in this scenario. With stronger unmeasured confounding bias increased to 10-15% in all models and again there was little difference between approaches. When either the outcome or propensity score model was mis-specified, TMLE provided unbiased estimates of the causal risk ratio 245 across, while methods that relied on the outcome model (e.g., G-computation) or the propensity score (e.g., IPW) were biased between 5-10%. Poisson regression performed slightly better than G-computation with mis-specified outcome model (bias of 5.7% vs 7.4%, respectively). Finally, in situations with missing interactions, misspecification of functional form of the continuous predictor, or both, all models exhibited small amounts of bias (1-3%) except TMLE with superlearner, which had none (Table 22, Figure 20). The coverage of the 95% confidence intervals was broadly good across simulation scenarios and models, with some notable exceptions. In the case of a strong unmeasured confounder, all models had severely suboptimal coverage, other than parametric TMLE (84.7%). Similarly, when the outcome model was mis-specified Poisson regression and G-computation had lower coverage (77.8% and 72.1%, respectively) as did IPW when the propensity score model was mis-specified (84.5%). Parametric TMLE was the most conservative with nearly all scenarios leading to coverage percentages >98%. This coincided with parametric TMLE having the largest width of intervals. Widths of confidence intervals for other methods were largely in agreement, with little difference between approaches (Table 23). From a total of 386,236 pregnancies between 2008-2020, 53,283 (13.8%) were from women with pre-pregnancy obesity. There were 228 (4.30 per 1000) stillbirths in obese women and 903 (2.72 per 1000) in non-obese women. All modeling approaches led to similar estimated risk ratios in both the analysis including all potential confounders, and the second analysis excluding pre-pregnancy hypertension and diabetes (which were slightly higher, e.g., aRR of 1.60 vs 1.55). 246 Confidence intervals had similar width across methods, except for parametric TMLE, which had a wider interval in both analyses (Table 24). D.4 Summary There are multiple approaches to modeling when one is interested in estimating a marginal causal effect. We conducted a simulation study across a wide variety of settings that were meant to mimic scenarios likely encountered in practice when conducting data analysis. Broadly speaking, across simulation scenarios we found only small differences between estimation methods that would be of relatively little practical importance in applied analysis of observational data, where other biases would likely lead to larger differences. That said, TMLE using flexible machine learning algorithms performed best across most metrics. In an applied example looking at the relationship between pre-pregnancy BMI and stillbirth, all models gave very similar results. 247 D.5 Tables Table 22: Summary statistics of estimated risk ratios and percent bias. All data are median and interquartile range from 1000 simulated data sets. Simulation scenario Poisson regression G-computation Inverse probability weighting TMLE parametric TMLE super learner Estimated risk ratio Bias (%) Estimated risk ratio Bias (%) Estimated risk ratio Bias (%) Estimated risk ratio Bias (%) Estimated risk ratio Bias (%) No unmeasured confounding 2.00 [1.94, 2.07] 0.1 [-3.1, 3.4] 2.03 [1.96, 2.10] 1.6 [-1.8, 4.9] 2.00 [1.93, 2.06] 0 [-3.4, 3.2] 2.01 [1.94, 2.07] 0.3 [-3.1, 3.5] 2.00 [1.94, 2.07] 0.2 [-3.2, 3.4] Weak unmeasured confounder 2.02 [1.96, 2.09] 1.1 [-1.8, 4.4] 2.05 [1.99, 2.12] 2.7 [-0.4, 6] 2.02 [1.95, 2.08] 0.8 [-2.4, 4.3] 2.02 [1.96, 2.09] 1.1 [-2.1, 4.6] 2.02 [1.96, 2.09] 1 [-2.2, 4.5] Strong unmeasured confounder 2.26 [2.19, 2.34] 13.2 [9.5, 17] 2.30 [2.23, 2.38] 15.1 [11.3, 19] 2.25 [2.17, 2.33] 12.4 [8.4, 16.6] 2.25 [2.17, 2.34] 12.6 [8.6, 16.9] 2.25 [2.17, 2.34] 12.6 [8.7, 16.8] Incorrect outcome model 2.11 [2.05, 2.19] 5.7 [2.3, 9.3] 2.15 [2.08, 2.22] 7.4 [3.9, 11] 2.00 [1.92, 2.06] -0.2 [-4, 3.1] 2.01 [1.94, 2.08] 0.7 [-2.9, 4] 2.01 [1.94, 2.08] 0.6 [-3.1, 3.9] Incorrect propensity score model 2.00 [1.94, 2.08] 0.2 [-2.9, 3.9] 2.04 [1.97, 2.11] 1.8 [-1.3, 5.6] 2.10 [2.03, 2.18] 5.1 [1.7, 9.1] 2.00 [1.94, 2.08] 0.2 [-3, 4.1] 2.00 [1.94, 2.08] 0.1 [-3.1, 4] Excluded interaction 2.02 [1.96, 2.08] 1 [-2.1, 4.2] 2.02 [1.96, 2.09] 1.2 [-1.8, 4.3] 2.00 [1.93, 2.06] -0.1 [-3.3, 3.2] 2.04 [1.98, 2.11] 2.2 [-1.1, 5.7] 2.00 [1.94, 2.07] 0.1 [-3, 3.7] Excluded nonlinearity 2.00 [1.95, 2.06] 0.2 [-2.7, 3.2] 2.01 [1.95, 2.07] 0.5 [-2.3, 3.4] 2.00 [1.94, 2.06] 0 [-3, 3.2] 2.03 [1.96, 2.09] 1.3 [-1.8, 4.5] 2.00 [1.94, 2.06] 0 [-3, 3.2] 248 Excluded interaction and nonlinearity 2.03 [1.97, 2.10] 1.6 [-1.3, 4.9] 2.03 [1.97, 2.10] 1.6 [-1.4, 4.9] 1.99 [1.92, 2.06] -0.3 [-3.7, 3.1] 2.06 [1.99, 2.13] 3 [-0.6, 6.6] 2.00 [1.94, 2.07] 0.1 [-3.2, 3.6] Table 23: Confidence interval coverage and confidence interval width across five simulated scenarios. Simulation scenario Poisson regression G-computation Inverse probability weighting TMLE parametric TMLE super learner Coverage proportion Width of 95% CI Coverage proportion Width of 95% CI Coverage proportion Width of 95% CI Coverage proportion Width of 95% CI Coverage proportion Width of 95% CI No unmeasured confounding 0.948 0.36 [0.35, 0.38] 0.942 0.37 [0.36, 0.39] 0.959 0.39 [0.38, 0.41] 0.996 0.55 [0.51, 0.58] 0.946 0.38 [0.36, 0.39] Weak unmeasured confounder 0.953 0.37 [0.36, 0.39] 0.936 0.39 [0.37, 0.4] 0.962 0.41 [0.39, 0.42] 0.999 0.58 [0.55, 0.62] 0.945 0.39 [0.38, 0.41] Strong unmeasured confounder 0.295 0.44 [0.42, 0.45] 0.222 0.45 [0.44, 0.47] 0.423 0.48 [0.46, 0.5] 0.847 0.76 [0.7, 0.81] 0.369 0.46 [0.44, 0.48] Incorrect outcome model 0.778 0.4 [0.38, 0.42] 0.721 0.41 [0.4, 0.43] 0.952 0.42 [0.41, 0.44] 0.998 0.61 [0.57, 0.65] 0.943 0.4 [0.39, 0.42] Incorrect propensity score model 0.961 0.38 [0.37, 0.4] 0.954 0.39 [0.38, 0.41] 0.845 0.44 [0.42, 0.45] 0.998 0.61 [0.57, 0.66] 0.957 0.4 [0.39, 0.42] Excluded interaction 0.947 0.36 [0.35, 0.38] 0.956 0.36 [0.34, 0.37] 0.962 0.40 [0.39, 0.42] 0.983 0.48 [0.45, 0.51] 0.945 0.36 [0.35, 0.38] Excluded nonlinearity 0.940 0.35 [0.34, 0.36] 0.946 0.35 [0.34, 0.36] 0.959 0.38 [0.37, 0.4] 0.986 0.47 [0.45, 0.5] 0.932 0.35 [0.34, 0.36] Excluded interaction and nonlinearity 0.932 0.37 [0.35, 0.39] 0.939 0.36 [0.35, 0.38] 0.961 0.42 [0.4, 0.43] 0.977 0.48 [0.45, 0.52] 0.948 0.37 [0.36, 0.39] 249 Table 24: Adjusted risk ratios comparing women with pre-pregnancy obesity to women without obesity in British Columbia Canada under varying estimation methods. Adjusted risk ratio 1 (95% CI) * Adjusted risk ratio 2 (95% CI) ** Poisson regression 1.53 (1.32, 1.78) 1.57 (1.36, 1.82) G-computation 1.53 (1.31, 1.76) 1.57 (1.34, 1.80) IPW 1.55 (1.34, 1.80) 1.60 (1.38, 1.86) TMLE parametric 1.55 (1.25, 1.92) 1.60 (1.29, 1.99) TMLE super learner 1.54 (1.33, 1.79) 1.59 (1.38, 1.84) *Risk ratio 1 adjusted for: Maternal age, maternal height, parity (nulliparous vs parous), alcohol or substance use, pre-pregnancy diabetes, pre-pregnancy hypertension, prior stillbirth **Risk ratio 2 adjusted for: Maternal age, maternal height, parity (nulliparous vs parous), alcohol or substance use, prior stillbirth 250 D.6 Figures Figure 20: Distributions of estimated risk ratios across eight simulation scenarios. PS model missing confounder Excluded interaction Excluded nonlinear term Excluded interaction and nonlinear termNo unmeasured confoudning Weak unmeasured confounder Strong unmeasured confounder Outcome model missing confounderPoisson G−comp IPW TMLE (param)TMLE (SL)Poisson G−comp IPW TMLE (param)TMLE (SL)Poisson G−comp IPW TMLE (param)TMLE (SL)Poisson G−comp IPW TMLE (param)TMLE (SL)1.82.12.42.71.82.12.42.7Estimation methodEstimated risk ratio 251 Appendix E: Supplementary data to Chapter 4 E.1 Tables Table 25: ICD-10 codes definitions of relevant variables. Variable ICD Codes Pre-pregnancy hypertension O10, O11 Diabetes mellitus E10,E11, O24.5, O24.6, O24.7 Chronic cardiac disease I05-I09, I34-I37, I39.1-I39.4, I25, Q20-26, I27.0, I27.2, I27.8, I27.9 Chonic renal disease N02.2, N03-N05, N08, N18, N25 Chronic hepatic disease K70-K77, B18 Systemtic lupus M32 Ashtma J44, J45 Gestational hypertension O24.8 Gestational diabetes O13 Placental previa O44 Placental disorders O43, O43.1, O43.2, O43.8, O43.9 Pre-eclampsia O141,O14.3-O14.9, O11 Congenital anomaly Q^^ Table 26: The amount of unmeasured confounding (as a risk ratio) between mediator and outcome needed to explain away total, direct and indirect effects. Estimates for underweight estimates are not presented as the total effects were not significant. The e-value is also not estimated when the effect is not significant. BMI Category Overweight Obese E-value point estimate E-value CI lower bound E-value point estimate E-value CI lower bound Total effect 1.77 1.43 2.59 2.18 Natural direct - - 1.54 1.17 Natural indirect 1.52 1.34 2.16 1.95 Adjusted* Total effect 1.74 1.39 2.48 2.06 Natural direct - - 1.63 1.28 Natural indirect 1.44 1.25 1.97 1.75 Adjusted** Total effect 1.73 1.39 2.47 2.05 Natural direct 1.45 1.05 1.65 1.29 252 Natural indirect 1.44 1.24 1.94 1.73 * adjusted for chronic hypertension, smoking, substance/alcohol use, prior stillbirth, prior preterm birth, parity, maternal age, year of birth, chronic diseases, asthma ** adjusted also for pre-pregnancy diabetes, congenital anomalies Table 27: Comparison of women with and without complete BMI data Complete BMI N = 392,820 Number (%) Missing BMI N= 153,855 Number (%) Standardized mean difference Maternal age (years) 0.083 <20 8899 (2.3) 5583 (3.6) 20-34 292618 (74.5) 112623 (73.2) 35-39 74903 (19.1) 28686 (18.6) ³40 16400 (4.2) 6963 (4.5) Year of delivery 0.214 2004-2006 75897 (19.3) 33191 (21.6) 2007-2009 84315 (21.5) 43773 (28.5) 2010-2012 92633 (23.6) 34983 (22.7) 2012-2014 64899 (16.5) 20183 (13.1) 2015-2017 75076 (19.1) 21725 (14.1) Male fetus 201528 (51.3) 79156 (51.4) 0.003 Nulliparous 191445 (48.7) 62270 (40.5) 0.167 Prior stillbirth 2706 (0.7) 1330 (0.9) 0.02 Prior low birth weight infant 6622 (1.7) 2783 (1.8) 0.009 Prior caesarean section 54847 (14.0) 27142 (17.7) 0.101 IVF** 6342 (2.4) 2175 (2.4) <0.001 Suspected IUGR*** 7689 (2.0) 2420 (1.6) 0.029 Pre-pregnancy diabetes mellitus 9243 (2.4) 2684 (1.7) 0.043 Gestational diabetes 48970 (12.5) 16706 (10.9) 0.05 Pre-pregnancy hypertension 2709 (0.7) 909 (0.6) 0.012 Gestational hypertension 33170 (8.4) 11070 (7.2) 0.047 Chronic disease 866 (0.2) 446 (0.3) 0.014 Asthma 2772 (0.7) 995 (0.6) 0.007 Placental previa 2664 (0.7) 1236 (0.8) 0.015 Placental disorders (excl. previa) 2816 (0.7) 1008 (0.7) 0.007 Preeclampsia 2421 (0.6) 779 (0.5) 0.015 253 Gestational age at delivery 20-27 0.076 28-33 1460 (0.4) 1161 (0.8) 34-36 3412 (0.9) 1554 (1.0) 37-38 26005 (6.6) 11019 (7.2) 39-41 122006 (31.1) 50643 (32.9) ³42 235249 (59.9) 87717 (57.0) Smoking during pregnancy* 31335 (8.0) 14195 (9.2) 0.045 Alcohol use during pregnancy* 3752 (1.0) 1789 (1.2) 0.02 Drug use during pregnancy* 11941 (3.0) 5524 (3.6) 0.031 Congenital anomaly (any)**** 1035 (0.3) 474 (0.3) 0.008 *Self-reported **In-vitro-fertilization, information available only for pregnancies delivered in 2008/2009 onwards *** Intrauterine growth restriction suspected on antenatal ultrasound (i.e., prior to delivery; antenatal ultrasound does not detect all growth-restricted fetuses theat are born as small-for-gestaional age) **** Only among live births, diagnosed at birth Table 28: Mediation analyses of BMI on perinatal death through gestational age at delivery after multiple imputation (n =10) of missing BMI values. BMI Category Underweight Overweight Obese Total effect (AOR-1) 1.09 (0.93,1.28) 1.20 (1.05,1.37) 1.49 (1.2,1.86) Natural direct - 1.13 (0.99,1.29) 1.23 (0.99,1.54) Natural indirect - 1.06 (0.93,1.21) 1.21 (0.97,1.51) Proportion mediated (indirect effect) 31% 47% Odds ratios were adjusted for chronic hypertension, smoking, substance/alcohol use, prior stillbirth, prior preterm birth, parity, maternal age, year of birth, chronic diseases, asthma Table 29: Mediation analyses for stillbirth and neonatal death. Outcome BMI Category Underweight Overweight Obese Stillbirth Total effect (AOR-1) 1.03 (0.8,1.33) 1.24 (1.08,1.43) 1.66 (1.41,1.94) Natural direct 0.92 (0.73,1.16) 1.13 (0.99,1.29) 1.26 (1.09,1.46) Natural indirect 1.13 (1.02,1.25) 1.10 (1.03,1.17) 1.32 (1.22,1.42) Proportion mediated (indirect effect) - 43% 54% Stillbirth antepartum Total effect (AOR-1) 1.00 (0.76,1.33) 1.25 (1.07,1.47) 1.64 (1.37,1.95) Natural direct 0.90 (0.68,1.17) 1.15 (0.99,1.33) 1.26 (1.06,1.49) 254 Natural indirect 1.12 (1.02,1.24) 1.09 (1.03,1.17) 1.30 (1.2,1.41) Proportion mediated (indirect effect) - 40.0% 53% Neonatal death Total effect (AOR-1) 1.31 (0.99,1.72) 1.16 (0.97,1.38) 1.41 (1.15,1.73) Natural direct 1.13 (0.89,1.44) 1.02 (0.88,1.19) 0.99 (0.83,1.18) Natural indirect 1.15 (1.00,1.33) 1.13 (1.04,1.24) 1.43 (1.28,1.59) Proportion mediated (indirect effect) - 87% - Odds ratios were adjusted for chronic hypertension, smoking, substance/alcohol use, prior stillbirth, prior preterm birth, parity, maternal age, year of birth, chronic diseases, asthma Figure S1: Study population E.2 Figures Figure 21: Directed acyclic graph representing assumed causal structure for causal mediation analysis. (C and L represent two sets of confounders: C confounders between the exposure and the outcomes; L confounders between the mediator and the outcome). Mediation analyses in the text assume that all C and L are measured and adjusted for, and for the identification of natural direct and indirect effects that BMI does not affect any of the gestational age and perinatal death confounders (L). When this last assumption is violated, ‘interventional effects’ may still be estimated. Pre-pregnancy BMICLGestational age at delivery Perinatal death1 255 Appendix F: Supplementary data to Chapter 5 F.1 Tables Table 30: Rates of the components of the composite outcome (perinatal mortality and/or severe neonatal morbidity) per 1000 women (stillbirth) or 1000 livebirths (other outcomes) with singleton birth by pre-pregnancy BMI, stratified by maternal age, parity, chronic hypertension and pre-pregnancy diabetes, USA, 2016-2017. Outcome Underweight (N = 254,225) Normal BMI (N = 3,220,432) Overweight (N = 1,918,480) Obese I (N = 1,062,177) Obese II (N = 516,693) Obese III (N = 365,357) Stillbirth 1033 (4.1) 12471 (3.9) 9495 (4.9) 6377 (6.0) 3594 (7.0) 3031 (8.3) Live birth 253192 (995.9) 3207961 (996.1) 1908985 (995.1) 1055800 (994.0) 513099 (993.0) 362326 (991.7) Neonatal death 729 (2.9) 7565 (2.4) 5244 (2.7) 3583 (3.4) 1925 (3.8) 1624 (4.5) Severe neonatal morbidity (any) 4001 (15.8) 44478 (13.9) 29596 (15.5) 19641 (18.6) 11392 (22.2) 9842 (27.2) Ventilation ≥ 6 hours 2938 (11.6) 32563 (10.2) 21576 (11.3) 14311 (13.6) 8385 (16.3) 7238 (20.0) Neonatal seizure 74 (0.3) 974 (0.3) 656 (0.3) 420 (0.4) 226 (0.4) 163 (0.4) Low Apgar score* 1212 (4.8) 13463 (4.2) 9173 (4.8) 6195 (5.9) 3504 (6.8) 3057 (8.4) * Apgar score at 5 min < 4 256 Table 31: Odds ratios expressing the joint association between pre-pregnancy BMI and parity, chronic hypertension and pre-pregnancy diabetes and the composite perinatal outcome singleton births, USA, 2016-2017. Underweight (N = 254,225) Normal BMI (N = 3,220,432) Overweight (N = 1,918,480) Obese I (N = 1,062,177) Obese II (N = 516,693) Obese III (N = 365,357) Stratified by parity Nulliparous 1.44 (1.38, 1.51) 1.51 (1.48, 1.53) 1.75 (1.71, 1.79) 2.13 (2.08, 2.18) 2.44 (2.37, 2.52) 2.67 (2.59, 2.76) Parous 1.21 (1.16, 1.26) 1.00 (ref) 1.04 (1.02, 1.06) 1.14 (1.12, 1.17) 1.24 (1.21, 1.28) 1.35 (1.32, 1.39) Stratified by chronic hypertension Chronic hypertension 3.44 (2.67, 4.43) 2.81 (2.64, 2.99) 2.72 (2.57, 2.87) 2.84 (2.70, 3.00) 2.79 (2.63, 2.96) 2.76 (2.62, 2.90) No chronic hypertension 1.07 (1.04, 1.10) 1.00 (ref) 1.10 (1.08, 1.11) 1.26 (1.24, 1.28) 1.41 (1.39, 1.44) 1.58 (1.55, 1.62) Stratified by pre-pregnancy diabetes Diabetes 3.81 (2.79, 5.2) 3.63 (3.37, 3.91) 3.70 (3.46, 3.95) 3.45 (3.22, 3.68) 3.42 (3.18, 3.69) 3.45 (3.23, 3.70) No Diabetes 1.07 (1.04, 1.10) 1.00 (ref) 1.10 (1.08, 1.11) 1.26 (1.24, 1.28) 1.41 (1.38, 1.44) 1.55 (1.52, 1.59) 257 Table 32: Strata-specific adjusted odds ratios for the association between pre-pregnancy BMI and each of the components of the composite perinatal outcome and quantification of the effect modification on the multiplicative and additive scale, singleton births, USA, 2016-2017. Underweight (N = 254,225) Normal BMI (N = 3,220,432) Overweight (N = 1,918,480) Obese I (N = 1,062,177) Obese II (N = 516,693) Obese III (N = 365,357) Stillbirth Overall unadjusted 1.05 (0.98, 1.12) 1.00 (ref) 1.28 (1.25, 1.31) 1.55 (1.51, 1.60) 1.80 (1.74, 1.87) 2.15 (2.07, 2.24) Overall adjusted 0.91 (0.83, 0.99) 1.00 (ref) 1.18 (1.14, 1.23) 1.41 (1.35, 1.48) 1.59 (1.50, 1.69) 1.70 (1.58, 1.81) Stratified by parity Nulliparous 0.81 (0.71, 0.92) 1.00 (ref) 1.24 (1.17, 1.32) 1.53 (1.42, 1.64) 1.78 (1.62, 1.96) 1.97 (1.77, 2.19) Parous 1.03 (0.91, 1.16) 1.00 (ref) 1.14 (1.08, 1.20) 1.34 (1.26, 1.42) 1.48 (1.37, 1.60) 1.53 (1.41, 1.67) Multiplicative interaction (ratio of aORs) 0.79 (0.66, 0.94) 1.00 (ref) 1.09 (1.01, 1.18) 1.14 (1.04, 1.25) 1.21 (1.07, 1.36) 1.28 (1.12, 1.47) Additive interaction (RERI) -0.29 (-0.48, -0.10) 0.00 (ref) 0.19 (0.08, 0.30) 0.39 (0.23, 0.55) 0.60 (0.35, 0.85) 0.80 (0.50, 1.10) Stratified by chronic hypertension Chronic hypertension 1.53 (0.52, 4.47) 1.00 (ref) 1.11 (0.82, 1.5) 1.25 (0.92, 1.71) 1.23 (0.89, 1.69) 1.36 (1.00, 1.84) No chronic hypertension 0.91 (0.83, 0.99) 1.00 (ref) 1.18 (1.14, 1.23) 1.41 (1.35, 1.48) 1.61 (1.51, 1.71) 1.71 (1.60, 1.84) Multiplicative interaction (ratio of aORs) 1.68 (0.57, 4.94) 1.00 (ref) 0.94 (0.69, 1.27) 0.89 (0.65, 1.22) 0.76 (0.55, 1.06) 0.79 (0.58, 1.08) Additive interaction (RERI) 1.22 (-2.26, 4.70) 0.00 (ref) 0.05 (-0.64, 0.74) 0.13 (-0.63, 0.89) -0.12 (-0.90, 0.66) 0.06 (-0.71, 0.83) 258 Stratified by pre-pregnancy diabetes Diabetes 0.60 (0.23, 1.57) 1.00 (ref) 1.27 (0.86, 1.89) 1.32 (0.89, 1.98) 1.47 (0.96, 2.25) 1.08 (0.72, 1.62) No Diabetes 0.91 (0.84, 1.00) 1.00 (ref) 1.18 (1.14, 1.23) 1.41 (1.35, 1.48) 1.59 (1.5, 1.69) 1.72 (1.61, 1.85) Multiplicative interaction (ratio of aORs) 0.66 (0.25, 1.72) 1.00 (ref) 1.08 (0.73, 1.60) 0.94 (0.63, 1.41) 0.92 (0.6, 1.42) 0.63 (0.42, 0.95) Additive interaction (RERI) -1.25 (-3.34, 0.84) 0.00 (ref) -1.25 (-3.34, 0.84) 0.68 (-0.90, 2.27) 1.00 (-0.83, 2.83) -0.45 (-1.88, 0.98) Neonatal death Overall unadjusted 1.22 (1.13, 1.32) 1.00 (ref) 1.17 (1.12, 1.21) 1.44 (1.38, 1.5) 1.59 (1.52, 1.68) 1.90 (1.81, 2.01) Overall adjusted 1.09 (1.01, 1.18) 1.00 (ref) 1.11 (1.07, 1.15) 1.3 (1.25, 1.36) 1.39 (1.32, 1.47) 1.55 (1.46, 1.64) Stratified by parity Nulliparous 0.99 (0.88, 1.11) 1.00 (ref) 1.24 (1.18, 1.31) 1.65 (1.55, 1.76) 1.91 (1.77, 2.06) 2.24 (2.07, 2.43) Parous 1.22 (1.10, 1.35) 1.00 (ref) 1.00 (0.95, 1.05) 1.08 (1.02, 1.14) 1.09 (1.01, 1.16) 1.15 (1.06, 1.24) Multiplicative interaction (ratio of aORs) 0.81 (0.69, 0.95) 1.00 (ref) 1.24 (1.16, 1.34) 1.53 (1.41, 1.66) 1.76 (1.59, 1.95) 1.96 (1.76, 2.18) Additive interaction (RERI) -0.23 (-0.42, -0.05) 0.00 (ref) 0.29 (0.2, 0.37) 0.68 (0.57, 0.8) 0.98 (0.81, 1.15) 1.32 (1.11, 1.52) Stratified by chronic hypertension Chronic hypertension 1.92 (1.05, 3.49) 1.00 (ref) 1.05 (0.83, 1.34) 1.11 (0.88, 1.41) 0.98 (0.76, 1.26) 1.05 (0.83, 1.32) No chronic hypertension 1.09 (1.00, 1.17) 1.00 (ref) 1.11 (1.07, 1.15) 1.30 (1.25, 1.36) 1.41 (1.34, 1.49) 1.59 (1.5, 1.68) Multiplicative interaction (ratio of aORs) 1.78 (0.97, 3.26) 1.00 (ref) 0.95 (0.75, 1.21) 0.85 (0.67, 1.09) 0.69 (0.53, 0.89) 0.66 (0.52, 0.84) Additive interaction (RERI) 1.69 (-0.44, 3.81) 0.00 (ref) 0.00 (-0.47, 0.47) -0.09 (-0.56, 0.38) -0.46 (-0.94, 0.02) -0.5 (-0.96, -0.04) Stratified by pre-pregnancy diabetes Diabetes 1.72 (0.78, 3.75) 1.00 (ref) 0.95 (0.70, 1.29) 1.09 (0.81, 1.47) 1.22 (0.90, 1.66) 1.19 (0.88, 1.60) 259 No Diabetes 1.09 (1.01, 1.18) 1.00 (ref) 1.11 (1.07, 1.15) 1.30 (1.25, 1.36) 1.39 (1.32, 1.46) 1.56 (1.47, 1.65) Multiplicative interaction (ratio of aORs) 1.57 (0.72, 3.45) 1.00 (ref) 0.86 (0.63, 1.18) 0.84 (0.62, 1.14) 0.89 (0.65, 1.21) 0.77 (0.57, 1.04) Additive interaction (RERI) 1.55 (-1.44, 4.54) 0.00 (ref) -0.21 (-0.91, 0.49) -0.08 (-0.8, 0.63) 0.15 (-0.64, 0.93) -0.1 (-0.85, 0.64) Severe neonatal morbidity Overall unadjusted 1.14 (1.11, 1.18) 1.00 (ref) 1.12 (1.10, 1.14) 1.35 (1.33, 1.37) 1.62 (1.58, 1.65) 1.99 (1.94, 2.03) Overall adjusted 1.08 (1.05, 1.12) 1.00 (ref) 1.08 (1.07, 1.10) 1.23 (1.21, 1.25) 1.38 (1.35, 1.41) 1.50 (1.46, 1.53) Stratified by parity Nulliparous 0.97 (0.92, 1.02) 1.00 (ref) 1.15 (1.12, 1.17) 1.39 (1.35, 1.42) 1.59 (1.54, 1.64) 1.72 (1.66, 1.78) Parous 1.23 (1.17, 1.20) 1.00 (ref) 1.03 (1.01, 1.05) 1.12 (1.09, 1.14) 1.23 (1.19, 1.26) 1.34 (1.30, 1.38) Multiplicative interaction (ratio of aORs) 0.79 (0.74, 0.84) 1.00 (ref) 1.12 (1.08, 1.15) 1.24 (1.20, 1.29) 1.29 (1.24, 1.35) 1.29 (1.23, 1.35) Additive interaction (RERI) -0.27 (-0.37, -0.18) 0.00 (ref) 0.20 (0.16, 0.25) 0.49 (0.44, 0.55) 0.69 (0.61, 0.78) 0.80 (0.70, 0.89) Stratified by chronic hypertension Chronic hypertension 1.12 (0.85, 1.48) 1.00 (ref) 0.94 (0.86, 1.03) 0.98 (0.90, 1.07) 0.97 (0.89, 1.06) 0.95 (0.87, 1.03) No chronic hypertension 1.09 (1.05, 1.12) 1.00 (ref) 1.08 (1.07, 1.10) 1.23 (1.21, 1.26) 1.40 (1.37, 1.43) 1.56 (1.53, 1.60) Multiplicative interaction (ratio of aORs) 1.03 (0.78, 1.36) 1.00 (ref) 0.87 (0.79, 0.95) 0.79 (0.73, 0.87) 0.69 (0.63, 0.76) 0.61 (0.56, 0.66) Additive interaction (RERI) 0.26 (-0.65, 1.17) 0.00 (ref) -0.26 (-0.50, -0.01) -0.29 (-0.54, -0.05) -0.49 (-0.74, -0.23) -0.72 (-0.96, -0.48) Stratified by pre-pregnancy diabetes Diabetes 1.07 (0.77, 1.50) 1.00 (ref) 1.01 (0.91, 1.12) 0.93 (0.84, 1.03) 0.90 (0.81, 1.01) 0.92 (0.83, 1.03) 260 No Diabetes 1.09 (1.05, 1.12) 1.00 (ref) 1.08 (1.07, 1.10) 1.24 (1.21, 1.26) 1.39 (1.36, 1.42) 1.53 (1.50, 1.57) Multiplicative interaction (ratio of aORs) 0.99 (0.71, 1.39) 1.00 (ref) 0.93 (0.84, 1.04) 0.75 (0.68, 0.84) 0.65 (0.58, 0.72) 0.60 (0.54, 0.67) Additive interaction (RERI) 0.19 (-1.14, 1.52) 0.00 (ref) -0.04 (-0.43, 0.34) -0.49 (-0.86, -0.11) -0.76 (-1.15, -0.37) -0.81 (-1.19, -0.44) Severe neonatal morbidity includes Apgar score < 4 at 5 minutes, neonatal seizure and/or assisted ventilation for ≥6 hours. RERI denotes Relative Excess of Risk due to Interaction; aOR denotes adjusted odds ratio, adjusted for race, education, type of health care insurance, smoking status during pregnancy, and prior neonatal death or stillbirth as well as other effect modifiers. 261 Table 33: Summary of missing data overall for included pregnancies and amongst fetal deaths. Data are presented as n (%). Those with missing outcome or BMI (N = 1605) were excluded (N = 30,737). Variable Missing (all pregnancies) N = 7535128 Missing (fetal deaths) N = 41288 Race* 0 (0.0) 0 (0.0) Education* 98752 (1.31) 4937 (12.0) Smoking status* 38248 (0.51) 2866** (6.94) Prior neonatal death ** 22916 (0.30) 2001** (4.84) Prior caesarian section** 6967 (0.0) 2191** (5.31) Age 0 (0.0) 0 (0.0) Nulliparous ** 23817 (0.32) 2107** (5.10) Chronic diabetes** 6967 (0.09) 2191** (5.31) Chronic hypertension** 6967 (0.09) 2191** (5.31) *Coded as unknown in primary analyses **Coded as ‘no’ in primary analyses ***Analysis of complete cases on covariates yielded near identical results to primary analyses. 262 Appendix G: Supplementary data to Chapter 6 G.1 Tables Table 34: Example of counting process-style data needed for PAMM modeling where cut points are set every 5 gestational weeks. The first patient has a livebirth between 35-40 weeks and the second a stillbirth at 31 weeks. Gestational age categories can be used in more granular intervals, as in the primary analyses. Patient ID Gestational week interval start Gestational week interval end BMI Stillbirth Offset term* 1 20 25 20.4 0 log(5) 1 25 30 20.4 0 log(5) 1 30 35 20.4 0 log(5) 1 35 40 20.4 0 log(5) 2 20 25 28.6 0 log(5) 2 25 30 28.6 0 log(5) 2 30 33 28.6 1 log(1) *This term is needed for the Poisson modeling and is generated automatically. It provides information (on the log-scale) of the actual observed survival time within each interval. Table 35: Comparison of risk factors (covariates) for stillbirth from multivariable Cox and PAMM models assuming proportional hazards. Variable Cox hazard ratio (95% CI) PAMM hazard ratio (95% CI) BMI (per 5-unit increase) 1.15 (1.15, 1.16) 1.15 (1.15, 1.16) Maternal age (per year) 1.01 (1.01, 1.02) 1.01 (1.01, 1.02) Race Asian/Pacific Islander Reference Reference Non-Hispanic Black 2.35 (2.23, 2.47) 2.35 (2.23, 2.47) Hispanic 1.12 (1.06, 1.18) 1.12 (1.06, 1.18) Alaskan Indian/Native American 1.65 (1.49, 1.84) 1.66 (1.49, 1.85) Non-Hispanic White 1.08 (1.03, 1.13) 1.08 (1.03, 1.13) Parity Parous Reference Reference Nulliparous 1.13 (1.11, 1.16) 1.13 (1.11, 1.16) 263 Table 36: Effective degrees of freedom for nonlinear terms for pre-pregnancy BMI, PAMM models. Model Estimate Effective degrees of freedom p-value Categorical BMI (time-constant effect) Time term 8.82 < 0.001 Linear effect of BMI (time-constant effect) Time term < 0.001 Linear effect of BMI varying nonlinearly over time Time varying coefficient 5.15 < 0.001 Nonlinear effect of BMI varying nonlinearly over time Tensor product between time and BMI splines 13.89 < 0.001 Time term 4.00 < 0.001 BMI term 3.91 < 0.001 Interaction term 7.60 < 0.001 Linear effect of number of cigarettes smoked (time-constant) Time term 1.98 < 0.001 Linear effect of number of cigarettes smoked varying nonlinearly over time Time varying coefficient 2.06 < 0.001 G.2 Figures Figure 22: Distribution of cigarettes smoked per day by trimester amongst women who smoked prior to pregnancy (left) and amongst those who smoked throughout pregnancy (right). 01020304050Pre−pregnancy (n = 693,020)1st trimester (n = 693,020)2nd trimester (n = 693,020)3rd trimester (n = 693,020)Average cigarettes per day (pre−pregnancy smokers)01020304050Pre−pregnancy (n = 521,529)1st trimester (n = 513,615)2nd trimester (n = 440,193)3rd trimester (n = 415,783)Average cigarettes per day (during pregnancy smokers) 264 Appendix H: Supplementary data to Chapter 7 H.1 Tables Table 37: Example gestational age-specific unadjusted and adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth in women with and without chronic hypertension. Unadjusted hazard ratio (95%CI)a Adjusted hazard ratio (95%CI)a,b Pre-pregnancy BMI Gestational age Hypertension No hypertension Hypertension No hypertension 25 31 0.99 (0.94, 1.03) 1.11 (1.07, 1.16) 0.94 (0.90, 0.98) 1.08 (1.04, 1.12) 25 34 1.01 (0.96, 1.07) 1.17 (1.12, 1.23) 0.96 (0.91, 1.02) 1.14 (1.09, 1.19) 25 37 1.15 (1.09, 1.22) 1.28 (1.22, 1.33) 1.10 (1.04, 1.17) 1.24 (1.18, 1.29) 25 40 1.32 (1.16, 1.50) 1.36 (1.28, 1.45) 1.26 (1.11, 1.44) 1.30 (1.23, 1.38) 30 31 0.97 (0.89, 1.06) 1.27 (1.21, 1.34) 0.88 (0.80, 0.96) 1.18 (1.12, 1.24) 30 34 1.03 (0.92, 1.14) 1.36 (1.28, 1.45) 0.93 (0.83, 1.03) 1.26 (1.19, 1.34) 30 37 1.33 (1.19, 1.49) 1.60 (1.51, 1.70) 1.21 (1.08, 1.36) 1.48 (1.40, 1.57) 30 40 1.74 (1.35, 2.25) 1.80 (1.64, 1.98) 1.59 (1.23, 2.07) 1.65 (1.51, 1.80) 35 31 0.96 (0.84, 1.10) 1.46 (1.38, 1.54) 0.83 (0.72, 0.95) 1.29 (1.22, 1.36) 35 34 1.04 (0.89, 1.22) 1.56 (1.46, 1.67) 0.89 (0.76, 1.05) 1.38 (1.29, 1.47) 35 37 1.54 (1.30, 1.82) 1.95 (1.83, 2.08) 1.34 (1.13, 1.58) 1.72 (1.61, 1.83) 35 40 2.30 (1.57, 3.38) 2.30 (2.08, 2.55) 2.01 (1.36, 2.97) 2.03 (1.84, 2.24) 40 31 0.95 (0.79, 1.13) 1.64 (1.54, 1.75) 0.78 (0.65, 0.93) 1.39 (1.30, 1.48) 40 34 1.05 (0.85, 1.30) 1.75 (1.62, 1.89) 0.86 (0.69, 1.07) 1.48 (1.37, 1.59) 40 37 1.78 (1.42, 2.22) 2.28 (2.12, 2.45) 1.47 (1.17, 1.85) 1.93 (1.80, 2.08) 40 40 3.04 (1.83, 5.07) 2.84 (2.53, 3.18) 2.54 (1.51, 4.27) 2.42 (2.16, 2.71) a) hazard ratios compared to a pre-pregnancy BMI of 20. b) adjusted for maternal age, maternal race/ethnicity, parity, smoking during pregnancy, maternal education and pregnancy diabetes mellitus. 265 Table 38: Example gestational age-specific unadjusted and adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth in women with and without pre-pregnancy diabetes. Unadjusted hazard ratio (95%CI)a Adjusted hazard ratio (95%CI)a,b Pre-pregnancy BMI Gestational age Diabetes No diabetes Diabetes No diabetes 25 31 1.05 (0.99, 1.11) 1.11 (1.07, 1.15) 0.98 (0.93, 1.03) 1.08 (1.04, 1.12) 25 34 1.06 (1.00, 1.13) 1.16 (1.11, 1.21) 0.99 (0.94, 1.04) 1.12 (1.07, 1.17) 25 37 1.11 (1.04, 1.19) 1.26 (1.21, 1.32) 1.03 (0.98, 1.09) 1.22 (1.17, 1.27) 25 40 1.17 (1.06, 1.30) 1.35 (1.27, 1.43) 1.09 (0.99, 1.20) 1.29 (1.22, 1.37) 30 31 1.10 (0.99, 1.23) 1.26 (1.20, 1.33) 0.96 (0.87, 1.06) 1.17 (1.11, 1.23) 30 34 1.13 (1.01, 1.27) 1.33 (1.26, 1.42) 0.98 (0.89, 1.08) 1.23 (1.16, 1.31) 30 37 1.24 (1.10, 1.39) 1.56 (1.47, 1.66) 1.07 (0.97, 1.18) 1.44 (1.36, 1.53) 30 40 1.37 (1.12, 1.67) 1.76 (1.61, 1.92) 1.19 (0.98, 1.44) 1.62 (1.48, 1.76) 35 31 1.16 (0.99, 1.35) 1.45 (1.37, 1.53) 0.94 (0.81, 1.08) 1.27 (1.20, 1.35) 35 34 1.19 (1.02, 1.40) 1.51 (1.42, 1.62) 0.97 (0.84, 1.12) 1.33 (1.24, 1.42) 35 37 1.37 (1.17, 1.62) 1.87 (1.76, 2.00) 1.10 (0.95, 1.28) 1.65 (1.54, 1.76) 35 40 1.60 (1.19, 2.14) 2.23 (2.02, 2.47) 1.30 (0.97, 1.74) 1.96 (1.78, 2.17) 40 31 1.21 (1.00, 1.48) 1.64 (1.54, 1.75) 0.92 (0.76, 1.11) 1.37 (1.28, 1.46) 40 34 1.26 (1.03, 1.55) 1.69 (1.57, 1.83) 0.96 (0.79, 1.16) 1.41 (1.31, 1.53) 40 37 1.52 (1.24, 1.87) 2.18 (2.03, 2.35) 1.14 (0.93, 1.39) 1.83 (1.70, 1.97) 40 40 1.87 (1.27, 2.74) 2.75 (2.46, 3.08) 1.41 (0.96, 2.09) 2.32 (2.07, 2.60) a) hazard ratios compared to a pre-pregnancy BMI of 20. b) adjusted for maternal age, maternal race/ethnicity, parity, smoking during pregnancy, maternal education and chronic hypertension. 266 Table 39: Example gestational age-specific unadjusted and adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth in women with and without both chronic hypertension and pre-pregnancy diabetes. Pre-pregnancy BMI Gestational age Unadjusted hazard ratio (95% CI)a Adjusted hazard ratio (95%CI)a,b 25 31 0.97 (0.90, 1.04) 0.94 (0.88, 1.02) 25 34 0.99 (0.92, 1.07) 0.96 (0.89, 1.04) 25 37 1.02 (0.93, 1.13) 0.99 (0.90, 1.09) 25 40 1.06 (0.91, 1.23) 1.02 (0.88, 1.19) 30 31 0.94 (0.81, 1.09) 0.89 (0.77, 1.03) 30 34 0.98 (0.84, 1.14) 0.92 (0.79, 1.08) 30 37 1.04 (0.86, 1.27) 0.98 (0.81, 1.19) 30 40 0.91 (0.73, 1.14) 0.84 (0.68, 1.05) 35 31 0.97 (0.77, 1.22) 0.89 (0.70, 1.12) 35 34 1.07 (0.80, 1.43) 0.97 (0.72, 1.31) 35 37 1.19 (0.75, 1.88) 1.07 (0.68, 1.70) 35 40 0.88 (0.66, 1.19) 0.80 (0.59, 1.07) 40 31 0.96 (0.70, 1.30) 0.85 (0.62, 1.17) 40 34 1.09 (0.74, 1.61) 0.96 (0.65, 1.43) 40 37 1.26 (0.68, 2.32) 1.10 (0.60, 2.03) 40 40 0.97 (0.90, 1.04) 0.94 (0.88, 1.02) a) hazard ratios compared to a pre-pregnancy BMI of 20. b) adjusted for maternal age, maternal race/ethnicity, parity, smoking during pregnancy, maternal education and chronic hypertension. Table 40: Example gestational age-specific adjusted hazard ratios expressing the association between pre-pregnancy BMI and stillbirth,; sensitivity analysis including complete cases. Adjusted hazard ratio (95% CI)a Pre-pregnancy BMI Gestational age Hypertension No hypertension Diabetes No diabetes 25 31 0.94 (0.90, 0.98) 1.08 (1.04, 1.12) 0.98 (0.93, 1.03) 1.08 (1.04, 1.12) 25 34 0.96 (0.91, 1.02) 1.13 (1.09, 1.19) 0.99 (0.94, 1.04) 1.12 (1.07, 1.17) 25 37 1.10 (1.04, 1.17) 1.23 (1.18, 1.29) 1.03 (0.98, 1.09) 1.22 (1.16, 1.27) 25 40 1.26 (1.11, 1.44) 1.30 (1.22, 1.38) 1.09 (0.99, 1.20) 1.28 (1.21, 1.36) 30 31 0.88 (0.80, 0.96) 1.18 (1.12, 1.24) 0.96 (0.87, 1.06) 1.17 (1.11, 1.23) 30 34 0.93 (0.83, 1.03) 1.26 (1.19, 1.34) 0.98 (0.89, 1.08) 1.23 (1.16, 1.31) 30 37 1.21 (1.08, 1.36) 1.48 (1.40, 1.57) 1.07 (0.97, 1.18) 1.44 (1.36, 1.53) 30 40 1.59 (1.23, 2.07) 1.65 (1.51, 1.8) 1.19 (0.98, 1.44) 1.61 (1.48, 1.76) 35 31 0.83 (0.72, 0.95) 1.29 (1.22, 1.36) 0.94 (0.81, 1.08) 1.27 (1.20, 1.35) 35 34 0.89 (0.76, 1.05) 1.37 (1.28, 1.47) 0.97 (0.84, 1.12) 1.32 (1.24, 1.42) 35 37 1.34 (1.13, 1.58) 1.73 (1.62, 1.84) 1.10 (0.95, 1.28) 1.65 (1.54, 1.76) 35 40 2.01 (1.36, 2.97) 2.03 (1.83, 2.25) 1.3 (0.97, 1.74) 1.96 (1.77, 2.17) 40 31 0.78 (0.65, 0.93) 1.39 (1.30, 1.48) 0.92 (0.76, 1.11) 1.37 (1.28, 1.46) 40 34 0.86 (0.69, 1.07) 1.47 (1.36, 1.59) 0.96 (0.79, 1.16) 1.41 (1.30, 1.52) 267 40 37 1.47 (1.17, 1.85) 1.94 (1.81, 2.09) 1.14 (0.93, 1.39) 1.83 (1.70, 1.97) 40 40 2.54 (1.51, 4.27) 2.43 (2.17, 2.73) 1.41 (0.96, 2.09) 2.33 (2.07, 2.61) a) Compared to a pre-pregnancy BMI of 20, adjusted for maternal age, maternal race/ethnicity, parity, smoking during pregnancy, maternal education and chronic hypertension or pre-pregnancy diabetes mellitus. H.2 Figures Figure 23: Weekly cumulative incidence rate of birth by gestational week in women with and without chronic hypertension. Obese I Obese II Obese IIIUnderweight Normal Overweight30 31 32 33 34 35 36 37 38 39 40 41 42 30 31 32 33 34 35 36 37 38 39 40 41 42 30 31 32 33 34 35 36 37 38 39 40 41 420250500750100002505007501000Gestational age (weeks)1−week cumulative incidence birth rate per 1000Chronic hypertension N Y 268 Figure 24: Weekly cumulative incidence rate of birth by gestational week in women with and without pre-pregnancy diabetes mellitus. Obese I Obese II Obese IIIUnderweight Normal Overweight30 31 32 33 34 35 36 37 38 39 40 41 42 30 31 32 33 34 35 36 37 38 39 40 41 42 30 31 32 33 34 35 36 37 38 39 40 41 420250500750100002505007501000Gestational age (weeks)1−week cumulative incidence birth rate per 1000Pre−pregnancy diabetes mellitus N Y 269 Appendix I: Supplementary data to Chapter 8 I.1 Statistical details of sensitivity analyses The main text refers to five primary sensitivity analyses, details of which are as follows: First, we repeated the analyses in the subset of women with twins of opposite sex, as this subset would include only dizygotic twins, which have a stronger association with environmental and maternal factors (such as BMI). Although we did not have information on zygocity and chorionicity for twin deliveries, opposite-sex twins constitute approximately half of all dizygotic twins (per Weinberg’s rule (440)). This analysis assumed that the same-sex and opposite-sex twin delivery ratio was not affected by pre-pregnancy BMI and all other factors, and that the analysis of opposite-sex twin deliveries would approximate the results for all dizygotic twin births. These results are summarized in Table 4 of the main text. Second, we performed complete-case analyses of records with known BMI to assess possible selection bias (Table 24 below). Third, we further carried out deterministic analyses for the missing data under four other missing-data assumptions. The first two scenarios corresponded to ‘worst case’ selection biases for the proportion mediated and assumed that all ART pregnancies with missing BMI were from women with obesity while all other women with missing data had normal BMI (scenario 1) and vice versa (scenario 2). The other two scenarios corresponded to ‘worst case’ selection biases for the total effect of BMI on twinning and assumed that all missing BMI values (regardless of ART 270 status) were from women with obesity (scenario 3) or were from women with normal BMI (scenario 4). Specifically: a) Scenario 1 –we assumed that all ART pregnancies missing BMI were obese with relative proportions of class I, II and III the same as in observed cases, while all others missing BMI had normal BMI. b) Scenario 2- we assumed that all ART pregnancies missing BMI were normal weight, while all others missing BMI had the same were obese with relative proportions of class I, II and III the same as in observed cases. c) Scenario 3 – in this scenario we assumed that all missing BMI values, regardless of ART status were obese with relative proportions of class I, II and III the same as in observed cases. d) Scenario 4 – in this scenario we assumed that the pregnancies missing BMI were those with normal BMI. These results are summarized in Table 25 below. Third, we repeated the mediation analyses adjusting for various levels of possible misclassification of BMI (441) as self-reported BMI is known to underestimate true BMI (42,44,392). There have been numerous studies indicating that self-reported BMI differs from clinical estimates, with most studies finding that self-reporting tends to underestimate BMI (42,44,392). This under-reporting may lead to misclassification of cases, e.g., some women listed as ‘normal’ actually being overweight, and some who are obese being listed as ‘normal’. We conducted this sensitivity analysis to assess this possible issue, assuming that most misclassification would be downwards (i.e., obese to overweight or overweight to normal) and that very few underweight women were misclassified as a higher BMI (441). 271 We used the following misclassification matrix where cells represent probability of misclassification (rounded). Due to high computation time and similarity between multiply imputed and complete case results we conducted these analyses only on complete cases. Confidence intervals were calculated from 100 bootstrap resamples. Results are summarized in Table 26. Underweight Normal Overweight Obese I Obese II Obese III Underweight 0.95 0.05 < 0.001 < 0.001 < 0.001 < 0.001 Normal 0.01 0.94 0.05 < 0.001 < 0.001 < 0.001 Overweight < 0.001 0.01 0.94 0.05 < 0.001 < 0.001 Obese I < 0.001 < 0.001 0.01 0.94 0.05 < 0.001 Obese II < 0.001 < 0.001 < 0.001 0.01 0.94 0.05 Obese III < 0.001 < 0.001 < 0.001 < 0.001 0.01 0.99 Fifth, our cohort only included pregnancies lasting to ≥20 weeks gestation. As has been documented in previous methodological studies, this can create a possible left-truncation bias if there is differential early pregnancy loss between groups. In this case, if obese women were more likely to miscarry before 20 weeks than those of normal BMI, then the association between BMI and twin pregnancy observed in our cohort would be biased. To assess the sensitivity of our primary result to this we conducted a probabilistic quantitative bias analysis assuming that the selection bias odds between higher levels of BMI and normal BMI women decreased with 272 increasing. We assumed triangular distribution with moderate amount of left truncation bias (see table below); 1.0 represents no left-truncation bias while smaller values represent higher degrees of bias. The bias analysis was run 100,000 times and we report the median bias adjusted risk ratio with corresponding confidence interval taken as the 2.5th and 97.5th percentiles of the resulting distribution. Results are summarized in Table 27 below. I.2 Tables Table 41: Comparison of women with and without information on pre-pregnancy BMI. Complete BMI (N = 392,046) Missing BMI (N = 132,799) Maternal age Median [IQR] 31.5 [27.9;34.9] 31.2 [27.2;35.0] < 20 7195 (1.84) 3865 (2.91) 20-24 39867 (10.2) 16344 (12.3) 25-34 247311 (63.1) 78787 (59.3) 35-39 80053 (20.4) 26974 (20.3) ≥40 17620 (4.49) 6829 (5.14) Parity Nulliparous 189557 (48.4) 53886 (40.6) 1 139007 (35.5) 50679 (38.2) ≥2 63444 (16.2) 28163 (21.2) Maternal height Median [IQR] 165 [160;170] 164 [160;168] < 165 194536 (49.6) 21650 (52.4) 165-168 93919 (24.0) 9548 (23.1) 169-172 46715 (11.9) 4658 (11.3) ≥173 56876 (14.5) 5495 (13.3) Smoking Yes 26424 (6.74) 10470 (7.88) No 365622 (93.3) 122329 (92.1) Use of ART Yes 11523 (2.94) 3872 (2.92) No 380523 (97.1) 128927 (97.1) Twin pregnancy Yes 5813 (1.48) 2482 (1.87) No 386233 (98.5) 130317 (98.1) IQR = interquartile range ART = assisted reproductive technology 273 Table 42: Rate of twin pregnancies of same and opposite sex by BMI group. Pre-pregnancy BMI Total deliveries N twin births overall (per 1000) N twin births same sex (per 1000) N twin births opposite sex (per 1000) Underweight 22396 253 (11.3) 182 (8.13) 71 (3.2) Normal 231583 3323 (14.4) 2221 (9.59) 1102 (4.8) Overweight 83887 1340 (16.0) 873 (10.41) 467 (5.6) Obese I 33263 531 (16.0) 334 (10.1) 197 (5.9) Obese II 13308 222 (16.7) 136 (10.2) 86 (6.5) Obese III 7609 144 (18.9) 92 (12.1) 52 (6.8) Table 43: Complete-case analysis. Pre-pregnancy BMI Total effect aRR (95% CI) Natural direct effect aRR (95% CI) Natural indirect effect aRR (95% CI) Proportion mediated (95% CI) Underweight 0.84 (0.74, 0.96) 0.85 (0.75, 0.97) 0.99 (0.97, 1.00) 7 (-2, 15) Normal 1.00 (ref) 1.00 (ref) 1.00 (ref) - Overweight 1.14 (1.07, 1.22) 1.13 (1.05, 1.20) 1.01 (1.01, 1.02) 10 (3, 17) Obese I 1.17 (1.07, 1.29) 1.14 (1.03, 1.25) 1.03 (1.02, 1.04) 20 (7, 33) Obese II 1.20 (1.05, 1.39) 1.16 (1.00, 1.33) 1.04 (1.02, 1.06) 23 (3, 43) Obese III 1.45 (1.22, 1.71) 1.48 (1.25, 1.75) 0.98 (0.96, 1.00) -7 (-14, 0) aRR = adjusted risk ratio. Effects are estimated from mediation analyses pooled across 20 multiply imputed data sets. Results are adjusted for maternal height, age, smoking status, parity and fiscal year. E-values represent strength of unmeasured confounder (on rate ratio scale) needed to bring the point estimate for total effects to 1.0. 274 Table 44: Results of mediation analysis based on imputation assuming 4 missing data scenarios. Total effect aRR (95% CI) Natural direct effect aRR (95% CI) Natural indirect effect for (95% CI) Proportion mediated (95% CI) Primary analysis Scenario 1 Scenario 2 Scenario 3 Scenario 4 Primary analysis Scenario 1 Scenario 2 Scenario 3 Scenario 4 Primary analysis Scenario 1 Scenario 2 Scenario 3 Scenario 4 Primary analysis Scenario 1 Scenario 2 Scenario 3 Scenario 4 Underweight 0.84 (0.74, 0.95) 0.80 (0.71, 0.92) 0.81 (0.71, 0.93) 0.84 (0.74, 0.96) 0.78 (0.69, 0.89) 0.85 (0.75, 0.96) 0.80 (0.70, 0.91) 0.84 (0.74, 0.96) 0.85 (0.75, 0.97) 1.07 (0.97, 1.17) 0.99 (0.98, 1.00) 1.01 (1.00, 1.02) 0.96 (0.95, 0.98) 0.99 (0.97, 1) 1.03 (1.02, 1.04) 6 (-2, 13) -4 (-10, 3) 17 (4, 30) 6 (2,15) 6 (0, 11) Normal 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) - - - - - Overweight 1.14 (1.07, 1.21) 1.09 (1.03, 1.17) 1.10 (1.03, 1.18) 1.14 (1.07, 1.22) 1.06 (1.00, 1.13) 1.12 (1.05, 1.19) 1.06 (1.00, 1.14) 1.12 (1.04, 1.19) 1.13 (1.06, 1.21) 1.05 (0.99, 1.12) 1.01 (1.01, 1.02) 1.03 (1.02, 1.04) 0.99 (0.98, 1.00) 1.01 (1.01, 1.02) 1.01 (1.00, 1.02) 11 (3, 18) 32 (9, 56) -11 (-21, -1) 9 (3, 16) 15 (-3,33) Obese I 1.16 (1.06, 1.27) 1.56 (1.34, 1.83) 1.30 (1.13, 1.49) 1.47 (1.30, 1.68) 1.09 (1.00, 1.20) 1.12 (1.03, 1.23) 1.42 (1.20, 1.67) 1.41 (1.23, 1.61) 1.48 (1.3, 1.68) 1.07 (0.97, 1.17) 1.03 (1.02, 1.04) 1.10 (1.06, 1.14) 0.92 (0.89, 0.96) 1 (0.98, 1.01) 1.03 (1.02, 1.04) 23 (7, 39) 26 (13, 38) -36 (-61, -11) -1 (-5, 3) 30 (-2, 63) Obese II 1.17 (1.02, 1.34) 1.33 (1.17, 1.51) 1.23 (1.11, 1.38) 1.40 (1.26, 1.55) 1.12 (0.98, 1.29) 1.13 (0.98, 1.29) 1.11 (0.96, 1.27) 1.30 (1.17, 1.44) 1.36 (1.23, 1.51) 1.08 (0.94, 1.25) 1.04 (1.02, 1.06) 1.20 (1.15, 1.25) 0.95 (0.94, 0.97) 1.03 (1.01, 1.04) 1.04 (1.02, 1.06) 27 (0, 54) 68 (36, 100) -27 (-43, -10) 9 (4, 13) 32 (-8, 73) Obese III 1.41 (1.19, 1.66) 1.29 (1.18, 1.40) 1.17 (1.08, 1.26) 1.30 (1.20, 1.39) 1.35 (1.14, 1.60) 1.44 (1.22, 1.69) 1.10 (1.00, 1.21) 1.22 (1.14, 1.32) 1.26 (1.17, 1.35) 1.38 (1.17, 1.64) 0.98 (0.96, 1.00) 1.17 (1.14, 1.20) 0.95 (0.94, 0.96) 1.03 (1.02, 1.04) 0.98 (0.96, 0.99) -7 (-15, 0) 64 (41, 87) -33 (-51, -15) 12 (8, 17) -9 (-17,0) aRR = adjusted risk ratio 275 Table 45 : Results of sensitivity analysis for body-mass-index misclassification compared to primary results (complete cases). Total effect aRR (95% CI) Natural direct effect aRR (95% CI) Natural indirect effect for (95% CI) Proportion mediated % (95% CI) Primary analysis Measurement error adjusted Primary analysis Measurement error adjusted Primary analysis Measurement error adjusted Primary analysis Measurement error adjusted Underweight 0.84 (0.74, 0.95) 0.81 (0.67, 0.96) 0.85 (0.75, 0.96) 0.81 (0.68, 0.97) 0.99 (0.98, 1.00) 0.99 (0.99, 1.00) 6 (-2, 13) 2 (-10, 6) Normal 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) - 1.00 (ref) Overweight 1.14 (1.07, 1.21) 1.17 (1.08, 1.27) 1.12 (1.05, 1.19) 1.16 (1.07, 1.26) 1.01 (1.01, 1.02) 1.01 (1.00, 1.01) 11 (3, 18) 3 (1, 8) Obese I 1.16 (1.06, 1.27) 1.18 (1.05, 1.32) 1.12 (1.03, 1.23) 1.16 (1.03, 1.30) 1.03 (1.02, 1.04) 1.01 (1.00, 1.02) 23 (7, 39) 8 (1, 15) Obese II 1.17 (1.02, 1.34) 1.20 (1.01, 1.43) 1.13 (0.98, 1.29) 1.18 (0.99, 1.40) 1.04 (1.02, 1.06) 1.02 (1.01, 1.03) 27 (0, 54) 9 (-1, 20) Obese III 1.41 (1.19, 1.66) 1.54 (1.27, 1.87) 1.44 (1.22, 1.69) 1.56 (1.29, 1.89) 0.98 (0.96, 1.00) 0.99 (0.98, 1.00) -7 (-15, 0) -3 (-5, 0) 276 Table 46: Results of sensitivity analysis for body-mass-index misclassification compared to primary results (complete cases). Total effect aRR (95% CI) Mode of bias odds (lower, upper limits) for triangular distribution Primary analysis Left-truncation bias adjusted Underweight 1.00 (0.90. 1.10) 0.84 (0.74, 0.95) 0.79 (0.68, 0.91) Normal - 1.00 (ref) 1.00 (ref) Overweight 0.98 (0.86, 1.00) 1.14 (1.07, 1.21) 1.18 (1.08, 1.30) Obese I 0.96 (0.84, 1.00) 1.16 (1.06, 1.27) 1.19 (1.07, 1.35) Obese II 0.94 (0.82, 1.00) 1.17 (1.02, 1.34) 1.27 (1.08, 1.49) Obese III 0.92 (0.80, 1.00) 1.41 (1.19, 1.66) 1.46 1.21, 1.77) I.3 Figures Figure 25: Directed acyclic graph representing mediation model. ACMYA (exposure): BMI categoryM (mediator): ARTY (outcome): Twin pregnancyC (confounders not affected by the exposure): Age, Parity, Smoking, Height, Fiscal year 277 Figure 26: Trends in twin rates amongst ART and non-ART conceptions during the study period. ART conception Non−ART conception08/0909/1010/1111/1212/1313/1414/1515/1616/1717/1818/1919/20 08/0909/1010/1111/1212/1313/1414/1515/1616/1717/1818/1919/2011.011.512.0120160200Fiscal yearRate of twin pregnancy per 1000 births 278 Figure 27: Rates of twin pregnancy by pre-pregnancy BMI stratified by ART conception. 5010015020020 30 40Pre−pregnancy body−mass−index (kg/m2)Number of twin pregnancies per 1000ART conception510152020 30 40Pre−pregnancy body−mass−index (kg/m2)Non−ART conception 279 Appendix J: Supplementary data to Chapter 9 J.1 Tables Table 47: Severe maternal morbidity (SMM) definitions. SMM group SMM conditions Data source ICD-10-CA, CCI codes and other variables SPE, eclampsia Severe pre-eclampsia, HELLP syndrome ICD-10-CA O14.1, O14.2 Eclampsia ICD-10-CA O15 Severe hemorrhage Placenta previa with hemorrhage and red cell transfusion ICD-10-CA and BCPDR O44.1 + blood_transfusion_flg (BCPDR)=‘Y’ Placental abruption with coagulation defect ICD-10-CA O45.0 Antepartum hemorrhage with coagulation defect ICD-10-CA O46.0 Intrapartum hemorrhage with coagulation defect ICD-10-CA O67.0 Intrapartum hemorrhage with red cell transfusion ICD-10-CA and BCPDR O67 + blood_transfusion_flg =‘Y’ Postpartum hemorrhage with red cell transfusion, procedures to the uterus or hysterectomy O72 + any of the following: • RBCTRNSF=‘Y’, or • (1.RM.13, 1.KT.51, 5.PC.91.LA or 5.PC.91.HV) + blood_transfusion_flg = “Y”, or • (5.MD.60.RC, 5.MD.60.RD, 5.MD.60.KE, 5.MD.60.CB or 1.RM.89.LAb), or • 1.RM.87.LA-GX bNote: 1.RM.89.LA is included only if codes 1.PL.74, 1.RS.74 or 1.RS.80 are NOT also present Curettage with red cell transfusion (5.PC.91.GA, 5.PC.91.GC or 5.PC.91.GD) + lo d_transfusion_flg =‘Y’ 280 Surgical complications Complications of obstetric surgery and procedures ICD-10-CA O75.4 Evacuation of incisional hematoma with RBC transfusion Procedure codes and BCPDR 5.PC.73.JS + blood_transfusion_flg =‘Y’ Repair of bladder, urethra, or intestine Procedure codes 5.PC.80.JR, 1.NK.80 or 1.NM.80 Reclosure of caesarean wound with RBC transfusion Procedure codes and BCPDR (5.PC.80.JM or 5.PC.80.JH) + blood_transfusion_flg =‘Y’ Hysterectomy Caesarean hysterectomy Procedure codes 5.MD.60.RC, 5.MD.60.RD, 5.MD.60.KE, 5.MD.60.CB Hysterectomy using an open approach (without bladder neck suspension, suspension of vaginal vault or pelvic floor repair) Procedure codes 1.RM.89.LAc (exclude if 1.PL.74, 1.RS.74 or 1.RS.80 code also present) or 1.RM.87.LA-GX Note: 1.RM.89.LA is included only if codes 1.PL.74, 1.RS.74 or 1.RS.80 are NOT also present Sepsis Puerperal sepsis ICD-10-CA O85 Septicemia during labour ICD-10-CA O75.3 Embolism, shock, DIC Obstetric shock ICD-10-CA O75.1, R57, T80.5 or T88.6 Obstetric embolism ICD-10-CA O88 Disseminated intravascular coagulation ICD-10-CA D65 Assisted ventilation Assisted ventilation through endotracheal tube Procedure codes 1.GZ.31.CA-ND Assisted ventilation through tracheostomy Procedure codes 1.GZ.31.CR-ND Acute cardiac conditions Cardiac complications of anesthesia ICD-10-CA O74.2, O89.1 281 Cardiomyopathy ICD-10-CA O90.3, I42, I43 Cardiac arrest and resuscitation ICD-10-CA I46, I49.0, 1.HZ.09, 1.HZ.30 Myocardial infarction ICD-10-CA I21, I22 Pulmonary edema and heart failure ICD-10-CA I50, J81 Acute renal failure Acute renal failure ICD-10-CA O90.4, N17, N19 or N99.0 Dialysis ICD-10-CA 1.PZ.21 Acute hepatic failure Toxic liver disease ICD-10-CA K71 Acute and subacute hepatic failure ICD-10-CA K72 Severe uterine rupture Rupture of the uterus with red cell transfusion, procedures to the uterus or hysterectomy ICD-10-CA (O71.0 or O71.1) + any of the following: • blood_transfusion_flg =‘Y’ , or • (1.RM.13, 1.KT.51, 5.PC.91.LA or 5.PC.91.HV) + blood_transfusion_flg =‘Y’ , or • (5.MD.60.RC, 5.MD.60.RD, 5.MD.60.KE, 5.MD.60.CB or 1.RM.89.LAa), or • 1.RM.87.LA-GX aNote: 1.RM.89.LA is included only if codes 1.PL.74, 1.RS.74 or 1.RS.80 are NOT also present Cerebrovascular morbidity Cerebral oedema or coma ICD-10-CA G93.6, R42.2 Cerebral venous thrombosis in pregnancy ICD-10-CA O22.5 Cerebral venous thrombosis in the puerperium ICD-10-CA O87.3 Subarachnoid and intracranial hemorrhage, cerebral infarction ICD-10-CA I60, I61, I62, I63 or I64 Other types Acute fatty liver with red cell transfusion or plasma transfusion ICD-10-CA and BCPDR O26.6 + (blood_transfusion_flg =‘Y’ or pp_fatty_liver (BCPDR) = “Y” PLSTRNSF=’Y’ --- we do not have info on plasma in bcpdr) 282 Liver hematoma BCPDR pp_liver_hematoma (BCPDR)=”Y” Pulmonary, cardiac, and CNS complications of anesthesia during pregnancy, labour, delivery or the puerperium ICD-10-CA O29.0, O29.1, O29.2, O89.0, O89.1, O89.2, O74.0, O74.1, O74.2 or O74.3 Status asthmaticus ICD-10-CA J45.01, J45.11, J45.81 or J45.91 Adult respiratory distress syndrome ICD-10-CA J80 Acute abdomen ICD-10-CA K35, K37, K65, N73.3 or N73.5 Surgical or manual correction of inverted uterus for vaginal births only Procedure codes 5.PC.91.HQ or 5.PC.91.HP, restricted to vaginal births (i.e., absence of caesarean code 5.MD.60) Sickle cell anemia with crisis ICD-10-CA D57.0 Acute psychosis ICD-10-CA F53.1 or F23 Status epilepticus ICD-10-CA G41 283 Table 48: Serious neonatal morbidity definitions. Neonatal morbidities Source Codes Intracranial non-traumatic hemorrhage ICD-10-CA P52, P912 Convulsions of the newborn ICD-10-CA P90 Hypoxic ischaemic encephalopathy ICD-10-CA P916 Bronchopulmonary dysplasia ICD-10-CA P271 Necrotizing enterocolitis ICD-10-CA P77 Sepsis ICD-10-CA P36 Severe birth trauma ICD-10-CA P100 -P104, P107, P109-P112 Severe respiratory distress syndrome ICD-10-CA and BCPDR P220, P228, P229** **only in infants who had mechanical ventilation for >3 days (BCPDR variable). BCPDR denotes BC Perinatal Database Registry 284 Table 49: Definitions of maternal chronic conditions. Maternal and pregnancy characteristics Data source ICD-10-CA code Risk factors Chronic hypertension ICD-10-CA or BCPDR O10, O11 Chronic diabetes ICD-10-CA or BCPDR O245, O246, O247, E10, E11 Assisted reproductive technology ICD-10-CA or BCPDR Z300, Z37101, Z37911, Z38001, Z38001, Z38201 Chronic cardiac conditions ICD-10-CA I05-I09, I25, I34-I37, I39, Q20-27 Chronic renal conditions ICD-10-CA N022, N03, N04, N05, N08, N18, N25 HIV ICD-10-CA B24, O987, Z21 Asthma ICD-10-CA J44, J45 Systemic lupus erythematosus ICD-10-CA M32 Chronic hepatic conditions ICD-10-CA K70 Sickle cell disease/thalassemia ICD-10-CA D56, D571-D578 Morbidly adherent placenta* ICD-10-CA O432 Placenta previa ICD-10-CA O44 BCPDR denotes BC Perinatal Database Registry Table 50: Outcome rates for women with and without a pre-pregnancy BMI measurement. Data are N (%). Outcome Complete BMI (N = 7,770 pregnancy, 15,440 fetuses) Missing BMI (N = 3113 pregnancies, 6526 fetuses) Severe maternal morbidity 232 (3.0) 95 (2.9) Perinatal composite 877 (5.6) 468 (7.2) Perinatal death 386 (2.5) 264 (4.0) Stillbirth 196 (1.3) 150 (2.3) Neonatal death 190 (1.2) 114 (1.8) Neonatal morbidity 525 (3.4) 228 (3.6) 285 Table 51: Sensitivity analysis comparing the results for complete cases and using Targeted Maximum Likelihood Estimation with and without imputation. Risk ratios are compared to women with normal pre-pregnancy BMI. Underweight Overweight Obese Unadjusted RR (95% CI) Adjusted RR (95% CI) Complete case adjusted RR (95% CI) TMLE adjusted RR (95% CI) Complete case TMLE adjusted RR (95% CI) Unadjusted RR (95% CI) Adjusted RR (95% CI) Complete case adjusted RR (95% CI) TMLE adjusted RR (95% CI) Complete case TMLE adjusted RR (95% CI) Unadjusted RR (95% CI) Adjusted RR (95% CI) Complete case adjusted RR (95% CI) TMLE adjusted RR (95% CI) Complete case TMLE adjusted RR (95% CI) Severe maternal morbidity/mortality 0.96 (0.57, 1.63) 1.01 (0.59, 1.71) 0.92 (0.45, 1.66) 1.60 (0.85, 3.03) 1.62 (0.83, 3.16) 0.87 (0.64, 1.17) 0.89 (0.66, 1.21) 0.86 (0.61, 1.19) 0.86 (0.62 1.18) 0.86 (0.62, 1.19) 0.84 (0.57, 1.23) 0.85 (0.57, 1.27) 0.82 (0.52, 1.23) 0.77 (0.50, 1.18) 0.77 (0.50, 1.18) Perinatal composite 1.34 (1.00, 1.8) 1.30 (0.97, 1.74) 1.51 (1.10, 2.08) 1.64 (1.21, 2.24) 1.79 (1.32, 2.43) 1.03 (0.86, 1.25) 1.04 (0.86, 1.26) 0.98 (0.80, 1.20) 1.02 (0.83, 1.25) 1.00 (0.82, 1.23) 1.07 (0.84, 1.36) 1.08 (0.85, 1.38) 1.07 (0.83, 1.37) 1.05 (0.81, 1.36) 1.04 (0.80, 1.34) Perinatal death 1.42 (0.93, 2.18) 1.38 (0.90, 2.11) 1.57 (0.98, 2.51) 1.66 (1.09, 2.53) 1.69 (1.11, 2.57) 1.12 (0.85, 1.48) 1.12 (0.85, 1.47) 1.00 (0.74, 1.37) 1.07 (0.79, 1.45) 1.05 (0.77, 1.43) 1.17 (0.84, 1.63) 1.17 (0.84, 1.63) 1.15 (0.8, 1.66) 1.10 (0.75, 1.60) 1.10 (0.75, 1.61) Stillbirth 1.08 (0.57, 2.05) 1.05 (0.55, 2.00) 0.82 (0.37, 1.81) 0.70 (0.28, 1.75) 1.64 (1.15, 2.34) 1.02 (0.69, 1.48) 1.02 (0.7, 1.49) 0.80 (0.53, 1.22) 0.89 (0.6, 1.34) 0.84 (0.55, 1.29) 1.07 (0.68, 1.69) 1.08 (0.68, 1.71) 0.98 (0.6, 1.61) 1.07 (0.66, 1.71) 1.11 (0.68, 1.80) Neonatal death 1.86 (1.08, 3.19) 1.79 (1.04, 3.08) 2.51 (1.38, 4.56) 3.14 (1.94, 5.10) 2.81 (1.64, 4.83) 1.25 (0.83, 1.88) 1.24 (0.82, 1.88) 1.28 (0.82, 1.99) 1.44 (0.98, 2.12) 1.38 (0.92, 2.07) 1.28 (0.81, 2.04) 1.29 (0.81, 2.05) 1.39 (0.85, 2.25) 1.16 (0.70, 1.91) 1.18 (0.71, 1.95) Neonatal morbidity 1.33 (0.90, 1.95) 1.30 (0.88, 1.91) 1.56 (1.03, 2.35) 1.66 (1.12, 2.46) 1.74 (1.17, 2.59) 0.98 (0.76, 1.26) 1.00 (0.77, 1.28) 0.98 (0.74, 1.28) 0.99 (0.76, 1.29) 0.98 (0.75, 1.27) 1.02 (0.76, 1.39) 1.05 (0.77, 1.43) 1.06 (0.77, 1.46) 1.04 (0.75, 1.43) 1.03 (0.74, 1.42) 286 Table 52: Outcome rates for maternal and perinatal outcomes by pre-pregnancy body-mass-index in twin pregnancies including both same sex and opposite sex twins. All data are N (rate per 10,000 pregnancies or livebirths). Underweight (N = 736) Normal (N = 9362) Overweight (N = 3408) Obese (N = 2032) Outcome Same sex (N = 512) Opposite sex (N = 224) Same sex (N = 6306) Opposite sex (N = 3058) Same sex (N = 2248) Opposite sex (N = 1160) Same sex (N = 1258) Opposite sex (N = 774) Severe maternal morbidity 6 (234.4) 4 (357.1) 99 (314) 51 (333.6) 33 (293.6) 13 (224.1) 12 (190.8) 14 (361.8) Perinatal composite 46 (898.4) 18 (803.6) 376 (596.5) 138 (451.3) 132 (587.2) 52 (448.3) 82 (651.8) 33 (426.4) Perinatal death 24 (468.8) 4 (178.6) 170 (269.7) 51 (166.8) 58 (258) 25 (215.5) 42 (333.9) 12 (155) Stillbirth 6 (117.2) < 5 (< 225) 101 (160.2) 24 (78.5) 25 (111.2) 13 (112.1) 21 (166.9) < 5 (< 94.5) Neonatal death 18 (355.7) 5 (< 225) 69 (111.2) 27 (89) 33 (148.4) 12 (104.6) 21 (169.8) 8 (103.9) Neonatal morbidity 25 (494.1) 15 (675.7) 216 (348.1) 93 (306.5) 80 (359.9) 28 (244.1) 43 (347.6) 25 (324.7) Table 53: Maternal and perinatal outcomes by pre-pregnancy body-mass-index in twin pregnancies with (sensitivity) analyses restricted to opposite sex twins. Adjusted risk ratio (95% CI)* Underweight Normal Overweight Obese Severe maternal morbidity 1.42 (0.84, 2.39) 1.00 (ref) 1.00 (0.70, 1.43) 0.97 (0.61, 1.53) Perinatal composite 1.18 (0.48, 2.89) 1.00 (ref) 1.21 (0.70, 2.11) 0.96 (0.47, 1.92) Perinatal death 1.21 (0.35, 4.24) 1.00 (ref) 1.34 (0.63, 2.83) 0.82 (0.28, 2.40) Stillbirth 1.08 (0.33, 3.59) 1.00 (ref) 1.09 (0.51, 2.34) 1.06 (0.46, 2.44) Neonatal death 1.60 (0.85, 3.01) 1.00 (ref) 0.83 (0.51, 1.34) 1.04 (0.61, 1.78) Neonatal morbidity 1.42 (0.84, 2.39) 1.00 (ref) 1.00 (0.70, 1.43) 0.97 (0.61, 1.53) *Per 10,000 pregnancies 287 J.2 Figures Figure 27: Standardized mean difference of covariates between women with and without missing BMI among women with a twin pregnancy.
Thesis/Dissertation
eng
Women+ and Children’s Health Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lisonkova, Sarka
Statistical modeling of the relationship between pre-pregnancy body-mass-index and maternal and perinatal outcomes
Text