Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Nethery, Elizabeth M K
Doctor of Philosophy - PhD
University of British Columbia
Background: Gestational diabetes mellitus (GDM) affects between 2-40% of pregnancies worldwide, depending on diagnostic and screening methods. Changes in screening practices are not well understood because administrative sources lack data on whether or how individuals were screened. The objectives of this thesis were to: 1) validate a method to identify prenatal screening for GDM and other conditions in administrative health data; 2) describe changes in GDM screening; 3) evaluate the relative contributions of screening and population characteristics to changes in GDM risk; 4) characterize the impact of the COVID-19 pandemic on pregnancy weight gain and infant birthweight. Methods: Laboratory billing records from BC’s universal health insurance system for prenatal screening tests were compared with medical records by calculating validation properties. All pregnancies (birth >20wks or >500g) in British Columbia, Canada, 2005-2019, with linked perinatal health and administrative data, were used to examine time trends in GDM screening methods, trends within subgroups, and the effect of screening changes on prevalence. A second cohort from Washington State, 2016-2020, was analyzed using an interrupted time series design, to assess COVID-19 impacts on pregnancy weight gain and infant birthweight using z-scores. Results: GDM screening in laboratory billing records had a high sensitivity (97% [95% CI: 90, 99]) and specificity (>99% [95% CI: 86, <99]) compared with medical records. GDM diagnoses in BC more than doubled from 7.2% in 2005 to 14.7% in 2019 (n=550,783 pregnancies). Most of this increase was explained by changes in screening; adjustment for population factors had minimal impact. In Washington state, using an interrupted time series, pregnancy weight gain z-score increased by 0.08 (95% CI 0.03, 0.13) after the COVID-19 pandemic onset and infant birthweight z-scores were unchanged (-0.004, 95% CI (-0.04, 0.03)). Conclusion: Prenatal screening tests can be accurately ascertained using BC insurance billing data. Changes in GDM screening completion and in screening methods accounted for most of the increase in GDM diagnosis in BC since 2005. Covid-19 pandemic countermeasures were associated with an increase in pregnancy weight gain but not infant birthweight. Public health and future researchers should understand how screening changes can directly affect disease prevalence.
Gestational diabetes screening changes and impacts on diagnosis by Elizabeth M K Nethery B.Ap.Sc., The University of British Columbia, 1998 M.Sc., The University of British Columbia, 2008 M.S.M, Bastyr University, 2014 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2022 © Elizabeth M K Nethery, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Gestational diabetes screening changes and impacts on diagnosis submitted by Elizabeth M.K. Nethery in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Population and Public Health Examining Committee: Dr. Patricia Janssen, Professor, School of Population and Public Health, UBC Supervisor Dr. Jennifer Hutcheon, Associate Professor, Department of Obstetrics & Gynecology, UBC Supervisory Committee Member Dr. Michael Law, Professor, School of Population and Public Health, UBC Supervisory Committee Member Dr. Geoff Cundiff, Professor, Department of Obstetrics & Gynecology, UBC University Examiner Dr. KS Joseph, Professor, School of Population and Public Health, UBC University Examiner Dr. M Karen Campbell, Professor, Department of Epidemiology and Biostatistics, Western University External Examiner Additional Supervisory Committee Members: Dr. Andrew Kotaska, Assistant Professor, Department of Obstetrics, University of Manitoba Supervisory Committee Member iii ABSTRACT Background: Gestational diabetes mellitus (GDM) affects between 2-40% of pregnancies worldwide, depending on diagnostic and screening methods. Changes in screening practices are not well understood because administrative sources lack data on whether or how individuals were screened. The objectives of this thesis were to: 1) validate a method to identify prenatal screening for GDM and other conditions in administrative health data; 2) describe changes in GDM screening; 3) evaluate the relative contributions of screening and population characteristics to changes in GDM risk; 4) characterize the impact of the COVID-19 pandemic on pregnancy weight gain and infant birthweight. Methods: Laboratory billing records from BC’s universal health insurance system for prenatal screening tests were compared with medical records by calculating validation properties. All pregnancies (birth >20wks or >500g) in British Columbia, Canada, 2005-2019, with linked perinatal health and administrative data, were used to examine time trends in GDM screening methods, trends within subgroups, and the effect of screening changes on prevalence. A second cohort from Washington State, 2016-2020, was analyzed using an interrupted time series design, to assess COVID-19 impacts on pregnancy weight gain and infant birthweight using z-scores. Results: GDM screening in laboratory billing records had a high sensitivity (97% [95% CI: 90, 99]) and specificity (>99% [95% CI: 86, <99]) compared with medical records. GDM diagnoses in BC more than doubled from 7.2% in 2005 to 14.7% in 2019 (n=550,783 pregnancies). Most of this increase was explained by changes in screening; adjustment for population factors had minimal impact. In Washington state, using an interrupted time series, pregnancy weight gain z-score increased by 0.08 (95% CI 0.03, 0.13) after the COVID-19 pandemic onset and infant birthweight z-scores were unchanged (-0.004, 95% CI (-0.04, 0.03)). Conclusion: Prenatal screening tests can be accurately ascertained using BC insurance billing data. Changes in GDM screening completion and in screening methods accounted for most of the increase in GDM diagnosis in BC since 2005. Covid-19 pandemic countermeasures were associated with an increase in pregnancy weight gain but not infant birthweight. Public health and future researchers should understand how screening changes can directly affect disease prevalence. iv LAY SUMMARY Gestational diabetes is a temporary state of high blood sugar that affected 7% of pregnancies in British Columbia (BC) in 2005 and rose to 14% in 2019. It is diagnosed by glucose screening tests but the specific tests have changed over the past 15 years. This work evaluated a new method to identify screening test data in BC and explained how changes in screening alone accounted for most of the rising rates of gestational diabetes. This thesis also demonstrated that the COVID-19 pandemic led to an increase in weight gain during pregnancy but no change in infant birthweights. v PREFACE This thesis presents the results of a program of research including two projects, that I identified and designed in consultation with my thesis committee. I independently applied for and obtained data access for both projects and designed data collection tools and conducted all primary data collection. In consultation with my thesis committee, I selected the research questions and selected analytic methods, based on my own review of the literature and original thought. In all work presented herein, I assembled and analyzed all data, conducted all statistical analyses, interpreted the results and drafted manuscripts for the research chapters. My thesis committee members collaborated with me, as lead researcher and author, on the research chapters presented in this thesis. The research projects conducted in my thesis were approved by the University of British Columbia’s Clinical Research Ethics Board, under ethics certificate #H20-00741, and data access was approved through a Student Data Access Request under Data Access Request #Janssen-21-018 via Population Data BC. All inferences, opinions, and conclusions drawn in this thesis are those of the authors, and do not reflect the opinions or policies of the Data Steward(s). Approval for data from the Obstetrical Care Outcomes Assessment Program (OB COAP) was granted by the OB COAP Research committee and the study protocol was also reviewed and approved under the University of British Columbia’s Clinical Research Ethics Board ethics certificate #H20-00741. A version of Chapter 3 has been published [Nethery E, Hutcheon JA, Law MR, Janssen PA. Validation of insurance billing codes for monitoring antenatal screening. Epidemiology. 2022, In press]. I was the lead investigator, responsible for designing the study, writing and obtaining ethics approval, obtaining administrative data access, conducting all data collection, analyzing the data, writing the manuscript, and interpreting the results. Hutcheon JA and Janssen PA were involved in designing the study and analytic approaches. All co-authors contributed to manuscript edits and interpretation. I was responsible for all manuscript reviews and editing in the peer-review process. A version of Chapter 6 has been published [Nethery E, Hutcheon JA, Kotaska A, Law MR, Janssen PA. Weight gain in pregnancy and infant birthweight after the onset of the COVID-19 pandemic: An interrupted time series analysis. The American Journal of Clinical Nutrition. 2022, In press]. I was the lead investigator, responsible for designing the study, obtaining administrative data access, analyzing the data, writing the manuscript and interpreting the results. Law MR, Hutcheon JA and Janssen PA were involved in designing the study and analytic approaches. All co-authors contributed to manuscript edits and interpretation. I was responsible for all manuscript reviews and editing in the peer-review process. vi TABLE OF CONTENTS Abstract ........................................................................................................................................................ iii Lay Summary ............................................................................................................................................... iv Preface .......................................................................................................................................................... v Table of contents .......................................................................................................................................... vi List of Tables ............................................................................................................................................. viii List of Figures ............................................................................................................................................... x List of Abbreviations .................................................................................................................................. xii Acknowledgements .................................................................................................................................... xiv Dedication ................................................................................................................................................... xv 1 Introduction ........................................................................................................................................... 1 1.1 Overarching theme and overview ................................................................................................................ 1 1.2 Gestational diabetes mellitus ........................................................................................................................ 2 1.3 Pregnancy weight gain and infant birthweight ............................................................................................. 6 1.4 Research questions and hypotheses .............................................................................................................. 9 1.5 Thesis overview ........................................................................................................................................... 9 1.6 Significance and knowledge gaps .............................................................................................................. 10 1.7 Disclaimer .................................................................................................................................................. 11 2 Literature review of gestational diabetes ............................................................................................ 12 2.1 Overview .................................................................................................................................................... 12 2.2 Risk-based v universal screening ............................................................................................................... 12 2.3 Glucose tests .............................................................................................................................................. 13 2.4 A one-step v two-step approach ................................................................................................................. 13 2.5 History of screening and diagnostic guidelines .......................................................................................... 14 2.6 Canadian context ........................................................................................................................................ 17 2.7 US context .................................................................................................................................................. 19 2.8 International context................................................................................................................................... 20 2.9 Being Unscreened ...................................................................................................................................... 20 2.10 Prevalence of GDM.................................................................................................................................... 21 2.11 Perceptions of GDM screening and diagnosis among pregnant people ..................................................... 22 2.12 Summary .................................................................................................................................................... 23 2.13 Tables ......................................................................................................................................................... 24 3 A validation of using insurance billings data to assess antenatal screening data against medical records data ................................................................................................................................................. 28 3.1 Synopsis ..................................................................................................................................................... 28 3.2 Background ................................................................................................................................................ 29 3.3 Methods ...................................................................................................................................................... 30 3.4 Analysis ...................................................................................................................................................... 31 3.5 Results ........................................................................................................................................................ 32 3.6 Discussion .................................................................................................................................................. 34 3.7 Tables ......................................................................................................................................................... 36 4 Trends in Gestational diabetes screening practices in British Columbia: a descriptive study ............ 39 4.1 Synopsis ..................................................................................................................................................... 39 4.2 Background ................................................................................................................................................ 40 4.3 Methods ...................................................................................................................................................... 41 4.4 Results ........................................................................................................................................................ 43 4.5 Interpretation .............................................................................................................................................. 44 4.6 Tables ......................................................................................................................................................... 47 4.7 Figures ........................................................................................................................................................ 51 5 The impact of changes in screening completion, screening methods and population demographics on the increase in gestational diabetes ............................................................................................................. 53 vii 5.1 Synopsis ..................................................................................................................................................... 53 5.2 Background ................................................................................................................................................ 54 5.3 Methods ...................................................................................................................................................... 55 5.4 Results ........................................................................................................................................................ 56 5.5 Discussion .................................................................................................................................................. 57 5.6 Tables ......................................................................................................................................................... 58 5.7 Figures ........................................................................................................................................................ 59 6 Weight gain in pregnancy and infant birthweight after the onset of the COVID-19 pandemic: an interrupted time series analysis ................................................................................................................... 61 6.1 Synopsis ..................................................................................................................................................... 61 6.2 Introduction ................................................................................................................................................ 63 6.3 Methods ...................................................................................................................................................... 64 6.4 Results ........................................................................................................................................................ 67 6.5 Discussion .................................................................................................................................................. 70 6.6 Tables ......................................................................................................................................................... 73 6.7 Figures ........................................................................................................................................................ 76 7 The case for “complete case”: A brief review of missing data in Chapter 6 ...................................... 79 7.1 Synopsis ..................................................................................................................................................... 79 7.2 Background ................................................................................................................................................ 80 7.3 Methods ...................................................................................................................................................... 81 7.4 Results ........................................................................................................................................................ 82 7.5 Discussion .................................................................................................................................................. 83 7.6 Tables ......................................................................................................................................................... 84 7.7 Figures ........................................................................................................................................................ 88 8 Conclusion .......................................................................................................................................... 90 8.1 Gestational diabetes screening and diagnoses ............................................................................................ 90 8.2 COVID-19 pandemic, pregnancy weight gain and infant birthweight ....................................................... 92 8.3 Significance and contribution..................................................................................................................... 93 8.4 Strengths and limitations ............................................................................................................................ 95 8.5 Applications of these findings .................................................................................................................... 97 8.6 Future research ........................................................................................................................................... 98 8.7 Conclusion ............................................................................................................................................... 100 8.8 Figures ...................................................................................................................................................... 101 Bibliography ............................................................................................................................................. 102 A. Validation study supplemental .......................................................................................................... 115 A.1 Supplemental methods ............................................................................................................................. 115 A.2 Tables ....................................................................................................................................................... 119 A.3 Figures ...................................................................................................................................................... 134 B. Descriptive study supplemental ........................................................................................................ 141 B.1 Tables ....................................................................................................................................................... 141 B.2 Figures ...................................................................................................................................................... 147 C. Gestational diabetes study supplemental .......................................................................................... 158 C.1 Methods .................................................................................................................................................... 158 C.2 Tables ....................................................................................................................................................... 159 C.3 Figures ...................................................................................................................................................... 166 D. Covid-19 Pandemic, weight gain and infant birthweight supplemental ........................................... 181 D.1 Methods .................................................................................................................................................... 181 D.2 Sensitivity analyses .................................................................................................................................. 183 D.3 Conditional quantile regression ................................................................................................................ 185 D.4 A re-analysis of the COVID-19 pandemic study using excess or inadequate pregnancy weight gain according to the Institute of Medicine criteria....................................................................................................... 186 D.5 Tables ....................................................................................................................................................... 188 D.6 Figures ...................................................................................................................................................... 199 viii LIST OF TABLES Table 2.1 Glucose tests used in gestational diabetes screening and diagnosis ............................................................ 24 Table 2.2 Major diagnostic and screening criteria for GDM (1960-2010) .................................................................. 24 Table 2.3 Key studies and randomized trials impacting GDM screening and diagnosis in the early 2000's ............... 25 Table 2.4 Overview of selected international and national guidelines for GDM diagnosis and screening .................. 26 Table 3.1 Population characteristics in a validation study of antenatal screening in BC, Canada 2014-2019............. 36 Table 3.2 Antenatal screening test outcomes for medical records and billing codes data in a validation study in British Columbia, Canada 2014-2019 ......................................................................................................................... 37 Table 3.3 Antenatal screening test validation parameters for billing codes compared to medical records data in a validation study in British Columbia, Canada 2014-2019 ........................................................................................... 38 Table 4.1 Prevalence of any glucose test v no test (unscreened) by subgroups and time periods in British Columbia, Canada, for glucose test dates or 28th week of pregnancy between June 1, 2004 and May 31, 2019 ......................... 47 Table 4.2 Prevalence of three gestational diabetes screening approaches among subgroups and time periods in British Columbia, Canada for glucose test dates from June 1, 2004 and May 31, 2019 (n=525,720) ......................... 49 Table 5.1 Gestational diabetes diagnosis, screen completion, methods and population characteristics for selected years ............................................................................................................................................................................. 58 Table 6.1 Pregnancy cohort demographics and outcomes by pre-pandemic (Jan 1, 2016 to Feb 23, 2020) and pandemic (Mar 23, 2020 to Dec 28, 2020) time periods for a Washington State cohort (n=77,411 pregnancies) ...... 73 Table 6.2. Infant cohort demographics and outcomes by pre-pandemic (Jan 1, 2016 to Feb 23, 2020) and pandemic (Mar 23, 2020 to Dec 28, 2020) pre-pandemic and pandemic (March 23, 2020) time periods for a Washington State cohort (n=104,936 infants) .......................................................................................................................................... 74 Table 6.3. Quantile regression results for 90th percentiles using an interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) ......................... 75 Table 7.1 Missing data patterns: Demographics for excluded cases, complete cases and full study population ......... 84 Table 7.2 Demographics comparing final cohort to excluded cases by pandemic time periods .................................. 85 Table 7.3 Pooled model estimates from analysis using imputed datasets (20) and complete cases ............................ 86 Table 7.4 Descriptive results for un-restricted weight gain cohort compared to complete case cohort ....................... 86 Table 7.5 ITS model results for unrestricted weight gain cohort v complete case cohort ........................................... 87 Table A.1 Detailed definitions of billing codes used from BC MSP for coding antenatal screening tests ................ 119 Table A.2 Pregnancy and obstetric characteristics for all linked records, records with incomplete insurance and final validation group (unweighted %) .............................................................................................................................. 120 Table A.3 Validation parameters for all screening tests using billing code derived screening tests compared to medical records abstracted data (gold standard) in subgroups................................................................................... 122 Table A.4 Cross-tabulations of medical records data against billings-based screening data for records available for a validation analysis of antenatal screening tests (n=127) ............................................................................................ 124 Table A.5 Study cohort characteristics and demographics comparing screened to unscreened for 3 antenatal screening tests (n=135 records) (unweighted %) ....................................................................................................... 125 Table A.6 Completion rates for secondary variables by data source (Chart-abstracted v. PDR) .............................. 127 Table A.7 Descriptive results for secondary variables by data source (Medical records abstracted v. PDR) ........... 128 Table A.8 Comparison of selected secondary variables with sample weighted results (medical records v BC-PDR) ................................................................................................................................................................................... 129 Table A.9 Comparison of secondary variables (categorical) from BC PDR against medical records data (unweighted results) ....................................................................................................................................................................... 129 Table A.10 Comparison of secondary variables (continuous) from BC PDR against medical records data (unweighted) .............................................................................................................................................................. 129 Table A.11 Detailed data dictionary and coding for medical records data ................................................................ 130 Table A.12 Detailed data dictionary and coding for MSP billing variables for antenatal health care professional type ................................................................................................................................................................................... 131 Table A.13 Detailed definitions of billing fee codes used for prenatal care utilization ............................................. 132 Table A.14 Data dictionary for PDR variables used for validation (secondary variables) ........................................ 133 Table B.1 Population demographics by gestational diabetes screening methods, BC, Canada, 2004-2019 .............. 141 Table B.2 Crude and adjusted Relative Risks (RR) for one-step gestational diabetes screening, BC, Canada, 2005-2019 ........................................................................................................................................................................... 143 ix Table B.3 Gestational diabetes diagnosis and treatment type by screening methods in BC, Canada, 2011-2019 (after one-step was implemented) ....................................................................................................................................... 144 Table B.4 One-step gestational diabetes screening rates by region in subgroups, time periods and by year ............ 145 Table B.5 One-step gestational diabetes screening rates by year, BC, Canada, 2005-2019 ...................................... 146 Table C.1 Model estimated risk of gestational diabetes in each study year (n=551,457) .......................................... 159 Table C.2 Population characteristics by gestational diabetes diagnosis in BC, Canada from 2005 to 2019 (n=551,457) ............................................................................................................................................................... 160 Table C.3 Population characteristics by gestational diabetes diagnosis in BC, Canada from 2005 to 2019 comparing full available study years to excluded groups for different reasons compared to the included cohort ...................... 162 Table C.4 Annual gestational diabetes counts and diagnoses (%) in excluded groups and full cohort ..................... 164 Table C.5 Annual gestational diabetes diagnoses in excluded groups and full cohort with 95% confidence intervals ................................................................................................................................................................................... 164 Table C.6 Gestational diabetes counts and diagnoses, screening status in excluded groups and full cohort ............. 165 Table C.7 Gestational diabetes counts and diagnoses, screening status in excluded groups and full cohort, rates with 95% CIs ..................................................................................................................................................................... 165 Table D.1 Model estimates from interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) (Primary analyses) ............................................ 188 Table D.2 Demographics and characteristics of excluded cases for a study of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) .......................................................... 189 Table D.3 Summary of sensitivity analyses results Covid-19 study .......................................................................... 190 Table D.4 Complete model terms for pregnancy weight gain models (kg) with sensitivity anlayses ....................... 190 Table D.5 Complete model terms for pregnancy weight gain z-score models with sensitivity analyses .................. 191 Table D.6 Complete model terms for all infant birthweight z-score models with sensitivity analyses ..................... 192 Table D.7 Quantile regression for 90th percentiles using an interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort ........................................................................................ 193 Table D.8 Quantile regression and mean regression results stratified by pregestational BMI (body mass index) categories for a study of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) .............................................................................................................................................. 194 Table D.9 Model estimates from sensitivity analyses for increasing exposure to the pandemic by excluding births from February 23, 2020 to April 27, 2020 (9 weeks) and from February 23, 2020 to June 8, 2020 (15 weeks) from interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort ....... 195 Table D.10 Baseline weight and other characteristics stratified by pregestational BMI categories .......................... 196 Table D.11 Institute of Medicine (2009) pregnancy weight gain classifications and pregestational BMI ................ 197 Table D.12 Full ITS model results for IOM excess or inadequate weight gain ......................................................... 198 x LIST OF FIGURES Figure 4.1 Monthly rates of gestational diabetes screening aggregated by month of screening tests, for a cohort of pregnancies in BC, Canada from June 2004 to May 2019 ........................................................................................... 51 Figure 4.2 Rates of gestational diabetes screening using a one-step method (75g glucose test only and IADPSG criteria) in BC from Jun 1, 2004 to May 31, 2019 in subgroups ................................................................................. 52 Figure 5.1 Gestational diabetes screening and diagnosis by screening method in BC, Canada .................................. 59 Figure 5.2 Predicted gestational diabetes diagnosis risk using adjusted models ......................................................... 60 Figure 6.1 Flow of study population and exclusions for a Washington State study of gestational weight gain, infant birthweight and the COVID-19 pandemic (January 1, 2016 to December 28, 2020). ................................................. 76 Figure 6.2 Interrupted time series graphs for pregnancy weight gain, z-score and infant birthweight z-score showing predicted trends and the COVID-19 pandemic onset (March 23, 2020) in a Washington State cohort (January 1, 2016 to December 28, 2020) ....................................................................................................................................... 77 Figure 6.3 Average pregnancy weight gain, z-score and infant birthweight z-score by month of conception across study years for a Washington State cohort (January 1, 2016 to December 28, 2020) ................................................. 78 Figure 7.1 Un-restricted weight gain cohort exclusions .............................................................................................. 88 Figure 7.2 Unrestricted weight gain cohort: Interrupted time series graphs and results .............................................. 89 Figure 8.1 Rates of gestational diabetes in Canada (excluding Quebec), 2017, from the Public Health Agency of Canada based on CIHI-DAD data ............................................................................................................................. 101 Figure A.1 Flowchart for linkage of chart-abstracted study data and administrative data......................................... 134 Figure A.2 Decision tree for 1st trimester ultrasound completion (screened, unscreened, missing) abstracted from medical records (n=127 for validation study) ............................................................................................................ 135 Figure A.3 Decision tree for gestational diabetes screen completion (screened, unscreened, missing) abstracted from medical records (n=127 linked for validation study) ................................................................................................. 136 Figure A.4 Decision tree for gestational diabetes screen methods (one-step or two-step) abstracted from medical records (n=127 linked for validation study)............................................................................................................... 137 Figure A.5 Decision tree for Group B streptococcus test abstracted from medical records ...................................... 138 Figure A.6 Decision tree for billings derived antenatal screening test results for 1st trimester ultrasound, GDM screening and GBS screening (screened v unscreened v missing) for n=135 records ............................................... 139 Figure A.7 Decision tree for billings derived antenatal screening test results for gestational diabetes screening method (one-step or two-step) (n=135 records)......................................................................................................... 140 Figure B.1 Study population derivation and exclusions ............................................................................................ 147 Figure B.2 Rates of GDM one-step screening by region and pre-pregnancy BMI .................................................... 148 Figure B.3 Rates of GDM one-step screening by region and health care professional type ...................................... 149 Figure B.4 Rates of GDM one-step screening by region and history of GDM .......................................................... 150 Figure B.5 Rates of GDM one-step screening by region and birth region of mother/birthing person ....................... 151 Figure B.6 Rates of GDM one-step screening by region and planned home birth .................................................... 152 Figure B.7 Rates of GDM one-step screening by region and adequacy of prenatal care usage index ....................... 153 Figure B.8 Rates of GDM one-step screening by region and Health services delivery areas .................................... 154 Figure B.9 Gestational diabetes diagnoses rates by screening type ........................................................................... 155 Figure B.10 Rates of one-step gestational diabetes screening in BC regions in 4 time periods by date of 1st glucose screening test for gestational diabetes by BC local health area ................................................................................. 156 Figure B.11 Rates of gestational diabetes diagnoses in BC regions in 4 time periods by date of 1st glucose screening test for gestational diabetes ........................................................................................................................................ 157 Figure C.1 Study population flow chart and exclusions ............................................................................................ 166 Figure C.2 Gestational diabetes screening and diagnosis rates with two-step by C-C and DC-2013 criteria ............ 167 Figure C.3 Gestational diabetes rates modeled with addition of prior history of gestational diabetes (sensitivity analysis) ..................................................................................................................................................................... 168 Figure C.4 Models with covariates added in reverse order ........................................................................................ 168 Figure C.5 Sensitivity analyses 2007 start year ......................................................................................................... 169 Figure C.6 Sensitivity analyses 2008 start year ......................................................................................................... 170 Figure C.7 Sensitivity analyses 2010 start year ......................................................................................................... 171 Figure C.8 Sensitivity analyses 2018 end year .......................................................................................................... 172 Figure C.9 Sensitivity analyses exclude 2011 (transition year) ................................................................................. 173 Figure C.10 Sensitivity analyses use monthly rates instead of year .......................................................................... 174 xi Figure C.11 Sensitivity analyses use monthly rates excluding transition period where methods changed................ 175 Figure C.12 Sensitivity analyses use two variables for two-step screening to account for change in diagnostic criteria after IADPSG adopted in 2010 (change from C-C to DC-2013 for two-step) .......................................................... 176 Figure C.13 Sensitivity analyses GDM screening rates in health regions over time ................................................. 177 Figure C.14 Sensitivity analyses GDM diagnosis risk in health regions over time ................................................... 178 Figure C.15 Predicted risk of GDM diagnosis by year (modeled) in stratified analyses by health region ................ 179 Figure C.16 Predicted risk of GDM diagnosis by year (modeled) in stratified analyses by alternate region classification .............................................................................................................................................................. 180 Figure D.1 COVID-19 policy indices (Containment Health Index, Stringency Index and Government Response Index) for Washington State, U.S. in 2020 using data from the Oxford COVID-19 Government Response Tracker ................................................................................................................................................................................... 199 Figure D.2 Directed acyclic graph for COVID-19 pandemic and pregnancy weight gain or infant birthweight ...... 200 Figure D.3 Seasonal trends year-by-year comparing conception and delivery month............................................... 201 Figure D.4 Quantile regression modeled level change parameters across percentiles of pregnancy weight gain (model adjusted for gestational age at delivery and seasonality) and z-score (adjusted for seasonality) .................. 202 Figure D.5 Subgroup analyses of pregnancy weight gain (kg) by pregestational BMI adjusted using an interrupted time series for a Washington State cohort (January 1, 2016 to December 28, 2020) ................................................ 203 Figure D.6 Subgroup analyses of pregnancy weight gain z-score by Distressed Communities Index using an interrupted time series for a Washington State cohort ............................................................................................... 204 Figure D.7 Subgroup analyses of weight gain z-score by Race/ethnicity using an interrupted time series for a Washington State cohort ............................................................................................................................................ 205 Figure D.8 Subgroup analyses of pregnancy weight gain z-score by pregestational BMI using an interrupted time series for a Washington State cohort ......................................................................................................................... 206 Figure D.9 Subgroup analyses of pregnancy weight gain z-score by insurance payor using an interrupted time series for a Washington State cohort ................................................................................................................................... 207 Figure D.10 Subgroup analyses of pregnancy weight gain z-score by Parity using an interrupted time series for a Washington State cohort ............................................................................................................................................ 207 Figure D.11 Subgroup analyses of infant birthweight z-score by Distressed Communities Index using an interrupted time series for a Washington State cohort ................................................................................................................. 208 Figure D.12 Subgroup analyses of infant birthweight z-score by Race/ethnicity using an interrupted time series for a Washington State cohort ............................................................................................................................................ 209 Figure D.13 Subgroup analyses of infant birthweight z-score by Race/ethnicity and parity adjusted using an interrupted time series for a Washington State cohort ............................................................................................... 210 Figure D.14 Subgroup analyses of infant birthweight z-score by insurance payor using an interrupted time series for a Washington State cohort ......................................................................................................................................... 211 Figure D.15 Subgroup analyses of infant birthweight z-score by parity using an interrupted time series for a Washington State cohort ............................................................................................................................................ 211 Figure D.16 Subgroup analyses of infant birthweight z-score by pregestational BMI using an interrupted time series for a Washington State cohort ................................................................................................................................... 212 Figure D.17 Comparison of primary analyses and Intergrowth 21 standards for weight gain and infant birthweight z-scores ......................................................................................................................................................................... 213 Figure D.18 Infant birthweight z-scores for primary cohort (infants) compared to a matched group with valid pregnancy weight gain ............................................................................................................................................... 214 Figure D.19 Parity-adjusted infant z-scores compared to primary outcome .............................................................. 214 Figure D.20 Rate of monthly Covid-19 Positive pregnancies in complete dataset .................................................... 215 Figure D.21 Interrupted time series plots excluding any known Covid-19 Positive cases ........................................ 216 Figure D.22 Interrupted time series for random sampled 1 birth per person for repeated pregnancies ..................... 217 Figure D.23 Excess and Inadequate weight gain using IOM criteria (full cohort, adjusted for weeks): Interrupted time series plots ......................................................................................................................................................... 218 Figure D.24 Excess and Inadequate weight gain using IOM criteria (>=37wks GA only): Interrupted time series plots ........................................................................................................................................................................... 219 xii LIST OF ABBREVIATIONS ACHOIS The Australian Carbohydrate Intolerance Study in Pregnant Women ACOG American College of Obstetricians and Gynecologists ADA American Diabetes Association ADIPS Australasia Diabetes in Pregnancy Study Group AIC Akaike’s Information Criterion APNCU Adequacy of Prenatal Care Utilization Index ARR Adjusted Relative risk BC British Columbia BC-PDR British Columbia Perinatal Data Registry BMI Body Mass Index, a ratio of weight (kg) /height (m)2 BSD Sociedade Brasileira de Diabetes/Brazilian Society of Diabetes CA/CMA/DA CA census agglomeration, CMA census metropolitan area, DA dissemination area C-C Carpenter-Coustan CI Confidence Interval CIHI Canadian Institute for Health Information COVID-19 SARS-COV-19 DC / CDA Diabetes Canada (formerly, Canadian Diabetes Association or CDA) DCI Distressed Communities Index DIPSI Diabetes in Pregnancy Study Group of India EASD European Association for the Study of Diabetes FP Family practice physician FPG Fasting plasma glucose GA Gestational age GBS Group B streptococcus GCT Glucose challenge test GDM Gestational diabetes mellitus HAPO Hyperglycemia Adverse Pregnancy Outcomes HbA1c Hemoglobin A1c test HIC High-income countries HSDA Health Services Delivery Area IADPSG International Association of Diabetes in Pregnancy Study Group ICD 10-CA International Classification of Diseases Version 10 (Canadian Edition) IOM Institute of Medicine IQR Interquartile range ITS Interrupted time series JSOG Japan Society of Obstetricians and Gynecologists LGA Large-for-gestational-age LHA Local Health Area LIC Low-income countries LMIC Low-middle income countries MAR Missing at random MCAR Missing completely at random MI Multiple imputation MSP Medical Services Plan NA Not applicable NDDG National Diabetes Data Group NICE National Institute for Health and Care Excellence NICHD National Institute of Child Health and Human Development NPV Negative predictive value NZSSD New Zealand Society for the Study of Diabetes OB Obstetrician OGTT Oral glucose tolerance test OR Odds ratio OxCGRT Oxford Covid-19 Government Response Tracker PPV Positive predictive value PSBC Perinatal Services British Columbia xiii RM Registered Midwife RR Relative Risk RUCA Rural-urban commuting areas SD Standard deviation SEMDSA Society for Endocrinology, Metabolism and Diabetes of South Africa SGA Small-for-gestational-age SOGC Society of Obstetricians and Gynecologists of Canada UK United Kingdom US United States WA Washington State wAG weighted Average Glucose WHO World Health Organization xiv ACKNOWLEDGEMENTS With gratitude, I acknowledge support over the years of my dissertation studies from the following individuals and institutions: • Dr. Patti Janssen, my thesis supervisor, for her patience, guidance, support and mentorship • My committee members: Dr. Jennifer Hutcheon for the ‘best critiques’ – contributing immensely to the rigour and foundation of this work; Dr. Michael Law for insight and general guidance especially for interrupted time series; and Dr. Andrew Kotaska for excellent general and clinical feedback. • Vanier Canada Graduate Scholars program for providing the funding that supported my studies. • The University of British Columbia and School of Population and Public Health for the 4-year graduate fellowship additional funding. • Population Data BC for a student waiver for data access fees. I also gratefully acknowledge assistance from the following organizations, staff and individuals for the projects in this thesis: • Staff from Fraser Health (FH) and Provincial Health Services Authority (PHSA) who assisted with obtaining approvals and access to medical records used for the validation study. • The Foundation for Health Care Quality and OB COAP for with in-kind contributions for data extracts for the Washington State study. Specifically, Kristin Sitcov, Dr. Vivian Souter. • Staff from Population Data BC who have assisted with data access and approvals. Last, I am deeply thankful for the support, mentorship and invaluable feedback throughout my studies from: • Dr. Laura Schummers • Jennifer Murray • Dr. Susan Kennedy xv DEDICATION To Nathan, Thank you for your love and support on this journey. For Sage and Kai, Always reminding me what’s truly important. 1 1 INTRODUCTION 1.1 Overarching theme and overview Antenatal health care1–3 is designed to optimize health for the pregnant person, the developing fetus and the neonate(s). A variety of screening tests and procedures are recommended1–3 during pregnancy, to monitor for, and diagnose, conditions that could adversely impact health. Important aspects of antenatal care include promotion of healthy weight gain and screening for gestational diabetes. Gestational diabetes is a temporary state of high blood sugar that occurs for the first time during pregnancy and is linked, in part, to maternal nutrition. Screening for this condition is usually recommended at the end of the second trimester (24-28 weeks gestation) as part of routine antenatal care. Pregnancy weight gain is also an important indicator of maternal nutrition and is generally screened for as part of routine care. Both excess weight gain and hyperglycemia in pregnancy have been associated with adverse perinatal and neonatal outcomes.4–6 My thesis examines how changes in policy and practice in antenatal care can affect health outcomes in pregnant women and birthing people. Two types of changes that I assessed in this work are 1) direct policy or guideline changes and 2) the onset of the COVID-19 pandemic. Both of these changes could directly impact pregnancy and birth outcomes. Specifically, I examined 1) impacts of gestational diabetes screening practices on gestational diabetes diagnosis and 2) impacts of a system-level event (the COVID-19 pandemic) on pregnancy weight gain and infant birthweight. The majority of this thesis is from a series of linked studies of gestational diabetes in British Columbia (BC), Canada that I designed and conducted. This project included primary data collection for the purposes of a validation study, and several follow-up studies using linked administrative datasets. In view of anticipated delays in access to BC’s administrative datasets during the recent COVID-19 pandemic, I utilized a clinical dataset from Washington State to study the effect of COVID-19 pandemic on pregnancy weight gain and infant birthweight. This chapter provides an overview of the thesis, the two topic areas, the study context for each topic and the primary research questions and hypotheses. 2 A brief note on gender-inclusive language Health outcomes discussed in my thesis are about people who experienced pregnancy. This can include people who identify as female (cis-women), as well as people who identify as male or non-binary. While a majority of those who become pregnant identify as female, other people (trans-men and non-binary people) may also experience pregnancy. None of the data sources used in my thesis captured information on gender identity, thus, I do not identify the study population as ‘women’ only. Throughout my thesis, in accordance with recent recommendations,7 I use the terms “pregnant women and other pregnant people”, “pregnant individuals”, “pregnant people”, “pregnant person” or “women and birthing people” to include all people who may become pregnant and who are included in my study groups. “We can recognize that this [pregnancy] impacts women while also recognizing that it also impacts other groups. Those things are not mutually exclusive.” – Dr. Khiara Bridges, Professor-UC Berkeley School of Law, July 12, 2022, speaking to the U.S. Senate Judiciary Committee 1.2 Gestational diabetes mellitus Gestational diabetes mellitus (GDM) is a pregnancy-associated condition which is usually screened for between 24-28 weeks of pregnancy6 and is broadly defined as any glucose intolerance resulting in a transient hyperglycemia (elevated glucose) with first onset during pregnancy.8 This is a temporary condition and is distinct from pre-existing type 1 or type 2 diabetes. GDM occurs during pregnancy and generally resolves after delivery, although, a history of a GDM diagnosis is associated with a 10-fold increased relative risk of developing type 2 diabetes post-partum compared to those without this condition.9,10 Physiologically, normal pregnancy is characterized by a progressive increase in insulin resistance designed to conserve glucose for the growing fetus.11 One explanation for gestational diabetes is that it represents an ‘unmasking’ of an underlying metabolic abnormality which results in excess insulin resistance during pregnancy which then precipitates a maternal state of hyperglycemia. As a result of this hyperglycemia, an excess of glucose is available to the fetus which can lead to fetal overgrowth.12 3 Gestational diabetes is one of the most commonly diagnosed pregnancy complications. A recent report by the International Diabetes Federation Atlas (2021) estimated the global standardized prevalence of GDM to be 14.2%,13 but in specific regions or subgroups, reported rates vary from 2% to 45% of pregnant individuals.14–17 By comparison, in 2011-2020, the global prevalence of pre-existing diabetes in pregnancy was estimated at 1.0%, with a regional range of 0.5% (Europe) to 2.4% (Middle East and North Africa).18 Despite extensive research efforts over the past 60 years, there remains widespread controversy regarding GDM.19–22 In this section, I introduce some of these controversies and discuss how they have impacted gestational diabetes research. In Chapter 2, I present a detailed literature review of topics related to GDM, specifically, the evolution of diagnostic and screening practices for GDM, the Canadian and international standards and guidelines, and an overview of qualitative research on patient experiences with GDM and screening. How to define gestational diabetes is a topic of substantial debate. GDM represents a heterogeneous group of metabolic disorders with varying levels of hyperglycemia12 and the underlying pathophysiology is complex.22 For example, current diagnostic approaches for GDM use blood test results from both a fasting state and after a glucose tolerance test (after a measured ‘loading’ of glucose is consumed). There is evidence that GDM in people with an abnormal fasting glycemia is more likely to represent one type of pathology, namely impaired β-cell function.23 This may be linked with an increased genetic susceptibility to type 2 diabetes. On the other hand, glycemia after a tolerance test may represent a different pathology, related to an underlying insulin sensitivity due to pre-existing inflammatory pathways triggered by obesity, unhealthy diet, environmental exposures or limited physical activity. Despite these apparent differences in the pathologies of GDM, the diagnostic label is applied uniformly. Further, there can be underlying differences in the severity of hyperglycemia and in whether the hyperglycemia is able to be controlled with diet/exercise, or requires medication. As Landon & Gabbe described it, “Gestational diabetes represents a heterogeneous group of metabolic disorders, which result in varying degrees of maternal hyperglycemia and pregnancy-associated risk.”12 A second controversy is around the overall benefit of GDM screening and treatment, especially for people with lower levels of hyperglycemia.24–26 In 2003, the Canadian Medical Association Journal (CMAJ) published an editorial27 stating that a number of physicians in Canada did not believe this condition exists, while others feel its importance is so low that screening is not justified. Some of this controversy stems from the fact that GDM presents with no overt symptoms. One perspective, is that hyperglycemia in pregnancy (not overt diabetes), is simply a risk factor that requires more intensive observation and testing to promote health.28 The other perspective is that gestational diabetes is an important condition, similar to 4 pre-existing diabetes in pregnancy, that conveys increased perinatal and postpartum risks. Furthermore, treatment of GDM decreases risks of shoulder dystocia, hypertensive disorders of pregnancy (e.g. pre-eclampsia) and having a large-for-gestational age infant.29,30 Both of these issues, how to define GDM and appropriate treatment are inextricably linked. For example, if diagnostic criteria are not well characterized, or heterogeneous, then it is difficult to conduct well-designed and robust research studies to identify treatments. It is also important to remember that the majority of the cases of GDM-associated adverse outcomes (large fetal size, shoulder dystocia and hypertensive disorders) occur in people who are normoglycemic, and factors such as maternal weight and weight gain may confer greater risk of these poor outcomes than GDM alone.31 Methodologically, the early studies of the association between GDM and perinatal health outcomes used inconsistent definitions of GDM with limited control for confounding by underlying factors such as pregestational nutritional status (BMI) and weight gain during pregnancy. Further, researchers over the past decades have struggled to agree on a definition of GDM, because they disagreed on the objectives related to a diagnosis. Was it to identify women who would be likely to develop later diabetes (adult-onset diabetes mellitus) or to identify those at risk for maternal and fetal adverse outcomes during pregnancy? Early criteria for GDM diagnosis were based solely on the prevention of adult-onset diabetes,32 therefore, diagnostic criteria were based on a relatively high threshold of hyperglycemia. In the late 1990’s, researchers began to examine both immediate maternal and fetal risks during pregnancy as well as future health risks.33,34 Others investigated GDM associations with pre-pregnancy obesity, pregnancy weight gain and fetal growth.35–37 In combination, these findings suggested that high levels of hyperglycemia in pregnancy conferred some adverse neonatal or pregnancy risks, but the effects of pregestational BMI and pregnancy weight gain were not always well controlled for. In 2008, a large multicenter study, the Hyperglycemia and Adverse Outcomes in Pregnancy (HAPO) Study, was the first well-designed study to find a continuous association between maternal glucose concentrations and some adverse pregnancy and infant outcomes. This led to a re-assessment in the GDM world as this study indicated that low levels of hyperglycemia also conferred some increased risk. In absence of a natural threshold, experts developed a new criteria38 for diagnosis of GDM. Unfortunately, application of this new criteria, in some regions, increased GDM diagnostic rates as high as 40% without a clear relationship of this diagnosis to adverse pregnancy outcomes.39 Therefore, the cost-benefit of adopting these new criteria continues to be the subject of much debate.40 Over the past 50 years, 20,39,41 numerous guidelines have been developed to define, and re-define, diagnostic criteria for gestational diabetes.15,42 If gestational diabetes is viewed as a spectrum of metabolic 5 abnormalities, then choice of guideline and diagnostic thresholds, will influence which individuals are diagnosed. Universal recommendations for diagnostic and screening criteria for GDM remain elusive and the most recent recommendations from the International Federation of Obstetrics and Gynecology (FIGO)43,44 favor a pragmatic approach that allows for regional variation within uniform guiding principles. This may be the best compromise for GDM in the international context. From an epidemiologic perspective, changing diagnostic guidelines and screening criteria are important to consider when comparing research studies done in different decades, or in regions where different standards are applied. Unfortunately, large population-based datasets that are often used for prevalence studies or surveillance rarely have any data on screening method or diagnostic criteria, therefore, are unable to account for these potential sources of bias. For example, screening is rarely conducted in all of the eligible pregnant population, a fact that is usually overlooked. Second, depending on which screening method and guidelines are followed, GDM is diagnosed at different thresholds of hyperglycemia. This leads to two problems with research on perinatal outcomes related to gestational diabetes; first, the population of those who are assumed to be negative for gestational diabetes may include people who were never screened at all, thus biasing the population of ‘normo-glycemic’ individuals. Second, the population of those who are diagnosed with GDM represents a range of severity of hyperglycemia and, depending on the time period or region, that range could have shifted during the study period. Careful study design and attention to information and selection biases are critical in future epidemiologic studies of gestational diabetes, particularly given the different guidelines used for screening and diagnosis. 1.2.1 Study context for GDM research in this thesis British Columbia (BC) is the westernmost province of Canada, with a population in 2019 of approximately 4.9 million people. As with all Canadian provinces, BC has a universal single-payor health insurance system, publicly-funded by the province, called the BC-Medical Services Plan (BC-MSP). All BC residents, with some exceptions, are covered by BC-MSP and have a province-wide Personal Health Number (PHN) which links health data and services throughout the province. Rates of GDM have increased across Canada,42,45,46 with the national prevalence increasing from 4.0% in 2004 to 9.0% in 2017.46 According to national hospitalization data, the province of British Columbia (BC) has the highest rate of GDM in Canada.47 Specifically, in 2017, the Canada-wide rate of GDM was 9%, compared to 13.2% in BC. BC is the only province (excluding Quebec, as no data was available for that province), where the GDM rate exceeded the national average. There is relatively little published research on inter-provincial variability in GDM. 6 In Alberta, GDM prevalence was reported as 4.9% from 2008-2012.48 A study of GDM among Chinese and South Asian populations in BC and Alberta (two western-most provinces of Canada) from 2004-2010 reported an overall GDM prevalence of 4.8% in Alberta and 7.2% in BC and concluded that differences in diagnostic practices likely contributed to this geographic difference in prevalence.49 One study in BC from Kong et al., reported that BC had changed diagnostic approaches in 2010, and the pre-2010 prevalence of GDM was 7.9% compared (2009) to 9.4% after the policy change (2011). A study from Ontario reported an age-adjusted prevalence in 2010 of 5.6% in that province. Perinatal health research in BC is strengthened by the linked administrative datasets available through Population Data BC.50 After a detailed data review process, Population Data BC may provide approved researchers with access to health insurance billings (from BC’s Medical Services Plan (MSP), perinatal data (from BC’s Perinatal Data Registry BC-PDR), vital statistics (birth certificate data) and other administrative databases that represent all individuals in the province. External, researcher-collected data, may also be linked to administrative sources within strict criteria for privacy considerations. All data are linked using BC’s Personal Health Numbers (PHNs) and other identifiers (date of birth, names, sex and postal codes) and de-identified prior to research access via a secure, remote platform.51 A broad range of perinatal health data, including gestational diabetes diagnoses and other demographic data, are available through the BC-PDR. However, as with most administrative datasets, the BC-PDR only reports gestational diabetes diagnoses and lacks data on gestational diabetes screening. To address this lack, I proposed to use BC-MSP laboratory insurance billings to capture gestational diabetes screening using a novel approach that I also planned to validate as part of this work. I chose to examine gestational diabetes and screening in the BC context, in part, because of the high prevalence compared to the rest of Canada, the availability of high-quality population-based datasets for research, the possibility of using insurance billings to obtain screening data and the previously described screening policy change in 2010. Within this context, this thesis addresses a research gap in understanding how changes in GDM screening directly impacts gestational diabetes diagnosis risk in a population. 1.3 Pregnancy weight gain and infant birthweight During pregnancy, a woman or birthing person’s body changes to accommodate the fetus, placenta, increased blood volume and other physiologic changes. One way to monitor for nutritional status and pregnancy health can include measuring weight gain over the course of a pregnancy.2,52 Serial weight gain measurements in pregnancy are not considered a perfect diagnostic or screening tool as these are 7 generally poor predictors, on their own, of adverse perinatal outcomes.52 However, despite limitations, high levels of pregnancy weight gain have been associated with poor outcomes compared to weight gain within recommendations.4,5 Deviations in maternal weight gain could be considered useful indicators of social or biological factors that relate to poor pregnancy or fetal outcomes. Further, recent research demonstrated that associations between fetal size and pregnancy weight gain are casual.53 This supports the consideration of pregnancy weight gain as a modifiable risk factor for impacting fetal size. Recommendations for weight gain in pregnancy are controversial. In the U.S in the 1960’s, clinicians recommended restricted weight gain, preferably to 15 lbs (6.8 kg) to reduce risks of ‘difficult births’ and toxemia.52 This was followed by a period of adjustment after this approach was linked to infant mortality, low birthweight and other poor outcomes. Later, the Institute of Medicine (IOM) defined target pregnancy weight gains by pre-pregnancy body mass index (BMI).54 These criteria remain in use today, although they are also widely criticized.55 Some argue the IOM guidelines are too high, especially for higher BMI categories and that they promote poor outcomes in this group.55,56 Further, only 30-40% of most North American pregnant individuals gain weight within this criteria.57 However, weight gain within IOM criteria is associated with lower rates of poor pregnancy outcomes than weight gain outside these ranges.57,58 Excess weight gain in pregnancy, perhaps most importantly, is also associated with postpartum weight retention and can have long term health implications for development of diabetes and cardiovascular disease.59 Other research has focused on the trajectory of weight gain in pregnancy and whether increased weight gain in some trimester(s) is more impactful for perinatal outcomes.60–62 Interactions between pre-pregnancy body mass index, gestational diabetes and weight gain are also widely investigated.36,63 Current evidence points to minimal or no weight gain in pregnancy as the best recommendation for women with very high BMI to reduce risks of poor pregnancy outcomes.55,57,64 Whether or not serial weight gain monitoring is useful on an individual level, from a population health surveillance perspective, pregnancy weight gain is a relatively simple measure that is associated with a range of maternal and fetal health outcomes.65 1.3.1 Study context for pregnancy weight gain and infant birthweight research in this thesis Following the COVID-19 pandemic onset in 2020, governments, health systems and policies changed rapidly to respond to concerns about infection risk.66,67 These policies and processes impacted people in different ways. Some examples include: reducing individuals’ mobility through lock-downs and ‘stay-at-home’ orders, closing schools and workplaces, restricting public events, and closing gyms or public recreation facilities. Health systems also changed dramatically with reductions in routine health care visits 8 and shift to telehealth, shortened hospital stays and cancellation of elective or non-urgent procedures. Some changes with potential benefit also occurred, such as, reduced commuting, more at-home cooking and more time with family. Early research following the initial months of the pandemic demonstrated weight increases among children and adults usually following pandemic-associated ‘lock-downs’.66,68–72 There is some limited local evidence that pregnant women and people also changed nutritional behaviors or increased food intake73,74 but the impact of the pandemic on weight gain in pregnant women or infants birthweight is not well studied. The Obstetrical Care Outcomes Assessment Program (OB COAP) is a research and quality improvement program in Washington, U.S.A. This database is abstracted from hospital and community-based medical records and currently represents approximately 1/3 of the hospitals in Washington State. While other perinatal data sources may have some data on pre-pregnancy weight, these data are often missing and may be inadequate for research purposes. Importantly, the OB COAP database contained relatively complete weight data for both an early and late pregnancy weight, along with a date indicator for when each value was collected. Further, this database is regularly updated on a quarterly basis, thus it was possible to obtain a dataset that contained a contemporaneous cohort of births until the end of 2020. Lastly, I was able to obtain deidentified OB COAP data with births by week of delivery for my study cohort. This was a key feature required for feasibility to conduct a time series analysis. The pandemic has provided a unique ‘natural experiment’ that is of interest to public health. Quasi-experimental methods are well suited to studying the effects of natural experiments or other policy interventions. To explore a research gap on the effect of the pandemic on pregnancy weight gain and infant birthweight, I used the OB COAP dataset from Washington State to conduct an interrupted time series analysis. This dataset provided granular clinical data including data on weight gain, was recently updated, and could be used for a time-series analysis at the week level. 9 1.4 Research questions and hypotheses The main objectives of my thesis were to answer the following 4 research questions: Research question 1: Is screening test completion and screening methods as assessed using laboratory insurance billing data valid when compared with medical records data in British Columbia (BC), Canada? Hypothesis 1: Laboratory insurance billings data can be used accurately obtain data on antenatal screening tests for gestational diabetes, early 1st trimester screening ultrasounds and Group B streptococcus screening tests. Research question 2: How did gestational diabetes screening practices change over time and within subgroups in BC, Canada following several policy and professional association guideline changes regarding gestational diabetes screening from 2005-2019? Hypothesis 2a: Gestational diabetes screening changed following policy and professional association guideline changes regarding methods of assessment. Hypothesis 2b: Gestational diabetes screening varied across population subgroups and following guideline changes. Research question 3: What was the relative contribution of gestational diabetes screening practices (screening rates and methods) and population demographics to increasing the risk of gestational diabetes? Hypothesis 3: Changes in screening practices explain most of the increase in gestational diabetes diagnoses rather than changes in population characteristics or changes in other unknown characteristics. Research question 4: What was the impact of the COVID-19 pandemic and associated public health countermeasures on pregnancy weight gain and infant birthweight? Hypothesis 4: COVID-19 associated policies and countermeasures that may have changed individual behaviors and changed prenatal care led to an increase in pregnancy related weight gain and infant birthweights. 1.5 Thesis overview This thesis is presented in seven chapters. Chapters 3 to 6 are written in the form of manuscripts for peer-reviewed journals. Thus, topic headings within each chapter align with different journal requirements. 10 Chapter 2 presents a more detailed literature review of topics related to GDM screening and diagnosis. Chapter 3 describes the validation study, answering my first research question. The validation study included primary data collection that I completed as part of this thesis. This included a chart review of medical records from 3 BC hospitals. The medical records data were linked through individual identifiers to provincial perinatal and administrative (billings) data for a validation analysis. Chapter 4 presents a descriptive analysis of gestational diabetes screening data in BC pregnancies and addresses the second research question related to changes in gestational diabetes screening over time. Chapter 5 examines how gestational diabetes screening methods impacted diagnosis in BC and addresses the third research question regarding the attribution of change in screening methods. Chapter 6 examines the fourth research question related to pregnancy weight gain during the COVID-19 using the cohort from Washington State. Chapter 7 presents additional analyses on missing data for the COVID-19 project. The final chapter (Conclusion) provides a general discussion and interpretations for the overall thesis. This chapter also discusses some implications of this work for policy, clinical practice and research. 1.6 Significance and knowledge gaps This research targets knowledge gaps related to: 1) gestational diabetes mellitus screening and diagnosis and 2) COVID-19 pandemic-associated impacts on pregnancy weight gain and infant birthweight. First, I address a gap in knowledge on gestational diabetes screening completion and methods, and the impact of screening on diagnoses. Most large administrative datasets used in population studies lack any data on screening. Thus, there is a gap in understanding how screening completion or screening methods could impact measurement of gestational diabetes prevalence, especially on a population level. In this thesis, I developed a novel approach to capture gestational diabetes screening data using administrative data sources, validated this method using medical records data (Chapter 3), applied this screening data to describe how policy changes impacted screening practices in BC (Chapter 4), and, last, quantified the impact of screening practices on the rising prevalence of gestational diabetes in BC over the past 15 years (Chapter 5). Second, I used a quasi-experimental study design to assess the impact of the COVID-19 pandemic on pregnancy weight gain and infant birthweight (Chapter 5). This study used rigorous analytic methods, an interrupted time series analysis, that improves over before-and-after study designs to examine an understudied topic area. Chapter 6 presents a brief review of issues with missing data in the study from Chapter 5. 11 1.7 Disclaimer Regarding data accessed through Population Data BC (BC data): Access to data provided by the Data Steward(s) is subject to approval, but can be requested for research projects through the Data Steward(s) or their designated service providers. All inferences, opinions, and conclusions drawn in this publication are those of the author(s), and do not reflect the opinions or policies of the Data Steward(s). 12 2 LITERATURE REVIEW OF GESTATIONAL DIABETES 2.1 Overview Current expert opinion supports the diagnosis and treatment of hyperglycemia in pregnancy to reduce risks of fetal overgrowth, neonatal hypoglycemia (a state of low-glucose that occurs in the neonate after delivery), associated delivery complications and maternal hypertensive disorders.8,38,75,76 However, criteria for diagnosis of gestational diabetes remain controversial, lack international consensus, and have shifted repeatedly since the first screening criteria were derived in the 1960’s.15 This leads to a complex landscape for interpreting both current and past research evidence. There remains significant debate on the key issues in gestational diabetes screening and diagnosis during pregnancy: 1) universal vs. risk-based screening programs;77 2) which screening test to use, timing of screening78 and usefulness of early screening prior to 24 weeks;21 3) screening approach (one-step v. two-step)79; 4) the most appropriate diagnostic test;15,80 5) the glucose thresholds that should be used for diagnosis;42 or 6) the number of abnormal values required to constitute a diagnosis.42,79,81,82 Further sections will expand on these issues. 2.2 Risk-based v universal screening Both selective or risk-based screening and universal screening are used for gestational diabetes screening.77 Selective screening means that only populations considered at risk of diabetes should be screened and low risk women and birthing people are exempt from screening. Low risk definitions vary, but generally include: younger age (< 25 years old), no prior GDM, no first-degree relatives with diabetes, no prior macrosomic infant, pre-pregnancy body mass index (<28 kg/m2), and belonging to a racial/ethnic group with a low prevalence of diabetes.77 Universal screening, where all pregnant individuals are recommended for screening, is most commonly used worldwide. However, current UK guidelines83 still use risk-based screening, as do some lower-resource settings.41 This change from selective to universal screening is important to consider for observational studies on gestational diabetes. Specifically, the proportion of people who are screened (v. unscreened), and the underlying risk profile of the group who are screened, differs between jurisdictions where universal screening is implemented compared to jurisdictions where risk-based screening is used. For example, a 2010 review of risk-based v. universal screening in 11 countries reported the proportion of people who would be exempt from screening according to risk-based criteria ranged from 5% to 86% in these different jurisdictions.84 Further, the proportion of GDM positive individuals who would be missed by risk-based screening varied from 46% to 1.3%.84 13 2.3 Glucose tests There are several test types used in gestational diabetes screening and diagnosis (Table 2.1). Most gestational diabetes screening and diagnoses criteria use either a glucose challenge test (GCT) or an oral glucose tolerance test (OGTT). Both tests use a glucose “challenge” approach whereby the person consumes a measured amount of glucose, and then blood tests are used to quantify the individual’s glycemic response. Other tests used to screen for and/or diagnose pre-gestational or gestational diabetes are: a fasting glucose test, a random glucose test or a HbA1c test. A fasting glucose test refers to a blood glucose test that is taken in a fasting state; after the person has refrained from eating for 8 to 12 hours. A random glucose is a glucose test taken at any time. A hemoglobin A1c test (abbreviated as HbA1c) measures the amount of glucose that is attached to hemoglobin and is considered to represent an average amount of glucose over the past 3 months (roughly the life-span of a red blood cell). The validity of a HbA1c in pregnancy is unclear; however, it is still recommended in some criteria for early first-trimester screening for pre-existing diabetes.8 HbA1c, random or fasting glucose tests are less commonly used for gestational diabetes diagnoses and screening and do not require a challenge component. A random or fasting glucose test is occasionally offered as an alternative for those who cannot tolerate the glucose drink, who decline recommended screening or who start prenatal care late in pregnancy.85,86 During the COVID-19 pandemic, some regions recommended these non-challenge tests as alternative methods of GDM screening to minimize potential infection risk and time spent in out-patient laboratories.87 A glucose challenge test (GCT) requires a 50g oral glucose load (in a standardized drink) and then a single blood sample taken 1-hour afterwards. An oral glucose tolerance test (OGTT) consists of consumption of a larger oral glucose load (100g or 75g), usually after fasting, and includes multiple blood samples. Specifically, an OGTT test usually includes a fasting sample and then several more samples taken after the glucose drink (e.g., at 1-hour, 2-hours and 3-hours). In all cases, samples are analyzed for glucose concentrations (mmol/L). The other tests use a single blood test only. A HbA1c is more commonly used to diagnose pre-existing diabetes in a non-pregnant person but some guidelines recommend this test for early pregnancy (prior to 24 weeks) testing for pre-existing diabetes.21 2.4 A one-step v two-step approach Screening for GDM can be broadly categorized as using either a two-step approach (a screening test followed by a diagnostic test if screen positive) or a one-step approach (a single diagnostic test)15. According to some guidelines, if extremely elevated results are identified on a screening test, then a diagnosis of either overt diabetes or gestational diabetes, may be made based on that test alone. A one-step approach has the advantage that everyone has only one diagnostic test. The disadvantage is that 14 diagnostic tests for GDM are generally more time-consuming, uncomfortable and costly than screening tests.88,89 Some argue that a one-step screening approach improves screening uptake rates since pregnant individuals only have to attend one laboratory visit for both screening and diagnosis.90–92 However, they must fast before and make time for at least a 2-hour visit. By contrast, a two-step screening program requires two visits, albeit only for the proportion of people who screen positive on the first test (usually 20%). Others have demonstrated good adherence with a two-step screening program within a universally funded health care system,78 but this may differ in health systems where individual or insurance costs vary.91 2.5 History of screening and diagnostic guidelines The early criteria for diagnosis of hyperglycemia in pregnancy (O’Sullivan, Table 2.2) were derived to identify pregnant women and people who would be at high risk of developing type 2 diabetes later in life.32,93 These criteria were generally used with risk-based screening programs. In the late 1970’s and early 1980’s, changes in laboratory techniques meant that samples were now analyzed in serum plasma rather than in whole blood. Two criteria for diagnosis were defined during this time period: the National Diabetes Data Group (NDDG) criteria94 and the Carpenter-Coustan (C-C) criteria.95 The former was designed to mimic the O’Sullivan criteria but adjusted the criteria for use in a serum plasma sample, the C-C criteria also lowered the diagnostic thresholds based on new research.95 The C-C criteria used a two-step approach with a 50g-GCT screening test, followed by a diagnostic test using a 100g, 3-hour OGTT. Over the following decades, a two-step approach with C-C diagnostic criteria was most commonly used in North America whereas the World Health Organization (WHO) criteria96 were more commonly used in Asia and in Europe. The early WHO criteria (Table 2.4) were based on thresholds from non-pregnant populations and were widely viewed to be invalid in pregnancy. During this time period, there was ongoing debate about the relevance of screening and treating gestational diabetes as it was defined and, specifically, debate about the importance of treating milder levels hyperglycemia than those that were diagnosed using existing criteria. Existing criteria (C-C/O’Sullivan) were derived based on identifying a pre-diabetic condition in the pregnant person rather than identifying those at risk of adverse perinatal outcomes.28 While there was evidence that untreated hyperglycemia was associated with fetal overgrowth97,98, there was debate as to whether hyperglycemia/GDM should be considered a risk factor or a unique metabolic abnormality/disease process. 15 Two randomized trials99,100 (Table 2.3) that examined whether treatment of hyperglycemia in pregnancy improved perinatal outcomes both demonstrated a benefit of treatment. It is important to note that one trial99 included individuals at thresholds much higher than current standards, whereas the other trial’s criteria100 were similar to the C-C standards. The Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) trial reported a reduced risk for their primary outcome (a perinatal composite), an increased risk of induction, and no change for cesarean delivery or shoulder dystocia in the untreated group compared with the treatment group.99 They also reported a lower risk of large for gestational age (LGA) (birthweight > 90th percentile by gestational age) in the treatment group. The second trial, by the National Institute of Child Health and Human Development (NICHD), found no difference in their primary perinatal composite outcome, but did find a reduction in LGA, cesarean birth, shoulder dystocia and hypertensive disorders.100 Both trials, however, had statistically significant reductions in pregnancy weight gain in the treatment groups.28,53,60 While weight gain is appropriately considered an intermediate variable (on the causal pathway) between a GDM diagnosis and perinatal adverse outcomes and should not be adjusted for, none of these studies presented sensitivity analyses with stratification on the intermediate variable (weight gain). Thus, it is unclear which treatment aspects of a GDM diagnosis (i.e. medication and glucose monitoring or nutritional/lifestyle counselling or simply a decrease in weight gain) confers health benefits.24,25 The studies also differed in the composition of racial/ethnic groups in their study populations. Specifically, the ACHOIS study population was reported as >75% White, 17% Asian and 8% “Other” and the NICHD study was reported as >55% Hispanic, 25% White, 11% Black and 5% Asian. The association between hyperglycemia and fetal growth varies by different racial/ethnic groups, this could be an important concern with generalizability for both studies.101,102 However, the authors of both studies concluded that treatment of moderate hyperglycemia in pregnancy was beneficial. A third large multi-center study (The Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) Study)33 was intended to clarify the risk of adverse outcomes by studying a continuum of hyperglycemia at levels lower than those considered to represent overt diabetes in pregnancy and by examining a range of perinatal outcomes. This study reported continuous associations between increasing hyperglycemia in pregnancy as measured by a 75g OGTT (one-step, 2-hour test) method and increasing risk(s) of cesarean delivery, neonatal hypoglycemia, LGA and cord C-peptide levels. Importantly, HAPO did not find clear evidence of a threshold at which risks increased, leading to debate about how to incorporate these findings into contemporary practice. Following the publication of the HAPO Study, the International Association of Diabetes in Pregnancy Study Group (IADPSG) consensus panel derived thresholds based on an increased risk relative to the 16 study mean values for the fasting, 1-hour and 2-hour glucose measurements.38 They defined a new diagnostic criteria for gestational diabetes based on an Odds Ratio (OR) of 1.75 relative to the mean glucose values as the reference group. However, the IADPSG authors acknowledged that the new criteria would result in an 17.8% incidence of gestational diabetes in the HAPO population. They also considered a threshold using an OR of 2.0 which had a modest increase in the risk of adverse outcomes (compared to the HAPO-OR1.75 threshold) but would decrease the incidence of GDM diagnosis to 10.5%. Nevertheless, the consensus recommendation was based on a HAPO-OR 1.75 thresholds and used a one-step screening approach with a 75g-OGTT (Table 2.3). The IADPSG criteria were published in 2010 and were subsequently adopted by many other professional association(s) guidelines and standards worldwide. Substantial increases in the prevalence of gestational diabetes after implementing the IADPSG criteria have been since demonstrated in numerous studies and systematic reviews.15,39,79,86,103–105 On average, switching to the IADPSG criteria increases diagnoses by at least 1.75-fold, although some regions have reported much larger increases.106,107 As one example, McIntyre and Jensen reported that applying the IADPSG criteria in Denmark would have resulted in an increase in GDM from 2.3% to >40% of all pregnancies.39 The authors found limited evidence of increased maternal or fetal risks in the Danish cohort who had ‘untreated’ GDM using the IADPSG criteria. Thus, they concluded that using the IADPSG criteria for GDM was inappropriate for Denmark and “would classify an unmanageable number of women as having GDM who are at low absolute risk of pregnancy complications and divert health resources from other areas.”39 On the other hand, a study in Spain reported fewer adverse outcomes after implementation of the IADPSG standards for GDM diagnosis and concluded that this change was cost-effective despite a 3.5-fold increase in prevalence.103 In part because of inconsistent results demonstrating improved perinatal outcomes after the switch to IADPSG criteria,39,86,108 GDM diagnostic criteria remain widely debated.15,108 A recent systematic review reported 15 national and international standards for diagnosis of gestational diabetes.15 Selected guidelines in Canada, the US and internationally (Table 2.4) demonstrate the variability in guidelines that still exists today. A few trends are notable. Most countries, with the exception of the UK (NICE guidelines)22,83, now recommend universal screening for GDM. Over time, more standards have adopted IADPSG criteria and diagnostic thresholds are decreasing (to capture more cases). Both one-step and two-step screening approaches are in widespread use today with some guidelines recommending either approach.109 A 50g-GCT is the most commonly used screening test, but some countries use a single 2-hour blood test after a 75g OGTT (non-fasting) as a screening test15,41 and others use a single fasting test.110 17 A recent 2022 study proposed a new diagnostic criteria for GDM using a weighted Average Glucose (wAG) that combined the results from a fasting, 1-hr and 2-hr result into one value and then assigned the wAG to 4 distinct diagnostic categories: Normal gestational glycemia (NGG), Impaired gestational glycemia (IGG), Gestational diabetes (GDM), and High risk GDM (hGDM).111 These four categories identified pregnant people at higher risk of immediate complications and subsequent development of post-pregnancy diabetes as validated with previously-collected data. This could be a new diagnostic tool to consider for future guidelines on GDM. 2.6 Canadian context In Canada, regional policies and professional association guidelines for GDM screening have changed several times in the past 20 years (Table 2.4).42 In the late 1990’s and early 2000’s, Canadian guidelines recommended selective (aka risk-based) screening. This meant that low-risk pregnant people were exempt from screening. “Low risk” was defined as less than 25 years old, having a non-obese pre-pregnant body mass index, no prior history of gestational diabetes or macrosomia, no family history of diabetes in first-degree relatives and not belonging to an ethnic group with high diabetes prevalence.42 Later Canadian guidelines (Diabetes Canada 2003, SOGC 2016)42 recommended universal screening. One study112 reported a 9-fold increase in GDM prevalence across Canada, from 0.3% in 1984 to 2.7% in 1996, after universal screening was implemented but noted this was likely an artifact of increased screening with no evidence of beneficial effects on pregnancy outcomes. In Canada, the two largest professional associations who have written guidelines for management and treatment of diabetes in pregnancy are Diabetes Canada (DC) (formerly known as the Canadian Diabetes Association (CDA)) and the Society of Obstetricians and Gynecologists of Canada (SOGC). Prior to the HAPO Study, Canadian guidelines113 used screening and diagnostic criteria generally comparable or the same as the C-C criteria. Following HAPO, Diabetes Canada in 2013 (DC-2013) recommended the use of a two-step screening approach (50g GCT followed by 75g OGTT) as the “preferred” option, with the alternate option being a one-step 75g OGTT using the IADPSG-2010 criteria.114 Interestingly, the DC-2013 guidelines specified diagnostic thresholds for the two-step “preferred” method using HAPO-OR 2.0 criteria. In the same guidelines, the alternate option specified the HAPO-OR 1.75 thresholds for diagnosis (as used in the IADPSG-2010 standards). Therefore, according to the DC-2013 guidelines, if an individual was screened with a 50g-GTT (two-step approach) they would be diagnosed with different 75g-OGTT criteria (HAPO-OR 2.0) than if they were not screened (one-step approach) (HAPO-OR 1.75). Thus, using one-step screening would result in a higher diagnostic prevalence than using two-step 18 screening. This was the first example of two different diagnostic criteria specified in the same guideline and within the same country. SOGC guidelines were updated in 20168 to match the DC-2013 criteria. Since then, the Canadian guidelines are generally aligned8,114, but allow for a preferred and alternate option for screening which means that characterizing screening method could be especially important in Canada because two different screening approaches are both accepted.42 I was unable to locate any Canadian research that describes relative uptake of the two guidelines-based screening approaches in practice. Thus, it is unknown to what extent different regions, provinces or health care professionals in Canada use the preferred or alternate screening approaches. If there are large regional differences in uptake of the different screening methods, this could have implications for both surveillance and health resource planning (i.e., access to diabetes clinics, nutritionists, and/or specialist physicians) across Canada. In British Columbia, the province eliminated the 100g OGTT with C-C criteria in 2010 and switched to offer only a 75g diagnostic OGTT using IADPSG diagnostic criteria.105 This change was in direct response to the HAPO Study and the IADPSG guidelines. Of interest, despite this provincial policy change, relevant Canadian professional association guidelines still referenced the C-C criteria and two-step screening. Canadian organizations did not endorse the IADPSG criteria until the Diabetes Canada 2013 guidelines. Following the 2010 policy in BC, clinicians were recommended to use a one-step screening approach with the 75g-OGTT and diagnostic criteria as per the IADPSG guidelines. However, the 50g-GTT was not eliminated, and clinicians could continue to use a two-step screening approach, albeit with a 75g-OGTT for diagnosis. One study, Kong et al.,105 used a before-and-after design to assess maternal and fetal outcomes following the change in policy in BC. They assessed births in two, 6-month blocks, from April 1 to September 30 in 2009 and in 2011 and found an increase in GDM prevalence from 7.9% to 9.4%. The authors did not find any statistically significant differences in maternal or fetal outcomes after the change in policy. In their discussion, they note the GDM prevalence in BC (9.4%), after the change to a policy recommending the one-step IADPSG screening, was lower than expected based on the HAPO Study (16%). These authors hypothesize that screening tests were likely under utilized (i.e. not everyone was screened)115 and that guidelines were not consistently followed, leading to lower than expected rates. However, this study lacked any information on screening, therefore they were unable to verify these hypotheses. In Alberta, Donovan et al. published two studies on gestational diabetes screening in this province. In one study,78 they examined primiparous pregnant individuals from 2008-2012 for gestational diabetes 19 screening completion. They found that 91% received a 50g-GTT screening test and that two-step screening was widely accepted and implemented in a timely manner within their universally funded health system. In this province, laboratory data was linked to population-level health data to allow screening uptake to be assessed. A second study48 examined perinatal outcomes in subgroups of different glucose test results after a two-step screening method and noted that a negative screening result using two-step screening with HAPO-OR 2.0 (DC-2013) criteria was associated with a low risk of adverse pregnancy outcomes. Several Canadian studies116 examined perinatal outcomes by glucose measurements according to different diagnostic criteria and report an increase in prevalence according to IADPSG criteria. However, none of these studies have examined regional or other differences in current screening practices, within current Canadian guidelines. 2.7 US context In the US, the two main professional associations have differing criteria on GDM diagnosis and screening.117 Following HAPO, in 2011, the American Diabetes Association (ADA) changed their guidelines to recommend only the IADPSG-2010 criteria (one-step 75g OGTT). By contrast, the American College of Obstetricians and Gynecologists (ACOG) (2013 and 2018 guidelines) has not adopted the IADPSG criteria and recommends screening with a two-step approach using a 50g GTT and 100g OGTT with either C-C or NDDG criteria for diagnosis. Likely to provide some coherence with ACOG, the 2014, 2018 and 2021 ADA guidelines include two options: either the IADPSG-2010 criteria (one-step 75g approach) or ACOG’s criteria (two-step with either NDGG or C-C). Thus, three different GDM diagnostic criteria could be used in the US, today, all of which would meet national professional association guidelines. Despite findings from the HAPO Study, the US has not widely adopted IADPSG criteria. A 2015 survey of obstetricians118 reported that 90.1% recommended a two-step screening for GDM (v. one-step). The majority of two-step screening used C-C criteria, but 12% used NDDG criteria. There were also significant regional differences in screening with higher use of one-step (24%) in the Western region. A 2021 systematic review and evidence update for the US Preventative Services Task Force117 examined 5 new randomized controlled trials (RCTs)81,82,109,119,120 (4/5 with US study groups) that compared universal screening with one-step IADPSG criteria to two-step C-C criteria. Their meta-analysis showed that one-step screening was significantly associated with a diagnosis of GDM in 11.5% v. 4.9% of pregnancies but was not associated with any differences in fetal/neonatal or maternal outcomes. These authors conclude 20 that the greater prevalence of GDM resulting from increased use of one-step screening may have led to overdiagnosis and overtreatment without associated benefits. 2.8 International context The diagnosis and treatment of GDM varies widely outside North America (Table 2.4).13 Because low and middle income countries (LIC and LMIC) may have limited health system resources,13,121 some use risk-based screening only or diagnostic criteria that result in much lower prevalence than the IADPSG-2010 criteria.122 This is a deliberate decision in many regions, stemming from access to limited resources for monitoring and treating gestational diabetes. For example, in high-income-countries, a GDM diagnosis is accompanied by referrals for additional services usually through a diabetes-based clinic. These often involve extra prenatal visits and follow-up, daily blood sugar monitoring using a glucometer, dietary and lifestyle counselling and referral to an endocrinologist or specialist for medication, if needed. In regions where there are other more pressing perinatal health concerns (i.e., pre-eclampsia, postpartum hemorrhage, malnutrition, infectious diseases, maternal or neonatal mortality) and health resources are scarce, lower diagnostic thresholds for GDM is not seen as a beneficial use of public health resources. This is one of the many reasons why standardized international criteria for GDM remain elusive. Many argue that cost-effectiveness and benefit of GDM treatment needs to be re-evaluated with carefully done randomized trials, before considering widespread use of standard diagnostic thresholds. As stated by Bilous et al., “each health care service [should] adopt diagnostic criteria based upon local available data on clinical and cost-effectiveness, practicality of test, and local resources.”26 Finally, ethnic and genetic variability in the effects of hyperglycemia in pregnancy on perinatal outcomes or long term outcomes is not well understood.123 This represents key knowledge gap when considering global standardization of GDM criteria. 2.9 Being Unscreened With risk-based screening, only those deemed at risk are screened. However, even in regions with recommended universal screening, 100% of the pregnant population is not screened in practice.78 First, there are people with pre-pregnancy diabetes (<1%) and other medical complications (e.g. bariatric surgery) for whom an OGTT is contraindicated and are not recommended for GDM screening.8 Second, there may be barriers that limit access to screening. For example, these could include travel distance to a laboratory for testing, language or cultural barriers in understanding screening information, not being able to take the time (off work or childcare) for testing, costs of testing or being unable to tolerate the test protocol because of nausea or vomiting.124 Third, pregnant people may decline screening (personal beliefs 21 or values),125 may decline the glucose drink (i.e. cultural, religious, personal, dietary reasons) and/or request alternative glucose loads (licorice, candy, honey tea, food)126,127. Finally, some pregnant people may elect to be tested and/or diagnosed with methods outside local guidelines such as self-monitoring with a glucometer128 or using a fasting blood glucose test.129 All of these could be classified as unscreened according to primary guidelines. While there is relatively little research on screening uptake,85,91,115 some estimates are that between 4-50% of pregnant people may be unscreened,78,115,125 even in the context of universal screening. Decision-making about screening is also influenced by the guidance given by health care professionals when discussing screening tests.130 While many pregnant women see a physician for health care during pregnancy, midwives and nurses may also provide prenatal care in many countries. In Canada, most pregnant people receive prenatal care from physicians, either general practice or obstetricians. Midwives in Canada are autonomous professionals who can care for women and birthing people during pregnancy, birth and postpartum and are involved with 30% of pregnancies in BC, ~10% across Canada.131 Midwifery care has a strong focus on the principles of informed choice and midwives are trained to engage in discussions with pregnant people about all aspects of prenatal care; including routine screening tests.132 Guidelines from midwifery organizations highlight the equivocal research evidence for screening and treatment of GDM133–136 but note that “all pregnant women should be offered screening between 24 to 28 weeks gestation.” In contrast, obstetric or physician-guidelines recommend universal screening and treatment based on evidence from expert consensus and state “all pregnant women not known to have pre-existing diabetes should be screened for GDM at 24-28 weeks of gestation.”8,114 Differences between midwifery and physician-led care have not been previously examined as a potential contributor to GDM screening uptake or screening method and is a research gap that I addressed in this work. 2.10 Prevalence of GDM Gestational diabetes risk, as measured in observational studies, appears to have increased in many jurisdictions over recent decades.14–16 Since hyperglycemia in pregnancy is also associated with maternal pre-pregnancy body mass index, maternal age and other maternal complications,137 population-level increases in these factors could be linked to rising prevalence of gestational diabetes. However, widespread changes in GDM screening criteria (Table 2.4) and in who is screened (i.e. risk-based v. universal screening) are also important.42 Unfortunately, many population-based data sources used for surveillance only capture information on gestational diabetes diagnosis. Often, these data sources use a ‘chart-by-exception’ approach, whereby only diagnoses are captured. Thus, population-level data to identify if people are unscreened, or how they 22 were screened (i.e., screening method or diagnostic criteria) is not readily available. In this thesis, these are gaps that I addressed, first, in developing a method to capture screening data for large population datasets and second, examining the contribution of screening to rising prevalence of GDM. 2.11 Perceptions of GDM screening and diagnosis among pregnant people There is relatively limited qualitative research on experiences with GDM screening in pregnancy.138,139 Side effects, experienced by up to 20% of people from glucose challenge tests, include: nausea, dizziness, syncope and hypoglycemia.81,140 In two qualitative studies specifically on screening (Germany138, Australia139), however, the majority of participants reported positive experiences with screening. Those who screened positive did report a negative psychological impact after diagnosis. The German study considered experiences of people who had a one-step approach compared to those who had a two-step approach and reported positive experiences after the non-fasting screening (two-step) test. Barriers to completing screening identified using qualitative and mixed methods include travel distance141, social/mental health issues, discomfort with the test, socio-economic barriers,138 and preference/choice85,124,142. A recent qualitative study in BC on the experiences of declining care in pregnancy, reported that gestational diabetes screening was declined in over 12% of pregnancies in their sample.125 This was the most commonly declined recommended prenatal test (slightly fewer than those who declined prenatal genetic testing which is considered optional/offered in BC). Last, Edwell and Jack89 published an important qualitative, rhetorical and narrative analysis of the overall debate around GDM. They reviewed scientific literature and patient-led blog posts to present both clinical and patient perspectives. The scientific literature, they noted, follows a “deferred quest” narrative trajectory as there are frequent references to how future research and data will eventually lead to medical consensus. On the other hand, patients’ perspectives showed that when they became aware of the lack of scientific consensus on GDM, this led to a rupture of trust in medical authorities. This perspective contributed to a general distrust in the medical system and has important implications for patient-provider relationships. These authors concluded, in part, that health care professionals should openly acknowledge the different diagnostic standards and lack of consensus to their patients, and explain why they adhere to a particular approach. Pregnant people who are diagnosed with a medical condition often seek to understand their diagnosis by gathering information from multiple sources. If they have not previously been exposed to the complexity and controversy in GDM by their health care team, this could lead to further distrust in biomedicine and have implications for long term health care. While there is relatively little qualitative research on GDM screening alone, the experiences of pregnant people who were diagnosed with GDM143,144 or who experienced diabetes (type 1 or 2) in 23 pregnancy143,145,146 are more well-studied. Other qualitative studies examined the experiences of treatment, socio-cultural implications147 and post-partum follow-up care.142 According to some qualitative research, the experience of a GDM diagnosis is profound and can result in an emotionally distressing pregnancy.148 Themes that emerged in the UK on the experiences of people with GDM diagnoses included: the disrupted pregnancy, projected anxiety, reproductive asceticism, women as baby machines, perceived stigma, lack of shared understanding and postpartum abandonment.146 Similar themes are reported in other studies, thus, it is important to acknowledge the deep impact this diagnosis can have for individual people. Some racialized participants reported that experiencing GDM treatment was especially difficult as it conflicted with their cultural food needs and felt they had received biased treatment by professionals.146 A Canadian qualitative study149 among an ethnically diverse group found both positive and negative themes. Negative effects included feeling pressured to fill multiple roles, financial impacts related to increasing costs of food to meet dietary requirements and a disconnect between the diabetes recommendations and their cultural practices. Positive effects were that women felt motivated to make health behavior changes after a diagnosis. Overall, the experience of GDM can be life-changing for women and other pregnant people. Experiences of screening alone are not well studied. 2.12 Summary Despite decades of research into gestational diabetes screening, diagnosis and treatment, there remains significant debate and lack of consensus on how best to screen and diagnose GDM. Importantly, universal v. risk-based screening, screening approaches (one-step v two-step), screening criteria and guidelines, as well as timing of early screening are all key issues that should be reported in epidemiologic studies of GDM. Knowing how people are screened/diagnosed (i.e., which guidelines are used) as well as whether or not they are screened are both critical when comparing GDM rates across groups or over time. Well-designed epidemiologic studies of GDM are needed that accurately control for screening status and screening method in order to understand population-level changes in GDM diagnoses and associated perinatal outcomes. 24 2.13 Tables Table 2.1 Glucose tests used in gestational diabetes screening and diagnosis Test type Glucose load Timing of blood sample(s) usually associated with this test Glucose challenge test (GCT) 50g 1-hour (after load) Oral glucose tolerance test (OGTT) 75g or 100g Fasting (before load) 1-hour (after load) 2-hour (after load) 3-hour (after load) Hemoglobin A1c (HbA1c) None 1 sample (anytime) Fasting glucose test None 1 sample (after fast) Random glucose None 1 sample (anytime) Table 2.2 Major diagnostic and screening criteria for GDM (1960-2010) Major criteria (1960-2010) Year Criteria defined for population to screen Overall screening approach Screening method (positive cut-off >) OGTT glucose load Criteria for diagnosis (> mmol/L) after OGTT > values for diagnosis of GDM fasting 1-hr 2-hr 3-hr O’Sullivan93 a 1964 Clinical risk One-step 100g 5.0 9.2 8.1 6.9 2 O’Sullivan + Mahan32 a 1973 Clinical risk Two-step 50g-GCT (7.2) 100g 5.0 9.1 8.0 6.9 2 NDDG94 b 1979 Two-step 50g-GCT (7.8) 100g 5.8 10.5 9.1 8.0 2 Carpenter-Coustan95 1982 Two-step 50g-GCT (7.8) 100g 5.3 10.0 8.6 7.8 2 IADPSG (HAPO OR-1.75)38 2010 Universal One-step 75g 5.1 10.0 8.5 1 a. Criteria for diagnosis based on samples in whole blood b. Abbreviations: National Diabetes Data Group = NDDG, Oral Glucose Tolerance Tests = OGTT, Glucose Challenge Test = GCT, International Association of Diabetes in Pregnancy Study Group =IADPSG, Hyperglycemia Adverse Pregnancy Outcomes =HAPO, OR = Odds Ratio 25 Table 2.3 Key studies and randomized trials impacting GDM screening and diagnosis in the early 2000's Key studies Year(s) of study Sample size Criteria defined for population to screen Overall screening approach Screening method (positive cut-off >) OGTT glucose load Criteria for inclusion in RCT (> mmol/L) after OGTT Criteria for treatment Fastingb 1-hr 2-hr 3-hr ACHOISa (Crowther RCT)99 2002-2007 485 / 473 treated / untreated Either clinical risk OR positive on screen positive on GTT Two-step 50g-GTT (7.8) 75g < 7.8 7.8 to 11 2-hour within criteria NICHDa (Landon RCT)100 1993-2003 490/510 treated / untreated Universal Two-step 50g-GTT (7.5-11.1) 100g < 5.3 >10 >8.6 >7.8 2 or 3 non-fasting values exceed criteria HAPOa Study33 2000-2006 23,316 Universal One-step 75g <5.8 <11.1 a. Acronyms: The Australian Carbohydrate Intolerance Study in Pregnant Women = ACHOIS, National Institute of Child Health and Human Development = NICHD, Hyperglycemia Adverse Pregnancy Outcomes =HAPO b. Fasting plasma glucose (FPG) 26 Table 2.4 Overview of selected international and national guidelines for GDM diagnosis and screening Organization or country Year Criteria defined for population to screen Overall screening approach Screening method (positive cut-off >) OGTT glucose load Criteria for diagnosis (> mmol/L) after OGTT # > for diag. Standard criteria fasting 1-hr 2-hr 3-hr International15,41,96 WHO 1980 NS Two-step 100g 5.8 10.6 9.2 8.1 2 WHO 1985 NS Two-step 100g 7.0 11.1 1 WHO 1999 NS One-step 75g 6.1 7.8 1 WHO 2006 Universal One-step 75g 6.1 7.8 1 WHO 2013 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG US117,150 ACOG 2013, 2018 Universal Two-step 50g-GCT (7.2, 7.5 or 7.8) 100g 5.3 or 5.8 10 or 10.5 8.6 or 9.1 7.8 or 8 2 C-C or NDDG ADA "Option 1" 2003 All but low risk Two-step 50g-GCT (7.8) 100g 5.3 10.0 8.6 7.8 2 ADA "Option 2" 2003 All but low risk One-step 75g 5.3 10.0 8.6 2 ADA 2011 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG ADA "Option 1" 2014, 2018, 2021 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG ADA "Option 2" 2014, 2018, 2021 Universal Two-step 50g-GCT (7.2, 7.5 or 7.8) 100g 2 C-C Canada8,42,114,151–153 DC "Preferred" 1998 Clinical risk Two-step 50g-GCT (7.8) 75g 5.3 10.6 8.9 2 DC "Alternate" 1998 Clinical risk Two-step 50g-GCT (7.8) 100g 5.3 10.0 8.6 7.8 2 C-C DC 2003 Universal Two-step 50g-GCT (7.8) 75g 5.3 10.6 8.9 2 DC 2008 Universal Two-step 50g-GCT (7.8) 75g 5.3 10.6 8.9 2 DC "Preferred" (HAPO OR 2.0) 2013 & 2018 Universal Two-step 50g-GCT (7.8 - 10.3) 75g 5.3 10.6 9.0 1 HAPO OR 2.0 DC "Alternate" 2013 & 2018 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG SOGC 1992 Universal Two-step 50g-GCT (7.8) 100g 5.3 or 5.8 10 or 10.5 8.6 or 9.1 7.8 or 8 2 C-C or NDDG SOGC-Option 1 2002 Clinical risk Two-step 50g-GCT (7.8) 100g 5.3 or 5.8 10 or 10.5 8.6 or 9.1 7.8 or 8 2 C-C or NDDG SOGC-Option 2 2002 Clinical risk Two-step 50g-GCT (7.8) 75g 5.3 10.6 8.9 2 DC-2003 SOGC "Preferred" 2016 & 2019 Universal Two-step 50g-GCT (7.8) 75g 5.3 10.6 9.0 1 HAPO OR 2.0 SOGC "Alternate" 2016 & 2019 Universal One-step 75g 5.1 10 8.5 1 IADPSG Europe15,83,154,155 EASD 1996 1996 NS One-step 6.0 9.0 1 NICE (UK) 2015 Clinical risk One-step 75g 5.6 7.8 1 Sweden-no standard criteria 2000-2015 Mixed One-step 75g 12.2, 10 or 8.6 1 Australasia156 ADIPS 1998 5.5 8.0 1 ADIPS 2013 & 2014 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG 2010 27 Organization or country Year Criteria defined for population to screen Overall screening approach Screening method (positive cut-off >) OGTT glucose load Criteria for diagnosis (> mmol/L) after OGTT # > for diag. Standard criteria fasting 1-hr 2-hr 3-hr NZSSD 2004 1998, 2004 Universal 5.5 9.0 NZ-MoH guideline 2014 Universal One-step 75g 5.5 9.0 1 Asia15,41,157,158 Chinese Medical Association 2007 Two-step 50g-GCT (7.8) 100g or 75g 2 ADA 2003 (either C-C or NDDG) Chinese Ministry of Health-preferred 2011 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG 2010 Chinese Ministry of Health-alternate 2011 Universal Two-step FPG 75g 5.1 10.0 8.5 1 IADPSG 2010 Chinese guidelines-preferred 2014 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG 2010 Chinese guidelines-alternate, low-resource 2014 Universal Two-step FPG (4.4-5.1) 75g 5.1 10.0 8.5 1 IADPSG 2010 (two-step) DIPSI 2009 NS One-step (non-fasting) 75g (*) 7.8 1 JSOG/JDS 1984 Universal Two-step OGTT 75g any GA 75g 5.5 10.0 8.3 2 JSOG 2013/2010 Universal Two-step OGTT 75g any GA 75g 5.1 10.0 8.5 1 South America110,159 BSD 2007 Universal FPG (4.7) 75g 7.0 BSD 2016 Universal FPG (5.1-6.9) (1st trimester<20wks) 75g 5.1 10.0 8.5 1 IADPSG 2010 Chile Ministry of Health previous and 2015 Universal One-step 75g 5.6 7.8 Africa121,160 Nigeria- no standard 2021 Mixed Mixed SEMDSA (not approved by OB group) 2007 Universal One-step 75g 5.1 10.0 8.5 1 IADPSG 2010 a. Abbreviations: World Health Organization=WHO; American College of Obstetricians and Gynecologists = ACOG; American Diabetes Association = ADA; Diabetes Canada = DC (formerly, Canadian Diabetes Association or CDA); Society of Obstetricians and Gynecologists of Canada = SOGC; European Association for the Study of Diabetes = EASD; National Institute for Health and Care Excellence = NICE; Australasia Diabetes in Pregnancy Study Group = ADIPS; New Zealand Society for the Study of Diabetes = NZSSD; Diabetes in Pregnancy Study Group of India = DIPSI; Japan Society of Obstetricians and Gynecologists= JSOG; Sociedade Brasileira de Diabetes/Brazilian Society of Diabetes = BSD; Society for Endocrinology, Metabolism and Diabetes of South Africa= SEMDSA 28 3 A VALIDATION OF USING INSURANCE BILLINGS DATA TO ASSESS ANTENATAL SCREENING DATA AGAINST MEDICAL RECORDS DATA To obtain data on gestational diabetes screening in BC, I planned to use billings data from BC’s Medical Services Plan. However, before billings data could be used in applied epidemiologic research, it was critical to understand their accuracy. Billings data are not collected for research purposes, and in some contexts, may not accurately define clinical conditions of interest. This study was the first of which we are aware to validate the use of antenatal screening and gestational diabetes screening billings data for epidemiologic research in Canada. Our finding that laboratory billings data for gestational diabetes screening and other antenatal screening tests have excellent validity against a gold standard of medical chart review opens up new avenues for researchers to advance our understanding of gestational diabetes. A version of this chapter is in In-press in the journal Epidemiology. 3.1 Synopsis Background: Prevalence statistics for pregnancy complications identified through screening such as gestational diabetes usually assume universal screening. However, rates of screening completion in pregnancy are not available in many birth registries or hospital databases. We validated screening test completion by comparing public insurance laboratory and radiology billing records with medical records at three hospitals in British Columbia, Canada. Methods: We abstracted a random sample of 140 medical records (2014-2019), and successfully linked 127 to valid provincial insurance billings and maternal-newborn registry data. Billing records for gestational diabetes screening, any ultrasound before 14 weeks gestational age, and Group B streptococcus screening during each pregnancy were compared to the gold standard of medical records by calculating sensitivity and specificity, positive predictive value, negative predictive value and prevalence with 95% confidence intervals (CIs). Results: Gestational diabetes screening (screened v. unscreened) in billing records had a high sensitivity (98% [95% CI: 93, 100]) and specificity (>99% [95% CI: 86, 100]). The use of specific glucose screening approaches (two-step v. one-step) were also well characterized by billing data. Other tests showed high sensitivity (ultrasound 97% [95% CI: 92, 99]; Group B streptococcus 96% [95%CI: 89, 99]) but lower negative predictive values (ultrasound 64% [95%CI: 33, 99]; Group B streptococcus 70% [95%CI: 40, 89]). Lower negative predictive values were due to the high prevalence of these screening tests in our sample. 29 Conclusions: Laboratory and radiology insurance billing codes accurately identified those who completed routine antenatal screening tests with relatively low false positive rates. 3.2 Background Routine health care during pregnancy involves a number of screening tests and procedures1 to optimize the health of the woman or pregnant person, and the developing fetus. Three recommended procedures and tests include: a first trimester ultrasound (<14 weeks) for gestational age estimation, a gestational diabetes screening test (usually between 24-28 weeks)8 and a Group B streptococcus test (recommended between 35-37 weeks of a term pregnancy).2 Most data sources for monitoring perinatal complications use a ‘chart by exception’ approach whereby individuals are assumed to not have a given condition unless specifically noted in their medical record. For conditions in which case status is established through screening, this approach makes the assumption that the population is 100% screened. However, screening uptake decreases when tests are uncomfortable, time-consuming, or controversial, or when individuals experience barriers to health care.85,124,141,161 As a result, if rates of a disease or condition ascertained by screening increase or decrease, public health officials cannot determine if this change is an artefact of changes in screening practices or a true change in disease prevalence. Accurate data on screening rates during pregnancy is critical to disentangle these factors. Recommendations for gestational diabetes screening, in particular among antenatal tests, have shifted repeatedly.42,78,105 Furthermore, rates of gestational diabetes have been increasing over time, for reasons which are not clearly understood.14,42,162 The reported prevalence of gestational diabetes assumes universal screening; however screening rates differ among populations.124,163,164 As a result, it is unclear whether current prevalence estimates are accurately reflecting population-level screening rates. We hypothesized that billing records from radiology and laboratory services with test-specific codes could potentially be used to monitor antenatal screening rates for routinely recommended pregnancy screening tests. Billing records have been previously used to characterize cancer screenings among non-pregnant groups165,166, for infectious disease screening during pregnancy167–169 and for post-partum diabetes screening.170 Billings from a commercial claims database in the United States have been used to characterize guideline-based routine antenatal screening tests; however, no validation of this method was cited.161 Therefore, we compared administratively-collected insurance billings data on the completion of three recommended antepartum screening tests conducted across different trimesters of pregnancy (1st trimester ultrasound, gestational diabetes screening and a Group B streptococcus test) with medical records in British Columbia, Canada to validate the use of billings records to characterize these antepartum screening tests. 30 3.3 Methods 3.3.1 Data 3.3.1.1 Medical records We obtained a stratified random sample of 140 medical records from births at 3 hospitals in British Columbia (BC), Canada from Jan 1, 2014 to Dec 31, 2019. This included two of five health regions in the province, and two high-volume urban teaching hospitals and one small rural hospital. The number of records was based on being able to calculate a 95% confidence interval with a precision of +/- 5% for an estimated prevalence of 90% for screening. Hospitals were selected to provide variability in case-mix, region and demographics while limiting to two health regions for feasibility. The requested records were evenly stratified by health care professional type at delivery (either Registered Midwife, Family Practice Physician, or Obstetrician) (Appendix A, Antenatal health care professional type coding). Data abstraction was performed by a BC Registered Midwife. This project was approved by the University of British Columbia Research Ethics Board (#H20-00741). 3.3.1.2 Administrative data Population Data BC is a multi-university data platform that provides researchers with access to population-level datasets for British Columbia. For this study, we accessed claims data from BC’s Medical Services Plan (MSP) and the Perinatal Data Registry (a chart-abstracted, quality-controlled dataset of all births >500g and >20 weeks)171,172. The universal Medical Services Plan covers all eligible residents except for a small population who are insured by the Federal government (estimated at <5% of total BC population)173. Antenatal care visits, out-patient laboratory, and hospital laboratory services are billed to the public health insurance plan through a fee-for-service model. Abstracted medical records from this study were linked to the administrative datasets by personal health number and date of birth, and deidentified by Population Data BC prior to analysis. We extracted billing codes for each pregnancy in the medical records study group. 3.3.2 Measures 3.3.2.1 Antenatal screening tests We assessed screening test status (screened/unscreened/missing) from medical records and from billing codes (Table A.1) for: any ultrasound prior to 14 weeks,174 any gestational diabetes screening, and any Group B streptococcus test by vaginal-rectal swab.175 We also assessed screen completion for two gestational diabetes screening methods as per current Canadian guidelines: either a “two-step” approach 31 (“preferred”), or a “one-step” approach (“alternate”). A “two-step” involved a preliminary 50g glucose challenge test, and if positive, a second diagnostic test with a 75g 2-hour fasting glucose tolerance test. The “one-step” approach required a single 75g 2-hour fasting test (details in Appendix A. Additional information on administrative data sources and linkages in this study).8 For each medical record, we examined any antenatal, labor or delivery records, consultant notes, or laboratory or radiology reports to ascertain completion of screening tests (Figure A.2, Figure A.3, Figure A.5 and Figure A.6 for detailed decision trees used to derive screening test status from medical records). To resolve any discrepancies in the medical record, we used primary sources such as laboratory reports. If there were no data on a test, a clear indication that a test was declined, or no antenatal care, this was coded as “unscreened”. If we were unable to determine test status due to missing or incomplete records, then the test result was coded as “missing”. To derive screening status from administrative data using billing codes (Figure A.6 and Figure A.7), we searched each pregnancy’s billing claims for the presence of specific fee codes. One-step screening was assigned if only a 75g test was billed, or if the 75g service date was before other glucose testing. Two-step screening was assigned if there was only a 50g test or if the 50g service date was within 45 days before a 75g test. If there was no billing for the specific test, and health insurance registration was active for this pregnancy, then screening status was set to “unscreened”. If health insurance was inactive, then the screening status was set to “missing”. 3.3.2.2 Other clinical data We used clinical and demographic data from the BC Perinatal Data Registry176 (BC-PDR) to stratify the results by subgroups. Antenatal health care professional type (“Family practice physician”, “Obstetrician” or “Registered Midwife”, or “Unknown and/or <3 visits”) was defined using billing codes via a previously published method.177 Generally, this required a minimum of 3 antenatal care visits or one full trimester of care with the specified provider type (Table A.12, Table A.13). Additional data for pregnancy characteristics was also extracted from the medical records and from the BC-PDR (Table A.11). 3.4 Analysis We present basic clinical characteristics, antenatal screening test results, and completion rates as abstracted from medical records and derived from billing codes. Tests for association used chi-squared and Fisher’s exact tests (when expected cell counts <5). We considered medical records as the gold standard as this reflected the primary record of patient care and use of billings to ascertain screening data 32 was a new approach. Using medical records as the gold standard, we calculated the sensitivity (probability of being identified as screened using billings, given a true screened result in medical records), specificity (probability of being identified as and unscreened using billings, given a true unscreened result in medical records), negative predictive value (probability that being unscreened by billings represents a true ‘unscreened’ case), and positive predictive value (probability that being screened by billings represents a true ‘screened’ case).178 Sensitivity and specificity were based on non-missing data from medical records. We also calculated the true prevalence as the percentage of pregnancies screened (v unscreened) for each test from the medical records data. For all our calculations, we derived sampling weights to account for our stratified sampling scheme (by care provider type at delivery) and applied weights to all calculations to enable our results to reflect the hospital populations from which they were sampled (see Appendix A. Sample size, strata and sample weights). Results are presented with 95% confidence intervals179,180 and analyses were performed using R (v 4.0.5)181. To inform future probabilistic bias analyses, we also calculated validation parameters with 95% confidence intervals by subgroups: antenatal health care professional type177, body mass index (>30 kg/m2 v. <30 kg/m2), maternal age (<35 years v. 35+) and parity (nulliparous v. multiparous). This could be used to quantify the contribution of systematic errors to research based on screening data. To discuss how having data on screening completion could impact diagnostic prevalence, we calculated true prevalence (excluding those unscreened) compared to an observed prevalence (including all individuals in the denominator regardless of screening status) for both gestational diabetes diagnoses and Group B streptococcus positivity ascertained from medical records. Some additional obstetric variables captured in the perinatal data registry were also validated although this was not the primary focus of this manuscript as these have been previously validated elsewhere176. Additional variables were validated with similar approaches for categorical results and calculated intraclass correlation coefficients for continuous results (e.g., infant birthweight, gestational age at delivery, weeks on 1st trimester ultrasound). We also compared gestational diabetes diagnosis, treatment method (diet or medication), Group B strep positivity, delivery outcomes and care provider type as ascertained from the perinatal data compared to medical records. 3.5 Results While 140 medical records were abstracted, only 135 medical records were linked to administrative data (Figure A.7). Unlinked records resulted from inconsistencies with personal health numbers or dates of birth between data sources (identifiers). The final sample for calculating sensitivity, specificity, negative 33 and positive predictive values was further reduced due to missing data in either medical records or billings-based data. Eight records were missing billings-based screening data because of incomplete insurance status throughout the pregnancy (Figure A.1), leaving a maximum of 127 records for the validation analysis. Detailed ascertainment of screening test data (screened, unscreened, and missing) from medical records are shown in Figure A.2 to Figure A.5. Of interest, being classified as ‘unscreened’ was most frequent for gestational diabetes screening (n=16, of which 12 were ‘declined’), followed by Group B strep screening (n=10 records, of which 9 were scheduled cesarean deliveries), and 1st trimester ultrasound (n=8, 4 declined). Demographics for the full sample (n=135) and those with complete insurance status (n=127) were similar (Table A.2). The study population was predominantly multiparous (63%), and slightly over half received care from a family practice physician (58%) (Table 3.1). The prevalence of screening completion for all tests was similar using both methods (medical records v. billing codes) (Table 3.2). Billing codes-based screening results had high completion rates (>94%). Medical records indicated that gestational diabetes screening had been completed but did not include specific glucose screening tests results in 42% (53/125) of cases, therefore these were coded as missing for screening approach (one-step or two-step). As a result, gestational diabetes screening test type was more difficult to ascertain (57% completion) using medical records, but well-captured by billing codes (94% completion). Billing codes methods (Table 3.3) had a high sensitivity and positive predictive (values between 92 to >99% depending on the screening test). For gestational diabetes screening, specificity (95 to >99% for different tests) and negative predictive values (95 to >99%) were also high. Billing codes-based 1st trimester ultrasounds had a lower specificity (78% [95% CI 33, 99]) and negative predictive value (64% [95% CI 33, 99]) than other tests. However, the overall prevalence of first trimester ultrasounds was very high (>94%) which decreases negative predictive values. When examining validation results (Table A.3), sensitivity was generally high in all subgroups. Specificity in subgroups varied by screening test. Specificity for gestational diabetes screening test type was also high (>87 to 100%) for all subgroups. Screening prevalence (weighted) based on medical records suggested lower screening among multiparous individuals, and by antenatal care provider type; however, confidence intervals were overlapping in most cases. The risk of diagnosis of gestational diabetes in our validation sample among those screened was 19% [95% CI 11, 27] compared to an observed risk of 14% [95% CI 9, 23] in the full sample. The true risk of positivity for Group B streptococcus was 25% [95% CI 15, 34] compared to an observed risk of 21% [95% CI 13, 29]. 34 Additional validation of variables available from the BC-PDR for diagnosis of gestational diabetes and other obstetric characteristics are reported in the Appendices (Table A.6, Table A.7, Table A.9, Table A.10). Of note, completion rate for weight of pregnant person (kg) (50%) at hospital admission was particularly low in the perinatal data registry (BC-PDR), otherwise, completion was similar in the BC-PDR to medical records. Specificity of Group B streptococcus testing (screened v unscreened v missing) in the PDR was very low (16%). Completion of a 1st trimester ultrasound also had lower specificity (60%) but high sensitivity in the PDR compared to medical records. Gestational diabetes diagnosis showed 100% agreement across the two data sources; however, mode of treatment (medication or diet controlled) had lower sensitivity (92% for diet controlled, 85% for medication controlled) but high specificity (99%, both). Intraclass correlations for all continuous secondary variables we assessed were generally high (>90%) except for pre-pregnancy weight (kg) (ICC 56%). 3.6 Discussion In this study, routinely-collected laboratory and radiology billing codes data accurately identified screening test completion for three recommended antenatal screening tests. Billing codes also accurately identified test completion of either a one-step or a two-step gestational diabetes screening approach in a population where both approaches could be used as per national guidelines.42 These results support the use of billing codes data as a strategy to help monitor trends in screening-based pregnancy complications such as gestational diabetes, and better understand the impact that choice of screening approach has on apparent disease rates. For example, the observed risk of diagnosis of gestational diabetes in our sample was 14% using a chart-by-exception approach, whereas the true diagnostic risk appeared higher (19%) after accounting for screening status. The study was limited to delivery-based medical records from only three hospitals. However, billings for laboratory tests or radiology are likely consistent across the province since all facilities, both outpatient laboratories and hospitals, bill for these tests using fee-for-service billings.29 Other limitations include: record abstraction by a single individual and no data for race or ethnicity. Our sample was randomly allocated, used both paper and electronic records and we carefully assessed screening completion from records using detailed algorithms. Screening may be implemented differently in some subgroups or regions. For example, gestational diabetes risk can be assessed using non-standard methods such as doing 1 week of daily glucometer monitoring. This approach to screening would not be captured in billings data, therefore our study may not be generalizable to those screening situations. However, findings from this study should be generalizable to other health care systems where codes (e.g., Current procedural terminology CPT) identify billings for reimbursement and with similar screening practices. In a single-35 payor system as in Canada, it is also important to consider that billings data is limited to those with active insurance registration. Billing codes also performed well across subgroups by parity, antenatal care provider, pre-pregnancy obesity and age and did not display any clear directional biases across subgroups or test types. However, our subgroup analyses were restricted to small samples and billing codes may be less accurate in subgroups where private-pay options are accessed more frequently. In particular, assisted reproductive technologies are not covered by public insurance in British Columbia and may include early ultrasounds.182 Thus, billing records may underestimate the prevalence of ultrasound completion prior to 14 weeks gestation in some subgroups, and we noted a low specificity (50%) in those with older maternal age at delivery. Characterizing those who are unscreened is critical for estimating the true prevalence of antenatal conditions and complications that follow from screening. This approach could help identify populations with low screening uptake, target health professionals to improve the use shared decision-making and unbiased communication,183,184 address screening barriers, or provide culturally appropriate resources.185 Both barriers to care, as well as individual values and preferences can impact screening uptake. Of note, a recent survey study in British Columbia found that 305/2100 (14.5%) declined gestational diabetes screening in pregnancy.125 The prevalence of gestational diabetes is increasing in many jurisdictions, but it is unclear whether this represents a true increase in disease incidence or an artefact of changes in screening practices, since the screening approach (one-step v. two-step) impacts prevalence (with a one-step screening having a lower diagnostic threshold).42 This billing codes-based method provides a novel way to identify the impact of screening approach (one-step v two-step) on disease incidence where individual laboratory test data is not available. Given ongoing debate around best practices for gestational diabetes screening, one-step screening may have decreased during the COVID-19 pandemic, as this approach requires more in-person time in an outpatient laboratory.186 Use of billing codes to capture screening approaches could be an important assessment tool for further studies on the impact of practice changes on gestational diabetes outcomes. Overall, these results support the use of billing codes to capture completion of three antenatal screening tests, as well as two different approaches to screen for gestational diabetes. The method we developed and validated in this study could be used to reduce information and diagnostic bias in future studies that rely on gestational age ascertainment using early ultrasound, or in research on gestational diabetes or Group B streptococcus disease in newborns. 36 3.7 Tables Table 3.1 Population characteristics in a validation study of antenatal screening in BC, Canada 2014-2019 Characteristic Linked to administrative data N = 135 n (%) weighted (%)a Parity P0 54 (40%) (37%) P1-P3 76 (56%) (60%) P4 or more 5 (4%) (3%) Age of birthing person at delivery (years) < 24 14 (10%) (9.5%) 25-29 36 (27%) (28%) 30-34 51 (38%) (38%) 35-39 28 (21%) (20%) 40+ 6 (4%) (5%) Pre-pregnancy body mass index (BMI) (kg/m2) b <24.9 (Underweight or Normal) 59 (44%) (43%) 25-29.9 (Overweight) 30 (22%) (20%) 30+ (Obese I, II, III) 19 (14%) (15%) missing data 27 (20%) (22%) Neighbourhood income quintiles per personc lowest income 29 (21%) (23%) mid-low income 28 (21%) (20%) middle income 18 (13%) (16%) mid-high income 33 (24%) (24%) highest income 13 (10%) (9%) missing or NA 14 (10%) (10%) Antenatal health care professional typed Family practice physician 54 (42%) (58%) Registered Midwife 52 (40%) (23%) Obstetrician 23 (18%) (19%) Unknown or <2 antenatal health care visits 6 Multifetal pregnancy <5f (3%) Gestational age at delivery preterm (<37 weeks) 12 (7%) (8%) term (37 + weeks) 123 (93%) (92%) Mode of deliverye Cesarean 46 (34%) (35%) Operative vaginal 13 (10%) (11%) Spontaneous vaginal 76 (56%) (54%) a. % weighted by sampling weights for stratified design as described in Appendix A Sample size, strata and sample weights. b. Pre-pregnancy body mass index (BMI) was ascertained from medical records which record a pre-pregnancy weight and height. We calculated BMI and reported by categories as defined by the Institute of Medicine standards.38 c. Neighbourhood income quintiles (based on Statistics Canada data)39 represent the average income in the area (census tract). Census tracts were linked by the data stewards at the residential postal code level for the residence of the birthing person/mother. d. Antenatal health care professional type was defined using billing codes27 and required a minimum of 3 routine antenatal care visits with the specified provider type. Consultation visits were not included. Number of visits were assessed using counts of prenatal care fee-for-service billing codes by professional type. e. Mode of delivery, gestational age at delivery, parity, is reported from medical records. Age is reported from administrative data. f. Cell sizes <5 suppressed as per Perinatal Services BC policy 37 Table 3.2 Antenatal screening test outcomes for medical records and billing codes data in a validation study in British Columbia, Canada 2014-2019 Medical records data N = 135 Billing codes data N = 135 Differences in screen prevalence p-valuea Screening test n screened/total non-missing (screened % in sample) n screened/total non-missing (screened % in sample) First ultrasound at <14 weeks 114/123 (93%) 113/127 (89%) 0.31 Completion rate 91 % 94 % GDM screened 114/131 (87%) 107/127 (84%) 0.53 Completion rate 97 % 94 % Two-step GDM screening approach 28/77 (36%) 53/127 (42%) 0.45 Completion rate 57 % 94 % One-step GDM screening approach 32/77 (42%) 54/127 (43%) 0.89 Completion rate 57 % 94 % Screened for GBS at term 116/130 (89%) 113/127 (89%) 0.95 Completion rate 96 % 94 % a. Pearson's Chi-squared test 38 Table 3.3 Antenatal screening test validation parameters for billing codes compared to medical records data in a validation study in British Columbia, Canada 2014-2019 Screening test Sensitivity Specificity Positive predictive value Negative predictive value Prevalence in medical records data Prevalence in billing codes data Ultrasound at <14 weeks gestation 97 (92, 99) 78 (33, 99) 99 (92, 99) 64 (33, 99) 94 (87, 98) 90 (83, 95) Gestational diabetes (GDM) screened 98 (93, 100) 100 (86, 100)a 100 (93, 100)a 88 (64, 97) 87 (79, 93) 84 (76, 90) Two-step GDM screening approach 100 (80, 100)a 95 (80, 100) 93 (74, 98) 100 (80, 100)a 40 (27, 53) 40 (31, 50) One-step GDM screening approach 92 (72, 99) 100 (80, 100)a 100 (71, 100) 95 (82, 100) 39 (27, 53) 44 (34, 54) Group B streptococcus test 96 (89, 99) 79 (47, 96) 97 (89, 99) 70 (40, 89) 88 (80, 94) 87 (79, 93) a. Confidence intervals reported with unweighted data because survey-weighted methods did not provide valid estimates due to 100% agreement. 39 4 TRENDS IN GESTATIONAL DIABETES SCREENING PRACTICES IN BRITISH COLUMBIA: A DESCRIPTIVE STUDY Having demonstrated that laboratory billing data can accurately capture gestational diabetes screening, we could then confidently use British Columbia's population-level billing records to explore time trends and variability of gestational diabetes screening across subgroups. This study was the first of which we are aware to present descriptive data on gestational diabetes screening across BC, both by time period and in the context of known policy changes. We showed differences in screening among population subgroups with particular regional differences. Descriptive epidemiology can generate new hypotheses to further advance our understanding of GDM screening patterns, uptake and barriers. These data are also important for public health surveillance in BC. 4.1 Synopsis Background: In October 2010, British Columbia shifted to recommending gestational diabetes screening use a one-step approach for all pregnant people. However, the province retained availability of a two-step approach. The shift to one-step screening was to align with new international guidelines and was expected to increase prevalence of GDM since the diagnostic threshold was lower than using a two-step approach. Later, in 2013, national guidelines recommended one-step as an alternate method only. The impact of these guideline and policy changes on rates of one-step screening is unknown and could differ by region or obstetric risk factors. Therefore, we assessed trends in one-step gestational diabetes screening over the study period and by subgroups. Methods: We conducted a descriptive study using de-identified linked perinatal and laboratory billing data. We included all pregnancies, delivered after 28 weeks gestation with screening dates from June 1 2004 to May 31, 2019. We assessed screening method prevalence with 95% Confidence Intervals, plotted time trends, and examined risks in subgroups and by region. Results: After BC’s policy change in 2010, use of one-step screening increased sharply from 2.0% (95% CI 1.9, 2.0) to a peak of 53.9% (95% CI 53.4, 54.5) in 2013. Following the Diabetes Canada 2013 update, one-step screening decreased to 39.3% (95% CI 38.8, 39.8) in 2015. Higher use was observed for people with risk factors and in the southwest, urban regions. Interpretation: One-step gestational diabetes screening uptake increased rapidly but did not reach 100%, despite provincial guidelines. Current gestational diabetes screening in BC demonstrates higher use of one-step screening among people with risk factors but there are strong regional disparities in screening practices. Since one-step screening increases diagnostic prevalence, more use of a one-step screening approach would require increased capacity for diabetes-associated treatment during pregnancy. 40 4.2 Background Gestational diabetes is defined as any glucose intolerance resulting in a transient hyperglycemia with first onset during pregnancy8 but specific thresholds to use for diagnosis are a subject of persistent debate.39 Gestational diabetes is one of the most commonly diagnosed complications of pregnancy but its prevalence varies from 2 to 40%,13 in part, based on screening and diagnostic practices in a particular region. Following the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study in 200833, the International Association of Diabetes in Pregnancy Study Group (IADPSG) released new guidelines38 recommending a one-step 75g diagnostic test for all pregnant people. In subsequent years, the IADPSG guidelines have been the subject of much controversy15,187 as there is clear evidence that this increases prevalence by up to 2-fold.15 Part of this debate stems from a desire to balance possible harms89,144 and system-level costs117 of diagnosing and treating a higher number of people with milder hyperglycemia against the potential reduction of some perinatal risks.33 In British Columbia, in October 2010, the Province switched from using a two-step screening approach to recommending a one-step approach using the IADPSG criteria for all pregnant people.105 However, two-step screening was still available at the patients’ or providers’ preference and the extent to which this policy recommendation (for one-step) was followed is not known. No Canadian guidelines or standards at this time specified one-step screening.42 In April 2013, Diabetes Canada revised their guidelines (DC-2013) with two-step screening as the “preferred” option (using updated set of thresholds) and one-step as an “alternative” (using IADPSG criteria).42,105 In July 2016, the Society of Obstetricians and Gynecologists of Canada updated their guidelines (SOGC-2016) for the first time since 2002 to align with the 2013 Diabetes Canada guidelines. Thus, there were three policy and guideline changes that may have impacted gestational diabetes screening practices in British Columbia between 2010 and 2016. Screening for gestational diabetes is not captured by perinatal data registries in this province, or in most jurisdictions, thus screening patterns are not well characterized. Understanding screening use, especially in response to guideline changes, could provide critical insights about system or individual-level barriers to care, provider practice patterns and/or opportunities for education or outreach. Therefore, we assessed the uptake of one-step screening across BC by region, health care professional type and other patient subgroups. 41 4.3 Methods 4.3.1 Data source We obtained data via Population Data BC, a multi-university platform50 that provides researchers with access to deidentified and linked population-level administrative datasets for British Columbia. For this study, we accessed insurance billing records from BC Medical Services Plan (MSP), chart-abstracted perinatal data from the BC Perinatal Data Registry176 (pregnancies resulting in liveborn infants or still-births >20 weeks and >500g), birth certificate data, and the MSP consolidation file that links individuals to census-derived data.50 Using MSP billing records, we defined active health insurance status if active coverage was maintained for over 90% of the duration of the pregnancy. The study population was restricted to linked pregnancies (> 28 weeks gestational age at birth), birthing parents or mothers who resided within the province, who had active health insurance, no pre-pregnancy diabetes, were screened for gestational diabetes and whose first prenatal care visit (by billings) occurred prior to the 7th month of pregnancy. We restricted to active provincial health insurance because our method to assess gestational diabetes screening required complete billings data for the pregnancy. The study population included pregnant individuals whose gestational diabetes screening test occurred between June 1, 2004 and May 31, 2019. 4.3.2 Measures Primary outcomes were gestational diabetes screening with either a one-step, two-step or an ‘other glucose test’ approach. A one-step approach was defined by billings for only a diagnostic glucose test which did not occur within 45 days after a glucose screening test billing. The timing limitation was to exclude cases where an early glucose test was included with a prenatal blood panel as this “early” test is not part of the usual second trimester (24-28 weeks) gestational diabetes screening. A two-step approach was defined by the presence of any billing for a gestational diabetes glucose screening test. An ‘other glucose test’ was identified if neither a one-step or a two-step was coded, and billing records for either a HbA1c or a random plasma glucose test were present. Our method to characterize gestational diabetes screening was previously validated against medical records with high sensitivity and specificity (Chapter 3). We obtained individual-level obstetric and demographic characteristics from the BC Perinatal Data Registry50,176 for parity (P0, P1-3, P4 or more), age at delivery (less than 25 years, 25-34 years, 35+ years), pre-pregnancy body mass index (in kg/m2, by Institute of Medicine criteria54), a measure of low/moderate medical and obstetric risk (yes v. no),177 local health region (rural v urban) and planned 42 home births (yes v. no). The risk variable was based on criteria derived by McRae177 with the inclusion of no prior adverse fetal/neonatal outcome (no prior congenital anomaly, neonatal death or stillbirth). Using antenatal billings from MSP, we derived prenatal care utilization using the Adequacy of Prenatal Care Utilization Index criteria177,188 and the antenatal health care professional (HCP) type during prenatal care (“Family practice physician”, “Obstetrician”, “Registered Midwife” or “missing”) using a previously developed approach.177 The HCP criteria required a minimum of three antenatal care visits with that HCP type and was intended to represent care provided during the time gestational diabetes screening would normally occur (24-28 weeks). The “missing” group included those with a low number of antenatal care visits (<3 visits billed or <1 full trimester of midwifery care) or who could not be assigned. We also classified birth region of mother/birthing person (Canada or USA v. Asia or Arab countries v. All others) using country indicated on the infant birth certificate.50 We classified region of residence (mapped by Population Data BC at the postal code level) of birthing person at the local health area (LHA) level in four groups to represent different levels of access to health services (Metro Vancouver or Victoria v. Northern v. Other southern cities v. Rest of province). Local health areas are geographic regions defined by the BC Ministry of Health. The “Other southern cities” group included medium sized cities outside of the Vancouver/Victoria area that also have a tertiary or regional hospital located in their city. The “Northern” group included all of the northern health region. We also reported neighbourhood income quintiles (based on Statistics Canada)50 representing the average income in the area (census tract). Rural (v. urban) was indicated by LHA having a population < 10,000 people. Last, we identified individuals with prior pregnancies in the study cohort and created a variable for the current pregnancy that represented a history of a gestational diabetes diagnoses for any prior pregnancy (any history of GDM, no previous GDM, unknown). Because this could only be assessed for a subset of pregnancies who had a prior delivery in BC during the study years, we maintained a separate category for those who could not be assessed. 4.3.3 Statistical analysis We calculated screening rates with 95% confidence intervals for each gestational diabetes screening approach and for those unscreened, in the whole study population and among subgroups defined by individual-level, health care systems and regional characteristics of interest. Monthly and yearly rates were defined using the date of the glucose test of interest for the identified screening approach. Thus, plots are based on the date of the first screening test for gestational diabetes. Using monthly rates, we plotted time trends for all screening approaches. We plotted annual one-step screening rates in subgroups by parity, age, body mass index, health care professional type, health region, 43 rural status and antepartum risk. Yearly rates were calculated for June 1 to May 31 in each year to report annual rates from the start and end dates of the study. On all plots, we also indicated dates of three policy and guideline changes: a BC-only policy change to using a one-step approach (October 2010)105, the Diabetes Canada 2013 guidelines (April 2013)189 and the SOGC 2016 guidelines (July 2016)152. We report prevalence rates of the one-step approach in four time periods relative to each guideline change and mapped regional trends at the Health Services Delivery Area level. Statistical analysis and mapping were done using R 4.0.6.181 Additional descriptive data for rates of gestational diabetes diagnosis and treatment, by screening approach, are reported from 2011 after one-step screening was included. We also mapped screening and diagnoses rates by health region. Sensitivity analyses were undertaken to confirm that descriptive results were not substantially altered after adjusting for multiple confounders. We also modeled univariate and adjusted relative risks (RR) for one-step gestational diabetes screening for each subgroup characteristic using a modified Poisson binomial190 regression approach. 4.4 Results A total of 525,720 pregnant people were screened for gestational diabetes after excluding 51,021 (8.8%) who were classified as unscreened (Table 4.1, Table B.1). Overall screening prevalence with any glucose test was 91.2% (95%CI 91.1, 91.2) and increased over the study period (from 89% to 95%). Screening prevalence was lower for those with high parity (P4 or more) (77.5%), inadequate antepartum care (77%) or who had midwife-led care (80%) (Table 4.1). Among those screened, prevalence of the two-step method was 69.4% (95%CI 69.2, 69.5), prevalence of the one-step method was 25.8% (95%CI 25.6, 25.9) and 4.9% (95%CI 4.8, 4.9) for other glucose methods (Table 4.1). Among all screened pregnancies, one-step screening uptake was lowest in the Northern region (4.1%), among rural residents (11.1%) and younger people (< 25 years old at delivery) (15.8%) (Table B.4). By contrast, one-step screening uptake was high for individuals with known risk factors for gestational diabetes: specifically, a previous diagnosis of gestational diabetes (51.1%), pre-pregnancy body mass index greater or equal to 35 kg/m2 (Obese II or III54) (34.3%), age > 35 years (30.8%), birthing parent born in Asia or Arabia (33.1%) and with moderate/high antepartum medical or obstetric risk (31.7%). An “other glucose test” was used more frequently for parous people (15.4% for P4 or more), midwife-led care (13.5%), inadequate prenatal care (11.6%) or planned home births (26.5%). 44 After October 2010 (Figure 4.1) use of the one-step screening method increased from 2% to over 50% of the screens conducted by the beginning of 2012. After the DC-2013 guidelines were published (specifying one-step an “alternate” method), one-step screening rapidly declined from 60% in early 2013 to 40% by 2014 (Table B.5). After the SOGC 2016 guidelines, one-step further declined to 37%. Following both guidelines publication dates, there appeared to be a 4-month delay before screening methods changed. One-step screening by subgroups revealed differences over time (Figure 4.2, Table 4.1). During the initial time period after BC’s policy change, there was less difference in uptake of one-step screening across risk strata. However, after the DC-2013 report was released, one-step uptake remained highest for those with risk factors for gestational diabetes (prior GDM, older age, higher pre-pregnancy BMI, medical risk (Figure B.3, Figure B.4, Figure B.5, Figure B.6, Figure B.7, Figure B.8). Irrespective of any changes in guideline, one-step screening was low across all subgroups in the northern region (~2-6%). All health care professional groups increased use of a one-step approach following the BC policy change but use only remained high (>50%) among obstetricians after the DC-2013 and SOGC-2016 guideline changes (Figure B.3). By 2013, (Figure 4.2) one-step screening was highest (66%) in the large urban cities (Vancouver and Victoria), lower in the other Southern cities and rural areas (33%) and lowest (3%) in the Northern region. Subgroups within regions showed similar trends to the full population relative to the baseline uptake in each region. The only characteristic that had substantially higher uptake of one-step screening across all regions was a prior history of gestational diabetes. Maps showed similar trends and that rates of diagnosis were also highest in the southern, urban regions (Figure B.10 and Figure B.11). Multiple regression analyses did not alter overall findings (Table B.2). Among those screened with a one-step method, 18% were diagnosed with gestational diabetes compared to 9% of those screened with a two-step approach. (Table B.3 and Figure B.9) 4.5 Interpretation Following a 2010 province-wide policy that recommended one-step screening approach for all pregnant people,105 only about 50% of eligible pregnancies were screened using this approach by 2012. Subsequently, the 2013 Diabetes Canada guideline countered the BC policy by labelling two-step as a “preferred” approach compared to one-step as an “alternate”. This led to a rapid decline of one-step screening to 40% of eligible people, and a trend towards a risk-based implementation of this screening option. The northern region had low uptake of one-step screening across all time periods, compared with high uptake in the urban centers (Vancouver and Victoria) and moderate uptake in the rest of BC. 45 Over the study period, 92% of pregnant people completed at least one glucose test during pregnancy and could be considered screened for gestational diabetes. We observed lower completion of any testing for Northern and rural residents, suggesting that these groups may be experiencing unique barriers compared to the rest of the province. Screening completion was also lower among midwife-led care. This could reflect both the low obstetric risk status of individuals under midwifery care and also the impact of a model of care which emphasizes principles of informed choice.132 Practice guidelines from midwifery organizations also have subtle differences in language (offer screening v should be screened) and content which may impact how care providers counsel clients on this topic.134,152,191 From October 2010 to March 2013, while a single approach (one-step) was recommended in BC, screening rates were similar across subgroups (BMI, age, risk composite, health care professional). In regions where one-step was implemented, most individuals were screened in accordance with the BC-2010 change, and personal or health care professional recommendations (based on risk status or other factors) may have had less effect on a choice of screening approach. However, the BC-2010 change had no/little impact in the northern region, suggesting that this change was generally not applied in this health region. Prior research has shown that patient experience89 and health system impacts/costs40 differ for a one-step, two-step or ‘other’ screening approach. Rural BC also experienced decreased access to prenatal care services.192 Perhaps rural or northern laboratories could not offer the longer, more involved one-step test as a routine option. Alternately, providers or pregnant people may prefer the two-step approach because the initial screening test is relatively brief. Given that northerners and rural people often have to travel long distances to/from health care services,193 a shorter test duration may be important in promoting any screening. Between 20-30% of people report significant side effects and inability to complete screening due to nausea or emesis from the glucose load, fainting or discomfort from repeated blood samples.124,127 We found rates of one-step screening, a test which is generally more invasive and time-consuming than a two-step approach, were low for those with high parity. People who were previously screened for gestational diabetes and who screened negative, could be choosing, in collaboration with their physician or midwife, screening approaches based on their presumably ‘lower risk’ status. During our study period, use of a random plasma glucose or a HbA1c (‘other glucose tests’) for gestational diabetes screening was not a recommended method. Thus, use of this screening approach is likely a result of patient or health care professional choice. Our findings of elevated rates of non-standard gestational diabetes screening for high parity, midwifery clients and planned home births suggest these 46 groups may be engaging with midwifery principles of informed choice in their prenatal care.194 There are more people under midwifery care who decline usual care195,196. Women and birthing people who plan a midwife-attended birth at home, rather than at the hospital, may want fewer interventions and tests in prenatal care, and in labor.197 Of note, despite the fact that an alternate screening was not standard during our study period, the SOGC recently endorsed the use of only a random plasma glucose or a HbA1c if health system resources are limited because of the COVID-19 pandemic.87 4.5.1 Limitations Specific data on barriers to care (i.e., travel distance, access to laboratory services, individual-level socio-economic indicators) or provider or patient decision-making were not available in our data sources and suggest some areas for future study. Finally, we did not have information on race or ethnicity thus we cannot explore potential associations between screening and racialized care. 4.5.2 Conclusion Our study demonstrates how changes in health policies and practice guidelines impacted gestational diabetes screening practices. Public health should be aware that changes in guidelines and policies may exacerbate rural-urban disparities in health care services. Gestational diabetes prevalence increases when more people are screened with a one-step v. a two-step screening approach.86 Thus, characterizing screening practices is critically important in understanding changes in gestational diabetes prevalence across BC. 47 4.6 Tables Table 4.1 Prevalence of any glucose test v no test (unscreened) by subgroups and time periods in British Columbia, Canada, for glucose test dates or 28th week of pregnancy between June 1, 2004 and May 31, 2019 Characteristic Any glucose screening test completed N = 525,720 % (95% CI) No glucose test (unscreened) N = 51,021 % (95% CI) All pregnancies 91.2% (91.1, 91.2) 8.8% (8.8, 8.9) Time period for guidelines on GDM screening Period 1 (Jun 1, 2004 – Sep 30, 2010) – Two-step 88.8% (88.6, 88.9) 11.2% (11.1, 11.4) Period 2 (Oct 1, 2010 – Mar 31, 2013) – BC 2010 one-step IADPSG 90.5% (90.3, 90.6) 9.5% (9.4, 9.7) Period 3 (Apr 1, 2013 – Jun 30, 2016) – DC 2013 guidelines 92.6% (92.5, 92.8) 7.4% (7.2, 7.5) Period 4 (Jul 1, 2016 – May 31, 2019) – SOGC 2016 guidelines 95.1% (95.0, 95.3) 4.9% (4.7, 5.0) Paritya P0 93.4% (93.4, 93.5) 6.6% (6.5, 6.6) P1-P3 89.6% (89.5, 89.7) 10.4% (10.3, 10.5) P4 or more 77.5% (76.7, 78.3) 22.5% (21.7, 23.3) Age of birthing person/mother (years) less than 25 87.7% (87.4, 87.9) 12.3% (12.1, 12.6) 25-34 91.6% (91.5, 91.7) 8.4% (8.3, 8.5) 35+ 91.9% (91.8, 92.1) 8.1% (7.9, 8.2) Pre-pregnancy body mass index (kg/m2)54 Under or normal (<24.9) 90.0% (89.8, 90.1) 10.0% (9.9, 10.2) Overweight (25.0-29.9) 93.6% (93.5, 93.8) 6.4% (6.2, 6.5) Obese I (30.0-34.9) 94.9% (94.7, 95.2) 5.1% (4.8, 5.3) Obese II & III (>=35.0) 96.4% (96.1, 96.6) 3.6% (3.4, 3.9) Missing data 90.2% (90.1, 90.4) 9.8% (9.6, 9.9) Region of birth of mother/birthing parentb Canada or USA 89.4% (89.3, 89.5) 10.6% (10.5, 10.7) Asia or Arabia 96.9% (96.8, 97.0) 3.1% (3.0, 3.2) All other regions 90.7% (90.4, 90.9) 9.3% (9.1, 9.6) Antepartum medical or obstetric riskc No/low risk 91.1% (91.0, 91.1) 8.9% (8.9, 9.0) Moderate/high risk 92.1% (91.8, 92.4) 7.9% (7.6, 8.2) History of gestational diabetes in previous pregnancy no 93.5% (93.4, 93.6) 6.5% (6.4, 6.6) no prior pregnancy in data 88.8% (88.7, 89.0) 11.2% (11.0, 11.3) yes 95.5% (95.1, 95.8) 4.5% (4.2, 4.9) Antenatal health care professional type177 Family practice 93.4% (93.3, 93.5) 6.6% (6.5, 6.7) Registered Midwife 79.9% (79.7, 80.2) 20.1% (19.8, 20.3) Obstetrician 95.2% (95.1, 95.4) 4.8% (4.6, 4.9) Missing or <2 antenatal visits 51.4% (49.7, 53.2) 48.6% (46.8, 50.3) Planned home birth Planned hospital (all births) 92.1% (92.0, 92.1) 7.9% (7.9, 8.0) Planned home (only available for RM care) 64.8% (64.1, 65.5) 35.2% (34.5, 35.9) Adequacy of prenatal care utilization index188 Adequate Plus 95.4% (95.2, 95.5) 4.6% (4.5, 4.8) Adequate 93.7% (93.6, 93.8) 6.3% (6.2, 6.4) Intermediate 88.2% (88.0, 88.3) 11.8% (11.7, 12.0) Inadequate 76.8% (76.4, 77.2) 23.2% (22.8, 23.6) Region of residence of birthing person/motherd Metro Vancouver or Victoria 92.2% (92.2, 92.3) 7.8% (7.7, 7.8) Northern region 87.7% (87.4, 88.0) 12.3% (12.0, 12.6) Other southern cities 90.2% (90.0, 90.4) 9.8% (9.6, 10.0) Rest of province 87.9% (87.6, 88.2) 12.1% (11.8, 12.4) 48 Characteristic Any glucose screening test completed N = 525,720 % (95% CI) No glucose test (unscreened) N = 51,021 % (95% CI) Rural or urban residence by local health area regione Urban 91.2% (91.2, 91.3) 8.8% (8.7, 8.8) Rural 88.3% (87.8, 88.7) 11.7% (11.3, 12.2) Neighbourhood income quintiles per personf lowest income quintile 91.0% (90.8, 91.1) 9.0% (8.9, 9.2) mid-low income quintile 91.8% (91.7, 92.0) 8.2% (8.0, 8.3) middle income quintile 91.4% (91.3, 91.6) 8.6% (8.4, 8.7) mid-high income quintile 91.5% (91.3, 91.6) 8.5% (8.4, 8.7) highest income quintile 90.1% (89.9, 90.3) 9.9% (9.7, 10.1) missing or NA 87.7% (87.0, 88.4) 12.3% (11.6, 13.0) a. Missing parity for n=17 pregnancies. b. Data obtained from infant birth certificate. Missing data for n=2423 (<1%) pregnancies. c. Antepartum medical or obstetric risk defined using methods proposed by McRae et al. with the addition of a history of fetal complications from BC-PDR data (prior neonatal death, stillbirth or anomaly). Full definition includes: CIHI discharge table ICD-10 codes: O991 O994 O99803/04/09 O101-4 O109 O266 O981 O984-9 O360 O361 and PDR: Prior prescription for anti-hypertensive medication, prior neonatal death, anomaly, stillbirth or 2 or more prior cesarean births. d. Regions derived as follows: Metro Vancouver or Victoria includes all local health areas within the greater Vancouver and Victoria metropolitan areas; Northern region includes all of the Northern health region; Other southern cities included all cities with regional or tertiary hospitals located in the city; Rest of province = all other local health regions. e. Rural (v. urban) was indicated by LHA having a population < 10,000 people. f. Neighbourhood income quintiles (based on Statistics Canada)50 represent the average income in the area (census tract). 49 Table 4.2 Prevalence of three gestational diabetes screening approaches among subgroups and time periods in British Columbia, Canada for glucose test dates from June 1, 2004 and May 31, 2019 (n=525,720) Characteristic One-step N = 135,427 % (95% CI) Two-step N = 364,698 % (95% CI) Other glucose test N = 25,595 % (95% CI) All pregnancies 25.8% (25.6, 25.9) 69.4% (69.2, 69.5) 4.9% (4.8, 4.9) Time period for guidelines on GDM screening Period 1 (Jun 1, 2004 – Sep 30, 2010) – Two-step 2.0% (1.9, 2.1) 93.1% (93.0, 93.2) 4.9% (4.8, 5.0) Period 2 (Oct 1, 2010 – Mar 31, 2013) – BC 2010 one-step IADPSG 45.2% (44.9, 45.6) 50.2% (49.8, 50.5) 4.6% (4.5, 4.7) Period 3 (Apr 1, 2013 – Jun 30, 2016) – DC 2013 guidelines 42.8% (42.5, 43.1) 51.9% (51.6, 52.2) 5.4% (5.2, 5.5) Period 4 (Jul 1, 2016 – May 31, 2019) – SOGC 2016 guidelines 37.7% (37.4, 38.0) 57.7% (57.4, 58.0) 4.5% (4.4, 4.7) Paritya P0 26.6% (26.5, 26.8) 69.8% (69.6, 70.0) 3.5% (3.5, 3.6) P1-P3 25.1% (25.0, 25.3) 69.1% (68.9, 69.2) 5.8% (5.7, 5.9) P4 or more 19.5% (18.6, 20.4) 65.1% (64.0, 66.2) 15.4% (14.6, 16.2) Age of birthing person/mother (years) less than 25 15.8% (15.5, 16.0) 77.9% (77.6, 78.3) 6.3% (6.1, 6.5) 25-34 25.8% (25.7, 26.0) 69.6% (69.4, 69.8) 4.6% (4.5, 4.6) 35+ 30.8% (30.5, 31.0) 64.3% (64.1, 64.6) 4.9% (4.8, 5.0) Pre-pregnancy body mass index (kg/m2)54 Under or normal (<24.9) 26.3% (26.2, 26.5) 68.7% (68.6, 68.9) 4.9% (4.8, 5.0) Overweight (25.0-29.9) 28.5% (28.2, 28.8) 67.2% (66.9, 67.5) 4.3% (4.2, 4.4) Obese I (30.0-34.9) 30.7% (30.3, 31.2) 64.4% (63.9, 64.9) 4.8% (4.6, 5.1) Obese II & III (>=35.0) 34.3% (33.7, 34.9) 59.5% (58.8, 60.2) 6.2% (5.9, 6.5) Missing datab 20.4% (20.2, 20.6) 74.7% (74.4, 74.9) 4.9% (4.8, 5.0) Region of birth of mother/birthing parentc Canada or USA 22.9% (22.8, 23.1) 71.2% (71.0, 71.3) 5.9% (5.8, 6.0) Asia or Arabia 33.1% (32.8, 33.3) 64.9% (64.6, 65.1) 2.0% (2.0, 2.1) All other regions 27.7% (27.3, 28.1) 67.7% (67.2, 68.1) 4.6% (4.4, 4.8) Antepartum medical or obstetric riskd No/low risk 25.3% (25.1, 25.4) 70.0% (69.8, 70.1) 4.8% (4.7, 4.8) Moderate/high risk 31.7% (31.2, 32.1) 62.4% (61.9, 62.8) 5.9% (5.7, 6.2) History of gestational diabetes in previous pregnancy no 26.6% (26.4, 26.8) 69.9% (69.7, 70.1) 3.5% (3.4, 3.6) no prior pregnancy in data 23.4% (23.2, 23.5) 70.8% (70.7, 71.0) 5.8% (5.7, 5.9) yes 51.1% (50.3, 51.8) 38.7% (38.0, 39.4) 10.2% (9.8, 10.7) Antenatal health care professional type177 Family practice 22.1% (21.9, 22.2) 74.6% (74.5, 74.8) 3.3% (3.2, 3.4) Registered Midwife 32.2% (31.8, 32.5) 54.3% (53.9, 54.6) 13.6% (13.3, 13.8) Obstetrician 36.8% (36.4, 37.1) 60.2% (59.8, 60.5) 3.1% (2.9, 3.2) Missing or <2 antenatal visits 12.2% (10.7, 13.9) 60.3% (58.0, 62.7) 27.4% (25.3, 29.7) Planned home birth Planned hospital (all births) 25.8% (25.7, 25.9) 69.8% (69.7, 70.0) 4.3% (4.3, 4.4) Planned home (only available for RM care) 23.1% (22.3, 23.8) 50.4% (49.5, 51.3) 26.5% (25.7, 27.3) Adequacy of prenatal care utilization index188 Adequate Plus 28.4% (28.1, 28.7) 67.3% (67.0, 67.7) 4.3% (4.1, 4.4) Adequate 26.6% (26.4, 26.7) 69.7% (69.5, 69.9) 3.7% (3.6, 3.8) Intermediate 24.2% (24.0, 24.5) 69.8% (69.6, 70.0) 6.0% (5.9, 6.1) Inadequate 19.8% (19.3, 20.2) 68.6% (68.1, 69.1) 11.6% (11.3, 12.0) Region of residence of birthing person/mothere Metro Vancouver or Victoria 31.4% (31.3, 31.6) 64.4% (64.3, 64.6) 4.1% (4.1, 4.2) Northern region 4.1% (3.9, 4.3) 89.0% (88.7, 89.3) 6.9% (6.7, 7.2) Other southern cities 14.9% (14.6, 15.2) 78.3% (78.0, 78.7) 6.8% (6.6, 7.0) Rest of province 17.5% (17.2, 17.7) 76.5% (76.2, 76.8) 6.1% (5.9, 6.2) Rural or urban residence by local health area regionf Urban 26.2% (26.1, 26.4) 69.0% (68.8, 69.1) 4.8% (4.7, 4.9) Rural 11.1% (10.7, 11.6) 81.7% (81.1, 82.3) 7.1% (6.7, 7.5) 50 Characteristic One-step N = 135,427 % (95% CI) Two-step N = 364,698 % (95% CI) Other glucose test N = 25,595 % (95% CI) Neighbourhood income quintiles per persong lowest income quintile 25.5% (25.2, 25.7) 69.3% (69.0, 69.6) 5.3% (5.1, 5.4) mid-low income quintile 26.2% (25.9, 26.4) 69.1% (68.8, 69.4) 4.7% (4.6, 4.9) middle income quintile 26.3% (26.0, 26.6) 69.0% (68.7, 69.2) 4.7% (4.6, 4.9) mid-high income quintile 26.5% (26.2, 26.7) 68.9% (68.6, 69.2) 4.6% (4.5, 4.8) highest income quintile 24.0% (23.7, 24.3) 71.1% (70.8, 71.4) 4.9% (4.7, 5.0) missing or NA 25.0% (24.0, 26.1) 68.7% (67.6, 69.8) 6.3% (5.7, 6.9) a. Missing parity for n=18 pregnancies b. Missing pre-pregnancy body mass index data for n=135,789 pregnancies (26%), therefore this was considered as an independent category for reporting c. Data obtained from infant birth certificate. Missing data for n=1914 (<1%) pregnancies d. Antepartum medical or obstetric risk defined using methods proposed by McRae et al. with the addition of a history of fetal complications from BC-PDR data (prior neonatal death, stillbirth or anomaly). Full definition includes: CIHI discharge table ICD-10 codes: O991 O994 O99803/04/09 O101-4 O109 O266 O981 O984-9 O360 O361 and PDR: Prior prescription for anti-hypertensive medication, prior neonatal death, anomaly, stillbirth or 2 or more prior cesarean births e. Regions derived as follows: Metro Vancouver or Victoria includes all local health areas within the greater Vancouver and Victoria metropolitan areas; Northern region includes all of the Northern health region; Other southern cities included all cities with regional or tertiary hospitals located in the city; Rest of province = all other local health regions f. Rural (v. urban) was indicated by LHA having a population < 10,000 people. g. Neighbourhood income quintiles (based on Statistics Canada)50 represent the average income in the area (census tract). 51 4.7 Figures Figure 4.1 Monthly rates of gestational diabetes screening aggregated by month of screening tests, for a cohort of pregnancies in BC, Canada from June 2004 to May 2019 52 Figure 4.2 Rates of gestational diabetes screening using a one-step method (75g glucose test only and IADPSG criteria) in BC from Jun 1, 2004 to May 31, 2019 in subgroups 53 5 THE IMPACT OF CHANGES IN SCREENING COMPLETION, SCREENING METHODS AND POPULATION DEMOGRAPHICS ON THE INCREASE IN GESTATIONAL DIABETES Having demonstrated that laboratory billing records can accurately capture gestational diabetes screening and having characterized screening trends in BC, we could then confidently use British Columbia's population-level linked datasets to explore the reasons for the province's rising rate of gestational diabetes. The rate of gestational diabetes in BC is not only increasing, but is among the highest in the country. As we demonstrated in Chapter 4, use of a one-step screening method has also increased in BC. This study is the first of which we know to examine the attribution of screening and population characteristics to rising rates of gestational diabetes. Our findings of a spurious association between screening method changes and rising incidence of GDM are important for public health as well as for future research. 5.1 Synopsis Importance: Gestational diabetes is increasing in British Columbia, Canada, but the reasons for these increases are poorly understood. Objective: To examine the relative contribution of gestational diabetes screening practices (screening completion and methods) and population demographics to rises in gestational diabetes risk. Design: Retrospective, population-based cohort linking a provincial perinatal data registry with population-based laboratory billing records. Setting: British Columbia, Canada, 2005-2019. Participants: All pregnancies without pre-existing diabetes, delivered >28 weeks gestational age. Interventions or exposures: Gestational diabetes screening completion, screening method (one-step 75g glucose test or two-step approach [50g glucose screening test followed by a diagnostic test if screen positive]), and population-level risk factors including maternal age, pre-pregnancy BMI, and maternal country of birth. Main outcomes or measures: Predicted annual gestational diabetes risk (relative to baseline 2005) sequentially adjusted with modified Poisson binomial regression, for screening completion, screening method and risk factors. 54 Results: Gestational diabetes risk more than doubled from 7.2% in 2005 to 14.7% in 2019 (n=550,783 pregnancies). Screening completion increased from 87.2% in 2005 to 95.5% in 2019. Use of a one-step screening approach increased from 0% in 2005 to 40% by 2019. Crude risk of gestational diabetes increased by 2-fold (95% CI: 1.9 to 2.1) in 2019 (vs. 2005). The magnitude of this estimated increase lessened to 1.9 (95% CI: 1.8 to 2.0) after controlling for changes in screening completion and to 1.34 (95% CI: 1.3 to 1.4) after further controlling for changes in screening method. Changes in screening methods had the largest impact on the increase in risk. Further adjustment for population factors had a small impact (1.25-fold increase, 95% CI: 1.2 to 1.3). Conclusions or relevance: In this population-based cohort, most of the apparent increase in gestational diabetes prevalence was attributable to changes in screening practices (primarily changes in screening methods). Changing population factors were not a large contributor to the increase. Accurate data on screening completion and methods are critical for population health surveillance efforts to monitor trends in gestational diabetes and identify true increases in risk. 5.2 Background Gestational diabetes is increasing worldwide.15 In the U.S., gestational diabetes increased from 4.8% in 2011 to 6.4% in 2019, a 33% increase in less than a decade.16 Although it is speculated that the rise in risk may be due to changing demographics and lifestyle (such as increasing maternal age, changes in racial/ethnic composition of the population, decreased physical activity and poor diet quality), increases have been observed in all racial/ethnic groups in the U.S. after adjusting for increases in maternal age over time.16,198 The reasons for the continued increased risk of diagnosis remain poorly understood. An initial diagnosis of gestational diabetes is dependant on completion of an antenatal glucose screening test. Most population-based studies of gestational diabetes, however, lack data on screening completion. Therefore, the rise in gestational diabetes diagnoses could reflect increased uptake of screening. Further, glucose screening options have expanded in recent years and professional organizations differ on recommended methods.80 Two-step screening (a 1-hour-50 g glucose challenge test followed by a diagnostic test using either a 3-hour-100g or a 2-hour-75g oral glucose tolerance test if screened positive) is the most commonly used method in the U.S. and is recommended by the American College of Obstetricians and Gynecologists.80 More recently, one-step screening (a single 2-hour-75g oral glucose tolerance test using International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria) was recommended by the American Diabetes Association80 and as an “alternate” screening test in Canada.42 As one-step screening is more sensitive, increased use of this method is expected to increase the diagnostic prevalence of gestational diabetes.15 55 Whether rising risk of gestational diabetes are explained by changes in screening practices or changes in population characteristics remains unknown. Therefore, we studied relative contributions of screening completion, screening method and changing population-level characteristics to rising risk of gestational diabetes. 5.3 Methods 5.3.1 Data source We identified all births at or beyond 28 weeks gestation in the Canadian province of British Columbia (BC), using the population-based BC Perinatal Data Registry (BC-PDR) and included all pregnancies between July 1, 2004 and June 30, 2019 (by screening date or 7th month if unscreened). We linked these records with public health insurance billings (BC Medical Services Plan), vital statistics data and census-derived data via Population Data BC199 using provincial health numbers. We excluded individuals with pre-existing diabetes (0.6%), late antenatal care (after 7th month) and inactive insurance status for >10% of the pregnancy (by month)50. This study was approved by University of British Columbia Research Ethics Board (#H20-00741). 5.3.2 Measures Gestational diabetes diagnosis was defined by an ICD 10-CA (code O24.8) in the discharge summary of the delivery hospitalization and from the BC-PDR. We obtained screening completion and type of gestational diabetes screening test using billings data via a validated method. Screen completion was defined by completion of a recommended gestational diabetes test versus having no screening test(s) (unscreened or unknown). Screen method was either a two-step approach (50g glucose challenge test followed by a diagnostic test) or a one-step approach (75g oral glucose tolerance test with IADPSG criteria).38 In October 2010 in BC, policy changed to recommend a one-step screening for all pregnancies.105 We also examined population characteristics associated with gestational diabetes risk:176 parity, age at delivery, pre-pregnancy body mass index, multi-fetal pregnancy, pre-existing medical/obstetric conditions composite, mother’s country of birth (all Asian or Arabian peninsula v. all others v. Canada/USA), antenatal care by a midwife and “Inadequate” prenatal care using the Adequacy of Prenatal Care Utilization Index.200 56 5.3.3 Statistical analysis We modeled annual gestational diabetes risk using generalized linear regression with a modified Poisson binomial approach. We built four nested models, which were sequentially adjusted for potential determinants of gestational diabetes to understand the contribution of each to temporal trends in prevalence: • Model (1): temporal trend: adjusted for each year as fixed, independent predictor • Model (2): adjusted for year + screen completion (screened v unscreened) • Model (3): adjusted for year + screen completion + method (two-step v one-step) • Model (4): adjusted for year + screen completion + method + population characteristics To assess the contribution of screening completion, method and population characteristics to temporal trends, we used model coefficients to predict yearly risk with 95% confidence intervals with all characteristics fixed at 2005 mean levels, and compared these to the observed 2005 risk (Model 1) by calculating risk ratios (RR). Thus, if a model explained all of the year-to-year variability in gestational diabetes risk, then there we would expect no increase in the predicted risk for each year relative to that observed for 2005. Additional sensitivity analyses and characteristics of excluded cases are described in the Supplemental. 5.4 Results Gestational diabetes diagnoses increased from 7.2% in 2005 to 14.7% in 2019 (n=550,783 pregnancies) (Figure 5.1 and Table 5.1). Screening completion increased from 87% in 2005 to 96% in 2019. Screening method changed over the study period: use of one-step screening increased from 0% in 2005 to 51% in 2012, then decreased to 40% in 2019. Some population characteristics changed from 2005 to 2019: pre-pregnancy body mass index over 30 kg/m2 increased from 11% to 16%; age 35+ years at delivery increased from 22% to 29%; medical/obstetric complications increased from 7% to 8%; mothers birth location in Asia or Arab countries increased from 22% to 27%; midwifery care increased from 4% to 26%. Unadjusted models estimated a 2-fold increase (95% CI 1.9 to 2.1) in gestational diabetes risk for 2019 compared with 2005 (Figure 5.2, Table C.1). After accounting for the increase in screening completion over time (Model 2), gestational diabetes in 2019 remained 1.9-fold higher (95% CI 1.8 to 2.0). Screening method (Model 3) explained more of the yearly increase than any other factors (1.34-fold increase for 2019 v. 2005; 95% CI 1.3 to 1.4). Further adjustment for trends in population characteristics had only a small impact on rising risk (1.25-fold increase; 95% CI 1.2 to 1.3). None of the sensitivity analyses altered the overall results (Appendix C. Gestational diabetes study supplemental). 57 5.5 Discussion In this population-based cohort from British Columbia, Canada, the increase in gestational diabetes from 2005 to 2019 was explained primarily by changes in screening and not by temporal changes in population characteristics. Despite global concerns over increases in high body mass index, older maternal age, and obstetric risk factors, these were not associated with annual increases in gestational diabetes in BC. Recent studies have examined trends in gestational diabetes worldwide.16,45,198,201–203 In jurisdictions where screening methods were unchanged, risks were relatively stable201,202 and/or increases have been explained by population changes (e.g. body mass index, age, ethnicity). In jurisdictions with variable screening practices, risks of diagnosis doubled or tripled, even when controlled for population changes.16,45,198 These increases persisted across subgroups.16,204 This study is limited by a lack of data on racialized/ethnic groups and incomplete data for body mass index. Strengths include having validated data for both gestational diabetes screening methods and completion in a large study population. Our findings lend support to the hypothesis that increases in the incidence of gestational diabetes may be spurious – caused by changes in screening practices rather than changes in true disease incidence. It is possible that our findings are not generalizable and that other regions have a true rise in rates; however, our study highlights the importance of having data on screening methods and completion to better understand the rising incidence of gestational diabetes in other jurisdictions. 58 5.6 Tables Table 5.1 Gestational diabetes diagnosis, screen completion, methods and population characteristics for selected years Gestational diabetes % (95% CIa) 2005 N = 33,340 2009 N = 37,702 2012 N = 37,158 2015 N = 37,089 2019 N = 36,414 Diagnosis 7.2% (6.9 to 7.5) 8.0% (7.8 to 8.3) 10.3% (10.0 to 10.7) 11.9% (11.6 to 12.3) 14.7% (14.3 to 15.0) Screening completion 87.2% (86.9 to 87.6) 89.0% (88.6 to 89.3) 90.2% (89.9 to 90.5) 92.3% (92.0 to 92.5) 95.5% (95.3 to 95.7) Screening method Two-step 87.2% (86.9 to 87.6) 89.0% (88.6 to 89.3) 44.7% (44.2 to 45.2) 54.0% (53.5 to 54.5) 57.8% (57.3 to 58.3) One-step (IADPSGb) 0.0% (0.0 to 0.0) 0.0% (0.0 to 0.0) 45.5% (45.0 to 46.0) 38.3% (37.8 to 38.8) 37.7% (37.2 to 38.2) Not screened/no data 12.8% (12.4 to 13.1) 11.0% (10.7 to 11.4) 9.8% (9.5 to 10.1) 7.7% (7.5 to 8.0) 4.5% (4.3 to 4.7) One step (IADPSG criteria) (among screened only) 0.0% (0.0 to 0.0) 0.0% (0.0 to 0.0) 50.5% (49.9 to 51.0) 41.5% (41.0 to 42.0) 39.5% (39.0 to 40.0) Population characteristics % (95% CI) Nulliparous 45.7% (45.1 to 46.2) 46.4% (45.9 to 46.9) 46.1% (45.6 to 46.6) 46.8% (46.3 to 47.3) 46.4% (45.9 to 46.9) Pre-pregnancy body mass index (kg/m2) (non-missing data) <24.9 68.3% (67.7 to 68.9) 66.2% (65.6 to 66.8) 66.1% (65.5 to 66.6) 65.1% (64.5 to 65.6) 60.4% (59.9 to 61.0) 25.0-29.9 20.3% (19.8 to 20.9) 21.2% (20.7 to 21.7) 21.0% (20.5 to 21.5) 21.2% (20.7 to 21.7) 23.8% (23.3 to 24.2) 30.0-34.9 7.1% (6.8 to 7.5) 7.9% (7.6 to 8.2) 8.1% (7.8 to 8.5) 8.4% (8.1 to 8.8) 9.6% (9.3 to 10.0) >35.0 4.2% (4.0 to 4.5) 4.7% (4.5 to 5.0) 4.8% (4.6 to 5.1) 5.3% (5.0 to 5.5) 6.2% (5.9 to 6.5) Missing body mass index 29.1% (28.6 to 29.6) 33.3% (32.8 to 33.8) 24.9% (24.4 to 25.3) 22.8% (22.4 to 23.2) 18.9% (18.5 to 19.4) Age of mother/birthing person at delivery Less than 25 years 17.3% (16.9 to 17.7) 16.1% (15.7 to 16.5) 13.2% (12.9 to 13.6) 11.1% (10.8 to 11.4) 8.4% (8.1 to 8.7) 25-34 years 60.7% (60.2 to 61.3) 60.7% (60.2 to 61.2) 63.0% (62.5 to 63.4) 64.1% (63.6 to 64.5) 62.5% (62.0 to 63.0) 35+ years 22.0% (21.6 to 22.4) 23.2% (22.7 to 23.6) 23.8% (23.4 to 24.3) 24.9% (24.4 to 25.3) 29.2% (28.7 to 29.6) Multifetal pregnancy (v. singleton) 1.5% (1.3 to 1.6) 1.5% (1.4 to 1.6) 1.6% (1.4 to 1.7) 1.5% (1.4 to 1.7) 1.4% (1.3 to 1.5) Medical/obstetric complications (composite)c 4.3% (4.1 to 4.5) 4.5% (4.3 to 4.7) 5.0% (4.7 to 5.2) 5.8% (5.6 to 6.1) 7.0% (6.7 to 7.2) Mother/birthing person’s region of birth (by country on infant birth certificate) All other regions 8.8% (8.5 to 9.1) 8.5% (8.3 to 8.8) 8.9% (8.6 to 9.2) 8.4% (8.1 to 8.7) 9.4% (9.1 to 9.7) Asia or Arabia 21.6% (21.1 to 22.0) 21.9% (21.5 to 22.3) 23.3% (22.9 to 23.8) 24.4% (23.9 to 24.8) 26.9% (26.5 to 27.4) Canada or USA (or missing)d 69.6% (69.1 to 70.1) 69.6% (69.1 to 70.0) 67.8% (67.3 to 68.2) 67.2% (66.8 to 67.7) 63.7% (63.2 to 64.2) Registered midwife prenatal care 3.7% (3.5 to 4.0) 11.2% (10.9 to 11.6) 16.4% (16.0 to 16.7) 21.1% (20.6 to 21.5) 26.3% (25.9 to 26.8) Inadequate prenatal care (v. all other)200 8.7% (8.4 to 9.0) 6.8% (6.5 to 7.0) 6.5% (6.2 to 6.7) 5.8% (5.6 to 6.1) 5.7% (5.5 to 6.0) a. CI = Confidence Interval using binomial proportions b. IADPSG = International Association of Diabetes in Pregnancy Study Groups screening criteria using a one-step 75g oral glucose tolerance test (OGTT) c. Medical/obstetric complications composite includes: pre-existing hypertension, pregnancy complicating conditions or diseases, previous stillbirth, neonatal death or anomaly. (ICD-10-CA codes in Appendix Table C.2). d. Missing data <0.5% 59 5.7 Figures Figure 5.1 Gestational diabetes screening and diagnosis by screening method in BC, Canada 60 Figure 5.2 Predicted gestational diabetes diagnosis risk using adjusted models 61 6 WEIGHT GAIN IN PREGNANCY AND INFANT BIRTHWEIGHT AFTER THE ONSET OF THE COVID-19 PANDEMIC: AN INTERRUPTED TIME SERIES ANALYSIS The COVID-19 pandemic and its associated countermeasures had profound consequences for provision of antenatal care. While the experience of pandemic-associated ‘lock-downs’ led to increases in weight gain for children and adults, little is known about the effects on pregnant people. This study was the first we know of to use a robust quasi-experimental design, interrupted time series, to examine this issue. Our findings of a modest pandemic-associated increase in weight gain, across all pregestational BMI categories, but no change in infant birthweight provide more information about how this global pandemic affected pregnancy. A version of this chapter is in In-press at the American Journal of Clinical Nutrition. 6.1 Synopsis Background: Increased weight gain and decreased physical activity has been reported for some populations since the COVID-19 pandemic but this has not been well characterized in pregnant populations. Objectives: Our objective was to characterize the impact of the COVID-19 pandemic and associated countermeasures on pregnancy weight gain and infant birthweight in a U.S. cohort. Design: Washington State pregnancies and births (Jan 1, 2016 to Dec 28, 2020) from a multi-hospital quality improvement organization were examined for pregnancy weight gain, pregnancy weight gain z-score adjusted for pregestational body mass index (BMI) and gestational age, and infant birthweight z-score, using an interrupted time series design which controls for underlying time –trends. We used mixed effects linear regression models, controlled for seasonality and clustered at the hospital level, to model weekly time trends and changes at March 23, 2020, the onset of local COVID-19 countermeasures. Results: Our analysis included 77,411 pregnant people and 104,936 infants with complete outcome data. The mean pregnancy weight gain was 12.1kg (z-score -0.14) during a pre-pandemic time period (March-December 2019) and increased to 12.4kg (z-score -0.09) after the onset of the pandemic (March-December 2020). Our time series analysis found that after the pandemic onset, mean weight gain increased by +0.49 kg (95% CI 0.25, 0.73), weight gain z-score increased by 0.080 (95% CI 0.031, 0.125) with no changes in the baseline yearly trend and infant z-scores (-0.004, 95% CI (-0.04, 0.03)) were unchanged. Overall results were unchanged in analyses stratified by pregestational BMI categories. 62 Conclusions: We observed a modest increase in weight gain after the onset of the pandemic among pregnant people, but no changes in infant birthweights. This modest weight change could be clinically most relevant for people with high pregestational BMI who are recommended a lower total weight gain during pregnancy. 63 6.2 Introduction In response to the COVID-19 pandemic, leaders introduced sweeping changes to health systems and service delivery, policies restricting individuals’ travel outside the home, and closures of schools and workplaces205. These have been associated with changes in exercise and nutrition, weight gain and weight loss 68,206–208 and worsening mental health 209,210, when compared to pre-pandemic time periods. Effects of the pandemic have not been uniform, with disproportionate impacts being felt by those living in poverty, those with existing chronic health conditions and racialized groups211. For pregnant people, the COVID-19 pandemic disrupted lifestyles and increased stress209.Antenatal stress may alter weight gain in pregnancy 5,212,213; furthermore, pandemic-related stress could have unique impacts 214 on weight change trajectories during pregnancy 4. Along with increased stress and disrupted lifestyles, health care delivery in the United States changed, with an increased reliance on telehealth and longer spacing between prenatal care visits 215. These may have decreased access to nutritional counseling and serial weight assessments during prenatal care. At the same time, food insecurity increased in the United States after the onset of the pandemic 216 and families may have had to increase their reliance on shelf-stable processed foods 217,218. This may have been particularly important for families living in poverty as both food insecurity and increased use of processed foods have been linked to higher rates of obesity 219. Weight gain during pregnancy is used as an indicator of nutritional health. Excess pregnancy weight gain is associated with a higher risk of large birthweight infants 53,57, pregnancy related diseases (e.g. hypertensive disorders of pregnancy and gestational diabetes) 220,221 and is a strong determinant of longer-term obesity 54. We hypothesized that the pandemic-associated countermeasures and stresses, which have impacted weight status among non-pregnant people 68,206–208, may have altered pregnancy weight gain and infant birthweights when compared to pre-pandemic time periods. Increases in maternal obesity or infant birthweights could have long term health consequences by impacting rates of post-pregnancy obesity, childhood obesity and chronic diseases such as diabetes. Therefore, we studied births from a multi-hospital initiative in Washington State, where some of the earliest U.S. cases of COVID-19 were detected. We aimed to assess the combined impact of the COVID-19 pandemic, pandemic-related policies, and health system disruptions on changes in weight during pregnancy and on infant birthweight. 64 6.3 Methods 6.3.1 Subjects We used data from a perinatal quality improvement initiative, the Obstetrical Care Outcomes Assessment Program (OB COAP), from January 1, 2016 through December 31, 2020, representing approximately one third of the births in Washington State, United States. The database was populated through both electronic health records and chart abstraction with real-time quality checks and validation 222. This study was reviewed by the University of British Columbia Harmonized Ethics Review Board and approved as a minimal risk study (#H20-00741) and was also approved by the OB COAP research committee. We restricted to singleton pregnancies with births that occurred at or beyond 24 weeks gestation. We excluded records with missing or implausible weight measurements (<30kg or >350kg), with no early or pre- pregnancy weight measurement (<14+0 weeks), with a last measured weight taken more than 28 days from delivery, and with missing or implausible gestational weight gain z-score (>6SD or < -6SD) using the z-score reference of Santos et al 223. We excluded infants with missing birthweight or sex, implausible infant birthweight according to criteria from Alexander 224 and z-scores (using the z-score chart from Aris 225) as per the approach of Basso & Wilcox 226 (<5SD or >-5SD for term, >4SD or <-3SD for preterm). 6.3.2 Study context The first confirmed case of COVID-19 in the United States was in Washington State on January 21, 2020. By March 12, 2020, social gatherings were banned, all educational institutions were closed, and on March 23, 2020, the state governor issued a 2 week ‘stay-at-home’ order. Most broad restrictions on public life remained until early June 2020, followed by a temporary loosening of some restrictions until September 2020 when there was a resurgence of COVID-19 cases. Schools were closed for in-person learning throughout 2020. Hospital-level and health-care provider-level infection control countermeasures were based on guidelines from national professional associations.227 COVID-19 public health and policy actions in Washington State were quantified using publicly available data from the Oxford Covid-19 Government Response Tracker (OxCGRT)205 to identify three time periods of interest: 1) a pre-pandemic period (January 1, 2016 to February 23, 2020), 2) a transition period where new policies were rapidly implemented (February 24, 2020 to March 23, 2020) and 3) a steady state period where a few pandemic measures were relaxed but most policies were maintained consistently across the state (March 24, 2020 – December 28, 2020) (Figure D.1). 65 6.3.3 Measurements 6.3.3.1 Pregnancy weight gain, z-scores and infant birthweight z-scores We examined pregnancy weight gain using two different measures: 1) total pregnancy weight gain in kilograms, defined as the difference between the last weight before delivery (within 28 days of delivery) and pre- or early pregnancy weight (<14+0 weeks) and 2) pregnancy weight gain z-scores which were standardized for pregestational body mass index (BMI) and gestational age using a weight-gain for gestational age chart223,228 that was derived from over 200,000 pregnant people from 33 cohorts in Europe, North America and Oceania. Pre- or early pregnancy weight was based on a self-reported pre-pregnancy weight or the measured weight at the first prenatal visit. The last pregnancy weight before delivery was recorded at the time of admission in labor. We calculated pregestational BMI using the pre- or early pregnancy weight and height (cm) 229. We calculated infant birthweight z-scores, standardized for gestational week at birth and infant sex, using the U.S. natality-based reference charts of Aris et al.225,230 6.3.3.2 Demographic and obstetric characteristics We obtained chart-abstracted data for self-reported race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic/Latinx, Asian or Pacific Islander, Native, Other or mixed race), maternal age at delivery (years), parity (nulliparous (no prior term delivery) or multiparous), pregestational body mass index (kg/m2), height (cm), gestational age at delivery (weeks, based on obstetric estimated due date), hospital site of delivery, insurance payor type (Medicaid v. others), rural-urban commuting areas (RUCA)231 collapsed to 2-levels (rural v. non-rural)232 and the Distressed Communities Index233 (DCI) quintiles (Prosperous, Comfortable, Mid-tier, At risk, Distressed) from the data registry. The DCI quintiles combine seven socioeconomic indicators into a measure of economic well-being in each zip-code relative to its peers.233 Pregestational body mass index was categorized as underweight or normal (<24.9 kg/m2), overweight (25-29.9 kg/m2), and grades 1 (30-34.9 kg/m2), 2 (35-39.9 kg/m2), and 3 (≥40 kg/m2) obesity. 6.3.4 Statistical analyses We used an interrupted time series design234,235 to assess pandemic-associated changes in the outcomes while controlling for underlying trends by week of study time. Interrupted time series is one of the strongest quasi-experimental designs to evaluate the effects of a policy, an intervention or a wide-spread systems impact. In this analysis, a time series establishes the underlying trend and two line segments are fitted simultaneously, separated by the onset of the COVID-19 pandemic. The hypothetical scenario if the impact had not occurred is referred to as the ‘counterfactual’. The time period of our study was January 1, 2016 to December 28, 2020 (261 complete weeks). We chose the COVID-19 pandemic onset as beginning on March 23, 2020 which we identified as the start of 66 a ‘steady-state’ of pandemic-related policies and restrictions in Washington State. Births in a 4-week period235 from February 24 to March 22, 2020 were excluded for two reasons. First, we hypothesized that an effect of the pandemic on either weight gain or infant birthweight would not occur immediately; and that at least 2 weeks of pandemic-associated policies would be needed to observe meaningful and detectable weight gain. Second, there were rapid changes in COVID-19 policies during this time segment. Excluding births in this 4-week period meant that for pregnant people who delivered after March 23, their total weight gain would include at least 2 weeks of the pandemic. We hypothesized the pandemic could cause both a change in level and in the time trend235 in our outcomes because the impact of the pandemic on weight at the population level could be immediate if diet or activity changed immediately, or delayed if there was a more gradual overall change in behavior. Potential confounders were identified using a directed acyclic graph (Figure D.2) and from prior studies.236–238 Pregnancy weight gain was adjusted a priori for gestational age at delivery, since gestational duration directly impacts an individual’s opportunity to gain weight.239 Neither weight gain z-scores or infant z-scores models were adjusted for gestational age since z-scores were gestational age specific. We considered Medicaid insurance payor, rural residence, distressed community indices, race/ethnicity, age, parity, antenatal health care professional type (midwife v. family practice v obstetrician) and pregestational body mass index (BMI) (in kg/m2) as potential confounders and plotted time series of all potential confounders. If we noted discontinuities at the pandemic onset and there was no plausible association between that factor and the pandemic, this justified inclusion in the model. We also adjusted for seasonal trends240,241 for both pregnancy weight gain and infant birthweight.238,242 Seasonality can be an important confounder in perinatal outcomes, as it may impact both birth rates and exposure-outcome relationships.243 We used week of conception243 rather than week of delivery to estimate the impact of season (Figure D.3). We also plotted mean data by month of conception and superimposed by year. Adjustment for seasonal trends used a single sine term, as this provided the best model fit based on lowest Akaike’s Information Criterion (AIC) compared to other approaches (multiple sine-cosine pairs, month indicator variables). We included, a priori, random effect terms for hospital intercept, slope and residuals to allow for hospital-level variation in baseline outcomes and over time. To account for sampling uncertainty and repeated measures within the sample study population, parametric bootstrapping techniques were used to calculate confidence intervals. All models were run as generalized linear regression mixed effect models in R.181 Final model specification is detailed in the online supplement. 67 6.3.5 Sensitivity analyses We hypothesized that the pandemic might differentially impact those who were at the higher end of the population distribution of pregnancy weight gain. To assess this, we examined the 90th percentile of all outcomes in quantile regression models244 using similar interrupted time series models. We also repeated pregnancy weight gain z-score and infant birthweight z-score analyses in subgroups (Distressed Communities Index quintiles, Medicaid status, parity, body mass index and by race/ethnicity). Last, we ascertained likely COVID-19 cases (identified via open-text fields as COVID-positive, COVID symptomatic and presumed positive or COVID-positive in pregnancy) and repeated our analyses excluding known cases. We also repeated the analyses stratified by pregestational BMI categories. Lastly, we examined the effect of increased duration of exposure to the pandemic by excluding 9 weeks of births, from Feb 23 to April 27, 2020, and 15 weeks, from Feb 23 to June 8, 2020, thereby including pregnancies with a longer exposure to pandemic-associated countermeasures. 6.4 Results Study population (Figure 6.1) There were 107,062 singleton pregnancies and infants (>24 weeks gestational age) during the study period. Overall, 21.7% (n=23,277) of pregnancies were missing weight gain data because their weight data was collected after 14 weeks of pregnancy and/or more than 28 days before delivery and 6.2% (n=5214) were excluded due to missing or implausible weight data. Only 0.5% (n=515) of infants were excluded due to missing or implausible birthweight data. The final sample included 77,411 pregnancies and 104,936 infants. Excluded cases were not biased by the pandemic time period (Table D.2). Population characteristics (Table 6.1 and Table 6.2): The demographics of the pregnancy and infant cohorts showed some minor differences between the time periods. Specifically, the pandemic period included a greater proportion of nulliparas, individuals beginning pregnancy with obesity, and individuals with older age at delivery. Time-series graphs for demographic or obstetric covariates revealed that any apparent differences were due to gradual trends over time rather than abrupt changes at the pandemic onset, thus no additional covariates were included in regression models. Pregnancy weight gain and infant birthweight: pre-pandemic compared to post-pandemic We compared the 40 weeks post-pandemic (March 23-Dec 28, 2020) to the same period in 2019. From March-December 2019, the mean pregnancy weight gain was 12.13 kg 95% CI (12.02, 12.24) and z-score was -0.14 95% CI (-0.16, -0.12) compared to the mean pregnancy weight gain 12.39 kg 95% CI (12.28, 68 12.51) and z-score -0.092 95% CI (-0.11, -0.07) in the post-pandemic period (March-December 2020). Infant birthweight z-scores were unchanged (0.075 95% CI 0.063, 0.086) compared to the post-pandemic period (0.068 95% CI 0.044, 0.091). Interrupted time series (Figure 6.2, Table D.1) models showed a decreasing yearly trend before the pandemic in both pregnancy weight gain (-0.12 kg/year 95%CI (-0.21, -0.03)) and z-score (-0.016, 95%CI (-0.03, 0.00) /year) which represents roughly 2% of the study population in 2019 having lower pregnancy weight gain when compared to 2016. Despite this yearly decrease in pregnancy weight gain, infant birthweight z-scores were stable (0.001, 95% CI (-0.01, 0.01) /year). Our models estimated that at the onset of the COVID-19 pandemic mean pregnancy weight gain increased (+0.49 kg, 95% CI (0.25, 0.73)) and pregnancy weight gain z-scores increased (0.08, 95% CI (0.04, 0.12)), but infant z-scores (-0.004, 95% CI (-0.04, 0.03)) were unchanged. We found no statistically significant change in the yearly time trends in infant z-scores after the pandemic onset. Seasonal effects Seasonal plots (Figure 6.3) revealed higher pregnancy weight gain and pregnancy weight gain z-scores for pregnancies conceived in the late spring (May-June) compared to those conceived in the late fall (November-December). Models estimated a seasonal trend where the difference from the maximum weight gain (births in the last week in February) to the minimum (births in the first week in September) was 0.32 kg 95% CI (0.26, 0.38) for weight gain, 0.066 (95% CI 0.055, 0.078) for weight gain z-score (~3 percentiles change) and non-significant (-0.002, 95% CI -0.010, 0.007) for infant z-score. Pregnancy weight gain at 90th percentile of distribution Examining the upper end of the weight gain distribution (90th percentile) identified an almost three-fold increase in both pregnancy weight gain (1.20 kg 95% CI (0.75, 1.65)) and pregnancy weight gain z-scores (0.20, 95% CI (0.12, 0.28)) only, after the pandemic onset, compared to the population average models (Figure D.4, Table D.7). Subgroup results Our observed finding of an increase in pregnancy weight gain z-score after the onset of the pandemic remained within quintiles of the Distressed Communities Index, although only the largest subgroup, “Prosperous” (0.12, 95% CI (0.061, 0.186)) had confidence intervals that excluded the null (Figure D.6). Subgroups by race and ethnicity (Figure D.7) showed a pandemic-related increased weight-gain z-score in the largest subgroup (Non-Hispanic, White) (0.12, 95% CI (0.060, 0.184) and marginally, in the Non-Hispanic Black and Other or mixed-race subgroups, despite small sample sizes. In the Asian, Pacific 69 Islander subgroup, there was a significant trend change (0.223 z-score/year, 95% CI (0.03, 0.42)) (almost a 9 percentile change per year) suggesting a gradual, rather than instantaneous, effect of the pandemic on increasing pregnancy weight gain z-scores. We observed no significant pandemic-related changes in the Hispanic/Latinx subgroup. Unlike the mean population models where infant birthweight z-scores were unaffected by the pandemic, in the DCI “Prosperous” subgroup (Figure D.11) the pandemic impact increased infant birthweight z-scores (0.056, 95% CI (0.001, 0.111)) and in the “Comfortable” DCI group, decreased infant birthweight z-scores (-0.069, 95% CI (-0.141, 0.002)). Infant birthweight z-scores were unchanged after the pandemic for all race-ethnicity groups (Figure D.12, Figure D.13) except in the Non-Hispanic Black subgroup (0.198, 95% CI (0.024, 0.371)) and in the Asian, Pacific-Islander subgroup (-0.087, 95% CI (-0.165, -0.008)). Stratifying by Medicaid payor demonstrated a stronger pandemic impact in the “not Medicaid” group (Figure D.9). Mean pregnancy weight gain z-scores were markedly different by parity, with mean z-scores near 0 in the nulliparous subgroup compared to -0.2 in the multiparous group (Figure D.10). For infant birthweight z-scores, none of the additional subgroup models (Figure D.14, Figure D.15, Figure D.16) showed statistically significant changes at the pandemic time point either for level or trend changes. Birthweight z-scores increased across subgroups by increasing pre- or early-pregnancy body mass index (Figure D.16). Excluding known Covid-19 positive cases (n=232), did not alter findings for the pandemic onset and key outcomes (pregnancy weight gain 0.506 kg, 95% CI (0.27, 0.75); pregnancy weight gain z-score 0.083, 95% CI (0.04, 0.13)) (Table D.4, Table D.5, Table D.6, Figure D.21). Results stratified by pregestational BMI: Stratified results for pregnancy weight gain by pregestational BMI (<25: +0.42 kg; 25-<30: +0.43; 30+: +0.49) were generally unchanged from the main results (+0.49 kg, 95% CI (0.25, 0.73)) by point estimates, although confidence intervals included 0 in the higher BMI categories with small sample sizes (Figure D.8). Results for z-scores and infant birthweight were also similar to the primary models. Stratified quantile regression results for the 90th percentiles of weight gain were also consistent across pregestational BMI categories (Table D.8, Figure D.5). Of interest, the model-predicted seasonal trend was most pronounced among those with normal or under-weight body mass index, suggesting that weight gain in this subgroup is most impacted by seasonality. Increased duration of exposure to the pandemic: 70 Overall results for all three outcomes were similar when excluding 9 or 15 weeks of births, although point estimates for the level change effect for weight gain and weight gain z-score moved away from the null (Table D.9). Additional sensitivity analyses (Appendix D) did not alter the overall findings. 6.5 Discussion Using rigorous analytic methods that control for underlying time trends, we found a modest (0.5 kg) increase in pregnancy weight gain and in pregnancy z-scores after the onset of the COVID-19 pandemic in a cohort of deliveries in Washington State. Pandemic-associated changes in pregnancy weight gain were more pronounced in people above the 90th percentile of weight gain. Despite pandemic-associated changes in pregnancy weight gain in this study group, and in subgroups, we found no pandemic-associated changes in infant birthweight z-scores at the population level. Results were consistent across pregestational BMI categories. The COVID-19 pandemic, and changes in health services, policies and governmental countermeasures have been linked to increases in stillbirth, maternal deaths and maternal depression and a decrease in preterm birth among high-income countries only 209. Weight increases after the pandemic have been reported for non-pregnant study groups (adults and children) 206,245 however, research on this topic has focused on qualitative measures 246, short post-pandemic time periods 54,73, ‘lock-downs’ 246 and used “pre-post” designs 247. Some countries reported fewer low-birthweight infants after ‘lockdowns’ 248 but these results have not been confirmed in pooled meta-analyses 209,249,250. Notably, one meta-analysis reported increased infant birthweights (mean 17g) after the pandemic 249. While our study found a statistically significant change, the magnitude of the mean increase in pregnancy weight gain (total of 0.5kg or 1.1lbs per pregnancy) was relatively small. Our findings are of interest for several reasons. First, this modest increase in pregnancy weight gain could be a part of the causal pathway for pandemic-associated decreases in low birthweight infants 209,249 noted in other settings. Despite no apparent shift in mean infant birthweight z-scores in our cohort, it is possible that a modest increase in pregnancy weight gain contributed to decreases in low birthweight seen elsewhere. Second, increased pregnancy weight gain could alter the trajectory of weight gain throughout pregnancy. For example, an higher trajectory of first trimester pregnancy weight gain has been associated with an increased risk of gestational diabetes 60. Third, this modest change in weight gain impacts the entire population 251, which could shift a relatively large number of people towards ‘excess’ weight gain and thus increase rates of chronic diseases such as hypertension and diabetes 252. For example, we noted a stronger pandemic impact (1.1 kg) for those who were already gaining “excess” pregnancy weight which may further impact chronic health risks. Importantly, the magnitude of both the pandemic-associated mean pregnancy weight 71 gain (0.5kg) and for the 90th percentile of weight gain (1.1 kg) was similar across all pregestational BMI categories. Therefore, pandemic-associated effects on weight gain may be most important for people with higher pregestational BMI for whom a lower overall weight gain is recommended (5-9kg) 54. Nevertheless, our findings are also generally reassuring, and suggest that the pandemic did not have a major impact on weight gain in pregnancy and/or that any effect was counteracted by decreases in commuting or other lifestyle changes 253. We also found a seasonal effect in pregnancy weight gain with 0.3 kg higher mean weight gain for pregnancies conceived in late winter compared to early summer conceptions. This seasonal effect highlights the importance of controlling for seasonality in weight gain research 243; otherwise, exposure-outcome or exposure-gestational age 254 relationships could be confounded by season – an issue which is critical when examining time-varying exposures, as in our case. While seasonal trends in weight gain 242,255 and infant birthweight 255 have most often been reported in countries where nutritional intake is correlated with the growing season, seasonal patterns also occurred in the U.S. 238,255 In our study, seasonal weight gain could be caused by changes in physical activity due to environmental factors; the fall-winter months in the Pacific Northwest are generally rainy and colder compared to summer months with moderate outdoor temperatures. Our analysis did not identify any changes in infant birthweight z-scores, either from the pandemic or from seasonality. It is possible that time-varying or neighbourhood-level exposures (e.g. air pollution) attenuated the impact of weight gain on infant birthweight in our cohort 256,257 or, more likely, that the small magnitudes of weight gain we observed were not sufficient to meaningfully impact infant birthweights. Strengths of this study include: an interrupted time series analysis which controls for underlying time trends, adjustment for seasonality 243, a large sample size, and a longer post-pandemic time period than many prior analyses. We also had several limitations. First, we restricted to those with valid weight measurements in the first trimester and near to delivery. However, the proportion of excluded cases was consistent in pre-pandemic and post-pandemic time periods. We do not expect that the association between the pandemic and either weight gain or infant birthweight is different in those excluded or included in the study, thus, this should not lead to substantial bias in our primary findings. Second, we were unable to control for individual-level factors such as diet, exercise, employment or stress. However, we see no indication that these factors would have changed in the population for reasons other than the pandemic onset which means our study is appropriately characterizing pandemic impacts on our outcomes. Last, our study population represents a single U.S. state where pandemic-associated 72 countermeasures were relatively widespread. This may impact generalizability to other U.S. regions where there were relatively few countermeasures that restricted physical activity or movement. Our study has implications for public health planning for future potential pandemic time periods where antenatal care and lifestyles are broadly disrupted. Virtual exercise programs 258 or fewer restrictions on the use of outdoor spaces (e.g. playgrounds and parks) with a low level of infection risk 259 could be two avenues to help promote physical activity and healthy pregnancies in any future pandemics. While the weight gain effect overall was modest, individuals with higher pregestational BMI were equally impacted, therefore public health efforts to promote healthy lifestyles after widespread disruptions such as the COVID-19 pandemic could be most relevant in this specific population. 73 6.6 Tables Table 6.1 Pregnancy cohort demographics and outcomes by pre-pandemic (Jan 1, 2016 to Feb 23, 2020) and pandemic (Mar 23, 2020 to Dec 28, 2020) time periods for a Washington State cohort (n=77,411 pregnancies) Characteristic Pre-pandemic Jan 1 2016- Feb 23, 2020 N=65,214 mean± SD or n (%) Pandemic Mar 23-Dec 28, 2020 N=12,197 mean± SD or n (%) Nulliparous 26,631 (40.8) 5256 (43.1) Race and ethnicity of birthing women/person: Non-Hispanic White 34,515 (52.9) 5967 (48.9) Non-Hispanic Black 2670 (4.1) 512 (4.2) Hispanic or Latinx 10713 (16.4) 2046 (16.8) Asian or Pacific Islander 13,060 (20.0) 2548 (20.9) Native American or Native Alaskan 696 (1.1) 103 (0.8) Other or mixed race 1963 (3.0) 372 (3.0) Missing 1597 (2.4) 649 (5.3) Rural zip code 5310 (8.1) 920 (7.5) Missing rural indicator 1649 (2.5) 351 (2.9) Medicaid insurance 19537 (30.0) 3351 (27.5) Missing insurance 2313 (3.5) 66 (0.5) Distressed Communities Index: Prosperous 29,543 (45.3) 5576 (45.7) Comfortable 16,137 (24.7) 3040 (24.9) Mid-tier 5931 (9.1) 1155 (9.5) At risk 10,097 (15.5) 1765 (14.5) Distressed 2839 (4.4) 534 (4.4) Missing 667 (1.0) 127 (1.0) Age of birthing person (year) 30.4 ± 5.4 30.8 ±5.4 Pregestational body mass index (kg/m2) 27.1 ± 6.6 27.4 ±6.7 Pregestational body mass index (kg/m2) categories Underweight or Normal weight (<24.9) 30,128 (46.2) 5454 (44.7) Overweight (25.0-29.9) 17,794 (27.3) 3288 (27.0) Obese class I (30.0-34.9) 9362 (14.4) 1809 (14.8) Obese class II (35.0-39.9) 4595 (7.0) 935 (7.7) Obese class III (>=40.0) 3335 (5.1) 711 (5.8) Height of birthing person (cm) 163.1 ±7.2 163.0 ±7.1 Total pregnancy weight gain (kg) 12.3 ± 6.1 12.4 ±6.5 Subgroup: pregestational BMI <24.9 13.8 ± 5.0 14.0 ± 5.2 Subgroup: pregestational BMI 25-<30 12.6 ± 5.9 12.8 ± 6.3 Subgroup: pregestational BMI 30+ 9.3 ± 6.9 9.4 ± 7.3 Total pregnancy weight gain (kg) 12.3 ± 6.1 12.4 ±6.5 Total pregnancy weight gain z-score -0.1 ± 1.1 -0.1 ±1.2 Gestational age at delivery (weeks) 38.8 ± 1.7 38.7 ±1.7 Infant birthweight (g) 3367.5 ±538.3 3354.0 ±533.4 74 Table 6.2. Infant cohort demographics and outcomes by pre-pandemic (Jan 1, 2016 to Feb 23, 2020) and pandemic (Mar 23, 2020 to Dec 28, 2020) pre-pandemic and pandemic (March 23, 2020) time periods for a Washington State cohort (n=104,936 infants) Pre-pandemic Jan 1 2016- Feb 23, 2020 N=88,904 mean± SD or n (%) Pandemic Mar 23-Dec 28, 2020 N= 16,032 mean± SD or n (%) Nulliparous pregnancy 35,722 (40.2) 6798 (42.4) Race or ethnicity of birthing person/woman: Non-Hispanic White 46,023 (51.8) 7571 (47.2) Non-Hispanic Black 4310 (4.8) 754 (4.7) Hispanic or Latinx 15,024 (16.9) 2702 (16.9) Asian or Pacific Islander 17,017 (19.1) 3058 (19.1) Native American or Native Alaskan 1241 (1.4) 197 (1.2) Other or mixed race 3113 (3.5) 547 (3.4) Missing 2176 (2.4) 1203 (7.5) Rural zip code: 7306 (8.2) 1285 (8.0) Missing rural indicator 2460 (2.8) 480 (3.0) Medicaid Insurance 30,921 (34.8) 5169 (32.2) Missing Insurance 2464 (2.8) 89 (0.6) Distressed Communities Index: Prosperous 37,144 (41.8) 6677 (41.6) Comfortable 22,308 (25.1) 4106 (25.6) Mid-tier 8981 (10.1) 1729 (10.8) At risk 14,906 (16.8) 2548 (15.9) Distressed 4415 (5.0) 767 (4.8) Missing 1150 (1.3) 205 (1.3) Age of birthing person (year) 30.2 ±5.6 30.5 ±5.6 Gestational age at delivery (weeks) 38.7 ±1.9 38.6 ±1.8 Infant birthweight (g) 3342.2 ±563.9 3332.0 ±556.4 Infant birthweight z-score 0.1 ±1.1 0.1 ±1.0 75 Table 6.3. Quantile regression results for 90th percentiles using an interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) Model terms Quantile regression (90th percentile) Estimate (95% CI) Pregnancy weight gain (kg) (n= 77, 411) Level change 1.20, 95%CI (0.75, 1.65) Trend change -0.72, 95%CI (-1.72, 0.27) Pregnancy weight gain z-score (n=77,411) Level change 0.20, 95%CI (0.12, 0.28) Trend change -0.11, 95%CI (-0.28, 0.06) Infant birthweight z-score (n=104,936) Level change -0.04, 95%CI (-0.10, 0.02) Trend change 0.07, 95%CI (-0.06, 0.20) 76 6.7 Figures Figure 6.1 Flow of study population and exclusions for a Washington State study of gestational weight gain, infant birthweight and the COVID-19 pandemic (January 1, 2016 to December 28, 2020). (Excluded data in blue text) 77 Figure 6.2 Interrupted time series graphs for pregnancy weight gain, z-score and infant birthweight z-score showing predicted trends and the COVID-19 pandemic onset (March 23, 2020) in a Washington State cohort (January 1, 2016 to December 28, 2020) Points (+) represent mean outcome by study week, modeled (predicted) trend is a solid line; counterfactual is dashed line; seasonal modeled effect is solid thin line; 95% confidence interval for modeled trends in light grey shading. Plotted trendlines are drawn based on models unadjusted by random effects. 78 Figure 6.3 Average pregnancy weight gain, z-score and infant birthweight z-score by month of conception across study years for a Washington State cohort (January 1, 2016 to December 28, 2020) 79 7 THE CASE FOR “COMPLETE CASE”: A BRIEF REVIEW OF MISSING DATA IN CHAPTER 6 Missing weight gain data was an important limitation in the study presented in Chapter 6. This chapter explores this issue in detail and presents several approaches to address missing data in the Washington State interrupted time series analysis of COVID-19 impacts on pregnancy weight gain and infant birthweight. 7.1 Synopsis Background: In the analysis in Chapter 5, the final cohort was restricted to pregnancies with complete and valid data (>22% of cases excluded). A “complete case” approach may lead to biased results; therefore, multiple imputation techniques are often used to address missing data and may yield valid results with correctly estimated standard errors. In this analysis, the missing data was in the outcome variable (weight gain) which is a special case. Objective: The aims of this section were to review the literature on multiple imputation with a lens to the specifics of imputing an outcome variable, to identify the type of the missing weight gain data (missing at random (MAR) or missing completely at random (MCAR)), to implement a multiple imputation (MI) approach if appropriate and recommend the use of either complete case or MI for this study. Methods: Patient data for excluded cases, complete cases and the full study population were compared descriptively and by pandemic time periods. A multiple imputation approach was implemented according to recommended methods and the interrupted time series analyses (ITS) (from Chapter 5) were repeated using the imputed datasets. Similar analyses were also repeated in sample without exclusions for ‘invalid’ weight gain data. Results: Based on the literature on imputation for outcome data, complete case analysis is preferred, unless missing data meet MCAR criteria. Missingness in this dataset did not meet criteria for MCAR as there was a higher proportion of Medicaid recipients in the missing cases. Using an MI approach, the overall findings were similar to Chapter 5, but standard errors increased and results did not reach statistical significance. By contrast, using an un-restricted cohort, the results were more pronounced (biased away from the null). Conclusions: Because the missing data did not meet MCAR criteria, complete case is preferred over an MI approach. Additional exploratory analyses also point to bias in the MI implementation. 80 7.2 Background There is a large body of literature on dealing with missing data in epidemiologic analyses.260,261 In general, there are two possible ways to deal with missing data: 1) restrict to a population with complete data (complete case analysis) or 2) use some form of “imputation” to replace missing values with plausible data. Statistical approaches to impute data range from very simple (aka replacement with a mean value) to complex (fully-conditional regression-based approaches, also known as multiple imputation (MI)). The decision to use complete case as compared to imputation is specific to each analysis. Some key issues are: whether missing data occurred at random (or not), if missingness is in the independent (X) or dependent variables (Y), and whether missing data techniques can be used with the proposed analysis. This section will expand on the decision to use a complete case approach as compared to multiple imputation using chained equations (fully conditional approach) which is often assumed to be the most robust approach to missing data.262 When assessing missing data for the study in Chapter 5, there were several unique considerations. First, this is an interrupted time series analysis, a specialized research design that has some features that differ from more standard approaches (e.g., linear or logistic regression). Multiple imputation has not been widely used for interrupted time series analysis, to date.262–264 Second, the missing data was in the dependent/outcome variable only. There has been some reluctance among epidemiologists to impute outcome data as this implies “treating cases in the analysis with unknown outcomes as if they were known”265; however, imputation is appropriate for dependent variables in many cases.265–267 According to van Buuren260 and Von Hippel261, when missing data is limited to the dependent variable Y, either complete case analysis260 or a multiple imputation with deletion (MID)261 is preferred to a standard MI approach. An MID approach uses standard MI imputation followed by deletion of cases with imputed Y, while retaining any cases with imputed independent (X) data. In this analysis, there is no missing data in the independent (X) (the COVID-19 pandemic time point), thus an MID technique is not applicable. Further, to use imputation for a dependent variable, if outcome data is missing at random (MAR), or missing completely at random (MCAR), then complete-case analysis is preferred. As noted by Little (1992) “if the Xs are complete and the missing values of Y are missing at random, then the incomplete cases contribute no information to the regression of Y on X1, ... ,Xp”. Von Hippel agrees, “If the X’s are complete and the missing values of Y are missing at random, then the incomplete cases contribute no information to the regression of Y on X”.261,268 And, as van Buuren 260 notes, “Suppose that the complete-data model is a regression with outcome Y and predictors X. If the missing data occur in Y only, 81 complete-case analysis and multiple imputation are equivalent, so then complete-case analysis is preferred since it is easier, more efficient and more robust. Quantities that depend on the correct marginal distribution of Y, such as the mean or R2, require the stronger MCAR assumption.” The primary outcome for this analysis was a mean, thus meeting the stronger MCAR assumption would be required to justify using a MI approach. Therefore, it was critical to understand whether missing data in this study population met MCAR assumptions. Last, the literature described one special case where MI may be advantageous over complete-case analysis when imputing outcome data and may lead to unbiased estimates when compared to a complete case analysis. This required additional predictors for Y, that are not part of the regression for Y on X.260 Further, these additional predictors variables should not have high proportions of missing data as this could increase standard errors/decrease efficiency in imputed data.260 The dataset used for the analysis in Chapter 5 did contain many potential auxiliary variables which could be used to impute Y, and which were not part of the regression. However, the ability of those auxiliary variables to usefully predict Y (weight gain) was unclear. After reviewing the literature on multiple imputation with a lens towards the particular features of this dataset and the analysis presented in Chapter 4, this chapter aimed to 1) assess the data for criteria for MAR or MCAR; 2) implement an exploratory MI approach assuming this data falls within the ‘special case’ noted above (acknowledging the uncertainty in whether these auxiliary variables provide sufficient information for imputation); 3) re-analyze the dataset without an exclusion for ‘valid’ weight gain and 4) justify the decision to use complete case analysis for the primary study. 7.3 Methods The complete case cohort (from Chapter 5) was derived by: a) excluding missing or implausible weight or body mass data and b) excluding weight gain outside the ‘study criteria’ (invalid weight gain). The ‘study criteria’ excluded cases with weight data, but where the initial weight measurement was after 14 weeks gestational age or the final weight was more than 4 weeks prior to delivery. To assess missing data patterns,264,269 I examined descriptive data for demographics and other relevant variables in the full study population and in three subgroups: 1) Cases excluded for missing/implausible data 2) Cases excluded for study criteria 3) Complete case cohort. I also examined the complete case cohort compared to the excluded subgroup, stratified by pandemic time periods. To impute missing data, I used an MI with chained equations approach260,270 with all possible covariates (with an absolute correlation with the response/imputed variable > 0.1) as predictors for imputation. 82 Because this is a data registry with a large number of potential auxiliary variables, I considered only variables which were measured prior to the weight data. I used multiple imputation with chained equations to impute missing initial and final weight measurements for 20 datasets using the R package “mice” (v. 3.3.1).260,270 After imputation, I recalculated weight gain and z-scores 223 and applied the same exclusion criteria as in the primary analysis, for ‘out of range’ weight gain or ‘z-scores’ (excluding cases with >6 standard deviations). For the 20 imputed datasets, I repeated the interrupted time series models (with random effect terms for hospital site as in the main analyses) for the imputed data sets and obtained pooled effect estimates for level and trend changes as reported in the main analyses using the R package “mitml”. To further investigate whether the study population was biased by the exclusion of cases outside the ‘study criteria’ for total pregnancy weight gain, I created an ‘un-restricted’ cohort that included cases that did not meet study criteria and repeated the primary ITS analysis. 7.4 Results There were 107,062 singleton pregnancies, >24 weeks gestational age fetus available for weight gain analysis (Figure 7.1). There were 23,277/107,062 (21.7%) pregnancies excluded for weight gain outside the study criteria. A smaller group, 5214/107062 (4.9%) pregnancies, were excluded because of a calculated weight gain, body mass index or z-score that was missing, invalid or implausible (Table 7.1). There were 78,571 pregnancies in the ‘complete case’ cohort. There were several differences for the excluded cases compared to the final cohort (Table 7.1). Excluded cases had substantially higher rates of Medicaid (48% in excluded cases v. 30% in final sample) and slightly more with low socio-economic status (as indicated by the DCI variables). Thus, missing weight gain data does not meet criteria for missing completely at random (MCAR) but is likely missing at random (MAR). Importantly for the ITS analysis, the proportion of cases relative to the exposure of interest (the pandemic) was similar for the complete case dataset (84% pre-pandemic and 16% pandemic cases) and the excluded cases (86% pre-pandemic, 14% pandemic) (Table 7.2). With the exception of missingness in race/ethnicity data, most characteristics were consistent in the excluded data across the pre-pandemic v. pandemic time periods. After implementing an MI approach, level and trend change terms from the pooled ITS models compared to the primary analysis (Table 7.3) were similar with an increase in pregnancy weight gain (level change), and no change in trend. However, using imputed data, effect estimates were attenuated towards the null. 83 Notably, results for pregnancy weight gain z-score no longer reach statistical significance. Further, standard errors were increased in the imputed results when compared to a complete case analysis. The ‘un-restricted cohort’ (Figure 7.1) (n=98,682) had a lower mean weight gain (11.3 kg) than the primary analytic cohort (12.3 kg) (Table 7.4). This is expected, since weight gain that did not meet study criteria includes people with weight gain over a shorter pregnancy time period. Overall findings were unchanged, but the magnitude of the level change almost doubled (0.12 (95%CI 0.08, 0.16) for pregnancy weight gain z-score) compared to the complete case cohort (0.05 (95%CI 06, 0.010) for pregnancy weight gain z-score) (Table 7.5). 7.5 Discussion Missing data did not meet criteria for missing completely at random (MCAR) but was classified as missing at random (MAR). Proportions of excluded cases compared to final analytic cohort (valid/plausible data) were unchanged by the exposure of interest (pandemic onset) and population characteristic differences were unlikely to cause residual confounding in the main analysis. A review of the literature found that imputation of outcome data was not recommended, unless MCAR criteria was met, which was not the case in this analysis. Using MI to impute missing data generally decreases standard errors, compared to complete case analysis, by increasing sample size and efficiency. In this analytic case, standard errors increased in the imputed results when compared to a complete case analysis. This suggests that the MI implementation in this context did not lead to improved efficiencies when compared to a complete-case analysis, as expected based on missing-at-random patterns 260,261,266. This may be because of the use of race/ethnicity (with a high degree of missingness) in the imputation or because the auxiliary variables are insufficient to derive appropriate values for the outcome data (Y). Excluding missing data for weight gain did not introduce differential bias, if anything, using a complete case cohort biased findings towards the null. In summary, a complete case analysis was recommended, over an MI approach, for this particular analysis. 84 7.6 Tables Table 7.1 Missing data patterns: Demographics for excluded cases, complete cases and full study population Excluded cases (outside study criteria) N=23,277 (21.7%) Excluded cases (missing or invalid data) N=5,214 (4.9%) Complete case cohort N=78,571 (73.4%) All available singleton pregnancies, >24 weeks, valid sites N=107,062 N (%) N (%) N (%) N (%) Nulliparous 9073 (39.0) 1895 (36.3) 32395 (41.2) 43363 (40.5) Race and ethnicity of birthing person: Non-Hispanic White 10894 (46.8) 2670 (51.2) 41107 (52.3) 54671 (51.1) Non-Hispanic Black 1592 (6.8) 355 (6.8) 3226 (4.1) 5173 (4.8) Hispanic or Latinx 4264 (18.3) 908 (17.4) 12934 (16.5) 18106 (16.9) Asian or Pacific Islander 3965 (17.0) 616 (11.8) 15843 (20.2) 20424 (19.1) Native American or Native Alaskan 551 (2.4) 111 (2.1) 808 (1.0) 1470 (1.4) Other or mixed race 1091 (4.7) 289 (5.5) 2374 (3.0) 3754 (3.5) Missing 920 (4.0) 265 (5.1) 2279 (2.9) 3464 (3.2) Rural zip code: Yes 1820 (7.8) 671 (12.9) 6316 (8.0) 8807 (8.2) Missing 658 (2.8) 301 (5.8) 2036 (2.6) 2995 (2.8) Insurance payer: Medicaid 11150 (47.9) 2563 (49.2) 23182 (29.5) 36895 (34.5) Missing 45 (0.2) 147 (2.8) 2381 (3.0) 2573 (2.4) Distressed Communities Index: Prosperous 7529 (32.3) 1417 (27.2) 35674 (45.4) 44620 (41.7) Comfortable 6084 (26.1) 1414 (27.1) 19451 (24.8) 26949 (25.2) Mid-tier 2964 (12.7) 789 (15.1) 7194 (9.2) 10947 (10.2) At risk 4729 (20.3) 1109 (21.3) 12027 (15.3) 17865 (16.7) Distressed 1605 (6.9) 271 (5.2) 3422 (4.4) 5298 (4.9) Missing 366 (1.6) 214 (4.1) 803 (1.0) 1383 (1.3) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Age of birthing person (year) 29.4 (6.0) 29.6 (5.8) 30.5 (5.4) 30.2 (5.6) Height of birthing person (cm) 162.6 (7.3) 162.8 (7.6) 163.1 (7.2) 163.0 (7.2) Gestational age at delivery (weeks) 38.6 (2.1) 37.8 (3.1) 38.8 (1.7) 38.7 (1.9) Weeks between weights (early or pre- and final weight) (pre-pregnancy weights assigned as time =0) 15.0 (8.1) 35.8 (4.8) 31.7 (4.6) 28.3 (8.9) Infant birthweight (g) 3292.9 (603.4) 3166.1 (757.1) 3365.6 (537.3) 3340.1 (566.8) 85 Table 7.2 Demographics comparing final cohort to excluded cases by pandemic time periods All excluded cases N=28,034 Complete case cohort N=77,411 Pre-pandemic COVID-19 pandemic Pre-pandemic COVID-19 pandemic N=24,144 (86%) N=3890 (14%) N=65,214 (84%) N=12,197 (16%) Nulliparous 9232 (38.2) 1559 (40.1) 26631 (40.8) 5256 (43.1) Race and ethnicity of birthing person: Non-Hispanic White 11755 (48.7) 1625 (41.8) 34515 (52.9) 5967 (48.9) Non-Hispanic Black 1667 (6.9) 245 (6.3) 2670 (4.1) 512 (4.2) Hispanic or Latinx 4406 (18.2) 669 (17.2) 10713 (16.4) 2046 (16.8) Asian or Pacific Islander 3999 (16.6) 517 (13.3) 13060 (20.0) 2548 (20.9) Native American or Native Alaskan 556 (2.3) 96 (2.5) 696 (1.1) 103 (0.8) Other or mixed race 1169 (4.8) 176 (4.5) 1963 (3.0) 372 (3.0) Missing 592 (2.5) 562 (14.4) 1597 (2.4) 649 (5.3) Rural zip code: Yes 2076 (8.6) 374 (9.6) 5310 (8.1) 920 (7.5) Missing 815 (3.4) 130 (3.3) 1649 (2.5) 351 (2.9) Insurance payor Medicaid 11624 (48.1) 1849 (47.5) 19537 (30.0) 3351 (27.5) Missing 168 (0.7) 23 (0.6) 2313 (3.5) 66 (0.5) Distressed Communities Index: Prosperous 7704 (31.9) 1115 (28.7) 29543 (45.3) 5576 (45.7) Comfortable 6293 (26.1) 1081 (27.8) 16137 (24.7) 3040 (24.9) Mid-tier 3117 (12.9) 584 (15.0) 5931 (9.1) 1155 (9.5) At risk 4924 (20.4) 795 (20.4) 10097 (15.5) 1765 (14.5) Distressed 1611 (6.7) 236 (6.1) 2839 (4.4) 534 (4.4) Missing 495 (2.1) 79 (2.0) 667 (1.0) 127 (1.0) Age of birthing person (year) 29.4 (5.9) 29.7 (6.1) 30.4 (5.4) 30.8 (5.4) Height of birthing person (cm) 162.7 (7.4) 162.6 (7.2) 163.1 (7.2) 163.0 (7.1) Gestational age at delivery (weeks) 38.4 (2.4) 38.3 (2.3) 38.8 (1.7) 38.7 (1.7) Pregnancy weight gain (kg) *only cases with non-missing weight gain data (N=21,560) 18,554 / 21,560 (86%) 3006 / 21,560 (14%) Median (IQR) 6.8 (2.7 – 11) 7.1 (2.5 – 11.7) 12.3 (8.6 – 15.9) 12.3 (8.6 – 16.3) 86 Table 7.3 Pooled model estimates from analysis using imputed datasets (20) and complete cases Pooled estimates from imputed datasets using MI Estimates from primary analysis (complete case analysis) Estimate (p-value) 95% CI SE Estimate (p-value) 95% CI SE Pregnancy weight gain (kg) Level change 0.232 (0.072) -0.020, 0.484 0.128 0.348 (0.006) 0.102, 0.593 0.125 Trend change 0.001 (0.825) -0.009, 0.011 0.005 0.001 (0.9) -0.009, 0.011 0.005 Pregnancy weight gain z-score Level change 0.016 (0.568) -0.040, 0.072 0.029 0.051 (0.028) 0.006, 0.096 0.023 Trend change 0.001 (0.314) -0.001, 0.003 0.001 0.001 (0.4) -0.001, 0.003 0.001 Table 7.4 Descriptive results for un-restricted weight gain cohort compared to complete case cohort Complete case cohort Un-restricted weight gain cohort N=77411 N=98682 Nulliparous 31887 (41.2) 40301 (40.8) Race and ethnicity of birthing person: Non-Hispanic White 40482 (52.3) 50340 (51.0) Non-Hispanic Black 3182 (4.1) 4666 (4.7) Hispanic or Latinx 12759 (16.5) 16619 (16.8) Asian or Pacific Islander 15608 (20.2) 19371 (19.6) Native American or Native Alaskan 799 (1.0) 1304 (1.3) Other or mixed race 2335 (3.0) 3295 (3.3) Missing data 2246 (2.9) 3087 (3.1) Payor is Medicaid: yes 22888 (29.6) 33001 (33.4) missing 2379 (3.1) 2418 (2.5) Distressed Communities Index: Prosperous 35119 (45.4) 42181 (42.7) Comfortable 19177 (24.8) 24617 (24.9) Mid-tier 7086 (9.2) 9782 (9.9) At risk 11862 (15.3) 16147 (16.4) Distressed 3373 (4.4) 4822 (4.9) missing 794 (1.0) 1133 (1.1) Age of birthing person (year) 30.5 (5.4) 30.3 (5.6) BMI in early pregnancy (kg/m2) 27.1 (6.6) 27.5 (6.7) Height of birthing person (cm) 163.1 (7.2) 163.0 (7.2) Pregnancy weight gain (kg) 12.3 (6.2) 11.3 (6.5) weight gain z-score -0.1 (1.1) -0.3 (1.2) Weeks between pre- or early pregnancy weight and final weight for weight gain calculation 31.7 (4.6) 28.1 (8.8) Gestational age at delivery (weeks) 38.8 (1.7) 38.7 (1.8) Infant birthweight (g) 3365.4 (537.5) 3349.5 (553.3) 87 Table 7.5 ITS model results for unrestricted weight gain cohort v complete case cohort Un-restricted weight gain cohort Complete case cohort Estimate (95% CI) p-value Estimate (95% CI) p-value Pregnancy weight gain (kg) Level change 0.688 (0.461, 0.915) <0.001 0.348 (0.102, 0.593) 0.006 Trend change 0.004 (-0.006, 0.013) 0.4 0.001(-0.009, 0.011) 0.9 Pregnancy weight gain z-score Level change 0.120 (0.077, 0.163) <0.001 0.051 (0.006, 0.096) 0.028 Trend change 0.001 (-0.001, 0.003) 0.2 0.001(-0.001, 0.003) 0.4 88 7.7 Figures Figure 7.1 Un-restricted weight gain cohort exclusions 89 Figure 7.2 Unrestricted weight gain cohort: Interrupted time series graphs and results 90 8 CONCLUSION The work presented in this thesis explored how policy changes during the time of antenatal care1–3 can impact pregnancy health and highlight new directions for further research in these areas. This theme was examined in the context of pregnancy nutrition and antenatal health care. My dissertation work included three studies on gestational diabetes in BC: (1) a validation of a novel approach to characterize antenatal screening data using insurance billings data; (2) a descriptive study of gestational diabetes screening patterns; (3) an analysis of the impacts of gestational diabetes screening completion, methods and population characteristics on rising rates of gestational diabetes. A final study (4) was an interrupted time series analysis of the impact of the COVID-19 pandemic on pregnancy weight gain and infant birthweight using a dataset with contemporary and detailed clinical data from hospitals in Washington State. 8.1 Gestational diabetes screening and diagnoses 8.1.1 (Chapter 3) A validation of using insurance billings data to assess antenatal screening data against medical records data In our validation study, we demonstrated that laboratory insurance billing data for three antenatal screening tests’ completion and methods were accurate when compared with medical record data. Specifically, billings data was able to capture screening completion for gestational diabetes, 1st trimester ultrasound and Group B streptococcus with high sensitivity (97 – 100%), specificity (68 – 100%), positive (97 – 100%), and negative (63 – 81%) predictive values. Gestational diabetes screening approaches (one-step or two-step) were also well characterized although 45% records did not report screening approach and thus sample size for validation was reduced. Validation parameters in subgroups did not point to key biases in the method but were generally consistent with the main results. There are some examples of billing data being used to characterize antenatal screening tests in the literature,161,167,168 but none report a formal validation of this approach. Both gestational diabetes and Group B streptococcus are widely studied conditions in perinatal epidemiology but issues of bias in screening completion are rarely discussed. In particular, most population-based gestational diabetes studies lack data on screening and note this as a significant limitation.16 While diagnostic codes based on billing or hospital discharge data (e.g., ICD-10 codes) have been validated,271 procedure codes are less commonly used. One other validation study examined the use of a procedure code for ultrasound in a pregnant population. This study272 validated a billing-based approach to ascertain prenatal conditions including GDM diagnosis and completion of ultrasound procedures in a 91 US setting. They did not examine GDM screening in this study. Similar to our findings, they reported a high sensitivity and PPV for both GDM diagnosis and ultrasounds. 8.1.2 (Chapter 4) A descriptive analysis of different approaches to gestational diabetes screening in BC, Canada In our study of screening practices for gestational diabetes, we demonstrated substantial variability over time and by subgroups for the uptake of a one-step screening method. The 2010 policy change to use one-step screening in BC was based on updated international IADPSG criteria38 but contemporaneous Canadian guidelines were not consistent42 with those criteria. Thus, there was a discrepancy between the province’s policy and Canadian professional association guidelines. Following the policy change in October 2010, one step screening uptake was concentrated in the large urban centers with over 70% of people receiving one step screening by 2012-2013. By contrast, rates in other areas increased slightly (~25%) or negligibly (5%) as compared with 2005-2009 (<2% across the province). We also noted a trend towards universal early uptake of one-step screening, at least within regions where it was applied at all. This was expected, given the policy was intended to apply to all pregnancies. Updates in 2013 and 2016 to Canadian guidelines included the IADPSG criteria using a one-step method but classified it as an “alternate” approach. After this, uptake patterns changed and became more “risk-based”. After 2013, one-step screening, which is a more sensitive approach to diagnosing people with GDM, was used primarily for people who also had underlying risk factors. Arguably, this is a reasonable and appropriate application of the current Canadian guidelines by clinicians who are recommending a one-step method for patients with additional risk factors for both gestational and type 2 diabetes. While there are many studies15,39,86,86,106,109 that demonstrate before-and-after effects of a change from two-step screening to one-step screening, most were done in closed systems, where one-step screening was applied uniformly, for example, without an alternate method or in a randomized trial where allocation was done by intent-to-treat. It is notable that several pragmatic studies reported lower adherence to any screening for groups that received a one-step method compared to a two-step method.82,273 In one recent randomized trial in the US (Hillier at al.82), over 11,000 people were assigned to each study arm (one-step v. two-step) but only 70% in the one-step group adhered to this method. The authors noted that “factors related to lower adherence included both maternal and provider characteristics as well as provider reliance on non-fasting tests to ensure that gestational diabetes screening was completed at a visit.”82 By contrast, >97% in the two-step arm completed two-step screening. Almost 9% of individuals in our BC cohort were not screened for gestational diabetes using recommended methods during the study period. This is similar to a 2008-2012 study in Alberta78 that 92 reported 8% had neither two-step or one-step screening. We were unable to locate other population-level studies that reported substantial variation in gestational diabetes screening methods (relative use of one-step and two-step) as we noted in our study in BC. In the Alberta study (Donovan et al.78), 99% of those screened (with recommended methods), were screened using a two-step approach. Kong et al.105 used a before-and-after design to assess changes in perinatal and neonatal outcomes in BC after the 2010 policy change, but they lacked any data on screening uptake. This was acknowledged as a limitation in their study. The 2013 Canadian guidelines that allowed for either one-step and two-step screening were among the first published guidelines to recommend multiple different approaches (Table 2.4). However, the US also has a mix of screening practices, in part, because the two main organizations recommended different methods (American Diabetes Association v. ACOG). One survey of US maternal-fetal medicine physicians reported only 10% of American MFM physicians118 preferred one-step screening, however regionally these preferences varied from 24% to <4%. 8.1.3 (Chapter 5) Changes in screening and population factors explained changes in diagnosis of GDM In this analysis, we used the data on screening in BC to explore how temporal changes in screening have impacted GDM risks by year. This BC cohort was unusual in having data on screening practices that changed substantially over time, a large cohort and individual level health data. Other studies have rarely had data on all three of the domains we assessed: screening completion, screening method and population risk factors. Prior research has demonstrated that where screening practices were stable, rates remained consistent and/or population level changes (increases in body mass index or older age) explained these changes.201,202 Several other recent studies have shown increasing trends across all subgroups;16,204 or increasing trends even after controlling for shifts in demographics.16,45,198 This latter group are from regions where screening data was not available and where current practice represents a mix of screening methods. Our study results demonstrate that the substantial, unexplained, increase in gestational diabetes in BC could be spurious. Specifically, this is attributed to screening changes, primarily, rather than underlying risk factor changes. 8.2 COVID-19 pandemic, pregnancy weight gain and infant birthweight This study reported a relatively modest (estimated as +0.49 kg) increase in weight gain after the onset of the COVID-19 pandemic in Washington state with no change in infant birthweights. However, we also 93 found a larger weight gain increase (1.13 kg) for the highest 90th percentile of weight gain. Pandemic-associated weight increases have been reported in other settings,73,246,274 but not with rigorous quasi-experimental methods as in our study. Stratification by pregestational BMI did not demonstrate any differences in the main findings. While both increases may appear relatively modest, this could contribute to changes in the trajectory of weight gain throughout a pregnancy. This altered trajectory has been associated with increase in risk of developing gestational diabetes. A recent study from Italy275 reported a statistically significant temporal relationship between experiencing ‘lock-down’ (using interrupted time series methods) in the first trimester of pregnancy and later GDM risk. In their study, the GDM diagnosis rate in 2019 was 9% compared to 13.5% in 2020 and there was a strong increase in monthly diagnoses for those who experienced lock-down in their first trimester. While an analysis of COVID-19 associated impacts on GDM in the Washington State cohort was out of the scope of this dissertation, preliminary data suggests a similar trend with higher GDM rates in the 6-months after the COVID-19 pandemic. On the other hand, our results could be viewed in a reassuring light, since there was only minimal change in weight gain and this did not appear to have any effect on infant birthweight. 8.3 Significance and contribution 8.3.1 Validation Validation studies are a critical component of epidemiologic research that is often under-utilized and important for addressing and mitigating information bias in epidemiologic analyses.276 Importantly, validation parameters estimated in this analysis could be used to inform other studies where billing data is used to assess screening status. In terms of the three screening tests validated in this study, each has specific applications in perinatal research. Ascertainment of gestational age at delivery is a critical component of clinical decision-making around timing of delivery and risk management in the context of obstetric complications.277 Further, gestational age (GA) is an important confounder (or mediator)243 in almost all perinatal outcomes. In clinical practice and in research, gestational age ascertained by an early (1st trimester) ultrasound is considered more accurate than either a last menstrual period or a later (2nd or 3rd trimester) ultrasound.174,278,279 Therefore, ascertainment of early ultrasound completion could address information bias in a broad range of perinatal research that rely on gestational age ascertainment. Characterizing screening status is also particularly important for studies where screening is directly associated with a diagnosis, such as with gestational diabetes. When a diagnosis is contingent on 94 screening, then it is crucial to correctly specify the denominator of the population at risk. Second, screening methods are rarely considered in epidemiologic studies of gestational diabetes despite known differences in screening methods. Therefore, the method we developed and validated in this study could be an important aspect of addressing information bias in future studies of gestational diabetes. Best practices for identifying mothers/people at risk for Group B sepsis in newborns is also widely debated; specifically, whether to use risk-based criteria for treatment as compared to screening.280 Therefore, application of our validated approach to identifying GBS screening status could also be informative to improving research in this domain. 8.3.2 Gestational diabetes The field of research impact is a developing area of study that explores how research generates benefit (in multiple domains) and concerns itself with what research impacts are and how to measure them.281 Descriptive research can contribute to the field of health policy and research impact by providing information about the population level effects of a policy change. There is relatively little available public documentation on the BC policy change in October 2010. For example, who decided on this policy? Was there consultation with experts, clinicians, patients or government? Was there some education or outreach across the province after it was implemented? Was there additional planning or resource allocation to address the increase in costs for laboratory testing and treatment (i.e., more diabetes would be diagnosed) after this policy was implemented? This could be an important example where research impact tools281,282 could be applied to understand both the harms and benefits of changes in GDM screening across the population. Importantly, the second gestational diabetes study (Chapter 5) demonstrates that current research on gestational diabetes-associated risk and any GDM-associated perinatal outcomes must consider underlying variability in screening practices. This is especially important given the mixed approach of Canadian guidelines. This may also have implications for large population studies in other countries as the impact of the IADPSG guidelines on clinical practice is highly variable. Some clinicians are strongly aligned with this guideline, whereas others remain skeptical. Despite over 14 years since the HAPO study was published, there is still no consensus about the best way to screen and diagnose GDM. In response to the two Canada-wide revisions of practice guidelines (2013 and 2016), there has been little published data on how this has impacted health systems across the country. While we have contributed information on this topic specific to British Columbia, our study also highlights a national-level research gap in this area. 95 8.3.3 COVID-19, pregnancy weight gain and infant birthweight The study on COVID-19 contributes to our general understanding of how the pandemic has impacted pregnant people and points to a need for ongoing surveillance on down-stream effects of increased weight gain such as hypertension and diabetes. The increase in pandemic-associated weight gain was strongest for those who were already on a high trajectory of weight gain z-score (>90th percentile). Our overall findings for the effect of the pandemic were unchanged when stratified by pregestational body mass index subgroups. This suggests that the pandemic effect on weight gain was the same across the population, regardless of pre-pregnancy nutritional status. However, people who have a high BMI before pregnancy are recommended to gain less weight overall so the small COVID-associated increase could be most impactful in this population. Our findings about seasonality in weight gain are also highlight this as a key issue with perinatal epidemiology, in general.239,243 As the long-term effects of the COVID-19 pandemic are studied and understood in public health, we can hope to learn from both the good, and the bad. On the one hand, we found a stronger impact for those who were already gaining more weight but, on the other hand, the population change in weight gain was small overall. 8.4 Strengths and limitations This research was strengthened by the completion of a validation study (Chapter 3) as the foundation of two analyses of gestational diabetes (Chapters 4 and 5). The potential for selection bias is minimized by the use of population-based data and billings from a universal health care system. Our analysis in Chapter 4 was strengthened by our access to individual-level health and screening data for the majority of the BC population over a 15-year period, representing over 500,000 pregnancies. We assessed the robustness of the gestational diabetes findings (Chapter 5) through multiple sensitivity analyses. The study on COVID-19 was strengthened by the use of a robust quasi-experimental study design and an interrupted time series analysis. This study also used z-scores to standardize the pregnancy weight gain for body mass index, gestational age. We also used a number of analytic approaches to investigate the robustness of the findings to different reference standards, selection bias, and missing data (Chapter 6). Overall, the studies in Chapters 4 and 5 were limited by using observational designs. Because Chapter 4 is purely a descriptive analysis, these findings highlight patterns we observed in the data and can be used for hypothesis generating. Unmeasured confounding is possible in both cases, in particular, we have no information on race/ethnicity. 96 The method used to ascertain screening status in both studies was based on billings for health care services. If an individual used health care services outside the province or did not have current insurance health care during their pregnancy near the point of screening, their screening billing might not be captured leading to bias in the screening data. Therefore, we excluded people without active health insurance during the majority of their pregnancy, with prenatal care that appeared to begin (in BC) late in pregnancy, and who resided outside the province. We also excluded pre-existing diabetes and deliveries (<28 weeks) that occurred before routine GDM screening. Our exclusions may introduce selection bias towards a less transient and possibly healthier population because we excluded people with incomplete care. This is unavoidable since the method to ascertain screening would inaccurately code people as unscreened if they did not have current insurance or if they were screened outside the province. Thus, the prevalence of GDM we reported may not be comparable to other settings; unless they used similar exclusion criteria (i.e., gestational age, insurance status etc.). Overall, the characteristics of the full available births did not differ substantively from the analytic cohort after exclusions (Table C.3, Table C.4, Table C.5, Table C.6). This selection bias is unlikely to impact the internal validity of the findings in Chapter 5. The key strengths of the COVID-19 study were the use of a quasi-experimental design and interrupted time series analysis. We also used a conception-time cohort rather than a delivery-date cohort to control for seasonality, employed bootstrapping to estimate confidence intervals, and used hospital-level random effects to account for systems-level variation. Additional sensitivity analyses assessed issues of missing data, reference standards, subgroup analyses and used conditional quantile regression to explore associations across the distribution of weight gain. As with all regional studies, there may be specific regional population characteristics that could differ from other locations. This is always important to consider and could affect generalizability of these results to other populations. In particular, for the COVID-19 study, pandemic-associated countermeasures were relatively widespread in Washington State, and may have been more restrictive than some other US States but less restrictive than in some parts of Europe. 8.4.1 Specific limitations In Chapter 4, one limitation of the validation study was the use of medical records from only three hospitals in BC. BC health services delivered by doctors and midwives are mostly on a fee-for-service basis, but there are some exceptions. However, we are unaware of any laboratories (either outpatient or in-hospital) that do not bill using a fee-for-service mode. This study was strengthened by the use of a stratified random sample of medical records from two different charting systems (paper charts and EMR) 97 from different health care regions. We also used sample-weights to adjust for underlying sample stratification. In Chapter 4, we were limited by available data sources for geographic and other characteristics. While it could be informative to report gestational diabetes screening or diagnoses among subgroups by individual-level data for income, ethnicity, dietary factors, exercise or relative to the location of food deserts (regions with limited availability of grocery stores with fresh food), this data is simply not available for this cohort. We observed a substantial difference in screening in the northern region and it is possible that this could be due to a differential measurement bias in our underlying method. Based on personal communication with several clinicians in the northern health region, they reported that one-step screening for GDM was rarely used in this region. In Chapter 5, a potential limitation of this analysis is unmeasured confounding, particularly by other underlying risk factors like family history of diabetes and race/ethnicity. Both are risk factors for GDM diagnosis and could be changing in the BC population. To address uncontrolled confounding by race/ethnicity, we used linked infants’ birth certificate data and controlled for the mother’s place of birth. While an imperfect indicator, we did observe screening differences by region of mother’s birth which suggests this variable is capturing some of the effect of unmeasured confounding by race/ethnicity. A key limitation of the COVID-19 study is selection bias due to the restriction to those with valid weight gain data as this excluded over 22% of pregnancies. In Chapter 7, I present a detailed review of this issue and argue that this bias is unlikely to be differential with respect to the intervention (the COVID-19 pandemic). We were also unable to control for unmeasured confounders but we see no reason that the key factors would have changed for reasons other than the pandemic, thus, our study is appropriately characterizing the pandemic impacts. 8.5 Applications of these findings 8.5.1 Applications to perinatal research and epidemiology The research in this thesis validated a method of ascertaining antenatal screening data that could be used in future research and has broad applications in perinatal or other health research. For gestational diabetes research, the work in my thesis demonstrated the critical importance of screening in understanding patterns of diagnosis of GDM. This also has important implications for planning new studies where GDM is considered and/or when examining perinatal or long-term health sequelae associated with a GDM diagnosis. 98 As we know, the COVID-19 pandemic will likely have long-lasting health effects on populations due to a myriad of factors: health system changes, mental health changes and physical health changes. The slight pandemic health effects on weight gain could be important to consider as a possible confounder in future studies that use weight gain either as an exposure or outcome. 8.5.2 Applications to public health practice, clinicians or pregnant women/people The descriptive data on GDM in BC may be helpful to public health and surveillance in the province and in health regions. This could inform health systems planning for gestational diabetes treatment services, clinician training and/or patient education or outreach. Follow-up re-screening in the first year after a gestational diabetes pregnancy is recommended, but there is little available data on this topic. This could be assessed using billing code methods, as we did in this study. Overall, the results and methods developed in this dissertation study could be applied to public health surveillance and to knowledge translation or dissemination tools at the hospital or community level. The controversy around gestational diabetes is easily seen in public discourse (blog posts, social media) by pregnant women/people who are diagnosed with this condition. As clinicians, we may not engage deeply with this controversy when discussing screening recommendations; however, this may not be ideal. Minimizing the controversy around GDM may erode individuals’ trust in traditional medical care.89 Shared decision-making tools could be developed that draw on the information in this work.184,283,284 Such tools could help pregnant people and health care professionals engage in a balanced and informed discussion about how, when and who to screen for gestational diabetes. For example, an excellent visual pamphlet on GDM published in 2014285 (https://www.renaissancemidwifery.ca/docs/gdm_pamphlet.pdf) is no longer aligned with current clinical guidelines. This pamphlet describes a risk-based approach to screening and assumes only a two-step approach to screening and diagnosis. Up to date patient information for BC should be developed to reflect current screening guidelines and the BC context. 8.6 Future research Validation studies are fundamental buildings blocks to future research studies. In this case, we hope that the method described and validated in this thesis could be applied in other regions. If applied in a region without universal health insurance/single-payor system, then there may be specific limitations for that context and additional validation could be useful. Alternately, other procedures that could be identified through billings (e.g., medication abortion, pap tests, intrauterine device (IUD) insertions, mammograms, cancer screening) can be characterized with a similar approach. 99 Using this cohort, we hope to study antenatal screening completion for all three antenatal procedures/tests, across BC. Further, this data can be applied to explore differences in perinatal outcomes among subgroups by screening status. Our method of capturing glucose tests using billing data could also be applied to understand the proportion of women in BC who receive a recommended post-partum diabetes screening in the first year after a gestational diabetes diagnosis. Gestational diabetes remains a widely debated and controversial topic. Future research should also focus on evaluation of the health systems impacts of the current Canadian guidelines and the extent to which the current approach benefits Canadian women and birthing people. There are numerous conceptual frameworks281,282 that could be applied to this explore question. Future research should also include mixed methods and/or qualitative methods to incorporate the views and preferences of birthing people in Canada with respect to gestational diabetes in pregnancy. Given the substantial personal and qualitative impacts of a gestational diabetes diagnosis89,144, and new research showing equivocal perinatal benefit of a one-step approach (vs two-step)81,82,117, arguably, we need to better understand the perspectives and values of Canadian women and birthing people on this topic. For mild hyperglycemia or diet-controlled GDM, in particular, explaining this diagnosis through a risk factor lens, rather than as a disease, may be helpful for pregnant people.144 Differences in screening across Canada may be important determinants of differences in prevalence. We described regional disparities in screening practices, possibly because of a concern about resource allocation or systems costs, but we lack any data to support this supposition. This would be an important hypothesis to test in future research in other Canadian regions. Prevalence of gestational diabetes across Canada is increasing and is substantially higher in BC than in the other Canadian provinces included in CIHI data (excludes Quebec). Specifically, a Perinatal Health Indicators data table, available from the Public Health Agency of Canada, showed that for 2017, the rate gestational diabetes in BC was 13.2% as compared to 9.0% across the country (using CIHI-DAD data, Figure 8.1).47 Using available 2019-2021 CIHI-DAD data from UBC’s data repository (a 10% random sample of the full CIHI-DAD), I reproduced a comparable sample. The 2019-2021 rate of GDM was 13.9%, again, substantially higher than the national average (9.7%). In our study, the 2019 GDM diagnosis rate was 14.7% (95% CI 14.3, 15.0) after exclusions (non-standard GDM screening, insurance status, gestational age and pre-existing diabetes) for study design issues. Using the full available births for this time period, I calculated an observed GDM diagnosis rate in 2019 in BC was 14.3% (95% CI 13.9, 14.6). 100 Based on CIHI-DAD data,47 and confirmed by the results from this study, BC has substantially higher rates of diagnosed GDM than other Canadian provinces. Another topic for future research would be to examine gestational diabetes screening practices across Canada and in subgroups such as First Nations or Inuit people. Differences in GDM screening could be a major cause of inter-provincial differences in GDM prevalence which could have a disproportionate burden, or benefit, across different health care systems. Drawing from the COVID-19 pandemic study, future research should explore the effect of any pandemic-associated weight gain on gestational diabetes. Further, using local nutrition data (i.e., grocery store data) or mobility data (i.e., from smartphones) could be an interesting and understand patterns of weight gain in pregnancy. Future research could also consider whether our results on seasonal trends in weight gain could be replicated using other data sources, for example, vital statistics data. 8.7 Conclusion This thesis broadly examined two topics that link to pregnancy, nutrition and antenatal health care. One chapter was a validation study, two chapters used a population-based cohort with descriptive and analytic methods, and the fourth chapter used a quasi-experimental design and an interrupted time series analysis. In the first three chapters, I developed and validated a novel approach to identify antenatal screening and applied this method to examine gestational diabetes in BC. The descriptive chapter demonstrated potential disparities in access or uptake of screening by region and highlighted how policy and guideline changes shift clinical practice. The second chapter revealed how these screening changes directly led to rising gestational diabetes diagnoses in BC. BC has the highest provincial rate of GDM diagnosis in Canada and screening practices in this province may differ from the rest of Canada. The final research chapter found a modest COVID-19 pandemic associated change in pregnancy weight gain in Washington State. This demonstrated how global events can lead to small, measurable changes in pregnancy health and nutrition. Overall, this research highlights the critical importance of characterizing screening in studies of gestational diabetes. Further studies of gestational diabetes and other antenatal conditions could apply these methods to incorporate screening data in their analyses. Pregnant women and people, clinicians and health planners in BC should understand the extent to which screening method directly impacts diagnosis risk and incorporate this information for systems planning and clinical decision-making. 101 8.8 Figures Figure 8.1 Rates of gestational diabetes in Canada (excluding Quebec), 2017, from the Public Health Agency of Canada based on CIHI-DAD data 7.1 7.9 7.8 7.3 8.3 9.2 7.3 8.6 13.2 8.4 8.7 4.50246810121416NL PE NS NB ON MB SK AB BC YT NT NURate of GDM (%, 95% CI)Province/TerritoryCanada-wide average 9.0% in 2017 102 BIBLIOGRAPHY 1. BC Perinatal Health Program. 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Unfortunately, the BC-PDR captures diagnosis of GDM but does not capture screening status, either completion or method. The BC-PDR does capture 1st trimester ultrasound and Group B strep screening and diagnosis status. Race and ethnicity data are not captured in this data registry, nor is it collected on most standard medical records in BC, therefore we were unable to report data on race or ethnicity. Pregnancies and births (live and stillbirths) are only captured in the BC-PDR if they happen >20 weeks gestational age or if birthweight of fetus or newborn is >500g. The BC-PDR does not include data for deliveries that are therapeutically or spontaneously delivered at <20 weeks gestation and are <500 grams fetal birth weight. This registry also includes all hospital births, all home births attended by Registered Midwives in BC and all births (e.g., en-route or unplanned home births) where hospital or emergency services were involved. Births outside of the hospital that were unattended, or not attended by a registered medical provider (aka “freebirth” or an intended home birth with an unlicensed attendant) in BC are not captured in the PDR. Since Registered Midwives in BC attend home births, are covered by insurance, and legal/licensed by the province (since 1998), we estimate that the proportion of unattended home births in BC is very small. 2) MSP-billings report all fees and procedures billed to the public health insurance system for each individual study id during all years. We restricted to billing records for services during each pregnancy time period and then identified all billings relevant to the codes of interest (listed in eTable1). 3) The MSP-registration file reports the start and end day of the year of active insurance registration for each person and year. Utilising this data, we identified months of active health insurance during the duration of the pregnancy. We did not count the first 2 months of the pregnancy, because we anticipated some error in our ascertainment of pregnancy start month and because most prenatal care services would not happen until after this point. We calculated a percentage of pregnancy months with active insurance coverage and considered those with less than 90% of months with active insurance as having “incomplete insurance for the pregnancy”. MSP covers all eligible BC residents except for a small population who are 116 insured by the Federal government (estimated at <5% of total BC population)173. These are: ‘Status Indians’1 (First Nations and Inuit), veterans, active-duty military and Royal Canadian Mounted Police (RCMP) who have separate (federally funded) health insurance. However, the billings file includes billings for these other federal payors, therefore these groups are included in the study population. Additionally, there a waiting period (at least 3 months) before people can register for BC-MSP for all new residents in the province. This applies to anyone with valid residency: Canadian citizens moving from another province, temporary workers on visas, refugees, new immigrants, students with valid visas. Interruptions in health insurance coverage are not uncommon in pregnancy.286 Last, there are people who pay directly or out-of-pocket. This may include citizens of other countries, who do not have residency status in BC. Uncertainty in linkage between the 3 data sources is not uncommon depending on the specific time period or cohort. For example, all births in the province are included in the BC-PDR, but not everyone is registered in the health insurance plan, therefore, some PDR records are completely unlinked to billings data. Further, some individuals have incomplete insurance coverage during their pregnancy, therefore their billings may not accurately contain all tests or screening procedures during their whole pregnancy. For example, if their insurance became active in the 6th month of pregnancy, then they may have paid out of pocket for lab services early in pregnancy, or they may have been covered by a different health insurance plan for part of their pregnancy before their BC insurance became active. Sample size, strata and sample weights Sample size was based on an estimated proportion (prevalence) between 90-95% for GDM screening and achieving a precision of +/- 4% and we requested a random sample evenly stratified by health care professional type at delivery (1/3 of cases by Registered Midwife, Family Practice Physician, or Obstetrician). A study in Alberta, Canada reported a screening completion rate78 for any diabetes screening in a population-based study of pregnant women of ~95% when including all possible tests. While we anticipated a similar population-based screening rate in BC, we anticipated lower screening completion rates among midwife-led care,125 thus we planned to oversample among Registered Midwife clients. While the proportion of midwife-involved care as of 2020 is roughly 30% in BC, the requested sample will oversample in the midwife group because we estimate a lower screening prevalence in this care provider group. A large chart validation study in BC from 2010-12 reported 100% completion for Group 1 ‘Status Indian’ is a legal term referring to the Indigenous identity of people registered under the Canadian government’s Indian Act and eligible for certain government benefits and services. 117 B Strep screening176 but only 83% had valid screening data. Therefore, we anticipated a true prevalence of GBS screening completion between 100-83%. Health authorities who provided the random sample identified health care professional type and pregnancy year from hospital discharge records. We used data from the full BC population (using administrative data) from 2014-2019 and calculated the total number of births in each sampling strata according to the health care professional groups from hospital discharge records. We calculated sample weights and applied these weights for validation statistics and sample demographics. This enabled our results to reflect the hospital populations from which they were sampled. Survey weighted methods (R library survey) were used to calculate all proportions reported for the validation and demographics, confidence intervals were calculated using an incomplete beta function (option “beta”) with an effective sample size based on the estimated variance of the proportion.287 In a few cases, R could not compute valid intervals and we re-calculated confidence intervals using proc surveyfreq in SAS (v 9.4) using design-based Clopper-Pearson confidence limits which apply a similar approach.287 Medical records chart abstraction procedure All medical records were abstracted by the first author of the study who is also a Registered Midwife in BC. We derived all procedures for data coding of screening data from charts, decision trees and an MS Access database (secured) with internal data checking/validation prior to beginning data abstraction. The variables and coding procedures for abstraction were reviewed by study co-authors prior to abstraction and also reviewed by two hospital ethics approval committees and the data stewards for the administrative datasets as part of their approvals process. Medical records from one hospital were paper-based while medical records at the other two hospitals were via an electronic medical records system (EMR). Prior to chart abstraction, training was provided for chart-based records by the medical records manager; however, the first author had previously had hospital privileges and was attending births at this site so was familiar with these medical records. For the EMR system, the health authority required completion of several online training modules before granting access to the EMR system. Background on screening and diagnosis of gestational diabetes in BC In Canada, the two main guidelines for management and treatment of diabetes in pregnancy are from Diabetes Canada (DC) (formerly known as the Canadian Diabetes Association (CDA)) and the Society of Obstetricians and Gynecologists of Canada (SOGC).42 Presently, both recommend a two-step screening approach (50g GCT followed by 75g OGTT) as the “preferred” option, with the “alternate” option being a 118 one-step 75g OGTT using the International Association of Pregnancy Study Groups (IADPSG) criteria.114 Thresholds for diagnosis of gestational diabetes differ depending on which approach: One-step screening is diagnosed when blood glucose levels exceed the IADPSG criteria (fasting 5.1, 1-hr 10.0, 2-hr 8.5 mmol/L) after a 75g OGTT. Two-step screening is diagnosed with a higher threshold (fasting 5.3, 1-hr 10.6, 2-hr 9.0 mmol/L) after the diagnostic 75g OGTT. There is no available data on prevalence of different screening approaches in Canada. Antenatal health care professional type coding We used a previously defined algorithm 177,288,289 to define health care professional type using billings data. The purpose of this criteria was to establish a minimum level of exposure to a single health care professional-type, as has been done in other comparison studies of midwifery v physician care. Details of this are: antenatal care with a family practice physician (FP) was defined as greater than or equal to three routine antenatal visits with this provider type, and less than or equal to one routine antenatal visit with an obstetrician (OB), or less than or equal to one partial trimester of midwifery care. Antenatal care with an OB was operationalised as greater than or equal to three routine antenatal visits with an OB, and less than or equal to one routine antenatal visit with FP, or less than or equal to one partial trimester of midwifery care. Antenatal midwifery care was operationalised as greater than or equal to two partial or full trimesters of midwifery care (equivalent to a minimum exposure of three routine antenatal physician visits), and less than or equal to one routine GP or OB antenatal visit. OB consultations were not included as routine antenatal visits. The “missing” or < 2 antenatal visits category was defined by cases when the algorithm could not identify a single care provider with 3 or more visits or where there were < 2 visits overall. 119 A.2 Tables Table A.1 Detailed definitions of billing codes used from BC MSP for coding antenatal screening tests Code Code description Antenatal screening test coded 8655 OBS. B-SCAN - LESS THAN 14 WKS. 1st trimester ultrasound 96782 APB OBS-B-SCAN UNDER 14 WEEKS GESTATION 1st trimester ultrasound 96785 APB OBS-B-SCAN <14WKS/NUCHAL TRANSLUCENCY-ADD FETU 1st trimester ultrasound 96786 APB OBS-B-SCAN <14WKS/NUCHAL TRANSLUCENCY-ADD FETU 1st trimester ultrasound 91690 GLUCOSE, GESTATIONAL ASSESSMENT 50g glucose test (GCT) (used for two-step screening) 91695 GLUCOSE TOLERANCE TEST-GESTATIONAL PROTOCOL 75g OGTT (used for both one-step and two-step) 91715 GLUCOSE TOLERANCE TEST, 2 - 5 HOURS 75g OGTT 91716 GLUCOSE TOLERANCE TEST > 6 HOURS 75g OGTT 90739 COMBINED VAGINO-ANORECTAL OR VAGINAL CULTURE Group B streptococcus test 120 Table A.2 Pregnancy and obstetric characteristics for all linked records, records with incomplete insurance and final validation group (unweighted %) Characteristic n (%) linked to administrative data N = 1351 available for validation N = 127 incomplete insurance N = 8 p-valuea Parity 0.13 P0 54 (40%) 48 (38%) 6 (75%) P1-P3 76 (56%) 74 (58%) <5f P4 or more 5 (3.7%) 5 (3.9%) <5 Age of birthing person at delivery (years) 0.61 < 24 14 (10%) 13 (10%) <5 25-29 36 (27%) 32 (25%) <5 30-34 51 (38%) 49 (39%) <5 35-39 28 (21%) 27 (21%) <5 40+ 6 (4.4%) 6 (4.7%) <5 Pre-pregnancy body mass index (kg/m2)b 0.95 <24.9 59 (44%) 56 (44%) <5 25-29.9 30 (22%) 28 (22%) <5 30+ 19 (14%) 18 (14%) <5 missing data 27 (20%) 25 (20%) <5 Neighbourhood income quintiles per personc 0.53 lowest income 29 (21%) 27 (21%) <5 mid-low income 28 (21%) 26 (20%) <5 middle income 18 (13%) 18 (14%) <5 mid-high income 33 (24%) 32 (25%) <5 highest income 13 (9.6%) 11 (8.7%) <5 missing or NA 14 (10%) 13 (10%) <5 Antenatal health care professional typed 0.33 Family practice physician 54 (42%) 53 (43%) <5 Registered Midwife 52 (40%) 50 (40%) <5 Obstetrician 23 (18%) 21 (17%) <5 Missing or <2 antenatal health care visits 6 <5 <5 Multifetal pregnancy 12 (7%) 11 (7%) <5 0.44 Gestational age at delivery: preterm (<37 weeks) 123 (93%) 116 (93%) 7 (88%) Mode of deliverye Cesarean 46 (34%) 43 (34%) <5 >0.99 Operative vaginal 13 (9.6%) 13 (10%) <5 Spontaneous vaginal 76 (56%) 71 (56%) 5 (62%) a. Fisher's exact test b. Pre-pregnancy BMI was ascertained from medical records. Standard medical records in BC collect data for a pre-pregnancy weight and height, we calculated BMI and reported by categories as defined by the Institute of Medicine standards.290 c. Neighbourhood income quintiles (based on Statistics Canada)50 represent the average income in the area (census tract). Census tracts were linked by the data stewards at the residential postal code level for the residence of the birthing person/mother. This data was obtained from administrative sources. For example, an individual living in the highest income quintile region is living in an overall ‘wealthier’ neighbourhood. d. Antenatal health care professional type (“Family practice physician”, “Obstetrician” or “Registered Midwife”, or “Unknown and/or <3 visits”) was defined using billing codes via a previously published method.177 Generally, this required a minimum of 3 routine antenatal care visits with the specified provider type. Consultation visits were not included. Number of visits were assessed using counts of prenatal care fee-for-service billing codes by professional type. 121 e. Mode of delivery, gestational age at delivery, parity, is reported from medical records. Age and labour type is reported from the perinatal data registry. f. Cell sizes <5 suppressed as per Perinatal Services BC reporting guidelines. 122 Table A.3 Validation parameters for all screening tests using billing code derived screening tests compared to medical records abstracted data (gold standard) in subgroups Subgroup Sensitivity Specificity Positive predictive value Negative predictive value Prevalence in medical records data Prevalence in billing codes data Ultrasound at <14 weeks gestation Antenatal health care professional type Midwife 96 (86, 100) 80 (23, 100) 98 (86, 100) 67 (23, 100) 91 (79, 97) 87 (75, 95) Family Practice 97 (89, 100) 77 (11, 100) 98 (89, 100) 63 (11, 100) 93 (81, 99) 88 (76, 95) Obstetrician 100 NA 100 NA 100 100 Parity Nulliparous 96 (85, 100) NA 100 (85, 100) 25 (0, 100) 99 (93, 100) 94 (85, 99) Multiparous 98 (92, 100) 76 (29, 99) 98 (92, 100) 76 (29, 99) 91 (81, 97) 88 (77, 95) Body mass index <30 kg/m2 96 (89, 99) 100 100 (89, 100) 40 (0, 84) 97 (93, 99) 92 (85, 96) >30 kg/m2 100 NA 100 NA 100 100 missing 100 69 (12, 99) 92 100 (12, 100) 77 (49, 94) 77 (51, 93) Age <35 years 100 100 100 100 95 (86, 99) 92 (83, 97) >35 years 88 (71, 97) 50 (6, 94) 94 (71, 97) 33 (6, 94) 90 (75, 97) 83 (66, 93) GDM screening (one or two-step) Antenatal health care professional type Midwife 100 100 100 100 83 (69, 93) 84 (69, 93) Family Practice 97 (89, 100) 100 100 (89, 100) 83 (72, 95) 87 (74, 95) 81 (68, 91) Obstetrician 100 NA 100 NA 95 (74, 100) 95 (75, 100) Parity Nulliparous 95 (83, 99) 100 100 (83, 100) 33 (0, 100) 98 (91, 100) 93 (82, 98) Multiparous 100 100 100 100 82 (70, 90) 79 (68, 88) Body mass index <30 kg/m2 100 100 100 100 95 (87, 98) 95 (87, 98) >30 kg/m2 100 100 100 100 72 (41, 93) 62 (34, 85) missing 90 (67, 99) 100 100 (67, 100) 76 76 (52, 92) 69 (45, 87) Age <35 years 97 (91, 100) 100 100 (91, 100) 83 (58, 100) 89 (80, 95) 84 (74, 92) >35 years 100 100 100 100 83 (62, 95) 83 (62, 95) Two-step GDM screening Antenatal health care professional type Midwife 100 95 (73, 100) 92 (73, 100) 100 (73, 100) 37 (18, 58) 51 (36, 66) Family Practice 100 93 (66, 100) 92 (75, 100) 100 (66, 100) 46 (27, 65) 43 (30, 58) Obstetrician 100 100 100 100 23 (5, 55) 14 (3, 37) Parity Nulliparous 100 100 100 100 46 (23, 71) 40 (24, 57) Multiparous 100 93 (74, 99) 89 (72, 100) 100 (74, 100) 37 (22, 54) 40 (29, 53) Body mass index <30 kg/m2 100 90 (64, 99) 90 (74, 100) 100 (64, 100) 47 (30, 65) 47 (35, 60) >30 kg/m2 100 100 100 100 17 (3, 46) 19 (4, 47) missing 100 100 100 100 36 (12, 67) 34 (15, 58) Age <35 years 100 100 100 100 46 (30, 62) 43 (32, 55) >35 years 100 87 (54, 99) 72 (26, 100) 100 (54, 100) 26 (9, 51) 31 (15, 52) 123 Subgroup Sensitivity Specificity Positive predictive value Negative predictive value Prevalence in medical records data Prevalence in billing codes data One-step GDM screening Antenatal health care professional type Midwife 90 (52, 100) 100 100 (52, 100) 95 (85, 100) 33 (16, 55) 33 (19, 48) Family Practice 88 (47, 100) 100 100 (47, 100) 95 (81, 100) 32 (16, 52) 38 (24, 52) Obstetrician 100 100 100 100 69 (38, 91) 81 (57, 95) Parity Nulliparous 100 100 100 100 50 (26, 74) 53 (36, 70) Multiparous 87 (56, 99) 100 100 (56, 100) 94 (84, 100) 35 (20, 51) 39 (27, 52) Body mass index <30 kg/m2 88 (57, 99) 100 100 (57, 100) 92 (73, 100) 43 (26, 61) 47 (35, 60) >30 kg/m2 100 100 100 100 43 (15, 74) 43 (18, 71) missing 100 100 100 100 29 (8, 58) 35 (16, 58) Age <35 years 100 100 100 100 35 (20, 52) 41 (30, 53) >35 years 80 (38, 99) 100 100 (38, 100) 84 (56, 100) 50 (26, 73) 52 (32, 72) GBS screening Antenatal health care professional type Midwife 100 100 100 100 96 (87, 100) 96 (87, 100) Family Practice 92 (81, 98) 70 (30, 95) 95 (81, 98) 60 (30, 95) 86 (73, 94) 85 (72, 93) Obstetrician 100 100 100 100 85 (61, 97) 86 (63, 97) Parity Nulliparous 93 (77, 99) NA 100 (77, 100) 27 (0, 100) 98 (87, 100) 91 (76, 98) Multiparous 97 (89, 100) 77 (44, 96) 95 (89, 100) 83 (44, 96) 83 (71, 91) 85 (74, 93) Body mass index <30 kg/m2 94 (84, 98) 69 (4, 100) 99 (84, 98) 32 (4, 100) 96 (87, 99) 91 (81, 97) >30 kg/m2 100 67 (4, 100) 94 (80, 100) 100 (4, 100) 83 (56, 97) 90 (66, 99) missing 100 87 (42, 100) 94 (83, 100) 100 (42, 100) 68 (44, 87) 74 (50, 91) Age <35 years 95 (86, 99) 68 (27, 95) 96 (86, 99) 63 (27, 95) 89 (79, 95) 89 (79, 95) >35 years 96 (81, 100) 100 100 (81, 100) 82 (37, 100) 85 (66, 96) 82 (63, 94) Abbreviations: GBS = Group B strep, GDM = gestational diabetes mellitus, Note: NA results occurred for subgroups with 100% agreement or because numbers in cell for validation statistic calculations were very small (<5) leading to modeled confidence intervals that exceeded relevant ranges and/or could not be calculated using survey-weighted methods. 124 Table A.4 Cross-tabulations of medical records data against billings-based screening data for records available for a validation analysis of antenatal screening tests (n=127) Billings-based data Medical records data Total yes unscreened Unknown 1st trimester ultrasound completed yes 104 2 7 113 no (unscreened) 4 6 4 14 Total, n 108 8 11 127 GDM screened yes 105 0 2 107 no (unscreened) 2 16 2 20 Total, n 107 16 4 127 Two-step GDM screening approach yes 27 2 24 53 no (unscreened) 0 42 32 74 Total, n 27 44 56 127 One-step GDM screening approach yes 26 0 28 54 no (unscreened) 2 43 28 73 Total, n 28 43 56 127 GBS screened yes 105 3 5 113 no (unscreened) 4 10 0 14 Total, n 109 13 5 127 Note: All validation calculations and prevalence results used weights based on sampling strata (health care professional type). 125 Table A.5 Study cohort characteristics and demographics comparing screened to unscreened for 3 antenatal screening tests (n=135 records) (unweighted %) Characteristic n (%) 1st trimester ultrasound completed Gestational diabetes screened Group B streptococcus test completed yes N = 114 no N = 9 p-valuea yes N = 114 no N = 17 p-valuea yes N = 116 no N = 14 p-valuea Parity <.001 <.001 .003 P0 48 (42%) <5f 50 (44%) <5 51 (44%) <5 P1-P3 65 (57%) <5 63 (55%) 10 (59%) 63 (54%) 9 (64%) P4 or more <5 <5 <5 <5 <5 <5 Age of birthing person at delivery (years) 0.031 0.91 0.76 < 24 10 (8.8%) <5 12 (11%) <5 12 (10%) <5 25-29 32 (28%) <5 30 (26%) <5 31 (27%) <5 30-34 47 (41%) <5 43 (38%) 6 (35%) 44 (38%) 5 (36%) 35-39 20 (18%) 5 (56%) 23 (20%) 5 (29%) 23 (20%) 5 (36%) 40+ 5 (4.4%) <5 6 (5.3%) <5 6 (5.2%) <5 Pre-pregnancy body mass index (BMI) (kg/m2) b 0.037 <5 0.026 0.001 <24.9 50 (44%) <5 53 (46%) 5 (29%) 55 (47%) <5 25-29.9 27 (24%) <5 28 (25%) <5 28 (24%) <5 30+ 19 (17%) <5 13 (11%) <5 15 (13%) <5 missing data 18 (16%) 5 (56%) 20 (18%) 7 (41%) 18 (16%) 8 (57%) Neighbourhood income quintiles per personc 0.46 0.35 0.95 lowest income 22 (19%) <5 25 (22%) <5 24 (21%) <5 mid-low income 24 (21%) <5 25 (22%) <5 24 (21%) <5 middle income 16 (14%) <5 14 (12%) <5 16 (14%) <5 mid-high income 27 (24%) <5 26 (23%) 6 (35%) 28 (24%) <5 highest income 11 (9.6%) <5 10 (8.8%) <5 11 (9.5%) <5 missing or NA 14 (12%) <5 14 (12%) <5 13 (11%) <5 Antenatal health care professional type 0.17 0.36 0.24 Family practice physician 46 (41%) <5 47 (42%) 5 (36%) 45 (40%) 6 (55%) Registered Midwife 46 (41%) 5 (83%) 43 (39%) 8 (57%) 49 (43%) <5 Obstetrician 19 (17%) <5 21 (19%) <5 19 (17%) <5 Missing or <2 antenatal health care visits <5 <5 <5 <5 <5 <5 Multifetal pregnancy <5 <5 >0.99 <5 <5 >0.99 <5 <5 0.37 Gestational age at delivery 0.44 0.31 >0.99 preterm (<37 weeks) 104 (94%) 7 (88%) 106 (94%) 14 (88%) 107 (93%) 12 (92%) term (37 + weeks) 7 (6.3%) <5 7 (6.2%) <5 8 (7.0%) <5 Unknown <5 <5 <5 <5 <5 <5 Labour type 0.45 0.071 <.001 Induced 36 (32%) <5 38 (33%) <5 39 (34%) <5 No labor (scheduled cesarean) 17 (15%) <5 14 (12%) 5 (29%) 11 (9.5%) 9 (64%) Spontaneous 61 (54%) 6 (67%) 62 (54%) 10 (59%) 66 (57%) <5 Mode of deliverye >0.99 0.25 0.015 Cesarean 41 (36%) <5 36 (32%) 8 (47%) 35 (30%) 10 (71%) Operative vaginal 10 (8.8%) <5 13 (11%) <5 13 (11%) <5 Spontaneous vaginal 63 (55%) 5 (56%) 65 (57%) 9 (53%) 68 (59%) <5 a. Bold indicates p>0.05 by Fisher’s exact test b. Pre-pregnancy BMI was ascertained from medical records. Standard medical records in BC collect data for a pre-pregnancy weight and height, we calculated BMI and reported by categories as defined by the Institute of Medicine standards.290 126 c. Neighbourhood income quintiles (based on Statistics Canada)50 represent the average income in the area (census tract). Census tracts were linked by the data stewards at the residential postal code level for the residence of the birthing person/mother. This data was obtained from administrative sources. For example, an individual living in the highest income quintile region is living in an overall ‘wealthier’ neighbourhood. d. Antenatal health care professional type (“Family practice physician”, “Obstetrician” or “Registered Midwife”, or “Unknown and/or <3 visits”) was defined using billing codes via a previously published method.177 Generally, this required a minimum of 3 routine antenatal care visits with the specified provider type. Consultation visits were not included. Number of visits were assessed using counts of prenatal care fee-for-service billing codes by professional type. e. Mode of delivery, gestational age at delivery, parity, is reported from medical records. Age and labour type is reported from the perinatal data registry. f. Cell sizes <5 suppressed as per Perinatal Services BC reporting requirements. 127 Table A.6 Completion rates for secondary variables by data source (Chart-abstracted v. PDR) Characteristic Medical records N = 135 BC-PDR N = 135 p-value1 Prenatal care provider type 99 % 100 % 0.50 Parity 100 % 100 % Parity -Number of previous liveborn infants 100 % 100 % Pre-pregnancy weight (kg) 90 % 82 % 0.080 Hospital admission weight (kg) 65 % 50 % 0.010 Height (cm) 79 % 90 % 0.013 Dx: pre-existing diabetes 100 % 100 % Dx: gestational diabetes 84 % 100 % <0.001 Dx: diet-controlled gestational diabetes 84 % 100 % <0.001 Dx: medication-controlled gestational diabetes 84 % 100 % <0.001 Screening: First ultrasound (GA in wks.) 86 % 87 % 0.72 Dx: GBS Positive at term 87 % 87 % >0.99 Screening: screened for GBS at term 96 % 89 % 0.020 Delivery: Mode 99 % 100 % >0.99 Delivery: CS scheduled or elective 34 % 34 % >0.99 Infant: birthweight (g) 96 % 99 % 0.21 Infant: Sex 99 % 100 % 0.50 Infant: GA at birth by best OB estimate 97 % 100 % 0.12 1 Fisher's exact test; Pearson's Chi-squared test 128 Table A.7 Descriptive results for secondary variables by data source (Medical records abstracted v. PDR) Characteristic Medical records N = 135 PDR N = 135 p-valuea Prenatal care provider type, n/N (%) <0.001 FP 44/133 (33%) n/a OB 35/133 (26%) n/a Otherb <5 83/135 (61%) RM 54/133 (41%) 52/135 (39%) Parity, n/N (%) 0.90 Multiparous 83/135 (61%) 82/135 (61%) Nulliparous 52/135 (39%) 53/135 (39%) Pre-pregnancy weight (kg), Mean (SD) 68 (15) 67 (15) 0.68 Hospital admission weight (kg), Mean (SD) 82 (16) 83 (14) 0.52 Height (cm), Mean (SD) 164 (7) 163 (7) 0.91 Pre-existing diabetes, n/N (%) <5 <5 >0.99 Gestational diabetes, n/N (%) 20/114 (18%) 20/135 (15%) 0.56 Diet-controlled gestational diabetes, n/N (%) 12/114 (11%) 12/135 (8.9%) 0.66 Medication-controlled gestational diabetes, n/N (%) 8/114 (7.0%) 8/135 (5.9%) 0.73 Screening: First ultrasound (GA in weeks), Mean (SD) 9.4 (2.0) 9.9 (2.4) 0.27 Screening: First ultrasound at <14 weeks completed, n/N (%) 114/123 (93%) 111/118 (94%) 0.67 GBS Positive at term among screened, n/N (%) 25/117 (21%) 28/117 (24%) 0.64 Screening: screened for GBS at term, n/N (%) 116/130 (89%) 117/120 (98%) 0.009 Delivery: Mode, n/N (%) >0.99 CS 46/134 (34%) 46/135 (34%) OVD 13/134 (9.7%) 13/135 (9.6%) SVB 75/134 (56%) 76/135 (56%) Delivery: CS scheduled or elective, n/N (%) 21/46 (46%) 15/46 (33%) 0.20 Infant: birthweight (g), Mean (SD) 3,407 (589) 3,383 (619) 0.84 Infant: Sex, n/N (%) 0.82 F 74/133 (56%) 77/135 (57%) M 59/133 (44%) 58/135 (43%) Infant: GA at birth by best OB estimate, Mean (SD) 38.7 (2.1) 38.4 (2.0) 0.087 a. Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test b. ‘Other’ for the PDR includes records not coded as “Midwife involved” in the BC-PDR b. Cell sizes <5 suppressed as per Perinatal Services BC reporting requirements. 129 Table A.8 Comparison of selected secondary variables with sample weighted results (medical records v BC-PDR) Comparison Sensitivity Specificity PPV NPV True Prevalence PDR Prevalence Group B streptococcus test 99 (94, 100) 16 (1, 53) 95 (94, 100) 50 (1, 53) 88 (80, 94) 98 (94, 100) GBS Positive result 100 99 (93, 100) 96 100 (93, 100) 23 (14, 33) 27 (17, 37) US 1st trimester completed 100 60 (6, 99) 99 100 (6, 99) 94 (87, 98) 96 (90, 99) GDM diagnosis 100 100 100 100 18 (10, 27) 15 (9, 23) GDM diagnosis diet-controlled 92 (59, 100) 99 (94, 100) 92 (59, 100) 99 (94, 100) 12 (6, 21) 10 (5, 18) GDM diagnosis medication-controlled 85 (35, 100) 99 (94, 100) 85 (35, 100) 99 (94, 100) 6 (2, 13) 5 (2, 11) Table A.9 Comparison of secondary variables (categorical) from BC PDR against medical records data (unweighted results) Comparison (Records v. BC-PDR) N Sensitivity Specificity PPV NPV True prevalence PDR prevalence First trimester ultrasound done 104 100 (98, 100) 75 (28, 97) 99 (95, 100) 100 (46, 100) 96 (91, 99) 97 (93, 99) GDM diagnosis 107 100 (87, 100) 100 (97, 100) 100 (87, 100) 100 (97, 100) 17 (11, 25) 17 (11, 25) GDM diet-controlled 107 91 (65, 99) 99 (95, 100) 91 (65, 99) 99 (95, 100) 10 (6, 17) 10 (6, 17) GDM med-controlled 107 86 (50, 98) 99 (95, 100) 86 (50, 98) 99 (95, 100) 7 (3, 12) 7 (3, 12) DM pre-pregnancy 127 100 (15, 100) 100 (98, 100) 100 (15, 100) 100 (98, 100) 1 (0, 4) 1 (0, 4) GBS screened 107 99 (95, 100) 33 (8, 71) 96 (91, 99) 67 (18, 96) 94 (89, 98) 97 (93, 99) GBS positive 101 100 (89, 100) 99 (94, 100) 96 (81, 100) 100 (97, 100) 22 (15, 31) 23 (15, 32) Nulliparous 127 99 (94, 100) 98 (90, 100) 99 (94, 100) 98 (90, 100) 63 (54, 71) 63 (54, 71) RM involved care 126 96 (88, 99) 100 (97, 100) 100 (95, 100) 97 (92, 99) 40 (32, 49) 39 (31, 48) SV delivery 126 100 (96, 100) 100 (96, 100) 100 (96, 100) 100 (96, 100) 56 (47, 64) 56 (47, 64) CS delivery 126 100 (94, 100) 100 (97, 100) 100 (94, 100) 100 (97, 100) 34 (26, 43) 34 (26, 43) Operative vaginal delivery 126 100 (83, 100) 100 (98, 100) 100 (83, 100) 100 (98, 100) 10 (6, 17) 10 (6, 17) Labor induction 111 91 (78, 97) 91 (83, 96) 81 (66, 91) 96 (90, 99) 30 (22, 39) 33 (25, 42) No labor or scheduled CS 127 86 (67, 96) 99 (96, 100) 95 (78, 99) 97 (93, 99) 17 (11, 24) 15 (10, 22) Infant sex 125 99 (94, 100) 94 (86, 98) 96 (89, 99) 98 (91, 100) 57 (48, 65) 58 (50, 67) Table A.10 Comparison of secondary variables (continuous) from BC PDR against medical records data (unweighted) Comparison (Chart v. PDR) ICC Lin's CCC Gestational age of fetus at 1st ultrasound (weeks) 91.8 (88.1, 94.4) 91.8 (88.0, 94.4) Gestational age of fetus at 1st ultrasound (days) 90.7 (86.1, 93.8) 90.6 (86.0, 93.8) Height (cm) 87.6 (82.0, 91.5) 87.5 (81.9, 91.4) Pre-pregnancy weight (kg) 55.8 (-9.19, 83.4) 55.3 (41.9, 66.4) Final pregnancy weight (kg) 99.0 (98.2, 99.4) 99.0 (98.2, 99.4) Early or pre-pregnancy body mass index (kg/m2) 90.8 (86.4, 93.9) 90.7 (86.3, 93.8) Gestational age of infant at delivery (weeks) 92.8 (86.2, 95.9) 92.8 (89.9, 94.9) Infant birthweight (g) 99.9 (99.9, 99.9) 99.9 (99.9, 99.9) 130 Table A.11 Detailed data dictionary and coding for medical records data Variables recoded/created Levels Definition/data source/coding GDM diagnosed and screened Yes Medical record (usually AN1/2 or LD notes) indicates patient was diagnosed with GDM during this pregnancy. No * derived If “GDM screening completed”!=”Yes” and no diagnosis of GDM from medical record, then GDM ==”No” Missing data derived If not screened or screening status unable to be determined, then GDM diagnosis is set to missing (true denominator) GDM diet-controlled Yes Note in the chart that this patient was diet-controlled GDM AND No medications (insulin or metformin) noted in prenatal records or LD notes No Not screened for GDM by chart information OR GDM was medication controlled GDM medication-controlled Yes Indication that this patient was taking medication for gestational diabetes during pregnancy (prenatal records, diabetes clinic referrals noted, other sources) No Not screened for GDM by chart information OR GDM was diet-controlled GBS screen completed (vaginal-rectal swab) Yes Chart reports at least 1 GBS test weeks OR a urine screening test with a positive GBS bacteriuria result OR chart indicates GBS positive No Chart reports GBS screening declined OR No GBS test noted and late-onset prenatal care OR No GBS test noted and scheduled CS noted OR No GBS test noted AND Positive GBS bacteriuria in urine sample Missing or no data Chart does not report GBS screening result or completion and no indication that screening was declined or not offered GBS positive Yes Chart indicates GBS positive (usually on AN1-2 or in LD notes) No* Derived from GBS Screening completed == “Yes” & NOT GBS positive Missing or no data No indication of GBS screening completed and no indication of GBS positive status Prenatal care provider type** RM Qualitative assessment by chart abstractor (EN) that the majority of prenatal care (>2 trimesters) was provided by RMs OB Qualitative assessment that >2 trimesters or MRP for prenatal care after 20 weeks was an Obstetrician ** Since prenatal care visits were not always well documented in the chart, this is difficult to determine FP Qualitative assessment that >2 trimesters of prenatal care was provided by a FP doctor who is not an OB. For some births it appears that family practice doctors transferred clients to an OB for delivery but did all prenatal care. Missing or no data No prenatal care record or no provider noted or late onset of prenatal care and no clear indication of a MRP who provided prenatal care for this pregnancy. Pre-pregnancy diabetes diagnosed Yes Usually from AN 1 – indication that this patient was diagnosed with diabetes pre-pregnancy, sometimes in other provider notes. * Re. “Screening=NO” results for all screening tests: Additional coding in unscreened was: Unscreened – declined screening; Unscreened – not eligible for screening (i.e. delivery occurred before screening would be offered or pre-pregnancy diabetes diagnosis); Unscreened – late prenatal care or no prenatal care indicated in chart; Unscreened – scheduled Cesarean delivery (for GBS only) ** Re prenatal care provider from charts: low degree of confidence in this for everyone except RM clients – quite difficult to determine for FP/OB mixed care who to indicate as MRP for prenatal care. Also for South Community Birth Program with shared care model (FP/RM) - were indicated as RM but may have mixed model of care. 131 Table A.12 Detailed data dictionary and coding for MSP billing variables for antenatal health care professional type Variables recoded/created Levels Definition/data source/coding Health care professional type * general method from288 OB Count of codes billed by OB (05) that are 14090, 14091, 04717 (** none of these are consult codes, all are ‘routine prenatal’) If > 2 billings by OB or FP for prenatal care and 3 or more OB prenatal visits and <3 FP visit ** If unable to assign, then additional check of 100 billings (routine office visit) and if >=3 by OB then assign as OB FP Count of codes billed by FP (00) that are 14090, 14091, 04717 If > 2 billings by OB or FP for prenatal care and 3 or more GP prenatal and <2 OB prenatal ** If unable to assign, then additional check of 100 billings (routine office visit) and if >=3 by FP then assign as FP RM Sum of codes: 36010 full =1; 36014 or 36016 partial 1st tri = 0.5 each 36020 full =1; 36024 or 36026 partial 2nd tri = 0.5 each 36030 full; 36034 or 36036 partial 3rd tri= 0.5 each If 2 or more full trimesters of RM care, regardless of whether there is also some OB or FP care, then classify as RM care primarily ** if unable to assign to OB, RM or FP in first pass, then use >=1 trimesters of RM billings Other or <2 AN visits < 2 trimesters of RM care OR <=2 prenatal billings by MDs OR billings by NP 132 Table A.13 Detailed definitions of billing fee codes used for prenatal care utilization Code Code description Count in chart-linked data For coding 14090 14090 PRENATAL VISIT- COMPLETE EXAMINATION 141 Prenatal care provider type 14091 14091 PRENATAL VISIT - SUBSEQUENT EXAMINATION 187 Prenatal care provider type 4717 4717 PRENATAL OFFICE VISIT -COMPLEX OBSTETRICAL PATIENT 23 Prenatal care provider type 36010 36010 MIDWIFE PHASE 1 (1ST TRIMESTER) - TOTAL CARE 73 Prenatal care provider type 36014 36014 MIDWIFE PHASE 1 (1ST TRIMESTER)-TRANS TO OTHR 40% . Prenatal care provider type 36016 36016 MIDWIFE PHASE 1 (1ST TRIMESTER)-TRANS FRM OTHR 60% 3 Prenatal care provider type 36020 36020 MIDWIFE PHASE 2 (2ND TRIMESTER) - TOTAL CARE 82 Prenatal care provider type 36021 36021 MIDWIFE PHASE 1 (1ST TRIMEST) 1ST VISIT IS IN PH 2 1 Prenatal care provider type 36024 36024 MIDWIFE PHASE 2 (2ND TRIMESTER)-TRANS TO OTHR 40% . Prenatal care provider type 36026 36026 MIDWIFE PHASE 2 (2ND TRIMESTER)-TRANS FRM OTHR 60% 5 Prenatal care provider type 36030 36030 MIDWIFE PHASE 3 (3RD TRIMESTER) - TOTAL CARE 87 Prenatal care provider type 36031 36031 MIDWIFE PHASE 3 (THIRD TRIMESTER) PHASE 3 SERVICES . Prenatal care provider type 36034 36034 MIDWIFE PHASE 3 (3ND TRIMESTER)-TRANS TO OTHR 40% . Prenatal care provider type 36036 36036 MIDWIFE PHASE 3 (3ND TRIMESTER)-TRANS FRM OTHR 60% 2 Prenatal care provider type 100 100 VISIT IN OFFICE (AGE 2 - 49) 235 Prenatal care provider type 101 101 IN OFFICE (AGE 2-49)-COMPLETE EXAMINATION 6 Prenatal care provider type 120 120 INDIVIDUAL COUNSELLING IN OFFICE (AGE 2-49) 53 Prenatal care provider type 133 Table A.14 Data dictionary for PDR variables used for validation (secondary variables) Variables recoded/created Levels Definition/data source/coding 1st trimester ultrasound completed (m.ustri1) Yes IF PDR GA at 1st ultrasound <14 and NOT missing No If PDR GA at 1st ultrasound >=14 weeks Missing or no data If no data for PDR GA at 1st Ultrasound GDM diagnosed (m.gdm) Yes If PDR GDM ID or Med = Positive No All remaining GDM medication-controlled (m.gdm.med) Yes If PDR GDM Med =Positive No If Not DM diagnosed and NOT positive for GDM-med controlled Missing DM positive or no data from PDR GDM diet-controlled (m.gdm.diet) Yes If PDR GDM diet-controlled =Positive No If Not DM diagnosed and NOT positive for GDM-diet controlled Missing DM positive or no data from PDR DM diagnosed (m.dm) Yes If PDR DM positive =yes No All other GBS screen completed (m.gbs.screen) Yes PDR GBS screening = Yes No PDR GBS screening = No Missing or no data PDR GBS screening = Unknown OR no data from PDR GBS positive in PDR (m.gbs.diag) Yes GBS Positive in PDR No Negative in PDR Missing No data in PDR for GBS result Prenatal care Midwife involved (m.midwifery.care) RM Midwife involved in care ** defined in PDR Other No midwife involved 134 A.3 Figures Figure A.1 Flowchart for linkage of chart-abstracted study data and administrative data Abbreviations: BC-PDR=BC Perinatal Data Registry; BC-MSP=BC Medical Services Plan, ID=identifiers Population Data BC (Data Stewards) reported that the 5 unmatched IDs were due to “linkage uncertainty” because of inconsistencies between the two identifiers provided from medical records (personal health number and date of birth) and the administrative data sources. No additional linkage identifiers (i.e., name) were available because of privacy restrictions. 135 Figure A.2 Decision tree for 1st trimester ultrasound completion (screened, unscreened, missing) abstracted from medical records (n=127 for validation study) Abbreviations: US=ultrasound; US1=1st trimester ultrasound (defined as <14 weeks gestational age) 136 Figure A.3 Decision tree for gestational diabetes screen completion (screened, unscreened, missing) abstracted from medical records (n=127 linked for validation study) Abbreviations: GCT= glucose challenge test; OCTT=oral glucose tolerance test; GDM=gestational diabetes mellitus 137 Figure A.4 Decision tree for gestational diabetes screen methods (one-step or two-step) abstracted from medical records (n=127 linked for validation study) Abbreviations: GDM =gestational diabetes mellitus; OGTT =oral glucose tolerance test; GCT = 50g glucose challenge test 138 Figure A.5 Decision tree for Group B streptococcus test abstracted from medical records Abbreviations: GBS= Group B streptococcus; V-R = vaginal-rectal 139 Figure A.6 Decision tree for billings derived antenatal screening test results for 1st trimester ultrasound, GDM screening and GBS screening (screened v unscreened v missing) for n=135 records Abbreviations: US1=1st trimester ultrasound, GDM=gestational diabetes mellitus, GBS = Group B streptococcus 140 Figure A.7 Decision tree for billings derived antenatal screening test results for gestational diabetes screening method (one-step or two-step) (n=135 records) 141 B. Descriptive study supplemental B.1 Tables Table B.1 Population demographics by gestational diabetes screening methods, BC, Canada, 2004-2019 Characteristic Full study N = 525,720 One-step screeninga N = 135,427 Two-stepb N = 364,698 Other glucose testc N = 25,595 Time period for guidelines on GDM screening Period 1 (Jun 1, 2004 – Sep 30, 2010) – Two-step 211,304 (40%) 4,208 (3.1%) 196,785 (54%) 10,311 (40%) Period 2 (Oct 1, 2010 – Mar 31, 2013) – BC 2010 one-step IADPSG 88,379 (17%) 39,972 (30%) 44,347 (12%) 4,060 (16%) Period 3 (Apr 1, 2013 – Jun 30, 2016) – DC 2013 guidelines 118,300 (23%) 50,606 (37%) 61,357 (17%) 6,337 (25%) Period 4 (Jul 1, 2016 – May 31, 2019) – SOGC 2016 guidelines 107,737 (20%) 40,641 (30%) 62,209 (17%) 4,887 (19%) Parity P0 249,629 (47%) 66,507 (49%) 174,316 (48%) 8,806 (34%) P1-P3 268,359 (51%) 67,416 (50%) 185,344 (51%) 15,599 (61%) P4 or more 7,714 (1.5%) 1,503 (1.1%) 5,022 (1.4%) 1,189 (4.6%) Missing data 18 <5 16 <5 Age of birthing person/mother (years) less than 25 68,079 (13%) 10,731 (7.9%) 53,061 (15%) 4,287 (17%) 25-34 327,553 (62%) 84,661 (63%) 227,969 (63%) 14,923 (58%) 35+ 130,088 (25%) 40,035 (30%) 83,668 (23%) 6,385 (25%) Pre-pregnancy body mass index (kg/m2)54 Under or normal (<24.9) 249,414 (47%) 65,675 (48%) 171,439 (47%) 12,300 (48%) Overweight (25.0-29.9) 85,229 (16%) 24,286 (18%) 57,282 (16%) 3,661 (14%) Obese I (30.0-34.9) 33,772 (6.4%) 10,384 (7.7%) 21,758 (6.0%) 1,630 (6.4%) Obese II & III (>=35.0) 21,206 (4.0%) 7,272 (5.4%) 12,620 (3.5%) 1,314 (5.1%) Missing data 136,099 (26%) 27,810 (21%) 101,599 (28%) 6,690 (26%) Region of birth of mother/birthing parent Canada or USA 352,453 (67%) 80,810 (60%) 250,936 (69%) 20,707 (82%) Asia or Arabia 126,215 (24%) 41,743 (31%) 81,887 (23%) 2,585 (10%) All other regions 45,137 (8.6%) 12,499 (9.3%) 30,545 (8.4%) 2,093 (8.2%) Missing data 1,915 375 1,330 210 Antepartum medical or obstetric riskd No/low risk 484,334 (92%) 122,316 (90%) 338,885 (93%) 23,133 (90%) Moderate/high risk 41,386 (7.9%) 13,111 (9.7%) 25,813 (7.1%) 2,462 (9.6%) Previous history of gestational diabetes No 245,310 (47%) 65,254 (48%) 171,454 (47%) 8,602 (34%) no prior pregnancy in data 263,667 (50%) 61,620 (46%) 186,765 (51%) 15,282 (60%) Yes 16,743 (3.2%) 8,553 (6.3%) 6,479 (1.8%) 1,711 (6.7%) Antenatal health care professional type177 Family practice 366,353 (70%) 80,838 (60%) 273,406 (75%) 12,109 (47%) Registered Midwife 78,118 (15%) 25,135 (19%) 42,393 (12%) 10,590 (41%) Obstetrician 79,562 (15%) 29,248 (22%) 47,881 (13%) 2,433 (9.5%) Missing or <2 antenatal visits 1,687 (0.3%) 206 (0.2%) 1,018 (0.3%) 463 (1.8%) Planned home birth Planned hospital (all births) 513,278 (98%) 132,557 (98%) 358,425 (98%) 22,296 (87%) Planned home (only available for RM care) 12,442 (2.4%) 2,870 (2.1%) 6,273 (1.7%) 3,299 (13%) Adequacy of prenatal care usage index188 Adequate Plus 65,600 (12%) 18,632 (14%) 44,172 (12%) 2,796 (11%) Adequate 282,612 (54%) 75,117 (55%) 197,001 (54%) 10,494 (41%) Intermediate 147,489 (28%) 35,742 (26%) 102,932 (28%) 8,815 (34%) Inadequate 30,019 (5.7%) 5,936 (4.4%) 20,593 (5.6%) 3,490 (14%) Region of residence of birthing person/mothere Metro Vancouver or Victoria 359,588 (68%) 113,020 (83%) 231,742 (64%) 14,826 (58%) Northern region 39,799 (7.6%) 1,644 (1.2%) 35,410 (9.7%) 2,745 (11%) Other southern areas (small towns and rural areas) 50,910 (9.7%) 7,588 (5.6%) 39,875 (11%) 3,447 (13%) Other southern cities 75,423 (14%) 13,175 (9.7%) 57,671 (16%) 4,577 (18%) 142 Characteristic Full study N = 525,720 One-step screeninga N = 135,427 Two-stepb N = 364,698 Other glucose testc N = 25,595 Rural or urban residence by local health area region Urban 509,204 (97%) 133,587 (99%) 351,197 (96%) 24,420 (95%) Rural 16,516 (3.1%) 1,840 (1.4%) 13,501 (3.7%) 1,175 (4.6%) Neighbourhood income per person lowest income quintile 111,692 (21%) 28,445 (21%) 77,383 (21%) 5,864 (23%) mid-low income quintile 111,263 (21%) 29,107 (21%) 76,884 (21%) 5,272 (21%) middle income quintile 107,022 (20%) 28,153 (21%) 73,798 (20%) 5,071 (20%) mid-high income quintile 107,065 (20%) 28,351 (21%) 73,756 (20%) 4,958 (19%) highest income quintile 81,950 (16%) 19,689 (15%) 58,252 (16%) 4,009 (16%) missing or NA 6,728 (1.3%) 1,682 (1.2%) 4,625 (1.3%) 421 (1.6%) a. One-step screening derived from laboratory billings as per Chapter 3; generally, if no screening test (91690) and/or 91695, 91715 or 91716 occurred > 45 days of 91690 (screening test) b. Two-step screening derived from laboratory billings as per Chapter 3, if code 91690 was present c. Other glucose tests derived from laboratory billings if neither one-step or two-step screening was identified and only codes 91745 (HbA1c) or 91707 or 91700 (Glucose quant or semi-quant) were present d. Antepartum medical or obstetric risk defined using methods proposed by McRae et al. with the addition of a history of fetal complications from BC-PDR data (prior neonatal death, stillbirth or anomaly). Full definition includes: ICD-10-CA codes O991 O994 O99803/04/09 O101-4 O109 O266 O981 O984-9 O360 O361 and PDR: Antepartum anti-hypertensives prescribed, Prior NND/anomaly/Stillbirth or 2+ prior Cesarean e. Regions derived as follows: Metro Vancouver or Victoria includes all local health areas within the greater Vancouver and Victoria metropolitan areas; Northern region includes all of the Northern health region; Other southern cities includes all cities with regional or tertiary hospitals located in the city; Rest of province = all other local health regions 143 Table B.2 Crude and adjusted Relative Risks (RR) for one-step gestational diabetes screening, BC, Canada, 2005-2019 Characteristic One-step screening for gestational diabetes Crude Relative Risks (RR) Adjusted Relative Risksa (ARR) GDM screening guidelines (BC 2010, DC 2013, SOGC 2016) Period 1 —b — Period 2 22.7 (22.0, 23.4) 22.2 (21.6, 22.9) Period 3 21.5 (20.8, 22.2) 20.9 (20.3, 21.6) Period 4 18.9 (18.4, 19.6) 18.2 (17.6, 18.8) PDR data: Parity categorized P0 — — P1-P3 0.94 (0.93, 0.95) 0.93 (0.92, 0.94) P4 or more 0.73 (0.69, 0.77) 0.80 (0.77, 0.83) PDR data: Mothers age categorized less than 25 — — 25-34 1.64 (1.61, 1.67) 1.13 (1.11, 1.14) 35+ 1.95 (1.91, 1.99) 1.22 (1.20, 1.24) Pre-pregnancy body mass index (kg/m2) Under or normal (<24.9) — — Overweight (25.0-29.9) 1.08 (1.07, 1.10) 1.12 (1.11, 1.13) Obese I (30.0-34.9) 1.17 (1.14, 1.19) 1.25 (1.23, 1.27) Obese II & III (>=35.0) 1.30 (1.27, 1.33) 1.42 (1.39, 1.44) missing 0.78 (0.77, 0.79) 0.97 (0.96, 0.98) Region of birth of mother/birthing person Canada or USA — — Asia or Arabia 1.44 (1.43, 1.46) 1.10 (1.09, 1.11) All other regions 1.21 (1.19, 1.23) 1.02 (1, 1.03) Antepartum risk No/low — — Moderate/high 1.25 (1.23, 1.28) 1.07 (1.05, 1.08) Antenatal health care professional type Family practice — — Registered Midwife 1.46 (1.44, 1.48) 1 (0.99, 1.01) Obstetrician 1.67 (1.64, 1.69) 1.25 (1.24, 1.26) Missing 0.55 (0.48, 0.63) 0.70 (0.62, 0.79) Planned home birth (full population) no — — yes 0.89 (0.86, 0.93) 0.76 (0.74, 0.78) APNCU index (MSP) Adequate Plus — — Adequate 0.94 (0.92, 0.95) 0.95 (0.94, 0.96) Intermediate 0.85 (0.84, 0.87) 0.91 (0.90, 0.93) Inadequate 0.70 (0.68, 0.72) 0.83 (0.82, 0.85) Region of residence of birthing person/mother Metro Vancouver or Victoria — — Northern region 0.13 (0.13, 0.14) 0.15 (0.14, 0.16) Other southern areas 0.47 (0.46, 0.49) 0.55 (0.54, 0.56) Other southern cities 0.56 (0.55, 0.57) 0.60 (0.59, 0.61) Rural or urban LHA Urban — — Rural 0.42 (0.41, 0.44) 0.82 (0.79, 0.86) a. Adjusted models include all covariates shown in the table; ARR = Adjusted Relative Risks; b. Baseline (-) all values relative to baseline category 144 Table B.3 Gestational diabetes diagnosis and treatment type by screening methods in BC, Canada, 2011-2019 (after one-step was implemented) Characteristic One-step N = 131,219 % (95% CIb) Two-step N = 167,913 % (95% CI) Other glucose test N = 15,284 % (95% CI) Gestational diabetes diagnosisa 18% (17.8, 18.2) 8.8% (8.7, 9.0) 10.0% (9.5, 10.5) Gestational diabetes diagnosis and treatment type No diabetes 82% (81.8, 82.2) 91% (91.0, 91.3) 90% (89.5, 90.5) Diet-controlled 12% (12.2, 12.6) 6.4% (6.3, 6.6) 4.3% (4.0, 4.7) Medication controlled 5.6% (5.5, 5.7) 2.4% (2.3, 2.4) 5.6% (5.3, 6.0) GDM treatment type (Among those with a diagnosis) GDM diet-controlled 69% (68.3, 69.5) 73% (72.4, 73.8) 43% (40.9, 45.9) GDM medication-controlled 31% (30.5, 31.7) 27% (26.2, 27.6) 57% (54.1, 59.1) a. GDM diagnosis and treatment data obtained from BC-PDR data b. CI = Confidence Interval 145 Table B.4 One-step gestational diabetes screening rates by region in subgroups, time periods and by year Characteristic Metro Vancouver and Victoria % (95% CI) Southern cities with Tertiary or Regional Hospitals % (95% CI) Southern small towns or rural areas % (95% CI) Northern region % (95% CI) One-step GDM screening / Total in region 31.4% (31.3, 31.6) 17.5% (17.2, 17.7) 14.9% (14.6, 15.2) 4.1% (3.9, 4.3) Screening guidelines (BC 2010, DC 2013, SOGC 2016) Period 1 2.1% (2.0, 2.1) 1.8% (1.6, 1.9) 1.8% (1.7, 2.0) 2.0% (1.8, 2.3) Period 2 57.6% (57.2, 58.0) 22.6% (21.8, 23.3) 22.3% (21.4, 23.2) 3.2% (2.8, 3.7) Period 3 51.1% (50.8, 51.4) 31.0% (30.3, 31.7) 28.5% (27.6, 29.3) 6.8% (6.3, 7.3) Period 4 44.9% (44.6, 45.3) 29.8% (29.1, 30.5) 21.4% (20.7, 22.3) 6.2% (5.7, 6.8) Parity P0 32.0% (31.7, 32.2) 18.2% (17.8, 18.6) 15.6% (15.2, 16.1) 3.9% (3.6, 4.2) P1-P3 31.0% (30.8, 31.2) 16.9% (16.5, 17.3) 14.4% (14.0, 14.8) 4.3% (4.0, 4.6) P4 or more 28.8% (27.4, 30.2) 12.9% (11.1, 15.0) 11.9% (10.0, 14.0) 4.6% (3.6, 5.9) Missing <5 <5 <5 <5 Age less than 25 23.1% (22.6, 23.5) 12.4% (11.9, 13.0) 10.8% (10.2, 11.5) 2.9% (2.6, 3.3) 25-34 31.2% (31.0, 31.4) 17.9% (17.6, 18.3) 15.0% (14.6, 15.4) 4.3% (4.1, 4.6) 35+ 34.6% (34.3, 34.9) 21.1% (20.4, 21.8) 18.7% (18.0, 19.5) 5.9% (5.2, 6.6) Pre-pregnancy body mass index (kg/m2) Under or normal (<24.9) 30.6% (30.4, 30.8) 16.5% (16.1, 16.9) 16.5% (16.0, 17.0) 3.4% (3.1, 3.8) Overweight (25.0-29.9) 34.3% (34.0, 34.7) 19.7% (19.0, 20.4) 18.3% (17.5, 19.3) 4.4% (3.9, 4.9) Obese I (30.0-34.9) 37.8% (37.1, 38.4) 23.3% (22.2, 24.4) 22.6% (21.2, 24.1) 5.7% (4.9, 6.5) Obese II & III (>=35.0) 42.5% (41.7, 43.4) 29.4% (28.0, 30.8) 25.4% (23.6, 27.2) 8.7% (7.7, 9.9) missing 27.9% (27.6, 28.2) 13.5% (13.1, 14.0) 9.5% (9.1, 9.9) 3.5% (3.2, 3.8) Region of birthplace of birthing person/mother Canada or USA 30.2% (30.0, 30.4) 17.0% (16.7, 17.3) 14.2% (13.8, 14.5) 4.0% (3.8, 4.2) Asia or Arabia 33.9% (33.6, 34.1) 25.7% (24.3, 27.1) 23.9% (22.4, 25.6) 7.5% (6.3, 8.9) All other regions 30.5% (30.0, 30.9) 18.3% (17.1, 19.6) 18.0% (16.7, 19.5) 4.3% (3.4, 5.4) Missing 312 40 19 <5 Antepartum risk (yes) 38.3% (37.7, 38.9) 23.1% (22.0, 24.2) 20.1% (18.8, 21.3) 6.4% (5.6, 7.3) History of GDM in previous pregnancy no 31.9% (31.7, 32.1) 18.2% (17.8, 18.6) 15.6% (15.1, 16.1) 3.9% (3.6, 4.2) no prior pregnancy in data 29.2% (28.9, 29.4) 15.9% (15.5, 16.3) 13.7% (13.3, 14.1) 3.7% (3.5, 4.0) yes 54.1% (53.2, 54.9) 46.0% (43.3, 48.7) 35.0% (31.9, 38.3) 22.3% (19.4, 25.6) Antenatal health care professional type Family practice 27.6% (27.4, 27.8) 16.1% (15.8, 16.4) 12.9% (12.6, 13.3) 3.6% (3.4, 3.8) Registered Midwife 38.0% (37.6, 38.4) 20.8% (20.2, 21.5) 20.2% (19.4, 21.1) 11.4% (10.2, 12.7) Obstetrician 40.2% (39.9, 40.6) 21.3% (20.3, 22.3) 24.0% (22.6, 25.3) 5.0% (4.2, 6.0) Missing 16.9% (14.6, 19.5) 7.3% (4.2, 12.1) 8.6% (5.5, 13.0) 3.9% (2.1, 6.8) Planned home birth 27.5% (26.5, 28.5) 17.1% (15.8, 18.5) 17.3% (15.6, 19.2) 6.5% (4.0, 10.3) APNCU index (MSP) Adequate Plus 34.1% (33.7, 34.5) 20.3% (19.6, 21.1) 16.1% (15.2, 17.1) 5.8% (5.1, 6.6) Adequate 31.9% (31.7, 32.1) 18.0% (17.6, 18.4) 15.3% (14.8, 15.7) 4.1% (3.8, 4.4) Intermediate 30.2% (29.9, 30.5) 15.5% (15.0, 16.0) 14.5% (14.0, 15.1) 3.8% (3.4, 4.1) Inadequate 26.6% (26.0, 27.3) 13.4% (12.3, 14.6) 12.0% (11.0, 13.1) 3.8% (3.3, 4.4) LHA type Urban 31.4% (31.2, 31.6) n.a.a 16.3% (15.9, 16.6) 4.2% (4.0, 4.4) Rural 37.1% (34.9, 39.4) 9.7% (9.1, 10.3) 3.7% (3.1, 4.3) 146 Characteristic Metro Vancouver and Victoria % (95% CI) Southern cities with Tertiary or Regional Hospitals % (95% CI) Southern small towns or rural areas % (95% CI) Northern region % (95% CI) Neighbourhood income per person lowest income quintile 31.4% (31.0, 31.7) 17.1% (16.5, 17.6) 13.9% (13.2, 14.6) 4.0% (3.6, 4.4) mid-low income quintile 31.5% (31.1, 31.8) 17.4% (16.8, 18.0) 13.8% (13.1, 14.4) 4.3% (3.8, 4.8) middle income quintile 31.5% (31.2, 31.9) 17.4% (16.8, 18.0) 14.7% (14.0, 15.4) 4.4% (3.9, 4.9) mid-high income quintile 32.0% (31.6, 32.3) 17.4% (16.8, 18.0) 14.3% (13.5, 15.0) 3.9% (3.5, 4.3) highest income quintile 30.2% (29.8, 30.6) 18.4% (17.7, 19.0) 18.1% (17.4, 18.9) 4.3% (3.9, 4.7) missing or NA 34.9% (33.4, 36.4) 16.0% (13.2, 19.2) 13.4% (11.6, 15.3) 3.5% (2.4, 5.0) Year 2005 1.9% (1.7, 2.1) 1.6% (1.3, 2.1) 2.3% (1.8, 2.9) 2.2% (1.7, 2.9) 2006 2.0% (1.8, 2.2) 1.9% (1.5, 2.4) 2.2% (1.7, 2.7) 2.3% (1.7, 3.0) 2007 2.0% (1.8, 2.2) 1.7% (1.4, 2.1) 1.8% (1.4, 2.3) 2.1% (1.6, 2.7) 2008 2.0% (1.8, 2.2) 1.8% (1.5, 2.3) 1.5% (1.1, 1.9) 2.7% (2.1, 3.4) 2009 2.0% (1.8, 2.1) 1.9% (1.5, 2.3) 1.4% (1.1, 1.9) 1.9% (1.4, 2.5) 2010 2.0% (1.9, 2.2) 1.8% (1.4, 2.2) 2.0% (1.6, 2.5) 1.4% (1.0, 1.9) 2011 30.5% (29.9, 31.0) 6.1% (5.5, 6.8) 6.3% (5.5, 7.1) 1.5% (1.1, 2.1) 2012 60.2% (59.6, 60.8) 23.1% (21.9, 24.3) 23.0% (21.6, 24.5) 3.4% (2.8, 4.2) 2013 65.9% (65.3, 66.5) 33.7% (32.4, 35.0) 33.2% (31.6, 34.8) 5.3% (4.4, 6.2) 2014 57.2% (56.6, 57.9) 32.6% (31.4, 34.0) 33.0% (31.4, 34.6) 9.2% (8.1, 10.3) 2015 46.6% (46.0, 47.3) 29.9% (28.7, 31.2) 26.6% (25.1, 28.2) 5.3% (4.5, 6.2) 2016 46.9% (46.3, 47.5) 30.4% (29.2, 31.7) 25.6% (24.1, 27.1) 5.6% (4.8, 6.5) 2017 46.0% (45.4, 46.6) 29.2% (28.0, 30.5) 22.4% (21.1, 23.8) 6.1% (5.2, 7.0) 2018 44.4% (43.8, 45.0) 29.8% (28.5, 31.0) 22.2% (20.9, 23.7) 6.3% (5.5, 7.3) 2019 44.6% (44.0, 45.3) 30.3% (29.0, 31.5) 19.8% (18.5, 21.2) 6.3% (5.4, 7.3) a. By definition, the Southern cities group included ‘urban’ local health areas. Table B.5 One-step gestational diabetes screening rates by year, BC, Canada, 2005-2019 Year One-step GDM screening % (95% CI) 2005 1.9% (1.8, 2.1) 2006 2.0% (1.9, 2.2) 2007 1.9% (1.8, 2.1) 2008 2.0% (1.8, 2.1) 2009 1.9% (1.7, 2.0) 2010 1.9% (1.8, 2.1) 2011 22.4% (22.0, 22.8) 2012 47.0% (46.5, 47.5) 2013 53.9% (53.4, 54.5) 2014 47.9% (47.4, 48.4) 2015 39.3% (38.8, 39.8) 2016 39.4% (38.9, 39.9) 2017 38.6% (38.1, 39.1) 2018 37.3% (36.8, 37.8) 2019 37.5% (37.0, 38.0) 147 B.2 Figures Figure B.1 Study population derivation and exclusions 148 Figure B.2 Rates of GDM one-step screening by region and pre-pregnancy BMI 149 Figure B.3 Rates of GDM one-step screening by region and health care professional type 150 Figure B.4 Rates of GDM one-step screening by region and history of GDM 151 Figure B.5 Rates of GDM one-step screening by region and birth region of mother/birthing person 152 Figure B.6 Rates of GDM one-step screening by region and planned home birth 153 Figure B.7 Rates of GDM one-step screening by region and adequacy of prenatal care usage index 154 Figure B.8 Rates of GDM one-step screening by region and Health services delivery areas 155 Figure B.9 Gestational diabetes diagnoses rates by screening type 156 Figure B.10 Rates of one-step gestational diabetes screening in BC regions in 4 time periods by date of 1st glucose screening test for gestational diabetes by BC local health area 157 Figure B.11 Rates of gestational diabetes diagnoses in BC regions in 4 time periods by date of 1st glucose screening test for gestational diabetes 158 C. Gestational diabetes study supplemental C.1 Methods I conducted several sensitivity analyses to test if these results were sensitive to particular characteristics of our study population. In British Columbia, prior to October 2010,105 the “two-step” method of screening included a 50g-GCT screening test, followed by a 3-hour, 100g OGTT diagnostic test using Carpenter-Coustan criteria. After October 2010, the “two-step” method continued to use a 50g-GCT but the diagnostic test used a 2-hour, 75g OGTT with the Diabetes Canada-2013 criteria291. These two screening approaches are similar, but not identical, and have different diagnostic prevalences. In the primary analysis, all two-step screening was treated as one screening method. In sensitivity analysis, we used an additional screening variable to control for the difference between the two-step methods (before October 2010, using C-C criteria and a 100g diagnostic OGTT compared to after October 2010 using the DC-2013 criteria). Overall results were unchanged and the additional variable was not statistically significant in the final models, thus we considered all two-step screening as one method for the main analysis. I assessed the models using different years as the baseline (2006, 2009) and study end (2018) to determine if the findings were sensitive to the choice of baseline year. I also included health region as potential covariates in the “population characteristics” and also repeated the analyses in subgroups by health region to assess for potential confounding by region. We also considered additional covariates for pregnancy-associated complications: > 2 prior cesarean deliveries or prior macrosomia. 159 C.2 Tables Table C.1 Model estimated risk of gestational diabetes in each study year (n=551,457) Model 1: Baseline Model 2: Add screen completiona Model 3: Add screen methodb Model 4: Add populationc factors Estimated Year RRd CIe RR CI RR CI RR CI 2005 Baseline Baseline Baseline Baseline 2006 0.96 0.91 to 1.01 0.96 0.91 to 1.01 0.96 0.91 to 1.01 0.96 0.91 to 1.01 2007 1.04 0.99 to 1.10 1.04 0.98 to 1.09 1.04 0.98 to 1.09 1.04 0.98 to 1.09 2008 1.06 1.01 to 1.12 1.05 1.00 to 1.11 1.05 1.00 to 1.11 1.04 0.99 to 1.09 2009 1.12 1.06 to 1.17 1.10 1.04 to 1.16 1.10 1.04 to 1.16 1.09 1.03 to 1.14 2010 1.05 1.00 to 1.11 1.03 0.98 to 1.09 1.03 0.98 to 1.09 1.02 0.97 to 1.07 2011 1.27 1.21 to 1.34 1.25 1.19 to 1.31 0.98 0.93 to 1.03 1.02 0.97 to 1.07 2012 1.44 1.37 to 1.51 1.40 1.33 to 1.47 0.91 0.87 to 0.96 0.98 0.93 to 1.03 2013 1.60 1.52 to 1.67 1.55 1.47 to 1.62 0.97 0.92 to 1.02 1.04 0.99 to 1.09 2014 1.51 1.44 to 1.58 1.45 1.38 to 1.52 0.96 0.91 to 1.01 1.01 0.96 to 1.06 2015 1.66 1.58 to 1.74 1.58 1.51 to 1.66 1.10 1.05 to 1.16 1.13 1.07 to 1.18 2016 1.74 1.66 to 1.82 1.65 1.57 to 1.73 1.15 1.09 to 1.20 1.16 1.11 to 1.22 2017 1.85 1.77 to 1.94 1.74 1.66 to 1.82 1.22 1.17 to 1.28 1.19 1.13 to 1.24 2018 1.85 1.77 to 1.94 1.73 1.65 to 1.81 1.23 1.17 to 1.28 1.16 1.11 to 1.22 2019 2.04 1.94 to 2.13 1.89 1.81 to 1.98 1.34 1.28 to 1.40 1.25 1.19 to 1.31 a. Screen completion was modeled as any gestational diabetes screen v. unscreened/no data b. Screen method was modeled as any two-step screening approach. In BC, until October 2010, two-step screening consisted of a 50g screening test followed by a 100g, 3-hour diagnostic test assessed using Carpenter-Coustan criteria.150 After October 2010, two-step screening changed to a 50g screening test followed by a 75g, 2-hour test using the International Association of Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria.38,105 c. Population factors included: parity (nulliparous v. multiparous), age at delivery of mother/birthing person (<25, 25-34, >35 years), pre-pregnancy body mass index (<24.5, 25-29.9, >30 kg/m2 or missing data), pre-existing complications (any of pre-existing disease,177 pre-existing hypertensive disorders, iso-immunization, prior stillbirth or neonatal death, >=2 prior Cesarean births), multifetal pregnancy, antenatal care by a Registered Midwife (v. any other), inadequate prenatal care200 and mother’s country of birth (all Asian or Arabian peninsula v. all others v. Canada/USA/missing). d. Relative risk = RR e. Confidence Intervals = CI, assessed using robust standard errors 160 Table C.2 Population characteristics by gestational diabetes diagnosis in BC, Canada from 2005 to 2019 (n=551,457) Characteristic Overall N = 551,457 No gestational diabetes diagnosed N = 495,175 Gestational diabetes diagnosed N = 56, 282 Nulliparous 254,588 (46%) 230,370 (47%) 24,218 (43%) Pre-pregnancy body mass index (kg/m2) <24.9 265,155 (48%) 244,370 (49%) 20,785 (37%) 25.0-29.9 87,533 (16%) 76,192 (15%) 11,341 (20%) 30.0-34.9 34,047 (6%) 28,060 (6%) 5,987 (11%) >35.0 20,765 (4%) 15,970 (3%) 4,795 (9%) Missing data 143,957 (26%) 130,583 (26%) 13,374 (24%) Age of birthing person/mother (years) less than 25 73,118 (13%) 70,291 (14%) 2,827 (5%) 25-34 342,918 (62%) 310,705 (63%) 32,213 (57%) 35+ 135,421 (25%) 114,179 (23%) 21,242 (38%) Multifetal pregnancy (v singleton) 8,488 (2%) 7,247 (1%) 1,241 (2%) Medical/obstetric complications (composite)b 39,945 (7%) 34,053 (7%) 5,892 (10%) Mother’s region of birth (infant birth certificate) All other regions 373,582 (68%) 346,355 (70%) 27,227 (49%) Asia or Arabia 127,945 (23%) 103,821 (21%) 24,124 (43%) Canada or USA (or missing (<0.5%)) 47,701 (9%) 42,943 (9%) 4,758 (8%) Registered Midwife (v other health care provider) 87,951 (16%) 82,120 (17%) 5,831 (10%) Inadequate prenatal care (APNCU index) 35,524 (6%) 32,485 (7%) 3,039 (5%) Neighbourhood income per person lowest income quintile 116,961 (21%) 103,426 (21%) 13,535 (24%) mid-low income quintile 115,957 (21%) 102,396 (21%) 13,561 (24%) middle income quintile 112,081 (20%) 100,630 (20%) 11,451 (20%) mid-high income quintile 112,230 (20%) 101,960 (21%) 10,270 (18%) highest income quintile 86,984 (16%) 80,149 (16%) 6,835 (12%) missing 7,244 (1%) 6,614 (1%) 630 (1%) Rural or urban local health area Urban 533,929 (97%) 478,568 (97%) 55,361 (98%) Rural 17,528 (3%) 16,607 (3%) 921 (2%) Year (July-June) 2005 33,341 (6%) 30,940 (6%) 2,401 (4%) 2006 34,284 (6%) 31,918 (6%) 2,366 (4%) 2007 35,955 (7%) 33,256 (7%) 2,699 (5%) 2008 36,496 (7%) 33,704 (7%) 2,792 (5%) 2009 37,703 (7%) 34,674 (7%) 3,029 (5%) 2010 38,115 (7%) 35,224 (7%) 2,891 (5%) 2011 38,153 (7%) 34,653 (7%) 3,500 (6%) 2012 37,158 (7%) 33,316 (7%) 3,842 (7%) 2013 37,263 (7%) 32,978 (7%) 4,285 (8%) 2014 36,927 (7%) 32,917 (7%) 4,010 (7%) 2015 37,089 (7%) 32,668 (7%) 4,421 (8%) 2016 37,606 (7%) 32,895 (7%) 4,711 (8%) 2017 37,670 (7%) 32,639 (7%) 5,031 (9%) 2018 37,280 (7%) 32,313 (7%) 4,967 (9%) 2019 36,417 (7%) 31,080 (6%) 5,337 (9%) Gestational diabetes screen completion (v. unscreened) 500,619 (91%) 445,255 (90%) 55,364 (98%) Screening method Two-step 368,178 (67%) 336,723 (68%) 31,455 (56%) One-step (IADPSG38 criteria) 132,441 (24%) 108,532 (22%) 23,909 (42%) Unscreened 50,838 (9%) 49,920 (10%) 918 (2%) Prior history of GDM 161 no 254,034 (46%) 229,879 (46%) 24,155 (43%) no prior pregnancy in data 281,459 (51%) 258,152 (52%) 23,307 (41%) yes 15,964 (3%) 7,144 (1%) 8,820 (16%) a. Gestational diabetes defined from the gestational diabetes diagnosis variable in the BC-PDR (99.9% of cases)176 with additional cases identified from the discharge summary of the delivery hospitalization data by ICD-10-CA codes (O24.8 – comparable to O24.4 in ICD-10-CM) (47, <0.1% additional cases) b. Medical/obstetric complications composite177 based ICD-10-CA codes in the discharge summary of the delivery hospitalization and the BC-PDR. Codes included pregnancy-complicating pre-existing diseases or conditions (O991, O994, O99803/04/09, O266, O981, O984 to 9, O360, O361), pre-existing hypertension (O100 to 4, O109) and from BC-PDR data: prior neonatal anomaly, stillbirth or neonatal death (direct coded variables) 162 Table C.3 Population characteristics by gestational diabetes diagnosis in BC, Canada from 2005 to 2019 comparing full available study years to excluded groups for different reasons compared to the included cohort Characteristic Full population N = 621,559 included N = 551,457 non-standard GDM screen N = 25,573 inactive insurance, out of BC or late PNC N = 37,908 DM or <29 wks N = 6,621 PDR: Nulliparous 287,690 (46%) 254,588 (46%) 8,606 (34%) 21,434 (57%) 3,062 (46%) Missing 17 14 <5 <5 <5 Pre-pregnancy body mass index (kg/m2) <24.9 298,041 (48%) 265,155 (48%) 12,295 (48%) 18,670 (49%) 1,921 (29%) 25.0-29.9 96,799 (16%) 87,533 (16%) 3,666 (14%) 4,447 (12%) 1,153 (17%) 30.0-34.9 37,861 (6%) 34,047 (6%) 1,630 (6%) 1,478 (4%) 706 (11%) >35.0 23,631 (4%) 20,765 (4%) 1,312 (5%) 804 (2%) 750 (11%) Missing data 165,227 (27%) 143,957 (26%) 6,670 (26%) 12,509 (33%) 2,091 (32%) Age of birthing person/mother (years) less than 25 87,129 (14%) 73,118 (13%) 4,277 (17%) 9,024 (24%) 710 (11%) 25-34 383,466 (62%) 342,918 (62%) 14,904 (58%) 21,982 (58%) 3,662 (55%) 35+ 150,964 (24%) 135,421 (25%) 6,392 (25%) 6,902 (18%) 2,249 (34%) Planned home birth 20,703 (3%) 15,976 (3%) 3,306 (13%) 1,396 (4%) 25 (0%) Multifetal pregnancy (v singleton) 9,732 (2%) 8,488 (2%) 321 (1%) 507 (1%) 416 (6%) Antepartum medical or obstetric risk 45,819 (7%) 39,945 (7%) 2,359 (9%) 2,365 (6%) 1,150 (17%) Gestational age at delivery Median (IQR) 39 (38, 40) 39 (38, 40) 39 (38, 40) 39 (38, 40) 35 (26, 37) Mean (SD) 38.57 (1.96) 38.64 (1.70) 38.55 (1.87) 38.80 (1.88) 31.71 (6.17) Missing <5 <5 <5 <5 <5 Region of birthplace of birthing person/mother Canada or USA 416,886 (67%) 373,582 (68%) 20,702 (82%) 18,786 (50%) 3,816 (68%) Asia or Arabia 145,832 (24%) 127,945 (23%) 2,570 (10%) 13,945 (37%) 1,372 (24%) All other regions 55,002 (9%) 47,701 (9%) 2,090 (8%) 4,752 (13%) 459 (8%) Missing 3,839 2,229 211 425 974 Inadequate prenatal care (APNCU) 71,650 (12%) 35,524 (6%) 3,482 (14%) 31,783 (84%) 861 (13%) Number of antenatal care visits Median (IQR) 9.0 (7.0, 11.0) 10.0 (8.0, 11.0) 9.0 (7.0, 12.0) 8.0 (5.0, 10.0) 7.0 (4.0, 10.0) Mean (SD) 9.4 (3.2) 9.5 (3.1) 9.4 (3.6) 7.8 (3.8) 7.4 (3.8) Missing 48,975 40,880 1,899 5,113 1,083 1st month of prenatal care visit Median (IQR) 2 (1, 3) 2 (1, 3) 2 (1, 3) 7 (5, 9) 2 (1, 3) Mean (SD) 2.52 (1.63) 2.26 (1.10) 2.42 (1.26) 6.81 (2.67) 2.14 (1.21) Missing 3,497 <5 <5 3,447 50 Percent of MSP coverage Median (IQR) 1.14 (1.12, 1.14) 1.14 (1.12, 1.14) 1.14 (1.12, 1.14) 0.71 (0.38, 1.12) 1.14 (1.14, 1.25) Mean (SD) 1.11 (0.14) 1.14 (0.02) 1.14 (0.02) 0.69 (0.37) 1.18 (0.05) Missing 216 <5 <5 216 <5 Total months of MSP Median (IQR) 10 (10, 11) 10 (10, 11) 10 (10, 11) 5 (3, 10) 9 (7, 10) Mean (SD) 9.94 (1.51) 10.23 (0.61) 10.24 (0.61) 5.71 (3.43) 8.73 (1.44) Missing <5 <5 <5 <5 <5 Health region of residence Interior 89,059 (14%) 79,371 (14%) 3,603 (14%) 5,301 (14%) 784 (12%) Fraser 246,036 (40%) 223,166 (40%) 7,177 (28%) 12,765 (34%) 2,928 (44%) Vancouver Coastal 146,154 (24%) 128,719 (23%) 5,356 (21%) 10,725 (28%) 1,354 (20%) Island 89,965 (14%) 77,566 (14%) 6,693 (26%) 4,697 (12%) 1,009 (15%) Northern 48,889 (8%) 42,635 (8%) 2,744 (11%) 2,964 (8%) 546 (8%) Unknown BC 1,075 (0%) <5 <5 1,075 (3%) <5 163 Out of province or out of country 203 (0%) <5 <5 203 (1%) <5 Unknown 178 (0%) <5 <5 178 (0%) <5 Other regional classification Metro Vancouver or Victoria 419,449 (67%) 375,266 (68%) 14,809 (58%) 24,738 (65%) 4,636 (70%) Northern region 48,889 (8%) 42,635 (8%) 2,744 (11%) 2,964 (8%) 546 (8%) Other southern areas 62,339 (10%) 54,503 (10%) 3,443 (13%) 3,833 (10%) 560 (8%) Other southern cities 89,426 (14%) 79,053 (14%) 4,577 (18%) 4,917 (13%) 879 (13%) Unknown or out of BC 1,456 (0%) <5 <5 1,456 (4%) <5 Neighbourhood income per person lowest income quintile 135,129 (22%) 116,961 (21%) 5,852 (23%) 10,680 (28%) 1,636 (25%) mid-low income quintile 130,666 (21%) 115,957 (21%) 5,261 (21%) 7,984 (21%) 1,464 (22%) middle income quintile 125,204 (20%) 112,081 (20%) 5,072 (20%) 6,712 (18%) 1,339 (20%) mid-high income quintile 124,376 (20%) 112,230 (20%) 4,961 (19%) 5,957 (16%) 1,228 (19%) highest income quintile 97,212 (16%) 86,984 (16%) 4,006 (16%) 5,371 (14%) 851 (13%) missing or NA 8,972 (1%) 7,244 (1%) 421 (2%) 1,204 (3%) 103 (2%) Region of residence Urban 599,751 (97%) 533,929 (97%) 24,398 (95%) 35,006 (96%) 6,418 (97%) Rural 20,352 (3%) 17,528 (3%) 1,175 (5%) 1,446 (4%) 203 (3%) Missing 1,456 <5 <5 1,456 <5 Labour type induced 136,613 (22%) 121,201 (22%) 4,813 (19%) 8,362 (22%) 2,237 (34%) none 92,844 (15%) 83,003 (15%) 3,449 (13%) 4,499 (12%) 1,893 (29%) spontaneous 392,094 (63%) 347,247 (63%) 17,310 (68%) 25,046 (66%) 2,491 (38%) unknown <5 <5 <5 <5 <5 Missing 8 6 <5 <5 <5 Mode of delivery Cesarean 200,160 (32%) 178,372 (32%) 6,808 (27%) 11,289 (30%) 3,691 (56%) Vaginal 421,399 (68%) 373,085 (68%) 18,765 (73%) 26,619 (70%) 2,930 (44%) Small for gestational age (SGA), <10th percentile 43,462 (7%) 37,380 (7%) 1,609 (6%) 3,875 (10%) 598 (9%) Missing 567 278 19 26 244 Large for gestational age (LGA), >90th percentile 78,772 (13%) 69,725 (13%) 3,716 (15%) 3,482 (9%) 1,849 (29%) Missing 567 278 19 26 244 164 Table C.4 Annual gestational diabetes counts and diagnoses (%) in excluded groups and full cohort Year n (%) All available births N=61,953 / 621,559 included N=56,282 / 551,457 insurance inactive N=3,118 / 37,908 DM or <28 wks N=209 / 6,621 non-standard GDM screen N=2,344 / 25,573 2005 2,686 (7.0%) 2,401 (7.2%) 154 (5.2%) 6 (2.0%) 125 (8.1%) 2006 2,657 (6.8%) 2,366 (6.9%) 164 (5.9%) 11 (3.2%) 116 (7.4%) 2007 2,963 (7.3%) 2,699 (7.5%) 158 (5.8%) <5 (<1.0%) 102 (6.6%) 2008 3,131 (7.5%) 2,792 (7.7%) 190 (6.4%) 8 (2.0%) 141 (7.1%) 2009 3,356 (7.9%) 3,029 (8.0%) 161 (6.0%) 8 (2.0%) 158 (8.0%) 2010 3,180 (7.6%) 2,891 (7.6%) 156 (6.9%) <5 (<0.5%) 131 (10.4%) 2011 3,838 (9.1%) 3,500 (9.2%) 155 (6.7%) 15 (3.6%) 168 (11.6%) 2012 4,235 (10.2%) 3,842 (10.3%) 233 (9.5%) 13 (2.8%) 147 (9.1%) 2013 4,723 (11.3%) 4,285 (11.5%) 230 (9.6%) 16 (3.2%) 192 (10.7%) 2014 4,423 (10.6%) 4,010 (10.9%) 218 (9.1%) 14 (2.8%) 181 (9.1%) 2015 4,859 (11.6%) 4,421 (11.9%) 240 (10.2%) 20 (4.2%) 178 (9.4%) 2016 5,180 (12.1%) 4,711 (12.5%) 276 (10.5%) 18 (3.7%) 175 (8.9%) 2017 5,494 (13.0%) 5,031 (13.4%) 259 (11.0%) 24 (4.6%) 180 (9.7%) 2018 5,391 (13.0%) 4,967 (13.3%) 236 (11.1%) 20 (3.9%) 168 (10.0%) 2019 5,837 (14.3%) 5,337 (14.7%) 288 (11.5%) 30 (6.0%) 182 (12.7%) Table C.5 Annual gestational diabetes diagnoses in excluded groups and full cohort with 95% confidence intervals Year n (%) All available births N=61,953 / 621,559 included N=56,282 / 551,457 insurance inactive N=3,118 / 37,908 DM or <28 wks N=209 / 6,621 non-standard GDM screen N=2,344 / 25,573 2005 7.0% (6.8, 7.3) 7.2% (6.9, 7.5) 5.2% (4.5, 6.1) 2.0% (0.8, 4.5) 8.1% (6.8, 9.6) 2006 6.8% (6.6, 7.1) 6.9% (6.6, 7.2) 5.9% (5.1, 6.9) 3.2% (1.7, 5.8) 7.4% (6.2, 8.9) 2007 7.3% (7.0, 7.6) 7.5% (7.2, 7.8) 5.8% (4.9, 6.7) 1.0% (0.3, 2.8) 6.6% (5.5, 8.0) 2008 7.5% (7.2, 7.7) 7.7% (7.4, 7.9) 6.4% (5.6, 7.4) 2.0% (1.0, 4.1) 7.1% (6.0, 8.3) 2009 7.9% (7.6, 8.1) 8.0% (7.8, 8.3) 6.0% (5.2, 7.0) 2.0% (0.9, 4.1) 8.0% (6.9, 9.3) 2010 7.6% (7.3, 7.8) 7.6% (7.3, 7.9) 6.9% (5.9, 8.0) 0.5% (0.1, 1.8) 10.4% (8.8, 12.2) 2011 9.1% (8.8, 9.3) 9.2% (8.9, 9.5) 6.7% (5.7, 7.8) 3.6% (2.1, 6.0) 11.6% (10.0, 13.3) 2012 10.2% (9.9, 10.5) 10.3% (10.0, 10.7) 9.5% (8.4, 10.8) 2.8% (1.6, 4.9) 9.1% (7.7, 10.6) 2013 11.3% (11.0, 11.6) 11.5% (11.2, 11.8) 9.6% (8.5, 10.9) 3.2% (1.9, 5.3) 10.7% (9.3, 12.2) 2014 10.6% (10.3, 10.9) 10.9% (10.5, 11.2) 9.1% (8.0, 10.3) 2.8% (1.6, 4.8) 9.1% (7.9, 10.5) 2015 11.6% (11.3, 11.9) 11.9% (11.6, 12.3) 10.2% (9.0, 11.5) 4.2% (2.7, 6.5) 9.4% (8.1, 10.8) 2016 12.1% (11.8, 12.4) 12.5% (12.2, 12.9) 10.5% (9.4, 11.7) 3.7% (2.3, 6.0) 8.9% (7.7, 10.2) 2017 13.0% (12.6, 13.3) 13.4% (13.0, 13.7) 11.0% (9.8, 12.3) 4.6% (3.0, 6.8) 9.7% (8.4, 11.2) 2018 13.0% (12.6, 13.3) 13.3% (13.0, 13.7) 11.1% (9.8, 12.5) 3.9% (2.5, 6.1) 10.0% (8.7, 11.6) 2019 14.3% (13.9, 14.6) 14.7% (14.3, 15.0) 11.5% (10.3, 12.8) 6.0% (4.1, 8.5) 12.7% (11.0, 14.5) 165 Table C.6 Gestational diabetes counts and diagnoses, screening status in excluded groups and full cohort Characteristic n (%) Overall N = 621,559 included N = 551,457 non-standard GDM screen N = 25,573 inactive insurance, out of BC or late PNC N = 37,908 DM or <29 wks N = 6,621 Gestational diabetes diagnosis 61,953 (10.0%) 56,282 (10.2%) 2,344 (9.2%) 3,118 (8.2%) 209 (3.2%) Gestational diabetes diagnosis combined with screening results Unscreened 64,597 (10.4%) 49,920 (9.1%) 0 (0.0%) 13,377 (35.3%) 1,300 (19.6%) GDM NEG: Alt glucose 23,650 (3.8%) <5 19,384 (75.8%) 1,886 (5.0%) 2,380 (35.9%) GDM NEG: HbA1c 5,417 (0.9%) <5 3,845 (15.0%) 309 (0.8%) 1,263 (19.1%) GDM NEG: HAPO 2.0 (Two-step) or C&C 349,779 (56.3%) 333,803 (60.5%) <5 15,043 (39.7%) 933 (14.1%) GDM NEG: HAPO 1.75 (75g only) 116,163 (18.7%) 111,452 (20.2%) <5 4,175 (11.0%) 536 (8.1%) GDM POS: HAPO 2.0 (Two-step) or C&C 31,562 (5.1%) 30,199 (5.5%) <5 1,302 (3.4%) 61 (0.9%) GDM POS: HAPO 1.75 (75g only) 26,142 (4.2%) 25,165 (4.6%) <5 895 (2.4%) 82 (1.2%) GDM POS: Alt glucose 1,960 (0.3%) <5 1,816 (7.1%) 114 (0.3%) 30 (0.5%) GDM POS: HbA1c 574 (0.1%) <5 528 (2.1%) 37 (0.1%) 9 (0.1%) GDM POS: Unscreened 1,715 (0.3%) 918 (0.2%) <5 770 (2.0%) 27 (0.4%) Table C.7 Gestational diabetes counts and diagnoses, screening status in excluded groups and full cohort, rates with 95% CIs Characteristic n (%) Overall N = 621,559 included N = 551,457 non-standard GDM screen N = 25,573 inactive insurance, out of BC or late PNC N = 37,908 DM or <29 wks N = 6,621 Gestational diabetes diagnosis 10.0% (9.9, 10.0) 10.2% (10.1, 10.3) 9.2% (8.8, 9.5) 8.2% (8.0, 8.5) 3.2% (2.8, 3.6) Gestational diabetes diagnosis combined with screening results Unscreened 10.4% (10.3, 10.5) 9.1% (9.0, 9.1) <5 35.3% (34.8, 35.8) 19.6% (18.7, 20.6) GDM NEG: Alt glucose 3.8% (3.8, 3.9) <5 75.8% (75.3, 76.3) 5.0% (4.8, 5.2) 35.9% (34.8, 37.1) GDM NEG: HbA1c 0.9% (0.8, 0.9) <5 15.0% (14.6, 15.5) 0.8% (0.7, 0.9) 19.1% (18.1, 20.0) GDM NEG: HAPO 2.0 (Two-step) or C&C 56.3% (56.2, 56.4) 60.5% (60.4, 60.7) 0.0% (0.0, 0.0) 39.7% (39.2, 40.2) 14.1% (13.3, 15.0) GDM NEG: HAPO 1.75 (75g only) 18.7% (18.6, 18.8) 20.2% (20.1, 20.3) <5 11.0% (10.7, 11.3) 8.1% (7.5, 8.8) GDM POS: HAPO 2.0 (Two-step) or C&C 5.1% (5.0, 5.1) 5.5% (5.4, 5.5) <5 3.4% (3.3, 3.6) 0.9% (0.7, 1.2) GDM POS: HAPO 1.75 (75g only) 4.2% (4.2, 4.3) 4.6% (4.5, 4.6) <5 2.4% (2.2, 2.5) 1.2% (1.0, 1.5) GDM POS: Alt glucose 0.3% (0.3, 0.3) <5 7.1% (6.8, 7.4) 0.3% (0.2, 0.4) 0.5% (0.3, 0.7) GDM POS: HbA1c 0.1% (0.1, 0.1) <5 2.1% (1.9, 2.2) 0.1% (0.1, 0.1) 0.1% (0.1, 0.3) GDM POS: Unscreened 0.3% (0.3, 0.3) 0.2% (0.2, 0.2) <5 2.0% (1.9, 2.2) 0.4% (0.3, 0.6) 166 C.3 Figures Figure C.1 Study population flow chart and exclusions 167 Figure C.2 Gestational diabetes screening and diagnosis rates with two-step by C-C and DC-2013 criteria 168 Figure C.3 Gestational diabetes rates modeled with addition of prior history of gestational diabetes (sensitivity analysis) Figure C.4 Models with covariates added in reverse order 169 Figure C.5 Sensitivity analyses 2007 start year 170 Figure C.6 Sensitivity analyses 2008 start year 171 Figure C.7 Sensitivity analyses 2010 start year 172 Figure C.8 Sensitivity analyses 2018 end year 173 Figure C.9 Sensitivity analyses exclude 2011 (transition year) 174 Figure C.10 Sensitivity analyses use monthly rates instead of year 175 Figure C.11 Sensitivity analyses use monthly rates excluding transition period where methods changed 176 Figure C.12 Sensitivity analyses use two variables for two-step screening to account for change in diagnostic criteria after IADPSG adopted in 2010 (change from C-C to DC-2013 for two-step) 177 Figure C.13 Sensitivity analyses GDM screening rates in health regions over time 178 Figure C.14 Sensitivity analyses GDM diagnosis risk in health regions over time 179 Figure C.15 Predicted risk of GDM diagnosis by year (modeled) in stratified analyses by health region 180 Figure C.16 Predicted risk of GDM diagnosis by year (modeled) in stratified analyses by alternate region classification 181 D. Covid-19 Pandemic, weight gain and infant birthweight supplemental D.1 Methods Covariate selection and DAG Interrupted time series studies are only vulnerable to confounding by factors with contemporaneous changes around the intervention time point – and not caused by the intervention. For example, if other factors associated with weight gain changed: 1) at the time point of the intervention and 2) due to some other cause (not the pandemic). To identify possible confounders for the association between the onset of the COVID-19 pandemic and pregnancy weight gain, pregnancy weight gain z-score and infant birthweight z-score, I reviewed existing literature 236–238 and then used a directed-acyclic graph (DAG) approach.292,293 After constructing a causal diagram (Figure D.2), and identifying confounders which could be modeled using available data, I plotted time-series for annual rates and yearly means across the study period. If I noted a discontinuity at the intervention time-point and there was no plausible association between that factor and the intervention (pandemic countermeasures), this justified inclusion in the model. The DAG represents the relationship between the COVID-19 pandemic and pandemic-associated countermeasures, on pregnancy weight gain and infant birthweight. The primary exposure is noted in yellow, the primary outcome in blue. Because most potential covariates (i.e. height, pre-pregnancy weight, history of GDM in previous pregnancy, antepartum risk status, health care professional type) occurred temporally prior to the pandemic onset, they are not potential confounders. Those on the causal pathway between the exposure and outcome should not be adjusted for in models to determine causal effects of the pandemic on weight gain/infant birthweight. Covariates without representative data sources in the OB COAP registry are indicated in white. I assessed the following covariates in time series: socio-economic factors (Medicaid insurance payor, a rural residence indicator, distressed community index), race/ethnicity, age, parity, antenatal health care professional type (midwife v. family practice v obstetrician) and pre- or early-pregnancy body mass index (BMI) (in kg/m2). None demonstrated a discontinuity at the time point of interest (the COVID-19 intervention time point (March 23, 2020) noted by the vertical blue line). 182 Conception week v Delivery week In order to model seasonal trends in pregnancy weight gain, comparable pregnancies were grouped together by duration of exposure to the seasonal effect (modeled as calendar month). As an initial approach, I grouped pregnancies by week of delivery and later analyses used the week of conception (calculated using the delivery week and subtracting gestational age at delivery (in weeks)). Monthly mean outcome data using the two methods (conception week or delivery week) (Figure D.3) were generally similar. Overall model fit was improved using the conception week seasonal term, although overall conclusions were unchanged. Final models used conception week as this more accurately controls for exposure over the course of the complete pregnancy.243 Modeling seasonality I considered several statistical approaches to model seasonality in the interrupted time series models. These included: a single sine term, a series of Fourier terms (sine and cosine pairs, 2, 4 and 8 terms), indicator variables for month, restricted cubic splines and restricted periodic cubic splines (using R library peRiodiCS in R).294 The most appropriate method was selected based on lowest Akaike’s Information Criterion (AIC) using generalized linear models (without random effects) and visualization of the fitted plots. For splines, I tested different numbers of knots (between 4 and 20 knots); 2 knots/year (for seasonal extremes (winter/summer)) and +2 for endpoints was the most reasonable fit (4 knots).241,294,295 Restricted cubic splines, however, did not provide a reasonable fit to the data after examining fitted plots; so this method was rejected. Model testing used fixed effects only, random effects for hospital site were added to the final models. Model specification(s) 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 (𝑇𝑇) = 𝛽𝛽0 + 𝜐𝜐0𝑖𝑖 + (𝛽𝛽1 + 𝜐𝜐1𝑖𝑖) × 𝑇𝑇𝑇𝑇𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖 + 𝛽𝛽2 × 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑂𝑂𝑂𝑂𝑇𝑇𝑂𝑂 𝑂𝑂𝑃𝑃𝑜𝑜𝑂𝑂𝑂𝑂𝑖𝑖+ 𝛽𝛽3 × 𝑃𝑃𝑂𝑂𝑜𝑜𝑂𝑂𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑂𝑂𝑂𝑂𝑇𝑇𝑂𝑂𝑇𝑇𝑇𝑇𝑂𝑂𝑂𝑂𝑖𝑖 + 𝛽𝛽4 × 𝑂𝑂𝑂𝑂𝑐𝑐𝑃𝑃𝑐𝑐𝑇𝑇𝑃𝑃𝑂𝑂𝑂𝑂𝑜𝑜 + 𝛽𝛽5 × 𝑜𝑜𝑂𝑂𝑃𝑃𝑜𝑜𝑂𝑂𝑃𝑃𝑃𝑃𝑠𝑠𝑇𝑇𝑂𝑂𝑠𝑠𝑖𝑖 + 𝜀𝜀𝑡𝑡𝑖𝑖 Where: Outcome = Outcome for pregnant person i or infant i at hospital j Model 1 Outcome = Pregnancy weight gain (kg) Model 2 Outcome = Gestational weight gain z-score Model 3 Outcome = Infant birthweight z-score Time= time in weeks from study start to delivery for pregnant person i or birth for infant i Pandemic onset = Level shift indicator for intervention time point (0 for time before Feb 23, 1 for time after March 23) 183 Post-pandemic Time = Time in weeks from March 23 at which delivery or birth occurred (for post-intervention time trend) Seasonality = sine ((2*pi/52.1429)*ConceptionWeek) υ0j and υ1j are random effect terms (intercept and slope) for hospital j εti ∼N(0, σ2) D.2 Sensitivity analyses I conducted a number of sensitivity analyses to examine potential sources of bias in the primary analysis presented in Chapter 5. For all sensitivity analyses, I modeled the interrupted time series (ITS) and compared results to the primary models (Chapter 5). Results are summarized in this section (Table D.3). Methods First, I assessed whether the findings were impacted by COVID-19 disease status. A variable for any COVID-19 diagnosis was created by searching in three open text fields (“Other Pregnancy Complications”, “Other Pregnancy Diagnoses” or in “Other Pre-pregnancy Diagnoses”) in the OB COAP data registry. Trained abstractors had been instructed to use these fields to indicate COVID-19 cases as there was no specific variable in the dataset at this time. This method identified 341 cases, with a COVID-19 positive rate of (2%%) from April to December of 2020. An analysis of COVID-19 prevalence in Washington state (March 1 – June 30, 2020) reported an infection rate of 1.4% among pregnant women.296 Time trends also demonstrates a substantial increase in COVID-positive (~4-5%) by the last two months of 2020 which aligns with data on infection in the region.297 After exclusions (valid weight gain and infant birthweight), 230 pregnancies and 340 infants from COVID-19 positive mothers remained. All ITS models (for all three outcomes) were repeated excluding these cases. Second, I considered the impact of known repeated pregnancies to the same individual during the study years. While this data registry did have a unique patient identifier, this had many limitations. Importantly, this identifier had not been validated, nor previously used for research. Within the limitations of this identifier, to conduct the sensitivity analysis, I randomly created a subset with only 1 birth per person and repeated all ITS models using this subset. Prior to any exclusions, only 22% of available pregnancies had more than 1 birth in the dataset which is much lower than the rate of multiparity (>50%). Third, I examined whether the results for the z-score based analyses were reproducible using a different reference standard. Thus, I calculated pregnancy weight gain z-scores using the INTERGROWTH 21 gestational weight gain228 standard and infant birthweight z-scores with the INTERGROWTH 21 fetal 184 growth birthweight standards 298 by infant sex and gestational age. Notably, the INTERGROWTH standard was only available for normal pre-pregnancy body mass index (BMI) (between 18.5 to 24.9kg/m2) thus this standard was only applied to a subgroup with normal BMI. Next, I conducted two more sensitivity analyses for only the infant birthweight z-score cohort. First, to investigate possible uncontrolled confounding by parity in the infant z-score analyses, I 1) calculated infant z-scores using parity-adjusted 225 reference charts and 2) included parity as a covariate in the ITS models. Second, to consider whether the findings for the infants were biased because a larger sample (n=104,936) was used for this cohort compared to the weight gain cohort (n=77,411) which was restricted to pregnancies with valid weight gain data, I restricted to a subset of infants with valid birthweight data (n=77,344) matched to pregnancies in the weight gain cohort. Results Overall, the primary study findings (modest impact of the COVID-19 pandemic towards increasing pregnancy weight gain, no change for infant birthweight) were unchanged (Table D.3, Table D.4, Table D.5, Table D.6) using sensitivity analyses. A few results were of interest and are summarized briefly. Excluding COVID-19 cases shifted the effect estimates for both pregnancy weight gain and pregnancy z-score further away from the null (Table D.4, Table D.5). Non-significant findings remained unchanged. Because of concerns with the reliability the COVID-19 case ascertainment, I did not exclude these from the main analysis; however, this suggests that including the COVID-positive cases is likely to have no impact, or to have biased findings towards the null. Using the Intergrowth 21 standard for infant birthweight (Table D.6) resulted in a mean z-score (0.38) substantially higher than with the reference chart used for the primary analysis (0.1) confirming that the INTERGROWTH 21 standard is not well calibrated to a US reference population299. Using the cohort where infants were matched to mothers in the weight gain (Table D.6), mean z-scores were slightly increased (0.11) compared to the full cohort (0.09). This suggests this cohort may represent a slightly different population with higher mean infant birthweights. Given the exclusions for valid weight gain required a pre- or initial- pregnancy weight that was taken <14 weeks of pregnancy, this would bias the sample towards individuals with earlier prenatal care; likely a higher socio-economic/income group. In other words, people with incomplete or late prenatal care who are at increased risk for prematurity and small for gestational age would be excluded. Thus, a slight increase in infant birthweight is understandable when restricting to those infants of mothers meeting pregnancy weight gain criteria. 185 D.3 Conditional quantile regression As a sensitivity analysis in the main manuscript, I reported the upper extremes (90th percentile) of weight gain and z-score (Table D.7). However, I also examined the overall distribution of weight gain outcomes using a conditional quantile approach (Table D.7, Figure D.4). Using conditional quantile regression modeled each percentile (0.05 to 0.95) using the ITS models. I included only the fixed effects from the mean regression models, because the quantile regression analytic library (R library quantreg) did not support random effect terms. Since the random effects terms had only negligible impacts on the effect estimates in the primary analyses, limiting to fixed effects only was reasonable. This approach demonstrated a generally non-significant pandemic shift in weight gain and z-score for the lower percentiles of weight gain (Figure D.4). Statistically significant pandemic-related impacts on pregnancy weight gain and z-scores were noted above the 60th percentile of weight gain. An increased level change, relative to the population mean (shaded blue line in the graphs) were noted for the distribution above the 90th percentile of weight gain (Table D.7). Quantile regressions revealed no differences when compared to the mean regression results for the infant birthweight z-scores. 186 D.4 A re-analysis of the COVID-19 pandemic study using excess or inadequate pregnancy weight gain according to the Institute of Medicine criteria Background In Chapter 6, I found a modest increase in the population mean pregnancy weight gain and body-mass index adjusted z-scores after the onset of the COVID-19 pandemic. While total pregnancy weight gain is a more direct measure, weight gain in pregnancy is often assessed clinically using the Institute of Medicine’s (IOM) categories for weight gain by pregestational BMI.54 Also based on the IOM criteria, either “excess” or “inadequate” weight gain in pregnancy56,57,300 are commonly used in research as an explanatory variable or as an outcome. While some have suggested these guidelines should be further revised to reflect current population norms, especially higher rates of obesity55, the IOM guidelines remain the most commonly used clinical standards for weight gain in pregnancy. The aim of this analysis was to assess whether there was an impact of the COVID-19 pandemic onset on either excess or inadequate weight gain as measured using these commonly referenced clinical criteria. This is one way to explore whether the findings of the mean regression analysis in Chapter 6 are sensitive to an alternate definition of weight gain. Methods The current (2009) IOM recommended ranges for weight gain in pregnancy are: underweight women (BMI < 18.5 kg/m2) 12.5—18.0 kg, normal weight women (BMI 18.5—24.9 kg/m2) 11.5—16.0 kg, overweight women (BMI 25.0—29.9) 7.0—11.5 kg, and obese women (BMI > 30.0 kg/m2) 5-9 kg. Using the pregnancy weight gain variable and pre-pregnancy body mass index from the cohort in Chapter 4, I defined categorical variables for “excess weight gain” (v. recommended weight gain) and for “inadequate weight gain” (v. recommended weight gain), applying the IOM weight gain criteria within categories by pregestational body mass index. Using a modified Poisson regression approach for binomial outcomes,190 I ran interrupted time series models similar to those in Chapter 4 but including weeks of gestation on delivery was included in the models because the IOM weight gain criteria is generally applied to term pregnancies. I restricted to a term cohort (>=37 weeks) without control for weeks on delivery. Models were adjusted for seasonality using the same approach as the generalized linear mixed regression models (single sine term using week of conception) and for hospital site (random slope and intercept). 187 Results A majority of pregnant people experienced inadequate weight gain (42%) compared to the recommended weight gain (32%) (Table D.11). Using the ITS approach, there was an increase in the risk of ‘excess weight gain’ (relative risk) 1.055, (0.99, 1.121) associated with the pandemic time point. This represented an increase from 41% of pregnancies in 2019, to 43% of pregnancies after the pandemic who experienced ‘excess weight gain’. There was no predicted change in ‘inadequate weight gain’ associated with the pandemic time point (Table D.12). Results were similar when restricted to term deliveries. Similar to the findings for mean pregnancy weight gain, there was a modest increase in the risk of “excess weight gain” among pregnant individuals in this cohort after the onset of the COVID-19 pandemic. 188 D.5 Tables Table D.1 Model estimates from interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) (Primary analyses) Model terms Estimate (95%CI) p-value Pregnancy weight gain (kg) (Intercept) -10.09 (-11.22, -9.070) <0.001 Sine week using conception time 0.156 (0.093, 0.218) <0.001 Gestational age at delivery (weeks) 0.582 (0.557, 0.607) <0.001 Baseline time trend (kg/year) -0.122, (-0.21, -0.03) 0.025 Level change at COVID-19 onset 0.486 (0.251, 0.730) <0.001 Trend change at COVID-19 onset (kg/year) -0.254, (-0.75, 0.25) 0.3 Pregnancy weight gain z-score (Intercept) -0.070 (-0.143, 0.012) 0.12 Sine week using conception time 0.033 (0.022, 0.045) <0.001 Baseline time trend (/year) -0.016, (-0.03, 0.00) 0.085 Level change at COVID-19 onset 0.080 (0.031, 0.125) <0.001 Trend change at COVID-19 onset (/year) -0.015, (-0.11, 0.08) 0.7 Infant birthweight z-score (Intercept) 0.119 (0.072, 0.167) <0.001 Sine week using conception time -0.001 (-0.009, 0.008) 0.8 Baseline time trend (/year) 0.001, (-0.01, 0.01) 0.8 Level change at COVID-19 onset -0.004 (-0.039, 0.034) 0.8 Trend change at COVID-19 onset (/week) -0.013, (-0.09, 0.06) 0.7 189 Table D.2 Demographics and characteristics of excluded cases for a study of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) All excluded cases N=28034 Complete case cohort (pregnancies) N=77411 Pre-pandemic COVID-19 pandemic Pre-pandemic COVID-19 pandemic N=24144 (86%) N=3890 (14%) N=65214 (84%) N=12197 (16%) Nulliparous 9232 (38.2) 1559 (40.1) 26631 (40.8) 5256 (43.1) Race and ethnicity of birthing person: Non-Hispanic White 11755 (48.7) 1625 (41.8) 34515 (52.9) 5967 (48.9) Non-Hispanic Black 1667 (6.9) 245 (6.3) 2670 (4.1) 512 (4.2) Hispanic or Latinx 4406 (18.2) 669 (17.2) 10713 (16.4) 2046 (16.8) Asian or Pacific Islander 3999 (16.6) 517 (13.3) 13060 (20.0) 2548 (20.9) Native American or Native Alaskan 556 (2.3) 96 (2.5) 696 (1.1) 103 (0.8) Other or mixed race 1169 (4.8) 176 (4.5) 1963 (3.0) 372 (3.0) Missing 592 (2.5) 562 (14.4) 1597 (2.4) 649 (5.3) Rural zip code: Yes 2076 (8.6) 374 (9.6) 5310 (8.1) 920 (7.5) Missing 815 (3.4) 130 (3.3) 1649 (2.5) 351 (2.9) Insurance payer: Medicaid* 11624 (48.1) 1849 (47.5) 19537 (30.0) 3351 (27.5) Missing 168 (0.7) 23 (0.6) 2313 (3.5) 66 (0.5) Distressed Communities Index: Prosperous 7704 (31.9) 1115 (28.7) 29543 (45.3) 5576 (45.7) Comfortable 6293 (26.1) 1081 (27.8) 16137 (24.7) 3040 (24.9) Mid-tier 3117 (12.9) 584 (15.0) 5931 (9.1) 1155 (9.5) At risk 4924 (20.4) 795 (20.4) 10097 (15.5) 1765 (14.5) Distressed 1611 (6.7) 236 (6.1) 2839 (4.4) 534 (4.4) Missing 495 (2.1) 79 (2.0) 667 (1.0) 127 (1.0) Age of birthing person (year) 29.4 (5.9) 29.7 (6.1) 30.4 (5.4) 30.8 (5.4) Height of birthing person (cm) 162.7 (7.4) 162.6 (7.2) 163.1 (7.2) 163.0 (7.1) Gestational age at delivery (weeks) 38.4 (2.4) 38.3 (2.3) 38.8 (1.7) 38.7 (1.7) Pregnancy weight gain (kg) *only cases with non-missing weight gain data (N=21,560) 18,554 / 21,560 (86%) 3006 / 21,560 (14%) Median (IQR) 6.8 (2.7 – 11) 7.1 (2.5 – 11.7) 12.3 (8.6 – 15.9) 12.3 (8.6 – 16.3) Note: Excluded cases over both time periods had a higher proportion of younger age, multiparas, Medicaid, Black, Latinx and Missing race data and lower DCI quintiles. We surmised that the excluded weight group represented a high proportion of people with late prenatal care (as the weight measurement was in the second trimester). However, the proportion of the population with missing or invalid data is stable across the time periods therefore these underlying differences would not impact our overall findings across the pandemic time period, because these characteristics were not subject to differential bias by the pandemic. 190 Table D.3 Summary of sensitivity analyses results Covid-19 study Sensitivity Total pregnancy weight gain (kg) Weight gain z-score Infant z-score Exclude COVID positivea No change No change No change 1 birth/ID No change No change No change Intergrowth21 Standards n/a No change No change (higher mean z-score) Parity (2 methods) n/a n/a No change Matched infants to mothers n/a n/a No change (higher mean z-score) a. COVID-positive cases identified from open text fields in either: “Other Pregnancy Complications”, “Other Pregnancy Diagnoses” or in “Other Pre-pregnancy Diagnoses”. I used text-searching to identify all pregnancies with “covid” (case independent) and NOT (“investigation”, “pui” or “unsure”) in any of these open text fields. Table D.4 Complete model terms for pregnancy weight gain models (kg) with sensitivity anlayses Total pregnancy weight gain (kg) Model terms Estimate (95%CI) p-value Primary analysis (Intercept) -10.09 (-11.22, -9.070) <0.001 Time trend, weekly -0.002 (-0.004, -0.001) 0.025 Level change 0.486 (0.251, 0.730) <0.001 Trend change -0.005 (-0.014, 0.004) 0.3 Gestational age at delivery (weeks) 0.582 (0.557, 0.607) <0.001 Sine week using conception 0.156 (0.093, 0.218) <0.001 Sensitivity analyses: Subset excluding COVID-19 Positive pregnancies (Intercept) -10.07 (-11.14, -9.001) <0.001 Time trend, weekly -0.002 (-0.004, -0.001) 0.024 Level change 0.506 (0.266, 0.747) <0.001 Trend change -0.005 (-0.015, 0.004) 0.3 Gestational age at delivery (weeks) 0.581 (0.556, 0.606) <0.001 Sine week using conception 0.154 (0.092, 0.216) <0.001 Subset to: 1 birth/patient identifier (random) (Intercept) -10.32 (-11.44, -9.199) <0.001 Time trend, weekly -0.002 (-0.003, 0.000) 0.11 Level change 0.506 (0.252, 0.761) <0.001 Trend change -0.007 (-0.018, 0.003) 0.2 Gestational age at delivery (weeks) 0.586 (0.560, 0.612) <0.001 Sine week using conception 0.159 (0.094, 0.224) <0.001 191 Table D.5 Complete model terms for pregnancy weight gain z-score models with sensitivity analyses Pregnancy weight gain z-score Model terms Estimate (95% CI) p-value Primary analysis: using Santos’ reference, adjusted for BMI and Gestational age (Intercept) -0.070 (-0.143, 0.012) 0.12 Time trend, weekly 0.033 (0.022, 0.045) <0.001 Level change 0.080 (0.031, 0.125) <0.001 Trend change 0.000 (-0.001, 0.000) 0.085 Sine week using conception 0.000 (-0.002, 0.002) 0.7 Sensitivity analyses: INTERGROWTH 21 standard (Intercept) 0.097 (0.030, 0.164) 0.018 Time trend, weekly 0.000 (-0.001, 0.000) 0.003 Level change 0.075 (0.009, 0.142) 0.026 Trend change 0.001 (-0.001, 0.004) 0.4 Sine week using conception 0.049 (0.032, 0.066) <0.001 Excluding COVID-19 Positive pregnancies (Intercept) -0.071 (-0.136, -0.005) 0.053 Time trend, weekly 0.000 (-0.001, 0.000) 0.084 Level change 0.083 (0.038, 0.127) <0.001 Trend change 0.000 (-0.002, 0.001) 0.7 Sine week using conception 0.033 (0.022, 0.044) <0.001 Restriction to 1 birth/patient identifier (Intercept) -0.078 (-0.149, -0.007) 0.052 Time trend, weekly 0.000 (-0.001, 0.000) 0.3 Level change 0.078 (0.031, 0.125) 0.001 Trend change -0.001 (-0.002, 0.001) 0.6 Sine week using conception 0.034 (0.022, 0.046) <0.001 192 Table D.6 Complete model terms for all infant birthweight z-score models with sensitivity analyses Infant birthweight z-score Model terms Estimate (95% CI) p-value Primary analysis: Aris’ standards, adjusted for infant sex and gestational age (Intercept) 0.119 (0.072, 0.167) <0.001 Time trend, weekly -0.001 (-0.009, 0.008) 0.8 Level change -0.004 (-0.039, 0.034) 0.8 Trend change 0.000 (0.000, 0.000) 0.8 Sine week using conception 0.000 (-0.002, 0.001) 0.7 Sensitivity analyses: INTERGROWTH 21 (Intercept) 0.421 (0.367, 0.474) <0.001 Time trend, weekly 0.000 (0.000, 0.000) >0.9 Level change 0.004 (-0.031, 0.038) 0.8 Trend change 0.000 (-0.002, 0.001) 0.6 Sine week using conception -0.001 (-0.010, 0.008) 0.8 Parity-adjusted z-score per Aris’ reference (Intercept) 0.135 (0.087, 0.184) <0.001 Time trend, weekly 0.000 (0.000, 0.000) 0.4 Level change -0.003 (-0.039, 0.033) 0.9 Trend change 0.000 (-0.002, 0.001) 0.7 Sine week using conception -0.001 (-0.010, 0.009) 0.9 Modeled adjustment for parity (Intercept) 0.238 (0.190, 0.285) <0.001 Time trend, weekly 0.000 (0.000, 0.000) 0.4 Level change -0.003 (-0.039, 0.032) 0.8 Trend change 0.000 (-0.002, 0.001) 0.8 Parity (nulliparous v. multiparous) -0.324 (-0.336, -0.311) <0.001 Sine week using conception -0.001 (-0.010, 0.008) 0.9 Matched infants to pregnancies from weight gain cohort (Intercept) 0.144 (0.091, 0.198) <0.001 Time trend, weekly 0.000 (0.000, 0.000) >0.9 Level change -0.008 (-0.050, 0.033) 0.7 Trend change 0.000 (-0.002, 0.001) 0.6 Sine week using conception -0.005 (-0.016, 0.005) 0.3 Excluding COVID-19 Positive (Intercept) 0.119 (0.063, 0.176) 0.004 Time trend, weekly 0.000 (0.000, 0.000) 0.9 Level change -0.007 (-0.043, 0.029) 0.7 Trend change 0.000 (-0.002, 0.001) >0.9 Sine week using conception -0.001 (-0.010, 0.008) 0.9 Restriction to 1 birth/patient identifier (Intercept) 0.119 (0.063, 0.176) 0.004 Time trend, weekly 0.000 (0.000, 0.000) 0.9 Level change -0.007 (-0.043, 0.029) 0.7 193 Infant birthweight z-score Model terms Estimate (95% CI) p-value Trend change 0.000 (-0.002, 0.001) >0.9 Sine week using conception -0.001 (-0.010, 0.008) 0.9 Table D.7 Quantile regression for 90th percentiles using an interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort Model terms Quantile regression (90th percentile) Estimate 95% CI Mean regression* (*fixed effects only) Estimate 95% CI Pregnancy weight gain (kg) Level change 1.20 (0.75, 1.65) 0.47 (0.23, 0.71) Trend change -0.72 (-1.72, 0.27) -0.16 (-0.66, 0.35) Pregnancy weight gain z-score Level change 0.20 (0.12, 0.28) 0.079 (0.03, 0.12) Trend change -0.11 (-0.28, 0.06) 0.008 (-0.09, 0.10) Infant birthweight z-score Level change -0.041 (-0.10, 0.02) -0.003 (-0.04, 0.03) Trend change 0.073 (-0.06, 0.20) -0.009 (-0.08, 0.07) 194 Table D.8 Quantile regression and mean regression results stratified by pregestational BMI (body mass index) categories for a study of the effect of the COVID-19 pandemic onset in a Washington State cohort (January 1, 2016 to December 28, 2020) Model terms – pregestational BMI subgroups 90th percentile estimate (95%CI) a p-value Mean estimate (95%CI) b p-value Pregestational BMI < 25 kg/m2 (Normal or underweight) pregnancy weight gain (kg) Level change 1.20 (0.63, 1.76) <0.001 0.42, (0.13, 0.71) 0.005 Trend change (/year) -0.53 (-1.69, 0.62) 0.367 0.14, (-0.48, 0.75) 0.7 pregnancy weight gain z-score Level change 0.23 (0.10, 0.35) <0.001 0.083, (0.02, 0.15) 0.016 Trend change (/year) -0.11 (-0.35, 0.14) 0.393 0.053, (-0.09, 0.19) 0.5 Infant birthweight z-score Level change 0.015 (-0.07, 0.10) 0.737 0.031, (-0.02, 0.09) 0.3 Trend change (/year) -0.016 (-0.20, 0.17) 0.862 -0.079, (-0.19, 0.04) 0.2 Pregestational BMI 25-<30 kg/m2 (Overweight) pregnancy weight gain (kg) Level change 0.99 (0.14, 1.84) 0.023 0.43, (-0.02, 0.87) 0.061 Trend change (/year) -0.75 (-2.66, 1.16) 0.441 -0.18, (-1.12, 0.76) 0.7 pregnancy weight gain z-score Level change 0.18 (0.04, 0.32) 0.013 0.072, (-0.01, 0.15) 0.089 Trend change (/year) -0.17 (-0.49, 0.15) 0.291 -0.039, (-0.21, 0.14) 0.7 Infant birthweight z-score Level change -0.028 (-0.13, 0.07) 0.588 -0.066, (-0.14, 0.00) 0.061 Trend change (/year) 0.11 (-0.11, 0.33) 0.314 0.16, (0.01, 0.30) 0.035 Pregestational BMI > 30 kg/m2 (Obese I, II, III) pregnancy weight gain (kg) Level change 1.03 (0.05, 2.00) 0.039 0.49, (-0.03, 1.01) 0.065 Trend change (/year) -0.40 (-2.19, 1.39) 0.663 -0.76, (-1.83, 0.32) 0.2 pregnancy weight gain z-score Level change 0.20 (0.07, 0.34) 0.003 0.070, (-0.01, 0.15) 0.094 Trend change (/year) -0.10 (-0.38, 0.19) 0.503 -0.075, (-0.25, 0.09) 0.4 Infant birthweight z-score Level change -0.019 (-0.11, 0.07) 0.674 0.013, (-0.06, 0.08) 0.7 Trend change (/year) 0.050 (-0.16, 0.26) 0.635 -0.056, (-0.20, 0.09) 0.4 a. Quantile regression models were run using library quantreg in R and assess the changes in the outcomes across the distribution of the outcome variable. Quantile regression models were restricted to fixed effects only and did not include hospital-level random effects; however, difference between mean models with and without random effects was minimal. Other percentiles (<90th) were not significantly different than the mean regression. b. Full models as described in Supplementary Methods and previous Supplementary Table 2. 195 Table D.9 Model estimates from sensitivity analyses for increasing exposure to the pandemic by excluding births from February 23, 2020 to April 27, 2020 (9 weeks) and from February 23, 2020 to June 8, 2020 (15 weeks) from interrupted time series analyses of the effect of the COVID-19 pandemic onset in a Washington State cohort April 27, 2020 pandemic onset models Estimate (95%CI) p-value Pregnancy weight gain (kg) n=75915 Level change at COVID-19 onset 0.562, (0.30, 0.82) <0.001 Trend change at COVID-19 onset (kg/year) -0.456, (-1.06, 0.15) 0.14 Pregnancy weight gain z-score n=75915 Level change at COVID-19 onset 0.095, (0.05, 0.14) <0.001 Trend change at COVID-19 onset (kg/year) -0.016, (-0.03, 0.00) 0.086 Infant birthweight z-score n=102953 Level change at COVID-19 onset -0.010, (-0.05, 0.03) 0.6 Trend change at COVID-19 onset (kg/year) -0.002, (-0.09, 0.09) >0.9 June 8, 2020 pandemic onset models Pregnancy weight gain (kg) n=74025 Level change at COVID-19 onset 0.549, (0.26, 0.84) <0.001 Trend change at COVID-19 onset (kg/year) -0.560, (-1.36, 0.24) 0.2 Pregnancy weight gain z-score n=74025 Level change at COVID-19 onset 0.099, (0.05, 0.15) <0.001 Trend change at COVID-19 onset (kg/year) -0.078, (-0.23, 0.07) 0.3 Infant birthweight z-score n=100544 Level change at COVID-19 onset -0.017, (-0.06, 0.03) 0.5 Trend change at COVID-19 onset (kg/year) 0.014, (-0.11, 0.14) 0.8 196 Table D.10 Baseline weight and other characteristics stratified by pregestational BMI categories Characteristic n (%) or Mean (SD) Pre pandemic Post pandemic BMI <25 N = 30,128 BMI 25.0-29.9 N = 17,794 BMI >30.0 N = 17,292 BMI <25 N = 5,454 BMI 25.0-29.9 N = 3,288 BMI >30.0 N = 3,455 Nulliparous 13,970 (46.4%) 6,879 (38.7%) 5,782 (33.4%) 2,721 (49.9%) 1,338 (40.7%) 1,197 (34.6%) Medicaid Payor 6,629 (23.0%) 5,355 (31.2%) 7,553 (44.7%) 1,047 (19.3%) 882 (27.0%) 1,422 (41.4%) Continuous Age of birthing person (year) 30.6 (5.3) 30.6 (5.4) 30.1 (5.6) 30.9 (5.3) 31.0 (5.3) 30.4 (5.6) BMI in early pregnancy (kg/m2) 21.9 (1.9) 27.2 (1.4) 36.0 (5.4) 22.0 (1.9) 27.2 (1.4) 36.1 (5.5) Height of birthing person (cm) 163.5 (7.0) 162.8 (7.2) 162.7 (7.6) 163.4 (6.9) 162.7 (7.2) 162.7 (7.3) Pregnancy weight gain (kg) 13.8 (5.0) 12.6 (5.9) 9.3 (6.9) 14.0 (5.2) 12.8 (6.3) 9.4 (7.3) weight gain z-score -0.1 (1.1) -0.2 (1.1) 0.0 (1.1) -0.1 (1.2) -0.2 (1.2) 0.0 (1.2) Gestational age at delivery (weeks) 38.9 (1.6) 38.8 (1.7) 38.6 (1.9) 38.9 (1.5) 38.7 (1.7) 38.5 (1.9) Infant birthweight (g) 3,317.0 (504.0) 3,390.0 (541.0) 3,432.3 (583.0) 3,313.9 (492.4) 3,372.2 (528.3) 3,400.0 (592.6) 197 Table D.11 Institute of Medicine (2009) pregnancy weight gain classifications and pregestational BMI Pre pandemic COVID-19 pandemic time period All (N=65214) (N=12197) (N=77411) Total pregnancy weight gain by IOM standards for BMI Excess weight gain 26966 (41.4%) 5265 (43.2%) 32231 (41.6%) Inadequate weight gain 17081 (26.2%) 3180 (26.1%) 20261 (26.2%) Recommended weight gain 21167 (32.5%) 3752 (30.8%) 24919 (32.2%) Pre- or Early- pregnancy body mass index (kg/m2) Underweight 1544 (2.4%) 272 (2.2%) 1816 (2.3%) Normal 28584 (43.8%) 5182 (42.5%) 33766 (43.6%) Overweight 17794 (27.3%) 3288 (27.0%) 21082 (27.2%) Obese I 9362 (14.4%) 1809 (14.8%) 11171 (14.4%) Obese II 4595 (7.0%) 935 (7.7%) 5530 (7.1%) Obese III 3335 (5.1%) 711 (5.8%) 4046 (5.2%) 198 Table D.12 Full ITS model results for IOM excess or inadequate weight gain Model terms Estimate (95%CI) p-value Excess weight gain (full cohort) (Intercept) 0.016, (0.012, 0.021) <0.001 Time trend, weekly 1.000, (1.000, 1.000) 0.8 Level change 1.055, (0.99, 1.121) 0.079 Trend change 1.000, (0.997, 1.002) 0.9 Gestational age at delivery (weeks) 1.089, (1.081, 1.097) <0.001 Sine week using conception 1.026, (1.009, 1.042) 0.002 Inadequate weight gain (full cohort) (Intercept) 12.08, (9.413, 15.49) <0.001 Time trend, weekly 1.000, (1.000, 1.000) 0.3 Level change 0.97, (0.895, 1.045) 0.4 Trend change 1.000, (0.997, 1.003) 0.9 Gestational age at delivery (weeks) 0.905, (0.899, 0.910) <0.001 Sine week using conception 0.97, (0.95, 0.99) 0.003 Excess weight gain (>=37 weeks GA only) (Intercept) 0.440, (0.413, 0.469) <0.001 Time trend, weekly 1.000, (1.000, 1.000) 0.6 Level change 1.065, (1.002, 1.133) 0.044 Trend change 1.000, (0.997, 1.002) 0.8 Sine week using conception 1.028, (1.011, 1.045) <0.001 Inadequate weight gain (>=37 weeks GA only) (Intercept) 0.240, (0.224, 0.257) <0.001 Time trend, weekly 1.000, (1.000, 1.001) 0.089 Level change 0.97, (0.891, 1.051) 0.4 Trend change 0.999, (0.996, 1.003) 0.7 Sine week using conception 0.97, (0.946, 0.99) 0.001 199 D.6 Figures Figure D.1 COVID-19 policy indices (Containment Health Index, Stringency Index and Government Response Index) for Washington State, U.S. in 2020 using data from the Oxford COVID-19 Government Response Tracker Source205 200 Figure D.2 Directed acyclic graph for COVID-19 pandemic and pregnancy weight gain or infant birthweight 201 Figure D.3 Seasonal trends year-by-year comparing conception and delivery month 202 Figure D.4 Quantile regression modeled level change parameters across percentiles of pregnancy weight gain (model adjusted for gestational age at delivery and seasonality) and z-score (adjusted for seasonality) Blue lines represent the mean regression (level change) parameter and dashed lines represent the 95% CIs. Shaded grey area represents 95% CI for the quantile regression model estimates. 203 Figure D.5 Subgroup analyses of pregnancy weight gain (kg) by pregestational BMI adjusted using an interrupted time series for a Washington State cohort (January 1, 2016 to December 28, 2020) 204 Figure D.6 Subgroup analyses of pregnancy weight gain z-score by Distressed Communities Index using an interrupted time series for a Washington State cohort 205 Figure D.7 Subgroup analyses of weight gain z-score by Race/ethnicity using an interrupted time series for a Washington State cohort 206 Figure D.8 Subgroup analyses of pregnancy weight gain z-score by pregestational BMI using an interrupted time series for a Washington State cohort 207 Figure D.9 Subgroup analyses of pregnancy weight gain z-score by insurance payor using an interrupted time series for a Washington State cohort Figure D.10 Subgroup analyses of pregnancy weight gain z-score by Parity using an interrupted time series for a Washington State cohort 208 Figure D.11 Subgroup analyses of infant birthweight z-score by Distressed Communities Index using an interrupted time series for a Washington State cohort 209 Figure D.12 Subgroup analyses of infant birthweight z-score by Race/ethnicity using an interrupted time series for a Washington State cohort 210 Figure D.13 Subgroup analyses of infant birthweight z-score by Race/ethnicity and parity adjusted using an interrupted time series for a Washington State cohort 211 Figure D.14 Subgroup analyses of infant birthweight z-score by insurance payor using an interrupted time series for a Washington State cohort Figure D.15 Subgroup analyses of infant birthweight z-score by parity using an interrupted time series for a Washington State cohort 212 Figure D.16 Subgroup analyses of infant birthweight z-score by pregestational BMI using an interrupted time series for a Washington State cohort 213 Figure D.17 Comparison of primary analyses and Intergrowth 21 standards for weight gain and infant birthweight z-scores 214 Figure D.18 Infant birthweight z-scores for primary cohort (infants) compared to a matched group with valid pregnancy weight gain Figure D.19 Parity-adjusted infant z-scores compared to primary outcome 215 Figure D.20 Rate of monthly Covid-19 Positive pregnancies in complete dataset 216 Figure D.21 Interrupted time series plots excluding any known Covid-19 Positive cases 217 Figure D.22 Interrupted time series for random sampled 1 birth per person for repeated pregnancies 218 Figure D.23 Excess and Inadequate weight gain using IOM criteria (full cohort, adjusted for weeks): Interrupted time series plots 219 Figure D.24 Excess and Inadequate weight gain using IOM criteria (>=37wks GA only): Interrupted time series plots
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Janssen, Patricia A.
Gestational diabetes screening changes and impacts on diagnosis
Text