Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Rich, Ashleigh
Doctor of Philosophy - PhD
University of British Columbia
Introduction: Given limited evidence of the epidemiology of chronic disease and multimorbidity for transgender people living with and without HIV, the purpose of this dissertation was to: 1) systematically review chronic disease burden for transgender populations globally; 2) assess methods for identifying transgender people with and without HIV in administrative data, to facilitate investigation of chronic disease-related and other healthcare utilization for this population; and 3) investigate multimorbidity burden for transgender compared to cisgender people living with HIV. Methods: Systematic review methods were used to review and narratively synthesize the empirical literature on chronic disease among transgender populations, globally. Healthcare records were used to develop and validate a computable phenotype to identity transgender people in a large linked provincial administrative dataset. Longitudinal analysis methods were used to examine multimorbidity prevalence and incidence for transgender compared to cisgender people in North America in a large longitudinal HIV cohort collaborative. Key Findings: The literature on chronic disease among transgender populations is focused on mental and sexual health, with sparse attention to chronic physical health, in particular age-related chronic conditions and inflammation-related disease. Identification of transgender people is feasible in administrative health data, with high specificity, concordance with transgender-specific healthcare utilization, and ability to reliably characterize transgender populations in terms of demographics and chronic disease. Transgender people living with HIV had elevated incidence and prevalence of multimorbidity compared to cisgender people living with HIV, though inconsistently over time and without strong evidence of statistically significant differences by gender identity. Conclusions: High quality evidence of chronic conditions among transgender populations is needed; in particular longitudinal, population-based and representative studies with appropriate comparison groups and rigorous measurement of both transgender status and chronic conditions. Development of validated measures for ascertaining transgender status in administrative data is an important contribution given the limited evidence and the methodological challenges in transgender health research. Given evidence of increased burden of multimorbidity for transgender people living with HIV, future research on chronic co- and multimorbidity for transgender populations is warranted with attention to vulnerabilities by racialization and gender, and the role of intervenable disease risk factors such as smoking.
EPIDEMIOLOGY OF CHRONIC DISEASE AND MULTIMORBIDITY FOR TRANSGENDER PEOPLE LIVING WITH AND WITHOUT HIV by Ashleigh Rich MPH, The University of British Columbia, 2013 B.A., Smith College, 2008 A THESIS 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) March 2021 © Ashleigh Rich, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The epidemiology of chronic disease and multimorbidity for transgender people living with and without HIV submitted by Ashleigh Rich in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Population & Public Health Examining Committee: Mieke Koehoorn, Interim Director and Professor, School of Population & Public Health, UBC Co-supervisor Robert Hogg, Professor, Faculty of Health Sciences, Simon Fraser University Co-supervisor Tonia Poteat, Assistant Professor, Deptartment of Social Medicine, University of North Carolina- Chapel Hill Supervisory Committee Member Joel Singer, Professor, School of Population & Public Health, UBC & University Examiner Dr. Elizabeth Saewyc, Director and Professor, School of Nursing, UBC University Examiner iii Abstract Introduction: Given limited evidence of the epidemiology of chronic disease and multimorbidity for transgender people living with and without HIV, the purpose of this dissertation was to: 1) systematically review chronic disease burden for transgender populations globally; 2) assess methods for identifying transgender people with and without HIV in administrative data, to facilitate investigation of chronic disease-related and other healthcare utilization for this population; and 3) investigate multimorbidity burden for transgender compared to cisgender people living with HIV. Methods: Systematic review methods were used to review and narratively synthesize the empirical literature on chronic disease among transgender populations, globally. Healthcare records were used to develop and validate a computable phenotype to identity transgender people in a large linked provincial administrative dataset. Longitudinal analysis methods were used to examine multimorbidity prevalence and incidence for transgender compared to cisgender people in North America in a large longitudinal HIV cohort collaborative. Key Findings: The literature on chronic disease among transgender populations is focused on mental and sexual health, with sparse attention to chronic physical health, in particular age-related chronic conditions and inflammation-related disease. Identification of transgender people is feasible in administrative health data, with high specificity, concordance with transgender-specific healthcare utilization, and ability to reliably characterize transgender populations in terms of demographics and chronic disease. Transgender people living with HIV had elevated iv incidence and prevalence of multimorbidity compared to cisgender people living with HIV, though inconsistently over time and without strong evidence of statistically significant differences by gender identity. Conclusions: High quality evidence of chronic conditions among transgender populations is needed; in particular longitudinal, population-based and representative studies with appropriate comparison groups and rigorous measurement of both transgender status and chronic conditions. Development of validated measures for ascertaining transgender status in administrative data is an important contribution given the limited evidence and the methodological challenges in transgender health research. Given evidence of increased burden of multimorbidity for transgender people living with HIV, future research on chronic co- and multimorbidity for transgender populations is warranted with attention to vulnerabilities by racialization and gender, and the role of intervenable disease risk factors such as smoking. v Lay Summary There is limited understanding of how chronic disease and multimorbidity, or having multiple chronic diseases at the same time, affects transgender people. Transgender people are those whose gender identity is partially or completely incongruent with their sex assigned at birth. The current research reviewed the state of the scientific knowledge of non-HIV chronic disease among transgender populations worldwide, assessed the feasibility of methods to identify transgender people in healthcare claims data without a gender identity measure, and investigated the relationship between gender identity and multimorbidity for people living with HIV. Collectively, these results provide methods for advancing transgender health research and improve our understanding of chronic disease and multimorbidity for transgender people living with and without HIV. vi Preface This statement is to certify that the work presented here was conceived, conducted, written and disseminated by Ashleigh J Rich, with supervisory guidance from dissertation committee members (Drs. Mieke Koehoorn, Robert Hogg, and Tonia Poteat), as well as Dr. Jeannie Shoveller, who served as my primary supervisor from 2016 to 2019, before leaving the University of British Columbia. The candidate was the lead researcher responsible for the identification of research objectives; literature review; study design; data preparation, manipulation and analyses; interpretation of results; and writing of first, subsequent and final versions of each chapter. All members of the dissertation committee provided guidance and input throughout all stages of the research process. Contributions from additional collaborators are as follows: 1) Dr. Ayden Scheim provided input on the literature search strategy, study selection eligibility, and served as second reviewer for the systematic review in Chapter 2; 2) Ms. Monica Ye and Jenny Li provided assistance with preparing and analyzing data for Chapter 3; 3) Ms. Andreea Bratu provided assistance with preparing the Data Access Request to Population Data BC for Chapter 3. As of March 2021, modified versions of Chapters 2 and 3 have been accepted for publication or published in peer-reviewed journals, and presented at scientific meetings. All co-authors made contributions only as is commensurate with the supervisory committee and co-author requirements. Specifically, a version of Chapter 2 has been published in Preventive Medicine Reports (Rich AJ, Scheim AI, Koehoorn M, Poteat T. Non-HIV chronic disease burden among transgender populations globally: A systematic review and narrative synthesis. Prev Med Rep 2020 Nov 28;20:101259. vii doi: http://doi.org/10.1016/j.pmedr.2020.101259). A modified version of Chapter 3 has been accepted for publication and is currently in press at BMJ Open (Rich AJ, Poteat T, Koehoorn M, Li J, Ye M, Sereda P, Salway T, Hogg R. Development of a computable phenotype to identify a transgender sample for health research purposes: A feasibility study in a large linked provincial healthcare administrative cohort in British Columbia, Canada. BMJ Open, accepted 2021 Mar 5) and preliminary findings from this Chapter were presented at the 25th World Professional Association for Transgender Health conference in Buenos Aires, Argentina in November 2018. All research included in this dissertation was approved by the University of British Columbia Research Ethics Board (certificate number H17-03225). Both COAST and the NA-ACCORD have received ethical approval from the University of British Columbia/Providence Health Care Research Ethics Board, harmonized with the Simon Fraser University Research Ethics Board (#H09-02905, #H06-00249). In addition to ethical approval, I also received scientific approval for my doctoral research study from Population Data BC that provides linked data to COAST, as well as from the NA-ACCORD International Steering Committee. The study complies with the British Columbia Freedom of Information and Protection of Privacy Act and did not require informed consent as it was conducted using retrospective administrative and anonymized data for research and statistical purposes only. Disclaimer All inferences, opinions, and conclusions in this dissertation are those of the candidate, and do not reflect opinions or policies of the Population Data BC Data Stewards. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Abbreviations ...................................................................................................................xv Acknowledgements .................................................................................................................... xvi Dedication ................................................................................................................................... xix Chapter 1: Introduction ................................................................................................................1 1.1 General overview ............................................................................................................ 1 1.2 Background and research rationale ................................................................................. 2 1.2.1 Limited data and methods on transgender populations........................................... 2 1.2.2 Transgender people and HIV .................................................................................. 3 1.2.3 Chronic disease multimorbidity and transgender people ........................................ 4 1.3 Theoretical framework .................................................................................................... 5 1.4 Dissertation research objectives and research questions ................................................ 7 1.4.1 Objectives ............................................................................................................... 7 1.4.2 Research questions .................................................................................................. 9 1.5 Study design and methods overview............................................................................... 9 1.5.1 Data sources and study samples .............................................................................. 9 ix 1.5.2 Primary Measures ................................................................................................. 11 1.5.2.1 Gender identity.................................................................................................. 11 1.5.2.2 Chronic disease and multimorbidity ................................................................. 12 1.5.3 Summary ............................................................................................................... 13 Chapter 2: Non-HIV chronic disease burden among transgender populations globally: A systematic review and narrative synthesis .................................................................................15 2.1 Introduction ................................................................................................................... 15 2.1.1 Chronic disease as a public health issue ............................................................... 15 2.1.2 Transgender populations and chronic disease....................................................... 16 2.1.3 Rationale and objectives for this review ............................................................... 16 2.2 Methods......................................................................................................................... 17 2.2.1 Protocol and registration ....................................................................................... 17 2.2.2 Eligibility criteria .................................................................................................. 17 2.2.3 Information sources .............................................................................................. 18 2.2.4 Literature search.................................................................................................... 18 2.2.5 Study selection ...................................................................................................... 18 2.2.6 Data extraction ...................................................................................................... 19 2.3 Results ........................................................................................................................... 19 2.3.1 Study selection ...................................................................................................... 19 2.3.2 Study characteristics ............................................................................................. 21 2.3.3 Synthesis of results ............................................................................................... 48 2.3.3.1 Mental health and substance use disorders ....................................................... 48 2.3.3.2 Cerebrovascular and/or cardiovascular diseases ............................................... 49 x 2.3.3.3 Cancer ............................................................................................................... 49 2.3.3.4 Metabolic and endocrine disorders ................................................................... 50 2.3.3.5 Respiratory disease ........................................................................................... 50 2.3.3.6 Chronic liver and kidney disease ...................................................................... 51 2.3.3.7 Other and unspecified diseases ......................................................................... 51 2.4 Discussion ..................................................................................................................... 52 2.4.1 Summary of evidence, gaps and future directions ................................................ 52 2.4.1.1 Chronic conditions ............................................................................................ 52 2.4.1.2 Study design ...................................................................................................... 54 2.4.1.3 Chronic disease measurement ........................................................................... 55 2.4.1.4 Transgender status measurement ...................................................................... 55 2.4.2 Limitations ............................................................................................................ 56 2.4.3 Conclusions ........................................................................................................... 57 Chapter 3: Development of a computable phenotype to identify a transgender sample for health research purposes: A feasibility study in a large linked provincial healthcare administrative cohort in British Columbia, Canada ................................................................59 3.1 Introduction ................................................................................................................... 59 3.1.1 Limited data on transgender people ...................................................................... 59 3.1.2 Computable phenotypes for transgender health research ..................................... 60 3.1.3 Summary of study rationale .................................................................................. 62 3.2 Methods......................................................................................................................... 62 3.2.1 Data sources and participants................................................................................ 62 3.2.1.1 The Comparative Outcomes and Service Utilization Trends Study (COAST) 62 xi 3.2.1.2 Drug Treatment Program .................................................................................. 63 3.2.1.3 Ministry of Health ............................................................................................. 63 3.2.2 Measures and analyses .......................................................................................... 63 3.2.2.1 Transgender computable phenotypes ................................................................ 63 3.2.2.2 Concordance ..................................................................................................... 64 3.2.2.3 Validation .......................................................................................................... 65 3.2.2.4 Demographics and chronic conditions .............................................................. 65 3.3 Results ........................................................................................................................... 66 3.3.1 Concordance ......................................................................................................... 68 3.3.2 Validation .............................................................................................................. 70 3.3.3 Transgender phenotype ......................................................................................... 72 3.3.4 Demographics and chronic conditions .................................................................. 72 3.4 Discussion ..................................................................................................................... 77 3.4.1 Concordance ......................................................................................................... 77 3.4.2 Computable phenotype development and validation ............................................ 77 3.4.3 Transgender status prevalence & ascertainment ................................................... 80 3.4.4 Demographics and chronic conditions .................................................................. 81 3.4.5 Limitations ............................................................................................................ 82 3.5 Conclusion .................................................................................................................... 84 Chapter 4: Chronic disease multimorbidity among transgender people living with HIV in North America ..............................................................................................................................85 4.1 Introduction ................................................................................................................... 85 4.2 Methods......................................................................................................................... 87 xii 4.2.1 Data Source ........................................................................................................... 87 4.2.2 Measures ............................................................................................................... 88 4.2.2.1 Gender Identity ................................................................................................. 88 4.2.2.2 Outcome Measures: Chronic Disease Multimorbidity ..................................... 88 4.2.2.3 Covariates ......................................................................................................... 89 4.2.3 Statistical Analyses ............................................................................................... 90 4.3 Results ........................................................................................................................... 91 4.4 Discussion ................................................................................................................... 102 Chapter 5: Discussion ................................................................................................................108 5.1 Summary of research findings .................................................................................... 108 5.2 Research strengths and contributions.......................................................................... 110 5.3 Dissertation research limitations and considerations .................................................. 115 5.4 Recommendations and future research directions ...................................................... 117 5.5 Conclusions ................................................................................................................. 122 References ...................................................................................................................................123 Appendix A Example search strategy: MEDLINE (Ovid) ................................................... 144 Appendix B COAST cohort data sources and description of data elements ....................... 145 Appendix C Prescription drugs with drug identification numbers (DIN)s ......................... 147 Appendix D Chronic condition case definitions ..................................................................... 153 xiii List of Tables Table 2.1 Studies and study characteristics included in systematic review of non-HIV chronic disease outcomes among transgender populations (n=93)............................................................ 25 Table 3.1 Concordance analyses for diagnoses and hormone measures in COAST (n=237) ...... 69 Table 3.2 Validation measures of transgender computable phenotype (CP) with provider-report transgender status measures, in COAST HIV-positive cohort (n=13,907) .................................. 71 Table 4.1 Baseline NA-ACCORD study sample characteristics, by gender identity (n=62,860) 92 Table 4.2 Multimorbidity prevalence over NA-ACCORD study period 2000-2016, by gender identity .......................................................................................................................................... 97 Table 4.3 Multimorbidity incidence over NA-ACCORD study period 2000-2016, by gender identity .......................................................................................................................................... 99 xiv List of Figures Figure 2.1 PRISMA flowchart for study screening for systematic search of the literature on non-HIV chronic disease outcome studies among transgender populations ........................................ 20 Figure 2.2 Chronic disease groups for transgender populations, captured in the systematic review (n=93 studies, 665 data points), proportion by study and disease datapoint ................................ 22 Figure 2.3 Chronic disease outcomes among transgender populations captured in the systematic review, proportion by disease datapoint (n=655) ......................................................................... 23 Figure 2.4 Global distribution of chronic disease outcomes among transgender populations, by disease datapoint (665); larger circles = more datapoints ........................................................... 47 Figure 3.1 Total transgender sample identified using a computable phenotype with electronic health records, in COAST study (n=528,859) .............................................................................. 67 Figure 3.2 Geographic distribution of transgender people across province, by health authority* 73 Figure 3.3 Age distribution of transgender sample, by HIV serostatus (n=188) .......................... 74 Figure 3.4 Co-morbidities among transgender sample, by HIV serostatus (n=188) .................... 76 Figure 4.1 Multimorbidity prevalence over NA-ACCORD study period, 2000-2016, by gender identity .......................................................................................................................................... 95 Figure 4.2 Cumulative incidence curves for multimorbidity over NA-ACCORD study period 2000-2016, by gender identity .................................................................................................... 101 xv List of Abbreviations ADHD = Attention-Deficit/ Hyperactivity Disorder aIRR = adjusted incidence rate ratio aPR = adjusted prevalence ratio ART = antiretroviral therapy BC = British Columbia CI = confidence interval CINAHL = Cumulated Index to Nursing and Allied Health Literature CM = cisgender men COAST = Comparative Outcomes and Service Utilization Trends Study CP = computable phenotype CW = cisgender women DSM = Diagnostic and Statistics Manual of Mental Disorders DTP = Drug Treatment Program EMR = electronic health records EMR = electronic medical record FIPPA = Freedom of Information and Protection of Privacy Act GAHT = gender affirming hormone treatment GAS = gender affirming surgery GD = gender dysphoria GID = Gender Identity Disorder HAART = highly active antiretroviral therapy HIV/AIDS = human immunodeficiency virus/ acquired immunodeficiency syndrome ICD = International Classification of Disease IDU = injection drug use IQR = interquartile range IRR = incidence rate ratio LGBT = lesbian, gay, bisexual, transgender MeSH = Medical Subject Headings NLP = natural language processing NA-ACCORD = North American AIDS Cohort Collaboration on Research and Design OBGYN = obstetrician/ gynecologist PI = protease inhibitor PPV = positive predictive value PR = prevalence ratio PRISMA = Preferred Reporting Items for Systematic Review and Meta-Analysis PROSPERO = International Prospective Register of Systematic Reviews STI = sexually transmitted infections TM = transgender men TW = transgender woman US = United States VHA = Veterans Health Administration xvi Acknowledgements First and foremost, I extend sincere thanks to my committee members, who have helped shape me into the researcher I am today. I would like to offer gratitude to my primary supervisor Dr. Mieke Koehoorn for taking me on, her wisdom and thoughtful guidance throughout the PhD process, and her enduring pedagogical and professional example. I would like to thank my co-supervisor Dr. Bob Hogg for his mentorship, support, and sharing of opportunities over my time at the British Columbia Centre for Excellence in HIV/AIDS and during the course of my PhD. I would also like to thank my committee member Dr. Tonia Poteat for invaluable input and support, as well as insightful clinical expertise. I would like to acknowledge the educational environment at the University of British Columbia School of Population and Public Health and the many students, staff, and faculty who have enriched my time there. In particular, I thank Dr. Jeannie Shoveller, Huiru Dong, Anita Minh, Kimberly Sharpe and Molly Sweeney Magee. Much of my research was conducted at, and with the support of, the British Columbia Centre for Excellence in HIV/AIDS, in particular on Dr. Bob Hogg’s team in the Epidemiology and Population Health Program. I am thankful for the many wonderful students, staff, faculty and other colleagues there with whom I have had the opportunity to collaborate and learn from over the years. In particular, I would like to thank Dr. Eric Roth- who first encouraged me to pursue a PhD. I would also like to thank my colleagues with the COAST team Dr. Oghenowede Eyawo, Dr. Kate Salters, Paul Sereda, Monica Ye, Jenny Li, and Andreea Bratu. Finally, thanks as well xvii to Dr. Keri Althoff, Co-Director of the NA-ACCORD, as well as other Johns Hopkins University Epi/Biostats Core team members Dr. Jen Lee and Brenna Hogan. I am fortunate to be a part of the larger global sexual and gender minority health research community and owe special thanks to a number of key members. In particular, I would like to thank friends and colleagues Dr. Ayden Scheim, Dr. Travis Salway, and Dr. Tara Lyons for their encouragement and inspiration along the way. I would also like to thank my friends and family for their love and support over the course of my PhD. In particular my partner, Jo, and my aunt, Dr. Paula Lane. I would like to acknowledge that my doctoral training and research was generously supported by a Doctoral Research Award and Michael Smith Foreign Study Award from the Canadian Institutes of Health Research; a Four-Year Fellowship, a Graduate Entrance Scholarship, and the Friedman Award for Scholars in Health from the University of British Columbia; as well as support from the British Columbia Centre for Excellence in HIV/AIDS. I would not have been able to complete this PhD without this essential funding. Specific funding support included, for the COAST study utilized in Chapter 3, from the Canadian Institutes of Health Research, and the British Columbia Centre for Excellence in HIV/AIDS. The Drug Treatment Program receives funding from the provincial government of British Columbia (PharmaCare). The funders had no role in the study design, analysis, interpretation of the data, or drafting of the dissertation. The NA-ACCORD, which provided data xviii for the analysis in Chapter 4, is supported by the National Center for Advancing Translational Sciences, the National Institutes for Health, the Centers for Disease Control and Prevention, the Agency for Healthcare Research and Quality, the Health Resources and Services Administration, the Canadian Institutes of Health Research, the Ontario Ministry of Health and Long Term Care, and the Government of Alberta, Canada. Additional support was provided by the National Cancer Institute, the National Institute for Mental Health, and the National Institute on Drug Abuse. xix Dedication This dissertation is dedicated to the transgender people whose experiences and data made this work possible, and whose lives and health this research is meant to improve. 1 Chapter 1: Introduction 1.1 General overview A growing number of international health organizations including the World Health Organization (WHO)1 and the Joint United Nations Programme on HIV/AIDS (UNAIDS),2 as well as locally by the British Columbia provincial government3 have identified transgender people, those whose gender identity is partially or fully incongruent with sex assigned at birth, as a key population affected by HIV. Despite the recognition by the aforementioned organizations, the health of transgender people remains understudied and underserved.4–7 Although the use of combination antiretroviral therapy (ART) has led to improved prognosis and survival, and reduced HIV-related morbidity and mortality for individuals living with HIV,8,9 non-HIV related comorbidities such as non-AIDS-related cancers, liver disease and cardiovascular disease are becoming more prevalent for people living with HIV.10,11 Multimorbidity, defined as the co-occurrence of two or more chronic medical conditions in an individual,12 is emerging as a key indicator for understanding the burden of non-HIV related chronic disease among those living with HIV.10 The current program of dissertation research represents one of the first opportunities to investigate the epidemiology of chronic disease multimorbidity among the population of transgender people living with and without HIV in British Columbia, Canada and the United States (US). 2 1.2 Background and research rationale 1.2.1 Limited data and methods on transgender populations Small population size, limited research design (e.g. insufficient sample size collected for meaningful analyses) and other institutional and methodological erasures (e.g. absent or poor gender identity ascertainment, inclusion of transgender women in groups of men who have sex with men) all contribute to the frequent exclusion or disregard of transgender people in epidemiological research.4,5,13 As recently as 2008, a US-based meta-analysis of HIV prevalence and risk behaviours among transgender persons found only 24 studies of transgender women and five additional studies of transgender men.14 A decade later, a 2018 updated review by Becasen and colleagues identified triple the number of studies reporting HIV prevalence estimates for the US transgender population (n=88),15 demonstrating growing interest in the area. Consistently, a review of published literature indexed in Medline between 1950 and 2016 found only 2,405 published articles including transgender people, nearly half of which had been published in the last ten years.7 However, in spite of the more recent transgender health research, co- and multi-morbid chronic disease remains understudied for this population. For example, a 2017 study using electronic medical record (EMR) data was able to identify only a single transgender person among 385,820 patients in a Minnesota cancer registry,16 and a review of research on the global health burden of transgender populations published the same year found the fewest publications in the general health category that included chronic disease.6 These gaps indicate the need for improved gender ascertainment and transgender inclusion in clinical and population research. While development and evaluation of best practices for measuring transgender status in survey research, such as the two-step method (capturing both sex assigned at birth and current gender identity), is an important advancement for transgender-inclusive population health research,17,18 3 novel research methods are needed to identify transgender people in existing database and data sources including long-standing cohorts, as well as EMR and other ‘big data’ sources such as medical billing records. 1.2.2 Transgender people and HIV Despite transgender people comprising approximately 0.15% to 1.6% of the general population,19–22 a meta-analysis of global HIV burden found that transgender women face 49 times the odds of HIV infection compared to cisgender adults.23 According to a 2016 systematic review, HIV prevalence estimates among transgender women in the United States range widely from 2.0% to 29.9% by self-report and as high as 40.1% by laboratory confirmed testing.24 There is limited data on HIV prevalence among Canadian transgender populations. The Ontario-based Trans PULSE study is the only study to publish HIV prevalence data for transgender women to date at 3.0% by self-report.25 For transgender men, HIV prevalence appears low. The only two available Canadian studies found no infection by self-report 25 and laboratory test.26 Prevalence estimates in the US range from 0.5% to 4.3% by laboratory test,24 although a 2011 study of transgender men at a community HIV/STI clinic in San Francisco found an HIV prevalence of 10.1% by laboratory test.27 Transgender people face a myriad of psychosocial (e.g. poor mental health), behavioural (e.g. sex work) and structural determinants (e.g. transphobia) for HIV vulnerability. This is in addition to limited access to HIV testing, treatment and care;28 and syndemic forces (i.e. co-occurring social inequity related health conditions) that determine the HIV epidemic among transgender people, such as substance use, psychosocial conditions, victimization and 4 discrimination.24 Among transgender people living with HIV compared to cisgender people, there is limited and conflicted evidence on HIV clinical care and treatment outcomes - with no difference in some clinical samples29,30 and poorer outcomes in community-based studies.31–36 1.2.3 Chronic disease multimorbidity and transgender people While other studies have described the multimorbidity burden of HIV seropositive status among cisgender populations,11,12 no studies to date have investigated this burden among transgender people. More generally, large US population-based studies using administrative medical record databases have demonstrated disparity in chronic disease burden and other mental and medical health conditions for transgender compared to cisgender patients.37,38 Specifically, transgender patients have an elevated prevalence of chronic conditions including asthma, autism spectrum disorder, chronic obstructive pulmonary disease, depression, hepatitis, HIV, schizophrenia, substance use disorders, chronic pain, obesity and liver conditions.38 Transgender veterans have more than a 10% greater prevalence of poor medical and mental health outcomes (e.g. depression, serious mental illness, suicidal ideation/attempt, post-traumatic stress disorder, alcohol and tobacco use, and obesity),37 and a 20 times greater suicide risk, compared to cisgender veterans in the US.39 Further, the same study of US transgender veterans found that this population was more likely to have experienced social marginalization and other social determinants of health disparities, specifically homelessness, sexual trauma and incarceration.37 This is consistent with other findings that racialization is associated with health disparity among transgender populations,14,40,41 as well as parallel evidence that structural factors including age, educational attainment and access to healthcare are associated with multimorbidity disparity in the general population.42 5 In the absence of previous research on the topic, the rationale for potential disparity in multimorbidity burden for transgender people living with and without HIV is based on the following evidence. In addition to the disproportionate HIV burden among transgender populations,6,14,24 and in particular transgender women,23,43 there are demonstrated disparities in non-HIV related health outcomes for this population.6,7,40,44 Furthermore, chronic disease risk is potentially exacerbated by both antiretroviral treatment and exogenous sex hormones (e.g. estrogen, testosterone) for transgender people living with HIV, the use of which is an important part of gender-affirming care for many transgender people.45 Both exogenous sex hormone use and antiretroviral treatment can affect bone density and inflammation that can in turn impact immune function, HIV disease progression and co-morbidities such as cardiovascular disease.45 Due to the combination of ART and hormone use in transgender people living with HIV, together with the structural and health vulnerabilities previously described, is it plausible that co- and multi-morbidity burden will be elevated for the population of transgender people living with and without HIV. 1.3 Theoretical framework This dissertation research is informed by the gender minority stress conceptual framework.46 The gender minority stress framework extends sexual minority stress theory, first put forward by Virginia Brooks in 1981,47,48 to gender minorities (i.e. transgender people). Building on previous psychological and social science research on minority stress for racialized people and other minoritized groups, Brooks developed a multi-level model of cultural and social stressors transmitting into biophysical and psychological stressors for sexual minorities (i.e., lesbian, gay 6 and bisexual people). These stress processes are mediated by access to social and economic resources, and moderated by differential stress exposure, different perception or internalization of stress, and varied access to stress mediating resources. Minority stress also varies by duration (i.e., cumulative duration of minority stress over a lifetime) as well as frequency and intensity of sub-group-based stressors (e.g., those based on sexual orientation, racialization, poverty). Ilan Meyer’s parallel minority stress model, first published in 1995,49,50 illustrates how sexual minorities (i.e. lesbian, gay, and bisexual people) are vulnerable to proximal (e.g. internalized homophobia) and distal stressors (e.g. heteronormativity, discrimination) that in turn contribute to health disparities compared to their heterosexual counterparts. Hendricks and Testa have more recently adapted and applied this model to gender minorities, demonstrating that gender minorities are likewise vulnerable to gender minority status related stressors (e.g. identity concealment, stigma, transphobia) that compound over time and become embodied51 to shape health disparities for gender minorities in relation to their cisgender peers.46,52 This extension of minority stress theory to gender minorities has included an important focus both on resiliency and access to gender-affirming care as key drivers of health disparity for this population.53 Minority stress theory has been modelled in a number of studies to date, providing evidence of links between experiences of minority stigma and discrimination with perceived stress and dysregulation of chronic stress biomarkers for sexual minorities,54,55 and gender minorities.56 Further, minority stress has been linked to poor mental health and cardiovascular disease among racial minorities,57–59 elevated cardiovascular disease risk for sexual minorities,60 and sub-optimal treatment outcomes for transgender people living with HIV.61 7 While this dissertation research does not aim to test or model gender minority stress, the gender minority stress theoretical framework informs this dissertation research aimed primarily at investigating whether there is a disparity in chronic disease and multimorbidity burden for transgender compared to cisgender populations. Minority stress theory underlies the objectives and research questions as any disparity evidenced is understood to be a function not of intrinsic pathological or otherwise aspects of being transgender, but of multi-level stressors that may shape chronic mental and physical health disparity for transgender people. The effects of minority stress in shaping health disparities may also be compounded by biopsychosocial impacts of exogenous sex hormone use and HIV for transgender people, as described above. 1.4 Dissertation research objectives and research questions 1.4.1 Objectives This dissertation addresses the knowledge gaps identified above by assessing the state of the evidence on chronic disease for transgender populations, testing the feasibility of identifying transgender people in administrative health claims data in the absence of a transgender status ascertainment measure, and investigating the relationship between gender identity and multimorbidity burden among people living with HIV. As such, this body of work aims to make contributions to three main areas of transgender health and chronic disease research: the state of knowledge, methodological innovation, and the evidence base. Specifically, objectives of this dissertation were to: 1. Review and characterize the epidemiological literature on non-HIV chronic disease burden, specifically prevalence, for transgender populations globally. Chronic 8 disease is a growing concern for research, policy, and clinical care. While the global burden of HIV for transgender populations has been comprehensively covered in recent systematic reviews, the same is not true for the burden of other chronic disease. This review aimed to address this gap in understanding chronic disease epidemiology for transgender populations. 2. Assess the feasibility of identification of transgender people in Canadian administrative databases through transgender-specific healthcare utilization, for the investigation of chronic disease and other health research purposes. Innovative methods are needed for identification of transgender people in administrative health records for answering important health research questions. This dissertation research aimed to investigate the feasibility of the development of a transgender case ascertainment algorithm, in Canadian population-based administrative data, and to use the algorithm to identify the proportion of transgender people within the population of people living with HIV in British Columbia, as a public health priority. 3. Investigate multimorbidity prevalence and incidence for transgender people living with HIV, compared to cisgender people. Multimorbidity is an area of growing focus in HIV research, with improved prognosis, survival and reduced HIV-related morbidity and mortality associated with the advent of combination antiretroviral therapy. While transgender people, particularly transgender women, are a high-risk HIV population, scarce research has investigated multimorbidity among this population. This dissertation research investigated multimorbidity burden for transgender people living with HIV in the United States and Canada, using data from a large longitudinal cohort collaborative representative of the HIV care-engaged population with cisgender comparators. 9 1.4.2 Research questions This PhD research had a number of research questions corresponding to the primary objectives. Specifically, the research questions were: 1. Is there limited literature on chronic disease among transgender populations globally, and what, if any, are the methodological gaps? 2. Can an algorithm using healthcare utilization data be used to identify transgender people in Canadian administrative data? 3. Is there a disparity in chronic disease multimorbidity burden for transgender people living with HIV in North America, compared to cisgender people living with HIV? 1.5 Study design and methods overview 1.5.1 Data sources and study samples A summary of the study design and methods used in this dissertation is provided here. Further details of the data source, study sample, measures and methods and analysis for each individual study included in this dissertation research are available in Chapters 2 through 4, corresponding to overall Objectives 1 through 3. Chapter 2 describes a systematic review and narrative synthesis of the non-HIV chronic disease burden for transgender populations worldwide, addressing Objective 1 of this dissertation research. A systematic search was conducted of Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO and LGBT Life bibliographic databases, to capture the peer-reviewed scientific literature reporting non-HIV chronic disease prevalence data 10 for transgender samples. Studies published to February 15, 2019 were eligible for inclusion in the review, without restriction on publication date, country or study design. After systematic screening for consistency with review eligibility criteria, the final systematic review dataset was used for narrative synthesis and characterization of the epidemiological literature. The Comparative Outcomes and Service Utilization Trends Study (COAST), a provincial healthcare administrative data-linked cohort study, provided the data for Chapter 3. The COAST cohort comprises a data linkage between two provincial data sources: The BC Centre for Excellence in HIV/AIDS Drug Treatment Program that coordinates HIV treatment dispensation across British Columbia; and Population Data BC, a provincial data repository holding individual, longitudinal data for all British Columbia residents (1996-2013).62 COAST includes British Columbia residents living with HIV (≥19 years of age) and a 10% random sample comparison group of the HIV-negative general population. Transgender people in COAST were identified using a transgender health services (i.e. transgender-specific diagnosis codes and hormone prescription use) utilization based computable phenotype, adapted from transgender case ascertainment algorithms developed in other jurisdictions, most notably the US Veterans Health Administration37,39 and Medicare systems.38,63 Addressing Objective 2, the study sample included all transgender patients in the HIV-positive and HIV-negative COAST cohorts. Data for Chapter 4 was drawn from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD). The NA-ACCORD is a US National Institutes of Health (NIH) funded longitudinal cohort collaborative, dedicated to investigation of long-term HIV clinical care questions.64 The NA-ACCORD is the largest HIV cohort collaborative in North America 11 and includes 25 US and Canada-based contributing clinical and interval cohorts, for a total cohort sample of over 150,000 patients who have initiated antiretroviral therapy since 2001. The NA-ACCORD is representative of HIV care and HIV infection demographics across North America and includes data on demographics, AIDS-defining clinical diagnoses, antiretroviral therapy and other medications, laboratory test results, vital status and viral resistance. 1.5.2 Primary Measures 1.5.2.1 Gender identity In light of the scarcity of literature in transgender health, and in particular in chronic disease for transgender populations, the systematic review described in Chapter 2 used a broad definition of transgender status, to capture a maximum selection of eligible studies for the systematic review. Measures of transgender status included diagnosis, self-report, healthcare provider report, and case-finding algorithm. Gender identity is not currently captured in COAST and was ascertained in the study presented in Chapter 3 via a computable phenotype using transgender-specific diagnosis codes and exogenous hormone prescription usage. This study used a combination of International Classification of Diseases (ICD) ninth and tenth edition codes (ICD-9, ICD-10)65,66 for transgender-specific diagnoses, and was unable to classify transgender people by gender (i.e. transgender women, transgender men). Gender identity is a new variable to the NA-ACCORD cohort collaborative, added to the data collection protocol in 2016, as a result of the 2015 successful transgender cohort feasibility 12 study.67 Both gender identity and sex assigned at birth were available, allowing for classification of transgender people by gender (i.e. transgender women, transgender men). Gender identity is routinely collected by 15 HIV clinical cohorts, including all four Canadian cohort members of the NA-ACCORD.67 Data collection methods vary between cohorts, and include use of an electronic medical record system to identity transgender-specific diagnosis codes, comparison of sex assigned at birth with records of cross-sex hormone use, inclusion of gender identity in patient intake questions, medical provider assessed transgender status based on asking the patient, a two-step method comparing sex assigned at birth and current gender identity, or medical provider assessment.67 1.5.2.2 Chronic disease and multimorbidity In Chapter 2, chronic disease outcomes captured by the systematic review and synthesis included a heterogeneity of measures across studies. These measures included the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnosis, structured clinical interviews, ICD treatment history, and clinical thresholds. Included studies also reported varied chronic disease definitions, including those based on several DSM editions, ICD revisions, and different structured clinical interview instruments. A diversity of recall periods for chronic disease outcomes was represented across included studies, including lifetime, current, past-year, past two-year, and past six-month recall periods. Of chronic disease prevalence measures reported, a majority of studies provided frequencies and proportions only. 13 In COAST and the NA-ACCORD cohorts used for the analyses presented in Chapters 3 and 4 respectively, chronic disease was measured by standardized or validated case definitions used in previous analyses, as available.11 1.5.3 Summary This dissertation comprises five chapters. Chapter 1 provides an overview of the literature on transgender health and chronic disease and HIV and the dissertation research rationale. This Chapter also provides the objectives and research questions, as well as a summary of the data sources, measures and methods for each study. Chapter 2 is a systematic review of the literature on non-HIV chronic disease among transgender populations globally. This chapter also represents a call for future research of greater methodological quality to better understand chronic disease prevalence in these populations. Using novel methods and unique data, Chapter 3 tests the development of a computable phenotype to identify transgender samples in healthcare utilization based administrative data, for the investigation of chronic disease and other health research questions. Importantly, using the phenotype, this chapter also provides an estimate of the proportion of people living with HIV in British Columbia who are transgender, and characterization of the demographics and chronic disease profile of this population. Chapter 4 provides an investigation of the relationship between gender identity and multimorbidity burden, specifically prevalence and incidence, in a long-standing longitudinal cohort collaborative of people living with HIV and engaged in HIV care in the United States and Canada. Finally, Chapter 5 summarizes key findings and unique contributions to the evidence on the health of transgender people, and the strengths and limitations of the overall body of work. Implications 14 for clinical practice and public health policy are also provided, along with recommendations and directions for future research. 15 Chapter 2: Non-HIV chronic disease burden among transgender populations globally: A systematic review and narrative synthesis 2.1 Introduction 2.1.1 Chronic disease as a public health issue Chronic disease is a leading cause of morbidity and mortality globally, accounting for 63% of deaths annually— with cardiovascular disease, diabetes, cancer, and chronic respiratory conditions responsible for 80% of morbidity.68 In addition to premature mortality, the consequences of chronic disease include medical complications, elevated risk of other co-morbidities, and disability.68,69 At the population level, chronic disease morbidity contributes to a growing burden on healthcare system demands and cost; to healthcare-cost related poverty; and to larger societal economic effects,68 such that each 10% increase in chronic disease burden corresponds with a 0.5% lower rate of annual economic growth globally.70 Despite advances in medicine, technology and healthcare systems that have eradicated many serious threats to human health, chronic diseases continue to rise worldwide, but increasingly so in middle and low-income countries.69 Mounting chronic disease burden is further fueled by population growth and ageing as the global population increasingly lives in a post epidemiologic transition context (i.e. a context in which chronic disease has replaced infectious disease as the primary driver of population morbidity and mortality).71 Importantly, key drivers of chronic disease include modifiable risk factors such as environmental exposures (e.g. workplace risks, sanitation services), access to social and material resources (e.g. healthy food, freedom from violence, adequate income, health care access) and health behaviours (e.g. substance use, exercise). As 16 such, may of the risk factors of chronic disease may be more intervenable than risk factors for acute diseases and other illnesses.69 This is therefore an important area for public health focus for the health of populations. 2.1.2 Transgender populations and chronic disease While chronic disease for transgender populations has been recognized as a priority research area,44 there is scarce literature in this area, particularly outside of HIV and mental health conditions.6,72 HIV among transgender populations has been thoroughly reviewed, recently and globally.6,14,15,23,24,73,74 A review of research on the global health burden of transgender populations published from 2008 to 2014 found only 7% included data on general health (e.g. chronic disease, mortality), mostly in the United States (US).6 Of the limited research, there is evidence that transgender people experience poorer physical health compared to cisgender people.6 Much of the existing research, beyond sexual and mental health, has focused on cardiovascular conditions among transgender people accessing gender affirming hormone therapy (GAHT), again largely in the US, although also in Europe.75 Studies have shown that transgender people have higher prevalence of any chronic disease76 as well as multimorbidity, compared to cisgender groups.77 Chronic disease burden may also be exacerbated for transgender people who are racialized,78,79 older,80 of lower socioeconomic status,78 have poor access to other social determinants of health81, and live with co-morbidities.63 2.1.3 Rationale and objectives for this review No systematic review of the literature has been conducted of chronic disease burden for transgender populations. This study sought to systematically review and synthesize all available 17 epidemiologic data on chronic disease burden, with a primary focus on prevalence measures, among transgender populations, to identify areas for future research, to inform healthcare practice, and to inform policy focus and action. 2.2 Methods 2.2.1 Protocol and registration The protocol has been registered with the International Prospective Register of Systematic Reviews (PROSPERO, https://www.crd.york.ac.uk/prospero), registration number CRD42020104077. The review is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.82 It is worth noting that while we also aimed to assess whether chronic disease burden was greater for transgender populations living with HIV compared to transgender populations living without HIV, the number of studies reporting both HIV and other chronic disease outcomes for transgender people captured in this review was too few to warrant a sub-analysis. 2.2.2 Eligibility criteria This review included peer-reviewed journal articles reporting quantitative data on the prevalence of chronic disease among transgender people. Studies with very small transgender sample sizes (n<10) and those published in languages other than English, French, Spanish, Italian, or Portuguese were excluded. Given the evidence need, no restrictions were placed on publication date, study country, or type of study design. If multiple publications reported the same chronic disease outcomes from a single study, the article reporting the most chronic disease data was included. For reported mental health and substance use outcomes, those based on symptomology 18 assessments were excluded (e.g., psychometric scales for depression), for consistency with study outcomes in other chronic disease groups. 2.2.3 Information sources MEDLINE, Embase, CINAHL, PsycINFO, and LGBT Life bibliographic databases were electronically searched for peer-reviewed literature published through Feburary 15, 2019. 2.2.4 Literature search The search strategy was developed in consultation with a research librarian at the University of British Columbia and included Medical Subject Headings (MeSH terms) and keywords related to the two main search concepts: transgender persons and chronic disease. Commonly used terms for both concepts were included as subject headings and keywords, such as “transsexual”, or “gender minority” for transgender; and “long-term illness”, “chronic disease”, or “diabetes” for chronic conditions. The complete MEDLINE search strategy is included in Appendix A. 2.2.5 Study selection Studies were screened in parallel by two independent reviewers using Covidence (Veritas Health Innovation, Melbourne). Reviewers rated studies for inclusion or exclusion at the title and abstract level first, then at the full text level. Disagreements at each screening phase were resolved through discussion and reasons for exclusion recorded to arrive at a consensus for inclusion or exclusion. 19 2.2.6 Data extraction Data elements extracted included participant characteristics, study design, sample size, and outcomes. One reviewer extracted data from included publications using a predetermined extraction form. The second reviewer assessed a 10% sample of extracted data for consistency and any discrepancies were resolved through discussion between the two reviewers (Inter-rater reliability, percent agreement: 97%). Both the number of unique studies and the number of relevant chronic disease datapoints (e.g. prevalence measure) reported in each study were captured. For example, a study reporting cancer for transgender people counted as n=1 at the study level. If this study reported the prevalence of multiple cancer types (e.g. lung cancer and skin cancer), it counted as n=2 at the datapoint level. Further, if the study reported cancer outcomes for multiple transgender sub-groups (e.g. by gender, race), each reported outcome was counted as a unique datapoint. For mental health and substance use outcomes, only diagnoses based on self-report, provider-report, or structured clinical interviews were included as the most reliable measures of the chronic disease conditions; assessments of symptomology (e.g., depression symptom scales) were excluded. 2.3 Results 2.3.1 Study selection As shown in Figure 2.1, 11,272 unique records were identified from the electronic database search. After title and abstract screening, 10,848 records were excluded with 424 articles assessed for eligibility via full text review. Of these, 93 articles were eligible for the review. 20 Figure 2.1 PRISMA flowchart for study screening for systematic search of the literature on non-HIV chronic disease outcome studies among transgender populations Records identified through database searching (n = 16348) Screening Included Eligibility Identification Records after duplicates removed (n = 11272) Records screened (n = 11272) Records excluded (n = 10848) Full-text articles assessed for eligibility (n = 424) Full-text articles excluded, with reasons (n = 304) No outcome of interest (n = 201) Abstract (n = 65) Duplicate (n = 14) No trans sample/trans not disaggregated (n = 12) Language (n = 4) Trans sample <10 (n = 3) No primary data (n = 3) Not peer-reviewed (n = 2) Duplicate data (n = 27) Studies included in review (n = 93) 21 2.3.2 Study characteristics The 93 studies included 665 datapoints for 48 unique chronic disease outcomes. Outcomes were grouped into seven chronic condition groups, adapted from the World Health Organization,68 for the purpose of narrative synthesis (Figures 2.2 to 2.3).83 22 Figure 2.2 Chronic disease groups for transgender populations, captured in the systematic review (n=93 studies, 665 data points), proportion by study and disease datapoint 23 Figure 2.3 Chronic disease outcomes among transgender populations captured in the systematic review, proportion by disease datapoint (n=655) 24 As summarized in Table 2.1, articles were published between 1980 and 2019: 57% were published in the five years preceding the search (2015-2019) and 85% in the decade preceding the search (2010-2019). Nearly half of included studies were clinical samples (44%), nearly one-quarter convenience samples (23%), 22% electronic medical record (EMR) samples, 6% respondent-driven samples (RDS) or snowball method samples, and 5% probability-based samples. A majority of studies employed cross-sectional designs (70%), approximately one quarter longitudinal designs (28%), one a matched case-control design, and one a randomized controlled trial (RCT). 25 Table 2.1 Studies and study characteristics included in systematic review of non-HIV chronic disease outcomes among transgender populations (n=93) Study ID Study sample Country Sampling method Trans sample size Chronic condition outcomes ASIA Chen 201984 Transgender people China Convenience 1309 Mental health and substance use disorders Kalra 201385 Hijras accessing an STI clinic India Clinical 50 Mental health and substance use disorders Baba 201186 Transgender men patients at a GID clinic Japan Clinical 178 Metabolic and endocrine disorders Hoshiai 201087 GID clinic patients Japan Clinical 579 Mental health and substance use disorders Rutledge 201888 Transgender women Malaysia Convenience 199 Mental health and substance use disorders Lim 201989 Transgender OBGYN clinic patients South Korea EMR 54 Mental health and substance use disorders EUROPE DeCuypere 199590 Applicants for GAS at a gender identity clinic Belgium Clinical 35 Mental health and substance use disorders 26 Defreyne 201791 Transgender endocrine clinic patients starting GAHT Belgium Clinical 1081 Metabolic and endocrine disorders Weyers 200992 Transsexual women accessing GAS Belgium Clinical 50 Unspecified and other diseases Wierckx 201393 Transgender gender clinic patients accessing GAHT, with cisgender control group Belgium Clinical 352 Cerebro/Cardiovascular diseases Simonsen 2016a94 Transsexual people accessing GAS at a public GID clinic Denmark EMR 104 Cancer; Cerebro/Cardiovascular diseases; Chronic liver and kidney disease; Other and unspecified diseases; Respiratory disease Simonsen 2016b95 Transsexual people accessing GAS at a public GID clinic Denmark EMR 104 Mental health and substance use disorders Holt 201696 Youth referred to a gender identity clinic England Clinical 218 Mental health and substance use disorders 27 Judge 201497 Endocrine clinic patients with suspected or confirmed GD referred for GAHT Ireland Clinical 218 Cerebro/Cardiovascular conditions; Metabolic and endocrine disorders; Mental health and substance use disorders Colizzi 201498 Transgender patients at a gender identity clinic Italy Clinical 118 Mental health and substance use disorders Fisher 201399 Patients with GID at a GID clinic, without previous GAS Italy Clinical 140 Mental health and substance use disorders Madeddu 2009100 Transsexual GID psychiatric clinic patients seeking GAS Italy Clinical 50 Mental health and substance use disorders 28 Heylens 2014101 Adults with GID seeking GAHT/GAS at gender clinics Multiple (The Netherlands, Belgium, Germany, Norway) Clinical 305 Mental health and substance use disorders Haraldsen 2000102 Transsexual patients with GAS history and cisgender personality disorder patients and cisgender healthy controls Norway Clinical 86 Mental health and substance use disorders Duisin 2014103 Transgender psychiatric clinic patients and cisgender heterosexual comparison group Serbia Clinical 30 Mental health and substance use disorders Becerra-Fernandez 2014104 Transgender men patients with GD diagnosis at GID clinic Spain Clinical 77 Metabolic and endocrine disorders 29 Bergero-Miguel 2016105 Transgender patients with GD at a public gender identity clinic Spain Clinical 210 Mental health and substance use disorders Gomez-Gil 2009106 Transsexual patients with GD seeking GAS/GAHT Spain Clinical 230 Mental health and substance use disorders Guzman-Parra 2016107 Transsexual patients of a gender identity clinic Spain Clinical 197 Mental health and substance use disorders Vilas 2014108 Transgender gender disorder clinic patients accessing GAHT Spain Clinical 157 Metabolic and endocrine disorders Bodlund 1996109 Transsexual patients accessing GAS Sweden Clinical 19 Mental health and substance use disorders Hepp 2005110 Psychiatric clinic patients with GID Switzerland Clinical 31 Mental health and substance use disorders 30 deVries 2011111 Adolescent gender identity clinic patients with GID The Netherlands Clinical 105 Mental health and substance use disorders Gooren 2013112 Transsexual patients accessing GAHT The Netherlands EMR 3102 Cancer Gooren 2014113 Transsexual women gender clinic patients with history of GAHT and orchiectomy The Netherlands EMR 2306 Cancer van Kesteren 1997114 Transsexuals receiving GAHT The Netherlands EMR 1109 Cerebro/Cardiovascular diseases Wallien 2007115 Dutch children referred to a GID clinic, and a comparison group of children referred to an ADHD clinic The Netherlands Clinical 120 Mental health and substance use disorders LATIN AMERICA Fontanari 2018116 Transgender women patients at a GD clinic Brazil Clinical 289 Mental health and substance use disorders 31 Lobato 2008117 Transsexual patients in a GID program Brazil Clinical 138 Mental health and substance use disorders MIDDLE EAST AND NORTHERN AFRICA REGION Aghabikloo 2012118 Patients with GID seeking GAS Iran Clinical 69 Mental health and substance use disorders Havar 2015119 Patients with GID accessing GAS Iran Clinical 108 Mental health and substance use disorders Mazaheri Meybodi 2014120 Transsexual psychiatric clinic patients with GID Iran Clinical 83 Mental health and substance use disorders Ibrahim 2016121 Transgender and cisgender adult controls Lebanon RDS/Snowball 20 Mental health and substance use disorders Basar 2016122 GD diagnosed psychiatric clinic patients Turkey Clinical 116 Mental health and substance use disorders 32 Yildirim 2017123 Children and adolescent psychiatric clinic patients with GID and a matched control group with non-GID diagnoses Turkey Clinical 20 Mental health and substance use disorders NORTH AMERICA Chiniara 2018124 Adolescents presenting to a transgender youth clinic Canada Clinical 203 Mental health and substance use disorders Drummond 2018125 Assigned female sex at birth patients referred to a gender identity clinic as youth Canada Clinical 25 Mental health and substance use disorders 33 Heard 2018126 Transgender youth patients of a GD program Canada Clinical 174 Mental health and substance use disorders Rotondi 2011a127 Transfeminine adults Canada RDS/Snowball 186 Mental health and substance use disorders Rotondi 2011b128 Transmasculine adults Canada RDS/Snowball 205 Mental health and substance use disorders Williams 2017129 Women and transgender/gender liminal people Canada Convenience 193 Mental health and substance use disorders Becerra-Culqui 2018130 Transgender youth patients United States EMR 1333 Mental health and substance use disorders 34 Beckwith C 2017131 HIV-positive adults with criminal justice system involvement history United States Convenience 20 Mental health and substance use disorders Beckwith N 2017132 Transgender patients at an LGBT community clinic United States EMR 145 Mental health and substance use disorders Brocksmith 2018133 Pediatric endocrine clinic patients with GD diagnosis United States Clinical 78 Metabolic and endocrine disorders Brown 201637 Veterans United States EMR 5135 Cancer; Cerebro/Cardiovascular diseases; Chronic liver and kidney disease; Mental health and substance use disorders; Metabolic and endocrine disorders; Other and unspecified diseases; Respiratory disease 35 Chen 2016134 Pediatric endocrinology clinic patients referred for GD United States Clinical 38 Mental health and substance use disorders Cole 1997135 Individuals presenting to a gender clinic with GD or transsexualism United States Clinical 435 Mental health and substance use disorders Dai 2019136 Adults United States Probability-based 669 Cancer; Cerebro/Cardiovascular diseases; Chronic liver and kidney disease; Mental health and substance use disorders; Metabolic and endocrine disorders; Other and unspecified chronic diseases; Respiratory conditions Dawson 2017137 Amazon Mechanical Turk workers United States Convenience 54 Mental health and substance use disorders 36 Dragon 201738 Medicare beneficiaries United States EMR 7454 Cancer; Cerebro/Cardiovascular conditions; Chronic liver and kidney disease; Mental health and substance use disorders; Metabolic and endocrine disorders; Other and unspecified diseases; Respiratory disease Flentje 2014138 People entering publicly funded substance abuse treatment United States EMR 199 Mental health and substance use disorders Flentje 2016139 Urban homeless sexual and gender minorities United States Convenience 49 Mental health and substance use disorders; Unspecified and other diseases 37 Gaither 2017140 Transgender women patients seeking GAS from a high-volume surgeon United States Clinical 330 Cerebro/Cardiovascular diseases; Mental health and substance use disorders; Metabolic and endocrine disorders; Respiratory disease; Unspecified and other diseases Hill 2016141 Transgender veterans and active duty service members United States Convenience 106 Cancer; Cerebro/Cardiovascular diseases; Mental health and substance use disorders; Metabolic and endocrine disorders; Other and unspecified diseases; Respiratory disease 38 Hutchison 2018142 Transgender patients in a state cancer registry United States EMR 230 Cancer Jarin 2017143 Adolescent patients with GD on GAHT United States EMR 116 Mental health and substance use disorders Katz-Wise 2017144 Transgender and gender non-conforming adults United States Convenience 452 Mental health and substance use disorders Katz-Wise 2018145 Transgender and gender non-conforming youth and their families United States Convenience 33 Mental health and substance use disorders Keuroghlian 2015146 Transgender and gender non-conforming adults United States Convenience 452 Mental health and substance use disorders Klemmer 2018147 Transgender women United States RDS/Snowball 233 Mental health and substance use disorders 39 Lindsay 2016148 Transgender veterans of Iraq or Afghanistan accessing the VHA United States EMR 332 Mental health and substance use disorders Liu 2019149 College students United States Probability-based 301 Mental health and substance use disorders Lothstein 1980150 Adolescent gender identity clinic patients seeking GAS United States Clinical 27 Mental health and substance use disorders Lytle 2016151 College students United States Probability-based 123 Mental health and substance use disorders Mustanski 2010152 LGBT youth United States Convenience 20 Mental health and substance use disorders 40 Nahata 2017153 Transgender adolescents at an urban clinical gender program, referred for GAHT United States EMR 79 Mental health and substance use disorders Nokoff 2018154 Adults United States Probability-based 764 Cerebro/Cardiovascular diseases; Metabolic and endocrine disorders Nuttbrock 2015155 Transgender women United States Convenience 230 Mental health and substance use disorders Odo 2001156 Native Hawaiian transgender women HIV prevention program clients and general population comparison group United States Clinical 100 Metabolic and endocrine disorders 41 Quinn 2017157 Transgender health system patients, and cisgender matched comparison group United States EMR 6456 Cancer; Cerebro/Cardiovascular diseases; Mental health and substance use disorders; Metabolic and endocrine disorders Qureshi 2018158 LGBT adults United States Convenience 27 Cerebro/Cardiovascular diseases Reisner 2013159 Transmasculine adult attendees of a transgender health conference United States Convenience 73 Mental health and substance use disorders; Metabolic and endocrine disorders; Respiratory disease Reisner 2014160 Transgender men accessing STI testing at an LGBT community clinic United States EMR 23 Mental health and substance use disorders 42 Reisner 2015161 Transgender youth/young adult patients of an adolescent urban community health center, and matched cisgender controls United States EMR 180 Mental health and substance use disorders Reisner 2016a162 Young transgender women United States Convenience 298 Mental health and substance use disorders Reisner 2016b163 Transmasculine adults who have sex with cisgender men United States Convenience 173 Mental health and substance use disorders 43 Seelman 2017164 Transgender and gender non-conforming adults United States Convenience 417 Cerebro/Cardiovascular diseases; Mental health and substance use disorders; Metabolic and endocrine disorders; Respiratory disease Silverberg 2017165 Transgender health system patients, and cisgender matched comparison group United States EMR 4889 Cancer Spack 2012166 Children and adolescents with GID referred for care to a multidisciplinary gender clinic United States Clinical 97 Mental health and substance use disorders 44 VanKim 2014167 College students United States Probability-based 53 Metabolic and endocrine disorders Warren 2016168 LGBT adults United States Convenience 208 Metabolic and endocrine disorders White-Hughto 2018169 Transgender and gender non-conforming older adults United States Convenience 452 Unspecified and other diseases Wilson 2015170 Transgender women United States RDS/Snowball 314 Mental health and substance use disorders OCEANIA Bolger 2014171 Transgender men Australia Convenience 279 Mental health and substance use disorders 45 Cheung 2018172 New patients with GD at an endocrine and a primary care clinic Australia EMR 540 Cancer; Cerebro/Cardiovascular diseases; Chronic liver and kidney disease; Mental health and substance use disorders; Respiratory disease Pitts 2009173 Transgender people Multiple: Australia, New Zealand Convenience 253 Mental health and substance use disorders MULTIPLE/GLOBAL Witten 201476 Transgender adults Multiple (24 countries including primarily United States, Canada, Australia, Sweden, United Kingdom) RDS/Snowball 1,963 Unspecified and other disease 46 Studies were conducted primarily in the US (46%), with 6% published in Canada, 5% each in Spain and The Netherlands, and all other countries contributing less than 5% of included studies (Figure 2.4). The global distribution of studies spanned Asia (6%), Europe (28%; including one study conducted in multiple countries in the region), Latin America (2%), the Middle Eastern and North African (MENA) region (6%), North America (53%), Oceania (3%; including one study conducted in multiple countries in the region); and one study spanning multiple regions (1%) conducted in 24 countries including primarily the US, Canada, Australia, Sweden and the United Kingdom. None were conducted in sub-Saharan Africa (Figure 2.4). 47 Figure 2.4 Global distribution of chronic disease outcomes among transgender populations, by disease datapoint (665); larger circles = more datapoints 48 A third of studies (33%) reported results for transgender samples without disaggregating by gender. Of those that did disaggregate by gender, nearly all included transgender women (89%) while 21% were conducted exclusively with transgender women. More than three-quarters (77%) of studies included transgender men, while 13% were conducted exclusively with transgender men. Only one study (2%) reported disaggregated chronic disease outcomes for gender non-conforming or non-binary people. Included studies employed a diversity of transgender status measures, but the predominant measure was diagnosis-based ascertainment using the Diagnostic and Statistical Manual of Mental Disorders (DSM III-IV; 54%), with an additional 5% using a combination of DSM diagnoses and clinical notes. A third of included studies used an identity measure and 8% used a two-step measure that assessed sex assigned at birth and current gender.18 The majority of studies sampled adult populations (67%), 16% were among youth, and 17% included mixed age groups. 2.3.3 Synthesis of results 2.3.3.1 Mental health and substance use disorders Mental health and substance use disorders comprised the majority of included data (80% of studies, n=74; 68% of datapoints, n=455). Mood, anxiety, and substance use disorders were the most commonly studied outcomes in this category (21%, 18%, and 10% of datapoints, respectively). Overall, the lowest prevalence for mood and anxiety disorders was reported in clinical studies with some finding no prevalence among the study samples. The highest prevalence was reported in EMR studies - specifically in US studies of transgender adolescents accessing gender affirming care in an urban center, Medicare beneficiaries, and Veteran Health Administration patients (mood disorder: up to 82%; anxiety disorder: up to 67%).153 However, 49 substance use disorder estimates varied more by study type, with the highest prevalence (50%) reported by a lifetime measure among transgender women (n=20) with a gender identity diagnosis in a Swiss cross-sectional clinical study of psychiatric morbidity.110 2.3.3.2 Cerebrovascular and/or cardiovascular diseases A total of 15% of studies (n=14) and 11% of datapoints (n=70) included cerebrovascular and/or cardiovascular diseases. The most commonly reported outcomes in this area were cardiovascular disease (7%), hypertension (2%), and hyperlipidemia (1%). Most datapoints in this area were provided via clinical or EMR studies (43%), with 19% from the US probability-based Behavioral Risk Factor Surveillance System (BRFSS) study,136,154 and 6% from several convenience samples. Prevalence of cardiovascular disease ranged from no cases in some studies to as high as 30% among a sample of 7,454 transgender adults in an EMR study of Medicare beneficiaries.38 Hyperlipidemia prevalence ranged from 9% to 64%, and hypertension from less than 1% to 60%, with no discernable pattern in terms of study design, sample, gender, or chronic condition measures or definitions. Cerebrovascular disease comprised three datapoints (0.45%), and prevalence ranged from <1% in a Netherlands EMR study of transgender people accessing GAHT114 to 8% in a matched case-control EMR study of US veterans.37 2.3.3.3 Cancer Cancer outcomes were included in 12% of studies (n=11) and 7% of datapoints (n=47). The most reported cancer conditions were general or unspecified cancer, breast cancer, viral infection-induced cancers, prostate cancer, and endocrine cancer (<2% of datapoints respectively). Three of the 11 studies reported incidence estimates for cancer outcomes, the remaining 73% reported 50 prevalence exclusively. Of the studies in this area, 55% included cisgender comparators for transgender groups, with mixed evidence on the comparative cancer burden for transgender individuals, both for overall cancer morbidity and specific cancer types. In other words, some studies found cancer disparity for transgender compared to cisgender people, some did not. EMR studies provided the majority of cancer estimates (82%), and many were longitudinal studies (23%). Nearly all cancer outcomes were measured directly via provider diagnosis (i.e. in clinical or EMR studies; 91%), with the remaining measured via self-report diagnosis. 2.3.3.4 Metabolic and endocrine disorders Metabolic and endocrine disorders represented 6% of datapoints (n=38) and 22% of studies (n=20). The most common outcome studied in this area was obesity (3% of datapoints), followed by diabetes (2%), polycystic ovary syndrome (PCOS) and metabolic syndrome (<1% each). Diabetes prevalence ranged widely from <1% to 6% in clinical studies using self-report and provider diagnosis for condition ascertainment, to 27% to 31% in EMR studies relying on clinical diagnosis. Of the two clinical studies reporting PCOS prevalence for transgender men, one in Japan and one in Spain, estimates were comparable (32 to 36%) and both used the 2003 Rotterdam criteria for condition measurement.86,104 2.3.3.5 Respiratory disease Respiratory diseases were included in 10% of studies (n=9) and 3% of datapoints (n=21). The most frequently reported respiratory outcomes were chronic lung disease (2% of datapoints) and asthma (1%). One-third of respiratory outcomes came from convenience samples, 29% EMR and probability-based samples respectively, and 10% clinical samples. Asthma prevalence was as 51 high as 33% for an age-standardized estimate via self-report in a community-based convenience sample of 73 transmasculine adults attending a transgender health conference in the US.159 Asthma prevalence was as low as 6%, via self-report in a clinical study of 330 transgender women seeking gender-affirming surgery from a high-volume US surgeon.140 2.3.3.6 Chronic liver and kidney disease The lowest proportion of studies (5%, n=5) and datapoints (2%, n=10) included chronic liver and kidney disease. All studies in this group were EMR-based except for one, using data from the probability-based BRFSS study in the US. Geographically, data was contributed from Europe, Oceania, and the US. Chronic kidney and liver disease were equally reported outcomes (0.75% of all datapoints), with prevalence estimates ranging widely from 3% by self-report provider diagnosis among transgender and gender non-conforming adults in a US probability sample,136 to 23% by provider diagnosis in an EMR study of transgender Medicare beneficiaries for chronic kidney disease.38 Chronic liver disease was as high as 12% by provider diagnosis, in the same EMR Medicare study.38 In studies that reported outcomes disaggregated by gender (40% of studies in this group), transgender women and men had a similar prevalence of chronic liver and kidney disease. 2.3.3.7 Other and unspecified diseases Unspecified chronic diseases and other chronic diseases not otherwise grouped were included in 4% of datapoints (n=24) and 13% of studies (n=12). Arthritis and unspecified chronic diseases were the most commonly reported outcomes in this group (0.75% of datapoints each). Remaining “other” chronic diseases not included in any other group included pain disorders, 52 osteoporosis, organic brain disorders and injuries, musculoskeletal diseases, gastroesophageal reflux disease, epilepsy, and benign prostatic hyperplasia. Of the five available arthritis datapoints, most were based on self-report diagnosis in probability-based or convenience sample surveys (80%). The remaining arthritis datapoint was the highest prevalence estimate (39%) based on a direct provider diagnosis from an EMR study of Medicare beneficiaries that included 7,454 transgender patients.38 Studies providing prevalence estimates of unspecified chronic conditions (e.g. “any chronic condition”, “chronic health condition”) used a diversity of sampling strategies but the majority were self-report measures (53%). Estimates ranged from 26% in a clinical study of 50 Dutch transgender women accessing gender-affirming hormone and surgical care,92 to 50% among transgender women in a convenience sample from a point-in-time population count survey of 1,027 homeless sexual and gender minorities in the US.139 2.4 Discussion 2.4.1 Summary of evidence, gaps and future directions 2.4.1.1 Chronic conditions This review of global chronic disease burden among transgender populations included 93 studies and 665 datapoints, reporting on 48 unique outcomes and spanning 23 countries and four decades of research. Findings include a predominance of research on mental health. This is consistent with known trends in transgender health research,72 including a 2016 review of the global health burden for transgender populations that found mental health to be the most commonly studied area in transgender health (31% of articles published between 2008 and 2014). The 2016 review called for research focused on neglected and urgent health areas for transgender populations (e.g. chronic physical disease).6 Our review indicates the focus on 53 mental health research among transgender people persists to the exclusion of other chronic disease. The continued focus on mental health in transgender research is in part a legacy of the historical psycho-pathologization of transgender status.174 Transgender-related diagnoses were only recently removed from the World Health Organization International Classification of Diseases (11th edition) chapter on mental and behavioural disorders in 2018, taking effect clinically in 2022.175 Hopefully, this change will contribute to a growing shift away from a disproportionate focus on the mental health of transgender people There is a specific need for future research on understudied chronic physical disease in transgender populations. This review found scarce research on age-related chronic conditions including dementia, cancer, hypertension and osteoporosis and inflammation-related chronic disease such as cardiovascular disease, diabetes, and arthritis. For example, in addition to the elevated prevalence of cardiovascular morbidity found in a previous review,93 there is evidence transgender women are more likely to experience cardiovascular risk factors as well as mortality compared to cisgender adults.176 Future research on inflammation-related chronic physical conditions is especially relevant for transgender populations. Inflammatory processes implicated in chronic disease morbidity may be compounded by exogenous sex hormone use (an important part of gender-affirming care for many transgender people),177 by HIV disease that disproportionately affects transgender populations,24 and by chronic disease drivers such as stress, stigma and discrimination.56–58 The impact of GAHT on health outcomes, including chronic disease, is an important area for future research.43 54 The prevailing focus on the sexual and mental health of transgender people has ignored some conditions with a high burden among transgender populations. For example, PCOS, which was reported by only two studies in this review, may affect a third of transgender men.86,104 Even within the mental health group, there is a disproportionate focus on mood, anxiety and substance use disorders, to the exclusion of other conditions such as PTSD (affecting up to 23% in an EMR study of transgender adolescents accessing gender affirming care in a US urban center)153. The current review found an increase in PTSD research compared to Reisner et al.’s 2016 global health burden review (n=24 versus n=3 datapoints, respectively), but there remains disproportionally little epidemiological evidence in this area. Many studies included in the current review were broad descriptive epidemiological studies characterizing the overall health of transgender samples that further highlights the limited number of studies (n=3) with an explicit focus on investigating the burden of specific chronic disease among transgender people. 2.4.1.2 Study design Caution is warranted when interpreting prevalence estimates from the existing body of evidence. The majority of included studies were cross-sectional, within-group analyses, using non-representative sampling. Fewer than a third (28%) included cisgender comparator groups to investigate health disparity by gender identity, with only five employing matching for more robust estimates. Much of the data in this review was based on transgender populations in the US, with a predominance of EMR-based studies, primarily those conducted among Medicare beneficiaries,63 veterans receiving care through the Veterans Health Administration system,39 and those insured through the Kaiser Permanente system.157 The health of transgender individuals in 55 these systems may not reflect the health status of the transgender general population, either in the US or in other regions. There is the need for the inclusion of core gender identity measures in nationally-representative probability-based studies.178,179 2.4.1.3 Chronic disease measurement Consistent with findings from Reisner et. al’s 2016 review,6 heterogeneity in chronic disease measures (e.g. DSM diagnosis, structural clinical interview for mental health conditions, ICD, treatment history, clinical thresholds for physical conditions) and definitions (e.g. variety in DSM editions, ICD revisions, structured clinical interview instruments) were found in the current review of the literature. Included studies also used varied recall periods (e.g. lifetime, current, past-year, past two-years, past six-months) and prevalence measures, with more than a third (37%) reporting only frequencies and proportions. Additionally, many outcome measures were developed for use with other populations and may not have been validated for use with transgender samples (e.g. chronic kidney disease).180 This may affect chronic disease prevalence estimates, particularly prevalence of conditions defined by sex-based clinical measures for transgender people on GAHT. This may also explain some of the variability in prevalence estimates for specific chronic disease outcomes observed in the current review of the literature. 2.4.1.4 Transgender status measurement This review found heterogeneity of transgender status measures across studies (e.g. DSM diagnosis, self-report) with few employing recommended two-step measures, all published within the last decade.17 The currently evolving context of transgender measurement in clinical and EMR studies is of particular relevance given the preponderance of EMR and clinical studies 56 in this review.181 Studies that reported results disaggregated by gender for transgender groups largely used binary operationalizations of gender. While gender minority health research generally suffers from small sample sizes, there is a growing recognition of the diversity of lived experience among transgender populations and an urgent need for evolution in how gender identity is measured to reflect the lived realities of gender diverse people.182–184 Gender non-conforming and non-binary people may experience mental health and substance use disparities even compared to binary-identified transgender groups.183,184 Rigorous, standardized transgender status measurement that reflects population gender diversity is key for future research. 2.4.2 Limitations The results of this review should be considered in light of its limitations. Importantly, this review sought to map the available evidence in the area of chronic disease among global transgender populations, employing narrative synthesis largely at the datapoint level. As such, this review did not endeavor to synthesize or meta-analyze chronic disease prevalence estimates and thus results may be limited in the extent to which they can inform direct clinical practice. The primary intent was to identify research gaps and evidence needs. Findings may also have been skewed by the ability of one study with many datapoints to disproportionately contribute data to this synthesis. In this review of published studies reporting quantitative prevalence estimates for transgender populations, there was also heterogeneity of quality of evidence, including in study design and prevalence measures used across studies. As such, evidence from this review may be susceptible to information and selection bias and thereby overall study validity, but this speaks to the need for more rigorous epidemiological research. Though, importantly, the quality of the captured 57 studies does not negate the identification of significant research gaps and evidence needs with regards to chronic disease, particularly physical chronic disease. Finally, the focus of this synthesis was on chronic disease outcomes and not chronic disease risk factors. Only 40% (n=37) of studies in this review reported significant correlates of chronic disease, including sociodemographics (e.g. sexual orientation, gender, age, race/ethnicity, socioeconomic status, country of origin) and psychosocial factors (e.g. history of violence, lifetime suicidal ideation), as well as trans-specific factors (e.g. experiences of transphobic victimization, access to a trans-affirming healthcare provider, social/medical transition status). A growing number of studies have demonstrated the importance of stigma, healthcare access, violence and syndemic or co-occurring factors in shaping health outcomes for transgender people.6,24,185 Future research is needed that can shed light on these drivers of disease burden, as well as the moderating and mediating impacts of intersecting social locations. Specifically, only approximately half (48%) of studies in this review reported race or ethnicity characteristics of the study sample, though, 22% of included studies were conducted in countries with majority or entirely non-white samples. Given evidence of racial disparities in chronic mental and physical health for transgender people,79 the intersection of race, ethnicity, and gender diversity is an important area of focus for future chronic disease research. 2.4.3 Conclusions This systematic review makes an important contribution as the first to focus on non-HIV chronic disease among global transgender populations. Findings from this review point to a number of critical gaps in the existing transgender health literature landscape in terms of chronic disease 58 burden knowledge, and important areas for future research. In particular, there is a need for research in areas other than sexual and mental health,6 areas with unique vulnerability for transgender people such as inflammation-related disease, and areas with little evidence to date such as age-related chronic conditions. There is also a need for well-designed longitudinal studies (particularly important for incident cancer and other age-related conditions); research making use of large, population-representative data and appropriate comparison groups,186 and use of consistent measurement for both transgender status186 and chronic disease ascertainment.80 The alignment of funding mechanisms to these research priorities is fundamental to continued progress in transgender chronic health research. Finally, research with an explicit focus on chronic disease in transgender populations, including multimorbidity, is warranted,80 as well as studies designed to investigate multi-level drivers of chronic disease burden. Given links between factors such as poverty, stigma, discrimination and barriers to healthcare access for transgender people,6,24,185 future research in this area should be rigorously designed to enable disentangling of the effects of these important determinants of health and point to opportunities for multi-level interventions to attenuate their effects in transgender populations. 59 Chapter 3: Development of a computable phenotype to identify a transgender sample for health research purposes: A feasibility study in a large linked provincial healthcare administrative cohort in British Columbia, Canada 3.1 Introduction 3.1.1 Limited data on transgender people Transgender people are often overlooked within epidemiological research and population health surveillance due to small population size, limited research designs (e.g. insufficient transgender sample size captured for meaningful analysis), and other institutional and methodological erasures.4,5,13 A 2017 review of Medline-indexed literature from 1950 to 2016 found 2405 published articles including transgender people, with almost half published in the last decade.7 A 2008 United States (US)-based meta-analysis of HIV prevalence among transgender populations found 24 studies of transgender women, and five additional studies of transgender men,14 though an updated review found 43 primary studies on transgender women and 15 on transgender men published between 2006 to 2017.15 Despite this recent increase in transgender health research in general and for HIV specifically, much of the literature has focused on transgender-specific care, mental health and HIV/sexual health,6,187 leaving the population understudied, in particular in the broader areas of physical health and healthcare utilization. The erasures or exclusions of transgender persons in health studies may be explained, in part, by methodological challenges. Specific to electronic medical record (EMR) data, a 2017 report identified only one transgender person among 38,5820 cancer cases in a Minnesota cancer 60 registry,16 clearly an undercount given that 0.4% of the US general population and 0.6% (95% credibility intervals: 0.5%-0.7%) of the Minnesota population is estimated to be transgender.21,22 This highlights the need for improved gender ascertainment and transgender inclusion in research relying on patient records and administrative data. The establishment of best practices for measuring transgender status in survey research, such as the two-step method (sex assigned at birth and current gender identity), points to a way forward for transgender-inclusive population health research.17,18 However, innovative research methods are needed to identify transgender people in studies that rely on existing data sources (in particular EMR) and that optimize the use of transgender respondents’ data in non-transgender specific research in order to meaningfully include transgender respondents in analyses. 3.1.2 Computable phenotypes for transgender health research Previous research in transgender health largely comprises cross-sectional studies, case reports, and qualitative or observational research.187 Many studies consist of clinic- or venue-based convenience samples, and most lack comparison groups.6,187 The literature is further characterized by inconsistent transgender status measurement,188 small sample sizes, and a focus on the United States (US).6 In response, researchers have called for advancing transgender health research methods - namely ascertainment of high-quality samples via systematic approaches - including for general population-based and health systems-based studies.189 One opportunity for the advancement of transgender health research methods is the emerging use of computable phenotypes190 or case ascertainment algorithms, to identify transgender samples in healthcare utilization data. A computable phenotype is an algorithm for identifying a clinical 61 feature, condition, or set of characteristics that can be determined directly from EMR and other ancillary health care data systems (e.g., disease registries, insurance claims data).191 Computable phenotypes are developed using a combination of data elements (e.g., sociodemographic variables, clinical diagnoses) and value sets (i.e., the selection of a set of relevant values for each data element). Development of computable phenotypes using standardized methods and definitions enables identification and inclusion of transgender persons in research, as well as replication of analyses across data sources, healthcare organizations/sites and studies. Computable phenotypes have application in clinical care, surveillance, and other health research. Recently, computable phenotype and other EMR-based algorithm methods have been applied in a number of settings primarily in the US to identify transgender samples for health research.188 Specifically, the STRONG study identified a transgender cohort (n = 6,456) using EMR data from Kaiser Permanente health plan members in California and Georgia, for investigation of general and transgender-specific health outcomes.157 Blosnich et al identified 3,177 people with a “gender identity disorder” diagnosis among military veterans accessing care through the US Veterans Health Administration healthcare system,39 for examination of mental health and other outcomes. Researchers with the US Centers for Medicare & Medicaid Services identified 4,098 transgender beneficiaries using national Medicare claims data,63 and researchers at Vanderbilt University identified 234 transgender patients in their university clinic EMR data.190 While these cohorts represent important opportunities for advancement of transgender health research, these methods have yet to be applied widely outside the US context. This is particularly important as different jurisdictions may vary in medical billing and coding practices, healthcare system patient populations, and representativeness of the general population. 62 Specifically, in Canada, healthcare is delivered through a provincially administered universal healthcare system. As such, research using EMR provides an opportunity to develop methods for population-based, representative estimates of transgender populations within the Canadian context. Coupled with the current absence of gender ascertainment measures in population-based routinely collected data (e.g., census, national government health surveys) in Canada and many other jurisdictions, this remains an evidence need. 3.1.3 Summary of study rationale This study investigated the application of emerging transgender health research methods, specifically computable phenotypes, in a Canadian context for the first time, testing the feasibility of identification of a transgender sample using EMR data from a provincial healthcare administrative data-linked cohort. 3.2 Methods 3.2.1 Data sources and participants 3.2.1.1 The Comparative Outcomes and Service Utilization Trends Study (COAST) COAST is a population-based cohort study focused on health services utilization research questions among all people known to be living with HIV in the province of British Columbia and a 10% random sample comparison group of the HIV-negative general population.62 The COAST cohort comprises individual-level, longitudinal data from people living with HIV who have ever accessed HIV treatment in British Columbia between 1996 and 2013, provided by Population Data BC (PopDataBC)192 . The cohort was identified via data linkage between two provincial 63 data sources using personal health number: the Drug Treatment Program (DTP)193 and the Ministry of Health. PopDataBC provides infrastructure for access to, and linkage of, longitudinal and individual-level administrative health data for all British Columbia residents.192 The HIV-negative general population cohort was drawn randomly from the Ministry of Health registry data by PopDataBC. COAST cohort data sources and a description of data elements is included in Appendix B. 3.2.1.2 Drug Treatment Program In British Columbia, antiretroviral therapy (ART) is provided to people living with HIV at no cost to the patient, and distributed through the DTP.193 The DTP contributed a provider-reported measure of transgender status for COAST. 3.2.1.3 Ministry of Health Ministry of Health data available via COAST included insured medical service billing records for outpatient visits,194,195 hospital (in-patient) visits,196 prescription medications,197,198 and vital statistics.199 3.2.2 Measures and analyses 3.2.2.1 Transgender computable phenotypes Identification of transgender cases was tested in COAST using International Classification of Disease (ICD) codes (9th and 10th editions) and exogenous sex hormone prescription use. Transgender-specific diagnoses in medical and hospital billing records included the ICD-9 codes 302.5 Trans-sexualism with unspecified history, 302.51 Trans-sexualism with asexual history, 64 302.52 Trans-sexualism with homosexual history, 302.53 Trans-sexualism with heterosexual history, 302.6 Gender Identity Disorder in children, 302.85 Gender Identity Disorder in adolescents or adults; and ICD-10 codes F64.0 Gender Identity Disorder of childhood, F64.2 Gender Identity Disorder of childhood, F64.8 Other Gender Identity Disorder, and F64.9 Gender Identity Disorder unspecified. The full list of androgen blockers and exogenous sex hormone prescriptions included in analyses is available in Appendix C. 3.2.2.2 Concordance To assess face validity and utility of diagnosis and prescription data over time in computable phenotype development (i.e. whether the identified transgender sample had exogenous sex hormone prescription use and other diagnoses patterns consistent with that of transgender populations in other studies), concordance analyses evaluated the presence of at least one included diagnosis and prescription during the COAST study follow-up period with the presence of at least one included diagnosis and prescription in the last study year. Concordance was assessed between transgender-specific diagnoses, exogenous sex hormone and androgen blocker prescriptions, and non-transgender specific diagnoses (ICD-9 259.9 Unspecified Endocrine Disorder and ICD-10 E34.9 Endocrine Disorder, Unspecified). Endocrine disorder diagnosis codes are sometimes preferred by medical providers treating transgender people in response to historic exclusions of transgender-specific care from insurance coverage and to combat the stigma of transgender-specific diagnosis codes that have historically been classified as psychiatric disorders in the DSM.200 Exogenous sex hormone use, while common in transgender populations,14,45 is not transgender-specific. Cisgender populations also use androgen blocker and sex hormone prescriptions (e.g. estrogen to treat menopausal symptoms in cisgender women, 65 spironolactone is used for hypertension), thus exogenous sex hormone and androgen blocker prescription use cannot independently identify transgender people. At the same time, not all transgender people use hormones and some access hormones via non-medical sources.177,201 3.2.2.3 Validation In British Columbia, transgender status is collected in the DTP via a provider-reported sex variable (“Male”, “Male to Female”, “Female to Male”, or “Female”). Patients reported as either “Male to Female” or “Female to Male” were classified as transgender. The provider-reported transgender measure, available for the HIV positive cohort only, was used as a ‘gold standard’ for computable phenotype validation. Sensitivity, specificity, positive predictive value and kappa statistics with corresponding 95% confidence intervals (CI) were calculated for identifying transgender people via the computable phenotypes, in the HIV positive cohort only. 3.2.2.4 Demographics and chronic conditions To further assess face validity of the transgender computable phenotype for future health research, descriptive statistics were calculated for the total transgender sample (both HIV-positive and HIV-negative) using the COAST study key sociodemographic and health variables, specifically laboratory confirmed HIV serostatus (HIV-positive/HIV-negative), baseline age, patient’s health authority (five provincial regions for the administration of health services that include large urban centres, suburban regions, and rural/remote areas), and chronic disease burden based on standardized case definitions from the British Columbia Ministry of Health 202 and the BC Cancer Agency.203 Full details of chronic case definitions used in available in Appendix D. 66 3.3 Results The total COAST cohort included 528,859 people, of which 514,952 were HIV-negative (10% general population random sample) and 13,907 were HIV-positive (Figure 3.1). 67 Figure 3.1 Total transgender sample identified using a computable phenotype with electronic health records, in COAST study (n=528,859) 68 3.3.1 Concordance Of the 237 people who had ever had a transgender-specific diagnosis during the study period, 19.4% also had a recent diagnosis in the last follow-up year (Table 3.1). None had an unspecified endocrine disorder diagnosis at any time; thus, this diagnosis was excluded from all computable phenotypes. Of the 237, 79.3% had an exogenous sex hormone or androgen blocker prescription at least once during the study period and 46.4% had one in the last year. 69 Table 3.1 Concordance analyses for diagnoses and hormone measures in COAST (n=237) N % ≥ Transgender ICD- ever 237 100 ≥ Transgender ICD- in last year 46 19.4 Unspecified endocrine disorder use- ever 0 0.0 Unspecified endocrine disorder use- in last year 0 0.0 ≥ Hormone/blocker use- ever 188 79.3 ≥ Hormone/blocker use-recent 110 46.4 70 3.3.2 Validation While no one computable phenotype consistently performed well across all validation metrics, the computable phenotype with the best overall performance across test statistics was based on having received at least one transgender-specific diagnosis and at least one androgen blocker/exogenous sex hormone prescription over the study follow-up period (Table 3.2). This computable phenotype had high specificity (99.8%, 95% CI: 99.6-99.8), low sensitivity (27.5%, 95% CI: 17.8-39.8), low to moderate Kappa coefficients (0.3, 95% CI: 0.2-0.5), and moderate positive predictive values (43.2%, 95% CI: 28.7-58.9). 71 Table 3.2 Validation measures of transgender computable phenotype (CP) with provider-report transgender status measures, in COAST HIV-positive cohort (n=13,907) CP Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) Kappa (95% CI) ≥ 1 transgender ICD- ever 27.5 (17.8, 39.8) 99.7 (99.6, 99.8) 40.4 (26.7, 55.7) 0.3 (0.2, 0.4) ≥ 1 transgender ICD- recent 8.7 (3.6, 18.6) 100.0 (99.9, 100.0) 85.7 (42.0, 99.4) 0.2 (0.1, 0.3) ≥ 1 transgender ICD AND ≥ 1 hormone/blocker use- ever 27.5 (17.8, 39.8) 99.8 (99.6, 99.8) 43.2 (28.7, 58.9) 0.3 (0.2, 0.5) ≥ 1 transgender ICD AND ≥ 1 hormone/blocker use- recent 7.3 (2.7, 16.8) 100.0 (99.9, 100.0) 83.3 (36.5, 99.1) 0.1 (0.0, 0.2) 72 3.3.3 Transgender phenotype Applying the best-performing computable phenotype, 137 HIV-negative people and 51 HIV-positive people (188 total) were identified as transgender in the respective COAST cohorts (Figure 3.1). 3.3.4 Demographics and chronic conditions Demographic characteristics and chronic conditions for the 188 transgender people identified via the best-performing computable phenotype are presented in Figures 3.2 to 3.4. Transgender people were geographically located throughout British Columbia health regions. The Vancouver Coastal Health Authority region, which includes the largest municipal area in British Columbia, had the highest concentration of transgender people (44.2%) while the Northern Health Authority region, a predominantly rural and remote area of the province, had the lowest (1.6%).204 The HIV-positive group had a higher median age than the HIV-negative group (35 years [Q1, Q3: 30,42] and 30 years [Q1, Q3: 19,42], respectively). For the HIV-negative sample, the largest proportion of transgender people were aged 19 to 29 years (44.5%) and the smallest proportion aged 55 years and older (<5%). For the HIV-positive sample, the largest proportion were aged 30 to 34 years (25.5%) and the smallest proportion aged 55 years and older (<5%). 73 Figure 3.2 Geographic distribution of transgender people across province, by health authority* *% of transgender individuals with known health authority (n=182) 74 Figure 3.3 Age distribution of transgender sample, by HIV serostatus (n=188) 75 Overall, transgender people living with HIV had a higher prevalence of at least one chronic condition (other than HIV) compared to HIV-negative transgender people (88.2% versus 85.4%, respectively), and of two or more chronic conditions (76.5% versus 52.6%, respectively). Most notable specific chronic disease differences included higher prevalence among transgender people living with HIV for cardiovascular disease, chronic kidney disease, osteoarthritis, schizophrenia and personality disorders, and chronic liver disease, and lower prevalence for hypertension, compared to the transgender people living without HIV. 76 Figure 3.4 Co-morbidities among transgender sample, by HIV serostatus (n=188) 77 3.4 Discussion This study demonstrates the feasibility of identification of a sample of transgender people in a large linked provincial healthcare administrative database, using a computable phenotype based on prescriptions and diagnoses. Among a growing number of studies using EMR and computable phenotype methods to identify transgender samples for health research purposes, this is the first to do so in Canada, to independently validate the computable phenotype using a ‘gold standard’ of provider-reported transgender status, and to do so using population-based data. 3.4.1 Concordance There was high concordance between transgender-specific diagnoses and exogenous sex hormone, or androgen blocker prescription use in this study. That nearly half of those with at least one transgender-specific diagnosis had been dispensed hormones or blockers in the past year is consistent with findings from other US and Canadian studies (48.9% and 43.0%, respectively)63,177,201 and suggesting face validity for the current computable phenotype. 3.4.2 Computable phenotype development and validation The best-performing computable phenotype overall successfully identified cisgender people who were truly cisgender (specificity) and correctly identified transgender people who were truly transgender (0.2% false positive rate, results not shown). However, the selected computable phenotype had relatively low sensitivity, missing approximately 72.5% of ‘true’ transgender people in COAST, as identified by the gold standard provider-based measure. Though a relatively small proportion of the ‘true’ transgender sample was identified in this study, the impact on future analyses comparing health outcomes for transgender and cisgender groups is likely negligible, as even the large proportion of ‘true’ transgender people misclassified as 78 cisgender (approximate n = 496) is a very small proportion of the total COAST sample. At worst, this misclassification would bias results related to disparities between transgender and cisgender health toward the null, producing a conservative attenuated effect. Further, as discussed below, gender identity classification will likely improve as transgender care shifts further into the family practice system in British Columbia. As in other Canadian administrative data studies, low sensitivity may be explained in part by provider and system billing preferences using 3-digit ICD diagnosis coding instead of the more specific 4-digit coding, and inconsistencies in the British Columbia billing management system.205 Despite the low sensitivity, computable phenotype development in this study with high specificity offers an advancement for transgender health research. A measure that correctly identifies cases for transgender samples in research with good success translates to better opportunities to include transgender people in health studies and to investigate their health relative to other groups. While future research may lead to improvements in computable phenotype development, the one identified in the current study with good specificity but poor sensitivity, has important utility in advancing opportunities in transgender health research. The limited agreement between the computable phenotype and provider-report transgender status in the current study may be due to the variability in the study design and ascertainment measures used.188 In the British Columbia context, the computable phenotype and the DTP measures are assessing transgender status in different ways and for different purposes. In the DTP, transgender status is ascertained in the context of HIV diagnosis and ART prescribing, during which demographics and HIV transmission risk factors are recorded. This differs from recording 79 diagnoses in EMR for those accessing transgender-specific care as utilized in the current computable phenotype. This may explain the lower PPV for the best-performing computable phenotype compared to that based on recent transgender diagnoses. This suggests that the DTP provider-reported transgender status measure may have better coverage for recent diagnosis and may prove beneficial in future computable phenotype development. Ultimately, a single computable phenotype may not be sufficient for all intended purposes and the best applicable computable phenotype (using different types of diagnoses, prescriptions or procedures) may differ depending on the intended healthcare, health research, or health policy application.191 There is limited literature on EMR-based studies with the ability to validate an administrative transgender measure using a ‘gold standard’ comparison measure.190 The two previous studies that have developed and validated algorithms to identify transgender individuals have both been conducted in non-representative samples in the US, one using Medicare data206 and one in a university medical center.190 Similar to the current study, the Medicare study found high specificity when comparing an EMR-based and a two-step survey-based transgender measure. However, the Medicare study found that the EMR measure performed consistently well with high sensitivity and a high Kappa statistic, unlike in the current study. Using chart review as the ‘gold standard’ for comparison of transgender status, Ehrenfeld et al. found a low false positive rate for their best-performing algorithm (3%), though not as low as the false positive rate in the current study. The overall high levels of agreement for transgender measures in the two previous studies is likely a function of the lack of independence between the ‘gold standard’ and the computable phenotype or algorithm measures. Specifically, only those classified as transgender in the Medicare EMR data were offered survey participation to complete the two-step ‘gold 80 standard’ survey measure, and only those cases identified as transgender in the university clinic EMR were included in chart review. Thus, previous studies could assess agreement between the two measures, but not robustly validate either. In the current study, the DTP provider-based transgender status measure is independent and thus could be used for robust computable phenotype validation. While not possible to incorporate free-text records in case-finding algorithms in the current study, as only structured EMR data is linked through COAST, it is worth noting the opportunities for using NLP and machine learning approaches as methods for identifying transgender samples in EMR data for future research. Outside of transgender health, the use of NLP and machine learning to mine unstructured free-text EMR data has demonstrated efficiency in improving case ascertainment algorithm accuracy .207 ‘Gold standard’ two-step sex assigned at birth and current gender identity measures of transgender status18 are slowly being implemented in routinely collected healthcare data sources. However, in the meantime, NLPs to extract free-text data can be used to produce gold standards against which to measure algorithm performance, as demonstrated by the Medicare study.206 3.4.3 Transgender status prevalence & ascertainment Based on a recent meta-analysis of transgender status prevalence in population-based probability samples,22 it was expected that an effective computable phenotype would identify 0.4% of the general population as transgender, or approximately 54 of the HIV-positive COAST cohort (n=13,907) and 3,098 of the HIV-negative cohort (n=516,340). Consistent with expectations, the best-performing computable phenotype identified 51 people living with HIV as transgender, 81 equivalent to a transgender status prevalence of 0.4% among people living with HIV. Contrary to expectations, the best-performing computable phenotype identified less than 5% of the estimated number of transgender persons expected in the HIV-negative cohort. This is likely a result of a number of factors including the inherent limitation of computable phenotypes, that they can only be applied to a subset of a population that is accessing care, and that most transgender people in British Columbia currently receive care outside the main fee-for-service healthcare delivery system. However, the underestimate of transgender people in the current study is also consistent with the underestimate of transgender populations using diagnostic criteria, compared to other methods of ascertainment, as demonstrated in other studies.188 Using the broadest computable phenotype algorithm (any transgender-specific diagnosis ever, n=56) and those identified by provider-report together (total n=106), the total transgender people living with HIV sample would represent as high as 0.88% (range: 0.73-1.1%) of the prevalent HIV infections in British Columbia in 2014.208 This overrepresentation of transgender people among people living with HIV is consistent with evidence of a disproportionate HIV burden for transgender populations globally,14,23,24 as well as in line with the only other available data on the proportion of people living with HIV who are transgender, from US national surveillance data (2012 data: 1.1%, 95%CI: 0.8-1.4).209 3.4.4 Demographics and chronic conditions Despite moderate to low performance by some validation metrics, particularly low sensitivity, the computable phenotype was able to detect meaningful results in the characterization of demographics and chronic condition burden for the transgender sample in support of the face validity of the computable phenotype. The population density by geographic region and age 82 distribution by HIV-status of transgender people in this study is largely consistent with general population patterns in British Columbia, as well as the larger COAST cohort.62,204 The overall higher burden of chronic disease for transgender people living with HIV versus without HIV in this study is consistent with elevated chronic disease risk and morbidity among cisgender people living with HIV.210 This higher chronic disease burden is linked to HIV disease processes and related inflammatory immune response.211 While a small but growing number of studies have begun to investigate the chronic disease burden for transgender populations in other industrialized settings,39,80,97,172,190 including using EMR data, findings vary widely due to differences in sampling, study design, setting and measurement. 3.4.5 Limitations Findings from this study should be interpreted in the context of a few key limitations. First, computable phenotypes are by design only applicable to people accessing healthcare services, often motivated by illness and aided by the ability to access care. As such, this study is limited to those transgender people accessing medical transition care in British Columbia and may only represent 24% to 47% of the total transgender population.177 This study was also limited by the inability to validate the transgender computable phenotype among the HIV-negative COAST cohort, as a ‘gold standard’ provider-based transgender measure was only available for the HIV-positive cohort. It is possible that the transgender computable phenotypes would perform differently in populations living without HIV, particularly as healthcare contact is higher among populations living with HIV. Additionally, this study should be considered in light of the context in which it was conducted, an environment in which transgender healthcare delivery in British Columbia is currently shifting from specialized care settings to the main primary care fee-for- 83 service settings. Given that COAST only includes fee-for-service data, this study was not able to capture transgender people who access care outside the fee-for-service system. However, fortunately, as the shift to the fee-for-service system occurs, transgender ascertainment via computable phenotypes in British Columbia will likely improve. The administrative data used in this study may also be susceptible to coding error (and coding biases/practices) across conditions and settings,212 potentially introducing misclassification bias in terms of transgender ascertainment. Furthermore, this study is limited by the inability to capture non-binary identity, for both transgender and cisgender people. Little is known about the health of non-binary people as a result of very few data sources that ascertain binary/non-binary identity and almost all research based on observational studies. As a result of the limited data available, the evidence is mixed and inconclusive with regards to the health outcomes for non-binary compared with binary transgender people.184 Improved ascertainment of both gender identity and gender, including non-binary identity, is needed in medical records and other administrative data sources, to enable investigation of potential health disparities for non-binary people. In the interim, administrative data sources are limited for research questions about the health of non-binary populations. Finally, chronic condition prevalence data reported in this study should be interpreted with caution, given potential selection bias by serostatus in the COAST cohort, though any such bias likely resulted in conservative estimates of chronic disease difference by serostatus in this analysis. 84 3.5 Conclusion This study makes a number of important contributions to the literature on innovative methods in transgender health. Major contributions include development and validation of a transgender computable phenotype, using a population-based representative source population, in the Canadian context. Another strength is the approximately complete ascertainment of the population of transgender people living with HIV in British Columbia, and capacity to estimate transgender status prevalence among people living with HIV. In a current funding environment of limited support for longitudinal transgender health studies in the US and none to date in Canada, this study and the methods employed offer an efficient, replicable and cost-effective way forward in creating electronic cohorts for advancing transgender health research.189 Moreover, the recent rollback of sexual orientation and gender identity data collection and legal changes in insurance coverage of transgender healthcare limit the use of claims coding for gender-affirming care in the US.200 These changes highlight the utility of transgender CP studies from other jurisdictions, particularly those with transgender-inclusive universal healthcare systems such as Canada. Future research should build upon the methods developed in this study and explore complimentary approaches for gender identity ascertainment in administrative and EMR data, such as machine learning approaches, as have been used to develop algorithms based on healthcare utilization data in other research areas. Finally, the current study lays the foundation for future work with the ability to study transgender health and healthcare use patterns over time, with linkage to laboratory data, as well as inclusion of appropriate comparison groups.186,189 85 Chapter 4: Chronic disease multimorbidity among transgender people living with HIV in North America 4.1 Introduction Multimorbidity, defined as the co-occurrence of two or more chronic conditions,12 is an area of emerging importance in HIV research. While the advent of combination antiretroviral therapy (ART) has improved prognosis and survival for people living with HIV, as well as reduced HIV-related morbidity and mortality, the prevalence of non-HIV related multimorbidity (e.g. non-AIDS related cancers, cardiovascular disease) has risen, particularly age-related chronic diseases.8,10 An increase in chronic diseases for people living with HIV may be related to adverse effects of some ART regimens, HIV disease processes-related inflammation,213 as well as elevated engagement in chronic disease risk factors (e.g. smoking). 214 The impact of multimorbidity includes increased disability, polypharmacy risks, and healthcare systems costs; and decreased quality of life.215,216 Compared to cisgender adults, transgender women face 49 times the odds of HIV infection globally, and 34 times the odds in the United States (US).23 HIV prevalence estimates among transgender women range from 2% to 30% by self-report, including 3% in Canada, and up to 40% by laboratory confirmed testing in the US.24 The burden appears lower among transgender men, primarily affecting those who have sex with men, with HIV prevalence ranging from 0% to 10% by laboratory confirmed testing in the US.24 Limited evidence suggests poorer HIV clinical 86 outcomes for transgender people living with HIV including challenges in retention in care,217 ART adherence,34 and treatment interruptions.61 While other studies have described multi-morbid chronic disease among people living with HIV, scarce research has investigated this area among transgender populations living with or without HIV. The evidence available has primarily been conducted in the US and Europe, with clinical and administrative study samples, and has focused largely on cardiovascular disease. While mixed, the evidence suggested an increased risk of cardiovascular disease morbidity and mortality for transgender people, and transgender women in particular.75,93,218 A European clinical cohort study found elevated type 1 diabetes mellitus prevalence among transgender compared to cisgender patients.91 Transgender women have also been found to have elevated cancer incidence, specifically endocrine, viral-induced, lymphatic and hematopoietic cancers; while transgender men have elevated incidence of breast and viral-induced cancers.165 There is also evidence of multimorbidity beyond that directly associated with HIV for transgender women, and that this excess morbidity may be shaped by age, racialization, structural stigma, and other multi-level factors.80 For transgender people, exogenous sex hormone use (e.g. estrogen, testosterone) may also drive chronic disease risk. While an important part of gender affirming care for many transgender people, and a protective factor for HIV care engagement, ART adherence and viral suppression,170 exogenous sex hormone use has been linked to inflammation related chronic disease such as cardiovascular disease and cancer.75 There is a dearth of literature examining multimorbidity for transgender people, and more specifically transgender people living with HIV. Of the research in this area, few studies have 87 been designed for longitudinal follow-up, ascertainment of both transgender men and transgender women, and comparisons with cisgender counterparts. The current study sought to address gaps in the literature by examining chronic disease multimorbidity for transgender people living with HIV in the United States and Canada, using data from a large and longitudinal cohort collaborative representative of the HIV care-engaged population. 4.2 Methods 4.2.1 Data Source This study used data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), the North American regional cohort collaborative of the International epidemiology Databases to Evaluate AIDS (IeDEA). The NA-ACCORD is a National Institutes of Health (NIH) funded longitudinal cohort collaborative designed for the investigation of HIV clinical care questions requiring large sample sizes. The NA-ACCORD is the largest HIV on-going cohort of people living with HIV in the US and Canada, comprising more than 20 single- and multisite clinical and interval cohorts with a total cohort of over 180,000 adults (18 years of age and older). Cohort sites submit data on participants to the Data Management Core (University of Washington, Seattle, Washington) for data harmonization and quality control before transmission to the Epidemiology/Biostatistics Core (Johns Hopkins University, Baltimore, Maryland) for further quality control and analytic file creation. The NA-ACCORD is designed to be representative of people with HIV infection and their care demographics across North America; further details have been published elsewhere.64 88 For this study, the sample included 12 of the 15 cohorts of the NA-ACCORD able to ascertain transgender status,217 or the North American Transgender Cohort Collaboration (NA-TRACC). In addition to reporting transgender status (i.e. transgender, cisgender), cohorts were included in this study if they reported data elements for the chronic conditions of interest. All data covered the study period from 1 January 2000 to 31 December 2016. Study entry was defined as the latest of: cohort site enrolment, beginning of cohort observation window for chronic conditions of interest or 1 January 2000. Study exit was defined as the earliest of: death, loss to follow-up, end of cohort observation window for chronic conditions of interest or 31 December 2016.219 4.2.2 Measures 4.2.2.1 Gender Identity Across NA-TRACC cohorts, transgender status was ascertained using diverse approaches including capture of transgender-specific diagnosis codes, comparison of sex assigned at birth with history of exogenous sex hormone prescriptions, gender identity self-report, and medical provider documentation in the clinical record.67 These multiple ascertainment methods were harmonized by the Epidemiology/Biostatistics Core, and gender identity (transgender women, transgender men, cisgender woman, cisgender man) was further categorized based on a combination of transgender status and sex (male, female). 4.2.2.2 Outcome Measures: Chronic Disease Multimorbidity Multimorbidity was defined as the co-occurrence of HIV with at least one of six chronic conditions (chronic kidney disease, liver fibrosis, Type 2 diabetes mellitus, hypertension, hyperlipidemia, anxiety/depression). Onset of chronic conditions was time-varying during the 89 study period but once present was considered irreversible during follow-up. For incidence analyses, the outcome was defined as first occurrence of at least one of the chronic conditions of interest, after excluding cases in the year of study entry. For prevalence analyses, all prevalent multimorbidity cases for the study period were included. NA-ACCORD standardized chronic condition definitions were used.11,220 Chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR) consistently ≤89 mL/minute/1.73m2 for at least three months, calculated by the Chronic Kidney Disease Epidemiology equation (CKD-EPI),221 using sex assigned at birth. Liver fibrosis was defined as a FIB-4 score of ≥1.46, calculated based on age, platelet count, AST and ALT.222 Type 2 diabetes mellitus was defined as either a glycosylated hemoglobin (HgA1c) level >6.5%; use of diabetes-specific medication; or Type 2 diabetes diagnosis and diabetes-related medication. Hypertension was defined as use of anti-hypertension medication and a hypertension diagnosis. Hyperlipidemia was defined as total cholesterol measurement ≥240 mg/dL, excluding total cholesterol measurements after first statin prescription for lipid-lowering medication users. Anxiety/depression was defined as a diagnosis of anxiety or depression, without a diagnosis of bipolar disorder. 4.2.2.3 Covariates Time-varying age was determined from year of birth. Race/ethnicity was classified at enrollment as White, Black, Hispanic, other, or unknown. HIV transmission risk group was categorized as sexual risk, injection drug use, other (e.g., perinatal), or unknown. Viral suppression was defined as an HIV RNA measurement ≤200 copies/mL at the last measurement in a calendar year. ART initiation era was categorized as pre-1996, 1996 to 1999, 2000 to 2005 or 2006 to 2016. ART prescription was classified according to US guidelines as ≥3 antiretroviral agents from ≥2 90 classes, or a triple nucleoside/nucleotide reverse transcriptase inhibitor (NRTI) regimen containing abacavir or tenofovir.223 The prescription regimen for the majority of a calendar year was categorized as protease inhibitor (PI) based (dual PI/non–nucleoside/nucleotide reverse-transcriptase inhibitor [NNRTI], PI-based, or PI-boosted regimens) or non–PI based (NNRTI-based; ≥3 NRTIs; or entry, fusion, or integrase inhibitor-based). Body mass index (BMI; kg / [height (m)]2) of ≥30 was categorized as obese, following CDC guidelines.224 For the subset of cohorts (n=10) that contributed smoking data, smoking status was time-fixed and defined as ever or never smoking tobacco at time of entry into the cohort. 4.2.3 Statistical Analyses Descriptive statistics characterized demographic and clinical characteristics of the baseline study sample. Generalized linear models examined the relationship between gender identity and multimorbidity, with generalized estimating equations to account for repeated measures. Poisson regression with robust error variance and an assumed exchangeable working correlation structure was used to produce crude and adjusted prevalence ratios (PR) with 95% confidence intervals, and incidence rate ratios (IRR) with 95% confidence intervals. Multivariable models were adjusted based on a 10% change-in-estimate strategy225, and inclusion of age, race and cohort based on a priori understanding of these as important confounders of the relationship between gender identity and chronic disease multimorbidity. Sensitivity analyses were conducted adjusting full models for the sub-cohorts with smoking status, given the known impact of smoking on elevated chronic disease morbidity among people living with HIV.226 Multimorbidity incidence rates (IR) per 1,000 person-years (PY) were calculated using time at 91 risk as the denominator, and cumulative incidence curves were produced by gender identity. All analyses were conducted using SAS statistical software (version 9.4, 2014). 4.3 Results As described in Table 4.1, the study sample included 301 transgender women, 26 transgender men, 11,504 cisgender women and 51,029 cisgender men. Transgender women and transgender men were younger (median, 36 years; interquartile range [IQR], 29, 43; 35 years; IQR, 29, 48; respectively) than cisgender women and cisgender men at baseline (38 years; IQR, 31-46; 39 years; IQR, 32, 47). More transgender women (41.20%) and transgender men (46.15%) were Black compared to cisgender men (29.99%), though cisgender women had the highest proportion of Black individuals (62.28%). Transgender women had the highest proportion of ever smokers (73.42%), and transgender men the lowest (60.00%). Cisgender women had the highest prevalence of obesity, followed by transgender women (38.91%, 21.03%; respectively). Transgender men had the highest prevalence of liver fibrosis, CKD and multimorbidity (4.35%, 34.62%, 65.38% respectively); transgender women the highest prevalence of anxiety/depression (33.89%); and cisgender women the highest prevalence of diabetes, hypertension and hyperlipidemia (7.22%, 21.16%, 11.07%; respectively). 92 Table 4.1 Baseline NA-ACCORD study sample characteristics, by gender identity (n=62,860) Gender Identity Overall (n=62860) Transgender women (n=301) Transgender men (n=26) Cisgender women (n=11504) Cisgender men (n=51029) Age (median, IQR) 39 (32-47) 36 (29-43) 35 (29-48) 38 (31-46) 39 (32-47) Race White 47.03 31.89 38.46 25.33 52.01 Black 35.96 41.20 46.15 62.28 29.99 Hispanic 7.15 13.29 3.85 4.95 7.61 Other 5.49 12.96 11.54 4.48 5.67 Unknown 4.38 0.66 0.00 2.96 4.72 HIV risk group Sexual 80.99 75.75 61.54 73.04 82.82 IDU 10.47 11.63 19.23 14.08 9.65 Other 3.08 0.66 7.69 6.95 2.22 Unknown 5.46 11.96 11.54 5.93 5.31 Viral suppression status Suppressed 53.21 53.47 50.00 48.81 54.20 Unsuppressed 46.79 46.53 50.00 51.19 45.80 ART initiation era pre-1996 10.30 8.87 8.33 8.12 10.80 1996-1999 20.44 12.41 4.17 20.43 20.50 2000-2016 23.64 26.24 29.17 26.41 23.00 2006-2016 45.62 52.48 58.33 45.05 45.70 ART regimen (of those on ART) PI/combo PI 51.07 47.33 60.87 55.09 50.20 Non PI 44.11 50.21 39.13 39.17 45.16 93 Non-HAART 3.63 0.00 0.00 5.06 3.34 Unknown 1.19 2.47 0.00 0.68 1.30 Ever Smoker (yes vs no) 70.42 73.42 60.00 70.87 70.31 Obesity (yes vs no) 20.77 21.03 13.04 38.91 16.62 Liver fibrosis 0.79 1.09 4.35 0.91 0.76 Kidney disease 19.78 13.04 34.62 18.13 20.19 Diabetes 5.10 4.32 3.85 7.22 4.62 Hypertension 16.23 17.28 19.23 21.16 15.11 Hyperlipidemia 10.48 4.98 7.69 11.07 10.38 Anxiety/Depression 27.25 33.89 23.08 29.08 26.80 Multimorbidity 52.71 53.49 65.38 55.18 52.14 * Of those with non-missing data 94 Overall, the prevalence of multimorbidity increased consistently over time for cisgender women and cisgender men, with a lower prevalence for cisgender men compared to cisgender women (Figure 4.1). There was greater variability in the prevalence of multimorbidity over the study period for transgender people, particularly for transgender men with a relatively small sample size. However, the prevalence of multimorbidity for transgender groups surpassed that of cisgender groups in 2010-2011, with highest prevalence for transgender men by the end of the study period (81.82%) followed by transgender women (74.90%). 95 Figure 4.1 Multimorbidity prevalence over NA-ACCORD study period, 2000-2016, by gender identity 96 As shown in Table 4.2, in unadjusted models, transgender men had the highest multimorbidity prevalence compared to cisgender men, though without evidence of a statistically significant difference. In models adjusted for age, race and cohort, transgender women (aPR = 1.14 [1.07, 1.23]), transgender men (aPR = 1.23 [1.00, 1.51]) and cisgender women (aPR = 1.07 [1.06, 1.09]) had a higher prevalence of multimorbidity, although the confidence interval included ‘1’ for transgender men indicating no evidence of a statistically significant difference in the prevalence of multimorbidity from cisgender men. In the sub-analysis also adjusted for smoking status, the prevalence was attenuated for transgender men (aPR = 1.22 [1.04, 1.44]) and for transgender women (aPR = 1.09 [1.01, 1.17]) but the prevalence remained elevated compared to cisgender men and the confidence intervals excluded 1. 97 Table 4.2 Multimorbidity prevalence over NA-ACCORD study period 2000-2016, by gender identity Unadjusted Adjusted* Sub-analysis† PR 95% CI aPR 95% CI aPR 95% CI Gender identity Cisgender men Ref Ref Ref Cisgender women 1.04 1.02 1.05 1.07 1.06 1.09 1.08 1.06 1.09 Transgender women 1.01 0.94 1.09 1.14 1.07 1.23 1.09 1.01 1.17 Transgender men 1.16 0.95 1.42 1.23 1.00 1.51 1.22 1.04 1.44 *Adjusted for age, race and cohort †Full model also adjusted for smoking, in sub-sample with reported smoking status 98 As shown in Table 4.3 for the analyses of incidence, cisgender men had the most person-years of follow-up (n = 165,063), followed by cisgender women (n = 36,251), and the smallest for transgender groups (transgender women: n = 804; transgender men: n = 52). Transgender women had the highest multimorbidity incidence rate (IR = 77.11 per 1,000 person-years), followed by transgender men (IR = 76.92), with similar rates for cisgender women and cisgender men (IR = 57.93; IR = 59.27; respectively). 99 Table 4.3 Multimorbidity incidence over NA-ACCORD study period 2000-2016, by gender identity Unadjusted Adjusted* Sub-analysis† Incident cases Person-years Incidence rate, per 1000 PY IRR 95% CI aIRR 95% CI aIRR 95% CI Gender Identity Cisgender men 9783 165063 59.27 Ref Ref Ref Cisgender women 2100 36251 57.93 0.97 0.93 1.02 1.03 0.97 1.09 1.09 1.02 1.16 Transgender women 62 804 77.11 1.20 0.95 1.51 0.91 0.70 1.17 0.97 0.74 1.27 Transgender men 4 52 76.92 1.24 0.46 3.33 0.92 0.28 2.98 1.29 0.40 4.22 *Adjusted for age, race, viral suppression, ART era, ART regimen, obesity and cohort †Full model also adjusted for smoking, in sub-sample with reported smoking status 100 In unadjusted multimorbidity incidence models, transgender women (IRR = 1.20 [0.95, 1.51]) and transgender men (IRR = 1.24 [0.46, 3.33]) had a higher incidence of multimorbidity compared to cisgender men, although there was no evidence of a statistically significant difference with confidence intervals that included ‘1’ for both estimates. In multivariable models adjusted for age, race, viral suppression, ART era, ART regimen, obesity and cohort, these effects were attenuated with lower incidence of multimorbidity but confidence intervals that still included ‘1’ (transgender women: aIRR = 0.91 [0.70, 1.17]; transgender men: aIRR = 0.92 [0.28, 2.98]. In the sub-analysis adjusted for smoking status, transgender men had a higher multimorbidity incidence (aIRR = 1.29 [0.40, 4.22]), followed by cisgender women (aIRR = 1.09, [1.02, 1.16]), compared to cisgender men; while transgender women had a lower incidence (aIRR = 0.97 [0.74, 1.27]). There was evidence of a statistically significant difference for cisgender women compared to cisgender men with a confidence interval that excluded ‘1’. Cumulative incidence curves (Figure 4.2) show elevated multimorbidity incidence for transgender women and transgender men over the study period, compared to cisgender women and cisgender men, though with no evidence of a statistically significant difference between groups (p value = 0.32). The curve for transgender men is shortest, as no transgender men in the study had more than seven years of follow up. 101 Figure 4.2 Cumulative incidence curves for multimorbidity over NA-ACCORD study period 2000-2016, by gender identity 102 4.4 Discussion This study provides one of the first analyses of multimorbidity for transgender people living with HIV, and that utilizes longitudinal data and analytic methods. Importantly, this study utilized cohort collaborative data designed to be representative of the population engaged in HIV clinical care in North America,64 and that ascertained transgender status with the inclusion of both transgender women and transgender men, as well as cisgender comparison groups. In this study of transgender people in the NA-ACCORD cohort, the gender distribution (8% transgender men, 92% transgender women) is similar to that reported by the Centers for Disease Control Medical Monitoring Project (CDC MPP) in 2015 (10%, 90% respectively),227 supporting NA-ACCORD as broadly representative of the population engaged in HIV clinical care in North America in terms of gender identity. However, 0.5% of the study sample was transgender which is less than the 1% to 2% transgender proportion of people living with HIV in the US reported by the CDC MPP between 2005 to 2016,209 suggesting transgender status is under-ascertained in the NA-ACCORD. Transgender women and transgender men in this study were younger than cisgender women and cisgender men, predominately Black and had acquired HIV primarily through sexual risk. This is consistent with findings previously published from NA-TRACC217 and adds to growing evidence that racialized transgender people, particularly young Black sexually active transgender women, are at the intersection of HIV vulnerability.228,229 A limited number of studies have reported chronic disease and multimorbidity prevalence statistics for transgender people living with and without HIV while addressing broader research questions. However, to my knowledge, no other studies have investigated the relationship 103 between gender identity and multimorbidity for people living with HIV or compared prevalence and incidence estimates. This study found elevated multimorbidity prevalence and incidence for transgender versus cisgender people living with HIV, though not consistently over time and without evidence of a statistically significant difference by gender identity. This is in line with a small number of other studies, including an analysis of 2,221 transgender and 523,080 cisgender respondents in the population-based US Behavioral Risk Factor Surveillance System (2014-2016) that found higher prevalence of multiple chronic conditions (≥2 conditions) for both transgender women (48.1%) and transgender men (46.7%) compared to cisgender women (45.8%) and cisgender men (38.3%).77 Similarly, a recently published large linked administrative data study of 2,085 transgender and 10,425 cisgender patients in Ontario, Canada found nearly double the prevalence of multiple conditions for transgender participants (6.6% versus 3.8% for cisgender), with corresponding elevated healthcare services use.230 An administrative claims study of transgender and cisgender Medicare beneficiaries in the US also found elevated prevalence of multiple chronic conditions for transgender patients (n = 7,454), and consistently for older-age patients as well as those on disability benefits.38 This emerging data suggests a chronic disease multimorbidity disparity for transgender women and transgender men, with implications for treatment, health systems demand, healthcare costs, disability and quality of life, as well as potentially early mortality. The impact of the adjustment for smoking tobacco status in the sub-analysis also suggests the need for specific prevention efforts to reduce intervenable chronic disease risk factors such as smoking in the transgender population.80 It is worth noting the heightened chronic disease multimorbidity for transgender men found in this study, compared to cisgender men, cisgender women and transgender women. Although, 104 there was variability around the estimate likely in part due to a relatively small sample size. Interestingly, the attention to transgender people in HIV research has focused on transgender women 24. Very little is known about transgender men living with HIV, though HIV risk for this group appears concentrated among those who have sex with cisgender men.24,26,231 The characteristics of transgender men living with HIV appear similar to those of transgender women living with HIV, including more likely to be racialized, sexually active, and living in poverty.232,233 Transgender men living with HIV report unmet care needs, and poor HIV clinical outcomes including sub-optimal viral suppression.233 A study using national population-based survey data in the US found that transgender men were more likely to report health risk behaviours (smoking, no exercise) and healthcare access barriers (cost-related healthcare avoidance, lack of primary care provider) compared with transgender women, though transgender women were more likely to report other health risk behaviours (heavy drinking, obesity).77 Transgender men are often ignored or excluded in HIV prevention interventions26,234,235 Of the limited available research, one study using national surveillance data in the US reported that transgender men living with HIV were much more likely than transgender women in the same study to report no known HIV transmission risk (56.8%, 4.9%, respectively).232 Transgender men, particularly those who have sex with cisgender men, may not realize they are engaging in HIV risk behaviours. It is possible that in addition to being at a unique intersection of HIV and other chronic disease vulnerability along with transgender women, transgender men living with HIV are at heightened risk of chronic disease multimorbidity, as found in the current study, driven in part at least by their invisibility in HIV prevention and response. The current finding of increased 105 multimorbidity for transgender men, coupled with syndemic and other multi-level factors that shape HIV sexual risk for other men who have sex with men, may be a result, in part, of barriers to engagement and retention in care and poorer HIV and other health outcomes for transgender men.26,163 However, it may also be that the elevated chronic disease multimorbidity prevalence for transgender men compared with transgender women found in this study is an artifact of the relatively small sample size for transgender men. Future research is needed to investigate potential unique chronic disease and multimorbidity risk for transgender men living with HIV, particularly with larger sample sizes. In the current study, there was also inconsistent evidence of statistical differences in multimorbidity incidence and prevalence for transgender compared to cisgender people living with HIV. This is in part related to small sample size and variability around the point estimates. However, it may be the case that transgender people in the NA-ACCORD are better engaged and retained in care than in other clinical or research settings resulting in no statistically significant difference in multimorbidity compared to cisgender people. In other words, the NA-ACCORD cohorts with the capacity to ascertain transgender status may be providing integrated HIV and gender-affirming care that is associated with improved HIV outcomes, including clinical care indicators.217 Findings from this study should be considered in light of a number of important limitations. The capture of chronic conditions as irreversible in the NA-ACCORD may have led to overestimation of multimorbidity prevalence, particularly over time as those classified as having a chronic condition remain in the numerator over the remainder of the study follow-up. However, 106 data from this study using the same variable definition has been used previously to successfully make inferences regarding multimorbidity burden for people living with HIV receiving clinical care in the US.11 Furthermore, inclusion of a conservative six chronic conditions in the multimorbidity definition in this study may have underestimated multimorbidity burden in light of heterogeneity of definitions of multimorbidity in the literature.12 Calculation of CKD based on sex assigned on birth for transgender individuals is another potential limitation, as CKD algorithms have not been validated for transgender people. Nevertheless, the findings highlight multimorbidities for people living with HIV for the most common chronic conditions in the population. The findings provide evidence of the relationship between gender identity and complex health status over time, with implications for clinical care, polypharmacy, and healthcare systems burden. However, further research is warranted with studies designed to ascertain other and reversible chronic conditions over time by transgender status, and larger samples for transgender men in particular. Other major limitations of the study relate to transgender status ascertainment and measurement. As described, the NA-ACCORD cohorts ascertained transgender status using a diversity of methods, generally collected at cohort enrollment.67 As such, participants could have been misclassified for a number of reasons including non-disclosure of transgender identity and/or sex assigned at birth, if transgender status was not noted or incorrectly assumed in the clinical record, or if transgender status changed over the course of study follow-up, after collection by the cohort site. However, any such misclassification likely resulted in underestimation of transgender sample sizes in this study and was likely non-differential by multimorbidity status. Nevertheless, the small sample sizes in this study may have limited representativeness of results 107 as well as the ability to detect statistically significant differences by gender identity, particularly when adjusted for a number of covariates. Future research designed to recruit or ascertain larger sample sizes of transgender women and transgender men is needed to enable more precise and complex statistical analyses. This study also had limited data on structural characteristics of participants (e.g. poverty and discrimination indicators), transgender specific clinical care (e.g. exogenous sex hormone prescribing); as well as gender transition timing, duration and related social, surgical and medical factors. Access to gender affirming care is protective for the health of transgender populations, particularly when provided in the context of HIV care, while experiences of multi-level stress and discrimination are associated with health disparities.61,229 In sum, the current study provides evidence of disparity in chronic disease multimorbidity for transgender populations, but further research is needed to adjust for the aforementioned factors in the context of a complex health status for transgender populations. 108 Chapter 5: Discussion 5.1 Summary of research findings The purpose of this dissertation research was to conduct a series of studies on the epidemiology of chronic disease and multimorbidity among transgender people living with and without HIV. While details of each study are presented in depth in the previous chapters, a summary of the overall research findings is provided here. Chapter 2 presents the results of a systematic review that sought to characterize the epidemiological literature on chronic disease among global transgender populations. A systematic search was conducted of Medline, Embase, CINAHL, PsycINFO and LGBT Life bibliographic databases for peer-reviewed scientific studies with non-HIV chronic disease prevalence data for transgender populations published up to February 15, 2019 without restriction on country or study design. A total of 93 studies and 665 datapoints were included in the review, comprising 48 distinct chronic disease outcomes in seven groups (cancer, cerebro/cardiovascular conditions, chronic liver and kidney disease, mental health and substance use conditions, metabolic and endocrine disorders, musculoskeletal and brain disorders, respiratory conditions, and unspecified and other conditions). The global transgender chronic disease literature focused on mental health morbidity, demonstrating an evidence gap on chronic physical health morbidity, particularly beyond that of sexual health. The review identified important gaps including in age-related conditions, inflammation-related disease and studies designed explicitly to investigate chronic disease burden among transgender populations. Further, this review found a need for high quality evidence in the area of chronic disease for 109 transgender people, including longitudinal population-based studies with appropriate comparison groups, and consistent measurement of both transgender status and chronic conditions. In light of a number of methodological gaps in transgender health research, including those identified by the systematic review presented in Chapter 2, Chapter 3 describes a transgender computable phenotype feasibility study. Specifically, the study in Chapter 3 assessed development of a computable phenotype for identifying transgender samples in Canadian population-based administrative health records data. Healthcare records were used to identify transgender people via a case ascertainment algorithm (diagnosis codes and androgen blocker/hormone prescriptions), to examine related diagnoses and prescription concordance, and to validate the computable phenotype using an independent provider-report transgender status measure. The best-performing computable phenotype identified 137 HIV-negative and 51 HIV-positive transgender people (total n = 188). In validity analyses, the best-performing computable phenotype had low sensitivity (27.5%, 95% CI: 17.8-39.8), high specificity (99.8%, 95% CI: 99.6-99.8), low agreement using Kappa statistics (0.3, 95% CI: 0.2-0.5), and moderate positive predictive value (43.2%, 95% CI: 28.7-58.9). There was high concordance between exogenous-sex hormone use and transgender-specific diagnoses. Demographics and chronic disease burden were characterized for the transgender sample identified using the algorithm. Importantly, this study also provided an estimate of the proportion of transgender people within the British Columbia population of people living with HIV (0.88%, range 0.73-1.1%), based on provincial surveillance data.208 The epidemiologic study presented in Chapter 4 investigated multimorbidity burden, specifically prevalence and incidence, for transgender people living with HIV in the US and Canada. This 110 study used data from a large longitudinal cohort collaborative representative of the HIV care-engaged population with cisgender comparators, the NA-ACCORD, over the study period January 2000 through December 2016. The study sample included 301 transgender women, 26 transgender men, 11,504 cisgender women and 51,029 cisgender men. Findings included an elevated prevalence and incidence of chronic disease multimorbidity among transgender versus cisgender people living with HIV, though inconsistently over time and without evidence of statistically significant differences by gender identity. 5.2 Research strengths and contributions This dissertation research makes contributions in three priority areas of transgender health and chronic disease research by contributing a review of the state of knowledge of chronic disease among transgender populations, methodological innovation, and epidemiological investigation of chronic disease burden. Specifically, this PhD research makes use of novel data, including a global systematic review, a large linked population-based administrative dataset, and the largest HIV cohort collaborative in North America. This dissertation research also makes use of innovative methods including evidence mapping and synthesis, computable phenotype development and validation, and epidemiological investigation of chronic disease for transgender people with appropriate cisgender comparison groups and classification by gender. In particular, this research represents an opportunity to apply emerging methods to research with small sample sizes in administrative data in Canada. The dissertation research also addressed a recurrent issue in transgender health research13 by using data from large non-transgender specific studies to maximize sample size and potential generalizability of findings. Furthermore, unlike much of the existing literature which is limited by the ability to investigate only mental or physical chronic 111 conditions,11 this research examines both physical and mental chronic health conditions for transgender people. The methodological gaps identified in the review in Chapter 2 point to future directions in transgender health research, indicating areas for focus in the development of high-quality rigorous research. This dissertation work is the first to map the evidence on chronic disease for transgender populations worldwide. This work provides researchers in transgender health and chronic disease with an improved understanding of the current state of knowledge and identified gaps for future research. This review joins a number of key reviews assessing the global epidemiology of HIV and other important health questions in transgender health,6,23,24 setting a baseline from which to measure growth in chronic disease research and its inclusion in broader transgender health research over time. This work informs research agenda setting in methods and measurement needed for higher quality epidemiological data on chronic disease for this key population, including within the context of HIV prevention and intervention. The computable phenotype study in Chapter 3 makes a number of important contributions. First and foremost, this work demonstrates the feasibility of identifying transgender samples for health research using computable phenotype methods in population-based and representative Canadian administrative healthcare data. This study demonstrated that transgender computable phenotype development is not only feasible, but can produce good face validity, concordance with expected transgender healthcare utilization patterns, and high specificity. While there is still room for improvement with regards to sensitivity, there is evidence to suggest this will improve over time with administrative health data. The first study of its kind in Canada, development of a validated 112 transgender computable phenotype algorithm lays the foundation for future investigation of transgender-specific research questions related to general and HIV-specific healthcare use and health outcomes for this key population. Use of such cost-effective secondary analysis methods capable of identifying transgender samples is an important opportunity given the persistent challenges of small sample size and underpowered analyses in transgender health research.13 Another contribution of this study is ascertainment of the population of transgender people living with HIV in the Canadian province of British Columbia, in a universal healthcare setting. Furthermore, an important contribution is also capacity to estimate the prevalence of transgender status among the population living with HIV in the province. The latter is an important achievement as transgender status is not currently captured by routine health surveillance systems in British Columbia or nationally in Canada. Any comparable estimates of the proportion of transgender people among people living with HIV are generally limited to community-based and clinical studies that may overestimate transgender people among the population living with HIV. The development of a validated computable phenotype opens up new opportunities for identifying transgender people for inclusion in population-based health research using administrative health data and offers the potential for much-needed and heretofore unavailable evidence on health status, including HIV status, and the healthcare use and needs of transgender people. Nested within a large linked British Columbia cohort and the largest HIV cohort collaborative in North America, the study of transgender people living with HIV in Chapter 4 identified potential chronic disease disparity, providing important clinical multimorbidity outcome data for this understudied and underserved population. Findings extend limited existing literature on excess 113 chronic disease and multimorbidity burden for transgender people in the US and Canada.80,230 Findings from the NA-ACCORD study in Chapter 4 inform evidence-based HIV and transgender-specific care for transgender people living with HIV. Scarce previous literature has focused on epidemiological questions of chronic disease for transgender people living with HIV, particularly with the ability to look at change over time. This is one of very few studies investigating chronic disease for transgender populations, and particularly to do so using longitudinal data. Much of the previous research in chronic disease has investigated specific outcomes, particularly cardiovascular disease for transgender people living with HIV or otherwise accessing clinical care.75 The existing literature on chronic mental and physical health more broadly for transgender people is challenged by reliance on clinical samples, limiting generalizability of findings. The literature is also challenged by predominance of cross-sectional data, limiting the ability to understand change over time and the temporality of primary relationships of interest. Additionally, overall findings from this PhD research are consistent with gender minority stress theory46,52 which framed the work. In particular, this dissertation research provides evidence of health disparity for transgender compared to cisgender people and the links between multilevel stressors and chronic disease for transgender people, and points to the potential importance of gender-affirming care in mediating chronic mental and physical health. In terms of transgender health disparity, the epidemiological study in Chapter 4 found evidence of chronic disease multimorbidity disparity for transgender compared to cisgender people living with HIV in the US and Canada. The study presented in Chapter 3 also found an over-representation of transgender people among those living with HIV in British Columbia. 114 Results of this PhD research also point to the role of multilevel drivers of health disparity for transgender people, as posited by gender minority stress theory. The systematic review presented in Chapter 2 found that nearly half of included studies reported significant correlates of chronic disease for transgender people. Consistent with gender minority stress theory, multilevel factors shape chronic disease burden for transgender people included individual (e.g. sociodemographics such as sexual orientation, gender, age, racialization and socioeconomic status), interpersonal (e.g. psychosocial factors such as exposure to violence), and structural (e.g. access to gender affirming healthcare) drivers. In the Chapter 4 study, transgender women and men were younger than cisgender women and men, predominately Black and had acquired HIV primarily through sexual risk. This is consistent with findings previously published from NA-TRACC,217 and adds to growing evidence that racialized transgender people, particularly young Black sexually active transgender women are at the intersection of unique HIV vulnerability.228,229 Findings from this dissertation research are also consistent with the importance of gender affirming care as a mediator of health disparity for transgender people, as posited by gender minority stress theory. As described above, a number of studies included in the systematic review in Chapter 2 reported significant correlates of chronic disease for transgender individuals, including transgender specific interpersonal and structural factors such as access to a gender-affirming care provider, and access to medical and surgical transition care. Relatedly, inconsistent evidence of statistically significant differences in multimorbidity incidence and prevalence for transgender compared to cisgender people living with HIV in the Chapter 4 study may be a result of better engagement and retention in care at cohort sites than in other clinical or 115 research settings. It may be the case that NA-ACCORD cohort sites with ability to ascertain transgender status may be providing integrated HIV and gender-affirming care, which is linked to improved HIV care and other health outcomes.217 While this PhD research was not designed to test underlying mechanisms as part of minority stress theory, the finding across all three studies are consistent with, and valid in the context of, gender minority stress theory. 5.3 Dissertation research limitations and considerations While limitations of each study are detailed in the previous chapters, overall methodological limitations of this dissertation research and considerations are summarized here, in particular those related to transgender ascertainment across studies and chronic disease measurement. A potential challenge to the internal validity of this research is instrumentation-related misclassification bias for transgender status measurement in the datasets used in Chapters 3 and 4. It is likely that this misclassification would be differential (i.e. transgender people would be more likely to be misclassified as cisgender than cisgender people as transgender), given that gender identity classification is based on medical provider assessment and (in)capacity of electronic/medical system to capture gender identity or transgender status. While the Chapter 4 study uses data from large administrative and multi-site HIV cohorts in British Columbia and North America-wide, results may not be generalizable to the general population as the study sample is relatively small compared to the national populations of Canada and the US. None of the studies included in this dissertation research consistently used best practice two step measures for ascertaining transgender status.17,18 While some of the studies included in the review in Chapter 2 did use a two-step measure, eligibility for study inclusion in the systematic 116 review included studies reporting chronic disease prevalence data for transgender samples regardless of transgender status measure. Similarly, there was a heterogeneity of transgender status measures across the NA-ACCORD cohort collaborative sites.67 Because the NA-ACCORD comprises a mix of clinical and interval cohorts, and the COAST study in Chapter 3 utilises a transgender-specific diagnosis based case ascertainment algorithm, likely only those transgender people accessing gender-affirming care or surgery or those who have disclosed trans status to their HIV care provider were captured in these two studies. As a result, findings may not be generalized to other sub-sets of transgender people of the transgender population as a whole. Thus, findings from this dissertation research may represent conservative estimates of multimorbidity burden among transgender people living with and without HIV in British Columbia and North America. A second methodological consideration of this dissertation research relates to chronic disease measurement across the included studies. The systematic review in Chapter 2 included studies employing a diversity of chronic disease measures (e.g. DSM diagnosis, structured clinical interviews, ICD, treatment history, self or provider report) and definitions (e.g. DSM editions, ICD revisions, clinical instruments). Many chronic disease outcomes included in the review were also based on self-report, though self-reported chronic health status has been showed to be reliable in measuring disease.236 Additionally, some chronic disease outcome measures included in the review in Chapter 2 and the analyses in Chapters 3 and 4 were developed for application in other populations and have not been validated for use with transgender samples.180 This limitation is further complicated by measures such as chronic kidney disease in Chapter 4, which are calculated using an algorithm that includes sex. In the case of the Chapter 4 analysis, chronic 117 kidney disease was calculated based on sex assigned at birth for transgender people. Complicating matters further is the use of exogenous sex hormone use among transgender populations as an important part of gender-affirming care.177 The prevalence of chronic kidney disease and other such sex based measures may differ when calculated based on sex assigned at birth compared to gender identity, and may further differ when calculated based exogenous sex hormone use, as has been demonstrated for cardiovascular risk measurement among transgender women living with HIV.218 5.4 Recommendations and future research directions Specific recommendations and directions for future research have been described in detail in the previous chapters of this dissertation, specific to each study included in this research. Here, recommendations are summarized, and overall future research directions are presented. The primary recommendations of this research are in the areas of methods, measurement, and clinical practice. The review presented in Chapter 2 identified a number of important limitations to the existing literature describing chronic disease for transgender populations. Much of the available literature is limited by cross-sectional, within-group analyses, using non-representative sampling. Few include cisgender comparator groups to enable investigation of disparity by gender identity, and even fewer employ robust methods (e.g. matching) to produce more reliable estimates. A majority of the literature describes clinical samples and relies on EMR data. The literature is also largely US based, indicating the need for further research in Canada, as well as other parts of the world. In particular, further research is needed in developing settings in Asia and Africa where 118 there is currently a lack of epidemiological literature on chronic disease among local transgender populations. Together, these gaps point to the need for more and better research on transgender health and improved methodological rigour and quality in the investigation of chronic disease for transgender populations. Specific recommendations include improvements in study design, funding and collection of longitudinal prospective cohort data, and inclusion of appropriate cisgender comparators to enable between group analyses. These recommendations are consistent with other large global reviews of the literature in transgender health, HIV, and chronic disease that have identified key methodological gaps in the evidence.6,75 Findings from this dissertation research also highlight measurement as a major area for future research. For one, further refinement of the transgender computable phenotype developed and validated in Chapter 3 is needed. This will be particularly important as transgender care in British Columbia shifts from specialty care to family practice, likely improving sensitivity of such computable phenotypes and consequently resulting in larger transgender samples. A key next step is to apply the developed computable phenotype to the full Population Data BC dataset, comprising the total provincial population in British Columbia. This would result in approximately ten times the sample size the study in Chapter 3 identified, enabling well-powered analyses in population-based representative administrative data, and ability to answer important health, care, and utilization questions requiring larger sample sizes. In particular, important questions remain unaddressed related to the management of complex chronic disease for transgender people, and also evaluation of the in-progress shift from speciality to primary care in terms of health services delivery and health human resources. 119 Very few previous computable phenotype studies have been able to develop a transgender case finding algorithm and to validate it, though some have attempted to do so using non-independent measures.63,190 In particular, few have used an independent ‘gold standard’ transgender status measure, such as the provider-based measure used in the computable phenotype study in Chapter 3. Findings from this study demonstrate the utility of computable phenotype methods in identifying transgender samples for health research. Development and validation of transgender case ascertainment algorithms in administrative healthcare data in other jurisdictions is important as computable phenotype methods gain popularity and more wide-spread use in transgender health. This is of particular importance in the absence of best practice gender identity measures in clinical systems, routinely collected data and surveillance. Findings from the Chapter 3 study extend emerging literature in the US and contribute to investigation of important social epidemiological questions in large population-based and representative data, key ‘big data’ sources for examination of the health of the small or ‘hidden’ transgender population. More wide-spread implementation of best practice gender identity measures in existing cohorts, such as the NA-ACCORD in Chapter 4 which has captured transgender status since 2016, is also recommended. Another important recommendation is inclusion of the ability to measure both transgender status and gender (man, woman) in transgender health research, and more specifically epidemiological studies of chronic disease burden. Findings from the study presented in Chapter 4 make clear the importance of inclusion of such measures, as differences in multimorbidity burden were found both for transgender compared to cisgender people, and for transgender men compared with transgender women. Gender and gender identity inclusive measures are needed in prospective 120 cohort and other survey research, particularly those with larger sample sizes, powered to investigate differences by gender among transgender samples and investigate underlying mechanisms. Furthermore, beyond the ability to capture gender identity and binary gender, inclusion of non-binary identities is also needed in this area. While datasets used in this dissertation research did not include ascertainment of non-binary identity, a study by Streed et al. using US population-based national data found elevated prevalence of poor health and health-related limitation for non-binary compared with binary transgender adults.184 While Streed et al. found no elevated prevalence or number of chronic conditions (defined as any one of several chronic conditions by self-report) for non-binary adults,184 further research is needed using clinical diagnostic data for chronic disease and ability to investigate specific chronic conditions independently. Findings of this dissertation research also have implications for clinical care. The epidemiological study in Chapter 4 found elevated multimorbidity burden for transgender compared to cisgender people living with HIV and engaged in clinical care in the US and Canada. These findings are consistent with the recent Canadian administrative data linked clinical cross-sectional study that found transgender people had higher rates of chronic disease, specifically asthma, Chronic Obstructive Pulmonary Disease, diabetes, HIV, and poor mental health; and a higher likelihood of multimorbidity in general and total number of comorbidities.230 This elevated chronic disease burden compared to the general population corresponded with greater healthcare utilization as transgender people in the same study were more likely to see a primary care provider, visit an emergency department and be admitted to the hospital.230 In the US, Swartz et al. conducted one of the only previous studies specifically designed explicitly to 121 investigate chronic health conditions among transgender people living with HIV, in this case women of colour, published in 2019.80 This latent class analysis using data from transgender women engaged in primary care in a large urban centre in the US found evidence of age, AIDS diagnosis and poor mental health as drivers of higher multimorbidity burden, and further points to the need for integrated HIV, primary and gender-affirming care for transgender people.80 Findings suggest the importance of further research aimed at understanding complex health status for transgender people, and how this may be further complicated by HIV infection. Future research should seek to elucidate how complex health status intersects with stress, discrimination and stigma and other determinants of health faced by transgender people,6,24,237 and disentangle underlying mechanisms of disparity to inform prevention and intervention efforts. Transgender people are medically underserved, facing numerous barriers to healthcare access and consequent healthcare avoidance and unmet need.238–240 Clinical implications of findings in this dissertation research go hand in hand with growing recommendations for transgender-competency training in medical education curriculum,241 to ensure providers treat transgender patients with respect, dignity and competent care- cognizant of the unique complexities of transgender healthcare, including the potential risk of elevated chronic disease morbidity and multi-morbidity among this population. Implementation of sexual and gender minority health core curriculum in medical education represents a step toward shifting systemic barriers to care and consequent health disparities for the underserved transgender population. 122 5.5 Conclusions Recent interest, funding, and improvement of sex and gender measurement tools have contributed to a growing body of research in transgender health. The work presented in this dissertation brought together a systematic review of the state of knowledge on chronic disease among transgender populations worldwide, development of innovative methods for identifying transgender samples for health research purposes in large administrative data in Canada, and an investigation of chronic disease multimorbidity burden for transgender compared to cisgender people living with HIV in the US and Canada. Collectively, these findings identified 1) a need for better quality methods and measurement in transgender health and chronic disease research, 2) that computable phenotypes are a feasible method for identifying transgender people in healthcare utilization data and a promising opportunity for the investigation of chronic disease and other key health research questions in this underserved population, and 3) a disparity in multimorbidity burden for transgender people living with HIV. This work has implications for methods and measurement in transgender health research, as well as clinical practice and public health policy. Recommendations for higher quality epidemiological research and directions for future research are also offered. Ultimately, the goal of this research is to contribute to ameliorating health inequities and improving health for transgender populations living with and without HIV in British Columbia, North America, and worldwide. 123 References 1. 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Adv Med Educ Pract. 2018;9:377-391. doi:10.2147/AMEP.S147183 144 Appendix A Example search strategy: MEDLINE (Ovid) -------------------------------------------------------------------------------- 1 *Chronic Disease/ or *Mental Disorders/ or *Substance-Related Disorders/ or *Hepatitis B, Chronic/ or *Hepatitis C, Chronic/ or *Obesity/ or *Chronic Pain/ or *Diabetes Mellitus, Type 1/ or *Diabetes Mellitus, Type 2/ (499181) 2 ((chronic* or longterm or long-term or "long term") adj2 (condition* or ill* or disease*)).ab,kw,ti. (224369) 3 (chronic* adj2 (hepat* or liver* or kidney* or obes* or mental* or anxiet* or depress* or substance* or drug* or alcohol* or respiratory or asthma or cardio* or heart or pain or diabet* or arthrit*)).ab,kw,ti. (235879) 4 (((mental or "mental health") adj1 (disorder* or condition*)) or depress* or anxiet* or ((substance* or drug* or alcohol*) adj2 (disorder* or dependence* or abuse* or use*))).ab,kw,ti. (738575) 5 (((respiratory or cardio* or heart or liver or kidney) adj2 (disease* or condition* or illness*)) or obes* or (cancer* or tumor* or malignan* or carcinoma*) or diabet*).ab,kw,ti. (3908116) 6 1 or 2 or 3 or 4 or 5 (4926803) 7 *Transgender persons/ or *Transsexualism/ or *Gender identity/ or *Health Services for * Transgender Persons/ (12588) 8 (Transgender* or Trans?exual* or Transvest*).ab,kw,ti. (6849) 9 ("Gender identity disorder" or "Gender dysphoria").ab,kw,ti. (1164) 10 (Gender adj1 (identity* or minorit$ or divers$ or var* or non-binary* or "non binary*")).ab,kw,ti. (4503) 11 (MTF or "Male to female transgender" or FTM or "Female to male transgender").ab,kw,ti. (3061) 12 (travesti* or koti* or hijira*).ab,kw,ti. (74) 13 7 or 8 or 9 or 10 or 11 or 12 (21230) 14 6 and 13 (3510) *************************** 145 Appendix B COAST cohort data sources and description of data elements Data source Data steward Description Data elements provided for this study Medical Services Plan British Columbia Ministry of Health For individuals covered by the provincial universal health insurance plan- Medically necessary services provided by fee-for-service physicians and other healthcare providers, laboratory services, diagnostic procedures, dental/oral surgery ICD-9 and ICD-10 codes Consolidation file British Columbia Ministry of Health Demographic data on individuals receiving or registered to receive care in BC, pooled from multiple PopData sources Sociodemographics Discharge Abstract Database British Columbia Ministry of Health Demographic, administrative and clinical data for inpatient hospital discharges and day surgeries ICD-9 and ICD-10 codes Vital statistics deaths British Columbia Vital Statistics Agency Records of all registered deaths in BC Death data Pharmacare British Columbia Ministry of Health Data related to prescription drugs dispensed under the BC public drug insurance program. Prescription drug name/identifier Pharmanet British Columbia Ministry of Health Data Stewardship Committee Data related to prescription drugs dispensed by community and outpatient pharmacies Prescription drug name/identifier 146 Drug Treatment Program and laboratory British Columbia Centre for Excellence in HIV/AIDS Antiretroviral therapy use history, laboratory testing, immunological and virologic testing, and demographic data on people living with HIV who have accessed antiretrovirals in BC Providers-reported transgender status, laboratory confirmed HIV serostatus 147 Appendix C Prescription drugs with drug identification numbers (DIN)s Generic Name DIN Transfeminine Androgen Blockers Spironolactone SPIRONOLACTONE 28606 SPIRONOLACTONE 613215 SPIRONOLACTONE 285455 SPIRONOLACTONE 613223 SPIRONOLACT/HYDROCHLOROTHIAZID 180408 SPIRONOLACT/HYDROCHLOROTHIAZID 613231 SPIRONOLACT/HYDROCHLOROTHIAZID 594377 SPIRONOLACT/HYDROCHLOROTHIAZID 657182 Cyproterone ETHINYL ESTRADIOL/CYPROTERONE 2233542 NO GENERIC FORMULARY 634514 CYPROTERONE ACETATE 704431 CYPROTERONE ACETATE 2229449 CYPROTERONE ACETATE 2229723 CYPROTERONE ACETATE 2232872 CYPROTERONE ACETATE 2245898 CYPROTERONE ACETATE 704423 Finasteride FINASTERIDE 2010909 FINASTERIDE 2238213 Dutasteride DUTASTERIDE 2247813 Estrogens Estrogen ESTROGENS,CONJUGATED 830240 ESTROGENS,CONJUGATED 831395 ETHYNODIOL D-ETHINYL ESTRADIOL 28630 ETHYNODIOL D-ETHINYL ESTRADIOL 469327 NORETHINDRONE-MESTRANOL 22608 NORETHINDRONE-MESTRANOL 22659 NORETHINDRONE A-E ESTRADIOL 297143 NORETHINDRONE A-E ESTRADIOL 315966 NORETHINDRONE-ETHINYL ESTRAD 317047 148 NORETHINDRONE-ETHINYL ESTRAD 372846 NORETHINDRONE-ETHINYL ESTRAD 373265 NORETHINDRONE-ETHINYL ESTRAD 531006 NORETHINDRONE-ETHINYL ESTRAD 538590 NORETHINDRONE-ETHINYL ESTRAD 602957 NORETHINDRONE-ETHINYL ESTRAD 620947 NORETHINDRONE-ETHINYL ESTRAD 2187086 NORETHINDRONE-ETHINYL ESTRAD 2187108 NORETHINDRONE-ETHINYL ESTRAD 2189054 NORGESTREL-ETHINYL ESTRADIOL 34207 NORGESTREL-ETHINYL ESTRADIOL 300640 LEVONORGESTREL-ETH ESTRA 579386 LEVONORGESTREL-ETH ESTRA 707600 LEVONORGESTREL-ETH ESTRA 782416 LEVONORGESTREL-ETH ESTRA 782432 LEVONORGESTREL-ETH ESTRA 2042320 NORGESTREL-ETHINYL ESTRADIOL 2043033 LEVONORGESTREL-ETH ESTRA 2043726 NORGESTIMATE-ETHINYL ESTRADIOL 2258560 NORETHINDRONE-MESTRANOL 30333 NORETHINDRONE-MESTRANOL 30341 LEVONORGESTREL-ETH ESTRA 2236974 ETHYNODIOL D-ETHINYL ESTRADIOL 471526 NORETHINDRONE-ETHINYL ESTRAD 340731 NORETHINDRONE-MESTRANOL 340758 NORETHINDRONE A-E ESTRADIOL 343838 NORETHINDRONE A-E ESTRADIOL 353027 NORETHINDRONE-ETHINYL ESTRAD 372838 NORETHINDRONE-ETHINYL ESTRAD 373273 NORETHINDRONE-ETHINYL ESTRAD 531014 NORETHINDRONE-ETHINYL ESTRAD 602965 NORETHINDRONE-ETHINYL ESTRAD 695734 NORETHINDRONE-ETHINYL ESTRAD 2187094 NORETHINDRONE-ETHINYL ESTRAD 2187116 NORETHINDRONE-ETHINYL ESTRAD 2189062 ETHINYL ESTRADIOL/NORETH AC 2242531 NORGESTREL-ETHINYL ESTRADIOL 340766 NORGESTREL-ETHINYL ESTRADIOL 342815 LEVONORGESTREL-ETH ESTRA 586609 149 LEVONORGESTREL-ETH ESTRA 707503 LEVONORGESTREL-ETH ESTRA 782424 LEVONORGESTREL-ETH ESTRA 782440 LEVONORGESTREL-ETH ESTRA 2042339 NORGESTREL-ETHINYL ESTRADIOL 2043041 LEVONORGESTREL-ETH ESTRA 2043734 NORGESTIMATE-ETHINYL ESTRADIOL 2258587 LEVONORGESTREL-ETH ESTRA 2236975 NORGESTIMATE-ETHINYL ESTRADIOL 1968440 NORGESTIMATE-ETHINYL ESTRADIOL 2028700 NORGESTIMATE-ETHINYL ESTRADIOL 1992872 NORGESTIMATE-ETHINYL ESTRADIOL 2029421 DESOGESTREL-ETHINYL ESTRADIOL 2042487 DESOGESTREL-ETHINYL ESTRADIOL 2042541 DESOGESTREL-ETHINYL ESTRADIOL 2042479 DESOGESTREL-ETHINYL ESTRADIOL 2042533 ESTRADIOL/NORETH AC 2241835 ESTRADIOL/NORETH AC 2241837 LEVONORGESTREL 2241674 ESTROGEN,CON/M-PROGEST ACET 2242878 ESTROGEN,CON/M-PROGEST ACET 2242879 ESTRADIOL/NORETH AC 2243529 ESTRADIOL/NORETH AC 2243530 ETHINYL ESTRADIOL/DROSPIRENONE 2261723 ETHINYL ESTRADIOL/DROSPIRENONE 2261731 ETONOGESTREL/ETHINYL ESTRADIOL 2253186 ETHINYL ESTRADIOL/NORELGEST 2248297 DIENESTROL 441295 DIETHYLSTILBESTROL 3360 DIETHYLSTILBESTROL 2091461 DIETHYLSTILBESTROL 2091488 ESTRADIOL 464791 ESTRADIOL 2148587 ESTRADIOL 464805 ESTRADIOL 2148595 ESTRADIOL VALERATE 29238 ESTRADIOL 756849 ESTRADIOL 2237807 ESTRADIOL 2243722 150 ESTRADIOL 2245676 ESTRADIOL 756857 ESTRADIOL 2204428 ESTRADIOL 2231509 ESTRADIOL 2237808 ESTRADIOL 2243724 ESTRADIOL 2244000 ESTRADIOL 2246967 ESTRADIOL 756792 ESTRADIOL 2204444 ESTRADIOL 2231510 ESTRADIOL 2244002 ESTRADIOL 2246969 ESTRADIOL 2168898 ESTRADIOL 2204436 ESTRADIOL 2244001 ESTRADIOL 2246968 ESTRADIOL 2225190 ESTRADIOL 2204401 ESTRADIOL 2238704 ESTRADIOL 2243999 ESTRADIOL 2241332 ESTRADIOL 2247499 ESTRADIOL 2247500 ESTROGENS,CONJUGATED 2569 ESTROGENS,CONJUGATED 2043394 ESTROGENS,CONJUGATED 2230891 ESTROGENS,CONJUGATED 2239654 ESTROGENS,CONJUGATED 2577 ESTROGENS,CONJUGATED 265470 ESTROGENS,CONJUGATED 587281 ESTROGENS,CONJUGATED 2043408 ESTROGENS,CONJUGATED 2089 ESTROGENS,CONJUGATED 2043440 ESTROGENS,CONJUGATED 403466 ESTROGENS,CONJUGATED 2043416 ESTROGENS,CONJUGATED 2230892 ESTROGENS,CONJUGATED 2239655 ESTROGENS,CONJUGATED 2585 151 ESTROGENS,CONJUGATED 265489 ESTROGENS,CONJUGATED 587303 ESTROGENS,CONJUGATED 2043424 ESTROGENS,CONJUGATED 2043432 ESTROGENS,CONJUGATED 2043386 ME-TESTOSTERONE/ESTROGEN,CON 53538 ESTROPIPATE 282685 ESTROPIPATE 2089769 ESTROPIPATE 282677 ESTROPIPATE 2089777 ESTROPIPATE 2089793 ESTRADIOL/NORETH AC 2108186 NORGESTIMATE-ETHINYL ESTRADIOL 2229064 NORGESTIMATE-ETHINYL ESTRADIOL 2229218 NORGESTIMATE-ETHINYL ESTRADIOL 2229226 ETHINYL ESTRADIOL/CYPROTERONE 2233542 ETHINYL ESTRADIOL/NORELGEST 2246340 NO GENERIC FORMULARY 66124057 NO GENERIC FORMULARY 66124058 NO GENERIC FORMULARY 66124060 NO GENERIC FORMULARY 66124061 NO GENERIC FORMULARY 66124062 NO GENERIC FORMULARY 66124063 NO GENERIC FORMULARY 66124064 Progestogens Progesterone PROGESTERONE,MICRONIZED 2241013 MEDROXYPROGESTERONE ACET 30848 MEDROXYPROGESTERONE ACET 30856 MEDROXYPROGESTERONE ACET 585092 NO GENERIC FORMULARY 66123240 MEDROXYPROGESTERONE ACET 708917 MEDROXYPROGESTERONE ACET 2148552 MEDROXYPROGESTERONE ACET 2221284 MEDROXYPROGESTERONE ACET 2229838 MEDROXYPROGESTERONE ACET 2244726 MEDROXYPROGESTERONE ACET 2246627 MEDROXYPROGESTERONE ACET 30937 MEDROXYPROGESTERONE ACET 2010739 152 MEDROXYPROGESTERONE ACET 2148560 MEDROXYPROGESTERONE ACET 2221292 MEDROXYPROGESTERONE ACET 2229839 MEDROXYPROGESTERONE ACET 2244727 MEDROXYPROGESTERONE ACET 2246628 MEDROXYPROGESTERONE ACET 729973 MEDROXYPROGESTERONE ACET 2010933 MEDROXYPROGESTERONE ACET 2148579 MEDROXYPROGESTERONE ACET 2221306 MEDROXYPROGESTERONE ACET 2229840 MEDROXYPROGESTERONE ACET 2246629 MEDROXYPROGESTERONE ACET 30945 MEDROXYPROGESTERONE ACET 2267640 NORETHINDRONE 37605 PROGESTERONE,MICRONIZED 2166704 PROGESTERONE 739952 PROGESTERONE 1977652 PROGESTERONE 2128470 LEVONORGESTREL 2243005 Transmasculine Testosterone TESTOSTERONE 2249499 TESTOSTERONE CYPIONATE 30783 TESTOSTERONE PROPIONATE 1977571 TESTOSTERONE CYPIONATE 1977601 TESTOSTERONE CYPIONATE 2220318 TESTOSTERONE CYPIONATE 2246063 TESTOSTERONE ENANTHATE 29246 TESTOSTERONE ENANTHATE 716936 TESTOSTERONE ENANTHATE 739944 TESTOSTERONE UNDECANOATE 782327 TESTOSTERONE ENANTHATE/ESTRAD 108278 TESTOSTERONE ENANTHATE/ESTRAD 2061031 TESTOSTERONE 2239653 TESTOSTERONE 2245346 TESTOSTERONE 2245345 TESTOSTERONE 2245972 153 Appendix D Chronic condition case definitions Chronic condition Case definition Codes Cardiovascular disease* Acute myocardial infarction 1 or more hospitalizations with relevant ICD codes ICD-10: I21 Acute myocardial infarction I22 Subsequent myocardial infarction ICD-9: 410 Acute myocardial infarction Ischemic heart disease At least one of the following: 2 medical visits with Angina ICD-9 code 413 plus 1 heart disease prescription in 365 days; or 1 specialist visit with Angina ICD-9 code 413 plus 1 prescription in 365 days; or 2 medical visits with two ICD9 codes 410, 411, 412, 413, 414 in 365 days; or 1 CCI/CCP CABG,PCI/PCTA procedure code; or 1 hospitalization with relevant ICD code. ICD-10: I20 Angina pectoris I21 Acute myocardial infarction I 22 Subsequent myocardial infarction I23 Certain current complications following acute myocardial infarction I24 Other acute ischaemic heart diseases I25 Chronic ischaemic heart disease ICD-9: 410 Acute myocardial infarction 411 Other acute and subacute forms of ischaemic heart disease 412 Old myocardial infarction 413 Angina pectoris 414 Other forms of chronic ischaemic heart disease Chronic heart failure 1 or more hospitalizations or 2 or more medical visits in 365 days with relevant ICD codes ICD-10: I50 Heart failure ICD-9: 428 Heart failure Stroke- hospital 1 or more hospitalizations with relevant ICD codes ICD-10: H34.1 Central retinal artery occlusion I60 Subarachnoid hemorrhage I61 Intracerebral haemorrhage 154 I63 Cerebral infarction (exclude I63.6 Cerebral infarction due to cerebral venous thrombosis, nonpyogenic) I64 Stroke, not specified as haemorrhage or infarction 362.3 Retinal vascular occlusion 430 Subarachnoid hemorrhage 431 Intracerebral hemorrhage 433.x1 Occlusion and stenosis of precerebral arteries 434.x Occlusion cerebral arteries 436 Acute but ill-defined cerebrovascular disease Excludes any traumatic brain injury Transient ischemic attack 1 or more hospitalizations with relevant ICD codes ICD-10: H34.0 Transient retinal artery occlusion G45.0 Vertebro-basilar artery syndrome G45.1 Carotid artery syndrome (hemispheric) G45.2 Multiple and bilateral precerebral artery syndromes G45.3 Amaurosis fugax G45.8 Other transient cerebral ischemic attacks and related syndromes G45.9 Transient cerebral ischemic attack, unspecified ICD-9: 435 Transient cerebral ischemia Excludes any traumatic brain injury Chronic kidney disease* 1 or more hospitalizations or 2 or more medical visits in 365 days with relevant ICD codes ICD-10: N01 Rapidly progressive nephritic syndrome N03 Chronic nephritic syndrome N04 Nephrotic syndrome N05 Unspecified nephritic syndrome 155 N06 Isolated proteinuria with specified morphological lesion N07 Hereditary nephropathy, not elsewhere classified N18 Chronic kidney disease N19 Unspecified kidney failure N26 Unspecified contracted kidney N27 Small kidney of unknown cause ICD-9: 581 Nephrotic syndrome 582 Chronic glomerulonephritis 583 Nephritis and nephropathy, not specified as acute or chronic 585 Chronic renal failure 586 Renal failure, unspecified 587 Renal sclerosis, unspecified 589 Small kidney of unknown cause Chronic liver disease 1 or more hospitalization or medical visit with relevant diagnosis within 365 days ICD-9: 571.0 Alcoholic fatty liver 571.2 Alcoholic cirrhosis of liver 571.3 Alcoholic liver damage, unspecified 571.4 Chronic hepatitis 571.5 Cirrhosis of liver without mention of alcohol 571.6 Billiary cirrhosis 571.8 Other chronic nonalcoholic liver disease 571.9 Unspecified chronic liver disease without mention of alcohol 070.3 Viral hepatitis B without mention of hepatic coma 070.30 Viral hepatitis B without mention of hepatic coma, acute or unspecified, without mention of hepatitis delta 070.31 Viral hepatitis B without mention of hepatic coma, acute or unspecified, with hepatitis delta 070.32 Viral hepatitis B without mention of hepatic coma, chronic, without mention of hepatitis delta 156 070.33 Viral hepatitis B without mention of hepatic coma, chronic, with hepatitis delta 070.52 Hepatitis delta without mention of active Hepatitis B disease or hepatic coma V02.61 Hepatitis B carrier 070.42 Hepatitis delta without mention of active Hepatitis B disease with hepatic coma 070.54 Chronic hepatitis C without mention of hepatic coma V02.62 Hepatitis C carrier Chronic Obstructive Pulmonary Disease* 1 or more hospitalization or 2 or more medical visits within 365 days ICD-10: J41 Simple and mucopurulent chronic bronchitis J42 Unspecified chronic bronchitis J43 Emphysema J44 Other chronic obstructive pulmonary disease ICD-9: 491 Chronic bronchitis 492 Emphysema 496 Chronic airways obstruction, not elsewhere classified Diabetes Mellitus* At least 1 of the following: 1 hospitalization or 2 medical visits in 365 days with relevant ICD codes; or 2 or more insulin prescriptions in 365 days; or 2 or more oral antihyperglycemic (not including metformin) prescriptions in 365 days; or 1 insulin and 1 oral antihyperglycemic (including ICD-10: E10 Type 1 diabetes mellitus E11 Type 2 diabetes mellitus E13 Other specified diabetes mellitus E14 Unspecified diabetes mellitus ICD-9: 250 Diabetes mellitus 157 metformin) in 365 days; or 2 metformin prescriptions and 1 medical visit in one year with relevant ICD codes. Excludes gestational diabetes. Hypertension* 1 or more hospitalizations or 2 or more medical visits within 2 years with relevant ICD codes. Excludes gestational hypertension. ICD-10: I10 Essential (primary) hypertension I11 Hypertensive heart disease I12 Hypertensive renal disease I13 Hypertensive heart and renal disease I15 Secondary hypertension ICD-9: 401 Essential hypertension 402 Hypertensive heart disease 403 Hypertensive renal disease 404 Hypertensive heart and renal disease 405 Secondary hypertension Mood and anxiety disorders* 1 or more hospitalizations with a relevant ICD code or 2 or more medical visits with a relevant code within 2 years ICD-10: F30 Manic episode F31 Bipolar affective disorder F32 Depressive episode F33 Recurrent depressive disorder F34 Persistent mood [affective] disorders F38 Other mood [affective] disorders F39 Unspecified mood [affective] disorder F40 Phobic anxiety disorders F41 Other anxiety disorders F42 Obsessive-compulsive disorder F43 Reaction to severe stress, and adjustment disorders F44 Dissociative (conversion) disorders F45 Somatoform disorders F48 Other neurotic disorders 158 F68 Other disorders of adult personality & behavior ICD-9: 296 Affective psychoses 300 Neurotic disorders 311 Depressive disorder, not elsewhere classified MSP DX Code: 50B Anxiety/Depression Non-AIDS defining cancer† All prevalent cancer cases were included, with the exception of AIDS defining malignancies (Kaposi’s sarcoma, non-Hodgkin’s lymphoma, invasive cervical cancer) Cancer case definition details available from the British Columbia Cancer Agency: http://www.bccancer.bc.ca/health-info/types-of-cancer Organic mental disorders 1 or more medical visits or hospitalizations with relevant diagnoses within 365 days ICD-9: 290.x Dementias 294.x Other organic psychotic conditions 331.x Alzheimer’s ICD-10: F00.x Dementia in Alzheimer’s disease F01.x Vascular Dementia F02.x Dementia in other disease classified elsewhere F03.x Unspecified dementia F04 Amnestic disorder due to physiological condition F06 Other mental disorders due to known physiological condition F09 Unspecified mental disorder due to known physiological condition 159 G30 Alzheimer’s disease with early onset Osteoarthritis* 1 or more hospitalization or 2 or more medical visits in 365 days with a relevant ICD code ICD-10: M15 Polyarthrosis M16 Coxarthrosis [arthrosis of hip] M17 Gonarthrosis [arthrosis of knee] M18 Arthrosis of first carpometacarpal joint M19 Other arthrosis ICD-9: 715 Osteoarthrosis and allied disorders Personality disorder 1 or more hospitalizations or medical visits with a relevant diagnosis within 365 days ICD-9: 301.x Personality disorders ICD-10: F60.x Specified personality disorders F62 Enduring personality changes, not attributable to brain damage and disease F68.1 Intentional production or feigning of symptoms or disabilities, either physical or psychological F68.8 Other specified disorders or adult personality and behaviour F69 Unspecified disorder or adult personality and behaviour Schizophrenia related disorder 1 or more medical visit or hospitalizations with relevant diagnoses within 365 days ICD-9: 295.x Schizophrenic disorders 297.0 Paranoid state, simple 297.1 Delusional disorder 297.2 Paraphrenia 297.3 Shared psychotic disorder ICD-10: F20.x Paranoid schizophrenia F21.x Schizotypal disorder F23.2 Acute schizophrenia-like psychotic disorder F25.x Schizoaffective disorders 160 * Case definition adapted from British Columbia Ministry of Health version 2017, April 4 2019 update † Case-definition adapted from British Columbia Cancer Agency
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Epidemiology of chronic disease and multimorbidity for transgender people living with and without HIV
Text