Pharmaceutical Sciences, Faculty of
DSpace
UBCV
Vo, Anh Khoa
Doctor of Philosophy - PhD
University of British Columbia
Background: Historically, race was believed to be biologically determined, which has permeated various facets of society, including health care and health research. Despite the contemporary notion of race as a social construct lacking a biological basis, racial disparities continue to exist in health outcomes and participation in health research. However, there are knowledge gaps with respect to racial disparities within the field of acute and chronic pain. Objective: To examine racial differences in health outcomes and health research participation within the framework of race as a social construct, and with a focus on acute and chronic pain contexts. Methods: Data were compiled from various sources including the Healthcare Cost and Utilization Project, Canadian Community Health Survey, Clinicaltrials.gov, PubMed, and others. Several analytical methods were used, including: multivariable logistic regression, multivariable linear regression, and causal mediation analysis. Results: Findings from Chapter 4 showed that emergency department visits (for complex regional pain syndrome type 1) by Black individuals were significantly negatively associated with immediate hospitalization and hospitalizations by Black patients were independently associated with increased length of stay. Chapter 5 demonstrated that racialized individuals in Canada were less likely to use prescription opioids (compared with non-racialized individuals), even after adjusting for potential confounders. In Chapter 6, the results showed that less than half of pain clinical trials in the US reported race, and that sample size, year of publication, and funding sponsorship were associated with race reporting practices. Moreover, Black participation in pain clinical trials aligned with national demographics in the US, with increased representation corresponding with health conditions more prevalent among Black individuals (e.g., cardiovascular disease) and with a diverse study team composition. Chapter 7 revealed that racialized individuals were less inclined to support open data sharing than non-racialized individuals, a relationship mediated by trust. Implications: In short, this thesis highlights the persistence of racial disparities in health care and health research, and underscores the multifaceted factors underlying these disparities. Further, it emphasizes the need to address race-based discrimination to achieve health equity for racialized groups.
RACIAL DIFFERENCES IN PAIN-RELATED HEALTH OUTCOMES AND HEALTHCARE PARTICIPATION by Anh Khoa Vo B.A., Kwantlen Polytechnic University, 2016 MSc, University of British Columbia, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Pharmaceutical Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2024 ©Anh Khoa Vo, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Racial Differences in Pain-Related Health Outcomes and Healthcare Participation submitted by Anh Khoa Vo in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Pharmaceutical Sciences Examining Committee: Jacquelyn Cragg, Assistant Professor, Faculty of Pharmaceutical Sciences, UBC Supervisor Brian Cairns, Professor, Faculty of Pharmaceutical Sciences, UBC Co-supervisor Gerry Veenstra, Professor, Department of Sociology, UBC University Examiner Donald Griesdale, Associate Professor, Faculty of Medicine, UBC University Examiner Hance Clarke, Associate Professor, Faculty of Medicine, University of Toronto External Examiner Additional Supervisory Committee Members: Annalijn Conklin, Associate Professor, Faculty of Pharmaceutical Sciences, UBC Supervisory Committee Member Lerato Chondoma, Associate Director, Indigenous Research Support Initiative, UBC Supervisory Committee Member Jonathan Loree, Assistant Professor, Faculty of Medicine, UBC Supervisory Committee Member iii Abstract Background: Historically, race was believed to be biologically determined, which has permeated various facets of society, including health care and health research. Despite the contemporary notion of race as a social construct lacking a biological basis, racial disparities continue to exist in health outcomes and participation in health research. However, there are knowledge gaps with respect to racial disparities within the field of acute and chronic pain. Objective: To examine racial differences in health outcomes and health research participation within the framework of race as a social construct, and with a focus on acute and chronic pain contexts. Methods: Data were compiled from various sources including the Healthcare Cost and Utilization Project, Canadian Community Health Survey, Clinicaltrials.gov, PubMed, and others. Several analytical methods were used, including: multivariable logistic regression, multivariable linear regression, and causal mediation analysis. Results: Findings from Chapter 4 showed that emergency department visits (for complex regional pain syndrome type 1) by Black individuals were significantly negatively associated with immediate hospitalization and hospitalizations by Black patients were independently associated with increased length of stay. Chapter 5 demonstrated that racialized individuals in Canada were less likely to use prescription opioids (compared with non-racialized individuals), even after adjusting for potential confounders. In Chapter 6, the results showed that less than half of pain clinical trials in the US reported race, and that sample size, year of publication, and funding sponsorship were associated with race reporting practices. Moreover, Black participation in pain clinical trials aligned with national demographics in the US, with increased representation corresponding with health conditions more prevalent among Black individuals (e.g., cardiovascular iv disease) and with a diverse study team composition. Chapter 7 revealed that racialized individuals were less inclined to support open data sharing than non-racialized individuals, a relationship mediated by trust. Implications: In short, this thesis highlights the persistence of racial disparities in health care and health research, and underscores the multifaceted factors underlying these disparities. Further, it emphasizes the need to address race-based discrimination to achieve health equity for racialized groups. v Lay summary We live in a racialized world such that racial disparities in health care and health research continue to exist today. Utilizing large datasets from the US and Canada, two key findings emerged. First, racialized individuals faced longer hospital stay and were less likely to receive appropriate pain management, including opioid prescriptions. These disparities suggest a presence of structural racism, hindering access to care for racialized individuals. Second, race and distrust can impact participation in health research, such as the decision to share data and participate in clinical trials. These findings reinforce the role of race as a social construct, influencing perception and behaviour in health care and research settings. Further, this work emphasizes the systemic need to address the complexity of race and practices to achieve health equity. vi Preface A version of chapter 6 has been published in Pain. The full citation of this manuscript is: Vo, A.K., Cerdeña, J.P., Loree, J., Cairns, B., Conklin, A., Kasewater, K., Chondoma, L., Cragg, J.J., Kramer, J.L.K. (2023). Race-based reporting and participation of Black individuals in registered pain clinical trials, United States 2000-2019. Pain. April 2023. Here, I was responsible for the study concept, study design, literature search, data cleaning, data extraction, data analysis, statistical programming, data interpretation, and drafting the manuscript. JJC and JLKK were responsible for the study concept, study design, data interpretation, and revising the manuscript for intellectual content. The remaining authors were responsible for interpretation of the data, and revising the manuscript for intellectual content. A version of chapter 4 is submitted for publication. The full citation of this manuscript is: Vo, A.K., Crispo J.A.G., Liu L.J.W., Thibault D., Willis A.W., Griesdale D., Kramer J.L.K., and Cragg J.J. (2024). The effects of race on outcomes of care for complex regional pain syndrome type 1 in the United States. For this component, I was responsible for the study concept, study design, literature search, data acquisition, data analysis plan, interpretation of results, and drafting the manuscript. JJC was responsible for the study concept, study design, data interpretation, and revising the manuscript for intellectual content. JAGC was responsible data analysis, data interpretation, and revising the manuscript for intellectual content. The remaining authors were responsible for interpretation of the data, and revising the manuscript for intellectual content. A version of chapter 5 is being prepared and expected for submission to a peer-reviewed journal. The full citation of this manuscript is: vii Vo, A.K., Jia, A., Griesdale, D., Conklin A., Cairns B., Loree, J., Chondoma, L., Kramer, J.L.K., Cragg, J.J. (2024). Prescription opioid use by race: a cross-sectional Canadian survey. In this manuscript, I was responsible for the study concept, study design, literature search, data acquisition, data cleaning, statistical programming, data analysis, and drafting the manuscript. JJC was responsible for the study concept, study design, data interpretation, and revising the manuscript for intellectual content. All other authors were responsible for interpretation of the data, and revising the manuscript for intellectual content. A version of chapter 7 is being prepared and expected for submission to a peer-reviewed journal. The full citation of this manuscript is: Vo, A.K., Kuramoto, L., Lim, R., Mello, M.M., Chondoma, L., Loree, J., Conklin, A.I., Cairns, B.E., Kramer, J.L.K., Cragg, J.J. (2024). Clinical Trial Participant Views of the Risks and Benefits of Open Data Sharing: Racial Differences and the Mediating role of Mistrust. For this manuscript, I was responsible for the study concept, study design, literature search, data acquisition, data cleaning, statistical programming, data analysis, and drafting the manuscript. JJC was responsible for the study concept, study design, data interpretation, and revising the manuscript for intellectual content. All other authors were responsible for interpretation of the data, and revising the manuscript for intellectual content. viii Table of contents Abstract............................................................................................................................................ iii Lay summary ................................................................................................................................... v Preface .............................................................................................................................................. vi Table of contents........................................................................................................................... viii List of tables ................................................................................................................................... xii List of figures ................................................................................................................................ xiii List of abbreviations .................................................................................................................... xiv Acknowledgements....................................................................................................................... xvi Dedication ..................................................................................................................................... xvii Chapter 1: General Introduction .................................................................................................. 1 1.1 Definition of race and social construct of race ............................................................. 1 1.2 Race and racialization .................................................................................................... 2 1.3 Self-identified race versus socially defined race .......................................................... 3 1.4 Race and racism .............................................................................................................. 4 1.5 Race and ancestry ........................................................................................................... 4 1.6 Race and ethnicity .......................................................................................................... 5 Chapter 2: Introduction- The Study of Race in Health Research .......................................... 6 2.1 Collection of race-based data......................................................................................... 6 2.2 Guidelines for referring to race in health research ....................................................... 8 2.3 Guidance of utilizing and reporting of race in health sciences research ................... 10 2.4 How race is utilized as a variable in health sciences research ................................... 12 2.5 Race and historic mistreatments in health care and research..................................... 14 2.6 Racial disparities in health outcomes .......................................................................... 15 2.7 Racial disparities in pain-related health outcomes ..................................................... 17 2.8 Race and participation in health research ................................................................... 18 2.9 Race and views on open data sharing.......................................................................... 19 2.10 Rationale for studying race ........................................................................................ 20 ix 2.11 Knowledge gaps ......................................................................................................... 21 2.12 Thesis objectives ........................................................................................................ 21 2.13 Thesis organization .................................................................................................... 23 Chapter 3: Methods Overview .................................................................................................... 24 3.1 Study design overview ................................................................................................. 24 3.2 Data sources .................................................................................................................. 24 3.3 Overview of statistical methodologies ........................................................................ 26 Chapter 4: The effects of race on outcomes of care for complex regional pain syndrome type 1 in the United States ........................................................................................................... 27 4.1 Introduction .................................................................................................................. 27 4.2 Methods ......................................................................................................................... 28 4.2.1 Ethics and reporting........................................................................................... 28 4.2.2 Data sources and study design .......................................................................... 28 4.2.3 CRPS-1 encounters............................................................................................ 29 4.2.4 Study outcomes.................................................................................................. 30 4.2.5 Primary analyses ................................................................................................ 31 4.2.6 Secondary analyses............................................................................................ 31 4.3 Results ........................................................................................................................... 32 4.3.1 Emergency department visits for CRPS-1 ....................................................... 32 4.3.2 Hospitalizations for CRPS-1 ............................................................................. 34 4.3.3 Emergency department visits and immediate hospital admission .................. 35 4.3.4 Hospitalizations and longer length of stay ....................................................... 36 4.4 Discussion ..................................................................................................................... 48 Chapter 5: Prescription opioid use by race: cross-sectional Canadian survey ................... 54 5.1 Introduction ................................................................................................................... 54 5.2 Methods ......................................................................................................................... 55 5.2.1 Data source and study design ............................................................................. 55 5.2.2 Study variables .................................................................................................... 56 5.2.3 Confounder and mediating variables ................................................................. 57 x 5.2.4 Statistical Analysis ............................................................................................. 58 5.2.4 Sensitivity and mediation analyses .................................................................... 58 5.2.5 Ethics approval .................................................................................................... 59 5.3 Results ........................................................................................................................... 59 5.3.1 Study sample ....................................................................................................... 59 5.3.2 Prescription opioid use: prevalence and odds ratios ......................................... 60 5.3.3 Sensitivity and mediation analysis ..................................................................... 60 5.4 Interpretation ................................................................................................................. 68 5.5 Conclusion .................................................................................................................... 70 Chapter 6: Race-based reporting and participation of Black individuals in registered pain clinical trials, United States 2000-2019 ............................................................................. 71 6.1 Introduction ................................................................................................................... 71 6.2 Methods ......................................................................................................................... 72 6.2.1 Identification of registered pain clinical trials and study inclusion/exclusion criteria ................................................................................ 72 6.2.2 Data extraction .................................................................................................... 72 6.2.3 Statistical analysis ............................................................................................... 73 6.3 Results ........................................................................................................................... 73 6.3.1 Descriptive summary .......................................................................................... 73 6.3.2 Race-based reporting in pain clinical trials ....................................................... 74 6.3.3 Participation of Black individuals in pain clinical trials .................................. 74 6.4 Discussion ..................................................................................................................... 86 6.5 Conclusion ................................................................................................................... 90 Chapter 7: Clinical Trial Participant Views of the Risks and Benefits of Open Data Sharing: Racial Differences and the Mediating role of Mistrust........................................... 92 7.1 Introduction .................................................................................................................. 92 7.2 Methods ......................................................................................................................... 93 7.2.1 Study design and data source ............................................................................. 93 7.2.2 Questionnaire and survey administration .......................................................... 93 xi 7.2.3 Outcome variables............................................................................................... 94 7.2.4 Race variable ....................................................................................................... 95 7.2.5 Mediating variables ............................................................................................. 95 7.2.6 Confounding variables ........................................................................................ 96 7.2.7 Statistical analysis and sensitivity analyses ....................................................... 96 7.3 Results ........................................................................................................................... 97 7.3.1 Sample characteristics......................................................................................... 97 7.3.2 Willingness to share data and trust .................................................................... 97 7.3.3 Mediating role of trust ........................................................................................ 97 7.3.4 Sensitivity analysis .............................................................................................. 98 7.4 Discussion .................................................................................................................. 104 7.5 Conclusion ................................................................................................................. 105 Chapter 8: Summary of findings and future directions ........................................................ 107 8.1 Overall summary ........................................................................................................ 107 8.2 Key findings ................................................................................................................ 108 8.3 General strength and limitations ................................................................................ 109 8.3.1 Strengths ........................................................................................................... 109 8.3.2 Limitations ......................................................................................................... 109 8.4 Implications and considerations ................................................................................ 110 8.5 Conclusion .................................................................................................................. 118 Bibliography ................................................................................................................................. 120 xii List of tables Table 2.1: Race categories used by the Canadian Institute for Health Information .................... 6 Table 2.2: Race categories used by the Canadian Community Health Survey ............................ 7 Table 2.3: Race categories used by the National Institutes of Health........................................... 7 Table 3.1: Overview of data sources, methodologies and analytical techniques used in each chapter ............................................................................................................................................. 26 Table 4.1: Associations between select factors and immediate hospital admission among emergency department encounters where complex regional pain syndrome type 1 was diagnosed, 2020. ................................................................................................................................................. 42 Table 4.2: Associations between select factors and longer length of stay among inpatient encounters where complex regional pain syndrome type 1 was diagnosed, 2020. ..................... 44 Table 5.1: Sociodemographic characteristics of CCHS study participants by prescription opioid use. ................................................................................................................................................... 63 Table 5.2: Logistic regression for the relationship between race and prescribed opioid use. ... 65 Table 5.3: Logistic regression for the relationship between each chronic condition and prescribed opioid use. ........................................................................................................................................ 67 Table 6.1: Extracted data elements in interventional pain clinical trials .................................... 76 Table 6.2: Characteristics of included pain interventional studies ............................................ 78 Table 6.3: Characteristics of pain clinical trials that reported Black participation .................... 80 Table 6.4: Factors associated with race-based reporting: logistic regression analysis .............. 82 Table 6.5: Factors associated with Black participation (% Black): linear regression ................ 85 Table 7.1: Sample characteristics by race ................................................................................... 100 Table 7.2: Odds ratios for willingness to share data. ................................................................. 101 Table 7.3: Odds ratios for trust in people among clinical trial participants ............................. 102 Table 7.4: Odds ratios for each domain of trust among clinical trial participants ................... 103 Table 8.1: Strengths and limitations............................................................................................ 110 xiii List of figures Figure 1.1 Overview of thesis objectives ..................................................................................... 22 Figure 4.1. Selection of CRPS-1 emergency department encounters (A) and hospitalizations (B) .................................................................................................................. 38 Figure 4.2. Adult emergency department visits and hospitalizations in 2020 for complex regional pain syndrome type 1 (CRPS-1) .................................................................................................... 39 Figure 5.1. Study sample from the 2015-2016 Canadian Community Health Survey .............. 62 Figure 7.1. Mediation analysis with trust in people as mediator ................................................. 99 Figure 8.1. Causal diagram explaining the relationship between race and health outcomes, participation in research, and views on open data sharing. ........................................................ 115 xiv List of abbreviations AACT Aggregate Analysis of ClinicalTrials.gov AHRQ Agency for Healthcare Research and Quality aOR Adjusted odds ratio BAME Black, Asian, and Minority Ethnic BIPOC Black, Indigenous, and People of Color BLM Black Lives Matter CCHS Canadian Community Health Survey CIHI Canadian Institute for Health Information CI Confidence interval CMAJ Canadian Medical Association Journal CRPS Complex Regional Pain Syndrome DAG Directed acyclic graph ED Emergency department FDA Food and Drug Administration HCUP Healthcare Cost and Utilization Project ICD-10 International Classification of Diseases Tenth Revision NEDS Nationwide Emergency Department Sample NIH National Institutes of Health NIS National Inpatient Sample OR Odds ratio PIs Principal Investigators PTSD Post-Traumatic Stress Disorder RECORD Reporting of studies Conducted using Observational Routinely collected Data RSD Reflex Sympathetic Dystrophy SQL Structured Query Language xv Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Jacquelyn Cragg for her continuous support over the years. Your dedication and attention to detail have inspired me to become a better scientist. Thank you for always being there and making sure that I have opportunities, and your support, despite tough times. I would also like to thank Dr. Kip Kramer. His stern but supportive words always keep me pushing forward. His words, “If you cannot lead after your PhD, then you’re wasting your time,” have become my cornerstone since 2020. I strive to become a better writer and scientist because of him. I would like to thank members of my supervisory committee for their continued guidance and support: Dr. Brain Cairns, who told me “It’s just another bad day, it won’t define you”; Dr. Jonathan Loree, who shared his expertise in conducting clinical trials with me; Dr. Annalijn Conklin, for our lively discussion on race, gender, and philosophy; and Lerato Chondoma, for our wonderful conversation and lived experience, and for believing in me, despite my doubts. I would also like to thank Dr. Mello at Stanford for providing the data used in Chapter 7. My gratitude also goes out to the lab members of both Kramer and Cragg labs: soon-to-be Dr. Analisa Jia for always being there and believing in me; Dr. Lukas Linde, your sharp wits and sarcasm have always made my day better; Dr. Freda Warner, for your wisdom and always listening to me; Dr. Jessica Archibald, for telling me, “Of course, you deserve to be in your PhD” and coming to my standup supporting me; Dr. Paulina Scheuren, for providing me your experiences and guidance in the academic world; other lab members (Ryan, Himali, Cassie, Jessie, Jaimie, Nicole, and Bobo) for adding sunshine into my turbulent times during my PhD. xvi Thank you to everyone at ICORD, especially Cheryl and Dr. Raza Malik, for making my experience at ICORD unforgettable. ICORD always has a special place in my heart, and I am grateful for ICORD electing me to become chair of the ICORD Trainee Committee. I also owe my gratitude to all my friends near and far who helped keep me grounded, and for making me realize that there is a world beyond research. Your support in my standup, dinners, movies, hanging out, and working out have tremendously lifted my spirits up. Finally, I would like to thank my family: my parents, who are always so supportive of me in my decision to continue my education and my brother, despite being younger, is wiser beyond his years. Our family chat is my favorite place to pick my mood up instantly. Now, the real journey begins. xvii Dedication Luận án này xin dành cho gia đình Văn Võ đã ủng hộ Dê trên con đường học vấn này. Đầu tiên là ba Nghĩa, má Thơm đã thúc đẩy động lực con học đến Tiến Sĩ. PÉ Gấu đã chịu đựng lắng nghe a2 than thở, và khóc lóc, đặt biệt trong khoảng thời gian khủng hoảng tinh thần. Gia đình bên Ngoại, nhóm group chat đã giúp Dzê giảm stress mỗi tối, trong những lúc suy nghĩ mém bỏ cuộc. Gia đình bên Nội, mỗi khi dc về quê thư giãn. Và luận án xin dành cho thế hệ đi sau, Dê là người đầu tiên, nhưng ko phải là người duy nhất. Cuối cùng, 2 câu thơ cuối trong "Độc Tiểu Thanh Kí" của Nguyễn Du: Bất tri tam bách dư niên hậu, Thiên hạ hà nhân khấp Tố Như? CHAPTER 1 GENERAL INTRODUCTION 1.1 Definition of race and social construct of race The concept of race is intricate and continually evolving, reflecting a nuanced understanding shaped by historical, social, political, and scientific perspectives. In its broadest sense, race serves as a classification system for people, typically grounded in perceived physical attributes like skin color, facial features, and hair texture.1,2 Historically, racial categories were used to classify individuals into groups, often with the implication of biological differences. The concept of race has been intertwined with colonialism, slavery, and discriminatory practices, where certain physical traits were used to justify social hierarchies and systemic inequalities.3–5 Over time, societies have grappled with the implications of such categorizations, leading to shifts in the conceptualization of race. From a scientific standpoint, advancements in genetics have challenged the view of race as a biological construct; the concept of race as an indicator for genetically distinct population groups has faced extensive scrutiny.2,6 The Human Genome Project and subsequent research have revealed that genetic diversity does not neatly align with traditional racial categories.7–9 It is noteworthy that there is a higher level of genetic variation within racial groups than between them.8,10 Specifically, in 2002, Stanford scientists conducted a ground breaking study published in Science, investigating the distribution of 4,000 alleles (variations of genes) across seven major geographical regions. Alleles determine features such as hair color and texture.11 The study found that over 92% of alleles were shared in two or more regions, and nearly half were present globally, highlighting the fundamental similarity among people worldwide. Contrary to expectations, only 7.4% of alleles were region-specific, and even when present, they occurred in just 1% of the 2 population, challenging the idea of distinct genetic identities for different racial groups. Thus, two individuals of European descent might be more genetically similar to an Asian person than to each other. This lack of "trademark" alleles across regions underscored the notion that common racial classifications lack a solid genetic basis, suggesting that race is a social construct, not a biological trait. 1.2 Race and racialization The term racialized encapsulates a socio-cultural process wherein social meanings and significance are assigned to racial categories, influencing individual experiences based on perceived racial attributes.12 Unlike the concept of race, which implies inherent biological differences, the use of the term racialized emphasizes the constructed nature of racial identity within societal contexts.13 It denotes the act of attributing social, economic, and political implications to individuals or groups based on perceived racial characteristics rather than inherent biological distinctions.14 This term highlights the dynamic and fluid nature of racial categorization, emphasizing that race is not an objective reality but a product of societal norms and power dynamics.15 People become racialized as they navigate societal structures, institutions, and interpersonal relationships, experiencing differential treatment based on societal interpretations of their racial identity.16,17 The term acknowledges the influence of historical legacies, colonization, and social constructs in shaping the experiences of individuals. Further, embracing the concept of racialized encourages a critical examination of the social processes that contribute to the unequal distribution of opportunities, resources, and privileges among diverse populations.15,18,19 3 1.3 Self-identified race versus socially defined race In tandem with the racialization process, it is important to distinguish between self-identified race and socially defined race. Self-identified race refers to an individual's personal perception and categorization of their own racial background.20 This identification often stems from a combination of cultural, familial, and personal experiences, reflecting a deeply subjective understanding of one's heritage and identity. On the other hand, socially defined race is a collective categorization imposed by society, influenced by historical, cultural, and institutional factors.20 This classification often relies on external markers such as physical characteristics, ancestry, or geographical origin.21 Socially defined race can be shaped by prevailing ideologies, power dynamics, and historical legacies, leading to the creation of racial categories that may not align with individual self-perception. Crucially, self-identified race allows individuals to assert agency in shaping their own narrative; whereas socially defined race can perpetuate stereotypes, reinforce systemic inequalities, and contribute to the construction of rigid categories that may not capture the complexity of individual experiences.20. This distinction emphasizes the need to critically examine the ways in which societal norms and structures influence the perception and categorization of race, while also acknowledging the autonomy individuals have in defining their own racial identity. In this thesis, both self-identified race and socially defined race will be utilized. 1.4 Race and racism Racism is a pervasive and deeply rooted social phenomenon that involves prejudice or discrimination, directed against individuals or groups based on perceived racial identity.22 It encompasses a range of beliefs, attitudes, and practices that uphold the notion of racial superiority or inferiority, contributing to systemic inequalities and social injustices. At its core, racism 4 operates on the premise of assigning value or worth to individuals solely based on their race, perpetuating stereotypes and reinforcing power dynamics. This discrimination can manifest in various forms, including institutional racism, interpersonal racism, and internalized racism.22–24 Institutional racism refers to policies, practices, and structures within societal institutions that systematically disadvantage certain racial groups. This can be observed in areas such as education, employment, criminal justice, and health care, where systemic biases contribute to unequal outcomes.22,23 Interpersonal racism involves acts of discrimination, bias, microaggressions, or prejudice directed at individuals or groups on the basis of their race.22,25 This can manifest in overt forms, such as racial slurs or acts of violence, as well as more subtle, implicit biases that influence decision-making and interactions.25 Internalized racism occurs when individuals from racialized groups internalize negative stereotypes and prejudices about their own racial identity.22 This can lead to a diminished sense of self-worth and contribute to perpetuating harmful narratives. 1.5 Race and ancestry Many researchers have reconsidered the framing of race, shifting towards a focus on genetic ancestry. A plethora of studies in various disease contexts (e.g., cancer, lung disease, bowel disease) recommend genetic ancestry as a tool for disease stratification/prediction.26–28 Some researchers have argued that ancestry is more precise, and should therefore be used in lieu of self-identified race. For example, a recent study advocated for the use of genetic ancestry into a spirometry study as a “racial correction” for lung function of racialized groups.27 However, ancestry differences ignore the racialization process that may have significant impacts on health. For example, a person whose genetic ancestry of only 25% “African” but socially-assigned as 5 “Black” will experience more structural discrimination, increasing their likelihood to be exposed to health risks.29 1.6 Race and ethnicity The terms race and ethnicity are often used interchangeably, or as a single, fused construct labelled race/ethnicity. While race and ethnicity are interconnected concepts, they refer to different aspects of human identity, and as such have distinct meanings and implications. As mentioned previously, race traditionally refers to a categorization of people based on perceived physical characteristics, such as skin color, facial features, and hair texture.1,2 On the other hand, the term ethnicity pertains to shared cultural characteristics and community belonging, such as language, religion, ancestry, and customs that distinguish one group from another.22 Ethnic groups can encompass people of various racial backgrounds who share a common cultural identity. 6 CHAPTER 2: INTRODUCTION- THE STUDY OF RACE IN HEALTH RESEARCH 2.1 Collection of race-based data The appropriate assessment of race is crucial for health sciences research. Data based on racial classifications are used by many federal and state agencies, public and private organizations, and the private business sector to allocate resources to needed areas, develop interventions designed to improve health outcomes, and to develop marketing plans.30–32 Generally in health sciences research, race is measured as respondent self-identification. While race-related questions have historically provided little instruction about defining one’s race, newer assessments of self-reported race include more specific information. For example, the Canadian Institute for Health Information (CIHI), which provides information on Canada’s health systems and health of Canadians, uses the following prompt for assessing self-report race: In our society, people are often described by their race or racial background. These are not based in science, but our race may influence the way we are treated by individuals and institutions, and this may affect our health. Which category(ies) best describe you? Check all that apply.33 Response categories for CIHI race categories are in Table 2.1: Table 2.1. Race categories used by the Canadian Institute for Health Information Response Category Examples Provided Black African, African Canadian, Afro-Caribbean descent East Asian Chinese, Japanese, Korean, Taiwanese descent Indigenous (First Nation, Inuk/Inuit, Métis) First Nations, Inuk/Inuit, Métis descent Latin American Hispanic or Latin American descent Middle Eastern Arab, Persian, West Asian descent (eg., Afghan, Egyptian, Iranian, Kurdish, Lebanese, Turkish) South Asian South Asian descent (eg. Bangladeshi, Indian, Indo-Caribbean, Pakistani, Sri Lankan) Southeast Asian Cambodian, Filipino, Indonesian, Thai, Vietnamese, or other Southeast Asian descent 7 White European descent Another race category Includes values not described above Do not know Not applicable Prefer not to answer Not applicable Similarly, Statistics Canada uses the following prompt for the recurring cycles of the national Canadian Community Health Survey (CCHS): People living in Canada come from many different cultural and racial backgrounds … Mark all that apply.34 Response categories in the CCHS are provided in Table 2.2. Table 2.2. Race categories used by the Canadian Community Health Survey Response Category Examples Provided White None provided Chinese None provided South Asian East Indian, Pakistani, Sri Lankan Black None provided Filipino None provided Latin American None provided Southeast Asian Cambodian, Indonesian, Laotian, Vietnamese West Asian Afghan, Iranian Japanese None provided Korean None provided Other None provided Lastly, the National Institutes of Health (NIH) classifies racial categories, and combines “racial and ethnic categories” in the US (Table 2.3).35 Table 2.3. Race categories used by the National Institutes of Health (NIH) Response Category Examples Provided American Indian or Alaska Native A person having origins in any of the original peoples of North and South America (including Central America), and now maintains tribal affiliation or community attachment Asian A person having origins in any of the original people of the Far East, Southeast Asia, or the Indian Subcontinent, including Cambodia, China, Indian, Japan, Korea, Malaysia, 8 Pakistan, the Philippine Islands, Thailand, and Vietnam Black or African American A person having origins in any of the Black racial groups of Africa. Terms such as “Haitian” or “Negro” can be used in addition to “Black or African American” Hispanic or Latino A person of Cuban, Mexican, Puertorican, South or Central American, or other Spanish culture or origin, regardless of race. The term “Spanish origin” can be used in addition to “Hispanic or Latino.” Native Hawaiian or Other Pacific Islander A person having origins in any of the original peoples of Hawaii, Guam, Samoa, of other Pacific Islands. White A person having origins in any of the original peoples of Europe, the Middle East, or North Africa. 2.2 Guidelines for referring to race in health research Significant efforts have been directed towards formulating guidelines regarding race-related terminology used in health research. Extensive discussions have ensued, particularly surrounding the application of race-related terminology, especially when collectively describing individuals who do not identify as White. Regarding the term minorities, prevailing guidelines advise against its usage due to its inherent vagueness and its implication of a hierarchical structure among groups,22 though the term minoritized may find appropriate use as an adjective, provided it is coupled with a specific noun, as in racial and ethnic minoritized group.22,36 In specific contexts, the term ‘groups that have been historically marginalized’ may be suitable, provided a rationale for the designation is offered, and the included categories are defined or described upon initial mention. Alternatively, the terms underserved populations or underrepresented populations can be employed, provided the categories are defined at the outset.22 The term people of color was introduced to encompass all groups not considered White or of European ancestry.22,23 However, concerns have been raised about its focus on skin colour and its connotation of erasing differences 9 among specific groups. Similar reservations apply to collective terms like Black, Indigenous, and People of Color (BIPOC) used in the US, and Black, Asian, and Minority Ethnic (BAME), commonly used in the United Kingdom.15,22 Critics argue that these terms neglect individual identities, fail to encompass all underrepresented groups, obliterate differences among them, and may imply a hierarchy in the order presented.15,22,37 The utilization of the term racialized in health sciences is helpful for fostering a more nuanced and accurate understanding of the complex intersections between race, health outcomes, and healthcare disparities.38 As previously mentioned, unlike traditional race-based terms that may inadvertently reinforce notions of biological determinism, the term racialized acknowledges the social construction of race, emphasizing the impact of societal processes on individual experiences and health.12,13 By embracing the term racialized, researchers can better capture the dynamic and context-dependent nature of racial categories. This facilitates a shift away from static and potentially misleading assumptions about genetic determinants of health toward an understanding that considers the multifaceted influences of social, economic, and environmental factors. Furthermore, the term racialized may promote a recognition of the diverse experiences within racial groups, dismantling the tendency to homogenize populations.15 This nuanced approach allows for a more accurate analysis of health disparities, considering the unique circumstances and challenges faced by individuals within specific racialized groups. Incorporating the term racialized into health research terminology also aligns with contemporary discourse on social justice and equity.14 It underscores the need to address systemic racism as a determinant of health outcomes, encouraging a more comprehensive examination of the structural factors contributing to health 10 disparities. As such, as well as through discussion with individuals with lived experiences, the term racialized will be used throughout this thesis where individual race categories were not available. 2.3 Guidance of utilizing and reporting of race in health sciences research With a more progressive understanding of the concept of race, including that race is a social construct and use of more carefully considered terms as racialized, many scientific scholarly journals have developed guidelines for reporting of race (and ethnicity) in research articles. While many scholars have discouraged race-based reporting in research articles, many scholars and journals are now embracing the concept of race-based reporting (with some caveats discussed in more detail below). For example, the Canadian Medical Association Journal (CMAJ) states: CMAJ encourages the collection, analysis and reporting of data on the race and ethnicity of research participants, in order to provide evidence regarding health effects, disparities and inequities experienced by different racial and ethnic groups…. CMAJ strongly encourages representation, as study partners, co-investigators and authors, of people from racial and ethnic groups affected by the health context being studied, especially for studies that explore racism, race and ethnicity as determinants of health.39 Guidelines from the CMAJ39 and other high impact journals such as JAMA include the following:22 1) In their study, authors are encouraged to elucidate the significance and intent behind the collection, analysis, and reporting of data related to race or ethnicity; 2) Authors should clarify the contextual meaning of race and ethnicity in relation to the research question; 3) As race and ethnicity are inherently social constructs, studies that analyze race and ethnicity should endeavour to explore their effects in the context of other sociodemographic variables and structures; 4) In the Methods section, authors are required to detail the process by which the race and ethnicity of study participants were ascertained and identify the individuals or entities responsible for this determination. Authors should clarify whether study participants self-identified their race and ethnicity or if identification was conducted by external parties. If self-identification was not employed, authors should offer justification for this choice; 5) Authors need to specify whether 11 participants had open-ended options or had to choose from fixed categories when indicating their race or ethnicity. If applicable, list the available categories and indicate whether participants were permitted to identify with more than one racial or ethnic group; 6) In the Interpretation/Discussion section, when addressing associations between race and ethnicity and health outcomes, authors should examine how their findings depict the intersection of race and ethnicity with other sociodemographic factors within the studied health context. Additionally, authors should explore the influence of structural racism in this context and propose potential avenues for addressing these issues; 7) Terms that imply a hierarchy among races (e.g., “non-White”) should be avoided and preferred terms (e.g., “under-served” or “underrepresented” populations, “historically marginalized groups”) used instead, as contextually appropriate; 8) Listing of racial and ethnic groups in tables should be ordered based on an empirical rationale rather than one that implies a hierarchy (e.g., “White” should not automatically be listed first); 9) Naming racial and ethnic categories as specifically as is appropriate to the study context is preferred over use of collective categories (e.g., “Indian” would be suitable in the context of some research questions, but in other contexts, “Punjabi” and “Malayali” could be more relevant; “Asian” is usually too generic to be sufficiently informative); 10) It is acceptable to pool racial and ethnic groups for analysis when necessary and appropriate, but authors should explain and justify the manner in which this is done and ensure that the individual racial and ethnic groups within each category are identified; 11) Racial and ethnic terms should be used in adjective form rather than in noun form (e.g., “Hispanic people,” not “Hispanics”); 12) Names of racial, ethnic or tribal groups should be capitalized; 13) Authors should use preferred contemporary names for racial and ethnic groups (e.g., White, not Caucasian); 14) As preferred names for racial and ethnic groups may vary and may change over time, authors 12 should be guided as much as possible by the preferences of study participants as to their expressed identity. 2.4 How race is utilized as a variable in health sciences research Once race-based data are collected, race can be incorporated into health sciences research design and analysis in multiple different ways, including the following:40,41 a) as an independent or grouping variable (to describe or compare groups, to explain or predict outcomes); for example, a study examining racial differences in cardiovascular disease; b) As a moderator (to see how race influences the strength or direction of the relationship between other variables (i.e., interacts with other variables); c) As a mediator (to see if race lies on the causal pathway between two variables); d) As a control (confounder) variable (to account for the effects of race on the relationship between two variables); e) As a dependent variable (to see if other variables affect race). This is considered inappropriate unless the study captures the fluidity of racial/ethnic identity over time. A systematic review of articles published in Epidemiology and the American Journal of Epidemiology from 2020 to 2021 examined the operationalization, utilization, and reporting of race, ethnicity, and similar social constructs, and assessed the quality of practices in using and reporting race data in terms of revealing or masking racism.42 The extraction process focused on how race was categorized, its roles with respect to the research question (as a descriptor, confounder, or for effect modification), and whether authors discussed racial differences and mechanisms which could perpetuate bias. Out of the 561 articles, 299 contained race data, and 192 (34.2%) utilized these data in their analyses. Within the 160 US-based studies, 81 different racial categorizations were employed. The predominant use of race was as a confounder (52%), followed 13 by effect modification (33%), and as a descriptive variable (12%). Alarming trends emerged as only 22.9% of articles demonstrated so-called “good practices” (effect modification combined with discussions on disparities and mechanisms), while 63.5% exhibited poor practices (confounding only or neglecting mechanism discussions), and 13.5% fell into a neither poor nor good practices category.42 Because race can be a potential confounding variable in many health sciences studies, epidemiologists have attempted to develop causal diagrams illustrating the effect of race on health outcomes.43 These diagrams attempt to understand the causal pathways by which race leads to differential health outcomes. One such purported causal pathway is that: membership in a marginalized racial group is posited to lead to heightened exposure to discrimination, emanating from both individuals and institutions/organizations.43 This escalated exposure, in turn, results in increased encounters with adverse conditions at both the non-individual level (e.g., neighborhood environments) and individual level (e.g., comorbidities), ultimately impacting health outcomes directly and indirectly via stress.43 Furthermore, non-individual factors, encompassing historical and contemporary structural/institutional racism and antiracism, are believed to shape an individual's exposure to interpersonal racism and adverse contexts or circumstances.43 Moreover, these causal diagrams also incorporate historical processes such that historical processes act as a common cause for both racial group assignment and health associations. For instance, as part of historical processes, Jim Crow laws in the United States reinforced socially defined racial categories (illustrated in the causal diagram as a direct arrow from historical processes to racial group43) and facilitated or perpetuated racist practices (depicted in the causal diagram as a direct arrow from historical processes to discrimination43), beliefs (depicted in the causal diagram as 14 direct arrows from historical processes to contemporary structural/institutional racism to internalized racism), and adverse circumstances (depicted in the causal diagram as a direct arrow from historical processes to adverse circumstances such as the absence of wealth43). Overall, these causal diagrams can be helpful to consider the multiple pathways and mechanisms by which race may impact health outcomes. 2.5 Race and historic mistreatments in healthcare and research Race and its purported biological implication of genetic differences in health and disease have been central to a dark chapter of many racially charged mistreatments in the realms of healthcare and health research throughout history. This included the experimentation on the enslaved Black bodies for use in anatomy lessons, forced sterilizations of Black and Indigenous women, and segregated hospitals that involved lower quality of care for Black patients.44–47 Among these historical injustices, the Tuskegee Syphilis Study stands out as a notorious example of racism and unethical research.48 Begun in 1932, this study withheld treatment for syphilis from economically disadvantaged Black/African American men, even after the discovery of penicillin as a viable cure, leading many to suffer sores, weight loss, paralysis, and even death.49,50 Lasting for 40 years until 1972, the study resulted in 100 deaths and many more suffered the devasting effects of untreated syphilis.49 The case of Henrietta Lacks also further highlights the exploitation of Black individuals in the realm of health research51,52 Lacks, a Black woman, had her cancer cells taken without her knowledge or consent in 1951.51–53 These cells, known as HeLa cells, became instrumental in numerous medical breakthroughs, including the development of the polio vaccine and advancements in cancer research.50,51 However, Lacks and her family were never informed about the use of her cells, and thus received no financial compensation.50 More recently, the tragic case 15 of Joyce Echaquan in 2020 also sheds light on racial disparities in healthcare treatment in Canada.54,55 An Indigenous women admitted into a Quebec hospital, she experienced racist taunts and did not receive adequate pain treatment, ultimately resulting in her death.54,55 2.6 Racial disparities in health outcomes Significant health disparities along racial lines persist, exemplified in the observation that Black individuals are twice as likely to have cardiovascular diseases compared to White individuals in the US.56–59 However, many seeming health disparities have been debunked. For example, scientists in the early 2000s proposed “Slavery Hypertension” theory to explain this phenomenon.60–62 Briefly, the theory claimed that the people of African descent had a genetic predisposition for salt retention due to the limited supply of salt and water, leading to an increase risk of hypertension.60,62 This theory, while attempting to explain health disparities, reinforced the notion that biological differences were the cause.60 The result of this perspective became tangible when the Food and Drug Administration (FDA) approved BiDil, a heart failure medication, which targeted Black patients.63 The FDA’s endorsement of BiDil for Black individuals raised several concerns, including that BiDil did not contain novel ingredients.63,64 Another seeming race-based health disparity pertains to sickle diseases.65,66 Black individuals are said to exhibit higher rates of sickle cell disease compared to White individuals, leading clinicians to label it as a “Black disease.”66–68 However, it is worth noting that this disease originated as an adaptation to genetic pressure for protection against malaria.29,69 Consequently, this disease is not exclusive to Black/African Americans descendants, but can also be found across the African continent, as well as in regions of the Middle East, South Asia, and Latin America.29,68 Even within the same continent, there are differences in its frequency, with a higher prevalence in West-Central African countries such as Nigeria, Cameroon, and Angola.29 16 Nonetheless, race-based disparities do exist in the United States. Firstly, maternal and child health disparities are particularly pronounced in Black communities. Black women are more likely to experience higher rates of maternal mortality and complications during childbirth compared to their White counterparts.70–72 The mortality rate for Black infants is also significantly higher, reflecting disparities in prenatal care, socio-economic factors, and the impact of chronic stressors related to racism.73,74 Secondly, Black Americans are disproportionately affected by chronic health conditions, including diabetes, hypertension, and cardiovascular disease.57,59,75 The prevalence of these conditions further contributes to the overall health gap between Black and White populations in the US.76 Thirdly, mental health disparities are a significant concern within the Black community in the US. Higher rates of depression, anxiety, and post-traumatic stress disorder (PTSD) are observed,77–79 often related to the experience of racism, discrimination, and socio-economic challenges. The stigma associated with mental health issues, coupled with limited access to culturally competent mental health services, exacerbates the disparities in mental health.80 Fourthly, Black Americans also face disparities in HIV/AIDS prevalence and care. The HIV infection rate is higher among Black individuals compared to White individuals,81,82 reflecting complex factors such as socio-economic status, access to healthcare, and stigma. In Canada, significant health disparities also exist between Indigenous and non-Indigenous peoples. The indigenous populations, comprising First Nations, Inuit, and Métis peoples, experience significant gaps in health outcomes compared to their non-Indigenous counterparts. These disparities stem from historical injustices, ongoing cultural genocide, socio-economic factors, and systemic Indigenous-specific racism that have perpetuated inequalities and trauma for 17 generations. Indigenous populations in Canada experience higher rates of chronic health conditions such as diabetes, cardiovascular disease, and respiratory illnesses.83–85 Contributing factors include lifestyle differences, limited access to nutritious food, and the prevalence of smoking. The impact of these chronic conditions is exacerbated by inadequate healthcare resources and preventive measures, leading to higher mortality rates and lower life expectancy among Indigenous peoples.86,87Moreover, mental health disparities are particularly pronounced within Indigenous communities.88,89 The historical and ongoing inter-generational trauma resulting from colonization and the forced removal of children from their families has led to high rates of mental health issues, including depression, anxiety, addiction, and post-traumatic stress disordered (PTSD). Limited access to culturally appropriate services further compounds these challenges, as traditional healing practices are often overlooked or not integrated into mainstream healthcare.90 2.7 Racial disparities in pain-related health outcomes In addition to chronic conditions such as cardiovascular disease and diabetes, racial disparities exist in the context of acute and chronic pain. Pain is a multifaceted and universal phenomenon rooted in the biopsychosocial framework.91,92 This perspective recognizes pain as complex and subjective, shaped by a combination of biological, psychological, and sociocultural factors. Within the context of pain, the consideration of race becomes a crucial dimension. Disparities in pain perception, management (e.g., prescribing patters of opioid analgesics), and pain types (e.g., chronic pain, cancer pain) among various racial groups have been consistently documented.93–99 A recurring theme is the undertreatment for racialized individuals, particularly Black individuals in the US, marked by fewer opioid prescriptions and longer length of hospital stay.100–102 Namely, a 2008 JAMA study demonstrated that White patients with pain were more likely to receive an 18 opioid than Black patients with pain, when seeking care in US emergency departments.103 Racial bias in pain management also extends to Black children, with Black children being significantly less likely to receive pain medications compared to White children in an emergency department setting.100,101 Emerging evidence also hints at racial differences in the occurrence of a chronic pain condition termed complex regional pain syndrome (CRPS). CRPS Type 1, formerly known as reflex sympathetic dystrophy (RSD), is a debilitating and poorly understood chronic pain condition characterized by severe, disproportionate pain typically affecting an arm or leg.104–106 While the exact cause remains elusive, CRPS is often triggered by trauma, surgery, or injury, though it can develop without an apparent cause.104 The hallmark of CRPS is persistent, intense pain that is disproportionate to the initial injury or inciting event.104,107 Individuals with CRPS may experience a range of symptoms, including changes in skin color and temperature, swelling, and abnormal sweating in the affected limb.107 The pain often presents as a continuous, burning sensation, and even light touch or movement can intensify the discomfort. Additionally, CRPS may lead to muscle atrophy and joint stiffness, further complicating the affected individual's mobility.108 Diagnosis of CRPS can be challenging due to its varied and subjective symptoms, and healthcare providers often rely on a combination of clinical history, physical examination, and diagnostic tests to confirm the condition. Management typically involve a multidisciplinary approach, incorporating physical therapy, medications, and interventions such as sympathetic nerve blocks.109 While some individuals may experience improvement with treatment, CRPS remains a chronic and challenging condition. While one study examined an array of predictors for CRPS,110 including race, a comprehensive analysis of race (e.g. disaggregated race categories, adjustment for confounding) remains a knowledge gap. 19 2.8 Race and participation in health research Clinical trials are important for medical advancements, and beneficial to society in the long run.111 They are used to determine safety and efficacy of treatments before broader introduction to society. Racial diversity in clinical trials is crucial, particularly in diverse regions, such as the US and Canada. Racial diversity in clinical trials is vital for generalizing study results, and subsequently, important to ensure equitable benefits in medical advances.112 Despite this imperative, there is vast evidence suggesting under documentation of race and under-representation of racialized individuals in clinical trials, across a wide range of health conditions.113–115 Lower rates of participation in clinical trials may be due to several factors such as costs of clinical trials (e.g., travel parking, loss of income from missing work, child care during trial participation), lack of health insurance, medical mistrust due to historic mistreatment, and low educational attainment (e.g., less educated participants may not fully comprehend research principles and informed consent).113,116–118 In an examination of trials pivotal to FDA approvals for oncology drugs, Loree and colleagues discovered that Black and Latinx patients with cancer were notably underrepresented in comparison to the expected rates based on US cancer incidence.113 Specifically, Black patients represented 22% and Latinx patients 44% of the expected numbers, while White patients with cancer closely aligned with expectations at 98%. However, a knowledge gap remains regarding race reporting and racial diversity specifically in pain clinical trials. 2.9 Race and views on open data sharing Once clinical trials are completed, their data can be reused and re-evaluated for different purposes through open data sharing.119 This refers to the act of making data widely available for researchers, 20 policy makers, and healthcare professionals.120 Open sharing of participant-level data is rapidly becoming the norm, such that ethics boards, publishers, and funding agencies increasingly require investigators to publicly release data.121 Particularly in the case of global health crises, such as the COVID-19 pandemic, the development of global standards for data sharing has been deemed a priority.122,123 The concept is that open data sharing increases transparency and rigor of science (e.g., allows confirmatory analyses) and leads to novel scientific discoveries.124,125 In turn, it has the potential to reduce the cost of research (e.g., a funded study is used beyond its original purpose) and the burden on subjects to participate in research (e.g., studies that can be addressed with existing data do not have to recruit new subjects).124 This suggests a paradigm shift as open data sharing challenges the notion that a patient’s data collected belongs to clinicians or researchers (or their universities), and advocating for patient/public ownership. However, a knowledge gap remains if racial differences exist in terms of views on open data sharing. Trust and privacy concerns are critical and multi-faceted components that can impact individuals’ willingness to share data.126–128 Evidence suggests that racialized participants believe they are less likely to benefit from research findings or may not trust that the research will be used to improve their health outcomes.129–131 This lack of trust can further complicate the relationship between racialized participants and data sharing preferences. However, it is a knowledge gap as to whether racial differences exist in views on open data sharing, and if this relationship is mediated by trust. 2.10 Rationale for studying race While it is crucial to acknowledge that race lacks biological significance, it still carries significant social meaning. Omitting the reporting of race in health sciences research overlooks the tangible existence of social stratification, injustices, and inequities, with profound implications for 21 population health. Excluding race from research endeavors might obscure essential health disparities. Therefore, the continued inclusion of race in health sciences research is essential for addressing and further comprehending health disparities and inequities in the present context. 2.11 Knowledge Gaps Several knowledge gaps exist within the realm of racial differences in health outcomes and healthcare participation, including the following: a) It is unknown whether there are racial differences in health outcomes related to CRPS-1 b) It is unknown whether there are racial differences in opioid prescription patterns in Canada c) It is unknown whether there are racial differences in participation in pain clinical trials, and predictors of participation in trials d) It is unknown if there are racial differences in open data sharing, and if so, if trust mediates the relationship between race and support for open data sharing 2.12 Thesis Objectives To address these knowledge gaps, this research seeks to deepen our understanding of racial differences in health outcomes and health care participation, through the lens of race as a social construct. This PhD thesis addresses these in the following themes, objectives, and corresponding hypothesizes (Figure 1): Theme 1: Racial differences in health outcomes 1) Objective 1: To investigate racial differences in hospitalization and hospital length of stay for CRPS-1 patients. Hypothesis: Racialized (especially Black) patients will be less likely to be admitted to hospital, but they will also be more likely to have longer length of stay, compared to White patients. 22 2) Objective 2: To examine opioid prescription patterns between racialized and White individuals in Canada. Hypothesis: racialized individuals will be less likely to be prescribed opioids compared to White individuals. Theme 2: Racial differences in healthcare participation 3) Objective 3: To evaluate the frequency of race reporting, racial diversity in pain clinical trials, and the association between clinical trials characteristics and these outcomes. Hypothesis: Representation of racialized individuals will be lower than that of national average. 4) Objective 4: To determine racial differences in views on data sharing and to determine the causal association between race and data sharing through the mediating role of distrust. Hypothesis: racialized individuals will be less likely to share data due to distrust. 23 Figure 1.1 Overview of thesis objectives. 2.13 Thesis Organization This paper-based thesis is organized as follows: Chapter 1 introduces a basic understanding of race, along with the transformation of race as biological trait to race as a social construct. Chapter 2 is a continuation of the introduction in chapter 1, with a focus on background relating to race in health research (and more specifically, within a pain context), and concludes with knowledge gaps and objectives. Chapter 3 is an overview of the general methodology used, including datasets and the statistical methods used in each chapter. Chapter 4 investigates racial differences in CRPS-1 in the US. Chapter 5 aims to quantify racial differences in pain management (i.e., opioid prescriptions) in Canada. Chapter 6 examines racial diversity and race reporting as well as associated factors in pain clinical trials in the US. Chapter 7 focuses on racial differences in views regarding data sharing and the mediating role of trust. Chapter 8 summarizes and synthesizes the 24 main findings of this thesis and discusses their implications. This chapter also summarizes the limitations and strengths of the thesis, as well as highlights the importance and relevance of race in health care and health research. 25 CHAPTER 3: METHODS OVERVIEW 3.1 Study design overview In epidemiological research, establishing causal relationships is crucial to understand the impact of exposures/treatments (independent variables) on health outcomes (dependent variables). Traditionally, this is achieved by randomized controlled experiments in which the independent variables are randomized to participants. However, investigation on racial differences necessitates the use of observational data. Observational data often rely on regression analyses to quantify associative relationships between exposures and outcomes, controlling for potential confounders. 3.2 Data Sources In this thesis, four datasets were utilized: the Healthcare Cost and Utilization Project, Canadian Community Health Survey, data from ClinicalTrials.gov and PubMed, and a secondary data analysis from a published study. Table 3.1 shows an overview of data sources, and methodologies included in this thesis. Specific details of each dataset and its methodologies are discussed below and in greater depth in each corresponding chapter. Healthcare Cost and Utilization Project In Chapter 4, data were utilized from acute and inpatient care settings using the 2020 Healthcare Cost and Utilization Project (HCUP), specifically from the Nationwide Emergency Department Sample (NEDS) and the National Inpatient Sample (NIS). The NEDS is the largest all-payer database of emergency department (ED) encounters, and the NIS is the largest publicly available all-payer inpatient care database in the US. Both NEDS and NIS contained detailed clinical and non-clinical data, including diagnoses according to the International Classification of Diseases, 26 demographics (e.g., age, sex, race, median household income), hospital characteristics (e.g., region, teaching centers), and insurance information (e.g., Medicaid, Medicare, private) Canadian Community Health Survey Data from Chapter 5 were derived from the Canadian Community Health Survey (CCHS). This is a national, cross-sectional survey conducted by Statistics Canada every two years. Designed to capture the broad spectrum of health-related data, the survey collected information pertaining to health status, healthcare utilization, and health determinants of Canadian population. To do so, this survey included respondents over the age of 12 years old residing in every province and territory within Canada, which covers approximately 97% of the population. ClinicalTrials.gov and PubMed In chapter 6, data were extracted from ClinicalTrials.gov using the Aggregate Analysis of ClinicalTrials.gov (AACT). Any clinical trials listing “pain” in their registration as “condition” were included. In addition, several inclusion criteria were also applied: interventional trials (as listed by study investigators), English language, and registered in the US. The data were also cross-referenced in PubMed using clinical trial registration numbers for any potential missing data. Secondary Analysis of Published Study Chapter 7 used data from a secondary analysis of a published study. In this study, respondents had been enrolled, or were the parent or guardian of someone who had been enrolled, in interventional clinical trials, both community-based and hospital-based, within the span of two years in the US. 27 3.3 Overview of statistical methodologies The majority of the analytical techniques used in this thesis were linear and logistic regression (Chapters 4 – 6). In addition, a causal mediation analysis was used in Chapters 5 and 7. Mediation analysis allows researchers to investigate the causal relationship between exposure and outcome (direct effect), and the extent of which this relationship can be explained by another mediating variable (indirect).Within indirect effects, partial mediation implies that the impact of exposure on outcome can be explained partially through mediators (i.e., both direct and indirect effects are significant), and full mediation suggests that the impact of exposure on outcomes is caused entirely by mediators (i.e., only indirect effect is significant). Table 3.1: Overview of data sources, methodologies and analytical techniques used in each chapter Thesis Chapter Data Source Total N Exposures Outcomes Confounders/Mediators Analytical Method 4 NEDS and NIS from HCUP 2020 23,040 ED and 15,225 hospitalizations Race/Ethnicity ED visits and length of stay Demographics, comorbidities, and hospital characteristics Logistic regression 5 CCHS 2015-2016 3,604 individuals Race Prescription Opioid Use Demographics, health conditions, and health care access Logistic regression and mediation analysis 6 ClinicalTrials.gov and PubMed 1200 Clinical trial characteristics, year of publication, and perceived race of investigators Race reporting and Black participation N/A Linear and logistic regression 7 Secondary Analysis of Published Study 771 Race Willingness to share data Demographics, trust variables Logistic regression and mediation analysis 28 CHAPTER 4: THE EFFECTS OF RACE ON OUTCOMES OF CARE FOR COMPLEX REGIONAL PAIN SYNDROME TYPE 1 IN THE UNITED STATES 4.1 Introduction Complex regional pain syndrome (CRPS) is a chronic pain disorder that is divided into two distinct clinical subtypes, CRPS-1 and CRPS-2. The vast majority of cases belong to CRPS-1, which occurs after an injury that does not involve direct nerve injury.132 Marked by symptoms such as swelling, impaired motor function, and sympathetic dysfunction, CRPS-1 poses a diagnostic challenge due to its complex etiology and variable clinical manifestations.104,105 A number of factors have been reported to be associated with the diagnosis of CRPS-1, including trauma to upper extremities, and certain sociodemographic characteristics, such as older age and female sex.110,133,134 Additionally, racial disparities in the diagnosis of CRPS-1 have been observed, whereby, compared with racialized patients, White patients experienced a higher rate of CRPS-1 diagnosis after adjusting for other factors.110 Even though previous studies have highlighted racial differences in management and outcome of other health conditions,135–137 little is known about whether similar differences exist in care for CRPS-1. Indeed, there is a paucity of information on potential disparities in CRPS-1 treatment and outcomes, such as differences, if any, in hospital admission and length of stay by race. To address knowledge gaps related to potential race-based disparities in CRPS-1, acute care and inpatient administrative data from the year 2020 were analyzed. The primary objective was to determine the total number of CRPS-1 ED visits and hospitalizations by race, as well as to assess the distribution of sex and age within race groups for each clinical setting. The secondary 29 objectives were to examine whether race, as well as other individual and hospital characteristics, are associated with immediate hospital admission and longer length of stay for CRPS-1 ED visits and hospitalizations, respectively. 4.2 Methods Ethics and Reporting The study utilized publicly available data that did not include any direct patient identifiers. It was therefore exempt from ethics board review by the Office of Research Ethics at the University of British Columbia. Our research was conducted in accordance with terms outlined in the United States Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP) Data Use Agreement, which required suppressing small cell counts less than or equal to ten. Study particulars were summarized using the Reporting of studies Conducted using Observational Routinely collected Data (RECORD) statement.138 Data Sources and Study Design We completed cross-sectional analyses within acute and inpatient care settings using 2020 HCUP observational data from the Nationwide Emergency Department Sample (NEDS) and the National (formerly Nationwide) Inpatient Sample (NIS). The NEDS is the largest all-payer database of emergency department (ED) encounters from American Hospital Association community, non-rehabilitation, hospital-owned EDs in the United States.139 Annual NEDS datasets are constructed from a 20% stratified sample of eligible EDs in a given year, with weights being derived for both individual facilities and visits. When weighted, the more than 28 million distinct ED visits in 2020 represent the roughly 123 million ED encounters in the US for that year. The NIS is the largest 30 publicly available all-payer inpatient care database in the US, and is available yearly dating back to 1988. Starting in 2012, NIS data originate from an approximate 20% sample of discharges from all HCUP-participating hospitals. Derived survey weights may subsequently be used to compute nationally representative estimates of hospital admission and associated outcomes for each year. For 2020, the NIS includes health information from more than seven million distinct hospitalizations. When weighted, these represent more than 35 million unique hospital admissions and account for more than 97% of the country’s population. The 2020 NEDS and NIS datasets contain in-depth clinical and nonclinical data, such as International Classification of Diseases (Tenth Revision; ICD-10) diagnosis, procedure, and external cause of morbidity codes; patient demographic details (such race/ethnicity, age, sex, and quartile of median household income); hospital characteristics (such as region, teaching status, trauma center designation, and ownership); and information about healthcare charges and the payer (such as Medicare, private insurance, or no charge). CRPS-1 Encounters Eligible complex regional pain syndrome type 1 (CRPS-1) ED visits for our study included all ED discharges between January 1, 2020 and December 31, 2020 where an ICD-10-CM diagnosis code for CRPS-1 was recorded in the NEDS as either a primary (DX1) or secondary (DX2-35) diagnosis: CRPS-1, unspecified (G90.50), CRPS-1 of upper limb (G90.51), CRPS-1of lower limb (G90.52), or CRPS-1 of other specified site (G90.59). Pediatric (<19 years) CRPS-1 ED visits were then excluded so that our findings may be compared with the largest epidemiological study of 31 CRPS-1 in the United States.110 We further excluded ED visits where patient race/ethnicity was missing to facilitate the reporting of patient race/ethnicity by age group. Similar to the selection of CRPS-1 ED visits, eligible CRPS-1 hospitalizations included all adult inpatient discharges between January 1, 2020 and December 31, 2020 where an ICD-10-CM diagnosis code for CRPS-1 was recorded in the NIS as a primary (DX1) or secondary (DX2-40) diagnosis. We subsequently excluded all elective hospitalizations, as well as hospitalizations with a missing elective/non-elective status, to focus on outcomes of non-elective CRPS-1 care. Hospitalizations were further excluded if patient race/ethnicity was missing. Lastly, eligible CRPS-1 ED visits and hospitalizations were excluded if a patient’s median household income or primary payer data was missing. Study Outcomes The primary study outcomes were the total number of nationwide ED visits and national hospitalizations for CRPS-1, respectively, by race/ethnicity group, as well as the distribution of sex and age within each race/ethnicity group. Secondary outcomes included immediate hospital admission for CRPS-1 ED visits and longer length of inpatient stay for CRPS-1 hospitalizations. Immediate hospital admissions included both admissions to the same hospital and transfers to other short-term hospitals; transfers to short-term hospitals were presumed to result in an inpatient stay. Inpatients stays greater than three days were defined as longer lengths of stay based on the distribution of time to discharge data and three days being the approximate midpoint of CRPS-1 hospitalizations. 32 Primary Analyses We estimated the total number of CRPS-1 ED visits and hospitalizations, as well as the number of CRPS-1 encounters in each clinical setting by race/ethnicity (White, Black, Hispanic, Other), using HCUP discharge weights for the 2020 NEDS and NIS, respectively. The distribution of sex (female, male) and age (19-29, 30-39, 40-49, 50-59, 60-69, and 70+ years) within CRPS-1 encounters for each setting were then examined and reported as percentages of total CRPS-1 encounters within each race/ethnicity group. Secondary Analyses Sociodemographic, clinical, and hospital characteristics of study encounters where a diagnosis of CRPS-1 was recorded were summarized using descriptive statistics, as were hospital admission and longer length of stay outcomes related to CRPS-1 ED visits and hospitalizations, respectively. Chi-square tests were used to assess whether the distribution of examined sociodemographic, clinical, and hospital variable values differed by encounter outcome. We used the HCUP Elixhauser Comorbidity Software Refined (version 2023.1) for ICD-10-CM to measure twenty distinct pre-existing medical conditions that did not require a present on admission indicator and were assumed to not arise from hospital care (such as metastatic cancer, dementia, and obesity).140 We used logistic regression to determine whether race/ethnicity, as well as other individual and hospital characteristics, were associated with examined secondary outcomes. For these analyses, we constructed the following four sets of models to measure exposure-outcome associations for each secondary outcome: 1) bivariable models for each reported characteristic; 2) a base adjusted model, which included race/ethnicity, age, sex, median household income, and primary payer; 3) 33 an intermediate adjusted model, which included all base adjusted model covariates and comorbidities identified as having a significant association with the study outcome in the bivariable models; and 4) a fully adjusted model, which included all intermediate adjusted model covariates and all other variables where one or more levels were significantly associated with the study outcome in the bivariable models. Covariates includes in the base adjusted model were selected a priori based on clinical knowledge and their presumed ability to confound examined exposure-outcome relationships. All statistical models accounted for the complexity of NEDS and NIS survey designs, including the clustering of discharges within hospitals, to generate precise variance estimates for reported odds ratios. A significance level of 0.05 was used for all analyses. SAS V.9.4 (SAS Institute Inc., Cary, North Carolina, USA) was used to complete all analyses. GraphPad Prism Version 9.2.0 (GraphPad Software LLC, San Diego, California, USA) was used to graphically report 2020 CRPS-1 counts and related proportions. 4.3 Results Emergency Department Visits for CRPS-1 The selection of emergency department (ED) encounters where complex regional pain syndrome type 1 (CRPS-1) was diagnosed is shown in Figure 4.1A. Between January 1, 2020 and December 31, 2020, there were 23,323 adult encounters where CRPS-1 was diagnosed. After excluding encounters with missing race/ethnicity data (283, 1.2%), a total of 23,040 ED CRPS-1 visits remained. The incidence proportion of CRPS-1 ED visits was 0.02%. White populations (19,139 visits; 83.1% of total) accounted for the greatest number of annual CRPS-1 ED visits, followed by Black (2,024 visits; 8.8% of total), Hispanic (1,231 visits; 5.3% of 34 total) and other race/ethnicity populations (646 visits; 2.8% of total) (Figure 4.2A). Overall, female patients visited the ED more often (17,067 visits; 74.1% of total) for CRPS-1 compared with males. There were minor differences in the proportion of ED visits by female patients across examined race/ethnicity groups, with the greatest proportion of females observed among patients of other race/ethnicity (79.1%) and the lowest proportion of females observed among Hispanic individuals (71.5%) or (Figure 4.2A). Few (1,806 visits; 7.8% of total) CRPS-1 ED visits were by younger adults (19-29 years) in 2020; the highest number of CRPS-1 visits were by individuals 50-59 years of age (6,004 visits; 26.1% of total). Within all distinct race/ethnicity groups, the proportion of CRPS-1 ED visits in each age category increased with increasing age, peaking with those aged 50-59 years, and then steadily declined (Figure 4.2B). Overall, age distributions in CRPS-1 ED visits were similar across examined race/ethnicity groups. For the secondary admission analyses (n = 22,676 visits), few ED visits for CRPS-1 from our primary analyses were excluded due to missing income quartile data (347; 1.5% of total) or missing primary payer data (17; 0.1% of total) (Figure 4.1A). The majority of CRPS-1 ED visits were by patients with Medicare (50.7%) or private insurance (26.0%) and occurred at hospitals in the South (31.3%) and Midwest (26.8%) (Table 1). Most hospitals where ED CRPS-1 care was provided were characterized as: 1) metropolitan teaching (69.5%), 2) privately controlled but not-for-profit (58.7%), and/or 3) non-trauma (52.9%) centres. Comorbidities commonly diagnosed during ED visits included uncomplicated hypertension (34.6%), chronic pulmonary disease (30.3%), depression (22.6%), obesity (18.0%), and hypothyroidism (14.8%). 35 Hospitalizations for CRPS-1 There were a total of 15,225 adult hospitalizations where CRPS-1 was recorded as a diagnosis between January 1, 2020 and December 31, 2020 (Figure 4.1B). Of these, 2,915 (19.1%) elective admissions were excluded from our analyses, as were 15 (0.1%) and 225 (1.5%) admissions where elective/non-elective status and race/ethnicity data, respectively, was missing. After exclusions, there were 12,070 CRPS-1 hospitalizations included in the primary analyses, corresponding to an incidence proportion of 0.04%. Overall, patterns of CRPS-1 hospitalizations by race/ethnicity were comparable to our findings from ED settings. White (10,000 admissions; 82.8% of total) and Black (1,035 admissions; 8.6% of total) patients comprised the majority of CRPS-1 hospitalizations, whereas a small number of admissions were by Hispanic (630 admissions; 5.2% of total) and other race/ethnicity patients (405 admissions; 3.3% of total) (Figure 4.2C). The majority of CRPS-1 hospitalizations were by female patients (8,605 admissions; 71.3% of total), and there were minimal fluctuations in the proportion of hospitalizations by female patients across examined race/ethnicity groups. As observed for CPRS-1 ED visits, CRPS-1 hospitalizations were infrequent (645; 5.3% of total) among younger adults. The proportion of CRPS-1 hospitalizations in each age category generally increased with increasing age until age 50-59 years for White, Black, and Hispanic populations, though did not peak until age 60-69 years for hospitalizations by White individuals (Figure 4.2D). Notable differences were observed in the age distribution of CRPS-1 hospitalizations among other race/ethnicity patients, whereby the youngest individuals comprised 13.6% of all admissions. 36 Few CRPS-1 hospitalizations (145; 1.2% of total) from our primary analyses were excluded for our secondary analyses (n = 11,925 admissions) due to missing income quartile data. No hospitalizations were excluded due to missing primary payer data (Figure 4.1B). Most CRPS-1 hospitalizations were subsidized by Medicare (56.5%) or private insurance (23.9%), originated in the ED (86.2%), did not (83.7%) include a major operating room procedure, and resulted in routine discharge (60.0%) (Table 2). Inpatients stays related to CRPS-1 commonly occurred at hospitals in the South (30.8%) and Midwest (25.1%), as well as centres that were classified as large (49.1%), urban teaching (76.7%), and/or privately controlled but not-for-profit (83.4%). Uncomplicated hypertension (37.4%), chronic pulmonary disease (34.1%), depression (29.0%), obesity (24.7%), and diabetes with chronic complications (20.9%) were the most frequently recorded comorbidities during CRPS-1 hospitalizations. Emergency Department Visits and Immediate Hospital Admission Descriptive statistics for our analyses of immediate hospitalization are provided in Table 4.1 along with observed estimates of association. Nearly half (10,868 visits, 47.9% of total) of all CRPS-1 ED visits in 2020 resulted in immediate hospitalization. Bivariable regression models identified a number of characteristics that were independently associated with being admitted to hospital after presenting to the ED with CRPS-1, including but not limited to age, sex, median household income, primary payer, hospital location/teaching status, and hospital trauma level designation. Race/ethnicity was not found to be associated with hospital admission in the bivariable model. Most examined Elixhauser comorbidities were independently associated with hospital admission, with diagnoses of metastatic cancer (odds ratio (OR) 4.91, 95% confidence interval (CI) 2.30 to 37 10.49) and dementia (OR 4.61, 95% CI 2.72 to 7.80) being most associated with immediate admission. The addition of covariates to our bivariable models generally resulted in initially observed associations being attenuated in the fully adjusted model, though this was not the case for all characteristics. A number of characteristics were found to be more strongly associated with hospitalization in our fully adjusted model. For example, compared with ED visits by White individuals, encounters by Black individuals were significantly negatively associated (adjusted odds ratio (AOR) 0.74, 95% CI 0.55 to 1.00) with immediate hospitalization. Similarly, despite women accounting for approximately three quarters of all CPRS-1 ED visits, ED visits by women were significantly less likely (AOR 0.70, 95% CI 0.58 to 0.85) to result in admission. The diagnosis of many individual comorbidities remained significantly positively associated with immediate hospitalization after adjustment for potential confounders, with estimates of association being greatest for the diagnosis of acquired immunodeficiency syndrome (AOR 4.90, 95% CI 1.15 to 20.81), drug abuse (AOR 4.67, 95% CI 3.53 to 6.18), and metastatic cancer (AOR 4.36, 95% CI 1.91 to 9.96). Hospitalizations and Longer Length of Stay Findings from our analyses of longer length of inpatient stay, including descriptive counts and observed estimates of association, are provided in Table 4.2. Slightly more than half (6,605 admissions, 55.4% of total) of all examined CRPS-1 hospitalizations were four or more days in length. Characteristics found to be associated with longer length of stay in our bivariable regression analyses included race/ethnicity, median household income, experiencing a major 38 operating room procedure, discharge disposition, hospital region, and hospital control/ownership. Additionally, some individual Elixhauser comorbidities were found to be significantly associated with longer lengths of stay, such as the diagnosis of dementia (OR 1.97, 95% CI 1.11 to 3.49) and drug abuse (OR 1.61, 95% CI 1.21 to 2.15). A number of originally observed associations were weakened with increasing adjustment for potential confounders in our multivariable models. Nevertheless, many characteristics, such as primary payer, experiencing a major operating room procedure, and discharge disposition, were found to be associated with longer length of stay in the fully adjusted model. Compared with CRPS-1 hospitalizations subsidized by private insurance, hospitalizations subsidized by Medicaid showed a significant negative association (AOR 0.68, 95% CI 0.51 to 0.92) with longer length of inpatient stay. Conversely, having a major operating room procedure (AOR 1.68, 95% CI 1.31 to 2.15) and being discharged to another facility (such as a skilled nursing facility relative to routine discharge; AOR 5.80, 95% CI 4.22 to 7.96) were significantly positively associated with increased time in hospital. Drug abuse was the only examined comorbidity to be significantly associated (AOR 1.81, 95% CI 1.34 to 2.46) with longer length of stay in the fully adjusted model. 39 Figure 4.1. Selection of CRPS-1 emergency department encounters (A) and hospitalizations (B). Abbreviations: CRPS-1, complex regional pain syndrome type 1; ED, Emergency Department; NEDS, Nationwide Emergency Department Sample; NIS, National (Nationwide) Inpatient Total number of NEDS encountersJanuary 1, 2020 - December 31, 2020n = 123,278,165Eligible CRPS-1 ED visitsn = 24,285 (0.02%)CRPS-1 Age Exclusions (n = 962, 3.9%)- Age < 19 years (n = 962)Other Exclusions (n = 647, 2.8%)- Missing race data (n = 283) - Missing income quartile data (n = 347)- Missing primary payer data (n = 17)Eligible CRPS-1 ED visits(individuals 19+ years) n = 23,323Eligible CRPS-1 ED visits included in analysesn = 22,676Total number of NIS hospitalizationsJanuary 1, 2020 - December 31, 2020n = 32,355,827Eligible CRPS-1 hospitalizationsn = 15,800 (0.05%)CRPS-1 Age Exclusions (n = 575, 3.6%)- Age < 19 years (n = 575)Other Exclusions (n = 3,300, 21.7%)- Elective admission (n = 2,915)- Missing elective/non-elective status (n = 15)- Missing race data (n = 225) - Missing income quartile data (n = 145)- Missing primary payer data (n = 0)Eligible CRPS-1 hospitalizations(individuals 19+ years) n = 15,225Eligible CRPS-1 hospitalizations included in analysesn = 11,925A. B. 40 Figure 4.2. Adult emergency department visits and hospitalizations in 2020 for complex regional pain syndrome type 1 (CRPS-1). Total number of CRPS-1 emergency department visits and proportion female by race/ethnicity (A), and age distribution of CRPS-1 by race/ethnicity (B). Total number of CRPS-1 hospitalizations and proportion female by race/ethnicity (C), and age distribution of CRPS-1 by race/ethnicity (D). Abbreviation: ED, Emergency Department. White Black Hispanic Other05,00010,00015,00020,00025,000020406080100Race / EthnicityNumber of VisitsPercent Female (%)ED VisitsFemaleWhite Black Hispanic Other05,00010,00015,00020,00025,000020406080100Race / EthnicityNumber of VisitsPercent Female (%)HospitalizationsFemaleWhite Black Hispanic Other010203040Race / EthnicityPercent Age Group (%)19 - 2930 - 3940 - 4950 - 5960 - 6970+White Black Hispanic Other010203040Race / EthnicityPercent Age Group (%)19 - 2930 - 3940 - 4950 - 5960 - 6970+A. B.C. D. 41 Table 4.1. Associations between select factors and immediate hospital admission among emergency department encounters where complex regional pain syndrome type 1 was diagnosed, 2020. Admitted to Hospital All ED Visits n (%) Yes n (%) No n (%) Characteristic n = 22,676 n = 10,868 n = 11,808 p-valuea OR AOR Model 1 AOR Model 2 AOR Model 3 Race/ethnicity White 18,871 (83.2) 9,183 (48.7) 9,688 (51.3) 0.1756 Reference Reference Reference Reference Black 1,959 (8.6) 858 (43.8) 1,102 (56.2) 0.82 (0.64 - 1.05) 0.87 (0.66 - 1.14) 0.81 (0.60 - 1.11) 0.74 (0.55 - 1.00)* Hispanic 1,199 (5.3) 495 (41.3) 704 (58.7) 0.74 (0.53 - 1.04) 0.85 (0.60 - 1.20) 0.89 (0.61 - 1.31) 0.87 (0.59 - 1.27) Otherb 646 (2.8) 332 (51.4) 314 (48.6) 1.12 (0.74 - 1.67) 1.18 (0.77 - 1.80) 1.31 (0.85 - 2.04) 1.28 (0.83 - 1.98) Age 19-29 1,792 (7.9) 546 (30.5) 1,246 (69.5) <.0001 Reference Reference Reference Reference 30-39 2,849 (12.6) 893 (31.4) 1,956 (68.6) 1.04 (0.70 - 1.55) 1.05 (0.70 - 1.58) 0.82 (0.55 - 1.24) 0.82 (0.53 - 1.25) 40-49 4,177 (18.4) 1,722 (41.2) 2,455 (58.8) 1.60 (1.08 - 2.38)* 1.58 (1.05 - 2.40)* 1.15 (0.76 - 1.73) 1.15 (0.75 - 1.76) 50-59 5,880 (25.9) 2,818 (47.9) 3,063 (52.1) 2.10 (1.45 - 3.04)*** 2.03 (1.37 - 2.99)*** 1.39 (0.94 - 2.05) 1.45 (0.98 - 2.15) 60-69 4,905 (21.6) 2,850 (58.1) 2,054 (41.9) 3.17 (2.15 - 4.67)*** 2.98 (1.95 - 4.56)*** 1.77 (1.16 - 2.73)** 1.88 (1.20 - 2.95)** 70-79 2,299 (10.1) 1,516 (65.9) 783 (34.1) 4.42 (2.91 - 6.71)*** 3.90 (2.42 - 6.30)*** 2.14 (1.30 - 3.52)** 2.28 (1.36 - 3.84)** 80-90 773 (3.4) 523 (67.6) 250 (32.4) 4.76 (2.79 - 8.11)*** 4.11 (2.29 - 7.38)*** 2.20 (1.13 - 4.31)* 2.51 (1.27 - 4.98)** Sex Male 5,877 (25.9) 3,139 (53.4) 2,738 (46.6) 0.0010 Reference Reference Reference Reference Female 16,799 (74.1) 7,729 (46.0) 9,070 (54.0) 0.74 (0.63 - 0.88)*** 0.73 (0.61 - 0.86)*** 0.71 (0.59 - 0.85)*** 0.70 (0.58 - 0.85)*** Median Household Income Quartile 4 ($86,000+) 5,673 (25.0) 2,941 (51.8) 2,732 (48.2) 0.2104 Reference Reference Reference Reference 42 Quartile 3 ($65,000 - $85,999) 5,957 (26.3) 2,866 (48.1) 3,091 (51.9) 0.86 (0.68 - 1.10) 0.91 (0.71 - 1.17) 0.92 (0.70 - 1.22) 0.92 (0.70 - 1.20) Quartile 2 ($50,000 - $64,999) 5,822 (25.7) 2,650 (45.5) 3,172 (54.5) 0.78 (0.61 - 0.99)* 0.80 (0.62 - 1.05) 0.83 (0.62 - 1.11) 0.94 (0.70 - 1.25) Quartile 1 ($1 - $49,999) 5,224 (23.0) 2,411 (46.2) 2,813 (53.8) 0.80 (0.61 - 1.04) 0.85 (0.64 - 1.12) 0.87 (0.63 - 1.20) 1.05 (0.77 - 1.43) Primary Payer Medicare 11,486 (50.7) 6,277 (54.7) 5,209 (45.3) <.0001 1.61 (1.34 - 1.93)*** 1.14 (0.93 - 1.40) 0.94 (0.76 - 1.16) 0.95 (0.76 - 1.18) Medicaid 3,633 (16.0) 1,522 (41.9) 2,110 (58.1) 0.96 (0.75 - 1.23) 1.08 (0.85 - 1.38) 0.90 (0.70 - 1.16) 0.91 (0.70 - 1.19) Private insurance 5,900 (26.0) 2,529 (42.9) 3,371 (57.1) Reference Reference Reference Reference Self-pay 717 (3.2) 157 (21.9) 559 (78.1) 0.38 (0.23 - 0.61)*** 0.38 (0.24 - 0.61)*** 0.39 (0.25 - 0.62)*** 0.38 (0.24 - 0.60)*** Other (including no charge) 941 (4.1) 382 (40.6) 559 (59.4) 0.91 (0.63 - 1.31) 0.89 (0.62 - 1.28) 0.87 (0.62 - 1.24) 0.90 (0.62 - 1.29) Comorbidities Acquired immune deficiency syndrome 94 (0.4) 74 (78.5) 20 (21.5) 0.0282 3.99 (1.25 - 12.75)* -- 4.20 (0.99 - 17.75) 4.90 (1.15 - 20.81)* Alcohol abuse 645 (2.8) 494 (76.7) 150 (23.3) <.0001 3.70 (2.47 - 5.55)*** -- 3.67 (2.31 - 5.84)*** 3.35 (2.11 - 5.32)*** Autoimmune conditions 1,443 (6.4) 937 (64.9) 506 (35.1) <.0001 2.11 (1.63 - 2.72)*** -- 1.87 (1.36 - 2.55)*** 1.85 (1.35 - 2.52)*** Lymphoma 76 (0.3) 55 (72.0) 21 (28.0) 0.0365 2.80 (1.02 - 7.65)* -- 2.54 (0.81 - 7.97) 2.49 (0.89 - 6.99) Leukemia 109 (0.5) 74 (67.6) 35 (32.4) 0.0929 2.28 (0.93 - 5.58) -- -- -- Metastatic cancer 239 (1.1) 195 (81.7) 44 (18.3) <.0001 4.91 (2.30 - 10.49)*** -- 4.80 (2.08 - 11.06)*** 4.36 (1.91 - 9.96)*** Solid tumor without metastasis, in situ ## ## ## ## ## -- -- -- Solid tumor without metastasis, malignant 324 (1.4) 218 (67.2) 106 (32.8) 0.0012 2.25 (1.33 - 3.81)** -- 1.35 (0.72 - 2.53) 1.31 (0.70 - 2.46) Dementia 453 (2.0) 365 (80.5) 88 (19.5) <.0001 4.61 (2.72 - 7.80)*** -- 2.48 (1.32 - 4.65)** 2.37 (1.25 - 4.49)** Depression 5,114 (22.6) 2,975 (58.2) 2,138 (41.8) <.0001 1.71 (1.41 - 2.06)*** -- 1.52 (1.24 - 1.85)*** 1.50 (1.23 - 1.82)*** Diabetes without chronic complications 1,923 (8.5) 893 (46.4) 1,030 (53.6) 0.5807 0.94 (0.74 - 1.18) -- -- -- Diabetes with chronic complications 3,301 (14.6) 2,483 (75.2) 818 (24.8) <.0001 3.98 (3.19 - 4.96)*** -- 2.20 (1.72 - 2.83)*** 2.17 (1.69 - 2.79)*** Drug abuse 1,908 (8.4) 1,484 (77.8) 424 (22.2) <.0001 4.25 (3.23 - 5.60)*** -- 4.85 (3.67 - 6.41)*** 4.67 (3.53 - 6.18)*** Hypertension, complicated 2,831 (12.5) 2,115 (74.7) 716 (25.3) <.0001 3.74 (2.94 - 4.77)*** -- 1.86 (1.41 - 2.45)*** 1.85 (1.40 - 2.44)*** 43 Hypertension, uncomplicated 7,852 (34.6) 4,107 (52.3) 3,745 (47.7) 0.0003 1.31 (1.14 - 1.51)*** -- 1.17 (1.01 - 1.35)* 1.15 (0.99 - 1.34) Chronic pulmonary disease 6,882 (30.3) 3,976 (57.8) 2,906 (42.2) <.0001 1.77 (1.51 - 2.07)*** -- 1.33 (1.12 - 1.57)** 1.29 (1.09 - 1.53)** Obesity 4,072 (18.0) 2,805 (68.9) 1,267 (31.1) <.0001 2.89 (2.36 - 3.55)*** -- 2.38 (1.91 - 2.95)*** 2.37 (1.91 - 2.94)*** Peripheral vascular disease 1,277 (5.6) 860 (67.3) 417 (32.7) <.0001 2.35 (1.62 - 3.40)*** -- 1.41 (0.88 - 2.25) 1.36 (0.87 - 2.12) Hypothyroidism 3,346 (14.8) 2,170 (64.8) 1,176 (35.2) <.0001 2.26 (1.83 - 2.77)*** -- 1.73 (1.37 - 2.17)*** 1.70 (1.35 - 2.13)*** Other thyroid disorders 371 (1.6) 227 (61.2) 144 (38.8) 0.0137 1.73 (1.11 - 2.70)* -- 1.37 (0.84 - 2.23) 1.36 (0.82 - 2.26) Hospital Region Northeast 5,123 (22.6) 2,685 (52.4) 2,437 (47.6) 0.1580 Reference -- -- -- Midwest 6,081 (26.8) 2,764 (45.5) 3,317 (54.5) 0.76 (0.53 - 1.07) -- -- -- South 7,089 (31.3) 3,501 (49.4) 3,588 (50.6) 0.89 (0.64 - 1.23) -- -- -- West 4,383 (19.3) 1,917 (43.7) 2,466 (56.3) 0.71 (0.50 - 1.00) -- -- -- Hospital Location/Teaching Status Metropolitan non-teaching 4,359 (19.2) 1,952 (44.8) 2,408 (55.2) <.0001 1.98 (1.44 - 2.72)*** -- -- 1.87 (1.31 - 2.66)*** Metropolitan teaching 15,761 (69.5) 8,174 (51.9) 7,587 (48.1) 2.63 (1.97 - 3.52)*** -- -- 2.07 (1.49 - 2.89)*** Non-metropolitan hospital 2,556 (11.3) 742 (29.0) 1,814 (71.0) Reference -- -- Reference Hospital Control/Ownership Government or private (collapsed category) 3,104 (13.7) 1,433 (46.2) 1,670 (53.8) 0.3733 0.91 (0.59 - 1.42) -- -- -- Government, non-federal (public) 1,825 (8.0) 757 (41.5) 1,067 (58.5) 0.76 (0.52 - 1.10) -- -- -- Private, not-for-profit (voluntary) 13,300 (58.7) 6,467 (48.6) 6,833 (51.4) 1.01 (0.74 - 1.37) -- -- -- Private, investor-owned (proprietary) 1,556 (6.9) 754 (48.4) 802 (51.6) Reference -- -- -- Private (collapsed category) 2,891 (12.7) 1,456 (50.4) 1,435 (49.6) 1.08 (0.74 - 1.59) -- -- -- Hospital Trauma Level Designation Not a trauma center 11,996 (52.9) 5,173 (43.1) 6,823 (56.9) 0.0002 0.59 (0.42 - 0.84)** -- -- 0.61 (0.43 - 0.87)** Trauma center level I 3,852 (17.0) 2,165 (56.2) 1,687 (43.8) Reference -- -- Reference 44 Abbreviations: AOR, adjusted odds ratio; ED, Emergency Department; OR, odds ratio. aChi-square test bIncludes Asian/Pacific Islander, Native American, & Other. ***p<0.001; **p<0.01; *p < 0.05. ## Data suppressed; 10 or fewer observations in some cells. Trauma center level II 3,593 (15.8) 2,078 (57.8) 1,515 (42.2) 1.07 (0.70 - 1.62) -- -- 0.94 (0.63 - 1.41) Trauma center level III 3,122 (13.8) 1,406 (45.0) 1,716 (55.0) 0.64 (0.41 - 0.99)* -- -- 0.60 (0.39 - 0.94)* Collapsed category (level I, II, or III) 112 (0.5) 45 (40.0) 67 (60.0) 0.52 (0.20 - 1.35) -- -- 1.18 (0.73 - 1.88) 45 Table 4.2. Associations between select factors and longer length of stay among inpatient encounters where complex regional pain syndrome type 1 was diagnosed, 2020. Longer Length of Stay (4+ days) All Admissions n (%) Yes n (%) No n (%) Characteristic n = 11,925 n = 6,605 n = 5,320 p-valuea OR AOR Model 1 AOR Model 2 AOR Model 3 Race White 9,900 (83.0) 5,425 (54.8) 4,475 (45.2) 0.0549 Reference Reference Reference Reference Black 1,020 (8.6) 650 (63.7) 370 (36.3) 1.45 (1.07 - 1.96)* 1.44 (1.06 - 1.94)* 1.43 (1.05 - 1.94)* 1.34 (0.98 - 1.83) Hispanic 605 (5.1) 335 (55.4) 270 (44.6) 1.02 (0.70 - 1.49) 1.06 (0.72 - 1.56) 1.07 (0.73 - 1.58) 1.15 (0.76 - 1.74) Otherb 400 (3.4) 195 (48.7) 205 (51.3) 0.79 (0.52 - 1.19) 0.80 (0.53 - 1.22) 0.85 (0.55 - 1.30) 0.84 (0.54 - 1.31) Age 19-29 645 (5.4) 325 (50.4) 320 (49.6) 0.7531 Reference Reference Reference Reference 30-39 1,060 (8.9) 570 (53.8) 490 (46.2) 1.15 (0.73 - 1.80) 1.10 (0.70 - 1.73) 1.02 (0.65 - 1.60) 1.00 (0.63 - 1.59) 40-49 1,775 (14.9) 1,000 (56.3) 775 (43.7) 1.27 (0.83 - 1.95) 1.17 (0.76 - 1.80) 1.08 (0.70 - 1.66) 1.04 (0.67 - 1.63) 50-59 3,095 (26.0) 1,690 (54.6) 1,405 (45.4) 1.18 (0.80 - 1.75) 1.08 (0.72 - 1.62) 0.99 (0.66 - 1.47) 0.86 (0.57 - 1.31) 60-69 3,225 (27.0) 1,780 (55.2) 1,445 (44.8) 1.21 (0.82 - 1.80) 1.07 (0.71 - 1.62) 0.95 (0.63 - 1.44) 0.77 (0.50 - 1.20) 70-79 1,570 (13.2) 905 (57.6) 665 (42.4) 1.34 (0.87 - 2.06) 1.15 (0.73 - 1.82) 0.99 (0.62 - 1.58) 0.66 (0.41 - 1.09) 80-90 555 (4.7) 335 (60.4) 220 (39.6) 1.50 (0.89 - 2.52) 1.27 (0.74 - 2.18) 1.02 (0.58 - 1.79) 0.66 (0.36 - 1.20) Sex Male 3,410 (28.6) 1,870 (54.8) 1,540 (45.2) 0.7384 Reference Reference Reference Reference Female 8,515 (71.4) 4,735 (55.6) 3,780 (44.4) 1.03 (0.86 - 1.24) 1.01 (0.84 - 1.22) 1.04 (0.87 - 1.26) 0.98 (0.81 - 1.19) Median Household Income Quartile 4 ($86,000+) 2,795 (23.4) 1,485 (53.1) 1,310 (46.9) 0.1373 Reference Reference Reference Reference Quartile 3 ($65,000 - $85,999) 3,270 (27.4) 1,760 (53.8) 1,510 (46.2) 1.03 (0.82 - 1.30) 1.03 (0.82 - 1.30) 1.02 (0.81 - 1.30) 1.02 (0.80 - 1.32) 46 Quartile 2 ($50,000 - $64,999) 2,970 (24.9) 1,635 (55.1) 1,335 (44.9) 1.08 (0.85 - 1.37) 1.08 (0.85 - 1.37) 1.06 (0.83 - 1.36) 1.05 (0.81 - 1.38) Quartile 1 ($1 - $49,999) 2,890 (24.2) 1,725 (59.7) 1,165 (40.3) 1.31 (1.02 - 1.68)* 1.27 (0.98 - 1.63) 1.28 (0.99 - 1.66) 1.17 (0.88 - 1.54) Primary Payer Medicare 6,735 (56.5) 3,870 (57.5) 2,865 (42.5) 0.0812 1.12 (0.91 - 1.37) 1.07 (0.86 - 1.33) 1.00 (0.80 - 1.24) 0.91 (0.72 - 1.15) Medicaid 1,690 (14.2) 820 (48.5) 870 (51.5) 0.78 (0.59 - 1.03) 0.74 (0.56 - 0.99)* 0.68 (0.51 - 0.91)** 0.68 (0.51 - 0.92)* Private insurance 2,850 (23.9) 1,560 (54.7) 1,290 (45.3) Reference Reference Reference Reference Self-pay 230 (1.9) 125 (54.3) 105 (45.7) 0.98 (0.56 - 1.73) 0.98 (0.55 - 1.76) 0.94 (0.54 - 1.64) 0.85 (0.46 - 1.56) Other (including no charge) 420 (3.5) 230 (54.8) 190 (45.2) 1.00 (0.64 - 1.57) 0.95 (0.61 - 1.50) 0.92 (0.58 - 1.45) 0.90 (0.55 - 1.48) Comorbidities Acquired immune deficiency syndrome 45 (0.4) 20 (44.4) 25 (55.6) 0.5176 0.64 (0.17 - 2.40) -- -- -- Alcohol abuse 580 (4.9) 355 (61.2) 225 (38.8) 0.2045 1.29 (0.87 - 1.90) -- -- -- Autoimmune conditions 965 (8.1) 545 (56.5) 420 (43.5) 0.7597 1.05 (0.77 - 1.43) -- -- -- Lymphoma 75 (0.6) 45 (60.0) 30 (40.0) 0.7147 1.21 (0.43 - 3.40) -- -- -- Leukemia 85 (0.7) 30 (35.3) 55 (64.7) 0.1317 0.44 (0.16 - 1.22) -- -- -- Metastatic cancer 285 (2.4) 200 (70.2) 85 (29.8) 0.0289 1.92 (1.07 - 3.46)* -- 2.13 (1.16 - 3.94)* 1.80 (0.90 - 3.60) Solid tumor without metastasis, in situ ## ## ## ## ## -- -- -- Solid tumor without metastasis, malignant 195 (1.6) 105 (53.8) 90 (46.2) 0.8453 0.94 (0.50 - 1.77) -- -- -- Dementia 290 (2.4) 205 (70.7) 85 (29.3) 0.0142 1.97 (1.11 - 3.49)* -- 1.77 (0.96 - 3.25) 1.30 (0.68 - 2.49) Depression 3,455 (29.0) 1,980 (57.3) 1,475 (42.7) 0.2397 1.12 (0.93 - 1.34) -- -- -- Diabetes without chronic complications 895 (7.5) 460 (51.4) 435 (48.6) 0.2546 0.84 (0.63 - 1.13) -- -- -- Diabetes with chronic complications 2,495 (20.9) 1,495 (59.9) 1,000 (40.1) 0.0313 1.26 (1.02 - 1.57)* -- 1.15 (0.92 - 1.45) 1.10 (0.87 - 1.39) Drug abuse 1,500 (12.6) 980 (65.3) 520 (34.7) 0.0008 1.61 (1.21 - 2.15)** -- 1.74 (1.30 - 2.34)*** 1.81 (1.34 - 2.46)*** Hypertension, complicated 2,480 (20.8) 1,535 (61.9) 945 (38.1) 0.0007 1.40 (1.15 - 1.71)*** -- 1.31 (1.06 - 1.64)* 1.20 (0.95 - 1.52) Hypertension, uncomplicated 4,465 (37.4) 2,385 (53.4) 2,080 (46.6) 0.1294 0.88 (0.75 - 1.04) -- -- -- Chronic pulmonary disease 4,065 (34.1) 2,240 (55.1) 1,825 (44.9) 0.8403 0.98 (0.83 - 1.16) -- -- -- 47 Obesity 2,940 (24.7) 1,690 (57.5) 1,250 (42.5) 0.2373 1.12 (0.93 - 1.35) -- -- -- Peripheral vascular disease 1,015 (8.5) 555 (54.7) 460 (45.3) 0.8369 0.97 (0.72 - 1.30) -- -- -- Hypothyroidism 2,340 (19.6) 1,295 (55.3) 1,045 (44.7) 0.9827 1.00 (0.81 - 1.23) -- -- -- Other thyroid disorders 310 (2.6) 145 (46.8) 165 (53.2) 0.1890 0.70 (0.41 - 1.19) -- -- -- Emergency Department Indicator Non-emergency department admission 1,650 (13.8) 950 (57.6) 700 (42.4) 0.4169 Reference -- -- -- Emergency department admission 10,275 (86.2) 5,655 (55.0) 4,620 (45.0) 0.90 (0.70 - 1.16) -- -- -- Major Operating Room Procedure No 9,985 (83.7) 5,265 (52.7) 4,720 (47.3) <.0001 Reference -- -- Reference Yes 1,940 (16.3) 1,340 (69.1) 600 (30.9) 2.00 (1.58 - 2.54)*** -- -- 1.68 (1.31 - 2.15)*** Discharge Disposition Routine 7,150 (60.0) 3,325 (46.5) 3,825 (53.5) <.0001 Reference -- -- Reference Short-term hospital 260 (2.2) 135 (51.9) 125 (48.1) 1.24 (0.71 - 2.17) -- -- 1.46 (0.82 - 2.58) Other facilityc 1,775 (14.9) 1,480 (83.4) 295 (16.6) 5.77 (4.29 - 7.76)*** -- -- 5.80 (4.22 - 7.96)*** Home health care 2,315 (19.4) 1,495 (64.6) 820 (35.4) 2.10 (1.69 - 2.60)*** -- -- 2.16 (1.72 - 2.71)*** Against medical advice 255 (2.1) 70 (27.5) 185 (72.5) 0.44 (0.23 - 0.83)* -- -- 0.43 (0.23 - 0.82)* Died 170 (1.4) 100 (58.8) 70 (41.2) 1.64 (0.81 - 3.33) -- -- 1.85 (0.90 - 3.77) Hospital Region Northeast 3,025 (25.4) 1,600 (52.9) 1,425 (47.1) 0.0024 Reference -- -- Reference Midwest 2,990 (25.1) 1,685 (56.4) 1,305 (43.6) 1.15 (0.91 - 1.46) -- -- 1.04 (0.80 - 1.35) South 3,675 (30.8) 2,225 (60.5) 1,450 (39.5) 1.37 (1.08 - 1.73)** -- -- 1.15 (0.88 - 1.49) West 2,235 (18.7) 1,095 (49.0) 1,140 (51.0) 0.86 (0.66 - 1.11) -- -- 0.76 (0.57 - 1.01) Hospital Bed Sized Small 2,545 (21.3) 1,380 (54.2) 1,165 (45.8) 0.0086 Reference -- -- -- Medium 3,525 (29.6) 1,795 (50.9) 1,730 (49.1) 0.88 (0.69 - 1.11) -- -- -- Large 5,855 (49.1) 3,430 (58.6) 2,425 (41.4) 1.19 (0.95 - 1.49) -- -- -- 48 Abbreviations: AOR, adjusted odds ratio; OR, odds ratio. aChi-square test bIncludes Asian/Pacific Islander, Native American, & Other. cIncludes skilled nursing facilities (SNF), intermediate care facilities (ICF), and other types of facilities. dBed size is based on a hospital’s total number of beds and is specific to the hospital's location and teaching status. ***p<0.001; **p<0.01; *p < 0.05. ## Data suppressed; 10 or fewer observations in some cells. Hospital Location/Teaching Status Metropolitan non-teaching 735 (6.2) 405 (55.1) 330 (44.9) 0.7820 Reference -- -- -- Metropolitan teaching 2,045 (17.1) 1,100 (53.8) 945 (46.2) 0.95 (0.64 - 1.41) -- -- -- Non-metropolitan hospital 9,145 (76.7) 5,100 (55.8) 4,045 (44.2) 1.03 (0.72 - 1.47) -- -- -- Hospital Control/Ownership Government, non-federal (public) 975 (8.2) 645 (66.2) 330 (33.8) 0.0122 1.63 (1.05 - 2.53)* -- -- 1.80 (1.13 - 2.86)* Private, not-for-profit (voluntary) 9,950 (83.4) 5,415 (54.4) 4,535 (45.6) 1.00 (0.73 - 1.37) -- -- 1.04 (0.73 - 1.48) Private, investor-owned (proprietary) 1,000 (8.4) 545 (54.5) 455 (45.5) Reference -- -- Reference 49 4.4 Discussion Using nationally representative data from acute care and inpatient settings, we performed cross-sectional analyses with the objectives to increase knowledge of potential race/ethnicity-based disparities in the treatment of complex regional pain syndrome type 1 (CRPS-1). Our primary findings were that, of the more than 23,000 emergency department (ED) visits and over 12,000 hospitalizations related to CRPS-1, most of all CRPS-1 care was provided to White, female patients. The proportion of females with CRPS-1 across examined race/ethnicity groups was comparable in both clinical settings. Similarly, within all race/ethnicity groups, the number of CRPS-1 ED visits and hospitalizations generally increased with increasing age, often peaking among those aged 50-59 years. Noteworthy secondary findings included: 1) ED visits by Black individuals (compared with White) were significantly negatively associated with immediate hospitalization; 2) ED visits by females were significantly less likely to result in admission; 3) hospitalizations by Black patients (compared with White) were independently associated with increased length of stay, though the observed association diminished with adjustment for presumed confounders; 4) hospitalizations subsidized by Medicaid (compared with private insurance) showed a significant negative association with longer length of inpatient stay; and 5) the diagnosis of drug abuse was significantly associated with both hospital admission and longer length of stay. To date, a small number of studies have examined the epidemiology of CRPS, with the incidence rate reported to range from 5.46 (CRPS-1) to 26.2 (CRPS) cases per 100,000 person years.141,142 The largest population-based study of CRPS-1 in the US was published in 2017 by Elsharydah et al.; it examined diagnoses of CRPS-1 during hospitalizations between 2007 and 2011 using NIS databases. For this study, investigators utilized a series of logistic regression models to determine 50 whether select characteristics were associated with hospitalization where CRPS-1 was diagnosed.110 Findings revealed racial/ethnicity differences in hospitalization for CRPS-1, whereby the odds of inpatient diagnosis of CRPS-1 were significantly greater during inpatient encounters by White patients as compared with admissions by Black, Hispanic, and Asian patients, respectively. Investigators also reported that there were 22,533 patients with a discharge diagnosis of CRPS-1 in an inpatient sample of 33,406,123 between 2007 and 2011,110 with other researchers extrapolating these findings without consideration of the source data and reporting the inpatient CRPS-1 incidence proportion to be 0.07%.143,144 Data analyzed by Elsharydah et al. are encounter-based, not patient-based, and it is not possible to longitudinally follow individual patients within or across multiple years of the NIS.145 As such, the reported patient numbers actually reflect distinct hospitalizations and not unique patients; the numbers also do not account for repeat hospitalizations by the same patient within or across examined years. Furthermore, the use of survey weighting is notably absent from counts reported by Elsharydah et al., which makes it challenging to estimate the actual annual number of adult hospitalizations where CRPS-1 was diagnosed. Our finding that a CRPS-1 diagnosis was documented during 0.04% (40 per 100,000 hospitalizations) of all non-elective adult hospitalizations in 2020 suggests that the inpatient CRPS-1 incidence proportion is considerably lower than previously reported, with the lower proportion not being attributed to the COVID-19 pandemic (data not shown). Additionally, our findings confirm previously reported CRPS-1 epidemiology, including that the majority of encounters/cases are by females and that CRPS-1 often peaks prior to age 60. To our knowledge, our study is the first to thoroughly examine sex and age distributions by race/ethnicity within CRPS-1. Further epidemiological studies are therefore necessary to determine whether similar patterns of acute and inpatient care for CRPS-1 by race/ethnicity are observed across other 51 healthcare settings, as such information may help to improve CRPS-1 diagnostic and treatment strategies. Our findings provide evidence to suggest that select sociodemographic characteristics, such as race, age, and sex may be associated with hospitalization among adults who presents to the ED with CRPS-1. Notably, in our fully adjusted model, we observed that ED visits by Black individuals (compared with White) and females were significantly negatively associated with immediate hospitalization. These findings complement prior evidence regarding race/ethnicity and sex differences in the diagnosis of CRPS-1, and signal that such disparities may extend beyond initial diagnosis to the subsequent delivery of health services. The observed disparities may be due in-part to numerous factors, including but not limited to differences in pain tolerance and health behaviours, as well as implicit bias among healthcare professionals.146–148 For example, while some studies have demonstrated no evidence of bias in the diagnosis of health conditions, there is growing evidence to suggest that significant differences in care are observed after considering interactions between multiple sociodemographic characteristics (such as race and gender, or race and socioeconomic status) or between sociodemographic characteristics and patient behaviour (such as race and challenging behaviour).148 We also observed differences in hospitalization by age in CRPS-1, whereby the odds of admission steadily increased with increasing age. This phenomenon may reflect differences in clinical presentation, comorbidities (including those not associated with the conditions chiefly responsible for precipitating the visit), and care provider practices. Future research should focus on exploring the effects of interactions between sociodemographic characteristics and behaviours on the diagnosis and treatment of CRPS-1. 52 After comprehensive adjustment for presumed confounders, we found few variables to be associated with length of stay for admissions with a CRPS-1 diagnosis. Race, sex, and age were not associated with length of stay after adjustment, which suggests that inpatient length of stay in CRPS-1 is largely determined by other factors, such as principal diagnosis, comorbidity, and post-operation recovery. Notwithstanding, some characteristics, including primary payer status, revealed associations with length of stay during CRPS-1 hospitalizations. Medicaid beneficiaries were found to have significantly shorter lengths of stay compared with CRPS-1 hospitalizations subsidized by private insurance. Although this contrasts previously reported associations between Medicaid insurance and longer hospitalization length of stay (compared with private insurance and uninsured patients, respectively), it may be attributed to the passage of the Affordable Care Act (ACA) and the subsequent expansion of Medicaid eligibility. The expansion of Medicaid eligibility has resulted in many studies examining the effects of insurance status on length of stay, with somewhat inconsistent findings. For example, one study of 2,314 adult inpatients with traumatic injuries found that expansion of Medicaid eligibility (relative to restrictive eligibility) was associated with a significant decrease in length of stay, and that the association between insurance status and length of stay varied by state.149 A separate study of 4,258,952 general medicine discharges from 211 academic medical centres found that length of stay pre- to post-ACA implementation did not significantly differ between hospitals in Medicaid-expansion compared with non-expansion states.150 As a result, future epidemiological studies are needed to better understand the impacts of expanded Medicaid eligibility on adult inpatient length of stay, including how expanded coverage may affect care for CRPS-1 by race/ethnicity. 53 There are numerous strengths to our study. To our knowledge, it is the most comprehensive investigation of CRPS-1 epidemiology to date that leverages nationally representative administrative health data from the US. We report on large, population-based analyses of CRPS-1 using data from both acute care and inpatient settings, which provides valuable insight into CRPS-1 care pathways. Both our selection of study CRPS-1 encounters and the identification of Elixhauser comorbidities were based on widely accepted ICD-10 definitions. All of our analyses accounted for the complex sampling design of the NIS and NEDS and utilized sampling weights. This allowed us to compute accurate estimates of ED visits and hospitalizations related to CRPS-1 for calendar year 2020, as well as precise variance estimates for reported associations. Furthermore, due to the availability of rich sociodemographic, clinical, and hospital data within the NIS and NEDS, we were able to assess many distinct exposure-outcome relationships and account for factors suspected of confounding modeled associations. Collectively, our findings provide much needed epidemiological data on ED and inpatient CRPS-1 care by race. They may serve as benchmarks for future studies that examine long-term health outcomes in CRPS-1 or the effectiveness of CRPS-1 interventions. Some limitations should be considered when interpreting our results. Our reported estimates of ED visits and hospitalizations related to CRPS-1 are based on information documented within administrative records; data from which our estimates are derived are encounter-based and do not contain unique patient identifiers. Moreover, as with other studies that utilize administrative health records, it is possible that a subset of our measured exposures and outcomes are misclassified or are not documented at all. Additionally, race/ethnicity is coded as a single variable (RACE) within the NEDS and NIS despite the possibility of some HCUP Partner Organizations providing separate 54 source data for race and ethnicity to HCUP. Ethnicity takes precedence when HCUP creates the uniform race values. As such, this would result in a patient who identifies as both Hispanic and Black being coded in the NEDS and NIS as Hispanic. Taken together, the aforementioned issues introduce the possibility that our reported findings are over or underestimates of actual healthcare encounters related to CRPS-1. This may also result in some of our reported associations being biased. Other study limitations include our inability to account for 1) time lived with CRPS-1; 2) use of prescribed and over-the-counter medications; and 3) other factors and behaviours that may bias modeled relationships. Finally, our study is exploratory in nature, and we therefore did not adjust for multiple comparisons in our statistical models. Notwithstanding these limitations, our study meaningfully adds to the limited literature on CRPS-1 care and may serve as foundation for future health services and outcomes research in this area. Overall, our study is the largest nationally representative US study to examine ED and inpatient care for CRPS-1, and to report CRPS-1 findings by race/ethnicity. We observed that CRPS-1 care was most often provided to White patients, irrespective of clinical setting, and that race/ethnicity was minimally associated with immediate hospitalization and longer length of stay. Additional studies are required to determine the impact of race/ethnicity on seeking care for CRPS-1, as well the effects of interactions between race/ethnicity and other factors on the diagnosis and treatment of CRPS-1. 55 CHAPTER 5: PRESCRIPTION OPIOID USE BY RACE: CROSS-SECTIONAL CANADIAN SURVEY 5.1 Introduction Research has consistently demonstrated racial disparities in healthcare access and service, particularly those related to pain management. For instance, Black and Hispanic individuals in the US, including children, are prescribed fewer opioids (e.g. oxycodone, hydrocodone, and codeine) than their White counterparts, despite similar pain intensity.93,102,151,152 A number of factors are thought to be associated with inequitable pain management—so called “pro-White treatment bias”—including access to healthcare and “pro-White empathy bias” whereby individuals report more empathy for White patients compared with racialized patients.135,153,154 A notable characteristic of studies examining racial disparities in pain management is that they have been conducted primarily in the US. Canada lacks empirical research examining potential inequities in pain management, despite a longstanding and well-documented history of limited healthcare access for racialized people.155–158 The objective of this study was therefore to evaluate racial disparities in pain management, evidenced by prescription opioid use, in Canada. A secondary objective was to examine if the effect of race on prescription opioid use was mediated by access to care. We hypothesized that racialized individuals (e.g., non-indigenous and non-White) will have a lower prevalence of prescription opioid use compared to White individuals, and that this relationship is mediated by access to care. A study examining racial differences in pain management in Canada is crucial for addressing disparities in healthcare and ensuring that all individuals receive equitable and effective treatment, regardless of their racial background. 56 5.2 Methods Data source and Study Design This study used a subset of data from the Canadian Community Health Survey (CCHS).34 The CCHS is a national, cross-sectional survey that is administered by Statistics Canada every two years. The survey collects information related to the health status, health care utilization, and health determinants of the Canadian population in all ten provinces and three territories. Participants are selected for in-person or phone interviews using a multi-stage cluster sample allocation strategy. The CCHS includes individuals 12 years and older that do not live on reserves or other Aboriginal settlements in the provinces and is representative of over 97% of the Canadian population. The 3% of Canadians that were not represented included full-time members of the Canadian Forces, youth aged 12 to 17 years living in foster homes, the institutionalized population, and persons living in the remote Quebec health regions of Région du Nunavik and Région des Terres-Cries-de-la-Baie-James.34 In total, 185,176 of the selected dwelling units in the 2015-2016 CCHS were in-scope for the survey, out of which a response was obtained for 110,095 individuals, resulting in a response rate of 59.5% (some units are not in-scope for the survey due to vacant, demolished, or non-residential dwellings). The CCHS includes core content asked of all respondents (the total number of respondents in the 2015/2016 cycle was 109,659),34 as well as optional modules that includes only respondents in provinces/territories which opted in. Because our study was focused on prescription medication use, only individuals in provinces which opted in to the “medication module” were included. For the medication module, proxy interviews (i.e., another member of household provided answers) were not used.34 57 Study Variables The primary outcome variable was prescribed opioid-based pain reliever use (from hereon referred to as “prescription opioid use”). Individuals in the CCHS medication module were asked three yes/no questions regarding opioid use: 1) During the past 12 months, have you used any codeine products like Tylenol #3, Tylenol #1, 292s or 222s?” (CCHS Variable MED_005); 2) During the past 12 months, have you used any oxycodone product such as Percocet or Percodan? (CCHS Variable MED_010); 3) During the past 12 months, have you used any other opioid products such as hydromorphone, Dilaudid, Hydromorph Contin, morphine, MS Contin, or Demerol? (CCHS Variable MED_015). For participants who responded “yes” to any of the above questions, they were then asked a follow-up question: Thinking about all the pain relievers you have used during the past 12 months, were they prescribed for you? (CCHS Variable MED_025). For our definition of “prescription opioid use,” the “yes” category included individuals who responded to this question as either “yes, they were all prescribed” or “some were prescribed and others were not.” Individuals who responded “no, none were prescribed” (e.g., used non-prescription opioids) or whose response was “don’t know” were excluded. The primary exposure was self-reported race (CCHS variable SDCDGCGT which includes 2 categories: “White” and “Non-White”). The “non-White” category included visible minorities (excluding Indigenous individuals). From hereon, we have relabelled the “non-White” category as “racialized” to be more consistent with contemporary views and guidelines on reporting race and decentering whiteness in research.159,160 58 Confounder and Mediating Variables Potential confounders, covariates, and mediating variables included: age (CCHS variable DHHGAGE, which we recategorized as 12-34, 35-64, and 65-80+ years); sex (CCHS variable DHH_SEX: male, female); BMI (CCHS variable HWTDGBCC: underweight, normal, overweight, and obese); total household income in Canadian dollars (CCHS variable INCDGHH: no income or less than $20,000, $20,000 to $39,000, $40,000 to $59,999, $60,000 to $79,000, or $80,000+); highest level of education in the household (CCHS variable EHG2DVH3: less than secondary school graduation, secondary school graduation only, and post-secondary education); prescription medication insurance coverage (CCHS variable INS_005: yes, no); having a regular healthcare provider (CCHS variable ACC_095: yes, no); Canadian active living Environments (CCHS variable ALECLASS, which we recategorized as low and moderate/high; immigrant status (CCHS variable SDCDVIMM: Canadian born, immigrant); anxiety/mood disorders, and health conditions. Anxiety/mood disorders were determined if participants answered “yes” to either of the following questions: Do you have a mood disorder such as depression, bipolar disorder, mania or dysthymia? (CCHS variable CCC_195); Do you have an anxiety disorder such as a phobia, obsessive-compulsive disorder or a panic disorder? (CCHS variable CCC_200). Health conditions were grouped into “painful” and “non-painful” health conditions. Painful conditions were defined by responding “yes” to any of the following questions: During the past 30 days, have you had any symptoms of pain, aching, or stiffness in or around a joint (CCHS variable CCC_005); Do you have scoliosis? (CCHS variable CCC_040); Do you have fibromyalgia? (CCHS variable CCC_045); Do you have arthritis for example osteoarthritis, rheumatoid arthritis, gout or any other type, excluding fibromyalgia? (CCHS variable CCC_050); Do you have back problems, excluding scoliosis, fibromyalgia and arthritis? (CCHS variable CCC_055); Do you have cancer? 59 (CCHS variable CCC_130); Do you have migraine headaches? (CCHS CCC_140). Non-painful health conditions were defined by responding “yes” to any of the following questions: Do you have osteoporosis? (CCHS variable CCC_060); Do you have asthma? (CCHS variable CCC_015); Do you have chronic bronchitis, emphysema or chronic obstructive pulmonary disease or COPD? (CCHS variable CCC_030); Do you have high blood pressure? (CCHS variable CCC_065); Do you have high blood cholesterol or lipids? (CCHS variable CCC_075); Do you have heart disease? (CCHS variable CCC_085); Do you have diabetes? (CCHS variable CCC_095); Do you suffer from the effects of a stroke? (CCHS variable CCC_090). Statistical Analyses Descriptive statistics were used to provide an overview of study sample characteristics. Bivariable logistic regression analysis was used to produce unadjusted odds ratios (ORs) and corresponding bootstrapped 95% confidence intervals (CIs). Multivariable logistic regression was then used to evaluate the relationship between race and prescription opioid use, adjusted for potential confounders/covariates listed above (excluding BMI since it was only captured in participants aged 18 years and older, not the entire sample, and was therefore used in a sensitivity analysis).34 The survey sampling design was accounted for in the regression analyses using sampling weights provided by Statistics Canada, to account for uneven probabilities of selection.34 Reported counts are unweighted and percentages and odds ratios are probability-weighted. All analyses were performed using R Statistical Software (R version 4.0.2). Sensitivity and Mediation Analyses Two sensitivity analyses were completed. Firstly, because BMI was only captured in participants aged 18 years and older (not the entire sample),34 we performed an additional analysis in those 60 with a valid BMI measure. Secondly, we used the disaggregated groupings for the health conditions, treating each health condition listed above separately. As a post hoc analysis, we sought to identify whether access to healthcare (CCHS variable ACC_095) mediates the relationship between race and prescription opioid use. Mediation analysis determines the direct relationship (direct effects) between race and prescription opioid use and the indirect relationship (indirect effects) between these two variables via access to care (potential mediator). If both direct and indirect effects are statistically significant, it is considered partial mediation; if only the indirect effect is statistically significant, it is considered full mediation.161 We employed a counterfactual framework of causal mediation analysis which is favourable because of its flexibility (e.g., non-parametric assumptions and can handle categorical variables).162–164 The mediation analysis was performed using the R Statistical Software package medflex. Ethics Approval This study was exempt from ethics approval via the clause governing the use of public release data outlined in the Tri-Council Policy Statement for Ethical Conduct for Research Involving Humans adopted by the University of British Columbia’s Policy #LR9: Research Involving Human Participants.165 5.3 Results Study Sample Of the 109,659 total CCHS 2015-2016 respondents, 3,802 (3.5%) were residents of provinces that opted in to the medication module (Figure 5.1). The 3,802 participants included residents from Prince Edward Island, Yukon, and the Northwest Territories. Of these 3,802 participants, 97 were 61 excluded for non-response (e.g., “don’t know” or “not stated”), 659 reported opioid use, and 3,046 reported no opioid use (Figure 5.1). Of the 659 who reported opioid use, 558 individuals reported prescription opioid use and 101 were excluded for non-response (e.g., “don’t know” or “not stated”) or for use of non-prescription opioids (Figure 5.1). The final study sample therefore included a total of 3,604 individuals (Figure 5.1). The study sample (N = 3,604) had a roughly even proportion of males and females (51.1% female), with 50% of participants aged 35-64. The majority of the sample were White (93.5%), post-secondary/university educated (77.6%), had access to regular healthcare providers (78.5%), had prescription insurance coverage (78.2%), and reported a painful health condition (74.2%) (Table 5.1). Prescription Opioid Use: Prevalence and Odds Ratios The prevalence of prescription opioid use in the entire study sample was 14.9% (15.1 and 3% for White and racialized participants, respectively). Racialized participants were significantly less likely to use prescription opioids compared to White participants (OR = 0.18, 95% CI = [0.07, 0.37]) (Table 5.2). This association persisted after adjustment for age, sex, prescription insurance coverage, access to a regular healthcare provider, active living environment, household income, household education, immigrant status, mood/anxiety disorders, and painful/non-painful health conditions (adjusted OR = 0.22, 95% CI = [0.05, 0.62]) (Table 5.2). Sensitivity and Mediation Analysis In the sensitivity analysis examining each health condition separately (as opposed to aggregated into “painful” and “non-painful” conditions, and adding BMI (only available on a subset of participants) as a covariate, the results remained consistent with the main analyses (Table 5.3): racialized participants were significantly less likely to use prescription opioids compared to White 62 participants (adjusted OR = 0.24, 95% CI = [0.05, 0.78], p = .038). In the post hoc mediation analysis, the relationship between race and prescription opioid use was partially mediated by healthcare access (ßdirect effect = -0.85; ßindirect effect = -0.07; ßtotal effect =-0.92; p<0.05 for direct and indirect effects). 63 Figure 5.1: Study sample from the 2015-2016 CCHS for the analysis of the relationship between race and prescription opioids use 64 Table 5.1: Sociodemographic characteristics of the CCHS study participant by prescription opioid use. Variables Overall N (%) Prescribed Opioids Na(%) Non-Opioids Users N (%) Study Sample 3604 (100%) 558 (14.9%) 3046 (85.1%) Race Racialized 203 (6.5%) 14 (3%) 189 (97%) White 2688 (93.5%) 429 (15.1%) 2259 (84.9%) Health Conditions * Painful Health Conditions 2166 (74.2%) 448 (19.7%) 1718 (80.3%) Non-painful Health Conditions 319 (10.4%) 30 (8.7%) 289 (91.3%) No Health Conditions 405 (15.4%) 21 (7.1%) 384 (92.9%) Anxiety-Mood Disorders** Yes 458 (11.9%) 111 (25.9%) 347 (74.1%) No 3143 (88.1%) 446 (13.4%) 2697 (86.6%) Age 12-34 years 1207 (33.3%) 136 (11.1%) 1071 (88.9%) 35-64 years 1623 (50%) 302 (17.3%) 1321 (82.7%) 65-80 years and older 774 (16.7%) 120 (15%) 654 (85%) Sex Male 1621 (48.9%) 256 (14.5%) 1365 (85.5%) Female 1983 (51.1%) 302 (15.2%) 1681 (84.8%) BMI Normal 881 (28.5%) 138 (14.7%) 743 (85.3%) Underweight 24 (0.7%) 1 (10%) 23 (90%) Overweight 1073 (37.7%) 169 (14.1%) 904 (85.9%) Obese 994 (33.1%) 200 (18.8%) 794 (81.2%) Income (household) 65 > $80,000 1602 (46.9%) 231 (13.2%) 1371 (86.8%) < $20,000 386 (8.3%) 79 (22.7%) 307 (77.3%) $20,000 - $39,999 607 (15.6%) 91 (15.9%) 516 (84.1%) $40,000 - $59,999 542 (15.5%) 77 (15.4%) 465 (84.6%) $60,000 - $79,999 441 (13.7%) 78 (14.5%) 363 (85.5%) Education (household) Less than secondary school 342 (7.2%) 61 (18.4%) 281 (81.6%) Secondary school graduation 509 (15.2%) 75 (13.5%) 434 (86.5%) Post-secondary or university degree 2626 (77.6%) 406 (15%) 2220 (85%) Insurance Coverage No 743 (21.8%) 120 (14.3%) 623 (85.7%) Yes 2786 (78.2%) 427 (14.9%) 2359 (85.1%) Regular Healthcare Providers No 943 (21.5%) 113 (11.4%) 830 (88.6%) Yes 2638 (78.5%) 444 (15.9%) 2194 (84.1%) Live in Active Environment Low 2724 (96%) 409 (14.8%) 2315 (85.2%) Moderate/High 101 (4%) 12 (8.9%) 89 (91.1%) Immigrant Status Canadian Born 3180 (93.2%) 497 (15.2%) 2683 (84.8%) Immigrant 273 (6.8%) 44 (12.4%) 229 (87.6%) a: Unweighted count; some column totals do note equal total due to missing values b: Weighted percentage taking into account CCHS survey weights. *Painful Condition is classified as having one of the following: Joint Pain, Scoliosis, Fibromyalgia, Arthritis, Back problems, Cancer, and Migraine; Non-painful Condition is classified as having one of the following: Osteoporosis, Asthma, COPD (Chronic obstructive pulmonary disease), High blood pressure, High blood cholesterol, Heart disease, Diabetes, and Stroke **Defined as having one of the following: Depression, Bipolar, Mania, Dysthymia, Phobia, Obsessive compulsive disorders, Panic. 66 Table 5.2: Logistic regression for the relationship between race and prescribed opioid use. Bivariable Analysis Multivariable Analysis Variable OR (95% CI) P OR (95% CI) P Race White Ref Racialized 0.18 (0.07, 0.37) <0.001 0.22 (0.05, 0.62) 0.012 Health Conditions * No Health Conditions Ref Painful Health Conditions 3.2 (2.2, 4.7) <0.001 2.88 (1.86, 4.64) <0.001 Non-painful Health Conditions 1.2 (0.72, 2.1) 0.4 1.05 (0.54, 2.01) 0.9 Anxiety-Mood Disorders ** No Ref Yes 2.27 (1.78, 2.87) <0.001 2.28 (1.66, 3.11) <0.001 Age 12-34 years Ref 35-64 years 1.67 (1.35, 2.08) <0.001 1.69 (1.19, 2.44) 0.004 65-80 years and older 1.40 (1.05, 1.87) 0.02 1.21 (0.79, 1.85) 0.4 Sex Male Ref Female 1.05 (0.88, 1.27) 0.6 0.92 (0.72, 1.17) 0.5 Income (household) > $80,000 Ref < $20,000 1.93 (1.41, 2.61) <0.001 3.01 (1.89, 4.77) <0.001 $20,000 - $39,999 1.25 (0.95, 1.63) 0.1 1.54 (1.04, 2.2.8) 0.024 $40,000 - $59,999 1.2 (0.91, 1.57) 0.2 1.54 (1.07, 2.20) 0.023 $60,000 - $79,999 1.12 (0.84, 1.49) 0.4 1.13 (0.77, 1.65) 0.5 Education (household) Less than secondary school Ref Secondary school graduation 0.69 (0.46, 1.04) 0.076 0.59 (0.33, 1.04) 0.065 67 Post-secondary or university degree 0.78 (0.56, 1.11) 0.2 1.19 (0.75, 1.93) 0.5 Insurance Coverage No Ref Yes 1.05 (0.84, 1.32) 0.7 1.02 (0.75, 1.41) 0.9 Regular Healthcare Providers No Ref Yes 1.46 (1.15, 1.87) 0.002 1.88 (1.21, 3.05) 0.007 Live in Active Environment Low Ref Moderate/High 0.56 (0.28, 1.02) 0.078 0.43 (0.18, 0.92) 0.043 Immigrant status Canadian Born Ref Immigrant 0.79 (0.52, 1.16) 0.3 1.14 (0.6, 2.02) 0.7 Abbreviations: OR = Odds Ratio, CI = Confidence Interval *Painful Condition was defined as having one of the following: joint pain, scoliosis, fibromyalgia, arthritis, back problems, cancer, and migraine; non-painful condition was classified as having one of the following: osteoporosis, asthma, COPD (chronic obstructive pulmonary disease), high blood pressure, high blood cholesterol, heart disease, diabetes, and stroke **Defined as having one of the following: depression, bipolar, mania, dysthymia, phobia, obsessive compulsive disorders, panic. 68 Table 5.3: Logistic regression for the relationship between each chronic condition and prescribed opioid use. Bivariable Analysis Multivariable Analysis * Variable OR (95% CI) P OR (95% CI) P Race White Ref Racialized 0.18 (0.07, 0.37) <0.001 0.24 (0.05, 0.78) 0.038 Disaggregated Health Conditions No Ref Non-cancer Pain** 2.89 (2.39, 3.49) <0.001 3.03 (2.22, 4.17) <0.001 Osteoporosis 2.27 (1.5, 3.39) <0.001 1.82 (1.07, 3.02) 0.023 Asthma 1.29 (0.92, 1.77) 0.12 1.28 (0.7, 2.25) 0.4 COPD 1.79 (1.12, 2.77) 0.012 0.95 (0.5, 1.73) 0.9 Cancer 3.37 (1.72, 6.37) <0.001 1.36 (0.46, 3.52) 0.5 Cardiovascular Diseases*** 1.36 (1.1, 1.66) 0.004 1.07 (0.77, 1.5) 0.7 Diabetes 1.42 (1.02, 1.94) 0.034 0.91 (0.56, 1.45) 0.7 Stroke 1.45 (0.52, 3.41) 0.4 0.68 (0.18, 2.09) 0.5 Abbreviations: OR = Odds Ratio, CI = Confidence Interval, COPD = Chronic obstructive pulmonary disease *Adjusted for healthcare provider access, insurance, active living environment, age, sex, BMI, income, education, and immigrant status **Defined as having one of the following: scoliosis, fibromyalgia, arthritis, back problems, and migraine ***Defined as having one of the following: high blood pressure, high blood cholesterol, and heart disease. 69 5.4 Interpretation In a population-based survey of opioid prescription use in Canada from 2015-2016, significant differences emerged on the basis of participant race. Racialized individuals (e.g., non-Indigenous and non-White) were less likely to report using prescribed opioids, compared to White individuals, even after adjusting for important covariates and confounders, including socioeconomic status and physical and mental health conditions. This relationship was partially mediated by healthcare access status, such that racialized individuals compared to White individuals were less likely to have regular access to healthcare providers, which then reduced the likelihood of prescription opioid use. To our knowledge, this is the first-time racial disparities in opioid prescriptions have been reported in a sample of individuals residing in Canada, mirroring that extensively reported in the United States.151,152,166 Similar to the United States, the population of Canada is diverse, with prominent visible minority groups, including South Asian, Chinese, and Black communities.167 Despite major differences in healthcare systems,168 our results are generally consistent with the United States, perhaps pointing to an even larger problem, with the odds of prescription opioid use nearly 80% less in racialized individuals compared to White individuals. Our results reflect the magnitude of the problem in Canada (represented by the Yukon, Northwest Territories and Prince Edwards Island), as well as differences in study design (i.e., self-reported prescription opioid use). In the US, racial disparities in pain management have been estimated on the basis of insurance claims and electronic health records of opioid prescriptions following encounters with healthcare provides (e.g., opioids dispensed after discharge from the emergency department).102,135,151 Such a study in the Canadian context is challenged by the lack of standardized collection of race information. Indeed, compared to the US, Canada has been slower in recognizing and collecting data on racial 70 disparities within provincial and territorial healthcare systems.169,170 Our study was only made possible by analyzing the CCHS, which has collected information on race dating back to 2000-2001 and the commitment of two territories (Yukon and NWT) and one province (PEI) to survey opioid prescription use in 2016.171 The Canadian Institute of Health Information only recently published standardized guidance on race-based data collection for health research in Canada – guidelines that remain only voluntary.169 The absence of robust race-based data are problematic, limiting the introduction of evidence-informed interventions and policies that address racial inequalities in healthcare, including those that could improve pain management.170,172–174 On the basis of our analysis, racial inequities of prescription opioid use in Canada are partially attributable to challenges in accessing healthcare. Previous studies have identified that individuals from racialized communities in Canada (e.g., Black, Chinese, South Asian) are less likely to have access to healthcare (e.g., healthcare providers),175–177 which was also evident in our analysis of the CCHS. Lack of access to healthcare creates an obvious challenge in acquiring any prescription medication, and in particular, opioids of which prescriptions are increasingly scrutinized.178–180 However, while significant, access to care only partially explains racial inequalities in opioid prescriptions in Canada. This suggests that initiatives to improve access to care will not likely resolve the problem of inequitable pain management. Other factors, such as implicit biases among healthcare providers to more aggressively manage “White pain”146,154 and fear of addiction in racialized individuals,99,181 still need to be considered in the Canadian context. The strength of our study lies in the ability to capture health outcomes in a large, population-based study in Canada. There are, however, a number of limitations to consider. First, our study did not include Indigenous individuals. Past research has indicated comparable or higher rates of prescription opioid use among Indigenous individuals compared to non-Indigenous counterparts.182 71 Second, there could be residual confounding due to a lack of information regarding pain intensity. For example, one possible scenario is that White individuals report their pain as more severe and debilitating, which then lends itself to more aggressive pain management (i.e., prescription opioids). However, several studies have demonstrated that racialized individuals experience increased sensitivity to pain, which is not mediated by biological differences but rather by way of discrimination.183–185 Third, a number of respondents were excluded because they provided invalid responses (e.g., “Don’t Know”) for questions regarding pain reliever use that could result in bias; however as a ‘positive control’, our data showed a strong relationship between pain-related conditions and pain medication use (Table 3), and since the medication module was not administered with proxy interviews, this adds to the validity to the responses. Fourth, we were unable to examine if opioid prescription represents an appropriate level of care. Finally, given that the pain medication questions were only asked to individuals in two territories and one province, the generalizability of these results to all Canadians, especially those in larger urban centres, could be limited. 5.5 Conclusion The results of our study demonstrated significant differences in opioid prescription use between racialized and White individuals in the Canadian publicly funded healthcare system. This racial inequity in prescription opioid use is partially mediated by access to regular healthcare providers. Future studies are needed to examine prescribing patterns in the wider Canadian population using electronic health records and insurance claims that collect standardized race-based information. 72 CHAPTER 6: RACE-BASED REPORTING AND PARTICIPATION OF BLACK INDIVIDUALS IN REGISTERED PAIN CLINICAL TRIALS, UNITED STATES 2000-2019 6.1 Introduction Racial discrimination in health care has been widely documented across a range of medical interventions, 95,186–188 including the management of pain (e.g., inequitable prescribing of analgesic medications).100,151,189,190 Racism in pain management is further demonstrated by false beliefs regarding biological differences based on race (e.g., Black people have thicker skin or Black bodies feel less pain than White bodies).95,147,191 In contrast, mounting evidence suggests that Black/African Americans may experience increased pain sensitivity compared to their White counterparts – an effect mediated, in part, by chronic stress associated with racial discrimination and learned behavioural responses to historic mistreatment.183,184 Before implementation in patients, interventions for pain are tested in clinical trials in research participants. Racial diversity in clinical trials is vital for generalizing results and ensuring equitable benefits to medical advances.112 However, racial diversity, particularly participation of Black individuals in the United States, may be low in pain clinical trials due to a variety of factors, including distrust in research related to past experimentation, lack of health insurance, comorbidities (used as exclusion criteria in clinical trials), and costs to participate (e.g. transportation, parking, missed work).113,116,117,192 Demonstrating a commitment to participant diversity in clinical trials, the NIH, FDA, Agency for Health Research and Quality, and the Centers for Disease Control and Prevention have developed guidelines advocating for greater inclusivity and diversity.193–195 Despite these efforts, failure to 73 comply with race-based reporting guidelines and the underrepresentation of racialized individuals in clinical trials remains a major societal issue.195–202 Several frameworks to overcome barriers of racial disparities in clinical trials have been addressed.117,203,204 Adherence to guidelines and racial diversity in pain clinical trials has not, however, been previously examined. The goal of the current study was to quantify the reporting of race, participation of Black individuals, and potential factors associated with race-based reporting and participation in pain clinical trials in the US. Our primary hypothesis was that the representation of Black individuals in pain clinical trials would be lower compared with national averages. 6.2 Methods Identification of Registered Pain Clinical Trials and Study Inclusion/Exclusion Criteria On July 3rd, 2020, the Aggregate Analysis of ClinicalTrials.gov (AACT) was downloaded. A query was performed to identify clinical trials listing “pain” in their registration as the “condition” of interest. Other inclusion criteria included: interventional trials (as identified by study investigators), English language, a start date from 2000 to 2019, and registered in the US. Because of their leadership in race reporting guidelines, the US was the focus of the present investigation. Trials were excluded if they did not address signs and symptoms of pain. For example, trials addressing opioid-induced constipation (e.g., NCT01993940), but were not directed at pain management, were excluded. Trials were reviewed for inclusion by AK and JK for suitability, both of whom were blinded to trial details pertaining to race-based reporting. Discrepancies were discussed until consensus was reached. Data Extraction 74 Information pertaining to trial characteristics (e.g., sponsorship) were extracted from the AACT database. A complete list of variables is provided in Table 6.1. If available, participant race was extracted directly from the AACT; otherwise, participant racial information was manually searched in PubMed using the clinical trial registration number. We also manually evaluated the perceived race of trial investigators. This was determined via online searches of investigator photographs, based on their names listed in the AACT database. A similar approach has been adopted previously.205 AK made the final determination of race (White or racialized). Statistical Analysis Race-based reporting (yes/no) and percentage of Black individuals were the primary outcomes. The focus was on Black participants due to the inconsistent nature of reporting other races. For the outcome of participation of Black individuals (expressed as a percentage), bivariable and multivariable linear regression were preformed; for race-based reporting, bivariable and multivariable logistic regression were used. P <.05 was considered statistically significant. Data extraction from the AACT database was conducted using Structured Query Language (SQL), and analyses were conducted using R Statistical Software Version 4.0.2. 6.3 Results Descriptive Summary The AACT database resulted in 12,688 trials on “pain” as the “condition.” Out of those, 1,433 trials reported results. The remaining 11,255 trials were manually searched in PubMed, which yielded 1,145 trial publications with results. A total of 2,578 trials with reported results were found. However, accounting for other inclusion/exclusion criteria, 1,200 trials were included in our analysis; 482 trials (40.2%) of which reported information pertaining to race (Table 6.2), with 428 75 trials (35.6%) specifically identifying participation among Black individuals (Table 6.3). In these 428 trials, 15,101 of 82,468 (18.3%) participants were Black (Table 6.3). Acute pain trials accounted for 210 of 428 total trials (49%), and most pain trials focused on the general population (283 out of 428 total trials, 66.1%) (Table 6.3). Race-based reporting in Pain Clinical Trials The results from logistic regression for race-based reporting in included studies are shown in Table 6.4. In bivariable analysis, the total number of participants, start year, and multi-state trials were significantly associated with reporting the race of study participants. Multivariable analysis revealed that the number of participants, trial funding sponsor, and trial star year were significantly associated with studies reporting on race: race-based reporting was more likely in larger, more recent, and publicly-funded (non-industry) trials (Table 6.4). Participation of Black Individuals in Pain Clinical Trials Results from bivariable and multivariable linear regression for the participation of Black individuals are shown in Table 6.5. In bivariable analysis, the percentage of Black participants enrolled in cancer pain clinical trials was significantly lower (-6.5%, p=0.033) compared with acute pain. Conversely, trials focused on cardiovascular disease pain (e.g., angina) reported significantly greater participation of Black individuals (+27%, p<0.001) compared with acute pain trials. Compared to trials with no special population of interest, those addressing pain in minoritized populations enrolled a significantly higher proportion of Black participants (+33%, p<0.001). There was also a trend for trials including only women to enroll more Black participants (+4%, p=0.065). Trials with federal sources of funding (compared to industry) and a team of diverse investigators (compared to only White investigators) enrolled a higher proportion of Black 76 participants. In the multivariable analysis, cardiovascular and chronic pain trials, trials focused in women, trials of opioid interventions, and those with a diverse study team remained significantly associated with participation of Black individuals (Table 6.5). 77 Table 6.1: Extracted data elements in interventional pain clinical trials Variable Name Definition Source Derived Categories Race-based Reporting Any racial information present in text or demographics table clinicaltrials.gov and pubmed.com Yes/No Total number of Black participants Total number of Black participants included (among only studies where race was reported) Table "baseline_measurements" from ACCT (https://aact.ctti-clinicaltrials.org/data_dictionary) Count Total number of participants Total number of participants per clinical trial Tables "baseline_counts" and "studies" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Count Type of pain International Association for the Study of Pain1 Tables "brief_summaries" and "conditions" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Acute, chronic, cancer, cardiovascular, palliative Pain intervention Whether the trial intervention was an opioid- or non-opioid substance2 Tables "brief_summaries" and "conditions" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Opioids/Non-opioids Study population Primary study population as defined by inclusion/exclusion criteria for each trial Tables "brief_summaries" and "conditions" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Elderly (65 and older), female only, healthy, children (aged 4-12), minority only, and veterans, or NA if the trial did not specify. Diverse team of investigators Subjective determination of trial investigator as Black/Indigenous/People of Colour (BIPOC or Table "facility_investigators" from AACT (https://aact.ctti-Yes/no 78 “diverse”) or Non-BIPOC, using methods as previously described3 clinicaltrials.org/data_dictionary) Funding sponsor Primary funding sponsor (i.e., funded by NIH, US Federal, others) Table "sponsors" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Industry/Non-industry Funding of clinical trial facilities Type of funding for clinical trial facility Homeland Infrastructure Foundation-Level Data (HIFLD) (https://hifld-geoplatform.opendata.arcgis.com/) Government/Non-Government Trials across different states If a trial setting was in more than one state, this was assigned as “multiple”. Otherwise, assigned as “single”. Table "facilities" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Multiple/Single Trial start year Year in which the trial started Table "studies" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Quartile of start year Trial phase The phase of each trial (phase 1, 1/2, 2, 2/3, 3, and 4) Table "studies" from AACT (https://aact.ctti-clinicaltrials.org/data_dictionary) Early/Late 1duration of pain symptoms greater than 3 months was characterized as chronic (and acute <3 months). Conditions classically associated with long-term pain symptoms, such as diabetic neuropathies, were also defined as chronic. Cancer, cardiovascular (i.e., chest), and palliative pain were characterized based on the study objective provided in the trial registration and from the abstract in published trials. 2To corroborate this classification, pharmacological interventions were cross-referenced with a list of common active opioids (https://www.canada.ca/en/health-canada/services/drugs-health-products/reports-publications/medeffect-canada/list-opioids.html). 3Perceived race of investigators was done by searching for their image via official verifiable sites such as LinkedIn and then determined by AK, Abbreviations: AACT= Aggregate Analysis of ClinicalTrials.org 79 Table 6.2: Characteristics of included pain interventional studies Study Factor Total No. Studies No. Studies Reporting Race (%) No. Studies Not Reporting Race (%) All trials 1,200 482 (40.2) 718 (59.8) Total participants 1,200 < 33 - 105 (35) 195 (65) 33 - 75 - 111 (36) 197 (64) 76 - 161 - 110 (37.5) 183 (62.5) > 161 - 156 (52.1) 143 (47.9) Funding Sponsor 1,200 Industry - 129 (41.7) 180 (58.3) Non-Industry - 353 (36.9) 538 (63.1) Trial start year 1,200 2000 – 2004 - 17 (25.7) 49 (74.3) 2005 – 2009 - 99 (30.1) 229 (69.9) 2010 – 2014 - 218 (41.2) 311 (58.8) 2015 - 2019 - 148 (53.4) 129 (46.6) Trial phase 701 Early (phase I/II) - 91 (38.2) 147 (61.8) Late (phase III/IV) - 191 (41.2) 272 (58.5) NA 499 200 299 Diverse team of investigators 916 No - 288 (39.6) 439 (60.4) Yes - 70 (37) 119 (63) NA 284 124 160 Trials across different states 1,177 80 Single state - 314 (37.4) 524 (62.6) Multiple states - 159 (46.9) 180 (53.1) NA 23 9 14 Funding of trial facilities 1,132 Government - 107 (43.4) 139 (56.6) Non-Government - 351 (39.6) 535 (60.4) NA 68 24 44 81 Table 6.3: Characteristics of pain clinical trials that reported Black participation Study Factor Total No. Studies Reported Black Participation Total No. Study Participants Black Participation (%) Non-Black Participation (%) All trials 428 82,468 15,101 (18.3) 67,367 (81.7) Type of Pain Acute 210 32,785 5,348 (16.3) 27,437 (83.7) Chronic 174 28,357 5,086 (17.9) 23,271 (82.1) Cancer 33 4,621 447 (9.6) 4,180 (90.4) Cardiovascular 7 10,140 2,319 (22.8) 7,821 (77.2) Palliative 4 6,565 1901 (29) 4,664 (71) Study Population Elderly 11 1,637 347 (21.2) 1,290 (78.8) General 283 65,477 11,709 (17.9) 53,768 (82.1) Pediatric 33 3,332 375 (11.3) 2,957 (88.7) Minority 4 810 390 (48.1) 420 (51.9) Veterans 25 3,726 740 (19.9) 2,986 (80.1) Women-Only 72 7,486 1,540 (20.6) 5,946 (79.4) Pain Intervention Opioid 79 13,921 2,689 (19.3) 11,232 (80.7) Non-opioid 349 68,547 12,412 (18.1) 56,135 (81.9) Diverse team of investigators Yes 61 6,389 1,642 (25.6) 4,747 (74.4) No 252 49,086 9,667 (19.6) 39,419 (80.4) NA 115 26,993 3,792 23,201 Funding sponsor Industry 124 31,249 4,146 (13.2) 27,103 (86.8) Non-industry 304 51,219 10,955 (21.3) 40,264 (78.7) Trials across different states One state 271 35,315 7,151 (20.2) 28,164 (79.8) Multiple states 148 44,945 7,799 (17.4) 37,146 (82.6) NA 9 2,208 151 2,057 Trial Start Year 2000 – 2004 9 2,766 393 (14.2) 2,373 (85.8) 2005 – 2009 83 31,311 5,680 (18.1) 25,631 (81.9) 2010 – 2014 198 32,449 6,202 (19.1) 26,247 (80.9) 2015 – 2019 138 15,942 2,826 (17.7) 13,116 (82.3) Trial phase Early (phase I/II) 79 7,800 1,205 (15.4) 6,595 (84.6) Late (phase III/IV) 177 47,628 7,622 (16) 40,006 (84) 82 NA 172 27,040 6,274 20,766 Funding of trial facilities Government 99 14,950 3,795 (25.4) 11,155 (74.6) Non-government 303 59,369 10,452 (17.6) 48,917 (82.4) NA 26 8,149 854 7,295 83 Table 6.4: Factors associated with race-based reporting: logistic regression analysis Bivariable Multivariable Variables OR (95% CI) P-value OR (95% CI) P-value Total number of participants (N =1200) < 33 - - 33 - 75 1.05 (0.75, 1.46) 0.8 0.79 (0.45, 1.37) 0.4 76 - 161 1.12 (0.80, 1.56) 0.5 1.49 (0.87, 2.56) 0.15 > 161 2.03 (1.46, 2.82) <0.001 3.63 (1.96, 6.85) <0.001 Funding Sponsor (N = 1200) Industry - - Non-Industry 0.92 (0.70, 1.19) 0.5 2.75 (1.50, 5.22) 0.001 Trial Start Year (N = 1200) 2000 – 2004 - - 2005 – 2009 1.25 (0.70, 2.33) 0.5 1.99 (0.86, 4.94) 0.12 2010 – 2014 2.02 (1.16, 3.70) 0.017 3.00 (1.34, 7.25) 0.01 2015 - 2019 3.31 (1.85, 6.17) <0.001 5.34 (2.17, 14.0) <0.001 Trial phase (N = 701) Early (phase I/II) - - Late (phase III/IV) 1.13 (0.82, 1.57) 0.4 0.83 (0.55, 1.27) 0.4 Diverse team of investigators (N = 916) No - - Yes 0.90 (0.64, 1.24) 0.5 1.28 (0.74, 2.22) 0.4 Trials across different states (N = 1,177) Single state - - Multiple states 1.47 (1.14, 1.90) 0.003 1.28 (0.74, 2.22) 0.4 84 Abbreviations: OR = Odds Ratio, CI = Confidence Interval Funding of trial facilities (N = 1,132) Government - - Non-government 0.85 (0.64, 1.14) 0.3 0.81 (0.48, 1.37) 0.4 85 Table 6.5: Factors associated with Black participation (% Black): linear regression Bivariable Multivariable Variable Beta (95% CI) P-value Beta (95% CI) P-value Type of Pain (N = 428) Acute - - Chronic 2.4 (-0.85, 5.6) 0.15 7.6 (0.35, 15) 0.040 Cancer -6.5 (-12, -0.54) 0.033 -1.5 (-11, 7.8) 0.7 Cardiovascular 27 (15, 39) <0.001 30 (5.1, 54) 0.018 Palliative -5.3 (-21, 11) 0.5 -6.3 (-39, 26) 0.7 Study Population (N = 428) General - - Elderly 4.2 (-5.7, 14) 0.4 -2.7 (-18, 12) 0.7 Pediatric -1.2 (-7.1, 4.7) 0.7 -2.8 (-12, 6.7) 0.6 Minority 33 (17, 50) <0.001 - - Veterans 3.8 (-2.9, 10) 0.3 10.0 (-3.9, 24) 0.2 Women-only 4.0 (-0.25, 8.2) 0.065 7.9 (1.1, 15) 0.023 Pain Intervention (N = 428) Non-opioid - - Opioid 3.3 (-0.73, 7.4) 0.11 7.5 (0.78, 14) 0.029 Funding Sponsor (N = 428) Industry - - Non-Industry 4.6 (1.2, 8.1) 0.008 0.25 (-8.2, 8.7) >0.9 Starting year (N = 428) 2000 – 2004 - - 2005 – 2009 -5.1 (-17, 6.3) 0.4 -3.5 (-22, 15) 0.7 2010 – 2014 -0.6 (-12, 10) >0.9 -0.37 (-17, 17) >0.9 2015 - 2019 -1.1 (-12, 10) 0.8 1.5 (-16, 19) 0.9 Trial phase (N = 256) Early (phase I/II) - - Late (phase III/IV) 0.26 (-3.7, 4.2) 0.9 1.1 (-4.9, 7.1) 0.7 Diverse team of investigators (N = 313) No - - Yes 5.6 (0.88, 10) 0.020 8.0 (1.5, 14) 0.016 Trials across different states (N = 419) Single state - - 86 Abbreviations: CI = Confidence Interval Multiple states -1.4 (-4.7, 2.0) 0.4 -0.44 (-7.7, 6.8) >0.9 Funding of trial facilities (N = 402) Government - - Non-Government -3.4 (-7.2, 0.46) 0.085 -1.3 (-9.1, 6.5) 0.7 87 6.4 Discussion Overall, the participation of Black individuals in pain clinicals trials over a 20-year period paralleled population demographics in the United States and general clinical trials demographics (~13%-16%).206,207 Participation of Black individuals was highest in trials aimed at interventions for the management of cardiovascular pain, opioid therapies, and in those conducted in minority populations and women. The diversity of the study team had a significant impact on the participation of Black individuals, such that trials with at least one perceived racialized investigator was associated with increased representation. The lack of reporting remains a major issue, with only 40% of trials providing a breakdown of participant enrollment by race. Numerous investigations, including those addressing participation in cancer, vaccine, acute leukemia, and heart failure clinical trials have documented low rates of compliance with the reporting of race.113,208–210 In close agreement with our results, only 43% of over 20,000 general clinical trials registered on ClinicalTrials.gov have adequately reported the race of participants. This figure only marginally improves by focusing on trials published in top-tier medical journals.211,212 Our analysis also identified a number of predictors of reporting race in included pain studies that have been documented elsewhere, including sample size and year of publication, with the latter reflecting a trend towards recent improvements.197,211,213,214 Also in alignment with a recent study that examined race-based reporting and racial representation over 20 years in the United States (not specific to pain trials), significantly lower race reporting was identified in industry-funded trials compared to those that were publicly-funded.211 This could be due to the reporting requirements for publicly-funded trials, per the NIH Revitalization Act.193 A number of factors may contribute to why race continues to go unreported in clinical trials for pain, despite a clear mandate.195,215 Trial investigators may either neglect to collect requisite 88 information pertaining to participant race—and thereby cannot report it—or collected these data and withheld it from published results. In both cases, failure to report may reflect lack of awareness on part of investigators of existing guidelines and inadequate recognition of the value of racial diversity in clinical trials (e.g., promoting equity, study generalizability, and building trust in racialized groups). Trials that obtained data on race, but did not report it, may also be concerned for the implications of underrepresenting racial minorities populations (e.g., issues raised during peer review). In these cases, research ethics boards, regulators, and academic publishers should ensure that race information is adequately reported regardless of enrollment outcomes. The benefits of race reporting not only advance the work of trial investigators for the broader population, but also facilitate self-determination in the races represented. Updated guidance from the American Medical Association on reporting of race and ethnicity in clinical and scientific publications may inform these efforts, as well as efforts to decenter Whiteness in research.22,216 Recent scholarship has called attention to the importance of race-consciousness in medical research and practice.217,218 However, prevailing assumptions regarding racial data tend toward treating race as a covariate or control to explain differences among racial groups as inherent, fixed biological characteristics (e.g., age).219,220 This results from longstanding flaws in research and education that essentialize racial health inequities as arising from genetic variation, rather than as a consequence of differential environmental exposure.112,195 Reporting race does not necessitate analysis of trial outcomes by race; indeed, we and others advise against using race to classify diverse clinical or biological outcomes.221 We do, however, emphasize the importance of maintaining—and increasing—diversity in clinical trials to promote generalizability across people with different experiences of racialization, to ensure the equitable benefits of clinical research, and to increase confidence in biomedical research and healthcare institutions.112 Interventions 89 regarding the use of race in clinical trials can target investigators’ conceptualization of race as a risk marker rather than as a risk factor, similar to socioeconomic status, and to emphasize the impact of structural racism as a determinant of divergent outcomes. In addition, there is also a growing movement within pain research calling for anti-racism. In this framework, several commitments were highlighted, including education (e.g., cultural humility, implicit bias), inclusion (e.g., changes in institutional culture), policy (e.g., journal guidelines), research design (e.g., biopsychosocial narrative, race as sociopolitical construct), dissemination of research (intentional language), and ongoing evaluation (e.g., regular assessment).222–224 In conjunction with low levels of reporting of race, previous investigations have demonstrated the underrepresentation of racialized individuals in clinical trials.113,196,197,199,202 This has been attributed to a variety of sociocultural factors, including the lack of access to health care, lower levels of health insurance, distrust in health care, and access to transportation.117,192,225–227 Moreover, stemming from a long-troubled past in medicine and cultural memory of experimentation, including experimentation on the enslaved, disinterment of Black bodies for use in anatomy lessons, forced sterilizations of Black women, segregated hospitals that involved lower quality of care for Black patients, and the Tuskegee syphilis study, mistrust in research may also limit participation.44–48,227 In contrast to our hypothesis, we observed overall participation on par with the racial breakdown of the United States (i.e., ~13-16%).206,207 In fact, representation of Black individuals was higher compared to national demographics for several pain conditions. These results correspond with elevated incidence of cardiovascular disease and chronic pain in the Black population.93,137,228,229 Studies addressing pain specifically in racially minoritized populations enrolled a greater number of Black participants – not particularly surprising given that Black individuals represent a target group. Black representation was also 90 higher in pain trials focused on women and opioids. This seemingly points to a paradox between research and clinical settings, where individuals that are Black are more likely to be involved in trials but less likely to be managed with opioids in real-world settings compared to their White counterparts.100,151,189 Collectively, these results raise the question as to why participation in pain trials has achieved or exceeded the national representation, whereas other health conditions often fail to achieve racial diversity (e.g., cancer)113,202 fail to do so? One possibility is that, after decades of research identifying racial disparities in managing pain, coupled with knowledge emphasizing that pain is a “biopsychosocial” phenomenon, researchers have adopted inclusive participant recruitment practices. In support of such a theory, other health conditions that prioritize a biopsychosocial model of disease (e.g., depression) have also demonstrated adequate representation of racialized individuals in clinical trials.230 Another potential explanation could be that compensation for participation in studies may differentially impact low-income populations, though we were not able to collect data on study compensation.231 In addition to pain and intervention type, and study population, perceived racial diversity among the investigator team significantly increased representation of Black patients in included trials. This observation corresponds with findings from a recent study addressing participation in heart failure trials, in which increased female representation on the study team was associated with an 8.4% increase in racialized individuals enrollment.205,232 The value of such an observation cannot be minimized: to facilitate diversity in clinical trials, diversifying investigators represents a critical step. In our analysis, only 14% of trials were classified as diverse. Recent reports have indicated that less than three percent of NIH trial investigators are racialized.233 Diversity among leadership teams could improve diversity in participation due to connection with the language, experiences, 91 and attitudes of the target participants.232,234 Thus, funders and investigators should seek to support, promote, and invest in diverse mentees and collaborators both to advance equity and to improve the quality of the science. Although our findings regarding racial inclusivity in pain clinical trials are encouraging, there are limitations to consider. Firstly, our study included 1200 registered clinical trials reporting results and conducted in the United States. Research not registered, unreported, and/or recruiting participants outside of the US may be less representative of racialized participation. We were also solely focused on Black participation, which means that our findings cannot be generalized to representation of other races in pain clinical trials. Thirdly, we acknowledge that investigator race was determined subjectively and may have introduced bias and misclassification. However, previous study has adopted this similar approach.205 Our protocol also raises an important point regarding whether racial demographics are self- or socially-assigned: The former conveys group belonging whereas the latter may indicate experience of racialization.235 Finally, our search of the registered trials was limited to trials that identified “pain” as the “condition” in clinical trial registration. This will have led to the exclusion of studies that were addressing pain but identified using other terms (e.g., arthritis). While a more comprehensive search strategy may have yielded more trials, >12,000 trials were initially queried, with a manual search of >11,000 trials performed in PubMed, from which a large sample (n=1200) were included in the final analysis. 6.5 Conclusion Overall rates of reporting race remain low in pain clinical trials; however, the proportion of those participating that are Black aligns with national averages (~13-16%). This broadly demonstrates a level of equitable participation achieved in pain clinical trials in the United States, which may 92 reflect widespread adoption of the biopsychosocial model of pain. Trials aiming to reach equitable representation of Black participants should consider a diverse study team composition. 93 CHAPTER 7: CLINICAL TRIAL PARTICIPANT VIEWS OF THE RISKS AND BENEFITS OF OPEN DATA SHARING: RACIAL DIFFERENCES AND THE MEDIATING ROLE OF MISTRUST 7.1 Introduction Open data sharing is the practice of making health-related information, such as medical records and research findings, accessible to the public or a broad audience while minimizing restrictions on its use.236,237Academics have proposed multiple benefits of open data sharing: it can accelerate scientific progress, reduce redundancy of scientific research, reduce research participant burden, and increase trust in science.124,236,238–240 On the contrary, multiple risks of data sharing, including breaches of confidentiality and the misuse of scientific data (so-called “junk science”) have also been raised.238,241–243 The bulk of discourse and research on open data sharing has been generally confined to scientists, health care professionals, policy makers, lawyers, and ethicists.238,243–246 In general, there has been little patient-centred research on the views on open data sharing by research participants. In 2018, however, Mello and colleagues published the first study to ask research participants their views on open data sharing.241 They found that the vast majority of surveyed participants believed that the potential advantages of open data sharing were greater than the potential drawbacks of open data sharing.241 Further, they found that 93% of participants indicated they were very or somewhat likely to permit their personal data to be shared with university researchers, and 82% expressed similar openness to sharing with scientists working in profit-driven enterprises.241 The inclination to share data remained fairly consistent regardless of the intended use, except for lower willingness to share for litigation purposes.241 Lastly, among the respondents, the primary concerns revolved around the possibility of data sharing discouraging people from participating in clinical trials (37% 94 very or somewhat concerned), the use of data for marketing (34%), or the risk of data theft (30%).241 While this study examined racial differences in some aspects of open data sharing (and noted no differences by race for some outcomes), the study did not examine racial differences in trust and willingness to share data, nor did they examine the causal role of potential mediators (i.e., trust) for racial differences on open data sharing. The aim of this study was therefore to examine the impact of race on data sharing practices and whether trust mediates this relationship. 7.2 Methods Study Design and Data Source This was secondary analysis of a cross-sectional survey study design.241 Survey participants had been enrolled, or were the parent or guardian of someone who had been enrolled, in an interventional clinical trial within the previous 2 years. The final sample included both community-based trials (e.g., involving smoking cessation or diabetes prevention) and hospital-based trials (e.g., involving cancer or kidney disease). Within these trials, all the participants were eligible for the survey unless the trial team judged them as having cognitive impairment or being unable to respond to questions in English. The cross-sectional study was approved by the institutional review boards at Stanford and at the medical centers that provided access to the trial participants. Questionnaire and Survey Administration A 10-page structured survey questionnaire was used to elicit clinical trial participant views on the sharing of data from clinical trials. Details of the survey development work, which included the use of focus groups, consultation with experts and community advisory boards, and pilot testing are provided elsewhere.241 The questionnaire provided plain-English definitions of clinical trial, 95 data sharing, and clinical trial data. It included reminders that the survey was asking about sharing of individual-level information about trial participants, not research results, and that respondents should assume that the data were deidentified. Agreement was obtained from nine principal investigators (PIs) in clinical trials at three academic medical centers to facilitate access to their trial participants. Clinical trial PIs were chosen from among three methods of survey delivery: email, regular mail, or in-person distribution in study clinic waiting rooms. Four PIs chose regular mail, four chose the clinic, and one used both. All surveys were completed on paper, and the clinic staff interaction with respondents was limited to a receptionist or research assistant handing out and collecting the questionnaires. The surveys were accompanied by informed consent information and a $40 gift card. The responses were identified by participant identification number only. Outcome Variables The first study outcome was willingness to share data with non-profit organizations, which was ascertained by asking: “How likely would you be to allow your anonymous, individuals clinical trial data to be shared with scientists in universities and other not-for-profit organizations?” The second study outcome was willingness to share data with for-profit organizations, which was ascertained by asking: “How likely would you be to allow your anonymous, individuals clinical trial data to be shared with scientists in companies developing medical products, such as prescription drugs?” For these questions, respondents could select "Very likely," "Somewhat likely," "Neither likely nor unlikely," "Somewhat unlikely," and "Very unlikely," respectively. We a priori recategorized the responses into two groups by combining the “Somewhat unlikely” and “Very unlikely” into “Unlikely” (reference category), and the remaining response categories were combined to form a “Likely” group as done previously.241 96 Race Variable The independent variable was race. In the survey, participants were asked “what is your race?” and could select one or more of: American Indian or Alaskan Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, or Other. For the primary analysis, we a priori collapsed the race categories into “Racialized” (encompassing all response categories except White or who selected more than once race category) and White (as the reference category). Mediating Variables We a priori hypothesized that trust would mediate the relationship between race and willingness to share data. To capture trust, the survey asked: “generally speaking, how often can you trust other people?” (response categories: always, most of the time, about half the time, once in a while, and never), which was recategorized as “low trust” (reference category, including “once in a while”, and “never”) and “high trust” (remaining responses). In a separate question that followed, specific domains of trust were captured as follows: “How much do you trust doctors?”; “How much do you trust universities?”; “How much do you trust drug companies?”; “How much do you trust government agencies that fund medical research?”; and “How much do you trust health insurance companies?” (response categories for each of these questions were: a great deal, a lot, a moderate amount, a little, and not at all). We recategorized these response categories as “low trust” (reference category to include responses such as a lot, a moderate amount, a little, and not at all) and “high trust” (remaining response categories). 97 Confounding Variables Potential confounding variables included age (dichotomized into <65 and >=65 years), sex (male, female, transgender), and advanced education (graduate degree or college degree vs. less than high school, high school diploma, or some college). Statistical Analysis and Sensitivity Analyses All statistical analyses were performed using R Statistical Software (R version 4.0.2 (2020-06-22)). Descriptive statistics (percentages) were used to describe sample characteristics. To examine the association between race and willingness to share data, bivariable and multivariable logistic regression (to account for potential confounders listed above) were used to produce unadjusted odds ratios (ORs) and adjusted odds ratios (aORs) with 95% confidence intervals. To examine whether trust mediates the relationship between race and willingness to share data, we used a counterfactual mediation analysis.247 Mediation analysis determines the direct relationship (direct effects) between race and willingness to share data and the indirect relationship (indirect effects) between these two variables via trust (potential mediator). If both direct and indirect effects are statistically significant, it is considered partial mediation; if only the indirect effect is statistically significant, it is considered full mediation, and if only the direct effect is statistically significant, there is no mediating effects of the variable.161 We employed a counterfactual framework of causal mediation analysis which is favourable because of its flexibility (e.g., non-parametric assumptions and can handle categorical variables).162–164 We also performed sensitivity analysis using disaggregated racial groupings, treating each racial group listed above separately in the mediation framework. The mediation analysis was performed using the R Statistical Software package medflex. 98 7.3 Results Sample Characteristics The study sample (N = 771) had a roughly even proportion of males and females (49.3% female) and the majority were less than 65 years of age (Table 7.1). The majority identified as White (67.2%), and the sample was roughly split by educational attainment and income level. When stratifying by race, White individuals tended to have higher educational attainment and household income compared with racialized individuals (Table 7.1). Willingness to share data and Trust Race was significantly associated with willingness to share data. Specifically, racialized participants were 62% less likely to share data with non-profit organizations (adjusted OR= 0.38, 95% C.I. [0.16, 0.9], p = .02), and approximately 50% less likely to share data with for-profit organizations (adjusted OR = 0.51, 95% C.I. [0.29, 0.92], p = .01) (Table 7.2). Race was also significantly associated with trust in people: racialized participants had a significantly lower odds of high trust in people compared to White participants (adjusted OR = 0.43, 95% C.I. [0.24, 0.75], p < .001; Table 7.3). However, when examining specific domains of trust, race was not significantly associated with trust in doctors, trust in universities, trust in companies, trust in insurers, but was associated with lower odds of high trust in government (Table 7.4). Mediating role of Trust Mediation analysis was performed to examine whether trust in people mediates the relationship between race and willingness to share data. The relationship between race and willingness to share data with with non-profit organizations was fully mediated by trust in people (ßindirect effect = -0.4 [-0.7, -0.11]) (Figure 7.1A), even after adjusting for confounders (Adjusted ßindirect effect = -0.35 [- 99 0.66, -0.02]) (Figure 7.1B). Similarly, the relationship between race and willingness to share data with for-profit organizations was fully mediated by trust in people (ßindirect effect = -0.19 [-0.37, -0.02]) (Figure 7.1C), after adjusting for confounders (Adjusted ßindirect effect = -0.18 [-0.34, -0.01]) (Figure 7.1D). Sensitivity Analysis When stratifying by race, trust had no mediating effect in any racial grouping except Black individuals (Adjusted ßindirect effect for non-profit organization = -0.48 [-0.9, -0.07]; Adjusted ßindirect effect for for-profit organization = -0.25 [-0.47, -0.02]). 100 Figure 7.1: Mediation analysis with trust in people as mediator. 7.1A: Unadjusted analysis of race and data sharing with non-profit organizations mediated by trust in people. 7.1B: Adjusted analysis of race and data sharing with non-profit organizations mediated by trust in people. 7.1C: Unadjusted analysis of race and data sharing with for-profit organizations mediated by trust in people. 7.1D: Adjusted analysis of race and data sharing with for-profit organizations mediated by trust in people. 101 Table 7.1: Sample characteristics by race Overall N (%) N=771 White N (%) N=518 Racialized N (%) N=218 Educational Attainment Advanced (graduate or college degree) 381 (50.6%) 290 (56.2%) 84 (38.8%) Lower (less than high school, high school, or some college) 371 (49.4%) 226 (43.8%) 132 (60.2%) Total Household Income < $55,000 379 (51%) 213 (42.2%) 153 (70.5%) >= $55,000 363 (49%) 291 (57.8%) 64 (29.5%) Age Group <65 years 526 (69%) 333 (64.7%) 170 (79%) >=65 years 236 (31%) 181 (35.3%) 45 (21%) Sex Female 380 (49.8%) 243 (47.2%) 115 (51.1%) Male 382 (50.2%) 271 (52.8%) 100 (48.9%) N in rows and columns don’t add up due to missing values 102 Table 7.2: Odds ratios for willingness to share data. Outcome Race Bivariable Analysis Multivariable Analysis* OR (95% CI) P OR (95% CI) P Willing to share data with non-profit organizations White Ref Racialized 0.34 (0.15, 0.75) 0.007 0.38 (0.16, 0.9) 0.02 Willing to share data with for-profit organizations White Ref Racialized 0.5 (0.28, 0.89) 0.01 0.51 (0.29, 0.92) 0.02 OR = Odds Ratio, CI = Confidence Interval * Adjusted for age, sex, and education 103 Table 7.3: Odds ratios for trust in people among clinical trial participants Outcome Race Bivariable Multivariable* OR (95% CI) P OR (95% CI) P High Trust in People White Ref Racialized 0.28 (0.17, 0.47) <0.001 0.43 (0.24, 0.75) 0.003 OR = Odds Ratio; CI = Confidence Interval; *adjusted for age, sex, income, and education 104 Table 7.4: Odds ratios for each domain of trust among clinical trial participants OR = Odds Ratio; CI = Confidence Interval; *adjusted for age, sex, income, and education Outcome Race Bivariable Multivariable* OR (95% CI) P OR (95% CI) P High Trust in Government White Ref Racialized 0.62 (0.43, 0.89) 0.01 0.62 (0.42, 0.91) 0.01 High Trust in Doctors White Ref Racialized 0.75 (0.36, 1.65) 0.46 0.92 (0.42, 2.1) 0.85 High Trust in University White Ref Racialized 0.6 (0.33, 1.12) 0.1 0.73 (0.37, 1.44) 0.36 High Trust in Companies White Ref Racialized 0.86 (0.62, 1.18) 0.36 0.83 (0.59, 1.17) 0.3 High Trust in Insurers White Ref Racialized 1.33 (0.96, 1.83) 0.07 1.22 (0.87, 1.72) 0.24 105 7.4 Discussion In recent years, data sharing has garnered increasing recognition and prominence within the realm of healthcare and health research. This shift in perspective is driven by a combination of factors, including wider recognition of open science, evolving attitudes/values, and the realization of its potential. Within the domain of clinical trials, data sharing plays a pivotal role in accelerating scientific progress. Specifically, data sharing can reduce redundancy and therefore increase efficiency: instead of prospectively collecting data by separate investigators, secondary analyses of existing data can be completed at a fraction of the cost and at a much higher speed.124 Data sharing also increases transparency and the ability to replicate results, which could increase confidence and trust in scientific results. However, open data sharing does not appear to be embraced by all individuals: our findings demonstrate that racialized individuals were less likely to share health data compared to White individuals, and this relationship is mediated by trust in people. Given that race is a social construct with no biological rationale,217,248,249 it is important to examine race correlates with support for data sharing beyond its surface-level. Employing mediation analysis, we gained a deeper understanding of data sharing from the perspective of racialized participants (especially Black participants), using the role of trust as a potential mediator. The historical mistreatment of Racialized individuals in scientific experimentation represents a dark chapter in human history, marred by ethical transgressions, and may impact trust. One example of such mistreatment occurred during the era of colonialism when European powers, driven by a sense of racial superiority, subjected indigenous populations to dehumanizing experiments. In the 19th century, for instance, European colonizers engaged in pseudo-scientific practices that aimed to establish a hierarchy of races, with white Europeans positioned at the 106 top.250,251 The Tuskegee Syphilis Study in the United States further exemplifies the historical mistreatment of racialized individuals. Initiated in 1932, this study involved withholding treatment for syphilis from Black men, even after the discovery of penicillin as a viable cure.48,227,252,253 Similarly, the case of Henrietta Lacks highlights the exploitation of African Americans in the realm of medical research. Lacks, an African American woman, had her cancer cells taken without her knowledge or consent in 1951.51,254 These cells, known as HeLa cells, became instrumental in numerous medical breakthroughs, including the development of the polio vaccine and advancements in cancer research.255,256 However, Lacks and her family were never informed about the use of her cells, raising profound ethical questions about informed consent and the rights of individuals.257 Our results demonstrate a mediating role of trust, though interestingly when examining specific domains of trust in relation to race, trust in doctors, universities, and companies were not significantly associated with race. Because race is a non-modifiable “risk factor,” our results highlight the importance of building trust to increase support for open data sharing. The strength of our study lies in the application of mediation analysis, allowing us explore the causal pathway between race and support for open data sharing. However, one potential limitation of our study is in a response bias, such that those are strongly against data sharing might opt out of clinical trials, potentially biasing our study sample. 7.5 Conclusion Our findings offer a more nuanced understanding of the causal mechanisms underlying the relationship between race and support for open data sharing. Since race is a social construct with no biological basis, understanding that trust mediates the relationship between racialized communities and their reluctance to share data is crucial for developing more equitable guidelines 107 for data sharing initiatives. Building trust within racialized communities is imperative to ensure their meaningful participation in research as well as participation in open data sharing initiatives. 108 CHAPTER 8: SUMMARY OF FINDINGS AND FUTURE DIRECTIONS 8.1 Overall summary This thesis seeks to examine two general themes, racial differences in health outcomes and health research participation, through the lens of race as a social construct. Rather than viewing race as a mere outcome of individuals, this thesis revolves around its role as a construct for a broader societal structure that can significantly impact health outcomes and participation in health research. Chapter 4 investigated racial disparities in hospitalization and length of hospital stay for patients with a neuropathic pain disorder (Complex Regional Pain Syndrome type 1). To do this, data from the National Emergency Department Sample (NEDS) and National Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP) were utilized. To our knowledge, this study was the first to incorporate both hospital admission and hospital length of stay for CRPS-1 using dual samples from the HCUP—a nationally representative sample. Chapter 5 investigated racial disparities in pain management (i.e., opioid prescriptions). Despite the similarities in sociocultural landscape between Canada and the US, the majority of studies on this topic have been conducted in the US. Thus, one key novel aspect of this study was to examine racial disparities in opioid prescription use in Canada, utilizing the CCHS from 2015 and 2016. Chapter 6 explored racial diversity and race reporting in pain clinical trials as well as factors associated with these outcomes. This study was conducting using published trials results from clinicaltrials.gov and PubMed. Despite the documented lack of racial diversity in clinical trials across various fields, this study was first to investigate this phenomenon within pain clinical trials. Chapter 7 focused on the relationship between race and views on data sharing. Given that race is a social construct, understanding the association between race and data sharing requires deeper exploration than simple bivariable analyses. A formal mediation analysis was employed in this 109 chapter, and found that trust acts as a mediator between the relationship between race and views on open data sharing, in part likely due to the historic mistreatment of racialized individuals. Overall, this thesis contributes to the nuanced understanding of racial dynamics in health outcomes and health research participation. 8.2 Key findings The key findings of this thesis are the following: 1. Black patients with CRPS-1 were less likely to be hospitalized but more likely to face longer hospitalization, compared to White patients. 2. Similar to the US, racialized individuals were less likely to use prescribed opioids, even after adjusting for potential confounders. This phenomenon was partially mediated by healthcare access. 3. Less than 50% of US-based pain clinical trials reported race. Sample size, year of publication, and funding sponsorship were associated with reporting race. 4. Black participation in US-based pain clinical trials generally aligned with national demographics in the US. Increased representation corresponded with health conditions more prevalent among Black individuals (e.g., cardiovascular disease) and with a diverse study team composition. 5. Racialized individuals were less likely to support open data sharing. This relationship was mediated by trust, likely in part due to historic mistreatment. 110 8.3 General strengths and limitations Each corresponding chapter contains details on strengths and limitations for each respective study; however, below is an overview of the strengths and limitations of the overall findings of this thesis (Table 8.1). Strengths This thesis has several strengths. First, the findings were supported from diverse data sources (i.e., ClinicalTrials.gov, PubMed, CCHS, NEDS, NIS). The diversity of datasets enhances the robustness and credibility of our findings, as well as allows a more comprehensive understanding of the relationship between race and health outcomes and between race and participation in health research. Second, this thesis offers a more holistic approach to examine the race in health outcomes and healthcare participation, ranging from hospitalization, pain management, and clinical trials, to data sharing attitudes. Finally, this thesis also addresses several knowledge gaps, notably racial disparities in CPRS 1, racial disparities in prescription opioid use in Canada, and views of clinical trial data sharing, with methodological rigor (i.e., mediation analysis), adding depth to the understanding of race as a social construct and the causal mechanisms relating race to health outcomes. Limitations This thesis is not without limitations. Chapter-specific limitations are discussed in more detail within each data chapter, and are summarized here with overall limitations. Specifically, a recurring constraint throughout chapter 4 to 6 is the narrow geographical and temporal scope. For example, in chapter 4, the analysis of the HCUP dataset took place in 2020, a time period profoundly impacted by the COVID-19 pandemic; however, this was the most recently available 111 dataset in HCUP. While chapter 5 focused on opioid prescriptions in Canada in 2015 and 2016, the dataset was confined to only 2 provinces and 1 territory (the regions which opted in to the Medication Module), limiting the generalizability of the results to the entire population Canada. Moreover, both datasets in chapter 4 and 5 use a cross-sectional study design, so time-dependent analyses were not possible. In chapter 6, the study investigated Black participation in pain clinical trials conducted in the US only. Thus, any other representation of racialized individuals in clinical trials outside of the US might not be applicable. In addition to these limitations, Chapter 7 may be subject to sampling bias, since it only included healthy respondents who had participated in clinical trials. Lastly, throughout this thesis, we were often not able to disaggregate race categories due to limited data availability (i.e., when the term “racialized” was used); this grouping could lead to generalizations that do not apply to all races included in the grouping. Table 8.1 below shows the strengths and limitations of the thesis. Table 8.1: Overview of thesis strengths and limitations 8.4 Implications and Considerations The findings of this thesis include broad implications for race reporting, management practices, and understanding of causal mechanisms related to race. While the primary focus of Chapter 4 was in CRPS-1, the implications contribute to the overwhelming landscape of inequitable healthcare management. Specifically, Black patients were less likely to be hospitalized, and more Strengths Limitations Diverse data sources Study design (cross-sectional study) Breadth of racial impacts on health Limited generalizability Methodological novelty Response and sampling bias 112 likely to have lengthier stays, compared to White patients. In addition, as a proxy for care management, prescribed opioids were less likely to be used by racialized individual compared to non-racialized individuals, even in Canada (Chapter 5). This was partially mediated by the lack of healthcare access. These findings suggest structural racism in healthcare access and management, disproportionately impacting racialized individuals. Consequently, addressing systemic racism is essential in equitable healthcare management in both the Canada and the US. However, combatting systemic racism is complex and difficult. In recent decades in the US, efforts to combat systemic racism have taken various forms, including legislative measures, grassroots activism, and cultural shifts. Affirmative action policies were introduced in the 1960s to promote equal opportunities for marginalized groups in education and employment.258 The Black Lives Matter (BLM) movement emerged in 2013 following the acquittal of George Zimmerman in the shooting death of Trayvon Martin.259 BLM gained widespread attention in 2020 following the killings of George Floyd, Breonna Taylor, and others, sparking protests against police brutality and racial injustice.260,261 The movement has called for police reform, the reallocation of funds from law enforcement to social services, and broader societal changes to address systemic racism.262 Further, numerous policy initiatives have been proposed and implemented to address systemic racism in the US.263–266 These include efforts to reform the criminal justice system, expand access to education and healthcare, and promote economic equity. The Obama administration launched initiatives such as My Brother's Keeper, aimed at supporting young Black men, and the Fair Sentencing Act, which aimed to reduce sentencing disparities for drug-related offenses.267,268 In 2021, President Joe Biden signed executive orders aimed at advancing racial equity, including actions to combat discrimination in housing, criminal justice, and healthcare.269–271 The administration also proposed legislative measures such as the George Floyd Justice in Policing 113 Act, which aims to increase police accountability and address systemic racism within law enforcement.270 In recent decades, Canada has also made strides in addressing systemic racism through legislative reforms, policy initiatives, and public awareness campaigns. The Canadian Charter of Rights and Freedoms, enshrined in the Constitution in 1982, guarantees equality rights and prohibits discrimination based on race, ethnicity, or other grounds.272 This foundational document has served as a framework for advancing racial equality and human rights in Canada. The Truth and Reconciliation Commission (TRC), established in 2008, aimed to address the legacy of residential schools and promote healing and reconciliation between Indigenous and non-Indigenous Canadians.273,274 The TRC's final report, released in 2015, included 94 calls to action related to education, health, justice, and other areas, aimed at addressing the ongoing impacts of colonialism and advancing Indigenous rights and self-determination.274,275 In addition to the TRC, the Canadian government has implemented various initiatives to address systemic racism and promote diversity and inclusion. This includes multiculturalism policies, affirmative action programs, and efforts to promote Indigenous languages and cultures.276–279 The Canadian Race Relations Foundation, established in 1996, works to combat racism and promote harmonious race relations through research, education, and community engagement.280 Policy initiatives have also played a crucial role in Canada's efforts to combat systemic racism, addressing inequalities and promoting equity in a health, as well as education, employment, and criminal justice. The Employment Equity Act, passed in 1986, requires federally regulated 114 employers to implement measures to achieve workplace diversity and address barriers faced by women, Indigenous peoples and other racialized individuals, and persons with disabilities.281 In the Canadian criminal justice system, similar efforts to the US are underway to address racial disparities in policing, sentencing, and incarceration. This includes initiatives such as bias training for law enforcement officers, community policing programs, and diversionary measures aimed at reducing the overrepresentation of Indigenous and other racialized individuals in the justice system.282,283 The Canadian government has also committed to addressing the socio-economic disparities facing Indigenous communities through initiatives such as the Indigenous Skills and Employment Training program, the Indigenous Housing Strategy, and investments in infrastructure, healthcare, and education in Indigenous communities.284–287 In addition to government action, civil society organizations, community groups, and individuals are working to address systemic racism through education, advocacy, and community organizing. Initiatives such as anti-racism training, cultural competency programs, and diversity and inclusion initiatives in workplaces and institutions are contributing to greater awareness and understanding of racism and its impacts.288–290 Another area of impact of this thesis is in race reporting. Since this work showed that less than 50% of US-based pain clinical trials reported race despite recommendations from major journals, researcher, editors, and funding organizations need more awareness about these recommendations.22,212 Although it was not an explicit objective of this thesis, in reviewing race collection practices, we also noted differences in the collection of race-based data. Race categories for the US and Canada markedly differ in the number of categories and their definitions. For example, NIH definition of “White” is “a person whose origins are in any of the original peoples of Europe, the Middle East, or North Africa” which does not align with CIHI or CCHS 115 descriptions/examples of White.33–35,291 Moreover, for some race-based collection of data, prompts are given (as mentioned in Chapter 2), while in others, prompts are not given.291 These differences make comparisons across regions difficult. Although some of the differences may be attributed to differences in the population (e.g. smaller Hawaiian population in Canada versus the US), future collection of race-based data should be standardized both with respect to prompts as well as race-based categories, even if the categories represent only a small fraction of the population. Thirdly, the collective work of this thesis also builds on our understanding of causal mechanisms relating race and disparities in health outcomes, health care participation, and views on open data sharing. Using the combined work of the individual chapters, and building on prior knowledge, I produced an overall diagram to better understand these complex casual relationships (Figure 8.1). This figure shows that several distinct pathways are theorized for racial differences in health outcomes, lack of racial diversity in clinical trials, and differences in views on open data sharing. Namely, primarily via discrimination, race (membership in a racialized group) affects socioeconomic factors (e.g., employment opportunities, income/education, insurance coverage, housing conditions), which could lead to differences in health outcomes (health status) directly and indirectly via stress.2,43,292 There is also a pathway from socioeconomic factors to stress. Health status is also affected by age and sex. Health status in turn impacts research participation since healthy individuals are more likely to participate in clinical trials. Research participation is also impacted by socioeconomic factors since participation in research involves costs due to missing work, childcare, and parking.117 Socioeconomic factors also influence research participation since participation in research requires access to information from doctors and online information (which requires computers and internet access).293 Lastly, views on open data sharing are therefore determined via past participation in research, as well as through the role of trust.128 116 Figure 8.1: Causal diagram explaining the relationship between race and health outcomes, participation in research, and views on open data sharing. : Exposure : Outcome : Ancestor of exposure : Ancestor of outcome : Ancestor of exposure and outcome : Adjusted variable 117 With this major role of trust, another implication of this work is the importance of building trust in a health care and health research context. Building trust within racialized communities should encompass the entire research process. Recognizing that racialized communities' experiences and challenges are influenced by their history, culture, and ongoing racial and ethnic inequalities in society, it is crucial to build trust, which may involve community engagement, more transparent communication, a reflection of personal biases, and furthered understanding of the ongoing effects of historical mistreatments. For example, hosting community forums to discuss research findings and their implications can also facilitate community engagement and build relationships. In acknowledgement of historic mistreatments, a more concerted effort, as well as including family members in the decision-making process can increase trust and comfort, making research more patient-centric and culturally sensitive.117 These recommendations ensure the concerns of racialized individuals are addressed in the research process, contributing to more equitable practices and outcomes. Another key implication of this thesis is that diversifying the team of investigators can enhance diversity in clinical trials. A diverse research team may foster trust and increase participation in clinical trials due to potential connection to language, experiences, and attitudes of the target population.232,234 Granting agencies, journals, investigators, and regulatory bodies should be conscious of the importance of diversity and include recommendations regarding the diversity of research teams in their guidelines. In addition to enhancing the participation of racialized individuals in research studies, diversity within scientific research teams has become increasingly recognized as an essential factor in driving innovation, problem-solving, and overall success in scientific endeavors.294 In recent years, there has been a growing acknowledgment of the importance of diversity, not only in terms race, 118 but also in terms of age, gender, disciplinary background, perspectives, and experiences.234,295–297 One of the primary benefits of diversity in scientific research teams is its ability to foster creativity and innovation. When individuals from different backgrounds, cultures, and disciplines come together, they bring with them unique perspectives, insights, and approaches to problem-solving.296 For example, research by Hong and Page (2004) demonstrated that diverse groups tend to outperform homogeneous groups in tasks that require complex problem-solving.298 By leveraging the cognitive diversity present within the team, diverse groups can overcome cognitive biases and explore a wider range of potential solutions.299 In scientific research, this ability to effectively address complex problems is crucial for advancing knowledge and addressing pressing societal issues.300 Moreover, diversity within research teams can help identify and mitigate unconscious biases that may otherwise go unnoticed in homogenous groups. By fostering an inclusive environment where diverse perspectives are valued and respected, teams can minimize the influence of bias and make more objective decisions.299 The importance of diversity within scientific research teams extends beyond the immediate benefits to the teams themselves. Diverse research teams contribute to a more inclusive and equitable scientific community, which is essential for addressing global challenges and maximizing the impact of scientific research.301 By promoting diversity within research teams, institutions can attract and retain talent from underrepresented groups, thereby enriching the scientific workforce and ensuring that diverse perspectives are represented in research endeavors. This not only enhances the quality of research but also promotes equity and inclusion within the scientific community. Furthermore, diverse research teams are better positioned to address the needs and concerns of diverse populations. By incorporating diverse perspectives into their work, researchers can develop 119 solutions that are more relevant, effective, and culturally sensitive.232 This is particularly important in fields such as health and more specifically in the context of acute and chronic pain, where disparities in healthcare access and outcomes disproportionately affect individuals from racialized communities. Lastly, diversity within research teams can enhance the dissemination and uptake of scientific findings. By engaging with diverse stakeholders and communities throughout the research process, teams can ensure that their findings are accessible and relevant to a wide range of audiences.117 This promotes greater trust and collaboration between the scientific community and society at large, leading to more informed decision-making and positive societal outcomes. However, addressing these issues requires proactive measures, such as implementing inclusive recruitment practices, providing support and mentorship for underrepresented individuals, and fostering a culture of inclusion within research institutions. 8.5 Conclusion In summary, the findings from this thesis significantly contribute to the broader understanding of racial disparities in healthcare and health research. Specifically, racialized individuals consistently experienced poorer health outcomes, marked by extended hospitalization and lower rates of prescription of opioids. These findings suggest the presence of structural racism, preventing optimal healthcare access and treatment within racialized communities. The achievement of these objectives was completed with large population datasets, namely HCUP and CCHS, representing a health outcomes landscape in both the United States and Canada. 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Thesis/Dissertation
eng
Pharmaceutical Sciences
Vancouver : University of British Columbia Library
Graduate
Cragg, Jacquelyn J.
Cairns, Brian E.
Racial differences in pain-related health outcomes and healthcare participation
Text