SC-14 - Emerging Topic: How to StudyHealth Disparities in Pharmacoepidemiology- An introduction to fundamentals and methods

1. Course Aim

Health disparities, defined as health differences caused or linked with social, economic and/or environmental disadvantages that adversely affect populations that systematically experience greater obstacles to health, have been documented worldwide. These disadvantaged populations, like racial and/or ethnic minority populations and sexual and gender minority populations, are more likely to experience social determinants of health (SDOH) and other forms of structural disadvantage, which create barriers to healthcare and put them at higher risk for poor outcomes.

This course will introduce participants to health disparities and health equity research, focusing on methods to study health disparities in drug utilization and outcomes. Applied examples will be used throughout to illustrate and connect concepts. Participants will also learn how health inequities are reflected in real-world data (RWD) and can introduce bias into our pharmacoepidemiologic studies, even when disparities are not the focus of study. This understanding is crucial for all researchers, as a foundational understanding on how SDOH and other forms of structural disadvantage impact access to healthcare and RWD quality – especially when applying advanced AI or machine learning methods – or researchers run the risk of perpetuating health disparities.

2. Requisites Statement

Entry level course – No prior experience in minority health, health disparities, or health equity research is needed.

This course may be most relevant for individuals who work hands-on with healthcare databases, have an interest in studying health disparities and health equity, are developing Clinical Trial Diversity Action plans, and those conducting pragmatic trials; however, all levels of expertise and experience are welcome.

Helpful pre-reads:
Braveman PA, Kumanyika S, Fielding J, LaVeist T, Borrell LN, Manderscheid R, Troutman A. (2011). Health disparities and health equity: the issue is justice. Am J Public Health; 101(Suppl 1): S249-S155.

Jackson JW. (2021). Meaningful causal decompositions in health equity research: definition, identification, and estimating through a weighting framework. Epidemiol; 32(2): 282-290.

3. Course Objectives

Attendees will learn how to:
1. Define health disparities, health equity, health determinants in the US and Europe
2. Learn how the drivers of health disparities can also bias pharmacoepidemiology research when using real world data (RWD), and ways to address this bias
3. Learn conceptual frameworks and how to apply them to evaluate the suitability of RWD sources for heath disparities research in pharmacoepidemiology.
4. Learn concepts and methodologies for measuring and understanding the sources of health disparities (to inform future interventions) in pharmacoepidemiology.

4. Syllabus Outline

1. Defining health disparities, health equity, social determinants of health
The first session of this course will define key concepts (e.g., health disparities, health equity), and provide some historical context for why health disparities exist in the United States and Europe. We will describe which populations experience health disparities in the US and Europe, and how these populations are defined. Finally, we will review facilitators and barriers for measuring and studying health disparities in the US and Europe.

2. How health determinants impact real-world data
Health determinants (e.g., SDOH) cause disadvantaged patients to experience more barriers to care and inadequate care, and inappropriate care. This means that health determinants can introduce differential missingness and selection bias into our RWD. This second session will review how health determinants impact our data and factors that need to be considered when designing and analyzing pharmacoepidemiologic studies. We will also review the current limitations of Z-codes and large language models (LLMs) to capture health determinants.

3. Applying conceptual frameworks to RWD for health disparities research
The third session will introduce health disparity conceptual frameworks and how to apply them to assess the suitability of RWD sources for health disparities research. Several applied examples will be provided. This session will also cover how data linkage can be used to address data limitations.

4. Using causal decomposition to identify health disparities and sources of health disparities to inform potential interventions
The final session will explain how applying conceptual frameworks, like target study emulation, and causal decomposition can be used to identify when health disparities exist and to estimate the effect potential interventions will have on observed health disparities. Attendees will be walked through applied examples, and code will be provided.