Our study seeks to advance methods to measure and analyze multiple types of discrimination for population health research. We will compare novel implicit vs. conventional explicit (self-report) measures of exposure, investigate different approaches to modeling exposure to multiple types of discrimination, and test novel hypotheses using causal mediation analyses. We are motivated by profound concerns that current widely- used approaches are underestimating the impact of discrimination on population health. Our population-based study directly tackles these issues, and focuses on two health outcomes linked to current and lifetime discrimination that harm quality of life and increase risk of both chronic disease and substance abuse: psychological distress and sleep disorders. We will employ a refined version of the first- ever implicit association test (IAT) for discrimination we recently developed, along with our prior validated self-report measure that is among the most widely-used measures in health research on discrimination. To ensure 350 participants per comparison group, we will recruit 1092 adult patient members and staff randomly selected from two diverse community health centers to address the following specific aims:
Aim 1. Assess exposure to discrimination using implicit and explicit (self-report) measures, using the validated brief IAT format, to enable time-efficient assessment of lifetime exposure in the US to discrimination based on: race/ethnicity, gender identity, age, sexual orientation, and weight, and assess different ways to combine individuals? exposure to multiple types of discrimination.
Aim 2. Test hypotheses about the health impact of exposure to discrimination, for single and combined measures across the social comparison groups (i.e., people of color vs. white; gender minority [GM/TGNC (transgender/non-conforming)] vs. cis-gender women vs. cis-gender men vs.; older vs. younger; sexual minority [LGBQ (lesbian/gay/bisexual/queer)/SM] vs. heterosexual; obese vs. non-obese) Aim 2.1. Test the hypotheses that the implicit and explicit discrimination measures will be independently associated with psychological distress and sleep disorders (insufficient sleep; sleep disordered breathing).
Aim 2. 2. Using causal mediation techniques, quantify the effects for discrimination as mediator of health inequities and determine which type and combination of discrimination measures yields the largest effects. Impact: If our hypotheses are supported, results would demonstrate that predominant measures and methods underestimate the health impacts of discrimination, and that better methods would entail use of implicit and explicit measures of multiple types of discrimination and counterfactual causal mediation techniques. We expect our study, designed for high internal validity, will significantly advance methods for feasibly measuring and rigorously analyzing, in large population-based studies, exposure to multiple types of discrimination, knowledge crucial for evidence to promote health equity, a priority for US health agencies.
Our study seeks to advance methods to measure and analyze multiple types of discrimination for population health research, motivated by profound concerns that current widely-used approaches are underestimating the impact of discrimination on population health. To test our hypotheses, we will recruit a random sample of 1092 diverse adults from two community health centers, who potentially have been exposed to discrimination in the US throughout their lifetimes, to: (1) analyze novel implicit vs. conventional explicit (self-report) measures of exposure to diverse types of discrimination based on race/ethnicity, gender identity, age, sexual orientation, and weight, both singly and combined, and (2) use new causal mediation analytic methods to analyze the health impact of discrimination on health inequities for both psychological distress and sleep disorders, specifically insufficient sleep and sleep disordered breathing. We expect our study will significantly advance methods for feasibly measuring and rigorously analyzing, in large population-based studies, exposure to multiple types of discrimination, knowledge crucial for evidence to promote health equity, a priority for US health agencies.
