# Advancing novel methods to measure and analyze multiple types of discrimination for population health research

> **NIH NIH R01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2022 · $651,321

## Abstract

Project Summary
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
o...

## Key facts

- **NIH application ID:** 10330589
- **Project number:** 5R01MD012793-04
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** NANCY KRIEGER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $651,321
- **Award type:** 5
- **Project period:** 2019-06-19 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330589

## Citation

> US National Institutes of Health, RePORTER application 10330589, Advancing novel methods to measure and analyze multiple types of discrimination for population health research (5R01MD012793-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10330589. Licensed CC0.

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