# Structural Racism and Disparities in Social Risk, Human Capital, Health Care Resources, and Health Outcomes: A Multi-level Analysis of Pathways and Policy Levers for Change

> **NIH NIH R01** · MEDICAL COLLEGE OF WISCONSIN · 2024 · $106,507

## Abstract

Project Summary/Abstract
Structural racism, the ways in which societies foster discrimination through mutually reinforcing inequitable
systems, has emerged as an important social risk factor and contributor to poor health outcomes for ethnic
minorities (Egede 2020, Bailey 2017, Paradies 2015). An important component of structural racism that has
been inadequately studied is historic redlining. Historic redlining refers to the previously legal practice
(initiated in 1934 by the Federal Housing Administration) of systematically denying credit access and insurance
for borrowers in neighborhoods that were economically disadvantaged and that were inhabited by primarily
racial minority groups (Richardson 2020, Rothstein 2017). While explicit redlining is now prohibited under the
Fair Housing Act of 1968, the residual effects of residential redlining ensures that the same areas with
exposure to historic structural racism are to this day disproportionately inhabited by residents that are unduly
subject to worse social risk factors (defined across: Housing Instability; Food Insecurity; Transport Needs;
Economic Needs; and Safety), lower human capital (defined as the totality of individuals’ knowledge and
skills), and lower health care resources (defined as the medical resource capabilities of areas which enable
them to handle broad and complex medical events/cases) (Brillioux 2017, Bailey 2021, Hidalgo 2021); and that
redlining may present an important link between historic structural racism and present day disparities in health
outcomes (defined across: mortality, health care cost, health care utilization) (Egede 2020).
 The goal of this project is to inform our understanding of the pathways between structural racism
(defined as historic redlining), social risk factors, human capital, health care resources and health outcomes;
and to evaluate policies that can help reduce the impact of structural racism on health disparities. The present
study will accomplish this objective by using recently developed causal and interpretable machine learning
methods, structural equation modelling, counterfactual analysis, along with stakeholder engagement, to
address four aims: 1) Examine the relationship between regional exposure to structural racism (defined as:
historic redlining) and present-day social risk factors, human capital, and health care resources. 2) Examine
the direct and indirect effects of regional exposure to structural racism on health outcomes via social risk,
human capital, and healthcare resources. 3) Assess policy levers of federal, state, and regional governments
that can be used to reduce area vulnerabilities to historic structural racism, and that can reduce present-day
health outcomes disparities. 4) Engage a diverse set of key stakeholders to identify and prioritize strategies for
mitigating social risk, building human capital, and improving health care resources and outcomes for regions
exposed to structural racism through historical redlini...

## Key facts

- **NIH application ID:** 10841659
- **Project number:** 5R01MD017574-02
- **Recipient organization:** MEDICAL COLLEGE OF WISCONSIN
- **Principal Investigator:** Leonard E. Egede
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $106,507
- **Award type:** 5
- **Project period:** 2023-05-13 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10841659

## Citation

> US National Institutes of Health, RePORTER application 10841659, Structural Racism and Disparities in Social Risk, Human Capital, Health Care Resources, and Health Outcomes: A Multi-level Analysis of Pathways and Policy Levers for Change (5R01MD017574-02). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10841659. Licensed CC0.

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