# Tradeoffs Between the Equal and Equitable Distribution of a Scarce Health Resource: Evidence from a Large Randomized Natural Experiment with Targeting by Health, Social Vulnerability, and Race

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $793,275

## Abstract

Tradeoffs Between the Equal and Equitable Distribution of a Scarce Health Resource: Evidence from a
Large Randomized Natural Experiment with Targeting by Health, Social Vulnerability, and Race
The recent pandemic and supply chain disruptions have created scarcities in life-saving health resources from
ventilators to vaccines, highlighting the need for evidence-based guidance on how to navigate tradeoffs in
distributing them. Policy responses to scarcity have important implications for large and widening disparities by
health, social vulnerability, and race. Our long-term goal is to inform evidence-based policies to address
inequities and improve public health when resources are scarce. Our objective is to use economics to
analyze a tradeoff between two key goals. One is equality: the same chance at early access for all subgroups
of a population. Another is equity: the same average outcomes for all subgroups of a population. We are
uniquely positioned to overcome a major barrier to understanding tradeoffs between the equal and equitable
distribution of vaccines and other scarce health resources by applying rigorous methods from economics to a
credible research design based on a large randomized natural experiment that randomized early access to
vaccines to a large population of patients age ≥65 when doses were extremely scarce. We have three specific
aims. Aim 1: Estimate inequities in vaccine takeup along many detailed dimensions of health, social
vulnerability, and race using equality in early access via random assignment and rich data that we
construct and disseminate. This aim will analyze inequities in a large novel dataset constructed by linking
randomized invitations to electronic health records, survey data, and population-level data from vaccination,
death, salary, and voter registries, which we will share. This aim will also contextualize inequities from our
randomized setting using national population-level vaccination, health, and voter data to inform the impact of
further policies that target by geographic vs. individual characteristics. Aim 2: Quantify how much of the
inequities by health, social vulnerability, and race that persist given equality in early access via random
assignment can be explained by various mechanisms that operate through health behaviors, health
conditions, and social and environmental factors available in our electronic health records and survey
data. This aim will quantify the potential equity impacts of policies that address various mechanisms such as
distance to vaccination sites using approaches that go beyond targeting greater numbers of invitations. Aim 3:
Characterize how the composition of the vaccinated population changes by health, social vulnerability,
and race as the share vaccinated expands with randomization, and relate those changes to inequities in
downstream impacts of vaccination on health and economic wellbeing. This aim will inform the potential
for vaccine distribution policies to affect c...

## Key facts

- **NIH application ID:** 10942243
- **Project number:** 1R01AG089084-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** AMANDA ELLEN KOWALSKI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $793,275
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10942243

## Citation

> US National Institutes of Health, RePORTER application 10942243, Tradeoffs Between the Equal and Equitable Distribution of a Scarce Health Resource: Evidence from a Large Randomized Natural Experiment with Targeting by Health, Social Vulnerability, and Race (1R01AG089084-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10942243. Licensed CC0.

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