# Reducing Evictions, Homelessness, and Poor Health Outcomes Among Washington State Veterans: Integrating Novel State Data into Analytic and Predictive Models of Housing Insecurity

> **NIH VA I01** · VA PUGET SOUND HEALTHCARE SYSTEM · 2024 · —

## Abstract

Background: There is a paucity of reliable, household-level data on evictions. We have no data on how
many Veterans are evicted each year, whether they regain housing or not after eviction, or the costs to VA of
rehousing evicted Veterans and treating eviction-related health conditions. If we knew these costs to be
substantial, we could better budget for programs, such as the Supportive Services for Veteran Families
(SSVF), that are designed to reduce housing insecurity.
Significance: We will acquire the names and addresses on all eviction filings starting in 2013 from Minnesota,
Indiana, Cook County (Chicago), IL and the Puget Sound (Seattle) region of Washington State. We will match
these filings by name and address to VA clinical and housing databases. This offers three major opportunities
for VA. First, it allows VA researchers to estimate the effects of eviction on health and housing outcomes and
VA costs. Knowing the costs of evictions informs the level of effort the VA should apply to mitigate those costs.
Second, it provides an opportunity to intervene proactively to prevent homelessness because the eviction data
can be extracted monthly. Third, because eviction data covers all Veterans and not just those receiving VA
care, it provides an opportunity for VA to proactively reach all enrolled Veterans, including non-VA users who
might not be aware of the housing, health, and social assistance available to them at VA.
Innovation and Impact: This is the first VA study to: carefully match evicted persons to non-evicted persons
at the time of eviction to minimize risks of reverse causation and confounding; estimate the probability of
homelessness following an eviction; comprehensively estimate the effect of evictions on health and healthcare
costs; pilot-test a system to identify Veterans before they become housing insecure that does not rely on a
Veteran having a healthcare visit with a housing screen; and use newly released data on the socioeconomic
status of Veterans from the USVETS database. If successful, we will be able to proactively contact Veterans
and refer them to VA housing services before they become homeless. We will also better understand the costs
of evictions to Veterans and VHA.
Specific Aims:
 1. Aim 1: Estimate the effect of evictions on Veterans’ health, housing outcomes and costs.
 Hypotheses: a) Veterans who were evicted were more likely to experience subsequent homelessness,
 have increased need for physical and mental health services, and to generate more costs to the VA
 than Veterans who were not evicted.; b) Black and Hispanic Veterans were more likely to face eviction
 than were white Veterans.
2. Aim 2: Conduct a prospective survey of evicted and non-evicted Veterans to estimate the association
 between evictions and patient reported outcomes:
 Hypothesis: Veterans who were evicted will have less favorable patient reported outcomes than
 Veterans who were not evicted.
3. Aim 3: Design and pilot test an intervention...

## Key facts

- **NIH application ID:** 10862247
- **Project number:** 1I01HX003754-01A2
- **Recipient organization:** VA PUGET SOUND HEALTHCARE SYSTEM
- **Principal Investigator:** Steven Bacchus Zeliadt
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862247

## Citation

> US National Institutes of Health, RePORTER application 10862247, Reducing Evictions, Homelessness, and Poor Health Outcomes Among Washington State Veterans: Integrating Novel State Data into Analytic and Predictive Models of Housing Insecurity (1I01HX003754-01A2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10862247. Licensed CC0.

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