246895 Veteran Homelessness 2000-2010: Examining a Comprehensive Model of Outcome Predictors

Wednesday, November 2, 2011: 9:16 AM

Roger Casey, PhD , National Center on Homeless Among Veterans, Tampa, FL
John Schinka, PhD , National Center on Homelessness among Veterans, Tampa, FL
Wes Kasprow, PhD , Homeless Programs, VA Northeast Program Evaluation Center, West Haven, CT
In this presentation, we examine a multi-domain model of predictors of housing intervention outcomes in a large VA-funded, transitional, community-based housing program. Data for this study were provided by the VA Northeast Program Evaluation Center and consisted of records of individuals receiving services in community-based housing intervention programs supported by the VA Grant and Per Diem program during 2000-2010. This program defrays the cost of housing support/supportive services for up to two years and is designed as a transitional program leading to permanent housing. The dataset was derived from comprehensive intake, admission, discharge, and facility descriptor data. Analyses were based on a dataset consisting of 100,167 records for 68,921 veterans who were admitted to one of 435 provider programs. We examined a comprehensive model of predictors for several intervention outcomes (e.g., program completion, employment on discharge, improvement in substance abuse). This model included predictors in the following domains: demographic characteristics, pre-admission status (e.g., employment, housing), substance use (e.g., recent use, hospitalizations), mental health status and history, health status (e.g., chronic illnesses, disease index), intervention components (e.g., length of stay, number of supportive services), and facility characteristics (e.g., program philosophy). Hierarchical logistic regression was used to examine outcome predictors. In order to estimate the amount of variance in outcomes (e.g., program completion) explained by each domain, we calculated the McKelvy-Zavoina index, which serves as a measure of effect size. We will present results with a focus on domains of predictors that have the most explanatory power in estimating housing intervention outcomes.

Learning Areas:
Other professions or practice related to public health

Learning Objectives:
To list and describe two major factors that contribute to housing intervention outcomes in homeless veterans

Keywords: Homeless, Veterans

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am the Director of a large VA housing intervention program and regularly provide training to healthcare providers working with homeless veterans.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.