221933 An analysis of marginalization, substance abuse and longitudinal service use among homeless adults using a Bayesian framework

Monday, November 8, 2010 : 9:30 AM - 9:45 AM

Ben Alexander-Eitzman, MSW, LCSW, PhD , Department of Social Work, Appalachian State University, Boone, NC
Carol North, MD, MPE , Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
David Pollio, PhD , School of Social Work, University of Alabama, Tuscaloosa, AL
Background: This NIDA funded study examined the complex longitudinal relationships between housing status, substance abuse, and service use among 400 homeless adults. The primary aim was to better understand how urban homeless adults engage in emergency and routine services based on changing indicators of marginalization, substance abuse, and housing status. Methods: Survey data from three interviews was matched with administrative service data collected from local and regional health, mental health, housing, and substance abuse service providers. To explore the proposed relationships and accurately account for high levels of over dispersion and zero inflation in the service count data, a flexible Bayesian framework was adopted, using a zero inflated Poisson Gamma model structure. Results: Key findings include consistent relationships between living on the streets, alcohol use, and increased marginalization, notably legal problems, shadow work, and victimization. The prevailing assumption that more marginalized individuals (legally, socially, and economically) would use more emergency services was not supported. Living on the streets and engaging in shadow work together predicted lower levels of both routine and emergency types of service use. In a similar manner, social isolation (i.e. not being able to count on family for help) predicted lower rates of routine service use. Discussion: These results underscore the importance of addressing legal problems, economic, and safety issues at a community level for homeless adults rather than simply focusing on adding more service options. Additional results and implications are discussed as are the benefits of Bayesian modeling strategies in exploring complex social problems.

Learning Areas:
Biostatistics, economics
Other professions or practice related to public health
Program planning
Social and behavioral sciences

Learning Objectives:
1. Describe how Bayesian statistical methods can be effectively used to analyze complex social problems and address non-normal data distributional assumptions. 2. Assess the influence of economic, social, and legal marginalization on emergency and routine service use over time.

Keywords: Homelessness, Statistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am an assistant professor in social work and have 10 years of experience working with homeless, substance using adults in health and mental health service settings.
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.