Abstract: Individual, Housing Development, and Neighborhood-Level Predictors of Health Behaviors Among Public Housing Residents (Society for Social Work and Research 25th Annual Conference - Social Work Science for Social Change)

All live presentations are in Eastern time zone.

Individual, Housing Development, and Neighborhood-Level Predictors of Health Behaviors Among Public Housing Residents

Thursday, January 21, 2021
* noted as presenting author
Margaret Thomas, PhD, Postdoctoral Research Scientist, Columbia University, New York, NY
Brena Figueiredo Sena, MPH, Project Manager, Harvard University, MA, Clinical Trials Manager, E25Bio, Cambridge, MA
Background: Public housing residents have some of the worst health outcomes in the United States. Despite stigma associated with living in public housing, poor health outcomes, and a rising acknowledgement of the spatial segregation of neighborhoods where public housing is located, little research has focused on housing developments or neighborhoods as levels of analysis that could predict health behaviors among public housing residents. The present study brings together established evidence on the health risks public housing residents often experience with knowledge regarding neighborhood environment and its association with health behaviors. The study assesses the impact of neighborhood and public housing development environments on the health behaviors of public housing residents.

Methods: The present study drew on a unique, combined dataset: individual-level data from the Resident Health and Wellness Survey, administered to adult residents of Boston Housing Authority public housing developments; housing development-level data from the U.S. Department of Housing and Urban Development; and neighborhood-level data from the U.S. Census Bureau’s American Community Survey. The data were merged into a person-level dataset, including development-level and neighborhood-level factors linked to individual-level observations (n=708). Multi-level modeling was used to test clustering at housing-development and neighborhood-levels; this analysis indicated that these hierarchical models were not the best fit for the data. Thus, multiple logistic regression was used to examine individual-, housing development-, and neighborhood-level predictors of two health behaviors: primary water source (tap vs. bottled water) and monthly sugar-sweetened beverage (SSB) consumption (moderate to significant vs. little to none).

Results: Results of the multiple logistic regression models identified a number of important individual-level and neighborhood-level predictors of both water source and SSB consumption. Development-level predictors were not significant in either model. In both models, older age predicted goal behaviors, specifically higher odds of tap water consumption and lower odds of greater SSB consumption. For SSB consumption only, Latinx ethnicity and being born in the US were significantly predictive of higher odds of greater SSB consumption. In both models, Black (vs. White) race categories were marginally predictive of behavior contrary to target goals, specifically lower odds of tap water consumption, and higher odds of greater SSB consumption.

While development factors were not significant in our analysis, higher odds of tap water consumption were predicted by neighborhood-level factors, including lower proportion of renters, higher proportion of college-level education, higher proportion of adults employed, and lower proportion of women employed. In contrast, higher odds of greater SSB consumption were predicted by a lower proportion of households with children, lower proportion of US-born individuals, lower proportion of employed women, and higher proportion of seniors living in poverty.

Conclusion: This study’s findings contribute conceptually, to the framing and understanding of factors that may impact health behaviors among a sample of adult public housing residents. Empirically, the study tests a number of factors across multiple levels of organization, capitalizing on the strengths of a novel, combined dataset. The findings suggest the importance of considering community-level factors in seeking to understand and respond to health disparities among public housing residents.