Spatial Associations of Alcohol Outlet Density with Neighborhood Crime and Child Opportunity: A Hierarchical Bayesian Ecological Study
Year: 2025
Type: article
Abstract: BACKGROUND: Passaic county, New Jersey (NJ) has a high population density and diverse racial composition, with significant socioeconomic disparities that vary by city. Such disparities expose residents, and particularly children, to environmental conditions that may be harmful to their health and wellbeing such as high exposure to crime, violence, and high amounts of alcohol outlets. This study explores the association between alcohol outlet density (AOD) and neighborhood-level crime rates and child opportunity index (COI) measures in Passaic county, NJ. METHODS: We applied a hierarchical Bayesian spatial approach to model the relationships between AOD and crime and child opportunity at the census tract level, controlling for neighborhood-level socioeconomic covariates (unemployment, access to health insurance, and living below the federal poverty level) and spatial correlation. RESULTS: In total there were 120 census tracts in Passaic county which had a total of 554 alcohol outlets. The median number of alcohol outlets in a single census tract was four; the maximum number of outlets in a single census tract was 26. Our results show that neighborhoods in the highest tertile of AOD experienced significantly higher rates of property crime compared to those in the lower tertiles. Furthermore, lack of access to health insurance was consistently associated with both an increase in crime and a lower child opportunity index. CONCLUSIONS: These findings suggest that reducing AOD and increasing access to health insurance in high-risk neighborhoods could mitigate crime and enhance opportunities for children. Policy interventions addressing these factors may be critical for improving community health and safety.
Source: Substance Use & Misuse
Institution: Yale University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Awards: Integrating Community Based Participatory Research and Machine Learning Methods to Predict Youth Substance Use Disorders for Urban Cities in New Jersey, Understanding the Role of Neighborhoods on Urban Youth's Substance Use and Mental Health: A Community-Based Substance Abuse Prevention Project, G8405901908
OpenAlex ID: W4409865844
