Neighborhood Typologies Associated with Alcohol Use among Adults in Their 30s: a Finite Mixture Modeling Approach
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There has been increasing interest in how neighborhood context may be associated with alcohol use. This study uses finite mixture modeling to empirically identify distinct neighborhood subtypes according to patterns of clustering of multiple neighborhood characteristics and examine whether these subtypes are associated with alcohol use. Neighborhoods were 303 census block groups in the greater Seattle, WA, area where 531 adults participating in an ongoing longitudinal study were residing in 2008. Neighborhood characteristics used to identify neighborhood subtypes included concentration of poverty, racial composition, neighborhood disorganization, and availability of on-premise alcohol outlets and off-premise hard liquor stores. Finite mixture models were used to identify latent neighborhood subtypes, and regression models with cluster robust standard errors examined associations between neighborhood subtypes and individual-level typical weekly drinking and number of past-year binge drinking episodes. Five neighborhood subtypes were identified. These subtypes could be primarily characterized as (1) high socioeconomic disadvantage, (2) moderate disadvantage, (3) low disadvantage, (4) low poverty and high disorganization, and (5) high alcohol availability. Adjusted for covariates, adults living in neighborhoods characterized by high disadvantage reported the highest levels of typical drinking and binge drinking compared to those from other neighborhood subtypes. Neighborhood subtypes derived from finite mixture models may represent meaningful categories that can help identify residential areas at elevated risk for alcohol misuse.
KeywordsFinite mixture model Neighborhood context Alcohol Latent class analysis
This work was supported the National Institute on Drug Abuse [grant numbers R01DA033956, R01DA09679]. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. The authors thank J. David Hawkins for his helpful comments on an earlier version of this manuscript. They also are grateful to Lawrence Frank and Jim Chapman at Urban Design 4 Health for their assistance with geocoding and creation of geospatial measures used in this study.
Compliance with Ethical Standards
This study was approved by the University of Washington Institutional Review Board.
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