Neighborhood Typologies Associated with Alcohol Use among Adults in Their 30s: a Finite Mixture Modeling Approach
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
- 8.Brenner AB, Diez Roux AV, Barrientos-Gutierrez T, Borrell LN. Associations of alcohol availability and neighborhood socioeconomic characteristics with drinking: cross-sectional results from the Multi-Ethnic Study of Atherosclerosis (MESA). Subst Use Misuse. 2015;50(12):1606–17.CrossRefPubMedPubMedCentralGoogle Scholar
- 13.Keyes K, Galea S. The limits of risk factors revisited: is it time for a causal architecture approach? Epidemiology. 2017;28(1):1–5.Google Scholar
- 14.Collins LM, Lanza ST. Latent class and latent transition analysis: with applications in the social, behavioral, and health sciences. Hoboken, NJ: Wiley; 2010.Google Scholar
- 15.Adams MA, Ding D, Sallis JF, et al. Patterns of neighborhood environment attributes related to physical activity across 11 countries: a latent class analysis. Int J Behav Nutr Phy. 2013;10:34.Google Scholar
- 16.Krieger N, Chen JT, Waterman PD, Rehkopf DH, Subramanian SV. Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures—the public health disparities geocoding project. Am J Public Health. 2003;93(10):1655–71.CrossRefPubMedPubMedCentralGoogle Scholar
- 19.Rubin DB. Multiple imputation for nonresponse in surveys. Hoboken, N.J: Wiley-Interscience; 2004.Google Scholar
- 21.R Development Core Team. R: a language and environment for statistical computing. [computer program]. Vienna, Austria: R Foundation for Statistical Computing. 2012. http://www.R-project.org. Accessed 7 January 2015.
- 22.Graham N, Arai M, Hagströmer B. multiwayvcov: multi-Way Standard Error Clustering [computer program]. R package version 1.2.3. 2016. http://CRAN.R-project.org/package=multiwayvcov. Accessed 7 January 2015.
- 23.Lumley T. mitools: tools for multiple imputation of missing data [computer program]. R package version 2.3. 2014. http://CRAN.R-project.org/package=mitools. Accessed 7 January 2015.