Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Box 354944, Seattle, WA, 98195, USA.
Social Development Research Group, University of Washington, 9725 3rd Ave NE, Suite 401, Seattle, WA, 98115, USA.
J Urban Health. 2017 Aug;94(4):542-548. doi: 10.1007/s11524-017-0161-2.
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.
人们越来越关注邻里环境如何与饮酒行为相关。本研究采用有限混合模型，根据多个邻里特征的聚类模式，实证识别不同的邻里亚类，并检验这些亚类是否与饮酒行为相关。研究的邻里环境是华盛顿州西雅图大都市区的 303 个普查街区组，2008 年有 531 名参与一项正在进行的纵向研究的成年人居住在这些街区组中。用于识别邻里亚类的邻里特征包括贫困集中程度、种族构成、邻里无序程度以及有证酒类专卖店和无证烈性酒商店的分布情况。有限混合模型用于识别潜在的邻里亚类，具有聚类稳健标准误差的回归模型检验了邻里亚类与个体层面典型每周饮酒量和过去一年 binge 饮酒次数之间的关联。本研究共识别出 5 种邻里亚类。这些亚类可以主要分为以下几类：（1）高社会经济劣势；（2）中等劣势；（3）低劣势；（4）低贫困和高无序；（5）高酒精供应。调整了协变量后，与来自其他邻里亚类的成年人相比，生活在高劣势特征邻里中的成年人报告的典型饮酒和 binge 饮酒量最高。从有限混合模型中得出的邻里亚类可能代表了有意义的类别，可以帮助识别出酒精滥用风险较高的居住区域。
