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酒精需求的两个因果驱动因素对社区环境中销售点密度的纵向影响:人口规模和收入效应。

Longitudinal impacts of two causal drivers of alcohol demand on outlet concentrations within community settings: Population size and income effects.

作者信息

Jin Zhuxuan, Chang Howard H, Ponicki William R, Gaidus Andrew, Waller Lance A, Morrison Christopher N, Gruenewald Paul J

机构信息

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, United States.

Prevention Research Center, Pacific Institute for Research and Evaluation, 2150 Shattuck Avenue, Suite 601, Berkeley, CA 94704, United States.

出版信息

Spat Spatiotemporal Epidemiol. 2018 Nov;27:21-28. doi: 10.1016/j.sste.2018.07.003. Epub 2018 Jul 17.

Abstract

We analyzed counts of licensed bars, restaurants and off-premise alcohol outlets within 53 California cities from 2000-2013. Poisson models were used to assess overall space-time associations between outlet numbers and population size and median household income in local and spatially adjacent block groups. We then separated covariate effects into distinct spatial and temporal components ("decomposed" models). Overall models showed that densities of all outlet types were generally greatest within block groups that had lower income, were adjacent to block groups with lower income, had greater populations, and were adjacent to block groups that had greater populations. Decomposed models demonstrate that over time greater income was associated with increased counts of bars, and greater population was associated with greater numbers of restaurants and off-premise outlets. Acknowledging the many negative consequences for populations living in areas of high outlet density, these effects are a predictable and powerful social determinant of health.

摘要

我们分析了2000年至2013年加利福尼亚州53个城市内持牌酒吧、餐馆和店外酒精销售点的数量。采用泊松模型评估销售点数量与当地及空间相邻街区组的人口规模和家庭收入中位数之间的总体时空关联。然后,我们将协变量效应分离为不同的空间和时间成分(“分解”模型)。总体模型显示,在收入较低、与收入较低的街区组相邻、人口较多且与人口较多的街区组相邻的街区组中,所有销售点类型的密度通常最大。分解模型表明,随着时间的推移,收入增加与酒吧数量增加相关,人口增加与餐馆和店外销售点数量增加相关。鉴于生活在高销售点密度地区的人群会产生许多负面后果,这些影响是健康的可预测且强大的社会决定因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c7c3/6233730/e41803402bee/nihms-1505611-f0001.jpg

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