The Relationship Between Neighborhood Poverty and Alcohol Use: Estimation by Marginal Structural Models

被引:118
作者
Cerda, Magdalena [1 ,2 ]
Diez-Roux, Ana V. [3 ]
Tchetgen, Eric Tchetgen [4 ]
Gordon-Larsen, Penny [5 ]
Kiefe, Catarina [6 ]
机构
[1] New York Acad Med, Atte CUES, Ctr Urban Epidemiol Studies, New York, NY 10029 USA
[2] Columbia Univ, Mailman Sch Publ Hlth, Dept Epidemiol, New York, NY USA
[3] Univ Michigan, Sch Publ Hlth, Dept Epidemiol, Ann Arbor, MI 48109 USA
[4] Harvard Univ, Harvard Sch Publ Hlth, Dept Epidemiol, Cambridge, MA 02138 USA
[5] Univ N Carolina, Dept Nutr, Chapel Hill, NC USA
[6] Univ Massachusetts, Sch Med, Dept Quantitat Hlth Sci, Boston, MA 02125 USA
关键词
SOCIAL EPIDEMIOLOGY; MULTILEVEL ANALYSIS; DRUG-USE; HEALTH; DISADVANTAGE; TIME;
D O I
10.1097/EDE.0b013e3181e13539
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Previous studies on the relationship of neighborhood disadvantage with alcohol use or misuse have often controlled for individual characteristics on the causal pathway, such as income-thus potentially underestimating the relationship between disadvantage and alcohol consumption. Methods: We used data from the Coronary Artery Risk Development in Young Adults study of 5115 adults aged 18-30 years at baseline and interviewed 7 times between 1985 and 2006. We estimated marginal structural models using inverse probability-of-treatment and censoring weights to assess the association between point-in-time/cumulative exposure to neighborhood poverty (proportion of census tract residents living in poverty) and alcohol use/binging, after accounting for time-dependent confounders including income, education, and occupation. Results: The log-normal model was used to estimate treatment weights while accounting for highly-skewed continuous neighborhood poverty data. In the weighted model, a one-unit increase in neighborhood poverty at the prior examination was associated with a 86% increase in the odds of binging (OR = 1.86 [95% confidence interval = 1.14-3.03]); the estimate from a standard generalized-estimating-equations model controlling for baseline and time-varying covariates was 1.47 (0.96-2.25). The inverse probability-of-treatment and censoring weighted estimate of the relative increase in the number of weekly drinks in the past year associated with cumulative neighborhood poverty was 1.53 (1.02-2.27); the estimate from a standard model was 1.16 (0.83-1.62). Conclusions: Cumulative and point-in-time measures of neighborhood poverty are important predictors of alcohol consumption. Estimators that more closely approximate a causal effect of neighborhood poverty on alcohol provided a stronger estimate than estimators from traditional regression models.
引用
收藏
页码:482 / 489
页数:8
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