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.
From the aCenter for Urban Epidemiologic Studies, New York Academy of Medicine, New York, NY; bDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY; cDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI; dDepartment of Epidemiology, Harvard School of Public Health, Harvard University, Cambridge, MA; eDepartment of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC; and fDepartment of Quantitative Health Sciences, University of Massachusetts Medical School, Boston, MA.
Submitted 22 May 2009; accepted 5 February 2010; posted 24 May 2010.
Supported by Robert Wood Johnson Foundation (Health and Society Scholars Program).
Editors' note: A commentary on this article appears on page 490.
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Correspondence: Magdalena Cerdá, New York Academy of Medicine, Atte: CUES, 1216 Fifth Ave, New York, NY 10029. E-mail: email@example.com.