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The relationship between neighborhood poverty and alcohol use: estimation by marginal structural models.

ABSTRACT: 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.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.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).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.

SUBMITTER: Cerda M

PROVIDER: S-EPMC3897210 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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The relationship between neighborhood poverty and alcohol use: estimation by marginal structural models.

Cerdá Magdalena M Diez-Roux Ana V AV Tchetgen Eric Tchetgen ET Gordon-Larsen Penny P Kiefe Catarina C

Epidemiology (Cambridge, Mass.) 20100701 4

<h4>Background</h4>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.<h4>Methods</h4>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 margi ...[more]

PMID: 20498603

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Project description:This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.

Dataset Information

The relationship between neighborhood poverty and alcohol use: estimation by marginal structural models.

Publications

The relationship between neighborhood poverty and alcohol use: estimation by marginal structural models.

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