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

Several studies have reported that alcohol abuse and dependence, as well as other risk behaviors, cluster in contexts of poverty, residential instability, and social isolation.1–5 Most of these studies are cross-sectional and do not account for the fact that neighborhoods change over time, or allow us to assess how such changes might affect alcohol misuse. The question remains whether such multilevel associations are actually due to the influence of neighborhood contextual characteristics on health outcomes such as alcohol abuse, or whether they merely reflect the selection of persons with similar socioeconomic characteristics and health problems into particular types of neighborhoods. Longitudinal studies that follow people and neighborhoods over time are needed to better estimate the nature of the association of neighborhood conditions with alcohol use. Traditionally, longitudinal studies examining the association between neighborhood characteristics and risk behaviors have attempted to address individual selection into neighborhoods by using standard regression models or propensity-score analysis to control tightly for individual-level characteristics (such as socioeconomic position) that are causally related both to the type of neighborhood a person lives in and to the person's use of alcohol. A major concern with such methods, however, is that many of the time-varying potential confounders are also affected by prior neighborhood conditions, and are thus in the causal pathway between the exposure of interest and the outcome, at the same time that they affect the types of neighborhoods that persons move into.6 Individual socioeconomic position, for example, not only contributes to the type of neighborhood a person can afford to live in and the level of alcohol consumed, but it is also a product of the types of income-generating opportunities afforded by the neighborhood socioeconomic environment.5 By controlling for the individual-level composition of neighborhoods, to address individual selection into neighborhoods, traditional regression analytic techniques run the risk of also controlling for individual-level mediators of earlier neighborhood characteristics and thus underestimating the impact that long-term cumulative neighborhood exposure has on health outcomes. Unadjusted estimates are confounded by individual-level characteristics related to selection of persons into neighborhoods.7 Marginal structural models offer a particularly useful tool for research on neighborhoods and health, where there are often time-dependent covariates that act simultaneously as confounders and as intermediate variables in the causal pathway between the neighborhood exposure of interest and the outcome.8 Marginal structural models describe the marginal causal relationship between a time-varying exposure such as neighborhood poverty and alcohol use, and therefore, allow us to control for time-varying confounders without conditioning on these variables. Formally, a marginal structural model for repeated measures is a parametric regression model relating any possible exposure history, up to time t, to the corresponding counterfactual outcome at time t. Marginal structural models are also useful in the case of loss-to-follow-up in longitudinal studies, because they allow us to account for differential loss to follow-up. Assuming ignorable treatment assignment and the absence of differential misclassification,9 the parameters of a marginal structural model can be estimated in an unbiased manner with inverse probability-of-treatment and censoring weighting. This is a product of inverse probability-of-treatment weights and inverse probability-of-censoring weights. Such weighting makes it possible to obtain a comparable “pseudopopulation” in terms of stable and time-varying confounders across levels of the exposure, and thus estimate the unconfounded association between the exposure and outcome without conditioning on the covariate through its inclusion as a predictor in the outcome model.8 A detailed example illustrating how weighting creates an unbiased “pseudopopulation” is provided in the eAppendix (http://links.lww.com/EDE/A397). Using data from a population-based longitudinal study of young adults, we investigated the potentially causal association of neighborhood poverty with 2 important aspects of alcohol consumption: frequency of alcohol consumption and binging. These 2 types of alcohol-related behavior may present contrasting etiologies,7 and neighborhood poverty may have a stronger impact on heavy alcohol consumption than on the consumption gradient. We used marginal structural models to estimate the relationship between cumulative and point-in-time neighborhood poverty and alcohol use behaviors, after appropriately accounting for time-dependent confounders and for loss to follow-up. A directed acyclic graph illustrating the relationship is found in eAppendix (http://links.lww.com/EDE/A397). Because neighborhood poverty is a continuous exposure, pooled logistic regression is not appropriate for estimating inverse probability-of-treatment and censoring weighting treatment weights. Instead, our approach makes use of an estimated log-normal exposure probability density function that correctly accounts for the highly skewed continuous nature of the exposure of interest.