Neighborhood education inequality and drinking behavior

Abstract

Background The neighborhood distribution of education (education inequality) may influence substance use among neighborhood residents. Methods Using data from the New York Social Environment Study (conducted in 2005; n=4,000), we examined the associations of neighborhood education inequality (measured using Gini coefficients of education) with alcohol use prevalence and levels of alcohol consumption among alcohol users. Analyses were adjusted for neighborhood education level, income level, and income inequality, as well as for individual demographic and socioeconomic characteristics and history of drinking prior to residence in the current neighborhood. Neighborhood social norms about drinking were examined as a possible mediator. Results In adjusted generalized estimating equation regression models, one-standard-deviationhigher education inequality was associated with 1.18 times higher odds of alcohol use (logistic regression odds ratio = 1.18, 95% confidence interval 1.08–1.30) but 0.79 times lower average daily alcohol consumption among alcohol users (Poisson regression relative rate = 0.79, 95% confidence interval 0.68–0.92). The results tended to differ in magnitude depending on respondents’ individual educational levels. There was no evidence that these associations were mediated by social drinking norms, although norms did vary with education inequality. Conclusions Our results provide further evidence of a relation between education inequality and drinking behavior while illustrating the importance of considering different drinking outcomes and heterogeneity between neighborhood subgroups. Future research could fruitfully consider other potential mechanisms, such as alcohol availability or the role of stress; research that considers multiple mechanisms and their combined effects may be most informative.

Modeling longitudinal drinking data in clinical trials: An application to the COMBINE study

“…To compare the zero drinking and risky drinking hurdle models to a commonly used longitudinal Poisson regression model (e.g., Le and Galea, 2010; Bandyopadhyay et al, 2011; Walley et al, 2012) three statistics were calculated. The Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to compare non-nested models.…”

“…These outcomes are usually analyzed using generalized linear models (GML) but zero-inflated Poisson or binomial (ZIP, ZIB) regression may be used to model consumption if data violate the assumptions of GLMs. There have been several applications of such zero inflated models in the substance use literature (Le and Galea, 2010; Hu et al, 2011; Meszaros et al, 2011; DeSantis et al, 2011; Fielder et al, 2012; Peeters et al, 2012; Walley et al, 2012). However, the statistical assumption of zero inflated models is that zero drinking arises from a set of patients who have zero risk of drinking.…”

Longitudinal and Concurrent Pathways to Alcoholism: The Importance of Perception of Neighborhood Disorder

“…Predicting vulnerability to alcoholism provides a point of departure in the creation of prevention and intervention initiatives, and it requires a longitudinal examination of behavioral, socioeconomic, and environmental factors associated with the development of these problems (Chermack & Giancola, ; Tarter & Vanyukov, ). Previous research has identified how individual‐level and neighborhood‐level factors jointly influence alcohol use ( Buu et al, ; Hill & Angel, ; Lê, Ahern, & Galea, ; Mulia, Zemore, & Greenfield, ; Stockdale et al, ); however, identifying precursors of alcoholism within a longitudinal, ecological framework has yet to be established despite its practical and theoretical implications for understanding how various factors shape this problematic outcome.…”

“…Studies linking census‐based assessments of neighborhood context to alcohol‐related outcomes are not unequivocal. Although some studies have found that factors associated with lower neighborhood SES, such as percentage of individuals living below the poverty line, neighborhood instability, and lowered neighborhood educational attainment, are associated with increased alcohol use (Buu et al, ; Hill & Angel, ; Jones‐Webb, Snowden, Herd, Short, & Hannan, ; Lê et al, ; Mulia et al, ; Stockdale et al, ), other studies have not supported this association (Brenner, Bauermeister, & Zimmerman, ; Galea, Ahern, Tracy, Rudenstine, & Vlahov, ; Gardner, Barajas, & Brooks‐Gunn, ; Snedker, Herting, & Walton, ; Trim & Chassin, ). These incongruent findings may exist for several reasons, including the age of the population under examination, the racial/ethnic composition of the sample, and the use of different indices of neighborhood SES.…”

Education level impact on cardiometabolic risk factors: a brief report

“…level, the more probability of adequate nutrition -and hence, less chance for presenting obesity and its comorbidities-due to the fact that these individuals are often more aware of the importance of nutritional habits and the practice of regular exercise [5,6]. On this regard, it has been established [12][13][14] that those with more years of education are less likely to smoke, drink heavily, or to be overweighed or obese. Similarly, the better educated subjects are more likely to exercise and use preventive care procedures such as vaccines, mammograms, Papanicolau tests and colonoscopies.…”