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Published on: June 23, 2023
An analysis of intersectional disparities in alcohol consumption in the US
Sophie Bright1, Charlotte Buckley2, Daniel Holman3
1Sheffield Centre for Health and Related Research (ScHARR), Faculty of Medicine, Dentistry & Health, University of Sheffield, Sheffield, UK.
Abstract:
Alcohol is one of the leading causes of preventable deaths in the United States (US). Prior research has demonstrated that alcohol consumption and related mortality are socially patterned; however, no study has investigated intersectional disparities in alcohol consumption, i.e., attending to how social positions overlap and interact. In this study, we used an innovative intersectional approach (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, MAIHDA) and data from a large nationally representative survey (the National Health Interview Survey, 2000-2018) to quantify inter-categorical disparities in alcohol consumption in the US (proportion of current drinkers, and average consumption amongst drinkers), along dimensions of sex, race and ethnicity, age, and level of education. Our analysis revealed significant intersectional disparities in both the prevalence of drinking and the average consumption by drinkers. Young, highly educated White men were the most likely to be current drinkers and consumed the highest amounts of alcohol on average, whilst racially and ethnically minoritized women with lower education were the least likely to drink and had the lowest levels of alcohol consumption, across all age categories. Notably, we found significant interaction effects for many intersectional strata, with much higher consumption estimated for some groups than traditional additive approaches would suggest. By identifying specific understudied groups with high consumption, such as young American Indian or Alaska Native (AI/AN) men, adult Black men with low education, and older White women with high education, this analysis has important implications for future research, policy, and praxis. This is the first known application of MAIHDA to account for a skewed outcome, highlighting and addressing critical methodological considerations.
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