An analysis of intersectional disparities in alcohol consumption in the US
Bibliographic record
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. • Rarely has intersectionality been applied to alcohol consumption disparities. • We used MAIHDA to predict alcohol outcomes by sex, race, age, and education level. • We found significant interaction effects for many intersectional strata. • Several understudied groups were found to have higher consumption than expected. • This is the first demonstration of intersectional MAIHDA with a skewed outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".