Gender differences in socioeconomic inequality of alcohol‐attributable mortality: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The present analysis contributes to understanding the societal distribution of alcohol-attributable harm by investigating socioeconomic inequality and related gender differences in alcohol-attributable mortality. DESIGN AND METHODS: A systematic literature search was performed on Web of Science, MEDLINE, PsycINFO and ETOH from their inception until February 2013. Articles were included when they reported data on alcohol-attributable mortality by socioeconomic status (SES), operationalised as education, occupation, employment status or income. Gender-specific relative risks (RR) comparing low with high SES were pooled using random effects meta-analyses. Gender differences were additionally investigated in random effects meta-regressions. RESULTS: Nineteen articles from 14 countries were included. For women, significant RRs across all measures of SES, except employment status, were found, ranging between 1.75 [95% confidence interval (CI) 1.21-2.54; occupation] and 4.78 (95% CI 2.57-8.87; income). For men, all measures of SES showed significant RRs ranging between 2.88 (95% CI 2.45-3.40; income) and 12.25 (95% CI 11.45-13.10; employment status). While RRs for men were in general slightly higher, only for occupation this gender difference was above chance (P = 0.01). Results refer to deaths 100% attributable to alcohol. DISCUSSION AND CONCLUSIONS: The results are predominantly based on data from high-income countries, limiting generalisability. Alcohol-attributable mortality is strongly distributed to the disadvantage of persons with a low SES. Marked gender differences in this inequality were found for occupation. Possibly male-dominated occupations of low SES were more strongly related to risky drinking cultures compared with female-dominated occupations of the same SES.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".