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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.026 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".