Socioeconomic differences in alcohol-attributable mortality compared with all-cause mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Factors underlying socioeconomic inequalities in mortality are not well understood. This study contributes to our understanding of potential pathways to result in socioeconomic inequalities, by examining alcohol consumption as one potential explanation via comparing socioeconomic inequalities in alcohol-attributable mortality and all-cause mortality. METHODS: Web of Science, MEDLINE, PsycINFO and ETOH were searched systematically from their inception to second week of February 2013 for articles reporting alcohol-attributable mortality by socioeconomic status, operationalized by using information on education, occupation, employment status or income. The sex-specific ratios of relative risks (RRRs) of alcohol-attributable mortality to all-cause mortality were pooled for different operationalizations of socioeconomic status using inverse-variance weighted random effects models. These RRRs were then combined to a single estimate. RESULTS: We identified 15 unique papers suitable for a meta-analysis; capturing about 133 million people, 3 741 334 deaths from all causes and 167 652 alcohol-attributable deaths. The overall RRRs amounted to RRR = 1.78 (95% confidence interval (CI) 1.43 to 2.22) and RRR = 1.66 (95% CI 1.20 to 2.31), for women and men, respectively. In other words: lower socioeconomic status leads to 1.5-2-fold higher mortality for alcohol-attributable causes compared with all causes. CONCLUSIONS: Alcohol was identified as a factor underlying higher mortality risks in more disadvantaged populations. All alcohol-attributable mortality is in principle avoidable, and future alcohol policies must take into consideration any differential effect on socioeconomic groups.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.044 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".