The role of alcohol use and drinking patterns in socioeconomic inequalities in mortality: a systematic review
Bibliographic record
Abstract
BACKGROUND: Individuals with low socioeconomic status (SES) experience disproportionately greater alcohol-attributable health harm than individuals with high SES from similar or lower amounts of alcohol consumption. Our aim was to provide an update of the current evidence for the role of alcohol use and drinking patterns in socioeconomic inequalities in mortality, as well as the effect modification or interaction effects between SES and alcohol use, as two potential explanations of this so-called alcohol-harm paradox. METHODS: We did a systematic review, searching Embase, Medline, PsycINFO, and Web of Science (published between Jan 1, 2013, and June 30, 2019) for studies reporting alcohol consumption, SES, and mortality. Observational, quantitative studies of the general adult population (aged ≥15 years) with a longitudinal study design were included. Two outcome measures were extracted: first, the proportion of socioeconomic inequalities in mortality explained by alcohol use; and second, the effect modification or interaction between SES and alcohol use regarding mortality risks. This study is registered with PROSPERO (CRD42019140279). FINDINGS: Of 1941 records identified, ten met the inclusion criteria. The included studies contained more than 400 000 adults, more than 30 000 deaths from all causes, and more than 3000 100% alcohol-attributable events. Alcohol use explained up to 27% of the socioeconomic inequalities in mortality. The proportion of socioeconomic inequalities explained systematically differed by drinking pattern, with heavy episodic drinking having a potentially significant explanatory value. Although scarce, there was some evidence of effect modification or interaction between SES and alcohol use. INTERPRETATION: To reduce socioeconomic inequalities in mortality, addressing heavy episodic drinking in particular, rather than alcohol use in general, is worth exploring as a public health strategy. FUNDING: Canadian Institutes of Health Research.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".