Mediating role of psychological distress and alcohol use in socioeconomic disparities in deaths of despair: a causal mediation analysis using record linkage data
Bibliographic record
Abstract
BACKGROUND: Deaths of despair - suicide, drug overdose and chronic liver disease mortality - are major contributors to premature mortality in the USA, disproportionately affecting individuals with low socioeconomic status (SES). The mechanisms underlying these disparities, particularly the roles of psychological distress and alcohol use, remain insufficiently understood. We assessed associations of SES, alcohol use and psychological distress with deaths of despair, along with the mediating roles of alcohol use and psychological distress in the SES-deaths of despair association in men and women. METHODS: We linked US National Health Interview Survey data (1997-2018) to mortality records until 31 December 2019 by following 3 11 508 women and 2 42 463 men for 10.5 years. Using counterfactual-based inverse probability-weighted marginal structural models, we decomposed the total effect of SES (education, income) into direct and indirect effects through psychological distress (Kessler-6) and alcohol use (daily consumption). Analyses were sex-stratified and adjusted for marital status, race and ethnicity and survey year. RESULTS: Severe psychological distress and high alcohol use were each associated with over a threefold increased risk of death of despair. In men, psychological distress and alcohol use mediated up to 16% and 14% of the SES-deaths of despair relationship, respectively. In women, psychological distress mediated up to 20% of the association, while alcohol use did not mediate the relationship. CONCLUSION: Low SES, psychological distress and alcohol use are key risk factors for deaths of despair. Intervention targeting mental health and alcohol use, especially through SES-specific and sex-specific approaches, may help reduce inequalities in these preventable causes of death.
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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 | 0.048 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".