Socioeconomic status, alcohol use disorders, and depression: A population-based study
Bibliographic record
Abstract
BACKGROUND: Depressive disorders (DD) and alcohol use disorders (AUD) frequently co-occur. They are key to understanding the current increases in "deaths of despair" among individuals with lower socioeconomic status (SES). The aim of this study was to assess the prospective bidirectional associations between AUD and DD, as well as the effect of SES on these two conditions. METHODS: The National Epidemiologic Survey on Alcohol and Related Conditions is a cohort study representative of the US adult population, which began in 2001-2002, with follow-up interviews conducted 3 years later. SES was primarily operationalized as educational attainment. AUD, DD, and their levels of severity were defined according to the DSM-5 criteria. RESULTS: The risk of developing an incident DD increased gradually with the recency and the severity of AUD at baseline, but the converse was not observed. Lower SES was an independent risk for incident AUD or DD. SES did not modify the prospective association between AUD and DD. LIMITATIONS: The absence of interaction between SES and moderate or severe AUD for the incident DD must be considered with caution due to the limited number of DD cases reported in these AUD categories. CONCLUSIONS: This result is consistent with a causal relationship between AUD and DD, and suggests that therapeutic interventions for AUD may also have beneficial effects to lower DD rates. The independent effects of a lower SES and AUD on DD may result in a vulnerable population cumulating disorders with heavy consequences on health and social well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".