Social disparities in hazardous alcohol use: self-report bias may lead to incorrect estimates
Bibliographic record
Abstract
BACKGROUND: Self-report bias in surveys of alcohol consumption is widely documented; however, less is known about the distribution of such bias by socioeconomic status (SES) and about the possible impact on social disparities. This study aims to assess social disparities in hazardous drinking (HD) and to analyze how correcting alcohol consumption data for self-report bias may affect estimates of disparities. METHODS: National survey data from 13 countries, Canada, England, Finland, France, Germany, Hungary, Ireland, Japan, Korea, New Zealand, Spain, Switzerland and USA, are used to examine social disparities in HD by SES and education level. Defining HD as drinking above 3 drinks/day for men and 2 for women, social disparities were assessed by calculating country-level concentration indexes. Aggregate consumption data were used to correct survey-based estimates for self-report bias. RESULTS: Survey data show that more-educated women are more likely than less-educated women to engage in HD, while the opposite is observed in men in most countries. Large discrepancies in alcohol consumption between survey-based and aggregate estimates were found. Correcting for self-report bias increased estimates of social disparities in women, and decreased them in men, to the point that gradients were reversed in several countries (from higher rates in low education/SES men to an opposite pattern). CONCLUSION: This study provides evidence of a likely misestimation of social disparities in HD, in both men and women, due to self-report bias in alcohol consumption surveys. This study contributes to a better knowledge of the social dimensions of HD and to the targeting of alcohol policies.
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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 | 0.089 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".