Estimating under‐ and over‐reporting of drinking in national surveys of alcohol consumption: identification of consistent biases across four English‐speaking countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Questions about drinking 'yesterday' have been used to correct under-reporting of typical alcohol consumption in surveys. We use this method to explore patterns of over- and under-reporting of drinking quantity and frequency by population subgroups in four countries. DESIGN: Multivariate linear regression analyses comparing estimates of typical quantity and frequency of alcohol consumption with and without adjustments using the yesterday method. SETTING AND PARTICIPANTS: Survey respondents in Australia (n = 26 648), Canada (n = 43 371), USA (n = 7969) and England (n = 8610). MEASUREMENTS: Estimates of typical drinking quantities and frequencies over the past year plus quantity of alcohol consumed the previous day. FINDINGS: Typical frequency was underestimated by less frequent drinkers in each country. For example, after adjustment for design effects and age, Australian males self-reporting drinking 'less than once a month' were estimated to have in fact drunk an average of 14.70 (± 0.59) days in the past year compared with the standard assumption of 6 days (t = 50.5, P < 0.001). Drinking quantity 'yesterday' was not significantly different overall from self-reported typical quantities during the past year in Canada, the United States and England, but slightly lower in Australia (e.g. 2.66 versus 3.04 drinks, t = 20.4, P < 0.01 for women). CONCLUSIONS: People who describe themselves as less frequent drinkers appear to under-report their drinking frequency substantially, but country and subgroup-specific corrections can be estimated. Detailed questions using the yesterday method can help correct under-reporting of quantity of drinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".