Who under‐reports their alcohol consumption in telephone surveys and by how much? An application of the ‘yesterday method’ in a national <scp>C</scp>anadian substance use survey
Bibliographic record
Abstract
BACKGROUND AND AIMS: Adjustments for under-reporting in alcohol surveys have been used in epidemiological and policy studies which assume that all drinkers underestimate their consumption equally. This study aims to describe a method of estimating how under-reporting of alcohol consumption might vary by age, gender and consumption level. METHOD: The Canadian Alcohol and Drug Use Monitoring Survey (CADUMS) 2008-10 (n = 43 371) asks about beverage-specific 'yesterday' consumption (BSY) and quantity-frequency (QF). Observed drinking frequencies for different age and gender groups were calculated from BSY and used to correct values of F in QF. Beverage-specific correction factors for quantity (Q) were calculated by comparing consumption estimated from BSY with sales data. RESULTS: Drinking frequency was underestimated by males (Z = 24.62, P < 0.001) and females (Z = 17.46, P < 0.001) in the QF as assessed by comparing with frequency and quantity of yesterday drinking. Spirits consumption was underestimated by 65.94% compared with sales data, wine by 38.35% and beer by 49.02%. After adjusting Q and F values accordingly, regression analysis found alcohol consumption to be underestimated significantly more by younger drinkers (e.g. 82.9 ± 1.19% for underage drinkers versus 70.38 ± 1.54% for those 65+, P < 0.001) and by low-risk more than high-risk drinkers (76.25 ± 0.34% versus 49.22 ± 3.01%, P < 0.001). Under-reporting did not differ by gender. CONCLUSIONS: Alcohol consumption surveys can use the beverage-specific 'yesterday method' to correct for under-reporting of consumption among subgroups. Alcohol consumption among Canadians appears to be under-reported to an equal degree by men and women. Younger drinkers under-report alcohol consumption to a greater degree than do older drinkers, while low-risk drinkers underestimate more than do medium and high-risk drinkers.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".