Why Is Per Capita Consumption Underestimated in Alcohol Surveys? Results from 39 Surveys in 23 European Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: The aims of the article are (a) to estimate coverage rates (i.e. the proportion of 'real consumption' accounted for by a survey compared with more reliable aggregate consumption data) of the total, the recorded and the beverage-specific annual per capita consumption in 23 European countries, and (b) to investigate differences between regions, and other factors which might be associated with low coverage (prevalence of heavy episodic drinking [HED], survey methodology). METHODS: Survey data were derived from the Standardised European Alcohol Survey and Harmonising Alcohol-related Measures in European Surveys (number of surveys: 39, years of survey: 2008-2015, adults aged 20-64 years). Coverage rates were calculated at the aggregated level by dividing consumption estimates derived from the surveys by alcohol per capita estimates from a recent global modelling study. Fractional response regression models were used to examine the relative importance of the predictors. RESULTS: Large variation in coverage across European countries was observed (average total coverage: 36.5, 95% confidence interval [CI] [33.2; 39.8]), with lowest coverage found for spirits consumption (26.3, 95% CI [21.4; 31.3]). Regarding the second aim, the prevalence of HED was associated with wine- and spirits-specific coverage, explaining 10% in the respective variance. However, neither the consideration of regions nor survey methodology explained much of the variance in coverage estimates, regardless of the scenario. CONCLUSION: The results reiterate that alcohol survey data should not be used to compare or estimate aggregate consumption levels, which may be better reflected by statistics on recorded or total per capita consumption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it