Coverage of alcohol consumption by national surveys in South Africa
Bibliographic record
Abstract
BACKGROUND AND AIMS: Evidence suggests that adult per-capita alcohol consumption, as estimated from self-reports of nationally representative surveys, underestimates 'true' consumption, as measured as the sum of recorded and unrecorded consumption. The proportion of total adult alcohol per capita reported in representative surveys is usually labelled 'coverage'. The aim of the present paper was to estimate coverage for South Africa under different scenarios of alcohol use assessment and 'true' consumption. DESIGN: Five nationally representative surveys from South Africa were used to estimate the prevalence of drinking and the grams per day among current drinkers. All surveys used a complex multi-stage sampling frame that was accounted for by using survey weights. The total (recorded and unrecorded), the recorded and the adjusted total adult per-capita alcohol consumption in South Africa served as different estimates of the 'true' consumption. SETTING: South Africa. PARTICIPANTS: Alcohol use information was assessed on a total of 8115, 16 398 and 13 181 adults (15 years or older) in surveys from the years 2003, 2005 and 2008, respectively. Two surveys in 2012 included 27 070 and 18 688 adults. MEASUREMENTS: Coverage of the alcohol use reported in the surveys was calculated, compared with the 'true' adult per-capita alcohol. FINDINGS: The survey data covered between 11.8% [2005; 95% uncertainty interval (UI) = 9.3-16.2%)] and 19.4% (2003; 95% UI = 14.9-24.2%) of the total alcohol used per capita. The highest coverage of 27.9% (95% UI = 22.4-36.8%) was observed when looking only at recorded alcohol in 2003. CONCLUSIONS: Evidence from five nationally representative surveys assessing alcohol use suggests that less than 20% of the total adult per-capita alcohol consumption in South Africa is reported in surveys.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".