Actual and predicted prevalence of alcohol consumption during pregnancy in the WHO African Region
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of alcohol consumption and binge drinking during pregnancy among the general population in the World Health Organization (WHO) African Region, by country. METHODS: First, a comprehensive systematic literature search was performed to identify all published and unpublished studies. Then, several meta-analyses, assuming a random-effects model, were conducted to estimate the prevalence of alcohol consumption and binge drinking during pregnancy among the general population for countries in the WHO African Region with two or more studies available. Lastly, for countries with less than two studies or no known data predictions were obtained using regression modelling. RESULTS: The estimated prevalence of alcohol consumption during pregnancy among the general population ranged from 2.2% (95% confidence interval [CI]: 1.6-2.8%; Equatorial Guinea) to 12.6% (95% CI: 9.9-15.4%; Cameroon) in Central Africa, 3.4% (95% CI: 2.6-4.3%; Seychelles) to 20.5% (95% CI: 16.4-24.7%; Uganda) in Eastern Africa, 5.7% (95% CI: 4.4-7.1%; Botswana) to 14.2% (95% CI: 11.1-17.3%; Namibia) in Southern Africa, 6.6% (95% CI: 5.0-8.3%; Mauritania) to 14.8% (95% CI: 11.6-17.9%; Sierra Leone) in Western Africa, and 4.3% (95% CI: 3.2-5.3%; Algeria) in Northern Africa. CONCLUSIONS: The high prevalence of alcohol consumption and binge drinking during pregnancy in some African countries calls for educational campaigns, screening and targeted interventions for women of childbearing age.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".