Alcohol per capita and prevalence of alcohol exposed pregnancies: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Alcohol is a teratogen that crosses the placenta. While studies have estimated the global burden of alcohol use in pregnancy, reliable and valid data on alcohol consumption during pregnancy in many countries is lacking. Alcohol consumption per capita (APC) data are available worldwide. This systematic review, by investigating the association between APC and alcohol consumption in pregnancy by country, aims to evaluate the accuracy of using APC data to predict the prevalence of prenatal alcohol exposure (PAE). Methods This study combines studies identified in a systematic review by Popova et al. 2017 and a systematic search of MEDLINE from 2015 through 6th October 2022, to identify additional papers. Papers identified in either search were on the prevalence of PAE or fetal alcohol syndrome (FAS). Two authors independently screened titles, abstracts, full-text articles and performed data extraction and quality assessment. APC data,per country, by year, was obtained from the World Health Organization (WHO) website. A meta-analysis, assuming a random effects model will be performed. Sensitivity analyses, including restricting by population, timing of exposure, time and method of ascertainment of alcohol consumption will be carried out. Results Of 24,935 PAE and 12,231 FAS prevalence articles identified, 400 PAE studies and 77 FAS studies met the inclusion criteria. Studies represent data from 1959-2020 from all 6 WHO regions, with APC between 0.012-14.78. Preliminary analysis suggests a statistically significant correlation between prevalence of alcohol consumption during pregnancy and APC. Conclusions Preliminary results from this study suggest that publicly available data on APC can be used to predict the population-based prevalence of alcohol use in pregnancy in different countries. Effective policies and interventions are required to reduce APC and thus, alcohol use in pregnancy and associated detrimental child health outcomes around the globe. Key messages • Knowing the prevalence of FAS and its main risk factor- prenatal alcohol use, will inform the setting of priorities for public health policy, public health initiatives, and health-care planning. • By investigating the association between alcohol intake in pregnancy and APC we will assess whether APC can predict alcohol intake in pregnancy.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".