Prevalence of alcohol consumption during pregnancy and Fetal Alcohol Spectrum Disorders among the general and Aboriginal populations in Canada and the United States
Bibliographic record
Abstract
Prenatal alcohol exposure may cause a number of health complications for the mother and developing fetus, including Fetal Alcohol Spectrum Disorders (FASD). This study aimed to estimate the pooled prevalence of i) alcohol use (any amount) and binge drinking (4 or more standard drinks on a single occasion) during pregnancy, and ii) Fetal Alcohol Syndrome (FAS) and FASD among the general and Aboriginal populations in Canada and the United States, based on the available literature. Comprehensive systematic literature searches and meta-analyses, assuming a random-effects model, were conducted. It was revealed that about 10% and 15% of pregnant women in the general population consume alcohol in Canada and the United States, respectively, and that about 3% of women engage in binge drinking during pregnancy in both countries. However, the prevalence of alcohol use during pregnancy in the Aboriginal populations of the United States and Canada were found to be approximately 3-4 times higher, respectively, compared to the general population. Even more alarmingly, it was estimated that approximately one in five women in the Aboriginal populations in both countries engage in binge drinking during pregnancy. Further, among the general population of Canada, the pooled prevalence was estimated to be about 1 per 1000 for FAS and 5 per 1000 for FASD. However, compared to the general population, the prevalence of FAS and FASD among the Aboriginal population in Canada was estimated to be 38 times and 16 times higher, respectively. With respect to the United States, the pooled prevalence of FAS and FASD was estimated to be about 2 per 1000 and 15 per 1,000, respectively, among the general population, and 4 per 1000 and 10 per 1,000, respectively, among the Aboriginal population. The FAS and FASD pooled prevalence estimates presented here should be used with caution due to the limited number of existing studies and their methodological limitations. Based on the results of the current study, it is evident that there is an urgent need for implementing more effective national prevention and surveillance strategies to monitor and lower the prevalence of alcohol consumption during pregnancy and FASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".