Prevalence of HIV/AIDS among pregnant women in North American region: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: As a major maternal health concern, the prevalence of human immunodeficiency virus (HIV) among pregnant women was previously investigated in European, African, and Latin American regions other than the North American region. This study analyzed the prevalence of HIV among pregnant women in the North American region including 3 major countries: the USA, Canada, and Mexico. METHODS: Relevant studies were screened from 3 online databases: Google Scholar, PubMed, and ScienceDirect using specific search keywords. Ultimately, 10 studies of the North American region were included with a total of 339,831 pregnant women residing in the USA, Canada, and Mexico. RESULTS: The overall pooled prevalence was 0.6% (95% confidence interval [CI]: 0.4-0.8) with a high degree of heterogeneity (I2 = 97%). Pooled prevalence rates of HIV among pregnant women in Canada, Mexico, and the USA were 0.3% (95% CI: 0.1-0.5), 0.5% (95% CI: 0.2-0.8), and 2.3% (95% CI: 0.0-5.7), respectively with high degrees of heterogeneity. CONCLUSION: The overall prevalence rate of HIV among pregnant women in the USA, Canada, and Mexico was minimal as compared with the countries of Eastern Europe, sub-Saharan Africa, or Latin America. Awareness, adequate testing and healthcare facilities, better socioeconomic, and geopolitical conditions might be crucial to lowering the prevalence of HIV among pregnant women.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".