Growth Patterns of HIV-Exposed and -Unexposed Infants in African Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: The purpose of this study is to understand the prevalence and odds of poor growth patterns among HIV-exposed but uninfected (HEU) versus HIV-unexposed (HUU) infants in the era of antiretroviral therapy (ART) and prevention of mother-to-child transmission (PMTCT) in Africa. Methods: We reviewed and meta-analyzed studies on growth patterns among HEU versus HUU infants in Africa. Evidence was gathered from the PubMed and Scopus databases following PRISMA guidelines. We independently evaluated the quality of included studies using Newcastle Ottawa guidelines. Data analysis was performed using an online meta-analysis tool, and the results are reported as odds ratios (OR) and prevalence with 95% confidence intervals (CI). Results: A total of 17 studies met the inclusion criteria for this review. The odds of stunting were significantly higher among HEU infants compared to HUU infants, with an odds ratio of 1.56 (95% CI: 1.23–1.97; p < 0.01). The pooled prevalence of stunting was 25% (95% CI: 17–33%) in HEU infants and 19% (95% CI: 12–26%) in HUU infants. In contrast, no significant differences were observed for underweight and wasting. The odds of being underweight in HEU infants compared to HUU was 0.85 (95% CI: 0.47–1.56; p = 0.60), with a pooled prevalence of 11% (95% CI: 5–17%) in HEU and 14% (95% CI: 5–24%) in HUU. Similarly, the odds of wasting were 1.10 (95% CI: 0.78–1.56; p = 0.58), with a pooled prevalence of 9% (95% CI: 3–14%) in HEU and 7% (95% CI: 3–12%) in HUU. Conclusions: Stunting was the most prevalent growth deficit among HEU infants compared to their HUU counterparts, with no significant differences observed in the rates of underweight and wasting. To improve postnatal growth outcomes, especially in the evolving landscape of HIV treatment and prevention, efforts should focus on educating and supporting mothers living with HIV.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.003 | 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".