Risk factors for stunting in children who are HIV‐exposed and uninfected after Option B+ implementation in Malawi
Bibliographic record
Abstract
Evidence suggests children HIV-exposed and uninfected (CHEU) experience poor growth. We analysed child anthropometrics and explored factors associated with stunting among Malawian CHEU. Mothers with HIV and their infants HIV-exposed were enroled in a nationally representative prospective cohort within the National Evaluation of Malawi's Prevention of Mother-to-Child HIV Transmission Programme after Option B+ implementation (2014-2018). Anthropometry was measured at enrolment (age 1-6 months), visit 1 (approximately 12 months), and visit 2 (approximately 24 months). Weight-for-age (WAZ) and length-for-age (LAZ) z-scores were calculated using World Health Organization Growth Standards; underweight and stunting were defined as WAZ and LAZ more than 2 standard deviations below the reference median. Multivariable logistic regression restricted to CHEU aged 24 months (±3 months) was used to identify factors associated with stunting. Among 1211 CHEU, 562/1211 attended visit 2, of which 529 were aged 24 months (±3 months) and were included. At age 24 months, 40.4% of CHEU were stunted and/or underweight, respectively. In multi-variable analysis, adjusting for child age and sex, the odds of stunting were higher among CHEU with infectious disease diagnosis compared to those with no diagnosis (adjusted odds ratio = 3.35 [95% confidence interval: 1.82-6.17]), which was modified by co-trimoxazole prophylaxis (p = 0.028). Infant low birthweight was associated with an increased odds of stunting; optimal feeding and maternal employment were correlated with reduced odds. This is one of the first studies examining CHEU growth since Option B+. Interventions to improve linear growth among CHEU should address their multi-faceted health risks, alongside maternal ART prescription, and follow-up of mother-child pairs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".