Poorer Longitudinal Growth Among HIV Exposed Compared With Unexposed Infants in Kenya
Bibliographic record
Abstract
BACKGROUND: Most infants born to women living with HIV are HIV exposed but uninfected exposed infants have poorer growth than HIV-unexposed uninfected children. Few large studies have compared children who are exposed (CHEU) and unexposed (CHUU) in the era of dolutegravir (DTG)-based antiretroviral treatment (ART). SETTING: Longitudinal study of mother-infant CHEU and CHUU pairs in Nairobi and Western Kenya. METHODS: Mother-infant pairs were enrolled at 6 weeks postpartum with 6-monthly growth assessments. We compared longitudinal growth between CHEU and CHUU infants during the first year and assessed biologic and social factors affecting growth [length- and weight-for-age z-scores (LAZ, WAZ) and weight-for-length z-scores (WLZ)] and stunting (LAZ <-2), underweight (WAZ <-2), and wasting (WLZ <-2) from birth to 1 year. RESULTS: Among 2000 infants (1000 CHEU and 1000 CHUU), CHEU infants had significantly lower LAZ at 6 months {-0.165 [95% confidence interval (CI): -0.274 to -0.056], P -value = 0.003} and 12 months (-0.195, 95% CI: -0.294 to -0.095, P -value = 0.0001; n = 1616). CHEU infants had a higher prevalence of stunting at 6 months compared with CHUU infants (prevalence ratio: 1.45, 95% CI: 1.14 to 1.85). Among all children, greater maternal BMI, education, and caregiver-perceived social support were positively associated with growth. Higher maternal and infant comorbidities were associated with growth deficits for CHEU infants. Among CHEU, ART timing (before versus during pregnancy), and ART regimen (dolutegravir -based, efavirenz-based, and protease inhibitor/other) did not affect growth. CONCLUSIONS: Growth deficits among CHEU persist, despite DTG-based ART. Addressing comorbidities, amplifying social support, and education may improve growth outcomes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".