Risk factors for postnatal mother–child transmission of HIV-1
Bibliographic record
Abstract
OBJECTIVE: To identify factors affecting HIV-1 breastfeeding transmission. DESIGN: Longitudinal observational cohort study. METHODS: HIV-1 seropositive pregnant women and seronegative controls were enrolled at a maternity hospital in Nairobi. Women and their children were followed from birth, and data on HIV-1 transmission, breastfeeding, clinical illness, and growth were collected. Specimens for HIV-1 serology and/or polymerase chain reaction were obtained at birth, 2, 6, and 14 weeks, 6, 9, 12, and 18 months, and every 6 months thereafter. Children were classified as HIV-1 uninfected, perinatally, or postnatally infected. Potentially breastfeeding transmission related risk factors were compared between postnatally infected and uninfected children. RESULTS: Among children born to seropositive or seroconverting mothers, 317 were uninfected, 51 infected perinatally and 42 infected postnatally. Identified risk factors for postnatal transmission were maternal nipple lesions (OR = 2.3, CI 95% 1.1-5.0), mastitis (OR = 2.7, CI 95% 1.1-6.7), maternal CD4 cell count < 400 mm3 (OR = 4.4, CI 95% 1.9-9.9), maternal seroconversion while breastfeeding (OR = 6.0, CI 95% 1.8-19.8), infant oral thrush at < 6 months of age (OR = 2.8, CI 95% 1.3-6.2) and breastfeeding longer than 15 months (OR = 2.4, CI 95% 1.2-5.1). All factors, except maternal seroconversion due to its rarity, were independently associated with an increased postnatal transmission risk by multivariate logistic regression analysis. CONCLUSION: In addition perinatal antiretroviral therapies, public health strategies should address: (i) prevention of maternal nipple lesions, mastitis and infant thrush; (ii) reduction of breastfeeding duration by all HIV-1-infected mothers; (iii) absolute avoidance of breastfeeding by those at high risk, and (iv) prevention of HIV-1 transmission to breastfeeding mothers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".