Child mortality associated with maternal HIV status: a retrospective analysis in Rwanda, 2005-2015
Bibliographic record
Abstract
INTRODUCTION: Child mortality remains highest in regions of the world most affected by HIV/AIDS. The aim of this study was to assess child mortality rates in relation to maternal HIV status from 2005 to 2015, the period of rapid HIV treatment scale-up in Rwanda. METHODS: We used data from the 2005, 2010 and 2015 Rwanda Demographic Health Surveys to derive under-2 mortality rates by survey year and mother's HIV status and to build a multivariable logistic regression model to establish the association of independent predictors of under-2 mortality stratified by mother's HIV status. RESULTS: In total, 12 010 live births were reported by mothers in the study period. Our findings show a higher mortality among children born to mothers with HIV compared with HIV negative mothers in 2005 (216.9 vs 100.7 per 1000 live births) and a significant reduction in mortality for both groups in 2015 (72.0 and 42.4 per 1000 live births, respectively). In the pooled reduced multivariable model, the odds of child mortality was higher among children born to mothers with HIV, (adjusted OR, AOR 2.09; 95% CI 1.57 to 2.78). The odds of child mortality were reduced in 2010 (AOR 0.69; 95% CI 0.59 to 0.81) and 2015 (AOR 0.35; 95% CI 0.28 to 0.44) compared with 2005. Other independent predictors of under-2 mortality included living in smaller families of 1-2 members (AOR 5.25; 95% CI 3.59 to 7.68), being twin (AOR 4.93; 95% CI 3.51 to 6.92) and being offspring from mothers not using contraceptives at the time of the survey (AOR 1.6; 95% CI 1.38 to 1.99). Higher education of mothers (completed primary school: (AOR 0.74; 95% CI 0.64 to 0.87) and secondary or higher education: (AOR 0.53; 95% CI 0.38 to 0.74)) was also associated with reduced child mortality. CONCLUSIONS: This study shows an important decline in under-2 child mortality among children born to both mothers with and without HIV in Rwanda over a 10-year span.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".