Integrating Household Water Treatment, Hand Washing, and Insecticide-Treated Bed Nets Into Pediatric HIV Care in Mombasa, Kenya: Impact on Diarrhea and Malaria Risk
Bibliographic record
Abstract
INTRODUCTION: In developing countries, HIV-infected children are at higher risk of morbidity and mortality from opportunistic infections than HIV-uninfected children. To address this problem, the Healthy Living Initiative (HLI) in Mombasa, Kenya distributed basic care packages (BCPs) containing improved water storage vessels, water treatment solution, soap, and insecticide-treated bed nets to prevent diarrhea and malaria in children, and had community health workers (CHWs) make bimonthly home visits to encourage adherence to HLI interventions and antiretroviral (ARV) medicine use. METHODS: To evaluate HLI, we enrolled 500 HIV-infected children from Bomu Hospital. In the implementation phase, from February to August 2011, we conducted surveys of caregivers, then provided free BCPs. In the evaluation phase, from September 2011 to August 2012, CHWs recorded observations of BCP use during home visits. We abstracted hospital data to compare diarrhea and malaria episodes, and pharmacy data on ARVs dispensed, between the 12-month preimplementation baseline phase (February 2010-January 2011) and the evaluation phase. RESULTS: The retention rate of children in HLI was 78.4%. In a multivariable logistic regression model adjusting for demographic characteristics, number of CHW home visits, distance to clinic, orphan status, and number of ARVs dispensed, children in HLI had 71% lower risk of diarrhea (relative risk 0.29, P < 0.001) and 87% lower risk of malaria (relative risk 0.13, P = 0.001) during the evaluation phase than the baseline phase; there was no independent association between ARV use and illness. CONCLUSIONS: HIV-infected children in HLI were less likely to experience diarrhea and malaria during the evaluation phase than the baseline phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".