Increasing coverage of pediatric diarrhea treatment in high-burden countries
Bibliographic record
Abstract
BACKGROUND: Diarrhea is the second leading cause of infectious deaths in children under-five globally. Oral rehydration salts (ORS) and zinc could avert an estimated 93% of deaths, but progress to increase coverage of these interventions has been largely stagnant over the past several decades. The Clinton Health Access Initiative (CHAI), along with donors and country governments in India, Kenya, Nigeria, and Uganda, implemented programs to scale-up ORS and zinc coverage from 2012 to 2016. The programs sought to demonstrate that increases in pediatric diarrhea treatment rates are possible at scale in high-burden settings through a holistic approach addressing both supply and demand barriers. We describe the overall program model and the activities undertaken in each country. The overall goal of the paper is to share the program results and lessons learned to inform other countries aiming to scale-up ORS and zinc. METHODS: We used a triangulation approach, using population-based household surveys, public facility audits, and private outlet surveys, to evaluate the program model. We used pre- and post-program population-based household survey data to estimate the changes in coverage of ORS and zinc for treatment of diarrhea cases in children under-five in program areas. We also conducted secondary analysis of Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) surveys in surrounding regions and compared annual coverage changes in the CHAI-supported program geographies to the surrounding regions. RESULTS: Across CHAI-supported focal geographies, the average ORS coverage across the program areas increased from 35% to 48% and combined ORS and zinc coverage increased from 1% to 24%. ORS coverage increases were statistically significant in the program states in India, from 22% (95% confidence interval CI = 21-23%) to 48% (95% CI = 47-50%) and program states in Nigeria, from 38% (95% CI = 32-40%) to 55% (95% CI = 51-58%). For combined ORS and zinc, coverage increases were statistically significant in all program geographies. Compared to surrounding regions, the estimated annual changes in combined ORS and zinc coverage were greater in program geographies. Using the Lives Saved Tool and based on the coverage changes during the program period, we estimated 76 090 diarrheal deaths were averted in the program geographies. CONCLUSIONS: Increasing ORS and zinc coverage at scale in high-burden countries and states is possible through a comprehensive approach that targets both demand and supply barriers, including pricing, optimal product qualities, provider dispensing practices, stocking rates, and consumer demand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".