Program evaluation of an ORS and zinc scale-up program in 8 Nigerian states
Bibliographic record
Abstract
BACKGROUND: In Nigeria, diarrhea is the second leading killer of children under five. Between 2012-2017, the Clinton Health Access Initiative, Inc. (CHAI) and the Government of Nigeria implemented a comprehensive program in eight states aimed at increasing the percentage of children under five with diarrhea who were treated with zinc and oral rehydration solution (ORS). The program addressed demand, supply, and policy barriers to ORS and zinc uptake through interventions in both public and private sectors. The interventions included: (1) policy revision and partner coordination; (2) market shaping to improve availability of affordable, high-quality ORS and zinc; (3) provider training and mentoring; and (4) caregiver demand generation. METHODS: We conducted cross-sectional household surveys in program states at baseline, midline, and endline and constructed logistic regression models with generalized estimating equations to assess changes in ORS and zinc treatment during the program period. RESULTS: In descriptive analysis, we found 38% (95% CI = 34%-42%) received ORS at baseline and 4% (95% CI = 3%-5%) received both ORS and zinc. At endline, we found 55% (95% CI = 51%-58%) received ORS and 30% (95% CI = 27%-33%) received both ORS and zinc. Adjusting for other covariates, the odds of diarrhea being treated with ORS were 1.88 (95% CI = 1.46, 2.43) times greater at endline. The odds of diarrhea being treated with ORS and zinc combined were 15.14 (95% CI = 9.82, 23.34) times greater at endline. When we include the interaction term to investigate whether the odds ratios between the endline and baseline survey were modified by source of care, we found statistically significant results among diarrhea episodes that sought care in the public and private sector. Among cases that sought care in the public sector, the predictive probability of treatment with ORS increased from 57% (95% CI = 50%-65%) to 83% (95% CI = 79%-87%). Among cases that sought care in the private sector, the predictive probability increased from 41% (95% CI = 34%-48%) to 58% (95% CI = 54%-63%). CONCLUSIONS: Use of ORS and combined ORS and zinc for treatment of diarrhea significantly increased in program states during the program period.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".