Sustainability of zinc coverage for acute childhood diarrhea in Bangladesh and other low- and middle-income countries: one decade following the SUZY project
Bibliographic record
Abstract
Oral zinc is a proven effective treatment for diarrheal illness, and long-term monitoring is key to evaluating the success of efforts to scale up zinc treatment. We examine zinc coverage for diarrheal illness in Bangladesh since the conclusion of the Scaling Up Zinc for Young Children (SUZY) project in 2008 and provide an overview of other countries' zinc scale-up programs to compare the long-term effectiveness of SUZY. We used data from the Bangladesh Demographic and Health Surveys from 2005-2022 to examine the proportion of children under five receiving zinc treatment for diarrheal illness and evaluate disparities in zinc coverage by urbanicity and wealth quintile. We used a qualitative framework synthesis to compare the SUZY project with national or large-scale zinc scale-up programs in other low- and middle-income countries (Ghana, India, Kenya, Nepal, Nigeria, Uganda). This method for synthesizing qualitative and quantitative data was used to break down components of the SUZY project and other national or large-scale zinc scale-up programs. In Bangladesh, zinc coverage has continued to increase since the conclusion of the SUZY project, disparities in coverage between urban and rural areas and across wealth quintiles have been resolved, and the prevalence of diarrheal illness has decreased from 10·8% in 2007 to 4·8% in 2022. The countries with the highest zinc coverage (Bangladesh, Kenya, Uganda) had national rather than regional scale-up campaigns. Our findings demonstrate the long-term success of the SUZY project and provide insights into best practices for impactful zinc scale-up programs including significant pre-launch implementation research addressing key knowledge gaps and partnering with research organizations. Long-term monitoring of scale-up campaigns is important to determine if these interventions can become socially embedded and self-sustaining, improving health outcomes in the long run.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".