Impact of an integrated community case management programme on uptake of appropriate diarrhoea and pneumonia treatments in Uganda: A propensity score matching and equity analysis study
Bibliographic record
Abstract
INTRODUCTION: Pneumonia and diarrhoea disproportionately affect children in resource-poor settings. Integrated community case management (iCCM) involves community health workers treating diarrhoea, pneumonia and malaria. Studies on impact of iCCM on appropriate treatment and its effects on equity in access to the same are limited. The objective of this study was to measure the impact of integrated community case management (iCCM) as the first point of care on uptake of appropriate treatment for children with a classification of pneumonia (cough and fast breathing) and/or diarrhoea and to measure the magnitude and distribution of socioeconomic status related inequality in use of iCCM. METHODS: Following introduction of iCCM, data from cross-sectional household surveys were examined for socioeconomic inequalities in uptake of treatment and use of iCCM among children with a classification of pneumonia or diarrhoea using the Erreygers' corrected concentration index (CCI). Propensity score matching methods were used to estimate the average treatment effects on the treated (ATT) for children treated under the iCCM programme with recommended antibiotics for pneumonia, and ORS plus or minus zinc for diarrhoea. FINDINGS: Overall, more children treated under iCCM received appropriate antibiotics for pneumonia (ATT = 34.7%, p < 0.001) and ORS for diarrhoea (ATT = 41.2%, p < 0.001) compared to children not attending iCCM. No such increase was observed for children receiving ORS-zinc combination (ATT = -0.145, p < 0.05). There were no obvious inequalities in the uptake of appropriate treatment for pneumonia among the poorest and least poor (CCI = -0.070; SE = 0.083). Receiving ORS for diarrhoea was more prevalent among the least poor groups (CCI = 0.199; SE = 0.118). The use of iCCM for pneumonia was more prevalent among the poorest groups (CCI = -0.099; SE = 0.073). The use of iCCM for diarrhoea was not significantly different among the poorest and least poor (CCI = -0.073; SE = 0.085). CONCLUSION: iCCM is a potentially equitable strategy that significantly increased the uptake of appropriate antibiotic treatment for pneumonia and ORS for diarrhoea, but not the uptake of zinc for diarrhoea. For maximum impact, interventions increasing zinc uptake should be considered when scaling up iCCM programmes.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".