The Long-Term Effect of the Integrated Care Model on Child Morbidity in Murewa District, Zimbabwe: A pragmatic Trial
Bibliographic record
Abstract
BACKGROUND: Poor access and utilisation of health care services remains a big challenge in rural communities in low to middle income countries leading to high prevalence of preventable childhood illnesses and death. Many community mobilisation models have been employed to address this. However, scientific evidence remain scanty on the long-term effects of such models to inform policy-makers on how to reduce preventable child morbidity and mortality and to provide guidance on what is pragmatic. The purpose of this study was to determine the long-term effects of the Integrated Care Model (ICM) on child morbidity and mortality in Zimbabwe. METHODS: This was a pragmatic trial that used a quasi-experimental design. The study used population based sampling to enrol villages into either intervention sites or control sites from two health centres in Murewa District. Target sampling was used to enrol children aged 0-48 months into the study. A total of 1380 children were enrolled and followed up prospectively for a period of 18 months. The disease condition that were being tracked were pneumonia, diarrhoea, fever and Malaria. RESULTS: We performed negative binomial logistic regression to determine the long-term effects of the intervention on child morbidity, adjusting for the number of under-fives in each village/cluster, village size, distance to the clinic and number of under-fives in each cluster. Overall, the intervention reduce the risk of general child morbidity by 83% [RR=0.17, 95% CI (0.14-0.23)]. The intervention reduced risk of incidence of pneumonia by 79% [RR=0.21, 95% CI (0.10-0.45)], risk of incidence of diarrhoea by 80% [RR=0.20 95% CI (0.15-0.29)], fever by 91%[RR=0.09, 95% CI (0.04-0.22)] and malaria by 73%[RR=0.27, 95% CI (0.14-0.51)].The incidence rate of childhood severe illnesses was reduced by 79%[RR=0.24, 95% CI (0.11-0.40)] through the intervention. CONCLUSION: This study sought to determine the long term effects of the Integrated Care Model on Child Morbidity in Murewa district, Zimbabwe. Study results revealed that indeed the ICM had a statistically significant impact on child morbidity in the long-term. Countries in low resource settings can benefit from the use of such a low-cost high impact model to reduce not only child morbidity and mortality, but also to address maternal health challenges.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".