Impact of a multifaceted intervention to improve emergency care on newborn and child health outcomes in Rwanda
Bibliographic record
Abstract
Implementing context-appropriate neonatal and paediatric advanced life support management interventions has increasingly been recommended as one of the approaches to reduce under-five mortality in resource-constrained settings like Rwanda. One such intervention is ETAT+, which stands for Emergency Triage, Assessment and Treatment plus Admission care for severely ill newborns and children. In 2013, ETAT+ was implemented in Rwandan district hospitals. We evaluated the impact of the ETAT+ intervention on newborn and child health outcomes. We used monthly time-series data from the DHIS2-enabled Rwanda Health Management Information System from 2012 to 2016 to examine neonatal and paediatric hospital mortality rates. Each hospital contributed data for 12 and 36 months before and after ETAT+ implementation, respectively. Using controlled interrupted time-series analysis and segmented regression model, we estimated longitudinal changes in neonatal and paediatric hospital mortality rates in intervention hospitals relative to matched concurrent control hospitals. We also studied changes in case fatality rate specifically for ETAT+-targeted conditions. Our study cohort consisted of 7 intervention hospitals and 14 matched control hospitals contributing 142 424 neonatal and paediatric hospital admissions. After controlling for secular trends and autocorrelations, we found that the ETAT+ implementation had no statistically significant impact on the rate of all-cause neonatal and paediatric hospital mortality in intervention hospitals relative to control hospitals. However, the case fatality rate for ETAT+-targeted neonatal conditions decreased immediately following implementation by 5% (95% confidence interval: -9.25, -0.77) and over time by 0.8% monthly (95% confidence interval: -1.36, -0.25) in intervention hospitals compared with control hospitals. Case fatality rate for ETAT+-targeted paediatric conditions did not decrease following the ETAT+ implementation. While ETAT+ focuses on improving the quality of hospital care for both newborns and children, we only found an impact on neonatal hospital mortality for ETAT+-targeted conditions that should be interpreted with caution given the relatively short pre-intervention period and potential regression to the mean.
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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".