Evidence of Impact: iCCM as a strategy to save lives of children under five
Bibliographic record
Abstract
BACKGROUND: In 2013, the World Health Organization (WHO) launched the Rapid Access Expansion (RAcE) programme in the Democratic Republic of Congo, Malawi, Mozambique, Niger, and Nigeria to increase coverage of diagnostic, treatment, and referral services for malaria, pneumonia, and diarrhea among children ages 2-59 months. In 2017, a final evaluation of the six RAcE sites was conducted to determine whether the programme goal was reached. A key evaluation objective was to estimate the reduction in childhood mortality and the number of under-five lives saved over the project period in the RAcE project areas. METHODS: The Lives Saved Tool (LiST) was used to estimate reductions in all-cause child mortality due to changes in coverage of treatment for the integrated community case management (iCCM) illnesses - malaria, pneumonia, and diarrhea - while accounting for other changes in maternal and child health interventions in each RAcE project area. Data from RAcE baseline and endline household surveys, Demographic and Health Surveys, and routine health service data were used in each LiST model. The models yielded estimated change in under-five mortality rates, and estimated number of lives saved per year by malaria, pneumonia and diarrhea treatment. We adjusted the results to estimate the number of lives saved by community health worker (CHW)-provided treatment. RESULTS: The LiST model accounts for coverage changes in iCCM intervention coverage and other health trends in each project area to estimate mortality reduction and child lives saved. Under five mortality declined in all six RAcE sites, with an average decline of 10 percent. An estimated 6200 under-five lives were saved by malaria, pneumonia, and diarrhea treatment in the DRC, Malawi, Niger, and Nigeria, of which approximately 4940 (75 percent) were saved by treatment provided by CHWs. This total excludes Mozambique, where there were no estimated under-five lives saved likely due to widespread stockouts of key medications. In all other project areas, lives saved by CHW-provided treatment contributed substantially to the estimated decline in under-five mortality. CONCLUSIONS: Our results suggest that iCCM is a strategy that can save lives and measurably decrease child mortality in settings where access to health facility services is low and adequate resources for iCCM implementation are provided for CHW services.
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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 | 0.047 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".