Using the Lives Saved Tool to aid country planning in meeting mortality targets: a case study from Mali
Bibliographic record
Abstract
BACKGROUND: Mali is one of four countries implementing a National Evaluation Platform (NEP) to build local capacity to answer evaluation questions for maternal, newborn, child health and nutrition (MNCH&N). In 2014-15, NEP-Mali addressed questions about the potential impact of Mali's MNCH&N plans and strategies, and identified priority interventions to achieve targeted mortality reductions. METHODS: The NEP-Mali team modeled the potential impact of three intervention packages in the Lives Saved Tool (LiST) from 2014 to 2023. One projection included the interventions and targets from Mali's ten-year health strategy (PDDSS) for 2014-2023, and two others modeled intervention packages that included scale up of antenatal, intrapartum, and curative interventions, as well as reductions in stunting and wasting. We modeled the change in maternal, newborn and under-five mortality rates under these three projections, as well as the number of lives saved, overall and by intervention. RESULTS: If Mali were to achieve the MNCH&N coverage targets from its health strategy, under-5 mortality would be reduced from 121 per 1000 live births to 93 per 1000, far from the target of 69 deaths per 1000. Projections 1 and 2 produced estimated mortality reductions from 121 deaths per 1000 to 70 and 68 deaths per 1000, respectively. With respect to neonatal mortality, the mortality rate would be reduced from 39 to 32 deaths per 1000 live births under the current health strategy, and to 25 per 1000 under projections 1 and 2. CONCLUSIONS: This study revealed that achieving the coverage targets for the MNCH&N interventions in the 2014-23 PDDSS would likely not allow Mali to achieve its mortality targets. The NEP-Mali team was able to identify two packages of MNCH&N interventions (and targets) that achieved under-5 and neonatal mortality rates at, or very near, the PDDSS targets. The Malian Ministry of Health and Public Hygiene is using these results to revise its plans and strategies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".