The costs and benefits of scaling up interventions to prevent poor birth outcomes in low-income and middle-income countries: a modelling study
Bibliographic record
Abstract
BACKGROUND: We estimated the benefits and costs of a set of preventive interventions that could be delivered during antenatal care to prevent poor birth outcomes, including small-for-gestational-age and preterm births. We built on the assumptions and analyses underlying the Lancet Series on small vulnerable newborns (SVNs) and extended that work by incorporating more recent data, focusing only on the subset of preventive interventions, and examining a broader range of effects. A primary aim of the study was to provide a framework that decision makers could use to design programmes for women and children. METHODS: The analyses used the Lives Saved Tool (LiST) to estimate the effects and costs of scaling up the 11 preventive interventions identified in the SVN Series to improve birth outcomes. We used LiST estimates of effects and costs to estimate benefit-cost ratios (BCRs) for two intervention packages (one with interventions proven to improve birth outcomes and one with proven interventions plus interventions with potential to improve birth outcomes) and for the individual interventions in these packages for 80 low-income and middle-income countries (LMICs). FINDINGS: Both packages of interventions had BCRs more than 1, with a proven package BCR of 7·3 (IQR 5·3-9·1) and a proven plus potential package BCR of 5·8 (4·4-6·9). We found that in all cases the individual interventions had BCRs more than 1, there was a wide range of BCR values for the different interventions, and the BCR varied depending on package and country. INTERPRETATION: The analyses presented in this Article provide evidence that there are preventive interventions that, if scaled up in LMICs, could have a large effect on child health and provide benefits that greatly exceed the costs. FUNDING: Global Affairs Canada.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".