Large gains in schooling and income are possible from minimizing adverse birth outcomes in 121 low- and middle-income countries: A modelling study
Bibliographic record
Abstract
While the global contributions of adverse birth outcomes to child morbidity and mortality is relatively well documented, the potential long-term schooling and economic consequences of adverse birth outcomes has not been estimated. We sought to quantify the potential schooling and lifetime income gains associated with reducing the excess prevalence of adverse birth outcomes in 121 low- and middle-income countries. We used a linear deterministic model to estimate the potential gains in schooling and lifetime income that may be achieved by attaining theoretical minimum prevalence of low birthweight, preterm birth and small-for-gestational age births at the national, regional, and global levels. We estimated that potential total gains across the 121 countries from reducing low birthweight to the theoretical minimum were 20.3 million school years (95% CI: 6.0,34.8) and US$ 68.8 billion (95% CI: 20.3,117.9) in lifetime income gains per birth cohort. As for preterm birth, we estimated gains of 9.8 million school years (95% CI: 1.5,18.4) and US$ 41.9 billion (95% CI: 6.1,80.9) in lifetime income. The potential gains from small-for-gestational age were 39.5 million (95% CI: 19.1,60.3) school years and US$113.6 billion (95% CI: 55.5,174.2) in lifetime income gained. In summary, reducing the excess prevalence of low birthweight, preterm birth or small-for-gestational age births in low- and middle-income countries may lead to substantial long-term human capital gains in addition to benefits on child mortality, growth, and development as well as on risk of non-communicable diseases in adults and other consequences across the life course.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".