Economics of preventing premature mortality and impaired cognitive development in children through home-fortification: A health policy perspective
Bibliographic record
Abstract
BACKGROUND: Home-fortification is a new strategy of adding micronutrients including zinc and iron to home-made foods. Zinc supplementation may prevent morbidity and mortality related to diarrheal illnesses, and iron supplementation may improve cognitive development, in children. OBJECTIVES: To project clinical and economic effects of home-fortification in children in an urban slum of Karachi, Pakistan. METHODS: This is a cost benefit analysis of 5,000 simulated male and female infants (6-12 months) assigned to micronutrients or placebo for 4 months and followed for 55 years. We linked the effect of zinc on longitudinal prevalence of diarrhea to mortality, and the effect of iron on hemoglobin to IQ scores and lifetime earnings. Cost estimates were based on volumes of resource utilization from the Pakistan Sprinkles Diarrhea study. Main outcome was incremental benefit defined as the gain in lifetime earnings after accounting for the incremental costs of micronutrients over placebo (societal perspective). RESULTS: Our model projected that the reduction in diarrhea and improvement in hemoglobin concentrations through home-fortification was associated with reduced child mortality, higher IQ scores, and higher earnings. The present value of incremental benefit was $106 (95 percent probability interval = $17 to $193) U.S. dollars, which corresponds to $464.79 ($74.54 to $846.27) international dollars using a purchasing power parity exchange rate. CONCLUSIONS: Home-fortification appears to improve clinical outcomes at a reasonable cost, and may actually be cost beneficial when lifetime earnings are considered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".