The cost of not breastfeeding in Southeast Asia
Bibliographic record
Abstract
Rates of exclusive breastfeeding are slowly increasing, but remain suboptimal globally despite the health and economic benefits. This study estimates the costs of not breastfeeding across seven countries in Southeast Asia and presents a cost-benefit analysis of a modeled comprehensive breastfeeding strategy in Viet Nam, based on a large programme. There have been very few such studies previously for low- and middle-income countries. The estimates used published data on disease prevalence and breastfeeding patterns for the seven countries, supplemented by information on healthcare costs from representative institutions. Modelling of costs of not breastfeeding used estimated effects obtained from systematic reviews and meta-analyses. Modelling of cost-benefit for Viet Nam used programme data on costs combined with effects from a large-scale cluster randomized breastfeeding promotion intervention with controls. This study found that over 12 400 preventable child and maternal deaths per year in the seven countries could be attributed to inadequate breastfeeding. The economic benefits associated with potential improvements in cognition alone, through higher IQ and earnings, total $1.6 billion annually. The loss exceeds 0.5% of Gross National Income in the country with the lowest exclusive breastfeeding rate (Thailand). The potential savings in health care treatment costs ($0.3 billion annually) from reducing the incidence of diarrhoea and pneumonia could help offset the cost of breastfeeding promotion. Based on the data available and authors' assumptions, investing in a national breastfeeding promotion strategy in Viet Nam could result in preventing 200 child deaths per year and generate monetary benefits of US$2.39 for every US$1, or a 139% return on investment. These encouraging results suggest that there are feasible and affordable opportunities to accelerate progress towards achieving the Global Nutrition Target for exclusive breastfeeding by 2025.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".