Scaling up prenatal nutrition could reduce the global burden of noncommunicable diseases in the next generation: a modeling analysis
Bibliographic record
Abstract
BACKGROUND: Nutritional conditions during pregnancy may influence the epigenetic development of an individual and consequently their later-life risk of noncommunicable disease (NCD). Improving nutrition for pregnant females may therefore serve the dual purpose of directly improving pregnancy outcomes and preventing NCDs in the next generation. OBJECTIVES: We estimated the impact of prenatal supplementation with iron and folic acid (IFA), multiple micronutrients (MMS), or calcium at 50%, 75%, or 90% coverage on future NCDs by age and sex in 2015. METHODS: We used secondary data sources from 132 countries to quantify the cases of diabetes and hypertension and the deaths from selected NCDs that could be averted or delayed by scaling up prenatal micronutrient supplementation. RESULTS: Globally, >51,000 NCD deaths, 6 million cases of hypertension, and 3 million cases of diabetes could be prevented per offspring birth cohort if mothers were prenatally supplemented with MMS at 90% coverage. For IFA these numbers would be roughly half. Calcium supplementation at 90% could delay 51,000 deaths per birth cohort. Our model suggests that substantial numbers of NCD deaths and cases of hypertension and diabetes could be prevented in future generations by scaling up micronutrient supplementation for mothers during pregnancy. CONCLUSIONS: Highlighting the additional benefits of proven nutrition interventions is critical in ensuring adequate and sustained investments, and programmatic integration. As the double burden of disease continues to grow, population-wide efforts to scale up micronutrient supplementation to pregnant females could help prevent both undernutrition and chronic disease.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".