Impact of scaling up breastfeeding on reducing the global burden of non-communicable diseases in mothers and children: a population-based modelling analysis for 132 low-income and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Growing evidence suggests breastfeeding offers long-term protective effects against non-communicable diseases (NCDs) later in life in mothers and their offspring. These benefits could be substantial at the population level but have not yet been rigorously quantified. We aimed to estimate the population-level effect of scaling up exclusive breastfeeding on long-term NCD burden in mothers and their offspring in 132 low-income and middle-income countries (LMICs). METHODS: In this population-based modelling analysis, we developed mathematical simulation models based on the population impact fraction estimator and leveraging available effect estimates and global input data. We conducted umbrella reviews to obtain pooled effect estimates from high-quality meta-analyses, with quality assessed using AMSTAR-2. Input data included cause-specific mortality (Global Burden of Diseases, Injuries, and Risk Factors Study), diabetes and hypertension prevalence (NCD Risk Factor Collaboration), baseline exclusive breastfeeding coverage (WHO-UNICEF), and demographics (UN Population Division). We quantified delayed cause-specific NCD deaths, averted diabetes and hypertension cases, and years of life gained (YLG) across four exclusive breastfeeding coverage scenarios. All future benefits were discounted (3% rate). FINDINGS: Scaling up exclusive breastfeeding coverage to 90% in 132 LMICs could delay 0·17% of NCD deaths across the two generations, equivalent to 72 300 delayed NCD deaths annually, yielding 1·04 million YLG. It substantially reduced type 2 diabetes prevalence by 1·29% (10 million cases averted across the lifespan of the cohort) and moderately reduced hypertension prevalence by 0·17% (3·8 million averted cases). The maternal generation constituted 42% of delayed deaths, 23% of averted diabetes cases, and approximately half of the total YLG. Regionally, southeast Asia, east Asia, and Oceania followed by south Asia had the largest absolute benefits due to population size; however, after adjusting for cohort and population size, sub-Saharan Africa and north Africa and the Middle East showed the largest benefits per million intervened mothers. Most delayed deaths were from ischaemic heart disease (43%) and stroke (33%), with cancer accounting for 18%. INTERPRETATION: Scaling up exclusive breastfeeding coverage could lead to benefits in reducing NCDs, complementing its established benefits for child mortality and early childhood development. FUNDING: Nutrition International.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".