Evidence map›Paper›PMID 39815319›Full record

ArticleBMC global and public health2025

Optimal allocation of antenatal and young child nutrition interventions: an individual-based global burden of disease calibrated microsimulation.

Alison Bowman, Sylvia Lutze, James Albright, Nathaniel Blair-Stahn, Hussain Jafari, Simar Kaur, Caroline Kinuthia, Rajan Mudambi, Patrick Nast, Alix Pletcher and 1 more

Abstract read
In one paragraph

Article in BMC global and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Alison BowmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Sylvia LutzeInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. lutzes@uw.edu.
James AlbrightInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Nathaniel Blair-StahnInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Hussain JafariInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Simar KaurInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Caroline KinuthiaInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Rajan MudambiInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Patrick NastInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Alix PletcherInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Abraham FlaxmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUndernutrition remains a global crisis and is a focus of Sustainable Development Goals. While there are multiple known, effective interventions, complex interactions between prevention and treatment and resource constraints can lead to difficulties in allocating funding. Simulation studies that use in silico simulation can help illuminate the interactions between interventions and provide insight into the cost-effectiveness of alternative packages of options.

methodsWe developed an individual-based microsimulation model based on the Global Burden of Disease (GBD) 2021 study data to test a range of nutrition interventions, including antenatal interventions (iron and folic acid, multiple micronutrients, and balanced energy protein supplementation) and child interventions (treatment for severe acute malnutrition, treatment for moderate acute malnutrition, and wasting prevention with small-quantity lipid-based nutrient supplements). We also developed an analytic approach to process the results of the microsimulation and identify the optimal intervention funding allocation for a given budget size. We use Ethiopia as an example in this paper.

resultsIn our illustrative example of Ethiopia, the reallocation of the baseline budget to minimize disability-adjusted life years (DALYs) resulted in first funding the antenatal multiple micronutrients to their maximum coverage and then funding treatment for severe child acute malnutrition. Relative to the baseline allocation, the reallocation optimized to minimizing DALYs resulted in 592,000 fewer annual DALYs, constituting an 8.3% reduction in total DALYs in Ethiopia. For budgets larger than the baseline, our model recommended funding first targeted moderate acute malnutrition treatment, second universal moderate acute malnutrition treatment, third wasting prevention with small-quantity lipid-based nutrient supplements, and fourth balanced energy protein supplementation.

conclusionsOur simulation is a novel model for estimating optimal allocation of spending on antenatal and child health nutrition interventions which accounts for the interaction between preventive and therapeutic approaches. Our illustrative results show that an optimized reallocation of current spending can substantially improve pregnancy-related and child health without additional funding. We hope this model can add validity and confidence to prior results to aid stakeholders in funding decisions.

Indexed as

Antenatal careChild nutritionHealth policyMaternal and child healthOptimal allocationSimulation modelYoung child feeding

Identifiers

PMID39815319
PMCPMC11737270

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.