Evidence map›Paper›PMID 42587332›Full record

ArticleBMC global and public health2026

Estimating the impact and cost-effectiveness of antenatal care approaches in 24 low- and middle-income countries: a modelling study.

Nisaa Wulan, Debra Ten Brink, Tharindu Wickramaarachchi, Sam Ali, Aris T Papageorghiou, Nick Scott

Abstract read
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Article in BMC global and public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Nisaa WulanBurnet Institute, Melbourne, Australia. nisaa.wulan@burnet.edu.au.ORCID http://orcid.org/0000-0002-4188-9761
Debra Ten BrinkBurnet Institute, Melbourne, Australia.
Tharindu WickramaarachchiBurnet Institute, Melbourne, Australia.
Sam AliDepartment of Obstetrics and Gynecology, Makerere University Hospital, Makerere University, Kampala, Uganda.
Aris T PapageorghiouNuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
Nick ScottBurnet Institute, Melbourne, Australia.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundAntenatal care (ANC) is a critical intervention for improving maternal and newborn health outcomes, particularly in low- and middle-income countries (LMICs). This study modelled the potential impact and cost-effectiveness of a theoretical community-based and risk-stratified model of ANC in LMICs.

methodsA decision-analytical model was developed to estimate health outcomes (maternal anemia, maternal deaths, newborn deaths, preterm births, stillbirths, low birth weight cases, and small for gestational age births) for 24 LMICs with demographic and health surveys conducted between 2018-2024. Three ANC scenarios were considered for the 2025-2034 period; status-quo (no changes in ANC-delivered intervention coverage); no ANC (a counterfactual where ANC-delivered intervention coverage was set equivalent to women reporting no ANC contacts); and a new ANC scenario (a community-based ANC delivery with risk stratification, assuming a 20% reduction in women with no ANC contacts, and equivalent quality/coverage of interventions for women with at least one ANC contact as those currently receiving four to seven contacts). Economic costs were estimated for each scenario from a healthcare provider's perspective using an ingredient-based approach, and the cost per disability-adjusted life year (DALY) averted was calculated relative to the status-quo. Probabilistic sensitivity analyses were conducted to reflect parameter uncertainty.

resultsCompared to the no ANC scenario, the status-quo scenario was estimated to cost US$75.16 per DALY averted across the 24 sample countries. Compared to the status-quo, the new ANC scenario was estimated to avert 11.9 million (4.1%) of maternal anemia cases, 0.1 million (4.1%) of maternal deaths, 0.3 million (1.9%) newborn deaths, 0.4 million (0.7%) preterm births, <0.1 million (<0.1%) stillbirths, 7.7 million (6.3%) low birth weight cases, and 8.0 million (5.4%) small for gestational age births. This was estimated to cost US$133.76 per DALY averted compared to the status-quo across the 24 sample countries, varying between 4% and 52% of gross domestic product per capita.

conclusionsA community-based risk-stratified ANC delivery model could be cost-effective in LMICs if it can achieve greater ANC coverage and consistency of interventions within ANC contacts. This analysis supports continued investigation of innovative ANC delivery approaches to improve maternal and newborn health outcomes in LMICs.

Indexed as

Antenatal careCost-effectivenessLow- and middle-income countriesLow birth weightMaternalModellingNewbornPreterm birthsSmall for gestational ageStillbirths

Identifiers

PMID42587332
PMCPMC13465528

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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.