Evidence map›Paper›PMID 40400047›Full record

ArticleMaternal & child nutrition2025

Influence of Maternal Education and Household Wealth on Double Burden of Malnutrition in South and Southeast Asia.

Ashis Talukder, Matthew Kelly, Darren Gray, Haribondhu Sarma

Abstract read
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Article in Maternal & child nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers 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

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ashis TalukderNational Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, Australia.ORCID 0000-0002-2205-0696
Matthew KellyNational Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, Australia.
Darren GrayNational Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, Australia.
Haribondhu SarmaNational Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, Australia.ORCID 0000-0003-1553-8498

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The double burden of malnutrition (DBM), defined by the coexistence of undernutrition and overnutrition within households, poses significant public health challenges in South and Southeast Asia. While previous studies have examined the individual effects of maternal education and household wealth on DBM, findings remain inconsistent, and little is known about how these factors interact across different contexts. To address this gap, we used nationally representative Demographic and Health Survey (DHS) data from Bangladesh (2017-2018), Cambodia (2022), Nepal (2022) and Timor-Leste (2016) to analyse the interaction between maternal education and household wealth in shaping DBM risk. These countries were selected due to their ongoing nutritional transition, which has contributed to the increasing prevalence of DBM. For the analysis, we used DHS data, which employs a standardised multistage cluster sampling method and probabilistic methods to ensure representativeness. In this study, we selected mother-child pairs from the DHS survey, focusing on children aged 0-59 months and nonpregnant mothers at the time of the survey. We included those pairs for which both the mother and child had valid weight and height measurements. Maternal education and household wealth were identified as the primary exposures. To examine their interaction and the impact of other covariates on DBM, we employed multivariable logistic regression models. Our study found that DBM prevalence was higher in urban and wealthier households, with the highest rates observed in Timor-Leste and Cambodia. Multivariable logistic regression indicated that lower maternal education increased DBM risk, especially in affluent households (OR 2.07-3.29). Conversely, higher maternal education was associated with lower DBM prevalence. Additionally, breastfeeding and antenatal care visits emerged as protective factors. These findings indicate the necessity for specific interventions aimed at enhancing maternal education and promoting healthy dietary practices, particularly in rich households. Additionally, reinforcing breastfeeding practices and increasing antenatal care visits are essential strategies to mitigate the risks associated with DBM.

Indexed as

Educational StatusFamily CharacteristicsMalnutritionMothersAdolescentAdultAsia, SoutheasternBangladeshCambodiaChild, PreschoolFemaleHealth SurveysHumansInfantInfant, NewbornMaledouble burden of malnutritioninteraction effectmaternal educationSouth and Southeast Asiawealth index

Identifiers

PMID40400047
PMCPMC12454179

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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.