Evidence map›Paper›PMID 41509810›Full record

ArticlePublic health in practice (Oxford, England)2026

Evaluating infant multimorbidity in Ethiopia through the international classification of functioning, disability, and health framework: Results from the performance monitoring for action survey.

E A Derso, S Fumagalli, M G Valsecchi, A Nespoli, P Rebora

Abstract read
In one paragraph

Article in Public health in practice (Oxford, England), 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

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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

E A DersoBicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
S FumagalliDepartment of Obstetric, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy.
M G ValsecchiBicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
A NespoliDepartment of Obstetric, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy.
P ReboraBicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Infant multimorbidity-defined as the co-occurrence of multiple symptoms or conditions-can negatively influence early development and population well-being. This study aimed to apply the International Classification of Functioning, Disability, and Health (ICF) framework to classify infant morbidity and examine its association with social and health-related risk factors. Study design: Cross-sectional analysis of population-based survey data. Methods: Data were drawn from the 2019 Performance Monitoring for Action Ethiopia (PMA-ET) community survey. Using multistage stratified cluster sampling, women aged 15-49 were screened, and those pregnant or postpartum within six weeks were eligible. Mothers reported infant illnesses in the preceding two weeks, which were then classified using the ICF framework. Multivariable weighted logistic regression was conducted to assess associations with selected risk factors. Results: Among 2514 infants, 24.7 % experienced morbidity in the cardiovascular, hematological, immunological, or respiratory domains (ICF code b4); 14.0 % in digestive, metabolic, or endocrine systems (b5); 4.5 % in skin and related structures (b8); 2.5 % in sensory functions and pain (b2); and 1.5 % in mental function (b1). Significant risk factors for b4 morbidity included poor sanitation (adjusted OR = 0.68; 95 % CI: 0.47-0.99), lack of cooking facilities (adjusted OR = 0.72; 95 % CI: 0.52-0.99), and a partner's disapproval of family planning (adjusted OR = 1.40; 95 % CI: 1.05-1.87). For b5 morbidity, lower maternal education and partner disapproval or indifference toward family planning were significant predictors. Conclusions: Addressing household-level determinants and empowering women through education and reproductive autonomy may reduce infant morbidity and support early childhood health.

Indexed as

ICF-CY checklistInfant multimorbidityMultivariable weighted logistic regressionPMA-Et

Identifiers

PMID41509810
PMCPMC12775979

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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.