ArticleGlobal health action2025
Beyond the labels: Classifying countries by child health outcomes - A cluster analysis of child mortality and child-health data.
Article in Global health action, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Catching Up on Mortality, Falling Behind on Disability: A Dynamic Decomposition of the Health-Adjusted Life Expectancy Gap between Higher-Income and Lower-Income Countries.Annals of global health · 2026Article
- The Clustering of Lifestyle Risk Factors in the Serbian Adult Population and Association with Self-Rated Health.Zdravstveno varstvo · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost health service classification systems are based on organisational components such as service provision, financing, and regulation. This study considers health systems using data focusing on child health outcomes, service provision, and selected social characteristics. This more accurately reflects the reality of health service provision for children, young people, and their families.
objectiveTo classify health systems based on child health data through cluster analysis and exploratory and descriptive data analysis.
methodData were extracted from the current version of the UNICEF (2023) State of the World's Children full dataset, concentrating on outcomes related to mortality. Cluster analyses were conducted, and a heatmap was produced to identify patterns and groups among countries and child health indicators. Row and column distances were calculated using the Euclidean distance, and clustering was performed using the complete linkage method. Each variable was centred and scaled using the scale command, allowing variables measured on different scales to be compared without those with large values being weighted more heavily. Countries that performed better or were less healthy than expected were identified through linear regression analysis using the ggplot2 package.
resultsAnalysis of countries by cluster reveals six main groups, characterised by child and maternal mortality rates, vaccination levels, access to maternal and child healthcare, access to water and sanitation, and population migration levels.
conclusionIdentifying patterns in outcomes and identifying countries that perform above or below expectations concerning child health can inform a more nuanced approach to improving a country's child health outcomes.
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Registered trials
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