Global health action · 2025
Beyond the labels: Classifying countries by child health outcomes - A cluster analysis of child mortality and child-health data.
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Purssell Edward, Frood Sharron, Sagoo Rohit
Аннотация
BACKGROUND: Most 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.
OBJECTIVE: To classify health systems based on child health data through cluster analysis and exploratory and descriptive data analysis.
METHOD: Data 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.
RESULTS: Analysis 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.
CONCLUSION: Identifying 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.
OBJECTIVE: To classify health systems based on child health data through cluster analysis and exploratory and descriptive data analysis.
METHOD: Data 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.
RESULTS: Analysis 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.
CONCLUSION: Identifying 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.