P Day1, J Pearce, D Dorling. 1. GeoHealth Laboratory, Department of Geography, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. peter.day@canterbury.ac.nz
Abstract
OBJECTIVE: The aim of this study was to identify clusters of nations grouped by health outcomes in order to provide sensible groupings for international comparisons. The utility of this approach is demonstrated by comparing life expectancy and a range of health system indicators within and between each cluster. METHODS: Age- and sex-specific mortality data for 190 member states were extracted from the Burden of Disease Estimates statistics produced by the World Health Organization. A hierarchical cluster method was used to identify groupings of countries that are homogeneous in terms of mortality rates. RESULTS: 12 clusters of countries were identified. The average life expectancy of each cluster ranged from 81.5 years (cluster 1) to 37.7 years (cluster 12). The two highest ranked clusters were dominated by Western European countries, Australia, Japan and Canada. Cluster 3 included the UK and USA. The four clusters with the lowest life expectancies were characterised by different configurations of African countries. Health system indicators for workforce, hospital beds, access to medicines and measles vaccination corresponded well with a clear association with cluster life expectancy. On a per capita basis, worldwide health spending was concentrated within the three highest life expectancy clusters, especially cluster 3 containing the USA. CONCLUSIONS: Considerable inequalities in life expectancy and healthcare are made clearer when viewed across clusters of countries grouped by health outcomes. This geo-demographic taxonomy of global mortality has advantages over traditional more ad hoc systems for comparing global health inequalities and for deciding which countries appear to have the most comparable health outcomes.
OBJECTIVE: The aim of this study was to identify clusters of nations grouped by health outcomes in order to provide sensible groupings for international comparisons. The utility of this approach is demonstrated by comparing life expectancy and a range of health system indicators within and between each cluster. METHODS: Age- and sex-specific mortality data for 190 member states were extracted from the Burden of Disease Estimates statistics produced by the World Health Organization. A hierarchical cluster method was used to identify groupings of countries that are homogeneous in terms of mortality rates. RESULTS: 12 clusters of countries were identified. The average life expectancy of each cluster ranged from 81.5 years (cluster 1) to 37.7 years (cluster 12). The two highest ranked clusters were dominated by Western European countries, Australia, Japan and Canada. Cluster 3 included the UK and USA. The four clusters with the lowest life expectancies were characterised by different configurations of African countries. Health system indicators for workforce, hospital beds, access to medicines and measles vaccination corresponded well with a clear association with cluster life expectancy. On a per capita basis, worldwide health spending was concentrated within the three highest life expectancy clusters, especially cluster 3 containing the USA. CONCLUSIONS: Considerable inequalities in life expectancy and healthcare are made clearer when viewed across clusters of countries grouped by health outcomes. This geo-demographic taxonomy of global mortality has advantages over traditional more ad hoc systems for comparing global health inequalities and for deciding which countries appear to have the most comparable health outcomes.
Authors: Howard K Koh; Sarah C Oppenheimer; Sarah B Massin-Short; Karen M Emmons; Alan C Geller; K Viswanath Journal: Am J Public Health Date: 2010-02-10 Impact factor: 9.308
Authors: Christine K Tashobya; Dominique Dubourg; Freddie Ssengooba; Niko Speybroeck; Jean Macq; Bart Criel Journal: Health Policy Plan Date: 2015-05-28 Impact factor: 3.344