Twelve worlds: a geo-demographic comparison of global inequalities in mortality
Identifieur interne : 004362 ( Istex/Corpus ); précédent : 004361; suivant : 004363Twelve worlds: a geo-demographic comparison of global inequalities in mortality
Auteurs : P. Day ; J. Pearce ; D. DorlingSource :
- Journal of Epidemiology and Community Health [ 0143-005X ] ; 2008-11.
English descriptors
- KwdEn :
- Absolute differences, Actual health outcomes, Adult mortality, Affordable drugs, African countries, African nations, Agglomeration process, Average life expectancy, Cluster, Cluster analysis, Cluster populations, Country groupings, Death rates, Democratic republic, Dendrogram, Discussion paper, Disease estimates statistics, Dissimilarity matrix, Epidemiol, Epidemiol community health, European countries, Expectancy, Geohealth laboratory, Global, Global health inequalities, Global health status, Global inequalities, Global mortality, Group nations, Grouping, Health inequalities, Health outcomes, Health system indicators, Health system performance indicators, Hierarchical cluster method, Hospital beds, Inequality, International comparisons, Life expectancies, Life expectancy, Life tables, Lower life expectancy, Lowest euclidean distance, Member states, Mortality, Mortality data, Mortality profiles, Mortality rates, Other clusters, Outlier group, Poorest countries, Public health expenditure, Redrawn globe, Registration system, Research report, Research report table, Social determinants, Third country, Total cluster, Traditional groupings, Western europe, Western european countries, Wider validity, World country, World health organization, World population, Zealand, Zimbabwe.
- Teeft :
- Absolute differences, Actual health outcomes, Adult mortality, Affordable drugs, African countries, African nations, Agglomeration process, Average life expectancy, Cluster, Cluster analysis, Cluster populations, Country groupings, Death rates, Democratic republic, Dendrogram, Discussion paper, Disease estimates statistics, Dissimilarity matrix, Epidemiol, Epidemiol community health, European countries, Expectancy, Geohealth laboratory, Global, Global health inequalities, Global health status, Global inequalities, Global mortality, Group nations, Grouping, Health inequalities, Health outcomes, Health system indicators, Health system performance indicators, Hierarchical cluster method, Hospital beds, Inequality, International comparisons, Life expectancies, Life expectancy, Life tables, Lower life expectancy, Lowest euclidean distance, Member states, Mortality, Mortality data, Mortality profiles, Mortality rates, Other clusters, Outlier group, Poorest countries, Public health expenditure, Redrawn globe, Registration system, Research report, Research report table, Social determinants, Third country, Total cluster, Traditional groupings, Western europe, Western european countries, Wider validity, World country, World health organization, World population, Zealand, Zimbabwe.
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.
Url:
DOI: 10.1136/jech.2007.067702
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ISTEX:CFCCCC877FF93C3A10DDA20118971F3AB5B01666Le document en format XML
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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. 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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.</abstract><qualityIndicators><score>6.889</score><pdfVersion>1.2</pdfVersion><pdfPageSize>595.276 x 793.701 pts</pdfPageSize><refBibsNative>false</refBibsNative><keywordCount>4</keywordCount><abstractCharCount>1748</abstractCharCount><pdfWordCount>3889</pdfWordCount><pdfCharCount>31419</pdfCharCount><pdfPageCount>9</pdfPageCount><abstractWordCount>254</abstractWordCount></qualityIndicators><title>Twelve worlds: a geo-demographic comparison of global inequalities in mortality</title><pmid><json:string>18854506</json:string></pmid><genre><json:string>research-article</json:string></genre><host><title>Journal of Epidemiology and Community 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mortality</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher scheme="https://publisher-list.data.istex.fr">BMJ Publishing Group Ltd</publisher><availability><licence><p>2008 the BMJ Publishing Group</p></licence><p scheme="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-7M42M2QJ-2">bmj</p></availability><date>2008-10-14</date></publicationStmt><notesStmt><note type="research-article" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</note><note type="journal" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</note><note>▸ An additional table is published online only at http://jech.bmj.com/content/vol62/issue11</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Twelve worlds: a geo-demographic comparison of global inequalities in mortality</title><author xml:id="author-0000"><persName><forename 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type="local">jech;62/11/1002</idno></analytic><monogr><title level="j">Journal of Epidemiology and Community Health</title><title level="j" type="abbrev">J Epidemiol Community Health</title><idno type="pISSN">0143-005X</idno><idno type="eISSN">1470-2738</idno><idno type="publisher-id">jech</idno><idno type="PublisherID-hwp">jech</idno><idno type="PublisherID-nlm-ta">J Epidemiol Community Health</idno><imprint><publisher>BMJ Publishing Group Ltd</publisher><date type="published" when="2008-11"></date><biblScope unit="volume">62</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="1002">1002</biblScope></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2008-10-14</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>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.</p></abstract><textClass><keywords scheme="Journal Subject"><list><head>hwp-journal-coll</head><item><term>Epidemiologic studies</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>hwp-journal-coll</head><item><term>Mortality and morbidity</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>hwp-journal-coll</head><item><term>Health economics</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>hwp-journal-coll</head><item><term>Health service research</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2008-10-14">Created</change><change when="2008-11">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/CFCCCC877FF93C3A10DDA20118971F3AB5B01666/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus bmj" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="no"</istex:xmlDeclaration><istex:docType PUBLIC="-//NLM//DTD Journal Archiving and Interchange DTD v2.3 20070202//EN" URI="archivearticle.dtd" name="istex:docType"></istex:docType><istex:document><article article-type="research-article" xml:lang="EN"><front><journal-meta><journal-id journal-id-type="hwp">jech</journal-id><journal-id journal-id-type="nlm-ta">J Epidemiol Community Health</journal-id><journal-id journal-id-type="publisher-id">jech</journal-id><journal-title>Journal of Epidemiology and Community Health</journal-title><abbrev-journal-title abbrev-type="publisher">J Epidemiol Community Health</abbrev-journal-title><issn pub-type="ppub">0143-005X</issn><issn pub-type="epub">1470-2738</issn><publisher><publisher-name>BMJ Publishing Group Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">ch67702</article-id><article-id pub-id-type="doi">10.1136/jech.2007.067702</article-id><article-id pub-id-type="other">jech;62/11/1002</article-id><article-id pub-id-type="pmid">18854506</article-id><article-id pub-id-type="other">1002</article-id><article-id pub-id-type="other">jech.2007.067702</article-id><article-categories><subj-group subj-group-type="heading"><subject content-type="original">Research reports</subject></subj-group><subj-group subj-group-type="hwp-journal-coll"><subject>Epidemiologic studies</subject></subj-group><subj-group subj-group-type="hwp-journal-coll"><subject>Mortality and morbidity</subject></subj-group><subj-group subj-group-type="hwp-journal-coll"><subject>Health economics</subject></subj-group><subj-group subj-group-type="hwp-journal-coll"><subject>Health service research</subject></subj-group></article-categories><title-group><article-title>Twelve worlds: a geo-demographic comparison of global inequalities in mortality</article-title><alt-title alt-title-type="running-head">Research report</alt-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Day</surname><given-names>P</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Pearce</surname><given-names>J</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Dorling</surname><given-names>D</given-names></name><xref ref-type="aff" rid="aff2">2</xref></contrib></contrib-group><aff id="aff1"><label>1</label><addr-line>GeoHealth Laboratory, Department of Geography, University of Canterbury, Private Bag 4800, Christchurch, New Zealand</addr-line> </aff><aff id="aff2"><label>2</label><addr-line>Department of Geography, University of Sheffield, Winter Street, Sheffield S10 2TN, UK</addr-line> </aff><author-notes><corresp>Mr P Day, GeoHealth Laboratory, Department of Geography, University of Canterbury, Private Bag 4800, Christchurch, New Zealand; <email xlink:type="simple">peter.day@canterbury.ac.nz</email></corresp><fn><p>▸ An additional table is published online only at <ext-link ext-link-type="uri" xlink:href="http://jech.bmj.com/content/vol62/issue11" xlink:type="simple">http://jech.bmj.com/content/vol62/issue11</ext-link></p></fn></author-notes><pub-date pub-type="ppub"><month>11</month><year>2008</year></pub-date><pub-date pub-type="epub"><day>14</day><month>10</month><year>2008</year></pub-date><volume>62</volume><volume-id pub-id-type="other">62</volume-id><volume-id pub-id-type="other">62</volume-id><issue>11</issue><issue-id pub-id-type="other">jech;62/11</issue-id><issue-id pub-id-type="other">11</issue-id><issue-id pub-id-type="other">62/11</issue-id><fpage>1002</fpage><history><date date-type="accepted"><day>3</day><month>2</month><year>2008</year></date></history><permissions><copyright-statement>2008 the BMJ Publishing Group</copyright-statement><copyright-year>2008</copyright-year></permissions><self-uri content-type="pdf" xlink:role="full-text" xlink:href="jech-62-1002.pdf"></self-uri><abstract><sec><title>Objective:</title><p>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.</p></sec><sec><title>Methods:</title><p>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.</p></sec><sec><title>Results:</title><p>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.</p></sec><sec><title>Conclusions:</title><p>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.</p></sec></abstract></article-meta></front></article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Twelve worlds: a geo-demographic comparison of global inequalities in mortality</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Twelve worlds: a geo-demographic comparison of global inequalities in mortality</title></titleInfo><name type="personal"><namePart type="given">P</namePart><namePart type="family">Day</namePart><affiliation>GeoHealth Laboratory, Department of Geography, University of Canterbury, Private Bag 4800, Christchurch, New Zealand</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">J</namePart><namePart type="family">Pearce</namePart><affiliation>GeoHealth Laboratory, Department of Geography, University of Canterbury, Private Bag 4800, Christchurch, New Zealand</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">D</namePart><namePart type="family">Dorling</namePart><affiliation>Department of Geography, University of Sheffield, Winter Street, Sheffield S10 2TN, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="research-article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>BMJ Publishing Group Ltd</publisher><dateIssued encoding="w3cdtf">2008-11</dateIssued><dateCreated encoding="w3cdtf">2008-10-14</dateCreated><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><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.</abstract><note type="footnotes">▸ An additional table is published online only at http://jech.bmj.com/content/vol62/issue11</note><relatedItem type="host"><titleInfo><title>Journal of Epidemiology and Community Health</title></titleInfo><titleInfo type="abbreviated"><title>J Epidemiol Community Health</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><subject><genre>hwp-journal-coll</genre><topic>Epidemiologic studies</topic></subject><subject><genre>hwp-journal-coll</genre><topic>Mortality and morbidity</topic></subject><subject><genre>hwp-journal-coll</genre><topic>Health economics</topic></subject><subject><genre>hwp-journal-coll</genre><topic>Health service research</topic></subject><identifier type="ISSN">0143-005X</identifier><identifier type="eISSN">1470-2738</identifier><identifier type="PublisherID">jech</identifier><identifier type="PublisherID-hwp">jech</identifier><identifier type="PublisherID-nlm-ta">J Epidemiol Community Health</identifier><part><date>2008</date><detail type="volume"><caption>vol.</caption><number>62</number></detail><detail type="issue"><caption>no.</caption><number>11</number></detail><extent unit="pages"><start>1002</start></extent></part></relatedItem><identifier type="istex">CFCCCC877FF93C3A10DDA20118971F3AB5B01666</identifier><identifier type="ark">ark:/67375/NVC-ZN5NJKDD-6</identifier><identifier type="DOI">10.1136/jech.2007.067702</identifier><identifier type="href">jech-62-1002.pdf</identifier><identifier type="ArticleID">ch67702</identifier><identifier type="PMID">18854506</identifier><identifier type="local">jech;62/11/1002</identifier><accessCondition type="use and reproduction" contentType="copyright">2008 the BMJ Publishing Group</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-7M42M2QJ-2">bmj</recordContentSource><recordOrigin>2008 the BMJ Publishing Group</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/document/CFCCCC877FF93C3A10DDA20118971F3AB5B01666/metadata/json</uri></json:item></metadata><annexes><json:item><extension>jpeg</extension><original>true</original><mimetype>image/jpeg</mimetype><uri>https://api.istex.fr/document/CFCCCC877FF93C3A10DDA20118971F3AB5B01666/annexes/jpeg</uri></json:item></annexes><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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