A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa
Sub Saharan Africa is confronted with a wide range of interlinked health and economic problems that include high levels of mortality and poor service delivery. The objective of the paper is to develop a spatial model for Sub-Saharan Africa that can quantify the mortality impact of (poor) service del...
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| Veröffentlicht in: | International journal of health geographics 2013-02, Vol.12 (1), p.8-8 |
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| description | Sub Saharan Africa is confronted with a wide range of interlinked health and economic problems that include high levels of mortality and poor service delivery. The objective of the paper is to develop a spatial model for Sub-Saharan Africa that can quantify the mortality impact of (poor) service delivery at sub-district level in order to integrate related health and local level policy interventions. In this regard, an expanded composite service delivery index was developed, and the data were analysed using a Bayesian Poisson spatial model.
The results indicate significant differences in the risk of mortality and poor service delivery at sub-district level. In particular, the results indicate clusters of high mortality and poor service delivery in two of the bigger, poorer provinces with large rural communities. Conversely, two of the wealthier provinces have lower levels of mortality and higher levels of service delivery, but income inequality is more widespread. The bivariate and multivariate models, moreover, reflect significant positive linkages (p |
| doi_str_mv | 10.1186/1476-072X-12-8 |
| format | Article |
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The results indicate significant differences in the risk of mortality and poor service delivery at sub-district level. In particular, the results indicate clusters of high mortality and poor service delivery in two of the bigger, poorer provinces with large rural communities. Conversely, two of the wealthier provinces have lower levels of mortality and higher levels of service delivery, but income inequality is more widespread. The bivariate and multivariate models, moreover, reflect significant positive linkages (p < 0.01) between increased mortality and poor service delivery after adjusting for HIV/AIDS, income inequality, population density and the protective influence of metropolitan areas. Finally, the hypothesized provision of a basket of services reduced the mortality rate in South Africa's 248 sub-districts by an average of 5.3 (0.3-15.4) deaths per 1000.
The results indicate that the model can accurately plot mortality and service delivery "hotspots' at sub-district level, as well as explain their associations and causality. A mortality reduction index shows that mortality in the highest risk sub-districts can be reduced by as much as 15.4 deaths per 1000 by providing a range of basic services. The ability to use the model in a wider SSA context and elsewhere is also feasible given the innovative use of available databases. Finally, the paper illustrates the importance of developing policy in SSA that can simultaneously solve both economic and health problems.</description><identifier>ISSN: 1476-072X</identifier><identifier>EISSN: 1476-072X</identifier><identifier>DOI: 10.1186/1476-072X-12-8</identifier><identifier>PMID: 23425437</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Acquired immune deficiency syndrome ; Africa South of the Sahara - epidemiology ; AIDS ; Analysis ; Bayes Theorem ; Databases, Factual - statistics & numerical data ; Delivery of Health Care - economics ; Delivery of Health Care - statistics & numerical data ; Economic aspects ; Environmental Monitoring - methods ; Health aspects ; Health sciences ; Humans ; Income distribution ; Income inequality ; Monte Carlo simulation ; Mortality ; Mortality - trends ; Parameter estimation ; Provinces ; Public health ; Quality of life ; Social aspects ; South Africa ; South Africa - epidemiology ; Studies ; Sub-Saharan Africa ; Sustainable living</subject><ispartof>International journal of health geographics, 2013-02, Vol.12 (1), p.8-8</ispartof><rights>COPYRIGHT 2013 BioMed Central Ltd.</rights><rights>2013 Sartorius and Sartorius; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright ©2013 Sartorius and Sartorius; licensee BioMed Central Ltd. 2013 Sartorius and Sartorius; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b610t-e8358c45e5d215b5925311764139e81c69a414bbaf3519c55ea34665c83793ab3</citedby><cites>FETCH-LOGICAL-b610t-e8358c45e5d215b5925311764139e81c69a414bbaf3519c55ea34665c83793ab3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607859/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3607859/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,27926,27927,53793,53795</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23425437$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sartorius, Kurt</creatorcontrib><creatorcontrib>Sartorius, Benn K D</creatorcontrib><title>A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa</title><title>International journal of health geographics</title><addtitle>Int J Health Geogr</addtitle><description>Sub Saharan Africa is confronted with a wide range of interlinked health and economic problems that include high levels of mortality and poor service delivery. The objective of the paper is to develop a spatial model for Sub-Saharan Africa that can quantify the mortality impact of (poor) service delivery at sub-district level in order to integrate related health and local level policy interventions. In this regard, an expanded composite service delivery index was developed, and the data were analysed using a Bayesian Poisson spatial model.
The results indicate significant differences in the risk of mortality and poor service delivery at sub-district level. In particular, the results indicate clusters of high mortality and poor service delivery in two of the bigger, poorer provinces with large rural communities. Conversely, two of the wealthier provinces have lower levels of mortality and higher levels of service delivery, but income inequality is more widespread. The bivariate and multivariate models, moreover, reflect significant positive linkages (p < 0.01) between increased mortality and poor service delivery after adjusting for HIV/AIDS, income inequality, population density and the protective influence of metropolitan areas. Finally, the hypothesized provision of a basket of services reduced the mortality rate in South Africa's 248 sub-districts by an average of 5.3 (0.3-15.4) deaths per 1000.
The results indicate that the model can accurately plot mortality and service delivery "hotspots' at sub-district level, as well as explain their associations and causality. A mortality reduction index shows that mortality in the highest risk sub-districts can be reduced by as much as 15.4 deaths per 1000 by providing a range of basic services. The ability to use the model in a wider SSA context and elsewhere is also feasible given the innovative use of available databases. Finally, the paper illustrates the importance of developing policy in SSA that can simultaneously solve both economic and health problems.</description><subject>Acquired immune deficiency syndrome</subject><subject>Africa South of the Sahara - epidemiology</subject><subject>AIDS</subject><subject>Analysis</subject><subject>Bayes Theorem</subject><subject>Databases, Factual - statistics & numerical data</subject><subject>Delivery of Health Care - economics</subject><subject>Delivery of Health Care - statistics & numerical data</subject><subject>Economic aspects</subject><subject>Environmental Monitoring - methods</subject><subject>Health aspects</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Income distribution</subject><subject>Income inequality</subject><subject>Monte Carlo simulation</subject><subject>Mortality</subject><subject>Mortality - trends</subject><subject>Parameter estimation</subject><subject>Provinces</subject><subject>Public health</subject><subject>Quality of life</subject><subject>Social aspects</subject><subject>South Africa</subject><subject>South Africa - epidemiology</subject><subject>Studies</subject><subject>Sub-Saharan Africa</subject><subject>Sustainable living</subject><issn>1476-072X</issn><issn>1476-072X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kk1v1DAQhiMEoqVw5YgscYFDShzb-eBQaVXxUakSEgsSN2vinWRdOfbWdiqWf8E_xlGXpQtFPng888xre2ay7DktTiltqjeU11Ve1OW3nJZ58yA73jse3rGPsichXBVFWVJePc6OSsZLwVl9nP1ckLCBqMGQ0a3QkOjI9QQ26n5L4hqT10cwOm6JHjegInE9CehvtEKSeH2DPoUsWU5dvoQ1eLBk0Xut4C1JJipn3JBOJtFBD5ZMURv9Q9uBrCAC6b0bydJNcb1Le5o96sEEfLbbT7Kv7999Of-YX376cHG-uMy7ihYxx4aJRnGBYlVS0Ym2FIzSuuKUtdhQVbXAKe866JmgrRICgfGqEqphdcugYyfZ2a3uZupGXCm00YORG69H8FvpQMvDiNVrObgbyaqibkSbBBa3Ap12_xE4jCg3yrklcm6JpKVsksar3SO8u54wRDnqoNAYsOimICkrG5G-xYqEvvwLvXKTt6lEM0Ubyrmo_1ADGJTa9i5drWZRuRBzATjjs9bpPVRaKxy1chZ7nfwHCa8PEhIT8XscYApBXiw_3yuuvAvBY78vCS3kPLT_FuHF3U7s8d9Tyn4Bdx7nBw</recordid><startdate>20130220</startdate><enddate>20130220</enddate><creator>Sartorius, Kurt</creator><creator>Sartorius, Benn K D</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7T2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>7U1</scope><scope>7U2</scope><scope>5PM</scope></search><sort><creationdate>20130220</creationdate><title>A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa</title><author>Sartorius, Kurt ; Sartorius, Benn K D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b610t-e8358c45e5d215b5925311764139e81c69a414bbaf3519c55ea34665c83793ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Acquired immune deficiency syndrome</topic><topic>Africa South of the Sahara - epidemiology</topic><topic>AIDS</topic><topic>Analysis</topic><topic>Bayes Theorem</topic><topic>Databases, Factual - statistics & numerical data</topic><topic>Delivery of Health Care - economics</topic><topic>Delivery of Health Care - statistics & numerical data</topic><topic>Economic aspects</topic><topic>Environmental Monitoring - methods</topic><topic>Health aspects</topic><topic>Health sciences</topic><topic>Humans</topic><topic>Income distribution</topic><topic>Income inequality</topic><topic>Monte Carlo simulation</topic><topic>Mortality</topic><topic>Mortality - trends</topic><topic>Parameter estimation</topic><topic>Provinces</topic><topic>Public health</topic><topic>Quality of life</topic><topic>Social aspects</topic><topic>South Africa</topic><topic>South Africa - epidemiology</topic><topic>Studies</topic><topic>Sub-Saharan Africa</topic><topic>Sustainable living</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sartorius, Kurt</creatorcontrib><creatorcontrib>Sartorius, Benn K D</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Environmental Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of health geographics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sartorius, Kurt</au><au>Sartorius, Benn K D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa</atitle><jtitle>International journal of health geographics</jtitle><addtitle>Int J Health Geogr</addtitle><date>2013-02-20</date><risdate>2013</risdate><volume>12</volume><issue>1</issue><spage>8</spage><epage>8</epage><pages>8-8</pages><issn>1476-072X</issn><eissn>1476-072X</eissn><abstract>Sub Saharan Africa is confronted with a wide range of interlinked health and economic problems that include high levels of mortality and poor service delivery. The objective of the paper is to develop a spatial model for Sub-Saharan Africa that can quantify the mortality impact of (poor) service delivery at sub-district level in order to integrate related health and local level policy interventions. In this regard, an expanded composite service delivery index was developed, and the data were analysed using a Bayesian Poisson spatial model.
The results indicate significant differences in the risk of mortality and poor service delivery at sub-district level. In particular, the results indicate clusters of high mortality and poor service delivery in two of the bigger, poorer provinces with large rural communities. Conversely, two of the wealthier provinces have lower levels of mortality and higher levels of service delivery, but income inequality is more widespread. The bivariate and multivariate models, moreover, reflect significant positive linkages (p < 0.01) between increased mortality and poor service delivery after adjusting for HIV/AIDS, income inequality, population density and the protective influence of metropolitan areas. Finally, the hypothesized provision of a basket of services reduced the mortality rate in South Africa's 248 sub-districts by an average of 5.3 (0.3-15.4) deaths per 1000.
The results indicate that the model can accurately plot mortality and service delivery "hotspots' at sub-district level, as well as explain their associations and causality. A mortality reduction index shows that mortality in the highest risk sub-districts can be reduced by as much as 15.4 deaths per 1000 by providing a range of basic services. The ability to use the model in a wider SSA context and elsewhere is also feasible given the innovative use of available databases. Finally, the paper illustrates the importance of developing policy in SSA that can simultaneously solve both economic and health problems.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>23425437</pmid><doi>10.1186/1476-072X-12-8</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Acquired immune deficiency syndrome Africa South of the Sahara - epidemiology AIDS Analysis Bayes Theorem Databases, Factual - statistics & numerical data Delivery of Health Care - economics Delivery of Health Care - statistics & numerical data Economic aspects Environmental Monitoring - methods Health aspects Health sciences Humans Income distribution Income inequality Monte Carlo simulation Mortality Mortality - trends Parameter estimation Provinces Public health Quality of life Social aspects South Africa South Africa - epidemiology Studies Sub-Saharan Africa Sustainable living |
| title | A spatial model to quantify the mortality impact of service delivery in Sub-Saharan Africa: an ecological design utilizing data from South Africa |
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