Dying in their prime: determinants and space-time risk of adult mortality in rural South Africa.
Identifieur interne : 001F90 ( Ncbi/Merge ); précédent : 001F89; suivant : 001F91Dying in their prime: determinants and space-time risk of adult mortality in rural South Africa.
Auteurs : Benn Sartorius [Afrique du Sud] ; Kathleen Kahn ; Mark A. Collinson ; Kurt Sartorius ; Stephen M. TollmanSource :
- Geospatial health [ 1970-7096 ] ; 2013.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Analyse spatiale, Cause de décès, Facteurs de risque, Facteurs socioéconomiques, Facteurs temps, Femelle, Humains, Jeune adulte, Mortalité (tendances), Mâle, Population rurale (), Répartition par sexe, Répartition par âge, République d'Afrique du Sud (épidémiologie), Surveillance de la population, Théorème de Bayes.
- MESH :
- tendances : Mortalité.
- épidémiologie : République d'Afrique du Sud.
- Adolescent, Adulte, Adulte d'âge moyen, Analyse spatiale, Cause de décès, Facteurs de risque, Facteurs socioéconomiques, Facteurs temps, Femelle, Humains, Jeune adulte, Mâle, Population rurale, Répartition par sexe, Répartition par âge, Surveillance de la population, Théorème de Bayes.
- Wicri :
- geographic : Afrique du Sud.
English descriptors
- KwdEn :
- Adolescent, Adult, Age Distribution, Bayes Theorem, Cause of Death, Female, Humans, Male, Middle Aged, Mortality (trends), Population Surveillance, Risk Factors, Rural Population (statistics & numerical data), Sex Distribution, Socioeconomic Factors, South Africa (epidemiology), Spatial Analysis, Time Factors, Young Adult.
- MESH :
- geographic , epidemiology : South Africa.
- statistics & numerical data : Rural Population.
- trends : Mortality.
- Adolescent, Adult, Age Distribution, Bayes Theorem, Cause of Death, Female, Humans, Male, Middle Aged, Population Surveillance, Risk Factors, Sex Distribution, Socioeconomic Factors, Spatial Analysis, Time Factors, Young Adult.
Abstract
A longitudinal dataset was used to investigate adult mortality in rural South Africa in order to determine location, trends, high impact determinants and policy implications. Adult (15-59 years) mortality data for the period 1993-2010 were extracted from the health and demographic surveillance system in the rural sub-district of Agincourt. A Bayesian geostatistical frailty survival model was used to quantify significant associations between adult mortality and various multilevel (individual, household and community) variables. It was found that adult mortality significantly increased over time with a reduction observed late in the study period. Non-communicable disease mortality appeared to increase and decrease in parallel with communicable mortality, whilst deaths due to external causes remained constant. Male gender, unemployment, circular (labour) migrant status, age and gender of household heads, partner and/or other household death, low education and low household socio-economic status were identified as significant and highly attributable determinants of adult mortality. Health facility remoteness was a risk for adult mortality and households falling outside a critical buffering zone were identified. Spatial foci of higher adult mortality risk were observed, indicating a strong non-random pattern. Communicable diseases differed from non-communicable diseases with respect to spatial distribution of mortality. Areas with significant excess mortality risk (hot spots) were found to be part of a complex interaction of highly attributable factors that continues to drive differential space-time risk patterns of communicable (HIV/AIDS and tuberculosis) mortality in Agincourt. The impact of HIV mortality and its subsequent lowering due to the introduction of antiretroviral therapy was found to be clearly evident in this rural population.
DOI: 10.4081/gh.2013.83
PubMed: 23733287
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Adult (15-59 years) mortality data for the period 1993-2010 were extracted from the health and demographic surveillance system in the rural sub-district of Agincourt. A Bayesian geostatistical frailty survival model was used to quantify significant associations between adult mortality and various multilevel (individual, household and community) variables. It was found that adult mortality significantly increased over time with a reduction observed late in the study period. Non-communicable disease mortality appeared to increase and decrease in parallel with communicable mortality, whilst deaths due to external causes remained constant. Male gender, unemployment, circular (labour) migrant status, age and gender of household heads, partner and/or other household death, low education and low household socio-economic status were identified as significant and highly attributable determinants of adult mortality. Health facility remoteness was a risk for adult mortality and households falling outside a critical buffering zone were identified. Spatial foci of higher adult mortality risk were observed, indicating a strong non-random pattern. Communicable diseases differed from non-communicable diseases with respect to spatial distribution of mortality. Areas with significant excess mortality risk (hot spots) were found to be part of a complex interaction of highly attributable factors that continues to drive differential space-time risk patterns of communicable (HIV/AIDS and tuberculosis) mortality in Agincourt. The impact of HIV mortality and its subsequent lowering due to the introduction of antiretroviral therapy was found to be clearly evident in this rural population.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23733287</PMID><DateCreated><Year>2013</Year><Month>06</Month><Day>04</Day></DateCreated><DateCompleted><Year>2014</Year><Month>02</Month><Day>10</Day></DateCompleted><DateRevised><Year>2017</Year><Month>09</Month><Day>22</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1970-7096</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>2</Issue><PubDate><Year>2013</Year><Month>May</Month></PubDate></JournalIssue><Title>Geospatial health</Title><ISOAbbreviation>Geospat Health</ISOAbbreviation></Journal><ArticleTitle>Dying in their prime: determinants and space-time risk of adult mortality in rural South Africa.</ArticleTitle><Pagination><MedlinePgn>237-49</MedlinePgn></Pagination><Abstract><AbstractText>A longitudinal dataset was used to investigate adult mortality in rural South Africa in order to determine location, trends, high impact determinants and policy implications. Adult (15-59 years) mortality data for the period 1993-2010 were extracted from the health and demographic surveillance system in the rural sub-district of Agincourt. A Bayesian geostatistical frailty survival model was used to quantify significant associations between adult mortality and various multilevel (individual, household and community) variables. It was found that adult mortality significantly increased over time with a reduction observed late in the study period. Non-communicable disease mortality appeared to increase and decrease in parallel with communicable mortality, whilst deaths due to external causes remained constant. Male gender, unemployment, circular (labour) migrant status, age and gender of household heads, partner and/or other household death, low education and low household socio-economic status were identified as significant and highly attributable determinants of adult mortality. Health facility remoteness was a risk for adult mortality and households falling outside a critical buffering zone were identified. Spatial foci of higher adult mortality risk were observed, indicating a strong non-random pattern. Communicable diseases differed from non-communicable diseases with respect to spatial distribution of mortality. Areas with significant excess mortality risk (hot spots) were found to be part of a complex interaction of highly attributable factors that continues to drive differential space-time risk patterns of communicable (HIV/AIDS and tuberculosis) mortality in Agincourt. The impact of HIV mortality and its subsequent lowering due to the introduction of antiretroviral therapy was found to be clearly evident in this rural population.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sartorius</LastName><ForeName>Benn</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. benn.sartorius@wits.ac.za</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kahn</LastName><ForeName>Kathleen</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>Collinson</LastName><ForeName>Mark A</ForeName><Initials>MA</Initials></Author><Author ValidYN="Y"><LastName>Sartorius</LastName><ForeName>Kurt</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>Tollman</LastName><ForeName>Stephen M</ForeName><Initials>SM</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>058893/Z/99/A</GrantID><Agency>Wellcome Trust</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>069683</GrantID><Agency>Wellcome Trust</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>069683/Z/02/Z</GrantID><Agency>Wellcome Trust</Agency><Country>United Kingdom</Country></Grant><Grant><GrantID>069683/Z/08/Z</GrantID><Agency>Wellcome Trust</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>Italy</Country><MedlineTA>Geospat 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RefType="Cites"><RefSource>Int J Epidemiol. 2009 Aug;38(4):1008-16</RefSource><PMID Version="1">19261659</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017677" MajorTopicYN="N">Age Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001499" MajorTopicYN="N">Bayes Theorem</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002423" MajorTopicYN="N">Cause of Death</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009026" MajorTopicYN="N">Mortality</DescriptorName><QualifierName UI="Q000639" MajorTopicYN="Y">trends</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012424" MajorTopicYN="N">Rural Population</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017678" MajorTopicYN="N">Sex Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012959" MajorTopicYN="N">Socioeconomic Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013019" MajorTopicYN="N" Type="Geographic">South Africa</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062206" MajorTopicYN="Y">Spatial Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">EMS53912</OtherID><OtherID Source="NLM">PMC3725424</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>6</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>6</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>2</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23733287</ArticleId><ArticleId IdType="pmc">PMC3725424</ArticleId><ArticleId IdType="mid">EMS53912</ArticleId><ArticleId IdType="doi">10.4081/gh.2013.83</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Afrique du Sud</li></country><region><li>Gauteng</li></region><settlement><li>Johannesbourg</li></settlement><orgName><li>Université du Witwatersrand</li></orgName></list><tree><noCountry><name sortKey="Collinson, Mark A" sort="Collinson, Mark A" uniqKey="Collinson M" first="Mark A" last="Collinson">Mark A. Collinson</name><name sortKey="Kahn, Kathleen" sort="Kahn, Kathleen" uniqKey="Kahn K" first="Kathleen" last="Kahn">Kathleen Kahn</name><name sortKey="Sartorius, Kurt" sort="Sartorius, Kurt" uniqKey="Sartorius K" first="Kurt" last="Sartorius">Kurt Sartorius</name><name sortKey="Tollman, Stephen M" sort="Tollman, Stephen M" uniqKey="Tollman S" first="Stephen M" last="Tollman">Stephen M. Tollman</name></noCountry><country name="Afrique du Sud"><region name="Gauteng"><name sortKey="Sartorius, Benn" sort="Sartorius, Benn" uniqKey="Sartorius B" first="Benn" last="Sartorius">Benn Sartorius</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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