A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia
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Auteurs : Lawrence N. KazembeSource :
- PLoS ONE [ 1932-6203 ] ; 2013.
Abstract
Despite remarkable gains in life expectancy and declining mortality in the 21st century, in many places mostly in developing countries, adult mortality has increased in part due to HIV/AIDS or continued abject poverty levels. Moreover many factors including behavioural, socio-economic and demographic variables work simultaneously to impact on risk of mortality. Understanding risk factors of adult mortality is crucial towards designing appropriate public health interventions. In this paper we proposed a structured additive two-part random effects regression model for adult mortality data. Our proposal assumed two processes: (i) whether death occurred in the household (prevalence part), and (ii) number of reported deaths, if death did occur (severity part). The proposed model specification therefore consisted of two generalized linear mixed models (GLMM) with correlated random effects that permitted structured and unstructured spatial components at regional level. Specifically, the first part assumed a GLMM with a logistic link and the second part explored a count model following either a Poisson or negative binomial distribution. The model was used to analyse adult mortality data of 25,793 individuals from the 2006/2007 Namibian DHS data. Inference is based on the Bayesian framework with appropriate priors discussed.
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DOI: 10.1371/journal.pone.0073500
PubMed: 24066052
PubMed Central: 3774685
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia</title><author><name sortKey="Kazembe, Lawrence N" sort="Kazembe, Lawrence N" uniqKey="Kazembe L" first="Lawrence N." last="Kazembe">Lawrence N. Kazembe</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24066052</idno><idno type="pmc">3774685</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3774685</idno><idno type="RBID">PMC:3774685</idno><idno type="doi">10.1371/journal.pone.0073500</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">002A53</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002A53</idno><idno type="wicri:Area/Pmc/Curation">002A52</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">002A52</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia</title><author><name sortKey="Kazembe, Lawrence N" sort="Kazembe, Lawrence N" uniqKey="Kazembe L" first="Lawrence N." last="Kazembe">Lawrence N. Kazembe</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Despite remarkable gains in life expectancy and declining mortality in the 21st century, in many places mostly in developing countries, adult mortality has increased in part due to HIV/AIDS or continued abject poverty levels. Moreover many factors including behavioural, socio-economic and demographic variables work simultaneously to impact on risk of mortality. Understanding risk factors of adult mortality is crucial towards designing appropriate public health interventions. In this paper we proposed a structured additive two-part random effects regression model for adult mortality data. Our proposal assumed two processes: (i) whether death occurred in the household (prevalence part), and (ii) number of reported deaths, if death did occur (severity part). The proposed model specification therefore consisted of two generalized linear mixed models (GLMM) with correlated random effects that permitted structured and unstructured spatial components at regional level. Specifically, the first part assumed a GLMM with a logistic link and the second part explored a count model following either a Poisson or negative binomial distribution. The model was used to analyse adult mortality data of 25,793 individuals from the 2006/2007 Namibian DHS data. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS One</journal-id><journal-id journal-id-type="iso-abbrev">PLoS ONE</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">plosone</journal-id><journal-title-group><journal-title>PLoS ONE</journal-title></journal-title-group><issn pub-type="epub">1932-6203</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24066052</article-id><article-id pub-id-type="pmc">3774685</article-id><article-id pub-id-type="publisher-id">PONE-D-13-14683</article-id><article-id pub-id-type="doi">10.1371/journal.pone.0073500</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia</article-title><alt-title alt-title-type="running-head">Bayesian Two Part Model for Adult Mortality</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Kazembe</surname><given-names>Lawrence N.</given-names></name><xref ref-type="aff" rid="aff1"></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Hsiao</surname><given-names>Chuhsing Kate</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1"><addr-line>National Taiwan University, Taiwan</addr-line></aff><author-notes><corresp id="cor1">* E-mail: <email>lkazembe@unam.na</email></corresp><fn fn-type="conflict"><p><bold>Competing Interests: </bold>The author has declared that no competing interests exist.</p></fn><fn fn-type="con"><p>Conceived and designed the experiments: LNK. Analyzed the data: LNK. Wrote the paper: LNK.</p></fn></author-notes><pub-date pub-type="collection"><year>2013</year></pub-date><pub-date pub-type="epub"><day>16</day><month>9</month><year>2013</year></pub-date><volume>8</volume><issue>9</issue><elocation-id>e73500</elocation-id><history><date date-type="received"><day>10</day><month>4</month><year>2013</year></date><date date-type="accepted"><day>22</day><month>7</month><year>2013</year></date></history><permissions><copyright-year>2013</copyright-year><copyright-holder>Lawrence N</copyright-holder><license><license-p>Kazembe. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p></license></permissions><abstract><p>Despite remarkable gains in life expectancy and declining mortality in the 21st century, in many places mostly in developing countries, adult mortality has increased in part due to HIV/AIDS or continued abject poverty levels. Moreover many factors including behavioural, socio-economic and demographic variables work simultaneously to impact on risk of mortality. Understanding risk factors of adult mortality is crucial towards designing appropriate public health interventions. In this paper we proposed a structured additive two-part random effects regression model for adult mortality data. Our proposal assumed two processes: (i) whether death occurred in the household (prevalence part), and (ii) number of reported deaths, if death did occur (severity part). The proposed model specification therefore consisted of two generalized linear mixed models (GLMM) with correlated random effects that permitted structured and unstructured spatial components at regional level. Specifically, the first part assumed a GLMM with a logistic link and the second part explored a count model following either a Poisson or negative binomial distribution. The model was used to analyse adult mortality data of 25,793 individuals from the 2006/2007 Namibian DHS data. Inference is based on the Bayesian framework with appropriate priors discussed.</p></abstract><funding-group><funding-statement>The author acknowledges financial support from the University of Namibia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement></funding-group><counts><page-count count="10"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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