Bayesian Spatial Semi-Parametric Modeling of HIV Variation in Kenya
Identifieur interne : 001F61 ( Main/Exploration ); précédent : 001F60; suivant : 001F62Bayesian Spatial Semi-Parametric Modeling of HIV Variation in Kenya
Auteurs : Oscar Ngesa [Afrique du Sud] ; Henry Mwambi [Afrique du Sud] ; Thomas Achia [Afrique du Sud]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Abstract
Spatial statistics has seen rapid application in many fields, especially epidemiology and public health. Many studies, nonetheless, make limited use of the geographical location information and also usually assume that the covariates, which are related to the response variable, have linear effects. We develop a Bayesian semi-parametric regression model for HIV prevalence data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (McMC). The model is applied to HIV prevalence data among men in Kenya, derived from the Kenya AIDS indicator survey, with n = 3,662. Past studies have concluded that HIV infection has a nonlinear association with age. In this study a smooth function based on penalized regression splines is used to estimate this nonlinear effect. Other covariates were assumed to have a linear effect. Spatial references to the counties were modeled as both structured and unstructured spatial effects. We observe that circumcision reduces the risk of HIV infection. The results also indicate that men in the urban areas were more likely to be infected by HIV as compared to their rural counterpart. Men with higher education had the lowest risk of HIV infection. A nonlinear relationship between HIV infection and age was established. Risk of HIV infection increases with age up to the age of 40 then declines with increase in age. Men who had STI in the last 12 months were more likely to be infected with HIV. Also men who had ever used a condom were found to have higher likelihood to be infected by HIV. A significant spatial variation of HIV infection in Kenya was also established. The study shows the practicality and flexibility of Bayesian semi-parametric regression model in analyzing epidemiological data.
Url:
DOI: 10.1371/journal.pone.0103299
PubMed: 25061669
PubMed Central: 4111556
Affiliations:
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id="aff1"><addr-line>School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal, South Africa</addr-line></nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, KwaZulu-Natal</wicri:regionArea><wicri:noRegion>KwaZulu-Natal</wicri:noRegion></affiliation></author><author><name sortKey="Achia, Thomas" sort="Achia, Thomas" uniqKey="Achia T" first="Thomas" last="Achia">Thomas Achia</name><affiliation wicri:level="1"><nlm:aff id="aff2"><addr-line>Division of Epidemiology and Biostatistics, University of Witwatersrand, Johannesburg, Gauteng, South Africa</addr-line></nlm:aff><country xml:lang="fr">Afrique du Sud</country><wicri:regionArea>Division of Epidemiology and Biostatistics, University of Witwatersrand, Johannesburg, Gauteng</wicri:regionArea><wicri:noRegion>Gauteng</wicri:noRegion></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Spatial statistics has seen rapid application in many fields, especially epidemiology and public health. Many studies, nonetheless, make limited use of the geographical location information and also usually assume that the covariates, which are related to the response variable, have linear effects. We develop a Bayesian semi-parametric regression model for HIV prevalence data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (McMC). The model is applied to HIV prevalence data among men in Kenya, derived from the Kenya AIDS indicator survey, with n = 3,662. Past studies have concluded that HIV infection has a nonlinear association with age. In this study a smooth function based on penalized regression splines is used to estimate this nonlinear effect. Other covariates were assumed to have a linear effect. Spatial references to the counties were modeled as both structured and unstructured spatial effects. We observe that circumcision reduces the risk of HIV infection. The results also indicate that men in the urban areas were more likely to be infected by HIV as compared to their rural counterpart. Men with higher education had the lowest risk of HIV infection. A nonlinear relationship between HIV infection and age was established. Risk of HIV infection increases with age up to the age of 40 then declines with increase in age. Men who had STI in the last 12 months were more likely to be infected with HIV. Also men who had ever used a condom were found to have higher likelihood to be infected by HIV. A significant spatial variation of HIV infection in Kenya was also established. The study shows the practicality and flexibility of Bayesian semi-parametric regression model in analyzing epidemiological data.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Auvert, B" uniqKey="Auvert B">B Auvert</name></author><author><name sortKey="Taljaard, D" uniqKey="Taljaard D">D Taljaard</name></author><author><name sortKey="Lagarde, E" uniqKey="Lagarde E">E Lagarde</name></author><author><name sortKey="Sobngwi Tambekou, J" uniqKey="Sobngwi Tambekou J">J Sobngwi-Tambekou</name></author><author><name sortKey="Sitta, R" uniqKey="Sitta R">R Sitta</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bailey, Rc" uniqKey="Bailey R">RC Bailey</name></author><author><name sortKey="Moses, S" uniqKey="Moses S">S Moses</name></author><author><name sortKey="Parker, Cb" uniqKey="Parker C">CB Parker</name></author><author><name sortKey="Agot, K" uniqKey="Agot K">K 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Kass</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Afrique du Sud</li></country></list><tree><country name="Afrique du Sud"><noRegion><name sortKey="Ngesa, Oscar" sort="Ngesa, Oscar" uniqKey="Ngesa O" first="Oscar" last="Ngesa">Oscar Ngesa</name></noRegion><name sortKey="Achia, Thomas" sort="Achia, Thomas" uniqKey="Achia T" first="Thomas" last="Achia">Thomas Achia</name><name sortKey="Mwambi, Henry" sort="Mwambi, Henry" uniqKey="Mwambi H" first="Henry" last="Mwambi">Henry Mwambi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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