Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling.
Identifieur interne : 000431 ( Ncbi/Curation ); précédent : 000430; suivant : 000432Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling.
Auteurs : Uwem F. Ekpo [Nigeria] ; Eveline Hürlimann ; Nadine Schur ; Akinola S. Oluwole ; Eniola M. Abe ; Margaret A. Mafe ; Obiageli J. Nebe ; Sunday Isiyaku ; Francisca Olamiju ; Mukaila Kadiri ; Temitope O S. Poopola ; Eka I. Braide ; Yisa Saka ; Chiedu F. Mafiana ; Thomas K. Kristensen ; Jürg Utzinger ; Penelope VounatsouSource :
- Geospatial health [ 1970-7096 ] ; 2013.
Descripteurs français
- Wicri :
- geographic : Nigeria.
English descriptors
- KwdEn :
- MESH :
- chemical , therapeutic use : Anthelmintics, Praziquantel.
- geographic , epidemiology : Nigeria.
- drug therapy : Schistosomiasis.
- epidemiology : Schistosomiasis.
- Bayes Theorem, Geographic Information Systems, Humans, Models, Theoretical, Prevalence, Risk, Spatial Analysis, Time Factors, Weather.
Abstract
Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is <10% in most states with a few reaching as high as 50%. It was estimated that 11.3 million children require praziquantel annually (95% CI: 10.3-12.2 million).
DOI: 10.4081/gh.2013.92
PubMed: 23733296
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Ekpo</name><affiliation wicri:level="1"><nlm:affiliation>Spatial Parasitology and Health GIS Group, Department of Biological Sciences, Federal University of Agriculture, Abeokuta, Nigeria. ekpouf@unaab.edu.ng</nlm:affiliation><country xml:lang="fr">Nigeria</country><wicri:regionArea>Spatial Parasitology and Health GIS Group, Department of Biological Sciences, Federal University of Agriculture, Abeokuta</wicri:regionArea><wicri:noRegion>Abeokuta</wicri:noRegion></affiliation></author><author><name sortKey="Hurlimann, Eveline" sort="Hurlimann, Eveline" uniqKey="Hurlimann E" first="Eveline" last="Hürlimann">Eveline Hürlimann</name></author><author><name sortKey="Schur, Nadine" sort="Schur, Nadine" uniqKey="Schur N" first="Nadine" last="Schur">Nadine Schur</name></author><author><name sortKey="Oluwole, Akinola S" sort="Oluwole, Akinola S" uniqKey="Oluwole A" first="Akinola S" last="Oluwole">Akinola S. Oluwole</name></author><author><name sortKey="Abe, Eniola M" sort="Abe, Eniola M" uniqKey="Abe E" first="Eniola M" last="Abe">Eniola M. Abe</name></author><author><name sortKey="Mafe, Margaret A" sort="Mafe, Margaret A" uniqKey="Mafe M" first="Margaret A" last="Mafe">Margaret A. Mafe</name></author><author><name sortKey="Nebe, Obiageli J" sort="Nebe, Obiageli J" uniqKey="Nebe O" first="Obiageli J" last="Nebe">Obiageli J. Nebe</name></author><author><name sortKey="Isiyaku, Sunday" sort="Isiyaku, Sunday" uniqKey="Isiyaku S" first="Sunday" last="Isiyaku">Sunday Isiyaku</name></author><author><name sortKey="Olamiju, Francisca" sort="Olamiju, Francisca" uniqKey="Olamiju F" first="Francisca" last="Olamiju">Francisca Olamiju</name></author><author><name sortKey="Kadiri, Mukaila" sort="Kadiri, Mukaila" uniqKey="Kadiri M" first="Mukaila" last="Kadiri">Mukaila Kadiri</name></author><author><name sortKey="Poopola, Temitope O S" sort="Poopola, Temitope O S" uniqKey="Poopola T" first="Temitope O S" last="Poopola">Temitope O S. Poopola</name></author><author><name sortKey="Braide, Eka I" sort="Braide, Eka I" uniqKey="Braide E" first="Eka I" last="Braide">Eka I. 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Kristensen</name></author><author><name sortKey="Utzinger, Jurg" sort="Utzinger, Jurg" uniqKey="Utzinger J" first="Jürg" last="Utzinger">Jürg Utzinger</name></author><author><name sortKey="Vounatsou, Penelope" sort="Vounatsou, Penelope" uniqKey="Vounatsou P" first="Penelope" last="Vounatsou">Penelope Vounatsou</name></author></analytic><series><title level="j">Geospatial health</title><idno type="eISSN">1970-7096</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Anthelmintics (therapeutic use)</term><term>Bayes Theorem</term><term>Geographic Information Systems</term><term>Humans</term><term>Models, Theoretical</term><term>Nigeria (epidemiology)</term><term>Praziquantel (therapeutic use)</term><term>Prevalence</term><term>Risk</term><term>Schistosomiasis (drug therapy)</term><term>Schistosomiasis (epidemiology)</term><term>Spatial Analysis</term><term>Time Factors</term><term>Weather</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Anthelmintics</term><term>Praziquantel</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Nigeria</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Schistosomiasis</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Schistosomiasis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Bayes Theorem</term><term>Geographic Information Systems</term><term>Humans</term><term>Models, Theoretical</term><term>Prevalence</term><term>Risk</term><term>Spatial Analysis</term><term>Time Factors</term><term>Weather</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Nigeria</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is <10% in most states with a few reaching as high as 50%. It was estimated that 11.3 million children require praziquantel annually (95% CI: 10.3-12.2 million).</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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