Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard.
BACKGROUND: Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty o...
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ftdoajarticles:oai:doaj.org/article:3e1ae3f6bc5b4fd8adc5ec0bee8c5796 2023-05-15T15:16:23+02:00 Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. Xian-Hong Wang Xiao-Nong Zhou Penelope Vounatsou Zhao Chen Jürg Utzinger Kun Yang Peter Steinmann Xiao-Hua Wu 2008-01-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0000250 https://doaj.org/article/3e1ae3f6bc5b4fd8adc5ec0bee8c5796 EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC2405951?pdf=render https://doaj.org/toc/1935-2735 1935-2735 doi:10.1371/journal.pntd.0000250 https://doaj.org/article/3e1ae3f6bc5b4fd8adc5ec0bee8c5796 PLoS Neglected Tropical Diseases, Vol 2, Iss 6, p e250 (2008) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2008 ftdoajarticles https://doi.org/10.1371/journal.pntd.0000250 2022-12-31T03:43:39Z BACKGROUND: Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. METHODOLOGY/PRINCIPAL FINDINGS: We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. CONCLUSION/SIGNIFICANCE: Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLoS Neglected Tropical Diseases 2 6 e250 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Xian-Hong Wang Xiao-Nong Zhou Penelope Vounatsou Zhao Chen Jürg Utzinger Kun Yang Peter Steinmann Xiao-Hua Wu Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
BACKGROUND: Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques. METHODOLOGY/PRINCIPAL FINDINGS: We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River. CONCLUSION/SIGNIFICANCE: Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales. |
format |
Article in Journal/Newspaper |
author |
Xian-Hong Wang Xiao-Nong Zhou Penelope Vounatsou Zhao Chen Jürg Utzinger Kun Yang Peter Steinmann Xiao-Hua Wu |
author_facet |
Xian-Hong Wang Xiao-Nong Zhou Penelope Vounatsou Zhao Chen Jürg Utzinger Kun Yang Peter Steinmann Xiao-Hua Wu |
author_sort |
Xian-Hong Wang |
title |
Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
title_short |
Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
title_full |
Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
title_fullStr |
Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
title_full_unstemmed |
Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
title_sort |
bayesian spatio-temporal modeling of schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2008 |
url |
https://doi.org/10.1371/journal.pntd.0000250 https://doaj.org/article/3e1ae3f6bc5b4fd8adc5ec0bee8c5796 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 2, Iss 6, p e250 (2008) |
op_relation |
http://europepmc.org/articles/PMC2405951?pdf=render https://doaj.org/toc/1935-2735 1935-2735 doi:10.1371/journal.pntd.0000250 https://doaj.org/article/3e1ae3f6bc5b4fd8adc5ec0bee8c5796 |
op_doi |
https://doi.org/10.1371/journal.pntd.0000250 |
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PLoS Neglected Tropical Diseases |
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2 |
container_issue |
6 |
container_start_page |
e250 |
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