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Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia
Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databas...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870142/ https://www.ncbi.nlm.nih.gov/pubmed/33432926 http://dx.doi.org/10.7554/eLife.59755 |
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author | Zhao, Ting-Ting Feng, Yi-Jing Doanh, Pham Ngoc Sayasone, Somphou Khieu, Virak Nithikathkul, Choosak Qian, Men-Bao Hao, Yuan-Tao Lai, Ying-Si |
author_facet | Zhao, Ting-Ting Feng, Yi-Jing Doanh, Pham Ngoc Sayasone, Somphou Khieu, Virak Nithikathkul, Choosak Qian, Men-Bao Hao, Yuan-Tao Lai, Ying-Si |
author_sort | Zhao, Ting-Ting |
collection | PubMed |
description | Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10–15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions. |
format | Online Article Text |
id | pubmed-7870142 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-78701422021-02-10 Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia Zhao, Ting-Ting Feng, Yi-Jing Doanh, Pham Ngoc Sayasone, Somphou Khieu, Virak Nithikathkul, Choosak Qian, Men-Bao Hao, Yuan-Tao Lai, Ying-Si eLife Epidemiology and Global Health Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10–15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions. eLife Sciences Publications, Ltd 2021-01-12 /pmc/articles/PMC7870142/ /pubmed/33432926 http://dx.doi.org/10.7554/eLife.59755 Text en © 2021, Zhao et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Epidemiology and Global Health Zhao, Ting-Ting Feng, Yi-Jing Doanh, Pham Ngoc Sayasone, Somphou Khieu, Virak Nithikathkul, Choosak Qian, Men-Bao Hao, Yuan-Tao Lai, Ying-Si Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia |
title | Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia |
title_full | Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia |
title_fullStr | Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia |
title_full_unstemmed | Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia |
title_short | Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia |
title_sort | model-based spatial-temporal mapping of opisthorchiasis in endemic countries of southeast asia |
topic | Epidemiology and Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870142/ https://www.ncbi.nlm.nih.gov/pubmed/33432926 http://dx.doi.org/10.7554/eLife.59755 |
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