Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Ting-Ting, Feng, Yi-Jing, Doanh, Pham Ngoc, Sayasone, Somphou, Khieu, Virak, Nithikathkul, Choosak, Qian, Men-Bao, Hao, Yuan-Tao, Lai, Ying-Si
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
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
_version_ 1783648755415777280
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
work_keys_str_mv AT zhaotingting modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT fengyijing modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT doanhphamngoc modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT sayasonesomphou modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT khieuvirak modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT nithikathkulchoosak modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT qianmenbao modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT haoyuantao modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia
AT laiyingsi modelbasedspatialtemporalmappingofopisthorchiasisinendemiccountriesofsoutheastasia