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The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction
BACKGROUND: Human echinococcosis is affected by natural environmental factors, and its prevalence shows a distinct geographical distribution. Western China has the highest endemicity of human echinococcosis worldwide, but the spatial pattern and environmental determinants of echinococcosis are still...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822772/ https://www.ncbi.nlm.nih.gov/pubmed/35130957 http://dx.doi.org/10.1186/s13071-022-05169-y |
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author | Yin, Jie Wu, Xiaoxu Li, Chenlu Han, Jiatong Xiang, Hongxu |
author_facet | Yin, Jie Wu, Xiaoxu Li, Chenlu Han, Jiatong Xiang, Hongxu |
author_sort | Yin, Jie |
collection | PubMed |
description | BACKGROUND: Human echinococcosis is affected by natural environmental factors, and its prevalence shows a distinct geographical distribution. Western China has the highest endemicity of human echinococcosis worldwide, but the spatial pattern and environmental determinants of echinococcosis are still unclear. METHODS: Hot/cold spot analysis was used to investigate the spatial distribution of human echinococcosis prevalence. Geodetector was used to identify key natural factors, and a structured additive regression model was used to analyse the relationship between natural factors and human echinococcosis prevalence and spatially predict echinococcosis epidemics. RESULTS: Hot spots for human echinococcosis prevalence include western and southeastern parts of Tibet Autonomous Region (henceforth Tibet) and the border areas between Tibet and the provinces of Qinghai and Sichuan. Spatial effects are crucial when modelling epidemics, and relative humidity, altitude and grassland area ratio were found to have the most evident effects on echinococcosis epidemics. The relationship between these three factors and echinococcosis prevalence was non-linear, and echinococcosis risk was higher in areas with high relative humidity, high altitude, and a high ratio of grassland to other land use types. The prevalence that was predicted from the investigated environmental factors was generally higher than the actual prevalence, and more epidemic hot spots were predicted for the Qinghai-Tibet Plateau, Inner Mongolia Autonomous Region, and the provinces of Yunnan and Sichuan than the rest of western China. These results indicate that prevention and control measures may effectively reduce echinococcosis prevalence. CONCLUSIONS: We suggest that the prevention and control of human echinococcosis should be prioritized in the hot spots identified here, through the rational allocation of limited medical resources to where they are most needed. Furthermore, the spatial epidemiological modelling methods used in this study can be employed in future studies on echinococcosis and similar diseases. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13071-022-05169-y. |
format | Online Article Text |
id | pubmed-8822772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88227722022-02-08 The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction Yin, Jie Wu, Xiaoxu Li, Chenlu Han, Jiatong Xiang, Hongxu Parasit Vectors Research BACKGROUND: Human echinococcosis is affected by natural environmental factors, and its prevalence shows a distinct geographical distribution. Western China has the highest endemicity of human echinococcosis worldwide, but the spatial pattern and environmental determinants of echinococcosis are still unclear. METHODS: Hot/cold spot analysis was used to investigate the spatial distribution of human echinococcosis prevalence. Geodetector was used to identify key natural factors, and a structured additive regression model was used to analyse the relationship between natural factors and human echinococcosis prevalence and spatially predict echinococcosis epidemics. RESULTS: Hot spots for human echinococcosis prevalence include western and southeastern parts of Tibet Autonomous Region (henceforth Tibet) and the border areas between Tibet and the provinces of Qinghai and Sichuan. Spatial effects are crucial when modelling epidemics, and relative humidity, altitude and grassland area ratio were found to have the most evident effects on echinococcosis epidemics. The relationship between these three factors and echinococcosis prevalence was non-linear, and echinococcosis risk was higher in areas with high relative humidity, high altitude, and a high ratio of grassland to other land use types. The prevalence that was predicted from the investigated environmental factors was generally higher than the actual prevalence, and more epidemic hot spots were predicted for the Qinghai-Tibet Plateau, Inner Mongolia Autonomous Region, and the provinces of Yunnan and Sichuan than the rest of western China. These results indicate that prevention and control measures may effectively reduce echinococcosis prevalence. CONCLUSIONS: We suggest that the prevention and control of human echinococcosis should be prioritized in the hot spots identified here, through the rational allocation of limited medical resources to where they are most needed. Furthermore, the spatial epidemiological modelling methods used in this study can be employed in future studies on echinococcosis and similar diseases. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13071-022-05169-y. BioMed Central 2022-02-08 /pmc/articles/PMC8822772/ /pubmed/35130957 http://dx.doi.org/10.1186/s13071-022-05169-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Yin, Jie Wu, Xiaoxu Li, Chenlu Han, Jiatong Xiang, Hongxu The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
title | The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
title_full | The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
title_fullStr | The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
title_full_unstemmed | The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
title_short | The impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
title_sort | impact of environmental factors on human echinococcosis epidemics: spatial modelling and risk prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822772/ https://www.ncbi.nlm.nih.gov/pubmed/35130957 http://dx.doi.org/10.1186/s13071-022-05169-y |
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