Cargando…
Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model
BACKGROUND: Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for res...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343143/ https://www.ncbi.nlm.nih.gov/pubmed/37440524 http://dx.doi.org/10.1371/journal.pntd.0011466 |
_version_ | 1785072668037349376 |
---|---|
author | Tong, Yixin Tang, Ling Xia, Meng Li, Guangping Hu, Benjiao Huang, Junhui Wang, Jiamin Jiang, Honglin Yin, Jiangfan Xu, Ning Chen, Yue Jiang, Qingwu Zhou, Jie Zhou, Yibiao |
author_facet | Tong, Yixin Tang, Ling Xia, Meng Li, Guangping Hu, Benjiao Huang, Junhui Wang, Jiamin Jiang, Honglin Yin, Jiangfan Xu, Ning Chen, Yue Jiang, Qingwu Zhou, Jie Zhou, Yibiao |
author_sort | Tong, Yixin |
collection | PubMed |
description | BACKGROUND: Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for resource deployment in practice. Our study aimed to identify the determinants for the seropositive rate of schistosomiasis at the village level and to explore their spatial variations in local space. METHODOLOGY: The seropositive rates of schistosomiasis were collected from 1714 villages or communities in Human Province, and six spatial regression models including ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), robust GWR (RGWR) and multiscale GWR (MGWR) were used to fit the data. PRINCIPAL/FINDINGS: MGWR was the best-fitting model (R(2): 0.821, AICc:2727.092). Overall, the nearest distance from the river had the highest mean negative correlation, followed by proportion of households using well water and the annual average daytime surface temperature. The proportions of unmodified toilets showed the highest mean positive correlation, followed by the snail infested area, and the number of cattle. In spatial variability, the regression coefficients for the nearest distance from the river, annual average daytime surface temperature and the proportion of unmodified toilets were significant in all villages or communities and varied little in local space. The other significant determinants differed substantially in local space and had significance ratios ranging from 41% to 70%, including the number of cattle, the snail infested area and the proportion of households using well water. CONCLUSIONS/SIGNIFICANCE: Our study shows that MGWR was well performed for the spatial variability of schistosomiasis in Hunan province. The spatial variability was different for different determinants. The findings for the determinants for the seropositive rate and mapped variability for some key determinants at the village level can be used for developing precision intervention measure for schistosomiasis control. |
format | Online Article Text |
id | pubmed-10343143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103431432023-07-14 Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model Tong, Yixin Tang, Ling Xia, Meng Li, Guangping Hu, Benjiao Huang, Junhui Wang, Jiamin Jiang, Honglin Yin, Jiangfan Xu, Ning Chen, Yue Jiang, Qingwu Zhou, Jie Zhou, Yibiao PLoS Negl Trop Dis Research Article BACKGROUND: Schistosomiasis is of great public health concern with a wide distribution and multiple determinants. Due to the advances in schistosomiasis elimination and the need for precision prevention and control, identifying determinants at a fine scale is urgent and necessary, especially for resource deployment in practice. Our study aimed to identify the determinants for the seropositive rate of schistosomiasis at the village level and to explore their spatial variations in local space. METHODOLOGY: The seropositive rates of schistosomiasis were collected from 1714 villages or communities in Human Province, and six spatial regression models including ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), robust GWR (RGWR) and multiscale GWR (MGWR) were used to fit the data. PRINCIPAL/FINDINGS: MGWR was the best-fitting model (R(2): 0.821, AICc:2727.092). Overall, the nearest distance from the river had the highest mean negative correlation, followed by proportion of households using well water and the annual average daytime surface temperature. The proportions of unmodified toilets showed the highest mean positive correlation, followed by the snail infested area, and the number of cattle. In spatial variability, the regression coefficients for the nearest distance from the river, annual average daytime surface temperature and the proportion of unmodified toilets were significant in all villages or communities and varied little in local space. The other significant determinants differed substantially in local space and had significance ratios ranging from 41% to 70%, including the number of cattle, the snail infested area and the proportion of households using well water. CONCLUSIONS/SIGNIFICANCE: Our study shows that MGWR was well performed for the spatial variability of schistosomiasis in Hunan province. The spatial variability was different for different determinants. The findings for the determinants for the seropositive rate and mapped variability for some key determinants at the village level can be used for developing precision intervention measure for schistosomiasis control. Public Library of Science 2023-07-13 /pmc/articles/PMC10343143/ /pubmed/37440524 http://dx.doi.org/10.1371/journal.pntd.0011466 Text en © 2023 Tong et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tong, Yixin Tang, Ling Xia, Meng Li, Guangping Hu, Benjiao Huang, Junhui Wang, Jiamin Jiang, Honglin Yin, Jiangfan Xu, Ning Chen, Yue Jiang, Qingwu Zhou, Jie Zhou, Yibiao Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model |
title | Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model |
title_full | Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model |
title_fullStr | Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model |
title_full_unstemmed | Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model |
title_short | Identifying determinants for the seropositive rate of schistosomiasis in Hunan province, China: A multi-scale geographically weighted regression model |
title_sort | identifying determinants for the seropositive rate of schistosomiasis in hunan province, china: a multi-scale geographically weighted regression model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10343143/ https://www.ncbi.nlm.nih.gov/pubmed/37440524 http://dx.doi.org/10.1371/journal.pntd.0011466 |
work_keys_str_mv | AT tongyixin identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT tangling identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT xiameng identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT liguangping identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT hubenjiao identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT huangjunhui identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT wangjiamin identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT jianghonglin identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT yinjiangfan identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT xuning identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT chenyue identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT jiangqingwu identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT zhoujie identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel AT zhouyibiao identifyingdeterminantsfortheseropositiverateofschistosomiasisinhunanprovincechinaamultiscalegeographicallyweightedregressionmodel |