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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...

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Autores principales: 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
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
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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.
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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
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