{"title":"中国钩虫病空间分布格局及危险因素:基于连续国家监测的研究","authors":"Huihui Zhu, Jilei Huang, Jinxin Zheng, Changhai Zhou, Tingjun Zhu, Mizhen Zhang, Luyuan Zhao, Xiaohong Wu, Jingbo Xue, Xiao-Nong Zhou, Shizhu Li, Menbao Qian","doi":"10.1371/journal.pntd.0013526","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hookworm infection, a neglected tropical disease (NTD) causing iron-deficiency anaemia and malnutrition in low-income populations with poor sanitation, poses a considerable public health challenge in China and worldwide.</p><p><strong>Methods: </strong>National surveillance across 31 provincial-level administrative divisions (PLADs) from 2016 to 2021 assessed regional and population-specific hookworm prevalence. Geospatial methods, such as global and local autocorrelation, hotspot detection, spatiotemporal clustering detection and standard deviation ellipse (SDE) analysis characterized distribution patterns. Machine learning identified key determinants and their associations with infection rates, revealing primary influence factors based on 7,929 township records and 40 environmental, climatic and anthropogenic variables.</p><p><strong>Results: </strong>Significant geographic disparities emerged, with the highest infection rates in south-western regions and the lowest in the Northeast. Spatial analyses demonstrated significant clustering, with persistent south-western hotspots and north-eastern coldspots (P < 0.001). Spatiotemporal scanning identified three significant clusters, while SDE analysis indicated stable northeast-southwest orientation with minimal centroid variation. Females and individuals ≥60 years showed elevated susceptibility. Machine learning demonstrated strong predictive capacity, with key risk factors identified as the frequency of barefoot farming, land cover, average relative humidity in the third quarter and average monthly sunshine duration in the third quarter.</p><p><strong>Conclusions: </strong>Hookworm disease clusters in south-western China, disproportionately affecting women and the elderly. Barefoot farming emerged as the primary risk factor, with infection rates positively associated with temperature, humidity and negatively with sunlight duration. The results support recommendations to target intervention zones in endemic areas, implement population-specific prevention programs and intensify health education to advance transmission control.</p>","PeriodicalId":49000,"journal":{"name":"PLoS Neglected Tropical Diseases","volume":"19 9","pages":"e0013526"},"PeriodicalIF":3.4000,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483247/pdf/","citationCount":"0","resultStr":"{\"title\":\"Spatial distribution patterns and risk factors of hookworm disease in China: A study based on successive national surveillance.\",\"authors\":\"Huihui Zhu, Jilei Huang, Jinxin Zheng, Changhai Zhou, Tingjun Zhu, Mizhen Zhang, Luyuan Zhao, Xiaohong Wu, Jingbo Xue, Xiao-Nong Zhou, Shizhu Li, Menbao Qian\",\"doi\":\"10.1371/journal.pntd.0013526\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hookworm infection, a neglected tropical disease (NTD) causing iron-deficiency anaemia and malnutrition in low-income populations with poor sanitation, poses a considerable public health challenge in China and worldwide.</p><p><strong>Methods: </strong>National surveillance across 31 provincial-level administrative divisions (PLADs) from 2016 to 2021 assessed regional and population-specific hookworm prevalence. Geospatial methods, such as global and local autocorrelation, hotspot detection, spatiotemporal clustering detection and standard deviation ellipse (SDE) analysis characterized distribution patterns. Machine learning identified key determinants and their associations with infection rates, revealing primary influence factors based on 7,929 township records and 40 environmental, climatic and anthropogenic variables.</p><p><strong>Results: </strong>Significant geographic disparities emerged, with the highest infection rates in south-western regions and the lowest in the Northeast. Spatial analyses demonstrated significant clustering, with persistent south-western hotspots and north-eastern coldspots (P < 0.001). Spatiotemporal scanning identified three significant clusters, while SDE analysis indicated stable northeast-southwest orientation with minimal centroid variation. Females and individuals ≥60 years showed elevated susceptibility. Machine learning demonstrated strong predictive capacity, with key risk factors identified as the frequency of barefoot farming, land cover, average relative humidity in the third quarter and average monthly sunshine duration in the third quarter.</p><p><strong>Conclusions: </strong>Hookworm disease clusters in south-western China, disproportionately affecting women and the elderly. Barefoot farming emerged as the primary risk factor, with infection rates positively associated with temperature, humidity and negatively with sunlight duration. The results support recommendations to target intervention zones in endemic areas, implement population-specific prevention programs and intensify health education to advance transmission control.</p>\",\"PeriodicalId\":49000,\"journal\":{\"name\":\"PLoS Neglected Tropical Diseases\",\"volume\":\"19 9\",\"pages\":\"e0013526\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483247/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PLoS Neglected Tropical Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1371/journal.pntd.0013526\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"PARASITOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS Neglected Tropical Diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1371/journal.pntd.0013526","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"PARASITOLOGY","Score":null,"Total":0}
Spatial distribution patterns and risk factors of hookworm disease in China: A study based on successive national surveillance.
Background: Hookworm infection, a neglected tropical disease (NTD) causing iron-deficiency anaemia and malnutrition in low-income populations with poor sanitation, poses a considerable public health challenge in China and worldwide.
Methods: National surveillance across 31 provincial-level administrative divisions (PLADs) from 2016 to 2021 assessed regional and population-specific hookworm prevalence. Geospatial methods, such as global and local autocorrelation, hotspot detection, spatiotemporal clustering detection and standard deviation ellipse (SDE) analysis characterized distribution patterns. Machine learning identified key determinants and their associations with infection rates, revealing primary influence factors based on 7,929 township records and 40 environmental, climatic and anthropogenic variables.
Results: Significant geographic disparities emerged, with the highest infection rates in south-western regions and the lowest in the Northeast. Spatial analyses demonstrated significant clustering, with persistent south-western hotspots and north-eastern coldspots (P < 0.001). Spatiotemporal scanning identified three significant clusters, while SDE analysis indicated stable northeast-southwest orientation with minimal centroid variation. Females and individuals ≥60 years showed elevated susceptibility. Machine learning demonstrated strong predictive capacity, with key risk factors identified as the frequency of barefoot farming, land cover, average relative humidity in the third quarter and average monthly sunshine duration in the third quarter.
Conclusions: Hookworm disease clusters in south-western China, disproportionately affecting women and the elderly. Barefoot farming emerged as the primary risk factor, with infection rates positively associated with temperature, humidity and negatively with sunlight duration. The results support recommendations to target intervention zones in endemic areas, implement population-specific prevention programs and intensify health education to advance transmission control.
期刊介绍:
PLOS Neglected Tropical Diseases publishes research devoted to the pathology, epidemiology, prevention, treatment and control of the neglected tropical diseases (NTDs), as well as relevant public policy.
The NTDs are defined as a group of poverty-promoting chronic infectious diseases, which primarily occur in rural areas and poor urban areas of low-income and middle-income countries. Their impact on child health and development, pregnancy, and worker productivity, as well as their stigmatizing features limit economic stability.
All aspects of these diseases are considered, including:
Pathogenesis
Clinical features
Pharmacology and treatment
Diagnosis
Epidemiology
Vector biology
Vaccinology and prevention
Demographic, ecological and social determinants
Public health and policy aspects (including cost-effectiveness analyses).