Evidence map›Paper›PMID 41026761›Full record

ArticlePLoS neglected tropical diseases2025

Spatial distribution patterns and risk factors of hookworm disease in China: A study based on successive national surveillance.

Huihui Zhu, Jilei Huang, Jinxin Zheng, Changhai Zhou, Tingjun Zhu, Mizhen Zhang, Luyuan Zhao, Xiaohong Wu, Jingbo Xue, Xiao-Nong Zhou and 2 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Huihui ZhuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Jilei HuangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Jinxin ZhengSchool of Global Health, Chinese Center for Tropical Diseases Research-Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Changhai ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Tingjun ZhuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Mizhen ZhangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Luyuan ZhaoNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Xiaohong WuSichuan Center for Disease Control and Prevention, Chengdu, Sichuan, China.
Jingbo XueNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Xiao-Nong ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.ORCID 0000-0003-1417-8427
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Menbao QianNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.

Funding

national natural science foundation
6 · The paper itself

Abstract

backgroundHookworm 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.

methodsNational 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.

resultsSignificant 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.

conclusionsHookworm 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.

Indexed as

Hookworm InfectionsAdolescentAdultAgedAncylostomatoideaAnimalsChildChild, PreschoolChinaEpidemiological MonitoringFemaleHumansInfantMaleMiddle AgedPrevalence

Identifiers

PMID41026761
PMCPMC12483247

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.