Evidence map›Paper›PMID 42152040›Full record

ArticleInfectious diseases of poverty2026

A county-level indicator framework for assessing schistosomiasis transmission risk in post-transmission-interruption China.

Andong Xu, Hong Zhu, Jing Xu, Xiaowei Shan, Yi Dong, Hongqiong Wang, Chao Lv, Shiyi Huo, Shizhu Li

Abstract read
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Article in Infectious diseases of poverty, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Andong XuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Hong ZhuHubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan, 430079, China.
Jing XuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Xiaowei ShanHubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan, 430079, China.
Yi DongDepartment of Schistosomiasis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention, Kunming, 650500, China.
Hongqiong WangDepartment of Schistosomiasis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention, Kunming, 650500, China.
Chao LvNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Shiyi HuoSchool of Global Health, Shanghai Jiao Tong University School of Medicine and Chinese Center for Tropical Diseases Research, Shanghai, 200025, China.
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China. lisz@chinacdc.cn.

Funding

National Key Research and Development Program of China No. 2024YFC2310902National Natural Science Foundation of China No. 32161143036, No.82473688Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai (2023-2025) Key Discipline Project No. GWVI-11.1-12
6 · The paper itself

Abstract

backgroundIn China's post-transmission-interruption stage of schistosomiasis control, infection signals are often sparse, and conventional surveillance indicators focused on infection detection may not adequately capture residual risk across heterogeneous ecological settings. This study aimed to develop an operational indicator framework for assessing schistosomiasis transmission risk and supporting routine risk assessment and management in this stage.

methodsA county-level indicator framework for schistosomiasis transmission risk assessment was developed through a two-round Delphi consultation and weighted using a combined Delphi-entropy weight method. Using longitudinal data from six pilot counties during 2020-2024, an annual composite risk index (R) was calculated by weighted linear aggregation and classified into risk levels with trapezoidal fuzzy membership functions. Robustness was evaluated by perturbing the subjective preference coefficient (α) and examining the consistency of county-year rankings across scenarios using Spearman's rank correlation.

resultsThe final framework comprised three first-level, twelve second-level, and thirty-nine third-level indicators spanning biological, environmental, and social domains. Across the six pilot counties, R ranged from 0.18 to 0.44 and the overall risk level was predominantly low. Nonetheless, distinct county-level risk profiles were observed between lake/marshland and mountainous settings. Risk rankings remained highly consistent under α perturbations (ρ = 0.909-0.998), indicating good robustness of the assessment results.

conclusionsThis framework translates multidimensional determinants relevant to the re-establishment of schistosomiasis transmission into interpretable county-level risk profiles. It provides an operational tool to support targeted surveillance and more efficient allocation of control resources in low-endemicity contexts.

Indexed as

SchistosomiasisAnimalsChinaHumansRisk AssessmentDelphiEntropy weightFuzzy membershipIndicator frameworkSchistosomiasisTransmission interruptionTransmission risk

Identifiers

PMID42152040
PMCPMC13182038

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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.