Evidence map›Paper›PMID 40108721›Full record

ArticleInfectious diseases of poverty2025

Optimal control strategies supported by system dynamics modelling: a study on hookworm disease in China.

Huihui Zhu, Jinxin Zheng, Jilei Huang, Mizhen Zhang, Changhai Zhou, Tingjun Zhu, Hongchun Tian, Xiaohong Wu, Yang Liu, Bo Zhong and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious diseases of poverty, 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

21 authors.

Huihui ZhuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Jinxin ZhengSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Jilei HuangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Mizhen ZhangNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Changhai ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Tingjun ZhuNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Hongchun TianSichuan Center for Disease Control and Prevention, Chendu, China.
Xiaohong WuSichuan Center for Disease Control and Prevention, Chendu, China.
Yang LiuSichuan Center for Disease Control and Prevention, Chendu, China.
Bo ZhongSichuan Center for Disease Control and Prevention, Chendu, China.
Hong XieSichuan Center for Disease Control and Prevention, Chendu, China.
Liping ZhangSichuan Center for Disease Control and Prevention, Chendu, China.
Lei TieSichuan Center for Disease Control and Prevention, Chendu, China.
Jingwen LuoSichuan Center for Disease Control and Prevention, Chendu, China.
Xiaoqin MaoHejiang Center for Disease Control and Prevention, Hejiang, China.
Bin ZhangLuxian Center for Disease Control and Prevention, Luxian, China.
Xiu DengSichuan Center for Disease Control and Prevention, Chendu, China.
Suping ZhangSichuan Center for Disease Control and Prevention, Chendu, China.
Menbao QianNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China.
Xiaonong ZhouNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025, China. zhouxn@nipd.chinacdc.cn.

Funding

National Natural Science Foundation of China 81973108
6 · The paper itself

Abstract

backgroundHookworm disease remains a global health issue. In China, it persists with a 0.67% infection rate and uneven distribution in 2021. Optimized control strategies are needed. This study aims to optimize intervention strategies for hookworm disease in China.

methodsStructural analysis and parameter estimation were conducted using system dynamics theory. Key variables were identified via the Delphi method, leading to the creation of a causal loop diagram (CLD) and stock flow chart (SFC). Based on the SFC, parameter estimation and quantitative relationships were established and the model was validated. A cost-effectiveness model was then integrated into the intervention mechanism model. Various intervention measures were tested in the model to determine their cost-effectiveness ratio (CER) and effectiveness. Generalized linear models were constructed from simulation data, accounting for the impact of survey sites. The results were used to develop an optimized strategy for hookworm disease control.

resultsIn comparing drug treatment methods, whole population deworming (WPD) and key population deworming (KPD) showed lower CERs than examination and voluntarily deworming (EVD), saving 384.79-504.64 CNY and 354.35-506.21 CNY per infection reduced, respectively (P < 0.001). For WPD or KPD alone, CER decreased with increased drug coverage. For examination and deworming (ED) and EVD, CER was highest at 30% coverage for a 1-year intervention, but at 90% coverage for 2-5 years (P < 0.05). WPD, ED, and EVD had higher infection reduction rates than KPD, with ratios of 0.14-0.25, 0.10-0.19, and 0.08-0.17, respectively, over 1-5 years (P < 0.001). Continuous health education over 1-5 years showed that increasing coverage from a 10% baseline led to enhancing cost-effectiveness and intervention outcomes.

conclusionsIn high-endemic areas (infection rate ≥ 20%) in China, prioritize WPD for better cost-effectiveness and outcomes. In medium-endemic areas (5% ≤ infection rate < 20%) where WPD isn't feasible, use ED for cost-effectiveness and KPD for infection reduction, based on local needs. In low-endemic areas (infection rate < 5%), encourage voluntary examination and treatment due to limited cost-effectiveness of mass treatment. Combining drug treatment with extensive health education can enhance long-term control effect. This strategy can guide control efforts for hookworm diseases in China. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Hookworm InfectionsAnthelminticsChinaCost-Benefit AnalysisHumansAnthelminticsChinaHookworm diseaseIntervention strategySystem dynamics

Identifiers

PMID40108721
PMCPMC11921666

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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.