Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning

Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study aimed to evaluate the prevalence and identify the risk factors of C. sinensis infection in Longsheng County, a mountainous area in northern Guangxi. In 2023, a comprehensive study was conducted in Longsheng County, utilizing longstanding inhabitants as study participants. Questionnaires were employed to gather data on fish consumption, awareness of C. sinensis, and residential coordinates, while fecal examinations were utilized to identify C. sinensis infection. Important risk factors for the C. sinensis infection were identified through the development of individual infection risk models using automated machine learning techniques. A total of 740 fecal samples were collected, revealing an overall C. sinensis infection rate of 69.59%. The gradient boosting machine (GBM) was the most accurate predictor with an area under the precision-recall Curve (AUPRC) of 0.997. The model identified years of raw fresh fish consumption, frequency of raw fresh fish consumption, elevation, and water distance as the top four predictors of C. sinensis infection risk. In conclusion, our study has revealed a high infection rate of C. sinensis in the mountainous areas of Longsheng County, with adults, men, and farmers particularly susceptible to both high incidence and infection severity. We developed a high-performance predictive model for individual C. sinensis infection within the county, identifying the key risk factors for local infections. These findings offer valuable guidance for the control and prevention of clonorchiasis.

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Publication Details

Journal
Parasitology Research
Published
2025-03-01
DOI
https://doi.org/10.1007/s00436-025-08470-8
Citations
3
Primary Topic
Parasite Biology and Host Interactions
Type
article
Field-Weighted Citation Impact
5.15

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article

Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning

Xiaohong Peng, Yu Chen, Xiaowen Li, Xuerong Sun et al.
3 citations
Parasitology Research
Parasite Biology and Host Interactions
5.15
article

Epidemiology and risk factors of Clonorchis sinensis infection in the mountainous areas of Longsheng County, Guangxi: insights from automated machine learning

Xiaohong Peng, Yu Chen, Xiaowen Li, Xuerong Sun, Guoyang Huang, Gang Mo
article en
3 citations

Abstract

Clonorchis sinensis (C. sinensis) is mainly prevalent in Northeast and South China, with Guangxi being the most severely affected region. This study aimed to evaluate the prevalence and identify the risk factors of C. sinensis infection in Longsheng County, a mountainous area in northern Guangxi. In 2023, a comprehensive study was conducted in Longsheng County, utilizing longstanding inhabitants as study participants. Questionnaires were employed to gather data on fish consumption, awareness of C. sinensis, and residential coordinates, while fecal examinations were utilized to identify C. sinensis infection. Important risk factors for the C. sinensis infection were identified through the development of individual infection risk models using automated machine learning techniques. A total of 740 fecal samples were collected, revealing an overall C. sinensis infection rate of 69.59%. The gradient boosting machine (GBM) was the most accurate predictor with an area under the precision-recall Curve (AUPRC) of 0.997. The model identified years of raw fresh fish consumption, frequency of raw fresh fish consumption, elevation, and water distance as the top four predictors of C. sinensis infection risk. In conclusion, our study has revealed a high infection rate of C. sinensis in the mountainous areas of Longsheng County, with adults, men, and farmers particularly susceptible to both high incidence and infection severity. We developed a high-performance predictive model for individual C. sinensis infection within the county, identifying the key risk factors for local infections. These findings offer valuable guidance for the control and prevention of clonorchiasis.

Parasitology ResearchVol. 124(3)
Guangxi University (CN), Guilin Medical University (CN), Chinese Center For Disease Control and Prevention (CN)
Natural Science Foundation of Guangxi Zhuang Autonomous Region, National Natural Science Foundation of China
Openalex Percentile: Top 6%
Parasite Biology and Host Interactions
5.15
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