Zhu Hui-Hui, Huang Ji-Lei, Zhou Chang-Hai, Zhu Ting-Jun, Zheng Jin-Xin, Zhang Mi-Zhen, Qian Men-Bao, Chen Ying-Dan, Li Shi-Zhu
National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); NHC Key Laboratory of Parasite and Vector Biology; WHO Collaborating Center for Tropical Diseases; National Center for International Research on Tropical Diseases, Shanghai 200025, China.
School of Global Health, Chinese Center for Tropical Diseases Research-Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lancet Reg Health West Pac. 2023 Apr 14;36:100766. doi: 10.1016/j.lanwpc.2023.100766. eCollection 2023 Jul.
Soil-transmitted helminthiasis is epidemic in China and many other countries of the world, and has caused substantial burdens to human health. We conducted successive national monitoring in China from 2016 to 2020 to analyze the prevalence, changing trends, and factors influencing soil-transmitted helminthiasis, which provided a reference for future control strategies.
Soil-transmitted helminth monitoring was carried out in 31 provinces (autonomous regions or municipalities, herein after referred to as "provinces") throughout China. Each province determined the number and location of monitoring sites (counties), and a unified sampling method was employed. At least 1,000 subjects were investigated in each monitoring county. Stool samples were collected and the modified Kato-Katz thick smear method was employed for stool examination. Infection data and the details of factors influencing soil-transmitted helminthiasis from 2016 to 2020 were collected from national monitoring sites. Additional influencing factors such as environment, climate and human activities were obtained from authoritative websites. Prevalence of soil-transmitted helminths was presented by species, province, sex, and age group. ArcGIS software was used to conduct spatial autocorrelation and hotspot analysis on the infection data. A Poisson distribution model and SaTScan software were used to analyze the infection data with retrospective spatiotemporal scan statistics. A database was built by matching village-level infection rate data with influencing factors. Subsequently, machine learning methods, including a Linear Regression (LR), a Random Forest (RF), a Gradient Boosted Machine (GBM), and an Extreme gradient boosting (XGBOOST) model was applied to construct a model to analyze the main influencing factors of soil-transmitted helminthiasis.
The infection rates of soil-transmitted helminths at national monitoring sites from 2016 to 2020 were 2.46% (6,456/262,380), 1.78% (5,293/297,078), 1.29% (4,200/326,207), 1.40% (5,959/424,766), and 0.84% (3,485/415,672), respectively. The infection rate of soil-transmitted helminths in 2020 decreased by 65.85% compared to that in 2016. From 2016 to 2020, the infection rate of soil-transmitted helminthiasis was relatively high in southern and southwestern China, including Hainan, Yunnan, Sichuan, Guizhou, and Chongqing. In general, the infection rate was higher in females than in males, with the highest rate in the population aged 60 years and above, and the lowest in children aged 0-6 years. Global autocorrelation and hotspot analyses revealed spatial aggregation in both the national and local distribution of soil-transmitted helminthiasis in China from 2016 to 2020. The hotspots were concentrated in southwestern China. The spatiotemporal scanning analysis revealed aggregation years from 2016 to 2017 located in southwestern China, including Yunnan, Sichuan, Chongqing, Guizhou and Guangxi. The RF model was the best fit model for the infection rate of soil-transmitted helminths in China. The top six influencing factors of this disease in the model were landform, barefoot farming, isothermality, temperature seasonality, year, and the coverage of sanitary toilets.
The overall infection rate of soil-transmitted helminths in China showed a decreasing trend from 2016-2020 due to the implementation of control measures and the economic boom in China. However, there are still areas with high infection rates and the distribution of such areas exhibit spatiotemporal aggregation. As a strategic next step, control measures should be adjusted to local conditions based on the main influencing factors and the prevalence of different sites to aid in the control and elimination of soil-transmitted helminthiasis.
This research was funded by the National Key Research and Development Program of China (Grant Nos. 2021YFC2300800 and 2021YFC2300804) and the National Natural Science Foundation of China (Grant No. 32161143036).
土源性蠕虫病在中国及世界其他许多国家流行，给人类健康造成了沉重负担。我们于2016年至2020年在中国开展了连续的全国监测，以分析土源性蠕虫病的流行率、变化趋势及影响因素，为未来的防控策略提供参考。
在中国31个省（自治区、直辖市，以下简称“省”）开展土源性蠕虫监测。每个省确定监测点（县）的数量和位置，并采用统一的抽样方法。每个监测县至少调查1000名对象。采集粪便样本，采用改良加藤厚涂片法进行粪便检查。收集2016年至2020年全国监测点土源性蠕虫病的感染数据及影响因素详情。环境、气候和人类活动等其他影响因素从权威网站获取。按虫种、省份、性别和年龄组呈现土源性蠕虫的流行率。使用ArcGIS软件对感染数据进行空间自相关和热点分析。采用泊松分布模型和SaTScan软件，通过回顾性时空扫描统计分析感染数据。通过将村级感染率数据与影响因素匹配建立数据库。随后，应用包括线性回归（LR）、随机森林（RF）、梯度提升机（GBM）和极端梯度提升（XGBOOST）模型在内的机器学习方法构建模型，分析土源性蠕虫病的主要影响因素。
2016年至2020年全国监测点土源性蠕虫的感染率分别为2.46%（6456/262380）、1.78%（5293/297078）、1.29%（4200/326207）、1.40%（5959/424766）和0.84%（3485/415672）。2020年土源性蠕虫的感染率与2016年相比下降了65.85%。2016年至2020年，中国南部和西南部地区，包括海南、云南、四川、贵州和重庆，土源性蠕虫病的感染率相对较高。总体而言，女性感染率高于男性，60岁及以上人群感染率最高，0 - 6岁儿童感染率最低。全局自相关和热点分析显示，2016年至2020年中国土源性蠕虫病在全国和局部地区的分布均存在空间聚集性热点集中在中国西南部。时空扫描分析显示，2016年至2017年聚集年份位于中国西南部，包括云南、四川、重庆、贵州和广西。RF模型是中国土源性蠕虫感染率的最佳拟合模型。该模型中该病的前六大影响因素为地形、赤脚务农、等温性、温度季节性、年份和卫生厕所覆盖率。
由于防控措施的实施和中国经济的繁荣，2016 - 2020年中国土源性蠕虫的总体感染率呈下降趋势。然而，仍有感染率较高的地区，且这些地区的分布呈现时空聚集性。作为下一步战略，应根据主要影响因素和不同地点的流行情况因地制宜调整防控措施，以助力控制和消除土源性蠕虫病。
本研究由中国国家重点研发计划（项目编号：2021YFC2300800和2021YFC2300804）和中国国家自然科学基金（项目编号：32161143036）资助。
