Risk mapping of clonorchiasis in the People’s Republic of China: A systematic review and Bayesian geostatistical analysis

Abstract

BackgroundClonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People’s Republic of China (P.R. China). Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions.MethodologyGeoreferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km).Principal findingsWe obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8–15.8 million) people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (≥ 20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang.Conclusions/SignificanceOur results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to pay more attention to the public health importance of clonorchiasis and to target interventions to high-risk areas.

Modeling the Transmission Dynamics of Clonorchiasis in Foshan, China

“…Zhou et al . 11 reported that the trend of infection risk is increasing from 2005 onwards and resulted in a threat to the public health in epidemic regions 12 , 13 . Specially, they estimated that around 14.8 million people in China were infected with C .…”

“…Specially, they estimated that around 14.8 million people in China were infected with C . sinensis in 2010 11 and there are two major endemic regions in China: provinces in the northeast such as Heilongjiang and Jilin; and provinces in the southeast including Guangdong and Guangxi 8 , 14 .…”

“…Lai et al . 11 carried out Bayesian variable selections to identify the most important predictors of C . sinensis risk and their results provide spatially relevant information for guiding clonorchiasis control interventions in China.…”

Assessment of control strategies against Clonorchis sinensis infection based on a multi-group dynamic transmission model

“…As a practical example, we applied the model in Fusha Town (coordinates: 22˚40'2.64''N, 113˚20'58.2''E, area: 37 km 2 , and population: 59,593 in the year 2017), located at the northern part of Zhongshan City, Guangdong Province (S1 Fig), where C. sinensis infection is highly endemic [5]. Situated among the Pearl River Delta's vast water network, the town is crisscrossed by rivers and the fishing industry is flourishing.…”

“…The life cycle of C. sinensis involves snails as the first intermediate hosts, fish or shrimp as the second, and humans or other piscivorous mammals as the definitive hosts [5]. The most common way of human getting infected with metacercariae of C. sinensis is eating the raw or insufficiently cooked infected fish/shrimp, the frequency and intensity of which can directly influence the probability of getting infection [6].…”