Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia

Abstract

Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10–15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.

The spatial-temporal risk profiling of Clonorchis sinensis infection in South Korea: A geostatistical modeling study

“…We identified several socioeconomic and environmental predictors significantly related with C. sinensis infection, which may provide insights in prevention and control of the disease. We found that community-based surveys were tended to report higher prevalence than that in schoolchildren, in line with the results of previous studies ( Lee et al, 1994a, 1994b; Min et al, 2002; Zhao et al, 2021 ). Some studies suggested that the youth eat raw fish far less frequently than the elderly, resulting in a generally lower prevalence among the youth ( June et al, 2013; Lee et al, 2020 ).…”

“…The national surveys mainly reported raw overall prevalence by calculating the proportion of positive individuals among all examined ones ( Korea Centers for Disease Control and Prevention and Korea National Institute of Health, 2013 ), which may be influenced by sampling framework ( Mitani et al, 2021; Zhao et al, 2021 ). Differently, our estimates relied on rigorous Bayesian geostatistical modeling of all available geo-referenced disease survey data along with important environmental and socioeconomic predictors, thus was able to take into account high-resolution spatial heterogeneous of both disease infection risk and population density across the country when compiling country-level prevalence ( Lai et al, 2019; Zhao et al, 2021 ). We did not provide estimates of infection risk for islands Jeju-do and Ulleung-gun, as sparse geo-referenced data were obtained, and they are quite a distance away from the continent of South Korea.…”

“…Bayesian geostatistical modeling is one of the most rigorous approaches to produce high-resolution disease risk maps ( Karagiannis-Voules et al, 2015 ). It has been applied in various studies to food-borne trematodiases or other neglected tropical diseases in many regions around the world, such as clonorchiasis in China, opisthorchiasis in Southeast Asia, schistosomiasis in Africa and soil-transmitted helminthiasis in South Asia ( Lai et al, 2015; Lai et al, 2019; Lai et al, 2017; Zhao et al, 2021 ). This approach usually models point-referenced disease data with potential risk factors (e.g., socioeconomic and environmental factors) and spatial-temporal random effects, thus estimates disease risk in areas without observed ( Gelfand and Banerjee, 2017; Lai et al, 2015 ).…”

“…This approach usually models point-referenced disease data with potential risk factors (e.g., socioeconomic and environmental factors) and spatial-temporal random effects, thus estimates disease risk in areas without observed ( Gelfand and Banerjee, 2017; Lai et al, 2015 ). Recently, more advanced Bayesian geostatistical models were developed to deal with data heterogeneities, such as jointly analyzing diseases data at both point- and areal levels ( Moraga et al, 2017; Smith et al, 2008 ), and joint modeling of disease data which is partially missed the total number examined ( Zhao et al, 2021 )…”

Monsters in our food: Foodborne trematodiasis in the Philippines and beyond

“…Opisthorchiasis and clonorchiasis are infections caused by the consumption of freshwater cyprinid fishes that carry the encysted metacercaria in their flesh or under their scales (Hung et al, 2013;Nithikathkul et al, 2021). Opisthorchiasis in the Asia-pacific region is caused by Opisthorchis viverrini and O. felineus (Sithithaworn et al, 2012;Waikagul et al, 2015;Xin et al, 2020;Zhao et al, 2021). The former is endemic in the Mekong River basin in Cambodia, Lao PDR, and northern Thailand (Waikagul et al, 2015;Phupiewkham et al, 2021;Kitphati et al, 2021).…”