The spatial-temporal risk profiling of Clonorchis sinensis infection over 50 years implies the effectiveness of control programs in South Korea: a geostatistical modeling study

Abstract

Background: Over the past 50 years, two national control programs on Clonorchis sinensis infection have been conducted in South Korea. Spatial-temporal profiles of infection risk provide useful information on assessing the effectiveness of the programs and planning spatial-targeted control strategies.

Methods: Advanced Bayesian geostatistical joint models with spatial-temporal random effects were developed to analyze disease data collecting by a systematic review with potential influencing factors, and to handle issues of preferential sampling and data heterogeneities. Changes of the infection risk were analyzed.

Findings: We presented the first spatial-temporal risk maps of C. sinensis infection at 5 × 5 km² resolution from 1970 to 2020 in South Korea. Moderate-to-high risk areas were shrunk, but temporal variances were shown in different areas. The population-adjusted estimated prevalence across the country was 5.99% (95% BCI: 5.09-7.01%) in 1970, when the first national deworming campaign began. It declined to 3.95% (95% BCI: 2.88-3.95%) in 1995, when the campaign suspended, and increased to 4.73% (95% BCI: 4.00-5.42%) in 2004, just before the Clonorchiasis Eradication Program (CEP). The population-adjusted prevalence was estimated at 2.77% (95% BCI: 1.67-4.34%) in 2020, 15 years after CEP started, corresponding to 1.42 (95% BCI: 0.85-2.23) million infected people.

Interpretation: The first nationwide campaign and the CEP showed effectiveness on control of C. sinensis infection. Moderate-to-high risk areas identified by risk maps should be prioritized for control and intervention.

Funding: The National Natural Science Foundation of China (project no. 82073665) and the Natural Science Foundation of Guangdong Province (project no. 2022A1515010042).

Keywords: Bayesian spatial-temporal joint model; Clonorchis sinensis; Control program; High-resolution risk map; Preferential sampling.

Fig. 1

Survey locations and observed prevalence of C. sinensis infection in South Korea. (a) 1970–1974, (b) 1975–1979, (c) 1980–1984, (d) 1985–1989, (e) 1990–1994, (f) 1995–1999, (g) 2000–2004, (h) 2005–2009, (i) 2010–2014, and (j) 2015 onward.

Fig. 2

Model-based estimated risk maps of C. sinensis infection across South Korea in selected years. Estimated prevalence was based on the median of the posterior estimated distribution of infection risk in (a) 1970, (b) 1975, (c) 1980, (d) 1985, (e) 1990, (f) 1995, (g) 2000, (h) 2005, (i) 2010, (j) 2015, and (k) 2020.

Fig. 3

The probability of risk reduction for C. sinensis infection across time periods. This probability was obtained by calculating the proportion of 500 posterior samples in each grid for which estimated prevalence of the latter year was lower than that of the former year. The probability (a) between 1970 and 1995, (b) between 1995 and 2005, and (c) between 2005 and 2020.

Fig. 4

Trends of estimated prevalence of C. sinensis infection in South Korea. (a) The trend of population-adjusted prevalence across the country, presented by the median and 95% Bayesian credible interval of the posterior distributions of population-adjusted estimated prevalence in each year. (b) The compositions of pixel-level areas with different levels of estimated prevalence (i.e., ≤1.00%, 1.01–5.00%, 5.01–20.00%, 20.01–50.00%, and >50.00%). The estimated prevalence was based on the median of the posterior distribution of infection risk in each grid.