Modeling the impact of environmental factors on the prevalence and risk of human echinococcosis in China

Accurate mapping of infection prevalence and risk is an essential tool to inform disease prevention and control strategies, as illustrated here for human echinococcosis in western China.

Human echinococcosis – distribution and impact
Echinococcosis is a worldwide widespread zoonotic disease caused by infection with larval stages of parasitic tapeworms of the genus Echinococcus.

Running WHO estimates suggest that worldwide more than one million people are infected with echinococcosis, resulting in an estimated 871,000 disability-adjusted life years (DALYs) each year. In addition to the substantial burden of disease and death associated with human echinococcosis, the treatment of livestock infections is estimated to cost more than $3 billion (USD) per year.

Treatment regimens are currently complicated and dangerous, and often require prolonged drug therapy and/or invasive surgery.

Echinococcus multilocularis adult worm. Source: Alan R Walker, Wikimedia commons, CC BY-SA 3.0.

Of all the regions affected by human echinococcosis, Western China has the highest prevalence anywhere in the world, and as such, the impact on public health in this region of China is enormous, especially in rural, pastoral and economically disadvantaged areas.

The high endemicity of human echinococcosis in western China has led researchers to study the geographical and environmental conditions of the region and to question whether specific environmental factors could influence the transmission of echinococcosis to humans. . For example, a study linked the prevalence of echinococcosis to average annual rainfall, and another one found a correlation between higher altitudes and increased prevalence of echinococcosis.

Despite extensive work linking specific environmental factors to human echinococcosis, no study has yet investigated how, or if, natural environmental factors may be predictors of human echinococcosis risk.

A recent study by Jie Yin and colleagues from the College of Global Change and Earth System Science in Beijing set out to do just that, and are the first to model and predict the spatial distribution of human echinococcosis in western China.

Create and populate the model
To study the spatial distribution and environmental risk factors of human echinococcosis, researchers first selected 344 counties in western China (see study map below) for inclusion in the study. study, and retrieved county-level human echinococcosis prevalence data from local data. CDC reports and epidemiological studies (surveyed) for later comparison with the prevalence predictions made by their model.

Map of the western China study area. Yin et al., 2022.

Nine environmental factors considered as natural risk factors for echinococcosis and four seasonal indices (spring, summer, autumn and winter) were selected for the prediction, which fall into two categories, climatic and geographical:

Climatic factors:

  • Temperature (T)
  • Precipitation (Pre)
  • Relative humidity (hr)
  • Sunshine duration (sun)

Geographic factors:

  • Elevation (measured as a Digital Elevation Model (DEM))
  • Vegetation density (measured by Normalized Difference Vegetation Index (NDVI))
  • Grassland Area Ratio (GrassR)
  • Forest area rate (ForestR)
  • Cultivated area ratio (CultivatedR)

In order to analyze the relationship between the nine environmental factors and the prevalence of echinococcosis in each county, and to predict potential hotspots/coldspots in human prevalence, the researchers constructed a mathematical model of Structured Additive Regression (STAR) using Bayesian inference.

Overview of human echinococcus risk provided by the model
The predicted prevalence and hot/cold spot pattern for each county was generally consistent with the prevalence and hot/cold spot reported by the survey results, supporting the constructed model.

The authors note, however, that the modeled prevalence based on environmental factors was in some cases higher than the studied prevalence, suggesting that past control measures implemented in western China have been effective in reducing the prevalence. of human echinococcosis compared to what is possible given the environmental conditions.

The highly endemic areas with the highest prevalence (>2%) were almost exclusively concentrated on the Qinghai-Tibet Plateau in south/southwest China. Counties outside the Plateau region generally had a low predicted prevalence of echinococcosis (

The three identified hotspots should therefore also be located on the high-altitude Qinghai-Tibet plateau, indicating that the environmental conditions are well suited for the transmission of human echinococcosis and that prevention and control should be concentrated in this region.

Predicted spatial distribution of human echinococcosis at the county level in western China: A = predicted prevalence; B = predicted hot/cold spots. Yin et al., 2022.

Main environmental factors influencing the transmission of echinococcosis
The model also revealed that climatic and geographic factors had a significant impact on the prevalence of human echinococcosis in the region.

In particular, altitude (DEM), vegetation density (NDVI) in spring, summer precipitation (pre) and sunshine duration (sun), relative humidity (hr) in winter, GrassR and ForestR have been shown to be key environmental factors.

These factors likely influence the survival, infectivity, and successful release of Echinococcus eggs into the environment, and act as drivers of parasite transmission in western China.

This work by Jie Yin and colleagues illustrates how useful spatial epidemiological modeling is in informing real-world disease prevention and control strategies, and that it can help improve the prioritization of prevention and control measures. control and lead to a better allocation of resources where they are most needed.

The authors note that additional risk factors for human echinococcosis, such as socioeconomic circumstances, population demographics, human behavior, habitat type, and animal infection status, could further improve predictions of echinococcosis prevalence made by the model and enable its use to predict hotspots for similar diseases. Neglected tropical diseases.

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