For disease prevention and control strategies, it is crucial to accurately map the prevalence and risk factors for infection. This is illustrated in this case study for human echinococcosis.
Gansu in Western China
image courtesy pixabay – user Liruoyingdoudou2009
Human echinococcosis – distribution and impact
Echinococcosis, a zoonotic disease that is widely spread worldwide, is caused by infection with the larval stages parasitic tapeworms of the genus. Echinoccocus.
Current Estimates by W.H.OGlobally, more than 1,000,000 people are infected by echinococcosis. This results in approximately 871 000 disability adjusted life-years (DALYs). Treatment for livestock infections, which are associated with human echinococcosis is estimated to cost over $3 billion annually, is a significant health risk and cause of death.
Current treatment options are complex and dangerous. They often require prolonged drug therapy or invasive surgery.
Echinococcus multilocularis adult worm. Source: Alan R Walker, Wikimedia commons, CC BY-SA 3.0.
Out of all the affected regions by human echinococcosisis, this is the one that has been most affected. The highest prevalence is found in Western ChinaAnywhere in the world, the impact on public healthcare in this area of China is enormous, especially in rural, pastoral, and economically disadvantaged regions.
Research has been conducted in Western China to examine the environmental and geographic conditions that exist in the region and to see if there are any environmental factors that might be influencing the transmission of human echinococcosis. For example: One studyIt was found that the prevalence of echinococcosis is linked to annual mean precipitation. AnotherThere was a correlation between higher altitudes, and higher echinococcosis prevalence.
Despite extensive research connecting certain environmental factors to human-echinococcosis risk, there are no studies that have yet been conducted to determine if or how these natural environmental factors can be used as predictors.
Jie Yin, and colleagues recently conducted a studyThe College of Global Change and Earth System Science, Beijing, has taken the initiative to do exactly that and is the first to model and predict Western China’s spatial distribution of human Echinococcosis.
Informing and creating the model
Researchers first selected 344 counties in Western China (see map below) to investigate the spatial distribution of human echinococcosis. They then retrieved county-level prevalence data from localities. CDCFor later comparison with their models, reports and epidemiological research (surveyed) can be used.
For the prediction, nine environmental factors were considered to be natural risk factors for echinococcosis. Four seasonal indices (spring/summer/autumn/winter) were used. These fall into two categories: climate and geographical.
Climate factors
- Temperature (T).
- Precipitation
- Relative humidity
- Sunshine duration (sun)
Geographical factors
- Elevation (measured using a digital elevation model (DEM).)
- Vegetation density (measured using the normalized difference vegetation indicator (NDVI).
- Ratio of grassland area to water (GrassR).
- Forest area ratio (ForestR)
- Cultivated land area ratio. (CultivatedR).
The researchers used Bayesian inference to create a structured additive regression model (STAR) that analysed the relationship between nine environmental factors and the prevalence of echinococcosis within each county.
The model provides insight into the human echinococcus threat
The predicted prevalence and hot/cold place model for each county was generally consistent with the surveyed findings. This supports the constructed model.
However, the authors note that the modelled prevalence was not always as high as the surveyed prevalence. This suggests that past control measures in Western China have been successful in reducing human Echinococcosis prevalence.
Highly endemic regions with highest prevalence (>2%), were almost exclusively found in South/Southwestern China’s Qinghai Tibet Plateau. Counties located outside the Plateau region had a lower predicted prevalence of echinococcosis (0.5%).
These hot spots were also predicted to be located at the Qinghai Tibet Plateau at high altitude, which indicates that the environment is well-suited for human echinococcosis infection and that prevention and control should focus in this area.
Factors that influence echinococcosis-related transmission
The model also revealed that both the climate and geographical landscape factors had significant impacts on human echinococcosis incidence in the region.
Particularly, elevation (DEM), vegetation densities (NDVII) in Spring, Summer precipitation and sun duration (sun), relative humidity in Winter, GrassR and ForestR were identified as key environmental factors.
These factors are likely to influence the survival, infection, and successful release Echinococcus eggs in the environment and act as drivers for parasite transmission in Western China.
Conclusion
Jie Yang and his colleagues have shown that spatial epidemiological modelling can be useful in forming real world disease prevention strategies.
The authors note that other risk factors for human echinococcosis such as socioeconomic conditions, population demographics and human behaviour could improve the model’s predictions of echinococcosis prevalence and allow it to be used in predicting hot spots. Neglected tropical diseases.
