Modeling the distribution of urolithiasis prevalence under projected climate change in Iran.

Abstract

Although studies support a positive correlation between temperature and stone risk, the precise relationship between these factors has not been elucidated. We modeled the current distribution of urolithiasis prevalence in Iran using 26 bioclimatic, climatic and topographic variables based on two multivariate linear regression models in geographical information system. The impact of climate change on the stone prevalence was predicted under the projections of GFDL-ESM2G, CCSM4 and HadGEM2-ES climate models by mid-century (2050). Extraterrestrial radiation and isothermality in the first regression model and annual mean temperature, precipitation seasonality and isothermality in the second model were the significant (P<0.01) predictors of urolithiasis prevalence. Both regression models provided good estimates of the stone prevalence (R2>0.9) and determined a mean urolithiasis prevalence of 6% (range of 1.5-10.8%) in Iran. The climate change under the projections of GFDL-ESM2G, CCSM4 and HadGEM2-ES models can, respectively, lead to an average increase of 5.7, 4.3 and 9% in the urolithiasis prevalence based on the second regression model by 2050. The highest increase of the prevalence will occur in the west, northwest and southwest provinces of the country. Predicting the impact of climate change on climate-related diseases can be useful for effective preventive measures.