Impact of environmental and socioeconomic factors on the prevalence of and DALYs due to cutaneous leishmaniasis globally from 1990 to 2021 based on remote sensing and GIS technologies

BACKGROUND: Leishmaniasis, a parasitic disease caused by Leishmania spp., is a major public health threat. The synergistic effects of environmental and socioeconomic factors on the global distribution of leishmaniasis are unknown. METHODS: Applying epidemiological data on cutaneous leishmaniasis (CL) from the Global Burden of Disease 2021 database, we used spatial autocorrelation and standard deviation ellipses to explore the spatiotemporal clustering and migration patterns of CL. Four remote sensing-retrieved environmental factors and five socioeconomic factors were selected for analysis. Spearman's correlation coefficient was used to screen for factors correlated with the prevalence of and disability-adjusted life years (DALYs) due to CL. Ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) were used to assess the impact of the influencing factors on the prevalence of and DALYs due to CL. RESULTS: values for prevalence reaching 0.841, 0.984, 0.839 and 0.972, and those for DALYs reaching 0.816, 0.966, 0.837 and 0.972 in Asia, Europe, the Americas and Africa, respectively. Regression coefficients further quantified the individual contributions of each factor to the prevalence of and DALYs due to CL, which could provide a scientific basis for governments to implement targeted control of CL. CONCLUSIONS: To our knowledge, this study is the first to analyze the global spatiotemporal distribution patterns of the prevalence of and DALYs due to CL and quantitatively study the spatiotemporal effects of environmental and socioeconomic factors on CL on a global scale. Environmental (temperature, SSR and humidity) and socioeconomic (GDP and IMR) factors were significantly correlated with the prevalence of and DALYs due to CL. The GTWR model outperformed the GWR and OLS models, further confirming the spatiotemporal effects of influencing factors on CL.