ArticleParasites & vectors2026
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.
Article in Parasites & vectors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLeishmaniasis, 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.
methodsApplying 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.
resultsFrom 1990 to 2008, the global prevalence of and DALYs due to CL exhibited significant positive spatial autocorrelation (Z > 1.96, P < 0.05). Prevalence and DALYs both had one cold spot, located in northern Africa, and two hot spots, located in Central America and Central Asia. Temperature, infant mortality rate (IMR) and humidity were significantly positively correlated with the prevalence of and DALYs due to CL, whereas gross domestic product (GDP) and surface solar radiation (SSR) were significantly negatively correlated with the latter. The GTWR model demonstrated the best regression performance, with adjusted R
conclusionsTo 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.
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