Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil

Abstract

Neglected tropical diseases characterized by skin lesions are highly endemic in the state of Mato Grosso, Brazil. We analyzed the spatial distribution of leprosy and Cutaneous Leishmaniasis (CL) and identified the degree of overlap in their distribution. All new cases of leprosy and CL reported between 2008 and 2017 through the national reporting system were included in the study. Scan statistics together with univariate Global and Local Moran’s I were employed to identify clusters and spatial autocorrelation for each disease, with the spatial correlation between leprosy and CL measured by bivariate Global and Local Moran’s I. Finally, we evaluated the demographic characteristics of the patients. The number of leprosy (N = 28,204) and CL (N = 24,771) cases in Mato Grosso and the highly smoothed detection coefficients indicated hyperendemicity and spatial distribution heterogeneity. Scan statistics demonstrated overlap of high-risk clusters for leprosy (RR = 2.0; P <0.001) and CL (RR = 4.0; P <0.001) in the North and Northeast mesoregions. Global Moran’s I revealed a spatial autocorrelation for leprosy (0.228; P = 0.001) and CL (0.311; P = 0.001) and a correlation between them (0.164; P = 0.001). Both diseases were found to be concentrated in urban areas among men aged 31-60 years, of brown-skinned ethnicity and with a low educational level. Our findings indicate a need for developing integrated and spatially as well as socio-demographically targeted public health policies.

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Published
2020-12-29
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Original Articles
Keywords:
Leprosy, cutaneous leishmaniasis, neglected tropical diseases, epidemiology, spatial analysis, Brazil.
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How to Cite
de Carvalho, A. G., Guimarães Luz, J. G., Leite Dias, J. V., Tiwari, A., Steinmann, P., & Ignotti, E. (2020). Hyperendemicity, heterogeneity and spatial overlap of leprosy and cutaneous leishmaniasis in the southern Amazon region of Brazil. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.892