4.1 Article

Interaction among environmental and socioeconomic determinants of risk for cutaneous leishmaniasis in Latin America

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PAN AMER HEALTH ORGANIZATION
DOI: 10.26633/RPSP.2021.49

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Leshmaniasis, cutaneous; cluster analysis; Latin America

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This study included 36.5% of municipalities in Latin America and successfully identified seven clusters of municipalities with different levels of risk for cutaneous leishmaniasis based on 18 environmental and socioeconomic variables. The results showed different associations between CL risk and clusters, providing valuable information for controlling CL.
Objective. Determine and characterize areas at potential risk for the occurrence of cutaneous leishmaniasis (CL) in Latin America. Method. Ecological observational study with observation units defined by municipalities with CL transmission during 2014-2018. Environmental and socioeconomic variables available for at least 85% of municipalities were combined in a single database, using R software. Principal component analysis was combined with hierarchical cluster analysis for the formation of clusters of municipalities according to their similarity. The V-test was used to define positive or negative association of variables with clusters and separation by natural divisions to determine which contributed more to each cluster. Cases were included to attribute CL risk for each cluster. Results. The study included 4 951 municipalities with CL transmission (36.5% of municipalities in Latin America); seven clusters were defined by their association with 18 environmental and socioeconomic variables. Historical risk of CL is associated positively and in descending order with the Amazonian, Andean, and Savanna clusters; and negatively with the Forest/perennial, Forest/cultivated, and Forest/populated clusters. The Agricultural cluster showed no association with CL cases. Conclusions. The study made it possible to identify and characterize CL risk by clusters of municipalities and to understand the characteristic epidemiological distribution patterns of transmission, providing program managers with better information for intersectoral interventions to control CL.

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