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

Maia-Elkhoury et al.


Determine and characterize areas at potential risk for the occurrence of cutaneous leishmaniasis
(CL) in Latin America.


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.


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.Se incluyeron en el estudio 4 951 municipios con transmisión de LC (36,5% del total en AL) y se definieron siete conglomerados por su asociación con 18 variables medioambientales y socioeconómicas. El riesgo histórico de LC se asocia de manera positiva y en forma decreciente con los conglomerados Amazónico, Andino y Sabana; y de manera negativa con los conglomerados Boscoso/perenne, Boscoso/cultivo y Boscoso/poblado. El conglomerado Agrícola no reveló ninguna asociación con los casos de LC.


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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