Fuzzy and spatial analysis of cutaneous leishmaniasis in Pará State, Brazilian Amazon: an ecological and exploratory study.

Autor:	Costa SB; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Miranda CDS; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., De Souza BC; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Guimarães HMMES; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Faria CM; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Da S Campos PS; Cyberspace Institute, Amazon Rural Federal University, Belém, Brazil., Koury TM; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Da Paixão JGM; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Leal AL; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., Carrera MFP; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil., De Brito SR; Cyberspace Institute, Amazon Rural Federal University, Belém, Brazil., Gonçalves NV; Amazon Epidemiology and Geoprocessing Laboratory, Faculty of Medicine, Pará State University, Belém, Brazil.
Jazyk:	angličtina
Zdroj:	Journal of infection in developing countries [J Infect Dev Ctries] 2024 Jul 29; Vol. 18 (7), pp. 1124-1131. Date of Electronic Publication: 2024 Jul 29.
DOI:	10.3855/jidc.18639
Abstrakt:	Introduction: This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022. Methodology: In this ecological and exploratory study, the microregions were used as spatial units because they are formed by contiguous municipalities with similar characteristics. The epidemiological, environmental, socioeconomic, and public health policy data employed were obtained from the official information systems at the Ministry of Health, National Institute for Space Research, and Brazilian Institute of Geography and Statistics. A fuzzy system was developed to identify risk factors for the disease, using Python programming language. The results were analyzed with the bivariate Global Moran spatial analysis technique. Results: It was observed that the Altamira microregion had the highest risk percentage for the disease, while Breves had the lowest, with significant differences in the relevance of its conditioning factors, mainly related to land use and cover patterns, in addition to demography and living conditions index, education and public health policies. Conclusions: The fuzzy system associated with the geostatistical technique was satisfactory for identifying areas with health vulnerability gradients related to deforestation, pasture, poverty, illiteracy, and health services coverage, as its conditioning variables. Thus, it was demonstrated that deforestation was the main risk factor for the disease. The system can also be used in environmental and epidemiological surveillance. Competing Interests: No Conflict of Interest is declared (Copyright (c) 2024 Simone BN Costa, Claudia do SC Miranda, Bruna C de Souza, Heloisa Maria M e S Guimarães, Camylle MC Faria, Pedro S da S Campos, Taiana MA Koury, José Gabriel M da Paixão, Alessandra L Leal, Maria de Fátima P Carrera, Silvana R de Brito, Nelson V Gonçalves.)
Databáze:	MEDLINE
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