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Visceral Leishmaniasis and Land Use and Cover in the Carajás Integration Region, Eastern Amazon, Brazil.

Claudia do Socorro Carvalho MirandaBruna Costa de SouzaTainara Carvalho Garcia Miranda FilgueirasAlder Mourão de SousaMaira Cibelle da Silva PeixotoTainã Carvalho Garcia Miranda FilgueirasFrederico José Carvalho MirandaSérgio Luiz AlthoffRaimundo Gladson Corrêa CarvalhoNelson Veiga Gonçalves
Published in: Tropical medicine and infectious disease (2022)
Human visceral leishmaniasis is a major public health problem in the Amazon. Thus, we analyzed the spatial distribution of this disease and its relationship with epidemiological, socioeconomic, and environmental variables in the Carajás Integration Region, Pará state, from 2011 to 2020. Epidemiological data for this ecological study were obtained from the State Public Health Secretariat, environmental data were obtained from the National Space Research Institute, and socioeconomic data were obtained from the Brazilian Geography and Statistics Institute. ArcGIS 10.5.1 software was used for classifying land use and cover and for the Kernel and Moran spatial analyses. It was observed in 685 confirmed cases that the epidemiological profile followed the national pattern of the disease occurrence, with a high prevalence in children who were not school-aged. The disease had a non-homogeneous distribution with clusters related to different human activities, such as urbanization, ranching, and mining. A spatial dependence between the disease prevalence and socioeconomic indicators was observed. The municipalities presented gradients of case densities associated with a direct relationship between areas with cases and deforestation. The disease is developing due to risk factors such as establishment and maintenance related to the non-sustainable development model implemented in the region, pointing to the need for its revision.
Keyphrases
  • public health
  • risk factors
  • endothelial cells
  • big data
  • physical activity
  • machine learning
  • young adults
  • quality improvement
  • climate change
  • human health
  • deep learning
  • data analysis
  • drug induced