Spatio-temporal analysis of leptospirosis in Eastern Amazon, State of Pará, Brazil

Rev Bras Epidemiol. 2020;23:e200041. doi: 10.1590/1980-549720200041. Epub 2020 Jun 1.


Introduction: Brazil has registered more than 62,000 confirmed cases of leptospirosis between 2001 and 2017, with more than 2,000 cases confirmed in the State of Pará. Despite a large number of cases, no study has been conducted to trace the spatio-temporal profile of the disease.

Methodology: Confirmed cases of leptospirosis from 2001 to 2017 from the state of Pará were the basis for this space-time study. The database of the Department of Informatics of the Ministry of Health was used to access data on leptospirosis. The spatio-temporal analysis was performed in the SaTScan software for the detection of clusters, and maps were generated in the QGIS software.

Results: The municipalities of Belém and Santarém were among the ones with the highest incidence rates of leptospirosis for the whole study period. Increased number of cases in Soure, Inhangapi, São João da Ponta and Magalhães Barata, Ponta de Pedras, Breves, Bragança, Castanhal, and São Domingos do Capim were identified in different time periods. Santarém and Belém are the main foci of leptospirosis because they are the most urbanized and densely populated municipalities in the State. The cases found in smaller municipalities may be associated with periods of more frequent rainfall and circulation of Leptospira sp. in marsupials and cattle, in the northeastern part of the State.

Conclusion: Further studies are needed to help identify the risk factors that contribute to the occurrence of leptospirosis in the State of Pará, particularly in areas with lower population density.

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Brazil / epidemiology
  • Child
  • Child, Preschool
  • Cities
  • Female
  • Geography
  • Humans
  • Infant
  • Leptospirosis / epidemiology*
  • Male
  • Middle Aged
  • Monte Carlo Method
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Rural Population
  • Sex Distribution
  • Spatio-Temporal Analysis
  • Time Factors
  • Urban Population
  • Young Adult