The acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010

  • André T. J. Alves | andrethiago@peb.ufrj.br Biomedical Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Flávio F. Nobre Biomedical Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.

Abstract

Despite increased funding for research on the human immunodeficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS), neither vaccine nor cure is yet in sight. Surveillance and prevention are essential for disease intervention, and it is recognised that spatio-temporal analysis of AIDS cases can assist the decision-making process for control of the disease. This study investigated the dynamic, spatial distribution of notified AIDS cases in the State of Rio de Janeiro, Brazil, between 2001 and 2010, based on the annual incidence in each municipality. Sequential choropleth maps were developed and used to analyse the incidence distribution and Moran’s I spatial autocorrelation statistics was applied for characterisation of the spatio-temporal distribution pattern. A significant, positive spatial autocorrelation of AIDS incidence was observed indicating that municipalities with high incidence are likely to be close to other municipalities with similarly high incidence and, conversely, municipalities with low incidence are likely to be surrounded by municipalities with low incidence. Two clusters were identified; one hotspot related to the State Capital and the other with low to intermediate AIDS incidence comprising municipalities in the north-eastern region of the State of Rio de Janeiro.

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Published
2014-05-01
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Original Articles
Keywords:
AIDS incidence, spatial analysis, disease mapping, cluster, Brazil.
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How to Cite
Alves, A. T. J., & Nobre, F. F. (2014). The acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010. Geospatial Health, 8(2), 437-443. https://doi.org/10.4081/gh.2014.32