Spatial analysis for identification of priority areas for surveillance and control in a visceral leishmaniasis endemic area in Brazil

• Published in: Acta tropica 2014-03, Vol.131, p.56-62
• Main Authors: Barbosa, David Soeiro, Belo, Vinícius Silva, Rangel, Maurício Eduardo Salgado, Werneck, Guilherme Loureiro
• Format: Article
• Language: English
• Subjects: Animals; Brazil - epidemiology; Cluster Analysis; Dog Diseases; Dogs; Endemic Diseases; Epidemiological Monitoring - veterinary; Epidemiological studies; Humans; Incidence; Leishmania donovani; Leishmaniasis, Visceral - epidemiology; Leishmaniasis, Visceral - parasitology; Leishmaniasis, Visceral - veterinary; Medical geography; Northeast Brazil; Residence Characteristics; Spatial Analysis; Spatial data analysis; Visceral leishmaniasis
• ISSN: 0001-706X; 1873-6254
• DOI: 10.1016/j.actatropica.2013.12.002

•Areas with high incidences of VL can be readily identified by spatial data analysis.•Spatial data analysis facilitates the prioritization of areas for disease control.•An action plan for the control of VL in priority areas is proposed. Spatial analysis of epidemiological data may be used to assist in the implementation of surveillance and control measures against visceral leishmaniasis (VL) in endemic areas. This ecological study aimed to identify priority areas for surveillance and control of VL in São Luís, the capital of the state of Maranhão in northeast Brazil, a highly endemic area for the disease. We evaluated the spatial structure of the incidence rates of human VL and of the mean number of human and canine cases occurring between 2005 and 2007 in 355 neighborhoods (aggregated into 203 geographical analytical units) within the municipality. The presence of spatial autocorrelation was explored using global and local Moran's I statistics. A local indicator of spatial autocorrelation was used to generate maps for the identification of VL clusters. The global Moran's I index revealed a weak, but statistically significant spatial autocorrelation for human VL incidence rates (I=0.138). A total of 43 geographical analytical units, encompassing 121 neighborhoods, were identified as priority areas for implementing surveillance and control actions. For the purpose of defining an action plan for the delivery of these measures, those 16 geographical analytical units (encompassing 54 neighborhoods) identified as clusters with high incidence rates of human VL should receive the highest priority. An additional nine geographical analytical units (comprising 28 neighborhoods) showed non-significant clustering of high rates of human, and might be considered as the next priority for VL management. Finally, a further 18 geographical analytical units (covering 39 neighborhoods) had records of coexisting human and canine VL cases during the study period, and these should receive priority attention when resources become available. Spatial data analysis is a valuable tool for defining priority areas for VL surveillance in high transmission areas contributing to a more effective management of financial and technical resources, increasing the sustainability and efficiency of control efforts.