Dataset used in the research: Spatiotemporal patterns and integrated approach to prioritise areas for surveillance and control of visceral leishmaniasis in a large metropolitan area in Brazil.
Description
We aimed to analyze the spatial and spatiotemporal patterns of VL occurrence and to identify priority risk areas for surveillance and control in the metropolitan region of Belo Horizonte-MG, Brazil. An ecological study was conducted considering all cases of VL in humans confirmed from 2006 to 2017, reported in Brazil’s national notification database (Notifiable Diseases Information System [SINAN]). We used databases from two different versions of SINAN: Windows version 2006 and TabNet version 2007–2017 (Ministry of Health, 2019). Therefore, we aggregated these two databases encompassing the entire period (2006–2017). Incidence rates were calculated annually and for 3-year periods for all MRBH municipalities. To calculate the incidence rates, each case was aggregated by municipality of residence (analytical unit). The population estimates were set considering projections of the Federal Court of Audit (Tribunal de Contas da União [TCU]) calculated yearly for each municipality, based on data from the IBGE (2018). The incidence rates were aggregated in 3 years, as follows: 1st triennium, 2006–2008; 2nd triennium, 2009–2011; 3rd triennium, 2012–2014; and 4th triennium, 2015–2017. The incidence rates per 100,000 inhabitants were calculated for each triennium. Cumulative incidence rates were re-estimated for the geographic analytical units and for each triennium using empirical Bayesian space smoothing. Calculations were performed using GeoDa version 1.10 software (Arizona State University / Center for Geospatial Analysis and Computation, n.d.). To perform the empirical Bayesian space smoothing analysis, the Moran’s Global Test and the Local Indicators of Spatial Association (LISA) were used to create the first order neighborhood matrix (Queen). The units of analysis that presented at p ≤ 0.05 in the LISA were considered statistically significant. In order to view the priority municipalities for surveillance, we made choropleth maps. The Moran’s Global and LISA were calculated using GeoDa software version 1.10 (Arizona State University / Center for Geospatial Analysis and Computation, n.d.), and maps were constructed using QGIS software version 2.18 (QGIS project, 2019). To identify spatiotemporal clusters, the SaTScan ™ 9.4.4 (Kulldorff, 2015) software scanning statistics was used. Statistical significance was considered when p <0.05 (Kulldorff and Nagarwalla, 1995). The spatial scan statistical analyzes for this study were performed using the case data set, population, and location. To perform the tests, the information regarding each municipality was inserted in the software: 1) number of cases, 2) year of infection, 3) population average of the 3 years that make up the 3-year period studied, and 4) geocode of each municipality. All this information was entered for the entire period.