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Background: Tuberculosis (TB) remains a major public health problem in many developing countries. Exploratory spatial analysis is a powerful instrument in spatial health research by virtue of its capacity to map disease distribution and associated risk factors at the population level. The aim of the present study was to describe the epidemiologic characteristics and spatial distribution of new cases of TB reported during the period 2002-2012 in Divinopolis, a midsized city located in the state of Minas Gerais, southeastern Brazil.Methods: Sociodemographic and clinical data relating to the study cases were retrieved from the national Brazilian database and geocoded according to residential address. Choropleth and kernel density maps were constructed and a spatial-temporal analysis was performed. Tracts defined by the 2010 national census were classified as sectors with higher or lower densities of new TB cases based on the kernel density map. Multivariate logistic analysis was used to compare the two types of sectors according to income, level of literacy and population density.Results: A total of 326 new cases of TB were reported during the study period. Residential addresses relating to 309 (94.8 %) of these were available in the SINAN database and the locations were geocoded and mapped. The average incidence of TB during the study period was 14.5/100,000 inhabitants. Pulmonary TB was the most predominant form (73.6 %) and 74.5 % of patients had been cured. The percentage of cases was highest in males (67.8 %) and individuals aged 25-44 years (41.1 %), and lowest in children aged less than 15 years (4.6 %). The disease was spatially distributed throughout the urban district. The incidence rate among urban census tracts ranged from 0.06 to 1.1 %, and the disease occurred predominantly in the downtown area (99.3 %). Higher population density was associated significantly with increased odds of living in a sector with a "higher density of cases", even after adjusting for income and education (odds ratio = 13.7).Conclusions: The highest density of cases was strongly associated with higher population density but not with lower income or level of literacy. [ABSTRACT FROM AUTHOR]

Background The physical characteristics of the environment influence the composition, distribution and behavior of the vectors and mammalian hosts involved in the transmission of visceral leishmaniasis (VL), thereby affecting the epidemiology of the disease. In Brazil, urbanization of human VL is a recent phenomenon and represents an issue of particular concern to local health authorities. The present study aimed to establish the degree of spatial dependency between canine and human VL in the municipality of Divinópolis, Minas Gerais, Brazil, and to identify priority risk areas in which stricter control measures should be implemented. Methods The selected canine population comprised 3,652 dogs distributed within 11 strata and 1,247 urban blocks. Serum samples were collected between March 2013 and February 2014. Serodiagnosis of dogs was performed using the enzyme-linked immunosorbent assay and the indirect fluorescent-antibody test. The blocks sampled for canine VL and the addresses of the 16 confirmed cases of human VL notified in Divinópolis during the period 2007-2013 were georeferenced. Spatial analysis of the data was performed using Kernel density estimation, Ripley's bivariate K-function and directional distribution methods. Results The overall prevalence of seropositive animals was 4.63% (range 3.95 - 5.31) (n =169) and varied in different strata between 0.9 (range 0.0 - 1.91) and 8.73% (range 5.65 - 11.81). A positive spatial dependency was detected between human and canine VL in which the occurrence of human cases of the disease tended to concentrate in locations that were close to areas with a higher incidence of canine VL. The priority risk area could be clearly distinguished from Kernel density estimation and standard deviational ellipse plots in which the human VL ellipse was totally enclosed within the canine VL ellipse. Conclusions The results presented herein will enable the Municipal Health Office of Divinópolis to devise a more effective management plan for human VL in which specific strategies would be applied to areas presenting different levels of risk. This spatial evaluation of leishmaniasis model could be applied in other urban areas of Brazil. [ABSTRACT FROM AUTHOR]

The proposal of prioritization is based on practical and feasible statistical tools and will contribute to a better understanding of VL distribution in urban settings. • Two criteria are proposed for prioritizing VL risk based on Moran's I & LISA indices. • Maximum risk areas showing significant spatial HVL/CVL correlation by bivariate LISA. • High risk areas showing significant spatial HVL autocorrelation by univariate LISA. • HVL and CVL data from 148 primary healthcare areas in Belo Horizonte were analyzed. • According to our proposals, 27 areas were classified maximum risk and 13 high risk. Human and canine visceral leishmaniasis (HVL and CVL, respectively) represent serious public health issues in Brazil. The surveillance and control measures currently employed have had limited effect in impeding the territorial expansion of the disease and in reducing the number of cases. We have investigated the space-time distribution of HVL incidence rates and CVL prevalence in the coverage areas of the 148 primary healthcare units in Belo Horizonte (MG, Brazil) during a 6-year period in order to identify those that should be prioritized for disease control actions. Data were smoothed using the empirical Bayes method and analyzed by space-time scanning and application of univariate global Moran's I index and local indicators of spatial association (LISA) statistics to identify spatial autocorrelations. Point data of CVL were analyzed using the Kernel method. Bivariate global Moran's I and LISA techniques were employed to identify spatial correlations between HVL and CVL. Based on our results, we were able to formulate two proposals for establishing the prioritization of coverage areas, namely: (i) classification of maximum priority areas as identified by bivariate LISA for HVL and CVL, and (ii) combination of maximum priority areas with high priority areas as identified by univariate LISA for HVL. According to our proposals, 27 coverage areas in Belo Horizonte were categorized as maximum priority and a further 13 were classified as high priority. Our proposals, which are based on practical, feasible and inexpensive statistical tools, will contribute to a better understanding of VL distribution in urban settings and improving the efficiency of governmental control programs. [ABSTRACT FROM AUTHOR]