Geospatial intelligence and health analitycs : its application and utility in a city with high tuberculosis incidence in Brazil

Abstract

Background: Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. Methods: We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area’s HDI (each one of the components — longevity, education, and income) with TB spatial distribution. Results: During the study period (2011–2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036). Conclusions: We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs.