Risk Areas and Spatial Variations in Temporal Trends of Pulmonary Tuberculosis and Their Determinants in a High Burden City From São Paulo State- Brazil.

Abstract BACKGROUNDTuberculosis is the leading cause of global deaths from a single infectious agent. This study aimed to identify areas of spatial and space-time risk for pulmonary tuberculosis, to identify areas with variation in the temporal tendency for this event, and to identify factors associated with the epidemiological situation in one municipality. METHODSAn ecological study carried out in Ribeirão Preto, São Paulo, Brazil. The population consisted of pulmonary tuberculosis cases reported in the Tuberculosis Patient Control System between 2006 to 2017. To check the behavior of tuberculosis over the period, the Seasonal Trend Decomposition using Loess decomposition method was used. Spatial and spatiotemporal scanning statistics were used to identify risk areas, and Spatial Variation in Temporal Trends (SVTT) was used to detect clusters with changes in the temporal trend. Finally, Pearson's chi-square test was performed to identify factors associated with the epidemiological situation in the municipality.RESULTSBetween 2006 and 2017, 1760 cases of pulmonary tuberculosis were reported in the municipality. With spatial scanning, four groups of clusters were identified with relative risks (RR) from 0.19 to 0.52 (95%CI:0.09-0.69); 1.73 (95%CI:1.49-1.99); 2.07 (95%CI:1.70–2.32) and from 2.68 to 2.72 (95%CI:2.00-3.77). With the space-time scan, four clusters were also identified with RR of 0.13 [2008-2013] (95%CI:0.03-0.26); 1.94 [2010-2015] (95%CI:1.77-2.11); 2.34 [2006 to 2011] (95%CI:1.67-2.94) and 2.84 [2014-2017] (95%CI:2.50-3.90). With the SVTT, a cluster was identified with RR 0.11, an internal time trend of growth (+0.09%/year) and an external one of decrease (-0.06%/year). Finally, three risk factors and three protective factors associated with the epidemiological situation in the municipality were identified, being: race/brown color (OR: 1.26), without education (OR: 1.71), retired (OR: 1.35), 15 years old or more of study (OR: 0.73), not having HIV (OR: 0.55) and not having diabetes (OR: 0.35).CONCLUSIONThe importance of using spatial analysis tools in the identification of areas that should be prioritized is highlighted, and greater attention is needed to individuals who fit the profile indicated as risk for the disease, in order to undertake social and health actions in an attempt decrease the mortality of the disease.