Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil

• Published in: BMC infectious diseases Vol. 22; no. 1; pp. 515 - 11
• Main Authors: Berra, Thaís Zamboni, Ramos, Antônio Carlos Vieira, Arroyo, Luiz Henrique, Delpino, Felipe Mendes, de Almeida Crispim, Juliane, Alves, Yan Mathias, dos Santos, Felipe Lima, da Costa, Fernanda Bruzadelli Paulino, dos Santos, Márcio Souza, Alves, Luana Seles, Fiorati, Regina Célia, Monroe, Aline Aparecida, Gomes, Dulce, Arcêncio, Ricardo Alexandre
• Format: Journal Article
• Language: English
• Published: London BioMed Central 02.06.2022; BioMed Central Ltd; BMC
• Subjects: Brazil; Chi-square test; Clusters; Confidence intervals; Control systems; Decomposition; Demographic aspects; Diabetes mellitus; Disease; Disease transmission; Ecological studies; Epidemics; Epidemiology; HIV; Human immunodeficiency virus; Hypotheses; Infectious Diseases; Internal Medicine; Medical Microbiology; Medicine; Medicine & Public Health; Parasitology; Population; Risk analysis; Risk factors; Scanning; Spatial analysis; Spatial variations; Statistical analysis; Statistical tests; Statistics; Temporal trend; Time series; Trends; Tropical Medicine; Tuberculosis; Urban areas; Variables; Brazil; Sao Paulo Brazil; Spatial analysis; Tuberculosis; Temporal trend
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-022-07500-5

Objectives To identify risk-prone areas for the spread of tuberculosis, analyze spatial variation and temporal trends of the disease in these areas and identify their determinants in a high burden city. Methods An ecological study was carried out in Ribeirão Preto, São Paulo, Brazil. The population was composed of pulmonary tuberculosis cases reported in the Tuberculosis Patient Control System between 2006 and 2017. Seasonal Trend Decomposition using the Loess decomposition method was used. Spatial and spatiotemporal scanning statistics were applied to identify risk areas. Spatial Variation in Temporal Trends (SVTT) was used to detect risk-prone territories 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. Results Between 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, 1.73, 2.07, and 2.68 to 2.72. With the space–time scan, four clusters were also identified with RR of 0.13 (2008–2013), 1.94 (2010–2015), 2.34 (2006 to 2011), and 2.84 (2014–2017). With the SVTT, a cluster was identified with RR 0.11, an internal time trend of growth (+ 0.09%/year), and an external time trend of decrease (− 0.06%/year). Finally, three risk factors and three protective factors that are 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 or more of study (OR: 0.73), not having HIV (OR: 0.55) and not having diabetes (OR: 0.35). Conclusion The importance of using spatial analysis tools in identifying areas that should be prioritized for TB control is highlighted, and greater attention is necessary for individuals who fit the profile indicated as “at risk” for the disease.