Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil
Abstract 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 po...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-022-07500-5 |
| _version_ | 1828328525852049408 |
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| author | Thaís Zamboni Berra Antônio Carlos Vieira Ramos Luiz Henrique Arroyo Felipe Mendes Delpino Juliane de Almeida Crispim Yan Mathias Alves Felipe Lima dos Santos Fernanda Bruzadelli Paulino da Costa Márcio Souza dos Santos Luana Seles Alves Regina Célia Fiorati Aline Aparecida Monroe Dulce Gomes Ricardo Alexandre Arcêncio |
| author_facet | Thaís Zamboni Berra Antônio Carlos Vieira Ramos Luiz Henrique Arroyo Felipe Mendes Delpino Juliane de Almeida Crispim Yan Mathias Alves Felipe Lima dos Santos Fernanda Bruzadelli Paulino da Costa Márcio Souza dos Santos Luana Seles Alves Regina Célia Fiorati Aline Aparecida Monroe Dulce Gomes Ricardo Alexandre Arcêncio |
| author_sort | Thaís Zamboni Berra |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T20:11:48Z |
| format | Article |
| id | doaj.art-48d0c9c8401e45dba7d55ffcb18f9ecd |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-04-13T20:11:48Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-48d0c9c8401e45dba7d55ffcb18f9ecd2022-12-22T02:31:50ZengBMCBMC Infectious Diseases1471-23342022-06-0122111110.1186/s12879-022-07500-5Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, BrazilThaís Zamboni Berra0Antônio Carlos Vieira Ramos1Luiz Henrique Arroyo2Felipe Mendes Delpino3Juliane de Almeida Crispim4Yan Mathias Alves5Felipe Lima dos Santos6Fernanda Bruzadelli Paulino da Costa7Márcio Souza dos Santos8Luana Seles Alves9Regina Célia Fiorati10Aline Aparecida Monroe11Dulce Gomes12Ricardo Alexandre Arcêncio13Department of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingDepartment of Neurosciences and Behavioral Sciences, Faculty of Medicine, University of São Paulo at Ribeirão PretoDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingMathematics Department, University of ÉvoraDepartment of Maternal-Infant and Public Health Nursing Graduate Program, University of São Paulo at Ribeirão Preto College of NursingAbstract 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.https://doi.org/10.1186/s12879-022-07500-5TuberculosisSpatial analysisTemporal trend |
| spellingShingle | Thaís Zamboni Berra Antônio Carlos Vieira Ramos Luiz Henrique Arroyo Felipe Mendes Delpino Juliane de Almeida Crispim Yan Mathias Alves Felipe Lima dos Santos Fernanda Bruzadelli Paulino da Costa Márcio Souza dos Santos Luana Seles Alves Regina Célia Fiorati Aline Aparecida Monroe Dulce Gomes Ricardo Alexandre Arcêncio Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil BMC Infectious Diseases Tuberculosis Spatial analysis Temporal trend |
| title | Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil |
| title_full | Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil |
| title_fullStr | Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil |
| title_full_unstemmed | Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil |
| title_short | Risk-prone territories for spreading tuberculosis, temporal trends and their determinants in a high burden city from São Paulo State, Brazil |
| title_sort | risk prone territories for spreading tuberculosis temporal trends and their determinants in a high burden city from sao paulo state brazil |
| topic | Tuberculosis Spatial analysis Temporal trend |
| url | https://doi.org/10.1186/s12879-022-07500-5 |
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