Temporal trend of tuberculosis incidence and its spatial distribution in Macapá – Amapá

Authors

DOI:

https://doi.org/10.11606/s1518-8787.2021055003431

Keywords:

Tuberculosis, epidemiology, Space-Time Clustering, Spatial Analysis, Ecological Studies

Abstract

OBJECTIVE: To evaluate the temporal trend of tuberculosis incidence after the implementation of the rapid molecular test (RMT-TB), to identify whether tuberculosis presents seasonal variation and to classify the territory according to case density and risk areas in Macapá, Amapá. METHODS: Ecological study of tuberculosis cases registered in the Sistema de Informação de Agravos de Notificação (SINAN – Information System for Notifiable Diseases) between 2001 and 2017. We used the Prais-Winsten test to classify the temporal trend of incidence and the interrupted time series to identify changes in the temporal trend before and after the implementation of the rapid molecular test, and to verify seasonality in the municipality. The Kernel estimator was used to classify case density and scan statistics to identify areas of tuberculosis risk. RESULTS: A total of 1,730 cases were identified, with a decreasing temporal trend of tuberculosis incidence (−0.27% per month, 95%CI −0.13 to −0.41). The time series showed no change in level after the implementation of the GeneXpert®MTB/RIF molecular test; however, the incidence increased in the post-test period (+2.09% per month, 95%CI 0.92 to 3.27). Regarding the seasonal variation, it showed growth (+13.7%/month, 95%CI 4.71 to 23.87) from December to June, the rainy season – called amazon winter season –, and decrease (−9.21% per month, CI95% −1.37 to −16.63) in the other periods. We classified areas with high density of cases in the Central and Northern districts using Kernel and identified three protection clusters, SC1 (RR = 0.07), SC2 (RR = 0.23) and SC3 (RR = 0.36), and a high-risk cluster, SC4 (RR = 1.47), with the scan statistics. CONCLUSION: The temporal trend of tuberculosis incidence was decreasing in the time series; however, detection increased after the introduction of RMT-TB, and tuberculosis showed seasonal behavior. The case distribution was heterogeneous, with a tendency to concentrate in vulnerable and risk territories, evidencing a pattern of disease inequality in the territory.

References

World Health Organization. Global Tuberculosis Report 2020. Geneva (CH): WHO; 2020 [cited 2021 Mar 15]. Available from: https://apps.who.int/iris/bitstream/handle/10665/336069/9789240013131-eng.pdf [ Links ]

Ministério da Saúde (BR), Secretaria de Vigilância em Saúde. Bol Epidemiol Tuberculose 2020. 2020 [cited 2021 Mar 15].];N° Espec:1-40. Available from: http://www.aids.gov.br/pt-br/pub/2020/boletim-epidemiologico-de-turbeculose-2020 [ Links ]

Amapá [Estado], Superintendência de Vigilância em Saúde, Unidade de Doenças Transmissíveis. Bol Epidemiol Tuberculose. 2020 [cited 2021 Mar 15]. Available from: https://editor.amapa.gov.br/arquivos_portais/publicacoes/SVS_c20f41e26fed90da418341d2d2135a3a.pdf [ Links ]

Ministério da Saúde (BR), Secretaria de Vigilância em Saúde, Departamento de Vigilância das Doenças Transmissíveis. Manual de recomendações para o controle da tuberculose no Brasil. 2. ed. atual. Brasília, DF: 2019 [cited 2021 Mar 15]. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/manual_recomendacoes_controle_tuberculose_brasil_2_ed.pdf [ Links ]

Ministério da Saúde (BR), Secretaria de Vigilância em Saúde, Departamento de Vigilância das Doenças Transmissíveis. Rede de Teste Rápido para Tuberculose no Brasil: primeiro ano da implantação. Brasília, DF; 2015 [cited 2021 Mar 15]. Available from: http://portalarquivos.saude.gov.br/images/pdf/2016/janeiro/19/rtr-tb-15jan16-isbn-web.pdf [ Links ]

Fares A. Seasonality of tuberculosis. J Glob Infect Dis. 2011;3(1):46-55. https://doi.org/10.4103/0974-777X.77296 [ Links ]

Fundação de Vigilância em Saúde do Amazonas (BR). Situação epidemiológica da Síndrome Respiratória Aguda Grave no Estado do Amazonas. Bol Epidemiol. 2021 [cited 2021 Mar 15];8(11). Available from: https://www.fvs.am.gov.br/media/publicacao/Boletim_Epidemiol%C3%B3gico_N%C2%BA_11.pdf [ Links ]

Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3. ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008. [ Links ]

Instituto Brasileiro de Geografia e Estatística. Censo 2010: cidades: Macapá. Rio de Janeiro: IBGE; 2016 [cited 2021 Mar 15]. Available from: https://cidades.ibge.gov.br/brasil/ap/macapa/panorama [ Links ]

Antunes JLF, Cardoso MRA. Uso da análise de séries temporais em estudos epidemiológicos. Epidemiol Serv Saude. 2015;24(3):565-76. https://doi.org/10.5123/S1679-49742015000300024 [ Links ]

Wagner AK, Soumerai SB, Zhang F, Ross-Degnan D. Segmented regression analysis of interrupted time series studies in medication use research. J Clin Pharm Ther. 2002;27(4):299-309. https://doi.org/10.1046/j.1365-2710.2002.00430.x [ Links ]

Environmental Systems Research Institute. ArcGIS for Desktop. How Kernel Density works. West Redlands, CA: Esri; c2016 [cited 2021 Mar 15]. Available from: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/how-kernel-density-works.htm [ Links ]

Oliveira U, Brescovit AD, Santos AJ. Delimiting areas of endemism through Kernel Interpolation. PLoS One. 2015;10(1):e0116673. https://doi.org/10.1371/journal.pone.0116673 [ Links ]

Yamamura M, Freitas IM, Santos-Neto M, Chiaravalloti-Neto F, Popolin MAP, Arroyo LH, et al. Análise espacial das internações evitáveis por tuberculose em Ribeirão Preto, SP (2006-2012). Rev Saude Publica. 2016;50:20. https://doi.org/10.1590/S1518-8787.2016050006049 [ Links ]

Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med. 1995;14(8):799-810. https://doi.org/10.1002/sim.4780140809 [ Links ]

Kulldorff M. SaTScanTM manual do usuário para versão 9.4: versão do original traduzido para o Português. Pelini, ACG tradutor. São Paulo; 2016 [cited 2021 Mar 15]. Available from: https://www.satscan.org/SaTScan_TM_Manual_do_Usu%C3%A1rio_v9.4_Portugues.pdf [ Links ]

Han J, Zhu L, Kulldorff M, Hostovich S, Stinchcomb DG, Tatalovich Z, et al. Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics. Int J Health Geogr. 2016;15(1):27. https://doi.org/10.1186/s12942-016-0056-6 [ Links ]

Hoshino H, Uchimura K, Yamauchi Y. [Comparison of TB incidence of young and middle age groups between urban/suburban prefectures and other prefectures]. Kekkaku. 2009;84(1):1-8. Japanese. https://doi.org/10.11400/kekkaku.84.1 [ Links ]

Fontes GJF, Silva TG, Sousa JCM, Feitosa ANA, Silva ML, Bezerra ALD, et al. Perfil epidemiológico da tuberculose no Brasil no período de 2012 a 2016. Rev Bras Educ Saude. 2019;9(1):19-26. https://doi.org/10.18378/rebes.v9i1.6376 [ Links ]

Santos JN, Sales CMM, Prado TN, Maciel EL. Fatores associados à cura no tratamento da tuberculose no estado do Rio de Janeiro, 2011-2014. Epidemiol Serv Saude. 2018;27(3):e2017464. https://doi.org/10.5123/s1679-49742018000300015 [ Links ]

Coriolano-Marinus MWL, Queiroga BAM, Ruiz-Moreno L, Lima LS. Comunicação nas práticas em saúde: revisão integrativa da literatura. Saude Soc. 2014;23(4):1356-69. https://doi.org/10.1590/S0104-12902014000400019 [ Links ]

World Health Organization. Global Tuberculosis Report 2019. Geneva (CH): WHO; 2019 [cited 2021 Mar 15]. Available from: https://apps.who.int/iris/bitstream/handle/10665/329368/9789241565714-eng.pdf [ Links ]

Boehme CC, Nabeta P, Hillemann D, Nicol MP, Shenai S, Krapp F, et al. Rapid molecular detection of tuberculosis and rifampin resistance. N Engl J Med. 2010;363(11):1005-15. https://doi.org/10.1056/NEJMoa0907847 [ Links ]

Lima TM, Belotti NCU, Nardi SMT, Pedro HSP. Teste rápido molecular GeneXpert MTB/RIF para diagnóstico da tuberculose. Rev Pan Amaz Saude 2017;8(2):67-78. [ Links ]

Pandey P, Pant ND, Rijal KR, Shrestha B, Kattel S, Banjara MR, et al. Diagnostic accuracy of GeneXpert MTB/RIF assay in comparison to conventional drug susceptibility testing method for the diagnosis of multidrug-resistant tuberculosis. PLoS One. 2017;12(1):e0169798. https://doi.org/10.1371/journal.pone.0169798 [ Links ]

Spagnolo LML, Tomberg JO, Vieira DA, Gonzales RIC. Detecção da tuberculose: fluxo dos sintomáticos respiratórios e resultados alcançados. Rev Bras Enferm. 2018;71(5):2543-51. https://doi.org/10.1590/0034-7167-2017-0457 [ Links ]

World Health Organization. Global Tuberculosis Report 2016. Geneva (CH): WHO; 2019 [cited 2021 Mar 15]. Available from: https://apps.who.int/iris/bitstream/handle/10665/250441/9789241565394-eng.pdf?sequence=1&isAllowed=y [ Links ]

Moreira ASR, Kinski AL, Carvalho ACC. Social determinants of health and catastrophic costs associated with the diagnosis and treatment of tuberculosis. J Bras Pneumol. 2020;46(5):e20200015. https://doi.org/10.36416/1806-3756/e20200015 [ Links ]

Veras RP, Almeida Filho N, Barreto ML, Veras RP, Barata RB. Teoria epidemiológica hoje: fundamentos, interfaces, tendências. Rio de Janeiro: Editora FIOCRUZ; 1998 [cited 2021 Mar 15]. (Séries Epidemiológicas, N° 2). Available from: http://books.scielo.org/id/5btwk [ Links ]

Melo GBT, Valongueiro S. Incompleteness of Mortality Information System records on deaths from external causes in Pernambuco, Brazil, 2000-2002 and 2008-2010. Epidemiol Serv Saude. 2015;24(4):651-60. https://doi.org/10.5123/S1679-4974201500040000 [ Links ]

Published

2021-12-01

Issue

Section

Original Articles

How to Cite

Giacomet, C. L., Santos, M. S., Berra, T. Z., Alves, Y. M., Alves, L. S., Costa, F. B. P. da, Ramos, A. C. V., Crispim, J. de A., Monroe, A. A. ., Pinto, I. C., Fiorati, R. C., Arcoverde, M. A. M., Gomes, D., Freitas, G. L. de, Yamamura, M., & Arcêncio, R. A. (2021). Temporal trend of tuberculosis incidence and its spatial distribution in Macapá – Amapá. Revista De Saúde Pública, 55, 96. https://doi.org/10.11606/s1518-8787.2021055003431

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