Impact of COVID-19 pandemic on tuberculosis mortality in Brazil: a time series analysis
DOI:
https://doi.org/10.11606/s1518-8787.2026060007226Palabras clave:
Tuberculosis, Mortality, Interrupted Time Series Analysis, COVID-19Resumen
OBJECTIVE: To evaluate changes in the trend of tuberculosis mortality in Brazil over recent decades and assess the impact of the COVID-19 pandemic on this indicator.
METHODS: We analyzed the national and regional time series of tuberculosis mortality from 2000 to 2023 using joinpoint regression. To assess the pandemic’s impact, we applied two Interrupted time series (ITS) approaches: segmented linear regression and the AutoRegressive Integrated Moving Average with eXogenous variables (ARIMAX) model). We also used Autoregressive Integrated Moving Average (ARIMA) modeling to estimate excess tuberculosis deaths linked to the pandemic and to forecast mortality trends through 2030.
RESULTS: An increase in tuberculosis mortality was observed starting in 2021, reaching 2.4 deaths per 100 thousand people in both 2022 and 2023 — similar to rates observed in 2011. This represents a reversal in the declining trend seen throughout the 2000s and 2010s, affecting all macroregions. The annual percentage change from 2019 to 2023 was +6.5% (95% confidence interval — 95%CI 4.42–9.98), contrasting with an average decline of -1.93% (95%CI -2.19 to -1.69) over the full period. Both ITS models consistently demonstrated a detrimental long-term reversal of the mortality trend after the pandemic. While a precise level change was not apparent using traditional segmented regression, the ARIMAX-based analysis successfully isolated a significant acute and lagged effect (β = +0.211; p = 0.0029). We estimated 6,540 excess tuberculosis deaths in Brazil between 2020 and 2023 (95%CI 3,950–9,130). The forecasting model with the pandemic effect projected higher mortality rates from 2024 to 2030, while the counterfactual scenario showed a continued decline.
CONCLUSIONS: The COVID-19 pandemic had a substantial negative impact on tuberculosis mortality in Brazil, representing a setback in achieving national and global elimination targets.
Referencias
1. World Health Organization. Global tuberculosis report [Internet]. World Health Organization; 2024 [cited 2025 Feb 1]. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports
2. Brasil. Ministério da Saúde. Brasil Livre da Tuberculose: Plano Nacional pelo Fim da Tuberculose como Problema de Saúde Pública [Internet]. Ministério da Saúde; 2017 [cited 2025 Dec 17]. Available from: www.saude.gov.br/bvs
3. Falzon D, Zignol M, Bastard M, Floyd K, Kasaeva T. The impact of the COVID-19 pandemic on the global tuberculosis epidemic. Front Immunol. 2023;14:1234785. https://doi.org/10.3389/fimmu.2023.1234785
4. Marco MH, Ahmedov S, Castro KG. The global impact of Covid-19 on tuberculosis: A thematic scoping review, 2020-2023. PLOS Global Public Health. 2024;4(7):e0003043. https://doi.org/10.1371/journal.pgph.0003043
5. Brasil. Ministério da Saúde. Boletim Epidemiológico – Tuberculose [Internet]. Ministério da Saúde; 2024 [cited 2025 Dec 17]. Available from: www.gov.br/saude
6. Instituto Brasileiro de Geografia e Estatística. Projeções da População do Brasil e Unidades da Federação: 2000–2070 [Internet]. Brazil: Instituto Brasileiro de Geografia e Estatística; 2024 [cited 2024 Nov 1]. Available from: https://www.ibge.gov.br/estatisticas/sociais/populacao/9109-projecao-da-populacao.html
7. Ahmad OB, Boschi-Pinto C, Lopez Christopher AD, Murray JL, Lozano R, Inoue M. Age standardization of rates: a new WHO standard. Geneva: World Health Organization; 2001.
8. Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335-51. https://doi.org/10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z
9. Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: A tutorial. Int J Epidemiol. 2017;46(1):348-55. https://doi.org/10.1093/ije/dyw098
10. Ashton RA, Bennett A, Yukich J, Bhattarai A, Keating J, Eisele TP. Methodological considerations for use of routine health information system data to evaluate malaria program impact in an era of declining malaria transmission. Am J Trop Med Hygiene. 2017;97(3 Suppl.):46-57. https://doi.org/10.4269/ajtmh.16-0734
11. Box GEP, Tiao GC. Intervention analysis with applications to economic and environmental problems. J Am Stat Assoc. 1975;70(349):70-9. https://doi.org/10.1080/01621459.1975.10480264
12. Zhou Q, Hu J, Hu W, Li H, Lin G. Interrupted time series analysis using the ARIMA model of the impact of Covid-19 on the incidence rate of notifiable communicable diseases in China. BMC Infect Dis. 2023;23:375. https://doi.org/10.1186/s12879-023-08229-5
13. Yoneoka D, Kawashima T, Tanoue Y, Nomura S, Eguchi A. Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during Covid-19 pandemic. BMC Med Res Methodol. 2022;22(1):202. https://doi.org/10.1186/s12874-022-01662-1
14. Schaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21(1):58. https://doi.org/10.1186/s12874-021-01235-8
15. Chen YP, Liu LF, Che Y, Huang J, Li GX, Sang GX, et al. Modeling and predicting pulmonary tuberculosis incidence and its association with air pollution and meteorological factors using an ARIMAX model: an ecological study in Ningbo of China. Int J Environ Res Public Health. 2022;19(9):5385. https://doi.org/10.3390/ijerph19095385
16. Cheng C, Jiang WM, Fan B, Cheng YC, Hsu YT, Wu HY, et al. Real-time forecasting of Covid-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models. BMC Public Health. 2023;23(1):1500. https://doi.org/10.1186/s12889-023-16419-8
17. Ali M, Khan DM, Aamir M, Khalil U, Khan Z. Forecasting Covid-19 in Pakistan. PLoS One. 2020;15(11):e0242762. https://doi.org/10.1371/journal.pone.0242762
18. Bierrenbach AL, Duarte EC, Gomes ABF, de Souza M de FM. Mortality trends due to tuberculosis in Brazil, 1980-2004. Rev Saúde Pública. 2007;41(Suppl. 1):15-23. https://doi.org/10.1590/s0034-89102007000800004
19. De Souza RA, Nery JS, Rasella D, Guimarães Pereira RA, Barreto ML, Rodrigues L, et al. Family health and conditional cash transfer in Brazil and its effect on tuberculosis mortality. Int J Tuberc Lung Dis. 2018;22(11):1300-6. https://doi.org/10.5588/ijtld.17.0907
20. de Paiva JPS, Magalhães MAFM, Leal TC, da Silva LF, da Silva LG, do Carmo RF, et al. Time trend, social vulnerability, and identification of risk areas for tuberculosis in Brazil: An ecological study. PLoS One. 2022;17(1):e0247894. https://doi.org/10.1371/journal.pone.0247894
21. Silva MT, Galvão TF. Tuberculosis incidence in Brazil: time series analysis between 2001 and 2021 and projection until 2030. Rev Bras Epidemiol. 2024;27:e240027. https://doi.org/10.1590/1980-549720240027
22. Li Y, de Macedo Couto R, Pelissari DM, Costa Alves L, Bartholomay P, Maciel EL, et al. Excess tuberculosis cases and deaths following an economic recession in Brazil: an analysis of nationally representative disease registry data. Lancet Glob Health. 2022;10(10):e1463-72. https://doi.org/10.1016/S2214-109X(22)00320-5
23. Mendenhall E, Kohrt BA, Logie CH, Tsai AC. Syndemics and clinical science. Nat Med. 2022;28(7):1359-62. https://doi.org/10.1038/s41591-022-01888-y
24. Instituto Brasileiro de Geografia e Estatística. Síntese de indicadores sociais: uma análise das condições de vida da população brasileira [Internet]. Rio de Janeiro; 2023 [cited 2025 Feb 1]. Available from: https://www.ibge.gov.br/estatisticas/sociais/trabalho/9221-sintese-de-indicadores-sociais.html
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Bernardo Bastos Wittlin, Felipe Bezerra Pimentel Araújo, Antônio Augusto Moura da Silva
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
