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. 2025 Jan 24:15:04012.
doi: 10.7189/jogh.15.04012.

Long-term impact of COVID-19-related nonpharmaceutical interventions on tuberculosis: an interrupted time series analysis using Bayesian method

Yongbin Wang  1 , Yue Xi  1 , Yanyan Li  1 , Peiping Zhou  1 , Chunjie Xu  2
Affiliations

Long-term impact of COVID-19-related nonpharmaceutical interventions on tuberculosis: an interrupted time series analysis using Bayesian method

Yongbin Wang et al. J Glob Health. .

Abstract

Background: The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic may inadvertently influence the epidemiology of tuberculosis (TB). (TB). However, few studies have explored how NPIs impact the long-term epidemiological trends of TB. We aimed to estimate the impact of NPIs implemented against COVID-19 on the medium- and long-term TB epidemics and to forecast the epidemiological trend of TB in Henan.

Methods: We first collected monthly TB case data from January 2013 to September 2022, after which we used the data from January 2013 to December 2021 as a training data set to fit the Bayesian structural time series (BSTS) model and the remaining data as a testing data set to validate the model's predictive accuracy. We then conducted an intervention analysis using the BSTS model to evaluate the impact of the COVID-19 pandemic on TB epidemics and to project trends for the upcoming years.

Results: A total of 590 455 TB cases were notified from January 2013 to September 2022, resulting in an annual incidence rate of 57.4 cases per 100 000 population, with a monthly average of 5047 cases (5.35 cases per 100 000 population). The trend in TB incidence showed a significant decrease during the study period, with an annual average percentage change of -7.3% (95% confidence interval (CI) = -8.4, -6.1). The BSTS model indicated an average monthly reduction of 25% (95% CI = 17, 32) in TB case notifications from January 2020 to December 2021 due to COVID-19 (probability of causal effect = 99.80%, P = 0.002). The mean absolute percentage error in the forecast set was 14.86%, indicating relatively high predictive accuracy of the model. Furthermore, TB cases were projected to total 43 584 (95% CI = 29 471, 57 291) from October 2022 to December 2023, indicating a continued downward trend.

Conclusions: COVID-19 has had medium- and long-term impacts on TB epidemics, while the overall trend of TB incidence in Henan is generally declining. The BSTS model can be an effective option for accurately predicting the epidemic patterns of TB, and its results can provide valuable technical support for the development of prevention and control strategies.

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Conflict of interest statement

Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.

Figures

Figure 1
Figure 1
Trends and cycle patterns of TB epidemic situation in Henan from January 2013 to September 2022 (the restriction period of COVID-19 is January 2020).
Figure 2
Figure 2
Joinpoint regression plot displaying the TB epidemiological trends from 2013–21. *APC is statistically significant.
Figure 3
Figure 3
Time series plot displaying the causal impacts of the COVID-19 outbreak on the decreases in TB cases from January – September 2022. Panel A. Reported TB cases and counterfactual expected figures for the post-outbreak period. Panel B. Pointwise causal impact that indicates the difference between reported cases and expected figures. Panel C. Cumulative effect of the COVID-19 pandemic on the decreases in TB cases via accumulating the pointwise effects from the second panel.
Figure 4
Figure 4
Incidence trend of TB in Henan from September 2022 to December 2023, as predicted by the BSTS model.
See this image and copyright information in PMC

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References

    1. GBD 2021 Demographics Collaborators Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:1989–2056. 10.1016/S0140-6736(24)00476-8 - DOI - PMC - PubMed
    1. GBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators. Antimicrobial Resistance C Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis. 2024;24:974–1002. 10.1016/S1473-3099(24)00176-2 - DOI - PMC - PubMed
    1. World Health Organization. Global tuberculosis report 2023. Geneva, Switzerland: World Health Organization; 2023. Available: https://www.who.int/publications/i/item/9789240083851. Accessed: 17 August 2024.
    1. Ding W, Li Y, Bai Y, Li Y, Wang L, Wang Y.Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis. Infect Drug Resist. 2021;14:4641–55. 10.2147/IDR.S337473 - DOI - PMC - PubMed
    1. Akter R, Rahman MH, Bhattacharya T, Kaushik D, Mittal V, Parashar J, et al. Novel coronavirus pathogen in humans and animals: an overview on its social impact, economic impact, and potential treatments. Environ Sci Pollut Res Int. 2021;28:68071–89. 10.1007/s11356-021-16809-8 - DOI - PMC - PubMed