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

IF 4.5 3区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Yongbin Wang, Yue Xi, Yanyan Li, Peiping Zhou, Chunjie Xu
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引用次数: 0

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.

与covid -19相关的非药物干预措施对结核病的长期影响:使用贝叶斯方法的中断时间序列分析
背景:在COVID-19大流行期间实施非药物干预措施(npi)可能会无意中影响结核病(TB)的流行病学。(结核病)。然而,很少有研究探讨非营利性项目如何影响结核病的长期流行病学趋势。我们的目的是评估新冠肺炎疫情防控措施对河南省中长期结核病流行的影响,并预测河南省结核病流行趋势。方法:我们首先收集2013年1月至2022年9月的每月结核病病例数据,然后将2013年1月至2021年12月的数据作为训练数据集来拟合贝叶斯结构时间序列(BSTS)模型,其余数据作为测试数据集来验证模型的预测准确性。然后,我们使用BSTS模型进行了干预分析,以评估COVID-19大流行对结核病流行的影响,并预测未来几年的趋势。结果:2013年1月至2022年9月,共报告结核病病例590455例,年发病率为57.4例/ 10万人口,月平均发病率为5047例(5.35例/ 10万人口)。在研究期间,结核病发病率呈显著下降趋势,年平均变化百分比为-7.3%(95%可信区间(CI) = -8.4, -6.1)。BSTS模型显示,从2020年1月到2021年12月,由于COVID-19,结核病病例报告平均每月减少25% (95% CI = 17,32)(因果效应概率= 99.80%,P = 0.002)。预测集的平均绝对百分比误差为14.86%,表明模型具有较高的预测精度。此外,预计2022年10月至2023年12月结核病病例总数为43 584例(95% CI = 29 471,57 291),显示持续下降趋势。结论:新冠肺炎疫情对结核病流行具有中长期影响,河南省结核病发病率总体呈下降趋势。BSTS模型可作为准确预测结核病流行模式的有效选择,其结果可为制定预防和控制策略提供有价值的技术支持。
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来源期刊
Journal of Global Health
Journal of Global Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
6.10
自引率
2.80%
发文量
240
审稿时长
6 weeks
期刊介绍: Journal of Global Health is a peer-reviewed journal published by the Edinburgh University Global Health Society, a not-for-profit organization registered in the UK. We publish editorials, news, viewpoints, original research and review articles in two issues per year.
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