Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis

Wenhao Ding,1,&ast; Yanyan Li,1,&ast; Yichun Bai,1 Yuhong Li,2 Lei Wang,3 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2National Center for Tuberculosis Control and...

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Autores principales:	Ding W, Li Y, Bai Y, Wang L, Wang Y
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	covid-19 tuberculosis bsts arima causal impact nowcasting and forecasting Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/c3500bd9587741799cce3074055cf248
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spelling	oai:doaj.org-article:c3500bd9587741799cce3074055cf2482021-11-07T18:42:56ZEstimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis1178-6973https://doaj.org/article/c3500bd9587741799cce3074055cf2482021-11-01T00:00:00Zhttps://www.dovepress.com/estimating-the-effects-of-the-covid-19-outbreak-on-the-reductions-in-t-peer-reviewed-fulltext-article-IDRhttps://doaj.org/toc/1178-6973Wenhao Ding,1,&ast; Yanyan Li,1,&ast; Yichun Bai,1 Yuhong Li,2 Lei Wang,3 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 3Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany&ast;These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453000, People’s Republic of ChinaEmail wybwho@163.comObjective: COVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the COVID-19 outbreak. The purposes of this study are to evaluate the impacts of the COVID-19 outbreak on the decreases in the TB case notifications and to forecast the epidemiological trends in China.Methods: The monthly TB incidents from January 2005 to December 2020 were taken. Then, we investigated the causal impacts of the COVID-19 pandemic on the TB case reductions using intervention analysis under the Bayesian structural time series (BSTS) method. Next, we split the observed values into different training and testing horizons to validate the forecasting performance of the BSTS method.Results: The TB incidence was falling during 2005– 2020, with an average annual percentage change of − 3.186 (95% confidence interval [CI] − 4.083 to − 2.281), and showed a peak in March–April and a trough in January–February per year. The BSTS method assessed a monthly average reduction of 14% (95% CI 3.8% to 24%) in the TB case notifications from January–December 2020 owing to COVID-19 (probability of causal effect=99.684%, P=0.003), and this method generated a highly accurate forecast for all the testing horizons considering the small forecasting error rates and estimated a continued downward trend from 2021 to 2035 (annual percentage change =− 2.869, 95% CI − 3.056 to − 2.681).Conclusion: COVID-19 can cause medium- and longer-term consequences for the TB epidemics and the BSTS model has the potential to forecast the epidemiological trends of the TB incidence, which can be recommended as an automated application for public health policymaking in China. Considering the slow downward trend in the TB incidence, additional measures are required to accelerate the progress of the End TB Strategy.Keywords: COVID-19, tuberculosis, BSTS, ARIMA, causal impact, nowcasting and forecastingDing WLi YBai YLi YWang LWang YDove Medical Pressarticlecovid-19tuberculosisbstsarimacausal impactnowcasting and forecastingInfectious and parasitic diseasesRC109-216ENInfection and Drug Resistance, Vol Volume 14, Pp 4641-4655 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 tuberculosis bsts arima causal impact nowcasting and forecasting Infectious and parasitic diseases RC109-216
spellingShingle	covid-19 tuberculosis bsts arima causal impact nowcasting and forecasting Infectious and parasitic diseases RC109-216 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
description	Wenhao Ding,1,&ast; Yanyan Li,1,&ast; Yichun Bai,1 Yuhong Li,2 Lei Wang,3 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 3Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany&ast;These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453000, People’s Republic of ChinaEmail wybwho@163.comObjective: COVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the COVID-19 outbreak. The purposes of this study are to evaluate the impacts of the COVID-19 outbreak on the decreases in the TB case notifications and to forecast the epidemiological trends in China.Methods: The monthly TB incidents from January 2005 to December 2020 were taken. Then, we investigated the causal impacts of the COVID-19 pandemic on the TB case reductions using intervention analysis under the Bayesian structural time series (BSTS) method. Next, we split the observed values into different training and testing horizons to validate the forecasting performance of the BSTS method.Results: The TB incidence was falling during 2005– 2020, with an average annual percentage change of − 3.186 (95% confidence interval [CI] − 4.083 to − 2.281), and showed a peak in March–April and a trough in January–February per year. The BSTS method assessed a monthly average reduction of 14% (95% CI 3.8% to 24%) in the TB case notifications from January–December 2020 owing to COVID-19 (probability of causal effect=99.684%, P=0.003), and this method generated a highly accurate forecast for all the testing horizons considering the small forecasting error rates and estimated a continued downward trend from 2021 to 2035 (annual percentage change =− 2.869, 95% CI − 3.056 to − 2.681).Conclusion: COVID-19 can cause medium- and longer-term consequences for the TB epidemics and the BSTS model has the potential to forecast the epidemiological trends of the TB incidence, which can be recommended as an automated application for public health policymaking in China. Considering the slow downward trend in the TB incidence, additional measures are required to accelerate the progress of the End TB Strategy.Keywords: COVID-19, tuberculosis, BSTS, ARIMA, causal impact, nowcasting and forecasting
format	article
author	Ding W Li Y Bai Y Li Y Wang L Wang Y
author_facet	Ding W Li Y Bai Y Li Y Wang L Wang Y
author_sort	Ding W
title	Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis
title_short	Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis
title_full	Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis
title_fullStr	Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis
title_full_unstemmed	Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis
title_sort	estimating the effects of the covid-19 outbreak on the reductions in tuberculosis cases and the epidemiological trends in china: a causal impact analysis
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/c3500bd9587741799cce3074055cf248
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Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis

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