Tuberculosis prevalence in children in the Northwestern Federal District of Russia before and after COVID-19 pandemic: prognosis and epidemiological models

Aim. To assess changes in the epidemic indicators of tuberculosis infection (TB) in children in the Northwestern Federal District of Russia before and after the COVID-19 pandemic based on mathematical modeling and forecasting.

Materials and methods. The main epidemiological indicators of TB were analyzed using the official statistical data for 2009–2021. A mathematical forecasting of epidemiological indicators was performed based on chest X-ray screening for TB. A statistical analysis was carried out using the software environment R (v.3.5.1) and the commercial software Statistical Package for Social Sciences (SPSS Statistics for Windows, version 24.0, IBM Corp., 2016). Time series forecasting was performed using the programming language of statistical calculations R, version 4.1.2 and the bsts package, version 0.9.8.

Results. The mean regression coefficient of a single predictor was found to differ in a model for TB morbidity in children is 0.0098. X-ray screening for TB was established to be a significant mortality predictor in children. At least 60% of the population should undergo TB screening in order for TB prevalence to be controlled in a country with a population above 140 million people.

Conclusions. The conducted study revealed a positive correlation between the incidence of tuberculosis in children in Russia and TB screening in at least 60% of the population. Under the current TB screening system in Russia, the epidemic TB situation will continue to improve, despite COVID-19 restrictions.  At the same time, in the Northwestern Federal District of Russia, preventive TB screening can be considered sufficient only in the Kaliningrad, Murmansk, and Pskov Oblasts.

1. WHO global lists of high burden countries for TB, multidrug/ rifampicin-resistant TB (MDR/RR-TB) and TB/HIV, 2021–2025. – 2021. – 16 p. ISBN 978-92-4-002943-9.

2. McQuaid CF, McCreesh N, Read JM, Sumner T, Houben RMGJ, White RG, Harris RC. The potential impact of COVID-19-related disruption on tuberculosis burden. Eur Respir J. 2020;56:2001718; doi: 10.1183/13993003.01718-2020

3. WHO consolidated guidelines on tuberculosis. Module 5: management of tuberculosis in children and adolescents. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO. ISBN: 978-92-4-004676-4.

4. Nechaeva OB. The state and prospects of the anti-tuberculosis service in Russia during the period of COVID-19. Tuberculosis and Lung Diseases. 2020;98(12):7–19 (In Russ.)]. doi: 10.21292/2075-12302020-98-12-7-19

5. Dovgalyuk IF, Starshinova АА, Galkin VB, Beltyukov MV, Kudlay DA, Yablonsky PK. Tuberculosis in children in the Northwestern Region of the Russian Federation: Epidemiology and diagnostic measures efficacy. Pediatria n.a. G.N. Speransky. 2019;98(3):274–9 (In Russ.)] doi: 10.24110/0031403X-2019-98-3-274-279

6. Starshinova AA, Kudlai DA, Dovgalyuk IF, Basantsova NYu, Zinchenko YuS, Yablonsky PK. The effectiveness of the use of new methods of immunodiagnostics of tuberculosis infection in the Russian Federation (literature review). Pediatria n.a. G.N. Speransky. 2019;4:229–35 (In Russ.)]. doi: 10.24110/0031-403X-2019-98-4-229-235

7. Comella-del-Barrioa P, De Souza-Galvãob ML, Prat-Aymericha C, Domínguez J. Impact of COVID-19 on Tuberculosis Control. 2021;57:S2. doi: 10.1016/j.arbres.2020.11.016

8. Аksenova VA, Baryshnikova LA, Klevno NI, Kudlay DA. Screening for tuberculosis infection in children and adolescents in Russia – past, present, future. Tuberculosis and Lung Diseases. 2019;97(9):59–67. (In Russ.) doi: 10.21292/2075-1230-2019-97-9-59-67

9. Slogotskaya LV, Bogorodskaya EM, Senchikhina OYu, Nikitina GV, Kudlay DA. Formation of risk groups for tuberculosis in various immunological methods for examining the child population. Russian pediatric journal. 2017;20(4):207–13 (In Russ.)]. doi:10.18821/15609561-2017-20-4-207-213

10. Yablonskii PK, Vizel AA, Galkin VB, Shulgina MV. Tuberculosis in Russia. Its history and its status today. Am J Respir Crit Care Med. 2015;191:372–6.

11. Tuberculosis in the Russian Federation 2011. Analytical review of statistical indicators used in the Russian Federation and in the world. Moscow: Triada, 2015. 280 p. (In Russ.)]

12. Starshinova AA, Dovgalyuk IF, Kudlay DA, Beltyukov MV, Yablonskiy PK. Tuberculosis in adults and children in the Northwestern Federal District: Changes in epidemiological rates and criteria for their assessment. Tuberculosis and Lung Diseases. 2022;100(9):46–58 (In Russ.)]. doi: 10.21292/2075-12302022-100-9-46-58

13. Harvey AC. Forecasting, structural time series models, and the Kalman filter. Cambridge University Press, 1989:268 p.

14. Glaziou P. Predicted impact of the COVID-19 pandemic on global tuberculosis deaths in 2020. medRxiv and bioRxiv. doi: 10.1101/2020.04.28.20079582

15. Qin ZZ, Ahmed S, Sarker SM, Paul K, Sikder SA, Naheyan T, Barrett R, Sayera Banu S, Creswell J. Tuberculosis detection from chest x-rays for triaging in a high tuberculosis burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digit Health. 2021;3:e543–54. doi: 10.1016/S2589-7500(21)00116-3