Laurynas Mockeliunas1, Rob C van Wijk2, Caryn M Upton3, Jonathan Peter4, Andreas H Diacon3, Ulrika SH Simonsson1
1Department of Pharmaceutical Biosciences, Uppsala University, 2Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, 3TASK, 4Allergy and Immunology Unit, University of Cape Town Lung Institute and Division of Allergy and Clinical Immunology, University of Cape Town
Introduction/Objectives: Respiratory tract infections (RTIs) cover a broad family of diseases, which also includes the coronavirus disease (COVID-19). COVID-19, declared a pandemic from 2020 to 2023, briefly became the world’s leading cause of death from an infectious agent, surpassing tuberculosis [1]. It is less known about the environmental and demographic factors leading to repeated RTIs (including COVID-19) as this requires data on repeated events within the same individual. Considering this, it is of interest to use all available information to identify risk factors for RTIs. The aim of this work was to describe the hazard of repeated non-specific RTI (including COVID-19) and to identify statistically significant covariates. Methods: Data from a prospective phase III study investigating Bacillus Calmette–Guérin (BCG) re-vaccination against COVID-19 were analysed [2]. In the trial, 1000 South African frontline healthcare workers were enrolled to receive either placebo or BCG vaccine, and were followed for one year after randomization. Data including health status, infection type, RTI start date, demographics and medical history were extracted, and the records were matched with daily reported COVID-19 burden in South Africa. Data was analysed using parametric repeated time-to-event (RTTE) analysis. Multiple hazard functions including a frailty parameter for the repeated events were tested, including the constant, Weibull, Gompertz and log-normal hazard functions. Covariate modelling was performed using stepwise covariate modelling with adaptive scope reduction (SCMplus) [3]. Tested risk factors were participant’s body mass index, age, sex, job category (doctor, nurse, or essential worker), medical history of diabetes mellitus, asthma, hypertension, or cardiovascular diseases, smoking status, self-reported expected exposure to COVID-19 patients, SARS-CoV-2 IgG serology status at enrolment, latent tuberculosis infection status at enrolment and end of the trial, Interferon Gamma Release Assay (IGRA) positive or negative seroconversion from baseline, and trial arm (BCG or placebo). Sampling importance resampling [4] with 2000 samples was used to derive 95% confidence intervals (CI). Hazard ratios (HR) were derived to compare the effect of statistically significant covariates. Model selection and evaluation were based on objective function value (OFV), parameter uncertainties, visual predictive checks and other goodness-of-fit plots. Modelling was performed using NONMEM version 7.5.1 (ICON, Hanover, MD, USA)[5] through Perl-speaks-NONMEM (v. 5.3.0) and R statistical software (v. 4.2.2). Results: In total, 958 RTI events occurred in 574 participants, with 0 to 7 RTI events per participant recorded. Of all events reported, 22.4% were confirmed as COVID-19. The final RTTE model included a Weibull distribution for baseline hazard, with final estimates for scale and shape of 0.00185 day^-1 (95% CI 0.00158–0.00217) and 0.818 (95% CI 0.773–0.867), respectively. Job category, COVID-19 burden in South Africa, sex, and positive SARS-CoV-2 serology at enrolment were identified as statistically significant covariates on the hazard function (p-value <0.01). The median HR for job category of doctor and nurse compared to essential worker were 1.64 (95% CI 1.35–2.00) and 1.31 (1.12–1.56), respectively. The median HR for COVID-19 burden in South Africa for 14 COVID-19 cases per 1,000 capita (representing the median number of reported COVID-19 cases during the trial) was 1.36 (95% CI 1.27–1.45), and the median HR for males was 0.72 (95% CI 0.61–0.86). Lastly, positive SARS-CoV-2 serology at baseline decreased the probability of the events, and the median HR was 0.64 (95% CI 0.50–0.81) for those with positive serology at baseline. The inclusion of the covariates led to a decrease of the frailty parameter from 71.7% coefficient of variation (CV) to 62.7% CV (95% CI 45.3–79.7) compared to the base model. Additionally, performing RTTE analysis allowed to differentiate the hazards for nurses and doctors which was not possible in the previously performed analysis [6]. Conclusions: Sex, job category, SARS-CoV-2 IgG serology status at baseline, and reported COVID-19 burden were identified as statistically significant covariates, and these findings allowed to characterize vulnerable groups prone to multiple RTI events.
[1] Global tuberculosis report 2024. Geneva: World Health Organization; 2024. Licence: CC BY-NC-SA 3.0 IGO. [2] Upton CM, van Wijk RC, Mockeliunas L, Simonsson USH, McHarry K, van den Hoogen G, Muller C, von Delft A, van der Westhuizen HM, van Crevel R, Walzl G, Baptista PM, Peter J, Diacon AH; BCG CORONA Consortium. Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers: A double-blind, randomised, controlled, phase 3 trial. EClinicalMedicine. 2022 Jun;48:101414. doi: 10.1016/j.eclinm.2022.101414. Epub 2022 May 12 [3] Svensson RJ, Jonsson EN. Efficient and relevant stepwise covariate model building for pharmacometrics. CPT Pharmacometrics Syst Pharmacol. 2022 Sep;11(9):1210-1222. Doi: 10.1002/psp4.12838. Epub 2022 Jul 19 [4] Dosne, A.-G.; Bergstrand, M.; Karlsson, M.O. An Automated Sampling Importance Resampling Procedure for Estimating Parameter Uncertainty. J. Pharmacokinet. Pharmacodyn. 2017, 44, 509–520. [5] Beal SL, Sheiner LB, Boeckmann AJ, Bauer RJ 1989-2011. NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA [6] Mockeliunas L, van Wijk RC, Upton CM, Peter J, Diacon AH, Simonsson USH. Risk Factors for COVID-19 and Respiratory Tract Infections during the Coronavirus Pandemic. Vaccines (Basel). 2024 Mar 19;12(3):329. doi: 10.3390/vaccines12030329
Reference: PAGE 33 (2025) Abstr 11579 [www.page-meeting.org/?abstract=11579]
Poster: Drug/Disease Modelling - Infection