Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers: A double-blind, randomised, controlled, phase 3 trial

Abstract

Background: BCG vaccination prevents severe childhood tuberculosis (TB) and was introduced in South Africa in the 1950s. It is hypothesised that BCG trains the innate immune system by inducing epigenetic and functional reprogramming, thus providing non-specific protection from respiratory tract infections. We evaluated BCG for reduction of morbidity and mortality due to COVID-19 in healthcare workers in South Africa.

Methods: This randomised, double-blind, placebo-controlled trial recruited healthcare workers at three facilities in the Western Cape, South Africa, unless unwell, pregnant, breastfeeding, immunocompromised, hypersensitivity to BCG, or undergoing experimental COVID-19 treatment. Participants received BCG or saline intradermally (1:1) and were contacted once every 4 weeks for 1 year. COVID-19 testing was guided by symptoms. Hospitalisation, COVID-19, and respiratory tract infections were assessed with Cox proportional hazard modelling and time-to-event analyses, and event severity with post hoc Markovian analysis. This study is registered with ClinicalTrials.gov, NCT04379336.

Findings: Between May 4 and Oct 23, 2020, we enrolled 1000 healthcare workers with a median age of 39 years (IQR 30-49), 70·4% were female, 16·5% nurses, 14·4% medical doctors, 48·5% had latent TB, and 15·3% had evidence of prior SARS-CoV-2 exposure. Hospitalisation due to COVID-19 occurred in 15 participants (1·5%); ten (66·7%) in the BCG group and five (33·3%) in the placebo group, hazard ratio (HR) 2·0 (95% CI 0·69-5·9, p = 0·20), indicating no statistically significant protection. Similarly, BCG had no statistically significant effect on COVID-19 (p = 0·63, HR = 1·08, 95% CI 0·82-1·42). Two participants (0·2%) died from COVID-19 and two (0·2%) from other reasons, all in the placebo group.

Interpretation: BCG did not protect healthcare workers from SARS-CoV-2 infection or related severe COVID-19 disease and hospitalisation.

Funding: Funding provided by EDCTP, grant number RIA2020EF-2968. Additional funding provided by private donors including: Mediclinic, Calavera Capital (Pty) Ltd, Thys Du Toit, Louis Stassen, The Ryan Foundation, and Dream World Investments 401 (Pty) Ltd. The computations were enabled by resources in project SNIC 2020-5-524 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement No. 2018-05,973.

Keywords: BCG; COVID-19; Pandemic; Respiratory tract infection; Trained immunity; Tuberculosis; Vaccine.

Conflict of interest statement

We declare no competing interests.

© 2022 The Author(s).

Figures

Figure 1

Trial participation, randomisation, and analysis. SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2, BCG = Bacillus Calmette–Guérin.

Figure 2

Kaplan-Meier plots (enhanced y-axis) for the time-to-first event for A: hospitalisation due to COVID-19 event, B: all-cause hospitalisation event, C: COVID-19 event, and D: RTI event. Shaded area around each curve represents the standard error, computed using the Greenwood method. Vertical dashes represent censoring, while downward steps represent events. Number at risk table represent the total number of participants without event or censoring. Some participants were followed up beyond 1 year due to ongoing events or challenges in contacting the participant. COVID-19 = coronavirus disease 2019, RTI = respiratory tract infection, BCG = Bacillus Calmette–Guérin.

Figure 3

Forest plots for the effect in the BCG group compared to placebo (reference) on COVID-19 hospitalisation, all-cause hospitalisation, PCR confirmed COVID-19, and RTI. Represented as a median hazard ratio (circle) and 95% confidence interval (whiskers). COVID-19 = coronavirus disease 2019, PCR = polymerase chain reaction, HR = hazard ratio, CI = confidence interval.

Figure 4

Cumulative probability plots of the effect in the BCG group on A: all-cause hospitalisation, B: COVID-19, and C: RTI events. Represented as the median (solid line) and 95% confidence interval (shaded area). No statistical difference was seen between the two groups (p > 0·05). BCG = Bacillus Calmette–Guérin, COVID-19 = coronavirus disease 2019, RTI = respiratory tract infection, PCR = polymerase chain reaction.

Figure 5

Probabilities of remaining in or transitioning between health status (HS) as predicted by the post hoc Markov Chain model for respiratory tract infections (RTIs). Because the majority of 958 RTIs (925, 97·0%) did not exceed HS 2 we grouped all remaining HS into HS 3–7. Participants without RTI remain in HS 0. Circles depict HS scores, arrows depict the transition from one HS to another, or remaining in the same HS for the curved arrows, with the corresponding probability noted. Dark red arrows and probabilities show the statistically significantly higher probabilities for participants on BCG to transit from HS 0 to HS 3–7 (2-fold increase, p = 0·02) and from HS 2 to HS 3–7 (2·9-fold increase, p = 0·02). Correspondingly, the probabilities of remaining in HS 0 and HS 2, respectively, decrease as shown in blue, as the probabilities of transitions originating from, and of remaining in each state sum up to 100% per state. BCG = Bacillus Calmette–Guérin.