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Association Between BCG Policy and COVID19 Infection Rates is Significantly Confounded by Age and is Unlikely to Alter Infection or Mortality Rates

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Abstract and Figures

Recently a number of publications looked at the association between COVID-19 morbidity and mortality on one hand and countries' policies with respect to BCG vaccination on the other. This connection arises from differences in the rates of infection in countries where BCG vaccination is mandatory compared to countries where mandatory vaccination no longer exists or was never implemented in the first place. In at least 2 preprint publications the authors expressed the view that the "known immunological benefits" of BCG vaccination may be behind the biological mechanism of such observation. One study accounted for different income levels in different groups. Another study did not attempted to do so, instead exploring the differences between countries where a booster shot is given vs others where no such practice exists (finding no connection). Both of these studies did not explore other potential confounding factors. Meanwhile the press has focused on these headlines and pushed the narrative that BCG vaccination is causally linked to infection and mortality rates. This poses a serious challenge, demonstrated by the recently initiated clinical trials on BCG vaccination within the COVID19 context. This study shows that population age is a very significant confounding factor that explains the rates of infections much better and has a solid biology mechanism which explains this correlation. It suggests that BCG vaccination may have little or no causal link to infection rates and advises that any follow up studies should control for several confounding factors, such as population age, ethnicity, rates of certain chronic diseases, time from community spread start date, major public policy decisions and income levels.
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Association Between BCG Policy
and COVID19 Infection Rates is
Significantly Confounded by Age and
is Unlikely to Alter Infection or
Mortality Rates
Stefan Kirov, PhD
Bristol Myers Squibb
Abstract
Recently a number of publications looked at the association between COVID-19 morbidity and
mortality on one hand and countries’ policies with respect to BCG vaccination on the other. This
connection arises from differences in the rates of infection in countries where BCG vaccination is
mandatory compared to countries where mandatory vaccination no longer exists or was never
implemented in the first place. In at least 2 preprint publications the authors expressed the view that the
“known immunological benefits” of BCG vaccination may be behind the biological mechanism of such
observation.
One study accounted for different income levels in different groups. Another study did not attempted to
do so, instead exploring the differences between countries where a booster shot is given vs others
where no such practice exists (finding no connection).
Both of these studies did not explore other potential confounding factors. Meanwhile the press has
focused on these headlines and pushed the narrative that BCG vaccination is causally linked to
infection and mortality rates. This poses a serious challenge, demonstrated by the recently initiated
clinical trials on BCG vaccination within the COVID19 context.
This study shows that population age is a very significant confounding factor that explains the rates of
infections much better and has a solid biology mechanism which explains this correlation. It suggests
that BCG vaccination may have little or no causal link to infection rates and advises that any follow up
studies should control for several confounding factors, such as population age, ethnicity, rates of certain
chronic diseases, time from community spread start date, major public policy decisions and income
levels.
Introduction
BCG vaccine has been associated in multiple studies with effects beyond protection against
tuberculosis, which is the original target of the intervention(1). As such, BCG vaccination has been
shown to enhance the protection provided by H1N1 vaccine(2). Another study observed that prior BCG
vaccination attenuates yellow fever vaccine associated viremia(3). These findings prompted two
independent studies(4,5) in which a strong correlation between the presence of BCG vaccination and a
reduced rate of COVID19 infections and/or death rates was observed.
These observations were also communicated by the press (6–9). While some of these publications
authors contacted additional subject matter experts who cautioned that the studies are not yet peer
reviewed and urged patience(7), there is danger that the observations will be over interpreted and used
for policy decisions. There are currently at least 2 ongoing studies focusing on the effect of BCG
vaccination in the context of COVID19 infections(10) any decisions should be taken only after
conclusive evidence is presented after the end of the trials.
At the same time, some of the established risk factors for COVID19 hospitalization and death such as
age(11) and BMI(12) are easy to evaluate in the same context (per country). Given the extreme urgency
and the potential for serious consequences I decided to explore this matter in greater depth.
Materials and Methods
Data on average population age per country and number of infections was collected from Wikipedia.
BCG policy and income level data was obtained from one of the original studies. BCG and rubella
immunization rates were obtained from WHO website(13).
Country names were cleaned and data was merged in R (Rstudio 1.1.456 on Ubuntu 16 Linux, R version
3.2.3). All scripts and data files are available from https://github.com/kirovsa/covid19-bcg.
As with one of the original studies the countries with population of less than 1M were excluded(4). For
analysis of mortality rates countries with no deaths on record were excluded as these are likely to be in
the very initial phase of the epidemic and will introduce significant noise.
To determine the effect of different factors I used lm function from stats package. Evaluating factors
effects on infections in the presence of random effect was done with lme from nlme package. Log
likelihood was tested with lrtest from lmtest package.
BCG policy and income level were coded according to one of the studies that found the association
with COVID19 infection(4):
BCG Policy
1 = current universal policy
2= used to recommend, not anymore
3 = never had universal policy
2018 FY income level:
Low income (L) -1
Lower middle income (LM)-2
Upper middle income (UM)-3
High income (H)-4
Results
Based on a significant amount of accumulated data, age is a significant factor predicting hospitalization
of COVID19 patients and fatal outcome. Younger people also seem more likely to remain
asymptomatic. Therefore I decided to evaluate a linear regression model that accounts for 3 factors-
BCG policy, income level and median age per country.
While the model as a whole explains very well the differences in infection rates across countries, the
most significant factor was income level, followed by median age. BCG policy was significant but
lagged behind the other factors (Figure 1). However, BCG immunization rates was not significant in
this model at alpha level at 0.05 (p=0.088). The likelihood test did find that the BCG policy had an
effect (p=0.0028) compared to the full model, however this was not true for BCG vaccination rates
(p=0.08). If there is a causal link between BCG vaccination and COVID19 infection rates one would
expect this association to hold or even get stronger, something I did not find evidence for.
The Pearson correlation between median age and infection rates was also much higher at R=0.774 than
the reported correlation between the BCG policy and the infection rates at R=0.521 or the reported
correlation between start date of BCG vaccination and infection rates (R=0.21).
The correlation between number of cases per million people with the median age in a country does not
change substantially between different policy categories (Figure 2A), though there was some separation
between categories 1 and 3. This can only be evaluated for countries with high rates of infection and
also higher median age. When the BCG immunization rates were used instead of the policy there was
no association (Figure 2B).
I also explored potential connection between countries with higher rubella immunization rates vs those
with lower rates (separated in categories by 50% threshold) and COVID19 infections. While this
variable on its own showed significant association (p<0.0001) with the observed infection rates per
country, it appeared that the effect is the opposite of what would be expected (Figure 2C) with
countries with low immunization rates scoring better in terms of infection rate. After the inclusion of
other factors such as median age and income level this association was not significant at alpha=0.05
(p=0.056).
Since income levels are unlikely to drive infection rates I decided to compare the performance of
median age and BCG policy. The data showed that median age explains the variance in the number of
COVID19 cases better than the BCG policy either with or without income level adjustment (Figure 3).
The median age explained 60% of the variability vs 30% for BCG policy. In a mixed model where
income levels are considered a random factor median age again appears to be more important than
BCG policy (Figure 4). BCG rates were again non-significant at p=0.0798.
Next, I looked at the median age distribution in different income levels and BCG policy categories
(Figure 5). There was a strong association between median age and BCG policy with or without
income level adjustment (p<0.0001). The same is true for median age and income level (p<0.0001).
I also explored associations with mortality rates (Figure 6). Again, there was demonstrably better
correlation between median age and mortality rates (R=0.653) compared to the correlation with start
date of BCG vaccination policy reported in one of the studies (R=0.54)(4).
BMI was another strong confounding factor in the context of mortality rates(Figure 7). Countries with
normal BMI were without exception in the policy category 1 (mandatory BCG vaccination). In
addition, death rates were substantially higher in countries with high BMI (p<0.0001).
Discussion
While observational studies are a valid and useful tool, there are also serious obstacles interpreting the
data correctly(14). In the specific case of the correlation between BCG vaccination policy and
COVID19 outcomes it is clear that important confounding factors may have been missed. An excellent
outline of these obstacles was given by Emily MacLean(15). In addition to missing hidden factors, the
critique in the blog goes further to challenge the biological plausibility of the BCG vaccine-COVID19
connection. It seems this is a very reasonable concern, given that the only established connection
between BCG vaccination and protection against viral infections seems to be within the scope of actual
anti-viral vaccines(2,3). On the other hand, the biological rational for causal link between age and
COVID19 morbidity and mortality seems a lot more straightforward; if we follow the Occam’s razor
we should prioritize this link over BCG vaccination. I also want to emphasize that the association
observed in this work between infection rates and rubella immunization are almost certainly spurious.
The arguments so far is that early childhood vaccinations might be protective, which is the opposite of
our observation. Prior preclinical research(16) that was done during one of the previous coronavirus
crisis shows clearly that childhood vaccinations are unlikely to drive different outcomes of COVID19
infections.
This is further enhanced by the data presented in this study. I need to emphasize that I have not
included a number of other potentially confounding factors such as blood pressure, public policy
(mandatory travel restrictions, use of masks, etc.) or time from first infection (start of community
spread). Finally, any conclusive study will need to address the disagreement between BCG policy and
actual BCG vaccination rates with the first still contributing to the regression model, whereas the
second did not.
References
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Vaccination Enhances the Immunogenicity of Subsequent Influenza Vaccination in Healthy
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15;212(12):1930–8.
3. Arts RJW, Moorlag SJCFM, Novakovic B, Li Y, Wang S-Y, Oosting M, et al. BCG Vaccination
Protects against Experimental Viral Infection in Humans through the Induction of Cytokines
Associated with Trained Immunity. Cell Host Microbe. 2018 Jan;23(1):89-100.e5.
4. (2) (PDF) Correlation between universal BCG vaccination policy and reduced morbidity and
mortality for COVID-19: an epidemiological study [Internet]. ResearchGate. [cited 2020 Apr 3].
Available from:
https://www.researchgate.net/publication/340263333_Correlation_between_universal_BCG_vaccin
ation_policy_and_reduced_morbidity_and_mortality_for_COVID-19_an_epidemiological_study
5. (PDF) BCG vaccination may be protective against Covid-19 [Internet]. ResearchGate. [cited 2020
Apr 3]. Available from:
https://www.researchgate.net/publication/340224580_BCG_vaccination_may_be_protective_again
st_Covid-19
6. Explainer: How an old tuberculosis vaccine might help fight the new coronavirus - Reuters
[Internet]. [cited 2020 Apr 2]. Available from: https://www.reuters.com/article/us-health-
coronavirus-tbvaccine-explaine/explainer-how-an-old-tuberculosis-vaccine-might-help-fight-the-
new-coronavirus-idUSKBN21K372
7. Coronavirus deaths are fewer in countries that mandate TB vaccine | Fortune [Internet]. [cited 2020
Apr 2]. Available from: https://fortune.com/2020/04/02/coronavirus-vaccine-tb-deaths/
8. BCG vaccine: US scientists link BCG vaccination with fewer Covid-19 cases, Indian scientists
hopeful but cautious - The Economic Times [Internet]. [cited 2020 Apr 2]. Available from:
https://economictimes.indiatimes.com/news/science/us-scientists-link-bcg-vaccination-with-fewer-
covid-19-cases-indian-scientists-hopeful-but-cautious/articleshow/74931591.cms
9. Rabin RC. Can an Old Vaccine Stop the New Coronavirus? The New York Times [Internet]. 2020
Apr 3 [cited 2020 Apr 6]; Available from:
https://www.nytimes.com/2020/04/03/health/coronavirus-bcg-vaccine.html
10. Australian researchers to trial BCG vaccine for Covid-19 [Internet]. [cited 2020 Apr 2]. Available
from: https://www.clinicaltrialsarena.com/news/australia-bcg-vaccine-trial-covid-19/
11. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of
adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020
Mar 28;395(10229):1054–62.
12. Zeng L, Li J, Liao M, Hua R, Huang P, Zhang M, et al. Risk assessment of progression to severe
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2020 Mar 30;2020.03.25.20043166.
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health care databases: problems and potential solutions – a primer for the clinician. Clin Epidemiol.
2017 Mar 28;9:185–93.
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Figures and tables
Figure 1. Linear regression model exploring policy, age and income level effect on infection rates
Call:
lm(formula = log(CasesPerM) ~ Median + Policy + IncomeLevel,
data = covid.stage2)
Residuals:
Min 1Q Median 3Q Max
-3.3978 -0.7759 -0.0137 0.9219 2.9194
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.16279 0.48224 -2.411 0.01738 *
Median 0.07980 0.01968 4.054 8.86e-05 ***
Policy2 1.06411 0.38036 2.798 0.00598 **
Policy3 1.48048 0.58576 2.527 0.01276 *
IncomeLevel2 0.56466 0.39583 1.427 0.15625
IncomeLevel3 2.25019 0.43483 5.175 8.99e-07 ***
IncomeLevel4 3.14738 0.54213 5.806 5.12e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.222 on 123 degrees of freedom
Multiple R-squared: 0.7463, Adjusted R-squared: 0.734
F-statistic: 60.32 on 6 and 123 DF, p-value: < 2.2e-16
Call:
lm(formula = log(CasesPerM) ~ Median + BCG + IncomeLevel, data = covid.stage2)
Residuals:
Min 1Q Median 3Q Max
-3.4935 -0.7091 0.0879 0.9235 2.9354
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.788849 0.594703 -1.326 0.1871
Median 0.083537 0.020396 4.096 7.54e-05 ***
BCG -0.005930 0.003453 -1.717 0.0884 .
IncomeLevel2 0.609350 0.411892 1.479 0.1416
IncomeLevel3 2.334206 0.455878 5.120 1.13e-06 ***
IncomeLevel4 3.365031 0.551903 6.097 1.26e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.258 on 124 degrees of freedom
Multiple R-squared: 0.7288, Adjusted R-squared: 0.7179
F-statistic: 66.64 on 5 and 124 DF, p-value: < 2.2e-16
Figure 2. Relationship between median age per country and infection rates in the context of BCG
policy
A
B
C
Figure 3. Effect of BCG policy or median age and infection rates in the presence and absence of
income level effect
> anova(lm(log(CasesPerM) ~ Policy + IncomeLevel, data= covid.stage2))
Analysis of Variance Table
Response: log(CasesPerM)
Df Sum Sq Mean Sq F value Pr(>F)
Policy 2 219.53 109.763 65.376 < 2.2e-16 ***
IncomeLevel 3 296.29 98.763 58.825 < 2.2e-16 ***
Residuals 124 208.19 1.679
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> anova(lm(log(CasesPerM) ~ Median + IncomeLevel, data= covid.stage2))
Analysis of Variance Table
Response: log(CasesPerM)
Df Sum Sq Mean Sq F value Pr(>F)
Median 1 433.67 433.67 269.662 < 2.2e-16 ***
IncomeLevel 3 89.31 29.77 18.511 5.329e-10 ***
Residuals 125 201.03 1.61
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> anova(lm(log(CasesPerM) ~ Policy , data= covid.stage2))
Analysis of Variance Table
Response: log(CasesPerM)
Df Sum Sq Mean Sq F value Pr(>F)
Policy 2 219.53 109.763 27.632 1.089e-10 ***
Residuals 127 504.48 3.972
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> anova(lm(log(CasesPerM) ~ Median, data= covid.stage2))
Analysis of Variance Table
Response: log(CasesPerM)
Df Sum Sq Mean Sq F value Pr(>F)
Median 1 433.67 433.67 191.19 < 2.2e-16 ***
Residuals 128 290.33 2.27
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Figure 4. Mixed models used to evaluate associations between infection rates, median age, BCG policy
and BCG immunization rates
Fixed effects: log(CasesPerM) ~ Median + Policy
Value Std.Error DF t-value p-value
(Intercept) 0.1470049 0.8923356 123 0.164742 0.8694
Median 0.0864087 0.0193181 123 4.472947 0.0000
Policy2 1.0936249 0.3796067 123 2.880942 0.0047
Policy3 1.5033315 0.5856559 123 2.566920 0.0115
Fixed effects: log(CasesPerM) ~ Median + BCG
Value Std.Error DF t-value p-value
(Intercept) 0.6196404 1.0028649 124 0.617870 0.5378
Median 0.0902190 0.0200332 124 4.503473 0.0000
BCG -0.0060820 0.0034438 124 -1.766091 0.0798
Figure 5. Relationship between median age and BCG policy in the context of income levels
Figure 6.
Figure 7. Association between normalized deaths from COVID19 and BMI (high>25) in the context of
BCG policy
Funding: None
Note: The opinions expressed in this paper are personal and do not represent in any way Bristol Myers
Squibb. No Bristol Myers Squibb resources were used to generate results or prepare this publication.
Acknowledgment: I would like to thank Max Lau, PhD for critically reading this work and Kamen
Kirov for editing the text.
... In addition, in the elderly population (Gursel and Gursel, 2020), BCG is suggestive of the notion that BCG protects the vaccinated elderly population. Its known protective immunological benefits, decreased incidences, hampered disease transmission and progression, and lowered mortality are suggestive of BCG vaccination as a potential nonspecific safe tool against COVID-19; however, various other factors make BCG efficacy against COVID-19 debatable (Dayal and Gupta, 2020;Gursel and Gursel, 2020;Hegarty et al., 2020;Hensel et al., 2020;Kirov, 2020;Miller et al., 2020). ...
... Thus, BCG induces sustained changes in the immune system associated with a nonspecific response to infections that could be beneficial against COVID-19. However, the beneficial role of BCG against COVID-19 remains debatable because of the variation in testing rate, population density, median age, TB incidence, urban population, public policy, and community spread check measures in different countries (Kirov, 2020). BCG as an adjuvant is proved very efficient in producing nonspecific immune responses; in the case of COVID-19, we should perform studies to evaluate the efficacy of various adjuvants, including BCG to elicit immunoprophylactic responses by candidate vaccines. ...
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... Hensel et al. included countries performing more than 2500 COV-2 tests per million population in their analysis and found no significant association between numbers of COVID-19 cases per million population with BCG vaccination. Kirov et al. [15] performed linear regression for cofactors and COVID-19 cases and mortality and significant correlation was observed with income level and median age but not with BCG policy. Szigeti et al. were unable to establish correlation between COVID-19 case fatality rates and the period of introduction of universal BCG vaccination programs. ...
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... Furthermore, it has been hypothesized that countries with nationalized BCG vaccination policies show decreased morbidity and mortality to COVID-19 when compared to those where no such uniform policy exists (such as the United States or Italy); however, these preliminary data are limited given it is yet to be peer-reviewed and fails to account for several confounding factors such as age, testing rates, 29,30 and the accuracy of the BGC World Atlas. 31 Nonetheless, given the safety of BCG vaccination and the well- Overall, the far-reaching effects of BCG vaccination testify to its dynamic role: Eliciting NSEs, curbing inflammation in cancer models, and reducing viremia in several distinct pathogens, including RSV, influenza A, yellow fever, and herpes simplex virus. 21,32 Altogether, this study aims to elucidate the relationship between BCG vaccination and SARS-CoV-2 through bioinformatic analysis of the biological and immunological pathways underlying both. ...
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To investigate the relationship between BCG vaccination and SARS‐CoV‐2 by bioinformatic approach. Two datasets for the SARS‐CoV‐2 infection group and BCG‐vaccinated group were downloaded. Differentially Expressed Genes were identified. Gene ontology and pathways were functionally enriched, and networking was constructed in NetworkAnalyst. Lastly, the correlation between post‐BCG vaccination and COVID‐19 transcriptome signatures was established. A total of 161 DEGs (113 upregulated DEGs and 48 downregulated genes) were identified in the SARS‐CoV‐2 group. In the pathway enrichment analysis, a cross‐reference of upregulated KEGG pathways in SARS ‐CoV‐2 with downregulated counterparts in the BCG‐vaccinated group, resulted in the intersection of 45 common pathways, accounting for 86.5% of SARS‐CoV‐2 upregulated pathways. Of these intersecting pathways, a vast majority were immune and inflammatory pathways with top significance in IL‐17, TNF, NOD‐like receptors, and NF‐κB signaling pathways. Given the inverse relationship of the specific DEG pathways highlighted in our results, BCG‐vaccine may incur a protective role against COVID‐19 by mounting a non‐specific immunological response and further investigation of this relationship is warranted. This article is protected by copyright. All rights reserved.
... 67 This correlation was met with a considerable amount of scepticism from the broader scientific community, with Kirov 68 suggesting that many of these studies did not accommodate for confounding factors such as race, income levels, population age and time from community spread, establishing that population age, was indeed a more significant factor influencing the COVID-19 epidemiological profile than BCG vaccination policy. 68 More recently, Aksu et al. 69 published the results of their retrospective study, which revealed that BCG vaccination was in fact not associated with severity of disease in COVID-19 patients in Turkey. Finally, as the pandemic progressed, many of the nations whose epidemiological profile initially supported this correlation, have also begun to witness a sharp rise in their incidence and mortality figures, suggesting at the very least that protective effects BCG vaccination on COVID-19, if any at all, is not long-lasting. ...
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... Hensel et al included countries performing more than 2500 COV-2 tests per million population in their analysis and found no signi cant association between numbers of COVID 19 cases per million population with BCG vaccination. Kirov et al [15] performed linear regression for cofactors and COVID-19 cases and mortality and signi cant correlation was observed with income level and median age but not with BCG policy. Szigeti et al was unable to establish correlation between COVID 19 case fatality rates and the period of introduction of universal BCG vaccination programs. ...
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Context: Lower morbidity and mortality in few geographic locations on the globe suffering with SARS-CoV-2 has been associated with the existing or previously followed long standing BCG vaccination policy amongst infants. But does that hold true that today after years of BCG vaccination few adults have better prognosis or is it just confounding due to differential disease burden, population density, testing facilities or improper reporting. The purpose was to evaluate and correlate this effect systematically. Evidence acquisition: Detailed electronic search for randomised controlled trials and observational studies in PubMed, Cochrane database and clinicaltrials.gov for eligible studies was performed. We performed a meta-analysis to provide pooled esimate of correlation of mortality with BCG vaccination policy from 4 studies. Results: 114 number of studies were yielded on search strategy and 28 observational studies were finally included for analysis. From our results we can say that BCG vaccination causes a decrease in COVID-19 incidence and mortality. But these results must be interpreted cautiously as lot of confounding factors were present in included studies, which can affect the outcome. Conclusion: The evidence of BCG vaccination for protection against COVID-19 can’t be ruled out as evidence from many studies support the hypothesis but the evidence of well conducted RCTs and observational studies can strengthen the evidence.
... However, such ecological studies that relate country aggregate and individual data should be interpreted with caution. Also, COVID-19 has shown a recent increase since publication of the analysis [56] in low-and middle-income countries and may still be underreported, confounders such as age were not taken into account, and variable BCG policies over time affect individual BCG coverage [57][58][59]. Several large randomised controlled trials (RCTs) currently evaluating the effect of BCG vaccination against COVID-19 in thousands of healthcare workers and elderly, in the Netherlands, Australia and other countries, will provide evidence to support or refute BCG as a cheap and rapidly scalable preventive measure against COVID-19 and other viral respiratory infections. ...
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Major epidemics, including some that qualify as pandemics, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), HIV, influenza A (H1N1)pdm/09 and most recently COVID-19, affect the lung. Tuberculosis (TB) remains the top infectious disease killer, but apart from syndemic TB/HIV little is known regarding the interaction of viral epidemics and pandemics with TB. The aim of this consensus-based document is to describe the effects of viral infections resulting in epidemics and pandemics that affect the lung (MERS, SARS, HIV, influenza A (H1N1)pdm/09 and COVID-19) and their interactions with TB. A search of the scientific literature was performed. A writing committee of international experts including the European Centre for Disease Prevention and Control Public Health Emergency (ECDC PHE) team, the World Association for Infectious Diseases and Immunological Disorders (WAidid), the Global Tuberculosis Network (GTN), and members of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Mycobacterial Infections (ESGMYC) was established. Consensus was achieved after multiple rounds of revisions between the writing committee and a larger expert group. A Delphi process involving the core group of authors (excluding the ECDC PHE team) identified the areas requiring review/consensus, followed by a second round to refine the definitive consensus elements. The epidemiology and immunology of these viral infections and their interactions with TB are discussed with implications for diagnosis, treatment and prevention of airborne infections (infection control, viral containment and workplace safety). This consensus document represents a rapid and comprehensive summary on what is known on the topic.
... Finally, a series of preprints has identified a potential link between BCG vaccination practice for Tuberculosis and COVID-19 mortality; see, for example, [64,65]. However, accounting for confounding factors such as age or testing policies, this correlation seems to be rather weak [66,67]; cf. a recent overview [68]. ...
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We present results on the mortality statistics of the COVID-19 epidemic in a number of countries. Our data analysis suggests classifying countries in five groups, (1) Western countries, (2) East Block, (3) developed Southeast Asian countries, (4) Northern Hemisphere developing countries and (5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerges in which the Western countries exhibit the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries reveals several subgroups within the Western countries and further emphasises the difference between the five groups. Analysing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries is found to be their larger population of elderly people, with exceptions such as Norway and Japan, for which other factors seem to dominate. Our comparison between countries at the same level of outbreak progress allows us to identify and quantify a measure of efficiency of the level of stringency of confinement measures. We find that increasing the stringency from 20 to 60 decreases the death count by about 50 lives per million in a time window of 20 days. Finally, we perform logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the “first wave” and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allows us to assess the outbreak progress in different countries, differentiating between those that are at a quite advanced stage and close to the end of the epidemic from those that are still in the middle of it. This raises many questions in terms of organisation, preparedness, governance structure and so on.
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COVID-19 is a pandemic caused by SARS-CoV-2 virus which is a very worrisome public health emergency. In this study, we compared the mortality rate and recovery rate in countries with and without BCG vaccination policy. The data of mortality of COVID-19 was extracted from worldometer (https://www.worldometers.info/coronavirus/) on 26th July 2020. The data of countries where BCG vaccination is being done for all individuals is taken from BCG world atlas (http://www.bcgatlas.org/index.php), updated in 2017. BCG vaccination policy recommended countries are intervention group versus countries without BCG vaccination policies which are regarded as control group. Pooled analysis of countries with and without BCG vaccination policy revealed mortality rate of 1.31% (95%CI - 1.31% to 1.32%; I2 = 100%, p<0.01) and 3.25% (95%CI - 3.23% to 3.26%; I2 = 100%, p<0.01), respectively. The recovery rate in two country groups were found to be 72.60% (95%CI - 72.57% to 72.63%) and 55.94% (95%CI - 55.90% to 55.98%), respectively. 52 individuals need to be BCG vaccinated to prevent one death (NNT = 52). In BCG vaccination program countries, there is statistically and clinically significant less mortality (p-value <0.001) as compared to countries without BCG policy. Our findings corroborate the hypothesis that BCG vaccination may provide protection from COVID-19. High quality evidence from randomised controlled trials are required to establish causality between BCG vaccination and protection from severe COVID-19.
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Background: The Bacillus Calmette-Guerin (BCG) vaccine has been in use for 99 years, and is regarded as one of the oldest human vaccines known today. It is recommended primarily due to its effect in preventing the most severe forms of tuberculosis, including disseminated tuberculosis and meningeal tuberculosis in children; however, its efficacy in preventing pulmonary tuberculosis and TB reactivation in adults has been questioned. Several studies however have found that asides from its role in tuberculosis prevention, the BCG vaccine also has protective effects against a host of other viral infections in humans, an effect which has been termed: heterologous, non-specific or off-target. Objectives: As we approach 100 years since the discovery of the BCG vaccine, we review the evidence of the non-specific protection offered by the vaccine against viral infections, discuss the possible mechanisms of action of these effects, highlight the implications these effects could have on vaccinology and summarize the recent epidemiological correlation between the vaccine and the on-going COVID-19 pandemic. Results: Several epidemiological studies have established that BCG does reduce all-cause mortality in infants, and also the time of vaccination influences this effect significantly. This effect has been attributed to the protective effect of the vaccine in preventing unrelated viral infections during the neonatal period. Some of such viral infections that have been investigated include: herpes simplex virus (HSV), human Papilloma virus (HPV), yellow fever virus (YFV), respiratory syncytial virus (RSV) and influenza virus type A (H1N1). These effects are thought to be mediated via induction of innate immune memory as well as heterologous lymphocytic activation. While epidemiological studies have suggested a correlation, the potential protection of the BCG vaccine against COVID-19 transmission and mortality rates is currently unclear. Ongoing clinical trials and further research may shed more light on the subject in the future. Conclusion: BCG is a multifaceted vaccine, with many numerous potential applications to vaccination strategies being employed for current and future viral infections. There however is a need for further studies into the immunologic mechanisms behind these non-specific effects, for these potentials to become reality, as we usher in the beginning of the second century since the vaccine's discovery.
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Bacillus Calmette-Guérin (BCG) vaccination may reduce the risk of a range of infectious diseases, and if so, it could protect against coronavirus disease 2019 (COVID-19). Here, we compared countries that mandated BCG vaccination until at least 2000 with countries that did not. To minimize any systematic effects of reporting biases, we analyzed the rate of the day-by-day increase in both confirmed cases (134 countries) and deaths (135 countries) in the first 30-day period of country-wise outbreaks. The 30-day window was adjusted to begin at the country-wise onset of the pandemic. Linear mixed models revealed a significant effect of mandated BCG policies on the growth rate of both cases and deaths after controlling for median age, gross domestic product per capita, population density, population size, net migration rate, and various cultural dimensions (e.g., individualism). Our analysis suggests that mandated BCG vaccination can be effective in the fight against COVID-19.
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The tuberculosis vaccine bacillus Calmette-Guérin (BCG) has heterologous beneficial effects against non-related infections. The basis of these effects has been poorly explored in humans. In a randomized placebo-controlled human challenge study, we found that BCG vaccination induced genome-wide epigenetic reprograming of monocytes and protected against experimental infection with an attenuated yellow fever virus vaccine strain. Epigenetic reprogramming was accompanied by functional changes indicative of trained immunity. Reduction of viremia was highly correlated with the upregulation of IL-1β, a heterologous cytokine associated with the induction of trained immunity, but not with the specific IFNγ response. The importance of IL-1β for the induction of trained immunity was validated through genetic, epigenetic, and immunological studies. In conclusion, BCG induces epigenetic reprogramming in human monocytes in vivo, followed by functional reprogramming and protection against non-related viral infections, with a key role for IL-1β as a mediator of trained immunity responses.
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A number of new tuberculosis (TB) vaccines have been or are entering clinical trials, which include genetically modified mycobacteria, mycobacterial antigens delivered by viral vectors, or mycobacterial antigens in adjuvant. Some of these vaccines aim to replace the existing BCG vaccine but others will be given as a boosting vaccine following BCG vaccination given soon after birth. It is clear that the existing BCG vaccines provide incomplete and variable protection against pulmonary TB. This review will discuss what we have learnt over the last 20 years about how the BCG vaccine induces specific and non-specific immunity, what factors influence the immune responses induced by BCG, and progress toward identifying correlates of immunity against TB from BCG vaccination studies. There is still a lot to learn about the BCG vaccine and the insights gained can help the development of more protective vaccines.
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Population-based health care databases are a valuable tool for observational studies as they reflect daily medical practice for large and representative populations. A constant challenge in observational designs is, however, to rule out confounding, and the value of these databases for a given study question accordingly depends on completeness and validity of the information on confounding factors. In this article, we describe the types of potential confounding factors typically lacking in large health care databases and suggest strategies for confounding control when data on important confounders are unavailable. Using Danish health care databases as examples, we present the use of proxy measures for important confounders and the use of external adjustment. We also briefly discuss the potential value of active comparators, high-dimensional propensity scores, self-controlled designs, pseudorandomization, and the use of positive or negative controls.
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Background: Influenza-related morbidity and mortality remain high. Seasonal vaccination is the backbone of influenza management but does not always result in protective antibody titers. Nonspecific effects of BCG vaccination related to enhanced function of myeloid antigen-presenting cells have been reported. We hypothesized that BCG vaccination could also enhance immune responses to influenza vaccination. Methods: Healthy volunteers received either live attenuated BCG vaccine (n = 20) or placebo (n = 20) in a randomized fashion, followed by intramuscular injection of trivalent influenza vaccine 14 days later. Hemagglutination-inhibiting (HI) antibodies and cellular immunity measured by ex vivo leukocyte responses were assessed. Results: In BCG-vaccinated subjects, HI antibody responses against the 2009 pandemic influenza A(H1N1) vaccine strain were significantly enhanced, compared with the placebo group, and there was a trend toward more-rapid seroconversion. Additionally, apart from enhanced proinflammatory leukocyte responses following BCG vaccination, nonspecific effects of influenza vaccination were also observed, with modulation of cytokine responses against unrelated pathogens. Conclusions: BCG vaccination prior to influenza vaccination results in a more pronounced increase and accelerated induction of functional antibody responses against the 2009 pandemic influenza A(H1N1) vaccine strain. These results may have implications for the design of vaccination strategies and could lead to improvement of vaccination efficacy.
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In contrast with adults, children infected by severe acute respiratory syndrome-corona virus (SARS-CoV) develop milder clinical symptoms. Because of this, it is speculated that children vaccinated with various childhood vaccines might develop cross immunity against SARS-CoV. Antisera and T cells from mice immunised with various vaccines were used to determine whether they developed cross reactivity against SARS-CoV. The results showed no marked cross reactivity against SARS-CoV, which implies that the reduced symptoms among children infected by SARS-CoV may be caused by other factors.
Article
Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/L (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/L could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
Australian researchers to trial BCG vaccine for Covid-19
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