key: cord-0855662-x8v23r0g
authors: Singh, Samer
title: BCG VACCINES MAY NOT REDUCE COVID-19 MORTALITY RATES
date: 2020-04-17
journal: nan
DOI: 10.1101/2020.04.11.20062232
sha: 6e8816a3f67b7afea18e21d13b8d30b42d83fae1
doc_id: 855662
cord_uid: x8v23r0g

The reason for the observed country-wise variability in incidence and severity of the COVID-19 outcome remains unknown. Few recent studies have suggested a positive protective correlation of the BCG vaccination policy of the countries with the observed COVID-19 severity. The current study was undertaken to reassess the existing data as of 4th April 2020. The incidence rates (cases per million population), Case Fatality Rates (CFR) and inherently more robust Infection Fatality Rates (IFR) were calculated across countries accounting for about 99% COVID-19 deaths. The initial scrutiny suggested a weaker association with BCG vaccination policy or BCG coverage, so positivity to the Tuberculin Sensitivity Test (TST)/ Interferon Gamma Release Assay (IGRA) as a measure of the potential protective effect of the resident populations exposure to Mycobacterium spp. whether from BCG vaccination or as a result of exposure to environmental mycobacteria was analyzed. The incidence rates (the number of cases per million population) decreased with an increase in % LTBI (TST/IGRA positivity) for the analyzed countries with R2 =0.6343, suggesting an exponentially negative covariation. However, the covariation of CFR estimates that ranged from 0.29% to 12.25 % (average 5.39%) among countries, was tenuous. Interim estimates of IFR (i-IFR), a more dependable measure for such studies, for the best and worst-case scenarios, i.e., i-IFR-l and i-IFR-h, predict on an average 20.57% to 30.15 % COVID-19 fatality rates globally, but individual country estimates display huge variation. Among countries accounting for 92.14% deaths (11 countries; top 20% countries included in current study) the estimate for lowest IFRs (i-IFR-l=4.16 (China) & i-IFR-h=4.61 (China)) and highest IFRs (i-IFR-l=96.39% (UK); & i-IFR-h=96.54% (UK)) displayed huge difference (average for the group: CFR=6.8 ± 3.6%; i-IFR-l=34.97 ± 30.55%; & i-IFR-h=44.20 ± 29.08%). Currently, the worst affected countries Italy (CFR=12.25%; i-IFR-l=42.63%; i-IFR-h=48.69%) and Spain (CFR=9.39%; i-IFR-l=26.85%; i-IFR-h=36.60%) would seemingly cope with COVID-19 better than UK, Netherlands and USA while the countries Germany (CFR=1.40%; i-IFR-l=4.93%; i-IFR-h=17.49%) and Switzerland (CFR=3.01%; i-IFR-l=10.87%; i-IFR-h=16.23%) along with China could fare the best. The rest of the 80% countries (accounting for 6.74% deaths), seemed to have reduced mortality (CFR=2.45 ± 2.01; i-IFR-l= 30.62 ± 28.24%; i-IFR-h=40.99 ± 30.47%) with associated high % LTBI (17.28 ± 8.87) than top 20% countries. The inherent issues in the data set (e.g., heterogeneity, non-random sampling, different criteria of sampling and reporting, access to health care, genetic composition, underlying co-morbidities, etc) need to be taken into account for making informed decisions.

Current COVID-19 pandemic caused by SARS-CoV-2, a coronavirus closely related to SARS and MERS, had a humble beginning in late 2019 in Wuhan, China. It rapidly spread to the majority of the nations by mid-March 2019. The COVID-19 pandemic has stirred worldwide havoc with higher infection rates and associated variable adverse outcomes across countries. So far the most affected countries had been the developed West and South European, North American countries along with China and Iran from Asian countries. It had already killed about 60 thousand people by 4 th April 2020 -when the analysis being communicated was planned [1] . With exponentially increasing deaths every passing day, the pandemic has sent the countries and their health services scrambling to decrease the fallout of COVID-19. The dedicated worldwide platforms had been created to provide up-to-date information about the pandemic. The overall recovery rate for COVID-19 had been about 79% making all countries panic to consolidate all resources to decrease the deaths. As of today, 11 th April 2020 the death ascribed to COVID-19 has already crossed the 100 thousand mark [1] . Various clinical trials to repurpose the existing drugs indicated for similar coronavirus or suggested pathology, as well as experimental vaccines, have been initiated/planned [2, 3] . A large number of studies are also being conducted and results published in journals or posted online on preprint servers medRxiv and bioRxiv to understand the basis of differential incidence and fatality rates in different countries. Few studies indicate the existence of a positive correlation between BCG vaccination policies to the inherent resistance of populations to COVID-19 and suggest clinical trials to evaluate its preventive potential in COVID-19 [4, 5] . The data available needs to be analyzed thoroughly to arrive at actionable contextspecific conclusions to ameliorate the devastating effect of COVID-19 on human lives.

The bacille Calmette-Guérin (BCG) vaccine is primarily given to protect against tuberculosis (TB) in countries with higher TB incidence [6] . TB remains the top cause of death from a single pathogen Mycobacterium tuberculosis (MTB). The vaccination of BCG or exposure to environmental nontuberculous mycobacteria is supposed to 'train' cell-mediated immunity to respond better on subsequent exposure to intracellular pathogen MTB by responding through CD4-and CD8-positive cells and the production of various cytokines including Interferon-γ (IFN-γ) which is also a key cytokine in innate and adaptive immunity against viral and bacterial infections [6, 7] . A more generalized longterm cross-reactive protective effect against several pathogens is suggested through long-term epigenetic programming and 'training' of immune cells particularly Macrophages and NK-cells by this mycobacterial exposure (BCG or environmental). The BCG vaccination has been found to provide protective immunity for a variable duration in different populations ranging from 15 years to 50-60 years [8, 9] . However, it should be noted that in about 90% of the cases MTB gets cleared even in the absence of BCG vaccination or prior 'training' of the immune system. BCG vaccination does not prevent primary infection of MTB, activation of latent TB infection (LTBI) and supposed to have only a limited effect on the prevention of MTB spread in a population [10] . The supposed protective immunity wanes away with time but subsequent exposures to environmental mycobacteria could act as a booster to keep the immunity intact. The exposure to Mycobacterium spp., whether it is MTB, BCG vaccine or environmental isolate, is usually tested by Tuberculin Sensitivity Test (TST) and

Interferon Gamma Release Assay (IGRA).

The current analysis was conducted to ascertain the effect of the prevalence of immune response against Mycobacterium spp. in select populations to their susceptibility to COVID-19. It intends to provide evidence to make an informed decision by the policymakers.

The April 2020 to universal BCG vaccination policy, actual estimated BCG vaccination coverage, Healthcare Access and Quality (HAQI) Index, Neglected tropical diseases (NTDs) and Tuberculosis incidence are presented in Figure 1 . When we consider countries contributing to the majority of the cases with an incidence rate of 25 to >200 cases per million, most of them appear in the higher bracket . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint of economies with HQI index of more than 70 (see Fig 1A with 1B.) Apparently, better healthcare and hygiene are puzzlingly translating into a higher incidence of COVID-19. Recently, it has been proposed that the earlier implementation of universal BCG policy could be responsible for the lower incidence of COVID-19 [4] . BCG vaccine is generally given to children in high TB prevalence countries to protect them from developing pulmonary TB. Its vaccination is estimated to protect different populations for variable durations ranging from 15 to 60 years [8, 9] . The BCG induced delayed T-cell mediated hypersensitivity is supposed to protect through training the immune system to respond in a more disciplined manner without getting overwhelmed with 'cytokine storm' when exposed to pathogens [7, 12] . When we compared the COVID-19 incidence among different countries with BCG vaccination policy implementation ( Figure 1C ) and the total BCG coverage (Figure 1 D) , they do not seem to affect the COVID-19 confirmed deaths per million population for specific countries (e.g., Australia, USA, Germany). Countries falling in less than 89% BCG coverage regions seemingly display more disparate outcomes concerning coronavirus deaths (e.g., Finland, Sweden, South Africa, Iran, Greenland, Iceland, etc).

We hypothesized, maybe the overall burden/incidence of TB and Neglected Tropical diseases (NTDs) may display a better correlation with deaths from COVID-19. When we compared the COVID-19 incidence with TB or NTD burden of the population, the COVD-19 seems to affect more the countries with lower disease burden especially with TB Overall, the observed total COVID-19 confirmed deaths per million of population among different countries seem to correlate (negatively) more with Tuberculosis incidence than coverage of BCG vaccination or BCG vaccination policy implementation as expected if these disease burden may provide some nonspecific protection against COVID-19.

We reasoned if there is some nonspecific protective immunity at work against COVID-19 due to author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint prominent tourist destinations and economic workhorses, had been inherently exchanging a large flux of visitors.

Case Fatality Rate/ Ratio (CFR) is an estimate of how probable it is that someone (case) may die after contracting a disease. CFR estimates are known to vary by location, phase of the outbreak, screening criteria adopted, the extent of screening, reporting issues -number of cases identified/reported, supporting health care, etc. As the COVID-19 pandemic is still underway, the true CFR cannot be reliably ascertained, rather just guessed to allow us to have some understanding of its evolving epidemiology. The CFR value for COVID -19 had been recently estimated between 0.2% (Germany) to 7.7% (Italy) [13] . To evaluate whether BCG vaccination may be providing a certain degree of protection (correlation again!) against COVID-19 to people in countries reporting high BCG coverage (high TST/IGRA positivity), we calculated the CFR in the same set of countries reporting more than 1000 cases, using definition CFR (in %) = [Total deaths from disease / Total number of cases reported] x 100 and plotted them vs % LTBI (TST/IGRA) positivity ( Figure 2B ). The CFR values ranged from 0.29% to 12.25 % among countries. The countries with lower %LTBI (<13% (6.07-12.97%); total 27) seem to have higher CFR than next 28 countries with higher %LTBI (>13% (13.25-47.64%). However, it should be noted that these lower CFR displaying countries (higher %LTBI) on an average are also supposed to be relatively at an earlier phase of the pandemic (except China) and accounting for <15 % of total deaths (including China) at the moment. Though a negative correlation can be obtained, it would be tenuous.

To get a more realistic actionable estimate of the current emerging COVID-19 situation for the projection purpose, we had estimated interim -Infection Fatality Rate/ Ratio (i-IFR) using definition i-IFR (in %) = [Total deaths from disease/ Total number of cases with outcomes] x 100. These estimates are required to assess the gravity of the situation. The lowest possible IFR (i-IFR-l) calculation, for the best possible outcome scenario, included both dead and recovered for the total number of cases with outcomes. The highest possible IFR (i-IFR-h) calculation, for the worst possible outcome scenario, considered the critically sick in the dead category (as a possibility) and rest was similar to i-IFR-l calculation. This was done to get a more realistic estimate of the problem for two reasons: currently, we do not have a complete picture of COVID-19's course and an estimate from the Chinese outbreak projects a period of 2 to 8 weeks from the appearance of first symptoms to death [14] . The predicted/projected death rate for COVID-19 under the best possible scenario (i-IFR-l), when all critically sick will recover, ranged from 1.28% (for Iceland) to 96.39% (For UK) while for the worstcase scenario estimate (i-IFR-h), when all critically sick may die, varied from 3.66 % (for S. Korea) to 96.54% (for UK) in (See Supplementary File sheet: Table for Figure 2 ). It is interesting to note that the difference observed in the CFR, i-IFR-l and i-IFR-h estimates are quite surprising across countries.

The CFR to i-IFR-l increase was lowest, less than 1%, for China (4.07-4.61%) while it witnessed more than 80 % increase for UK (9.44 to 96.54%) and Netherlands (9.45% to 91.83%). Every countries . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint context is different and those variables need to be identified to better manage the COVID-19.

However, when i-IFR-l and i-IFR-h estimates were plotted vs increasing % LTBI positivity of different countries' populations, no apparent correlative inference could be made ( Figure 2C and 2D) about the % LTBI positivity of countries and the severity (IFR) of the COVID-19.

The large variation observed in the estimates of COVID-19 infection fatality rate, i.e., i-IFR-l and i-IFR-h, for different countries (about 5% to almost 97 %) cannot be credibly explained with available data. We speculate this large variation could be the result of countries being in different stages of the pandemic as well as inherent differences in the affected population demography, co-morbidities and genetic makeup as also indicated for select populations such as South Korea, Spain, China, Italy [11, 14] . Detailed comparative analysis of the personal health records of patients with final outcomes (i.e., dead, recovered, sick) along with genetics if available need to be performed wherever possible in all affected countries to better understand the underlying confounding variables affecting COVID-19 outcomes and also design a better strategy to manage it.

Next, for getting a better picture of the underlying mechanics if any concerning role of % LTBI of the target population, we divided the selected countries into top 20 % (11 countries; each accounting for Surprisingly countries which are seemingly worst-hit now with regard to total number no of deaths, will seemingly have lower IFRs (i-IFR and i-IFR-h), e.g., Italy (CFR: 12.25%; i-IFR-l=42.63%; i-IFR-h=48.69%), Spain (CFR: 9.39%; i-IFR-l=26.86%; i-IFR-h=36.60%) as compared to many others who currently seem to be less severely affected, e.g., UK (CFR: 9.45%; i-IFR-l=96.39%; i-IFR-h=96.54%), Netherlands (CFR= 9.46%; i-IFR-l=85.61%; i-IFR-h= 91.83%). Current combined estimates for these countries stand as CFR=6.83.9, i-IFR-l=34.9730.55%; i-IFR-h=44.2029.08%. Next, we compared these countries' fatality rate estimates with that of the bottom 80% countries (44 countries; accounted . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint for 6.74 % COVID-19 deaths) and presented the comparison as a bar diagram ( Figure 3B) There is an urgent need to identify the risk factors which may be promoting the COVID-19 adverse outcomes. Some risk variables such as age, cardiovascular disease, chronic respiratory disease, diabetes have been already identified as associated with adverse outcome in select populations, e.g., Italy, South Korea, Spain, China etc [11, 14] . These potential causative association needs to be verified in other populations as well. The underlying mechanisms responsible for adverse outcome needs to be elucidated and the causes precisely identified for taking any corrective measures to decrease the overall COVID-19 mortality. It would be also interesting to investigate the underlying issues with the reporting, data collection or identify protective contributory factors which may be responsible for such disparate outcomes. Difference in the virulence of virus strain currently affecting different population and the 'training status' of immune system to deal with intracellular pathogens (exposure to mycobacteria/BCG/TST positivity) to mount measured immune response without buckling under the 'cytokine storm' as predicted for SARS-CoV-2 infection, could be some of the other risk factors for select populations/groups.

The BCG vaccination may not be the primary cause for the lower infection and mortality rate per million population observed in select countries who had adopted the universal BCG policy early on or covering the entire population for vaccination. Though the idea that the exposure to environmental Mycobacterium spp. or BCG vaccine could train the body to respond to intracellular pathogens better cannot be completely discounted but the marginal benefit, if any, which could be supposedly gained would be limited to specific population subgroup susceptible to developing TB.

There are countries with hugely disparate case fatality rates among both low % LTBI or BCG coverage countries (High: Italy, Spain, USA etc. vs Low: Germany, Switzerland, Austria, Iceland) and high %LTBI or BCG coverage countries (High: Brazil, Philippines, Indonesia, etc vs Low: Japan, China, Mexico, S. Africa, S. Korea, Thailand etc.). The large variation in CFR and more importantly in estimated interim IFRs among countries could be resulting from the inherent population constitution, e.g., genetics, age, underlying co-morbidities (e.g., cardiovascular disease, diabetes, age, HIV status, . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint inflammatory disease like rheumatoid arthritis) or due to different inherent virulence/pathogenicity of SARS-CoV-2 virus strains currently circulating in different regions. The underlying causes of the difference in the fatality rates among different country populations need to be understood. The government agencies may collect and make available more patient specific data pertaining to COVID-19 to allow its closer scrutiny for making better informed decisions. For correlative analysis, Immune Marker Variation/ HLA typing and genetic background information may be already available or readily obtained in the developed countries who are also currently some of the most affected countries with the COVID-19. The studies exploring the correlation of the genetic makeup of the COVID-19 patients (i.e., critical, recovered, deceased, along with asymptomatic and those displaying non-serious symptoms) may be actively promoted or taken up on a priority basis. It is time for various international bodies, government agencies and philanthropists to come forward and allow this kind of retrospective/prospective work to be undertaken to better understand the adversary SARS-CoV-2019.

Current situation, warrants the research organizations and groups with required resources to be brought together to work in unison towards the common goal of containing the COVID-19 spread and devastation on a war footing as the window of opportunity may be closing at a rate faster than perceived.

The funding support from Banaras Hindu University to the laboratory of SS is acknowledged. The author gratefully acknowledges the fruitful constructive discussions on the subject with Professor Rakesh Bhatnagar, Jawaharlal Nehru University, New Delhi, India that helped it take the current form. Figure 1 : A, B, E and F are from https://ourworldindata.org/coronavirus while C and D are from https://www.who.int/images/default-source/maps/global_tb_bcg_vaccination_ respective entities. They are being used here for non-commercial purpose as fair use entity. Wherever applicable, permission from the copyholders may be sought for their commercial reproduction).

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.11.20062232 doi: medRxiv preprint

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