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

STEFAN Kirov

The copyright holder has placed this preprint (which was not peer-reviewed) . https://doi.org/10.1101/2020.04.06.20055616 doi: medRxiv preprint 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