Discussion
To the best of our knowledge, this is the first study assessing the impact of BCG vaccination on the diffusion and mortality of Covid-19 at the global level by controlling for a comprehensive set of social, economic, geographic and demographic variables. This allows to greatly reduce the risk of spurious correlations among variables and confers high statistical robustness to the results which are therefore more amenable to causal interpretation.
From the second and third column in Table 2, containing the statistically significant Tobit coefficients of relative incidence, we observe that the number of positive cases 15 days earlier has a very significant and sizeable positive coefficient, capturing the different stage of the epidemic across countries (a larger number of cases implies a higher probability of contagion).
Among the demographic variables, population in the largest city has a very sizeable, positive and statistically significant coefficient, indicating that high urban density fosters the epidemic. Other demographic variables do not reach statistical significance, possibly in view of high variability in data. Lastly, the percentage of immigrant over total population is negatively correlated with the extent of the epidemic, but with a non statistically significant coefficient. Its sign, however, might be read in the light of the impact of BCG vaccine, as explained below. Or it might be read as resulting from the lower probability of those immigrant communities being tested, as suggested by Borjas (17).
Among geographical variables, the dummy for Summer (countries below Equatorial Line) is negative, large, and highly statistically significant. This strongly reinforces results in Ozdemir and colleagues (7), finding that the mean of cases per population ratio was higher in the Northern hemisphere. The magnitude of the coefficient is really large, and indicates that countries in the Southern hemisphere have, on average and all else equal, over 200 cases per million less (against a mean value of confirmed cases per million inhabitants of about 470). Seasonality is a key question for describing the trend of pandemic and predicting future transmission dynamics (18).
Among the economic and health policy variables, GDP has a large and highly significant coefficient in all specifications, which corresponds to our a priori expectations, in view of the maintained hypothesis of a positive correlation between income and number of tests performed. In other words, including the per capita GDP variable allows to control for different testing policies implemented across countries. Domestic private health expenditure also features a positive coefficient, which can most likely be explained in the same way.
The main and well taken criticism, addressed by Curtis and colleagues (6) and Faust and colleagues (https://naturemicrobiologycommunity.nature.com/users/36050-emily-maclean/posts/64892-universal-bcgvaccination-and-protection-against-covid-19-critique-of-an-ecological-study), to most of the recent ecological studies assessing the impact of BCG vaccination upon Covid-19 related outcomes goes as follows: different testing policies, induced by nation-wide economic situations, might impair the validity of results, by spuriously inducing a negative correlation between BCG vaccination (more frequent in low or middle income countries, that on average have less economic means to carry out a substantial testing policy), reported cases and deaths.
In our study we avoid this problem by explicitly accounting for the relative wealth of countries and of their respective health systems, thus netting out the relationship between income and testing policies, and by using a large dataset including quite a few high income countries where BCG vaccine is also mandatory (Japan, as a notable example).
Closer economic ties to China exert an ambiguous effect. On the one hand, imports from China have a negative and statistically significant effect on the extent of the epidemic, most likely because countries closer to China were affected to a limited extent. On the other, FDI from China have a positive, but limited effect, and might derive from the more frequent personal contacts between Chinese and western businessmen, around the start of the crisis. Anyway, if ties with China might have explained the diffusion of the epidemic at earlier stages, they no longer seem to do so.
The HFI has a strong and significant impact on the incidence of the epidemic, suggesting that in freer countries lockdown measures were milder, and that compliance might have been lower in such countries.
Most importantly, the BCG dummy variable has a strong and negative impact in the Tobit specifications. This finding corroborates in a more comprehensive and robust framework those presented in some recent contributions (7-10) and arguing in favor of a correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19. Another study by Dayal and Gupta (11) obtains similar conclusions, comparing CFR’s of countries where BCG re-vaccination is adopted vs. those countries where vaccination is practiced only once in lifetime.
As the significance of BCG might be spurious, driven by the correlation with other vaccinations, the third column in Table 2 contains a second Tobit specification, where the adoption of other vaccination policies (namely, B Hepatitis, Measles and DPT) is also controlled for. Results are substantially unaltered, hinting at a very robust effect of BCG vaccination in reducing the (symptomatic) diffusion of the epidemic.
Tables 3 contains results for CFR’s and MRs in the fractional model specifications. The second and fourth column include two additional vaccination variables (B Hepatitis has not been omitted here in view of its high correlation with DPT) to account for the potential endogeneity of the BCG variable, in view of the possibility of omitted vaccination variables.
Looking at the estimated coefficients, we notice that general government health expenditure, as a percentage of GDP, has a positive and significant effect, possibly revealing a more accurate control of deaths, while closer ties with China have once again ambiguous effects. Negative for imports and for FDI to China, and positive for FDI from China, most likely related to the relative stance of Asian and Western countries in those areas. The impact of all those variables, however, is quite limited in size (less than 1%).
Population wise, while concentration in largest city exerts, not surprisingly, a positive effect on CFRs and MRs (but only significant for CFRs), the percentage of migrant population has a significant and negative effect. For example, one standard deviation of migrant population share would reduce the CFR by about 1.5%. This might be interpreted in two different ways. On the one hand, paralleling the arguments in Borjas (17), immigrants’ communities might be less checked, and report less fatalities. However, migrants to western countries usually come from countries where BCG is mandatory, which might result associated with lower MRs. More disaggregated data - possibly at individual level - may provide additional information on vulnerability drivers involved in Covid-19 related outcomes (19).
In fact, the most important effect on CFRs and MRs, as for relative incidence, is exerted by the BCG variable, which is associated to a strongly significant reduction of CFR and MR by, respectively, -4.5 percentage points (both with and without additional vaccination controls) and -1.9 (-1.2 with additional vaccination controls) percentage points. To get a relative idea of the magnitude of these estimates, let us just notice that the mean values of CFRs and MRs in the estimation sample are, respectively, 4.1% and 2.8%.
Anecdotal evidence, especially in Europe, seems largely consistent with the large explanatory power of BCG on CFRs and MRs. We mentioned the wide and puzzling differences in CFRs and MRs between contiguous countries, such as Portugal and Spain, Ireland and UK, Norway/Finland and Sweden. In all those pairs of countries, the first has mandatory BCG vaccination or had it until recent times, the second has not.
Last but not least, the HFI turns out to have a negative and significant effect on the CFR. This, however, might only be due to the results reported above, i.e. the positive effect of HFI on the number of reported cases, which is the denominator of CFR.
As a further control of the robustness of these results, more checks have been performed by replacing the BCG dummy with two different continuous variables related to this vaccination policy. One is BCG coverage, as reported in national surveys. Another is the number of years of missing vaccination, until 2020. Unfortunately, data on coverage were available for a more limited number of countries, but even so the results seem noteworthy. Table 4 contains the results of two Tobit regressions of the relative number of cases, by using the alternative BCG variables. Table 5 reports the results of fractional regressions on both CFRs and MRs, using the two alternative BCG variables. Tables 4 and 5 show a strong and negative impact, of magnitude comparable to that obtained for the BCG variable in the previous analyses, for the BCG coverage variable. Missing years of vaccination also feature a coefficient with the expected positive sign, but only significant in the case of relative incidence and CFRs.