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.