Understanding socio-epidemiological background of infection disease
dynamics is an extremely difficult task and this problem was professionally
approached in a paper published in several Lancetlatest papers [1–3]. However, some concerns in interpretation and
limitation need additional discussion. As authors used fit-all-size
methodology, they could lose precision due to heterogeneity of given regions.
Thus, estimated variation in
the COVID-19 outcomes in geography on global scale could be far from reality on
local scale.
Many scientists attempt to reveal
the “epidemiological mystery” of COVID-19: why the coronavirus hit some regions
so much harder than others and how it corresponds with measurable
epidemiological indexes. This and a few other modelling and epidemiological
paradoxes (in which different regions are compared on populational level) such
as effectiveness of pharmaceutical intervention (e.g. vaccination) or NPI (e.g.
lockdowns) need to be carefully assessed if they are interpreted correctly
according to epidemiological knowledge. Unfortunately,
even with a correct statistical model (without understanding underlying
socio-epidemiological variables), by comparing incidences in different regions
one could easily underestimate effectiveness of NPI [4] or vaccination [5]
(even unintentionally) or it could also lead to manipulation (for instance one
could pick up only these indexes, which support a given thesis).
Let us look closer into post-communistic, Slavic majority Eastern
Europe countries as an example, because their public health systems, political
transformations, alternative social capitals and second demographic transitions
have similar patterns [6]. The red light should turn on, if we compare reported
case notifications [7] with infection standardised estimates (in brackets [1])
per 1000 inhabitants for the same time period for Slovakia 147 (166), Poland 76
(469) and Russia 50 (1165). Thus, cumulative cases till the end of the
so-called European 3rd wave (so called “dark figure”) were estimated to be from
1.13 in Slovakia
to 23.3
in Russia fold greater than the confirmed cases. Even reminding that Slovakia
performed all country cross-sectional studies on COVID-19 prevalence and Russia
has been known since the HIV epidemic [8] to manipulate infection registries,
such a difference is rather impossible to be true. Moreover, death toll (if no
vaccination is applied) based on crude death registry as well as exceed death
estimation (in bracket) per 1000 inhabitants for the first year of vaccination
roll out were calculated [3] for Poland 7.9 (9.0), Russia 5.2 (12.8), Czechia
9.9 (10.0). Thus, the corrected death tolls using exceed death were estimated
from 1.01
in Czechia to 2.46 in Russia fold greater than the crude deaths. One more time,
death registries in Russia are known to be flawed [9] and on the other hand the
Czech mortality registry is working relatively well [10], but again such a
variation is extremely unlikely. This could lead to the conclusion that even a
well performing estimation method on a global scale may be really poor in some
particular regions. I guess that possibly Healthcare Access (sensu lato with affective and
behavioural components [11]), which was found not to be associated with epidemiological outcomes [1] should be revisited.
Therefore, published models have a great advantage of
modelling from a global perspective, but on the other hand, due limitations
raised here, together with those already indicated by authors [1–3], more
emphasis should be given on regional research, especially on intra-country
variability.
References
1. Bollyky TJ, Hulland EN, Barber RM, Collins JK, Kiernan S, Moses M,
et al. Pandemic preparedness and COVID-19: an exploratory analysis of infection
and fatality rates, and contextual factors associated with preparedness in 177
countries, from Jan 1, 2020, to Sept 30, 2021. The Lancet. 2022
Feb;S0140673622001726.
2. Barber RM,
Sorensen RJD, Pigott DM, Bisignano C, Carter A, Amlag JO, et al. Estimating global, regional, and national daily and cumulative
infections with SARS-CoV-2 through Nov 14, 2021: a statistical analysis. The
Lancet. 2022 Jun;399(10344):2351–80.
3. Watson OJ, Barnsley G, Toor J, Hogan AB, Winskill P, Ghani AC.
Global impact of the first year of COVID-19 vaccination: a mathematical
modelling study. Lancet Infect Dis. 2022 Jun;S1473309922003206.
4. Herby J, Jonung L, Hanke SH. A Literature Review and Meta-Analysis
of the Effects of Lockdowns on COVID-19 Mortality [Internet]. Available from: https://sites.krieger.jhu.edu/iae/files/2022/01/A-Literature-Review-and-Meta-Analysis-of-the-Effects-of-Lockdowns-on-COVID-19-Mortality.pdf
5. Subramanian SV, Kumar A. Increases in COVID-19 are unrelated to
levels of vaccination across 68 countries and 2947 counties in the United
States. Eur J Epidemiol,2021 Dec;36(12):1237-40. https://link.springer.com/10.1007/s10654-021-00808-7
6. Jarynowski A. Phenomenon of participatory “guerilla” epidemiology
in post-communist European countries. Balt Rim Econ. 2021;3:25–6. https://sites.utu.fi/bre/phenomenon-of-participatory-guerilla-epidemiology-in-post-communist-european-countries/
7. Our World in data. Coronavirus (COVID-19) Cases [Internet]. 2022. Available from: https://ourworldindata.org/covid-cases
8. Rechel B. HIV/AIDS in the Countries of the Former Soviet Union:
Societal and Attitudinal Challenges. Cent Eur J Public Health. 2010 Jun
1;18(2):110–5.
9. Danilova I, Shkolnikov VM, Jdanov DA, Meslé F, Vallin J.
Identifying potential differences in cause-of-death coding practices across
Russian regions. Popul Health Metr. 2016 Dec;14(1):8.
10. Kossarova L, Holland W, Mossialos E. ‘Avoidable’ mortality: a
measure of health system performance in the Czech Republic and Slovakia between
1971 and 2008. Health Policy Plan. 2013 Aug 1;28(5):508–25.
11. Jarynowski A, Belik V. Access to healthcare as an important moderating variable for understanding geography of immunity levels for COVID-19 – preliminary insights from Poland [Internet]. Available from: http://medrxiv.org/lookup/doi/10.1101/2021.12.08.21267167 (accepted to EJTCM)
