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Studies on COVID-19 contradict each other. Who is right, and on what basis?

Text updated on 2020-06-19


Conflicting information circulates and it is sometimes difficult to find one's way around, even within the scientific community. Nevertheless, a few simple steps can give an idea of the significance of a scientific result. And be patient...history (mankind) will eventually decide!

Some scientific studies contradict each other. How then can you make up your own mind if even researchers disagree with each other? There are many factors to consider:

1. The COVID-19 disease is recent (identified in December 2019) so tools and methods to characterize the infection and the associated immune response are being refined. As a result, tests are not performed in the same way in all countries and are constantly evolving. Differences in testing protocols, sampling methods, criteria for identifying patients who have been infected COVID-19 and methods for counting patients in hospitals, homes and nursing homes can lead to discrepancies in the conclusions.

There are wide variations in the number of diagnostic tests performed per million inhabitants and in the criteria used to perform the tests: whether or not the degree of symptom severity is taken into account, and whether or not asymptomatic persons are included in the tests. These differences have an impact on the case fatality rate, which fluctuates between 1% and 20% depending on the country.

2. There are many factors that influence the severity of the COVID-19 disease that must be considered before concluding.

Because the case-fatality rate of infection is highly dependent on age, gender, and co-morbidity factors, it can vary greatly from country to country or even region to region, depending on the age and health of the individuals involved. For example, it is expected that Africa, where 40% of the population is under 14 years of age and there are very few elderly people, is expected to be less impacted by COVID-19 than in Japan, where 33% of the population is over 60 years of age.

Furthermore, not all countries offer the same conditions for the reception and treatment of patients, particularly in terms of access to resuscitation services, which will have a major impact on the case-fatality rate.

In the future, we will discover whether genetic or environmental factors also influence the transmission of the coronavirus or the severity of symptoms.

3. There are many factors influencing the transmission of the COVID-19 disease that must be considered before concluding.

The transmission of the disease COVID-19 has a strong cultural component: people in Asian countries easily put on a mask, which reduces the spread of the coronavirus, while Western populations are sometimes still reluctant.

Moreover, population density and the nature of interactions differ between countries and even regions, so that the "spontaneous" social distance is not the same from one place to another. Before the COVID-19 pandemic, the average distance between two people was greater in Northern Europe than in Southern Europe, where the population density is higher and people are more used to talking to each other in close proximity and hugging each other.

The impact of COVID-19 within a population depends greatly on the density of housing and the socio-economic level of the inhabitants. Ethnic differences have been observed, for example, in the United States, the United Kingdom, and France in Saint-Denis (African-Americans and Latin Americans are more affected from COVID-19 than Caucasians). These differences are largely due to socio-economic differences which are correlated with population density, the preponderance of essential at-risk occupations, and certain co-morbidity factors. However, genetic factors are still poorly understood and could impact the severity or transmission of the coronavirus.

4. Intrinsic variability is very important in experimental science. An experiment under the same conditions can give different results depending on a large number of uncontrolled parameters. It is therefore necessary to repeat the experiments in several laboratories to replicate the results and to carry out meta-analyses that synthesize a large number of studies.


In conclusion
, scientific work must always be reduced to the dimension of the study and the critical factors for the severity and transmission of the COVID-19 disease (age, gender, co-morbidity, culture, ethnicity). Comparisons must be made between what is comparable: same test, same population, same criteria for analysis.

Before concluding, it takes time for scientists to test an effect on large cohorts of individuals, taking into account all the parameters that may influence the conclusions. The inclusion of controls and the reproducibility of results by independent research teams on large scale and on diverse populations is crucial.


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Sources

Synthesis of scientific data on COVID-19 : Our World in Data, SciLine, EurekAlert.

Origin of the number of cases and number of COVID-19 victims between countries

Reference showing the difference in infection rate by age, sex, and co-morbidity factors. Meta-analysis of COVID-19 cases in Chinese hospitals: older people or people with co-morbidities (diabetes, hypertension, cardiovascular or respiratory problems) were more likely to have severe symptoms.

Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., ... Zhou, Y. (2020). Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. International Journal of Infectious Diseases, 94, 91-95.

Studies in China suggest that men are more affected by COVID-19 than women.

Cai, H. (2020). Sex difference and smoking predisposition in patients with COVID-19. The Lancet Respiratory Medicine, 8(4), e20.

Impact of population density on disease attack rate The COVID-19 disease spreads much faster in cities with higher population densities.

Berman, M. G., Bettencourt, L. M., & Stier, A. J. (2020). COVID-19 attack rate increases with city size. MedRxiv. PREPRINT

Differences observed in the rate of transmission within the family according to countries and cultures. In China, secondary transmission of SARS-CoV-2 occurred in 16.3% of household contacts. The secondary attack rate among household contacts with reference patients quarantined by themselves since the onset of symptoms was 0%, compared to 16.9% among contacts without quarantined reference patients.

Li, W., Zhang, B., Lu, J., Liu, S., Chang, Z., Cao, P., ... & Chen, J. (2020). The characteristics of household transmission of COVID-19. Clinical Infectious Diseases.

Countries with a culture that provides for the wearing of masks or that have imposed them (Taiwan, Japan, South Korea, several regions of China, Slovakia, Slovenia) show a smaller increase in the number of COVID cases.

Kai, D., Goldstein, G.-P., Morgunov, A., Nangalia, ishal, & Rotkirch, A. (2020). Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations. ArXiv.

The typical distance between people varies between cultures.

Sorokowska, A., Sorokowski, P., Hilpert, P., Cantarero, K., Frackowiak, T., Ahmadi, K., ... & Blumen, S. (2017). Preferred interpersonal distances: a global comparison. Journal of Cross-Cultural Psychology, 48(4), 577-592.

In the Crépy-en-Valois high school (Oise, France), 38% of the students, 43% of the teachers, and 59% of the staff working in the school who were given a serological test were positive, confirming SARS-CoV-2 infections. The rate of secondary intra-familial transmission was estimated at 11% to parents and 10% to siblings.

Fontanet, A., Shearer, L., Madec, Y., Grant, R., Besombes, C., Jolly, N., ... & Temmam, S. (2020). Cluster of COVID-19 in northern France: A retrospective closed cohort study. medRxiv.

The presence of an older population in a population and the frequency of intergenerational contact within a culture are relevant to the transmission and case-fatality rate of COVID-19.

Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., ... & Mills, M. C. (2020). Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences, 117(18), 9696-9698.

Study on the factors that influence variations in case fatality rates between countries.

Ward, D. (2020). Sampling Bias: Explaining Wide Variations in COVID-19 Case Fatality Rates.

Data from the Regional Observatory of Health in Ile-de-France highlighted a very significant excess mortality in the department of Seine Saint-Denis, with the highest change in mortality in Ile-de-France compared to the same period in 2019 (+ 69.4% between March 1 and 31, 2020 and + 118.4% between March 1 and April 10, 2020). In comparison, mortality in Paris increased by 89.8%. In this department, the densest in Ile de France but also the poorest, housing is often small (for a quarter of the population of Seine Saint-Denis, the surface area per inhabitant is 14m2 compared to 17m2 in Paris) and occupied by larger families (42.1% of housing is occupied by 3 or more people, compared to 21.8% in Paris), making social distancing difficult to implement. It is also in this department, compared to other departments of Ile de France, that the greatest number of workers exposed to risk situations (hospital workers, orderlies, cashiers, delivery personnel) reside, with more travel than in other departments (more than 50% of the inhabitants of Seine Saint-Denis work in another department, compared to only 24.4% of Parisians working in another department). Finally, often linked to difficult social conditions, the prevalence of certain pathologies (diabetes, chronic disease, overweight) is higher than in other departments. The social and health inequalities from which Seine Saint-Denis suffers explain the excess mortality so high in Seine Saint-Denis compared to other departments of Ile de France.

Mangeney, C., Bouscaren, N.,Telle-Lamberton, M., Saunal, A., Féron, V.La excess mortality during the epidemic of COVID-19 in the departments of France, Regional Health Observatory Ile de France, April 2020.

Data from the United Kingdom's Office of National Statistics (ONS) show similar results to French data from the Ile de France Regional Health Observatory. Comparison of the mortality rate between March 1 and April 17 2020 in disadvantaged areas in terms of wages, employment, health, education level, environment etc. and in privileged areas, shows that the risk of dying from COVID-19 in disadvantaged areas is 2.1 times higher than in favoured areas.

Deaths involving COVID-19 by local area and socioeconomic deprivation: deaths occurring between March 1 and April 17 2020, Office for National Statistics, May 1 2020.

Further reading

How many people are infected without showing symptoms?

What is the risk of dying from this COVID-19 for an infected person?

What is the test to find out if I am infected with SARS-CoV-2?

What are the different types of serological tests?