Comparing the incidence of SARS-CoV-2 across age groups considering sampling biases - use of testing data of autumn 2021 in Belgium

Author:

Lajot Adrien,Cornelissen Laura,Van Cauteren Dieter,Meurisse Marjan,Brondeel Ruben,Dupont-Gillain Christine

Abstract

Abstract Background To design efficient mitigation measures against COVID-19, understanding the transmission dynamics between different age groups was crucial. The role of children in the pandemic has been intensely debated and involves both scientific and ethical questions. To design efficient age-targeted non-pharmaceutical interventions (NPI), a good view of the incidence of the different age groups was needed. However, using Belgian testing data to infer real incidence (RI) from observed incidence (OI) or positivity ratio (PR) was not trivial. Methods Based on Belgian testing data collected during the Delta wave of Autumn 2021, we compared the use of different estimators of RI and analyzed their effect on comparisons between age groups. Results We found that the RI estimator’s choice strongly influences the comparison between age groups. Conclusion The widespread implementation of testing campaigns using representative population samples could help to avoid pitfalls related to the current testing strategy in Belgium and worldwide. This approach would also allow a better comparison of the data from different countries while reducing biases arising from the specificities of each surveillance system.

Publisher

Springer Science and Business Media LLC

Subject

Public Health, Environmental and Occupational Health

Reference27 articles.

1. Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nat Nat Publishing Group. 2020;584:257–61.

2. Braekman E, Charafeddine R, Demarest S, Drieskens S, Gisle L, Hermans L. Quatrième Enquête de santé COVID-19. Résultats préliminaires [Internet]. Sciensano; 2020. Available from: https://www.sciensano.be/node/65423

3. Backhaus A. Common Pitfalls in the interpretation of COVID-19 Data and Statistics. Intereconomics. 2020;2020:162–6.

4. Belgium COVID-. 19 Dashboard - Sciensano [Internet]. Google Data Studio. [cited 2022 Jun 24]. Available from: http://datastudio.google.com/reporting/c14a5cfc-cab7-4812-848c-0369173148ab/page/tpRKB?feature=opengraph

5. National Academies of Sciences, Engineering, and Medicine. Evaluating Data Types: A Guide for Decision Makers using Data to Understand the Extent and Spread of COVID-19 [Internet]. Washington, D.C.: National Academies Press. ; 2020 [cited 2022 May 24]. Available from: https://www.nap.edu/catalog/25826

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