Online study of health professionals about their vaccination attitudes and behavior in the COVID-19 era: addressing participation bias

• Main Authors: Pierre Verger (Author), Dimitri Scronias (Author), Yves Fradier (Author), Malika Meziani (Author), Bruno Ventelou (Author)
• Format: Book
• Published: Taylor & Francis Group, 2021-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2164-5515; 2164-554X; 10.1080/21645515.2021.1921523
• Keywords: health care professionals; online surveys; selection bias; attitudes; practices; covid-19; weighting; heckman method; Immunologic diseases. Allergy; RC581-607; Therapeutics. Pharmacology; RM1-950
• Source: Human Vaccines & Immunotherapeutics, Vol 17, Iss 9, Pp 2934-2939 (2021)

Online surveys of health professionals have become increasingly popular during the COVID-19 crisis because of their ease, speed of implementation, and low cost. This article leverages an online survey of general practitioners' (GPs') attitudes toward the soon-to-be-available COVID-19 vaccines, implemented in October-November 2020 (before the COVID-19 vaccines were authorized in France), to study the evolution of the distribution of their demographic and professional characteristics and opinions about these vaccines, as the survey fieldwork progressed, as reminders were sent out to encourage them to participate. Focusing on the analysis of the potential determinants of COVID-19 vaccine acceptance, we also tested if factors related to survey participation biased the association estimates. Our results show that online surveys of health professionals may be subject to significant selection bias that can have a significant impact on estimates of the prevalence of some of these professionals' behavioral, opinion, or attitude variables. Our results also highlight the effectiveness of reminder strategies in reaching hard-to-reach professionals and reducing these biases. Finally, they indicate that weighting for nonparticipation remains indispensable and that methods exist for testing (and correcting) selection biases.