Scoping review of the methodology of large health surveys conducted in Spain early on in the COVID-19 pandemic - Sánchez-Cantalejo Garrido, Carmen; Yucumá Conde, Daniela; Rueda, María del Mar; Olry-de-Labry-Lima, Antonio; Martín-Ruiz, Eva; Higueras-Callejón, Camila; Cabrera-León, Andrés (Frontiers in Public Health, 2023)
Background The use of health surveys has been key in the scientific community to promptly communicate results about the health impact of COVID-19. But what information was collected, where, when and how, and who was the study population? Objective To describe the methodological characteristics used in large health surveys conducted in Spain early on in the COVID-19 pandemic. Methods Scoping review. Inclusion criteria: observational studies published between January 2020 and December 2021, with sample sizes of over 2,000 persons resident in Spain. Databases consulted: PubMed, CINAHL, Literatura Latinoamericana y del Caribe en CC de la Salud, Scopus, PsycINFO, Embase, Sociological Abstracts, Dialnet and Web of Science Core Collection. We analyzed the characteristics of the literature references, methodologies and information gathered in the surveys selected. Fifty five studies were included. Results Sixty percentage of the studies included had mental health as their main topic and 75% were conducted on the general adult population. Thirteen percentage had a longitudinal design, 93% used the internet to gather information and the same percentage used non-probability sampling. Thirty percentage made some type of sampling correction to reduce coverage or non-response biases, but not selection biases. Sixty seven percentage did not state the availability of their data. Conclusions Consistent with the extensive use of non-probability sampling without any bias correction in the extraordinary setting created by COVID-19, quality population frameworks are required so that probability and representative samples can be extracted quickly to promptly address other health crises, as well as to reduce potential coverage, non-response and particularly selection biases by utilizing reweighting techniques. The low data accessibility despite the huge opportunity that COVID-19 provided for Open Science-based research is striking.