Scoping review of the methodology of large health surveys conducted in Spain early on in the COVID-19 pandemic

Abstract

BackgroundThe 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?ObjectiveTo describe the methodological characteristics used in large health surveys conducted in Spain early on in the COVID-19 pandemic.MethodsScoping 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.ResultsSixty 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.ConclusionsConsistent 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.

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

“…This has often led to the creation of survey studies which did not meet the criteria for being considered probabilistic, entailing many sources of error that may affect the final estimates obtained from them. In this sense, a recent scoping review on the methodological characteristics of the health surveys conducted in Spain early on in the COVID-19 pandemic included 55 studies (among over 3000 initially identified) [4]. An outcome of this review worth noting is the low proportion of longitudinal surveys identified (12.7%) and the implementation of some type of sampling adjustment (30.9%), even though most of the surveys were based on non-probability sampling (92.7%).…”

“…These surveys allow valid inferences to be made about the population without having to incorporate hypotheses into the models, which is of great practical benefit [3]. Regarding the COVID-19 pandemic, most of the surveys created were based on non-probability sampling to provide a quick and efficient assessment of the situation based on predicting and quantifying the main parameters involved in this phenomenon [4].…”