Comparability of Control and Comparison Groups in Studies Assessing Long COVID.

Background: Awareness of long coronavirus disease (COVID) began primarily through media and social media sources, which eventually led to the development of various definitions based on methodologies of varying quality. We sought to characterize comparison groups in long COVID studies and evaluate comparability of the different groups.

Methods: We searched Embase, Web of Science, and PubMed for original research articles published in high-impact journals. We included studies on human patients with long COVID outcomes, and we abstracted study-related characteristics, as well as long COVID characteristics.

Results: Of the 83 studies, 3 were randomized controlled trials testing interventions for long COVID, and 80 (96.4%) were observational studies. Among the 80 observational studies, 76 (95%) were trying to understand the incidence, prevalence, and risk factors for long COVID, 2 (2.5%) examined prevention strategies, and 2 (2.5%) examined treatment strategies. Among those 80 studies, 45 (56.2%) utilized a control or comparison group and 35 (43.8%) did not. Compared with 95% of observational studies that documented symptoms or assessed risk factors, all randomized studies assessed treatment strategies. We found 48.8% of observational studies did any adjustment for covariates, including demographics or health status. Of those that did adjust for covariates, 15 (38.5%) adjusted for 4 or fewer variables. We found that 26.5% of all studies and 45.8% of studies with a control/comparator group matched participants on at least 1 variable.

Conclusion: Long COVID studies in high-impact journals primarily examine symptoms and risk factors of long COVID; often lack an adequate comparison group and often do not control for potential confounders. Our results suggest that standardized definitions for long COVID, which are often based on data from uncontrolled and potentially biased studies, should be reviewed to ensure that they are based on objective data.