Author:
Davis Katrina A. S.,Carr Ewan,Leightley Daniel,Vitiello Valentina,Bergin-Cartwright Gabriella,Lavelle Grace,Wickersham Alice,Malim Michael H.,Oetzmann Carolin,Polling Catherine,Stevelink Sharon A. M.,Razavi Reza,Hotopf Matthew
Abstract
Abstract
Background
Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators.
Methods
We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators.
Results
Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 (n = 509/1882, 27%), a positive symptom algorithm (n = 298/1882, 16%), study antibody lateral flow positive (n = 124/1882, 7%) and a positive external test result (n = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%).
Conclusions
Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.
Funder
NIHR Maudsley Biomedical Research Centre
King's College London
Wellcome Trust
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health
Reference49 articles.
1. Anderson RM, Hollingsworth TD, Baggaley RF, Maddren R, Vegvari C. COVID-19 spread in the UK: the end of the beginning? Lancet. 2020;396:587–90.
2. Davies NG, Kucharski AJ, Eggo RM, Gimma A, Edmunds WJ, Jombart T, et al. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. Lancet Public Health. 2020;5(7):e375–85.
3. Neil Ferguson, Azra Ghani, Wes Hinsley, Erik Volz, Imperial College COVID-19 response team. Hospitalisation risk for Omicron cases in England. https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-50-severity-omicron/: MRC Centre for Global Infection Disease Analysis; 2021. [Accessed 30 May 2022].
4. Cunniffe NG, Gunter SJ, Brown M, Burge SW, Coyle C, De Soyza A, et al. How achievable are COVID-19 clinical trial recruitment targets? A UK observational cohort study and trials registry analysis. BMJ Open. 2020;10(10): e044566.
5. de Erausquin GA, Snyder H, Carrillo M, Hosseini AA, Brugha TS, Seshadri S, et al. The chronic neuropsychiatric sequelae of COVID‐19: The need for a prospective study of viral impact on brain functioning. Alzheimer's & Dementia. 2021 https://doi.org/10.1002/alz.12255.