Abstract
Background: Cohort studies of people with a history of COVID-19 infection and controls will be essential to understand the epidemiology of long-term effects. However, clinical diagnosis requires resources that are frequently restricted to the severely ill. Cohort studies may have to rely on surrogate indicators of COVID-19 illness. We describe the prevalence and overlap of five potential indicators: self-reported suspicion, self-reported core symptoms, symptom algorithm, self-reported routine test results, and home antibody testing.
Methods: An occupational cohort of staff and postgraduate students at a large London university who participated in surveys and antibody testing. Self-report items cover March to June 2020 and antibody test results from ‘lateral flow’ IgG/IgM antibody test cassettes sent to participants in June 2020.
Results: Valid antibody test results were returned for 1882 participants. Of the COVID-19 indicators, the highest prevalence was core symptoms (770 participants positive, 41%), followed by participant suspicion of infection (n=509, 27%), a symptom algorithm (n=297, 16%), study antibody positive test (n=124, 6.6%) and self-report of a positive external test (n=39, 2.1%). Study antibody positive result was rare in people who had no suspicion they had experienced COVID-19 (n=4, 0.7%) or did not experience core symptoms (n=10, 1.6%). When study antibody test results were compared with earlier external antibody results in those who had reported them, the study antibody results agreed in 88% cases (kappa= 0.636), with a lower proportion testing positive on this occasion (proportion with antibodies detected 15% in study test vs 24% in external testing).
Discussion: Our results demonstrate that there is some agreement between different COVID indicators, but that they a more complete story when used together. Antibody testing may provide greater certainty and be one of the only ways to detect asymptomatic cases, but is likely to under-ascertain due to weak antibody responses to mild infection, which wane over time. Cohort studies will need to review how they deal with different and sometimes conflicting indicators of COVID-19 illness in order to study the long-term outcomes of COVID-19 infection and related impacts.
Methods: An occupational cohort of staff and postgraduate students at a large London university who participated in surveys and antibody testing. Self-report items cover March to June 2020 and antibody test results from ‘lateral flow’ IgG/IgM antibody test cassettes sent to participants in June 2020.
Results: Valid antibody test results were returned for 1882 participants. Of the COVID-19 indicators, the highest prevalence was core symptoms (770 participants positive, 41%), followed by participant suspicion of infection (n=509, 27%), a symptom algorithm (n=297, 16%), study antibody positive test (n=124, 6.6%) and self-report of a positive external test (n=39, 2.1%). Study antibody positive result was rare in people who had no suspicion they had experienced COVID-19 (n=4, 0.7%) or did not experience core symptoms (n=10, 1.6%). When study antibody test results were compared with earlier external antibody results in those who had reported them, the study antibody results agreed in 88% cases (kappa= 0.636), with a lower proportion testing positive on this occasion (proportion with antibodies detected 15% in study test vs 24% in external testing).
Discussion: Our results demonstrate that there is some agreement between different COVID indicators, but that they a more complete story when used together. Antibody testing may provide greater certainty and be one of the only ways to detect asymptomatic cases, but is likely to under-ascertain due to weak antibody responses to mild infection, which wane over time. Cohort studies will need to review how they deal with different and sometimes conflicting indicators of COVID-19 illness in order to study the long-term outcomes of COVID-19 infection and related impacts.
| Original language | English |
|---|---|
| Journal | medRxiv preprint server |
| DOIs | |
| Publication status | Published - 8 Dec 2020 |
Keywords
- COVID-19
- Cohort studies
- community
- antibody
- Methodology
- Universities