Agreement Between Self‐Reported COVID‐19 and Dried Blood Spot Serology: A Cross‐Sectional Study

Background and Aims: Identifying COVID-19 cases with high accuracy is essential for epidemiological research on the pandemic's health effects. We aimed to investigate the agreement between a validated self-report questionnaire for COVID-19 and dried blood spot serology for SARS-CoV-2 antibodies. Methods: We conducted a cross-sectional analysis of combined survey and SARS-CoV-2 antibody data. Between June and October 2023, 311 adults completed a validated self-report COVID-19 questionnaire and provided fingertip blood samples, which underwent Enzyme Linked Immunosorbent Assay to quantify IgG antibodies to SARS-CoV-2 nucleocapsid (N)-proteins. We applied several statistical approaches to assess agreement: Cohen's κ of inter-rater reliability; positive (PPV) and negative (NPV) predictive values; and logistic and linear regressions of the year of most recent self-reported COVID-19 on serostatus and N-protein antibody concentrations. Results: Two-thirds (203, 65%) of participants self-reported a history of COVID-19 whereas one-third (98, 32%) were seropositive for SARS-CoV-2 N-protein antibodies, reflecting only "fair" agreement (κ = 0.23 [95% CI 0.15-0.31]). Self-reported COVID-19 had a PPV of 41% and an NPV of 87% for SARS-CoV-2 seropositivity. PPV was low for those whose most recent self-reported cases were in 2020-21 (36%) and 2022 (33%), but higher for 2023 (75%). Compared to participants with no self-reported history of COVID-19, those reporting SARS-CoV-2 infection in 2023 had 23 times greater odds of being seropositive (95% CI: 9.18-62.5) and had 1078% (617-1836%) higher N-protein concentrations, after adjustment for confounders. Conclusion: While overall agreement self-reported COVID-19 and serostatus is only fair, there was a strong relationship exhibited between the two for recent self-reported cases, when serology is most accurate. This suggests that self-reported COVID-19 is reasonably accurate for identifying people who have previously had COVID-19 as well as determining roughly when infections occurred.