Associations between self-reported SARS-CoV-2 infection status, serology and common longer-term COVID-19 symptoms among adults in Canada, a cross-sectional study
Bibliographic record
Abstract
Background: A variety of methods, including self-report and antibody testing, has been used to estimate the prevalence of SARS-CoV-2 infections and related longer-term symptoms, but the impact of employed methods on conclusions has not been thoroughly explored. Objective: We examined associations between self-report and antibody findings in the Canadian adult (aged 18 years and older) population. Methods: We used data from a large population-based cross-sectional probability survey conducted between April and August 2022. Self-reported infection status and experiences with common longer-term COVID-19 symptoms since the start of the pandemic was collected, as well as a dried blood spot to measure SARS-CoV-2 antibodies. Results: As of August 2022, the number of adults reported having had a confirmed or suspected infection was 37.9% (95% CI: 36.8%-39.1%), while the overall mean probability of having infection-related antibodies was 52.9% (95% CI: 51.8%-54.0%) and increased with respondent certainty they had been infected. However, the mean probability of having infection-related antibodies was not associated with infection severity or the reporting of common longer-term COVID-19 symptoms. More than one in five adults were unaware they had been infected. Conclusion: Self-report surveys may misclassify the SARS-CoV-2 infection status of a substantial proportion of untested people and may bias estimates of the percentage infected, the severity of infections and the risk of developing infection-related longer-term symptoms. Common longer-term COVID-19 symptoms reported by some could have been caused by other infections or diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".