Long COVID risk and severity after COVID-19 infections and reinfections: A retrospective cohort study among healthcare workers
Bibliographic record
Abstract
OBJECTIVE: We evaluated long COVID risk, prevalence and severity in a population-based cohort of Quebec healthcare workers (HCWs), in Canada, and the effect of reinfections, variants, and acute infection's severity. METHODS: Retrospective survey-based cohort study conducted in Spring 2023. HCWs self-reporting COVID-19-attributed symptoms lasting ≥12 weeks were long COVID cases, classified as mild, moderate, or severe based on self-perceived symptom intensity and compared to controls. We estimated long COVID prevalence and cumulative incidence by number of infections, and following first and subsequent infections stratified by infecting variant and acute COVID-19 severity. Adjusted risk ratios were computed using robust Poisson models. RESULTS: 22 496 (of 397,222 invited HCWs) and 3 978 (of 10,500) participated in the electronic and telephone surveys. Long COVID cumulative risk among infected participants was 17.0% (95% CI:16.3-17.6%), increasing with number of infections. Estimated prevalence was 5.6% (95% CI:4.9-6.3%). Reinfections (vs first infection), Omicron (vs ancestral variant) and nonsevere acute infection (vs severe) were associated, respectively, with 40%, 34% and 72% lower long COVID risk. Severe cases exhibited multiple symptoms and substantial functional limitations. CONCLUSION: Long COVID is a common and disabling condition. With the ongoing SARS-CoV-2 transmission, interventions targeting the frequency and severity of reinfections may reduce future long COVID burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".