Prevalence, risk factors and clinical presentations of post-COVID-19 condition: a follow-up study of reported COVID-19 infections in Montreal, Canada
Bibliographic record
Abstract
Abstract Background Meta-analyses suggest that post-COVID-19 condition (PCC) mostly takes three clinical presentations: fatigue, cognitive and respiratory. We sought to estimate the prevalence of these presentations and the strength of their associations with potential risk/protective factors, activity limitations and healthcare utilization. Methods Follow-up study by telephone re-interview, ≥5 months after initial interview, of a random sample of Montrealers aged 18 years or more, with a PCR-confirmed COVID-19 infection reported between July 18 and December 4, 2021. The re-interview covered age, sex, comorbidities, signs and symptoms (SS), activity limitations, healthcare utilization, perceived stigmatization and psychological distress. Results Of a sample of 2000 adults, 652 (39.1%) completed the questionnaire. Of 518 with only 1 acute episode of COVID-19, 32.2% met the WHO definition of PCC. Of these, 45.5% reported SS that fit the fatigue presentation, 24.6% the cognitive presentation and 16.8% the respiratory presentation. Neither age nor COVID-19 immunization was associated with PCC, compared to COVID-19 without PCC. However, being female (OR=2.28), hospitalization (OR=2.44) and intensive care (OR=3.21) for the acute COVID-19 episode were. Various activity limitations and types of healthcare utilization were also associated with PCC. All presentations were associated with serious psychological distress. The respiratory presentation was particularly associated with hospitalization for the acute episode (aOR=7.72) and was the only one associated with later hospitalization (aOR=23.6). Interpretation Our findings suggest that caring for patients with PCC requires adapted organizational models. If they favoured excellence in research, these models could help future studies meet the recommended methodological standards.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".