Factors associated with the development, severity, and resolution of post COVID-19 condition in adults living in Canada, January 2020 to August 2022
Bibliographic record
Abstract
OBJECTIVES: We aimed to characterize the burden of post COVID-19 condition (PCC) among adults in Canada and identify factors associated with its occurrence, severity, and resolution. METHODS: We used self-report data from a population-based cross-sectional probability survey of adults in Canada conducted between April and August 2022. Incidence and prevalence of PCC were estimated using confirmed infections, as well as confirmed and suspected combined. Multivariable modeling using confirmed cases identified associated factors. RESULTS: As of August 2022, 17.2% (95% CI 15.7, 18.8) of adults with confirmed infections and 16.7% (95% CI 15.5, 18.0) of adults with confirmed or suspected infections experienced PCC, translating to 3.3% (95% CI 3.0, 3.6) and 4.4% (95% CI 4.1, 4.8) of all adults, respectively. Age less than 65 years (aORs of 1.75 to 2.14), more pre-existing comorbidities (aORs of 1.75 to 3.57), and a more severe initial infection (aORs of 3.52 to 9.69) were all associated with higher odds of PCC, while male sex at birth (aOR = 0.54, 95% CI 0.41, 0.70), identifying as Black (aOR = 0.23, 95% CI 0.11, 0.51), and being infected after 2020 (aORs of 0.24 to 0.55) were associated with lower odds. Those residing in a rural area (aOR = 2.31, 95% CI 1.35, 3.93), or reporting a disability (aOR = 2.87, 95% CI 1.14, 7.25), pre-existing chronic lung condition (aOR = 5.47, 95% CI 1.85, 16.12) or back problem (aOR = 2.34, 95% CI 1.26, 4.36), or PCC headache (aOR = 2.47, 95% CI 1.60, 3.83) or weakness (aOR = 2.27, 95% CI 1.41, 3.68) had higher odds of greater limitations in daily activities, while males had lower odds (aOR = 0.54, 95% CI 0.34, 0.85). Two or more pre-existing chronic conditions (aHRs from 0.33 to 0.38), or PCC symptoms relating to the heart (aHR = 0.25, 95% CI 0.07, 0.90), brain fog (aHR = 0.44, 95% CI 0.23, 0.86), or stress/anxiety (aHR = 0.48, 95% CI 0.24, 0.96) were associated with a decreased rate of symptom resolution. CONCLUSION: Over the first two and a half years of the pandemic, a substantial proportion of infected adults in Canada reported PCC. Females and people with comorbidities were disproportionately impacted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".