Long COVID and Associated Factors Among Chinese Residents Aged 16 Years and Older in Canada: A Cross-Sectional Online Study
Bibliographic record
Abstract
As the COVID-19 pandemic evolved, long COVID emerged as a significant threat to public health, characterized by one or more persistent symptoms impacting organ systems beyond 12 weeks of infection [9-12]. Informative research has been derived from assessments of long COVID among the Chinese populace. However, none of these studies considered the COVID-19 experience of Chinese residents in Canada. Objectives: We aimed to fill this literature gap by delineating the long COVID experience, prevalence, and associated factors among a sample of Chinese residing in Canada during the pandemic. Methods: The present study employed a cross-sectional online survey questionnaire distributed to a sample of Canadian Chinese using a convenience sampling procedure from December 22, 2022, to February 15, 2023. Respondents were probed for sociodemographic background, health-, COVID-, and vaccine-related characteristics. Logistic LASSO regression was used for model building and multivariate logistic regression was used to identify factors associated with developing long COVID. Results: Among 491 eligible participants, 63 (12.83%) reported experiencing long COVID with a mean duration of 5.31 (95% CI: 4.06–6.57) months, and major symptoms including difficulty concentrating (21.67%), pain/discomfort (15.00%), as well as anxiety/depression (8.33%). Our final model identified significant associations between long COVID and two or more COVID-19 infections (OR = 23.725, 95% CI: 5.098–110.398, p < 0.0001), very severe/severe symptoms (OR = 3.177, 95% CI: 1.160–8.702, p = 0.0246), over-the-counter medicine (OR = 2.473, 95% CI: 1.035–5.909, p = 0.0416), and traditional Chinese medicine (OR = 8.259, 95% CI: 3.016–22.620, p < 0.0001). Further, we identified a significant protective effect of very good/good health status (OR = 0.247, 95% CI: 0.112–0.544, p = 0.0005). Conclusions: Long COVID adversely effected a notable proportion of Canadian Chinese for a prolonged period during the pandemic. Our findings underscore the importance of preexisting health status and reinfection prevention when managing long COVID. Moreover, our work indicates the need for culturally accessible guidance and services pertaining to effective COVID-19 treatment modalities through infection, recovery, and beyond.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".