Association between pre-existing chronic conditions and severity of first SARS-CoV-2 infection symptoms among adults living in Canada: a population-based survey analysis from January 2020 to August 2022
Bibliographic record
Abstract
BACKGROUND: Individuals living with chronic conditions (CC) typically have a higher risk of more severe outcomes when exposed to infection. Although many studies have investigated the relationship between CCs and COVID-19 severity, they are generally limited to clinical or hospitalized populations. There is a need to estimate the impact of pre-existing CCs on the severity of acute SARS-CoV-2 infection symptoms among the general population. METHODS: Data from the Canadian COVID-19 Antibody and Health Survey - Cycle 2, a population-based cross-sectional probability survey across 10 provinces capturing the COVID-19 experiences of respondents from January 2020 to August 2022, were used to assess whether pre-existing CCs increased the odds of more severe self-reported infection symptoms among adults living in Canada. Multivariable regression modelling identified which CCs were independently associated with more severe infection symptoms after adjusting for sex, age at infection, and other significant covariates. RESULTS: Chronic lung disease (aOR = 1.64, 95% CI: 1.09, 2.46), high blood pressure (aOR = 1.35, 95% CI: 1.13, 1.62), weakened immune system (aOR = 1.46, 95% CI: 1.08, 1.98), chronic fatigue syndrome or fibromyalgia (aOR = 2.20, 95% CI: 1.39, 3.50), and arthritis (aOR = 1.28, 95% CI: 1.04, 1.56) were associated with a higher odds of more severe infection, whereas osteoporosis (aOR = 0.58, 95% CI: 0.39, 0.87) was associated with a lower odds. Limiting modelling to adults with confirmed SARS-CoV-2 infections affected some of the variables retained and adjusted associations. CONCLUSION: Our findings contribute to a growing evidence base of associations between pre-existing CCs and adverse outcomes after SARS-CoV-2 infection. Identifying factors associated with more severe infection allows for more targeted prevention strategies and early interventions that can minimize the impact of infection.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".