Associations between chronic conditions and death in hospital among adults (aged 20+ years) during first acute care hospitalizations with a confirmed or suspected COVID-19 diagnosis in Canada
Bibliographic record
Abstract
PURPOSE: We aimed to quantify life course-specific associations between death in hospital and 30 chronic conditions, and comorbidity among them, in adults (aged 20+ years) during their first acute care hospitalization with a confirmed or suspected COVID-19 diagnosis in Canada. METHODS: We identified 35,519 first acute care hospitalizations with a confirmed or suspected COVID-19 diagnosis in the Discharge Abstract Database as of March 31, 2021. For each of five life-course age groups (20-34, 35-49, 50-64, 65-79, and 80+ years), we used multivariable logistic regression to examine associations between death in hospital and 30 chronic conditions, comorbidity, period of admission, and pregnant status, after adjusting for sex and age. RESULTS: About 20.9% of hospitalized patients with COVID-19 died in hospital. Conditions most strongly associated with in-hospital death varied across the life course. Chronic liver disease, other nervous system disorders, and obesity were statistically significantly associated (α = 0.05) with in-hospital death in the 20-34 to 65-79 year age groups, but the magnitude of the associations decreased as age increased. Stroke (aOR = 5.24, 95% CI: 2.63, 9.83) and other inflammatory rheumatic diseases (aOR = 4.37, 95% CI: 1.64, 10.26) were significantly associated with in-hospital death among 35 to 49 year olds only. Among 50+ year olds, more chronic conditions were significantly associated with in-hospital death, but the magnitude of the associations were generally weaker except for Down syndrome in the 50 to 64 (aOR = 8.49, 95% CI: 4.28, 16.28) and 65 to 79 year age groups (aOR = 5.19, 95% CI: 1.44, 20.91). Associations between comorbidity and death also attenuated with age. Among 20 to 34 year olds, the likelihood of death was 19 times greater (aOR = 18.69, 95% CI: 7.69, 48.24) in patients with three or more conditions compared to patients with none of the conditions, while for 80+ year olds the likelihood of death was two times greater (aOR = 2.04, 95% CI: 1.70, 2.45) for patients with six or more conditions compared to patients with none of the conditions. CONCLUSION: Conditions most strongly associated with in-hospital death among hospitalized adults with COVID-19 vary across the life course, and the impact of chronic conditions and comorbidity attenuate with age.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".