COVID-19 Demographics, Acute Care Resource Use and Mortality by Age and Sex in Ontario, Canada: Population-based Retrospective Cohort Analysis
Bibliographic record
Abstract
Abstract Background Understanding resource use for COVID-19 is critical. We conducted a population-based cohort study using public health data to describe COVID-19 associated age- and sex-specific acute care use, length of stay (LOS), and mortality. Methods We used Ontario’s Case and Contact Management (CCM) Plus database of individuals who tested positive for COVID-19 in Ontario from March 1 to September 30, 2020 to determine age- and sex-specific hospitalizations, intensive care unit (ICU) admissions, invasive mechanical ventilation (IMV) use, LOS, and mortality. We stratified analyses by month of infection to study temporal trends and conducted subgroup analyses by long-term care residency. Results During the observation period, 56,476 COVID-19 cases were reported (72% < 60 years, 52% female). The proportion of cases shifted from older populations (> 60 years) to younger populations (10-39 years) over time. Overall, 10% of individuals were hospitalized, of those 22% were admitted to ICU, and 60% of those used IMV. Mean LOS for individuals in the ward, ICU without IMV, and ICU with IMV was 12.8, 8.5, 20.5 days, respectively. Mortality for individuals receiving care in the ward, ICU without IMV, and ICU with IMV was 24%, 30%, and 45%, respectively. All outcomes varied by age and decreased over time, overall and within age groups. Interpretation This descriptive study shows acute care use and mortality varying by age, and decreasing between March and September in Ontario. Improvements in clinical practice and changing risk distributions among those infected may contribute to fewer severe outcomes among those infected with COVID-19.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".