Age-dependence of healthcare interventions for COVID-19 in Ontario, Canada
Bibliographic record
Abstract
Abstract Background: Patient age is the most salient clinical indicator of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for many regions. Less attention has been given to the age distributions of serious medical interventions administered to COVID-19 patients, which could reveal sources of potential pressure on the healthcare system should SARS-CoV-2 prevalence increase. Methods: We analysed 97,957 known SARS-CoV-2 infection records for Ontario, Canada, from 23 January 2020 to 26 November 2020 and estimated the age distributions of hospitalizations, Intensive Care Unit admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization. Results: The distribution of hospitalizations peaks with a wide plateau covering ages 54–90, whereas deaths are concentrated in very old ages. The estimated probability of hospitalization given known infection reaches a maximum of 30.9% at age 80 (95% CI 28.0%–33.9%). The probability of survival given hospitalization is near 100% for adults younger than 40, but declines substantially after this age; for example, a hospitalized 54-year-old patient has a 91.5% chance of surviving COVID-19 (95% CI 87.0%–94.9%). Conclusions: Ontario’s healthcare system has not been overstretched by COVID-19 thanks to wide-spread infection control efforts, yet the probability of survival given hospitalization for COVID-19 is lower than is generally perceived for patients over 40. As pervalence continues to increase during this most recent wave of infection, healthcare capacities are at risk of being exceeded. Survival of individuals in the broad age range requiring acute care could decrease, potentially expanding the distribution of COVID-19-related deaths toward younger ages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".