SARS-CoV-2 Infection, Hospitalization, and Mortality in Adults With and Without Cancer
Bibliographic record
Abstract
Importance: Patients with cancer are at increased risk of SARS-CoV-2-associated adverse outcomes. Objective: To determine the associations of tumor type with SARS-CoV-2 infection, hospitalization, intensive care unit (ICU) admission, and death. Design, Setting, and Participants: This retrospective, population-based cohort study included community-dwelling adults aged at least 18 years in Ontario, Canada, ICES-linked provincial health databases from January 1, 2020, to November 30, 2021. Data were analyzed from December 1, 2021, to November 1, 2022. Exposures: Cancer diagnosis. Main Outcomes and Measures: The primary outcome was SARS-CoV-2 infection, and secondary outcomes included all-cause 14-day hospitalization, 21-day ICU admission, and 28-day death following SARS-CoV-2 infection. Cox proportional hazards models were used to obtain adjusted hazard ratios (aHRs) and 95% CIs. Results: Of 11 732 108 people in the ICES-linked health databases, 279 287 had cancer (57.2% female; mean [SD] age, 65.9 [16.1] years) and 11 452 821 people did not have cancer (45.7% female; mean [SD] age, 65.9 [16.0] years). Overall, 464 574 individuals (4.1%) developed SARS-CoV-2 infection. Individuals with hematologic malignant neoplasms (33 901 individuals) were at increased risk of SARS-CoV-2 infection (aHR, 1.19; 95% CI, 1.13-1.25), 14-day hospitalization (aHR, 1.75; 95% CI, 1.57-1.96), and 28-day mortality (aHR, 2.03; 95% CI, 1.74-2.38) compared with the overall population, while individuals with solid tumors (245 386 individuals) were at lower risk of SARS-CoV-2 infection (aHR, 0.93; 95% CI, 0.91-0.95) but increased risk of 14-day hospitalization (aHR, 1.11; 95% CI, 1.05-1.18) and 28-day mortality (aHR, 1.31; 95% CI, 1.19-1.44). The 28-day mortality rate was high in hospitalized patients with hematologic malignant neoplasms (163 of 321 hospitalized patients [50.7%]) or solid tumors (486 of 1060 hospitalized patients [45.8%]). However, the risk of 21-day ICU admission in patients with hematologic malignant neoplasms (aHR, 1.14; 95% CI, 0.93-1.40) or solid tumors (aHR, 0.93; 95% CI, 0.82-1.05) was not significantly different from that among individuals without cancer. The SARS-CoV-2 infection risk decreased stepwise with increasing numbers of COVID-19 vaccine doses received (1 dose: aHR, 0.63; 95% CI, 0.62-0.63; 2 doses: aHR, 0.16; 95% CI, 0.16-0.16; 3 doses: aHR, 0.05; 95% CI, 0.04-0.06). Conclusions and Relevance: These findings highlight the importance of prioritization strategies regarding ICU access to reduce the mortality risk in increased-risk populations, such as patients with cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".