Timeliness and Modality of Treatment for New Cancer Diagnoses During the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
Importance: The impact of COVID-19 on the modality and timeliness of first-line cancer treatment is unclear yet critical to the planning of subsequent care. Objective: To explore the association of the COVID-19 pandemic with modalities of and wait times for first cancer treatment. Design, Setting, and Participants: This retrospective population-based cohort study using administrative data was conducted in Ontario, Canada, among adults newly diagnosed with cancer between January 3, 2016, and November 7, 2020. Participants were followed up from date of diagnosis for 1 year, until death, or until June 26, 2021, whichever occurred first, to ensure a minimum of 6-month follow-up time. Exposures: Receiving a cancer diagnosis in the pandemic vs prepandemic period, using March 15, 2020, the date when elective hospital procedures were halted. Main Outcomes and Measures: The main outcome was a time-to-event variable describing number of days from date of diagnosis to date of receiving first cancer treatment (surgery, chemotherapy, or radiation) or to being censored. For each treatment modality, a multivariable competing-risk regression model was used to assess the association between time to treatment and COVID-19 period. A secondary continuous outcome was defined for patients who were treated 6 months after diagnosis as the waiting time from date of diagnosis to date of treatment. Results: Among 313 499 patients, the mean (SD) age was 66.4 (14.1) years and 153 679 (49.0%) were male patients. Those who were diagnosed during the pandemic were less likely to receive surgery first (subdistribution hazard ratio [sHR], 0.97; 95% CI, 0.95-0.99) but were more likely to receive chemotherapy (sHR, 1.26; 95% CI, 1.23-1.30) or radiotherapy (sHR, 1.16; 95% CI, 1.13-1.20) first. Among patients who received treatment within 6 months from diagnosis (228 755 [73.0%]), their mean (SD) waiting time decreased from 35.1 (37.2) days to 29.5 (33.6) days for surgery, from 43.7 (34.1) days to 38.4 (30.6) days for chemotherapy, and from 55.8 (41.8) days to 49.0 (40.1) days for radiotherapy. Conclusions and Relevance: In this cohort study, the pandemic was significantly associated with greater use of nonsurgical therapy as initial cancer treatment. Wait times were shorter in the pandemic period for those treated within 6 months of diagnosis. Future work needs to examine how these changes may have affected patient outcomes to inform future pandemic guideline development.
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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 | 0.000 |
| 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".