Measuring the impact of COVID-19 on time to treatment for individuals diagnosed with cancer in Manitoba, Canada using an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Delays in treatment can lead to increases in cancer morbidity and mortality. There were many concerns that the COVID-19 pandemic led to delays in cancer treatment. Several studies have examined this issue but with serious limitations. We conducted a study that addressed many of these limitations and evaluated changes in first treatment, surgery, systemic therapy, and radiation therapy (RT) during the pandemic for individuals diagnosed with cancer in Manitoba, Canada. METHODS: A population-based, quasi-experimental cross-sectional study with an interrupted time series analysis was used to examine time to first treatment (expressed as treatment rates accounting for the competing risk of death) before (January 2015-September 2019) and after (April 2020-December 2022) the COVID-19 pandemic. RESULTS: When compared to the counterfactual scenario without COVID-19, first treatments were not received significantly later during the COVID-19 period for any cancer site. Individuals diagnosed with breast, colon, endocrine, or head and neck cancer had their first treatment within 90 days of diagnosis significantly sooner when comparing the COVID-19 period to the counterfactual. When examining type of treatment within one year of diagnosis, individuals diagnosed with breast cancer had surgery significantly later from April to June 2020 and systemic therapy significantly sooner from April 2020 to September 2021. Individuals diagnosed with colon cancer had surgery significantly sooner from April 2020 to June 2021 and individuals diagnosed with rectal cancer had RT significantly later from January to June 2021. CONCLUSIONS: Although significantly impacted by COVID-19, the cancer care system in Manitoba was able to prioritize individuals diagnosed with cancer and modify treatment modalities resulting in no significant delays in first treatment between April 2020 and December 2022. POLICY SUMMARY: It will be important to assess long-term survival and if unaltered, the ongoing use of strategies first used in the pandemic might be justified.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".