Incident Cancer Detection During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Resource restrictions were established in many jurisdictions to maintain health system capacity during the COVID-19 pandemic. Disrupted healthcare access likely impacted early cancer detection. The objective of this study was to assess the impact of the pandemic on weekly reported cancer incidence. PATIENTS AND METHODS: This was a population-based study involving individuals diagnosed with cancer from September 25, 2016, to September 26, 2020, in Ontario, Canada. Weekly cancer incidence counts were examined using segmented negative binomial regression models. The weekly estimated backlog during the pandemic was calculated by subtracting the observed volume from the projected/expected volume in that week. RESULTS: The cohort consisted of 358,487 adult patients with cancer. At the start of the pandemic, there was an immediate 34.3% decline in the estimated mean cancer incidence volume (relative rate, 0.66; 95% CI, 0.57-0.75), followed by a 1% increase in cancer incidence volume in each subsequent week (relative rate, 1.009; 95% CI, 1.001-1.017). Similar trends were found for both screening and nonscreening cancers. The largest immediate declines were seen for melanoma and cervical, endocrinologic, and prostate cancers. For hepatobiliary and lung cancers, there continued to be a weekly decline in incidence during the COVID-19 period. Between March 15 and September 26, 2020, 12,601 fewer individuals were diagnosed with cancer, with an estimated weekly backlog of 450. CONCLUSIONS: We estimate that there is a large volume of undetected cancer cases related to the COVID-19 pandemic. Incidence rates have not yet returned to prepandemic levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".