Cancer incidence, stage shift and survival during the 2020 <scp>COVID</scp>‐19 pandemic: A population‐based study in Belgium
Bibliographic record
Abstract
The COVID-19 pandemic was associated with a profound decline in cancer diagnoses in 2020 in Belgium. Disruption in diagnostic and screening services and patient reluctance to visit health facilities led to fewer new cases and concerns that cancers may be diagnosed at more advanced stages and hence have poorer prognosis. Using data from mandatory cancer registration covering all of Belgium, we predicted cancer incidence, stage distribution and 1-year relative survival for 2020 using a Poisson count model over the preceding years, extrapolated to 2020 for 11 common cancer types. We compared these expected values to the observed values in 2020 to specifically quantify the impact of the COVID-19 pandemic, accounting for background trends. A significantly lower incidence was observed for cervical, prostate, head and neck, colorectal, bladder and breast cancer, with limited or no recovery of diagnoses in the second half of 2020 for these cancer types. Changes in stage distribution were observed for cervical, prostate, bladder and ovarian and fallopian tube tumours. Generally, changes in stage distribution mainly represented decline in early-stage than in late-stage tumours. One-year relative survival was lower than predicted for lung cancer and colorectal cancer. Stage shifts are hypothesised to result from alterations in access to diagnosis, potentially due to prioritisation of symptomatic patients, and patient reluctance to contact a physician. Since there were over 5000 fewer cancer diagnoses than expected by the end of 2020, it is critical to monitor incidence, stage distribution and survival for these cancers in the coming years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".