New Cancer Diagnoses Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: Disruptions to health care during the COVID-19 pandemic may have led to missed cancer diagnoses. It is critical to evaluate the association between the COVID-19 pandemic and cancer incidence to address public and patient anxiety, inform recovery efforts, and identify strategies to reduce the system's vulnerability to future disruptions. Objective: To examine the association between the COVID-19 pandemic and cancer incidence in Manitoba, Canada. Design, Setting, and Participants: A population-based cross-sectional study design was conducted using data from the Manitoba Cancer Registry and an interrupted time-series analysis. All individuals diagnosed with cancer in Manitoba, Canada, from January 1, 2015, until December 31, 2021, were included. Individuals diagnosed with breast, colon, rectal, or lung cancer were grouped by age as follows: younger than 50 years, 50 to 74 years, and 75 years and older. Exposures: COVID-19 pandemic. Main Outcomes and Measures: Age-standardized cancer incidence rates and the estimated cumulative difference between the number of cases in the absence of COVID-19 and observed (fitted) number of cancer cases. Results: A total of 48 378 individuals were included. The median (IQR) age at diagnosis was 68 (59-77) years and 23 972 participants (49.6%) were female. In April 2020, there was a 23% decrease in overall cancer incidence. Cancer incidence decreased by 46% for breast, 35% for colon, 47% for rectal, 50% for head and neck, 65% for melanoma, and 33% for endocrine cancer diagnoses and increased by 12% for hematological cancer diagnoses and 8% for diagnoses of cancers with an unknown primary site. Lung cancer incidence remained stable until December 2020 when it decreased by 11%. Brain and central nervous system and urinary cancer diagnoses decreased consistently over time from April 2020 to December 2021 by 26% and 12%, respectively. No association was observed with gynecologic (1% increase), other digestive (1% decrease), or pancreatic (7% increase) cancer incidence. As of December 2021, Manitoba had an estimated deficit of 692 (5.3%) cancers. The largest estimated deficits were for breast (273 cases, 14.1% deficit), colon (133 cases, 12.2% deficit), and lung cancers (132 cases, 7.6% deficit). Conclusions and Relevance: In this study, the COVID-19 pandemic was associated with an initial decrease in cancer diagnosis incidence followed by a recovery for most cancer sites. However, the cumulative deficit for some cancers with high fatality needs immediate attention.
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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 | 0.001 | 0.001 |
| 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".