Measuring the Association Between the COVID-19 Pandemic and Cancer Incidence by Sex Using a Quasi-Experimental Study Design
Bibliographic record
Abstract
PURPOSE: This study examined the association between COVID-19 and cancer incidence by sex in Manitoba, Canada. METHODS: We used a population-based quasi-experimental study design and an interrupted time-series analysis to compare the rate of new cancer diagnoses between males and females before (January 2015 until December 2019) and after the start of the COVID-19 pandemic (April 2020 until December 2022). RESULTS: A total of 16,200 females and 20,631 males diagnosed with cancer between 2015 and 2022 in Manitoba were included. Colon cancer incidence decreased by 34% for males and females from April to September 2020. Incidence then remained stable for males but decreased by 22% from October 2021 to December 2022 for females. Brain and CNS cancer incidence decreased by 37% for males during 2021 and 2022 but only for females during the last quarter of 2020 and the first quarter of 2021 (77%). Urinary cancer decreased by 18% for males from April 2020 to December 2022 but was stable for females. Head and neck cancers decreased by 22% for males during 2020, but was stable for females. As of December 2022, the largest estimated cumulative differences in the number of cases occurred for males diagnosed with brain and CNS cancer (31.6% deficit for males, 76 cases), urinary cancer (18.4% deficit, 186 cases), and endocrine cancer (52.4% surplus, 56 cases), and females diagnosed with colon cancer (19.7% deficit, 187 cases). CONCLUSION: Sex-based differences in the association between age-standardized cancer incidence and the COVID-19 pandemic exist for several cancer sites. Sex-based differences on postpandemic cancer incidence, especially for brain, CNS, urinary, and colon cancers, need follow-up because of the ongoing deficits documented in this study.
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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 | 0.005 | 0.003 |
| 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 | 0.001 |
| 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".