Cancer incidence during the COVID‐19 pandemic by region of residence in Manitoba, Canada: A cancer registry‐based interrupted time series study
Bibliographic record
Abstract
INTRODUCTION: Health care in Manitoba, Canada is divided into five regions, each with unique geographies, demographics, health care access, and health status. COVID-19-related restrictions and subsequent responses also differed by region. To understand the impact of the pandemic on cancer incidence in the context of these differences, we examined age-standardized cancer incidence rates by region over time before and after the COVID-19 pandemic. METHODS: We used a population-based quasi-experimental study design, population-based data, and an interrupted time series analysis to examine the rate of new cancer diagnoses before (January 2015 until December 2019) and after the start of COVID-19 and the interventions implemented to mitigate its impact (April 2020 until December 2021) by region. RESULTS: Overall cancer incidence differed by region and remained lower than expected in Winnipeg (4.6% deficit, 447 cases), Prairie Mountain (6.9% deficit, 125 cases), and Southern (13.0% deficit, 238 cases). Southern was the only region that had a significantly higher deficit in cases compared to Manitoba (ratio 0.92, 95% CI 0.86, 0.99). Breast and colorectal cancer incidence decreased at the start of the pandemic in all regions except Northern. Lung cancer incidence decreased in the Interlake-Eastern region and increased in the Northern region. Prostate cancer incidence increased in Interlake-Eastern. CONCLUSIONS: The impact of the COVID-19 pandemic on cancer incidence differed by region. The deficit in the number of cases was largest in the southern region and was highest for breast and prostate cancers. Cancer incidence did not significantly decrease in the most northern, remote region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".