Modelling the Potential Impact of Weight Gain During the COVID-19 Pandemic on the Future Burden of Cancer
Bibliographic record
Abstract
To understand potential long-term consequences of the impacts of the COVID-19 pandemic and resulting public health measures on daily life, specifically shifts in health behaviours which contribute to weight gain. Data on unintentional weight gain among adults during the first year of the COVID-19 pandemic was applied to national survey data to simulate pre and post weight gain body mass index (BMI). Population impact measures were estimated using OncoSim, a web-based microsimulation tool, which simulates the trajectory of cancer, calibrated using Canadian cancer incidence and mortality data along with measurable risk for specified cancers from lifestyle risk factors. Projections were estimated until 2042, assuming a 12-year latency period. Following a mean weight gain of 11.4 lbs, the proportion of underweight, overweight, obese and morbidly obese BMI were: 37%, 36%, 18% and 9%, respectively. The projected excess cancer cases would reach 8,651 and 16,915 by 2037 and 2042. The additional cancer burden will disproportionately impact women. The largest projected increases were observed for uterine, kidney and liver cancers among women, with mean potential impact fractions (PIF) of 4.26%, 2.58% and 2.08%, respectively. Among men, the largest mean PIFs were observed for esophageal (3.03%), kidney (2.28%) and liver (1.81%) cancers. The projected excess cancer deaths would reach 6,254 by 2042, with the largest burden projected for colorectal, esophageal and pancreatic cancer (N = 1,087, N = 945, and N = 813). These projections highlight the possible long-term consequences of changes in health behavior during the COVID-19 pandemic on the burden of cancer in Canada. This underscores the critical need for timely investment into effective cancer prevention strategies, to minimize the likelihood that unhealthy lifestyle changes during the COVID-19 pandemic are sustained. Michael Smith Foundation for Health Research, the Canadian Partnership Against Cancer, Health Canada.
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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 | 0.000 |
| 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".