The projected health and economic impact of increased colorectal cancer screening participation among Canadians by income quintile
Bibliographic record
Abstract
OBJECTIVES: Disparities in colorectal cancer (CRC) screening uptake by socioeconomic status have been observed in Canada. We used the OncoSim-Colorectal model to evaluate the health and economic outcomes associated with increasing the participation rates of CRC screening programs to 60% among Canadians in different income quintiles. METHODS: Baseline CRC screening participation rates were obtained from the 2017 Canadian Community Health Survey. The survey participants were categorized into income quintiles using their reported household income and 2016 Canadian Census income quintile thresholds. Within each quintile, the participation rate was the proportion of respondents aged 50-74 who reported having had a fecal test in the past two years. Using the OncoSim-Colorectal model, we simulated an increase in CRC screening uptake to 60% across income quintiles to assess the effects on CRC incidence, mortality, and associated economic costs from 2024 to 2073. RESULTS: Increasing CRC screening participation rates to 60% across all income quintiles would prevent 69,100 CRC cases and 36,600 CRC deaths over 50 years. The improvement of clinical outcomes would also translate to increased person-years and health-adjusted person-years. The largest impact was observed in the lowest income group, with 22,200 cases and 11,700 deaths prevented over 50 years. Increased participation could lead to higher screening costs ($121 million CAD more per year) and lower treatments costs ($95 million CAD less per year), averaged over the period 2024-2073. CONCLUSION: Increased screening participation will improve clinical outcomes across all income groups while alleviating associated treatment costs. The benefits of increased participation will be strongest among the lowest income quintile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".