Resource Implications for a Population-Based Colorectal Cancer Screening Program in Canada: A Study of the Impact on Colonoscopy Capacity and Costs in London, Ontario
Bibliographic record
Abstract
OBJECTIVE: Cancer Care Ontario has recommended a population-based colorectal cancer (CRC) screening program using fecal occult blood testing. Patients who test positive should undergo further investigation, preferably colonoscopy. So far, no studies have been performed to quantify the costs or demands on the health care system at the community level. The number of consultations, colonoscopies and polypectomies, and the corresponding direct medical costs generated by the CRC screening program, between 2006 and 2015 in London, Ontario, were estimated using a decision analysis model in comparison with the population health model. METHODS: A faxed survey study was conducted to examine the current CRC screening practice among family physicians in London. Data from the survey and randomized studies were applied to a decision analysis model, which simulated the steps involved in population-based biennial and annual CRC screening between 2006 and 2015. The number of consultations, colonoscopies and polypectomies, and their associated costs were calculated. RESULTS: For a cohort population of 140,000, between 50 and 74 years of age, in 2006 to 2015, it is estimated that an average of 412 consultations, 463 colonoscopies and 174 polypectomies will be performed per 100,000 screen eligible population per year in biennial screening, and double in annual screening, reflecting an average of 8.7% or 17.6% increase annually in outpatient colonoscopies, respectively, compared with 2003. A mean of $285,000 or $562,000 per year would be required to support the extra consultation and endoscopic procedures generated by the biennial or annual screening. CONCLUSION: Population-based fecal occult blood testing screening for CRC appears to be a manageable strategy if a modest increase in endoscopic resources is allocated.
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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 | 0.000 | 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 | 0.000 |
| 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".