A174 THE DIRECT HEALTHCARE COST OF INFLAMMATORY BOWEL DISEASE IN CANADA: A POPULATION-BASED ANALYSIS OF ADMINISTRATIVE DATA
Bibliographic record
Abstract
Abstract Background The prevalence of inflammatory bowel disease (IBD) in Canada is rising rapidly, with an estimated 0.86% of the population living with IBD in 2025. With this rise in prevalence comes a rise in the direct costs to healthcare systems. Aims To assess direct healthcare costs associated with IBD in Canada. Methods We analyzed population-based administrative healthcare costing data from AB, BC, MB, and SK from fiscal year (FY) 2010/11 to 2016/17. Costs were adjusted to 2020 CAD$ using the Consumer Price Index. Average annual costs were calculated for: annual per IBD person cost (All), and medication costs (on a biologic ± other IBD-related medications, and only other IBD-related medication [eg. mesalamine] (AB,BC,MB)). Per event costs were calculated by outcome: IBD-related hospitalization or surgery (AB,MB), emergency department visit (AB, BC), and colonoscopy (AB,BC). We calculated the average annual percentage change (AAPC) with 95% confidence intervals (CI) using weighted costs in log-gamma models. Autoregressive moving average models to forecasted costs to FY2025/26 with 95% prediction intervals (PI). Results In FY 2016/17, the annual average cost per IBD person was $11186 (95%CI:11052,11320), significantly increased from FY2010/11 (5.12%;95%CI:4.75,5.48). Biologics were the largest cost, accounting for 45.11% (95%CI:44.23,45.98) of the total costs and significantly increasing (2.13%; 95%CI:1.43,2.84). Costs for other IBD medications significantly decreased (−2.00%; 95%CI:−3.47,−0.51), contributing 5.46% (95%CI:5.09,5.64) of total costs. Costs of emergency department visits and colonoscopies significantly increased, while costs for IBD-related hospitalizations and surgeries remained stable. By FY2025/26, the annual cost per IBD person is forecasted to be $15345 (95%PI:14915,15775). Conclusions The direct healthcare costs of IBD are rising, largely driven by the costs of biologics. As IBD prevalence continues to grow, the burden on healthcare systems is expected to escalate. Proactive measures are essential to address this burden and ensure individuals with IBD receive the necessary care. ¥Proportions will not add up to 100% as individuals can be captured in multiple cost categories each year (e.g., an IBD-related surgery cost is also a hospitalization). * p<0.05 Funding Agencies CCC, CIHR
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".