P104 THE INCIDENCE OF INFLAMMATORY BOWEL DISEASE: ANALYZING HISTORICAL TRENDS TO PREDICT THE FUTURE
Bibliographic record
Abstract
Incidence of Inflammatory Bowel Disease (IBD)—Crohn’s Disease (CD) and ulcerative colitis (UC)—is decreasing in some provinces, but increasing in pediatrics. Even with this decrease in incidence, prevalence will continue to rise until incidence equals to mortality. Decision makers require accurate data on the current and future burden of IBD for resource planning to ensure IBD patients receive proper care. 1) To assess current trends in IBD incidence and forecast future trends; 2) to determine the mortality rate of IBD; and, 3) to calculate the threshold that incidence would need to approximate mortality in order to stabilize the prevalence of Crohn’s disease (CD) and ulcerative colitis (UC). Using population-based data from Alberta (AB), per year incidence is calculated from 2010 to 2015 with an eight-year washout period, stratified by pediatric (<18), adult (18-64), and elderly (65+). Incidence is calculated for CD and UC separately, as well as for total IBD, which includes IBD type unclassifiable. Data is standardized based on annual Canadian age and sex distributions from Statistics Canada. Poisson regression (or negative binomial regression, when appropriate) is used to analyze historical trends and calculate average annual percentage change (AAPC) with 95% confidence intervals (CI). Log-linear models are used to forecast incidence to 2030 with 95% prediction intervals (PI). Overall standardized mortality ratios (SMR) with 95% CI are calculated for IBD, CD, and UC from 2010 to 2015—as compared to the Canadian population. The incidence threshold is calculated to determine an incidence rate that approximates mortality, which would stabilize the prevalence of IBD. Age-stratified IBD, CD and UC incidence with AAPC are provided in Table 1. The incidence of IBD in Alberta is 27.8 per 100,000 in 2015.The overall IBD incidence is stable from 2010 to 2015 (AAPC= −2.00, 95%CI: −4.15, 0.20) (Table 1). However, the subtype-specific incidence of IBD in adults is decreasing for both CD (AAPC = −5.50; 95%CI: −7.71, −3.23) and UC (AAPC = −4.78; 95%CI: −8.57, −0.84). Figure 1 illustrates the historical and forecasted incidence of IBD, CD, and UC. The SMR is 1.41 (95%CI: 1.34, 1.48) for IBD, 1.48 (95%CI: 1.38, 1.59) for CD, and 1.20 (95%CI: 1.09, 1.31) for UC. The threshold whereby incidence approximates mortality such that it would stabilize the prevalence of IBD is 7.82 per 100,000. Based on our forecasting models, the incidence of IBD (21.63 per 100,000; 95%PI: 10.85, 32.41) exceeds this threshold in 2030. The 2030 forecasted incidence (21.6 per 100,000 persons) exceeds the threshold required to reduce the prevalence of IBD. Future interventional research focused on prevention is urgently required to mitigate the rising burden of IBD. Incidence and Average Annual Percentage Change of IBD, CD, and UC stratified by age Historical incidence (per 100,000 persons) and average annual percentage change (AAPC)—with associated 95% confidence interval (CI)—for Inflammatory Bowel Disease, Crohn’s disease, and ulcerative colitis stratified by all, pediatric (<18), adult (18-64), and elderly (65+). Figure 1: Historical and forecasted incidence (per 100,000 persons) of Inflammatory Bowel Disease, Crohn’s disease, and ulcerative colitis. Historical data is from 2010 to 2015 and forecasted from 2016 to 2030, with 95% prediction intervals represented by shaded area around forecasted incidence (dashed line).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.002 |
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".