EPIDEMIOLOGY OF INFLAMMATORY BOWEL DISEASE AMONG EARLY-INDUSTRIALIZED REGIONS OF THE WORLD: CHALLENGES IN SUSTAINABLE HEALTHCARE DELIVERY
Bibliographic record
Abstract
Abstract BACKGROUND Early-industrialized regions (e.g., North America, Europe, Oceania) are currently in stage 3 of the epidemiologic evolution of inflammatory bowel disease (IBD), compounding prevalence wherein incidence stabilizes, but prevalence rises rapidly. This epidemiologic stage predicts the trajectory of healthcare infrastructure needs over the coming decades. AIMS 1. Describe current epidemiologic trends in representative stage 3 regions; 2. discuss ways to provide accessible, equitable, and quality IBD care in a sustainable manner; and 3. strategize future steps to prevent disease. METHODS The Canadian Gastro-Intestinal Epidemiology Consortium (CanGIEC) hosted IBD epidemiologists from representative stage 3 regions: Catalonia, Canada, Denmark, Hungary, Israel, New Zealand, Scotland, and the United States and address the rising burden of IBD. Epidemiologic data were reviewed in context using methodological differences between case ascertainment in each region to help explain heterogeneity (Table 1). Attendees engaged in a round-table consensus meeting, voting on priority needs to sustain IBD healthcare delivery given the trajectory of epidemiologic data. Four groups were formed with attendees of various backgrounds to encompass many perspectives, including epidemiologists, clinicians, IBD nurse practitioners, and patient advocates. RESULTS Table 1 describes IBD incidence and prevalence at the most recent year of historical data for each region. Canadian and Scottish studies included the forecasting of future prevalence estimates, a method that allows for proactive decision-making to accommodate the increasing number of individuals living with IBD. In 2028, the forecasted prevalence of IBD in Lothian, Scotland, was 1,023 per 100,000; and in 2030, the forecasted prevalence of IBD in Canada was 981 per 100,000. Table 2 describes the topics brought up in response to the aims, and the level of priority. Limited resources for a growing IBD population are a major cause for concern, negatively impacting access to and quality of care. To optimize quality of care, a multidisciplinary team approach is necessary. Suggestions for support in managing mild IBD cases include virtual care and remote monitoring, nurse-driven clinics, and cooperation with primary care physicians; this would help free-up in-person appointments with specialists for moderate-to-severe cases. Interventions that prevent IBD through modifying environmental and behavioral determinates, will have the largest impact on decelerating prevalence rates over time. DISCUSSION Forefront at the meeting was the fact of the continuing rise of IBD prevalence, augmenting the challenges faced by healthcare systems and practitioners. There are plausible and actionable solutions to achieve successful management of IBD upon initiating changes in healthcare policy.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".