MODELING THE TRANSITION TO THE FOURTH EPIDEMIOLOGIC STAGE OF INFLAMMATORY BOWEL DISEASE: PREVALENCE EQUILIBRIUM
Bibliographic record
Abstract
Abstract BACKGROUND A theoretical framework for population-level transition across four epidemiologic stages has been proposed for inflammatory bowel disease (IBD): 1. Emergence (low incidence/prevalence); 2. Acceleration in Incidence (rapid rising incidence); 3. Compounding Prevalence (stabilizing incidence, rapid rising prevalence); and 4. Prevalence Equilibrium (decelerated prevalence), which no region has entered yet. Many regions of the early-industrialized world (North America, Europe, Oceania) are currently in stage 3. AIM To model the transition to the 4th epidemiologic stage of IBD based on real-world data. METHODS Transition to stage 4 involves deceleration of prevalence after a period of rapid increase. Age-specific, population-based incidence and prevalence data from Canada (2007–2014, >97% of population) were used to model changing prevalence using a partial differential equation (PDE) derived from a compartment model (Figure 1). The PDE is built on an average historical incidence value (2007–2014) and accounts for changing population age distributions over time. The PDE prevalence output was internally validated by comparing observed historical prevalence values with model outputs. After validation, the model was used to forecast future age-stratified prevalence and provide an estimate for when the transition to stage 4 is expected. Although incidence is predicted to stabilize in stage 3, the PDE was run on five scenarios to account for variability in future incidence values; therefore, we modeled annual incidence increases of 2% and 1%, stable incidence, and annual incidence decreases of 1% and 2%. RESULTS Internal validation of the model compared observed to predicted prevalence values in 2014: The observed prevalence was 0.677%; the model output was 0.678%. The model predicts gradual prevalence deceleration across 2020–2043 (Figure 2). In 2043, prevalence is modeled to range between 1.101% and 1.293% across incidence scenarios (−2% to 2%). Assuming a stable incidence, as predicted by stage 3, the increase in prevalence slows from a difference in annual percent change of 0.021% in 2020 to 0.011% in 2043, signalling transition towards stage 4 (Figure 2). DISCUSSION Stage 3 (Compounding Prevalence) is characterized by a stabilizing incidence trend, and rapid growth in prevalence. Without a growing incidence rate, the IBD population continues to age until mortality approximates incidence, thereby allowing prevalence to stabilize (Stage 4). Understanding the markers of epidemiologic transition and predicting the future population distribution (age, prevalence) of IBD allows healthcare administrators to anticipate the future needs of gastroenterology clinics to continue offering timely, high-quality care and prepare for an aging IBD population with complications from long-standing disease, age-related comorbidities, and polypharmacy. Figure 1 Compartment model that feeds the partial differential equation. The compartment model has three states: healthy (H), diseased (S), and dead (D); the i = incidence; r = remission, and m0 = mortality rates are dependent on age (a) and time (i), where as m1 = mortality rate is dependent on age, time, and possibly duration of disease (d). Figure 2 Modeled time-dependent prevalence of IBD in Canada for 2%, 1%, 0%, −1%, and −2% change in annual incidence (2015–2043).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".