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Record W4391232113 · doi:10.1093/ibd/izae020.073

MODELING THE TRANSITION TO THE FOURTH EPIDEMIOLOGIC STAGE OF INFLAMMATORY BOWEL DISEASE: PREVALENCE EQUILIBRIUM

2024· article· en· W4391232113 on OpenAlexaboutno aff
Joseph W. Windsor, Lindsay Hracs, Julia Gorospe, Michael Buie, Joshua Quan, Ante Markovinović, Léa Caplan, Quinn Goddard, Tyler Williamson, Yvonne Abbey, María T. Abreu, Raja Affendi Raja Ali, Murdani Abdullah, Mansour Altuwaijri, Vineet Ahuja, Domingo Balderramo, Rupa Banerjee, Eric I. Benchimol, Çharles N. Bernstein, Eduard Brunet, Johan Burisch, Iris Dotan, Usha Dutta, Sara El Ouali, Angela Forbes, Richard B. Gearry, Juanda Leo Hartono, Ida Hilmi, Fabian Julian-Banos, Jamilya Kaibullayeva, Paul Kelly, Paulo Gustavo Kotze, Péter L. Lakatos, Charlie W. Lees, Julajak Limsrivilai, Edward V. Loftus, Jonas F. Ludvigsson, Joyce Wing Yan Mak, Ka Kei Ng, Ola Olén, Remo Panaccione, Mukesh S Paudel, Abel Botelho Quaresma, David T. Rubin, Marcellus Simadibrata, Yang Sun, Hidekazu Suzuki, Martín Toro, Dan Turner, Beatriz Iade Vergara, Shu‐Chen Wei, Jesús K. Yamamoto‐Furusho, Kuk-Kyun Yang, Siew C. Ng, Stephanie Coward, Gilaad G. Kaplan

Bibliographic record

VenueInflammatory Bowel Diseases · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsInflammatory bowel diseaseMedicineInflammatory Bowel DiseasesStage (stratigraphy)DiseaseGastroenterologyInternal medicineBiology

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.015
GPT teacher head0.260
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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