MODELING THE TRANSITION TO THE FOURTH EPIDEMIOLOGIC STAGE OF INFLAMMATORY BOWEL DISEASE: PREVALENCE EQUILIBRIUM
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».