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Enregistrement W4323351052 · doi:10.1093/jcag/gwac036.210

A210 THE BURDEN OF IBD HOSPITALIZATION IN CANADA: AN ASSESSMENT OF THE CURRENT AND FUTURE BURDEN IN A NATION-WIDE ANALYSIS

2023· article· en· W4323351052 sur OpenAlexaffabout
Stephanie Coward, Eric I. Benchimol, C N Bernstein, J. Antonio Aviña‐Zubieta, Alain Bitton, Lindsay Hracs, Jennifer Jones, E Kuenzig, L. Lu, Sanjay K. Murthy, Z Nugent, Anthony Otley, Remo Panaccione, Juan Nicolás Peña-Sánchez, Harmeet Singh, Laura E. Targownik, Joseph W. Windsor, Gilaad G. Kaplan

Notice bibliographique

RevueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueInflammatory Bowel Disease
Établissements canadiensUniversity of SaskatchewanUniversity of TorontoDalhousie UniversityUniversity of CalgaryMcGill UniversityHospital for Sick ChildrenUniversity of British ColumbiaOttawa HospitalResearch CanadaUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésMedicineInflammatory bowel diseasePoisson regressionComorbidityConfidence intervalPopulationRheumatoid arthritisDemographyDiseaseInternal medicineEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Abstract Background Hospitalizations pose a significant burden on both the individual and the healthcare system. Those with inflammatory bowel disease (IBD) are at increased risk of hospitalization as compared to the general population due to flaring of disease activity and complications related to IBD. The advent of biologics over the past twenty years may have influenced the rates of hospitalization for IBD. Purpose To assess current and forecast the overall hospitalization rates of those with IBD stratified by types of hospitalizations (all cause hospitalizations, IBD-related, and IBD-specific). Method Population-based administrative data on hospitalization of IBD (2002-2014) were obtained from: AB, BC, MB, and SK. Data were age and sex standardized to the matching year and aggregated into a representative sample of the Canadian population. Hospitalization rates were assessed as follows: 1. All cause hospitalizations: all admissions regardless of indication; 2. IBD-specific: an admission directly resulting from IBD (e.g., IBD-flare); 3. IBD-related: an admission for IBD, or a symptom or comorbidity associated with IBD (e.g. rheumatoid arthritis). Using prevalence estimates from the provinces, hospitalization rates (per 100 persons with IBD) were calculated, with 95% confidence intervals (CI). Autoregressive Integrated Moving Average models were created to estimate number of hospitalizations and corresponding prevalence to forecast hospitalization rates to 2030 with 95% prediction intervals (PI). Poisson (or negative binomial) regression estimated the Average Annual Percentage Change (AAPC), with 95% CIs, of the forecasted data. Result(s) In 2002 there were 35.3 per 100 (95%CI: 34.7, 35.9) all cause hospitalizations for IBD patients and this decreased to 24.9 per 100 (24.5, 25.2) in 2014. Similar trends were seen for IBD-specific hospitalizations [16.8 per 100 (95%CI: 16.4, 17.2) in 2002 to 8.7 per 100 (95%CI: 8.5, 9.0) in 2014] and IBD-related (22.6 per 100 (95%CI: 22.1, 23.1) in 2002 to 13.4 per 100 (95%CI: 13.2, 13.7) in 2014). When forecasted out to 2030 all hospitalization types were significantly decreasing—the AAPC for all cause hospitalizations was -2.12% (95%CI: -2.31, -1.93), -3.77% (95%CI: -4.63, -3.08) for IBD-specific, and -3.09% (95%CI: -3.65, -2.62) for IBD-related. By 2030, the rates of hospitalization are forecasted to be 17.0 per 100 (95%PI: 16.2, 17.9), 4.6 per 100 (95%PI: 3.7, 5.4), and 7.9 per 100 (95%PI: 6.9, 8.9) for all cause, IBD-specific, and IBD-related, respectively. Image Conclusion(s) In Canada, rates of hospitalizations for those with IBD have decreased from 2002 to 2014. The use of anti-TNF therapy in conjunction with the evolution of clinical monitoring, management and guidelines, likely has contributed to dropping hospitalization rates. Forecast models estimate a continued drop in hospitalization rates out to 2030. Importantly, healthcare resource planning should account for the shift from hospital-based to clinic-centric models of IBD care. Please acknowledge all funding agencies by checking the applicable boxes below CIHR Disclosure of Interest S. Coward: None Declared, E. Benchimol Consultant of: Hoffman La-Roche Limited and Peabody & Arnold LLP for matters unrelated to medications used to treat inflammatory bowel disease and McKesson Canada and the Dairy Farmers of Ontario for matters unrelated to medications used to treat inflammatory bowel disease., C. Bernstein Grant / Research support from: Unrestricted educational grants from Abbvie Canada, Janssen Canada, Pfizer Canada, Bristol Myers Squibb Canada, and Takeda Canada. Has received research grants from Abbvie Canada, Amgen Canada, Pfizer Canada, and Sandoz Canada and contract grants from Janssen, Abbvie and Pfizer, Consultant of: Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, JAMP Pharmaceuticals, Janssen Canada, Pfizer Canada, Sandoz Canada, and Takeda., Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, J. A. Avina-Zubieta: None Declared, A. Bitton: None Declared, L. Hracs: None Declared, J. Jones Consultant of: Janssen, Abbvie, Pfizer, Takeda, Speakers bureau of: Janssen, Abbvie, Pfizer, Takeda, E. Kuenzig: None Declared, L. Lu: None Declared, S. Murthy: None Declared, Z. Nugent: None Declared, A. Otley Grant / Research support from: Unrestricted educational grants from AbbVie Canada and Janssen Canada, Consultant of: Advisory boards of AbbVie Canada, Janssen Canada and Nestle, R. Panaccione Consultant of: Abbott, AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Celltrion, Cosmos Pharmaceuticals, Eisai, Elan, Eli Lilly, Ferring, Galapagos, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pendopharm, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz, Satisfai Health, Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Theravance Biopharma, Trellus, Viatris, UCB. Advisory Boards for: AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Speakers bureau of: AbbVie, Amgen, Arena Pharmaceuticals, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Gilead Sciences, Janssen, Merck, Organon, Pfizer, Roche, Sandoz, Shire, Takeda Pharmaceuticals, J.-N. Pena-Sanchez: None Declared, H. Singh Consultant of: Pendopharm, Amgen Canada, Bristol Myers Squibb Canada, Roche Canada, Sandoz Canada, Takeda Canada, and Guardant Health, Inc.,, L. Targownik Grant / Research support from: Investigator initiated funding from Janssen Canada, Consultant of: [Advisory board] AbbVie Canada, Takeda Canada, Merck Canada, Pfizer Canada, Janssen Canada, Roche Canada, and Sandoz Canada, J. Windsor: None Declared, G. Kaplan Grant / Research support from: Ferring, Janssen, AbbVie, GlaxoSmith Kline, Merck, and Shire, Consultant of: Gilead, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, and Takeda

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,066
Score d'incertitude au seuil0,478

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0030,011
Études des sciences et des technologies0,0020,000
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,004
Tête enseignante GPT0,233
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

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