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Enregistrement W4323350630 · doi:10.1093/jcag/gwac036.182

A182 PREDICTING HIGH DIRECT HEALTHCARE COSTS IN PEDIATRIC PATIENTS WITH INFLAMMATORY BOWEL DISEASE IN THE FIRST YEAR FOLLOWING DIAGNOSIS

2023· article· en· W4323350630 sur OpenAlexafffundabout
E Kuenzig, R Duchen, T D Walters, David R. Mack, A M Griffiths, C N Bernstein, Gilaad G. Kaplan, Anthony Otley, W Yu, X Wang, Jun Guan, Stephen Fung, Eric I. Benchimol

Notice bibliographique

RevueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueAdolescent and Pediatric Healthcare
Établissements canadiensUniversity of ManitobaResearch ManitobaDalhousie UniversitySickKids FoundationUniversity of TorontoAgricultural Research Institute of OntarioUniversity of OttawaUniversity of CalgaryInstitute for Clinical Evaluative Sciences
Organismes subventionnairesJanssen CanadaTakeda CanadaAbbVie CanadaSandoz CanadaPfizer CanadaDairy Farmers of OntarioAmgen CanadaAmgenPfizerEli Lilly and CompanyBristol-Myers Squibb
Mots-clésMedicineHealth careInflammatory bowel diseaseDiseaseLogistic regressionPercentileCrohn's diseasePediatricsInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Background The incidence of inflammatory bowel disease (IBD) continues to rise rapidly among Canadian children. The care of children results in higher direct healthcare costs than adults with IBD. It is imperative that we identify individuals who will become the highest-cost users of the health system in order to intervene early and decrease the individual- and system-level burden of IBD. Purpose To develop a predictive-model for high-cost health system users and (2) identify factors associated with high-cost healthcare use. Method Incident cases of IBD diagnosed ≤17y residing in Ontario and enrolled in the Canadian Children IBD Network (CIDsCaNN) between Dec 31 2013 and Jan 31 2019 were linked deterministically using health card number to health administrative data. Using a validated algorithm, direct healthcare costs accumulated between the 31st and 365th day after diagnosis were calculated using data from CIDsCaNN (medications) and health administrative data (health system encounters, including surgery). A predictive model was created to determine high-cost (≤25th percentile) and medium-cost (26th to 75th) users, compared to low-cost users (>75th) using ordinal logistic regression. Potential predictive variables were determined a priori based on clinical significance and magnitude of univariable association, based on sample size-informed degrees of freedom. Variables from CIDsCaNN data included: IBD type, age at diagnosis, sex, first line of therapy (steroids, aminosalicylates, exclusive enteral nutrition; yes or no, not mutually exclusive), disease activity (severe vs. moderate vs. none/mild based on PUCAI [UC] or wPCDAI [Crohn’s]). Predictive variables from the health administrative data included: rural/urban residence, hospitalization at diagnosis, intestinal resection or colectomy within 3 months of diagnosis, emergency department visit ±1 month of diagnosis, and a mental health encounter within the first year following diagnosis. Anti-TNF treatment was excluded from models due to the strong correlation with the outcome (direct costs). Overall model fit was estimated with a c-statistic. Result(s) Among the 487 (57% Crohn’s) children included in the study, the mean (sd) direct costs accumulated between the 31st and 365th days following IBD diagnosis was $14,451 (14,665). The mean cost among high-cost users was $33,533 (16,530); medium-cost users, $11,038 (5322); low-cost users, $2530 (831). The predictive model identified high-cost users of the health system with acceptable model fit (c-statistic 0.69). The relative contribution of individual variables, as measured by odds ratio (OR), is reported in the Table. Image Conclusion(s) The direct healthcare costs of pediatric IBD are substantial. Children with IBD who become high-cost users of the health system were identifiable using characteristics at diagnosis (e.g., need for mental health care, emergency visits, older age). Further research should assess whether interventions in patients at-risk for becoming high-cost users may help to reduce costs. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; Ontario Academic Health Sciences Centres Alternate Funding Plan Innovation Fund Disclosure of Interest E. Kuenzig: None Declared, R. Duchen: None Declared, T. Walters Grant / Research support from: Janssen, Abbvie, Psfizer, Ferring, Amgen, Consultant of: Janssen, Abbvie, Psfizer, Ferring, Amgen, D. Mack: None Declared, A. Griffiths Grant / Research support from: Abbvie, Consultant of: Abbvie, Amgen, BristolMyersSquibb, Janssen, Lilly, Takeda, Speakers bureau of: Abbvie, Janssen, Takeda, C. Bernstein Grant / Research support from: 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, Takeda, Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, G. Kaplan Grant / Research support from: Ferring, Consultant of: AbbVie, Janssen, Pfizer, Amgen, Sandoz, Pendophram, and Takeda, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, Sandoz, Pendophram, and Takeda, A. Otley Grant / Research support from: Research support: AbbVie Global. Research site: AbbVie, Pfizer, Eli-Lily, Janssen, Consultant of: AbbVie Canada, W. Yu: None Declared, X. Wang: None Declared, J. Guan: None Declared, S. Fung: None Declared, E. Benchimol Consultant of: McKesson Canada, Dairy Farmers of Ontario (unrelated to medications used to treat inflammatory bowel disease)

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)0,000
score de la tête « metaresearch » (Gemma)0,003
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 enseignants0,450
Score d'incertitude au seuil0,895

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,013
Tête enseignante GPT0,282
Écart entre enseignants0,270 · 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

Citations3
Publié2023
Routes d'admission3
Résumé présentoui

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Même revueJournal of the Canadian Association of GastroenterologyMême sujetAdolescent and Pediatric HealthcareTravaux en français237 207