Association of Patient, Prescriber, and Region With the Initiation of First Prescription of Biologic Disease-Modifying Antirheumatic Drug Among Older Patients With Rheumatoid Arthritis and Identical Health Insurance Coverage
Notice bibliographique
Résumé
Importance: Prescribing the first biologic treatment for rheumatoid arthritis (RA) is an important decision for patients, their physicians, and payers, with considerable costs and clinical implications. Conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) have known effectiveness and safety profiles and are less expensive; therefore, determining the variables contributing to csDMARD treatment duration is an essential question for patients, physicians, and payers. Objectives: To describe access to the first biologic DMARD prescription in a population of patients with RA and identical comprehensive health insurance coverage in Ontario, Canada, and to explore the associations of patient, prescriber, and geographic region with differences in time to first biologic prescription. Design, Setting, and Participants: This cohort study of incident patients with RA used administrative data with surveillance and patient-level data collected at yearly intervals. A total of 17 672 patients were included in the study; they were residents of Ontario, Canada, had an incident RA diagnosis at age 67 or older between 2002 and 2015, and received at least 1 csDMARD. Data were analyzed in November 2017. Exposure: Patient variables were age, sex, disease duration, socioeconomic status, distance to care, and supply of care in the patient's area of residence. Prescriber covariates were year of graduation, specialty of practice, and supply of rheumatologic care in the patient's geographic region. Main Outcomes and Measures: Time from first csDMARD prescription to receipt of first biologic medication. Results: Of 17 672 patients, 11 598 (65.6%) were women, and the mean (SD) age was 75.2 (5.8) years. Characteristics associated with longer time to receipt of a biologic prescription were older age (HR for every 5-year increase, 0.66; 95% CI, 0.62-0.71; P < .001), male sex (HR, 0.76; 95% CI, 0.66-0.89; P < .001), and distance to the nearest rheumatologist (HR per 10-km increase, 0.99; 95% CI, 0.98-0.99; P < .001). Prescribers were primarily rheumatologists (151 of 214 [70.6%]) and primary care physicians (26 of 214 [12.1%]). After adjusting for the number of patients eligible to receive biologic DMARDs, rheumatologists' preferences (ie, yearly prescription rates) for using biologic DMARDs increased over time, from 1.7% in 2001 to 4.9% in 2015. After adjusting for calendar year and patient-, prescriber-, and region-level characteristics, substantial variation between prescribers in rates of prescribing a first biologic DMARD were found (65% variance). Conclusions and Relevance: This study found variation in time to receipt of first biologic DMARD after prescription of first csDMARD in a population with RA after adjustment for individual-level patient, prescriber, and geographic area covariates, despite identical universal health insurance coverage.
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».