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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

2019· article· en· W2992069601 on OpenAlexafffundabout
Mark Tatangelo, George Tomlinson, J. Michael Paterson, Vandana Ahluwalia, Alex Kopp, Tara Gomes, Nick Bansback, Claire Bombardier

Bibliographic record

VenueJAMA Network Open · 2019
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of British ColumbiaSt. Michael's HospitalWilliam Osler Health SystemInstitute for Clinical Evaluative SciencesUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareArthritis SocietyJanssen PharmaceuticalsCelgeneUnion Chimique BelgeSanofiAmgenPfizerEli Lilly and Company
KeywordsRheumatoid arthritisMedicineAntirheumatic drugsMedical prescriptionAntirheumatic AgentsHealth insuranceDrugDiseasePhysical therapyInternal medicinePharmacologyHealth care

Abstract

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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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations25
Published2019
Admission routes3
Has abstractyes

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