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Impact of the Adalimumab Patient Support Program's Care Coach Calls on Persistence and Adherence in Canada: An Observational Retrospective Cohort Study

2018· article· en· W2792610153 on OpenAlexafffundabout
John K. Marshall, Louis Bessette, Carter Thorne, Neil H. Shear, Gerald Lebovic, Sebastien K. Gerega, Brad Millson, Driss Oraichi, Tania Gaetano, Sandra Gazel, Martin G. Latour, Marie-Claude Laliberté

Bibliographic record

VenueClinical Therapeutics · 2018
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsABB (Canada)St. Michael's HospitalUniversity of TorontoHealth Sciences CentreSunnybrook Health Science CentrePopulation Health Research InstituteUniversité LavalSouthlake Regional Health CenterMcMaster University
FundersUniversity of TorontoAbbVie
KeywordsMedicineAdalimumabRetrospective cohort studyObservational studyPersistence (discontinuity)CohortCohort studyFamily medicineEmergency medicineInternal medicineTumor necrosis factor alpha

Abstract

fetched live from OpenAlex

Purpose Adalimumab (ADA) is a tumor necrosis factor-α inhibitor indicated for use in various immune-mediated inflammatory diseases. Patients receiving ADA in Canada are eligible to enroll in the AbbVie Care's Patient Support Program (PSP), which provides personalized services, including tailored interventions in the form of nurse-provided care coach calls (CCCs), with the goal of improving patients' experiences and outcomes. The primary objective of this study was to evaluate the impact of PSP services, including CCCs and patient characteristics, on persistence with and adherence to ADA for those patients enrolled in the PSP. A secondary objective was to estimate the effect of initial CCCs on treatment-initiation abandonment (ie, failure to initiate therapy after enrollment in the PSP). Methods An observational retrospective cohort study was conducted. A patient linkage algorithm based on probabilistic matching was developed to link the AbbVie Care PSP database to the QuintilesIMS longitudinal pharmacy transaction database. Patients who started ADA therapy between July 2010 and August 2014 were selected, and their prescriptions were evaluated for 12 months after the date of ADA start to calculate days until drug discontinuation, that is, the end of persistence, defined as >90 days without therapy. Cox proportional hazards modeling was used for estimating hazard ratios for the association between persistence and patient characteristics and each PSP service. Adherence, measured by medication possession ratio, was calculated, and multivariate logistic regression provided adjusted odds ratios for the relationship between being adherent (medication possession ratio ≥80%) and patient characteristics and each PSP service. Treatment-initiation abandonment among patients who received an initial CCC compared with those who did not was analyzed using the χ 2 test. Findings Analysis of 10,857 linked patients yielded statistically significant differences in the hazard ratio of discontinuation and the likelihood of being adherent across multiple variables between patients who received CCCs in comparison to patients who did not. Patients receiving CCCs were found to have a 72% decreased risk for therapy discontinuation (hazard ratio=0.282; P < 0.0001), and a greater likelihood of being adherent (odds ratio=1.483; P < 0.0001), when compared with those patients who did not receive CCCs. The rate of treatment-initiation abandonment was significantly higher in patients who did not receive initial CCCs ( P < 0.0001). Implications Ongoing CCCs, provided by AbbVie Care PSP, were associated with greater patient persistence and adherence over the first 12 months of treatment, while initial CCCs were associated with a lower rate of treatment-initiation abandonment. Results may inform the planning of interventions aimed at improving treatment adherence and patient outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.008
Science and technology studies0.0070.002
Scholarly communication0.0040.002
Open science0.0040.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.174
GPT teacher head0.433
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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".

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Citations21
Published2018
Admission routes3
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