Adherence and Persistence with Drug Therapy among Fibromyalgia Patients: Data from a Large Health Maintenance Organization
Bibliographic record
Abstract
OBJECTIVE: To assess 1-year persistence and adherence rates with drug therapy among patients with fibromyalgia (FM) and to identify factors associated with therapy discontinuation. METHODS: This retrospective, cohort study included members ≥ 21 years old from the Maccabi Healthcare Services, a large health maintenance organization in Israel, who were diagnosed with FM from 2008 through 2011. Medications of interest included the anticonvulsant pregabalin, antidepressants [selective serotonin reuptake inhibitor (SSRI), serotonin/norepinephrine reuptake inhibitor (SNRI)], and tricyclic antidepressants (TCA). Time to treatment discontinuation and proportion of days covered (PDC) with FM-specific therapies during the year from first dispensed were analyzed. PDC < 20% was considered low adherence and PDC ≥ 80% was considered high adherence. Logistic regression models were constructed for multivariable analyses. RESULTS: Overall, 3932 patients with FM were included; 88.7% were female. Pre-diagnosis use of medication of interest was documented in 41% of the study population. Of the remaining 2312 patients, 56.1% were issued a prescription, 45.0% were dispensed at least 1 medication in the year following diagnosis, and only 28.8% had prescriptions filled twice within the first year from diagnosis. Among newly prescribed patients, 1-year discontinuation was highest for TCA (91.0%) and lowest for SSRI/SNRI antidepressants (73.7%). Over half of the patients (60.5%) had fewer than 20% of the days covered by any medication during the year and only 9.3% were very adherent (PDC ≥ 80%). CONCLUSION: This study clearly shows that in an Israeli "real-life" population of patients with FM, persistence and adherence with FM therapy in the year following diagnosis is remarkably low.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".