Adherence and persistence to drug therapies for multiple sclerosis: A population-based study
Bibliographic record
Abstract
OBJECTIVE: We aimed to estimate the prevalence and predictors of optimal adherence and persistence to the disease-modifying therapies (DMT) for multiple sclerosis (MS) in 3 Canadian provinces. METHODS: We used population-based administrative databases in British Columbia (BC), Saskatchewan, and Manitoba. All individuals receiving DMT (interferon-B-1b, interferon-B-1a, and glatiramer acetate) between 1-January-1996 and 31-December-2011 (BC), 31-March-2014 (Saskatchewan), or 31-March-2012 (Manitoba) were included. One-year adherence was estimated using the proportion of days covered (PDC). Persistence was defined as time to DMT discontinuation. Regression models were used to assess predictors of adherence and persistence; results were pooled using random effects meta-analysis. RESULTS: 4830 individuals were included. When results were combined, an estimated 76.4% (95% CI: 69.1-82.4%) of subjects exhibited optimal adherence (PDC ≥80%). Median time to discontinuation of the initial DMT was 1.9 years (95% CI: 1.6-2.1) in Manitoba, 2.8 years (95% CI: 2.5-3.0) in BC, and 4.0 years (95% CI: 3.5-4.6) in Saskatchewan. Age, sex and socioeconomic status were not associated with adherence or persistence. Individuals who had ≥4 physician visits during the year prior to the first DMT dispensation were more likely to exhibit optimal adherence compared to those with fewer (0-3) physician visits. CONCLUSIONS: We observed adherence that is higher than what has been reported for other chronic diseases, and other non-population-based MS cohorts. Closer examination as to why adherence appears to be relatively better in MS and how adherence influences disease outcomes could contribute to our understanding of MS, and prove useful in the management of other chronic diseases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".