Disease-modifying therapy use and health resource utilisation associated with multiple sclerosis over time: A retrospective cohort study from Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Estimating multiple sclerosis (MS) prevalence and incidence, and assessing the utilisation of disease-modifying therapies (DMTs) and healthcare resources over time is critical to understanding the evolution of disease burden and impacts of therapies upon the healthcare system. METHODS: A retrospective population-based study was used to determine MS prevalence and incidence (2003-2019), and describe utilisation of DMTs (2009-2019) and healthcare resources (1998-2019) among people living with MS (pwMS) using administrative data in Alberta. RESULTS: Prevalence increased from 259 (95% confidence interval [CI]: 253-265) to 310 (95% CI: 304, 315) cases per 100,000 population, and incidence decreased from 21.2 (95% CI: 19.6-22.8) to 12.7 (95% CI: 11.7-13.8) cases per 100,000 population. The proportion of pwMS who received ≥1 DMT dispensation increased (24% to 31% annually); use of older platform injection therapies decreased, and newer oral-based, induction, and highly-effective therapies increased. The proportion of pwMS who had at least one MS-related physician, ambulatory, or tertiary clinic visits increased, and emergency department visits and hospitalizations decreased. CONCLUSIONS: Alberta has one of the highest rates of MS globally. The proportion of pwMS who received DMTs and had outpatient visits increased, while acute care visits decreased over time. The landscape of MS care appears to be rapidly evolving in response to changes in disease burden and new highly-effective therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".