Direct Health Care Costs Associated With Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS), a leading cause of nontraumatic neurologic disability in young adults, exerts a substantial economic burden on the health care system. The objective of this study was to quantify the excess health care costs of MS in British Columbia, Canada. METHODS: A retrospective-matched cohort study of patients with MS was conducted using population-based administrative health data from 2001 to 2020. Patients with MS who satisfied a validated case definition were matched to 5 unique controls without MS on sex, age, and cohort entry date. Patients and controls were followed to the end of 2020 or to their last health care resource use, whichever came first. We calculated the direct medical costs for each individual, including outpatient services use, hospital admissions, and dispensed medications. We used generalized linear models with an identity link and normal distribution to estimate the excess cost of MS as the mean cost difference between patients with MS and controls. All costs were reported in 2020 Canadian dollars. RESULTS: A total of 17,071 patients with MS were matched to 85,355 controls. Overall, 72.4% were female, and the mean age at cohort entry date was 46.1 years. The excess cost of MS was $6,881 (95% CI: $6,713, $7,049) per patient-year. Inpatient, outpatient, and medication costs accounted for 25%, 10%, and 65% of excess costs, respectively. Excess costs were higher in patients with MS with at least one disease-modifying therapy (DMT) prescription ($13,267; 95% CI: $12,992-$13,542) compared with non-DMT users ($3,469; 95% CI: $3,297-$3,641) and even higher among frequent DMT users ($24,835; 95% CI: $24,528-$25,141). Patients with MS with a history of at least one relapse requiring hospitalization had higher excess costs ($10,543; 95% CI: $10,136-$10,950) compared with patients with MS without a relapse; hospitalizations accounted for 51% of the costs in this group. The excess cost of hospitalizations was $1,391 lower among frequent DMT users than non-DMT users. DISCUSSION: The economic burden of MS is considerable, with medications, particularly DMTs, being the largest cost driver. Future studies should investigate how disease management strategies, including early diagnosis and timely use of DMTs, could offset future and ongoing costs while improving patients' quality of life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".