Healthcare Cost of Multiple Sclerosis and in Relation to Disability Level in Alberta
Bibliographic record
Abstract
BACKGROUND: We aimed to (1) report updated estimates of direct healthcare costs for people living with MS (pwMS), (2) contrast costs to a control population and (3) explore differences between disability levels among pwMS. METHODS: Administrative data were used to identify adult pwMS (MS cohort) and without (control cohort) in Alberta, Canada; disability level (based on the Expanded Disability Status Scale) among pwMS was estimated. One- and two-part generalized linear models with gamma distribution were used to estimate the incremental direct healthcare cost (2021 $CDN) of MS during a 1-year observation period. RESULTS: = 150,080) (5.24 [95% CI: 5.08, 5.41]) with a predicted incremental cost of $15,016 (95% CI: $14,497, $15,535) per person-year. Among the MS cohort, total predicted direct healthcare costs were higher with greater disability, $14,430 (95% CI: $13,980, $14,880) to $58,697 ($51,514, $65,879) per person-year in mild and severe disability, respectively. The primary health resource cost component shifted from disease-modifying therapies in mild disability to supportive care in moderate and severe disability. CONCLUSION: Adult pwMS had greater direct healthcare costs than those without. Extrapolating to the population level (where 14,485 adult pwMS were identified in the study), it is estimated that $218 million per year in healthcare costs may be attributable to MS in Alberta. The significantly larger economic impact associated with greater disability underscores the importance of preventing or delaying disease progression and functional impairment in MS.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".