Association between disease-modifying therapies for multiple sclerosis and healthcare utilisation on a population level: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Disease-modifying therapy (DMT) use in multiple sclerosis (MS) has increased significantly. However, the impact of DMTs on healthcare use is limited and conflicting, and rarely examined at a population level. This study examined the association between DMTs and healthcare utilisation at the population level. DESIGN: Retrospective cohort. SETTING: Health administrative data from Saskatchewan, Canada (1997-2016). PARTICIPANTS: To test for associations at the population level, we identified two cohorts. The general population cohort included all Saskatchewan residents ≥18 years who were drug plan beneficiaries. The MS cohort included individuals ≥18 years, identified using a validated definition (≥3 hospital, physician or drug claims for MS). MAIN OUTCOME MEASURES AND METHODS: To test for an association between the total number of DMT dispensations per year and the total number of hospitalisations we used negative binomial regression fitted with generalised estimating equations (GEE); only hospitalisations that occurred after the date of MS diagnosis (date of first claim for MS or demyelinating disease) were extracted. To test for an association between the number of DMT dispensations and physician claims, negative binomial distributions with GEE were fit as above. Results were reported as rate ratios (RR), with 95% CIs, and calculated for every 1000 DMT dispensations. RESULTS: The number of DMT dispensations was associated with a decreased risk for all-cause (RR=0.994; 95% CI 0.992 to 0.996) and MS-specific (RR=0.909; 95% CI 0.880 to 0.938) hospitalisations. The number of DMT dispensations was not associated with the number of all-cause (RR=1.006; 95% CI 0.990 to 1.022) or MS-specific (RR=0.962; 95% CI 0.910 to 1.016) physician claims. CONCLUSION: Increased DMT use in Saskatchewan was associated with a reduction in hospitalisations, but did not impact the number of physician services used. Additional research on cost-benefit and differing treatment strategies would provide further insight into the true impact of DMTs on healthcare utilisation at a population level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".