Disease-modifying drugs, multiple sclerosis and infection-related healthcare use in British Columbia, Canada: a population-based study
Bibliographic record
Abstract
Background: Much remains unknown surrounding the disease-modifying drugs (DMDs) used to treat multiple sclerosis and infection-related healthcare use in the 'real-world' setting. We examined if DMD exposure was associated with altered infection-related healthcare use. Methods: We assessed if DMD (versus no) exposure was associated with altered infection-related hospitalizations, physician claims, and prescriptions filled in British Columbia, Canada (1996-2017). Healthcare use was assessed using negative binomial and proportional means regression models, reported as sex-/age-/comorbidity-/calendar year-/socioeconomic-adjusted rate and hazard ratios [aRR, aHR], with 95% confidence intervals [CIs]). Findings: We identified 19,360 multiple sclerosis cases (13,940/19,360; 72.0% women; mean age at study start = 44.5 standard deviation, SD = 13.3; mean follow-up = 11.7 [SD = 7.3] years). Relative to unexposed periods, exposure to any DMD was associated with a lower infection-related rate of physician claims (aRR = 0.88; 95% CI:0.85-0.92) and hazard of hospitalization (aHR = 0.64; 95% CI:0.56-0.73), and a higher rate of infection-related prescriptions (aRR = 1.14; 95% CI:1.08-1.20). Exposure to any injectable or oral DMD was associated with a lower infection-related rate of physician claims (injectable aRR = 0.88; 95% CI:0.84-0.92, oral aRR = 0.83; 95% CI:0.77-0.90) and hazard of hospitalization (injectable aHR = 0.65; 95% CI:0.56-0.75, oral aHR = 0.54; 95% CI:0.38-0.77), whereas intravenous DMD exposure was not (aRR = 0.99; 95% CI:0.86-1.14, aHR = 0.73; 95% CI:0.49-1.09). Exposure to any injectable or intravenous DMD was associated with a higher rate of infection-related prescriptions (injectable aRR = 1.15; 95% CI:1.08-1.22, intravenous = 1.34; 95% CI:1.15-1.56), whereas oral DMDs were not (aRR = 0.98; 95% CI:0.91-1.05). Interpretation: DMD exposure for the treatment of MS was associated with differences in infection-related healthcare use. While infection-related hospitalizations and physician visits were lower, prescription fills were higher. How these differences in infection-related healthcare use affect outcomes in persons with multiple sclerosis warrants consideration. Funding: Canadian Institutes of Health Research (CIHR); German Research Foundation (DFG).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".