Disease-modifying drugs for multiple sclerosis and infection risk: a cohort study
Bibliographic record
Abstract
OBJECTIVE: Little is known about disease-modifying treatments (DMTs) for multiple sclerosis (MS) and infection risk in clinical practice. We examined the association between DMTs and infection-related medical encounters. METHODS: Using population-based administrative data from British Columbia, Canada, we identified MS cases and followed them from their first demyelinating event (1996-2013) until emigration, death or study end (December 2013). Associations between DMT exposure (by DMT generation or class) and infection-related physician or hospital claims were assessed using recurrent time-to-events models, adjusted for age, sex, socioeconomic status, index year and comorbidity count. Results were reported as adjusted HRs (aHRs). RESULTS: Of 6793 MS cases, followed for 8.5 years (mean), 1716 (25.3%) were DMT exposed. Relative to no DMT, exposure to any first-generation DMT (beta-interferon or glatiramer acetate) was not associated with infection-related physician claims (aHR: 0.96; 95% CI 0.89 to 1.02), nor was exposure to these drug classes when assessed separately. Exposure to any second-generation DMT (oral DMT or natalizumab) was associated with an increased hazard of an infection-related physician claim (aHR: 1.47; 95% CI 1.16 to 1.85); when assessed individually, the association was significant for natalizumab (aHR: 1.59; 95% CI 1.19 to 2.11) but not the oral DMTs (aHR: 1.17; 95% CI 0.88 to 1.56). While no DMTs were associated with infection-related hospital claims, these hospitalisations were also uncommon. CONCLUSION: Exposure to first-generation DMTs was not associated with an altered infection risk. However, exposure to the second-generation DMTs was, with natalizumab associated with a 59% increased risk of an infection-related physician claim. Continued pharmacovigilance is warranted, including an investigation of the DMT-associated infection burden on patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".