Causes that Contribute to the Excess Mortality Risk in Multiple Sclerosis: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Lifespan is 6-10 years shorter in multiple sclerosis (MS), but the reasons remain unclear. Using linked clinical- and population-based administrative health databases, we compared cause-specific mortality in an MS cohort to the general population. METHODS: MS patients in British Columbia (BC), Canada, were followed from the later of first MS clinic visit or January 1, 1986, to the earlier of death, emigration, or December 31, 2013. Comprehensive mortality information was obtained by linkage to BC's multiple-cause-of-death mortality data. Causes were grouped using International Classification of Disease codes. Standardized mortality ratios (SMRs) were calculated for underlying cause, and relative mortality ratios (RMRs) for any mention cause, by comparison to mortality rates in the age-, sex-, and calendar year-matched general population. Cause-specific relative mortality was explored by sex and disease course (relapsing onset and primary progressive). RESULTS: Among 6,629 MS patients with 104,236 patient-years of follow-up, 1,416 died. The all-cause mortality risk was increased relative to the general population (SMR 2.71; 95% CI 2.55-2.87). MS was the underlying cause in 50.4%, and a mentioned cause in 77.9%, of deaths. Mortality by underlying cause was higher than expected for genitourinary disorders/infections (SMR 3.55; 95% CI 2.25-5.32), respiratory diseases/infections (SMR 2.69; 95% CI 2.17-3.28), suicide (SMR 2.40; 95% CI 1.61-3.45), cardiovascular disease (SMR 1.57; 95% CI 1.36-1.81), and other infections/septicemia (SMR 1.83; 95% CI 1.15-2.78). Risks of death due to overall cancer, accidents, digestive system disorders, and endocrine/nutritional diseases as underlying causes were similar to the general population. However, mortality with any mention of accidents (RMR 2.71; 95% CI 2.22-3.29) or endocrine/nutritional diseases (RMR 1.75; 95% CI 1.46-2.09) was greater. Bladder cancer mortality was increased in women (SMR 3.87; 95% CI 1.42-8.42) but not men. No notable differences were observed by disease course. CONCLUSIONS: MS itself was the most frequent underlying cause of death. Infections (genitourinary, respiratory, and septicemia), suicides, cardiovascular disease, and accidents contributed significantly to the increased risk of death. Some findings differed by sex, but not disease course. Multiple-cause death data offer advantages over "traditional" use of underlying cause only.
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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 | 0.001 |
| 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) | 0.001 | 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".