When does the increased mortality risk appear in rheumatoid arthritis? A distributed data analysis comparing mortality in two Canadian provinces
Bibliographic record
Abstract
IntroductionRheumatoid arthritis (RA) is chronic inflammatory arthritis. For decades studies showed that RA patients died earlier than their general population counterparts. Some inception cohorts have failed to detect an increased mortality risk, possibly due to limited follow-up or to improvement in mortality risk in cohorts of more recent onset. Objectives and ApproachWe evaluated mortality risk in RA patients and estimated when the increased risk appears. Using a common protocol, we conducted distributed analyses using administrative data, of incident RA patients in British Columbia (BC) and Ontario (ON) over 2000-2015. We identified all RA patients (using validated criteria), and identified non-RA comparators, matched 1:2 on age, sex and index years. Adjusted hazard ratios (HRs) were estimated using multivariable Cox regression, controlling for comorbidities and other factors. To estimate when the increased risk appeared we included an interaction with follow-up time, to detect if and how the HR varied by RA duration. ResultsAmong 13834 RA patients in BC (27668 comparators), 66% were female with a mean age of 58 years at cohort entry. Among 27405 RA patients in ON (54810 comparators), 70% were female with a mean age of 56 years. The prevalence of individual comorbidities was comparable across RA cohorts. During follow-up, 23% of RA patients in each province died, with corresponding crude mortality rates of 2.3 deaths per 100 person-years in both provinces. Multivariable analyses detected an increased mortality risk in RA by 6 years of follow-up, with a linear relationship suggesting further increase over time. By 10 years, the adjusted HR was 1.14 (95% CI 1.07,1.22) in BC and 1.13 (95% CI 1.08,1.18) in ON. Conclusion/ImplicationsIn 2 large Canadian RA inception cohorts, a small increased mortality risk appeared after 6 years of RA duration and increased to a 14% (in BC) and 13% (in ON) increased mortality risk after 10 years, suggesting increased efforts to prevent disease progression and optimizing comorbidity management are needed.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".