Rheumatoid Arthritis Surveillance in Ontario: Monitoring the Burden, Quality of Care and Patient Outcomes through Linkage of Administrative Health Data
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is the most common chronic inflammatory joint disease.Using the Ontario administrative health data housed at the Institute for Clinical Evaluative Sciences, researchers have quantified the population-level burden and epidemiology of RA, mapped its geographic distribution in relation to rheumatologist supply, studied trends in access to rheumatology care and treatment and evaluated patient outcomes.The findings highlight the excess morbidity and mortality associated with the growing burden of RA in the face of a strained rheumatology supply, and raise urgent questions about how best to meet the needs of Ontarians with RA. The IssueGiven the rising levels of chronic disease and multimorbidity in Canada, researchers and decision-makers are increasingly looking to administrative health data as an information source for chronic disease detection and surveillance.Rheumatoid arthritis (RA) is just one condition for which administrative data-based research on disease burden, quality of care and patient outcomes is helping to transform the lives of Canadians with chronic disease.RA is the most common chronic inflammatory joint disease, affecting millions of people worldwide, and one of the most disabling and costly of all chronic diseases (Cross et al. 2014).At disease onset, RA is considered a medical emergency requiring prompt referral to a rheumatologist.To control symptoms and inhibit disease progression, treatment guidelines include disease-modifying antirheumatic drugs (DMARDs), biologic agents such as anti-tumour necrosis factor agents and corticosteroids (Bykerk et al. 2012).However, uncontrolled RA activity can lead to irreversible joint damage requiring surgery, multiple comorbidities and premature mortality.Many factors contribute to increased morbidity and mortality in patients with RA, including immune abnormalities, organ system manifestations associated with the disease, genetic predisposition, the immunosuppressive and cytotoxic effects of treatments and, possibly, poor quality of care.Here we highlight some recent Institute for Clinical Evaluative Sciences (ICES) research aimed at helping to improve the care and outcomes of patients with RA. Key Findings Optimizing Administrative Data AccuracyValidation of administrative data for identifying patients with various health states improves our understanding of when and how such data can be used for chronic disease surveillance and research.To assess the accuracy of administrative data for identifying RA patients, two independent validation studies were performed at ICES (Widdifield et al. 2013a(Widdifield et al. , 2014a)).This foundational work led to the establishment of the Ontario Rheumatoid Arthritis Database (ORAD), a validated, population-based registry.Derived by linking physician service claims,
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".