An Update on Arthritis in Canada — National and Provincial Data Regarding the Past, Present, and Future
Bibliographic record
Abstract
OBJECTIVE: To provide updated arthritis estimates for Canada given a change in wording in the 2015 Canadian Community Health Survey (CCHS) arthritis question. METHODS: Prevalence data from the 2000 to 2016 CCHS were used to document trends in the prevalence of arthritis over time. Projections of arthritis prevalence were also calculated using data from CCHS 2015 in conjunction with Statistics Canada's published population projections. Data for 2015 were also used to provide summary data on the effect of arthritis. RESULTS: Between 2000 and 2014 there were some fluctuations in the prevalence of arthritis (age ≥ 15 yrs), with the range of prevalence varying between 15.4% and 17.6%. There was a significant increase in overall prevalence to over 20% with the 2015 and 2016 surveys (6 million Canadians), coinciding with a revised wording of the arthritis question. This increase was observed in all age and sex groups, except for men aged 85+. The overall characteristics of the 2015 arthritis population were similar to those in 2007/08. Using the updated 2015 CCHS arthritis data, projection estimates suggest the population prevalence of arthritis will increase to just over 24% by 2040, with the number of Canadians living with arthritis projected to increase by about 50% from 2015 to 2040. CONCLUSION: The revised question likely increased ascertainment of arthritis owing to inclusion of examples of arthritis diagnoses in the CCHS question and more explicit wording in the French version, resulting in a large increase in the estimated prevalence and numbers of people with arthritis in Canada.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".