Arthritis and arthritis‐attributable activity limitations in the United States and Canada: A cross‐border comparison
Bibliographic record
Abstract
OBJECTIVE: To compare directly the prevalence and risk factors for arthritis and arthritis-attributable activity limitations (AAL) between the US and Canada, and to estimate the population attributable risk percentage (PAR%) for obesity and leisure time physical inactivity. METHODS: We conducted analyses of the 2002-2003 Joint Canada/US Health Survey, which asked about health professional-diagnosed arthritis, and arthritis reported as a cause of disability in specified activities of daily living. We used log-Poisson regression to obtain prevalence ratios for arthritis and AAL, adjusting for education, income, having a regular doctor, physical inactivity, and obesity. PAR% for obesity and physical inactivity were calculated. RESULTS: The estimated crude prevalence of arthritis and AAL were 18.7% and 9.3%, respectively, in the US and 16.9% and 7.4%, respectively, in Canada. Being American was a significant bivariate predictor of arthritis and AAL, but not after adjustment for obesity and physical inactivity. PAR% for obesity were 14% and 20% for arthritis and AAL, respectively, for Americans and 13% and 17%, respectively, for Canadians, and for physical inactivity were 15% and 21%, respectively, for Americans and 4% and 5%, respectively, for Canadians, with estimates being higher among women. CONCLUSION: The higher prevalence of arthritis and AAL in the US may be accounted for by the higher prevalence of obesity and physical inactivity, particularly in women. The high PAR% related to obesity in both countries, and physical inactivity in the US, point to the importance of public health initiatives to reduce obesity and increase physical activity to reduce the prevalence of arthritis and AAL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".