Population‐Based Study of Changes in Arthritis Prevalence and Arthritis Risk Factors Over Time: Generational Differences and the Role of Obesity
Bibliographic record
Abstract
OBJECTIVE: To investigate cohort effects in arthritis prevalence across 4 birth cohorts: World War II (born 1935-1944), older and younger baby boomers (born 1945-1954 and 1955-1964, respectively), and Generation X (born 1965-1974), and to determine whether birth cohort effects in arthritis prevalence were associated with differences in risk factors over time or period effects. METHODS: Analysis of biannually collected data from the longitudinal Canadian National Population Health Survey, 1994-2011 (n = 8,817 at baseline). Data included self-reported arthritis diagnosed by a health professional, risk factors (years of education, household income, smoking, physical activity, sedentary behavior, body mass index [BMI]), and survey year as an indicator of period. We used hierarchical age-period-cohort analyses to compare the age trajectory of arthritis by birth cohort and to examine the contribution of changes in risk factors and period to cohort differences. RESULTS: More recent cohorts had successively a greater prevalence of arthritis. Risk factors were significantly associated with arthritis prevalence independently of cohort differences. The effects of increasing education and income over time on potentially reducing the arthritis prevalence were almost counter-balanced by effects of increasing BMI. Significant cohort-BMI and age-BMI interactions indicated an earlier age of arthritis onset for obese individuals than those of normal weight. CONCLUSION: Projections that only take into account the changing age structure of the population may underestimate future trends. Our understanding of the impact of BMI on arthritis is likely an underestimate. Cohort differences focus attention on the need to target arthritis management education to young and middle-aged adults.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".