Trends in hip fracture rates in Canada: An age-period-cohort analysis
Bibliographic record
Abstract
Age-standardized rates of hip fracture in Canada declined during the period 1985 to 2005. We investigated whether this incidence pattern is explained by period effects, cohort effects, or both. All hospitalizations during the study period with primary diagnosis of hip fracture were identified. Age- and sex-specific hip fracture rates were calculated for nineteen 5-year age groups and four 5-year calendar periods, resulting in 20 birth cohorts. The effect of age, calendar period, and birth cohort on hip fracture rates was assessed using age-period-cohort models as proposed by Clayton and Schiffers. From 1985 to 2005, a total of 570,872 hospitalizations for hip fracture were identified. Age-standardized rates for hip fracture have progressively declined for females and males. The annual linear decrease in rates per 5-year period were 12% for females and 7% for males (both p < 0.0001). Significant birth cohort effects were also observed for both sexes (p < 0.0001). Cohorts born before 1950 had a higher risk of hip fracture, whereas those born after 1954 had a lower risk. After adjusting for age and constant annual linear change (drift term common to both period and cohort effects), we observed a significant nonlinear birth cohort effect for males (p = 0.0126) but not for females (p = 0.9960). In contrast, the nonlinear period effect, after adjustment for age and drift term, was significant for females (p = 0.0373) but not for males (p = 0.2515). For males, we observed no additional nonlinear period effect after adjusting for age and birth cohort, whereas for females, we observed no additional nonlinear birth cohort effect after adjusting for age and period. Although hip fracture rates decreased in both sexes, different factors may explain these changes. In addition to the constant annual linear decrease, nonlinear birth cohort effects were identified for males, and calendar period effects were identified for females as possible explanations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".