Global Epidemiology of Hip Fractures: Secular Trends in Incidence Rate, Post‐Fracture Treatment, and All‐Cause Mortality
Bibliographic record
Abstract
In this international study, we examined the incidence of hip fractures, postfracture treatment, and all-cause mortality following hip fractures, based on demographics, geography, and calendar year. We used patient-level healthcare data from 19 countries and regions to identify patients aged 50 years and older hospitalized with a hip fracture from 2005 to 2018. The age- and sex-standardized incidence rates of hip fractures, post-hip fracture treatment (defined as the proportion of patients receiving anti-osteoporosis medication with various mechanisms of action [bisphosphonates, denosumab, raloxifene, strontium ranelate, or teriparatide] following a hip fracture), and the all-cause mortality rates after hip fractures were estimated using a standardized protocol and common data model. The number of hip fractures in 2050 was projected based on trends in the incidence and estimated future population demographics. In total, 4,115,046 hip fractures were identified from 20 databases. The reported age- and sex-standardized incidence rates of hip fractures ranged from 95.1 (95% confidence interval [CI] 94.8-95.4) in Brazil to 315.9 (95% CI 314.0-317.7) in Denmark per 100,000 population. Incidence rates decreased over the study period in most countries; however, the estimated total annual number of hip fractures nearly doubled from 2018 to 2050. Within 1 year following a hip fracture, post-hip fracture treatment ranged from 11.5% (95% CI 11.1% to 11.9%) in Germany to 50.3% (95% CI 50.0% to 50.7%) in the United Kingdom, and all-cause mortality rates ranged from 14.4% (95% CI 14.0% to 14.8%) in Singapore to 28.3% (95% CI 28.0% to 28.6%) in the United Kingdom. Males had lower use of anti-osteoporosis medication than females, higher rates of all-cause mortality, and a larger increase in the projected number of hip fractures by 2050. Substantial variations exist in the global epidemiology of hip fractures and postfracture outcomes. Our findings inform possible actions to reduce the projected public health burden of osteoporotic fractures among the aging population. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".