The Incidence of Hip Fractures in Long-Term Care Homes in Saskatchewan from 2008 to 2012: an Analysis of Provincial Administrative Databases
Bibliographic record
Abstract
BackgroundHip fractures (HFs) represent an important cause of morbidity and mortality among adults in long-term care (LTC), but lack of detailed epidemiological data poses challenges to intervention planning. We aimed to determine the incidence of HFs among permanent LTC residents in Saskatchewan between 2008 and 2012, using linked, provincial administrative health databases, exploring associations between outcomes and basic individual and institutional characteristics.MethodsWe utilized the Ministry of Health databases to select HF cases based on ICD 10 diagnoses fracture of head and neck of femur, pertrochanteric fracture and subtrochanteric fracture of femur. HF incidence rates in LTC were compared to older adults in the general population.ResultsLTC residents were more likely to be female overall (65.5%), although this varied by age, with only 46.6% female in those under 65, but 77% female among those 90 years and older. Mean age of residents was highest in rural centres (85.2 yrs) and lowest in medium–large centres (81.0 yrs). Of 6,230 cases of HFs in the province during the study period, 2,743 (44%) were in the LTC cohort. Incidence rates per 1,000 person years increased with age and were higher in the LTC group (F = 68.6, M = 49.8) than the overall population (F = 1.62, M = 0.73). Rates of HFs in the province and in LTC were higher in females than males in all age groups, except for the youngest (< 65 years), where males had higher rates, and the oldest category (90+) where rates were similar. Women 90+ years in larger LTC had significantly higher (p = .035) HF rates than those in smaller LTC, and also had significantly (p = .001) higher rates in medium-large compared to smaller population centres. However, after age standardization to the overall SK population, it was apparent that the larger LTC facilities and the medium-large population centres had overall lower HF rates than the small and medium LTC facilities and the small urban and rural PCs, respectively. One health region had particularly high rates, even when accounting for age and sex composition.ConclusionBoth HF numbers and incidence rates were higher in LTC compared to the overall population, with higher rates in older women, small to medium size LTC, and particular health regions. Our data suggest the need for further exploration of potentially remediable factors for HFs in smaller LTCs, and for targeting specific facilities and regions with outlying HF rates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".