Increased acute care utilisation, comorbidities and mortality in adults with haemophilia: A population‐based cohort study from 2012 to 2019
Bibliographic record
Abstract
INTRODUCTION: Improvements in treatment strategies have led to increased life expectancy of persons with haemophilia (PWH). Consequently, age-related comorbidities become increasingly relevant. AIM: To evaluate the prevalence of age-related comorbidities, mortality, health service utilisation and predictors of hospitalisation in PWH compared to the general population. METHODS: We conducted a population-based retrospective cohort study using linked administrative data. Men with haemophilia were identified in Alberta, Canada (2012-2019) with a validated case definition and were age-matched with male population controls. We calculated the prevalence of major comorbidities, all-cause mortality, and examined health service utilisation including Emergency Department visits and hospitalisations. Logistic regression was applied to identify predictors of hospitalisation. RESULTS: We identified 198 and 329 persons with moderately severe haemophilia and mild/moderate, respectively. Moderately severe haemophilia had a higher risk of death (standardised mortality ratio 3.2, 95% confidence interval [CI] 1.4-6.3) compared to the general population. PWH had a significantly higher prevalence of hypertension, liver diseases and malignancies than controls. Moderately severe haemophilia was associated with significantly higher rates of hospitalisations (52.5% vs. 14.5%), Emergency Department visits (89.1% vs. 62.7%) and intensive care admissions (8.9% vs. 2.3%). Age > 65 years (adjusted odds ratio [aOR] 6.8) and presence of multiple comorbidities (aOR 3.9) were significant predictors of hospitalisations among PWH. CONCLUSION: Despite advanced care, haemophilia is associated with higher acute care utilisation than the general population, highlighting the substantial burden of illness on patients and the health care system.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".