Sex Differences in High-Cost Users of Healthcare for Atrial Fibrillation
Bibliographic record
Abstract
Background Healthcare resource use for atrial fibrillation (AF) is high, but it may not be equivalent across all patients. We examined whether sex differences exist for AF high-cost users (HCUs), who account for the top 10% of total acute care costs. Methods All patients aged ≥ 20 years who presented to the emergency department (ED) or were hospitalized with AF were identified in Alberta, Canada, between 2011 and 2015. The cohort was categorized by sex into HCUs and non-HCUs. Healthcare utilization was defined as ED, hospital, and physician visits, and costs included those for hospitalization, ambulatory care, physician billing, and drugs. All costs were inflated to 2022 Canadian dollars (CAD$). Results Among 48,030 AF patients, 45.1% were female. Of these, 31.8% were HCUs, and the proportions of female and male patients were equal (31.9% vs 31.7%). Female HCUs were older, more likely to have hypertension and heart failure, and had a higher stroke risk than male HCUs. Mean healthcare utilization did not differ among HCUs by sex, except for number of ED visits, which was higher in male patients (12.7% vs 9.2%, P < 0.0001). Overall, HCUs accounted for 65.8% of the total costs (CAD$3.4 billion). Almost half of total HCU costs were attributable to female HCUs (CAD$966.1 million). Significant differences were present in the distributions of HCU-related costs (male patients: 74.6% hospitalization, 9.5% ambulatory care, 12.4% physician billing, 3.5% drugs; female patients: 77.7% hospitalization, 7.4% ambulatory care, 11.5% physician billing, 3.5% drugs, P < 0.0001). Conclusions Despite having a lower AF prevalence, female patients represent an equal proportion of HCUs, and account for almost half the total HCU costs. Interventions targeted at reducing the number of AF HCU are needed, particularly for female patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".