The current and future total health care costs of atrial fibrillation
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, yet detailed information on the cost burden remains sparse. These data are essential for determining resource allocation, benchmarking care, identifying areas for more efficient healthcare delivery and to measure effects of alternative treatment strategies. Purpose We sought to examine costs for inpatient, ambulatory, physician, and drugs for AF and model future costs. Methods In this retrospective population-based cohort study, we used linked administrative databases to identify all adult patients presenting to any healthcare setting with nonvalvular AF (NVAF) as the most responsible diagnosis in our city, Canada, from fiscal years 2010-2018. Costs for inpatient, ambulatory, physician, and drugs were estimated, in 2019 Canadian dollars, by using Statistics Canada Consumer Price Index. A two-part cost model with logit and gamma generalized linear model was developed to predict costs from 2019 to 2030. Results There were 48,854 NVAF patients. The median age was 70 [59.0,80.0] years, 55% were male, and median CHADS-Vasc score = 3.0 [1.0,4.0]. NVAF-related costs were $1.4 billion dollars (inpatient $1.1 billion, ambulatory $107.0 million, physician $59.6 million, drugs $140.1 million) and represented 36.8% of total costs (Figure 1). The per patient cost of NVAF was $30.1 thousand. Over the study period, costs increased by 2.2% for inpatient, 40.5% for ambulatory, 67.1% for physician, and 214.2% for drugs. By 2030, we estimate there will be 54,523 NVAF patients. NVAF-related costs from 2019 to 2030 are estimated to be $3.6 billion (inpatient $2.5 billion, ambulatory $310.5.0 million, physician $197.5 million, drugs $610.4 million) and will represent 32.1% of total costs (Figure 1). Conclusions Costs for AF are on the rise and the distribution of costs are changing. Although inpatient costs represent the highest proportion of total AF costs, they are projected to decrease while costs due to ambulatory, physician and drug costs are increasing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".