The current and future financial burden of hospital admissions for heart failure in Canada: a cost analysis
Bibliographic record
Abstract
BACKGROUND: Heart failure is a costly health condition and a major public health concern. We sought to examine the costs of hospital admissions for heart failure between fiscal years 2004 and 2013 in Canada and to model the future costs to 2030. METHODS: Canadian Institutes for Health Information Discharge Abstract Database was used to identify admissions to hospital with heart failure as the primary diagnosis between fiscal years 2004 and 2013. Multiple linear regression models were used to calculate the trend in prevalence and extrapolate these to 2030. Canadian Institutes for Health Information patient cost estimates were used to identify costs of hospital admissions for heart failure. Generalized linear models were used to estimate average annual costs per heart failure patient. We conducted a sensitivity analysis including all admissions for heart failure in any diagnostic field. RESULTS: In 2013, 45 600 (95% confidence interval [CI]: 43 800-47 200) patients were admitted with heart failure as the primary diagnosis, accounting for $482 (95% CI $464-$500) million. By 2030, we estimate 54 000 (95% CI 49 000-60 000) patients and costs of $722 (95% CI $650-$801) million, with older adults (age ≥ 80 yr) accounting for 52% of costs. Including admissions for which heart failure was a secondary diagnosis increases the total cost to $2.8 (95% CI $2.6-$3.0) billion in 2030. INTERPRETATION: As in other developed countries, hospital costs related to heart failure in Canada are on the rise. Older adults are the main consumers of such hospital services. Strategies to improve outpatient care to reduce rates of admission for heart failure are needed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".