Economic Impact of Subsequent Depression in Patients With a New Diagnosis of Stable Angina: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: Depression is strongly linked to increased morbidity and mortality in patients with chronic stable angina; however, its associated healthcare costs have been less well studied. Our objective was to identify the characteristics of chronic stable patients found to have depression and to determine the impact of an occurrence of depression on healthcare costs within 1 year of a diagnosis of stable angina. METHODS AND RESULTS: In this population-based study conducted in Ontario, Canada, we identified patients diagnosed with stable angina based on angiogram between October 1, 2008, and September 30, 2013. Depression was ascertained by physician billing codes and hospital admission diagnostic codes contained within administrative databases. The primary outcome was cumulative mean 1-year healthcare costs following index angiogram. Generalized linear models were developed with a logarithmic link and γ distribution to determine predictors of cost. Our cohort included 22 917 patients with chronic stable angina. Patients with depression had significantly higher mean 1-year healthcare costs ($32 072±$41 963) than patients without depression ($23 021±$25 741). After adjustment for baseline comorbidities, depression was found to be a significant independent predictor of cost, with a cost ratio of 1.33 (95% confidence interval, 1.29-1.37). Higher costs in depressed patients were seen in all healthcare sectors, including acute and ambulatory care. CONCLUSIONS: Depression is an important driver of healthcare costs in patients following a diagnosis of chronic stable angina. Further research is needed to understand whether improvements in the approach to diagnosis and treatment of depression will translate to reduced expenditures in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".