The Importance of Indirect Costs in Primary Cardiovascular Disease Prevention
Bibliographic record
Abstract
BACKGROUND: The losses in productivity due to cardiovascular disease (CVD) are substantial but rarely considered in health economic analyses. We compared the cost-effectiveness of lipid level modification in the primary prevention of CVD with and without these indirect costs. METHODS: We used the Cardiovascular Life Expectancy Model to estimate the long-term benefits and cost-effectiveness of lipid level modification with atorvastatin calcium, including 28% and 38% reductions in total cholesterol and low-density lipoprotein cholesterol levels, respectively, and a 5.5% increase in high-density lipoprotein cholesterol level. The direct costs included all medical care costs associated with CVD. The indirect costs represented the loss of employment income and the decreased value of housekeeping services after different manifestations of CVD. All costs were expressed in 2000 Canadian dollars. RESULTS: When only direct medical care costs were considered, the incremental cost-effectiveness ratios for lifelong therapy with atorvastatin calcium, 10 mg/d, were generally positive, ranging from a few thousand to nearly $20 000 per year of life saved. When the societal point of view was adopted and indirect costs were included, the total costs were generally negative, representing substantial cost savings (up to $50 000) and increased life expectancy for most groups of individuals. CONCLUSIONS: Lipid therapy with statins can reduce CVD morbidity and mortality as demonstrated in a number of clinical trials. Adding the indirect CVD costs associated with productivity losses at work and home can result in forecasted cost savings to society as a whole such that lipid therapy could potentially save lives and money.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".