Estimating the payoffs from cardiovascular disease research in Canada: an economic analysis
Bibliographic record
Abstract
BACKGROUND: Investments in medical research can result in health improvements, reductions in health expenditures and secondary economic benefits. These "returns" have not been quantified in Canada. Our objective was to estimate the return on cardiovascular disease research funded by public or charitable organizations. METHODS: Our primary outcome was the internal rate of return on cardiovascular disease research funded by public or charitable sources. The internal rate of return is the annual monetary benefit to the economy for each dollar invested in cardiovascular disease research. Calculation of the internal rate of return involved the following: measuring expenditures on cardiovascular disease research, estimating the health gains accrued from new treatments for cardiovascular disease, determining the proportion of health gains attributable to cardiovascular disease research and the time lag between research expenditures and health gains, and estimating the spillovers from public- or charitable-sector investments to other sectors of the economy. RESULTS: Expenditures by public or charitable organizations on cardiovascular disease research from 1981 to 1992 amounted to $392 million (2005 dollars). Health gains associated with new treatments from 1994 to 2005 (13-yr lag) amounted to 2.2 million quality-adjusted life-years. We calculated an internal rate of return of 20.6%. CONCLUSION: Canadians obtain relatively high health and economic gains from investments in cardiovascular disease research. Every $1 invested in cardiovascular disease research by public or charitable sources yields a stream of benefits of roughly $0.21 to the Canadian economy per year, in perpetuity.
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 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".