The impact of pharmaceutical innovation on the burden of disease in Canada, 2000–2016
Bibliographic record
Abstract
We perform an econometric assessment of the role that pharmaceutical innovation-the introduction and use of new drugs-has played in reducing the burden of disease in Canada, by investigating whether diseases for which more new drugs were launched had larger subsequent reductions in disease burden. Since utilization of a drug reaches a peak about 12-14 years after it was launched, we allow for considerable lags in the relationship between new drug launches and the burden of disease. We analyze the impact of new drug launches on a comprehensive measure of disease burden-the age-standardized disability-adjusted life-years lost (DALY) rate-and on its two components: the age-standardized years of life lost (YLL) and years lost to disability (YLD) rates. We also analyze the impact of new drug launches on the number of hospital discharges and on the average length of hospital stays. The number of DALYs lost is significantly inversely related to the number of drugs that had ever been launched 9-20 years earlier, and the number of YLLs is significantly inversely related to the number of drugs that had ever been launched 11-20 years earlier. The launch of a drug has the largest (most negative) impact on the number of DALYs and YLLs 15 years after it was launched. The estimates indicate that if no drugs had been launched during 1986-2001, the age-standardized DALY rate would not have declined between 2000 and 2016; it might even have increased. Almost all (93%) of the reduction in DALYs was due to a reduction in YLL. The estimates imply that new drug launches during 1986-2001 reduced DALYs in 2016 by 21%, reduced YLLs in 2016 by 28%, and reduced YLDs in 2016 by 3%. We estimate that drugs launched during 1986-2001 reduced the number of DALYs lost in 2016 by 2.31 million. Expenditure in 2016 on drugs launched during 1986-2001 per DALY gained in 2016 from those drugs was 2842 USD. Interventions that avert one DALY for less than average per capita income for a given country or region are generally considered to be very cost-effective; Canada's per capita GDP was 42,158 USD in 2016, so our estimates indicate that the new drugs launched during 1986-2001 were very cost-effective, overall. Moreover, 2842 USD may be an overestimate of the true net cost in 2016 per DALY of drugs launched during 1986-2001. A previous study based on U.S. data showed that about 25% of the cost of new drugs is offset by reduced expenditure on old drugs. Also, our estimates indicate that, if no drugs had been launched during 1986-2001, the average length of 2016 hospital stays would have been about 16% higher. The reduction in hospital expenditure due to shorter average length of stay may have been larger than the expenditure on the drugs responsible for shorter hospital stays.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".