How is Funding Medical Research Better for Patients?
Bibliographic record
Abstract
With rising health care costs, often health research is viewed as a major cost driver, calling to question the role and value of provincial funding of health research. Most agree that the quality of healthcare provided is directly linked to our ability to conduct quality research; however currently there is little empirical evidence supporting the link between engagement in health research and healthcare performance. In Canada this has resulted in funding for health research that varies over time and between provinces. While medical knowledge is a public good, we hypothesize there are local benefits from health research, such as the attraction of a specialized human capital workforce, which fosters a culture of innovation in clinical practice. To address this question, we look at whether health outcomes are impacted by changes in provincial research funding in Alberta compared to other provinces. Provincial funding for medical research, which varies greatly over time and among provinces, is used as a proxy for medical treatment inputs. Trend rates of reduction in mortality from potentially avoidable causes (MPAC) (comprised of mortality from preventable causes (MPC) and mortality from treatable causes (MTC)), are used as a proxy health outcome measure sensitive to the contributions of technological progress in medical treatment. Our analysis suggests that investment in health research has payback in health outcomes, with greater improvements in the province where the research occurs. The trend declines seen in age standardized MPAC rates in different Canadian provinces may be impacted by shifts in provincial research funding investment, suggesting that knowledge is not transferred without cost between provinces. Up until the mid-1980s, Alberta had the most rapid rate of decline in MPAC compared to the other provinces. This is striking given the large and unique investment in medical research funding in Alberta in the early 1980s through AHFMR, the only provincial health research funding agency at the time. However in recent years, Alberta’s rate of decrease in MPAC has occurred at a rate slower than the other provinces (British Columbia, Ontario or Quebec) with provincial medical research funding. This is striking at a population level, where Alberta’s failure to achieve a reduction in age standardized rates of MTC comparable to British Columbia, Ontario or Quebec after 1985 represents 240 unnecessary deaths in 2011 and 48,250 Potential Life Years Lost worth around $4.8 billion. The findings from our study suggest that some of the divergence in the rates of reduction in MPAC between provinces may be due to beneficial changes in institutional structure and human capital, resulting in differences across provinces in the capacity to adopt new effective healthcare innovations. While health indicators such as MPAC are the result of complex interactions between the patient, treatment and the healthcare system, as well as socioeconomic and demographic factors, this analysis suggests that a different capacity for health research within the provinces impacts health outcomes. The findings from this analysis are limited by the lack of data related to research funding and the health research workforces within provinces. This analysis has important implications for health research policy and funding allocations, suggesting that decision makers should consider the long-term impact provincial funding for health research has on health outcomes. This study also highlights the lack of longitudinal public data available for provincial health research funding. This information is critical to inform future health research policy.
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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 | 0.046 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".