Predictive Genetic Tests and Health Care Costs: A Policy Framework and Illustrative Estimates
Bibliographic record
Abstract
Genetic testing has long been part of Canada’s health system, but the scope of genetic testing is growing into new areas. Whereas traditional tests predominantly foretell the health of future generations, new tests increasingly tell individuals about their own health and risks. And whereas traditional tests have focused on rare, single-gene, genetically determined disorders, new tests target common, complex, and multifactorial diseases in which genetics plays only a part. These trends lead to unprecedented clinical and popular interest in genetic tests, and the expanded use of testing will affect both population health and health care costs. Whether the net effects will be positive or negative is a matter of heated debate. Early policy decisions about how tests will be disseminated, provided, and funded will greatly influence the cost and other impacts of new predictive genetic tests. This report examines the potential effect of new predictive genetic test services on health care costs. We offer a general framework that identifies key factors determining the cost impact of a predictive genetic test service and suggests how the choices of health system decision makers influence costs. We also present cost analyses of four specific predictive genetic tests. The report focuses solely on financial cost implications from the formal health care system’s point of view, and does not address the very important questions of impacts on health, wellbeing, productivity, societal costs, or informal care giving. The cost impact of a predictive genetic test depends on, among other things, characteristics of the test, the scope of its application, and the changes in health care utilization (disease surveillance, prevention, and treatment) induced by the test result. For many tests, the cost of performing the test itself makes up only a small proportion of the total health care costs that follow from its use.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".