Evaluation of out‐of‐pocket pay genetic testing in a publicly funded healthcare system
Bibliographic record
Abstract
When genetic tests are not funded publicly, out-of-pocket (OOP) pay options may be discussed with patients. We evaluated trends in genetic testing and OOP pay for two publicly funded British Columbia clinical programs serving >12 000 patients/year (The Hereditary Cancer Program [HCP] and Provincial Medical Genetics Program [PMGP]) between 2015-2019. Linear and regression models were used to explore the association of OOP pay with patient demographic variables at HCP. An interrupted time series and linear and logistic regression models were used on PMGP data to examine the effect of a change in the funding body. The total number of tests completed through PMGP, and HCP increased by 260% and 320%, respectively. OOP pay increased at HCP by 730%. The mean annual income of patients who paid OOP at HCP was ≥$3500 higher than in the group with funded testing (p < 0.0001). The likelihood of OOP pay increased at PMGP before the funding body change (OR per month: 1.07; 95% CI: 1.04, 1.10); while this likelihood had an immediate 87% drop when the change occurred (OR: 0.13; 95% CI: 0.06, 0.32). Patients with higher incomes are more likely to pay OOP. Financial barriers can create disparities in clinical outcomes. Funding decisions have a significant impact on rate of OOP pay.
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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 | 0.074 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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; both teacher heads agree on what is shown here.
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".