Addressing the affordability of cancer drugs: using deliberative public engagement to inform health policy
Bibliographic record
Abstract
BACKGROUND: Health system expenditure on cancer drugs is rising rapidly in many OECD countries given the costly new treatments and increased rates of use due to a growing and ageing population. These factors put considerable strain on the sustainability of health systems worldwide, sparking public debate among clinicians, pharmaceutical companies, policy-makers and citizens on issues of affordability and equity. We engaged Canadians through a series of deliberative public engagement events to determine their priorities for making cancer drug funding decisions fair and sustainable in Canada's publicly financed health system. METHODS: An approach to deliberation was developed based on the McMaster Health Forum's citizen panels and the established Burgess and O'Doherty model of deliberative public engagement. Six deliberations were held across Canada in 2016. Transcripts were coded in NVivo and analysed to determine where participants' views converged and diverged. Recommendations were grouped thematically. RESULTS: A total of 115 Canadians participated in the deliberative events and developed 86 recommendations. Recommendations included the review and regular re-review of approved drugs using 'real-world' evidence on effectiveness and cost-effectiveness; prioritisation of treatments that restore patients' independence, mental health and general well-being; ensuring that decision processes, results and their rationales are transparent; and commitment to people with similar needs receiving the same care regardless of where in Canada they live. CONCLUSIONS: The next steps for policy-makers should be to develop mechanisms for (1) re-reviewing effectiveness and cost-effectiveness data for all cancer drugs; (2) making disinvestments in cancer drugs that satisfy requirements relating to grandfathering and compassionate access; (3) ensuring fair and equitable access to cancer drugs for all Canadians; and (4) fostering a pan-Canadian approach to cancer drug funding decisions.
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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 | 0.104 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".