Access to novel drugs and therapeutics for children and youth: Eliciting citizens' values to inform public funding decisions
Bibliographic record
Abstract
INTRODUCTION: The unique evidentiary, economic and ethical challenges associated with health technology assessment (HTA) of precision therapies limit access to novel drugs and therapeutics for children and youth, for whom such challenges are amplified. We elicited citizens' perspectives about values-based criteria relevant to the assessment of paediatric precision therapies to inform the development of a child-tailored HTA framework. METHODS: We held four citizen panels virtually in May-June 2021, informed by a plain-language citizen brief summarizing global and local evidence about the challenges, policy and programmatic options and implementation strategies related to enhancing access to precision therapies for Canadian children and youth. Panellists were recruited through a nationally representative database, medical/patient networks and social media. We inductively coded and thematically analysed panel transcripts to generate themes and identify priority values. RESULTS: The perspectives of panellists (n = 45) coalesced into four overlapping themes, with attendant subthemes, relevant to a child-tailored HTA framework: (1) Childhood Distinctions: vulnerability, 'fair innings', future potential, family impacts; (2) Voice: agency of children and youth; lived versus no lived experience; (3) One versus Many: disease severity, rarity, equity, unmet need and (4) Health System Governance: funding, implementation inequities, effectiveness and safety. Participants broadly agreed that childhood distinctions, particularly family impacts, justify child-tailored HTA. Dissent arose over whose voice should inform HTA and how such perspectives are best incorporated. CONCLUSIONS: Citizens can offer unique insights into criteria relevant to the development or revision of HTA frameworks to capture holistic, societally responsive dimensions of value attached to unique contexts or populations, including children. Balancing the hopes and expectations of patients and caregivers for access to expensive but potential life-altering therapies against the opportunity costs borne by encompassing health systems is a fundamental challenge that will require rigorous methods to elicit, weigh and reconcile varied views. PATIENT OR PUBLIC CONTRIBUTION: A patient advocate served on the steering committee of this study and co-authored this article. Key informants for the Citizen Brief included patient advocates and caregivers; a separate patient advocate reviewed the Brief before dissemination. Qualitative and quantitative data were collected from the general public and caregivers of children, with written consent.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".