Improving the Affordability of Prescription Medications for People with Chronic Respiratory Disease: An Official American Thoracic Society Policy Statement
Bibliographic record
Abstract
BACKGROUND: Mounting evidence indicates that out-of-pocket costs for prescription medications, particularly among low- and middle-income patients with chronic diseases, are imposing financial burden, reducing medication adherence, and worsening health outcomes. This problem is exacerbated by a paucity of generic alternatives for prevalent lung diseases, such as asthma and chronic obstructive pulmonary disease, as well as high-cost medicines for rare diseases, such as cystic fibrosis. Affordability and access challenges are especially salient in the United States, as citizens of many other countries pay lower prices for and have greater access to prescription medications. METHODS: The American Thoracic Society convened a multidisciplinary committee comprising experts in health policy pharmacoeconomics, behavioral sciences, and clinical care, along with individuals providing industry and patient perspectives. The report and its recommendation were iteratively developed over a year of in-person, telephonic, and electronic deliberation. RESULTS: The committee unanimously recommended the establishment of a publicly funded, politically independent, impartial entity to systematically draft evidence-based pharmaceutical policy recommendations. The goal of this entity would be to generate evidence and action steps to ensure people have equitable and affordable access to prescription medications, to maximize the value of public and private pharmaceutical expenditures on health, to support novel drug development within a market-based economy, and to preserve clinician and patient choice regarding personalized treatment. An immediate priority is to examine the evidence and make recommendations regarding the need to have essential medicines with established clinical benefit from each drug class in all Tier 1 formularies and propose recommendations to reduce barriers to timely generic drug availability. CONCLUSIONS: By making explicit, evidence-based recommendations, the entity can support the establishment of coherent national policies that expand access to affordable medications, improve the health of patients with chronic disease, and optimize the use of public and private resources.
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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 | 0.114 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.113 | 0.065 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".