Modelling the potential financial impacts of expanding access to immune checkpoint inhibitors as monotherapy for treating advanced non-small cell lung cancer
Bibliographic record
Abstract
Background: Access to immune checkpoint inhibitors remains limited due to cost-effectiveness and affordability concerns. This study evaluates the financial impacts of expanding global access to PD1/PD-L1 inhibitors as first-line monotherapy for patients aged 40-74 years with advanced unresectable non-small cell lung cancer (NSCLC), with wildtype EGFR and ≥50% of tumour cells with PD-L1 expression. Methods: The potential usage and associated costs were assessed from 2024 to 2040 through repeated cross-sectional assessments. The base case assumed treatment rates in countries with access to PD1/PD-L1 inhibitors in 2023, while expanded-access scenarios projected coverage increases to 30% in low-income, 50% in lower-middle-income, 80% in upper-middle-income (UMICs), and 95% in high-income countries over 10 years. Findings: The model estimated that 200,000-250,000 individuals are treatment-eligible, with only about one-fifth receiving PD1/PD-L1 inhibitors in the base case. Expanding access would increase global treatment coverage to 75% by 2040, particularly in middle-income countries. The largest increases would be in UMICs (+100,700) and the Western Pacific region (+82,400). At an estimated per-patient lifetime cost of US$37,600-US$75,100, total costs could reach US$14,087 million with fixed dosing, or US$9080 million with weight-based dosing. PD-L1 testing costs would add <1% to the total. Interpretation: Expanding access to PD1/PD-L1 inhibitors for advanced NSCLC over 10 years demands significant funding, making equitable access in lower-income countries doubtful without a significant price reduction. Policymakers should negotiate lower prices to ensure cost-effectiveness and affordability, improve spending efficiency by optimised dosing and treatment duration, and enhance health system capacity, including ensuring appropriate use and introducing biosimilars. Funding: This publication was made available as open access through WHO funding provided by two projects: the Universal Health Coverage Partnership (Award 74812, the European Union, the Grand Duchy of Luxembourg, Irish Aid, the Government of Japan, the French Ministry for Europe and Foreign Affairs, the United Kingdom's Foreign, Commonwealth & Development Office, the Government of Belgium, the Government of Canada, and the Government of Germany) and the Increasing Global Equitable Access to Health Products & Health Technologies project (Award 72913, the Government of Belgium).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".