• Lancet · Dec 2024

    Population-health impact of new drugs recommended by the National Institute for Health and Care Excellence in England during 2000-20: a retrospective analysis.

    • Huseyin Naci, Peter Murphy, Beth Woods, James Lomas, Jinru Wei, and Irene Papanicolas.
    • Department of Health Policy, London School of Economics and Political Science, London, UK. Electronic address: h.naci@lse.ac.uk.
    • Lancet. 2024 Dec 12.

    BackgroundHealth systems experience difficult trade-offs when paying for new drugs. In England, funding recommendations by the National Institute for Health and Care Excellence (NICE) for new drugs might generate health gains, but inevitably result in forgone health as the funds cannot be used for alternative treatments and services. We aimed to evaluate the population-health impact of NICE recommendations for new drugs during 2000-20.MethodsFor this retrospective analysis, we identified technology appraisals for new drugs in England published in NICE's publicly available database of appraisals between 2000 and 2020. We excluded products with terminated appraisals, not recommended, or subsequently withdrawn from the market and excluded appraisals in programmes focusing on medical devices, diagnostics, or interventional procedures. We included drugs that underwent NICE appraisal within 5 years of initial regulatory approval. We collected data on drug name, appraised indication, and specific features of both the drug and its appraisal. We noted the value for money offered by new drugs, expressed as the incremental cost-effectiveness ratio (ICER), and data on health benefits, expressed as quality-adjusted life-years (QALYs). We estimated the number of patients receiving new drugs recommended by NICE using proprietary data on the total volumes of new drugs sold in England between Jan 1, 2000, and Dec 31, 2020. We calculated the net health effect of each appraisal using the difference between the incremental QALY gains from implementing the new drug within the National Health Service (NHS) and the estimated QALYs that could hypothetically be obtained by reallocating the same funds to other NHS services or treatments. We obtained forgone QALYs by dividing the incremental cost of the new drug by the health-opportunity cost of NHS expenditure.FindingsNICE appraised 332 unique pharmaceuticals between 2000 and 2020; 276 (83%) had positive recommendations. Of these 276, 207 (75%) had a NICE appraisal within 5 years of regulatory approval. We included 183 (88%) of 207 drugs in this analysis, after excluding drugs that did not meet eligibility criteria. The median QALY gain across all 339 appraisals was 0·49 (IQR 0·15-1·13), equivalent to an additional half a year in full health. Median ICER for recommending new drugs increased from £21 545 (IQR 14 175-26 173) per QALY gained for 14 appraisals published between 2000 and 2004 to £28 555 (19 556-33 712) for 165 appraisals published between 2015 and 2020 (p=0·014). Median ICER varied by therapeutic area, ranging from £6478 (3526-12 912) for 12 appraisals of anti-infective drugs to £30 000 (22 395-45 870) for 144 appraisals of oncology drugs (p<0·0001). New drugs generated an estimated 3·75 million additional QALYs across 19·82 million patients who received new drugs recommended by NICE. The use of new drugs resulted in an estimated additional cost to the NHS of £75·1 billion. If the resources allocated to new drugs had been spent on existing services in the NHS, an estimated 5·00 million additional QALYs could have been generated during 2000-20. Overall, the cumulative population-health impact of drugs recommended by NICE was negative, with a net loss of approximately 1·25 million QALYs.InterpretationDuring 2000-20, NHS coverage of new drugs displaced more population health than it generated. Our results highlight the inherent trade-offs between individuals who directly benefit from new drugs and those who forgo health due to the reallocation of resources towards new drugs.FundingThe Commonwealth Fund.Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.

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