Abstract
Background: A common way of describing UK National Institute for Health and Clinical Excellence (NICE) decisions is to distinguish between cases where NICE recommended use of a healthcare technology by all relevant patients (‘yes’); those where it did not recommend use (‘no’); and those where its decisions are a mixture of ‘yes’ to some patient subgroups, and ‘no’ to others. Over half of NICE’s decisions are of this mixed type, which involve restricting (or‘optimizing’) patient use in some way.
Objective: To report an attempt to develop a robust and defensible means of measuring and describing the degree of patient access in mixed NICE decisions.
Methods: A list of mixed decisions made from 2006 to the end of 2009 was identified using HTAinSite™. The following calculation was used: M= (p/P) × 100, where M is a measure of the level of patient access (0 = no access, 100 = full access), P is the set of patients considered in the guidance as Potential candidates for treatment (given the licensed use and the scope of NICE’s appraisal), and p is a subset of those patients, for whom NICE did recommend treatment.Mcan be estimated either for a specific product or for a group of technologies (Multiple Technology Appraisals). Both productspecific and overall M were estimated, using estimates of p obtained from NICE costing templates. These data are subject to some important limitations, so the results should be regarded as illustrative.
Results: Of the 69 medicines that have received a mixed decision since January 2006, 34 included details that allowed the estimation of M. Of these 34 decisions, 24 (71%) had a product-specific M ≤50, 16 (47%) M ≤25 and 11 (32%) M ≤10. That is, in just under three-quarters of the mixed decisions for which P and p were available, NICE recommended use for less than half of patients for whom the medicine is licensed, and in nearly one-third of these sorts of decisions, NICE recommended use in ≤10% of potential patients. The estimates of M for groups of technologies provide a slightly different picture: for example, grouped M was ≤10 in <20% of decisions.
Conclusions: The measure of patient access, M, proposed here has the potential to provide a more informative way of reporting all NICE decisions, particularly ‘restricted’ (or ‘optimized’) decisions.
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Acknowledgements
The authors acknowledge with thanks, helpful comments received on an earlier draft of this paper from Jon Sussex and Adrian Towse (Office of Health Economics), Peter Littlejohn and Sarah Garner (NICE) and three anonymous referees for PharmacoEconomics. The usual disclaimers apply; in particular, responsibility for the conclusions drawn in this paper rests solely with the authors. We are grateful to HTAinSite™ for providing access to its data. This work was undertaken as part of the Office of Health Economics’ ongoing research on HTA decision making, which is funded in part by the Association of the British Pharmaceutical Industry (ABPI).
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O’Neill, P., Devlin, N.J. An Analysis of NICE’s ‘Restricted’ (or ‘Optimized’) Decisions. Pharmacoeconomics 28, 987–993 (2010). https://doi.org/10.2165/11536970-000000000-00000
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DOI: https://doi.org/10.2165/11536970-000000000-00000