Tuesday, October 21, 2014: 3:45 PM

Alexander Thompson, BA, MSc1, Katherine Payne, BPharm, MSc, PhD2, Moray Nairn, MA, PhD3, PhIl Alderson, PhD4, Matthew Sutton, BA, MSc, PhD1 and Bruce Guthrie, MB BChir, MRCGP, PhD5, (1)The University of Manchester, Manchester, United Kingdom, (2)Manchester Centre for Health Economics, The University of Manchester, Manchester, United Kingdom, (3)Healthcare Improvement Scotland, Edinburgh, United Kingdom, (4)National Institute for Health and Care Excellence, Manchester, United Kingdom, (5)University of Dundee, Dundee, United Kingdom
Purpose: Healthcare interventions can have immediate and persistent harms but deferred benefits, with the risk of harm greater in multimorbid and older populations. The ‘pay-off time’ framework, defined as the time when the cumulative incremental benefits outweigh the cumulative incremental harms for an intervention, offers a potentially useful tool to inform the development of national guidelines and clinical decision-making, with particular relevance when prioritising treatments.  This study aimed to demonstrate the application of the pay-off time framework to an existing economic model which captures benefit and harms over time for a population requiring prescribed treatments for hypertension.

Method: A Markov model (six-month cycle-length; life-time horizon; NHS perspective), developed by the National Clinical Guideline Centre to inform the National Institute for Health and Care Excellence (NICE) guidelines, on treatment options for adults with primary hypertension (CG127) was used as the basis for this analysis. The model structure captured seven relevant health states for patient cohorts (starting age 55-years) with hypertension prescribed either a: thiazide diuretic (TD); calcium-channel blocker (CCB); beta-blocker (BB); angiotensin-converting enzyme inhibitor/angiotensin-II receptor blocker (ACE/ARB). The relevant comparator was defined as ‘no intervention’. Pay-off times were numerically captured, by assessing when the cumulative benefits associated with treatment exceeded the cumulative harms. Both benefits and harms were measured using the quality-adjusted life-year.  The model was re-run (‘iteration’) for: starting age; patient gender; assumed harm-related utility decrement (0 or 0.01 per cycle). Adaptations were also made to the Excel® produced Markov model, to generate visual profiles of cumulative QALY gains which clearly show the benefit/harm trade-off over time. 

Result: The pay-off time was calculated for each iteration of the model. In the base-case analysis (no harms; utility decrement = 0) the estimated pay-off time was positive in cycle one (at six-months) for each of the five anti-hypertensives in a 55-year old male patient cohort. In comparison, when there was an assumed ongoing treatment-induced harm (constant 0.01 utility decrement), the estimated pay-off time was: 8.3 years for CCB; 8.6 years for ACE/ARB; 8.7 years for TD; 10.1 years for BB.

Conclusion: This study illustrates the potential for implementing quantitative estimates of the ‘pay-off time’, together with visual cumulative QALY-gain profiles, within current economic models. Both concepts represent potentially useful tools to aid decision-making when prioritising treatment options.