Distribution-Weighted Cost-Effectiveness Analysis Using Lifetime Health Loss

Ulrikke J. V. Hernæs (), Kjell A. Johansson, Trygve Ottersen and Ole F. Norheim
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Ulrikke J. V. Hernæs: Haukeland University Hospital
Kjell A. Johansson: University of Bergen
Trygve Ottersen: University of Bergen
Ole F. Norheim: Haukeland University Hospital

PharmacoEconomics, 2017, vol. 35, issue 9, No 10, 965-974

Abstract: Abstract Background It is widely acknowledged that concerns for the worse off need to be integrated with the concern for cost effectiveness in priority setting, and several countries are seeking to do so. In Norway, a comprehensive framework for priority setting was recently proposed to specify the worse off in terms of lifetime loss of quality-adjusted life-years (QALYs). However, few studies have shown how to calculate such health losses, how to integrate health loss into cost-effectiveness analyses (CEAs) and how such integration impacts the incremental cost-effectiveness ratios (ICERs). The aim of this study was to do so. Methods The proposed framework was applied to data from 15 recent economic evaluations of drugs. Available data were used to calculate the lifetime health loss of the target groups, and the proposed marginal weighting function was employed to adjust standard ICERs according to the size of this loss. Standard and weighted ICERs were compared to a threshold of US$35,000 per QALY gained. Results Lifetime health loss can be calculated with the use of available data and integrated by a marginal weighting function with CEAs. Such integration affected standard ICERs to a varying degree and changed the number of interventions considered cost effective from three to eight. Conclusion Calculation of lifetime health loss and its integration with CEA is feasible and can influence the reimbursement and ranking of interventions. To facilitate regular integration, guidelines for economic evaluations could require (i) adjustment according to distributional concerns and (ii) that data on health loss are extracted directly from the models and reported. Generic databases on health loss could be developed alongside such efforts.

Date: 2017
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DOI: 10.1007/s40273-017-0524-2

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