22 research outputs found
Development of Dose-Response Models of Creutzfeldt-Jakob Disease Infection in Nonhuman Primates for Assessing the Risk of Transfusion-Transmitted Variant Creutzfeldt-Jakob Disease
Unconventional settings and uses of human enhancement technologies: A non‐systematic review of public and experts' views on self‐enhancement and DIY biology/biohacking risks
Associations of C-reactive protein and fibrinogen with mortality from all-causes, cardiovascular disease and cancer among U.S. adults
Symposium on Integrating the Science of Environmental Justice into Decision-Making at the Environmental Protection Agency: An Overview
Assessing the Benefits and Costs of Homeland Security Research: A Risk‐Informed Methodology with Applications for the U.S. Coast Guard
Valuing avoided morbidity using meta-regression analysis: what can health status measures and QALYs tell us about WTP?
Many economists argue that willingness-to-pay (WTP) measures are most appropriate for assessing the welfare effects of health changes. Nevertheless, the health evaluation literature is still dominated by studies estimating nonmonetary health status measures (HSMs), which are often used to assess changes in quality-adjusted life years (QALYs). Using meta-regression analysis, this paper combines results from both WTP and HSM studies applied to acute morbidity, and it tests whether a systematic relationship exists between HSM and WTP estimates. We analyze over 230 WTP estimates from 17 different studies and find evidence that QALY-based estimates of illness severity - as measured by the Quality of Well-Being (QWB) Scale - are significant factors in explaining variation in WTP, as are changes in the duration of illness and the average income and age of the study populations. In addition, we test and reject the assumption of a constant WTP per QALY gain. We also demonstrate how the estimated meta-regression equations can serve as benefit transfer functions for policy analysis. By specifying the change in duration and severity of the acute illness and the characteristics of the affected population, we apply the regression functions to predict average WTP per case avoided. Copyright © 2006 John Wiley & Sons, Ltd.