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Valuing avoided morbidity using meta‐regression analysis: what can health status measures and QALYs tell us about WTP?
Author(s) -
Van Houtven George,
Powers John,
Jessup Amber,
Yang JuiChen
Publication year - 2006
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.1105
Subject(s) - willingness to pay , meta regression , economics , quality adjusted life year , regression analysis , econometrics , regression , population , logistic regression , health economics , meta analysis , eq 5d , welfare , medicine , statistics , actuarial science , health care , health related quality of life , environmental health , mathematics , cost effectiveness , operations management , market economy , disease , pathology , microeconomics , economic growth
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.