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March 2003

Valuation of Morbidity Losses: Meta-Analysis of Willingness-to-Pay

and Health Status Measures

Final Report Task Order 2

Prepared for

Amber Jessup Food and Drug Administration

Center for Food Safety and Applied Nutrition HFS-726

5100 Paint Branch Parkway College Park, MD 20740

Prepared by

George Van Houtven Matthew Rousu Jui-Chen Yang

Charles Pringle Wanda Wagstaff

Jason DePlatchett RTI

Health, Social, and Economics Research Research Triangle Park, NC 27709

Contract Number 223-01-2466

RTI Project Number 08184.002

cannada

Contract Number 223-01-2466 RTI Project Number 08184.002

Valuation of Morbidity Losses: Meta-Analysis of Willingness-to-Pay

and Health Status Measures

Final Report Task Order 2

March 2003

Prepared for

Amber Jessup

Food and Drug Administration Center for Food Safety and Applied Nutrition

HFS-726 5100 Paint Branch Parkway

College Park, MD 20740

Prepared by

George L. Van Houtven Matthew Rousu Jui-Chen Yang

Charles Pringle Wanda Wagstaff

Jason DePlatchett RTI

Health, Social, and Economics Research Research Triangle Park, NC 27709

iii

Contents

1. Introduction 1-1

1.1 Background....................................................................... 1-2

1.2 Summary of Results ........................................................... 1-3

2. Conceptual Framework for Morbidity Valuation 2-1

2.1 Basic Framework for Health Valuation............................... 2-1

2.2 Extensions of the Basic Framework .................................... 2-3

2.2.1 Health Production ................................................. 2-4

2.2.2 Uncertainty ........................................................... 2-6

2.2.3 Lifetime Utility and QALYs .................................... 2-8

2.3 Determinants of Health Values ........................................ 2-13

2.3.1 Measures of Health Changes ................................ 2-13

2.3.2 Study Population Characteristics .......................... 2-19

2.3.3 Price Effects ......................................................... 2-20

2.3.4 Valuation Method................................................ 2-20

2.4 Summary ........................................................................ 2-22

3. Analytical Approach—Meta-Analysis 3-1

3.1 Meta-Analysis in Nonmarket Valuation.............................. 3-1

3.2 Procedures for Conducting Meta-Analyses ......................... 3-3

4. Data Collection and Evaluation 4-1

4.1 Review and Selection of Studies on WTP for Improved Health............................................................................... 4-2

4.1.1 Literature Search and Screening ............................. 4-2

4.1.2 Annotated Bibliography of Selected Studies............ 4-3

iv

4.2 WTP for Health Database .................................................. 4-4

4.2.1 Database Design.................................................... 4-5

4.2.2 Data Summary and Evaluation ............................. 4-20

4.3 Database of Health Status Measures................................. 4-23

4.3.1 Overview of HSMs .............................................. 4-23

4.3.2 MAUS Database Description................................ 4-31

5. Meta-Analysis Results 5-1

5.1 Meta-Analysis of Value Estimates for Acute Effects ............. 5-1

5.1.1 Data Selection and Description.............................. 5-2

5.1.2 Meta-Regression Models and Results...................... 5-6

5.1.3 Implications of Results for Benefit Transfer ........... 5-16

5.2 Meta-Analysis of Value Estimates for Chronic Effects ........ 5-22

5.2.1 Data Selection and Description............................ 5-22

5.2.2 Meta-Regression Models and Results.................... 5-27

5.3 Summary and Conclusions .............................................. 5-30

6. Summary and Discussion of Results 6-1

6.1 Illustrative Applications of the Estimated Benefit Transfer Function for Acute Effects..................................... 6-3

6.2 Conclusions ...................................................................... 6-6

References R-1

Appendixes

A Bibliography and Summary of Morbidity Valuation Studies ..............................................................................A-1

B Annotated Bibliography of Publications Included in the Morbidity Value Meta-Analyses ......................................... B-1

C Summary Statistics for the Morbidity Value Database .........C-1

v

Figures

Figure 4-1 Three-Level Database Design ..................................................... 4-5

vi

Tables

Table 2-1 Comparison of Health Status Measures..................................... 2-14

Table 4-1 Number of Publications per Study .............................................. 4-6

Table 4-2 Number of Value Estimates per Publication ................................ 4-6

Table 4-3 Study-Level Data Fields (Spreadsheet 1)..................................... 4-6

Table 4-4 Publication-Level Data Fields (Spreadsheet 2)............................. 4-7

Table 4-5 Value-Level Data Fields (Spreadsheet 3.1) .................................. 4-9

Table 4-6 Value-Level Data Fields (Spreadsheet 3.2) ................................ 4-11



Table 4-9 Value-Level Data Fields—Stated Preference Methods (Spreadsheet 3.5) ..................................................................... 4-18

Table 4-10 Value-Level Data Fields—Hedonic Method (Spreadsheet 3.6) ..................................................................... 4-19

Table 4-11 Value-Level Data Fields—Averting Behavior Method (Spreadsheet 3.7) ..................................................................... 4-20

Table 4-12 Number of Publications by Year............................................... 4-21

Table 4-13 Number of Publications by Type of Publication ........................ 4-21

Table 4-14 Valuation Methods Used (Number of Value Estimates per Method.................................................................................... 4-21

Table 4-15 Number of Value Estimates by Country .................................... 4-22

Table 4-16 Number of Value Estimates by Type of Health Condition Valued..................................................................................... 4-23

Table 4-17 Symptom and Problem Complexes (CPX) for the Quality of Well-Being Scale ..................................................................... 4-25

Table 4-18 Dimensions, Function Levels, and Weights of the Quality of Well-Being Scale ..................................................................... 4-26

Table 4-19 Multiattribute Health Status Classification System: Health Utilities Index Mark 3 (HUI-3) .................................................. 4-28

vii

Table 4-20 The EuroQol Descriptive System .............................................. 4-31

Table 4-21 Summary of MAUS Studies and Scores for Selected Health Conditions ............................................................................... 4-33

Table 5-1 Descriptions of Variables Used in the Meta-Analysis................... 5-4

Table 5-2 Summary Statistics for Variables Used in the Meta-Analysis ........ 5-5

Table 5-3 Meta-Regression Results—WTP for Avoided Acute Effects Using the Total QWB Score ....................................................... 5-9

Table 5-4 Meta-Regression Results—WTP for Avoided Acute Effects Using the Total QWB Score ..................................................... 5-10

Table 5-5 Meta-Regression Results—WTP for Avoided Acute Effects Using the Four-Dimensional QWB Scores ................................ 5-14

Table 5-6 Meta-Regression Results—WTP for Avoided Acute Effects and Four-Dimensional QWB Scores ......................................... 5-15

Table 5-7 Benefit Transfer Function Estimates........................................... 5-18

Table 5-8 Out-of-Sample WTP Predictions with BT Function 1................. 5-19

Table 5-9 Out-of-Sample WTP Predictions with BT Function 2................. 5-20

Table 5-10 Chronic Health Effect Descriptions and Scores.......................... 5-24

Table 5-11 Descriptions of Variables Used in the Meta-Analysis................. 5-26

Table 5-12 Summary Statistics for Variables Used in the Meta-Analysis ...... 5-27

Table 5-13 Meta-Regression Results—WTP for Avoided Acute Effects and Total QWB Score .............................................................. 5-28

Table 6-1 Three Illustrative Applications of the Meta-Analytic Benefit Transfer Function for Acute Effects.............................................. 6-5

1-1

1 Introduction

A primary objective of the Food and Drug Administration’s (FDA’s) Center for Food Safety and Applied Nutrition (CFSAN) is to protect and improve public health through a variety of food safety regulations. It is well recognized, however, that regulatory actions will typically impose both costs and benefits on society. Therefore, CFSAN has the responsibility to develop methods for accurately assessing these costs and benefits.

The purpose of this project is to assist CFSAN in strengthening its capabilities for assessing the health benefits, in monetary terms, of its regulatory alternatives. To conduct regulatory impact analyses (RIAs), CFSAN must have at its disposal reliable and cost-effective benefits assessment methods. Unfortunately, CFSAN does not have the resources to conduct original research on the value of all of the health outcomes affected by its actions. Consequently, it must make best use of the existing research on health valuation to inform its decisions. In other words, it must rely to a large extent on “benefit transfer” approaches.

This report summarizes RTI’s efforts and results in developing a systematic benefit transfer method for valuing changes in morbidity. This method is based on an integrated statistical analysis (“meta-analysis”) of results from the existing health valuation literature. By combining the findings from multiple studies, we are able to specify and demonstrate a benefit transfer function for acute health effects. We are also able to compare value estimates based on this function with estimates based on separate benefit transfer approaches that CFSAN has used for recent regulatory analysis. In addition, the

The purpose of this project is to assist CFSAN in strengthening its capabilities for assessing the health benefits, in monetary terms, of its regulatory alternatives.

Valuation of Morbidity Losses: Meta-Analysis of Willingness-to-Pay and Health Status Measures

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meta-analysis for acute effects lays an important foundation for assessing values for chronic health effects as well.

1.1 BACKGROUND Regulatory action by CFSAN can protect against a wide variety of adverse health outcomes. Morbidity outcomes can range from short-term acute conditions, such as incidents of food poisoning or allergic reactions, to long-term chronic conditions, such as reactive arthritis from food contamination or diabetes associated with poor nutrition. These outcomes can also vary considerably in terms of their severity, and in more extreme cases, they can progress beyond illness (morbidity) and cause death (mortality).

Assessing the health benefits of its regulations is a challenge for CFSAN in part because the monetary values for avoiding many of these health effects have not been well quantified. This lack of value information is particularly the case for morbidity outcomes. Although valuation of mortality effects continues to be somewhat controversial and contains important areas of uncertainty, compared to morbidity valuation it is relatively well researched and summarized (Viscusi, 1993; Mrozek and Taylor, 2002).

A number of general methods for valuing morbidity effects exists; however, each has important disadvantages. As we discuss in more detail in Section 2 of this report, it is generally accepted that a complete accounting of losses due to ill health must capture direct costs (e.g., medical expenditures), indirect costs (e.g., lost income or productivity), and nonpecuniary losses such as those from pain and suffering. Cost of illness (COI) methods are often used to monetize losses from illness, but these methods do not capture the potentially important category of nonpecuniary losses. As we also discuss in Section 2, methods that focus on measuring individual’s willingness to pay (WTP) for avoiding illness are considered to be more conceptually correct and comprehensive. However, one of the main drawbacks of these methods is that they are relatively expensive to implement.

To address resource constraints and make best use of existing research, CFSAN and other regulatory agencies must often use benefit transfer approaches. These approaches involve identifying, selecting, and adapting value estimates from studies conducted in

To address resource constraints and make best use of existing research, CFSAN and other regulatory agencies must often use benefit transfer approaches.

Section 1 — Introduction

1-3

one context and applying them to estimate the benefits of effects (usually policy related) in a separate context. Transferring values from one context to another inevitably introduces additional uncertainties into the benefit estimation process; therefore, it is particularly important to evaluate these transfer methods carefully.

In recent years, CFSAN has predominantly used one benefit transfer approach for valuing changes in morbidity. This approach combines nonmonetary measures of the severity and duration of illness—quality-adjusted life years (QALYs) lost—with monetary measures of the value of avoided mortality—value of statistical life (VSL) estimates. As we discussed in a previous report (RTI, 2002), this approach has a number of appealing qualities, but it also imposes a number of relatively stringent assumptions regarding individuals’ preferences for health. For this reason and because of FDA’s interest in strengthening its regulatory decisions, CFSAN has asked RTI to reexamine this approach and to assist them in continuing to develop sound benefit transfer approaches for morbidity valuation.

1.2 SUMMARY OF RESULTS To address CFSAN’s needs, RTI conducted the following activities:

Z developed a conceptual framework that describes the microeconomic foundations for health valuation and identifies the key expected determinants of health values (Section 2);

Z reviewed the empirical literature on health valuation (including over 600 publications) and compiled a detailed bibliography of the most relevant 136 publications (Section 4);

Z selected WTP estimates from these studies and constructed a database of health values, which currently contains 389 WTP estimates (and corresponding data) from 44 publications (Section 4);

Z used meta-analysis methods to analyze subsets of the database—236 WTP estimates for avoiding acute illness and 38 WTP estimates for avoiding chronic illness (Section 5); and

Z applied the meta-analysis results to specify benefit transfer functions for acute illnesses (Section 5).


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Through this process, we also tested hypotheses regarding the determinants of WTP estimates. The results of the meta-analysis indicate that WTP estimates for avoided acute effects vary in systematic and expected ways with respect to key explanatory variables. We found a strong statistical relationship between the value estimates and corresponding measures of the severity and duration of the health effects. In addition, we find generally positive and significant income effects and age effects.

The meta-analysis results also provide a simple but informative test of the assumptions underlying the QALY valuation approach for assessing morbidity values. The results strongly reject the assumption of a constant value per QALY and the assumption that the duration and the severity of illness have equivalent and proportional effects on WTP.

In Section 6, we illustrate how the results of the analysis can be applied to estimate the benefits of avoiding specific acute conditions often associated with foodborne illness. We also discuss other implications and limitations of the analysis.

The data and analyses assembled for this project provide a foundation for health benefits analysis that should extend beyond this report. The bibliography and databases described in Section 4 should serve as general resources for identifying, summarizing, or transferring estimates from the health valuation literature. The databases also provide a structure for organizing data that can easily be used to include information from more studies. As such, they should support additional analyses (including meta-analyses) of the health valuation literature and continued development of benefit transfer tools.

The results of the meta-analysis indicate that WTP estimates for avoided acute effects vary in systematic and expected ways with respect to key explanatory variables.

2-1

Conceptual Framework for 2 Morbidity Valuation

To provide CFSAN with a morbidity valuation approach that is not only practical to use but is also theoretically sound, it is important to establish an appropriate conceptual foundation for the approach. In this section, we

Z present a relatively simple theoretical model describing how measures of morbidity can be related to individuals’ preferences, and

Z use this framework to identify and describe the main factors expected to explain and influence estimates of morbidity values.

In other words, we describe the expected relationships and linkages between key variables, and we identify key hypotheses to be tested in the statistical analysis (described in Section 5).

2.1 BASIC FRAMEWORK FOR HEALTH VALUATION To formalize the way in which individuals derive value from changes in health, we begin with a simple conceptual framework that links an individual’s private utility (U) with his/her health status (H). This simple single period framework, which assumes that H is exogenously determined and that there is no uncertainty regarding H or other determinants of utility, is summarized in the following utility function:

U = U(H, X) (2.1)

In this function, X represents a vector of other goods that contribute to utility. Individuals are assumed to maximize this utility function


2-2

subject to the budget constraint Y = PX, where Y is exogenously determined income and P is a vector of prices corresponding to the X goods.

The indirect utility function associated with this maximization process can be written as follows:

V = V(H, Y, P) (2.2)

The monetary value, or WTP, associated with an improvement in health from initial health, H0, to new health, H1, can be expressed by the compensating variation term (CV) in the following equation:

V(H0, Y, P) = V(H + ∆H, Y – CV, P) = V(H1, Y – CV, P) (2.3)

CV represents the reduction in income that would exactly offset the increase in utility resulting from health improvement, such that there would be no net gain in utility. Similarly, the WTP associated with avoiding a decline in health from H0 to H2 can be expressed by the equivalent variation term (EV) in the following equation:

V(H0 – ∆H, Y, P) = V(H2, Y, P) = V(H0, Y – EV, P) (2.4)

EV represents the reduction in income that would reduce utility by exactly the same amount as the health decline.

Both CV and EV are considered to be conceptually correct welfare measures for changes in health; however, even for equivalent gains or losses in health (i.e., holding ∆H constant), they will not necessarily have the same value.1 In particular, if the marginal

utility of health decreases with H ( 0H

V2

2

<∂∂ ) or if the marginal

utility of income increases with H ( 0HY

V2

>∂•∂

∂ ), EV is generally

expected to be greater than CV. Nonetheless, for small changes in health, the two measures should be roughly equivalent. Large differences between CV and EV are possible, but they may also

1CV can also be expressed as the increase in income that would exactly offset the

utility loss associated with a decline in health (i.e., an individual’s minimum willingness to accept [WTA] compensation for a decline in health). Although much attention has been devoted in the literature to discrepancies between WTP and WTA measures (see for example, Hanemann [1991] and Johansson [1995], in practice relatively few studies have estimated WTA values for health changes.

Section 2 — Conceptual Framework for Morbidity Valuation

2-3

indicate deviations from the standard utility model, such as the loss aversion and reference dependence models proposed by Tversky and Kahneman (1991). According to this alternative framework, individuals value changes with respect to a reference point (such as their status quo condition) and place a much higher (negative) weight on losses than they do on equivalent gains.2

Both Eqs. (2.3) and (2.4) can be rearranged to derive the following corresponding value functions:

CV = CV(H0, H1, P, Y) (2.3’)

EV = EV(H0, H2, P, Y) (2.4’)

These equations are essentially “variation” or WTP functions with respect to a change in health status. Consequently, they provide a conceptual basis for constructing and statistically estimating the meta-analytic functions of health values, which are described in Section 5. The expected properties of these functions and their implications for meta-analysis are described in more detail below.

2.2 EXTENSIONS OF THE BASIC FRAMEWORK At least three extensions of this basic framework are useful for establishing the conceptual basis for health values. The first extension involves using a “health production” function (HPF) to account for how individuals’ actions influence their health outcomes. Including a health production function allows for a more explicit link between individuals’ WTP to avoid a particular health outcome and the losses—both pecuniary and nonpecuniary—associated with that outcome. The second extension incorporates uncertainty into the model, through the use of an expected utility (EU) framework. This extension is particularly helpful for conceptualizing values that are based on changes in the probability (i.e., risk) rather than the certainty of a particular health outcome. The third extension introduces a temporal dimension to health-related utility. A specific and frequently used framework for

2Note that with a standard utility model, an individual’s WTP to avoid a loss from

H0 to H2 (EV in Eq. [2.4]) should be identical to his/her WTP for an increase in health (CV) going from H2 to H0. However, with reference dependence—shifting the status quo from H0 to H2—the gain may be treated differently from the avoided loss.

The basic framework can be usefully extended by including Z a health production

framework,

Z uncertainty regarding health outcomes, and

Z a temporal dimension to health outcomes.


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characterizing health-related utility over time is the quality-adjusted life year (QALY) method. Although QALYs have important practical appeal, their relationship to WTP measures of health is a complex one. Each of these three extensions is discussed in more detail below.

2.2.1 Health Production

First developed by Grossman (1972), the HPF is now commonly used as the conceptual basis for explaining health behaviors and values. In contrast to the basic framework described above, where health outcomes are assumed to be exogenously determined, the HPF clearly distinguishes between exogenous and endogenous determinants of health. As a simple example, we define the following HPF:

S = S(H, M) (2.5)

As in Eq. (2.1), H is an exogenous variable, which can be thought of as a measure of a person’s health status (or accumulated health capital) at a point in time. For example, H could be a measure of the presence and/or severity of a chronic illness such as asthma. M is a choice variable representing purchasable goods, such as medication, which can be used to alleviate the symptoms of adverse health conditions associated with H. These are sometimes referred to as “mitigating” goods/activities. However neither H nor M directly affects utility. Rather, they jointly determine the health outcome (S) that does matter to an individual.

In other words, S is the health measure that directly affects a person’s level of utility such that Eq. (2.1) can be reformulated as follows:

U = U(S(H, M), X) (2.6)

For this example, we assume that S is a measure of the number of sick days experienced. In this case, the budget constraint can then be defined as follows:

Y = I + w(T – L – S) = X + PmM (2.7)

where I is exogenous income, w is the wage rate, T is total time, L is leisure time, S is sick time (such that T – L – S is labor time), and Pm is the unit price of M. The price of X is normalized at 1.


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Given this formulation, it can be shown (see, for example, Freeman [1993]) that the individual’s WTP for an improvement in health status (the reduction in I that would exactly offset the utility gain from a change in H) can be expressed by the following equation:

WTP = dIdH = Pm

∂M∂H + w

dSdH –

∂U/∂Sλ

dSdH (2.8)

The first term on the right-hand side of this equation represents the change (savings) in expenditures on M associated with an improvement in H. These savings represent avoided direct costs. The second term represents the avoided loss of wage income (opportunity cost) due to the reduction in sick days. It captures avoided indirect costs. The last term represents the nonpecuniary utility gain (e.g., avoided pain and suffering) due to improved health, which is converted to monetary terms through the marginal utility of income term ( ).

This decomposition of WTP highlights at least two important points. First, it emphasizes the distinction between the pecuniary effects (direct and indirect costs), which are typically captured by COI measures, and nonpecuniary effects (disutility effects), which are not. WTP estimates are interpreted to be more comprehensive than COI estimates because they include both effects.

Second, this decomposition implies that WTP is positively related to both the monetary costs of illness avoided and the amount of pain and suffering avoided. It is important to note, however, that an individual’s private WTP is only related to the portion of the costs that he or she bears. Through public and private health insurance and sick leave policies, individuals may only bear a small portion of the direct and indirect costs associated with an additional day of illness. Under these conditions, costs are shifted to others in society (e.g., employers or taxpayers), and the effective marginal costs faced by individuals for mitigating goods and sick days are less than their full prices (Pm and w, respectively). As can be seen in Eq. (2.8), lowering the prices of mitigating goods and sick days decreases WTP. When marginal costs of illness are subsidized in this way, private WTP to avoid an illness is less than the full societal WTP.

If individuals’ marginal costs of illness are subsidized—e.g., through sick leave or health insurance policies—then private WTP to avoid illness will be less than the full societal WTP.


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2.2.2 Uncertainty

The previously described models treat changes in health as if they were known with certainty; however, it is often the case that individuals make choices and reveal or express values for changes that are not known with certainty. The traditional approach for including uncertainty in a conceptual model of health is to treat health outcomes (and thus utility) in a probabilistic manner. Using an EU framework, we assume that there are multiple (N) possible health states, that each has a defined probability (πi) of occurring, and that all of these probabilities sum to 1. As in the following expression, EU can be expressed as the probability weighted average utility associated with the various possible health states:

EU = ∑i=1

N πiV(Hi, Y, P) (2.9)

where πi is the probability associated with health state i.3

The EU framework has been widely questioned and tested, and several empirical violations of EU have been noted in the academic literature (Weber and Camerer, 1987). Despite its shortcomings, the simplicity and generalizability of EU continue to make it most useful (and most widely used) as a basic conceptual structure. Most violations of EU have been attributed to either systematic biases in individual risk judgments or to behavior that contradicts the assumption that utility is linearly related to the probabilities of outcomes, as expressed in Eq. (2.9). Alternatives to the EU framework have been proposed, such as theories involving decision weights, reference dependent preferences, and nontransitive and nonmonotonic preferences; however, none of these alternatives has emerged as a clearly superior framework for explaining individual decision making under uncertainty. For more on the arguments against the EU framework and some non-EU models, we refer the interested reader to Starmer (2000).

Eq. (2.9) can be used to develop an adapted measure of compensating (or equivalent) surplus. In this instance we are interested in the value of a reduction in health risk (i.e., a reduction in the probability of an adverse health outcome) rather than a

3For simplicity, we revert to the previous form of the model (without the HPF) and

assume here that health and income are exogenous.

Despite its shortcomings, the simplicity and generalizability of EU continue to make it most useful (and most widely used) as a basic conceptual structure.


2-7

certain improvement in health status. For simplicity, we assume that there are two possible and mutually exclusive health states, good health, Hg, and bad health, Hb. We further assume that the initial risk associated with Hb is πb

0. WTP for a reduction in risk to πb

1 (< πb0) can be expressed by the CV measure in the following

equation:

πb0V(Hb, Y, P) + (1 – πb

0)V(Hg, Y, P) =

πb1V(Hb, Y – CVπ, P) + (1 – πb

1)V(Hg, Y – CVπ, P) (2.10)

Similarly, WTP to avoid an increase in risk to πb2(> πb

0) can be expressed by the EV measure in the following equation:

πb0V(Hb, Y – EVπ, P) + (1 – πb

0)V(Hg, Y – EVπ, P) =

πb2V(Hb, Y, P) + (1 – πb

2)V(Hg, Y, P) (2.11)

Both of these equations can also be rearranged to derive the following corresponding WTP functions with respect to change in health risks, health outcomes, income, and prices:

CVπ = CVπ (πb0, πb

1, Hg, Hb, P, Y) (2.10′)

EVπ = EVπ (πb0, πb

2, Hg, Hb, P, Y) (2.11′)

In this context, CVπ and EVπ are referred to as ex ante (or “option price”) health values because they are defined from a perspective where the relevant health outcomes have not yet been resolved. In contrast, the CV and EV measures in Eqs. (2.3) and (2.4) are ex post health values because they are defined for health changes that are known with certainty.

Because WTP for a specific health change can be expressed and measured either as an ex post or ex ante value, it is important to consider how the two measures are related. For example, one approach to estimating the value of avoiding a chronic illness is to estimate how much an individual with a chronic illness would be willing to pay for a cure. This approach provides an ex post CV measure of going from Hb to Hg (with πb

0 = 1 and πb1 = 0). An

alternative approach is to estimate how much an individual who is at risk of contracting the chronic illness would be willing to pay for a risk reduction. This approach provides an ex ante CVπ measure, such as in Eq. (2.10’), where πb

0 < 1 and πb1 � 0. Dividing CVπ by


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the risk reduction (CVπ /(πb0 – πb

1)) in effect rescales the ex ante measure to provide an estimate of WTP for avoiding a “statistical” case of the illness.

Both approaches provide conceptually valid estimates of private WTP to avoid a case of the illness, but they will not necessarily generate equivalent values. Johannesson (1996) shows that when the ex ante measure is based on reducing the risk of illness to zero (πb

1 = 0), it should be less than or equal to the ex post measure, as long as the individual is risk averse with respect to income (i.e., the marginal utility of income declines as income increases).4 However, ex ante measures may exceed ex post measures in cases where risks are not reduced to zero and where the marginal utility of income is higher in the better health state. Therefore, in many cases it is difficult to establish strong priors regarding the relative magnitudes of the two measures.

2.2.3 Lifetime Utility and QALYs

The QALY framework has been developed primarily to address the need for a simplified and summary measure of individuals’ health-related quality of life (HRQL) over time. Measures of this type are often needed to compare and evaluate health outcomes of alternative treatments or public health programs.

Measuring health status, particularly over the long term, is complicated by the fact that individuals generally experience a variety of “health states” over the course of their life span. The time path of these health states can be captured in a “health profile,” which describes the sequence of health states across time periods. According to the QALY framework, the HRQL corresponding to any possible health state (i = 1 to N) can be represented by a single numerical index value (qi), typically ranging between 0 (death) to 1 (prefect health). If Ti presents the number (or fraction) of life years spent in each health state, i, then the number of QALYs corresponding to a lifetime health profile can be expressed as

QALY = ∑i=1

N qi * Ti (2.12)

4This conclusion is based on a standard expected utility framework. It does not

allow, for example, for the presence of a “certainty premium,” whereby individuals are willing to pay a premium to reduce risks all the way to zero (Viscusi, 1989).

The QALY framework has been developed primarily to address the need for a simplified and summary measure of individuals’ health-related quality of life (HRQL) over time.


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Because of their relative simplicity as a measure of individuals’ health over an extended period, QALYs have been widely used to compare and evaluate health interventions. Estimates of the number of QALYs gained due to an intervention provide a convenient summary measure of effectiveness; consequently, QALY-based measures have been particularly applied in cost-effectiveness analyses (CEA).

Despite the popularity and relative simplicity of QALYs as a measure of effectiveness, a number of questions have arisen regarding their normative implications. In particular, under what conditions is maximizing QALY gains equivalent to maximizing human welfare? That is, to what extent can the QALY equation described above represent an actual utility function. This question is fundamentally the same as examining the equivalence between CEA and cost-benefit analysis (CBA).

The general conclusion from the literature exploring these issues is that QALYs only represent a valid utility function under very restrictive conditions. Many of the original studies on this topic began with the assumption that preferences over health and longevity could be defined independently of income and other personal characteristics. Even under these conditions, the validity of QALYs as a utility function requires strong assumptions.

Pliskin, Shepard, and Weinstein (1980) define a lifetime utility function, U(q,L), where q represents a scalar index of long-term (e.g., average lifetime) health status and L represents longevity (e.g., number of years in one’s life span). They conclude that the QALY model is consistent with this utility formulation if three main assumptions hold:

Z “Risk neutrality” over life expectancy, which implies, for example, that one is indifferent between (1) living 25 more years with certainty and (2) a gamble offering 50 percent chance of living 50 more years and a 50 percent chance of dying immediately.

Z “Constant proportional trade-off” of longevity for health, which implies, for example, that if one is willing to give up 10 out of 50 years remaining in life for a specific improvement in health, then one should be willing to give up 1 year for the same health improvement if one’s remaining life is 5 years.

Z “Mutual utility independence” between life years and health status, which implies that (1) preferences between lotteries

The general conclusion from the literature exploring these issues is that QALYs only represent a valid utility function under very restrictive conditions.


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involving different health statuses for the same life span do not depend on the length of the life span and (2) preferences between lotteries on life span in a constant health status do not depend on the level of the health status.

A number of studies have demonstrated empirical violations of these assumptions; however, these findings are most likely not sufficient by themselves to invalidate the use of QALYs, at least as an approximation of utility.

Establishing consistency between QALYs and individual preferences is even more difficult if one allows for discounting of future utilities. Johannesson, Pliskin, and Weinstein (1994), for example, use a multiperiod discounted utility model of the form

U = ∑t=0

T qt / (1–d)t (2.13)

where d is a constant discount rate, qt is the HRQL index for period t, and T is a fixed remaining life span. They demonstrate that the QALY approach is not consistent with this commonly assumed lifetime utility structure (with discounted and additive utility). Consistency will only be maintained if one adapts the QALY model to include discounted life years in each health state.

Bleichrodt and Quiggin (1999) extend these analyses even further by evaluating QALYs under conditions where lifetime utility depends not only on health status, but also on consumption (and thus income or wealth) as well. Using a life-cycle model, they conclude that CEA and CBA are mutually consistent (QALY maximization is consistent with utility maximization) if lifetime utility is additive over time and is multiplicative in the utility of consumption and the utility of health status, and if the utility of consumption is constant over time:

U = ∑t=0

T 1 / (1 – d)t * u(c) * qt. (2.14)

If individuals are allowed to optimize lifetime utility by selecting consumption levels in each period, then the main condition under which consumption levels will be constant over time is when the rate of individual time preference is equal to the discount rate. By including consumption in the lifetime utility function, Bleichrodt


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and Quiggin are also able to explore the relationship between WTP for health improvements and QALY gains. One of the main implications of their analysis is that only under conditions of constant consumption over time is it possible to define a constant WTP per QALY gained, which is necessary for the equivalence between QALY maximization and utility maximization for an individual.5

Given the rather strong assumptions that are required to establish equivalence between QALY maximization, utility maximization, and WTP, a slightly different question is whether QALYs can be included at all within a utility theoretic framework that includes both health status and consumption. In other words, rather than defining conditions under which the QALY function is a utility function or conditions under which QALY maximization is equivalent to utility maximization, is it possible to define a utility-theoretic preference structure that includes both consumption and QALYs as an argument? If so, then how is WTP related to QALY gains?

Hammitt (2002) uses a simplified lifetime utility structure (similar to Pliskin, Shepard, and Weinstein [1980])) to define utility functions for health, longevity, and wealth that are “admissible” if one assumes that preferences for health and longevity can be represented by QALYs. To define admissible functions, he introduces the concept of “HRQL invariance,” which essentially means that it is possible to define a utility index for health that is independent of wealth. For example, he defines the following lifetime utility function with respect to health (H), longevity (L), and wealth (w):

U(H,L,w) = [q(H)L]r a(w) + b(w) (r > 0). (2.15)

Note that, within preference specification, the marginal utility of wealth is not independent of health, nor is the marginal utility of health independent of wealth. Even so, if q(death) = 0 and q(full health) = 1, then q(H*)r will be the utility index (i.e., QALY weight)

5More recently, Dolan and Edlin (2002) have extended this model to include other

nonhealth and nonwealth factors in the utility function. They also take a societal perspective rather than an individual perspective in comparing QALY maximization and welfare maximization. They conclude that in a broad welfare economic framework it is essentially impossible to link CBA and CEA.


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for health state H* estimated by a standard gamble (see Section 2.3.1.1), regardless of wealth level.

Defining QALYs as Q = q(H)*L, Hammitt then explores how the marginal WTP per QALY gain is affected by total QALYs, wealth, and the parameter r, which is like a risk aversion coefficient. The main conclusion is that WTP per QALY is not constant. Under most conditions, WTP per QALY is diminishing with respect to total QALYs and increasing with respect to wealth.

Klose (2002) investigates similar issues as Hammitt (2002) using a multiperiod model of utility. This model defines lifetime utility as the discounted sum utilities across time periods:

U = ∑t=0

T 1 / (1 – d)t *u(Ht, wt) (2.16)

Klose does not specify a functional form for within-period utility, u(w,h); however, he does specify conditions under which it is possible to derive utility indexes for health states (QALY weights) that are consistent with utility theory and independent of wealth level. As in Hammitt’s analysis, defining “wealth-standardized” QALY weights that are independent of wealth does not require the marginal utility of wealth to be independent of health status.

Klose also examines the relationship between WTP and QALY gains and comes to the same fundamental conclusion: WTP per QALY gain is not constant. In particular, as long as health has a positive effect on the marginal utility of wealth, the WTP per QALY gain decreases with health status and with the size of the gain.

In summary, the QALY framework continues to be widely used because of its simplicity and intuitive appeal, but its use as a tool for welfare analysis has raised a number of important concerns. Most analyses indicate that QALYs are not equivalent to the preferred welfare measure, WTP. For this analysis, a more important issue than the equivalence of WTP and QALYs, is whether it is possible to define a consistent relationship between WTP and QALYs. The recent work by Hammitt and Klose, which we have described above, come closest to addressing this issue. The potentially testable hypotheses raised by their analyses are discussed in more detail below.

For this analysis, a more important issue than the equivalence of WTP and QALYs, is whether it is possible to define a consistent relationship between WTP and QALYs.


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2.3 DETERMINANTS OF HEALTH VALUES Starting with the framework described above, we expect health value estimates to be primarily influenced by

Z the magnitude of changes in health outcomes or health risks;

Z the characteristics of the study population, including

X income effects,

X health status, and

X other socio-demographic characteristics;

Z price effects; and

Z the valuation method used.

Below we describe how these determinants of health values can be measured and how they are expected to affect the magnitude of value estimates.

2.3.1 Measures of Health Changes

As shown by Eqs. (2.3′), (2.4′), (2.10′), and (2.11′), the magnitude of changes in health outcomes (∆H) and/or changes in health risks (∆π) is a key determinant of health values. Therefore, even though health is a complex and multidimensional concept, for the purposes of economic evaluation, it is necessary to develop somewhat simplified characterizations and measures of these factors.

Severity Measures

To characterize and assess health status (i.e., HRQL) in a systematic and standardized way, health economists, psychometricians, and other health experts have developed a wide array of health status measures (HSMs). HSMs generally use standardized questionnaires to assess various aspects of illness or disability. Although some of these measures have been developed for specific diseases, many are designed for more generic use.

Table 2-1 describes how some of the more widely used and broadly applicable (i.e., generic) HSMs are used to classify health status. In each case, they characterize health status (or severity of illness) according to multiple health dimensions, including physical, mental, and emotional elements. In many cases, they also divide each of these dimensions into discrete levels. For example, one component of the Quality of Well-Being (QWB) asks respondents to indicate whether (two levels) they have certain symptoms/problems,


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Table 2-1. Comparison of Health Status Measures

HSM Health Dimension Levels Health States Preference-Based Index

SF-36 Physical functioning, role limitations due to physical health, role limitations due to emotional health, social functioning, bodily pain, mental health, vitality, general health

NA NA No

QWB Mobility, physical activity, social functioning 3 1,170 Yes

27 symptoms/problems 2

HUI-III Vision, hearing, speech, ambulation, dexterity, emotion, cognition, pain/discomfort

5 to 6 972,000 Yes

EuroQol Mobility, self-care, usual activity, pain/discomfort, anxiety/depression

3 243 Yes

Source: Adapted from Brazier, J., M. Deverill, C. Green, R. Harper, and A. Booth. 1999. “A Review of the Use of Health Status Measures in Economic Evaluation.” Health Technology Assessment 3(9).

such as a cough or the need for eyeglasses or contact lenses. A component of the HUI-III asks respondents to rate their speech according to three levels—ability to be (1) completely understood by strangers and friends, (2) partially understood by strangers and completely understood by friends, or (3) partially understood by all. These various dimensions and levels can combine to define a multitude of health states. For example, with five to six possible levels for eight health dimensions, the HUI-III defines as many as 972,000 unique health states.

In addition to these methods for categorizing health states, a number of survey-based scoring techniques have also been developed for measuring and comparing the severity of health states. These techniques use preference elicitation methods to produce “utility weights”—typically varying between 0 (immediate death) and 1 (perfect health)—for specifically defined health states. Three of the most commonly used scoring techniques can be briefly described as follows:

Z Time trade-off (TTO)—asks respondents how many years of perfect health would be equivalent to a given number of years of compromised health.

Z Standard gamble (SG)—asks respondents the risk of death that would make them indifferent between a lottery between death and perfect health relative to the certainty of a given level of compromised health.


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Z Visual analog scale (VAS)—asks respondents to rate their health on a visual representation of the zero to one scale, commonly in a thermometer-type format.

The last column in Table 2-1 highlights a key difference between the SF-36 and the other HSMs. SF-36 does not provide a “preference-based” measure of health. It does not make use of any of the preference-based scoring techniques described above, and, as a result, it does not provide a systematic way to aggregate the various health dimensions into a single utility index.6 In contrast, the other HSMs in Table 2-1 can all be characterized as preference-based measures (or multiattribute utility scales [MAUSs]) because they have used one or more of these techniques to develop utility scores for specifically defined health states. For instance, the EuroQol team used a combination of VAS and TTO techniques to elicit preferences from a sample population for approximately 45 of the health states. The sample weights were then used to extrapolate and define utility scores for each of the 243 possible health states described by the EuroQol (Gudex et al., 1997).

The advantages and limitations of the various scoring techniques and MAUSs have been widely analyzed in the health economics literature (see Brazier et al. [1999] for a good summary). None of these methods has emerged as a clearly superior alternative for measuring health, but as a group they provide the most promising set of tools for quantifying changes in the severity of health outcomes.

Duration Measures

In addition to severity, the duration of illness can also be an important factor affecting utility. Fortunately, duration is much more straightforward to measure than severity, but selection of the appropriate time scale is still an issue. For example, it may be critical to distinguish between acute and chronic morbidity. Whereas chronic morbidity refers to long-term or recurring conditions that can extend over several years or even a person’s lifetime, acute morbidity refers to discrete and more short-term health events, sometimes lasting 1 day or less. Furthermore, in some cases, acute conditions, such as asthma attacks, may be

6Scoring systems have been developed for the SF-36, for each of its eight health

dimensions and for composite physical and mental health dimensions; however, the resulting scores cannot be interpreted as a utility index.


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directly related to underlying chronic conditions. In other cases, such as foodborne illness, there may be no relation to a long-term condition. Therefore, measures of duration changes may need to account for how both long-term and short-term conditions are affected.

Combined Measures of Severity and Duration

Severity and duration measures, such as the ones described above, can be combined in a multitude of ways to describe specific health outcomes, but the most common method is the QALY approach. As described above in Eq. (2.12), this approach assumes a simple multiplicative relationship between duration and severity.

A QALY measure for a specified health state is constructed by multiplying the time spent (in years) in the health state by its corresponding utility weight. This weight can be estimated directly for a specific health state (or health profile) using one of the preference scoring techniques described above. Alternatively, a weight can be derived from one of the MAUSs, by mapping the health state to the MAUS classification system and calculating or selecting the corresponding weight.

As discussed in detail above, the relative simplicity of the QALY approach has advantages in terms of understandability and ease of use, but it also imposes strict and perhaps unrealistic assumptions about individual preferences. Somewhat less restrictive, but slightly more complex, versions of QALYs have been proposed. For example, Pliskin, Shepard, and Weinstein (1980) define two forms of “risk-adjusted” QALYs, which include a risk-aversion parameter, r:

RA-QALY1 = (q(H)*L)r (2.17a)

RA-QALY2 = q(H)*(L)r (2.17b)

Unless r = 1, both of these forms relax the assumption of linearity with respect to the duration of the health state. Another adaptation of the QALY approach is to include a discounting factor for future time periods. “Discounted QALYs” are calculated by replacing the duration of the health state—number of life-years—with discounted life years (Johannesson, Pliskin, and Weinstein, 1994). These adapted versions are less commonly used because they require


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additional assumptions about the risk aversion or discount factors; however, they do provide potentially useful alternatives to the simple QALY approach.

Risk Measures

Under conditions of uncertainty, expected utility can be affected, not only by changes in health outcomes, but also by changes in the risk of experiencing poor health states (see Eqs. [2.10′] and [2.11′]). Defining an appropriate metric for health risks is, in general, more straightforward than for health outcomes. In the EU framework, risks are uniquely characterized and measured by the mathematical probabilities (π) associated with each health state. Whether one relies on subjective estimates of these probabilities or more scientifically based (“objective”) estimates is often an issue in measuring risk preferences, but the risk metric is the same in both cases.

Hypotheses for Health Change Measures

To the extent that health changes can be characterized in the three dimensions described above—severity, duration, and risk—economic theory and the conceptual model described in Sections 1 and 2 help to define hypotheses regarding how these dimensions should affect health values. More formally, they inform our expectations about the first and second derivatives of H and π with respect to WTP in Eqs. (2.3′), (2.4′), (2.10′), and (2.11′).

In particular, we expect larger reductions in the severity, duration, and/or risk of illness to generate larger values (positive first derivatives). The test of this relationship is commonly referred to as the “scope test.”

The assumptions underlying the QALY model also define hypotheses regarding how changes in severity and duration should affect values. As discussed in Section 2.2.3, under the most restrictive set of assumptions where QALYs are interpreted as a utility function, WTP should increase in direct proportion to the gain in QALYs. This implies the following expression:

WTP = α * (∆QALY)β, where β = 1 (2.18)

In this expression, α can be interpreted as a constant value per QALY gain. In practice, a constant value per QALY or “QALY

In particular, we expect larger reductions in the severity, duration, and/or risk of illness to generate larger values (positive first derivatives). The test of this relationship is commonly referred to as the “scope test.”


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valuation” approach has been frequently used (see, for example, Zarkin et al. [1993] and Cutler and Richardson [1997]) for valuing national-level health changes. In most of these applications, the value per QALY is derived by annualizing empirical estimates of the value per statistical life (VSL) (Viscusi, 1993), resulting in values of roughly $100,000 per QALY.

Maintaining the constant value per QALY assumption, if the QALY change is composed of a specific change in HRQL (∆q) over a specific time period (∆t), then (Eq. 2.18) can be further decomposed into the following expression:

WTP = α * (∆q *∆t) β, where β =1 (2.19)

This formulation implies that the elasticities of WTP with respect to ∆q and ∆t are both equal to 1.

As shown by Hammitt (2002) and Klose (2002), it is possible to include QALYs (or QALY weights) in a utility theoretic framework without imposing assumptions that imply a constant WTP per QALY. In other words, QALY weights that are invariant to wealth and longevity can be derived from plausible preference structures that include both health and consumption. Under these conditions WTP is systematically but not linearly related to QALY gains. In particular, under most plausible conditions, WTP per QALY gain is declining with respect to health status and the size of the QALY gain.

For ex ante values, the relationships between WTP and risk reductions can be derived from the EU framework. This framework assumes a linear relationship between risks (for specific health states) and EU. Therefore, for relatively small changes in risk (and/or less severe health outcomes), we expect a roughly proportional relationship between WTP and risk changes.7

7With EU, nonlinearity of WTP with respect to risk changes is primarily attributable

to differences in the marginal utility of income across health states. Consequently, one would only expect to observe significant nonlinearities if one is dealing with relatively large changes in health status and relatively large changes in the risk of severe health outcomes.


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2.3.2 Study Population Characteristics

Values for health changes are also expected to depend importantly on the characteristics of the individual in question, including income, age, and other characteristics.

Income/wealth effects. Income and wealth are primarily expected to have a positive effect on health values. First, individuals’ WTP for health changes is constrained by their available budget; therefore, other things equal and as long as health is a normal good, it is expected that individuals with higher incomes will also have higher values for health changes. Second, as shown in Eq. (2.8), a potentially critical component of WTP is the avoided opportunity cost (lost wages) associated with illness. To the extent that higher incomes imply higher opportunity costs, there is further reason to expect a positive relationship between income and WTP for health.

Age. An individual’s age can potentially affect his/her WTP for health improvements in a number of ways, both positive and negative. First, in general terms, age is negatively related to health status. As discussed above, WTP for a specific health improvement is generally expected to lower when starting from a higher level of health status; therefore, to the extent that age serves as an indicator (inverse) of health status, a positive relationship between WTP and age is expected. Second, age can also affect the nature of the health improvement in a way that decreases WTP. For example, because of a shorter remaining life expectancy, an older individual with moderate arthritis may have a lower total WTP for curing his/her illness than a younger individual with the same condition. In this case, age may serve as a proxy for the duration of the health improvement, in which case it would have a negative effect on WTP. Third, age may capture aspects of socio-economic status that are not captured by annual income measures. For example, elderly individuals may have higher accumulated wealth relative to younger individuals with comparable income. If wealth measures are not adequately controlled for, then age may increase WTP for health improvements through a wealth effect.

Other characteristics. Several other factors can influence people’s perceptions and/or preferences for health and, in turn, their WTP. It is not feasible to catalog all of these possible effects, but a few are worth considering for meta-analysis. First, experience and


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familiarity with a health condition can certainly influence a person’s WTP to avoid the condition, but the effect of these factors on WTP are not always predictable. For example, with experience may come adaptation to the condition and thus a lower WTP. On the other hand, experience may create a greater appreciation for the potential pain and discomfort associated with the condition and thus a higher WTP. Second, education and cognitive abilities can also influence one’s ability to comprehend the risks and outcomes associated with illness. The direction of these effects, however, will depend on the context.

2.3.3 Price Effects

As shown in Eq. (2.8), at least two price effects are potential factors influencing WTP: wages and prices of mitigating goods. Above, in the section on income effects, we discussed how wages can represent opportunity costs of illness. Prices of mitigating goods, such as medication and treatment, can affect the direct cost of illness. Consequently, as shown in Eq. (2.8), higher prices for these goods are expected to increase the WTP to avoid or reduce morbidity. It is important, however, to emphasize that these prices may be partially or totally covered by employers or health insurers, in which case the marginal effect of these prices on private WTP should be smaller.

2.3.4 Valuation Method

The magnitude of value estimates for changes in health is also expected to depend on the way in which they are estimated. A number of nonmarket valuation approaches are potentially applicable for assessing WTP for changes in morbidity outcomes or risks, but most fall under the categories of either stated preference (SP) and revealed preference (RP) approaches.

SP methods such as the contingent valuation method (CVM) and, to a lesser extent, conjoint analysis (CA) are most commonly used for morbidity valuation. Using surveys of individuals, these methods present respondents with hypothetical scenarios involving tradeoffs between monetary gains/losses and health gains/losses. Respondents are then asked to rate, rank, or select their preferred options, or they are asked to directly state their maximum WTP (or minimum WTA) relating to the proposed scenarios. A variety of statistical and econometric techniques can then be used to analyze


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responses and estimate the size, distribution, and key determinants of values for specified health changes.

In contrast, RP methods rely on information from actual human behavior to estimate individuals’ tradeoffs between monetary gains/losses and health gains/losses. For example, the hedonic approach examines the relationship between market wages and job risks (for injury and fatality) across occupational categories to infer how much income individuals are on average willing to accept/forgo to accept higher/lower risks. The household production approach typically examines individuals’ purchase behavior with respect to goods that allow them to avoid or mitigate adverse health effects (e.g., water filters to avoid drinking water risks or medication to treat symptoms). These behaviors are also used to infer WTP or WTA for health changes. Although value estimates based on RP methods are sometimes given more credence because they are not based on hypothetical settings, in practice they are less widely used. Compared to SP methods, it is generally more difficult with RP methods to acquire the necessary data and to control conditions necessary to isolate the monetary-health tradeoffs of interest.

A number of features and issues associated with SP methods have the potential to influence the values that are estimated with these approaches. The implications for designing and interpreting SP studies have been widely debated and studied in the literature. A complete listing and evaluation of these issues is beyond the scope of this report; however a few of them are worth highlighting:

Z Elicitation format—a key distinction is between open-ended (OE) and dichotomous choice (DC) formats, both of which can induce respondent bias.

Z Protest responses—stated values of zero may reflect scenario rejection rather than true WTP, in which case WTP is downward biased.

Z Response rates—low response rates, particularly for the WTP or choice questions, can induce bias in WTP estimation if nonrespondents have unobserved differences from those who do respond.

Z Payment method—stated values may be influenced by the way in which payment is made (e.g., out of pocket payment versus insurance premium).


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Z Scenario description—the type and level of detail regarding the health effect of interest and the hypothetical choice scenario may influence responses.

Z Scenario comprehension—internal consistency checks can be used to identify and address “invalid” responses that reflect a lack of understanding of the scenario.

Through the application of meta-analysis, it is possible to test for systematic biases associated with some of these points. In particular, the effects of elicitation format can be examined. The relative advantages of OE and DC question formats for CVM studies have been widely discussed and analyzed. Carson (2000), for example, argues in favor of the DC format. He suggests that individuals will tend to understate their WTP with the OE format because respondents do not have the incentive and are not accustomed to conditions where they have to “find” their maximum point. However, if respondents are answering questions strategically, OE responses may overstate or understate WTP (Smith, 2000). For example, if respondents feel that their response will influence the availability of a public or private good without actually affecting the price they have to pay, the OE format offers more of an opportunity to overstate WTP. Meta-analysis offers a framework for testing whether systematic differences do exist between DC- and OE-based estimates of health values.

2.4 SUMMARY This section provides a simple theoretical framework describing how measures of morbidity can be related to individuals’ preference. We have used this framework to identify and describe the factors that are primarily expected to explain and influence estimates of morbidity values. As a result, the framework provides a basis for our empirical model, which is described in Section 5, and for identifying potential hypotheses to be tested in the analysis.

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Analytical Approach— 3 Meta-Analysis

Over the last two decades, a large and diverse body of empirical research in the area of health valuation has emerged in the economics and health literature. In particular, an increasing number of studies have focused on measuring individuals’ willingness to pay for specifically defined health improvements. All of these studies provide potentially useful information for evaluating the benefits of policies designed to improve public health; however, finding ways to integrate all of this information in a systematic way presents a key challenge to policymakers. Meta-analysis provides an approach for addressing this challenge.

Below, we define meta-analysis and describe how it has typically been used to integrate research findings in the area of nonmarket (including health) valuation. Importantly, meta-analysis can be used to define a benefit transfer function. This function can then be applied to estimate values associated with a wide variety of health improvements, including those associated with food safety policies and programs.

3.1 META-ANALYSIS IN NONMARKET VALUATION Meta-analysis refers to the practice of using a collection of formal and informal statistical methods to synthesize the results found in a well-defined class of empirical studies. Glass (1976), in an early review of the method, described it as “the statistical analysis of a large collection of results from individual studies for the purposes of integrating the findings. It connotes a rigorous alternative to the


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casual, narrative discussion of research studies, which typify our attempts to make sense of the rapidly expanding research literature.” It is an analytical approach that has primarily evolved and been most commonly applied in the area of health sciences; however, it is increasingly being used in social sciences, including in the field of economics (Stanley, 2001).

Smith and Pattanayak (2002) provide a recent summary and evaluation of how this set of techniques has been used in the area of nonmarket valuation, including health valuation. They argue that meta-analyses in this field have generally served three main purposes:

Z research synthesis,

Z hypothesis testing, and

Z prediction (benefit transfer).

Research synthesis is the most common objective of these analyses. In contrast to most literature reviews, however, the approach is to define quantitative measures that can be defined consistently across studies and to then provide statistical summaries of these measures. In these cases, the primary measure of interest is the monetary value of a particular welfare effect, such as the average WTP to reduce one’s mortality risk. For example, Viscusi (1993) provides an often cited summary of the VSL literature, which defined the most relevant range of VSL estimates for policy evaluation purposes.

Hypothesis testing takes the statistical analysis of the meta-data one step further. Depending on the context, a number of statistical methods can be used to test a variety of hypotheses. Regression analysis (“meta-regression”) is a tool that is particularly used in economic applications of meta-analysis. In contrast to clinical trial analyses, for example, where conditions are carefully controlled and duplicated across multiple studies, economic studies typically vary in several respects, each of which may exert a significant influence on the result of interest. Regression analysis offers a way to simultaneously control for a variety of factors and to test for how these factors influence the results. In Section 2 of this report, we identified a number of hypotheses regarding factors that we expect to influence health values. In Section 5, we report on the results of meta-regressions which we have used to test some of these hypotheses.

Section 3 — Analytical Approach—Meta-Analysis

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Through the process of meta-regression and hypothesis testing, it is possible to determine whether, across studies, there exists a systematic relationship between key explanatory variables and the primary measure of interest. In the area of nonmarket (e.g., health) valuation, meta-analyses have typically focused on whether WTP estimates vary in systematic and expected ways with respect to the “commodity” of interest and the characteristics of the study population and study methods. To the extent that one is able to uncover such a systematic relationship, the results provide a basis for specifying values for benefit transfer.

One benefit transfer approach is to use the results to define an unconditional average unit-value (with confidence interval), such as the mean value of statistical life (Mrozek and Taylor, 2002). This unit value benefit transfer approach is possible when the commodity of interest is well-defined and relatively homogeneous, and the statistical analysis supports the validity of the underlying value estimates (i.e., to demonstrate that they do not simply represent random “noise”).

Alternatively, it may be possible to use the regression results to define a mean value that is conditional on the type of change and the context of interest. This benefit function transfer approach may be more appropriate when there is substantial heterogeneity across values and the meta-regression analysis is able to account for this variation in a statistically significant manner. For example, Johnson, Fries, and Banzhaf (1997) have conducted a meta-analysis of morbidity valuation studies, and used the results to define a benefit transfer function, which uses measures of duration and severity of illness as explanatory variables. The analysis described in Section 5 of this report uses a similar approach.

3.2 PROCEDURES FOR CONDUCTING META-ANALYSES The steps most commonly used in conducting a meta-analysis are similar to those used in most forms of empirical research with primary data. Researchers have proposed various clusters of essentially the same sets of activity that constitute a meta-analysis (Cooper, 1988; Rosenthal, 1991). Cooper’s approach, perhaps the most used categorization, includes the following primary steps:


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Z problem formulation

Z data collection

Z data evaluation

Z analysis and interpretation

Z public presentation

These are precisely the steps which guide the analysis described in this report. In Sections 1 and 2, we have formulated the primary issues to be addressed and hypotheses to be examined. The next section describes the processes we have used to identify, collect, and organize the valuation data. Selecting and preparing the data for meta-analysis has also required several steps of screening and study evaluation. The process of results of this data evaluation process is also described in Section 4. In Section 5, we discuss and interpret the results of two meta-analyses of value estimates, one for acute health effects and another for chronic effects. This report addresses the final step of the process by communicating and presenting the results of our analysis.

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Data Collection and 4 Evaluation

This section describes the main data collection and evaluation activities that have been conducted to support the meta-analysis and to provide CFSAN with tools that can be used in assessing the health benefits of its regulations. The first major group of activities has been to develop a comprehensive bibliography of health valuation studies from the existing literature and to screen and evaluate these studies with respect to their usefulness for this project. These activities are described in Section 4.1. Next, selecting from the set of studies in our bibliography, we have created a database of health-based WTP estimates. Section 4.2 describes the design and contents of this database.

On a separate but related track, we have also collected information on nonmonetary measures of health. In particular, we have compiled a bibliography of studies that have used established MAUS techniques, such as QWB and EuroQol, to estimate health indexes for selected health effects and populations. We have also created a database that summarizes the results of these studies. The objective of these data collection activities, which are described in Section 4.3, is to provide a consistent set of HSMs that can be used to predict WTP for avoided selected health effects.

Although the bibliographies and databases described in this section have primarily been assembled to support the analysis described in Section 5, their applicability should extend beyond this report. The information currently contained in these files can serve as a more general resource for identifying, summarizing or transferring estimates from the health valuation literature. The databases also

Although the bibliographies and databases described in this section have primarily been assembled to support the analysis described in Section 5, their applicability should extend beyond this report.


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provide organizing structures that can easily be used to include information from more WTP and HSM studies. As such, they can support additional and expanded analyses of the health valuation literature.

4.1 REVIEW AND SELECTION OF STUDIES ON WTP FOR IMPROVED HEALTH To begin identifying and collecting estimates on the WTP for health improvements, we conducted a through literature search and developed an annotated bibliography of WTP for health improvements. This bibliography provides general descriptions of the methods used and issues addressed in the studies. It is primarily designed to screen and evaluate studies for potential inclusion in a meta-analysis.

This section describes the literature search and screening methods we used to identify and select studies. It also provides an overview of the 136 publications that are included in the bibliography.

4.1.1 Literature Search and Screening

Beginning with RTI’s current bibliography of nonmarket and health valuation studies, we systematically identified as many empirical studies as possible that have been applied to or relate in some way to WTP for health improvements. We initially started by looking at previous literature reviews (e.g., Johnson, Fries, and Banzhaf, 1997; Diener, O’Brien, and Gafni, 1998; Olsen and Smith, 2001). We then expanded the search using several search engines, including

Z PubMed—a service of the National Library of Medicine that provides access to MEDLINE citations and life science journals

Z Ingenta—a website specifically designed for searching and delivering research articles (http://www.ingenta.com)

Z Econlit—a detailed indexed bibliography with selected abstracts in economics

These searches were conducted using several key words such as WTP, WTA, health value, health valuation, contingent valuation, and conjoint analysis. We then scanned the reference lists of these articles and used our personal contacts to identify additional candidate studies.

Section 4 — Data Collection and Evaluation

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In screening and selecting studies for inclusion in our bibliography, we focused on empirical studies that examined an individual’s WTP to improve his/her own health. Our focus was on the WTP to reduce morbidity. Thus, many potential candidate studies were excluded, including studies that focused only on the WTP to avoid death (mortality), studies that were purely theoretical, review/summary articles, studies that did not use WTP to value health improvements, studies that primarily valued health-related information, and studies that focused strictly on the COI.

4.1.2 Annotated Bibliography of Selected Studies

We examined over 600 studies to compile this bibliography. Of the publications screened, we currently have 136 in our bibliography.

Appendix A contains an annotated bibliography of the 136 publications; each publication is separately identified and described according to the following fields:

Z Priority Code: This field includes a numerical indicator of the priority that is currently being given to the publication for more thorough review and inclusion in the WTP for health database (see Section 4.2 for more details). The code values have the following meaning:

1. Value estimates from these publications have been included in the WTP for improved health database. (1a indicates publications that were also used in the Johnson, Fries, and Banzhaf [1997] study). They were identified as the most likely candidates for inclusion in a meta-analysis.

2. Value estimates from these publications are the next “in line” to be included in the WTP for health database. For various reasons (e.g., lack of specificity regarding the health change evaluated), these studies are less likely candidates for meta-analysis.

3. These are documents with supporting information to supplement data from other publications (with priority code equal to 1 or 2) in the same study, but it does not provide additional independent value data (i.e., additional records/observations) to the WTP for health database.

Z Study ID Number and Publication ID number: Each study has a separate ID number, and, to the extent that individual studies have resulted in multiple publications, each publication is also numbered. The combination of these two numbers provides a unique identifier for each publication. For example, Mark Dickie and his coauthors conducted a


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single study (study ID = 7) that yielded four publications (pub ID = 1–4), all of which are in our bibliography.

Z Publication Name: This field includes standard bibliographic reference information, including authors names, date, title, etc.

Z Valuation Method: This field describes the empirical valuation method applied in the publication. The studies either use the CVM, conjoint analysis, the hedonic method, or the averting behavior method.

Z Health Effect/Change: This field describes the type of health change that each study was evaluating.

Z Risk Based: This field indicates whether the WTP was for a reduction in the risk of a health outcome (ex ante analysis), or a change in the health outcome itself (ex post analysis).

Z Country: This field indicates the country where the study was conducted.

Z HSM: This field indicates which, if any, nonmonetary HSMs were used in the study to evaluate the same health effect.

The assembled bibliography, summarized in Appendix A, reveals a large and diverse body of empirical literature on this topic. The large number of studies/publications is encouraging, especially when one considers the multiple value estimates that many of the studies contain. This large number of studies makes meta-analysis possible.

4.2 WTP FOR HEALTH DATABASE After collecting and screening the health valuation studies, the next step in preparing for meta-analysis is to design a database that efficiently stores and codes detailed information from each publication. The key field in this database contains the WTP estimate(s) for health improvements from each publication. Several other fields are included to further characterize the valuation methods, sample population, type of illness being considered, and whether the WTP was for a reduction in the probability of an illness (ex ante) or the elimination of an illness once obtained (ex post). This section describes the design of the database and the data entry and verification process. It also summarizes the contents of the database, which includes 389 values from 44 separate publications.


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4.2.1 Database Design

This database is based on a three-level nested structure, as shown in Figure 4-1. We first distinguish between studies and publications, because one study can lead to many publications. We then distinguish between publications and values, because one publication can have many values.

Figure 4-1. Three-Level Database Design

Study i

Publication i1

Publication ij

Publication iM

Value i11

Value i10

Value ij1

Value ijP

Value iM1

Value iMQ

•••

•••

•••

•••

•••

This database currently contains value information from 35 studies and 44 publications. The frequency distribution of publications per study is shown in Table 4-1. Eight of these studies (23 percent) have more than one publication included in the database. The frequency distribution of value estimates per publication is shown in Table 4-2. Most publications (59 percent) have from 1 to 5 value estimates.

The first spreadsheet contains study-level information for each of the publications included in the spreadsheet file. As shown in Table 4-3, this information includes a study identification number, names of the key authors, and names of the primary sponsors/funders of the study (if available and relevant).


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Number of Publications per Study

Number of Studies in Category Percent

1 27 77.1

2 7 20.0

3 1 2.9

Total 35 100.0

Number of Value Estimates per Publication

Number of Publications in Category Percent

1 to 5 26 59.1

6 to 10 6 13.6

11 to 15 2 4.5

16 to 20 6 13.6

21 to 25 1 2.3

26+ 3 6.8

Total 44 100.0

Description Field Type Field Name

Study ID Number integer studyid

Key Author 1 (last name) character kauthor1



Study Sponsor/Funder 1 character sponsor1



The second spreadsheet contains publication-specific information for each of the publications included in the spreadsheet file. This information is listed in Table 4-4. In addition to study and publication identification numbers, it includes author identifiers and information about the data and type of publication.

Table 4-1. Number of Publications per Study

Table 4-2. Number of Value Estimates per Publication

Table 4-3. Study-Level Data Fields (Spreadsheet 1)


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Publication ID Number integer pubid

First Author Last Name character pubauthor1

First Author First Name Initial character pubauthorfn1

Second Author Last Name character pubauthor2

Second Author First Name Initial character pubauthorfn2

Third Author Last Name character pubauthor3

Third Author First Name Initial character pubauthorfn3

Fourth Author Last Name character pubauthor4

Fourth Author First Name Initial character pubauthorfn4

Total Number of Authors integer numauthor

Publication Year integer pubyr

Publication Type pubtype

Journal article (peer reviewed) dummy pubjrl

Book dummy pubbk

Book chapter dummy pubbkchap

Technical report dummy pubtech

Working paper dummy pubwp

Ph.D. dissertation dummy pubphdd

Master’s thesis dummy pubmt

Conference presentation dummy pubconf

Other dummy pubother

Other description character pubdes

The remaining seven spreadsheets (3.1 through 3.7) contain information that is specific to the value estimates. This information is split into multiple sheets, primarily to facilitate the entry and viewing of data. The first five data fields in each of these studies are identical—study ID number, publication ID number, value ID number, lead author last name, and publication year. Together, these fields create a unique identifier for each value estimate. In each of the seven sheets, value-specific information is entered, by column, beneath this identifier. Each of these seven sheets has the same value identifiers in each column (e.g., column F contains the same value identifier and includes information about the same value estimate in each sheet).

Table 4-4. Publication-Level Data Fields (Spreadsheet 2)


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The first value-level sheet (spreadsheet 3.1) contains information about the value estimate of WTP for health improvements. The specific data fields are listed in Table 4-5. The WTP for improved health can either be entered as a mean or a median (or both) in dollar terms, and the range and error estimates can also be included. The currency and currency year are also specified here. Farther down, fields are included to distinguish between the value concept. That is, was this the WTP to avoid an illness or the willingness of a person to accept an illness for a certain dollar amount (equivalent or compensating variation measures).

The second value-level sheet (spreadsheet 3.2) contains information about the changes in the health outcome. There are several measures for the change in the intensity/severity of the illness. There are also descriptors to measure the frequency and the duration of the illness. For studies that measured the WTP to reduce the risk of illness (ex ante WTP), there are several descriptors to measure the change in the probability of getting ill. These fields are listed in Table 4-6.

The third value-level sheet (spreadsheet 3.3) contains information on the specific characteristics of the illness. In particular, there is the type of illness, the specific symptoms, and the cause of the illness. These fields are listed in Table 4-7.

The fourth value-level sheet (spreadsheet 3.4) contains information on how the WTP data were collected. It specifies the analysis period and the WTP methods applied. Many types of valuation methods could be used, among them the CVM and the hedonic pricing method. This spreadsheet also specifies the size, socio-demographic, and other characteristics of the study sample/population, along with the methods used to recruit and gather information from this population. These fields are listed in Table 4-8.


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Table 4-5. Value-Level Data Fields (Spreadsheet 3.1)




Value ID Number integer valueid

Lead Author Last Name character pubauthor1


Value

Mean real mean

Outliers trimmed dummy trimmean

Nonresponse corrected dummy corrected

Protest response corrected dummy protcorrected

Inconsistency corrected dummy inconcorrected

Turnbull lower-bound estimate dummy turnbull

Median real median

Low (95%CI) real lowerci

High (95%CI) real upperci

Standard error real stderr

Currency character currency

Currency year integer currencyyr

Payment time frame

Frequency

Every X days integer tfday

One time dummy tfmonth

Duration

X days integer tfyear

Duration of illness dummy tfduration

Permanent dummy tfpermanent

Present value dummy tfpv

Discount rate (%) integer tfpvdiscount

Number of years integer tfpvyrs

(continued)


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Table 4-5. Value-Level Data Fields (Spreadsheet 3.1) (continued)


Other dummy tfother

Other description character tfotherdes

Value concept

WTP (compensating variation) dummy wtp

WTP to avoid (equivalent variation) dummy wtpavoid

WTA (equivalent variation) dummy wta

WTA to forgo (compensating variation) dummy wtaforgo

Marginal rate of substitution dummy mrs

Other dummy other

Other description character valueotherdes

Health Change Valued

Description character hcvdes

General health effect

Mortality dummy hcvmortality

Acute morbidity/disability dummy hcvacutemorb

From chronic condition dummy hcvchrocon

From treatment dummy hcvtrtmt

Chronic morbidity/disability dummy hcvchronic


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Ex Post Health Change

Health outcome character exposthlthoc

Number of health outcome measures changed integer hlthocnumchg

Change in intensity/severity dummy sevchgint

Severity measure character sevmsr

Before change level real sevbefore

Before change description character sevbeforedes

After change level real sevafter

After change description character sevafterdes

Change in severity measure character sevchgmsr

Change real sevchg

Severity change description character sevchgdes

Change in duration dummy durationchg

Duration measure character durmsr

Before change level real durbefore

Before change description character durbeforedes

After change level real durafter

After change description character durafterdes

Change in duration measure character durchgmsr

Change real durchg

Duration change description character durchgdes

Change in frequency dummy frequencychg

Frequency measure character freqmsr

Before change level real freqbefore

Before change description character freqbeforedes

After change level real freqafter

After change description character freqafterdes (continued)


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Change in frequency measure character freqchgmsr

Change real freqchg

Frequency change description character freqchgdes

Ex Ante Health Change dummy exante

Risk-related health outcome character riskhlthoc

Risk measure character riskmsr

Before change risk real riskbefore

After change level real riskafter

Change in risk measure character riskcghmsr

Change real riskchg

Health Status Measure

Visual Analog Scale (VAS) dummy vas

Average baseline real vasavgbasln

Average with change real vasavgwchng

Standard Gamble (SG) dummy sg

Time frame used character sgtimeframe

Average baseline real sgbasln

Average with change real sgavgwchg

Time Trade Off (SG) dummy tto

Time frame used character ttotimeframe

Average baseline real ttobasln

Average with change real ttoavgwchg

Other dummy hsmother

Other description character hsmotherdes

Average baseline real hsmotherbasln

Average with change real hsmotheravgwchg


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Study ID Number Integer studyid





Illness Category

AIDS/HIV dummy aids

Alcoholism and related diseases dummy alcohol

Allergies dummy allergy

Back and neck injuries dummy backneck

Birth defects dummy birfthdef

Blindness dummy blind

Blood disorders (anemia) dummy blood

Bone diseases dummy bone

Bowel syndromes dummy bowel

Cancer dummy cancer

Circulatory/metabolic disorder dummy circulat

Dental conditions dummy dental

Depressive disorders dummy depress

Diabetes diabetes

Digestion and nutrition disorders dummy digest

Ear and eye disorders dummy eareye

Eating disorders dummy eating

Epilepsy dummy epilepsy

Gastrointestinal diseases dummy gastroint

Genetic disorders dummy genetic

Glaucoma dummy glaucoma

Heart (cardiovascular) diseases, stroke, aneurysm dummy heart

Hepatitis dummy hepatitis

High cholesterol dummy cholesterol

Hypertension, anemia, etc. dummy hypertense

Infertility dummy infertility (continued)


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Leprosy dummy leprosy

Liver and kidney diseases dummy liverkidney

Lupus dummy lupus

Malaria, polio, smallpox, etc. dummy malaria

Meningitis dummy meningitis

Mental disorders dummy mental

Mood disorders dummy mood

Multiple sclerosis (MS) dummy ms

Nervous system disorder dummy nervoussys

Neurological diseases dummy neurological

Obesity dummy obesity

Osteoporosis dummy osteoporosis

Palsy (cerebral and Bell), facial paralysis dummy palsy

Parkinson’s diseases dummy parkinsons

Pregnancy-associated conditions dummy pregnancy

Psoriasis dummy psoriasis

Respiratory diseases dummy respiratory

Schizophrenia dummy schizo

Sexually transmitted diseases (STDs) dummy sexual

Skin diseases dummy skindis

Sleep disorders dummy sleepdis

Speech disorders dummy speechdis

Sports injuries dummy sportsinjuries

Substance abuse and addiction dummy substanceabuse

Thyroid disease dummy thyroid

Not specified dummy illnessnotspec

Other dummy illnessother

Other specify character illnessotherdes

Specific Symptoms

Cough dummy cough

Pain dummy pain

Headache dummy headache

Nausea/stomach upset dummy nausea

Vomiting dummy vomit (continued)


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Fever/aching dummy fever

Disorientation/light-headedness dummy disorient

Chest pain dummy chestpain

Shortness of breath dummy shortbreath

Throat irritation dummy throat

Eye irritation dummy eyeirritation

Itching dummy itching

Not specified dummy sympnotspec

Other dummy sympother

Other specify character symotherdes

Cause of Health Effect

Environment/air dummy environair

Environment/water dummy environwater

Food/nutrition dummy food

Occupational dummy occupation

Product safety dummy productsafety

Substance abuse dummy causesubstance

Transportation dummy transportation

Natural disaster dummy naturaldisaster

Genetic/hereditary dummy causegenetic

Infectious disease dummy infectious

Not specified dummy causenotspec

Other dummy causeother

Other specify character causeotherdes


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Analysis Period

Year begin integer apyrbegin

Year end integer apyrend

Valuation Method

Contingent valuation dummy cv

Hedonic dummy hedonic

Conjoint/paired comparison dummy conjoint

Averting behavior dummy mktv

Other dummy othervm

Other specify character othervmspec

Study Sample/Population

Unit/number of observations

Individuals/households dummy person

Number integer numperson

Choice occasions dummy choice

Number integer numchoice

Other dummy othersample

Other description character othersampledes

Number integer numothersample

Sample size integer samplesize

Response rate (%) integer responserate

Total number of observations integer numobs

Sampling approach

Country

U.S. dummy sampleus

Canada dummy samplecan

Other dummy sampcntryother

Other specify character sampcntryotherspec

City/state character samplecitystate

Recruitment

Random digit dial dummy randial

Random mailing dummy ranmail (continued)


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Mall intercept dummy mall

Patient list dummy patientrecruit

Other dummy otherrecruit

Other description character otherrecruitdes

Inclusion criteria

Age dummy inclcritage

Gender dummy inclcritgender

Parent dummy inclcritparent

Race dummy inclcritrace

Health condition dummy inclcrithlthcond

Health condition specify character inclcrithlthspec

Other dummy inclcirtother

Other specify character inclcritotherspec

Sample characteristics

Income

Mean integer incomemean

Median integer incomemedian

Currency year integer incomeyr

Gender (% male) integer gendermale

Ethnicity (% white) integer racewhite

Age

Mean integer agemean

Median integer agemedian

Minimum integer agemin

Maximum integer agemax

Education (average number of years) integer avgedu

Survey method

Mail dummy mail

In-person interview dummy inperson

Telephone dummy phone

Computer interview dummy computer

Internet dummy internet

Other dummy othersurvey

Other description character othersurveydes


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The final three sheets are used to summarize information that is specific to one of the three valuation method categories—stated preference, hedonic, or averting behavior methods. Data are only entered in these fields if the valuation method was used to derive the value estimate. As shown in Table 4-9, the fifth value level sheet (spreadsheet 3.5) contains information for stated preference method applications. In this sheet, the value estimates are categorized according to the manner in which they were elicited from survey respondents and the method proposed in the survey to pay for the improvement in health.

Table 4-9. Value-Level Data Fields—Stated Preference Methods (Spreadsheet 3.5)







Elicitation Format

Open ended dummy openend

Closed ended dummy closedend

Dichotomous choice dummy dichochoice

Double bounded dummy doublebond

Iterative bidding dummy bidding

Open-ended follow up dummy followup

Payment card dummy card

Ranking dummy ranking

Rating dummy rating

Other dummy otherformat

Other description character otherformatdes

Payment Vehicle

Tax dummy payctax

Out-of-pocket payment dummy payoutofpock

Insurance payment dummy payinsurance

Cost of living dummy paycsurcharge

User fee dummy paycfee

Voluntary contribution dummy payv

Not specified dummy paynotv

Other dummy paycother

Other specify character paycotherpaydes


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The sixth value level sheet (spreadsheet 3.6) contains information for the hedonic pricing method. This includes information on job characteristics and wage of participants in the sample population. It also includes some methodological variables, including dummy variables to indicate what type of econometric techniques were used. These fields are listed in Table 4-10.

Table 4-10. Value-Level Data Fields—Hedonic Method (Spreadsheet 3.6)







Job characteristics

% union integer jobpercunion

Average wage real jobavgwage

Average wage income (per yr) real jobavgwageinc

Average nonwage income (per yr) real jobavginc

Average hrs worked (per yr) real jobavghrs

Other character jobother

Specification

Linear dummy linear

Log-linear (Cobb-Douglas) dummy loglinear

Semi-log dummy semilog

rhs dummy rhs

lhs dummy lhs

Number of risk characteristics integer numriskchar

Number of other job characteristics integer numjobchar

Estimation Method

First Stage dummy stage1

Ordinary least squares (OLS) dummy stage1ols

Other dummy stage1other

Other specify character stage1otherdes

Second Stage dummy stage2

OLS dummy stage2ols

Other dummy stage2other

Other specify character stage2otherdes

Labor income/supply data source character laborincomedata

Job risk data source character jobriskdata


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The seventh value level sheet (spreadsheet 3.7) contains information for the averting behavior method. This includes information on the types of goods purchased, prices, and quantity purchased. It also includes information on total expenditures on goods. These fields are listed in Table 4-11.

Table 4-11. Value-Level Data Fields—Averting Behavior Method (Spreadsheet 3.7)







Averting good characteristics

Type of good character typeofgood

Average price per unit real avgunitprice

Average units purchased (per yr) real avgunitpurch

Average expenditure (per yr) real avgexpperyr

Other character otheravert

4.2.2 Data Summary and Evaluation

As previously indicated, the database currently contains value information from 44 publications (and 35 studies), and a brief description of these publications can be found in Appendix A. In this section, we summarize and evaluate the compiled data from these studies. Additional descriptive statistics for the variables included in the database are included in Appendix C.

The distribution of these publications by year and by type of publication is shown in Tables 4-12 and 4-13. The earliest publication date is 1979, and 73 percent of the publications have been published in the last 8 years. Thirty-six of the articles are from peer-reviewed journal articles (81.8 percent).

The database contains a total of 389 WTP estimates from these publications. As shown in Table 4-14, these estimates have been generated using different valuation techniques; however, a large majority have used CVM—77 percent. Due to the focus of our


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Publication Year Number of Publications Percent

1975–1979 1 2.3

1980–1984 0 0.0

1985–1989 8 18.2

1990–1994 3 6.8

1995–1999 17 38.6

2000–2002 15 34.1

Total 44 100.0

Publication Type Number of Publications Percent

Journal article (peer reviewed) 36 81.8

Technical report 5 11.4

Working paper 3 6.8

Total 44 100.0

Valuation Method Number of Value Estimates Percent

Contingent valuation 299 76.9

Conjoint 83 21.3

Averting behavior 7 1.8

Total 389 100.0

analysis, relatively little data from RP studies have thus far been collected and added to the database. Spreadsheets 3.6 and 3.7 include very few data entries as a result. Nonetheless, the database is designed to provide an organizing structure that can be used to include RP data and to support broader analyses of the valuation literature.

Table 4-12. Number of Publications by Year

Table 4-13. Number of Publications by Type of Publication

Table 4-14. Valuation Methods Used (Number of Value Estimates per Method


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This database contains studies from 11 countries, and the distribution of value estimates to each country is shown in Table 4-15. Almost 45 percent of the value estimates are from the United States. Canada has the second highest percentage with 20 percent. Seven European countries—Denmark, Great Britain, Netherlands, Norway, Portugal, Spain, and Sweden—have 29 percent of the value estimates. Australia and Taiwan have the remaining 6 percent of the estimates.

Country Number of Value Estimates Percent

U.S. 177 44.9

Canada 79 20.0

Norway 37 9.4

Sweden 28 7.1

Great Britain 18 4.6

Netherlands 18 4.6

Australia 16 4.1

Taiwan 7 1.8

Portugal 6 1.5

Spain 5 1.3

Denmark 3 0.8

Totala 394 100.0

aFive value estimates were based on samples from both the U.S. and Canada.

The type of health condition that is valued is also considered. The majority of value estimates are considering acute morbidity (79 percent). The distribution of value estimates is shown in Table 4-16. Two publications also included value estimates for avoided mortality. For completeness, these 3 estimates were also included in the database.

Table 4-15. Number of Value Estimates by Country


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Table 4-16. Number of Value Estimates by Type of Health Condition Valued

Health Condition Valued Number of Value Estimates Percent

Acute morbidity 308 79.2

Acute morbidity from chronic condition 31

Acute morbidity from treatment 1

Chronic morbidity 78 20.1

Mortality (only) 3 0.8

Total 389 100.0

4.3 DATABASE OF HEALTH STATUS MEASURES As described above, the database of WTP estimates includes values covering a wide range of acute and chronic health conditions. To integrate and compare these values in a systematic way it is important to identify a common metric for characterizing the various health states. In Section 2, we introduced three possible candidates for such a metric—the QWB Index, the HUI, and the EuroQol. Below we describe each of the MAUSs in more detail.

The QWB Index is of particular interest because this is the metric we have used as a severity index in our meta-analysis of WTP estimates for acute health effects (see Section 5). Nevertheless, we have examined each of these three measures as a way of characterizing chronic health effects. To support our evaluation of these measures, we compiled a bibliography of studies that have applied these measures for selected health effects, and we developed a database that summarizes the results of these studies. Section 4.3.2 provides a description of this database.

4.3.1 Overview of HSMs

QWB

Developed at the University of California San Diego, the QWB grew out of the initial endeavor of creating the General Health Policy Model. The goal was to find a way to assess quality of life using a general instrument, because most of the available instruments at the time were condition specific. Researchers, notably Bush, Kaplan,


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Anderson, and colleagues, began development of the QWB, formerly the Index of Well-Being, in the early 1970s.

Scoring Approach. Health is given a value on a 0.0 (death) to 1.0 (perfect health) point scale, with negative values representing health states worse than death. Individual health is rated on three dimensions: Mobility, Physical Activity, and Social Functioning. Each of these dimensions has three levels on which health can be assessed, varying from no limitations to extreme debilitation. Each level for each dimension corresponds to a weight, which is subtracted from 1.0 (perfect health). Weights increase as disability increases. Another element to the QWB is the list of Symptoms/Problems used to further describe the health state. The worst symptom present out of the 27 possible symptoms determines which weight is used. Included in the list are death (highest weight) and no symptoms (lowest weight). The numerous combinations of dimensions, levels, and symptoms result in a total possible 1,170 health states.

Tables 4-17 and 4-18 help describe the dimensions of the QWB Index. Table 4-17 describes the symptom and problem complexes, which range from excessive worry or anxiety to death. Table 4-18 describes the dimensions for the mobility scale, the physical activity scale, and the social activity scale.

Construction of the Weights. The weights used for the current version of the QWB were developed using an ethnically representative sample of the general community in the San Diego, CA region. Trained scorers interviewed 866 people from 1974 to 1975. Surveys used the VAS, such as a rating scale in the form of a thermometer, to rate respondent valuations of 343 case descriptions. Approximately 100 respondents valued each health state. These values were used to determine the values for other health states using a method of modeling (Spilker, 1996).

Current Use. The current questionnaire is given by interview and measures individual health experienced in the past 6 days. A large sample group allows researchers to find an accurate value for average utility for that health state. The QWB is constantly being tested for validity and reliability (Anderson et al., 1989). One issue with the QWB is that the interviewer-administered version, which takes 15 to 35 minutes to complete, is too cumbersome to use in a


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Table 4-17. Symptom and Problem Complexes (CPX) for the Quality of Well-Being Scale

CPX No. CPX Description Weight

1 Death (not on respondent’s card) –0.727

2 Loss of consciousness such as seizure (fits), fainting, or coma (out cold or knocked out)

–0.407

3 Burn over large areas of face, body, arms, or legs –0.387

4 Pain, bleeding, itching, or discharge (drainage) from sexual organs—does not include normal menstrual bleeding

–0.349

5 Trouble learning, remembering, or thinking clearly –0.340

6 Any combination of one or more hands, feet, arms, or legs either missing, deformed (crooked), paralyzed (unable to move), or broken—includes wearing artificial limbs or braces

–0.333

7 Pain, stiffness, weakness, numbness, or other discomfort in chest, stomach (including hernia or rupture), side, neck, back, hips, or any joints or hands, feet, arms or legs

–0.299

8 Pain, burning, bleeding, itching, or other difficulty with rectum, bowel movements, or urination (passing water)

–0.292

9 Sick or upset stomach, vomiting or loose bowel movement, with or without chills, or aching all over

–0.290

10 General tiredness, weakness, or weight loss –0.259

11 Cough, wheezing or shortness of breath, with or without fever, chills, or aching all over

–0.257

12 Spells of feeling upset, being depressed, or of crying –0.257

13 Headache, or dizziness, or ringing in ears, or spells of feeling hot, nervous or shaky

–0.244

14 Burning or itching rash on large areas of face, body, arms, or legs –0.240

15 Trouble talking, such as lisp, stuttering, hoarseness, or being unable to speak –0.237

16 Pain or discomfort in one or both eyes (such as burning or itching) or any trouble seeing after correction

–0.230

17 Overweight for age and height or skin defect of face, body, arms, or legs, such as scars, pimples, warts, bruises or changes in color

–0.188

18 Pain in ear, tooth, jaw, throat, lips, tongue; several missing or crooked permanent teeth—includes wearing bridges or false teeth

–0.170

19 Took medication or stayed on a prescribed diet for health reasons –0.144

20 Wore eyeglasses or contact lenses –0.101

21 Breathing smog or unpleasant air –0.101

22 No symptoms or problems (not on respondent’s card) –0.000

23 Standard symptom/problem –0.257

24 Trouble sleeping –0.257 (continued)


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Table 4-17. Symptom and Problem Complexes (CPX) for the Quality of Well-Being Scale (continued)

CPX No. CPX Description Weight

25 Intoxication –0.257

26 Problems with sexual interest or performance –0.257

27 Excessive worry or anxiety –0.257

Note: Reproduced with permission from an original supplied by Dr. Kaplan.

Table 4-18. Dimensions, Function Levels, and Weights of the Quality of Well-Being Scale

Step Step Definition Weight

Mobility Scale (MOB)

5 No limitations for health reasons –0.000

4 Did not drive a car, health related; did not ride in a car as usual for age (younger than 15 years), health related and/or did not use public transportation, health related; or had or would have used more help than usual for age to use public transportation, health related

–0.062

2 In hospital, health related –0.090

Physical Activity Scale (PAC)


3 In wheelchair, moved or controlled movement of wheelchair without help from someone else; or had trouble or did not try to lift, stoop, bend over, or use stairs or includes, health related; and/or had any other physical limitation in walking, or did not try to walk as far as or as fast as others the same age are able, health related

–0.060

1 In wheelchair, did not move or control the movement of wheelchair without help from someone else, or in bed, chair, or couch for most or all of the day, health related

–0.077

Social Activity Scale (SAC)


4 Limited in other (e.g., recreational) role activity, health related –0.061

3 Limited in major (primary) role activity, health related –0.061

2 Performed no major role activity, health related, but did perform self-care activities

–0.061

1 Performed no major role activity, health related, and did not perform or had more help than usual in performance of one or more self-care activities, health related

–0.106

Note: Reproduced with permission from an original supplied by Dr. Kaplan.


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survey with a large sample size. A self-administered version of the QWB (QWB-SA) that takes only 10 minutes to complete has been developed to solve this problem (Kaplan et al., 1996).

Health Utility Index (HUI)

Designed by Torrance et al. in Canada in 1982, the HUI now encompasses versions 1, 2, and 3. The preference-based scoring system is the component of the HUI that generates the health utility score. The HUI Mark 3 (or HUI-3) is the most often used today.

Scoring Approach. The HUI-3 uses eight dimensions: Vision, Hearing, Speech, Ambulation, Dexterity, Emotion, Cognition, and Pain/Discomfort. The five to six levels of each dimension range from full ability to complete lack of ability. The description of individual health depends on the utility values described by the level in each dimension. All together there are 972,000 health states with utility values ranging from 0.0 to 1.0. Table 4-19 describes the states available in each of the eight dimensions.

Construction of the Weights. The surveys used to determine the weights for the HUI-3 were administered in Hamilton, Ontario, Canada. The VAS and SG were used, through a two-sided feeling thermometer and flip chance board, respectively. Two surveys, the HUI-3 Modelling Survey given to 256 respondents and the HUI-3 Direct Measurement Survey given to 248 respondents, were presented to different groups of people through interviewers (Furlong et al., 1998). Respondents were instructed to assume that the health conditions were chronic and would persist throughout the rest of their lives. Remaining life expectancy was determined and recorded by respondents. A total of 74 health states values were gathered. The MAUF, used to generate utility scores for the remaining health states, was derived from these data using an algebraic method (Furlong et al., 1998).

Current Use. The HUI-3 is the most current version. The versions differ greatly from one another, such as in the number of dimensions used to describe the health states. Many national health surveys have included some form of the HUI, which makes it a valuable tool in gathering large-scale health utilities for a population.


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Table 4-19. Multiattribute Health Status Classification System: Health Utilities Index Mark 3 (HUI-3)

Attribute Description

Vision 1. Able to see well enough to read ordinary newsprint and recognize a friend on the other side of the street, without glasses or contact lenses.

2. Able to see well enough to read ordinary newsprint and recognize a friend on the other side of the street, but with glasses.

3. Able to read ordinary newsprint with or without glasses but unable to recognize a friend on the other side of the street, even with glasses.

4. Able to recognize a friend on the other side of the street with or without glasses but unable to read ordinary newsprint, even with glasses.

5. Unable to read ordinary newsprint and unable to recognize a friend on the other side of the street, even with glasses.

6. Unable to see at all.

Hearing 1. Able to hear what is said in a group conversation with at least three other people, without a hearing aid.

2. Able to hear what is said in a conversation with one other person in a quiet room without a hearing aid, but requires a hearing aid to hear what is said in a group conversation with at least three other people.

3. Able to hear what is said in a conversation with one other person in a quiet room with a hearing aid, and able to hear what is said in a group conversation with at least three other people, with a hearing aid.

4. Able to hear what is said in a conversation with one other person in a quiet room, without a hearing aid, but unable to hear what is said in a group conversation with at least three other people even with a hearing aid.

5. Able to hear what is said in a conversation with one other person in a quiet room with a hearing aid, but unable to hear what is said in a group conversation with at least three other people even with a hearing aid.

6. Unable to hear at all.

Speech 1. Able to be understood completely when speaking with strangers or friends.

2. Able to be understood partially when speaking with strangers but able to be understood completely when speaking with people who know me well.

3. Able to be understood partially when speaking with strangers or people who know me well.

4. Unable to be understood when speaking with strangers but able to be understood partially by people who know me well.

5. Unable to be understood when speaking to other people (or unable to speak at all).

Ambulation 1. Able to walk around the neighborhood without difficulty, and without walking equipment.

2. Able to walk around the neighborhood with difficulty; but does not require walking equipment or the help of another person.

3. Able to walk around the neighborhood with walking equipment, but without the help of another person.

(continued)


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Table 4-19. Multiattribute Health Status Classification System: Health Utilities Index Mark 3 (HUI-3) (continued)


Ambulation (continued)

4. Able to walk only short distances with walking equipment, and requires a wheelchair to get around the neighborhood.

5. Unable to walk alone, even with walking equipment. Able to walk short distances with the help of another person, and requires a wheelchair to get around the neighborhood.

6. Cannot walk at all.

Dexterity 1. Full use of two hands and ten fingers.

2. Limitations in the use of hands or fingers, but does not require special tools or help of another person.

3. Limitations in the use of hands or finders, is independent with use of special tools (does not require the help of another person).

4. Limitations in the use of hands or fingers, requires the help of another person for some tasks (not independent even with use of special tools).

5. Limitations in use of hands or fingers, requires the help of another person for most tasks (not independent even with use of special tools).

6. Limitations in use of hands or fingers, requires the help of another person for all tasks (not independent even with use of special tools).

Emotion 1. Happy and interested in life.

2. Somewhat happy.

3. Somewhat unhappy.

4. Very unhappy.

5. So unhappy that life is not worthwhile.

Cognition 1. Able to remember most things, think clearly, and solve day-to-day problems.

2. Able to remember most things, but have a little difficulty when trying to think and solve day-to-day problems.

3. Somewhat forgetful, but able to think clearly and solve day-to-day problems.

4. Somewhat forgetful, and have a little difficulty when trying to think or solve day-today problems.

5. Very forgetful, and have great difficulty when trying to think or solve day-to-day problems.

6. Unable to remember anything at all, and unable to think or solve day-to-day problems.

Pain/ Discomfort

1. Free of pain and discomfort.

2. Mild to moderate pain that prevents no activities.

3. Moderate pain that prevents a few activities.

4. Moderate to severe pain that prevents some activities.

5. Severe pain that prevents most activities.

Source: Feeny, D., W. Furlong, M. Boyle, and G.W. Torrance. 1995. “Multi-attribute Health Status Classification Systems.” Pharmaco Economics 7(6):490-502.


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EuroQol

The creation of the EuroQol occurred because of a joint effort among many European researchers to develop a general index to measure quality of life. The EuroQol Group began developing the index in 1990. Generally speaking, EuroQol refers to the combination of a five-item questionnaire (the EQ-5D) and a visual analogue rating scale (EQ-VAS).

Scoring Approach. The EQ-5D, the questionnaire portion of the EuroQol, is used to generate a health utility score between 0.0 and 1.0. The five dimensions tested by the questionnaire are meant to cover all aspects of health: Mobility, Self-Care, Usual Activity, Pain/Discomfort, and Anxiety/Depression. The dimensions are subdivided into three levels each, from no problems (level 1) to extreme problems (level 3). A score of 11111 describes perfect health, and a score of 33333 describes the worst health state possible. There are 243 health states defined by the EuroQol. Table 4-20 describes the EuroQol dimensions.

Construction of the Weights. The weights used for the EuroQol (or EQ-5D) are based on the results of a representative survey consisting of 3,395 people in the UK conducted from August to December of 1993. Face-to-face interviews by trained interviewers in respondents’ homes lasting approximately 1 hour were used to present the survey. Respondents were asked to imagine themselves in a given health state for a time period of 10 years. They then rated that health condition using the VAS and TTO methods. Values were elicited for 45 health states (15 states per respondent). Utility weights for the remaining health states were identified using modeling and generalized least squares regression (Dolan, 1997).

Current Use. Both the EQ-5D and EQ-VAS are available for use, although only the EQ-5D produces utility scores. The EQ-5D is self-completed by the respondent and can be completed in a few minutes. The EuroQol is routinely updated and validated by members of the EuroQol group, which has grown to include people in other European countries and around the world.


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Table 4-20. The EuroQol Descriptive System


Mobility 1. No problems walking about

2. Some problems walking about

3. Confined to bed

Self-Care 1. No problems with self-care

2. Some problems washing or dressing self

3. Unable to wash or dress self

Usual Activities 1. No problems with performing usual activities (e.g., work, study, housework, family, or leisure activities)

2. Some problems with performing usual activities

3. Unable to perform usual activities

Pain/Discomfort 1. No pain or discomfort

2. Moderate pain or discomfort

3. Extreme pain or discomfort

Anxiety/Depression 1. Not anxious or depressed

2. Moderately anxious or depressed

3. Extremely anxious or depressed

Note: For convenience each composite health state has a five-digit code number relating to the relevant level of each dimension, with the dimensions always listed in the order given above. Thus 11223 means: 1 No problems walking about 1 No problems with self-care 2 Some problems with performing usual activities 2 Moderate pain or discomfort 3 Extremely anxious or depressed

Source: Dolan, Paul. 1997. “Modeling Valuations for EuroQol Health States.” Medical Care 35(11):1095-1108.

4.3.2 MAUS Database Description

Each of the previously described measures has been used to quantify, on a zero to one scale, health status associated with a wide variety of health conditions. Therefore, each one provides a potential metric for severity of illness that can used in a meta-analysis of WTP estimates. For example, Section 5 describes how we specifically use the QWB Index in a meta-analysis of WTP estimates for avoiding acute effects.

Because of data limitations and the heterogeneity of individuals’ experiences with long-term illness, identifying an appropriate index for chronic effects is particularly challenging. To address this issue, we searched the relevant literature and compiled a database of estimated MAUS scores for selected illnesses.


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To identify potentially applicable empirical studies we relied on several sources, including existing reviews of the literature (Brazier et al., 1999), on-line databases (primarily PubMed), and reference lists from acquired articles. We focused our data collection efforts on studies that have applied at least one of these measures to estimate an average health index for individuals with selected health conditions. The health conditions that are of particular interest to us are (1) those for which we have corresponding WTP estimates and (2) those that CFSAN has identified as priorities, including diabetes, reactive arthritis, and peanut allergies.

The database contains over 700 scores collected from over 60 studies. For each record, the database includes fields to describe the corresponding

Z health condition (including an ICD-9 code);

Z study reference;

Z MAUS method;

Z mean score, as well as the corresponding ranges (i.e., low and high score, and confidence interval);

Z sample size;

Z average age (and range);

Z gender distribution;

Z study country; and

Z study year.

Table 4-21 provides summary statistics for a selected subset of the database, focusing on 35 health conditions.1 Although the QWB Index, followed by EQ-5D, has been applied in the largest number of studies, the largest number of scores have been estimated using HUI-3, followed by EQ-5D. Partly for this reason, HUI-3 is a primary candidate to be used as a metric in the meta-analysis of WTP estimates for avoiding chronic conditions (see Section 5 for details).

1The database includes a number of estimated scores using EQ-VAS, HUI-1, and

HUI-2, and it includes scores for a wide range of other health conditions that were assigned lower priority. These observations are not included in the statistics for Table 4-21.


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Table 4-21. Summary of MAUS Studies and Scores for Selected Health Conditions

Number of Studies Number of Scores ICD 9 Illness

Category Health Condition QWB HUI-3 EQ-5D QWB HUI-3 EQ-5D 153 Colon cancer 1 0 25 0 201 Hodgkin’s Disease 1 0 0 5 239 AIDS/cancer 2 1 2 2 11 14 246 Thyroid disorder 1 1 0 0 250 Diabetes 3 1 7 15 11 57 277 Cystic fibrosis (CF) 1 1 0 0 340 Multiple sclerosis 1 1 1 0 6 345 Epilepsy 2 2 0 12 3 401 Hypertension 2 1 1 8 11 3 410 Myocardial infarction 1 1 0 0 413 Angina pectoris 1 1 3 0 3 428 Congestive heart failure 2 2 0 0 429 Heart disease 1 0 11 0 436 Stroke 1 0 1 0 490 Bronchitis/emphysema 1 0 11 0 491 Chronic bronchitis 1 1 0 0 492 Emphysema 1 1 0 0 493 Asthma 1 1 2 1 11 9 496 Chronic obstructive pulmonary

disease 2 6 0 0

518 Interstitial lung disease 2 4 0 0 531 Ulcer 1 1 1 11 0 533 Drug rash or diarrhea 1 1 0 0 555 Colitis 1 1 0 0 627 Hormone replacement therapy 1 2 0 0 706 Acne 1 1 0 5 4 714 Rheumatoid arthritis 1 3 1 0 11 715 Osteoarthritis 2 3 5 0 4 716 Arthritis/rheumatism 2 2 1 10 12 1 719 Rheumatic disorder 2 0 0 6 729 Fibromyalgia 1 1 1 0 1 797 Old age disability 1 0 18 0 995 Miscellaneous allergies 1 1 0 0 V15 Food allergy 1 0 11 0 V15 Other allergy (not food) 1 0 11 0 V15 Peanut allergy 0 0 0

Total: 32 17 28 70 172 127

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Meta-Analysis 5 Results

Using information from the collection of valuation studies described in the previous section, RTI conducted two meta-analyses of WTP values for health improvements. The first meta-analysis builds on work by Johnson, Fries, and Banzhaf (1997). It focuses on values for acute conditions and uses regression analysis to explain variation in the value estimates. The second meta-analysis uses a similar approach to analyze value estimates for chronic conditions.

This section presents the results of the two meta-analyses. In both cases, we begin by discussing how the data were selected and analyzed. We then describe in detail the results and implications of the analysis. The findings of the meta-analyses indicate that, for the most part, WTP estimates for avoided adverse health effects vary in systematic and expected ways with respect to key explanatory variables. However, these results are stronger and more conclusive for acute effects than for chronic effects. We describe how the results can be applied for benefit transfer and discuss what they imply about the use of a QALY valuation approach.

5.1 META-ANALYSIS OF VALUE ESTIMATES FOR ACUTE EFFECTS Building on work by Johnson, Fries, and Banzhaf (1997), the analysis described in this section focuses on values for acute conditions. Most importantly, the analysis finds a strong statistical relationship between WTP and measures of severity and duration of acute illness. As a result, the estimated meta-regression functions


5-2

also provide a basis for specifying benefit transfer functions for estimating WTP to avoid acute effects.

5.1.1 Data Selection and Description

As Smith and Pattanayak (2002) note in their review of meta-analyses in nonmarket valuation, “Synthesis requires the ability to define a common concept to be measured” (p. 274). Unfortunately, because of the heterogeneity in valuation approaches and health effects across value estimates (see Section 4), defining such a common concept for all 389 value estimates currently included in the value database is difficult. Instead, using an approach similar to Johnson, Fries, and Banzhaf (1997), we selected a subset of more closely related values and used these in a meta-analysis.

In particular, we selected WTP values if

Z they were estimated for well-defined acute health effects,

Z they were estimated using stated preference methods,

Z the severity of the acute health effect could be expressed as and converted to a QWB score, and

Z the change in duration or frequency of the acute effect could be quantified in terms of discrete days and/or episodes.

We began by selecting the 53 observations included in the Johnson, Fries, and Banzhaf study. These values (see Table A-1 in Appendix A) were taken from five CV studies conducted in the United States in the late 1970s and 1980s. The studies were predominantly conducted for cardio-respiratory health effects associated with air pollution.

We then supplemented these values with 183 additional values taken from 12 other studies. These additional studies were, for the most part, conducted after 1990, and they include research conducted both in the United States and in other countries. In Appendix B, Table B-1 provides more detailed descriptions of some of the key characteristics of these studies.

As is commonly done in meta-regressions analyses, we did not define strict quality criteria for including or excluding studies from the analysis. Rather, we followed the recommendation of Stanley (2001) who writes “when in doubt, it is best to err on the side of inclusion…. Differences in quality, data, or methods do not provide a valid justification for omitting studies [from meta-analysis]” (p.

Section 5 — Meta-Analysis Results

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135). Excluding studies based on quality would require making judgments that might introduce more bias into the results than it would avoid. Moreover, to the extent possible, we have included explanatory variables in the analysis to control for methodological differences.

For the 236 selected values, Tables 5-1 and 5-2 describe and summarize the main variables that we used in the meta-analysis. WTPACUTE was the key variable of interest. It represents individuals’ WTP to avoid or to reduce the duration or frequency of a specific acute condition over the course of a year. All WTP estimates were converted to 2000 dollars using the consumer price index (CPI) and, if they were originally measured in a foreign currency, we first converted the estimates to dollars using the purchasing power parity (PPP) index. Most of the selected studies estimate and report average WTP values. If only median WTP values were reported, we included these estimates in WTPACUTE.

Following the approach used by Johnson, Fries, and Banzhaf, we characterized the change in acute health outcomes associated with each WTP value in two main dimensions. First, we created the variable ∆DAYS to capture changes in the duration or frequency of the health effect. In most cases, this variable represents the reduction in the number of days, over the course of a year, that one experiences a given condition, such as shortness of breath, nausea, or headache. In a relatively small number of cases (N = 17), this variable represents a reduction in the number of acute events, such as asthma, angina, or allergy attacks.1

Second, we used the QWB index to characterize the severity of the acute health effect. As described in Section 4, the QWB index characterizes health outcomes in four dimensions—symptoms, mobility, social activity, and physical activity—each of which can be scored separately. Using the health effect descriptions from the valuation studies and following the approach by Johnson, Fries, and Banzhaf, we assigned each health effect to a defined level for all four dimensions. We then used the premeasured QWB weights (see Tables 4-17 and 4-18) to assign a numerical score to each level.

1In statistical tests of the regression results, the effect of ∆DAYS on WTP was not

found to be significantly different for these observations.


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Table 5-1. Descriptions of Variables Used in the Meta-Analysis

Variables Description

WTPACUTE Mean WTP for health change (in 2000 dollars)a

∆DAYS Reduction in duration (in days) or number of episodes of acute effect

∆QWB Improvement in health-related quality of life on affected days (= 1 – total QWB index)

QWBSYMSCORE QWB Symptom Score (27 symptoms)

QWBMOBSCORE QWB Mobility Score (3 levels)

QWBSACSCORE QWB Social Activity Score (3 levels)

QWBPACSCORE QWB Physical Activity Score (3 levels)

INCOME Mean household income (in 2000 dollars)a

AGE Mean age

% MALE Percent male

US = 1 if study was conducted in the U.S.

WTPAVOID = 1 if value was stated for avoiding a decrease in health

OPEN ENDED = 1 if an open-ended value elicitation method was used

PAYMENT CARD = 1 if a payment card value elicitation method was used

IN PERSON = 1 if survey was conducted as an in-person interview

JOURNAL = 1 if publication was published in a peer-reviewed journal

SAMPLE SIZE Number of respondents used to estimate the WTP value

aConverted to dollars using purchasing power parity (PPP) if in foreign currency; median WTP used if mean value not reported.

The resulting four scores were captured in the variables QWBSYMSCORE, QWBMOBSCORE, QWBSACSCORE, and QWBPACSCORE. A higher score in each of these cases indicates a more severe condition.2 As shown in Table 5-2, the symptom scores are on average higher than the other three, but the standard deviation of this score is slightly less.3

The variable ∆QWB provided a summary measure of the reduction in severity of the health effect. It was calculated as the sum of the four scores. This additive assumption is somewhat arbitrary, but it is

2The absolute values of the weights are used in the analysis. They represent the

amount that is deducted from 1.0 (perfect health) to arrive at the specified level of health.

3For one study (Liu et al., 2000), the health effect of interest could not be mapped into the four separate scores, but an overall QWB score was estimated in the study. As a result, one observation is missing for each of the four scores.


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Table 5-2. Summary Statistics for Variables Used in the Meta-Analysis

Variables N Mean SD Min Median Max

WTPACUTE 236 270.22 322.06 2.70 145.62 2927.69

∆DAYS 236 11.9 20.5 1 5 90

∆QWB 236 0.37 0.11 0.17 0.36 0.57

QWBSYMSCORE 235 0.26 0.03 0.17 0.257 0.30

QWBMOBSCORE 235 0.03 0.03 0 0 0.09

QWBSACSCORE 235 0.05 0.04 0 0.061 0.11

QWBPACSCORE 235 0.03 0.03 0 0 0.08

INCOME 236 46,348 13,618 21,891 47,067 88,020

AGE 236 45.2 6.8 35.4 44.5 68

% MALE 236 49.25 14.74 0 47.7 100

US 236 0.39 0.49 0 0 1

WTPAVOID 236 0.88 0.33 0 1 1

OPEN ENDED 236 0.14 0.35 0 0 1

PAYMENT CARD 236 0.29 0.46 0 0 1

IN PERSON 236 0.42 0.49 0 0 1

JOURNAL 236 0.67 0.47 0 1 1

SAMPLE SIZE 236 316.04 151.91 20 399 832

consistent with the way a total QWB index is typically estimated for a specified health condition.4

The variables INCOME, AGE, %MALE, and US were included to account for potentially influential characteristics of the study population. Most studies report summary statistics for these characteristics, but this information is not always reported for the specific subsample that is used to calculate the WTP value. When the subsample information was not available, we used summary statistics for the full study sample.

4The total QWB score is typically calculated as 1 – QWBSYMSCORE –

QWBMOBSCORE – QWBSACSCORE – QWBPACSCORE, such that a higher index represents better health. Therefore, ∆QWB is equal to 1 – the total QWB score.


5-6

The dummy variable WTPAVOID was specified to distinguish between WTP values that were estimated for avoiding a decline in health, as opposed to those for improving health from current conditions. A positive effect for this variable would be consistent with declining marginal utility of health. However, as discussed in Section 2, a large positive effect for this variable may also indicate deviations from the standard utility model, such as the loss aversion and reference dependence models proposed by Tversky and Kahneman (1991).

The remaining dummy variables were included to control for study design effects and for potential publication bias. Finally, we included SAMPLE SIZE, not as an explanatory variable, but as a weighting variable, so that WTP estimates based on larger samples could be given more weight in the regression analysis. The results of including these variables in meta-regressions are described below.

As discussed in Section 2, other factors not included in our list of variables may influence WTP. For example, prices for medical care, wages (opportunity cost of sick time), and average education are all potentially influential factors. Unfortunately, the amount of information reported in the original studies is not adequate to include these factors in the analysis.

5.1.2 Meta-Regression Models and Results

Tables 5-3 through 5-6 describe the regression results for several model specifications, all of which share the same basic structure. We included a measure of WTP in all models as the dependent variable. Measures of the change in duration and severity of the corresponding health effect were included as explanatory variables. Additional explanatory variables include characteristics of the study population, valuation method, study design, and publication outlet.

To explore the robustness of the results across model specifications, 16 sets of results are reported in these tables.5 Four model estimation/specification issues in particular are addressed in the

5The robustness of the results described in these tables is confirmed by the fact that

the size and statistical significance of the key variables change very little when the two highest WTP estimates (over $1,000 each) are excluded from the regression analysis.

Four model estimation/ specification issues in particular were addressed in conducting the meta-analysis: Z functional form

specification

Z health index specification

Z regression weights

Z panel data clustering effects


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regression results tables. Each of these issues is described separately below.

Functional form specification. To evaluate the robustness of model results, we applied linear, semi-log, and log-linear specifications to analyze the data. Although all of these approaches are reasonable for approximating the relationship between WTP estimates and the other variables described in Table 5-1, the log-linear approach has a few conceptual advantages. First, it implies that, as changes in severity and duration of illness (and income) approach zero, WTP also approaches zero. Second, it implies that the marginal effect of income on WTP and the marginal effect of the severity/duration change on WTP are not mutually independent. So, for example, the additional WTP associated with a larger health improvement is not assumed to be independent of an individual’s budget. Third, as discussed in more detail below, the log-linear form allows for a more explicit statistical test of the QALY-based valuation approach.

Health index specification. All of the results reported in these tables rely on the QWB index to describe the severity of acute illness. However, using the composite QWB index by itself implies that the four subcomponent scores—the symptoms, mobility, social activity, and physical activity scores—each have the same marginal effect on WTP. A less restrictive specification allows the four scores to enter the functional relationship separately and independently. In this way it is possible to directly test the simple additive assumption underlying the composite QWB score.

Regression weights. For each functional form and health index specification, we used two different weighted regression approaches. In the first approach (No Weight), each value estimate was weighted equally in the regression. This approach is perhaps too restrictive because it does not account for the fact that some estimates are based on larger sample sizes. Larger samples are likely to contribute more “information” to the analysis. Therefore, in the second approach (Sample Size), we weighted each observation in direct proportion to its sample size. Although there is no explicit test of these alternative approaches, we believe that the assumptions underlying the second weighting approach are inherently more defensible.


5-8

Panel data clustering effects. To account for the panel nature of the data, we estimated all of the models using clustered robust regression. Because the data used in the analysis are characterized by multiple observations from individual studies, they are likely to violate the OLS assumptions of independent and identically distributed errors. Therefore, we used clustered regressions, by study ID, to correct the standard error estimates in a way that accounts for error correlation within study clusters and unequal variance of errors across clusters.

Tables 5-3 and 5-4 report results for linear, log-linear, and semi-log specifications of WTPACUTE with respect to ∆DAYS and the single composite QWB score (∆QWB). All of the models show a reasonably good fit, with R-squared statistics between 38 and 65 percent. More importantly, several of the coefficients have the expected sign and are statistically significant.

The results indicate that the WTP estimates “pass” the scope test. The coefficients for ∆DAYS and ∆QWB, in both linear and log forms, are consistently positive and predominantly significant across specifications at a 0.05 level. In other words, average WTP to avoid acute effects increases with the number of days/episodes avoided and with the severity of the conditions avoided. The coefficients for the linear specification with sample size regression weighting (second specification in Table 5-3) indicate that WTP increases by an average of $4.80 for each additional day/episode avoided and increases by $16 for each 0.01 change (between 0 and 1) in the total QWB index. Using a linear specification implies that these marginal values for ∆DAYS and ∆QWB are constant and independent of one another; therefore, these estimates must be interpreted as average effects across the range of ∆DAYS and ∆QWB. The log-linear specification discussed below relaxes these restrictions.

As expected, income also has a consistently positive and, for the most part, significant effect on WTP. The income coefficient in the log-linear specification (last specification in Table 5-4) can be interpreted as an elasticity of WTP with respect to income. The elasticity estimate is 0.7, which implies that income has a positive but relatively inelastic effect on WTP. Notably, controlling for this


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Table 5-3. Meta-Regression Results—WTP for Avoided Acute Effects Using the Total QWB Score

Dependent Variable: WTPACUTE (N = 236)

Regression Weight: No Weight Sample Size No Weight Sample Size

Explanatory Variable Coef. t-stata Coef. t-stata Coef. t-stata Coef. t-stata

∆DAYS 5.88 2.78 4.78 3.59

LN(∆DAYS) 106.69 5.51 97.40 4.48

∆QWB 1,577.65 2.97 1,613.27 2.82

LN(∆QWB) 428.20 2.36 454.20 2.23

INCOME 0.00 0.43 0.00 0.57

LN(INCOME) 249.99 2.02 197.20 1.61

AGE 5.19 0.32 –0.96 –0.05

LN(AGE) 432.17 0.71 –58.22 –0.08

%MALE –3.63 –0.93 –2.90 –0.70 –2.64 –0.84 –1.61 –0.58

US 79.35 0.53 18.87 0.14 22.60 0.17 –41.88 –0.38

WTPAVOID –67.07 –0.20 –122.39 –0.34 17.06 0.06 –43.27 –0.15

OPEN ENDED 28.03 0.17 51.77 0.28 57.91 0.43 93.63 0.84

PAYMENT CARD –92.48 –1.20 –15.06 –0.16 –28.85 –0.53 31.94 0.51

IN PERSON –21.50 –0.17 –42.98 –0.33 –48.24 –0.44 –124.55 –1.08

JOURNAL –36.08 –0.24 43.13 0.26 –22.74 –0.15 7.91 0.05

CONSTANT –445.16 –0.46 –256.01 –0.22 –3,623.47 –1.10 –1,168.54 –0.31

R2 38.41% 44.47% 42.88% 48.00%

aBased on robust standard error estimates, corrected for clustering by study ID.

income effect, the dummy variable for studies done in the United States does not have a significant effect (at a 0.05 level) on WTP in any of these specifications.

The average age of the sample also tends to have a positive effect, although the significance of this variable varies across specifications. To the extent that the age is an inverse proxy for health status, this result is consistent with declining marginal utility of health. In other words, this result suggests that older and less healthy individuals are willing to pay more for an increment in health. The estimated size of this age effect is surprisingly large,


5-10

Table 5-4. Meta-Regression Results—WTP for Avoided Acute Effects Using the Total QWB Score

Dependent Variable: LN(WTPACUTE) (N = 236)



∆DAYS 0.03 3.05 0.02 4.36

LN(∆DAYS) 0.54 10.33 0.50 12.59

∆QWB 6.59 5.69 7.23 4.89

LN(∆QWB) 1.70 3.59 1.97 3.26

INCOME 0.00 1.34 0.00 1.82

LN(INCOME) 0.69 2.17 0.70 2.13

AGE 0.09 2.56 0.09 2.20

LN(AGE) 3.55 3.30 2.56 1.78

%MALE –0.03 –2.12 –0.03 –2.05 –0.01 –1.85 –0.01 –1.36

US –0.11 –0.37 –0.19 –0.64 –0.33 –1.41 –0.41 –1.48

WTPAVOID 1.02 1.63 0.75 0.99 1.12 2.48 0.78 1.33

OPEN ENDED 0.25 0.55 0.33 0.85 0.17 0.60 0.20 0.76

PAYMENT CARD –0.33 –1.30 –0.06 –0.22 –0.21 –1.36 –0.02 –0.09

IN PERSON –0.17 –0.45 –0.11 –0.32 –0.46 –1.29 –0.47 –1.28

JOURNAL –0.84 –1.78 –0.54 –1.37 –0.94 –2.27 –0.71 –1.88

CONSTANT –1.69 –0.73 –1.75 –0.64 –14.26 –2.21 –10.34 –1.29

R2 55.92% 57.19% 65.20% 64.48%

aBased on robust standard error estimates, corrected for clustering by study ID.

however. In the log-linear specification, the elasticity of WTP with respect to age is 2.56.

The coefficient on WTPAVOID is generally not statistically significant. Its sign varies across specifications, although it is more often positive. Therefore, these results do not indicate the presence of loss aversion or strong reference effects.

None of the other variables characterizing the study approach show significant effects on WTP. However, in a few specifications, the JOURNAL coefficient is negative and significant at a 0.10 level,

The assumptions implicit in the QALY valuation approach are not supported by the meta-analysis results.


5-11

which suggests that values published in peer-reviewed journals are generally lower.

The results of the log-linear specification (last specification in Table 5-4) can also be used to test the assumptions of the QALY valuation approach for estimating morbidity values. The results do not support these assumptions.

According to the QALY valuation approach, WTP is assumed to increase in direct proportion to the gain in QALYs. This relationship can be expressed as

WTP = α * (∆QALY). (5.1)

In this expression, α can be interpreted as the unit value per QALY (which is often assumed to be approximately $100,000).

Under less restrictive assumptions, the relationship between WTP and QALYs can be expressed as

WTP = α * (∆QALY)β. (5.2)

In other words, the QALY valuation approach restricts β to be equal to one. If β is greater than or equal to one, then WTP will increase more or less than proportionately with respect to QALY gains.

If we assume that the QWB index is an appropriate health utility index, and we assume that the QALY gain of interest is brought about by avoiding a health effect of specific duration and severity, then Eq. (5.2) can be expanded as follows:

WTP = α * (∆DAYS/365 * ∆QWB)β. (5.3)

In this expression, the QALY gain is expressed as the constant per-period utility gain (from less than 1 to 1) times the duration of the utility gain (converted to years). In log-linear form, this equation becomes

ln(WTP) = ln(α) + β*ln(1/365) + β*ln(∆DAYS) +

β*ln(∆QWB). (5.4)

This expression implies that, with a log-linear model, the restrictions imposed by the QALY valuation approach can be directly tested. The null hypothesis is that coefficients on ln(∆DAYS) and ln(∆QWB) are both equal to one. An F-test of this restriction for the log-linear


5-12

specification in Table 5-4 can be strongly rejected (p – value < 0.001). In the last specification, the coefficient estimate for ln(∆DAYS) is considerably less than one (0.5), and the coefficient estimate for ln(∆QWB) is considerably greater than one (1.97). Therefore, even the less restrictive assumptions implied by Eq. (5.2)—that coefficients on ln(∆DAYS) and ln(∆QWB) are equal to one another (but not necessarily equal to one)—can be strongly rejected (p – value = 0.026).

In contrast to the assumptions of Eqs. (5.1) through (5.4), the results of the log-linear specification in Table 5-4 indicate that WTP increases less than proportionately with respect to changes in duration, as measured by ∆DAYS. This result is consistent with a declining marginal disutility with respect to duration of illness. Furthermore, the coefficient for ln(∆QWB) implies that WTP increases more than proportionately with the severity of illness. This result is consistent with a decreasing marginal utility with respect to health status.

Tables 5-5 and 5-6 report results for very similar specifications; however, the main difference is that the QWB score is decomposed into its four components—scores for symptoms, mobility, social activity, and physical activity.

For the most part, the results using the four QWB scores continue to show significant scope effects. The estimated coefficients for the scores are mostly positive and, particularly for the mobility score, are often statistically significant. For example, the results of the second specification in Table 5-5 indicate that average annual WTP increases by $24 and $29 for each 0.01 increment in the physical activity and mobility scores respectively.

The decomposition of ∆QWB in these specifications also allows us to test one of the underlying assumptions of the QWB index (i.e., that each of the four separate dimensions of the index contribute equally to utility [and thus WTP]). In the linear specifications (first two specifications in Table 5-5) and the semilog specification using ln(WTP) as the dependent variable (first two specifications in Table 5-6), ∆QWB is linearly decomposed. Separate coefficients are estimated for each of the four scores.

The results of the decomposed model indicate that the mobility score and the physical activity score have larger and consistently

The meta-analysis results indicate that the QWB mobility and physical activity scores have larger and more statistically significant effects on WTP than the social activity and symptom scores.


5-13

significant and positive effects on WTP. The symptom score and, to a lesser extent, the social activity score have lower and generally insignificant effects on WTP. It is worth reemphasizing that the regressions estimate the marginal effects of these scores on private WTP. If individuals are directly compensated for lost work time through sick leave policies, then the additional WTP of employers or society to avoid these conditions is not captured by these measures. If, in contrast, the costs of lost work time were to be fully internalized by the individuals experiencing the health effect, then it is likely that the mobility, physical activity, and social activity scores would have a larger effect on private WTP. It is also possible that the relative effects of these scores would be different (e.g., social activity restrictions might have a larger effect on WTP relative to physical activity restrictions).

This finding—that the different scores have different effects on individuals’ utility and WTP—contradicts the implicit assumptions of the composite ∆QWB score. F-tests of the restriction that all four of the scores have the same marginal effect on WTP can be rejected at a 0.05 level for each of these specifications.

A comparable analysis of the other specifications is somewhat more complicated. In these cases, a linear decomposition of ∆QWB results in nonlinear models. For example, the simple log-linear model can be written as

ln(WTP) = *ln(∆QWB) + α*X, (5.5)

where X represents the vector of all the other explanatory variables. When ∆QWB is linearly decomposed, it results in the following nonlinear form:

ln(WTP) = β*ln( 1*QWBSYMSCORE + 2*QWBMOBSCORE +

3*QWBSACSCORE + 4*QWBPACSCORE ) + α*X, (5.6)

We estimated the nonlinear models using the QWB subcomponent scores using maximum likelihood estimation and report the results in the last four columns of Table 5-5 and Table 5-6. In each of these specifications, one of the coefficients (in this case 4) is restricted to be equal to one. This restriction implies that, if the marginal effects of the four scores on WTP are the same, then the other three coefficients will also equal one. Furthermore, if these


5-14

Table 5-5. Meta-Regression Results—WTP for Avoided Acute Effects Using the Four-Dimensional QWB Scores

Dependent Variable: WTPACUTE (N=235)



∆DAYS 5.69 2.83 4.66 3.70

LN(∆DAYS) 104.88 7.66 95.09 4.22

βb 221.95 1.40 289.81 1.65

QWBSYSCORE –336.28 –0.51 –88.88 –0.11 0.51c 0.58 0.50c 0.86

QWBMOBSCORE 3,176.32 4.45 2,896.72 5.86 5.10c 0.68 2.78c 1.34

QWBSACSCORE 1,646.13 2.61 880.54 1.15 0.91c 0.59 0.31c 0.93

QWBPACSCORE 1,698.67 1.30 2,380.64 1.86

INCOME 0.00 0.51 0.00 0.62

LN(INCOME) 302.17 2.60 240.60 1.86

AGE 9.74 0.60 1.93 0.10

LN(AGE) 514.78 1.66 52.72 0.07

%MALE –4.59 –1.21 –5.47 –1.39 –3.86 –1.90 –3.87 –1.33

US 126.32 0.84 40.36 0.30 50.83 1.01 –24.52 –0.21

WTPAVOID –110.36 –0.32 –205.62 –0.55 5.15 0.06 –65.84 –0.22

OPEN ENDED 18.21 0.11 40.00 0.22 75.12 0.99 121.37 1.13

PAYMENT CARD –64.28 –0.85 10.67 0.12 –3.30 –0.07 63.47 0.99

IN PERSON 37.25 0.30 –14.55 –0.11 –29.90 –0.43 –119.09 –0.91

JOURNAL –20.31 –0.14 44.73 0.29 0.81 0.01 19.41 0.12

CONSTANT –187.30 –0.18 209.85 0.17 –4,623.95 –2.06 –1,978.06 –0.52

R2 41.19% 47.07% NA NA

aBased on robust standard error estimates, corrected for clustering by study ID. bSee Eq. (5.5) for interpretation of this coefficient. cRefers to η coefficient in Eq. (5.5).

coefficients are all equal to one, then the specification in Eq. (5.6) simplifies to the one Eq. (5.5).

The results of these nonlinear specifications again cast doubt on the assumption underlying the composite QWB score. The coefficients on the mobility and physical activity scores are distinctly larger than for the other two QWB scores. To test the restrictions implied by


5-15

Table 5-6. Meta-Regression Results—WTP for Avoided Acute Effects and Four-Dimensional QWB Scores

Dependent Variable: LN(WTPACUTE) (N=235)



∆DAYS 0.03 3.13 0.02 4.67

LN(∆DAYS) 0.53 10.87 0.49 13.15

βb 0.80 1.88 1.18 1.61

QWBSYSCORE –0.38 –0.17 1.09 0.39 0.26c 0.78 0.47c 0.95

QWBMOBSCORE 11.03 8.58 10.94 9.93 3.00c 1.96 2.73c 3.92

QWBSACSCORE 5.05 1.53 4.88 1.24 0.38c 1.55 0.45c 1.24

QWBPACSCORE 10.16 4.34 10.49 3.65

INCOME 0.00 1.40 0.00 2.08

LN(INCOME) 0.83 2.25 0.88 2.46

AGE 0.11 3.10 0.10 2.70

LN(AGE) 3.90 3.42 3.04 2.16

%MALE –0.03 –2.52 –0.04 –3.84 –0.02 –2.63 –0.03 –3.51

US 0.05 0.17 –0.12 –0.40 –0.22 –0.90 –0.35 –1.27

WTPAVOID 0.86 1.35 0.30 0.43 1.06 2.47 0.57 1.03

OPEN ENDED 0.21 0.53 0.33 0.98 0.24 0.97 0.33 1.48

PAYMENT CARD –0.25 –1.11 0.08 0.29 –0.11 –0.75 0.14 0.68

IN PERSON 0.01 0.03 0.02 0.04 –0.40 –1.06 –0.43 –1.07

JOURNAL –0.82 –1.71 –0.50 –1.31 –0.87 –2.20 –0.65 –1.79

CONSTANT –0.60 –0.26 0.22 0.09 –17.18 –2.24 –13.58 –1.59

R2 58.48% 59.46% NA NA

aBased on robust standard error estimates, corrected for clustering by study ID. bSee Eq. (5.5) for interpretation of this coefficient. cRefers to η coefficient in Eq. (5.5).

using the sum of the four scores (∆QWB) as a composite index, in these cases we can compare the log-likelihood values for the restricted models (in Tables 5-3 and 5-4) with those for the unrestricted models (in Tables 5-5 and 5-6). Based on likelihood


5-16

ratio tests, the restricted models can all be rejected at a 0.05 level of significance.

In the models reported in Tables 5-5 and 5-6, the effects of duration income, age, and other variables are, for the most part, consistent with the previous results. The coefficients on ∆DAYS (linear and log form) are virtually unchanged and consistently positive and significant. The effect of LN(INCOME) is also consistently positive and significant, although the coefficient is somewhat larger. The final specification in Table 5-6 implies an income elasticity of almost 0.9. The estimated elasticity of WTP with respect to age continues to be positive and significant, and once again it is surprisingly large (i.e., greater than 3 in Table 5-6). The effect of gender composition (%MALE) continues to be negative but is somewhat larger (in absolute value) and more significant.

The final (log-linear) specification in Table 5-6 can also be used to test the restrictions implied by the QALY valuation approach. By replacing ∆QWB in Eq. (5.4) with a reweighted composite score based on the results in Table 5-6 ( 1*QWBSYMSCORE +

2*QWBMOBSCORE + 3*QWBSACSCORE + 1*QWBPACSCORE), it is again possible to test the restriction that the coefficient for LN(DAYS) is equal to the parameter. Using a likelihood ratio test, this restriction can again be rejected at a 0.05 level of significance.

5.1.3 Implications of Results for Benefit Transfer

The overall goodness of fit and statistical significance of the models reported in Tables 5-3 through 5-6 suggest that they provide a reasonable foundation for constructing a predictive WTP function. In other words, the models can be used to develop and test alternative benefit transfer functions.

The advantage of being able to specify a WTP function is that it can be used to extrapolate beyond the existing set of WTP estimates in the literature. That is, it can be used to estimate values for any number of avoided acute illnesses, as long as these illnesses can be described according to their severity (in this case, using the QWB classification system) and their duration. In addition, depending on the model specification and econometric results, it can be used to tailor WTP estimates according to the socio-demographic

The overall goodness of fit and statistical significance of the models suggest that they provide a reasonable foundation for constructing a predictive WTP function.


5-17

characteristics of the affected population (e.g., average age, gender composition, and/or average income).6

To demonstrate and evaluate how the results can be used to predict WTP for avoiding acute effects under specific conditions, we selected two specifications—the log-linear specification using the composite QWB score (last specification with sample size weighting in Table 5-4) and the log-linear form using the four separate scores (last specification in Table 5-6). We selected these specifications because, as argued previously, they are most defensible from an a priori standpoint and because they provide strong empirical results in terms of goodness-of-fit and statistical significance.

In both cases, we conducted specification tests and dropped the least significant variables. The revised specifications are reported in Table 5-7 as BT Function 1 and BT Function 2, respectively. For BT Function 1, the three variables related to survey method (OPEN ENDED, PAYMENT CARD, and IN PERSON) and the gender proportion variable (%MALE) were dropped from the original specification. Based on a F-test, the coefficients for these four variables were jointly not significantly different from zero. For BT Function 2, only the three survey method variables were dropped from the original specification. Using a likelihood ratio test, a restricted model that also held the %MALE coefficient at zero could not be rejected at a 0.05 level of significance.

To explore the implications of the BT functions under selected out-of-sample conditions, we applied each functions to estimate average individual WTP under eight scenarios. The scenarios and results are summarized in Tables 5-8 and 5-9. The scenarios are defined by specifying values for the relevant explanatory variables.

Specifically, eight combinations of ∆DAYS (1 or 10), ∆QWB (0.1 or 0.4), and age (40 or 60) were used. In all cases, income is set to $45,000 per year, and for BT Function 2 the %MALE is 50 percent. The values are all estimated for the U.S. population (US = 1) and for avoiding health declines (WTPAVOID = 1). Furthermore, assuming

6Developing value estimates that vary according to income levels can raise difficult

ethical issues. Nonetheless, it may be useful, for example, to estimate WTP for a person with average income or to estimate how average WTP will be affected by growth in per-capita income levels.


5-18

Table 5-7. Benefit Transfer Function Estimates

Dependent Variable: LN(WTPACUTE)

BT Function: (Regression Weight):

BT Function 1a (Sample Size)

BT Function 2b (Sample Size)

Explanatory Variable Coefficient t-statc Explanatory Variable Coefficient t-statc

LN(∆DAYS) 0.501 13.07 LN(∆DAYS) 0.477 12.93

LN(∆QWB) 2.339 6.23 βd 1.239 2.22

QWBSYSCORE ( 1)d 0.357 1.31

QWBMOBSCORE ( 2)d 2.120 4.45

QWBSACSCORE ( 3)d 0.476 2.22

QWBPACSCORE ( 4)d 1.000

LN(INCOME) 0.777 3.01 LN(INCOME) 0.833 3.96

LN(AGE) 2.591 2.06 LN(AGE) 2.987 2.49

%MALE –0.017 –2.61

US –0.181 –1.52 US –0.101 –0.73

WTPAVOID 0.799 1.72 WTPAVOID 0.516 1.25

JOURNAL –0.357 –2.05 JOURNAL –0.382 –1.96

CONSTANT –12.031 –1.66 CONSTANT –13.293 –2.02

R2 63.50% NA

N 236 235

aEstimated with Weighted Least Squares Regression (SAMPLE SIZE weight). bEstimated with Weighted Maximum Likelihood Regression (SAMPLE SIZE weight). cBased on robust standard error estimates, corrected for clustering by study ID. dSee Eq. (5.5) for interpretation of coefficient.

that values published in peer-reviewed articles are most defensible, we set JOURNAL equal to one as well.

For BT Function 1, the predicted mean WTP values range from $2.88 (95% C.I.: $1.05 – $4.1) to $669.25 (95% C.I.: $268.61 – $863.03).7 The 10-fold increase in ∆DAYS has a positive but relatively small effect on predicted WTP. In contrast, increasing ∆QWB by a factor of four increases predicted WTP substantially, by

7To appropriately transform the model prediction of mean WTP in logarithmic form

to mean WTP in nonlogarithmic form, we applied a smearing factor to the model predicted values (Duan, 1983). The smearing factor is equal to the average of the exponentiated residuals for the 235-6 observations in the estimation sample.

Generally speaking, the WTP estimates based on the QALY valuation approach are larger than WTP estimates based on the meta-analysis benefit transfer functions.


5-19

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5-21

a factor of more than 25. A change in ∆QWB from 0.1 to 0.4 is comparable to the difference between avoiding breathing unpleasant air and avoiding a severe angina attack. Increasing age from 40 to 60 also has a large impact on WTP, increasing it by a factor of roughly 3.

For comparative purposes, value estimates based on a simple QALY valuation approach (using QWB as the health utility index) are also included in Table 5-8 for each scenario. In seven of the eight scenarios, these QALY-based estimates are higher. They are particularly large compared to the BT function estimates when the duration change is large (e.g., 10 days). This difference occurs because the QALY-based approach assumes that values increase in direct proportion to duration, whereas the BT function has a lower elasticity with respect to duration. Also, in contrast to the BT function estimates, the QALY-based values for acute effects do not increase with respect to age. As a result, the QALY-based estimates are less likely to exceed the BT function estimates in the higher age scenarios.

As reported in Table 5-9, similar scenarios are run for BT Function 2, but in this case values for each of the four QWB component scores are specified. Each score is assumed to vary by the same amount, such that the total ∆QWB (sum of the four scores) again varies between 0.1 and 0.4 across scenarios. For these scenarios, the BT Function 2 predictions are consistently higher than the BT Function 1 estimates.8 The predicted WTP values range from $26.77 (95% C.I.: $4.88 – $80.41) to $1500.51 (95% C.I.: $672.81 – $1833.42). The 10-fold increase in ∆DAYS has a similar positive effect with this function. Increasing overall ∆QWB by a factor of four again increases predicted WTP substantially, by a factor of more than 6. Increasing age from 40 to 60 increases WTP by a factor of roughly 3.4.

Table 5-9 also includes comparisons with QALY-based estimates. These estimates are the same as in Table 5-8 because the overall ∆QWB scores are the same for each scenario. In this table, the QALY-based values exceed the BT function values in only four of

8Compared to BT Function 1, BT Function 2 tends to generate higher estimates in

the upper range of WTP and lower estimates in the lower range. The eight scenarios described here tend toward the upper range of WTP.


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the eight scenarios—those with larger duration changes and lower age.

5.2 META-ANALYSIS OF VALUE ESTIMATES FOR CHRONIC EFFECTS Conducting a comparable analysis of WTP estimates for avoiding chronic health effects is hampered by two main factors. First, relatively few WTP estimates are available in the literature. Of the 389 estimates identified and included in our health value database, less than 75 were for avoiding or reducing the risk of chronic health conditions. Second, it is difficult to define a single index (such as the QWB for acute effects) that can be used to characterize all of the chronic effects addressed by these few estimates.

In spite of these limitations, we are able to conduct a small-scale meta-analysis. Through this analysis, we find that WTP estimates for chronic effects are related in expected and statistically significant ways with respect to a few explanatory factors. Although these results are somewhat limited for the purposes of benefit transfer, they do provide a foundation for developing functions to predict WTP for avoiding chronic effects.

5.2.1 Data Selection and Description

To conduct a meta-analysis of values for chronic effects, we initially identified 74 WTP estimates from 15 studies that were candidates for inclusion in the analysis. That is, they estimated individuals’ WTP to either (1) avoid a specific chronic condition with certainty (e.g., a cure for asthma) or (2) reduce the probability of acquiring or experiencing a chronic condition in the future. In other words, we included both ex ante and ex post estimates.

For a variety of reasons, several of the initially identified values needed to be excluded from further consideration. For example, O’Brien and Viramontes (1994) estimate WTP for a treatment of chronic obstructive pulmonary disease (COPD); however, the treatment also would entail a small increase in mortality risk. Because it is not possible to separate the mortality and morbidity values from the resulting WTP estimates, they could not be included. In another case, Krabbe, Essink-Bot, and Bonsel (1997) estimate values for 13 broadly defined conditions; however, their

A meta-analysis of WTP estimates for chronic effects is limited by Z a lack of WTP data

and

Z difficulties in defining a single common health index.


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analysis was based on a sample of university students. We judged this sample to be too specialized for inclusion in the meta-analysis.

After this second round of screening, we ended up with 38 value estimates from 10 studies (12 publications). A description of key characteristics of these studies is included in Appendix B, Table B-2. Table 5-10 summarizes the health changes associated with these 38 WTP estimates.

Table 5-10 also includes health index estimates for the chronic health effects addressed in the 38 WTP estimates. Whereas it was relatively straightforward to use a MAUS like the QWB to generate severity scores for selected acute effects, it is generally more difficult to assign severity scores for chronic conditions. One reason is that for many chronic illnesses, such as asthma, the severity (i.e., disutility) of illness can vary substantially across time and across individuals. Fortunately, as described in Section 3, a number of empirical studies have administered surveys using MAUS methods and developed average health scores for a wide variety of health conditions. Three of the most commonly used MAUS methods are the EQ-5D, the HUI-3, and the QWB. Unfortunately, none of the three MAUS methods has been applied to more than 5 of the roughly 10 health conditions addressed by the 38 WTP estimates. As a result, it is not possible to assign illness-specific severity scores to each of the conditions using a common, standardized method.

As an alternative, it is possible to group illnesses more broadly according to three categories of illness—mild, moderate, and severe—using an approach based on Kopec et al. (2000). It is also possible, using the results of that study, to assign average severity scores for these three broad categories. Kopec et al. used a sample of 11,372 Canadians drawn from the 1994/5 National Population Health Survey. This survey included health status questions that corresponded to the HUI classification system. It also inquired about the presence of 20 chronic health conditions. Kopec et al. used the responses to first group individuals into the three general health categories (plus a “none” category). They then used the HUI responses to generate average HUI-3 scores for each category (0.93 for mild, 0.89 for moderate, and 0.806 for severe). As shown in Table 5-10, we used this same basic approach to categorize (and score) the illnesses corresponding to the 38 WTP estimates.


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Table 5-10. Chronic Health Effect Descriptions and Scores

Average MAUS Scores

Publication Country Health Change Description Number of Estimates Severity EQ5D HUI QWB

Blumenschein and Johannesson (1998)

U.S. Cure for asthma 2 Moderate 0.79a 0.84b 0.68c

Kartman et al. (1996) Sweden Reduced risk of experiencing symptoms of reflux oesophagitis

4 Mild

Krupnick and Cropper (1992)

U.S. Reduced risk of chronic bronchitis

2 Severe 0.79d 0.67 c

Reduced risk of chronic respiratory disease as experienced by relative

1 Severe

Lundberg et al. (1999) Sweden Cure for psoriasis 2 Mild 0.92d

Cure for atopic eczema 2 Mild 0.92d

Sloan et al. (1998) U.S. Reduced risk of acquiring multiple sclerosis

8 Severe 0.185e 0.6f

Stavem (1999) Norway Cure for epilepsy 1 Moderate 0.78d

Stavem (2002) Norway Cure for COPD 1 Severe 0.61g

Thompson (1986) U.S. Cure for rheumatoid arthritis (for patients able to climb steps without any difficulty)

1 Moderate 0.73h 0.78d 0.60i

Cure for rheumatoid arthritis (for patients able to climb steps with some difficulty)

1 Moderate 0.47h 0.78d 0.60i

Cure for rheumatoid arthritis (for patients able to climb steps with much difficulty)

1 Severe 0.24h 0.78d 0.60i

Cure for rheumatoid arthritis (for patients unable to climb steps)

1 Severe 0.02h 0.78d 0.60i

Viscusi et al. (1991) U.S. Reduced chronic bronchitis risk

1 Severe 0.79 d 0.67 c

Zethraeus (1998) Sweden Hormone replacement therapy

2 Mild

(continued)


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Table 5-10. Chronic Health Effect Descriptions and Scores (continued)

Average MAUS Scores

Publication Country Health Change Description Number of Estimates Severity EQ5D HUI QWB

Zethraeus et al. (1997) Sweden Hormone replacement therapy (for mild menopause)

1 Mild

Hormone replacement therapy (for severe menopause)

1 Moderate

Zillich et al. (2002) U.S. Cure for mild asthma 2 Mild 0.79a 0.84b 0.68c

Cure for moderate asthma 2 Moderate 0.79a 0.84b 0.68c

Cure for severe asthma 2 Severe 0.79a 0.84b 0.68c

aGarratt, A.M., A. Hutchinson, and I. Russell. 2000. “Patient-Assessed Measures of Health Outcome in Asthma: A Comparison of Four Approaches.” Respiratory Medicine 94(6):597-606.

bLeidy, N.K., and C. Coughlin. 1998. “Psychometric Performance of the Asthma Quality of Life Questionnaire in a U.S. Sample.” Quality of Life Research 7(2):127-134.

cFryback, D.G., W.F. Lawrence, P.A. Martin, R. Klein, and B.E. Klein. 1993. “The Beaver Dam Health Outcomes Study: Initial Catalog of Health-State Quality Factors.” Medical Decision Making 13:89-102.

dMittmann, N., K. Trakas, N. Risebrough, and B.A. Liu. 1999. “Utility Scores for Chronic Conditions in a Community-Dwelling Population.” Pharmacoeconomics 15(4):369-376.

eForbes, R.B., A. Lees, N. Waugh, and R. J. Swingler. 1999. “Population Based Cost Utility Study of Interferon Beta-1b in Secondary Progressive Multiple Sclerosis.” British Medical Journal 319(7224):1529-1533.

fSchwartz, C.E., R.M. Kaplan, J.P. Anderson, T. Holbrook, and M.W. Genderson. 1999. “Covariation of Physical and Mental Symptoms Across Illnesses: Results of a Factor Analytic Study.” Annals of Behavioral Medicine 21(2):122-127.

gKaplan, R.M., C.J. Atkins, and R. Timms. 1984. “Validity of a Quality of Well-Being Scale as an Outcome Measure in Chronic Obstructive Pulmonary Disease.” J Chronic Dis 37(2):85-95.

hHurst, N.P., P. Kind, D. Ruta, M. Hunter, and A. Stubbings. 1997. “Measuring Health-Related Quality of Life in Rheumatoid Arthritis: Validity, Responsiveness and Reliability of EuroQol (EQ-5D).” British Journal of Rheumatology 36(5):551-559.

iBombardier, C., and J. Raboud, The Auranofin Cooperating Group. 1991. “A Comparison of Health-Related Quality-of-Life Measures for Rheumatoid Arthritis Research.” Control Clin Trials 12(4 Suppl):243S-256S.

Table 5-11 describes the variables that were created to describe the 38 WTP estimates and were used in a small-scale meta-analysis. In this case, WTPCHRONIC is the key variable of interest. It represents individuals’ WTP to avoid or to reduce the risk of chronic illness. As with WTPACUTE, all WTP estimates (and mean INCOME values) were converted to 2000 dollars using the CPI and, if they were originally measured in a foreign currency, we first converted the estimates to dollars using the PPP index. Special attention was also given to the time and risk dimensions of the


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Table 5-11. Descriptions of Variables Used in the Meta-Analysis

Variables Description

WTPCHRONIC Mean WTP for avoiding chronic health condition (in 2000 dollars)a

MODERATE = 1 if classified as a severe condition (see Table 5-10)

SEVERE = 1 if classified as a moderate condition (see Table 5-10)

HUI-3 Mean HUI (Mark III) score for mild, moderate, and severe conditions (based on Kopec et al., 2000)

INCOME Mean household income (in 2000 dollars)a

AGE Mean age

EXANTE = 1 if value was ex ante

SAMPLESIZE Number of survey respondents used in calculating WTP

aConverted to dollars using PPP if in foreign currency; median WTP used if mean value not reported.

health change. All values were converted to annual WTP to avoid a lifetime case (statistical or actual) of illness. One-time payments were annualized assuming a 20-year payment period and a 5 percent discount rate.9 To approximate WTP for avoiding a “statistical” case of illness, ex ante values for risk reductions were divided by the corresponding reduction in probability of illness. Table 5-12 provides summary statistics for the variables described in Table 5-11. WTPCHRONIC varies considerably, from as little as $595 per year for mild asthma (Zillich et al., 2002) to almost$200,000 per year to avoid a statistical case of chronic bronchitis (Krupnick and Cropper, 1992).

Almost 45 percent of the WTP estimates are for health effects classified as severe, and 21 percent are for moderate effects. Using the results from Kopec et al., this translates to an overall average HUI-3 score of 0.87. About 42 percent of the estimates are ex ante values. The SAMPLESIZE variable, which is used as one of the regression weights, varies between 26 and 400 observations.

9Annual payments for contemporaneous annual risk reductions were assumed to

be equivalent to a one-time payment for a lifetime risk reduction, and they were annualized in the same manner.


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Table 5-12. Summary Statistics for Variables Used in the Meta-Analysis

Variables N Mean SD Min Median Max

WTPCHRONIC 38 18,206.53 44,655.65 595.36 3,328.78 194,153.70

MODERATE 38 0.21 0.41 0 0 1

SEVERE 38 0.45 0.50 0 0 1

HUI-3 38 0.87 0.06 0.81 0.89 0.93

INCOME 38 39,185.25 17,437.45 16,308.18 37,765.73 78,545.42

AGE 38 46.21 9.68 24.36 48.9 60

EXANTE 38 0.42 0.50 0 0 1

SAMPLESIZE 38 153.52 125.82 26 87 400

5.2.2 Meta-Regression Models and Results

Table 5-13 describes regression results for a number of model specifications. All of the models were estimated using weighted least squares, and the standard error estimates were corrected to account for clustering by study ID.

Two types of regression weights were included in this analysis. First, as in the analysis of acute effects, estimates based on larger samples were assumed to contain relatively more information. Thus, each estimate was assigned a weight equal to the size of the sample used to estimate the value (weight1 = SAMPLESIZE). Second, several of the WTP studies for chronic effects included multiple estimates for the same health change and the same sample of respondents (i.e., same “group”). For example, in some cases, different values were calculated for the same sample of individuals using different model specifications or assumptions. To avoid assigning too much weight to each of these estimates, they were down-weighted in proportion to the number of estimates coming from the same group (i.e., same sample and health change) (weight2 = 1/GROUPSIZE). The results reported in Table 5-13 include and combine both weights (as the product, weight1*weight2).

As might be expected because of the relatively small number of observations, the regression results are quite sensitive to model specification. Nevertheless, the results do provide some evidence of a systematic and theoretically consistent relationship between


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Table 5-13. Meta-Regression Resultsa—WTP for Avoided Acute Effects and Total QWB Score

Dependent Variable (N=38):

WTPCHRONIC LN(WTPCHRONIC)

(1) (2) (3) (4) (5) Explanatory Variable Coef. t-stata Coef. t-stata Coef. t-stata Coef. t-stata Coef. t-stata

Moderate –16447 –1.19 0.11 0.3

Severe –5182 –0.35 0.65 1.23

HUI-3 –13849 –0.13 –5.60 –1.47

Ln(HUI3) –4.15 –1.3

Income 1.376 3.07 1.054 3.03 0.0001 6.92 0.0001 7.95

Ln(Income) 1.65 6.33

Age –1275 –2.98 –1419 –3.46 0.0027 0.13 0.0018 0.09

Ln(Age) –0.72 –1.01

Ex Ante 12741 1.11 19803 2.2 0.34 0.75 0.39 1.03 0.45 1.24

Constant 32810 1.71 55122 0.5 6.30 6.03 11.56 3.02 –6.38 –1.76

R2 72.41% 70.72% 65.80% 65.74% 64.31%

aWeighted least squares with robust standard error estimates, corrected for clustering by study ID.

WTP and a certain explanatory factors. These relationships are examined in specifications (1) and (2) using WTPCHRONIC as the dependent variables and in specifications (3), (4), and (5) using WTPCHRONIC in logarithmic form.

To test for scope effects with respect to the severity of the chronic condition, we use two approaches. Specifications (1) and (3) control for differences in the severity of illness using two dummy variables—MODERATE and SEVERE. None of the estimated coefficients for these variables are statistically significant at a level less than 0.25, and they have unexpected signs in the first specification. In contrast, specifications (2), (4), and (5) use HUI-3 as an index of severity. In accordance with expectations, the sign of this coefficient is always negative—lower scores imply more severe conditions and higher WTP—and the level of significance varies between 0.18 and 0.22 when WTPCHRONIC is expressed in logarithmic form. Thus, there is some, albeit weak, evidence that the variation in WTP across studies is related to the severity of illness.


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The results show consistently positive and statistically significant income effects. Specification (5) estimates an income elasticity of 1.65, which suggests a high sensitivity of WTP to income. Although the level of significance is somewhat lower, the EXANTE coefficient is also consistently positive across specifications. The sign of this effect may be interpreted in different ways. On the one hand, it may relate to cognitive differences in how people perceive of and respond to small changes in probabilities. For example, respondents may have upwardly biased perceptions of small risks and thus overstate their WTP. It may also relate to individuals’ experiences with disease. Ex post valuations are typically asked of individuals with the illness, whereas the opposite is true of ex ante valuations. Therefore, if individuals adapt well to having an illness, this may have a dampening effect on ex post WTP. Furthermore, as discussed for example by Johannesson (1996), the sign of the difference between ex post and ex ante WTP to avoid illness depends importantly on whether the individual is risk averse with respect to income. A positive sign for EXANTE suggests risk aversion.

The sign and size of the age effect vary considerably across specifications. In the first two regression equations, age has a negative and statistically significant effect on WTP. This is the opposite effect from what was observed for acute effects. One reason may be that “background” quality of life is lower for older individuals, such that avoiding a chronic illness would have a lower net positive effect on their utility compared to younger people. Another reason may be that older individuals have lower life expectancy and thus fewer years to enjoy the improved health. When the logarithmic form of WTPCHRONIC is used as a dependent variable in specifications (3) – (5), the effect of age is indeterminate; therefore, it is difficult to draw strong conclusions about how age affects WTP to avoid chronic illness.

The results show consistently positive and statistically significant income effects.


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5.3 SUMMARY AND CONCLUSIONS Generally speaking, the results of our meta-analyses indicate that WTP estimates for avoided adverse health effects vary in systematic and expected ways with respect to key explanatory variables. These results are stronger and more conclusive for acute effects than for chronic effects; however, this difference may be largely due to the larger sample size and the ability to more easily characterize the severity and duration of illness for acute effects.

The analysis of acute effects was based on over 230 WTP estimates from 17 separate stated preference studies, most of which were conducted in the United States, Canada, and Northern Europe. We found a strong statistical relationship between the value estimates and corresponding measures of the severity and duration of the health effects. These results provide evidence to support the theoretical validity of WTP estimates in the literature. Furthermore, we found generally positive and significant income effects and age effects. Positive income effects support the hypothesis that health is a normal good, and positive age effects are consistent with declining marginal utility with respect to health status. However, in most cases, the estimated magnitude of the age effect on WTP is surprisingly high, with an elasticity of around 3.

The regression results for acute effects also provide a simple test of the assumptions underlying the QALY valuation approach for assessing morbidity values. If the composite QWB score is accepted as an appropriate health utility index for calculating QALYs, then the elasticities of WTP with respect to the change in QWB and the change in duration should both be equal to one. This parameter restriction was tested and could be strongly rejected. However, at the same time, the regression results cast doubt on the QWB as an appropriate health utility index. The four component scores were found to have statistically different effects on WTP, which contradicts the assumptions underlying the QWB index.

The meta-analysis for chronic effects is inherently more limited due primarily to data limitations. Nevertheless, based on 38 estimates from 10 studies in the United States, Sweden, and Norway, we did find preliminary indications of systematic and theoretically consistent relationships between WTP and other factors. In particular, we found a strong positive and statistically significant

Generally speaking, the results of our meta-analyses indicate that WTP estimates for avoided adverse health effects vary in systematic and expected ways with respect to key explanatory variables.


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effect of income. Using a crude measure of differences in HUI-3 scores between health conditions, we found that WTP estimates were positively related to severity of illness but with low levels of statistical significance.

Although both of these meta-analyses warrant further investigation, the results for acute effects provide a foundation for developing benefit transfer functions to predict WTP. In particular, we have proposed two benefit transfer functions for acute effects and examined how they perform under alternative scenarios. Developing comparable functions for chronic effects will require additional analysis and, most likely, additional data.

6-1

Summary and Discussion of 6 Results

The main purpose of this analysis has been to assist CFSAN in strengthening its capabilities for valuing the health benefits of its regulatory alternatives. Because of constraints on the availability of resources for regulatory analysis, the most practical approach for systematically evaluating a wide range of morbidity impacts is to develop of a flexible and broadly applicable benefit transfer method.

This report discusses the steps that RTI has taken to develop such a benefit transfer tool. These steps include

Z developing a conceptual framework that describes the microeconomic foundations for health valuation, identifies the key expected determinants of health values, and explores the conceptual links between HSMs (and QALYs) and WTP measures;

Z reviewing the empirical literature on health valuation and compiling a detailed bibliography of the most relevant 136 publications;

Z selecting WTP estimates from these studies and constructing a database of health values, which currently contains 389 WTP estimates (and corresponding data) from 44 publications;

Z using meta-regressions to analyze subsets of the database—236 WTP estimates for avoiding acute illness and 38 WTP estimates for avoiding chronic illness; and

Z applying the meta-regression results to specify benefit transfer functions. These functions can be used to predict WTP for avoiding acute illness, based on characteristics of the illness (through the QWB scoring system) and


6-2

characteristics of the affected population (age, gender, income).

Through this process, we have also been able to test hypotheses regarding the determinants of WTP estimates. The results of the meta-analysis indicate that WTP estimates for avoided acute effects vary in systematic and expected ways with respect to key explanatory variables. We find a strong statistical relationship between the value estimates and corresponding measures of the severity (using the QWB scale) and duration of the health effects. In addition, we find generally positive and significant income effects and age effects. Positive income effects support the hypothesis that health is a normal good, and positive age effects are consistent with declining marginal utility with respect to health status.

The meta-analysis results also provide a simple but informative test of the assumptions underlying the QALY valuation approach for assessing morbidity values. The results strongly reject the assumption of a constant value per QALY and the assumption that the duration and the severity of illness have equivalent and proportional effects on WTP.

The results also indicate that the four health utility scores underlying the QWB index have statistically different effects on WTP. The mobility and physical activity dimensions were found to have stronger effects on WTP than the symptoms or social activity dimensions. This finding contradicts the equal weighting assumption, which is typically used in constructing the composite QWB health status index.

It should be emphasized that the strength of these results depends in large part on the strength of the underlying WTP estimates and QWB scores. It is encouraging that the convergent validity of the two measures is supported by the finding that WTP is positively and significantly related to QWB scores. Nevertheless, the results would be weakened if either of these two preference measures includes systematic biases. Although the empirical accuracy and reliability of both methods have been questioned (see Hausman, [1993] for a critique of CVM methods and Brazier [1999] for a review of QWB evaluations), there is no conclusive evidence that such systematic biases exist.

The databases developed for this report can be expanded to include more WTP and HSM studies. Consequently, they should support the continued development of benefit transfer tools.

Section 6 — Summary and Discussion of Results

6-3

Below, we illustrate how the results of the analysis can be applied to estimate the benefits of avoiding specific acute conditions often associated with foodborne illness. However, the data and analyses assembled for this project should extend beyond this report in other ways. The information currently contained in the bibliography and databases described in Section 4 can serve as more general resources for identifying, summarizing, or transferring estimates from the health valuation literature. The databases also provide organizing structures that can easily be used to include information from more WTP and HSM studies. As such, they should support additional analyses (including meta-analyses) of the health valuation literature and continued development of benefit transfer tools.

6.1 ILLUSTRATIVE APPLICATIONS OF THE ESTIMATED BENEFIT TRANSFER FUNCTION FOR ACUTE EFFECTS The final objective of this analysis is to demonstrate how our findings can be used to estimate values for avoiding specific illnesses of interest. At the outset of this project, CFSAN identified four health conditions of particular concern:

Z acute symptoms of foodborne illness,

Z reactive arthritis,

Z diet-related diabetes, and

Z peanut allergy.

Based on our assessment of the data, we have concluded that it is not feasible at this stage to specify a reliable benefit transfer function for avoiding chronic conditions. As discussed in Section 5, the availability of WTP estimates for avoiding chronic illness is currently too limited for this purpose. Consequently, the current analysis does not support estimation of values for avoiding long-term conditions, such as arthritis, diabetes, or allergies.

However, all of the four conditions listed above involve acute outcomes. To the extent that CFSAN’s activities are helpful in limiting the incidence of these acute effects (as opposed to necessarily preventing the associated chronic illness), the benefit transfer functions described in Section 5 can be used to evaluate their benefits.


6-4

BT Function 2 is particularly suited for estimating the benefits of avoiding these types of acute effects (as shown more generally in Table 5-9). According to this function, WTP can be defined as a function of the four QWB scores, duration of illness, income, age, plus a number of other factors. Through our review of the HSM literature, we were not able to identify any studies that have specifically assigned QWB scores for these four categories of foodborne illness. Nevertheless, the descriptions of the QWB health dimensions and levels (see Tables 4-17 and 4-18) are general enough to be used in a few illustrative case examples.

Table 6-1 summarizes the results of applying BT Function 2 to estimate benefits for three illustrative cases:

Z avoiding 10,000 cases of acute gastrointestinal illness (GI), lasting on average 5 days;

Z avoiding 10,000 severe allergic reactions/attacks requiring 5 days of hospitalization; and

Z reducing by 10 the number of days with moderate arthritis symptoms for 5,000 older individuals (averaging 60 years old).

The four QWB scores for each case were assigned using the descriptions in Table 4-17. For acute GI illness we assign QWB symptom (“CPX No.”) 9, corresponding to general stomach ailments, and assume moderate mobility, physical activity, and social activity restrictions. For allergy attack, we assign QWB symptom 2, which includes loss of consciousness, and assume hospitalization with severe mobility, physical activity, and social activity restrictions. For moderate arthritis symptoms, we assign QWB symptom 7, including joint pain, and assume no mobility restrictions (i.e., able to drive as usual) and moderate physical and social activity restrictions.

In all three cases, we assume that the average annual household income of the affected population is roughly equal to the national average in 2000.1 We also assume an equal distribution between males and females affected. For GI illness and allergy attack we assume that the average age of the affected population is close to

1Even if the average income of the affected population is lower or higher than the

U.S. average, it is arguably most appropriate to apply average U.S. income in the transfer function for estimating national benefits.


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Table 6-1. Three Illustrative Applications of the Meta-Analytic Benefit Transfer Function for Acute Effects

Acute Gastro-

intestinal Illness Severe Allergy Attack Moderate Arthritis

Symptom Days

DAYS 5 5 10

QWBSYSCOREa 0.29 (9) 0.407 (2) 0.299 (7)

QWBMOBSCOREa 0.062 (4) 0.09 (2) 0 (5)

QWBSACSCOREa 0.06 (3) 0.077 (1) 0.06 (3)

QWBPACSCOREa 0.061 (3) 0.106 (1) 0.061 (3)

INCOME $57,000 $57,000 $57,000

AGE 40 40 60

%MALE 50 50 50

US 1 1 1

WTPAVOID 1 1 1

JOURNAL 1 1 1

Number of affected individuals 10,000 10,000 5,000

Mean Annual WTP/Personb $306.37 $496.00 $767.71

95% CI Lower $1,185,827 $1,366,987 $3,163,076

90% CI Lower $1,313,872 $1,598,176 $3,470,165

Total Annual Benefitsb $3,063,700 $4,960,000 $3,838,600

90% CI Upper $3,913,835 $8,433,439 $9,305,009

95% CI Upper $4,336,451 $9,859,722 $10,208,391

aNumber in parentheses corresponds to the level of the QWB health dimension (see Table 4-17). bIn 2000 dollars.

the U.S. adult average (40 years old); however, for arthritis, we assume an older population.

By transforming (out of logarithmic form) the results reported in Table 5-7, median WTP per person can be calculated as follows:

Median WTPACUTE = EXP {–13.29 + 0.477*LN(DAYS) +

1.239*LN[QWBPACSCORE + 0.357*QWBSYSSCORE

+ 2.12*QWMOBSCORE + 0.476*QWBSACSORE] +

0.833*LN(INCOME) + 2.987*LN(AGE) – 0.017*%MALE -

0.101*US + 0.516* WTPAVOID – 0.382*JOURNAL} (6.1)


6-6

To properly estimate the mean (expected) WTP reported in Table 6-1, we multiplied the median WTP estimate by the smearing factor of 1.351 (see footnote 5 in Section 5).

Total benefits were then calculated by simply multiplying the estimated mean WTP by the size of the affected population.

6.2 CONCLUSIONS The three case examples summarized in Table 6-1 demonstrate how the estimated meta-regression function can provide CFSAN with a flexible benefit transfer tool for assessing benefits of avoided acute morbidity. This function can be used to assess benefits for any number of avoided health impacts, as long as one is able to specify values for the explanatory variables—in particular the QWB health dimensions and duration of illness—and the size of the affected population.

A number of limitations and uncertainties must also be recognized in applying this function. First, the function provides point estimates of median and/or mean individual WTP. Confidence intervals for the model predictions can also be estimated by taking into account the variance of the estimated parameters. However, other types and sources of uncertainty, such as model specification uncertainty (e.g., functional form) or sample selection effects, will inevitably be present, and they are much more difficult to quantify. As the number of published WTP studies increases, this function can (and should) be re-estimated and refined. This process should improve the precision and accuracy of the WTP predictions, but it will not eliminate the various sources of uncertainty.

Second, the function does not directly address WTP to avoid chronic illness. In our judgment, the number of available WTP estimates is currently too limited to develop a comparable meta-analytic function for chronic illness. Two of the illustrative examples described above—allergy attacks and arthritis symptoms—demonstrate how the transfer function can be used to assess acute effects associated with chronic illness. However, extending this approach to assess long-term changes in acute effects (i.e., over 1 year in duration) would most likely entail an unreasonable extrapolation beyond the range of the data. Furthermore, important differences in values may exist between


6-7

(1) preventing or curing a chronic illness and (2) limiting the number and severity of the acute conditions associated with the chronic illness. As more WTP estimates for avoiding acute and chronic conditions become available, it may eventually be possible to pool these data and formally test this hypothesis.

Third, it is important to reemphasize that the meta-analysis function only includes estimates of adults’ WTP to avoid morbidity outcomes. As a result, it may not include all of the value components that are of potential interest to policy makers. To the extent that costs of illness are externalized to other members of society, for example, through social insurance, this analysis will not capture the benefits of avoiding these external costs. Moreover, the benefit transfer function cannot be applied to specifically estimate values for protecting children’s health. In other words, it would certainly not be appropriate to specify values of less than about 20 years old for the AGE variable in Eq. (6.1).2 Doing so would again entail an unreasonable extrapolation beyond the range of the data.3 Values for children’s health are best measured by assessing parents’ WTP to protect their children. Although the number of studies using this approach is still limited, it would eventually be of interest to include these types of estimates in a meta-analysis.

Finally, the data and analyses assembled for this report have intentionally focused on morbidity-related values, rather than values for reducing mortality risks.4 However, morbidity- and mortality-related values are not always entirely separable. This is especially the case for more serious acute and chronic conditions. Although it is reasonable to assume that the values included in our meta-analysis for acute effects and the WTP predictions based on these functions do not include significant mortality-related values, a more cautious approach will be required for chronic effects. As new WTP data become available for chronic illnesses, it should eventually be possible to develop reliable benefit transfer functions for this category of morbidity. However, to apply these functions

2In the absence of more precise estimates for children, however, it may be useful to

estimate values for young or average age adults and apply them, with all the necessary caveats, to children.

3Note from Table 5-2 that the minimum average age from the estimation sample is 35 years old.

4For good summaries of the mortality valuation literature see, for example, Viscusi (1993) and Mrozek and Tayler (2002).


6-8

for benefits analysis, it will most likely be necessary to separate the mortality-related values from these WTP estimates. Through the continued use of meta-analysis, it should be possible to control for mortality effects.

R-1

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R-4

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Appendix A: Bibliography and Summary of Morbidity Valuation Studies

A-1

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

6 2

1a

Che

stnu

t, La

urai

ne G

., St

even

D. C

olom

e, L

. Rob

in

Kel

ler,

Will

iam

E. L

ambe

rt, B

art O

stro

, Rob

ert D

. Row

e,

and

Sand

ra L

. Woj

ciec

how

ski.

198

8. H

eart

Dis

ease

Pa

tient

s’ A

vert

ing

Beh

avio

r, C

osts

of I

llnes

s, a

nd

Will

ingn

ess

to P

ay to

Avo

id A

ngin

a Ep

isod

es.

Fina

l re

port

pre

pare

d fo

r U

.S. E

nvir

onm

enta

l Pro

tect

ion

Age

ncy.

Doc

umen

t No.

EPA

-230

-10-

88-0

42.

CV

M

Ang

ina

atta

cks

No

US

7 4

1a

Dic

kie,

M.,

Ger

king

, S.,

McC

lella

nd, G

., Sc

hulz

e, W

. 19

88.

”Con

tinge

nt V

alua

tion:

The

Val

ue o

f For

mat

ion

Proc

ess.

” U

npub

lishe

d m

anus

crip

t. C

VM

A

cute

sym

ptom

s N

o U

S

29

1 1a

Loeh

man

, E.T

., S.

V. B

erg,

A.A

. Arr

oyo,

R.A

. Hed

inge

r,

J.M. S

chw

artz

, M.E

. Sha

w, R

.W. F

ahie

n, V

.H. D

e, R

.P.

Fish

e, D

.E. R

io, W

.F. R

ossl

ey, a

nd A

.E.S

. Gre

en.

1979

. “D

istr

ibut

iona

l Ana

lysi

s of

Reg

iona

l Ben

efits

and

Cos

t of

Air

Qua

lity

Con

trol

.” J

ourn

al o

f Env

iron

men

tal

Econ

omic

s an

d M

anag

emen

t 6:2

22-2

43.

CV

M

Acu

te s

ympt

oms

No

US

30

1 1a

Row

e, R

ober

t D.,

and

Laur

aine

G. C

hest

nut.

198

5.

Oxi

dant

s an

d A

sthm

atic

s in

Los

Ang

eles

: A

Ben

efits

A

naly

sis.

Fin

al r

epor

t pre

pare

d fo

r U

.S. E

nvir

onm

enta

l Pr

otec

tion

Age

ncy.

Doc

umen

t No.

EPA

/230

/7-8

5/01

0.

CV

M

Ast

hma

atta

cks

No

US

32

1 1a

Tolle

y, G

eorg

e, L

yndo

n B

abco

ck, e

t al.

198

6.

“Val

uatio

n of

Red

uctio

ns in

Hum

an H

ealth

Sym

ptom

s an

d R

isk.

” In

Vol

. 3:

Con

tinge

nt V

alua

tion

Stud

y of

Li

ght S

ympt

oms

and

Ang

ina.

Fin

al r

epor

t pre

pare

d fo

r U

.S. E

nvir

onm

enta

l Pro

tect

ion

Age

ncy.

CV

M

Acu

te s

ympt

oms

No

US

1 1

1

Alb

erin

i, A

., an

d A

. Kru

pnic

k. 1

998.

“A

ir Q

ualit

y an

d Ep

isod

es o

f Acu

te R

espi

rato

ry Il

lnes

s in

Tai

wan

Citi

es:

Evid

ence

from

Sur

vey

Dat

a.”

Jour

nal o

f Urb

an

Econ

omic

s 4

4(1)

:68-

92.

CV

M

Acu

te r

espi

rato

ry

sym

ptom

s N

o Ta

iwan

(con

tinue

d)


A-2

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

1 2

1

Alb

erin

i, A

nna,

Mau

reen

Cro

pper

, Tsu

-Tan

Fu,

Ala

n K

rupn

ick,

Jin-

Tan

Liu,

Dai

gee

Shaw

, and

Win

ston

H

arri

ngto

n. 1

997.

“V

alui

ng H

ealth

Effe

cts

of A

ir

Pollu

tion

in D

evel

opin

g C

ount

ries

: Th

e C

ase

of

Taiw

an.”

Jou

rnal

of E

nvir

onm

enta

l Eco

nom

ics

and

Man

agem

ent

34:1

07-2

6.

CV

M

Acu

te r

espi

rato

ry

sym

ptom

s N

o Ta

iwan

2 1

1

Bal

a, M

ohan

V.,

Lisa

L. W

ood,

Gar

y A

. Zar

kin,

Edw

ard

C. N

orto

n, A

mir

am G

afni

, and

Ber

nie

O’B

rien

. 19

98.

“Val

uing

Out

com

es in

Hea

lth C

are:

A C

ompa

riso

n of

W

illin

gnes

s to

Pay

and

Qua

lity-

Adj

uste

d Li

fe-Y

ears

.”

Jour

nal o

f Clin

ical

Epi

dem

iolo

gy 5

1(8)

:667

-676

.

CV

M

Pain

from

shi

ngle

s N

o U

S SG

, Q

ALY

32

2 1

Ber

ger,

Mar

k C

., G

lenn

C. B

lom

quis

t, D

on K

enke

l, an

d G

eorg

e S.

Tol

ley.

198

7. “

Val

uing

Cha

nges

in H

ealth

R

isks

: A

Com

pari

son

of A

ltern

ativ

e M

easu

res.

”

Sout

hern

Eco

nom

ic Jo

urna

l 53

(4):9

67-9

84.

CV

M

Acu

te s

ympt

oms

No

US

4 1

1

Blu

men

sche

in, K

., Jo

hann

esso

n, M

. 19

98.

“Rel

atio

nshi

p B

etw

een

Qua

lity

of L

ife In

stru

men

ts,

Hea

lth S

tate

Util

ities

, and

Will

ingn

ess

to P

ay in

Pat

ient

s w

ith A

sthm

a.”

Ann

als

of A

llerg

y, A

sthm

a, a

nd

Imm

unol

ogy

80:

189-

194.

CV

M

Ast

hma

cure

N

o U

S V

AS,

SG

, TT

O

5 1

1

Car

thy,

Tre

vor,

Sus

an C

hilto

n, Ju

dith

Cov

ey, L

orra

ine

Hop

kins

, Mic

hael

Lee

-Jon

es, G

raha

m L

oom

es, N

ick

Pidg

eon,

and

Ann

e Sp

ence

r. 1

999.

“O

n th

e C

ontin

gent

V

alua

tion

of S

afet

y an

d th

e Sa

fety

of C

ontin

gent

V

alua

tion:

Par

t 2—

The

CV

/SG

‘Cha

ined

’ App

roac

h.”

Jo

urna

l of R

isk

and

Unc

erta

inty

17(

3):1

87-2

13.

CV

M

Hos

pita

lizat

ion

for

inju

ry

No

UK

SG

6 1

1

Che

stnu

t, La

urai

ne G

., L.

Rob

in K

elle

r, W

illia

m E

. La

mbe

rt, a

nd R

ober

t D. R

owe.

199

6. “

Mea

suri

ng H

eart

Pa

tient

s’ W

illin

gnes

s to

Pay

for

Cha

nges

in A

ngin

a Sy

mpt

oms.

” M

edic

al D

ecis

ion

Mak

ing

16:

65-7

7.

CV

M

Ang

ina

atta

cks

No

US

(con

tinue

d)

Appendix A — Bibliography and Summary of Morbidity Valuation Studies

A-3

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

8 1

1

Dic

kie,

M.,

and

V. U

lery

. 20

02.

“Par

enta

l Altr

uism

and

th

e V

alue

of C

hild

Hea

lth:

Are

Kid

s W

orth

Mor

e Th

an

Pare

nts?

” R

epor

t pre

pare

d fo

r U

.S. E

nvir

onm

enta

l Pr

otec

tion

Age

ncy.

CV

M

Acu

te s

ympt

oms

No

US

7 2

1

Dic

kie,

Mar

k, S

helb

y G

erki

ng, D

avid

Bro

oksh

ire,

Don

C

ours

ey, W

illia

m S

chul

ze, A

nne

Cou

lson

, and

Don

ald

Tash

kin.

198

7. “

Rec

onci

ling

Ave

rtin

g B

ehav

ior

and

Con

tinge

nt V

alua

tion

Ben

efit

Estim

ates

of R

educ

ing

Sym

ptom

s of

Ozo

ne E

xpos

ure.

” In

Impr

ovin

g A

ccur

acy

and

Red

ucin

g C

osts

of E

nvir

onm

enta

l Ben

efit

Ass

essm

ent.

Was

hing

ton,

DC

: U

.S. E

nvir

onm

enta

l Pr

otec

tion

Age

ncy.

CV

M/A

VB

A

cute

sym

ptom

s N

o U

S

7 3

1

Dic

kie,

Mar

k, S

helb

y G

erki

ng, W

illia

m S

chul

ze, A

nne

Cou

lson

, and

Don

ald

Tash

kin.

198

6. “

Val

ue o

f Sy

mpt

oms

of O

zone

Exp

osur

e: A

n A

pplic

atio

n of

the

Ave

rtin

g B

ehav

ior

Met

hod.

” In

Impr

ovin

g A

ccur

acy

and

Red

ucin

g C

osts

of E

nvir

onm

enta

l Ben

efit

Ass

essm

ents

, U

.S. E

nvir

onm

enta

l Pro

tect

ion

Age

ncy.

AV

B

Acu

te s

ympt

oms

No

US

9 1

1 G

an T

., F.

Slo

an, e

t al.

2001

. “H

ow M

uch

Are

Pat

ient

s W

TP to

Avo

id P

osto

pera

tive

Nau

sea

and

Vom

iting

?”

Ane

sthe

sia

& A

nalg

esia

92(

2):3

93-4

00.

CV

M

Post

-ope

rativ

e na

usea

N

o U

S

10

1 1

Hen

son,

Spe

ncer

. 19

96.

“Con

sum

er W

illin

gnes

s to

Pay

fo

r R

educ

tions

in th

e R

isk

of F

ood

Pois

onin

g in

the

UK

.”

Jour

nal o

f Agr

icul

tura

l Eco

nom

ics

47(

3):4

03-4

20.

CV

M

Food

poi

soni

ng

Yes

U

K

39

1 1

Jaco

bs, R

. Jak

e, R

onal

d J.

Mol

eski

and

Alle

n S.

M

eyer

hoff.

200

2. “

Val

uatio

n of

Sym

ptom

atic

Hep

atiti

s A

in A

dults

.” P

harm

acoe

cono

mic

s 2

0(11

):739

-47.

C

VM

H

epat

itis

A

Yes

U

S

(con

tinue

d)


A-4

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

13

1 1

Kar

tman

B.,

et a

l. 1

996.

”V

alua

tion

of H

ealth

Cha

nges

w

ith th

e C

V M

etho

d.”

Hea

lth E

cono

mic

s 5

:531

-41.

C

VM

R

eflu

x oe

soph

agiti

s Y

es

Swed

en

12

1 1

Kar

tman

, Ber

nt, F

redr

ik A

nder

sson

, and

Mag

nus

Joha

nnes

son.

199

6. “

Will

ingn

ess

to P

ay fo

r R

educ

tions

in

Ang

ina

Pect

oris

Atta

cks.

” M

edic

al D

ecis

ion

Mak

ing

16

(3):2

48-2

53.

CV

M

Ang

ina

atta

cks

No

Swed

en

14

1 1

Kei

th P

.L.,

et a

l. 2

000.

”A

Cos

t-B

enef

it A

naly

sis

Usi

ng

a W

TP Q

uest

ionn

aire

of I

ntra

nasa

l Bud

eson

ide

for

Seas

onal

Alle

rgic

Rhi

nitis

.” A

nn A

llerg

y A

sthm

a Im

mun

ol 8

4:55

-62.

CV

M

Alle

rgic

rhi

nitis

N

o U

S

22

1 1

Kra

bbe,

Pau

l F.,

Mar

ie-L

ouis

e Es

sink

-Bot

, and

Gou

ke J.

B

onse

l. 1

997.

“Th

e C

ompa

rabi

lity

and

Rel

iabi

lity

of

Five

Hea

lth-S

tate

Val

uatio

n M

etho

ds.”

Soc

ial S

cien

ce

and

Med

icin

e 4

5(11

):164

1-52

.

CV

M

Bro

adly

def

ined

he

alth

sta

tes

No

Net

herl

and

s SG

, TTO

23

1 1

Kru

pnic

k, A

lan

J., a

nd M

aure

en C

ropp

er.

1992

. “T

he

Effe

ct o

f Inf

orm

atio

n on

Hea

lth R

isk

Val

uatio

ns.”

Jo

urna

l of R

isk

and

Unc

erta

inty

5(1

):29-

48.

Con

join

t B

ronc

hitis

Y

es

US

37

1 1

Lee,

Pat

rick

Y.,

Dav

id M

atch

ar, D

enni

s C

lem

ents

, Joe

l H

uber

, Joh

n H

amilt

on, a

nd E

ric

Pete

rson

. 20

02.

“Eco

nom

ic A

naly

sis

of In

fluen

za V

acci

natio

n an

d A

ntiv

iral

Tre

atm

ent f

or H

ealth

y W

orki

ng A

dults

.”

Ann

als

of In

tern

al M

edic

ine

137

(4):2

25-3

1.

Con

join

t

One

day

of

influ

enza

sy

mpt

oms

no

US

15

1 1

Liu

Jin-T

an, e

t al.

200

0. ”

Mot

her’

s W

TP fo

r H

er O

wn

and

Her

Chi

ld’s

Hea

lth:

A C

ontin

gent

Val

uatio

n St

udy

in T

aiw

an.”

Hea

lth E

cono

mic

s 9

:319

-26.

C

VM

C

old

No

Taiw

an

QW

B

35

1 1

Lund

berg

L.,

et a

l. 19

99.

“Qua

lity

of L

ife, H

ealth

-Sta

te

Util

ities

and

WTP

in P

atie

nts

with

Pso

rias

is a

nd A

topi

c Ec

zem

a.”

Br

J Der

mat

ol 1

41(6

):106

7-75

. C

VM

Ps

oria

sis

and

atop

ic e

czem

a N

o Sw

eden

SG, T

TO,

VA

S,

Euro

Qol

, 15

D,S

F-36

(c

ontin

ued)


A-5

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

16

1 1

O’B

rien

, Ber

nie

J., a

nd Jo

se L

uis

Vir

amon

tes.

199

4.

“Will

ingn

ess

to P

ay:

A V

alid

and

Rel

iabl

e M

easu

re o

f H

ealth

Sta

te P

refe

renc

es?”

Med

ical

Dec

isio

n M

akin

g 14

(3):2

89-2

97.

CV

M

Hea

lthy

lung

fu

nctio

n Y

es

Can

ada

SG

26

1 1

Perr

eira

, Kri

sta

M.,

and

Fran

k Sl

oan.

200

2. “

Livi

ng

Hea

lthy

and

Livi

ng L

ong:

Val

uing

the

Non

pecu

niar

y Lo

ss fr

om D

isab

ility

and

Dea

th.”

The

Jour

nal o

f Ris

k an

d U

ncer

tain

ty 2

4(1)

:5-2

9.

US

Dis

abili

ty

Yes

U

S

17

1 1

Rea

dy, R

icha

rd C

., St

ale

Nav

rud,

and

W. R

icha

rd

Dub

ourg

. 20

01.

“How

do

Res

pond

ents

with

Unc

erta

in

Will

ingn

ess

to P

ay A

nsw

er C

ontin

gent

Val

uatio

n Q

uest

ions

?” L

and

Econ

omic

s 7

7(3)

:315

-26.

CV

M

Acu

te s

ympt

oms

No

Nor

way

17

2 1

Rea

dy, R

icha

rd, S

tale

Nav

rud,

Bre

tt D

ay, R

icha

rd

Dub

ourg

, Fer

nand

o M

acha

do, S

usan

a M

oura

to, F

rank

Sp

anni

nks,

and

Mar

ia X

ose

Vaz

quez

Rod

rigu

ez.

1999

. “B

enef

it Tr

ansf

er in

Eur

ope:

Are

Val

ues

Con

sist

ent

Acr

oss

Cou

ntri

es?”

Wor

king

Pap

er.

CV

M

Acu

te s

ympt

oms

No

Nor

way

, N

ethe

rlan

ds,

Port

ugal

, Sp

ain,

En

glan

d

27

1 1

Sloa

n, F

rank

A.,

W. K

ip V

iscu

si, H

arre

ll W

. Che

sson

, C

hris

toph

er J.

Con

over

, and

Kat

hryn

Whe

tten-

Gol

dste

in.

1998

. “A

ltern

ativ

e A

ppro

ache

s to

Val

uing

Inta

ngib

le

Hea

lth L

osse

s: T

he E

vide

nce

for

Mul

tiple

Scl

eros

is.”

Jo

urna

l of H

ealth

Eco

nom

ics

7:4

75-4

97.

Con

join

t M

ultip

le S

cler

osis

Y

es

US

19

1 1

Slot

huus

U.,

et a

l. 2

000.

“W

TP fo

r A

rthr

itis

Sym

ptom

A

llevi

atio

n.”

Inte

rnat

iona

l Jou

rnal

of T

echn

olog

y A

sses

smen

t in

Hea

lth C

are

16(

1):6

0-72

. C

VM

A

rthr

itis

sym

ptom

s N

o D

enm

ark

19

2 1

Slot

huus

U.,

et a

l. 2

000.

WTP

in A

rthr

itis:

A D

anis

h C

ontr

ibut

ion.

” R

heum

atol

ogy

(Oxf

ord)

39(

7):7

91-9

. C

VM

A

rthr

itis

No

Den

mar

k

(con

tinue

d)


A-6

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

28

1 1

Stav

em K

. 19

99. ”

WTP

: A

Fea

sibl

e M

etho

d fo

r A

sses

sing

Tre

atm

ent B

enef

its in

Epi

leps

y?”

Sei

zure

8:

14-1

9.

CV

M

Epile

psy

cure

N

o N

orw

ay

SG,

TTO

, V

AS,

Eu

roQ

ol,

15D

34

1 1

Stav

em, K

. 20

02.

“Ass

ocia

tion

of W

illin

gnes

s to

Pay

w

ith S

ever

ity o

f Chr

onic

Obs

truc

tive

Pulm

onar

y D

isea

se, H

ealth

Sta

tus

and

Oth

er P

refe

renc

e M

easu

res.

”

Inte

rnat

iona

l Jou

rnal

of T

uber

culo

sis

and

Lung

Dis

ease

6(

6):5

42-5

49.

CV

M

CO

PD c

ure

No

Nor

way

SG,

TTO

, V

AS,

SF-

36

33

1 1

Thom

pson

, Mar

k S.

198

6. “

Will

ingn

ess

to P

ay a

nd

Acc

ept R

isks

to C

ure

Chr

onic

Dis

ease

.” A

mer

ican

Jo

urna

l of P

ublic

Hea

lth 7

6(4)

:392

-396

.

CV

M

Art

hriti

s cu

re

No

US

SG

38

1 1

Torr

ance

, Geo

rge,

Val

lery

Wal

ker,

Ron

ald

Gro

ssm

an,

Jaya

nti M

ukhe

rjee

, Dav

id V

augh

an, J

aque

s La

For

ge,

and

Noe

l Lam

pron

. 19

99.

“Eco

nom

ic E

valu

atio

n of

C

ipro

floxa

cin

Com

pare

d w

ith th

e U

sual

Ant

ibac

teri

al

Car

e fo

r th

e Tr

eatm

ent o

f Acu

te E

xace

rbat

ions

of

Chr

onic

Bro

nchi

tis in

Pat

ient

s Fo

llow

ed fo

r 1

Yea

r.”

Ph

arm

acoe

cono

mic

s 1

6(5

Pt. 1

):499

-520

.

CV

M

Acu

te

exac

erba

tion

of

chro

nic

bron

chiti

s no

C

anad

a

21

1 1

Vis

cusi

, W. K

ip, W

esle

y A

. Mag

at, a

nd Jo

el H

uber

. 19

91.

“Pri

cing

Hea

lth R

isks

: Su

rvey

Ass

essm

ents

of

Ris

k-R

isk

and

Ris

k-D

olla

r Tr

adeo

ffs.”

Jou

rnal

of

Envi

ronm

enta

l Eco

nom

ics

and

Man

agem

ent

21(1

):32-

51.

Con

join

t C

hron

ic b

ronc

hitis

Y

es

US

31

2 1

Zet

hrae

us N

., et

al.

199

7. “

The

Impa

ct o

f Hor

mon

e R

epla

cem

ent T

hera

py o

n Q

ualit

y of

Life

and

WTP

.” B

r J

Obs

tet G

ynae

col

104(

10):1

191-

5.

CV

M

Hor

mon

e re

plac

emen

t th

erap

y N

o Sw

eden

V

AS,

TT

O

31

1 1

Zet

hrae

us, N

ikla

s. 1

998.

“W

illin

gnes

s to

Pay

for

Hor

mon

e R

epla

cem

ent T

hera

py.”

Hea

lth E

cono

mic

s 7:

31-3

8.

CV

M

Hor

mon

e re

plac

emen

t th

erap

y N

o Sw

eden

V

AS,

TT

O

(con

tinue

d)


A-7

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Age

e, M

.D.,

and

T.D

. Cro

cker

. 19

96.

“Par

enta

l A

ltrui

sm a

nd C

hild

Lea

d Ex

posu

re:

Infe

renc

es fr

om th

e D

eman

d fo

r C

hela

tion

Ther

apy.

” Jo

urna

l of H

uman

R

esou

rces

31(

3):6

77-6

91.

AV

B

Che

latio

n th

erap

y Y

es

US

2

App

el, L

.J., S

tein

berg

E.P

., Po

we

N.R

., et

al.

199

0.

“Ris

k R

educ

tion

From

Low

Osm

ality

Con

tras

t Med

ia:

Wha

t Do

Patie

nts

Thin

k It

Is W

orth

?” M

edic

al C

are

28:3

24.

CV

M

Side

effe

cts

from

ra

diol

ogy

Yes

U

S

2

Ara

na, J

orge

E.,

and

Car

mel

o J.

Leon

. 20

02.

“Will

ingn

ess

to P

ay fo

r H

ealth

ris

k R

educ

tion

in th

e C

onte

xt o

f Altr

uism

.” H

ealth

Eco

nom

ics

in p

ress

. C

VM

R

educ

e pr

obab

ility

of

get

ting

flu

Yes

Sp

ain

2

Ari

stid

es, M

ike,

Jack

Che

n, M

ak S

chul

z, E

ve

Will

iam

son,

and

Ste

phen

Cla

rke.

200

2. “

Con

join

t A

naly

sis

of a

New

Che

mot

hera

py.”

Ph

arm

acoe

cono

mic

s 20

(110

:775

-84.

C

onjo

int

2 Ty

pes

of m

outh

ul

cera

tions

from

ch

emot

hera

py

Yes

A

ustr

alia

2 B

iddl

e, J.

E.,

and

G. Z

arki

n. 1

988.

“W

orke

r Pr

efer

ence

s an

d M

arke

t Com

pens

atio

n fo

r Jo

b R

isk.

” R

evie

w o

f Ec

onom

ics

and

Stat

istic

s 7

0(4)

:660

-667

. H

edon

ic

Inju

ry

Yes

U

S

2

Blu

men

sche

in K

., et

al.

200

1. ”

Hyp

othe

tical

ver

sus

Rea

l Will

ingn

ess

to P

ay in

the

Hea

lth C

are

Sect

or:

Res

ults

from

a F

ield

Exp

erim

ent.”

J H

ealth

Eco

nom

ic

20:4

41-4

57.

CV

M

Ast

hma

man

agem

ent

prog

ram

N

o U

S

2 C

ross

M.J.

, et a

l. 2

000.

”D

eter

min

ants

of W

TP fo

r H

ip

and

Kne

e R

epla

cem

ent S

urge

ry fo

r O

steo

arth

ritis

.”

Rhe

umat

olog

y (O

xfor

d) 3

9(11

):124

2-8.

C

VM

H

ip a

nd k

nee

repl

acem

ent

surg

ery

No

Aus

tral

ia

2 C

unni

ngha

m S

.J., a

nd N

.P. H

unt.

200

0. ”

Rel

atio

nshi

p B

etw

een

Util

ity V

alue

s an

d W

TP in

Pat

ient

s U

nder

goin

g Tr

eatm

ent.”

Com

mun

ity D

enta

l Hea

lth 1

7(2)

:92-

6.

CV

M

Ort

hagn

atic

tr

eatm

ent

U

K

SG

(con

tinue

d)


A-8

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Dic

kie,

Mar

k, a

nd S

helb

y G

erki

ng.

1996

. “D

efen

sive

A

ctio

n an

d W

illin

gnes

s to

Pay

for

Red

uced

Hea

lth R

isk:

In

fere

nces

from

Act

ual a

nd C

ontin

gent

Beh

avio

r.”

Pa

per

pres

ente

d at

the

1996

AER

E W

orks

hop,

Tah

oe

City

, CA

.

AV

B

Skin

can

cer

Yes

U

S

2

Dic

kie,

Mar

k, a

nd S

helb

y G

erki

ng.

1996

. “F

orm

atio

n of

Ris

k B

elie

fs, J

oint

Pro

duct

ion

and

Will

ingn

ess

to P

ay

to A

void

Ski

n C

ance

r.”

Rev

iew

of E

cono

mic

s an

d St

atis

tics

451

-463

.

AV

B

Skin

can

cer

Yes

U

S

2 D

iez

L. 1

998.

”A

sses

sing

the

Will

ingn

ess

of P

aren

ts to

Pa

y fo

r R

educ

ing

Post

oper

ativ

e Em

esis

in C

hild

ren.

”

Phar

mac

o-Ec

onom

ics

13(

5 pa

rt 2

):589

-596

. C

VM

Po

st o

pera

tive

emes

is in

chi

ldre

n Y

es

UK

2

Don

alds

on, C

., T.

Map

p, M

. Rya

n, a

nd K

. Cur

tin.

1996

. “E

stim

atin

g th

e Ec

onom

ic B

enef

its o

f Avo

idin

g Fo

od

Bor

ne R

isk:

Is

Will

ingn

ess

to P

ay F

easi

ble?

” E

pide

mio

l. In

fect

. 11

6:28

5-94

.

CV

M

Elim

inat

e ri

sk o

f po

ultr

y bo

rne

illne

ss

Yes

U

K

2

Don

alds

on, C

am, a

nd P

hil S

hack

ley.

199

7. “

Doe

s ‘P

roce

ss U

tility

’ Exi

st?

A C

ase

Stud

y of

Will

ingn

ess

to

Pay

for

Lapa

rosc

opic

Cho

lecy

stec

tom

y.”

Soc

ial S

cien

ce

and

Med

icin

e 4

4(5)

:699

-707

.

CV

M

Gal

l bla

dder

tr

eatm

ent

No

UK

2

Dra

nits

aris

G.,

et a

l. 2

000.

”Th

e Ec

onom

ic V

alue

of a

N

ew In

sulin

Pre

para

tion,

Hum

alog

Mix

25.

Mea

sure

d by

a W

TP A

ppro

ach.

” P

harm

aco-

Econ

omic

s

18(3

):275

-87.

CV

M,

Con

join

t N

ew in

sulin

fo

rmul

atio

n Y

es

Can

ada

2

Dra

nits

aris

G.

1997

. ”A

Pilo

t Stu

dy to

Eva

luat

e th

e Fe

asib

ility

of U

sing

WTP

as

a M

easu

re o

f Val

ue in

C

ance

r Su

ppor

tive

Car

e: A

n A

sses

smen

t of A

mifo

stin

e C

ytop

rote

ctio

n.”

Sup

port

Car

e C

ance

r 5

(6):4

89-9

9.

CV

M

Che

mot

hera

py

toxi

city

Y

es

Can

ada

(con

tinue

d)


A-9

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2 Ea

stau

gh S

.R.

1991

. “V

alua

tions

of t

he B

enef

its o

f Ris

k-Fr

ee B

lood

.” I

nter

natio

nal J

ourn

al o

f Tec

hnol

ogy

Ass

essm

ent

7:51

. C

VM

R

isk

free

blo

od

Yes

C

anad

a

2 Ea

stau

gh, S

.R.

2000

. ”W

TP in

Tre

atm

ent o

f Ble

edin

g D

isor

ders

.” I

ntl J

Tec

hnol

ogy

Ass

essm

ent i

n H

ealth

C

are

16(

2):7

06-1

0.

CV

M

Ble

edin

g di

sord

er

trea

tmen

t Y

es

US

2

Eber

hart

, L.H

.J., M

. Mau

ch, A

. M. M

orin

, H. W

ulf,

and

G. G

eldn

er.

2002

. “I

mpa

ct o

f Mul

timod

al A

nti-

Emet

ic

Prop

hyla

xis

on P

atie

nt S

atis

fact

ion

in H

igh-

Ris

k Pa

tient

s fo

r Po

stop

erat

ive

Nau

sea

and

Vom

iting

.” A

naes

thes

ia

57:1

022-

27.

CV

M

Red

uce

risk

of

post

-ope

rativ

e na

usea

Y

es

UK

2

Evan

s, W

illia

m N

., an

d W

. Kip

Vis

cusi

. 19

91.

“Est

imat

ion

of S

tate

-Dep

ende

nt U

tility

Fun

ctio

ns U

sing

Su

rvey

Dat

a.”

Rev

iew

s of

Eco

nom

ics

and

Stat

istic

s 73

:94-

104.

CV

M

Occ

upat

iona

l in

jury

ris

k Y

es

US

2

Gay

er, T

ed, J

ames

T. H

amilt

on, a

nd W

. Kip

Vis

cusi

. 20

00.

“Pri

vate

Val

ues

of R

isk

Trad

eoffs

at S

uper

fund

Si

tes:

Hou

sing

Mar

ket E

vide

nce

on L

earn

ing

abou

t R

isk.

” T

he R

evie

w o

f Eco

nom

ics

and

Stat

istic

s 82

(3):4

39-5

1.

Hed

onic

(P

rope

rty

Val

ue)

Can

cer

Yes

U

S

2

Gay

er, T

ed, J

ames

T. H

amilt

on, a

nd W

. Kip

Vis

cusi

. 20

02.

“The

Mar

ket V

alue

of R

educ

ing

Can

cer

Ris

k:

Hed

onic

Hou

sing

Pri

ces

with

Cha

ngin

g In

form

atio

n.”

So

uthe

rn E

cono

mic

Jour

nal

69(2

):266

-289

.

Hed

onic

(P

rope

rty

Val

ue)

Can

cer

Yes

U

S

2

Gra

nber

g, M

aria

, Mat

ts W

ikla

nd, L

ars

Nils

son,

and

Lar

s H

ambe

rger

. 19

95.

“Cou

ple’

s W

illin

gnes

s to

Pay

for

IVF/

ET.”

Act

a O

bste

tric

ia e

t Gyn

ecol

ogic

a Sc

andi

navi

ca

74:1

99-2

02.

2

Gyl

dmar

k, M

arle

ne, a

nd G

wen

doly

n C

. Mor

riso

n.

2001

. “D

eman

d fo

r H

ealth

Car

e in

Den

mar

k: R

esul

ts o

f a

Nat

iona

l Sam

ple

Surv

ey U

sing

Con

tinge

nt V

alua

tion.

”

Soci

al S

cien

ce a

nd M

edic

ine

53:

1023

-36.

CV

M

Insu

ranc

e to

cov

er

hosp

ital c

osts

for

four

chr

onic

co

nditi

ons

Yes

D

enm

ark


A-10

(con

tinue

d)

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Joha

nnes

son,

M. a

nd B

. Fag

erbe

rg.

1992

. “A

Hea

lth

Econ

omic

Com

pari

son

of D

iet a

nd D

rug

Trea

tmen

t in

Obe

se M

en w

ith M

ild H

yper

tens

ion.

” Jo

urna

l of

Hyp

erte

nsio

n 1

0:10

63-7

0.

CV

M

Die

t and

/or

drug

tr

eatm

ent t

o re

duce

ris

k of

ca

rdio

vasc

ular

di

seas

e fr

om

obes

ity

Yes

Sw

eden

V

AS

2

Joha

nnes

son,

Mag

nus,

Ben

gt Jö

nsso

n, a

nd L

ars

Bor

gqui

st.

1991

. “W

illin

gnes

s to

Pay

for

Ant

ihyp

erte

nsiv

e Th

erap

y: R

esul

ts o

f a S

wed

ish

Pilo

t St

udy.

” Jo

urna

l of H

ealth

Eco

nom

ics

10:

461-

473.

CV

M

Ant

ihyp

erse

nsiti

ve

ther

apy

No

Swed

en

2

Joha

nnes

son,

Mag

nus,

H. A

berg

, L. A

greu

s, L

. Bor

quis

t, an

d B

. Jon

sson

. 19

91.

“Cos

t-B

enef

it A

naly

sis

of N

on-

phar

moc

olog

ical

Tre

atm

ent o

f Hyp

erte

nsio

n.”

Jour

nal

of In

tern

al M

edic

ine

230

:307

-312

.

CV

M

Ant

ihyp

erse

nsiti

ve

ther

apy

No

Swed

en

VA

S

2

Joha

nnes

son,

Mag

nus,

Per

-Olo

v Jo

hans

son,

Ben

gt

Kri

strö

m, a

nd U

lf-G

. Ger

dtha

m.

1993

. “W

illin

gnes

s to

Pa

y fo

r A

ntih

yper

tens

ive

Ther

apy—

Furt

her

Res

ults

.”

Jour

nal o

f Hea

lth E

cono

mic

s 1

2:95

-108

.

CV

M

Ant

ihyp

erse

nsiti

ve

ther

apy

No

Swed

en

VA

S

2

Joha

nnes

son,

Mag

nus,

R.M

. O’C

onor

, G. K

obel

t-N

guye

n, a

nd A

. Mat

tiass

on.

1997

. “W

illin

gnes

s to

Pay

fo

r R

educ

ed In

cont

inen

ce S

ympt

oms.

” B

ritis

h Jo

urna

l of

Uro

logy

80:

557-

562.

CV

M

Red

uctio

n in

Le

akag

es

No

Swed

en

SF-3

6

2 Jo

hann

esso

n, M

agnu

s. 1

992.

“Ec

onom

ic E

valu

atio

n of

H

yper

tens

ion

Trea

tmen

t.” I

nter

natio

nal J

ourn

al o

f Te

chno

logy

Ass

essm

ent i

n H

ealth

Car

e 8(

3):5

06-5

23.

C

VM

A

ntih

yper

sens

itive

th

erap

y N

o Sw

eden

2 Jo

hann

esso

n, M

agnu

s. 1

992.

“Ec

onom

ic E

valu

atio

n of

Li

pid

Low

erin

g—A

Fea

sibi

lity

Test

of t

he C

ontin

gent

V

alua

tion

App

roac

h.”

Hea

lth P

olic

y 2

0:30

9-32

0.

CV

M

Trea

tmen

t to

redu

ce c

hole

ster

ol

leve

ls to

nor

mal

fo

r lif

e

No

Swed

en

(con

tinue

d)


A-11

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

John

son,

F. R

eed,

and

Kri

sty

E. M

atth

ews.

200

1.

“Sou

rces

and

Effe

cts

of U

tility

-The

oret

ic In

cons

iste

ncy

in

Stat

ed-P

refe

renc

e Su

rvey

s.”

Am

eric

an Jo

urna

l of

Agr

icul

tura

l Eco

nom

ics

83(

5):1

328-

33.

Con

join

t G

luco

se c

ontr

ol

Yes

U

S

2

Kle

inm

an, L

eah,

Em

ma

McI

ntos

h, M

andy

Rya

n, Jo

rdan

Sc

hmie

r, Jo

seph

Cra

wle

y, G

. Ric

hard

Loc

ke, a

nd

Gre

gory

de

Liss

ovoy

. 20

02.

“Will

ingn

ess

to P

ay fo

r co

mpl

ete

Sym

ptom

Rel

ief o

f Gas

troe

soph

agea

l Ref

lux

Dis

ease

.” A

rch

Inte

rn M

ed 1

62:1

361-

66.

Con

join

t

Rel

ief o

f ga

stro

esop

hage

al

reflu

x di

seas

e sy

mpt

oms

No

US

2

Kob

elt,

Gis

ela.

199

7. “

Econ

omic

Con

side

ratio

ns a

nd

outc

ome

Mea

sure

men

t in

Urg

e In

cont

inen

ce.”

Uro

logy

50

(6A

):100

-107

.

CV

M

25 a

nd 5

0%

impr

ovem

ent i

n in

cont

inen

ce

sym

ptom

s N

o Sw

eden

Eu

roQ

OL

2

Kup

perm

ann

M.,

et a

l. 2

000.

”Pa

rent

s’ P

refe

renc

es fo

r O

utco

mes

Ass

ocia

ted

with

Chi

ldho

od V

acci

natio

ns.”

Pe

diat

r In

fect

Dis

19:

129-

33.

2 Le

e S.

J., e

t al.

199

8. ”

Perc

eptio

ns a

nd P

refe

renc

es o

f A

utol

ogou

s B

lood

Don

ors.

” T

rans

fusi

on 3

8(8)

:757

-63.

2

Lee,

S.J.

, B. L

iljas

, W.H

. Chu

rchi

ll, M

.A. P

opov

sky,

C.P

. St

owel

l, M

.E. C

anno

n, a

nd M

. Joh

anne

sson

. 19

98.

“Per

cept

ions

and

Pre

fere

nces

of A

utol

ogou

s B

lood

D

onor

s.”

Tra

nsfu

sion

38:

757-

763.

CV

M

Fee

for

patie

nt

stor

age

of th

eir

own

bloo

d

Red

uce

risk

of

infe

cted

blo

od

No

US

2

Lee,

Ste

phan

ie J.

Jun

e 19

98.

“Pat

ient

s’ W

illin

gnes

s to

Pa

y fo

r A

utol

ogou

s B

lood

Don

atio

n.”

Ris

k in

Pe

rspe

ctiv

e 6(

6):.

(c

ontin

ued)


A-12

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Lee,

Ste

phan

ie, J

., Pe

ter

J. N

eum

ann,

W. H

allo

wel

l C

hurc

hill,

Mar

ie E

. Can

non,

Milt

on C

. Wei

nste

in, a

nd

Mag

nus

Joha

nnes

son.

199

7. “

Patie

nts’

Will

ingn

ess

to

Pay

for

Aut

olog

ous

Blo

od D

onat

ion.

” H

ealth

Pol

icy

40:1

-12.

CV

M

Red

uce

risk

of

infe

cted

blo

od

No

US

2 Li

eu T

A, e

t al.

200

0. ”

The

Hid

den

Cos

ts o

f Inf

ant

Vac

cina

tion”

Vac

cine

19(

1):3

3-41

. C

VM

Num

ber

of

vacc

ine

inje

ctio

ns

and

adve

rse

sym

ptom

s

No

US

2

Lipt

on, R

icha

rd B

., Sa

ndra

W. H

amel

sky,

and

Jeffr

ey M

. D

ayno

. 20

02.

“Wha

t do

Patie

nts

with

Mig

rain

e W

ant

from

Acu

te M

igra

ine

Trea

tmen

t?”

Hea

dach

e 4

2(S1

). C

VM

M

igra

ine

no

US

2 Lo

ngo

C.J.

199

9. ”

Cho

ices

of M

etho

dolo

gy in

Ph

arm

acoe

cono

mic

s St

udie

s.”

(bas

ed o

n ab

stra

ct) M

ed

Car

e 3

7(4

Supp

l Lill

y):A

S32-

5.

CV

M

Neu

trop

enia

, ne

urot

oxic

ity,

neph

roto

xici

ty

Yes

C

anad

a

2

Mag

at, W

esle

y A

., W

. Kip

Vis

cusi

, and

Joel

Hub

er.

1988

. “P

aire

d C

ompa

riso

n an

d C

ontin

gent

Val

uatio

n A

ppro

ache

s to

Mor

bidi

ty R

isk

Val

uatio

n.”

Jour

nal o

f En

viro

nmen

tal E

cono

mic

s an

d M

anag

emen

t 15

(4):3

95-

411.

CV

M

Con

sum

er p

rodu

ct

safe

ty (a

ccid

ents

/ po

ison

ing)

Y

es

US

2

Mag

at, W

esle

y, a

nd W

. Kip

Vis

cusi

. 19

92.

“Inf

orm

atio

nal A

ppro

ache

s to

Reg

ulat

ion.

” R

egul

atio

n of

Eco

nom

ic A

ctiv

ity S

erie

s, V

ol 1

9. C

ambr

idge

and

Lo

ndon

: M

IT P

ress

.

CV

M

Inju

ry

Yes

U

S

2

Mat

thew

s, D

ebor

a, A

ngel

a R

occh

i, an

d A

mir

am G

afni

. 20

02.

“Put

ting

You

r M

oney

Whe

re Y

our

Mou

th Is

: W

illin

gnes

s to

Pay

for

Den

tal G

el.”

Ph

arm

acoe

cono

mic

s 2

0(4)

:245

-55.

CV

M

Non

-inj

ecte

d (n

o pa

in) d

enta

l an

aest

hetic

Y

es

Can

ada

(con

tinue

d)


A-13

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Mitc

hell,

Rob

ert C

., an

d R

icha

rd T

. Car

son.

198

6.

“Val

uing

Dri

nkin

g W

ater

Ris

k R

educ

tions

Usi

ng th

e C

ontin

gent

Val

uatio

n M

etho

d: A

Met

hodo

logi

cal S

tudy

of

Ris

ks fr

om T

HM

and

Gia

rdia

.” P

repa

red

for

the

U.S

. En

viro

nmen

tal P

rote

ctio

n A

genc

y un

der

Coo

pera

tive

Agr

eem

ent C

R81

0466

-01-

6.

CV

M

Can

cer

Yes

U

S

2 M

ulle

r A

., an

d T.

J Reu

tzel

. 19

84.

”WTP

for

Red

uctio

n in

Fat

ality

Ris

k: A

n Ex

plor

ator

y Su

rvey

.” A

m J

Publ

ic

Hea

lth 7

4(8)

:808

-12.

C

VM

In

jury

and

fata

lity

Yes

U

S

2 N

arbr

o K

., an

d Sj

ostr

om L

. 200

0. ”

WTP

for

Obe

sity

Tr

eatm

ent.”

Int

l J T

echn

olog

y A

sses

smen

t in

Hea

lth

Car

e 1

6(1)

:50-

59.

CV

M

Rel

ieve

obe

sity

-re

late

d pr

oble

ms

No

Swed

en

2

Neu

man

n, P

eter

J.,

and

Joha

nnes

son,

Mag

nus.

199

4.

“The

Will

ingn

ess

to P

ay fo

r In

Vitr

o Fe

rtili

zatio

n: A

Pilo

t St

udy

Usi

ng C

ontin

gent

Val

uatio

n.”

Med

ical

Car

e 32

(7):6

86-6

99.

2 N

orin

der,

A.,

et a

l. 2

001.

”Sc

ope

and

Scal

e In

sens

itivi

ties

in a

CV

Stu

dy o

f Ris

k R

educ

tions

.” H

ealth

Po

licy

57(

2):1

41-5

3.

CV

M

Fata

l and

non

-fat

al

Yes

Sw

eden

2

O’B

rien

, Ber

nie

J., R

on G

oere

e, A

mir

am G

afni

, Geo

rge

W. T

orra

nce,

Mar

k V

. Pau

ly, H

aim

Erd

er, J

im

Rus

thov

en, J

ane

Wee

ks, M

iliss

a C

ahill

, and

Bru

ce

LaM

ont.

199

8. “

Ass

essi

ng th

e V

alue

of a

New

Ph

arm

aceu

tical

: A

Fea

sibi

lity

Stud

y of

Con

tinge

nt

Val

uatio

n in

Man

aged

Car

e.”

Med

ical

Car

e.

CV

M

Neu

trop

enia

from

ch

emot

hera

py

Yes

U

S

2

O’B

rien

, Ber

nie

J., S

neza

na N

ovos

el, G

eorg

e To

rran

ce,

and

Dav

id S

trei

ner.

199

5. “

Ass

essi

ng th

e Ec

onom

ic

Val

ue o

f a N

ew A

ntid

epre

ssan

t: A

Will

ingn

ess-

to-P

ay

App

roac

h.”

Pha

rmac

oEco

nom

ics

8(1

):34-

45.

CV

M

Adv

erse

effe

cts

of

antid

epre

ssan

t Y

es

Can

ada

(con

tinue

d)


A-14

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

O’B

ryne

, Pau

l, La

uren

Cud

dy, D

. Way

ne T

aylo

r,

Step

hen

Bir

ch, J

oann

e M

orri

s, a

nd Je

rry

Syro

tuik

. 19

96.

“Effi

cacy

and

Cos

t Ben

efit

of In

hale

d C

ortic

oste

roid

s in

Pa

tient

s C

onsi

dere

d to

Hav

e M

ild A

sthm

a in

Pri

mar

y C

are

Prac

tice.

” C

anad

ian

Res

pira

tory

Jour

nal

3(3)

:169

-17

5.

CV

M

Ast

hma

Yes

C

anad

a

2

O’C

onor

, Ric

hard

M.,

and

Gle

nn C

. Blo

mqu

ist.

199

7.

“Mea

sure

men

t of C

onsu

mer

-Pat

ient

Pre

fere

nces

Usi

ng a

H

ybri

d C

ontin

gent

Val

uatio

n M

etho

d.”

Jour

nal o

f H

ealth

Eco

nom

ics

16:

667-

683.

CV

M

Ast

hma

drug

ef

ficac

y, d

eath

Y

es

US

2 O

’Con

or, R

.M.,

et a

l. 1

998.

”U

rge

Inco

ntin

ence

: Q

ualit

y of

Life

and

Pat

ient

s’ V

alua

tion

of S

ympt

om

Red

uctio

n.”

Pha

rmac

o-Ec

onom

ics

14(

5):5

31-5

39.

CV

M

Mic

turi

tions

and

le

akag

es

Yes

U

S SF

-36

2 O

lson

, C.A

. 19

81.

“An

Ana

lysi

s of

Wag

e D

iffer

entia

ls

Rec

eive

d by

Wor

kers

on

Dan

gero

us Jo

bs.”

Jou

rnal

of

Hum

an R

esou

rces

16:

167-

185.

he

doni

c In

jury

Y

es

US

2

Ort

ega,

Ana

, Geo

rge

Dra

nits

aris

, and

Ani

tash

a L.

V.

Puod

siun

as.

1998

. “W

hat A

re C

ance

r Pa

tient

s W

illin

g to

Pay

for

Prop

hyla

ctic

Epo

etin

Alfa

? A

Cos

t-B

enef

it A

naly

sis.

” C

ance

r 8

3(12

):258

8-25

96.

CV

M

Blo

od tr

ansf

usio

n fo

r ca

ncer

ane

mia

Y

es

Can

ada

2

Penn

ie, .

R.A

, et a

l. 1

991.

”Fa

ctor

s In

fluen

cing

the

Acc

epta

nce

of H

epat

itis

B V

acci

ne b

y St

uden

ts in

H

ealth

Dis

cipl

ines

in O

ttaw

a.”

Can

J Pu

blic

Hea

lth

82(1

):12-

5.

CV

M

Hep

atiti

s B

Y

es

Can

ada

2

Poin

er, T

.F.,

et a

l. 2

000.

”Pa

tient

Atti

tude

s to

Top

ical

A

ntip

sori

atic

Tre

atm

ent w

ith C

alci

potr

iol a

nd

Dith

rano

l.” J

of t

he E

urop

ean

Aca

dem

y of

Der

mat

olog

y &

Vin

ereo

logy

14(

3):1

53-8

.

CV

M

antip

sori

atic

tr

eatm

ent

Yes

U

K

(con

tinue

d)


A-15

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2 R

amse

y, S

.D.,

et a

l. 1

997.

”W

TP fo

r A

ntih

yper

tens

ive

Car

e: E

vide

nce

from

a S

taff-

Mod

el H

MO

.” (

base

d on

ab

stra

ct)

Soc

Sci M

ed 4

4(12

):191

1-7.

C

VM

A

ntih

yper

sens

itive

th

erap

y Y

es

US

2

Rea

d, D

anie

l, an

d N

icol

eta

Lilia

na R

ead.

200

1. “

An

Age

-Em

bedd

ing

Effe

ct: T

ime

Sens

itivi

ty a

nd T

ime

Inse

nsiti

vity

Whe

n Pr

icin

g H

ealth

Ben

efits

.” A

cta

Psyc

holo

gica

108

:177

-136

.

CV

M

Cur

e fo

r co

nditi

on

that

wou

ld li

mit

driv

ing

abili

ty

No

UK

2

Rya

n, M

. 19

98.

“Val

uing

Psy

chol

ogic

al F

acto

rs in

the

Prov

isio

n of

Ass

iste

d R

epro

duct

ive

Tech

niqu

es U

sing

th

e Ec

onom

ic In

stru

men

t of W

illin

gnes

s to

Pay

.”

Jour

nal o

f Eco

nom

ic P

sych

olog

y 1

9:17

9-20

4.

2

Rya

n, M

andy

, and

Fer

nand

o Sa

n M

igue

l. 2

000.

“T

estin

g fo

r C

onsi

sten

cy in

Will

ingn

ess

to P

ay

Expe

rim

ents

.” J

ourn

al o

f Eco

nom

ic P

sych

olog

y 2

1:30

5-17

.

CV

M

Men

orha

gia

trea

tmen

t Y

es

UK

2

Rya

n, M

andy

, and

Julie

Rat

cliff

e. 2

000.

“So

me

Issu

es

in th

e A

pplic

atio

n of

Clo

sed-

Ende

d W

illin

gnes

s to

Pay

St

udie

s to

Val

uing

Hea

lth G

oods

: A

n A

pplic

atio

n of

A

nten

atal

Car

e in

Sco

tland

.” A

pplie

d Ec

onom

ics

32

:643

-51.

CV

M

Ant

enat

al c

are

No

UK

2 R

yan,

Man

dy.

1996

. “U

sing

Will

ingn

ess

to P

ay to

Pay

to

Ass

ess

the

Ben

efits

of A

ssis

ted

Rep

rodu

ctiv

e Te

chni

ques

.” E

cono

mic

Eva

luat

ion

5:5

43-5

58.

2

Sans

om, S

teph

anie

L.,

L. B

arke

r, P

.S. C

orso

, C. B

row

n,

and

R. D

euso

n. “

Rot

avir

us V

acci

ne a

nd

Intu

ssus

cept

ion:

How

Muc

h R

isk

Will

Par

ents

in th

e U

nite

d St

ates

Acc

ept t

o O

btai

n V

acci

ne B

enef

its.”

A

mer

ican

Jour

nal o

f Epi

dem

iolo

gy 1

54(1

1):1

077-

1085

.

CV

M

Side

effe

ct (b

owel

bl

ock)

from

chi

ld

vacc

ine

Yes

U

S

(con

tinue

d)


A-16

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Scha

fer,

T.,

A. R

iehl

e, H

.-E.

Wic

hman

n, a

nd J.

Rin

g.

2002

. “A

ltern

ativ

e M

edic

ine

in A

llerg

ies

- Pr

eval

ence

, Pa

ttern

s of

Use

, and

Cos

ts.”

Alle

rgy

57:

694-

700.

C

VM

C

ompl

ete

heal

ing

of a

llerg

y sy

mpt

oms

No

Ger

man

y

2

Schw

ab-C

hris

te, N

atha

lie G

., an

d N

ils C

. Sog

uel.

199

6.

“The

Pai

n of

Roa

d-A

ccid

ent V

ictim

s an

d th

e B

erea

vem

ent o

f the

ir R

elat

ives

: A

Con

tinge

nt-V

alua

tion

Expe

rim

ent.”

Jou

rnal

of R

isk

and

Unc

erta

inty

13:

277-

291.

CV

M

Red

uced

ris

k of

ro

ad a

ccid

ent f

or

self

and/

or

rela

tive

Yes

Sw

itzer

land

2

Sevy

, Ser

ge, K

ay N

atha

nson

, Cly

de S

chec

hter

, and

G

eorg

e Fu

lop.

200

1. “

Con

tinge

ncy

Val

uatio

n an

d Pr

efer

ence

s of

Hea

lth S

tate

s A

ssoc

iate

d w

ith S

ide

Effe

cts

of A

ntip

sych

otic

Med

icat

ions

in S

chiz

ophr

enia

.”

Schi

zoph

reni

a B

ulle

tin 2

7(4)

:643

-52.

CV

M

All

side

effe

cts

from

an

tipsy

chot

ic

med

icat

ion

no

US

2

Slot

huus

, Ulla

, Met

te L

. Lar

sen,

and

Pet

er Ju

nker

. 20

02.

“The

Con

tinge

nt R

anki

ng M

etho

d- A

Fea

sibl

e an

d V

alid

M

etho

d W

hen

Elic

iting

Pre

fere

nces

for

Hea

lth C

are?

”

Soci

al S

cien

ce M

edic

ine

54:

1601

-160

9.

Con

tinge

nt

rank

ing

33%

, 66%

, 100

%

impr

ovem

ent o

f rh

eum

atoi

d ar

thri

tis s

ympt

oms

No

Den

mar

k

2

Soru

m P

. 199

9. ”

Mea

suri

ng P

atie

nt P

refe

renc

es b

y W

illin

gnes

s to

Avo

id:

The

Cas

e of

Acu

te O

titis

Med

ia.”

M

edic

al D

ecis

ion

Mak

ing

19(

01):2

7-37

.

2

Stie

b, D

., P.

Civ

ita, F

. Joh

nson

, M. M

anar

y, A

. Ani

s.

“Val

uing

Acu

te C

ardi

ores

pira

tory

Mor

bidi

ty A

ssoc

iate

d w

ith A

ir P

ollu

tion

Usi

ng C

ost o

f Illn

ess

and

Indi

vidu

al

Will

ingn

ess

to P

ay.”

Con

join

t C

ardi

ores

pira

tory

m

orbi

dity

N

o C

anad

a

2 Ta

mbo

ur M

., an

d N

. Zet

hreu

s. 1

998.

”N

onpa

ram

etri

c W

TP M

easu

res

and

Con

fiden

ce S

tate

men

ts.”

Med

ical

D

ecis

ion

Mak

ing

18(

3):3

30-3

36.

CV

M

Hor

mon

e re

plac

emen

t th

erap

y Y

es

Swed

en

(con

tinue

d)


A-17

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Tang

, J.,

B. W

ang,

P.F

. Whi

te, M

.F. W

atch

a, J.

Qi,

and

R.H

. Wen

der.

199

8. “

The

Effe

ct o

f Tim

ing

of

Oda

nset

ron

Adm

inis

trat

ion

on it

s Ef

ficac

y, C

ost-

Effe

ctiv

enes

s, a

nd C

ost-

Ben

efit

as a

Pro

phyl

actic

A

ntie

met

ic in

the

Am

bula

tory

Set

ting.

” A

nest

hesi

a A

nalg

esia

86:

274-

82.

CV

M

post

ope

rativ

e em

esis

(nau

sea,

vo

miti

ng)

No

US

2 Te

lser

, Har

ry, a

nd P

eter

Zw

eife

l. 2

002

“M

easu

ring

W

illin

gnes

s-to

-Pay

for

Ris

k R

educ

tion:

An

App

licat

ion

of C

onjo

int A

naly

sis.

” H

ealth

Eco

nom

ics

11:

129-

139.

C

onjo

int

Elde

rly

fem

ur

frac

ture

Y

es

Switz

erla

nd

34

1 2

Thom

pson

, M.S

., J.L

. Rea

d, a

nd M

. Lia

ng.

1984

. “F

easi

bilit

y of

Will

ingn

ess

to P

ay M

easu

rem

ent i

n C

hron

ic A

rthr

itis.

” M

edic

al D

ecis

ion

Mak

ing

4:1

95-

215.

CV

M

Art

hriti

s N

o U

S

2

Vis

cusi

, W. K

ip, a

nd W

illia

m N

. Eva

ns.

1990

. “U

tility

Fu

nctio

ns th

at D

epen

d on

Hea

lth S

tatu

s: E

stim

ates

and

Ec

onom

ic Im

plic

atio

ns.”

Am

eric

an E

cono

mic

Rev

iew

80

(3):3

53-3

74.

Hed

onic

In

jury

Y

es

US

2 V

iscu

si, W

. Kip

. 19

78.

“Lab

or M

arke

t Val

uatio

ns o

f Li

fe a

nd L

imb:

Em

piri

cal E

stim

ates

and

Pol

icy

Impl

icat

ions

.” P

ublic

Pol

icy

26(

3):3

59-3

86.

Hed

onic

In

jury

Y

es

US

2

Vis

cusi

, W. K

ip.,

and

C. O

’Con

nor.

198

4. “

Ada

ptiv

e R

espo

nses

to C

hem

ical

Lab

elin

g: A

re W

orke

rs B

ayes

ian

Dec

isio

n M

aker

s?”

Am

eric

an E

cono

mic

Rev

iew

74

(5):9

42-9

56.

Hed

onic

In

jury

Y

es

US

2

Vis

cusi

, W.K

., W

. Mag

at, a

nd A

. For

rest

. 19

88.

“Altr

uist

ic a

nd P

riva

te V

alua

tions

of R

isk

Red

uctio

n.”

Jo

urna

l of P

olic

y A

naly

sis

and

Man

agem

ent

7(2)

:227

-24

5.

CV

M

Pair

ed e

ffect

: In

hala

tion,

ski

n-po

ison

ing

Yes

U

S

(con

tinue

d)


A-18

Ta

ble

A-1

. B

iblio

gra

ph

y a

nd

Su

mm

ary

of

Mo

rbid

ity

Va

lua

tio

n S

tud

ies

(co

nti

nu

ed

)

Stud

y ID

Pu

b ID

Pr

iori

ty

Stud

y N

ame

Val

uati

on

Met

hod

Hea

lth

Effe

ct/C

hang

e R

isk-

base

d C

ount

ry

Hea

lth

Stat

us

Mea

sure

2

Wer

ner,

P.,

and

I. V

ered

. 20

02.

“Wom

en’s

WTP

Out

-of

-Poc

ket f

or D

rug

Trea

tmen

t for

Ost

eopo

rosi

s B

efor

e an

d A

fter

the

Enac

tmen

t of R

egul

atio

ns P

rovi

ding

Pub

lic

Fund

ing:

Evi

denc

e fr

om a

Nat

ural

Exp

erim

ent i

n Is

rael

.”

Osp

eopo

rosi

s In

t 13

:228

-34.

CV

M

Ost

eopo

rosi

s: h

ip

frac

ture

Y

es

Isra

el

1 3

3

Alb

erin

i, A

., an

d A

. Kru

pnic

k. 2

000.

“C

ost-

of-I

llnes

s an

d W

illin

gnes

s-to

-Pay

Est

imat

es o

f the

Ben

efits

of

Impr

oved

Air

Qua

lity:

Evi

denc

e fr

om T

aiw

an.”

Lan

d Ec

onom

ics

76(

1):3

7-53

.

CV

M

Res

pira

tory

illn

ess

No

Taiw

an

2 2

3

Bal

a, M

ohan

V.,

Lisa

L. W

ood,

and

Gar

y A

. Zar

kin.

19

97.

Val

uing

Out

com

es in

Hea

lth C

are:

A

Com

pari

son

of W

illin

gnes

s to

Pay

and

Qua

lity-

Adj

uste

d-Li

fe Y

ears

.” W

orki

ng P

aper

.

CV

M

Pain

from

shi

ngle

s N

o U

S SG

, Q

ALY

29

2 3

Gre

en, A

.E.S

., et

al.

197

8. “

An

Inte

rdis

cipl

inar

y St

udy

of th

e H

ealth

, Soc

ial a

nd E

nvir

onm

enta

l Eco

nom

ics

of

Sulfu

r O

xide

Pol

lutio

n in

Flo

rida

.” I

nter

disc

iplin

ary

Cen

ter

for

Aer

onom

y an

d (o

ther

) Atm

osph

eric

Sci

ence

s.

Prep

ared

for

Flor

ida

Sulfu

r O

xide

s St

udy,

Inc.

CV

M

Acu

te s

ympt

oms

No

US

3 M

orri

s J.,

Per

ez D

. 20

00.

”WTP

for

New

C

hem

othe

rapy

for

Adv

ance

d O

vari

an C

ance

r.”

New

Z

eala

nd M

edic

al Jo

urna

l 11

3(11

08):1

43-6

. C

VM

C

hem

othe

rapy

N

o N

ew

Zea

land

30

3 3

Row

e, R

ober

t D. a

nd L

aura

ine

G. C

hest

nut.

198

4.

“Val

uing

Cha

nges

in M

orbi

dity

: WTP

ver

sus

CO

I M

easu

re.”

Pap

er p

repa

red

for

the

Am

eric

an E

cono

mic

A

ssoc

iatio

n/A

ssoc

iatio

n of

Env

iron

men

tal a

nd R

esou

rce

Econ

omic

s Jo

int M

eetin

gs, D

alla

s, T

exas

12/

27-1

2/30

.

CV

M

Ast

hma

No

US

30

2 3

Row

e, R

ober

t D.,

and

Laur

aine

G. C

hest

nut.

198

6.

Add

endu

m to

: Oxi

dant

s an

d A

sthm

atic

s in

Los

Ang

eles

: A

Ben

efits

Ana

lysi

s. R

epor

t pre

pare

d fo

r U

.S. E

PA.

Doc

umen

t No.

EPA

-230

-09-

86-0

17.

CV

M

Ast

hma

No

US

a Inc

lude

d in

the

John

son,

Fri

es, a

nd B

anzh

af (1

997)

met

a-an

alys

is.

Appendix B: Annotated Bibliography of Publications Included in the Morbidity Value Meta-Analyses

B-1

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

lud

ed

in

th

e A

cu

te E

ffe

cts

Me

ta-A

na

lysi

s

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

1 1

Alb

erin

i, A

an

d K

rupn

ick,

A “A

ir Q

ualit

y Ep

isod

es o

f Acu

te

Res

pira

tory

Illn

ess

in T

aiw

an C

ities

: Ev

iden

ce fr

om

Surv

ey D

ata.

”

1998

Jou

rnal

of

Urb

an

Econ

omic

s 44

: 68-

92 C

V

Tele

phon

e an

d In

-pe

rson

inte

rvie

w;

Nov

embe

r 19

91 -

Ja

nuar

y 19

92;

Sept

embe

r 19

92.

Mos

t rec

ent

expe

rien

ce o

f ac

ute

resp

irat

ory

illne

ss a

s de

fined

by

the

resp

onde

nt

Gen

eral

po

pula

tion

of

Taip

ei,

Kao

hsiu

ng,

and

Hua

lien,

Ta

iwan

52.3

3% o

f re

spon

dent

s re

port

ed

sym

ptom

s du

ring

th

e st

udy

peri

od

with

a m

ean

of

0.96

epi

sode

s pe

r re

spon

dent

and

a

mea

n du

ratio

n of

3.

97 d

ays

Dic

hoto

-m

ous

choi

ce

The

mea

n W

TP

ran g

ed fr

om $

26

to $

54 (1

992

US$

) to

avoi

d fr

om 1

to 1

0 sy

mpt

om d

ays

3

1 2

Alb

erin

i, A

et

al.

“Val

uing

Hea

lth

Effe

cts

of A

ir

Pollu

tion

in

Dev

elop

ing

Cou

ntri

es: T

he

Cas

e of

Tai

wan

”

1997

Jou

rnal

of

Envi

ron-

men

tal

Econ

omic

s an

d M

anag

eme

nt 3

4: 1

07-

126

CV

In

per

son

inte

rvie

w;

Sept

embe

r 19

92.

Mos

t rec

ent

expe

rien

ce o

f ac

ute

resp

irat

ory

illne

ss a

s de

fined

by

the

resp

onde

nt

Gen

eral

po

pula

tion

of

Taip

ei,

Kao

hsiu

ng,

and

Hua

lien,

Ta

iwan

Med

ian

dura

tion

of a

n ep

isod

e w

as

4 da

ys a

nd m

ean

was

6.8

day

s.

Dur

ing

an e

piso

de

the

med

ian

num

ber

of

sym

ptom

s ex

peri

ence

d w

as

1 an

d th

e m

ean

was

2.2

.

Dic

hoto

mo

us c

hoic

e M

edia

n W

TP to

av

oid

a re

curr

ence

of t

he

aver

age

epis

ode

was

$39

(199

2 U

S$),

Mea

n W

TP fo

r th

e av

era g

e of

1 a

nd

5 da

y ep

isod

es

whe

re th

e ep

isod

e is

or

is

not a

col

d w

as

$42

(199

2 U

S$)

2

2 1

Bal

a M

V, e

t al

. “V

alui

ng

Out

com

es in

H

ealth

Car

e: a

C

ompa

riso

n of

W

TP a

nd Q

ALY

s”

1998

Jou

rnal

of

Clin

ical

Ep

idem

i-ol

ogy

51(8

): 66

7-67

6

CV

C

ompu

ter

inte

ract

ive

inte

rvie

w, y

ear

not

avai

labl

e

WTP

for

3 di

ffere

nt

trea

tmen

ts: T

1:

no tr

eatm

ent =

m

ild p

ain

for

2 w

eeks

; tr

eatm

ent =

no

pain

. T2

: no

trea

tmen

t =

seve

re p

ain

for

2 w

eeks

fo

llow

ed b

y m

ild p

ain

for

1 w

eek;

trea

tmen

t =

mild

pai

n fo

r 65

-70

year

ol

ds in

Sa

raso

ta a

nd

Ft. M

yers

, FL

12%

had

pri

or

shin

gles

ex

peri

ence

and

ov

er 8

0% h

ad a

n ac

quai

ntan

ce w

ho

had

expe

rien

ced

shin

gles

Dou

ble-

boun

ded

dich

oto-

mou

s ch

oice

Med

ian

WTP

ra

nged

from

$3

79 (T

reat

men

t 1)

to $

1,19

8 (T

reat

men

t 3)

(199

6 U

S$)

1

(con

tinue

d)


B-2

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he A

cute

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

Bal

a M

V, e

t al

. (co

nt’d

)

2 w

eeks

. T3

: no

trea

tmen

t =

seve

re p

ain

for

2 m

onth

s fo

llow

ed b

y m

ild p

ain

for

1 m

onth

; tr

eatm

ent =

se

vere

pai

n fo

r 2

wee

ks

follo

wed

by

mild

pai

n fo

r 1

wee

k

6 1

Che

stnu

t LG

, et a

l. “M

easu

ring

Hea

rt

Patie

nts’

WTP

for

Cha

nges

in A

ngin

a Sy

mpt

oms”

1996

M

edic

al

Dec

isio

n M

akin

g 16

: 65-

77 C

V

Tele

phon

e in

terv

iew

. 198

6 A

void

ance

of

eith

er 4

or

8 ad

ditio

nal

angi

na e

piso

des M

ale

angi

na

patie

nts

who

ha

d be

en

trea

ted

at a

m

edic

al c

ente

r or

a V

A

hosp

ital i

n th

e Lo

s A

ngel

es

area

.

43 s

ubje

cts

wer

e cu

rren

tly

expe

rien

cing

an

gina

and

all

subj

ects

had

ex

peri

ence

d an

gina

with

in th

e pr

evio

us tw

o ye

ars.

Bid

ding

ga

me

follo

wed

by

open

-end

ed M

ean

WTP

to

avoi

d 4

or 8

ad

ditio

nal

angi

na

epis

odes

ra

nged

from

$2

03 to

$21

8 w

ith a

med

ian

of $

100

(US$

)

2

6 2

Che

stnu

t LG

, et

al.

“Hea

rt D

isea

se

Pati

ents

’ Ave

rtin

g B

ehav

ior,

Cos

t of

Il

lnes

s, a

nd

Will

ingn

ess

to P

ay

to A

void

Ang

ina

Epis

odes

”

1988

U

.S. E

PA

Rep

ort

CV

Te

leph

one

inte

rvie

w; 1

986

Avo

idan

ce o

f an

gina

sy

mpt

oms

for

eith

er o

ne o

r tw

o ep

isod

es

Mal

e an

gina

pa

tien

ts w

ho

had

been

tr

eate

d at

a

med

ical

cen

ter

or a

VA

ho

spit

al in

the

Lo

s A

ngel

es

area

.

43 s

ubje

cts

wer

e cu

rren

tly

expe

rien

cing

an

gina

and

all

subj

ects

had

ex

peri

ence

d an

gina

wit

hin

the

prev

ious

tw

o ye

ars.

Dic

hoto

-m

ous

choi

ce

wit

h op

en-

ende

d fo

llow

up

Mea

n W

TP t

o av

oid

one

epis

ode

was

$1

00 a

nd $

165

to a

void

tw

o ep

isod

es (

US$

)

2

7 4

Dic

kie

M,

Ger

king

S,

McC

lella

nd

G, S

chul

ze

W

“Con

ting

ent

Val

uati

on: T

he

Val

ue o

f Fo

rmat

ion

Proc

ess”

1988

U

.S. E

PA

Rep

ort

CV

Te

leph

one

inte

rvie

w; 1

986

Avo

id

sym

ptom

s of

O

3 ex

posu

re

and

othe

r sy

mpt

oms

for

a du

rati

on o

f one

da

y

Part

icip

ants

of

the

CO

RD

st

udy

from

the

Lo

s A

ngel

es,

CA

are

a co

mm

unit

ies

of G

lend

ora

and

Bur

bank

30%

of

resp

onde

nts

had

been

dia

gnos

ed

wit

h ch

roni

c re

spir

ator

y ai

lmen

ts.

Ope

n-en

ded

Mea

n W

TP

rang

ed f

rom

$2

to

$27

per

sym

ptom

day

(1

986

US$

)

12

(con

tinue

d)

Appendix B — Annotated Bibliography of Publications Included in the Morbidity Value Meta-Analysis

B-3

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he A

cute

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

8 1

Dic

kie,

M

and

V

Ule

ry.

“Par

enta

l Altr

uism

an

d th

e V

alue

of

Chi

ld H

ealth

: Are

K

ids

Wor

th M

ore

Than

Par

ents

?”

2002

U

.S. E

PA

Rep

ort

CV

In

-Per

son

Inte

rvie

ws.

June

-Ju

ly, 2

000.

Avo

id

com

bina

tions

of

sym

ptom

s (c

ough

, sh

ortn

ess

of

brea

th, c

hest

pa

in, f

ever

); du

ratio

n (o

ne

day,

one

wee

k,

and

one

mon

th);

pers

on

expe

rien

cing

it

(chi

ld o

r pa

rent

).

Gen

eral

po

pula

tion

of

pare

nts

in

Hat

tiesb

urg,

M

issi

ssip

pi

Phys

icia

n-di

agno

sed

asth

ma

10%

, cou

gh w

ith

phle

gm in

pas

t ye

ar 4

6%,

shor

tnes

s of

br

eath

with

w

heez

ing

in p

ast

year

13%

, che

st

pain

whe

n co

ugh

or b

reat

he d

eep

in

past

yea

r 25

%,

feve

r in

pas

t yea

r 46

%, m

ean

sym

ptom

day

s 11

.16,

mea

n co

ugh

days

4.4

8,

mea

n sh

ortn

ess

of

brea

th w

ith

whe

ezin

g da

ys

2.74

, mea

n ch

est

pain

day

s 2.

72,

mea

n fe

ver

days

2.

73

Dou

ble-

boun

ded

dich

oto-

mou

s ch

oice

w

ith o

pen-

ende

d fo

llow

up.

Mea

n W

TP to

av

oid

diffe

rent

sy

mpt

om/d

urat

ion

co

mbi

natio

ns

rang

ed fr

om

$53

to $

218

(200

0 U

S$)

15

11

1 Jo

hnso

n FR

, et

al.

“WTP

for

Impr

oved

R

espi

rato

ry a

nd

Car

diov

ascu

lar

Hea

lth:

A

Mul

tiple

-For

mat

, St

ated

-Pre

fere

nce

App

roac

h”

2000

H

ealth

Ec

onom

ics

9: 2

95-

317.

CA

C

ompu

ter

inte

rvie

w; M

arch

–Ju

ly, 1

997

Avo

id

com

bina

tions

of

sym

ptom

s (7

), D

urat

ion

(3)

and

activ

ity

leve

ls (4

).

Gen

eral

po

pula

tion

of

the

Toro

nto

area

Not

spe

cifie

d G

rade

d-pa

ir

and

disc

rete

-ch

oice

Mea

n W

TP to

av

oid

the

diffe

rent

co

mbi

natio

ns o

f sy

mpt

oms,

du

ratio

n, a

nd

activ

ity le

vels

ra

nged

from

$3

to $

1002

(199

7 C

an$)

67

(con

tinue

d)


B-4

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he A

cute

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

12

1 K

artm

an B

, et

al.

“WTP

for

Red

uctio

ns in

A

ngin

a Pe

ctor

is

Atta

cks”

1996

M

edic

al

Dec

isio

n M

akin

g 16

(3):

248-

53

CV

Te

leph

one

Inte

rvie

ws,

199

3-19

94

Red

uctio

ns

(50%

, 25%

, 75

%) i

n an

gina

pe

ctor

is w

eekl

y at

tack

rat

e ov

er

a pe

riod

of 3

m

onth

s

Swed

ish

angi

na p

ecto

ris

patie

nts

rece

ivin

g dr

ug

trea

tmen

t in

tend

ed fo

r an

gina

pec

tori

s91.2

% o

f pat

ient

s ha

d st

able

ang

ina

and

the

rem

aini

n g

8.8%

had

un

stab

le a

ngin

a.

The

mea

n w

eekl

y at

tack

rat

e w

as

4.8

atta

cks

per

wee

k.

Dis

cret

e ch

oice

with

a

bidd

ing

gam

e fo

llow

up

Mea

n W

TP

rang

ed fr

om

1145

to 3

350

(199

4 SE

K) f

or

vary

ing

perc

ent

redu

ctio

ns

12

14

1 K

eith

PL,

et

al.

“A C

BA

Usi

ng a

W

TP

Que

stio

nnai

re o

f In

tran

asal

B

udes

onid

e fo

r Se

ason

al A

llerg

ic

Rhi

nitis

”

2000

A

nn

Alle

rgy

Ast

hma

Imm

un

84: 5

5-62

CV

Se

lf-ad

min

iste

red

ques

tionn

aire

. 199

3 In

tran

asal

be

deso

nide

for

trea

tmen

t of

seas

onal

al

lerg

ic r

hini

tis

Patie

nts

olde

r th

an 1

8 w

ith

seas

onal

al

lerg

y sy

mpt

oms

(pos

itive

ski

n pr

ick

for

ragw

eed)

Mod

erat

e se

ason

al a

llerg

y sy

mpt

oms

Ope

n-en

ded

Prio

r to

tr

eatm

ent m

ean

WTP

$1

5.89

/wee

k,

follo

win

g tr

eatm

ent

$12.

95/w

eek

(199

3 C

an$)

.

1

15

1 Li

u Jin

-Tan

, et

al.

“Mot

her’

s W

TP fo

r H

er O

wn

and

Her

C

hild

’s H

ealth

: a

Con

tinge

nt

Val

uatio

n St

udy

in

Taiw

an”

2000

H

ealth

Ec

onom

ics

9: 3

19-2

6

CV

In

-per

son

surv

ey,

1995

Pr

even

tion

of

mot

hers

and

th

eir

child

ren

from

sym

ptom

s eq

uiva

lent

to

the

mos

t rec

ent

cold

ex

peri

ence

d by

th

e re

spon

dent

.

Mot

hers

of

prim

ary

scho

ol

stud

ents

in

Taiw

an

Perc

ent o

f re

spon

dent

s w

ith

sym

ptom

s du

ring

la

st c

old:

57.

5%

with

hea

dach

e,

61.5

% w

ith

coug

h, 1

2% w

ith

feve

r, 7

2.6%

with

a

doct

or v

isit,

12

% lo

st a

day

of

wor

k. A

vera

ge

QW

B w

as 0

.66.

Th

e av

erag

e du

ratio

n of

the

last

col

d w

as 6

.48

days

.

Dic

hoto

-m

ous

with

2

follo

w-u

p qu

estio

ns

yiel

ding

a

trip

le-

boun

ded

bina

ry

choi

ce

form

at

Med

ian

WTP

to

avoi

d th

e sy

mpt

oms

and

dura

tion

of th

e la

st c

old

was

$3

7.3

(199

5 U

S$).

Med

ian

WTP

to in

crea

se

QW

B in

dex

from

0.6

6 to

1.

00 w

as $

37.5

(1

995

US$

).

1

(con

tinue

d)


B-5

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he A

cute

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

17

1 R

eady

RC

, N

avru

d S,

an

d W

R

Dub

org.

“How

do

Res

pond

ents

with

U

ncer

tain

W

illin

gnes

s to

Pa

y A

nsw

er

Con

tinge

nt

Val

uatio

n Q

uest

ions

.”

2001

La

nd

Econ

omic

s 77

(3):

315-

26.

CV

In

-Per

son

Inte

rvie

ws,

199

8

Avo

id: a

) one

da

y of

cou

gh b

) th

ree

days

of f

lu

and

c)

Res

pira

tory

sy

mpt

oms

requ

irin

g ho

spita

lizat

ion

for

3 da

ys

follo

wed

by

5 da

ys a

t hom

e in

be

d

Gen

eral

po

pula

tion

of

Osl

o, N

orw

ay N

ot s

peci

fied

Dic

hoto

-m

ous

choi

ce a

nd

Paym

ent

Car

d fo

rmat

s w

ith

cert

aint

y-fo

llow

up

Mea

n W

TP to

av

oid

a) o

ne d

ay

of c

ough

ran

ged

from

93

NO

K to

14

3 N

OK

; b)

thre

e da

ys o

f flu

ra

n ged

from

380

N

OK

to 6

29

NO

K; a

nd c

) re

spir

ator

y sy

mpt

oms

rang

ed fr

om

1016

NO

K to

10

86 N

OK

(1

998

valu

es)

10

17

2 R

eady

RC

, N

avru

d S,

D

ay B

, D

ubor

g W

R,

Mac

hado

F,

et a

l.

Ben

efit

Tran

sfer

in

Euro

pe: A

re

Val

ues

Con

sist

ent

Acr

oss

Cou

ntri

es?

1999

W

orki

ng

Pape

r C

V

In-p

erso

n in

terv

iew

s, 1

998.

A

dditi

onal

day

s of

sev

en li

ght

heal

th

sym

ptom

s (c

ough

ing,

sin

us

cong

estio

n,

thro

at

cong

estio

n, e

ye

irri

tatio

n,

head

ache

, sh

ortn

ess

of

brea

th a

nd

acut

e br

onch

itis)

and

as

thm

a.

Gen

eral

po

pula

tion

of

Am

ster

dam

, N

ethe

rlan

ds;

Osl

o, N

orw

ay;

Vig

o, S

pain

; Li

sbon

, Po

rtug

al; a

nd

Engl

and

Not

spe

cifie

d Pa

ymen

t ca

rd,

itera

tive

bidd

ing

WTP

to a

void

va

riou

s co

mbi

natio

ns o

f sy

mpt

oms

and

dura

tions

ran

ged

from

13.

56 to

42

5.7

(199

8 B

ritis

h po

unds

)

27

25

1 N

avru

d S.

“V

alui

ng H

ealth

Im

pact

s fr

om A

ir

Pollu

tion

in

Euro

pe.”

2001

En

viro

n-m

enta

l an

d R

esou

rce

Econ

omic

s 20

:305

-29.

CV

In

-per

son

inte

rvie

ws.

199

6.

Avo

id

addi

tiona

l day

s (1

or

14 d

ays)

of

seve

n lig

ht

heal

th

sym

ptom

s (c

ough

ing,

sin

us

cong

estio

n,

thro

at

Rep

rese

ntat

ive

sam

ple

of

Nor

weg

ians

ol

der

than

15

year

s of

age

.

Not

spe

cifie

d O

pen-

ende

d M

ean

WTP

to

avoi

d ad

ditio

nal

days

of o

ne o

f se

ven

sym

ptom

s or

ast

hma

rang

ed fr

om 9

9 N

OK

to 1

772

NO

K (1

996

NO

K)

18

(con

tinue

d)


B-6

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he A

cute

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

Nav

rud

S.

(con

t’d)

co

nges

tion,

eye

ir

rita

tion,

he

adac

he,

shor

tnes

s of

br

eath

and

ac

ute

bron

chiti

s) a

nd

asth

ma.

29

1 Lo

ehm

an

ET, e

t al

. “D

istr

ibut

iona

l A

naly

sis

of

Reg

iona

l Ben

efit

s an

d C

ost

of A

ir

Qua

lity

Con

trol

”

1979

Jo

urna

l of

Envi

ron-

men

tal

Econ

omic

s an

d M

anag

e-m

ent

6:

222-

243.

CV

M

ail q

uest

ionn

aire

, 19

77

Avo

id

sym

ptom

s of

ex

posu

re t

o ai

r po

lluti

on

vary

ing

in

seve

rity

and

du

rati

on

Gen

eral

po

pula

tion

of

Tam

pa B

ay

area

Not

spe

cifie

d C

lose

d-en

ded;

pa

ymen

t ca

rd

Med

ian

WTP

to

avoi

d sy

mpt

oms

of v

aryi

ng

seve

rity

and

du

rati

on r

ange

d fr

om $

2.31

to

$493

.16

(US$

).

36

30

1 R

owe

RD

an

d C

hest

nut

L

“Oxi

dant

s an

d A

sthm

atic

s in

Los

A

ngel

es: A

Ben

efit

A

naly

sis”

1985

U

.S. E

PA

Rep

ort

CV

In

per

son

inte

rvie

w; 1

983

Fift

y pe

rcen

t re

duct

ion

in

bad

asth

ma

days

Ast

hma

pati

ents

who

ha

d pa

rtic

ipat

ed in

th

e C

OR

D

stud

y an

d liv

e in

Gle

ndor

a,

CA

All

pati

ents

wit

h as

thm

a C

lose

d-en

ded;

pa

ymen

t ca

rd

Mea

n W

TP w

as

$401

(19

83

US$

)

1

32

1 To

lley

G

and

Bab

cock

L

“Val

uati

on o

f R

educ

tion

s in

H

uman

Hea

lth

Sym

ptom

s an

d R

isks

”

1986

U

.S. E

PA

Rep

ort

CV

In

per

son

inte

rvie

w; 1

985

Ligh

t sy

mpt

om

redu

ctio

ns a

nd

angi

na r

elie

f for

va

ryin

g du

rati

ons

Ran

dom

se

lect

ion

of

resi

dent

s of

C

hica

go I

L an

d D

enve

r C

O

Not

spe

cifie

d C

lose

d en

ded

iter

ativ

e bi

ddin

g w

ith

open

-en

ded

follo

wup

Mea

n W

TP t

o av

oid

rang

ed

from

$25

.2 t

o $8

68.8

9 fo

r va

riou

s sy

mpt

oms

and

dura

tion

s

20

37

1 Le

e PY

, M

atch

ar D

, C

lem

ents

D

, Hub

er J,

et

al.

Econ

omic

Ana

lysi

s of

Influ

enza

V

acci

natio

n an

d A

ntiv

iral

Tr

eatm

ent f

or

Hea

lthy

Wor

king

A

dults

2002

A

nnal

s of

In

tern

al

Med

icin

e 13

7(4)

: 22

5-31

.

Con

join

t an

alys

is

In p

erso

n in

terv

iew

, ye

ar n

ot a

vaila

ble

One

day

of

relie

f fro

m

influ

enza

, na

usea

, and

di

zzin

ess

Adu

lts a

ged

18-5

0 in

Nor

th

Car

olin

a w

ith

flu e

xper

ienc

e an

d no

ser

ious

ch

roni

c co

nditi

on

No

sign

ifica

nt

com

orbi

d co

nditi

ons

Perc

enta

ge

chan

ce th

at

resp

onde

nt

wou

ld

choo

se a

pa

rtic

ular

op

tion

Mea

n W

TP to

av

oid

influ

enza

w

as $

15.4

9,

naus

ea $

61.7

9,

and

dizz

ines

s $5

6.39

(200

1 U

S$)

3

(con

tinue

d)


B-7

Ta

ble

B-1

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he A

cute

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b

ID

Aut

hors

A

rtic

le T

itle

Y

ear

Sour

ce

Val

ue

Elic

itat

ion

Met

hod

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

St

udy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Acu

te M

eta-

Ana

lysi

s

38

1 To

rran

ce G

, W

alke

r V

, G

ross

man

R

, et a

l.

Econ

omic

Ev

alua

tion

of

Cip

roflo

xaci

n C

ompa

red

with

th

e U

sual

A

ntib

acte

rial

Car

e fo

r th

e Tr

eatm

ent

of A

cute

Ex

acer

batio

ns o

f C

hron

ic

Bro

nchi

tis in

Pa

tient

s Fo

llow

ed

for

1 Y

ear

1999

Ph

arm

aco

econ

omic

s 16

(5 P

t. 1)

: 499

-52

0.

CV

Q

uest

ionn

aire

at

phys

icia

n vi

sit,

Nov

embe

r, 1

993–

June

, 199

4.

Sym

ptom

day

s of

acu

te

exac

erba

tion

of

chro

nic

bron

chiti

s

Out

patie

nt

adul

t men

and

w

omen

age

d 18

or

olde

r w

ith c

hron

ic

bron

chiti

s pa

rtic

ipat

ing

in

a cl

inic

al tr

ial

for

cipr

oflo

xaci

n

18%

mild

chr

onic

br

onch

itis,

82%

m

oder

ate,

15

%

seve

re

Ope

n-en

ded

with

ce

rtai

nty

follo

wup

.

Mea

n W

TP

amon

g th

ose

in

the

trea

ted

with

ci

prof

loxa

cin

grou

p w

as

$1,2

35 (1

994

Can

$) a

nd $

868

(199

4 C

an$)

am

ong

thos

e in

th

e us

ual c

are

grou

p

2

39

1 Ja

cobs

R,

Mol

eski

RJ,

and

AS

Mey

erho

ff

Val

uatio

n of

Sy

mpt

omat

ic

Hep

atiti

s A

in

Adu

lts

2002

Ph

arm

aco

econ

omic

s 20

(11)

: 73

9-47

.

CV

M

ail s

urve

y, 2

001.

R

isk

free

pr

even

tion

of

all d

isea

se

sym

ptom

s fr

om

hepa

titis

A

Gen

eral

po

pula

tion

of

Uni

ted

Stat

es

31%

had

a

pers

onal

or

fam

ily

hist

ory

of

hepa

titis

. Th

e m

ean

SF-3

6 ph

ysic

al h

ealth

sc

ore

was

48

and

the

mea

n SF

-36

men

tal h

ealth

sc

ore

was

50.

Paym

ent

card

M

ean

WTP

is

$3,0

11 (2

001

US$

)

1

Not

e: S

tudi

es in

clud

ed in

the

John

son

(199

7) M

eta-

Ana

lysi

s ar

e in

bol

d.


B-8

Ta

ble

B-2

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

lud

ed

in

th

e C

hro

nic

Eff

ec

ts M

eta

-An

aly

sis

Stud

y ID

Pu

b ID

A

utho

rs

Art

icle

Tit

le

Yea

r So

urce

V

alue

El

icit

atio

n M

etho

d

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

Stu

dy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Chr

onic

Met

a-A

naly

sis

4 1

Blu

men

sche

in,

K a

nd

Joha

nnes

son,

M “R

elat

ions

hip

betw

een

qual

ity o

f life

in

stru

men

ts,

heal

th s

tate

ut

ilitie

s an

d w

illin

gnes

s to

pa

y in

pa

tient

s w

ith

asth

ma.

”

1998

A

nn A

llerg

y A

sthm

a Im

mun

80:

18

9-94

.

CV

In

-Per

son

Inte

rvie

ws,

ye

ar n

ot

avai

labl

e

Trea

tmen

t tha

t w

ill c

ure

resp

onde

nts

from

ast

hma

for

one

mon

th

Ast

hma

patie

nts

in

Cen

tral

and

Ea

ster

n K

entu

cky

age

18 a

nd o

lder

All

patie

nts

have

ast

hma

Dic

hoto

-m

ous

choi

ce

alon

e an

d di

chot

omou

s ch

oice

w

ith b

iddi

n g

gam

e fo

llow

-up.

Mea

n W

TP

rang

es fr

om

$189

to $

343

(US$

)

2

4 2

Zill

ich

AJ,

Blu

men

sche

in

K, J

ohan

ness

on

M, a

nd

Free

man

P

“Ass

essm

ent

of th

e R

elat

ions

hip

Bet

wee

n M

easu

res

of

Dis

ease

Se

veri

ty,

Qua

lity

of

Life

, and

W

illin

gnes

s to

Pay

in

Ast

hma”

2002

Ph

arm

aco-

econ

omic

s 20

(4):

257-

265.

CV

In

-Per

son

Inte

rvie

ws,

ye

ar n

ot

avai

labl

e

Cur

e fo

r as

thm

a cl

assi

fied

as

mild

, m

oder

ate,

or

seve

re.

Ast

hma

patie

nts

18

year

s or

old

er

in C

entr

al a

nd

East

ern

Ken

tuck

y.

All

patie

nts

with

a

diag

nosi

s of

as

thm

a ob

ject

ivel

y cl

assi

fied

as

mild

, mod

erat

e or

sev

ere,

re

ceiv

ing

an

inha

led

B2

agon

ist

med

icat

ion

with

or

with

out

inha

led

cort

icos

tero

id

med

icat

ion.

Dic

hoto

-m

ous

choi

ce

Mea

n W

TP

ran g

es fr

om $

48

to $

331

(US$

) fo

r a

cure

for

mild

, mod

erat

e,

or s

ever

e as

thm

a

6

13

1 K

artm

an B

, et

al.

“Val

uatio

n of

H

ealth

C

han g

es w

ith

the

CV

M

etho

d”

1996

H

ealth

Ec

onom

ics

5: 5

31-4

1

CV

Te

leph

one

inte

rvie

w.

Oct

ober

, 199

4 –S

epte

mbe

r,

1995

(1) A

sho

rt-

term

trea

tmen

t th

at in

crea

ses

the

prob

abili

ty

of b

eing

free

fr

om

sym

ptom

s af

ter

4 w

eeks

; (2)

a

long

-ter

m

trea

tmen

t tha

t re

duce

s th

e ri

sk o

f hav

ing

a re

laps

e on

ce

reco

vere

d; (3

) a

med

icat

ion

Patie

nts

with

re

flux

oeso

phag

itis

in

Swed

en

All

patie

nts

diag

nose

d w

ith

and

rece

ivin

g m

edic

atio

n fo

r re

flux

oeso

phag

itis

Dic

hoto

-m

ous

choi

ce w

ith

open

-end

ed

follo

w-u

p

Mea

n W

TP

rang

ed fr

om

431

to 1

023

(SEK

199

5) fo

r re

duct

ion

in r

isk

of s

ympt

oms

and

from

261

to

912

(SEK

199

5)

for

redu

ctio

n in

ri

sk o

f rel

apse

4

(con

tinue

d)


B-9

Ta

ble

B-2

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he C

hro

nic

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b ID

A

utho

rs

Art

icle

Tit

le

Yea

r So

urce

V

alue

El

icit

atio

n M

etho

d

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

Stu

dy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Chr

onic

Met

a-A

naly

sis

Kar

tman

B, e

t al

. (co

nt’d

)

that

can

be

take

n w

ith

mea

ls, a

s co

mpa

red

with

a

med

icat

ion

that

mus

t be

take

n at

leas

t 1

hour

bef

ore

a m

eal.

21

1 V

iscu

si W

K,

Ma g

at W

A, a

nd

J Hub

er.

“Pri

cing

En

viro

nmen

t-al

Hea

lth

Ris

ks: S

urve

y A

sses

smen

ts

of R

isk-

Ris

k an

d R

isk-

Dol

lar

Trad

e-O

ffs fo

r C

hron

ic

Bro

nchi

tis.”

1991

Jo

urna

l of

Envi

ron-

men

tal

Econ

omic

s an

d M

anag

e-m

ent 2

1:

32-5

1.

CJ/P

aire

d-C

ompa

riso

n In

tera

ctiv

e co

mpu

ter

ques

tionn

aire

, ye

ar n

ot

avai

labl

e.

Ris

k of

co

ntra

ctin

g ch

roni

c br

onch

itis

and

risk

of f

atal

au

to a

ccid

ent.

Gre

ensb

oro

mal

l sho

pper

s N

ot s

peci

fied

Itera

tive

bidd

ing

R

espo

nden

ts

wer

e w

illin

g to

su

bstit

ute

a m

ean

cost

of

livin

g in

crea

se

of $

8.83

(US$

) pe

r ye

ar fo

r a

redu

ctio

n in

the

risk

of g

ettin

g ch

roni

c br

onch

itis

and

of $

81.8

4 (U

S$)

per

year

to

redu

ce th

e ri

sk

of g

ettin

g in

a

fata

l aut

o ac

cide

nt

1

23

1 K

rupn

ick

AJ

and

M C

ropp

er “T

he E

ffect

of

Info

rmat

ion

on H

ealth

R

isk

Val

uatio

ns.”

1992

Jo

urna

l of

Ris

k an

d U

ncer

tain

ty

5: 2

9-48

.

CJ/P

aire

d-C

ompa

riso

n C

ompu

ter

inte

rvie

w, y

ear

not a

vaila

ble

Ris

k of

co

ntra

ctin

g ch

roni

c br

onch

itis

Was

hing

ton,

D

C a

rea

resi

dent

s ov

er

age

18 th

at

had

a re

lativ

e ov

er 2

1 ye

ars

old

who

had

a

chro

nic

resp

irat

ory

cond

ition

.

Non

e of

the

resp

onde

nts

had

a hi

stor

y of

ch

roni

c re

spir

ator

y co

nditi

on

Itera

tive

bidd

ing

M

ean

cost

of

livin

g in

crea

se

rang

ing

from

$1

1 to

$21

(U

S$)

3

(con

tinue

d)


B-10

Ta

ble

B-2

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he C

hro

nic

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b ID

A

utho

rs

Art

icle

Tit

le

Yea

r So

urce

V

alue

El

icit

atio

n M

etho

d

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

Stu

dy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Chr

onic

Met

a-A

naly

sis

27

1 Sl

oan

FA, e

t al.

“Alte

rnat

ive

App

roac

hes

to v

alui

n g th

e In

tang

ible

H

ealth

Lo

sses

: The

Ev

iden

ce o

f M

ultip

le

Scle

rosi

s.”

1998

Jo

urna

l of

Hea

lth

Econ

omic

s 17

: 475

-97.

CJ/P

aire

d-C

ompa

riso

n In

-per

son

and

com

pute

r in

terv

iew

s,

1995

Ris

k-ris

k an

d ri

sk d

olla

r tr

adeo

ffs fo

r de

ath

and

mul

tiple

sc

lero

sis

Mal

l int

erce

pt

in G

reen

sbor

o,

NC

re

pres

enta

tive

of th

e ge

nera

l po

pula

tion;

M

embe

rs o

f th

e Ea

ster

n N

orth

Car

olin

a M

ultip

le

Scle

rosi

s So

ciet

y in

O

rang

e an

d D

urha

m

Cou

ntie

s, N

C.

13%

hav

e m

ultip

le

scle

rosi

s. T

he

mea

n ra

nk o

f cu

rren

t hea

lth

(on

a sc

ale

of 0

w

orst

to 1

00

best

) was

73.

79. Ite

rativ

e bi

ddin

g

Med

ian

WTP

pe

r ye

ar r

ange

d fr

om $

419,

000

to $

510,

000

for

the

low

-pr

obab

ility

sc

enar

io in

the

gene

ral s

ampl

e an

d fr

om

$346

,000

to

$420

,000

for

the

high

-pr

obab

ility

sc

enar

io in

the

gene

ral s

ampl

e.

It ra

nged

from

$5

83,0

00 to

$8

81,0

00 fo

r th

e lo

w-

prob

abili

ty

scen

ario

in th

e M

S sa

mpl

e an

d fr

om $

375,

000

to $

566,

000

for

the

high

-pr

obab

ility

sc

enar

io in

the

MS

sam

ple

(199

6 U

S$)

8

28

1 St

avem

K

“Will

ingn

ess

to P

ay: A

Fe

asib

le

Met

hod

for

Ass

essi

ng

Trea

tmen

t B

enef

its in

Ep

ileps

y?”

1999

Se

izur

e 8:

14

-19.

C

V

In-p

erso

n in

terv

iew

s,

year

not

av

aila

ble

Perm

anen

t cu

re fo

r ep

ileps

y

Patie

nts

aged

18

-67

who

ha

d be

en

adm

itted

to th

e ou

tpat

ient

cl

inic

for

epile

psy

at th

e C

entr

al

Hos

pita

l of

Aer

shus

in

Nor

way

be

twee

n 19

87

and

1994

24.5

% h

ad

seiz

ures

last

ye

ar,7

5.4%

us

ing

med

icat

ion

Ope

n-en

ded

Med

ian

WTP

is

$20,

000

(US$

) to

cur

e ep

ileps

y.

1

(con

tinue

d)


B-11

Ta

ble

B-2

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he C

hro

nic

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b ID

A

utho

rs

Art

icle

Tit

le

Yea

r So

urce

V

alue

El

icit

atio

n M

etho

d

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

Targ

et

Popu

lati

on

Bas

elin

e H

ealt

h of

Stu

dy

Popu

lati

on

Que

stio

n Fo

rmat

W

TP E

stim

ate

# of

Val

ues

in

Chr

onic

Met

a-A

naly

sis

31

1 Z

ethr

aeus

N

“Will

ingn

ess

to P

ay fo

r H

orm

one

Rep

lace

men

t Th

erap

y.”

1998

H

ealth

Ec

onom

ics

7: 3

1-38

.

CV

In

-per

son

inte

rvie

ws.

19

95-1

996.

Hor

mon

e re

plac

emen

t th

erap

y

Wom

en

betw

een

age

45-6

5 th

at h

ad

been

trea

ted

with

HR

T fo

r at

leas

t 1

mon

th.

Rec

ruite

d fr

om

the

depa

rtm

ent

of G

ynec

olog

y at

the

Sode

rtal

e H

ospi

tal i

n Sw

eden

Not

spe

cifie

d C

lose

d-en

ded

with

ce

rtai

nty

follo

w u

p

Mea

n W

TP fo

r H

RT

rang

ed

from

365

1 SE

K

to 3

772

SEK

(1

996

valu

es)

2

31

2 Z

ethr

aeus

N,

Joha

nnes

son

M,

Hen

riks

son

P,

and

RT

Stra

nd

“The

Impa

ct

of H

orm

one

Rep

lace

men

t Th

erap

y on

Q

ualit

y of

life

an

d W

illin

gnes

s to

Pay

.”

1997

H

ealth

Ec

onom

ics

7: 3

1-38

.

CV

In

-per

son

inte

rvie

ws.

19

95-1

996.

Hor

mon

e re

plac

emen

t th

erap

y fo

r a

redu

ctio

n in

m

enop

ause

sy

mpt

oms

Wom

en

betw

een

age

45-6

5 th

at h

ad

been

trea

ted

with

HR

T fo

r at

leas

t 1

mon

th.

Rec

ruite

d fr

om

the

depa

rtm

ent

of G

ynec

olog

y at

the

Sode

rtal

e H

ospi

tal i

n Sw

eden

Self-

rate

d (b

ased

on

inte

rvie

wer

de

scri

ptio

n)

mild

or

seve

re

men

opau

sal

sym

ptom

s, 5

6 w

ith m

ild a

nd

48 w

ith s

ever

e sy

mpt

oms

Clo

sed-

ende

d w

ith

cert

aint

y fo

llow

up

Mea

n W

TP fo

r H

RT

and

a re

duct

ion

in

mild

sym

ptom

s w

as 2

346

SEK

an

d 48

38 S

EK

for

seve

re

sym

ptom

s (1

996

valu

es)

2

33

1 Th

omps

on M

S,

et a

l. “

WTP

and

A

ccep

t Ris

ks

to C

ure

Chr

onic

D

isea

ses”

1986

A

mer

ican

Jo

urna

l of

Publ

ic

Hea

lth

76(4

):392

-39

6

CV

In

-per

son

Inte

rvie

w, y

ear

not a

vaila

ble

Cur

e fo

r rh

eum

atoi

d ar

thri

tis w

ith a

ri

sk o

f dea

th

Patie

nts

with

ad

ult-

onse

t rh

eum

atoi

d ar

thri

tis,

enro

lled

in a

ra

ndom

ized

co

ntro

lled

drug

tria

l, w

ho

had

mai

ntai

ned

for

at le

ast t

hree

m

onth

s on

ba

sic

All

patie

nts

with

ad

ult-

onse

t, un

rem

ittin

g rh

eum

atoi

d ar

thri

tis

Ope

n-en

ded

Mea

n W

TP

rang

ed fr

om

$4,4

75 to

$7

,752

(US$

)

4

(con

tinue

d)


B-12

Ta

ble

B-2

. A

nn

ota

ted

Bib

lio

gra

ph

y o

f S

tud

ies

Inc

luded in t

he C

hro

nic

Eff

ects

Me

ta-A

na

lysi

s (c

on

tin

ue

d)

Stud

y ID

Pu

b ID

A

utho

rs

Art

icle

Tit

le

Yea

r So

urce

V

alue

El

icit

atio

n M

etho

d

Mod

e of

A

dmin

istr

atio

n an

d Y

ear

Com

mod

ity

Def

init

ion

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4

Appendix C: Summary Statistics for the Morbidity Value Database

C-1

Table C-1. Summary Statistics for the Morbidity Value Database

Variable Name Number of

Observations Mean Standard Deviation Minimum Maximum

numauthor 388 5.332474 4.495795 1 18

pubyr 389 1,994.47 7.424692 1,979 2,002

pubjrl 268 1 0 1 1

pubbk 0

pubbkchap 0

pubtech 59 1 0 1 1

pubwp 62 1 0 1 1

pubphdd 0

pubmt 0

pubconf 0

pubother 0

valueid 389 13.01542 14.26208 1 67

mean 336 866.6709 2,540.2 0.3 3,3746

trimmean 54 1 0 1 1

corrected 7 1 0 1 1

protcorrected 68 1 0 1 1

inconcorrected 40 1 0 1 1

turnbull 10 1 0 1 1

median 129 2,384.436 17,736.46 0 200,000

lowerci 7 15,001.86 37,490.76 24 100,000

upperci 7 43,754.29 11,2999.7 55 300,000

stderr 121 384.7199 1,061.301 0.26 10,310

currencyyr 304 1,994.497 4.62023 1,983 2,001

tfday 88 155.6705 156.5961 7 365

tfonetime 301 1 0 1 1

tfyear 2 42 0 42 42

tfduration 11 1 0 1 1

tfpermanent 1 1 . 1 1

tfpv 0

tfpvdiscount 0

tfpvyrs 0

tfother 2 1 0 1 1

wtp 109 1 0 1 1

wtpavoid 273 1 0 1 1

wta 2 1 0 1 1

(continued)


C-2

Table C-1. Summary Statistics for the Morbidity Value Database (continued)



wtaforgo 0

mrs 5 1 0 1 1

other 0

hcvmortality 6 1 0 1 1

hcvacutemorb 308 1 0 1 1

hcvchrocon 31 1 0 1 1

hcvtrtmt 1 1 . 1 1

hcvchronic 78 1 0 1 1

hlthocnumchg 195 2.066667 1.377532 1 15

sevchgint 181 1 0 1 1

sevbefore 16 1.791 0.744197 0.656 3

sevafter 16 2.5625 1.931105 0 4

sevchg 0

durationchg 300 1 0 1 1

durbefore 213 10.60909 21.40374 1 90

durafter 227 0.729339 3.497302 0 33.15

durchg 83 –55.1139 440.17 –4016 –1

frequencychg 28 1 0 1 1

freqbefore 18 7.477778 16.71732 0 74

freqafter 7 11.25714 7.694772 0 16

freqchg 15 35.86667 36.13836 –50 75

exante 46 1 0 1 1

riskbefore 28 0.232381 0.349496 1.67E–07 0.8

riskafter 17 0.188378 0.211651 0 0.5

riskchg 42 –0.06944 0.146584 –0.5 0.01

vas 23 1 0 1 1

vasavgbasln 23 35.25522 29.623 0.32 76

vasavgwchng 7 43.33714 53.00325 0.82 100

sg 17 1 0 1 1

sgbasln 14 25.63857 40.91571 0.13 95

sgavgwchg 3 100 0 100 100

tto 11 1 0 1 1

ttobasln 28 3.993929 17.05241 0.18 91

ttoavgwchg 4 0.9225 0.020616 0.9 0.95

hsmother 6 1 0 1 1

hsmotherbasln 5 0.748 0.087293 0.69 0.9

(continued)

Appendix C — Summary Statistics for the Morbidity Value Database

C-3




hsmotheravgwchg 0

aids 0

alcohol 0

allergy 7 1 0 1 1

backneck 0

birfthdef 0

blind 0

blood 0

bone 0

bowel 0

cancer 0

circulat 0

dental 0

depress 0

diabetes 0

digest 0

eareye 2 1 0 1 1

eating 0

epilepsy 1 1 . 1 1

gastroint 8 1 0 1 1

genetic 0

glaucoma 0

heart 89 1 0 1 1

hepatitis 1 1 . 1 1

cholesterol 0

hypertense 0

infertility 0

leprosy 0

liverkidney 0

lupus 0

malaria 0

meningitis 0

mental 5 1 0 1 1

mood 0

ms 8 1 0 1 1

(continued)


C-4




nervoussys 0

neurological 0

obesity 0

osteoporosis 0

palsy 0

parkinsons 0

pregnancy 0

psoriasis 0

respiratory 165 1 0 1 1

schizo 0

sexual 0

skindis 0

sleepdis 2 1 0 1 1

speechdis 1 1 . 1 1

sportsinjuries 0

substanceabuse 0

thyroid 0

illnessnotspec 125 1 0 1 1

illnessother 81 1 0 1 1

cough 90 1 0 1 1

pain 25 1 0 1 1

headache 39 1 0 1 1

nausea 16 1 0 1 1

vomit 4 1 0 1 1

fever 51 1 0 1 1

disorient 12 1 0 1 1

chestpain 21 1 0 1 1

shortbreath 71 1 0 1 1

throat 17 1 0 1 1

eyeirritation 21 1 0 1 1

itching 1 1 . 1 1

sympnotspec 71 1 0 1 1

sympother 0

environair 132 1 0 1 1

environwater 0

(continued)


C-5




food 8 1 0 1 1

occupation 0

productsafety 0

causesubstance 0

transportation 5 1 0 1 1

naturaldisaster 0

causegenetic 0

infectious 7 1 0 1 1

causenotspec 220 1 0 1 1

causeother 1 1 . 1 1

apyrbegin 360 1,991.681 6.933481 1,977 2,001

apyrend 360 1,991.842 6.973482 1,977 2,001

cv 299 1 0 1 1

hedonic 0

conjoint 83 1 0 1 1

mktv 7 1 0 1 1

othervm 19 1 0 1 1

person 373 1 0 1 1

numperson 389 268.3599 217.1858 5 1,250

choice 30 1 0 1 1

numchoice 30 5.966667 2.470283 1 8

othersample 0

numothersample 0

samplesize 236 566.178 585.609 50 1,800

responserate 117 71.95218 31.73554 6.1 99

numobs 385 1,192.2 2,018.049 3 5,504

sampleus 177 1 0 1 1

samplecan 79 1 0 1 1

sampcntryother 141 1 0 1 1

randial 27 1 0 1 1

ranmail 45 1 0 1 1

mall 17 1 0 1 1

patientrecruit 66 1 0 1 1

otherrecruit 136 1 0 1 1

inclcritage 91 1 0 1 1

(continued)


C-6




inclcritgender 10 1 0 1 1

inclcritparent 17 1 0 1 1

inclcritrace 1 1 . 1 1

inclcrithlthcond 0

inclcirtother 186 1 0 1 1

incomemean 311 84,235.82 90,009.96 13,577 334,080

incomemedian 22 47,002.42 1,611.329 42,553.19 52,500

incomeyr 288 1,991.698 7.583385 1977 2,001

gendermale 310 49.43839 19.48298 0 100

racewhite 50 77.66 7.215658 57 90

agemean 307 45.96344 7.386145 24.36 68

agemedian 4 53.5 5 46 56

agemin 11 43 1.949359 41 45

agemax 11 68.72727 7.072353 65 83

avgedu 185 13.67982 1.462819 9.098 16.1

mail 45 1 0 1 1

inperson 205 1 0 1 1

phone 74 1 0 1 1

computer 87 1 0 1 1

internet 0

othersurvey 13 1 0 1 1

openend 91 1 0 1 1

closedend 209 1 0 1 1

dichochoice 75 1 0 1 1

doublebond 21 1 0 1 1

bidding 73 1 0 1 1

followup 63 1 0 1 1

card 79 1 0 1 1

ranking 1 1 . 1 1

rating 67 1 0 1 1

otherformat 106 1 0 1 1

payctax 1 1 . 1 1

payoutofpock 316 1 0 1 1

payinsurance 0

paycsurcharge 16 1 0 1 1

(continued)


C-7




paycfee 0

payv 0

paynotv 49 1 0 1 1

paycother 5 1 0 1 1

jobpercunion 0

jobavgwage 0

jobavgwageinc 0

jobavginc 0

jobavghrs 0

linear 0

loglinear 0

semilog 0

rhs 0

lhs 0

numriskchar 0

numjobchar 0

stage1 0

stage1ols 0

stage1other 0

stage2 0

stage2ols 0

stage2other 0

avgunitprice 0

avgunitpurch 7 7.515714 2.2867 4.16 11.35

avgexpperyr 7 27.83857 32.10604 11 100

valuation of morbidity losses: meta-analysis of ...(rias), cfsan must have at its di sposal reliable...

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