101F90050
THE APPLICATION OF UTILITY THEORY TO
THE VALUING OF
AIR POLLUTION-RELATED HEALTH EFFECTS:
Three Proposed Pilot Studies on Subjective
Judgments of Asthma
Prepared for
Harvey M. Richmond
Environmental Protection Specialist
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Prepared by
Adam C. Johnston
EPA NNEMS Graduate Student Intern
University of North Carolina, Chapel Hill
Dept Environmental Science and Engineering
December 1990 y g Environmental Protection Agency
Region 5.Library (PL-12J)
77 West Jackson Boulevard, I2trt FJoof
Chicago, IL 60604-3590
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DISCLAIMER
This report was furnished to the U.S. Environmental Protection
Agency by the student identified on the cover page, under a National
Network for Environmental Management Studies fellowship.
The contents are essentially as received from the author. The
opinions, findings, and conclusions expressed are those of the author
and not necessarily those of the U.S. Environmental Protection
Agency. Mention, if any, of company, process, or product names is
not to be considered as an endorsement by the U.S. Environmental
Protection Agency.
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ACKNOWLEDGMENTS
This National Network for Environmental Studies fellowship
was conducted for Harvey Richmond, Environmental Protection
Specialist, Office of Air Quality Planning and Standards, U.S.
EPA, Research Triangle Park, NC.
I would like to recognize several contacts and reviewers who
have assisted greatly on this project. This includes Leland
Deck, David McKee, Thomas Feagans, Ron Whitfield, Robert Winkler,
Howard Kehrl, Deborah Amaral, and Tom Wallsten.
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EXECUTIVE SUMMARY
Utility under uncertainty is a field of decision theory that
has received increasing attention in the field of public health.
This report reviews its uses during the past decade and suggests
its possible use in national ambient air quality standard setting
procedures. It is common practice in standard setting to assess
the likelihood of air pollution effects on sensitive populations.
One such population, asthmatics, is selected in this report and
the relationship between air pollution and asthma is reviewed.
In addition, three possible pilot studies are suggested which use
aspects of utility under uncertainty theory to elicit values
concerning asthma health effects. The results of such studies
would provide U.S. EPA with information for their ambient air
quality standard setting and increase the awareness of the
possible uses of utility theory in such applications.
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INTRODUCTION
During the past twenty years significant efforts have been
made to create a method of measuring health status on a single
scale for comparisons over time, and between individuals-and
groups in an attempt to overcome what Bergner (1) calls the
"single-continuum dilemma." This dilemma has arisen in the past
because of the diverse and complex nature of many health states,
making it difficult to make comparisons between individuals in
the same or different health conditions. Such a means of
measurement would be useful in making decisions, creating health
indices, setting priorities, and developing a basis for
regulatory standards. One area which has been suggested for this
purpose is utility theory. Spitzer (2) in his keynote address to
the Portugal Conference on the measurement of quality of life
stressed the importance of utility theory in such applications by
stating that "utilities and trade-off methods hold enormous
promise in the field of public health, [and] there is a strict
discordance between the logical strength of the utility strategy
for the assessment of quality of life or health status and the
extent to which the methods have been demonstrated."
As Spitzer has stated, the health field appears to be an
excellent area for the application of utility theory and its
methods. In many fields, when an individual is making a decision
or comparing a series of possible outcomes, comparable units
representing the worth of each outcome, such as dollars, lives
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saved, or cases of disease averted are clear. In the health
field, however, effects are often refered to in intangible terms
such as pain and suffering (3). These outcomes do not have
obvious, uni-dimensiona.l measurable values which can serve as a
single unit of comparison. This may frequently cause
difficulties. In addition, in policy or regulatory environments
it is desirable to compare these intangibles with each other or
with more concrete ideas of money or time. To overcome this
problem, it is possible to employ some aspects of utility theory
as a means of valuing outcomes which have varying likelihoods of
occurrance, but do not have inherent quantifiable measurements.
This area of utility theory is referred to as utility under
conditions of uncertainty and is considered a specific
application of the broader theory. Within this specific
application it is common to use various assessment techniques to
elicit subjective values from an individual for any number of
different outcomes. These values, or utility levels, can then be
compared on a single scale. Utility, therefore, can be thought
of as a subjective measurement whereas money and lives saved are
uni-dimensional, objective measurements (4). For this reason,
utility has become particularly useful in the health field; it is
often used in the field of medical decision making to compare
possible health outcomes, treatments, or health states. In this
case, utility theory is used to provide a means of comparison
through the assignment of probabilities and preference weights to
the various possible outcomes.
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Only a small volume of workin the field, however, has been
associated with health-related environmental decisions. Keeney,
a major contributor to the field of utility under uncertainty,
and collegues have done, some work related to environmental
decisions, especially in the area of air pollution control and
regulations (5,6). There is, however, much room for additional
work to be done.
This report, therefore, reviews the theory, biases, and
various methods of assessment of utility under uncertainty, and
its applications to health-related outcomes. In addition, the
specific health state of asthma and its association with air
pollution is discussed and a series of possible studies applying
utility analysis techniques to air pollution health effects
valuing are suggested. Specifically, three pilot studies are
outlined which attempt to provide information to the U.S. EPA on
the valuing by asthmatics of air pollution-related health
effects. These studies are intended as a means of displaying the
ease of use of utility assessment and to provide specific
information on subjective valuing of asthma and its symptoms by
asthmatics. Furthermore, such studies may provide valuable input
during the assessment of effects on sensitive populations as a
part of national ambient air quality standard setting.
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UTILITY THEORY
Utility theory has its roots in more than two hundred years
of economic thought. Qne of the early developers of the idea of
utility was Daniel Bernoulli. In 1738 he developed the .
St.Petersburg Paradox which provided a source for early
discussions about utility. The paradox involves a game in which
a coin is tossed n times until it comes up heads at which time
the player of the game is paid $2n. The question that must be
asked , therefore, is how much should an individual be willing to
pay to play this game? It would be reasonable to predict the
amount by calculating the expected payoff of the game by summing
the products of all the possible payoff outcomes and their
respective probabilities of occurance. As it turns out, the
expected payoff for this game is an infinite sum. The player,
therefore, should be willing to pay anything to participate in
this game. Intuitively, however, this does not make sense.
Bernoulli, therefore, stated that one should consider the "moral
worth" of an alternative and not just its expected monetary
value. In other words it could be said that in the process of
gaining an item, a point is reached where an additional item is
not worth as much as the item preceeding it. For example, if you
receive a pizza you place some value on that pizza. The second
and third pizza that you receive you may also value equally;
however, you will quickly reach a point where an additional pizza
is not worth as much to you as those previously received. Money
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can be considered similarly. The value or utility of money (or
pizzas) will decrease as more is gained. If this idea can be
accepted then a finite solution to the above game may result.
The St. Petersburg Paradox showed a need for the idea of
utility. It wasn't until the 1940's, however, when von Neumann
and Morgenstern published Theory of Games and Economic Behavior
(7) that utility theory under uncertainty became established.
Watson and Beude (8) state that von Neumann and Morgenstern
developed utility "to prescribe how people should evaluate
options about which they were uncertain." Specifically, von
Neumann and Morgenstern thought that individuals should assign
utility values to possible outcomes. Then, when faced with
decisions which include risky alternatives, the individual should
select the outcome with the highest expected utility. The
expected utility is the product of the assigned utility level and
the probability that the outcome will occur.
The theory is justified by a series of axioms. These axioms
include assumptions about how an individual ought to behave while
making a decision under uncertainty, and, therefore, are
normative. They do not, however, describe how individuals
actually behave. If a subject accepts these axioms, then his
only rational action during decision making is to select an
outcome with the maximum expected utility. This point, however,
is considered somewhat controversial. It is often argued whether
individuals should even necessarily conform to the axioms.
Furthermore, when they do not behave consistently with the
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axioms, it is often out of choice and not because they are "being
fooled by cognitive illusions-" or not considering all the
dimensions (9) .
Certain specific decisions which are often cited by scholars
are the various paradoxes that go against the axioms. The axioms
of utility theory assume that the value of an outcome and the
probability of its occurance are independent. This assumption,
however, may not always be true in real decision making as shown
by the results of the Allais Paradox. The paradox involves
several choices between gambles. In the first an individual is
offered a choice between (A) a guaranteed payoff or (B) a chance
at a higher payoff that also involves a very small risk of
receiving nothing. In the second decision the individual must
choose between (C) a chance at receiving a moderate payoff with
some risk of receiving nothing or (D) a much greater prize at a
slightly higher probability of receiving nothing. If the
expected payoffs are calculated for each case, it appears that
the best choices are (B) and (C). When this problem is actually
presented to people the predominant choices by far are (A) and
(C). The problem involved here is that most people overweigh the
very small probability that exists in (B) and would rather
receive a moderate guaranteed payoff than risk getting nothing
for a chance at a much higher payoff. Furthermore, even when the
inconsistency of their choice is pointed out, many people stay
with their original choice. This is a case in which the axioms
are clearly violated by choice. The original idea of utility and
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the modern von Neumann-Morgenstern uncertainty utility therory
still have not resolved this-paradox; however, it should be
remember that axioms do not attempt to describe decisions exactly
but instead provide a target for rational behavior. For the
purpose of this paper these controversies are set aside, but it
should be noted that the field of utility under uncertainty is
still under development and its validity is often questioned and
discussed by both its critics and proponents.
The following is a list of axioms compiled from three
sources (9-11) and should not be considered exhaustive. These
axioms are not all equally important. Hershey and Baron (12)
state, for example, that the two most important axioms are
transitivity and independence and Bell and Farquhar (9) include
continuity as a basic axiom also. Each of the axioms, however,
is necessary at least in some specific case.
Transitivity - given the outcomes A, B, and C, in a
preference relationship such that A is preferred to B and B is
preferred to C, then it is assumed that A is preferred to C.
Independence - if A is preferred to B then a gamble where A
is the prize is preferred to a gamble where B is the prize if the
two gambles have equivalent alternatives to the prize and equal
probabilities of outcomes.
Continuity - it must be possible for indifference to exist
between a certain outcome, C, and a pair of uncertain outcomes, A
and B, given that C is preferred to B and A is preferred to C.
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Reduction of Compound Uncertain Events - a mixture of
gambles may be reduced or simplified using standard probability
manipulations without affecting preferences.
Connectivity - an individual is able to make judgments about
preferences when faced with a gamble (i.e. preferences exist).
Sure Thing - preferences for gamble A over gamble B should
not depend on events for which A and B have identical outcomes.
Substitutability - indifference exists between a certain
outcome, C, and one formed by substituting for C with an outcome
which may be a lottery judged equivalent to C.
Monotonicity - given a choice between two gambles with
equivalent outcomes, A and B, the gamble with the higher
probability of winning the better outcome is preferred.
Completeness - outcome values and probabilities are required
to determine preferences between uncertain outcomes.
Boundedness - outcomes cannot be infinitely bad or
infinitely good.
The validity or strength of a number of these axioms is
often questioned. Further, as mentioned above, the axioms are
considered controversial because individuals in their usual
decision making process do not necessarily adhere to these axioms
and it is also argued whether an individual even ought to conform
when able. There may, in fact, be very rational reasons for
violating an axiom. A brief discussion of the psychology and
biases which cause these differences is presented later in this
paper.
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The usually intended result of the application of the theory
and the axioms is the assignment -of utility levels or
measurements to specific outcomes. If it is assumed that an
individual's choices in simple cases obey the axioms then one may
infer from these choices the individual's utility levels.-. These
utilities may then be used in more complex cases (12) .
Similarly, von Neumann and Morgenstern state that the utility of
an outcome is equal to the probability of winning a gamble such
that an individual is indifferent between accepting the gamble
and receiving an outcome with certainty. In other words, an
individual may be confronted with two possibilities, A and B,
and, in addition, there exists a third possibility, C, which is
preferred to B but not to A (i.e. C is preferred to B and A is
preferred to C). It is possible under these conditions to find a
level of C for which the individual is indifferent between
receiving C with absolute certainty or taking a gamble between
the two extremes, A and B. It is common to represent such a
decision with a following decision tree (Figure 1).
FIGURE 1
Basic Decision Tree
outcome
B
C
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where the box (decision node) represents a choice between C and
the A/B gamble and the circle (chance node) represents possible
outcomes of a decision. Furthermore, numerical measures can be
introduced by assigning probabilities to the two possible
outcomes (Figure 2). Again, C can be found for which the
individual is indifferent to receiving outcome C or taking the
gamble of A with probability, p, and B with probability, 1-p.
Figure 2
Decision Tree with Outcomes and Probabilities
outcome probability
A p
B 1-p
C 1
It is often true in utility assessment that the values of A
and B are the maximum and minimum (most preferred and least
preferred) of a range of possible outcomes. When this is true, A
and B are assigned utility values of 1 and 0, respectively, to
facilitate the measurement of the utility levels of intermediate
outcomes. This can be written as u(A) =1 and u(B) =0. The
utility value of C, therefore, should fall between these values
as would be expected given the preference relationship: B is less
preferred than C which is less preferred than A. Using simple
algebra and utility theory the value of p (the probability of A
occurring) is defined as being equivalent to the utility of C if
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indifference between the options exists. According to utility
theory, A, B, and C can be represented by utilities, and because
A has been assigned a value of 1 and B a value of zero, the
following equation holds true:
u(A)p + u(B) (1-p) = u(C)
and simplifies to:
P = u(C)
where u(C) is the utility of outcome C. Utility, therefore,
provides a means of assessing the strength of preference for an
outcome relative to other possible outcomes. In fact, it is
possible to take any number of intermediate outcomes and assess
their individual utility values. Further coverage of the
principles of utility theory can be found in a number of works
(5,7,8,10,11,13-15) .
In summary, it is useful to outline an application which
incorporates the theory discussed above. First, the outcomes
which are to be assigned utilities must be clearly defined.
Next, a scale is constructed which is based on the outcomes of
interest. Often this is an arbitrary scale of zero to 1 with the
most appealing option assigned the maximum value and the least
preferred option the minimum value of zero. A final step may be
the elicitation of utilities for the various intermediate
outcomes on the specified scale using utility assessment methods.
Several of these methods are described in the following section.
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UTILITY ASSESSMENT METHODS
Single-attribute Utility Assessment Methods
Many different methods of directly estimating utility levels
exist; some are based on the indifference gambles of uncertainty
utility theory, whereas others have been developed as the need
for different methods has arisen.
Standard Gamble Methods
The group of methods that is most often used are called
standard gamble methods (SG). These techniques are based on the
utility theory relationship between various outcomes, outlined in
the previous section. The general form of the decision tree as
described earlier is shown in Figure 3.
Figure 3
Standard Gamble Decision Tree
outcome probability utility
B 1-p
unknown
According to utility theory the utility of outcome C is
equivalent to the probability, p, of the occurrence of outcome A
under indifference conditions. In the above relationship there
are four possible variables: A, B, C, and p. In utility
assessment, three of the four variables are held constant while
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the final one is varied until indifference exists between the two
choices. Consequently, three-main types of standard gamble
elicitation procedures have been developed.
Certainty Equivalence Method
The first technique is called the certainty equivalence
method (CE). This is employed when the outcomes, A, B, C, are
continuous variables, such as money or time. It is common in
this method to set p at 0.5 (50%), therefore, giving equal
probability to A and B in the gamble. A and B are generally the
extreme values of the entire possible range of outcomes and C is
an intermediate whose value is varied until indifference exists
between the options. The outcome value of C is then given a
utility of p, or in this case, 0.5. The analyst continues in
this manner, but replaces outcome B with C. The 0.5 utility
value of the bisection of the C to A outcome range is assessed
and can be called D. Using the same equation as before, but
replacing B with C:
u(A)p + u(C) (1-p) = u(D)
The utility of A, u(A), has already been set at 1, u(C) has been
previously assessed at 0.5, and the probability remains at 0.5,
therefore:
u(D) = 0.75
On the overall utility scale between B (0) and A (1) this second
assessed point, therefore, has a utility of 0.75. The analyst
continues this process, assessing as many bisecting utilities as
is desired until a smooth utility function is determined. The
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utility function or curve is commonly represented by a graph of
utility on the y-axis (0 - 1). and outcomes on the x-axis (B - A),
This provides a graphical representation for all outcomes within
the range of B to A.
Figure 4
Basic Utility Curve
Utility
Level
Outcome Range (B to A)
Probability Equivalence Method
In some cases the outcomes are discrete, such as objects or
health states. In these cases a different method must be used.
This second technique is called the probability equivalence
method (PE). In this method, the analyst sets A and B as the
most and least preferable options, respectively, and selects and
intermediate option C. This is represented in Figure 5.
Figure 5
Probability Equivalence Decision Tree
outcome probabI I Ity utI I Ity
1-p
0
unknown
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The value of p is varied until the decision maker is indifferent
between the certain outcome C. and the gamble over A and B. This
method may also be used when the outcomes are continuous and is
often used as a consistency check for the CE method. Just as
before the utility of C is equivalent to the elicited
probability, p, and a utility curve for the various discrete
outcomes between A and B is drawn.
Value Equivalence Method
A third method of standard gamble techniques is the value
equivalence method (VE). In this technique the values of B, C,
and p are held constant and A is varied. This method, however,
is rarely used in the literature and will not be discussed
further. There are also equivalent versions of the PE and VE
methods which involve the comparison of two gambles (Figure 6)
Figure 6
'Gamble Comparison Decision Tree
B l-p
c q
D l-q
These are also not popularly used because it often believed that
many people have difficulty comparing gambles. Further
information is available concerning standard gamble methods
(5,8,10,11,13,16).
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Utility Curves
In using each of these standard gamble methods it is
possible to create a utility curve. A number of points should be
made, however, concerning the shape of the resulting curve. If,
for example, the midpoint of the outcome range is given a.utility
value of 0.5 the individual is said to be risk neutral. A series
of such risk neutral utility elicitations would result in a
linear utility curve from (B,0), the origin, to the point (A,l).
Similarly, if the intermediate outcomes are given utility values
greater than those expected from risk neutrality then the
resulting curve will be concave downward and the decision maker
is said to be risk averse. Conversely, if the curve is convex
downward then the decision maker is said to be risk seeking.
These types of risk attitudes are shown graphically in Figures 7
and 8.
Figure 7
Utility Curve Representing
Risk Seeking Behavior
Utility
Level
Outcome Range (B to A)
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Utility
Level
Figure 8
Utility Curve Representing
Risk Averse Behavior
Outcome Range (B to A)
Category Scaling Method
A second type of single attribute utility assessment method
is category scaling (CS). This technique generally involves the
use of a visual analogue or "feeling thermometer". First, the
individual ranks the outcomes from worst to best. The worst is
given a value of zero and the best a value of 1. The remaining
outcomes are rated, relative to the extremes, on the visual
scale. Torrance (17), for example, suggests a 100 unit scale, 10
cm in length. In addition, small arrows are provided to the
individual to be used as indicators to identify his relative
positioning of outcomes on the scale. Further explanation and
discussion on the use of category scaling is provided by (18-23).
Some studies have shown that category scaling is best for
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obtaining ordinal rankings only and should not be employed to
elicit cardinal utility values (22,23) and is not grounded in the
principles of von Neumann-Morgenstern utility theory.
Multiattribute Utility Assessment Methods
In the example of single attribute analyses the outcomes
(A,B,C) are each at different levels of one attribute, and are
compared, therefore, on a single utility scale. In many cases,
however, an outcome will have several different attributes
associated with it, each at a different level. When faced with
comparing such outcomes with multiple value dimensions,
difficulty may arise because of the presence of conflicting
goals. The best possible option may involve maximizing one
attribute while minimizing another. In such instances the
application of multiattribute utility theory (MAUT) may be
employed for the comparison or valuing of outcomes. Basically,
the outcome being valued must be broken down into its component
parts - the different single attributes. Each individual
attribute may then be assessed as described previously. A model
to reaggregate the single attribute utility functions, which
generally includes weights or scaling factors that also must be
assessed, is necessary.
There are three main types of aggregation models: additive,
multiplicative, and multilinear. Each is progressively more
complex but requires a less restrictive independence condition
than the previous one. Associated with each model is a utility
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independence condition that must be met for the model to be
applicable. The three types of independence conditions are
called: additive independence, mutual utility independence, and
order-one utility independence. They correspond, respectively,
to the three models listed above.
Additive Model
The additive model is the easiest to use but its additive
independence restriction is the most difficult to meet. For
every attribute, x^, additive independence must hold between it
and all other attributes (y, z, etc). Additive independence
holds if changing the values of one attribute does not alter the
preference for the attribute being assessed. For example, in a
two attribute system, where x and y are the attributes, the
individual must be indifferent between the following pair of
choices shown in Figure 9 if each branch has an equal probability
of occurrence.
Figure 9
Requirement for Additive Independence
<(Xi ,YO D// CXl 'Yz)
cxT
u'3 (X2,Y2) °'5 CX2,Yi)
where Xj and yj are different levels of attributes x and y,
Changing the value of attribute y should have no effect on
preferences for attribute x.
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Once additive independence is found to exist between all
attributes or groups of attributes, the additive model may then
be applied.
The form of the model is quite simple, but requires a lot of time
to assess.
U(X) = E kiui (x,)
where,
U(X) = the multiattribute utility
kj = weight or scaling factor
u-j(x-j) = utility of level x of attribute j
n = number of attributes
and,
Multiplicative Model
To use the multiplicative model of utility aggregation,
mutual utility independence must hold. Mutual utility
independence exists when preferences for uncertain choices
i
involving different levels of attribute x are independent of the
level of attribute y. For example, if x-^ is a minimum value, x.-$
is the maximum, and X2 is an intermediate value then the
indifference (~) relationship shown in Figure 10 must hold for
utility independence to exist in this two attribute system:
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Figure 10
Requirement for Mutual Utility Independence
(Xz.Yi)
For more than two attributes, mutual utility independence holds
if there is "no interaction between preferences for lotteries on
some attribute and the fixed level on the other attributes" (11) .
If this condition is met, the following multiplicative model
equation can be used:
l+JcZ7(.x)=n
where,
and,
U(X) = the multiattribute utility
Uj (Xj) = utility of level x of attribute j
n = number of attributes
k and k_i are scaling factors where
kj is a scaling factor that makes single attribute
assessments consistent with overall assessment, and
k is a scaling factor that is a solution to:
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Multilinear Model
The third model type is -called 'multilinear . It is the most
complicated to use, but requires the least restrictive
independence condition, order-one utility independence. This is
simply met by insuring that each single attribute is independent
of all other attributes (i.e. pairs or groups of attributes need
not be independent of remaining attributes) . The multilinear
model is superficially similar to the multiplicative model, but
has a series of interaction parameters, k. In most cases,
however, the multilinear model is complicated and one study has
shown that generally the simplest model, the additive, is
sufficient (24) . This, of course, cannot be accepted as a
general rule.
Assessing Independence and MAU Functions
For each of the three models discussed above the method for
assessing the multiattribute utility function is quite similar.
First, the single attribute utility functions for each of the
attributes are assessed using single attribute methods. Next,
the k-:S are assessed by eliciting the utility of setting one
attribute at its best level while the others are at their worst.
For example, in a three attribute system:
(b) ,x2 (w) ,x3 (w) ]
k2 = utx-L (w) ,x2 (b) ,x3 (w) ]
k3 = utx-L (w) ,x2 (w) ,x3 (b) ]
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where,
Xj(b) = best possible level of attribute j
xj (w) = worst possible, level of attribute j
Finally, when using the multiplicative and the multilinear
models, the scaling constants, k.^, are calculated iteratively.
In addition, Klein et al. (25) have developed a method of
assessing multiattribute utility models with mathematical
programming.
It is also necessary to demonstrate that the specific
independence condition holds for the model being used. Because
it is very difficult and time consuming to demonstrate additive
or mutual utility independence, Keeney and Raiffa (5) have
developed a number of simpler methods to demonstrate
independence. Torrance (17) also outlines some of these. For
example, Torrance states that additive independence can be
determined by first demonstrating that mutual utility
independence exists. It is then necessary to establish additive
independence for only any two attributes with all other
attributes held constant. Similarly, the demonstration of mutual
utility independence can be simplified by showing that utility
independence, as described above, holds between one attribute and
the others, and then showing that the remaining pairs of
attributes which involve the one tested for utility independence
are preferentially independent. Preferential independence exists
when an individual is indifferent to outcomes involving one or
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more attributes regardless of the level of the remaining
attributes. In a three attribute system, for example, with x-j_,
x2, anc* x? as attributes, mutual utility independence can be
shown by demonstrating .the following:
is utility independent of (Xo,x3)
x2) is preferentially independent of (x3)
x3) is preferentially independent of (x2)
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UTILITY ASSESSMENT of HEALTH OUTCOMES
The basic concepts and methods of utility theory remain
unchanged when applied .to the field of health; however, it has
been necessary in some cases for new or slightly altered -methods
to be employed. These changes are generally considered valid if
the axioms of utility theory still hold. In fact, Torrance (26)
restates the axioms to fit in the context of health states and
comments on their validity. He states that the axiom of
continuity has been empirically tested and appears to be
applicable while the others seem to be reasonable, but none has
been rigorously tested.
The use of uncertainty utility theory in the health field
has grown dramatically in the past two decades. Many of the
medical applications of single and multiattribute utility
analysis prior to 1980 are reviewed by Krischer (27). Since that
time the use of utility and its traditional methods has continued
to expand and new methods have been created.
Single-attribute Approaches to Health Status Assessment
Single attribute utility analysis has been used
significantly to assign values to health states for the purpose
of clinical, technological, and health program decision making.
Also, it has been widely used in the creation of health indexes
as a way of prioritizing health states. There are many examples
of such uses in the literature. Utility values have been
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assessed for various types of cancer including ovarian (28),
gastric (29), rectal (30), laryngeal (31), endometrial (32), lung
(33,34), and various other cancer states (35,36). It has also
been used with such chronic conditions as prostatic hypertrophy
(37), Hodgkin's lymphoma (38), arthritis (39,40), kidney-disease
(41), osteoporosis (32), and generally poor health or disability
(41-43). It has also been applied to other areas including
outcomes from coronary artery bypass surgery (23), menopausal
symptoms (32), childbirth (44), genetic counseling (45-47) and
drug effects (48,49).
In most of these cases, an expert, physician, or patient was
asked to rate these conditions on a single attribute utility
scale of quality-of-life using one of the elicitation methods
described earlier. For example, category scaling was employed by
Llewellyn-Thomas et al (31) when they asked subjects to indicate
on a 100 mm line the relative desirability of several health
state scenarios. Similarly, Boyd et al. (30) utilized a standard
gamble method to elicit the value of living with colostomy.
Various groups of subjects were presented with the choice between
two alternatives: A - living a full lifetime with a colostomy
(certain outcome) or B - taking a gamble of perfect health for a
lifetime with probability p or immediate death with probability
1-p. The value for p was elicited from the subjects until there
was indifference between the choices, A and B.
27
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This is represented schematically in Figure 11.
Figure ll
Decision Tree with Health Outcomes
outcome probability utility
perfect
health H
death 1-P 0
llfewlth 1 unknown
co I ostotny
This is equivalent to the PE method described earlier. In
examples such as this the CE method cannot be used because it
requires a continuous variable, whereas the PE method can
accommodate discrete values such as health states.
Multiattribute Approaches to Health Status Assessment
Single-attribute utility assessment has been used
significantly in the health field; however, some groups have
found it necessary to decompose quality-of-life into several
attributes. Consequently, in assessing a subject's utility
function for the quality-of-life in a health state, a
multiattribute utility (MAU) model must be used. An excellent
review of the application MAU theory to health state evaluation
is found in (50). Torrance et al. (51) have created a four
attribute classification system to categorize various levels of
health. The four attributes each represent a different facet of
health which must be considered in determining value. In their
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opinion, the value of health states depends upon: (1) physical
function (mobility and physical activity); (2) role function
(self care and role activity); (3) social-emotional function
(emotional well-being and social activity); and (4) health
problems. As is done in traditional MAU analysis, each of the
utility functions for these attributes must be assessed
individually and then aggregated using one of the models.
Keeney and Ozernoy (6) employed an additive multiattribute
model to assess the relative values of the possible health
impacts of several carbon monoxide (CO) standards. Judgments
about the utility function and scaling factors for four
attributes were elicited from an EPA staff person. These
attributes concerned four possible health effects of CO exposure:
heart attacks, angina attacks, peripheral vascular attacks, and
vigilance impairment.
The MAU method has also been used in the creation of health
status indices (52). Gustafson et al. (53) have used the
additive model to develop a severity index. In this case the
attributes are various indicators of the severity of heart
disease. Similarly, Gustafson's model has been applied by Choi
et al. (54) to assess the severity of non-traumatic chest pain.
Other methods of valuing health effects have been developed,
such as the index of well-being (55). This index is based on an
ordinal ranking of various health states from which scores
similar to utility are derived.
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Incorporating Health State Quality and Duration
In both the single and multiattribute methods described
above the main purpose was to value specific health states
independent of any other factors. It has been suggested by Dowie
(56), however, that this may not be an accurate representation.
He argues that it is not appropriate to value life and health
directly, but instead each should be seen as a demand for time
(i.e. nothing is consumed independent of time). Dowie suggests,
therefore, that it is time that should be valued (in terms of
utility).
Single Attribute Approaches
Torrance (57) also felt that incorporating time was
important and devised the single attribute time trade-off method
(TTO) which evaluates the value of health states in the context
of time. This method has been used extensively and was developed
specifically for health outcomes. The technique was created
because many people have difficulty understanding and working
with probabilities. The method is not based on von Neumann-
Morgenstern utility axioms and requires an additional assumption
that the subject's utility function for additional healthy years
be linear over time (39) . This assumption has been a serious
point of controversy.
In the TTO method the subject is provided with two
alternatives. The first alternative is living the rest of one's
lifetime (t) in a specified health state, such as arthritis. The
second alternative is living in perfect health but for a shorter
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period of time (x). The number of years of perfect health are
varied until the subject is indifferent between the two
alternatives. The preference or utility value is defined then as
x/t. This method has been used and described by Torrance and
other researchers (21,22,29,37,38,40,57-60). For example.,
Krumins et al. (37) used the TTO method to value the possible
outcome of prostate surgery. In a situation such as this, TTO is
very good because patients and physicians must often try to
balance between quality of life and prospects for survival.
The balancing of the duration and quality of life is a
common goal in the health field. In fact, a unit of measurement
has been devised which incorporates these two values and has been
called quality-adjusted life years (QALY). QALY is basically a
measure of expected time of survival adjusted for varying states
of health. For example, living ten years with a disability or
disease is worth only a fraction of living ten years in good
health. QALYs provide a means, therefore, of comparing survival
in two states of health. A review of QALYs is presented in
(60,61) .
QALYs are not necessarily used when time and quality are
being accounted for. For example, McNeil et al. (62) studied the
differences in the utility of survival with artificial and normal
speech. First, the CE method was used to determine a utility
curve for duration of survival. In the next step the TTO method
was employed to create a graph of years of survival with normal
speech (y-axis) and years of survival with artificial
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speech (x-axis). If the two were 'valued equally then a linear
relationship with a slope of 1 would result. In this manner
utility values for varying years with artificial speech were
determined based on the CE and TTO utility curves. Consequently,
two curves were drawn which compared the utility for years of
life in each of these two states. The effect of a poorer health
state over time, therefore, could be observed graphically. The
curves produced by this method are shown in Figure 12 (62). This
procedure, however, does not produce the QALY measure that is
often desired.
Figure 12
Utilitv Curve Construction for Survival with Artificial Speech
100
5 f 10 15 20 25
YEARS OF SURVIVAL
(NORMAL SPEECH)
10 15 20 25
YEARS OF SURVIVAL
(ARTIFICIAL SPEECH)
5 10 15 20 25
YEARS OF SURVIVAL
Note: above graphs taken fron reference (62)
When such a measure is required the utility level for a
specific health state is multiplied by the average duration of
the health state to get QALYs. Utility measures for health
states derived from TTO, SG, or CS methods may be used for
calculating QALYs but only the TTO method considers quality in
the context of time. This simple single attribute method of
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is used only to obtain weights and does not fully incorporate
preferences for time (63). This controversy is discussed later
in this report.
Multiattribute Approaches
In an attempt to compensate for the weaknesses of the single
attribute approach to quality of life and health status duration
incorporation, a number of different models have been developed
to calculate functional equivalents to QALYs using multiattribute
utility (MAU) or MAU-like methods. Pliskin et al. (64,65)
developed a MAU model for this purpose which is based on several
assumptions. First, it is assumed that mutual utility
independence exists between survival time and health status. The
resulting model they use is a "quasi-additive" form of a MAU
function:
U(Y,Q) = au(Y) + bu(Q) + (l-a-b)u(Y)u(Q)
where,
u(Y) = utility for survival time
u(Q) = utility for health status
and a and b are weights.
It is also assumed that "constant proportional tradeoff" is
valid. This means that an individual is willing to give up the
same proportion of his remaining life for an improvement in
health regardless of how many years remain in his lifetime. For
example, if an individual is willing to give up 3 of 15 remaining
years of expected lifetime for an improvement in health, then he
must also be willing to give up 4 of 20 remaining years for the
same improvement.
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The second assumption implies that the utility function,
U(Y,Q), exhibits constant proportional risk aversion. It will,
according to Pliskin et al., therefore, take one of three general
forms (concave, convex,, or linear) of which the linear form is
selected. Miyamoto and Eraker (66) develop further this•model
and state the following relationship between the QALY-like
measure, U(Y,Q), and a bivariate utility function:
U(Y,Q) = bYrH(Q)
where,
Y . = survival time
H(Q) = utility of survival in a health state, Q
b = scaling constant
r = risk attitude factor with respect to survival
duration
Using the PE or the CE method the value for r is determined
and the TTO method is employed to determine the utility function
for health status, H(Q). Extensive examples and proofs are
provided in (64-66).
Loomes and McKenzie (67) criticize this method for
determining QALYs. They first attack the constant proportional
time trade-off assumption on the grounds of empirical evidence
from (41,43,62), which show that individual's preferences change
depending on the expected time in the health state. Furthermore,
the above model does not account for individual preference for
present consumption over future consumption; this is called time
preference (68). Loomes and McKenzie (67) suggest alternative
methods that do not involve traditional utility analysis.
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Other Methods
Other alternatives to QALYs have been suggested in response
to criticisms of QALYs (63,67,69-71). One of these alternatives
to QALYs is Healthy-Years Equivalents (HYE), suggested by Mehrez
and Gafni (63). HYEs cannot be assumed to be equivalent to QALYs
and Mehrez and Gafni warn that it may not be possible to
construct conversion curves. They support HYEs because even
though they are more difficult to elicit, they include
preferences for both time and quality of survival. In addition,
they provide several examples where QALYs would incorrectly
describe a subject's true preferences and they provide a full
description of the method for eliciting HYEs in the appendix of
(63) .
A final method that should be mentioned briefly is the DEALE
method (Declining exponential approximation of life expectancy).
Several groups have used this method to assess what they call
utilities (72-78). This method assumes that life expectancy can
be approximated by a simple exponential function. Therefore,
survival is calculated using the reciprocal of the age, sex, and
race specific mortality rate combined with the reciprocal of the
rate of disease in question. Quality adjustments for long and
short term morbidity are then made to the calculated life
expectancy. This value is used as a "utility". The result is a
measure of quality adjusted life expectancy.
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COMMENTS ON UTILITY ASSESSMENT AND QUALYs
As mentioned above, both utility assessment and QALYs are
often criticized for various reasons. Included in these
criticisms are general comments concerning decision analysis
overall, perception, and the psychology behind stating
preferences (79-81). In the following section, the specific
criticisms concerning utility analysis and QALYs are discussed.
Utility Assessment
The advantages and disadvantages of utility assessment are
frequently enumerated. There appear to be some points that are
repeatedly stressed. For example, Albert (3) comments that
"utility functions for one individual cannot be carried over to
another individual or group" while Kassirer (82) states the
opposite, but qualifies it by saying that comparing utilities is
only acceptable in a non-rigorous analysis. Furthermore, Albert
states that utilities cannot be associated with a monetary amount
because different individuals have differing utilities for money.
Feeny and Torrance (39), however, clearly state that an advantage
of utilities is their ability to be integrated into economic
evaluations.
Other pros and cons of utility assessment have been stressed
by a number of authors (39,83,84). Utility can be a very
comprehensive measure, taking into account all the many
attributes and tradeoffs presented by a problem and aggregating
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them on one scale. Furthermore, various evaluators or raters can
be elicited. These may include experts, patients, policy makers,
or even the general public. There has been some discussion as to
whether an individual c.an truly give a value (or disvalue) to
something he has not experienced. Drummond (84) states that the
V
issue of "whose values" may depend upon the purpose of the study,
as some have stated (58,83), but says that further investigation
is required. If such a variability in raters can exist it allows
utility assessments to be applied to hypothetical situations
which greatly increases the applicability of the method.
Finally, utility can be combined with outcome probabilities to
get expected utilities with which comparisons with other possible
outcomes can be made.
The advantages are, of course, balanced by a number of
disadvantages. There is complaint of a lack of precision in that
the same rater's utility levels may change greatly from one
measurement to the next. In addition, the elicitation process
consumes a lot of time and labor, and requires trained
professionals to attain the best results. It is also common to
find that the results^ of utility studies are not easily
interpretable, and there have even been suggestions that the
method of decomposing a problem as utility analysis does, may
change an individual's preferences (28).
Sources of Bias in Utility Assessment
When performing a specific study there are significant
sources of bias or error which may greatly affect the results.
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There have been quite a few studies which have pointed to the
fact that significant differences may result from the various
methods of utility elicitation. Again, this is a source of
controversy and there are studies which support both sides
(14,19,53,80-88) .
Much of the discussion in the literature on the
comparability of assessment methods concerns the three methods:
SG,CS, and TTO. Read et al. (23) found that the three methods
produce significantly different results, with SG resulting in the
highest utility and CS, the lowest. Large differences between CS
and SG were also noted in (18,89,90). In one of these studies,
however, Torrance did not find statistical differences between SG
and TTO (89). Wolfson et_al (18) found that TTO results were
close to those found using CS. Conversely, the SG method has
been shown to agree with direct evaluation (CS) results (55,91).
Other comparisons have been made between the different types
of SG methods. For example, Hershey et al. (12,92) cite several
studies that show differences in the shape of utility curves
created by CE and PE methods. CE resulted in more risk seeking
behavior and PE resulted in aversion to risk.
The ability to point out differences between methodological
results, however, does not provide judgment of which method is
best. Economists generally support the use of SG techniques
because they have the strongest foundation in utility theory
axioms (21,23) and they involve risk. Psychologists and
regulators argue, however, that visual scales are superior
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because of their ease of use and because risk is not always a
factor in a decision. These strong'preferences for specific
methods and the obvious differences in results from these methods
may restrict the use of certain methods to specific areas or
studies. In an attempt to explain some of the discrepancies and
provide assistance in selecting a method, much work has been done
on the psychology of utility assessment and the factors that
affect decision making abilities.
One area that has received significant attention is the
effect of time on an individual's utility values
(21,31,34,41,43,68,93). Time preference is very important
because of its potential effects on utility values. Time
preference is the desire for gains at the present as opposed to
equal gains at some point in the future. Some utility assessment
methods assume that time preference does not exist. This is not
supported by empirical studies (21,34,68); however, there are
methods of accounting for or measuring time preference. Most
commonly, the CE standard gamble method is employed. Using this
technique the utility of varying years of survival can be
measured.
Utilities have also been shown to be affected by the length
of time that an individual is in a specific health state. As the
length of time increases, the utility for being in that health
state decreases (31,41,93). Furthermore, it has been found that
in very disabling states, individuals often reach a point of
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"maximum endurable time" after which death becomes more
preferable (43). . "
Another factor that has been investigated for its effects on
utility is personal position or context. Christensen-Szalanski
has shown that utilities of women in childbirth change with
regard to preferences for anesthesia (44). It has not been
proven, however, that age, sex, social position, or professional
status have any effect on the utility values elicited for
specific health states (36,41).
It has also been shown that individuals in specific health
states value the health state differently than objective
observers or medical experts (30,41). The question arises,
therefore, of which group of people should be used to elicit
utility levels. Studies have been done which employed medical
experts (18,23), patients (18,35,41,42,94), and objective raters
(41,55,95). It can be argued that "the choice of raters should
be influenced by who are the stakeholders" (83) , but it can also
be argued whether these "stakeholding" raters can really provide
an informed judgment. Many opinions exist on this question, but
there may be no right or wrong answer. This topic, however, must
still be considered when conducting a utility assessment.
A final area that has received significant attention is the
psychology behind the assessment of utilities. It is often
believed that individuals allow biases to affect their decision
making (79) , therefore, it should be no different in the
assessment of utilities. Extensive work in this area has exposed
40
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a number of psychological factors that affect the valuing of
outcomes. The factors include framing, regret, outcome bias, and
range and probability effects among others. The factor that
receives the most attention is framing or context bias
(33,36,92,93,95-98). This type of bias can be controlled.by the
analyst because it is caused by the way in which questions or
elicitations are posed to the subject. For example, Llewellyn-
Thomas et al. (97) have shown that the form of the health state
description affects utility values being elicited. They found
that assessed utilities for health states described in the first
person narrative form were significantly different from utilities
assessed for the same condition described in standard point form.
Furthermore, the tone of the health state description, whether it
is framed in positive or negative terms, can affect the outcome
(33,36). Regret (12,32,92,93) is the tendency to shy away from
or devalue outcomes that could cause the decision maker to regret
making a wrong decision. This is especially common in the field
of medical decision making where physicians must value possible
outcomes or treatment options. For example, treatments or
therapies that have some potential for disabling effects may be
devalued because of regret bias even though they may be highly
unlikely. Outcome bias refers to the shifting of risk attitude
(risk aversion or risk seeking) depending on the whether the
outcome is a pure loss, pure gain or mixed situation (49,92,96).
The range of the outcomes has also been shown to affect the
assessment of health state utilities. Sutherland et al. (42)
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found differences in utility values depending on whether perfect
health and death were used as anchors or a smaller range
including an intermediate effect and perfect health were used.
Others have also described the occurrence of this bias
(87,94,96).
Other biases which have been identified include aversion to
gambles over health outcomes (89), the spreading of responses
across entire utility scales (23) (especially with CS technique);
the overweighting of low probability events (32,79,92); thinking
in only one dimension (i.e. not including all attributes) (92);
and overweighting the value of past events because they are
easily recalled or remembered (23,96). There are, of course,
many other sources of error or bias and not all those listed
above apply in all situations. Each one, however, should be
considered and accounted for, if possible, during any elicitation
process.
Quality Adjusted Life Years (QALYs)
The use of QALYs as a decision making tool has increased
significantly during the past decade; consequently, so have the
criticisms of this method of measurement. As stated earlier,
QALYs have been attacked because of the restrictive assumptions
(67): utility independence, risk neutrality for time, and
constant proportion trade-off; and because of their simplicity:
utility used only as a quality weight for life years (63).
Several authors have brought up ethical questions in addition to
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the problems with validity. They have even gone as far as
stating that QALYs are unjust (69-71). They complain that QALYs
value time over individual lives, too narrowly define the meaning
of quality of life, and do not treat all age groups equally.
Conversely, equally strong arguments are made in support of QALYs
(100-103). These authors argue that even though some problems
may exist with QALYs, they are usually successful in their goal
of comparing specific health states or treatment programs. More
time, they state, should be spent on standardizing the methods
and measuring sensitivity.
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ASTHMA. AS A HEALTH STATE OF CONCERN
Many varied diseases and physical conditions have been the
object of research using the utility under uncertainty
techniques. One health state, however, which apparently.has not
been addressed in this manner is asthma. Asthma is a condition
that may be characterized by many different attributes,
therefore, it should lend itself to the use of utility assessment
methods. Furthermore, the valuing of these various attributes by
asthmatics should be given some importance considering the
significance of asthmatics in air pollution research and standard
setting.
Definition of Asthma
Asthma, in its many and varied forms, is a condition that
affects at one time or another approximately 20 million
individuals in the United States and accounts for over 6 million
doctors visits, nearly 2 million emergency room visits, and 1
million work days lost in a single year (104). Often assumed to
be a specific disease, the term asthma, in fact, "encompasses
many patterns of response to a variety of stimuli, which find
common expression in variable airflow obstruction" (105).
Many attempts have been made to formulate an accurate and
all-encompassing definition for this disease, yet discussion and
controversy concerning the true description of asthma have
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continued for decades. A frequently accepted definition is that
of the American Thoracic Society (1962) which states that asthma
is a "disease characterized by an increased responsiveness of the
trachea and bronchi to .a variety of stimuli and manifested by
narrowing of the airways that changes in severity either•.
spontaneously or as a result of therapy" (106) . This definition
was rejected by a study 'group in 1971 because it allegedly was
not a definition of a "disease". Furthermore, in 1980, Gross,
following the philosophy of Karl Popper, suggested that asthma
should remain undefined (107) .
Because of the lack of agreement over a specific definition
it may be useful, instead, to provide a simple broad definition,
but include defining characteristics in an explanatory paragraph.
Scadding does just this by defining asthma as "a disease
characterized by wide variations over short periods of time in
resistance to flow in the airways of the lungs" (108). To this
definition one may add various symptoms and causes to better
characterize the disease. For example, the airways of asthmatics
are often referred to as being hypersensitive to various stimuli
or environmental factors in concentrations that would not be
expected to affect non-asthmatics. The disease may also be
episodic or persistent and may or may not be associated with
atopy, an Immunoglobulin-E (IgE) mediated allergic response. IgE
refers to a specific antibody that reacts to inhaled allergens.
Furthermore, the symptoms may be augmented by the swelling of
airway tissue or the collection of mucus in addition to the
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constricting of the smooth muscle of the air pathways.
The causes for the narrowing of the airways is somewhat
unclear. It appears, however, that asthmatics are genetically
predisposed and that various humoral, neurogenic, and
environmental factors exaggerate the effect (109). These, include
such physical, chemical, pharmacological, and immunological
stimuli as allergens, infectious agents, cold air, exercise,
emotional stress, laughter, inhaled substances such as
methacholine and histamine, and atmospheric pollution. It is
this latter one that is of particular interest to regulators and
environmental scientists. Many studies have been performed on
the effects of air pollutants on the lung function of asthmatics;
several of these will be discussed later in this report.
Asthma Severity Measurement
The broad definition and many variations of asthma have led
to several attempts at categorizing asthmatics. Dawson and Simon
(109), for example, divide asthmatics into several groups
including those that have no bronchial obstruction but react
abnormally to inhalation challenge testing with stimuli,
individuals who are acutely episodic, and those that are chronic.
Scadding (108) suggests that separate categories should also be
made for those whose asthma is and is not affected by inhaled
allergens (IgE mediated), and asthmatics whose condition is only
brought on by heavy exercise.
A more common method of categorization has been to use some
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sort of severity scale. In this manner, those that are mildly,
moderately, or severely affected can be identified for routine
medical and emergency purposes. It has been estimated that a
majority of asthmatics .are only mildly affected, 25% are
moderately affected, and as many as 15% are severely affected
(110). Many of the symptoms of asthma, such as airways
narrowing, cannot be quantified without the use of clinical
measurements. Several of these objective means of measurement
have been developed in an attempt to reflect the degree of
severity of a patient's asthma. The forced expiratory volume in
one second (FEV^) is the volume of air that an individual can
expire in one second. This is a good measure for asthma because
the narrowed airways of an asthmatic do not allow for rapid
expiration. A second measure is the forced vital capacity (FVC
or VC) which is the total volume of air that can be expelled from
the lungs. Each of these measures can be used independently or
as a ratio FEV1/FVC. The ratio has the advantage of allowing for
better comparison between individuals because it will not be
dependent on body size as FVC may be. It is also common to
measure the flow rate of expired air using a spirometer and note
the peak expiratory flow rate (PEFR). In measuring both the flow
and the volume it is helpful to portray the measurements
graphically (Figure 13):
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Figure 13
Graphical Representation, of Peak Expiratory Flow Rate and
Forced Expiratory Volume Measurements
FVC
Expiratory
Flow
Volume Expired
An additional measure that is used is the specific resistance of
the airways (SRAW). This value, however, is not as easily
attainable because of the need for a body plethysmograph, a
complex piece of equipment, and is less reproducible than those
described above. Several other measurements exist which are
slight variations of those described.
It is often the case that ranges of these measurements are
used to indicate the presence of asthma. For example, an FEV-^ to
FVC ratio of greater than 0.7 is often considered the normal
range. Similarly, a PEFR of 400 - 600 1/min is considered
normal, whereas 200 - 400 1/min may indicate the presence of
asthma. Bateman et al. (Ill) proposes bounds that roughly
identify healthy or asthmatic individuals. An example is shown
below.
FEV-, (% predicted)
FEV-L/FVC
PEFR (% predicted)
Asthmatic
85 ± 6
0.74 ± 0.4
76 + 5
Healthy
119 i 4
0.88 ± 0.2
99 + 3
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These objective clinical measures used in conjunction with
frequency and degree of asthmatic attacks have been used to
determine asthma severity in patients. Often these are presented
in the form of severity scales. A rather simple scale, commonly
called the Aas scale (112), is based primarily on the number and
duration of asthmatic episodes in a year, but it also considers
drug consumption and clinical evaluation. Godard et al. (113)
claim to increase the correlation between severity and this scale
by including the objective FEVj measurement. Juji et al. (114)
attempted to find what measures best correlate with asthma
severity. They found that low respiration threshold to
acetylcholine, high maximal histidine release by anti-IgE, high
serum IgE, and high eosinophil counts in the blood all correspond
well with severity. Pereira et al. (115) examined the
coefficient of variation (COV) of PEFR in asthmatics. It was
found that the COV of PEFR has a bimodal distribution which was
interpreted as a natural separation between mild and severe
asthmatics. Those individuals with severe asthma had greater
variations in their expiatory flow rate than mild or non-
asthmatics.
In many cases a more subjective aspect is included in the
severity scale. Scadding (108) bases a severity categorization
on necessary treatment and lifestyle interferences. Mild asthma,
according to Scadding, is controllable with a bronchodilator and
there is no interference with normal activities. Moderate
severity causes occasional interferences and necessitates the use
49
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of systemic corticosteroids, and severe asthma is defined as
seriously interfering lifestyle and' may include life threatening
episodes. Similarly, Jones (116) uses effects to lifestyle and
the ability to perform housework or one's job as a part of a
severity scale.
A broad and encompassing scale was developed by Brooks et
al. (117) to estimate the clinical status of asthmatic subjects.
Included were ratings of frequency and degree of four symptoms:
cough, wheeze, chest tightness, and shortness of breath. In
addition, frequency of attacks and therapy use were considered.
Scores were given to various levels within each of these
categories to indicate asthma severity. Similarly, Donnelly et
al. (118) identified variables which could be used to rate
patients severity. In their study multivariate cluster analysis
was used to select eight variables which correlated well with
severity from a group of 256 seasonal and environmental factors
gleaned from patient questionnaires. The selected variables
included four symptoms and four lifestyle interferences. The
symptoms included cough, chest tightness, wheeze, and shortness
of breath, and the lifestyle interferences were hospitalizations,
days of school missed, nights of sleep missed, and degree of
physical activity tolerated. Several other severity scales have
been developed which are also based on degree and frequency of
asthmatic attacks and clinical evaluations (119,120).
Often, patients are asked to rate their own severity or
subjective feeling about a specific asthma symptom. One commonly
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used scale was developed by Hackney (121). On this scale,
specific symptoms are rated on a scale of zero to 40 on the basis
of lifestyle interferences and meication use. Various levels are
shown below.
Hackney Scale
0 Symptoms not present
5 Minimal (not noticeable unless asked about)
10 Mild (noticeable but not bothersome)
20 Moderate (bothersome, consider taking medication to
alleviate)
30 Severe (interferes with activities)
40 Incapacitating
Other subjective scales have been developed using visual analog
scales (VAS) on which various symptoms are rated on a scale of
zero to 100 where zero represented no breathing problems and 100
represented total disability. For example, shortness of breath
was rated by patients on a VAS scale and the VAS score was then
correlated with PEFR measurements. In three studies (122-124)
the r values were found to range between -0.7 and -0.85 showing a
negative correlation between the two. This implies that
individuals who had greater difficulty in breathing and felt more
disabled also had lower PEFRs. Similar symptom rating techniques
have been used by patients as a part of more encompassing
severity scales which also included other criteria such as
clinical examination information and drug consumption (125-127).
Air Pollution Effects on Asthamtics
As stated earlier, many diiferent airborne contaminants can
adversely effect the functioning of an asthmatic's lungs and may
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even bring on an attack. There has been a significant amount of
research in this area by environmental scientists, especially
with regard to air pollutants such as sulfur dioxide and ozone.
Regulators are also concerned with the results of such studies
because of the need to consider the effects of pollutants, on
sensitive groups in the ppulation during standard setting. The
Environmental Protection Agency (EPA) does just this by reviewing
all pertinent studies in order set national ambient air quality
standards (NAAQS).
The importance of regulating these pollutants in the
atmosphere becomes obvious when one examines reports of severe
exposure episodes. In London in 1952 a brief period of S02-
polluted fog was blamed for hundreds, perhaps thousands of deaths
of sensitive individuals. Similarly, 88% of the asthmatic
patients in Donora, Pennsylvania experienced exacerbations during
a period of high S02 concentrations in 1948 (128). These are
isolated incidents; however, high S02 concentrations have been
reported near S02 point sources, in indoor air of kerosene-heated
homes, and in industrial plants such as paper-pulp mills,
smelters, and food processing plants (128) .
Sulfur dioxide (S02) is one such atmospheric pollutant which
has received significant attention. EPA, in an assessment of
available health S02 health effects data (129) states that
asthmatics as a group are more sensitive to S02 than non-
asthmatic individuals. Furthermore they state that moderate to
heavy execise with exposure to S02 is likely to cause
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bronchoconstriction within 5 minute at concentrations of less
than 1 ppm. These assertions are based on the results of various
inhalation studies which measured several objective, clinical
lung function criteria .including SRAW, FEV-^, and PEFR as a means
of quantifying the observed decreased lung function and •.
bronchoconstriction (130-133).
Similarly, studies on the inhalation of ozone (03) by
asthmatics were reviewed by EPA during an assessment of effects
on sensitive individuals (134). Recent studies were pointed out
as reporting airway resistance after Og exposure (135-136). EPA
concluded that the lungs and airways of asthmatics are more
responsive to 63 than are those of normal subjects.
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SUGGESTED PILOT STUDIES
The final part of this project is the design of a number of
pilot studies that apply utility under uncertainty techniques to
the valuing of asthma health states associated with air .
pollution. Specifically, three pilot studies have been developed
to accomplish two main objectives. The first is to evaluate the
possible uses of utility assessment for common problems faced by
the EPA. The second is to collect subjective data about how
asthmatics value (or disvalue) asthma and its associated
symptoms. These two main areas are discussed individually in the
following sections.
Evaluation of Utility Assessment
Utility assessment has been shown to be useful in many
different fields and even as a possible application to
environmental standard setting procedures (6). To further the
study of the applicability and ease of use in areas such as this,
three pilot studies have been designed to test several
methodological variables (which I will call "utility assessment
variables") that may affect the results of such tests. Three
variables in particular have been selected: subject group,
elicitation method, and elicitation format, and each is
elaborated upon below.
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Subject Group
As mentioned earlier there is much debate about what group
should be the decision makers in a utility assessment problem.
Some have said that it should be the stakeholders that should be
elicited, while others think that the general public or ..
regulators should also have a say. It would be interesting,
therefore, to compare the results of studies using one or the
other of these general groups. The questions being pursued in
this report concern the elicitation of various uncertainty
utility levels of asthma and its symptoms. Severe asthmatics are
used in the present studies as the stakeholders and non-
asthmatics familiar with utility assessment as the other group.
This provides a good basis of comparison as to which is more
important, to experience a health state first hand or to be
knowledgeable about the assessment techniques.
Elicitation Method
There are several types of elicitation methods that are used
commonly in the literature. Two of these, the standard gamble
method and the time trade-off technique, however, receive much of
the attention and appear to have been well accepted. The purpose
of the pilot tests is not to compare the results from each method
quantitatively but more to weigh their individual advantages and
disadvantages. For example, the ease with which subjects can
comprehend the technique or the difficulty for the interviewer in
performing the elicitation would provide valuable information for
later studies.
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Elicitation Format
There has been much discussion in the literature concerning
biases or errors that may be associated with the format of the
study. The pilot studies attempt to test two different format
methods: interview and questionnaire. It is expected that a well
designed interview should provide more valid results than a
questionnaire. For an agency such as the EPA, however, where
time and money are often important criteria, it may be helpful to
know whether a questionnaire format is a possible option and what
limitations may be associated with it.
Subjective Information on Asthma
The second area of interest is information on how various
aspects of asthma are judged (i.e., the utility of changes in a
health state which may be brought on by exposure to air
pollution). From discussions with members of EPA who are
associated with the study of asthma and air pollution it appears
that there are several areas of primary interest concerning
asthma and subjective judgment. These include the severity of
asthma, the severity of its symptoms, and the frequency of asthma
attacks. Each of these health state variables may be adversely
affected by exposure to air pollutants, therefore, subjective
judgment concerning them may prove valuable.
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Asthma Severity
There is much discussion, in the literature about the
severity of asthma and the various methods of placing asthmatics
in such levels. It may be useful, therefore, to elicit
uncertainty utility curves for changes in severity which.may be
expected to occur upon exposure to air pollutants. For example,
this could quantify the difference in effects between mild and
moderate (or severe) asthmatics. Or, studies may be designed to
elicit judgments of how undesirable it is to remain a severe
asthmatic for a certain period of time as compared to a moderate
or mild asthmatic over that same duration.
As t hma S ympt oms
A second area of interest concerns the various symptoms
associated with asthma. Because asthma varies greatly from
person to person, it is difficult to pinpoint only a few defining
symptoms. Studies which were discussed earlier, however, have
shown that several symptoms correlated well with asthma severity.
In controlled human exposure studies these included cough,
wheeze, chest tightness, and shortness of breath (dyspnea). It
is expected that each of these symptoms is of greater or less
concern than the others. It would be helpful, therefore, to use
utility under uncertainty methods to elicit the severity
relationships that exist between them.
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Frequency of Attacks
The use of utility assessment under uncertainty would also
appear to be a good vehicle to elicit subjective valuing of
additional asthma attacks. It is possible, for example, to find
what range of asthma attack frequency is relatively acceptable to
an asthmatic or at what point one additional attack would become
intolerable.
Pilot Study Protocols
Three pilot studies have been designed to test the effects
of each of the various utility assessment variables discussed
above and listed in Table 1.
Table 1
Utility Assessment Variables
Subject Group: Asthmatics
Non-asthmatics
Elicitation Method: Time Trade-Off (TTO)
Standard Gamble (SG)
Elicitation Format: Interview
Questionnaire
Each of the pilot studies also addresses one of the three areas
concerning asthma with different combinations from Table 1.
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Pilot Study 1
The first study attempts- to elicit the preference
relationship of three severity levels of asthma (severe,
moderate, mild) over differing periods of time. The order is
expected to remain the same; however, it is anticipated that the
relative differences on a quantifiable scale may change over
time.
Elicitation Method
Of the two elicitation methods that have been discussed, the
TTO method is better suited for the type of test in which time is
involved. In fact, it was specifically designed for this type of
application. The time trade-off technique is used to elicit
preferences for various lengths of time in a specific health
state. It involves presenting the subject with a choice of two
options, and in the case of severe asthma, for example, the
options would appear as follows:
A B
A guarantee A guarantee
of x years of or of y years of
good health severe asthma
The number of years in option A is varied until the subject
is indifferent between A and B. In each of the two options the
health state is guaranteed for only the time indicated. After
that period, what might happen is unknown; it may be asthma, good
health, or immediate death. It is expected that the subject will
prefer fewer years of guaranteed good health to a greater number
of years with asthma. The process is repeated for various time
59
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periods (y^), and in each case a corresponding good health
duration (x^) is elicited from the individual. The resulting
values may be plotted on an x - y axis.
The same process may also be performed for the various
severity levels of asthma, therefore creating a comparison of the
three. It is hoped that a comparison of the curves would
indicate whether the preference relationship of the three
severity levels would remain in equal ratios over time or if the
more severe levels become disvalued at the greater durations.
For the sake of clarity the subject is provided with a
written description of the various health states being considered
' in the study. Good health is defined as the lack of any major
illnesses and each of the severity levels by changes in lifestyle
caused by the asthma. These lifestyle effects are listed in
Table 2.
Table 2
Criteria Used to Define Asthma Severity
Symptom Frequency: wheeze
cough
chest tightness
dyspnea
Frequency of Attacks
Number of hospital/doctor visits per year
Necessary Treatment (frequency, type, possible side effects)
Lifestyle Changes: work or school missed
physical activity tolerated
avoidance of precipitating factors
For each of the three levels of asthma a different description is
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provided to the subject using the terms in Table 2 to define it.
Subject Group
Because this is only a pilot study and only very general
trends are of interest, it is possible to select a small. .sample
size of subjects. Consequently, it is expected that the number
of participants will be kept between three and ten. This first
pilot study is designed to compare responses for asthmatics and
non-asthmatics; therefore, a group of each must be selected. The
asthmatic group will consist of several asthmatics who will be
contacted through their physician and will be interviewed during
a regular hospital clinic visit. The non-asthmatics consist of a
similar sized group of EPA staff members and other individuals
who are familiar with the methods of utility under uncertainty.
Elicitation Format
This test also compares the two types of elicitation
formats: interview and questionnaire. The interview consists of
a series of questions in which the subject is presented with
options (A or B) as described above. For example, the subject is
asked whether y years of asthma or x years of good health are
preferred. The interviewer reduces the number of years of good
health until indifference between the two exists. To control
bias, however, it is necessary that this not be done in a
stepwise manner. The interviewer, therefore, must alter the
years of asthma (y) for each question. To assist in this, a
61
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tally sheet is provided to the interviewer which consists of a
grid with the years of asthma (y) oh the y-axis and the years of
good health (x) on the x-axis. Each box in the grid represents a
comparison of an x with a y. The interviewer may easily select
any box and ask the preference of the subject. Once the..
preference is recorded, the interviewer chooses another box in a
predetermined random order. A different tally sheet is necessary
for each of the three asthma severity levels and, switching
between the three levels is also necessary during the random
sequence.
The questionnaire does not involve the tally sheet; however,
it does involve the presentation of a random sequence of options
for which the subject must indicate a preference or indifference.
Pilot Study 2
The second of the three pilot tests investigates the
preference relationships of four symptoms: wheeze, cough, chest
tightness, and dyspnea. Once again, both asthmatics and non-
asthmatics are interviewed. The questionnaire format, however,
is not used in this test. In addition, this test only uses the
standard gamble technique. To explain its use in this
application one symptom, dyspnea, is discussed as an example.
The severity of dyspnea is best described by discrete levels
as shown in Table 3.
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Table 3
Severity of Dyspnea
None no shortness of breath at any time
Mild shortness of breath with strenuous exertion
Moderate shortness of breath with moderate exertion
(e.g., climbing one to two flights of stairs or
walking four or five blocks)
Severe shortness of breath with minimal exertion
(e.g., climbing one-half to one flight of stairs
or walking half a block or performing housework)
Dyspnea
at rest shortness of breath without any physical activity
Source: reference (117)
Because of the lack of continuity it is possible to use only
the probability equivalence method. This involves presenting the
subject with a pair of options to elicit a preference of
indifference. For example, the subject may be asked to choose
between two options, A and B:
A B
A guarantee of A chance of no
moderate dyspnea or dyspnea with a
for the remainder probability of p
of one's life and dyspnea at rest
with probability 1-p
The value of p is altered by the interviewer until indifference
between the two options exists. This may be done in much the
same manner as in pilot test 1 (i.e., using a tally sheet). This
process should produce utility levels for each of the various
severities of dyspnea.
The other three symptoms are also tested in a similar
manner. In some cases, however, it is possible to use the
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certainty equivalence method as well. The severity of wheeze and
chest tightness, for example,-can be considered continuous
variables if frequency of occurrence is used to determine the
severity of the symptom.
Pilot Study 3
The final pilot study attempts to elicit utility curves of
the frequency of asthma attacks. Because it may be difficult to
accurately describe to a non-asthmatic what an asthma attack is
like, this test is restricted to asthmatics. Furthermore, the
information of interest lends itself better to standard gamble
methods, and because the variable being tested is continuous (#
of attacks) both the certainty equivalence and probability
equivalence methods may be used.
As in pilot test 2 , the subjects are presented with two
options, A and B. For example,
A B
z attacks per An equal chance of
year with or the maximum possible
certainty imaginable attacks in
one year or no attacks
during the year
The value of z is altered by the interviewer in much the same
manner as in pilot test 1 and 2 using a tally sheet as an aid. A
utility curve is drawn from the resulting data.
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CONCLUSIONS
The assessment of the likelihood of effects of air pollution
on sensitive populations, such as asthmatics, plays an important
role in the regulatory process and standard setting by the EPA.
The subjective judgments of the nature and value of these effects
by the individuals affected may also add valuable input to these
processes. It is useful, therefore, to attempt to elicit utility
under uncertainty judgments from such populations. To do this
several possible pilot tests have been suggested. It is
anticipated that the results of these pilot tests will provide
information to the EPA concerning how asthmatics value
effects. Furthermore, performing such valuations will also
increase the awareness of EPA about the use of utility under
i
uncertainty, its advantages, disadvantages, and areas for further
study.
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REFERENCES CITED
1. Bergner, M. Measurement of health status. Med. Care, 1985,
23, 5, 696-704.
2. Spitzer, W.O. State of science 1986: Quality of life
and functional status as target variables for research. J.
Chron. Dis., 1986, 40, 465.
3. Albert, D.A. Decision theory in medicine: A review and
critique. Milbank Mem. Fund Quarterly / Health and Society.
1978, 56, 3, 362-401.
4. Clarke, J.R. Decision making in surgical practice. World J.
Surg., 1989, 13, 245-251.
5. Keeney/ R.L., and Raiffa, H. Decisions with Multiple
Objectives: Preferences and Value Tradeoffs, Wiley, New
York, 1976.
6. Keeney, R.L., Ozernoy, V.M. An illustrative analysis of
ambient carbon monoxide standards. J. Opl. Res. Soc. 1982,
33, 365-375.
7. von Neumann, J., and Morgenstern, 0. Theory of Games and
Economic Behavior, 2nd edition, Princeton Univ. Press,
Princeton, NJ, 1947.
8. Watson, S.R., and Buede, D.M. Decision Synthesis, Cambridge
Univ. Press, Cambridge, U.K., 1987.
9. Bell, D., P. Farquhar. Perspectives on utility theory. Oper.
Res. 1986. 34,1,179-183.
10. Clemen, R. Making Hard Decisions. An Introduction to
Decision Analysis (In publication)
11. von Winterfeldt, D., and Edwards, W. Decision Analysis and
Behavioral Research, Cambridge Univ. Press, Cambridge,
U.K., 1986.
12. Hershey, J.C., and Baron, J. Clinical reasoning and
cognitive process. Med. Dec. Mak., 1987, 7, 203-211.
13. Raiffa, H. Decision Analysis. Introductory Lectures on
Choice Under Uncertainty. Addison-Wesley, Reading, MA,
1968.
66
-------
14. French, S. Decision Theory: An Introduction to the
Mathematics of Rationality, Ellis Horwood, Ltd.,Chichester,
U.K., 1968.
15. Fisburn, P.C. Utility Theory for Decision Making. Wiley, New
York, New York, 1970.
16. Farquhar, P. Utility assessment methods. Management Sci.,
1984, 30, 11, 1283-1300.
17. Torrance, G.W. "Multiattribute Utility Theory as a Method
of Measuring Social Preferences for Health Status in Long-
term Care" in Values and Long-term Care. Robert L. Kane and
Rosalie A. Kane, eds., Lexington Books D.C. Heath and Co.,
Lexington MA, 1982.
18. Wolfson, A.D., Sinclair, A.J., Bombardier, C., and
McGreer, A. "Preference Measurement for Functional Status
in Stroke Patients" in Values and Long-term Care, Robert L.
Kane and Rosalie A. Kane, eds., Lexington Books D.C. Heath
and Co., Lexington, MA, 1982.
19. Kaplan, R.M., Bush, L.W., Berry, C.C., et. al. Health
status index: Category rating versus magnitude estimation
for measuring levels of well-being. Med. Care, 27, 5,
1979
20. Patrick, D.L., Bush, J.W., and Chen, M.M. Methods for
measuring levels of well-being for a health status index.
Health Serv. Res., 8, 3, 1973.
21. McNutt, R.A. Measuring patient preference for health
outcomes: A decision analytic approach. Patient Educ.
and Counseling, 1989, 13, 271-279.
22. Feeny, D., Labelle, R., and Torrance, G.W. Integrating
economic evaluations and quality of life assessments. in
Quality of Life Assessments in Clinical Trials, B. Spiker,
ed., Raven Press, Ltd., NY, 1990.
23. Read, J.L., Quinn, R.J., Berwick, D.M., et. al. Preferences
for health outcomes: Comparisons of assessment methods.
Med. Dec. Mak., 1984, 4, 3, 315-329.
24. Leung, P. Sensitivity analysis of the effect of variations
in the form and parameters of a multivariate attribute
utility model: A survey. Behav. Sci., 1978, 23, 478-485.
25. Klein, G., Moskowitz, H., Mahesh, S., et. al. Assessments of
multiattribute measuring value and utility functions via
mathematical programming. Decision Sciences, 1985, 16, 309-
324.
67
-------
26. Torrance, G.W. Toward a utility theory foundation for
health status index models. Health Serv. Res., 1976, 11,
349-369.
27. Krischer, J.P. An annotated bibliography of decision
analytic application to health care. Oper. Res., 1979,
28, 1, 97-113.
28. Simes, R.J. Treatment selection for cancer patients:
Application of statistical decision theory to the treatment
of advanced ovarian cancer. J. Chron. Dis., 1985, 38, 2,
171-186.
29. Lane, D.A. Utility, decision and quality of life. J.
Chron. Dis., 1987, 40, 6, 585-591.
30. Boyd, N.F., Sutherland, H.J., Heasmann, K.Z., et. al. Whose
utilities for decision analysis? Med. Dec. Mak., 1990, 10,
1, 58-67.
31. Llewellyn-Thomas, H.A., Sutherland, H.J., Ciampi, A., et.
al. The assessment of values in laryngeal cancer:
Reliability of measurement methods. J. Chron. Dis., 1984,
37, 4, 283-291.
32. Einstein, A.S., Holzman, G.B., Ravitch, M.M., et. al.
Comparison of physicians decisions regarding estrogen
replacement therapy for menopausal women and decisions
derived from a decision analytic model. Am. J. Med., 1986,
80, 246-258.
33. McNeil, B.J., Pauker, S.G., Sox, H.C., et. al. On the
elicitation of preferences for alternative therapies. N.
Eng. J. Med., 1982, 306, 21, 1259-1262.
34. McNeil, B.J., Weichselbaum, R., and Pauker, S.G. Fallacy of
the five-year survival in lung cancer. N. Eng. J. Med.,
1978, 299, 25, 1387-1401.
35. Llewellyn-Thomas, H.A., Sutherland, H.J., Tibshivani,R., et.
al. Describing health states: Methodologic issues in
obtaining values for health states. Med. Care, 1984, 22,
6, 543-552.
36. O'Connor, A.M.C., Boyd, N.F., Tritchler, D.L., et. al.
Eliciting preferences for alternative cancer drug
treatments: The influence of framing, medium, and rate
variables. Med. Dec. Mak., 1985, 5, 4, 453-463.
68
-------
37. Krumins, P.E., Finn, S.D., and Kent, D.L. Symptom
severity and patients' values in the decision to perform a
transurethral resection of the prostate. Med. Dec. Mak.,
1988, 8, 1-8.
38. Corder, M.P., and Ellwein, L.B. A decision-analysis
methodology for consideration of morbidity factors in
clinical decision making. Am. J. Clin. Oncol., 1984, 6,
19-32.
39. Feeny, D.H., and Torrance, G.W. Incorporating utility-
based quality-of-life assessment measures in clinical
trials. Med. Care, 1989, 27, 3, S190-S204.
40. Bombardier, C., Ware, J., Russell, I.J., et. al. Auranofine
therapy and quality of life in patients with rheumatoid
arthritis. Am. J. Med., 1986, 81, 4, 565-578.
41. Sackett, D.L., and Torrance, G.W. The utility of different
health states as perceived by the general public. J. Chron.
Dis., 1978, 31, 697-704.
42. Sutherland, H.J., Dunn, V., and Boyd, N.F. Measurement of
values for state of health with linear analog scales, ed.
Dec. Mak., 1983, 3, 4, 477-487.
43. Sutherland, H.J., Llewellyn-Thomas, H., Boyd, N.F., et. al.
Attitudes toward quality of survival: The concept of
maximum endurable time. Med. Dec. Mak., 1982, 2, 3, 299-
309.
44. Christensen-Szalanski, J.J.J. Discount functions and the
measurement of patients' values: Women's decisions during
childbirth. Med. Dec. Mak., 1984, 4, 1, 47-58.
45. Pauker, S.P., and Pauker, S.G. Prenatal diagnosis: A
directive approach to genetic counseling using decision
analysis. Yale J. Biol. Med., 1977, 50, 275-289.
46. Pauker, S.P., and Pauker, S.G. The amniocentesis decision:
An explicit guide for parents. in Risk, Communication, and
Decision Making in Genetic Counseling. Epstein, C.J.,
Curry, C.J.R., Packman, S., Sherman, S., and Hall, B.D.,
eds. New York,Alan R. Liss, 1982, 289-324.
47. Pauker, S.G., Pauker, S.P., and McNeil, B.J. The effect of
private attitudes on public policy: Prenatal screening
for neural tube defects as a prototype. Med. Dec. Mak.,
1981, 1, 103-114.
69
-------
48. Lane, D.L., and Hutchinson, T.A. The notion of "acceptable
risk:" The role of utility in drug management. J.
Chron. Dis., 1987, 40, 6, 621-625.
49. Eraker, S.A., and Sox, H.C. Assessment of patients'
preferences for therapeutic outcomes. Med. Dec. Mak., 1981,
1, 1, 29-39.
50. Torrance, G.W. "Multiattribute Utility Theory as a Method
of Measuring Social Preferences for Health Status in Long-
term Care" in Values and Long-term Care. Robert L. Kane
and Rosalie A. Kane, eds., Lexington Books B.C. Heath and
Co., Lexington Mass, 1982.
51. Torrance, G.W., Boyle, M.H., and Horwood, S.P. Application
of multi-attribute utility theory to measure social
preferences for health states. Oper. Res., 1982, 30, 6,
1043-1069.
52. Boyle, M.H., and torrance, G.W. Developing multiattribute
health indexes. Med. Care, 1984, 22, 11, 1045-1057.
53. Gustafson, D.H., Fryback, D.G., Rose, J.H., et. al. A
decision theoretic methodology for severity index
development. Med. Dec. Mak., 1986, 6, 27-35.
54. Choi, T., Brekka, M.L., Campion, B.C., et. al. Feasibility
of simulating physicians' judgments of patient severity.
Med. Care, 1984, 22, 1, 30-41.
55. Kaplan, R.M., Bush, J.W., and Berry, C.C. Health status:
Types of validity and the index of well-being. Health Serv.
Res., 1976, 11, 478-507.
56. Dowie, J.A. Valuing the benefits of health improvement.
Austral. Econ. Papers, 9:21 June, 1970.
57. Torrance, G.W., Thomas, W.H., and Sackett, D.L. A utility
maximization model for evaluation of health care programs.
Health Serv. Res., 1972, 7, 118-133.
58. Torrance, G.W. Utility approach to measuring health-
related quality of life. J. Chron. Dis., 1987, 40, 6, 593-
600.
59. Torrance, G.W. Measurement of health state utilities for
economic appraisal. J. Health Econ., 1986, 5, 1-30.
60. Torrance, G.W. and Feeny, D. Utilities and quality-
adjusted life years. Intl. J. of Tech. Assess, in Health
Care, 1989, 5, 559-575.
70
-------
61. Weinstein, M.C., and Fineberg, H.V. Clinical Decision
Analysis, W.B. Saunders Company, Philadelphia, 1980, 184-
227.
62. McNeil, B.J., Weichselbaum, R., and Pauker, S.G. Speech and
survival: Tradeoff between quantity and quality of life in
laryngeal cancer. New Eng. J. Med., Oct. 22, 1981, 982-987.
63. Mehrez, A., and Gafni, A. Quality-adjusted life years,
utility theory, and healthy-years equivalents. Med. Dec.
Mak., 1989, 9, 2, 142-149.
64. Pliskin, J.S., Shepard, D.S., and Weinstein, M.C. Utility
functions for life years and health status. Oper. Res.,
1980, 28, 1, 206-224.
65. Pliskin, J.S., Stason, W.B., Weinstein, M.C., et. al.
Coronary artery bypass graft surgery: Clinical decision
making and cost-effectiveness analysis. Med. Dec. Mak.,
1981, 1, 1, 10-28.
66. Miyamoto, J.M., and Eraker, S.A. Parameter estimates for a
QALY utility model. Med. Dec. Mak., 1985, 5, 2, 191-213.
67. Loomes, G., and McKenzie, L. The use of QALYs in health
care decision making. Soc. Sci. Med., 1989, 28, 4, 299-
308.
68. Gafni, A., and Torrance, G.W. Risk attitudes and time
preference in health. Management Sci., 1984, 30, 4, 440-
451.
69. Harris, J. Life: quality, value and justice. Health Policy,
1988, 10, 259-266.
70. Smith, A. Qualms about QALYs. The Lancet, May 16, 1987,
1134-1136.
71. Rawles, J. Castigating QALYs. J. of Med. Ethics., 1989,
15, 143-147.
72. Beck, R., Kassiner, J.P., and Pauker, S.G. A convenient
approximation of life expectancy (the DEALE). I.
Validation of the method. Am. J. Med., 1982, 73, 883- 888.
73. Beck, R., Pauker, S.G., Gottlieb, J.E., et. al. A
convenient approximation of life expectancy (the DEALE).
II. Use in medical decision-making. Am. J. Med., 1982,
73, 889-897.
71
-------
74. Molitch, M.E., Beck, R., Dreisman, M., et. al. The cold
thyroid nodule: An analysis of diagnostic and therapeutic
options. Endocrine Rev., 1984,'5, 2, 185-199.
75. Cummings, E.R., Lillington, G.A., Richard, R.J. Managing
solitary pulmonary nodules. Am. Rev. Resp. Dis. 1986, 134,
453-460.
76. Eckman, M.H., Robert, N.J., Parkinson, D.R. Eaton-Lambert
syndrome and small cell lung cancer. Side effects and
certainty. Med. Dec. Mak. 1986, 6, 174-186.
77. Mandelblatt, J.S., Fahs, M.C. The cost-effectiveness of
cervical cancer screening for low-income elderly women. J.
Am. Med. Assoc. 1988, 259, 2409-2413.
78. Plante, D.A., Piccirillo, J.F., Sofferman, R.A. Decision
analysis of treatment options in pyriform sinus carcinoma.
Med. Dec. Mak. 1987, 7, 74-83.
79. Tversky, A., and Kahneman, D. Judgment under
uncertainty: Heuristics and biases. Science, 1974, 185,
1124-1131.
80. Slovic, P. Perception of Risk. Science, 1981, 236, 280-
285.
81. Ravitch, M.M. Subjectivity in decision making: Common
problems and limitations. World J. Surg., 1989, 13, 281-
286.
82. Kassiver, J.P. The principles of clinical decision making:
An introduction to decision analysis. Yale J. Bio. and
Med., 1976, 2, 149-164.
83. Mulley, A.G. Assessing patients' utilities: Can the ends
justify the means? Med. Care, 1989, 27, 3, Supplement,
5269-5281.
84. Drummond, M.F. Discussion: Torrance's "Utility approach
to measuring health-related quality of life." J. Chron.
Dis., 1987, 40, 6, 601-603.
85. Rifkin, R.D. Classical statistical considerations in
medical decision models. Med. Dec. Mak., 1983, 3, 2, 197-
214.
86. Mehrez, A., and Gafni, A. An empirical evaluation of two
assessment methods for utility measurement for life years.
Socio-Econ. Plann. Sci., 1987, 21, 6, 371-375.
72
-------
87. Krzysztofowicz, R., and Duckstein, L. Assessment errors in
multiattribute utility functions. Org. Behav. Hum. Perf.,
1980, 26, 326-348.
88. Bursztajn, H., and Hamm, R.M. The clinical utility of
utility assessment. Med. Dec. Mak., 1982, 2, 2, 161- 165.
89. Torrance, G.W. Social preference for health states, an
empirical evaluation of three measurement techniques.
Socio-Econ. Plann. Sci., 1976, 10, 129-136.
90. Quinn, R.J. The effect of measurement method on preference
scales in a medical decision making context. Med.-Dec.
Mak., 1981, 1, 431.
91. Fischer, G.W. Multidimensional utility models for risky and
riskless choice. Organ. Behav. Hum. Perf., 1976, 17, 127-
146.
92. Hershey, J.C., Konreuther, H.C., and Schoemaker, P.J..
Sources of bias in assessment procedures for utility
functions. Management Sci., 1982, 28, 8, 936-954.
93. Zarin, D.A., and Pauker, S.G. Decision analysis as a basis
for medical decision-making: The true hippocrates. J. Med.
Philos., 1984, 9, 181-213.
94. Llewellyn-Thomas H., Sutherland, H.J., Tibishirani, R.,
et. al. The measurement of patients' values in medicine.
Med. Dec. Mak., 1982, 2, 4, 449-462.
95. Ciampi, A., Silberfeld, M., and Till, J.E. Measurement
of individual preferences: The importance of "situation-
specific" variables. Med. Dec. Mak., 1982, 2, 4, 483- 495.
96. Eraker, S.A., and Pollster, P. How decisions are reached:
Physician and patient. Ann. Int. Med., 1982, 97, 262-268.
97. Llewellyn-Thomas, H., Sutherland, H.J., Tibishirani, R.,
et. al. Describing health states: Methodologic issues in
obtaining values for health states. Med. Care, 1984, 22, 6,
543-552.
98. O'Connor, A. Effects of framing and level of probability on
patients' preferences for cancer chemotherapy. J. Clin.
Epid., 1989, 42, 2, 119-126.
99. Bellinger, F.J. Expected utility theory and risky choices
with health outcomes. Med. Care, 1989, 27, 3, 273-279.
100. Kawachi, I. QALYs and justice. Health Policy, 1989, 13,
115-120.
73
-------
101. Mendeloff, J. Measuring elusive benefits: On the value
of health. J. of Health Politics, Policy and Law, 1983,
8, 3, 554-580.
102. Mooney, G. QALYs: Are they enough? J. of Med. Ethics,
1989, 15, 148-152.
103. Williams, A.; Evans, R.W.; Drummond, M.F. Letters: Quality-
adjusted life years. The Lancet, June 13, 1987.
104. Merchant, J., Memorandum: Opening remarks, workshop
findings, and summary for proceedings of the workshop on
environmental and occupational asthma.
105. Josephs, L., Gregg, I., Holgate, S. Does non-specific
bronchial responsiveness indicate the severity of asthma?
Eur. Respir. J. 1990, 3, 2, 220 - 227.
106. O'Connor, G., Weiss, S., Speizer, F. The epidemiology of
asthma. In Bronchial Asthma: Principals of Diagnosis and
Treatment. 2nd edition, M. Eric Gershwin editor, Grune and
Stratton, Inc. Orlando, 1986.
107. Gross, N. What is this thing called love? - or, defining
asthma. Am. Rev. Respir. Dis. 1980, 121, 203.
108. Scadding, J. Definition and clinical categorization. In
Bronchial Asthma: Mechanisms and Therapeutics, 2nd edition,
E.B. Weiss, M. S. Segal, M. Stein editors. Little, Brown,
and Company, Boston, 1985
109. Dawson, A., Simon, R. Bronchospastic disorders: An overview.
In The Practical Management of Asthma. A. Dawson and R.
Simon editors, Grune and Stratton, Inc. Orlando, 1984.
110. Cohen, J. Memorandum: Assessing asthmatic response to S02:
Summary of expert opinions. 1984. United States
Environmental Protection Agency, Office of Air Quality
Planning and Standards, Ambient Standards Branch, Research
Triangle Park, NC.
111. Bateman, J., Pavia, D., Sheehan, N., et al. Impaired
tracheobronchial clearance in patients with mild stable
asthma. Thorax, 1983, 38, 463 - 467.
112. Aas, K. Heterogeneity of bronchial asthma. Subpopulations or
different stages of disease. Allergy, 1981, 36, 3-14.
113. Godard, P., Chaney, P., Hors, V., et al. Evaluation of a
symptom-indication score or chronic asthma. J. Allerg. Clin.
Immun. 1989, 83, 175.
74
-------
114. Juji, F, Takashima, H., Takaishi, T. Clinical study of
asthma in adolescents and in young adults. 'Correlation
between lab findings and severity of asthma. Annals of
Allergy, 1989, 63,427 -433.
115. Pereira, J., Carswell, F. Measurement and prediction of
asthma severity. J. Allerg. Clin. Immun. 1989, 83, 249
116. Jones, E. The intensive therapy of asthma. Proc. Roy.. Soc.
Med. 1971, 64, 1151 - 1152.
117. Brooks, S., Bernstein, I., Raghuprasad, P., et al.
Assessment o airway hyperresponsiveness in chronic stable
asthma. J. Allerg. Clin. Immun. 1990, 85 (1 pt 1), 17 - 26.
118. Donnelly, W., Donnelly, J., Thong, Y. Guidelines for
maintenance treatment o childhood asthma: Developments of a
score card system by multivariate cluster analysis. Soc.
Sci. Med. 1987, 25, 9, 1033 - 1038.
119. Iwamoto, I., Nawata, Y., Koike, T., et al. Relationship
between anti-IgE autoantibody and severity of bronchial
asthma. Int. Arch. Allergy Appl. Immun. 1989, 90, 4, 414 -
416.
120. Baker, M. Pitfalls in the use of clinical asthma scoring.
Am. J. Dis. Child. 1988, 142, 183 - 185.
121. Hackney, J., et al. Replicated dose-response study of'sulfur
dioxide efffets in normal, atopi, and asthmati volunteers,
Interim Special Report for Researh Project 1225 prepared by
the professional Staff Association of Ranho Los Amigos
Medical enter Inc., for EPRI, Palo Alto, CA, July 1987.
122. Dhand, R., Kalva, S., Malik, S. Use of visual analogue
scales for assessment of the severity o asthma. Respiration,
1988, 54, 255 - 262.
123. Gift, A. Validation of a vertical visual analogue scale as a
measure of clinical dyspnea. Am. R. Resp. Dis. 1986, 133, 4,
A163
124. Stark, R., Gambles, S., Lewis, J. Method to assess
breathlessness in healthy subjects: A clinical evaluation
and application to analyze the acute effects of diazepam and
promethazine on breathlessness induced by exercise or by
exposure to raised levels of C02- Clin. Sci. 1981, 61, 429 -
439.
75
-------
125. McManus, M., Koenig, J., Altman, L., et al. Pulmonary
effects of sulfur dioxide exposure and ipratropium bromide
pretreatment in adults with noh-allergenic asthma. J.
Allerg. Clin. Immun. 1989, 83, 3, 619 - 626.
126. Selcow, J., Mendelson, L., Rosen, J. A comparison of
cromyolyn and bronchodilators in patients with mild to
moderately severe asthma in an office practice. Annals of
Allergy, 1983, 50, 13 - 18.
127. Kotses, H., Harver, A., Creer, T., et al. Measures of asthma
severity recorded by patients. J. Asthma, 1988, 25, 6, 373 -
376.
128. Sheppard, D. Sulphur-dioxide and asthma - A double edged
sword? J. Allerg. Clin. Immun. 1988, 82, 961-964.
129. U.S.EPA. 2nd Addendum to air quality criteria for
particulate matter and sulfur oxides. Assessment of newly
available health effects information. 1986. Environmental
Criteria and Assessment Office, Research Triangle Park, NC.
' 600/8-86-020F
130. Linn, W.S., T.G. Venet, D.A. Shamoo, et al. Respiartory
effects of sulfur-dioxide in heavily exercising asthmatics -
a dose-response study. Am R Resp Dis, 1983, 127, 278-283.
131. Schachter, E.N., T.J. Witek, G.J. Beck, et al. Airways
effects of low concentrations of sulfur-dioxide: Dose-
response characteristics. Arch. Env. Health, 1985, 39, 34-
42.
132. Koenig, J.Q., W.E.Pierson, M. Horike, R. Frank. A comparison
of the pulmonary effects of 0.5 ppm versus 1.0 ppm sulfur-
dioxide plus sodium chloride droplets in asthmatic
adolescents. J. Tox. Env. Health, 1983, 11, 129-139.
133. Roger, L.J., H.R. Kehrl, M. Hazucha, D.H. Horstman.
Bronchoconstriction in asthmatics exposed to sulfur-dioxide
during repeated exercise. J. Appl Phys, 1985, 59, 784-791:
134. U.S.EPA. Review of the national ambient air quality
standards for ozone. Assessment of scientific and technical
information. OAQPS staff paper. Air Quality Management
Division, Office of Air Quality Planning and Standards.
U.S. Environmental Protection Agency, Research Triangle
Park, N.C. 1989
76
-------
135. McDonnell, W.F., D.H. Horstman, S. Abdul-Salaam, L. Raggio,
J.A. Green. The respiratory responses of subjects with
allergic rhinitis to ozone exposure and their relationship
to nonspecific airway reactivity. Toxicol. Ind. Health 1987,
3, 507-517.
136. Kreit, J.W., K.B. Gross, T.B. Moore, et al. Ozone-induced
changes in pulmonary function and bronchial responsiveness
of asthmatics. J. Appl. Phys., 1988, 66, 217-222.
77
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