Document ID: EPA-HQ-OAR-2007-0225-0034
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2007-08-02T04:00Z

Appendix Chapter 6b: Health-Based Cost-Effectiveness of Reductions in
Ambient PM2.5 Associated with Illustrative Ozone NAAQS 0.070ppm
Attainment Strategy

6b.1	Summary

Health-based cost-effectiveness analysis (CEA) and cost-utility analysis
(CUA) have been used to analyze numerous health interventions but have
not been widely adopted as tools to analyze environmental policies.  The
Office of Management and Budget (OMB) recently issued Circular A-4
guidance on regulatory analyses, requiring federal agencies to
“prepare a CEA for all major rulemakings for which the primary
benefits are improved public health and safety to the extent that a
valid effectiveness measure can be developed to represent expected
health and safety outcomes.”  Environmental quality improvements may
have multiple health and ecological benefits, making application of CEA
more difficult and less straightforward.  For the Ozone NAAQS, CEA may
provide a useful framework for evaluation:  non-health benefits are
substantial, but the majority of quantified benefits come from health
effects.  Therefore, EPA is including in the Ozone NAAQS RIA a
preliminary and experimental application of one type of CEA—a modified
quality-adjusted life-years (QALYs) approach.

This cost effectiveness analysis considers the PM2.5 benefits resulting
from the illustrative ozone control strategies only. Estimation of QALY
or Morbidity Inclusive Life Year (MILY, discussed below) impacts
associated with reducing ozone concentrations is difficult for several
reasons.  First, with the exception of premature death, the set of
ozone-related health endpoints includes only acute diseases and impacts.
 As discussed below, there are a number of reasons that the QALY method
is not appropriate for valuing acute health effects.  Second,
calculation of QALY or MILY impacts for premature mortality is
complicated by a lack of information, including the change in life
expectancy associated with the risk reduction (for MILYs and QALYs) and
the baseline quality of life for individuals experiencing the risk
reduction (for QALY calculations).  The EPA has recently asked the
National Academies of Sciences for advice on characterizing the
mortality risk reduction benefits of reducing ozone concentrations.  In
their evaluation, the NAS Committee on Estimating Mortality Risk
Reduction Benefits from Decreasing Tropospheric Ozone Exposure will
provide advice on, among other topics, the adequacy of a basis for
estimating the likely impact on life expectancy from reductions in
short-term daily exposures to ozone.  If there is an adequate basis,
they will, to the extent practicable, estimate the magnitude and
associated uncertainties of this impact.  While awaiting the
recommendations of the NAS committee, EPA is electing to not calculate
QALY or MILY impacts for ozone related health effects.  As a result, the
overall $/MILY estimates for attainment of alternative ozone NAAQS
reported in this appendix will overstate the expected $/MILY
incorporating ozone effects.

QALYs were developed to evaluate the effectiveness of individual medical
treatments, and EPA is still evaluating the appropriate methods for CEA
for environmental regulations.  Agency concerns with the standard QALY
methodology include the treatment of people with fewer years to live
(the elderly); fairness to people with preexisting conditions that may
lead to reduced life expectancy and reduced quality of life; and how the
analysis should best account for non-health benefits, such as improved
visibility.

The Institute of Medicine (a member institution of the National
Academies of Science) established the Committee to Evaluate Measures of
Health Benefits for Environmental, Health, and Safety Regulation to
assess the scientific validity, ethical implications, and practical
utility of a wide range of effectiveness measures used or proposed in
CEA.  This committee prepared a report titled “Valuing Health for
Regulatory Cost-Effectiveness Analysis” which concluded that CEA is a
useful tool for assessing regulatory interventions to promote human
health and safety, although not sufficient for informed regulatory
decisions (Miller, Robinson, and Lawrence, 2006  XE "Miller, Robinson,
and Lawrence, 2006"  ).  They emphasized the need for additional data
and methodological improvements for CEA analyses, and urged greater
consistency in the reporting of assumptions, data elements, and analytic
methods.  They also provided a number of recommendations for the conduct
of regulatory CEA analyses.  EPA is evaluating these recommendations and
will determine a response for upcoming analyses.  For this analysis, we
use the same approach that was applied in the CEA that accompanied the
RIA’s for the Clean Air Interstate Rule and the PM NAAQS.

The methodology presented in this appendix is not intended to stand as
precedent either for future air pollution regulations or for other EPA
regulations where it may be inappropriate.  It is intended solely to
demonstrate one particular approach to estimating the cost-effectiveness
of reductions in ambient PM2.5 in achieving improvements in public
health.  Reductions in ambient PM2.5 likely will have other health and
environmental benefits that will not be reflected in this CEA.  Other
EPA regulations affecting other aspects of environmental quality and
public health may require additional data and models that may preclude
the development of similar health-based CEAs.  A number of additional
methodological issues must be considered when conducting CEAs for
environmental policies, including treatment of nonhealth effects,
aggregation of acute and long-term health impacts, and aggregation of
life extensions and quality-of-life improvements in different
populations.  The appropriateness of health-based CEA should be
evaluated on a case-by-case basis subject to the availability of
appropriate data and models, among other factors.

Attainment of the revised Ozone NAAQS is expected to result in
substantial reductions in potential population exposure to ambient
concentrations of PM by 2020.  The benefit-cost analysis presented in
the RIA shows that partial attainment of the revised 0.070 ppm ozone
standard achieves substantial health benefits whose monetized value is
roughly equal to costs (net benefits are between -$6B and $4.1B). 
Despite the risk of oversimplifying benefits, cautiously-interpreted
cost-effectiveness calculations may provide further evidence of whether
the costs associated with attainment strategies for the Ozone NAAQS are
a reasonable health investment for the nation.

This analysis provides estimates of commonly used health-based
effectiveness measures, including lives saved, life years saved (from
reductions in mortality risk), and QALYs saved (from reductions in
morbidity risk) associated with the reduction of ambient PM2.5 due to
illustrative attainment strategies for a more stringent annual ozone
standard.  In addition, we use an alternative aggregate effectiveness
metric, Morbidity Inclusive Life Years (MILY) to address some of the
concerns about aggregation of life extension and quality-of-life
impacts.  It represents the sum of life years gained due to reductions
in premature mortality and the QALY gained due to reductions in chronic
morbidity.  This measure may be preferred to existing QALY aggregation
approaches because it does not devalue life extensions in individuals
with preexisting illnesses that reduce quality of life.  However, the
MILY measure is still based on life years and thus still inherently
gives more weight to interventions that reduce mortality and morbidity
impacts for younger populations with higher remaining life expectancy. 
This analysis focuses on life extensions and improvements in quality of
life through reductions in two diseases with chronic impacts:  chronic
bronchitis (CB) and nonfatal acute myocardial infarctions.  Monte Carlo
simulations are used to propagate uncertainty in several analytical
parameters and characterize the distribution of estimated impacts. 
While the benefit-cost analysis presented in the RIA characterizes
mortality impacts using a number of different sources for the PM
mortality effect estimate, for this analysis, we focus on the mortality
results generated using the effect estimates derived from the Pope et
al. (2002  XE "Pope et al. (2002"  ) and Laden et al. (2005) studies.

Presented in three different metrics, the analysis suggests the
following:

In 2020 the illustrative attainment strategy for the revised 0.070 ppm
standards will result in:

Between 550 (95% CI:  215 – 890) and 1,300 (95% CI: 680 – 1,800)
premature deaths avoided using the Pope (2002) and Laden (2006) studies,
respectively, or

Between 6,100 (95% CI:  2,400 – 9,800) and 14,000 (95% CI:  7,500 –
20,000) life years gained (discounted at 3 percent) using the Pope
(2002) and Laden (2006) studies, respectively, or

Between 9,100 (95% CI:  3,100 – 16,000) and  17,000 (95% CI:  8,200
– 27,000) MILYs gained (discounted at 3 percent) using the Pope (2002)
and Laden (2006) studies, respectively.

Using a 7 percent discount rate, mean discounted life years gained are
between 4,600 and 10,400 using the Pope (2002) and Laden (2006) studies,
respectively; mean MILYs gained are 6,800 and 13,000 using the two
studies (The estimates of premature deaths avoided are not affected by
the discount rate.)

The associated reductions in CB and nonfatal acute myocardial
infarctions will reduce medical costs by approximately $140 million
based on a 3 or 7 percent discount rate.

Direct private compliance costs for the 0.070 ppm partial attainment
strategy, are $3.9 billion in 2020. Based on these costs, the
incremental cost effectiveness (net of cost of illness and other health
and visibility benefits) of the 0.070 ppm partial attainment strategy is
$430,000/MILY using a 3 percent discount rate and $580,000/MILY using a
7 percent discount rate if one calculates MILY’s using the Pope (2002)
mortality estimate. The incremental cost effectiveness (again, net of
cost of illness and other health and visibility benefits) of the 0.070
ppm partial attainment strategy is $230,000/MILY using a 3 percent
discount rate and $310,000/MILY using a 7 percent discount rate if one
calculates MILY’s using the Laden (2006) mortality estimate.  See
Chapters 3 and 5 of this RIA for more discussion of the control
strategies and cost estimates.

6b.2	Introduction

Analyses of environmental regulations have typically used benefit-cost
analysis to characterize impacts on social welfare.  Benefit-cost
analyses allow for aggregation of the benefits of reducing mortality
risks with other monetized benefits of reducing air pollution, including
acute and chronic morbidity, and nonhealth benefits such as improved
visibility.  One of the great advantages of the benefit-cost paradigm is
that a wide range of quantifiable benefits can be compared to costs to
evaluate the economic efficiency of particular actions.  However,
alternative paradigms such as CEA and CUA analyses may also provide
useful insights.  CEA involves estimation of the costs per unit of
benefit (e.g., lives or life years saved).  CUA is a special type of CEA
using preference-based measures of effectiveness, such as QALYs.

CEA and CUA are most useful for comparing programs that have similar
goals, for example, alternative medical interventions or treatments that
can save a life or cure a disease.  They are less readily applicable to
programs with multiple categories of benefits, such as those reducing
ambient air pollution, because the cost-effectiveness calculation is
based on the quantity of a single benefit category.  In other words, we
cannot readily convert improvements in nonhealth benefits such as
visibility to a health metric such as life years saved.  For these
reasons, environmental economists prefer to present results in terms of
monetary benefits and net benefits.

However, QALY-based CUA has been widely adopted within the health
economics literature (  XE “Neumann, 2003”  Neumann, 2003;   XE
“Gold et al., 1996”  Gold et al., 1996) and in the analysis of
public health interventions (US FDA, 2004  XE "U.S. FDA, 2004"  ). 
QALY-based analyses have not been as accepted in the environmental
economics literature because of concerns about the theoretical
consistency of QALYs with individual preferences (  XE “Hammitt,
2002”  Hammitt, 2002), treatment of nonhuman health benefits, and a
number of other factors (  XE “Freeman, Hammitt, and De Civita,
2002”  Freeman, Hammitt, and De Civita, 2002).  For environmental
regulations, benefit-cost analysis has been the preferred method of
choosing among regulatory alternatives in terms of economic efficiency. 
Recently several academic analyses have proposed the use of life
years-based benefit-cost or CEAs of air pollution regulations (  XE
“Cohen, Hammitt, and Levy, 2003”  Cohen, Hammitt, and Levy, 2003;  
XE “Coyle et al., 2003”  Coyle et al., 2003;   XE “Rabl, 2003” 
Rabl, 2003;   XE “Carrothers, Evans, and Graham, 2002”  Carrothers,
Evans, and Graham, 2002).  In addition, the World Health Organization
has adopted the use of disability-adjusted life years, a variant on
QALYs, to assess the global burden of disease due to different causes,
including environmental pollution (  XE “Murray et al., 2002” 
Murray et al., 2002;   XE “de Hollander et al, 1999”  de Hollander
et al., 1999).

Recently, the U.S. OMB (Circular A-4,   XE “U.S. OMB, 2003”  2003)
issued new guidance requiring federal agencies to provide both CEA and
benefit-cost analyses for major regulations.  The OMB Circular A-4
directs agencies to “prepare a CEA for all major rulemakings for which
the primary benefits are improved public health and safety to the extent
that a valid effectiveness measure can be developed to represent
expected health and safety outcomes.”  We are including a CEA for the
illustrative PM NAAQS attainment strategies to illustrate one potential
approach for conducting a CEA.  EPA is still evaluating the appropriate
methods for CEA for environmental regulations with multiple outcomes.

The methodology presented in this appendix is not intended to stand as
precedent either for future air pollution regulations or for other EPA
regulations governing water, solid waste, or other regulatory
objectives.  It is intended solely to demonstrate one particular
approach to estimating the effectiveness of reductions in ambient PM2.5
in achieving improvements in public health.  This analysis focuses on
effectiveness measured by improvements in life expectancy and reductions
in the incidence of two diseases with chronic impacts on quality of
life:  CB and nonfatal acute myocardial infarctions.  Other EPA
regulations affecting other aspects of environmental quality and public
health may require additional data and models that may preclude the
development of similar QALY-based analyses.  The appropriateness of
QALY-based CEA should be evaluated on a case-by-case basis subject to
the availability of appropriate data and models.

Preparation of a CEA requires identification of an appropriate measure
of rule effectiveness.  Given the significant impact of reductions in
ambient PM2.5 on reductions in the risk of mortality, lives saved is an
important measure of effectiveness.  However, one of the ongoing
controversies in health impact assessment regards whether reductions in
mortality risk should be reported and valued in terms of statistical
lives saved or in terms of statistical life years saved.  Life years
saved measures differentiate among premature mortalities based on the
remaining life expectancy of affected individuals.  In general, under
the life years approach, older individuals will gain fewer life years
than younger individuals for the same reduction in mortality risk during
a given time period, making interventions that benefit older individuals
seem less beneficial relative to similar interventions benefiting
younger individuals.  A further complication in the debate is whether to
apply quality adjustments to life years lost.  Under this approach,
individuals with preexisting health conditions would have fewer QALYs
lost relative to healthy individuals for the same loss in life
expectancy, making interventions that primarily benefit individuals with
poor health seem less beneficial to similar interventions affecting
primarily healthy individuals.

In addition to substantial mortality risk reduction benefits, strategies
for attaining the revised PM NAAQS will also result in significant
reductions in chronic and acute morbidity.  Several approaches have been
developed to incorporate both morbidity and mortality into a single
effectiveness metric.  The most common of these is the QALY approach,
which expresses all morbidity and mortality impacts in terms of quality
of life multiplied by the duration of time with that quality of life. 
The QALY approach has some appealing characteristics.  For example, it
can account for morbidity effects as well as losses in life expectancy
without requiring the assignment of dollar values to calculate total
benefits.  By doing so it provides an alternative framework to
benefit-cost analysis for aggregating quantitative measures of health
impacts.

While used extensively in the economic evaluation of medical
interventions (  XE “Gold et al., 1996”  Gold et al., 1996), QALYs
have not been widely used in evaluating environmental health
regulations.  A number of specific issues arise with the use of QALYs in
evaluating environmental programs that affect a broad and heterogeneous
population and that provide both health and nonhealth benefits.  The
U.S. Public Health Service report on cost-effectiveness in health and
medicine notes the following:

	For decisions that involve greater diversity in interventions and the
people to whom they apply, cost-effectiveness ratios continue to provide
essential information, but that information must, to a greater degree,
be evaluated in light of circumstances and values that cannot be
included in the analysis.  Individuals in the population will differ
widely in their health and disability before the intervention, or in
age, wealth, or other characteristics, raising questions about how
society values gains for the more and less health, for young and old,
for rich and poor, and so on.  The assumption that all QALYs are of
equal value is less likely to be reasonable in this context.  (  XE
“Gold et al., 1996”  Gold et al., 1996, p. 11)

Use of QALYs as a measure of effectiveness for environmental regulations
is still developing, and while this analysis provides one framework for
using QALYs to evaluate environmental regulations, there are clearly
many issues, both scientific and ethical, that need to be addressed with
additional research.  The Institute of Medicine panel evaluating QALYs
and other effectiveness measures prepared a report titled “Valuing
Health for Regulatory Cost-Effectiveness Analysis” which concluded
that “the QALY is the best measure at present on which to standardize
Health Adjusted Life Year estimation because of its widespread use,
flexibility, and relative simplicity” (Miller, Robinson, and Lawrence,
2006  XE "Miller, Robinson, and Lawrence, 2006"  ).  EPA is evaluating
this recommendation and will determine a response for upcoming analyses.
 For this analysis, for reasons discussed in the text, we use the same
MILY approach that was applied in the CEA that accompanied the RIA for
the Clean Air Interstate Rule.

This appendix presents cost-effectiveness methodologies for evaluating
programs such as attainment strategies for the revised Ozone NAAQS that
are intended to reduce both ozone and PM2.5 precursors, such as NOx and
VOCs, starting from the standard QALY literature and seeking a parallel
structure to benefit-cost analysis in the use of air quality and health
inputs (see   XE “Hubbell [2004a”  Hubbell [2004a] for a discussion
of some of the issues that arise in comparing QALY and benefit-cost
frameworks in analyzing air pollution impacts).  For the purposes of
this analysis, we calculate effectiveness using several different
metrics, including lives prolonged, life years gained, and modified
QALYs.  For the life years and QALY-type approaches, we use life table
methods to calculate the change in life expectancy expected to result
from changes in mortality risk from PM.  We use existing estimates of
preferences for different health states to obtain QALY weights for
morbidity endpoints associated with air pollution.  In general,
consistent with the   XE “Gold et al., 1996”  Gold et al. (1996)
recommendations, we use weights obtained from a societal perspective
when available.  We explore several different sources for these weights
to characterize some of the potential uncertainty in the QALY estimates.
 We follow many of the principles of the reference case analysis as
defined in   XE “Gold et al., 1996”  Gold et al. (1996), although in
some cases we depart from the reference case approach when data
limitations require us to do so (primarily in the selection of
quality-of-life weights for morbidity endpoints).  We also depart from
the reference case (and the recommendations of the IOM report) in the
method of combining life expectancy and quality-of-life gains.

Results in most tables are presented only at a discount rate of 3
percent, rather than at both 3 percent and 7 percent as recommended in
EPA and OMB guidance.  This is strictly for ease of presentation. 
Aggregate results at 7 percent are presented in the summary, and the
impact of using a 7 percent discount rate instead of 3 percent rate is
summarized in a sensitivity analysis.

Monte Carlo simulation methods are used to propagate uncertainty in
several of the model parameters throughout the analysis.  We
characterize overall uncertainty in the results with 95 percent
confidence intervals based on the Monte Carlo simulations.  In addition,
we examine the impacts of changing key parameters, such as the discount
rate, on the effectiveness measures and the cost-effectiveness metrics.

The remainder of this appendix provides an overview of the key issues
involved in life year- and QALY-based approaches for evaluating the
health impacts of air pollution regulations, provides detailed
discussions of the steps required for each type of effectiveness
calculation, and presents the CEA for the PM NAAQS illustrative
attainment strategies.  Section G.3 introduces the various effectiveness
measures and discusses some of the assumptions required for each. 
Section G.4 details the methodology used to calculate changes in life
years and quality adjustments for mortality and morbidity endpoints. 
Section G.5 provides the results for the illustrative attainment
strategies for the revised and more stringent alternative PM NAAQS and
discusses their implications for cost-effectiveness of these attainment
strategies.

6b.3	Effectiveness Measures

Three major classes of benefits are associated with reductions in air
pollution:  mortality, morbidity, and nonhealth (welfare).  For the
purposes of benefit-cost analysis, EPA has presented mortality-related
benefits using estimates of avoided premature mortalities, representing
the cumulative result of reducing the risk of premature mortality from
long-term exposure to PM2.5 for a large portion of the U.S. population. 
Morbidity benefits have been characterized by numbers of new incidences
avoided for chronic diseases such as CB, avoided admissions for
hospitalizations associated with acute and chronic conditions, and
avoided days with symptoms for minor illnesses.  Nonhealth benefits are
characterized by the monetary value of reducing the impact (e.g., the
dollar value of improvements in visibility at national parks).

For the purposes of CEA, we focus the effectiveness measure on the
quantifiable health impacts of the reduction in PM2.5.  Treatment of
nonhealth benefits is important and is discussed in some detail later in
this section.  If the main impact of interest is reductions in mortality
risk from air pollution, the effectiveness measures are relatively
straightforward to develop.  Mortality impacts can be characterized
similar to the benefits analysis, by counting the number of premature
mortalities avoided, or can be characterized in terms of increases in
life expectancy or life years.  Estimates of premature mortality have
the benefit of being relatively simple to calculate, are consistent with
the benefit-cost analysis, and do not impose additional assumptions on
the degree of life shortening.  However, some have argued that counts of
premature mortalities avoided are problematic because a gain in life of
only a few months would be considered equivalent to a gain of a many
life years, and the true effectiveness of an intervention is the gain in
life expectancy or life years (  XE “Rabl, 2003”  Rabl, 2003;   XE
“Miller and Hurley, 2003”  Miller and Hurley, 2003).

Calculations of changes in life years and life expectancy can be
accomplished using standard life table methods (  XE “Miller and
Hurley, 2003”  Miller and Hurley, 2003).  However, the calculations
require assumptions about the baseline mortality risks for each age
cohort affected by air pollution.  A general assumption may be that air
pollution mortality risks affect the general mortality risk of the
population in a proportional manner.  However, some concerns have been
raised that air pollution affects mainly those individuals with
preexisting cardiovascular and respiratory disease, who may have reduced
life expectancy relative to the general population.  This issue is
explored in more detail below.

Air pollution is also associated with a number of significant chronic
and acute morbidity endpoints.  Failure to consider these morbidity
effects may understate the cost-effectiveness of air pollution
regulations or give too little weight to reductions in particular
pollutants that have large morbidity impacts but no effect on life
expectancy.  The QALY approach explicitly incorporates morbidity impacts
into measures of life years gained and is often used in health economics
to assess the cost-effectiveness of medical spending programs (  XE
“Gold et al., 1996”  Gold et al., 1996).  Using a QALY rating
system, health quality ranges from 0 to 1, where 1 may represent full
health, 0 death, and some number in between (e.g., 0.8) an impaired
condition.  QALYs thus measure morbidity as a reduction in quality of
life over a period of life.  QALYs assume that duration and quality of
life are equivalent, so that 1 year spent in perfect health is
equivalent to 2 years spent with quality of life half that of perfect
health.  QALYs can be used to evaluate environmental rules under certain
circumstances, although some very strong assumptions (detailed below)
are associated with QALYs.  The U.S. Public Health Service Panel on Cost
Effectiveness in Health and Medicine recommended using QALYs when
evaluating medical and public health programs that primarily reduce both
mortality and morbidity (  XE “Gold et al., 1996”  Gold et al.,
1996).  Although there are significant nonhealth benefits associated
with air pollution regulations, over 90 percent of quantifiable
monetized benefits are health-related, as is the case with the
attainment strategies for the PM NAAQS.  Thus, it can be argued that
QALYs are more applicable for these types of regulations than for other
environmental policies.  However, the value of nonhealth benefits should
not be ignored.  As discussed below, we have chosen to subtract the
value of nonhealth benefits from the costs in the numerator of the
cost-effectiveness ratio.

In the following sections, we lay out a phased approach to describing
effectiveness.  We begin by discussing how the life-extending benefits
of air pollution reductions are calculated, and then we incorporate
morbidity effects using the QALY approach.  We also introduce an
alternative aggregated health metric, Morbidity Inclusive Life Years
(MILY) to address some of the ethical concerns about aggregating life
extension impacts in populations with preexisting disabling conditions.

The use of QALYs is predicated on the assumptions embedded in the QALY
analytical framework.  As noted in the QALY literature, QALYs are
consistent with the utility theory that underlies most of economics only
if one imposes several restrictive assumptions, including independence
between longevity and quality of life in the utility function, risk
neutrality with respect to years of life (which implies that the utility
function is linear), and constant proportionality in trade-offs between
quality and quantity of life (  XE “Pliskin, Shepard, and Weinstein,
1980”  Pliskin, Shepard, and Weinstein, 1980;   XE “Bleichrodt,
Wakker, and Johannesson, 1996”  Bleichrodt, Wakker, and Johannesson,
1996).  To the extent that these assumptions do not represent actual
preferences, the QALY approach will not provide results that are
consistent with a benefit-cost analysis based on the Kaldor-Hicks
criterion.  Even if the assumptions are reasonably consistent with
reality, because QALYs represent an average valuation of health states
rather than the sum of societal WTP, there are no guarantees that the
option with the highest QALY per dollar of cost will satisfy the
Kaldor-Hicks criterion (i.e., generate a potential Pareto improvement [ 
XE “Garber and Phelps, 1997”  Garber and Phelps, 1997]).

Benefit-cost analysis based on WTP is not without potentially troubling
underlying structures as well, incorporating ability to pay (and thus
the potential for equity concerns) and the notion of consumer
sovereignty (which emphasizes wealth effects).  Table G-1 compares the
two approaches across a number of parameters.  For the most part, WTP
allows parameters to be determined empirically, while the QALY approach
imposes some conditions a priori.

Table 6b-1:	Comparison of QALY and WTP Approaches

Parameter	QALY	WTP

Risk aversion	Risk neutral	Empirically determined

Relation of duration and quality	Independent	Empirically determined

Proportionality of duration/ quality trade-off	Constant	Variable

Treatment of time/age in utility function	Utility linear in time
Empirically determined

Preferences	Community/Individual	Individual

Source of preference data	Stated	Revealed and stated

Treatment of income and prices	Not explicitly considered	Constrains
choices

6b.4	Changes in Premature Death, Life Years, and Quality of Life

To generate health outcomes, we used the same framework as for the
benefit-cost analysis described in Chapter 6.  For convenience, we
summarize the basic methodologies here.  For more details, see Chapter 6
and the BenMAP user’s manual
(http://www.epa.gov/ttn/ecas/benmodels.html).

BenMAP uses health impact functions to generate changes in the incidence
of health effects.  Health impact functions are derived from the
epidemiology literature.  A standard health impact function has four
components:  an effect estimate from a particular epidemiological study,
a baseline incidence rate for the health effect (obtained from either
the epidemiology study or a source of public health statistics like
CDC), the affected population, and the estimated change in the relevant
PM summary measure.

A typical health impact function might look like this:

	

where y0 is the baseline incidence, equal to the baseline incidence rate
times the potentially affected population;  is the effect estimate;
and x is the estimated change in PM2.5.  There are other functional
forms, but the basic elements remain the same.

6b.4.1	Calculating Reductions in Premature Deaths

As in several recent air pollution health impact assessments (e.g.,   XE
“Kunzli et al., 2000”  Kunzli et al., 2000;   XE “U.S. EPA,
2004”   EPA, 2004), we focus on the prospective cohort long-term
exposure studies in deriving the health impact function for the estimate
of premature mortality.  Cohort analyses are better able to capture the
full public health impact of exposure to air pollution over time (  XE
“Kunzli et al., 2001”  Kunzli et al., 2001;   XE “NRC, 2002” 
NRC, 2002).  We selected effects estimate from the extended analysis of
the ACS cohort (  XE “Pope et al., 2002”  Pope et al., 2002) as well
as from the Harvard Six City Study (Laden et al., 2006).  Given the
focus in this analysis on developing a broader expression of
uncertainties in the benefits estimates, and the weight that was placed
on both the ACS and Harvard Six-city studies by experts participating in
the PM2.5 mortality expert elicitation, we have elected to provide
estimates derived from both Pope et al. (2002  XE "Pope et al. (2002"  )
and Laden et al. (2006  XE "Laden et al. (2006"  ).

This latest re-analysis of the ACS cohort data (Pope et al, 2002)
provides additional refinements to the analysis of PM-related mortality
by (a) extending the follow-up period for the ACS study subjects to 16
years, which triples the size of the mortality data set; (b)
substantially increasing exposure data, including consideration for
cohort exposure to PM2.5 following implementation of PM2.5 standard in
1999; (c) controlling for a variety of personal risk factors including
occupational exposure and diet; and (d) using advanced statistical
methods to evaluate specific issues that can adversely affect risk
estimates, including the possibility of spatial autocorrelation of
survival times in communities located near each other.  The effect
estimate from   XE “Pope et al., 2002”  Pope et al. (2002)
quantifies the relationship between annual mean PM2.5 levels and
all-cause mortality in adults 30 and older.  We selected the effect
estimate estimated using the measure of PM representing average exposure
over the follow-up period, calculated as the average of 1979–1984 and
1999–2000 PM2.5 levels.  The effect estimate from this study is
0.0058, which is equivalent to a relative risk of 1.06 for a 10 g
change in PM2.5.  

Very recently, a follow up to the Harvard 6-city study was published
(Laden et al., 2006  XE "Laden et al., 2006"  ), that both confirmed the
effect size from the first study and provided additional confirmation
that reductions in PM2.5 directly result in reductions in the risk of
premature death.  This additional evidence stems from the observed
reductions in PM2.5 in each city during the extended follow-up period. 
Laden et al. (2006  XE "Laden et al. (2006"  ) found that mortality
rates consistently went down at a rate proportionate to the observed
reductions in PM2.5.  The effect estimate obtained from the Laden et al
is 0.0148, which is equivalent to a relative risk of 1.16 for a 10
μg/m3 change in PM2.5.

Age, cause, and county-specific mortality rates were obtained from CDC
for the years 1996 through 1998.  CDC maintains an online data
repository of health statistics, CDC Wonder, accessible at
http://wonder.cdc.gov/.  The mortality rates provided are derived from
U.S. death records and U.S. Census Bureau postcensal population
estimates.  Mortality rates were averaged across 3 years (1996 through
1998) to provide more stable estimates.  When estimating rates for age
groups that differed from the CDC Wonder groupings, we assumed that
rates were uniform across all ages in the reported age group.  For
example, to estimate mortality rates for individuals ages 30 and up, we
scaled the 25- to 34-year old death count and population by one-half and
then generated a population-weighted mortality rate using data for the
older age groups.

The reductions in incidence of premature mortality within each age group
associated with the illustrative attainment strategies for the revised
and more stringent alternative Ozone NAAQS in 2020 are summarized in
Table G-2.

6b.4.2	Calculating Changes in Life Years from Direct Reductions in
PM2.5-Related Mortality Risk

To calculate changes in life years associated with a given change in air
pollution, we used a life table approach coupled with age-specific
estimates of reductions in premature mortality.  We began with the
complete unabridged life table for the United States in 2000, obtained
from CDC (  XE “CDC, 2002”  CDC, 2002).  For each 1-year age
interval (e.g., zero to one, one to two) the life table provides
estimates of the baseline probability of dying during the interval,
person years lived in the interval, and remaining life expectancy.  From
this unabridged life table, we constructed an abridged life table to
match the age intervals for which we have predictions of changes in
incidence of premature mortality.  We used the abridgement method
described in   XE “CDC, 2002”  CDC (2002).  Table G-3 presents the
abridged life table for 10-year age intervals for adults over 30 (to
match the   XE “Pope et al. 2002”  Pope et al. [2002] study
population).  Note that the abridgement actually includes one 5-year
interval, covering adults 30 to 34, with the remaining age intervals
covering 10 years each.  This is to provide conformity with the age
intervals available for mortality rates.

Table 6b-2:	Estimated Reduction in Incidence of All-cause Premature
Mortality Associated with Illustrative Attainment Strategies for the
Revised and More Stringent Alternative Ozone NAAQS in 2020

	Reduction in All-Cause Premature Mortality 

(95% CI)

Age Interval	Pope (2002)	Laden (2006)

30 – 34	4

(2 – 6)	9

(5 – 13)

35 – 44	12

(5 – 20)	28

(15 – 40)

45 – 54	26

(10 – 42)	60

(32 – 87)

55 – 64	70

(27 – 110)	160

(86 – 230)

65 – 74	120

(48 – 200)	280

(150 – 400)

75 – 84	140

(56 – 230)	320

(180 – 470)

85+	170

(68 – 280)	390

(210 – 570)

Total	550

(220 – 890)	1,300

(680 – 1,800)

From the abridged life table (Table 6b-3), we obtained the remaining
life expectancy for each age cohort, conditional on surviving to that
age.  This is then the number of life years lost for an individual in
the general population dying during that age interval.  This information
can then be combined with the estimated number of premature deaths in
each age interval calculated with BenMAP (see previous subsection). 
Total life years gained will then be the sum of life years gained in
each age interval:

	

where LEi is the remaining life expectancy for age interval i, Mi is the
change in incidence of mortality in age interval i, and N is the number
of age intervals.

For the purposes of determining cost-effectiveness, it is also necessary
to consider the time-dependent nature of the gains in life years. 
Standard economic theory suggests that benefits occurring in future
years should be discounted relative to benefits occurring in the
present.  OMB and EPA guidance suggest discount rates of three and seven
percent.  As noted earlier, we present gains in future life years
discounted at 3 percent.  Results based on 7 percent are included in the
summary and the overall impact of a 7 percent rate is summarized in
Table 6b-16.  Selection of a 3 percent discount rate is also consistent
with recommendations from the U.S. Public Health Service Panel on Cost
Effectiveness in Health and Medicine (  XE “Gold et al., 1996”  Gold
et al., 1996).

Table 6b-3:	Abridged Life Table for the Total Population, United States,
2000

Age Interval	Probability of Dying Between Ages x to x+1	Number Surviving
to Age x	Number Dying Between Ages x to x+1	Person Years Lived Between
Ages x to x+1	Total Number of Person Years Lived Above Age x	Expectation
of Life at Age x

Start Age	End Age	qx	Ix	dx	Lx	Tx	ex

30	35	0.00577	97,696	564	487,130	4,723,539	48.3

35	45	0.01979	97,132	1,922	962,882	4,236,409	43.6

45	55	0.04303	95,210	4,097	934,026	3,273,527	34.4

55	65	0.09858	91,113	8,982	872,003	2,339,501	25.7

65	75	0.21779	82,131	17,887	740,927	1,467,498	17.9

75	85	0.45584	64,244	29,285	505,278	726,571	11.3

85	95	0.79256	34,959	27,707	196,269	221,293	6.3

95	100	0.75441	7,252	5,471	20,388	25,024	3.5

100+

1.00000	1,781	1,781	4,636	4,636	2.6

Discounted total life years gained is calculated as follows:

	

where r is the discount rate, equal to 0.03 in this case, t indicates
time, and LE is the life expectancy at the time when the premature death
would have occurred.  Life years are further discounted to account for
the lag between the reduction in ambient PM2.5 and the reduction in
mortality risk.  We use the same 20-year segmented lag structure that is
used in the benefit-cost analysis (see Chapter 6).

The most complete estimate of the impacts of PM2.5 on life years is
calculated using the   XE “Pope et al. 2002”  Pope et al. (2002) C-R
function relating all-cause mortality in adults 30 and over with ambient
PM2.5 concentrations averaged over the periods 1979–1983 and
1999–2000.  Use of all-cause mortality is appropriate if there are no
differences in the life expectancy of individuals dying from air
pollution-related causes and those dying from other causes.  The
argument that long-term exposure to PM2.5 may affect mainly individuals
with serious preexisting illnesses is not supported by current empirical
studies.  For example, the   XE “Krewski et al. (2000”  Krewski et
al. (2000) ACS reanalysis suggests that the mortality risk is no greater
for those with preexisting illness at time of enrollment in the study. 
Life expectancy for the general population in fact includes individuals
with serious chronic illness.  Mortality rates for the general
population then reflect prevalence of chronic disease, and as
populations age the prevalence of chronic disease increases.

The only reason one might use a lower life expectancy is if the
population at risk from air pollution was limited solely to those with
preexisting disease.  Also, note that the OMB Circular A-4 notes that
“if QALYs are used to evaluate a lifesaving rule aimed at a population
that happens to experience a high rate of disability (i.e., where the
rule is not designed to affect the disability), the number of life years
saved should not necessarily be diminished simply because the rule saves
lives of people with life-shortening disabilities.  Both analytic
simplicity and fairness suggest that the estimate number of life years
saved for the disabled population should be based on average life
expectancy information for the relevant age cohorts.”  As such, use of
a general population life expectancy is preferred over
disability-specific life expectancies.  Our primary life years
calculations are thus consistent with the concept of not penalizing
individuals with disabling chronic health conditions by assessing them
reduced benefits of mortality risk reductions.

For this analysis, direct impacts on life expectancy are measured only
through the estimated change in mortality risk based on the   XE “Pope
et al., 2002”  Pope et al. (2002) C-R function.  The SAB-HES has
advised against including additional gains in life expectancy due to
reductions in incidence of chronic disease or nonfatal heart attacks ( 
XE “U.S. EPA Science Advisory Board, 2004” 
EPA-SAB-COUNCIL-ADV-04-002).  Although reductions in these endpoints are
likely to result in increased life expectancy, the HES has suggested
that the cohort design and relatively long follow-up period in the Pope
et al. study should capture any life-prolonging impacts associated with
those endpoints.  Impacts of CB and nonfatal heart attacks on quality of
life will be captured separately in the QALY calculation as years lived
with improved quality of life.  The methods for calculating this benefit
are discussed below.

6b.4.2.1	Should Life Years Gained Be Adjusted for Initial Health Status?

The methods outlined above provide estimates of the total number of life
years gained in a population, regardless of the quality of those life
years, or equivalently, assuming that all life years gained are in
perfect health.  In some CEAs (  XE “Cohen, Hammitt, and Levy, 2003”
 Cohen, Hammitt, and Levy, 2003;   XE “Coyle et al., 2003”  Coyle et
al., 2003), analysts have adjusted the number of life years gained to
reflect the fact that 1) the general public is not in perfect health and
thus “healthy” life years are less than total life years gained and
2) those affected by air pollution may be in a worse health state than
the general population and therefore will not gain as many “healthy”
life years adjusted for quality, from an air pollution reduction.  This
adjustment, which converts life years gained into QALYs, raises a number
of serious ethical issues.  Proponents of QALYs have promoted the
nondiscriminatory nature of QALYs in evaluating improvements in quality
of life (e.g., an improvement from a score of 0.2 to 0.4 is equivalent
to an improvement from 0.8 to 1.0), so the starting health status does
not affect the evaluation of interventions that improve quality of life.
 However, for life-extending interventions, the gains in QALY will be
directly proportional to the baseline health state (e.g., an individual
with a 30-year life expectancy and a starting health status of 0.5 will
gain exactly half the QALYs of an individual with the same life
expectancy and a starting health status of 1.0 for a similar
life-extending intervention).  This is troubling because it imposes an
additional penalty for those already suffering from disabling
conditions.    XE “Brock (2002”  Brock (2002) notes that “the
problem of disability discrimination represents a deep and unresolved
problem for resource prioritization.”

  XE “U.S. OMB, 2003”  OMB (2003) has recognized this issue in their
Circular A-4 guidance, which includes the following statement:

	When CEA is performed in specific rulemaking contexts, you should be
prepared to make appropriate adjustments to ensure fair treatment of all
segments of the population.  Fairness is important in the choice and
execution of effectiveness measures.  For example, if QALYs are used to
evaluate a lifesaving rule aimed at a population that happens to
experience a high rate of disability (i.e., where the rule is not
designed to affect the disability), the number of life years saved
should not necessarily be diminished simply because the rule saves the
lives of people with life-shortening disabilities.  Both analytic
simplicity and fairness suggest that the estimated number of life years
saved for the disabled population should be based on average life
expectancy information for the relevant age cohorts.  More generally,
when numeric adjustments are made for life expectancy or quality of
life, analysts should prefer use of population averages rather than
information derived from subgroups dominated by a particular demographic
or income group. (p. 13)

This suggests two adjustments to the standard QALY methodology:  one
adjusting the relevant life expectancy of the affected population, and
the other affecting the baseline quality of life for the affected
population.

In addition to the issue of fairness, potential measurement issues are
specific to the air pollution context that might argue for caution in
applying quality-of-life adjustments to life years gained due to air
pollution reductions.  A number of epidemiological and toxicological
studies link exposure to air pollution with chronic diseases, such as CB
and atherosclerosis (  XE “Abbey et al., 1995”  Abbey et al., 1995; 
 XE “Schwartz, 1993”  Schwartz, 1993;   XE “Suwa et al., 2002” 
Suwa et al., 2002).  If these same individuals with chronic disease
caused by exposure to air pollution are then at increased risk of
premature death from air pollution, there is an important dimension of
“double jeopardy” involved in determining the correct baseline for
assessing QALYs lost to air pollution (see   XE “Singer et al.
(1995”  Singer et al. [1995] for a broader discussion of the
double-jeopardy argument).

Analyses estimating mortality from acute exposures that ignore the
effects of long-term exposure on morbidity may understate the health
impacts of reducing air pollution.  Individuals exposed to chronically
elevated levels of air pollution may realize an increased risk of death
and chronic disease throughout life.  If at some age they contract heart
(or some other chronic) disease as a result of the exposure to air
pollution, they will from that point forward have both reduced life
expectancy and reduced quality of life.  The benefit to that individual
from reducing lifetime exposure to air pollution would be the increase
in life expectancy plus the increase in quality of life over the full
period of increased life expectancy.  If the QALY loss is determined
based on the underlying chronic condition and life expectancy without
regard to the fact that the person would never have been in that state
without long-term exposure to elevated air pollution, then the person is
placed in double jeopardy.  In other words, air pollution has placed
more people in the susceptible pool, but then we penalize those people
in evaluating policies by treating their subsequent deaths as less
valuable, adding insult to injury, and potentially downplaying the
importance of life expectancy losses due to air pollution.  If the risk
of chronic disease and risk of death are considered together, then there
is no conceptual problem with measuring QALYs, but this has not been the
case in recent applications of QALYs to air pollution (  XE
“Carrothers, Evans, and Graham, 2002”  Carrothers, Evans, and
Graham, 2002;   XE “Coyle et al., 2003”  Coyle et al., 2003).  The
use of QALYs thus highlights the need for a better understanding of the
relationship between chronic disease and long-term exposure and suggests
that analyses need to consider morbidity and mortality jointly, rather
than treating each as a separate endpoint (this is an issue for current
benefit-cost approaches as well).

Because of the fairness and measurement concerns discussed above, for
the purposes of this analysis, we do not reduce the number of life years
gained to reflect any differences in underlying health status that might
reduce quality of life in remaining years.  Thus, we maintain the
assumption that all direct gains in life years resulting from mortality
risk reductions will be assigned a weight of 1.0.  The U.S. Public
Health Service Panel on Cost Effectiveness in Health and Medicine
recommends that “since lives saved or extended by an intervention will
not be in perfect health, a saved life year will count as less than 1
full QALY” (  XE “Gold et al., 1996”  Gold et al., 1996). 
However, for the purposes of this analysis, we propose an alternative to
the traditional aggregate QALY metric that keeps separate quality
adjustments to life expectancy and gains in life expectancy.  As such,
we do not make any adjustments to life years gained to reflect the less
than perfect health of the general population.  Gains in quality of life
will be addressed as they accrue because of reductions in the incidence
of chronic diseases.  This is an explicit equity choice in the treatment
of issues associated with quality-of-life adjustments for increases in
life expectancy that still capitalizes on the ability of QALYs to
capture both morbidity and mortality impacts in a single effectiveness
measure.

6b.5	Calculating Changes in the Quality of Life Years (Morbidity)

In addition to directly measuring the quantity of life gained, measured
by life years, it may also be informative to measure gains in the
quality of life.  Reducing air pollution also leads to reductions in
serious illnesses that affect quality of life.  These include CB and
cardiovascular disease, for which we are able to quantify changes in the
incidence of nonfatal heart attacks.  To capture these important
benefits in the measure of effectiveness, they must first be converted
into a life-year equivalent so that they can be combined with the direct
gains in life expectancy.

For this analysis, we developed estimates of the QALYs gained from
reductions in the incidence of CB and nonfatal heart attacks associated
with reductions in ambient PM2.5.  In general, QALY calculations require
four elements:

1.	the estimated change in incidence of the health condition,

2.	the duration of the health condition,

3.	the quality-of-life weight with the health condition, and

4.	the quality-of-life weight without the health condition (i.e., the
baseline health state).

The first element is derived using the health impact function approach. 
The second element is based on the medical literature for each health
condition.  The third and fourth elements are derived from the medical
cost-effectiveness and cost-utility literature.  In the following two
subsections, we discuss the choices of elements for CB and nonfatal
heart attacks.

The preferred source of quality-of-life weights are those based on
community preferences, rather than patient or clinician ratings (  XE
“Gold et al., 1996”  Gold et al., 1996).  Several methods are used
to estimate quality-of-life weights.  These include rating scale,
standard gamble, time trade-off, and person trade-off approaches (  XE
“Gold, Stevenson, and Fryback, 2002”  Gold, Stevenson, and Fryback,
2002).  Only the standard gamble approach is completely consistent with
utility theory.  However, the time trade-off method has also been widely
applied in eliciting community preferences (  XE “Gold, Stevenson, and
Fryback, 2002”  Gold, Stevenson, and Fryback, 2002).

Quality-of-life weights can be directly elicited for individual specific
health states or for a more general set of activity restrictions and
health states that can then be used to construct QALY weights for
specific conditions (  XE “Horsman et al., 2003”  Horsman et al.,
2003;   XE “Kind, 1996”  Kind, 1996).  For this analysis, we used
weights based on community-based preferences, using time trade-off or
standard gamble when available.  In some cases, we used patient or
clinician ratings when no community preference-based weights were
available.  Sources for weights are discussed in more detail below. 
Table G-4 summarizes the key inputs for calculating QALYs associated
with chronic health endpoints.

6b.5.1	Calculating QALYs Associated with Reductions in the Incidence of
Chronic Bronchitis

CB is characterized by mucus in the lungs and a persistent wet cough for
at least 3 months a year for several years in a row.  CB affects an
estimated 5 percent of the U.S. population (  XE “American Lung
Association, 1999”  American Lung Association, 1999).  For gains in
quality of life resulting from reduced incidences of PM-induced CB,
discounted QALYs are calculated as

	

where CBi is the number of incidences of CB avoided in age interval
i, wi is the average QALY weight for age interval i,  is the QALY weight
associated with CB,  ADVANCE \d 6   ADVANCE \u 6   is the discounted
duration of life with CB for individuals with onset of disease in age
interval i, equal to   ADVANCE \d 11   ADVANCE \u 11 , where Di is the
duration of life with CB for individuals with onset of disease in age
interval i.

g change in PM2.5.  Table G-5 presents the estimated reduction in new
incidences of CB associated with the illustrative PM NAAQS attainment
strategies.

Table 6b-4:	Summary of Key Parameters Used in QALY Calculations for
Chronic Disease Endpoints

Parameter	Value(s)	Source(s)

Discount rate	0.03 (0.07 sensitivity analysis)	  XE “Gold et al.,
1996”  Gold et al. (1996),   XE “U.S. EPA (2000)”  U.S. EPA
(2000),   XE “U.S. OMB (2003)”  U.S. OMB (2003)

Quality of life preference score for chronic bronchitis	0.5 – 0.7
Triangular distribution centered at 0.7 with upper bound at 0.9 (  XE
“Vos, 1999a”  Vos, 1999a) (slightly better than a mild/moderate
case) and a lower bound at 0.5 (average weight for a severe case based
on   XE “Vos, 1999a”  Vos [1999a] and   XE “Smith and Peske
[1994”  Smith and Peske [1994])

Duration of acute phase of acute myocardial infarction (AMI)	5.5 days
– 22 days	Uniform distribution with lower bound based on average
length of stay for an AMI (  XE “AHRQ, 2000”  AHRQ, 2000) and upper
bound based on   XE “Vos, 1999b”  Vos (1999b).

Probability of CHF post AMI	0.2	  XE “Vos, 1999a”  Vos, 1999a (WHO
Burden of Disease Study, based on   XE “Cowie et al., 1997”  Cowie
et al., 1997)

Probability of angina post AMI	0.51	  XE “American Heart Association,
2003”  American Heart Association, 2003

(Calculated as the population with angina divided by the total
population with heart disease)

Quality-of-life preference score for post-AMI with CHF (no angina)	0.80
– 0.89	Uniform distribution with lower bound at 0.80 (  XE “Stinnett
et al., 1996”  Stinnett et al., 1996) and upper bound at 0.89 (  XE
“Kuntz et al., 1996”  Kuntz et al., 1996).  Both studies used the
time trade-off elicitation method.

Quality-of-life preference score for post-AMI with CHF and angina	0.76
– 0.85	Uniform distribution with lower bound at 0.76 (  XE “Stinnett
et al., 1996”  Stinnett et al., 1996, adjusted for severity) and upper
bound at 0.85 (  XE “Kuntz et al., 1996”  Kuntz et al., 1996).  Both
studies used the time trade-off elicitation method.

Quality-of-life preference score for post-AMI with angina (no CHF)	0.7
– 0.89	Uniform distribution with lower bound at 0.7, based on the
standard gamble elicitation method (  XE “Pliskin, Stason, and
Weinstein, 1981”  Pliskin, Stason, and Weinstein, 1981) and upper
bound at 0.89, based on the time trade-off method (  XE “Kuntz et al.,
1996”  Kuntz et al., 1996).

Quality-of-life preference score for post-AMI (no angina, no CHF)	0.93
Only one value available from the literature.  Thus, no distribution is
specified.  Source of value is   XE “Kuntz et al., 1996”  Kuntz et
al. (1996).

CB is assumed to persist for the remainder of an affected individual’s
lifespan.  Duration of CB will thus equal life expectancy conditioned on
having CB.  CDC has estimated that COPD (of which CB is one element)
results in an average loss of life years equal to 4.26 per COPD death,
relative to a reference life expectancy of 75 years (  XE “CDC,
2003”  CDC, 2003).  Thus, we subtract 4.26 from the remaining life
expectancy for each age group, up to age 75.  For age groups over 75, we
apply the ratio of 4.26 to the life expectancy for the 65 to 74 year
group (0.237) to the life expectancy for the 75 to 84 and 85 and up age
groups to estimate potential life years lost and then subtract that
value from the base life expectancy.

Table 6b-5:	Estimated Reduction in Incidence of Chronic Bronchitis
Associated with Illustrative Attainment Strategies for the Revised and
More Stringent Alternative PM NAAQS in 2020

	Reduction in Incidence (95% Confidence Interval)

Age Interval	070 ppm Partial Attainment Strategy

25 – 34	74

(14 – 140)

35 – 44	85

(16 – 160)

45 – 54	82

(15 – 150)

55 – 64	88

(16 – 160)

65 – 74	62

(12 – 640)

75 – 84	31

(6 – 56)

85+	14

(3 – 25)

Total	440

(80 – 790)

Quality of life with chronic lung diseases has been examined in several
studies.  In an analysis of the impacts of environmental exposures to
contaminants,   XE “de Hollander et al, 1999”  de Hollander et al.
(1999) assigned a weight of 0.69 to years lived with CB.  This weight
was based on physicians’ evaluations of health states similar to CB.  
 XE “Salomon and Murray (2003”  Salomon and Murray (2003) estimated
a pooled weight of 0.77 based on visual analogue scale, time trade-off,
standard gamble, and person trade-off techniques applied to a
convenience sample of health professionals.  The Harvard Center for Risk
Analysis catalog of preference scores reports a weight of 0.40 for
severe COPD, with a range from 0.2 to 0.8, based on the judgments of the
study’s authors (  XE “Bell et al., 2001”  Bell et al., 2001). 
The Victoria Burden of Disease (BoD) study used a weight of 0.47 for
severe COPD and 0.83 for mild to moderate COPD, based on an analysis by 
 XE “Stouthard et al. (1997”  Stouthard et al. (1997) of chronic
diseases in Dutch populations (  XE “Vos, 1999a”  Vos, 1999a). 
Based on the recommendations of   XE “Gold et al. (1996”  Gold et
al. (1996), quality-of-life weights based on community preferences are
preferred for CEA of interventions affecting broad populations.  Use of
weights based on health professionals is not recommended.  It is not
clear from the Victoria BoD study whether the weights used for COPD are
based on community preferences or judgments of health professionals. 
The Harvard catalog score is clearly identified as based on author
judgment.  Given the lack of a clear preferred weight, we select a
triangular distribution centered at 0.7 with an upper bound at 0.9
(slightly better than a mild/moderate case defined by the Victoria BoD
study) and a lower bound at 0.5 based on the Victoria BoD study.  We
will need additional empirical data on quality of life with chronic
respiratory diseases based on community preferences to improve our
estimates.

Selection of a reference weight for the general population without CB is
somewhat uncertain.  It is clear that the general population is not in
perfect health; however, there is some uncertainty as to whether
individuals’ ratings of health states are in reference to a perfect
health state or to a generally achievable “normal” health state
given age and general health status.  The U.S. Public Health Service
Panel on Cost Effectiveness in Health and Medicine recommends that
“since lives saved or extended by an intervention will not be in
perfect health, a saved life year will count as less than 1 full QALY”
(  XE “Gold et al. (1996”  Gold et al., 1996).  Following   XE
“Carrothers, Evans, and Graham (2002”  Carrothers, Evans, and Graham
(2002), we assumed that the reference weight for the general population
without CB is 0.95.  To allow for uncertainty in this parameter, we
assigned a triangular distribution around this weight, bounded by 0.9
and 1.0.  Note that the reference weight for the general population is
used solely to determine the incremental quality-of-life improvement
applied to the duration of life that would have been lived with the
chronic disease.  For example, if CB has a quality-of-life weight of 0.7
relative to a reference quality-of-life weight of 0.9, then the
incremental quality-of-life improvement in 0.2.  If the reference
quality-of-life weight is 0.95, then the incremental quality-of-life
improvement is 0.25.  As noted above, the population is assumed to have
a reference weight of 1.0 for all life years gained due to mortality
risk reductions.

We present discounted QALYs over the duration of the lifespan with CB
using a 3 percent discount rate.  Based on the assumptions defined
above, we used Monte Carlo simulation methods as implemented in the
Crystal Ball™ software program to develop the distribution of QALYs
gained per incidence of CB for each age interval.  Based on the
assumptions defined above, the mean 3 percent discounted QALY gained per
incidence of CB for each age interval along with the 95 percent
confidence interval resulting from the Monte Carlo simulation is
presented in Table G-6.  Table G-6 presents both the undiscounted and
discounted QALYs gained per incidence.

Table 6b-6:	QALYs Gained per Avoided Incidence of CB

Age Interval	QALYs Gained per Incidence

Start Age	End Age	Undiscounted	Discounted (3%)

25	34	12.15

(4.40-19.95)	6.52

(2.36-10.71)

35	44	9.91

(3.54-16.10)	5.94

(2.12-9.66)

45	54	7.49

(2.71-12.34)	5.03

(1.82-8.29)

55	64	5.36

(1.95-8.80)	4.03

(1.47-6.61)

65	74	3.40

(1.22-5.64)	2.84

(1.02-4.71)

75	84	2.15

(0.77-3.49)	1.92

(0.69-3.13)

85+

0.79

(0.27-1.29)	0.77

(0.26-1.25)

6b.5.2	Calculating QALYs Associated with Reductions in the Incidence of
Nonfatal Myocardial Infarctions

Nonfatal heart attacks, or acute myocardial infarctions, require more
complicated calculations to derive estimates of QALY impacts.  The
actual heart attack, which results when an area of the heart muscle dies
or is permanently damaged because of oxygen deprivation, and subsequent
emergency care are of relatively short duration.  Many heart attacks
result in sudden death.  However, for survivors, the long-term impacts
of advanced CHD are potentially of long duration and can result in
significant losses in quality of life and life expectancy.

In this phase of the analysis, we did not independently estimate the
gains in life expectancy associated with reductions in nonfatal heart
attacks.  Based on recommendations from the SAB-HES, we assumed that all
gains in life expectancy are captured in the estimates of reduced
mortality risk provided by the   XE “Pope et al. (2002”  Pope et al.
(2002) analysis.  We only estimate the change in quality of life over
the period of life affected by the occurrence of a heart attack.  This
may understate the QALY impacts of nonfatal heart attacks but ensures
that the overall QALY impact estimates across endpoints do not
double-count potential life-year gains.

Our approach adapts a CHD model developed for the Victoria Burden of
Disease study (  XE “Vos, 1999b”  Vos, 1999b).  This model accounts
for the lost quality of life during the heart attack and the possible
health states following the heart attack.  Figure G-1 shows the heart
attack QALY model in diagrammatic form.

The total gain in QALYs is calculated as:

		

, the duration of the acute phase of the AMI, and   ADVANCE \d 11  
ADVANCE \u 11 , is the discounted value of , the duration of post-AMI
health status j.

 

Figure 6b-1.	Decision Tree Used in Modeling Gains in QALYs from Reduced
Incidence of Nonfatal Acute Myocardial Infarctions

Nonfatal heart attacks have been linked with short-term exposures to
PM2.5 in the United States (  XE “Peters et al., 2001”  Peters et
al., 2001) and other countries (  XE “Poloniecki et al., 1997” 
Poloniecki et al., 1997).  We used a recent study by   XE “Peters et
al., 2001”  Peters et al. (2001) as the basis for the impact function
estimating the relationship between PM2.5 and nonfatal heart attacks. 
Peters et al. is the only available U.S. study to provide a specific
estimate for heart attacks.  Other studies, such as   XE “Samet et al.
(2000”  Samet et al. (2000) and   XE “Moolgavkar (2000” 
Moolgavkar (2000), show a consistent relationship between all
cardiovascular hospital admissions, including for nonfatal heart
attacks, and PM.  Given the lasting impact of a heart attack on
longer-term health costs and earnings, we chose to provide a separate
estimate for nonfatal heart attacks based on the single available U.S.
effect estimate.  The finding of a specific impact on heart attacks is
consistent with hospital admission and other studies showing
relationships between fine particles and cardiovascular effects both
within and outside the United States.  These studies provide a weight of
evidence for this type of effect.  Several epidemiologic studies (  XE
“Liao et al., 1999”  Liao et al., 1999;   XE “Gold et al., 2000”
 Gold et al., 2000;   XE “Magari et al., 2001”  Magari et al., 2001)
have shown that heart rate variability (an indicator of how much the
heart is able to speed up or slow down in response to momentary
stresses) is negatively related to PM levels.  Heart rate variability is
a risk factor for heart attacks and other CHDs (  XE “Carthenon et
al., 2002”  Carthenon et al., 2002;   XE “Dekker et al., 2000” 
Dekker et al., 2000;   XE “Liao et al., 1997”  Liao et al., 1997,  
XE “Tsuji et al., 1996”  Tsuji et al., 1996).  As such, significant
impacts of PM on heart rate variability are consistent with an increased
risk of heart attacks.

The number of avoided nonfatal AMI in each age interval is derived from
applying the impact function from   XE “Peters et al., 2001”  Peters
et al. (2001) to the population in each age interval with the
appropriate baseline incidence rate.  The effect estimate from the   XE
“Peters et al., 2001”  Peters et al. (2001) study is 0.0241, which,
based on the logistic specification of the model, is equivalent to a
relative risk of 1.27 for a 10 g change in PM2.5.  Table 6b-7
presents the estimated reduction in nonfatal AMI associated with the
illustrative Ozone NAAQS attainment strategies.

Table 6b-7:	Estimated Reduction in Nonfatal Acute Myocardial Infarctions
Associated  with Illustrative Attainment Strategies for the Revised and
More Stringent Alternative PM NAAQS in 2020

	Reduction in Incidence*(95% Confidence Interval)

Age Interval	070 ppm Attainment Strategy

18 – 24	1

(1 – 2)

25 – 34	4

(3 – 6)

35 – 44	37

(20 – 53)

45 – 54	110

(61 – 170)

55 – 64	290

(160 – 430)

65 – 74	350

(190 – 500)

75 – 84	280

(150 – 410)

85+	150

(80 – 220)

Total	1,200

(660 – 1,800)

Acute myocardial infarction results in significant loss of quality of
life for a relatively short duration.  The WHO Global Burden of Disease
study, as reported in   XE “Vos, 1999b”  Vos (1999b), assumes that
the acute phase of an acute myocardial infarction lasts for 0.06 years,
or around 22 days.  An alternative assumption is the acute phase is
characterized by the average length of hospital stay for an AMI in the
United States, which is 5.5 days, based on data from the Agency for
Healthcare Research and Quality’s Healthcare Cost and Utilization
Project (HCUP).  We assumed a distribution of acute phase duration
characterized by a uniform distribution between 5.5 and 22 days, noting
that due to earlier discharges and in-home therapy available in the
United States, duration of reduced quality of life may continue after
discharge from the hospital.  In the period during and directly
following an AMI (the acute phase), we assigned a quality of life weight
equal to 0.605, consistent with the weight for the period in treatment
during and immediately after an attack (  XE “Vos, 1999b”  Vos,
1999b).

During the post-AMI period, a number of different health states can
determine the loss in quality of life.  We chose to classify post-AMI
health status into four states defined by the presence or absence of
angina and congestive heart failure (CHF).  This makes a very explicit
assumption that without the occurrence of an AMI, individuals would not
experience either angina or CHF.  If in fact individuals already have
CHF or angina, then the quality of life gained will be overstated.  We
do not have information about the percentage of the population have been
diagnosed with angina or CHF with no occurrence of an AMI.  Nor do we
have information on what proportion of the heart attacks occurring due
to PM exposure are first heart attacks versus repeat attacks. 
Probabilities for the four post-AMI health states sum to one.

Given the occurrence of a nonfatal AMI, the probability of congestive
heart failure is set at 0.2, following the heart disease model developed
by   XE “Vos, 1999b”  Vos (1999b).  The probability is based on a
study by   XE “Cowie et al. (1997”  Cowie et al. (1997), which
estimated that 20 percent of those surviving AMI develop heart failure,
based on an analysis of the results of the Framingham Heart Study.

The probability of angina is based on the prevalence rate of angina in
the U.S. population.  Using data from the American Heart Association, we
calculated the prevalence rate for angina by dividing the estimated
number of people with angina (6.6 million) by the estimated number of
people with CHD of all types (12.9 million).  We then assumed that the
prevalence of angina in the population surviving an AMI is similar to
the prevalence of angina in the total population with CHD.  The
estimated prevalence rate is 51 percent, so the probability of angina is
0.51.

Combining these factors leads to the probabilities for each of the four
health states as follows:

I.	Post AMI with CHF and angina = 0.102

II.	Post AMI with CHF without angina = 0.098

III.	Post AMI with angina without CHF = 0.408

IV.	Post AMI without angina or CHF = 0.392

Duration of post-AMI health states varies, based in part on assumptions
regarding life expectancy with post-AMI complicating health conditions. 
Based on the model used for established market economies (EME) in the
WHO Global Burden of Disease study, as reported in   XE “Vos, 1999b”
 Vos (1999b), we assumed that individuals with CHF have a relatively
short remaining life expectancy and thus a relatively short period with
reduced quality of life (recall that gains in life expectancy are
assumed to be captured by the cohort estimates of reduced mortality
risk).  Table 6b-8 provides the duration (both discounted and
undiscounted) of CHF assumed for post-AMI cases by age interval.

Table 6b-8:	Assumed Duration of Congestive Heart Failure

Age Interval	Duration of Heart Failure (years)

Start Age	End Age	Undiscounted	Discounted (3%)

18	24	7.11	6.51

25	34	6.98	6.40

35	44	6.49	6.00

45	54	5.31	4.99

55	64	1.96	1.93

65	74	1.71	1.69

75	84	1.52	1.50

85+

1.52	1.50

Duration of health states without CHF is assumed to be equal to the life
expectancy of individuals conditional on surviving an AMI.    XE “Ganz
et al. (2000”  Ganz et al. (2000) note that “Because patients with a
history of myocardial infarction have a higher chance of dying of CHD
that is unrelated to recurrent myocardial infarction (for example,
arrhythmia), this cohort has a higher risk for death from causes other
than myocardial infarction or stroke than does an unselected
population.”  They go on to specify a mortality risk ratio of 1.52 for
mortality from other causes for the cohort of individuals with a
previous (nonfatal) AMI.  The risk ratio is relative to all-cause
mortality for an age-matched unselected population (i.e., general
population).  We adopted the same ratios and applied them to each
age-specific all-cause mortality rate to derive life expectancies (both
discounted and undiscounted) for each age group after an AMI, presented
in Table 6b-9.  These life expectancies are then used to represent the
duration of non-CHF post-AMI health states (III and IV).

Table 6b-9:	Assumed Duration of Non-CHF Post-AMI Health States

Age Interval	Post-AMI Years of Life Expectancy (non-CHF)

Start Age	End Age	Undiscounted	Discounted (3%)

18	24	55.5	27.68

25	34	46.1	25.54

35	44	36.8	22.76

45	54	27.9	19.28

55	64	19.8	15.21

65	74	12.8	10.82

75	84	7.4	6.75

85+

3.6	3.47

For the four post-AMI health states, we used QALY weights based on
preferences for the combined conditions characterizing each health
state.  A number of estimates of QALY weights are available for post-AMI
health conditions.

The first two health states are characterized by the presence of CHF,
with or without angina.  The Harvard Center for Risk Analysis catalog of
preference scores provides several specific weights for CHF with and
without mild or severe angina and one set specific to post-AMI CHF. 
Following the Victoria Burden of Disease model, we assumed that most
cases of angina will be treated and thus kept at a mild to moderate
state.  We thus focused our selection on QALY weights for mild to
moderate angina.  The   XE “Harvard Center for Risk Analysis” 
Harvard database includes two sets of community preference-based scores
for CHF (  XE “Stinnett et al., 1996”  Stinnett et al., 1996;   XE
“Kuntz et al., 1996”  Kuntz et al., 1996).  The scores for CHF with
angina range from 0.736 to 0.85.  The lower of the two scores is based
on angina in general with no delineation by severity.  Based on the
range of the scores for mild to severe cases of angina in the second
study, one can infer that an average case of angina has a score around
0.96 of the score for a mild case.  Applying this adjustment raises the
lower end of the range of preference scores for a mild case of angina to
0.76.  We selected a uniform distribution over the range 0.76 to 0.85
for CHF with mild angina, with a midpoint of 0.81.  The same two studies
in the Harvard catalog also provide weights for CHF without angina. 
These scores range from 0.801 to 0.89.  We selected a uniform
distribution over this range, with a midpoint of 0.85.

The third health state is characterized by angina, without the presence
of CHF.  The Harvard catalog includes five sets of community
preference-based scores for angina, one that specifies scores for both
mild and severe angina (  XE “Kuntz et al., 1996”  Kuntz et al.,
1996), one that specifies mild angina only (  XE “Pliskin, Stason, and
Weinstein, 1981”  Pliskin, Stason, and Weinstein, 1981), one that
specifies severe angina only (  XE “Cohen, Breall, and Ho, 1994” 
Cohen, Breall, and Ho, 1994), and two that specify angina with no
severity classification (  XE “Salkeld, Phongsavan, and Oldenburg,
1997”  Salkeld, Phongsavan, and Oldenburg, 1997;   XE “Stinnett et
al., 1996”  Stinnett et al., 1996).  With the exception of the
Pliskin, Stason, and Weinstein score, all of the angina scores are based
on the time trade-off method of elicitation.  The Pliskin, Stason, and
Weinstein score is based on the standard gamble elicitation method.  The
scores for the nonspecific severity angina fall within the range of the
two scores for mild angina specifically.  Thus, we used the range of
mild angina scores as the endpoints of a uniform distribution.  The
range of mild angina scores is from 0.7 to 0.89, with a midpoint of
0.80.

For the fourth health state, characterized by the absence of CHF and/or
angina, there is only one relevant community preference score available
from the Harvard catalog.  This score is 0.93, derived from a time
trade-off elicitation (  XE “Kuntz et al., 1996”  Kuntz et al.,
1996).  Insufficient information is available to provide a distribution
for this weight; therefore, it is treated as a fixed value.

Similar to CB, we assumed that the reference weight for the general
population without AMI is 0.95.  To allow for uncertainty in this
parameter, we assigned a triangular distribution around this weight,
bounded by 0.9 and 1.0.

defined above, we used Monte Carlo simulation methods as implemented in
the Crystal Ball™ software program to develop the distribution of
QALYs gained per incidence of nonfatal AMI for each age interval.  For
the Monte Carlo simulation, all distributions were assumed to be
independent.  The mean QALYs gained per incidence of 

nonfatal AMI for each age interval is presented in Table 6b-10, along
with the 95 percent confidence interval resulting from the Monte Carlo
simulation.  Table 6b-10 presents both the undiscounted and discounted
QALYs gained per incidence.

Table 6b-10:	QALYs Gained per Avoided Nonfatal Myocardial Infarction

Age Interval	QALYs Gained per Incidencea

Start Age	End Age	Undiscounted	Discounted (3%)

18	24	4.18

(1.24-7.09)	2.17

(0.70-3.62)

25	34	3.48

(1.09-5.87)	2.00

(0.68-3.33)

35	44	2.81

(0.88-4.74)	1.79

(0.60-2.99)

45	54	2.14

(0.67-3.61)	1.52

(0.51-2.53)

55	64	1.49

(0.42-2.52)	1.16

(0.34-1.95)

65	74	0.97

(0.30-1.64)	0.83

(0.26-1.39)

75	84	0.59

(0.20-0.97)	0.54

(0.19-0.89)

85+

0.32

(0.13-0.50)	0.31

(0.13-0.49)

a	Mean of Monte Carlo generated distribution; 95% confidence interval
presented in parentheses.

6b.6	Cost-Effectiveness Analysis

Given the estimates of changes in life expectancy and quality of life,
the next step is to aggregate life expectancy and quality-of-life gains
to form an effectiveness measure that can be compared to costs to
develop cost-effectiveness ratios.  This section discusses the proper
characterization of the combined effectiveness measure and the
appropriate calculation of the numerator of the cost-effectiveness
ratio.

6b.6.1	Aggregating Life Expectancy and Quality-of-Life Gains

To develop an integrated measure of changes in health, we simply sum
together the gains in life years from reduced mortality risk in each age
interval with the gains in QALYs from reductions in incidence of CB and
acute myocardial infarctions.  The resulting measure of effectiveness
then forms the denominator in the cost-effectiveness ratio.  What is
this combined measure of effectiveness?  It is not a QALY measure in a
strict sense, because we have not adjusted life-expectancy gains for
preexisting health status (quality of life).  It is however, an
effectiveness measure that adds to the standard life years calculation a
scaled morbidity equivalent.  Thus, we term the aggregate measure
morbidity inclusive life years, or MILYs.  Alternatively, the combined
measure could be considered as QALYs with an assumption that the
community preference weight for all life-expectancy gains is 1.0.  If
one considers that this weight might be considered to be a “fair”
treatment of those with preexisting disabilities, the effectiveness
measure might be termed “fair QALY” gained.  However, this implies
that all aspects of fairness have been addressed, and there are clearly
other issues with the fairness of QALYs (or other effectiveness
measures) that are not addressed in this simple adjustment.  The MILY
measure violates some of the properties used in deriving QALY weights,
such as linear substitution between quality of life and quantity of
life.  However, in aggregating life expectancy and quality-of-life
gains, it merely represents an alternative social weighting that is
consistent with the spirit of the recent OMB guidance on CEA.  The
guidance notes that “fairness is important in the choice and execution
of effectiveness measures” (  XE “U.S. OMB, 2003”  OMB, 2003). 
The resulting aggregate measure of effectiveness will not be consistent
with a strict utility interpretation of QALYs; however, it may still be
a useful index of effectiveness.

Applying the life expectancies and distributions of QALYs per incidence
for CB and AMI to estimated distributions of incidences yields
distributions of life expectancy and QALYs gained due to the Ozone NAAQS
illustrative attainment strategies.  These distributions reflect both
the quantified uncertainty in incidence estimates and the quantified
uncertainty in QALYs gained per incidence.

For the attainment strategy for the revised 070 ppm standards, Table
6b-11 presents the mean 3 percent discounted MILYs gained for each age
interval, broken out by life expectancy and quality-of-life categories. 
Note that quality-of-life gains occur from age 18 and up, while life
expectancy gains accrue only after age 29.  This is based on the ages of
the study populations in the underlying epidemiological studies.  It is
unlikely that such discontinuities exist in reality, but to avoid
overstating effectiveness, we chose to limit the life-expectancy gains
to those occurring in the population 30 and over and the morbidity gains
to the specific adult populations examined in the studies.  

It is worth noting that around a third of mortality-related benefits are
due to reductions in premature deaths among those 75 and older, while
only 7 percent of morbidity benefits occur in this age group.  This is
due to two factors:  (1) the relatively low baseline mortality rates in
populations under 75, and (2) the relatively constant baseline rates of
chronic disease coupled with the relatively long period of life that is
lived with increased quality of life without CB and advanced heart
disease.

The relationship between age and the distribution of MILYs gained from
mortality and morbidity is shown for the 070 ppm  attainment strategy in
Figure _-2.  Because the baseline mortality rate is increasing in age at
a much faster rate than the prevalence rate for CB, the share of MILYs
gained accounted for by mortality is proportional to age.  At the oldest
age interval, avoiding incidences of CB leads to only a few MILYs
gained, due to the lower number of years lived with CB.  MILYs gained
from avoided premature mortality is low in the youngest age intervals
because of the low overall mortality rates in these intervals, although
the number of MILYs per incidence is high.  In later years, even though
the MILYs gained per incidence avoided is low, the number of cases is
very high due to higher baseline mortality rates.

Table 6b-11.  Estimated Gains in 3 Percent Discounted MILYs Associated
with Illustrative Attainment Strategies for the Revised Ozone NAAQS
(0.070 ppm) in 2020: Pope (2002) Estimate of Mortalitya

Age	Life Years Gained from Mortality Risk 

Reductions

(95% CI)	QALY Gained from Reductions in Chronic Bronchitis

(95% CI)	QALY Gained from Reductions in Acute Myocardial Infarctions

(95% CI)	Total Gain in 

MILYs

(95% CI)

18–24	—	—	3

(1 – 5)	3

(1 – 5)

25–34	100

(41 – 170)	490

(91 – 1,100)	8

(3 – 15)	600

(130 – 1,200)

35–44	310

(120 – 490)	500

(93 – 1,100)	70

(23 – 120)	870

(240 – 1,700)

45–54	580

(230 – 930)	420

(77 – 890)	170

(60 – 320)	1,200

(360 – 2,100)

55–64	1,300

(500 – 2,100)	350

(64 – 760)	330

(110 – 620)	2,000

(680 – 3,400)

65–74	1,700

(680 – 2,800)	180

(33 – 380)	280

(100 – 530)	2,200

(810 – 3,700)

75–84	1,400

(540 – 2,200)	60

(11 – 130)	150

(54 – 280)	1,600

(610 – 2,600)

85+	690

(270 – 1,100)	10

(2 – 22)	43

(16 – 80)	740

(290 – 1,200)

Total	6,100

(2,400 – 9,800)	2,000

(370 – 4,300)	1,100

(370 – 2,000)	9,100

(3,100 – 16,000)

a	Note that all estimates have been rounded to two significant digits.

Table 6b-12.  Estimated Gains in 3 Percent Discounted MILYs Associated
with Illustrative Attainment Strategies for the Revised Ozone NAAQS
(0.070 ppm) in 2020: Laden (2006) Estimate of Mortalitya

Age	Life Years Gained from Mortality Risk 

Reductions

(95% CI)	QALY Gained from Reductions in Chronic Bronchitis

(95% CI)	QALY Gained from Reductions in Acute Myocardial Infarctions

(95% CI)	Total Gain in 

MILYs

(95% CI)

18–24	—	—	3

(1 – 5)	3

(1 – 5)

25–34	240

(130 – 340)	490

(91 – 1,100)	8

(3 – 15)	730

(220 – 1,400)

35–44	690

(380 – 1,000)	500

(93 – 1,100)	70

(23 – 120)	1,300

(490 – 2,200)

45–54	1,400

(710 – 1,900)	420

(77 – 890)	170

(60 – 320)	1,900

(850 – 3,100)

55–64	2,900

(1,600 – 4,200)	350

(64 – 760)	330

(110 – 620)	3,600

(1,800 – 5,600)

65–74	3,900

(2,100 – 5,700)	180

(33 – 380)	280

(100 – 530)	4,400

(2,300 – 6,600)

75–84	3,100

(1,700 – 4,600)	60

(11 – 130)	150

(54 – 280)	3,300

(1,800 – 5,000)

85+	1,600

(840 – 2,300)	10

(2 – 22)	43

(16 – 80)	1,600

(860 – 2,400)

Total	14,000

(7,500 – 20,000)	2,000

(370 – 4,300)	1,100

(370 – 2,000)	17,000

(8,200 – 26,000)

a	Note that all estimates have been rounded to two significant digits.

Summing over the age intervals provides estimates of total MILYs gained
for the Ozone NAAQS illustrative attainment strategies.  The total
number of discounted (3 percent) MILYs gained for the 070 ppm attainment
strategy using the Pope (2002) estimate is 9,100 (95% CI:  3,100 –
16,000). Using the Laden (2006) estimate, the total number of discounted
(3 percent) MILYs is 17,000 (95% CI:  8,200 – 26,000).

6b.6.2	Dealing with Acute Health Effects and Nonhealth Effects

Health effects from exposure to particulate air pollution encompass a
wide array of chronic and acute conditions in addition to premature
mortality (  XE “U.S. EPA, 1996”  EPA, 1996).  Although chronic
conditions and premature mortality generally account for the majority of
monetized benefits, acute symptoms can affect a broad population or
sensitive populations (e.g., asthma exacerbations in asthmatic children.
 In addition, reductions in air pollution may result in a broad set of
nonhealth environmental benefits, including improved visibility in
national parks, increased agricultural and forestry yields, reduced acid
damage to buildings, and a host of other impacts.  QALYs address only
health impacts, and the OMB guidance notes that “where regulation may
yield several different beneficial outcomes, a cost-effectiveness
comparison becomes more difficult to interpret because there is more
than one measure of effectiveness to incorporate in the analysis.”

With regard to acute health impacts,   XE “Bala and Zarkin (2000” 
Bala and Zarkin (2000) suggest that QALYs are not appropriate for
valuing acute symptoms, because of problems with both measuring utility
for acute health states and applying QALYs in a linear fashion to very
short duration health states.    XE “Johnson and Lievense (2000” 
Johnson and Lievense (2000) suggest using conjoint analysis to get
healthy-utility time equivalences that can be compared across acute
effects, but it is not clear how these can be combined with QALYs for
chronic effects and loss of life expectancy.  There is also a class of
effects that EPA has traditionally treated as acute, such as hospital
admissions, which may also result in a loss of quality of life for a
period of time following the effect.  For example, life after asthma
hospitalization has been estimated with a utility weight of 0.93 (  XE
“Bell et al., 2001”  Bell et al., 2001;   XE “Kerridge, Glasziou,
and Hillman. 1995”  Kerridge, Glasziou, and Hillman, 1995).

How should these effects be combined with QALYs for chronic and
mortality effects?  One method would be to convert the acute effects to
QALYs; however, as noted above, there are problems with the linearity
assumption (i.e., if a year with asthma symptoms is equivalent to 0.7
year without asthma symptoms, then 1 day without asthma symptoms is
equivalent to 0.0019 QALY gained).  This is troubling from both a
conceptual basis and a presentation basis.  An alternative approach is
simply to treat acute health effects like nonhealth benefits and
subtract the dollar value (based on WTP or COI) from compliance costs in
the CEA.

Figure 6b-2.	Distribution of Mortality and Morbidity Related MILY Across
Age Groups for Illustrative Attainment Strategy for the Revised PM NAAQS
(3 percent Discount Rate)

To address the issues of incorporating acute morbidity and nonhealth
benefits, OMB suggests that agencies “subtract the monetary estimate
of the ancillary benefits from the gross cost estimate to yield an
estimated net cost.”  As with benefit-cost analysis, any unquantified
benefits and/or costs should be noted and an indication of how they
might affect the cost-effectiveness ratio should be described.  We will
follow this recommended “net cost” approach in the illustrative
exercise, specifically in netting out the benefits of health
improvements other than reduced mortality and chronic morbidity, and the
benefits of improvements in visibility at national parks (see Chapter 5
for more details on these benefit categories).

6b.6.3	Cost-Effectiveness Ratios

Construction of cost-effectiveness ratios requires estimates of
effectiveness (in this case measured by lives saved, life years gained,
or MILYs gained) in the denominator and estimates of costs in the
numerator.  The estimate of costs in the numerator should include both
the direct costs of the controls necessary to achieve the reduction in
ambient PM2.5 and the avoided costs (cost savings) associated with the
reductions in morbidity (  XE “Gold et al., 1996”  Gold et al.,
1996).  In general, because reductions in air pollution do not require
direct actions by the affected populations, there are no specific costs
to affected individuals (aside from the overall increases in prices that
might be expected to occur as control costs are passed on by affected
industries).  Likewise, because individuals do not engage in any
specific actions to realize the health benefit of the pollution
reduction, there are no decreases in utility (as might occur from a
medical intervention) that need to be adjusted for in the denominator. 
Thus, the elements of the numerator are direct costs of controls minus
the avoided COI associated with CB and nonfatal AMI.  In addition, to
account for the value of reductions in acute health impacts and
nonhealth benefits, we net out the monetized value of these benefits
from the numerator to yield a “net cost” estimate.  For the MILY
aggregate effectiveness measure, the denominator is simply the sum of
life years gained from increased life expectancy and the sum of QALYs
gained from the reductions in CB and nonfatal AMI.

Avoided costs for CB and nonfatal AMI are based on estimates of lost
earnings and medical costs.  Using age-specific annual lost earnings and
medical costs estimated by   XE “Cropper and Krupnick (1990” 
Cropper and Krupnick (1990) and a 3 percent discount rate, we estimated
a lifetime present discounted value (in 2000$) due to CB of $150,542 for
someone between the ages of 27 and 44; $97,610 for someone between the
ages of 45 and 64; and $11,088 for someone over 65.  The corresponding
age-specific estimates of lifetime present discounted value (in 2000$)
using a 7 percent discount rate are $86,026, $72,261, and $9,030,
respectively.  These estimates assumed that 1) lost earnings continue
only until age 65, 2) medical expenditures are incurred until death, and
3) life expectancy is unchanged by CB.

Because the costs associated with a myocardial infarction extend beyond
the initial event itself, we consider costs incurred over several years.
 Using age-specific annual lost earnings estimated by   XE “Cropper
and Krupnick (1990”  Cropper and Krupnick (1990) and a 3 percent
discount rate, we estimated a present discounted value in lost earnings
(in 2000$) over 5 years due to a myocardial infarction of $8,774 for
someone between the ages of 25 and 44, $12,932 for someone between the
ages of 45 and 54, and $74,746 for someone between the ages of 55 and
65.  The corresponding age-specific estimates of lost earnings (in
2000$) using a 7 percent discount rate are $7,855, $11,578, and $66,920,
respectively.    XE “Cropper and Krupnick (1990”  Cropper and
Krupnick (1990) do not provide lost earnings estimates for populations
under 25 or over 65.  Thus, we do not include lost earnings in the cost
estimates for these age groups.

Two estimates of the direct medical costs of myocardial infarction are
used.  The first estimate is from Wittels, Hay, and Gotto (1990  XE
"Wittels, Hay, and Gotto (1990"  ), which estimated expected total
medical costs of MI over 5 years to be $51,211 (in 1986$) for people who
were admitted to the hospital and survived hospitalization (there does
not appear to be any discounting used).  Using the CPI-U for medical
care, the Wittels estimate is $109,474 in year 2000$.  This estimated
cost is based on a medical cost model, which incorporated therapeutic
options, projected outcomes, and prices (using “knowledgeable
cardiologists” as consultants).  The model used medical data and
medical decision algorithms to estimate the probabilities of certain
events and/or medical procedures being used.  The second estimate is
from   XE “Russell et al. (1998”  Russell et al. (1998), which
estimated first-year direct medical costs of treating nonfatal
myocardial infarction of $15,540 (in 1995$), and $1,051 annually
thereafter.  Converting to year 2000$, that would be $23,353 for a
5-year period (without discounting).

The two estimates from these studies are substantially different, and we
have not adequately resolved the sources of differences in the
estimates.  Because the wage-related opportunity cost estimates from  
XE “Cropper and Krupnick (1990”  Cropper and Krupnick (1990) cover a
5-year period, we used estimates for medical costs that similarly cover
a 5-year period.  We used a simple average of the two 5-year estimates,
or $65,902, and add it to the 5-year opportunity cost estimate.  The
resulting estimates are given in Table 6b-13.

Table 6b-13:	Estimated Costs Over a 5-Year Period (in 2000$) of a
Nonfatal Myocardial Infarction

Age Group	Opportunity Cost	Medical Costa	Total Cost

0 – 24	$0	$65,902	$65,902

25-44	$8,774b	$65,902	$74,676

45 – 54	$12,253b	$65,902	$78,834

55 – 65	$70,619b	$65,902	$140,649

>65	$0	$65,902	$65,902

a	An average of the 5-year costs estimated by   XE “Wittels, Hay, and
Gotto (1990”  Wittels, Hay, and Gotto (1990) and   XE “Russell et
al. (1998”  Russell et al. (1998).

b	From   XE “Cropper and Krupnick (1990”  Cropper and Krupnick
(1990), using a 3 percent discount rate.

The total avoided COI by age group associated with the reductions in CB
and nonfatal acute myocardial infarctions is provided in Table 6b-14. 
Note that the total avoided COI associated with the revised PM NAAQS is
$520 million and is $1,200 million for the more stringent alternative. 
Note that this does not include any direct avoided medical costs
associated with premature mortality.  Nor does it include any medical
costs that occur more than 5 years from the onset of a nonfatal AMI. 
Therefore, this is likely an underestimate of the true avoided COI
associated with strategies for attainment of the PM NAAQS.

Table 6b-14:	Avoided Costs of Illness Associated with Reductions in
Chronic Bronchitis and Nonfatal Acute Myocardial Infarctions Associated
with Attainment Strategies for the 0.070 ppm alternative Ozone NAAQS in
2020

	Avoided Cost of Illness 

(in millions of 1999$)

Age Range	Chronic Bronchitis	Nonfatal Acute Myocardial Infarction

18-24	—	$0.07

25-34	$11	$0.3

35-44	$13	$2.6

45-54	$7	$8.6

55-64	$8	$40

65-74	$0.7	$22

75-84	$0.3	$18

85+	$0.1	$9.4

Total	$41	$100

6b.7	Discount Rate Sensitivity Analysis

A large number of parameters and assumptions are necessary in conducting
a CEA.  Where appropriate and supported by data, we have included
distributions of parameter values that were used in generating the
reported confidence intervals.  For the assumed discount rate, we felt
it more appropriate to examine the impact of the assumption using a
sensitivity analysis rather than through the integrated probabilistic
uncertainty analysis.

The choice of a discount rate, and its associated conceptual basis, is a
topic of ongoing discussion within the academic community.  OMB and EPA
guidance require using both a 7 percent rate and a 3 percent rate.  In
the most recent benefit-cost analyses of air pollution regulations, a 3
and 7 percent discount rate have been adopted in the primary analysis. 
A 3 percent discount rate reflects a “social rate of time
preference” discounting concept.  A 3 percent discount rate is also
consistent with the recommendations of the NAS panel on CEA (  XE
“Gold et al., 1996”  Gold et al., 1996), which suggests that “a
real annual (riskless) rate of 3 percent should be used in the Reference
Case analysis.”  We have also calculated MILYs and the implicit cost
thresholds using a 7 percent rate consistent with an “opportunity cost
of capital” concept to reflect the time value of resources directed to
meet regulatory requirements.  Further discussion of this topic appears
in Chapter 7 of   XE “Gold et al., 1996”  Gold et al. (1996), in
Chapter 6 of the EPA Guidelines for Economic Analysis, and in OMB
Circular A-4.

Table 6b-15:	Summary of Results for the Illustrative Partial Attainment
Strategies for the Alternative Ozone Standard of 0.070 ppm in 2020a

	Result Using 3% Discount Rate (95% Confidence Interval)

	070 ppm  Partial Attainment Strategy

Life years gained from mortality risk reductions

		Pope et al. (2002)	6,100

(2,400 – 9,800)

	Laden et al. (2006)	14,000

(7,500 – 20,000

QALY gained from reductions in chronic bronchitis	2,000

(370 – 4,300)

QALY gained from reductions in acute myocardial infarctions	1,100

(370 – 2,000)

Total gain in MILYs

		Pope et al. (2002)	9,100

(3,100 – 16,000)

	Laden et al. (2006)	17,000

(8,200 – 26,000)

Avoided cost of illness

	Chronic bronchitis	$41 million

($7.6 million – $75 million)

Nonfatal AMI	$100 million

($63 million – $220 million)

Implementation strategy costsb	$3.9 billion

Net cost per MILY

		Pope et al. (2002)	$410,000

($220,000 – $1,300,000)

	Laden et al. (2006)	$220,000

($140,000 – $470,000)

a	Consistent with recommendations of   XE “Gold et al. (1996”  Gold
et al. (1996), all summary results are reported at a precision level of
two significant digits to reflect limits in the precision of the
underlying elements.

b	Costs are the private firm costs of control, as discussed in Chapter
6, and reflect discounting using firm specific costs of capital.

Table 6b-16 presents a summary of results using the 7 percent discount
rate and the percentage difference between the 7 percent results and the
base case 3 percent results.  Adoption of a 7 percent discount rate
decreases the estimated life years and QALYs gained from implementing
the PM NAAQS.  Adopting a discount rate of 7 percent results in a 35
percent reduction in the estimated total MILYs gained in each year,
while the cost per MILY increases by approximately 60 percent.

Table 6b-16:	Impacts of Using a 7 Percent Discount Rate on Cost
Effectiveness Analysis for the Illustrative Attainment Strategies for
the Revised and More Stringent PM NAAQS in 2020

	Result Using 7 Percent Discount Rate 	Percentage Change Relative to
Result Using 3 Percent Discount Rate 

Life years gained from mortality risk reductions

	4,600	–24%

	10,000	-24%

QALY gained from reductions in chronic bronchitis	1,300	–35%

QALY gained from reductions in acute myocardial infarctions	830	–21%

Total gain in MILYs

	Pope et al. (2002)	6,500	-26%

	Laden et al. (2006)	12,000	-25%

Avoided cost of illness

Chronic bronchitis	$27 million	–36%

Nonfatal AMI	$111 million	+10%

Net cost per MILY

	Pope et al. (2002)	$550,000	+36%

	Laden et al. (2006)	$300,000	+33%

6b.8	Conclusions

We calculated the effectiveness of PM NAAQS attainment strategies based
on reductions in premature deaths and incidence of chronic disease.  We
measured effectiveness using several different metrics, including lives
saved, life years saved, and QALYs (for improvements in quality of life
due to reductions in incidence of chronic disease).  We suggested a new
metric for aggregating life years saved and improvements in quality of
life, morbidity inclusive life years (MILY) which assumes that society
assigns a weight of one to years of life extended regardless of
preexisting disabilities or chronic health conditions.

CEA of environmental regulations that have substantial public health
impacts may be informative in identifying programs that have achieved
cost-effective reductions in health impacts and can suggest areas where
additional controls may be justified.  However, the overall efficiency
of a regulatory action can only be judged through a complete
benefit-cost analysis that takes into account all benefits and costs,
including both health and nonhealth effects.  The benefit-cost analysis
for the PM NAAQS attainment strategies, provided in Chapter 9, shows
that the attainment strategies we modeled have potentially large net
benefits, indicating that implementation of the revised PM NAAQS will
likely result in improvements in overall public welfare.

6b.9	References

Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancuren, and P.K. Mills. 
1995.  “Estimated Long-Term Ambient Concentrations of PM(10) and
Development of Respiratory Symptoms in a Nonsmoking Population.” 
Archives of Environmental Health 50(2):  139-152.

Agency for Healthcare Research and Quality.  2000.  HCUPnet, Healthcare
Cost and Utilization Project.

American Lung Association.  2002.  Trends in Chronic Bronchitis and
Emphysema:  Morbidity and Mortality.  American Lung Association, Best
Practices and Program Services, Epidemiology and Statistics Unit.

American Heart Association.  2003.  Heart Disease and Stroke
Statistics—2003 Update.  Dallas, Texas:  American Heart Association.

Bala, M.V., and G.A. Zarkin.  2000.  “Are QALYs an Appropriate Measure
for Valuing Morbidity in Acute Diseases?”  Health Economics 9:177-180.

Bell, C.M., R.H. Chapman, P.W. Stone, E.A. Sandberg, and P.J. Neumann. 
2001.  “An Off-the-Shelf Help List:  A Comprehensive Catalog of
Preference Scores from Published Cost-Utility Analyses.”  Medical
Decision Making 21:288-94.

Bleichrodt, H., P.P. Wakker, and M. Johannesson.  1996. 
“Characterizing QALYs by Risk Neutrality.”  Journal of Risk and
Uncertainty 15:107-114.

Brock, D.W.  2002.  “Chapter 14.3:  Fairness and Health.”  In
Summary Measures of Population Health:  Concepts, Ethics, Measurement
and Applications, C.J.L. Murray, J.A. Salomon, C.D. Mathers, and A.D.
Lopez (eds.).  Geneva:  World Health Organization.

Carrothers, T. J., J.S. Evans, and J.D. Graham.  2002.  “The
Lifesaving Benefits of Enhanced Air Quality.”  Harvard Center for Risk
Analysis Working Paper.

Carnethon, M.R., D. Liao, G.W. Evans, W.E. Cascio, L.E. Chambless, W.D.
Rosamond, and G. Heiss.  2002.  “Does the Cardiac Autonomic Response
to Postural Change Predict Incident Coronary Heart Disease and
Mortality?  The Atherosclerosis Risk in Communities Study.”  American
Journal of Epidemiology 155(1):48-56.

Centers for Disease Control and Prevention (CDC) Health, United States. 
2003.  Table 30.  Years of Potential Life Lost before Age 75 for
Selected Causes of Death, According to Sex, Race, and Hispanic Origin: 
United States, Selected Years 1980–2000.

Centers for Disease Control and Prevention (CDC).  2002.  National Vital
Statistics Reports 51(3).

Cohen, D.J., J.A. Breall, and K.K. Ho.  1994.  “Evaluating the
Potential Cost-Effectiveness of Stenting as a Treatment for Symptomatic
Single-Vessel Coronary Disease:  Use of a Decision-Analytic Model.” 
Circulation 89(4):1859-1874.

Cohen, J.T., J.K. Hammitt, and J.I. Levy.  2003.  “Fuels for Urban
Transit Buses:  A Cost-effectiveness Analysis.”  Environmental Science
and Technology 37:1477-1484.

Cowie, M.R., A. Mosterd, D.A. Wood, et al.  1997.  “The Epidemiology
of Heart Failure.”  European Heart Journal 18:208-25.

Coyle, D., D. Stieb, R.T. Burnett, P. DeCivita, D. Krewski, Y. Chen, and
M.J. Thun.  2003.  “Impact of Particulate Air Pollution on Quality
Adjusted Life Expectancy in Canada.”  Journal of Toxicology and
Environmental Health, Part A 66:1847-1863.

Cropper, M.L., and A.J. Krupnick.  1990.  “The Social Costs of Chronic
Heart and Lung Disease.”  Discussion Paper QE 89-16-REV.  Washington,
DC:  Resources for the Future.

de Hollander, A.E.M., J.M. Melse, E. Lebret, and P.G.N. Kramers.  1999. 
“An Aggregate Public Health Indicator to Represent the Impact of
Multiple Environmental Exposures.”  Epidemiology 10:606-617.

Dekker, J.M., R.S. Crow, A.R. Folsom, P.J. Hannan, D. Liao, C.A. Swenne,
and E.G. Schouten.  2000.  “Low Heart Rate Variability in a 2-Minute
Rhythm Strip Predicts Risk of Coronary Heart Disease and Mortality From
Several Causes:  The ARIC Study.”  Circulation 2000 102:1239-1244.

Freeman, A.M., J.K. Hammitt, and P. De Civita.  2002.  “On Quality
Adjusted Life Years (QALYs) and Environmental/Consumer Safety
Valuation.”  AERE Newsletter 22(1):7-12.

Ganz, D.A., K.M. Kuntz, G.A. Jacobson, and J. Avorn.  2000. 
“Cost-Effectiveness of 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase
Inhibitor Therapy in Older Patients with Myocardial Infarction.” 
Annals of Internal Medicine 132:780-787.

Garber, A.M., and C.E. Phelps.  1997.  “Economic Foundations of
Cost-Effectiveness Analysis.”  Journal of Health Economics 16:1-31.

Gentile, J.E.  1998.  Random Number Generation and Monte Carlo Methods. 
New York, NY:  Springer Verlag.

Gold, M.R., J.E. Siegel, L.B. Russell, and M.C. Weinstein.  1996. 
Cost-effectiveness in Health and Medicine.  New York:  Oxford University
Press.

Gold, D.R., A. Litonjua, J. Schwartz, E. Lovett, A. Larson, B. Nearing,
G. Allen, M. Verrier, R. Cherry, and R. Verrier.  2000.  “Ambient
Pollution and Heart Rate Variability.”  Circulation 101(11):1267-1273.

Gold, M.R., D. Stevenson, and D.G. Fryback.  2002.  “HALYS and QALYS
and DALYS, Oh My:  Similarities and Differences in Summary Measures of
Population Health.”  Annual Review of Public Health 23:115–34.

Hammitt, J.K.  2002.  “QALYs versus WTP.”  Risk Analysis
22(5):985-1001.

Harvard Center for Risk Analysis, Catalog of Preference Scores. 
Available at:  http://www.hcra.harvard.edu/pdf/preferencescores.pdf.

Horsman, J., W. Furlong, D. Feeny, and G. Torrance.  2003.  “The
Health Utilities Index (HUI):  Concepts, Measurement Properties, and
Applications.”  Health and Quality of Life Outcomes 1:54.

Johnson, F.R., and K. Lievense.  2000.  Stated-Preference Indirect
Utility and Quality-Adjusted Life Years.  Report prepared for Health
Canada, July.

Kerridge, R.K., P.P. Glasziou, and K.M. Hillman.  1995.  “The Use of
“Quality-Adjusted Life Years” (QALYs) to Evaluate Treatment in
Intensive Care.”  Anaesthia and Intensive Care 23:322-31.

Kind, P.  1996.  The EuroQoL Instrument:  An Index of Health-Related
Quality of Life.  Quality of Life and Pharmacoeconomics in Clinical
Trials, Second Edition, B. Spilker (ed.), pp. 191-201.  Philadelphia,
PA:  Lippincott-Raven Publishers.

Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M.
Jerrett, M. Abrahamowicz, and W.H. White.  2000.  Reanalysis of the
Harvard Six Cities Study and the American Cancer Society Study of
Particulate Air Pollution and Mortality.  Special Report to the Health
Effects Institute, Cambridge, MA.

Kuntz, K.M, J. Tsevant, L. Goldman, and M.C. Weinstein.  1996. 
“Cost-Effectiveness of Routine Coronary Angiography after Acute
Myocardial Infarction.”  Circulation  94(5):957-65.

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger,
M. Herry, F. Horak Jr., V. Puybonnieux-Texier, P. Quenel, J. Schneider,
R. Seethaler, J-C Vergnaud, and H. Sommer.  2000.  “Public-Health
Impact of Outdoor and Traffic-Related Air Pollution:  A European
Assessment.”  The Lancet 356:795-801.

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr, and M.
Studnicka.  2001.  “Assessment of Deaths Attributable to Air
Pollution:  Should We Use Risk Estimates Based on Time Series or on
Cohort Studies?”  American Journal of Epidemiology 153(11):1050-55.

Liao, D., J. Cai, W.D. Rosamond, R.W. Barnes, R.G. Hutchinson, E.A.
Whitsel, P. Rautaharju, and G. Heiss.  1997.  “Cardiac Autonomic
Function and Incident Coronary Heart Disease:  A Population-Based
Case-Cohort Study.  The ARIC Study.  Atherosclerosis Risk in Communities
Study.”  American Journal of Epidemiology 145(8):696-706.

Liao, D., J. Creason, C. Shy, R. Williams, R. Watts, and R. Zweidinger. 
1999.  “Daily Variation of Particulate Air Pollution and Poor Cardiac
Autonomic Control in the Elderly.”  Environmental Health Perspectives
107:521-5.

Magari, S.R., R. Hauser, J. Schwartz, P.L. Williams, T.J. Smith, and
D.C. Christiani.  2001.  “Association of Heart Rate Variability with
Occupational and Environmental Exposure to Particulate Air Pollution.”
 Circulation 104(9):986-91.

Miller, B.G., and J.F. Hurley.  2003.  “Life Table Methods for
Quantitative Impact Assessments in Chronic Mortality.”  Journal of
Epidemiology and Community Health 57:200-206.

Moolgavkar, S.H.  2000.  “Air Pollution and Hospital Admissions for
Diseases of the Circulatory System in Three U.S. Metropolitan Areas.” 
Journal of the Air Waste Management Association 50:1199-206.

Murray, C.J.L., J.A. Salomon, C.D. Mathers, and A.D. Lopez.  2002. 
“Summary Measures of Population Health:  Concepts, Ethics, Measurement
and Applications.”  Geneva:  World Health Organization.

National Research Council (NRC).  2002.  Estimating the Public Health
Benefits of Proposed Air Pollution Regulations.  Washington, DC:  The
National Academies Press.

Neumann, P.  2003.  “A Web-based Registry of Cost-utility Analyses.”
 Risk in Perspective 11(3).

Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mittleman.  2001. 
“Increased Particulate Air Pollution and the Triggering of Myocardial
Infarction.”  Circulation 103:2810-2815.

Pliskin, J.S., D.S. Shepard, and M.C. Weinstein.  1980.  “Utility
Functions for Life Years and Health Status.”  Operations Research
28:206-224.

Pliskin, J.S., W.B. Stason, and M.C. Weinstein.  1981.  “Coronary
Artery Bypass Graft Surgery:  Clinical Decision Making and
Cost-Effective Analysis.”  Medical Decision Making 1(1):10-28.

Poloniecki, J.D., R.W. Atkinson, A.P. de Leon, and H.R. Anderson.  1997.
 “Daily Time Series for Cardiovascular Hospital Admissions and
Previous Day’s Air Pollution in London, UK.”  Occupational and
Environmental Medicine 54(8):535-40.

Pope, D.A., R.T. Burnett, M.J. Thun, E.E. Cale, D. Krewski, K. Ito, and
G.D. Thurston.  2002.  “Lung Cancer, Cardiopulmonary Mortality, and
Long-Term Exposure to Fine Particulate Air Pollution.”  Journal of the
American Medical Association 287:1132-1141.

Rabl, A.  2003.  “Interpretation of Air Pollution Mortality:  Number
of Deaths or Years of Life Lost?”  Journal of the Air and Waste
Management Association 53:41-50.

Russell, M.W., D.M. Huse, S. Drowns, E.C. Hamel, and S.C. Hartz.  1998. 
“Direct Medical Costs of Coronary Artery Disease in the United
States.”  American Journal of Cardiology 81(9):1110-5.

Salkeld, G., P. Phongsavan, and B. Oldenburg.  1997.  “The
Cost-Effectiveness of a Cardiovascular Risk Reduction Program in General
Practice.”  Health Policy 41:105-19.

Salomon, J.A., and C.J.L. Murray.  2003.  “A Multi-Method Approach to
Measuring Health-State Valuations.”  Health Economics, online.

Samet, J.M., S.L. Zeger, F. Dominici, F. Curriero, I. Coursac, D.W.
Dockery, J. Schwartz, and A. Zanobetti.  June 2000.  The National
Morbidity, Mortality and Air Pollution Study:  Part II:  Morbidity,
Mortality and Air Pollution in the United States.  Research Report No.
94, Part II.  Cambridge, MA:  Health Effects Institute.

Schwartz, J.  1993.  “Particulate Air Pollution and Chronic
Respiratory Disease.”  Environmental Research 62:7-13.

Singer, P., J. McKie, H. Kuhse, J. Richardson, and J. Harris.  1995. 
“Double Jeopardy and the Use of QALYs in Health Care Allocation.” 
Journal of Medical Ethics 21:144-157.

Smith, K.J., and R.R. Pesce.  1994.  “Pulmonary Artery Catheterization
in Exacerbations of COPD Requiring Mechanical Ventilation:  A
Cost-Effectiveness Analysis.”  Respiratory Care 39:961-7.

Stinnett, A.A., M.A. Mittleman, M.C. Weinstein, K.M. Kuntz, D.J. Cohen,
L.W. Williams, P.A. Goldman, D.O. Staiger, M.G.M. Hunink, J. Tsevat,
A.N.A. Tosteson, and L. Goldman.  1996.  “The Cost-effectiveness of
Dietary and Pharmacologic Therapy for Cholesterol Reduction in
Adults.”  In Cost-effectiveness in Health and Medicine, Gold, M.E., J.
E. Siegel, L.B. Russell, and M.C. Weinstein (eds.).  New York:  Oxford
University Press.

Stouthard, M.E.A., J.L. Essink-Bot, G.J. Bonsel, J.J. Barendregt, P.G.N.
Kramers, H.P.A. Van de Water, and L.J. Gunning-Schepers.  1997. 
“Disability Weights for Diseases in the Netherlands.”  Rotterdam: 
Department of Public Health, Erasmus University Rotterdam.

Suwa, T. J.C. Hogg, K.B. Quinlan, A. Ohgami, R. Vincent, and S.F. van
Eeden.  2002.  “Particulate Air Pollution Induces Progression of
Atherosclerosis.”  Journal of the American College of Cardiology
39:935-942.

Tsuji, H., M.G. Larson, F.J. Venditti, Jr., E.S. Manders, J.C. Evans,
C.L. Feldman, and D. Levy.  1996.  “Impact of Reduced Heart Rate
Variability on Risk for Cardiac Events.  The Framingham Heart Study.” 
Circulation 94(11):2850-2855.

U.S. Environmental Protection Agency (EPA).  1996.  Review of the
National Ambient Air Quality Standards for Particulate Matter: 
Assessment of Scientific and Technical Information.  Report no.
EPA/4521R-96-013.  Research Triangle Park, NC:  Office of Air Quality
Planning and Standards.

U.S. Environmental Protection Agency (EPA).  2000.  Guidelines for
Preparing Economic Analyses.  EPA 240-R-00-003.

U.S. Environmental Protection Agency (EPA). 2004. Final Regulatory
Analysis:  Control of Emissions from Nonroad Diesel Engines.

U.S. Environmental Protection Agency (EPA) Science Advisory Board (SAB).
 EPA-SAB-COUNCIL-ADV-04-002.  March 2004.  Advisory on Plans for Health
Effects Analysis in the Analytical Plan for EPA’s Second Prospective
Analysis—Benefits and Costs of the Clean Air Act, 1990-2020:  Advisory
by the Health Effects Subcommittee of the Advisory Council on Clean Air
Compliance Analysis.

U.S. Food and Drug Administration (FDA). 2004.  Bar Code Label
Requirements for Human Drug Products and Biological  Products; Final
Rule.  Federal Register:  February 26, 2004 Volume 69 (38):  9119-9171.

U.S. Office of Management and Budget (OMB).  2003.  Circular A-4: 
Regulatory Analysis, September 17, 2003.  Available at: 
http://www.whitehouse.gov/omb/ circulars/a004/a-4.pdf.

Vos, T.  1999a.  Final worksheet VM1 COPD.xls, prepared for Victoria
Burden of Disease Study.  Available at: 
http://www.dhs.vic.gov.au/phd/bod/daly.htm.

Vos, T.  1999b.  Final worksheet VL2 IHD.xls, prepared for Victoria
Burden of Disease Study.  Available at: 
http://www.dhs.vic.gov.au/phd/bod/daly.htm.

Wittels, E.H., J.W. Hay, and A.M. Gotto, Jr.  1990.  “Medical Costs of
Coronary Artery Disease in the United States.”  American Journal of
Cardiology 65(7):432-40.

 	National Academy of Sciences (2007) Project Scope.  Estimating
Mortality Risk Reduction Benefits from Decreasing Tropospheric Ozone
Exposure.  Division on Earth and Life Studies, Board on Environmental
Studies and Toxicology. Available at:   HYPERLINK
"http://www8.nationalacademies.org/cp/projectview.aspx?key=48768" 
http://www8.nationalacademies.org/cp/projectview.aspx?key=48768  

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e entire population, while life years gained measures life years gained
only for those experiencing the life extension.

 The Kaldor-Hicks efficiency criterion requires that the “winners”
in a particular case be potentially able to compensate the “losers”
such that total societal welfare improves.  In this case, it is
sufficient that total benefits exceed total costs of the regulation. 
This is also known as a potential Pareto improvement, because gains
could be allocated such that at least one person in society would be
better off while no one would be worse off.

 Prevalence rates for CB were obtained from the 1999 National Health
Interview Survey (American Lung Association, 2002  XE "American Lung
Association, 2002"  ).  Prevalence rates were available for three age
groups:  18–44, 45–64, and 65 and older.  Prevalence rates per
person for these groups were 0.0367 for 18–44, 0.0505 for 45–64, and
0.0587 for 65 and older.  The incidence rate for new cases of CB
(0.00378 per person) was taken directly from Abbey et al. (1995  XE
"Abbey et al. (1995"  ).

 Monte Carlo simulation uses random sampling from distributions of
parameters to characterize the effects of uncertainty on output
variables.  For more details, see Gentile (1998  XE "Gentile (1998"  ).

 Daily nonfatal myocardial infarction incidence rates per person were
obtained from the 1999 National Hospital Discharge Survey (assuming all
diagnosed nonfatal AMI visit the hospital).  Age-specific rates for four
regions are used in the analysis.  Regional averages for populations 18
and older are 0.0000159 for the Northeast, 0.0000135 for the Midwest,
0.0000111 for the South, and 0.0000100 for the West.

 Average length of stay estimated from the HCUP data includes all
discharges, including those due to death.  As such, the 5.5-day average
length of stay is likely an underestimate of the average length of stay
for AMI admissions where the patient is discharged alive.

 Gold et al. (1996  XE "Gold et al. (1996"  ) recommend not including
lost earnings in the cost-of-illness estimates, suggesting that in some
cases, they may be already be counted in the effectiveness measures. 
However, this requires that individuals fully incorporate the value of
lost earnings and reduced labor force participation opportunities into
their responses to time-tradeoff or standard-gamble questions.  For the
purposes of this analysis and for consistency with the way
costs-of-illness are calculated for the benefit-cost analysis, we have
assumed that individuals do not incorporate lost earnings in responses
to these questions.  This assumption can be relaxed in future analyses
with improved understanding of how lost earnings are treated in
preference elicitations.

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