# Health-Based Cost-Effectiveness of Reductions in Ambient PM~2.5~ Associated with Illustrative Ozone NAAQS 0.070ppm Attainment Strategy

## \_.1 Summary {#summary .App-Heading-2}

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 PM~2.5~ 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[^1] 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). 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 PM~2.5~ in achieving improvements in public
health. Reductions in ambient PM~2.5~ 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 far
exceeds costs (net benefits are over \_\_ billion in 2020). 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 PM~2.5~ 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) 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:

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-   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.

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```
-   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.

-   

-   D irect private compliance costs for the 0.070 ppm partial
    attainment strategy, are \$6.4 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 \$700,000/MILY using a 3 percent discount
    rate and \$920,000/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 \$380,000/MILY using a 3 percent discount
    rate and \$500,000/MILY using a 7 percent discount rate if one
    calculates MILY's using the Laden (2006) mortality estimate. See
    Chapters \_\_ and \_\_ of this RIA for more discussion of the
    control strategies and cost estimates.

## \_.2 Introduction {#introduction .App-Heading-2}

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 (Neumann, 2003; Gold et al., 1996) and in the
analysis of public health interventions (US 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 (Hammitt, 2002), treatment of nonhuman
health benefits, and a number of other factors (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 (Cohen, Hammitt, and Levy, 2003; Coyle et al.,
2003; Rabl, 2003; 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 (Murray et
al., 2002; de Hollander et al., 1999).

Recently, the U.S. OMB (Circular A-4, 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 PM~2.5~ 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 PM~2.5~ 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 (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. (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). 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 PM~2.5~ precursors, such as NOx
and VOC's, 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 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 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 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.

## \_.3 Effectiveness Measures {#effectiveness-measures .App-Heading-2}

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 PM~2.5~ 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 PM~2.5~. 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.[^2] 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 (Rabl, 2003; Miller and Hurley, 2003).

Calculations of changes in life years and life expectancy can be
accomplished using standard life table methods (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 (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 (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 (Pliskin, Shepard, and Weinstein, 1980;
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.[^3] 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 \[Garber and Phelps, 1997\]).

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 \_-1: Comparison of QALY and WTP Approaches

  ------------------------------ ---------------------- --------------------
  Parameter                      QALY                   WTP

  Risk aversion                  Risk neutral           Empirically
                                                        determined

  Relation of duration and       Independent            Empirically
  quality                                               determined

  Proportionality of duration/   Constant               Variable
  quality trade-off                                     

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

  Preferences                    Community/Individual   Individual

  Source of preference data      Stated                 Revealed and stated

  Treatment of income and prices Not explicitly         Constrains choices
                                 considered             
  ------------------------------ ---------------------- --------------------

## \_.4 Changes in Premature Death, Life Years, and Quality of Life {#changes-in-premature-death-life-years-and-quality-of-life .App-Heading-2}

To generate health outcomes, we used the same framework as for the
benefit-cost analysis described in Chapter \_\_. For convenience, we
summarize the basic methodologies here. For more details, see Chapter
\_\_ 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:

![](media/image1.wmf){width="1.4166666666666667in"
height="0.2604166666666667in"}

where y~0~ 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 PM~2.5~. There are other
functional forms, but the basic elements remain the same.

### \_.4.1 Calculating Reductions in Premature Deaths {#calculating-reductions-in-premature-deaths .App-Heading-3}

As in several recent air pollution health impact assessments (e.g.,
Kunzli et al., 2000; 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 (Kunzli et al., 2001; NRC, 2002). We selected effects estimate from
the extended analysis of the ACS cohort (Pope et al., 2002) as well as
from the Harvard Six City Study (Laden et al., 2006). This latest
re-analysis of the ACS cohort data 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 PM~2.5~ following
implementation of PM~2.5~ 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 Pope et al. (2002)
quantifies the relationship between annual mean PM~2.5~ 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 PM~2.5~ 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 PM~2.5~. Although there are other cohort-based studies of the
relationship between PM~2.5~ and mortality, none provide the same level
of population and geographic coverage as the ACS study.

[\[Insert description of Laden 2006 study here\]]{.mark}

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.

### \_.4.2 Calculating Changes in Life Years from Direct Reductions in PM~2.5~-Related Mortality Risk {#calculating-changes-in-life-years-from-direct-reductions-in-pm2.5-related-mortality-risk .App-Heading-3}

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 (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 CDC (2002). Table
G-3 presents the abridged life table for 10-year age intervals for
adults over 30 (to match the 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 \_-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\                           9\
                 (2 -- 6)                     (5 -- 13)

  35 -- 44       12\                          28\
                 (5 -- 20)                    (15 -- 40)

  45 -- 54       26\                          60\
                 (10 -- 42)                   (32 -- 87)

  55 -- 64       70\                          160\
                 (27 -- 110)                  (86 -- 230)

  65 -- 74       120\                         280\
                 (48 -- 200)                  (150 -- 400)

  75 -- 84       140\                         320\
                 (56 -- 230)                  (180 -- 470)

  85+            170\                         390\
                 (68 -- 280)                  (210 -- 570)

  Total          550\                         1,300\
                 (220 -- 890)                 (680 -- 1,800)
  -------------- ---------------------------- ----------------------------

From the abridged life table (Table \_-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:

![](media/image2.wmf){width="2.270138888888889in"
height="0.4895833333333333in"}

where LE~i~ is the remaining life expectancy for age interval *i*, M~i~
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 \_-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 (Gold et al., 1996).

Table G-\_: Abridged Life Table for the Total Population, United States,
2000

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

  Start Age  End   q~x~          I~x~        d~x~      L~x~      T~x~        e~x~
             Age                                                             

  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:

![](media/image3.wmf){width="1.9694444444444446in"
height="0.3645833333333333in"}

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 PM~2.5~ 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 \_\_\_).

The most complete estimate of the impacts of PM~2.5~ on life years is
calculated using the Pope et al. (2002) C-R function relating all-cause
mortality in adults 30 and over with ambient PM~2.5~ 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
PM~2.5~ may affect mainly individuals with serious preexisting illnesses
is not supported by current empirical studies. For example, the 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 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 (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.

#### \_.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 (Cohen, Hammitt, and Levy, 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. Brock (2002) notes that "the problem of disability
discrimination represents a deep and unresolved problem for resource
prioritization."

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 (Abbey et al., 1995; Schwartz, 1993; 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 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 (Carrothers,
Evans, and Graham, 2002; 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"
(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.

## \_.5 Calculating Changes in the Quality of Life Years (Morbidity) {#calculating-changes-in-the-quality-of-life-years-morbidity .App-Heading-2}

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 PM~2.5~. 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 (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 (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 (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 (Horsman et al., 2003; 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.

### \_.5.1 Calculating QALYs Associated with Reductions in the Incidence of Chronic Bronchitis {#calculating-qalys-associated-with-reductions-in-the-incidence-of-chronic-bronchitis .App-Heading-3}

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 (American Lung Association,
1999). For gains in quality of life resulting from reduced incidences of
PM-induced CB, discounted QALYs are calculated as

![](media/image4.wmf){width="4.333333333333333in"
height="0.3951388888888889in"}

where CB~i~ is the number of incidences of CB avoided in age interval
i, w~i~ is the average QALY weight for age interval i,
![](media/image5.wmf){width="0.3125in" height="0.2604166666666667in"} is
the QALY weight associated with CB,
![](media/image6.wmf){width="0.28125in" height="0.3013888888888889in"}
is the discounted duration of life with CB for individuals with onset of
disease in age interval i, equal to
![](media/image7.wmf){width="0.7604166666666666in" height="0.4375in"},
where D~i~ is the duration of life with CB for individuals with onset of
disease in age interval i.

A limited number of studies have estimated the impact of air pollution
on new incidences of CB. Schwartz (1993) and Abbey et al. (1995) provide
evidence that long-term PM exposure gives rise to the development of CB
in the United States. Because this analysis focuses on the impacts of
reducing ambient PM~2.5~, only the Abbey et al. (1995) study is used,
because it is the only study focusing on the relationship between
PM~2.5~ and new incidences of CB. The number of cases of CB in each age
interval is derived from applying the impact function from Abbey et al.
(1995), to the population in each age interval with the appropriate
baseline incidence rate.[^4] The effect estimate from the Abbey et al.
(1995) study is 0.0137, which, based on the logistic specification of
the model, is equivalent to a relative risk of 1.15 for a 10 g change
in PM~2.5~. Table G-5 presents the estimated reduction in new incidences
of CB associated with the illustrative PM NAAQS attainment strategies.

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

  ------------------ ------------- -----------------------------------------
  Parameter          Value(s)      Source(s)

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

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

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

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

  Probability of     0.51          American Heart Association, 2003\
  angina post AMI                  (Calculated as the population with angina
                                   divided by the total population with
                                   heart disease)

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

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

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

  Quality-of-life    0.93          Only one value available from the
  preference score                 literature. Thus, no distribution is
  for post-AMI (no                 specified. Source of value is Kuntz et
  angina, no CHF)                  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 (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 \_-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 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, 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. 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 (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 Stouthard et al. (1997) of
chronic diseases in Dutch populations (Vos, 1999a). Based on the
recommendations of 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" (Gold et al.,
1996). Following 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.[^5] 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 \_-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\            6.52\
                                      (4.40-19.95)      (2.36-10.71)

  35                44                9.91\             5.94\
                                      (3.54-16.10)      (2.12-9.66)

  45                54                7.49\             5.03\
                                      (2.71-12.34)      (1.82-8.29)

  55                64                5.36\             4.03\
                                      (1.95-8.80)       (1.47-6.61)

  65                74                3.40\             2.84\
                                      (1.22-5.64)       (1.02-4.71)

  75                84                2.15\             1.92\
                                      (0.77-3.49)       (0.69-3.13)

  85+                                 0.79\             0.77\
                                      (0.27-1.29)       (0.26-1.25)
  ----------------- ----------------- ----------------- -----------------

### \_.5.2 Calculating QALYs Associated with Reductions in the Incidence of Nonfatal Myocardial Infarctions {#calculating-qalys-associated-with-reductions-in-the-incidence-of-nonfatal-myocardial-infarctions .App-Heading-3}

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 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 (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:

![](media/image8.wmf){width="5.333333333333333in" height="0.75in"}

where AMI~i~ is the number of nonfatal acute myocardial infarctions
avoided in age interval *i*, ![](media/image9.wmf){width="0.375in"
height="0.25in"}is the QALY weight associated with the acute phase of
the AMI, p~j~ is the probability of being in the *j*th post-AMI status,
![](media/image10.wmf){width="0.5416666666666666in"
height="0.2777777777777778in"}is the QALY weight associated with
post-AMI health status *j*, w~i~ is the average QALY weight for age
interval i, ![](media/image11.wmf){width="1.3465277777777778in"
height="0.3888888888888889in"} , the discounted value of
![](media/image12.wmf){width="0.3888888888888889in" height="0.25in"},
the duration of the acute phase of the AMI, and
![](media/image13.wmf){width="1.625in" height="0.3888888888888889in"},
is the discounted value of
![](media/image14.wmf){width="0.5555555555555556in"
height="0.2777777777777778in"}, the duration of post-AMI health status
*j*.

![](media/image17.wmf){width="6.25in" height="5.122222222222222in"}

**Figure** **\_-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
PM~2.5~ in the United States (Peters et al., 2001) and other countries
(Poloniecki et al., 1997). We used a recent study by Peters et al.
(2001) as the basis for the impact function estimating the relationship
between PM~2.5~ 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 Samet et al. (2000) and 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 (Liao et al., 1999; Gold et al., 2000; 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 (Carthenon et al., 2002; Dekker et al.,
2000; Liao et al., 1997, 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 Peters et al. (2001) to the population
in each age interval with the appropriate baseline incidence rate.[^6]
The effect estimate from the 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 PM~2.5~. Table \_-7
presents the estimated reduction in nonfatal AMI associated with the
illustrative Ozone NAAQS attainment strategies.

Table \_-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 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).[^7] 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 (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 Vos (1999b). The probability is based on a study by 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 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 \_‑8 provides the
duration (both discounted and undiscounted) of CHF assumed for post-AMI
cases by age interval.

Table \_-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. 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 \_-9. These life expectancies are then used
to represent the duration of non-CHF post-AMI health states (III and
IV).

Table \_-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 Harvard database includes two sets of community
preference-based scores for CHF (Stinnett 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 (Kuntz et al., 1996), one that specifies mild
angina only (Pliskin, Stason, and Weinstein, 1981), one that specifies
severe angina only (Cohen, Breall, and Ho, 1994), and two that specify
angina with no severity classification (Salkeld, Phongsavan, and
Oldenburg, 1997; 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 (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.

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 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 \_-10, along
with the 95 percent confidence interval resulting from the Monte Carlo
simulation. Table \_-10 presents both the undiscounted and discounted
QALYs gained per incidence.

Table \_-10: QALYs Gained per Avoided Nonfatal Myocardial Infarction

  ----------------- ----------------- ----------------- -----------------
  Age Interval                        QALYs Gained per  
                                      Incidence^a^      

  *Start Age*       *End Age*         *Undiscounted*    *Discounted (3%)*

  18                24                4.18\             2.17\
                                      (1.24-7.09)       (0.70-3.62)

  25                34                3.48\             2.00\
                                      (1.09-5.87)       (0.68-3.33)

  35                44                2.81\             1.79\
                                      (0.88-4.74)       (0.60-2.99)

  45                54                2.14\             1.52\
                                      (0.67-3.61)       (0.51-2.53)

  55                64                1.49\             1.16\
                                      (0.42-2.52)       (0.34-1.95)

  65                74                0.97\             0.83\
                                      (0.30-1.64)       (0.26-1.39)

  75                84                0.59\             0.54\
                                      (0.20-0.97)       (0.19-0.89)

  85+                                 0.32\             0.31\
                                      (0.13-0.50)       (0.13-0.49)
  ----------------- ----------------- ----------------- -----------------

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

## U.6 Cost-Effectiveness Analysis {#u.6-cost-effectiveness-analysis .App-Heading-2}

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.

### \_.6.1 Aggregating Life Expectancy and Quality-of-Life Gains {#aggregating-life-expectancy-and-quality-of-life-gains .App-Heading-3}

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" (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
\_-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 \_-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 Mortality^a^

  -------- ---------------- ---------------- ----------------- --------------
  Age      Life Years       QALY Gained from QALY Gained from  Total Gain in\
           Gained from      Reductions in    Reductions in     MILYs\
           Mortality Risk\  Chronic          Acute Myocardial  (95% CI)
           Reductions\      Bronchitis\      Infarctions\      
           (95% CI)         (95% CI)         (95% CI)          

  18--24   ---              ---              3\                3\
                                             (1 -- 5)          (1 -- 5)

  25--34   104\             490\             8\                600\
           (41 -- 170)      (91 -- 1,100)    (3 -- 15)         (130 -- 1,200)

  35--44   305\             500\             70\               870\
           (120 -- 490)     (93 -- 1,100)    (23 -- 120)       (240 -- 1,700)

  45--54   580\             420\             170\              1,200\
           (230 -- 930)     (77 -- 890)      (60 -- 320)       (360 -- 2,100)

  55--64   1,300\           350\             330\              2,000\
           (500 -- 2,100)   (64 -- 760)      (110 -- 620)      (680 -- 3,400)

  65--74   1,700\           180\             280\              2,200\
           (680 -- 2,800)   (33 -- 380)      (100 -- 530)      (810 -- 3,700)

  75--84   1,400\           60\              150\              1,600\
           (540 -- 2,200)   (11 -- 130)      (54 -- 280)       (610 -- 2,600)

  85+      690\             10\              43\               740\
           (270 -- 1,100)   (2 -- 22)        (16 -- 80)        (290 -- 1,200)

  Total    6,100\           2,000\           1,100\            9,100\
           (2,400 -- 9,800) (370 -- 4,300)   (370 -- 2,000)    (3,100 --
                                                               16,000)
  -------- ---------------- ---------------- ----------------- --------------

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

Table \_-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 Mortality^a^

  -------- ---------------- ---------------- ----------------- --------------
  Age      Life Years       QALY Gained from QALY Gained from  Total Gain in\
           Gained from      Reductions in    Reductions in     MILYs\
           Mortality Risk\  Chronic          Acute Myocardial  (95% CI)
           Reductions\      Bronchitis\      Infarctions\      
           (95% CI)         (95% CI)         (95% CI)          

  18--24   ---              ---              3\                3\
                                             (1 -- 5)          (1 -- 5)

  25--34   240\             490\             8\                730\
           (130 -- 340)     (91 -- 1,100)    (3 -- 15)         (220 -- 1,400)

  35--44   690\             500\             70\               1,300\
           (380 -- 1,000)   (93 -- 1,100)    (23 -- 120)       (490 -- 2,200)

  45--54   1,400\           420\             170\              1,900\
           (710 -- 1,900)   (77 -- 890)      (60 -- 320)       (850 -- 3,100)

  55--64   2,900\           350\             330\              3,600\
           (1,600 -- 4,200) (64 -- 760)      (110 -- 620)      (1,800 --
                                                               5,600)

  65--74   3,900\           180\             280\              4,400\
           (2,100 -- 5,700) (33 -- 380)      (100 -- 530)      (2,300 --
                                                               6,600)

  75--84   3,100\           60\              150\              3,300\
           (1,700 -- 4,600) (11 -- 130)      (54 -- 280)       (1,800 --
                                                               5,000)

  85+      1,600\           10\              43\               1,600\
           (840 -- 2,300)   (2 -- 22)        (16 -- 80)        (860 -- 2,400)

  Total    14,000\          2,000\           1,100\            17,000\
           (7,500 --        (370 -- 4,300)   (370 -- 2,000)    (8,200 --
           20,000)                                             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).

### \_.6.2 Dealing with Acute Health Effects and Nonhealth Effects {#dealing-with-acute-health-effects-and-nonhealth-effects .App-Heading-3}

Health effects from exposure to particulate air pollution encompass a
wide array of chronic and acute conditions in addition to premature
mortality (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, 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.
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 (Bell
et al., 2001; 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.

![](media/image18.wmf){width="5.622916666666667in"
height="3.8493055555555555in"}

**Figure** **\_-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).

### \_.6.3 Cost-Effectiveness Ratios {#cost-effectiveness-ratios .App-Heading-3}

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 PM~2.5~ and the avoided costs (cost savings) associated with the
reductions in morbidity (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.[^8] Using age-specific annual lost earnings
and medical costs estimated by 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 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. 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), 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 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
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 G-13.

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

  ----------------- ----------------- ----------------- -------------------
  Age Group         Opportunity Cost  Medical Cost^a^   Total Cost

  0 -- 24           \$0               \$65,902          \$65,902

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

  45 -- 54          \$12,253^b^       \$65,902          \$78,834

  55 -- 65          \$70,619^b^       \$65,902          \$140,649

  \>65              \$0               \$65,902          \$65,902
  ----------------- ----------------- ----------------- -------------------

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

> ^b^ From 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 G-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 \_-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  | *Chronic Bronchitis*          | *Nonfatal Acute Myocardial  |
| R     |                               | Infarction*                 |
| ange* |                               |                             |
+-------+-------------------------------+-----------------------------+
|       |                               |                             |
+-------+-------------------------------+-----------------------------+
| 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                       |
+-------+-------------------------------+-----------------------------+

## \_.7 Discount Rate Sensitivity Analysis {#discount-rate-sensitivity-analysis .App-Heading-2}

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 (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 Gold et al. (1996), in Chapter 6
of the EPA *Guidelines for Economic Analysis*, and in OMB Circular A-4.

Table \_-15: Summary of Results for the Illustrative Attainment
Strategies for the Revised and More Stringent PM NAAQS in 2020^a^

+------------------------+---------------------------------------------+
|                        | Result Using 3% Discount Rate (95%          |
|                        | Confidence Interval)                        |
+------------------------+---------------------------------------------+
|                        | *070 ppm* *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       | 2,000\                                      |
| reductions in chronic  | (370 -- 4,300)                              |
| bronchitis             |                                             |
+------------------------+---------------------------------------------+
| QALY gained from       | 1,100\                                      |
| reductions in acute    | (370 -- 2,000)                              |
| myocardial infarctions |                                             |
+------------------------+---------------------------------------------+
| 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         | \$\_\_\_ billion                            |
| strategy costs^b^      |                                             |
+------------------------+---------------------------------------------+
| Net cost per MILY      | \$\_\_\_\                                   |
|                        | (\$\_\_\_ -- \$\_\_\_)                      |
+------------------------+---------------------------------------------+

^a^ Consistent with recommendations of 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 \_-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 \_-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

## \_.8 Conclusions {#conclusions .App-Heading-2}

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.

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[Not Cited.]{.mark} Teng, T.O., M.E. Adams, J.S. Pliskin, D.G. Safran,
J.E. Siegel, M.C. Weinstein, and J.D. Graham. 1995. "Five Hundred
Life-Saving Interventions and Their Cost-Effectiveness." *Risk Analysis*
15:369-390.

[^1]: 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:
    <http://www8.nationalacademies.org/cp/projectview.aspx?key=48768>

[^2]: Life expectancy is an *ex ante* concept, indicating the impact on
    an entire population's expectation of the number of life years they
    have remaining, before knowing which individuals will be affected.
    Life expectancy thus incorporates both the probability of an effect
    and the impact of the effect if realized. Life years is an *ex post*
    concept, indicating the impact on individuals who actually die from
    exposure to air pollution. Changes in population life expectancy
    will always be substantially smaller than changes in life years per
    premature mortality avoided, although the total life years gained in
    the population will be the same. This is because life expectancy
    gains average expected life years gained over the entire population,
    while life years gained measures life years gained only for those
    experiencing the life extension.

[^3]: 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.

[^4]: Prevalence rates for CB were obtained from the 1999 National
    Health Interview Survey (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).

[^5]: 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).

[^6]: 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.

[^7]: 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.

[^8]: 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.