This chapter reports EPA’s analysis of a subset of the public health
and welfare impacts and associated monetized benefits to society of
illustrative implementation strategies to attain alternative NAAQS for
fine particulate matter (PM2.5).  EPA is required by Executive Order
(E.O.) 12866 to estimate the benefits and costs of major new pollution
control regulations.  Accordingly, the analysis presented here attempts
to answer three questions:  (1) what are the estimated nationwide
physical health and welfare effects of changes in ambient air quality
resulting from reductions in precursors to particulate matter (PM)
including carbonaceous particle, NOx, SO2, and NH3 emissions? (2) what
is the estimated monetary value of the changes in these effects
attributable to the final promulgated standards and an alternative,
tighter annual standard? and (3) how do the estimated monetized benefits
compare to the costs?  It constitutes one part of EPA’s thorough
examination of the relative merits of this regulation.

The analysis presented in this chapter uses a methodology generally
consistent with benefits analyses performed for the recent analysis of
the Clean Air Interstate Rule (EPA, 2005  XE "U.S. EPA, 2005"  ).  The
methodology diverges in three areas:

Rather than presenting both a “primary” estimate of the benefits and
a separate characterization of the uncertainty associated with that
estimate, the current analysis follows the recommendation of  NRC’s
2002 report “Estimating the Public Health Benefits of Proposed Air
Pollution Regulations” to begin moving the assessment of uncertainties
from its ancillary analyses into its main benefits  presentation through
the conduct of probabilistic analyses.

Since the publication of CAIR, we have completed a the full- scale
expert elicitation designed to more fully characterize the state of our
understanding of assessing the uncertainty in the concentration-response
function for PM-related premature mortality.  The elicitation results
form a major component of the current effort to use probabilistic
assessment techniques to Consistent with the recommendations of the NRC
report “Estimating the Public Health Benefits of Proposed Air
Pollution Regulations,” we are integrateing uncertainty into the main
benefits analysisthe results of this probabilistic assessment into the
main benefits analysis.

We have updated our projections of mortality incidence rates to be
consistent with the U.S. Census population projections that form the
basis of our future population estimates.  This change will result in a
reduction in mortality impacts in future years, as overall mortality
rates are projected to decline for most age groups.

We are providing additional characterizations of the impacts of assuming
alternative thresholds in the concentration-response functions derived
from the epidemiology literature.  Unless specifically noted, our base
premature mortality benefits estimates are based on an assumed cutpoint
in the long-term mortality concentration-response function at 10 µg/m3,
and an assumed cutpoint in the short-term morbidity
concentration-response functions at 10 µg/m3. We also show the results
of a sensitivity analysis for premature mortality, with 3 alternative
cutpoints, at 3 µg/m3, 7.5 µg/m3, and 12 µg/m3.

The benefits analysis takes as inputs the results of the CMAQ air
quality modeling described in Chapter 4.  Reductions in certain PM2.5
precursors such as NOx and VOC may also lead to changes in ambient
concentrations of ozone.  These changes in ozone will also have health
and welfare effects.  However, for this RIA, because the majority of the
illustrative strategies evaluated do not affect NOx and VOC emissions
(with the exception of nonattainment areas in parts of the western U.S.,
where we do not have adequate models for ozone), we focus on estimating
the health and welfare effects associated with changes in ambient PM2.5.
 This adds some uncertainty to the overall results, but given the likely
small magnitude of the impacts, this uncertainty will likely be small
relative to other modeling uncertainties.

A wide range of human health and welfare effects are linked to ambient
concentrations of PM2.5.  Potential human health effects associated with
PM2.5 range from premature mortality to morbidity effects linked to
long-term (chronic) and shorter-term (acute) exposures (e.g.,
respiratory and cardiovascular symptoms resulting in hospital
admissions, asthma exacerbations, and acute and chronic bronchitis
[CB]).  Welfare effects potentially linked to PM and its precursors
include materials damage and visibility impacts, as well as the impacts
associated with deposition of nitrates and sulfates.  Although methods
exist for quantifying the benefits associated with many of these human
health and welfare categories, not all can be evaluated at this time
because of limitations in methods and/or data.  Table 5-1 summarizes the
annual monetized health and welfare benefits associated with the
illustrative implementation strategies for alternative 15/35 and 14/35
standards in 2020, when the standards are expected to be fully attained.
 Table 5-2 lists the full complement of human health and welfare effects
associated with PM (and its precursors) and identifies those effects
that are quantified for the primary estimate and those that remain
unquantified because of current limitations in methods or available
data.

	Rangec of Mediand Estimates for Total Benefitsa, b (billions 1999$)

	15/35	14/35

Using a 3% discount rate	$XXX- $ xxx + B	$XXX + B

Using a 7% discount rate	$XXX + B	$XXX + B

a	For notational purposes, unquantified benefits are indicated with a
“B” to represent the sum of additional monetary benefits and
disbenefits.  A detailed listing of unquantified health and welfare
effects is provided in Table 5-2.

b	Results reflect the use of two different discount rates:  3% and 7%,
which are recommended by EPA’s Guidelines for Preparing Economic
Analyses (EPA, 2000b  XE "U.S. EPA, 2000b"  ) and OMB Circular A-4 (OMB,
2003  XE "OMB, 2003"  ).  Results are rounded to three significant
digits for ease of presentation and computation.

c   The minimums and maximums in the range are driven by the results of
the individual responses of 12 experts regarding the likely magnitude
and form of the concentration response function between fine PM and
premature mortality.

d   Although medians are presented here, it is important to note that
the full uncertainty in the benefits estimates, based on the
probabilistic analysis conducted, is characterized by looking at the 5th
and 95th percentiles for these estimates.  Those numbers can be found in
tables…..

Figure 5-1 illustrates the major steps in the benefits analysis.  Given
baseline and post-control emissions inventories for the emission species
expected to affect ambient air quality, we use sophisticated
photochemical air quality models to estimate baseline and post-control
ambient concentrations of PM, visibility, and deposition of nitrates and
sulfates for each year.  The estimated changes in ambient concentrations
are then combined with monitoring data to estimate population-level
potential exposures to changes in ambient concentrations for use in
estimating health effects.  Modeled changes in ambient data are also
used to estimate changes in visibility and changes in other air quality
statistics that are necessary to estimate welfare effects.  Changes

Table 5-2:  Human Health and Welfare Effects of Pollutants Controlled
to Simulate Attainment with PM2.5 Standardsa

Pollutant/Effect	Quantified and Monetized in Base Estimatesb	Quantified
and/or Monetized Effects in Sensitivity Analyses	Unquantified Effects -
Changes in

PM/Healthc	Premature mortality based on cohort study estimatesd

Bronchitis:  chronic and acute

Hospital admissions:  respiratory and cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial infarction)

Lower and upper respiratory illness

Minor restricted-activity days

Work loss days

Asthma exacerbations (asthmatic population)

Respiratory symptoms (asthmatic population)

Infant mortality	Subchronic bronchitis cases

	Low birth weight

Pulmonary function

Chronic respiratory diseases other than chronic bronchitis

Nonasthma respiratory emergency room visits

UVb exposure (+/-)e

PM/Welfare	Visibility in Southeastern, Southwestern, and California
Class I areas	Visibility in Northeastern, Midwestern, and Rocky Mountain
Class I areas

Household soiling	Visibility in residential and non-Class I areas

UVb exposure (+/-)e

Global climate impacts (+/-)e

Nitrogen and Sulfate Deposition/ Welfare

	Commercial forests due to acidic sulfate and nitrate deposition

Commercial freshwater fishing due to acidic deposition

Recreation in terrestrial ecosystems due to acidic deposition

Commercial fishing, agriculture, and forests due to nitrogen deposition

Recreation in estuarine ecosystems due to nitrogen deposition

Ecosystem functions

Passive fertilization

(continued)

Table 5-2:  Human Health and Welfare Effects of Pollutants Controlled to
Simulate Attainment with PM2.5 Standardsa (continued)

Pollutant/Effect	Quantified and Monetized in Base Estimatesb	Quantified
and/or Monetized Effects in Sensitivity Analyses	Unquantified Effects -
Changes in

SO2/Health

	Hospital admissions for respiratory and cardiac diseases

Respiratory symptoms in asthmatics

NOx/Health

	Lung irritation

Lowered resistance to respiratory infection

Hospital admissions for respiratory and cardiac diseases

a	Reductions in certain PM2.5 precursors such as NOx and VOC may also
lead to changes in ambient concentrations of ozone.  These changes in
ozone will also have health and welfare effects.  However, for this RIA,
because the majority of the illustrative strategies evaluated do not
affect NOx and VOC emissions, we focus on estimating the health and
welfare effects associated with changes in ambient PM2.5.  For a full
listing of health and welfare effects associated with ozone exposures,
see the Ozone Criteria Document (U.S. EPA, 2006  XE "U.S. EPA, 2006"  ),
and Chapter 4 of the RIA for the Clean Air Interstate Rule (U.S. EPA,
2005  XE "U.S. EPA, 2005"  ).

b	Base quantified and monetized effects are those included when
determining the base estimate of total monetized benefits of
implementing the PM2.5 NAAQS.  See Appendix C for a more complete
discussion of the benefit estimates.

c	In addition to primary economic endpoints, there are a number of
biological responses that have been associated with PM health effects
including morphological changes and altered host defense mechanisms.
The public health impact of these biological responses may be partly
represented by our quantified endpoints.

d	Cohort estimates are designed to examine the effects of long term
exposures to ambient pollution, but relative risk estimates may also
incorporate some effects due to shorter term exposures (see Kunzli, 2001
 XE "Kunzli, 2001"   for a discussion of this issue). While some of the
effects of short term exposure are likely to be captured by the cohort
estimates, there may be additional premature mortality from short term
PM exposure not captured in the cohort estimates included in the primary
analysis.

e	May result in benefits or disbenefits.  See Section 5.3.4 for more
details.

Figure 5-1.  Key Steps in Air Quality Modeling Based Benefits Analysis

in population exposure to ambient air pollution are then input to impact
functions to generate changes in the incidence of health effects, or
changes in other exposure metrics are input to dose-response functions
to generate changes in welfare effects.  Because these estimates contain
uncertainty, we characterize the benefits estimates probabilistically
when appropriate information is available. The resulting effects changes
are then assigned monetary values, taking into account adjustments to
values for growth in real income out to the year of analysis (values for
health and welfare effects are in general positively related to real
income levels).  Finally, values for individual health and welfare
effects are summed to obtain an estimate of the total monetary value of
the changes in emissions.

The benefits discussed in this chapter represent the estimates based
upon illustrative attainment strategies for the final PM2.5 standards
(and an alternative set of standards), comprised of sets of controls on
sources of PM2.5 precursor emissions in areas expected to contribute to
residual nonattainment in locations in the U.S. in 2020.  These
strategies are evaluated after application of existing federal, state,
and local programs (such as CAIR).  As noted in earlier chapters,
benefits (and costs) for the final PM2.5 standards are evaluated
incrementally relative to an illustrative scenario of full attainment
with the current PM2.5 standards (15 µg/m3 annual mean and 65 µg/m3
daily 98th percentile).  Based on the nature of the air quality problems
in different parts of the U.S. (see Chapter 2), we have divided the
nation into three regions, the Eastern U.S., California, and the Western
U.S. excluding California.  Benefits will be presented separately for
each region, as well as for the nation as a whole.

As noted in previous chapters, we were not able to completely model
attainment in several locations due to limitations in the data and
modeling.  In these areas, we extrapolate from existing information to
develop estimates of the air quality changes that might result from
fully attaining the alternative standards in residual nonattainment
areas.  To reflect different levels of confidence in the underlying data
and models, benefits will be presented as two components, representing
the fully modeled partial attainment component, and the extrapolated
component.

EPA is currently developing a comprehensive integrated strategy for
characterizing the impact of uncertainty in key elements of the benefits
modeling process (e.g., emissions modeling, air quality modeling, health
effects incidence estimation, valuation) on the health impact and
monetized benefits estimates that are generated.  A recently completed
componentsubset of this effort, which has recently been completed and
peer reviewed, iswas an expert elicitation designed to characterize more
fully our understanding uncertainty in the estimation of PM-related
mortality resulting from both short-term and long-term exposure.  We
include the results of the formal expert elicitation among the sources
of information used in developing health impact functions for this
benefits analysis. The results of the ‘pilot’ for this The expert
elicitation  project included a pilot elicitation, completed and
peer-reviewed in 2004, the results of which were presentedapplied in
RIAs for both the Nonroad Diesel and Clean Air Interstate Rules (U.S.
EPA, 2004, 2005  XE "U.S. EPA, 2004, 2005"  ).  The main focus of the
project was the formal expert elicitation, completed in summer 2006.
The results of theseis elicitation projects, including peer review
comments, are available on EPA’s Web site, at XXXXXXXXXX.  In
addition,  We include the results of the formal expert elicitation among
the sources of information used in developing health impact functions
for this benefits analysis.

Ssimilar to our approach in the Nonroad Diesel RIA, forand the CAIR
RIA’s , we present a distribution of benefits estimates using two
types of probabilistic approaches.  The first approach generates a
distribution of benefits based on a more limited set of uncertainties,
those characterized by the sampling error and variability in the
underlying health and economic valuation studies used in the benefits
modeling framework.  We note that incorporating only the uncertainty
from random sampling error omits important sources of uncertainty (e.g.,
in the functional form of the model, as discussed below)..in The second
approach uses the results from a pilot expert elicitation project
designed to characterize key aspects of uncertainty in the ambient
PM2.5/mortality relationship.  Both  Uuse of the expert elicitation and
incorporation of the standard errors approaches provide insights into
the likelihood of different outcomes and about the state of knowledge
regarding the benefits estimates.  Both approaches have different
strengths and weaknesses, that are summarized later in this chapter.

The benefits estimates generated for the final PM2.5 NAAQS rule are
subject to a number of assumptions and uncertainties, which are
discussed throughout this document.  For example, key assumptions
underlying the concentration response functions primary estimate for the
mortality category include the following:

1.	Inhalation of fine particles is causally associated with premature
death at concentrations near those experienced by most Americans on a
daily basis.  Although biological mechanisms for this effect have not
yet been specifically identifiedcompletely established, the weight of
the available epidemiological, toxicological, and experimental evidence
supports an assumption of causality.  The impacts of including a
probabilistic representation of causality are explored using the results
of in the expert elicitation based results.

2.	All fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality.  This is an important
assumption, because the composition of PM produced via transported
precursors emitted from EGUs may differs significantly from direct PM
released from automotive engines and other industrial sources.  However,
no clear scientific grounds exist for supporting differential effects
estimates by particle type.

3.	The C-R function for fine particles is approximately linear within
the range of ambient concentrations under consideration (above the
assumed threshold of 10 µg/m3).  Thus, we assume that the CR functions
are applicable to estimates ofinclude health benefits associated with
from reducing fine particles in areas with varied concentrations of PM,
including both regions that are in attainment with PM2.5 standards and
those that do not meet the standards. However, we examine the
sensitivity of this assumption by looking at alternative thresholds in a
sensitivity analysis.

In addition, a key assumption underlying the entire analysis is that the
forecasts for future emissions and associated air quality modeling are
valid.  Because we are projecting emissions and air quality out to 2020,
there are inherent uncertainties in all of the factors that underlie the
future state of emissions and air quality levels.  Although recognizing
the difficulties, assumptions, and inherent uncertainties in the overall
enterprise, these analyses are based on peer-reviewed scientific
literature and up-to-date assessment tools, and we believe the results
are highly useful in assessing the impacts of this rule.

In addition to the quantified and monetized benefits summarized above, a
number of additional categories associated with PM2.5 and its precursor
emissions are not currently amenable to quantification or valuation.
These include reduced acid and particulate deposition damage to cultural
monuments and other materials, and environmental benefits due to
reductions of impacts of acidification in lakes and streams and
eutrophication in coastal areas.  Additionally, we have not quantified a
number of known or suspected health effects linked with PM for which
appropriate health impact functions are not available or which do not
provide easily interpretable outcomes (i.e., changes in heart rate
variability).  As a result, monetized benefits generated for the primary
estimate may underestimate the total benefits attributable to attainment
of alternative standards.

Benefits estimates for attaining alternative standards were generated
using BenMAP, a computer program developed by EPA that integrates a
number of the modeling elements used in previous RIAs (e.g.,
interpolation functions, population projections, health impact
functions, valuation functions, analysis and pooling methods) to
translate modeled air concentration estimates into health effects
incidence estimates and monetized benefits estimates.  BenMAP provides
estimates of both the mean impacts and the distribution of impacts
(information on BenMAP, including downloads of the software, can be
found at http://www.epa.gov/ttn/ecas/ benmodels.html).

In general, this chapter is organized around the steps illustrated in
Figure 5-1.  In Section 5.1, we provide an overview of the data and
methods that were used to quantify and value health and welfare
endpoints and discuss how we incorporate uncertainty into our analysis.
In Section 5.2, we report the results of the analysis for human health
and welfare effects (the overall benefits estimated for the final
NAAQSCAIR are summarized in Table 5-1).  Details on the emissions
inventory and air modeling are presented in Chapter 3.

5.1	Benefit Analysis—Data and Methods

Given changes in environmental quality (ambient air quality, visibility,
nitrogen, and sulfate deposition), the next step is to determine the
economic value of those changes.  We follow a “damage-function”
approach in calculating total benefits of the modeled changes in
environmental quality.  This approach estimates changes in individual
health and welfare endpoints (specific effects that can be associated
with changes in air quality) and assigns values to those changes
assuming independence of the individual values.  Total benefits are
calculated simply as the sum of the values for all nonoverlapping health
and welfare endpoints.  This imposes no overall preference structure and
does not account for potential income or substitution effects (i.e.,
adding a new endpoint will not reduce the value of changes in other
endpoints).  The “damage-function” approach is the standard approach
for most cost-benefit analyses of environmental quality programs and has
been used in several recent published analyses (Banzhaf, Burtraw, and
Palmer, 2002  XE "Banzhaf, Burtraw, and Palmer, 2002"  ; Hubbell et al.,
2004  XE "Hubbell et al., 2004"  ; Levy et al., 2001  XE "Levy et al.,
2001"  ; Levy et al., 1999  XE "Levy et al., 1999"  ; Ostro and
Chestnut, 1998  XE "Ostro and Chestnut, 1998"  ).

To assess economic value in a damage-function framework, the changes in
environmental quality must be translated into effects on people or on
the things that people value.  In some cases, the changes in
environmental quality can be directly valued, as is the case for changes
in visibility.  In other cases, such as for changes in ozone and PM, a
health and welfare impact analysis must first be conducted to convert
air quality changes into effects that can be assigned dollar values.
Inherent in each of these steps is a high degree of uncertainty, due
both to the randomness of environmental factors such as meteorology, and
the difficulty in measuring and predicting model inputs such as
pollutant emissions.  As such, where possible, we incorporate
probabilistic representations of model inputs and outputs.  However, in
many cases, probabilistic representations are not available.  In these
cases, we use the best available science and models, and characterize
uncertainty using sensitivity analyses.

For the purposes of this RIA, the health impacts analysis is limited to
those health effects that are directly linked to ambient levels of air
pollution and specifically to those linked to PM2.5.  There may be
other, indirect health impacts associated with implementing emissions
controls, such as occupational health impacts for coal miners.  These
impacts may be positive or negative, but in general, for this set of
control options, they are expected to be small relative to the direct
air pollution-related impacts.

The welfare impacts analysis is limited to changes in the environment
that have a direct impact on human welfare.  For this analysis, we are
limited by the available data to examine impacts of changes in
visibility.  We also provide qualitative discussions of the impact of
changes in other environmental and ecological effects, for example,
changes in deposition of nitrogen and sulfur to terrestrial and aquatic
ecosystems, but we are unable to place an economic value on these
changes.

We note at the outset that EPA rarely has the time or resources to
perform extensive new research to measure either the health outcomes or
their values for this analysis.  Thus, similar to Kunzli et al. (2000
XE "Kunzli et al. (2000"  ) and other recent health impact analyses, our
estimates are based on the best available methods of benefits transfer.
Benefits transfer is the science and art of adapting primary research
from similar contexts to obtain the most accurate measure of benefits
for the environmental quality change under analysis.  Where appropriate,
adjustments are made for the level of environmental quality change, the
sociodemographic and economic characteristics of the affected
population, and other factors to improve the accuracy and robustness of
benefits estimates.

5.1.1	Valuation Concepts

In valuing health impacts, we note that reductions in ambient
concentrations of air pollution generally lower the risk of future
adverse health effects by a fairly small amount for a large population.
The appropriate economic measure is willingness to pay (WTP) for changes
in risk prior to the regulation (Freeman, 1993  XE "Freeman, 1993"  ).
Adoption of WTP as the measure of value implies that the value of
environmental quality improvements depends on the individual preferences
of the affected population and that the existing distribution of income
(ability to pay) is appropriate.  For some health effects, such as
hospital admissions, WTP estimates are generally not available.  In
these cases, we use the cost of treating or mitigating the effect as the
measure of benefits.  These cost of illness (COI) estimates generally
understate the true value of reductions in risk of a health effect,
because they do not include the value of avoided pain and suffering from
the health effect (Harrington and Portney, 1987  XE "Harrington and
Portney, 1987"  ; Berger et al., 1987  XE "Berger et al., 1987"  ).

One distinction in environmental benefits estimation is between use
values and nonuse values.  Although no general agreement exists among
economists on a precise distinction between the two (see Freeman [1993
XE "Freeman [1993"  ]), the general nature of the difference is clear.
Use values are those aspects of environmental quality that affect an
individual’s welfare directly.  These effects include changes in
product prices, quality, and availability; changes in the quality of
outdoor recreation and outdoor aesthetics; changes in health or life
expectancy; and the costs of actions taken to avoid negative effects of
environmental quality changes.

Nonuse values are those for which an individual is willing to pay for
reasons that do not relate to the direct use or enjoyment of any
environmental benefit but might relate to existence values and bequest
values.  Nonuse values are not traded, directly or indirectly, in
markets.  For this reason, measuring nonuse values has proven to be
significantly more difficult than measuring use values.  The air quality
changes produced by attainment strategies to attain the PM2.5 NAAQS
cause changes in both use and nonuse values, but the monetary benefits
estimates are almost exclusively for use values.

More frequently than not, the economic benefits from environmental
quality changes are not traded in markets, so direct measurement
techniques cannot be used.  There are three main nonmarket valuation
methods used to develop values for endpoints considered in this
analysis:  stated preference (including contingent valuation [CV]),
indirect market (e.g., hedonic wage), and avoided cost methods.

The stated preference method values endpoints by using carefully
structured surveys to ask a sample of people what amount of compensation
is equivalent to a given change in environmental quality.  There is an
extensive scientific literature and body of practice on both the theory
and technique of stated preference-based valuation.  Well-designed and
well-executed stated preference studies are valid for estimating the
benefits of air quality regulations.  Stated preference valuation
studies form the basis for valuing a number of health and welfare
endpoints, including the value of mortality risk reductions, CB risk
reductions, minor illness risk reductions, and visibility improvements.

Indirect market methods can also be used to infer the benefits of
pollution reduction.  The most important application of this technique
for our analysis is the calculation of the VSL for use in estimating
benefits from mortality risk reductions.  No market exists where changes
in the probability of death are directly exchanged.  However, people
make decisions about occupation, precautionary behavior, and other
activities associated with changes in the risk of death.  By examining
these risk changes and the other characteristics of people’s choices,
it is possible to infer information about the monetary values associated
with changes in mortality risk (see Section 5.1.5).

Avoided cost methods are ways to estimate the costs of pollution by
using the expenditures made necessary by pollution damage.  For example,
if buildings must be cleaned or painted more frequently as levels of PM
increase, then the appropriately calculated increment of these costs is
a reasonable lower-bound estimate (under most conditions) of true
economic benefits when PM levels are reduced.  Avoided costs methods are
also used to estimate some of the health-related benefits related to
morbidity, such as hospital admissions (see Section 5.1.5).

5.1.2	Growth in WTP Reflecting National Income Growth Over Time

Our analysis accounts for expected growth in real income over time.
Economic theory argues that WTP for most goods (such as environmental
protection) will increase if real incomes increase.  There is
substantial empirical evidence that the income elasticity of WTP for
health risk reductions is positive, although there is uncertainty about
its exact value.  Thus, as real income increases, the WTP for
environmental improvements also increases.  Although many analyses
assume that the income elasticity of WTP is unit elastic (i.e., a 10%
higher real income level implies a 10% higher WTP to reduce risk
changes), empirical evidence suggests that income elasticity is
substantially less than one and thus relatively inelastic.  As real
income rises, the WTP value also rises but at a slower rate than real
income.

The effects of real income changes on WTP estimates can influence
benefits estimates in two different ways:  through real income growth
between the year a WTP study was conducted and the year for which
benefits are estimated, and through differences in income between study
populations and the affected populations at a particular time.
Empirical evidence of the effect of real income on WTP gathered to date
is based on studies examining the former.  The Environmental Economics
Advisory Committee (EEAC) of the Science Advisory Board (SAB) advised
EPA to adjust WTP for increases in real income over time but not to
adjust WTP to account for cross-sectional income differences “because
of the sensitivity of making such distinctions, and because of
insufficient evidence available at present” (EPA-SAB-EEAC-00-013).  A
recent advisory by another committee associated with the SAB, the
Advisory Council on Clean Air Compliance Analysis, has provided
conflicting advice.  While agreeing with “the general principle that
the willingness to pay to reduce mortality risks is likely to increase
with growth in real income. The same increase should be assumed for the
WTP for serious nonfatal health effects (EPA-SAB-COUNCIL-ADV-04-004, p.
52),” they note that “given the limitations and uncertainties in the
available empirical evidence, the Council does not support the use of
the proposed adjustments for aggregate income growth as part of the
primary analysis (EPA-SAB-COUNCIL-ADV-04-004, p. 53).”  Until these
conflicting advisories have been reconciled, EPA will continue to adjust
valuation estimates to reflect income growth using the methods described
below, while providing sensitivity analyses for alternative income
growth adjustment factors.

Based on a review of the available income elasticity literature, we
adjusted the valuation of human health benefits upward to account for
projected growth in real U.S. income.  Faced with a dearth of estimates
of income elasticities derived from time-series studies, we applied
estimates derived from cross-sectional studies in our analysis.  Details
of the procedure can be found in Kleckner and Neumann (1999  XE
"Kleckner and Neumann (1999"  ).  An abbreviated description of the
procedure we used to account for WTP for real income growth between 1990
and 2020 is presented below.

Reported income elasticities suggest that the severity of a health
effect is a primary determinant of the strength of the relationship
between changes in real income and WTP.  As such, we use different
elasticity estimates to adjust the WTP for minor health effects, severe
and chronic health effects, and premature mortality.  Note that because
of the variety of empirical sources used in deriving the income
elasticities, there may appear to be inconsistencies in the magnitudes
of the income elasticities relative to the severity of the effects
(apriori one might expect that more severe outcomes would show less
income elasticity of WTP).  We have not imposed any additional
restrictions on the empirical estimates of income elasticity. We also
expect that the WTP for improved visibility in Class I areas would
increase with growth in real income.  The relative magnitude of the
income elasticity of WTP for visibility compared with those for health
effects suggests that visibility is not as much of a necessity as
health, thus, WTP is more elastic with respect to income.  The
elasticity values used to adjust estimates of benefits in 2020 are
presented in Table 5-3.

Table 5-3:  Elasticity Values Used to Account for Projected Real Income
Growtha

Benefit Category	Central Elasticity Estimate

Minor Health Effect	0.14

Severe and Chronic Health Effects	0.45

Premature Mortality	0.40

Visibility	0.90

a	Derivation of estimates can be found in Kleckner and Neumann (1999  XE
"Kleckner and Neumann (1999"  ) and Chestnut (1997  XE "Chestnut (1997"
).  COI estimates are assigned an adjustment factor of 1.0.

In addition to elasticity estimates, projections of real gross domestic
product (GDP) and populations from 1990 to 2020 are needed to adjust
benefits to reflect real per capita income growth.  For consistency with
the emissions and benefits modeling, we used national population
estimates for the years 1990 to 1999 based on U.S. Census Bureau
estimates (Hollman, Mulder, and Kallan, 2000  XE "Hollman, Mulder, and
Kallan, 2000"  ).  These population estimates are based on application
of a cohort-component model applied to 1990 U.S. Census data projections
(U.S. Bureau of Census, 2000  XE "U.S. Bureau of Census, 2000"  ).  For
the years between 2000 and 2020, we applied growth rates based on the
U.S. Census Bureau projections to the U.S. Census estimate of national
population in 2000.  We used projections of real GDP provided in
Kleckner and Neumann (1999  XE "Kleckner and Neumann (1999"  ) for the
years 1990 to 2010.  We used projections of real GDP (in chained 1996
dollars) provided by Standard and Poor’s (2000  XE "Standard and
Poor’s (2000"  ) for the years 2010 to 2020.

Using the method outlined in Kleckner and Neumann (1999  XE "Kleckner
and Neumann (1999"  ) and the population and income data described
above, we calculated WTP adjustment factors for each of the elasticity
estimates listed in Table 5-4.  Benefits for each of the categories
(minor health effects, severe and chronic health effects, premature
mortality, and visibility) are adjusted by multiplying the unadjusted
benefits by the appropriate adjustment factor.  Table 5-4 lists the
estimated adjustment factors.  Note that, for premature mortality, we
applied the income adjustment factor to the present discounted value of
the stream of avoided mortalities occurring over the lag period.  Also
note that because of a lack of data on the dependence of COI and income,
and a lack of data on projected growth in average wages, no adjustments
are made to benefits based on the COI approach or to work loss days and
worker productivity.  This assumption leads us to underpredict benefits
in future years

Table 5-4:  Adjustment Factors Used to Account for Projected Real Income
Growtha

Benefit Category	2020

Minor Health Effect	0.14

Severe and Chronic Health Effects	0.45

Premature Mortality	0.40

Visibility	0.90

a	Based on elasticity values reported in Table 5-3, U.S. Census
population projections, and projections of real GDP per capita.

because it is likely that increases in real U.S. income would also
result in increased COI (due, for example, to increases in wages paid to
medical workers) and increased cost of work loss days and lost worker
productivity (reflecting that if worker incomes are higher, the losses
resulting from reduced worker production would also be higher).

5.1.3	Methods for Describing Uncertainty

A. Sources of Uncertainty

In any complex analysis using estimated parameters and inputs from
numerous models, there are likely to be many sources of uncertainty.
This analysis is no exception.  As outlined both in this and preceding
chapters, many inputs were used to derive the final estimate of
benefits, including emission inventories, air quality models (with their
associated parameters and inputs), epidemiological health effect
estimates, estimates of values (both from WTP and COI studies),
population estimates, income estimates, and estimates of the future
state of the world (i.e., regulations, technology, and human behavior).
Each of these inputs may be uncertain and, depending on its role in the
benefits analysis, may have a disproportionately large impact on final
estimates of total benefits.  For example, emissions estimates are used
in the first stage of the analysis.  As such, any uncertainty in
emissions estimates will be propagated through the entire analysis.
When compounded with uncertainty in later stages, small uncertainties in
emission levels can lead to large impacts on total benefits.

Some key sources of uncertainty in each stage of the benefits analysis
are the following:

gaps in scientific data and inquiry;

variability in estimated relationships, such as epidemiological effect
estimates, introduced through differences in study design and
statistical modeling;

errors in measurement and projection for variables such as population
growth rates;

errors due to misspecification of model structures, including the use of
surrogate variables, such as using PM10 when PM2.5 is not available,
excluded variables, and simplification of complex functions; and

biases due to omissions or other research limitations.

Some of the key uncertainties in the benefits analysis are presented in
Table 5-5.

Integrate what is currently section entitled “Uncertainties
Associated with Health Impact Functions based on Reported Effect
Estimates from the Epidemiological Literature”  [begins on page 5-32
of the 8-11 draft]  It seems that having some of this information up
front will both help the reader interpret this table, prevent revisiting
things in different terms you’ve already talked about, and give the
background material for interpreting later sections.  I’ve put some
comments in that section with an eye toward moving it here.

Table 5-5:  Primary Sources of Uncertainty in the Benefits Analysis

1.	Uncertainties Associated with Impact Functions

●	The value of the PM effect estimate in each impact function.

●	Application of a single impact function to pollutant changes and
populations in all locations.

●	Similarity of future-year impact functions to current impact
functions.

●	Correct functional form of each impact function.

●	Extrapolation of effect estimates beyond the range of PM
concentrations observed in the source epidemiological study.

●	Application of some impact functions only to those subpopulations
matching the original study population.

2.	Uncertainties Associated with PM Concentrations

●	Responsiveness of the models to changes in precursor emissions
resulting from the control policy.

●	Projections of future levels of precursor emissions, especially
organic carbonaceous particle emissions.

●	Model chemistry for the formation of ambient nitrate concentrations.

●	Lack of speciation monitors in some areas requires extrapolation of
observed speciation data.

●	CMAQ model performance in the Western U.S., especially California
indicates significant underprediction of PM2.5.

3.	Uncertainties Associated with PM Mortality Risk

●	Differential toxicity of specific component species within the
complex mixture of PM has not been determined.

●	The extent to which adverse health effects are associated with
low-level exposures that occur many times in the year versus peak
exposures.

●	The extent to which effects reported in the long-term exposure
studies are associated with historically higher levels of PM rather than
the levels occurring during the period of study.

●	Reliability of the limited ambient PM2.5 monitoring data in
reflecting actual PM2.5 exposures.

4.	Uncertainties Associated with Possible Lagged Effects

●	The portion of the PM-related long-term exposure mortality effects
associated with changes in annual PM levels that would occur in a single
year is uncertain as well as the portion that might occur in subsequent
years.

5.	Uncertainties Associated with Baseline Incidence Rates

●	Some baseline incidence rates are not location specific (e.g., those
taken from studies) and therefore may not accurately represent the
actual location-specific rates.

●	Current baseline incidence rates may not approximate well baseline
incidence rates in 2020.

●	Projected population and demographics may not represent well
future-year population and demographics.

6.	Uncertainties Associated with Economic Valuation

●	Unit dollar values associated with health and welfare endpoints are
only estimates of mean WTP and therefore have uncertainty surrounding
them.

●	Mean WTP (in constant dollars) for each type of risk reduction may
differ from current estimates because of differences in income or other
factors.

7.	Uncertainties Associated with Aggregation of Monetized Benefits

●	Health and welfare benefits estimates are limited to the available
impact functions.  Thus, unquantified or unmonetized benefits are not
included.

B. Methods Used to Characterize Uncertainty

NRC (2002) report on EPA’s benefits analysis methodology highlighted
the need for EPA to conduct rigorous quantitative analysis of
uncertainty in its benefits estimates as well as the need for presenting
these estimates to decision makers in ways that foster an appropriate
appreciation of their inherent uncertainty.  The NRC report In response
to these comments, EPA has initiated the development of a comprehensive
methodology for characterizing the aggregate impact of uncertainty in
key modeling elements on both health incidence and benefits estimates.
For this analysis of the final rule, EPA has developed a limited
probabilistic simulation approach based on Monte Carlo methods to
propagate the impact of a limited set of sources of uncertainty through
the modeling framework.  Issues such as correlation between input
parameters and the identification of reasonable upper and lower bounds
for input distributions characterizing uncertainty in additional model
elements will be addressed in future versions of the uncertainty
framework.

One component of EPA’s uncertainty analysis methodology that is
partially reflected in this analysis is our work using the results of an
expert elicitation to characterize uncertainty in the effect estimates
used to estimate premature mortality resulting from exposures to PM.
This expert elicitation was aimed at evaluating uncertainty in the
underlying causal relationship, the form of the mortality impact
function (e.g., threshold versus linear models) and the fit of a
specific model to the data (e.g., confidence bounds for specific
percentiles of the mortality effect estimates).  Additional issues, such
as the ability of long-term cohort studies to capture premature
mortality resulting from short-term peak PM exposures, were also
addressed in the expert elicitation.  In collaboration with OMB, EPA
completed a pilot expert elicitation in 2004, which was used to
characterize uncertainty in the PM mortality C-R function in the Nonroad
Diesel and CAIR RIAs.  EPA has recently completed a full-scale expert
elicitation that incorporated peer-review comments on the pilot
application, and that provides a more robust characterization of the
uncertainty in the premature mortality function.

In the current analysis EPA continues to move forward on one of the key
recommendations of the  NRC –  moving the assessment of uncertainties
from its ancillary analyses into its main benefits  presentation through
the conduct of probabilistic analyses.  InFor this final rule, EPA
addressed key sources of uncertainty bythrough  Monte Carlo propagation
of uncertainty in the C-R functions and economic valuation functions
through its base estimates as well as by continuing its practice of
conducting and through a series of ancillary sensitivity analyses
examining the impact of alternate assumptions on the benefits estimates
that are generated.  It should be noted that the Monte Carlo-generated
distributions of benefits reflect only some of the uncertainties in the
input parameters. Uncertainties associated with emissions, air quality
modeling, populations, and baseline health effect incidence rates are
not represented in the distributions of benefits of attaining
alternative standards.  Issues such as correlation between input
parameters and the identification of reasonable upper and lower bounds
for input distributions characterizing uncertainty in additional model
elements will be addressed in future versions of the uncertainty
framework.

Therefore, in characterizing the uncertainty related to the estimates of
total benefits it is particularly important to attempt to characterize
the uncertainties associated with this endpoint. As such the analysis
for this rule incorporates the results of our recent expert elicitation
to characterize uncertainty in the effect estimates used to estimate
premature mortality resulting from exposures to PM into the main
analysis.  This expert elicitation was aimed at evaluating uncertainty
in the underlying causal relationship, the form of the mortality impact
function (e.g., threshold versus linear models) and the fit of a
specific model to the data (e.g., confidence bounds for specific
percentiles of the mortality effect estimates).  Additional issues, such
as the ability of long-term cohort studies to capture premature
mortality resulting from short-term peak PM exposures, were also
addressed in the expert elicitation.  In collaboration with OMB, EPA
completed a pilot expert elicitation in 2004, which was used to
characterize uncertainty in the PM mortality C-R function in the Nonroad
Diesel and CAIR RIAs.  Now EPA has recently completed a full-scale
expert elicitation that incorporated peer-review comments on the pilot
application, and that provides a more robust characterization of the
uncertainty in the premature mortality function. This expert elicitation
was designed to evaluate uncertainty in the underlying causal
relationship, the form of the mortality impact function (e.g., threshold
versus linear models) and the fit of a specific model to the data (e.g.,
confidence bounds for specific percentiles of the mortality effect
estimates).  Additional issues, such as the ability of long-term cohort
studies to capture premature mortality resulting from short-term peak PM
exposures, were also addressed in the expert elicitation.

Our distribution point estimates of total benefits are uncertain because
of the uncertainty in model elements discussed above (see Table 5-5).
Uncertainty about specific aspects of the health and welfare estimation
models is discussed in greater detail in the following sections.  The
distributionspoint estimates of total benefits may understate or
overstate actual benefits of attaining alternative standards.  Likewise,
the estimated distributions of total benefits may not completely capture
the shape and location of the actual distribution of total benefits.

In considering the monetized benefits estimates, the reader should
remain aware of the many limitations of conducting the analyses
mentioned throughout this RIA.  One significant limitation of both the
health and welfare benefits analyses is the inability to quantify many
of the effects listed in Table 5-1.  For many health and welfare
effects, such as changes in ecosystem functions and PM-related materials
damage, reliable impact functions and/or valuation functions are not
currently available.  In general, if it were possible to monetize these
benefit categories, the benefits estimates presented in this analysis
would increase, although the magnitude of such an increase is highly
uncertain.  Unquantified benefits are qualitatively discussed in the
health and welfare effects sections.  In addition to unquantified
benefits, there may also be environmental costs (disbenefits) that we
are unable to quantify.  These endpoints are qualitatively discussed in
the health and welfare effects sections as well.  The net effect of
excluding benefit and disbenefit categories from the estimate of total
benefits depends on the relative magnitude of the effects.

In addition to unquantified and unmonetized health benefit categories,
Table 5-2 shows a number of welfare benefit categories that are omitted
from the monetized benefit estimates for this rule.  Only a subset of
the expected visibility benefits-those for Class I areas in the
southeastern and southwestern (including California) United States are
included in the monetary benefits estimates we project for this rule.
We believe the benefits associated with these non-health benefit
categories are likely significant.  For example, we are able to quantify
significant visibility improvements in Class I areas in the Northeast
and Midwest, but are unable at present to place a monetary value on
these improvements.  Similarly, we anticipate improvement in visibility
in urban areas for which we are currently unable to monetize benefits.
For the Class I areas in the southeastern and southwestern U.S., we
estimate annual benefits of $XXX billion beginning in 2020 for
visibility improvements.  The value of visibility benefits in areas
where we were unable to monetize benefits could also be substantial.

We conduct supplemental analyses related to visibility and household
cleaning costs later in this chapter.  Based on these analyses, expanded
coverage of these benefit categories could increase total benefits by
over $XXX million.  (See Appendix C for more details.)

5.1.4	Demographic Projections

Quantified and monetized human health impacts depend on the demographic
characteristics of the population, including age, location, and income.
We use projections based on economic forecasting models developed by
Woods and Poole, Inc.  The Woods and Poole (WP) database contains
county-level projections of population by age, sex, and race out to
2025.  Projections in each county are determined simultaneously with
every other county in the United States to take into account patterns of
economic growth and migration.  The sum of growth in county-level
populations is constrained to equal a previously determined national
population growth, based on Bureau of Census estimates (Hollman, Mulder,
and Kallan, 2000  XE "Hollman, Mulder, and Kallan, 2000"  ).  According
to WP, linking county-level growth projections together and constraining
to a national-level total growth avoids potential errors introduced by
forecasting each county independently.  County projections are developed
in a four-stage process.  First, national-level variables such as
income, employment, and populations are forecasted.  Second, employment
projections are made for 172 economic areas defined by the Bureau of
Economic Analysis, using an “export-base” approach, which relies on
linking industrial-sector production of nonlocally consumed production
items, such as outputs from mining, agriculture, and manufacturing with
the national economy.  The export-based approach requires estimation of
demand equations or calculation of historical growth rates for output
and employment by sector.  Third, population is projected for each
economic area based on net migration rates derived from employment
opportunities and following a cohort-component method based on fertility
and mortality in each area.  Fourth, employment and population
projections are repeated for counties, using the economic region totals
as bounds.  The age, sex, and race distributions for each region or
county are determined by aging the population by single year of age by
sex and race for each year through 2015 based on historical rates of
mortality, fertility, and migration.

The WP projections of county-level population are based on historical
population data from 1969 through 1999 and do not include the 2000
Census results.  Given the availability of detailed 2000 Census data, we
constructed adjusted county-level population projections for each future
year using a two-stage process.  First, we constructed ratios of the
projected WP populations in a future year to the projected WP population
in 2000 for each future year by age, sex, and race.  Second, we
multiplied the block-level 2000 Census population data by the
appropriate age-, sex-, and race-specific WP ratio for the county
containing the census block for each future year.  This results in a set
of future population projections that is consistent with the most recent
detailed Census data.

As noted above, values for environmental quality improvements are
expected to increase with growth in real per capita income.  Accounting
for real income growth over time requires projections of both real GDP
and total U.S. populations.  For consistency with the emissions and
benefits modeling, we used national population estimates based on the
U.S. Census Bureau projections.

5.1.5	Health Benefits Assessment Methods

The largest monetized benefits of reducing ambient concentrations of PM
are attributable to reductions in health risks associated with air
pollution.  EPA’s Criteria Documents for ozone and PM lists numerous
health effects known to be linked to ambient concentrations of these
pollutants (EPA, 1996a; 1996b  XE "U.S. EPA, 1996a\; 1996b"  ).  As
illustrated in Figure 5-1, quantification of health impacts requires
several inputs, including epidemiological effect estimates
(concentration-response functions), baseline incidence and prevalence
rates, potentially affected populations, and estimates of changes in
ambient concentrations of air pollution.  Previous sections have
described the population and air quality inputs.  This section describes
the effect estimates and baseline incidence and prevalence inputs and
the methods used to quantify and monetize changes in the expected number
of incidences of various health effects.

Selection of Health Endpoints

Talk about SAB/HES review/use of their recommendations for which
endpoints and general criteria they use.  Point out any deviations from
CAIR RIA

Refer to Table 5-2 for list of quantified vs unquantifed endpoints

 addition to premature mortality, based on the available epidemiological
data, we quantified nonfatal heart attacks, chronic bronchitis, acute
bronchitis, hospital admissions, emergency room visits for asthma, upper
and lower respiratory symptoms, asthma exacerbations, minor restricted
activity days and days of work lost.

 health effects are excluded from this analysis for three reasons:  the
possibility of double-counting (such as hospital admissions for specific
respiratory diseases), uncertainties in applying effect relationships
based on clinical studies to the affected population, or a lack of an
established relationship between the health effect and pollutant in the
published epidemiological literature.  An improvement in ambient PM and
ozone air quality may reduce the number of incidences within each
unquantified effect category that the U.S. population would experience.
Although these health effects are believed to be PM or ozone induced,
effect estimates are not available for quantifying the benefits
associated with reducing these effects.  The inability to quantify these
effects lends a downward bias to the monetized benefits presented in
this analysis.

Sources of Information for Selecting Health Endpoints and Determining
Effect Estimates

There are several types of data that can support the determination of
types and magnitude of health effects associated with air pollution
exposures.  These sources of data include toxicological studies
(including animal and cellular studies), human clinical trials, and
observational epidemiology studies.  All of these data sources provide
important contributions to the weight of evidence surrounding a
particular health impact, however, only epidemiology studies provide
direct concentration-response relationships which can be used to
evaluate population-level impacts of reductions in ambient pollution
levels. In the case of PM exposure, we are fortunate that there are
epidemiological studies available in the literature that provide an
appropriate starting point for deriving concentration response functions
for likely health effects.

In recent years, EPA has begun investigating how expert elicitation
studies can be used to integrate the other sources of data in developing
C-R functions that can be applied for regulatory benefits analyses.
However, Sstandard environmental epidemiology studies provide estimates
of the precision with which the parameters of the C-R function are
estimated.  This provides only a limited representation of the
uncertainty associated with a specific C-R function, measuring only the
statistical error in the estimates, and usually relating more to the
power of the underlying study (driven largely by population size and the
frequency of the outcome measure).  There are many other sources of
uncertainty in the relationships between ambient pollution and
population level health outcomes, including many sources of model
uncertainty, such as model specification, potential confounding between
factors that are both correlated with the health outcome and each other,
and many other factors.  As such, in recent years, EPA has begun
investigating how expert elicitation methods can be used to integrate
across various sources of data in developing C-R functions for
regulatory benefits analyses.

Expert elicitation is useful in integrating the many sources of
information about uncertainty in the C-R function, because it allows
experts to synthesize these data sources using their own mental models,
and provide a probabilistic representation of their synthesis of the
data in the form of a probability distribution of the C-R function.
Figure 5-2 shows how expert elicitation builds on both the direct
empirical data on C-R relationships and other less direct evidence to
develop probabilistic distributions of C-R functions.  EPA has used
expert elicitation to inform the decision  process in the past (for lead
(ref), for carbon monoxide (ref), and for ozone (ref). In the cCurrently
analysis, we have only used expert elicitation to characterize the C-R
function for the relationship between fine PM and premature mortality.
However, similar methods could be used to characterize C-R functions for
other health outcomes.  In addition to premature mortality, based on the
available epidemiological data, we quantified nonfatal heart attacks,
chronic bronchitis, acute bronchitis, hospital admissions, emergency
room visits for asthma, upper and lower respiratory symptoms, asthma
exacerbations, minor restricted activity days and days of work lost.

Figure 5-2.  Sources and Integration of Scientific Data in Informing
Development of Health Impact Functions

Information Selected for Quantifying C-R Functions

We relied on the published scientific literature to ascertain the
relationship between PM and adverse human health effects.  We evaluated
epidemiological studies using the selection criteria summarized in Table
5-6.  These criteria include consideration of whether the study was
peer-reviewed, the match between the pollutant studied and the pollutant
of interest, the study design and location, and characteristics of the
study population, among other considerations.  The selection of C-R
functions for the benefits analysis is guided by the goal of achieving a
balance between comprehensiveness and scientific defensibility.

Some health effects are excluded from this analysis for three reasons:
the possibility of double-counting (such as hospital admissions for
specific respiratory diseases), uncertainties in applying effect
relationships based on clinical studies to the affected population, or a
lack of an established relationship between the health effect and
pollutant in the published epidemiological literature.  An improvement
in ambient PM and ozone air quality may reduce the number of incidences
within each unquantified effect category that the U.S. population would
experience.  Although these health effects are believed to be PM or
ozone induced, effect estimates are not available for quantifying the
benefits associated with reducing these effects.  The inability to
quantify these effects lends a downward bias to the monetized benefits
presented in this analysis.

In general, the use of results from more than a single study can provide
a more robust estimate of the relationship between a pollutant and a
given health effect.  However, there are often differences between
studies examining the same endpoint, making it difficult to pool the
results in a consistent manner.  For example, studies may examine
different pollutants or different age groups.  For this reason, we
consider very carefully the set of studies available examining each
endpoint and select a consistent subset that provides a good balance of
population coverage and match with the pollutant of interest.  In many
cases, either because of a lack of multiple studies, consistency
problems, or clear superiority in the quality or comprehensiveness of
one study over others, a single published study is selected as the basis
of the effect estimate.  When possible in these cases, a sensitivity
analysis is provided to examine the impact of choosing alternative C-R
functions.

When several effect estimates for a pollutant and a given health
endpoint have been selected, they are quantitatively combined or pooled
to derive a more robust estimate of the relationship.  The BenMAP
User’s Manual provides details of the procedures used to combine
multiple impact functions (Abt Associates, 2003  XE "Abt Associates,
2003"  ).  In general, we used fixed or random effects models to pool
estimates from different studies of the same endpoint.  Fixed effects
pooling simply weights each study’s estimate by the inverse variance,
giving more weight to studies with greater statistical power (lower
variance).  Random effects pooling accounts for both within-study
variance and between-study variability, due, for example, to differences
in population susceptibility.  We used the fixed effects model as our
null hypothesis and then determined whether the data suggest that we
should reject this null hypothesis, in which case we would use the
random effects model.  Pooled impact functions are used to estimate
hospital admissions and asthma exacerbations.  For more details on
methods used to pool incidence estimates, see the BenMAP User’s Manual
(Abt Associates, 2003  XE "Abt Associates, 2003"  ).

Table 5-6:  Summary of Considerations Used in Selecting C-R Functions

because they control for important individual-level confounding
variables that cannot be controlled for in cross-sectional studies.

Study Period	Studies examining a relatively longer period of time (and
therefore having more data) are preferred, because they have greater
statistical power to detect effects.  More recent studies are also
preferred because of possible changes in pollution mixes, medical care,
and lifestyle over time.  However, when there are only a few studies
available, studies from all years will be included.

Population Attributes	The most technically appropriate measures of
benefits would be based on impact functions that cover the entire
sensitive population but allow for heterogeneity across age or other
relevant demographic factors.  In the absence of effect estimates
specific to age, sex, preexisting condition status, or other relevant
factors, it may be appropriate to select effect estimates that cover the
broadest population to match with the desired outcome of the analysis,
which is total national-level health impacts.  When available,
multi-city studies are preferred to single city studies because they
provide a more generalizable representation of the C-R function.

Study Size	Studies examining a relatively large sample are preferred
because they generally have more power to detect small magnitude
effects.  A large sample can be obtained in several ways, either through
a large population or through repeated observations on a smaller
population (e.g., through a symptom diary recorded for a panel of
asthmatic children).

Study Location	U.S. studies are more desirable than non-U.S. studies
because of potential differences in pollution characteristics, exposure
patterns, medical care system, population behavior, and lifestyle.

Pollutants Included in Model	When modeling the effects of ozone and PM
(or other pollutant combinations) jointly, it is important to use
properly specified impact functions that include both pollutants.  Using
single-pollutant models in cases where both pollutants are expected to
affect a health outcome can lead to double-counting when pollutants are
correlated.

Measure of PM	For this analysis, impact functions based on PM2.5 are
preferred to PM10 because of the focus on reducing emissions of PM2.5
precursors, and because air quality modeling was conducted for this size
fraction of PM.  Where PM2.5 functions are not available, PM10 functions
are used as surrogates, recognizing that there will be potential
downward (upward) biases if the fine fraction of PM10 is more (less)
toxic than the coarse fraction.

Economically Valuable Health Effects	Some health effects, such as forced
expiratory volume and other technical measurements of lung function, are
difficult to value in monetary terms.  These health effects are not
quantified in this analysis.

Nonoverlapping Endpoints	Although the benefits associated with each
individual health endpoint may be analyzed separately, care must be
exercised in selecting health endpoints to include in the overall
benefits analysis because of the possibility of double-counting of
benefits.

The eEffect estimates selected for a pollutant and a given health
endpoint were applied consistently across all locations nationwide.
This applies to both impact functions defined by a single effect
estimate and those defined by a pooling of multiple effect estimates.
Although the effect estimate may, in fact, vary from one location to
another (e.g., because of differences in population susceptibilities or
differences in the composition of PM), location-specific effect
estimates are generally not available.  The recent study of hospital
admissions reported in Domenici et al. (2006  XE "Domenici et al. (2006"
 ) reports separate effect estimates for Eastern and Western states.  We
examine the impact of using these region specific estimates in a
sensitivity analysis.

The specific studies from which effect estimates for the primary
analysis are drawn are included in Table 5-7.  In all cases where effect
estimates are drawn directly from epidemiological studies, standard
errors are used as a partial representation of the uncertainty in the
size of the effect estimate.  Below we provide the basis for selecting
these studies.

Adult Premature Mortality – Available Epidemiological Studies.  Both
long- and short-term exposures to ambient levels of air pollution have
been associated with increased risk of premature mortality.  The size of
the mortality risk estimates from epidemiological studies, the serious
nature of the effect itself, and the high monetary value ascribed to
prolonging life make mortality risk reduction the most significant
health endpoint quantified in this analysis.

Although a number of uncertainties remain to be addressed by continued
research (NRC, 1998  XE "NRC, 1998"  ), a substantial body of published
scientific literature documents the correlation between elevated PM
concentrations and increased mortality rates (EPA CD Doc 2004).
Time-series methods have been used to relate short-term (often
day-to-day) changes in PM concentrations and changes in daily mortality
rates up to several days after a period of elevated PM concentrations.
Cohort methods have been used to examine the potential relationship
between community-level PM exposures over multiple years (i.e.,
long-term exposures) and community-level annual mortality rates.
Researchers have found statistically significant associations between PM
and premature mortality using both types of studies.  In general, the
risk estimates based on the cohort studies are larger than those derived
from time-series studies.  Cohort analyses are thought to better capture
the full public health impact of exposure to air pollution over time,
because they capture the effects of long-term exposures and possibly
some component of short-term exposures (Kunzli et al., 2001  XE "Kunzli
et al., 2001"  ; NRC, 2002  XE "NRC, 2002"  ).  This section discusses
some of the issues surrounding the estimation of premature mortality.

Over a dozen studies have found significant associations between various
measures of long-term exposure to PM and elevated rates of annual
mortality, beginning with Lave and Seskin (1977  XE "Lave and Seskin
(1977"  ).  Most of the published studies found positive (but not always
statistically significant) associations with available PM indices such
as total suspended particles (TSP).  However, exploration of alternative
model specifications sometimes raised questions about causal
relationships (e.g., Lipfert, Morris, and Wyzga [1989  XE "Lipfert,
Morris, and Wyzga [1989"  ]).  These early “ecological
cross-sectional” studies (e.g., Lave and Seskin [1977  XE "Lave and
Seskin [1977"  ]; Ozkaynak and Thurston [1987  XE "Ozkaynak and Thurston
[1987"  ]) were criticized for a number of methodological limitations,
particularly for inadequate control at the individual level for
variables that are potentially important in causing mortality, such as
wealth, smoking, and diet.  Over the last 10 yearsMore recently, several
studies using “prospective cohort” methods have been published that
appear to be consistent with the earlier body of literature.   that use
improved approaches and appear to be consistent with the earlier body of
literature.  These new “prospective cohort” studies reflect a
significant improvement over the earlier work because they include
individual-level information with

Table 5-7:  Endpoints and Studies Used to Calculate Total Monetized
Health Benefits

Endpoint	Pollutant	Study	Study Population

Premature Mortality

Premature mortality —cohort study, all-cause	PM2.5 (annual)	Pope et
al. (2002  XE "Pope et al. (2002"  )	>29 years

Premature mortality, total exposures	PM2.5 (annual)	Expert Elicitation
(IEc, 2006  XE "IEc, 2006"  )	>24 years

Premature mortality — all-cause	PM2.5 (annual)	Woodruff et al. (1997
XE "Woodruff et al. (1997"  )	Infant (<1 year)

Chronic Illness

Chronic bronchitis	PM2.5 (annual)	Abbey et al. (1995  XE "Abbey et al.
(1995"  )	>26 years

Nonfatal heart attacks	PM2.5 (daily)	Peters et al. (2001  XE "Peters et
al. (2001"  )	Adults

Hospital Admissions

Respiratory	PM2.5 (daily)	Pooled estimate:

Moolgavkar (2003  XE "Moolgavkar (2003"  )—ICD 490-496 (COPD)

Ito (2003  XE "Ito (2003"  )—ICD 490-496 (COPD)	>64 years

	PM2.5 (daily)	Moolgavkar (2000  XE "Moolgavkar (2000"  )—ICD 490-496
(COPD)	20–64 years

	PM2.5 (daily)	Ito (2003  XE "Ito (2003"  )—ICD 480-486 (pneumonia)
>64 years

	PM2.5 (daily)	Sheppard (2003  XE "Sheppard (2003"  )—ICD 493 (asthma)
<65 years

Cardiovascular	PM2.5 (daily)	Pooled estimate:

Moolgavkar (2003  XE "Moolgavkar (2003"  )—ICD 390-429 (all
cardiovascular)

Ito (2003  XE "Ito (2003"  )—ICD 410-414, 427-428 (ischemic heart
disease, dysrhythmia, heart failure)	>64 years

	PM2.5 (daily)	Moolgavkar (2000  XE "Moolgavkar (2000"  )—ICD 390-429
(all cardiovascular)	20–64 years

Asthma-related ER visits	PM2.5	Norris et al. (1999  XE "Norris et al.
(1999"  )	0–18 years

Other Health Endpoints

Acute bronchitis	PM2.5	Dockery et al. (1996  XE "Dockery et al. (1996"
)	8–12 years

Upper respiratory symptoms	PM10	Pope et al. (1991  XE "Pope et al.
(1991"  )	Asthmatics, 9–11 years

Lower respiratory symptoms	PM2.5	Schwartz and Neas (2000  XE "Schwartz
and Neas (2000"  )	7–14 years

Asthma exacerbations	PM2.5	Pooled estimate:

Ostro et al. (2001  XE "Ostro et al. (2001"  ) (cough, wheeze and
shortness of breath)

Vedal et al. (1998  XE "Vedal et al. (1998"  ) (cough)	6–18 yearsa

Work loss days	PM2.5	Ostro (1987  XE "Ostro (1987"  )	18–65 years

MRADs	PM2.5	Ostro and Rothschild (1989  XE "Ostro and Rothschild (1989"
)	18–65 years

a	The original study populations were 8 to 13 for the Ostro et al. (2001
 XE "Ostro et al. (2001"  ) study and 6 to 13 for the Vedal et al. (1998
 XE "Vedal et al. (1998"  ) study.  Based on advice from the SAB-HES, we
extended the applied population to 6 to 18, reflecting the common
biological basis for the effect in children in the broader age group.

respect to health status and residence.  The most extensive analyses
have been based on data from two prospective cohort groups, often
referred to as the Harvard “Six-Cities Study” (Dockery et al., 1993;
Laden et al . 2006  XE "Dockery et al., 1993"  ) and the “American
Cancer Society or ACS study” (Pope et al., 1995; Pope et al 2002; Pope
et al. 2004  XE "Pope et al., 1995"  ); these studies have found
consistent relationships between fine particle indicators and premature
mortality across multiple locations in the United States.  A third major
data set comes from the California-based 7th Day Adventist Study (e.g.,
Abbey et al., 1999, add others  XE "Abbey et al., 1999"  ), which
reported associations between long-term PM exposure and mortality in
men.  Results from this cohort, however, have been inconsistent, , and
the air quality results are not geographically representative of most of
the United States, and the lifestyle of population is not reflective of
much of the U.S. population.  Analysis is also available fromMore
recently, a cohort of adult male veterans diagnosed with hypertension
has been examined (Lipfert et al., 2000; Lipfert et al 2006a and b  XE
"Lipfert et al., 2000"  ).  The characteristics of this group differ
from the cohorts in the Six-Cities, ACS, and 7th Day Adventist studies
with respect to income, race, health status, and smoking status.  Unlike
previous long-term analyses, this study found some associations between
mortality and ozone but found inconsistent results for PM indicators.
Because of the selective nature of the population in the veteran’s
cohort, we have chosen not to include any effect estimates from the
Lipfert et al. (2000  XE "Lipfert et al. (2000"  )this study in our
benefits assessment.

Given their consistent results and broad geographic coverage and
importance for informing the NAAQS development process, the Six-Cities
and ACS data have been particularly important in benefits analyses.  The
credibility of these two studies is further enhanced by the fact that
they were subject to extensive reexamination and reanalysis of the Pope
et al 1995 and Dockery et al 1993 results by an independent team of
scientific experts commissioned by HEI (Krewski et al., 2000  XE
"Krewski et al., 2000"  ).  The final results of the reanalysis were
then independently peer reviewed by a Special Panel of the HEI Health
Review Committee.  The results of these reanalyses confirmed and
expanded those of the original investigators.  This intensive
independent reanalysis effort was occasioned both by the importance of
the original findings and concerns that the underlying individual health
effects information has never been made publicly available.

While the HEI reexamination lends credibility to the original studies,
it also highlights sensitivities concerning the relative impact of
various pollutants[add a phrase about SO2 effect], the potential role of
education in mediating the association between pollution and mortality,
and the influence of spatial correlation modeling.

Rextended eanalyses of the ACS cohort data (Pope et al., 2002; 2004  XE
"Pope et al., 2002"  ) provide additional refinements to the analysis of
PM-related mortality by a) extending the follow-up period for the ACS
study subjects to 16 years, which triples the size of the mortality data
set; b) substantially increasing exposure data, including consideration
for cohort exposure to PM2.5 following implementation of the PM2.5
standard in 1999; c) controlling for a variety of personal risk factors
including occupational exposure and diet; and d) using advanced
statistical methods to evaluate specific issues that can adversely
affect risk estimates including the possibility of spatial
autocorrelation of survival times in communities located near each
other.

.  That panel recommended using long-term prospective cohort studies in
estimating mortality risk reduction (EPA-SAB-COUNCIL-ADV-99-005, 1999B
XE "U.S. EPA-SAB-COUNCIL-ADV-99-005, 1999B"  ).  This recommendation has
been confirmed by a recent report from the National Research Council,
which stated that “it is essential to use the cohort studies in
benefits analysis to capture all important effects from air pollution
exposure” (NRC, 2002  XE "NRC, 2002"  , p. 108).  More specifically,
the SAB recommended emphasis on the ACS study because it includes a much
larger sample size and longer exposure interval and covers more
locations (e.g., 50 cities compared to the Six-Cities Study) than other
studies of its kind.

Reanalyses of the ACS cohort data (Pope et al., 2002  XE "Pope et al.,
2002"  ) provides additional refinements to the analysis of PM-related
mortality by a) extending the follow-up period for the ACS study
subjects to 16 years, which triples the size of the mortality data set;
b) substantially increasing exposure data, including consideration for
cohort exposure to PM2.5 following implementation of the PM2.5 standard
in 1999; c) controlling for a variety of personal risk factors including
occupational exposure and diet; and d) using advanced statistical
methods to evaluate specific issues that can adversely affect risk
estimates including the possibility of spatial autocorrelation of
survival times in communities located near each other.  Because of these
refinements, the SAB-HES recommends using the Pope et al. (2002  XE
"Pope et al. (2002"  ) study as the basis for the primary mortality
estimate for adults and suggests that alternate estimates of mortality
generated using other cohort and time-series studies could be included
as part of the sensitivity analysis (SAB-HES, 2004  XE "SAB-HES, 2004"
).

As noted above, since the most recent SAB review, an extended follow-up
of the Harvard Six-cities study has been published (Laden et al., 2006
XE "Laden et al., 2006"  ).  Given the focus in this analysis on
developing a broader expression of uncertainties in the benefits
estimates, and the weight that was placed on both the ACS and Harvard
Six-city studies by experts participating in the PM2.5 mortality expert
elicitation, we have elected to provide estimates derived from both Pope
et al. (2002  XE "Pope et al. (2002"  ) and Laden et al. (2006  XE
"Laden et al. (2006"  ).

The NRC also recommended that EPA review the database of cohort studies
and consider developing a weighted mean estimate based on selected
studies.  Because of the differences in the study designs and
populations considered in the ACS and Harvard Six-cities studies, we
have elected to not pool the results of the studies, instead presenting
a range of estimates reflecting the different sources of impact
estimates.

A number of additional analyses have been conducted on the ACS cohort
data (Jarrett et al., 2005  XE "Jarrett et al., 2005"  ; Krewski et al.,
2005  XE "Krewski et al., 2005"  ; Pope et al., 2004  XE "Pope et al.,
2004"  ).  These studies have continued to find a strong significant
relationship between PM2.5 and mortality outcomes.  Specifically,
However, much of the recent research has suggested a stronger
relationship between cardiovascular mortality and lung cancer mortality
with PM2.5, and a less significant relationship between
respiratory-related mortality and PM2.5.  We investigate the impacts of
using effect estimates derived from these more recent studies in
sensitivity analyses.

The SAB-HES also recommended using the estimated relative risks from the
Pope et al. (2002  XE "Pope et al. (2002"  ) study based on the average
exposure to PM2.5, measured by the average of two PM2.5 measurements,
over the periods 1979–1983 and 1999–2000.  In addition to relative
risks for all-cause mortality, the Pope et al. (2002  XE "Pope et al.
(2002"  ) study provides relative risks for cardiopulmonary, lung
cancer, and all-other cause mortality.  Because of concerns regarding
the statistical reliability of the all-other cause mortality relative
risk estimates, we calculated mortality impacts for the primary analysis
based on the all-cause relative risk.  However, we provide separate
estimates of cardiopulmonary and lung cancer deaths to show how these
important causes of death are affected by reductions in PM2.5.

Adult Premature Mortality – Expert Elicitation Study

ADD 2 PARAGRAPH DISCUSSION OF THE EXPERT ELICITATION HERE.

Based on the responses of the 12 experts (designated A through L), we
constructed a corresponding set of 12 health impact functions for
premature mortality.  For those experts providing log-linear
non-threshold functions, construction of a health impact function was
straightforward, and directly matched the construction of health impact
functions based on the epidemiology literature.  In these cases, the
expert’s function can be translated into a health impact function of
the form:

 ,

Where y0 is the baseline incidence, equal to the baseline incidence rate
time the potentially affected population, β is the effect estimate
provided by the expert, and ΔPM is the change in PM2.5.

Some experts specified a piecewise log-linear function, in which case we
developed health impact functions that incorporate ambient concentration
levels.  For example, Expert B specified a piecewise function with two
segments, representing the concentration-response function for ambient
concentrations between 4 and 10 µg/m3 and between 10 and 30 µg/m3.  In
this case, the expert’s function can be translated into a health
impact function of the form:

 ,

Where Q0 is the baseline concentration of PM2.5, y01 is the baseline
incidence for populations living in areas with baseline concentrations
of PM2.5 less than 10 µg/m3, y02 is the baseline incidence for
populations living in areas with baseline concentrations of PM2.5
greater than or equal to10 µg/m3, and β1 and β2 are the effect
estimates corresponding to the segments of the C-R function relating to
ambient concentrations between 4 and 10 µg/m3 and 10 and 30 µg/m3,
respectively.

A third form specified by one expert (Expert K) included both a
piecewise log-linear function and a probabilistic threshold.  Expert K
did not provide a full set of information about the shape of the
distribution of the threshold, providing only the probability that a
threshold existed between 0 and 5 µg/m3 (equal to 0.4) and the
probability that a threshold existed between 5 and 10 µg/m3 (equal to
0.1).  The probability that a threshold above 10 existed was set to
zero, and the probability that there was no threshold was specified as
0.50.  We assumed that the probability distribution across the range 0
to 5 was uniform, such that the probability of a threshold between 0 and
1, 1 and 2, etc. was equal.  Likewise, we assumed that the probability
distribution across the range 5 to 10 was uniform.  Expert K also
provided a two segment piecewise log-linear function, with the segments
defined over the ranges 4 to 16 µg/m3, and 16 to 30 µg/m3.  Using this
information, we translated Expert K’s responses into the following
three conditional health impact functions:

Function K1 is associated with a no threshold segmented log-linear
specification with a knot at 16 µg/m3.  Function K2 represents the
segmented log-linear function with a threshold between 0 and 5 µg/m3,
with the cumulative probability of a threshold at or below the initial
concentration Q0 increasing as Q0 decreases (this results in a declining
expected value of the impact at lower initial concentrations).
Likewise, function K3 represented the segmented log-linear function with
a threshold between 5 and 10 µg/m3.  The results of applying the three
conditional functions are then combined using Monte Carlo analysis with
weights of 0.5, 0.4, and 0.1 assigned to conditional functions K1, K2,
and K3, respectively.

In addition to specifying a function form, each expert provided a
representation of the distribution (or distributions for those who
specified piecewise functions) of the effect size (in terms of the
percent change in premature mortality associated with a one microgram
change in annual mean PM2.5).  Six of the experts simply chose a normal
distribution, which is completely specified with two parameters, the
mean and standard deviation (see Figure XX).  In one case, the expert
specified a triangular distribution, which is represented by a minimum,
maximum, and most likely value (see Figure XX).  In another case, the
expert specified a Weibull distribution, which has three parameters
representing scale, location, and ???? (see Figure XX2).  Four of the
experts did not choose a parametric distribution, preferring instead to
provide only effect estimates at particular percentiles of their
distributions.  In these cases, we constructed custom distributions to
represent their percentiles.  For these custom distributions, we assume
a continuous and smooth transition of the distribution between the
reported percentiles (see Figure XX3 for example).

In some cases, experts included in their reported distributions the
likelihood that the relationship between PM2.5 and mortality was not
causal, e.g., that reducing PM2.5 would not actually reduce the risk of
premature death.  In these cases, the distributions are unconditional,
and included zero with some probability to reflect views on less than
certain causality.  In most cases, the experts chose to specify a
conditional distribution, such that the distribution of the effect
estimate is conditional on there being a causal relationship.  In these
cases, the final estimated distribution of avoided incidence of
premature mortality will be the expected value of the unconditional
distribution.  In practice, we implement this by estimating each
expert’s conditional distribution and then, using Monte Carlo
sampling, construct an unconditional distribution using the expert’s
reported probability of a causal relationship.  In the results section,
we provide an example of how this process works using the reported
distributions from Expert K.

Infant Mortality.  Recently published studies have strengthened the case
for an association between PM exposure and respiratory
inflamationinflammation and infection leading to premature mortality in
children under 5 years of age.  Specifically, the SAB-HES (2004??) noted
the release of the WHO Global Burden of Disease Study focusing on
ambient air, which cites several recently published time-series studies
relating daily PM exposure to mortality in children (SAB-HES, 2003 or
2004?  XE "SAB-HES, 2003"  ).  The SAB-HES also cites the study by
Belanger et al. (2003  XE "Belanger et al. (2003"  ) as corroborating
findings linking PM exposure to increased respiratory
inflamationinflammation and infections in children.  Recently, a study
by Chay and Greenstone (2003  XE "Chay and Greenstone (2003"  ) found
that reductions in TSP caused by the recession of 1981–1982 were
related to reductions in infant mortality at the county level.  With
regard to the cohort study conducted by Woodruff et al. (1997  XE
"Woodruff et al. (1997"  ), the SAB-HES notes several strengths of the
study, including the use of a larger cohort drawn from a large number of
metropolitan areas and efforts to control for a variety of individual
risk factors in infants (e.g., maternal educational level, maternal
ethnicity, parental marital status, and maternal smoking status).  Based
on these findings, the SAB-HES recommends that EPA incorporate infant
mortality into the primary benefits estimate and that infant mortality
be evaluated using an impact function developed from the Woodruff et al.
(1997  XE "Woodruff et al. (1997"  ) study (SAB-HES, 2004  XE "SAB-HES,
2004"  ).  A more recent study by Woodruff et al. (2006  XE "Woodruff et
al. (2006"  ) continues to find associations between PM2.5 and infant
mortality.  This study focused on premature deaths in infants in
California from 1999 to 2000, and found an overall relative risk of
premature death associated with PM2.5 of 1.07 per 10 µg/m3 of PM2.5.
The study also found the most significant relationships with
respiratory-related causes of death. [fix wording so that you end by
indicating whether you are using the newer study or the older study]

Chronic Bronchitis.  CB is characterized by mucus in the lungs and a
persistent wet cough for at least 3 months a year for several years in a
row.  CB affects an estimated 5% of the U.S. population (American Lung
Association, 1999  XE "American Lung Association, 1999"  ).  A limited
number of studies have estimated the impact of air pollution on new
incidences of CB.  Schwartz (1993  XE "Schwartz (1993"  ) and Abbey et
al. (1995  XE "Abbey et al. (1995"  ) provide evidence that long-term PM
exposure gives rise to the development of CB in the United States.
Because the CAIR is expected to reduce primarily PM2.5, tThis analysis
uses only the Abbey et al. (1995  XE "Abbey et al. (1995"  ) study,
because it is the only study focusing on the relationship between PM2.5
and new incidences of CB.

Nonfatal Myocardial Infarctions (heart attacks).  Nonfatal heart attacks
have been linked with short-term exposures to PM2.5 in the United States
(Peters et al., 2001  XE "Peters et al., 2001"  ) and other countries
(Poloniecki et al., 1997  XE "Poloniecki et al., 1997"  ).  We used a
recent study by Peters et al. (2001  XE "Peters et al. (2001"  ) as the
basis for the impact function estimating the relationship between PM2.5
and nonfatal heart attacks.  A more recent study by Zanobetti and
Schwartz (2005  XE "Zanobetti and Schwartz (2005"  ) used a similar
method to Peters et al. (2001  XE "Peters et al. (2001"  ), but focused
on adults 65 and older, and used PM10 as the PM indicator.  They found a
significant relationship between nonfatal heart attacks and PM10,
although the magnitude of the effect was much lower than Peters et al.
This may reflect the use of PM10, the more limited age range, or the
less precise diagnosis of heart attack used in defining the outcome
measure.  Other studies, such as Domenici et al. (2006  XE "Domenici et
al. (2006"  ), Samet et al. (2000  XE "Samet et al. (2000"  ), and
Moolgavkar (2000  XE "Moolgavkar (2000"  ), show a consistent
relationship between all cardiovascular hospital admissions, including
those for nonfatal heart attacks, and PM.  Given the lasting impact of a
heart attack on long-term health costs and earnings, we provide a
separate estimate for nonfatal heart attacks.  The estimate used in the
this CAIR analysis is based on the single available U.S. PM2.5 effect
estimate from Peters et al. (2001  XE "Peters et al. (2001"  ).  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. Several epidemiologic studies (Liao et al., 1999  XE "Liao et
al., 1999"  ; Gold et al., 2000  XE "Gold et al., 2000"  ; Magari et
al., 2001  XE "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 coronary heart diseases (Carthenon et al., 2002  XE "Carthenon et
al., 2002"  ; Dekker et al., 2000  XE "Dekker et al., 2000"  ; Liao et
al., 1997  XE "Liao et al., 1997"  ; Tsuji et al., 1996  XE "Tsuji et
al., 1996"  ).  As such, significant impacts of PM on heart rate
variability are consistent with an increased risk of heart attacks.

Hospital and Emergency Room Admissions.  Because of the availability of
detailed hospital admission and discharge records, there is an extensive
body of literature examining the relationship between hospital
admissions and air pollution.  Because of this, many of the hospital
admission endpoints use pooled impact functions based on the results of
a number of studies.  In addition, some studies have examined the
relationship between air pollution and emergency room visits.  Since
most emergency room visits do not result in an admission to the hospital
(the majority of people going to the emergency room are treated and
return home), we treat hospital admissions and emergency room visits
separately, taking account of the fraction of emergency room visits that
are admitted to the hospital.

The two main groups of hospital admissions estimated in this analysis
are respiratory admissions and cardiovascular admissions.  There is not
much evidence linking ozone or PM with other types of hospital
admissions.  The only type of emergency room visits that have been
consistently linked to ozone and PM in the United States are
asthma-related visits.

To estimate avoided incidences of PM2.5 related cardiovascular hospital
admissions in populations aged 65 and older, we use a new multicity
study by Domenici et al. (2006  XE "Domenici et al. (2006"  ).  In
recent analyses, we have used effect estimates from studies by
Moolgavkar (2003  XE "Moolgavkar (2003"  ) and Ito (2003  XE "Ito (2003"
 ).  However, the Domenici et al. (2006  XE "Domenici et al. (2006"  )
analysis provides a better effect estimate for our national analysis
because it is based on pooling data from across 204 counties in the U.S.
 In addition, the Domenici analysis provides consistent estimates for 7
regions of the country, allowing us to examine sensitivity of responses
to PM2.5 reductions in different areas of the U.S.  The Domenici,
Moolgavkar, and Ito studies provide effect estimates for populations
over 65, however, only Moolgavkar (2000  XE "Moolgavkar (2000"  )
provided a separate effect estimate for populations 20 to 64.  Total
cardiovascular hospital admissions are thus the sum of the Domenici et
al. (2006  XE "Domenici et al. (2006"  ) based impacts for populations
over 65 and the Moolgavkar (2000  XE "Moolgavkar (2000"  ) based impacts
for populations aged 20 to 64.  Cardiovascular hospital admissions
include admissions for myocardial infarctions.  To avoid double-counting
benefits from reductions in myocardial infarctions when applying the
impact function for cardiovascular hospital admissions, we first
adjusted the baseline cardiovascular hospital admissions to remove
admissions for myocardial infarctions.

To estimate total avoided incidences of respiratory hospital admissions,
we used impact functions for several respiratory causes, including
chronic obstructive pulmonary disease (COPD), pneumonia, and asthma.  As
with cardiovascular admissions, additional published studies show a
statistically significant relationship between PM10 and respiratory
hospital admissions.  We used only those focusing on PM2.5.  Both
Moolgavkar (2000  XE "Moolgavkar (2000"  ) and Ito (2003  XE "Ito (2003"
 ) provide effect estimates for COPD in populations over 65, allowing us
to pool the impact functions for this group.  Only Moolgavkar (2000  XE
"Moolgavkar (2000"  ) provides a separate effect estimate for
populations 20 to 64.  Total COPD hospital admissions are thus the sum
of the pooled estimate for populations over 65 and the single study
estimate for populations 20 to 64.  Only Ito (2003  XE "Ito (2003"  )
estimated pneumonia and only for the population 65 and older.  In
addition, Sheppard (2003  XE "Sheppard (2003"  ) provided an effect
estimate for asthma hospital admissions for populations under age 65.
Total avoided incidences of PM-related respiratory-related hospital
admissions is the sum of COPD, pneumonia, and asthma admissions.

To estimate the effects of PM air pollution reductions on asthma-related
ER visits, we use the effect estimate from a study of children 18 and
under by Norris et al. (1999  XE "Norris et al. (1999"  ).  As noted
earlier, there is another study by Schwartz examining a broader age
group (less than 65), but the Schwartz study focused on PM10 rather than
PM2.5.  We selected the Norris et al. (1999  XE "Norris et al. (1999"  )
effect estimate because it better matched the pollutant of interest.
Because children tend to have higher rates of hospitalization for asthma
relative to adults under 65, we will likely capture the majority of the
impact of PM2.5 on asthma emergency room visits in populations under 65,
although there may still be significant impacts in the adult population
under 65.

Acute Health Events and Work Loss Days.  As indicated in Table 5-1, in
addition to mortality, chronic illness, and hospital admissions, a
number of acute health effects not requiring hospitalization are
associated with exposure to ambient levels of ozone and PM.  The sources
for the effect estimates used to quantify these effects are described
below.

Around 4% of U.S. children between the ages of 5 and 17 experience
episodes of acute bronchitis annually (American Lung Association, 2002c
XE "American Lung Association, 2002c"  ).  Acute bronchitis is
characterized by coughing, chest discomfort, slight fever, and extreme
tiredness, lasting for a number of days.  According to the MedlinePlus
medical encyclopedia, with the exception of cough, most acute bronchitis
symptoms abate within 7 to 10 days.  Incidence of episodes of acute
bronchitis in children between the ages of 5 and 17 were estimated using
an effect estimate developed from Dockery et al. (1996  XE "Dockery et
al. (1996"  ).

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in
children aged 7 to 14 were estimated using an effect estimate from
Schwartz and Neas (2000  XE "Schwartz and Neas (2000"  ).

Because asthmatics have greater sensitivity to stimuli (including air
pollution), children with asthma can be more susceptible to a variety of
upper respiratory symptoms (e.g., runny or stuffy nose; wet cough; and
burning, aching, or red eyes).  Research on the effects of air pollution
on upper respiratory symptoms has thus focused on effects in asthmatics.
 Incidences of upper respiratory symptoms in asthmatic children aged 9
to 11 are estimated using an effect estimate developed from Pope et al.
(1991  XE "Pope et al. (1991"  ).

Exposure to air pollution can result in restrictions in activity levels.
 These restrictions range from relatively minor changes in daily
activities to serious limitations that can result in missed days of work
(either from personal symptoms or from caring for a sick family member).
 We include two types of restricted activity days, minor restricted
activity days (MRAD) and work loss days (WLD).  MRAD result when
individuals reduce most usual daily activities and replace them with
less strenuous activities or rest, yet not to the point of missing work
or school.  For example, a mechanic who would usually be doing physical
work most of the day will instead spend the day at a desk doing paper
and phone work because of difficulty breathing or chest pain.  The
effect of PM2.5 and ozone on MRAD was estimated using an effect estimate
derived from Ostro and Rothschild (1989  XE "Ostro and Rothschild (1989"
 ).  Work loss days due to PM2.5 were estimated using an effect estimate
developed from Ostro (1987  XE "Ostro (1987"  ).

In analyzing attainment strategies for the PM NAAQS, we have followed
the SAB-HES recommendations regarding asthma exacerbations in developing
the primary estimate.  To prevent double-counting, we focused the
estimation on asthma exacerbations occurring in children and excluded
adults from the calculation.  Asthma exacerbations occurring in adults
are assumed to be captured in the general population endpoints such as
work loss days and MRADs.  Consequently, if we had included an
adult-specific asthma exacerbation estimate, we would likely
double-count incidence for this endpoint.  However, because the general
population endpoints do not cover children (with regard to asthmatic
effects), an analysis focused specifically on asthma exacerbations for
children (6 to 18 years of age) could be conducted without concern for
double-counting.

To characterize asthma exacerbations in children, we selected two
studies (Ostro et al., 2001  XE "Ostro et al., 2001"  ; Vedal et al.,
1998  XE "Vedal et al., 1998"  ) that followed panels of asthmatic
children.  Ostro et al. (2001  XE "Ostro et al. (2001"  ) followed a
group of 138 African-American children in Los Angeles for 13 weeks,
recording daily occurrences of respiratory symptoms associated with
asthma exacerbations (e.g., shortness of breath, wheeze, and cough).
This study found a statistically significant association between PM2.5,
measured as a 12-hour average, and the daily prevalence of shortness of
breath and wheeze endpoints.  Although the association was not
statistically significant for cough, the results were still positive and
close to significance; consequently, we decided to include this
endpoint, along with shortness of breath and wheeze, in generating
incidence estimates (see below).  Vedal et al. (1998  XE "Vedal et al.
(1998"  ) followed a group of elementary school children, including 74
asthmatics, located on the west coast of Vancouver Island for 18 months
including measurements of daily peak expiratory flow (PEF) and the
tracking of respiratory symptoms (e.g., cough, phlegm, wheeze, chest
tightness) through the use of daily diaries.  Association between PM10
and respiratory symptoms for the asthmatic population was only reported
for two endpoints:  cough and PEF.  Because it is difficult to translate
PEF measures into clearly defined health endpoints that can be
monetized, we only included the cough-related effect estimate from this
study in quantifying asthma exacerbations.  We employed the following
pooling approach in combining estimates generated using effect estimates
from the two studies to produce a single asthma exacerbation incidence
estimate.  First, we pooled the separate incidence estimates for
shortness of breath, wheeze, and cough generated using effect estimates
from the Ostro et al. study, because each of these endpoints is aimed at
capturing the same overall endpoint (asthma exacerbations) and there
could be overlap in their predictions.  The pooled estimate from the
Ostro et al. study is then pooled with the cough-related estimate
generated using the Vedal study.  The rationale for this second pooling
step is similar to the first; both studies are attempting to quantify
the same overall endpoint (asthma exacerbations).

Additional epidemiological studies are available for characterizing
asthma-related health endpoints (the full list of epidemiological
studies considered for modeling asthma-related incidence is presented in
Table 5-8).  However, based on recommendations from the SAB-HES, we
decided not to use these additional studies in generating the primary
estimate.  In particular, the Yu et al. (2000  XE "Yu et al. (2000"  )
estimates show a much higher baseline incidence rate than other studies,
which may lead to an overstatement of the expected impacts in the
overall asthmatic population.  The Whittemore and Korn (1980  XE
"Whittemore and Korn (1980"  ) study did not use a well-defined
endpoint, instead focusing on a respondent-defined “asthma attack.”
Other studies looked at respiratory symptoms in asthmatics but did not
focus on specific exacerbations of asthma.

Treatment of Potential Thresholds in Health Impact Functions

, to demonstrate the sensitivity of our assumptions regarding
thresholds, The primary analysis for each regulatory alternative we
present the benefits based on five different cutpoints (assumed
thresholds) for premature mortality (the basline assumption of 10ug/m3
and four alternative assumptions).   –shows the benefits estimates
using long-term mortality at the long-term mortality 10 µg/m3 cutpoint.
We also show the results of our sensitivity analysis, with the 5 various
cutpoints, for each alternative to illustrate the impact of the
different assumptions.

.  Within-study variation refers to the precision with which a given
study estimates the relationship between air quality changes and health
effects.  Health effects studies provide both a “best estimate” of
this relationship plus a measure of the statistical uncertainty of the
relationship.  The size of this uncertainty, expressed in the standard
error, depends on factors such as the number of subjects studied and the
size of the effect being measured.  The results of even the most
well-designed epidemiological studies are characterized by this type of
uncertainty, though

Table 5-8:  Studies Examining Health Impacts in the Asthmatic Population
Evaluated for Use in the Benefits Analysis

1 mild asthma symptom:  wheeze, cough, chest tightness, shortness of
breath	PM10, PM1.0	Yu et al. (2000  XE "Yu et al. (2000"  )	Asthmatics,
5–13

Cough	Prevalence of cough	PM10	Vedal et al. (1998  XE "Vedal et al.
(1998"  )	Asthmatics, 6–13

Other Symptoms/Illness Endpoints

Upper respiratory symptoms	1 of the following:  runny or stuffy nose;
wet cough; burning, aching, or red eyes 	PM10	Pope et al. (1991  XE
"Pope et al. (1991"  )	Asthmatics, 9–11

Moderate or worse asthma	Probability of moderate (or worse) rating of
overall asthma status	PM2.5	Ostro et al. (1991  XE "Ostro et al. (1991"
)	Asthmatics, all ages

Acute bronchitis	1 episodes of bronchitis in the past 12 months	PM2.5
McConnell et al. (1999  XE "McConnell et al. (1999"  )	Asthmatics,
9–15

Phlegm	“Other than with colds, does this child usually seem congested
in the chest or bring up phlegm?”	PM2.5	McConnell et al. (1999  XE
"McConnell et al. (1999"  )	Asthmatics, 9–15

Asthma attacks	Respondent-defined asthma attack	PM2.5, ozone	Whittemore
and Korn (1980  XE "Whittemore and Korn (1980"  )	Asthmatics, all ages

well-designed studies typically report narrower uncertainty bounds
around the best estimate than do studies of lesser quality.  In
selecting health endpoints, we generally focus on endpoints where a
statistically significant relationship has been observed in at least
some studies, although we may pool together results from studies with
both statistically significant and insignificant estimates to avoid
selection bias.

Across-Study Variation.  Across-study variation refers to the fact that
different published studies of the same pollutant/health effect
relationship typically do not report identical findings; in some
instances the differences are substantial.  These differences can exist
even between equally well designed and executed reputable studies and
may result in health effect estimates that vary considerably.
Across-study variation can result from a variety oftwo possible causes.
Such differences might simply be associated with different measurement
techniques.  Sources of variation can be introduced by the air quality
monitoring technique,  measurement averaging times, health endpoint data
sources (differences in the way medical records are kept at different
institutions or questionnaire wording).  OneAnother possibility is that
studies report different estimates of the single true relationship
between a given pollutant and a health effect because of differences in
study design, random chance, or other factors.  For example, a
hypothetical study conducted in New York and one conducted in Seattle
may report different C-R functions for the relationship between PM and
mortality, in part because of differences between these two study
populations (e.g., demographics, activity patterns).  Alternatively,
study results may differ because these two studies are in fact
estimating different relationships; that is, the same reduction in PM in
New York and Seattle may result in different reductions in premature
mortality.  The latter is may result from a number of factors, such as
differences in the relative sensitivity of these two populations to PM
pollution and differences in the composition of PM in these two
locations.  In either case, where we identified multiple studies that
are appropriate for estimating a given health effect, we generated a
pooled estimate of results from each of those studies.

.  Regardless of the use of impact functions based on effect estimates
from a single epidemiological study or multiple studies, each impact
function was applied uniformly throughout the United States to generate
health benefit estimates.  However, to the extent that pollutant/health
effect relationships are region specific, applying a location-specific
impact function at all locations in the United States may result in
overestimates of health effect changes in some locations and
underestimates of health effect changes in other locations.  It is not
possible, however, to know the extent or direction of the overall effect
on health benefit estimates introduced by applying a single impact
function to the entire United States.  This may be a significant
uncertainty in the analysis, but the current state of the scientific
literature does not allow for a region-specific estimation of health
benefits for most health outcomes.  In the specific case of
cardiovascular hospital admissions, the Domenici et al. (2006  XE
"Domenici et al. (2006"  ) analysis does provide effect estimates for
seven regions of the U.S., and we examine sensitivity of results to the
assumption of regional heterogeneity.

.  A substantial body of published scientific literature demonstrates a
correlation between elevated PM concentrations and increased premature
mortality.  However, much about this relationship is still uncertain.
These uncertainties include the following:

.

Other Pollutants:  PM concentrations are correlated with the
concentrations of other criteria pollutants, such as ozone and CO.  To
the extent that there is correlation, this analysis may be assigning
mortality effects to PM exposure that are actually the result of
exposure to other pollutants.  Recent studies (see Thurston and Ito
[2001  XE "Thurston and Ito [2001"  ] and Bell et al. [2004  XE "Bell et
al. [2004"  ]) have explored whether ozone may have mortality effects
independent of PM.  EPA is currently evaluating the epidemiological
literature on the relationship between ozone and mortality.

:  The shape of the true PM mortality C-R function is uncertain, but
this analysis assumes the C-R function has a non-threshold log-linear
form throughout the relevant range of exposures.  If this is not the
correct form of the C-R function, or if certain scenarios predict
concentrations well above the range of values for which the C-R function
was fitted, avoided mortality may be misestimated.

  Although the consistent advice from EPA’s Science Advisory Board
(SAB) that provides advice on benefits analysis methods has been to
model premature mortality associated with PM exposure as a non-threshold
effect, that is, with harmful effects to exposed populations regardless
of the absolute level of ambient PM concentrations.  , EPA’s most
recent PM2.5 Criteria Document concludes that “the available evidence
does not either support or refute the existence of thresholds for the
effects of PM on mortality across the range of concentrations in the
studies” (U.S. EPA, 2004  XE "U.S. EPA, 2004"  , p. 9-44).  Some
researchers have hypothesized the presence of a threshold relationship.
The nature of the hypothesized relationship is the possibility that
there exists a PM concentration level below which further reductions no
longer yield premature mortality reduction benefits.

Regional Differences:  As discussed above, significant variability
exists in the results of different PM/mortality studies.  This
variability may reflect regionally specific C-R functions resulting from
regional differences in factors such as the physical and chemical
composition of PM.  If true regional differences exist, applying the
PM/mortality C-R function to regions outside the study location could
result in misestimation of effects in these regions.

Strategies that reduce a wide array of types of PM and precursor
emissions will have more certain health benefits than strategies that
are more narrowly focused. EPA’s national rules follow this risk
management insight by requiring reductions in a number of sources.
?????CAIR reduces SO2 and NOx, precursors to sulfates and nitrates,
non-road and on-road reduce directly emitted PM from diesels, and MACT
standards reduce PM and its precursors from a wide variety of source
categories. Similarly, all strategies analyzed in this RIA are for
reductions in a wide array of control factors. Until a more robust
scientific basis exists for making reliable judgments about the relative
toxicity of PM, it will not be possible to determine whether the
strategy of reducing a wide array of PM types is suboptimal or not.

Many of the national rules designed to reduce PM have estimated
benefit-cost ratios significantly larger than one, suggesting that the
conclusion that benefits exceed costs for these rules is robust to
modest deviations from the assumption that all particles are equally
potent.

.

(the amount of time between exposure and onset of a health effect)  may
inform our understanding of cessation lags. I THINK THERE IS SOME GOOD
LANGUAGE YOU COULD USE FROM THE DISINFECTION BIPRODUCTS RULE
“Intervention” studies in which changes in exposure levels are
associated with changes in health endpoints provide some insight. In
section …. We discuss recent studies that document reductions in
exposure over time have been associated with reduction in the premature
mortality (refs).  In addition, However, current scientific literature
on adverse health effects similar to those associated with PM (e.g.,
smoking-related disease) and the difference in the effect size between
chronic exposure studies and daily mortality studies suggests that all
incidences of premature mortality reduction associated with a given
incremental change in PM exposure probably would not occur in the same
year as the exposure reduction.  The smoking-related literature also
implies that lags of up to a few years or longer are plausible, although
it is worth noting that in the case of ambient air pollution we are
predicting the effects of reduced exposure rather than complete
cessation.  The SAB-HES suggests that appropriate lag structures may be
developed based on the distribution of cause-specific deaths within the
overall all-cause estimate.  Diseases with longer progressions should be
characterized by long-term lag structures, while impacts occurring in
populations with existing disease may be characterized by short-term
lags.

A key question is the distribution of causes of death within the
relatively broad categories analyzed in the cohort studies used.  While
we may be more certain about the appropriate length of cessation lag for
lung cancer deaths, it is not clear what the appropriate lag structure
should be for different types of cardiopulmonary deaths, which include
both respiratory and cardiovascular causes.  Some respiratory diseases
may have a long period of progression, while others, such as pneumonia,
have a very short duration.  In the case of cardiovascular disease,
there is an important question of whether air pollution is causing the
disease, which would imply a relatively long cessation lag, or whether
air pollution is causing premature death in individuals with preexisting
heart disease, which would imply very short cessation lags.

The SAB-HES provides several recommendations for future research that
could support the development of defensible lag structures, including
the use of disease-specific lag models, and the construction of a
segmented lag distribution to combine differential lags across causes of
death.  The SAB-HES recommended that until additional research has been
completed, EPA should assume a segmented lag structure characterized by
30% of mortality reductions occurring in the first year, 50% occurring
evenly over years 2 to 5 after the reduction in PM2.5, and 20% occurring
evenly over the years 6 to 20 after the reduction in PM2.5.  The
distribution of deaths over the latency period is intended to reflect
the contribution of short-term exposures in the first year,
cardiopulmonary deaths in the 2- to 5-year period, and long-term lung
disease and lung cancer in the 6- to 20-year period.  For future
analyses, the specific distribution of deaths over time will need to be
determined through research on causes of death and progression of
diseases associated with air pollution.  It is important to keep in mind
that changes in the lag assumptions do not change the total number of
estimated deaths but rather the timing of those deaths.

Baseline Health Effect Incidence Rates

g/m3 decrease in daily PM2.5 levels might decrease hospital
admissions by 3%.  The baseline incidence of the health effect is
necessary tTo then convert this relative change into a number of cases
the baseline incidence of the health effect is necessary.  The baseline
incidence rate provides an estimate of the incidence rate (number of
cases of the health effect per year, usually per 10,000 or 100,000
general population) in the assessment location corresponding to baseline
pollutant levels in that location.  To derive the total baseline
incidence per year, this rate must be multiplied by the corresponding
population number (e.g., if the baseline incidence rate is number of
cases per year per 100,000 population, it must be multiplied by the
number of 100,000s in the population).

Some epidemiological studies examine the association between pollution
levels and adverse health effects in a specific subpopulation, such as
asthmatics or diabetics.  In these cases, it is necessary to develop not
only baseline incidence rates, but also prevalence rates for the
defining condition (e.g., asthma).  For both baseline incidence and
prevalence data, we use age-specific rates where available.  Impact
functions are applied to individual age groups and then summed over the
relevant age range to provide an estimate of total population benefits.

In most cases, because of a lack of data or methods, we have not
attempted to project incidence rates to future years, instead assuming
that the most recent data on incidence rates is the best prediction of
future incidence rates.  In recent years, better data on trends in
incidence and prevalence rates for some endpoints, such as asthma, have
become available.  We are working to develop methods to use these data
to project future incidence rates.  However, for our primary benefits
analysis, we continue to use current incidence rates.  The once
exception is in the case of premature mortality.  In this case, we have
projected mortality rates such that future mortality rates are
consistent with our projections of population growth (Abt Associates,
2005  XE "Abt Associates, 2005"  ).  Compared with previous analyses,
this will result in a reduction in the mortality related impacts of air
pollution in future years.

Table 5-9 summarizes the baseline incidence data and sources used in the
benefits analysis.  We use the most geographically disaggregated data
available.  For premature mortality, county-level data are available.
For hospital admissions, regional rates are available.  However, for all
other endpoints, a single national incidence rate is used, due to a lack
of more spatially disaggregated data.  In these cases, we used national
incidence rates whenever possible, because these data are most
applicable to a national assessment of benefits.  However, for some
studies, the only available incidence information comes from the studies
themselves; in these cases, incidence in the study population is assumed
to represent typical incidence at the national level.

Table 5-9:  Baseline Incidence Rates and Population Prevalence Rates for
Use in Impact Functions, General Population

Endpoint	Parameter	Rates

Value	Sourcea

Mortality	Daily or annual mortality rate	Age-, cause-, and
county-specific rate	CDC Wonder (1996–1998)

Hospitalizations	Daily hospitalization rate	Age-, region-, and
cause-specific rate	1999 NHDS public use data filesb

Asthma ER Visits	Daily asthma ER visit rate	Age- and region- specific
visit rate	2000 NHAMCS public use data filesc; 1999 NHDS public use data
filesb

(continued)

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潩牐癥污湥散删瑡獥映牯唠敳椠浉慰瑣䘠湵瑣潩獮
敇敮慲潐異慬楴湯⠠潣瑮湩敵⥤䔍摮潰湩ݴ慐慲敭
整ݲ慒整ݳ܇嘇污敵匇畯捲慥܇桃潲楮牂湯档瑩獩䄇湮
慵牰癥污湥散爠瑡数⁲数獲湯

	Aged 18–44

	Aged 45–64

	Aged 65 and older

0.0367

0.0505

0.0587	1999 NHIS (American Lung Association, 2002b  XE "American Lung
Association, 2002b"  , Table 4)

	Annual incidence rate per person	0.00378	Abbey et al. (1993  XE "Abbey
et al. (1993"  , Table 3)

Nonfatal Myocardial Infarction (heart attacks)	Daily nonfatal myocardial
infarction incidence rate per person, 18+

	Northeast

	Midwest

	South

	West

0.0000159

0.0000135

0.0000111

0.0000100	1999 NHDS public use data filesb; adjusted by 0.93 for
probability of surviving after 28 days (Rosamond et al., 1999  XE
"Rosamond et al., 1999"  )

Asthma Exacerbations	Incidence (and prevalence) among asthmatic
African-American children

	daily wheeze

	daily cough

	daily dyspnea

0.076 (0.173)

0.067 (0.145)

0.037 (0.074)	Ostro et al. (2001  XE "Ostro et al. (2001"  )

	Prevalence among asthmatic children

	daily wheeze

	daily cough

	daily dyspnea

0.038

0.086

0.045	Vedal et al. (1998  XE "Vedal et al. (1998"  )

Acute Bronchitis	Annual bronchitis incidence rate, children	0.043
American Lung Association (2002c  XE "American Lung Association (2002c"
, Table 11)

Lower Respiratory Symptoms	Daily lower respiratory symptom incidence
among childrend	0.0012	Schwartz et al. (1994  XE "Schwartz et al.
(1994"ᔠ慔汢⤲܇灕数⁲敒灳物瑡牯⁹祓灭潴獭䐇楡
祬甠灰牥爠獥楰慲潴祲猠浹瑰浯椠据摩湥散愠潭杮愠瑳
浨瑡捩挠楨摬敲ݮ⸰㐳㤱倇灯瑥愠⹬⠠㤱ㄹ–䕘∠潐
数攠⁴污ㄨ㤹∱ᔠ慔汢⤲܇潗歲䰠獯⁳慄獹䐇楡
祬圠䑌椠据摩湥散爠瑡数⁲数獲湯⠠㠱㚖⤵

	Aged 18–24

	Aged 25–44

	Aged 45–64

0.00540

0.00678

0.00492	1996 HIS (Adams, Hendershot, and Marano, 1999  XE "Adams,
Hendershot, and Marano, 1999"  , Table 41); U.S. Bureau of the Census
(2000  XE "U.S. Bureau of the Census (2000"  )

Minor Restricted-Activity Days	Daily MRAD incidence rate per person
0.02137	Ostro and Rothschild (1989  XE "Ostro and Rothschild (1989"  ,
p. 243)

a	The following abbreviations are used to describe the national surveys
conducted by the National Center for Health Statistics:  HIS refers to
the National Health Interview Survey; NHDS—National Hospital Discharge
Survey; NHAMCS—National Hospital Ambulatory Medical Care Survey.

b	See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHDS/.

c	See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/.

d	Lower respiratory symptoms are defined as two or more of the
following:  cough, chest pain, phlegm, and wheeze.

Baseline age, cause, and county-specific mortality rates were obtained
from the U.S. Centers for Disease Control and Prevention (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.

To estimate age- and county-specific mortality rates in years 2000
through 2050, we calculated adjustment factors, based on a series of
Census Bureau projected national mortality rates, to adjust the CDC
Wonder age- and county-specific mortality rates in 1996-1998 to
corresponding rates for each future year.  For the analysis year 2020,
these adjustment factors ranged across age categories from 0.76 to 0.86

For the set of endpoints affecting the asthmatic population, in addition
to baseline incidence rates, prevalence rates of asthma in the
population are needed to define the applicable population.  Table 5-9
lists the baseline incidence rates and their sources for asthma symptom
endpoints.  Table 5-10 lists the prevalence rates used to determine the
applicable population for asthma symptom endpoints.  Note that these
reflect current asthma prevalence and assume no change in prevalence
rates in future years.  As noted above, we are investigating methods for
projecting asthma prevalence rates in future years.  However, it should
be noted that current trends in asthma prevalence do not lead us to
expect that asthma prevalence rates will be more than 4% overall in
2020, or that large changes will occur in asthma prevalence rates for
individual age categories (Mansfield et al., 2005  XE "Mansfield et al.,
2005"  ).

Table 5-10:  Asthma Prevalence Rates Used to Estimate Asthmatic
Populations in Impact Functions

Population Group	Asthma Prevalence Rates

	Value	Source

All Ages	0.0386	American Lung Association (2002a  XE "American Lung
Association (2002a"  , Table 7)—based on 1999 HIS

< 18	0.0527	American Lung Association (2002a, Table 7)—based on 1999
HIS

5–17	0.0567	American Lung Association (2002a, Table 7)—based on 1999
HIS

18–44	0.0371	American Lung Association (2002a, Table 7)—based on
1999 HIS