### 5D

- 1

APPENDIX 5D : DISCUSSION OF EFFECT ESTIMATES REFLECTED IN THE DEVELOPMENT OF DOLLAR - PER - TON VALUES USED IN MODELING PM 2.5 

### COBENEFITS Overview This section describes how we selected effect estimates to estimate the benefits of reducing PM 2.5 

### (see section 5.4.4). T his section mirrors that covering effect estimate selection for ozone presented in section 5.6.3 in terms of organization (in some cases, we have repeated introductory/setup content here to facilitate review by the reader).
The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base our selection of health endpoints on consistency with the EPA’s Integrated Science Assessments (which replace previous “Criteria Documents”), with input an d advice from the HES, a scientific review panel specifically established to provide advice on the use of the scientific literature in developing benefits analyses for the EPA’s Report to Congress on The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a). In addition, we have included more recent epidemiology studies from the PM ISA (U.S. EPA, 2009b), the PM Provisional Assessment (U.S. EPA, 2012b). 1 

### In general, we follow a weight of evidence approach, based on the biological plausibilit y of effects, availability of concentration

- response functions from well conducted peer

- reviewed epidemiological studies, cohesiveness of results across studies, and a focus on endpoints reflecting public health impacts (like hospital admissions) rather th an physiological responses (such as changes in clinical measures like Forced Expiratory Volume [FEV1]). There are several types of data that can support the determination of types and magnitude of health effects associated with air pollution exposures. The se 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 parti cular health impact. However, only epidemiology studies provide direct concentration

- response relationships that can be used to evaluate population

- level impacts of reductions in ambient pollution levels in a health impact assessment.

1 The peer

- reviewed studies in the Provisional Assessment have not yet undergone external review by the SAB.

### 5D

- 2

For the data

- derived estimates, we relied on the published scientific literature to ascertain the relationship between PM 2.5 

### and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 5

- 6 (see section 5.6.3). These c riteria 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. In general, the use of concentration

- response functions from more than a single study can provide a more representative distribution of the effect estimate. 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 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 Manual Technical Appendices for an earlier version of the program provides details of the procedures used to combine multiple impact functions (Abt Associates, 2012). In general, we used fixed or random effects models to pool estimates from dif ferent single city studies of the same endpoint. Fixed effect 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 with in

- study variance and between

- study variability, due, for example, to differences in population susceptibility. We used the fixed effect model as our null hypothesis and then determined whether the data suggest that we should reject this null hypothesis, i n which case we would use the random effects model. 2 

### Pooled impact functions are used to estimate hospital admissions and asthma exacerbations. When combining

2 EPA recently changed the algorithm BenMAP uses to calculate study variance, which is used in the pooling process. Prior versions of the model calculated population variance, while the version used here calculated sample variance. This change did not affect the selection of random or fixed effects for the pooled incidence estimates between the pro posal and final RIA.

### 5D

- 3

evidence across multi

- city studies (e.g., cardiovascular hospital admission studies), we use equa l weights pooling. The effect estimates drawn from each multi

- city study are themselves pooled across a large number of urban areas. For this reason, we elected to give each study an equal weight rather than weighting by the inverse of the variance reporte d in each study. For more details on methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2012). Effect estimates selected for a given health endpoint were applied consistently across all locations nationwide. This ap plies 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 suscept ibilities or differences in the composition of PM), location

- specific effect estimates are generally not available. The specific studies from which effect estimates for the core analysis for PM 2.5 

### (generated using the dollar

- per - ton approach

– see section 5.4.4) are presented in Table 5

- 8 and repeated below for ease of access in in Table 5D

- 1. We highlight in red those studies that have been added since the benefits analysis conducted for the O 3 

### reconsideration (U.S. EPA, 2010 d ) or the O 3 

### NAAQS RIA (U.S. EP A, 2008 ). 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.

### 5D

- 4

Table 5D - 1 . Health Endpoints and Epidemiological St udies Used to Quantify PM 2.5 

### -

related Health Impacts in the Core Analysis a
Endpoint Study Study Population Relative Risk or Effect Estimate ( β)

(with 95 th Percentile Confidence Interval or SE, respectively) Premature Mortality Premature mortality

— cohort study, all

- cause Krewski et al. (2009) > 29 years > 24 years RR = 1.06 (1.04

– 1.06) per 10 μg/m 3 Lepeule et al. (2012) RR = 1.14 (1.07

– 1.22) per 10 μg/m 3 Premature mortality

— all

- cause Woodruff et al. (1997) Infant (< 1 year) OR = 1.04 (1.02

– 1.07) per 10 μg/m 3 Chronic Illness Nonfatal heart attacks Peters et al. (2001) Adults (> 18 years) OR = 1.62 (1.13

– 2.34) per 20 μg/m 3 Pooled estimate: Pope et al. (2006) β = 0.00481 (0.00199) Sullivan et al. (2005) β = 0.00198 (0.00224) Zanobetti et al. (2009) β = 0.00225 (0.000591) Zanobetti and Schwartz (2006) β = 0.0053 (0.00221) Hospital Admissions Respiratory Zanobetti et al. (2009)

— ICD 460

- 519 (All respiratory) > 64 years β=0.00207 (0.00446) Kloog et al. (2012)

— ICD 460

- 519 (All Respiratory β=0.0007 (0.000961) Moolgavkar (2000)

— ICD 490

– 496 (Chronic lung disease) 18

– 64 years 1.02 (1.01

– 1.03) per 36 μg/m 3 Babin et al. (2007)

— ICD 493 (asthma) < 19 years β=0.002 (0.004337) Sheppard (2003)

— ICD 493 (asthma) < 18 RR = 1.04 (1.01

– 1.06) per 11.8 μg/m 3 Cardiovascular Pooled estimate: Zanobetti et al. (2009)

— ICD 390

- 459 (all cardiovascular) > 64 years β=0.00189 (0.000283) Peng et al. (2009)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

and peripheral vascular disease) β=0.00068 (0.000214)
Peng et al. (2008)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

and peripheral vascular disease) β=0.00071 (0.00013)
Bell et al. (2008)

— ICD 426

- 427; 428; 430

- 438; 410

- 414; 429; 440

- 449 (Cardio

- , cerebro

and peripheral vascular disease) β=0.0008 (0.000107) Moolgavkar (2000)

— ICD 390

– 429 (all cardiovascular) 20

– 64 years RR=1.04 (t statistic: 4.1) per 10 μg/m 3 Asthma

- related emergency department visits Pooled estimate: Mar et al. (2010) All ages RR = 1.04 (1.01

– 1.07) per 7 μg/m 3 Slaughter et al. (2005) RR = 1.03 (0.98

– 1.09) per 10 μg/m 3 Glad et al. (2012) β=0.00392 (0.002843) Other Health Endpoints Acute bronchitis Dockery et al. (1996) 8

– 12 years OR = 1.50 (0.91

– 2.47) per 14.9 μg/m 3

### 5D

- 5

Asthma exacerbations Pooled estimate: Ostro et al. (2001) (cough, wheeze and shortness of breath) b 6

– 18 years b OR = 1.03 (0.98

– 1.07) OR = 1.06 (1.01

– 1.11) OR = 1.08 (1.00

– 1.17) per 30 μg/m 3 Mar et al. (2004) (cough, shortness of breath) RR = 1.21 (1

– 1.47) per RR = 1.13 (0.86

– 1.48) per 10 μg/m 3 Work loss days Ostro (1987) 18

– 65 years β=0.0046 (0.00036) Acute respiratory symptoms (MRAD) Ostro and Rothschild (1989) (Minor restricted activity days) 18

– 65 years β=0.00220 (0.000658) Upper respiratory symptoms Pope et al. (1991) Asthmatics, 9

– 11 years 1.003 (1

– 1.006) per 10 μg/m 3 Lower respiratory symptoms Schwartz and Neas (2000) 7

– 14 years OR = 1.33 (1.11

– 1.58) per 15 μg/m 3

a Studies highlighted in red represent updates incorporated since the O 3 NAAQS RIA (U.S. EPA, 2008). These updates were introduced in the PM NAAQS RIA (U.S. EPA, 2012). b The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) study. Based on advice from t he SAB

- HES, we extended the applied population to 6

- 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA

- SAB (2004) and NRC (2002).


### 5D.1 PM 2.5 

### Premature Mortality Effect Coefficients Core Mortality Effect Coefficients for Adults. A substantial body of published scientific literature documents the association between elevated PM 2.5 

### concentrations and increased premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA completed as part of the most recent review of the PM standards, which was twice r eviewed by the SAB

- CASAC (U.S. EPA

- SAB, 2009, 2009b ), concluded that there is a causal relationship between mortality and both long

- term and short

- term exposure to PM 2.5 

### based on the entire body of scientific evidence (U.S. EPA, 2009b). The size of the mor tality effect 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.
Research ers have found statistically significant associations between PM 2.5 

### and premature mortality using different types of study designs. Time

- series methods have been used to relate short

- term (often day

- to

- day) changes in PM 2.5 

### concentrations and changes in da ily mortality rates up to several days after a period of elevated PM 2.5 

### concentrations. Cohort methods have been used to examine the potential relationship between community

- level PM 2.5 

### exposures over multiple years (i.e., long

- term exposures) and communit y - level annual mortality rates that have been adjusted for individual level risk factors. When choosing between using short

- term studies or cohort studies for estimating mortality benefits, cohort analyses are thought

### 5D

- 6

to capture more of the public health i mpact of exposure to air pollution over time because they account for the effects of long

- term exposures as well as some fraction of short

- term exposures (Kunzli et al., 2001; NRC, 2002). The NRC stated that “it is essential to use the cohort studies in be nefits analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). The NRC further notes that “ the overall effect estimates may be a combination of effects from long

- term exposure plus some fraction from short

- term exposure. The amount of overlap is unknown” (NRC, 2002, p. 108

- 9). To avoid double counting, we focus on applying the risk coefficients from the long

- term cohort studies in estimating the mortality impacts of reductions in PM 2.5 

### . Over the last two decades, several studies using “prospective cohort” designs have been published that are consistent with the earlier body of literature. Two prospective cohort studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; Lepeule e t al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2009), provide the most extensive analyses of ambient PM 2.5 

### concentrations and mortality. These studies have found consi stent relationships between fine particle indicators and premature mortality across multiple locations in the United States. The credibility of these two studies is further enhanced by the fact that the initial published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effect Institute (HEI) and by a Special Panel of the HEI Health Review Committee (Krewski et al., 2000). Publicati on of studies confirming and extending the findings of the 1993 Six Cities Study and the 1995 ACS study using more recent air quality and a longer follow

- up period for the ACS cohort provides additional validation of the findings of these original studies (Pope et al., 2002, 2004; Laden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The SAB

- HES also supported using these two cohorts for analyses of the benefits of PM reductions, and concluded, “the selection of these cohort studies as the underl ying basis for PM mortality benefit estimates to be a good choice. These are widely cited, well studied and extensively reviewed data sets” (U.S. EPA

- SAB, 2010a). As both the ACS and Six Cities studies have inherent strengths and weaknesses, we present ben efits estimates using relative risk estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other recent RIAs

### 5D

- 7

(e.g., U.S. EPA, 200 6, 2010c, 2011b, 2011c ). The PM ISA concludes that the ACS and Six Cities cohorts provide the strongest evidence of the association between long

- term PM 2.5 

### exposure and premature mortality with support from a number of additional cohort studies (described below). The extended analyses of the ACS cohort data (Krewski et al., 2009) provides additional refinements to the analysis of PM

- related mortality by (a) extending the follow

- up period by 2 years to the year 2000, for a total of 18 years; (b) incorporatin g almost double the number of urban areas (c) addressing confounding by spatial autocorrelation by incorporating ecological, or community

- level, co

- variates; (d) performing an extensive spatial analysis using land use regression modeling in two large urban areas. These enhancements make this analysis well

suited for the assessment of mortality risk from long

- term PM 2.5 

### exposures for the EPA’s benefits analyses. In 2009, the SAB

- HES again reviewed the choice of mortality risk coefficients for benefits analy sis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the SAB

- HES recommends that EPA not use the Krewski et al. (2009) findings for ge nerating the Primary Estimate” (U.S. EPA

- SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone additional peer review, which we believe strengthens the support for including this study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the key mortality studies. In addition, the risk assessment supporti ng the PM NAAQS (U.S. EPA, 2010a ) utilized risk coefficients drawn from the Krewski et al. (2009) study, the most recent reanalysis of the ACS cohort data. The ri sk assessment cited a number of advantages that informed the selection of the Krewski et al. (2009) study as the source of the core effect estimates, including the extended period of observation, the rigorous examination of model forms and effect estimates , the coverage for ecological variables, and the large dataset with over 1.2 million individua ls and 156 MSAs (U.S. EPA, 2010a ). The CASAC also provided extensive peer review of the risk assessment and supported the use of effect estimates from this study (U.S. EPA

- SAB, 2009 , 2010b, c).

### 5D

- 8

Consistent with the Quantitative Health Risk Assessment for Par ticulate Matter (U.S. EPA, 2010a ) which was reviewed by the CASAC (U.S. EPA

- SAB, 2009), we use the all

- cause mortality risk estimate based on the random

- effects Cox proportional hazard model that incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04

– 1.08 per 10μg/m 3 

### increase in PM 2.5 

### ). The relative risk estimate (1.06 per 10μg/m 3 

### increase in PM 2.5 

### ) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et al. (2009) risk estimate is tighter. In the most recent Six Cities study, which was published after the last SAB

- HES review, Lepeule et al. (2012) evaluated the sensitivity of previous Six

- Cities results to model specifications, lower exposures, and averaging time using eleven additional year s of cohort follow

- up that incorporated recent lower exposures. The authors found significant associations between PM 2.5 

### exposure and increased risk of all

- cause, cardiovascular and lung cancer mortality. The authors also concluded that the concentration

- r esponse relationship was linear down to PM 2.5 

### concentrations of 8 μg/ m 3 

### , and that mortality rate ratios for PM 2.5 

### fluctuated over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the all

cause mortality risk estima te based on a Cox proportional hazard model that incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07

– 1.22 per 10 μg/m 3 

### increase in PM 2.5 

### ). The relative risk estimate is slightly smaller than the risk estimate drawn from Laden et
a l. (2006), with relatively smaller confidence intervals. Implicit in the calculation of PM 2.5 

### - related premature mortality impacts are several key assumptions, which are described in further detail later in this chapter. First, we assume that there is a “ce ssation” lag in time between the reduction in PM exposure and the full reduction in mortality risk that affects the timing (and thus discounted monetary valuation) of the resulting premature deaths (see section 5.6.4.1). Second, following conclusions of th e PM ISA, we assume that all fine particles are equally potent in causing premature mortality (see section 5.7.3). Third, following conclusions of the PM ISA, we assume that the health impact function for fine particles is linear within the range of ambien t concentrations affected by these standards (see section 5.7.3).

### 5D

- 9

Alternate Mortality Effect Coefficients for Adults. In addition to the ACS and Six Cities cohorts, several recent cohort studies conducted in North America provide evidence for the relations hip between long

- term exposure to PM 2.5 

### and the risk of premature death. Many of these additional cohort studies are described in the PM ISA (U.S. EPA, 2009 b ) and the Provisional Assessment (U.S. EPA, 2012b) (and thus not summarized here). 3 , 4 

### Table 5D

- 2 pr ovides the effect estimates from each of these cohort studies for all

- cause, cardiovascular, cardiopulmonary, and ischemic heart disease (IHD) mortality as well as the lowest measured air quality level (LML) and mean concentration in the study.
We also dr aw upon the results of the 2006 expert elicitation sponsored by the EPA (Roman et al., 2008; IEc, 2006) to demonstrate the sensitivity of the benefits estimates to 12 expert

- defined concentration

- response functions. The PM 2.5 

### expert elicitation and the der ivation of effect estimates from the expert elicitation results are described in detail in the 2006 PM 2.5 

### NAAQS RIA (U.S. EPA, 2006 ), the elicitation summary report (IEc, 2006) and Roman et al. (2008), and so we summarize here the key attributes of this study relative to the interpretation of the estimates of PM

- related mortality reported here. We describe also how the epidemiological literature has evolved since the expert elicitation was conducted in 2005 and 2006.

3 It is important to note that the newer studies in the Provisional Assessment are published in peer

- reviewed journals and meet our study selection criteria, but they have not been assessed in the context of an Integrated Science Asse ssment nor gone through review by the SAB. In addition, only the ACS and H6C cohort studies have been recommended by the SAB as appropriate for benefits analysis of national rulemakings. 4 In this chapter, we only describe multi

- state cohort studies. There are additional cohort studies that we have not included in this list, including cohort studies that focus on single cities (e.g., Gan et al., 2012) and cohort studies focusing on methods development. In Appendix 5A, we provide additional information regar ding cohort studies in California, which is the only state for which we identified single state cohorts.

### 5D

- 10

Table 5D - 2 . Summary of Effect estimates from Associated with Change in Long - Term

Exposure to PM 2.5 

### in Recent Cohort Studies in North America
Study Cohort (age) LML (μg/ m 3 ) Mean (μg/ m 3 ) Hazard Ratios per 10 μg/m 3 Change in PM 2.5

(95 th percentile confidence intervals) All Causes Cardiovascular Cardiopulmonary IHD Pope et al. (2002) ACS

(age >30) 7.5 18.2 1.06 (1.02

– 1.11) 1.12

(1.08

– 1.15) 1.09

(1.03

– 1.16) N/A Laden et al. (2006) Six Cities

(age > 25) 10 16.4 1.16 (1.07

– 1.26) 1.28

(1.13

– 1.44) N/A N/A Lipfert et al. (2006) a Veterans

(age 39

– 63) <14.1 14.3 1.15 (1.05

– 1.25) N/A N/A N/A Miller et al. (2007) b WHI

(age 50

– 79) 3.4 13.5 N/A 1.76 (1.25

– 2.47) N/A 2.21 (1.17

– 4.16) Eftim et al. (2008) Medicare (age >

65) 6 13.6 1.21 (1.15

– 1.27) N/A N/A N/A Zeger et al. (2008) c Medicare (age >

65) <9.8 13.2 1.068 (1.049

– 1.087) N/A N/A N/A Krewski et al. (2009) d ACS

(age >30) 5.8 14 1.06 (1.04

– 1.08) N/A 1.13 (1.10

– 1.16) 1.24

(1.19

– 1.29) Puett et al. (2009) b NHS

(age 30

– 55) 5.8 13.9 1.26 (1.02

– 1.54) N/A N/A 2.02 (1.07

– 3.78) Crouse et al. (2011) d,e Canadian

census 1.9 8.7 1.06 (1.01

– 1.10) N/A N/A N/A Puett et al. (2011) f Health

Professionals (age 40

– 75) <14.4 17.8 0.86 (0.70

– 1.00) 1.02

(0.84

– 1.23) N/A N/A
Lepeule et al. (2012) d Six Cities

(age > 25) 8 15.9 1.14 (1.07

– 1.22) 1.26

(1.14

– 1.40) N/A N/A

a Low socio

- economic status (SES) men only. Used traffic proximity as a surrogate of exposure. b Women only.
c Reflects risks in the Eastern U.S. Risks in the Central U.S. were higher, but the authors found no association in the Western U.S. d Random effects Cox model with individual and ecologic covariates.
e Canadian population. f Men with high socioeconomic status only. 

### The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing uncertainty in estimates of the reduction in mortality among the adult U.S. population result ing from reductions in ambient annual average PM 2.5 

### levels. These distributions were obtained through a formal interview protocol using methods designed to elicit subjective expert judgments. These experts were selected through a peer

- nomination process an d included experts in epidemiology, toxicology, and medicine. The elicitation interview consisted of a protocol of carefully structured questions, both qualitative and quantitative, about the nature of the PM 2.5 

### - mortality relationship designed to build twe lve individual distributions for the coefficient (or slope) of the C

- R function relating changes in annual average PM 2.5 

### exposures to annual, adult all

- cause mortality. The elicitation also provided useful information regarding uncertainty characterization in the PM 2.5 

### - mortality relationship.

### 5D

- 11

Specifically, during their interviews, the experts highlighted several uncertainties inherent within the epidemiology literature, such as causality, concentration thresholds, effect modification, the role of short

- and long

- term exposures, potential confounding, and exposure misclassification. In Appendix 5C, we evaluate each of these uncertainties in the context of this health impact assessment. For several of these uncertainties, such as causality, we are able to use the expert

derived functions to quantify the impacts of applying different assumptions. The elicitation received favorable peer review in 2006 (Mansfield and Patil, 2006). Prior to providing a quantitative estimate of the risk of premature death associated with long

- term PM 2.5 

### exposure, the experts answered a series of “conditioning questions.” One such question asked the experts to identify which epidemiological studies they found most informative. The “ideal study attributes” 5 

### according to the experts included:
1. Geographic representation of the entire U.S. (e.g., monitoring sites across the country) 2. Collection of information on individual risk factors and residential information both at the beginning and throughout the follow

- up period 3. Large sample size that is representative of the general U.S. population 4. Collection of genetic information from cohort members to identify and assess potential effect modifiers 5. Monitoring of individual exposures (e.g., with a personal monitor) 6. Collection of data on levels of several co

- pollutants (not only those that are monitored for compliance purposes 7. Accurate characterization of outcome (i.e., cause of death) 8. Follow

- up for a long period of time, up to a lifetime 9. Prospective study design Although no single epidemiological study completely satisfies each of these criteria, the experts determined that the ACS and Six Cities cohort studies best satisfy a majority of these ideal attributes. To varying degrees the studies examining these two cohorts: are geographically represent ative; have collected information on individual risk factors; include a large sample size; have collected data on co

- pollutants in the case of the ACS study; have accurately characterized the health outcome; include a long (and growing) follow

- up period; a nd, are prospective in nature. The experts also noted a series of limitations in these two cohort studies. In the case of

5 These criteria are substantively similar to EPA’s study selection criteria identified in Table 5

- 5 of this chapter.

### 5D

- 12

the Six Cities study (Laden et al., 2006), the experts identified the “small sample size, limited number of cities, and concerns about representativeness of the six cities for the U.S. as a whole” as weaknesses. When considering the ACS study (Pope et al., 2002), the experts indicated that the “method of recruitment for the study, which resulted in a group with higher income, more educat ion, and a greater proportion of whites than is representative of the general U.S. population” represented a shortcoming. Several experts also argued that because the ACS study relied upon “...whatever monitors were available to the study...a single monitor re present[ed] exposure for an entire metropolitan area...whereas [the Six Cities study] often had exposures assigned at the county level.” Despite these limitations, the experts considered the Pope et al. (2002) extended analysis of the ACS cohort and the Lade n et al. (2006) extended analysis of the Six Cities cohort to be particularly influential in their opinions (see Exhibit 3

- 3 of the elicitation summary report [IEc, 2006]). Please note that the benefits estimates results presented are not the direct resul ts from the studies or expert elicitation; rather, the estimates are based in part on the effect coefficients provided in those studies or by experts. In addition, the experts provided distributions around their mean PM 2.5 

### effect estimates, which provides more information regarding the overall range of uncertainty, and this overall range is larger than the range of the mean effect estimates from each of the experts. Since the completion of the EPA’s expert elicitation in 2006, additional epidemiology litera ture has become available, including 9 new multi

- state cohort studies shown in Table 5

- 12. This newer literature addresses some of the weaknesses identified in the prior literature. For example, in an attempt to improve its characterization of population e xposure the most recent extended analysis of the ACS cohort Krewski et al. (2009) incorporates two case studies that employ more spatially resolved estimates of population exposure. In light of the availability of this newer literature, we have updated th e presentation of results in the RIA. Specifically, we focus the core analysis on results derived from the two most recent studies of the ACS and Six Cities cohorts (Krewski et al., 2009; Lepeule et al., 2012). Because the other multi

- state cohorts general ly have limited geography and age/gender representativeness, these limitations preclude us from using these studies in our core benefits

### 5D

- 13

results, and we instead present the risk coefficients from these other multi

- state cohorts in Table 5D

- 2. In addition, we now include as a sensitivity analysis, mortality estimates based on application of the full set of expert

- derived effect estimates (see Appendix 5B, section 5B.2). We do not combine the expert results in order to preserve the breadth and diversity of op inion on the expert panel (Roman et. al., 2008). This presentation of the expert

- derived results is generally consistent with SAB advice (U.S. EPA

- SAB, 2008), which recommended that the EPA emphasize that “scientific differences existed only with respect t o the magnitude of the effect of PM 2.5 

### on mortality, not whether such an effect existed” and that the expert elicitation “supports the conclusion that the benefits of PM 2.5 

### control are very likely to be substantial”. Although it is possible that the newer literature could revise the experts’ quantitative responses if elicited again, we believe that these general conclusions are unlikely to change. Mortality Effect Coefficients for Infants. In addition to the adult mortality studies described above, several studies show an association between PM exposure and premature mortality in children under 5 years of age. 6 

### The PM ISA states that less evidence is available regarding the potential impact of PM 2.5 

### exposure on infant mortality than on adult mortality and th e results of studies in several countries include a range of findings with some finding significant associations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the post

- neonatal period and for respiratory

- related mortalit y, although this trend is not consistent across all studies. In addition, compared to avoided premature deaths estimated for adult mortality, avoided premature deaths for infants are significantly smaller because the number of infants in the population is much smaller than the number of adults and the epidemiology studies on infant mortality provide smaller risk coefficients associated with exposure to PM 2.5 

### . In 2004, the SAB

- HES noted the release of the WHO Global Burden of Disease Study focusing on ambien t air, which cites several recently published time

- series studies relating daily PM exposure to mortality in children (U.S. EPA

- SAB, 2004 ). The SAB

- HES also cites the study by Belanger et al. (2003) as corroborating findings linking PM exposure to increase d respiratory inflammation and infections in children. A study by Chay and Greenstone (2003) found that reductions in TSP caused by the recession of 1981

– 1982 were statistically associated with

6 For the purposes of this analy sis, we only calculate benefits for infants age 0

– 1, not all children under 5 years old.

### 5D

- 14

reductions in infant mortality at the county level. With regar d to the cohort study conducted by 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 factor s in infants (e.g., maternal educational level, maternal ethnicity, parental marital status, and maternal smoking status). Based on these findings, the SAB

- HES recommended that the EPA incorporate infant mortality into the primary benefits estimate and tha t infant mortality be evaluated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA

- SAB, 2004 ). In 2010, the SAB

- HES again noted the increasing body of literature relating infant mortality and PM exposure and supported the in clusion of infant mortality in the monetized benefits (U.S. EPA

- SAB, 2010a). The SAB

- HES generally supported the approach of estimating infant mortality based on Woodruff et al. (1997) and noted that a more recent study by Woodruff et al. (2006) continued to find associations between PM 2.5 

### and infant mortality in California. The SAB

- HES also noted, “when PM 10 

### results are scaled to estimate PM 2.5 

### impacts, the results yield similar risk estimates.” Consistent with the Costs and Benefits of th e Clean Air Act ( U.S. EPA, 2011a ), we continue to rely on the earlier 1997 study in part due to the national

– scale of the earlier study.
5D.2 Hospital Admissions and Emergency Department Visits Because of the availability of detailed hospital admission and discharge record s, there is an extensive body of literature examining the relationship between hospital admissions and air pollution. For this reason, we pool together the incidence estimates using several different studies for many of the hospital admission endpoints. In addition, some studies have examined the relationship between air pollution and emergency department visits. Since most emergency department visits do not result in an admission to the hospital (i.e., most people going to the emergency department are trea ted and return home), we treat hospital admissions and emergency department visits separately, taking account of the fraction of emergency department visits that are admitted to the hospital. Specifically, within the baseline incidence rates, we parse out the scheduled hospital visits from unscheduled ones as well as the hospital visits that originated in the emergency department.

### 5D

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The two main groups of hospital admissions are estimated in this analysis for PM 2.5 

### : respiratory admissions and cardiovascular a dmissions . There is not sufficient evidence linking PM 2.5 

### with other types of hospital admissions. Both asthma

- and cardiovascular

- related visits have been linked to PM 2.5 

### in the United States, though as we note below, we are able to assign an economic val ue to asthma

- related events only. To estimate the effects of PM 2.5 

### air pollution reductions on asthma

- related ER visits, we use the effect estimate from a study of children 18 and under by Mar et al. (2010), Slaughter et al. (2005), and Glad et al. (2012). The first two studies examined populations 0 to 99 in Washington State, while Glad et al. examined populations 0

- 99 in Pittsburgh, PA. Mar and colleagues perform their study in Tacoma, while Slaughter and colleagues base their study in Spokane. We apply r andom/fixed effects pooling to combine evidence across these two studies. To estimate avoided incidences of cardiovascular hospital admissions associated with PM 2.5 

### , we used studies by Moolgavkar (2000), Zanobetti et al. (2009), Peng et al. (2008, 2009) and Bell et al., (2008). Only Moolgavkar (2000) provided a separate effect estimate for adults 20 to 64, while the remainder estimate risk among adults over 64. 7 

### Total cardiovascular hospital admissions are thus the sum of the pooled estimate for adults ov er 65 and the single study estimate for adults 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 car diovascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions. We applied equal weights pooling to the multi

- city studies assessing risk among adults over 64 because these studies already incorporated pooling across the city

- level estimates. One potential limitation of our approach is that while the Zanobetti et al. (2009) study assesses all cardiovascular risk, Bell et al. (2008), and Peng et al., (2008, 2009) studies esti mate a subset of cardiovascular hospitalizations as well as certain cerebro

- and peripheral

- vascular diseases. To

7 Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence criteria. However, given that no other estimates are availab le for this age group, we chose to use the existing study. Given the very small (<5%) difference in the effect estimates for people 65 and older with cardiovascular hospital admissions between the original and reanalyzed results, we do not expect this choi ce to introduce much bias. For a discussion of the GAM convergence criteria, and how it affected the size of effect coefficients reported by time series epidemiological studies using NMMAPS data, see: http://www.healtheffects.org/Pubs/st

timeseries.htm

.

### 5D

- 16

address the potential for the pooling of these four studies to produce a biased estimate, we match the pooled risk estimate with a baseline in cidence rate that excludes cerebro

- and peripheral

vascular disease. An alternative approach would be to use the Zanobetti et al. (2009) study alone, though this would prevent us from drawing upon the strengths of the three multi

- city studies.
To estimate avoided incidences of respiratory hospital admissions associated with PM 2.5 

### , we used a number of studies examining total respiratory hospital admissions as well as asthma and chronic lung disease. We estimated impacts among three age groups: adults over 65 , adults 18 to 64 and children 0 to 17. For adults over 65, the multi

- city studies by Zanobetti et al. (2009) and Kloog et al. (2012) provide effect coefficients for total respiratory hospital admissions (defined as ICD codes 460

– 519). We pool these two st udies using equal weights. Moolgavkar et al. (2003) examines PM 2.5 

### and chronic lung disease hospital admissions (less asthma) in Los Angeles, CA among adults 18 to 64. For children 0 to 18, we pool two studies using random/fixed effects. The first is Babin et al. (2007) which assessed PM 2.5 

### and asthma hospital admissions in Washington, DC among children 1 to 18; we adjusted the age range for this study to apply to children 0 to 18. The second is Sheppard et al. (2003) which assessed PM 2.5 

### and asthma hospita lizations in Seattle, Washington, among children 0 to 18.
5D.3 Acute Health Events and School/Work Loss Days In addition to mortality, chronic illness, and hospital admissions, a number of acute health effects not requiring hospitalization are associated w ith exposure to PM 2.5 

### . The sources for the effect estimates used to quantify these effects are described below.
Asthma exacerbations. For this RIA, we have followed the SAB

- HES recommendations regarding asthma exacerbations in developing the core estimate (U.S. EPA

- SAB, 2004 ). Although certain studies of acute respiratory events characterize these impacts among only asthmatic populations , others consider the full population, including both asthmatics and non

asthmatics. For this reason, incidence estimates derived from studies focused only on asthmatics cannot be added to estimates from studies that consider the full population

— to do so w ould double

- count impacts. To prevent such double

- counting, we estimated the exacerbation of asthma among children and excluded adults from the calculation. Asthma exacerbations occurring in adults are assumed to be captured in the general population endpo ints such as work

### 5D

- 17

loss days and MRADs. Finally, we note the important distinction between the exacerbation of asthma among asthmatic populations, and the onset of asthma among populations not previously suffering from asthma; in this RIA, we quantify the e xacerbation of asthma among asthmatic populations and not the onset of new cases of asthma.
Based on advice from the SAB

- HES ( 2004 ), regardless of the age ranges included in the source epidemiology studies, we extend the applied population to ages 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. This age range expansion is also supported by NRC (2002, pp. 8, 116). To characterize asthma exacerbations in children from exposure to PM 2.5 

### , we selected two stud ies (Ostro et al., 2001; Mar et al., 2004) that followed panels of asthmatic children. 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 as thma exacerbations (e.g., shortness of breath, wheeze, and cough). This study found a statistically significant association between PM 2.5 

### , measured as a 12

- hour average, and the daily prevalence of shortness of breath and wheeze endpoints. Although the ass ociation 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).
Ma r et al. (2004) studied the effects of various size fractions of particulate matter on respiratory symptoms of adults and children with asthma, monitored over many months. The study was conducted in Spokane, Washington, a semiarid city with diverse sources of particulate matter. Data on respiratory symptoms and medication use were recorded daily by the study’s subjects, while air pollution data was collected by the local air agency and Washington State University. Subjects in the study consisted of 16 adult s

— the majority of whom participated for over a year

— and nine children, all of whom were studied for over eight months. Among the children, the authors found a strong association between cough symptoms and several metrics of particulate matter, including PM 2.5 

### . However, the authors found no association between respiratory symptoms and PM of any metric in adults. Mar et al. therefore concluded that the discrepancy in results between children and adults was due either to the way in which air quality

### 5D

- 18

was monito red, or a greater sensitivity of children than adults to increased levels of PM air pollution. We employed the following pooling approach in combining estimates generated using effect estimates from the two studies to produce a single estimate for PM

- relat ed asthma exacerbation incidence. First, we used random/fixed effects pooling to combine the Ostro and Mar estimates for shortness of breath and cough. Next, we pooled the Ostro estimate of wheeze with the pooled cough and shortness of breath estimates to derive an overall estimate of asthma exacerbation in children. Acute Respiratory Symptoms. We estimate three types of acute respiratory symptoms related to PM 2.5 

### exposure: lower respiratory symptoms, upper respiratory symptoms, and minor restricted activit y days (MRAD). Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 7 to 14 were estimated for PM 2.5 

### using an effect estimate from Schwartz and Neas (2000). Incidences of upper respiratory symptoms in asthmatic children a ged 9 to 11 are estimated for PM 2.5 

### using an effect estimate developed from Pope et al. (1991). 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. Minor restricted activity days (MRAD) result when indivi duals 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 PM 2.5 

### on MRAD was estimated using an effect estimate derived from Ostro and Rothschild (1989). More recently published literature examining the relationship bet ween short

- term PM 2.5 

### exposure and acute respiratory symptoms was available in the PM ISA (U.S. EPA, 2009 b ), but proved to be unsuitable for use in this benefits analysis. In particular, the best available study

### 5D

- 19

(Patel et al., 2010) specified a population aged 13

– 20, which overlaps with the population in which we asses asthma exacerbation. As we describe in detail below, to avoid the chance of double

- counting impacts, we do not estimate changes in acute respiratory symptoms and asthma exacerbation among pop ulations of the same age.
Acute Bronchitis. Approximately 4% of U.S. children between the ages of 5 and 17 experience episodes of acute bronchitis annually (ALA, 2002). Acute bronchitis is characterized by coughing, chest discomfort, slight fever, and extr eme tiredness, lasting for a number of days. According to the MedlinePlus medical encyclopedia, 8 

### 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 o f 5 and 17 were estimated using an effect estimate developed from Dockery et al. (1996). Work Loss Days. Health effects from air pollution can also result in missed days of work (either from personal symptoms or from caring for a sick family member). Day s of work lost due to PM 2.5 

### were estimated using an effect estimate developed from Ostro (1987). Children may also be absent from school because of respiratory or other diseases caused by exposure to air pollution, but we have not quantified these effects for this rule. Work loss days. Health effects from air pollution can also result in missed days of work (either from personal symptoms or from caring for a sick family member). Days of work lost due to PM 2.5 

### were estimated using an effect estimate develope d from Ostro (1987). Ostro (1987) estimated the impact of PM 2.5 

### on the incidence of work loss days in a national sample of the adult working population, ages 18 to 65 living in metropolitan areas. Ostro reported that two

- week average PM 2.5 

### levels were sign ificantly linked to work loss days, but there was some year

- to

- year variability in the results.
5D.4 Nonfatal Acute Myocardial Infarctions (AMI) (Heart Attacks) Nonfatal heart attacks have been linked with short

- term exposures to PM 2.5 

### in the United States (Mustafić et al., 2012; Peters et al., 2001; Sullivan et al., 2005; Pope et al., 2006; Zanobetti and Schwartz, 2006; Zanobetti et al., 2009) and other countries (Poloniecki et al.,

8 See http://www.nlm.nih.gov/medlineplus/ency/article/001087.htm, accessed April 2012.

### 5D

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1997; Barnett et al., 2006; Peters et al., 2005). In previous health impac t assessments, we have relied upon a study by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM 2.5 

### and nonfatal heart attacks. The Peters et al. (2001) study exhibits a number of strengths. In particular, it i ncludes a robust characterization of populations experiencing acute myocardial infarctions (AMIs). The researchers interviewed patients within 4 days of their AMI events and, for inclusion in the study, patients were required to meet a series of criteria i ncluding minimum kinase levels, an identifiable onset of pain or other symptoms and the ability to indicate the time, place and other characteristics of their AMI pain in an interview. Since the publication of Peters et al. (2001), a number of other single and multi

- city studies have appeared in the literature. These studies include Sullivan et al. (2005), which considered the risk of PM 2.5 

### - related hospitalization for AMIs in King County, WA; Pope et al. (2006), based in Wasatch Range, UT; Zanobetti and Sch wartz (2006), based in Boston, MA; and, Zanobetti et al. (2009), a multi

- city study of 26 U.S. communities. Each of these single and multi

- city studies, with the exception of Pope et al. (2006), measure AMIs using hospital discharge rates. Conversely, the Pope et al. (2006) study is based on a large registry with angiographically characterized patients

— arguably a more precise indicator of AMI. Because the Pope et al. (2006) study reflected both myocardial infarctions and unstable angina, this produces
a mor e comprehensive estimate of acute ischemic heart disease events than the other studies. However, unlike the Peters study (Peters et al., 2006), Pope and colleagues did not measure the time of symptom onset, and PM 2.5 

### data were not measured on an hourly bas is. As a means of recognizing the strengths of the Peters study while also incorporating the newer evidence found in the four single and multi

- city studies, we present a range of AMI estimates. The upper end of the range is calculated using the Peters stud y while the lower end of the range is the result of an equal

- weights pooling of these four newer studies. It is important to note that when calculating the incidence of nonfatal AMI, the fraction of fatal heart attacks is subtracted to ensure that there is no double

- counting with premature mortality estimates. Specifically, we apply an adjustment factor in the concentration

- response function to reflect the probability of surviving a heart attack. Based on recent data from the Agency for Healthcare Research and Quality’s Healthcare Utilization Project National Inpatient Sample database (AHRQ, 2009), we identified death rates for adults hospitalized with acute myocardial infarction

### 5D

- 21

stratified by age (e.g., 1.852% for ages 18

– 44, 2.8188% for ages 45

– 64, and 7.4 339% for ages 65+). These rates show a clear downward trend over time between 1994 and 2009 for the average adult and thus replace the 7% survival rate previously applied across all age groups from Rosamond et al. (1999).
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