1 UNITED STATES ENVIRONMENTAL PROTECTION AGENCY Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

April 7, 2011

MEMORANDUM

SUBJECT: PM 2.5 Distributional Statistical Analyses

FROM: Pradeep Rajan, OAR/OAQPS/HEID /s/ Mark Schmidt, OAR/OAQPS/AQAD /s/ Beth Hassett-Sipple, OAR/OAQPS/HEID /s/

TO: PM NAAQS Review Docket EPA-HQ-OAR-2007-0492

Overview

This memorandum documents the analyses of population-level data from epidemiological studies conducted by EPA staff for the review of the particulate matter (PM) national ambient air quality standards (NAAQS). The purpose of th ese analyses was to employ distributional statistics to inform staff conclusions in the Policy Assessment on a range of alternative standard levels that is appropriate to consid er in identifying a primary annual PM 2.5 standard that provides appropriate protection for health effects as sociated with both long- and short-term PM 2.5 exposures. 1

In reaching staff conclusions on the range of annual standa rd levels appropriate to consider, we expanded our general approach for identifying alternative standard levels beyond the consideration of a single stat istical metric (i.e., mean) from an epidemiological study, to the extent such information was available. In so doing, we employed dist ributional statistics to identify the broader range of PM 2.5 concentrations that were most influential in generating health effect estimates in epidemiological studies as we ll as to characterize where the bulk of the data exist. Thus, we considered the range of PM 2.5 concentrations where the data (i.e., populationlevel) from epidemiological studies were most dense, and at what range below the long-term mean PM 2.5 concentrations where there is a comparativ e lack of data and our confidence in the associations is appreciably less. We recognize there is no one percentil e value within a given distribution that is the most a ppropriate or “correct” way to ch aracterize where our confidence in the associations becomes appreciably lower. C onsequently, we have determined that the range from the 25 th to 10 th percentiles is a reasona ble range to consider as a region where we have appreciably less confidence in th e associations observed in the epidemiological studies.

1 US EPA (2011). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA 452/R-11-003. April 2011.2 In considering distributional statistics from epidemiological studies, we recognized that there are two types of study populati on-level metrics that are useful to consider in identifying the PM 2.5 concentrations most influential in generatin g the health effect estimates reported in the epidemiological studies. The most relevant information to consider is the distribution of health events (e.g., deaths, hospitaliza tions) occurring within a study population in relation to the distribution of PM 2.5 concentrations. However, in recognizing that access to event data can be restricted, and consistent with ad vice from CASAC on the second draft Policy Assessment we also considered the number of st udy participants within each study area as a reasonable surrogate for health event data. 2,3

In the absence of health event and study population data, we also considered the distribution of PM 2.5 concentrations across study areas as representative of the PM 2.5 concentrations likely experienced by study participants, which are integral to the generation of effect estimates. This approach is particul arly relevant for iden tifying the range of PM 2.5 concentrations most influential in generating the effect estimates in short-term exposure studies (e.g., time-series studies), but, as noted by CASAC, is more “complex” for long-term PM 2.5 exposure studies (Samet, 2010, p. 2). In short-term exposure studies, a similar number of events are likely occurring on a daily basi s and, thus, information on the PM 2.5 air quality distributions provides a better approximation of a study par ticipant’s likely exposur e in relation to the observed health effects as compared to l ong-term exposure studies where individual study participant’s exposures change over the course of the study follow-up period (Samet, 2010, p. 2).

Specific analyses conducted, including task s, assumptions, caveats, and processing methodologies are described in more details belo w. In summary, these analyses address the following:

o Analysis 1 – Distributions of air quality and associ ated health event and population data from selected long-term epidemiological studies

o Analysis 2 – Distributions of air quality and associ ated health event and population data from selected short-term epidemiological studies

General Data Processing

These analyses built on previous air quality analyses described in earlier EPA staff memoranda (Schmidt et al., 2010; Hassett-Sipple, et al., 2010). 4 For the current analyses, we

2 Samet J. (2010, p. 2). Letter from Dr. Jonathan M. Sa met, Chair, Clean Air Scientific Advisory Committee to the Honorable Lisa P. Jackson, Administrator, US EPA. CASAC Review of Policy Assessm ent for the Review of the PM NAAQS – Second External Review Draft (Jun e 2010). September 10, 2010. Available at: http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/CCF9F4C0500C500F8525779D 0073C593/$File/EPA-CASAC-10-015-unsigned.pdf.

3 A limitation of applying population data as a substitute for the number of the health events occurring in a study area is the recognition that baseline incidence rates of health events will vary across study areas.

4 Schmidt M; Hassett-Sipple B; Rajan P (2010). PM 2.5 Air Quality Analyses. Memorandum to PM NAAQS review docket EPA-HQ-OAR-2007-0492-0131. July 22, 2010; Hassett-Sipple, B, Schmidt, M, and Rajan, P. (2010) Analyses of PM 2.5 Data for the PM NAAQS Review. Memorandum to the PM NAAQS review docket. Docket ID number EPA-HQ-OAR-2007-0492-0077. March 29, 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html.3 requested population-level data fr om investigators of the six multi -city U.S. studies included in Analysis 2 of Schmidt et al. (2010). As describe d in Hassett-Sipple et al . (2010) these six studies were selected because they considered multiple locations representing varying regions across several seasons that provide evidence on the influe nce of climate and particle mixtures on health effects associated with long- or short-term PM 2.5 exposures. In addition, these multi-city studies considered relatively more recent air quality conditions (1999 to 2005). 5

Specifically, the population-level data for these analyses originated from the authors of the following four multi-city long- and short-term PM 2.5 exposure epidemiological studies 6 :

Long-term PM 2.5 exposure studies:

 ACS Reanalysis II

o Krewski D, Jerrett M, Burnett RT, Ma R, H ughes E, Shi Y, Turner MC, Pope AC III, Thurston G, Calle EE, Thun MJ. (2009). Extended Follow-Up and Spatial Analysis of the American Cancer Society Study Linking Part iculate Air Pollution and Mortality. HEI Research Report 140, Health Effects In stitute, Boston, MA. Available: http://pubs.healtheff ects.org/view.php?id=315

.

o Burnett R (2010). Personal communication w ith Dr. Rick Burnett: Fw: Urgent Request for Information from your 2009 study. Se ptember 13, 2010. Document ID EPA-HQOAR-2007-0492-0304.

 Women’s Health Initiative (WHI)

o Miller KA, Siscovick DS, Sheppard L, She pherd K, Sullivan JH, Anderson GL, Kaufman JD (2007). Long-term exposure to air pollution and incidence of cardi ovascular events in women. N Engl J Med, 356: 447-458.

o Miller K (2010). Personal communication wi th Kristin Miller: Re: Urgent Request for Information from your 2007 study. Augus t 24, 2010. Document ID EPA-HQ-OAR2007-0492-0306.

Short-term PM 2.5 exposure studies:

 Medicare Air Pollution Study (MCAPS)

o Bell ML, Ebisu K, Peng RD, Walker J, Same t JM, Zeger SL, Dominic F (2008). Seasonal and regional short-term effects of fine particles on hospital admissions in 202 U.S. counties, 1999-2005. Am J Epidemiol, 168: 1301-1310.

5 The network of PM 2.5 FRMs reporting to the AQS has been operational since 1999. 6 We requested but did not receive population-level data from the investigators of the following 2 studies: Eftim SE; Samet JM; Janes H; McDermott A; Dominici F (2008). Fine Particulate Matter and Mortality: A Comparison of the Six Cities and American Cancer Society Cohorts With a Medicare Cohort. Epidemiology, 19: 209- 216., and Dominici F; Peng RD; Bell ML; Pham L; McDermott A; Ze ger SL; Samet JL (2006). Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA, 295: 1127-1134.4 o Bell M (2010). Personal communication with Dr . Michelle Bell: Re: Urgent Request for Information from your 2008 study. Augus t 30, 2010. Document ID EPA-HQ-OAR2007-0492-0305.

 112-Cities Mortality Study

o Zanobetti A, Schwartz J (2009). The effect of fine and coarse partic ulate air pollution on mortality: A national analysis. Envi ron Health Perspect, 117: 1-40.

o Zanobetti A (2010). Personal communication with Dr. Antonella Zanobetti; email to Pradeep Rajan, US EPA, OAQPS. September 15, 2010. Document ID EPA-HQ-OAR2007-0492-0308, EPA-HQ-OAR-2007-0492-0308.1.

We requested that investigators from the thr ee mortality studies noted above (Krewski et al., 2009; Miller et al., 2007; Zanobetti and Schwartz 2009) provide us with the total number of all-cause, non-accidental deaths and the total number of cohort members for each of the study areas (i.e., MSA, cities). In addition, we reque sted that investigators for one morbidity study noted above (Bell et al., 2008) provide us with the number of cardiovascular-related and respiratory-related hospitalizations and the total number of cohort members for each of the 202 study areas (i.e., counties).

To maintain continuity and consistency, the same air quality database included in prior analyses was utilized for the current analyses. The air quality data for this project originated from EPA’s Air Quality System (AQS) database , the official repository of NAAQS-comparable ambient measurements. Data were extracted and processed as described in Hassett-Sipple et al. (2010).

In computing PM 2.5 concentrations corresponding w ith health event and population distributions for the four epidem iological studies noted above, th e number of health events and study participants were combined fo r those study areas with identical PM 2.5 concentrations. A description of study-area specific PM 2.5 concentrations can be found in Attachment A of HassettSipple et al. (2010). Additional data processing details are described in the analysis-specific descriptions below.

Analysis 1 – Distributions of air quality and a ssociated health event and population data from selected long-term epidemiological studies In the analysis documented here, we present distributions of health event (i.e., deaths, hospitalizations) and population (i .e. study participants) data from Krewski et al. (2009) and distributions of population data fr om Miller et al. (2007). Inve stigators provided us with MSAspecific counts of deaths and study participants for Krewski et al. ( 2009), and MSA-specific counts of study participants for Miller et al. (2007). These two long-term PM 2.5 exposure epidemiological studies provided a continuous measur e of the density of the data from multi-city studies as a function of the annua l mean concentrations [14.0 μg/m 3 (Krewski et al., 2009); 12.95 μg/m 3 (update of Miller et al. (2007) PM 2.5 data included in Curl (2009) 7 ] across the cities in each study. Specifically, we considered the dist ributions of air qual ity data as well as distributions of event and st udy population data across air qu ality concentrations.

In the absence of event data we consider ed the distribution of population data as a surrogate for the occurrence of health events acro ss study areas. Specificall y, our analysis of the distributions of health events and population data from Krewsk i et al. (2009) examined the reliability of utilizing study population data as a surrogate for event data. Table 1 includes the years of air quality data, to tal study areas, and the annual PM 2.5 concentrations computed from the site-level 24-hour average PM 2.5 concentrations that corres pond with study areas contributing to the 10 th , 25 th , and 50 th percentiles of the health event and study population data. Since the 10 th , 25 th , and 50 th percentiles for the health events a nd study population distri butions are nearly identical for Krewski et al. (2009) , we conclude that the distribu tion of population data can be a useful surrogate for event data, and in particular for Miller et al. (2007), for which we did not have health event data.

In Figure 1, the cumulative variable (i.e ., frequency of deaths, study population) for Krewski et al. (2009) and Miller et al. (2009) is plotted on the y-axis as a percentage of the total study variable (i.e., as a percenta ge of the total study area-period count of health events or study population). The x-axis repr esents the annual mean con centration in units of μg/m 3

.
Analysis 2 – Distributions of air quality and a ssociated health event and population data from selected short-term epidemiological studies This analysis built on previous air quality anal yses described in Schmidt et al. (2010). In the analysis documented here, we present dist ributions of health events (i.e., deaths, hospitalizations) and population (i.e. study pa rticipants) data from two short-term PM 2.5 exposure studies (Zanobetti and Schw artz, 2009; Bell et al., 2008). The long-term mean PM 2.5 concentrations for Zanobetti and Schwartz (2009) and Bell et al. (2008) were 13.2 μg/m 3 and 12.9 μg/m 3 , respectively.

Table 1. Summary Results for Select Long-and Short-term PM 2.5 Exposure Studies

Cite Years of Air Quality

Data Study Areas Population-Level Data PM 2.5 (μg/m 3 ) a
Distributional Statistics
10 th 25 th 50 th

Krewski et al. (2009) 1999- 2000 116 US MSAs Deaths 10.2 12.0 14.4

Study Population 10.2 12.1 14.4

7 Personal communication with Cynthia Curl, MESA Air Project Manager, University of Washington; email to Beth Hassett-Sipple, US EPA, OAQPS regarding request for PM air quality data. August 10, 2009. Document ID EPAHQ-ORD-2007-0517-0113.6 Miller et al. (2007) 2000 36 MSAs Study Population 9.7 11.2 12.8

Zanobetti & Schwartz (2009) 1999- 2005 99 US cities b Deaths 10.3 12.5 14.5

Study Population 10.3 12.5 14.5

Bell et al. (2008) 1999- 2005 202 US countie

s CV-related HA 9.8 11.5 13.5 Respiratory-related HA 9.8 11.4 13.5

Study Population 9.6 11.2 13.3

a Annual mean PM 2.5 concentration correspond ing with study areas contributing to the 10 th , 25 th

, and 50 th percentiles of the distributi on of study population-level data.
b AQS data were available for 99 of 112 study areas included in Zanobetti and Schwartz (2009).

Investigators provided us with county- or ci ty-specific counts of cardiovascular-related and respiratory-related hospitaliz ations (Bell et al., 2008) and de aths (Zanobetti and Schwartz, 2009). In addition, investigators for the Bell et al. (2008) study provided us with county-specific counts of study participants. For the Zanobe tti and Schwartz (2009) study populations were estimated for each study by summing the county popul ations (per 2000 Census) for the cities where sites had valid contributi ng data for the area-period air quality metric estimates.

Table 1 includes for these two studies the year s of air quality data , total study areas, and the annual PM 2.5 concentrations computed from the site-level 24-hour average PM 2.5 concentrations that correspond with study areas contributing to the 10 th , 25 th , and 50 th percentiles of the health event and p opulation data. We note that as in the case of Analysis 1 , the 10 th , 25 th

, and 50 th percentiles for the health events and popul ation distributions are nearly identical for Zanobetti and Schwartz (2009) and Bell et al. (2 008), and thus provide further support for utilizing the distribution of population data as a surrogate for event data.

In Figure 2, the cumulative variable (i.e., fr equency of deaths, cardi ovascular and hospital admissions, study population) is plotted on the y-axis as a percentage of th e total study variable (i.e., as a percentage of the tota l study area-period count of health events or study population). The x-axis represents the annual m ean concentration in units of μg/m 3

.7Percentage of Total Data (i.e., health event or study population)

Corresponding Annual Mean (μg/m 3 ) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Health Event Data Krewski et al., 2009 (ACS) - deaths (116 study areas)

Population Data Krewski et al., 2009 (ACS) Miller (WHI, 36 study areas) Figure 1. Distribution of Population-Level Data and Corresponding PM 2.5 Concentrations for Selected Long-term PM 2.5 Epidemiological Studies

In the absence of health event and study population data, th e distribution of PM 2.5 concentrations across study areas co uld be used to represent the PM 2.5 concentrations likely experienced by study participants. However, we were unable to determine from the methodologies published for these studies how the air quality data were sta tistically weighted to account for variations in th e availability of daily PM 2.5 measurements by study area. Consequently, we are unable to cons ider this type of information as part of our effort to identify the broader range of PM 2.5 concentrations that were most influential in generating the health effect estimates in epidemiological studies.

Figure 3 includes the distribution of health event data for Kr ewski et al. (2009), Zanobetti and Schwartz (2009), and Bell et al. (2008), respectively, and corresponding PM 2.5 concentrations. We also include the distribution of study participan ts for Miller et al. (2007) and corresponding PM 2.5 concentrations.8 Corresponding Annual Mean (μg/m 3 ) 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Figure 2. Distribution of Population-Level Data and Corresponding PM 2.5 Concentrations for Selected Short-term PM 2.5 Epidemiological StudiesPercentage of Total Data (i.e., health event or study population)

Health Event Data
 Bell et al., 2008 (MCAPS) - cardiovascular-related hospitalizations (202 study areas) Bell et al., 2008 (MCAPS) - respiratory-related hospitalizations Zanobetti and Schwartz, 2009 – mortality (99 study areas)

Population Data

Bell et al., 2008 (MCAPS) Zanobetti and Schwartz, 20099 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Percentage of Total Data (i.e., health event or study population)

Corresponding Annual Mean (μg/m 3 ) Figure 3. Distribution of Population-Level Data and Corresponding PM 2.5 Concentrations for Selected Multi-City Epidemiological Studies

Health Event Data



## • Krewski et al., 2009 (ACS) - deaths (116 study areas)

Zanobetti & Schwartz, 2009 - deaths (99 study areas)

 Bell et al., 2008 (MCAPS) - cardiovascular-related hospitalizations (202 study areas) Population Data
♦ Miller et al., 2007 (WHI, 36 study areas)