UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON D.C. 20460

OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD June 16, 2010

EPA-COUNCIL

- 10-001

The Honorable Lisa P. Jackson

Administrator U.S. Environmental Protection Agency 1200 Pennsylvania Avenue, N.W. Washington, D.C. 20460

Subject: Review of EPA’s DRAFT Hea lth Benefits of the Second Section 812 Prospective Study of the Clean Air Act

Dear Administrator Jackson:

In response to a request from EPA’s Office of Air and Radiation (OAR), the Advisory Council on Clean Air Compliance Analysis (Council) convened the Health Effects Subcommittee (HES) with additional experts from the Council to review EPA’s draft benefits and uncertainty documents supporting the second pr ospective study of the be nefits and costs of the Clean Air Act. The study was conducted in concordance with Section 812 of the Clean Air Act Amendments of 1990. Specifically, the HE S provided advice on the data chosen for the analysis, the selection of models used to conduc t the analysis, and the validity of resulting estimates of Clean Air Act program benefits.

The HES compliments EPA on a thorough analysis of a very complex issue. The Second Prospective Study has been under development for se veral years. In the early planning phases, the analytical blueprint and preliminary da ta were reviewed by the Council and its subcomponents. EPA has thoughtfully and thorou ghly considered previ ous advice in conducting this analysis.

The HES finds the data sources and analyti cal methodology for estimating health benefits to be generally sound and well conceived. In particular, the HES notes that reductions in particulate matter (PM)-related mortality that can be attributed to the Clean Air Act are a major

driver of benefits. The bene fit estimates that EPA derives for PM are based on two wellresearched and highly respected co hort studies of air pollution h ealth effects, the Harvard Six Cities Study and the American Cancer Study (ACS) Cancer Prevention Study. These are good

foundations for the health benefits estimates fo r PM and these studies are supported by other recent studies and expert evidence gathered by th e EPA project team. This is encouraging, and

the HES makes some recommendations on how to express a best estimate and the uncertainty2 around that estimate. The HES generally agrees with other decisions made by the EPA project team with respect to PM, in particular, the PM mo rtality effect threshold model, the cessation lag model, the inclusion of infant mortality estimation, and differential toxicity of PM.

The HES supports the inclusion of mortality related to ozone exposure in the Section 812 Prospective Analysis and supports EPA’s data choices for estimating health benefits from reduction in ozone exposure. As with PM, the HES makes recommendations on a statistical approach to better capture th e best estimate and uncertainty around the ozone concentrationresponse function.

The HES finds that the draft documents presente d to it for review requi re a fair amount of technical editing to make the analyses clea rer. The details of the conclusions and

recommendations are detailed in the Subcommitt ee’s report. We appreciate the opportunity to provide EPA advice on its analysis of the health benefits of the Clean Air Act regulations.

Sincerely,

/Signed/ /Signed/

Dr. James K. Hammitt, Chair Dr. John Bailar, Chair Advisory Council on Clean Air Health Effects Subcommittee Compliance AnalysisNOTICE

This report has been written as part of the ac tivities of the EPA Advi sory Council on Clean Air Compliance Analysis (Council), a public advi sory group providing extramural scientific

information and advice to the Administrator and other officials of the Environmental Protection Agency. The Council is structured to provide balanced, expert a ssessment of scientific matters related to problems facing the Agency. This re port has not been reviewed for approval by the Agency and, hence, the contents of this report do not necessarily represen t the views and policies of the Environmental Protection Agency or of other agencies in the Executive Branch of the Federal government, nor does mention of trade names of commercial pr oducts constitute a recommendation for use. Reports of the Council are posted on the EPA website at http://www.epa.gov/advisorycouncilcaa

.

iU.S. Environmental Protection Agency Advisory Council on Clean Air Compliance Analysis Health Effects Subcommittee, Augmen ted with Members of the Council

COUNCIL MEMBERS

Dr. John Bailar , Chair of the Health Eff ects Subcommittee, Scholar in Residence, The National Academies, Washington, DC

Dr. Michelle Bell , Associate Professor, School of Fore stry and Environmental Studies, Yale University, New Haven, CT

Dr. James K. Hammitt , Professor, Department of Hea lth Policy and Management, Harvard School of Public Health, Boston, MA

Dr. Jonathan Levy , Associate Professor, Department of Environmental Health, Harvard School of Public Health, Boston, MA

Dr. Arden Pope , Professor, Department of Economics , Brigham Young University, Provo, UT

SUBCOMITTEE MEMBERS Mr. John Fintan Hurley , Research Director, Institute of Occupational Medicine (IOM), Edinburgh, United Kingdom, UK

Dr. Patrick Kinney , Professor, Department of Environm ental Health Sciences, Mailman School of Public Health , Columbia University, New York, NY

Dr. Michael T. Kleinman , Professor, Department of Medicine, Division of Occupational and Environmental Medicine, University of California, Irvine, Irvine, CA

Dr. Bart Ostro , Chief, Air Pollution Epidemiology Unit, Office of Environmental Health Hazard Assessment, California Environm ental Protection Agency, Oakland, CA

Dr. Rebecca Parkin , Professor and Associate Dean, Envi ronmental and Occupational Health,

School of Public Health and Health Services, The George Washington University Medical Center, Washington, DC

SCIENCE ADVISORY BOARD STAFF Dr. Marc Rigas , Designated Federal Officer, 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-343-9978, Fax: 202-233-0643, (rigas.marc@epa.gov)

iiU.S. Environmental Protection Agency Advisory Council on Clean Air Compliance Analysis

CHAIR

Dr. James K. Hammitt , Professor, Center for Risk Anal ysis, Harvard University, Boston, MA

COUNCIL MEMBERS

Dr. John Bailar , Scholar in Residence, The Na tional Academies, Washington, DC

Dr. Michelle Bell , Associate Professor, School of Fore stry and Environmental Studies, Yale University, New Haven, CT

Dr. Sylvia Brandt , Assistant Professor, Department of Resource Economics, University of Massachusetts, Amherst, MA

Dr. Linda Bui , Associate Professor, Department of Economics, Brandeis University, Waltham,

MA

Dr. Dallas Burtraw , Senior Fellow, Resources for the Future, Washington, DC

Dr. Ivan J. Fernandez , Professor, Department of Plant, Soil and Environmental Sciences, University of Maine, Orono, ME

Dr. Shelby Gerking , Professor, Department of Economics, University of Central Florida, Orlando, FL

Dr. Wayne Gray , Professor, Department of Economics , Clark University, Worcester, MA

Dr. D. Alan Hansen , Independent Consultant, Fremont, CA

Dr. Nathaniel Keohane , Director of Economic Policy and Analysis, Climate and Air,

Environmental Defens e Fund, New York, NY

Dr. Jonathan Levy , Associate Professor, Department of Environmental Health, Harvard School of Public Health, Boston, MA

Mr. Richard L. Poirot , Environmental Analyst, Air Pollutio n Control Division, Department of Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT

Dr. Arden Pope , Professor, Department of Economics , Brigham Young University, Provo, UT

Dr. Armistead (Ted) Russell , Professor, Department of Civ il and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA

iiiMr. Michael Walsh 1 , Independent Consultant, Arlington, VA

SCIENCE ADVISORY BOARD STAFF Ms. Stephanie Sanzone , Designated Federal Officer, 1200 Pennsylvania Avenue, NW, Washington, DC, Phone: 202-343-9697, Fax: 202-233-0643, (sanzone.stephanie@epa.gov)

1 Did not participate in this review.

ivv 

### TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

............................................................................................................................... .1

2. BACKGROUND

............................................................................................................................... .................4

3. GENERAL COMMENTS

............................................................................................................................... ..6

4. RESPONSE TO SPECIFIC CHARGE QUESTIONS ....................................................................................9

4.1. PM M ORTALITY C ONCENTRATION -R ESPONSE F UNCTION (CRF) ................................................................9

4.2. C ESSATION L AG

............................................................................................................................... .........11

4.3. PM-R ELATED I NFANT M ORTALITY ...........................................................................................................12

4.4. PM M ORTALITY E FFECT T HRESHOLD .......................................................................................................13

4.5. O ZONE M ORTALITY C ONCENTRATION -R ESPONSE F UNCTION (CRF) ........................................................13

4.6. O ZONE C ESSATION L AG ............................................................................................................................15

4.7. O ZONE M ORTALITY E FFECT T HRESHOLD .................................................................................................16

4.8. B ASELINE I NCIDENCE / P REVALENCE E STIMATES .....................................................................................17

4.9. PM D IFFERENTIAL T OXICITY S ENSITIVITY A NALYSIS ..............................................................................17

4.10. D YNAMIC P OPULATION M ODELING ...........................................................................................................18

REFERENCES

............................................................................................................................... ........................ R-1

APPENDIX A: TECHNICAL CORRECTIONS FROM INDIVIDUAL MEMBERS .................................... A-1

### 1. EXECUTIVE SUMMARY

The Clean Air Act Amendments of 1990 in cluded specific language in Section 812 directing the EPA to perform benef it-cost studies of the overall e ffects of the Clean Air Act. In response to Section 812, on October 15, 1997, EPA i ssued a retrospective study (1970 – 1990) of

benefits and costs of the Clean Air Act. EPA issued a first prospective study (1990 – 2010) on November 15, 1999 and is now completing a second prospective study (1990 – 2020).

The Health Effects Subcom mittee (HES) of the Advisory Council on Clean Air Compliance Analysis (Council) held a public meeting on December 15-16, 2009 to review and provide guidance to the EPA on th e draft human health effect es timates in chapters from the second Section 812 Prospective Analysis benefi ts report (1990 – 2020) as well as the human health components of the draft stand-alone uncertainty analysis report. Specifically, the OAR asked the HES to consider: (1) the data choices and methodologies used to develop mortality estimates as a function of air pollution concentr ation for both particulate matter (PM) and ozone; (2) The methods and data used to develop es timates of the lag time between cessation of exposure and reduction in health effects for bot h PM and ozone; (3) the Agency’s assumptions about a threshold concentration for mortality eff ects for both PM and ozone; (4) the estimates of infant mortality related to PM exposure; (5) Baseline incidence and prevalence of relevant disease conditions in the population; (6) the sensitivity analysis of the analytical results to differences in PM composition; and (7) the a pproach for dynamic modeling of U.S. population demographic changes.

Overall, the HES finds the EPA analyses, da ta choices, and methodologies to be sound. EPA bases its estimates of the mortality benefits of reducing fine particulate matter (PM 2.5 ) on data analyzed from two large, long-running la ndmark studies on the health effects of air pollution, the Harvard Six Cities Study and th e American Cancer Society (ACS) Cancer Prevention Study (CPS). The HES finds the selecti on of these cohort studies as the underlying basis for PM mortality benefit estimates to be a good choice. These are wi dely cited, well studied and extensively reviewed data sets. EPA further bolsters its PM mortality benefit estimates by gathering information on the PM mortality c oncentration-response function in an expert

elicitation of twelve clinicians , epidemiologists, and air pollu tion scientists. HES suggests an

alternative to EPA’s choice of the Pope et al. (2002) study (b ased on the ACS cohort) for the primary estimates of differences in incidence of PM-related premature mortality and the Laden et al. (2006) study (based on the Six Cities cohort) as an altern ative estimate. EPA found that these choices fell at approximately the 25 th and 75 th percentile of the mortality effect estimates garnered from the expert elic itation. EPA proposed a st atistical approach us ing copula functions to combine the 12 expert elicitation estimates in to a single mortality effects distribution. The HES, however, recommends a simpler approac h. EPA should define a distribution, perhaps a truncated normal distribution, with th e Pope and Laden studies at the 25 th and 75 th percentiles respectively. The mean of the new distribution s hould then be close to the mean of the central estimates of both Pope and Laden. This generally will be consistent with the distribution identified in the expert elicitation.

1The HES agrees with EPA’s contention that a majority of health effect benefits from reduction of exposure to PM occurs within the first few years following reduced exposure. The HES therefore supports EPA’s choice of a 20-year distributed cessation lag structure, skewed towards benefit gains in the first few years. However, EPA should further examine the decay function and determine whether a more rapid decay model is appropriate. Further, the HES fully supports EPA’s use of a no-threshold model to estim ate the mortality reduc tions associated with reduced PM exposure.

EPA estimates an association between PM exposure and respiratory inflammation and infection leading to mortality in children unde r 5 years of age, re lying on a cohort study (Woodruff et al., 1997). The HES ge nerally supports this approach with some caveats. The age range of interest for respirator y-related mortality should be 1 m onth of age (since infections earlier than this are not likely to be from air pollution) to about 12 months of age. There are

limited data available to estimate these eff ects and EPA should bette r describe the data challenges and uncertainties in these estimates.

The HES supports the inclusion of mortality due to ozone exposure in the Section 812 Prospective Analysis, given the evidence in the time -series literature and the consistency of these findings with morbidity studies. With respect to the concentration-response estimates, it is premature to use the cohort mortality eviden ce from Jerrett et al., 2009 as the basis for the Primary Estimate, because it has not yet been subjected to the vigorous critique that now supports, for example, analyses using the Pope and Laden studies for PM. For the time-series ozone mortality evidence, the HES supports the c onsideration of both the multi-city studies and the literature meta-analyses when generating the Primary Estimate. As there is not a clear basis for choosing one set of studies over the other, th e HES recommends that the Primary Estimate be

derived as an intermediate value between the multi-city studies and the meta-analyses. The uncertainty distribution can then reflect methodological uncertainties as well as the uncertainties reported within individual studies.

The HES supports the EPA decision not to apply a cessation lag for ozone and also supports the assumption of no threshold for ozone-re lated mortality effects, as evidence is not

conclusive to demonstrate such a threshold.

In their analysis, EPA incorporates data on the baseline incidence and prevalence of the various health endpoints that ar e the basis of the benefits. The HES generally supports the choices made by the EPA. The most important data , in terms of driving the benefit estimates, are the baseline mortality data, which are of relatively high quality. There is some concern about the noise and inconsistency in data us ed to estimate baseline school loss and work loss days. There may be too much noise to make reasonably relia ble quantitative estimates of air pollution work loss days.

The Council had previously recommended th at EPA perform an analysis of the sensitivity of their benefit results to differing PM composition in terms of both size and chemical composition. EPA determined that there are insuffi cient data to perform such an analysis. The HES supports this conclusion, given the state of th e evidence but encourages EPA to continually

revisit this conclusion as the literature evolves.

2EPA developed a dynamic population simulatio n model to explore how changes in population age structure change po llution-related premature mortal ity risks. EPA asked HES to comment on the methodology and results and to consider providing advice on the potential utility of further development of this a pproach. Overall, the HES commends EPA for demonstrating how dynamic population modeling can be used in estimating the mortality impacts of long-term exposure to PM 2.5 . If practical, the approach would be a preferred one. However, the HES recognizes that these methods are complicated and computationally intensive, necessitating trade-offs between resolution of th e results and run-time for the models. The HES

recommends that the EPA describe in more de tail the methods used and the reasons why EPA chose these particular methods over others.

Overall, the HES finds the EPA analysis thor ough and generally consis tent with previous

recommendations from the Council. The HES ma kes some minor suggestions for improvement and notes that the draft documents presented to the HES need technical editing to make the analyses and associated uncertainties more clear.

3

### 2. BACKGROUND

The reauthorization and amendment of th e Clean Air Act in 1990 included specific language in Section 812 directi ng the EPA to perform benefit- cost studies of the programs affiliated with the Clean Air Act as a whole, rela tive to a consistent baseline. Congress expressed their intent that the comprehensiveness of the 812 studies should encourage and enable EPA to develop and continually refine its capabilities in clean air program assessment. Congress’ stated objective was to ensure that EPA could provide better information on clea n air program benefits and costs in support of future rounds of Clean Air Act reauthorizati on, whenever they may

occur.

Included in the Section 812 amendments was specific language requiring EPA to convene a panel of outside experts in a range of relevant disciplines to advise the Administrator on the data chosen for the analysis, the selection of models used to conduc t the analysis, and the validity of resulting estimates of Clean Air Act program benefits and costs. The Advisory Council on Clean Air Act Compliance Analysis (C ouncil) was established in 1991 to fulfill this goal of providing multi-disciplinary outside expert review. Separate subcommittees have since been established to advise the parent Council on particular technical as pects of the Section 812 studies. The Air Quality Modeling Subcommittee (AQMS) was formed to advise the Council on issues of emissions estimation and air quality modeling. A Health Effect s Subcommittee (HES) and an Ecological Effects Subcommittee (EES) exist today to advise the Council on issues

associated with human health effects and eco logical effects compone nts, respectively.

In response to the Section 812 legisl ation, on October 15, 1997, EPA issued a retrospective study (1970 – 1990) of the benefits and costs of the Clean Air Act. EPA issued a first prospective study (1990 – 2010) on Novemb er 15, 1999 and is now completing a second prospective study (1990 – 2020).

In the Charge to the Committee, EPA requested that the HES review the human healthrelated chapters and appendices of the draft Section 812 Second Prospective Study benefits and uncertainty reports, and respond to the following general questions:

a. Does the Council HES support the data choices made by the 812 Project Team for the development of the human health-related chap ters and appendices of the draft benefits and uncertainty reports? If not, are there alternative data sets the Council HES recommends should be applied instead?

b. Does the Council HES support the methodologi cal choices made for analyzing those data and developing the human h ealth effect estimates for the relevant scenarios, and for characterizing their uncertain ty? If not, are there altern ative methodologies the Council

HES recommends should be applied instead?

c. What advice does the HES have for the Counc il regarding the validity and utility of the human health effect analyses incorporated in the draft benefits report and the uncertainty

4analyses incorporated in the dr aft uncertainty report? If the va lidity and/or utility of the reports and their underlying analyses could be improved, what specific improvements

does the Council HES recommend that the 812 Project Team consider, either for the present analysis or as part of a longer term research and development program?

Additional detailed questions also were posed that pertain to specific subsections of the

review materials.

The purpose of this Advisory is to review and provide guidance on the EPA draft human health effect estimates in chap ters from the Second Section 812 Prospective analysis benefits report (1990 – 2020) as well as the human health component s of the draft stand-alone uncertainty analysis report.

The Health Effects Subcom mittee (HES) held a public meeting on December 15-16, 2009 to receive briefings and discuss the charge questions provided by the Agency. In addition to the Chair of the HES, who represents the HES on the Council, severa l members of the Council participated in the meeting, including Dr. Jame s Hammitt, Chair of the Council, Dr. Michelle Bell, Dr. Jonathan Levy, and Dr. Arden Pope. A subsequent conference call of this committee was held on March 2, 2010, for the purposes of di scussing recommendations made in this report. The HES report was discussed and approved by the full Council at the public meeting on May 4,

2010.

5

### 3. GENERAL COMMENTS

The Council HES found the draft report to be generally sound, and members commend EPA on the quality of the present version. The very quality of this draft has made detailed

criticism more feasible. EPA had to make an enormous number of choices of data collected from a variety of sources, and, using a variety of m odels, further processed the data. The scope and complexity of the data cannot be fully documented in a report of manageable size. Insofar as we have been able to probe the data, the Council HES generally supports th e data approaches and judgments that have been made.

In particular, the HES supports the presentati on of the PM mortality effect estimates and the data choices using the Pope et al. (2002) and Laden et al. (2006) studies. The HES notes that using the Pope studies (which are based on th e ACS studies) for the Primary Estimate would provide lower health benefits estimates than th e Six Cities study, which is considered by EPA to be equally applicable for health benefits anal ysis. The HES supports EPA’s aim to give unbiased benefits estimates where possible. The HES proposes an alternative simple combination of the Pope and Laden studies. This alternative is disc ussed in the HES response to charge question 2a on the PM mortality concentration response f unction. With respect to PM, the HES also generally supports the Agency’s proposed 20-year distributed cessation lag model, though most of the benefits of cessation appear to occur in the first few year s after cessation of exposure. The HES commends the efforts to quantify infant mortality related to PM, even though, as the EPA acknowledges, these do not add a large amount to the overall benefits in the 812 Analysis, and the estimates are based on only a small number of studies. The HES also supports the Agency’s

choice of a no-threshold model for PM-related effects.

The generation of a concentration-response f unction to estimate mortality effects of ozone is more difficult. The HES supports EP A’s data choices for the Primary Estimates.

Because time series studies were used for the Pr imary Estimate, a cessation lag for effects is not relevant, and the HES supports the Agency’s use of a no-lag assumption for the primary mortality effect estimates for ozone. Finally, the HES supports EPA’s use of a no-threshold model for ozone mortality effects, based on the time-series results. The HES supports the inclusion of, as an alternate es timate of benefits, a concentrat ion-response function (CRF) from long-term exposure based on Jerrett et al. (2009).

There should be a short but thoughtful discussion of the purposes of the benefits report and how it may be used beyond the initial determ ination of the overall impact of the Clean Air

Act and its amendments. There should also be some consideration of the likely background of expected readers (Congressional sta ffers? State health officials? Other?), and the report should be written to be comprehensible to them. This includes spelling out the meaning of, and assumptions underlying concepts such as Value of Statistical Life (VSL), which appear easy to understand but which have quite pr ecise and non-intuitiv e technical meanings. Overall the report needs technical editing, and two s ections (Chapter 6, PM mortality cessation lag and Chapter 7, Dynamic population modeling) are difficult to foll ow; these sections may need a complete rewrite to achieve better clarity for non-experts.

6The HES makes some general observations and recommendations for improvement of the presentation in the draft docum ents. EPA generally models health effects in ways that imply multiplicative effects. The report should discuss this and say why other models (e.g., additive) were not used. The report should also explicitly say why it is limited to PM and ozone, when EPA regulates a much larger number of pollutants.

There is no discussion of the differences betw een values at central monitors and what people actually inhale. The HES recognizes that broad data on inhaled levels of pollutants do not now exist, but some comment is in order. (Elementary considerations of epidemiology suggest that better measures of what is inhale d would probably lead to bigger estimates of effects).

The characterization of uncertainty is confus ing and incomplete. It would be helpful to have an integrated assessment of overall uncertainty. Are the es timates within 10% of the true

value? 50%? 200%? Does it matte r? Missing is a sense of what level of accuracy is needed in this report and how close the report may come to that . It is understandable that this is difficult to say, so uncertainty is broken down into components. However, the appendices are not comprehensive and the scope for “comprehensiveness” is not stated. It is not clear how the authors determined “key uncertainties”. Rather than brief footnoted statements, it would be helpful to have introductory text describing the scope of the uncertainties listed, the significance classification definitions (e.g., “potentially majo r” and “probably minor”), and rationales for these choices. Further, it would be helpful to know the likely direction of bias and EPA’s confidence in the assessment components.

On a related note, there are inconsistenc ies in presenting assumptions between the Benefits and Uncertainty draft reports. A compar ison of the assumptions (using a simple search on the “assum” in both documents) reveals a mismat ch that may not be a problem but is a matter for clarification. Two sets of authors may have used different appro aches or boundaries for identifying and/or recording assumptions. Perh aps the authors of the benefits document sought to present all assumptions they made in th eir estimation processes; e.g., in extending studyspecific or regional results to the national scale. If s o, the rationale for some assumptions is not always clear (e.g., use of mother’s median wage rather than an average of mother and father median wages, which may yield a higher cost es timate). In contrast, the authors of the uncertainty report indicate that they intended to record only the most important assumptions made during their estimation processes.

The HES suggests that a section be added to th e report that compiles, in a brief form, the primary and secondary data sources and the mode l sources used. This section should include links to detailed descriptions of the data and models used. Finally, the report reveals a large number of research needs. It would help read ers to add some discussion of the most important gaps, especially if EPA is to repeat this exercise at some future time.

Overall, total mortality benefits from the Cl ean Air Act appear large in relation to total US mortality (about 7%). The repo rt should note that this result is driven in part by the rise in expected pollution-related mortality in the absenc e of Clean Air Act, and not entirely by actual

7reductions in mortality. If EPA can estimate thes e separately, the report will be stronger and perhaps more credible.

8

### 4. RESPONSE TO SPECIFIC CHARGE QUESTIONS

4.1. PM Mortality Concentration-Response Function (CRF)

Agency-supplied background : The current draft benefits re port reflects adoption of the Pope et al. 2002 study as the basis for the Primar y Estimates of the difference in incidences of PM-related premature mortality. Also within the ma in benefits report, an Alternative Estimate is presented prominently which is based on the La den et al. 2006 study. Furthermore, the Project Team is currently assessing the potential signific ance of the recent Krewski et al. (2009) publication since it appears to st rengthen the evidence for PM-related ischemic heart disease and lung cancer mortality and could pr ovide the basis for a revised Primary Estimate or an additional Alternative Estimate. Uncertainty in the Primar y Estimate is further described in the draft uncertainty report through graphica l presentation of results obta ined by applying each of the 12 expert elicitation study f unctions to the differences in PM exposure estimated for the withCAAA90 and without-CAAA90 core scenarios. In addition, the Project T eam has recently been considering an approach develope d by Industrial Economics, which uses a copula function to generate results representing th e 12 expert functions. This appr oach is summarized in a draft briefing which the Project Team proposes to present to the HES on December 15 for its

consideration.

Charge question 2a: Does the Council HES support th ese study selections and the organization and presentation of PM mortality estimates in the draft benefits and uncertainty reports? In addition, a particul ar question for which the Project Team seeks HES advice is whether the application of mortality risk coeffi cients drawn from the Krewski et al. (2009) study should be considered for use in generating the Pr imary Estimate, or at least as the foundation for

an Alternative Estimate. If the answer to either or both of these two questions is negative, are there alternative study choices an d/or methods for generating, or ganizing, and presenting results

which the Council HES recommends EPA consider?

HES response: The HES emphasizes the continued im portance of the American Cancer Society (ACS) and Harvard Six Cities cohort studies for underp inning our understanding of the mortality effects of PM2.5 in the U.S. Findings from both cohorts have be en robust to extensive analyses and independent re-analy ses. The HES believes that the Pope et al. (2002) and Laden et al. (2006) analyses of the ACS and Six Cities cohort s, respectively, are at present the most useful findings from these studies. The Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as ha ve the aforementioned studies. Thus, the HES

recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary

Estimate.

The HES also reviewed the findings from EPA’s Expert Elicitation (EE) study. The central effect estimates from the 12 experts co ver a range that encompasses the Pope et al. (2002) effect estimate at the low end, and the Laden et al. (2006) effect estimate at the high end.

This is illustrated in Figure 1 (reproduced from an EPA T echnical Memorandum presented to the HES), which shows the monetized PM-related mort ality benefits associated with meeting a hypothetical 50 ppb NO 2 standard in 2020 for the studies of P ope al. and Laden et al. as well as

9the twelve experts consulted in the EE st udy. Further, a quantit ative analysis by EPA

consultants demonstrates that the Pope et al. (2002) effect estimate falls at about the 25 th
percentile of the EE distribution; the Laden et al . (2006) effect estimate falls at about the 75 th
percentile of the EE distribution. HES review ed the proposed copula function approach to combine the 12 response functions from the EE. A c opula function is a useful method to describe the dependence among a set of rando m variables. When information about dependence is limited or absent, a joint distribution can be constructed from the marginal distributions of the variables by making assumptions about the copula functio n. While supporting EPA’ s investigation of alternatives for combining evidence using adva nced quantitative methods, the HES feels that adoption of the copula function approach as a prominent feature of the current prospective analysis might hinder interpretabi lity and transparency of the findings for a general audience, given the very technical nature of the method.

The HES finds that the EE results from 2006 remain relevant; i.e., that new evidence since 2006 informs, but does not fundamentally cont radict, the earlier data. On that basis, the HES supports the EE as the most comprehensive a ssessment to date of the mortality effect of

PM 2.5 and so, given EPA’s stated objective of ge nerating unbiased benefits estimates in the Section 812 prospective analysis, HES recomm ends that EPA adopt a new approach for developing its primary mortality benefits estimates. The new approach should reflect the range of EE opinions in a transparent way, while groundi ng the risk estimates in results from the two major contributing US cohorts. We suggest an approach along the following lines:

Define a distribution of possi ble coefficients (perhaps a truncated normal distribution with zero probability below a value of zero, or a gamma distribution), with 25 th percentile equal to a 0.6 percent cha nge in mortality per 1 μg/m 3 change in annual average PM2.5 (the Pope et al., 2002 central estimate), with 75 th percentile equal to 1.5 percent change in mortality per 1 μg/m 3 change in annual average PM2.5 (the Laden et al., 2006 central estimate), and with mean equal to approximate ly the mean of thes e two values. Such a distribution would be adopted for the primary estimates.

The HES considered a formal method of integrat ing the opinions of th e elicited experts, using a copula function. However, the HES deci ded not to recommend th is approach, favoring instead a simpler and more transparent approach as described above.

10$0 $100 $200 $300 $400 $500 $600 $700 $800

Pope et al Laden et alMillions(2006$)

PM 2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR 7% DR

Figure 1. PM-related monetized benefits of attaining an NO 2 standard of 50 ppb nationwide in 2020.

Blue bars are based on a 3% discount rate ; red bars represent a 7% discount rate (Source: Fann, 2009).

4.2. Cessation Lag

Agency-supplied background : The Primary Estimates for PM mortality reflect an assumed lag between cessation of exposure and r ealization of the change in health effect incidence. Based in part on prior Council HES advi ce, the primary estimates in the draft benefits report reflect a 20-year distributed lag. Specifically, 30 percent of th e total reduced incidences is assumed to occur in the first year following th e exposure change. Another 50 percent of the total incidence changes is be spread evenly over year s two through five. The remaining 20 percent of the incidence change is spread evenly over year s six through twenty. The effect of the cessation lag is realized through discounti ng (at a 5 percent rate) of the monetized value of future-year incidence changes (i.e., there is no need, and no intent, to represent the discounted values as reflecting direct discounting of incidences per se ). In addition, the draft uncertainty report

evaluates the effect of alternativ e lag structures. These alternatives include the 5-year distributed lag applied in the First Prospective Study and a set of smoothed lag functions derived from

consideration of the resu lts of available cohort and intervention studies.

Charge question 2b: Does the Council HES support the us e of the 20-year distributed lag structure described above for gene ration of the Primary Estimates of the monetary value of PM mortality incidence reduction and the specific al ternative lag functions presented in the draft uncertainty report? If not, are th ere alternative study choices a nd/or methods for organizing and presenting results that the Counc il HES recommends EPA consider?

HES response: EPA has done an admirable job res ponding to the suggestions of earlier reviews by the Council and NAS. However, EPA should cite and include information from the

11recent analyses of the Nurses’ Health Study (Pue tt et al., 2009) and the Harvard Six Cities Study (Schwartz et al., 2008; Laden et al., 2006). These st udies suggest that most of the health effects of exposure (and benefits from reduction) occur within a few years. EPA assumes that 80% of the risk reduction occurs in the first five y ears. However, the EPA analysis of alternative assumptions about the lag using a given cohort st udy indicates that the 20-year distributed lag default assumption generates a result that is close to the mean of a range of reasonable assumptions. Therefore, in the face of uncertainty, this lag structure is appropriate.

The HES suggests that if the decay function ap proach is used, EPA should ensure that its choice of parameter k is consistent with its choice of ri sk coefficient, in terms of the cohort studies used to generate both.

4.3. PM-Related Infant Mortality

Agency-supplied background: EPA’s current approach to estimating the association between PM exposure and respiratory inflammation and infection leading to premature mortality in children under 5 years of age relies on th e cohort study conducted by Woodruff et al. (1997). This is based in part on prior (SAB-HES) advi ce, which noted several strengths of the study, including the use of a larger c ohort drawn from a large number of metropolitan areas and efforts to control for a variety of indi vidual risk factors in infants (e .g., maternal educational level, maternal ethnicity, parental marital status, a nd maternal smoking status). A more recent study by Woodruff et al. (2006) continues to find associations between PM 2.5 and infant mortality, and also found the most significant relationships with respiratory-rela ted causes of death.

Charge question 2c: Does the Council HES recommend continued reliance on the Woodruff et al. (1997) study to characterize the associati on between PM exposure and respiratory inflammation and infection leading to premature mortality in children under 5 years of age, or recommend that the relationship be characterized by the more recent Woodruff et al. (2006) study, or recommend some other approach th at relies on a third study or some combined consideration of multiple studies? Are there specific reasons to favor the results of one of these studies or of another study?

HES response: The HES supports EPA’s decision to include infant mortality in its analysis. Although its inclusion has only a small impact on overall benefits, compared with PM effects on adult mortality, incorporating infant mortality not only is consistent with the Agency’s goal of comprehensiveness but al so demonstrates the impacts of PM across the entire human lifespan.

An increasing body of literature relates infant mortality and PM exposure. For example, the Ritz et al., 2006 study of S outhern California data further informs the PM-infant mortality relationships found in the Woodruf f et al. (1997, 2006) studies. Wh ile there are some important

differences between the available studies within and outside of the United States, the results consistently show positive asso ciations between PM (both PM 10 and PM 2.5 ) and infant mortality.

When PM 10 results are scaled to estimate PM 2.5 impacts, the results yiel d similar risk estimates.

The number of studies now availabl e may be sufficient to consider pooling results, rather than relying on a single study, thereby deriving a mo re robust risk estimate. The strengths and

12weaknesses of each study should be assessed to determine whether pooling or using a single study is the appropriate appro ach for this analysis.

To summarize, several studies, both within and outside of the U.S., have resulted in similar risk estimates for PM impacts on infantmortality. The committee recommends that EPA

do a reasoned evaluation of relevant studies and synthesize evidence across the studies.

4.4. PM Mortality Effect Threshold

Agency-supplied background: Consistent with prior Council and NAS advice, the Project Team did not attempt to alter the Pope 2002 CRF to reflect an assumed concentration threshold below which PM concentration changes would yield no change in estim ated incidences. In addition to the lack of compelling evidence for part icular effects thresholds, the Project Team is not aware of any valid procedure for the altering the CRF above an assumed threshold. In other words, the Project Team presumed that imposition of an (arbitrary) threshold would require respecification of the CRF to en sure a “with threshold” CRF slope that would accurately account for the total change in incide nce expected based on the epidem iological study from which the CRF was derived. Prior efforts to apply a thre shold simply truncated the incidence change estimated from a no-threshold CRF, though prior SAB advice indicates this is improper and the Project Team chose not to apply such an adjustment in the current analysis.

Charge question 2d: Does the Council HES support the use of a no-threshold model for generation of the Primary Estimates of PM mortality incidence reduction? If not, are there methods for estimating and applying an effect s threshold that the Council HES recommends EPA consider, either for the Primary Estimates or for presentation in the dr aft uncertainty report?

HES response: The HES fully supports EPA’s decisi on to use a no-threshold model to estimate mortality reductions. This decision is su pported by the data, which are quite consistent in showing effects down to the lo west measured levels. Analyses of cohorts using data from more recent years, during which time PM concentr ations have fallen, continue to report strong associations with mortality. Ther efore, there is no evidence to support a tr uncation of the CRF.

4.5. Ozone Mortality Concentration-Response Function (CRF)

Agency-supplied background: Based in part on prior C ouncil and NAS advice, EPA has included changes in ozone-related premature mo rtality as part of the Primary Estimate of

benefits in the draft benef its report. Recognizing the ongoi ng uncertainty regarding the appropriate study or studies from which a quantitative CRF should be derived, the Project Team adopted a placeholder function for the Primary Es timate of changes in ozone mortality which encourages focus on several key factors: st udy selection, pooling across studies, and pooling methodology. Given the particular uncertainties regarding the r easonableness of pooling across the multi-city NMMAPS 11 studies and the meta-analyses, the Project Team specified a CRF for the Primary Estimate which reflects inverse va riance-weighted pooling of the Bell et al. 2004 and Schwartz 2005 mortality effect estimates, both of which reflect an all-cause mortality endpoint. In addition, the draft uncertainty report presents al ternative results obtained by applying CRFs derived from each of the three indi vidual multi-city time-seri es studies and three meta-analyses. Furthermore, EPA has developed an alternative CRF based on the Jerrett et al.

13(2009) long-term ozone mortality study. This approach is described in the technical memorandum included in the package of review documents.

Charge question 2e: Does the Council HES support the use of the ozone mortality CRF derived by pooling the Bell et al . (2004) and Schwartz (2005) st udies for the Primary Estimate and the presentation of the six a lternative estimates in the draft uncertainty report? A particular question for which the Project Team seeks HES a dvice is whether application of the respiratory mortality risk estimate drawn from Jerrett et al. (2009) might be su itable for use in generating the Primary Estimate, or at least for generation of an Alternative Estimate. If the answer to either, or both, of these two questions is negative, are there alternative study se lection and/or pooling approaches the Council HES recommends EPA cons ider for the Primary Estimate in the draft main benefits report and/or for the Alternative Estimates presented in the draft uncertainty report?

HES response: The HES supports the inclusion of ozone mortality in the Section 812 Prospective Analysis, given the growth of ev idence in the time-series literature and the consistency of these findings with morbidity stud ies. In terms of the most appropriate ozone CRF, the HES finds that it is premature to use the cohort mortality evidence from Jerrett et al. (2009) as the basis for the Primary Estimate, in light of the lack of corroboration from other cohort studies. However, the HES supports its in clusion as an Alternative Estimate or other sensitivity analysis, as it would be valuable to co nvey its implications if, as we expect, the cohort mortality findings are co rroborated elsewhere.

For the time-series ozone mortality evidence, the HES supports the consideration of both the multi-city studies and the literature meta-analyses when generating the Primary Estimate. The multi-city studies have the advantage of a consistent methodology across cities and the possible reduction of publication bias, but meta -analytic approaches are the foundation of the CRFs elsewhere in the prospective analysis. In a ddition, some investigators have noted that the National Mortality, Morbidity, a nd Air Pollution Study (NMMAPS) produces significantly lower CRFs than other epidemiological investigations, whic h may be in part attributable to the question investigated and the methodological choices made by various investigators. As there is not a clear basis for choosing one set of studies over the other, the HE S recommends that the Primary

Estimate be derived as an intermediate valu e between the multi-city studies and the metaanalyses. The uncertainty distribut ion can then reflect methodologica l uncertainties as well as the uncertainties reported within individual studies.

Elaborating on this point, on page 2-9 the repo rt indicates that the mean of the estimates derived from the three meta-analyses and the m ean of the estimates derived from the three NMMAPS-based studies will be presented. However, the Primary Estimate is instead derived from a pooling of a 95-city NMMAPS-based st udy (Bell et al., 2004) and a 14-city casecrossover study not directly tie d to NMMAPS (Schwartz, 2005), without weight on the other NMMAPS-based study (Huang et al., 2005) or the th ree meta-analyses. It would be better to present a Primary Estimate reflecting an interm ediate value among the multi-city studies and the meta-analyses, with explicit consid eration of the full body of evidence.

14The uncertainty analyses presented in Exhi bit 4-4 of the uncertainty report raised additional questions regarding the CRFs chosen a nd their rationale. For example, the Schwartz (2005) paper reported a 0.23% increase in mort ality per 10 ppb increase in 1-hour maximum ozone concentrations. Using thes e same metrics, Bell et al. (2 004) reported a value of 0.34%, yet the mortality incidence estimate in Exhibit 4-4 was lower. This could be e xplained if the Section 812 Prospective Analysis used the one-week aver age ozone findings from Bell et al. (2004), butthis goes against the stated averaging time preferences in the report. Similarly, the three metaanalyses report corresponding values of 0.34% , 0.39% and 0.41%, but the incidence estimates are substantially greater for these meta-analyses than for the multi-city studies. More information about the CRFs derived from each study (and the assumptions regarding averaging times) would help to clarify why the benefits estimates appear to vary more substantially than the original studies, and in general, efforts to choose CRFs c onsistent with this full body of literature would be supported.

The HES notes that ‘attributable deaths’ as estimated using cohort mortality evidence (from long-term exposure) for PM and time-se ries mortality evidence (from short-term exposure) for ozone have potential ly very different implications in loss of life expectancy, with mortality from long-term exposure likely to be much more significant. The HES is concerned about the implications for the ec onomic valuation of mo rtality. In line with other guidance, EPA has adopted a VSL approach (as opposed to a life-year approach) within the Section 812 Prospective Analysis. The VSL a pproach treats all deaths as equi valent, irrespective of whether the associated loss of life expectancy is large or small. While accepting that this is standard practice, some HES members see it as counter-intui tive. The HES asks that EPA spell out, clearly, the assumptions underlyi ng its valuation methods, and in particular why the average extent of life expectancy is c onsidered irrelevant despite differe nces in method (time series vs. cohort), age, or other matters, so that readers can better unde rstand the Benefits method and results. In addition to differences by age in life-years lost, the HES accepts that because economic valuation of life-years would not likely be constant with age, it is not immediately

obvious to what extent differentia l values should be applied to time-series vs. cohort mortality

evidence. This should be addressed within the valuation uncertainty an alysis, which was not provided to the HES for review.

4.6. Ozone Cessation Lag

Agency-supplied background: Based on a perceived lack of empirical data to support specification of a cessation lag structure for ozone -related mortality effects, the Project Team has not attempted to apply a cessation lag structur e for the Primary Estimate of ozone mortality

reduction benefits in the draft be nefits report, and alternatives are not evaluated in the draft uncertainty report.

Charge question 2f: Does the Council HES support the use of a no-lag assumption for the Primary Estimate of ozone mortality benefits pres ented in the draft benefits report? If not, are there methods for estimating and applying a cessa tion lag structure for ozone mortality that the Council HES recommends EPA consider, either for the Primary Estimates or for presentation in the draft uncertainty report?

15HES response: The HES concludes that time-series data should be used for the Primary Estimate for ozone mortality. Time-series studies investigate health cond itions or effects in a given cohort over time. Therefore, any lag in cess ation of effects is already accounted for by the studies, and no further adjustment for cessation lag would be appropriate. The HES therefore supports EPA’s position regarding cessation lag.

If Alternative Estimates are derived using cohort mortality evidence, there is no evidence in the literature to support a different cessati on lag between ozone and particulate matter. The HES therefore recommends using the same ce ssation lag structure and assumptions as for particulate matter when utilizing co hort mortality evidence for ozone.

4.7. Ozone Mortality Effect Threshold

Agency-supplied background: Based on a perceived lack of empirical data to support application of a concentration th reshold for ozone-related premature mortality effects, the Project Team did not attempt to apply an effect thresh old for the Primary Estimate of ozone mortality

reduction benefits.

Charge question 2g: Does the Council HES support the use of a no-threshold model for generation of the Primary Estimates of ozone mo rtality incidence reduction? If not, are there methods for estimating and applying an effect s threshold, which the Council HES recommends EPA, consider, either for the Primary Estimates or for presentation in the draft uncertainty report?

HES response: The HES supports the use of a no-threshold model for ozone and mortality. The current scientific literature does not support a population-based threshold, as studies have found no supporting evidence for short- term exposure and only suggestive evidence for long-term exposure. For example, time-series analysis of ozone a nd mortality in 98 U.S.

urban studies examined four model structures for the concentration-response curve: linear; subset; threshold; and spline models (Bell et al ., 2006). All findings suppor t the application of a no-threshold model, and also support the traditio nally used shape of th e concentration-response

curve. Associations between oz one and mortality were present at low concentrations, nearing natural background levels. If a threshold for short-term ozone expos ure and mortality exists, it is likely below the range of regulatory interest.

With respect to increased mortality risk from long-term ozone exposure, there is

inconclusive evidence that a threshold may be present. A stu dy of long-term health effects of ozone in 96 U.S. metropolitan areas for almo st 450,000 persons for the ozone season (April to September) identified an association between ozon e and respiratory-related mortality (Jerrett et al., 2009). A threshold analysis included a model in which a linear relationship between ozone and respiratory-related mortality risk is assumed for ozone levels above the specified threshold,

and no association is assumed for levels belo w the threshold. Model fit was improved under the threshold model, compared to a no-threshold m odel (p-value 0.06), providing weak evidence of a threshold at 56 ppb daily maximum ozone concen tration. Given this limited evidence for a threshold, the HES recommends that analyses based on findings from this study be conducted

16both with the no-threshold model and with an assumed threshold model, as an alternative analysis.

4.8. Baseline Incidence / Prevalence Estimates

Agency-supplied background: Baseline incidence / prevalence are key determinants of the estimated changes in health effect incidence described in the draft benefits and uncertainty

reports.

Charge question 2h: Does the Council HES support the choices made by the Project Team regarding baseline incide nce / prevalence across the va rious human health endpoints incorporated in the Primary Estima te of benefits? If not, are ther e alternative baseline incidence / prevalence data which the Council HES recomme nds EPA consider, either for the Primary Estimates or for presentation in the draft uncertainty report?

HES response: The HES generally supports the c hoices made by the project team regarding baseline incidence/pr evalence across the various huma n health endpoints. The HES recognizes that many of these choices are judgments within significant data constraints, and that the projected estimates have substantial uncertain ties. The most important data, in terms of driving the benefit estimates, are the baseline mort ality data, which are of relatively high quality.

There is some concern about the noise and inconsistency in data used to estimate baseline school loss and work loss days. There may be too much noi se to make reasonably reliable quantitative estimates of air pollution work loss days.

4.9. PM Differential Toxicity Sensitivity Analysis

Agency-supplied background: In its review of the Sec ond Prospective Study analytical blueprint, the Council recognized that the state of the science did not support development and application of assumptions rega rding the potential differential toxicity of PM components suitable for informing the present analysis. However, the Council did encourage the Project Team to explore the f easibility of conducting a sensitivity analysis to gauge the potential significance of differential toxici ty. After extensive review of the literature and analysis of options, the Project Team concl uded that currently available data and methodologies remain insufficient to meet the challenge of developi ng a reasonably valid a nd usefully informative sensitivity analysis, even on a notional basis. Indeed, the Project Team concluded that the potential research utility of such a sensitivity an alysis in the end did not appear to justify the risks from potential misinterpretation and misapp lication of the results of such a sensitivity analysis. The Project Team’s evaluation of the is sue of differential toxicity is presented in chapter 5 of the draf t uncertainty report.

Charge question 2i: Does the Council HES support the Project Team’s decision to defer quantitative sensitivity analysis of potential PM component differen tial toxicity? If not, are there data or methods for conducting a quantitative analys is of PM component differential toxicity that the HES recommends EPA consider, or are ther e other aspects of differential PM component toxicity which the HES recommends should be addr essed in the draft benefits and/or uncertainty reports?

17HES response: The Council had encouraged the EP A Project Team to explore the feasibility of conducting a sensitiv ity analysis to gauge the poten tial significance of differential toxicity. The Project Team has determined after a review of th e literature that the currently

available data are insufficient for devel oping an informative sensitivity analysis.

The Uncertainty Analysis document object ively reviews the evidence for various components, although research beyond 2007 does not seem to be included. Several recent papers could be reviewed (e.g., Ostro et al., 2007, 2008; Smith et al., 2009). The Project Team found a limited but growing literature addressing the health effects of various PM components, including (but not limited to) su lfate, nitrate, elemental carb on, organic carbon, and metals. They conclude that none of the components show consis tently greater effects than PM as a whole; however, the epidemiological evidence base wa s clearly limited by the high correlations among many PM components (and between those components and PM as a whole). The Project Team

concluded that “for this evidence base to be applicable to a differential toxicity analysis, it would need to be able to provide quantitative CRFs fo r all of the key components, derived in a manner

so that the total reflected the observed effect s of PM2.5 and so that the estimates reflected possible interactions among components.”

The HES agrees that the evidence base at this time does not currently support this sort of assessment. Additionally, the HES finds that diffe rential assessment – even if feasible – would not lead to substantially different results in an assessment such as the 812 Analysis, which deals with changes in the pollution mixture as a whole; and so it supports EPA’s decision to omit differential toxicity from the pr esent 812 analysis. However, th e HES recognizes that benefits analysis of specific measures may affect particul ar constituents of PM only, and that – for other analyses – the issue of differential toxicity should be considered anew.

4.10. Dynamic Population Modeling

Agency-supplied background: Chapter 7 of the draft uncertainty report describes the results of the Project Team’s application of a dynamic popul ation simulation model to the evaluation of changes in pollution-related prematur e mortality risks. The Project Team continues to consider the potential util ity of dynamic population modeling approaches and respectfully requests that the HES review the methodology and results and consider providing advice regarding the potential utility of further development and future application of this approach.

Charge question 2j: Does the Council HES have recommendations regarding the potential value for future analyses of the dynami c population approach desc ribed in chapter 7, or any alternative approaches the HES may suggest for addressing the issu e of population changes during a study’s reference period?

HES response: The HES appreciates the work EPA has done to illustrate the use of dynamic population modeling for estimating the mo rtality impacts of long-term exposure to PM2.5. In general, HES supports the use of dynamic population modeling where practicable because it provides the most realistic availa ble modeling of how, over time, changes in population risk lead to changes in the size and age distribution of the population, with consequent implications for estimated mortality impacts, whether expressed as deaths or life

years.

1819 We note EPA’s concern that a full dynami c population implementati on requires detailed projections for every year up to 2020 (compa red with currently for 2000, 2010 and 2020 only), and that this is very resource-intensive if carried out at the small spatial scale of the current core methods. EPA should focus its dynamic population modeling on estimating mortality effects forward in time using one-year changes in exposure, based on the years 2000, 2010 and 2020 (i.e., the approach reported by EPA in the Uncertainty Report), when comparing dynamic population modeling with core BenMap methods. As noted by EPA, this is an intermediate strategy, which for a modest increase in effort captures many (though not all) of the gains of dynamic population modeling.

The HES discussed how the dynamic population approach highlights some subtleties in how monetary values are linked with the mortality implications of different pollution scenarios. HES requests EPA to describe, in substantially more detail than at present, the methods used, and the reasons why these methods rather than othe rs were selected. This includes, but is not limited to, describing (1) the current (‘static popula tion’) BenMap approach to estimating deaths postponed; (2) how, within this approach, VSL values are linked with changes in risk or deaths, and the assumptions underlying th is approach; (3) why EPA favors valuation based on deaths / VSL rather than life years / VSLY; (4) whether EP A can link monetary values to the results of dynamic population modeling, and if so how this might be done; and in particular (5) whether the changing year-on-year pattern of deaths , illustrated by dynamic population modeling, can be incorporated into the mone tary analysis.

### REFERENCES

Bell, ML, McDermott A, Zeger SL, Samet JM, and Dominici F 2004. Oz one and short-term mortality in 95 US urban communities, 1987-2000. Journal of the American Medical

Association 292(19):2372-2378.

Bell ML, Peng RD, and Dominici F 2006. The exposure- response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environmental Health Perspectives 114:532-536.

Fann, N. 2009. Estimating PM2.5 and Ozone-Related Premature Mortality Based on Risk Estimates from the Jerrett et al. (2009) a nd Krewski et al. (20 09) Studies. Technical Memorandum from Neal Fann, EPA/ OAR/OAQPS, to Jim DeMocker, EPA/OAR/OPAR. November 15, 2009.

H uang Y, Dominici F, and Bell ML 2005. Bayesian hierar chical distributed lag models for summer ozone exposure and car dio-respiratory mortality. Envirometrics 16:547-562.

Jerrett M, BurnettRT, Pope CA 3rd, Ito K, Thurston G, Krewski D, Shi Y, Calle E, and Thun M 2009. Long-term ozone and mortality. New England Journal of Medicine 360:1085-1095.

Krewski D, Jerrett M, Burnett R T, Ma R, Hughe s E, et al. 2009. Extended follow-up and spatial analysis of the American Cancer Societ y study linking particulat e air pollution and mortality. HEI Research Report , 140, Health Effects Institute, Boston, MA.

Laden , F, Schwartz J, Speizer FE, and Docker y DW 2006. Reduction in fine particulate air pollution and mortality. American Journal of Respiratory and Critical Care Medicine

173:667-672.

Ostro BD, Feng WY, Broadwin R, Green S, and Li psett M 2007. The effects of components of fine particulate air pollution on mortality in California: results from CALFINE. Environmental Health Perspectives 115(1):13-19.

Ostro BD, Feng WY, Broadwin R, Malig BJ, Green RS, Lipsett MJ 2008. The impact of components of fine particulate matter on cardiovascular mortality in susceptible subpopulations. Occupational and Environmental Medicine 65:750-756.

Pope, CA III, Burnett RT, Thun MT, Calle EE, Krewski DD, Ito K, and Thurston GD 2002.
Lung cancer, cardiopulmonary mortality, and long -term exposure to fine particulate air pollution. Journal of the American Medical Association 287:1132-1141.

Puett RC, Schwartz J, Hart JE, Yanosky JD, Spei zer FE, Suh H, Paciorek CJ, Neas LM, and Laden F 2008. Chronic particulate exposure, mortality, and coronary he art disease in the Nurses’ Health Study. American Journal of Epidemiology, 168(10):1161-1168.

R-1R-2 Ritz B, Wilhelm M, Zhao Y 2006. Air pollution and infant death in southern California, 1989-

2000. Pediatrics 118:493-502.

Schwartz J, Coull B, Laden F, and Ryan L 2008. The effect of dose and timing of dose on the

association between airborne particles and survival. Environmental Health Perspectives 116(1):64-69.

Schwartz, J. 2005. How sensitive is the association between ozone a nd daily deaths to control for temperature? American Journal of Respiratory and Critical Care Medicine 171(6):27-

631.

Smith KR, Jerrett M, Anderson HR, Brunett RT, St one V, Derwent R, Atkinson RW, Cohen A, Shonkoff SB, Krewski D, Pope CA 3 rd , Thun MJ, and Thurston G 2009. Public health benefits of strategies to reduce greenhouse- gas emissions: health implications of shortlived greenhouse pollutants. The Lancet 374 (9707): 2091-2103.

Woodruff TJ, Grillo J, and Schoendorf KC 1997. The Re lationship Between Selected Causes of Postneonatal Infant Mortality and Partic ulate Air Pollution in the United States. Environmental Health Perspectives 105(6): 608-612.

Woodruff TJ, Parker JD, and Schoendor f KC 2006. Fine particulate matter (PM2.5) air pollution and selected causes of postneonatal infant mortality in California. Environmental Health Perspectives 114(5):786-90.

### APPENDIX A: Technical Correct ions from Individual Members

The Subcommittee’s advice and resp onses to the charge questions are contained in the body of this report. However, in the course of the review , the following technical issues were noted in the materials provided by the Agency. This is no t intended to be an exhaustive list.

Regarding the health benefits chapters from the draft Health and Welfare Benefits Analyses to Support the Second Section 812 Benefit- Cost Analysis of the Clean Air Act (dated November 2009):

P. 1-6: It appears that the pr imary benefits result includes CA IR, but this has not yet been promulgated, and would not have influenced the year 2000 benefits in any event. It is later mentioned in the qualitative uncertainty analysis th at the issue of assuming CAIR when it is not yet in place is a potentially majo r uncertainty, but it would be nice to provide even more insight about this question (since CAIR itself has been associated with benefits of $70B in 1999 dollars and using some other “older” assumptions). Doin g new runs assuming no CAIR is impractical,

but this should be able to be quantified at first order gi ven the previous RIA of CAIR.

P. 2-7, Exhibit 2-2: It is not cl ear why “low birth weight” is not quantified; it is a birth outcome which has long lasting impacts on development. Ar e there too few studies from which reliable

estimates can be derived? Some mention of th e decision for this outcome should be included in

the text or as a footnote to this exhibit.

P. 2-8, Exhibit 2-3, footnote a: In the last sent ence “biological similarity” is puzzling. Children between the ages of 5 and 17 continue to develo p biologically, including in the nasal-respiratory tract. Is EPA saying that the NRC determined that the differences in respiratory system developmental stages for ages 5-17 were not significant for estimating PM-related health effects? Please clarify or correct this statement; as currently writte n it is too vague and broad.

P. 2-10: It is not true that th e individual time-series studies e ither used 24-hour average or 1-hour maximum levels for exposures; multiple studies did use 8-hour maximum. For example, Fairley (2003), Klemm (2000), Michelo zzi (1998), Saez (2002), Anders on (2001), Bremmer (1999), Roemer (2001) all used 8-hour maximum. These studies (and probably others) are embedded in the meta-analyses.

P. 2-12, School absence section:

The recommendation from the NRC should be brie fly stated so the reader can understand the justification for extending the cite d studies to a wider age range. Further, in the last sentence of this section the logic used for derivi ng the estimate should be expanded.

P. 2-15: It seems strange to argue that there is an extensive body of literature on CHA and RHA and then to choose only 2 studies for the estimates . Why not use, for example, the meta-analyses conducted in Europe in 2006 by th e Committee on the Medical Effects of Air Pollutants, which pooled 50+ studies of CHA in a very careful manner?

P. 2-17 to 2-18, Baseline Incidence Rates section:

A-1The title for this section does not match that of the related exhib it. More importantly, some of

the data sources and their limitations are not descri bed in this section. Fo r example, the “School Loss Days” database parameters (e.g., ages c overed) and challenges in using them are not

included in the text.

P. 2-18, last paragraph: The disc ussion of the asthma prevalence rates and assumptions needs to

be clarified. Does “in future years” refer to post-1999 (e.g., after the ALA data), or from 2009 based on 1999, or something else? It is not clear whether the basi s for the “current trends” in the

first half of the last sentence is the study cited or whether anot her study and/or national database (Ostro et al, CDC, ALA, etc) was/ were used for this comment. Fu rther, a trend requires at least two points in time; what were t hose points? There is no recogniti on of whether rates have been affected by changes in diagnostic, treatment and/or reporting practices that may affect predicted rates. Last, the 4% chosen may be relevant for the 2020 national population but not for at-risk subpopulations, such as those noted in Exhib it 2-6. Are demographic changes in these subpopulations expected to result in overall population prevalence rates that would exceed 4%? Clarification of these several issues would help the reader understand the bases for and therefore the validity of the asthma data used and the 2020 prevalence rate assumption.

P. 2-20: Exhibit 2.5. Baseline Incidence/Prevalence Rates

• In the first row (Mortality), the scope and years of data used should be clarified further. Because “infant mortality” was not specified as a row item, it was not clear to this reviewer whether infant mortality data were included. Wonder includes infant mortality data for 1995-2005; were any of these data (or only 1996-1998? ) used to derive the risk estimate for the “< 18” rate? If infant mortality data were included, the Morality ro w needs to include an “infant mortality” line for the data source and y ears of data used. If not, the ages for the “<

18” column need to be clarified.

• In the row marked “School Loss Days,” the Na tional Center for Education Statistics (1996) database is cited. However, this data sour ce is not easily located on the Internet. The

citation for this database is cu rrently missing and needs to be li sted in the References section of this chapter.

P. 2-27 and Exhibit 2.14: This reviewer assume s that “School Loss Days” on p. 2-27 became the “Work Loss Days” in the exhibit. Clarify in the text whether this is the case or not. Further, if “Work Loss Days” includes several endpoints, then a ll of the inclusions shoul d be clearly stated.

P. 2-29 to 2-31: Applying the eyeball test, it seem s strange that mortality incidence is about 3-4 times higher at the mean than the 5 th percentile, while valuation is about 70 times higher. This patterning doesn’t exist for morbidity endpoints or for PM mortality, and the text did not mention (that I noticed) any us e of lower valuation for ozone mortality even as a bounding

calculation.

A-2A-3 Regarding excerpted materials from the draft Uncertainty Analyses to Support the Scond Section 812 Benefit-Cost Anal ysis of the Clean Air Act (dated November 2009):

P. C-8: I’m not sure if I agree with some of the “major/minor” conclusions drawn in Table C-4. For example, the exclusion of populations under 30 from the mortality health impact assessment

will have a trivial effect on the risk calculations , given the very low baseline mortality rate in these populations. It seems unlikely that the exclusi on of air toxics has a pot entially major effect on the net benefit estimate, as previous studies have shown air toxics cancer risks to be orders of magnitude less than criteria pollutant risks. The two ozone mortality in cidence estimates that were pooled were from the same study and diffe red minimally, so this would not be a major

effect.

P. C-8-11, Table C-4: The rationale for the ch oice of health effects and their related

uncertainties documented in this table are no t apparent. While adult mortality-related assumptions and uncertainties must be describe d because they drive th e overall estimate, the table also presents information about an adul t morbidity factor. Chronic bronchitis (the second most important contributo r to the overall estimate) is in cluded in the table, but there is no mention of “nonfatal myocardial infarcti on,” which is another morbidity outcome and a

close third contributor to the health effects es timate. Additionally, there is no mention of infant mortality or other childhood health effects. Whether the purpose of this table was to captu re the “key uncertainti es” for the top three

contributors to the health estim ate or for adult outcomes only, th ere are inconsistencies within either scope. The rationale for the choice of health effects and their related uncertainties

documented in this table should be clearly stated in supporting text.