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 Health 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 prospective study of the benefits 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 HES provided advice on the data chosen for the 

analysis, the selection of models used to conduct 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 several years. In the early planning phases, 

the analytical blueprint and preliminary data were reviewed by the Council and its 

subcomponents. EPA has thoughtfully and thoroughly considered previous advice in conducting 

this analysis. 

The HES finds the data sources and analytical 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 benefit estimates that EPA derives for PM are based on two wellresearched and highly respected cohort studies of air pollution health effects, the Harvard Six 

Cities Study and the American Cancer Study (ACS) Cancer Prevention Study. These are good 

foundations for the health benefits estimates for PM and these studies are supported by other 

recent studies and expert evidence gathered by the EPA project team. This is encouraging, and 

the HES makes some recommendations on how to express a best estimate and the uncertainty 
2

around that estimate. The HES generally agrees with other decisions made by the EPA project 

team with respect to PM, in particular, the PM mortality 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 the best estimate and uncertainty around the ozone concentrationresponse function. 

The HES finds that the draft documents presented to it for review require a fair amount of 

technical editing to make the analyses clearer. The details of the conclusions and 

recommendations are detailed in the Subcommittee’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 Analysis
NOTICE 

This report has been written as part of the activities of the EPA Advisory Council on Clean Air 

Compliance Analysis (Council), a public advisory 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 assessment of scientific matters 

related to problems facing the Agency. This report has not been reviewed for approval by the 

Agency and, hence, the contents of this report do not necessarily represent 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 products constitute a 

recommendation for use. Reports of the Council are posted on the EPA website at 

http://www.epa.gov/advisorycouncilcaa. 

i
U.S. Environmental Protection Agency 

Advisory Council on Clean Air Compliance Analysis 

Health Effects Subcommittee, Augmented with Members of the Council 

COUNCIL MEMBERS 

Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in Residence, The National 

Academies, Washington, DC 

Dr. Michelle Bell, Associate Professor, School of Forestry and Environmental Studies, Yale 

University, New Haven, CT 

Dr. James K. Hammitt, Professor, Department of Health 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 Environmental 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 Environmental Protection Agency, Oakland, CA 

Dr. Rebecca Parkin, Professor and Associate Dean, Environmental 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)

ii
U.S. Environmental Protection Agency 

Advisory Council on Clean Air Compliance Analysis 

CHAIR 

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

 

COUNCIL MEMBERS 

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

Dr. Michelle Bell, Associate Professor, School of Forestry 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 Defense 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 Pollution 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 Civil and Environmental Engineering, 

Georgia Institute of Technology, Atlanta, GA 

 

iii
Mr. Michael Walsh1

, 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. 

iv
v

TABLE OF CONTENTS 

1. EXECUTIVE SUMMARY................................................................................................................................1

2. BACKGROUND ................................................................................................................................................4

3. GENERAL COMMENTS.................................................................................................................................6

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

4.1. PM MORTALITY CONCENTRATION-RESPONSE FUNCTION (CRF)................................................................9

4.2. CESSATION LAG ........................................................................................................................................11

4.3. PM-RELATED INFANT MORTALITY...........................................................................................................12

4.4. PM MORTALITY EFFECT THRESHOLD .......................................................................................................13

4.5. OZONE MORTALITY CONCENTRATION-RESPONSE FUNCTION (CRF)........................................................13

4.6. OZONE CESSATION LAG ............................................................................................................................15

4.7. OZONE MORTALITY EFFECT THRESHOLD .................................................................................................16

4.8. BASELINE INCIDENCE / PREVALENCE ESTIMATES .....................................................................................17

4.9. PM DIFFERENTIAL TOXICITY SENSITIVITY ANALYSIS ..............................................................................17

4.10. DYNAMIC POPULATION MODELING...........................................................................................................18

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

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

The Clean Air Act Amendments of 1990 included specific language in Section 812 

directing the EPA to perform benefit-cost studies of the overall effects of the Clean Air Act. In 

response to Section 812, on October 15, 1997, EPA issued 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 Subcommittee (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 the draft human health effect estimates in chapters from the 

second Section 812 Prospective Analysis benefits 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 concentration for both particulate matter (PM) and ozone; 

(2) The methods and data used to develop estimates of the lag time between cessation of 

exposure and reduction in health effects for both PM and ozone; (3) the Agency’s assumptions 

about a threshold concentration for mortality effects 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 approach for dynamic modeling of U.S. population 

demographic changes. 

Overall, the HES finds the EPA analyses, data choices, and methodologies to be sound. 

EPA bases its estimates of the mortality benefits of reducing fine particulate matter (PM2.5) on 

data analyzed from two large, long-running landmark studies on the health effects of air 

pollution, the Harvard Six Cities Study and the American Cancer Society (ACS) Cancer 

Prevention Study (CPS). The HES finds the selection of these cohort studies as the underlying 

basis for PM mortality benefit estimates to be a good choice. These are widely cited, well studied 

and extensively reviewed data sets. EPA further bolsters its PM mortality benefit estimates by 

gathering information on the PM mortality concentration-response function in an expert 

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

alternative to EPA’s choice of the Pope et al. (2002) study (based 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 alternative estimate. EPA found that 

these choices fell at approximately the 25th and 75th percentile of the mortality effect estimates 

garnered from the expert elicitation. EPA proposed a statistical approach using copula functions 

to combine the 12 expert elicitation estimates into a single mortality effects distribution. The 

HES, however, recommends a simpler approach. EPA should define a distribution, perhaps a 

truncated normal distribution, with the Pope and Laden studies at the 25th and 75th percentiles 

respectively. The mean of the new distribution should 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. 

1 
The 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 estimate the mortality reductions associated with 

reduced PM exposure. 

EPA estimates an association between PM exposure and respiratory inflammation and 

infection leading to mortality in children under 5 years of age, relying on a cohort study 

(Woodruff et al., 1997). The HES generally supports this approach with some caveats. The age 

range of interest for respiratory-related mortality should be 1 month 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 effects and EPA should better 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 evidence 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 consideration 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, the 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-related 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 are 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 used to estimate baseline school loss and work loss days. There 

may be too much noise to make reasonably reliable quantitative estimates of air pollution work 

loss days. 

The Council had previously recommended that 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 insufficient data to perform such an analysis. The 

HES supports this conclusion, given the state of the evidence but encourages EPA to continually 

revisit this conclusion as the literature evolves. 

2 
EPA developed a dynamic population simulation model to explore how changes in 

population age structure change pollution-related premature mortality 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 approach. Overall, the HES commends EPA for 

demonstrating how dynamic population modeling can be used in estimating the mortality 

impacts of long-term exposure to PM2.5. 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 the results and run-time for the models. The HES 

recommends that the EPA describe in more detail the methods used and the reasons why EPA 

chose these particular methods over others. 

Overall, the HES finds the EPA analysis thorough and generally consistent with previous 

recommendations from the Council. The HES makes 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 the Clean Air Act in 1990 included specific 

language in Section 812 directing the EPA to perform benefit-cost studies of the programs 

affiliated with the Clean Air Act as a whole, relative 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 clean air program benefits 

and costs in support of future rounds of Clean Air Act reauthorization, 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 conduct 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 (Council) 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 aspects 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 Effects Subcommittee (HES) 

and an Ecological Effects Subcommittee (EES) exist today to advise the Council on issues 

associated with human health effects and ecological effects components, respectively. 

In response to the Section 812 legislation, 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 November 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 chapters 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 methodological choices made for analyzing those 

data and developing the human health effect estimates for the relevant scenarios, and for 

characterizing their uncertainty? If not, are there alternative methodologies the Council 

HES recommends should be applied instead? 

c. What advice does the HES have for the Council regarding the validity and utility of the 

human health effect analyses incorporated in the draft benefits report and the uncertainty 

4 
analyses incorporated in the draft uncertainty report? If the validity 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 chapters from the Second Section 812 Prospective analysis benefits 

report (1990 – 2020) as well as the human health components of the draft stand-alone 

uncertainty analysis report. 

The Health Effects Subcommittee (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, several members of the Council 

participated in the meeting, including Dr. James 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 discussing 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 models, 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 the data approaches and 

judgments that have been made. 

In particular, the HES supports the presentation 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 the ACS studies) for the Primary Estimate would 

provide lower health benefits estimates than the Six Cities study, which is considered by EPA to 

be equally applicable for health benefits analysis. 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 discussed in the HES response to charge question 2a 

on the PM mortality concentration response function. 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 years 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 function to estimate mortality effects of 

ozone is more difficult. The HES supports EPA’s data choices for the Primary Estimates. 

Because time series studies were used for the Primary 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 estimate of benefits, a concentration-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 determination 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 staffers? 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 precise and non-intuitive technical meanings. Overall the report 

needs technical editing, and two sections (Chapter 6, PM mortality cessation lag and Chapter 7, 

Dynamic population modeling) are difficult to follow; these sections may need a complete rewrite to achieve better clarity for non-experts. 

6 
The HES makes some general observations and recommendations for improvement of 

the presentation in the draft documents. 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 between 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 inhaled would probably lead to bigger estimates of 

effects). 

The characterization of uncertainty is confusing and incomplete. It would be helpful to 

have an integrated assessment of overall uncertainty. Are the estimates within 10% of the true 

value? 50%? 200%? Does it matter? 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 major” 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 inconsistencies in presenting assumptions between the 

Benefits and Uncertainty draft reports. A comparison of the assumptions (using a simple search 

on the “assum” in both documents) reveals a mismatch that may not be a problem but is a matter 

for clarification. Two sets of authors may have used different approaches or boundaries for 

identifying and/or recording assumptions. Perhaps the authors of the benefits document sought 

to present all assumptions they made in their estimation processes; e.g., in extending studyspecific or regional results to the national scale. If so, 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 estimate). 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 the report that compiles, in a brief form, the 

primary and secondary data sources and the model 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 readers 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 Clean Air Act appear large in relation to total 

US mortality (about 7%). The report should note that this result is driven in part by the rise in 

expected pollution-related mortality in the absence of Clean Air Act, and not entirely by actual 

7 
reductions in mortality. If EPA can estimate these 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 report reflects adoption of the 

Pope et al. 2002 study as the basis for the Primary Estimates of the difference in incidences of 

PM-related premature mortality. Also within the main benefits report, an Alternative Estimate is 

presented prominently which is based on the Laden et al. 2006 study. Furthermore, the Project 

Team is currently assessing the potential significance of the recent Krewski et al. (2009) 

publication since it appears to strengthen the evidence for PM-related ischemic heart disease and 

lung cancer mortality and could provide the basis for a revised Primary Estimate or an additional 

Alternative Estimate. Uncertainty in the Primary Estimate is further described in the draft 

uncertainty report through graphical presentation of results obtained by applying each of the 12 

expert elicitation study functions to the differences in PM exposure estimated for the withCAAA90 and without-CAAA90 core scenarios. In addition, the Project Team has recently been 

considering an approach developed by Industrial Economics, which uses a copula function to 

generate results representing the 12 expert functions. This approach 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 these study selections and the 

organization and presentation of PM mortality estimates in the draft benefits and uncertainty 

reports? In addition, a particular question for which the Project Team seeks HES advice is 

whether the application of mortality risk coefficients drawn from the Krewski et al. (2009) study 

should be considered for use in generating the Primary 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 and/or methods for generating, organizing, and presenting results 

which the Council HES recommends EPA consider? 

HES response: The HES emphasizes the continued importance of the American Cancer 

Society (ACS) and Harvard Six Cities cohort studies for underpinning our understanding of the 

mortality effects of PM2.5 in the U.S. Findings from both cohorts have been robust to extensive 

analyses and independent re-analyses. The HES believes that the Pope et al. (2002) and Laden et 

al. (2006) analyses of the ACS and Six Cities cohorts, 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 have 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 cover 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 Technical Memorandum presented to the 

HES), which shows the monetized PM-related mortality benefits associated with meeting a 

hypothetical 50 ppb NO2 standard in 2020 for the studies of Pope al. and Laden et al. as well as 

9 
the twelve experts consulted in the EE study. Further, a quantitative analysis by EPA 

consultants demonstrates that the Pope et al. (2002) effect estimate falls at about the 25th

percentile of the EE distribution; the Laden et al. (2006) effect estimate falls at about the 75th

percentile of the EE distribution. HES reviewed the proposed copula function approach to 

combine the 12 response functions from the EE. A copula function is a useful method to describe 

the dependence among a set of random 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 function. While supporting EPA’s investigation of 

alternatives for combining evidence using advanced quantitative methods, the HES feels that 

adoption of the copula function approach as a prominent feature of the current prospective 

analysis might hinder interpretability 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 contradict, the earlier data. On that basis, the 

HES supports the EE as the most comprehensive assessment to date of the mortality effect of 

PM2.5 and so, given EPA’s stated objective of generating unbiased benefits estimates in the 

Section 812 prospective analysis, HES recommends 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 grounding the risk estimates in results from the two 

major contributing US cohorts. We suggest an approach along the following lines: 

Define a distribution of possible coefficients (perhaps a truncated normal distribution 

with zero probability below a value of zero, or a gamma distribution), with 25th percentile 

equal to a 0.6 percent change in mortality per 1 μg/m3 change in annual average PM2.5 

(the Pope et al., 2002 central estimate), with 75th percentile equal to 1.5 percent change in 

mortality per 1 μg/m3 change in annual average PM2.5 (the Laden et al., 2006 central 

estimate), and with mean equal to approximately the mean of these two values. Such a 

distribution would be adopted for the primary estimates. 

The HES considered a formal method of integrating the opinions of the elicited experts, 

using a copula function. However, the HES decided not to recommend this 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 al

Millions (2006$)

PM2.5 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 NO2 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 realization of the change in health effect 

incidence. Based in part on prior Council HES advice, the primary estimates in the draft benefits 

report reflect a 20-year distributed lag. Specifically, 30 percent of the total reduced incidences is 

assumed to occur in the first year following the exposure change. Another 50 percent of the total 

incidence changes is be spread evenly over years two through five. The remaining 20 percent of 

the incidence change is spread evenly over years six through twenty. The effect of the cessation 

lag is realized through discounting (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 alternative 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 results of available cohort and intervention studies. 

Charge question 2b: Does the Council HES support the use of the 20-year distributed lag 

structure described above for generation of the Primary Estimates of the monetary value of PM 

mortality incidence reduction and the specific alternative lag functions presented in the draft 

uncertainty report? If not, are there alternative study choices and/or methods for organizing and 

presenting results that the Council HES recommends EPA consider? 

HES response: EPA has done an admirable job responding to the suggestions of earlier 

reviews by the Council and NAS. However, EPA should cite and include information from the 

11 
recent analyses of the Nurses’ Health Study (Puett et al., 2009) and the Harvard Six Cities Study 

(Schwartz et al., 2008; Laden et al., 2006). These studies 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 years. However, the EPA analysis of alternative 

assumptions about the lag using a given cohort study 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 approach is used, EPA should ensure that its 

choice of parameter k is consistent with its choice of risk 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 the cohort study conducted by Woodruff et al. (1997). 

This is based in part on prior (SAB-HES) advice, which noted several strengths of the study, 

including the use of a larger cohort drawn from a large number of metropolitan areas and efforts 

to control for a variety of individual risk factors in infants (e.g., maternal educational level, 

maternal ethnicity, parental marital status, and maternal smoking status). A more recent study by 

Woodruff et al. (2006) continues to find associations between PM2.5 and infant mortality, and 

also found the most significant relationships with respiratory-related causes of death. 

Charge question 2c: Does the Council HES recommend continued reliance on the 

Woodruff et al. (1997) study to characterize the association 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 that 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 also 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 Southern California data further informs the PM-infant mortality 

relationships found in the Woodruff et al. (1997, 2006) studies. While there are some important 

differences between the available studies within and outside of the United States, the results 

consistently show positive associations between PM (both PM10 and PM2.5) and infant mortality. 

When PM10 results are scaled to estimate PM2.5 impacts, the results yield similar risk estimates. 

The number of studies now available may be sufficient to consider pooling results, rather than 

relying on a single study, thereby deriving a more robust risk estimate. The strengths and 

12 
weaknesses of each study should be assessed to determine whether pooling or using a single 

study is the appropriate approach 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 infant mortality. 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 estimated incidences. In 

addition to the lack of compelling evidence for particular 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 ensure a “with threshold” CRF slope that would accurately account 

for the total change in incidence expected based on the epidemiological study from which the 

CRF was derived. Prior efforts to apply a threshold 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 effects threshold that the Council HES recommends 

EPA consider, either for the Primary Estimates or for presentation in the draft uncertainty report? 

HES response: The HES fully supports EPA’s decision to use a no-threshold model to 

estimate mortality reductions. This decision is supported by the data, which are quite consistent 

in showing effects down to the lowest measured levels. Analyses of cohorts using data from 

more recent years, during which time PM concentrations have fallen, continue to report strong 

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF. 

4.5. Ozone Mortality Concentration-Response Function (CRF)

Agency-supplied background: Based in part on prior Council and NAS advice, EPA has 

included changes in ozone-related premature mortality as part of the Primary Estimate of 

benefits in the draft benefits report. Recognizing the ongoing 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 Estimate of changes in ozone mortality which 

encourages focus on several key factors: study selection, pooling across studies, and pooling 

methodology. Given the particular uncertainties regarding the reasonableness 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 variance-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 alternative results obtained by 

applying CRFs derived from each of the three individual multi-city time-series 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) studies for the Primary Estimate 

and the presentation of the six alternative estimates in the draft uncertainty report? A particular 

question for which the Project Team seeks HES advice is whether application of the respiratory 

mortality risk estimate drawn from Jerrett et al. (2009) might be suitable 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 selection and/or pooling 

approaches the Council HES recommends EPA consider 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 evidence in the time-series literature and the 

consistency of these findings with morbidity studies. 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 inclusion as an Alternative Estimate or other 

sensitivity analysis, as it would be valuable to convey its implications if, as we expect, the cohort 

mortality findings are corroborated 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 addition, some investigators have noted that the 

National Mortality, Morbidity, and Air Pollution Study (NMMAPS) produces significantly lower 

CRFs than other epidemiological investigations, which 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 HES recommends that the Primary 

Estimate be derived as an intermediate value between the multi-city studies and the metaanalyses. The uncertainty distribution can then reflect methodological uncertainties as well as the 

uncertainties reported within individual studies. 

Elaborating on this point, on page 2-9 the report indicates that the mean of the estimates 

derived from the three meta-analyses and the mean 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 study (Bell et al., 2004) and a 14-city casecrossover study not directly tied to NMMAPS (Schwartz, 2005), without weight on the other 

NMMAPS-based study (Huang et al., 2005) or the three meta-analyses. It would be better to 

present a Primary Estimate reflecting an intermediate value among the multi-city studies and the 

meta-analyses, with explicit consideration of the full body of evidence. 

14 
The uncertainty analyses presented in Exhibit 4-4 of the uncertainty report raised 

additional questions regarding the CRFs chosen and their rationale. For example, the Schwartz 

(2005) paper reported a 0.23% increase in mortality per 10 ppb increase in 1-hour maximum 

ozone concentrations. Using these same metrics, Bell et al. (2004) reported a value of 0.34%, yet 

the mortality incidence estimate in Exhibit 4-4 was lower. This could be explained if the Section 

812 Prospective Analysis used the one-week average ozone findings from Bell et al. (2004), but 

this 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 consistent 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-series mortality evidence (from short-term 

exposure) for ozone have potentially 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 economic valuation of mortality. 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 approach treats all deaths as equivalent, 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-intuitive. The HES asks that EPA spell out, 

clearly, the assumptions underlying its valuation methods, and in particular why the average 

extent of life expectancy is considered irrelevant despite differences in method (time series vs. 

cohort), age, or other matters, so that readers can better understand 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 differential values should be applied to time-series vs. cohort mortality 

evidence. This should be addressed within the valuation uncertainty analysis, 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 structure for the Primary Estimate of ozone mortality 

reduction benefits in the draft benefits 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 presented in the draft benefits report? If not, are 

there methods for estimating and applying a cessation 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? 

15 
HES response: The HES concludes that time-series data should be used for the Primary 

Estimate for ozone mortality. Time-series studies investigate health conditions or effects in a 

given cohort over time. Therefore, any lag in cessation 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 cessation lag between ozone and particulate matter. The 

HES therefore recommends using the same cessation lag structure and assumptions as for 

particulate matter when utilizing cohort 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 threshold for ozone-related premature mortality effects, the Project 

Team did not attempt to apply an effect threshold 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 mortality incidence reduction? If not, are there 

methods for estimating and applying an effects 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 and 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 support the application of a 

no-threshold model, and also support the traditionally used shape of the concentration-response 

curve. Associations between ozone and mortality were present at low concentrations, nearing 

natural background levels. If a threshold for short-term ozone exposure 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 study of long-term health effects of 

ozone in 96 U.S. metropolitan areas for almost 450,000 persons for the ozone season (April to 

September) identified an association between ozone 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 below the threshold. Model fit was improved under the 

threshold model, compared to a no-threshold model (p-value 0.06), providing weak evidence of a 

threshold at 56 ppb daily maximum ozone concentration. Given this limited evidence for a 

threshold, the HES recommends that analyses based on findings from this study be conducted 

16 
both 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 incidence / prevalence across the various human health endpoints 

incorporated in the Primary Estimate of benefits? If not, are there alternative baseline incidence / 

prevalence data which the Council HES recommends EPA consider, either for the Primary 

Estimates or for presentation in the draft uncertainty report? 

HES response: The HES generally supports the choices made by the project team 

regarding baseline incidence/prevalence across the various human health endpoints. The HES 

recognizes that many of these choices are judgments within significant data constraints, and that 

the projected estimates have substantial uncertainties. 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 used to estimate baseline school 

loss and work loss days. There may be too much noise 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 Second Prospective Study analytical 

blueprint, the Council recognized that the state of the science did not support development and 

application of assumptions regarding the potential differential toxicity of PM components 

suitable for informing the present analysis. However, the Council did encourage the Project 

Team to explore the feasibility of conducting a sensitivity analysis to gauge the potential 

significance of differential toxicity. After extensive review of the literature and analysis of 

options, the Project Team concluded that currently available data and methodologies remain 

insufficient to meet the challenge of developing a reasonably valid and usefully informative 

sensitivity analysis, even on a notional basis. Indeed, the Project Team concluded that the 

potential research utility of such a sensitivity analysis in the end did not appear to justify the 

risks from potential misinterpretation and misapplication of the results of such a sensitivity 

analysis. The Project Team’s evaluation of the issue of differential toxicity is presented in 

chapter 5 of the draft uncertainty report. 

Charge question 2i: Does the Council HES support the Project Team’s decision to defer 

quantitative sensitivity analysis of potential PM component differential toxicity? If not, are there 

data or methods for conducting a quantitative analysis of PM component differential toxicity that 

the HES recommends EPA consider, or are there other aspects of differential PM component 

toxicity which the HES recommends should be addressed in the draft benefits and/or uncertainty 

reports? 

17 
HES response: The Council had encouraged the EPA Project Team to explore the 

feasibility of conducting a sensitivity analysis to gauge the potential significance of differential 

toxicity. The Project Team has determined after a review of the literature that the currently 

available data are insufficient for developing an informative sensitivity analysis. 

The Uncertainty Analysis document objectively 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) sulfate, nitrate, elemental carbon, organic carbon, and metals. They 

conclude that none of the components show consistently greater effects than PM as a whole; 

however, the epidemiological evidence base was 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 for all of the key components, derived in a manner 

so that the total reflected the observed effects 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 differential 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 present 812 analysis. However, the HES recognizes that benefits 

analysis of specific measures may affect particular 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 population simulation model to the 

evaluation of changes in pollution-related premature mortality risks. The Project Team continues 

to consider the potential utility 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 dynamic population approach described in chapter 7, or 

any alternative approaches the HES may suggest for addressing the issue 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 mortality 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 available 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. 

18 
19 

We note EPA’s concern that a full dynamic population implementation requires detailed 

projections for every year up to 2020 (compared 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 others were selected. This includes, but is not 

limited to, describing (1) the current (‘static population’) 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 this approach; (3) why EPA favors valuation based on deaths / 

VSL rather than life years / VSLY; (4) whether EPA 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 monetary analysis. 
REFERENCES 

Bell, ML, McDermott A, Zeger SL, Samet JM, and Dominici F 2004. Ozone 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) and Krewski et al. (2009) Studies. Technical 

Memorandum from Neal Fann, EPA/OAR/OAQPS, to Jim DeMocker, 

EPA/OAR/OPAR. November 15, 2009. 

Huang Y, Dominici F, and Bell ML 2005. Bayesian hierarchical distributed lag models for 

summer ozone exposure and cardio-respiratory mortality. Envirometrics 16:547-562. 

Jerrett M, Burnett RT, 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, Hughes E, et al. 2009. Extended follow-up and spatial 

analysis of the American Cancer Society study linking particulate air pollution and 

mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 

Laden, F, Schwartz J, Speizer FE, and Dockery 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 Lipsett 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, Speizer FE, Suh H, Paciorek CJ, Neas LM, and 

Laden F 2008. Chronic particulate exposure, mortality, and coronary heart disease in the 

Nurses’ Health Study. American Journal of Epidemiology, 168(10):1161-1168. 

R-1 
R-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 and 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, Stone V, Derwent R, Atkinson RW, Cohen A, 

Shonkoff SB, Krewski D, Pope CA 3rd, 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 Relationship Between Selected Causes of 

Postneonatal Infant Mortality and Particulate Air Pollution in the United States. 

Environmental Health Perspectives 105(6): 608-612. 

Woodruff TJ, Parker JD, and Schoendorf KC 2006. Fine particulate matter (PM2.5) air pollution 

and selected causes of postneonatal infant mortality in California. Environmental Health 

Perspectives114(5):786-90.
APPENDIX A: Technical Corrections from Individual Members 

The Subcommittee’s advice and responses 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 not 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 primary benefits result includes CAIR, 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 that the issue of assuming CAIR when it is not 

yet in place is a potentially major 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). Doing new runs assuming no CAIR is impractical, 

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

P. 2-7, Exhibit 2-2: It is not clear why “low birth weight” is not quantified; it is a birth outcome 

which has long lasting impacts on development. Are there too few studies from which reliable 

estimates can be derived? Some mention of the 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 sentence “biological similarity” is puzzling. Children 

between the ages of 5 and 17 continue to develop 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 written it is too vague and broad. 

P. 2-10: It is not true that the individual time-series studies either used 24-hour average or 1-hour 

maximum levels for exposures; multiple studies did use 8-hour maximum. For example, Fairley 

(2003), Klemm (2000), Michelozzi (1998), Saez (2002), Anderson (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 briefly stated so the reader can understand the 

justification for extending the cited studies to a wider age range. Further, in the last sentence of 

this section the logic used for deriving 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 the 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-1 
The title for this section does not match that of the related exhibit. More importantly, some of 

the data sources and their limitations are not described in this section. For example, the “School 

Loss Days” database parameters (e.g., ages covered) and challenges in using them are not 

included in the text. 

P. 2-18, last paragraph: The discussion 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 basis for the “current trends” in the 

first half of the last sentence is the study cited or whether another study and/or national database 

(Ostro et al, CDC, ALA, etc) was/were used for this comment. Further, a trend requires at least 

two points in time; what were those points? There is no recognition 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 Exhibit 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 row needs to include an 

“infant mortality” line for the data source and years of data used. If not, the ages for the “< 

18” column need to be clarified. 

• In the row marked “School Loss Days,” the National Center for Education Statistics (1996) 

database is cited. However, this data source is not easily located on the Internet. The 

citation for this database is currently missing and needs to be listed in the References section 

of this chapter. 

P. 2-27 and Exhibit 2.14: This reviewer assumes 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 all of the inclusions should be clearly stated. 

P. 2-29 to 2-31: Applying the eyeball test, it seems strange that mortality incidence is about 3-4 

times higher at the mean than the 5th 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 use of lower valuation for ozone mortality even as a bounding 

calculation. 

A-2 
A-3 

Regarding excerpted materials from the draft Uncertainty Analyses to Support the Scond 

Section 812 Benefit-Cost Analysis 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 exclusion of air toxics has a potentially 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 incidence estimates that 

were pooled were from the same study and differed minimally, so this would not be a major 

effect. 

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

uncertainties documented in this table are not apparent. While adult mortality-related 

assumptions and uncertainties must be described because they drive the overall estimate, the 

table also presents information about an adult morbidity factor. Chronic bronchitis (the 

second most important contributor to the overall estimate) is included in the table, but there 

is no mention of “nonfatal myocardial infarction,” which is another morbidity outcome and a 

close third contributor to the health effects estimate. Additionally, there is no mention of 

infant mortality or other childhood health effects. 

Whether the purpose of this table was to capture the “key uncertainties” for the top three 

contributors to the health estimate or for adult outcomes only, there 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.