Technical Support Document for the 

Expanded Expert Judgment Assessment of 

the Concentration-Response Relationship 

Between PM2.5 Exposure and Mortality:

Expert Interview Summaries 

September 30, 2006

prepared for: 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 27711 

prepared by: 

Industrial Economics, Incorporated 

2067 Massachusetts Avenue 

Cambridge, MA 02140 
Table of Contents 

Introduction..........................................................................................................................1 

Expert A Interview Summary ......................................................................................... A-1 

Expert B Interview Summary ..........................................................................................B-1 

Expert C Interview Summary ..........................................................................................C-1 

Expert D Interview Summary ......................................................................................... D-1 

Expert E Interview Summary...........................................................................................E-1 

Expert F Interview Summary...........................................................................................F-1 

Expert G Interview Summary ......................................................................................... G-1 

Expert H Interview Summary ......................................................................................... H-1 

Expert I Interview Summary.............................................................................................I-1 

Expert J Interview Summary ............................................................................................J-1 

Expert K Interview Summary ......................................................................................... K-1 

Expert L Interview Summary...........................................................................................L-1 
1

INTRODUCTION 

As part of the Expanded Expert Judgment Assessment of the Concentration-Response 

Relationship Between PM2.5 Exposure and Mortality (IEc, 2006), the elicitation team 

conducted day-long personal interviews with 12 health experts who have conducted 

research on the relationship between PM2.5 exposures and mortality. This technical 

support document consists of summaries of the discussions that took place during each of 

these interviews. The summaries provide additional detail and context that may be 

useful for understanding each expert's judgments. 

Exhibit 1 presents the list of the experts interviewed. As described in the main report, 

experts are identified by a randomly assigned letter from A to L in order to preserve 

confidentiality. The section headings in the interview summaries correspond to those 

found in the elicitation protocol (See IEc, 2006, Appendix A). Each expert was given the 

opportunity to review his summary and confirm that it accurately captured the views he 

expressed in the interview. In addition, if an expert opted to change any of his responses 

to the questions in the protocol following the June 2006 Post-elicitation Workshop, the 

changes are reflected in a “Modification to Expert Judgment” form, appended to the end 

of his interview summary. 

EXHIBIT 1: FINAL EXPERT LIST 

NAME AFFILIATION 

Dockery, Doug W. Harvard School of Public Health 

Ito, Kazuhiko New York University School of Medicine 

Krewski, Daniel University of Ottawa 

Künzli, Nino 

University of Southern California Keck School of Medicine (currently 

at Institut Municipal d'Investigació Mèdica (IMIM) - Center for 

Research in Environmental Epidemiology, Barcelona, SPAIN) 

Lippmann, Morton New York University School of Medicine 

Mauderly, Joe Lovelace Respiratory Research Institute 

Ostro, Bart D. California Office of Environmental Health Hazard Assessment 

Pope, C. Arden III Brigham Young University 

Schlesinger, Richard Pace University 

Schwartz, Joel Harvard School of Public Health 

Thurston, George D. New York University School of Medicine 

Utell, Mark University of Rochester School of Medicine and Dentistry 
2

 

IEc prepared these summaries to document both the views expressed by each expert in his 

interview and the process by which he arrived at the quantitative distribution presented in 

the main report (IEc, 2006).1

 As part of documenting the process of developing an 

expert's final quantitative distribution, the summaries may include additional distributions 

generated by the IEc elicitation team during the interview that were shown to the expert 

to assist him in evaluating his responses. For example, if an expert indicated during the 

interview that his distribution was conditional on the existence of a causal relationship 

between PM2.5 and mortality, the elicitation team showed the expert a distribution 

generated by statistically combining his distribution with his stated probability of a causal 

relationship, assuming that the probabilities are independent. All of these additional 

distributions are marked as "IEc-generated" in the summaries and do not reflect the 

expert's final judgments, which are presented in the main report. We also note that 

content in the interview summaries may differ from that presented in the main report if an 

expert modified his judgments following the Post-elicitation Workshop. As noted above, 

these changes are documented in a modification form appended to the end of the 

summary of each expert who revised his judgments. 

 

1

 IEc prepared all of the interview summaries, with the exception of Expert D. Expert D wrote his own summary, to which IEc 

added the tables and graphs and a short introductory paragraph. 
Expert A 

Interview Summary 
A-1 

Interview Summary 

Expert A 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects from Exposure to PM2.5

Expert A thought that there were separate mechanisms for mortality associated with 

short-term and long-term exposures to PM2.5. However, he did feel that there was some 

overlap between them. He began by discussing mortality related to short-term exposures, 

which he felt to be the most compelling. He then discussed potential mechanisms of 

long-term exposures, and ended with a discussion of mechanisms for intermediate length 

exposures. He felt that the intervention studies (Clancy et al. (2002) in Dublin and Pope 

et al. (1996) in Utah Valley), involving intermediate time scales of months up to a year, 

actually provided more insight into how the effects measured in short- and long-term 

studies might be tied together and help understand the total effect of reducing particulate 

air pollution on mortality. 

Short-Term Exposures

For short-term exposures, Expert A thought that the major causes of death, in order of 

importance were: acute cardiac events; respiratory disease; stroke; and lung cancer deaths 

(where people are frail from lung cancer and die from acute exposure to air pollution). 

Expert A argued that the most compelling evidence for an association between particles 

and acute cardiac events came from the time-series cardiovascular studies. In particular, 

he thought the pooled studies (e.g., the National Morbidity, Mortality, and Air Pollution 

Study (NMMAPS), Air Pollution and Health – A European Approach (APHEA)) 

provided the strongest evidence because they are not as subject to confounding by copollutants and weather as other study designs. He cited as supporting evidence the 

studies involving implantable defibrillator discharges (e.g., Peters et al., 2000 and Rich et 

al., 2005 & 2006) and case-cross over studies of myocardial infarction survivors (Peters 

et al., 2001 and Foresteri et al., 2005 in Rome). He thought their strengths include 

unambiguous outcome measures and the fact that by “...looking at the same individual 

you eliminate by design possibilities for confounding by individual characteristics, such 

as smoking. You can also largely eliminate seasonal effects, temperature. You can 

eliminate factors that potentially confound time-series studies. On the other hand, there 

are some other subtle issues that enter with the selection of case and control periods, and 

there is a loss of power in case-crossover compared to a time-series analysis.” He noted 

that a weakness of both types of studies is that they have a selected population that is not 

representative of the general population (e.g., frail, high risk of cardiac instability). Other 

case-crossover studies have not supported a relationship between myocardial events and 

air pollution (e.g., Sullivan et al., Checkoway et al., 2003, and Levy et al., 2001). 

Though these studies had good outcome measures, he was not convinced that the 

nephelometry measures of particles were good measures of air pollution. 
A-2 

In terms of mechanistic evidence for a role of particles in acute cardiac events, he was not 

sure of the mechanisms. He noted that several mechanistic pathways had been proposed 

(e.g., “inflammatory responses, change in autonomic function, release of hormones, 

cytokines, chemokines, and other chemicals delivered to the heart”). Experimental 

evidence had not ruled any out any pathways. He cited experimental studies in animals 

(Godleski et al., 2000) showing changes in cardiovascular responses in dogs; Watkinson 

and others at EPA showing electrical abnormalities and dysfunction in rodents; Devlin et 

al. chamber studies measuring intermediate endpoints, such as changes in heart rate 

variability and other electrical disturbances in human subjects. 

The next three causes of death related to short-term exposures (respiratory disease, 

strokes, lung cancer) were discussed in less detail and the expert did not feel the evidence 

was as strong. Expert A cited respiratory disease as the next most important, though he 

did not discuss specific evidence. He noted that death certificate coding would tend to 

place acute cardiac death as the primary cause of death even if the individual had 

underlying chronic obstructive pulmonary disease (COPD). Therefore respiratory effects 

might not show up as associations in mortality studies. 

Expert A was less certain about strokes as a cause of death related to particles. Strokes 

can be hemorrhagic or ischemic and it is often difficult to determine the type from death 

certificates. He would not expect to see an association with hemorrhagic stroke, but 

thought that ischemic strokes might follow a similar pathway as ischemic heart attacks, 

and therefore are more plausible. He suggested that most of the evidence for stroke 

associations with air pollution comes from Asian countries where the underlying rates of 

stroke deaths are much higher than in the U.S. 

The final category of death that Expert A discussed was lung cancer. “People with lung 

cancer are more likely to respond to air pollution and die acutely.” However, he noted 

that there were no short-term studies that have actually looked at lung cancer specifically. 

Long-Term Exposures

For long-term exposures, Expert A used the analogy of smoking and environmental 

tobacco smoke (ETS) as a conceptual model. Airborne particles might impact human 

health through “changes in lung function, deterioration of lung volumes, development of 

chronic obstructive pulmonary disease, and a long-term debilitation, loss of respiratory 

reserve”; development of atherosclerosis, plaques and increased risk of myocardial 

infraction (MI); vascular changes; and cancer. However, he noted that “in terms of air 

pollution, [there is] somewhat limited evidence for those effects... The prospective 

studies [including new Adventist Health and Smog (ASHMOG) data] have suggested that 

you can see some changes in pulmonary function, but I don't think the evidence is very 

compelling ... [A]ir pollution at the levels we're talking, is even less than the typical 

exposures from environmental tobacco smoke. So I don't think this is a likely pathway 

for major disability.” 
A-3 

He cited some evidence suggesting the development of atherosclerosis as a pathway for 

the influence of chronic exposure to particles on increased risk of mortality. “There are 

new studies coming out, the Kunzli study for example, that are looking at carotid IMT, 

intima-media thickness. The carotid IMT, looking at deposition of plaques in the carotid 

arteries, shows more plaque in those people living in the higher air pollution levels.” 

Expert A thought that the recent work by Sun and Lippmann in atherosclerotic mice has 

provided experimental evidence for the cardiac pathways. At the same time he expressed 

some skepticism about this pathway contributing to major increases in risk or death from 

atherosclerosis, stating, “there are other, larger factors.” 

Intermediate Exposures

Expert A thought that the intervention studies (the Pope et al. Utah Valley study and 

Clancy et al. Dublin study) tie the concept of the smoking analogy and cumulative 

exposures to air pollution together as well as point to larger mortality effects in the 

intermediate time scale than have been typically captured in time-series or cohort studies. 

He thought that the time-series studies show effects on the same day, as well as two to 

four days later “[b]ut if you ... go out to longer time periods, you can still see residual 

associations from the short-term exposures. And when you add up all those effects, 

they're much larger than ... short-term effects. Given that we're normally exposed to 

repeated days of events, I think that these air pollution events are not independent ... 

Usually we'll see two, three, four, maybe a week of air pollution. I think that's where the 

action is.” As with smoking cessation, he thought most of the intermediate effects are in 

reduction of deaths from cardiovascular disease with a smaller impact on respiratory and 

cancer deaths, which are results of longer-term exposures. 

3.2 Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

When asked to describe his conceptual framework for the relationship between types of 

death caused by air pollution, he indicated that he would prefer if the Kunzli framework 

incorporated an intermediate effect. He indicated that categories A and C constitute 

short-term effects but would include larger effect windows up to weeks or months. He 

thought categories A and C constitute the bulk of the mortality effect. He indicated that 

category B, the mortality effect from long term exposures, was relatively small compared 

to A and C. 

3.3 The Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert A thought that, to the extent that short-term exposures are correlated with longterm exposures, prospective cohort studies provide information on the effects of both 

types of associations, although they would not capture deaths advanced by just a few 

days. He thought time-series studies are restricted to the short term. Given the focus of 

our question on the impact of a change in PM2.5 concentrations, he thought the 

intervention studies (Utah Valley, Dublin, Hong Kong) would be most informative 
A-4 

because he thought most of the mortality effects occur in weeks to months. However he 

noted that the re-analysis of the Six-City data (Laden et al., 2006), which looks over the 

last 10 years, might capture some long-term effects as well. 

3.4 Epidemiologic Evidence for the Impact of Exposures to PM2.5 on Mortality 

Specifically, Expert A relied on the estimates from the original Six Cities cohort study 

(Dockery et al., 1993) and the extended follow-up (Laden et al., 2006), a reanalysis of the 

American Cancer Society (ACS) cohort study in Los Angeles (Jerrett et al., 2005), and 

the NMMAPS (Samet et al., 2000) for his quantitative estimates. 

3.5 Confounding 

Expert A generally felt that most confounders had been adequately controlled in the 

epidemiologic literature and therefore this did not have a large effect on his quantitative 

estimates. He noted that the Krewski re-analysis had done a better job of controlling for 

confounding than the original ACS and Six Cities analyses, and that it strengthened the 

findings. However, he did list some factors that might not have been fully accounted for 

in particular studies. The table below lists the factors he discussed. 
A-5 

SUMMARY OF KEY POTENTIAL CONFOUNDERS IN PM MORTALITY EPIDEMIOLOGIC

Study (author, date) Potential Confounder Rationale 

Overestimated RRs 

Six Cities Study  Dockery et al., 1993 Ethnic differences between cities 

Laden et al., 2006 Ethnic differences between cities Results of this study suggest tha

is unlikely to affect the results. 

Intervention Studies  Laden et al., 2006 (Six Cities) Time-varying trends in healthcare  Clancy et al., 2002 (Dublin) Time-varying trends in healthcare Includes only a single study and 

pollution levels dropped, but did again. 

 Pope et al., 1996 (Utah Valley) Time-varying trends in healthcare American Cancer Society  Jerrett et al., 2005 (Los  Angeles) 

Co-pollutants 

 Pope et al. (1995) Occupational Exposures 

Uncertain Direction of Bias 

American Cancer Society Community characteristics 

 Socioeconomic Status (SES)  Healthcare  Contextual 

a The scores are defined by the magnitude of their effect on the published estimates: 1 = minimal effect; 2 = medium effect; and 3 = ma
A-6 

3.6 Effect Modification 

Expert A discussed the potential for effect modification by race, educational level, and 

housing characteristics. While he thought there might be an argument for effect 

modification by race (due more to underlying differences in SES and health status), he 

ultimately did not think that race intrinsically affected susceptibility to air pollution and 

that therefore the effect estimates in the cohort studies were likely to be representative. 

He noted that educational attainment per se was not an effect modifier but likely to be an 

indicator for other factors. He thought the Six Cities studies had a representative sample 

so it was not an important issue. He thought that there could be some minimal effect 

modification by housing characteristics (e.g., air conditioning) in the cohort studies 

leading to a possible underestimate of the effect estimate. 

3.7 Exposure Issues 

Expert A thought that exposure misclassification (i.e., problems with use of central site 

monitors to characterize individual exposures to ambient PM2.5) was the biggest issue of 

concern. He thought that evidence suggests that exposure misclassification causes the 

published effect estimates to be biased downwards. “The evidence we have from the epi 

studies is that when we have improved measures of PM2.5 exposures they get much 

stronger associations. Specifically comparing the Six Cities study to the [original and 

extended] ACS [Pope et al., 1995 & 2002], comparing to the Jerrett [et al., 2005] study to 

the [original and extended] ACS studies, you see substantially higher effect estimates. 

When asked if variation in composition affected published effect estimates, he did not 

think so. He basically argued that compositional differences do not seem likely to be 

resulting in an overestimate of mortality effects. He thought similar mortality effects had 

been estimated in various areas of the country with different PM compositions (e.g., 

sulfates in the northeast and Utah Valley, nitrates in Los Angeles) as well as in 

intervention studies where the PM composition changes within a community over time, 

suggesting PM is at worst a good surrogate for whatever the toxic agent might be. If the 

agent were known, Expert A thought it would more likely underestimate, rather than 

overestimate the relative risks attributable to that agent. 

He thought that the measurement method in the Laden et al. (2006) extended analysis of 

the Six Cities data, in which the PM2.5 is estimated rather than measured directly, might 

be an important limitation of the study. He thought it might introduce some bias though 

he was unable to define a direction of bias. 

3.8 Causality 

Expert A discussed a number of the standard criteria for supporting a causal relationship. 

He particularly focused on the consistency of results across studies and across different 

study designs (e.g., time series, prospective cohort, intervention). He thought the 

intervention studies are important because they are semi-experimental designs. In 

addition, he thought clinical and toxicological studies have provided supporting evidence. 
A-7 

Expert A specified a range of values for the likelihood of a causal relationship between 

PM2.5 and mortality of 70 – 99.5 percent, with a most likely value of 95 percent (1 in 20). 

He selected the low end of the range based on the concern that there is a lack of 

specificity in the mechanism (i.e., every pathway proposed seems to have an association) 

and possibility that there just might be some alternate explanation for the effects, though 

it is difficult to imagine what it might be. The upper end of the range was based on 

Expert A’s confidence in there being a mortality effect of PM2.5 exposures. “There’s 

lingering doubt, but not as much as there was in [the past].” 

3.9 Thresholds 

While Expert A felt strongly that individual thresholds exist (i.e., concentrations of 

particles that are sufficient to overcome and individual’s defense mechanisms), he also 

argued that there is a continuum of individual responses, based on variety of genetic, 

environmental, and SES factors. Therefore, he did not expect to see evidence of a 

population threshold in epidemiologic studies. “That's a construct of experimental 

studies, but it's not something I expect to see in epidemiology.” He indicated that 

epidemiological studies, not clinical or toxicological, are the appropriate tool for 

exploring population thresholds because they allow investigators to look at the full range 

of susceptible individuals in a population. 

Expert A indicated that the epidemiologic literature has shown no evidence of a threshold 

in the C-R function. In addition, he thought that study designs were becoming 

increasingly sophisticated, and therefore more able to detect effects at lower levels, 

further supporting the lack of threshold in the C-R relationship. He thought that 

arguments for thresholds that focus on lack of statistical significance for relationships at 

low concentrations were confusing detectability with the likelihood that a threshold 

exists. He thought that the population data indicate a linear relationship at low doses. 

3.10 Other Influential Factors 

Expert A thought that an additional source of uncertainty arises from his concern that 

there is a lack of independent statistical and epidemiological expertise to provide 

adequate external criticism of the particulate matter (PM) studies. He thought that many 

working in this field are very self-critical and are better at identifying weaknesses in their 

studies than the critics. Nonetheless, it concerns him that they are all “moving in the 

same direction ... There actually are quite a few studies coming along, replicating [the 

Six Cities and ACS studies]. That's where the scientific certainty, I think, comes from, 

that is from other people really challenging thee results [in other] populations.”
A-8 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert A thought that the C-R function was linear (expressed in percent change per 

1μg/m3) and consistent over the entire range of annual average PM2.5 concentrations that 

were the focus of the study (4-30 μg/m3

). 

Expert A chose to provide a C-R function that was conditional on the existence of a 

causal relationship (with a 95 percent likelihood of a causal relationship). The elicitation 

team then combined his elicited distribution with his likelihood of causality using Monte 

Carlo simulation to create a final distribution.2

Expert A preferred to discuss estimates in terms of mortality impact per 10 μg/m3

 change 

in PM2.5. The elicitation team later scaled these estimates to a 1 μg/m3

 change. 

Expert A began by thinking about the studies he thought were most informative. “ [T]he 

benchmarks that I would use [are] the Six Cities original analysis, which would give me a 

13 percent increase per 10 micrograms; the Jerrett study, which gives me 17 percent 

increase per 10 micrograms; the extended Six Cities analysis (Laden), gives me 16 

percent; the ACS extended analysis, which gives me 6.2 percent and the Six Cities 

change analysis, which gives me actually 37 percent. So these kind of define the upper 

bounds. I suppose also there's another bound, which might be the NMMAPS study, 

which was about half a percent, 0.4 percent, approximately, for 10.” 

Expert A iterated through the process of developing his distribution, relying on results 

from these studies to guide him. He initially identified the NMMAPS estimate, 1.004, 

and the 1.37 relative risk from the Laden et al. 2006 as plausible lower and upper bounds, 

respectively.3

 He chose the NMMAPS estimate, 1.004, as a lower bound, noting that the 

NMMAPS is probably over controlled. He argued for using the Jerrett (2005) study 

relative risk estimate (1.17), supported by the (1.16) extended analysis for the Six-Cities 

study, as the basis for the central estimate. He chose these studies because he thought 

they did not suffer from the biases created by non-representativeness, educational 

attainment levels, and exposure errors inherent to the ACS original study and re-analysis. 

Using the natural log of these values, he then estimated a standard deviation based on an 

assumed normal distribution. The median and standard deviation then were converted 

into percent change per 1 μg/m3

 change in PM2.5. He then decided to increase his 

standard deviation because he was not satisfied with the spread of the distribution. He 

settled on a median of 1.6 percent increase in mortality per 1 μg/m3 change in PM2.5 and a 

standard deviation of 0.78. From these values, he calculated 5th and 95th percentile values 

of 0.29 and 2.9, respectively. The elicitation team entered these values into Crystal Ball 

and using a normal distribution, calculated the remaining percentiles. He ultimately set 

his minimum value at zero and his maximum at 4 percent per 1 μg/m3

 change in PM2.5. 

His final distribution in Exhibit 1 incorporates his estimate that there is a 95 percent 

likelihood that the relationship is causal, as noted above. 

 

2

 Using Monte Carlo simulation in Crystal BallTM, 95 percent of the iterations take a value drawn from the 

elicited distribution and 5 percent are assigned a value of zero. 

3

 Expert A briefly considered using as his upper bound the upper 95 percent confidence limit (1.75) on the 

1.37 RR from Laden et al, 2006, but rejected it as implausibly large. 
A-9 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations

Percentile Percent Change in Mortality 

Elicited Distributiona 

Percent Change in Mortality 

Incorporating Causality 

(IEc Generated)b 

Minimum 0 0 

5th 0.29 0 

25th 1.1 0.97 

50th 1.6 1.6 

75th 2.1 2.1 

95th 2.9 2.9 

Maximum 4.0 4.0 

a. Assuming or conditional on a causal relationship 

 b. Incorporating 5 percent likelihood of a non-causal relationship using Monte Carlo simulation. 
A-10 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 percent Confidence Intervals for Various Studies to Distributions from 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

3

Expert A -

Elicited

Expert A -

Incorporating

Causality

(IEc Generated)

ACS Original ACS Extend ACS Reanalysis ACS LA

Analysis

Six Cities

Original

Six Cities

Reanalysis

% Decrease in Mortality per 1 ug/m3 PM2.5
A-11 

Elicited Distribution 

Distribution Incorporating Causality - Probability Density Function (IEc 

Generated) 
A-12 

Distribution Incorporating Causality - Cumulative Density Function (IEc 

Generated) 
A-13 

U.S. EPA EXPERT ELICITATION STUDY OF THE CONCENTRATION-RESPONSE 

RELATIONSHIP BETWEEN ANNUAL AVERAGE PM2.5 EXPOSURE AND 

MORTALITY 

Modification to Expert Judgments

Expert A

Date: 11 July 2006

Section of Protocol Affected (Section Number and/or Title):

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Description of Change (e.g. to a specific percentile, or to a qualitative opinion or 

statement of belief):

C-R function is NOT conditional on the existence of a causal relationship 
Expert B 

Interview Summary 
B-1 

Interview Summary 

Expert B 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert B discussed the biological mechanisms for short-term and long-term exposures 

separately, preferring to begin with long-term. 

Long-term Exposures

Expert B thought that the two main causes of death associated with long-term exposures 

were lung cancer and cardiopulmonary disease. He cited evidence from studies of 

concentrated ambient particles (CAPs) showing the mutagenicity of these complex 

mixtures (e.g., the 1992 International Programme on Chemical Safety (IPCS) study in 

which CAPs from Washington, D.C. were analyzed (Krewski et al., 1992)4

) as the basis 

for a hypothesis of a relationship between exposure to airborne particles and cancer. The 

extended analysis of the American Cancer Society (ACS) cohort (Pope et al., 2002) then 

provided clear epidemiologic evidence for increases in lung cancer associated with PM2.5

exposures. For cardiopulmonary mortality, he noted that as recently as 1998, when the 

National Research Council (NRC) set forth research priorities for particulate matter, the 

mechanism for cardiopulmonary mortality was “a mystery.” However, he indicated that 

he now felt that inflammatory responses to particles (and release of cytokines affecting 

endothelial cells) leading to accelerated formation of atherosclerotic plaque is now the 

prime hypothesis for the mechanism for this cause of death, citing the paper by Pope et 

al. published in Circulation (2004), and toxicological studies in rabbits conducted at the 

University of British Columbia. While he thought cardiopulmonary mortality is the 

dominant cause of mortality, he indicated that the weight of evidence was stronger for the 

causal mechanism for cancer mortality. He noted respiratory mortality as a third cause of 

death, but he thought that less epidemiological evidence exists for the respiratory effect, 

and the evidence that is available is weaker. 

Short-term Exposures

Expert B did not think that there was sufficient evidence to discuss the specific 

mechanisms underlying the causes of death associated with short-term exposures, 

although he did feel that there were some deaths associated with short-term exposures 

that were not captured by the long-term studies. He did not feel that the time-series 

studies allowed for a clear understanding of the specific role of PM2.5. The issue of 

harvesting remained uncertain in his mind. “There's a lot of conversation about this 

harvesting hypothesis, frail individuals or in poor health, and you hit them with this little 

additional insult and it just is enough to push them over the edge. But if you look at 

studies that Mark Goldberg has done in Montreal, where you examine the association 

 

4

 Krewski et al. (1992) Mutation Research/Reviews in Genetic Toxicology 276: 33-59. 
B-2 

between mortality and pre-existing co-morbidities, you don't see a real strong predictive 

effect there ... If harvesting was a real phenomenon, you should be able to show that 

people with pre-existing co-morbidities are at greater risk than the general population.” 

He did not feel that toxicological or clinical studies shed light on the mechanisms for 

mortality related to short-term exposures. 

3.2. Conceptual Framework for Mortality Effects of Short-term and Long-Term 

PM2.5 Exposures 

Expert B thought the Künzli diagram was a good conceptualization of the relationship 

between long- and short-term exposures, although he did not think it adequately captured 

the influence of other co-factors and he would alter the relative magnitude of the circles. 

He thought the biggest contribution of PM2.5 to mortality would be from long-term 

exposures, and that the overlap between long- and short-term exposures, if any, would be 

very small. 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert B thought that a combination of cohort, time-series, case-crossover, case-control, 

and intervention studies were appropriate for capturing the mortality effects of changes in 

annual average PM2.5 concentrations. The effects captured by each study design are 

shown in the table below: 

Study Design Type of Effects Captured (e.g., shortterm, long-term, or both) 

Cohort Studies Long-term, and possibly some acute effects 

that are the end stages of long-term exposure. 

Missing true acute effects related to peak 

pollution episodes. 

Time-Series Studies Short-term 

Case-Crossover Studies Short-term 

Intervention Studies Long-term 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert B thought that the following characteristics would be part of an ideal 

epidemiologic study to characterize the PM2.5-mortality relationship in the U.S. 

population: 

• Individual exposure measurements (ideally, each participant would have a 

personal dosimeter that could distinguish between ambient and indoor exposures, 

though a dosimeter that gives average annual ambient exposures might be 

sufficient); 

• Large number of individuals; and 
B-3 

• Collection of information on a series of co-factors (e.g., indoor air pollution, 

gaseous co-pollutants, and confounders such as diet and occupation). “Any of 

them that had significant temporal heterogeneity should be longitudinally 

measured.” 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert B focused primarily on the estimates from the original ACS 

cohort study (Pope et al., 1995) and the reanalysis in Los Angeles (Jerrett et al., 2005), 

and the Six Cities Cohort Study (Dockery et al., 1993). 

“[N]umber one would be Arden Pope's 1995 first analysis of the ACS cohort, which had 

as the two key strengths the very large sample size, both in terms of number of people, 

about a half a million it’s less than that in the PM2.5 cohort ... But more importantly ... 

[t]here were about 50 cities in the PM2.5 cohort. The big limitation of that first [ACS] 

analysis was the ecologic measure of the exposure. You've got one measure of exposure 

for each of those 50 cities .... The second most important study is Mike Jerrett's 2005 

study of intra-urban variation in Los Angeles, where there were some 30-odd pollution 

monitors around the city. People were allocated to the closest monitor. And the risk 

estimates went up by a factor of two- to three-fold when you got a more localized 

measure of exposure.” 

Expert B also mentioned a “what if” analysis by Mallik et al. (2002) of the Six Cities data 

that found essentially the same effect. He indicated that he authors “postulated a certain 

level of exposure misclassification by using ecologic rather than personal dosimeters [and 

by] adjusting for it analytically, ... actually got risk estimates that were two- to three-fold 

higher.” 

His third choice was the Harvard Six Cities study, which has the limitation of only having 

six cities and ... “there's such a co-linearity among all the pollutants measured that you 

get almost the same concentration-response function for any pollutant that you look at.” 

He also did not believe it was demographically representative of the U.S. (e.g., it does not 

represent Washington D.C., which is more white collar and less industrially based). 

Expert B also briefly discussed the Adventists Health and Smog (AHSMOG) and the 

Veteran’s cohort (VA) studies but he was not familiar with the methodological details. 

He also discussed Moolgavkar’s criticisms that the cohort studies and their re-analyses 

have focused too heavily on PM2.5 as a prior, and have therefore potentially missed 

effects of other gaseous co-pollutants. Expert B argued that the Health Effects Institute 

(HEI) re-analyses did study other co-pollutants but only found effects associated with 

SO2. He felt that Moolgavkar’s views are based on time-series studies primarily and 

argued that “it's harder to disentangle the effects of multiple pollutants in the acute 

studies because typically you're focusing on a much narrower geographic area, where you 

don't have the heterogeneity of the pollution mixes that you get in the ACS cohort.” 

Expert B felt the most influential published studies of the effects of short-term exposures 

to date were the multi-city studies (Canadian studies of 8-11 cities by Burnett et al. (2000 
B-4 

& 2003), the National Morbidity, Mortality and Air Pollution Study (NMMAPS) (Samet 

et al., 2000), and the Air Pollution and Health – A European Approach (APHEA) study). 

3.5 and 3.6. Confounding and Effect Modification 

Expert B preferred to discuss confounding and effect modification together. He 

discussed the following factors as possible confounders or effect modifiers of the 

relationship between PM2.5 and mortality: 

Cohort Studies

• Confounding by Occupational Exposures: Expert B did not think that this was 

a confounder because of extensive analysis done on the ACS (Siemiatycki et al., 

2003) and Six Cities studies examining this factor. 

• Confounding by SO2: Expert B thought SO2 was correlated with both PM2.5 and 

mortality. “Another key limitation ... of the ACS study, was this powerful effect 

of SO2, which can almost negate, almost but not quite, the PM2.5 effect ... There's 

no mechanism; obviously it's a marker for something else. So at this point I don't 

have any ... clear explanation for why that SO2 effect is so strong, but it is a very 

strong effect that does require some kind of investigation.” He felt that failing to 

account for SO2 could lead to a “major” (i.e., a score of 3) overstatement of the 

PM2.5 effect. 

• Effect Modification by Educational Attainment: Expert B thought effect 

modification by educational attainment was a “major” (i.e., a score of 3) issue for 

the ACS study and to a lesser extent for the Six Cities Study. He thought that 

individuals with less than high school education were under-represented in the 

ACS study and effect estimates for this group were nearly six times as high as the 

group with greater than a high school education. He felt that educational 

attainment was an indicator for a complex set of lifestyle factors (e.g., exercise, 

access to health services, diet, and occupational exposures). A similar type of 

effect, though smaller and non-significant, was also found in the Six Cities study. 

He indicated that he has not seen clear statistical evidence of effect modification by other 

factors that might be expected to modify risk, such as gender, smoking status, or preexisting co-morbidities. 

Short-Term Studies

Expert B thought that confounding by gaseous co-pollutants (NOx and SOx, especially) 

could lead to a “moderate” (i.e., a score of 2, or on the order of a 25 percent) overestimate 

of the relative risk for PM2.5 in these studies. He noted that time-series studies examining 

mortality and reproductive effects have shown strong associations with both PM and 

primary gaseous pollutants. “We don't have a good scientific basis, in my view, to 

distinguish between the two.” However, he thought “if the dominant circle in the Künzli 

diagram is the long-term mortality and the acute effects are much smaller, then really our 

understanding ... is much better in the long-term effects. So we're capturing much more 
B-5 

of the population health impact, and the uncertainty caused by lack of ability to 

disentangle co-pollutants in the short term effect contributes less to the overall 

uncertainty in terms of overall mortality.” He thought effect modification by weather or 

seasonal effects is possible, which is why he thought these variables should be included 

in the time-series models, but he thought it would be minimal (i.e., a score of 1) for 

PM2.5. 

3.7. Exposure Issues 

Expert B focused on two issues 1) exposure misclassification; and 2) temporal variation 

in exposure. He also discussed instrument measurement error and the role of indoor 

versus outdoor measurements of exposure, but thought these were trivial issues. 

Exposure Misclassification

Expert B thought that exposure misclassification, particularly differences between central 

site monitoring exposure estimates and actual individual exposures, could cause the 

published effect estimates to be biased downward. He saw evidence for this in a series of 

studies: “[First,] when you take one monitor or the average of all monitors in an urban 

area to represent individual exposure from everybody in a city of hundreds of thousands 

or several million people, just intuitively doesn't seem like a good thing to do. And when 

we actually do find associations with that very crude ecologic measure, it's first of all 

quite surprising ... Second is [the Willis et al. (2003) reanalysis of the] ACS cohort,5

actually had some data within urban areas at the county level ... for about half the cohort 

... [The analysis] showed that the risk estimates were approximately double when you 

use ecologic measures on a county level as opposed to an SMA level ... So that was the 

second chapter in the story, having gone [from] an association with such a crude measure 

of exposure, to showing that we get stronger associations going down to the county level. 

Third chapter was [a paper by Mallick et al. (2002)] ... which was this sort of 

hypothetical, what if the exposure misclassification from using one fixed-site monitor 

representing all of an urban area on actual personal exposures of such and such a 

magnitude?” [She] gauged that by looking at inter-monitor variation in cities, where you 

have multiple monitors, and then did a regression calibration kind of adjustment, and the 

risk estimates went up by a factor of two- to three-fold. And then the fourth and final 

piece was Mike Jerrett's Los Angeles analyses, where a totally different way of adjusting 

for exposure misclassification [than the] Mallick paper [was taken] and got almost the 

same effect.” 

Expert B thought this type of exposure misclassification was more of an issue in the ACS 

cohort study (Pope et al., 1995 & 2002) than in the Six Cities study, which had better 

spatial resolution of exposure. 

 

5

 Willis, A.J. et al. (2003) Journal of Toxicology and Environmental Health 66: 1605-1624. 
B-6 

Temporal Variation in Exposure

Expert B thought that temporal variation in exposure was the second area of concern; that 

is to understand “what the long-term effects of air pollution are from exposure in the last 

year, the last five years, the last ten years – When in the past does exposure actually 

contribute most to mortality? And if we believe the accelerated atherosclerosis 

hypothesis for cardiopulmonary effects ...you would expect maybe 5, 10, 15 years ago 

would be the most important window for current mortality.” 

Expert B believed we currently do not know the answer to these questions. He indicated 

that Villeneuve et al. (2002) attempted to look at temporal variation in exposure in the 

Six Cities study taking into account people who moved, but few of the original cohort 

moved and the author was not “able to tease out much of a temporal effect of exposure on 

risk.” He did not think that the Pope et al. (2002) extended analysis of the ACS cohort 

really revealed significant differences between effect estimates based on exposures from 

1980 versus the later exposures up to 2000, because of correlation between exposures for 

the two time periods. He indicated that work is being done on a nutritional sub-cohort of 

the ACS study that will take into account both mobility and temporal changes in PM, but 

data are not available yet from that study. 

 

3.8. Causality 

Expert B felt a causal relationship would be best supported by a “strong, statistically 

significant, and robust association. Robust in the sense that adjustment for confounders 

and effect modifiers still leaves the association intact ... replication of the results in 

independent studies ... a plausible biological mechanism by which those effects could 

occur ... a clear exposure response gradient.” 

“[I]f we look at long-term exposure, I think we have strong, statistically significant, 

robust associations that have been replicated in the Harvard Six Cities study, the 

American Cancer Society cohort, and the Seventh Day Adventist. [These associations 

are r]obust against adjustment for a whole host of factors.” Expert B noted that the 

Health Effects Institute in their reanalysis “has looked at the ACS data just about every 

way imaginable, and have had extensive opportunity to control for 140 covariates. [W]e 

do have a plausible ... biologic mechanism, by which particles can cause 

cardiopulmonary mortality and lung cancer mortality.” He thought the exposure response 

gradient had been observed in both the ACS studies and the Six Cities study. 

Expert B thought there was less strength of evidence for causal effects of short-term 

exposures “even though there are only a handful of chronic studies and lots of short-term 

studies.” For example, he pointed out that there are not statistically significant results for 

all 90 cities in NMMAPS. 

Expert B specified a range and most likely value for the likelihood of a causal 

relationship for long-term and short-term exposures separately. For long-term, he 

initially specified a range of 80-95 percent with a most likely value of 95 percent. “The 

main factors that would [contribute to the 80 percent] are the SO2 effect on PM2.5, which 
B-7 

is quite substantial as we discussed earlier” and to a lesser extent the ecologic measures 

of exposure. For short-term, he initially specified a range of 70-90 percent with a most 

likely value of 90 percent. After further consideration of his views on the overall strength 

of the evidence, he expressed greater confidence in the strength of the evidence for a 

long-term impact, and ultimately provided a range of 90-99 percent and a most likely 

value of 98 percent for the likelihood of a causal relationship for long-term and shortterm exposures combined. This estimate reflects a greater emphasis on causality for 

long-term effects, which he thought were the dominant contributor to effects seen for 

changes in annual average PM2.5. 

3.9 Thresholds 

Expert B summarized his views on thresholds with four main points “Number one, 

conceptually, looking at mechanisms of lung cancer and cardiopulmonary mortality, 

particularly for lung cancer, I think there's a strong argument to be made that 

conceptually there is no population threshold. Number two, for acute effects ... without 

knowing those mechanisms, just drawing analogies with other causes of acute mortality 

due to environmental exposures, I think there's a stronger argument to be made for the 

existence of a threshold. Number three, observationally, for both long-term and shortterm exposures, I don't think we have much of a chance of demonstrating a threshold 

because of the limits of resolution of epidemiologic data at low exposure levels. We see 

in the chronic studies, as we go down the concentration-response curve, the uncertainty 

bands widening to the point where you get to the lower end of that 4 to 30 μg/m3 range, 

you really can't tell definitively if there is an increased risk or not, unless you believe that 

trend line holds even with confidence bounds at the possibility of no risk at the lower end 

of the range.” He indicated that Cakmak et al. (1999) did an analysis published in Risk 

Analysis showing empirically how difficult it would be to identify a threshold even in 

acute studies. 

He thought the population exposure-response relationship might flatten out some at lower 

exposures, including levels below 4μg/m3

. In essence, he thought it was more an issue of 

some non-linearity at low doses rather than a threshold concept. 

He concluded: “[A]nother overarching observation here is that the question of thresholds 

may not be a question that's really worth trying to resolve because of the limits of 

observational studies and defining that threshold, because any conceptual arguments that 

you can put forward will remain conceptual unless you've worked out the biological 

mechanism by which particles cause mortality in full detail and embed a sensitive marker 

that you can trace right down to the very low exposure levels. And I don't think we're 

there yet.” 

3.10 Other Influential Factors 

Expert B discussed additional sources of uncertainty that were not part of the protocol. 

He thought that more research into biological mechanisms was warranted. In addition, he 

thought that errors in outcome ascertainment (misinterpretation of cause of death, 

ascertainment of vital status) could add uncertainty to the concentration-response (C-R) 
B-8 

function (although all-cause mortality would not be affected by miscoding of cause of 

death). Expert B did not think publication bias was an issue. Nor did he think ecologic 

community covariates were an alternative explanation for the mortality effect seen in the 

ACS study, in particular. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert B thought the C-R relationship was largely linear on the basis of observational 

data. He noted that work by Abramowitz et al. (2003) using flexible exposure-response 

curves with no constraints on the prior form of the distribution showed a largely linear CR relationship. 

Expert B wanted to characterize his C-R function in a piece-wise linear fashion, 

specifying two C-R functions over the range of annual average PM2.5 concentrations that 

were the focus of the study (4-30 μg/m3

), one that applied to concentrations of 4-10 

μg/m3 (hereafter, “Range 1”), and one that applied to concentrations of >10-30 μg/m3 

(hereafter, “Range 2”). He did so to reflect his greater uncertainty about the shape of the 

concentration response function in the lower concentration range. 

Expert B chose to provide C-R functions that were conditional on the existence of a 

causal relationship. The elicitation team then combined his conditional distributions with 

his percent likelihood of causality specified in Section 3.8 (98 percent likelihood of a 

causal relationship). In addition, the two distributions were applied to a distribution of 

population-weighted annual average PM2.5 concentrations in the U.S. from EPA’s 

BenMap model to create a combined distribution (hereafter, “Example Applied 

Distribution”). He stated that his effect estimates represented primarily the effects of 

long-term exposure (on the order of less than 10 percent attributable to effects of shortterm exposure). 

Expert B specified the same 50th, 75th, 95th and maximum values for both Range 1 and 2. 

Expert B estimated his 50th percentile by adjusting the original ACS relative risks (1.06 

per 10 μg/m3

) upward by a factor of two to account for exposure misclassification using 

as justification the Mallick et al. (2002) analysis and the Jerrett et al. (2005) studies. 

The 95th percentile was based on adjusting the ACS cohort study estimates to account for 

the influence of a non-representative population with respect to socioeconomic status 

(SES) (educational attainment), for uncertainty about the true basis of SO2 effect, and for 

some statistical uncertainty. His 75th percentile was chosen largely as an intermediate 

value between the 50th and 95th percentiles. Finally, his maximum values for both ranges 

were based on allowing for uncertainties that he had not already taken into account, such 

as measurement error in the monitoring science, unidentified covariates, a larger SES 

adjustment, and model uncertainty. 

Expert B specified different values for the minimum, 5th, and 25th percentiles for Ranges 

1 and 2. Expert B began with the upper range (Range 2) and set his minimum value at 

0.1 percent per 1 μg/m3

. For the 5th percentile, Expert B adjusted the 50th percentile 

downward first by 0.4 to account for an “appropriate” confidence limit. This took the 

estimate from 1.2 to 0.8. He then decreased this by 75 percent to account for an SO2
B-9 

effect, ending up with 0.2 percent. For the 25th percentile in Range 2, he again adjusted 

the median to 0.8 for the confidence limit and then reduced it 37.5 percent (halfway 

between 25 and 50 percent) to account for an SO2 effect, ending up with a value of 0.5. 

For Range 1, he decreased the minimum to show his increased uncertainty about the C-R 

relationship at low concentrations. He reduced the 5th percentile to account for the fact 

that there may be no acute effects at these concentrations, and the potential flattening out 

of the C-R function for cardiopulmonary deaths. He reduced the 25th percentile effect 

estimate to account for the potential absence of acute effects at these concentrations, and 

the potential flattening out of the C-R function for cardiopulmonary deaths.

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Range 1 (4 – 10 μg/m3

)

Percentile Percent Change in Mortality 

Elicited Distribution 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood 

(IEc Generated) 

Minimum 0.01 0 

5th 0.10 0.07 

25th 0.20 0.19 

50th 1.2 1.2 

75th 2.1 2.1 

95th 2.6 2.6 

Maximum 2.8 2.8 

Exhibit 2: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Range 2 (>10 – 30 μg/m3

)

Percentile Percent Change in Mortality 

Elicited Distribution 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood 

(IEc Generated) 

Minimum 0.1 0 

5th 0.2 0.16 

25th 0.5 0.47 

50th 1.2 1.2 

75th 2.1 2.1 

95th 2.6 2.6 

Maximum 2.8 2.8 
B-10 

Exhibit 3: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3 Change in Ambient Annual Average PM2.5 Concentrations 

After Incorporating Causality and Applying the C-R functions to the PopulationWeighted Annual Average PM2.5 Concentration Distribution in the U.S. from 

BenMap - Example Applied Distribution (IEc Generated) 

Percentile Percent Change in Mortality 

Minimum 0 

5th 0.12 

25th 0.43 

50th 1.2 

75th 2.1 

95th 2.6 

Maximum 2.8 
B-11 

Exhibit 4: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 percent Confidence Intervals for Various Studies to Distributions from 

* = Distribution incorporating causality and applying the C-R functions from Ranges 1 and 2 to a 2002 population-weighted annual av

distribution in the U.S. from BenMap. • = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

3

Range 1 - Elicited Range 2 - Elicited Example Applied

Distribution

Incorporating

Causality* (IEc

Generated)

ACS Original ACS Extend ACS LA Analysis Six Cities Original

% Decrease in Mortality per 1 ug/m3 PM2.5
B-12 

Elicited Distribution – Range 1 

Elicited Distribution – Range 2 
B-13 

Range 1 Incorporating Causality - Probability Density Function (PDF) (IEc Generated) 

Range 1 Incorporating Causality - Cumulative Density Function (CDF) (IEc Generated) 
B-14 

Range 2 Incorporating Causality – PDF (IEc Generated) 

Range 2 Incorporating Causality – CDF (IEc Generated) 
B-15 

Example Applied Distribution – PDF (IEc Generated) 

Example Applied Distribution – CDF (IEc Generated) 
Expert C 

Interview Summary
C-1 

Interview Summary 

Expert C 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure PM2.5

Expert C discussed the biological mechanisms for the effects of long-term and short-term 

exposures separately, focusing first on long-term. 

Long-Term Exposures

He thought that the two main causes of death associated with long-term exposures were 

cardiac disease and lung cancer. In particular, he cited the Pope et al. 2004 paper in 

Circulation as influential in showing that most of the excess mortality due to PM 

exposure was related to cardiac disease. He described the cardiac mechanism as an 

accelerated aging process brought on by the contribution of the body’s natural defense 

mechanisms to inflammation, to long-term functional changes, and ultimately to changes 

in viability. He thought that the exact intermediate steps for the mechanism “are pretty 

well undefined” but are believed to involve deposition of the particles into the lung, 

which stimulate mediator release, leading to endothelial inflammation. He cited the Sun 

et al. (2005) work (in JAMA) and the nine papers in the Mar.-Apr. 2005 issue of 

Inhalation Toxicology describing earlier CAPs inhalation research conducted at NYU. 

These studies showed histological evidence of accelerated arteriosclerotic lesions in 

ApoE-/- mice exposed to concentrated air particles (CAPs) as important evidence for a 

mechanism for cardiac disease. Although the exposures to mice involved high 

concentrations (~100 μg/m3

) for 6 hours a day, 5 days a week, he thought that it was the 

cumulative average exposure (on average 15-17 μg/m3

) that was important for the effects 

seen. 

For lung cancer, he thought that the Pope et al., 2002 study showed a significant excess 

of cancer; this work has been supported by other cohort studies which have shown 

consistent elevated risk, though the others were not statistically significant. He thought 

the mechanisms here are not certain but may be related to mutagenic or carcinogenic 

particle components and whether they are initiators or promoters. He noted that there is 

some excess risk for pulmonary disease but since pulmonary disease is less prevalent, the 

total impact on numbers of deaths is smaller than for cardiac disease and cancer. 

Short-Term Exposures

For short-term exposures, he thought that PM acted analogously to ozone and could cause 

an irritant response (broncho-constriction and inflammation in the airways resulting from 

stimulation of epithelial cells to release mediators). He noted that, unlike ozone, particles 

can remain in the lung for a long time and may contribute to chronic as well as acute 

conditions. He thought that deaths from short-term exposures could occur in people with 

little reserve capacity because underlying pulmonary disease (inability to ventilate 
C-2 

effectively) can contribute to cardiac deaths. He also cited evidence from Annette Peters’ 

epidemiological work and from animal studies showing declines in heart rate variability, 

a factor in viability, which could be leading to acute cardiac deaths. 

3.2 Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

Expert C generally found the Kunzli framework very useful and the definitions of the 

categories of death relevant, though he thought it could be enhanced to include different 

causes of death. He would want to change the size of the ovals to show effects of longterm exposure to be much greater than the effects of short-term exposure. 

3.3 The Role of Epidemiological Study Design in Characterizing the Total Impacts 

of PM2.5 Exposures on Mortality 

Expert C thought the cohort studies are the best way to estimate total mortality, 

represented by Categories B (long-term effects) and A (mixed effects) in the Kunzli 

framework. He though total short-term effects are best captured by the time-series 

studies with lags of up to 40 days (e.g., as shown by Joel Schwartz and his colleagues). 

The intervention studies (Sugiri et al., 2005; Oglesby et al., 2005; Avol et al., 2001; 

Clancy et al., 2002; and Hedley et al., 2002) suggest an intermediate effect on daily or 

longer-term functional changes and mortality. He noted that the Clancy et al. study saw 

reductions in both respiratory and cardiac deaths; the Avol et al. study showed changes in 

lung growth related to changes in PM exposures. He did not have a clear sense of what 

the dominant mechanisms were for these changes, although findings of inflammation and 

atherosclerosis development in the Sun et al (2005) six month mouse study (discussed 

earlier) may provide part of the explanation. He thought the composition of the particles 

may turn out to be relevant and discussed unpublished work suggesting the importance of 

nickel and vanadium in the mortality changes observed in intervention studies. 

 

3.4 Epidemiological Evidence for the Impact of Exposures to PM2.5 on Mortality 

Expert C thought an ideal study for characterizing the U.S. concentration-response (C-R) 

function for PM2.5 and mortality would resemble the Six Cities study but with a broader 

range of cities that would be more geographically representative of the U.S. He would 

want the study to be prospective, to involve a representative sample of the U.S. 

population, to include measurements of multiple air quality parameters (e.g., acid vapors, 

organics, elemental carbon, PM2.5), and to collect data on personal risk factors both on 

enrollment and on follow-up. 

Expert C made the argument that ACS (Pope et al., 2002) and Six Cities studies (Dockery 

et al., 1993 (original) Laden et al., 2006 (follow-up)) were essentially a “complementary 

pair of studies which found differences in [the] magnitude [of effects that] can be 

explained. ... [We] can decide some of their biases and correct [for] them.” 
C-3 

Expert C thought that the ACS study (Pope et al., 2002) was the most useful for 

answering the causal question because it is geographically more representative of the 

whole U.S., though it is “less than ideal” in terms of its measurement of pollutants (use of 

single monitors for broad areas, lack of detail on other pollutants than those included for 

compliance monitoring, lack of information about where members of the cohort lived 

after enrollment), and the non-representativeness of its population with respect to 

socioeconomic status (SES). He thought the Jerrett et al., 2005 ACS reanalysis in Los 

Angeles (LA) used a more precise measure of individual exposure. 

He thought the Six Cities Study had a more representative population with respect to SES 

and better exposure measurements (use of investigator controlled monitoring stations, 

located to be more representative of populations). However he was concerned that the 

six cities were not geographically representative of the U.S. 

Expert C thought the intervention studies were very important but “they’re most useful 

for ... plausibility ... that the effects of the particles are substantial and real, rather than 

in terms of quantitation.” His point was that they each use different measures of particle 

pollution, and do not directly address PM2.5. The Hong Kong study (Hedley et al., 2002) 

looked primarily at SO2, but Expert C views SO2 as a surrogate for fine PM in this study; 

the Dublin study used black smoke and the correlation with PM2.5 was uncertain; and the 

Utah Valley study used PM10 – the results could be corrected for PM2.5 though he was not 

aware that they had been. 

Expert C also discussed the Adventist Health and Smog (AHSMOG) and Veteran’s 

studies. He thought the AHSMOG study was generally supportive of a PM effect though 

he was concerned about the non-representative population and the indirect construction 

of the PM2.5 metric. He characterized the Veteran’s study as difficult to understand based 

on available publications and he was concerned about its non-representative population. 

3.5 Confounding 

Focusing on the ACS and Six Cities studies, Expert C discussed a number of theoretically 

influential confounders and then evaluated whether or not he thought they had been dealt 

with adequately in the two studies. The initial discussion identified the following: 

• Criteria pollutants; 

• Smoking; 

• Occupation; 

• Other pollutants (fresh motor vehicle exhaust, ultrafines, products of incomplete 

combustion, others depending on the source (e.g., wood smoke)); 

• Differential migration; 

• SES factors (e.g., access to health care, financial status); and 

• Secular trends in obesity. 

Of these, he thought both the ACS and the Six Cities studies had done a good job of 

exploring the impacts of the first three. His view was that neither smoking nor 

occupation was influential in biasing the results. As a result, he thought even the early 
C-4 

cross-sectional studies needed to be re-considered. Nor did he think differences in 

criteria pollutants made a “demonstrable difference.” "[The time-series data] ... show the 

same PM2.5 coefficient across high and low ozone, NO2, SO2 and CO communities.” 

When asked about the SO2 effect shown in the ACS study, Expert C thought that it was 

not likely to be a “true confounder, in that it is a precursor ... [with a] significant 

correlation [with fine PM], but that it’s probably not causal.” Similarly, “[s]ulfate has 

been an extremely good index of fine particles over years, but is it because it's causal or 

because it's a surrogate for something else, like perhaps nickel and vanadium? Or other 

pollutants from coal [or oil] combustion.”

The remaining issues were ones that he saw as more hypothetical concerns, ones that he 

lacked information to characterize more definitively. 

We discussed whether other, currently unmeasured, pollutants might be confounders for 

the PM effect. Expert C did not think that there was likely to be a differential across 

cities in terms of indoor-generated pollutants. He thought wood smoke “could be a 

confounder, but probably not in terms of large population influence.” He thought the 

ACS and Six Cities studies reflect largely urban populations with relatively little wood 

burning. 

He thought that a recent series of studies looking at traffic as a risk factor raised the 

question of possible confounding by “fresh” motor vehicle exhaust (which could include 

ultra-fines and/or products of incomplete combustion). Ultimately, this factor contributed 

to his uncertainty about the PM2.5 effect on mortality (he was uncertain of the direction of 

bias) and he gave this factor a score of 2 (which he equated to plus or minus 20-30 

percent). 

Expert C engaged in a discussion of obesity (and with it diabetes) as a risk factor. During 

this discussion Expert C made comments indicating that it might be a confounder of the 

mortality effect (e.g., higher rates of obesity in low PM2.5 areas, as in the rural south, 

which could lead to an underestimate of the PM2.5 relative risk), and that it could function 

as an effect modifier of the mortality risk estimate ("... if the relative risk is greater in 

obese people, then [the effect estimate] would be understated if we didn't acknowledge 

that obesity was increasing [in the U.S. population].” He gave obesity a score of 2. 

Finally, Expert C discussed differential migration and its impact on the PM-mortality 

effect estimate. He thought it might bias the estimate toward the null because he thought, 

on average, people might be more likely to move from the “Rust Belt” to the “Sun Belt” 

and therefore from higher to lower exposures. He cited the Roosli et al. and Schwartz et 

al. papers that suggest that, “it’s the most recent years of exposure which determine 

mortality rates ... [and if the population] moved to a cleaner environment more than three 

or four years ago, that would ... bias it downward.” He ultimately thought it would have 

a minimal effect on the PM2.5 relative risk (score = 1 which he characterized as <10 

percent change). 
C-5 

3.6 Effect Modification 

Expert C discussed two potential effect modifiers, race and SES (as indicated by 

educational attainment). Race as an effect modifier was discussed extensively; Expert C 

did not think that race, per se, would necessarily predict response to air pollution but that 

it might be a proxy for other risk factors (e.g., SES, access to health care, diet) for early 

mortality. He noted that the Six Cities and ACS cohorts were largely white. Expert C 

felt it was largely beyond his expertise to assess the impact of excluding other races from 

these cohorts, although his intuition was that it would tend to bias the PM effect estimate 

downward. However, given that race was not reported in the Krewski re-analysis of the 

ACS as a significant risk factor, he thought the impact might be minimal (score = 1). 

Expert C’s views on the effect of educational attainment (as a proxy for SES factors) on 

the relative risks for PM2.5 were influenced by and expert's discussion at the preelicitation workshop, in which he presented estimates showing that different approaches 

for adjusting for educational attainment increased the relative risks by 30-50 percent. 

Expert C consequently thought this factor merited a score of 3. 

3.7 Exposure Issues 

Expert C initially raised several exposure issues that might impact the PM2.5-mortality 

effect estimate: 

• Exposure measurement error; 

• Compositional variation across the country; 

• The time course of exposure, relevant exposure period for the mortality effect; 

and 

• Air conditioning usage. 

Expert C thought exposure measurement error, resulting from use of single compliance 

monitors for a large population was an important potential source of bias for the ACS 

study. He thought the Six Cities Study did a better job of exposure measurement. 

Although he thought the Jerrett et al. (2005) study’s improvement in exposure 

measurement was at least partly responsible for the increased PM2.5-mortality effect 

estimate relative to the original ACS study, he was not certain that it was all attributable 

to improved exposure metrics. He thought a difference in components of the PM2.5 in the 

Los Angeles area might also have played an important role. He scored the exposure 

measurement error issue a 3. 

Expert C cited the National Morbidity, Mortality and Air Pollution Study (NMMAPS) 

publications with daily mortality showing that the mortality coefficients vary by region of 

the country (e.g., higher in the northeast) which suggests to him a role for the 

composition of PM2.5. For example, he thought that there is more acidic aerosol in the 

northeast than in the rest of the country. However, he recognized that this “doesn't 

necessarily mean it has an influence on annual mortality” although he thought the Jerrett 

et al. paper might suggest, in part, the influence of more toxic PM2.5 in southern 
C-6 

California. For the Six Cities Study, he argued that the PM mixture “is not that different 

from a lot of the U.S. and Europe, and therefore it’s a reasonable estimate for ... the U.S. 

effect ... There probably is some bias, but it’s indeterminate.” 

Expert C also acknowledged that the relevant period exposure for estimating the PM2.5- 

mortality effect was not known. He thought the intervention studies, as in Hong Kong, 

suggest that more recent exposure is more relevant to mortality risk, rather than the 

previous 20-40 years, although he found that judgment somewhat counterintuitive. He 

indicated that one rationale might be that the mortality risk is higher among the older, 

frailer population, than in the young. 

Noting unpublished work [by George Thurston] showing higher effect estimates between 

areas where air conditioning use is low (e.g., San Francisco) compared to where air 

conditioning use is more prevalent (e.g., Houston), Expert C thought air conditioning 

could be an important factor in determining effect size by reducing the correlation 

between central site monitoring data and personal exposures. Furthermore, he noted that 

use of air conditioning has been increasing nationwide over time. He thought it might be 

influential in the ACS study and could be reducing the PM effect in southern cities. 

Expert C expressed the view that air conditioning would have led, therefore, to a negative 

bias in the overall ACS effect estimate as compared to a true national PM effect. But it 

appears that his “true national effect” in this case was for a hypothetical U.S. population 

that did not use air conditioning. Expert C was not sure that the distribution of air 

conditioning use in the ACS study was un-representative of the US distribution of air 

conditioning usage. Thus the issue of whether the ACS effect estimate was biased in 

relation to the actual U.S. adult population remained unclear. He went on to suggest that 

air conditioning was likely to be less prevalent among the poor and that, because the ACS 

study was non-representative of the U.S. with respect to educational attainment, air 

conditioning use and its influence on exposure may underlie some of the effect 

modification by educational attainment observed there. For the Six Cities study he 

thought that there was minimal bias but that it was in the direction of overestimating the 

mortality risk in the U.S. since there is probably less air conditioning use in those six 

cities compared to the U.S. 

3.8 Causality 

Expert C argued that both animal studies and human epidemiological data are important 

for establishing plausibility of the PM2.5/mortality association. The recent experiments 

(discussed under mechanisms) with the atherosclerotic mouse model (the ApoE-/- mouse) 

showing accelerated development of atherosclerosis in mice exposed to CAPs as well as 

“prompt and substantial changes in heart rate variability and heart rate” add a “new 

increment of plausibility for particles being causally related to cardiac disease endpoints.” 

For epidemiological data, “the best evidence is the so-called ‘found’ experiments, where 

there have been sudden changes in pollution [Dublin, Hong Kong, European studies of 

children showing changes in respiratory function]” corresponding to changes in 

mortality. Expert C felt that the fact that studies all around the world have demonstrated 

an excess daily mortality associated with fine particles, an association that has not been 
C-7 

successfully challenged, establishes plausibility. He thought that the ACS cohort, Six 

Cities cohort, the Netherlands cohort (Hoek et al., 2002), AHSMOG, and even the 

Veterans cohort all “provide essentially the same message” for annual mortality. 

He thought both relationships with short-term and long-term exposures were very likely 

to be causal though he thought it was stronger for long-term because of the greater 

amount of mortality associated with long-term exposures. He was certain in both cases 

that the relationships were likely to be causal, comparable to his certainty that smoking 

increases the risk of mortality. 

In developing his final uncertainty distribution, he ultimately settled on 99 percent 

likelihood of a causal relationship, which he felt accommodated his views on the strength 

of the evidence and was consistent with the probabilistic distribution of effect estimates 

he specified. 

3.9 Thresholds 

Expert C thought cohort studies based on large, representative populations, not clinical or 

animal studies would, in theory, be most suitable for detecting a threshold if one existed, 

but that it would require such a large population and so much clinical follow-up that it 

would be prohibitive. 

While Expert C thought that there are “clearly” thresholds for individuals, he would not 

expect to see a population threshold, even if one existed, given the population variability 

in sensitivity (due to genetic differences or pre-existing conditions, for example), in the 

general population of humans. He did not think there was a conceptual basis for arguing 

that a population threshold might exist in the 4-30 μg/m3

 range for this study. Although 

he noted that the ACS study was directly informative about exposures only down to 

around 10 μg/m3

, and that a “modest extrapolation, even down as far as 4, is not 

unreasonable...[compared to] orders of magnitude as you do in carcinogen risk 

assessment.” 

His opinion on thresholds did not vary for short-term and long-term exposures. He did 

not elect to incorporate a threshold into his C-R function. 

3.10 Other Influential Factors 

As discussed to some extent earlier, Expert C thought that PM composition was likely to 

be influential. He thought the operating assumption of the protocol was that 

“composition made no difference, but that clearly can’t be correct. Not that I can tell you 

in specific terms.” He stated that recent studies have shown that composition might be 

influential on mortality estimates. He thought that future regulatory scenarios would 

likely be designed to deal with source-related or individual components as data improves. 
C-8 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert C chose to base his judgments on a linear, non-threshold (log concentration, linear 

response) function over the entire range of concentrations included in the study (4-30 

μg/m3

). “I'm not absolutely sure that it is truly linear, but the linear approximation is the 

best estimate that can be made, in my judgment at this time. Clearly it no longer is linear 

as you get to much higher concentrations.” He cited the Pope et al. (2002), and Laden et 

al. (2006) comparison of the original and extended relationships as influential. Based on 

our earlier discussion, the likelihood of a causal relationship is assumed to be 99 percent. 

Although we began by trying to establish the maximum or upper bound for the PM2.5-

mortality effect, Expert C found beginning with the extremes very difficult. He asked to 

start with a midpoint and work out to the extremes considering the uncertainties we had 

discussed. 

He approached his estimation of the midpoint in two ways; one beginning with the 

central estimate from the Six Cities study (Dockery et al., 1993) and the other beginning 

with the Pope et al. (2002) ACS study. He began with the estimate of 13 percent per 

10μg/m3

 from the original Six-Cities study rather than the ACS study, whose estimate he 

thought was low given the non-representative population and degree of exposure 

misclassification. He was concerned that the Six Cities study might be biased a little high 

relative to a national estimate given its focus on the northeast where he thought the 

toxicity of PM might be greater and because of likely less use of air conditioning. But 

after some discussion of these issues, he did not consider them large enough to change his 

estimate, so he chose to maintain his central estimate at around 13 percent based on the 

Six Cities study. 

As an alternative approach to deriving the median estimate, based on the ACS study 

estimate of 6.2 percent per 10μg/m3

, he argued that it should be increased to about 9 

percent as a crude adjustment for educational attainment as discussed in the workshop. 

Adjusting further for exposure misclassification, he arrived at about 13 percent again, 

noting that although the Jerrett et al. (2005) study reported a measure of 17 percent, he 

thought that composition played some role in that effect measure. 

Considering both studies and the inexact rationale for various adjustments, he settled on a 

value of 12 percent per 10μg/m3

 for his 50th percentile. 

He then worked on developing an upper 95th percentile, which he estimated by not quite 

doubling his median estimate to about 20 percent. These were converted to values per 

1μg/m3

 change in PM2.5. He then asked that a Gaussian distribution be fit to these two 

percentiles, and the elicitation team generated the remaining percentiles of his uncertainty 

distribution using Crystal BallTM. He allowed the distribution to be truncated at zero, 

leaving about 1 percent probability of a non-causal relationship. He felt this was largely 

trivial and consistent with his views on the strength of the PM2.5-mortality relationship. 
C-9 

Expert C chose not to specify a maximum value for his distribution because the Gaussian 

distribution, on which his C-R function distribution was based, has an asymptotic 

maximum. 

Comparing his distribution to some of the other studies on which he relied, he felt that 

specifying an upper 95th percentile that was comparable to those based on statistical error 

for the Six Cities Study was justifiable. “You could be more certain. No reason why 

weighing all the evidence doesn't shift you to be more or less certain.” 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

Percentile Percent Change in 

Mortality 

Minimum 0.0 

5th 0.40 

25th 0.90 

50th 1.2 

75th 1.5 

95th 2.0 

Maximum - 
C-10 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 percent Confidence Intervals for Various Studies to Distribution from 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

Expert C ACS

Original

ACS Extend ACS

Reanalysis

ACS LA

Analysis

Six Cities

Original

Six C

Reana

% Decrease in Mortality per 1 ug/m3 PM2.5
C-11 

Expert C Distribution - Probability Density Function 
Expert D 

Interview Summary
 

D-1 

Interview Summary 

Expert D6

Expert D began our discussion by providing a conceptual framework, informed by evidence, for 

explaining the possible relationship between mortality and exposure to fine particles. He 

essentially described a microenvironmental model in which individuals spend varying amounts 

of time in different microenvironments defined broadly by differing proximity to roadways. It 

was within this context that Expert D discussed remaining uncertainties in the PM mortality 

relationship. 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

A. Epidemiological estimates considered most influential to my decision: 

 1. Six Cities Study (Dockery et al., 1993; Laden et al., 2006; Krewski et al., 2000) 

 Six single citywide PM2.5 data for first study (‘74-’89) 

 Estimated PM2.5 for follow-up (‘90-‘98) 

 Only white adults 

 PM2.5 ↓ from first to follow-up 

 RR ↓ from first to follow-up (using numbers in Laden) 

 1.7 to 1.3 %/μg 

 2. ACS Study (Pope et al., 1995, 2002; Krewski et al., 2000) 

 50 metropolitan area PM2.5 from zip code for ‘82-‘89 

 Estimated PM2.5 for extension to ‘98 

 Enrolled adults 

 PM2.5 ↓ in all areas, with much greater ↓ for areas over 20 μg/m3

 RR ↑ from first to follow-up (using numbers in Pope ‘02) 

 0.4 to 0.6 %/μg 

 RR higher if adjusted for education level 

 3. L.A. ACS cohort (Jerrett et al., 2005) 

 L.A. by zip code (267 zip codes interpolated from 23 area monitors) 

 RR was lowered by progressive controls in model 

1.1 %/μg after controlling for the most variables 

 No apparent freeway proximity effect (??) 

 4. Elderly in L.A. (Enstrom et al., 2005) 

 Two periods: ‘73-‘82, and ‘83-‘02 

 RR ↓ between periods 

 0.4 to 0.0 %/μg 

 

6

 Expert D provided his own summary. We have supplemented it, where necessary to provide details for 

comparison with other experts. 
 

D-2 

B. Epidemiological studies providing useful supporting information: 

1. Pope et al., 2004 

 ACS nationwide – cardiovascular 

 RR for CV + diabetes (?) higher in former and current smokers vs. never smokers 

 1.2, 1.3, and 1.9 %/μg, respectively 

2. Lipfert et al., 2006 

 Men – VA – nationwide 

 RR was higher for traffic proximity than for PM2.5

 

 3. Pope et al. Utah Valley (1989; 1991) 

C. Toxicological support for quantitative estimates of RR for human mortality 

 

None suitable for quantitative estimates 

D. Toxicological support for plausibility and mechanisms (among many) 

1. Utah Valley Studies (reviewed in Ghio, 2004) 

 Coherence of toxicological and epidemiologic studies 

 Inflammation 

Importance of soluble transition metals 

2. Dog model of myocardial ischemia (Wellenius et al., 2003) 

 Boston CAPs @ 345 μg/m3

 x 6 hr x 5 d 

 Alterations in ST segment 

 Multivariate analysis > Si 

3. CV effects in ApoE mice (Sun et al., 2005) 

 Regional NY CAPs @ 85 μg/m3

 x 6 hr x 5 d x 6 mo 

 Increased HR and atheromatous vascular changes 

 4. LRRI studies 

a. DOE studies of PM and co-pollutants in engine emissions 

 Both PM and non-PM fractions are important to several effects 

b. NERC repeated exposures to combustion emissions at relevant levels: 

 Altered cardiac electrophysiology (diesel, gasoline) 

 Oxidative vascular injury (gasoline ± PM) 

Enhanced atheromatous responses (gasoline ± PM) 

Altered systemic immune competence (diesel and wood smoke) 

Altered resistance to respiratory infection (diesel and gasoline) 

E. Slope of exposure-response function 

 1. Best sources of information 
 

D-3 

 

EPA PM Staff Paper (2005), Figure 3-4, p 3-57 

 Pope and Dockery (2006), Figure 1 

2. Shape and assumptions 

 No biologist would believe that the D-R curve is actually linear throughout its length. 

Current epidemiological data do not provide enough information to select any other 

than a linear function in the range of current annual concentrations. 

If anything, the slope is estimated to steepen in the low range, but the uncertainty 

expands too much in the low range to estimate a non-linear function. 

F. Major Sources of Uncertainty 

 1. Responsibility of co-pollutants for a portion of effects ascribed to PM 

Only a few co-pollutants are measured. Epidemiologists have no way of accurately 

distinguishing effects of pollutants that are not measured. An indirect assessment can 

be made by assessing “PM” effects in locations having different co-pollutant levels. 

There are some data that may satisfy this strategy for co-pollutants that are typically 

measured (e.g., NAAQS pollutants), but I am not aware of studies that have proposed 

to have done this for unmeasured co-pollutants. 

Evidence from toxicological studies, strongly suggests to me (but does not prove) that 

unmeasured co-pollutants are responsible for a portion of effects attributed to PM. 

These effects include inflammation, atherogenic vascular responses, and resistance to 

respiratory infection, and may include immune/allergic effects and developmental 

effects. 

This view is based almost exclusively on studies of fresh, or relatively fresh 

traffic emissions. Because I believe that current evidence points toward 

engine emissions PM as the most toxic PM overall (toxicity x prevalence of 

exposure), the most important exposure to unmeasured co-pollutants occurs 

concurrent with the most important exposure to PM. This concurrence adds to 

the plausibility that co-pollutants cause a portion of the effects ascribed to 

PM. 

Current data do not allow the confident parsing of effects among different 

unmeasured co-pollutants, measured co-pollutants, and PM. However, based 

on current data, I consider gas and vapor-phase organics (VOCs, SVOCs) to 

be very likely among the culprits. 

It is true that many VOC and SVOC species will migrate into the PM 

population with distance from source; moreover, the organics will be changed 
 

D-4 

by oxidation, chemical reactions, etc. Some portions of this material will 

become more toxic and some will become less toxic. We don’t know enough 

yet to deal with this in a quantitative manner. Regardless, it is also true that 

all exposures in close proximity (on-road or near roadways) to engine 

emissions include exposures to these unmeasured non-PM emissions. There 

will be a declining concentration of these emissions with distance from the 

roadway, which parallels the declining concentration of PM (and especially 

ultrafine PM). This concurrence of distribution lends itself to ascribing the 

effects of unmeasured co-pollutants to PM. 

Credible epidemiologists (e.g., Samet, Krewski) acknowledge that existing 

epidemiological data cannot completely separate effects of PM from co-pollutants 

Credible groups (e.g., NRC committees, HEI) acknowledge need to consider 

exposures as mixtures, reflecting an eroding confidence that one can confidently 

ascribe effects to single pollutants (based on current data), or disentangle effects 

among the hundreds of physical-chemical species that people actually breathe. 

Elicitation ground rule assume no change in nature of co-pollutants. This is 

interpreted to mean that the proportional “silent” contribution of co-pollutants will 

remain the same, and that any overestimate of PM effects due to this factor will 

remain the same. Of course, the nature of both PM and co-pollutants will change.

2. Accuracy of PM exposure estimates 

None of the predominant epidemiological studies actually measured personal 

exposures 

 

Portions of the data are not even direct measures of PM2.5. 

It is usually assumed that all personal PM exposure is proportional to, or identical to, 

the average PM concentration at the place of residence. 

The most exposure to the most toxic PM is probably in or near traffic, and that 

is likely to differ substantially from exposure at the residences of people living 

more than 100-300 meters from a major roadway. 

The most exposure to co-pollutants most likely to add to the effect (silently, 

because they are unmeasured) would also occur on or near roadways. 

The “traffic effect” is believable with a fairly high level of confidence. The 

Lipfert (2005) study, and many others (e.g., multiple studies from the 

Brunekreef group, the Peters MI study, etc.), support the notion that traffic 

emissions are likely to be key to many health effects associated with PM. 

Most of these studies either did not attempt to single out the effects of PM, or 

had low power to do so. Nonetheless, the importance of proximity to traffic 

has been convincingly demonstrated 
 

D-5 

To the extent that the effects are indeed due to PM, and if most of the most 

important PM exposure (C x T x toxicity) occurs on or near roads and is thus 

underestimated by exposure at residence, and further, if that exposure parallels 

the estimated exposure at the residence in a roughly linear fashion, then the 

“unit risk” has been overestimated because the most important exposure has 

been underestimated. The proportional change in mortality with decreasing 

PM could still be approximately correct, even if the unit risk, and thus the 

body count, attributed to PM is overestimated. 

No epidemiology studies of PM2.5 have been able to deal seriously with PM 

composition; incrimination of traffic PM is almost exclusively by location, not 

composition. My (unproven) view is that good composition data for personal 

exposure would support my view of the importance of traffic PM. 

 3. Changes in risk with time 

 

Both the 6-cities and ACS studies, as well as the Enstrom study, indicate that unit 

risks have changed with time. 

 Risk went down in 6-cites and Enstrom 

 Risk went up in ACS 

Regardless of the real, or if the direction has been different in different 

locations, this adds to uncertainty about impact of future changes 

The nature of PM2.5 will likely change with time, which could change risk; however, 

the elicitation ground rules preclude this consideration. 

 4. Plausibility (as defined by identification of potential mechanisms) 

There is not much of a roadblock now to believing that PM may contribute to the 

effects associated with it by epidemiology – at least superficially. Findings with time 

are providing increasing, rather than decreasing, mechanistic plausibility. 

Experimental exposures of humans tend to support, rather than detract from, 

plausibility. The problem is that we can’t conduct the studies that would be most 

informative – exposure of the most sensitive people to the most toxic PM, under the 

most hazardous conditions, using the most discriminating endpoints. 

Toxicology is providing increasing mechanistic plausibility, but still very little “dose 

plausibility”. The biggest problem with plausibility today is confidence in high dose 

to low dose extrapolation and confidence in animal model to human extrapolation. 

The former can be fixed by improved study design. The latter will probably improve, 

but will never be eliminated. Overall, we have enough confidence in the general 

utility of the models to do informative work - if investigators would extend the dose 
 

D-6 

range downward. The problem of course, is that when that is done, effects are very 

hard, and sometimes impossible to demonstrate - especially inexpensively (e.g., short 

exposures, simple atmospheres, few measured variables, small groups of animals, 

rapid-response models for chronic human effects, lack of methodical comparisons 

among exposures). There may be a message here – in fact, it is probably true that 

some (perhaps many) of the effects that are published in the toxicology literature do 

not occur in either animals or humans under realistic exposure regimes. Of course, 

the alternate outcome is also possible; it could be that repeated exposures of large 

numbers of animals of diverse susceptibility would, in fact, reproduce the 

epidemiological findings under realistic exposure conditions. 

Expert D characterized the likelihood of a causal relationship as ranging between 90 

and 100% with a most likely value of 95%. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS

G. My answer: % change in all cause mortality associated with a 1.0 μg/m3

 change in PM 

It all boils down to selecting the studies in which I have the most confidence and 

generating a distribution from those results using my biases regarding their likelihood of 

having overestimated or underestimated the actual value. 

I pick the 6-cities and ACS bodies of work as the most likely to approximate the truth, 

although both involve uncertainties. I pick these because: 

 Multiple locations in U.S.; 

 Large numbers of people; 

 Credibility of investigators; 

 Repeated analysis with extended follow-ups; and 

 Withstanding lots of scrutiny by other credible analysts. 

 

Other studies add texture to our understanding and expansion of updated methods (e.g., 

more localized exposure estimates, further parsing of the population, better characterization 

of regional differences in PM and response slopes) may yield different results. However, I 

choose to answer on the basis of existing data, not by imagining where evolving knowledge 

will take us. 

I start with a “most likely” value derived by a simplistic average, as shown below. These 

values have some numerical justification because they are derived from published results, 

and they are the most recent from the two bodies of work that I have selected as most 

informative. There are arguments from more recent published work for higher values, but I 

avoid going higher for two reasons. First, the more recent results are not derived from 

studies of comparably large populations in multiple U.S. locations. Second, and perhaps 

most reflecting my bias, I believe that the effect of PM has been overestimated across the 

entire epidemiological database, for the reasons I state above. On the other hand, I don’t 
 

D-7 

have sufficient scientific basis for selecting an arbitrary “most likely” value that has no 

basis in published work. I do not have high confidence in this number. 

6-cities (most recent) 1.3 

ACS (most recent) 0.6

Mean (most likely value) 0.95 

I next provide the following perspectives on the distribution around the above “most likely” 

value: 

Both of the 6-Cities and ACS values above must be plausible; i.e., within the bounds 

of 90-95% confidence limits. 

If the real value is not the “most likely” value of 0.95, it is more likely below that 

value than above it; thus the distribution of probability must be weighted between the 

“most likely” value and the lower bound of the distribution. I base this on my bias 

that the current data most likely miss-assign at least a portion of the effects of copollutants to PM, and that there is as much, if not more, evidence that the unit risk for 

PM is decreasing with time, rather than increasing. 

The lower bound of the actual value could be very low. On the basis of present 

knowledge, it could not plausibly be zero, but I don’t exclude the possibility that it 

could be near zero. I arbitrarily select a lower bound of 0.1. 

In keeping with my biases regarding current data, I don’t think that the upper bound 

of the actual value could be a great deal higher than the “most likely” value I selected. 

I do not have a solid rationale for picking an upper bound. I arbitrarily select a value 

of 1.6, which is near, but slightly below, the original 6-cities estimate of 1.7. 

I next selected a probability distribution that best fits the above criteria. 

I relied on the elicitation staff to provide a range of distribution functions that might 

fit my criteria. Most standard distributions do not fit the criteria very well. Although 

my criteria are sufficiently arbitrary that I could have elected to change them to better 

fit a standard distribution function, I did not do so. 

All things considered, a simple triangular distribution fit my criteria best, using the 

“most likely” value as the mode and fitting to the upper and lower bounds. 
 

D-8 

Elicited Distribution 

Min 5th %ile 25th %ile 50th %ile 75th %ile 95th %ile Max 

0.10 0.35 0.66 0.90 1.1 1.4 1.6 

Expert D chose to provide a C-R function that was conditional on the existence of a 

causal relationship. The elicitation team then combined his conditional distributions with 

his percent likelihood of causality (95% likelihood of a causal relationship). A 

probability density function (PDF) and a cumulative density function (CDF) of Expert 

D’s distribution incorporating causality as well as the are below: 
 

D-9 

Distribution Incorporating Causality –PDF (IEc Generated) 

Distribution Incorporating Causality – CDF (IEc Generated) 
 

D-10 

Percentiles of Distribution Incorporating Causality (IEc Generated): 

Min 5th %ile 25th %ile 50th %ile 75th %ile 95th %ile Max 

0 0 0.62 0.88 1.1 1.4 1.6 
D-11 

Exhibit 1: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 % Confidence Intervals for Various Studies to Distributions from Exp

• = median □ = interquartile range │= 90 % confidence interval 

* Estimate for entire follow-up period.

0

0.5

1

1.5

2

2.5

Expert D -

Elicited

Expert D -

Incorporating

Causality (IEc

Generated)

ACS Original ACS Extend ACS Reanalysis ACS LA

Analysis

Six Cities

Original

Six Cities

Reanalysis

% Decrease in Mortality per 1 ug/m3 PM2.5
 

Expert E 

Interview Summary
 

E-1 

Interview Summary 

Expert E 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert E discussed the biological mechanisms for short-term and long-term exposures 

together because he thought that it was difficult to separate them, since the physiology is 

not well understood enough to do so. He first discussed cardiovascular disease as a cause 

of death from particulate matter (PM). He thought the main mechanisms related to this 

cause of death were oxidative stress and inflammation leading to atherosclerosis, changes 

in autonomic function, and arterial reactivity. He also thought chronic obstructive 

pulmonary disease (COPD) and lung cancer could be potential causes of death. 

Cardiovascular Disease 

He first discussed a series of recent studies that he thought lent biological plausibility for 

a relationship between particle exposure, oxidative stress and inflammation, and 

ultimately, atherosclerosis and plaque instability. He cited an animal study by Gurgueira 

et al. (2002) that involved exposing rats to concentrated ambient particles (CAPs) and 

showed increases in reactive oxygen species in the lungs and heart. He cited another 

study by Evelson & Gonzalez-Flecha (2000) that placed animals who had been exposed 

to typical ambient particulate levels (~11 μg/m3

) in filtered chambers, thereby removing 

their PM exposure, and found that the reactive oxygen species levels decreased. In 

addition, he cited in vitro and in vivo studies showing that “part of [the body’s] defense 

mechanism against oxidative stress, seem to modify the effects of particles [i.e., reduction 

of pro-inflammatory cytokines, lung inflammation]” (Gurgueira et al., 2004; Rhoden et 

al., 2004). He indicated that “to me, that makes endothelial dysfunction plausible; it 

makes myocardial infarction plausible; it makes arrhythmias plausible.” He thought that 

a study that found in populations with lower defenses against reactive oxygen species 

(e.g., individuals with genetic polymorphisms of genes that impair the ability to defend 

against oxidative stress and obese individuals), there are higher effects from PM 

(Schwartz et al., 2005) further supported the reactive oxygen species theory. 

Expert E thought that recent work by Kunzli et al. showing a relationship between PM 

exposure and intima-media thickness suggests another connection to oxidative stress and 

a risk factor for cardiac mortality. He indicated that there is evidence from studies in rats 

that suggests that PM–related oxidative stress contributes to inflammation in the 

endothelium (the lining of the arteries), which could lead to atherosclerosis (Rhoden et 

al., 2004). As further evidence of PM’s inflammatory role, he cited a CAP study in rats 

by Dvonch et al. (2004) that showed plasma asymmetric dimethylarginine (ADMA) 

levels to be significantly elevated in rats exposed to CAPs versus those exposed to 

filtered air. 
 

E-2 

Expert E indicated that recent studies of brachial artery reactivity, a measure of the ability 

of the arteries to dilate in response the body’s demand for blood flow, suggests a 

connection between both long- and short-term exposures with cardiac mortality. The 

more “stiff and impaired the arteries get,” the less responsive they can become. A recent 

study in Circulation (O’Neill et al., 2005) found decreases in brachial artery reactivity 

related to the previous couple of day’s air pollution (both black carbon and sulfates). He 

indicated that the percent increase in arterial dilation was less in diabetics and individuals 

with coronary artery disease. 

Expert E added that studies by Van Eeden et al. exposed animals with atherosclerosis to 

PM and found that short-term exposures lead to decreased stability of plaques. He 

thought that this, coupled with thickening of the arteries, could increase the risk of a 

myocardial infarction. On a related note, he indicated that there was increasing evidence 

for associations between ischemic stroke and PM. He said that there were multiple 

studies done in Korea where there is a higher incidence of stroke than in the U.S., so 

there is more power to see associations in that population. He cited studies by Zeka et al. 

(2005), Wellenius et al. (2005), and Dominici et al. (2006), all of which found significant 

associations between PM and stroke. “Ischemic stroke is the same thing as a heart attack, 

only in the brain. Something’s blocking some arteries, say a plaque got ruptured, and it 

cuts off the supply of blood to some of the brain tissue.” 

He also thought that both animal and human studies have shown that PM could cause 

changes in heart rate variability (HRV). In animals PM seems to increase HRV (rather 

than decrease, as in humans), but he thought there was evidence that it occurs through the 

inflammatory pathway described above; when animals are simultaneously given Nacetylcysteine, a precursor to the anti-oxidant glutathione, no changes in HRV are 

observed. He indicated that he thought the changes in HRV could be connected to lung 

inflammation, in that HRV is controlled by the autonomic nervous system, which has 

nerve endings in the lungs that feed back to the control of the heart. 

COPD

Expert E thought that, “the fact that particles are associated with increased reactive 

oxygen species in the lungs seems to make sense for COPD. COPD is chronic lung 

inflammation, and if you have more inflammation going on in the lung, you’re going to 

tend to get mucus hypersecretion. You’re going to tend to get structural damage. And 

so, generating reactive oxygen species, increasing inflammatory cells and cytokines, it all 

seems like it would help contribute to develop COPD.” He cited studies that have shown 

epidemiological associations between particles and COPD, including the Adventist 

Health and Smog (AHSMOG) study, a 1993 study by Schwartz examining National 

Health And Nutrition Examination Study (NHANES) data, and a Chinese study. He also 

mentioned the Children’s Health Study that found when children moved to a less polluted 

area, their lung function growth accelerated and when they moved to a more polluted 

area, their lung function decreased (Avol et al., 2001). In addition, he cited a study by 

Paolo Saldiva and John Godleski (2002) that showed that the capillary walls in the lungs 

of the animals that were exposed to CAPs were thicker than those that were unexposed. 
 

E-3 

Lung Cancer

Expert E discussed lung cancer as a potential cause of mortality related to PM. He 

indicated that the cohort studies show increased relative risks with lung cancer mortality, 

but with large standard errors. He also indicated that, “there is a long history of studies 

showing that there’s an urban-rural gradient in lung cancer among non-smokers, which 

[could be due to] ... air pollution.” He also mentioned studies looking at measures of air 

pollution in cities that looked at the distance from the inner city and from industrial zones 

in the city and risk (Barbone et al., 1995; Biggeri et al., 1996). He thought that the 

evidence was pointing towards the traffic particles and diesel exhaust as causal agents of 

lung cancer. He also thought that, “inflammation is definitely a promoter. So, you would 

think that even when other things are causing the fundamental mutations, that in the 

presence of chronic lung inflammation and oxidative stress, it’s going to develop faster.” 

3.2. Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

Expert E generally thought the Künzli diagram was a good conceptualization of the 

relationship between long- and short-term exposures. “I think it’s a fine conceptual view 

to think about how air pollution affects mortality. What’s less clear is whether there are 

regression coefficients that come out of epidemiological studies that ... relate to 

[individual categories] or whether they’re all mixed.” In reality, he thought it was 

difficult to see a clear distinction between categories (A) and (C) because “there’s no one 

who’s completely unexposed.” He thought one concept not captured by the diagram is 

the possibility that long-term exposures to PM could modify the effects of short-term 

exposures. In theory, one might expect that individuals who are chronically exposed to 

high levels of PM would be more susceptible to short-term effects than those with lower 

long-term exposures. Paradoxically, the empirical evidence to-date points towards bigger

short-term PM effects in cities with lower long-term PM exposures.

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert E thought that cohort studies looking at changes in air pollution over time were 

the most directly relevant for measuring the mortality effects of changes in annual 

average PM2.5 concentrations. He thought that the Six Cities follow-up (Laden et al., 

2006) is the only such study that has looked directly at mortality changes. “The general 

pattern that ... you see in the cross-sectional studies is buttressed by similar findings for 

other health endpoints, like lung function and chronic respiratory symptoms, where 

similar study designs have been done” (e.g., in Germany (Heinrich et al., 2002) and 

Switzerland (Oglesby et al., 2000)). He indicated that these types of studies not only 

measure what we are asking for in the elicitation study (risks associated with a change in 

exposure) but also eliminate confounding by factors that vary across cities. However he 

noted the weakness is that potential confounding can still exist if some risk factors 

changed from one period to another. 
 

E-4 

He thought the next best study design for answering the question posed by the protocol 

was cross-sectional cohort studies. This study design is less optimal than the previous 

studies because it requires making the inference that differences in mortality between 

cities with high and low exposure also translate into similar mortality changes given 

temporal declines in air pollution. He thought the Laden et al. study (2006), having both 

temporal and cross-sectional designs, provided evidence that this was a reasonable 

inference. 

He thought that neither the cohort studies with temporal changes in air pollution nor 

cross-sectional studies would capture deaths “brought forward by a relatively short period 

of time ... but if the people who die today would have lived three years had they not had 

their heart attack today, then I would think that the cohort study would ... capture that.” 

When asked about the Utah Valley (Pope et al., 1996) and Dublin (Clancy et al., 2002) 

“intervention” studies, Expert E’s view was that they were supporting information but did 

not provide the coefficients relevant to estimating the impact of long-term changes in 

PM. He indicated that the Utah Valley study included a dummy variable for year the 

steel mill closed; “the fact that it was significant tells you that we’re not just looking at 

short-term harvesting and that if you change annual average pollution levels, something 

happens, but it doesn’t tell you what happens to the equilibrium mortality rate after you 

change pollution for a long time.” The Dublin study looks at a longer period of time and 

so gives you a “more useful coefficient,” but “unlike the cohort studies,” lacked control 

for risk factors that might change over time. 

He then mentioned time-series studies with long distributed lags (1-2 months). He 

thought that these did not provide useful quantitative coefficients to estimate the effects 

of long-term exposure, but show that “acute effects aren’t all short-term harvesting ... 

that they persist over months.” Finally, Expert E discussed time-series studies with short 

lags (1-2 days). He thought that these provide “qualitative support” for the hypothesis 

that “air pollution can kill people” but because they do not capture “any longer-term 

effects,” and therefore, the coefficients are underestimating the total mortality effects. 

Expert E’s views on the mortality effects captured by each study design are shown in the 

table below: 

Study Design Type of Effects Captured (e.g., shortterm, long-term, or both) 

Cohort studies looking at changes over time Long-term 

Cross-sectional cohort studies Long-term 

Intervention studies (Utah Valley, Dublin) Supporting evidence 

(Intermediate 1+ years) 

Time-series studies with long distributed lags Intermediate (1-2 months) 

Time-series studies with short lags (1-2 days) Short-term 
 

E-5 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert E thought that the following characteristics would be part of an ideal 

epidemiologic study to characterize the PM2.5-mortality relationship in the U.S. 

population: 

• Examines changes in long-term exposure to PM in multiple locations with 

different magnitude of changes, “so that your delta exposure is variable”; 

• Controls for potential confounding; 

• Intermediate time scale (“more than a year or so, but less than 20 years, where 

there are fewer things that might have changed over time”); 

• Representative geographic sampling of the U.S.; 

• Information about individual risk factors and modifiers of exposure (e.g., window 

air conditioning); 

• Population that is representative of the general U.S. population (e.g., follow-up on 

NHANES); and 

• Collects information on genotype to understand mechanisms and susceptibility. 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert E discussed the following studies: 

Study 

(author, 

date) 

Key findings Strengths Limitations 

Six Cities 

Follow-up 

(Laden et al., 

2006) 

Significant association 

of change in long-term 

exposure with change 

in risk of dying in the 

cohort 

• Directly addresses question posed 

by the protocol 

• Eliminates across cities 

confounding by design 

• Less exposure misclassification 

than cross-sectional cohort studies 

• Potential confounding by 

factors that may have 

changed over time within 

the cities 

• Not geographically 

representative (does not 

include west coast) 

American 

Cancer Society 

(ACS) Los 

Angeles (Jerrett 

et al., 2005) 

Examined zip code 

levels exposure 

estimates and found a 

larger slope than 

original ACS study 

• Better measure of exposure than 

original analysis 

• Only included Los 

Angeles, which has a 

different PM mix than the 

rest of the U.S. (higher 

fraction of secondary 

organic aerosol and 

nitrates – traffic related) 

The Netherlands 

Cohort study 

(Hoek et al., 

2002) 

Examined mortality 

effects of traffic 

particles using 

geocoding to enhance 

exposure assessment. 

Found larger 

coefficients than the 

original Six Cities 

study. 

• Better measure of exposure than 

the ACS or Six Cities studies 

• Uses black smoke and is a 

measure of traffic 

particles, rather than 

PM2.5 

• The Netherlands is 

homogenous for nontraffic particles 

Willis et al., 

2003 

Used the ACS data but 

restricted it to people 

• Better measure of exposure than 

the original ACS analysis 

• Did not provide results for 

PM2.5 (only sulfates) 
 

E-6 

Study 

(author, 

date) 

Key findings Strengths Limitations 

who lived in the same 

county as the monitor. 

Coefficient for sulfates 

increased two-fold. 

AHSMOG Qualitatively sees 

signals with mortality 

and particles as the 

mortality in the cohort 

has increased over time 

• Provides qualitative support for 

mortality effects of PM 

• Low mortality (healthy 

cohort) which leads to 

low statistical power 

• Non-representative 

population 

• Manipulations of data 

decreases the applicability 

of results 

Expert E thought the key strength of the Six Cities study is that the population is a 

random sample of the area of study. He thought that this was important because 

epidemiology contrasts mortality experiences of samples in different locations with 

pollution concentration differences. If the sampling scheme produced nonrandom 

samples, then we need to be assured that it did so in a uniform way, so that it is 

reasonable to ask whether pollution contrasts across locations are associated with 

survival. In the case of the Six Cities study, this is not necessary, as the samples were 

representative. In the case of the ACS study, the sample was not representative because 

it was a convenience sample with a bias towards high socioeconomic status (SES), and 

possible other biases, as they were recruited by volunteers. Fortunately, Pope et al. have 

compared the results of the ACS analysis to a result comparing age standardized 

mortality rates by city against the pollution differences by city, and found comparable 

results. This means that the sample is the same subsample in each location, and the 

relative mortality risks are preserved compared to the general population. Without this, 

we cannot consider it reasonable to look for pollution as an explanation of differences, 

since the differences would reflect sampling differences across community, rather 

differences in risk across community. This supports the use of those two studies. The Six 

Cities study has the added advantage that it was designed to look at air pollution, and 

therefore sampled people in defined neighborhoods of each community, with monitors 

within each neighborhood, and generally within a few miles of each subject. 

Given the different aerosol mix in the Jerrett study in Los Angeles (LA) (2005), Expert E 

argued that, “while directionally it suggests that as you reduce exposure error, you get 

higher coefficients, it would be [difficult] to take that coefficient and say that that should 

be applied nationally.” On the other hand, he thought that work by Willis et al. (2003) 

(published in Toxicology and Environmental Health) on sulfates was supportive evidence 

for the exposure error hypothesis. This study found that when you include only those 

individuals in the ACS cohort who lived in the same county as the exposure monitor, 

there was a doubling of the sulfate coefficient for all-cause mortality compared to 

including all individuals in the metropolitan-area. He noted that this study did not 

include the full ACS cohort, but was a more nationally representative sample than the 

Jerrett et al. (2005) study. 
 

E-7 

Expert E was also asked about what reliance he might have on the AHSMOG study, the 

Lipfert et al. series of studies and the recent Enstrom et al. (2005) study. He indicated 

that he would not want to rely on coefficients from the AHSMOG study to represent the 

U.S. population because that cohort was highly non-representative (i.e., non-smokers, 

healthier dietary patterns). In addition, he thought the exposure measure was nonstandard, the study size is small, and not many deaths have been observed. He noted, 

however, that as the number of deaths has increased, the mortality coefficients have 

increased. He concluded by saying, “I think it's qualitatively supportive.” 

When asked about the cohort studies by Lipfert et al., Expert E indicated that he did not 

give any weight to the results; he felt strongly that the industry funding sources were 

likely to bias the findings and that they are not published in mainstream epidemiologic 

journals (where they would get rigorous peer-review). In addition, he indicated that the 

Lipfert study differs substantially from the Six Cities study in the sample design issue. It 

is clear that the relative ranking of mortality rates across cities for members of the cohort 

is quite different from the relative ranking of the general population. For example, 

Lipfert has reported a relatively high mortality rate in Salt Lake City, which in general 

has a low mortality rate compared to other cities. This violation of the principle that the 

sampling frame has to sample the same way in each city means that one cannot contrast 

the community exposure against the mortality experience of biased samples not 

representative of the communities. A second major concern Expert E mentioned is that 

the cohort was basically recruited from hypertensive subjects and did not control for 

cardiovascular medication. There is documented wide variability in the aggressiveness of 

the use of cardiovascular medication by region of the country, and by deliberately 

sampling a cohort that maximizes the potential for confounding exposures that are 

defined geographically, it could lead to bias, unless medications are controlled in the 

study. Hence findings of no association with sulfates, which are driven by the lower than 

average mortality rates in the Northeast in this cohort, could be entirely due to the well 

documented more aggressive use of cardiovascular medications in the Northeast. 

He was not as familiar with the Enstrom et al. (2005) work but had similar concerns as 

for the Lipfert work. He noted that given that Jerrett has reported almost as much 

variation in PM2.5 exposure within Los Angeles county as exists across the US in the 

ACS study, it is clear that the use of county level averages represents much greater 

exposure misclassification in California (CA) than elsewhere in the US. For example, 

there is little difference between PM2.5 concentrations in the county north of Boston and 

the county to the south, and there are fairly similar levels in Philadelphia and 

Washington, DC. Given the topography in CA and the dominance of traffic as opposed 

to long range transported particles, he felt that this study moves in the exact opposite 

direction as that suggested by the Six Cities study, Willis et al., and Jerrett et al. 

3.5 Confounding 

In general, Expert E thought that all of the potential confounders he discussed had a 

minimal impact on published effect estimates (less than a 20 percent change) and were 

well-controlled in the studies that he relied upon for his quantitative estimate. He thought 

a key point in this discussion is that many people first focus on whether or not a factor is 
 

E-8 

correlated with outcome but in order for their to be confounding, the factor also has to 

correlated with exposure. He did not think that there is much evidence that many 

potential confounding factors actually meet this second criterion. His discussion of 

confounders in the ACS and Six Cities cross-sectional studies included the following: 

• Age: Expert E thought this could be an important factor if it is not controlled for 

adequately. He thought that “in general, cities with older populations, other than 

in Arizona and Florida, tend to be older industrial cities, where young people are 

moving out ... so it’s plausible that exposure and age are correlated.” He thought 

this was potentially more of a problem in the Six Cities study than the ACS study. 

He indicated that not controlling for age would result in an overestimate of the 

pollution effect. He thought that the Six Cities and ACS studies adequately 

controlled for this factor. 

• Smoking: He indicated that “[s]moking is ... a pretty good predictor of mortality, 

but there’s not much evidence in these cohort studies of a correlation between 

smoking and exposure ... and it didn’t look like, in either the Six Cities study or 

the ACS study, the controlling for smoking did much to the PM effect.” 

• SO2: Expert E indicated that when SO2 was added to the model in the ACS study, 

the particle coefficient changed significantly. However, he thought it was 

unlikely to be contributing to mortality (“concentrations of S02 in the U.S. are 

extremely low, and 90 percent of it is stripped out in [the] nasal passages, ... [it’s] 

a somewhat reactive gas, the indoor-outdoor ratio is small.”) He thought that 

“SO2 could only be standing for something else, and the most plausible thing it’s 

standing for is particles. Because SO2 and particles are highly correlated. I just 

view that as a model that had two different indices of particles in it.” 

• Socioeconomic Status: He thought that socioeconomic status (SES) could be a 

potential confounder in the cross-sectional cohort studies, but not in the “change” 

studies. He said that most epidemiologic studies control for SES by using a 

variable for education. He thought that family income might be a useful measure 

to include in the models. He also thought that examining the census block groups, 

which are “extremely homogeneous on socioeconomic position” would be 

informative. 

• Diet/BMI: Expert E indicated that diet was not included in the Six Cities analysis, 

which was a “major failing” although he noted that it did include alcohol and 

body mass index (BMI), which does a “pretty good job.” He thought that there 

could be some residual confounding by diet, even after controlling for BMI, but 

he was not sure how it could be correlated with exposure. 

In general, he thought that residual confounding by SES and age in the Six Cities and 

ACS studies, if it existed, would be more likely to contribute to over estimates of the 

mortality effect coefficient, but thought it was a minimal impact (score ~1; < 20 percent). 

He did not think that occupational history or pre-existing disease were potential 

confounders in the cross-sectional cohort studies. 
 

E-9 

Expert E then discussed potential confounding in the Six Cities follow-up (Laden et al., 

2006). The first factor he mentioned was smoking, since the study did not record 

smoking status during the most recent follow-up period. He thought that, most likely, 

people are quitting rather than starting to smoke, and it’s “not obvious that people would 

quit smoking more or less in places with more or less change in air pollution, although 

it’s always possible.” He thought SES and diet were probably not confounders in this 

study because they would have to change over time. He indicated that it was possible in 

this type of study design to have confounding if there were changes in medical care and 

life expectancy over time. He said that the Laden et al. study controlled for a period two 

versus a period one effect, which attempted to capture all of these factors. Expert E 

pointed out, however, “if the changes were different in the different cities, and those 

differences happened to be correlated with the differences in air pollution, then we have a 

potential for confounding.” In general, he thought the direction of confounding in the 

Laden et al. paper was uncertain and not very large (i.e., that these factors might cause 

the coefficient to shift by 20 percent one way or another but not by as much as 50 

percent). 

3.6 Effect Modification 

Expert E began his discussion of effect modification using acute studies which he felt 

provided mainly qualitative insights, particularly about the influence of underlying 

susceptibilities on PM-related mortality (diabetes, obesity, systemic inflammatory 

disease, genetic susceptibility). He thought short-term studies have shown effect 

modification by diabetes (e.g., hospital admissions, flow-mediated dilation studies: 

Goldberg et al. in Montreal, Rome case-crossover studies), and some evidence of effect 

modification by obesity. In general, Expert E thought that there are a number of 

biological phenomena affecting susceptibility (including other systemic inflammatory 

diseases such as rheumatoid arthritis) that track with age so that age is a possible 

surrogate for these factors. 

Expert E thought that genetic polymorphisms related to susceptibility for developing 

atherosclerosis (e.g., GSTM1-null mutation) were potential effect modifiers. He noted 

that the existing cohorts did not have genotypes for the population, but that in the future, 

“we’re going to find that there’s actually a fair amount of genetic heterogeneity in the 

population with respect to things that may turn out to modify the effects of PM2.5.” He 

also indicated that, “there are genetic differences between races” and that one would 

logically expect race to be a surrogate for these, although he acknowledged that shortterm studies have not really detected any evidence of racial differences (Zeka et al., 2006 

and Zanobetti et al., 2000). He did not think race was something that could be examined 

in the cohort studies given that the Six Cities study cohort, including the Laden et al. 

follow-up, did not include minorities and the ACS cohort underrepresented minorities. In 

theory, he thought that this would lead to an underestimate in the effect estimates, 

although there was limited evidence that this was occurring. 

Expert E discussed potential effect modification by education, which was found 

particularly in the in the ACS study and to a lesser extent in the Six Cities study. He said 
 

E-10 

he was “a little skeptical” about this factor coming out of the extensive Krewski et al. 

reanalysis (2000) in part because of the multiple comparisons problem but also because 

the Six Cities extended follow-up found effect modification by education for some types 

of mortality and not others. Still, he thought that it was plausible to some degree because 

“poor people have a lot of stresses ... poor diet, ... less air conditioning. So, there are 

both exposure-related differences and host susceptibility-related differences.” He thought 

that this was more of an issue in the ACS study than the Six Cities study. 

Expert E discussed effect modification by air conditioning, citing a paper by Jeremy 

Sarnat (2000) that found “a significant association between personal PM2.5 and outdoor 

PM2.5, in all three groups [low, medium, and high ventilation in the home], with a factor 

of two difference in the slope between the high ventilation and the low ventilation 

group.” He also indicated that studies in Atlanta often did not find positive associations 

between exposure and ill-health, which he thought was due to air conditioning use 

(studies that were part of the Aerosol Research and Inhalation Epidemiology Study 

(ARIES) and by Paige Talbert’s group). He thought this was a large effect modifier that 

would have a “factor of two impact on what the true health effects are.” 

Expert E thought that underlying disease, such as diabetes or COPD could be an effect 

modifier in the long-term studies. He thought that those with COPD have difficulty 

breathing and therefore, would be more susceptible to the effects of particles. In 

addition, he said that those with COPD have “ventilation/perfusion inhomogeneity,” 

meaning that parts of the lung are exposed to larger volumes of air than others. This 

could lead to enhanced particle deposition in parts of the lung that get higher airflow. He 

thought the evidence for effect modification from COPD was mixed, which in part could 

be due to the variability in the diagnosis. 

The following table summarizes the discussion on potential impacts of effect modifiers: 

Study (author date) Effect Modifier Score (1-3)* 

Six Cities SES 

Obesity 

Other susceptibilities 

(diabetes, COPD) 

1 (underestimated RR) 

1 (underestimated RR) 

1 (uncertain whether these 

groups were under or over 

represented in cohort) 

ACS Educational attainment 

Obesity 

Poverty 

Race 

2 (underestimated RR)** 

2 (underestimated RR)** 

2 (underestimated RR)** 

2 (underestimated RR)** 

* 1= minimal, 2= moderate, 3= major 

**Expert E was not sure whether these all had independent effects of similar magnitude. 

He thought they might be correlated and be represented essentially by educational 

attainment (collectively account for ~50 percent underestimate, crudely estimated) 
 

E-11 

3.7. Exposure Issues 

Expert E thought that exposure misclassification due to central site versus individual 

exposures was a significant exposure issue. He indicated that “the gradient in effect size 

estimate from ... neighborhood-level measurements, to the county [level] to the metro 

area measurements ... suggests that more geographically localized assessment of 

exposure results in a larger effect size estimate.” He thought the ACS study results were 

likely to be biased downwards due to exposure error, based on the results from Jerrett et 

al. (2005) and Willis et al. (2003), which both showed higher effect estimates than the 

original study with increased spatial resolution of exposure assignment. While the Jerrett 

study showed some effect estimates that were higher by a factor of three or more 

depending on the model, he acknowledged that other factors like particle composition 

might account for some of the difference. He thought that the ACS study estimates 

should be increased by a factor of two to account for this issue. 

Expert E next discussed differential toxicity of particles. He thought that the Six Cities 

study was not representative geographically of the U.S. in that it only spanned from 

Topeka to the east. Given that time-series study effects tend to be smaller in the west 

(National Morbidity, Mortality, and Air Pollution Study (NMMAPS)), whereas the Jerrett 

paper found higher cross-sectional effects in LA, he thought uncertainty exists about the 

representativeness of the effect estimates in the Six Cities study. He was not sure of the 

direction of the uncertainty caused by this issue, but assigned it a score of 2. He did not 

think that this affected the ACS study, because it included cities all across the U.S. 

He thought that another exposure issue was whether the exposure in the cohort studies 

was measured at the appropriate time to see the full mortality effects. He thought the 

ACS study exposures were somewhat inadequate because they only included a few years 

of data and “that creates some additional uncertainty as to how relevant that exposure was 

to the mortality follow-up.” He indicated that the Six Cities study’s measure of average 

exposure was significantly better than the ACS study because “for the first follow-up, 

they monitored for most of the time throughout the period ... and therefore presumably, 

[there was] less measurement error, less downward bias in the slope.” However, for the 

Laden et al. (2006) follow-up period, there were no PM2.5 monitors, and the 

concentrations were actually predicted by a regression model that included PM10 and 

airport visibility. He still thought this was an improvement over the ACS study in that 

there were continuous exposure estimates throughout the follow-up, but acknowledged 

that direct PM2.5 measurements would have been superior, had they been available for the 

extended follow-up. He indicated that the use of estimated PM2.5 would probably have 

added random measurement error, which could have caused a downward bias in the 

Laden et al. (2006) estimates. 

When asked about differences in the slope over time, Expert E thought it wasn’t 

necessarily just the timing of exposure measurements relative to outcome, but could be 

other factors, such as: 1) “confidence intervals may in fact overlap and may just be 

noise”; 2) differences in particle composition; and 3) air conditioning prevalence 

increases over time. He indicated that in the Six Cities study, “when [the investigators] 
 

E-12 

looked at difference between period one and period two ... [there was] a bigger 

coefficient ... for the change.” He thought “that the Dublin study says that you've got to 

see some of [the mortality effect] next year, and [the Laden study] says you're basically 

seeing all of it within ten years.” 

3.8. Causality 

Expert E thought that there was a “web of evidence” supporting a causal relationship. He 

indicated that there were many different study designs with different strengths and 

weaknesses and that the evidence was consistent across all of them. He mentioned 

epidemiologic studies, chamber studies, animal studies, and cell culture studies. He 

indicated that the animal studies (Sun et al., 2005; Van Eeden et al.) show changes in 

oxidative stress, inflammation, destabilization of atherosclerotic plaques, and changes in 

electrocardiogram patterns, all of which contributes to the biological plausibility. He 

thought the chamber studies in people (though under-powered and over-controlled) 

provided evidence of changes in fibrinogen levels, systemic inflammation, and 

endothelial function. He thought these studies are consistent with adverse effects on 

mortality and with the mechanistic pathways that are suggested by the animal studies. In 

addition, he mentioned epidemiologic studies on PM and mortality, including time-series, 

cross-sectional cohort, and change cohorts all have different limitations. “Individual 

studies have individual flaws, but that’s the advantage of different study designs with 

different kinds of vulnerabilities. You can now build this web and say the web is a lot 

less vulnerable.” He did not think that there could be another factor to explain the 

consistency across all of the study designs. 

Expert E specified a range of the likelihood of a causal relationship of 80 – 99 percent 

with a most likely value of 95 percent. He set his upper limit at 99 percent because “I’m 

pretty convinced ... but nothing’s for certain.” Maybe “a substantial amount of the effect 

is flowing down pathways that haven’t been identified.” He noted some inconsistencies 

in studies of inflammatory markers (e.g., C-Reactive Protein) for example. He noted, 

however that the medical community accepted a causal relationship between smoking 

and cancer long before many of the data gaps (e.g., an animal model) were filled in. 

 In order to get at his lower bound and most likely value, he drew an analogy with 

environmental tobacco smoke (ETS). He thought that, “the evidence for PM is stronger 

than for environmental tobacco smoke” because the evidence for ETS is based mostly on 

case-control studies, which he thought could be biased because of the difficulty in 

selecting appropriate controls. He also thought that PM was a more randomly assigned 

exposure than ETS. He thought for ETS and lung cancer, his causal likelihood would 

range between the high 60 percents to the low 90 percents with a most likely value of 80 

percent. Since he thought the evidence for PM was greater than ETS, he selected his 

minimum based on the most likely value for ETS, and his most likely value for PM based 

on the upper bound for ETS. 
 

E-13 

3.9 Thresholds 

Expert E thought that conceptually, the idea of an individual threshold in animal studies 

(i.e., that a specific dose causes death or not) is too deterministic. He thought that the 

probability of an animal dying from a given dose at a given time follows a Poisson 

distribution. Therefore, he thought that the fact that an animal died in a study from being 

administered a specific dose was not entirely based upon their individual threshold or 

susceptibility level, but rather was a compilation of several different stochastic factors. 

With regard to a population threshold, he explained that, “in a population that's 

genetically diverse, and diverse with respect to all these effect modifiers that we spoke 

about ... I think it's mathematically impossible. So the probability that something is 

mathematically impossible could happen, is extremely low. So I think that there isn't a 

threshold in a population. That doesn't mean the slope can't change. That doesn't mean 

the probability of dying, on average over the population as a function of dose, has to be 

linear. But I don't think it's going to be zero for something that at some dose can kill 

you.” 

He mentioned that epidemiologic studies, such as Pope et al., 2002 that plotted the 

concentration-response (C-R) function and suggested a steeper curve at lower levels. He 

also discussed an expert’s presentation at the pre-elicitation workshop of a model 

averaging approach, the results of which put most of the weight on the linear term and the 

overall curve looked linear down to about 5 μg/m3

. Expert E concluded that “in this 

range [4 to 30 μg/m3

], it looks pretty linear to me from all the data” and therefore he did 

not chose to incorporate a threshold into his C-R function. 

3.10 Other Influential Factors 

Expert E discussed additional sources of uncertainty that were not part of the protocol. 

He did not think that methodology was major issue. He indicated that “Cox proportional 

hazards models are pretty standard for cohort studies. There’s actually a lot to be said for 

fitting a parametric survival model, because there’s not huge evidence of a need to go 

non-parametric for the underlying survival curve. A two-parameter Weibull model is 

pretty flexible to get it fitting any shape you want.” He also did not think that spatial 

auto-correlation had much impact on the analyses. Expert E did not think there were any 

other outstanding issues not already covered by the protocol. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert E thought the C-R relationship was linear over the entire range of concentrations 

that were the focus of the study (4-30 μg/m3

) based on the current evidence. He indicated 

that the Pope et al., 2002 and some acute studies suggest that the C-R function is steeper 

at lower levels. However, he thought that, “the evidence for a deviation from linearity is 

very weak.” 

Expert E chose to provide a C-R function that was conditional on the existence of a 

causal relationship. The elicitation team then combined his conditional distributions with 
 

E-14 

his percent likelihood of causality specified in Section 3.8 (95 percent likelihood of a 

causal relationship). 

Expert E began with the 50th percentile value because he felt it offered the advantage of 

thinking about different studies and integrating them. He based his estimate largely on 

three studies: the change estimate from the Six Cities extended follow-up by Laden et al. 

(2006) of 3 percent per 1 μg/m3

; the Six Cities cross-sectional cohort study (about 1.5 

percent per μg/m3

); and the ACS study (0.6 percent per μg/m3). He adjusted the ACS 

estimate first by doubling to account for exposure misclassification, to get 1.2 percent. 

He based the doubling on the paper by Willis et al., 2003 that found coefficients for 

sulfate from the ACS data that were twice as high as the original analysis with greater 

spatial resolution of exposure assignment (he assumed that the exposure error in PM2.5

would be at least as high as with sulfate). He then increased the ACS estimate 50 percent 

more (based on Pope’s estimates at the pre-elicitation workshop) to 1.8 percent to 

account for under-representation of low SES, diabetes and obesity within the ACS study 

population. He then indicated that qualitatively, the Jerrett et al., 2005 estimate and the 

Hoek et al., 2002 study estimates support higher estimates with finer geographic 

resolution. He took all of this information and combined it roughly into an “average” 

estimate of 2 percent for the 50th percentile. 

To get at the distribution about the median estimate, Expert E proceeded by thinking 

about how the variance between studies and within studies might be accounted for in 

coming up with an overall distribution. He reasoned that the overall variance would be 

the sum of stochastic within-study variance and the variance due to heterogeneity across 

studies. He further reasoned that these two variances were roughly equal in magnitude. 

For the stochastic portion of the variance, he indicated that the variance of a metaanalysis estimate is the sum of 1 over the weights. The weights in turn are 1 over the 

individual within-study variances. Assuming that the within-study variances were all the 

same, and if there were five studies contributing to his estimate, he calculated that the 

variance of the average is “going to be roughly half the variance of the typical study.” 

Based on his earlier assumption that the heterogeneity variance is about the same size as 

a typical within-study variance, it follows that the total variance is about 1.5 times the 

typical within-study variance (1 part for the heterogeneity variance and 0.5 part for the 

meta-analytic stochastic variance). Taking the square root, he came up with an estimate 

of roughly 1.3 (times the typical within-study SE) for the standard error around his 

central effect estimate. He then discussed the concept that since he had increased the 

estimate to correct for measurement error, he would need to inflate the standard error 

further because “I’ve added some additional uncertainty by doing this correction factor. 

And so, the standard error of my now unbiased estimate of beta is bigger than the 

standard error of the biased estimate, using the exposure that was measured with error.” 

He estimated that this would inflate the standard error by an additional 30 percent. But 

this applies only to the stochastic “within-study” standard error (0.3). Multiplying that by 

1.3 to account for this additional source of uncertainty yielded a new stochastic error of 

0.4, and a total SE of 1.4. 
 

E-15 

After combining these estimates of variance, he concluded “we might want to take our 

typical standard error from these studies and multiply it by 1.4 to generate an estimated 

range of uncertainty.” He decided to use the Pope et al., 2002 standard error to represent 

a “typical” standard error from a study. Therefore, he took the standard error from the 

Pope study (0.0023) and multiplied it by 1.4 to get 0.32 percent. He then adjusted the 

standard error further to take into consideration the adjustment for under-representation 

of lower education in the ACS cohort by multiplying the standard error from Pope by 1.5, 

instead of 1.4 to get 0.345 percent. 

Expert E then stipulated that a normal curve be fit to a distribution with a 50th percentile 

at 2 and a SE of 0.345. After the elicitation team plotted this curve, he decided that the 

estimate from Laden et al., 2006 was not receiving enough probability weight. 

Therefore, he altered the normal distribution so that the 50th percentile was at 2, and a 

95th percentile was at 3. When asked to reconcile placing the Laden change estimate at 

the 95 percentile given how directly he thought it addressed the question, Expert E 

explained that he had some concerns about the size of the estimate relative to all the other 

studies. The interquartile range was estimated from the distribution. He truncated his 

distribution at zero but did not specify a maximum value. 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Expert E

Percentile Percent Change in Mortality 

Elicited Distribution 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood (IEc 

Generated) 

Minimum 0 0 

5th 1.0 0 

25th 1.6 1.5 

50th 2.0 2.0 

75th 2.4 2.4 

95th 3.0 3.0 

Maximum - - 
E-16 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distributions from 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Expert E - Elicited Expert E -

Incorporating

Causality (IEc

Generated)

ACS Original ACS Extend ACS LA Analysis Six Cities Original Six Cities Follow

% Decrease in Mortality per 1 ug/m3 PM2.5
 

E-17 

Elicited Distribution 

Distribution Incorporating Causality - Probability Density Function (IEc Generated) 
 

E-18 

Distribution Incorporating Causality - Cumulative Density Function (IEc Generated) 
 

E-19 

U.S. EPA EXPERT ELICITATION STUDY OF THE CONCENTRATION-RESPONSE 

RELATIONSHIP BETWEEN ANNUAL AVERAGE PM2.5 EXPOSURE AND 

MORTALITY 

Modification to Expert Judgments

Expert E

Date: July 7, 2006

Section of Protocol Affected (Section Number and/or Title):

Causality—dose response 

Description of Change (e.g. to a specific percentile, or to a qualitative opinion or 

statement of belief):

In the post elicitation workshop I was struck by an expert’s statement that the assigning 

of some probability of no effect was really an assigning of a finite probability that there 

was not even a very small effect, that the size and probability interacted. In thinking 

about this, I would like to change my previous assumption that there was a 5 percent 

probability of zero effect to there being a 1 percent probability of there being zero effect. 

I would leave all the other assumptions about dose-response and its uncertainty the same. 
 

Expert F 

Interview Summary
 

F-1 

Interview Summary 

Expert F 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS

3.2. Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

Expert F wanted to discuss the effects of short-term and long-term exposures separately, 

and chose to begin with Kunzli’s framework as a way of structuring the discussion. 

Expert F generally agreed with the Kunzli framework although he differed on the relative 

magnitude of the effects. He agreed that most chronic long-term exposure effects fall 

into Category B and that deaths due acute exposures largely fall into Category C. He 

argued that chronic exposures account for the majority of effects; he estimated the ratio 

of long-term to short-term effects to be about 3:1. Expert F thought that Category A, 

where air pollution is both the underlying cause of frailty and the acute cause of death 

was likely to be a much smaller percentage (e.g., around 3 percent of all long-term 

effects, 10 percent of short-term effects). This judgment was based on a view that the 

“acute effects ... are largely independent of the long-term [effects]” and that the majority 

of individuals who get sick over the long-term are not getting sick due to air pollution, 

and that therefore the joint probability of both B and A is likely to be very small. 

In essence, Expert F viewed “estimates from the long-term studies and estimates from the 

short-term studies to be basically, not completely ... additive.” In other words, he 

thought the long-term studies do not capture all of the short-term effects, partly because 

of the statistical argument given above but also because of the underlying mechanisms, 

and the fact that the life-shortening of the acute PM exposure effects was likely much 

shorter than for the effects of long-term exposure. 

3.1 Mechanisms for Effects from Exposure to PM2.5

Expert F thought that the mechanisms for the effects of short-term and long-term 

exposures were “relatively” independent. He began with a discussion of long-term 

exposures. 

Long-Term Exposures

In order of contribution to overall deaths from PM2.5, he discussed deaths from 

cardiovascular disease, respiratory disease, and cancer. He saw the lung as being 

primarily the “portal” through which other systemic effects occur, rather than the primary 

site for most of the mortality effects. 

Expert F thought the mechanistic pathway for cardiovascular disease involved chronic 

lung inflammation, leading to release of cytokines and other indicators of oxidative 

stress, which lead to increased plaque formation in the vascular system, which can 

ultimately increase the likelihood of thrombosis and death. He cited the Sun et al. (2005) 
 

F-2 

paper in atherosclerotic mice (presented at the pre-elicitation workshop) as influential in 

providing evidence for this pathway. He also thought animal studies by Costa and 

Kodavanti, (2003) and other work done at EPA showing the oxidative stress and damage 

to tissues caused by exposure to acids and transition metals were also supportive. He 

argued that such studies point to the importance of fossil fuel combustion as a “dominant 

source of the problem.” 

Impact on the respiratory system was the second cause of death Expert F thought could 

be associated with air pollution, although more weakly. Expert F suggested that the 

overall mechanism involves both direct damage to the lung and impacts on lung 

development that in turn contribute to increased risks of respiratory infections. He 

thought damage can occur via different routes; particles initiate chronic inflammation 

which itself can damage lung tissue (Ghio et al., 2000) and macrophages that take up 

particles may then die releasing enzymes that can cause scarring in the lung. He cited a 

study by Plopper and Fanucchi (2000) that showed altered immune defenses in animals 

where the lungs did not develop both physically (e.g., development of the alveoli and size 

of airways) and immunologically. He pointed to some epidemiological evidence of 

increases in death from influenza and pneumonia related to air pollution as supportive of 

this mechanism. In general, he noted that there are fewer deaths from respiratory disease, 

so the statistical power is weaker to assess this association. 

The third cause of death Expert F discussed was lung cancer, which has been associated 

with PM2.5 in epidemiological studies (Pope et al., 2002; Jerrett, 2005). He did not think 

that the same degree of mechanistic evidence has been established in animals for lung 

cancer as it has for cardiac and respiratory effects. His argument for the mechanism was 

largely based on general cancer principles linking together pieces of evidence; particles 

contain known carcinogens (e.g., benzo[a]pyrene and other organics) which have been 

shown to be mutagenic in Ames-type assays and which act as initiators; metals and sulfur 

can then act as promoters by contributing to oxidative stress and cell damage and cell 

turnover, as shown in animal studies. He did not know of any long-term animal studies 

that have addressed the question of the carcinogenicity of inhaled particles. 

Short-Term Exposures

Expert F expressed the opinion that the primary causes of death associated with shortterm exposures to PM2.5 were related to cardiac and respiratory mechanisms. He thought 

the mechanisms for cardiac deaths included not only ischemic heart attacks (as for longterm) but also effects related to arrhythmias or changes in heart rate variability (HRV). 

Expert F also discussed evidence that PM exposures lead to acute changes in blood 

coagulation (Devlin et al., 2004) leading to increased risk of coronary thrombosis, and to 

increases in C-reactive protein (CRP) (Peters et al., 2001; Ruckerl et al., 2006). He cited 

work by Annette Peters (2000, 2001), a study by Robert Devlin (2003) in elderly subjects 

exposed to CAPs, and a study by William Penn Watkinson (2000) in animals as 

influential evidence of the impact of PM on reducing HRV. Expert F speculated that the 

impact on the autonomic nervous system might be mediated by the oxidative stress and 
 

F-3 

release of fibrinogen (Schwartz et al., 2001 National Health and Nutrition Examination 

Survey (NHANES) study) and of CRP (Seaton, 1999; Riediker et al., 2004). 

Expert F thought the arguments for respiratory-related deaths was more compelling for 

short-term exposures to PM than for long-term exposures. He cited studies by Gilmour et 

al. (2002) and Zelikoff et al. (2003), which showed increased mortality rates in animals 

exposed to both streptococcus and particles compared to those in animals exposed to 

streptococcus alone. The rate of inactivation of the bacterium was also reduced 

suggesting impairment of the immune system by exposure to high levels of particles. 

Expert F thought acute inflammation by high exposures to particles was a separate 

mechanism contributing to respiratory-related deaths, primarily in individuals suffering 

from asthma or other chronic respiratory diseases. Expert F did not think cancer was a 

cause of death related to short-term exposures, but indicated that cancer patients could be 

more susceptible to effects from acute exposures to particles. 

However, he noted the biggest (but not unusual or prohibitive) limitation in the evidence 

for the mechanistic pathways is that much of it relies on animal studies; the mechanisms 

are shown to be biologically plausible, but cannot be absolutely proven in human subjects 

because of the practical limitations on such human studies. 

3.3 The Role of Epidemiological Study Design in Characterizing the Total Impacts 

of PM2.5 Exposures on Mortality 

Expert F thought that prospective cohort studies represent a “gold standard” for 

estimating the effects of long-term exposures. These were closely followed, in his view, 

by the cross-sectional cohort study designs, which he thought provided qualitative 

support for the quantitative effect estimates reported in prospective cohort studies. In 

both types of studies, he estimated that roughly 97 percent of the mortality reported was 

due to long-term exposures with the remaining 3 percent attributable to cumulative shortterm exposure effects. 

For estimating the mortality effects of short-term exposures to PM2.5, he thought the timeseries and case-crossover designs were most useful. He thought the panel studies were 

important for demonstrating that the effects that are observed in population studies are 

also observed on an individual level. 

When asked about intervention studies or “natural experiments” (such as Pope’s Utah 

Valley study, the Clancy study in Dublin, and the Hedley Hong Kong study), he thought 

that they were not as directly appropriate to the quantification of mortality effects, though 

they do speak to the issue of cessation lag; such studies suffer from questions about 

temporal confounding (i.e., changes in other factors that could also affect mortality). 

3.4 Epidemiological Evidence for the Impact of Exposures to PM2.5 on Mortality 

Expert F’s ideal epidemiologic study included the following attributes: 
 

F-4 

• Representative of the U.S. 

o Appropriate sampling to include important subgroups (e.g., age, race, 

education level, socioeconomic status (SES)); and 

o Geographically. 

• Collection of individual level data at both beginning and follow-up (e.g., personal 

risk factors related to cardiac, respiratory disease, and lung cancer). 

• Exposure assessment 

o Representative of lower and higher exposures; 

o Personal exposures to outdoor PM2.5, not personal exposure overall; and 

o Includes information on mobility. 

The specific studies that Expert F thought would be most informative for his quantitative 

estimates were the extended American Cancer Society (ACS) cohort studies (Pope et al., 

2002 & 2004), the reanalysis in Los Angeles (LA) (Jerrett et al., 2005), and the original 

Six Cities cohort study (Dockery et al., 1993) as well as the follow-up (Laden et al., 

2006). He thought the strengths of the ACS studies included their large study population 

that is inclusive of an age range relevant to the elicitation, more nationally representative, 

detailed individual level data on risk factors, and exposure measurements at the 

beginning and end of study that were well correlated. Expert F noted that limitations of 

the ACS studies related primarily to effect modification by education and exposure 

misclassification (this topic is discussed in greater detail below). He indicated that 

strengths of the Six Cities study were that it was specifically designed for investigating 

the relationship between air pollution and health, that its exposure estimates did not rely 

on central site monitors only (they have some personal-level monitoring), inclusion of 

more co-pollutant information, data on potential confounders, and a population that is 

more representative of the general U.S. population (regarding educational level, 

industrial, and non-industrial America). He thought its limitations included relatively 

small sample size, restriction to the eastern half of the U.S. (i.e., no representation of the 

west, especially California), and non-representative racial composition (mostly white). 

Expert F thought the Jerrett (2005) study of the LA component of the ACS study 

improved measures of exposure within LA and, by focusing on one metropolitan 

statistical area (MSA), eliminated some of the issues with between-city comparisons that 

existed in the parent study. He thought potential limitations of the Jerrett analysis were 

that it is representative only of one city, that spatial correlations might exist between SES 

variables and more polluted parts of LA, and that there may be within-MSA mobility 

impacts on exposure. He argued that the Jerrett study essentially trades one exposure 

measurement problem for another, yet the fact that it finds similar results provides 

evidence that the PM2.5 mortality association is robust. He thought the Laden 2006 

follow-up to the Six Cities study has similar strengths and weaknesses of the original Six 

Cities study, though it has the benefit of longer follow-up period. 

Finally, Expert F noted that the ecological studies (e.g., Ozkaynak and Thurston; Evans, 

Tosteson, and Kinney, 1984) showing an air pollution effect on mortality have been 

recently exonerated by all the subsequent cohort studies. He thought the concerns about 
 

F-5 

confounding that were raised about the ecological studies have not turned out to be 

substantiated. 

Expert F briefly discussed papers that he felt were less conclusive about the relationship 

between PM2.5 and mortality, such as the Lipfert studies and the Enstrom (2005) study, 

neither of which Expert F wanted to place much emphasis on. He basically argued that, 

although Lipfert's later study found results similar to some of the studies above when he 

used more comparable measures of exposure, his studies lack credibility given the 

funding source (Electric Power Research Institute (EPRI)), and that the papers were not 

published in a journal where they were likely to be peer-reviewed by epidemiologists 

(e.g., Inhalation Toxicology and Atmospheric Environment). He had similar concerns 

about the Enstrom (2005) paper in Inhalation Toxicology. Although not fully familiar 

with this study, he had concerns with the way exposure was measured (at point of 

residence as opposed to an area average), the highly educated population, and large loss 

to follow-up. He thought if one assumes that women spend more time at home than men, 

then the exposure error might not be so great and might explain why they saw effects in 

women but not men. 

When asked about evidence provided by Moolgavkar papers against the hypothesis of a 

PM-mortality effect, he stated simply that he did not believe the work was credible. 

Although Expert F initially thought his estimates of the total mortality effect would have 

to include some additional increment from the short-term studies, he ultimately decided 

to rely only on the cohort studies. He felt that it was difficult to know what the protocol 

question was really asking for. Nevertheless, he argued that even though strategies to 

reduce the annual average PM2.5 concentration could affect peaks as well, annual average 

measures of mortality would not pick up much acute mortality and that any additional 

contribution from acute mortality to the cohort estimates would be small, relative to the 

chronic exposure reduction benefits. 

3.5 Confounding 

As a backdrop to the discussion on confounding, Expert F discussed the results of an 

analysis conducted by Zidek et al. (1996) entitled, “Causality, measurement error, and 

multi-colinearity in epidemiology” in Envirometrics. He indicated that the study 

basically found that if both the predictor variable and the outcome variable are measured 

with error, the impact of confounding is diminished. In order to have substantial 

influence on the regression estimates by a confounder, the degree of correlation between 

the confounder and the predictor variable had to be very high (e.g., around 0.9). Expert 

F’s conclusion was that although confounding is something to be concerned about and to 

control for, the fear that modest correlations with confounding variables could fully 

account for effects measured in epidemiological studies does not appear to be warranted. 

The few variables that Expert F thought might be of some residual concern in the studies 

he planned to rely on for his quantitative estimates are summarized in the table below: 
 

F-6 

Study Confounders potentially 

leading to overestimate of 

the true RR 

Score (1-3)* 

ACS Contextual SES 2 (lower end) 

Gaseous co-pollutants, 

occupational, smoking 

1 

Contextual SES 1 

Occupational 1-2 

Six Cities (original and 

follow-up) 

Gaseous co-pollutants, 

smoking 

1 

Jerrett, 2005 Contextual SES 2 (higher end) 

* Expert F defined the scores in the following way: 1= <10 percent, 2= 10-15 percent, 3 

= >20 percent 

Expert F generally did not think that gaseous co-pollutants (CO, NOx, SO2) were “big 

confounders to the PM effect.” The reason for this was that “these pollutants are 

correlated because they’re coming from the same sources and not because they cause the 

effect.” He predicted that, as we begin to investigate exposures by source categories, CO 

and NOx would be related to traffic particles and SO2 would be related primarily to coal 

combustion. He did not think there were plausible arguments for mechanisms by which 

these co-pollutants could affect mortality over the long-term (he thought CO might have 

a plausible mechanism for affecting mortality during acute exposures). Although the 

ACS study and subsequent re-analyses found a strong SO2 effect, Expert F did not think 

there is a plausible explanation for how SO2 actually causes the mortality effects (i.e., it 

does not get deep in the lung, does not cause increases in CRP or plaque development). 

He thought it is more likely that SO2 is acting as an effect modifier, by making the 

particles more toxic. He similarly thought smoking had been well-controlled for in the 

ACS studies (examined using different smoking variable in different models) and Six 

Cities studies though he could not rule out some minor residual confounding. The 

concern he raised here was as to whether measures of smoking and risk fit the 

proportionality assumptions of the Cox Proportional Hazard model. 

 

He thought there could be some residual confounding by “contextual SES” variables (i.e., 

that there is some correlation between living in a more polluted location and being poor), 

particularly in the ACS study. He did not think confounding by contextual SES variables 

was likely to be as great in the full ACS study which averaged variables across whole 

metropolitan areas, as in the Jerrett, 2005 study of Los Angeles which looked within a 

particular metropolitan area. 

He expected confounding by occupational exposure to be minimal as well in all of these 

studies although it might rise to the level “moderate [2]” for the Six Cities study given the 

statistical influence of Steubenville, originally a highly industrial city, on the mortality 

effect estimate. 
 

F-7 

3.6 Effect Modification 

Expert F discussed primarily the ACS and Six cities study in his review of effect 

modification. He discussed the potential for effect modification by SES status (i.e., as 

indicated by educational attainment in the ACS study), SO2, and smoking. With the 

exception of SES status in the ACS study and possibly SO2 in the Six Cities study, he 

thought it unlikely that effect modification would have more than a minimal effect on the 

mortality effect estimates: 

Study Effect modifiers Score (1-3)* 

SO2 1 (overestimated RR) 

Education (SES) 3 (underestimated RR) 

ACS 

Smoking 1 (uncertain direction of bias) 

Six Cities (original and SO2 2 (overestimated RR) 

follow-up) Smoking 1 (uncertain direction of bias) 

* Expert F defined the scores in the following way: 1= <10 percent, 2= 10-15 percent, 3 

= >20 percent 

Expert F thought the most influential effect modifier was the educational effect observed 

in the ACS study where most of the mortality effect was observed in the portion of the 

cohort with less than a high school education. Since the ACS cohort was more highly 

educated than the U.S., Expert F thought the relative risks were probably underestimated 

by as much as 50 percent or more in the ACS study (in part based on discussion from the 

pre-elicitation workshop). 

He thought effect modification by SO2 might be a larger problem in the Six Cities study 

than in the ACS study. His argument was that higher mortality has been observed in 

Steubenville in the Six Cities study (as it has been in the Pittsburgh-Steubenville area for 

the ACS study) and that these higher mortality rates may relate to effect modification of 

the SO2 on the PM effect because SO2 adsorption on the surface of PM might well make 

the particles more toxic. He thought this modification effect might be absent in other 

areas of the country (e.g., Seattle where SO2 levels and coal combustion are lower), 

which are not as well represented in the Six Cities study and, to a lesser extent, in the 

ACS study. As a result, he thought the relative risks (RRs) might be somewhat 

overestimated in each of these studies. However, the influential weight of the 

Steubenville data in the Six Cities studies discussed earlier led Expert F to believe that 

effect modification by SO2 might be greater in those studies than for the ACS studies. He 

did not think this was an issue for the Jerrett study though noted that recent source 

apportionment studies in the LA area have seen increasing evidence of exposure to 

sulfates from cargo ships in the port of LA. 

While he thought there might remain some potential for effect modification by smoking 

in the ACS and Six Cities studies, he thought it was likely to be minimal (a score of 1) 

and he was uncertain which way it might influence the effect estimates if at all. 
 

F-8 

3.7 Exposure Issues 

The types of measurement error Expert F focused on in his discussion of the ACS and Six 

Cities studies (including the follow-up paper) included the impacts of mobility of the 

population on measures of exposure, central site versus individual site monitors, and 

choice of the appropriate monitoring time period given historical trends in exposures. In 

general, Expert F thought the ACS study was more affected by exposure measurement 

error, in particular the first two issues listed above than the Six Cities studies: 

Study Exposure Issues Score (1-3)* 

Mobility (coupled with 

effect modification by 

education) 

3 (underestimated RR) 

Central site v. individual 

exposures 

2 (underestimated RR) 

ACS 

Historical trends 1 (if use correct time 

period) (underestimated 

RR) 

Six Cities, original Mobility 1 (underestimated RR) 

Central site v. individual 

exposures 

Laden et al. (2006) Six 1 (underestimated RR) 

Cities follow-up 

Estimation method for 

PM2.5 central site value

1 (underestimated RR) 

* Expert F defined the scores in the following way: 1= <10 percent, 2= 10-15 percent, 3 

= >20 percent 

Expert F thought the issue of mobility of the study population is essentially one of 

measurement error. If people move after their exposure has been assigned, and their new 

location and exposure is unknown and cannot be adjusted for (as in the ACS study), there 

is more measurement error. The impact of the error could be different depending 

whether people, on average, moved to a cleaner or a more polluted area, and might differ 

by economic status if people of lower economic status are less likely to move out of the 

study area, as census data suggest. Expert F thought this factor could be influential in 

underestimating the relative risks for the ACS study, as well as contributing the reported 

effect modification by education level. 

Expert F thought the degree to which the central site monitors reflected individual 

ambient or outdoor exposures was the other important source of measurement error for 

the ACS study. He thought it could contribute to underestimate of the RR in the study; 

he assigned it a score of 2 (“at most”). He did not think that the Jerrett study provided a 

clean measure of the impact of more precise geographic estimates on the effect estimates 

(i.e., he thought the higher effect estimate in the Jerrett study might also be due in part to 

more spatial confounding by SES). 

He discussed the relative importance of outdoor, indoor, and personal exposures as 

exposure metrics. Basically Expert F believed that outdoor air is what predicts indoor air 
 

F-9 

quality and it is outdoor air that is ultimately of interest for regulatory purposes. Expert F 

did not consider differential ability of particles to penetrate indoors in different climates 

to be a big issue because he did not think air conditioning was a very efficient way to 

remove particles. 

Expert F thought that choice of the exposure period used to estimate mortality effects 

could influence effect estimates, given historical changes in particle concentrations with 

time. He thought there exists the potential to “underestimate the effect size when you use 

[exposures from] the beginning of the study because you’re overestimating the 

exposure.” He noted how the reanalysis of the ACS study estimated the mortality effects 

using two exposure periods, early and late in the study, and that the effect estimates were 

higher when the later exposure period was used. He thought the average of the two 

periods made the most sense. However, he also pointed out that Roosli et al.’s work 

suggests that the last 5-10 years before death are the most influential for the effect 

estimate. He was not sure how this finding could be reconciled with the mechanisms for 

developing cardiac disease that involve chronic inflammation and accumulation of 

atherosclerotic plaques. For cancer development, he thought the longer-term exposures 

would be more important. He concluded that if the correct exposure period were used 

(e.g., using the average of the two exposure periods in the ACS re-analysis study), the 

effect estimates would not be biased or mis-estimated, giving larger estimates than those 

generally used from the ACS study (i.e., using those based on the 1980’s exposures, as 

reported in the manuscripts’ abstracts). 

3.8 Causality 

Expert F would ideally have the following types of evidence to support a conclusion of a 

causal relationship: 

• Specificity (e.g., would like to see an impact on cardiovascular and, with 

sufficient follow-up, lung cancer, but not other causes); 

• Consistency across studies that are done well; 

• Plausible biological mechanisms that are consistent with the epidemiological 

evidence; 

• Concentration-response relationship; 

• Coherence across different types of study designs; and 

• A temporal relationship (as observed in intervention studies). 

Expert F’s views on the strength of the causal relationship differed for long-term and 

short-term exposures. He felt that the likelihood of a causal relationship was 100 percent 

for the effects of long-term exposures; it would take his ideal study, done well and 

showing no relationship between particles and mortality to convince him otherwise. For 

the effects of short-term exposures, Expert F thought that, under the least optimal 

conditions (if one believed that weather and ozone were confounding the relationship) the 

likelihood of causal relationship for between short-term exposures and mortality was 

about 60 percent. At best, believing all other relevant factors to be controlled or analyzed 
 

F-10 

for, he could be 100 percent certain of a causal relationship. His best estimate was that it 

was highly likely, around 95 percent. 

3.9 Thresholds 

In theory, Expert F could imagine that a threshold might exist at a natural background 

level (if one could eliminate all fossil fuel combustion). At the same time, conceptually, 

there exists an “infinite pool” of susceptible individuals, who could be “knocked off” at 

various concentrations. 

He thought that the most appropriate types of studies to assess the existence of population 

thresholds would be long-term studies in large populations experiencing a wide range of 

pollutant levels. They should look at indicators of disease development (e.g., intimamedia thickness (IMT) of arteries, as in Kunzli’s latest work). He thought toxicological 

studies were useful for studying mechanisms, but not for determining population 

thresholds or effect estimates. 

He did not think that a threshold is detectable in any of the epidemiological studies 

currently available. He did not want to incorporate a threshold into his characterization 

of the concentration-response (C-R) relationship. 

3.10 Other Influential Factors 

Expert F briefly discussed other factors that were of residual concern to him. He 

mentioned again some uncertainty about how well the Cox proportional hazards model 

deals with potential confounding by smoking. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert F chose to characterize the C-R function as piece-wise linear across the range of 

annual average PM2.5 concentrations that were the focus of the study (4-30 ug/m3

). He 

estimated a distribution for the first slope that applied to concentrations of 4-7 ug/m3 

(hereafter, “Range 1”), and one that applied to concentrations of >7-30 ug/m3 (hereafter, 

“Range 2”). Although he indicated that there was not enough data to specify other than 

one linear range, he thought that there was probably more uncertainty about the slope in 

the lower portion of the range (i.e., below the range of observed PM2.5 data in the ACS 

study). 

For purposes of comparison to other experts who specified a single distribution for the 

whole PM range for this study, the elicitation team simulated a combined distribution 

(hereafter, “Example Applied Distribution”) by linking his two distributions via a 2002 

distribution of population-weighted annual average PM2.5 concentrations in the U.S. from 

EPA’s BenMap model. 

Expert F began by specifying the percentiles for Range 2 (note all values were initially 

given per 10 μg/m3

 change in PM2.5 and converted later to a 1 μg/m3 change). His 
 

F-11 

minimum value was based on an estimate that lay between the effect estimates from the 

ACS earlier and later exposure periods (around 6 percent). His 5th percentile was based 

on an estimate from the ACS study for those with less than a high school education 

(around 8 percent - estimated from Figure 4 in Pope et al., 2002). Expert F’s 50th

percentile was based initially on estimates from the original Six Cities study (Dockery et 

al., 1993) as well as the (Laden et al., 2006) follow-up (around 14 percent), but he 

adjusted it downward to 12 percent to account for concerns about residual confounding 

by occupation and effect modification by SO2. 

Expert F’s 95th percentile was based on an estimate from the Jerrett et al., (2005) (17 

percent per 10 μg/m3

 based on the model with 44 individual covariates). He argued that 

it was appropriate to think of this estimate as an upper 95th percentile because, although 

he thought the Jerrett study estimates represented improvements in exposure 

measurement, he thought they also might still be confounded somewhat by contextual 

SES factors. To generate his maximum value, he adjusted this estimate upwards by an 

additional 3 percent from his 95 percentile estimate to account for additional uncertainty, 

because he could not see the “true” effect estimate of all anthropogenic PM effects for the 

U.S. being greater than 20-25 percent of total mortality. Expert F calculated his 

interquartile range by interpolating roughly between the 50th and the 5th percentile values, 

as well as between the 50th and the 95th.

To calculate the percentiles for Range 1, Expert F reduced the percentile values from 

Range 2 by 25 percent to indicate that he thought slope might be lower. He then made 

adjustments to the 50th, 75th, 95th, and maximum to ensure that the two distributions had 

overlapping interquartile ranges and to express greater uncertainty about the magnitude 

of the slope in the lower range.

He was satisfied that his Example Applied Distribution essentially encompassed the 

distributions from the main studies on which he relied, even though the 90 percent 

confidence intervals were not much wider. He thought they represented essentially a 

mental meta- analysis and that would reduce, not increase the uncertainty distribution. 
 

F-12 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1 ug/m3

 Change in Ambient Annual Average PM2.5

Concentrations 

Percentile Percent Change 

in Mortality 

(Range 1) 

Percent Change 

in Mortality 

(Range 2) 

Percent Change in 

Mortality -Example 

Applied Distribution 

(IEc Generated) 

Minimum 0.37 0.49 0.37 

5th 0.58 0.77 0.71 

25th 0.73 0.96 0.94 

50th 0.93 1.1 1.1 

75th 1.1 1.4 1.3 

95th 1.4 1.6 1.6 

Maximum 1.7 1.8 1.8 
F-13 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1 ug/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distributions from 

* = Distribution incorporating causality and applying the C-R functions from Ranges 1 and 2 to a 2002 population

PM2.5 concentration distribution in the U.S. from BenMap (IEc Generated). 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

Range 1 - Elicited Range 2 - Elicited Example Applied

Distribution (IEc

Generated)*

ACS Original ACS Extend ACS LA Analysis Six Cities Original Six Cit

% Decrease in Mortality per 1 ug/m3 PM2.5
 

F-14 

Elicited Distribution – Range 1 

Elicited Distribution – Range 2 
 

F-15 

Example Applied Distribution - Probability Density Function (IEc Generated) 

Example Applied Distribution - Cumulative Density Function (IEc Generated) 
 

Expert G 

Interview Summary
 

G-1 

Interview Summary 

Expert G 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert G discussed the biological mechanisms for short-term and long-term exposures 

separately, beginning with short-term exposures. 

Short-Term Exposures

Expert G primarily discussed two types of effects in the context of short-term exposures 

to PM: cardiovascular effects and acute respiratory infection. 

Expert G thought that cardiovascular effects were the “most compelling in terms of [the 

ability of short-term exposures] to cause mortality.” He focused on “cardiovascular 

dysfunction,” which he defined as involving arrhythmia or other changes in heart 

function, “including changes in the ability of the heart to respond to nervous system 

stimulation.” He thought that this effect would be more significant in susceptible 

populations such as “[p]eople with pre-existing cardiac or respiratory disease, and 

elderly.” He thought that a body of evidence provides support for the mechanism 

underlying this cause of death. “Looking at the overall database ... epidemiological 

studies ... looking at populations that have changes in certain aspects of EKG, changes in 

heart rhythm, changes in heart rate variability, which suggest effects on the nervous 

system control ... [and] I look at the toxicology studies, which have shown similar effects 

... in terms of change in heart rate variability or changes in heart rhythm ... this 

consistency between the epidemiology and the toxicology.” He thought that limitations 

of this database included differences in dosimetry and between ambient exposures and 

controlled experimental exposures. 

When asked about how the cardiovascular mechanism for deaths related to PM worked, 

Expert G indicated that there are two hypotheses 1) that the particles “get out of the 

respiratory tract and actually end up in the cardiovascular system”; and 2) “the oxidative 

stress hypothesis ... the chemical nature of the particles is such that it causes some sort of 

biochemical effect in the lungs, such as release of certain mediators, like interleukins ... 

which will then travel out of the respiratory tract, go systemic, and then affect the heart.” 

He also thought that “pro-inflammatory mediators can cause tissue damage, which can 

alter organ susceptibility to other physiological influences and/or cause structural damage 

... hypothetically that can affect an organ’s ability to responds to nervous system 

stimulation.” But he thought there was “still a gap [in understanding what the] structural, 

biochemical changes within the tissue [are] that result in its inability to respond normally 

to nervous system input.” 

Expert G thought the second most important cause of death from short-term exposures is 

acute respiratory infection, such as bronchitis or pneumonia in individuals “already 
 

G-2 

stressed either by age or pre-existing disease” because their defense mechanisms may be 

compromised. He thought this mechanism was supported by evidence from 

epidemiology as well as toxicology (Zelikoff’s work, studies from NYU). 

Expert G also briefly discussed another mechanism for cardiovascular impacts involving 

PM’s impacts on blood coagulation ability. He noted that there were a few studies that 

seem to support this pathway. When asked about stroke as a potential cause of death from 

short-term exposures, Expert G thought this was related to this cardiovascular mechanism 

in that changes in blood coagulation ability could also cause strokes. 

Long-Term Exposures

Expert G indicated that he viewed effects from long-term exposures as “increasing 

biological stress levels on a long-term basis ... [s]o you have cumulative damage over the 

years ... [which] appears to reduce life span.” “The effects on organs, especially the 

heart and perhaps the lung as well, may be similar to aging, in that the additional stress 

induced by sub-clinical release of pro-inflammatory mediators could result in the 

inability to handle additional stress to a certain point, and that would cause death either 

by heart attack or maybe lung disease.” He discussed the Children’s Health study in 

California that “infers that [children] have compromised lung function development,” and 

reasoned that it might contribute to increased risk of mortality in adulthood. 

For cardiovascular disease, Expert G discussed the Sun et al. (2005) study experiments at 

NYU with the apoE-/- atherosclerotic mouse model. While he noted that this research 

showed that PM accelerates the development of atherosclerosis, his critique of this work 

was that the apoE-/- mouse is a sensitive model and he questioned how representative it 

might be of human experience. Expert G also discussed Künzli et al. (2005) study that 

found changes in intima-media thickness of the carotid arteries associated with PM. He 

thought that this could be related to “inflammatory effects causing damage to the wall, 

which then gets repaired by fibrotic tissue, which is thicker than normal tissue.” 

Overall, Expert G thought the body of evidence was more complete for short-term 

exposures than for long-term exposures: “I think that ... there is a coherence between the 

epidemiology and the controlled exposure studies, which include human clinical and the 

tox, in terms of acute responses to PM mechanisms, potential mechanisms, that would 

explain mortality. [For] long-term exposures ... there is a lack of chronic studies in 

controlled exposure. [The Sun et al. study] at NYU is ... the longest-term controlled 

exposure study and is showing responses that could explain long-term exposure effects. 

But ... it's still only a six month study ... [T]he results are consistent with what one would 

think would be going on in the population exposed chronically, but the data set isn't as 

strong as it is for the acute, yet.” 

Expert G discussed lung cancer as a potential cause of death from long-term exposures. 

He thought that there was some epidemiological support for this. For a plausible 

mechanism, he drew a parallel to chronic cigarette smoking. He thought PM contains 

carcinogens and the combination of years of exposure to the carcinogens and a genetic 
 

G-3 

pre-disposition to cancer make it plausible that PM could cause cancer. However, he 

noted that cancer did not appear to be the largest risk associated with PM. 

3.2. Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

Expert G thought the Künzli diagram was a good conceptualization of the relationship 

between long- and short-term exposures. He elaborated on the definition of Category B, 

long-term effects, to say that he thought that the long-term effects of air pollution could 

result, not just from long-term average exposures as is commonly assumed, but from 

long-term exposures to a series of peak PM levels, “but the effects my be different and 

affect different people.” In the peak model, short-term peak exposures could lead to subclinical development of disease over time. “Some of those peaks might also kill people 

... Quantitating the overlap [between the categories] is the question.” 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert G’s views on the mortality effects captured by each study design are shown in the 

table below: 

Study Design Type of Effects Captured (e.g., shortterm, long-term, or both) 

Cohort Studies Long-term and some short-term (Categories B 

and A, respectively) 

Intervention Studies Short-term and mixed (Categories A and C) 

Time-Series Studies Short-term and mixed (Categories A and C) 

Expert G thought that he would most want to rely on the cohort studies for quantifying 

the effects of long-term exposures. He led an extensive discussion to assess to what 

extent the cohort design could distinguish whether increased mortality from long-term 

exposures was due to average levels or to a series of peaks. He ultimately concluded that 

they could not make such a distinction. After initially thinking cohort studies would pick 

up purely short-term effects (C), he concluded that they would not necessarily pick them 

all up. 

Expert G thought the intervention studies were an important “component of a portfolio of 

epidemiology studies used to develop some sort of quantitative risk assessment.” He 

indicated that they show the effect of an intervention and ideally, if the difference in 

components were known, it might be possible to understand more about which 

components of the PM are producing the change in mortality. He thought they would 

largely capture short-term effects. 

Expert G thought time-series studies would largely capture the effects of short-term or 

acute exposures. Ultimately, Expert G thought estimating the mortality effects of 
 

G-4 

changes in annual average PM2.5 concentrations might require a combination of results 

from cohort, intervention and time-series design. He found it difficult, however, to 

determine what the basis might be for the relationship between the magnitudes of the 

effect estimates seen using different study designs and thus, was unsure of how they 

should be combined. 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert G’s ideal study is one that would allow him answer the question he had raised in 

section 3.3, “what's the relationship between acute versus long-term exposure in terms of 

mortality?” At this point, he was not considering the representativeness of the study. 

Expert G thought that to characterize the total mortality effects from long-term and shortterm exposures, he would want to conduct a cohort and time-series study in the same 

population, to “control for ... genetic differences” between populations. He would 

conduct the studies in the same three cities, one with a low PM level that’s constant, one 

with a high PM level that has peaks, and one with an intermediate level. Ideally, all three 

cities would have PM with similar physical and chemical properties to avoid the 

difficulty of comparing across studies in areas with different PM component mixtures. In 

terms of exposure measurement, he would want the placement of monitors and types of 

measurements to reflect what the study population is being exposed to. 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert G first indicated that a limitation of PM/mortality 

epidemiologic studies in general is that PM is correlated with other pollutants, and 

therefore the investigators “can’t separate out the PM [effect].” 

Expert G thought that the American Cancer Society (ACS) study by Pope et al., the Six 

Cities study (Dockery et al., 1993) as well as the Krewski et al. (2000) reanalyses of these 

studies were the most informative for his quantitative estimates. He also discussed 

intervention studies (Utah Valley (Pope et al., 1996); Hong Kong (Hedley et al., 2002)) 

but thought these were more important for determining effects of different PM 

components, rather than for quantifying mortality effects in the U.S. 

Expert G indicated that the strengths of the ACS study were its large population size, 

geographical distribution of the population, and its examination of the effect of copollutants. He thought the limitations were uncertainty related to the “monitoring sites 

versus where the population is” as well as downward bias due to effect modification by 

education and race. He thought the Six Cities had more representative exposure 

monitoring but was less geographically representative. 

3.5 Confounding 

Expert G chose to discuss confounders that affect the cohort studies generically, rather 

than describing the effect on individual studies. 
 

G-5 

Expert G first discussed confounding by gaseous co-pollutants, in particular ozone, NO2, 

SO2, and semi-volatile organics. He thought the gaseous co-pollutants were problematic 

because they are often correlated with PM and they “move like PM,” making them 

difficult to control. However, he acknowledged that “[i]t’s an open issue as to what role 

the gaseous co-pollutants do have in PM toxicology, if any.” He expressed the view that 

SO2 could be a surrogate for PM. He thought that correlation of PM with gaseous copollutants could cause the published effect estimates to be overestimated, and assigned 

this confounder a score of 3. He cited a similar problem with semi-volatile organics that 

can adsorb to particles making difficult to isolate the PM effect. 

He then discussed other potential confounders that could affect study estimates. He 

thought that weather could be an issue in short-term studies because it “clearly will 

increase mortality in certain populations” and “PM does tend to go up when it’s hot.” 

However, he thought that this had been reasonably controlled in the epidemiologic 

studies. 

He thought socioeconomic status (SES) could be a confounder, indicating that those 

living in inner cities could have higher exposures and lower SES. A related issue he 

raised was urban stress (e.g., related to living in close quarters, noise, job/money 

problems), which could reduce immune competence, could also be greater in inner cities. 

He was uncertain about whether it had been dealt with in epidemiologic studies using 

education as a proxy. He assigned SES a score of 2. In addition, he thought pre-existing 

health status could be a confounder as also assigned it a score of 2. 

Expert G thought smoking, alcohol, and diet were well-controlled in the cohort studies. 

3.6 Effect Modification 

Expert G discussed potential effect modification in the ACS study (Pope et al., 2002). 

After extensive discussions, he stated that he thought that this study population 

underrepresented non-whites, low educational attainment, and low SES although he 

thought educational attainment might be capturing the other two factors to some degree. 

Therefore, he thought that the published effect estimates might be biased downwards. 

Expert G thought that ozone could potentially be an effect modifier, in that those exposed 

to both PM and ozone would have higher mortality than PM alone. However, he could 

not cite specific evidence from the ACS study to support ozone as an effect modifier. He 

was not sure how much adjustment should be made, if any, to the published relative risk 

estimates, or whether any differences might already be captured in the statistical 

uncertainty of the estimates or whether he might need to expand the confidence interval. 

3.7. Exposure Issues 

Expert G thought that the differences in exposures measured by central monitors and 

actual exposures to the study population could be a major (score = 3) source of 

uncertainty. He thought that local sources, such as traffic or point sources could cause 

individual exposures to differ from exposure measured at a regional central site. He 
 

G-6 

thought that this could bias the results in either direction, depending on the placement of 

the central site and the types of local sources. He also thought that there could be 

differences in PM composition at the central site and individual exposures. Therefore, he 

concluded that a greater number of monitors or more representative monitors would 

improve study estimates. He thought that this could be a major factor, however, he did 

not feel that he had sufficient data to determine how this would affect the published 

relative risks. Although he was not familiar with the details of the Jerrett et al. (2005) LA 

study, for example, he generally thought that its findings might not necessarily be 

representative of findings in other parts of the country. 

Expert G discussed the issue of whether the studies had measured the exposure period 

relevant to the mortality effects observed. He thought that using historical exposure 

estimates could cause an overestimate in the mortality effect. He also indicated that he 

was unsure of what the relevant exposure period should be, which contributed to overall 

uncertainty in the effect estimates, but to a lesser extent (score = 1). 

3.8. Causality 

When asked what type of evidence he relied upon to evaluate the strength of the causal 

relationship between PM and mortality, Expert G answered, 1) “reliable epidemiological 

studies having minimal confounders and be able to adjust for any confounders or effect 

modifiers and other factors to the best of their ability; ... [2)] biological plausibility, 

meaning are there reasonable or believable mechanisms that could underlie the causes of 

death reported in the epidemiologic studies?”; and 3) coherence between epidemiology 

and toxicology, which includes both human controlled studies and animal studies as well 

as in vitro studies. “And even if there’s a dosimetry issue between ... the epidemiology 

and the controlled exposure ... as long as there’s a mechanism [to explain] how the PM 

could be doing this in human.” He indicated that he did not view epidemiology as causal, 

but rather a “statistical relationship.” 

Expert G thought that the current state of science “strongly suggests a causal relationship 

between PM exposure ... and increased mortality.” This position represents a change in 

his views since the first Irvine colloquium in 1994 when the relationship had only been 

based on epidemiology and had little credibility in his view. He indicated that he put 

most weight on recent toxicological studies using concentrated ambient particles (CAPs), 

such as those published by Godleski et al. at Harvard, Devlin’s group at EPA, and subchronic studies from New York University (NYU) by Lippmann and Chen. He indicated 

that the exposure levels are higher than ambient levels, but not by as much. 

Expert G chose to discuss causality separately for short-term and long-term exposures 

because “I have more confidence in the short-term than in the long-term.” Expert G 

thought that the likelihood of a causal relationship between PM and mortality for shortterm exposures fell between 50 and 80 percent, with a most likely value of 70 percent. 

For long-term exposures, he did not feel comfortable providing a range of values because 

“at some point, I think the chronic turns into acute. I think the acute effects may override 

the chronic if levels are above a certain concentration,” but provided a most likely value 
 

G-7 

of 60 percent. When asked what evidence he would need to make him 100 percent 

certain, he suggested that we would like to have more controlled exposure studies 

conducted at ambient concentrations, perhaps in sensitive animals, that demonstrated 

effects on mortality. He used smoking and its impact on heart disease and lung cancer as 

an example of a relationship that was 100 percent likely or causal. 

When asked for an overall likelihood of causality for a change in annual average PM 

exposures, Expert G specified a most likely value of 70 percent, based on the “whole 

dataset together.” He later provided a range of values around this estimate from 60 to 80 

percent reflecting his uncertainty in the relative contribution of short-term and long-term 

exposures to overall mortality. 

Expert G’s discussion of the likelihood of a causal relationship was somewhat 

complicated by his belief that the strength of the causal relationship might differ within 

the range of 4-30 μg/m3

 and by the sense that the likelihood of a change in mortality 

might differ if one asked about a larger incremental change in PM concentration than 1 

μg/m3

. 

3.9 Thresholds 

Expert G first discussed what evidence he would like to have to determine whether there 

is a threshold in the PM/mortality relationship. “You can do epidemiological studies, 

which can show an exposure-response relationship at different exposures, and then 

develop a dose-response curve as low as you can go. And you can look at animal and/or 

human controlled exposure studies to see with specific materials or ... CAPs, to look at 

the dose-response there. So ... for the population study, it needs to be a big enough 

group ... to be representative of reality ... [to include the] hypersensitive and 

hyposensitive [individuals].” He thought that animal studies could be informative “as to 

sensitivity of different populations to PM in sensitive groups within the population, and 

slopes of dose-response curves, but they probably won’t be able to determine whether in 

reality there is a threshold for human populations exposed to ambient air.” 

On a conceptual basis, Expert G thought that “if there’s some damage that the body is 

always able to repair, the repair mechanisms will come into play to fix things ... what’s 

disturbing from a mechanistic standpoint is that no matter how low you go, that there 

doesn’t seem to be the ability to repair whatever is going on, to the extent that it mitigates 

any mortality.” 

When asked if his views on threshold differed for long-term and short-term exposures, 

Expert G thought there could be different thresholds. He felt there was a greater 

likelihood that there is no threshold for long-term effects than short-term effects “because 

... repeated ... sub-clinical damage by PM, which could occur at low concentrations, 

would just build up and be cumulative ... [w]hile for short-term exposure, I found it hard 

to believe, say, 2 μg/m3

 is going to kill somebody ... So, conceptually, the no-threshold 

model makes sense to me for the long-term. There probably would be a threshold for the 

short-term.” 
 

G-8 

He did not think that thresholds have been detected in epidemiologic studies, for example 

the ACS and Six Cities studies. “In the tox studies there are thresholds, but ... [y]ou 

can’t directly apply the toxicology studies to the epidemiology.” Expert G did not elect 

to incorporate a threshold into his concentration-response (C-R) function. 

3.10 Other Influential Factors 

Expert G discussed additional sources of uncertainty that were not part of the protocol. 

When asked whether he thought that there was any publication or investigator bias, he 

answered that he thought that some investigators were biased, but he did not think that 

this affected the studies that he relied upon for his quantitative estimates. Expert G did 

not think there were any other outstanding issues not already covered by the protocol. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert G thought the C-R relationship was linear over the entire range of concentrations 

that were the focus of the study (4-30 μg/m3

). He elected to provide a C-R function that 

was conditional on the existence of a causal relationship. The elicitation team then 

combined his most likely causal likelihood (70 percent) with the conditional C-R 

function. 

 Expert G began by specifying his maximum. He specified a value of 1.5 percent based 

on a rough interpolation between the upper confidence intervals on the ACS and Six 

Cities studies, weighted on their respective strengths and weaknesses. He then specified 

a 95th percentile value of 1.3. He chose a value relatively close to his maximal value, 

because he assumed that the C-R function was normally distributed and that the max and 

95th percentile would be close together. Expert G then specified a 50th percentile value of 

1.0 percent based on his view that the Six Cities estimate is biased upward due to 

confounding by co-pollutants and the ACS study was biased low due to effect 

modification of education and race. Although the issue of exposure misclassification was 

again discussed, Expert G remained unconvinced that the evidence supported a need for 

additional adjustment of the effect estimate. He suggested it be discussed at the postelicitation workshop. 

He then had the elicitation team fit the 50th and 95th percentiles to a normal distribution in 

Crystal BallTM to determine his 5th, 25th, and 75th percentiles. Expert G did not specify a 

minimum value. This distribution, which is conditional on the assumption of a causal 

relationship, is shown in the first column below. The second distribution is the 

probabilistic combination of the first with Expert G’s likelihood of a causal relationship. 
 

G-9 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

Percentile Percent Change in Mortality 

Elicited Distribution 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood (IEc 

Generated) 

Minimum - 0 

5th 0.70 0 

25th 0.88 0 

50th 1.0 0.90 

75th 1.1 1.1 

95th 1.3 1.3 

Maximum 1.5 1.5 
G-10 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distributions from 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.20.40.60.8

1

1.21.41.61.8

2

Expert G - Elicited Expert G -

Incorporating

Causality (IEc

Generated)

ACS Original ACS Extend Six Cities Original Six Cities R

% Decrease in Mortality per 1 ug/m3 PM2.5
 

G-11 

Elicited Distribution 

Distribution Incorporating Causality - Probability Density Function (IEc Generated) 
 

G-12 

Distribution Incorporating Causality - Cumulative Density Function (IEc Generated) 
 

G-13 

U.S. EPA EXPERT ELICITATION STUDY OF THE CONCENTRATION-RESPONSE 

RELATIONSHIP BETWEEN ANNUAL AVERAGE PM2.5 EXPOSURE AND 

MORTALITY 

Modification to Expert Judgments

Expert G

Date: 7/11/06 

Section of Protocol Affected (Section Number and/or Title):

Part 4 – Elicitation of Quantitative Judgments 

Description of Change (e.g. to a specific percentile, or to a qualitative opinion or 

statement of belief):

The causality factor should be disassociated from the final numbers I gave you. 
 

Expert H 

Interview Summary
 

H-1 

Interview Summary 

Expert H 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert H discussed the biological mechanisms for short-term and long-term exposures 

separately, but he acknowledged that, “they may overlap.” 

Long-Term Exposures

He began by discussing causes of death from long-term exposures, indicating that the 

main causes of death were cardiovascular disease, respiratory disease, and lung cancer. 

Expert H first discussed possible mechanisms for cardiovascular deaths related to PM. 

He thought that particles could cause systemic inflammation leading to increased arterial 

intima-media thickness (IMT) and atherosclerosis. He cited a combination of studies to 

support this theory, including an animal study by Sun et al. (2005), an epidemiologic 

study by Kunzli et al. (2005) of PM2.5 and carotid artery IMT, and a study of 

environmental tobacco smoke (ETS) and IMT by Howard et al. (1990). He noted that 

before the Sun et al (2005) study, “I didn't see a very strong connection ... between 

toxicological evidence and cardiovascular long-term mortality because many of the 

toxicological, human studies appeared to be more about acute response.” Expert H 

mentioned that one complication with the Sun et al. study was that detection of increased 

IMT in mice with high fat diet suggests a major effect modifier and he wasn’t sure the 

ACS or Six Cities studies had really looked at diet (other than using body mass index 

(BMI) as an indicator). 

Expert H also thought that chronic exposures to PM could cause respiratory deaths, “even 

though it’s not a major fraction of [overall] mortality.” He cited a study by Ghio et al. 

(2000) that examined humans exposed to concentrated particles and found respiratory 

inflammation indicated by increased blood fibrinogen. He thought that chronic lung 

inflammation could eventually lead to respiratory mortality. He noted, however, that, 

“respiratory mortality is not ... showing up [strongly] in long-term epi studies.” He 

thought this could be due to the small numbers of deaths attributed to respiratory disease 

or that PM could be related only to short-term respiratory mortality. 

He also thought it was plausible that particles could cause lung cancer although he did 

not think there were specific studies connecting PM2.5 to lung cancer, except perhaps 

Pope et al. (2002). He drew an analogy to cigarette smoking and lung cancer, indicating 

that it was “conceivable ... that combustion-related PM2.5 may be as toxic or even more 

toxic than cigarette smoke.” However, he also acknowledged that he was uncertain about 

how similar PM2.5 is to cigarette smoke. He thought that occupational studies have also 

shown positive associations between lung cancer and exposure to diesel exhaust, 

although he was less clear about this. 
 

H-2 

Short-Term Exposures

Expert H thought that short-term exposures to PM were related mostly to cardiovascular 

deaths, specifically with outcomes such as heart failure, myocardial infarction (MI), and 

arrhythmia. He cited the defibrillator study by Peters et al. (2000), studies linking 

changes in heart rate variability (HRV) to changes in PM (e.g., Gold et al., 2000), and 

studies on increased blood viscosity by Peters et al. (1997). He thought that the strength 

of these studies was that they use human subjects; however, he thought their limitation 

was that the findings were not always specific to PM2.5. For example, in the Peters 

(2000) he thought that the strongest association was with NO2 or perhaps black smoke, 

which he still thought was indicative of PM2.5. Similarly, he thought the Dockery et al. 

(2005) follow-up of that study also showed associations with sulfates, CO and SO2, 

implicating both stationary and traffic-related sources. He thought the HRV studies were 

“less convincing” than the defibrillator studies because they had less consistent results. 

3.2. Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

Expert H thought the Künzli diagram was a good conceptualization of the relationship 

between long- and short-term exposures. He thought that cohort studies would capture 

categories A, B and C but that not all of the short-term effects were captured by the 

cohort studies. In particular, he thought that, “cohort stud[ies] can miss some of 

[Category] C, and that is the small ... portion of the short-term association that [could 

potentially account for] harvesting.” He considered harvesting to include deaths pushed 

forward by just a few days and that it may be concentration dependent. 

Although the notion of harvesting was conceptually appealing, he did not think that there 

was evidence in the literature to support it. He cited an analysis by Zeger et al. (1999) 

that simulated the potential effect of harvesting on a dataset. Zeger et al. then analyzed 

actual data and could not demonstrate the harvesting effects he anticipated. In addition, 

he indicated that papers by Joel Schwartz (2000) found increasing coefficient size with 

longer time window, which “is the opposite of [the] harvesting situation.” 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert H’s views on the types of mortality effects captured using different study designs 

is summarized in the table below. 

Study Design Type of Effects Captured (e.g., shortterm, long-term, or both) 

Cohort Studies Long-term and short-term minus harvesting 

Cross-Sectional Studies Long-term, and short-term minus harvesting 

Intervention Studies Short-term and some long-term 

Time-Series Studies Short-term 
 

H-3 

He thought cross-sectional studies would likely capture similar types of mortality as the 

cohort studies but expressed concerns about their ability to deal with individual level 

confounding. 

He stated that time-series and case-crossover studies cover mortality effects of short-term 

exposures only. However, because he thought the short-term was largely captured by the 

cohort studies, he found the real value of the time-series studies to be their contribution to 

establishing a causal relationship. He indicated that they have less confounding to deal 

with and demonstrate the temporal relationship between exposure and effect. 

When asked about the intervention studies, he thought they captured long-term effects 

but over a shorter period of time than the cohort studies. He thought of the intervention 

studies as a “special type of time-series study where you are estimating sort of long-term 

trends that usually [are not] estimated in time series [studies],” although he thought they 

had the potential limitation that there may be changes in certain factors that are 

concurrent with the change in air pollution (e.g., in behaviors) that could confound the 

PM/mortality relationship. 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert H thought that the following characteristics would be part of an ideal 

epidemiologic study to characterize the PM2.5-mortality relationship in the U.S. 

population: 

• Geographically representative of the U.S.; 

• Population that is representative of the general U.S. population with respect to 

race and socioeconomic status (SES); 

• Large sample size; and 

• Exposure assessment/monitor placement appropriate to source types. “I think ... 

[it’s important to] have monitors that can possibly capture several major source 

types.” 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert H first discussed the American Cancer Society (ACS) 

cohort study by Pope et al. (2002). He thought the strengths of this study were the long 

follow-up period, which allowed for a large number of deaths, the use of PM2.5

monitoring data, large study size, and geographic coverage of the U.S. He thought the 

main limitation of the study was in the recruitment of subjects and that the population 

was more highly educated than the general U.S. population. He indicated that the ACS 

cohort had about 12 percent with less than high school education versus 28 percent in Six 

Cities. 

 

He next discussed the Six Cities study (Dockery et al., 1993). Expert H thought that the 

strengths of this study were that the study authors carefully selected the six cities to 

include a wide range of mean PM exposure levels, that they placed their monitors in 
 

H-4 

central locations and in such a way as to avoid proximity to major point sources. He also 

thought that, although the limited geographic representation of the study could be seen as 

a limitation, it could also be seen as a strength because it provides “continuous 

geographic coverage of a certain area in the U.S.” and thus “eliminate[s] factors that can 

actually confound” if they had chosen individual cities from different regions. He 

thought the Krewski et al. (2000) reanalysis of this paper resolved many of the questions 

surrounding its statistical analysis. 

When asked to discuss evidence against a hypothesis of PM related mortality, Expert H 

argued that the debate is more “about good design versus bad design which is different 

from having several [well-designed] studies [with evidence] for and against.” He briefly 

discussed two papers that he thought were not well-designed, the Adventist Health and 

Smog (AHSMOG) study and the Veterans (VA) study. He thought the AHSMOG study 

was well designed to look at the population included in that specific cohort (non-smoking 

non-Hispanic white Seventh Day Adventist in California), but not useful for quantifying 

the C-R function for the general U.S. population. He indicated that the cohort in the 

Veteran's study was too specialized (mild to moderate hypertensives, with a large fraction 

of former and current smokers) to be useful, not just for this elicitation project, but even 

for the general public health research agenda, and in that sense, it was not well designed. 

He thought that the information from the intervention studies could be qualitatively 

useful as support for effects responding to a reduction in exposure, but he did not rely on 

them for his quantitative estimates. 

3.5 Confounding 

Expert H’s review of confounding applied to both the ACS and Six Cities studies. He 

indicated that in the Krewski et al. (2000) reanalysis of the ACS and Six Cities studies, 

no individual level confounders were found to significantly affect the original 

quantitative estimates. However, some ecologic or contextual variables did change the 

estimates when included in the models. In particular, he thought that “population 

change” and spatial auto-correlation could be potential confounders. 

He indicated that “population change” occurs because survival times of individuals may 

be more similar in closer distances than those who live farther apart. This creates spatial 

(positive) autocorrelation. If this pattern is created by some unmeasured risk factors that 

also coincide with spatial autocorrelation in the PM levels, then confounding may occur. 

However, he thought both the Krewski et al.’s (2000) re-analysis of ACS data and Pope 

et al.’s (2002) extended analysis addressed this issue by fitting spatial smooth functions 

and the results did not change much. This led him to believe that confounding by this 

factor was small. He assigned both population change and spatial auto-correlation a 

score of minimal (1) for both the ACS and Six Cities studies because although he thought 

they could be correlated with PM, he could not see how they would be able to cause 

death, and therefore were not true confounders. Although he also discussed the fact that 

SO2 affected the mortality estimates in the Krewski et al. (2000) analysis, he thought SO2

was actually a precursor of, and thus a surrogate for PM2.5, rather than a confounder. 
 

H-5 

On a more speculative basis, Expert H also thought that high fat diet and environmental 

tobacco smoke (ETS) could be potential confounders that would lead to an overestimate 

of the PM effect in the ACS study. He assigned a both a score of minimal (1) given his 

perceived lack of strong evidence. 

Expert H also discussed the potential for residual confounding in the Jerrett et al. (2005) 

paper. He speculated that the Jerrett et al. study might be affected by residual 

confounding because “I see additional uncertainty in the Jerrett et al study, in comparison 

to the Krewski et al. (2000) re-analysis or the Pope et al. (2002).” He provided the 

following rationale: 

(1) The geographic scale in the Jerrett et al study is much smaller than that for the 

nationwide ACS studies, and while this scale has the advantage of examining the 

within-city variation of PM2.5, judging from Figure 1 in the Jerrett et al study, 

there appears to be strong spatial autocorrelation in the PM2.5 data in this locale at 

this scale; 

(2) Jerrett et al noted that in models with only individual covariates and PM2.5, some 

residual spatial autocorrelation was present, but inclusions of ecologic 

(contextual) variables eliminated residual spatial autocorrelation. To me, this 

suggests that the ecologic variables do have spatial autocorrelation that coincide 

with the spatial autocorrelation in the survival times of the cohort; and 

(3) In the Jerrett et al study (Table 1), the addition of ecologic variables did result in 

reductions of PM2.5 risk estimates (e.g., from 1.17 with individual covariates only 

to 1.11 with ecologic variables). In contrast, in Krewski et al’s sensitivity 

analysis of nationwide ACS data, the addition of similar ecologic variables did 

not seem to affect PM2.5 risk estimates. 

Expert H concluded that, “I cannot think of a major confounder that would influence this 

estimate. I guess [the] more important question to answer is about effect modifiers.” 

3.6 Effect Modification 

Expert H thought that the main effect modifier of the PM/mortality relationship in both 

the ACS and Six Cities studies was educational attainment. He indicated that Krewski et 

al. had examined a number of different variables but education was the only one that 

exhibited strong effect modification. He indicated that education was probably a 

surrogate for other factors related to healthier lifestyles, such as diet, access to medical 

care, and housing characteristics. 

He thought that the Six Cities cohort overrepresented those with less than a high school 

education (leading to an overestimate) and the ACS study underrepresented this group 

(leading to an underestimate) in comparison with the general U.S. population. He 

assigned a score of 2 for this factor in each of the two studies. 
 

H-6 

Expert H indicated that the size and importance of this adjustment really depended on 

what the real statistics are about educational attainment in the study populations versus 

the U.S. population, as well as the relevant measurement period. The Six Cities study 

had 28 percent with less than a high school education, whereas the ACS study had 12 

percent. Expert H found a statistic of 20 percent for the U.S. population from the U.S. 

Census and the elicitation briefing book had a graph from the U.S census bureau showing 

that in 1980, about 32 percent of the population aged 25 and over had less than high 

school education. By 2003, only 15 percent had less than high school education. If the 

relevant point in time is the educational level of the cohort at enrollment, then the Six 

Cities study is more representative of the U.S. at that time. If the U.S. average for 1980 

(or whatever the right comparison time is) is closer to 20 percent, both studies are off but 

in opposite directions. He also noted that there was about 45 percent with less than a 

high school education in Steubenville versus 12 percent in Topeka raising the possibility 

of confounding rather than effect modification. 

He also discussed the possibility of effect modification by SO2. However he indicated 

that studies by Joel Schwartz (2000) and the National Morbidity, Mortality and Air 

Pollution Study (NMMAPS) examined this issue and did not find supporting evidence. 

In addition, he thought that in the short-term “it’s conceivable that the areas where you 

have certain components of PM2.5, you may see a strong association of PM2.5,” but he did 

not think it would affect the overall effect estimates. 

3.7. Exposure Issues 

Expert H thought that differences between central site concentrations versus individual 

exposures could be an important exposure issue. He indicated that the ACS cohort had 

been reanalyzed by Jerrett et al. (2005) and had found that in Los Angeles (LA), better 

spatial resolution of exposure lead to increased effect estimates. However, he thought 

that it was “not straightforward” to interpret the increased effect as being attributable 

solely to the central site versus individual exposure issue because of the potential 

pollutant mix and because these results were specific to one city. He noted that the as yet 

unpublished Jerrett work in New York found mixed results: no association for all-cause 

mortality and an association as large or larger than the LA study for cardiovascular 

mortality. Overall, he thought this type of exposure misclassification would lead to an 

underestimation of mortality effects in both the short-term and long-term studies. 

However, given the disparity in the ACS results in the two cities he felt that he could not 

determine by what magnitude the effects might be off. He did state that the LA study 

might provide an upper limit on the magnitude of the downward bias and that it might be 

an overestimation. He assigned a score of 1 to 2 to the ACS study for exposure 

misclassification but a score of 1 to the Six Cities study because he thought the 

investigators selected more representative monitoring sites in the latter study. 

When asked about the relevant time window of exposure, Expert H expressed the view 

that for cardiovascular deaths, the critical window would be 10 years, and it would be 

longer for cancer. He thought that since “the ranking of PM levels don’t change much 
 

H-7 

over time across these cities, I don’t think it’s much of a problem.” He thought it would 

be more of a problem if the critical period is 40 years or so before, but he thought the 

evidence was more consistent with a 10-20 year period. When asked whether the 

intervention studies suggested a shorter critical time window of a few years, he said that 

he did not think so. He thought it was difficult to discern “how much of the intervention 

drop is accumulation of short term process [or] long-term effects.” 

He did not think any of the other exposure issues listed in the protocol were influential. 

3.8. Causality 

In general, Expert H thought a causal inference would be supported by epidemiological 

studies that include testing of all kinds for confounders and other factors. He would want 

to see strong and consistent associations, not just for mortality, but also for subclinical 

markers like IMT. He would like to see evidence demonstrating an appropriate temporal 

relationship between exposure and mortality effects as in the intervention studies. 

Ideally, he would like clinical or other studies that demonstrate a plausible biological 

mechanism. “But I don’t think it’s absolutely necessary.” 

He thought the ACS and Six Cities studies, in particular were important evidence of a 

causal relationship between long-term PM2.5 exposures and mortality. 

Expert H thought it was easier to think about a causal relationship for short-term 

exposures and mortality than for long-term exposures. In particular, Expert H felt 

intervention studies provide potential support for a causal relationship for short-term 

exposures and mortality. He indicated that, “I think it gets more difficult with [the] 

longer period [of time] you try to estimate risk reduction for.” He also mentioned a study 

by Clancy et al. (2002) that examined changes in mortality in Dublin after a coal ban, and 

a study by Hedley et al. (2002) in Hong Kong. Although in the Hong Kong study the 

effects appeared to be more related to SO2 than PM, he thought that SO2 might be serving 

as an indicator for something else in the source emissions, in particular metals. He cited 

a poster by Hedley showing that nickel levels had been reduced along with SO2 and 

inferred that nickel might be the important agent. He also discussed a laboratory study 

that exposed cells to ambient PM samples in the Utah Valley during the time when area 

steel mills were functioning and found an inflammatory response that was not present for 

particles from the mill closure period (Dye et al., 2001). Transition metals are believed to 

play a role in the inflammatory response. 

He indicated that the studies that he discussed when answering questions in Section 3.1 

regarding biologic mechanisms were also supportive of a causal relationship (Sun et al. 

(2005); Howard et al. (1990); Ghio et al. (2000); and Kunzli et al. (2005). 

Expert H specified a range of the likelihood of a causal relationship of 70 – 95 percent 

with a most likely value of 80 percent. His lower bound was based on “[j]ust the fact that 

there’s no counterpart to intervention studies for the long-term effects. It’s hard to 

establish direct, convincing proof.” His upper bound was based on “the results from the 
 

H-8 

sensitivity analysis of the ACS and Six Cities studies [which] alone give me a lot of 

confidence.” However, in later characterization of his uncertainty in the magnitude of the 

C-R relationship, he did not think that an 80 percent likelihood of a causal relationship 

was consistent with his views that there are no major confounders of the PM/mortality 

relationship in the key epidemiological studies. He adjusted his likelihood upward to 90 

percent for his most likely value, and changed his range of values to 80 – 95 percent. 

3.9 Thresholds 

Expert H thought, “[c]onceptually, you can come up with [a] threshold for some ... very 

narrowly defined sub-group that [has a] uniform level of frailty [or] sensitivity ... [b]ut 

when you are talking about a population that is diverse in terms of susceptibility, effect 

modifying factors, confounding factors, I think you are bound to come up with something 

that is monotonic ... I cannot think of any reason why we should see a population 

threshold in this range.” His views were the same for short-term and long-term 

exposures. 

When asked what types of evidence would be informative for assessing a threshold level, 

Expert H thought that, “it would be easier to find a threshold in toxicological studies, 

only because you can have uniform levels of susceptibility.” However, he did not think 

that the results of toxicological studies could be used to support a U.S. population 

threshold. 

He did not think that a population threshold was detectable in any of the studies currently 

available. 

Expert H did not opt to include a threshold in his C-R function. 

3.10 Other Influential Factors 

Given his emphasis on the cohort studies, Expert H did not think there were any other 

influential factors within those studies that would change the estimates. He did mention 

that it would be helpful in the future to examine the factors that are involved in the effect 

modification by education, since it was still somewhat vague. He indicated that he would 

have been more skeptical about the statistical modeling in the epidemiologic studies if it 

were not for the Health Effects Institute’s extensive sensitivity analysis of the cohort 

studies’ data. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert H thought the C-R relationship was log-linear over the entire range of 

concentrations that were the focus of the study (4-30 μg/m3

) based on the statistical 

model used in the ACS and Six Cities studies and because it was consistent with his view 

that there is no threshold. 

Expert H based the values for his uncertainty distribution on a subjective weighting of the 

ACS (Pope et al., 2002) and the Six Cities Study (Dockery et al., 1993) and adjustment 
 

H-9 

for exposure misclassification and for differences in educational attainment relative to the 

U.S. population. He began by specifying his 50th percentile because he first wanted to 

take into account the education and exposure issues in his midpoint before characterizing 

the other percentiles. He started with an estimate from Pope et al. (2002) of 5.9 percent 

per 10 μg/m3

. He then decided to adjust it upward to account for exposure 

misclassification, which he supported with the Jerrett et al. (2005) ACS LA analysis. 

This study found an effect estimate of 11 percent per 10 μg/m3 for the model including 44 

individual covariates and parsimonious contextual covariates. However, given that the 

study was conducted in just one city and that he thought there was epidemiological 

evidence suggesting all-cause mortality is higher in LA than, for example, in the 

Midwest, he was reluctant to adjust his basic estimate up by a factor of 2. He also 

thought that even this model could be overstating the effect because of some residual 

confounding or for spatial representativeness or “spatial correctional factor taking into 

consideration it was LA where educational attainment is not the same as U.S. general.” 

He therefore moved his central estimate up to 7 percent per 10 μg/m3

. 

Expert H raised an interesting question about the adjustment for effect modification by 

education. Initially, he thought that educational attainment could be causing the Pope et 

al. estimates to be underestimated. But, after learning that the U.S. Census bureau7

reported in 2003 that 15 percent of the U.S. population aged 25 and older had less than a 

high school diploma, a percentage which does not differ drastically from the percentage 

in the ACS cohort (12 percent), Expert H decided not to adjust for this factor. Therefore, 

his 50th percentile value remained at 7 percent per 10 μg/m3 (0.7 percent per 1 μg/m3

). 

Expert H then specified his 75th percentile value at 1.3 percent per 1 μg/m3 based on the 

estimate from the original Six Cities study. When thinking about his 95th percentile 

value, he relied on the estimate from Jerrett et al., 2005 (adjusted for 44 individual 

covariates) of 1.7. He thought it could be slightly higher and set the value at 2. He then 

set his maximum at 3 percent to ensure that his distribution would be smooth. 

After reviewing his assumptions on causality, and electing to change his most likely 

value for the likelihood of causality from 80 percent to 90 percent as discussed in an 

earlier section, Expert H set his minimum and 5th percentile values at zero and his 25th

percentile at 0.4 percent per 1 μg/m3. 

 

7

 U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplement, 2003 

(http://www.census.gov/rod/2004pubs/p20-550.pdf). 
 

H-10 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Expert H

Percentile Percent Change in Mortality 

Minimum 0 

5th 0 

25th 0.40 

50th 0.70 

75th 1.3 

95th 2.0 

Maximum 3.0 
H-11 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distribution from 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

Expert H ACS Original ACS Extend ACS LA Analysis

(Individual

Covariates)

ACS LA Analysis

(Individual and

Contextual

Covariates)

Six Cities Original 

% Decrease in Mortality per 1 ug/m3 PM2.5
 

H-12 

Expert H Distribution 
 

Expert I 

Interview Summary
 

I-1 

Interview Summary 

Expert I 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert I discussed the biological mechanisms for short-term and long-term exposures 

together because he thought that, “there are ... similar mechanisms involved.” Expert I 

thought that the main cause of death from particulate matter (PM) was cardiovascular 

disease. He thought the key mechanisms related to this cause of death were oxidative 

stress and inflammation leading to atherosclerosis, changes in autonomic function, and 

other markers of heart disease. 

He first discussed oxidative stress and inflammation leading to plaque development and 

atherosclerosis, stating that this mechanism was first brought to light in a study by Seaton 

et al. (1995) that showed “inflammation and blood coagulation relating to the 

accumulation of particles as a potential pathway.” He thought papers by Pope et al. 

(2004) and Jerrett et al. (2005), although they do not shed light on specific pathways and 

mechanisms, provide supporting evidence for mortality from various cardiovascular 

disease outcomes as a result of long-term exposures. Additionally, he cited 

epidemiologic studies that give insight into mechanistic pathways, such as Kunzli et al.’s 

(2005) study on atherosclerosis, and animal studies such as Wellenius et al. (2003), 

showing ischemic heart disease in dogs as a result of fine particles, and Sun et al. (2005), 

that found plaque development, inflammation, and mortality in mice. 

He then discussed autonomic function effects. He indicated that this mechanism was 

supported by studies that have found changes in heart rate variability (HRV) associated 

with PM, such as Pope et al. (1999 & 2000), Gold et al. (2000), Creason et al. (2000), and 

Devlin et al. (2003). He indicated that, “heart rate variability, the different measures, 

both the time domain and the frequency domain, are good predictors of congestive heart 

failure and sudden death.” While some have argued that acute changes in HRV are really 

transient effects, he thought a recent study by Schwartz (2005) provided evidence for a 

longer term role; the study essentially looked at HRV in individuals with different 

genotypes that make them more or less susceptible to oxidative stress. Individuals with 

genotypes more likely to experience oxidative stress had greater changes in effective 

HRV than those who did not. 

Expert I thought that studies examining other markers of heart disease provided evidence 

for a link to air pollution, although they have not been as well-studied. For instance, he 

cited a paper by Zanobetti et al. (2004) that found blood pressure changes, and a paper by 

Brook et al. (2002) examining arterial vasoconstriction, a study by Schwartz et al. (2005) 

looking at changes in inflammatory markers in the blood such as fibrinogen and white 

blood cells, and the Peters et al. (2000) implantable defibrillator studies. 
 

I-2 

He thought that respiratory deaths from the progression of Chronic Obstructive 

Pulmonary Disease (COPD) and lung cancer were secondary causes of death from PM. 

He indicated that the Six Cities cohort study extended analysis (Laden et al., 2006) did 

not find a separate effect for respiratory deaths. However, he thought there was some 

evidence for a mechanism by which inhalation of particles could have effects on the 

respiratory system. He drew an analogy with smoking, which involves higher 

concentrations but over shorter periods of time than air pollution, which is a more 

constant exposure. He cited studies by Gaudermann et al. (2004) that showed long-term 

effects on lung function in children exposed to PM2.5 and a paper by van Eeden et al. 

(2005) whose results indicated that, “effects on the COPD from long-term exposures 

could also have cardiac effects.” He indicated that epidemiologic evidence for lung 

cancer as a result of PM is “quite reasonable and robust” specifically citing the American 

Cancer Society (ACS) cohort study (Pope et al., 2002) and the Adventist Health and 

Smog study (AHSMOG) (Abbey et al., 1999). 

Expert I discussed whether these mechanisms were related to long-term exposures, acute 

exposures or both. He thought that both types of exposure could contribute to mortality, 

noting a parallel with smoking. He thought that the pathway involving inflammation and 

build-up of atherosclerotic plaque leading to increased risk of death from heart attack was 

likely a long-term process. However, the evidence of shorter-term exposures on changes 

in HRV and “increased markers of inflammation, like fibrinogen [and] white blood cells 

... [suggests that acute exposures could affect individuals who are] already compromised 

with heart disease, whether the disease is brought on from exposures to air pollution or 

not.” He thought the development of COPD and lung cancer was related to long-term 

exposures. He thought the mechanism for PM’s impact on lung cancer was analogous to 

the mechanisms for smoking and lung cancer. 

3.2. Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

Expert I thought the Künzli diagram was a good conceptualization of the relationship 

between long- and short-term exposures. “I’m willing to accept this diagram. I think it 

describes adequately the possible scenarios.” He thought that not all of the short-term 

effects were captured by the long-term studies, but was not certain enough of the exact 

amount that he would want to add in additional deaths related to short-term effects to 

those reported in the long-term studies. He thought it was a small percentage, no more 

than ten percent. 

We had a lengthy discussion of the challenges to estimating how many deaths fall into 

different exposure windows, the related years of life lost, and what deaths are ultimately 

captured by various study designs. Expert I suggested these topics would be good ones 

for discussion at the Post-elicitation Workshop. 
 

I-3 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert I thought that a combination of cohort, cross-sectional, intervention, and shortterm studies looking at distributed lags of various lengths were appropriate for capturing 

the mortality effects of changes in annual average PM2.5 concentrations. The effects 

captured by each study design are shown in the table below: 

Study Design Type of Effects Captured (e.g., shortterm, long-term, or both) 

Cohort Studies Long-term, and potentially some short-term 

Cross-Sectional Studies Long-term, and potentially some short-term 

Intervention Studies Short and Intermediate-term 

Time-Series Studies with Distributed Lag Short (multi-day) and Intermediate-term (days 

to weeks) 

Expert I also expressed the opinion that there was need for a better understanding of the 

biological explanation for the different mortality effect sizes observed in short-term, 

intermediate-term (intervention), and long-term exposure studies. Regarding the 

intervention studies, for example, “they’re telling us that something other than same-day 

exposures or same week exposures seem to be important, but it’s not clear whether it’s 

several months or several years.” 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert I thought that the following characteristics would be part of an ideal 

epidemiologic study to characterize the PM2.5-mortality relationship in the U.S. 

population: 

• Includes a cohort population that is representative of the national population; 

• PM2.5 measured over the lifetime of the cohort members; 

• Personal monitors (home, workplace, everywhere) in addition to central site 

monitors with which the personal monitors could be related; 

• Measures of fine, coarse, and ultrafine particles, gases, benzenes, and benzopyrenes; 

• Measures several confounding factors such as body mass index (BMI), age, 

gender, race, alcohol, tobacco, smoking histories, occupational exposures, 

socioeconomic status (SES), extreme weather events, diet, psychological 

measures, hormone replacement, and neighborhood effects; 

• Periodic follow-ups including residential histories; and 

• Large samples to allow for stratification on several different factors. 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert I discussed the following studies: 
 

I-4 

Study 

(author, 

date) 

Key findings Strengths Limitations 

ACS Cohort 

(Pope et al., 

2002 & 2004) 

• Effects on 

cardiopulmonary 

disease and lung cancer 

• Effects for 

cardiovascular diseasespecific subsets (Pope 

et al., 2004) 

• National sample (includes variety 

of particle mixes) 

• Controlled for many confounders 

• Replication by Krewski et al. 

(2000) 

• Large sample size 

• Historical data on pollution 

• Range of exposures 

• Well-established cohort 

• Not representative of the 

general U.S. population, 

with respect to lowerincome, minority, and 

education 

• Gaps in exposure estimates 

• Exposure assessment 

problems 

Jerrett et al., 

2005 

With improved exposure 

metric, found associations 

with cardiopulmonary 

disease and lung cancer 

• Improved exposure metric 

• Careful consideration of spatial 

auto-correlation 

• Less inter-city variation and 

confounding 

• No historical pollution data 

• Possibly not representative 

of national mixes of PM 

• Not representative of the 

general U.S. population 

Laden et al., 

2006 

Found both long-term and 

relatively recent exposures 

to fine particles associated 

with cardiovascular disease 

• Monitors set up for the purpose of 

the study and well-followed 

• Representative population 

• Random population design 

• Longer-term and recent set of data 

on pollution 

• Only included 6 cities 

• Limited geographic 

representation of the U.S. 

• Extrapolation from PM10 to 

PM2.5

Krewski et al., 

2000 

Replicated original findings 

of the ACS and Six Cities 

studies with extensive 

sensitivity analysis. Found 

that results were robust. 

• Objectivity in approach and 

analysis 

• Extensive analysis including 

multiple confounders, such as 

smoking and occupation. 

• Potential for multiple 

comparisons (i.e., over 

manipulation of the data) 

AHSMOG 

(Abbey et al., 

1999; 

McDonnell et al., 

2000) 

Generally inconsistent 

effects, often genderspecific 

• Less confounding from alcohol, 

smoking, and environmental 

tobacco smoke (ETS) 

• Frequent follow-up allows for a 

complete exposure profile 

• McDonnell et al. analysis of this 

cohort had improved exposure 

estimates 

• Very small sample 

• Not representative of the 

general U.S. population 

• Used airport visibility as 

exposure measure 

Enstrom et al., 

2005 

Effect found in the younger 

portion (43-65 at 

enrollment) of the cohort. 

Found smaller effect 

estimates than other cohort 

studies. 

• Moderate sample size • Potential healthy survivor 

effect 

• Elderly cohort (many 65-99 

at enrollment) 

• Questionable objectivity 

• Potential confounding (ETS, 

smoking) 

European studies 

(Hoek et al., 

2002; Filleul et 

al., 2005) 

Found effects related to 

either traffic or to measured 

pollutants 

• Long-term follow-up • Relatively small sample 

sizes 

• PM mixture potentially 

differs from that in the U.S. 

• Different SES/demographics 

factors than in the U.S. (more 

central city, less suburban) 

• No direct measure of PM2.5 

• Small sample size 
 

I-5 

Study 

(author, 

date) 

Key findings Strengths Limitations 

Intervention 

studies (Clancy 

et al., 2002 – 

Dublin; Pope et 

al., 1996 – Utah 

Valley; Hedley 

et al., 2002 - 

Hong Kong) 

Associations between 

relatively short-term 

exposures and mortality. 

Effect estimates higher than 

short-term studies. 

• Large change in exposures, and 

exposure conditions very clear 

• Not capturing all of the 

long-term changes 

(intermediate between shortterm and long-term) 

Veteran’s Cohort 

(Lipfert et al., 

2000 & 2006) 

• Used population at risk 

(hypertensive) so more power to 

detect associations 

• Population not 

representative of the general 

U.S. population 

• Small sample size 

• Unclear and perhaps 

inappropriate statistical 

analysis 

3.5 Confounding 

Expert I thought that, based on Krewski’s reanalysis of the ACS and Six Cities studies 

(2000) and the Jerrett et al. (2005) ACS L.A. reanalysis, many of the factors commonly 

thought to be confounders did not substantially affect the estimates. He thought that 

there were three remaining issues of “particular interest”: 

• SES: Expert I thought that SES could be a marker of a “constellation” of other 

confounders, such as alcohol, diet, occupational exposure, smoking, and exercise. 

He thought that SES could be a proxy for exposure, because those with higher 

SES are not likely to live near a major roadway or other pollution sources. 

“When we do find the effect modification of SES ... the SES could be picking up 

pure income [or] health habits, [but] it also could just be modeling exposure.” 

• Pre-Existing Health Status: Expert I thought that conceptually, “people with 

pre-existing health status [e.g., cardiovascular disease] should probably have a 

large effect, because they’re on the pathway.” He indicated that Krewski et al. 

(2000) had examined this issue and did not find higher effect estimates in people 

with pre-existing health issues. However, he did point out that those with preexisting health problems could be susceptible to short-term exposures, so the 

effects might not be easily captured by the long-term studies. 

• Temporal Trends: Expert I thought that exposure changes over time could 

potentially affect the published estimates, although the implications for the 

mortality estimates are not entirely clear. On the one hand, one might expect that 

early childhood exposures might be very important. However, Jerrett’s analysis 

of this issue for the L.A. portion of the ACS cohort, as well as some calculations 

done by Expert I, convinced him that this potential source of error would not 

make a big difference in the effect estimates even under the most extreme 

assumptions. He indicated that results from Laden et al. (2006) found that the 

previous one or two years of exposure were the most influential for effects. He 
 

I-6 

was unsure of the disease process that could explain the bigger effects found 

when a more recent exposure period is used. 

In addition to those three topics discussed above, Expert I also touched on the question of 

co-pollutants and contextual ecologic variables. 

• Co-pollutants: Expert I thought the issue of co-pollutants had been handled 

statistically, though “the explanations have not been developed yet.” He was 

concerned that it was difficult to measure co-pollutants well in long-term settings 

with central site monitors because they are too localized in terms of exposure. He 

discussed only SO2 specifically. He thought that SO2 might be a confounder (but 

representing something other than pure SO2). That is, he did not think there was a 

plausible biological mechanism linking SO2 and mortality. His understanding 

was that it was unlikely to get into the deep lung and cause the types of 

inflammation that we discussed as part of the mechanisms for health effects. He 

said that clinical studies have shown health effects to occur only in asthmatics that 

are exercising while exposed to high levels. He thought it might be more of an 

effect modifier. He thought SO2’s role would be a good subject to discuss in the 

Post-elicitation Workshop. 

• Ecologic Variables: Expert I thought that ecologic covariates, or factors outside 

the individual level, such as neighborhood effects or air conditioning use, were 

potential confounders. However, he thought that the issue of how much to control 

for them remains unclear. He thought there is a strong likelihood of over-control 

if these factors are correlated with pollutants. He also discussed the possibility 

that unmeasured spatial variables cause mortality rates to correlate among closely 

spaced cities. He indicated that Jerrett et al. (2005) found reduced effect 

estimates after controlling for spatial auto-correlation in LA, but that the Pope et 

al., 2002 study found that it did not affect results for the full cohort. 

Ultimately, Expert I did not want to assign scores, or indicate the degree of adjustment he 

might make as a result of any of these issues, indicating that he would rely on effect 

estimates from studies that had adjusted or accounted for these factors. 

3.6 Effect Modification 

Expert I thought that the only variable for which empirical evidence existed for effect 

modification was educational attainment. He indicated that the effect estimates in the 

ACS study were biased downwards because the cohort population overrepresented highly 

educated people. He discussed the possibility of effect modification by criteria copollutants. He did not think that there was evidence for effect modification or interaction 

effects (e.g., SO2 with particles). The one exception he cited was the Air Pollution and 

Health – A European Approach (APHEA) study of short-term exposures that found 

greater effects for PM in cities with high NO2. He thought that the NO2 effect has been 

interpreted to be a proxy for traffic and diesel, meaning that “that in those areas where 

particles are dominated by traffic, the effects might be higher.” 
 

I-7 

3.7. Exposure Issues 

Expert I discussed three issues: 1) relevant time period of exposure; 2) exposure 

misclassification related to location of monitors (i.e., central site versus averages of 

monitors versus monitors closest to the home); and 3) pollutants measured (e.g., PM10, 

PM2.5, PM1, elemental (EC), organic carbon (OC), metals) 

Expert I discussed potential exposure misclassification resulting from monitoring PM2.5

during time windows that may not be most relevant for the effect. He thought that if 

childhood exposures were important and past concentrations were higher, risk would be 

overestimated based on monitoring in the years immediately preceding death. On the 

other hand, he though that if very recent exposures were the critical window and an 

annual average or several year average was used, risks could be underestimated. He 

thought that this was not a major influence, and thought it was “a neutral bias, slightly 

weighted toward overestimation.” He then went on to quantify the influence of this 

factor, stating that the effect estimates could be overestimated by about 5 to 10 percent, 

based on information from the Jerrett et al. (2005) paper. 

Expert I then discussed the impact of location of PM monitors and techniques for 

estimating local exposures. Based on the “Hoek [2002] study and the Jerrett [2005] 

study, which used localized measures [of exposure], there’s some reason to believe that 

the estimates from the other studies are too low, based on measurement 

misclassification.” He thought that this was more of an issue in studies that use few 

monitors for a large geographic area, such as the ACS study. He thought that this could 

be a potentially large bias, and published effect estimates from the ACS study could be 

underestimated by a factor of two (for example, the ACS estimate is 6 percent but the 

Jerrett study finds 15 percent per 10 μg/m3

). 

Finally, Expert I discussed the role of the pollutant mix, specifically the potential that we 

are not measuring the causal pollutant. He was not sure whether this is a big factor in 

estimating the mortality effect. On one hand, if the mis-measured causal pollutant is 

correlated with PM, he did not think there would be an issue. He thought that it could be 

an issue if that pollutant’s concentration relative to PM varies by region. He cited studies 

of short-term exposures by Laden et al. (2003) and Mar et al. (2000 & 2003) that suggest, 

“traffic-related particles seem to be a little worse than oil residual particles.” He noted 

the difficulty of knowing, for example whether particle constituents (or traffic related 

particles) were a possible explanation for the higher effect estimates measured in the 

Jerrett (2005) study in L.A. However, he indicated that overall, the evidence on the 

relative toxicity of PM components was limited and he would not feel comfortable 

adjusting his quantitative estimates of the mortality effect related to long-term exposures 

based on this issue. 

 

3.8. Causality 

Expert I thought that a causal relationship would be best supported by “replicated epi 

studies that are well designed and carefully done.” In addition, he said it is useful but not 
 

I-8 

necessary to have good mechanistic hypotheses. He also mentioned temporality, exposure 

response, and plausible quantitative results. 

“First of all, based on my own criteria, there has been replication in different forms of the 

original studies[;] the fact that [the ACS and Six Cities studies] were replicated by 

Krewski ... is a very powerful evidence for a real effect ... And then second, when all the 

different sensitivity analyses were conducted, the findings were basically upheld ... 

Then, the existence of the other epi studies that have now been conducted support it. The 

parallels between [PM-mortality] and the models of smoking and ETS and the fact that 

you see similar types of endpoints, cardiovascular and lung cancer effects, help support 

causality. And then I think the toxicological studies that have been conducted over the 

last five years (most recently the Sun studies and Lippmann studies) showing the effects 

in mice in terms of plaque development are a help, as well as some of the other previous 

studies. The Godleski studies and the Ghio studies and the van Eeden studies and the 

Seaton studies and some of these other studies, both in humans and in animals, [support] 

causality. Even the short-term exposure studies, where there's much less of a likelihood 

of confounding, and where you see effects on mortality and morbidity, [support my belief 

in] causality... [S]ome of the short-term studies and ... the endpoints that they found, 

like MIs and defibrillations and changes in blood plasma and inflammatory markers [are 

also supportive]. [So,] observational studies as well as controlled human studies, and 

animal studies also give me some evidence. And finally I would say that, regarding my 

last criter[ion], when [one] examines what the quantitative implications are of these 

studies, [they’re] entirely plausible in terms of the magnitude of the effect[s] that are 

predicted.” 

Expert I specified a range of the likelihood of a causal relationship of 80 – 100 percent 

with a most likely value of 95 percent. His lower bound was based on having some 

residual doubt. “You have these monitors, and who knows what those are representing? 

And you've got all sorts of competing risk factors and nothing is ever fully measured, and 

you've got people commuting all over the county and who knows what they're exposed 

to? And you get different penetration rates. And you get tons of pollution driving in 

your car, and in your bus. And you get people taking all sorts of medications or not ... I 

factor all that in and how high could be my doubt? I could say as high as ... 20 percent.” 

Expert I’s upper bound was based on the strength of the evidence such as cohort studies, 

and the addition of more recent toxicological studies (e.g., Sun et al., 2005 study in 

mice), and epidemiologic studies providing evidence on mechanisms (e.g., studies of 

IMT (Kunzli et al., 2005), genetic predisposition to oxidative stress, myocardial 

infarctions (MI’s), and work by van Eeden and Seaton). 

3.9 Thresholds 

Conceptually, Expert I thought “that [al]though individuals can have thresholds, for such 

a huge distribution of individuals and such heterogeneity across individuals, it's unlikely 

at the population level to have a particular threshold.” The types of studies he thought 

would be most informative about the quantitative level of a threshold included a 
 

I-9 

combination of epidemiological studies, clinical studies of markers of inflammation, 

studies of disease with long-term follow-up, and long-term primate studies. 

However, Expert I thought “epi evidence ... from both the mortality studies and 

morbidity studies and lung function studies, failed to show any kind of threshold or even 

non-linearity in most of the cases. So I think the scientific [evidence] at this point does 

not suggest any kind of threshold concentration.” Further, he noted that there has been a 

simulation study in Risk Analysis by Brauer et al. (2002) with short-term exposures 

showing that if there is significant exposure measurement error, thresholds would be 

difficult to detect, even if one existed. However, he did think that there was the 

possibility of non-linearity at the low end of the concentration range because limited data 

exists for the C-R function at concentrations below 8-10 μg/m3

 and the uncertainty 

bounds are wider. He concluded that his best estimate was that the C-R function shape 

was linear based on the existing evidence and did not elect to incorporate a threshold into 

his quantitative response. 

3.10 Other Influential Factors 

Expert I did not think there were any other outstanding sources of uncertainty not already 

covered by the protocol. He did not think that publication bias was an issue because all 

of the existing cohorts are well-known and followed. He also thought that the statistical 

modeling used in published studies is “appropriate.” He indicated that the use of Cox 

proportional hazard models for survival analysis was “well established theoretically and 

empirically.” He noted that Krewski’s re-analysis evaluated other statistical models and 

found the results to be similar. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert I thought the C-R relationship was log-linear over the entire range of 

concentrations that were the focus of the study (4-30 μg/m3

) based on the current 

evidence. He indicated that, “those who have done specific statistical tests, chi-square 

tests ... for linearity, seem to be able to rule out non-linearity.” 

Expert I felt the most comfortable basing his C-R function distribution on empirical 

estimates from the literature. He indicated that rather than attempt to adjust for specific 

factors (e.g., confounding, effect modification, exposure issues), his approach to 

incorporating uncertainty would be by weighting different studies whose estimates reflect 

different conditions or approaches about which he is uncertain. He selected three 

estimates and their associated standard errors and assigned each one a subjective weight.8

 

The elicitation team then combined the estimates in Crystal BallTM using a Monte Carlo 

simulation, according to the assigned weights. The trial values from the Monte Carlo run 

were then fit to a Beta distribution, which was ranked the most highly by Crystal BallTM 

based on the Anderson-Darling goodness-of-fit test. The elicitation team then combined 

the Beta distribution with Expert I’s estimate for the likelihood of a causal relationship. 

 

8

 The estimates and standard errors from the three studies were each fit to a normal distribution. 
 

I-10 

For his first modeling approach, Expert I chose the first estimate from Pope et al., 2002 

(β (SE) - 0.0583 (0.02157)) because it has a large sample size and the cities includes are 

geographically representative of the US. However, he thought the population was not 

representative of the entire U.S. because of oversampling of highly educated people and 

therefore, the estimates were biased downward. The second estimate chosen by Expert I 

was from the ACS L.A. reanalysis by Jerrett et al., 2005, in (0.14 (0.0574)) based on the 

model using principal components analysis for the ecological covariates. He chose this 

study because it has “better modeling of exposure, less potential inter-city-related 

confounding, regional confounding.” He thought the limitations of this study were that it 

only included the “L.A. basin, so the pollution mix could be different. The people could 

be different. Smaller sample size.” His final estimate was taken from Six Cities 

extended reanalysis (Laden et al., 2006 (Table 3, Model 1: 0.1484 (0.0417)). 

“Advantages: biggest one is the monitor placement and the city size that's used. The city 

sizes are small, so the monitors are probably most representative. And the sample is 

more random and representative of those areas. Third, it's been replicated by Krewski. 

The downsides are ... small sample [size that only includes the] East Coast [and the] 

Midwest.” 

Expert I based his maximum value on adding two standard deviations to the mean effect 

estimates from the Laden et al., 2006 study, arguing that it would be “hard to believe” it 

could be higher. 

Expert I ran through the combination of these three studies with three different weighting 

schemes. The first run included equal weights for each study. For the second run, he 

placed a weight of 0.25 on the Pope et al., 2002 estimate and a weight of 0.375 on both 

the Jerrett et al., 2005 and the Laden et al., 2006 estimates. His final run included an 

estimate from the Pope et al., 2002 study adjusted for educational attainment as an 

alternative to the original Pope estimate; this run also assigned equal weights to each 

study. After examining the resulting distributions from each run, Expert I ultimately 

decided to use the results from the second run, which did not weight Pope et al., 2002 as 

highly as the other two studies, for the following two reasons: “One is that the 

measurement error might be a little bit more [in the Pope et al., 2002 study] ... And then 

two, because of the [lack of] national representation.” 
 

I-11 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Expert I

Percentile Percent Change in Mortality 

Elicited Distribution** 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood (IEc 

Generated) 

Minimum 0 0 

5th 0.30 0 

25th 0.80 0.70 

50th 1.2 1.2 

75th 1.6 1.6 

95th 2.2 2.2 

Maximum 2.3 2.3 

** Parameters of fitted beta distribution: α = 2.9, β= 3.7, min = -0.16, max = 3.0 
I-12 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distributions from 

• = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

Expert I - Elicited Expert I -

Incorporating

Causality (IEc

Generated)

ACS Original ACS Extend ACS LA Analysis Six Cities Original Six Cities 

% Decrease in Mortality per 1 ug/m3 PM2.5
 

I-13 

Elicited Distribution 

Distribution Incorporating Causality - Probability Density Function (IEc Generated) 
 

I-14 

Distribution Incorporating Causality - Cumulative Density Function (IEc Generated) 
 

I-15 

U.S. EPA EXPERT ELICITATION STUDY OF THE CONCENTRATION-RESPONSE 

RELATIONSHIP BETWEEN ANNUAL AVERAGE PM2.5 EXPOSURE AND 

MORTALITY 

Modification to Expert Judgments

Expert I

Date: 7/7/06 

Section of Protocol Affected (Section Number and/or Title):

Part 4 – Elicitation of Quantitative Judgments 

Description of Change (e.g. to a specific percentile, or to a qualitative opinion or 

statement of belief):

1. Expert I chose to use Run 3 of the meta-analysis for his final distribution, rather 

than Run 2. This entailed using estimates for the beta (slope) and standard error, 

from Pope et al., 2002 that he adjusted upward for educational attainment. He 

then applied equal weights to the three studies used in the meta-analysis (Pope et 

al., 2002; Jerrett et al., 2005; and Laden et al., 2006). 

2. Expert I decided to fit the weighted average data to a normal distribution, rather 

than a beta distribution, as in the original interview. 

3. Expert I selected a minimum value of 0.2 based on the lower 95 percent 

confidence limit on the original Pope et al., 2002 all-cause mortality effect 

estimate for the average of the two exposure periods (1979-83 & 1999-2000). 

Rationale for Change:

1. He thought that first adjusting the Pope et al. upwards for education and then 

weighing the three studies equally was a better approach then weighting the 

original Pope estimate lower than the other two studies. He also wanted to clarify 

that although the Jerrett et al. study is a subset of the ACS study, the exposure 

assessment was so different than the original study, that he considered it to be an 

independent analysis. Therefore, he chose to weight it equally along with the 

other two studies. 

2. His rationale was that the weighted average data fit to the normal distribution was 

almost equally as good (the normal distribution was the second most highly 

ranked distribution in terms of fit according to the Anderson-Darling test), the 

values of his final uncertainty distribution differed very little from those resulting 
 

I-16 

from the fit of the beta distribution, and that the normal distribution was more 

intuitive and straightforward to explain. 

3. He selected this minimum value based on the lower 95 percent confidence 

interval bound of the Pope et al. (2002) all-cause estimate for the average of the 

two exposure periods (1979-83 & 1999-2000). 
 

Expert J 

Interview Summary
 

J-1 

Interview Summary 

Expert J 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5 

Relying on a schematic diagram, Expert J outlined the general pathophysiologic 

mechanisms and pathways that he thought could explain the mortality effects of PM2.5

exposure. In order to be plausible, Expert J noted, mechanisms should be consistent with 

what is observed in the epidemiologic studies (respiratory and cardiovascular mortality 

for short-term exposures and cardiovascular and to a lesser extent, respiratory mortality, 

in the studies of long-term exposures). He also thought the mechanisms should explain 

the findings of morbidity studies (primarily respiratory and hospital admissions 

associated with short-term exposures). His views have been shaped particularly by the 

emergence over the last six years of numerous studies from different disciplines that 

seem to converge on this set of pathways. Expert J chose to discuss mechanisms for 

short-term and long-term exposures together. “I think that it's hard to talk about [shortterm and long-term exposures] individually. There's no question that some of the 

pathophysiologic links are different whether you're talking short-term exposure versus 

long-term exposure. But there's a lot of it that's very much the same.” 

The primary health effects that Expert J thought might be linked by the same 

pathophysiologic mechanisms included chronic obstructive pulmonary disease (COPD) 

and ischemic heart disease. He discussed data showing both acute and chronic impacts 

on lung function (Harvard 24-city study (Raizenne et al., 1996), Adventist Health and 

Smog (AHSMOG) study, Children’s Health Study (Gaudermann et al., 2004)). In 

particular, he thought there was “substantial evidence that long-term exposure to air 

pollution increases the progression of chronic obstructive pulmonary disease...[and] that 

short-term elevated exposure exacerbates existing COPD. [T]here is also substantial 

literature, more recently, that suggests that the existence of COPD substantially increases 

[the] risk of heart disease and complications related to heart disease, especially ... 

ischemic heart disease and death.” He thought the mechanism for exacerbation of COPD 

was likely to involve pulmonary inflammation. 

The second key hypothesis for the impact of PM on mortality that Expert J discussed was 

that “long-term, repeated exposure to fine particulate matter may help initiate and 

accelerate the progression of atherosclerosis and that short-term elevated PM exposures 

may also contribute to the acute thrombotic complications of atherosclerosis.” He noted 

that this hypothesis is not independent of the COPD hypothesis, but rather that the 

process begins in the lung with pulmonary inflammation leading to systemic 

inflammation and oxidative stress that contribute to development of atherosclerosis, 

plaque instability, and increasing vulnerability to ischemic events. He cited Godleski et 

al (2004), Brook et al. (2004), Kunzli’s atherosclerosis work in LA (2005), Sun et al.’s 

(2005) work at New York University (NYU) in mice, and work done in Hogg’s lab work 

looking at bone marrow responses (van Eeden et al., 2001 & 2002; Terashima et al., 
 

J-2 

1997; Mukae et al., 2000 & 2001; Fujii et al., 2002; Goto et al., 2004) and in 

hyperlipidemic rabbits (Suwa et al., 2002) as some of the evidence supporting this 

hypothesis. 

Expert J thought that another, related set of intermediate effects on the vascular system 

that several studies have associated with exposures to PM contribute to the risk of 

ischemic events (i.e., endothelial dysfunction, vasoconstriction, and hypertension). He 

noted that the PM findings are “at least qualitatively consistent with” the smoking 

literature in which one sees that “smoking accelerates COPD ... increases respiratory 

symptoms, [reduces] lung function. You see systemic inflammation, ... oxidative stress, 

... ischemic heart disease, ... ischemic cerebral vascular disease, ... changes in the 

vasculature.” 

He discussed an additional hypothesis for cardiovascular mortality related to impacts on 

the autonomic nervous system, which has arisen out of the work of Annette Peters et al. 

and others. He noted there are now about a dozen studies showing a relatively robust 

association between short-term exposures to PM (mostly PM2.5 but sometimes PM10) and 

short-term changes in heart rate variability (HRV). However, Expert J indicated that it is 

not yet clear to him how these observations relate to the other pathophysiologic 

mechanisms discussed above. 

Two other hypotheses for which he found the evidence to be less compelling included 

those involving systemic translocation of ultrafines, altered immune responses, and 

hypoxemia (resulting from declines in lung function and oxygen saturation of the blood). 

3.2. Conceptual Framework for Mortality Effects of Short-Term and Long-Term 

PM2.5 Exposures 

While he thought Kunzli’s paper on this topic was an important contribution, he thought 

that, in practice, allocation of health effects would be difficult to achieve using this 

framework. He preferred to view the effects of short- and long-term exposures in terms 

of time scale. Referring to the work by Schwartz et al. (2000 & 2001), and others (Zeger 

et al., 1999; Kelsall et al., 1999; Dominici et al., 2003) exploring the question of 

harvesting, he thought that the evidence suggests “that daily time-series studies utilizing 

only short-term time, day to day variability, are observing more than just the phenomena 

of short-term harvesting or mortality displacement. These results suggest the daily timeseries studies capture only a small amount of the overall health effect of long-termrelated exposure to particulate air pollution. Because the adverse health effects of 

particulate air pollution are dependent on both exposure concentrations and the length of 

exposure, it's fully expected that long-term repeated exposures would have larger, more 

persistent cumulative effects than short-term transient exposures.” He thought that the 

data are consistent with the view that long-term chronic exposure contributes to the 

progression of disease and that short-term exposure exacerbates underlying disease and 

contributes to short-term changes in mortality. He argued that the exposure-response 

relationships are essentially linear and that if the right time scale and weighting were 

known, one could estimate the long-term effects exposure from the time-series studies; if 
 

J-3 

it were not linear, and the effects of short-term exposures only occurred above some 

level, the results of two types of studies would be additive. He concluded that the timeseries studies are only giving a “glimpse” of the total health effects of PM and that the 

long-term cohort studies are capturing nearly all of the effects. 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

The following table summarizes the discussion of the types of impacts that Expert J 

thought were observable using different study designs: 

Study Design Type of Exposure Effects Captured 

Prospective Cohort • Long-term 

• Cumulative short-term 

Intervention Studies 

(Intermediate term length) 

• Cumulative short-term 

• Intermediate long-term 

• Misses effects of long-term exposures 

Ecological Studies • Cumulative short-term 

• Long-term 

Multi-city, intermediate time-series studies 

(distributed lag, unconstrained distributed 

lag ~ 5-40 days) 

• Cumulative short-term up to length of 

lag 

• Not capturing effects of long-term 

exposure to chronic disease and 

mortality 

Overall, he thought the American Cancer Society (ACS) cohort studies (Pope et al., 1995, 

2002 & 2004; Krewski et al., 2000; Jerrett et al., 2005) and the Six Cities cohort studies 

(Dockery et al., 1993; Krewski et al., 2000; Laden et al., 2006) were the most suitable for 

estimating the total health impacts of a 1 μg/m3

 reduction in annual average PM2.5. He 

thought they were likely to capture both the mortality effects of long-term exposures but 

also the cumulative effects of short-term exposures (distributed lag). 

He thought the intervention studies (Pope et al., 1996 (“Utah Valley” study); Clancy et 

al., 2002 (“Dublin” study)) were primarily indicative of the effects of cumulative shortterm exposures and intermediate exposures. He did not think they captured the full 

effects of chronic exposure (contribution to progression of chronic disease and mortality). 

Expert J thought that the “ecological studies” (Lave and Seskin; Thurston and Ozkaynak; 

Evans, Tosteson and Kinney) have come to represent much the same findings as the 

prospective cohort studies. Their results were dismissed early on because they did not 

control for smoking or other individual level risk factors (several contextual ecologic 

variables were examined). He thought it was ironic that subsequent studies including 

Krewski’s re-analysis of the ACS study (2000) and the ACS LA analysis Jerrett et al. 

(2005) have suggested that these individual factors are not likely to have had a huge 

impact on the results. 
 

J-4 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM2.5 on Mortality 

Expert J noted that none of the studies currently published really represent the ideal study 

one would want for predicting the impact of PM2.5 on U.S. mortality. He thought an ideal 

study should have a larger sample size, include more cities, include cities where air 

pollution differs across communities and over time, and should collect information on 

clinical and subclinical measures of disease (e.g., atherosclerosis) or markers of 

inflammation. He thought the ideal study would also have not just ambient monitors but 

monitors in homes, in workplaces, and some personal monitoring. 

He noted that the Six Cites study had many characteristics of an ideal study in that, unlike 

most epidemiologic studies that tend to be opportunistic, it was specifically designed to 

look at the relationship between air pollution exposure and health. He indicated that it 

included cities with a range of exposures, and with the extended follow-up, a few of the 

cities have shown substantial reductions in exposure. He felt that the study’s limitations 

are relatively small study size, and that it did not anticipate the link with cardiovascular 

disease, and therefore did not include some of the key clinical and subclinical measures 

of disease. 

Expert J thought that the ACS study and its reanalysis, although more opportunistic that 

the Six Cities study, had the strong advantage of representing a much larger number 

cities, included a large population, and collected very good information on individual risk 

factors. The ACS study, like the Six Cities study, has also undergone extensive reanalysis and has been corroborated by independent investigators. 

When asked which studies he would most like to focus on during our later discussions of 

confounding, effect modification, and exposure issues, he indicated that he would most 

like to rely on the ACS study and then the Six Cities studies. For the ACS study, he 

included the original study its reanalysis (Pope et al., 1995; Krewski et al., 2000) its 

extended analysis (Pope et al., 2002), the analysis of cardiovascular disease (Pope et al., 

2004) as well as the analysis of spatial resolution of exposure in LA by Jerrett et al. 

(2005). For the Six Cities study, he relied upon the original analysis and reanalysis 

(Dockery et al., 1993; Krewski et al. 2000) and the extended analysis (Laden et al., 

2006). 

Expert J discussed more briefly a second set of studies: the AHSMOG cohort studies 

(Abbey et al., 1991 & 1999 and Chen et al., 2005); Hoek et al.’s work in the Netherlands 

looking at proximity to roadways (2002); the Finklestein et al. (2004) study in Ontario, 

Canada also looking at proximity to roadways and finding similar results as Hoek et al.; 

and the series of work staring with Woodruff (1997) looking at infant mortality. We then 

discussed the series of papers based on the Veterans Administration (VA) cohort by 

Lipfert et al., 2000, 2003 & 2006. He expressed concern about the lack of robustness of 

the results, the convoluted nature of the analysis and the write-up, and their publication in 

journals where they are less likely to have received peer review by epidemiologists. He 
 

J-5 

had not had an opportunity to evaluate fully the Enstrom et al. (2005) study in California, 

but expressed some concerns similar to those he had for the VA studies. 

3.5 Confounding 

We conducted a lengthy discussion of confounding in both time-series analyses and in 

cohort studies. Expert J generally felt that most confounders had been adequately 

controlled in the studies upon-which he would rely for his quantitative estimates of the 

PM mortality effect. 

For time-series studies, the potential confounder that remained a concern for Expert J was 

the issue of co-pollutants (SO2, NO2, CO, and ozone). Expert J thought that co-pollutants 

are positively correlated with PM and inadequately controlling for co-pollutants will tend 

to overstate the importance of PM alone. However, he argued that if one is using PM as 

an indicator for air pollution in general, excluding the other co-pollutants might 

underestimate the effects of air pollution. He wanted to clarify that this argument about 

co-pollutants can only be carefully evaluated in the context of large, multi-city studies 

like the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) or Air 

Pollution and Health – A European Approach (APHEA). He thought analyses of copollutant effects in single cities (e.g., by Moolgavkar et al.) are problematic given that 

individual cities are likely to differ in the relative levels of co-pollutants. In general, he 

argued that the literature suggests that PM is driving more of the mortality effect seen 

than the co-pollutants. 

For the cohort studies, Expert J discussed smoking, occupation, weather, diet, 

socioeconomic variables, contextual variables, and differential migration. He thought 

smoking had been well-controlled in the ACS and Six Cities studies, and the AHSMOG 

study included only non-smokers. He thought occupation, weather, and diet were wellcontrolled for in both ACS and Six Cities studies (score = 1). Neither study controlled 

for exercise directly, though each assumed body mass index (BMI) was at least a partial 

measure for exercise. 

He had some concern about general socioeconomic status (SES) variables and the fact 

that the Jerrett et al. (2005) study found lower effect estimates when it controlled for 

more contextual SES variables. He thought it was unclear whether these analyses were 

evidence of residual confounding or over-fitting the models. “Probably some of both.” 

Expert J thought that of all the confounders discussed, co-pollutants and differential 

migration had been addressed the least in his key studies. He thought co-pollutants had 

the potential to be a major factor (score ≈ 3) in overstating the impact of PM alone (as 

discussed above). Differential migration he thought had the potential to be a major 

negative confounder (score ≈ 3) if for example, sick people are more likely to move to 

the cleaner areas of the country (e.g., to Arizona) whereas the healthy people do not 

move. 
 

J-6 

3.6 Effect Modification 

After an extensive discussion, Expert J summarized his views on effect modification by 

saying that the only effect modifier that has emerged from the analysis and re-analysis of 

the Six Cities and ACS studies has been educational attainment. He thought the relative 

risks associated with PM were clearly higher in the population with less than high school 

education. He explained that educational attainment per se is not a risk factor but that it 

correlates with various socioeconomic variables that may more directly bear on exposure 

or health outcomes (e.g., housing, income, access to medical care, or use of air 

conditioning). 

Expert J thought that the higher level of educational attainment in the ACS cohort relative 

to the U.S. population could lead to the conclusion that the overall ACS risk estimate 

likely understates the true relative risks in the broader U.S. population. He cited a 

presentation at the Pre-elicitation Workshop in which the Pope et al., 2002 effect 

estimates were adjusted to account for the distribution of educational attainment in the 

U.S. population. Depending on the data used for the adjustment, the original estimates 

rose by about 30-50 percent or from “six, seven percent per 10 μg/m3 estimate to about a 

nine or 10.” 

Expert J also discussed whether race is another potential effect modifier given that 

neither the Six Cities or the ACS study were representative of the racial mix in the U.S. 

On the one hand, he noted that work by Joel Schwartz’s group has shown a role for 

genetic differences in response to particulate pollution and changes in HRV. On the 

other hand, he did not think that the epidemiologic evidence in studies in populations 

around the world suggests that different nationalities respond differently to air pollution. 

He did not think sufficient evidence exists to speculate whether and/or how differences in 

racial composition of the cohorts might affect the generalizability of the results to the US 

population. 

3.7 Exposure Issues 

Expert J raised five potential issues with regard to how exposure is measured in the 

cohort studies: 

• Central versus personal exposure monitors; 

• Spatial resolution of exposure monitoring; 

• Migration of the population; 

• Co-pollutants; and 

• Temporal changes in exposure. 

Expert J thought that the first three issues were most important as each could contribute 

to a substantial underestimate of the PM mortality effect (score = 3). “To the extent that 

variability in central site monitoring only gives you part of the variability in exposure, we 

have substantial exposure measurement error that likely is biasing our effect estimates 

downward.” He thought a related issue is the degree of spatial resolution of the exposure 

measurements. He referred to an expert’s presentation at the Pre-elicitation Workshop, 
 

J-7 

which suggested that finer spatial resolution of the Six Cities study relative to the ACS 

study might account in part for the higher relative risks observed in that study. He noted 

that the spatial resolution in the L.A. sub-cohort of the ACS study (Jerrett et al., 2005) 

was finer than in the original ACS study and found results that are more comparable to 

the Six Cities study. He pointed out, however, that it is difficult to know what the “right” 

spatial resolution should be in these studies; if it’s too big, there is exposure measurement 

error, but it it’s too small, there may be exposure measurement error because individuals 

are not staying in the vicinity of the monitor to which they’re assigned (e.g., they go to 

work elsewhere). He also thought migration of individuals in the cohort over the course 

of the study could lead to exposure error and underestimation of the “true” relative risk. 

He indicated that if, on average, some individuals in high pollution areas move to lower 

pollution areas and some move from low to high pollution areas, it is another form of 

random exposure misclassification. He noted another expert’s presentation on 

differential migration patterns according to socioeconomic status at the Pre-elicitation 

Workshop, which offers a “reasonable hypothesis” for the differences in relative risk 

estimates by education. This expert’s analysis of U.S. census data indicated that 

individuals with higher SES were more likely to move outside than within Metropolitan 

Statistical Areas (MSAs) as compared with individuals with lower SES, the implication 

being that their exposure might be subject to more misclassification. Expert J did not 

think the data were available to know how to estimate the magnitude of this issue’s 

effects on relative risks. 

In his discussion of co-pollutants, Expert J raised the same issues as he had under 

confounding. In essence, the question is whether one is focusing on a pure PM effect or 

an air pollution effect for which PM is an indicator. Expert J argued that a weakness of 

the elicitation question is that it is hard to know which of these two is truly the focus of 

the study. He noted that, in reality, the evidence does not allow us to understand what 

happens if PM changes without any other change in co-pollutants or components; when 

the sources are reduced, the mix of co-pollutants changes. If one wants the marginal 

effect of PM alone, which is the actual focus of the elicitation, the PM effect estimates in 

the studies could be moderate over-estimates (score = 2) of the true PM effect for the US 

population. 

Finally, we raised the question of the time course of historical exposures that were 

relevant to the health effects observed in the follow-up period. Expert J did not think this 

issue would matter much in terms of hypothesis testing, though it might matter some in 

the magnitude of the relative risks. Theoretically, “if the time scale [for long-term 

effects] is on the order of decades and we use more recent PM2.5 measures where the rank 

ordering is the same, but the [concentrations] are lower, then we are overestimating the 

effects.” However, he believed that the health effects observed in the cohort studies are 

primarily due to exposures over the previous 5 to 10 years (based on looking at the 

progressive size of mortality effects over studies looking at the past few days (time 

series), to few years (intervention studies), to eight years (the Six Cities) where the 

largest effects are seen). He therefore did not think he would want to adjust his estimates 

to account for this issue (score = 1). 
 

J-8 

3.8 Causality 

Expert J described how the development of the body of evidence over the past several 

years has convinced him that it is highly likely that the relationship between exposure to 

fine particles and mortality is a causal one. The literature has gone from the early, naïve 

epidemiological studies to a variety of study designs showing a consistent and coherent 

relationship between particulate air pollution and respiratory and cardiovascular disease 

as measured by various health endpoints (“changes in lung function, changes in cardiac 

autonomic function, changes in hospitalization for both respiratory and cardiovascular 

disease, changes in mortality for respiratory hospitalization and mortality”). He indicated 

that there are no studies showing associations with health endpoints that one would 

consider a priori to be unrelated. In addition, he stated that there are “semi-controlled 

[studies] ... looking for sub-clinical measures of disease” such as measures of pulmonary 

and systemic inflammation, C-reactive protein, and cardiac autonomic function. He 

thought that the toxicological studies by Hogg et al and the NYU group looking at such 

things as “changes in inflammation, changes in bone marrow responses, changes in 

atherosclerosis, etcetera” lend further credibility to the whole hypothesis. Ultimately, 

Expert J noted that he is not able to define an alternative explanation for the phenomena 

observed across these studies; nor has he heard one put forward by others. For example, 

the early criticism of publication or analytical bias in the individual time series studies 

has been muted by the large multi-city studies (e.g., NMMAPS, APHEA). 

Expert J did not draw a distinction between short-term and long-term exposures and 

mortality. While he expressed the view that, at some level, it has been more difficult to 

imagine that a short-term exposures to PM could lead to a cardiac death than to believe 

that chronic exposure to PM could, like exposure to environmental tobacco smoke (ETS), 

lead to an increased risk of cardiovascular disease. However, he felt that the empirical 

evidence is too strong to ignore. 

Expert J’s quantitative characterizations of the causal relationship reflect his inherent 

conceptual difficulty with this question. In the initial discussions on causality, Expert J 

expressed the view that the overall likelihood of a causal relationship between annual 

average PM2.5 exposures and mortality ranged between 75 percent and 98 percent with a 

best estimate of 90-95 percent. However, in the development of his quantitative 

estimates, Expert J revised these views, ultimately stating that he could not argue for a 5 

percent likelihood of a non-causal relationship. He then provided an estimate of no more 

than a 1 percent likelihood of a non-causal relationship (i.e., his final estimate of the 

percent likelihood of a causal relationship was 99 percent). 

3.9 Thresholds 

Expert J’s views on thresholds were driven by the empirical data. While conceptually he 

might have difficulty believing that low levels of PM2.5 could cause damage, he stated 

that the empirical evidence does not point to an observable threshold for the effects of 

either short-term or long-term exposures. Piecing together the literature on smoking, 

occupational exposures to particulates (e.g., coke ovens), ETS, and ambient air pollution, 
 

J-9 

there’s a trend that “basically looks linear.” He thought the epidemiological studies with 

sufficient power to make statistical inferences about population thresholds (e.g., multicity time series studies, the ACS study) have not been able to detect them even with using 

more sophisticated statistical techniques (e.g., meta-smoothing in multi-city time series 

studies, Krewski et al., 2000 reanalysis of the ACS study). He indicated that these 

studies consistently support a generally linear relationship. He did not think that 

toxicological studies or clinical studies were useful for finding population thresholds 

because they lack sufficient statistical power and are conducted at the high end of, or well 

beyond the range of environmentally relevant exposures. 

3.10 Other Influential Factors 

Expert J discussed additional sources of uncertainty that were not raised as part of the 

protocol. He expressed concern about whether the PM index most commonly used in the 

literature (PM2.5) is the best measure in terms of both understanding potential health 

comes and directing regulatory efforts. For example, he expressed the opinion that if 

there are characteristics of combustion or non-combustion related PM more 

toxicologically relevant, and PM is not a good index for them, we may be 

underestimating the real effects of exposure. He thought that an example of this was with 

historical measurements of PM10 or total suspended particles (TSP). “When we were 

using PM10, we were underestimating the effect estimates, especially in those studies 

where PM10 was not highly correlated with PM2.5 ... and with TSP, there [were] no 

effects.” Furthermore, he thought we might be regulating the wrong sources. 

Expert J also discussed the fundamental issues raised by the 2002 National Research 

Council (NRC) study about application of probability models that assume random 

behavior or sampling to observational data in epidemiologic studies and whether the 

standard error is therefore an appropriate expression of uncertainty. He noted that 

analysts are aware that this approach is not “exactly right” but that in the “real world, the 

actual data never matches the ideal.” He argued that none of the people who do these 

studies “thinks that any one of the studies precisely defines not only the point estimate 

[or] the uncertainty around that estimate.” 

He also expressed a lingering concern about scientific and human fallibility and that the 

current scientific consensus might eventually be proven wrong. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert J thought that the concentration-response (C-R) function was log-linear and 

consistent over the entire range of annual average PM2.5 concentrations that were the 

focus of the study (4-30 μg/m3

). 

As discussed in Section 3.8, Expert J ultimately chose to provide a C-R function that 

directly incorporated his assumptions about the likelihood of a causal relationship. 
 

J-10 

Expert J specified his maximum, minimum, 5th, 50th, and 95th percentiles and then used 

the Weibull distribution in Crystal BallTM to generate the interquartile range (25th and 75th

percentiles). 

Expert J placed his minimum at zero in order to be consistent with his views that the 

likelihood of a causal relationship was less than 100 percent. He loosely based his 

estimate of the 5th percentile on published estimates from time-series studies with 

distributed lags (meta-analyses from Levy et al., 2000; Steib et al., 2002; Anderson et al., 

2005; Ostro et al., 2005; Schwartz et al., 1996 (Six cities), Klemm et al., 2000; Burnett et 

al., 2003) He thought NMMAPS value (0.04 percent per μg/m3

) represented somewhat of 

an outlier in this group and more of a minimum value. 

For his 95th percentile value, he first discussed results from the Hoek et al. (2002) work in 

the Netherlands which, though based on measurements of black smoke, suggested effects 

on the order of 3 percent per μg/m3

 which he thought was on the high end. He next 

considered results from the Six Cities cohort studies and the Jerrett et al. (2005) paper 

(1.7 per μg/m3

) and observed that one standard deviation or so above the primary 

estimates lead to values of roughly 2-2.5 percent. The maximum value (3 percent) was 

based on estimates from Hoek et al., (2002) as well as unpublished findings from the 

Women’s Health Initiative study. 

His 50th percentile was initially based on adjusting the ACS cohort estimate (0.62 percent 

per μg/m3 from Pope et al., 2002 (average of exposure periods, all-cause mortality)) 

upward to account for exposure misclassification due to central site monitoring and 

migration, and effect modification by education and also giving consideration to the 

Harvard Six Cities and ACS-L.A. studies. He then down-weighted that estimate to take 

into account some of the more negative studies (AHSMOG, VA, Enstrom). When 

elicitors pointed out that his estimate was only slightly higher than that in the ACS, he 

indicated that he was relying on the fact that the ACS study was larger than the other 

published studies. 

Exhibit 1: Subjective Estimate of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations

Percentile Percent Change in Mortality 

Minimum 0 

5th 0.15 

25th 0.53 

50th 0.90 

75th 1.3 

95th 2.0 

Maximum 3.0 
J-11 

Exhibit 2: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distribution from 

• = median □ = interquartile range │= 90 percent confidence intervals 

* Note that this estimate is for a 1 μg/m3 increase in black smoke for cardiopulmonary mortality. 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Expert J ACS Original ACS Extend

ACS Reanalysis ACS LA Analysis Six Cities Original

Six Cities Reanalysis Six Cities Follow-up Netherlands Study* AHSMOG AHSMOG Extend 

% Decrease in Mortality per 1 ug/m3 PM2.5
 

J-12 

Expert J Distribution 
 

Expert K 

Interview Summary
 

K-1 

Interview Summary 

Expert K 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert K discussed the biological mechanisms for short-term and long-term exposures 

separately. “I think the mechanisms probably have some relationship between acute and 

chronic. But [since] we’re talking about mortality, then I think there are some 

differences.” He began with a discussion about short-term mechanisms. 

Short-Term Exposures

Expert K thought that short-term mortality from PM is caused by “an exacerbation ... of 

an underlying disease process.” 

He discussed cardiovascular mortality, specifying two plausible underlying mechanisms 

for acute events: arrhythmias and endothelial dysfunction. He discussed studies looking 

at PM concentrations and ventricular arrhythmias in patients with defibrillators (Peters et 

al., 2000; Dockery et al., 2005). He thought that this could be related to effects on the 

autonomic nervous system but “how that happens exactly I don’t think we know at this 

point.” He thought another plausible mechanism for cardiovascular death was 

endothelial dysfunction. He cited a study by Brooks et al. from Michigan (2002) that 

measured brachial artery diameter and found that this vessel constricted following 

exposure to ozone and particles. He thought that “[i]f it happens in a peripheral or a 

central artery, there’s some evidence that these kinds of things can happen in coronary 

arteries as well.” He thought that this “vasoconstriction or absence of normal dilation” in 

an individual with significant vascular disease is a possible explanatory mechanism for an 

acute event. Expert K thought that the “attractive basic science mechanism right now” 

for these responses is that particles could induce inflammation, which could stimulate 

production of reactive oxygen species (ROS). He indicated that in vitro studies have 

shown production of ROS with cellular exposure to PM (Nel et al., 2001). The ROS 

could trigger a series of events in someone with underlying endothelial dysfunction, or in 

the case of arrhythmias, could an effect on the autonomic nervous system. 

Expert K indicated that it was conceivable that respiratory mortality may be related to 

particles if one argued that particles cause inflammation in the lung, resulting in bronchoconstriction and possibly mortality. However, he had less confidence in this mechanism, 

pointing out that asthmatics, although they could have particle-induced exacerbations of 

their disease, do not usually die from asthma. 

Expert K discussed the limitations and/or gaps in the mechanistic literature. His major 

concern was about exposure particularly in epidemiological studies, which he thought 

were uncertain because they are measures of ambient, rather than personal exposures. He 

thought it was possible that those with defibrillators could be spending their time indoors, 
 

K-2 

for example, and would not be exposed to ambient pollution. He thought the in vitro

studies that provide evidence on the “more basic science or the cell biology question, the 

production of reactive oxygen species” could not be directly extrapolated to humans. He 

indicated that although the exposures are more controlled, these studies rely on isolated 

cell systems whereas “the way most things happen in biology is that ... there’s a signal 

from one cell to the next ... [This gap] creates great uncertainty.” 

When asked about data suggesting C-reactive proteins (CRP) or other factors as a 

markers of inflammation related to air pollution exposures, he explained that CRP levels 

are non-specific; they may be elevated in response to an infection, to chronic artery 

disease, or to other chronic inflammatory diseases like lupus. Similarly, there is “some 

very interesting data showing that when you breathe ultrafine particles and you don't 

vasodilate, it affects your nitric oxide production [Shah et al., submitted for publication]. 

Nitric oxide can certainly be related to reactive oxygen products. But it isn't a specific 

marker.” 

Long-Term Exposures

Expert K indicated that he thought the mortality effects from PM are “largely cardiac” 

although he expressed uncertainty about whether there was a true independent chronic 

effect of long-term exposure and of acute exposures, or whether the long-term effects 

published were more the result of acute exacerbation of existing chronic disease. He 

cited the Sun et al. (2005) study looking at the impact of six-month exposures to 

concentrated ambient particles (CAPs) in mice in which the authors found evidence for 

the “development of vascular response and atherosclerosis.” While he thought this study 

had the advantage of controlled exposure and a genetically modified mouse model, he 

also thought it still raised questions. “Is it something specific about the particles or ... 

are there lots of other things that would just do this because of [the animals’] genetic 

susceptibility? ... [I]f we gave [them] salt every day or something else, would we see 

plaque formation just because most any inflammatory event might initiate this kind of 

change?” He thought the Kunzli et al. (2005) epidemiologic study of intima-media 

thickness in carotid arteries, as a measure of atherosclerosis progression, was intriguing 

but that exposure uncertainties limit its usefulness. 

He thought the series of studies by Godleski et al. looking at myocardial infarction (MI) 

in animals with underlying chronic disease after exposure to particles were indicative that 

“in fact you’d need the chronic disease to lead on to the acute.” Expert K thought Devlin 

et al.’s study in Baltimore that found acute changes in heart rate variability (HRV) and 

some arrhythmia in an elderly population exposed to CAPs provided evidence of “the 

potential that particles would put you at risk, [but] is it acute or chronic? I don’t know.” 

Expert K also discussed possible evidence for the effect of long-term exposures on 

respiratory mortality. He cited a study by Diaz-Sanchez et al. (1999) that he felt provided 

some evidence not just for exacerbation but possibly development of asthma. In this 

study, the investigators showed IgE production, a marker of allergy/immunological 

response, following nasal exposures to diesel; the response was enhanced with joint 
 

K-3 

exposure to ragweed and diesel. He thought Gauderman’s paper in the New England 

Journal of Medicine on the effect of air pollution on lung function were not specific to 

particles but indicated a NO2 effect as well. 

When asked about lung cancer, Expert I answered that he did not think there was a strong 

link with particle exposures. He thought the signal from smoking is so strong, and selfreporting on smoking histories are so often inaccurate (i.e., self identification as a nonsmoker is unreliable) that he thought associations found in cohort studies that did not 

involve a formal medical history taking could be the result of residual confounding by 

smoking. He noted, for example that the ACS study relied on a questionnaire filled out 

at one point in time (1982). 

3.2. Conceptual Framework for Mortality Effects of Short-term and Long-Term 

PM2.5 Exposures 

Expert K thought the Künzli diagram was a good conceptualization of the relationship 

between long- and short-term exposures. “I don’t know if it’s valid or not, but it’s a good 

way to say, if indeed short-term or long-term or perhaps both can affect mortality, how 

can we envision that that can happen? I think this is a very reasonable sort of framework 

for it.” 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert K thought that cohort, time-series, and intervention studies each provided 

somewhat different information about the potential mortality effects of changes in annual 

average PM2.5 concentrations, as indicated in the table below: 

Study Design Type of Mortality Effects Captured (e.g., 

short-term, long-term, or both) 

Cohort Studies Long-term 

Time-Series Studies Short-term 

Intervention/ “Accountability” Studies Short-term, 

Long-term, if studied long enough 

Expert K thought the intervention studies were certainly capturing effects of short-term 

reductions in PM2.5 on both morbidity and mortality and that they offered the hope of 

understanding the effects of long-term reductions. However, he would want to see 

longer-term follow-up, use of clear markers of cardiac disease (e.g., cardiac 

catheterization) and comparison to a community without the same intervention. 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert K thought that the following characteristics would be part of an ideal 

epidemiologic study to characterize the PM2.5-mortality relationship in the U.S. 

population: 
 

K-4 

• Good exposure data documenting the link between ambient exposures and the 

specific outcome; 

• Prospective study design (similar to the Six Cities); 

• An intervention in exposure; 

• Good characterization of underlying cause of death (e.g., details on myocardial 

infarction or ventricular arrhythmia, not just cardiac arrest); and 

• Broad geographic coverage of the U.S. 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert K mentioned the American Cancer Society (ACS) study, the 

Six Cities study, the Clancy et al. (2002) study in Dublin, and the Jerrett et al. studies in 

Los Angeles (L.A.) and New York. He felt the Six Cities study was strengthened by 

being specifically designed to test the hypothesis of a relationship between air pollution 

levels and health, although he would have liked to have seen more personal monitoring. 

Among the analyses of the Six Cities studies, he focused on the original study (Dockery 

et al., 1993) and its reanalysis by Krewski et al. (2000). He was not as familiar with the 

Laden et al. (2006) follow-up to the Six Cities study, but felt that it found similar results 

as the original and reanalysis. 

He preferred the prospective design to what he characterized as “data mining,” which he 

defined as the use of large data sets to look retrospectively for associations. He expressed 

a general discomfort with the fact that investigators often find associations with PM and 

several outcomes. “Every time I see an association between breathing and an adverse 

outcome [e.g., reproduction, central nervous system, birth outcomes], it makes me a little 

uncomfortable. I could be wrong, but I worry that it’s in the way the data are analyzed, 

and that there are so many unknowns in terms of the actual exposures. And yet every 

time the analysis is done, it shows an effect.” In general, he thought it would be 

beneficial if the clinical endpoints of these studies were more fully understood by the 

investigators. 

He acknowledged that his concern about the role of the statistical analysis in somehow 

generating the associations primarily applied to time-series studies. He thought the 

Krewski re-analysis of the Six Cities and ACS studies was very credible. 

He thought the Dublin intervention study (Clancy et al., 2002) was important because of 

the potential advantages of intervention studies discussed previously. 

He thought the Jerrett et al. analyses of the ACS data in L.A. and New York were 

intriguing because they had “a better handle on the exposure.” However, he wasn’t sure 

what the implications of the improved exposure were given that the two studies appeared 

to find very different relationships. 
 

K-5 

3.5 Confounding 

In his discussion of confounding, Expert K focused on the ACS and Six Cities studies. 

He initially first discussed several factors that he thought could still be potential 

confounders of the mortality effect (smoking, socioeconomic status, pre-existing health 

status, indoor air exposures, co-pollutants, and diet). He ultimately reasoned that 

smoking, co-pollutants, and diet were the most important of these for each study and 

assigned them scores according to the table below. 

Study Potential 

Confounder 

Direction of impact Score 

Smoking Overestimate 1-2 

Diet Overestimate 1 

Co-pollutants Uncertain 2 

Pope et al., 2002 

Indoor Air Exposures Uncertain 2 

Smoking Overestimate 1 

Diet Overestimate 1 

Dockery et al., 1993 

Co-pollutant Uncertain 1 

Expert K first discussed smoking. He acknowledged that the ACS study had a variable 

for smoking in the model but thought that there could be some residual confounding (see 

discussion under mechanisms) that could lead to inflation of the effect estimate (score of 

1 or 2, smaller than the effect of co-pollutants). 

He also thought that cardiovascular mortality from PM could be overestimated due to 

confounding by diet; he thought poverty, smoking, poor diet are probably highly 

correlated. He indicated that the effects of diet on the estimation of the PM/mortality 

relationship are likely to be a major focus of future research. 

Expert K raised co-pollutants as an issue of concern. “You have a very large population 

and there you could have fairly different exposures in terms of the co-pollutants.” He 

discussed the Pope et al. (2002) and the Krewski et al. (2000) reanalysis, which indicated 

that SO2 was the only co-pollutant that could account for a lot of the PM2.5 effect, but 

ultimately Expert K did not think there was a sound biological explanation for an 

independent SO2 role in mortality at ambient concentrations. Expert K then indicated 

that he did not think that the National Morbidity, Mortality, and Air Pollution Study 

(NMMAPS) had not eliminated the possible role of co-pollutants. He particularly 

thought that ozone has not been ruled out as a factor. In summarizing his views, Expert 

K indicated that the issue of co-pollutants is not straightforward. He thought there are 

recent data relating elevated ozone levels to increased mortality rates. But more 

importantly, there may be unregulated co-pollutants that are neither regulated nor 

measured which could important confounders. He thought that these unmeasured copollutants could have a moderate effect (score = 2) on the mortality effect estimate for the 

ACS study estimates. 
 

K-6 

Finally, he thought differential exposures in indoor air across study areas could cause the 

effect estimates to be confounded. He was uncertain about either the direction or the 

magnitude of the effect since indoor air was not monitored in either the ACS or Six Cities 

study. However, he argued that it was more likely to be an issue in the ACS study 

because it spanned a broader geographic area than the Six Cities study. 

Expert K thought the Six Cities study could be affected in a similar way by the 

confounders discussed for the ACS study. He thought that this study controlled for 

smoking and diet a little bit better than the ACS study because they interviewed the 

cohort participants rather than just had them fill out questionnaires, but still thought that 

there could be some residual confounding. He did not think indoor air affected the Six 

Cities results because he did not think there was as much potential for differential 

exposures across the cities given their geographic distribution largely in the East. 

3.6 Effect Modification 

Expert K discussed three issues that he thought could be effect modifiers in specific 

studies: educational attainment, co-pollutants, and weather (as it affects prevalence of 

seasonal viruses and building effects). 

Expert K thought that the ACS study results could be modified by educational attainment, 

since the ACS cohort was a more highly educated population than the general U.S. 

population. He thought this differential in educational attainment could cause the 

mortality effects to be underestimated and, given the Krewski analysis, assigned it a score 

of 2. 

Expert K thought that differences in the co-pollutant/component mix in the Six Cities 

study relative to the rest of the country could result in potential effect modification of 

their results. Specifically, he thought that the SO2 and acid particles could be higher in 

the six cities selected for the study than in the rest of the U.S., but thought that the ozone 

levels could be lower. Therefore, he was uncertain of the direction of bias, and assigned 

it a score of 2. 

He thought differences in viral epidemics (e.g., adeno-viruses (not influenza)) could lead 

to an overestimate because of “certain kind[s] of winter and seasonal viruses which may 

be different in this temperate zone that in the southern temperate zone ... It might be a 

problem when you’re extrapolating outside of those cities.” 

He also thought that temperature could be an effect modifier, but was uncertain of the 

direction. “And weather ... the ability of outdoor air to penetrate indoors, depending on 

the amount of insulation and how tight your windows are and so on. I don't know if we're 

stretching or those are real issues, but there are differences that exist.” 

3.7. Exposure Issues 

Expert K thought a major exposure issue in both the ACS and Six Cities studies was 

central site versus individual exposures. “Is there any confidence that what we say we’re 
 

K-7 

monitoring is actually what people are exposed to? ... The other exposure issue ... that’s 

covered in this is going to be ... where you live [with respect to a roadside].” He 

discussed Jerrett et al. work with the ACS data that attempted to improve spatial 

resolution of exposure. He indicated that the L.A. analysis (2005) seemed to show that 

improved exposure measurement leads to higher effect estimates. However, the analysis 

in New York (unpublished) did not find the same relationship. He did not necessarily 

think that the New York results refuted the L.A. results, because of city-specific 

characteristics such as the existence of tall vertical buildings that could cause differences 

in the exposures. He thought that exposure misclassification was probably more of a 

problem in the ACS study, and assigned it a score of 2 to 3. He thought the Six Cities 

study had better exposure measurement and placement of central site monitors, and 

assigned a score of 2. There was further discussion of the statistical arguments about the 

influence of exposure misclassification on effect estimates. He was uncertain about the 

direction of influence central site versus individual monitors might have on the effect 

estimates. 

3.8. Causality 

Expert K expressed the view that “causality doesn’t simply arrive from statistical 

correlations.” He indicated that there are several time-series and cohort studies that 

“suggest or indicate an association between PM concentrations and ... mortality,” but he 

thought of them as associations rather than evidence for causality. He thought that there 

are several mechanistic explanations for a PM/mortality effect, but he did not feel that 

there was a single, well-established pathway. “Reactive oxygen species, as we talked 

about this morning, [is] a nice model. There are some very elegant in vitro work that 

show these kinds of effects. There are whole animal studies [where] one can find lots of 

different mediators, lots of different electrophysiological responses, inflammation. But I 

don't think we have a clear understanding of a mechanism that goes from exposure to 

whatever it is in the ambient air to an effect that we want to establish as mortality or 

certainly morbidity. I think those are the two ... major reasons that I'm still not certain 

about a causal relationship.” 

He thought that there were some differences in causality between short-term and longterm exposures, in that he thought the evidence was stronger for short-term. He cited the 

defibrillator studies indicating that if these studies had personal exposures measurements 

so that, “if I knew that the people who develop the arrhythmia, that they were the people 

that we actually knew that their exposure went up, not at a central site ... that to me 

would get very close to establishing causality.” As for the long-term studies, he found 

the kind of questionnaires and correlations not as powerful, particularly in the ACS study, 

for establishing a causal link. Furthermore, the exposure issues (discussed in Section 3.7) 

also undermined his belief in a causal relationship. He indicated that, “I don’t know that 

there is a clear model that’s going to get us there on the long-term studies until we 

actually are able to more carefully define the exposure.” 

Expert K estimated the likelihood of a causal relationship between PM and mortality to 

be about 20 percent. His overall estimate the range was rooted in his low confidence in 

the completeness of the existing science. In terms of cardiovascular mortality, he felt 
 

K-8 

there was uncertainty in both the link between exposure to particles and atherosclerotic 

plaque and in the progression from that stage to death. He observed that, “the only 

observations that really tie the long-term exposure with the plaque are the NYU studies.” 

Although he thought these studies represent “an initially powerful set of studies,” he 

thought that there were problems with extrapolating from studies performed on mice 

genetically altered to be at high risk for atherosclerosis to humans. In particular, he did 

not think that it was possible to tell yet whether it was just the particles or whether there 

were a number of other factors that could equally well trigger atherosclerosis in this 

sensitive mouse model. 

He thought the likelihood of a causal relationship could be as high as 50 percent. To be 

more convinced, he would want a study to find similar results using a more normal 

animal model. 

After quantifying his uncertainty in the C-R function, Expert K revised his estimates for 

the likelihood of a causal relationship. He thought that his original estimate of 20 percent 

was too low. He then specified a range of 20 to 50 percent likelihood with a most likely 

value of 35 percent. 

3.9 Thresholds 

Expert K thought that it is possible to make a conceptual argument that a population 

threshold exists. He drew an analogy with smoking, indicating that among heavy 

smokers, only a proportion of them gets lung cancer or demonstrates an accelerated 

decline in lung function. “I think that the idea that somehow there’s no level that 

biologically is safe just doesn’t fit with toxicology, where in fact we usually think about 

the dose making the toxicity of the material, and normal host defenses, normal responses, 

I think that there is going to be a level below which the population is fine.” 

When asked about what types of studies would be appropriate for determining values for 

a potential population threshold, Expert K answered that short-term studies with personal 

exposure monitoring data and clear outcome (e.g., arrhythmia) that could be associated 

with mortality would be helpful. He also thought that an animal model that did not use 

animals that were genetically altered to be highly susceptible to disease and that used 

exposures that were progressively closer to ambient levels would be necessary. 

However, he thought the likelihood that such a model would find no threshold would be 

small. 

He did not think that a population threshold was detectable in the currently available 

epidemiologic studies. He indicated that in some of the cohort studies showed greater 

uncertainty in the shape of the C-R function at lower levels, which could be indicative of 

a threshold. 

Expert K chose to incorporate a threshold into his C-R function. He indicated that he was 

50 percent sure that a threshold existed. If there were a threshold, he thought that there 

was an 80 percent chance that it falls between 0 and 5, and a 20 percent chance that it 
 

K-9 

falls between >5 and 10. The elicitation team took this information and created a 

probabilistic distribution in Crystal BallTM with 50 percent of the weight at zero, 40 

percent of the weight between >0 and 5, and 10 percent of the weight between >5 and 10. 

3.10 Other Influential Factors 

Expert K discussed additional sources of uncertainty that were not part of the protocol. 

Expert K indicated that he was unsure if publication bias existed, but noted that there 

were a large number of studies published with positive results on this topic (primarily 

time-series studies). In addition, Expert K expressed the view that the fact that 

investigators are finding associations with PM and “different, totally unrelated outcomes” 

such as reproductive outcomes, effects on fertility, central nervous system, stroke, and 

Alzheimer’s disease made him uncomfortable. He thought that there was a possibility 

that, “there is a systematic problem that results in these kinds of associations.” 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert K chose to characterize his C-R function within the specified range of annual 

average PM2.5 concentrations (4-30 μg/m3

) in a piece-wise linear fashion. He specified 

one set of numbers that applied to concentrations of 4-16 μg/m3 (hereafter, “Range 1”), 

and another set that applied to concentrations of >16-30 μg/m3 (hereafter, “Range 2”). 

He chose the split point, 16 μg/m3

, because it was at about the mid-point of the range. 

Expert K elected to provide his initial distributions conditional on the existence of a 

causal relationship and on being above a threshold. The elicitation team then combined 

his conditional distributions with his percent likelihood of causality specified in Section 

3.8 and his probability distribution for a threshold specified in Section 3.9. In order to 

create a single distribution (hereafter, “Example Applied Distribution”) representing the 

incorporation of all these concepts, the elicitation team probabilistically combined the 

distributions for threshold, causal likelihood, and concentration response with a 

distribution of population-weighted annual average PM2.5 concentrations in the U.S. from 

EPA’s BenMap model using Monte Carlo simulation. 

Expert K began by specifying the percentiles for the slope of Range 2. He indicated that 

he put more weight on the ACS study than the Six Cities study because it was more 

geographically representative. He thought that the maximum value would be 1.5, slightly 

above the mortality estimates for the original Six Cities study. In order to specify his 

median, he indicated that he would start with an estimate of 0.6 from the ACS study and 

1.25 for the Six Cities. We discussed again his view that smoking at diet might not have 

been fully controlled for in both studies and his uncertainty about the role of copollutants. He chose to weight the ACS study roughly twice as much as the Six Cities 

study and arrived at a median of 0.7. He then selected a 5th percentile of 0.1 after 

considering a range of confidence intervals from the cohort studies. He then had the 

elicitation team fit a normal distribution to his estimated 5th and 50th percentile values. 

The resulting 25th percentile value was 0.45, his 75th was 0.95, and his 95th percentile was 

1.3. 
 

K-10 

He then specified the percentiles for Range 1, which overall he thought should reflect his 

view that at “these lower levels, there should be less effect for a small change.” He 

provided a theoretical maximum of 0.8. He specified a 5th percentile of 0.1 and a 50th of 

0.4. The elicitation team again fit a normal distribution to the 5th and 50th percentile 

values. His resulting 25th percentile was 0.28, his 75th was 0.52, and his 95th was 0.7. 

Viewing his distribution with causality incorporated prompted further discussion of his 

assumption about the likelihood of causality. He reconsidered his views thinking his 

initial estimate of 20 percent was perhaps an “overreaction.” He still thought he couldn’t 

place more than 50 percent likelihood of a causal relationship but decided to change his 

most likely estimate from 20 percent to 35 percent, with a range of 20-50 percent. 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Range 1 (4 – 16 μg/m3)

Percentile Percent Change in 

Mortality 

Elicited Distribution 

Percent Change in 

Mortality 

Distribution 

Incorporating Causal 

Likelihood (IEc 

Generated) 

Percent Change 

in Mortality 

Distribution 

Incorporating 

Causal 

Likelihood and 

Threshold (IEc 

Generated) 

Minimum - 0 0 

5th 0.10 0 0 

25th 0.28 0 0 

50th 0.40 0 0 

75th 0.52 0.29 0.28 

95th 0.70 0.59 0.58 

Maximum 0.80 0.80 0.80 
 

K-11 

Exhibit 2: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Range 2 (>16 – 30 μg/m3

)

Percentile Percent Change in 

Mortality 

Elicited Distribution 

Percent Change in 

Mortality 

Distribution 

Incorporating Causal 

Likelihood (IEc 

Generated) 

Percent Change 

in Mortality 

Distribution 

Incorporating 

Causal 

Likelihood and 

Threshold (IEc 

Generated) 

Minimum - 0 0 

5th 0.10 0 0 

25th 0.45 0 0 

50th 0.70 0 0 

75th 0.95 0.48 0.48 

95th 1.3 1.1 1.1 

Maximum 1.5 1.5 1.5 

Exhibit 3: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

After Incorporating Causality and Threshold and Applying the C-R functions to the 

Population-Weighted Annual Average PM2.5 Concentration Distribution in the U.S. 

from BenMap - Example Applied Distribution (IEc Generated) 

Percentile Percent Change in Mortality 

Minimum 0 

5th 0 

25th 0 

50th 0 

75th 0.29 

95th 0.63 

Maximum 1.5 
K-12 

Exhibit 4: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distributions from 

* = Distribution incorporating causality and applying the C-R functions from Ranges 1 and 2 to a 2002 population-weighted annual av

distribution in the U.S. from BenMap. • = median □ = interquartile range │= 90 percent confidence interval 

0

0.20.40.60.8

1

1.21.41.61.8

2

Range 1 - Elicited Range 2 - Elicited Example Applied

Distribution -

Incorporating

Causality and

Threshold (IEc

Generated)*

ACS Original ACS Extend ACS Reanalysis Six Cities Original 

% Decrease in Mortality per 1 ug/m3 PM2.5
 

K-13 

Elicited Distribution – Range 1 

Elicited Distribution – Range 2 
 

K-14 

Range 1 Incorporating Causality - Probability Density Function (PDF) (IEc Generated) 

Range 1 Incorporating Causality - Cumulative Density Function (CDF) (IEc Generated) 
 

K-15 

Range 2 Incorporating Causality – PDF (IEc Generated) 

Range 2 Incorporating Causality – CDF (IEc Generated) 
 

K-16 

Range 1 Incorporating Causality and Threshold – PDF (IEc Generated) 

Range 1 Incorporating Causality and Threshold – CDF (IEc Generated) 
 

K-17 

Range 2 Incorporating Causality and Threshold – PDF (IEc Generated) 

Range 2 Incorporating Causality and Threshold – CDF (IEc Generated) 
 

K-18 

Example Applied Distribution – PDF (IEc Generated) 

Example Applied Distribution – CDF (IEc Generated) 
 

K-19 

U.S. EPA EXPERT ELICITATION STUDY OF THE CONCENTRATION-RESPONSE 

RELATIONSHIP BETWEEN ANNUAL AVERAGE PM2.5 EXPOSURE AND 

MORTALITY 

Modification to Expert Judgments

Expert K

Date: 7/10/06 

Section of Protocol Affected (Section Number and/or Title):

3.8 Causality 

Description of Change (e.g. to a specific percentile, or to a qualitative opinion or 

statement of belief):

Expert K changed his range of values for the likelihood of a causal relationship. The new 

range is from 5 to 50 percent. The most likely value of 35 percent remains the same. 

Rationale for Change:

My confidence (or lack thereof) in the data showing that reductions in PM2.5 levels 

translate into changes in mortality (e.g., annual averages of 4-30 μg/m3

) goes back to my 

concerns about both exposure measurements (errors) as well as all of the other 

unaccounted for variables that are changing in the environment. Unfortunately, your 

question is somewhat confusing to me: Are you asking about a 1 μg/m3 reduction or as 

much as a 25 μg/m3 reduction. I would certainly have more confidence that a large 

reduction could impact mortality and virtually no confidence that we could meaningfully 

measure the impact of a reduction of 1 μg/m3

. It would simply be an exercise in more 

modeling and extrapolation at such low level reductions. My estimates will certainly 

increase for larger decreases in PM2.5 levels. 
 

Expert L 

Interview Summary
 

L-1

Interview Summary 

Expert L 

PART 3. CONDITIONING STEP: PRELIMINARY QUESTIONS 

3.1 Mechanisms for Effects of Exposure to PM2.5

Expert L discussed the biological mechanisms for short-term and long-term exposures 

separately because he thought, “different mechanisms come into play.” He preferred to 

begin with a discussion of the mechanisms of mortality related to short-term exposures 

because “long-term effects are to some extent also the result of a mechanism that 

happened in the acute domain.” 

Short-Term Exposures

Expert L’s views on causes of death related to fine particle exposure were informed by a 

broad overview of the scientific evidence, which he thought provides support to the idea 

that “seemingly different pathways are interconnected.” He thought smoking is a good 

parallel for fine particles because both are complex exposures; particles are “markers for 

something for that is much more complex than just the mass ... [which makes it] very 

possible to assume that [a] whole range of pathways are initiated.” 

Expert L thought that a major cause of death from short-term exposures was 

cardiovascular disease, specifically myocardial infarction (MI) and stroke. He thought 

work by Annette Peters (2001), Joel Schwartz, and others on “a whole range of ... acute 

effect studies” was informative for these outcomes (Wellenius et al., 2005; Tsai et al., 

2003; Hong et al., 2002; Zanobetti et al., 2000; Schwartz et al., 2003). He also thought 

the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) was very 

important because of the wide geographic coverage and standardization of methods. He 

felt that the U.S. findings are strengthened by the European studies, particularly the large 

multi-center studies like Air Pollution and Health – A European Approach (APHEA), 

where publication bias is not an issue. 

 He thought one pathway for cardiovascular disease included particle-induced oxidative 

stress leading to systemic inflammatory responses and release of fibrinogen, which are 

associated with outcomes like MI, and to a lesser degree, with stroke. He also thought 

cardiovascular outcomes could be caused by particle-induced arrhythmias or changes in 

autonomic function that might also be linked to inflammatory pathways. To support this, 

he cited work by Joel Schwartz, which found that changes in HRV are modified by 

factors on the oxidative stress or inflammatory pathway (Schwartz et al., 2005). He 

indicated that there are fewer studies on stroke, but they are of a high quality. Expert L 

thought that the mechanistic evidence is strengthened by the fact that “where we expect 

MIs [to] happen, strokes should happen as well, and the fact that [published] studies show 

this, I think this very much [strengthens] the evidence.” 
 

L-2

Expert L thought that another cause of death related to short-term exposures was 

respiratory disease and that oxidative stress and inflammation were again part of the 

pathway. “The changes in [neuro] reflexes in the lung ... structur[al] changes [like] 

narrowing of the airways.” He thought these impacts create particular challenges for 

asthmatics. Expert L also indicated that PM could cause weakening of clearance and the 

immune system, lowering an individual’s ability to fight infection. 

In addition, he thought there were subpopulations that are particularly susceptible to the 

effects of PM, including people who are in “terminal health states,” such as those with 

lung cancer or diabetes. He noted Mark Goldberg’s time-series studies showing that lung 

cancer patients die earlier when air pollution is high. He thought diabetics were more 

likely to have underlying cardiovascular disease or atherosclerosis, in addition to reduced 

defense mechanisms against infections and therefore would be vulnerable to acute 

exposures to PM. 

Long-Term Exposures

Expert L thought that overall, “long-term effects ... are really changes in the underlying 

pathophysiology that ultimately leads to ailments, to chronic conditions which we know 

lead to premature death.” 

He first discussed lung cancer as a cause of death related to long-term exposures. He 

cited a Swedish study by Nyberg et al. (Epidemiology, 2000: Vol. 487) that found lung 

cancer associated with air pollution exposures 20-30 years in the past. He also thought 

that there was “toxicological evidence for some of the relevant pathways that are welldescribed for carcinogenesis, that they also relate to toxicity of particles, leading to 

chronic persistent inflammation in the lung and airways, with genotoxic and proliferative 

effects in the lung tissue and remodeling.” He indicated carcinogens can adsorb to the 

particles and individuals can be exposed in that way as well. 

Expert L next discussed cardiovascular disease as a cause of death related to PM. He 

cited four groups of researchers that published recent animal studies providing evidence 

that particles contribute to the progression of atherosclerosis (a study in rabbits by Suwa 

et al., 2002; a particle instillation study in rabbits showing inflammation by Goto et al., 

2004; a study from Mort Lippmann’s group (Sun et al., 2005) showing progression of 

atherosclerosis in rats; and a rat model by Lemos et al. 2006). He also mentioned a 

Japanese study by Yamawaki et al. (2006) that found endothelial dysfunction with 

exposures to carbon black, which he thought was an important part of developing 

atherosclerosis. He thought additional support of this mechanism was provided by an 

epidemiologic study by Kunzli et al. (2005) in Los Angeles (L.A.) showing increasing 

carotid intima-media thickness (CIMT) associated with outdoor particles. 

He thought another mechanism related to long-term exposures was chronic lung 

inflammation leading to decreased lung function, although he acknowledged that lung 

pathologists would agree that it is difficult to line up all the pathways that lead to reduced 

lung function as it may involve air ways, lung parenchyma, and the pulmonary 
 

L-3

vasculature. He indicated that, “[l]ower lung function is one of the strongest predictors of 

survival, of life expectancy, of mortality.” He thought one of the most important pieces 

of evidence for this mechanism comes from the Children’s Health Study, which found 

that children’s lung function develops more slowly if they reside in areas with poor air 

quality. It also showed that when children moved into higher pollution areas, the 

development of their lung function decreased. He thought in general, respiratory deaths 

were less correlated with air pollution in the major cohort studies than other causes. For 

instance, the Six Cities follow-up (Laden et al., 2006) had found a positive signal with 

respiratory deaths that was non-significant. He indicated that some studies have found 

signals for chronic obstructive pulmonary disease (COPD), but in general the signals are 

weak. He thought an explanation for this could be misclassification of cause of death 

(e.g., older people are more likely to be classified as dying from cardiovascular disease) 

or a lack of clarity in the definition of COPD itself. He indicated that there was one 

German study that observed an association between air pollution and lung function 

markers of COPD (Schikowski et al., 2006). In addition, he said that the Adventist 

Health and Smog (AHSMOG) study shows incidence of COPD associated with PM. 

“From the mechanistic perspective ... it is very important to use lung function studies in 

assessing the plausibility ... of deaths being related to particles or to air pollution.” 

Finally, he discussed the development of asthma – a story he thought was not as clear. 

He indicated that several recent studies (Children’s Health Study (McConnell et al., 

2006); a European study (Venn et al., 2001); a study in Alaska (Gordian et al., 2006)) are 

suggesting a connection between traffic related exposures and development of asthma. 

He thought these studies were indicative air pollution’s role in the development of 

chronic disease 

3.2. Conceptual Framework for Mortality Effects of Short-term and Long-Term 

PM2.5 Exposures 

Expert L thought the Künzli diagram was a good conceptualization of the relationship 

between long- and short-term exposures. “I think it is ... a framework that is useful ... 

and it allows us to think, for each death ... about what story might be important.” He 

thought the difficulty is how the literature relates to the diagram and he indicated that one 

has to be careful in discussing this relationship. 

3.3. Role of Epidemiologic Study Design in Characterizing the Total Impacts of 

PM2.5 Exposures on Mortality 

Expert L thought that none of the study designs alone were sufficient for capturing “total” 

mortality from PM exposures. He thought time-series studies with a 2-3 day exposure 

window are useful measures of the most acute effects. He indicated that these estimates 

are certainly not a measure of “total” mortality, nor even of total acute if one imagines 

that “air pollution ... triggers an MI, many MI [patient]s do not die within 3 days but end 

up in a phase of treatment ... and some of them die within six weeks, or during their 

convalescence.” He thought Ari Rabel’s work arguing that we don’t observe the true 

total acute effect was a useful theoretical contribution. 
 

L-4

The next class of studies he discussed was the cohort studies which he thought provided 

some measure of the effects of long-term exposures, but which were unlikely to capture 

the cases where life is shortened by less than five to ten days up to two or three weeks, 

for example. He noted that we have to be “clear and honest that these are very, very 

crude approaches in terms of characterizing exposure, and also in terms of measuring 

time lost ... [loss of] life expectancy. When asked how much mortality the cohorts might 

miss, he responded, “I don't know the signal exactly, but maybe it's the NMMAPS type 

of signal, which is ...[about] a half percent [per 10 μg/m3

] ... It seems to me this is [part 

of the] noise of what we discuss for the cohort [studies].” 

He thought case-control studies had value for estimating the risks for specific health 

outcomes like lung cancer, or possibly COPD. He did not think they were appropriate for 

assessing the effects of exposure on total mortality. 

The final type of studies he discussed was the intervention study, which he thought 

provided strong evidence of a benefit of reducing air pollution, particularly in the shortterm. However, he thought they might pose a challenge for assessing the long-term 

effects of reduction. For example, he thought the Utah Valley study (Pope et al., 1996) 

provides “particularly strong evidence for the acute, sub-acute domain.” He thought the 

Clancy et al. (2002) study in Dublin was the appropriate design for assessing the effects 

of long-term exposures but that the longer the follow-up time, the more difficult it 

becomes to isolate the effects of PM changes alone. It might be argued that changes in 

other factors, for example, economic development, diet, smoking rates, or ETS could also 

contribute to improvement, or declines (in the case of obesity) in health status and 

susceptibility. Thus, the intervention studies could not necessarily be a “gold standard” 

for chronic effect studies in his view. 

The effects captured by each study design are shown in the table below: 

Study Design Type of Effects Captured (e.g., shortterm, long-term, or both) 

Cohort Studies Long-term, excludes very short-term effects 

Case-Control Studies Long-term for specific health outcomes only 

Intervention Studies Acute, sub-acute, intermediate acute 

(depending on study) 

Time-Series Studies with a 2-3 day lag; 

distributed lag (up to 3 months) Short-term; subacute 

3.4. Epidemiologic Evidence for the Impact of Exposures to PM on Mortality 

Expert L thought that the following characteristics would be part of an ideal 

epidemiologic study to characterize the PM2.5-mortality relationship in the U.S. 

population: 

• Based in the U.S.; 

• Large sample size covering the entire U.S.; 
 

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• Random population sample, including susceptible individuals; 

• Spatially resolved exposure data at the neighborhood level; 

• Lifetime, or at least 10, 20, or 30 years of individually assigned exposure; 

• Recent cohort (with PM exposures at current levels); 

• Control of potential confounders and susceptibility factors (e.g., smoking, 

physical activity, diet, obesity, body mass index (BMI), family history of disease); 

When asked to review the epidemiologic studies that have been most informative about 

the percent change in all-cause mortality related to a reduction in annual average ambient 

PM2.5 concentrations, Expert L first discussed Pope et al’s work with the American 

Cancer Society (ACS) cohort (Pope et al., 1995 & 2002). He thought that strengths of 

these studies included large population, geographic representation of the U.S., long-term 

follow-up, control of confounding, and the degree of scrutiny and re-analysis that they 

have undergone. He thought that limitations of the studies included the very large areas 

that were assigned exposure from a single monitor and the fact that the population was 

not a random sample. He also discussed the ACS reanalysis in L.A. He thought that this 

study had very strong exposure assessment and control of confounders. However, he 

thought that the fact that it only included one city was a limitation. “[I]t opens the 

question of whether these effects are larger because of the better exposure, or whether it's 

a population ... with different types of more toxic exposures from traffic, more toxic PM 

due to the ozone environment or the susceptibility [in] the population.” 

He next discussed the Six Cities study, both the original (Dockery et al., 1993) and the 

extended analysis (Laden et al., 2006). He thought a strength of these studies were that 

the cohort was a more random sample than the ACS study, with better exposure 

assignments (smaller areas assigned to each monitor). However, he thought small sample 

size and geographic focus on the eastern U.S. were limitations. 

Expert L discussed the AHSMOG, the Enstrom et al., 2005, and the Veteran’s cohort 

studies by Lipfert et al. Although he considered these studies for this exercise, he did not 

end up relying on the estimates when quantifying his C-R function. However, he did 

discuss the following strengths and limitations: 

• AHSMOG: Expert L thought that this study was overall supportive of a PM 

effect but less clear. He indicated that the population was not a random sample 

and had restricted geography, small sample size, and was non-representative in 

terms of susceptibility factors. He did think the study had full control of smoking, 

good exposure assignment, making “attempts to assign exposure on an individual 

level, more than any other [current] study.” 

• Enstrom et al., 2005: He thought that this study was limited geographically, in 

that it only included 11 counties in California. In addition, he indicated that they 

used a single monitor for each county, which could have introduced exposure 

error. It is also limited by having a very elderly population so the relevant periods 

of exposure may extend back a long time. 

• Veteran’s cohort: He said that this study used traffic as a surrogate for PM, 

which was consistent with a Canadian study by Finkelstein et al. (2004) and the 

Netherlands cohort study by Hoek et al. (2002). He thought that the statistical 
 

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analysis was a limitation, indicating that the authors used models that included 

both traffic and PM2.5, which would yield uncertain results since the two factors 

are correlated. He also indicated that the study population was not representative 

of the general population because it included a large percentage of smokers and 

included only hypertensive individuals, which could cause uncertain results due to 

potential treatment interaction effects. 

• Other work that he thought was supportive of a quantitative relationship included 

the Finkelstein et al. study and the Hoek et al. (2002) study, which are more 

“traffic-driven” and therefore not directly applicable given the relationship to PM. 

3.5 Confounding 

Although Expert L discussed potential confounding by smoking and SES, he thought that 

confounding did not affect the published estimates from the ACS studies (Pope et al., 

1995 & 2002), the ACS L.A. reanalysis (Jerrett et al., 2005), and Six Cities Studies 

(Dockery et al., 1993; Laden et al., 2006) in a significant way. “It’s my impression that 

confounding has been addressed ... I don’t see much strong evidence for an uncontrolled 

confounder that one could claim, and convince me that [it is] really confounding. [That it 

is both] associated with air pollution and these outcomes.” In particular, he felt that the 

Krewski et al. (2000) reanalyses of the Six Cities and the ACS studies, as well as the 

ACS L.A. reanalysis (Jerrett et al., 2005) included extensive sensitivity analyses with 

several different variables without finding a large effects on the estimates. For example, 

Jerrett et al. included 44 individual covariates in the analysis. Expert L thought that it 

was possible that the Jerrett analysis could have overadjusted. In particular, he was 

concerned about the models with 44 individual covariates plus contextual covariates 

because they include variables that are indicators of exposure, such as air conditioning 

other pollutants. The contextual covariates include things like SES (a complex covariate), 

or urban land use that could also be indicators for exposure. He thought SES could also 

be a surrogate for susceptibility (e.g., correlated with obesity, diet, antioxidants) but was 

likely not a confounder per se. In general he argued the quantitative effects of possible 

confounding were within the range of effect estimates presented in the key studies. He 

thought the model from the Jerrett study that included 44 individual covariates and 

parsimonious contextual covariates, with relative risk of 1.11, represented the “lowest 

end” of the PM effect estimates. 

3.6 Effect Modification 

Expert L framed his discussion of effect modification by stating that he sees two different 

aspects: 1) real biological modifiers of the PM effect (i.e., that influence susceptibility to 

PM) and 2) the presence of co-pollutants in the ambient environment that may modify the 

effect of particles. For this discussion, he focused on the first issue. The second issue, 

which he felt related more to regulatory approaches, was left for discussion of exposure 

issues. 

Expert L’s discussion focused on the ACS study. Expert L thought that the ACS study 

population was not reflective of the actual U.S. distribution of SES; that is that it 
 

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underrepresented individuals with lower SES as indicated by educational attainment 

(those with less than a high school education). He thought this could be one of the 

reasons that the effect estimates in this study are lower than those found in the Six Cities 

study. He reiterated that SES could be a marker for several factors, such as obesity, diet, 

or underlying diseases. He thought it would be useful to adjust the ACS study for 

educational attainment, which might get at some of the underlying SES effect 

modification. He would want to include it in his list of effect estimates as part of 

characterizing his uncertainty. He thought the sample adjustments presented at the Preelicitation Workshop, showing 30-50 percent increases in the ACS mortality effect 

estimate when adjusted for educational attainment, were consistent with what he would 

have expected. 

Effect modification in the Jerrett et al. (2005) and the Six Cities studies were not 

discussed, and he thought that the paper did not disclose much about heterogeneities. 

Expert L expressed the general concern that there are important heterogeneities (patterns 

in susceptibility) in populations that are likely to change over time. “This type of risk 

assessment [the expert elicitation] would be much less uncertain if our data would show 

no heterogeneity. And that’s not true. The evidence is really increasing that we have 

heterogeneity.” 

3.7. Exposure Issues 

Expert L thought that exposure misclassification due to central monitors versus 

individual exposures caused the ACS study results to be biased downwards. He 

discussed the ACS L.A. reanalysis by Jerrett et al. (2005), indicating that he thought that 

it was “strong evidence that improvement of exposure assignment matters [quite a bit].” 

However, he thought that “there is a limitation [in the Jerrett paper] ... we are not sure to 

what extent [the] much bigger signal [is] a result of being in California rather than in the 

rest of the states, or in another susceptibility domain than the rest.” 

He thought another exposure issue was changes in the characteristics of ambient air 

pollution over time. He thought that “exposures, emissions change with technology. 

And [it] is ... not ... possible to take into [this into] account with the current type of data, 

which is just mass-based.” 

Expert L discussed his theoretical concern that “in heavily polluted centers, over the last 

30 years, improvements in air quality have been much ... bigger than in the cleaner areas, 

where it might even have deteriorated or [become] more or less stable. This has 

implications on the estimate, and not knowing exactly which time window is the relevant 

one for the mortality, this becomes an unresolvable challenge. Exposure gradients and 

errors in assignment also change with the time window of exposure.” He thought this 

probably could be more of a problem in the Six Cities study because it was smaller, so 

“the large change in one city has a big impact.” He thought that this was the only 

argument he could find that “could lead to an overestimation of the main effects.” 

However, he noted that the Laden et al. analysis, by looking into how changes in air 
 

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quality affected the outcomes, controlled for the problem to some extent. He thought this 

issue was less of a problem in the ACS study, pointing to the sensitivity analysis in the 

Krewski reanalysis and a comparison of effect estimates from different time periods in a 

paper by Jerrett et al. (2003). 

Expert L discussed regional differences in the concentration-response (C-R) function. He 

indicated there are not enough data in the studies of long term exposures to stratify by 

region so “one can go to the acute effect studies ... for evidence of ... geographic 

heterogeneity.” He noted that NMMAPS (Samet et al., 2000) found evidence that “there 

are geographic patterns” although the reason for these differences was not easily 

explained. He thought they could be due to differences in PM toxicity, or susceptibilities 

of the population. He thought that the ACS study was “a decent estimate for the [entire] 

U.S.,” and although he thought the Six Cities study was limited by the inclusion of only 

six cities and was probably an estimate of east coast exposures, he thought “these 

differences [in PM composition] are not sufficiently big to expect too much of a 

difference in this estimate.” He went on to say that, “I'm convinced [the constituents of 

PM] does matter, but it's very difficult to say how. And the answer might be different for 

the different outcomes. Might be different in acute and in the chronic domain. And 

given all these uncertainties, it still seems that measuring the mass concentration in the 

real world of real exposures that happen today, real emissions, it still seems to be a pretty 

useful marker for a much more complex story.”

3.8. Causality 

Expert L indicated that he relied on a variety of evidence to evaluate the strength of the 

causal relationship between PM and mortality: 

o Cohort and time-series mortality studies; 

o Intervention studies (epidemiological); 

o Studies examining the underlying mechanisms, “in terms of morbidity or 

intermediate outcomes, [it] is extremely important to support the evidence”; and 

o Toxicological studies, especially those using concentrated ambient particle (CAP) 

exposures. In particular, he mentioned an animal study by McDonald et al. 

(2004) that involved an experimental intervention “changing the diesel emissions 

with a filter [or] sulfur content of the gasoline and showing that all these acute 

effects on the molecular and cellular level are almost disappearing or very ... 

strongly reduced, all the inflammatory responses, oxidative stress-related 

responses.” 

His overall conclusion was that he does not “see any coherent way to argue that this is a 

fluke that has nothing to do with air pollution and particle pollution, but would rather be 

explained by SES factors or the weather or smoking.” 

He thought that there was strong evidence of a causal relationship with mortality for both 

long-term and short-term exposures. He thought that “there is a continuum, and given 

that we have this everyday exposure, we repeat the short-term effects every day in the 
 

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long run. [B]ased on the mechanism discussion we had yesterday, I think evidence is 

really increasing almost every month in the direction of leading to the conclusion that 

these short-term acute mechanistic pathways that are initiated, they have longer-term 

effects.” 

Expert L thought that the likelihood of a causal relationship between PM and mortality 

fell between 90 and 100 percent, with a most likely value of 99 percent. He based these 

estimates on the fact that he did not think that there was an alternate explanation for the 

effects seen, particularly in light of the more recent evidence. He cited studies showing 

atherosclerosis (“this piece was entirely missing and now it’s here”), oxidative stress and 

DNA changes due to diesel particles (Knaapen et al., 2004; Borm et al., 2004) as well as 

new epidemiologic studies looking at cardiovascular disease and other outcomes. In fact, 

he suggested that he would probably have provided different numbers three years ago. 

3.9 Thresholds 

Expert L thought that in order to determine whether there is a threshold in the 

PM/mortality relationship, he would want to rely on the published epidemiological 

studies that include scatter plots of their data, that are inclusive of a wide range of PM, 

and those that try more formally to investigate the existence of a threshold. He thought 

that a formal investigation of threshold would be difficult because of the small effect 

sizes seen. He thought that this had been done to some extent in the time-series studies, 

with authors concluding that, “there is no evidence for any well-defined threshold.” 

However, he thought that this type of assessment was more difficult in the long-term 

studies, although some had attempted it and had also come to the same conclusion as in 

the time series studies. “And if there is one, it must be at the very lower end of this range 

... based on the lack of evidence for a clear threshold, and the positive findings in 

[relatively] clean air studies.” He thought it might be possible to assess the presence of a 

threshold if one were to focus on a susceptible population and follow them for long 

periods of time. Or he thought the threshold question might be addressed by repeated 

acute effect time-series approaches. “Just to repeat ... every five years under the new 

pollution conditions, to see whether one still sees the signal and whether one sees it down 

to ... very low exposures.” He did not think toxicological or clinical study designs were 

likely to resolve the question given concerns about interspecies comparisons, high to low 

dose extrapolations, differences in susceptibilities, and interaction of effects of different 

pollutants. 

When asked if his views on threshold differed for long-term and short-term exposures, 

Expert L thought that they were related, in that if “we are clear about the effects and 

shape of the response function in the very short-term domain, this answers part of the 

long-term question ... if it is true that in the very short-term, there is no threshold, this 

would be true for the long-term public health impact to the extent that the latter is in part 

also the accumulation of acute effects.” 

He indicated that overall, he did not think there was evidence for a threshold, indicating 

that the scatter plots in the ACS study do not show a threshold across the range of 

exposures (about 6 up to 23 μg/m3

 for the later exposures (1999-2000) and about 10 to 30 
 

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μg/m3 (1979-1983) for the earlier exposures). Nor did he think they are statistically 

detectable by current methods. Expert L did not elect to incorporate a threshold into his 

C-R function. 

3.10 Other Influential Factors 

Expert L discussed additional sources of uncertainty that were not part of the protocol. 

When asked whether he felt that there had been adequate exploration of alternative ways 

to model PM/mortality effects, he answered that the ACS and Six Cities studies had been 

analyzed and scrutinized extensively and that the overall conclusions of the main 

published estimates remained the same. Expert L did not think there were any other 

outstanding issues not already covered by the protocol. 

PART 4. ELICITATION OF QUANTITATIVE JUDGMENTS 

Expert L chose to characterize his C-R function within the specified range of annual 

average PM2.5 concentrations (4-30 μg/m3

) in a piece-wise linear fashion. He specified 

one set of numbers that applied to concentrations of 4-10 μg/m3 (hereafter, “Range 1”), 

and another set that applied to concentrations of >10-30 μg/m3 (hereafter, “Range 2”). 

He did so to reflect his greater uncertainty about the shape of the C-R function in the 

lower concentration range. 

Expert L elected to provide C-R functions conditional on the existence of a causal 

relationship. He thought that the discussion of causality should be separate from that of 

the C-R function itself. “I don’t see how to integrate the probability of the causality with 

the probability of these estimates, because I honestly do not believe that this is a 

multiplication of probabilities. [There is] [t]he causality, and then these estimates.” The 

elicitation team then combined his conditional distributions with his percent likelihood of 

causality specified in Section 3.8 (99 percent likelihood of a causal relationship). In 

addition, the two distributions were applied to a distribution of population-weighted 

annual average PM2.5 concentrations in the U.S. from EPA’s BenMap model to create a 

combined distribution (hereafter, “Example Applied Distribution”). 

His general approach was to characterize his uncertainty using the “best estimates” from 

a variety of studies, reflecting different strengths and weaknesses. He did not think the 

published statistical confidence intervals were as relevant to answering the elicitation 

question, given that they reflect precision of the data, choice of model, and covariates. 

He began by specifying the percentiles for Range 2. He started with a minimum value of 

0.2 percent per 10 μg/m3

, basing it on the NMMAPS paper (Samet et al., 2000) because 

“biologically, mechanistically, and epidemiologically, it is really clear that there must be 

more than just these immediate acute effects that you observe in two days ... evidence is 

very strong that things happen in a longer-term way.” His 5th percentile value, 2 percent 

per 10 μg/m3

, was based on a study by Schwartz et al. (2000), which used a distributed 

lag model up to 3 months. To determine his 25th percentile, he began with estimates from 

the cohort studies. He selected a 25th percentile value of 4 percent per 10 μg/m3

 based on 

the lower estimate from the Pope et al., 2002 ACS paper. He noted that the maximum is 
 

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a particularly difficult value to estimate. He ultimately chose a maximum of 30 percent 

per 10 μg/m3 based on a rough average of the upper 95 percent confidence limits from 

several models published in the Jerrett et al. (2005) paper. For the 95th percentile value, 

he used the individual covariate-adjusted estimate from the Jerrett et al. (2005) paper, 17 

percent per 10 μg/m3

. For the 75th percentile value, Expert L elected to use 15 percent 

per 10 μg/m3 based on the estimate adjusted for social factors from the Jerrett et al. 

(2005) study as well as the Laden et al. (2006) extension of the Six Cities study. He then 

chose his 50th percentile value based on the fully adjusted estimate from Jerrett et al. 

(2005) as well as the fact that it fell between the other Jerrett et al. estimates, the Laden et 

al. estimates, and the Pope et al. estimates. For Range 1, Expert L specified the same 

percentiles, with the exception of the minimum, which he placed at zero to account for 

the uncertainty in the threshold at low levels. We discussed how published error bars 

tend to be greater around the lower and upper ends of a range, but Expert L felt that those 

were statistically driven and did not make sense for these estimates. 

After viewing his distributions for Ranges 1 and 2 incorporating causality in Crystal Ball, 

he felt that the they did not accurately reflect his views about where the probability mass 

should be centered. For example, his interquartile range was between 4 and 15 percent 

per 10 μg/m3

, which would indicate that 50 percent of the time, the true mortality effect 

estimate should fall within that range. However, he felt that the probability weight 

assigned to that range should be greater than 50 percent, and opted to change the weight 

to 70 percent. Therefore, his original 25th and 75th percentiles became his 15th and 85th

percentiles. The elicitation team then built a custom distribution in Crystal Ball in order 

to calculate his new interquartile range, displayed below. 

Exhibit 1: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Range 1 (4 – 10 μg/m3)

Percentile Percent Change in Mortality 

Elicited Distribution 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood (IEc 

Generated) 

Minimum 0 0 

5th 0.20 0.16 

25th 0.60 0.54 

50th 1.0 0.98 

75th 1.4 1.4 

95th 1.6 1.6 

Maximum 2.7 2.7 
 

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Exhibit 2: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

for Range 2 (>10 – 30 μg/m3

)

Percentile Percent Change in Mortality 

Elicited Distribution 

Percent Change in Mortality 

Distribution Incorporating 

Causal Likelihood (IEc 

Generated) 

Minimum 0.02 0 

5th 0.20 0.17 

25th 0.60 0.56 

50th 1.0 0.99 

75th 1.4 1.4 

95th 1.6 1.6 

Maximum 2.7 2.7 

Exhibit 3: Subjective Estimates of the Percent Change in Annual Mortality 

Associated with a 1μg/m3

 Change in Ambient Annual Average PM2.5 Concentrations 

After Incorporating Causality and Applying the C-R functions to the PopulationWeighted Annual Average PM2.5 Concentration Distribution in the U.S. from 

BenMap - Example Applied Distribution (IEc Generated) 

Percentile Percent Change in Mortality 

Minimum 0 

5th 0.20 

25th 0.55 

50th 1.0 

75th 1.4 

95th 1.6 

Maximum 2.7 
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Exhibit 4: Percent Change in Annual Mortality Associated with a 1μg/m3 Change in Ambient An

Concentrations: Comparison of 90 Percent Confidence Intervals for Various Studies to Distributions from 

* = Distribution incorporating causality and applying the C-R functions from Ranges 1 and 2 to a 2002 population-weighted annual av

distribution in the U.S. from BenMap. • = median □ = interquartile range │= 90 percent confidence interval 

0

0.5

1

1.5

2

2.5

Range 1 - Elicited Range 2 - Elicited Example Applied

Distribution

Incorporating

Causality (IEc

Generated) *

ACS Original ACS Extend ACS LA Analysis

(Individual

Covariates)

ACS LA Analysis

(Social Factors)

Six Cities Original Six Cit

% Decrease in Mortality per 1 ug/m3 PM2.5
 

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Elicited Distribution – Range 1 

Elicited Distribution – Range 2 
 

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Range 1 Incorporating Causality - Probability Density Function (PDF) (IEc Generated) 

Range 1 Incorporating Causality - Cumulative Density Function (CDF) (IEc Generated) 
 

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Range 2 Incorporating Causality – PDF (IEc Generated) 

Range 2 Incorporating Causality – CDF (IEc Generated) 
 

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Example Applied Distribution – PDF (IEc Generated) 

Example Applied Distribution – CDF (IEc Generated) 
 

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U.S. EPA EXPERT ELICITATION STUDY OF THE CONCENTRATION-RESPONSE 

RELATIONSHIP BETWEEN ANNUAL AVERAGE PM2.5 EXPOSURE AND 

MORTALITY 

Modification to Expert Judgments

Expert L

Date: July 7th, 2006 

Section of Protocol Affected (Section Number and/or Title):

Causality 

Description of Change (e.g. to a specific percentile, or to a qualitative opinion or 

statement of belief) (Causality versus Change of Mortality):

1. Regarding the range of probabilities of a CAUSAL RELATIONSHIP I would like to 

express two issues: 

In the range of >10 μg/m3

, my Min/Max values are 90 and 100 with the ‘more likely’ 

being 99 because we can never be “100 percent sure.” 

In the range of 0-10 μg/m3

 my Min/Max range estimates are 0 and 95 with the ‘most 

likely value’ being 75. 

ARGUMENTS: 

It is very hard to me to find a plausible coherent alternative explanation for all the 

findings – at least in the acute effect domain – showing effects of PM in the range 

>10 μg/m3

. It is inherently more difficult to know what is going on in the lowest 

ranges. If the theory of ‘distributions of susceptibilities’ does apply (which I endorse), 

it means to have no threshold on the population level, thus effects among susceptible 

individuals are likely to occur in the 0-10 μg/m3

 range, too. Empirical evidence is just 

not abandoned to disregard the minute probability of ‘no effect’ in this lowest range 

(or maybe in the lowest range of this low range, e.g., 0-5 μg/m3

. 

2. I ask for strict separation of the “causality distribution” and of the response function 

distribution (in the two concentration strata). It is important to not provide any 

figures or tables that combine “Percent Change in Mortality” with “Causality.” What 

might be done are combinations of the overall distributions (percent change, number 

of cases etc.) with causality in the estimation/presentations of costs. 
 

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ARGUMENTS: 

As discussed at the Post-elicitation Workshop, the science to establish causality has 

its own research domains, which may lead to a steady increase (as in case of air 

pollution) in favor of ‘causality,’ or the opposite could happen as well. The question 

of the quantitative association between an agent and an outcome needs an 

independent assessment. Evidence for causality may, e.g., increase while the updated 

literature may give no indication at all that concentration response functions changed 

– or if they do change in future studies it may be due to constituents or susceptibility 

changes rather than ‘causality.’