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R e s e a r c h

R e p o r t

H E A L T H

EF F ECTS

INSTITUTE

Number 140

May 2009

Extended Follow-Up and Spatial Analysis of 

the American Cancer Society Study Linking 

Particulate Air Pollution and Mortality

Daniel Krewski, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, 

Yuanli Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, 

Eugenia E. Calle, and Michael J. Thun

with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, 

K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara Tempalski

with a Commentary by the HEI Health Review Committee

Research Report 140

Health Effects Institute 

Boston, Massachusetts

Trusted Science · Cleaner Air · Better Health
Publishing history: The Web version of this document was posted at www.healtheffects.org in May 2009 and

then finalized for print. 

Citation for whole document:

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, Turner MC, Pope CA III, 

Thurston G, Calle EE, Thun MJ. 2009. Extended Follow-Up and Spatial Analysis of 

the American Cancer Society Study Linking Particulate Air Pollution and Mortality. 

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

When specifying a section of this report, cite it as a chapter of the whole document. 

© 2009 Health Effects Institute, Boston, Mass., U.S.A. Cameographics, Belfast, Me., Compositor. Printed by 

Recycled Paper Printing, Boston, Mass. Library of Congress Catalog Number for the HEI Report Series: WA 

754 R432.

 Cover paper: made with at least 50% recycled content, of which at least 15% is post-consumer waste; 

free of acid and elemental chlorine.Text paper: made with at least 50% recycled content, of which at least 

30% is post-consumer waste; acid free; no chlorine used in processing. The book is printed with soy-based 

inks and is of permanent archival quality.
CONTENTS

About HEI v

About This Report vii

HEI STATEMENT 1

INVESTIGATORS’ REPORT by Krewski et al. 3

ABSTRACT

INTRODUCTION

The Harvard Six Cities Study and the American Cancer 

Society Study of Particulate Air Pollution and Mortality

The Particle Epidemiology Reanalysis Project: Objectives 

and Findings

Geographic Scale of Analysis

Refinement of Exposure Estimates

Limitations of the Random Effects Cox Model

Post-Reanalysis Studies of the ACS Cohort

Exposure Time Windows

SPECIFIC AIMS OF THE CURRENT STUDY

NATIONWIDE ANALYSIS

Materials and Methods

Study Population

Air Pollution Exposure Data

Ecologic Covariates

Statistical Methods and Data Analysis

The Random Effects Cox Model

Auxiliary Random Effects Poisson Models

Orthodox, Best Linear, Unbiased Predictor, Approach

Results

Sensitivity of Air Pollution Risk to Error Structure of the 

Random Effects Cox Model

Alternative Formulation of the Concentration–Response 

Function

Discussion and Conclusions

INTRA-URBAN ANALYSIS FOR THE NEW YORK CITY REGION

A Land-Use Regression Model for Predicting 

PM2.5 Concentrations

Background

Materials and Methods

Dependent Variable: Ambient PM2.5 Data

Independent Variables: Traffic, Land-Use, Population, and 

Local Emissions Data

Statistical Data Analysis

Results

Descriptive Statistics for PM2.5
Research Report 140

LUR Model Building and Results

Discussion

Spatial Analysis of Air Pollution and Mortality in New York

Materials and Methods

Study Population

Assessment of Exposure to PM2.5

Ecologic Covariates

Statistical Methods and Data Analysis

Results

LUR Exposure Models

Mortality Models

Discussion

INTRA-URBAN ANALYSIS FOR THE LOS ANGELES REGION

A Land-Use Regression Model for Predicting 

PM2.5 Concentrations

Background

Materials and Methods

Study Area

Methods for Handling Spatial Data

Modeling Methods

Visualizing the Surface

Results

Discussion

Accuracy of Predictions Using the LUR Model

Effects of Buffer Size Section

Modeling Spatial Variability in Exposure

Spatial Analysis of Air Pollution and Mortality in Los Angeles

Materials and Methods

Study Population

Assessment of PM2.5 Exposure

Traffic Data

Assessment of O3 Exposure

Statistical Methods and Data Analysis

Results

Discussion and Conclusions

CRITICAL EXPOSURE TIME WINDOWS

Materials and Methods

Study Population

Individual Exposure Profiles

Statistical Methods and Data Analysis

Results

Discussion and Conclusions

IMPLICATIONS OF THE FINDINGS

Phase I

Phase II

Phase III

Summary of Results from Phase III

Nationwide Analysis

Intra-Urban Analysis

Critical Exposure Time Windows

Revisions to Cox Models
Research Report 140

Comparison of Data Sets and Analytic Methods for the 

Three-Follow-Up Time Periods

Policy Implications

ACKNOWLEDGMENTS

REFERENCES

APPENDIX A. HEI QUALITY ASSURANCE REPORT

APPENDICES AVAILABLE ON THE WEB

ABOUT THE AUTHORS

OTHER PUBLICATIONS RESULTING FROM THIS RESEARCH

ABBREVIATIONS AND OTHER TERMS

GLOSSARY OF STATISTICAL TERMS

COMMENTARY by the Health Review Committee xx

INTRODUCTION

BACKGROUND 

The Harvard Six Cities Study 

The ACS Study 

The Reanalysis Project 

The Updated Analysis 

The Extended Analysis

STUDY SUMMARY

Specific Aims

Sources of Data

Study Population

Air Pollutants

METHODS FOR EACH ANALYSIS

Nationwide Analysis (Specific Aims 1 and 2)

Intra-Urban Analyses (Specific Aim 4)

New York City Analysis

Los Angeles Analysis

Critical Exposure Time Windows Analysis (Specific Aim 3)

KEY FINDINGS

Nationwide Analysis

Intra-Urban Analyses

Critical Exposure Time Windows Analysis

Nationwide Analysis

Intra-Urban Analyses

Critical Exposure Time Windows Analysis
Research Report 140

DISCUSSION

Nationwide Analysis

Study Design 

Statistical Methods 

Results

Intra-Urban Analyses

Study Design 

Statistical Methods 

Exposure Assessment 

Results

Critical Exposure Time Windows Analysis 

Study Design 

Statistical Methods 

Exposure Assessment 

CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

RELATED HEI PUBLICATIONS xx

HEI BOARD, COMMITTEES, AND STAFF xx
ABOUT HEI

vii

The Health Effects Institute is a nonprofit corporation chartered in 1980 as an independent 

research organization to provide high-quality, impartial, and relevant science on the effects of air 

pollution on health. To accomplish its mission, the institute

• Identifies the highest-priority areas for health effects research;

• Competitively funds and oversees research projects;

• Provides intensive independent review of HEI-supported studies and related 

research;

• Integrates HEI’s research results with those of other institutions into broader 

evaluations; and

• Communicates the results of HEI research and analyses to public and private 

decision makers.

HEI receives half of its core funds from the U.S. Environmental Protection Agency and half 

from the worldwide motor vehicle industry. Frequently, other public and private organizations in 

the United States and around the world also support major projects or certain research 

programs. HEI has funded more than 280 research projects in North America, Europe, Asia, and 

Latin America, the results of which have informed decisions regarding carbon monoxide, air 

toxics, nitrogen oxides, diesel exhaust, ozone, particulate matter, and other pollutants. These 

results have appeared in the peer-reviewed literature and in more than 200 comprehensive 

reports published by HEI.

HEI’s independent Board of Directors consists of leaders in science and policy who are 

committed to fostering the public–private partnership that is central to the organization. The 

Health Research Committee solicits input from HEI sponsors and other stakeholders and works 

with scientific staff to develop a Five-Year Strategic Plan, select research projects for funding, and 

oversee their conduct. The Health Review Committee, which has no role in selecting or 

overseeing studies, works with staff to evaluate and interpret the results of funded studies and 

related research.

All project results and accompanying comments by the Health Review Committee are widely 

disseminated through HEI’s Web site (www.healtheffects.org), printed reports, newsletters, and 

other publications, annual conferences, and presentations to legislative bodies and public agencies.

ABOUT THIS REPORT

ix

Research Report 140, Extended Follow-Up and Spatial Analysis of the American Cancer Society 

Study Linking Particulate Air Pollution and Mortality, presents a research project funded by the 

Health Effects Institute and conducted by Dr. Daniel Krewski of the McLaughlin Centre for 

Population Health Risk Assessment, University of Ottawa, in Ottawa, Ontario, Canada, and his 

colleagues. This report contains three main sections.

The HEI Statement, prepared by staff at HEI, is a brief, nontechnical summary of the 

study and its findings; it also briefly describes the Health Review Committee’s 

comments on the study.

The Investigators’ Report, prepared by Krewski et al., describes the scientific 

background, aims, methods, results, and conclusions of the study.

The Commentary is prepared by members of the Health Review Committee with 

the assistance of HEI staff; it places the study in a broader scientific context, points out 

its strengths and limitations, and discusses remaining uncertainties and implications of 

the study’s findings for public health and future research.

This report has gone through HEI’s rigorous review process. When an HEI-funded study is 

completed, the investigators submit a draft final report presenting the background and results of 

the study. This draft report is first examined by outside technical reviewers and a biostatistician. 

The report and the reviewers’ comments are then evaluated by members of the Health Review 

Committee, an independent panel of distinguished scientists who have no involvement in 

selecting or overseeing HEI studies. During the review process, the investigators have an 

opportunity to exchange comments with the Review Committee and, as necessary, to revise 

their report. The Commentary reflects the information provided in the final version of the 

report.

Synopsis of Research Report 140

HEI STATEMENT

This Statement, prepared by the Health Effects Institute, summarizes a research project funded by HEI and conducted by Dr. Daniel Krewski

at the McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada. Research Report 140 contains

both the detailed Investigators’ Report and a Commentary on the study prepared by the Institute’s Health Review Committee.

1

Extended Analysis of the American Cancer Society 

Study of Particulate Air Pollution and Mortality

INTRODUCTION

The American Cancer Society (ACS) Cancer Prevention Study II (CPS-II), a large ongoing prospective study of mortality in adults initiated in 1982,

was one of two U.S. cohort studies central to the

1997 debate on the National Ambient Air Quality

Standard (NAAQS) for fine particulate air pollution

in the United States. Because of the high importance of the original ACS study in formulating regulations and the controversy generated by the

limitations of that study, the U.S. Environmental

Protection Agency (U.S. EPA), the Congress and

industry requested the Health Effects Institute to

conduct the Particle Epidemiology Reanalysis

Project with the objective of independently and rigorously assessing the original data and findings.

The results of the Reanalysis Project validated the

quality of the original data (which included 7 years

of follow-up), replicated the original results, and

tested those results against alternative risk models

and analytic approaches.

After the Reanalysis Project, Pope and colleagues

undertook an Updated Analysis of the ACS cohort

using an additional 10 years of follow-up and exposure data. Recent advances in statistical modeling

were incorporated into these analyses.

More recently, with HEI’s support, Dr. Daniel

Krewski and colleagues have conducted an

Extended Analysis of the same cohort. This

research increases the follow-up period for the ACS

cohort to 18 years (1982 to 2000) — 11 years more

than the original study. The investigators have produced national estimates of the risks of death from

various causes and have extended the range of analyses to include refinements of statistical methods

and incorporate sophisticated control of bias and

confounding.

SUMMARY

The cohort for the current study consists of

approximately 360,000 participants residing in

areas of the country that have adequate monitoring

information on levels of particulate matter with an

aerodynamic diameter of 2.5 μm or less (PM2.5) for

1980 and about 500,000 participants in areas with

adequate information for 2000. The causes of death

obtained from death certificates during follow-up

that were analyzed included all causes, cardiopulmonary disease (CPD), ischemic heart disease (IHD,

reduction of blood supply to the heart, leading to

heart attack), lung cancer, and all other causes. Data

for 44 personal, or individual-level, covariates,

based on participants’ answers to a 1982 enrollment

questionnaire, were also used for the analyses. Dr.

Krewski’s research team also collected data for

seven ecologic (neighborhood-level) covariates,

each of which represents local factors known or

suspected to influence mortality, such as poverty

level, education Educational ?attainment, and

unemployment (at both Zip Code and city levels).

Long-term average exposure variables were constructed for PM2.5 from monitoring data for two

periods: 1979–1983 and 1999–2000. Similar variables were constructed for long-term exposure to

other pollutants of interest from single-year (1980)

averages, including total suspended particles,

ozone (O3), nitrogen dioxide, and sulfur dioxide

(SO2). Exposure was averaged for all monitors

within a metropolitan statistical area (MSA) and

assigned to participants according to their Zip Code

area (ZCA) of residence.

Dr. Krewski’s team chose the standard Cox proportional-hazards model (and a variation to allow

for random effects) to calculate hazard ratios for

various cause-of-death categories associated with
Research Report 140

 2

the levels of air pollution exposure in the cohort.

They extended the random effects Cox model to

accommodate two levels of information each for

clustering and ecologic covariates. Three main analyses were conducted: a Nationwide Analysis, IntraUrban Analyses in the New York City (NYC) and

Los Angeles (LA) regions, and an analysis designed

to clarify whether critical time windows of exposure to pollutants might have affected mortality in

the cohort.

Nationwide Analysis

For the Nationwide Analysis using the standard

Cox model, the associations between average PM2.5

concentrations in both 1979–1983 and 1999–2000

and mortality from all causes (except the category of

“all other causes”) was statistically significant. The

hazard radio (HR) for death was elevated by 3% to

15%, depending on the cause of death, for each

increase of 10-μg/m3 in PM2.5. When the random

effects Cox model was used with added control for

ecologic covariates, the effect estimates increased

slightly and remained significant; the strongest estimate was for IHD (HR = 1.24, 95% confidence

interval [CI], 1.19–1.29). These effect estimates

were, in general, higher than those found in some

previous analyses of this cohort. The association of

mortality with summer O3 levels (calculated from

concentrations measured from April to September

1980) was small, but significant, for deaths from all

causes (HR = 1.02; 95% CI, 1.01–1.03) and from

CPD (HR = 1.03; 95% CI, 1.02–1.04).

In earlier analyses of this cohort, investigators

found that increasing education levels appeared to

reduce the effect of PM2.5 exposure on mortality.

Results from the current study show a similar pattern, although with somewhat less certainty, for all

causes of death except IHD, for which the pattern

was reversed.

Intra-Urban Analyses

 For the NYC Analysis, land-use regression

(LUR) models were created to estimate exposure to

PM2.5 using concentrations averaged over 3 years

or over the winter months only for 1 year. Annual

average concentrations were calculated for each of

62 monitors from 3 years of daily monitoring data

for 1999 through 2001. Those data were combined

with land-use data collected from traffic-counting

systems, roadway network maps, satellite photos of

the study area, and local government planning and

tax-assessment maps to assign estimated exposures

to the ACS participants. As with the Nationwide

Analysis, the team used the random effects Cox

model for statistical analysis and incorporated the

44 individual-level covariates as well as the 7 ecologic covariates at the ZCA and MSA scales.

In the LA Analysis, the investigators used both

LUR and kriging (a method of interpolating missing

values) to estimate exposure concentrations for

cohort members. The Cox models used to calculate

associations between exposure and mortality

included the same individual-level and ecologic

covariates as in the NYC Analysis. The LA Analysis

reported results separately for analyses that used

exposure based on LUR and that based on kriging of

monitored concentrations. The investigators assembled data from several sources for the LUR models,

including the California EPA’s 23 PM2.5 monitors

and the California Air Resources Board’s database

for 42 sites monitoring O3.

Despite the common methodologic basis for the

NYC and LA Analyses, the resulting LUR exposure

models and associations between exposure and

mortality were strikingly dissimilar. The LA results

show much larger hazard ratios than the NYC

results, except for mortality due to IHD (LA: HR =

1.33; 95% CI, 1.08–1.63; NYC: HR = 1.47; 95% CI,

1.00–2.00; both per 10 μg/m3 increase in PM2.5).

These differences may arise from the range of exposures derived for cohort members residing in each

city, the relative uniformity of PM2.5 exposure in

the NYC region, and the differences between the

land-use variables selected as the most appropriate

for inclusion in the LUR models that were constructed for the two cities.

Critical Periods of Exposure Analysis

Dr. Krewski’s team performed an analysis

designed to explore whether more recent exposures

to air pollution are more or less strongly associated

with mortality than exposures further in the past.

Exposure profiles for this analysis were constructed

from average PM2.5 and SO2 concentrations for

periods 1 to 5 years, 6 to 10 years, and 11 to 15 years

before death. As with all other analyses, the investigators used the standard Cox model including individual-level covariates.

The investigators considered the time window

with the best-fitting model (judged by the lowest

Akaike information criterion [AIC] statistic, which

is a measure of how well a model fits the available
3

Research Report 140

data) to be the period during which pollution had the

strongest influence on mortality. Overall, differences

in model fit, HRs, and CIs among the three 5-year

exposure periods were small and demonstrated no

definitive patterns. High correlations between exposure levels in the three periods may have reduced the

ability of this analysis to detect any differences in the

relative importance of the time windows.

DISCUSSION

The basic Cox proportional-hazards model used

for the mortality analyses has two major limitations

that the investigators addressed in innovative ways

developed specifically for this study: confounding

by ecologic factors and spatial autocorrelation. Ecologic confounders are risk factors for mortality that

are relevant at the neighborhood level, rather than

the individual level. In the current study, in contrast to the Reanalysis Project, ecologic information

was collected at the ZCA level as well as the city

level, although not all ecologic covariates considered previously were included in this analysis. Spatial autocorrelation arises from the way values for

certain variables tend to be similar for people (or

areas) that are geographically close. For example,

people who live in the same household or neighborhood — or even in similar neighborhoods in the

same city — tend to have similar health risks (diet,

smoking habits, access to health care), as well as

similar proximity to sources of exposure (e.g., freeways and industrial areas). The spatial models in

this analysis differed from those used in the Reanalysis by including random effects at the ZCA, city,

and state levels and by adjusting for correlation

between adjacent ZCAs, cities, and states.

In its evaluation of the study by Krewski and colleagues, the Review Committee agreed with the

investigators that key results were robust when

adjusted for spatial autocorrelation in the statistical

models. In a recently published follow-on study of

O3 and respiratory outcomes in the ACS data,

including the same individual and ecologic covariates as the current study, Jerrett and associates found

no indication of important residual spatial autocorrelation of the association between O3 and mortality.

Because the Reanalysis Project tested extensively

for confounding by gaseous pollutants of the relationship between fine particles and mortality,

Krewski et al. instead focused the current study on

an extensive exploration of spatial autocorrelation

in a series of one-pollutant models. The Committee

thought that the inclusion of some two-pollutant

analyses would have strengthened the study. The

authors note, however, that the available data on

most gaseous pollutants were not sufficient for such

analyses, since they came from only a few locations

in each city and could not adequately represent the

high degree of spatial variability of pollutant levels

in a given metropolitan area.

The present report combines deaths from cardiovascular and respiratory causes—a decision that is

important for continuity with earlier studies but

one that makes the results more difficult to interpret

biomedically. The report singled out the associations between PM2.5 and IHD, consistent with previous investigations with this cohort, but the

Committee felt it would be useful in the future to

see the results for other categories of cardiovascular

disease, such as stroke and heart failure, presented

alongside those for IHD.

The fundamental difference in exposure between

the two Intra-Urban Analyses lies in the different

relative influence of regional background concentrations of PM2.5. The intra-urban studies primarily

investigated variability in local exposure within the

cities that was driven by local near-source effects

such as traffic, industry, and residential or commercial emissions. Despite the substantial differences in

how the LUR models were constructed and the

likely quality of the data used, the LUR models for

LA and NYC were both successful in explaining a

moderate percentage of variability (60 to 65 %) in

PM2.5 concentrations measured at the monitoring

sites. The range of average annual monitored PM2.5

concentrations considered in developing the models

was not very different between LA (9.5–28 μg/m3)

and NYC (10–20 μg/m3). However, the resulting

ranges of exposure assigned by the LUR models in

LA ( < 10 – > 125 μg/m3) and NYC (8–20 μg/m3), by

comparison, suggest that levels of PM2.5 are regionally determined in NYC and highly locally variable

in LA.

The intra-urban results for the two cities were

very different, with a strong positive and significant

association between mortality from CPD and PM2.5

in LA and no significant association in NYC. Both

the LA and NYC results showed significant associations between PM2.5 and mortality from IHD, consistent with the results of the nationwide study. The

authors note that differences in the estimated HRs

for LA and NYC were partially attributable to the
Research Report 140

 4

different — and opposite — ways that mortality not

explained by the individual and ecologic variables

in the Cox models was distributed relative to the

varying PM2.5 exposure levels in the two cities. The

higher exposures in LA tended to occur in areas

characterized by low socioeconomic status (and relatively high expected mortality), whereas the higher

exposures in NYC were generally found in areas of

high socioeconomic status (and relatively low

expected mortality).

The Committee noted that the inconclusive

results from the NYC Analysis (aside from that for

IHD) were probably due to too little variation in

PM2.5 exposure across the NYC area, owing to the

regional nature of PM2.5 exposure in the Northeastern United States. Relatively uniform exposures

would reduce the ability of the statistical models to

detect patterns of mortality relative to exposure and

to estimate HRs with precision. As for the LA

results, the authors believe that the higher estimates

are due to reduced error in the assignment of exposures. However, the Committee saw no persuasive

argument that exposure measurement error would

be expected to be less in the LA or NY studies than

the Nationwide Analysis. Therefore, the Committee

believes that the most likely explanation for the

largely null results for the NYC Analysis and their

divergence from the LA and nationwide results was

the low variability in PM2.5 exposure levels across

the NYC region.

The epidemiologic design used in the analysis of

Critical Periods of Exposure was more complex

than that of the full Nationwide Analysis because it

used two distinct subcohorts of subjects from the

main ACS cohort, rather than the whole cohort as in

the Nationwide Analysis. For each deceased ACS

participant in each subcohort, time windows of

exposure were calculated as average exposures

during successive five-year periods preceding the

date of death.

The use of AIC to compare models including different five-year windows of past exposure is

broadly reasonable, since the number of variables in

the each model being compared was the same. The

Committee was somewhat disappointed that the

investigators did not present results for “multiwindow” models, in which the effects of exposure

in one time window are controlled for the effects of

exposure in another time window. Although it is

important to know whether more recent exposure

has a greater effect on risk than earlier exposure, the

Committee considered that the evidence presented

was not substantial enough to draw conclusions

based on the extremely small differences in AIC

resulting from exchanging exposure in one time

window with another.

CONCLUSIONS

This report represents a sound and innovative

analysis of extended follow-up data from an already

important cohort study. Dr. Krewski and his colleagues have successfully designed, constructed,

and implemented advanced statistical methods for

survival modeling that include nested random

effects variables and control for autocorrelation in a

single model. Furthermore, they have added to the

body of evidence linking PM2.5 exposure and mortality, confirming earlier findings and reporting

higher estimates of effects with their new statistical

techniques than had been found in some previous

analyses of this cohort. The results of this study

support earlier findings regarding lung cancer risk

associated with long-term PM2.5 exposure. Associations between mortality from CPD and long-term

exposure to O3, first identified in this study in the

standard Cox model results, have been confirmed

and refined in a recently published analysis of longterm O3 exposure and pulmonary mortality.

The LA Intra-Urban Analysis demonstrates that

observed associations between mortality and PM2.5

exposure may be higher than national estimates

when variations in local exposures are estimated.

When results from the NYC and LA Analyses are

taken together, however, they underscore that cities

may differ markedly in their local exposure situations, and in the relative importance of the contributions of local and regional sources of PM2.5.

Thus, care must be taken in extrapolating from any

one such analysis to other cities.

The HEI Statement for the Reanalysis Project (HEI

Special Report, 2000), which covered the ACS and

Harvard Six Cities Studies, commented that no

single study can be the basis for determining a

causal relationship between air pollution and mortality. The Committee considers that, with the emergence of new cohort evidence from the United

States and Europe, there is a need to examine

closely the similarities and differences between the

results of the various studies. The size and character

of the ACS cohort and the contributions of this most

recent analysis by Dr. Krewski and colleagues make

it likely that these studies will remain pre-eminent.
Health Effects Institute Research Report 140 © 2009 5

INVESTIGATORS’ REPORT

Extended Follow-Up and Spatial Analysis of the American Cancer 

Society Study Linking Particulate Air Pollution and Mortality

Daniel Krewski, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, 

Yuanli Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, 

and Michael J. Thun

with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, 

K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara Tempalski

McLaughlin Centre for Population Health Risk Assessment, Institute of Population Health, University of Ottawa (D.K., R.T.B., Y.S.,

M.C.T.); Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa (D.K., R.T.B); Division

of Environmental Health Sciences, School of Public Health, University of California–Berkeley (M.J.); Healthy Environments and

Consumer Safety Branch, Health Canada (R.T.B); Department of Mathematics and Statistics, University of New Brunswick, Fredericton (R.M.); Edward Hughes Consulting (E.H.); Department of Economics, Brigham Young University (C.A.P.); Nelson Institute

of Environmental Medicine, New York University School of Medicine (G.T.); Department of Epidemiology and Surveillance

Research, American Cancer Society (E.E.C., M.J.T.)

ABSTRACT

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS)* Cancer Prevention Study II (CPS-II) cohort in order to further

examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The

current study sought to clarify outstanding scientific

issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined

(1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution–mortality association; (2) how spatial autocorrelation

and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects

Cox model; (3) how using land-use regression to refine

measurements of air pollution exposure to the within-city

(or intra-urban) scale might affect the size and significance

of health effects in the Los Angeles and New York City

regions; and (4) what exposure time windows may be most

critical to the air pollution–mortality association.

The 18 years of follow-up (extended from 7 years in the

original study [Pope et al. 1995]) included vital status data

for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through

December 31, 2000 and more recent exposure data from air

pollution monitoring sites for the metropolitan areas.

In the Nationwide Analysis, the influence of ecologic

covariate data (such as education attainment, housing

characteristics, and level of income; data obtained from

the 1980 U.S. Census; see Ecologic Covariates sidebar on

page 14) on the air pollution–mortality association were

examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference

between each ZCA value and the mean MSA value (DIFF).

In contrast to previous analyses that did not directly include

ecologic covariates at the ZCA scale, risk estimates

increased when ecologic covariates were included at all

scales. The ecologic covariates exerted their greatest effect

on mortality from ischemic heart disease (IHD), which was

also the health outcome most strongly related with exposure

to PM2.5 (particles 2.5 μm or smaller in aerodynamic diameter), sulfate (SO4

2), and sulfur dioxide (SO2), and the only

outcome significantly associated with exposure to nitrogen

dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the

This Investigators’ Report is one part of Health Effects Institute Research

Report 140, which also includes a Commentary by the Health Review Committee and an HEI Statement about the research project. Correspondence

concerning the Investigators’ Report may be addressed to Dr. Daniel

Krewski, McLaughlin Centre for Population Health Risk Assessment, Room

320, University of Ottawa, One Stewart Street, Ottawa, ON K1N 6N5, Canada. E-mail: cphra@uottawa.ca.

Although this document was produced with partial funding by the United

States Environmental Protection Agency under Assistance Award CR–

83234701 to the Health Effects Institute, it has not been subjected to the

Agency’s peer and administrative review and therefore may not necessarily

reflect the views of the Agency, and no official endorsement by it should be

inferred. The contents of this document also have not been reviewed by private party institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and

no endorsement by them should be inferred.

* A list of abbreviations and other terms appears at the end of the Investigators’ Report.
6

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

hazard ratio (HR) for mortality from IHD associated with

PM2.5 exposure (average concentration for 1999–2000)

increased by 7.5% and that associated with SO4

2exposure

(average concentration for 1990) increased by 12.8%. The

two covariates found to exert the greatest confounding

influence on the PM2.5–mortality association were the percentage of the population with a grade 12 education and the

median household income.

Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended

random effects Cox model (see Glossary of Statistical

Terms at end of report that is capable of clustering up to

two geographic levels of data. Using this model tended to

increase the HR estimate for exposure to air pollution and

also to inflate the uncertainty in the estimates. Including

ecologic covariates decreased the variance of the results at

both the MSA and ZCA scales; the largest decrease was in

residual variation based on models in which the MSA and

DIFF levels of data were included together, which suggests

that partitioning the ecologic covariates into betweenMSA and within-MSA values more completely captures

the sources of variation in the relationship between air

pollution, ecologic covariates, and mortality.

Intra-Urban Analyses were conducted for the New York

City and Los Angeles regions. The results of the Los

Angeles spatial analysis, where we found high exposure

contrasts between ZCAs within the Los Angeles region,

showed that air pollution–mortality risks were nearly 3

times greater than those reported from earlier analyses.

This suggests that chronic health effects associated with

intra-urban gradients in exposure to PM2.5 may be even

larger between communities within an MSA than the associations between MSAs that have been previously

reported. However, in the New York City spatial analysis,

where we found very little exposure contrast between

ZCAs within the New York region, mortality from all

causes, cardiopulmonary disease (CPD), and lung cancer

was not elevated. A positive association was seen for PM2.5

exposure and IHD, which provides evidence of a specific

association with a cause of death that has high biologic

plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS

enrollment questionnaire in 1982; see Individual-Level

Covariates sidebar on page 22) and (2) spatial clustering

using the random effects Cox model. Effects were mildly

lower when unemployment at the ZCA scale was

included.

To examine whether there is a critical exposure time

window that is primarily responsible for the increased

mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for

particulate and gaseous air pollutants (PM2.5 and SO2) for a

subset of the ACS CPS-II participants for whom residence

histories were available. The relevance of the three exposure time windows we considered was gauged using the

magnitude of the relative risk (HR) of mortality as well as

the Akaike information criterion (AIC), which measures

the goodness of fit of the model to the data. For PM2.5, no

one exposure time window stood out as demonstrating the

greatest HR; nor was there any clear pattern of a trend in

HR going from recent to more distant windows or vice

versa. Differences in AIC values among the three exposure

time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most

recent time window (1 to 5 years), although none of these

HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets.

This study provides additional support toward developing cost-effective air quality management policies and

strategies. The epidemiologic results reported here are

consistent with those from other population-based studies,

which collectively have strongly supported the hypothesis

that long-term exposure to PM2.5 increases mortality in the

general population. Future research using the extended

Cox–Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

INTRODUCTION

THE HARVARD SIX CITIES STUDY AND THE 

AMERICAN CANCER SOCIETY STUDY OF 

PARTICULATE AIR POLLUTION AND MORTALITY

Epidemiologic studies conducted over several decades

have provided evidence to suggest that long-term exposure

to elevated ambient levels of particulate air pollution is

associated with increased mortality. Two U.S. cohort

studies, the Harvard Six Cities Study (Dockery et al. 1993),

a 20-year prospective cohort study, and the ACS Study

(Pope et al. 1995), a larger retrospective cohort study,

reported that mortality from all causes increased in association with an increase in the concentration of PM2.5.

Both studies came under intense scrutiny in 1997 when

the results were used by the U.S. Environmental Protection Agency (U.S. EPA) to support new National Ambient

Air Quality Standards for PM2.5 and to maintain the

standards for PM10 that were already in effect. The findings

of these two studies were the subject of debate regarding

the following factors: possible residual confounding by
D. Krewski et al.

7

individual risk factors (e.g., sedentary lifestyle, active or

passive cigarette smoke exposure) or ecologic risk factors

(e.g., education, unemployment, poverty); inadequate

characterization of the long-term exposure of study

subjects; different kinds of bias in allocating exposure to

separate cities; and robustness of the results to changes in

the specification of statistical models (Gamble 1998;

Lipfert and Wyzga 1995). To address growing public

controversy concerning the studies’ methods and their

results, Harvard University and the ACS requested that the

Health Effects Institute organize an independent

Reanalysis of these studies.

Through a competitive process, a Reanalysis Team led

by Dr. Daniel Krewski of the McLaughlin Centre for Population Health Risk Assessment at the University of Ottawa

was selected by an independent Expert Panel appointed by

the HEI Board of Directors, with support from the U.S.

EPA, industry, Congress, and other stakeholders. The

Reanalysis Project was overseen by the Expert Panel,

which was chaired by Dr. Arthur Upton from the University of Medicine and Dentistry of New Jersey and former

Director of the National Cancer Institute, with assistance

by a broad-based Advisory Board of stakeholders and scientists. The findings of the Reanalysis (Phase I and Phase

II) were published in an HEI Special Report in 2000

(Krewski et al. 2000a,b). The final results were extensively

peer reviewed by an independent Special Panel of the HEI

Review Committee, which was chaired by Dr. Millicent

Higgins of the University of Michigan.

THE PARTICLE EPIDEMIOLOGY REANALYSIS 

PROJECT: OBJECTIVES AND FINDINGS

The overall objective of the Reanalysis Project was to

conduct a rigorous and independent assessment of the

findings of the Harvard Six Cities and ACS Studies of air

pollution and mortality. Phase I: Replication and Validation involved a quality assurance (QA) audit of a sample of

the original data and validation of the original numeric

results. Phase II: Sensitivity Analyses tested the robustness

of the original analyses to alternate risk models and analytic methods.

In Phase I, the Reanalysis assured the quality of the original data, replicated the original results, and tested those

results against alternative risk models of the Cox proportional-hazards family and other analytic approaches

without substantively altering the original findings of an

association between indicators of PM air pollution and

mortality (Krewski et al. 2000a, 2003a).

Phase II of the Reanalysis made innovative contributions to understanding the air pollution–mortality association by developing new methods of spatial analysis for

cohort studies that involve both individual-level and ecologic covariates. Most of the Phase II analysis used the

standard Cox model, which assumes that the probability of

death is independent among subjects. We challenged this

assumption in a number of ways by introducing largely ad

hoc statistical approaches that were developed for this

specific dataset. (In the current Extended Analysis, we

have formalized these statistical models that include

extensions of the standard Cox model to include random

effects at multiple levels of clustering, such as MSA and

ZCA. In addition, we have developed models and estimation methods to allow the random effects to have spatial

structure such that clusters of data that are geographically

close are assumed to be more correlated than those more

spatially distant.)

Key findings from the Reanalysis indicated that (1) educational status significantly modifies the risk of mortality

associated with exposure to PM2.5 in that the risk declines

as education attainment rises; (2) SO2 may exert a more

robust effect on mortality than SO4

2; (3) other possible

ecologic confounders have no significant effect in models

that control for spatial autocorrelation; and (4) spatial risk

models attenuate the air pollution effect, both in terms of

size and certainty.

The implications of the findings for air quality risk management were significant and pointed to the vital need for

further study of the role that ecologic covariates have in

the association between air pollution and mortality.

Although the methods developed in the Reanalysis were

useful for exploring the spatial structure of the data and

the impact of spatial autocorrelation on estimates of risk

associated with exposure to PM2.5, further work was

required to determine how robust the results would be to

more sophisticated spatial models.

GEOGRAPHIC SCALE OF ANALYSIS

As an initial step toward understanding the effects of

ecologic covariates in confounding or modifying the relationship between particulate air pollution and mortality,

the Reanalysis Team first used data at the MSA (city) scale

in order to match the work by the original investigators

(Pope et al. 1995). The Reanalysis demonstrated that several ecologic covariates significantly influenced health

outcomes when incorporated into the standard Cox model

(Krewski et al. 2000a,b). One of the more surprising results

was the lack of confounding effect that ecologic covariates

exerted on the air pollution–mortality relationship in

models that controlled for spatial autocorrelation. For

example, when SO4

2and SO2 were included as ecologic

variables in spatial regression models with PM2.5 as the
8

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

main exposure variable, they were the only ecologic covariates that showed a significant impact at the MSA scale.

The Reanalysis Team next relied on multi-level data

(individual-level and MSA-level covariates) in a two-stage

analysis with a random effects Cox model. The extensive

battery of individual-level variables included in the first

stage may have removed most of the possible confounding

effects before the ecologic covariates were tested in the

second stage. This seems unlikely, however, because of

other compelling studies that point to the importance of

contextual or community-level variables in assessing mortality (Duncan et al. 1996; Curtis and Taket 1996; Macintyre and Ellaway 2000).

Other methodologic limitations in the Reanalysis

Project probably also contributed to the unexpected lack of

statistical effect when the ecologic covariates were incorporated in the models. At the MSA scale of aggregation, for

example, many ecologic covariates may be too dissimilar

across the city for a mean value to represent the socioeconomic or environmental phenomenon of interest (e.g.,

income level) without large measurement error. A growing

number of studies implicate neighborhood-scale ecologic

covariates as confounders of health outcomes (Macintyre

et al. 1993; Macintyre and Ellaway 1998, 2000; Eyles

1999). In many analyses, data gathered on county and

census-tract scales vary within a large MSA more widely

than they do between MSAs (Jerrett et al. 1997, 2001).

Another aggregation issue, referred to as the modifiable

areal unit problem (MAUP), emphasizes the need for

choosing the correct scale because the size and boundaries

of the zones influence the reported values. For example, if

the boundary of an ecologic unit—such as a census block

or a ZCA—includes a neighborhood with a high poverty

level, changing the boundary to exclude that neighborhood would substantially lower the mean poverty level for

the ecologic unit. (This is referred to as the zoning effect.)

An observed spatial pattern might reflect the zone boundaries chosen for analysis rather than a true underlying spatial pattern. Spatially aggregated data are more uncertain

than the individual data on which the aggregations are

based; and an observed pattern may result from artifacts of

aggregation (Fotheringham et al. 2000). Even variables

measured at the same scale may display different spatial

patterns because of the zones chosen for analysis.

Aggregation of data can also produce changes in the

statistical values computed on the variables because

information is lost when individual data are aggregated

into ecologic zones and fewer data are in the model

(Amrhein and Reynolds 1997). (This is referred to as the

scale effect.) The scale effect also suggests that some

changes in statistical results occur because the aggregated

data refer to different levels in the geographic hierarchy

(e.g, states, metropolitan areas, cities, ZCAs) and each

level contains different information about the geographic

variable of interest (Steel and Holt 1996). Each scale can

have a different spatial pattern for mortality as well as for

the ecologic covariates that influence mortality.

To minimize these aggregation problems, some

researchers suggest that the smallest available unit of analysis should be used unless earlier evidence indicates that

larger units will reveal more about the effect in question

(Bailey and Gatrell 1995). In related studies of environmental justice that have investigated whether disadvantaged and minority groups suffer greater pollution

exposure than wealthier and majority groups, empirical

evidence and compelling conceptual arguments suggest

that geographic scale affects the outcome of the analysis

(Greenberg 1993; Cutter et al. 1996; Jerrett et al. 1997;

McMaster et al. 1997). Likewise, some of the observed relationships between air pollution and health may be

reduced or modified by the context of ecologic covariates

measured at scales finer than metropolitan areas or measured at many scales. Further analyses of ecologic covariates in the ACS study at multiple scales would answer

lingering questions about whether these variables exert a

significant influence and would provide important guidance for location-specific air quality management policies.

REFINEMENT OF EXPOSURE ESTIMATES

Previous studies using the ACS database have relied on

comparing between communities the central monitor estimates that assign the same level of exposure to an entire

MSA. Recent studies have recognized that exposure to air

pollution may vary spatially within a city (Briggs et al.

2000a; Jerrett et al. 2001; Brunekreef and Holgate 2002;

Zhu et al. 2002; Brauer et al. 2003), and these variations

may follow social gradients that influence susceptibility to

environmental exposures (Jerrett et al. 2003). For example,

residents of poorer neighborhoods may live closer to point

sources of industrial pollutants or roadways with higher

traffic density (O’Neil et al. 2003). Health effects may be

higher around such sources, and these effects are diminished when using average pollutant concentrations for the

entire community.

Recent studies of PM2.5 have shown that intra-urban

exposure gradients can be associated with atherosclerosis

(Künzli et al. 2005) and a high risk of reduced life expectancy (Jerrett et al. 2005a). Those studies have used geostatistical interpolation models that capture regional patterns of

pollution well, but often fail to account for the near-source

impact from local traffic and industry. Given the large

health effects reported in these and in European studies
D. Krewski et al.

9

(Hoek et al. 2002; Nafstad et al. 2003), estimates of intraurban exposure need to be refined to reduce uncertainties

associated with the modeling processes.

Several recent studies have demonstrated that land-use

regression (LUR) has the potential to supply accurate,

small-area estimates of air pollutant concentrations

without the data entry and monetary expense of dispersion

or exposure modeling (Brauer et al. 2003; Briggs et al.

2000b). The goal of LUR is to explain, to the extent possible, the variation in existing air quality data for a given

pollutant using data on nearby traffic, land use, and population variables. In most cases, multiple linear regression

is used to develop a model with data from existing monitors that can be applied to unmonitored locations if the

appropriate geographic data are available.

Ross and colleagues (2006) developed LUR models

using traffic data, distance to the coast, and road length

measurements to predict NO2 levels in San Diego, California. When the predicted concentrations were compared

with measured concentrations at validation locations—

data from sites that were not included in creating the

model—the values matched to within, on average, 2.1 ppb.

The model explained nearly 80% of the variation in NO2

levels in San Diego. LUR models used to predict NO2

levels using traffic and other variables in Montréal and in

several European cities also produced accurate predictions

(Jerrett et al. 2005b).

In contrast to localized gases such as NO2, PM mass has

a significant regional component that includes smaller

contributions from local sources (Bari et al. 2003). This

complicates the estimation of intra-urban exposure with

LUR. LUR models have been used with some success to

predict PM2.5 concentrations in Europe as part of the

Traffic Related Air Pollution and Childhood Asthma Study

(TRAPCA) (Brauer et al. 2003). However, North American

cities have vastly different transportation and land-use

patterns and the applicability of LUR to predict PM2.5 concentrations is unknown (Gilbert et al. 2005).

LIMITATIONS OF THE RANDOM EFFECTS 

COX MODEL

Although the Reanalysis made progress toward understanding the influence of spatial autocorrelation on the

health effects of SO4

2exposure, the methods used were

criticized on a number of grounds (HEI Health Review

Committee 2000). In particular, all methods assumed that

the relationship between exposure and health outcome

was fixed over space. For example, our spatial filtering

method used a 600-km buffer to remove significant spatial

autocorrelation before we used weighted-least-squares

methods to estimate effects. Even after applying this filter,

though, the relationship between air pollution and mortality may still differ depending on the location within the

United States (known as nonstationarity behavior over

space). A more flexible modeling strategy was needed to

address such nonstationarity.

Furthermore, reliance on one autocorrelation parameter

may have effectively removed variables that operate at the

broad regional scale, such as SO4

2concentrations, but it

may not have controlled autocorrelation from pollutants

that have a more spatially concentrated or local distribution, such as SO2 (HEI Health Review Committee 2000;

Krewski et al. 2000a,b). The inability of the spatial regression methods to deal simultaneously with variables that

exhibit different spatial patterns may have contributed to

the second key finding of the Reanalysis: The effect of SO2

exposure changed less than the effect of SO4

2exposure

when spatial autocorrelation and the ecologic covariates

were accounted for in the model (HEI Health Review Committee 2000). Models capable of adapting to the available

data and accounting for spatial autocorrelation may alter

these results and show that the effects of SO2 and of SO4

2

exposure are equally robust to adjustment; or such models

may further confirm the results from the Reanalysis.

In either case, the implications for policy formulation

and regulatory intervention are considerable. In the Phase

II Reanalysis we developed an approach for Cox models

with two levels of nested random effects (referred to as the

random effects Cox model; Ma et al. 2003). This method

allowed us to characterize the clustering of spatial effects

at two geographic levels. In the Extended Analysis

described in this report, our random effects Cox model

needed to be expanded to fully describe complex spatial

patterns in the ACS data in order to accommodate more

than two levels of geographic nesting (e.g., neighborhood

within county, county within MSA, MSA within state).

POST-REANALYSIS STUDIES OF THE ACS COHORT

After the Reanalysis, Pope and associates (2002) undertook a subsequent analysis using an additional 10 years of

data, which doubled the follow-up time to more than 16

years and tripled the number of deaths (referred to as the

Updated Analysis). Exposure data were expanded to

include gaseous copollutants and new PM2.5 data that had

been collected since 1999 as a result of the NAAQS for

PM2.5 enacted in 1997. Recent advances in statistical modeling were incorporated in the analyses, especially the use

of random effects (relaxation of the assumption of independent observations) and control for spatial autocorrelation.

Results from that Updated Analysis provided the strongest evidence to date that long-term exposure to PM2.5 air
10

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

pollution common to many metropolitan areas is an

important risk factor for death from lung cancer and CPD.

For each 10-μg/m3 increase in long-term average PM2.5

ambient concentrations, the associated risk of death from

all causes, CPD, and lung cancer increased by approximately 4%, 6%, or 8%, respectively. No evidence of statistically significant spatial autocorrelation was found in the

survival data after PM2.5 air pollution and the various individual risk factors were controlled. Graphical examination

of the correlations of the residual mortality with distance

between metropolitan areas also revealed no significant

spatial autocorrelation after controlling residual spatial

patterns in mortality using a smoothed function of latitude

and longitude.

Pope and colleagues (2004) examined pathways by

which inhaled particles may increase CPD deaths in the

ACS cohort, although it is difficult to make empirical

observations from epidemiologic data about possible

mechanistic pathways of disease. The results of that analysis are largely consistent with others: Pathways that link

long-term PM exposure with risk of death from CPD

include pulmonary and systemic inflammation, accelerated atherosclerosis, and altered cardiac autonomic function. Künzli and associates (2005) have published the first

epidemiologic evidence to support the suggestion that the

systemic effects of PM exposure result in a chronic vascular response.

EXPOSURE TIME WINDOWS

Although the Harvard Six Cities Study and the ACS

Study have demonstrated an association between longterm exposure to PM air pollution and mortality (Dockery

et al. 1993, Pope et al. 1995, 2002), none of those studies

provided an indication of whether there may be a critical

time period of exposure responsible for the observed association (Goddard et al. 1995). Investigations by Zeger and

associates (1999) and by Schwartz (2000) have shown that

mortality cannot be attributed entirely to the effects of

short-term peak exposures, which may affect sensitive

individuals with preexisting conditions (Brunekreef 1997;

Goldberg et al. 2000, 2001a, b). During the Reanalysis, we

developed individual temporal exposure profiles for some

subjects in the Harvard Six Cities Study by coding their

residence histories; however, limited data about population mobility and limited variation in individual timedependent exposure profiles precluded identifying critical

exposure time windows (Villeneuve et al. 2002).

Identifying these time windows has important implications for establishing time lines for policy interventions

that will maximize public health benefits. Doing so

requires information on temporal patterns of exposure at

the individual level. Given the regulatory importance of the

results, further work to develop individual time-dependent

exposure profiles for ACS cohort participants is needed.

SPECIFIC AIMS OF THE CURRENT

EXTENDED ANALYSIS

A Phase III study was launched by the Reanalysis Team

in 2002 to conduct an Extended Analysis of the association

between particulate air pollution and mortality in large

U.S. cities using alternative spatial models and extended

follow-up data from the ACS CPS-II database. For the original study (Pope et al. 1995) and the Reanalysis (Krewski et

al. 2000a,b), vital status data were only available for

approximately 7 years of follow-up (through December 31,

1989). The Extended Analysis included vital status data

with multiple cause-of-death codes for approximately 18

years (through December 31, 2000). In addition, more

recent exposure data were compiled based on mean concentrations of air pollutants from various monitoring sites

for the metropolitan areas (Krewski et al. 2000a,b).

The Phase III Extended Analysis program addressed the

following four key questions (the fourth aim was added in

year 2 with supplementary funding):

1. Do social, economic, and demographic ecologic covariates confound or modify the relationship between

particulate air pollution and mortality?

The analysis of ecologic covariates at multiple scales

would provide greater understanding of the potential confounding and modifying effects of these variables.

Although the Reanalysis suggested that these variables are

unlikely to exert a significant confounding influence when

the analysis is also controlled for spatial autocorrelation,

we planned to directly address several unresolved issues:

(a) the scale and spatial boundaries for ecologic covariate

data; (b) nested spatial effects (neighborhood effects

within MSA effects) and (c) operational variables that represent the separate and combined effects of many ecologic

confounders at once. (This aim was pursued in the Nationwide Analysis.)

2. How can spatial autocorrelation and multiple levels

of data be taken into account within the random

effects Cox model?

The standard Cox regression model commonly used to

analyze cohort mortality data is based on the assumption

that individual data are independent. However, in Phase II

of the Reanalysis, spatial autocorrelation (data for neighboring ACS cohort participants are not independent due to

complex spatial patterns) showed that this assumption

was not true. Ignoring such spatial autocorrelation —
D. Krewski et al.

11

applying statistical models that assume all data are independently measured and are not correlated — has important implications about bias and the precision of modelbased estimates of risk. In Phase II, a random effects Cox

model was developed to take into account spatial patterns

in the data that could be described at either one (e.g., city)

or two (e.g., city and county) levels of clustering. Computer software capable of efficiently fitting the random

effects Cox model was also developed. In Phase III, the

random effects model would be extended to include spatial autocorrelation of the random effects at two levels of

clustering. This extended random effects Cox model

would permit us to explore much more complex spatial

patterns in the ACS data and may lead to improved estimates of risk. (This aim was pursued in the Nationwide

Analysis.)

3. What critical exposure time windows affect the association between air pollution and mortality?

The overall objective was to develop individual timedependent exposure profiles for a subset of the ACS cohort

in order to determine which exposure time windows may

be most critical to the association between air pollution

and mortality from all causes, CPD, and lung cancer.

Whereas almost no information on population mobility

was available for Phase II of the Reanalysis, the additional

follow-up data available for this Extended Analysis

included information on residence changes within the

CPS-II Nutrition Cohort (n = 184,194), which was established in 1992 as a subgroup of the larger CPS-II cohort. As

in the Harvard Six Cities Study (Dockery et al. 1993), residence histories would be used to develop time-dependent

exposure profiles by matching residences to particulate air

pollution monitors at the MSA level. The construction of

the time-dependent exposure profiles would make use of

national exposure data (Lall et al. 2004). (This aim was

pursued in the Critical Exposure Time Windows Analysis.)

4. How would refining the exposure gradient to the

intra-urban level affect the size and significance of

health effects?

A growing body of evidence suggests that refining the

scale of exposure estimates and assigning them to cohort

members, especially at the intra-urban scale (within cities),

will elevate estimates of pollutant-related health effects.

For example, Hoek and associates (2002) demonstrated

that CPD mortality risk was nearly twice as high for subjects living near major roads than for those living farther

away; and Nafstad and colleagues (2004) reported an estimated increase in male mortality risk of over 18% across

the gradient of plausible modeled exposures to NO2. These

and similar findings summarized elsewhere (Jerrett et al.

2005b) have demonstrated a need to investigate exposures

at the intra-urban scale within the ACS cohort. (This aim

was pursued in Intra-Urban Analyses for the New York

City and Los Angeles regions.)

NATIONWIDE ANALYSIS

In the Reanalysis (Krewski et al. 2000a,b), we determined values of the ecologic covariates only at the MSA

level and did not consider the spatial distribution of ACS

cohort members within each MSA. In the current analyses,

we determined ecologic covariate values at the ZCA level,

which is much smaller geographically than the MSA and

thus may be more representative of the economic and

social environment of the cohort members. 

The cohort follow-up is from 1982 to 2000, thus adding

11 additional years of follow-up to our previous analyses

of these data for which we examined follow-up from 1982

to 1989 (Krewski et al. 2000a,b).

Finally, we extended our random effects Cox model,

which we had introduced in the Reanalysis (Krewski et al.

2000a,b), to include spatial autocorrelation on the random

effects themselves. This more realistic stochastic model

specification allowed us to examine the spatial correlation

structure of mortality within the cohort and to assess the

sensitivity of the association between air pollution and

mortality to the spatial structure of the data.

MATERIALS AND METHODS

Study Population

The original ACS study (Pope et al. 1995), the HEI-sponsored Reanalysis (Krewski et al. 2000a,b), and the

Extended Analysis reported here all have relied on data

from the ACS CPS-II database, an ongoing prospective

mortality study of approximately 1.2 million adults. 

Cohort participants were enrolled by ACS volunteers

beginning in the fall of 1982; most were friends, neighbors,

or acquaintances of the volunteers. Enrollment was

restricted to persons who were at least 30 years of age and

who were members of households with at least one individual 45 years of age or older. Participants completed a

confidential questionnaire that included questions about

several demographic characteristics, including lifestyle

factors such as smoking history and alcohol use, occupational exposures, and level of education.

Participants resided in all 50 states, the District of

Columbia, and Puerto Rico. For all analyses, however, the

cohort has been restricted to include only those who

resided in U.S. metropolitan areas within the 48 contiguous states (including the District of Columbia) for which
12

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

air pollution data were available. The number of metropolitan areas to be analyzed differs depending on the pollutant of interest, available data, time period, and the

quality control criteria used to compile the data.

Mortality of the participants was ascertained by volunteers in 1984, 1986, 1988, and biannually thereafter using

the National Death Index (Calle and Terrell 1993). At each

time point, death certificates or multiple cause-of-death

codes were obtained for participants known to have died.

This Extended Analysis, with an additional 11 years of

follow-up, contributes substantially more data on deaths

and thereby enhance the statistical power of the analyses.

Air Pollution Exposure Data

For Phase II of the Reanalysis and this Extended Analysis in Phase III, several air pollution variables were examined; data were obtained from these sources (Krewski et al.

2000a,b).

• PM2.5 (1979–1983) average concentrations from the 

Inhalable Particle Monitoring Network (IPMN) 

between 1979 and 1983;

• PM2.5 (1999–2000) average concentrations from the 

Aerometric Information Retrieval System (AIRS) network from 1999 to 2000;

• PM15 (1979–1983) average concentrations from the 

IPMN between 1979 and 1983;

• TSP (1980) (total suspended particulate) mass from 

the National Aerometric Database (NAD) for 1980;

• SO4

2(1980–1981) concentrations from the IPMN and 

NAD (adjusted for a sampling artifact) for 1980 and 

1981;

• SO4

2(1990) concentrations computed by part of our 

team at NYU for 1990;

• SO2 (1980) concentrations from AIRS for 1980;

• NO2 (1980) concentrations from AIRS for 1980;

• CO (1980) concentrations from AIRS for 1980;

• O3 (1980) concentrations from AIRS for 1980; and

• O3 (1980 summer) concentrations from AIRS for 

April–September, 1980.

Having estimates of PM2.5 concentrations for both 1979–

1983 and 1999–2000 allowed us to compare exposure at

the start and end of the follow-up years. Both annual and

summertime O3 (ozone) levels were estimated because O3

is generally higher in warm months when people spend

more time outdoors or with windows open and thus have

higher exposure. Furthermore, in many cities O3 is monitored only from April to September.

For any pollutant, the average concentration calculated

from all available monitoring data within each MSA was

used as a summary measure of exposure and assigned to

each subject within the MSA. Thus all subjects residing in

an MSA were assigned the same exposure value.

Ecologic Covariates

Data Collection and Assembly A major component of

this research dealt with improving analytic control of confounding variables over time and across space, particularly

at the intra-urban scale. We obtained information about

ecologic covariates at the neighborhood scale for approximately 12,000 ZCAs listed in the 1980 U.S. Census database to amass one of the largest sets of ecologic covariates

data ever assembled for an air pollution or population

health study. These ZCAs covered our MSAs of interest —

the 156 cities used for the Reanalysis Project (Krewski et

al. 2000a,b) and in the Updated Analysis by Pope and associates (2002).

Compilation of these data for ecologic covariates

required that we completely recheck the coverages used

for the Updated Analysis by Pope and colleagues in 2002

using the ArcView 9 (2004) Geographic Information

System (GIS) software because some census definitions

have changed during intervening years.

Identifying ZCAs To collect intra-urban ecologic covariate data, we purchased the complete 1980 census database from the U.S. Census Bureau (USCB), which includes

Zip Code tabulation. This census contains data that have

not been in subsequent census years. Of particular interest

were variables such as the proportion of housing units

with air conditioning. These two variables may influence

indoor exposures (especially particle penetration) and

may confound the air pollution–mortality association if

left uncontrolled. Moreover, because the lowest level of

geographic identity available for ACS respondents was the

Zip Code of residence, we had to rely on aggregating data

at the ZCA level for neighborhood analyses. None of the

commercial vendors who process historic census data

could provide data by Zip Code.

To facilitate finding and extracting data from the USCB

database, we developed a relational database management

system format and a program that would convert the flattext USCB file into a relational database. The program was

developed using Microsoft Visual Basic to load the USCB

file, store it in a logical format analogous to the schema of

the database, and output the data of each logical table to

the appropriate table in a Microsoft Access database. From

this convertedUSCB.file, we extracted data for most of the

ecologic covariates for approximately 12,000 ZCAs that
D. Krewski et al.

13

cover the 156 MSAs for which we had both pollution and

ACS data.

Extracting the ecologic data presented considerable

challenges. The most significant obstacle was the incongruity of spatial designations between the United States

Postal Service (USPS) and Census Bureau. Zip Codes are

defined by the USPS for delivering mail, not for collecting

and analyzing the socioeconomic data the census requires.

Zip Codes lack definitive boundaries and change frequently at the discretion of postal officials. They do not

conform to boundaries of standard geographies such as

counties, cities, or census units. Likewise, the Census

Bureau does not have maps or digital files that show the

boundaries of Zip Codes and they have no file that relates

MSAs to Zip Codes for any time period.

We needed to find maps to integrate both sets of boundaries so we could compare the positions of each Zip Code’s

exact geographic center; we preferred to use maps from

1980, close to the inception of the ACS study (in 1982)

when participants’ residences were registered with Zip

Codes. We contacted, without success, at least six organizations to obtain digital maps of the 1980 Zip Code boundaries: the U.S. Postmaster General, the USCB, Quick Data,

Environmental Systems Research Institute (ESRI), the U.S.

Geological Survey (USGS) time-series database for

watersheds, and the University of Michigan census Web

site. We also contacted colleagues at George Washington

University who have direct access to the USCB.

Various companies have created maps by interpolating

boundaries between Zip Codes. However, the companies

we contacted could not provide what we needed: GDT

(Lebanon, NH) did not have data available for 1980 (personal communication with Norm Finkelstein, March,

2003); and GeoLytics Inc. (East Brunswick, NJ) produced a

1980 Census CD but without the data we needed (personal

communication with Pat Deluca, June, 2003). Neither the

USPS Web site nor the ESRI Web site had maps; and the

map librarians at McMaster University and the University

of Waterloo (both in Ontario, Canada) had nothing we

could use. (McMaster University does have a 1980 U.S.

Gazetteer book, but the resolution is not very good.)

Despite so many obstacles, out of about 12,000 Zip

Codes in which CPS-II participants lived, we were ultimately successful in matching about 10,000 with spatial

boundaries consistent with USCB data.

We used a similar process for the Los Angeles area; the

ACS data showed that participants resided in 373 ZCAs.

When we compared the ACS ZCAs with the USCB Zip

Code data, we found that only 275 of them appeared in our

USCB file for California (convertedUSCBforCA.file). To

identify the discrepancies between the ACS and USCB

lists, and to consider any impact on the validity of the

data, we created a computer query to match the 373 ZCAs

in the ACS table with the Zip Codes in census records contained in convertedUSCBforCA.file. This query produced a

data set that lists the 373 ACS ZCAs along with the corresponding Zip Codes from convertedUSCBforCA.file.

This analysis showed that 98 of the 373 ACS ZCAs do

not appear in the convertedUSCBforCA.file. To assess how

excluding participants in these 98 Zip Codes would affect

the validity of further analyses based on converted USCB

.file, we used the USPS “Zip Code Lookup” tool online. By

checking the status of each of the missing 98 Zip Codes, we

found the following:

• 50 could not be found in the USPS database.

• 36 were described as Post Office [PO] Boxes.

• 3 were described as UNIQUE (serving a discrete building or installation).

• 9 were described as STANDARD (serving a collection 

of buildings or homes found in the network of streets 

the Zip Code represents).

We excluded most of these missing ZCAs because we

surmised they probably did not contain residential

addresses. If the missing ZCAs are randomly distributed,

excluding any or all of the cohort members who live in the

98 missing Zip Codes should not adversely affect our

results because we are already using a non-random sample

that has been arbitrarily cut down to half its original size

due to the availability of pollution data. Using the ACS

participants residing in 80% or more of the original ACS

Zip Codes, we still had ample data to detect effects and, as

shown in our subsequent analyses, we did not lose many

ACS subjects.

A further complication in reconciling ZCAs with census

data was the imperfect correspondence between where

people live and where they get their mail. Some people

live in rural areas where there is no mail delivery and they

collect mail at a post office in a nearby town. The boundaries of such PO Box Zip Codes (about 5,000 of them) are

not formally defined. Some urban residents pick up mail at

a PO Box, perhaps near their work place, and reside in one

Zip Code but receive mail in another.

Extracting Ecologic Covariate Data by ZCA Next we

focused on compiling data for the ecologic covariates for

analysis. For this study we limited the ecologic covariates

to those that had been found to be important as predictors

in the Reanalysis (Krewski et al. 2000a,b; also see Ecologic

Covariates sidebar):

• median household income;
14

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

ECOLOGIC COVARIATES

Ecologic covariates are variables or factors known or suspected to influence mortality that represent the social, economic, and environmental settings (“contextual conditions”) at 

community and neighborhood levels where individuals live, 

work, or spend time.

These data are typically collected for the United States Census 

and were extracted from the U.S. Census Bureau 1980 database for the Zip Code areas in which participants lived.

Air Conditioning

Percentage of homes with air conditioning 

Availability of air conditioning is a good proxy for type of home 

construction; buildings with air conditioning typically have a relatively low level of infiltration of outdoor air into the structure.

Grade 12 Education

Percentage of adults with less than a grade 12 education

Nationwide Analysis — three stages: less than high school, finished high school, high school plus more

New York and Los Angeles Analyses — two stages: grade 12 

completed or not

Ethnic/Racial Identification

Self-reported identification of ethnic/racial group 

Nationwide Analysis — percentage not white

New York Analysis — percentage white

Los Angeles Analysis — percentages for white, black, and Hispanic

Unemployment

Percentage of persons over the age of 16 years who are 

unemployed

Household Income 

Median household income

Reported as $000s (U.S. dollars)

Income Disparity

A measure of the inequality or equality of income distribution 

within neighborhoods and cities 

Reported as the Gini coefficient

Poverty

Percentage of people with income # 125% of the poverty level

(The Los Angeles Analysis included total population instead of 

poverty.)

• poverty: percentage of people with  125% of poverty-level income;

• unemployment: percentage of persons over the age of 

16 years who are unemployed;

• education: percentage of adults with less than a grade 

12 education;

• percentage of homes with air conditioning;

• income disparity as evaluated by the Gini coefficient; 

and

• percentage of the population who are not white 

(according to self-reported information).

Because we were concerned that comparing ZCA characteristics between cities does not fully control for ecologic confounding, we also created two other variables to

include in the Cox models. The first was to aggregate,

within an MSA, all the ZCAs in which ACS subjects lived

to obtain an average estimate (MSA mean) of the ecologic

covariates of interest. The second was to differentiate the

MSA mean from the respective ZCA-specific values. The

difference value (DIFF) for a ZCA is the specific value in

each ZCA minus the MSA mean. This DIFF value ensured

that comparisons could be made for local communities,

where the ecologic covariates were most likely to be interpretable because local factors that affect the comparisons

(such as cost of living, housing conditions, economic

opportunities) may be similar.

STATISTICAL METHODS AND DATA ANALYSIS

To examine the association between ambient concentrations of air pollutants and mortality on a national scale, we

initially used the standard Cox model to link pollution

levels to survival, and adjusted for potentially confounding risk factors. We included data from the CPS-II

cohort questionnaires for 44 individual-level covariates:

• eight variables to represent active smoking habits 

including nonlinear terms for cigarettes per day and 

number of years smoked;

• seven variables to characterize former smoking habits;
D. Krewski et al.

15

• one variable for exposure to passive smoke;

• two variables to represent marital status;

• two variables to represent linear and squared terms for 

body mass index;

• six variables to characterize consumption of beer, 

wine, and other alcohol;

• seven variables to characterize the subject’s main lifetime occupation and his or her possible exposure to 

PM in the workplace;

• one variable to represent self-reported exposure to 

dust and fumes in the workplace;

• eight variables to represent diet; and

• two variables to characterize level of education.

We also examined the seven ecologic covariates measured at the five-digit ZCA scale. The baseline HR was

stratified by 1-year age groups, gender, and race. This standard Cox model assumes that all observations are statistically independent, an assumption that was relaxed in

subsequent analyses.

However, it is possible that survival may cluster by community or neighborhood. That is, longevity among subjects

within the same community or neighborhood may be more

similar than longevity between these subjects and those in

different geographic locales; this would remain true even

after controlling for all known and available personal risk

factor information, such as smoking habits, diet, education, and occupation. Furthermore, subjects who live close

together typically share similar longevity patterns. Lack of

statistical control for these factors can bias both the estimate of air pollution’s effect on health and the associated

standard errors.

To characterize the statistical error structure of survival

data, statistical methods and computer software that incorporate two levels of spatial clustering (e.g., MSA, and ZCA

within MSA) have been developed. At each of the two

clustering levels we incorporated a spatial autocorrelation

structure such that the correlation in survival after

adjusting for known risk factors is dependent on the distance between clusters. This distance can be defined in

ordinary units (kilometers or miles) or as adjacency, or it

can be based on other notions of distance in economic or

social terms. The association between concentrations of

ambient air pollutants and survival can be examined at the

spatial scales both between MSAs and between ZCAs

within an MSA, thus permitting a simultaneous exposure

assessment of health effects at the macro and micro level.

The Random Effects Cox Model

The standard Cox model, proposed by Sir David Cox

(1972), assumes that the survival times for individuals are

statistically independent. In our earlier Reanalysis of the

ACS cohort, we found evidence of spatial autocorrelation

in the data (Krewski et al. 2000a,b) that needed to be considered in the current Extended Analysis. Ma and

coworkers (2003) developed a modification of the standard

Cox model that incorporates random effects to represent

spatial patterns in the data (the random effects Cox model),

and established large-sample properties of the maximum

likelihood estimates of the model parameters.

Here, we consider a Cox model with two levels of spatially

correlated random effects (e.g., MSA, and ZCA within

MSA). Suppose that the cohort of interest is composed of

m spatially correlated clusters indexed by i. Within the ith

cluster, there are Ji spatially correlated subclusters indexed

by (i,j). Specifically, we assume that the cluster-level

random effects U1, ..., Um are positive random effects with

expectation and covariance

(1)

where 2 is the MSA-level variance of the random effects,

0 < 1 < 1, and d(s,i) indicates the distance between clusters indexed by s and i. This distance between two independent clusters is defined as d(s,i) = . Negative ρ1 can be

estimated if the distances are integers.

We further assume that, given the cluster-level random

effects U* = u* = (u1, ..., um), the subcluster-level random

effects U11, ..., UmJm are positive and spatially dependent

with

, (2)

where 2 is the ZCA-level variance of the random effects, 0

< 2 < 1, and r[(i,t),(i,j)] indicates the distance between subclusters indexed by (s,t) and (i,j). The Kronecker notation

(s,i) is 1 if s = i, and 0 otherwise. In addition, the conditional distribution of Uij, given is assumed to

depend on ui only.

Furthermore, within each subcluster (i,j) there are nij

individuals. Suppose that the cohort is stratified on the

basis of one or more relevant covariates and these strata are

indexed by s = 1, 2, ..., a. The (i,j,k) denotes the kth subject

within the jth subcluster within the ith cluster. The (i,j,k)

and s notations do not imply any fixed relationship

between strata and clusters; for example, males and females

2 () E( ) = 1 and Cov( ) = , 1, , dsi U UU i si  

E( | ) = and 

Cov( | ) = ( )

*

2

2

[(

UU U

U U U si

ij i

st ij

r s , , *,   t ij ),( )] ,

= , U u * *
16

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

could correspond to different strata, whereas clusters and

subclusters could be communities and families that

include both males and females. Let the hazard function

for individual (i,j,k) from stratum s at time t be denoted by

	ijk(s)

(t). Given the random effects, we assume that the individual hazard functions are conditionally independent,

with

	ijk(s)

(t) = 	0

(s)

(t)uij exp(
Txijk(s)). (3)

The survival times, either observed or censored, are spatially correlated. The distribution of random effects is

assumed to not depend on the regression parameter 
.

Without loss of generality, we assume that the design

matrix X = (x111(1), ..., x(a)

mJmnmJm)

T is of full rank. A

random effects Cox model with a single level of spatially

correlated random effects is obtained as a special case of

the Cox model with two levels of random effects by setting

2 = 0 and Ji = 1 for all i.

Our assumptions (1) and (2) on random effects concern

the spatial dependence and the first two moments only.

This is desirable since the mechanism by which the unobserved random effects were generated is usually not completely known (Ma et al. 2003).

Auxiliary Random Effects Poisson Models

As in Ma and associates (2003), we make inferences on

the random effects Cox models by fitting random effects

Poisson models. Let denote the distinct failure

times in the sth stratum, with msh (s = 1, ..., a; h = 1, ..., qs)

indicating the multiplicity of failures occurring at time

sh(s = 1, ..., ; h = 1, ..., qs). The risk set at time sh is a

subset of stratum s: = [(i,j,k) : tijk  sh], where tijk is

the observed survival time for individual (i,j,k) from the

sth stratum. In addition, let Yijk,h(s) be 1 if a failure occurs

for individual (i,j,k) from the sth stratum at time sh and 0

otherwise. Let Y and U denote the vectors of the Yijk,h(s)

and the random effects Ui and Uij, respectively. Given the

random effects U = u, Peto’s version of the conditional partial likelihood (Cox and Oakes 1984) is 

(4)

We now define an auxiliary random effects Poisson

model. Assume that the components of Y are conditionally

independent, given random effects U = u, with Po (the

Poisson likelihood)

(5)

Given the random effects, the conditional likelihood for

the random effects Poisson model is

(6)

Since the random effects vector does not depend on the

regression parameter vector, as in Ma and colleagues

(2003), we can show that

regardless of the covariance structures assumed for

random effects. The term in braces on the right-hand side

does not depend on the parameters of interest. This demonstrates that the maximum joint Poisson likelihood estimators for the regression parameter vector 
 from equation

(6) are the maximum joint partial likelihood estimators for

the regression parameter vector 
 from equation (4). In

addition, the nonparametric estimator of the cumulative

baseline hazard function remains the same as given in Ma

and associates (2003).

Orthodox, Best-Linear, Unbiased Predictor Approach

Prediction of Random Effects As in Ma and colleagues

(2003), we can predict the random effects by the following

orthodox, best-linear, unbiased predictor of U given Y.

Û = E(U) + Cov(U,Y) Cov1(Y,Y) (Y E[Y]), (7)

where Cov(Y,Y) denotes the marginal covariance of Y

instead of the conditional covariance of Y given U. This is

the linear unbiased predictor of U given Y, which minimizes the mean squared distance between the random

effects U and their predictor within the class of linear

functions of Y.

Unlike the case of nested random effects in Ma and associates (2003), the explicit expression for the inverse of

1, ..., s sqs  

R( ) sh

p

ijk ij

Y

ijk

T Y

Y u

u x sh

ijk h i

s

jk h





 

;

exp 

|

, ,

, ,

( ) =

( ) ∈ R { } ( ) ( )

( )




s

sh

sh

s m

u x

sh

ij ijk

T

ijk

m

h

q

s

a

( )

( )

{ } ( ) ∈ R ( ) ( ) = = ∑

∏ ∏

!

, , exp 
  1 1

 .

Y Po u x i j k ijk h

s

ij sh

T

, jik sh

( ) ~ [ exp( )] ( , , ) ( ) 
  + ∈R . 

 


 
 

, |

exp , ,

,

;

 sh

Y u

u x ijk ij

Y

sh ijk

T Y ijk h

s

( ) =

{ } ( ) + ( ) ∈ R( )

( )


 ijk h

s

sh

u x ij sh ijk

T

ijk

h

qs

,

exp exp , ,

( )

{ } ( ) + ( ) ∈ R( ) = ∑

∏ .

  
  s 1

a

=

∏

1

ˆ,ˆ , exp

! 
 
 ;Y U ˆ m m

m

sh

m

sh

h sh

q

s

a

p

s sh

( ) = ⎧ ( ) − ⎨

⎪

⎩⎪

⎫

⎬

⎪

= = ⎭⎪ ∏ ∏1 1

( ; , Y U, )
D. Krewski et al.

17

Var(Y) is no longer available with spatially correlated

random effects. We therefore compute the random effects

predictor of U given Y shown in equation (7) through

numerically inverted Var(Y). However, because the order

of covariance matrix Var(Y) includes all ACS data of air

pollution and mortality between 1982 and 2000, the size

and amount of data in this matrix is exceptionally large (on

the order of over 47 million). Since the matrices Cov(Y,Y)

and Cov(U,Y) are dense, even the computer memory

required to process these matrices may create a serious

problem when analyzing the ACS study data.

To facilitate the computation, we have derived the following sparse representations for Cov(U,Y) and Cov(Y,Y)

after some algebra:

Cov(U,Y) = Cov(U,U)BT and 

Cov(Y,Y) = diag(E[Y])+ B Cov(U,U)BT, 

(8)

where diag(E[Y]) denotes the diagonal matrix with E[Y] on

its diagonal. The matrix B is a sparse matrix of the same

order as that of Cov(Y,U) = Cov(U,Y)

T in which column i of

B corresponds to cluster i. The elements of column i of B

are zeros except being μijk,h(s)

 = exp(sh + 
Txijk(s)) at the

positions corresponding to those of Cov(Yijk,h(s),Ui) in the

matrix Cov(Y,U).

These sparse representations not only make the amount

of computer memory feasible, but also make inverting

Cov(Y,Y) possible as follows. Let matrices diag(E[Y]) and

Cov(U,U) be denoted by A and D; we have

Cov1(Y,Y) = (A + BDBT)

1

= A1 A1B(BTA1B + D1) 1BTA1,

where A1 = diag(E[Y])1 and D = Cov(U,U) is generally

small enough to be inverted numerically. In fact, there is a

similar sparse representation of Cov(U,U); therefore the

inverse of Cov(U,U) can be obtained through inverting

numerically the much smaller covariance matrix of

cluster-level random effects. (See Appendix B for the algorithm description and Appendix C for the computer program; both are available on the HEI Web site).

The mean squared distances between the random effects

U and its predictor can now be evaluated through the following equation:

Cov(Û U,Û U) = 

Cov(U,U) Cov(U,Y) Cov1(Y) Cov(Y,U).

In addition, we have the following two desirable orthogonality properties concerning the orthodox best linear unbiased predictor:

Cov(Û U,Û) = 0 and Cov(Û U,Y) = 0.

Estimation of Regression Parameters Consider the first

estimation of the regression parameters in the case of

known dispersion parameters.

As in Ma and colleagues (2003), we can estimate the

regression parameters through an optimal estimating function. Differentiating the joint log-likelihood of the auxiliary model for the data and random effects yields the joint

score function. By replacing the random effects with their

predictors, we have an unbiased estimating function for

the regression parameters  = (T,
T)

T:

(9)

The sensitivity matrix S() and the variability matrix

V() are defined by

According to Ma and associates (2003), we have the following global matrix expression for estimating function

() because their proof holds regardless of the covariance

structure assumed for random effects:

() = XT diag(E[Y]) Cov1 (Y,Y) (Y Y). (10)

Similarly, we have

S() = V()

= 0 XT diag(E[Y]) Cov1(Y,Y) diag(E[Y])X. (11)

With an appropriate partition of matrix

C = XT diag(E[Y]) Cov1(Y,Y) = (C1, ..., Cm),

it follows from (10) that

  



( ) = − { } () () () ()

( ) ∈ R( ) =

 ∑

 sh

Y U ijk h

s

ij ijk h

s

h ijk

, ,

, ,

ˆ

1

q

s

a s

∑ ∑ =1

.

S E

V E

T

T

  



  





( ) = ∂ ( )

∂

⎧

⎨

⎪

⎩⎪

⎫

⎬

⎪

⎭⎪

( ) = { } () ()

,

.

   ( ) = − ( ) = ( ) = =

∑ ∑ C Y Yi i i

i

m

i

i

m

E[ , ]

1 1
18

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

where the unbiased estimating function i() = Ci(Yi 

E[Yi

]) corresponds to the ith cluster. The estimating function can easily be shown to be optimal in the

sense that it attains the minimum asymptotic covariance

for the estimator among a certain class of linear functions of Y (Crowder 1986, 1987).

The solutions of the estimating equation 

provide estimators of the regression parameters; however,

the unbiased estimating functions i(), ..., m() are no

longer independent because of spatial dependence. Under

mild regularity conditions, the component-wise asymptotic normality of parameter estimator can be shown (He

and Shao 2000; Chen and Shao 2004). Specifically, for any

constant vector of appropriate dimension b, bT is asymptotically normal with asymptotic mean bT and asymptotic

variance given by bTS1()b as m .

A modification of the Newton scoring algorithm introduced by Jørgensen and colleagues (1996) can be used to

solve this estimating equation: In the modified algorithm, the derivative of is replaced

by its expectation S(γ) and produces the following updated

value for γ:

γ* = γ S1(γ)(γ).

The computation of S(γ) can be realized through equation

(11).

Estimation of Dispersion Parameters When dispersion

parameters are unknown, we use moment estimates for the

dispersion parameter with bias correction to give an unbiased estimating function for dispersion parameters. (The

detailed process of estimating dispersion parameters is

given in Appendix B, which is available on the HEI Web

site.) Unlike other reported approaches, the asymptotic

variance of our regression parameter estimator is not

affected by variability in the dispersion parameter estimators because our estimation function is insensitive to dispersion parameters (Jørgensen and Knudsen, 2004).

The computational procedures regarding initial values

and iteration steps are exactly as outlined in Ma and associates (2003).

RESULTS

Distributive statistics for the pollutants are given in

Table 1. Of special note is the decline in PM2.5 concentrations

over 20 years. The mean PM2.5 measured in 58 MSAs

between 1979 and 1983 was 21.2 μg/m3; when measured in

116 MSAs between 1999 and 2000, it had declined to

14.0 μg/m3.

Descriptive statistics for the seven ecologic covariates are

given in Table 2. Ecologic covariate data were missing for a

small percentage (< 5%) of ZCAs due to small population

sizes, for which the USCB randomly assigned values.

We initially conducted a screening analysis using the

standard Cox model that assumes observations are independent. We then selected only those relationships between a

pollutant and a cause of death that were statistically significant (P < 0.05; 95% CIs) and conducted further analyses

using our random effects Cox model. We chose this two-step

approach because the standard Cox model can be performed

in a few minutes, whereas our random effects Cox model

with two cluster levels requires approximately one day to

calculate each pollutant–outcome relationship.

The HRs associated with specific changes in air pollutant concentrations or with the scale (MSA) of the ecologic covariates are presented in Table 3 for all, CPD, IHD,

lung cancer, and all other causes of death. The number of

subjects and MSAs are included.

Positive and statistically significant associations between both measures of PM2.5 mass and both measures of

SO4

2were observed for all, CPD, and IHD causes of death

(Table 3). Significant but weaker associations were observed

with these same causes of death for the particle mass measurements of larger sizes of particles (PM15 and TSP) and for

the concentration of SO2.

Lung cancer deaths were more strongly associated with

both measures of PM2.5 mass than with other pollutants.

All other causes of death tended to be negatively correlated with air pollutants except for both measures of SO4

2

and SO2, which all displayed a positive association.

Annual average O3 concentrations were not clearly

linked with mortality; but a positive and statistically significant association was observed between O3 measured in

the April-to-September period and both all and CPD

causes of death; no association was observed between O3

and IHD deaths. Neither NO2 nor CO was strongly associated with any cause-of-death category.

Overall, most ecologic covariates were individually associated with mortality. Increases in the percentage of homes

with air conditioning within the ZCA were negatively associated with mortality for all causes of death examined

except lung cancer. Unemployment and poverty levels

were positively associated with all five causes of death;

household income was negatively associated with all five;

and income disparity was not associated with any cause.

The correlations between the seven ecologic covariates

and air pollutants, except for CO and annual O3 (which

were not related to mortality), are given in Table 4. These

correlations were determined at the ZCA and MSA scales.

Pollutant levels were determined at the MSA scale and

assigned to each participant in the MSA. Ecologic covariate

  i i

m ∑ = ( ) = 1 0

ˆ

ˆ   i i

m ∑ = ( ) = 1 0

ˆ

ˆ

→ ∞

  i i

m ( ) = ∑ =1 0.

  i i

m ( ) = ∑ =1 0.
D. Krewski et al.

19

Table 1. Distribution of Air Pollutants at the Individual Levela

Pollutant Monitoring 

Periodb

MSAs /

Participants

(n) Mean Variance

Percentiles

0 5 10 25 50 75 90 95 100

PM2.5 (1979–1983)

(μg/m3)

58

351,338

21.20 21.40 10.77 13.73 14.91 17.85 21.69 24.12 26.75 27.89 30.01

PM2.5 (1999–2000) 

(μg/m3)

116

499,968

14.02 9.12 5.80 8.80 10.20 11.80 14.40 16.00 17.90 20.00 22.20

SO4

2(1980–1981)

(μg/m3)

147

572,312

6.54 7.86 1.40 2.69 2.86 4.36 6.45 8.26 10.71 11.03 15.64

SO4

2(1990) (μg/m3) 52

268,336

6.17 3.85 1.96 2.38 2.86 4.87 6.82 7.37 8.39 8.79 10.65

SO2 (1980) (ppb) 115

513,450

9.71 23.65 0.02 1.62 3.24 6.61 9.60 12.33 15.13 18.78 29.32

PM15 (1979–1983)

(μg/m3)

57

345,824

59.70 110.9 34.23 43.77 46.79 51.56 61.65 66.95 73.38 73.88 100.8

TSP (1980) (μg/m3) 152

578,704

68.37 287.0 41.93 49.14 50.66 56.75 64.81 72.39 90.55 106.9 126.5

O3 (1980) (ppb) 118

531,826

22.91 21.46 10.40 15.06 18.30 20.61 22.46 25.27 27.96 31.24 41.14

O3 (Summer 1980) (ppb) 118

531,185

30.15 40.91 11.73 17.05 22.75 26.75 30.67 32.71 37.30 40.66 56.36

NO2 (1980) (ppb) 76

406,917

27.90 85.25 7.75 14.59 15.88 23.12 26.06 33.71 37.14 51.06 51.06

CO (1980) (ppm) 108

508,538

1.68 0.43 0.19 0.75 0.94 1.17 1.72 2.13 2.58 3.05 3.95

a

 The mean MSA concentration of a pollutant was determined by averaging data from all monitors within the MSA. The mean was then assigned as the 

exposure level to each participant in the MSA. The subject-specific distribution of PM2.5 concentrations is shown.

b Dates are the monitoring years from which data averages were derived. Both annual and summer data for O3 are given because O3 concentrations are higher 

in warm seasons; people tend to be more exposed because they spend more time outdoors or with windows open. Also, many cities monitor O3 only in the 

warm months.

Table 2. Distribution of Ecologic Covariates Based on 1980 U.S. Census Data and Determined at the ZCA Scale

Ecologic

Covariate

Participants

(n) Mean Variance

Percentiles

0 5 10 25 50 75 90 95 100

Air conditioning (%) 571,643 61.0 26.8 0 9.8 20.7 42.1 65.8 85.5 90.1 97.5 100

Grade 12 education (%) 571,351 51.9 8.2 0 36.5 40.5 47.2 53.2 57.7 61.4 63.2 100

Not white (%) 571,485 10.7 16.1 0 0.5 0.8 1.8 4.7 11.8 28.0 45.0 100

Unemployment (%) 571,361 11.6 3.1 0 7.1 8.0 9.7 11.4 13.3 15.5 17.0 60.0

Median household 

income ($000s)

571,206 20.4 6.4 2.5 11.8 13.2 15.9 19.6 23.7 28.5 32.1 75.0

Income disparity (Gini) 571,197 0.4 0.04 0 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.6

Poverty (%) 571,494 11.6 8.1 0 2.2 3.3 5.9 9.6 15.2 22.1 27.3 41.4
20

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

values were determined at the ZCA scale and assigned to

each participant in the ZCA. Ecologic covariate values for

all subjects in ZCAs were averaged to arrive at the MSA

value.

The correlations between pollutants and ecologic covariates vary considerably. For example, for grade 12 education, the correlation with both measures of PM2.5 mass and

with both measures of SO4

2is negative at both the ZCA

and MSA levels, whereas the correlation is positive with

PM15 and TSP. Although the correlations tended to have

the same sign at both the ZCA and MSA scales, the MSAbased correlations tended to be more extreme than the

ZCA-based correlations because the same pollutant concentration had been assigned to all ZCAs within an MSA.

Table 3. HRs of Pollutants and Ecologic Risk Factors for Selected Causes of Death from the Standard Cox Modela

Covariate

MSA / 

Participants

(n)

Incremental 

Changeb

All

Causes

Cardiopulmonary IHDLung

Cancer

All Other 

Causes

PM2.5 (1979–1983) 58

351,338

10 μg/m3 1.03

(1.01–1.04)

1.06

(1.04–1.08)

1.12

(1.09–1.16)

1.08

(1.03–1.14)

0.98

(0.96–1.00)

PM2.5 (1999–2000) 116

499,968

10 μg/m3 1.03

(1.01–1.05)

1.09

(1.06–1.12)

1.15

(1.11–1.20)

1.11

(1.04–1.18)

0.97

(0.94–1.00)

SO4

2(1980) 147

572,312

5 μg/m3 1.04

(1.03–1.05)

1.04

(1.02–1.05 )

1.06

(1.04–1.08)

1.05

(1.02–1.09)

1.03

(1.02–1.05)

SO4

2(1990) 52

268,336

5 μg/m3 1.07

(1.05–1.09)

1.06

(1.03–1.09)

1.14

(1.10–1.19)

1.04

(0.97–1.11)

1.08

(1.05–1.11)

SO2 (1980) 115

513,450

5 ppb 1.02

(1.02–1.03)

1.02

(1.01–1.03)

1.04

(1.02–1.05)

1.00

(0.98–1.02)

1.02

(1.02–1.03)

PM15 (1979–1983) 57

345,824

15 μg/m3 1.01

(1.00–1.02)

1.03

(1.02–1.05)

1.06

(1.04–1.08)

1.00

(0.97–1.04)

0.99

(0.97–1.00)

TSP (1980) 152

578,704

15 μg/m3 1.00

(1.00–1.01)

1.01

(1.01–1.02)

1.01

(1.00–1.01)

0.98

(0.97–1.00)

0.99

(0.99–1.00)

O3 (1980) 118

531,826

10 ppb 1.00

(0.99–1.01)

1.01

(1.00–1.03) 

1.01

(0.98–1.03)

1.00

(0.96–1.04)

0.99

(0.97–1.00)

O3 (Summer 1980) 118

531,185

10 ppb 1.02

(1.01–1.02)

1.03

(1.02–1.04)

1.01

(0.99–1.02)

0.99

(0.96–1.02)

1.01

(1.00–1.02)

NO2 (1980) 76

406,917

10 ppb 0.99

(0.99–1.00)

1.01

(1.00–1.02)

1.02

(1.00–1.03)

0.99

(0.97–1.01)

0.98

(0.97–0.99)

CO (1980) 108

508,538

1 ppm 1.00

(0.99–1.01)

1.00

(0.99–1.01)

1.01

(0.99–1.03)

0.99

(0.97–1.03)

0.99

(0.98–1.01)

Air conditioning (%) 133

574,725

40% 0.98

(0.97–0.99)

0.98

(0.96–0.98)

0.97

(0.95–0.98)

1.00

(0.97–1.02)

0.98

(0.97–0.99)

Grade 12 education 

(%) 

130

571,352

10% 1.01

(1.00–1.02)

1.01

(1.01–1.02)

1.02

(1.00–1.03)

1.00

(0.98–1.02)

1.01

(1.00–1.01)

Not white (%) 131

572,745

10% 1.01

(1.01–1.01)

1.01

(1.01–1.02)

1.00

(0.99–1.01)

1.01

(1.00–1.02)

1.01

(1.00–1.01)

Unemployment (%) 130

571,362

5% 1.04

(1.03–1.05)

1.05

(1.04–1.06)

1.05

(1.03–1.06)

1.04

(1.01–1.07)

1.03

(1.02–1.04)

Median household 

income ($000s)

130

571,217

$10,000 0.95

(0.94–0.96)

0.95

(0.93–0.96)

0.93

(0.91–0.94)

0.94

(0.91–0.97)

0.96

(0.95–0.97)

Income disparity 

(Gini)

130

571,208

0.05 0.99

(0.99–1.00)

1.00

(0.99–1.00)

1.00

(0.99–1.02)

1.00

(0.98–1.02)

0.99

(0.98–1.00)

Poverty (%) 133

574,664

0.1 1.03

(1.02–1.03)

1.03

(1.02–1.04)

1.03

(1.02–1.05)

1.04

(1.02–1.06)

1.02

(1.01–1.03)

a Model adjusted for 44 individual-level covariates. The baseline hazard function was stratified by age (1-year groupings), gender, and race. HRs are followed 

by 95% confidence intervals. Bolded values refer to text. Some upper confidence limits have been rounded down and no longer appear larger than the HR. 

HRs are lower than the upper confidence limit. 

b Incremental change on which the HR is based: pollutant level or covariate level in a ZCA.
D. Krewski et al.

21

The correlations between pairs of the seven ecologic

covariates are given in Table 5 by ZCA, MSA, and DIFF

(the ZCA values after being adjusted for the MSA mean).

Large differences in correlations between ecologic covariates were observed for the three levels. For example, the

correlation between grade 12 education and unemployment is near zero at the ZCA (4%) and DIFF (4%) levels

but clearly positive at the spatially broader MSA level

(31%). Air conditioning is negatively correlated with

grade 12 education at the MSA level (30%) but positively

correlated at the DIFF level (26%). Unemployment has an

unexpected positive correlation (42%) with household

income at the ZCA level, but the expected negative correlation was observed after the broader MSA association was

removed (a 53% correlation at the DIFF level).

We note that the correlation between an ecologic covariate at the DIFF level and an air pollutant is zero because

for analyses at the MSA level, all ZCAs within an MSA

were assigned the same pollutant concentration. However,

variation in survival between subjects can be explained, in

part, by ecologic covariates that vary both between and

within MSAs. Thus, incorporating the ecologic covariates

at both the MSA and DIFF levels together in a model (noted

as “MSA & DIFF”) may more fully account for the risks

associated with social determinants of health and thus

yield less biased risk estimates of exposure to air pollution.

All combinations of a pollutant and a cause of death that

were statistically significant at the 5% level based on the

results given in Table 3 are presented in Table 6. HRs were

calculated in four ways: without adjustment for the ecologic covariates, and with adjustment for all seven covariates simultaneously at the ZCA, the MSA, or the MSA &

DIFF levels together. The air pollution risks tended to be

equally sensitive to inclusion of the seven ecologic covariates regardless of their level (ZCA, MSA, or MSA & DIFF).

Adjustment for the seven ecologic covariates tended to

either inflate the HR for an air pollutant exposure or have

little impact, except for six cases in which adjustment

reduced the air pollutant HR: PM15 and TSP with CPD

deaths; SO4

2(both 1980 and 1990) with other causes of

death; and summertime O3 with all and CPD causes of death.

In Table 7, the HRs for PM2.5 (1999–2000) are used to

examine in more detail the sensitivity of the association

between air pollution and mortality. HRs related to PM2.5

exposure are given for all causes of death and for IHD

deaths either not adjusted or adjusted for each of the seven

Table 4. Pearson Correlations Between Pollutants Determined at the MSA Scale and Ecologic Covariates Enumerated at 

the ZCA Scale and Averaged for Each MSAa

Ecologic 

Covariate

Geographic 

Level

PM2.5

(1979–

1983)

PM2.5

(1999–

2000)

SO4

2

(1980)

SO4

2

(1990)

SO2

(1980)

PM15

(1979–

1983)

TSP

(1980)

O3

(Summer 

1980)

NO2

(1980)

Air conditioning (%) ZCA

MSA

7

8

13

16

4

6

15

20

16

19

6

7

1

2

18

22

24

31

Grade 12 education (%) ZCA

MSA

12

22

18

32

20

34

20

42

1

1

19

35

21

38

3

6

3

6

Not white (%) ZCA

MSA

10

28

15

36

1

2

1

2

8

19

2

6

2

5

5

13

5

13

Unemployment (%) ZCA 

MSA

6

17

2

4

4

11

14

33

5

12

12

31

11

25

2

5

7

17

Median household income 

($000s)

ZCA

MSA

12

25

10

18

6

9

1

2

6

10

1

2

0

1

6

11

19

35

Income disparity (Gini) ZCA

MSA

5

13

0

1

8

18

10

29

15

36

3

9

2

6

2

4

5

12

Poverty (%) ZCA

MSA

5

13

2

4

2

4

2

4

9

18

2

7

1

3

7

14

13

28

a

 Correlations ( 100) were based on pollutant and ecologic covariate data assigned to each subject. Pollutant concentrations were calculated at the MSA 

scale and assigned to all participants within the MSA. Ecologic covariate data were determined at the ZCA scale and averaged for the MSA value. Bolded 

values refer to text. 
22

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

44 INDIVIDUAL-LEVEL COVARIATES

From the 1982 ACS Enrollment Questionnaire

The 1982 enrollment questionnaire for the ACS CPS-II study 

collected data for variables that measure lifestyle, diet, demographic, occupational, and educational factors that may confound the air pollution–mortality association.

****************

Eight variables represent active smoking habits including 

nonlinear terms for cigarettes per day and number of years 

smoked, and seven variables characterize former smoking 

habits (percentage who are current or former smokers; cigarettes per day; years of smoking; started smoking at younger 

or older than 18 yrs; pipe/cigar smoker).

One variable assesses exposure to passive smoke 

(hours/day exposed to smoking).

****************

Seven variables characterize the subject’s main lifetime 

occupation and his or her possible exposure to PM in the 

workplace.

One variable represents self-reported exposure to dust and 

fumes in the workplace.

****************

Two variables represent marital status (separated/divorced/widowed, single versus married).

Two variables characterize level of education (high school, 

more than high school versus less than high school).

****************

Two variables represent linear and squared terms for body 

mass index.

Six variables characterize consumption of beer, wine, and 

other alcohol (beer, missing beer, wine, missing wine, liquor, 

missing liquor).

Eight variables represent diet (dietary fat and dietary fiber 

indices).

****************

Table 5. Pearson Correlations Between Ecologic Covariates Enumerated at the ZCA, MSA, or DIFF Levelsa

Ecologic

Covariate

Geographic 

Levelb

Air ConditioningGrade 12 

Education

Not

white

UnemploymentHousehold 

Income

Income 

Disparity Poverty

Air conditioning (%) ZCA

MSA

DIFF

1.0 1

30

26

3

38

34

30

37

32

24

2

50

9

14

29

21

12

55

Grade 12 education (%) ZCA

MSA

DIFF

1.0 33

51

28

4

31

4

12

8

14

49

26

57

4

37

48

Not white (%) ZCA

MSA

DIFF

1.0 36

1

44

33

0

45

37

35

38

60

43

65

Unemployment (%) ZCA 

MSA

DIFF

1.0 42

11

53

18

12

20

42

17

49

Median household income 

($000s)

ZCA

MSA

DIFF

1.0 56

70

53

75

81

72

Income disparity (Gini) ZCA

MSA

DIFF

1.0 64

80

60

Poverty (%) ZCA

MSA

DIFF

1.0

a

 Correlations ( 100) were based on ecologic covariate data estimated at the ZCA scale and assigned to each subject. Bolded values refer to text.

b The MSA value is the average of all ZCA values within the MSA. The DIFF value for a ZCA is the ZCA value minus the MSA mean. 
D. Krewski et al.

23

Table 6. HRs for Pollutant and Cause-of-Death Relationships With and Without Adjustment for All Seven Ecologic 

Covariates

Cause of 

Death

Ecologic Covariate Adjustmenta

 None ZCA MSA MSA & DIFF

PM2.5 (1979–1983)

All causes 1.028 (1.013–1.042) 1.043 (1.027–1.058) 1.040 (1.024–1.056) 1.043 (1.027–1.060)

CPD 1.066 (1.044–1.089) 1.085 (1.062–1.109) 1.086 (1.062–1.112) 1.089 (1.064–1.114)

IHD 1.132 (1.099–1.166) 1.168 (1.132–1.204) 1.176 (1.139–1.215) 1.183 (1.145–1.221)

Lung cancer 1.073 (1.019–1.130) 1.090 (1.034–1.150) 1.084 (1.025–1.145) 1.090 (1.031–1.153)

PM2.5 (1999–2000)

All causes 1.034 (1.016–1.053) 1.054 (1.035–1.075) 1.053 (1.031–1.074) 1.056 (1.035–1.078)

CPD 1.094 (1.065–1.124) 1.126 (1.095–1.158) 1.126 (1.093–1.161) 1.129 (1.095–1.164)

IHD 1.153 (1.111–1.197) 1.210 (1.163–1.258) 1.231 (1.181–1.284) 1.240 (1.189–1.293)

Lung cancer 1.108 (1.037–1.183) 1.135 (1.059–1.216) 1.130 (1.050–1.216) 1.137 (1.056–1.225)

All other causes 0.969 (0.944–0.995) 0.978 (0.952–1.006) 0.975 (0.946–1.004) 0.979 (0.950–1.008)

SO4

2(1980)

All 1.037 (1.027–1.046) 1.044 (1.034–1.054) 1.043 (1.032–1.054) 1.045 (1.034–1.056)

CPD 1.037 (1.023–1.051) 1.049 (1.034–1.064) 1.052 (1.036–1.069) 1.054 (1.038–1.071)

IHD 1.064 (1.044–1.085) 1.085 (1.063–1.107) 1.100 (1.077–1.124) 1.104 (1.081–1.128)

Lung cancer 1.055 (1.020–1.090) 1.060 (1.024–1.097) 1.074 (1.035–1.115) 1.077 (1.037–1.118)

All other causes 1.034 (10.21–1.048) 1.038 (1.024–1.053) 1.031 (1.015–1.046) 1.032 (1.017–1.048)

SO4

2(1990)

All causes 1.069 (1.049–1.090) 1.089 (1.066–1.112) 1.082 (1.056–1.109) 1.086 (1.060–1.113)

CPD 1.057 (1.027–1.088) 1.095 (1.061–1.130) 1.110 (1.070–1.152) 1.114 (1.074–1.156)

IHD 1.142 (1.097–1.189) 1.196 (1.145–1.248) 1.282 (1.219–1.349) 1.288 (1.225–1.355)

All other causes 1.086 (1.056–1.117) 1.090 (1.057–1.124) 1.065 (1.027–1.104) 1.068 (1.030–1.107)

SO2 (1980) 

All causes 1.021 (1.016–1.027) 1.022 (1.016–1.028) 1.019 (1.013–1.025) 1.020 (1.014–1.026)

CPD 1.020 (1.012–1.028) 1.021 (1.012–1.029) 1.021 (1.012–1.031) 1.022 (1.013–1.032)

IHD 1.037 (1.026–1.049) 1.043 (1.031–1.055) 1.057 (1.044–1.071) 1.059 (1.046–1.072)

All other causes 1.025 (1.017–1.033) 1.025 (1.017–1.034) 1.019 (1.009–1.028) 1.019 (1.010–1.029)

PM15 (1979–1983)

CPD 1.034 (1.019–1.048) 1.034 (1.018–1.049) 1.037 (1.019–1.054) 1.025 (1.013–1.037)

IHD 1.061 (1.040–1.082) 1.074 (1.052–1.097) 1.095 (1.069–1.122) 1.064 (1.048–1.082)

TSP (1980)

CPD 1.013 (1.007–1.020) 1.012 (1.005–1.019) 1.010 (1.002–1.018) 1.007 (1.002–1.012)

All other causes 0.992 (0.986–0.999) 0.993 (0.986–1.000) 0.996 (0.988–1.003) 0.998 (0.993–1.003)

O3 (Summer 1980)

All causes 1.016 (1.008–1.024) 1.014 (1.006–1.023) 1.006 (0.998–1.015) 1.008 (0.999–1.017)

CPD 1.028 (1.016–1.041) 1.027 (1.014–1.040) 1.015 (1.002–1.028) 1.016 (1.002–1.029)

NO2 (1980)

IHD 1.018 (1.004–1.031) 1.030 (1.015–1.045) 1.033 (1.016–1.051) 1.035 (1.017–1.053)

All other causes 0.982 (0.972–0.991) 0.986 (0.976–0.997) 0.990 (0.978–1.002) 0.991 (0.979–1.003)

a

 Determined with the standard Cox model, which included the 44 individual-level covariates. The models with no adjustment for ecologic covariates 

included only those participants who resided in ZCAs for which ecologic covariate data were available. Ecologic covariate data were derived for each 

ZCA and averaged for the MSA value. The DIFF value for a ZCA is the ZCA value minus the MSA mean. MSA & DIFF indicates both levels of data were 

included in the model together. HRs are followed by 95% confidence intervals. Bolded values refer to text. 
24

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

ecologic covariates separately. The all-cause mortality HR

increased after adjustment for six of the seven covariates;

only percentage of population who are not white reduced

the risk from 1.034 (95% CI, 1.016–1.053) to 1.028 (95%

CI, 1.010–1.047). The IHD risk increased after adjustment

for any of the seven ecologic covariates.

The Reanalysis and Updated Analysis (Krewski et al.

2000a,b; Pope et al. 2004) had shown that the subjects’

level of education modified the effect of PM2.5 exposure on

mortality. To explore this further, we stratified educational

status based on the individual-level data gathered at ACS

enrollment: low education (less than grade 12), medium

education (grade 12 completed), and high education (some

education beyond grade 12 completed). Models included

42 of the individual-level covariates used in other analyses

(two variables that represented education attainment were

removed) with and without the seven ecologic covariates.

Results are presented in Table 8.

For all-cause and CPD mortality, we observed a slight

effect modification by level of education, although in the

case of CPD, the effect modification was confounded by

including the ecologic covariates.

For lung cancer deaths, the analyses with high education showed the lowest effects of PM2.5 exposure on mortality, but there appeared to be little difference between

risks in the two lowest strata of education attainment.

These findings follow a pattern similar to what was

reported in the earlier studies, but the patterns are less

clear with the longer follow-up period used here (all P

values for effect modification were > 0.10).

For IHD mortality, in contrast to previous findings, level

of education produced a positive modification effect; the

largest effects were observed in the high education

stratum. This pattern was consistent between results from

models both with and without controlling all seven ecologic covariates, but was more pronounced when the ecologic covariates were included, which had the effect of

increasing the HR in all strata. Yet, the ratio between the

high and low educational strata is roughly the same in

results from models with and without the ecologic control.

SENSITIVITY OF AIR POLLUTION RISK TO THE 

ERROR STRUCTURE OF THE RANDOM EFFECTS 

COX MODEL

We illustrate the sensitivity of the risk estimates and

their standard errors to model specification using the ACS

data with follow-up from 1982 to 2000. We selected PM2.5

(1999–2000) as the pollutant measure and all causes of

death and IHD deaths as the outcomes to illustrate the

random effects Cox model and the sensitivity of an air pollution–mortality association to the model specification.

The spatial structure of the random effects Cox model

was specified by a nearest-neighbor approach at both the

MSA and ZCA levels. Here, two ZCAs are assumed to be

neighbors if any part of their boundaries are connected.

For each ZCA, we constructed a Thiessen polygon, which

has the property that any point inside the polygon is closer

Table 7. HRs for All and IHD Causes of Death Associated with a 10-μg/m3 Change in PM2.5 (1999–2000) from the 

Standard Cox Model Unadjusted or Adjusted for a Single Ecologic Covariatea

Ecologic Covariate 

Adjustment

 All Causes IHD

PM2.5 (1999–2000) Ecologic Covariateb PM2.5 (1999–2000) Ecologic Covariateb

No adjustment 1.034 (1.016–1.053) — 1.153 (1.111–1.197) —

Air conditioning (%) 1.040 (1.021–1.059) 0.976 (0.968–0.984) 1.160 (1.118–1.204) 0.972 (0.955–0.989)

Grade 12 education (%) 1.043 (1.025–1.063) 1.016 (1.009–1.023) 1.171 (1.127–1.216) 1.031 (1.016–1.046)

Not white (%) 1.028 (1.010–1.047) 1.009 (1.006–1.013) 1.158 (1.115–1.203) 0.997 (0.989–1.005)

Unemployment (%) 1.036 (1.018–1.055) 1.040 (1.032–1.049) 1.155 (1.113–1.199) 1.046 (1.027–1.064)

Median household income 

($000s)

1.048 (1.030–1.068) 0.950 (0.942–0.959) 1.177 (1.134–1.222) 0.924 (0.906–0.942)

Income disparity (Gini) 1.036 (1.017–1.055) 0.993 (0.987–1.000) 1.156 (1.113–1.200) 1.001 (0.988–1.014)

Poverty (%) 1.036 (1.018–1.055) 1.027 (1.020–1.035) 1.156 (1.113–1.199) 1.029 (1.015–1.045)

a

 Model included 44 individual-level covariates and adjusted for a single ecologic covariate at the ZCA scale. The baseline hazard function was stratified by 

age (1-year groupings), gender, and race. HRs are followed by 95% confidence intervals. Bolded values refer to text.

b HRs for the ecologic covariate itself when in the model with PM2.5.
D. Krewski et al.

25

to the geographic center (centroid) of the ZCA than to the

centroid of any other ZCA. ZCA polygons were joined to

create each MSA polygon; following this pattern, the 48

contiguous United States were covered by polygons. The

random effects associated with two MSAs (or ZCAs) are

assumed to have a common correlation if they are neighbors and to be uncorrelated if they are not neighbors. Two

ZCAs in different MSAs are assumed to be uncorrelated.

The HRs for a 10-μg/m3 change in PM2.5 (1999–2000) are

given in Table 9 under selected error specifications for the

random effects Cox model with: (a) no adjustment for the

seven ecologic covariates, (b) adjustment for the ecologic

covariates defined at only the ZCA level, and (c) adjustment for the covariates defined at both the MSA & DIFF

levels. All and IHD causes of death were examined.

Inclusion of clustering at both the MSA & DIFF levels

and at the ZCA level increased the estimate of the PM2.5

(1999–2000) HR, with or without adjustment for the ecologic covariates. This is likely due to the manner in which

the random effects Cox model weights the importance of

the observations. The standard Cox model gives equal

weight to all subjects’ information whereas the random

effects Cox model used here weights the information differently for each cluster. The variation in weight among

clusters is positively associated with the cluster variance.

Thus the larger MSAs are given less weight in the random

effects model than in the standard Cox model in which

responses are assumed to be statistically independent. An

increase in the HR using the random effects model implies

that the larger MSAs did not clearly follow our model specification, particularly the assumptions about there being a

log-linear relationship between mortality and the associated exposure to PM2.5 (1999–2000). Thus they are given

less weight even though their sample size may be larger.

The inclusion of additional variance due to clustering of

survival experience at the MSA level also inflates the

uncertainty in the PM2.5 (1999–2000) HR estimate, as evidenced by wider confidence intervals (CIs) compared with

the case in which the MSA and ZCA variances are set to 0

(equivalent to the standard Cox model assumption). There

was more unexplained variation in survival between ZCAs

than between MSAs for all causes of death, but this pattern

is not replicated for IHD deaths.

Inclusion of the seven ecologic covariates defined at the

ZCA level reduced both the MSA and ZCA random effects

variances. However, a further reduction was observed

when these covariates were defined by their MSA & DIFF

levels together.

The inclusion of spatial autocorrelation at both the MSA

and ZCA levels lowered the PM2.5 (1999–2000) HR and widened the CIs, which suggests that there may be some evidence of spatial clustering of residual mortality coinciding

with the spatial pattern of PM2.5 (1999–2000). However, our

estimate of spatial autocorrelation at the MSA level (0.36)

was at the boundary of allowable values. This implies that

the model is ill-specified, likely due to the very large

number of nearest neighbors for each MSA, particularly in

the Eastern U.S. Although we need to interpret these results

with caution, we can view them as a situation with the maximum spatial autocorrelation permissible, and thus we

included them in our sensitivity analysis as an extreme case.

Table 8. Modification of HRs by Education at Individual Level for a 10-μg/m3 Change in PM2.5 (1999–2000) from the 

Standard Cox Model by Cause of Deatha

Level of 

Education / 

Participants 

(n)

Ecologic 

Covariates 

in Model All Causes CPD IHD Lung Cancer All Other Causes

< Grade 12 

education

(59,168)

No 1.064 (1.004–1.126) 1.127 (1.044–1.216) 1.111 (0.979–1.260) 1.187 (1.020–1.381) 0.955 (0.883–1.034)

Yes 1.082 (1.024–1.144) 1.157 (1.077–1.245) 1.173 (1.022–1.347) 1.217 (1.025–1.446) 0.960 (0.886–1.040)

Grade 12 

education 

(152,024)

No 1.060 (1.013–1.110) 1.086 (1.016–1.160) 1.195 (1.072–1.333) 1.192 (1.057–1.344) 0.999 (0.942–1.061)

Yes 1.072 (1.020–1.127) 1.112 (1.040–1.189) 1.248 (1.109–1.404) 1.236 (1.081–1.413) 0.995 (0.931–1.062)

> Grade 12 

education 

(274,941)

No 1.041 (1.001–1.083) 1.083 (1.023–1.146) 1.225 (1.121–1.339) 1.024 (0.912–1.150) 1.005 (0.956–1.058)

Yes 1.055 (1.018–1.094) 1.115 (1.060–1.173) 1.331 (1.215–1.458) 1.043 (0.934–1.164) 1.006 (0.956–1.060) 

a

 Model included 42 individual-level covariates (the two covariates for education attainment were omitted for this analysis) and with or without adjustment 

for the seven ecologic covariates at the MSA & DIFF levels together. The baseline hazard function was stratified by age (1-year groupings), gender, and race. 

HRs are followed by 95% confidence intervals. Bolded values refer to text. All P values > 0.10 for effect modification.
26

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

We also considered the sensitivity of our results to the

spatial definition of clusters. Here, we used “state” and

“MSAs within state” to define the cluster levels. There are

far fewer nearest neighbors for the state specification than

for the MSA specification. We postulate that state is a natural cluster definition because many health care programs

and resources that influence health, and thus longevity, are

administered at the state level.

PM2.5 data for 1999–2000 were available for 44 states and

116 MSAs. We included the 44 individual-level covariates

in random effects Cox models with adjustment for the

seven ecologic covariates defined at the MSA & DIFF

levels together. Including both state and MSA-within-state

clusters increased the PM2.5 (1999–2000) HRs compared

with results from the standard Cox model (Table 10, top

row under each cause of death) and also increased the

uncertainty in the estimates, as evidenced by the much

wider CIs. Including spatial autocorrelation at both cluster

levels reduced the HRs compared with a model that

included both state and MSA random effects but no spatial

autocorrelation. Similar patterns were observed with and

without adjustment for the ecologic covariates and for both

all causes of death and IHD deaths (results not shown).

ALTERNATIVE FORMULATION OF THE 

CONCENTRATION–RESPONSE FUNCTION

We investigated the form of the concentration–response

function between PM2.5 (1999–2000) and selected causes

of death. Our alternative formulation of the function is the

natural logarithm of concentration. We selected this for

two reasons. First, the logarithm of concentration yields a

Table 9. Sensitivity Analysis of the Deterministic and Stochastic Parameters of the Random Effects Cox Model for All 

and IHD Causes of Deatha

Ecologic Covariate 

Adjustment

HR for a 10-μg/m3 Change 

in PM2.5 (1999–2000)

MSA Variance

(103)

MSA

Autocorrelation

ZCA Variance

(103)

ZCA

Autocorrelation

All Causes

Standard Cox model

None 1.034 (1.016–1.053) 0 0 0 0

ZCA 1.054 (1.035–1.075) 0 0 0 0

MSA & DIFF 1.056 (1.035–1.078) 0 0 0 0

No autocorrelation at MSA or ZCA level

None 1.057 (1.014–1.101) 1.62 0 4.35 0

ZCA 1.080 (1.047–1.114) 1.21 0 3.06 0

MSA & DIFF 1.075 (1.041–1.110) 0.95 0 3.01 0

Autocorrelation at MSA and ZCA levels

None 1.048 (0.989–1.110) 1.66 0.36 5.65 0.29

ZCA 1.074 (1.036–1.112) 1.25 0.36 3.61 0.23

MSA & DIFF 1.071 (1.032–1.111) 1.06 0.36 3.45 0.22

IHD

Standard Cox model

None 1.153 (1.111–1.197) 0 0 0 0

ZCA 1.210 (1.163–1.258) 0 0 0 0

MSA & DIFF 1.240 (1.189–1.293) 0 0 0 0

No autocorrelation at MSA or ZCA level

None 1.181 (1.092–1.278) 10.51 0 10.36 0

ZCA 1.243 (1.147–1.346) 10.24 0 8.40 0

MSA & DIFF 1.287 (1.179–1.404) 9.47 0 8.05 0

Autocorrelation at MSA and ZCA levels

None 1.168 (1.065–1.280) 10.49 0.36 15.61 0.30

ZCA 1.229 (1.120–1.347) 10.15 0.36 10.64 0.28

MSA & DIFF 1.276 (1.156–1.409) 9.79 0.36 10.06 0.27

a

 Model included 44 individual-level covariates. The baseline hazard function was stratified by age (1-year groupings), gender, and race. A 0 indicates the 

parameter was not included in the model. HRs are followed by 95% confidence intervals.
D. Krewski et al.

27

flexible mathematical form to relate exposure to survival

within the proportional-hazards family of models. Here

we have

where the unknown parameter γ is a scalar. If  < 1, the

relationship between PM2.5 and mortality concaves downward, which yields larger HRs at lower concentrations;

and if  > 1, the association concaves upward, which

yields smaller HRs at lower concentrations. This formulation predicts HRs that are a function of concentration and

thus a single risk value will not represent the association

between exposure and response throughout the concentration range. The second reason is that the Updated Analysis

(Pope et al. 2002) suggested that the logarithm of PM may

be a slightly better predictor of risk than the linear form.

The HRs associated with a 10-μg/m3 change in PM2.5

(1999–2000) concentrations based on linear and log-linear

functions of PM are given in Table 11 for selected causes

of death with and without adjustment for the seven ecologic covariates defined at both the MSA & DIFF levels

together. Two separate spans of a 10-μg/m3 change in

PM2.5 are examined for the logarithmic formulation: from

5 to 15 μg/m3 and from 10 to 20 μg/m3 since the HR based

on the logarithmic function varies with concentration. A

concentration of 5 μg/m3 represents the minimum of the

distribution of PM2.5 (1999–2000) and a concentration of

20 μg/m3 represents the 95th percentile of the concentration distribution.

Our random effects Cox model was used to estimate the

HRs with only one level of clustering at the MSA scale. For

PM2.5, the logarithmic function was a slightly better predictor of the variation in survival among MSAs than the

linear function because the MSA random effect variance is

somewhat smaller (than that for the linear function) for

each cause-of-death category except all other causes. As

	 	 
 e re e ( ) ( ) exp{log( ) } t tU X ( )

. = + 0 2 5 PM

		 


 e re e ( ) ( ) exp( ) ( ) t tU X ( )

. = ∗ 0 2 5 PM

Table 10. Sensitivity Analysis of the Deterministic and Stochastic Parameters of the Random Effects Cox Model Due to 

Geographic Cluster Definition for All and IHD Causes of Deatha

Without or 

With Spatial 

Autocorrelation

HR for a 10-μg/m3

Change in PM2.5

(1999–2000)

State Variance

(103)

State

Autocorrelation

MSA Variance

(103)

MSA

Autocorrelation

All Causes

Standard Cox model 1.056 (1.035–1.078) 0 0 0 0

Clustering by state only

Without 1.055 (1.019–1.092) 1.61 0 0 0

With 1.045 (1.009–1.083) 1.90 0.23 0 0

Clustering by state and MSA

Without 1.072 (1.031–1.116) 1.22 0 0.32 0

With 1.066 (1.021–1.112) 1.26 0.20 0.43 0.29

IHD

Standard Cox model 1.240 (1.189–1.293) 0 0 0 0

Clustering by state only

Without 1.284 (1.189–1.387) 13.14 0 0 0

With 1.232 (1.141–1.330) 14.04 0.39b 0 0

Clustering by state and MSA

Without 1.320 (1.192–1.460) 9.74 0 3.05 0

With 1.241 (1.112–1.382) 8.83 0.39b 4.07 0.15

a

 Analysis based on 44 states and 116 MSAs. The model included 44 individual-level covariates and adjusted for seven ecologic covariates simultaneously. 

defined at the MSA and DIFF levels. The baseline hazard function was stratified by age (1-year groupings), gender, and race. A 0 indicates the parameter 

was not included in the model. HRs are followed by 95% confidence intervals.

b The estimation algorithm yielded a value of autocorrelation at the boundary of acceptable values, which indicates that the model was not properly 

specified and the results should be interpreted with caution.
28

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

expected, the HR based on the logarithm formulation

yielded higher values for the difference between 5 and

15 μg/m3 compared with the linear formulation; and lower

values were observed for the difference between 10 and

20 μg/m3 except for other causes of death in which the logarithmic formulation yielded larger HRs compared with

the linear formulation.

The choice of functional relationship between PM exposure and mortality can make a considerable difference in

the predicted risk at lower concentrations. For example,

the HR for lung cancer adjusted for the ecologic covariates

based on the linear formulation is 1.142 (95% CI, 1.057–

1.234), whereas the HR based on the logarithmic formulation is 1.236 (95% CI, 1.114–1.372), a 66% increase in risk.

DISCUSSION AND CONCLUSIONS

Our overall objective was to further analyze the association between particulate air pollution and all-cause and

cause-specific mortality in large U.S. cities using alternative

spatial models and extended follow-up data from the ACS

CPS-II cohort. Specifically, we examined whether social,

economic, and demographic ecologic covariates confound

the relationship between particulate air pollution and

mortality, and we explored complex spatial patterns in the

CPS-II data with an extended random effects Cox model.

Results of the Reanalysis Project showed that ecologic

covariates estimated on the MSA level were unlikely to

exert a significant confounding influence on the association between particulate air pollution and mortality

(Krewski et al. 2000a,b). Nevertheless, unresolved questions remained about the scale and the construct validity

of the variables used. Therefore, in the current study we

examined the influence of ecologic covariates at several

scales: the ZCA scale (data obtained from the 1980 U.S.

Census), the MSA scale (by averaging information on all

ZCAs within an MSA), and by the value of the difference

obtained between the ZCA-specific value and the MSA

mean value (DIFF).

Table 11. Comparison of HRs for Selected Causes of Death Associated with a 10-μg/m3 Change in PM2.5 (1999–2000) 

Concentrations Based on Inclusion of Linear PM2.5 or log(PM2.5) in the Random Effects Cox Model with Clustering at the 

MSA Levela

Ecologic 

Covariate 

Adjustment

Linear PM2.5 (1999–2000) Log (PM2.5 [1999–2000])

HR for

10-μg/m3

Change

MSA

Variance

(103)

HR for

Change from 

5 to 15 μg/m3

HR for

Change from 

10 to 20 μg/m3

MSA

Variance

(103)

All Causes

None 1.060 (1.024–1.097) 1.86 1.095 (1.044–1.148) 1.059 (1.028–1.091) 1.81

MSA & DIFF 1.078 (1.043–1.115) 1.07 1.128 (1.077–1.183) 1.079 (1.048–1.112) 1.00

CPD

None 1.094 (1.040–1.150) 3.96 1.145 (1.068–1.228) 1.089 (1.042–1.138) 3.87

MSA & DIFF 1.128 (1.077–1.182) 1.86 1.208 (1.132–1.290) 1.127 (1.081–1.174) 1.66

IHD

None 1.196 (1.103–1.298) 12.13 1.315 (1.175–1.470) 1.188 (1.107–1.275) 11.73

MSA & DIFF 1.287 (1.177–1.407) 10.56 1.484 (1.311–1.680) 1.283 (1.186–1.387) 9.80

Lung Cancer

None 1.122 (1.040–1.210) 2.03 1.193 (1.071–1.330) 1.118 (1.044–1.197) 1.90

MSA & DIFF 1.142 (1.057–1.234) 0.56 1.236 (1.114–1.372) 1.143 (1.071–1.221) 0.45

All Other Causes

None 1.008 (0.968–1.051) 1.95 1.023 (0.965–1.083) 1.014 (0.978–1.052) 1.97

MSA & DIFF 1.010 (0.968–1.055) 1.37 1.026 (0.970–1.085) 1.016 (0.981–1.053) 1.39

a

 Model included 44 individual-level covariates with and without adjustment for seven ecologic covariates simultaneously. MSA & DIFF indicates both 

levels of data were included in the model together. The baseline hazard function was stratified by age (1-year groupings), gender, and race. HRs are 

followed by 95% confidence intervals. Bolded values refer to text.
D. Krewski et al.

29

Results from the CPS-II cohort with an additional eleven

years of follow-up in this Extended Analysis revealed positive and significant associations between a 10-μg/m3

change in PM2.5 mass or a 5-μg/m3 in SO4

2mass (with the

exception of SO4

2[1990]) and all-cause, CPD, IHD, and

lung cancer deaths. Particles of larger size (PM15 and TSP)

were associated with positive, although smaller, increases

in risk of all-cause, IHD, and CPD deaths. Mortality from

IHD compared with other causes of death was consistently

associated with the largest HR estimates. Indeed, other

recent investigations have supported an association

between particles and adverse cardiovascular effects

(Krewski et al. 2000a,b; Pope et al. 2004; Künzli et al.

2005). Further investigations to examine the cardiovascular health effects of particulate air pollution are critical

to understanding the nature of the association.

Fewer significant associations were observed with the

gaseous pollutants. SO2, which is likely to be an indicator

of other pollutants related to mortality, was significantly

associated with higher mortality from all causes, CPD, and

IHD, although the strength of the associations tended to be

weaker than the associations observed with particles.

Summertime O3 was related with all-cause and CPD mortality in this study; this relationship was statistically significant in the Reanalysis (relative risk = 1.08, 95% CI,

1.01–1.16; Krewski et al. 2000a,b).

Due to differences in modeling methods and use of

multi-level variables, HR estimates presented here are not

directly comparable with the Updated Analysis using

alternate modeling strategies (Pope et al. 2002) or with the

Reanalysis (Krewski et al. 2000a,b), in which different

scales or levels of pollutant increases were used to calculate relative risks. This Expanded Analysis evaluated the

level of risk associated with a 10-μg/m3 increase in PM2.5

and a 5-μg/m3 increase in SO4

2. The Reanalysis evaluated

a 24.5-μg/m3 increase in PM2.5 and a 19.9-μg/m3 increase

in SO4

2(those values were equal to the difference in

mean concentrations between the most- and least-polluted

cities from the Harvard Six Cities Study [Krewski et al.

2000a]).

We wanted to directly compare the HRs between the

three follow-up periods that have been examined: 1982–

1989 (the Reanalysis, Krewski et al. 2000a,b); 1982–1998

(the Updated Analysis, Pope et al. 2002); and 1982–2000

(the current Extended Analysis) (see also the section

Implications of the Findings / Comparison of Data Sets and

Analytic Methods for the Three Follow-Up Time Periods).

To do so, we estimated the HR for a 10-μg/m3 increase in

the average PM2.5 concentration for 1979–1983 in 58

MSAs, and for 1999–2000 in 116 MSAs, and controlled for

the 44 individual-level covariates using the standard Cox

model (both with and without control for the ecologic

covariates) for mortality from all causes, CPD, IHD, lung

cancer, and all other causes. Over the three follow-up time

periods, deaths from all causes increased to 90,783 in the

58 MSAs and 128,954 in the 116 MSAs; and deaths from

IHD increased to 20,651 in the 58 MSAs and 29,989 in the

116 MSAs (see Table 32). For the PM2.5 (1979–1983) sampling period (58 MSAs), we observed a small decrease in

the HR estimate for deaths from all causes between the initial follow-up period (1.048, 95% CI, 1.022–1.076) and the

later two periods (1.031, 95% CI, 1.015–1.047; and 1.028,

95% CI, 1.014–1.043) (see Table 33). We found no temporal

pattern in risk for IHD deaths (1.122, 95% CI, 1.066–1.181;

1.130, 95% CI, 1.094–1.166; and 1.133, 95% CI, 1.100–

1.167). Declining risk could be due to exposure misclassification increasing over time. We assigned exposure based

on residence location in 1982; and the percentage of cohort

members who have moved has to have increased over

time. Also, PM2.5 concentrations have clearly declined

over the follow-up period, so assigning a single exposure

measure would not have fully captured this temporal pattern. Finally, as the cohort ages, air pollution effects may

decline due to the healthy survivor effect.

The ecologic covariates evaluated were associated with

overall and cause-specific mortality and correlated with

specific air pollutants, which is consistent with the definition of a possibly confounding variable. Poverty and

unemployment were positively associated and household

income was negatively associated with all cause-of-death

categories. Historically, indicators of community-level

socioeconomic status such as these have been associated

with overall and cause-specific mortality (Kaplan et al.

1996; Boyd et al. 1999; Winkleby and Cubbin 2003). They

likely reflect a complex interaction of environmental,

social, and behavioral factors and of available health services that all influence health status and longevity. However, income disparity (a related measure of economic

status) was not associated with mortality in the ACS CPSII cohort. This is surprising; we would have expected

income disparity to be positively associated with mortality, as in some previous studies (Cooper et al. 2001; Ram

2005; Singh and Siahpush 2006), although in the Reanalysis (Krewski et al. 2000a,b), income disparity was also not

strongly related with mortality. It may be that controlling

the 44 individual-level covariates (including such factors

as marital status, smoking, and diet) removed any potential effect of income disparity on mortality in that study; or

it may be that an inadequate variable was used.

All ecologic covariates were based on data from the

1980 U.S. Census. Spatial patterns of mortality and ecologically defined sociodemographic factors could change
30

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

independently of each other over follow-up time and thus

lead to misclassification of their values and relationships.

Discussion of the impact of population mobility in the

ACS cohort is presented elsewhere (Jerrett et al. 2007).

Both the positive and negative influences of different ecologic covariates on mortality require careful interpretation

as to their potential confounding effects on the air pollution–mortality association.

Spatial patterns of ecologically defined sociodemographic factors and air pollution for the CPS-II cohort are

complex. Ecologic covariates were both positively and

negatively correlated with concentrations of certain specific air pollutants (see Table 4). Having less than a grade

12 education was most frequently strongly correlated with

air pollutant indices — negatively correlated with concentrations of PM2.5 and SO4

2, but positively correlated with

particles of larger size. Conversely, percentage of the population that is not white and median household income

was positively correlated with PM2.5 mass.

Correlations between ecologic covariates varied

according to the unit of aggregation (ZCA or MSA), which

indicates that spatial patterns in the data differ according

to scale. Interestingly, the largest correlations were not

necessarily found in the smallest unit of observation

(ZCA) as might be expected; this could indicate that complex spatial patterns of sociodemographic factors are also

involved. Strong correlations were observed between some

of the ecologic covariates; therefore, caution is required in

interpreting models that include all of the ecologic covariates simultaneously due to the possible resulting statistical instability.

Since variation in survival between subjects can be

explained, in part, by ecologic covariates that vary both

between and within MSAs, we conducted analyses that

adjusted for the ecologic covariates at both the MSA & DIFF

levels together. We hoped to account for sociodemographic

risks more completely and thus yield less biased risk estimates of exposure to air pollution. In nearly all models that

adjusted for the seven ecologic covariates simultaneously,

the HR tended to increase in comparison to models with no

adjustment, although many of the differences were small.

The largest increases were observed for mortality from IHD

associated with PM2.5 (1999–2000) and SO4

2(1990); the

magnitude of the inflation of HR was 7.5% and 12.8%,

respectively; this model included ecologic covariates at

both the MSA & DIFF levels simultaneously.

When we evaluated the independent effect of each ecologic covariate at the ZCA scale on the HR for the association

between PM2.5 and mortality from all causes and from IHD,

we found that each covariate independently increased the

strength of the association (except for percentage who are

not white with all-cause mortality). We found the largest

increases when household income was the only ecologic

covariate included — from 1.034 (1.016–1.053) to 1.048

(1.030–1.068) for all causes of death, and from 1.153

(1.111–1.197) to 1.177 (1.134–1.222) for deaths from IHD

(Table 7). Household income was positively associated

with exposure to PM2.5 (1999–2000) and negatively associated with all-cause mortality. (No individual-level information on income was available for CPS-II participants;

therefore, we relied on the ecologic-level indicator of

income status.)

Percentage of the population with a grade 12 education

exerted the next largest influence on HR estimates. The

random effects Cox model was adjusted for education

attainment at the individual level. This analysis, therefore,

suggests that neighborhood education attainment measured at the ZCA level exerts additional influence on the

air pollution–mortality association beyond that of individual-level education attainment.

ZCA-level median household income and grade 12 education, therefore, represent the most important sociodemographic ecologic factors that may confound the PM2.5–

mortality association, although their effects individually

are slight.

One of the more remarkable findings in this set of analyses was that when ecologic covariates were included, the

HRs increased. This finding likely represents a case of variance suppression (Tabachnick and Fidell 2001), which is

documented as one way in which confounding variables

may increase the size of their effects on the primary risk

variable in the model. Ecologic confounders and the pollutants share some of the model variance, and their common

variability also partially overlaps with the variability of the

mortality outcome. The part of the variance shared by the

ecologic and pollutant variables is the component of variance with mortality that has a relatively less-defined association with pollution. When the ecologic covariates are

included in the model, the component of the variance

overlap between pollution and mortality is removed, and

the remaining relationship becomes more pronounced.

These results imply that simultaneous consideration of

ecologic covariates and air pollution may be important in

models of chronic health effects.

Results from the Reanalysis (Krewski et al. 2000a,b) suggested that SO2 may exert a more robust effect on mortality

than SO4

2. In the current Extended Analysis, including

ecologic covariates at multiple scales exerted little influence on HR estimates for the association between SO2 and

all-cause and cause-specific mortality. An exception was

for IHD mortality, in which including ecologic covariates
D. Krewski et al.

31

at the MSA & DIFF levels together increased the HR value

slightly (by 2.1%).

The ecologic covariates exerted their greatest effect on

mortality from IHD, which was also the outcome most

strongly related with PM2.5 (1979–1983 and 1999–2000),

SO4

2(1980–1981 and 1990), and SO2 exposure, and was

the only outcome significantly associated with NO2 exposure. IHD, a significant contributor to mortality in the

United States, is associated with a multitude of behavioral

risk factors such as smoking, lack of exercise, obesity, and

diet, which are likely related to spatial patterns in sociodemographics such as income, education level, and unemployment. Spatial patterns in health services available to

diagnose and treat IHD are possibly another important

factor not explicitly evaluated here. The HR of IHD mortality associated with air pollution exposure remained elevated even after adjustment for a variety of individuallevel and ecologic covariates. The fact that IHD mortality

was the outcome most influenced by the ecologic covariates suggests the importance of controlling for these influences in future studies.

Another interesting finding was the fact that, in general,

the geographic unit of analysis (ZCA or MSA) of ecologic

covariates tended to not make an appreciable difference in

their influence on HR estimates. In many cases, though,

HR estimates tended to be highest from models with the

ecologic covariate considered at the MSA & DIFF levels

simultaneously. The sociodemographic ecologic covariates

at simply the ZCA or MSA levels, compared with the MSA

& DIFF levels, did not substantially alter results of the air

pollution–mortality association found in the current study.

The association between air pollution and lung-cancer

mortality was found to vary somewhat by level of education attainment recorded at the inception of the cohort.

The risk of lung-cancer mortality associated with each 10-

μg/m3 change in PM2.5 (as measured in 1999–2000) was

approximately 20% higher for participants who completed

only grade 12 education compared with no association

observed among those who completed more than high

school. Including ecologic covariates increased the point

estimates and width of CIs slightly. Similar results were

reported in the Reanalysis (Krewski et al. 2000a,b): relative

risks of 1.41 (95% CI, 0.87–2.29) and 1.39 (95% CI, 0.90–

2.15) were reported for lung-cancer mortality associated

with each 24.5-μg/m3 change in PM2.5 among those who

completed less than high school and those who completed

high school, respectively, whereas a relative risk of 0.66

(95% CI, 0.46–0.95) was found among those who completed more than high school. Indeed, this finding of

increasing mortality from PM2.5 exposure associated with

declining education attainment represented a key finding

of the Reanalysis. In the Updated Analysis, Pope and

coworkers (2002) also reported a similar trend. Although

the reasons for this finding are unknown, it was suggested

that level of education attainment may likely indicate the

effects of complex and multifactorial socioeconomic processes on mortality or may reflect disproportionate pollution exposures.

Although the direction of the trend for all-cause and

CPD mortality in the current study was similar to that of

lung-cancer mortality, a significantly elevated HR was

found for these outcomes in each educational stratum and

the magnitude of the difference was small. This suggests

that education attainment may exert less of a modifying

force on these mortality categories. A surprising finding in

the current study was increasing IHD mortality associated

with PM2.5 (1999–2000) and increasing education attainment (Table 8). HR estimates for IHD mortality also

increased up to 11% among those with more than high

school education with the inclusion of ecologic covariates

in the model. Large socioeconomic differences in coronary

mortality have been observed in the United States, where

individuals of lower socioeconomic status exhibit greater

coronary mortality compared with individuals of higher

socioeconomic status (Kunst et al. 1999; Mackenbach et al.

2001; Armstrong et al. 2003). The finding of a greater effect

of PM2.5 exposure on IHD mortality among those with

greater education attainment in the current study may suggest that there are a number of individual- or communitylevel influences on mortality among those with lower education that are more powerful than that of PM2.5.

We also explored complex spatial patterns in the CPS-II

data. The 2000 Reanalysis, using a random effects Cox

model that could handle up to two levels of clustering,

showed that spatial risk models attenuate the air pollution

effect, both in terms of size and certainty. The presence of

spatial autocorrelation has important implications with

respect to bias and precision of model-based estimates of

risk. In this project we extended the random effects Cox

model used in the Reanalysis to be capable of handling

more than two levels of clustering in order to evaluate how

robust the results from the Reanalysis would be to more

sophisticated spatial models.

The spatial random effects Cox model captures additional variation beyond that explained by the standard Cox

model. We included random effects at both the MSA and

ZCA levels in order to capture variation in survival due to

unexplained variation between MSAs and between ZCAs

within MSAs. We note that including these random effects

tends to increase the estimated HR of death associated with

air pollution exposure and to inflate uncertainty in these

estimates, as evidenced by wider confidence intervals.
32

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

We further note that including the ecologic covariates

decreases the variance of the random effects at both the

MSA and ZCA level; the largest decrease in residual variation is based on models with the MSA & DIFF specification,

which suggests that partitioning the ecologic covariates into

between-MSA and within-MSA values more completely

captures these sources of variation.

INTRA-URBAN ANALYSIS FOR THE NEW YORK 

CITY REGION

A LAND-USE REGRESSION MODEL FOR PREDICTING 

PM2.5 CONCENTRATIONS

Background

Exposure to PM2.5 has been linked to a wide array of

health outcomes such as aggravation of existing heart and

lung disease and premature death (Pope et al. 2002). Such

health studies, though, often rely on pollutant exposure

estimates that are based on a city’s central monitors; that

same level of exposure is then assigned to the entire population in a metropolitan area and exposure estimates are

compared between cities. Recent studies of PM2.5 have

shown, however, that intra-urban exposure gradients can

also be associated with atherosclerosis (Künzli et al. 2005)

and with high risk of premature death (Jerrett et al. 2005a).

These studies have used geostatistical interpolation models

that capture regional patterns of pollution well, but often

fail to account for the near-source impact from local traffic

and industry. Given the high risks of health effects reported

in these studies and others conducted in Europe (Hoek et

al. 2002; Nafstad et al. 2003), we need to refine the estimates of pollutant exposures and reduce uncertainties that

could be associated with measurement error.

Several recent studies have demonstrated the potential

of LUR to supply accurate, small-area estimates of air pollutant concentrations without the financial expense of dispersion or exposure modeling (Brauer et al. 2003; Briggs et

al. 2000a). The goal of LUR is to explain, to the extent possible, the variation in existing air quality data for a given

pollutant using data on nearby traffic, land use, emissions

from local sources, and population variables. In most cases,

multiple linear regression is used to develop and validate a

model using data from existing monitors and land-use data

that can then be applied to unmonitored locations provided the appropriate geographic data are available.

Ross and associates (2006) developed LUR models using

traffic data, distance to the coast, and road length measurements to predict NO2 levels in San Diego, California, that

explained nearly 80%of the variation in measured exposure levels. When the predicted levels were compared

with measured levels at validation locations — locations

that were not included in developing the model — they

were accurate to within, on average, 2.1 ppb. LUR models

to predict NO2 levels using traffic and other variables in

Montréal and several European cities also produced accurate predictions (Jerrett et al. 2005b).

In contrast to a more localized pollutant such as NO2,

however, PM mass has a significant regional component

that includes smaller contributions from local sources

(Bari et al. 2003). This complicates estimating intra-urban

exposure with LUR. Models of PM2.5 in three European

cities produced mixed results. One of the only studies to

attempt prediction of fine particle concentrations with the

LUR methods to date was undertaken in Europe as part of

the TRAPCA (Brauer et al. 2003). Researchers measured

PM2.5 for representative temporal periods over one year in

the Netherlands; Munich, Germany; and Stockholm

County, Sweden. They found significant differences in the

ability of the model to predict monitoring data from region

to region: values from LUR models could predict from

73% of the variation of monitored values (the Netherlands)

down to 56% and 50% (Munich and Stockholm, respectively). The limited variability of the monitoring sites in

Stockholm County was suggested as an explanation for the

difference in the Stockholm data (Brauer et al. 2003).

When combined with health data, an LUR model was able

to predict some childhood respiratory outcomes (Brauer et

al. 2002). More recently, in Germany, Hochadel and

coworkers (2006) found that LUR predicted PM2.5 absorbance (R2 = 65%–82%), but failed to predict PM2.5 mass

very well (R2 = 9%–17%).

North American cities have vastly different transportation and land-use patterns than those in Europe, and the

applicability of LUR to predict PM2.5 is unknown (Gilbert

et al. 2005). To our knowledge this study was the first

attempt to apply LUR to analyze PM2.5 in North America.

Materials and Methods

LUR uses concentrations of ambient pollutants at monitoring locations as the dependent variable. Surrounding

land-use, transportation, emissions from local sources,

and population data were obtained using GIS and

included in a regression equation as predictor variables. In

this study we assembled a database of information on land

use and transportation around the PM2.5 monitors in the

New York City region.

We constructed three models: one covering a 9-county

urban area using 3-year averages; another covering all

28 counties using 3-year averages; and a third covering the
D. Krewski et al.

33

28 counties using data for only the winter months of 2000.

After describing the data we collected, we present our statistical modeling strategy.

Dependent Variable: Ambient PM2.5 Data For the 28-

county models, counties were chosen to match those

included in the Best Practice Model (BPM; a tool for forecasting regional travel demand) used by the New York Metropolitan Transportation Council (NYMTC), the regional

council of governments established to help make transportation-related decisions in the region. Of those, 9 counties

were selected to represent the more urbanized area of New

York City’s five boroughs and adjacent New Jersey and

Connecticut counties.

In general, the 28 counties ranged from very urban to

relatively rural; 12 counties had fewer than 500,000 people

(with a minimum population of 96,000 in Putnam County)

and 6 counties had more than 1,000,000 people (maximum

population of 2,470,000 in Kings County). Most counties

had heavily urban populations (based on population per

square mile) — the populations were greater than 90%

urban in 22 counties and greater than 99% in 12 of those.

The 6 least-urban counties (Hunterdon, Warren, Sussex,

Orange, Dutchess, and Putnam), all of which are more than

50 km west or north of downtown Manhattan, range from

40%–76% urban. For comparison, the average county population for the three states is 340,000 and the average

urban percentage is 64%.

We calculated 3-year averages (from data in 1999–2001)

of PM2.5 for monitors in the 28-county region around New

York City (Figure 1) using data from the U.S. EPA Air

Quality Subsystem (AQS; a repository for ambient air

quality data made available for analysis). For each monitor

that had at least eight observations in a quarter (i.e., at least

half of the scheduled monitoring days), we first computed

quarterly means. When we could calculate a complete set

of four quarterly means for three years running, we computed 3-year averages from the 12 quarterly means. (We lost

only about 5% of the monitors due to incomplete data.)

We were also interested in assessing seasonal variations

in exposure, especially for SO4

2. New York City experiences long-range transport of secondary aerosols with a

high proportion of SO4

2in the warmer months. Because

SO4

2is also formed regionally, the exposure gradient

within cities is more spatially homogenous during the

warmer months when secondary aerosols are transported.

By contrast, in the winter months, SO4

2concentrations are

strongly influenced by local emissions from automobile and

truck traffic, shipping, heating, and industry. The exposure

gradient within the city is therefore generally steeper

during the winter months when the dominant regional

exposure is absent. The steeper gradients increase the statistical power of our health effects models to detect associations between air pollution and mortality.

Thus, for winter of 2000 in 28 counties, we computed

pollutant averages using data from January, February, and

March. We limited the winter-2000 analysis to those sites

that could provide both 3-year and winter-2000 averages.

Data from 62 monitors in the 28-county region were

included in the 3-year analysis of the 28-county area (1999–

2001), data from 36 in the 3-year analysis of the 9-county

area (1999–2001), and data from 45 for the 28-county area in

winter (2000).

At nine monitoring locations, two types of monitors

were used concurrently: the Federal Reference Method

(FRM) and the tapered-element oscillating microbalance

(TEOM) continuous monitoring method. When both TEOM

and FRM data were collected at the same site, we used the

FRM data.

For most monitors, the AQS included latitudes and longitudes for the monitors, but some were inaccurate. We

therefore used locations provided directly by the appropriate state agencies (the Department of Environmental

Conservation in New York and the Departments of Environmental Protection in New Jersey and Connecticut). In

most instances, the locations were identified by the agencies with either a global positioning system (GPS) or orthophotos in a GIS. For a limited number of older sites, the

more accurate methods had not been used by the agencies,

so we manually verified their locations in a GIS using a

combination of road layers, orthophotos, New York State

Department of Transportation Raster Quadrangles, and

USGS Digital Raster Graphics.

Independent Variables: Traffic, Land-Use, Population, 

and Local Emissions Data For each air monitoring location in the AQS for which we had adequate data on PM2.5,

using our GIS software we generated circular buffers

around the monitor location with different radii (50 m, 100

m, 300 m, 500 m, and 1000 m). All of the mapped data in

our GIS layers (as described below) were then linked with

the circular buffers, and the traffic and land-use data for all

buffers around each air monitoring location were calculated. Calculations were performed with a vector data

structure using ArcGIS 9.1 (ESRI 2004).

Traffic and Road Data The NYMTC provided traffic

estimates for 2002 for New York City and surrounding

counties that covered approximately 40,000 road links in

28 counties using the BPM, a tool used for forecasting

regional travel demand in the metropolitan area. The

BPM makes use of traffic data from more than a dozen
34

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

municipal and other sources. The road network used in

the BPM was developed to include all minor arterial roads

or larger from five separate network databases. The five

had been conflated to match the LION street centerline file

for New York’s five boroughs (from the City of New York

Department of City Planning 2009) and the USCB Topologically Integrated Geographic Encoding and Referencing

Database. A complete description of the BPM model and

development of mapped roadways can be found in Parsons

Brinckerhoff Quade & Douglas, Inc. (2005). The traffic estimates, divided by vehicle type (heavy- and light-duty

trucks, buses, vans, cars), are available for the four time

periods of morning, midday, afternoon, and night. We used

estimates for afternoon rush-hour traffic flow because a

complete set of estimates (for all 40,000 links) for the

other time periods was not yet available. We calculated

totals for truck traffic and total traffic for each buffer zone

around each monitor. Traffic data were scaled to 1000s of

vehicle-kilometers per hour. We also used this data source

to calculate road density (total length of roadway in kilometers) in each buffer area.

Land-Use Data Layers of land-use data were assembled

from several sources: extremely detailed (1 inch = ~250

feet) tax-lot data from the New York City Department of

City Planning for all tax lots in the five boroughs (2003);

medium-scaled (1 inch = ~40,000 feet) land-use data for

New Jersey from the New Jersey Department of Environmental Conservation (1995/1997); and coarse scale (1 inch

= ~100,000 feet) land-use data from the USGS National

Land Cover Data 1997 (based on Landsat satellite images

from 1992 and confirmed using aerial photos). For each

land-use layer we calculated the total area of an individual

land-use category (e.g., industrial, forest, residence) in

each buffer. Although the tax-lot and New Jersey land-use

data layers were more detailed, only the data from the

USGS covered the entire area of interest.

Figure 1. Locations of PM2.5 monitors in the New York City region.
D. Krewski et al.

35

Similar uses within each land-use layer were grouped to

create aggregate variables. The land-use categories within

the three layers were not identical but comparable groupings were tabulated. We generated industrial and residential categories from all three land-use layers. In addition,

for the New Jersey Department of Environmental Conservation data and the National Land Cover Data, we created

water, vegetation, and barren land-use variables.

The “Industrial” category, discussed throughout most of

this section, comprises the “Industrial / Commercial /

Transportation” category from USGS data and the New

Jersey land-use data. We created a comparable grouping

using the New York City tax-lot data by combining the categories denoted “Industrial and Manufacturing”, “Transportation and Utility” and “Commercial and Office Buildings.”

The New York City tax-lot data and the New Jersey land-use

data were used only for sensitivity analysis because they

did not cover the entire region. Land-use areas were converted to acres (1 acre = 4047 m2).

Population Data Census data for the New York City

study area were acquired at the block-group level from

the USCB 2000 Summary File 1 (for population and

housing data) and Summary File 3 (for income data). All

population and housing data are in 1000s or percentages;

income data were not scaled (U.S. Census Bureau 2000).

These data were used as possible predictor variables in

the LUR model.

Local Emissions Data We calculated primary emissions

of PM2.5 (area, point, off-road mobile, and on-road mobile)

for each county and assigned to each monitor the value

associated with its county as a possible predictor variable.

In addition, for each buffer area, we calculated the number

of point sources and the amount of PM2.5 emissions using

data for 1999 from the U.S. EPA National Emissions Inventory (1999).

Statistical Data Analysis We developed three separate

models: two that included data from all 28 counties, one of

which used the 3-year-average PM2.5 data and one that

used the winter-2000 PM2.5 data; and a third that used 3-

year-average PM2.5 but was geographically limited to the

nine more urbanized counties.

We started with 62 monitors in the 28 counties. First,

based on each monitor’s land-use code in the AQS, we

divided them into agricultural/forest, commercial, industrial/mobile, and residential categories. Next, to validate

our predictions later, we randomly removed 20% of the

monitors to use for validating the predictions that would

be generated. Samples (monitors) were removed from each

of the 28-county and 9-county models separately using the

same criteria. The remaining monitors were used to

develop the models.

The 28-county 3-year model included 49 monitors for

developing the model (plus 13 removed for validation; 62

total), the 28-county winter-2000 model included 36 monitors for modeling (plus 9 removed for validation; 45 total),

and the 3-year 9-county model included 29 monitors for

modeling (plus 7 removed for validation; 36 total) (Table 12).

We then considered more than 25 separate variables

within five different buffer distances around each monitor

for possible inclusion in the final three models. These

included total traffic, total truck traffic, industrial land

use, residential land use, total county-wide emissions,

point emissions, total number of point sources of PM2.5,

total population, number of housing units, median income

of population, and the percentage of the population who

are not white.

Table 12. Modeling and Validation Monitors by Land-Use Category

Land-Use 

Categorya

3-Year 

Models

Winter-2000 

Average

28-County

Total

28-County

Validation

9-County

Total

9-County

Validation

28-County

Total

28-County

Validation

Industrial / mobile 8 2 6 1 5 1

Residential 32 6 19 4 24 5

Commercial 18 4 11 2 12 2

Agricultural / forest 4 1 0 0 4 1

Total 62 13 36 7 45 9

a

 Based on codes in the U.S. EPA’s AQS.
36

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

All of the predictor variables discussed above were

included in a combination of forward, stepwise, and allsubsets selection procedures. We included variables in the

model based on the explained sums of squares, the Mallows Cp statistic, the variance inflation factor (VIF)

method, and other diagnostic tools. In the forward selection process, as the strongest variables are added to the

model the remaining variables are reevaluated for inclusion. As such, the same variable at a different buffer distance may be included if it outperformed other related

variables. (For example, in a model that included total

traffic in the 300-m buffer, total traffic in the 500-m buffer

might also be used if it outperformed residential land use.)

The sensitivity of model parameters to the sample selection procedure was evaluated with a bootstrap method in

which five random samples were excluded, the model was

run, the coefficients were recorded, the five samples were

returned to the pool, and another random sample of five

was removed. This process was repeated 10,000 times.

To evaluate the validity of the assumption that all data

points are independent, we tested the spatial autocorrelation in the 3-year-average and winter-2000 PM2.5 monitor

values themselves and then in the residuals from our final

28-county models using the Moran I statistic (Bailey and

Gatrell 1995) and two neighborhood constructions — a

Queen contiguity matrix based on Thiessen polygons, and

a nearest-neighbor approach in which we limited the analysis to the three nearest monitors. Statistical significance

was tested using a permutation test with 999 iterations. We

also qualitatively assessed the extent of spatial autocorrelation in the PM2.5 concentrations using variograms of the

residuals from our final models.

For visualization purposes, we created a smooth interpolated surface by predicting PM2.5 concentrations at 5600

random point locations (200 per county) with the LUR formula and kriged these predictions. To compare our LUR

estimates to the exposure interpolation method that is

commonly used in health studies, we also kriged the PM2.5

values from the monitors. All the statistical analysis was

conducted using R statistical software and the GSTAT

library (Pebesma 2004; R Development Core Team 2005).

We evaluated the quality of the predictions at validation

locations by calculating the root-mean-squared error

(RMSE) at each location. We assessed the quality of the

final models (those generated after returning the validation

locations to the pool of samples) by calculating the RMSE

based on fitted values in a leave-one-out cross validation.

Results

Descriptive Statistics for PM2.5 PM2.5 3-year-average

values for both the 28- and 9-county regions were approximately normally distributed (Figure 2) with a mean for

the 28-county region of 14.3 μg/m3 (median = 14.3, SD =

1.78) and a mean for the 9-county region of 15.3 μg/m3

(median = 15.1, SD = 1.42). The highest concentrations

were located in 4 counties including Manhattan, New

York; Bergen and Hudson, New Jersey; and New Haven,

Connecticut. Most of these locations are situated in close

proximity to major highways. Locations with higher concentrations tended to be located closer to Manhattan.

Winter-2000 averages were also approximately normally

distributed with a mean of 14.0 μg/m3 (median = 14.3, SD

= 2.55); three of the four highest concentrations were

Figure 2. Distributions of mean PM2.5 concentrations. Mean for the 28-county 3-year average was 14.3 μg/m3 (median = 14.3; SD ± 1.78), for the 9-county

3-year average was 15.3 μg/m3 (median = 15.1; SD ± 1.42), and for the 28-county winter-2000 average was 14.0 μg/m3 (median = 14.3; SD ± 2.55). Analyses were based on the full set of samples with no validation samples removed.
D. Krewski et al.

37

located in Manhattan and one in New Haven. These statistics are based on the full set of samples with no validation

samples removed.

LUR Model Building and Results

28-County and 9-County Models Based on 3-Year-Average

Concentrations For both regions, the total traffic variable

was the strongest predictor of PM2.5 concentrations (see

Table 13 for distributional statistics on final predictors).

Total traffic in the 500-m buffer and total traffic in the 300-

m buffer, in particular, led all other variables in explanatory power. Urbanization-related variables, primarily in

the 500- and 1000-m buffers, were also strong predictors of

PM2.5 concentrations both with and without total traffic in

the 500-m buffer included in the model. These included

total population in both the 500- and 1000-m buffers and

numbers of both households and housing units in the

1000-m buffer.

With total traffic in the 500-m buffer and total population in the 500-m and 1000-m buffers in the model, the

next strongest predictor was industrial land use in the 300-

m buffer for both the 28-county and 9-county regions.

The final models for both of these regions therefore

included total traffic in the 500-meter buffer, total population in the 1000-meter buffer, and industrial land use in

the 300-meter buffer (Table 14).

The 28-county 3-year model predicted PM2.5 at validation locations to within, on average, 0.93 μg/m3 (6.5%) of

actual concentrations with a RMSE of 1.10. The 9-county

model predicted PM2.5 at validation locations to within,

on average, 0.77 μg/m3 (5.0%) of actual concentrations

with a RMSE of 0.87. The predictions for validation sites

showed some bias, particularly in the 28-county 3-year

model, which was likely to over-predict PM2.5 values,

though no bias was apparent in the models after including

the validation samples (Figure 3).

Within the 28-county 3-year model, the predictions

were better for those validation sites located in the more

urbanized 9-county area. The nine validation sites located

within the 9 counties had a RMSE of 0.90, whereas the four

sites located in the other 19 counties had a RMSE of 1.45.

This distinction, however, is due primarily to a single large

residual in Waterbury, Connecticut, at a site with the

second highest industrial land use among all the 62 locations in the 28-county area and relatively high traffic.

When the validation samples were returned to the pool

of modeling samples and the models were run again, the

regression parameters remained similar; the parameter for

the industrial land-use variable changed most between

runs. The average absolute value residuals based on the

fitted values (for observations included in the modeling

rather than the prediction of validation locations) from the

full models (no excluded samples) were 0.85 μg/m3 or

6.0% of actual concentrations for the 28-county 3-year

model (with an RMSE based on a leave-one-out cross validation of 1.15), and 0.69 μg/m3 or 4.5% of actual concentrations for the 9-county 3-year model (RMSE of 1.00). The

final 28-county 3-year model explained 64% of the variation in the PM2.5 measured values from the validation

locations, and the 9-county 3-year model explained 62%

of the variation. Plots of the fitted and observed values are

pictured in Figure 4.

Models included data from a mix of FRM and TEOM

monitors: for the 28-county 3-year model, 50 FRM and 12

TEOM; for the winter-2000 model, 42 FRM and 3 TEOM;

for the 9-county 3-year model, 25 FRM and 11 TEOM.

Although plots of the nine sampling locations with both

FRM and TEOM monitors showed little or no bias (four

Table 13. Distribution Statistics for Final Predictor Variablesa

28-County 3 Years 9-County 3 Years 28-County Winter-2000

Total

Traffic 

(500 m)

Total

Population 

(1000 m)

Industrial 

Land Use 

(300 m)

Total

Traffic 

(500 m)

Total

Population 

(1000 m)

Industrial 

Land Use 

(300 m)

Total

Traffic 

(300 m)

Total

Population 

(1000 m)

Vegetation 

Land Use 

(1000 m)

Minimum 0.00 0.21 0.00 0.00 0.21 0.00 0.00 0.21 4.92

1st quartile 1.46 6.31 1.17 2.41 16.75 3.40 0.33 6.09 31.36

Median 4.55 14.53 9.23 5.38 36.47 7.87 1.07 11.19 96.66

Mean 6.41 27.52 14.26 8.03 42.60 12.85 2.21 22.97 125.90

3rd quartile 9.80 42.28 20.34 13.48 55.01 16.59 3.83 27.22 122.10

Maximum 24.06 119.40 52.70 24.06 119.40 45.77 8.35 119.40 697.10

a

 Traffic units: 1000s of vehicle-km/hour; total population units: 1000s of people; land-use units: acres.
38

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Table 14. Statistics for Final LUR Models

Parameter

Value SE t Statistic P Value VIF

28-County 3 Years

Intercept 12.273 0.261 46.965 0.000 —

Total traffic (500 m) 0.121 0.027 4.530 0.000 1.344

Total population (1000 m) 0.031 0.006 5.704 0.000 1.378

Industrial land use (300 m) 0.028 0.010 2.721 0.009 1.253

Multiple R2 0.642

Model P value 0.000

9-County 3 Years

Intercept 13.171 0.364 36.232 0.000 —

Total traffic (500 m) 0.098 0.025 3.967 0.000 1.196

Total population (1000 m) 0.020 0.006 3.547 0.001 1.359

Industrial land use (300 m) 0.040 0.013 3.005 0.005 1.321

Multiple R2 0.617

Model P value 0.000

28-County Winter 2000

Intercept 12.841 0.509 25.214 0.000 —

Total traffic (300 m) 0.463 0.106 4.370 0.000 1.106

Total population (1000 m) 0.033 0.010 3.355 0.002 1.181

Vegetation land use (1000 m) 0.005 0.002 2.453 0.019 1.200

Multiple R2 0.607

Model P value 0.000

Figure 3. Predicted versus actual values for the validation samples. 13 samples for the 28-county 3-year model; 7 for the 9-county 3-year model; and 9 for

the 28-county winter-2000 model.
D. Krewski et al.

39

locations had higher TEOM values and five had higher FRM

values), we tested the inclusion of an indicator variable in

the models discussed above to control for a possible effect.

This indicator was not significant in any of the models and

was excluded.

The 9-county 3-year model was not used for the spatial

analysis of mortality and pollution. It was a means of

testing whether less detailed land-use data would affect

model predictions and whether the same variables would

be strong in a more urbanized context (if the more rural

counties were eliminated).

28-County Model Based on Winter-2000 Concentrations

Developing the model of winter-2000 concentrations led to

a very similar model as those based on 3-year-average

PM2.5 concentrations. Length of road in the 1000-m buffer,

total area of high-density residential land use in the 1000-

m buffer, and total traffic within the 1000- and 500-m

buffers were, singly, the top predictors of winter PM2.5

concentrations after validation samples were removed.

Although these variables appeared to individually predict

well, their collinearity with other strong candidate predictors and highly influential observations pointed us to more

robust variables with slightly less predictive power.

The final 28-county winter-2000 model included total

traffic in the 300-m buffer, total population in the 1000-m

buffer, and vegetative land use in the 1000-m buffer (Table

14). The inclusion of the vegetative land-use variable was

strongly influenced by two rural monitors in New Jersey.

Removing these two points reduced the level of statistical

significance of the variable to P < 0.15. The value of the

parameter for vegetative land use remained very similar

(0.00457 vs. 0.00460) with and without these points

and we opted to retain the vegetative land use variable

because it helped to account for rural–urban differences.

The average absolute value residual from the predictions

at validation locations was 1.37 μg/m3 (within an average

of 11% of actual concentrations) with a RMSE of 1.72.

These numbers were inflated by one large residual at a

Brooklyn location with an unusually high population density. Without this one location, the average absolute value

residual is 1.08 μg/m3 (9% of actual) with a RMSE of 1.27.

For the 28-county winter-2000 model, the predictions

were, on average, better at the five sites outside of the 9

urbanized counties than at the four validation sites inside

(RMSE of 1.30 outside vs. 2.14 inside). The distinction

between highly urbanized and less urbanized areas, again,

is primarily a product of the single large residual at the

Brooklyn location. Among the 36 modeling samples used

for the winter-2000 model in the 28-county area, 19 were

inside the 9 more-urbanized counties and 17 outside.

When the validation samples were returned to the pool

of modeling samples and the models were run again, the

parameters remained similar in that the greatest change

(approximately 15%) occurred in the parameter for vegetative land use. The average absolute value of the residuals

based on the fitted data from the full models (no excluded

samples) was 1.19 μg/m3, which represents an average of

9% of actual concentrations with a RMSE of 1.69 μg/m3

based on a leave-one-out cross validation. This final 28-

county winter-2000 model explained 61% of the variation

in PM2.5 concentrations. Interpolated surfaces of LUR predictions are shown in Figure 5.

Figure 4. Fitted versus actual values for final models.
40

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 5. Interpolated LUR predictions for the 28-county and 9-county 3-year models and the 28-county winter-2000 model. (Figure continues next

page.)
D. Krewski et al.

41

Figure 5 (Continued).
42

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 5 (Continued).
D. Krewski et al.

43

Bootstrap Analysis The bootstrap results for all three

models showed that they were relatively stable to the

choice of samples (Figure 6). We observed a slight bimodal

shape in the industrial variable in the 9-county model. In

all cases, alterations from normality were caused by a

single sample (monitor) (AIRS ID 340030004) located near

Fort Lee, New Jersey, just 1.5 miles from Manhattan and

extremely close to Interstate 95 and the George Washington Bridge. This one point had the highest PM2.5 concentration of all samples. We had no reason to believe that

the underlying data for this station were inaccurate and

the monitor was not excluded.

Figure 6. Results of bootstrap analyses for final parameters in the 28-county and 9-county 3-year models and the 28-county winter-2000 model. Example:

For total traffic in the 500-m buffer (upper right panel), 0.121 was the parameter from the 28-county 3-year model; the histogram shows how that parameter

changed when sites were randomly removed. (Figure continues next page.)
44

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 6 (Continued).
D. Krewski et al.

45

Figure 6 (Continued).
46

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Spatial Autocorrelation As expected, we found that the

PM2.5 values were highly autocorrelated as shown by the

Moran I values of 0.52 (P = 0.001), 0.27 (P = 0.003), and

0.43 (P = 0.001) for the 28-county 3-year, 9-county 3-year,

and 28-county winter-2000 models, respectively, when we

used the Queen’s contiguity matrix, and 0.49 (P = 0.001),

0.38 (P = 0.002), and 0.37 (P = 0.002) for the same models

when we used a three-nearest-neighbor approach. The spatial autocorrelation in our residuals from the full models

was considerably diminished and generally nonsignificant, which suggests that the models did not violate the

independence assumption and that the included covariates accounted for the autocorrelation. The residuals produced Moran I values of 0.21 (P = 0.003), 0.09 (P = 0.108),

and 0.08 (P = 0.105) for the 28-county 3-year, 9-county 3-

year, and 28-county winter-2000 models, respectively,

when we used the Queen’s contiguity matrix, and 0.10 (P =

0.101), 0.01 (P = 0.324), and 0.03 (P = 0.496) when we

used the three-nearest-neighbor approach. Variograms of

the raw PM2.5 values showed considerable spatial autocorrelation but the pattern was significantly diminished and

barely visible in variograms of the residuals from the full

models (variograms not shown).

Kriging PM2.5 Values from Monitors Despite the limited

number of samples (49, 29, and 36 for the 28-county 3-

year, 9-county 3-year, and 28-county winter-2000 models,

respectively), kriging based on exponential models performed surprisingly well, and even outperformed LUR at

many locations. For the 28-county 3-year model we calculated an average absolute value residual of 0.68 and a

RMSE on these predictions of 0.90 (compared with 1.10 for

LUR). For the 9-county model, the average absolute value

residual was 0.48, with a RMSE of 0.61 (compared with

0.87 for LUR). For the 28-county winter-2000 model, we

calculated an average absolute value residual of 1.39 and a

RMSE of 1.55 (compared with 1.72 for LUR).

Although kriging performed well when predicting measured PM2.5 concentrations at validation locations using

only the modeling samples, the LUR model outperformed

kriging based on the results of cross validation analyses

using the full set of samples. The RMSEs of cross validation predictions, for example, are 1.15, 1.00, and 1.69 for

the LUR compared with 1.30, 1.47, and 2.04 for the kriging

(28-county 3-year, 9-county 3-year, and 28-county winter2000 models, respectively).

Discussion

We developed three LUR models for predicting PM2.5

concentrations in New York City and surrounding counties

using a combination of traffic, land-use, population, and

local emissions variables. These models explained more

than 60% of the variability in measured PM2.5 concentrations and they predicted concentrations at validation locations that were generally within 10% of actual values.

All models included total traffic in the 500-m or 300-m

buffer and total population in the 1000-m buffer. The 28-

county and 9-county 3-year models also included the

industrial land-use in the 300-m buffer variable, whereas

the 28-county winter-2000 model included a variable representing vegetative land use in the 1000-m buffer. There

was little difference between the 28-county models based

on winter-2000 concentrations and on the 3-year averages.

The relative strength of a smaller (300-m vs. 500-m) buffer

for total traffic in the 28-county winter-2000 model does

suggest a stronger local influence; but otherwise, the difference in regional and local contributions did not appear

to strongly influence the model.

Given the presumed impact of local traffic, we would

have expected variables in smaller buffers to be the strongest predictors of PM2.5 concentrations. Although they

were indeed good predictors (traffic in the 100-m buffer,

for example, explained approximately 25% of variation in

the 28-county 3-year concentrations), they did not perform as well as variables in larger buffers. This is attributable to a number of factors. Since PM2.5 is a pollutant with

both regional and local contributors, it is possible that

strong predictor variables are able to explain some variation for both sources. Predictors based on relatively small

buffers may also perform better when modeling highly

concentrated samples (which was not the case in this

analysis). Finally, as demonstrated through European

studies (Hochadel et al. 2006), PM2.5 varies more gradually over space than elemental carbon and therefore the

strength of the larger buffers may reflect this large-area

variation in the pollutant.

In general, these variables (total traffic 500 m and 300 m,

total population 1000 m, industrial 1000 m, and vegetation

1000 m) were robust regardless of which specific

monitoring locations were omitted from the model to use

for validation, although we found that the industrial landuse variable was affected by the inclusion or exclusion of a

New Jersey site that had the highest PM2.5 concentration.

Nevertheless, the fact that the variable for industrial land

use in the 300-m buffer in the five New York City boroughs

was also significant when modeled without the influence of

the New Jersey sample lends support to the important role

played by industrial land use. Furthermore, we found that

an industrial land-use variable derived from New York

City's tax-lot data — which are limited to the five boroughs,

have a very different resolution, and have slightly different

land-use characterizations — was also a statistically
D. Krewski et al.

47

significant predictor of PM2.5 levels. This supports the

assertion that industrial land use is indeed explaining

variation in PM2.5 and that the result we found was not due

to the idiosyncratic nature of the data we included.

The 28-county 3-year model without validation samples

exhibited some bias in that it over-predicted ten values

and under-predicted three. The bias was less evident, but

still existed, in the full model that included the validation

samples (34 were over-predicted and 28 were under-predicted). The under-prediction appears to have occurred in

the more urbanized New Jersey counties and over-prediction appears to have occurred in areas distant from New

York City and, to a lesser extent, in eastern New York City

and Long Island. The bias did not exist in the 9-county

model. Although slight bias was present, the models performed well overall on cross-validation.

Although the RMSEs for the 28-county 3-year and

winter-2000 models suggest that predictions at validation

locations were more precise for the 3-year values than for

the winter values, they also disguise differences in variance and spread. The winter-2000 PM2.5 values had

approximately twice the variance and a 24% greater interquartile range than the 3-year values. The mean absolute

percentage error (MAPE) may allow more appropriate

comparison; it reveals that the predictions are more similar in percentage terms: The MAPE for the predictions at

validation locations was 6.5% for the 28-county 3-year

model and 6.2% for the 28-county winter-2000 model (and

5.0% for the 3-year 9-county 3-year model).

We found that when the validation samples (about 20%

of the total) were omitted, kriging models more accurately

predicted the monitored values at the validation sites

than the LUR models; but when the validation samples

were returned to the pool of modeling samples, LUR performed better. Since ordinary kriging employs only data

on the variable of interest, it is not vulnerable to unusual

values in potential predictor variables. The extreme

values for traffic in some areas of New York City, for

example, could inflate (or deflate) LUR parameters, particularly in models based on a limited number of observations. At the same time, kriging the raw monitoring data

limits the potential for kriging to capture small-area variation such as an intersection of major highways. Kriging,

as a result, may “over-smooth” and is likely to miss areas

with unusually high PM2.5 concentrations. It is also possible that kriging in this context violates the fundamental

assumption of stationarity — that relationships between

exposure levels and pollutant sources are constant over

time and space. Points close to high-traffic freeways, for

example, may exhibit a different relationship between

PM2.5 and distance than would points in rural areas far

from any major source of PM2.5.

PM2.5 exhibits strong seasonal and diurnal patterns in

New York City; concentrations are higher in the summer

months and during the morning (6 AM to 9 AM) and late

evening (5 PM to 10 PM) hours (DeGaetano and Doherty

2004). Although this set of analyses makes use of PM2.5

concentrations averaged over time, future analyses may

wish to consider these patterns and develop, for instance,

daily or seasonal maps that could highlight short-term

variability (Christakos and Serre 2003).

As mentioned in the Materials and Methods section,

incomplete data provided by the NYMTC precluded our

use of traffic data for all time periods. For example, it is

likely that models based on average daily traffic, rather

than average daily afternoon rush-hour traffic, would lead

to different parameters. It is also possible that an analysis

that relies on rush-hour traffic alone (such as this one)

could lead to a wider spread of PM2.5 predictions because

areas of high traffic in off-peak times may also have a significantly larger increase in traffic during rush hour. Nevertheless, we would expect relative rates of traffic for

different areas to remain similar. We found, for example,

the Spearman rank correlation coefficient for evening

(4 PM to 8 PM) rush-hour traffic compared with night-time

(8 PM to 6 AM) traffic for the same road segments is 0.93;

this suggests that the results would likely have been similar had we used total average daily traffic.

These models demonstrate that PM2.5 can be predicted

using LUR in North America. Using a combination of

traffic, population, land-use, and local point-source emissions variables we were able to predict PM2.5 concentrations well at validation locations and predict more than

60% of the variation in PM2.5 measurements over a wide

area. Although the three models are not identical in variables or parameters, their similarity reinforces the relationship between PM2.5 concentrations and land use near

monitors. Given the strong predictive power of both LUR

and kriging, we also applied kriging with external drift (a

technique that combines aspects of LUR with kriging) to

the data. The limited residual autocorrelation from the

LUR, however, precluded adequately fitting variograms

and predictions were not improved using this technique.

These models, and LUR models in general, hold particular promise in epidemiologic settings in which small-area

variations can be associated with significant health effects.

Whereas these models seem to predict well, data limitations hampered our ability to investigate all potentially

useful predictors. In particular, further research is needed

on the possible effects of street canyons and of seasonal

variations in PM2.5.
48

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

SPATIAL ANALYSIS OF AIR POLLUTION AND 

MORTALITY IN NEW YORK

Materials and Methods

Study Population Mortality data were extracted from the

ACS CPS-II database for subjects in 746 ZCAs in the New

York City region (28 counties) (Figure 7). In total, 43,930

subjects lived in these ZCAs and 10,525 deaths were

recorded during the follow-up from 1982 to 2000.

Assessment of Exposure to PM2.5 Regression equations

were developed to predict PM2.5 concentrations around air

monitoring locations in the New York City region using

proximate traffic, population, and land-use data (see the

section A Land-Use Regression Model for Predicting PM2.5

Concentrations for details). The dependent variable in our

LUR was PM2.5; 3-year averages for 1999 through 2001

were calculated using daily data from the EPA AQS.

In total we assembled complete data for 62 sites in 28

counties. Models were fitted for the 3-year averages and

the winter-2000 averages (January through March). The

winter average was used to reduce the impact of regional

transport of secondary SO4

2aerosols; this allowed us to

assess particles that may be more toxic and are likely to

result from local traffic and industry.

Figure 7. New York City study region. 
D. Krewski et al.

49

Land-use data were assembled from several sources at

several resolutions. These included detailed (1 inch =

~250 feet) tax-lot data from the New York City Department

of City Planning (2003), medium scale (1 inch = ~40,000

feet) land-use data for New Jersey from the New Jersey

Department of Environmental Conservation (1995/1997),

and coarse scale (1 inch = ~100,000 feet) land-use data

(National Land Cover Data) from the USGS (1997, based on

Landsat images from 1992 and confirmed using aerial

photos). Data on traffic and road conditions were supplied

by the NYMTC. (We also conducted sensitivity analyses to

compare the results of the LUR models of exposure with

results from ordinary kriging models. See details of the

model derivation and validation in the section A Land-Use

Regression Model for Predicting PM2.5 Concentrations;

also published in Ross et al. 2007).

Ecologic Covariates Contextual conditions for the ZCAs

where subjects lived at enrollment were controlled with

the ecologic covariates (from USCB 2000; see sidebar),

which included poverty, income disparity, unemployment, median household income, education attainment,

and three descriptors of ethnic origin: percentage who are

white, Hispanic-American, and African-American (we

used only “percentage white” in the statistical models due

to its high inverse correlations with the other ethnic categories). Finally, percentage of homes with air conditioning

was used to assess possible infiltration of particles; a

greater proportion of air conditioning use could lead to

lower indoor exposure levels.

Statistical Methods and Data Analysis

Detailed methods are reported in an earlier krigingbased exposure study (Jerrett et al. 2005a) and in the

Nationwide Analysis section of this report. Briefly, we

used the random effects Cox model with random effects

defined at the ZCA scale and assuming positive correlation

between random effects in neighboring areas. Sensitivity

analyses were also conducted using two levels of clustering (e.g., ZCA and MSA) as well as with controls for the

likely correlation of observed values for the cohort (nonindependence) in the random effects model, similar to the

approach used for the Nationwide Analysis.

Exactly the same 44 individual-level covariates identified

in earlier ACS studies of the health effects of air pollution

were included. These variables (see sidebar) measure lifestyle, diet, demographics, occupational, and educational

factors that may confound the air pollution–mortality

association (Pope et al. 2002). There were 12 different

smoking variables included in every model. Controls for

various ecologic covariates were also included.

Results

LUR Exposure Models The final 28-county 3-year LUR

model included the three predictors of total traffic within

500 m, total population within 1000 m, and industrial land

use within 300 m of the monitors. This model predicted

66% of the variation in PM2.5 and estimated PM2.5 concentrations at the 13 validation locations to within 0.93 μg/m3

(6.5%) of actual concentrations. Concentrations were generally highest in the central parts of New York City, especially in Manhattan. The 28-county winter-2000 model

used similar independent variables, but explained slightly

less of the variation, with a correlation coefficient of ~

0.60.

Mortality Models Table 15 summarizes the health effects

estimates for all-cause and cause-specific deaths in the 28-

county 3-year model. Only IHD had a significant positive

association with PM2.5. The IHD results were insensitive to

control for the 44 individual-level variables and for most

of the ecologic covariates. HR estimates were lower, but

positive (with confidence intervals including unity) for

models that contained percentage of unemployment,

median household income, or all seven ecologic covariates

together in the model with the 3-year average PM2.5 concentration For the 28-county winter-2000 model, effects

were significant and relatively stable for IHD with all variable specifications, including those that controlled for all

seven ecologic covariates and for copollutant exposure to

O3 (see Table 16).

We compared the 28-county 3-year average PM2.5 concentration at the 90% decile and at the 10% decile in the

exposure distribution and found a difference of 1.5 μg/m3.

For this exposure contrast with the fully adjusted model,

the HR for PM2.5 and IHD was 1.06 (95% CI, 1.00–1.12). For

the 28-county winter-2000 average PM2.5 exposure, the difference between the 10% and 90% deciles was 3.9 μg/m3

PM2.5, and the HR for IHD was 1.21 (95% CI, 1.07–1.34;

Table 16). Diabetes and endocrine deaths also had large HR

estimates (in the range of 1.2 to 1.4; Table 16), although

these were confounded by the ecologic covariates.
50

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality Table 15. HRs by Cause of Death Associated with an Interdecile Comparison Value of 1.5 μg/m3 from the 28-County 3-Year Modela Covariates All Cause IHD ICD-9: 410–414 CPD ICD-9: 400–440, 460–519 Lung Cancer ICD-9: 162 Diabetes ICD-9: 250 Endocrine Disorders ICD-9: 240–279 Deaths (

n) 10,559 2,735 4,625 853 118 206

PM2.5 onlyb 1.011(0.935–1.047) 1.109 (1.039–1.182)c 0.977 (0.926–1.031) 1.036 (0.911–1.179) 1.326 (0.999–1.759) 1.249 (1.000–1.561)+ 44 Individual-level 

covariates

0.984 (0.948–1.020) 1.072 (1.003–1.147) 0.953 (0.902–1.007) 0.955 (0.836–1.091) 1.329 (0.983–1.797) 1.224 (0.965–1.552)

Ecologic covariatesd

Air conditioning (%) 0.987 (0.951–1.024) 1.071 (1.001–1.147) 0.956 (0.904–1.011) 0.962 (0.841–1.102) 1.332 (0.980–1.809) 1.235 (0.973–1.567)White (%) 0.987 (0.950–1.026) 1.088 (1.013–1.169) 0.950 (0.895–1.008) 1.002 (0.872–1.151) 1.273 (0.920–1.762) 1.206 (0.937–1.554)Unemployed (%) 0.970 (0.933–1.009) 1.066 (0.992–1.146) 0.927 (0.874–0.984) 0.962 (0.835–1.108) 1.239 (0.891–1.723) 1.172 (0.905–1.518)Median household 

income ($000’s) 0.968 (0.930–1.007) 1.050 (0.976–1.130) 0.922 (0.868–0.979) 0.950 (0.824–1.095) 1.249 (0.892–1.751) 1.165 (0.895–1.517)

Grade 12 education (%) 1.006 (0.968–1.045) 1.139 (1.059–1.224) 0.983 (0.927–1.042) 0.986 (0.858–1.135) 1.236 (0.892–1.714) 1.191 (0.923–1.537)Income disparity (Gini) 0.998 (0.959–1.039) 1.102 (1.023–1.188) 0.969 (0.912–1.029) 0.961 (0.832–1.110) 1.293 (0.918–1.823) 1.218 (0.933–1.591)Poverty (%) 0.979 (0.940–1.021) 1.080 (1.000–1.165) 0.929 (0.873–0.990) 1.004 (0.866–1.163) 1.247 (0.875–1.778) 1.188 (0.902–1.565)

All 7 ecologic covariates 0.977 (0.932–1.025) 1.072 (0.980–1.172) 0.940 (0.875–1.011) 0.985 (0.832–1.166) 1.083 (0.723–1.621) 1.143 (0.835–1.564)Ozonee 0.997 (0.955–1.042) 1.058 (0.975–1.148) 0.963 (0.902–1.029) 0.967 (0.828–1.129) 1.325 (0.918–1.912) 1.319 (0.992–1.754)Ozone + all 7 ecologic 

covariates

0.982 (0.934–1.034) 1.054 (0.957–1.161) 0.938 (0.868–1.014) 1.000 (0.836–1.196) 1.099 (0.705–1.711) 1.221 (0.871–1.712)

a The PM2.5 concentration of 1.5 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposure distribution from the 28-

county 3-year model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. 

N = 44,056 CPS-II participants living in the 28 

counties. Bolded data refer to text. HRs are followed by 95% confidence intervals.

b Predicted using the 28-county 3-year model.c Significant positive association.d Ecologic covariates were included one at a time to a base model with PM2.5 + 44 individual-level covariates. e Summer O3 values for 1999–2000.
D. Krewski et al.

51

Table 16. HRs by Cause of Death Associated with an Interdecile Comparison Value of 3.9 μg/m3 PM2.5 from the 28-County Winter-2000 ModelaCovariates All Cause

IHD

ICD-9: 410–414

CPD

ICD-9: 400–440, 

460–519

Lung Cancer

ICD-9: 162

Diabetes 

ICD-9: 250

Endocrine 

Disorders

ICD-9: 240–279

Deaths (n) 10,559 2,735 4,625 853 118 206

PM2.5 onlyb 1.036 (0.989–1.085) 1.263 (1.157–1.379) 1.047 (0.978–1.122) 0.984 (0.832–1.164) 1.476 (0.959–2.271) 1.289 (0.927–1.793)+ 44 Individual-level 

covariates

1.000 (0.954–1.048) 1.203 (1.099–1.316) 1.013 (0.945–1.087) 0.893 (0.754–1.058) 1.423 (0.907–2.232) 1.214 (0.863–1.708)

Ecologic covariatesc

Air conditioning (%) 1.001 (0.953–1.052) 1.194 (1.087–1.312) 1.022 (0.950–1.099) 0.886 (0.739–1.063) 1.404 (0.878–2.247) 1.253 (0.881–1.781)White (%) 1.002 (0.953–1.053) 1.230 (1.117–1.354) 1.011 (0.938–1.089) 0.941 (0.786–1.126) 1.307 (0.805–2.124) 1.173 (0.815–1.688)Unemployed (%) 0.981 (0.933–1.032) 1.200 (1.089–1.322) 0.983 (0.912–1.060) 0.894 (0.746–1.072) 1.258 (0.772–2.050) 1.127 (0.781–1.628)Median household 

income ($000’s) 0.985 (0.938–1.035) 1.180 (1.073–1.297) 0.986 (0.917–1.061) 0.891 (0.747–1.063) 1.290 (0.799–2.082) 1.138 (0.796–1.629)

Grade 12 education (%) 1.015 (0.967–1.066) 1.261 (1.148–1.386) 1.037 (0.964–1.116) 0.917 (0.769–1.092) 1.295 (0.810–2.071) 1.171 (0.822–1.668)Income disparity (Gini) 1.011 (0.962–1.062) 1.233 (1.120–1.358) 1.029 (0.955–1.109) 0.898 (0.752–1.073) 1.332 (0.822–2.157) 1.182 (0.823–1.697)Poverty (%) 0.996 (0.946–1.047) 1.216 (1.102–1.341) 0.997 (0.924–1.076) 0.930 (0.775–1.117) 1.270 (0.772–2.091) 1.144 (0.788–1.660)

All 7 ecologic covariates 0.989 (0.929–1.054) 1.220 (1.082–1.375) 1.034 (0.941–1.135) 0.891 (0.710–1.117) 1.026 (0.563–1.870) 1.107 (0.706–1.736)Ozoned 1.024 (0.970–1.082) 1.224 (1.100–1.362) 1.058 (0.974–1.148) 0.892 (0.736–1.082) 1.376 (0.816–2.318) 1.282 (0.863–1.905)Ozone + all 7 ecologic 

covariates

0.999 (0.935–1.068) 1.212 (1.066–1.377) 1.047 (0.948–1.157) 0.897 (0.707–1.139) 1.028 (0.539–1.960) 1.182 (0.732–1.909)

a The PM2.5 concentration of 3.9 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposure distribution from the 28-

county winter-2000 model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. N = 44,056 CPS-II participants living in the 

28 counties. HRs are followed by 95% confidence intervals.

b Predicted using the 28-county winter-2000 model.c Ecologic covariates were included one at a time to a base model with PM2.5 + 44 individual-level covariates.d Summer O3 values for 1999–2000.
52

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Results for all models in which the 44 individual-level

variables were controlled are shown in Figure 8 for all

cause-of-death categories. Figure 9 summarizes the results

for causes of death that appeared to be associated with

PM2.5 exposures. Neither all-cause nor any cause-specific

deaths had a positive, significant association with PM2.5.

All-cause and CPD deaths did have positive HRs, but these

were attenuated by some ecologic covariates.

We investigated the sensitivity of the results to alternative model specifications using the random effects models

with clustering at the ZCA scale and at the ZCA and MSA

scales together, and with the latter model including a spatial autocorrelation parameter based on nearest-neighbor

adjacency. These results for IHD are shown in Figure 10.

The 3-year average exposure effect was slightly reduced by

the clustering on the random effects, but the HRs for IHD

were largely unaffected by alternative model specification.

Effect modification by education was investigated based

on earlier findings (Krewski et al. 2000a,b) and the results

are presented in Tables 17–20. There was a mild suggestion of effect modification; the less-educated group had

slightly higher effects, but the differences were not large.

The differences between educational groups seemed more

pronounced for the 3-year-average PM2.5 exposure than for

the winter-2000 exposure; the group with grade 12 completed or not (Table 17) had point estimates nearly twice

the size of the group with more than a grade 12 education

(Table 19). For the winter models effects were similar for

both groups.

Discussion

In the New York City region, we did not observe elevated PM2.5-associated risk of mortality for all-cause, CPD,

or lung cancer deaths, but IHD did show a significant positive association with PM2.5 exposure. The large and significant effects for IHD provide additional evidence of a

specific association with a cause of death that has high biologic plausibility (Brook et al. 2004). The random effects

from the model with one level of clustering (ZCA scale)

displayed a spatial pattern of lower residual variance in

the central parts of the city and higher residual variance in

the suburban and outlying areas. This pattern visually

appeared to be the opposite of the pattern for PM2.5 concentrations, which had higher levels in the city center and

lower levels in the suburbs (see Figure 11).

Despite this visual contrast between residual mortality

(mortality that was not explained or predicted by the

model) and the pattern of PM2.5 concentrations, IHD did

have a significant positive association with exposure to

PM2.5. The IHD results were insensitive to control for the

44 individual-level covariates and control for clustering

(nonindependence) in the random effects model (see

Figure 11 for a map of the random effects from a model

with only individual-level covariates). Effects were mildly

reduced when we included unemployment at the ZCA

scale (Tables 15–20). There was a suggestion of effect modification that showed larger risks in the lower-education

groups in the 28-county 3-year model.

Although the HRs for IHD and PM2.5 appeared to be

smaller than those reported for Los Angeles, this resulted

from the limited range of exposures in New York City. If

the coefficient is reported for the same 10-μg/m3 exposure

contrast used in Los Angeles, the significant HR increases

to 1.56 for the 3-year average, which is somewhat higher

than the HR of 1.39 for the Los Angeles region. This is a

key point about the difference between the two regions: In

Los Angeles we found a strong positive association

between areas of high pollution and areas of high residual

mortality that was not explained by the 44 individual-level

covariates. In New York City, the pattern was reversed —

pollution appeared worst in areas where people seemed to

be healthier and wealthier. Therefore pollution is less

likely to have a major effect on health because it must compete with many positive health attributes in the individuals such as good nutrition, clean employment, and access

to medical care.

The pattern of high urban and low rural PM2.5 exposure

and the opposite pattern for mortality that was not

accounted for by individual factors complicates the association between air pollution and mortality in New York

City. Nevertheless, the association between PM2.5 exposure and IHD mortality is biologically plausible and is still

evident in this analysis despite these competing gradients

of mortality and exposure. Other recent studies have suggested specific links between cardiovascular disease and

traffic-related pollution (Hoek et al. 2002) and between

PM2.5 concentrations and preclinical indicators of atherosclerosis (Künzli et al. 2005), which underlies many of the

IHD deaths. Consequently, the results from New York City

lend corroborative evidence of an association between

PM2.5 exposure and mortality for a cause of death likely

associated with air pollution exposure.

We also investigated an alternative exposure model in

New York City based on spatially kriging the data from the

62 fixed-site air pollution monitors (Ross et al. 2006), as

had been done by Jerrett and coworkers (2005a) for Los

Angeles. The kriging and LUR models performed similarly

in cross-validation studies, although the LUR model was

slightly superior for predicting PM2.5 concentrations at the

validation locations that had not been included in the

model formulation. In the health effects assessment, we

also tested the kriging exposure model based on the 3-year
D. Krewski et al.

53

Figure 8. HRs by cause of death associated with an interdecile comparison value of 1.5 μg/m3 PM2.5 from the 28-county 3-year model and 3.9 μg/m3 PM2.5

from the winter-2000 model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. N =

44,056 participants living in the 28 counties; number of deaths for each cause is given beneath the ICD-9 codes. (See also Tables 15 and 16.)

Figure 9. HRs by selected causes of death associated with an interdecile

comparison value of 1.5 μg/m3 PM2.5 from the 28-county 3-year model

and 3.9 μg/m3 PM2.5 from the winter-2000 model. Analysis was based on

746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year

groupings), gender, and race. N = 44,056 participants living in the 28

counties; number of deaths for each cause is given beneath the ICD-9

codes. (See also Tables 15 and 16.)

Figure 10. HR results for IHD deaths from analyses with one (ZCA) or two

(ZCA and MSA) levels of clustering and with or without  (a statistical

estimate of the homogeneity of PM2.5 exposure within a cluster). Deaths

were associated with an interdecile comparison value of 1.5 μg/m3 PM2.5

from the 28-county 3-year model and 3.9 μg/m3 PM2.5 from the winter-2000

model. Analysis was based on 746 ZCAs with ACS CPS-II residents and

was stratified by age (in 1-year groupings), gender, and race.

average PM2.5 concentrations. Risk estimates were similar to

but slightly smaller than those obtained with the LUR model.

Because of concerns about attenuation of risk estimates due

to exposure measurement error with spatial kriging, we

based our risk estimates on LUR. This suggests that differences in results between the New York City and Los Angeles

Analyses (see the next section) are likely not due to different

methods used to ascertain levels of exposures.
54

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality Table 17. HRs by Cause of Death Associated with an Interdecile Comparison Value of 1.5 

μg/m3 PM2.5 from the 28-County 3-Year Model as Modified by 

Grade 12 Education Completed or Nota

Covariates All Cause

IHD

ICD-9: 410–414

CPD

ICD-9: 400–440, 

460–519

Lung Cancer

ICD-9: 162

Diabetes

ICD-9: 250

Endocrine 

Disorders

ICD-9: 240–279

Deaths (

n) 5,602 1,537 2,580 469 66 117

PM2.5 onlyb 0.993 (0.940–1.049) 1.130 (1.026–1.243) 0.982 (0.907–1.062) 0.892 (0.725–1.097) 1.078 (0.660–1.762) 1.064 (0.734–1.541)+ 44 Individual-level 

covariates

0.976 (0.924–1.031) 1.105 (1.003–1.218) 0.968 (0.894–1.048) 0.826 (0.669–1.020) 1.091 (0.663–1.797) 1.051 (0.721–1.533)

Ecologic covariatesc

Air conditioning (%) 0.982 (0.929–1.038) 1.104 (1.000–1.220) 0.969 (0.894–1.050) 0.814 (0.654–1.012) 1.116 (0.679–1.837) 1.104 (0.760–1.605)White (%) 0.987 (0.930–1.047) 1.131 (1.017–1.256) 0.956 (0.877–1.043) 0.864 (0.691–1.080) 1.015 (0.595–1.732) 1.061 (0.711–1.584)Unemployed (%) 0.972 (0.916–1.030) 1.094 (0.985–1.214) 0.941 (0.864–1.025) 0.851 (0.683–1.062) 0.991 (0.582–1.690) 1.037 (0.695–1.545)Median household income 

($000’s) 0.962 (0.906–1.022) 1.104 (0.990–1.230) 0.935 (0.856–1.022) 0.836 (0.665–1.050) 1.028 (0.589–1.795) 1.022 (0.674–1.549)

Grade 12 education (%) 1.003 (0.946–1.063) 1.173 (1.056–1.303) 0.998 (0.917–1.087) 0.880 (0.705–1.100) 1.015 (0.602–1.712) 1.045 (0.706–1.549)Income disparity (Gini) 1.000 (0.942–1.061) 1.146 (1.029–1.276) 0.987 (0.905–1.077) 0.858 (0.684–1.076) 1.066 (0.619–1.834) 1.053 (0.701–1.580)Poverty (%) 0.981 (0.921–1.045) 1.136 (1.015–1.272) 0.941 (0.858–1.033) 0.906 (0.716–1.146) 0.942 (0.527–1.683) 1.005 (0.654–1.544)

All 7 ecologic covariates 0.967 (0.900–1.039) 1.096 (0.963–1.247) 0.933 (0.840–1.037) 0.863 (0.657–1.133) 0.804 (0.416–1.554) 0.960 (0.594–1.554)Ozoned 1.000 (0.938–1.066) 1.110 (0.979–1.235) 0.994 (0.905–1.092) 0.831 (0.654–1.056) 1.135 (0.638–2.018) 1.123 (0.725–1.741)Ozone + all 7 ecologic 

covariates

0.982 (0.910–1.059) 1.090 (0.950–1.252) 0.948 (0.847–1.060) 0.872 (0.656–1.159) 0.851 (0.420–1.724) 1.026 (0.614–1.716)

a The PM2.5 concentration of 1.5 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposure distribution from the 28-

county 3-year model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. 

N = 18,963 CPS-II with grade 12 education or less. 

HRs are followed by 95% confidence intervals.

b Predicted using the 28-county 3-year model.c Ecologic covariates were included one at a time to a base model with PM2.5 + 44 individual-level covariates.d Summer O3 values for 1999–2000.
D. Krewski et al.

55

Table 18. HRs by Cause of Death Associated with an Interdecile Comparison Value of 3.9 μg/m3 PM2.5 from the 28-County Winter-2000 Model as 

Modified by Grade 12 Education Completed or Nota

Covariates All Cause

IHD

ICD-9: 410–414

CPD

ICD-9: 400–440, 

460–519

Lung Cancer

ICD-9: 162

Diabetes

ICD-9: 250

Endocrine

Disorders

ICD-9: 240–279

Deaths (n) 5,602 1,537 2,580 469 66 117

PM2.5 onlyb 1.003 (0.938–1.073) 1.248 (1.100–1.416) 1.042 (0.945–1.149) 0.867 (0.683–1.100) 1.435 (0.765–2.692) 1.234 (0.773–1.970)+ 44 Individual-level 

covariates

0.984 (0.920–1.053) 1.220 (1.075–1.385) 1.027 (0.931–1.133) 0.803 (0.632–1.020) 1.423 (0.741–2.732) 1.203 (0.746–1.938)

Ecologic covariatesc

Air conditioning (%) 0.978 (0.911–1.050) 1.189 (1.041–1.357) 1.021 (0.922–1.132) 0.762 (0.587–0.988) 1.435 (0.730–2.823) 1.302 (0.795–2.131)White (%) 0.988 (0.919–1.062) 1.241 (1.083–1.422) 1.008 (0.906–1.121) 0.853 (0.662–1.100) 1.287 (0.637–2.603) 1.216 (0.729–2.029)Unemployed (%) 0.970 (0.902–1.042) 1.194 (1.043–1.368) 0.986 (0.887–1.096) 0.843 (0.654–1.088) 1.249 (0.617–2.529) 1.181 (0.707–1.973)Median household income 

($000’s) 0.969 (0.902–1.040) 1.203 (1.051–1.376) 0.993 (0.895–1.102) 0.827 (0.644–1.061) 1.342 (0.667–2.701) 1.189 (0.717–1.971)

Grade 12 education (%) 0.998 (0.930–1.070) 1.258 (1.102–1.436) 1.040 (0.939–1.153) 0.850 (0.663–1.088) 1.320 (0.671–2.599) 1.209 (0.737–1.983)Income disparity (Gini) 0.996 (0.928–1.069) 1.239 (1.083–1.418) 1.034 (0.932–1.147) 0.840 (0.654–1.078) 1.378 (0.691–2.750) 1.217 (0.737–2.009)Poverty (%) 0.983 (0.914–1.058) 1.235 (1.075–1.419) 1.001 (0.899–1.115) 0.880 (0.680–1.139) 1.247 (0.608–2.555) 1.173 (0.698–1.970)

All 7 ecologic covariates 0.954 (0.872–1.044) 1.168 (0.988–1.381) 0.993 (0.871–1.132) 0.803 (0.583–1.106) 1.041 (0.433–2.505) 1.194 (0.642–2.221)Ozoned 1.017 (0.941–1.099) 1.240 (1.070–1.437) 1.084 (0.967–1.215) 0.812 (0.621–1.063) 1.584 (0.760–3.301) 1.345 (0.780–2.319)Ozone + all 7 ecologic 

covariates

0.971 (0.883–1.067) 1.166 (0.976–1.392) 1.021 (0.889–1.172) 0.810 (0.579–1.132) 1.160 (0.461–2.921) 1.326 (0.689–2.554)

a The PM2.5 concentration of 3.9 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposure distribution from the 28-

county winter-2000 model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. N = 18,963 CPS-II participants with grade 12 

education or less. HRs are followed by 95% confidence intervals.

b Predicted using the 28-county winter-2000 model.c Ecologic covariates were included one at a time to a base model with PM2.5 + 44 individual-level covariates. d Summer O3 values for 1999–2000.
56

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality Table 19. HRs by Cause of Death Associated with an Interdecile Comparison Value of 1.5 

μg/m3 PM2.5 from the 28-County 3-Year Model as Modified by 

More Than Grade 12 Educationa

Covariates All Cause

IHD

ICD-9: 410–414

CPD

ICD-9: 400–440, 

460–519

Lung Cancer

ICD-9: 162

Diabetes

ICD-9: 250

Endocrine

Disorders

ICD-9: 240–279

Deaths (

n) 4,957 1,198 2,045 384 52 89

PM2.5 onlyb 1.022 (0.974–1.072) 1.089 (0.993–1.195) 0.964 (0.892–1.041) 1.121 (0.949–1.324) 1.608 (1.134–2.282) 1.490 (1.121–1.979)+ 44 Individual-level 

covariates

0.986 (0.938–1.036) 1.032 (0.937–1.136) 0.924 (0.854–1.000) 1.055 (0.891–1.250) 1.411 (0.959–2.077) 1.393 (1.026–1.893)

Ecologic covariatesc

Air conditioning (%) 0.988 (0.940–1.039) 1.038 (0.943–1.144) 0.930 (0.859–1.008) 1.080 (0.910–1.282) 1.326 (0.884–1.989) 1.363 (0.997–1.863)White (%) 0.985 (0.935–1.038) 1.044 (0.943–1.155) 0.931 (0.857–1.013) 1.095 (0.917–1.306) 1.225 (0.791–1.898) 1.271 (0.905–1.785)Unemployed (%) 0.964 (0.913–1.017) 1.034 (0.932–1.147) 0.901 (0.827–0.981) 1.037 (0.862–1.247) 1.183 (0.746–1.873) 1.275 (0.898–1.810)Median household income 

($000’s) 0.968 (0.919–1.021) 1.004 (0.906–1.112) 0.898 (0.825–0.977) 1.044 (0.871–1.251) 1.227 (0.791–1.904) 1.255 (0.888–1.774)

Grade 12 education (%) 1.000 (0.950–1.054) 1.094 (0.987–1.212) 0.949 (0.873–1.031) 1.054 (0.883–1.259) 1.229 (0.791–1.910) 1.302 (0.929–1.824)Income disparity (Gini) 0.989 (0.937–1.045) 1.052 (0.945–1.171) 0.935 (0.856–1.020) 1.014 (0.841–1.223) 1.288 (0.803–2.067) 1.307 (0.908–1.882)Poverty (%) 0.966 (0.913–1.021) 1.013 (0.909–1.128) 0.900 (0.823–0.984) 1.055 (0.873–1.275) 1.200 (0.748–1.927) 1.233 (0.852–1.784)

All 7 ecologic covariates 0.968 (0.907–1.034) 1.036 (0.909–1.181) 0.923 (0.831–1.024) 0.998 (0.799–1.247) 1.040 (0.577–1.875) 1.243 (0.804–1.921)Ozoned 0.982 (0.925–1.043) 1.001 (0.889–1.127) 0.910 (0.827–1.002) 1.060 (0.862–1.304) 1.268 (0.763–2.108) 1.414 (0.958–2.087)Ozone + all 7 ecologic 

covariates

0.959 (0.893–1.029) 0.998 (0.866–1.151) 0.895 (0.799–1.003) 1.020 (0.802–1.298) 0.940 (0.484–1.827) 1.257 (0.780–2.024)

a The PM2.5 concentration of 1.5 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposure distribution from the 28-

county 3-year model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. 

N = 25,093 CPS-II participants with more than 

grade 12 education. HRs are followed by 95% confidence intervals.

b Predicted using the 28-county 3-year model.c Ecologic covariates were included one at a time to a base model with PM2.5 + 44 individual-level covariates. d Summer O3 values for 1999–2000.
D. Krewski et al.

57

Table 20. HRs by Cause of Death Associated with an Interdecile Comparison Value of 3.9 μg/m3 PM2.5 from the 28-County Winter-2000 Model as 

Modified by More Than Grade 12 Educationa

Covariates All Cause

IHD

ICD-9: 410–414

CPD

ICD-9: 400–440, 

460–519

Lung Cancer

ICD-9: 162

Diabetes

ICD-9: 250

Endocrine

Disorders

ICD-9: 240–279

Deaths (n) 4,957 1,198 2,045 384 52 89

PM2.5 onlyb 1.053 (0.987–1.124) 1.261 (1.110–1.432) 1.035 (0.937–1.144) 1.056 (0.831–1.340) 1.576 (0.845–2.941) 1.096 (0.655–1.834)+ 44 Individual-level 

covariates

1.013 (0.948–1.082) 1.177 (1.033–1.341) 0.987 (0.891–1.092) 0.998 (0.785–1.268) 1.347 (0.721–2.519) 1.047 (0.610–1.800)

Ecologic covariatesc

Air conditioning (%) 1.025 (0.957–1.099) 1.206 (1.052–1.381) 1.012 (0.910–1.126) 1.037 (0.802–1.340) 1.218 (0.628–2.360) 0.966 (0.556–1.679)White (%) 1.015 (0.946–1.089) 1.216 (1.059–1.396) 1.007 (0.904–1.122) 1.032 (0.798–1.333) 1.035 (0.516–2.076) 0.916 (0.462–1.814)Unemployed (%) 0.990 (0.922–1.063) 1.203 (1.046–1.385) 0.970 (0.869–1.081) 0.954 (0.735–1.238) 0.984 (0.488–1.983) 1.099 (0.611–1.977)Median household income 

($000’s) 1.000 (0.934–1.071) 1.162 (1.014–1.330) 0.971 (0.874–1.078) 0.977 (0.761–1.255) 1.068 (0.547–2.085) 0.871 (0.421–1.804)

Grade 12 education (%) 1.028 (0.960–1.101) 1.255 (1.094–1.439) 1.017 (0.915–1.130) 0.989 (0.771–1.268) 1.086 (0.559–2.108) 1.385 (0.849–2.260)Income disparity (Gini) 1.021 (0.951–1.095) 1.223 (1.062–1.409) 1.012 (0.907–1.128) 0.945 (0.733–1.219) 1.094 (0.544–2.202) 1.233 (0.748–2.034)Poverty (%) 0.998 (0.929–1.072) 1.181 (1.025–1.361) 0.978 (0.876–1.092) 0.976 (0.752–1.267) 1.003 (0.495–2.034) 1.233 (0.739–2.059)

All 7 ecologic covariates 1.014 (0.927–1.110) 1.274 (1.066–1.524) 1.068 (0.930–1.226) 0.921 (0.663–1.280) 0.746 (0.302–1.842) 1.013 (0.586–1.751)Ozoned 1.022 (0.945–1.104) 1.194 (1.020–1.398) 1.008 (0.893–1.138) 0.972 (0.733–1.288) 1.035 (0.485–2.209) 1.016 (0.590–1.752)Ozone + all 7 ecologic 

covariates

1.011 (0.919–1.112) 1.251 (1.034–1.515) 1.057 (0.912–1.224) 0.937 (0.662–1.326) 0.614 (0.229–1.650) 1.029 (0.609–1.738)

a The PM2.5 concentration of 3.9 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposure distribution from the 28-

county winter-2000 model. Analysis was based on 746 ZCAs with ACS CPS-II residents and stratified by age (in 1-year groupings), gender, and race. N = 25,093 CPS-II participants with more 

than grade 12 education. HRs are followed by 95% confidence intervals.

b Predicted using the 28-county winter-2000 model.c Ecologic covariates were included one at a time to a base model with PM2.5 + 44 individual-level covariates.d Summer O3 values for 1999–2000.
58

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

INTRA-URBAN ANALYSIS FOR THE 

LOS ANGELES REGION

A LAND-USE REGRESSION MODEL FOR PREDICTING 

PM2.5 CONCENTRATIONS

Background

Developing exposure models that would predict the spatial variation of air pollution within cities has been identified as a research priority (Brunekreef and Holgate 2002).

The LUR method is a promising approach that uses monitored pollutant concentrations as the dependent variable

and a comprehensive set of land-use, traffic, population,

and physical geography variables as predictors of PM2.5

concentrations (Briggs et al. 2000a). Most LUR models

have been calibrated with NO2 as a marker for traffic exposure (Briggs et al. 2000a; Gilbert et al. 2005; Sahsuvaroglu

et al. 2006). Two European studies that have estimated

ambient particles with an LUR model used a reflectance

method to measure the presence of ambient particles. This

method is more likely to show local variation due to traffic

than would PM2.5 mass measurements (Brauer et al. 2003;

Hochadel et al. 2006). To date, no North American studies

have attempted to use the LUR method for deriving PM2.5

estimates. In this project, we developed an LUR model for

predicting PM2.5 mass over the 5-county MSA of Los

Angeles (see also Moore et al. 2007).

Figure 11. Residual variance in mortality from IHD for 28 counties in the NYC region (shown by ZCA). Mortality levels were predicted by the random

effects model with PM2.5 exposure and the 44 individual-level covariates. The predicted values are compared with actual measured mortality. Values > 1

indicate higher-than-expected and < 1 indicate lower-than-expected residual mortality.
D. Krewski et al.

59

Materials and Methods

Study Area At the 2000 U.S. census, the Los Angeles MSA

had a population of about 16.7 million people (Southern California Association of Governments [SCAG] 2004). This

MSA encompasses Los Angeles, San Bernardino, Ventura,

Riverside, and Orange counties and covers a total area of

98,500 km2. The Los Angeles MSA is the most congested

area in the United States and has some of the highest levels

of air pollution (Southern California Compass 2005). As

illustrated in Figure 12, the region also has wide variation

in PM2.5 concentrations, which makes it a suitable site to

explore the use of LUR models in North America. Once calibrated, an LUR model can be used to derive a spatially continuous surface map of PM2.5 concentrations.

Methods for Handling Spatial Data We used the

ArcGIS 9.1 (ESRI 2004) GIS to integrate data about PM2.5

concentrations, land use, traffic, population, and physical

geography.

Dependent Variable: Ambient PM2.5 In 1998, a PM2.5 monitoring network was implemented throughout California;

we used data from 2000 because it was the first complete

year of data collected. The placement of each site was

determined by specific criteria: (1) to provide measurements of pollutant concentrations in the air basins to be

used for regulatory purposes; (2) to represent populated

areas with high pollutant concentrations; and (3) to characterize emission sources in areas of high concentrations.

Most (14) of the 23 monitors that we used to estimate

Figure 12. Map including the most densely populated areas in Los Angeles MSA showing PM2.5 concentrations at air quality monitoring sites used to 

support the analysis. 
60

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

exposure were placed to determine concentrations in populated areas; five were set up close to mountain passes

(e.g., Fontana, San Bernardino) to determine the regional

transport of pollutants; and four monitors were dedicated

to chemical speciation of pollutants (Los Angeles, Fontana, Anaheim, and Rubidoux) (California Environmental

Protection Agency and Air Resources Board 2003).

In the Los Angeles MSA, 23 sites measure PM2.5 (Figure

12). All of these sites use FRM monitors and collect 24-

hour mass data on 1-, 3-, and 6-day cycles. Since the network was established in 1998, annual average PM2.5 concentrations have ranged from 9.49 μg/m3 in Lancaster (an

arid flat agricultural area with generally clear air) to 28.22

μg/m3 in Riverside (a large inland city with high pollutant

levels). The mean and median for the Los Angeles MSA

were 18.42 μg/m3 (SD = 6.01) and 19.31 μg/m3, respectively. In much of the MSA, PM2.5 levels exceed the annual

average limits for both federal (15 μg/m3) and state

(12 μg/m3) standards.

Land-Use Data Land use is a significant factor in predicting air pollution levels (Briggs et al. 2000a; Jerrett et al.

2003) because different uses involve certain transportation

and other activities and produce different levels of emissions. For example, industrial and heating sources may

expel gaseous precursor air pollutants and primary PM,

resulting in a relatively high PM2.5 concentration surrounding the source; whereas parks and open spaces have

fewer sources of pollution and therefore low concentrations of PM2.5.

We followed methods from a study completed in

Hamilton, Ontario, Canada by Sahsuvaroglu and

coworkers (2006) to delineate the land-use categories that

would have a reasonable probability of covering an adequate number of pollution monitors: commercial, industrial, residential, agricultural, airports, water, parks, open

space, and roads.

We obtained digital land-use data from the SCAG

(2004). (These data were first collected in 1993 and were

updated in 2000.) We cleaned and aggregated the data into

land-use and transportation categories that have been used

in other studies and found to be associated with ambient

pollutant concentrations (Briggs et al. 2000a; Brunekreef

and Holgate 2002; Jerrett et al. 2005b; Ross et al. 2006).

Each of the land-use categories was analyzed as a possible predictor of PM2.5 concentrations. We created buffers

at 50 to 5000 m in radius around each of the 23 PM2.5 monitors. The larger buffer areas were selected based on an

analysis of the semivariance of a kriging model, which suggested that monitoring sites within 5 to 10 km would have

correlated pollutant levels. In keeping with the focus of

deriving local estimates, we used the lower bound of the

range (5 km) as the maximum distance to test. The area (in

hectares [Ha]; 1 Ha = 2.471 acres or 0.01 km2) of each separate land-use category was measured within each buffer

zone surrounding a monitor.

Traffic Data Personal and commercial vehicles emit primary particulates and precursors such as nitrogen oxides

that react with the ambient environment to produce secondary particulate air pollution (Brauer et al. 2003). To

improve estimates of traffic-generated particles, we

obtained a geographically coded digital road network file

from TeleAtlas (Reitscheweg, The Netherlands). This file

covered major highways, minor highways, major roads,

arterial roads, collector roads, and local roads. We used the

same buffering technique used for the land-use data to

measure the total length (in kilometers) of specific road

types around a given monitor.

Through ESRI, we obtained average annual daily traffic

(AADT) counts (of all vehicles) from Business Analyst

(ArcView 9.1, ESRI, San Diego, CA 2004) for major traffic

corridors in Southern California from 1990 to 2002. The

traffic corridors were defined in six categories: (1) major

freeways, (2) minor freeways, (3) highways, (4) connector

roads, (5) major local roads, and (6) minor local roads. We

used a total of 34,310 measurement locations (traffic

points) for analyses in the metropolitan area (Los Angeles,

Ventura, Orange, San Bernardino, and Riverside). The

AADT traffic-point data were spatially assigned to the

closest individual road segments in the ESRI Streetmap

data set. Each road class included many speed limit categories. The road classes and speed limits were used to calculate an accurate traffic density. We calculated an average

traffic count at each road segment for each class of road

and speed limit. For those segments that had no traffic

count available, we assigned the average traffic count for

road segments with the corresponding road class and

speed limits. For example, a major road would have a class

of 3 and a variation of speed limits; for a class-3 road with

a speed limit of 35 mph but no traffic-count data, we

assigned the average traffic count of all class-3 roads with a

speed limit of 35 mph. The average traffic count for all

road segments within each buffer was calculated and

assigned to each monitor. Using this method we were able

to impute an accurate measurement of AADT throughout

the MSA.

Population Data Population density — the number of

people, volume of traffic, number of businesses, and area

of green space — is an important factor in determining

how much and what type of pollution is produced in a

given area. Densely populated areas contribute more
D. Krewski et al.

61

traffic-related pollution than sparsely populated areas, and

within a city density may also influence the content of

emissions (Ross et al. 2006). Higher population density

generates more pollution from heating buildings by combustion and from traffic for commuting and traveling to

commercial areas.

To determine the population density across the SCAG

area (the Los Angeles MSA plus Imperial County), we calculated a kernel estimate by assigning the U.S. Census

population data for 2000 for each census tract to the

census tract centroid. The radius of the kernel was determined through analyses with a semivariogram. This process revealed that analyzing spatial autocorrelation in the

range of 5 to 10 km would be optimal.

Physical Geography Proximity to a large body of water,

such as an ocean, reduces the concentrations of pollutants,

and the onshore marine breeze helps maintain pollution at

lower levels near the coast (Bay Area Air Quality Management District [BAAQMD] 2005). The distance to the

Pacific ocean was calculated for each monitor location and

was analyzed for its possible relationship to measured pollutant levels in the LUR model. Elevation data were

acquired from the USGS at 30-m resolution and each monitoring area was assigned an elevation for analysis in the

LUR model.

Modeling Methods Regression and spatial analysis were

used to create an interpolated PM2.5 pollutant surface. ArcView v3.3, ArcMap v9.0, ArcInfo v9.0 (Redlands, CA), SPLUS 2000 (Boston, MA) and Stata v8 (College Station, TX)

were used for these analyses. We used an inverse distance

weighting (IDW) method to create an accurate surface.

Linear regression was conducted using the natural logarithm transformation of the PM2.5 measurements.

Bivariate linear regressions were first used to determine

which variables were most strongly related to PM2.5. This

first step tested over 140 independent variables because

land-use and road variables covered a large number of categories and several sizes of buffers around 23 monitors. A

multiple linear regression model was developed using the

significant parameters from the bivariate linear regressions with a manual forward selection process based on

the highest t test score for each variable. The VIF was then

examined to identify variables that were collinear and

could be eliminated. Variables with the highest VIF and

variables with the lowest t scores were removed until a

parsimonious model with the highest R2 value and

acceptable levels of collinearity between included variables was derived.

Using the bootstrap method, we completed a sensitivity

analysis to test the stability of the estimates from the multiple regression model (Burrough and McDonnell 1998).

From the 23 monitor data points, a random sample of 15

data points was selected with replacement, for 1000 repetitions. This enabled us to determine bias in the estimates

and how accurately the model would predict PM2.5 concentrations when multiple locations (or monitors) were

excluded from the regression analysis. Additional model

diagnostics included the Cook-Weisberg test for heteroskedasticity and the degrees of freedom Betas and Cook distance to examine outliers.

Visualizing the Surface Visualization is an important

diagnostic tool for assessing the face validity of a predicted

LUR model. We used GIS software to identify approximately 18,000 lattice points for the SCAG area with 2.3 km

between lattice points. We created lattice points that were

relatively close together in distance to more finely estimate

the PM2.5 surface, but we were partly constrained by computational capacity. Buffers for the land-use variables in

the final LUR model were created around each lattice

point, where the areas of each variable were once again

calculated in hectares. Using the fitted regression equation, we calculated a predicted PM2.5 value for each of the

18,000 lattice points. This method allowed us to visualize

the PM surface; for subsequent health analysis, geographic points corresponding to study subjects could

serve as the lattice assignment points to minimize exposure assignment error.

We then used the IDW method to interpolate the pollutant surface. The IDW interpolator assumes that at each

PM2.5 sample point the local influence lessens with distance (ESRI 2004). A specified number of predicted PM2.5

points, or all points within a specified radius, can be used

to determine the output value for each location, creating a

surface. The power in the IDW interpolation determines

how influential surrounding points are to the point being

interpolated (Burrough and McDonnell 1998). A higher

power results in less influence from distant points (Burrough and McDonnell 1998). For this analysis we used the

default power of 2, which is a common power used in

inverse distance squared weighting.

Results

The land-use variables most correlated with PM2.5 concentrations from monitors were used to develop the multivariate model; correlation coefficients are shown in

Table 21. The final LUR model included three independent variables (Table 22): (1) traffic count within 300 m

(as a direct proxy for particulate emissions and gaseous
62

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

precursors), (2) industrial areas within 5000 m (to represent

primary and precursor emissions), and (3) government areas

within 5000 m (as a proxy for traffic intensity around major

destinations such as schools, government service offices,

and hospitals). The R2 value for the LUR was 0.69 (F =

14.07, P < 0.00001), with a few geographic areas near the

intersections of freeways displaying over-prediction (see

Figure 13).

Regression diagnostics were used to assess the validity

of the LUR model. Table 22 shows little collinearity

among the independent variables; the average VIF was

1.06. The visualization surface in Figure 14 shows a 63%

R2 between the measured and predicted values of PM2.5

from the LUR model. Although the residuals showed a

slightly greater variance with higher PM2.5 concentrations

(Figure 15), the Cook-Weisberg test for heteroskedasticity

indicated a nonsignificant difference in the variance

around the residuals (P = 0.81), and the degrees-offreedom Betas-and-Cook distance indicated few outliers in

the data. An additional sensitivity analysis used the bootstrap method, in which we resampled 15 locations with

1000 repetitions and jackknife residual statistics. For the

bootstrap, the bias values and standard errors were

extremely close to 0, and the statistics for the jackknife

residuals were less than 0.01, indicating that no single

PM2.5 monitor’s data drove the values predicted.

Discussion

Accuracy of Predictions Using the LUR Model We derived

a multiple linear regression model that explained 69% of

the variance in PM2.5 concentrations, with the main predictors being traffic count within 300 m, industrial areas

within 5000 m, and government areas within 5000 m

(Table 22). In some areas, the LUR predicted PM2.5 concentrations slightly higher than those measured — within the

Los Angeles Basin near the intersection of high-traffic freeways (605 and 210, 110 and 405, and 405 and 55) and in

Long Beach near the two ports (Los Angeles and Long

Beach) and the 710 freeway (Figures 13 and 14).

All of the observed over-predictions were located near

intersections of major freeways (Figure 13) and some were

upward of 126 μg/m3. Due to unregulated fuel combustion,

ships are one of the leading sources of particulate air pollution in the Los Angeles Basin (Hricko 2004). Surrounding the ports is a network of freeways and railroad

Table 21. Land Use Variables with R  0.4 That Were 

Significantly Associated with PM2.5 Concentrationsa

Description

Buffer Size

(m) R Value

Commercial areas 5000 0.67

Industrial areas* 5000 0.58

Parks and recreation 5000 0.55

Population density 20 km 0.54

Collector roads 5000 0.53

Arterial roads 3000 0.52

Government areas* 5000 0.51

Population density 10 km 0.48

Arterial roads 2000 0.48

Traffic* 300 0.48

Arterial roads 5000 0.47

Collector roads 2000 0.46

Arterial roads 1000 0.45

Collector roads 3000 0.45

Industrial areas 3000 0.42

Arterial roads 750 0.4

Minor roads 750 0.39

Commercial areas 750 0.35

Industrial areas 50 0.32

Open areas 2000 0.43

Open areas 3000 0.48

Secondary roads 2000 0.51

Secondary roads 1000 0.54

Open areas 5000 0.55

a

 Arranged from highest to lowest correlation. An * indicates the three 

variables used in the final model. 

Table 22. Land-Use Regression Model Statistics Along with Collinearity Diagnostics

Variables

Buffer

(m) 
 t Value SE P Value VIF

Constant 2.28621 22.81 0.1002109 0.000

Traffic 300 0.00001 2.99 0.0000034 0.007 1.09

Industrial area 5000 0.00032 3.33 0.0000958 0.004 1.05

Government area 5000 0.00072 3.35 0.0002139 0.003 1.04
D. Krewski et al.

63

tracks used to disperse cargo to distribution and storage

centers in inland areas. More transport trucks, which combust diesel fuel, travel the 710 freeway than any other

freeway in Los Angeles (Meyer 2003). Westerdahl and colleagues (2005) found that measured freeway concentrations of PM2.5 had a range of 60 to 820 μg/m3 on the 10 East

freeway and that concentrations along major roadways

with high-traffic density were up to 20 times higher than

residential concentrations. Those measurements were

taken over a 5-day period in April of 2003, whereas our

LUR model estimates were based on data from 2000. Moreover, in the Westerdahl study, increased concentrations of

PM2.5 were associated with high diesel traffic along freeways. Thus, the over-predicted values in our analysis are

at plausible levels, although further field validation work

is needed to assess whether annual average levels on or

near freeway intersections are indeed this high.

Brauer and associates (2003) and Hochadel and colleagues (2006) have also used LUR to predict PM2.5 concentrations in Europe. Brauer’s team modeled air pollution

in communities throughout the Netherlands, in Munich,

Germany, and in Stockholm, Sweden. Although they used

only traffic indicators and did not use land-use classifications in their multivariate regression model, they were

able to derive significant prediction models for each of the

three locations, for both PM2.5 mass and PM2.5 filter absorbance, which is used as a marker for diesel exhaust (Brauer

et al. 2003). For the Netherlands, Munich, and Stockholm,

the R2 values for the PM2.5 mass prediction model were

0.78, 0.76, and 0.63, respectively; and 0.9, 0.83, and 0.76,

respectively, for the PM2.5 absorbance prediction model.

Since absorbance models are typically used to assess

traffic-related pollutants, the absorbance model results are

a better predictor of measured PM2.5 than the PM2.5 mass

model because the variables used in the absorbance model

were chosen specifically to measure traffic. Hochadel and

coworkers (2006) conducted a study in Wesel, Germany,

using primarily traffic-based indicators as the geographic

factors. The regression models predicted strongly for PM2.5

absorbance (R2 = 0.65), but not for PM2.5 mass (R2 = 0.094).

Figure 13. Map of over-predicted values from the LUR model, shown as > 50, 75, or 100 μg/m3 higher than the measured value. Most over-predictions

occurred at freeway intersections. PM2.5 monitor positions are marked with a +. 
64

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Effects of Buffer Size Selection The buffers we used to

measure areas of land-use categories were quite large compared with other studies (generally 100 to 300 meters;

Brauer et al. 2003; Hochadel et al. 2006; Ross et al. 2006;

Sahsuvaroglu et al. 2006). The Los Angeles MSA is a massive and sprawling urban area and has one of the highest

levels of employment and population dispersion in the

United States (Gordon and Richardson 1996). This dispersed urban structure, transected by major highways and

commercial areas, leads to a broader regional scale of

influence for processes that generate PM pollution, and the

2000- to 5000-m buffers surrounding the PM2.5 monitors

probably reflect this dispersed form of urban development.

The study that is most closely related geographically to

this Los Angeles Analysis was done by Ross and associates

(2006) in San Diego County, California, in which they predicted ambient NO2 concentrations. The study area in San

Figure 14. PM2.5 surface created by IDW using predicted PM2.5 concentrations from an LUR model with the variables of traffic within a 300-m radius,

industrial areas within a 5000-m radius, and government areas within a 5000-m radius. PM2.5 monitor positions are marked with a +. Notice the high

PM2.5 concentrations near intersections of major highways.

Figure 15. Graph showing predicted PM2.5 versus measured PM2.5 concentrations from the LUR model. The measured and predicted values for

each monitor are plotted on the x and y axes.
D. Krewski et al.

65

Diego County was much smaller than the Los Angeles

MSA — 11,721 km2 versus 98,500 km2 for the SCAG

region. The traffic-count variable and the industrial landuse variable were common to both models, but the Ross

study found that smaller-radius buffers were more effective predictors than those we used in Los Angeles. This

makes sense because NO2 arises from local sources and is

known to vary over smaller areas in proximity to traffic

(Gilliland et al. 2005), whereas PM2.5 is a mixture generated by primary and secondary sources that we would

expect to be more regionally dispersed. Data from the larger

buffer sizes in this study may reflect either an inherent spatial scale of variation in the pollutants or the more dispersed

urban structure of Los Angeles.

Modeling Spatial Variability in Exposure A kriging

model was previously developed for the Los Angeles Basin

based on PM2.5 values from the same 23 monitors (Jerrett et

al. 2005). That model did not show as much of the local-area

variations in exposure as did the LUR model developed for

this study (see Figures 14 and 16). The success of this LUR

model demonstrates that traffic and land uses are strong

predictors of PM2.5 concentrations. Although the scale of

variation of PM2.5 values around an emissions source

appears to be larger than what we might expect from purely

local processes, the covariance of PM2.5 concentrations

with land-use predictors suggests that local land uses and

traffic have a significant role in distributions of PM2.5

across this large urban area. The kriging model may not be

generalizable, because the monitors were established by

government agencies in certain locations for the purpose

of regulatory compliance and therefore may not reflect the

true variability in PM2.5 levels represented by the mixture

of land uses in the LUR model.

If the monitoring network is not representative of the

spatial variability in actual pollutant exposures, we would

expect to see bias in the pollutant levels predicted from

models based on those monitoring locations. Our use of

the bootstrap method demonstrated that it is unlikely that

single influential points drove the results of the LUR

model. For example, none of the monitors were specifically located to measure traffic impact, and the closest

proxy we have is monitors located in densely populated

urban areas. Although there is some overlap between

dense population and areas of heavy traffic, we would be

able to create kriging models to reflect greater local variation if we could include data from monitors located near

heavy-traffic sites.

Although the spatially modeled exposures depend

intrinsically on land use and local emissions sources such

as traffic, the fact that some covariates are related to factors

Figure 16. Universal kriging map of PM2.5 in the Los Angeles area. PM2.5 monitor positions are marked with a circled dot (). (Adapted from Kuenzli et

al. [2005].)
66

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

that affect health and mortality (such as socio-economic

position) means that the LUR model may include some

hidden confounders. Consequently, to use LUR for pollution and health analyses, researchers must adjust for other

contextual confounders that may be related to the land-use

and traffic input to ensure unbiased estimates of the health

effects of air pollution.

We found that LUR predicted 69% of the variance in

PM2.5 mass in the Los Angeles MSA. With sensitivity analysis, we observed that few monitors influenced the results.

Traffic, industrial, and government areas were of most significance because of their associated particulate emissions

from increased traffic and industrial point sources. Further

investigation is needed into the extraordinarily high levels

of PM2.5 predicted around the intersections of freeways;

these levels may constitute a significant threat to public

health if they are validated by monitoring.

SPATIAL ANALYSIS OF AIR POLLUTION AND 

MORTALITY IN LOS ANGELES

Materials and Methods

Study Population Mortality data were extracted from the

ACS CPS-II cohort database for metropolitan Los Angeles at

the ZCA scale (average population per ZCA in Los Angeles

is approximately 35,000; average area is approximately

22.5 km2). ZCA centroids were weighted by population

distribution using spatial boundary files based on 1980

and 1990 USBC data. The centroids were used to assign

PM2.5 and O3 exposure levels to the 22,905 ACS subjects

living in the 267 ZCAs (this cohort included 5,856 deaths

based on follow-up from 1982 through 2000). Some subjects reported only PO Box addresses and were therefore

excluded. As in earlier ACS analyses (Pope et al. 2000; Jerrett et al. 2005), availability of air pollution data and other

relevant information (e.g., ZCA-level data about ecologic

covariates) led to the subset of study subjects to be used in

the health effects assessment. Although the ACS cohort is

not representative of the general population, the cohort

allows for internally valid comparisons within large samples of the American population.

Assessment of PM2.5 Exposure An LUR prediction model

was developed to predict PM2.5 from 23 monitoring locations in the Los Angeles MSA using GIS to integrate data

from land use, transportation, and physical geography (see

A Land-Use Regression Model for Predicting PM2.5 in the

Los Angeles Region). The LUR method explained 69% of

the variance in PM2.5 with three predictors around each

monitoring site: (1) traffic count within 300 m, (2) industrial land area within 5000 m, and (3) government land

area within 5000 m. Model validation suggested that only

a few areas were over-predicted in the downtown section

and near freeway interchanges. (See above for details of

the LUR model’s derivation and validation [also described

in Moore et al. 2007]).

Assessment of O3 Exposure In earlier ACS studies that

used between-city comparisons, few associations have

been found between O3 exposure and mortality (Pope et al.

1995, 2002; Krewski et al. 2000a,b). Nevertheless, exposure to this pollutant is considered a health threat in the

Los Angeles region, which has some of the highest levels

in the United States (Künzli et al. 2003). For O3, we

obtained data at 42 sites in and around the Los Angeles

Basin from the California Air Resources Board (ARB) database. We interpolated two surfaces using a universal

kriging algorithm: one surface based on the average of the

four highest 8-hour concentrations during 2000 at each

monitoring site, and another based on the expected peak

daily concentrations (EPDC). Expected daily peaks have

been used as a statistical measure to assess the likelihood

of exceeding the 8-hour average California standard at

each site; expected peaks are based on data recorded at

each site on a specific date in the previous 3 years (1999–

2001). Both 8-hour concentrations and daily peaks are

used for federal and state designations of nonattainment

areas. They both capture extreme events, but the expected

daily peak concentration provides more stability for estimating spatial patterns than the one-year measures based

on the four highest days. Few studies of long-term health

effects have found significant association with O3 exposure, although acute effects of a small magnitude have

been observed (Bell et al. 2004). Thus, it seems plausible

that an O3 effect would be manifest in those areas most

likely to experience exceedances.

Traffic Data To assess the impact of traffic, we defined

buffers at both 500 and 1000 meters of a freeway. Each

ZCA centroid within those buffers was assigned a value of

1 and others were assigned 0. The resulting variables were

used as indictors of proximity to freeways in the health

effects models. For each ZCA centroid within those buffers

we collected land-use and PM2.5 data from monitors. The

U.S. Census Feature Class Codes define freeways as having

limited access, a numbered assignment (e.g., a major state

or U.S. route), and a speed limit higher than 50 miles per

hour (USCB 2004). The distance from the ZCA centroid to

the freeway was used as a proxy for exposure to trafficrelated pollution, which may exert effects independent of

and in addition to specific pollutants such as PM2.5 and O3

that vary over larger areas (Hoek et al. 2002). 
D. Krewski et al.

67

Ecologic Covariates We assembled data from the 2000

USCB for seven ecologic covariates for the ZCAs to control

for contextual neighborhood covariates (see sidebar). Contextual effects occur when individual differences in a

health outcome are associated with the group of variables

that represent the social, economic, and environmental

settings where the individuals live, work, or spend time

(e.g., poverty or unemployment in a neighborhood) (Curtis

and Taket 1996; Macintyre and Ellaway 2000; Pickett and

Pearl 2001; Diez Roux 2002). These contextual effects

often operate independently from (or interactively with)

the individual-level covariates, such as smoking and diet,

from the ACS enrollment questionnaire. We used the ecologic covariates that have been identified as important in

the population health literature and have been previously

tested as potential confounders with the ACS dataset at the

metropolitan scale (Evans and Kantrowitz 2002; Willis et

al. 2003). These included income, income disparity, grade

12 education, total population, racial composition (black,

white, Hispanic in percentages), and unemployment (Jerrett et al. 2003).

We used the proportion of homes that have air conditioning to measure possible exposure misclassification on

the premise that air-conditioned houses are more tightly

sealed and have lower penetration of particles from outdoors. Similar housing variables have been used in a metaanalysis of acute effects (Levy et al. 2000); and a recent

study of personal exposures in Los Angeles reported that

particle penetration is largely reduced in air-conditioned

homes (Meng et al. 2005). This variable added partial control for the impact of air conditioning, which may relate

both to health outcomes (through prevention of heat stress)

and to air pollutant concentrations (because high concentrations and low proportions of air conditioning are related

in our study area). We thus expected the proportion of air

conditioning in the ZCA to correlate with lower PM exposures and effects.

We also computed principal components from the seven

ecologic covariates (excluding AC) to provide maximal

control for confounding while avoiding multicollinearity

among the ecologic covariates (Luginaah et al. 2001;

Krieger et al. 2002).

Statistical Methods and Data Analysis

The statistical methods are similar to those employed by

Jerrett and colleagues (2005a) in a previous intra-urban

analysis of data from Los Angeles using spatial kriging to

estimate air pollutant concentrations at the ZCA scale.

(The underlying statistical model is the random effects

Cox model described in the section Nationwide Analysis /

Statistical Methods and Data Analysis / The Random

Effects Cox Model.)

In brief, we used the 44 individual-level covariates (see

sidebar) identified as possible confounders of the air pollution–mortality association in the Updated Analysis

(Pope et al. 2002). These variables included lifestyle, diet,

demographics, occupational, and educational factors

along with 12 variables that measure aspects of smoking.

Sensitivity analyses revealed that removing individual

variables had little influence on the estimated risk of pollution exposure; therefore, to promote comparability with

results from earlier studies, we report the results for our

analyses that included this standard set of 44 individuallevel variables. Eight ecologic covariates (discussed above)

were also included.

We used the standard Cox model for our main analyses

of association between air pollution and mortality

(Hosmer and Lemeshow 1999). Because the units of analyses were small ZCAs and previous analyses had indicated spatial autocorrelation in the residual variation of

some health effects analyses, we also used the spatial

random effects Cox model as a cross-validation of the standard Cox model. We have previously shown that survival

experience is clustered by community and the clusters are

spatially autocorrelated between communities (Krewski et

al. 2000a,b; Jerrett et al. 2003). Lack of statistical control

for these factors can bias the estimates of the effects of air

pollution and underestimate associated standard errors

(Jerrett et al. 2003; Ma et al. 2003). We specified a model in

which the random effects were assumed to be positively

correlated for neighboring ZCAs. Thiessen polygons,

which ensure that all points within the polygon are closer

to the centroid of that polygon than to any other centroid,

were used to assign first-order nearest-neighbor contiguity

between the ZCAs. The polygons were derived using ArcView 3.2 (ESRI Corp., Redlands, CA).

Results

Using the new LUR model documented in the last section, we assigned exposure concentrations based on the

traffic, government, and industrial land use for the 267

ZCAs (see also Jerrett et al. 2005a). These exposure assignments were then compared with the earlier analysis, with

exactly the same causes of death, individual and ecologic

covariates, and copollutants. The results are shown in

Table 23 (six causes of death are shown in each section of

the table).

The results show the effects of LUR-predicted PM2.5

exposure with different levels of control for confounding

variables. HRs are expressed for a 10-μg/m3 change in
68

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality Table 23. HRs by Cause of Death Associated with Each 10-μg/m3 Change in PM2.5 2000 Concentrations (from LUR Model) Based on 267 Los Angeles ZCAsa Covariates All Causes

(n = 5856)

IHD

ICD-9: 410–414

(n = 1462)

CPD

ICD-9: 400–440, 

460–519

(n = 3136)

Lung Cancer

ICD-9: 162

(n = 434)

Digestive

Cancer

(n = 429)

Other

Cancers

(n = 992)

PM2.5 only 1.197 (1.082–1.325) 1.415 (1.154–1.735) 1.179 (1.025–1.356) 1.460 (1.013–2.105) 1.277 (0.880–1.854) 1.036 (0.814–1.319)

+ 44 Individual-level covariates 1.143 (1.033–1.266) 1.331 (1.084–1.634) 1.114 (0.968–1.282) 1.392 (0.964–2.010) 1.191 (0.816–1.738) 1.010 (0.791–1.289)Ecologic covariatesb

Air conditioning (%) 1.142 (1.031–1.265) 1.333 (1.085–1.638) 1.121 (0.974–1.290) 1.376 (0.952–1.989) 1.165 (0.797–1.702) 1.002 (0.784–1.281)Black (%) 1.145 (1.033–1.269) 1.347 (1.096–1.656) 1.120 (0.972–1.289) 1.411 (0.976–2.041) 1.188 (0.812–1.738) 1.009 (0.789–1.290)White (%) 1.151 (1.036–1.278) 1.362 (1.103–1.682) 1.127 (0.976–1.302) 1.471 (1.008–2.147) 1.162 (0.787–1.717) 1.027 (0.798–1.321)Hispanic (%) 1.132 (1.016–1.261) 1.322 (1.065–1.641) 1.113 (0.960–1.290) 1.415 (0.956–2.096) 1.117 (0.749–1.667) 1.021 (0.788–1.323)Unemployment (%) 1.127 (1.015–1.252) 1.328 (1.075–1.641) 1.129 (0.977–1.305) 1.279 (0.879–1.862) 1.187 (0.803–1.755) 0.966 (0.751–1.243)Median household income ($000s) 1.146 (1.035–1.268) 1.332 (1.086–1.635) 1.115 (0.970–1.283) 1.388 (0.963–2.001) 1.195 (0.823–1.735) 1.014 (0.796–1.293)Total population 1.141 (1.030–1.264) 1.322 (1.076–1.624) 1.108 (0.963–1.275) 1.396 (0.967–2.016) 1.202 (0.823–1.755) 1.012 (0.792–1.292)Income disparity (Gini) 1.110 (0.999–1.234) 1.254 (1.014–1.552) 1.056 (0.913–1.222) 1.306 (0.893–1.910) 1.213 (0.823–1.786) 1.015 (0.790–1.304)Grade 12 education (%) 1.144 (1.033–1.266) 1.334 (1.087–1.637) 1.118 (0.972–1.286) 1.386 (0.961–2.000) 1.183 (0.811–1.725) 1.006 (0.788–1.284)

Covariate groupings

All social factors 1.142 (1.026–1.272) 1.322 (1.064–1.642) 1.107 (0.954–1.285) 1.399 (0.949–2.061) 1.185 (0.800–1.755) 1.037 (0.803–1.340)

Air conditioning, median household 

income, > grade 12 education, social factor

1.115 (1.003–1.239) 1.263 (1.020–1.563) 1.072 (0.926–1.241) 1.290 (0.881–1.890) 1.189 (0.809–1.748) 1.011 (0.786–1.299)

Parsimonious ecologic covariatesc 1.126 (1.014–1.251) 1.264 (1.022–1.563) 1.086 (0.939–1.256) 1.311 (0.897–1.915) 1.199 (0.817–1.758) 1.012 (0.788–1.299)

Copollutant controld

44 Individual-level covariates + ozone 

(EPDCe

)

1.191 (1.069–1.327) 1.455 (1.171–1.810) 1.187 (1.023–1.378) 1.446 (0.982–2.128) 1.163 (0.777–1.742) 1.059 (0.818–1.371)

44 Individual-level covariates + ozone 

(averagef

)

1.176 (1.057–1.307) 1.431 (1.155–1.772) 1.152 (0.996–1.334) 1.489 (1.018–2.178) 1.166 (0.784–1.734) 1.053 (0.816–1.358)

44 Individual-level covariates + freeways 

within 500 mg

1.170 (1.054–1.299) 1.393 (1.127–1.721) 1.134 (0.982–1.310) 1.439 (0.989–2.095) 1.208 (0.819–1.783) 1.057 (0.824–1.356)

Copollutant risk estimates

Ozone (EPDCe

) 0.985 (0.964–1.006) 0.973 (0.932–1.015) 0.966 (0.938–0.994) 0.989 (0.917–1.068) 1.008 (0.933–1.089) 0.989 (0.941–1.040)

Ozone (averagef

) 0.993 (0.977–1.010) 0.984 (0.952–1.017) 0.985 (0.963–1.008) 0.970 (0.912–1.032) 1.007 (0.947–1.070) 0.993 (0.954–1.033)

Freeway within 500 mg 0.987 (0.875–1.113) 0.898 (0.706–1.143) 0.915 (0.775–1.081) 1.440 (0.939–2.208) 0.841 (0.526–1.346) 1.184 (0.883–1.588)Freeway within 1000 mg 0.974 (0.894–1.062) 1.048 (0.885–1.241) 0.982 (0.874–1.104) 0.942 (0.685–1.295) 0.879 (0.634–1.220) 0.897 (0.721–1.117)
D. Krewski et al.

69

Table 23 (Continued). HRs by Cause of Death Associated with Each 10-μg/m3 Change in PM2.5 2000 Concentrations (from LUR Model) Based on 267 Los 

Angeles ZCAsa

Covariates

Endocrine Disorders

ICD-9: 240–279

(n = 95)

Diabetes

ICD-9: 250

(n = 57)

Digestive

Disorders

(n = 151)

Male

Accidents

(n = 75)

Female

Accidents

(n = 47)

All Other

Causes

(n = 497)

PM2.5 only 3.344 (1.565–7.148) 2.313 (0.803–6.660) 1.868 (0.993–3.514) 1.226 (0.512–2.938) 1.126 (0.367–3.456) 0.978 (0.692–1.381)+ 44 Individual-level covariates 3.037 (1.384–6.664) 2.022 (0.687–5.948) 1.709 (0.902–3.238) 1.078 (0.443–2.627) 0.928 (0.284–3.035) 1.006 (0.709–1.427)Ecologic covariatesb

Air conditioning (%) 3.025 (1.374–6.660) 2.021 (0.685–5.969) 1.683 (0.885–3.199) 1.156 (0.470–2.847) 1.070 (0.322–3.563) 0.977 (0.688–1.387)Black (%) 2.919 (1.328–6.419) 1.991 (0.680–5.830) 1.734 (0.918–3.277) 1.029 (0.417–2.535) 0.987 (0.302–3.222) 0.981 (0.689–1.396)White (%) 2.815 (1.256–6.306) 1.971 (0.657–5.915) 1.682 (0.875–3.236) 0.973 (0.387–2.447) 0.943 (0.279–3.192) 0.979 (0.682–1.405)Hispanic (%) 2.949 (1.283–6.780) 2.006 (0.640–6.286) 1.456 (0.744–2.853) 1.088 (0.424–2.793) 0.693 (0.198–2.425) 1.033 (0.713–1.496)Unemployment (%) 2.359 (1.065–5.225) 1.664 (0.568–4.877) 1.572 (0.818–3.022) 1.190 (0.474–2.987) 0.944 (0.279–3.193) 0.975 (0.679–1.399)Median household income ($000s) 2.907 (1.329–6.357) 1.992 (0.681–5.829) 1.709 (0.897–3.256) 1.075 (0.439–2.734) 0.885 (0.261–2.999) 1.021 (0.723–1.441)Total population 3.090 (1.401–6.816) 2.017 (0.686–5.924) 1.735 (0.916–3.286) 1.083 (0.445–2.638) 0.835 (0.249–2.797) 1.000 (0.704–1.421)Income disparity (Gini) 3.121 (1.423–6.844) 2.048 (0.692–6.059) 1.691 (0.875–3.271) 1.027 (0.411–2.563) 0.920 (0.267–3.178) 1.013 (0.705–1.453)Grade 12 education (%) 3.058 (1.384–6.759) 2.038 (0.682–6.089) 1.705 (0.901–3.228) 1.096 (0.449–2.680) 0.923 (0.279–3.059) 1.002 (0.708–1.417)

Covariate groupings

All social factors 2.676 (1.213–5.904) 1.985 (0.669–5.895) 1.571 (0.804–3.071) 0.927 (0.355–2.420) 0.650 (0.175–2.414) 1.057 (0.733–1.525)

Air conditioning, median household 

income, > grade 12 education, social factor

2.796 (1.203–6.496) 1.885 (0.584–6.080) 1.311 (0.649–2.649) 1.088 (0.432–2.745) 0.910 (0.256–3.230) 1.006 (0.702–1.441)

Parsimonious ecologic covariatesc 2.559 (1.131–5.791) 1.735 (0.569–5.290) 1.330 (0.660–2.679) 1.210 (0.488–3.002) 1.096 (0.320–3.746) 0.988 (0.690–1.413)

Copollutant controld

44 Individual-level covariates + ozone 

(EPDCe)

2.926 (1.305–6.557) 2.063 (0.686–6.200) 1.630 (0.839–3.167) 1.097 (0.424–2.841) 1.157 (0.319–4.195) 0.939 (0.645–1.368)

44 Individual-level covariates + ozone 

(averagef)

3.040 (1.376–6.715) 2.154 (0.735–6.311) 1.575 (0.814–3.049) 1.060 (0.414–2.712) 1.014 (0.292–3.525) 0.967 (0.667–1.401)

44 Individual-level covariates + freeways 

within 500 mg

2.813 (1.252–6.321) 1.820 (0.595–5.574) 1.869 (0.981–3.560) 1.114 (0.448–2.769) 0.761 (0.221–2.618) 1.010 (0.704–1.450)

Copollutant risk estimates

Ozone (EPDCe) 1.044 (0.884–1.232) 0.982 (0.791–1.220) 1.036 (0.910–1.179) 1.015 (0.843–1.224) 0.872 (0.682–1.114) 1.063 (0.988–1.144)

Ozone (averagef) 0.999 (0.877–1.137) 0.944 (0.795–1.121) 1.063 (0.959–1.178) 1.037 (0.892–1.206) 0.931 (0.766–1.132) 1.043 (0.986–1.104)Freeway within 500 mg 0.660 (0.264–1.651) 0.439 (0.116–1.665) 2.490 (1.082–5.730) 0.559 (0.167–1.874) 0.868 (0.279–2.704) 0.868 (0.584–1.290)Freeway within 1000 mg 1.515 (0.855–2.687) 1.774 (0.835–3.766) 0.495 (0.246–0.995) 1.077 (0.527–2.202) 2.011 (0.883–4.579) 1.152 (0.868,1.529)

a Baseline HRs were stratified by age (in 1-year groupings), gender, and race. Total number of participants was 22,905. HRs are followed by 95% confidence intervals. Bolded data refer to text. b Ecologic covariates were included one at a time in the model with PM2.5 and the 44 individual-level covariates. c Parsimonious individual-level (23) and ecologic (4) covariates were considered. The ecologic covariates were air conditioning, household income, grade 12 education, and the predicted values of 

the third principal component analysis based on all social variables (except air conditioning).

d All models included PM2.5. e Based on expected peak daily concentrations.f Average of four highest 8-hour maximum readings. g Intersection with a freeway within 500 or 1000 m of a ZCA centroid. 
70

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

PM2.5 exposure (followed by the 95% CI). For example, we

follow the successive analyses for all-cause mortality:

• with PM2.5 alone (stratified for age, sex, and race), the 

HR was 1.197 (95% CI, 1.082–1.325);

• whereas with the 44 individual-level covariates the 

HR was 1.143 (95% CI, 1.033–1.266).

• All subsequent results include the 44 individual-level 

covariates and one or more ecologic covariates.

• With the 44 individual-level covariates and the ecologic covariate of unemployment, the HR of PM2.5 was 

1.127 (95% CI, 1.015–1.252).

• When we added four social factors extracted from the 

principal component analysis (which account for 81% 

of the total variance in the social variables), the HR 

was 1.142 (95% CI, 1.026–1.272).

• Including all ecologic covariates that were individually associated with mortality in bivariate models 

with PM2.5 exposure (air conditioning, median 

income, and education attainment) reduced the HR to 

1.115 (95% CI, 1.003–1.239).

• For the parsimonious model that included ecologic 

confounder variables that both reduced the pollution 

coefficient and had associations with mortality, the 

HR was 1.126 (95% CI, 1.014–1.251).

• When controlled for the expected peak daily O3 levels 

the HR was 1.191 (95% CI, 1.069–1.327). (Thus, similar to earlier results [Jerrett et al. 2005], O3 had no primary effect in these models for all-cause mortality.)

Discussion and Conclusions

In general, the associations for PM2.5 and mortality in

this set of analyses are similar in magnitude with those

reported earlier from geostatistical kriging estimates (Jerrett et al. 2005a) with three minor differences. First, the HR

for PM2.5 alone is somewhat smaller than the risks from

the kriging model. Second, although the risk estimates

were smaller in this study, the CIs were tighter. Third, the

results from this study appear to be less sensitive to ecologic confounding. For example, in the earlier study, the

parsimonious model for all-cause mortality that included

all the individual-level variables and the significant ecologic confounders resulted in the lower confidence bound

below 1. In this analysis, the same model had a lower confidence bound higher than 1 (1.126, 95% CI, 1.014–1.251;

only the income disparity variable confounded the estimate

to below 1, but that estimate was still extremely close to 1;

see Table 23). We consider the model containing the social

factors from the principal components analysis to be the

most reasonable on the grounds that it maximally controls

for confounding without inducing as much collinearity.

The estimates from the model that contains only the 44

individual-level covariates (1.143, 95% CI, 1.033–1.266)

and the model with all social factors (1.142, 95% CI,

1.026–1.272) produce nearly identical point estimates.

The CIs are only slightly wider for the model with social

factors from the principal component analysis incorporated in the model in the same manner as the ecologic factors. Thus, results from this LUR exposure model appear to

be more robust to adjustment for confounders than the earlier results using the kriging surface.

The Los Angeles Analysis was extended using the

random effects Cox model with and without allowance for

spatial autocorrelation. The earlier study (Jerrett et al.

2005a) using the kriging estimates demonstrated significant autocorrelation in the risks, which was diminished to

acceptable levels when the ecologic covariates were

included in the model. That analysis did not explicitly

incorporate a spatial autocorrelation parameter in the

model, but in the current analysis we used the full random

effects formulation.

Table 24 displays results from the random effects

models, organized to compare results between models

with and without allowance for spatial autocorrelation. It

also compares models with various levels of control for

confounding, beginning with a model that contains only

the 44 individual-level covariates and no pollutant variable (all models were stratified for age, sex, and race). The

initial model (with only the 44 individual-level covariates)

thus demonstrates the residual random effects variance

attributable to variability in mortality patterns between

ZCAs. For all-cause mortality, when PM2.5 was included in

the model, random effects variance declined about 37%

for the model with no autocorrelation (7.008 vs. 4.386) and

about 24% for the model including autocorrelation (10.02

vs. 7.591). This large decline suggests that approximately

one-third to one-quarter of the residual variation is

accounted for by the PM2.5 variable. The drop in the

random effects variance for models including ecologic

covariates was much larger, which indicates that these

variables account for much of the residual variation in

mortality across Los Angeles. Including the freeway intersection variable (distance from a ZCA centroid to a

freeway) yielded only a minimal change in the random

effects variance. Substantial autocorrelation was still

found in the data after inclusion of all the variables, as evidenced by the fact that our estimate of ZCA autocorrelation reached the maximum value allowable in the model

formulation.

This high level of residual autocorrelation, even with a

very small random effects variance, may have resulted

from a number of phenomena. First, the nearest-neighbor
D. Krewski et al.

71

formulation of the random effects Cox model may not capture what is likely to be a more complex residual spatial

structure. Second, the autocorrelation may not be stationary over the entire study area, and more flexible

models based on spatial moving averages may be needed

to capture this nonstationarity. Third, the presence of spatial autocorrelation may also suggest that one or more

important covariates have been excluded from the model.

We have visually examined the residuals, and there seems

to be a clustering of autocorrelation in residual mortality

on the downwind side of roadways with high volumes of

truck traffic. The assignment of residence locations to the

ZCA centroid prevented us from precisely assigning exposures, but this autocorrelation suggests that possible effects

from truck traffic merit attention in future studies. Although

these theoretical possibilities are present, the point estimates and CIs are insensitive to model specification.

In analyses of IHD mortality, including PM2.5 had a

marked effect on the residual random effects variance,

with a drop of more than 86% (4.357 vs. 0.582). The subsequent decline in the random effects variance due to inclusion of the ecologic covariates was also large, but much

less pronounced than with all-cause mortality. For IHD,

the residual autocorrelation was negligible. We expected

this in a model in which the individual, ecologic, and air

pollution variables predicted the mortality data more completely than they did for the analysis of all-cause mortality.

With IHD we did observe some confounding effect of the

point estimates when we included the ecologic and

freeway variables. Models with and without the autocorrelation parameter produced nearly identical results, as

would be expected with such a small amount of residual

autocorrelation.

Results from the more refined exposure surface from the

LUR model largely confirm the earlier findings from the

kriging models. This is somewhat surprising given the different visual appearance of each surface, with the LUR

detecting smaller variations in areas around highways and

industry. The effect sizes were slightly diminished compared with the earlier study, but the effects shown in this

study tended to be less sensitive to control for confounding and to alternative model specification. The larger

decline in the random effects variance for IHD mortality

supports a growing body of research on the mechanisms of

systemic pulmonary inflammation and atherogenesis

(Brook et al. 2004; Künzli et al. 2005).

Table 24. Sensitivity Analysis Using the Random Effects Cox Model for a 10-μg/m3 Change in PM2.5 Exposure with 

Allowance for Spatial Autocorrelationa

Covariatesb

No Spatial 

Autocorrelation 

Spatial 

Autocorrelation 

HR

ZCA

Variance 

( 103) HR

ZCA 

Variance 

( 103)

ZCA

Autocorrelation

All Causes

44 Individual-level covariates — 7.008 — 10.02 0.3307c

+ PM2.5 1.158 (1.035–1.295) 4.386 1.160 (1.021–1.317) 7.591 0.3307c

+ Parsimonious ecologic covariatesd 1.152 (1.034–1.283) 0.229 1.152 (1.032–1.286) 0.623 0.3307c

+ Freeway intersectione 1.156 (1.036–1.289) 0.233 1.156 (1.034–1.293) 0.847 0.3307c

IHD

44 Individual-level covariates — 4.357 — 4.357 0.000f

+ PM2.5 1.402 (1.137–1.728) 0.582 1.402 (1.137–1.728) 0.582 0.000f

+ Parsimonious ecologic covariatesd 1.347 (1.083–1.675) 0.189 1.347 (1.083–1.675) 0.189 0.000f

+ Freeway intersectione 1.339 (1.074–1.669) 0.189 1.339 (1.074–1.669) 0.189 0.000f

a

 Bolded data refer to text.

b Covariates accumulate in the model with each addition.

c

 Estimate of ZCA autocorrelation reached its maximum value.

d Parsimonious individual-level (23) and ecologic (4) covariates were considered. The ecologic covariates were air conditioning, household income, grade 

12 education, and the predicted values of the third principal component analysis based on all social variables (except air conditioning). 

e

 Intersection with a freeway within 500 or 1000 m of a ZCA centroid.

f

 Estimate of ZCA autocorrelation was < 0.
72

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

CRITICAL EXPOSURE TIME WINDOWS

We constructed individual time-dependent exposure

profiles for particulate and gaseous air pollution for a subcohort of the ACS CPS-II in order to examine if there is a

critical exposure time window that is primarily responsible for the increased mortality associated with ambient

air pollution. We examined air pollution exposures in

three separate time windows: 1 to 5, 6 to 10, and 11 to 15

years in the past.

MATERIALS AND METHODS

Study Population

The ACS CPS-II cohort consists of nearly 1.2 million

Americans enrolled in 1982. In 1992 and 1993, the Nutrition Cohort was established within the larger ACS CPS-II

cohort (Calle et al. 2002). The purposes of selecting the

Nutrition Cohort were to update a variety of behavioral,

medical, and demographic information that had first been

collected in 1982, and to collect more detailed data

regarding diet, physical activity, and other lifestyle factors

that had not been obtained at the time of enrollment. The

ACS cohorts and survey questionnaires are detailed elsewhere (Pope et al. 1995; Calle et al. 2002). Briefly, CPS-II

participants who were alive and between the ages of 50

and 74 years and who resided in one of the following 21

states that have population-based cancer registries were

included in the Nutrition Cohort: California, Connecticut,

Florida, Georgia, Illinois, Iowa, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Jersey,

New Mexico, New York, North Carolina, Pennsylvania,

Utah, Virginia, Washington, and Wisconsin. A total of

184,194 participants from the CPS-II cohort who lived in

these states completed the self-administered questionnaire

mailed in 1992–1993, and were contacted again in 1997–

1998, 1999–2000, and every 2 years thereafter for followup information. Mortality of the study participants was

ascertained by volunteers in 1984, 1986, and 1988, and

subsequently by ACS staff using the National Death Index

(Calle and Terrell 1993). The Emory University School of

Medicine Human Investigations Committee approved all

aspects of the CPS II study.

Individual Exposure Profiles

Previous analyses of the ACS cohort have assumed that

each individual’s exposure was determined by the average

level of ambient air pollution in that person’s city of residence at one point in time, either the beginning or the end

of the study (Pope et al. 1995, 2002; Jerrett et al. 2005a;

Krewski et al. 2000a,b). In this project, we sought to establish time-dependent exposure profiles for each individual

in the study and to investigate the association between

mortality and ambient air pollutant concentrations in specific time windows that could reflect past exposures. We

hypothesized that a critical exposure time window may be

primarily responsible for the increased mortality associated with ambient air pollution.

Individual time-dependent exposure profiles for PM2.5

and SO2 were constructed for each participant by

matching the reported addresses at different times with the

air pollutant exposure for the MSA where they lived. Individual exposures were determined using mean annual

PM2.5 and SO2 levels that may have been experienced by

the individual during specific time windows. For

example, to test the possibility that current excess mortality is primarily due to pollution exposure 5 to 10 years

ago, a person’s exposure would be modeled as the average

of his or her annual exposures during the interval

extending from 5 to 10 years before the current time.

Residential Histories Residential histories for the Nutrition Cohort participants were constructed based on the

place of residence when enrolled in the CPS-II study

(1982), again when enrolled in the Nutrition Cohort (1992–

1993), and at subsequent follow-up times in 1997–1998

and 1999–2000. Zip Code information was used to assign

participants to an MSA. Since no information was collected on the number of moves between follow-up stages,

we assumed that if a new Zip Code was reported at the

time of follow-up, the participant had moved only once

directly from the previous Zip Code to the new one

(Hansen 1998). Since no information on the time of a move

was collected between questionnaires, we randomly

assigned a year of move to each participant (Schachter

2000) and assumed they had moved mid-calendar year

(Hansen 1998). We constructed residential histories before

enrollment in 1982 under the assumption that participants

had lived in the same residence they reported in 1982.

PM2.5 A total of 60,941 Nutrition Cohort participants

resided in one of 83 MSAs for which average annual PM2.5

exposure data had been estimated based on measured

values of TSP and PM10 for the period of 1972–2000 (calculated by Lall and colleagues [2004]). For each MSA,

either annual TSP or PM10 data were used in addition to

the specific PM2.5/TSP or PM2.5/PM10 ratio. This subcohort was designated as the PM2.5-A group and PM2.5 exposure levels were assigned to each participant (Figure 17). A

total of 8181 (13.4%) participants in the PM2.5-A group

moved at least once during the study period. A small proportion of these participants (8.1%) did not reside in one

of the 83 MSAs for some portion of the follow-up period.
D. Krewski et al.

73

Figure 17. Profile of time trends (1972–2000) in average annual PM2.5 concentrations based on measured values of TSP and PM10 in 83 MSAs and on

linear interpolation of measured PM2.5 data for the years 1979–1983 and 1999–2000 for an additional 23 MSAs. (Figure continues next page.)
74

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 17 (Continued).
D. Krewski et al.

75

Figure 17 (Continued).
76

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 17 (Continued).
D. Krewski et al.

77

For those individuals, average annual PM2.5 concentrations were estimated using linear interpolation of data

from an additional 23 MSAs for which measured PM2.5

data were available for the periods 1979–1983 and 1999–

2000 (Pope et al. 2002). The first participant in this group

died in 1992; therefore, the entire exposure period we

examined for this group started in 1977 (15 years before

the first death) and ended in 2000 (the end of follow-up).

A second analytic cohort was formed to include 81,466

deceased participants from the full CPS-II cohort who (1)

were not in the PM2.5-A group, and (2) died in the same

MSA where they had resided at enrollment in 1982. They

were designated as the PM2.5-B group. We assumed that

these participants had not moved between enrollment and

death. PM2.5 values were assigned using average annual

data from Lall and colleagues (2004) for 50,915 participants

in 30 MSAs, and by linear interpolation of data for 23

MSAs from Pope and colleagues (2002) for the remaining

30,551 participants (Figure 18). The first participant in this

group died in 1982; therefore the exposure period ranged

from 1967 (15 years before the first death) to 2000 (end of

follow-up). The 1972 PM2.5 concentrations were used to

represent exposures before 1972. Participants in the PM2.5-

A and PM2.5-B groups were mutually exclusive.

SO2 A third analytic group was formed with 80,711

Nutrition Cohort participants who resided in one of 120

MSAs for which at least 20 years of annual average SO2

measurements were available. For most of the period from

1972 to 2000, measured average annual SO2 data were

available from the EPA AIRS (Pope et al. 2002). Linear

interpolation between adjacent time points was used for

years in which average annual measurements were

missing (Figures 19 and 20). As with the PM2.5-A group,

the entire exposure period examined was 1977 to 2000. A

total of 5762 (7.1%) participants moved at least once

during the exposure period.

STATISTICAL METHODS AND DATA ANALYSIS

Standard Cox models with time-dependent PM2.5 and

SO2 exposure data were used to obtain adjusted HRs and

95% CIs (SAS version 9.1; SAS Institute, Cary, NC) for

all-cause, lung-cancer (ICD9: 162), and CPD (ICD9: 400–

440, 460–519) mortality. For the PM2.5-A group and the

SO2 group, HRs were calculated from enrollment in the

Nutrition Cohort in 1992–1993 through 2000. For the

PM2.5-B group, the HRs covered the period from enrollment in the CPS-II full cohort in 1982 through end of

follow-up in 2000.

The baseline hazard function was stratified by age (in 1-

year groupings), sex, and race (white/other; from CPS-II

enrollment questionnaire). Models were adjusted for the

standard suite of 44 lifestyle and demographic variables

collected at enrollment in 1982 (see sidebar) including:

smoking, education, marital status, body mass index,

alcohol consumption, occupational exposure, and diet

(Jerrett et al. 2005a; Pope et al. 2002).

When fitting the standard Cox model, the partial likelihood is updated each time a death occurred in a group. For

each of the three groups (PM2.5-A, PM2.5-B, and SO2), the

risk set for the partial likelihood consisted of all subjects in

the group who were alive at the time a member died. The

exposure for each subject in the risk set was based on their

time-dependent exposure profile up to the death of a

cohort member and was updated at each subsequent death

time. Estimates of the parameters of the standard Cox

model with time-dependent covariates were then obtained

by maximizing the product of the partial likelihoods that

corresponded to each death time.

Differences in HRs and AIC values between separate 5-

year time-window models allowed us to investigate which

time window would be most relevant to the association

between air pollution and mortality. AIC values provide a

measure of goodness of fit, or show how well the model

that relates exposure to outcome fits the available data,

taking into account the complexity of the model. Models

with lower AIC values imply that the model has a better fit

(Burnham and Anderson 2002). In this investigation, the

number of parameters did not change between the separate

time-window models. Models were fitted for an overall 15-

year time window, and separately for 1- to 5-year, 6- to 10-

year, and 11- to 15-year time windows. Since education

attainment had been found to be a powerful modifier of the

air pollution–mortality association in the Reanalysis

(Krewski et al. 2000a,b), results were also examined by

level of education recorded at enrollment in 1982 (less

than high school compared with high school or more).

A multiple exposure-time-window model was also

tested as a type of weighted model to consider the three 5-

year time windows simultaneously. In this model, the logHR was modeled as a linear combination of average annual

exposures that had occurred during each of the three time

windows. If each of the three fitted effects (the three 5-year

periods) were positive, we could interpret them as a single

overall pollutant effect calculated as a weighted average of

the exposures in the three time windows. The weights, represented by the coefficients in the linear combination,

would represent the “relative effectiveness” of exposure

during that time window (Goddard et al. 1995). To be interpretable, this model would require the additional constraint

that all of the weights be nonnegative, or, equivalently, that
78

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 18. Profile of time trends (1972–2000) in average annual PM2.5 concentrations for 53 MSAs in which PM2.5-B participants (from the full CPS-II

cohort) lived. PM2.5 exposure concentrations assigned to the participants were based on (1) average annual data for 50,915 participants in 30 MSAs and

(2) linear interpolation of data for the remaining 30,551 participants in 23 MSAs. The 1972 PM2.5 concentrations were used to represent exposures before

1972. (Figure continues next page.)
D. Krewski et al.

79

Figure 18 (Continued).
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Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 19. Profile of time trends (1972–2000) in SO2 concentrations for 120 MSAs. Measured average annual SO2 data for most of the period were available.

Linear interpolation between adjacent time points was used for years in which average annual measurements were missing. (Figure continues next page.)
D. Krewski et al.

81

Figure 19 (Continued).
82

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 19 (Continued).
D. Krewski et al.

83

Figure 19 (Continued).
84

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 19 (Continued).

Figure 20. Distribution of measured average annual SO2 data for 1972–2000. Note that only 5 MSAs had 29 years of SO2 measurements for which no

imputations were needed.

the direction of the effect be the same for each of the three

time windows.

Because SAS does not provide a means of fitting the

constrained model, we fit the unconstrained model to see

if all three fitted effects had the same sign. Unfortunately,

this produced a combination of both positive and negative weights making interpretation problematic. For

example, two positive effects in the more distant time

windows and a negative effect for the most recent time

window would imply that current exposure is deleterious

only until 5 years have passed, at which time it becomes

beneficial. In the absence of established software to fit the

correct, constrained model, we were obliged to abandon

this line of inquiry.

RESULTS

Tables 25 (15-year time window) and 26 (three separate

5-year time windows) present adjusted HRs (with 95%

CIs) and AIC values for all-cause, lung-cancer, and CPD

mortality associated with PM2.5 or SO2 exposure. HRs
D. Krewski et al.

85

associated with PM2.5 exposure in several of the 15-year

windows were elevated, and significantly so for the PM2.5-

B cohort group for lung-cancer and CPD mortality. Results

for analyses stratified by level of education attainment are

presented in Tables 27–29.

An examination of HRs revealed no clear pattern. For

PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of

a trend in HRs going from most recent to most distant windows or vice versa. For SO2, the HRs were highest in the

most recent time window (1 to 5 years), although none of

the HRs was significantly elevated.

Differences in AIC values among the three 5-year exposure time windows were small. For both PM2.5 groups the

AIC value tended to be lowest (the model with the best fit

to the data) in either the most recent time window (1 to 5

years) or the middle time window (6 to 10 years). This was

apparent for all three cause-of-death categories in analyses

with all subjects (Table 26) and in analyses stratified by

education attainment (Tables 28 and 29). The exception

was lung cancer in the PM2.5-A group (Table 26). For the

SO2 group, AIC values were lowest in the most recent time

window for nearly all cause-of-death categories although

differences in AIC values were again small. The exceptions

were all-cause and lung-cancer mortality among those

with a lower level of education attainment (Table 28).

DISCUSSION AND CONCLUSIONS

Overall, no clear pattern emerged as to which of the time

windows of exposure may be most responsible for the

PM2.5–mortality association. Although for SO2 exposure,

models that used exposure data from the most recent 5

years provided a slightly better fit to available data on mortality from all causes, lung cancer, and CPD, as evidenced by

lower AIC values; these differencewere small. The HRs for

mortality associated with exposure to SO2 were highest in

the most recent time window; however none of them were

significantly elevated.

To be able to identify which exposure time windows are

associated with mortality attributable to air pollution, the

patterns of exposure among individuals in the cohort need

to be sufficiently varied. Variation can be spatial (different

exposure profiles experienced by individuals living in different MSAs) and temporal (exposure profiles for a given

MSA changing over time). In this data set, both spatial and

temporal variations in exposure patterns were limited by

the ecologic nature of the exposure measure used. Greater

variability in temporal exposure patterns may be expected

for individuals who move from one MSA to another during

the study period; however, relatively few participants

moved during the time span of this analysis.

On average, the spatial (between MSAs) and temporal

(within MSAs) variations in exposure patterns were similar for the PM2.5-A group (some of whom moved during

the study period) and the PM2.5-B group (who were

assumed to not have moved between enrollment and

death). For spatial variation in both groups, the SD around

the annual average PM2.5 concentrations was 5.0; and for

temporal variation it was 3.5 for group A and 3.4 for group

B. Spatial variation of within-MSA temporal variation,

however, was somewhat higher in the PM2.5-B group than

in the PM2.5-A group as shown by SDs of 1.9 for group B

and 1.7 for group A. For the SO2 group, SDs were 3.7 for

spatial variation, 2.6 for temporal variation, and 1.9 for

spatial–temporal variation.

Table 25. HRs by Cause of Death Associated with a 10-μg/m3 Change in PM2.5 Concentrations or a 5-ppb Change in SO2

Concentrations Over the 15-Year Time Windowa

Cause of Death

PM2.5-A Group

(n = 60,941)

PM2.5-B Group

(n = 81,466)

SO2 Group

(n = 80,711)

All causes

Deaths (n) 6,117 81,466 7,702

HR 0.98 (0.92–1.06) 1.01 (0.99–1.02) 0.99 (0.96–1.01)

Lung cancer

Deaths (n) 599 6,038 763

HR 1.08 (0.87–1.35) 1.07 (1.02–1.13) 0.96 (0.88–1.05)

CPD

Deaths (n) 2,478 40,496 3,145

HR 1.00 (0.90–1.11) 1.05 (1.03–1.07) 1.01 (0.97–1.05)

a

 Model included the 44 individual-level covariates. Baseline hazard function was stratified by age (1-year groupings), sex, and race and was adjusted for 

smoking, marital status, body mass index, alcohol, occupational exposures, and diet. Bolded values refer to text. HRs are followed by 95% confidence 

intervals.
86

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Correlations between exposures assigned to the same

subject in different time windows also tended to be high (r

ranged from 0.75 to 0.98 for PM2.5 and 0.11 to 0.92 for

SO2). These correlations indicate that the amount of independent information available for each time window was

somewhat limited.

Two recent studies by Jerrett and colleagues (2007) and

Laden and associates (2006) have attempted to examine

temporal variation of mortality risk associated with longerterm air pollution exposure in cohort studies. In both

studies, relative risk estimates for mortality were found to

vary with follow-up time. For the ACS CPS-II cohort, Jerrett

and coworkers (2007) reported that the relative risk of mortality from all causes and from CPD associated with SO4

2

exposure declined from the 1980s to the 1990s, possibly

due to air quality improvements. In contrast, PM2.5 HRs

were largest for exposures in more recent time windows,

possibly due to changing patterns of PM2.5 emissions. Complex patterns of population mobility, individual susceptibility, and exposure measurement error have also been

suggested as possible explanations for the observed temporal variation in risk. In contrast, HRs for lung-cancer

mortality exhibited an inverted U-shaped pattern over

time, possibly reflecting a prolonged multifactorial etiology

Table 26. HRs and AIC Values by Cause of Death Associated with a 10-μg/m3 Change in PM2.5 Concentrations or a 5-ppb 

Change in SO2 Concentrations for Three 5-Year Time Windowsa

Exposure 

Time Windowb

PM2.5-A Group

(n = 60,941)

PM2.5-B Group

(n = 81,466)

SO2 Group

(n = 80,711)

All Causes

Years 1–5

HR 1.01 (0.94–1.08) 1.01 (0.99–1.03) 1.03 (0.97–1.09)

(Rank) AIC (3) 81,144.310 (1) 933,094.00 (1) 102,074.03

Years 6–10

HR 0.98 (0.91–1.04) 1.01 (0.99–1.02) 0.99 (0.95–1.03)

(Rank) AIC (1) 81,143.776 (2) 933,094.94 (3) 102,074.37

Years 11–15

HR 0.98 (0.92–1.04) 1.01 (0.99–1.02) 0.99 (0.95–1.02)

(Rank) AIC (2) 81,143.970 (3) 933,095.03 (2) 102,074.19

Lung Cancer

Years 1–5

HR 1.12 (0.89–1.40) 1.10 (1.04–1.17) 1.12 (0.94–1.35)

(Rank) AIC (2) 7,541.342 (1) 67,541.515 (1) 9,661.684

Years 6–10

HR 1.02 (0.83–1.25) 1.06 (1.01–1.12) 1.03 (0.91–1.16)

(Rank) AIC (3) 7,542.180 (2) 67,545.732 (2) 9,662.963

Years 11–15

HR 1.10 (0.91–1.33) 1.05 (1.01–1.10) 0.98 (0.87–1.09)

(Rank) AIC (1) 7,541.275 (3) 67,546.285 (3) 9,662.990

CPD

Years 1–5

HR 1.02 (0.91–1.14) 1.06 (1.03–1.08) 1.06 (0.96–1.17)

(Rank) AIC (2) 32,234.695 (3) 462,773.21 (1) 40,854.733

Years 6–10

HR 0.98 (0.89–1.09) 1.05 (1.03–1.07) 0.99 (0.93–1.05)

(Rank) AIC (1) 32,234.694 (1) 462,771.08 (2) 40,856.150

Years 11–15

HR 0.99 (0.90–1.09) 1.04 (1.02–1.06) 1.00 (0.94–1.05)

(Rank) AIC (3) 32,234.791 (2) 462,772.56 (3) 40,856.224

a

 Model included the 44 individual-level covariates. Baseline hazard function was stratified by age (1-year groupings), sex, and race and was adjusted for 

smoking, marital status, body mass index, alcohol, occupational exposures, and diet. HRs are followed by 95% confidence intervals.

b The AIC value is a measure of how well the model fits the available data; the time window with the lowest AIC value (number 1 in rank) best represents 

the patterns of mortality.
D. Krewski et al.

87

for lung cancer. In a Swedish case–control study, Nyberg

and colleagues (2000) reported that NO2 exposure more

than 20 years in the past was most important for lung

cancer development compared with more recent exposure.

In an analysis of the Harvard Six Cities Study participants over a follow-up period similar to the one used in

the current study, Laden and colleagues (2006) reported

that relative risk estimates of mortality associated with

PM2.5 exposure declined in accordance with a decline in

exposure; this was true for overall, cardiovascular, and respiratory mortality but not for lung-cancer mortality.

Whether PM2.5 exposure was modeled as the annual

average in the year of death or as the average over the

entire follow-up period, it had similar effects on mortality.

The results from the study suggest that since PM2.5 exposure may affect sensitive individuals with preexisting conditions and play a role in the development of chronic

disease, as exposure declines so may the excess mortality

related to it. The importance of the effect of recent air pollution exposure on mortality has also been emphasized in

other studies using different modeling approaches

(Schwartz and Laden 2004; Roosli et al. 2005). Most

recently, Schwartz and associates (2008), in a further analysis of data from the Harvard Six Cities Study, reported

that mortality associated with exposure to fine particles

was observed largely within 1 to 2 years of exposure.

The purpose of the present analysis was not to provide

estimates of the relative risk of mortality associated with

air pollution, but rather to examine the relative effectiveness of exposures at different times in the past to affect

mortality associated with air pollution in the present.

Therefore, these HR estimates for the Nutrition Cohort are

not directly comparable with those from previous analyses

based on the full ACS CPS-II cohort. 

The results of the present study are subject to certain

limitations. Whereas the SO2 concentrations were all measured data, the PM2.5 concentrations were predicted from

PM10 and TSP measurements and are therefore subject to a

certain degree of exposure measurement error. For some

subjects, PM2.5 data were interpolated based on two time

points, at baseline (1982) and the end of follow-up (2000);

and for the larger PM2.5-B group, we extrapolated back

from 1972 exposures for models that considered exposures

up to 15 years in the past, which sometimes extended to

1967. It is possible that increasingly older air pollution

measurements may have been subject to a greater degree of

measurement error than more recent measurements; if so,

Table 27. HRs by Cause of Death Associated with a 10-μg/m3 Change in PM2.5 Concentrations or a 5-ppb Change in SO2

Concentrations Over the 15-Year Time Window as Modified by Education Attainmenta

Cause of Death PM2.5-A Group PM2.5-B Group SO2 Group

Less than High School n = 2,902 n = 16,479 n = 4,026

All Causes

Deaths (n) 533 16,479 699

HR 1.13 (0.90–1.42) 1.02 (0.99–1.06) 1.02 (0.94–1.12)

Lung Cancer

Deaths (n) 66 1,199 82

HR 1.30 (0.67–2.52) 1.08 (0.97–1.21) 1.03 (0.78–1.36)

CPD

Deaths (n) 231 9,281 307

HR 1.00 (0.70–1.42) 1.08 (1.04–1.13) 1.03 (0.90–1.18)

High School or More n = 58,039 n = 64,987 n = 76,685

All Causes

Deaths (n) 5,584 64,987 7,003

HR 0.97 (0.90–1.05) 1.00 (0.99–1.02) 0.98 (0.96–1.01)

Lung Cancer

Deaths (n) 533 4,839 681

HR 1.06 (0.84–1.35) 1.07 (1.01–1.13) 0.95 (0.87–1.04)

CPD

Deaths (n) 2,247 31,215 2,838

HR 0.99 (0.89–1.11) 1.04 (1.02–1.07) 1.01 (0.97–1.06)

a

 Model included the 44 individual-level covariates (the two covariates for education attainment were omitted for this analysis). Baseline hazard function 

was stratified by age (1-year groupings), sex, and race and was adjusted for smoking, marital status, body mass index, alcohol, occupational exposures, and 

diet. HRs are followed by 95% confidence intervals.
88

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Table 28. HRs and AIC Values by Cause of Death Associated with a 10-μg/m3 change in PM2.5 Concentrations or a 5-ppb 

Change in SO2 Concentrations for Three 5-Year Time Windows for Those with an Education of Less than High Schoola

Exposure 

Time Windowb

PM2.5-A Group

(n = 2,902)

PM2.5-B Group

(n = 16,479)

SO2 Group

(n = 4,026)

All Causes

Years 1–5

HR 1.11 (0.87–1.41) 1.03 (0.99–1.07) 1.00 (0.80–1.24)

(Rank) AIC (3) 3,930.551 (3) 134,703.47 (3) 5,319.918

Years 6–10

HR 1.15 (0.93–1.42) 1.03 (0.996–1.06) 1.04 (0.90–1.19)

(Rank) AIC (1) 3,929.531 (1) 134,702.42 (2) 5,319.643

Years 11–15

HR 1.10 (0.90–1.33) 1.02 (0.99–1.05) 1.04 (0.92–1.17)

(Rank) AIC (2) 3,930.464 (2) 134,703.30 (1) 5,319.545

Lung Cancer

Years 1–5

HR 1.15 (0.56–2.34) 1.10 (0.97–1.26) 1.12 (0.61–2.04)

(Rank) AIC (3) 486.585 (1) 9,346.718 (3) 601.746

Years 6–10

HR 1.33 (0.72–2.46) 1.07 (0.96–1.20) 1.12 (0.74–1.69)

(Rank) AIC (1) 485.900 (3) 9,347.238 (2) 601.602

Years 11–15

HR 1.29 (0.72–2.31) 1.07 (0.98–1.17) 1.18 (0.82–1.71)

(Rank) AIC (2) 486.021 (2) 9,346.887 (1) 601.098

CPD

Years 1–5

HR 1.10 (0.76–1.60) 1.09 (1.04–1.14) 1.08 (0.77–1.50)

(Rank) AIC (1) 1,689.729 (3) 76,074.461 (1) 2,354.605

Years 6–10

HR 0.99 (0.71–1.38) 1.08 (1.04–1.13) 1.01 (0.82–1.25)

(Rank) AIC (3) 1,690.004 (1) 76,071.375 (3) 2,354.778

Years 11–15

HR 0.94 (0.69–1.28) 1.07 (1.03–1.10) 0.97 (0.80–1.17)

(Rank) AIC (2) 1,689.836 (2) 76,072.491 (2) 2,354.686

a

 Model included the 44 individual-level covariates (the two covariates for education attainment were omitted for this analysis). Baseline hazard function 

was stratified by age (1-year groupings), sex, and race and was adjusted for smoking, marital status, body mass index, alcohol, occupational exposures, and 

diet. HRs are followed by 95% confidence intervals.

b The AIC value is a measure of how well the model fits the available data; the time window with the lowest AIC value (number 1 in rank) best represents 

the patterns of mortality.

that could result in higher AIC values and a bias of HRs

toward the null in more distant exposure time windows.

We may have also misclassified exposure with certain

assumptions about individuals’ residential histories, such

as that the place of residence remained unchanged before

1982. The PM2.5-B group was composed entirely of participants who had died and were assumed to have never

moved, whereas the PM2.5-A and SO2 groups were composed of self-selected CPS-II participants who had survived until 1992. Although this may introduce some type

of a selection bias, we think it does not influence the identification of critical exposure time windows, which are

determined by the pathophysiological disease processes.

Updated information for certain demographic and lifestyle covariates have been collected in follow-up surveys

for Nutrition Cohort participants. Although data on these

covariates were not available for all study participants

included in this analysis, most of the covariates do not
D. Krewski et al.

89

Table 29. HRs and AIC Values by Cause of Death Associated with a 10-μg/m3 Change in PM2.5 Concentrations or a 5-ppb 

Change in SO2 Concentrations for Three 5-Year Time Windows for Those with an Education of High School or Morea

Exposure 

Time Windowb

PM2.5-A Group

(n = 58,039)

PM2.5-B Group

(n = 64,987)

SO2 Group

(n = 76,685)

All Causes

Years 1–5

HR 1.00 (0.93–1.08) 1.01 (0.99–1.03) 1.03 (0.96–1.10)

(Rank) AIC (3) 73,721.769 (1) 722,351.83 (1) 92,415.750

Years 6–10

HR 0.96 (0.90–1.03) 1.00 (0.98–1.02) 0.99 (0.95–1.03)

(Rank) AIC (1) 73,720.383 (3) 722,352.50 (3) 92,416.128

Years 11–15

HR 0.97 (0.91–1.03) 1.00 (0.99–1.02) 0.99 (0.95–1.02)

(Rank) AIC (2) 73,720.888 (2) 722,352.49 (2) 92,416.016

Lung Cancer

Years 1–5

HR 1.12 (0.87–1.42) 1.10 (1.03–1.18) 1.13 (0.93–1.37)

(Rank) AIC (1) 6,702.496 (1) 52,588.969 (1) 8,620.221

Years 6–10

HR 0.99 (0.79–1.23) 1.06 (1.00–1.12) 1.04 (0.91–1.18)

(Rank) AIC (3) 6,703.257 (2) 52,592.574 (3) 8,621.479

Years 11–15

HR 1.08 (0.88–1.33) 1.05 (0.998–1.10) 0.96 (0.86–1.08)

(Rank) AIC (2) 6,702.698 (3) 52,593.565 (2) 8,621.350

CPD

Years 1–5

HR 1.01 (0.90–1.14) 1.05 (1.02–1.08) 1.06 (0.96–1.18)

(Rank) AIC (2) 29,075.089 (1) 346,041.73 (1) 36,658.773

Years 6–10

HR 0.98 (0.88–1.09) 1.04 (1.02–1.07) 0.99 (0.93–1.06)

(Rank) AIC (1) 29,074.988 (2) 346,042.27 (2) 36,660.093

Years 11–15

HR 0.99 (0.90–1.10) 1.04 (1.01–1.06) 1.00 (0.95–1.06)

(Rank) AIC (3) 29,075.102 (3) 346,043.24 (3) 36,660.141

a

 Model included the 44 individual-level covariates (the two covariates for education attainment were omitted for this analysis). Baseline hazard function 

was stratified by age (1-year groupings), sex, and race and was adjusted for smoking, marital status, body mass index, alcohol, occupational exposures, and 

diet. HRs are followed by 95% confidence intervals.

b The AIC value is a measure of how well the model fits the available data; the time window with the lowest AIC value (number 1 in rank) best represents 

the patterns of mortality.

appreciably affect the air pollution–mortality relationship

(education attainment is the major exception) and were

not expected to complicate the identification of critical

time windows of exposure (Krewski et al. 2000a,b). In the

present study, stratification of the analysis by level of education attainment (less than high school versus high

school or greater) did not shed light on the question of

which time windows might be most important. However,

consistent with previous studies that did not account for

residential mobility information, we also observed larger

mortality HRs among those with a lower level of education

attainment. Levels of smoking cessation may have also differed between analytic groups.

Overall, identification of critical exposure time windows, even among large national cohorts, remains a challenge and further research to identify them with other

relevant data sets is needed. Such work would improve

our understanding of the time frame surrounding the

human health benefits from reduced exposure to long-term

air pollution.
90

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

IMPLICATIONS OF THE FINDINGS

PHASE I

This report provides the results of Phase III of the Particle Epidemiology Reanalysis Project, which began in

1998. Phases I and II together are referred to as the Reanalysis. In Phase I, we validated the data used in the Harvard

Six Cities Study (follow-up 1974–1991; Dockery et al.

1993) and the American Cancer Society Study (follow-up

1982–1989; Pope et al. 1995) of long-term exposure to particulate air pollution and mortality and, using the same

analytic methods, replicated the numerical results

reported by the original investigators. Our findings in

Phase I established the integrity of the original data and

confirmed, with trivial discrepancies, the original risk estimates (Krewski et al. 2000a; 2003b; 2004; 2005a).

PHASE II

In Phase II, we conducted a detailed sensitivity analysis

to assess the robustness of the original findings to alternative analytic approaches (Krewski et al. 2000b). Specific

sensitivity analyses focused on the impact on risk estimates

of including additional individual-level covariates in the

risk models that relate particulate air pollution with mortality; including new ecologic covariates related to demographic factors, socioeconomic indicators, availability of

health services, climate variables, characteristics of the

physical environment, and gaseous copollutants (Krewski

et al. 2000b; 2003a,b); possible confounding due to occupational exposures (Siemiatycki et al. 2003); and population mobility.

These sensitivity analyses produced four main results:

(1) Adjusting for most ecologic covariates did not substantially alter mortality risk estimates related to exposure to

particulate and gaseous air pollutants. An exception was

population change (movement of people from one area of

the country to another), which notably reduced the risk estimates for all-cause mortality related to SO4

2exposure,

although the effects of population change were completely

attenuated by control for residual autocorrelation. (2) The

risk estimates did not vary with adjustment for most individual-level covariates. However, education attainment was

found to modify the effect — risk estimates for all causes of

death studied decreased with increasing education attainment for both PM2.5 and SO4

2exposures. (3) Adjustment

for ambient SO2 concentrations at the MSA scale markedly

Figure 21. Spatial distribution (kriged) of PM2.5 concentrations based on data from 1982–1986. Analysis done in Phase II (Krewski et al. 2000b).
D. Krewski et al.

91

Figure 22. Spatial distribution (kriged) of PM2.5 concentrations based on data from 1999–2000. Analysis done in the current project. 

reduced the mortality risk estimates for both PM2.5 and

SO4

2exposures. (4) Adjusting for occupational exposures

based on an occupational “dirtiness” score and an index of

occupational exposure to known lung carcinogens did not

materially alter air pollution risk estimates, thereby all but

ruling out residual confounding by occupation.

New methods of analysis that take into account spatial

patterns in the ACS data were also developed and applied

in Phase II (Krewski et al. 2000b; Burnett et al. 2001;

Cakmak et al. 2003; Jerrett et al. 2003; Ma et al. 2003).

These methods, which consider different patterns of spatial autocorrelation in both mortality rates and air pollutant levels, resulted in slightly higher mortality risk

estimates with wider confidence limits compared with

those found using standard Cox regression methods based

on the assumption of no spatial autocorrelation. The

results of flexible exposure–response models indicated

that air pollution risk estimates were particularly sensitive

to the use of alternate risk projection models (Abrahamowicz et al. 2003). The effects of using ecologic (neighborhood-level) rather than individual-level covariates

were also explored (Abrahamowicz et al. 2004); this analysis showed that although biases due to aggregation of

individual-level covariates can occur, those biases were

unlikely to account for the observed associations between

air pollution exposure and mortality.

In Phase II, we also explored the effect of the geographic

unit of scale for spatial analyses, and noted that mortality

risk estimates using PM2.5 concentrations at the county

scale were higher than estimates based on pollutant concentrations at the MSA scale (Willis et al. 2003). Therefore,

we explored the MAUP in more detail in Phase III using

Intra-Urban Analyses for the Los Angeles and New York

City regions.

PHASE III

The Phase III Extended Analysis was intended to further

explore the association between ambient air pollution and

mortality by using additional follow-up data on vital status

in the ACS cohort through 2000, thereby providing 11 more

years of follow-up beyond that considered in Phase II.

Additional data on exposure to ambient air pollution

was also available for use in Phase III. In Phase II, all air

pollution exposure data we considered were related to

baseline exposures at the time the ACS cohort was

enrolled in 1982 (Figure 21). Levels of air pollution have

since changed; Figure 22 presents the spatial distribution

of PM2.5 in 2000. Part of our team at NYU constructed
92

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

annual average estimates of PM2.5 concentrations for

1972–2000, and we used them in the analysis of possible

critical exposure time windows. Measured annual average

SO2 concentrations were also available from the EPA AIRS

database for the same period.

The specific objectives of Phase III were threefold: (1)

Do social, economic, and demographic ecologic variables

confound or modify the relationship between particulate

air pollution and mortality? (2) How can spatial autocorrelation and multiple geographic levels be taken into

account within the random effects Cox models? (3) What

critical exposure time windows affect the association

between air pollution and mortality?

During the course of Phase III, a fourth objective of

focusing on Intra-Urban Analyses in the Los Angeles and

New York City regions was added. These Intra-Urban Analyses were intended to explore the MAUP by using more

geographically refined indicators of air pollution exposure.

Specifically, the relatively dense grid of air pollution monitors in both of these large cities permitted an assessment of

exposure to ambient air pollution at the ZCA scale.

The extensive series of analyses conducted during the

course of Phase III provided a number of important findings concerning the association between exposure to

ambient air pollutants and mortality.

SUMMARY OF RESULTS FROM PHASE III

Nationwide Analysis

The first set of analyses focused on further characterizing the relationship between air pollution and mortality

using the most recent follow-up data for the ACS cohort.

Nationwide Analysis of the full ACS cohort confirmed

that mortality increases with exposure to PM2.5; the HR for

death from all causes was 1.033 (95% CI, 1.015–1.052), for

death from CPD was 1.091 (95% CI, 1.063–1.120), for

death from IHD was 1.152 (95% CI, 1.111–1.196), and for

death from lung cancer was 1.110 (95% CI, 1.040–1.185)

(see Table 3). These risk estimates were based on annual

average air pollutant concentrations in 1999–2000 and were

calculated based on a 10-μg/m3 change in PM2.5. (Similar

although slightly lower risk estimates were obtained using

air pollutant concentrations for 1979–1983.)

The Reanalysis showed stronger and more robust associations between SO2 exposure and all causes of mortality

compared with those observed for PM2.5 exposure (Krewski

et al. 2000b). A plausible mechanism by which SO2 could

increase mortality risk, however, has not yet been identified. One possibility is that SO2 may serve as a marker for

particulate air pollution formed by the atmospheric transformation of SO2 into SO4

2particles. In contrast, the

present analysis showed stronger mortality associations

for PM2.5 than for SO2 exposure. We did not investigate

sources of particles or particle transformation; however,

source apportionment is an important avenue for future

research.

In analyses conducted at the national level, the largest

mortality risk estimates were consistently associated with

IHD; a similar result was noted in the Reanalysis Project.

Whereas lung-cancer mortality in Phase II was found not

to be significantly elevated in relation to PM2.5 exposure,

the additional number of lung-cancer deaths during the

further 11 years of follow-up used for Phase III resulted in

a significant association between PM2.5 exposure and lung

cancer. A significant increase in lung-cancer mortality was

also noted in the Updated Analysis of the ACS cohort with

follow-up through 1998 (Pope et al. 2002).

We adjusted models for ecologic covariates to account

for sociodemographic risks, which would yield less biased

mortality risk estimates of air pollution exposure. In nearly

all models that adjusted for the seven ecologic covariates

simultaneously, the HR tended to increase in comparison

to models with no adjustment, although many of the differences were small. The geographic unit of analysis of ecologic covariates (ZCA, MSA, or MSA & DIFF) tended not to

make an appreciable difference in influence on HR estimates; however, in many cases HR estimates tended to be

highest in models with the ecologic covariates considered

at the MSA & DIFF levels simultaneously.

The air pollution–lung-cancer mortality association

was also found to vary somewhat by the level of education

attainment recorded when subjects were enrolled. Specifically, participants who completed high school or less had

a significant increase in risk of lung-cancer mortality of

approximately 20% associated with each 10-μg/m3

change in PM2.5 (as measured in 2000) compared with no

association observed for those who completed more than

high school.

Intra-Urban Analysis

The Intra-Urban Analysis for the Los Angeles region

using the LUR-based exposure assignments resulted in

notably larger mortality risk estimates than those found in

the Nationwide Analysis. Such differences were suggested

by an earlier simulation study of the effects of exposure

measurement error in studies of this type conducted by

Mallick and associates (2002). Mortality risk estimates

using the LUR-based exposure estimates were somewhat

smaller than those based on the exposure estimates from

the kriging model, but were generally comparable. The HR

of mortality due to exposure to PM2.5 in Los Angeles from
D. Krewski et al.

93

all causes was 1.143 (95% CI, 1.033–1.266), from CPD was

1.114 (95% CI, 0.968–1.282), from IHD was 1.331 (95% CI,

1.084–1.634), and from lung cancer was 1.392 (95% CI,

0.964–2.010) (see Table 23). These risk estimates are based

on PM2.5 concentrations in Los Angeles in 1999–2000 and

correspond to a change of 10 μg/m3 PM2.5. Mortality was

not significantly higher in relation to exposure to O3 concentrations or with proximity to freeways within the Los

Angeles MSA.

The Intra-Urban Analysis for the New York City region

produced a somewhat different picture of the association

between air pollution and mortality than in Los Angeles.

In New York City, the HR of mortality due to exposure to

PM2.5 from all causes was 0.984 (95% CI, 0.948–1.020),

from CPD was 0.953 (95% CI, 0.902–1.007), from IHD was

1.072 (95% CI, 1.003–1.147), and from lung cancer was

0.955 (95% CI = 0.836–1.091) (see Table 15). Whereas in Los

Angeles PM2.5 exposure was related to all cause-of-death

categories, in New York IHD was the only cause of death

associated with PM2.5 exposure (which is consistent with

the mechanistic hypothesis put forward by Pope and associates [2004] in the Updated Analysis). These HR estimates

are based on 3-year average PM2.5 concentrations from the

28 counties of New York City in 1999–2001 and correspond to a difference of 1.5 μg/m3 of PM2.5 between the

highest level of exposure and the lowest. The exposure

estimates were based on LUR models, taking into account

traffic density, population density, and industrial land use.

Previously, we have used kriging methods to estimate

levels of ambient PM2.5 concentrations (Jerrett et al.

2005a). Table 30 presents a comparison of HRs obtained

from analyses using LUR and kriging exposure assignments in Los Angeles and LUR in New York City. The comparison suggests that the method used for exposure

assignment did not affect HR estimates appreciably and

was not responsible for differences between the two cities

that have been previously observed. The differences in

mortality risks may be attributable to fundamental differences in the topographical, geographical, and urban

attributes of these two metropolitan areas.

Comparing the mortality risk estimates obtained from the

Nationwide Analysis with those from the Intra-Urban Analysis indicates that the national risk estimates cannot be

directly applied to all urban areas within the United States

and that mortality risk estimates can vary appreciably

among large urban areas with different characteristics.

In an effort to understand the variation in risk estimates

between the Los Angeles, New York, and Nationwide

Analysis, we compared the subjects' characteristics in the

three locations (see Table 31). As shown in the table, few

remarkable differences were apparent among the groups

of the ACS cohort used for each analysis. (1) The New

York City cohort had slightly fewer deaths, largely due to

a lower number of CPD deaths. (2) The Los Angeles cohort

had fewer subjects who are white. (3) Subjects in Los

Angeles had higher education levels, a slightly healthier

Table 30. A Comparison of HRs by Cause of Death Associated with a 10-μg/m3 Change of PM2.5 (Los Angeles) or with an 

Interdecile Comparison Value of 1.5 μg/m3 PM2.5 (New York City) with Exposure Estimated Using LUR or Kriginga

Covariates

Los Angeles New York City

LUR Kriging LUR

All Causes

PM2.5 only 1.20 (1.08–1.32) 1.24 (1.11–1.37) 1.01 (0.94–1.05)

+ 44 Individual-level 1.14 (1.03–1.27) 1.17 (1.05–1.30) 0.98 (0.95–1.02)

IHD

PM2.5 only 1.42 (1.15–1.74) 1.49 (1.20–1.85) 1.11 (1.04–1.18)

+ 44 Individual-level 1.33 (1.08–1.63) 1.39 (1.12–1.73) 1.07 (1.00–1.15)

CPD

PM2.5 only 1.18 (1.02–1.36) 1.20 (1.04–1.39) 0.98 (0.93–1.03)

+ 44 Individual-level 1.11 (0.97–1.28) 1.12 (0.97–1.30) 0.95 (0.90–1.01)

Lung Cancer

PM2.5 only 1.46 (1.01–2.10) 1.60 (1.09–2.33) 1.04 (0.91–1.18)

+ 44 Individual-level 1.39 (0.96–2.01) 1.44 (0.98–2.11) 0.96 (0.84–1.09)

a

 The PM2.5 concentration of 1.5 μg/m3 for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in 

the exposure distribution from the 28-county 3-year model. HRs are followed by 95% confidence intervals.
94

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

diet, and higher levels of alcohol consumption. (4) Occupational exposures were slightly lower in Los Angeles

and New York than in the national cohort. (5) With the

exception of a lower proportion of white subjects, the

slight differences of the characteristics in the Los Angeles

cohort compared with the New York cohort were likely to

increase survival, which would likely decrease susceptibility to air pollution; yet the health effects observed in Los

Angeles were generally higher than those observed in

either of the other two groups. Based on these comparisons, it appears unlikely that the differences in results

observed between the Nationwide Analysis and the two

Intra-Urban Analyses are attributable to differences in the

underlying characteristics in each cohort group. The possibility of confounding by non-PM effects of mobile sources,

such as noise, remains.

Critical Exposure Time Windows

A unique aspect of Phase III was the use of the ACS

Nutrition Cohort to examine the time window of exposure

that may be primarily responsible for the association

between air pollution and mortality. We constructed an

individual time-dependent exposure profile for each participant in this large cohort for which population mobility

information was available. We evaluated air pollutant

exposure concentrations experienced by individuals

Table 31. Descriptive Statistics of Risk Factors for the Nationwide, New York City, and Los Angeles Data Sets

Variable Nationwidea New York City Los Angeles 

Participants (n) 488,370 44,056 22,905

Participants died from (%)

All causes 26.4 24.0 25.6

CPD 13.1 10.5 13.7

Lung cancer 2.0 1.9 1.9

All other causes 11.3 11.1 10.0

Ageb 56.6 (10.5) 55.3 (10.7) 57.0 (10.6)

Male (%) 56.5 56.3 57.0

White (%) 94.0 95.0 89.1

Education (%)

< High School 12.1 11.8 8.2

High School 31.2 31.2 22.9

> High School 56.7 57.0 68.9

Current Smoker (%) 21.9 23.5 19.3

Cigarettes per day 22.0 (12.4) 21.8 (12.6) 21.3 (12.6)

Years of smoking 33.6 (11.0) 32.8 (11.0) 34.0 (11.1)

Former smoker (%) 30.3 33.5 33.0

Cigarettes per day 21.6 (14.6) 22.1 (15.1) 20.9 (14.8)

Years of smoking 22.2 (4.1) 25.2 (4.0) 24.7 (4.0)

Age when started smoking (%)

< 18 yrs (current smoker) 9.3 11.5 8.4

 18 yrs (current smoker) 13.0 12.4 11.3

< 18 yrs (former smoker) 11.8 15.3 12.0

 18 yrs (former smoker) 18.4 18.3 20.8

Hours per day exposed to smokingb 3.2 (4.4) 3.6 (4.5) 2.8 (4.2)

Table continues next page

a

 Based on the 116 MSAs for which PM2.5 (1999–2000) data were available in the national dataset and follow-up of participants through 2000.

b Mean (SD).

c

 Occupational exposure to PM increases with increasing index number.

d Dietary fat consumption increases with increasing index number.

e

 Dietary fiber consumption increases with increasing index number.
D. Krewski et al.

95

dynamically, during three separate exposure time windows reflecting individual exposure 1–5, 6–10, and 11–15

years in the past. Overall, no clear findings emerged for

either PM2.5 or SO2. Identification of critical exposure time

windows, even among large national cohorts, remains a

challenge and further work on identifying them with other

relevant datasets — including other cohorts with different

exposure circumstances — is needed.

REVISIONS TO THE COX MODELS

An important methodologic contribution from Phase III

is the extension of the random effects Cox regression

models to handle multiple levels of clustering. The software developed for this purpose in Phase II has been significantly enhanced and allows for a broader class of

spatial autocorrelation models. We expect the extended

random effects Cox regression model software to find

application in a wide variety of research directions outside

of the ACS cohort.

Application of the random effects Cox model to the

updated ACS cohort data in Phase III tended to modestly

increase the air pollution risk estimates over those from

the standard Cox model, and to inflate the uncertainty in

the estimates, as is reflected in the associated broader confidence limits.

Table 31 (Continued). Descriptive Statistics of Risk Factors for the Nationwide, New York City, and Los Angeles Data Sets

Variable Nationwidea New York City Los Angeles 

Marital status (%)

Married 83.9 83.0 80.9

Separated 3.5 4.5 3.6

Divorced 12.6 12.5 15.5

Body mass indexb 25.2 (4.1) 25.2 (4.0) 24.7 (4.0)

Occupational exposure to PM indexc

 (%)

Level 1 13.1 13.9 13.1

Level 2 11.3 10.2 12.4

Level 3 4.7 4.5 4.6

Level 4 6.4 4.9 3.8

Level 5 4.2 3.7 4.1

Level 6 1.1 0.6 0.8

Not able to access 8.7 10.1 9.2

Self-reported exposure to dust or fumes (%) 19.9 19.2 19.5

Dietary fat consumption indexd (%)

Level 1 15.9 18.2 17.8

Level 2 17.5 18.6 18.3

Level 3 21.4 21.5 21.3

Level 4 30.9 25.2 24.5

Dietary fiber consumption indexe

 (%)

Level 1 19.8 20.0 18.1

Level 2 18.8 19.3 17.4

Level 3 22.8 23.3 22.9

Level 4 22.0 21.2 26.4

Alcohol consumption (%)

Drink beer 22.9 23.2 22.4

Drink liquor 27.6 30.4 31.3

Drink wine 23.2 29.9 34.2

a

 Based on the 116 MSAs for which PM2.5 (1999–2000) data were available in the national dataset and follow-up of participants through 2000.

b Mean (SD).

c

 Occupational exposure to PM increases with increasing index number.

d Dietary fat consumption increases with increasing index number.

e

 Dietary fiber consumption increases with increasing index number.
96

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

COMPARISON OF DATA SETS AND ANALYTIC 

METHODS FOR THE THREE FOLLOW-UP TIME 

PERIODS

We further examined how a variety of analytic factors

may influence the estimation of the long-term air pollution–mortality association in the ACS cohort over time.

Table 32 provides some descriptive statistics about three

major analyses conducted with the ACS cohort data at different points in time.

In the Reanalysis Project (Krewski et al. 2000b), PM2.5

exposure data were available for the time period 1979–

1983 for 50 MSAs containing 298,825 study participants.

Follow-up of participants had been performed through

1989 and had documented a total of 23,180 deaths from all

causes, 11,262 from CPD, 2,001 from lung cancer, and

5,968 from IHD.

In the Updated Analysis by Pope and colleagues (2002),

the follow-up period had been extended by 9 years until

1998. Additional PM2.5 data had been obtained and exposure could be estimated for two time periods (1979–1983

and 1999–2000); these corresponded to a total of 61 and 116

MSAs, respectively. Due in large part to the longer followup period, more deaths had occurred; over 80,000 deaths

from all causes were recorded among those subjects for

whom PM2.5 exposure data for 1979–1983 were available.

Finally, for the current Extended Analysis, the ACS

follow-up period had been extended by 2 more years,

until 2000. Because we examined several additional ecologic covariates at different levels of analysis, the total

number of MSAs and participants available for the

Nationwide Analysis was slightly lower than the number

available for the Updated Analysis by Pope and colleagues (2002). The two additional years of follow-up,

however, included more deaths: over 90,000 from all

causes among the same group for whom PM2.5 exposure

data were available for 1979–1983.

Table 33 compares HR estimates for the five main causeof-death categories using different cohort follow-up

periods (until 1989, 1998, and 2000) that correspond with

the analytic time periods used for the Reanalysis Project

(Krewski et al. 2000a,b), the Updated Analysis (Pope et al.

2002), and the current Extended Analysis. By holding the

number of MSAs and study subjects constant, we were

better able to examine the independent influence of

changing the length of the follow-up period on study findings. The PM2.5 (1979–1983) results were obtained using

available data for 342,521 subjects residing in 58 MSAs,

and the PM2.5 (1999–2000) results were obtained using

available data for 488,370 subjects residing in 116 MSAs.

Results are presented with adjustment for the standard

suite of 44 individual-level covariates and with and

without adjustment for the seven ecologic covariates

examined in the current study.

With models adjusted for individual-level covariates

only, estimated HRs associated with exposure to PM2.5

(1979–1983) ranged from 1.03–1.05 for all causes, 1.07–1.10

for CPD, 1.12–1.13 for IHD, and 1.05–1.09 for lung cancer.

HR estimates were slightly higher for IHD and lung cancer

Table 32. Characteristics of the Analytic Cohorts in the Previous and Current Analysesa

Characteristic

Cohort Data Set 

Used in 

Reanalysis (2000)

Cohort Data Sets 

Used by 

Pope and Associates

(2002, 2004)

Cohort Data Sets

Used in 

Current Nationwide Analysis 

(2009)

Years of PM2.5 exposure data 1979–1983 1979–1983 1999–2000 1979–1983 1999–2000

Years of cohort follow-up 1982–1989 1982–1998 1982–1998 1982–2000 1982–2000

MSAs (n) 50 61 116 58 116

Study participants (n) 298,825 360,682 499,779b 342,521 488,370b

Person-years 2,109,750 5,302,337 7,350,011 5,542,998 7,908,283

Deaths (n)

All causes 23,180 80,819 111,677 90,783 128,954

CPD 11,262 35,782 49,539 44,866 63,917

Lung cancer 2,001 6,335 8,754 6,827 9,788

IHD 5,968 14,691 20,791 20,651 29,989

a

 Bolded data refer to text.

b For the PM2.5 1999–2000 exposure data, the 116 MSAs used by Pope and in the current analysis are the same. The number of study participants differs 

because several additional ecologic covariates were analyzed in the current study and those data were not available for the full cohort. Bolded values refer 

to text. 
D. Krewski et al.

97

associated with the PM2.5 (1999–2000) exposure data. With

models including ecologic covariates, HR estimates

increased to 1.04–1.06 for all causes, 1.09–1.13 for CPD,

1.18 for IHD, and 1.07–1.10 for lung-cancer mortality associated with exposure to PM2.5 (1979–1983) over the different follow-up time periods. Overall, there was little

change in risk estimates over different periods of followup time; those for PM2.5 (1979–1983) tended to either

remain fairly similar or decrease slightly with increasing

follow-up time. An exception was lung-cancer mortality

for which HRs increased and were significant when

follow-up was extended beyond 1989.

It is possible that there may be increasing exposure

misclassification due to population mobility during the

11 years of follow-up from 1989 to 2000. A recent analysis by Jerrett and colleagues (2007) showed that population mobility in the ACS cohort demonstrates that areas

of relatively low improvements in air pollution have

higher population gains.

Table 34 shows the influence of (1) the number of MSAs

and participants included in the analysis and (2) the

choice of statistical method (either the standard Cox model

or the random effects Cox model). For these analyses, we

either held the number of MSAs and participants constant

across the different follow-up periods (using the same

number of MSAs and participants as those in the current

study) or we allowed the number of MSAs and participants to vary across the different follow-up periods (using

the number of MSAs and participants that corresponded

with those used in the earlier analyses [Krewski et al.

2000a,b; Pope et al. 2002; see Table 32]). For PM2.5 (1979–

1983) both the number of MSAs and participants could

vary; but for PM2.5 (1999–2000), the number of MSAs was

Table 33. HRs by Cause of Death for a 10-μg/m3 Change in PM2.5 Covering Three Follow-Up Time Periods, Using the 

Same Number of MSAs and Study Participants Within PM2.5 Exposure Categories, and With and Without Adjustment for 

the Seven Ecologic Covariatesa

Covariates

in Model

Follow-Up 

Through 1989b

Follow-Up 

Through 1998c

Follow-Up 

Through 2000d

PM2.5 1979–1983 1979–1983 1999–2000 1979–1983 1999–2000

MSAs (n) 58 58 116 58 116

Participants (n) 342,521 342,521 488,370 342,521 488,370

All Causes

44 Individual 1.048 (1.022–1.076) 1.031 (1.015–1.047) 1.032 (1.012–1.053) 1.028 (1.014–1.043) 1.036 (1.017–1.054)

+ 7 Ecologic 1.061 (1.031–1.091) 1.047 (1.029–1.064) 1.057 (1.033–1.080) 1.044 (1.028–1.060) 1.057 (1.036–1.079)

CPD

44 Individual 1.101 (1.061–1.143) 1.071 (1.048–1.095) 1.092 (1.063–1.123) 1.070 (1.049–1.092) 1.100 (1.073–1.129)

+ 7 Ecologic 1.129 (1.084–1.175) 1.098 (1.073–1.125) 1.134 (1.099–1.170) 1.094 (1.070–1.118) 1.138 (1.106–1.172)

IHD

44 Individual 1.122 (1.066–1.181) 1.130 (1.094–1.166) 1.143 (1.099–1.190) 1.133 (1.100–1.167) 1.155 (1.113–1.199)

+ 7 Ecologic 1.183 (1.119–1.250) 1.183 (1.143–1.225) 1.234 (1.179–1.291) 1.184 (1.146–1.222) 1.242 (1.191–1.295)

Lung Cancer

44 Individual 1.053 (0.963–1.150) 1.089 (1.031–1.151) 1.116 (1.041–1.197) 1.075 (1.021–1.132) 1.109 (1.039–1.185)

+ 7 Ecologic 1.070 (0.973–1.177) 1.104 (1.040–1.171) 1.152 (1.065–1.247) 1.092 (1.033–1.154) 1.138 (1.057–1.225)

All Other Causes

44 Individual 0.998 (0.958–1.040) 0.981 (0.957–1.005) 0.953 (0.924–0.982) 0.979 (0.957–1.000) 0.953 (0.927–0.980)

+ 7 Ecologic 0.992 (0.949–1.037) 0.984 (0.958–1.010) 0.953 (0.920–0.988) 0.983 (0.960–1.007) 0.953 (0.923–0.984)

a

 Based on a standard Cox model with the 44 individual-level covariates with and without adjustment for the seven ecologic covariates at MSA & DIFF 

levels. The baseline hazard function was stratified by age (1-year groupings), gender, and race. All analyses were conducted using the same 58 MSAs 

(342,521 participants) or 116 MSAs (488,370 participants) that were used in the current analysis. HRs are followed by 95% confidence intervals. Bolded 

data refer to text. 

b Data for the 44 individual-level covariates are from the ACS enrollment questionnaire. Data for the seven ecologic covariates were extracted from the 1980 

U.S. Census Bureau database for the Nationwide Analysis of the current study.
98

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Table 34. HRs by Cause of Death for a 10-μg/m3 Change in PM2.5 Based on Either a Standard Cox or a Random Effects Cox 

Model Covering Three Follow-Up Time Periods and Using the Same or Different Numbers of MSAs and Participantsa

Model and 

Number of 

MSAs

Follow-Up

Through 1989b

Follow-Up

Through 1998c

Follow-Up

Through 2000d

PM2.5 data 1979–1983 1979–1983 1999–2000 1979–1983 1999–2000

Samee

MSAs (n) 58 58 116 58 116

Participants (n) 342,521 342,521 488,370 342,521 488,370

Differente

MSAs (n) 50 61 116 58 116

Participants (n) 298,825 360,682 499,779 342,521 488,370

All Causes

 Standard Cox

Same 1.048 (1.022–1.076) 1.031 (1.015–1.047) 1.032 (1.012–1.053) 1.028 (1.014–1.043) 1.036 (1.017–1.054)

Different 1.067 (1.037–1.099) 1.027 (1.012–1.043) 1.028 (1.009–1.048)

 Random Effects Cox

Same 1.074 (1.028–1.122) 1.046 (1.014–1.080) 1.061 (1.023–1.101) 1.042 (1.012–1.073) 1.063 (1.026–1.102)

Different 1.101 (1.046–1.157) 1.044 (1.011–1.078) 1.058 (1.020–1.098)

CPD

 Standard Cox

Same 1.101 (1.061–1.143) 1.071 (1.048–1.095) 1.092 (1.063–1.123) 1.070 (1.049–1.092) 1.100 (1.073–1.129)

Different 1.109 (1.063–1.157) 1.060 (1.036–1.084) 1.079 (1.049–1.111)

 Random Effects Cox

Same 1.116 (1.055–1.180) 1.075 (1.032–1.120) 1.100 (1.044–1.159) 1.073 (1.031–1.116) 1.105 (1.050–1.162)

Different 1.130 (1.063–1.201) 1.061 (1.018–1.105) 1.081 (1.025–1.141)

IHD

 Standard Cox

Same 1.122 (1.066–1.181) 1.130 (1.094–1.166) 1.143 (1.099–1.190) 1.133 (1.100–1.167) 1.155 (1.113–1.199)

Different 1.122 (1.059–1.189) 1.119 (1.081–1.159) 1.141 (1.091–1.193)

 Random Effects Cox

Same 1.167 (1.062–1.282) 1.160 (1.074–1.252) 1.198 (1.099–1.305) 1.155 (1.074–1.124) 1.200 (1.106–1.301)

Different 1.174 (1.064–1.295) 1.140 (1.053–1.235) 1.192 (1.085–1.310)

Lung Cancer

 Standard Cox

Same 1.053 (0.963–1.150) 1.089 (1.031–1.151) 1.116 (1.041–1.197) 1.075 (1.021–1.132) 1.109 (1.039–1.185)

Different 1.001 (0.907–1.104) 1.072 (1.017–1.130) 1.117 (1.042–1.197)

 Random Effects Cox

Same 1.117 (0.979–1.274) 1.102 (1.032–1.177) 1.129 (1.045–1.220) 1.085 (1.019–1.156) 1.124 (1.041–1.213)

Different 1.062 (0.913–1.235) 1.083 (1.014–1.157) 1.126 (1.044–1.214)

All Other Causes

 Standard Cox

Same 0.998 (0.958–1.040) 0.981 (0.957–1.005) 0.953 (0.924–0.982) 0.979 (0.957–1.000) 0.953 (0.927–0.980)

Different 1.040 (0.993–1.089) 0.992 (0.971–1.013) 0.971 (0.944–0.998)

 Random Effects Cox

Same 1.017 (0.960–1.077) 1.001 (0.961–1.043) 0.991 (0.946–1.039) 0.999 (0.962–1.037) 0.963 (0.879–1.055) 

Different 1.063 (0.995–1.135) 1.014 (0.975–1.054) 1.010 (0.967–1.055)

a

 Models included the 44 individual-level covariates. The baseline hazard function was stratified by age (1-year groupings), gender, and race. HRs are 

followed by 95% confidence intervals. Bolded values refer to text.

b Follow-up period used for the Reanalysis Project (Krewski et al. 2000).

c

 Follow-up period used for the Updated Analysis (Pope et al. 2002, 2004).

d Follow-up period used for this Extended Analysis. 

e

 Rows marked “Same” use the MSAs and participants from the current Nationwide Analysis. Rows marked “Different” use the MSAs and participants 

included in the earlier analyses (as outlined in Table 32).
D. Krewski et al.

99

the same in both follow-up periods, and only the number

of participants varied.

As noted previously, risk estimates obtained with the

random effects Cox model were slightly higher than those

obtained with the standard Cox model; the comparisons in

Table 34 show this pattern fairly consistently.

In results obtained with the standard Cox model, there

was only slight variation in the mortality HR estimates comparing calculations with the same number or different

number of MSAs and participants. One of the largest

changes in the HRs was found for mortality from lungcancer associated with PM2.5 (1979–1983) with follow-up

through 1989 with a HR of 1.00 (95% CI, 0.91–1.10) when

using data from 50 MSAs (“different” row), which increased

to 1.05 (95% CI, 0.96–1.15) when we included the 8 additional MSAs for which PM2.5 data had later been obtained

(“same” row), although it remained nonsignificant.

A similar pattern was observed with the random effects

Cox model. Again, the difference in risk estimates was

slight whether using the same number or a different

number of MSAs and participants. The largest difference

was also observed for mortality from lung cancer associated with PM2.5 (1979–1983) with follow-up through 1989

with a HR of 1.062 (95% CI, 0.913–1.235) found when

using data from 50 MSAs (“different” row), which

increased, although again remained nonsignificant, to

1.117 (95% CI, 0.979–1.274) reported when we included 8

additional MSAs (“same” row).

Table 35 presents a comparison of the modifying effect

of education attainment on the association between PM2.5

(1979–1983) and mortality over the three follow-up time

periods with and without the ecologic covariates examined as part of the Nationwide Analysis. (Note that this set

of analyses uses the same 58 MSAs and 342,521 subjects as

the current Extended Analysis.) In the two most recent

periods (through 1998 and 2000), risk of mortality

decreased with increasing level of education attainment

for all-cause and cause-specific mortality and, with the

exception of IHD, was no longer significant in those with

the highest level of education attainment. When the

follow-up time period was truncated at 1989, although the

decreasing trend was less clear, the lowest risk estimates

were also consistently reported in the highest education

group. A similar pattern of results was obtained with or

without adjustment for the seven ecologic covariates evaluated in the current study.

POLICY IMPLICATIONS

The results of Phase III of the Particle Epidemiology

Reanalysis Project have important implications for air

quality risk management policy. The extended follow-up

of the ACS cohort through 2000 supports earlier findings

of a positive association between long-term exposure to

PM2.5 in ambient air and increased mortality rates in urban

centers in the United States. The earlier results have figured prominently in establishing the current National

Ambient Air Quality Standard for PM2.5 in the United

States (U.S. EPA 2004). Although short-term exposures to

PM2.5 are also associated with higher mortality, one of the

effects of long-term exposure (Cohen et al. 2003) is to predispose some people to the effects attributed to short-term

exposures (Network for Environmental Risk Assessment

and Management [NERAM]2002; Krewski et al. 2005b).

Particulate air pollution represents a major health issue

at the international level as well (Cohen et al. 2005). The

World Health Organization (WHO 2002) has estimated that

particulate air pollution is responsible for approximately

2% of all deaths worldwide. WHO has assumed a leadership role in global air quality management and has

recently updated its air quality guidelines for both PM2.5

and O3 (WHO 2006).

Although the precise magnitude of the risk associated

with long-term exposure to air pollution remains subject to

some uncertainty, these and earlier results from the ACS

cohort have generally demonstrated elevated mortality

from CPD, IHD, and lung cancer. Mortality from all causes

is also clearly associated with long-term exposure to

ambient PM2.5 in U.S. urban centers. Phases II and III of

the Particle Epidemiology Reanalysis Project have both

shown these estimates to be robust to alternative analytic

methods and to adjustment for other variables that may

impact the association between air pollution and mortality.

In an attempt to address the uncertainty about the actual

magnitude of the increase in mortality due to long-term

exposure to fine particles, the U.S. EPA has recently conducted an expert elicitation involving leading researchers

in the health effects of air pollution, especially those with

experience evaluating data from large cohorts including

the ACS, the Harvard Six Cities Study (Laden et al. 2006),

and the Adventist Health Study of Smog (Abbey et al.

1999). This expert elicitation process demonstrated a clear

consensus among the participating scientists that longterm exposure to particulate air pollution is associated

with increased mortality (Industrial Economics, Incorporated 2006).

The policy implications of the current body of scientific

evidence linking air pollution to morbidity and mortality

were recently discussed at the fifth and final NERAM

Collo-quium on Air Quality and Health (NERAM 2006).

Whereas NERAM I (Cohen et al. 2003), II (Krewski et al.

2005a), and III (Craig et al. 2007) focused more on the
100

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality Table 35. Modification by Education Attainment: HRs by Cause of Death for a 10-

μg/m3 Change in PM2.5 (1979–1983) Covering Three Follow-Up Time 

Periods, With and Without Seven Ecological Covariates, and Using the Same Number of MSAs and Study Participantsa

Level ofEducation

Follow-Up

Through 1989

Follow-Up

Through 1998

Follow-Up

Through 2000

Covariatesb 42 Individual + 7 Ecologic 42 Individual + 7 Ecologic 42 Individual + 7 Ecologic

All Causes< High school 1.081 (1.024–1.140) 1.104 (1.042–1.170) 1.074 (1.038–1.112) 1.099 (1.059–1.141) 1.072 (1.038–1.107) 1.095 (1.058–1.134)

High school 1.092 (1.042–1.145) 1.090 (1.036–1.147) 1.050 (1.021–1.079) 1.065 (1.033–1.097) 1.045 (1.018–1.071) 1.057 (1.029–1.087)

>High school 1.007 (0.969–1.046) 1.016 (0.974–1.060) 1.001 (0.979–1.023) 1.011 (0.987–1.036) 1.000 (0.980–1.021) 1.011 (0.989–1.034)CPD<High school 1.103 (1.027–1.185) 1.122 (1.038–1.213) 1.125 (1.074–1.178) 1.154 (1.098–1.213) 1.129 (1.081–1.178) 1.157 (1.104–1.212)High school 1.153 (1.075–1.236) 1.184 (1.099–1.275) 1.103 (1.060–1.148) 1.132 (1.084–1.182) 1.087 (1.048–1.128) 1.112 (1.069–1.157)>High school 1.066 (1.007–1.127) 1.089 (1.023–1.158) 1.024 (0.992–1.057) 1.042 (1.006–1.080) 1.029 (1.000–1.060) 1.044 (1.011–1.079)IHD<High school 1.148 (1.040–1.268) 1.189 (1.068–1.324) 1.183 (1.106–1.264) 1.229 (1.144–1.321) 1.182 (1.110–1.260) 1.224 (1.143–1.311)High school 1.170 (1.064–1.286) 1.232 (1.114–1.362) 1.173 (1.107–1.242) 1.219 (1.146–1.296) 1.153 (1.093–1.216) 1.195 (1.129–1.266)>High school 1.070 (0.991–1.156) 1.133 (1.041–1.232) 1.073 (1.024–1.125) 1.129 (1.072–1.188) 1.093 (1.047–1.142) 1.149 (1.095–1.205)Lung Cancer<High school 1.095 (0.911–1.317) 1.115 (0.912–1.363) 1.175 (1.034–1.335) 1.186 (1.034–1.360) 1.155 (1.024–1.302) 1.142 (1.004–1.298)High school 1.196 (1.022–1.400) 1.189 (1.003–1.409) 1.151 (1.047–1.267) 1.163 (1.049–1.288) 1.152 (1.054–1.259) 1.168 (1.061–1.286)>High school 0.931 (0.815–1.064) 0.992 (0.860–1.144) 1.012 (0.935–1.096) 1.040 (0.954–1.135) 0.993 (0.921–1.070) 1.028 (0.948–1.116)All Other Causes<High school 1.046 (0.954–1.148) 1.085 (0.983–1.198) 0.995 (0.939–1.055) 1.015 (0.952–1.081) 0.987 (0.935–1.042) 1.008 (0.950–1.070)High school 1.021 (0.949–1.098) 0.989 (0.914–1.070) 0.981 (0.940–1.025) 0.981 (0.936–1.028) 0.986 (0.948–1.026) 0.984 (0.943–1.027)>High school 0.972 (0.917–1.030) 0.956 (0.896–1.019) 0.978 (0.945–1.011) 0.977 (0.941–1.015) 0.973 (0.943–1.003) 0.975 (0.942–1.009)

a Based on a standard Cox model with 42 individual-level covariates. The baseline hazard function was stratified by age (1-year groupings), gender, and race. HRs are followed by 95% confidence 

intervals. All analyses were conducted using the same 58 MSAs and 342,521 participants used in the current Extended Analysis.

b Data for the 42 individual-level covariates are from the ACS enrollment questionnaire. Data for the seven ecologic covariates (at MSA & DIFF levels) were extracted from the 1980 U.S. Census 

Bureau database for the Nationwide Aanalysis in the current study.
D. Krewski et al.

101

scientific evidence of the adverse health effects of ambient

air pollution, NERAM IV (2005) and V (2006) focused on air

quality management. The conference statement for NERAM

V includes the following policy guidelines:

1. Current scientific evidence on adverse health impacts

is sufficient to support air quality management action.

2. Air quality management actions have been shown to

be cost-beneficial in a number of circumstances.

3. Although additional scientific evidence will be useful

in targeted areas, such as characterizing the effects of

traffic-related pollution, air quality management

actions are needed now.

4. Successful air quality management requires local,

regional, national, international, and global actions.

The results of the present analysis support the call for

greater efforts to reduce exposure to air pollution embodied in the NERAM V conference statement. In developing

air quality management policy, the following additional

elements of the NERAM V conference statement are also

relevant. 

1. Harmonization of air quality measurement methods

and health assessments is desirable.

2. Different air quality management strategies may be

needed for developed and developing countries.

3. Air quality and climate change are closely linked.

4. Cross-sectoral strategies (air, climate, transportation,

energy, agriculture) based on integrated assessments

are needed.

5. Air quality management strategies should support

sustainable development.

6. The impact of air quality management interventions

needs to be evaluated.

The accountability framework proposed by the HEI

Accountability Working Group (2003) provides an excellent basis for addressing the last item.

In conclusion, Phase III of the Particle Epidemiology

Reanalysis Project has provided additional support for the

development of effective air quality management policies

and strategies. The epidemiologic results reported here are

consistent with those from other population-based studies,

which collectively support the hypothesis that long-term

exposure to PM2.5 increases mortality in the general population. We also now know mechanisms by which particles

can increase mortality from CPD (Pope et al. 2004;

National Research Council 2004), which provides a biologic basis for the associations observed.

ACKNOWLEDGMENTS

The authors would like to thank the following individuals for their contributions to the study: Huixia Jiang for

providing programming support, Lorraine Craig for editorial assistance, and Dan Crouse, Karen King, and Richard

Hamilton for assistance with statistical analyses, cartography, and graphic design. The following individuals contributed to the New York City Intra-Urban Analysis: Dr.

John Mbwana, Cornell University, assisted with traffic data

analysis; Michael Chiume, Sangeeta Bhowmick, and Alan

Gershowitz, NYMTC, prepared traffic data.

Finally, the authors would like to acknowledge project

funding support from the following sponsors: Health

Effects Institute, the National Institute of Environmental

Health Sciences (Grants 5 R01 ES 09560, 5 P30 ES07048,

5P01ES011627, 5P01 ES11627, 5P01 ES09581), the California Air Resources Board (Grant 55245A), and the U.S.

EPA (RD-83184501-0, RD-8318610-1). Additional support

was provided through the Verna Richter Chair in Cancer

Research (Duncan Thomas).

REFERENCES

Abbey DE, Nishino N, McDonnell WF, Burchette RJ,

Knutsen SF, Beeson WL, Yang JX. 1999. Long-term inhalable particles and other air pollutants related to mortality

in nonsmokers. Am J Respir Crit Care Med 159:373–382.

Abrahamowicz M, Du Berger R, Krewski K, Burnett R,

Bartlett G, Tamblyn RM, Leffondre K. 2004. Bias due to

aggregation of individual covariates in the Cox regression

model. Am J Epidemiol 160:696–706.

Abrahamowicz M, Schopflocher T, Leffondre K, du Berger

R, Krewski D. 2003. Flexible modeling of exposureresponse relationship between long-term average levels of

particulate air pollution and mortality in the American

Cancer Society study. J Toxicol Environ Health A 66:1625–

1654.

Amrhein C, Reynolds H. 1997. Using the Getis statistic to

explore aggregation effects in metropolitan Toronto census

data. The Canadian Geographer 41:137–149.

Armstrong DL, Strogatz D, Barnett E, Wang R. 2003. Joint

effects of social class and community occupational structure on coronary mortality among black men and white

men, upstate New York, 1988-92. J Epidemiol Community

Health 57:373–378.
102

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Bailey TC, Gatrell AC. 1995. Interactive Spatial Data Analysis. John Wiley and Sons, New York, NY.

Bari A, Ferraro V, Wilson L, Luttinger D, Husain L. 2003.

Measurement of gaseous HONO, HNO3, SO2, HCL, NH3,

particulate sulfate and PM2.5 in New York, NY. Atmos

Environ 37:2825–2835.

Bay Area Air Quality Management District [BAAQMD].

2005. Climate, physiography and air pollution potential:

Bay area and its subregions. Bay Area Air Quality Management District, San Francisco, CA.

Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F.

2004. Ozone and short-term mortality in 95 U.S. urban

communities, 1987–2000. JAMA 292:2372–2378.

BoundarySeer User Guide. 2001. Biomedware, Ann Arbor,

MI.

Boyd C, Zhang-Salomons JY, Groome PA, Mackillop WJ.

1999. Associations between community income and

cancer survival in Ontario, Canada, and the United States.

J Clin Oncol 17:2244–2255.

Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P,

Brunekreef B. 2002. Air pollution from traffic and the

development of respiratory infections and asthmatic and

allergic symptoms in children. Am J Respir Crit Care Med

166:1092–1098.

Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P,

Gehring U, Heinrich J, Cyrys J, Bellander T, Lewne M,

Brunekreef B. 2003. Estimating long-term average particulate air pollution concentrations: Application of traffic

indicators and geographic information systems. Epidemiology 14:228–239.

Briggs D, de Hoogh C, Gulliver J, Wills J, Elliott P, Kingham

S, Smallbone K. 2000a. A regression-based method for

mapping traffic-related air pollution: Application and

testing in four contrasting urban environments. Sci Total

Environ 253:151–167.

Briggs D, Paul E, Shaddick G, Fraser A. 2000b. Changing

geographic relationships between air pollution and cardiorespiratory mortality in Great Britain, 1962-1996. Epidemiology 11:S103–S103.

Brook RD, Franklin B, Cascio W, Hong Y, Howard G,

Lipsett M, Luepker R, Mittleman M, Samet J, Smith SC Jr,

Tager I, Expert Panel on Population and Prevention Science of the American Heart Association. 2004. Air pollution and cardiovascular disease. A statement for

healthcare professionals from the expert panel on Population and Prevention Science of the American Heart Association. Circulation 109:2655–2671.

Brunekreef B. 1997. Air pollution and life expectancy: Is

there a relation? Occup Environ Med 54:781–784.

Brunekreef B, Holgate S. 2002. Air pollution and health.

Lancet 360:1233–1242.

Burnett R, Ma R, Jerrett M, Goldberg MS, Cakmak S, Pope

CA III, Krewski D. 2001. The spatial association between

community air pollution and mortality: A new method of

analyzing correlated geographic cohort data. Environ

Health Perspect 109:375–380.

Burnham KP, Anderson DR. 2002. Model Selection and

Multimodel Inference: A Practical-Theoretic Approach,

2nd ed. Springer-Verlag, New York, NY.

Burrough PA, McDonnell RA. 1998. Principals of Geographical Information Systems. Oxford University Press,

Oxford.

Cakmak SR, Burnett T, Jerrett M, Goldberg MS, Pope C III,

Ma R, Gultekin T, Thun MJ, Krewski D. 2003. Spatial

regression models for large-cohort studies linking community air pollution and health. J Toxicol Environ Part Health

A 66:1811–1823.

Calle EE, Rodriguez C, Jacobs EJ, Almon ML, Chao A,

McCullough ML, Feigelson HS, Thun MJ. 2002. The American Cancer Society Cancer Prevention Study II Nutrition

Cohort: Rationale, study design, and baseline characteristics. Cancer 94:2490–2501.

Calle EE, Terrell DD. 1993. Utility of the National Death

Index for ascertainment of mortality among Cancer Prevention Study II participants. Am J Epidemiol 137:235–

241.

California Environmental Protection Agency and Air

Resources Board. 2003. 2002 California PM2.5 monitory

network description. R. Rothacker, California Environment Protection Agency: 76.

Chen LHY, Shao Q. 2004. Normal approximation under

local dependence. Ann Probab 32:1985–2028.

Christakos G, Serre ML. 2003. Efficient mapping of California mortality fields at different spatial scales. J Expos

Anal Environ Epidemiol 13:120–133.

City of New York Department of City Planning. 2009.

DCPLION. Available from http://home2.nyc.gov/

html/dcp/html/bytes/meta_lion.shtml. Last updated September 2008.
D. Krewski et al.

103

Cohen AJ, Krewski D, Samet J, Willes R (eds.). 2003. Health

and air quality: Interpreting science for decision makers. J

Toxicol Environ Health Part A 66:1489–1903.

Cohen AJ, Ross Anderson H, Ostro B, Pandey KD, Krzyzanowski M, Künzli N, Gutschmidt, K, Pope A, Romieu I,

Samet JM, Smith K. 2005. The global burden of disease due

to outdoor air pollution. J Toxicol Environ Health A

68:1301–1307.

Cooper RS, Kennelly JF, Durazo-Arvizu R, Oh HJ, Kaplan

G, Lynch J. 2001. Relationship between premature mortality and socioeconomic factors in black and white populations of U.S. metropolitan areas. Public Health Rep

116:464–473.

Cox DR. 1972. Regression models and life tables (with Discussion). J R Stat Soc Ser B 55:187–220.

Cox DR, Oakes D. 1984. Analysis of Survival Data, p. 103.

Chapman and Hall, New York.

Craig L, Krewski D, Samet J, Shortreed J, van Bree L. 2007.

Strategies for clean air and health. J Toxicol Environ

Health A 70:183–364.

Crowder MJ. 1986. On consistency and inconsistency of

estimating equations. Econometrics Theory 3:305–330.

Crowder MJ. 1987. On linear and quadratic estimating

function. Biometrika 74:591–597.

Curtis S, Taket A. 1996. Health and Societies: Changing

Perspectives. Edward Arnold, London.

Cutter SL, Holm D, Clark L. 1996. The role of geographic

scale in monitoring environmental justice. Risk Anal

16:517–526.

DeGaetano A, Doherty O. 2004. Temporal, spatial and

meteorological variations in hourly PM2.5 concentration

extremes in New York City. Atmos Environ 38:1547–1558.

Diez Roux AV. 2002. A glossary for multilevel analysis. J

Epidemiol Community Health 56:588–594.

Dockery DW, Pope CA III, Xu X, Spengler JD, Ware JH, Fay

ME, Ferris BG, Speizer FE. 1993. An association between

air pollution and mortality in six U.S. cities. N Engl J Med

329:1753–1759.

Duncan C, Jones K, Moon G. 1996. Health-related behaviour in context: A multi-level modeling approach. Soc Sci

Med 42: 817–830.

ESRI. 2004. Deterministic Methods for Spatial Interpolation. Environmental Systems Research Institute, Redlands,

California, USA.

Evans GW, Kantrowitz E. 2002. Socioeconomic status and

health: The potential role of environmental risk exposure.

Annu Rev Public Health 23:303–331.

Eyles J. 1999. Health, environmental assessment and population health: Tools for a complex process. Can J Public

Health 90:S31–S34.

Fotheringham AS, Brunsdon C, Charlton M. 2000. Quantitative Geography: Perspectives on Spatial Data Analysis.

Thousand Oaks: Sage Publications, London.

Gamble JF. 1998. PM2.5 and mortality in long-term prospective cohort studies: Cause-effect or statistical associations? Environ Health Perspect 106:535–549.

Gauderman WJ, Avol E, Gilliland F, Vora H, Thomas D,

Berhane K, McConnell R, Künzli N, Lurmann F, Rappaport

E, Margolis H, Bates D, Peters J. 2004. The effect of air pollution on lung development from 10 to 18 years of age. N

Engl J Med 351:1057–1067.

Gilbert NL, Goldberg MS, Beckerman B, Brook JR, Jerrett

M. 2005. Assessing spatial variability of ambient nitrogen

dioxide in Montreal, Canada, with a land-use regression

model. J Air Waste Manage Assoc 55:1059–1063.

Gilliland F, Avol E, Kinney P, Jerrett M, Dvonch T,

Lurmann F, Buckley T, Breysse P, Keeler G, de Villiers T,

McConnell R. 2005. Air pollution exposure assessment for

epidemiologic studies of pregnant women and children:

Lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research. Environ

Health Perspect 113:1447–1454.

Goddard MJ, Murdoch DJ, Krewski D. 1995. Temporal

aspects of risk characterization. Inhal Toxicol 7:1005–

1018.

Goldberg MS, Bailar JC III, Burnett R, Brook J, Tamblyn R,

Bonvalot Y, Ernst P, Flegel KM, Singh R, Valois MF. 2000.

Identifying subgroups of the general population that may

be susceptible to short-term increases in particulate air

pollution: A time series study in Montreal, Quebec.

Research Report 97. Health Effects Institute, Cambridge,

MA.

Goldberg MS, Burnett R, Bailar JC III, Brook J, Bonvalot Y,

Tamblyn R, Singh R, Valois MF. 2001a. The association

between daily mortality and short-term effects of ambient

air particulate pollution in Montreal, Quebec: 1. Nonaccidental mortality. Environ Res 86:12–25.

Goldberg MS, Burnett R, Bailar JC III, Brook J, Bonvalot Y,

Tamblyn R, Singh R, Valois MF, Vincent R. 2001b. The

association between daily mortality and short-term effects
104

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

of ambient air particle pollution in Montreal, Quebec: 2.

Cause-specific mortality. Environ Res 86:26–36.

Gordon P, Richardson H. 1996. Beyond polycentricity the

dispersed metropolis, Los Angeles, 1970-1990. J Am Plann

Assoc 62:289–295.

Greenberg M. 1993. Proving environmental inequity in

siting locally unwanted land uses. Risk Issues Health Saf

4:235–252.

Hansen KA. 1998. Seasonality of moves and duration of

residence. U.S. Census Bureau Report. Curr Popul Rep p.

70–66.

He X, Shao Q. 2000. On parameters of increasing dimension. J Multivar Anal 73:120–135.

HEI Accountability Working Group. 2003. Assessing

Health Impact of Air Quality Regulations: Concepts and

Methods for Accountability Research. Communication 11.

Health Effects Institute, Boston, MA.

HEI Health Review Committee. 2000. Commentary. In:

Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and

Mortality (Krewski D, Burnett R, Goldberg M, Hoover K,

Siemiatycki J, Jerrett M, Abrahamowicz M, White W).

Health Effects Institute, Cambridge, MA.

Hochadel M, Heinrich J, Gehring U, Morgenstern V,

Kuhlbusch T, Link E, Wichmann HE, Krämer U. 2006. Predicting long-term average concentrations of traffic related

air pollutants using GIS-based information. Atmos

Environ 40:542–553.

Hoek G, Brunekreef B, Goldbohm S, Fischer P, van den

Brandt PA. 2002. Association between mortality and indicators of traffic-related air pollution in The Netherlands: A

cohort study. Lancet 360:1203–1209.

Hosmer W, Lemeshow S. 1999. Applied Survival Analysis.

Wiley and Sons, New York, NY.

Hricko, A. 2004. Road to an unhealthy future for Southern

California's children. University of Southern California

Urban Initiative, p. 1–11.

Industrial Economics, Incorporated. 2006. Expanded

expert judgment assessment of the concentration-response

relationship between PM2.5 exposure and mortality. Final

Report. Available from: www.epa.gov/ttn/ecas/regdata/

Uncertainty/pm_ee_report.pdf (Accessed January 3, 2008).

Janssen N, van Vliet P, Aarts F, Harssema H, Brunekreef B.

2001. Assessment of exposure to traffic related air pollution of children attending schools near motorways. Atmos

Environ 35:3875–3884.

Jerrett M, Arain A, Kanaroglou P, Beckerman B, Potoglou

D, Sahsuvaroglu T, Korrison J, Giovis C. 2005a. A review

and evaluation of intraurban air pollution exposure

models. J Expo Anal Environ Epidemiol 15:185–204.

Jerrett M, Burnett RT, Kanaroglou P, Eyles J, Finkelstein N,

Giovis C, Brook J. 2001. A GIS-environmental justice analysis of particulate air pollution in Hamilton, Canada.

Environ Plan A 33:955–973.

Jerrett M, Burnett RT, Ma RJ, Pope CA 3rd, Krewski D,

Newbold KB, Thurston G, Shi YL, Finkelstein N, Calle EE,

Thun MJ. 2005b. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16:727–736.

Jerrett M, Burnett R, Willis A, Krewski D, Goldberg MS,

DeLuca PF, Finkelstein N. 2003. Spatial analysis of the air

pollution mortality relationship in the context of ecologic

confounders. J Toxicol Environ Health 66:1735–1777.

Jerrett M, Eyles J, Cole D, Reader S. 1997. Environmental

equity in Canada: An empirical investigation into the

income distribution of pollution in Ontario. Environ Plan

A 29:1777–1800.

Jerrett M, Newbold KB, Burnett RT, Thurston G, Lall R,

Pope CA III, Ma R, DeLuca P, Thun M, Calle EE, Krewski D.

2007. Geographies of uncertainty in the health benefits of

air quality improvements. Stoch Environ Res Risk Assess

21:511–522.

Jørgensen B, Lundbye-Christensen S, Song X-K, Sun L.

1996. A longitudinal study of emergency room visits and

air pollution for Prince George, British Columbia. Stat Med

15 23–836.

Jørgensen B, Knudsen SJ. 2004. Parameter orthogonality

and bias adjustment for estimating functions. Scand J Stat

31:93–114.

Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL.

1996. Inequality in income and mortality in the United

States: Analysis of mortality and potential pathways. BMJ

312:999–1003.

Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J, Abrahamowicz M, White WH. 2000a. Part I: Replication and Validation. In: Reanalysis of the Harvard Six

Cities Study and the American Cancer Society Study of

Particulate Air Pollution and Mortality. A Special Report

of the Institute's Particle Epidemiology Reanalysis Project.

Health Effects Institute, Cambridge, MA.
D. Krewski et al.

105

Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J, Jerrett M, Abrahamowicz M, White WH. 2000b.

Part II. Sensitivity Analysis. In: Reanalysis of the Harvard

Six Cities Study and the American Cancer Society Study of

Particulate Air Pollution and Mortality. A Special Report

of the Institute's Particle Epidemiology Reanalysis Project.

Health Effects Institute, Cambridge, MA.

Krewski D, Burnett RT, Goldberg MS, Abrahamowicz M,

Siemiatycki J, Jerrett M, Hoover K. 2003a. Rejoinder:

Reanalysis of the Harvard six cites study and American

Cancer Society study of particulate air pollution and mortality. J Toxicol Environ Health Part A 66:1715–1722.

Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J, Jerrett M, Abrahamowicz M, White W H. 2003b.

Overview of the reanalysis of the Harvard Six Cities study

and American Cancer Society study of particulate air pollution and mortality. J Toxicol Environ Health A 66:1507–

1552.

Krewski D, Burnett RT, Goldberg MS, Hoover BK, Siemiatycki J, Abrahamowicz M, White WH. 2004. Validation of

the Harvard six cities study of air pollution and mortality.

N Engl J Med 350:198–199.

Krewski D, Burnett R, Jerrett M, Pope CA III, Rainham D,

Calle E, Thurston G, Thun G. 2005a. Mortality and longterm exposure to ambient air pollution: Ongoing analyses

based on the American Cancer Society Cohort. J Toxicol

Environ Health A 68:1093–1109.

Krewski D, Samet, Craig L (eds.). 2005b. Interpreting epidemiologic studies of acute and long-term exposure to particulate matter for policy development. J Toxicol Environ

Health A 68:1049–1307.

Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. 2002. Geocoding and monitoring of

U.S. socioeconomic inequalities in mortality and cancer

incidence: Does the choice of area-based measure and geographic level matter? The public health disparities geocoding project. Am J Epidemiol 156:471–482.

Kunst AE, Groenhof F, Andersen O, Borgan JK, Costa G,

Desplanques G, Filakti H, Giraldes Mdo R, Faggiano F, Harding S, Junker C, Martikainen P, Minder C, Nolan B, Pagnanelli F, Regidor E, Vagero D, Valdonen T, Machenbach JP.

1999. Occupational class and ischemic heart disease mortality in the United States and 11 European countries. Am

J Public Health 89:47–53.

Künzli N, Jerrett M, Mack WJ, Beckerman B, LaBree L, Gilliland F, Thomas D, Peters J, Hodis HN. 2005. Ambient air

pollution and atherosclerosis in Los Angeles. Environ

Health Perspect 113:201–206.

Künzli N, McConnell R, Bates D, Bastain T, Hricko A, Lurmann F, Avol E, Gilliland F, Peters J. 2003. Breathless in

Los Angeles: The exhausting search for clean air. Am J

Public Health 93:1494–1499.

Künzli N, Medina S, Kaiser R, Quenel P, Horak F Jr, Studnicka M. 2001. Assessment of deaths attributable to air

pollution: Should we use risk estimates based on time

series or on cohort studies? Am J Epidemiol 153:1050–

1055.

Laden F, Schwartz J, Speizer FE, Dockery DW. 2006.

Reduction in fine particulate air pollution and mortality:

Extended follow-up of the Harvard Six Cities study. Am J

Respir Crit Care Med 173:667–672.

Lall R, Kendall M, Ito K, Thurston GD. 2004. Estimation of

historical annual PM2.5 exposures for health effects assessment. Atmos Environ 38:5217–5226.

Levy JI, Hammitt JK, Spengler JD. 2000. Estimating the

mortality impacts of particulate matter: What can be

learned from between-study variability? Environ Health

Perspect 108(2):109–117.

Lipfert FW, Wyzga RE. 1995. Air pollution and mortality:

Issues and uncertainties. J Air Waste Manage Assoc

45:949–966.

Luginaah I, Jerrett M, Elliott S, Eyles J, Parizeau K, Birch S,

Abernathy T, Veenstra G, Hutchinson B, Giovis C. 2001.

Health profiles of Hamilton: Characterizing neighbourhoods for health investigations. GeoJournal 53:135–147.

Ma R, Krewski D, Burnett RT. 2003. Random effects Cox

models: A Poisson modelling approach. Biometrika

90:157–169.

Macintyre S, Ellaway A. 1998. Social and local variations

in the use of urban neighbourhoods: A case study in

Glasgow. Health Place 4:91–94.

Macintyre S, Ellaway A. 2000. Ecological approaches:

Rediscovering the role of the physical and social environments. In: Social Epidemiology (Berkman L, Kawachi I,

eds). Oxford University Press, Oxford.

Macintyre S, Maciver S, Sooman A. 1993. Area, class and

health: Should we be focusing on places or people? J Soc

Policy 22:213–234.

Mackenbach JP, Cavelaars AE, Kunst AE, Groenhof F. 2001.

Socioeconomic inequalities in cardiovascular disease mortality; an international study. Eur Heart J 21: 1141–1151.
106

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Mallick R, Fung K, Krewski D. 2002. Adjusting for measurement error in the Cox proportional hazards regression

model. J Cancer Epidemiol Prev 7;155–164.

McMaster R, Leitner H, Sheppard E. 1997. GIS-based environmental equity and risk assessment: Methodological

problems and prospects. Cartography and Geographic Info

Systems 24:172–189.

Meng Q, Turpin B, Korn L, Weisel CP, Morandi M, Colome

S, Zhang JJ, Stock T, Spektor D, Winer A, Zhang L, Lee JH,

Giovanetti R, Cui W, Kwon J, Alimokhtari S, Shendell D,

Jones J, Farrar C, Maberti S. 2005. Influence of ambient

(outdoor) sources on residential indoor and personal PM2.5

concentrations: Analyses of RIOPA data. J Expo Anal Env

Epid 15:17–28.

Meyer M. 2003. Transportation Study. Meyer Mohaddes

Associates, Inc., Los Angeles, California, USA.

Moore DK, Jerrett M, Mack MJ, Künzli N. 2007. A land use

regression model for predicting ambient fine particulate

matter across Los Angeles, CA. J Environ Monit 9:246–252.

Nafstad P, Håheim LL, Oftedal B, Gram F, Holme I, Hjermann I, Leren P. 2003. Lung cancer and air pollution: A 27

year follow up of 16,209 Norwegian men. Thorax 58:1071–

1076.

Nafstad P, Håheim LL, Wisløff T, Gram F, Oftedal B, Holme

I, Hjermann I, Leren P. 2004. Urban air pollution and mortality in a cohort of Norwegian men. Environ Health Perspect 112:610–615.

National Research Council. 2004. Research Priorities for

Airborne Particulate Matter: IV. Continuing Research

Progress. National Academy Press, Washington, DC.

Network for Environmental Risk Assessment and Management. 2002. NERAM II. Interpreting Epidemiological

Studies of Acute & Long Term Exposure to Particulate

Matter for Policy Development. November 21-22, 2002,

Baltimore, Maryland. www.irr-neram.ca/CQ2Baltimore/

CQ2.html. Last accessed 2/19/08.

Network for Environmental Risk Assessment and Management. 2005. NERAM IV Colloquium Statement. International Perspectives on Air Quality: Risk Management

Principles for Policy Development. www.irr-neram

.ca/pdf_files/Mexico_Statement.pdf. Last accessed 1/3/08.

Network for Environmental Risk Assessment and Management. 2006. Program and Guidance Document. NERAM V

Colloquium Strategic Policy Directions for Air Quality

Risk Management October 16-18, 2006. www.irr-neram

.ca/Vancouver/NERAM-V_%20handout.pdf (Accessed

1/3/08).

Nyberg F, Gustavsson P, Järup L, Bellander T, Berglind N,

Jakobsson R, Pershagen G. 2000. Urban air pollution and

lung cancer in Stockholm. Epidemiology 11:487–495.

O’Neill M, Jerrett M, Kawachi I, Levy JI, Cohen AJ, Gouveia

N, Wilkinson P, Fletcher T, Cifuentes L, Schwartz J. 2003.

Health, wealth, and air pollution. Environ Health Perspect

111:1861–1870.

Parsons Brinckerhoff Quade & Douglas, Inc. 2005. Transportation Models and Data Initiative: General Final Report,

New York Best Practice Model. January 30, New York.

Pebesma EJ. 2004. Multivariable geostatistics in S: The

gstat package. Comput Geosci 30:683–691.

Pickett KE, Pearl M. 2001. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: A critical review. J Epidemiol Community Health 55:111–122.

Pope CA III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito

K, Thurston GD. 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287:1132–1141.

Pope CA III, Burnett RT, Thurston GD, Thun M, Calle E,

Krewski D, Godleski J. 2004. Cardiovascular mortality and

long-term exposure to particulate air pollution: Epidemiological evidence of general pathophysiological pathways

of disease. Circulation 109:71–77.

Pope CA III, Thun MJ, Namboodiri MM, Dockery DW,

Evans JS, Speizer FE, Heath CW. 1995. Particulate air pollution as a predictor of mortality in a prospective study of

U.S. adults. Am J Respir Crit Care Med 151:669–674.

R Development Core Team. 2005. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available from

http://www.R-project.org. 

Ram R. 2005. Income inequality, poverty, and population

health: Evidence from recent data for the United States.

Soc Sci Med 61:2568–2579.

Roosli M, Künzli N, Braun-Fahrlander C, Egger M. 2005.

Years of life lost attributable to air pollution in Switzerland: Dynamic exposure-response model. Int J Epidemiol

34:1029–1035.
D. Krewski et al.

107

Ross Z, English P, Jerrett M, Scalf R, Gunier RB, Smorodinsky S, Wall S. 2006. Nitrogen dioxide prediction in

southern California using land use regression modeling:

Potential for environmental health analyses. J Expo Anal

Env Epid 16:106–114.

Ross Z, Jerrett M, Ito K, Tempalski B, Thurston G. 2007. A

land use regression model for predicting fine particulate

matter concentrations in the New York City region. Atmos

Environ 41:2255–2269.

Sahsuvaroglu T, Arain A, Kanaroglou P, Finkelstein N,

Newbold B, Jerrett M, Beckerman B, Brook J, Finkelstein

M, Gilbert NL. 2006. A land use regression model for predicting ambient concentrations of nitrogen dioxide in

Hamilton, Ontario, Canada. J Air Waste Manage Assoc

56:1059–1069.

Southern California Association of Governments. 2004.

2001 SCAG County Population Forecasts. Los Angeles,

CA. http://www.scag.ca.gov. Last updated February 19,

2008. Last accessed March 13, 2009.

Southern California Compass. 2005. 2004 Growth Vision

Report. Southern California Association of Governments,

Los Angeles, CA. www.smartgrowth.org/news/article.asp?

art=4169&state=5&res=800. Last accessed 3/12/2009.

Schachter JP. 2000. Geographical mobility, 1990–1995:

Special studies. Current Popul Rep: 23–200. U.S. Bureau

of the Census, Washington, DC.

Schwartz J. 2000. Harvesting and long term exposure

effects in the relation between air pollution and mortality.

Am J Epidemiol 151:440–448.

Schwartz, J, Laden F. 2004. Dose, time and death: A Bayesian model averaging approach to estimating the association between particles and survival. Epidemiology

15:S152–S153.

Siemiatycki J, Krewski D, Shi Y, Goldberg MS, Nadon L,

Lakhani R. 2003. Controlling for potential confounding by

occupational exposures. J Toxicol Environ Health

66:1591–1604.

Singh GK, and Siahpush M. 2006. Widening socioeconomic inequalities in U.S. life expectancy, 1980-2000. Int J

Epidemiol 35:969–979.

Steel GG, Holt D. 1996. Rules for random aggregation.

Environ Plan A 28:957–978.

Tabachnick BG, Fidell LS. 2001. Using Multivariate Statistics, 4th edition. Allyn and Bacon, Needham Heights,

MA.U.S. Census Bureau. 2000. Census 2000, Summary

File 1 (SF 1) and Summary File 3 (SF 3). Available from

http://factfinder.census.gov/home/saff/main.html

?_lang=en.

U.S. Census Bureau. 2004. First Edition TIGER/Line Technical Documentation: U.S. Bureau of the Census Feature

Class Codes (CFCC). Available from http://www.census

.gov/geo/www/tiger/.

U.S. Environmental Protection Agency. 1999. National Emission Inventory Documentation and Data—Final Version 3.0.

Technology Transfer Network Clearinghouse for Inventories

& Emissions Factors web site. Available from: www

.epa.gov/ttn/chief/net/1999inventory.html. Last accessed

2/1/05.

U.S. Environmental Protection Agency. 2004. Air Quality

Criteria for Particulate Matter (October 2004). EPA 600/P99/002aF-bF. U.S. Environmental Protection Agency,

Washington, DC.

Villeneuve PJ, Krewski D, Burnett RT, Goldberg MS, Chen

C, Siemiatycki J. 2002. Fine particulate air pollution and

all-cause mortality with the Harvard Six-cities Study: Variations in risk by period of exposure. Ann Epidemiol

12:568–576.

Westerdahl D, Fruin S, Sax T, Fine PM, Sioutas C. 2005.

Mobile platform measurements of ultrafine particles and

associated pollutant concentrations on freeways and residential streets in Los Angeles. Atmos Environ 39:3597–

3610.

World Health Organization. 2002. World Health Report.

World Health Organization, Geneva, Switzerland.

World Health Organization. 2006. WHO Air Quality

Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide

and Sulfur Dioxide. Global Update 2005. Summary of Risk

Assessment. World Health Organization, Geneva, Switzerland.

Willis A, Krewski D, Jerrett M, Goldberg MS. 2003. Selection of ecologic covariates in the American Cancer Society

study. J Toxicol Environ Health 66:1591–1604.

Winkleby MA, Cubbin C. 2003. Influence of individual

and neighbourhood socioeconomic status on mortality

among black, Mexican-American, and white women and

men in the United States. J Epidemiol Community Health

57:444–452.
108

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Zeger SL, Dominici F, Samet J. 1999. Harvesting-resistant

estimates of air pollution effects on mortality. Epidemiology 10:171–175.

Zhu Y, Hinds W, Kim S, Shen S, Sioutas C. 2002. Study on

ultrafine particles and other vehicular pollutants near a

major highway with heavy-duty diesel traffic. Atmos

Environ 36:4323–4335.

APPENDIX A. HEI QUALITY 

ASSURANCE REPORT

The conduct of this study was subject to independent

QA oversight by Drs. W. Cary Eaton, Richard Kwok, and

James Flanagan of RTI International (RTI), Research Triangle Park, NC. Dr. Eaton, who led the project, is a chemist

with over 35 years of relevant experience in air monitoring

and quality assurance and is the supervisor of the Quality

Systems Programs at RTI. Dr. Flanagan is also a chemist

with a specialization in air quality monitoring, data analysis, and quality assurance. Dr. Kwok is a research epidemiologist with applications to air and water pollution;

cardiovascular, cancer, and respiratory outcomes; and

quality assurance auditing. Other participants on the RTI

QA oversight team included Dr. William Wheaton, a Geographic Information Systems specialist, who participated

in a conference call with ACS participants, and Drs. Abhik

Das and Breda Munoz, statisticians who reviewed the

model code and the final report, respectively.

The QA oversight program that RTI conducted of this

project consisted of a single on-site audit at the

McLaughlin Centre for Population Health Risk Assessment, Ottawa, ON, Canada, conducted by Dr. Flanagan and

Dr. Kwok. The audit included interviews with key project

staff, and review of study activities for conformance to the

study protocol and operating procedures. The investigators provided written responses to the findings of the audit

report, which are included as an appendix in the investigators’ final report.

In addition to the on-site audit, QA oversight activities

included conference calls to coinvestigators and review of

the final report.

DATE AND PHASE OF STUDY AUDITED

June 30–October 1, 2004

The auditors conducted an on-site audit at the University of Ottawa. Staffing and internal quality assurance procedures were reviewed. Audit observations consisted

primarily of recommendations for improving study documentation and some data processing and security issues. A

copy of code was taken for review with an audit team

member, Dr. Abhik Das, who did not participate in the onsite portion.

October 14, 2004

Conference call with NYU collaborators

November 22, 2004

Conference call with collaborators at ACS Atlanta

February 3, 2005

Audit report delivered to HEI with findings based on the

above activities.

January–March, 2009

Review of Final Report. At HEI's request, the same RTI

personnel who conducted the QA 2004 oversight activities

(Kwok, Eaton, and Flanagan) reviewed the investigators’

final report for the project. In addition, mathematical

details in the report were reviewed by Dr. Breda Munoz of

RTI’s Genomics, Statistical Genetics, and Environmental

Research Group. No serious issues were raised during the

review, but a list of minor comments and editorial suggestions was submitted to HEI.

Written reports of each activity were provided to the HEI

Project Manager, who transmitted the findings to the Principal Investigator. Responses received from the project team

demonstrated that the study was conducted by a highly

experienced team of scientists with a high regard for the

quality and credibility of their work. The final report

appears to be an accurate representation of the study.

James B. Flanagan, Ph.D.

Chemist and Quality Assurance Specialist

Richard Kwok, Ph.D.

Epidemiologist

Auditors
D. Krewski et al.

109

APPENDICES AVAILABLE ON THE WEB

Appendices B and C contain supplemental material not

included in the printed report. They are available on the

HEI Web site http://pubs.healtheffects.org. They may also

be requested by contacting the Health Effects Institute at

101 Federal Street, Suite 500, Boston, MA 02110, phone

+1-617-488-2300, fax +1-488-2335, or e-mail

(pubs@healtheffects.org). Please give (1) the first author,

full title, and number of the Research Report and (2) the

title of the appendix requested.

APPENDIX B. Algorithmic Description of the Cox–

Poisson Program

APPENDIX C. Computer Program for Random Effects

Cox Model Using the Cox–Poisson Program

ABOUT THE AUTHORS

Daniel Krewski is professor and director of the R. Samuel

McLaughlin Centre for Population Health Risk Assessment

and holds the Natural Sciences and Engineering Research

Council/McLaughlin Chair in Risk Science at the University of Ottawa. Dr. Krewski has also served as adjunct

research professor of statistics in the Department of Mathematics and Statistics at Carleton University since 1984.

Before joining the Faculty of Medicine at the University of

Ottawa in 1998, Dr. Krewski was director of risk management in the Health Protection Branch of Health Canada.

While with Health Canada, he also served as acting

director of the Bureau of Chemical Hazards and as chief of

the Biostatistics Division in the Environmental Health

Directorate. Dr. Krewski obtained his Ph.D. in statistics

from Carleton University and subsequently completed an

MHA at the University of Ottawa. His professional interests include epidemiology, biostatistics, risk assessment,

and risk management.

Michael Jerrett, Ph.D., is an associate professor in the Division of Environmental Health Sciences, School of Public

Health, University of California–Berkeley. Building on

expertise in medical geography, Geographic Information

Systems, and spatial analysis, Dr. Jerrett currently

assesses air pollution–health associations in the United

States, Canada, and Mexico with special reference to

social–spatial confounders and intra-urban exposure gradients. He is also engaged in determining the role of the

built environment as a contributor to childhood obesity

formation. He was awarded the 2004 Dangermond

Endowed Speaker in Geographic Information Science

honor at the Environmental Research Systems Institute

and the University of California–Santa Barbara.

Richard Thomas Burnett received his Ph.D. in 1982 from

Queen’s University in mathematical statistics. He is a

senior research scientist with the Healthy Environments

and Consumer Safety Branch of Health Canada, where he

has been working since 1983 on issues relating to the

health effects of outdoor air pollution. He is an adjunct

professor in the Department of Epidemiology and Community Medicine, an affiliate scientist in the Institute of Population Health, and a scientist with the McLaughlin Centre

for Population Health Risk Assessment, Institute of Population Health, all at the University of Ottawa. Dr. Burnett’s

work has focused on the use of administrative health and

environmental information to determine the public health

impacts of combustion-related pollution using nonlinear

random effects models and time-series and spatial analytic

techniques.

Renjun Ma is an associate professor of statistics in the

Department of Mathematics and Statistics and a research

fellow in the Canadian Research Institute for Social Policy

at the University of New Brunswick. He received his Ph.D.

in statistics from the University of British Columbia. His

primary research interests lie in the area of latent variables

multivariate statistical models for spatial and longitudinal

data. He collaborates with epidemiologists on spatial analysis of adverse effects of air pollution and works with

social scientists on spatio-temporal analysis of healthy

development of children and adolescents.

Edward Hughes received his Ph.D. in mathematics from

the University of Wisconsin, and served a term as postdoctoral fellow in the University of British Columbia mathematics department. After several years on the Carleton

University faculty, he left academic life and worked for two

consulting firms and for the research division of Ontario

Hydro. Since 1988, he has operated Edward Hughes Consulting, specializing in statistics and applied mathematics.

He has worked with clients in the fields of engineering,

economics, accounting, finance, and health research. Current research interests focus on developing efficient algorithms for large-scale epidemiologic computations.

Yuanli Shi is a research associate at the McLaughlin

Centre for Population Health Risk Assessment, Institute of

Population Health, University of Ottawa. She received her

M.D. in China and has been trained in applied statistics

from University of Reading and University of Salford,

England. Her research interests are in environmental,

cancer, and reproductive health epidemiology and risk

assessment.
110

Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Michelle C. Turner is a Ph.D. candidate in Population

Health at the University of Ottawa. She received her M.Sc.

in epidemiology from the University of Ottawa. Her

research focus is on environmental epidemiology, cancer

epidemiology, and risk perception. She is supported with a

Canada Graduate Scholarship from the Canadian Institutes

of Health Research.

C. Arden Pope III is the Mary Lou Fulton professor of economics at Brigham Young University. He has a Ph.D. from

Iowa State University (1981) where he studied economics

and statistics. He has had appointments at Iowa State and

Texas A&M Universities and has been an Interdisciplinary

Programs in Health fellow and visiting scientist in Environmental Health and Public Policy at the Harvard School

of Public Health. He has conducted research dealing with a

wide range of natural resource and environmental issues,

but over the last 15 years has focused on evaluating health

effects of air pollution. He has conducted or collaborated

on many of the key, pioneering studies of short-term and

long-term effects of air pollution.

George Douglas Thurston is a tenured professor at the

New York University School of Medicine's Department of

Environmental Medicine. He conducts epidemiologic

research into the human health effects of air pollution. Dr.

Thurston received his undergraduate degrees in environmental engineering and environmental studies from

Brown University, and received his masters and doctorate

degrees in environmental health sciences from the Harvard School of Public Health. Dr. Thurston was a contributing author to the most recent U.S. Criteria Document for

Particulate Matter (2004) and has testified before the U.S.

Congress regarding the human health effects of air pollution in the United States.

Eugenia E. Calle received her Ph.D. (1982) in epidemiology from the Ohio State University. She worked as an

epidemiologist at the Oak Ridge National Laboratory in

the area of cancer risk assessment (1979–1984) and at the

Centers for Disease Control on the Agent Orange projects

(1984–1989) before joining the American Cancer Society

in 1989. Since 1994, she was the managing director of analytic epidemiology; in that capacity, she oversaw all program activities including data collection, management,

and analyses for the Cancer Prevention Study cohorts. Her

research was primarily in the areas of breast cancer risk

factors, hormone-replacement therapy and cancers in

women, and adiposity and cancer incidence and mortality. Dr. Calle retired from the American Cancer Society

on January 30, 2009, and died unexpectedly on February

17, 2009.

Michael J. Thun received a B.A. degree from Harvard College, an M.D. from the University of Pennsylvania School

of Medicine, and a Master of Science in epidemiology from

the Harvard School of Public Health. He has worked for 28

years in epidemiology and disease prevention, first as a

medical officer investigating toxic exposures at the New

Jersey State Health Department, and then as an epidemic

intelligence service officer and staff scientist for the Centers for Disease Control and Prevention at the National

Institute for Occupational Safety and Health (1980–1988).

In 1989, Dr. Thun became director of analytic epidemiology for the ACS in Atlanta. Since 1998 he has served as

vice president of epidemiology and surveillance research,

overseeing both cancer surveillance and studies on the

causes and prevention of cancer. He is an adjunct professor

in the Divisions of Epidemiology and of Environmental

and Occupational Health at Emory University, Rollins

School of Public Health, and at the Winship Cancer Center.

OTHER PUBLICATIONS RESULTING FROM THIS 

RESEARCH

Jerrett M, Burnett RT, Pope CA III, Ito K, Thurston G,

Krewski D, Shi Y, Calle E, Thun M. 2009. Long-term ozone

exposure and mortality. N Engl J Med 360:1085–1095.

Jerrett M, Newbold KB, Burnett RT, Thurston G, Lall R,

Pope CA III, Ma R, DeLuca P, Thun M, Calle EE, Krewski D.

2007. Geographies of uncertainty in the health benefits of

air quality improvements. Stoch Environ Res Risk Assess

21:511–522.

Moore DK, Jerrett M, Mack MJ, Künzli N. 2007. A land use

regression model for predicting ambient fine particulate

matter across Los Angeles, CA. J Environ Monit 9:246–252.

Ross Z, Jerrett M, Ito K, Tempalski B, Thurston G. 2007. A

land use regression model for predicting fine particulate

matter concentrations in the New York City region. Atmos

Environ 41:2255–2269.

Jerrett M, Burnett RT, Ma R, Pope CA III, Krewski D, Newbold

KB, Thurston G, Shi Y, Finkelstein N, Calle EE, Thun MJ.

2005. Spatial analysis of air pollution and mortality in Los

Angeles. Epidemiology 16:727–736.

Künzli N, Jerrett M, Mack WJ, Beckerman B, LaBree L, Gilliland F, Thomas D, Peters J, Hodis HN. 2005. Ambient air

pollution and atherosclerosis in Los Angeles. Environ

Health Perspect 113:201–206.

Krewski D, Burnett R, Jerrett M, Pope CA, Rainham D, Calle

E, Thurston G, Thun M. 2005. Mortality and long-term
D. Krewski et al.

111

exposure to ambient air pollution: Ongoing analyses based

on the American Cancer Society cohort. J Toxicol Environ

Health 68:1093–1109.

Pope CA III, Burnett R, Thurston G, Thun M, Calle E,

Krewski D, Godleski JJ. 2004. Cardiovascular mortality and

long-term exposure to particulate air pollution: Epidemiological evidence of general pathophysiological pathways

of disease. Circulation 109:71–77.

Ma R, Krewski D, Burnett RT. 2003. Random effects Cox

models: A Poisson modelling approach. Biometrika

90:157–169.

O’Neill M, Jerrett M, Kawichi I, Levy JI, Cohen AJ, Gouveia

N, Wilkinson P, Fletcher T, Cifuentes L, Schwartz J. 2003.

Health, wealth, and air pollution. Environ Health Perspect

111:1861–1870.

Ramsay T, Burnett R, Krewski D. 2003. Exploring bias in a

generalized additive model for spatial air pollution data.

Environ Health Perspect 111:1283–1288.

ABBREVIATIONS AND OTHER TERMS

AADT average annual daily traffic

ACS American Cancer Society

AIC Akaike information criteria

AIRS Aerometric Information Retrieval System

AQS Air Quality Subsystem

ARB Air Resources Board

BPM Best Practice Model

CI confidence interval

CPD cardiopulmonary disease

CPS-II cancer prevention study

DIFF difference between the ZCA value 

and the MSA mean value for an 

ecologic covariate

EPDC expected peak daily concentrations

ESRI Environmental Systems Research Institute

FRM Federal Rference Method

GIS Geographic Information System

GPS global positioning system

Ha hectare

HR hazard ratio

ICD-9 International Classification of Diseases, 

9th edition

IDW inverse distance weighting

IHD ischemic heart disease

IPMN Inhalable Particle Monitoring Network

LUR land-use regression

MAPE mean absolute percentage error

MAUP modifiable areal unit problem

MCD minor civil division

MSA metropolitan statistical area

NAAQS National Ambient Air Quality Standard

NAD National Aerometric Database

NERAM Network for Environmental Risk 

Assessment and Management

NYMTC New York Metropolitan Transportation 

Council

NYU New York University

O3 ozone

PM2.5 particles of 2.5 μm or less in aerodynamic 

diameter

R2 coefficient of determination for multivariate 

analyses

RMSE root mean squared error

SCAG Southern California Area Governments

SO2 sulfur dioxide

SO4

2sulfate

TEOM tapered-element oscillating microbalance

TRAPCA Traffic Related Air Pollution and Childhood 

Asthma [study]

TSP total suspended particulates

USCB U.S. Census Bureau

U.S. EPA U.S. Environmental Protection Agency

USGS United States Geological Survey

USPS U.S. Postal Service

VIF variance inflation factor

WHO World Health Organization

ZCA Zip Code area

GLOSSARY OF STATISTICAL TERMS

Absolute value The absolute value of a number is its

numerical value without regard to its sign. For example, 3

is the absolute value of both 3 and 3.

Bootstrapping A resampling method for estimating the

sampling distribution of an estimator by sampling with

replacement from the original sample; most often done

with the purpose of deriving robust estimates of standard
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Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

errors and confidence intervals of population parameters,

such as the mean, median, and proportion. It may also be

used for constructing hypothesis tests. See also jackknifing

and sample with replacement.

Census block The smallest geographic unit used by the

United States Census Bureau for tabulating data collected

from all houses (rather than a sample of houses). Several

blocks make up block groups, which make up census

tracts.

Centroid The intersection of all straight lines that divide

a two-dimensional figure (such as a polygon) into two

parts.

Confounder An extraneous or “unstudied” factor that

may be responsible for the observed effect; it is unmeasured, yet is statistically associated (positively or negatively) with both the observed outcome and the exposure

of interest (both the dependent and independent variables). For example, regular coffee drinkers are more likely

to smoke than people who do not drink coffee. Therefore,

any study to link coffee drinking with cancer would need

to take participants’ smoking into account as a possible

confounder. A confounder must be accounted for in an

analysis so you know if the association observed is not

entirely attributable to the confounder.

Construct validity The agreement between the theoretical properties of the object being studied and the properties of the specific measure of that object used in a study.

Dependent and independent variables In an experiment,

the dependent variable is the “event” being studied that is

expected to change when the independent variable is

changed. An independent variable is one whose value is

controlled or selected by the experimenter to determine its

relationship to the dependent variable. In the current study,

the dependent variable is mortality data, and the independent variables are PM2.5 exposure data, individual-level

covariates, and information about communities.

Explanatory power The ability of a model or theory to

account for the observed phenomenon.

Geostatistics Originally applied to problems in geology;

it involves statistical procedures to describe and analyze

patterns and associations in spatially defined (geographic)

data (that is, data whose values are associated with locations in space).

Gini coefficient A measure of statistical dispersion most

prominently used as a measure of inequality of income or

wealth distribution within a defined area (such as a city,

state, or country). It is presented as a ratio with values

between 0 and 1: A low value indicates relatively uniform

income or wealth distribution and a high value indicates

unequal distribution.

Heteroskedasticity In statistics, a sequence of random

variables displays heteroskedasticity if the variables have

different variances.

Inverse distance weighting (IDW) A process of assigning

values to unknown points by using values usually from a

scattered set of known points; used for multivariate interpolation.

Jackknifing A resampling method that estimates the precision of sample statistics (medians, variances, percentiles) using subsets of available data with each data point

systematically removed and replaced. Jackknifing assesses

the sensitivity of calculated statistics to the presence or

absence of a single data point. See also bootstrapping.

Kernel A mathematical method used to identify the

underlying structure of complex data. Nonlinear clustering methods based on kernels provide a common means

of resolving patterns in spatially correlated data.

Kriging A group of geostatistical techniques for interpolating values (estimating a value between known values). It

uses the distance between data points and the degree of

variation to estimate values across a surface (e.g., geographic area).

Manual forward selection process A process of manually

adding variables one by one to a regression model, instead

of allowing the statistics program to automatically select

which variables to add.

Multiple linear regression model A model used to analyze the relationship between a set of independent variables and a dependent variable.

Nested analysis An analysis that statistically accounts

for the effects of the structure of the data, and in which the

values for a variable are dependent on the values of the

class of variables to which they belong. For example,

health status may be associated with one’s neighborhood

of residence or city or state; but it may also be related to

which neighborhood within the city, and which city

within the state, and even to the neighborhood within that

city and state together. A nested analysis accounts for

these joint effects at different geographic levels.

Orthogonality The quality of being completely independent. When performing statistical analysis, variables that
D. Krewski et al.

113

affect a particular result are said to be orthogonal if they are

uncorrelated; that is, their statistical relationship is zero.

Orthophoto An aerial photograph that has been geometrically corrected so the scale is uniform; like a map, it lacks

distortion. Unlike an uncorrected aerial photograph, an

orthophoto can be used to measure true distances, because

it is an accurate representation of the earth’s surface,

having been adjusted for topographic relief, lens distortion, and camera tilt.

Principal components A statistical procedure that combines a number of possibly correlated variables and transforms them into a smaller number of uncorrelated

variables called principal components. The resulting principal component variables may then be included as independent variables in statistical analyses.

Queen contiguity matrix A matrix in which each element is considered to be the neighbor of another element

if it shares a border or vertices with the observation of

interest. It is used to characterize spatial contiguity

between spatial units (such as a Thiessen polygon or a Zip

Code area) in which both border and vertices are used to

define contiguity.

Random effects Cox model In statistics, many models

proceed from the assumption that observed values are

independent of one another — that information for any

given participant is completely unrelated to information

on another. A random effects model does not assume that

observations are independent. For example, it does not

assume that people living in the same neighborhood or

household have completely unrelated dietary or smoking

habits, pollution exposures, or health status.

Residual (or fitting error) An observable estimate of the

unobservable statistical error. For example, if you have a

random sample of n men whose heights are measured, and

the mean of the sample is used as an estimate of the (unobservable) population mean, then:

the difference between the height of each man in the

sample and the unobservable population mean is a statistical error; and

the difference between the height of each man in the

sample and the observable sample mean (predicted height)

is a residual.

See also statistical error.

Root mean squared error A frequently used measure of

the differences between values predicted by a model or an

estimator and the values actually observed.

Sample with replacement A sampling method that

involves randomly selecting a specified number of data

points for analysis, and returning them to the population

before randomly selecting another set of data points.

Because these data points are not permanently removed,

the population remains intact. If the randomly sampled set

of data points had been deleted, the probability for

selecting an equivalent set of points in the next sample

would be changed. For example, suppose you have 5 blue,

5 red, and 5 yellow jelly beans in a jar. If you remove 3

beans (1 red and 2 yellow) and then return them to the jar,

you have the same odds of getting 1 red and 2 yellow beans

the next time as you did originally. If you eat them, the

odds of getting 1 red and 2 yellow beans has been changed.

Semivariogram / variogram A mathematical function

used in geostatistics to visualize the spatial or the temporal

correlation of data.

Spatial analysis Any of the formal techniques for

studying entities using their topological, geometric, or geographic properties.

Spearman rank correlation coefficient A measure of correlation (relationship) between two variables that does not

make any assumptions about the shape of the distribution

of the observed values. It measures the correspondence

between the rankings of observations for each variable in

the data and assesses its significance.

Standard Cox proportional-hazards regression model of

survival A proportional-hazards model is a subclass of

statistical survival models. Survival analysis deals with

the time until an event, death, or failure. Survival models

consist of two parts: the underlying hazard function,

which describes how hazard (or risk) changes over time;

and the effect parameters, which describe how hazard may

be affected by other factors, such as the choice of treatment

for an illness. The proportional-hazards assumption is that

effect parameters multiply hazard in a constant manner

over the study follow-up time: For example, if taking drug

X reduces your hazard by half at time 0, it also does so at

time 1, or time 0.5, or at any time t. The effect parameter(s)

estimated by any proportional-hazards model can be

reported as hazard ratios.

Sir David Cox observed that if the proportional-hazards

assumption holds (or is assumed to hold), then it is possible to estimate the effect parameter(s) without any consideration of the hazard function. This approach to

survival data is referred to as applying the Cox proportional-hazards model (referred to as standard Cox model

in this report).
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Stationarity / nonstationarity Qualities of a random process: the statistical properties (such as mean and standard

deviation) of a process that exhibit stationarity do not

change over time or across space. If the process exhibits

nonstationarity, the statistical properties do change.

Statistical error The amount by which an observation

differs from its expected value, which is based on the

whole population from which the statistical unit was randomly chosen. The expected value — for example, the

mean of the entire population — is typically unobservable.

See also residual.

Thiessen polygons A type of Voronoi diagram used to

analyze spatially distributed data (such as air pollution

measurements). A Thiessen polygon defines the area of

influence around one of a set of points (e.g., an air pollution monitor). The boundaries of the polygon define the

area that is closest to each point relative to all other points.

The center of the polygon is called the centroid.

Variance The amount of variation among the data measured for one variable. It is a measure of the distribution of

values.

Variance inflation factor (VIF) An index that measures

how much the variance of a coefficient (square of the standard deviation) is increased because of multicollinearity.

Multicollinearity is a statistical phenomenon in which two

or more predictor variables in a multiple regression model

are highly correlated.

(Glossary prepared by Health Effects Institute)
Health Effects Institute Research Report 126 © 2004 115

COMMENTARY

Health Review Committee

Research Report 140, Extended Follow-Up and Spatial Analysis of the American Cancer 

Society Study Linking Particulate Air Pollution and Mortality, D. Krewski et al.

INTRODUCTION

In the report to HEI entitled Extended Follow-up and

Spatial Analyses of the American Cancer Society Study

Linking Particulate Air Pollution and Mortality, Dr. Daniel

Krewski and colleagues provide details of Phase III of the

Particle Epidemiology Evaluation Project, their research

with the cohort from the American Cancer Society (ACS*)

Cancer Prevention Study II (CPS-II), an ongoing prospective study of mortality among adults residing across the

United States and in Puerto Rico. In Phases I and II of their

research (referred to here as the Reanalysis Project; Krewski

et al. 2000), Krewski and colleagues validated and replicated earlier results from other investigators of associations

between exposure to particulate air pollution and mortality

and then used their data to try new methods of analysis that

incorporated individual-level and ecologic covariates. 

Dr. Krewski submitted a preliminary application to HEI

in February 2001, requesting funds to conduct a third

phase of his analysis of the ACS cohort. Later that year, he

submitted a full application, and in February 2002, HEI

approved the project. 

The HEI Research Committee chose to fund Phase III

because they believed it would yield further important

information about the effects of long-term exposure to air

pollution on mortality. During the Reanalysis Project,

Krewski and colleagues had developed new statistical

models to incorporate city-level ecologic covariates and

control for spatial autocorrelation in the data. For this

Extended Analysis, they planned to apply updated statistical methods to new follow-up data (through 2000) for the

ACS cohort. This cohort remains a key source of information on the effects of long-term exposure to air pollution on

mortality from chronic disease. Among many uses of the

ACS results in U.S., European, and global risk analyses,

health risk estimates that have resulted from the Reanalysis Project were used by the U.S. Environmental Protection Agency (U.S. EPA) in the development of the cost–

benefit analyses for regulations of emissions from heavyduty diesel engines. Past results from studies of this cohort

along with the Phase III results are also being considered

in the current U.S. EPA process for reviewing the National

Ambient Air Quality Standard (NAAQS) for fine particles

(particulate matter 2.5 μm or smaller in aerodynamic

diameter [PM2.5]). 

BACKGROUND 

Epidemiologic studies conducted over several decades

have suggested that long-term exposure to elevated ambient

levels of particulate air pollution is associated with

increased premature mortality. Commentary Table 1 summarizes the details of some key cohort studies that have

found and continue to corroborate and further define the

relationship between exposure to PM2.5 and mortality. 

Two U.S. cohort studies became central to the 1997 debate

on the NAAQS for PM2.5 pollution in the United States: the

Harvard Six Cities Study (Dockery et al. 1993), a 20-year prospective cohort study begun in the 1970s, and the ACS study

(Pope et al. 1995), a larger retrospective cohort study initiated in 1982 and involving data from 151 cities. Both of

these studies estimated exposure to PM2.5 based on ambient

air monitoring data, and both reported that the risk of death

from all causes increased in association with exposure to

higher annual average concentrations of PM2.5.

Several analyses of these two cohorts were also considered in the EPA review of the NAAQS for PM2.5 in 2006.

Other important cohort studies that were considered in that

review included the Seventh-Day Adventist Health Study of

Smog (AHSMOG; Abbey et al. 1999) and the Veterans study

(Lipfert et al. 2006; U.S. EPA 2006). Results from the

AHSMOG cohort provided some suggestive but less conclusive evidence, and evidence from the Veterans cohort was

inconsistent. Nevertheless, looking at all the available evidence, the U.S. EPA concluded that overall the epidemiologic evidence of an association between long-term

exposure to PM2.5 and mortality was strong (U.S. EPA 2004).

Dr. Krewski’s 4-year study, “Extended Follow-Up and Spatial Analysis of

the American Cancer Society Study Linking Particulate Air Pollution and

Mortality,” began in May 2002. Total expenditures were $425,000. The draft

Investigators’ Report from Krewski and colleagues was received for review

in January 2007. A revised report, received in January 2008, was accepted

for publication in June 2008. During the review process, the HEI Health

Review Committee and the investigators had the opportunity to exchange

comments and to clarify issues in both the Investigators’ Report and in the

Review Committee’s Commentary.

This document has not been reviewed by public or private party institutions, including those that support the Health Effects Institute; therefore, it

may not reflect the views of these parties, and no endorsements by them

should be inferred.

* A list of abbreviations and other terms appears at the end of the Investigators’ Report.
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Commentary on Investigators’ Report by Krewski et al.

Commentary Table 1. Key Prospective Long-Term Cohort Studies of Air Pollution and MortalityStudy Cohort Exposure Data Analysis ResultsStudies That Used Data from Cohort Studies Other Than ACS Harvard Six 

Cities(Dockery et al. 1993)

8,111 adults in the 

study of health effects of air pollution; cohort followed prospectively for14–16 yearsstarting in 1974 

Ambient concentrations measured in each 

community at a centrally located air monitoring station: TSP, SO2, O3, suspended SO4

2for 

1977–1985; inhalable and fine particles 1979–85; SO4

2particles 1979–1984; aerosol acidity 1985–

1988

Adjusted mortality rate ratios for 

air pollution estimated by simultaneously adjusting for other risk factors in standard Cox model; subjects stratified according to sex and 5-year age group

Adjusted mortality rate ratio for the 

most-polluted city compared with the least-polluted city was 1.26 (95% CI 1.08–1.47); air pollution weakly associated with death from lung cancer and cardiopulmonary disease; PM2.5 

and SO4

2strongly associated with 

mortality

Seventh-Day 

Adventist Health Study of Smog (AHSMOG; Abbey et al. 1999)

6,338 nonsmokers, 

white, ages 25–97 years, residents of California, enrolled before 1977

Days per year when PM10 exceeded 100 μg/m3 Relative risks calculated using 

standard Cox model, controlled for age, smoking, passive smoke exposure, occupation, diet, body/mass index, and family history of chronic disease

Relative risk for an increase of 43 days per 

year above 100 μg/m3 PM10 and 

mortality from all natural causes was 1.12 (95% CI 1.01–1.24) for males for mortality from nonmalignant respiratory disease was 1.28 (95% CI 1.03–1.57 for males; no significant associations found for women or for PM10 exposure 

concentration

Veterans’ 

Cohort Mortality Study (Lipfert et al. 2000)

50,000 male U.S. 

military veterans diagnosed with hypertension in the mid-1970s; average age at recruitment 51 ± 12 years; cohort followed for ~21 years

Pollutant levels averaged by year and county for 

PM2.5, coarse particles, and PM15 data from the U.S. 

EPA Inhalable Particle Monitoring Network 1979–1984

Standard Cox model controlled for 

race, smoking, age, blood pressure, body/mass index, and “selected ecologic variables” at the Zip Code and county levels

PM2.5 was not associated with any 

significant (positive) excess in mortality risk for any of the models evaluated

Women’s 

Health Initiative (Miller et al. 2007)

65,893 postmenopausal women 

without preexisting cardiovascular disease in 36 U.S. metropolitan areas; cohort followed 1994–1998 

Exposure to PM2.5 assessed at monitor nearest to 

residence

Hazard ratios estimated for the first 

cardiovascular event or for death from cardiovascular disease; adjusted for age, race, diabetes, hypertension, and hypercholesterolemia

Difference of 10 μg/m3 PM2.5 associated 

with 24% increase in risk of a cardiovascular event (relative risk 1.24; 95% CI 1.09–1.41) and 76% increase in risk of cardiovascular mortality (RR 1.76; 95% CI 1.25–2.47)

*NLCS-AIR 

(Beelen etal. 2008; Brunekreefet al. 2009) 

120,740 residents of 

the Netherlands, ages 55–69, enrolled in 1986; cohort followed 1987–1996

Combination of regional, urban, and local exposure 

(1976–1996 averages) determined from monitoring station data and land-use regression and assigned to participants, residence locations; separate background (urban plus regional) level used with various measures of exposure to traffic

Relative risks calculated using 

standard Cox model; analyses controlled for personal risk factors, and for community income levels at the local and regional scales

 Relative risks for a 10 μg/m3 change in 

black-smoke exposure; associated with lung cancer incidence in nonsmokers (RR 1.47; 95% CI 1.01–2.61), mortality from natural causes (RR 1.05; 95% CI 1.00–1.11), and respiratory causes (RR 1.22; 95% CI 0.99–1.50)

Table continues next page

* Study funded by HEI
117

Health Review Committee

Commentary Table 1 (Continued). Key Prospective Long-Term Cohort Studies of Air Pollution and Mortality

Study Cohort Exposure Data Analysis Results

Studies That Used Data from the ACS CPS-II CohortOriginal 

Analysis ACS CPS-II (Pope et al. 1995)

552,138 adults 

residing in these areas when enrolled 1982 included data for individual risk factors; deaths ascertained through 1989

Ambient data for 151 U.S. metropolitan areas in 

1980; SO42and PM2.5 estimated from national 

data bases and used as indices of exposure to combustion-source ambient PM

Multivariate analysis used to 

examine relationships of air pollution with mortality from all causes, lung cancer, and cardiopulmonary disease; analysis controlled for smoking, education, and other risk factors

Increased mortality associated with 

SO42and PM2.5 air pollution at levels 

commonly found in U.S. cities

*Reanalysis 

Project (Krewski et al. 2000)

Same cohort data as in 

the Initial Analysis (Pope et al. 1995) but subjected to additional quality control procedures

Same exposure data and assignment as in the Initial 

Analysis but with additional quality control 

procedures and data validation by audit

Phase I — Replication and 

validation of Six Cities and ACS CPS-II results

Phase II — Sensitivity analysis 

compare results from alternative risk models using different hazard functions and spatial factors

Phase I — Mortality association with 

SO42– and PM2.5 compared closely with 

findings from Initial Analysis

Phase II — Positive associations between 

mortality and pollutants robust to control for confounding, analytic methods used, and adjustment for spatial autocorrelation

Updated ACS 

Analysis (Pope et al. 2002)

500,000 adults in 

ACS CPS-II enrolled in 1982; included data for individual risk factors; deaths ascertained through 1998

Mean concentrations of air pollution for 

metropolitan areas compiled from various primary 

data sources including EPA AIRS, IPMN, and NAD; extracted from IPMN for 1979–1983 and AIRS for 1999–2000

Adjusted relative risk ratios for 

mortality estimated using standard Cox model including metropolitan-based random effects component; estimated for mortality from all causes, cardiopulmonary disease, lung cancer, and all other causes

PM2.5, SO42, and SO2 pollution 

associated with mortality from all causes, cardiopulmonary disease, and lung cancer; PM2.5 found to be an 

important environmental risk factor for death from cardiopulmonary disease and lung cancer

Updated ACS 

Analysis (Pope et al. 2004)

319,000 adults in 

areas with PM2.5

monitoring data for 1979–1983 and 1999–2000; 500,000 ACS adults in areas with PM2.5

monitoring data for 1999–2000

PM2.5 monitoring data from IPMN for 1979–1983 

and from AIRS for 1999–2000 and assigned annual average PM2.5 exposure to participants

Standard Cox model used to 

calculate relative risk ratios per 10−μg/m3 change in PM2.5 for subcategories of cardiopulmonary 

disease; controlled for available individual risk factors

Long-term PM2.5 exposures most strongly 

associated with mortality attributed to ischemic heart disease, dysrhythmias, heart failure, and cardiac arrest; weak associations with respiratory mortality 
118

Commentary on Investigators’ Report by Krewski et al.

THE HARVARD SIX CITIES STUDY 

As part of the Harvard Six Cities Study, Dockery and colleagues (1993) had prospectively followed a cohort of 8,111

adult subjects in the Northeast and Midwest regions of the

United States for 14 to 16 years, beginning in the mid1970s. For these analyses, data from the most-polluted city

were compared with those from the least-polluted city and

the results were described as being associated with the difference in concentrations between the two cities. A difference of 18.6 μg/m3 PM2.5 was associated with a 26%

increase in mortality from all causes. The same difference

in PM2.5 was associated with a 37% increase in deaths from

cardiopulmonary disease. A follow-up study of this cohort

conducted in a period of lower air pollution and including

8 additional years of mortality data showed that associations between pollutant exposure and risk of mortality

were also lower (Laden et al. 2006).

THE ACS STUDY 

In the past 15 years, the ACS cohort has been the subject

of numerous analyses and a primary source of information

on air pollution exposure and associated mortality for risk

estimates and policy making throughout the world. The

various ACS cohort analyses, including those from the

study reported here, are listed in Commentary Table 2. (A

more detailed description of each study, except the current

one, is found in Commentary Table 1.)

In the original ACS study, Pope and colleagues (1995)

followed 552,138 adult subjects in 151 U.S. cities from

1982 through 1989. Again, higher ambient levels of PM2.5

were associated with increased mortality from all causes

and from cardiopulmonary disease in the 50 cities for

which PM2.5 data were available (data from monitors for

1979 to 1983). Higher ambient SO4

2levels were associated

with increased mortality from all causes, cardiopulmonary

disease, and lung cancer in the 151 cities for which SO4

2

data were available (data from monitors for 1980 to 1982).

The difference in all-cause mortality between the most-polluted city and the least-polluted city was 17% for PM2.5 and

15% for SO4

2(the pollutant range among the cities was

24.5 μg/m3 for PM2.5 and 19.9 μg/m3 for SO4

2).

Although these two cohort studies produced similar

results, they differed in design and limitations. Important

strengths of the Six Cities Study included the random

selection of study subjects; the high response rate of participants enrolling (> 70%); the personal interviews conducted with respondents at the time of enrollment and

subsequent follow-up interviews at intervals of 3, 6, and

12 years; lung-function measurements at baseline; and the

gathering of residential histories. The air pollution measurements were collected by the research team, who had

designed the Six Cities Study to monitor a range of air pollutants that was nearly as large as that used for the ACS

study. A limitation was that exposure for each pollutant was

represented in each city by one average level for the span of

years, so only 6 air pollutant data points were used to estimate the exposure–response function. 

Important strengths of the ACS study were the number of

cities studied (151); the very large cohort of subjects

enrolled; and the extensive information collected for the

enrollment questionnaire on health status, demographic

characteristics, smoking history, alcohol use, and occupational exposure. One limitation was that these subjects were

enrolled by ACS volunteers from among their friends and

relatives so the subjects may not be representative of the general population. Another was that the air quality measurements were not designed for this study; they were obtained

from monitors set up and maintained by the U.S. EPA.

In 1997, the two studies came under intense scrutiny

when the U.S. EPA used the results to support new

NAAQS standards for PM2.5 and to maintain the standards

for particles of 10 μm or smaller in aerodynamic diameter

(PM10) that were already in effect. Members of Congress

and industry, the scientific community, and others interested in the regulation of air quality scrutinized the studies’

methods and their results. Some insisted that any data used

in studies that were conducted with federal funding should

be made public. Others argued that the individual health

and mortality data had been gathered with assurances of

confidentiality for the individuals who had agreed to participate. Still others claimed that the concept of public

access to federally funded data did not take into account

the intellectual property rights of the investigators and

their supporting institutions. To address the public controversy, Harvard University, the ACS, Congress, the U.S. EPA,

and representatives of the motor vehicle industry

Commentary Table 2. Studies of Air Pollution and Public 

Health That Used the ACS CPS-II Cohort

Principal Investigator

Publication 

Year

End (Years) of 

Follow Upa

Pope (Initial Original) 1995 1989 (7)

Krewskib (Reanalysis) 2000 1989 (7)

Pope (Updated) 2002 1998 (16)

Pope (Updated) 2004 2000 (16)

Krewski (Extended) 2009 2000 (8)

a

 Cohort was enrolled 1982–1983.

b Part of the HEI-funded Reanalysis of both the initial original ACS study 

(Pope et al. 1995) and the Harvard Six Cities Study (Dockery et al. 1993).
119

Health Review Committee

requested that the Health Effects Institute organize an

independent reanalysis of the data from these studies. The

investigators agreed to provide access to their data to a

team of analysts to be selected by HEI through a competitive process. HEI’s Board of Directors approved the

request. HEI then assembled an Expert Panel to provide

scientific oversight of the Reanalysis Project on HEI’s

behalf and to ensure that the Reanalysis would be conducted by independent and impartial investigators. The

Panel recommended that Dr. Daniel Krewski of the University of Ottawa and his team conduct the Reanalysis. The

HEI Board of Directors approved the Panel’s recommendation of Dr. Krewski in November 1997.

THE REANALYSIS PROJECT 

The overall objective of the Particle Epidemiology

Reanalysis Project (Krewski et al. 2000) was to conduct a

rigorous and independent assessment of the findings of the

Six Cities and ACS studies of air pollution and mortality

(Dockery et al. 1993; Pope et al. 1995). This objective was

met in two phases. In Phase I: Replication and Validation,

the Reanalysis Team sought to replicate the initial studies

through a quality assurance audit of a sample of the original data and to validate the original numerical results. In

Phase II: Sensitivity Analyses, they tested the robustness of

the original analyses to alternative risk models and analytic

approaches, including models that used different hazard

functions and models that incorporated spatial factors

(Krewski et al. 2000). Overall, the Reanalysis confirmed the

quality of the original data, replicated the original results,

and validated those results using alternative risk models

and analytic approaches; none of them substantively

altered the original finding of an association between indicators of particulate air pollution and mortality. 

THE UPDATED ANALYSIS 

After the Reanalysis Project, Pope and colleagues undertook a subsequent set of analyses of the ACS cohort data

using an additional 10 years of data (covering 1982

through 1998), thus doubling the follow-up time to more

than 16 years and tripling the number of deaths. Exposure

data were expanded to include data on gaseous copollutants and new PM2.5 data that had been collected since the

enactment of the new air quality standards. Recent

advances in statistical modeling were incorporated in the

analyses, including the introduction of random effects and

nonparametric spatial smoothing components into the

standard Cox proportional-hazards regression model of

survival (referred to as the standard Cox model; see Glossary of Statistical Terms in the Investigators’ Report). This

Updated Analysis provided the strongest evidence that the

long-term exposure to fine particulate air pollution that is

common to many metropolitan areas is an important risk

factor for death from lung cancer and cardiopulmonary

disease (Pope et al. 2002). Each 10-μg/m3 increase in longterm average ambient PM2.5 concentrations was associated

with approximately a 4%, 6%, or 8% increase in risk of

death from all causes, cardiopulmonary disease, and lung

cancer, respectively. There was no evidence of a threshold

exposure level within the range of observed PM2.5 concentrations. There was also no statistically significant evidence

that the survival data were spatially autocorrelated after

controlling for fine particulate air pollution and the various

individual-level risk factors. Graphic examination of the

residual mortality also revealed no significant spatial autocorrelation with distance between metropolitan areas.

In further analyses of the ACS cohort data published in

2004 (Updated Analysis), Pope and colleagues refined

their classification of causes of death within the “cardiopulmonary” category by separately analyzing ischemic

heart disease, dysrhythmias, hypertensive disease, atherosclerosis, chronic obstructive pulmonary disease, pneumonia, and other subcategories of cardiovascular and

respiratory causes of death. They found significantly

increased risks of death (ranging from 8% to 18% per 10-

μg/m3 increase in PM2.5) for the various subcategories of

cardiovascular causes of death (ischemic heart disease,

dysrhythmias, heart failure, and cardiac arrest) and only

nonsignificant associations for all respiratory subcategories. This study demonstrated that the strongest association between PM2.5 exposure and mortality within the

broadly defined “cardiopulmonary” category is for death

from ischemic heart disease.

THE EXTENDED ANALYSIS 

The current study, conducted by Daniel Krewski and

colleagues, extends the follow-up period for the ACS

cohort through 2000 (18 years). The investigators have

continued to produce nationwide estimates of the risk of

death from various causes using additional years of data.

In addition, this project extended the range of analyses to

include the following:

• A Nationwide Analysis integrating control for spatial 

autocorrelation and socioeconomic covariates at the 

neighborhood (Zip Code area [ZCA]) and city (metropolitan statistical area [MSA]) scales in a single statistical model;

• An Intra-Urban Analyses for the New York City and 

Los Angeles regions featuring land-use regression 

(LUR) techniques to refine estimates of exposure from 

local sources (e.g., traffic and industry); and
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• An exploration of Critical Exposure Time Windows 

through analyzing estimated average exposures for up 

to 5 years, 6 to 10 years, and 11 to 15 years before death 

to test whether longer-term or more recent exposures 

might have a stronger effect on premature mortality.

These refinements of methods and additional analyses

were intended to further explore the relationships between

air pollution exposure and mortality by incorporating

sophisticated controls for known sources of bias and confounding in the statistical models.

STUDY SUMMARY

SPECIFIC AIMS

Phase III of the Particle Epidemiology Reanalysis

Project, consisting of analyses of data that are presented in

this report, was designed to further analyze associations

between air pollution and mortality in U.S. cities using

alternative spatial models and to extend the follow-up of

the ACS CPS-II cohort to 18 years. Dr. Krewski and his

team proposed to address four key research questions for

this study:

1. Do social, economic, and demographic (ecologic)

variables confound or modify the relationship

between particulate air pollution and mortality?

2. How can spatial autocorrelation and multiple levels

of spatial analysis be taken into account within the

random effects Cox model?

3. What critical time periods of exposure affect the association between air pollution and mortality?

4. How does refining the exposure gradient to the intraurban level affect the size and significance of health

effects estimates? 

To achieve Specific Aims 1 and 2, the investigators performed a Nationwide Analysis of air pollution exposure

and mortality using an innovative extension to the standard

Cox model. It relaxed the assumption that cohort observations are independent, thus enabling the researchers to

include multiple levels of nested variables and control for

spatial autocorrelation within a single model (referred to as

the random effects Cox model). This method contrasts with

earlier two-stage analyses of this cohort that used the standard Cox model to produce city-specific estimates of mortality risk and then calculated a nationwide risk estimate

from a regression of these city estimates and ecologic covariates (variables that represent local social, economic, and

environmental conditions that are known or suspected to

influence mortality). 

The researchers addressed the Specific Aim 3 by calculating average exposure concentrations in 5-year time windows and analyzing their relationship with patterns of

mortality in the cohort. The Specific Aim 4, added in year

2 of the study, was carried out in collaboration with a team

of researchers led by Dr. Michael Jerrett. This team built

separate land-use regression models to estimate exposure

for the New York City and Los Angeles regions using local

land-use and traffic data to supplement ambient monitoring data for PM2.5. ACS cohort mortality data for these

cities were analyzed with the locally refined exposure

assessments using the same methods used in the Nationwide Analysis. Each of these research efforts is described

in greater detail below.

SOURCES OF DATA

Study Population

In late 1982, volunteers from the ACS recruited participants in the 50 U.S. states, Puerto Rico, and the District of

Columbia for a large prospective cancer prevention study

(ACS CPS-II) of 1.2 million adults. The ACS allowed air

pollution researchers access to the cohort data for the original study of air pollution and mortality (Pope et al. 1995),

the HEI-sponsored Reanalysis Project (Krewski et al.

2000), the Updated Analysis (Pope et al. 2002, 2004), and

the current Extended Analysis reported here. For the purposes of air pollution research, the cohort has been

restricted to participants who reside in metropolitan areas

of the contiguous 48 states and District of Columbia that

have adequate air pollution monitoring data for the study

period. This yielded approximately 575,000 total participants for the air pollution study, including about 360,000

and 500,000 who resided in areas with adequate PM2.5

monitoring information in 1980 and in 2000, respectively.

At enrollment in 1982, participants were at least 30

years of age and were members of households with at least

one person 45 years of age or older. Participants filled out

an extensive questionnaire that included personal demographic characteristics, personal habits, occupational history and exposures, tobacco and alcohol use, and other

factors possibly related to mortality from cancer. These

questionnaires provided data for the 44 individual-level

covariates (see details in the 44 Individual-Level Covariates sidebar in the Investigators’ Report) used in these and

other analyses of the cohort. Covariate data are summarized in Table 31 of the Investigators’ Report.

The ACS has followed this cohort using volunteers to

contact participants in 1984, 1986, and 1988, and to gather

data through the National Death Index thereafter. They

obtained death certificates for participants who were
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known to have died and compiled cause-of-death information. Causes of death analyzed in this study fall into these

categories: all causes, cardiopulmonary disease, ischemic

heart disease, lung cancer, and all other causes. The original study and the Reanalysis of this cohort contained

follow-up information through 1989, the Updated Analysis through 1998, and this Extended Analysis through

2000 — 11 additional years of follow-up that were unavailable for the initial studies.

In addition to the individual-level covariate information

from the ACS enrollment questionnaires, Dr. Krewski’s

research team gathered data for seven ecologic covariates

to use in their analyses. Ecologic covariates are intended to

represent local neighborhood-level conditions or factors

known to or suspected of influencing mortality. For

example, poverty, level of education attainment, and

unemployment in a person’s neighborhood are known to

influence an individual’s health beyond that predicted by

a person’s own attributes and situation (e.g., diet and exposure to tobacco smoke). Ecologic covariates are included in

health research analyses because of a growing awareness

that the possible influences of local neighborhood or urban

conditions need to be directly accounted for in a health

assessment in order to eliminate them as the primary cause

of a health outcome (referred to as control of confounding).

For this study, the researchers enumerated ecologic

covariates for both the ZCA and city of residence for each

participant based on demographic data from the 1980 U.S.

Census. These neighborhood factors included prevalence

of air conditioning use, percentage of residents who completed high school, percentage of residents who were not

white, unemployment, median household income, relative

income disparity (income distributed within neighborhoods and cities), and percentage of persons who live in

poverty (see details in the Ecologic Covariates sidebar and

Table 2 of the Investigators’ Report). 

Air Pollutants

Air pollutant exposure was estimated from data gathered by several air pollution monitoring systems throughout the continental United States. These networks are run

either by the U.S. EPA or by state and local organizations

in conjunction with the U.S. EPA. Pollutants and the distributions of exposure to them appear in Table 1 of the

Investigators’ Report.

PM2.5 data were obtained for cities in the Inhalable Particle Monitoring Network (IPMN) and the Aerometric

Information Retrieval System (AIRS). Long-term exposure

variables were constructed to study how exposure early

and late in the follow-up years might have affected the

association between particulate exposure and mortality

and to compare results from the Nationwide and IntraUrban Analyses: PM2.5 (1979–1983) and PM15 (1979–1983)

averaged IPMN data for 6those years, and PM2.5 (1999–

2000) averaged data from the AIRS. Dr. Krewski’s team

constructed similar variables for long-term exposure to

other pollutants of interest from single-year (1980) averages: total suspended particles (TSP) from National Aerometric Database (NAD) data, and ozone (O3), nitrogen

dioxide (NO2), and sulfur dioxide (SO2) from AIRS data.

Additional O3 data for April through September of 1980

represented the peak seasonal average exposure and was

calculated from third-quarter 1980 AIRS data. SO4

2

(1980–1981) averaged IMPN and NAD data from 1980 and

1981, and SO4

2(1990) data were supplied by the New

York University investigators.

METHODS FOR EACH ANALYSIS

NATIONWIDE ANALYSIS (SPECIFIC AIMS 1 AND 2) 

In order to address Specific Aim 1 (Do social, economic,

and demographic [ecologic] variables confound or modify

the relationship between particulate air pollution and mortality?), Dr. Krewski’s team used pollutant data for MSAs

and ecologic covariate data at both the ZCA and MSA

scales.

As shown in Table 1 of the Investigators’ Report, the

number of MSAs with sufficient monitoring data for exposure assessment and the resulting total number of CPS-II

participants varied for the different pollutants. For each

monitor site, averages of measurements for each pollutant

for each of the years of interest were computed and averaged to obtain a summary concentration for the monitor. If

a metropolitan area included more than one monitor, the

mean concentration of each pollutant from all available

monitoring sites was calculated in order to obtain the

exposure level to assign to each participant who lived in

the MSA (see also Pope et al. 2004).

For ecologic covariates, the research team encountered

difficulties extracting information from the 1980 U.S.

Census database at the ZCA scale. Zip Codes are used by the

U.S. Postal Service to simplify the delivery of mail and are

not necessarily areas with well-defined boundaries. The U.

S. Census Bureau has therefore defined ZCAs to approximate the land areas covered by Zip Codes. Some Zip Codes

were for post office boxes and commercial areas, not for residences, and others could not be matched to Census ZCAs,

paper maps, or private data sources. Despite these difficulties, approximately 10,000 of the nearly 12,000 Zip Codes

listed for CPS-II participants in 1982 were successfully connected with ZCAs listed for the 1980 Census. 

For each ecologic covariate, ZCA-scale data from the

U.S. Census Bureau files were averaged to obtain a value
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for each MSA. The difference between the ZCA value and

the MSA value for each ecologic covariate is referred to as

DIFF. The DIFF values represent the variability of the ecologic covariates at the ZCA level within their MSA. As the

MSA value and ZCA values are highly correlated by definition (the MSA value is the mean of ZCA values), statistical instability might arise from using the MSA and the

ZCA values together if the ZCA values were not adjusted

for the MSA value.

Survival analyses are frequently used in long-term

cohort studies of air pollution exposure and mortality. In

this survival analysis, a fixed number of participants are

followed over time; mortality data are collected as members die, and no new members are recruited. Groups of

participants are classified according to exposure level, and

subgroups are compared with each other to understand

how levels of exposure may affect the number of cohort

members who die and from which causes over the life of

the cohort. Risk analyses are typically adjusted for age,

smoking status, sex, and other variables known to affect

longevity that could confound the relationship between

pollutant exposure and death. The resulting comparisons

yield a hazard ratio (HR) or a relative risk of mortality

based on exposure to the pollutant of interest. 

Dr. Krewski’s team chose the standard Cox model to calculate hazard ratios for the various causes of death associated with the levels of air pollution exposure experienced

by the cohort. They used two variations of this model for

their statistical analysis: the standard Cox model and the

random effects Cox model. 

The standard Cox model assumes that the mortality experience of individuals in the study is independent (after

adjusting for covariates), even for participants living in the

same household or neighborhood. However, it is possible

that mortality experience for people in the same community

will be more similar than that for people in different communities, even after controlling for all available risk factors.

Lack of statistical control for these factors can bias the estimate of air pollution’s effect on health. Despite these limitations, the researchers used the standard Cox model to test

each of the pollutant and cause-of-death combinations of

interest in the Nationwide Analysis to find those with the

strongest associations. They then explored the selected pollutant–cause of death combinations with the far more computationally powerful random effects Cox model, which

allowed them to account for spatial autocorrelation and

include multiple levels of spatially defined covariates (Specific Aim 2). 

The random effects Cox model relaxes the assumption

that every participant’s mortality experience is independent of that for his or her neighbors. It permits the

researcher to account for (1) the statistical similarity in the

risk of mortality often found in persons living near each

other (also known as spatial autocorrelation), and (2) the

joint effects of neighborhood-level and city-level variables.

Dr. Krewski and colleagues developed the random effects

Cox model as an extension to the standard Cox model specifically to analyze the complex spatial patterns of risk in

the CPS-II data.

INTRA-URBAN ANALYSES (SPECIFIC AIM 4) 

In order to address Specific Aim 4 (How does refining

the exposure gradient to the intra-urban level affect the

size and significance of health effects estimates?), the

research team collaborated with investigators in New York

City and Los Angeles, two very large metropolitan areas that

have extensive pollution monitoring networks. Their goal

was to estimate mortality risks associated with exposure to

PM2.5 in urban areas and compare them with those obtained

from the Nationwide Analysis. In the Intra-Urban Analyses,

however, they would use land-use data to estimate exposure

locally and comprehensively, whereas in the Nationwide

Analysis, they had assigned exposure according to metropolitan area averages. These analyses differed from the

Nationwide Analysis in the following ways: 

• Exposure was restricted to PM2.5 in a single large metropolitan area.

• Additional monitoring data were available at local 

levels. 

• A variety of markers of emissions (most notably 

nearby vehicle emissions) and local exposures were 

used to estimate participants’ exposures.

• Kriging and land-use regression statistical methods 

were used to estimate and assign local exposures to 

participants rather than assigning the same MSA average value to everyone.

New York City Analysis 

The investigators used New York City data on pollutants

and the CPS-II cohort mortality data that were used for the

Nationwide Analysis. A total of 43,930 CPS-II participants

lived in the New York City region at enrollment in 1982,

and a total of 10,525 deaths had been recorded for this

group during the 18 years of follow-up (1982 through

2000). The team chose to analyze mortality from all causes,

ischemic heart disease, cardiopulmonary disease, lung

cancer, endocrine disorders, diabetes, digestive cancers

and disorders, and accidental deaths. 

To estimate pollutant levels, the research team calculated 3-year averages for each monitor from daily monitoring data collected for the U.S. EPA’s Air Quality Sub-
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system (AQS) for 1999 through 2001. In addition, they

used LUR, a relatively new statistical process that estimates pollutant exposures at specific locations by incorporating air pollution monitoring data, road and traffic data,

land-use information (e.g., population density, industry,

vegetation), and data on emissions from local sources. The

ultimate goal was to be able to explain the variations in

local exposures over a broad area. In New York City, land

use was assessed using traffic counting systems, roadway

network maps, satellite photos, and local government

planning and tax-assessment maps. 

Developing a LUR model requires evaluating land use

around pollution monitors using different types of landuse variables to find which ones best predict monitored

pollutant levels. Data for a selected set of monitors were

used to develop the model, and then the model was used

to predict pollutant concentrations at the remaining monitors that had not been used. When the predicted value was

compared with the actual measured value at a validation

site, the team could assess how accurately the model predicted pollutant exposure at locations without monitors

but with similar land-use characteristics. The final LUR

model for New York City predicted 66% of the variation in

monitored PM2.5 concentrations across the study area (R2 =

0.66).

Three LUR models were created for the New York City

region: two covering 28 counties (one using a 3-year exposure average and one using only winter exposure averaged

over 1 season) and one covering 9 counties using a 3-year

exposure average. Only the 28-county models were used in

the mortality analysis; the 9-county model was used only

for testing methods.

The LUR model results were compared with the results

of a spatial interpolation of measured values known as

kriging. Using kriging, the investigators produced estimates of exposure for the entire study area by interpolating

data from the 62 pollution monitoring sites in the 28-

county area. The LUR and kriging models produced similar estimates of pollution levels in cross-validation

studies in which data from one monitor were removed

from the data set and data from the other monitors were

used to estimate exposure at the “missing” monitor’s location (the procedure was repeated for each monitor in turn).

The authors still chose to use their LUR model to assign

exposure to participants (for analyzing mortality) because

the LUR was more likely to account for highly local variations in exposure due to traffic patterns and land use in the

areas where participants actually resided, whereas kriging

relied only on data near pollution monitors.

As with the Nationwide Analysis, the investigators used

both the standard Cox and random effects Cox models for

analyzing pollutant exposure and mortality. They incorporated the 44 individual-level covariates from the ACS

enrollment questionnaire as well as data for the seven ecologic covariates at the ZCA and MSA scales. Even in the

28-county winter-2000 model, the team chose to include

the percentage of homes with air conditioning as an ecologic covariate because (1) the availability of air conditioning is a good proxy for the type of home construction;

and (2) buildings with air conditioning typically have a

relatively low level of infiltration of outdoor air into the

structure (and therefore less exposure to outdoor ambient

air pollution) in the winter as well as in the summer.

Los Angeles Analysis 

The Intra-Urban Analysis in the Los Angeles region

included 22,905 ACS CPS-II participants in 267 ZCAs covering five counties; 5,856 deaths had been recorded at the

end of 2000. As with the New York City Analysis, associations between mortality and exposure were analyzed for

mortality from all causes, ischemic heart disease, cardiopulmonary disease, lung cancer, endocrine disorders, and

diabetes. In addition, the Los Angeles Analysis included

digestive cancer, other cancers, digestive disorders, accidents, and all other causes. The investigators used both

LUR and kriging methods to estimate exposure concentrations at the center of each ZCA. The standard Cox and

random effects Cox models were used to calculate exposure–mortality associations; they included the 44 individual-level covariates (from the ACS enrollment

questionnaire) and seven ecologic covariates. Unlike the

New York City Analysis, the Los Angeles researchers did

not include a seasonal (winter-only) model and they did

not construct a test model for a smaller portion of the study

area; did, however, control for O3 concentrations.

In their attempt to model fine geographic variations in

pollutant exposure, the team led by Dr. Jerrett assembled

data from a wide variety of sources. For the LUR and

kriging models, they started with air monitoring data from

several sources including the California EPA data for 23

PM2.5 monitors and the California Air Resources Board 42

sites that monitor O3. Although PM2.5 was of primary

interest, the Los Angeles Analysis included O3 concentrations as a copollutant in the health analyses. 

To develop the LUR models, they used data for digital

land use, mapped road networks, road classes and speed

limits, traffic counts, population, and topography (elevation). The investigators constructed a LUR model for the

Los Angeles area that predicted 69% of the variation in

local PM2.5 concentrations when evaluated using regression diagnostics (as described in the Investigators’ Report).

Primary predictors of exposure in the LUR model were
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Commentary on Investigators’ Report by Krewski et al.

traffic volume, industrial areas, and government areas (as a

proxy for traffic intensity around major destinations such

as schools, government service offices, and hospitals).

They do note, however, that this model tended to predict

excessively high values in certain high-pollution areas,

including several freeway intersections with heavy traffic

and the Long Beach area near the port facilities for Los

Angeles and Long Beach.

As with the New York City Analysis, the team led by Dr.

Jerrett built a kriging model based on data from the 23 PM2.5

monitors in the Los Angeles metropolitan area. The results

lacked much of the local-area variation in concentrations

found with the LUR model. The investigators expected this

difference because the monitors had been placed for purposes of federal and state regulatory compliance and did not

specifically monitor sources of pollution, such as nearby

industry, or traffic, or pollutant concentrations in residential areas. A brief comparison of the associations (hazard

ratios) for death from various causes and PM2.5 exposures

modeled using both kriging and LUR for Los Angeles and

LUR for New York City is presented in Table 30 of the Investigators’ Report and in Commentary Table 3.

The investigators also built two kriging models for exposure to O3 using data from the 42 monitors. One model

mapped the interpolated exposure values based on an

average of the four highest 8-hour concentrations for each

monitor in 2000. The second one mapped the interpolated

values for the expected peak daily concentrations (based

on the average daily concentration for each monitor for

1999 through 2001). 

The team led by Dr. Jerrett applied the same standard

Cox and random effects Cox models used for the Nationwide and New York City Analyses of mortality and exposure concentrations to the Los Angeles region. Unlike the

other analyses, Dr. Jerrett’s team also included covariates

for O3 exposure because it is sufficiently high in the Los

Angeles Basin to be a potential confounder of the relationship between PM2.5 exposure and mortality. 

CRITICAL EXPOSURE TIME WINDOWS ANALYSIS 

(SPECIFIC AIM 3) 

To address Specific Aim 3 (What critical time periods of

exposure affect the association between air pollution and

mortality?), Dr. Krewski’s team performed an analysis of

how exposure to pollutant concentrations in different time

intervals might be associated with mortality patterns. This

analysis was designed to address hypotheses about longterm exposures — that some time periods of exposure may

be more critical than others to possible health outcomes —

and whether more recent exposures to air pollution are

more or less associated with mortality than exposures much

further in the past. The researchers selected three time

periods to investigate: 1 to 5 years, 6 to 10 years, and 11 to

15 years before a participant died.

The investigators used the same CPS-II data set used for

the Nationwide Analysis to analyze critical time windows

of exposure, but with some important distinctions. For one

exposure group, they selected participants in the Nutrition

Cohort, a subcohort of the CPS-II, to analyze the effect of

Commentary Table 3. Selected Hazard Ratios from the Nationwide and Intra-Urban Analysesa

Nationwideb

Intra-Urban

New York Cityc

Intra-Urban

Los Angelesb

Exposure assignment method MSA Average LUR LUR Kriging

3-Year

Model

Winter-2000

Modeld

Cause of death

All causes 1.04 (1.03–1.06) 0.87 (0.67–1.13) 0.97 (0.82–1.13) 1.14 (1.03–1.27) 1.17 (1.05–1.30)

Ischemic heart disease 1.18 (1.15–1.22) 1.47 (1.00–2.00) 1.56 (1.21–1.97) 1.33 (1.08–1.63) 1.39 (1.12–1.73)

Cardiopulmonary disease 1.09 (1.06–1.11) 0.67 (0.33–1.07) 1.08 (0.85–1.36) 1.11 (0.97–1.28) 1.12 (0.97–1.30)

Lung cancer 1.09 (1.03–1.15) 0.73 (0.00–1.60) 0.72 (0.26–1.31 1.39 (0.96–2.01) 1.44 (0.98–2.11)

a

 Results are from random effects Cox models, stratified by age (1-year groupings), sex, and race; adjusted for the 44 individual-level covariates; and adjusted 

for seven ecologic covariates at the MSA & DIFF levels. HRs are followed by 95% confidence intervals.

b HRs for the Nationwide and Los Angeles analyses were calculated per 10-μg/m3 change in PM2.5.

c

 For easier comparison with Nationwide and Los Angeles results, HRs for the NYC analyses were calculated for a 10-μg/m3 change in PM2.5 converted from 

data reported for a 1.5-μg/m3 change in PM2.5 for the 3-year model and from data reported for a 3.9-μg/m3 change in PM2.5 for the winter-2000 model.

d Based on data for January, February, and March 2000.
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relocation on exposure and mortality. The Nutrition

Cohort was formed in 1992–1993 and composed of

189,194 CPS-II participants who were between the ages of

50 and 74 years and resided in one of 21 states with population-based cancer registries. These subcohort members

completed follow-up questionnaires with detailed questions on diet, physical activity, and other individual-level

factors not covered in the initial 1982 enrollment questionnaire and provided updated information in 1997–1998,

1999–2000, and every 2 years thereafter. 

From the Nutrition Cohort, the investigators identified

approximately 61,000 participants who resided in 106

MSAs with enough available average PM2.5 monitoring

data; of those, 8181 were known to have moved to another

MSA during follow-up. This group is referred to as the

PM2.5-A group. 

A second analysis group (81,466 members in 53 MSAs)

was formed from members of the full CPS-II cohort who

were not in the Nutrition Cohort and who died while

residing in the same MSA in which they were enrolled in

1982 and were assumed to have lived continuously in that

MSA from enrollment until death. This group is referred to

as the PM2.5-B group; the PM2.5-B group and the PM2.5-A

group are mutually exclusive. 

A 15-year exposure profile for each member of the

PM2.5-A and PM2.5-B groups was constructed from PM2.5

concentrations for the MSA of residence for the three time

periods (1 to 5 years, 6 to 10 years, and 11 to 15 years)

before his or her death. For participants who were known

to have moved, exposures in the new MSAs of residence

were also used to create the exposure profiles. For this purpose, PM2.5 levels were derived by Lall and colleagues

(2004) from average annual measured TSP and PM10 concentrations for 1972 through 2000.

A third group included 80,711 Nutrition Cohort participants in 120 MSAs for which at least 20 years of annual

average SO2 measurements were available; 5762 (7.1%)

participants moved at least once during the exposure

period. Similar exposure profiles for SO2 were constructed

for each participant based on the MSA of residence for

1972 through 2000. 

As with all other analyses, the investigators used the

standard Cox model to analyze the data, with stratification

and adjustment for the 44 individual-level variables from

the ACS 1982 enrollment questionnaire. In this set of analyses, however, they used the three 5-year time-period

exposure variables instead of the exposure averages for

1980 or 2000. Only one exposure window (1 to 5 years, 6 to

10 years, or 11 to 15 years) was analyzed at a time because

the model was designed to avoid statistical complications

that would arise if two or more were included together.

The PM2.5-A and PM2.5-B groups were also analyzed separately because they were distinct risk groups. 

Dr. Krewski’s research team considered the time

window with the best-fitting model to be the period during

which pollution could have had the strongest influence on

mortality. The best-fitting model was judged by the HRs

and CIs and by the Akaike information criterion [AIC], a

statistical measure of how well a model fits the available

data; the lowest AIC value indicates the time window most

strongly connected with the pattern of mortality. For

example, in Table 26 of the Investigators’ Report, the combination of the PM2.5-B group, all causes of death, and

average exposure 1 to 5 years before death shows an AIC of

933,094.00; for exposure 6 to 10 years in the past, the AIC

is 933,094.94; and for exposure 11 to 15 years in the past,

the AIC is 933,095.03. The investigators thus ranked the

exposure window 1 to 5 years before death as potentially

more important than earlier exposure periods, although

both the AIC values and HRs were sufficiently close as to

be inconclusive.

KEY FINDINGS

NATIONWIDE ANALYSIS

The researchers investigated a suite of U.S. EPA criteria

pollutants using the standard Cox model. The primary focus

of this project, however, was to extensively evaluate the

analytic results for exposure to PM2.5 and mortality

obtained from the random effects Cox models that were

adjusted for ecologic covariates and spatial autocorrelation. 

Generally speaking, use of the random effects Cox

model leads to somewhat different results than the standard Cox model, most notably small increases in the

hazard ratios and slightly wider confidence intervals (CIs).

Results of standard Cox and random effects Cox models for

the associations of PM2.5 with various causes of death are

presented in Commentary Table 4. 

For the standard Cox models that included the 44 individual-level covariates, the associations between average

PM2.5 concentrations for both 1979–1983 and 1999–2000

and all listed causes of death (except “all other causes”) were

significant. When random effects Cox models were used

with controls for ecologic covariates (in addition to the

44 individual-level covariates included in the standard Cox

models), the effect estimates increased and remained significant; the largest hazard ratio (1.24; 95% CI, 1.19–1.29) was

noted for ischemic heart disease. Additional controls for

these ecologic covariates at the MSA and ZCA scales

appeared to remove some confounding effects, even as the
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switch to the random effects model, with its relaxed assumptions, resulted in decreased certainty for some estimates. 

Although conventional statistical models, including the

standard Cox model, assume that all data points are independent of one another, it is unlikely that individuals who

live in the same household or neighborhood are independent with respect to measured and unmeasured factors

that affect health and mortality. A number of sensitivity

analyses were conducted to allow for nonindependence of

observations for CPS-II participants living close together.

The results demonstrate relatively unchanged estimates of

risk with widened confidence intervals (see Table 9 in the

Investigators’ Report). This pattern suggests that some

clustering of mortality is not explained by the ecologic

covariates for MSAs and ZCAs. 

Results for some pollutants other than PM2.5 are worth

noting. In the Reanalysis Project (Krewski et al. 2000), the

research team had investigated whether the relationship

between PM2.5 exposure and mortality changed when O3

was included in the model and had found little effect.

Therefore, the investigators did not repeat those combinations and analyzed O3 in single-pollutant models. Summer

O3 levels (calculated from concentrations measured from

April to September in 1980) were significantly associated

with mortality from all causes (HR = 1.02; 95% CI, 1.01–

1.03) and from cardiopulmonary disease (HR = 1.03; 95%

CI, 1.02–1.04). O3 exposure has recently been linked with

mortality from pulmonary causes (Jerrett et al. 2009),

whereas PM2.5-related mortality has been shown to be

dominated by cardiovascular causes (Pope et al. 2002).

This implies that exposure to O3 does not confound the

association between PM2.5 and mortality. Dr. Krewski’s

team did not construct a random effects Cox model for O3

with ecologic covariates, nor did they compare mortality

from respiratory causes with mortality from other causes

of death. They have recently published the results of further analyses of O3 exposure and death from pulmonary

causes in this cohort (Jerrett et al. 2009).

Hazard ratios for exposure to SO4

2and SO2 were significantly elevated for all analyzed causes of death in the

standard Cox models (Table 3 of the Investigators’ Report);

the research team did not analyze them with random

effects Cox models because of the project’s limited computing resources and their focus on PM2.5 analyses.

Because SO2 concentrations have been reduced across the

United States during the follow-up years, however, SO2

was evaluated separately for possible critical periods of

exposure, described below.

Level of education attainment has been identified as a

possible surrogate indicator of socioeconomic status and

occupational exposure. In the Reanalysis Project (Krewski

et al. 2000) and some parts of the Updated Analysis (Pope

et al. 2002, 2004), the education level of the cohort members appeared to modify the effect of exposure on mortality: Participants with less than a high school education

had substantially higher estimates of risk, and those with

more than a high school education had no apparent

increase in risk. In this analysis, with substantial additional follow-up data, there is a moderate suggestion of

this same trend (most notably for mortality from lung

Commentary Table 4. Associations Between Various Causes of Death and Long-Term Exposure to PM2.5 in Two Time 

Periods from the Nationwide Analysisa

Cause of Death Standard Cox Model Random Effects Cox Modelb

HR per 10-μg/m3 Change in PM2.5 Exposure Level (Average for 1979–1983)

All causes 1.03 (1.01–1.04) 1.04 (1.03–1.06)

Ischemic heart disease 1.12 (1.09–1.16) 1.18 (1.15–1.22)

Cardiopulmonary disease 1.06 (1.04–1.08) 1.09 (1.06–1.11)

Lung cancer 1.08 (1.03–1.14) 1.09 (1.03–1.15)

HR per 10-μg/m3 Change in PM2.5 Exposure Level (Average for 1999–2000)

All causes 1.03 (1.01–1.05) 1.06 (1.04–1.08)

Ischemic heart disease 1.15 (1.11–1.20) 1.24 (1.19–1.29)

Cardiopulmonary disease 1.09 (1.06–1.12) 1.13 (1.10–1.16)

Lung cancer 1.11 (1.04–1.18) 1.14 (1.06–1.23)

a

 Analyses with the standard Cox model were stratified by age (1-year groupings), sex, and race and were adjusted for the 44 individual-level covariates. 

Analyses with the random effects Cox model were also adjusted for the 7 ecologic covariates at the MSA & DIFF levels. HRs are followed by 95% 

confidence intervals. Values are from Tables 3 and 6 in the Investigators’ Report.

b Results are for the MSA & DIFF combination of ecologic covariates. The DIFF value for each ZCA is the ZCA-specific value minus the MSA mean. This 

model allowed nested ecologic information to be analyzed.
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cancer), but the hazard ratios for participants with more

than a high school education were positive for all evaluated causes of death. For ischemic heart disease, the trend

seemed to be reversed — those with higher education had

the highest risk. Full results for the analyses of effect modification by education attainment are presented in Table 8

of the Investigators’ Report.

INTRA-URBAN ANALYSES 

The Intra-Urban Analyses produced very different

hazard ratios and confidence intervals for New York City

and Los Angeles. Commentary Table 3 presents hazard

ratios by cause-of-death category for different methods of

estimating and assigning exposure levels to participants:

the New York City 28-county LUR-based models for the 3-

year (1998–2000) and winter-2000 pollutant levels, and

the Los Angeles models with exposure estimated by

kriging and LUR. For easier comparison with the hazard

ratios for the Los Angeles and Nationwide Analyses,

hazard ratios for the New York City Analysis have been

converted to a 10-μg/m3 exposure change from 1.5-μg/m3

change for the 3-year model and from a 3.9-μg/m3 for the

winter-2000 model. The results for the fully adjusted

Nationwide Analysis are included for comparison.

Even though the New York City and Los Angeles Analyses share common theoretical and methodologic

approaches, the results of LUR models for assigning exposure and the estimates of attributable health effects are

strikingly dissimilar. The hazard ratios are much higher for

Los Angeles than for New York City, except for ischemic

heart disease. Possible reasons for this difference may be

the range of PM2.5 exposures derived for the Los Angeles

area and the relative uniformity of exposures in the New

York City region. Annual average measured PM2.5 concentrations from air monitors in the Los Angeles region ranged

from 9.5 to 28 μg/m3, and annual average exposure values

predicted by the LUR at high-exposure locations such as

freeway interchanges were in excess of 125 μg/m3. In contrast, the interdecile (10th to 90th percentile) range of

exposure for the New York City region was 1.9 μg/m3 for

the 3-year model and 3.9 μg/m3 for the winter-2000 model.

These differences may stem, in part, from the different

LUR models constructed for the two cities because LUR

models are inherently driven by land-use factors that predict exposure and are specific to each urban environment.

CRITICAL EXPOSURE TIME WINDOWS ANALYSIS

This set of analyses was an innovative start at understanding how the timing of exposure may influence health

outcomes. Unfortunately, it did not yield conclusive

results. Some interesting contrasts were noted between the

PM2.5-A group, composed of Nutrition Cohort participants, and the PM2.5-B group, made up of CPS-II cohort

members who were assumed to have died without moving

from their 1982 MSAs of residence. Table 26 in the Investigators’ Report details the results for PM2.5 and SO2 exposure in 5-year segments.

Overall, differences in model-fit statistics and in the

hazard ratios and confidence intervals for combinations of

two exposures, two cohorts, and three 5-year time periods

of exposure are modest and demonstrate no definitive patterns. However, some possible trends can be discerned.

Deaths from cardiopulmonary disease were most strongly

associated with SO2 exposure 1 to 5 years before death and

with PM2.5 exposure 6 to 10 years before death. Associations between exposure to PM2.5 and mortality from lung

cancer showed some interesting patterns for all time windows for both PM2.5-A and PM2.5-B groups, and some associations for the PM2.5-B group were significant. Lung

cancer is known to be a disease of long latency (typically

15 to 30 years) and yet the strongest statistical association

with PM exposure was reported for the time window 5 to

10 years before death.

DISCUSSION

The Extended Analysis was a complex and important

project that addressed several research questions

including (1) statistical issues related to confounding and

modification of effects by community-level covariates, (2)

developing improved models to accommodate spatial

autocorrelation in the data, and (3) within a subcohort

with detailed residential data, developing methods of

investigating critical time periods of exposure. Because

this project included more years of follow-up and more

deaths than previous analyses, it further consolidates

existing knowledge about mortality risks in this cohort.

Although the intra-urban associations obtained using

kriged exposure data for Los Angeles have been published

previously (Jerrett et al. 2005), this report describes more

refined methods of exposure assessment. The results for

the New York City region are entirely new.

NATIONWIDE ANALYSIS

Study Design 

The broad study design for this Extended Analysis of the

nationwide database was the same as that used in the previous Updated Analysis of this cohort (Pope et al. 2002) and

fully appropriate for the task. We have no reason to believe
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that limitations in the ascertainment of outcomes would

have had any important impact on the study results. The

additional years of follow-up added substantially to the

data available at the time of the original study (Pope et al.

1995) and the Reanalysis (Krewski et al. 2000), and modestly to the Updated Analysis (Pope et al. 2002, 2004). 

Statistical Methods

The model that underlay all mortality analyses was the

standard Cox model, which was appropriate for

addressing the study’s specific aims. The influence of risk

factors on which information had been collected through

the ACS enrollment questionnaire (including age, sex,

diet, education attainment, and occupational exposures)

could be controlled at the individual level by including

the factors in the model. When an air pollutant variable

was considered, the investigators could assume that

including these 44 individual-level covariates would substantially control confounding that could arise if one of

these risk factors were associated with the pollution variable (for example, if diets are generally poorer where exposures are higher). 

The standard Cox model has two major limitations that

the investigators addressed in innovative ways: ecologic

confounding and spatial autocorrelation. Ecologic covariates are risk factors for mortality that are measured at the

neighborhood level rather than at the individual level. Typically, in spatial studies of health, such variables are found

to predict mortality even when — as here — adjustment is

made for individual-level risk factors. Ecologic confounding arises when such variables are associated with air

pollution levels — a matter of particular concern here,

because air pollution was estimated at the larger MSA

scale. In this study, the ecologic covariates included were

aggregate measures derived from the 1980 U.S. Census;

they included measures of income, unemployment, availability of air conditioning, and race. This information was

collected at the smaller ZCA scale rather than the MSA

scale, although MSA-scale values were calculated from

ZCA-scale values and included in the analysis.

The inclusion of ZCA-level covariates made the evaluation of ecologic confounding more refined in this

Extended Analysis than for the Reanalysis (for which only

MSA-scale ecologic covariates were considered). However,

not all previously considered ecologic covariates were

included in this analysis. In particular, population migration and ambient temperature had both been shown to be

determinants of mortality with some evidence of confounding in the Reanalysis and were not further evaluated

in this project. 

Spatial autocorrelation is the tendency for variables to

have similar values for people or areas that are geographically (spatially) close. Experience with spatial analysis of

health data generally has led us to expect this pattern, even

when individual and ecologic covariates are controlled.

Spatial autocorrelation is of particular concern with

respect to the mortality data used in this study because the

Reanalysis documented that air pollution levels are spatially autocorrelated. Spatial autocorrelation has two types

of effects that might be relevant here: Its presence suggests

the existence of determinants of mortality otherwise unaccounted for in the model, which might (though need not)

confound the association between air pollution and mortality; and its presence distorts (and usually exaggerates)

the precision of the estimates.

In this and previous analyses of the ACS study data, the

investigators incorporated spatial components into the statistical model in order to reduce such adverse impacts of

spatial autocorrelation. The spatial models in this analysis

differed from those used in the Reanalysis and in the

Updated Analysis by Pope and coworkers (2002). Both of

those studies used a variety of approaches; in particular,

they included a regional fixed effect; and they fitted a spatially smooth surface to mortality that was unexplained by

covariates in the models (Pope et al. 2002). The model

used in the current Extended Analysis includes (1)

random effects at the ZCA, MSA, and state scales, and (2)

correlation between adjacent ZCAs, MSAs, and states

(although only two levels could be incorporated in the

model simultaneously). (In the Nationwide Analysis section Statistical Methods and Data Analysis, the investigators note that the degree of correlation can, alternatively,

be assumed to depend on distance; but results for distancebased models are not presented.) 

Technical problems in fitting spatial models, such as

these, have previously prevented their use in as complex a

formulation as that in which they were used here. The

investigators are to be congratulated on overcoming these

technical complexities to produce what in many ways is a

more comprehensive representation of spatial structure in

these data. It remains very difficult, however, to be sure

that the range of models considered is broad enough that

all of the adverse impacts described above are controlled

by the incorporation of spatial patterns of mortality due to

unmeasured risk factors. In particular, though the models

used seem well formulated to capture local spatial patterns,

it is less obvious that they captured larger-scale patterns.

For example, the Reanalysis found important residual variation in mortality across regions and incorporated regional

terms (as fixed and random effects) in the model. There does

not appear to have been any analogous direct representation
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of larger-scale patterns in this Extended Analysis, although

allowing for correlation between adjacent states may have

done this to some extent. One also wonders what the impact

would be of allowing for the effects of latitude or longitude,

which were not considered. 

In summary the approach used to allow for spatial autocorrelation in these analyses was technically innovative

and provides quite comprehensively for patterns at the

local scale. However, the absence of any investigation of

larger-scale spatial patterns due to factors other than air

pollution makes it impossible to rule out an impact of such

factors on results. 

Results 

We agree with the investigators that including ecologic

covariates (social and economic factors) — measured at the

ZCA scale for the first time in this study — changed the

risk estimates relatively little (Commentary Figure 1). This

lack of strong effect adds to the reassurance provided in

previous reports on this cohort that the effects on health of

these variables did not bias the risk estimates. However, as

noted above, not all ecologic covariates considered in previous analyses were assessed in this study. Some variables

not considered in this study — population change and climate (temperature) — did have an impact on pollution risk

estimates in some previous analyses (Commentary

Figure 2). Although we do not know that including them

would have had a similar influence in the current analyses,

omitting them exemplifies the uncertainty due to possible

residual confounding by ecologic covariates that were not

included in the models. We also agree with the investigators that key results were robust when spatial autocorrelation in mortality was incorporated in the statistical models

(Commentary Figure 3). This is reassuring, particularly

because the spatial autocorrelation models developed in

the course of this work represent a refinement of those used

previously. However, such reassurance cannot be absolute.

It remains possible that spatial patterns in mortality due to

unmeasured risk factors could bias estimates of risk. In particular it is unclear to what extent the models used allowed

for large-scale geographic patterns, such as those presented

in the Reanalysis by fitting regional effects (Krewski et al.

2000). However, an analysis of semivariance presented in a

follow-on study of O3 (Jerrett et al. 2009) using the same

individual and ecologic covariates showed no indication of

important residual spatial autocorrelation of the O3 and

mortality relationship in this same data set when individual

and ecologic covariates were included. In this Extended

Analysis the research team adhered to the practice in early

reports of combining deaths from cardiovascular and respiratory causes. This is important for reasons of continuity,

but it can be questioned from a biomedical point of view.

Commentary Figure 1. Adjustments to control confounding by ecologic

covariates for mortality from all causes related to PM2.5 exposure. 

Commentary Figure 2. Ecologic variables found to influence the relationship between PM2.5 exposure and mortality from all causes in the

Reanalysis (data are Independent Observations from Table 46 of Krewski

et al. 2000).

Commentary Figure 3. Adjustment for spatial autocorrelation with different statistical methods. Random effects Cox models were used with

covariates clustered at ZCA, MSA, and state levels with and without spatial autocorrelation. HRs are shown for PM2.5 exposure and mortality from

all causes.
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Commentary on Investigators’ Report by Krewski et al.

Although there may well be overlap of ICD classifications

between the two categories, it would be more prudent to

present them separately, as well as in combined categories,

until more is known about the biologic mechanisms

involved in effects. The recent report from a Dutch cohort

study (Brunekreef et al. 2009) stated that, depending on the

exposure metric used, risks tended to be higher for respiratory than for cardiovascular mortality. 

The current study, although focused on particles, reports

the emergence of a relationship between O3 and cardiopulmonary mortality that recently has been further analyzed by

examining causes of cardiovascular and respiratory mortality separately. The more recent analysis (Jerrett et al. 2009)

found an association between O3 exposure and respiratory

but not cardiovascular mortality, which further supports the

desirability of presenting results for the two categories separately. Similarly, the Health Review Committee would have

liked to see the results for other categories of cardiovascular

disease, such as stroke and heart failure, presented alongside those for ischemic heart disease. As more cohort studies

are conducted, such a disaggregated presentation would

facilitate a degree of meta-analysis.

The Investigators’ Report singled out the associations

between PM2.5 and ischemic heart disease; although other

cardiovascular causes of death were not presented, mortality from ischemic heart disease appears to be the category

of cardiovascular disease where much of the association

between cardiovascular mortality and air pollution exposure is concentrated, as reflected in previous investigations

with this cohort (Pope et al. 2004). The investigators

referred to this association as having “high biologic plausibility,” but the Review Committee thought that the existing

limited epidemiologic and mechanistic evidence, though

suggestive, does not justify such a strong assertion. 

Given that the Reanalysis (Krewski et al. 2000) had extensively tested the potential for the gaseous pollutants to confound the relationship between exposure to PM2.5 and

mortality and had not found any significant confounding

(other than by SO2), it is understandable that the current

investigators chose to focus their limited resources on the

extensive exploration of spatial autocorrelation in a series

of one-pollutant models. In this case, including gasses

such as NO2 and SO2 as copollutants would have presented some challenges in a model with PM2.5 because

(1) levels of NO2 recorded at central monitoring stations do

not reflect the fine scale on which these gases vary; (2) NO2

and SO2 are generated by some of the same sources that generate PM2.5 (NO2 and traffic; SO2 and power plants) and

their levels will be correlated with those of PM2.5; and (3)

the gases are important precursors to components of fine

nitrate and SO4

2particles. Still, it is always reassuring if

two-pollutant analyses can be conducted. Further analyses

of O3 and respiratory mortality that were completed

recently by members of Dr. Krewski’s team included a twopollutant analysis for PM2.5 and O3 (Jerrett et al. 2009).

INTRA-URBAN ANALYSES

Study Design 

Epidemiologically, the design for the two Intra-Urban

Analyses (in the New York City and Los Angeles regions)

was essentially the same as that for the Nationwide Analysis, from which the data for these two analyses were

selected. The differences and methodologic innovations

lie in the methods of exposure assessment. 

The Nationwide Analysis compared MSAs across a wide

range of air shed regions; and because some cities have a

common regional background (e.g., New England), this

study compared effects of air pollution between cities and

between air sheds. By contrast, the Intra-Urban Analyses

primarily investigated variations in local exposures added

to common regional and urban background exposures. Spatial variations within urban areas are driven more by local

near-source emissions such as traffic, industry, and residential space heating; the resulting analyses can tell us little

about the health effects of chronic exposure to the background pollutant levels. 

Statistical Methods 

The statistical analysis for these studies had two components. The LUR analysis, used to estimate exposures, will

be discussed below. The epidemiologic analysis, designed

to estimate the effects of air pollution after controlling the

influence of ecologic covariates, followed essentially the

same methods as those used in the Nationwide Analysis,

with some differences due to the local scale (e.g., statelevel components of the Nationwide Analysis were not relevant to assessing the impact of spatial autocorrelation).

For this set of analyses, as for the Nationwide Analysis, the

Review Committee considered these methods very appropriate and probably adequate to control most potential

confounding. Climate is more homogeneous in these IntraUrban Analyses, and possible unmeasured determinants

of large-scale spatial confounding are not an issue, so

residual confounding from these sources is much less of a

concern than in the Nationwide Analysis.

Exposure Assessment 

Both Intra-Urban Analyses used LUR and kriging techniques to predict exposure at each ZCA centroid (the central point in a ZCA); this is clearly described for the Los

Angeles model in the Investigators’ Report, but less clearly
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Health Review Committee

stated for the New York City model. These studies are

among the first to use LUR to predict PM2.5 concentrations

at the local level. Unlike NO2 and elemental carbon, which

remain localized near their sources, PM2.5 has a large

regional component and a smaller local-source component

(e.g., traffic emissions), which make it challenging to estimate the contribution of local sources to air pollution

exposure. Including specific land uses in the assessment

may help make local estimates more precise. 

The LUR models differed substantially between the two

cities in several important ways, including the number of

ambient monitoring sites used (23 in Los Angeles, 62 in

New York City); the size of the buffers surrounding each

monitoring site (50 m to 5000 m) for determining which

land-use variables were significant predictors of monitored levels; and the sources of information available for

constructing the independent variables of traffic, land use,

and population for each location. In addition, the New

York City model used county-wide emissions inventory

data to estimate PM2.5 emissions for certain types of industrial point sources. In developing the New York City

models, the investigators considered about 30 variables,

whereas for the Los Angeles model they considered over

140. The three final models for New York City (Table 14 of

the Investigators’ Report) included single indicators for

traffic (within 500 m of each monitor for the 3-year 28- and

9-county models and within 300 m for the 28-county

winter-2000 model), total population (within 1000 m for

all three models), industrial land use (within 300 m for the

3-year 28- and 9-county models) and vegetation land use

(within 1000 m for the 28-county winter-2000 model). The

New York City models were able to explain 60% to 64% of

the variation in local PM2.5 measurements. The final Los

Angeles model also included three variables: traffic within

300 m, industrial area within 5000 m, and government

area within 5000 m of each monitor; it explained 63% of

the variation. 

Despite the substantial differences in how the LUR

models were constructed and the probable quality of available data, they were both successful in explaining a moderate degree of variability (60% to 64%) in PM2.5 at the

ZCA scale. Each model is unique to the area where it was

developed and is not likely to be transferable to other metropolitan areas.

In addition to the number and spatial distribution of the

air-quality monitoring sites, the type of independent variables selected, and the quality of the available data, a

number of additional factors need to be considered when

assessing the applicability of LUR for providing better spatial estimates of PM2.5 concentrations. The ranges of

average annual monitored PM2.5 concentrations were

moderately different between Los Angeles (9.5–28 μg/m3)

and New York City (10–20 μg/m3) and suggest that the

PM2.5 levels in New York City were more strongly influenced by regional transport than by local emissions. The

models reflected this difference. The intercepts in the New

York City models varied between 12.3 and 13.2 μg/m3,

whereas the intercept in the Los Angeles model was

2.3 μg/m3. It is difficult to compare coefficients for the

independent variables between cities, particularly for

traffic, since the variables were constructed from different

forms of land-use and traffic data.

LUR has the potential to provide a better estimate of

PM2.5 levels on a finer spatial scale than simple spatial

averaging using central-site air pollution monitors; thus it

is somewhat disappointing that the LUR models in validation studies have only slightly out-performed kriging

models. For a regional pollutant such as PM2.5, it is not

clear that there are substantial benefits in using LUR

models, compared with spatial averaging models, in

extending the analysis of data from regional air pollutant

monitors to a finer spatial scale. 

Two additional issues arise in using a LUR models. As

the authors report, the LUR method may include hidden or

unmeasured factors that relate to health care, socioeconomic status, or mortality risks and can confound the estimated exposure. This means that although LUR may be

good at predicting exposure, confounding may occur if the

LUR-derived exposure estimate is then used in a healthassessment model that contains the same or similar factors

as variables. (For example, population density may be

independently associated with both mortality and air pollution.) This issue deserves further attention. 

It is reassuring that the results from the random effects

Cox model for Los Angeles obtained using LUR-based

exposure assignments were similar to those obtained using

kriging. Residential location data for the ACS cohort were

available only at the ZCA scale, thus LUR-modeled exposures were appropriate for the central point of the ZCA,

but not necessarily where residences were likely to be

located throughout a ZCA. If data for the independent variables were to be applied at the residential addresses of the

participants, the modeled exposures might be very different from exposures assigned to the ZCA of residence. 

Results

The Intra-Urban results for the two cities were very different. Whereas for Los Angeles there was a strong positive

and significant association between PM2.5 exposure and

mortality from cardiopulmonary disease, the result of the

corresponding analysis for New York City was negative and

not significant. The confidence intervals were, however,
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quite wide for both Intra-Urban Analyses (although more

so for New York City) and when the results are compared

for the same exposure range, they are both consistent with

the Nationwide Analysis result and with one another

(Commentary Figure 4). One explanation for the differences could be the play of chance. Other explanations are

offered by the authors. In the case of Los Angeles, they suggest that the higher estimate is due to less error in estimating exposure, although this assertion can be

questioned. In the case of New York City, the investigators

suggest that there was too little heterogeneity in the PM2.5

estimates to detect differences in the mortality patterns at

high and low ends of the range of exposures; this statement

is consistent with our comments on the width of confidence intervals. The authors further suggest that differences between the Los Angeles and New York City

Analyses lie in the underlying geographic distribution of

social and economic factors in the population relative to

exposure levels. For example, in New York City, pollution

appears to be worst in areas where people are wealthier

and are likely to be in better overall health, whereas pollution in Los Angeles tends to be worst in areas of low socioeconomic status.

Both the Los Angeles and New York City results showed

significant associations between PM2.5 exposure and mortality from ischemic heart disease, in line with the results of

the Nationwide Analysis. The Review Committee noted,

however, that the range of results for cause-specific mortality that are presented differ among the three analyses,

making comparisons more difficult. The Committee also

noted that there was a strong and robust association between

PM2.5 exposure and deaths from endocrine disorders (but

not diabetes) in Los Angeles, but not New York City. This

comparison is intriguing; it would be helpful to see the corresponding results from the Nationwide Analysis. 

In the section Implications of the Findings / Summary of

Results from Phase III / Intra-Urban Analyses, the investigators advance the hypothesis that the impact of exposure

measurement error on the results of PM–mortality regression coefficients will be less in small-scale spatial studies

than in large-scale studies. This suggests that the hazard

ratios calculated for the Intra-Urban Analyses would be

less subject to the statistical effects of measurement error

than the hazard ratios from the Nationwide Analysis

would be. There is an intuitive plausibility to this suggestion; but it is not persuasive for reasons elaborated below. 

Two types of statistical error are important to this analysis: classical measurement error, which biases coefficients

toward the null; and Berkson error, which does not bias

coefficients appreciably, though it increases standard errors,

widening confidence intervals. It is thus important to identify which type of measurement error operates to what

extent in the Intra-Urban and the Nationwide Analyses. 

In this context, Berkson error is that due to differences

in individual true exposures and in the true means for the

areas used in analysis. Zeger and colleagues (2000)

explored this concept for time-series studies; the principle

is the same for geographic studies. Thus, though using

larger spatial units will increase Berkson error, Berkson

error does not cause bias in coefficients; therefore this

source of error would lead us to expect coefficients to be

less biased in small-scale studies. 

In this context, classical error, which does bias coefficients downwards, is that due to differences between the

true mean exposures for the areas and the values actually

used in the regression. Zeger argued that for ecologic timeseries studies, classical error will have less variance than

Berkson error, and similar arguments seem likely to lead to

the same conclusion for geographic studies. That these

observed means will be less precisely measured as areas get

bigger has some plausibility, though the presumption is

much weaker than for the increase in Berkson error in

larger-scale studies. Furthermore, if bias due to measurement error in the coefficients based on MSAs in the Nationwide Analysis is compared with that in an analysis based on

ZCAs in one region (Los Angeles or New York City), one

also has to allow for the greater variation in (true) exposures

across the nation, as compared with the variation in one

region. The bias due to classical error is (to a reasonable

approximation) a function of the ratio of the standard deviation of the error to the standard deviation of the true exposure (Armstrong 1998). It is not at all clear that this ratio is

bigger for MSAs nationally than for ZCAs in Los Angeles or

New York City. Thus, we should not expect that measurement error bias due to classical error will be greater in

small-scale studies than in large-scale studies. 

In summary, we saw no persuasive argument that bias

due to random measurement error would be expected to be

less in the Los Angeles or New York City Analysis than in

the Nationwide Analysis. The extent of bias in each remains

Commentary Figure 4. Comparison of results from Nationwide and IntraUrban Analyses. The New York City data were adjusted from a 1.5-μg/m3

change in PM2.5 to a 10-μg/m3 change to match the scale of the other analyses. HRs are shown for PM2.5 exposure and mortality from all causes.
133

Health Review Committee

an open question, as does the reason for the differences

between New York City and Los Angeles observations.

Overall, the divergent results argue for caution in using

results from any such analysis of only one city to extrapolate to what those results might be in another city. 

CRITICAL EXPOSURE TIME WINDOWS ANALYSIS 

Study Design

The epidemiologic design (specifically, the selection of

study subjects) for the analyses of critical time periods of

exposure was more complex than the selection for the

Nationwide Analysis, which considered only single estimates of exposure. For this set of analyses to explore exposure over time, the investigators needed to have

participants with residential histories, and those were

available for only the Nutrition Cohort, a subset of the full

ACS study. Therefore, the PM2.5-A group comprised some

of the participants for whom information was available on

changes in residence since enrollment in 1982. Dates of

moving were approximate, and no information was available for before 1982, so these sources of approximation are

likely to have resulted in misclassification of exposures in

specific time windows. 

The PM2.5-B group is the most unusual epidemiologically. This group comprised subjects who were not in the

Nutrition Cohort (and for whom ongoing residential information was therefore not available) but who had died and

were assumed to have lived continuously in the same MSA

in which they had been living at enrollment in 1982. The

investigators reasonably inferred that it was most likely that

these subjects did not move in the intervening time, and

their time-specific exposures could therefore be assumed to

be those determined for the MSA in which they resided in

1982 and in which they died. However, a usual epidemiologic principle for cohort studies is that a subject’s presence

in the cohort (his or her person-time at risk) should not

depend on subsequent death. Because persons who did not

die were not included, this group did not adhere to this

principle. Though it is not obvious that the nonstandard

nature of the group would have caused bias in this context,

the validity of the analysis cannot be deduced from the

usual “guarantees” of standard cohort analyses.

Statistical Methods

Although fitting a standard Cox model with time-dependent exposures is not as typical as is fitting the model with

time-fixed exposures, it is well established (notably for

occupational epidemiology), and the approach used by the

investigators seemed to fall within the established methods.

It is understandable that the compounded complexity of

handling both time-dependent exposures and random

effects (to reflect spatial autocorrelation) proved to be too

much of a technical challenge, and we concur with the

investigators that this is a minor limitation here. 

The investigators’ use of AIC to compare models using

different windows of exposure is broadly reasonable,

though it is curious that more was not made of comparisons of deviance statistics, for which the magnitude as

well as the direction of difference between models can be

interpreted. Happily, however, because the numbers of

parameters in the models compared are the same, differences in AIC are the same as differences in deviance and

may be interpreted as such. 

The Review Committee was somewhat disappointed

that the investigators did not present results for “multiwindow” models, which would be analogous to multi-pollutant models. The investigators discuss using such

models, but they decided that the resulting estimation of

negative coefficients for some time windows precluded

their useful interpretation. It seemed to the Review Committee that the use of such multi-window models (well

established in occupational epidemiology) in conjunction

with single-window models would provide the best basis

for assessing the evidence for the independent effects of

each window (for example, using likelihood ratio tests in

nested models). The investigators favored “constrained”

multi-window models, in which all coefficients are constrained to be non-negative. Such models are of interest

but, particularly because the technical difficulty in fitting

them was apparently insurmountable, unconstrained

multi-window models would also be of interest. 

For the purposes of formulating and evaluating air pollution policy, it is very important to know whether more

recent exposure has a greater effect on risk than earlier

exposure. However, the Review Committee thought that

the evidence presented was not substantial enough for any

conclusions to be drawn from these analyses. This view is

mainly based on the extremely small differences in AIC

values between the time windows for each pollutant and

group. For example, for PM2.5 exposure, the three AIC

values for the models with exposure in windows 1 to 5, 6

to 10, or 11 to 15 years before death differed by little more

than 1 for PM2.5-A group and for PM2.5-B group (Table 26

in the Investigators’ Report). As noted above, differences

in AIC values for these models will closely approximate

differences in deviance, and differences of less than 3.84

(the 5% point of the chi-squared distribution with 1 degree

of freedom) can easily be explained by chance. 

In only one of the comparisons were differences in AIC

large enough to show any more than very weak, suggestive

evidence for the greater importance of one time window

over another. The exception was for lung cancer and PM2.5
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Commentary on Investigators’ Report by Krewski et al.

exposure for PM2.5-B group; the AIC value (9346.729) for

the time window of 1 to 5 years was 0.515 lower than that

for the 6 to 10 year window (9347.244). However, the

direction of the difference (showing that recent exposure is

the better predictor) does not correspond with expectations for lung cancer (a disease with a latency of 20 years

or longer); therefore, as one “significant” pattern among

many results, it cannot be considered as strong evidence. 

When the group was restricted to participants who died

in the same city where they lived in 1982 (PM2.5-B group),

no attempt was made to adjust for the effects of exposure

earlier or later than the time period of interest. Given the

potentially high correlations among exposures 1 to 5, 6 to

10, and 11 to 15 years before death within each city, differences among the effect estimates reported for each separate

time window cannot be accepted as meaningfully different

from the others. 

Patterns of risk estimates across time windows also

showed only very weak evidence for the superior predictive value of one window over another, though the width

of the confidence intervals indicated that the precision of

the study — despite its size — was not sufficient to conclusively determine if there were significant differences. 

Exposure Assessment

Average exposures were calculated for each of the

deceased participants in 5-year windows of exposure proceeding back from the date of death and are based on previously published work (Lall et al. 2004). Because there were

no comprehensive exposure monitoring data available

before 1973, cohort members who died before 1987 had data

for 1973 substituted for 1967 through 1972 in the calculations of the earliest exposure time window. This practice

has the potential to introduce uncertainty in exposure estimations for the most distant time window for the earliest

decedents because those estimates may or may not reflect

actual exposure concentrations for 1967 through 1972. 

CONCLUSIONS

The current Extended Analysis, described in the Investigators’ Report, represents a broadly sound and thorough

analysis of extended follow-up of an already important

cohort study, with several innovative features. The results

of the Nationwide Analysis presented in this report will be

of great importance world-wide. This is the case because

coefficients from earlier analyses (Pope et al. 2002) have

been central to the calculation of burden-of-disease estimates, policy evaluation, and comparison of policy

options not only in the United States, but in countries and

regions throughout the world (e.g., Global Burden of Disease; Clean Air for Europe; U.K. Department for Environment, Food, and Rural Affairs; U.S. EPA). The ACS study

remains paramount owing to its size and the richness of

the data. 

The results presented in this report consolidate earlier

findings by showing that the application of state-of-the-art

statistical approaches to controlling confounders and spatial autocorrelation do not materially change risk estimates;

important residual confounding (by climate and possibly

other unmeasured determinants of large-scale spatial variation) cannot be excluded, however, particularly in the

Nationwide Analysis. In analyzing the extended follow-up

data for mortality, the report also provides new risk estimates, including — for the first time — an estimate for O3

and premature mortality.

Previous analyses of this ACS cohort have identified

subjects with lower education attainment (and probably

lower socioeconomic status) as having a higher risk of

mortality associated with exposure to PM2.5, although this

trend was not as consistent as in the Six Cities Study

(Krewski et al. 2000). In this Extended Analysis, effect

modification by level of education attainment was considerably less marked than it was in the Reanalysis (Krewski

et al. 2000); for ischemic heart disease, however,

increasing levels of education had the reverse effect (i.e.,

the association of mortality with air pollution exposure

increased as education attainment increased). The Review

Committee thought that this recent evidence suggests that

effect modification by education is probably not as important as was previously thought and that some of the associations may have been at least partly due to chance.

Nonetheless, it is interesting that the recent report on the

Netherlands Cohort Study of diet and cancer found some

indication that lower education attainment was associated

with higher risk (Brunekreef et al. 2009). 

The Review Committee’s Commentary for the Reanalysis Report mentioned that no single study can be the basis

for accepting the existence of a causal relationship

between air pollution and mortality. With this in mind, the

Committee thought that — with the emergence of new

cohort evidence from the United States and Europe — the

similarities and differences among the results of the various studies need to be examined closely. Nevertheless, the

size and character of the ACS cohort makes it likely that it

will remain preeminent. 

The number of intra-urban studies is growing along with

increased interest in the potential effects of exposure to

pollutants from local sources. It is important to recognize,

however, that such studies address the effects of a different

pattern of exposure from that addressed in intercity
135

Health Review Committee

studies, such as those conducted with the national ACS

cohort, and that they employ different exposure-assessment techniques. Although the results of the Los Angeles

and New York City Intra-Urban Analyses contribute to this

body of evidence, their precision is limited. 

Information on the time period of exposure that might

be critically associated with illness and mortality from various causes, if any, remains elusive in the absence of

cohort data with better residential histories. Since very

few cohorts are as large as the ACS cohort to begin with,

future attempts to find and track a sufficiently large subpopulation to document critical periods of exposure while

accounting for the separate effects of earlier or later exposure periods will require careful statistical design and

power calculations at the inception. Although many questions remain about the timing of exposures in terms of the

development of diseases associated with air pollutants

(e.g., the relationship between actions to improve air

quality and subsequent improvement in health), this

Extended Analysis serves to underscore the difficulty of

assessing the relative importance of the timing of exposure

in a cohort study, even with a large base population.

The results from this Extended Analysis will be very

influential in the coming years, despite the limitations discussed in this Commentary. Dr. Krewski and his colleagues

have successfully designed, constructed, and implemented advanced statistical methods that include nested

random effects variables and control for autocorrelation in

a single model of survival. Furthermore, they have confirmed our understanding that exposure to PM2.5 is associated with increased mortality and have again validated

earlier findings. These results also bolster earlier reports of

increased risk of lung cancer associated with long-term

exposure to PM2.5. For the first time, the association

between long-term O3 exposure and mortality has been

identified with a standard Cox model; in further, more

recent analyses, the same association was evident with a

random effects Cox model (Jerrett et al. 2009).

The Intra-Urban Analysis for Los Angeles suggests that

mortality risks associated with PM2.5 exposure may be elevated when there is a strong local component of exposure.

When the New York City and Los Angeles Analyses are

taken together, however, they underscore the important

point that cities differ markedly in their local exposure

conditions and emphasize the variable importance of the

contributions of local sources to the overall risk of mortality associated with PM2.5 exposure. These divergent

results argue for caution in extrapolating from such studies

in any one metropolitan area to other areas. 

In summary, the additional follow-up years and methodologic developments used in the Nationwide Analysis

have confirmed with remarkable consistency the association of mortality and exposure to PM2.5 reported in previous studies of the ACS data starting 15 years ago. In

addition to consolidating evidence for a causal association, the new analyses have added precision, especially for

the evidence that ischemic heart disease is a cause of death

particularly affected by exposure. The lack of support for

previous findings of greater risks of mortality for underprivileged population groups adds importantly to evidence on this topic, though no doubt the debate will

continue. The Intra-Urban Analyses in Los Angeles and

New York City advance methods for such studies; the differences between results from the two cities could be

explained by chance, but we must be careful about overgeneralizations, given the very local nature of each study.

The search for greater precision on critical periods of exposure was well conducted, but power limitations preclude

clear conclusions. The richness of these latest findings

from the ACS cohort confirms the vital role of such large,

ongoing epidemiologic studies in assessing and protecting

human health in relation to air pollution.

ACKNOWLEDGMENTS

The Health Review Committee thanks the ad hoc

reviewers for their help in evaluating the scientific merit of

the Investigators’ Report. The Committee is also grateful to

Kate Adams for her assistance in preparing its Commentary,

to Virgi Hepner for science editing of this Report and Commentary, and to Ruth Shaw, Flannery McDermott, Melissa

Norcross, Bernard Jacobsen, and Fred Howe for their roles

in preparing this Research Report for publication.

REFERENCES

Abbey DE, Nishino N, McDonnell WF, Burchette RJ,

Knutsen SF, Beeson WL, Yang JX. 1999. Long-term inhalable particles and other air pollutants related to mortality

to non-smokers. Am J Respir Crit Care Med 159:373–382.

Armstrong BG. 1998. Effect of measurement error on epidemiological studies of environmental and occupational

exposures. Occup Environ Med 55:651–656.

Brunekreef B, Beelen R, Hoek J, Schouten L, BauschGoldbohm S, Fischer P, Armstrong B, Hughes E, Lerrett M,

van den Brandt P. 2009. Effects of Long-Term Exposure to

Traffic-Related Air Pollution on Respiratory and Cardiovascular Mortality in the Netherlands: The NLCS-AIR

Study. Research Report 139. Health Effects Institute,

Boston, MA.
136

Commentary on Investigators’ Report by Krewski et al.

Dockery DW, Pope CA III, Xu X, Spengler JD, Ware JH, Fay

ME, Ferris BG Jr, Speizer FE. 1993. An association between

air pollution and mortality in six U.S. cities. N Engl J Med

329:1753–1759.

Jerrett M, Burnett RT, Ma R, Pope CA III, Krewski D, Newbold KB, Thurston G, Shi Y, Finkelstein N, Calle EE, Thun

MJ. 2005. Spatial analysis of air pollution and mortality in

Los Angeles. Epidemiology 16:727–736.

Jerrett M, Burnett RT, Pope CA III, Ito K, Thurston G,

Krewski D, Shi Y, Calle E, Thun M. 2009. Long-term ozone

exposure and mortality. N Engl J Med 360:1085–1095.

Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J, Jerrett M, Abrahamowicz M, White WH. 2000.

Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and

Mortality. A Special Report of the Institute's Particle Epidemiology Reanalysis Project. Health Effects Institute,

Cambridge, MA. 

Laden F, Schwartz J, Speizer FE, Dockery DW. 2006.

Reduction in fine particulate air pollution and mortality:

Extended follow-up of the Harvard Six Cities study. Am J

Respir Crit Care Med 173:667–672.

Lall R, Kendall M, Ito K, Thurston GD. 2004. Estimation of

historical annual PM2.5 exposures for health effects assessment. Atmos Environ 38:5217–5226.

Lipfert FW, Perry HM Jr., Miller JP, Baty JD, Wyzga RE,

Carmody SE. 2000. The Washington University-EPRI Veterans’ Cohort Mortality Study: Preliminary Results. Inhal

Toxicol 12 Suppl 4:41–73.

Lipfert FW, Wyzga RE, Baty JD, Miller JP. 2006. Traffic density as a surrogate measure of environmental exposures in

studies of air pollution health effects: Long-term mortality

in a cohort of US Veterans. Atmos Environ 40:154–169.

Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan

JH, Anderson GL, Kaufman JD. 2007. Long-term exposure

to air pollution and incidence of cardiovascular events in

women. N Engl J Med 356:447–458.

Pope CA III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito

K, Thurston GD. 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287:1132–1141.

Pope CA III, Burnett RT, Thurston GD, Thun MJ, Calle EE,

Krewski D, Godleski JJ. 2004. Cardiovascular mortality and

long-term exposure to particulate air pollution: Epidemiological evidence of general pathophysiological pathways

of disease. Circulation 109:71–77. 

Pope CA III, Thun MJ, Namboodiri MM, Dockery DW,

Evans JS, Speizer FE, Heath CW Jr. 1995. Particulate air

pollution as a predictor of mortality in a prospective study

of U.S. adults. Am J Respir Crit Care Med 151:669–674.

U.S. Environmental Protection Agency. 2004. Air Quality

Criteria for Particulate Matter. EPA/600/P–99/002aF and

EPA/600/P–99/002bF. National Center for Environmental

Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC.

U.S. Environmental Protection Agency. 2006. Provisional

Assessment of Recent Studies on Health Effects of Particulate Matter Exposure. EPA/600/R-06/063. National Center

for Environmental Assessment, Office of Research and

Development, U.S. Environmental Protection Agency,

Research Triangle Park, NC.

Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J,

Dockery D, Cohen A. 2000. Exposure measurement error in

time-series studies of air pollution: Concepts and consequences. Environ Health Perspect 108(5):419–426.
* Reports published since 1998. 

Copies of these reports can be obtained from the Health Effects Institute and many are available at www.healtheffects.org.

Principal

Number Title Investigator Date*

RELATED HEI PUBLICATIONS: PARTICULATE MATTER AND TRAFFIC

137

Research Reports

139 Effects of Long-Term Exposure to Traffic-Related Air Pollution on Respiratory B. Brunekreef 2009

and Cardiovascular Mortality in the Netherlands: the NLCS-AIR Study

138 Health Effects of Real-World Exposure to Diesel Exhaust in Persons with Asthma J. Zhang 2009

137 The Influence of Improved Air Quality on Mortality Risks in Erfurt, Germany A. Peters 2009

133 Characterization of Metals Emitted from Motor Vehicles J.J. Schauer 2006

131 Characterization of Particulate and Gas Exposures of Sensitive Subpopulations P. Koutrakis 2005

Living in Baltimore and Boston

127 Personal, Indoor, and Outdoor Exposures to PM2.5 and Its Components B. Brunekreef 2005

for Groups of Cardiovascular Patients in Amsterdam and Helsinki

124 Particulate Air Pollution and Nonfatal Cardiac Events 2005

Part I. Air Pollution, Personal Activities, and Onset of Myocardial Infarction A. Peters 

in a Case–Crossover Study

Part II. Association of Air Pollution with Confirmed Arrhythmias D. Dockery 

Recorded by Implanted Defibrillators

118 Controlled Exposures of Healthy and Asthmatic Volunteers to Concentrated H. Gong Jr. 2003

Ambient Particles in Metropolitan Los Angeles

104 Inhalation Toxicology of Urban Ambient Particulate Matter: R. Vincent 2001

Acute Cardiovascular Effects in Rats

97 Identifying Subgroups of the General Population That May Be Susceptible M.S. Goldberg 2000

to Short-Term Increases in Particulate Air Pollution: A Time-Series Study 

in Montreal, Quebec

95 Association of Particulate Matter Components with Daily Mortality M. Lippmann 2000 

and Morbidity in Urban Populations

91 Mechanisms of Morbidity and Mortality from Exposure to Ambient J.J. Godleski 2000

Air Particles

Continued
RELATED HEI PUBLICATIONS: PARTICULATE MATTER AND TRAFFIC

Principal

Number Title Investigator Date*

* Reports published since 1998. 

Copies of these reports can be obtained from the Health Effects Institute and many are available at www.healtheffects.org.

138

Special Reports

17 Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, In Press

Exposure, and Health Effects

16 Mobile-Source Air Toxics: A Critical Review of the Literature on Exposure and Health Effects 2007

Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate 2000

Air Pollution and Mortality: A Special Report of the Institute’s Particle Epidemiology 

Reanalysis Project

Diesel Emissions and Lung Cancer: Epidemiology and Quantitative 1999

Risk Assessment

HEI Communications

8 The Health Effects of Fine Particles: Key Questions and the 2003 Review 1999 

(Report of the Joint Meeting of the EC and HEI)

10 Improving Estimates of Diesel and Other Emissions for Epidemiologic Studies 2003

HEI Research Program Summaries

Research on Diesel Exhaust and Other Particles 2003

Research on Particulate Matter 1999

HEI Perspectives

Understanding the Health Effects of Components of the Particulate Matter Mix: 2002

Progress and Next Steps
HEI BOARD, COMMITTEES, and STAFF

139

Board of Directors

Richard F. Celeste, Chair President, Colorado College

Enriqueta Bond President Emeritus, Burroughs Wellcome Fund

Michael T. Clegg Professor of Biological Sciences, University of California–Irvine

Purnell W. Choppin President Emeritus, Howard Hughes Medical Institute

Jared L. Cohon President, Carnegie Mellon University

Stephen Corman President, Corman Enterprises

Gowher Rizvi Vice Provost of International Programs, University of Virginia

Linda Rosenstock Dean, School of Public Health, University of California–Los Angeles

Warren M. Washington Head, Climate Change Research, National Center for Atmospheric Research

Archibald Cox, Founding Chair 1980–2001

Donald Kennedy, Vice Chair Emeritus Editor-in-Chief Emeritus, Science; President Emeritus and 

Bing Professor of Biological Sciences, Stanford University

Health Research Committee

Mark J. Utell, Chair Professor of Medicine and Environmental Medicine, University of Rochester 

Medical Center

Kenneth L. Demerjian Ray Falconer Endowed Chair, Director and Professor, Atmospheric Sciences Research Center 

and Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York

Joe G.N. Garcia Lowell T. Coggeshall Professor of Medicine, University of Chicago

Uwe Heinrich Professor, Medical School Hannover; Executive Director, Fraunhofer Institute for Toxicology and 

Experimental Medicine, Hanover, Germany

Grace LeMasters Professor of Epidemiology and Environmental Health, University of Cincinnati 

College of Medicine

Sylvia Richardson Professor of Biostatistics, Department of Epidemiology and Public Health, Imperial College 

School of Medicine, London, United Kingdom

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University of Pittsburgh

James A. Swenberg Kenan Distinguished Professor of Environmental Sciences, Department of Environmental 

Sciences and Engineering, University of North Carolina–Chapel Hill

Ira B. Tager Professor of Epidemiology, School of Public Health, University of California–Berkeley
HEI BOARD, COMMITTEES, and STAFF

 140

Health Review Committee

Homer A. Boushey, Chair Professor of Medicine, Department of Medicine, University of California–San Francisco

Ben Armstrong Reader in Epidemiological Statistics, Public and Environmental Health Research Unit, Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, United Kingdom

Michael Brauer Professor, School of Environmental Health, University of British Columbia, Canada

Bert Brunekreef Professor of Environmental Epidemiology, Institute of Risk Assessment Sciences, 

University of Utrecht, the Netherlands

Alan Buckpitt Professor of Toxicology, Department of Molecular Biosciences, School of Veterinary Medicine, 

University of California–Davis

John R. Hoidal Professor of Medicine, Clarence M. and Ruth N. Birrer Presidential Endowed Chair, 

Department of Medicine, University of Utah Health Sciences 

Stephanie London Senior Investigator, Epidemiology Branch, National Institute of Environmental Health Sciences

William N. Rom Sol and Judith Bergstein Professor of Medicine and Environmental Medicine and 

Director of Pulmonary and Critical Care Medicine, New York University Medical Center 

Armistead Russell Georgia Power Distinguished Professor of Environmental Engineering, School of Civil and 

Environmental Engineering, Georgia Institute of Technology

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