# R e s e a r c h R e p o r t

H E A L T H E F F E CT S I N ST I T U T E

Includes a Commentary by the Institute’s Health Review Committee 101 Federal Street, Suite 500 Boston, MA 02110, USA
+1-617-488-2300 www.healtheffects.org



# R e s e a r c h R e p o r t 

# H E A L T H E F F E CT S I N ST I T U T E

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. Thunt

with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara TempalskiMay 2009Extended Analysis of the ACS Study of Particulate Air Pollution and Mortalityreport 139



### Number 140 May 2009

WEB VERSION Posted May 21, 2008

# 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 HE I Health Review Committee



# Research Report 140
Health Effects Institute
Boston, Massachusetts



# Trusted Science 

# · Cleaner Air 

# · Better HealthPublishing 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. Exte nded 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

PM 2.5 Concentrations

Background Materials and Methods

Dependent Variable: Ambient PM 2.5 Data Independent Variables: Traffic, Land-Use, Population, and Local Emissions Data Statistical Data Analysis Results

Descriptive Statistics for PM 2.5

# 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 PM 2.5 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

PM 2.5 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 PM 2.5 Exposure Traffic Data Assessment of O 3 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 RESULT ING 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 corp oration chartered in 1980 as an independent research organization to provide high-quality, impa rtial, and relevant science on the effects of air pollution on health. To accomplish its mission, the institute • Identifies the highest-priority ar eas for health effects research; • Competitively funds and ov ersees research projects; • Provides intensive independent review of HEI-supported studies and related research;
• Integrates HEI’s research results with th ose of other institutions into broader evaluations; and

• Communicates the results of HEI resear ch 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. Freque ntly, other public and pr ivate 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 li terature 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 partners hip 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 result s 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 presenta tions to legislative bodi es and public agencies.

# ABOUT THIS REPORT

ix 

### Research Report 140, Extended Follow-Up and Spatial Analys is 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, result s, and conclusion s 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 fu ture research.

This report has gone through HEI’s rigorous re view process. When an HEI-funded study is completed, the investigators submit a draft final report presenting the ba ckground 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 th e review process, the investigators have an opportunity to exchange comments with the Re view 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, summar izes a research project funded by HEI and conducted by Dr. Dani el Krewski at the McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada. Research Report 140 c ontains 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

INTRODUCTIONThe 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 Nation al Ambient Air Quality Standard (NAAQS) for fine particulate air pollution in the United States. B ecause 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 He alth Effects Institute to conduct the Particle Epi demiology Reanalysis Project with the objective of independently and rigorously assessing the original data and findings. The results of the Reanalys is 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 Anal ysis of the ACS cohort using an additional 10 y ears of follow-up and exposure data. Recent advances in statistical modeling were incorporated in to these analyses. More recently, with HE I’s support, Dr. Daniel Krewski and colleague s have conducted an Extended Analysis of the same cohort. This research increases the fo llow-up period for the ACS cohort to 18 years (1982 to 2000)

— 11 years morethan the original study. The investigators have produced national estimates of the risks of death fromvarious causes and have extended the range of analyses to include refinements of statistical methods and incorporate sophisticat ed control of bias and confounding. SUMMARY
The cohort for the curre nt study consists of approximately 360,000 part icipants residing in areas of the country that have adequate monitoring information on levels of particulate matter with an aerodynamic diameter of 2.5 μ m or less (PM 2.5 ) for 1980 and about 500,000 partic ipants in areas with adequate information for 200 0. The causes of death obtained from death certificates during follow-upthat were analyzed included all causes, cardiopulmonary disease (CPD), is chemic 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 PM 2.5 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 tota l suspended particles, ozone (O 3 ), nitrogen dioxide, and sulfur dioxide (SO 2 ). 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 ca lculate 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 forclustering and ecologic covariates. Three main analyses were conducted: a Nationwide Analysis, IntraUrban Analyses in the Ne w York City (NYC) and Los Angeles (LA) regions, and an analysis designedto 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 PM 2.5 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- μ g/m 3 in PM 2.5 . When the random effects Cox model was used with added control for ecologic covariates, the effect estimates increased slightly and remained sign ificant; 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 O 3 levels (calculated from concentrations measured from April to September 1980) was small, but signifi cant, 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 PM 2.5 exposure on mortality.Results from the current study show a similar pattern, although with somewh at 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 PM 2.5 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 analys is and incorporated the44 individual-level covariates as well as the 7 ecologic covariates at th e 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 mo dels used to calculate associations between exposure and mortality included the same indi vidual-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 PM 2.5 monitors and the California Air Re sources Board’s database for 42 sites monitoring O 3 .

Despite the common methodologic basis for the NYC and LA Analyses, th e 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 μ g/m 3 increase in PM 2.5 ). These differences may arise from the range of exposures derived for cohort members residing in each city, the relative uniformity of PM 2.5 exposure in the NYC region, and the differences between the land-use variables selected as the most appropriatefor 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 whet her more recent exposures to air pollution are more or less strongly associated with mortality than exposu res further in the past. Exposure profiles for this analysis were constructed from average PM 2.5 and SO 2 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 cons idered 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 available3 

# 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 sm all and demonstrated nodefinitive 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 proportion al-hazards model used for the mortality analyses has two major limitations that the investigators addressed in innovative ways developed specifically fo r this study: confoundingby ecologic factors and spatial autocorrelation. Ecologic confounders are risk factors for mortality that are relevant at the neigh borhood level, rather thanthe individual level. In the current study, in contrast to the Reanalysis Pr oject, ecologic information was collected at the ZCA level as well as the citylevel, although not all ecologic covariates considered previously were includ ed in this analysis. Spatial autocorrelation arises from the way values for certain variables tend to be similar for people (or areas) that are geographica lly 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 assimilar 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 re sults were robust when adjusted for spatial autoco rrelation in the statistical models. In a recently published follow-on study of O 3 and respiratory outcomes in the ACS data, including the same indivi dual and ecologic covariates as the current study, Jerrett and associates foundno indication of important residual spatial autocorrelation of the association between O 3 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 fo cused the current study on an extensive exploration of spatial autocorrelation in a series of one-pollutant models. The Committee thought that the inclusio n of some two-pollutant analyses would have strengthened the study. The authors note, however, th at 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 combin es 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 interpretbiomedically. The report singled out the associations between PM 2.5 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 cat egories of cardiovascular disease, such as stroke and heart failure, presented alongside those for IHD.

The fundamental differen ce in exposure between the two Intra-Urban Analys es lies in the different relative influence of regi onal background concentrations of PM 2.5 . 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 succ essful in explaining a moderate percentage of variability (60 to 65 %) in PM 2.5 concentrations measur ed at the monitoring sites. The range of av erage annual monitored PM 2.5 concentrations considered in developing the models was not very different between LA (9.5

– 28 μ g/m 3 ) and NYC (10

– 20 μ g/m 3 ). However, the resulting ranges of exposure as signed by the LUR models in LA ( < 10

– > 125 μ g/m 3 ) and NYC (8

– 20 μ g/m 3 ), by comparison, suggest that levels of PM 2.5 are regionally determined in NYC a nd highly locally variable in LA.

The intra-urban results for the two cities were very different, with a stro ng positive and significant association between mortality from CPD and PM 2.5 in LA and no significant association in NYC. Both the LA and NYC results sh owed significant associations between PM 2.5 and mortality from IHD, consistent with the results of the nationwide study. The authors note that differen ces 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 individu al and ecologic variables in the Cox models was distributed relative to the varying PM 2.5 exposure levels in the two cities. The higher exposures in LA tended to occur in areas characterized by low socio economic status (and relatively high expected mort ality), whereas the higher exposures in NYC were gen erally found in areas of high socioeconomic status (and relatively low expected mortality).

The Committee noted that the inconclusive results from the NYC Analys is (aside from that for IHD) were probably due to too little variation in PM 2.5 exposure across the NYC area, owing to the regional nature of PM 2.5 exposure in the Northeastern United States. Rela tively 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 estimatesare 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 PM 2.5 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 Natio nwide Analysis because it used two distinct subcohorts of subjects from the main ACS cohort, rather th an the whole cohort as in the Nationwide Analysis. For each deceased ACS participant in each subcohort, time windows of exposure were calculate d as average exposures during successive five-year periods preceding the date of death.

The use of AIC to compar e models including different five-year window s of past exposure is broadly reasonable, since th e number of variables in the each model being compared was the same. The Committee was somewhat disappointed that theinvestigators did not present results for “multiwindow” models, in which the effects of exposure in one time window are cont rolled for the effects of exposure in another tim e 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 sm all differences in AIC resulting from exchanging exposure in one time window with another.

CONCLUSIONS This report represents a sound and innovative analysis of extended follo w-up data from an alreadyimportant 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 contro l for autocorrelation in a single model. Furthermore, they have added to the body of evidence linking PM 2.5 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 re garding lung cancer risk associated with long-term PM 2.5 exposure. Associations between mortality from CPD and long-term exposure to O 3 , first identified in this study in the standard Cox model results , have been confirmed and refined in a recently pu blished analysis of longterm O 3 exposure and pulmonary mortality. The LA Intra-Urban Analysis demonstrates that observed associations between mortality and PM 2.5 exposure may be higher than national estimates when variations in local exposures ar e estimated. When results from the NY C and LA Analyses are taken together, however, they underscore that citiesmay differ markedly in their local exposure situations, and in the relative importance of the contributions of local and regional sources of PM 2.5 . 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 Studie s, commented that no single study can be the basis for determining acausal relationship between air pollution and mortality. The Committee cons iders 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. Kr ewski 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 Analys is 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 Po pe 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, Fa culty of Medicine, University of Ottawa (D.K., R.T.B); Division of Environmental Health Sciences, School of Pu blic 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 furtherexamine 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-spon sored Reanalysis of the original ACS stud y data (the Particle Epidemiology Reanalysis Project). Specifically, we examined(1) how ecologic covariates at the community and neighborhood levels might confou nd 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 accoun t 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 Lo s Angeles and New York City regions; and (4) what exposu re time windows may be most critical to the air polluti on–mortality association.

The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 19 95]) included vital status datafor the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recen t exposure data from air pollution monitoring sites for the metropolitan areas.

In the Nationwide Analysis , the influence of ecologic covariate data (such as e ducation 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 ar ea (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 analys es 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 PM 2.5 (particles 2.5 μm or smaller in aerodynamic diameter), sulfate (SO 4 2

), and sulfur dioxide (SO 2 ), and the only outcome significantly associated with exposure to nitrogen dioxide (NO 2 ). When ecologic covariates were simultaneously included at both th e MSA and DIFF levels, the This Investigators’ Report is one part of Health Effects Institute Research Report 140, which also includes a Comm entary by the Health Review Committee and an HEI Statement about th e research project. Correspondence concerning the Investigators’ Report may be addressed to Dr. Daniel Krewski, McLaughlin Centre for Popul ation Health Risk Assessment, Room320, 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 beinferred. 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 vi ews or policies of these parties, and no endorsement by them should be inferred.
* A list of abbreviations and other term s 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 PM 2.5 exposure (average concentration for 1999–2000) increased by 7.5% and that associated with SO 4 2

exposure (average concentration for 19 90) increased by 12.8%. The two covariates found to exer t the greatest confounding influence on the PM 2.5 –mortality association were the percentage of the popula tion 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 ex posure 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 in cluded together, which suggests that partitioning the ecologi c 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 region s. The results of the Los Angeles spatial analysis, wh ere we found high exposure contrasts between ZCAs within the Los Angeles region, showed that air po llution–mortality risk s were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic h ealth effects associated with intra-urban gradients in exposure to PM 2.5 may be even larger between communities with in 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 dise ase (CPD), and lung cancer was not elevated. A positive association was seen for PM 2.5 exposure and IHD, which pr ovides 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 re sponsible for the increasedmortality associated with ambient air pollution, we constructed individual time-depen dent exposure profiles for particulate and gaseous air pollutants (PM 2.5 and SO 2 ) for a subset of the ACS CPS-II par ticipants for whom residencehistories were available. The relevance of the three exposure time windows we consid ered 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 PM 2.5 , 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 va lues among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO 2 were highest in the most recent time window (1 to 5 year s), although none of these HRs were significantly elevat ed. Identifying critical exposure time windows remains a ch allenge that warrants further work with other relevant data sets. This study provides addition al support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from ot her population-based studies, which collectively have strong ly supported the hypothesis that long-term exposure to PM 2.5 increases mortality in the general population. Future research using the extended Cox–Poisson random effects me thods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will pr ovide 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 su ggest 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 al l causes increased in association with an increase in the concentration of PM 2.5 .

Both studies came under in tense scrutiny in 1997 whenthe results were used by the U.S. Environmental Protection Agency (U.S . EPA) to support new National Ambient Air Quality Standards for PM 2.5 and to maintain the standards for PM 10 that were already in effect. The findings of these two studies were the subject of debate regarding the following factors: possi ble residual confounding byD. 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 statis tical models (Gamble 1998; Lipfert and Wyzga 1995). To address growing public controversy concerning the studies’ methods and their results, Harvard University an d the ACS requested that the Health Effects Institute organize an independent Reanalysis of these studies. Through a competitive process, a Reanalysis Team ledby Dr. Daniel Krewski of the McLaughlin Centre for Population Health Risk Assessment at the University of Ottawa was selected by an independ ent Expert Panel appointed by the HEI Board of Directors, with support from the U.S. EPA, industry, Congress, an d other stakeholders. The Reanalysis Project was ove rseen by the Expert Panel,which was chaired by Dr. Arthur Upton from the University of Medicine and Dentis try of New Jersey and former Director of the National Cancer Institute, with assistance by a broad-based Advisory Bo ard 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 independ ent 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 inde pendent 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 robustnessof the original analyses to alternate risk models and analytic methods.

In Phase I, the Reanalysis as sured 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 ot her 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 ai r pollution–mortality association by developing new methods of spatial analysis for cohort studies that involve both individual-level and ecologic covariates. Most of th e Phase II analysis used the standard Cox model, which as sumes that the probability of death is independent among su bjects. We challenged this assumption in a numb er of ways by introducing largely ad hoc statistical approaches th at were developed for this specific dataset. (In the cu rrent Extended Analysis, we have formalized these statis tical models that include extensions of the standard Cox model to include random effects at multiple levels of clustering, such as MSA andZCA. In addition, we have developed models and estimation methods to allow the rand om 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 PM 2.5 in that the risk declines as education attainment rises; (2) SO 2 may exert a more robust effect on mortality than SO 4 2

; (3) other possible ecologic confounders have no significant effect in models that control for spatial autoco rrelation; and (4) spatial risk models attenuate the air poll ution effect, both in terms of size and certainty.

The implications of the findi ngs for air quality risk management were significant and poin ted 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 spatia l structure of the data and the impact of spatial autocorrelation on estimates of risk associated with exposure to PM 2.5 , 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 pa rticulate air pollut ion 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 Reanalys is demonstrated that several ecologic covariates si gnificantly 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 e ffect that ecologic covariates exerted on the air pollution– mortality relationship in models that controlled for sp atial autocorrelation. For example, when SO 4 2

and SO 2 were included as ecologic variables in spatial regr ession models with PM 2.5 as the8 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

main exposure variable, they were the only ecologic covariates that showed a signifi cant 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 effect s 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; Cu rtis and Taket 1996; Macintyre and Ellaway 2000). Other methodologic limita tions in the Reanalysis Project probably also contribu ted to the unexpected lack ofstatistical effect when the ecologic covariates were incorporated in the models. At th e MSA scale of aggregation, for example, many ecologic covari ates may be too dissimilar across the city for a mean valu e to represent the socioeconomic or environmental pheno menon of interest (e.g., income level) without large m easurement error. A growing number of studies implicat e 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, refe rred to as the modifiable areal unit problem (MAUP), emphasizes the need for choosing the correct scale b ecause the size and boundaries of the zones influence the repo rted 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 lowe r the mean poverty level for the ecologic unit. (This is refe rred to as the zoning effect.) An observed spatial pattern might reflect the zone boundaries chosen for anal ysis 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 patter n 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 zo nes 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). (T his is referred to as the scale effect.) The scale eff ect also suggests that some changes in statistical resu lts 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 info rmation 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 sma llest 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 in vestigated whether disadvantaged and minority groups suffer greater pollution exposure than wealthier and majority groups, empirical evidence and compelling con ceptual 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 poll ution and health may be reduced or modified by the cont ext of ecolog ic covariatesmeasured 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 whet her these variables exert a significant influence and wo uld 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 communitie s the central monitor estimates that assign the same level of exposure to an entire MSA. Recent studies have recogn ized that exposure to air pollution may vary spatially wi thin 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 th at influence susceptibility to environmental exposures (Jerre tt et al. 2003). For example, residents of poorer neighborho ods may live closer to point sources of industrial polluta nts or roadways with higher traffic density (O’Neil et al. 2003). Health effects may behigher around such sources, and these effects are diminished when using av erage pollutant concen trations for the entire community.

Recent studies of PM 2.5 have shown that intra-urban exposure gradients can be associated with atherosclerosis (K ü nzli 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 studiesD. 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 po tential to supply accurate, small-area estimates of ai r pollutant concentrations without the data entry and mo netary 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 regressionis 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 NO 2 levels in San Diego, California. When the predicted co ncentrations 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 NO 2 levels in San Diego. LUR mo dels used to predict NO 2 levels using traffic and other variables in Montr é al and in several European cities also produced accurate predictions (Jerrett et al. 2005b).

In contrast to localized gases such as NO 2 , PM mass has a significant regional component that includes smaller contributions from local sour ces (Bari et al. 2003). This complicates the estimation of intra-urban exposure with LUR. LUR models have been used with some success to predict PM 2.5 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 PM 2.5 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 SO 4 2

exposure, the methods used were criticized on a number of grounds (HEI Health Review Committee 2000). In particul ar, all methods assumed that the relationship between ex posure 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. Ev en after applying this filter, though, the relationship between air pollution and mortality may still differ dependin g on the location within the United States (known as no nstationarity behavior over space). A more flexible modeling strategy was needed to address such nonstationarity.

Furthermore, reliance on one autocorrelation parameter may have effectively removed va riables that operate at the broad regional scale, such as SO 4 2

concentrations, but it may not have controlled autocorrelation from pollutants that have a more spatially co ncentrated or local distribution, such as SO 2 (HEI Health Revi ew Committee 2000; Krewski et al. 2000a,b). The in ability 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 SO 2 exposure changed less than the effect of SO 4 2

exposure when spatial autocorrelation and the ecologic covariateswere 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 SO 2 and of SO 4 2

exposure are equally robust to adjustment; or such models may further confirm the results from the Reanalysis. In either case, the implica tions for policy formulation and regulatory intervention ar e considerable. In the Phase II Reanalysis we developed an approach for Cox models with two levels of nested rando m 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, ou r random effects Cox model needed to be expanded to fu lly describe complex spatial patterns in the ACS data in order to accommodate more than two levels of geographi c 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 us ing 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 PM 2.5 data that had been collected since 1999 as a result of the NAAQS for PM 2.5 enacted in 1997. Recent ad vances in statistical modeling were incorporated in th e analyses, especially the useof random effects (relaxation of the assumption of independent observations) and cont rol for spatial autocorrelation. Results from that Updated An alysis provided the strongest evidence to date that long-term exposure to PM 2.5 air10 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

pollution common to many metropolitan areas is an important risk factor for d eath from lung cancer and CPD. For each 10- μ g/m 3 increase in long-term average PM 2.5 ambient concentratio ns, 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 auto correlation was found in the survival data after PM 2.5 air pollution and the various individual risk factors were cont rolled. 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 epidemio logic data about possible mechanistic pathways of diseas e. 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. K ü nzli and associates (2005) have published the first epidemiologic evidence to su pport the suggestion that the systemic effects of PM expo sure result in a chronic vascular response.

EXPOSURE TIME WINDOWS Although the Harvard Six Cities Study and the ACSStudy have demonstrated an association between longterm exposure to PM air pollu tion and mortality (Dockery et al. 1993, Pope et al. 1995, 2002), none of those studies provided an indication of wh ether there may be a critical time period of exposure resp onsible for the observed association (Goddard et al. 1995). Investigations by Zeger and associates (1999) and by Schw artz (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 (V illeneuve 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 temp oral patterns of exposure at the individual level. Given the regulatory importance of the results, further work to deve lop individual time-dependent exposure profiles for ACS coho rt 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 CP S-II database. For the original study (Pope et al. 1995) and the Reanalysis (Krewski et al. 2000a,b), vital status da ta were only available for approximately 7 years of fo llow-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, morerecent exposure data were compiled based on mean concentrations of air pollutants fr om various monitoring sites for the metropolitan areas (Krewski et al. 2000a,b).

The Phase III Extended Analys is 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 scaleswould provide greater understanding of the potential confounding and modifying effects of these variables. Although the Reanalysis sugges ted that these variables are unlikely to exert a significan t confounding influence when the analysis is also controlle d 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) op erational variables that represent the separate and combined effects of many ecologic confounders at once. (This ai m 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 inde pendent. However, in Phase IIof the Reanalysis, spatial autocorrelation (data for neighboring ACS cohort participants are not independent due to complex spatial patterns) sh owed that this assumption was not true. Ignoring such spatial autocorrelation —D. Krewski et al.

11applying 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 spatialpatterns in the ACS data and may lead to improved estimates of risk. (This aim was pursued in the Nationwide Analysis.) 3. What critical exposure tim e windows affect the association between air pollution and mortality?The overall objective was to develop individual timedependent exposure profiles fo r a subset of the ACS cohort in order to determine whic h exposure time windows may be most critical to the association between air pollution and mortality from all cause s, 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 Stud y (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 (Lal l et al. 2004). (This aim was pursued in the Critical Exposu re 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) demonstratedthat 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 morta lity risk of over 18% across the gradient of plausible modeled exposures to NO 2 . These and similar findings summarized elsewhere (Jerrett et al. 2005b) have demonstrated a ne ed 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 th e spatial distribution of ACS cohort members within each MS A. In the curr ent analyses, we determined ecologic covari ate values at the ZCA level, which is much smaller geographically than the MSA and thus may be more representa tive 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 th e 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 databa se, an ongoing prospective mortality study of approx imately 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 andwho 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 hist ory and alcohol use, occupational exposures, and level of education. Participants resided in all 50 states, the District of Columbia, and Puerto Rico. Fo r all analyses, however, the cohort has been restricted to include only those whoresided in U.S. metropolitan areas within the 48 contiguous states (including the Dist rict of Columbia) for which12 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

air pollution data were availabl e. 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 partic ipants known to have died. This Extended Analysis, with an additional 11 years of follow-up, contributes substa ntially more data on deaths and thereby enhance the statis tical 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).
• PM 2.5 (1979–1983) average concentrations from the Inhalable Particle Mon itoring Network (IPMN) between 1979 and 1983;
• PM 2.5 (1999–2000) average concentrations from theAerometric Information Retrieval System (AIRS) network from 1999 to 2000;
• PM 15 (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;
• SO 4 2

(1980–1981) concentratio ns from the IPMN and NAD (adjusted for a sampling artifact) for 1980 and 1981;
• SO 4 2

(1990) concentrations co mputed by part of our team at NYU for 1990;
• SO 2 (1980) concentrations from AIRS for 1980;
• NO 2 (1980) concentrations from AIRS for 1980; • CO (1980) concentrations from AIRS for 1980;
• O 3 (1980) concentrations fr om AIRS for 1980; and
• O 3 (1980 summer) concentr ations from AIRS for April–September, 1980. Having estimates of PM 2.5 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 O 3 (ozone) levels were estimated because O 3 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 O 3 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. Th us all subjects residing in an MSA were assigned th e same exposure value.

Ecologic Covariates

Data Collectio n and Assembly A major component of this research dealt with improv ing analytic control of confounding variables over time and across space, particularly at the intra-urban scale. We obtained info rmation about ecologic covariates at the ne ighborhood scale for approximately 12,000 ZCAs listed in the 1980 U.S. Census database to amass one of the lar gest 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 Update d 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 th e 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 pollutio n–mortality association if left uncontrolled. Moreover, because the lowest level of geographic identity available for ACS respondents was the Zip Code of residence, we ha d to rely on aggregating data at the ZCA level for neighbor hood analyses. None of the commercial vendors who proc ess 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 th at would convert the flattext USCB file into a relational database. The program was developed using Microsoft Visu al 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 Mi crosoft Access database. From this convertedUSCB.file , we extracted data for most of the ecologic covariates for appr oximately 12,000 ZCAs thatD. Krewski et al.

13 cover the 156 MSAs for which we had both pollution and ACS data.

Extracting the ecologic data presented considerablechallenges. 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 deliv ering mail, not for collecting and analyzing the socioeconomi c data the census requires. Zip Codes lack de finitive boundaries and change frequently at the discretion of postal officials. They do not conform to boundaries of st andard 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 th e 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 Ge neral, 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 co lleagues at George Washington University who have direct access to the USCB.

Various companies have creat ed maps by interpolating boundaries between Zip Codes. However, the companies we contacted could not provi de what we needed: GDT(Lebanon, NH) did not have data available for 1980 (personal communication with Norm Finkelstein, March, 2003); and GeoLytics Inc. (Eas t Brunswick, NJ) produced a 1980 Census CD but without th e 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 Un iversity and the University of Waterloo (both in Ontario, Canada) had nothing we could use. (McMaster Univer sity does have a 1980 U.S. Gazetteer book, but the reso lution is not very good.)

Despite so many obstacles, out of about 12,000 ZipCodes 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 partic ipants 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 im pact 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 th ese 98 Zip Codes would affect the validity of further analyses based on converted USCB .file , we used the USPS “Zip Co de 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 ZCA s are randomly distributed, excluding any or all of the co hort members who live in the 98 missing Zip Codes should not adversely affect our results because we are alread y 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 anal yses, we did not lose many ACS subjects. A further complication in r econciling 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 ther e is no mail delivery and they collect mail at a post office in a nearby town. The boundaries of such PO Box Zip Co des (about 5,000 of them) are not formally defined. Some urban residents pick up mail at a PO Box, perhaps n ear 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 variab les 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 collecte d for the United States Censusand 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 homeconstruction; 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 educationNationwide 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 whiteLos 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 eq uality 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 adu lts 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 ZCA s 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 we re 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 ad justed for potentially confounding risk factors. We included data from the CPS-II cohort questionnaires for 44 individual-level covariates:
• eight variables to repres ent active smoking habits including nonlinear terms for cigarettes per day and number of years smoked;
• seven variables to charac terize former smoking habits;D. Krewski et al.

15 • one variable for expo sure 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 subject s 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. Furtherm ore, subjects who live close together typically share simila r longevity patterns. Lack ofstatistical control for these factors can bias both the estimate of air pollutio n’s effect on health and the associated standard errors.

To characterize the statistical error structure of survivaldata, 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 incorpor ated a spatial autocorrelation structure such that the correlation in survival after adjusting for known risk fact ors 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 surv ival 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 surviv al 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. 20 00a,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 i th cluster, there are J i spatially correlated subclusters indexed by ( i,j )

. Specifically, we assume that the cluster-level random effects U 1 , ..., U m 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 ρ 1 can be estimated if the distances are integers.

We further assume that, given the cluster-level random effects U
* = u * = ( u 1 , ..., u m ), the subcluster-level random effects U 11 , ..., U mJm are positive and sp atially 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 U ij , given is assumed to depend on u i only.

Furthermore, within each subcluster ( i,j ) there are n ij 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 k th subject within the j th subcluster within the i th cluster. The ( i,j,k ) and s notations do not imply any fixed relationship between strata and clusters; fo r example, males and females 2 ()

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

E( | ) = and
Cov( | ) = ( ) *
2 2 [( UU U

U U U si ij i

st ij r s

, , * ,   t ij ),( )]

,

= , * * U u16 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

could correspond to different strata, whereas clusters and subclusters could be commu nities and families that include both males and female s. 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 ) u ij exp( T x

ijk ( s ) ). (3)

The survival times, either obs erved 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 = ( x 111 (1)

, ..., x ( a ) mJ m n mJ m ) 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 J i = 1 for all i. Our assumptions (1) and (2) on random effects concern the spatial depend ence and the first two moments only. This is desirable since the mechanism by which the unobserved random effects were ge nerated is usually not completely known (Ma et al. 2003).

Auxiliary Random Ef fects 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 s th stratum, with m sh ( s = 1, ..., a ; h = 1, ..., q s ) indicating the multiplicity of failures occurring at time

sh ( s = 1, ..., ; h = 1, ..., q s ). The risk set at time sh is a subset of stratum s: = [( i,j,k ) : t ijk  sh ], where t ijk is the observed survival time for individual ( i,j,k ) from the

s th stratum. In addition, let Y ijk,h ( s ) be 1 if a failure occurs for individual ( i,j,k ) from the s th stratum at time sh and 0 otherwise. Let Y and U denote the vectors of the Y ijk,h ( s ) and the random effects U i and U ij , 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 effect s 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 parame ters of interest. This demonstrates that the maximum joint Poisson likelihood estimators for the regression parameter vector from equation (6) are the maximum joint partia l likelihood estimators for the regression parameter vector from equation (4). In addition, the nonparametric estimator of the cumulative baseline hazard function remain s the same as given in Ma and associates (2003).

Orthodox, Best-Linear, Un biased Predictor Approach

Prediction of Random Effects As in Ma and colleagues (2003), we can predict the ra ndom effects by the following orthodox, best-linear, unb iased predictor of U given Y

.

Û = E( U ) + Cov( U,Y ) Cov

1 ( Y,Y ) ( Y

E[ Y ]), (7)

where Cov( Y
, Y

) denotes the marginal covariance of Y instead of the cond itional 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 sq s

R 

## ( ) sh

p

ijk ij Y ijk T Y Y u

u x sh i i jk h 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 

## ( ) ( ) i jk h s

sh u x ij sh ijk T ijk h q s ,

exp exp

, , ( )

+ 

# ( ) 

# { } ( ) ∈ R 

## ( ) = 

## ∑ ∏

.

1 s a

= 

## ∏

1

ˆ , ˆ

, exp ! ˆ ; Y U m m

m sh m sh
sh h q

s a
p sh s 

# ( )
= − 

## ( ) ⎧ ⎨ ⎪ ⎩ ⎪ ⎫ ⎬ ⎪ ⎭ ⎪

= = 

## ∏ ∏

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 ma trix 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 ) B T and Cov( Y,Y ) = diag(E[ Y ]) + B Cov( U,U ) B T

, (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 μ ijk,h ( s ) = exp( sh + T x

ijk ( s ) ) at the positions correspondin g to those of Cov( Y ijk,h ( s )

, U i ) 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

Cov 1 ( Y,Y ) = ( A + BDB T )

1

= A 1

A 1 B ( B T A 1 B + D 1 )

1 B T A 1

,

where A 1 = diag(E[ Y ])

1 and D = Cov( U,U ) is generally small enough to be inverted nu merically. 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 ofcluster-level random effects. (See Appendix B for the algorithm description and Append ix 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

) Cov

1 ( Y ) Cov( Y
, U

). In addition, we have the fo llowing two desirable orthogonality properties concerning th e 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 (2 003), we can estimate the regression parameters throug h an optimal estimating function. Differentiating the joint log-likelihood of the auxiliary model for the data and ra ndom 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 associat es (2003), we have the following global matrix expression for estimating function  (  ) because their proof holds re gardless of the covariance structure assumed for random effects:

 (  ) = X T diag(E[ Y ]) Cov 1 ( Y
, Y

) ( Y

Y )

. (10)

Similarly, we have

S (  ) =

V (  )
= 0

X T diag(E[ Y ]) Cov 1 ( Y
, Y

) diag(E[ Y ]) X

. (11)

With an appropriate partition of matrix

C

= X T diag(E[ Y ]) Cov 1 ( Y
, Y

) = ( C 1 , ..., C m ),

it follows from (10) that   



## ( ) = − 

## () () 

# { } () () 

### ( ) ∈ R 

## ( ) = 

## ∑

sh Y U ijk h s ij ijk h s ijk h , ,

, , ˆ

1 q

s a s 

## ∑ ∑

= 1 .

S E

V E T

T

  

    

 

## ( ) = ∂ 

## ( ) ∂ ⎧ ⎨ ⎪ ⎩ ⎪ ⎫ ⎬ ⎪ ⎭ ⎪ 

## ( ) = 

## () () 

# { } ,

.

  



## ( ) = − 

## ( ) = 

## ( )

= = 

## ∑ ∑ C Y Yi

i i i m
i i m E[ , ]

1 118 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

where the unbiased estimating function  i (  ) = C i ( Y i E[ Y i ]) corresponds to the i th cluster. The estimating function can easily be shown to be optimal in the sense that it attains the minimum asymptotic covariancefor 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 co mponent-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
, b T is asymptotically normal with asymptotic mean b T  and asymptotic variance given by

b T S 1 (  ) b as m

.A modification of the Newton scoring algorithm introduced by J ø rgensen and colleagues (1996) can be used to solve this estimating eq uation: In the modified algorithm, the derivative of is replaced by its expectation S ( γ ) and produces th e following updated value for γ :

γ * = γ

S 1 ( 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 fo r 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 notaffected by variability in the dispersion parameter estimators because our estimation function is insensitive to dispersion parameters (J ø rgensen and Knudsen, 2004). The computational procedures regarding initial values and iteration steps ar e exactly as outlined in Ma and associates (2003).

RESULTS

Distributive statistics fo r the pollutants are given in Table 1 . Of special note is the decline in PM 2.5 concentra tions over 20 years. The mean PM 2.5 measured in 58 MSAs between 1979 and 1983 was 21.2 μ g/m 3 ; when measured in 116 MSAs between 1999 and 2000, it had declined to
14.0 μ g/m 3

. 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 assu mes observations are independent. We then selected only those relationships between apollutant and a cause of death that were statistically significant ( P < 0.05; 95% CIs) and co nducted further analyses using our random effects Cox mo del. We chose this two-step approach because the standard Cox model can be performed in a few minutes, whereas ou r random effects Cox model with two cluster levels requir es 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 PM 2.5 mass and both measures of SO 4 2

were observed for all, CP D, and IHD causes of death (Table 3). Significant but weaker associations were observedwith these same causes of death for the particle mass measurements of larger si zes of particles (PM 15 and TSP) and for the concentration of SO 2 . Lung cancer deaths were mo re strongly associated with both measures of PM 2.5 mass than with other pollutants.All other causes of death tended to be negatively correlated with air pollutants except for both measures of SO 4 2

and SO 2 , which all displayed a positive association.

Annual average O 3 concentrations were not clearlylinked with mortality; but a positive and statistically significant association was observed between O 3 measured in the April-to-September period and both all and CPD causes of death; no associa tion was observed between O 3 and IHD deaths. Neither NO 2 nor CO was strongly associated with any cause-of-death category. Overall, most ecologic covariat es were individually associated with mortality. Increase s in the percentage of homes with air conditioning within th e ZCA were negatively associated with mortality for all causes of death examined except lung cancer. Unem ployment and poverty levels were positively associated with all five causes of death; household income was negatively associated with all five; and income disparity was no t associated with any cause.

The correlations between the seven ecologic covariates and air pollutants, excep t for CO and annual O 3 (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 Level a

Pollutant Monitoring Period b MSAs / Participants ( n ) Mean Variance Percentiles

0 5 10 25 50 75 90 95 100

PM 2.5 (1979–1983)

(μg/m 3 ) 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

PM 2.5 (1999–2000)

(μg/m 3 ) 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

SO 4 2

(1980–1981)

(μg/m 3 ) 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

SO 4 2

(1990) (μg/m 3 ) 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

SO 2 (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

PM 15 (1979–1983)

(μg/m 3 ) 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/m 3 ) 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

O 3 (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

O 3 (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

NO 2 (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 aver aging 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 PM 2.5 concentrations is shown.

b Dates are the monitoring years from which data averages were derived. Both annual and summer data for O 3 are given because O 3 concentrations are higher

in warm seasons; people tend to be more exposed because they spend more time outd oors or with windows open. Also, many cities monitor O 3 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 41.420 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 po llutants and ecologic covariates vary considerably. For example, for grade 12 education, the correlation with both measures of PM 2.5 mass and with both measures of SO 4 2

is negative at both the ZCA and MSA levels, whereas the correlation is positive with PM 15 and TSP. Although the co rrelations 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 beca use 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 St andard Cox Model a

Covariate MSA / Participants ( n ) Incremental Change b All Causes Cardiopulmonary IHD Lung Cancer All Other Causes

PM 2.5 (1979–1983) 58 351,338 10 μg/m 3 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) PM 2.5 (1999–2000) 116 499,968 10 μg/m 3 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) SO 4 2

(1980) 147 572,312 5 μg/m 3 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) SO 4 2

(1990) 52 268,336 5 μg/m 3 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)

SO 2 (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) PM 15 (1979–1983) 57 345,824 15 μg/m 3 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/m 3 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)

O 3 (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) O 3 (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) NO 2 (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), gende r, 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 app ear 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 correlatio ns between ecologic covariates were observed for the th ree levels. For example, the correlation between grade 12 education and unemployment is near zero at th e 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 correl ation (42%) with household income at the ZCA level, bu t 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 pollu tant 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 estimate s of exposure to air pollution. All combinations of a pollut ant 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: with out 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 beequally sensitive to inclusion of the seven ecologic covariates regardless of their leve l (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: PM 15 and TSP with CPD deaths; SO 4 2

(both 1980 and 1990) with other causes of death; and summertime O 3 with all and CPD causes of death.

In Table 7 , the HRs for PM 2.5 (1999–2000) are used to examine in more detail the se nsitivity of the association between air pollution and mo rtality. HRs related to PM 2.5 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 th e MSA Scale and Ecologic Covariates Enumerated at the ZCA Scale and Aver aged for Each MSA a

Ecologic Covariate Geographic Level PM 2.5 (1979– 1983) PM 2.5 (1999– 2000) SO 4 2

(1980) SO 4 2

(1990) SO 2 (1980) PM 15 (1979– 1983) TSP (1980) O 3 (Summer

1980) NO 2 (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 fo r 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 studycollected 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, si ngle 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 Levels a
Ecologic Covariate Geographic Level b Air Conditioning Grade 12 Education Not white Unemployment Household 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 covari ate 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 Ad justment for All Seven Ecologic Covariates

Cause of Death Ecologic Covariate Adjustment a

None ZCA MSA MSA & DIFF

PM 2.5 (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)

PM 2.5 (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 (0.9 52–1.006) 0.975 (0.946–1.004) 0.979 (0.950–1.008)

SO 4 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)

SO 4 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)

SO 2 (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 (1.0 17–1.034) 1.019 (1.009–1.028) 1.019 (1.010–1.029)

PM 15 (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 (1.0 02–1.018) 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)

O 3 (Summer 1980) All causes 1.016 (1.008–1.024) 1.014 (1.006–1.023) 1.006 (0.9 98–1.015) 1.008 (0.999–1.017) CPD 1.028 (1.016–1.041) 1.027 (1.014–1.040) 1.015 (1.0 02–1.028) 1.016 (1.002–1.029)

NO 2 (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 (0.9 76–0.997) 0.990 (0.978–1.002) 0.991 (0.979–1.003)

a Determined with the standard Cox model, which included the 44 i ndividual-level covariat es. The models with no adjustment for e cologic 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 lev els 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) ha d shown that the subjects’ level of education modified the effect of PM 2.5 exposure on mortality. To explore this further, we stratified educational status based on the individual- level data gathered at ACS enrollment: low education (l ess than grade 12), medium education (grade 12 completed), and high education (some education beyond grade 12 completed). Models included 42 of the individual-level covari ates used in other analyses (two variables that represente d education attainment were removed) with and without the seven ecologic covariates. Results are presented in Table 8

. For all-cause and CPD mort ality, we observed a slight effect modification by level of education, although in the case of CPD, the effect modification was confounded by including the ecolo gic covariates.

For lung cancer deaths, the analyses with high education showed the lowest effects of PM 2.5 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 pos itive modification effect; the largest effects were observed in the high education stratum. This pattern was co nsistent between results from models both with and withou t 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 an d 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 mo del specification using the ACS data with follow-up from 1982 to 2000. We selected PM 2.5 (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 th e sensitivity of an air pollution–mortality association to the model specification.

The spatial structure of th e random effects Cox model was specified by a nearest-neigh bor 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 poin t inside the polygon is closer Table 7. HRs for All and IHD Causes of Death Associated with a 10-μg/m 3 Change in PM 2.5 (1999–2000) from the Standard Cox Model Unadjusted or Adju sted for a Single Ecologic Covariate a

Ecologic Covariate Adjustment All Causes IHD

PM 2.5 (1999–2000) Ecologic Covariate b PM 2.5 (1999–2000) Ecologic Covariate b

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 (1.00 6–1.013) 1.158 (1.115–1.203) 0.997 (0.989–1.005) Unemployment (%) 1.036 (1.018–1.055) 1.040 (1.0 32–1.049) 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 (1.02 0–1.035) 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 haz ard function was stratified by

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

b HRs for the ecologic covariate itself when in the model with PM 2.5 .D. Krewski et al.

25 to the geographic center (centroi d) of the ZCA than to the centroid of any other ZCA. ZCA polygons were joined to create each MSA polygon; foll owing this pattern, the 48 contiguous United States we re covered by polygons. The random effects associated with two MSAs (or ZCAs) are assumed to have a common co rrelation 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- μ g/m 3 change in PM 2.5 (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 ecologiccovariates defined at only the ZCA level, and (c) adjustment for the covariates defi ned 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 increa sed the estimate of the PM 2.5 (1999–2000) HR, with or with out adjustment for the ecologic covariates. This is likel y 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’ in formation 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 give n less weight in the random effects model than in the st andard Cox model in which responses are assumed to be statistically independent. An increase in the HR using the random effects model impliesthat the larger MSAs did not clearly follow our model specification, particularly the as sumptions about there being a log-linear relationship between mortality and the associated exposure to PM 2.5 (1999–2000). Thus they are given less weight even though their sample size may be larger.

The inclusion of additional va riance due to clustering of survival experience at the MSA level also inflates the uncertainty in the PM 2.5 (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 cause s of death, but this pattern is not replicated for IHD deaths.

Inclusion of the seven ecologi c covariates defined at the ZCA level reduced both the MSA and ZCA random effects variances. However, a furt her 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 PM 2.5 (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 PM 2.5 (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 resultswith 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- μ g/m 3 Change in PM 2.5 (1999–2000) from the Standard Cox Model by Cause of Death a

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 covariat es 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 fo r the state specification thanfor the MSA specification. We postulate that state is a natural cluster definition because many health care programs and resources that influence h ealth, and thus longevity, are administered at the state level.

PM 2.5 data for 1999–2000 were avai lable for 44 states and 116 MSAs. We included the 44 individual-level covariates in random effects Cox mode ls with adjustment for the seven ecologic covariates defined at the MSA & DIFF levels together. Including bo th state and MSA-within-state clusters increased the PM 2.5 (1999–2000) HRs compared with results from the standard Cox model ( Table 10 , top row under each cause of deat h) and also increased the uncertainty in the estimates, as evidenced by the much wider CIs. Including spatial au tocorrelation at both cluster levels reduced the HRs compared with a model that included both state and MSA ra ndom effects but no spatial autocorrelation. Similar patterns were observed with and without adjustment fo r the ecologic covariates and for both all causes of death and IHD de aths (results not shown).

ALTERNATIVE FORMULATION OF THE CONCENTRATION–RE SPONSE FUNCTION

We investigated the form of the concentration–response function between PM 2.5 (1999–2000) and selected causes of death. Our alternative formulation of the function is the natural logarithm of concentra tion. 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 Death a
Ecologic Covariate Adjustment HR for a 10-μg/m 3 Change in PM 2.5 (1999–2000) MSA Variance (  10 3 ) MSA Autocorrelation ZCA Variance (  10 3 ) 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 (1.03 2–1.111) 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 (1.15 6–1.409) 9.79 0.36 10.06 0.27

a Model included 44 individual-level covari ates. 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. HR s are followed by 95% confidence intervals.D. Krewski et al.

27 flexible mathematical form to relate exposure to survival within the proportio nal-hazards family of models. Here we have

where the unknown parameter γ is a scalar. If  < 1, the relationship between PM 2.5 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 fu nction 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 th at the logarithm of PM may be a slightly better predictor of risk than the linear form. The HRs associated with a 10- μ g/m 3 change in PM 2.5 (1999–2000) concentrations based on linear a nd log-linear functions of PM are given in Table 11 for selected causes of death with and without adju stment for the seven ecologic covariates defined at both the MSA & DIFF levels together. Two separate spans of a 10- μ g/m 3 change in PM 2.5 are examined for the loga rithmic formulation: from
5 to 15 μ g/m 3 and from 10 to 20 μ g/m 3 since the HR based on the logarithmic function varies with concentration. A concentration of 5 μ g/m 3 represents the minimum of the distribution of PM 2.5 (1999–2000) and a concentration of
20 μ g/m 3 represents the 95th pe rcentile 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 PM 2.5 , the logarithmic function was a slightly better predictor of the variation in su rvival 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

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

. = + 0 2 5 PM

 e re e

t tU X ( ) ( ) exp( ) ( ) ( )

. = ∗ 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 Death a

Without or With Spatial Autocorrelation HR for a 10-μg/m 3 Change in PM 2.5

(1999–2000) State Variance (  10 3 ) State Autocorrelation MSA Variance (  10 3 ) 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 0.39 b 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 0.39 b 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 co variates 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 accepta ble values, which indicates that the mod el 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 μ g/m 3 compared with the linear formulation; and lower values were observed for th e difference between 10 and
20 μ g/m 3 except for other causes of death in which the logarithmic formulation yielded larger HRs compared with the linear formulation.
The choice of functional re lationship between PM exposure and mortality can make a considerable difference in the predicted risk at lower concentrations. For example, the HR for lung can cer adjusted for the ecologic covariates based on the linear formula tion is 1.142 (95% CI, 1.057– 1.234), whereas the HR base d 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 eco logic covariates confound the relationship between pa rticulate 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 Pr oject 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). Ne vertheless, unresolved questions remained about the s cale and the construct validity of the variables used. Theref ore, 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 Ca uses of Death Associated with a 10- μ g/m 3 Change in PM 2.5 (1999–2000) Concentrations Based on Inclusion of Linear PM 2.5 or log(PM 2.5 ) in the Random Effects Cox Model with Clustering at the MSA Level a

Ecologic Covariate Adjustment Linear PM 2.5 (1999–2000) Log (PM 2.5 [1999–2000])

HR for 10-μg/m 3 Change MSA Variance (  10 3 ) HR for Change from 5 to 15 μg/m 3 HR for Change from 10 to 20 μg/m 3 MSA Variance (  10 3 )

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 1.12 8 (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 1.20 8 (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 1.48 4 (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 1.02 6 (0.970–1.085) 1.016 (0.981–1.053) 1.39

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

levels of data were included in the model together. The base line hazard function was stratified by age (1-year groupings), gend er, and race. HRs are followed by 95% confidence intervals. Bolded values refer to text.D. Krewski et al.

29 Results from the CPS-II coho rt with an additional eleven years of follow-up in this Ex tended Analysis revealed positive and significant associations between a 10- μ g/m 3 change in PM 2.5 mass or a 5- μ g/m 3 in SO 4 2

mass (with the exception of SO 4 2

[1990]) and all-cause, CPD, IHD, and lung cancer deaths. Particles of larger size (PM 15 and TSP) were associated with positive, although smaller, increases in risk of all-cause, IHD, and CPD deaths. Mortality from IHD compared with other cause s 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; K ü nzli et al.2005). Further investigations to examine the cardiovascular health effects of particulate air pollution are critical to understanding the natu re of the association. Fewer significant associations were observed with the gaseous pollutants. SO 2 , 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 associatio ns observed with particles. Summertime O 3 was related with all-cause and CPD mortality in this study; this relationship was statistically significant in the Reanalysis (r elative 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 (P ope et al. 2002) or with the Reanalysis (Krewski et al. 2000a,b), in which differentscales or levels of pollutant increases were used to calculate relative risks. This Expanded Analysis evaluated the level of risk associated with a 10- μ g/m 3 increase in PM 2.5 and a 5- μ g/m 3 increase in SO 4 2

. The Reanalysis evaluated
a 24.5- μ g/m 3 increase in PM 2.5 and a 19.9- μ g/m 3 increase in SO 4 2

(those values were equal to the difference in mean concentrations between the most- and least-polluted cities from the Harvard Six Ci ties 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 Analys is) (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

- μ g/m 3 increase in the average PM 2.5 concentration for 1979–1983 in 58 MSAs, and for 1999–2000 in 116 MSAs, and controlled for the 44 individual-level covariat es 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 thre e follow-up time periods, deaths from all caus es increased to 90,783 in the 58 MSAs and 128,954 in the 116 MSAs; and deaths from IHD increased to 20,651 in th e 58 MSAs and 29,989 in the 116 MSAs (see Table 32). For the PM 2.5 (1979–1983) sampling period (58 MSAs), we observed a small decrease in the HR estimate for deaths fr om 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 deat hs (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 ha s to have increased over time. Also, PM 2.5 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 eval uated 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 associ ated 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 w ith mortality in the ACS CPSII cohort. This is surprising; we would have expectedincome disparity to be positively associated with mortality, as in some previous studies (Cooper et al. 2001; Ram 2005; Singh and Siahpu sh 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 covariat es (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 patte rns of mortality and ecologically defined sociodemographic factors could change30 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 confound ing 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 co ncentrations of certain specific air pollutants (see Table 4). Having less than a grade 12 education was most frequen tly strongly correlated with air pollutant indices — negativ ely correlated with concentrations of PM 2.5 and SO 4 2

, but positively correlated with particles of larger size. Conv ersely, percentage of the population that is not white and median household income was positively correlated with PM 2.5 mass. Correlations between ecologic covariates varied according to the unit of aggrega tion (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 sm allest 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; ther efore, caution is required in interpreting models that includ e all of the ecologic covariates simultaneously due to th e possible resulting statistical instability.
Since variation in survival between subjects can be explained, in part, by ecolog ic covariates that vary both between and within MSAs, we conducted analyses that adjusted for the ecologic covari ates at both the MSA & DIFF levels together. We hoped to account for sociodemographicrisks more completely and thus yield less biased risk estimates of exposure to air pollu tion. In nearly all models that adjusted for the seven ecolog ic 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 obse rved for morta lity from IHD associated with PM 2.5 (1999–2000) and SO 4 2

(1990); the magnitude of the inflation of HR was 7.5% and 12.8%, respectively; this model incl uded 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 PM 2.5 and mortality from a ll causes and from IHD, we found that each covariat e independently increased the strength of the association (except for percentage who are not white with all-cause mort ality). 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) fo r deaths from IHD (Table 7). Household income was positively associated with exposure to PM 2.5 (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 in fluence on HR estimates. The random effects Cox model was adjusted for education attainment at the individual le vel. This anal ysis, therefore, suggests that neighborhood education attainment measured at the ZCA level exerts additional influence on the air pollution–mortality associ ation beyond that of individual-level education attainment.
ZCA-level median househol d income and grade 12 education, therefore, represent the most important sociodemographic ecologic factors that may confound the PM 2.5 – 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 like ly 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 riskvariable in the model. Ecologic confounders and the pollutants share some of the mode l variance, and their common variability also partially overla ps with the variability of the mortality outcome. The part of the variance shared by the ecologic and pollutant variable s 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, th e 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 (K rewski et al. 2000a,b) suggested that SO 2 may exert a more robust effect on mortality than SO 4 2

. In the current Extended Analysis, includingecologic covariates at multiple scales exerted little influence on HR estimates for the association between SO 2 and all-cause and cause-specific mortality. An exception was for IHD mortality, in which including ecologic covariatesD. Krewski et al.

31 at the MSA & DIFF levels to gether 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 PM 2.5 (1979–1983 and 1999–2000), SO 4 2

(1980–1981 and 1990), and SO 2 exposure, and was the only outcome significan tly associated with NO 2 exposure. IHD, a significant contri butor 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 evalua ted here. The HR of IHD mortality associated with air po llution exposure remained elevated even after adjustment for a variety of individuallevel and ecologic covariates. The fact that IHD mortality was the outcome most influen ced by the ecologic covariates suggests the importance ofcontrolling 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 le vels, compared with the MSA & DIFF levels, did not substantially alter results of the air pollution–mortality associatio n 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 mort ality associated with each 10-
μ g/m 3 change in PM 2.5 (as measured in 1999–2000) was approximately 20% higher for participants who completed only grade 12 education co mpared 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 (Kre wski 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- μ g/m 3 change in PM 2.5 among those who completed less than high sc hool 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 PM 2.5 exposure associated with declining education attainme nt represented a key finding of the Reanalysis. In the Updated Analysis, Pope and coworkers (2002) al so reported a similar trend. Although the reasons for this finding are unknown, it was suggested that level of education attainment may likely indicate theeffects 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 sign ificantly elevated HR was found for these outc omes 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 catego ries. A surprising finding in the current study was increasing IHD mortality associated with PM 2.5 (1999–2000) and increasing education attainment (Table 8). HR estimates for IHD mortality also increased up to 11% among th ose with more than high school education with the in clusion of ecologic covariates in the model. Large socioeco nomic 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). Th e finding of a greater effect of PM 2.5 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 PM 2.5 .

We also explored complex sp atial 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 mode ls 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 ex plained 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 larges t 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 PM 2.5 CONCENTRATIONS

Background

Exposure to PM 2.5 has been linked to a wide array of health outcomes such as aggr avation of existing heart and lung disease and premature d eath (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 PM 2.5 have shown, however, that intra-urban exposure gradients can also be associated with atherosclerosis (K ü nzli et al. 2005) and with high risk of premat ure death (Jerrett et al. 2005a). These studies have used geost atistical interpolation models that capture regional patterns of pollution well, but often fail to account for the near-sou rce impact from local traffic and industry. Given the high ri sks 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 potentialof LUR to supply accurate, small-area estimates of air pollutant concentrations without the financial expense of dispersion or exposure modeling (B rauer et al. 2003; Briggs et al. 2000a). The goal of LUR is to explain, to the extent possible, the variation in existin g air quality data for a given pollutant using data on nearby traffic, land use, emissions from local sources, and populatio n variables. In most cases, multiple linear regression is used to develop and validate a model using data from existin g 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 co ast, and road length measurements to predict NO 2 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 NO 2 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 loca lized pollutant such as NO 2 , however, PM mass has a significant regional component that includes smaller cont ributions from local sources (Bari et al. 2003). This comp licates estimating intra-urban exposure with LUR. Models of PM 2.5 in three European cities produced mixed results. One of the only studies to attempt prediction of fine par ticle concentrations with the LUR methods to date was undert aken in Europe as part of the TRAPCA (Brauer et al. 2003). Researchers measured PM 2.5 for representative temporal periods over one year in the Netherlands; Munich, Germany; and Stockholm County, Sweden. They found significant d ifferences in the ability of the model to predict monitoring data from region to region: values from LU R models could predict from 73% of the variation of monitored values (the Netherlands) down to 56% and 50% (Mun ich 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 re spiratory outcomes (Brauer et al. 2002). More recently, in Germany, Hochadel and coworkers (2006) found that LUR predicted PM 2.5 absorbance ( R 2 = 65%–82%), but failed to predict PM 2.5 mass very well ( R 2 = 9%–17%).
North American cities have vastly different transportation and land-use pa tterns than those in Europe, and the applicability of LUR to predict PM 2.5 is unknown (Gilbert et al. 2005). To our knowle dge this study was the first attempt to apply LUR to analyze PM 2.5 in North America.

Materials and Methods
LUR uses concentrations of ambient pollutants at monitoring locations as the de pendent variable. Surrounding land-use, transportation, em issions from local sources, and population data were obtained using GIS and included in a regression equa tion as predictor variables. In this study we assembled a database of information on land use and transportation around the PM 2.5 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 theD. 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 PM 2.5 Data For the 28- county models, counties were chosen to match thoseincluded in the Best Practice Model (BPM; a tool for forecasting regional travel demand ) used by the New York Metropolitan Transportation Co uncil (NYMTC), the regional council of governments establis hed to help make transportation-related decisions in th e 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 countie s 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 populatio ns 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 do wntown Manhattan, range from 40%–76% urban. For comparis on, 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 PM 2.5 for monitors in the 28-co unty region around New York City ( Figure 1 ) using data from the U.S. EPA Air Quality Subsystem (AQS; a re pository 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 monitori ng 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 SO 4 2

. New York City experiences long-range transport of secondary aerosols with a high proportion of SO 4 2 in the warmer months. Because SO 4 2

is also formed regionally, the exposure gradient within cities is more spatially homogenous during the warmer months when secondar y aerosols are transported. By contrast, in the winter months, SO 4 2

concentrations are strongly influenced by local emissions from automobile and truck traffic, shipping, heatin g, and industry. The exposure gradient within the city is therefore generally steeper during the winter months wh en the dominant regional exposure is absent. The steep er 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-ye ar analysis of the 9-county area (1999–2001), and data from 45 for the 28-county area in winter (2000). At nine monitoring locati ons, two types of monitors were used concurrently: th e Federal Reference Method (FRM) and the tapered-element oscillating microbalance (TEOM) continuous monitori ng 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 prov ided directly by the appropriate state agencies (the Department of EnvironmentalConservation in New York and the Departments of Environmental Protection in New Jersey and Connecticut). Inmost instances, the locations were identified by the agencies with either a global positio ning 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: Tra ffic, Land-Use , Population, and Local Emissions Data For each air monitoring location in the AQS for which we had adequate data on PM 2.5 , 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 tra ffic and land-use data for allbuffers around each air monitoring location were calculated. Calculations were performed with a vector data structure using ArcG IS 9.1 (ESRI 2004).

Traffic and Road Data The NYMTC provided traffic estimates for 2002 for New York City and surrounding counties that covered approxim ately 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 dozen34 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 incl ude 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 YorkDepartment 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 roadwa ys can be found in Parsons Brinckerhoff Quade & Douglas, Inc. (2005). The traffic estimates, divided by vehicle ty pe (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 (tota l length of roadway in kilometers) in each buffer area.

Land-Use Data Layers of land-use data were assembled from several sources: extrem ely 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 forNew 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 usin g aerial photos). For each land-use layer we calculated the total area of an individual land-use category (e.g., indust rial, 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 PM 2.5 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 id entical but comp arable 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, di scussed throughout most of this section, comprises the “Industrial / Commercial / Transportation” category from USGS data and the New Jersey land-use data. We cr eated a comparable grouping using the New York City tax-lo t data by combining the categories denoted “Industrial an d 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 theydid not cover the entire region. Land-use areas were converted to acres (1 acre = 4047 m 2 ).

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 PM 2.5 (area, point, off-road m obile, 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 PM 2.5 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 PM 2.5 data and one that used the winter-2000 PM 2.5 data; and a third that used 3- year-average PM 2.5 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 agricultura l/forest, commercial, industrial/mobile, and residential categories. Next, to validate our predictions later, we randomly removed 20% of the monitors to use for validatin g 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 remainin g 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 rem oved for validation; 45 total), and the 3-year 9-county mode l 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 di stances 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 PM 2.5 , total population, nu mber of housing units, median income of population, and the percentage of the population who are not white.

Table 12. Modeling and Validation Mon itors by Land-Use Category

Land-Use Category a 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 predic tor variables disc ussed above wereincluded 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 stronges t variables are added to themodel 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 an other 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 te sted the spatial autocorrelation in the 3-year-average and winter-2000 PM 2.5 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 neigh borhood constructions — a Queen contiguity matrix base d on Thiessen polygons, and a nearest-neighbor approach in which we limited the analysis to the three nearest mo nitors. Statistical significance was tested using a permutation test with 999 iterations. Wealso qualitatively assessed the extent of spatial autocorrelation in the PM 2.5 concentrations using variograms of the residuals from our final models. For visualization purposes, we created a smooth interpolated surface by predicting PM 2.5 concentrations at 5600 random point locations (200 pe r county) with the LUR formula and kriged these pred ictions. To compare our LUR estimates to the exposure in terpolation method that is commonly used in health studi es, we also kriged the PM 2.5 values from the monitors. All the statistical analysis was conducted using R statis tical software and the GSTAT library (Pebesma 2004; R Development Core Team 2005).

We evaluated the quality of the predictions at validation locations by calculating th e 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 sa mples) by calculating the RMSE based on fitted values in a l eave-one-out cross validation.

Results

Descriptive Statistics for PM 2.5 PM 2.5 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 μ g/m 3 (median = 14.3, SD = 1.78) and a mean for the 9-county region of 15.3 μ g/m 3 (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 loca tions are situated in close proximity to major highways . Locations with higher concentrations tended to be lo cated closer to Manhattan.

Winter-2000 averages were also approximately normally distributed with a mean of 14.0 μ g/m 3 (median = 14.3, SD = 2.55); three of the four highest concentrations were

Figure 2. Distributions of mean PM 2.5 concentrations. Mean for the 28-county 3-year average was 14.3 μ g/m 3 (median = 14.3; SD ± 1.78), for the 9-county 3-year average was 15.3 μ g/m 3 (median = 15.1; SD ± 1.42), and for the 28-county winter-2000 average was 14.0 μ g/m 3 (median = 14.3; SD ± 2.55). Analyses were based on the full set of samp les with no validati on samples removed.D. Krewski et al.

37located 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 PM 2.5 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 a ll other variables in explanatory power. Urbanization-related variables, primarily in the 500- and 1000-m buffers, were also strong predictors of PM 2.5 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 in dustrial land use in the 300- m buffer for both the 28-coun ty 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 PM 2.5 at validation locations to within, on average, 0.93 μ g/m 3 (6.5%) of actual concentrations with a RMSE of 1.10. The 9-county model predicted PM 2.5 at validation locations to within, on average, 0.77 μ g/m 3 (5.0%) of actual concentrations with a RMSE of 0.87. The pred ictions for validation sites showed some bias, particul arly in the 28-county 3-year model, which was likely to over-predict PM 2.5 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, wh ereas 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 thesecond 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 exclude d samples) were 0.85 μ g/m 3 or 6.0% of actual concentratio ns for the 28-county 3-year model (with an RMSE based on a leave-one-out cross validation of 1.15), and 0.69 μ g/m 3 or 4.5% of actual concentrations for the 9-county 3-y ear model (RMSE of 1.00). The final 28-county 3-year model explained 64% of the variation in the PM 2.5 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 fr om a mix of FRM and TEOM monitors: for the 28-county 3- year model, 50 FRM and 12 TEOM; for the winter-2000 mo del, 42 FRM and 3 TEOM; for the 9-county 3-year mode l, 25 FRM and 11 TEOM. Although plots of the nine sampling locations with both FRM and TEOM monitors show ed little or no bias (four

Table 13. Distribution Statistics for Final Predictor Variables a
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 popul ation 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 (3 00 m) 0.028 0.010 2.721 0.009 1.253 Multiple R 2 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 (3 00 m) 0.040 0.013 3.005 0.005 1.321 Multiple R 2 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 R 2 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 valu es 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 signifi cant 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 whethe r 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 winte r-2000 concentrations led to a very similar model as those based on 3-year-average PM 2.5 concentrations. Length of road in the 1000-m buffer, total area of high-density resi dential land use in the 1000- m buffer, and total traffic within the 1000- and 500-m buffers were, singly, the top predictors of winter PM 2.5 concentrations after valida tion samples were removed. Although these variables appear ed to individually predict well, their collinearity with ot her strong candidate predictors and highly influential obs ervations pointed us to more robust variables with slig htly 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 us e in the 1000-m buffer (Table 14). The inclusion of the veget ative land-use variable was strongly influenced by two ru ral monitors in New Jersey. Removing these two points re duced 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 μ g/m 3 (within an average of 11% of actual concentrat ions) with a RMSE of 1.72. These numbers were inflated by one large residual at a Brooklyn location with an un usually high population density. Without this one locatio n, the average absolute value residual is 1.08 μ g/m 3 (9% of actual) w ith a RMSE of 1.27. For the 28-county winter-2 000 model, the predictions were, on average, better at the five sites outside of the 9 urbanized counties than at th e four validation sites inside (RMSE of 1.30 outside vs. 2.14 inside). The distinction between highly urbanized an d less urbanized areas, again, is primarily a product of the single large residual at the Brooklyn location. Among th e 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 μ g/m 3 , which represents an average of 9% of actual concentrat ions with a RMSE of 1.69 μ g/m 3 based on a leave-one-out cros s validation. This final 28- county winter-2000 model explained 61% of the variation in PM 2.5 concentrations. Interpol ated 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 andextremely close to Interstate 95 and the George Washington Bridge. This one point had the highest PM 2.5 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.

F igure 6. Results of bootstrap analyses for final parameters in the 28-county and 9-county 3-year models and the 28-county winte r-2000 model. Example: F or total traffic in the 500-m buffer (upper right panel), 0.121 was the parameter from the 28-county 3-year model; the histogra m 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 PM 2.5 values were highly auto correlated 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 mode ls, respectively, when we used the Queen’s contigu ity 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-neares t-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 theindependence 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 co ntiguity matrix, and 0.10 ( P = 0.101), 0.01 ( P = 0.324), and

0.03 ( P = 0.496) when we used the three-nearest-neigh bor approach. Variograms of the raw PM 2.5 values showed consid erable spatial autocorrelation but the pattern was significantly diminished and barely visible in variograms of the residuals from the full models (variograms not shown).

Kriging PM 2.5 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, an d even outperformed LUR at many locations. For the 28-cou nty 3-year model we calculated an average absolute value residual of 0.68 and a RMSE on these predic tions 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-c ounty winter-2000 model, we calculated an average absolute va lue residual of 1.39 and a RMSE of 1.55 (compared with 1.72 for LUR). Although kriging performed well when predicting measured PM 2.5 concentrations at va lidation locations using only the modeling samples, the LUR model outperformed kriging based on the results of cross validation analysesusing 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 PM 2.5 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 PM 2.5 concentrations and they predicted con centrations at validation locations that were generally within 10% of actual values.
All models included total tr affic 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 doessuggest 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 wouldhave expected variables in smaller buffers to be the strongest predictors of PM 2.5 concentrations. Although they were indeed good predictors (traffic in the 100-m buffer, for example, explained approxi mately 25% of variation inthe 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 PM 2.5 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. Predicto rs 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), PM 2.5 varies more gradually over space than elementa l 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, indust rial 1000 m, and vegetation 1000 m) were robust regardless of which specific monitoring locations were om itted from the model to usefor validation, although we found that the industrial landuse variable was affected by th e inclusion or exclusion of a New Jersey site that had the highest PM 2.5 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 ar e limited to the five boroughs, have a very different resolution , and have slig htly different land-use characterizations — was also a statisticallyD. Krewski et al.

47 significant predictor of PM 2.5 levels. This supports the assertion that industrial la nd use is indeed explaining variation in PM 2.5 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-pred icted and 28 were under-predicted). The under-prediction appears to have occurred in the more urbanized New Jers ey 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 th e 28-county 3-year and winter-2000 models suggest that predictions at validation locations were more precise fo r the 3-year values than for the winter values, they also disguise differences in variance and spread. The winter-2000 PM 2.5 values hadapproximately twice the variance and a 24% greater interquartile range than the 3-year values. The mean absolute percentage error (MAPE) ma y allow more appropriatecomparison; 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-co unty 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 predic tor variables. The extreme values for traffic in some areas of New York City, for example, could inflate (or de flate) LUR parameters, particularly in models based on a limited number of observations. At the same time, kr iging the raw monitoring data limits the potential for krigin g 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 PM 2.5 concentrations. It is also possible that kriging in this co ntext 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 PM 2.5 and distance than would points in rural areas far from any major source of PM 2.5 .

PM 2.5 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 PM 2.5 concentrations averaged over time, future analyses may wish to consider these pattern s 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 tim e 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 traf fic alone (such as this one) could lead to a wider spread of PM 2.5 predictions becauseareas of high traffic in off-peak times may also have a significantly larger increase in tr affic during rush hour. Nevertheless, we would expect relative rates of traffic for different areas to remain si milar. We found, for example, the Spearman rank correlatio n 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 PM 2.5 can be predicted using LUR in North America. Using a combination of traffic, population, land-use, and local poin t-source emissions variables we were able to predict PM 2.5 concentrations well at validation locations and predict more than 60% of the variation in PM 2.5 measurements over a wide area. Although the three models are no t identical in variables or parameters, their similarity reinforces the relationship between PM 2.5 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 asp ects of LUR with kriging) to the data. The limited residual autocorrelation from the LUR, however, precluded adequately fitting variograms and predictions were not impr oved 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 particul ar, further research is needed on the possible effects of st reet canyons and of seasonal variations in PM 2.5 .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 fo llow-up from 1982 to 2000.

Assessment of Exposure to PM 2.5 Regression equations were developed to predict PM 2.5 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 Regressi on Model for Predicting PM 2.5 Concentrations for details). Th e dependent variable in our LUR was PM 2.5 ; 3-year averages for 1999 through 2001 were calculated using daily data from the EPA AQS. In total we assembled comple te data for 62 sites in 28 counties. Models were fitted for the 3-year averages and the winter-2000 averages (J anuary through March). The winter average was used to re duce the impact of regional transport of secondary SO 4 2

aerosols; 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), medi um 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) fr om 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 condu cted sensitivity analyses to compare the results of the LU R models of exposure with results from ordinary krigin g models. See details of the model derivation and validation in the section A Land-Use Regression Model for Predicting PM 2.5 Concentrations; also published in Ross et al. 2007).

Ecologic Covariates Contextual condi tions for the ZCAs where subjects lived at enro llment were controlled with the ecologic covariates (f rom USCB 2000; see sidebar),which included poverty, income disparity, unemployment, median household inco me, 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 dueto 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 co nditioning use could lead to lower indoor exposure levels.

Statistical Methods and Data Analysis

Detailed methods are reported in an earlier krigingbased exposure study (Jerre tt et al. 2005a) and in the Nationwide Analysis section of this report. Briefly, we used the random effects C ox 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 e ffects model, similar to the approach used for th e Nationwide Analysis.

Exactly the same 44 individual-level covariates identified in earlier ACS studies of the health effects of air pollutionwere included. These variables (see sidebar) measure lifestyle, diet, demographics, oc cupational, 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 predic tors of total traffic within 500 m, total population within 1000 m, and industrial land use within 300 m of the moni tors. This model predicted 66% of the variation in PM 2.5 and estimated PM 2.5 concentrations at the 13 validatio n locations to within 0.93 μ g/m 3 (6.5%) of actual concentratio ns. Concentratio ns were generally highest in the central parts of New York City, especially in Manhattan. The 28-county winter-2000 model used similar independent vari ables, 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 cau se-specific deaths in the 28- county 3-year model. Only IHD had a significant positive association with PM 2.5 . The IHD results were insensitive to control for the 44 individua l-level variables and for most of the ecologic cova riates. HR estimates were lower, but positive (with confidence inte rvals including unity) for models that contained pe rcentage of unemployment, median household income, or all seven ecologic covariates together in the model with the 3-year average PM 2.5 concentration For the 28-county winter-2000 model, effectswere significant and relatively stable for IHD with all variable specifications, including those that controlled for all seven ecologic cova riates and for copollutant exposure to O 3 (see Table 16 ).

We compared the 28-county 3-year average PM 2.5 concentration at the 90% decile and at the 10% decile in the exposure distribution and found a difference of 1.5 μ g/m 3

. For this exposure contrast w ith the fully adjusted model, the HR for PM 2.5 and IHD was 1.06 (95% CI, 1.00–1.12). For the 28-county winter-2000 average PM 2.5 exposure, the difference between the 10% and 90% deciles was 3.9 μ g/m 3
PM 2.5 , and the HR for IHD was 1.21 (95% CI, 1.07–1.34; Table 16). Diabetes and endocr ine 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 MortalityTable 15.HRs by Cause of Death Associated with anInterdecile Comparison Value of 1.5 μg/m3from the 28-County 3-Year ModelaCovariates All CauseIHDICD-9: 410–414CPDICD-9: 400–440,460–519Lung CancerICD-9: 162DiabetesICD-9: 250Endocrine DisordersICD-9: 240–279Deaths (n)10,559 2,735 4,625 853 118 206PM2.5onlyb1.011(0.935–1.047) 1.109 (1.039–1.182)c0.977 (0.926–1.031) 1.036 (0.911–1.179) 1.326 (0.999–1.759) 1.249 (1.000–1.561)+ 44 Individual-levelcovariates0.984 (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 covariatesdAir 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 householdincome ($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 covariates0.977 (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)Ozonee0.997 (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 ecologiccovariates0.982 (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)aThe PM2.5concentration of 1.5μg/m3for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposuredistribution from the 28-county 3-year model. Analysis was based on 746 ZCAs with ACSCPS-II residents and stratified by age (in 1-year groupings), gender, and race.N= 44,056 CPS-II participants living in the 28counties. Bolded data refer to text. HRs are followed by 95% confidence intervals.bPredicted using the 28-county 3-year model.cSignificant positive association.dEcologic covariates were included one at a time to a base model with PM2.5+ 44 individual-level covariates.eSummer O3values for 1999–2000.D. Krewski et al.

51Table 16.HRs by Cause of Death Associated with anInterdecile Comparison Value of 3.9μg/m3PM2.5from the 28-County Winter-2000 ModelaCovariates All CauseIHDICD-9: 410–414CPDICD-9: 400–440,460–519Lung CancerICD-9: 162DiabetesICD-9: 250EndocrineDisordersICD-9: 240–279Deaths (n)10,559 2,735 4,625 853 118 206PM2.5onlyb1.036 (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-levelcovariates1.000 (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 covariatescAir 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 householdincome ($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 covariates0.989 (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)Ozoned1.024 (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 ecologiccovariates0.999 (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)aThe PM2.5concentration of 3.9μg/m3for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposuredistribution from the 28-county winter-2000 model. Analysis was basedon 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 the28 counties. HRs are followed by 95% confidence intervals.bPredicted using the 28-county winter-2000 model.cEcologic covariates were includedone at a time to a base model with PM2.5+ 44 individual-level covariates.dSummer O3values 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 PM 2.5 exposures. Neither all-cause nor any cause-specific deaths had a positive, significant association with PM 2.5 . 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 th e ZCA and MSA scales together, and with th e 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 e ffect 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 educa tion 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 edu cational groups seemed more pronounced for the 3-year-average PM 2.5 exposure than for the winter-2000 exposure; th e group with grade 12 completed or not (Table 17) had point estimates nearly twice the size of the group with mo re than a grade 12 education (Table 19). For the winter models effects were similar for both groups.

DiscussionIn the New York City region, we did not observe elevated PM 2.5 -associated risk of mort ality for all-cause, CPD, or lung cancer deaths, but IH D did show a significant positive association with PM 2.5 exposure. The large and significant effects for IHD provide additional evidence of aspecific 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 PM 2.5 concentrations, which had higher le vels 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 PM 2.5 concentrations, IHD did have a significant positive association with exposure to PM 2.5 . The IHD results were inse nsitive to control for the 44 individual-level covariates and cont rol 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 wa s 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 PM 2.5 appeared to be smaller than those reported for Los Angeles, this resulted from the limited range of ex posures in New York City. If the coefficient is reported for the same 10- μ g/m 3 exposure contrast used in Los Angele s, 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 stro ng 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 ar eas where people seemed to be healthier and wealthier. Therefore pollution is lesslikely 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 PM 2.5 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 PM 2.5 exposure and IHD mortality is biologically plausible and is still evident in this analysis desp ite these competing gradients of mortality and exposure. Ot her recent studies have suggested specific links between cardiovascular disease and traffic-related pollution (H oek et al. 2002) and between PM 2.5 concentrations and preclinical indicators of atherosclerosis (K ü nzli et al. 2005), which underlies many of the IHD deaths. Consequently, the results from New York City lend corroborative evidence of an association between PM 2.5 exposure and mortality for a cause of death likely associated with ai r pollution exposure.

We also investigated an alternative exposure model in New York City based on spatia lly kriging the data from the 62 fixed-site air pollution mon itors (Ross et al. 2006), as had been done by Jerrett a nd 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 PM 2.5 concentrations at the validation locations that had not been included in the model formulation. In the health effects assessment, we also tested the kriging exposu re model based on the 3-yearD. Krewski et al.

53 Figure 8. HRs by cause of death associated with an interdecile comp arison value of 1.5 μ g/m 3 PM 2.5 from the 28-county 3-year model and 3.9 μ g/m 3 PM 2.5 from the winter-2000 model. Analysis was based on 746 ZCAs with ACS CPS-II residents a nd 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 Tabl es 15 and 16.)

Figure 9. HRs by selected causes of death associated with an interdecile comparison value of 1.5 μ g/m 3 PM 2.5 from the 28-county 3-year model and 3.9 μ g/m 3 PM 2.5 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 PM 2.5 exposure within a cluster). Deaths were associated with an interdec ile comparison value of 1.5 μ g/m 3 PM 2.5 from the 28-county 3-year model and 3.9 μ g/m 3 PM 2.5 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 PM 2.5 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 erro r 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 MortalityTable 17.HRs by Cause of Death Associated with anInterdecile Comparison Value of 1.5μg/m3PM2.5from the 28-County 3-Year Model as Modified byGrade 12 Education Completed or NotaCovariates All CauseIHDICD-9: 410–414CPDICD-9: 400–440,460–519Lung CancerICD-9: 162DiabetesICD-9: 250EndocrineDisordersICD-9: 240–279Deaths (n)5,602 1,537 2,580 469 66 117PM2.5onlyb0.993 (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-levelcovariates0.976 (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 covariatescAir 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 covariates0.967 (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)Ozoned1.000 (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 ecologiccovariates0.982 (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)aThe PM2.5concentration of 1.5μg/m3for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposuredistribution from the 28-county 3-year model. Analysis was based on 746 ZCAs with ACSCPS-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.bPredicted using the 28-county 3-year model.cEcologic covariates were included one at a time to a base model with PM2.5+ 44 individual-level covariates.dSummer O3values for 1999–2000.D. Krewski et al.

55Table 18.HRs by Cause of Death Associated with anInterdecile Comparison Value of 3.9μg/m3PM2.5from the 28-County Winter-2000 Model asModified by Grade 12 Education Completed or NotaCovariates All CauseIHDICD-9: 410–414CPDICD-9: 400–440,460–519Lung CancerICD-9: 162DiabetesICD-9: 250EndocrineDisordersICD-9: 240–279Deaths (n)5,602 1,537 2,580 469 66 117PM2.5onlyb1.003 (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-levelcovariates0.984 (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 covariatescAir 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 covariates0.954 (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)Ozoned1.017 (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 ecologiccovariates0.971 (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)aThe PM2.5concentration of 3.9μg/m3for New York City represents the difference between the concentrations at the 90% decile and at the 10% decile in the exposuredistribution 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 12education or less. HRs are followed by 95% confidence intervals.bPredicted using the 28-county winter-2000 model.cEcologic covariates were includedone at a time to a base model with PM2.5+ 44 individual-level covariates.dSummer O3values for 1999–2000.56 Extended Analysis of ACS Study of Particulate Air Pollution and MortalityTable 19.HRs by Cause of Death Associated with anInterdecile Comparison Value of 1.5μg/m3PM2.5from the 28-County 3-Year Model as Modified byMore Than Grade 12 EducationaCovariates All CauseIHDICD-9: 410–414CPDICD-9: 400–440,460–519Lung CancerICD-9: 162DiabetesICD-9: 250EndocrineDisordersICD-9: 240–279Deaths (n)4,957 1,198 2,045 384 52 89PM2.5onlyb1.022 (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-levelcovariates0.986 (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 covariatescAir 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 covariates0.968 (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)Ozoned0.982 (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 ecologiccovariates0.959 (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)aThe PM2.5concentration of 1.5μg/m3for New York City represents the difference between the concentrations at the 90% decile and atthe 10% decile in the exposuredistribution 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 thangrade 12 education. HRs are followed by 95% confidence intervals.bPredicted using the 28-county 3-year model.cEcologic covariates were included one at a time to a base model with PM2.5+ 44 individual-level covariates.dSummer O3values for 1999–2000.D. Krewski et al.

57Table 20.HRs by Cause of Death Associated with anInterdecile Comparison Value of 3.9 μg/m3PM2.5from the 28-County Winter-2000 Model asModified by More ThanGrade 12 EducationaCovariates All CauseIHDICD-9: 410–414CPDICD-9: 400–440,460–519Lung CancerICD-9: 162DiabetesICD-9: 250EndocrineDisordersICD-9: 240–279Deaths (n)4,957 1,198 2,045 384 52 89PM2.5onlyb1.053 (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-levelcovariates1.013 (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 covariatescAir 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 covariates1.014 (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)Ozoned1.022 (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 ecologiccovariates1.011 (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)aThe PM2.5concentration of 3.9μg/m3for New York City represents the difference between the concentrations at the 90% decile and atthe 10% decile in the exposuredistribution 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 morethan grade 12 education. HRs are followed by 95% confidence intervals.bPredicted using the 28-county winter-2000 model.cEcologic covariates were included one at a time to a base model with PM2.5+ 44 individual-level covariates.dSummer O3values 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 PM 2.5 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 promisin g approach that uses monitored pollutant concentrations as the dependent variable and a comprehensive set of la nd-use, traffic, population, and physical geography vari ables as predictors of PM 2.5 concentrations (Briggs et al. 2000a). Most LUR models have been calibrated with NO 2 as a marker for traffic exposure (Briggs et al. 2000a; G ilbert 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 PM 2.5 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 PM 2.5 estimates. In this project, we developed an LUR model for predicting PM 2.5 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 PM 2.5 exposure and the 44 individual-level covariat es. 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 km 2 . 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 PM 2.5 concentrations, which ma kes it a suitable site toexplore the use of LUR models in North America. Once calibrated, an LUR model can be us ed to derive a spatially continuous surface map of PM 2.5 concentrations. Methods for Handling Spatial Data We used the ArcGIS 9.1 (ESRI 2004) GIS to integrate data about PM 2.5 concentrations, land use, tr affic, population, and physical geography.

Dependent Variable: Ambient PM 2.5 I n 1998, a PM 2.5 moni toring network was implemen ted throughout California; we used data from 2000 becaus e it was the first complete year of data collected. The placement of each site was determined by specific criteri a: (1) to provide measurements of pollutant concentratio ns in the air basins to be used for regulatory purposes; (2) to represent populated areas with high pollutant con centrations; 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 PM 2.5 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 dete rmine 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 dedicatedto chemical speciation of pollutants (Los Angeles, Fontana, Anaheim, and Rubidoux ) (California Environmental Protection Agency and Ai r Resources Board 2003). In the Los Angeles MSA, 23 sites measure PM 2.5 (Figure 12). All of these sites use FRM monitors and collect 24- hour mass data on 1-, 3-, a nd 6-day cycles. Since the network was established in 1998, annual average PM 2.5 concentrations have ranged from 9.49 μ g/m 3 in Lancaster (an arid flat agricultural area with gen erally clear air) to 28.22
μ g/m 3 in Riverside (a large inland city with high pollutant levels). The mean and median for the Los Angeles MSA
were 18.42 μ g/m 3 (SD = 6.01) and 19.31 μ g/m 3 , respectively. In much of the MSA, PM 2.5 levels exceed the annual average limits for both federal (15 μ g/m 3 ) and state (12 μ g/m 3 ) 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 transportationand 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 PM 2.5 concentration surrounding the source; whereas parks and open spaces have fewer sources of pollution and therefore low concentrations of PM 2.5 .

We followed methods from a study completed in Hamilton, Ontario, Canada by Sahsuvaroglu and coworkers (2006) to delineate the land-use categories thatwould 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 transpor tation categories that have been used in other studies and found to be associated with ambient pollutant concentrations (Bri ggs 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 PM 2.5 concentrations. We created buffers at 50 to 5000 m in radius around each of the 23 PM 2.5 monitors. The larger buffer areas were selected based on an analysis of the semivariance ofa 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 km 2 ) 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 precurs ors such as nitrogen oxides that react with the ambient environment to produce secondary particulate air pollu tion (Brauer et al. 2003). To improve estimates of traffic -generated particles, we obtained a geographically code d 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 vehicl es) from Business Analyst (ArcView 9.1, ESRI, San Dieg o, 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 m easurement locations (traffic points) for analyses in the me tropolitan area (Los Angeles, Ventura, Orange, San Bernar dino, and Riverside). The AADT traffic-point data were spatially assigned to the closest individual road segm ents in the ESRI Streetmap data set. Each road class in cluded many spee d limit categories. The road classes and sp eed 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 s egments 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 ma jor 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. Usin g this method we were able to impute an accurate meas urement of AADT throughout the MSA.

Population Data Population density — the number of people, volume of traffic, nu mber of businesses, and area of green space — is an impo rtant factor in determining how much and what type of pollution is produced in a given area. Densely populat ed areas contribute moreD. 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 fo r commuting and traveling to commercial areas.

To determine the populatio n 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 thecensus 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 th e 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 PM 2.5 pollutant surface. ArcView v3.3, ArcMap v9.0, ArcInfo v9.0 (Redlands, CA), SPLUS 2000 (Boston, MA) and St ata 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 PM 2.5 measurements. Bivariate linear regressions we re first used to determine which variables were most strongly related to PM 2.5 . This first step tested over 140 independent variables becauseland-use and road variables covered a large number of categories and several sizes of bu ffers around 23 monitors. A multiple linear regression mo del was developed using thesignificant 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 R 2 value andacceptable 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 (Bur rough and McDonnell 1998). From the 23 monitor data po ints, a random sample of 15 data points was selected with replacement, for 1000 repetitions. This enabled us to de termine bias in the estimates and how accurately the model would predict PM 2.5 concentrations when multiple locations (or monitors) were excluded from the regression analysis. Additional model diagnostics included the Cook-W eisberg 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 th e face validity of a predictedLUR 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 PM 2.5 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 e ach variable were once again calculated in hectares. Usin g the fitted regression equation, we calculated a predicted PM 2.5 value for each of the 18,000 lattice points. This method allowed us to visualizethe PM surface; for subsequent health analysis, geographic points corresponding to study subjects couldserve as the lattice assignment points to minimize exposure assignment error.

We then used the IDW meth od to interpolate the pollutant surface. The IDW interpolator assumes that at each PM 2.5 sample point the local influence lessens with distance (ESRI 2004). A specified number of predicted PM 2.5 points, or all points within a specified radius, can be used to determine the output valu e for each location, creating a surface. The power in the IDW interpolation determines how influential surrounding points are to the point being interpolated (Burrough a nd 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 PM 2.5 concentrations from monitors we re used to develop the multivariate model; correlation coefficients are shown in Table 21 . The final LUR model in cluded three independent variables ( Table 22 ) : (1) traffic co unt within 300 m (as a direct proxy for particulate emissions and gaseous62 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

precursors), (2) industrial areas within 5000 m (to represent primary and precursor emission s), and (3) government areas within 5000 m (as a proxy for tr affic intensity around major destinations such as schools, government service offices, and hospitals). The R 2 value for the LUR was 0.69 ( F = 14.07, P < 0.00001), with a few geo graphic areas near the intersections of freeways di splaying 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 variab les; the average VIF was 1.06. The visualiz ation surface in Figure 14 shows a 63% R 2 between the measured a nd predicted values of PM 2.5 from the LUR model. Although the residuals showed a slightly greater variance with higher PM 2.5 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 sensitiv ity 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 PM 2.5 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 PM 2.5 concentrations, with the main predictors being traffic count w ithin 300 m, industrial areas within 5000 m, and governme nt areas within 5000 m (Table 22). In some areas, the LUR predicted PM 2.5 concentrations slightly higher than those measured — within theLos 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 po rts (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 freeway s (Figure 13) and some were upward of 126 μ g/m 3 . 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 PM 2.5 Concentrations a

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 co rrelation. 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 0.00 032 3.33 0.0000958 0.004 1.05 Government area 5000 0.00 072 3.35 0.0002139 0.003 1.04D. Krewski et al.

63 tracks used to disperse carg o to distribution and storage centers in inland areas. More transport trucks, which combust diesel fuel, travel th e 710 freeway than any other freeway in Los Angeles (Meyer 2003). Westerdahl and colleagues (2005) found that me asured freeway concentrations of PM 2.5 had a range of 60 to 820 μ g/m 3 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 ourLUR model estimates were based on data from 2000. Moreover, in the Westerdahl study, increased concentrations of PM 2.5 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 (200 3) and Hochadel and colleagues (2006) have also used LUR to predict PM 2.5 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 di d not use land-use classifications in their multivariate re gression model, they were able to derive significant pred iction models for each of the three locations , for both PM 2.5 mass and PM 2.5 filter absorbance, which is used as a marker for diesel exhaust (Brauer et al. 2003). For the Netherla nds, Munich, and Stockholm, the R 2 values for the PM 2.5 mass prediction model were 0.78, 0.76, and 0.63, respectively; and 0.9, 0.83, and 0.76, respectively, for the PM 2.5 absorbance prediction model. Since absorbance models are typically used to assess traffic-related pollutants, th e absorbance model results are a better predictor of measured PM 2.5 than the PM 2.5 mass model because the variables us ed in the absorbance model were chosen specifically to measure traffic. Hochadel and coworkers (2006) co nducted a study in Wesel, Germany, using primarily traffic-based indicators as the geographic factors. The regression mode ls predicted strongly for PM 2.5 absorbance ( R 2 = 0.65), but not for PM 2.5 mass ( R 2 = 0.094). Figure 13. Map of over-predicted values f rom the LUR model, shown as > 50, 75, or 100 μ g/m 3 higher than the measured value. Most over-predictions occurred at freeway intersections. PM 2.5 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 tomeasure areas of land-use categories were quite large compared with other studies (gen erally 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, trans ected by major highways and commercial areas, leads to a broader regional scale of influence for processes that gen erate PM pollu tion, and the 2000- to 5000-m buffers surrounding the PM 2.5 monitors probably reflect this disperse d form of urban development. The study that is most closely related geographically to this Los Angeles Analysis wa s done by Ross and associates (2006) in San Diego County, California, in which they predicted ambient NO 2 concentrations. The study area in San Figure 14. PM 2.5 surface created by IDW using predicted PM 2.5 concentrations from an LUR model with the variables of traffic within a 300-m radius, industrial areas within a 5000-m radius, a nd government areas within a 5000-m radius. PM 2.5 monitor positions are marked with a +. Notice the high PM 2.5 concentrations near intersections of major highways.

Figure 15. Graph showing predicted PM 2.5 versus measured PM 2.5 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 km 2 versus 98,500 km 2 for the SCAG region. The traffic-count vari able and the industrial landuse variable were common to both models, but the Rossstudy found that smaller-radius buffers were more effective predictors than those we used in Los Angeles. This makes sense because NO 2 arises from local sources and is known to vary over smaller areas in proximity to traffic (Gilliland et al. 2005), whereas PM 2.5 is a mixture generated by primary and secondar y sources that we would expect to be more regionally dispersed. Data from the largerbuffer 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 develope d for the Los Angeles Basin based on PM 2.5 values from the same 23 monitors (Jerrett et al. 2005). That model did not sh ow as much of the local-area variations in exposure as di d the LUR model developed for this study (see Figure s 14 and 16 ). The success of this LUR model demonstrates that tra ffic and land uses are strong predictors of PM 2.5 concentrations. Although the scale of variation of PM 2.5 values around an emissions source appears to be larger than what we might expect from purely local processes, the covariance of PM 2.5 concentrations with land-use predictors suggests that local land uses and traffic have a significant role in distributions of PM 2.5 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 PM 2.5 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 po llutant exposures, we would expect to see bias in the pollutant levels predicted from models based on th ose monitoring locations. Our use of the bootstrap method demonstr ated that it is unlikely that single influential points drove the results of the LUR model. For example, none ofthe 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 an d local emissions sources such as traffic, the fact that some covariates are related to factors

Figure 16. Universal kriging map of PM 2.5 in the Los Angeles area. PM 2.5 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 somehidden confounders. Consequently, to use LUR for pollution and health analyses, resear chers must adjust for other contextual confounders that ma y be related to the land-use and traffic input to ensure un biased estimates of the health effects of air pollution.

We found that LUR predicted 69% of the variance in PM 2.5 mass in the Los Angeles MSA. With sensitivity analysis, we observed that few mo nitors influenced the results.Traffic, industrial, and government areas were of most significance because of their asso ciated particulate emissions from increased traffic and indu strial point sources. Further investigation is needed into the extraordinarily high levels of PM 2.5 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 popula tion per ZCA in Los Angeles is approximately 35,000; average area is approximately 22.5 km 2 ). ZCA centroids were weighted by population distribution using spatial boundary files based on 1980 and 1990 USBC data. The cent roids were used to assign PM 2.5 and O 3 exposure levels to the 22,905 ACS subjects living in the 267 ZCAs (this co hort included 5,856 deathsbased on follow-up from 1982 through 2000). Some subjects reported only PO Box addresses and were therefore excluded. As in earlier ACS anal yses (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 gen eral population, the cohortallows for internally valid comparisons within large samples of the American population.

Assessment of PM 2.5

Exposure An LUR prediction model was developed to predict PM 2.5 from 23 monitoring locations in the Los Angeles MSA using GIS to integrate data from land use, transportatio n, and physical geography (see A Land-Use Regression Mo del for Predicting PM 2.5 in the Los Angeles Region). The LU R method explained 69% of the variance in PM 2.5 with three predictors around each monitoring site: (1) traffic co unt within 300 m, (2) industrial land area within 5000 m, and (3) government land area within 5000 m. Model va lidation 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 O 3 Exposure In earlier ACS studies that used between-city comparisons, few associations have been found between O 3 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 (K ü nzli et al. 2003). For O 3 , 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 concen trations 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 m easure 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 concentrat ions and daily peaks are used for federal and state designations of nonattainment areas. They both cap ture extreme events , but the expected daily peak concentr ation 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 O 3 exposure, although acute effects of a small magnitude have been observed (Bell et al. 20 04). Thus, it seems plausible that an O 3 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 buffe rs was assigned a value of 1 and others were assigned 0. The resulting variables were used as indictors of proximi ty to freeways in the health effects models. For each ZCA centroid within those buffers we collected land-use and PM 2.5 data from monitors. The U.S. Census Feature Class Co des define freeways as having limited access, a numbered assignment (e.g., a major state or U.S. route), and a speed li mit higher than 50 miles per hour (USCB 2004). The distance from the ZCA centroid to the freeway was used as a pr oxy for exposure to trafficrelated pollution, which may exert effects independent of and in addition to speci fic pollutants such as PM 2.5 and O 3 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 covariat es for the ZCAs to controlfor 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 fr om (or interactively with) the individual-level covariates, such as smoking and diet, from the ACS enrollment ques tionnaire. 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 inco me, income disparity, grade 12 education, total population, racial composition (black, white, Hispanic in percent ages), 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-conditio ned houses are more tightly sealed and have lower penetr ation of particles from outdoors. Similar housing variable s 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-conditionedhomes (Meng et al. 2005). This variable added partial control for the impact of air conditioning, which may relate both to health outcomes (throug h prevention of heat stress) and to air pollutant concentra tions (because high concentrations and low proportions of air conditioning are related in our study area). We thus ex pected the proportion of air conditioning in the ZCA to co rrelate with lower PM exposures and effects.

We also computed principal components from the seven ecologic covariates (excludi ng AC) to provide maximal control for confounding wh ile avoiding multicollinearity among the ecologic covariates (Luginaah et al. 2001; Krieger et al. 2002).

Statistical Methods and Data Analysis

The statistical methods are si milar to those employed by Jerrett and colleagues (2005a) in a previous intra-urban analysis of data from Los An geles 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 s ection Nationwide Analysis / Statistical Methods and Data Analysis / The Random Effects Cox Model.) In brief, we used the 44 indi vidual-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 individualvariables 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 includ ed 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 pr evious 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 pr eviously shown that survival experience is clustered by co mmunity 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 a ssociated 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 polygo n than to any other centroid, were used to assign first-ord er nearest-neighbor contiguitybetween the ZCAs. The polygons were derived using ArcView 3.2 (ESRI Corp., Redlands, CA).

Results Using the new LUR model docu mented in the last section, we assigned exposure concentrations based on the traffic, government, and i ndustrial land use for the 267 ZCAs (see also Jerrett et al. 2 005a). 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 ar e shown in each section of the table).
The results show the effe cts of LUR-predicted PM 2.5 exposure with different leve ls of control for confounding variables. HRs are expressed for a 10- μ g/m 3 change in68 Extended Analysis of ACS Study of Particulate Air Pollution and MortalityTable 23.HRs by Cause of Death Associated with Each 10-μg/m3Change in PM2.52000 Concentrations (from LURModel) Based on 267 Los AngelesZCAsaCovariatesAllCauses(n=5856)IHDICD-9: 410–414(n=1462)CPDICD-9: 400–440,460–519(n=3136)Lung CancerICD-9: 162(n=434)DigestiveCancer(n=429)OtherCancers(n=992)PM2.5only1.197 (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 covariates1.143 (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)EcologiccovariatesbAir 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 population1.141 (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 groupingsAll social factors1.142 (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 householdincome, > grade 12 education, socialfactor1.115 (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 covariatesc1.126 (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 controld44 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 + freewayswithin 500 mg1.170 (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 estimatesOzone (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 mg0.987 (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 mg0.974 (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.

69Table 23 (Continued).HRs by Cause of Death Associated with Each 10-μg/m3Change in PM2.52000 Concentrations (from LUR Model) Based on 267 LosAngeles ZCAsaCovariatesEndocrine DisordersICD-9: 240–279(n=95)DiabetesICD-9: 250(n=57)DigestiveDisorders(n=151)MaleAccidents(n=75)FemaleAccidents(n=47)All OtherCauses(n=497)PM2.5only3.344 (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 covariates3.037 (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 covariatesbAir 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 population3.090 (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 groupingsAll social factors2.676 (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 householdincome, > grade 12 education, socialfactor2.796 (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 covariatesc2.559 (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 controld44 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 + freewayswithin 500 mg2.813 (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 estimatesOzone (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 mg0.660 (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 mg1.515 (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)aBaseline HRs were stratified by age (in 1-year groupings), gender, and race. Total number ofparticipants was 22,905. HRs arefollowed by 95% confidence intervals.Bolded data refer to text.bEcologic covariates were included one at a time in the model with PM2.5and the 44 individual-level covariates.cParsimonious individual-level (23) and ecologic (4) covariates were considered. The ecologic covariates were air conditioning,household income, grade 12 education, and the predicted values ofthe third principal component analysis based onall social variables (except air conditioning).dAll models included PM2.5.eBased on expected peak daily concentrations.fAverage of four highest 8-hour maximum readings.gIntersection with a freeway within 500 or 1000 m of a ZCA centroid.70 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

PM 2.5 exposure (followed by the 95% CI). For example, we follow the successive analyses for all-cause mortality:

• with PM 2.5 alone (stratified for age, sex, and race), the HR was 1.197 (95% CI, 1.082–1.325);
• whereas with the 44 indivi dual-level covariates the HR was 1.143 (95% CI, 1.033–1.266). • All subsequent re sults include the 44 individual-level covariates and on e or more ecologic covariates.
• With the 44 individual-leve l covariates and the ecologic covariate of unemployment, the HR of PM 2.5 was 1.127 (95% CI, 1.015–1.252).
• When we added four social factors extracted from the principal component analys is (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 morta lity in bivariate models with PM 2.5 exposure (air conditioning, median income, and education attain ment) reduced the HR to 1.115 (95% CI, 1.003–1.239). • For the parsimonious mode l 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 O 3 levels the HR was 1.191 (95% CI, 1.069–1.327). (T hus, similar to earlier results [Jerrett et al. 2005], O 3 had no primary effect in these models for all-cause mortality.)

Discussion and Conclusions In general, the associations for PM 2.5 and mortality in this set of analyses are si milar in magnitude with those reported earlier from geosta tistical kriging estimates (Jerrett et al. 2005a) with three mi nor differences. First, the HR for PM 2.5 alone is somewhat smaller than the risks from the kriging model. Second, a lthough the risk estimates were smaller in this study, the CIs were tighter. Third, theresults 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 includedall the individual-level variables and the significant ecologic confounders resulted in the lower confidence boundbelow 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 va riable confounded the estimate to below 1, but that estimate was still extremely close to 1; see Table 23). We consider th e model containing the social factors from the principal comp onents 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 wid er for the model with socialfactors 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 confounder s than the earlier results using the kriging surface.

The Los Angeles Analysis was extended using the random effects Cox model wit h and without allowance for spatial autocorrelation. The earlier study (Jerrett et al.2005a) using the kriging estimates demonstrated significant autocorrelation in the ri sks, 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 anal ysis we used the full random effects formulation.

Table 24 displays results from the random effects models, organized to comp are results between models with and without allowance for spatial autocorrelation. It also compares models with va rious levels of control for confounding, beginning with a model that contains onlythe 44 individual-level covariates and no pollutant variable (all models were stratifie d for age, sex, and race). The initial model (with only the 44 individual-level covariates) thus demonstrates the resi dual random effects variance attributable to variability in mortality patterns between ZCAs. For all-cause mortality, when PM 2.5 was included in the model, random effects variance declined about 37% for the model with no autoco rrelation (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 PM 2.5 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 inclusio n of all the variables, as evidenced by the fact that ou r 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 va riance, may have resulted from a number of phenomena. First, the nearest-neighborD. Krewski et al.

71formulation of the random effects Cox model may not capture what is likely to be a more complex residual spatial structure. Second, the auto correlation 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 pr esence of spatial autocorrelation may also suggest that one or more important covariates have b een excluded from the model. We have visually examined th e residuals, and there seems to be a clustering of autocorrelation in residual mortality on the downwind side of ro adways with hi gh volumes of truck traffic. The assignment of residence locations to the ZCA centroid prevented us fr om 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 PM 2.5 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 covariat es 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 airpollution variables predicted the mortality data more completely than they did for the an alysis 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 somewh at surprising given the different visual appearance of each surface, with the LUR detecting smaller variations in areas around highways andindustry. The effect sizes were slightly diminished compared with the earlier study, but the effects shown in thisstudy 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 re search on the mechanisms of systemic pulmonary infl ammation and atherogenesis (Brook et al. 2004; K ü nzli et al. 2005). Table 24. Sensitivity Analysis Usin g the Random Effects Cox Model for a 10-μg/m 3 Change in PM 2.5 Exposure with Allowance for Spa tial Autocorrelation a

Covariates b No Spatial Autocorrelation Spatial Autocorrelation

HR ZCA Variance (  10 3 ) HR ZCA Variance (  10 3 ) ZCAAutocorrelation

All Causes 44 Individual-level covariates — 7.008

— 10.02 0.3307 c + PM 2.5 1.158 (1.035–1.295) 4.386 1.160 (1.021–1.317) 7.591 0.3307 c + Parsimonious ecologic covariates d 1.152 (1.034–1.283) 0.229 1.152 (1.032–1.286) 0.623 0.3307 c + Freeway intersection e 1.156 (1.036–1.289) 0.233 1.1 56 (1.034–1.293) 0.847 0.3307 c

IHD 44 Individual-level covariates — 4.357 — 4.357 0.000 f
+ PM 2.5 1.402 (1.137–1.728) 0.582 1.402 (1.137–1.728) 0.582 0.000 f + Parsimonious ecologic covariates d 1.347 (1.083–1.675) 0.189 1.347 (1.083–1.675) 0.189 0.000 f + Freeway intersection e 1.339 (1.074–1.669) 0.189 1.339 (1.074–1.669) 0.189 0.000 f

a Bolded data refer to text.

b Covariates accumulate in the model with each addition.

c Estimate of ZCA autocorrelati on reached its maximum value.

d Parsimonious individual-level (23) and ecologic (4) covariates were considered. The ecologic c ovariates were air conditioning, household income, grade

12 education, and the predicted values of the third principal co mponent analysis based on all social variables (except air cond itioning).

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 gas eous air pollution for a subcohort of the ACS CPS-II in or der to examine if there is a critical exposure time window that is primarily responsible for the increased morta lity 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 consis ts of nearly 1.2 millionAmericans 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 upda te a variety of behavioral, medical, and demographic information that had first been collected in 1982, and to co llect more detailed data regarding diet, physical activity, and other lifestyle factors that had not been obtained at the time of enrollment. TheACS 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 Coho rt: California, Connecticut,Florida, Georgia, Illinois, Iowa, Louisiana, Maryland, Massachusetts, Michigan, Minnes ota, Missouri, New Jersey, New Mexico, New York, North Carolina, Pennsylvania, Utah, Virginia, Washington, and Wisconsin. A total of 184,194 participants from th e 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 Investigatio ns Committee approved all aspects of the CPS II study.

Individual Exposure Profiles

Previous analyses of the AC S cohort have assumed that each individual’s exposure was determined by the averagelevel of ambient air pollution in that person’s city of residence at one point in time, e ither 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 pollu tant 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 PM 2.5 and SO 2 were constructed for each participant by matching the reported addresse s at different times with the air pollutant exposure for th e MSA where they lived. Individual exposures were dete rmined using mean annual PM 2.5 and SO 2 levels that may have been experienced by the individual during specific time windows. For example, to test the possib ility that curr ent excess mortality is primarily due to poll ution exposure 5 to 10 years ago, a person’s exposure woul d be modeled as the average of his or her annual expo sures during the interval extending from 5 to 10 year s before the current time.

Residential Histories Residential histories for the Nutrition Cohort participants we re 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 assignparticipants to an MSA. Since no information was collected on the number of moves between follow-up stages, we assumed that if a new Zi p 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 ha d moved mid-calendar year (Hansen 1998). We constructed residential histories before enrollment in 1982 under the as sumption that participants had lived in the same residence they reported in 1982.

PM 2.5 A total of 60,941 Nutritio n Cohort participants resided in one of 83 MSAs for which average annual PM 2.5 exposure data had been estimated based on measured values of TSP and PM 10 for the period of 1972–2000 (calculated by Lall and colleag ues [2004]). For each MSA, either annual TSP or PM 10 data were used in addition to the specific PM 2.5 /TSP or PM 2.5 /PM 10 ratio. This subcohort was designated as the PM 2.5 -A group and PM 2.5 exposure levels were assigned to each participant ( Figure 17 ). A total of 8181 (13.4%) participants in the PM 2.5 -A groupmoved 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 port ion of the follow-up period.D. Krewski et al.

73 Figure 17. Profile of time tre nds (1972–2000) in average annual PM 2.5 concentrations based on measured values of TSP and PM 10 in 83 MSAs and on linear interpolation of measured PM 2.5 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 individual s, average annual PM 2.5 concentrations were estimated using linear interpolation of data from an additional 23 MSAs for which measured PM 2.5 data were available for the periods 1979–1983 and 1999– 2000 (Pope et al. 2002). The fir st participant in this group died in 1992; therefore, the entire exposure period we examined for this group star ted 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 th e full CPS-II cohort who (1) were not in the PM 2.5 -A group, and (2) died in the same MSA where they had resided at enrollment in 1982. They were designated as the PM 2.5 -B group. We assumed that these participants had not moved between enrollment and death. PM 2.5 values were assigned using average annual data from Lall and colleagues (2 004) for 50,915 participants in 30 MSAs, and by linear interpolation of data for 23 MSAs from Pope and colleagu es (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 PM 2.5 concentrations were used to represent exposures before 1 972. Participants in the PM 2.5 - A and PM 2.5 -B groups were mutually exclusive.

SO 2 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 SO 2 measurements were available. For most of the period from 1972 to 2000, measured average annual SO 2 data were available from the EPA AIRS (Pope et al. 2002). Linear interpolation between adjacen t time points was used for years in which average annual measurements were missing ( Figures 19 and 20 ). As with the PM 2.5 -A group, the entire exposure period ex amined was 1977 to 2000. A total of 5762 (7.1%) particip ants moved at least once during the exposure period.

STATISTICAL METHODS AND DATA ANALYSIS Standard Cox models with time-dependent PM 2.5 and SO 2 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 PM 2.5 -A group and the SO 2 group, HRs were calculate d from enrollment in the Nutrition Cohort in 1992–1993 through 2000. For the PM 2.5 -B group, the HRs covere d the period from enrollment in the CPS-II full coho rt 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). Mo dels were adjusted for the standard suite of 44 lifestyle and demographic variables collected at enrollment in 1982 (see sidebar) including: smoking, education, mari tal status, body mass index, alcohol consumption, occupa tional 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 deat h occurred in a group. For each of the three groups (PM 2.5 -A, PM 2.5 -B, and SO 2 ), the risk set for the partial likelihoo d consisted of all subjects in the group who were alive at the time a member died. The exposure for each subject in th e risk set was based on their time-dependent exposure pr ofile up to the death of a cohort member and was update d at each subsequent death time. Estimates of the parame ters of the standard Cox model with time-dependent cova riates 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 allowe d us to investigate which time window would be most re levant to the association between air pollution and mort ality. 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 tim e windows. Since education attainment had been found to be a powerful modifier of the air pollution–mortality asso ciation in the Reanalysis (Krewski et al. 2000a,b), re sults 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 simultaneous ly. 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 calcu lated as a weighted average ofthe exposures in the three time windows. The weights, represented by the coefficients in the linear combination, would represent the “relative effectiveness” of exposureduring that time window (Goddard et al. 1995). To be interpretable, this model would requ ire the additional constraint that all of the weights be nonnegative, or, equivalently, that78 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 18. Profile of time trends (1972–2000) in average annual PM 2.5 concentrations for 53 MSAs in which PM 2.5 -B participants (from the full CPS-II cohort) lived. PM 2.5 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 PM 2.5 concentrations were used to represent exposures before 1972. ( Figure continues next page .)D. Krewski et al.

79 Figure 18 ( Continued ).80 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Figure 19. Profile of time trends (1972–2000) in SO 2 concentrations for 120 MSAs. Measured average annual SO 2 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 SO 2 data for 1972–2000. Note that only 5 MSAs had 29 years of SO 2 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 combinatio n 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 curren t 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 PM 2.5 or SO 2 exposure. HRsD. Krewski et al.

85 associated with PM 2.5 exposure in several of the 15-year windows were elevated, and significantly so for the PM 2.5 - B cohort group for lung-cance r 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 PM 2.5 , no one exposure time win dow stood out as demonstrating the greatest HR; nor was there any clear pattern ofa trend in HRs going from most recent to most distant windows or vice versa. For SO 2 , 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 am ong the three 5-year exposure time windows were small. For both PM 2.5 groups the AIC value tended to be lowe st (the model with the best fit to the data) in either the most recent time window (1 to 5 years) or the middle time windo w (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 PM 2.5 -A group (Table 26). For the SO 2 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 emer ged as to which of the time windows of exposure may be most responsible for the PM 2.5 –mortality association. Although for SO 2 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 differe ncewere small. The HRs for mortality associated with exposure to SO 2 were highest in the most recent time window; however none of them were significantly elevated.

To be able to identify whic h exposure time windows are associated with mortality attr ibutable to air pollution, the patterns of exposure among in dividuals in the cohort need to be sufficiently varied. Variation can be spatial (different exposure profiles experienced by individuals living in different MSAs) and temporal (e xposure profiles for a given MSA changing over time). In this data set, both spatial and temporal variations in expo sure patterns were limited by the ecologic nature of the ex posure 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 PM 2.5 -A group (some of whom moved during the study period) and the PM 2.5 -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 PM 2.5 concentrations was 5.0; and for temporal variation it was 3.5 for group A and 3.4 for group B. Spatial variation of with in-MSA temporal variation, however, was somewhat higher in the PM 2.5 -B group than in the PM 2.5 -A group as shown by SDs of 1.9 for group B and 1.7 for group A. For the SO 2 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/m 3 Change in PM 2.5 Concentrations or a 5-ppb Change in SO 2 Concentrations Over the 15-Year Time Window a

Cause of Death PM 2.5 -A Group ( n = 60,941) PM 2.5 -B Group ( n = 81,466) SO 2 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 h azard 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 di et. Bolded values refer to text. HRs are follo wed 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 PM 2.5 and 0.11 to 0.92 for SO 2 ). These correlations indicat e 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 examinetemporal 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 fr om CPD associated with SO 4 2

exposure declined from the 1980s to the 1990s, possibly due to air quality improve ments. In contrast, PM 2.5 HRs were largest for exposures in more recent time windows, possibly due to changing patterns of PM 2.5 emissions. Complex patterns of population m obility, individual susceptibility, and exposure measurement error have also beensuggested as possible explanations for the observed temporal variation in risk. In contrast, HRs for lung-cancer mortality exhibited an inve rted 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/m 3 Change in PM 2.5 Concentrations or a 5-ppb Change in SO 2 Concentrations for Three 5-Year Time Windows a
Exposure Time Window b PM 2.5 -A Group ( n = 60,941) PM 2.5 -B Group ( n = 81,466) SO 2 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 hazar d function was stratified by age (1-year groupings), sex, and race and was adjusted for

smoking, marital status, body mass index, alcohol, occupationa l exposures, and diet. HRs are followed by 95% confidence interva ls.

b The AIC value is a measure of how well the model fits the av ailable 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 NO 2 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 PM 2.5 exposure declined in accordance with a decline in exposure; this was true for ove rall, cardiovascular, and respiratory mortality but not for lung-cancer mortality. Whether PM 2.5 exposure was modeled as the annual average in the year of death or as the average over the entire follow-up period, it had si milar effects on mortality. The results from the study suggest that since PM 2.5 exposure may affect sensitive in dividuals with preexisting conditions and play a role in the development of chronic disease, as exposure declines so may the excess mortalityrelated 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 Harv ard 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 ex amine 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 Nu trition 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 SO 2 concentrations were all measured data, the PM 2.5 concentrations were predicted from PM 10 and TSP measurements and are therefore subject to a certain degree of exposure measurement error. For some subjects, PM 2.5 data were interpolated based on two time points, at baseline (1982) an d the end of follow-up (2000); and for the larger PM 2.5 -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/m 3 Change in PM 2.5 Concentrations or a 5-ppb Change in SO 2 Concentrations Over the 15-Year Time Wi ndow as Modified by Education Attainment a

Cause of Death PM 2.5 -A Group PM 2.5 -B Group SO 2 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 co variates (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 ad justed for smoking, marital st atus, 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/m 3 change in PM 2.5 Concentrations or a 5-ppb Change in SO 2 Concentrations for Three 5-Year Time Windows for Those with an Education of Less than High School a
Exposure Time Window b PM 2.5 -A Group ( n = 2,902) PM 2.5 -B Group ( n = 16,479) SO 2 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 (0.9 7–1.26) 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 covariat es (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 av ailable 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 di stant exposure time windows.

We may have also misclassifi ed exposure with certain assumptions about individuals’ residential histories, such as that the place of residence remained unchanged before 1982. The PM 2.5 -B group was composed entirely of participants who had died and we re assumed to have never moved, whereas the PM 2.5 -A and SO 2 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 pathophysi ological disease processes.

Updated information for ce rtain demographic and lifestyle covariates have been collected in follow-up surveys for Nutrition Cohort participan ts. Although data on these covariates were not available for all study participants included in this analysis, most of the covariates do notD. Krewski et al.

89 Table 29. HRs and AIC Values by Cause of Death Associated with a 10-μg/m 3 Change in PM 2.5 Concentrations or a 5-ppb Change in SO 2 Concentrations for Three 5-Year Time Windows for Those with an E ducation of High School or More a
Exposure Time Window b PM 2.5 -A Group ( n = 58,039) PM 2.5 -B Group ( n = 64,987) SO 2 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 co variates (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 ad justed 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 ava ilable 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 (Krews ki 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 stud ies that did not account for residential mobility information, we also observed larger mortality HRs among those with a lower level of educationattainment. 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 re duced exposure to long-term air pollution.90 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality



### IMPLICATIONS OF THE FINDINGS

PHASE IThis report provides the results of Phase III of the Particle Epidemiology Reanalysis Project, which began in1998. 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-up1982–1989; Pope et al. 1995) of long-term exposure to particulate air pollution and mort ality 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 discrepa ncies, 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 th e original findings to alternative analytic approaches (Kre wski 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 par ticulate 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 estim ates related to exposure to particulate and gaseous air po llutants. 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 mo rtality related to SO 4 2

exposure, although the effects of popu lation change were completely attenuated by control for resi dual autocorrelation. (2) Therisk estimates did not vary with adjustment for most individual-level covariates. However, education attainment was found to modify the effect — ri sk estimates fo r all causes of death studied decreased with increasing education attainment for both PM 2.5 and SO 4 2

exposures. (3) Adjustment for ambient SO 2 concentrations at the MSA scale markedly

Figure 21. Spatial distribution (kriged) of PM 2.5 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 PM 2.5 concentrations based on data from 1999–2000. Analysis done in the current project. reduced the mortality risk estimates for both PM 2.5 and SO 4 2

exposures. (4) Adjusting for occupational exposures based on an occupational “dir tiness” score and an index of occupational exposure to kn own lung carcinogens did not materially alter air pollution ri sk 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 s lightly higher mortality risk estimates with wider confidence limits compared with those found using standard C ox regression methods based on the assumption of no sp atial autocorrelation. The results of flexible exposure–response models indicated that air pollution ri sk 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 (Abrahamo wicz et al. 2004); this analysis showed that although biases due to aggregation of individual-level cova riates can occur, those biases were unlikely to account for the obs erved associations between air pollution exposure and mortality.

In Phase II, we also explored the effect of the geographic unit of scale for spatial anal yses, and noted that mortality risk estimates using PM 2.5 concentrations at the county scale were higher than estim ates based on pollutant concentrations at the MSA scale (Willis et al. 2003). Therefore, we explored the MAUP in mo re detail in Phase III using Intra-Urban Analyses for th e 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 PM 2.5 in 2000. Part of our te am at NYU constructed92 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

annual average estimates of PM 2.5 concentrations for 1972–2000, and we used them in the analysis of possible critical exposure time windows . Measured annual average SO 2 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 demo graphic ecologic variables confound or modify the rela tionship 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 windo ws affect the association between air pollution and mortality? During the course of Phas e III, a fourth objective of focusing on Intra-Urban Anal yses in the Los Angeles andNew York City regions was added. These Intra-Urban Analyses were intended to explore the MAUP by using more geographically refined indicator s 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 anal yses conducted during the course of Phase III provided a number of important findings concerning the associ ation between exposure to ambient air pollutants and mortality.

SUMMARY OF RESULTS FROM PHASE III

Nationwide AnalysisThe first set of analyses focused on further characterizing the relationship between air pollution and mortality using the most recent follow-u p data for the ACS cohort. Nationwide Analysis of the full ACS cohort confirmed that mortality increases with exposure to PM 2.5 ; the HR for death from all causes was 1.03 3 (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 concen trations in 1999–2000 and were calculated based on a 10- μ g/m 3 change in PM 2.5 . (Similar although slightly lower risk estimates were obtained using air pollutant concentrations for 1979–1983.)

The Reanalysis showed stron ger and more robust associations between SO 2 exposure and all causes of mortality compared with those observed for PM 2.5 exposure (Krewski et al. 2000b). A plausibl e mechanism by which SO 2 couldincrease mortality risk, however, has not yet been identified. One possibility is that SO 2 may serve as a marker forparticulate air pollution formed by the atmospheric transformation of SO 2 into SO 4 2

particles. In contrast, the present analysis showed stro nger mortality associations for PM 2.5 than for SO 2 exposure. We did not investigate sources of particles or part icle 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 note d in the Reanalysis Project. Whereas lung-cancer mortality in Phase II was found not to be significantly elev ated in relation to PM 2.5 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 PM 2.5 exposure and lung cancer. A significant increase in lung-cancer mortality was also noted in the Updated An alysis 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 ai r pollution exposu re. 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 geograph ic unit of analysis of ecologic covariates (ZCA, MSA, or MSA & DIFF) tended not tomake an appreciable difference in influence on HR estimates; however, in many cases HR estimates tended to be highest in models with the ec ologic covariates considered at the MSA & DIFF levels simultaneously.

The air pollution–lung-can cer mortality association was also found to vary somewhat by the level of education attainment recorded when subj ects were enrolled. Specifically, participants who comple ted high school or less had a significant increase in risk of lung-cancer mortality of approximately 20% associated with each 10- μ g/m 3 change in PM 2.5 (as measured in 2000) compared with no association observed for th ose who completed more than high school.

Intra-Urban Analysis The Intra-Urban Analysis for the Los Angeles region using the LUR-based exposu re 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 associ ates (2002). Mortal ity 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 PM 2.5 in Los Angeles fromD. 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). Thes e risk estimates are based on PM 2.5 concentrations in Lo s Angeles in 1999–2000 and correspond to a change of 10 μ g/m 3 PM 2.5 . Mortality was not significantly higher in relation to exposure to O 3 concentrations or with proximity to freeways within the Los Angeles MSA. The Intra-Urban Analysis for the New York City region produced a somewhat differen t 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 PM 2.5 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 PM 2.5 exposure was related to all cause-of-death categories, in New York IHD was the only cause of death associated with PM 2.5 exposure (which is consistent with the mechanistic hypothesis put forward by Pope and associates [2004] in the Updated An alysis). These HR estimates are based on 3-year average PM 2.5 concentrations from the28 counties of New York City in 1999–2001 and correspond to a difference of 1.5 μ g/m 3 of PM 2.5 between the highest level of exposure and the lowest. The exposure estimates were based on LUR models, taking into account traffic density, population dens ity, and industrial land use. Previously, we have used kriging methods to estimate levels of ambient PM 2.5 concentrations (Jerrett et al. 2005a). Table 30 presents a comparis on of HRs obtained from analyses using LUR an d 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 differe nces between the two cities that have been previously observed. The differences in mortality risks may be attribut able to fundamental differences in the topographical, geographical, and urban attributes of these two metropolitan areas.

Comparing the mortality risk estimates obtained from theNationwide 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 subj ects' 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- μ g/m 3 Change of PM 2.5 (Los Angeles) or with an Interdecile Comparison Value of 1.5 μ g/m 3 PM 2.5 (New York City) with Exposure Estimated Using LUR or Kriging a

Covariates Los Angeles New York City
LUR Kriging LUR

All Causes
PM 2.5 only 1.20 (1.08–1.32) 1.24 (1.11–1.37) 1.01 (0.94–1.05) + 44 Individual-level 1.1 4 (1.03–1.27) 1.17 (1.05–1.30) 0 .98 (0.95–1.02)

IHD PM 2.5 only 1.42 (1.15–1.74) 1.49 (1.20–1.85) 1.11 (1.04–1.18) + 44 Individual-level 1.3 3 (1.08–1.63) 1.39 (1.12–1.73) 1 .07 (1.00–1.15)

CPD PM 2.5 only 1.18 (1.02–1.36) 1.20 (1.04–1.39) 0.98 (0.93–1.03) + 44 Individual-level 1.1 1 (0.97–1.28) 1.12 (0.97–1.30) 0 .95 (0.90–1.01)
Lung Cancer PM 2.5 only 1.46 (1.01–2.10) 1.60 (1.09–2.33) 1.04 (0.91–1.18) + 44 Individual-level 1.3 9 (0.96–2.01) 1.44 (0.98–2.11) 0 .96 (0.84–1.09)

a The PM 2.5 concentration of 1.5 μ g/m 3 for New York City represents the difference between the co ncentrations 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 al cohol 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 woul d likely decrease susceptibility to air pollution; yet the health effects observed in Los Angeles were generally higher than those observed ineither 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 attrib utable 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 resp onsible for the association between air pollution and mort ality. We constructed anindividual 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 Natio nwide, New York City, an d Los Angeles Data Sets

Variable Nationwide a 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 Age b 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 smoking b 3.2 (4.4) 3.6 (4.5) 2.8 (4.2)

Table continues next page

a Based on the 116 MSAs for which PM 2.5 (1999–2000) data were available in the national dataset and follo w-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.

95dynamically, 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 PM 2.5 or SO 2 . Identification of critical exposure time windows, even among large na tional cohorts, remains a challenge and further work on identifying them with other relevant datasets — including other cohorts with different exposure circumstan ces — 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 purpos e in Phase II has been significantly enhanced and allo ws 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 Na tionwide, New York City, and Los Angeles Data Sets

Variable Nationwide a 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 index b 25.2 (4.1) 25.2 (4.0) 24.7 (4.0) Occupational exposure to PM index c (%)

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 index d (%)

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 index e (%)

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 PM 2.5 (1999–2000) data were available in the national da taset and follow-up of pa rticipants through 2000.

b Mean (SD).

c Occupational exposure to PM increases with increasing index number.

d Dietary fat consumption increa ses with increasing index number.

e Dietary fiber consumption increas es 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 factorsmay 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), PM 2.5 exposure data were availa ble for the time period 1979– 1983 for 50 MSAs containing 298,825 study participants. Follow-up of participants ha d been performed through 1989 and had documented a tota l 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 coll eagues (2002), the follow-up period had been extended by 9 years until 1998. Additional PM 2.5 data had been obtained and exposure could be estimated fo r two time periods (1979–1983 and 1999–2000); these corresponded to a total of 61 and 116MSAs, 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 PM 2.5 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 exam ined several additional ecologic covariates at different levels of analysis, the total number of MSAs and partic ipants available for the Nationwide Analysis was slightly lower than the number available for the Updated Analysis by Pope and colleagues (2002). The two add itional years of follow-up, however, included more deaths: over 90,000 from all causes among the same group for whom PM 2.5 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 U pdated Analysis (Pope et al. 2002), and the current Extend ed Analysis. By holding the number of MSAs and study subjects constant, we were better able to examine the independent influence ofchanging the length of the follow-up period on study findings. The PM 2.5 (1979–1983) results were obtained using available data for 342,521 subjects residing in 58 MSAs, and the PM 2.5 (1999–2000) results were obtained using available data for 488,370 subj ects 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 th e current study.

With models adjusted for individual-level covariates only, estimated HRs associated with exposure to PM 2.5 (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 Analyses a

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 PM 2.5 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 ) 298,825 360,682 499,779 b 342,521 488,370 b 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 PM 2.5 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 ful l cohort. Bolded values refer
to text.D. Krewski et al.

97 associated with the PM 2.5 (1999–2000) exposure data. With models including ecologic covariates, HR estimates increased to 1.04–1.06 for a ll causes, 1.09–1.13 for CPD, 1.18 for IHD, and 1.07–1.10 fo r lung-cancer mortality associated with exposure to PM 2.5 (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 PM 2.5 (1979–1983) tended to either remain fairly similar or de crease slightly with increasing follow-up time. An exception was lung-cancer mortality for which HRs increased and were significant when follow-up was extend ed beyond 1989.

It is possible that there may be increasing exposure misclassification due to popula tion mobility during the11 years of follow-up from 1989 to 2000. A recent analysis by Jerrett and colleague s (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 MS As and participants constant across the different follow- up periods (using the same number of MSAs and participan ts as those in the currentstudy) or we allowed the number of MSAs and participants to vary across the di fferent follow-up periods (using the number of MSAs and par ticipants that corresponded with those used in the earlier analyses [Krewski et al. 2000a,b; Pope et al. 2002; see Table 32]). For PM 2.5 (1979– 1983) both the number of MS As and participants could vary; but for PM 2.5 (1999–2000), the number of MSAs was

Table 33. HRs by Cause of Death for a 10- μ g/m 3 Change in PM 2.5 Covering Three Follow-Up Time Periods, Using the Same Number of MSAs and St udy Participants Within PM 2.5 Exposure Categories, and With and Without Adjustment for the Seven Ecologic Covariates a

Covariates in Model Follow-Up Through 1989 b Follow-Up Through 1998 c Follow-Up Through 2000 d

PM 2.5 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 co variates with and without adjustme nt for the seven ecologic covar iates at MSA & DIFF

levels. The baseline hazard function was stratified by age (1-year groupings), gender, and race. All analyses were conducted us ing the same 58 MSAs (342,521 participants) or 116 MSAs (488,370 part icipants) that were used in the current analysis. HRs are followed by 95% confi dence intervals. Bolded data refer to text.

b Data for the 44 individual-level covariates are from the AC S enrollment questionnaire. Data fo r the seven ecologic covariates were extracted from the 1980

U.S. Census Bureau database for the Na tionwide 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- μ g/m 3 Change in PM 2.5 Based on Either a Standard Cox or a Random Effects Cox Model Covering Three Follow-Up Time Periods and Using th e Same or Different Number s of MSAs and Participants a

Model and Number of MSAs Follow-Up Through 1989 b Follow-Up Through 1998 c Follow-Up Through 2000 d

PM 2.5 data 1979–1983 1979–1983 1999–2000 1979–1983 1999–2000 Same e

MSAs ( n ) 58 58 116 58 116 Participants ( n ) 342,521 342,521 488,370 342,521 488,370 Different e

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), gen der, and race. HRs are

followed by 95% confidence intervals. Bolded values refer to text.

b Follow-up period used for the Reanal ysis Project (Krews ki 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 curr ent Nationwide Analysis. Rows marked “Different” use the MSAs an d 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 C ox model; the comparisons in Table 34 show this pattern fairly consistently.

In results obtained with the standard Cox model, therewas only slight variation in the mortality HR estimates comparing calculations with the same number or different number of MSAs and partic ipants. One of the largest changes in the HRs was found for mortality from lungcancer associated with PM 2.5 (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) wh en we included the 8 additional MSAs for which PM 2.5 data had later been obtained (“same” row), although it remained nonsignificant.

A similar pattern was observed with the random effects Cox model. Again, the differ ence in risk estimates was slight whether using the same number or a different number of MSAs and particip ants. The largest difference was also observed for mortality from lung cancer associated with PM 2.5 (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) repo rted when we included 8 additional MSAs (“same” row).

Table 35 presents a comparison of the modifying effect of education attainment on the association between PM 2.5 (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 report ed in the highest education group. A similar pattern of results was obtained with orwithout 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 poli cy. The extended follow-up of the ACS cohort through 200 0 supports earlier findings of a positive association be tween long-term exposure to PM 2.5 in ambient air and increased mortality rates in urbancenters in the United States. The earlier results have figured prominently in estab lishing the current National Ambient Air Quality Standard for PM 2.5 in the United States (U.S. EPA 2004). Alth ough short-term exposures to PM 2.5 are also associated with higher mortality, one of the effects of long-term exposure (C ohen et al. 2003) is to predispose some people to the e ffects attributed to short-term exposures (Network for Envi ronmental Risk Assessment and Management [NERAM]2002; Krewski et al. 2005b).

Particulate air pollution repr esents a major health issue at the international level as well (Cohen et al. 2005). The World Health Organization (W HO 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 PM 2.5 and O 3 (WHO 2006).

Although the precise magnitude of the risk associated with long-term exposure to ai r pollution remains subject to some uncertainty, these and earlier results from the ACS cohort have generally demonstrated elevated mortality from CPD, IHD, and lung can cer. Mortality from all causes is also clearly associated with long-term exposure to ambient PM 2.5 in U.S. urban centers. Phases II and III of the Particle Epidemiology Re analysis Project have both shown these estimates to be r obust 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 th e uncertainty about the actual magnitude of the increase in mortality due to long-term exposure to fine particles, th e U.S. EPA has recently conducted an expert elicitation in volving leading researchers in the health effects of air pollution, especially those with experience evaluati ng data from large cohorts including the ACS, the Harvard Six Cities Study (Laden et al. 2006), and the Adventist Health St udy of Smog (Abbey et al. 1999). This expert elicitation process demonstrated a clear consensus among the participat ing scientists that longterm exposure to particulate air pollution is associated with increased mortality (Indus trial 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 the100 Extended Analysis of ACS Study of Particulate Air Pollution and MortalityTable 35.Modification by Education Attainment: HRs by Cause of Death for a 10-μg/m3Change in PM2.5(1979–1983) Covering Three Follow-Up TimePeriods, With and Without Seven Ecological Covariates, and Using the Same Number of MSAs and Study ParticipantsaLevel ofEducationFollow-UpThrough 1989Follow-UpThrough 1998Follow-UpThrough 2000Covariatesb42 Individual + 7 Ecologic 42 Individual+ 7 Ecologic 42 Individual + 7 EcologicAll 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)aBased on a standard Cox model with 42 individual-level covariates. The baseline hazard function was stratified by age (1-yeargroupings), gender, and race. HRs are followed by 95% confidenceintervals. All analyses were conducted using the same 58 MSAsand 342,521 participants used in the current Extended Analysis.bData for the 42 individual-level covariates are from the ACSenrollment questionnaire. Data for the seven ecologic covariates(at MSA & DIFF levels) were extracted from the 1980 U.S. CensusBureau 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 conf erence statement for NERAM V includes the followin g policy guidelines:

1. Current scientific evidence on adverse health impacts is sufficient to support ai r quality management action. 2. Air quality management ac tions have been shown to be cost-beneficial in a number of circumstances. 3. Although additional scie ntific 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 ma nagement requires local, regional, national, international, and global actions. The results of the present an alysis support the call for greater efforts to reduce expo sure to air pollution embodied in the NERAM V conference statement. In developing air quality management policy, the following additional elements of the NERAM V co nference 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 (a ir, climate, transportation, energy, agriculture) based on integrated assessments are needed. 5. Air quality management strategies should support sustainable development. 6. The impact of air qua lity management interventions needs to be evaluated.
The accountability framewor k proposed by the HEI Accountability Working Grou p (2003) provides an excellent basis for addressing the last item.

In conclusion, Phase III of the Particle Epidemiology Reanalysis Project has provid ed additional su pport for the development of effective air quality management policies and strategies. The epidemiologi c results reported here are consistent with those from ot her population-based studies, which collectively support th e hypothesis that long-term exposure to PM 2.5 increases mortality in the general population. We also now know m echanisms 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 st atistical analyses, cartography, and graphic design. The following individuals contributed to the New York C ity Intra-Urban Analysis: Dr. John Mbwana, Cornell Univers ity, assisted with traffic data analysis; Michael Chiume, Sangeeta Bhowmick, and Alan Gershowitz, NYMTC, prepared traffic data.

Finally, the authors woul d like to acknowledge project funding support from the following spon sors: 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 Ve rna Richter Chair in Cancer Research (Duncan Thomas).



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### APPENDIX A. HEI QUALITY ASSURANCE REPORT
The conduct of this study was subject to independent QA oversight by Drs. W. Ca ry Eaton, Richard Kwok, andJames 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 chemistwith 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. Ot her participants on the RTIQA 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 sing le on-site audit at the McLaughlin Centre for Popul ation 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 ac tivities for conformance to the study protocol and operatin g 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 in ternal quality assurance procedures were reviewed. Audit observations consisted primarily of recomm endations for impr oving study documentation and some data proces sing and security issues. A copy of code was taken for review with an audit team member, Dr. Abhik Das, who di d 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 Re port. 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 i ssues were raised during thereview, but a list of minor comments and editorial suggestions was submitted to HEI.

Written reports of each activity were provided to the HEIProject Manager, who transmitted the findings to the Principal Investigator. Responses received from the project team demonstrated that the stud y was conducted by a highly experienced team of scientists with a high regard for the quality and credibility of th eir 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
AuditorsD. 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 Re search Report and (2) the title of the appendix requested.

APPENDIX B. Algorithmic Description of the Cox– Poisson Program

APPENDIX C. Computer Pr ogram for Random Effects Cox Model Using the Cox–Poisson Program



### ABOUT THE AUTHORS

Daniel Krewski is professor and dir ector of the R. Samuel McLaughlin Centre for Popula tion Health Risk Assessment and holds the Natural Scien ces and Engineering ResearchCouncil/McLaughlin Chair in Risk Science at the University of Ottawa. Dr. Krewski has also served as adjunctresearch professor of statistics in the Department of Mathematics and Statistics at Carl eton University since 1984. Before joining the Faculty of Medicine at the University of Ottawa in 1998, Dr. Krewski wa s 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 Ch emical Hazards and as chief of the Biostatistics Division in the Environmental Health Directorate. Dr. Krewski obtained his Ph.D. in statistics from Carleton University an d subsequently completed an MHA at the University of Ot tawa. His professional interests include epidemiology, biostatistics, risk assessment, and risk management.

Michael Jerrett , Ph.D., is an associat e 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 pollutio n–health associatio ns in the United States, Canada, and Mexico with special reference to social–spatial confounders an d intra-urban exposure gradients. He is also engaged in determining the role of the built environment as a cont ributor to childhood obesity formation. He was awarded the 2004 Dangermond Endowed Speaker in Geogra phic 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 scie ntist in the Institute of Population Health, and a scientist with the McLaughlin Centre for Population Health Risk As sessment, 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 tim e-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 Brun swick. 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 epid emiologists on spatial analysis of adverse effects of air pollution and works with social scientists on spatio-te mporal 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 re search division of Ontario Hydro. Since 1988, he has op erated 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 ep idemiologic 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 inte rests are in environmental, cancer, and reproductive hea lth 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 enviro nmental epidemiology, cancer epidemiology, and risk perceptio n. She is supported with a Canada Graduate Scholarship from the Canadian Institutes of Health Research.

C. Arden Pope III is the Mary Lou Fulto n 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 appo intments at Iowa State and Texas A&M Universities and has been an Interdisciplinary Programs in Health fellow a nd visiting scientist in Environmental Health and Public Policy at the Harvard School of Public Health. He has cond ucted 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 underg raduate degrees in environmental engineering and en vironmental studies from Brown University, and received his masters and doctorate degrees in environmental heal th sciences from the Harvard School of Public Health. Dr. Thurston was a contributing author to the most rece nt 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 Pr evention Study cohorts. Her research was primarily in the areas of breast cancer risk factors, hormone-replacemen t therapy and cancers inwomen, 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 Univ ersity 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 di sease prevention, first as a medical officer investigating toxic exposures at the New Jersey State Health Department, and then as an epidemic intelligence service officer an d staff scientist for the Centers for Disease Control an d Prevention at the National Institute for Occupational Safe ty and Health (1980–1988). In 1989, Dr. Thun became dire ctor of analytic epidemiology for the ACS in Atlanta. Since 1998 he has served as vice president of epidemiology and surveilla nce research, overseeing both cancer surve illance 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, Th un M. 2009. Long-term ozone exposure and mortality. N Engl J Med 360:1085–1095.
Jerrett M, Newbold KB, Burnet t 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. St och Environ Res Risk Assess
21:511–522.
Moore DK, Jerrett M, Mack MJ, K ü nzli N. 2007. A land use regression model for predictin g ambient fine particulate matter across Los Angeles, CA. J Environ Monit 9:246–252.
Ross Z, Jerrett M, Ito K, Temp alski B, Thurston G. 2007. A land use regression model fo r predicting fine particulate matter concentrations in the New York City region. Atmos Environ 41:2255–2269.

Jerrett M, Burnett RT, Ma R, Po pe CA III, Krewski D, Newbold KB, Thurston G, Shi Y, Fink elstein N, Calle EE, Thun MJ. 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16:727–736.

K ü nzli N, Jerrett M, Mack WJ, Beckerman B, LaBree L, Gilliland F, Thomas D, Peters J, Hodis HN. 2005. Ambient air pollution and atherosclerosi s 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-termD. Krewski et al.

111 exposure to ambient air pollu tion: Ongoing analyses based on the American Cancer Soci ety cohort. J Toxicol Environ Health 68:1093–1109.
Pope CA III, Burnett R, Thur ston G, Thun M, Calle E, Krewski D, Godleski JJ. 2004. Cardiovascular mortality and long-term exposure to particul ate 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 modell ing 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 pollutio n. Environ Health Perspect
111:1861–1870. Ramsay T, Burnett R, Krewski D. 2003. Exploring bias in a generalized addi tive 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 Syst ems 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 En vironmental Risk Assessment and Management NYMTC New York Metropolitan Transportation Council NYU New York University

O 3 ozone PM 2.5 particles of 2.5 μm or less in aerodynamic diameter
R 2 coefficient of determination for multivariate analyses RMSE root mean squared error
SCAG Southern California Area Governments

SO 2 sulfur dioxide
SO 4 2

sulfate 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 standard112 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

errors and confidence interval s of population parameters, such as the mean, median, and proportion. It may also be used for constructing hypo thesis 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 gro ups, which make up census
tracts.

Centroid The intersection of a ll straight lines that divide a two-dimensional figure (such as a polygon) into two parts.

Confounder An extraneous or “unstudied” factor thatmay be responsible for the observed effect; it is unmeasured, yet is statistically a ssociated (positively or negatively) with both the observed outcome and the exposure of interest (both the dependent and independent variables ). For example, regular coff ee 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’ smokin g into account as a possible confounder. A confounder mu st 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 mo rtality data, and the independent variables are PM 2.5 exposure data, individual-level covariates, and informat ion about communities.
Explanatory power The ability of a model or theory to account for the obse rved phen omenon.

Geostatistics Originally applied to problems in geology; it involves statistical procedur es 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 measur e of inequality of income or wealth distribution within a de fined area (such as a city, state, or country). It is pr esented 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 st atistics to the presence or absence of a single data point. See also bootstrapping

.
Kernel A mathematical method used to identify the underlying structure of comp lex data. Nonlinear clustering methods based on ke rnels provide a common means of resolving patterns in spatially correlated data.

Kriging A group of geostatistical techniques for interpolating values (estimating a va lue between known values). It uses the distance between data points and the degree ofvariation 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 depe ndent 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 thatD. 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 uncorr ected 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 topo graphic relief, lens distortion, and camera tilt.
Principal components A statistical procedure that combines a number of possibly co rrelated variables and transforms them into a smaller number of uncorrelated variables called principal co mponents. 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 bord er 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 complete ly 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 exposure s, 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 he ight of each man in thesample and the unobservable population mean is a statistical error; and

the difference between the he ight 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 valu es actually observed. Sample with replacement A sampling method that involves randomly selecting a specified number of data points for analysis, and retu rning them to the population before randomly selecting an other 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 th en return them to the jar, you have the same odds of getting 1 red and 2 yellow beans the next time as you did orig inally. 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 visua lize 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 th e shape of the distribution of the observed values. It measures the correspondence between the rankings of obse rvations for each variable in the data and assesses its significance.

Standard Cox proportional-ha zards 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, wh ich describe how hazard may be affected by other factors, such as the choice of treatment for an illness. The proportio nal-hazards assu mption 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-hazardsassumption 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 tosurvival data is referred to as applying the Cox proportional-hazards model (referred to as standard Cox model in this report).114 Extended Analysis of ACS Study of Particulate Air Pollution and Mortality

Stationarity / nonstationarity Qualities of a random process: the statistical propertie s (such as mean and standard deviation) of a process that exhibit stationarity do not change over time or across sp ace. 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 thewhole 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 ofpoints (e.g., an air pollution monitor). The boundaries of the polygon define the area that is closest to each poin t 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 measureshow much the variance of a coefficient (square of the standard deviation) is increased because of multicollinearity. Multicollinearity is a statisti cal phenomenon in which two or more predictor variables in a multiple regression model are highly correlated.
(Glossary prepared by He alth Effects Institute)Health Effects Institute Research Report 126 © 2004 115 

# COMMENTARY Health Review Committee

Research Report 140, Extended Follow-Up and Spatial Anal ysis 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 Am erican Cancer Society Study Linking Particulate Air Pollution and Mortality , Dr. Daniel Krewski and colleagues provide details of Phase III of the Particle Epidemiolog y Evaluation Project, their research with the cohort from the Amer ican Cancer Society (ACS*) Cancer Prevention Study II (C PS-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 coll eagues valida ted and replicated earlier results from othe r investigators of associations between exposure to particulat e air pollution and mortality and then used their data to try new methods of analysis that incorporated individual-leve l and ecologic covariates.

Dr. Krewski submitted a preliminary application to HEI in February 2001, requestin g funds to conduct a third phase of his analysis of the ACS cohort. Later that year, he submitted a full a pplication, and in February 2002, HEI approved the project.

The HEI Research Committee chose to fund Phase III because they believed it wo uld yield further important information about the effects of long-term exposure to air pollution on mortality. During the Reanalysis Project, Krewski and colleagues ha d developed new statistical models to incorporate city-le vel ecologic covariates and control for spatial autocorrel ation in the data. For this Extended Analysis, they plan ned to apply updated statistical methods to new follow-up data (through 2000) for theACS cohort. This cohort remains a key source of information on the effects of long-te rm exposure to air pollution on mortality from chronic diseas e. 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 th e U.S. Environmental Protection Agency (U.S. EPA) in the development of the cost– benefit analyses for regulati ons of emissions from heavyduty diesel engines. Past resu lts from studies of this cohort along with the Phase III resu lts are also being considered in the current U.S. EPA process for reviewing the National Ambient Air Quality Standard (NAAQS) for fi ne particles (particulate matter 2.5 μ m or smaller in aerodynamic diameter [PM 2.5 ]).



### 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 corrobor ate and further define the relationship between exposure to PM 2.5 and mortality. Two U.S. cohort studies became central to the 1997 debate on the NAAQS for PM 2.5 pollution in the United States: the Harvard Six Cities Study (Docke ry 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 da ta from 151 cities. Both of these studies estimated exposure to PM 2.5 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 PM 2.5 .Several analyses of these two cohorts were also considered in the EPA review of the NAAQS for PM 2.5 in 2006. Other important cohort studies that were considered in that review included the Seventh-Da y 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, looki ng at all the available evidence, the U.S. EPA conclude d that overall the epidemiologic evidence of an asso ciation between long-term exposure to PM 2.5 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 Li nking 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 investigat ors 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.116 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 ACSHarvard SixCities(Dockery etal. 1993)8,111 adults in thestudy of healtheffects of airpollution; cohortfollowedprospectively for14–16 yearsstarting in 1974Ambient concentrations measured in eachcommunity at a centrally located air monitoringstation: TSP, SO2, O3, suspended SO42for1977–1985; inhalable and fine particles 1979–85;SO42particles 1979–1984; aerosol acidity 1985–1988Adjusted mortalityrate ratios forair pollution estimated bysimultaneously adjusting for otherrisk factors in standard Coxmodel; subjects stratifiedaccording to sex and 5-year agegroupAdjusted mortalityrate ratio for themost-polluted city compared with theleast-polluted city was 1.26 (95% CI1.08–1.47); air pollution weaklyassociated with death from lung cancerand cardiopulmonary disease; PM2.5andSO42strongly associated withmortalitySeventh-DayAdventistHealth Studyof Smog(AHSMOG;Abbey et al.1999)6,338 nonsmokers,white, ages 25–97years, residents ofCalifornia, enrolledbefore 1977Days per year when PM10exceeded 100 μg/m3Relative risks calculated usingstandard Cox model, controlled forage, smoking, passive smokeexposure, occupation, diet,body/mass index,and familyhistory of chronic diseaseRelative risk for an increase of 43 days peryear above 100 μg/m3PM10andmortality from all natural causes was1.12 (95% CI 1.01–1.24) for males formortality from nonmalignant respiratorydisease was 1.28 (95% CI 1.03–1.57 formales; no significant associations foundfor women or for PM10exposureconcentrationVeterans’CohortMortalityStudy(Lipfert et al.2000)50,000 male U.S.military veteransdiagnosed withhypertension in themid-1970s; averageage at recruitment 51± 12 years; cohortfollowed for ~21yearsPollutant levels averaged by year and county forPM2.5, coarse particles, and PM15data from the U.S.EPA Inhalable Particle Monitoring Network 1979–1984Standard Cox model controlled forrace, smoking, age, blood pressure,body/mass index,and “selectedecologic variables”at the Zip Codeand county levelsPM2.5was not associated with anysignificant (positive) excess in mortalityrisk for any of the models evaluatedWomen’sHealthInitiative(Miller et al.2007)65,893 postmenopausal womenwithout preexistingcardiovasculardisease in 36 U.S.metropolitan areas;cohort followed1994–1998Exposure to PM2.5assessed at monitor nearest toresidenceHazard ratios estimated for the firstcardiovascular event or for deathfrom cardiovascular disease;adjusted for age, race, diabetes,hypertension, andhypercholesterolemiaDifference of 10 μg/m3PM2.5associatedwith 24% increase in risk of acardiovascular event (relative risk 1.24;95% CI 1.09–1.41) and 76% increase inrisk of cardiovascular mortality (RR1.76; 95% CI 1.25–2.47)*NLCS-AIR(Beelen etal. 2008;Brunekreefet al. 2009)120,740 residents ofthe Netherlands,ages 55–69, enrolledin 1986; cohortfollowed 1987–1996Combination of regional, urban, and local exposure(1976–1996 averages) determined from monitoringstation data and land-use regression and assignedto participants, residence locations; separatebackground (urban plus regional) level used withvarious measures of exposure to trafficRelative risks calculated usingstandard Cox model; analysescontrolled for personal riskfactors, and for communityincome levels at the local andregional scalesRelative risks for a 10 μg/m3change inblack-smoke exposure; associated withlung cancer incidence in nonsmokers(RR 1.47; 95% CI 1.01–2.61), mortalityfrom natural causes (RR 1.05; 95% CI1.00–1.11), and respiratory causes (RR1.22; 95% CI 0.99–1.50)Table continues next page* Study funded by HEI117 Health Review CommitteeCommentary Table 1 (Continued).Key Prospective Long-Term Cohort Studies of Air Pollution and MortalityStudy Cohort Exposure Data Analysis ResultsStudies That Used Data from the ACS CPS-II CohortOriginalAnalysis ACSCPS-II (Popeet al. 1995)552,138 adultsresiding in theseareas when enrolled1982 included datafor individual riskfactors; deathsascertained through1989Ambient data for 151 U.S. metropolitan areas in1980; SO42and PM2.5estimated from nationaldata bases and used as indices of exposure tocombustion-source ambient PMMultivariate analysis used toexamine relationships of airpollution with mortality from allcauses, lung cancer, andcardiopulmonary disease; analysiscontrolled for smoking, education,and other risk factorsIncreased mortality associated withSO42and PM2.5air pollution at levelscommonly found in U.S. cities*ReanalysisProject(Krewski etal. 2000)Same cohort data as inthe Initial Analysis(Pope et al. 1995) butsubjected toadditional qualitycontrol proceduresSame exposure data and assignment as in the InitialAnalysis but with additional quality controlprocedures and datavalidation by auditPhase I — Replication andvalidation of Six Cities and ACSCPS-II resultsPhase II — Sensitivity analysiscompare results from alternativerisk models using different hazardfunctions and spatial factorsPhase I — Mortality association withSO42–and PM2.5compared closely withfindings from Initial AnalysisPhase II — Positive associations betweenmortality and pollutants robust tocontrol for confounding, analyticmethods used, and adjustment for spatialautocorrelationUpdated ACSAnalysis(Pope et al.2002)500,000 adults inACS CPS-II enrolledin 1982; includeddata for individualrisk factors; deathsascertained through1998Mean concentrations of air pollution formetropolitan areas compiled from various primarydata sources including EPA AIRS, IPMN, andNAD; extracted from IPMN for 1979–1983 andAIRS for 1999–2000Adjusted relative risk ratios formortality estimated usingstandard Cox model includingmetropolitan-based randomeffects component; estimated formortality fromall causes, cardiopulmonarydisease, lung cancer, and all othercausesPM2.5, SO42, and SO2pollutionassociated with mortality from allcauses, cardiopulmonary disease, andlung cancer; PM2.5found to be animportant environmental risk factor fordeath from cardiopulmonary diseaseand lung cancerUpdated ACSAnalysis(Pope et al.2004)319,000 adults inareas with PM2.5monitoring data for1979–1983 and1999–2000; 500,000ACS adults in areaswith PM2.5monitoring data for1999–2000PM2.5monitoring data from IPMN for 1979–1983and from AIRS for 1999–2000 and assigned annualaverage PM2.5exposure to participantsStandard Cox model used tocalculate relative risk ratios per10−μg/m3change in PM2.5for subcategories of cardiopulmonarydisease; controlled for availableindividual risk factorsLong-term PM2.5exposures most stronglyassociated with mortality attributed toischemic heart disease, dysrhythmias,heart failure, and cardiac arrest; weakassociations with respiratory mortality118 Commentary on Investigators’ Report by Krewski et al.

THE HARVARD SIX CITIES STUDYAs part of the Harvard Six Cities Study, Dockery and colleagues (1993) had prospectivel y followed a cohort of 8,111 adult subjects in the Northeas t 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 andthe results were described as being associated with the difference in concentrations between the two cities. A difference of 18.6 μ g/m 3 PM 2.5 was associated with a 26% increase in mortality from all causes. The same difference in PM 2.5 was associated with a 37% increase in deaths from cardiopulmonary disease. A fo llow-up study of this cohort conducted in a period of lowe r 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 AC S 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 PM 2.5 were associated with increased mortality from all causes and from cardiopulmonary di sease in the 50 cities for which PM 2.5 data were available (data from monitors for 1979 to 1983). Higher ambient SO 4 2

levels were associated with increased mortality from all causes, cardiopulmonary disease, and lung cancer in the 151 cities for which SO 4 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-po lluted city was 17% for PM 2.5 and 15% for SO 4 2

(the pollutant range among the cities was
24.5 μ g/m 3 for PM 2.5 and 19.9 μ g/m 3 for SO 4 2

).

Although these two cohort studies produced similar results, they differed in desi gn and limitations. Important strengths of the Six Cities Study included the randomselection of study subjects; the high response rate of participants enrolling (> 70%); the personal interviews conducted with respondents at th e time of enrollment and subsequent follow-up interviews at intervals of 3, 6, and 12 years; lung-function measur ements at baseline; and the gathering of residential hist ories. The air pollution measurements were collected by the research team, who had designed the Six Citi es Study to monitor a range of air pollutants that was nearly as la rge 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 ofyears, 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 in formation collected for the enrollment questionnaire on health status, demographiccharacteristics, smoking history, alcohol use, and occupational exposure. One limitation was that these subjects were enrolled by ACS volunteers fr om among their friends and relatives so the subjects may no t 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 st udies came under intense scrutiny when the U.S. EPA used the results to support new NAAQS standards for PM 2.5 and to maintain the standards for particles of 10 μ m or smaller in aerodynamic diameter (PM 10 ) that were already in effect. Members of Congressand 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 argu ed that the individual health and mortality data had been gathered with assurances ofconfidentiality 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 righ ts of the investigators andtheir 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 Up a

Pope (Initial Original) 1995 1989 (7) Krewski b (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 Si x 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 ateam 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’sbehalf and to ensure that the Reanalysis would be conducted by independent and im partial investigators. ThePanel recommended that Dr. Daniel Krewski of the University of Ottawa and his team conduct the Reanalysis. The HEI Board of Directors appr oved 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: Replicat ion and Validation
, the Reanalysis Team sought to replicate the initial studies through a quality assurance audi t 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 altern ative risk models and analytic approaches, including models that used different hazard functions and models that in corporated 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; no ne of them substantively altered the original finding ofan association between indicators of particulate air pollution and mortality.

THE UPDATED ANALYSIS

After the Reanalysis Project , Pope and colleagues undertook a subsequent set of anal yses 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 th e number of deaths. Exposure data were expanded to includ e data on gaseous copollutants and new PM 2.5 data that had been collected since the enactment of the new air quality standards. Recent advances in statistical modelin g were incorporated in the analyses, including the introduction of random effects and nonparametric spatial smoothing components into the standard Cox proportional-h azards regression model of survival (referred to as the standard Cox model; see Glossary of Statistical Terms in th e Investigators’ Report). This Updated Analysis provided th e strongest evidence that the long-term exposure to fine pa rticulate 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- μ g/m 3 increase in longterm average ambient PM 2.5 concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death from all causes, cardio pulmonary dise ase, and lung cancer, respectively. There wa s no evidence of a threshold exposure level within th e range of observed PM 2.5 concentrations. There was also no st atistically 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 theresidual 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 refinedtheir classification of causes of death within the “cardiopulmonary” category by separately analyzing ischemic heart disease, dysrhy thmias, hypertensive disease, atherosclerosis, chronic obstructiv e pulmonary di sease, pneumonia, and other subcategor ies of cardiovascular and respiratory causes of death. They found significantly increased risks of death (rangi ng from 8% to 18% per 10-
μ g/m 3 increase in PM 2.5 ) for the various subcategories of cardiovascular causes of deat h (ischemic heart disease, dysrhythmias, heart failure, and cardiac arrest) and only nonsignificant associations fo r all respiratory subcategories. This study demonstrated that the strongest association between PM 2.5 exposure and mo rtality within the broadly defined “cardiopulmonary” category is for death from ischemic heart disease.

THE EXTENDED ANALYSIS The current study, conducte d by Daniel Krewski and colleagues, extends the follo w-up period for the ACS cohort through 2000 (18 years). The investigators have continued to produce nationwid e estimates of the risk of death from various causes usin g additional years of data. In addition, this pr oject extended the range of analyses to include the following:
• A Nationwide Analysis inte grating control for spatial autocorrelation and socioeco nomic covariates at the neighborhood (Zip Code ar ea [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 featur ing land-use regression (LUR) techniques to refine es timates of exposure from local sources (e.g., tra ffic and industry); and120 Commentary on Investigators’ Report by Krewski et al.

• 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 incorporatingsophisticated controls for known sources of bias and confounding in the st atistical 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 exposu re 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 standardCox 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 ci ty estimates and ecologic covariates (variables that repres ent 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 concen trations 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 localland-use and traffic data to supplement ambient monitoring data for PM 2.5 . ACS cohort mortal ity 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 prosp ective 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 wh o reside in metropolitan areas of the contiguous 48 states and District of Columbia that have adequate air pollution mo nitoring data for the study period. This yielded approx imately 575,000 total participants for the air pollution st udy, including about 360,000 and 500,000 who resided in areas with adequate PM 2.5 monitoring information in 198 0 and in 2000, respectively.

At enrollment in 1982, par ticipants 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 th at included personal demographic characteristics, pers onal habits, occupational history and exposures, tobacco and alcoho l use, and other factors possibly related to mortality from cancer. These questionnaires provided data for the 44 individual-levelcovariates (see details in the 44 Individual-Level Covariates sidebar in the Investigator s’ Report) used in these andother 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 were121 Health Review Committee

known to have died and compiled cause-of-death information. Causes of death analyzed in this study fall into these categories: all causes, cardiop ulmonary disease, ischemicheart disease, lung cancer, and all other causes. The original study and the Reanalysis of this cohort contained follow-up information throug h 1989, the Updated Analysis through 1998, and this Extended Analysis through2000 — 11 additional years of follow-up that were unavailable for the initial studies.

In addition to the individu al-level covariate information from the ACS enrollment ques tionnaires, Dr. Krewski’s research team gathered data for seven ecologic covariates to use in their analyses. Ecolog ic 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 he alth beyond that predicted by a person’s own attributes and si tuation (e.g., diet and exposure to tobacco smoke). Ecolog ic covariates are included in health research analyses b ecause of a growing awareness that the possible influences of local neighborhood or urban conditions need to be direc tly accounted for in a health assessment in order to elimin ate them as the primary cause of a health outcome (referred to as control of confounding).

For this study, the resear chers enumerated ecologic covariates for both the ZCA and city of residence for each participant based on demograp hic data from the 1980 U.S. Census. These neighborhood factors included prevalenceof air conditioning use, percentage of residents who completed high school, percentage of residents who were not white, unemployment, median household income, relativeincome 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 organizationsin conjunction with the U.S. EPA. Pollutants and the distributions of exposure to th em appear in Table 1 of the Investigators’ Report.

PM 2.5 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 ex posure early and late in the follow-up years might have affected the association between particul ate exposure and mortality and to compare results from the Nationwide and IntraUrban Analyses: PM 2.5 (1979–1983) and PM 15 (1979–1983) averaged IPMN data for 6those years, and PM 2.5 (1999– 2000) averaged data from th e AIRS. Dr. Krewski’s team constructed similar variable s 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 (O 3 ), nitrogen dioxide (NO 2 ), and sulfur dioxide (SO 2 ) from AIRS data. Additional O 3 data for April through September of 1980 represented the peak season al average exposure and was calculated from third- quarter 1980 AIRS data. SO 4 2

(1980–1981) averaged IMPN an d NAD data from 1980 and 1981, and SO 4 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 [eco logic] variables confound or modifythe 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 sufficien t 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 pollutantfor each of the years of interest were computed and averaged to obtain a summary con centration 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 sc ale. Zip Codes are used by the U.S. Postal Service to simplify the delivery of mail and are not necessarily areas with we ll-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 liste d for the 1980 Census. For each ecologic covariate, ZCA-scale data from the U.S. Census Bureau files were averaged to obtain a value122 Commentary on Investigators’ Report by Krewski et al.

for each MSA. The difference between the ZCA value and the MSA value for each ecologic covariate is referred to asDIFF. 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 ZC A values were not adjusted for the MSA value. Survival analyses are frequently used in long-term cohort studies of air pollutio n exposure and mortality. In this survival analysis, a fixed number of participants arefollowed over time; mortality data are collected as members die, and no new members are recruited. Groups of participants are classified acco rding to exposure level, and subgroups are compar ed 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 ar e typically adjusted for age, smoking status, sex, and othe r variables known to affect longevity that could confound the relationship between pollutant exposure and deat h. 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 assume s that the mortality experience of individuals in the study is independent (after adjusting for covariates), even for participants living in the same household or neighborho od. However, it is possible that mortality experience for people in the same communitywill 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 th en explored the selected pollutant–cause of death combina tions with the far more computationally powerful random effects Cox model, which allowed them to account for spatial autocorrelation andinclude multiple levels of spatially defined covariates (Specific Aim 2).

The random effects Cox model relaxes the assumptionthat 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 PM 2.5 in urban areas and compare them with those obtained from the Nationwide Analysis. In the Intra-Urban Analyses, however, they would use land-us e 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 PM 2.5 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 regr ession statistical methods were used to estimate and assign local exposures to participants rather than as signing the same MSA average value to everyone.

New York City Analysis The investigators used New Yo rk 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 re gion 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 anal yze mortality from all causes, ischemic heart disease, car diopulmonary 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-123 Health Review Committee

system (AQS) for 19 99 through 2001. In addition, theyused 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 traffi c counting systems, roadway network maps, satellite photos, and local government planning and tax-assessment maps.

Developing a LUR model requ ires evaluating land use around pollution monitors us ing different types of landuse variables to find which on es 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 concentra tions at the remaining monitors that had not been used. When the predicted value was compared with the actual me asured 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 ch aracteristics. The final LUR model for New York City predicted 66% of the variation in monitored PM 2.5 concentrations across the study area ( R 2 = 0.66).

Three LUR models were creat ed for the New York Cityregion: two covering 28 counties (one using a 3-year exposure average and one using on ly winter exposure averaged over 1 season) and one coveri ng 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 entir e 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 us e their LUR model to assign exposure to participants (for analyzing mortality) becausethe LUR was more likely to account for highly local variations in exposure du e to traffic patterns and land use in the areas where participants actua lly resided, whereas kriging relied only on data near pollution monitors.
As with the Nationwide Anal ysis, the investigators used both the standard Cox and rand om effects Cox models for analyzing pollutant exposure and mortality. They incorporated the 44 individual-level covariates from the ACS enrollment questionnaire as we ll 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, isch emic heart disease, cardiopulmonary disease, lung cancer , endocrine disorders, and diabetes. In addition, the Lo s Angeles Analysis includeddigestive cancer, other cancers, digestive disorders, accidents, and all other causes. The investigators used bothLUR and kriging methods to estimate exposure concentrations at the center of each ZCA. The standard Cox andrandom effects Cox models were used to calculate exposure–mortality associations; they included the 44 individual-level covariates (fr om the ACS enrollment questionnaire) and seven ecol ogic 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 O 3 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 th e California EPA data for 23 PM 2.5 monitors and the Californ ia Air Resources Board 42 sites that monitor O 3 . Although PM 2.5 was of primary interest, the Los Angeles Analysis included O 3 concentrations as a copollutant in the health analyses. To develop the LUR models, they used data for digital land use, mapped road netwo rks, road classes and speedlimits, traffic counts, population, and topography (elevation). The investigators cons tructed a LUR model for the Los Angeles area that predicted 69% of the variation in local PM 2.5 concentrations when evaluated using regression diagnostics (as described in the Investigators’ Report). Primary predictors of expo sure in the LUR model were124 Commentary on Investigators’ Report by Krewski et al.

traffic volume, industrial areas , and government areas (as a proxy for traffic intensity ar ound major des tinations 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 PM 2.5 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 monito rs had been placed for purposes of federal and state regu latory compliance and did not specifically monitor sources of pollution, such as nearby industry, or traffic, or pollut ant concentrations in residential areas. A brief comparison of the associations (hazard ratios) for death from various causes and PM 2.5 exposures modeled using both kriging and LUR fo r Los Angeles andLUR 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 O 3 using data from the 42 monitors. One model mapped the interpol ated 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 concen tration for each monitor for 1999 through 2001). The team led by Dr. Jerrett applied the same standardCox and random effects Cox models used for the Nationwide and New York City Anal yses of mortality and exposure concentrations to the Los Angeles region. Unlike the other analyses, Dr. Jerrett’s team also included covariates for O 3 exposure because it is su fficiently high in the LosAngeles Basin to be a potential confounder of the relationship between PM 2.5 exposure and mortality.

CRITICAL EXPOSURE TIME WINDOWS ANALYSIS (SPECIFIC AIM 3)

To address Specific Aim 3 (Wha t 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 con centrations in different time intervals might be associated with mortality patterns. This analysis was designed to ad dress hypotheses about longterm exposures — that some ti me 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 rese archers 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 im portant 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 Na tionwide and Intra-Urban Analyses a

Nationwide b Intra-Urban New York City c Intra-Urban Los Angeles b
Exposure assignment method MSA Average LUR LUR Kriging

3-Year Model Winter-2000 Model d

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 individua l-level covariates; and adjusted

for seven ecologic covari ates at the MSA & DIFF levels. HRs ar e followed by 95% confidence intervals.

b HRs for the Nationwide and Los Angeles analyses were calculated per 10-μg/m 3 change in PM 2.5 .

c For easier comparison with Nationwide and Los Angeles resu lts, HRs for the NYC analyses were calculated for a 10-μg/m 3 change in PM 2.5 converted from

data reported for a 1.5-μg/m 3 change in PM 2.5 for the 3-year model and from data reported for a 3.9-μg/m 3 change in PM 2.5 for the winter-2000 model.

d Based on data for January, February, and March 2000.125 Health Review Committee

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 questionna ires with detailed questions on diet, physical activi ty, and other i ndividual-levelfactors 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 particip ants who resided in 106 MSAs with enough available average PM 2.5 monitoring data; of those, 8181 were know n to have moved to another MSA during follow-up. This group is referred to as the PM 2.5 -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 PM 2.5 -B group; the PM 2.5 -B group and the PM 2.5 -A group are mutually exclusive.

A 15-year exposure profile for each member of the PM 2.5 -A and PM 2.5 -B groups was constructed from PM 2.5 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 ex posure profiles. For this purpose, PM 2.5 levels were derived by Lall and colleagues (2004) from average annu al measured TSP and PM 10 concentrations for 1972 through 2000.

A third group included 80,7 11 Nutrition Cohort participants in 120 MSAs for which at least 20 years of annual average SO 2 measurements were available; 5762 (7.1%) participants moved at leas t once during the exposure period. Similar exposure profiles for SO 2 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 in dividual-level variables from the ACS 1982 enrollment questionn aire. 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 PM 2.5 -A and PM 2.5 -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 mode l 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 info rmation 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 PM 2.5 -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 inve stigators 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 PM 2.5 and mortality obtained from the random effe cts Cox models that were adjusted for ecologic covariates and spatial autocorrelation.

Generally speaking, use of the random effects Coxmodel leads to somewhat different results than the standard Cox model, most nota bly small increases in the hazard ratios and s lightly wider confid ence intervals (CIs). Results of standard Cox and ra ndom effects Cox models for the associations of PM 2.5 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 PM 2.5 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 cova riates (in add ition to the 44 individual-leve l 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 diseas e. Additional controls for these ecologic covariates at the MSA and ZCA scales appeared to remove some conf ounding effects, even as the126 Commentary on Investigators’ Report by Krewski et al.

switch to the random effects model, with its relaxed assumptions, resulted in decreased ce rtainty for some estimates. Although conventional statis tical models, including the standard Cox model, assume th at all data points are independent of one another, it is unlikely that individuals wholive in the same household or neighborhood are independent with respect to meas ured 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 relati vely 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 poll utants other than PM 2.5 are worth noting. In the Reanalysis Proj ect (Krewski et al. 2000), the research team had investigated whether the relationship between PM 2.5 exposure and morta lity changed when O 3 was included in the model and had found little effect.Therefore, the investigators did not repeat those combinations and analyzed O 3 in single-pollutant models. Summer O 3 levels (calculated from concentrations measured from April to September in 1980) were significantly associated with mortality from all cause s (HR = 1.02; 95% CI, 1.01

– 1.03) and from cardiopulmona ry disease (HR = 1.03; 95% CI, 1.02

– 1.04). O 3 exposure has recently been linked with mortality from pulmonary ca uses (Jerrett et al. 2009), whereas PM 2.5 -related mortality ha s been shown to be dominated by cardiovascular causes (Pope et al. 2002). This implies that exposure to O 3 does not confound the association between PM 2.5 and mortality. Dr. Krewski’s team did not construct a ra ndom effects Cox model for O 3 with ecologic covariates, nor did they compare mortality from respiratory cau ses with mortality from other causes of death. They have recently published the results of further analyses of O 3 exposure and death from pulmonary causes in this cohort (Jerrett et al. 2009).

Hazard ratios for exposure to SO 4 2

and SO 2 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 randomeffects Cox models because of the project’s limited computing resources and their focus on PM 2.5 analyses. Because SO 2 concentrations have been reduced across the United States during the follow-up years, however, SO 2 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 (Popeet 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 estim ates of risk, and those with more than a high school education had no apparentincrease 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 PM 2.5 in Two Time Periods from the Nationwide Analysis a

Cause of Death Standard Cox Mo del Random Effects Cox Model b

HR per 10-μg/m 3 Change in PM 2.5 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/m 3 Change in PM 2.5 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-y ear groupings), sex, and race and were adjusted for the 44 ind ividual-level covariates.

Analyses with the random effects Cox model we re also adjusted for the 7 ecologic covariates at the MSA & DIFF levels. HRs are f ollowed by 95% confidence intervals. Values are from Tabl es 3 and 6 in the Investigators’ Report.

b Results are for the MSA & DIFF combination of ecologic covariat es. The DIFF value for each ZCA is the ZCA-specific value minus the MSA mean. This

model allowed nested ecologic information to be analyzed.127 Health Review Committee

cancer), but the hazard ratio s for participants with morethan a high school education were positive for all evaluated causes of death. For isch emic heart disease, the trend seemed to be reversed — those with higher education had the highest risk. Full results fo r 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 exposu re 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 Angele s and Nationwide Analyses, hazard ratios for the New York City Analysis have been converted to a 10- μ g/m 3 exposure change from 1.5- μ g/m 3 change for the 3-year model and from a 3.9- μ g/m 3 for the winter-2000 model. The resu lts for the fully adjusted Nationwide Analysis are in cluded for comparison.

Even though the New York City and Los Angeles Analyses share common theoretical and methodologic approaches, the results of LU R models for assigning exposure and the estimates of attributable health effects are strikingly dissimilar. The haza rd ratios are much higher for Los Angeles than for New York City, except for ischemic heart disease. Possible reas ons for this difference may be the range of PM 2.5 exposures derived for the Los Angeles area and the relative uniformity of exposures in the New York City region. Annual average measured PM 2.5 concentrations from air monitors in the Los Angeles region ranged from 9.5 to 28 μ g/m 3 , and annual average exposure values predicted by the LUR at high -exposure locations such as freeway interchanges were in excess of 125 μ g/m 3 . In contrast, the interdecile (10th to 90th percentile) range of exposure for the New York City region was 1.9 μ g/m 3 for the 3-year model and 3.9 μ g/m 3 for the winter-2000 model. These differences may stem, in part, from the different LUR models constructed for the two cities because LURmodels 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 PM 2.5 -A group, composed of Nutrition Cohort participants, and the PM 2.5 -B group, made up of CPS-II cohort members who were assumed to have died without moving from their 1982 MSAs of reside nce. Table 26 in the Investigators’ Report details the results for PM 2.5 and SO 2 exposure in 5-year segments.

Overall, differences in model-fit statistics and in the hazard ratios and confidence in tervals for combinations of two exposures, two cohorts, and three 5-year time periodsof exposure are modest and demonstrate no definitive patterns. However, some possib le trends can be discerned. Deaths from cardiopulmonary disease were most strongly associated with SO 2 exposure 1 to 5 y ears before death and with PM 2.5 exposure 6 to 10 years before death. Associations between exposure to PM 2.5 and mortality from lungcancer showed some interesting patterns for all time windows for both PM 2.5 -A and PM 2.5 -B groups, and some associations for the PM 2.5 -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 se veral 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 a ssociations 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. Th e results for the New York City region are entirely new.

NATIONWIDE ANALYSIS

Study Design

The broad study design for this Extended Analysis of thenationwide 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 believe128 Commentary on Investigators’ Report by Krewski et al.

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 (Kre wski 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, wh ich 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 co uld arise if one of these risk factors were asso ciated 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 spa tial autocorrelation. Ecologic covariates are risk factors for mortal ity that are measured at theneighborhood 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 ri sk factors. Ecologic confounding arises when such vari ables 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 fr om the 1980 U.S. Census; they included measures of income, unemployment, availability of air conditioning, an d 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 confoundin g more refined in this Extended Analysis than for the Reanalysis (for which only MSA-scale ecologic covariates were considered). However, not all previously consider ed ecologic covariates were included in this analysis. In particular, population migration and ambient temperature had both been shown to bedeterminants of mortality with some evidence of confounding in the Rean alysis 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 ecologi c covariates are controlled. Spatial autocorrelation is of particular concern with respect to the mortality data us ed in this study because theReanalysis documented that air pollution levels are spatially autocorrelated. Spatial autocorrelation has two types of effects that might be relevant here: Its presence suggeststhe existence of determinants of mortality otherwise unaccounted for in the model, which might (though need not) confound the association betw een 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 spat ial components into the statistical model in order to reduce such adverse impacts of spatial autocorrelation. The spa tial models in this analysis differed from those used in the Reanalysis and in the Updated Analysis by Pope an d 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 mort ality 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, MS A, and state scales, and (2) correlation between adjace nt ZCAs, MSAs, and states (although only two levels could be incorporated in the model simultaneously). (In th e Nationwide Analysis section Statistical Methods and Data Analysis, the investigators note that the degree of correlation can, alternatively, be assumed to depend on dist ance; but results for distancebased models are not presented.) Technical problems in fitting spatial models, such as these, have previously preven ted their use in as complex a formulation as that in which they were used here. The investigators are to be congra tulated on overcoming these technical complexities to produ ce what in many ways is a more comprehensive representa tion of spatial structure in these data. It remains very difficult, however, to be sure that the range of models cons idered is broad enough that all of the adverse impacts de scribed above are controlled by the incorporation of spatia l 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 capt ured larger-scale patterns. For example, the Reanalysis found important residual variation in mortality across region s and incorporated regional terms (as fixed and random effect s) in the model. There does not appear to have been any analogous direct representation129 Health Review Committee

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 ef fects 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 an d 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 co variates 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 influ ence in the current analyses, omitting them exemplifies the un certainty 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 ro bust 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 repr esent a refinement of those used previously. However, such rea ssurance cannot be absolute. It remains possible that spatial patterns in mortality due to unmeasured risk factors could bi as estimates of risk. In particular it is unclear to what extent the models used allowed for large-scale geographic pattern s, 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 O 3 (Jerrett et al. 2009) using the same individual and ecologic covariates showed no indication of important residual spatial autocorrelation of the O 3 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 fr om 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 PM 2.5 exposure.

Commentary Figure 2. Ecologic variables found to influence the relationship between PM 2.5 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 dif

ferent statistical methods. Random effects Cox models were used wit h covariates clustered at ZCA, MSA, and state levels with and without spa

tial autocorrelation. HRs are shown for PM 2.5 exposure and mortality fro m all causes.130 Commentary on Investigators’ Report by Krewski et al.

Although there may we ll be overlap of ICD classifications between the two categories, it would be more prudent to present them separately, as we ll 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 tend ed to be higher for respiratory than for cardiovascular mortality. The current study, although fo cused on partic les, reports the emergence of a relationship between O 3 and cardiopulmonary mortality that recently has been further analyzed by examining causes of cardiovas cular and respiratory mortality separately. The more rece nt analysis (Jerrett et al. 2009) found an association between O 3 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 othe r categories of cardiovascular disease, such as stroke and heart failure, presented alongside those for ischemic heart di sease. As more cohort studies are conducted, such a disagg regated presentation would facilitate a degree of meta-analysis.

The Investigators’ Report singled out the associations between PM 2.5 and ischemic heart disease; although other cardiovascular causes of de ath were not presented, mortality from ischemic heart di sease appears to be the category of cardiovascular disease where much of the association between cardiovascular morta lity and air pollution exposure is concentrated, as reflect ed in previous investigations with this cohort (Pope et al. 2004). The investigatorsreferred to this association as having “high biologic plausibility,” but the Review Comm ittee thought that the existing limited epidemiologic and mechanistic evidence, though suggestive, does no t justify such a strong assertion.

Given that the Reanalysis (Kre wski et al. 2000) had extensively tested the potential for the gaseous pollutants to confound the relationship between exposure to PM 2.5 and mortality and had not found an y significant confounding (other than by SO 2 ), 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 NO 2 and SO 2 as copollutants would have presented some challenges in a model with PM 2.5 because (1) levels of NO 2 recorded at central monitoring stations do not reflect the fine scale on which these gases vary; (2) NO 2 and SO 2 are generated by some of the same sources that generate PM 2.5 (NO 2 and traffic; SO 2 and power plants) and their levels will be correlated with those of PM 2.5 ; and (3) the gases are important precur sors to compon ents of fine nitrate and SO 4 2

particles. Still, it is always reassuring if two-pollutant analyses can be conducted. Further analyses of O 3 and respiratory mortalit y that were completed recently by members of Dr. Kr ewski’s team included a twopollutant analysis for PM 2.5 and O 3 (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 an d 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 backgro und (e.g., New England), this study compared effects of air pollution between cities and between air sheds. By contra st, the Intra-Urban Analyses primarily investigated variatio ns in local exposures added to common regional and urba n background exposures. Spatial variations within urban ar eas are driven more by local near-source emissions such as traffic, industry, and residential space heating; the resultin g analyses can tell us little about the health effects of ch ronic exposure to the background pollutant levels.

Statistical Methods

The statistical analysis for these studies had two components. The LUR analys is, used to estimate exposures, will be discussed below. The epid emiologic 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 Natio nwide Analysis were not relevant to assessing the impact of spatial autocorrelation). For this set of analyses, as for the Nationwide Analysis, theReview 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 thes e sources is much less of a concern than in the Nationwide Analysis.

Exposure AssessmentBoth 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 Investigat ors’ Report, but less clearly131 Health Review Committee

stated for the New York City model. These studies are among the first to us e LUR to predict PM 2.5 concentrations at the local level. Unlike NO 2 and elemental carbon, which remain localized near their sources, PM 2.5 has a large regional component and a sm aller local-source component(e.g., traffic emissions), which make it challenging to estimate the contribution of lo cal sources to air pollution exposure. Including specific la nd uses in the assessment may help make local estimates more precise. The LUR models differed substantially between the two cities in several important wa ys, 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 5 000 m) for determining whichland-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 loca tion. In addition, the New York City model used count y-wide emissions inventory data to estimate PM 2.5 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) in cluded single i ndicators 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 mode ls) 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 PM 2.5 measurements. The final Los Angeles model also included three variables: traffic within 300 m, industrial area w ithin 5000 m, and government area within 5000 m of each monitor; it explained 63% of the variation.

Despite the substantial d ifferences in how the LUR models were constructed and th e probable quality of available data, they were both successful in explaining a moderate degree of variability (60% to 64%) in PM 2.5 at the ZCA scale. Each model is un ique to the area where it wasdeveloped and is not likely to be transferable to other metropolitan areas.
In addition to the number an d spatial distribution of theair-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 PM 2.5 concentrations. The ranges of average annual monitored PM 2.5 concentrations were moderately different be tween Los Angeles (9.5–28 μ g/m 3 ) and New York City (10

– 20 μ g/m 3 ) and suggest that the PM 2.5 levels in New York City were more strongly influenced by regional transport th an by local emissions. The models reflected this differen ce. The intercepts in the New York City models varied between 12.3 and 13.2 μ g/m 3

, whereas the intercept in the Los Angeles model was
2.3 μ g/m 3 . It is difficult to compare coefficients for the independent variables between cities, particularly for traffic, since the variables we re constructed from different forms of land-use and traffic data.

LUR has the potential to provide a better estimate of PM 2.5 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 sl ightly out-performed kriging models. For a regional pollutant such as PM 2.5 , 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 me thod may include hidden or unmeasured factors that relate to health care, socioeconomic status, or mortality ri sks 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 deserv es further attention.

It is reassuring that the results from the random effects Cox model for Los Angeles obtained using LUR-based exposure assignments were simi lar to those obtained using kriging. Residential location data for the ACS cohort were available only at the ZCA s cale, 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 da ta for the independent variables were to be applied at the residential addresses of theparticipants, 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 PM 2.5 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,132 Commentary on Investigators’ Report by Krewski et al.

quite wide for both Intra-Urban Analyses (although more so for New York City) and wh en 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 PM 2.5 estimates to detect differences in the mortality patterns at high and low ends of the range of exposures; this statementis consistent with our comments on the width of confidence intervals. The author s further suggest that differences between the Los Angeles and New York City Analyses lie in the underlyi ng geographic di stribution 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 Ne w York City results showed significant associations between PM 2.5 exposure and mortality from ischemic heart dis ease, 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 PM 2.5 exposure and deaths from endocrine disorders (but not diabetes) in Los Angeles, but not New York City. Thiscomparison is intriguing; it would be helpful to see the corresponding results from the Nationwide Analysis. In the section Implications of the Findings / Summary ofResults from Phase III / Intra-Urban Analyses, the investigators advance the hypothesis that the impact of exposure measurement error on the resu lts 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 Analysiswould 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 inte rvals. It is thus important to identify which type of measurem ent 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 st udies. Thus, though using larger spatial units will incr ease 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 argu ments 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. Furtherm ore, 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 th e 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 shou ld 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 pers uasive 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 remainsCommentary Figure 4. Comparison of results from Nationwide and IntraUrban Analyses. The New York City data were adjusted from a 1.5- μ g/m 3 change in PM 2.5 to a 10- μ g/m 3 change to match the scale of the other analyses. HRs are shown for PM 2.5 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 usingresults 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 (s pecifically, 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 co nsidered 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 PM 2.5 -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, an d no information was available for before 1982, so thes e sources of approximation are likely to have resulted in misclassification of exposures in specific time windows.

The PM 2.5 -B group is the most unusual epidemiologically. This group comprised su bjects 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 co ntinuously in the same MSA in which they had been livin g 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 co hort 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 st andard cohort analyses.

Statistical Methods

Although fitting a standard Cox model with time-dependent exposures is not as ty pical as is fitting the model with time-fixed exposures, it is we ll established (notably for occupational epidemiology), a nd the approach used by the investigators seemed to fall within the established methods. It is understandable that th e compounded complexity of handling both time-dependent exposures and random effects (to reflect spatial autoco rrelation) proved to be too much of a technical challeng e, and we concur with the investigators that this is a minor limitation here.

The investigators’ use of AI C to compare models using different windows of exposu re 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 diff erence between models can be interpreted. Happily, however, because the numbers ofparameters 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 disappointedthat the investigators did not present results for “multiwindow” models, which would be analogous to multi-pollutant models. The inves tigators discuss using such models, but they decided th at the resulting estimation of negative coefficients for some time windows precludedtheir 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 fo r the independent effects of each window (for example, using likelihood ratio tests in nested models). The investig ators 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 insu rmountable, unconstrained multi-window models would also be of interest. For the purposes of formula ting and evaluating air pollution policy, it is very impo rtant 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 PM 2.5 exposure, the three AIC values for the models with expo sure in windows 1 to 5, 6 to 10, or 11 to 15 years be fore death differed by little more than 1 for PM 2.5 -A group and for PM 2.5 -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 d ifferences of less than 3.84 (the 5% point of the chi-squ ared distribution with 1 degree of freedom) can easily be explained by chance.

In only one of the comparis ons 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 wa s for lung cancer and PM 2.5134 Commentary on Investigators’ Report by Krewski et al.

exposure for PM 2.5 -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 (sho wing 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 on e “significant” pattern among many results, it cannot be cons idered as strong evidence.

When the group was restricted to participants who died in the same city where they lived in 1982 (PM 2.5 -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 to10, and 11 to 15 years before death within each city, differences among the effect estimat es reported for each separate time window cannot be accepte d as meaningfully different from the others.

Patterns of risk estimates across time windows alsoshowed only very weak evidence for the superior predictive value of one window over another, though the width of the confidence intervals in dicated 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-ye ar 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 datafor 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 estim ates 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 Anal ysis presented in this report will be of great importance world-wid e. This is th e case because coefficients from earlier analyses (Pope et al. 2002) havebeen 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 Affair s; 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 ap plication of state-of-the-art statistical approach es to controlling confounders and spatial autocorrelation do not materially change risk estimates; important residual confounding (by climate and possiblyother unmeasured determinants of large-scale spatial variation) cannot be excluded, however, particularly in the Nationwide Analysis. In analyzing the extended follow-updata for mortality, the report also provides new risk estimates, including — for the first time — an estimate for O 3 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 PM 2.5 , although this trend was not as consistent as in the Six Cities Study (Krewski et al. 2000). In th is Extended Analysis, effect modification by level of educa tion 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 attain ment increased). The Review Committee thought that this recent evidence suggests that effect modification by educatio n 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 morta lity. With this in mind, the Committee thought that — with the emergence of new cohort evidence from the United States and Europe — thesimilarities and differences among the results of the various studies need to be examin ed closely. Nevertheless, the size and character of the ACS cohort makes it likely that it will remain preeminent.
The number of intra-urban st udies 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 intercity135 Health Review Committee

studies, such as those conducted with the national ACScohort, and that they employ different exposure-assessment techniques. Although th e 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 whileaccounting for the separate effects of earlier or later exposure periods will require careful statistical design and power calculations at the incep tion. Although many questions remain about the timing of exposures in terms of the development of diseases associated with air pollutants (e.g., the relationship betw een actions to improve air quality and subsequent improvement in health), this Extended Analysis serves to underscore the difficulty of assessing the relative importan ce of the timing of exposure in a cohort study, even w ith a large base population.

The results from this Extended Analysis will be veryinfluential in the coming years, despite the limitations discussed in this Commentary. Dr. Krewski and his colleagueshave 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 PM 2.5 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 PM 2.5 . For the first time, the association between long-term O 3 exposure and mo rtality has been identified with a standard Co x model; in further, more recent analyses, the same asso ciation was evident with a random effects Cox model (Jerrett et al. 2009). The Intra-Urban Analysis fo r Los Angeles suggests that mortality risks associated with PM 2.5 exposure may be elevated when there is a strong local component of exposure. When the New York City an d Los Angeles Analyses are taken together, however, th ey underscore the important point that cities differ marked ly in their local exposure conditions and emphasi ze the variable importance of the contributions of local sources to the overall risk of mortality associated with PM 2.5 exposure. These divergent results argue for caution in extr apolating 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 remarkab le consistency the association of mortality and exposure to PM 2.5 reported in previous studies of the ACS da ta starting 15 years ago. In addition to consolidating ev idence for a causal association, the new analyses have ad ded precision, especially for the evidence that ischemic hear t disease is a cause of death particularly af fected by exposure. Th e 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 An alyses in Los Angeles andNew York City advance methods for such studies; the differences between results from the two cities could beexplained 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 limitatio ns preclude clear conclusions. The richne ss 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 Commi ttee thanks the ad hoc reviewers for their help in evaluating the scientific merit of the Investigator s’ 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, a nd Fred Howe for their roles in preparing this Research Report for publication.



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# RELATED HEI PUBLICATIONS: PARTICULATE MATTER AND TRAFFIC

137 

### Research Reports 139 Effects of Long-Term Exposure to Traffic-Relat ed Air Pollution on Respir atory B. Brunekreef 2009 and Cardiovascular Mortality in th e Netherlands: the NLCS-AIR Study 138 Health Effects of Real-World Exposure to Di esel Exhaust in Person s 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 Exposu res of Sensitive Subpopulations P. Koutrakis 2005 Living in Baltimore and Boston 127 Personal, Indoor, and Outdoor Exposures to PM 2.5 and Its Components B. Brunekreef 2005 for Groups of Cardiovascular Pa tients in Amsterdam and Helsinki

124 Particulate Air Pollution and Nonfatal Cardiac Events 2005

Part I. Air Pollution, Personal Activities, and On set of Myocardial Infarction A. Peters in a Case–Crossover Study
Part II. Association of Air Pollution with Confirmed Arrhythmias D. Dockery Recorded by Impl anted Defibrillators 118 Controlled Exposures of Hea lthy and Asthmatic Volunteers to Concentrated H. Gong Jr. 2003 Ambient Particles in Metropolitan Los Angeles 104 Inhalation Toxicology of Urban Am bient Particulate Matter: R. Vincent 2001 Acute Cardiovascular Effects in Rats 97 Identifying Subgroups of the Ge neral Population That May Be Susceptible M.S. Goldberg 2000 to Short-Term Increases in Particul ate Air Pollution: A Time-Series Study in Montreal, Quebec 95 Association of Particulate Matter Compon ents 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 He alth 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 th e Literature on Exposure and Health Effects 2007 Reanalysis of the Harvard Six Citie s Study and the American Cancer So ciety Study of Particulate 2000 Air Pollution and Mortality: A Special Report of the Institu te’s Particle Epidemiology Reanalysis Project

Diesel Emissions and Lu ng Cancer: Epidemiolo gy 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 Mee ting of the EC and HEI) 10 Improving Estimates of Diesel and Othe r Emissions for Epidem iologic Studies 2003 

### HEI Research Program Summaries
Research on Diesel Exhaust and Other Particles 2003

Research on Particulate Matter 1999 

### HEI Perspectives Understanding the Health Ef fects of Components of th e 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 Pr ograms, University of Virginia
Linda Rosenstock Dean, School of Public Health, Univ ersity of California–Los Angeles Warren M. Washington Head, Climate Change Research, Nation al 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 Environmenta l Medicine, University of Rochester Medical Center Kenneth L. Demerjian Ray Falconer Endowed Chair, Di rector and Professor, Atmosphe ric 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 Di rector, Fraunhofer Institute for Toxicology and Experimental Medicine, Hanover, Germany
Grace LeMasters Professor of Epidemiology and Environmen tal Health, Universi ty of Cincinnati College of Medicine Sylvia Richardson Professor of Biostatistics, Department of Epid emiology and Public Health, Imperial College School of Medicine, Lo ndon, United Kingdom
Howard E. Rockette Professor, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh James A. Swenberg Kenan Distinguished Professor of Environmen tal Sciences, Department of Environmental Sciences and Engineering, Universi ty 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 Medici ne, 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 Enviro nmental 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 Me dicine and Environmental Medicine and Director of Pulmonary and Critical Care Me dicine, New York University Medical Center
Armistead Russell Georgia Power Distinguished Professor of Envi ronmental Engineering, School of Civil and Environmental Engineering, Geor gia Institute of Technology



## Officers and Staff

Daniel S. Greenbaum President Robert M. O’Keefe Vice President Rashid Shaikh Director of Science Barbara Gale Director of Publications Jacqueline C. Rutledge Director of Finance and Administration Helen I. Dooley Corporate Secretary

Kate Adams Staff Scientist Marian Berkowitz Research Associate Aaron J. Cohen Principal Scientist Maria G. Costantini Principal Scientist Philip J. DeMarco Compliance Manager Terésa Fasulo Science Administration Manager Hope Green Editorial Assistant (part time) L. Virgi Hepner Senior Science Editor Francine Marmenout Senior Executive Assistant Flannery Carey McDermott Editorial Assistant Teresina McGuire Accounting Assistant Sumi Mehta Senior Scientist Nicholas Moustakas Policy Associate Kelly Pitts Research Assistant Hilary Selby Polk Science Editor Robert A. Shavers Operations Manager Geoffrey H. Sunshine Senior Scientist Annemoon M.M. van Erp Senior Scientist Katherine Walker Senior Scientist

# R e s e a r c h R e p o r t

H E A L T H E F F E CT S I N ST I T U T E

Includes a Commentary by the Institute’s Health Review Committee 101 Federal Street, Suite 500 Boston, MA 02110, USA
+1-617-488-2300 www.healtheffects.org



# R e s e a r c h R e p o r t 

# H E A L T H E F F E CT S I N ST I T U T E

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. Thunt

with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara TempalskiMarch 2009Extended Analysis of the ACS Study of Particulate Air Pollution and Mortalityreport 139



### Number 140 May 2009