Final Report: Chronic and Acute Exposure to Ambient Fine Particulate Matter and Other Air Pollutants: National Cohort Studies of Mortality and Morbidity

EPA Grant Number: R830548
Title: Chronic and Acute Exposure to Ambient Fine Particulate Matter and Other Air Pollutants: National Cohort Studies of Mortality and Morbidity
Investigators: Samet, Jonathan M.
Institution: The Johns Hopkins University , Decipher , Health Systems Innovations
EPA Project Officer: Chung, Serena
Project Period: February 1, 2003 through January 31, 2006 (Extended to January 31, 2007)
Project Amount: $1,033,646
RFA: Epidemiologic Research on Health Effects of Long-Term Exposure to Ambient Particulate Matter and Other Air Pollutants (2002) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Health Effects , Particulate Matter , Air

Objective:

This project was carried out to explore the utility of using the Medicare files of the Center for Medicare & Medicaid Services for carrying out epidemiological studies of the longer term effects of air pollution on health. The Medicare files include information on hospitalizations and mortality for nearly all U.S. residents 65 years and older.  By joining these files with routine air pollution monitoring data available from the U.S. Environmental Protection Agency, meteorological data, and national Census data, we created a database that could be used to create cohorts of Medicare participants, assigning pollution exposure on the basis of residence location. We then assessed whether average air pollutant concentrations at the residence location were associated with mortality, both overall and cause-specific.  Some chronic diseases, e.g., DM and COPD, have been postulated as increasing the risk of air pollution exposure.  We explored the use of hospitalization for particular conditions to assess whether mortality was subsequent to hospitalization for the condition.

Summary/Accomplishments (Outputs/Outcomes):

Initially, we addressed the data management issues involved in assembling the analytic databases.  This step involved the conversion of large databases of claims files into an individual-based longitudinal record suitable for cohort analysis.  Additionally, the various databases were merged.  Three sets of analyses are described below:  1) an initial analysis of PM2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) and mortality in the counties corresponding to those of the Harvard Six Cities Study and the American Cancer Study cohorts; 2) an analysis of PM2.5 and mortality at the Zip code level; and 3) an analysis of modification of the risk for hospitalization for chronic obstructive pulmonary disease (COPD) associated with PM2.5 by the presence of diabetes mellitus. The findings of these analyses are set out below. We also used the Medicare data to assess PM2.5 and short-term risk for hospitalization for cardiac and respiratory causes (Dominici et al., 2006).

Comparability of findings in the Medicare cohort and the Harvard Six Cities and American Cancer Society studies:  The American Cancer Society study (ACS) and the Harvard Six Cities study (SCS) are two landmark cohort studies for estimating the chronic effects of fine particulate matter, PM2.5, on mortality.  From the Medicare data set, we have constructed two cohorts for the locations corresponding to those included in the ACS and SCS, denoted as Med-ACS and Med-SCS, respectively. These two cohorts differ from the original ACS and SCS with respect to study design, study period, availability of individual and area-level confounders, and modeling approach (see Table 1). The original cohorts were “closed” so that new members were not added after their inception, whereas Medicare has new enrollees each month and the Med-ACS and Med-SCS cohorts are dynamic.  We compared findings from these two new cohorts, drawn from Medicare participants, with the results from the two earlier cohort studies.  We calculated the mortality rates for the same geographical locations included in the ACS and SCS.  We used the county-specific mortality rates for COPD and lung cancer as a surrogate for the pattern of smoking in the county along with indicators of socioeconomic status at the county-level.  The data were analyzed with a log-linear regression model that accounted for the geographic clustering of the data. Table 2 summarizes the numbers of geographic locations, numbers of deaths and people at risk, average levels of PM2.5, and exposure periods of the Med-ACS, ACS, Med-SCS, and SCS.

Table 3 compares the results of the comparative analysis of Med-SCS and Med-ACS with the first reports of the SCS and ACS, the reanalysis conducted for the Health Effects Institute (HEI), and the follow-up of the SCS and ACS.  In the Med-SCS, we found that a 10 μg/m3 increase in average PM2.5 is associated with a 20.8% increase in the overall mortality rate (95% CI: 12.3, 30.0), adjusted for age and gender. In the Med-ACS, we estimated that a 10 μg/m3 increase in average PM2.5 is associated with a 10.8% increase in the mortality rate (95% CI: 8.9, 12.8), adjusted for age and gender.  Without adjustment for area-level covariates, the Med-SCS and Med-ACS provide results similar to the ACS and SCS. In the Med-ACS, with adjustment for area-level covariates, we estimated a 10.9% increase in the mortality rate (95% CI: 9.0, 12.8), slightly larger than the ACS estimates from the re-analysis.10  Estimates from the Med-ACS obtained by using the 50 SMAs as geographical units were smaller than from the Med-ACS obtained by using the 110 counties and much closer to the ACS estimates. The findings were similar by year, with and without adjustment for the rates of COPD and lung cancer, and after adjustment for area-level SES covariates.

In summary, we have used the Medicare files to assemble cohorts for the same geographic locations included in the SCS and ACS cohorts.  Although the analyses of the Med-SCS and Med-ACS cohorts only considered age and gender as individual-level covariates, risk estimates were comparable for the original and Medicare cohorts. We suggest that the periodic assembly of cohorts from Medicare participants can provide tracking of the risks of air pollution in a large, representative, and susceptible population. Appling this approach to other geographical areas where the recruited cohorts has provided negative or mixed findings  27-29 could also provide valuable information.

Analyses of Mortality at the Zip-Code Level:In this analysis we linked PM2.5 monitoring data to the Zip codes of residence of all U.S. Medicare enrollees to develop a new retrospective cohort study, the Medicare Air Pollution Cohort Study (MCAPS).  The matching identified a cohort of 13.0 million Medicare participants living in the 4,846 Zip codes within 5 miles of 861 PM2.5 monitors over the period 2000-2005. The MCAPS cohort experienced 2.77 million deaths during 51.9 million person-years of follow-up. We estimated log-linear regression models having as outcome the age-specific mortality rate for each Zip code and as main predictor for the average PM2.5 level for the period 2000-2005 for the monitor within 5 miles of the Zip code’s centroid. The models also included Zip-code-level variables from the 2000 U.S. Census to adjust for SES and county-level standardized mortality rates for COPD to adjust for smoking, as in the MED-SCS and MED-ACS cohort analyses. We also carried out separate analyses by gender and SES stratum for each of the three large U.S. geographical regions (eastern, central and west coast) but found little evidence of effect-modification for either gender or Zip-averaged SES.

As shown in Table 4, the MCAPS data provide strong evidence for the eastern and central regions that mortality is higher in Zip codes with higher PM2.5.  In the western region, there is no observed association between mortality and particulate levels. In the analysis that controls for both SES and COPD (as a surrogate for smoking) rates, we estimate that an eastern Zip code with 10 μg/m3 per cubic meter higher long-term average PM2.5 has 5.1 percent higher (95% CI: 3.27 to 6.93% higher) mortality than another easter Zip with comparable age distribution, SES and COPD rates. In the central region, the difference is estimated to be 13.2% per 10 μg/m3 (95% CI: 9.64 to 16.74). In the west, the estimate is 1.14 and does not achieve statistical significance. Control for SES indicators and COPD standardized mortality rate calculated at either the Zip or county levels results in a non-trivial change from 13.5 down to 5.10 in the east but a relatively smaller change in the other two regions.

Because of the substantially different relative risks in the three regions, it is inappropriate to ignore region and then estimate a common value. However, for completeness, Table 5 also presents the value obtained when we do so. After control for SES, there is no evidence of an effect of PM2.5 when Zip codes are compared without regard to their region. Note: This estimate is not a pooled version of the three region-specific values because region was not included as a main-effect in the model that estimates the national value.

Table 6 presents the estimated region-specific log relative risks of death separately for the three age groups. In the west, there is no evidence of an association for any of the three age groups. In the east and central regions, the largest effect in the regions is for the youngest group, 65-74 year olds (8.9 and 22.4%  per 10 μg/m3, respectively). The effects are smaller for the 75-84 year olds and close to 0 for the oldest group, 85 and above. Hence, there is no evidence of an effect of PM2.5 for persons above 85 years of age in any region.

The strengths of this study are the large number of deaths and national at-risk population; the major weakness is the potential for ecologic bias due to unmeasured personal characteristics that co-vary with personal exposures. Nevertheless, this study raises important questions about heterogeneity in the risk of chronic exposure to air pollution by region and by age. Like all cohort studies of chronic exposure, the evidence about the mortality effects of chronic exposure to air pollution derives entirely from comparisons of mortality rates among urban locations and is therefore subject to bias by unmeasured factors that are geographically correlated with PM.

Diabetes and Risk for COPD Hospitalization Associated with PM2.5. The National Research Council’s Committee on Research Priorities for Airborne Particulate Matter designated the identification of susceptible subpopulations as a research priority. Prior studies suggest that DM may increase susceptibility to adverse health effects of air pollution exposure, although whether DM enhances the short-term effect of PM2.5 on COPD has not been investigated using a national database.  Consequently, we used the Medicare database to test whether DM increases risk for COPD hospitalization associated with PM2.5.  The hypothesis has substantial public health relevance because both diabetes and COPD are highly prevalent in older populations.  Additionally, we wanted to explore the utility of the Medicare database for identifying high-risk subgroups. 

To test the hypothesis, we conducted a multi-site daily time-series study using data from the Medicare National Claims History Files on 2.6 million enrollees.  We constructed daily time series of ambient PM2.5, temperature, and dew point temperature for the period 1999-2002 for each of the largest 204 U.S. counties. We used two approaches for investigating whether DM increases susceptibility to the short-term effect of PM2.5 on COPD.  In the first, we estimated and compared the national average effects of PM2.5 on COPD for the following two outcomes: hospitalizations with COPD as primary diagnosis and DM as secondary diagnosis and  hospitalizations for COPD as a  primary diagnosis. In the second approach, we used a 5% Medicare outpatient sample in addition to the 100% Medicare hospitalization sample to construct sub-populations of diabetics and non-diabetics. More specifically, we enroll a person in the sub-population of diabetics on the date that this person has been discharged from the hospital or had an outpatient or a doctor visit for a diagnosis of diabetes. Within each sub-population, we then constructed daily time-series of COPD hospitalizations. We estimated and compared the national average effects of PM2.5 on COPD among the two sub-populations of diabetics and non diabetics.

We found evidence that DM increases the short-term effect of PM2.5 on COPD.  With the first approach, a 10μg/m3 increase in PM2.5 was associated with a 2.46% increase in risk for hospitalization for COPD with secondary coding for DM (95% CI: 1.19%, 3.73%) and with 0.77% increase in hospitalization for COPD without secondary coding for DM (95% CI: 0.21%, 1.33%,).  We found evidence that the first relative rate estimate is statistically significant higher than the second (one-side p = 0.036).  Under the second approach, a 10μg/m3 increase in PM2.5 was associated with a 1.64% increase in hospitalizations for COPD among the diabetics (95% CI: 0.23%, 3.05%) and with a 0.28%, increase in hospitalizations for COPD among the non diabetics (95%CI: -0.55%, 1.10%). However, these two rates are not statistically significantly different (p > 0.05).

These analyses illustrate the application of two approaches for identifying susceptible populations. There is an indication that DM increases the short-term effect of PM2.5 on risk for hospitalization for COPD. In follow-up studies, we intend to extend this approach to other susceptible groups.

Table 1: Comparison of characteristics between the Medicare study versus the American Cancer Society (ACS) study and the Six Cities Study (SCS).

  Medicare ACS and SCS
Study design open to enrollment closed to enrollment
Geographical areas counties metropolitan statistical areas
Population age 65 years and older 25 years and older
Exposure measured PM2.5 only measured PM2.5  and estimated PM2.5 from PM10
Time scale of exposure concurrent with the study period preceding and concurrent with the study period
Individual-level risk factors age, gender age, race, gender, education, smoking, and more
Statistical model Log-linear regression Cox proportional hazards regression

Table 2: Study characteristics: Med-ACS, ACS, 3  Med-SCS and SCS.5 

Characteristics Med-ACS ACS Med-SCS SCS

No. of counties

110a 50b 6c 6

No. of subjectsd

7, 333, 040d 295,223 341, 099d 8,096

No. of deathse

1,122,311 62,000 54,160 2,732

Average PM2.5, μg/m3

13.6 17.7 14.1 16.4f

(standard deviation)

(2.8) (3.7) (3.1) (5.6)f

Range

6.0- 25.1 9- 33.5 9.6- 19.1 10.2- 29.0f

Study period

2000-2002 1982-1998 2000-2002 1974-1998

Period of measured exposure

2000-2002 1979-1983,

1999-2000

2000-2002 1979-1988

1990-1998

a Counties identified by the Reanalysis team16 as being within the 50 metropolitan statistical areas included in the ACS2.
b These are metropolitan statistical areas.
c The six counties that include the six cities in the SCS.
d The number of subject for the Med-ACS and Med-SCS datasets is the number of persons at risk in year 2000. For ACS and SCS, it is the number of persons enrolled at the beginning of the study period.
e Total deaths occurred during the entire study period. For ACS 3, the number of deaths is approximately triple the number of deaths in the original ACS2.
f Calculated based on Table 1 and Figure 1 from Laden et al.5

Table 3: Comparison of results across studies:  estimated % increase in mortality rate per 10μg/m3 increase in PM2.5

Study

Primary source

Duration of measured exposure (PM2.5 )

Change in mortality risk per 10μg/m3 increase in average PM2.5

SCSa

SCSb

SCSa

Med-SCSb

(Dockery et al., 1993)

(Krewski et al., 2000c)

(Laden et al., 2006)

1979-1988

1979-1988

1979-1988, 1990-1998

2000-2002

13.2 (4.2-23.0)

16.6 (7.3-26.1)

16.0 (7.0-26.0)

20.8.(14.8-27.1)

ACSc

ACSd

ACSe

ACSf 

Med-ACSb

Med-ACSg

(Pope et al., 1995)

(Krewski et al., 2000c)

(Krewski et al., 2000c)

(Pope et al., 2002)

 

1979-1983

1979-1983

1979-1983

1979-1983,1999-2000

2000-2002

2000-2002

6.6 (3.5-9.8)

10.2 (7.0-13.7)

7.4 (4.4-10.6)

6.2 (1.6-11.0)

10.8 (8.6-13.0)

10.9 (9.0-12.8)

Med-ACSh,b

Med-ACSh,g

 

2000-2002

2000-2002

    6.3 (3.8-8.9)

8.9 (6.9-10.9)

aAdjusted for individual-level age, gender, cigarette smoking, BMI, education.

bAdjusted for individual-level age and gender. 

cAdjusted for individual-level age, gender, cigarette smoking, BMI, education, race, alcohol

consumption and occupational exposure.

dAdjusted for individual-level age, race, and gender.

eAdjusted for population change, income, poverty, income disparity, unemployment and education

Table 4. Numbers of zip-codes, counties, monitoring sites, Medicare enrollees, person-years of follow-up, deaths and crude death rates stratified by region for the MCAPS data.

 

Eastern U.S.

Central U.S.

Western U.S.

Total

zip-codes

3,133

1,083

630

4,846

counties

420

181

47

648

monitoring sites

533

245

98

861

persons (millions)

8.7

2.4

1.9

13.0

person-years (millions)

34.7

9.4

7.8

51.9

deaths (millions)

1.88

0.51

0.39

2.77

crude rate (deaths/1,000 person years)

54.1

53.8

49.5

53.4

Table 5. Percentage increase (95% confidence interval) in mortality rate per 10 micrograms/cubic meter of PM2.5 estimated from the log-linear regression separately for the 3,133 zip-codes in the eastern U.S, the 1,083 zip-codes in the central U.S. and the 630 zip-codes in the western U.S. for MCAPS data for three levels of adjustment for demographic and socioeconomic variables.

Adjustment

All

Eastern U.S. (3,133)

Central U.S.

(1,083)

Western U.S. (630)

Age

1.12 2.45 3.78

11.09 13.50 15.90

12.75 16.88 21.01

-2.04 -0.11 1.82

Age + SES

-0.48 0.68 1.84

6.54 8.48 10.43

6.12 9.62 13.12

-2.11 -0.43 1.25

Age+SES+COPD

-1.25 -0.07 1.11

3.27 5.10 6.93

9.64 13.19 16.74

-2.84 -1.14 0.57

Table 6. Percentage increase (95% confidence interval) in mortality rate per 10 micrograms/cubic meter of PM2.5 estimated from the log-linear regression using data from all three regions for MCAPS data for three levels of adjustment for demographic and socioeconomic variables.

 

 

 

Ages

 

All

65-74

75-84

85+

Eastern U.S.

Age

11.1 13.5 16.0

24.9 29.2 33.4

12.0 14.7 17.3

-4.1 -1.8 0.4

Age + SES

6.6  8.5 10.5

12.0 14.7 17.4

7.2  9.4 11.6

-1.3  0.9 3.1

Age+SES+COPD

3.3  5.1  7.0

6.3  8.9 11.6

4.1  6.2  8.3

-0.9  1.2 3.3

 

Central U.S.

Age

12.7 16.8 21.0

30.1 38.6 47.1

10.3 15.0 19.8

-5.7 -1.6 2.5

Age + SES

6.1  9.6 13.1

15.3 20.8 26.4

3.5  7.7 11.8

-5.0 -1.2 2.5

Age+SES+COPD

9.6 13.1 16.7

17.0 22.4 27.7

6.2 10.6 15.0

-3.6  0.3 4.3

 

Western U.S.

Age

-2.0 -0.1 1.8

1.0  4.4  7.8

-2.2 -0.1 2.0

-6.1 -4.2 2.4

Age + SES

-2.1 -0.4 1.3

-5.6 -2.9 -0.2

-2.4 -0.4 1.6

0.3  2.0 3.8

Age+SES+COPD

-2.8 -1.1 0.6

-4.8 -2.3  0.3

-2.9 -0.8 1.1

-1.2  0.9 3.1

Conclusions:

Benefits

While we know that air pollution harms people, we still need to learn more about lasting, long-term effects. This study tests if information from Medicare participants and routine air pollution monitoring could be used to investigate longer term effects of air pollution. We were successful in doing so, finding that particulate air pollution was associated with risk for dying. The results will be useful as part of the evidence for setting air quality standards. The same approach could be used in the future to track risks over time.


Journal Articles on this Report : 11 Displayed | Download in RIS Format

Other project views: All 12 publications 12 publications in selected types All 12 journal articles
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Journal Article Bell ML, Peng RD, Dominici F. The exposure-response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environmental Health Perspectives 2006;114(4):532-536. R830548 (Final)
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  • Journal Article Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM. Spatial and temporal variation in PM2.5 chemical composition in the United States for health effects studies. Environmental Health Perspectives 2007;115(7):989-995. R830548 (Final)
    R832417 (Final)
    R832417C001 (2006)
    R832417C001 (2007)
    R832417C001 (2008)
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    R832417C001 (Final)
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  • Journal Article Bell ML, Kim JY, Dominici F. Potential confounding of particulate matter on the short-term association between ozone and mortality in multisite time-series studies. Environmental Health Perspectives 2007;115(11):1591-1595. R830548 (Final)
    R832417 (Final)
    R832417C001 (2007)
    R832417C001 (2008)
    R832417C001 (2009)
    R832417C001 (Final)
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  • Journal Article Dominici F, Peng RD, Ebisu K, Zeger SL, Samet JM, Bell ML. Does the effect of PM10 on mortality depend on PM nickel and vanadium content? A reanalysis of the NMMAPS data. Environmental Health Perspectives 2007;115(12):1701-1703. R830548 (Final)
    R832417 (Final)
    R832417C001 (2008)
    R832417C001 (2009)
    R832417C001 (Final)
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  • Journal Article Dominici F, Peng RD, Zeger SL, White RH, Samet JM. Particulate air pollution and mortality in the United States: did the risks change from 1987 to 2000? American Journal of Epidemiology 2007;166(8):880-888. R830548 (Final)
    R832417 (Final)
    R832417C001 (2007)
    R832417C001 (2008)
    R832417C001 (2009)
    R832417C001 (Final)
    R833622 (2008)
    R833622 (2009)
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  • Journal Article Janes H, Dominici F, Zeger SL. Trends in air pollution and mortality: an approach to the assessment of unmeasured confounding. Epidemiology 2007;18(4):416-423. R830548 (Final)
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  • Journal Article Peng RD, Dominici F, Louis TA. Model choice in time series studies of air pollution and mortality. Journal of the Royal Statistical Society Series A-Statistics in Society 2006;169(2):179-203. R830548 (Final)
    R832417 (Final)
    R832417C001 (2006)
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    R832417C001 (2008)
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  • Journal Article Peng RD, Dominici F, Zeger SL. Reproducible epidemiological research. American Journal of Epidemiology 2006;163(9):783-789. R830548 (Final)
    R832417 (Final)
    R832417C001 (2006)
    R832417C001 (2007)
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  • Journal Article Symons JM, Wang L, Guallar E, Howell E, Dominici F, Schwab M, Ange BA, Samet J, Ondov J, Harrison D, Geyh A. A case-crossover study of fine particulate matter air pollution and onset of congestive heart failure symptom exacerbation leading to hospitalization. American Journal of Epidemiology 2006;164(5):421-433. R830548 (Final)
    R832417 (Final)
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  • Journal Article Thomas DC, Jerrett M, Kuenzli N, Louis TA, Dominici F, Zeger S, Schwartz J, Burnett RT, Krewski D, Bates D. Bayesian model averaging in time-series studies of air pollution and mortality. Journal of Toxicology and Environmental Health-Part A 2007;70(3-4):311-315. R830548 (Final)
    R831861 (2005)
    R832417 (Final)
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  • Journal Article Zhou Y, Dominici F, Louis TA. A smoothing approach for masking spatial data. Annals of Applied Statistics 2010;4(3):1451-1475. R830548 (Final)
    R833622 (Final)
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  • Other: ResearchGate-Abstract and Full Text PDF
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  • Supplemental Keywords:

    epidemiological studies, Medicare,  air pollution, health effects, mortality, morbidity, particulate matter, RFA, Health, Scientific Discipline, PHYSICAL ASPECTS, Air, HUMAN HEALTH, particulate matter, air toxics, Environmental Chemistry, Health Risk Assessment, Exposure, Epidemiology, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, Physical Processes, copollutant exposures, sensitive populations, atmospheric particulate matter, airway epithelial cells, cardiopulmonary responses, fine particles, PM 2.5, long term exposure, inhaled pollutants, acute cardiovascular effects, acute lung injury, morbidity, air pollution, susceptible subpopulations, cardiac arrest, chronic health effects, lung inflammation, time series analysis, particulate exposure, National Cohort Studies, cardiopulmonary response, human exposure, Acute health effects, inhaled, human susceptibility, cardiotoxicity, cardiopulmonary, mortality, concentrated particulate matter, air contaminant exposure, air quality, environmental hazard exposures, toxics, airborne urban contaminants, cardiovascular disease, acute exposure

    Progress and Final Reports:

    Original Abstract
  • 2003 Progress Report
  • 2004 Progress Report
  • 2005 Progress Report