Original Contribution

Ambient Air Pollution and Cardiovascular Emergency Department Visits in

Potentially Sensitive Groups

Jennifer L. Peel1,2, Kristi Busico Metzger1, Mitchel Klein1,3, W. Dana Flanders3, James A.

Mulholland4

, and Paige E. Tolbert1,3

1 Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA.

2 Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO.

3 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA.

4 School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA.

Received for publication September 9, 2005; accepted for publication August 9, 2006.

Limited evidence suggests that persons with conditions such as diabetes, hypertension, congestive heart failure,

and respiratory conditions may be at increased risk of adverse cardiovascular morbidity and mortality associated

with ambient air pollution. The authors collected data on over 4 million emergency department visits from 31

hospitals in Atlanta, Georgia, between January 1993 and August 2000. Visits for cardiovascular disease were

examined in relation to levels of ambient pollutants by use of a case-crossover framework. Heterogeneity of risk

was examined for several comorbid conditions. The results included evidence of stronger associations of dysrhythmia and congestive heart failure visits with comorbid hypertension in relation to increased air pollution levels

compared with visits without comorbid hypertension; similar evidence of effect modification by diabetes and

chronic obstructive pulmonary disease (COPD) was observed for dysrhythmia and peripheral and cerebrovascular

disease visits, respectively. Evidence of effect modification by comorbid hypertension and diabetes was observed

in relation to particulate matter less than 10 lm in aerodynamic diameter, nitrogen dioxide, and carbon monoxide,

while evidence of effect modification by comorbid COPD was also observed in response to ozone levels. These

findings provide further evidence of increased susceptibility to adverse cardiovascular events associated with

ambient air pollution among persons with hypertension, diabetes, and COPD.

air pollution; arrhythmia; cardiovascular diseases; cerebrovascular disorders; diabetes mellitus; emergency

service, hospital; lung diseases, obstructive; peripheral vascular diseases

Abbreviations: CI, confidence interval; COPD, chronic obstructive pulmonary disease; ICD-9, International Classification of

Diseases, Ninth Revision; OR, odds ratio; PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

Limited but growing evidence from recent epidemiologic

studies suggests that persons with comorbid conditions, including diabetes, hypertension, congestive heart failure, recent myocardial infarction, and respiratory conditions, may

be at increased risk of cardiovascular morbidity and mortality

in relation to ambient air pollution levels (1–12). However,

considerable uncertainty is included in these risk estimates

for susceptible populations (13). The most recent report

by the National Research Council emphasized the need for

continued examination of the most susceptible subgroups,

including persons with underlying cardiovascular and respiratory disease (14).

This case-crossover analysis examined the association

of ambient air pollution levels and cardiovascular morbidity in visits with and without specific secondary conditions,

that is, diabetes, hypertension, dysrhythmia, congestive heart

Correspondence to Dr. Jennifer L. Peel, Department of Environmental and Radiological Health Sciences, Colorado State University, 1681

Campus Delivery, Fort Collins, CO 80523-1681 (e-mail: jpeel@colostate.edu).

625 Am J Epidemiol 2007;165:625–633

American Journal of Epidemiology

Copyright a 2006 by the Johns Hopkins Bloomberg School of Public Health

All rights reserved; printed in U.S.A.

Vol. 165, No. 6

DOI: 10.1093/aje/kwk051

Advance Access publication December 28, 2006

 at EPA-RTP Main Library on April 1, 2010 http://aje.oxfordjournals.org Downloaded from 
failure, atherosclerosis, chronic obstructive pulmonary disease (COPD), pneumonia, upper respiratory infections, and

asthma, by use of a large database of emergency department

visits compiled for the Study of Particles and Health in

Atlanta (referred to as ‘‘SOPHIA’’).

MATERIALS AND METHODS

Ambient air quality data

For the period January 1, 1993, through August 31, 2000,

we obtained ambient air quality data for 24-hour particulate

matter with an average aerodynamic diameter of less than

10 lm (PM10), 8-hour maximum ozone, and 1-hour maximum nitrogen dioxide, sulfur dioxide, and carbon monoxide

from several existing monitoring networks, including the

Air Quality System, the Georgia Department of Natural

Resources, and the Metro Atlanta Index. Ozone levels were

not monitored during the winter months when ozone levels

in Atlanta are low; the remaining pollutants were measured

daily throughout the year. These air quality data have been

described previously (15, 16). For gaseous pollutants, hourly

data were used; for PM10 mass, daily gravimetric data were

used. For each pollutant, data from the most central monitoring site were used. On days when measurements were

missing at the central site, data were imputed by use of measurements from at least one other monitoring site in the Atlanta metropolitan statistical area, as well as meteorologic

and time variables (values imputed for 17 percent, 2 percent,

14 percent, 6 percent, and 9 percent of PM10, ozone, nitrogen

dioxide, carbon monoxide, and sulfur dioxide measurements,

respectively).

The average temperature and dew point temperature (average of the daily minimum and maximum), as well as additional meteorologic data measured at the Hartsfield-Atlanta

International Airport, were obtained from the National

Climatic Data Center network.

Emergency department data

Of the 41 hospitals in the 20-county Atlanta metropolitan

statistical area, 37 agreed to participate, and 31 provided

usable computerized billing records for at least part of the

study period. Computerized billing records for all emergency department visits between January 1, 1993, and August

31, 2000, were collected, including primary International

Classification of Diseases, Ninth Revision (ICD-9), diagnostic codes, secondary ICD-9 diagnostic codes, age, date of

birth, sex, race, and residential ZIP code. The study was approved and was granted a waiver of consent by the Emory

University Institutional Review Board. Residents of the Atlanta metropolitan statistical area, determined by residential

ZIP code at the time of the visit, were included in the analyses. Repeat visits within a day were counted as a single visit.

Using the primary ICD-9 diagnosis code, we defined several primary cardiovascular disease groups: ischemic heart

disease (codes 410–414), dysrhythmia (code 427), congestive heart failure (code 428), and peripheral vascular and

cerebrovascular disease (codes 433–437, 440, 443, 444,

451–453). The combined cardiovascular disease case group

pooled the ICD-9 diagnoses of the preceding case groups.

The comorbid health conditions for each visit were defined by use of all secondary ICD-9 diagnosis codes listed

for the same visit as the primary diagnosis code. The comorbid health conditions that we examined were defined as

follows: hypertension (codes 401–405), diabetes (code 250),

dysrhythmia (code 427), congestive heart failure (code 428),

atherosclerosis (code 440), COPD (codes 491, 492, 496),

pneumonia (codes 480–486), upper respiratory infections

(codes 460–465, 466.0), and asthma (codes 493, 786.09).

Statistical analysis

All analyses were performed using SAS, version 9.1, statistical software (SAS Institute, Inc., Cary, North Carolina).

All odds ratios and confidence intervals were calculated for

an increase of approximately 1 standard deviation in the

pollutant measure.

Using a case-crossover framework, we modeled the association between air pollution and cardiovascular visits. We

used a time-stratified approach to select referent days (17–

20); referent days were selected on the same day of week

and within the same calendar month as the cardiovascular

visit of interest. For example, if the visit occurred on a Tuesday in March (the case day), we selected all other Tuesdays

in March as the control days. A subject could have an emergency department visit for the outcome of interest on a

selected control day; because this situation occurred so infrequently (0.7 percent of the strata), these days were retained as control days (and were also counted as a case day

in another stratum).

For each air quality variable, the moving average of the

0-, 1-, and 2-day lags was used as the a priori lag structure.

We used conditional logistic regression for the analysis

(PROC PHREG procedure in SAS, version 9.1, software);

each stratum consisted of the case day and all selected control days. Cubic splines for average daily temperature and

dew point temperature (average of values lagged 0, 1, and 2

days) with knots at the 25th and 75th percentiles were also

included in the models.

We compared the results from the case-crossover analysis

with those from our previous time-series analysis, which

used Poisson generalized linear modeling (15). Then, we

examined potentially susceptible subgroups of the primary

cardiovascular disease visits with comorbid health conditions as defined by the secondary ICD-9 diagnosis codes

as described above. For example, the association of air pollution and emergency visits for dysrhythmia was examined

separately in visits with and without a secondary diagnosis

of hypertension. We calculated chi-squared statistics and corresponding two-sided p values to assess the heterogeneity of

the pollution regression coefficients from the two strata.

RESULTS

Table 1 provides descriptive statistics for the daily concentrations of the air quality measures, as well as for the

absolute difference between air pollution levels on event

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days and the average concentrations on the controls days.

More detailed descriptions of the emergency department and

air quality data for this time period have been presented

elsewhere (15, 16).

Thirty-one hospitals provided data on 4,407,535 emergency department visits by Atlanta residents for the study

period. These 31 hospitals were estimated to receive 79 percent of emergency department visits in the Atlanta metropolitan statistical area. Five hospitals provided data for the entire

study period; the mean length of time the hospitals provided

data was 4.5 years (range: 2–7.5 years). There was an average of 37 cardiovascular disease visits per day; subgroups had

between 7.2 visits per day (congestive heart failure) and 11.7

visits per day (ischemic heart disease). Table 2 presents the

number and percentage of the secondary diagnoses for each

of the primary outcome groups. Hypertension was the largest

of the secondary diagnosis groups, with over 30 percent of

visits for cardiovascular disease having a secondary diagnosis

of hypertension (table 2).

We observed 2–3 percent increases in cardiovascular visits, including the subgroups ischemic heart disease, peripheral and cerebrovascular disease, and congestive heart failure,

associated with standard deviation increases of nitrogen dioxide and carbon monoxide in single-pollutant models using

3-day moving averages (pollution lagged 0, 1, and 2 days)

(table 3). Associations of peripheral and cerebrovascular disease visits with PM10 and sulfur dioxide were also elevated

but not significant, as was the association of dysrhythmia and

nitrogen dioxide. Results from the case-crossover analysis

were largely similar to results from the previous time-series

analysis (15) (table 3). The greatest inconsistencies were

observed for congestive heart failure, the smallest of the subgroups, and for ozone across the outcome groups.

Results from analyses examining visits for cardiovascular

disease with comorbid diagnoses are presented in tables

4–8. There were low numbers of visits with comorbid atherosclerosis, asthma, pneumonia, and upper respiratory infection, resulting in unstable models; therefore, the results

for these comorbid conditions are not presented. The estimated associations of cardiovascular disease, specifically

visits for congestive heart failure and dysrhythmia, in relation to nitrogen dioxide, carbon monoxide, and PM10 were

substantially higher among patients with a secondary diagnosis of hypertension than for patients without comorbid

hypertension (table 4). The strongest associations among

hypertensive patients were observed for dysrhythmia visits

in relation to nitrogen dioxide (per 20 ppb: odds ratio

(OR) 1⁄4 1.095, 95 percent confidence interval (CI): 1.030,

1.165) and in relation to carbon monoxide (per 1 ppm: OR 1⁄4

1.065, 95 percent CI: 1.015, 1.118). Generally, the estimated

associations were stronger in patients with hypertension

compared with patients without hypertension. A similar, although weaker, pattern was observed for comorbid diabetes

(table 5); the association of dysrhythmia visits in relation

TABLE 1. Mean, standard deviation, and selected percentiles of daily ambient air quality

levels and of the absolute differences between daily levels on event days and the average

concentrations on the control days, Atlanta, Georgia, 1993–2000

Mean Standard

deviation

Percentile

10th 90th

Daily levels

24-hour PM10* (lg/m3

)y 27.9 12.3 13.2 44.7

8-hour ozone (ppb)y,z 55.6 23.8 26.8 87.6

1-hour nitrogen dioxide (ppb)y 45.9 17.3 25.0 68.0

1-hour carbon monoxide (ppm)y 1.8 1.2 0.5 3.4

1-hour sulfur dioxide (ppb)y 16.5 17.1 2.0 39.0

Average temperature (C) 17.5 8.3 6.1 27.2

Average dew point (C) 10.5 8.9 2.2 20.8

Absolute differences between the

average level on case days and the

average level on control days

24-hour PM10 (lg/m3

) 9.1 7.5 1.4 19.1

8-hour ozone (ppb) 17.5 13.9 2.7 39.2

1-hour nitrogen dioxide (ppb) 16.3 12.7 2.8 32.8

1-hour carbon monoxide (ppm) 1.0 0.8 0.2 2.1

1-hour sulfur dioxide (ppb) 12.6 12.6 1.7 25.8

Average temperature (C) 7.2 6.0 1.0 15.2

Average dew point (C) 9.3 7.8 1.0 20.2

* PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

y Data were imputed for 17 percent (458 of 2,703) of PM10 values, 2 percent (46 of 1,892) of

ozone values, 14 percent (398 of 2,775) of nitrogen dioxide values, 6 percent (161 of 2,758) of

carbon monoxide values, and 9 percent (237 of 2,775) of sulfur dioxide values.

z Ozone was measured from March through October only.

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to nitrogen dioxide among patients with diabetes was markedly stronger than that among patients without diabetes

(per 20 ppb: OR 1⁄4 1.158, 95 percent CI: 1.046, 1.282 vs.

OR 1⁄4 1.014, 95 percent CI: 0.988, 1.040). Associations of

peripheral and cerebrovascular disease visits in relation to

ozone, nitrogen dioxide, and carbon monoxide were considerably larger among patients with comorbid COPD

than in patients without COPD (table 6). The estimated associations for these pollutants among patients with comorbid

COPD expressed as a range were odds ratios of 1.11–1.24

per unit increase of pollutant compared with 1.01–1.03 per

same unit increase in patients without comorbid COPD.

TABLE 2. Number and percentage of cardiovascular emergency department visits with the comorbid

conditions listed at the same visit, Atlanta, Georgia, 1993–2000

Comorbid

condition

Primary diagnosis

All cardiovascular

disease

(n 1⁄4 103,551)

Ischemic heart

disease

(n 1⁄4 32,731)

Dysrhythmia

(n 1⁄4 27,342)

Peripheral and

cerebrovascular

disease

(n 1⁄4 23,411)

Congestive

heart failure

(n 1⁄4 20,073)

No. % No. % No. % No. % No. %

Hypertension 30,658 30 11,592 35 4,218 15 8,574 37 6,227 31

Diabetes 15,796 15 5,705 17 1,562 6 3,737 16 4,793 24

Dysrhythmia 12,839 12 5,407 17 NA* NA 2,741 12 4,692 23

COPD* 8,378 8 2,651 8 993 4 1,405 6 3,329 17

Congestive heart

failure 5,746 6 3,475 11 1,111 4 1,160 5 NA NA

Pneumonia 1,507 2 404 1 153 1 322 1 628 3

Asthma 1,552 2 347 1 362 1 264 1 579 3

Upper respiratory

infection 756 1 151 1 155 1 126 1 324 2

Atherosclerosis 941 1 302 1 60 0 357 1 222 1

* NA, not applicable; COPD, chronic obstructive pulmonary disease.

TABLE 3. Estimated odds ratios and 95% confidence intervals for the association of cardiovascular disease visits with daily ambient

air quality measurements (average of pollution levels lagged 0, 1, and 2 days), Atlanta, Georgia, 1993–2000

Air quality

measurement (unit)

and method

All cardiovascular

disease

Ischemic heart

disease Dysrhythmia

Peripheral and

cerebrovascular

disease

Congestive

heart failure

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

PM10* (10 lg/m3)

Case-crossover 1.010 1.000, 1.020 1.009 0.991, 1.027 1.011 0.991, 1.031 1.017 0.996, 1.039 1.001 0.978, 1.024

Time seriesy 1.009 0.998, 1.019 1.011 0.992, 1.030 1.008 0.989, 1.029 1.020 0.999, 1.043 0.992 0.968, 1.016

Ozone (25 ppb)

Case-crossover 1.000 0.980, 1.020 1.001 0.966, 1.038 1.012 0.973, 1.052 1.021 0.979, 1.064 0.952 0.908, 0.997

Time seriesy 1.008 0.987, 1.030 1.019 0.981, 1.059 1.008 0.967, 1.051 1.028 0.985, 1.073 0.965 0.918, 1.014

Nitrogen dioxide (20 ppb)

Case-crossover 1.025 1.012, 1.038 1.026 1.003, 1.049 1.022 0.997, 1.047 1.033 1.006, 1.061 1.017 0.988, 1.047

Time seriesy 1.025 1.012, 1.039 1.029 1.005, 1.053 1.019 0.994, 1.044 1.041 1.013, 1.069 1.010 0.981, 1.040

Carbon monoxide (1 ppm)

Case-crossover 1.020 1.010, 1.030 1.016 0.999, 1.034 1.017 0.998, 1.036 1.031 1.010, 1.052 1.019 0.997, 1.041

Time seriesy 1.017 1.008, 1.027 1.016 0.999, 1.034 1.012 0.993, 1.031 1.031 1.010, 1.052 1.010 0.988, 1.032

Sulfur dioxide (20 ppb)

Case-crossover 1.009 0.995, 1.024 1.013 0.988, 1.039 1.003 0.975, 1.031 1.024 0.993, 1.055 0.993 0.961, 1.026

Time seriesy 1.007 0.993, 1.022 1.007 0.981, 1.033 1.001 0.975, 1.028 1.028 0.999, 1.059 0.992 0.961, 1.025

* PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

y Time-series results from Metzger et al. (15).

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The results from models assessing effect modification by

comorbid congestive heart failure provided little evidence of

effect modification with the notable exception of primary

ischemic heart disease visits (table 7). The associations for

ischemic heart disease among persons with comorbid congestive heart failure were substantially more negative in

TABLE 4. Estimated odds ratios and 95% confidence intervals for the association of cardiovascular disease visits with daily ambient

air quality measurements (average of pollution levels lagged 0, 1, and 2 days) in visits with and without comorbid hypertension,

Atlanta, Georgia, 1993–2000

Ischemic heart

disease Dysrhythmia

Peripheral and

cerebrovascular

disease

Congestive

heart failure

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

With comorbid

hypertension*

PM10y 1.003 0.973, 1.034 1.037 0.988, 1.089 1.024 0.990, 1.060 1.041z 0.999, 1.084

Ozone 1.022 0.962, 1.086 0.980 0.888, 1.082 1.006 0.939, 1.077 0.969 0.890, 1.054

Nitrogen dioxide 1.036 0.997, 1.076 1.095z 1.030, 1.165 1.031 0.987, 1.076 1.037 0.985, 1.090

Carbon monoxide 1.007 0.978, 1.037 1.065z 1.015, 1.118 1.038 1.004, 1.074 1.037 0.997, 1.079

Sulfur dioxide 1.024 0.980, 1.070 1.034 0.964, 1.110 1.041 0.989, 1.095 1.012 0.954, 1.074

No comorbid

hypertension*

PM10 1.013 0.991, 1.036 1.006 0.985, 1.028 1.013 0.987, 1.040 0.982z 0.955, 1.010

Ozone 0.991 0.948, 1.036 1.018 0.975, 1.063 1.029 0.977, 1.084 0.943 0.891, 0.998

Nitrogen dioxide 1.021 0.992, 1.050 1.009z 0.982, 1.036 1.035 1.001, 1.070 1.007 0.973, 1.043

Carbon monoxide 1.022 1.000, 1.043 1.008z 0.988, 1.029 1.027 1.002, 1.054 1.010 0.985, 1.037

Sulfur dioxide 1.008 0.976, 1.040 0.997 0.968, 1.028 1.015 0.978, 1.053 0.985 0.947, 1.024

* Determined from secondary International Classification of Diseases, Ninth Revision, codes listed for the same visit.

y PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

z Comparing the pollution regression coefficients for visits with and without comorbid hypertension: p < 0.05.

TABLE 5. Estimated odds ratios and 95% confidence intervals for the association of cardiovascular disease visits with daily ambient

air quality measurements (average of pollution levels lagged 0, 1, and 2 days) in visits with and without comorbid diabetes, Atlanta,

Georgia, 1993–2000

Ischemic heart

disease Dysrhythmia

Peripheral and

cerebrovascular

disease

Congestive

heart failure

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

With comorbid diabetes*

PM10y 1.022 0.979, 1.067 1.049 0.968, 1.137 1.016 0.965, 1.069 1.029 0.982, 1.078

Ozone 1.025 0.940, 1.118 1.010 0.859, 1.189 0.958 0.862, 1.065 0.956 0.868, 1.053

Nitrogen dioxide 1.003 0.950, 1.059 1.158z 1.046, 1.282 1.012 0.947, 1.082 1.017 0.959, 1.078

Carbon monoxide 0.985 0.945, 1.027 1.058 0.976, 1.146 1.065 1.012, 1.121 1.020 0.975, 1.067

Sulfur dioxide 0.995 0.934, 1.060 1.025 0.911, 1.153 1.026 0.951, 1.106 1.018 0.952, 1.090

No comorbid diabetes*

PM10 1.006 0.987, 1.026 1.009 0.989, 1.029 1.018 0.995, 1.042 0.992 0.966, 1.019

Ozone 0.996 0.958, 1.037 1.012 0.972, 1.054 1.033 0.987, 1.080 0.950 0.900, 1.003

Nitrogen dioxide 1.030 1.005, 1.057 1.014z 0.988, 1.040 1.037 1.008, 1.068 1.018 0.985, 1.052

Carbon monoxide 1.023 1.004, 1.042 1.014 0.995, 1.034 1.025 1.003, 1.048 1.018 0.993, 1.044

Sulfur dioxide 1.017 0.989, 1.045 1.002 0.973, 1.031 1.023 0.990, 1.058 0.986 0.949, 1.023

* Determined from secondary International Classification of Diseases, Ninth Revision, codes listed for the same visit.

y PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

z Comparing the pollution regression coefficients for visits with and without comorbid diabetes: p < 0.05.

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relation to air pollution than those for patients without comorbid congestive heart failure, particularly for PM10, nitrogen dioxide, and carbon monoxide. Results from models

assessing effect modification by comorbid dysrhythmia did

not provide evidence of effect modification (table 8).

DISCUSSION

Despite numerous studies providing evidence of an association between ambient air pollution and acute cardiovascular morbidity in the general population, relatively little is

known regarding potentially susceptible populations. Using a

case-crossover framework in this investigation, we took advantage of a large database of emergency department visits

collected over a 7-year time period to examine potential heterogeneity of risk for adverse cardiovascular events in relation

to air pollution in people with specific comorbid conditions.

Stronger associations were observed for cardiovascular visits

in relation to ambient air pollution levels among patients with

comorbid hypertension, diabetes, and COPD compared with

patients without these comorbid conditions.

Our results provide evidence that underlying hypertension may increase the risk for cardiovascular morbidity, specifically for dysrhythmia and congestive heart failure, in

relation to increased air pollution levels, particularly PM10,

nitrogen dioxide, and carbon monoxide. Relatively few studies have examined comorbid hypertension as a potential

effect modifier. Results from D’Ippoliti et al. (2) did not provide evidence of effect modification by hypertension for the

association of air pollution and risk of myocardial infarction.

Goldberg et al. (3) reported no increased association of mortality in relation to air pollution among persons with hypertension. However, several studies have found evidence of

effect modification by hypertensive status when examining

the association of air pollution and heart rate variability (4, 8,

21), suggesting that hypertension may increase the risk of

cardiovascular events in relation to air pollution via a reduction in cardiac autonomic control. This hypothesis is consistent with our results, in which we observed the strongest

evidence for effect modification by hypertensive status for

dysrhythmia visits.

Our results suggesting that existing diabetes modifies the

association of air pollution and cardiovascular outcomes are

generally consistent with previously reported associations

among persons with diabetes (1, 2, 7, 8, 11, 12). The plausibility of this association is strengthened by evidence that

air pollution exposure is associated with reduced heart rate

variability (4, 21–26), increased C-reactive protein levels

(27), increased fibrinogen levels (2832), and elevated inflammatory markers (31, 32). Diabetes is associated with

similar changes in these cardiovascular risk factors (33–35).

Our results also provide evidence that persons with comorbid COPD may be at increased risk of adverse cardiovascular

events in relation to air pollution. These results are similar

to those reported previously (3, 10, 36). Zanobetti et al.

(10) reported similar increased risk for hospital admissions

for cardiovascular disease in relation to PM10 among persons

with COPD as well as with pneumonia and acute respiratory

infections. The number of visits for cardiovascular disease in

our data set with an underlying diagnosis of pneumonia or

acute respiratory infections was very low, so we were not able

TABLE 6. Estimated odds ratios and 95% confidence intervals for the association of cardiovascular disease visits with daily ambient

air quality measurements (average of pollution levels lagged 0, 1, and 2 days) in visits with and without comorbid chronic obstructive

pulmonary disease, Atlanta, Georgia, 1993–2000

Ischemic heart

disease Dysrhythmia

Peripheral and

cerebrovascular

disease

Congestive

heart failure

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

With comorbid COPD*,y

PM10* 0.981 0.921, 1.044 0.984 0.889, 1.088 1.086 0.998, 1.181 1.010 0.954, 1.069

Ozone 0.938 0.825, 1.067 0.959 0.779, 1.180 1.237z 1.039, 1.473 0.972 0.864, 1.093

Nitrogen dioxide 0.960 0.886, 1.040 1.064 0.934, 1.213 1.142 1.026, 1.271 1.051 0.980, 1.128

Carbon monoxide 0.996 0.938, 1.057 0.972 0.878, 1.077 1.113 1.027, 1.205 1.058 1.003, 1.115

Sulfur dioxide 0.991 0.905, 1.086 1.085 0.936, 1.256 1.065 0.944, 1.202 1.035 0.956, 1.122

No comorbid COPDy

PM10 1.012 0.993, 1.031 1.012 0.992, 1.032 1.013 0.991, 1.035 0.999 0.974, 1.025

Ozone 1.007 0.970, 1.045 1.014 0.974, 1.055 1.009z 0.967, 1.053 0.948 0.900, 0.998

Nitrogen dioxide 1.032 1.007, 1.056 1.020 0.995, 1.046 1.026 0.998, 1.054 1.011 0.979, 1.043

Carbon monoxide 1.018 1.000, 1.036 1.018 0.999, 1.038 1.026 1.004, 1.047 1.011 0.987, 1.036

Sulfur dioxide 1.015 0.988, 1.042 1.000 0.972, 1.029 1.020 0.989, 1.053 0.985 0.950, 1.021

* COPD, chronic obstructive pulmonary disease; PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

y Determined from secondary International Classification of Diseases, Ninth Revision, codes listed for the same visit.

z Comparing the pollution regression coefficients for visits with and without comorbid COPD: p < 0.05.

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TABLE 7. Estimated odds ratios and 95% confidence intervals for the association of cardiovascular

disease visits with daily ambient air quality measurements (average of pollution levels lagged 0, 1, and 2

days) in visits with and without comorbid congestive heart failure, Atlanta, Georgia, 1993–2000

Ischemic heart

disease Dysrhythmia

Peripheral and

cerebrovascular

disease

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

With comorbid congestive

heart failure*

PM10y 0.927z 0.877, 0.980 1.016 0.924, 1.117 1.076 0.979, 1.183

Ozone 1.015 0.910, 1.132 0.981 0.815, 1.180 1.089 0.903, 1.314

Nitrogen dioxide 0.911z 0.850, 0.977 1.136 1.006, 1.282 1.043 0.928, 1.172

Carbon monoxide 0.956z 0.907, 1.007 1.065 0.968, 1.173 1.072 0.981, 1.172

Sulfur dioxide 0.981 0.905, 1.063 1.034 0.902, 1.186 1.067 0.931, 1.223

No comorbid congestive

heart failure*

PM10 1.020z 1.000, 1.039 1.011 0.991, 1.031 1.014 0.993, 1.036

Ozone 1.000 0.963, 1.039 1.013 0.973, 1.055 1.017 0.975, 1.061

Nitrogen dioxide 1.041z 1.016, 1.066 1.017 0.992, 1.043 1.032 1.005, 1.061

Carbon monoxide 1.024z 1.006, 1.042 1.015 0.996, 1.034 1.029 1.008, 1.051

Sulfur dioxide 1.017 0.990, 1.045 1.002 0.974, 1.030 1.021 0.990, 1.053

* Determined from secondary International Classification of Diseases, Ninth Revision, codes listed for the same

visit.

y PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

z Comparing the pollution regression coefficients for visits with and without comorbid congestive heart failure:

p < 0.05.

TABLE 8. Estimated odds ratios and 95% confidence intervals for the association of cardiovascular

disease visits with daily ambient air quality measurements (average of pollution levels lagged 0, 1, and 2

days) in visits with and without comorbid dysrhythmia, Atlanta, Georgia, 1993–2000

Ischemic heart

disease

Peripheral and

cerebrovascular

disease

Congestive heart

failure

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

Odds

ratio

95%

confidence

interval

With comorbid dysrhythmia*

PM10y 1.019 0.975, 1.065 1.047 0.985, 1.113 0.983 0.939, 1.030

Ozone 0.969 0.887, 1.060 1.033 0.913, 1.169 0.914 0.830, 1.006

Nitrogen dioxide 1.059 1.001, 1.120 1.067 0.988, 1.152 1.014 0.956, 1.076

Carbon monoxide 1.028 0.985, 1.072 1.072 1.011, 1.138 1.004 0.960, 1.051

Sulfur dioxide 1.015 0.953, 1.082 1.000 0.914, 1.092 0.992 0.927, 1.061

No comorbid dysrhythmia*

PM10 1.007 0.987, 1.027 1.014 0.991, 1.036 1.006 0.980, 1.034

Ozone 1.008 0.969, 1.048 1.019 0.975, 1.065 0.964 0.913, 1.017

Nitrogen dioxide 1.019 0.994, 1.045 1.028 1.000, 1.058 1.018 0.985, 1.052

Carbon monoxide 1.014 0.995, 1.033 1.026 1.004, 1.048 1.023 0.998, 1.049

Sulfur dioxide 1.013 0.985, 1.041 1.027 0.994, 1.060 0.994 0.957, 1.032

* Determined from secondary International Classification of Diseases, Ninth Revision, codes listed for the same

visit.

y PM10, particulate matter with an average aerodynamic diameter of less than 10 lm.

Air Pollution and Sensitive Populations 631

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to examine this stratification. Sunyer et al. (36) reported an

increased risk of mortality in relation to PM10 among patients

with severe COPD; Bateson and Schwartz (1), however, reported no increased risk of mortality in relation to PM10 in

persons with existing COPD.

Our results did not corroborate the results from Mann

et al. (6), who reported an increased risk of hospital admissions for ischemic heart disease in relation to carbon monoxide among persons with a secondary diagnosis of congestive

heart failure. We observed the opposite trend in our results;

patients with comorbid congestive heart failure had a decreased risk of emergency department visits for ischemic

heart disease compared with patients without comorbid congestive heart failure. These differences may have resulted

from the different populations; Mann et al. (6) examined

hospital admissions among members of a health maintenance organization in southern California, while our study

was a population-based assessment of emergency department visits. Different air pollution mixtures in the two areas

or instability due to low numbers of patients with comorbid

congestive heart failure may have also contributed to the

contrasting results. Additionally, the different sources of information for primary diagnosis and comorbid conditions

(medical records in Mann et al. (6) vs. emergency department billing records in our study) may have different

amounts of measurement error, particularly in assessing secondary diagnoses. Goldberg et al. (3) also reported an increased risk of death in relation to particles among persons

with existing congestive heart failure, while D’Ippoliti et al.

(2) did not observe an increased risk of acute myocardial

infarction admissions in relation to air pollution among patients with congestive heart failure. Moreover, consistent

with our results, D’Ippoliti et al. (2) did not observe evidence of effect modification by dysrhythmia of the association of air pollution and acute myocardial infarction.

There were limitations in our assessment of comorbid

illness. Hospitals provided various numbers of secondary

diagnostic codes (the number provided ranged from three

to 35 secondary codes); moreover, we used only the concurrent secondary diagnoses rather than including diagnoses

from previous visits, potentially reducing the sensitivity of

our comorbid illness assessment and subsequently limiting

power to observe any effect modification by these conditions. However, Zanobetti et al. (10) reported little or no

difference when using concurrent or previous admissions

when assessing secondary conditions. Additionally, we examined only one comorbid illness at a time; an alternative

method would be to examine multiple secondary diagnoses

(e.g., patients with secondary diabetes and hypertension

compared with patients with either one or none of the underlying conditions). However, the proportions of cardiovascular disease visits with combinations of comorbid

conditions were too low for reliable models. An additional

limitation in our assessment of comorbid illness stems from

the possibility that the diagnosis of certain primary outcomes may be affected by the presence of one or more

comorbid conditions, or vice versa, making certain primary

outcomes more or less likely to have comorbid conditions

listed at the same visit in the billing records. We are not able

to examine this issue in our database.

Additional limitations of this study include the use of a

central monitor for pollution measurements and the potential for spurious associations due to the number of statistical

tests performed. The measurement error resulting from using

centrally located monitors for ambient air pollution measures

could potentially attenuate observed associations but is not

likely to be responsible for spurious associations. We have

attempted to reduce the potential problems associated with

multiple testing by using an a priori approach for choosing

the pollutant metric, pollutant lag structure, comorbid conditions, and modeling approach. Additionally, behavior such

as air conditioning use or time spent outdoors may affect

personal exposure levels. This could affect the magnitude

of the observed associations in comparison with other geographic locations.

The magnitude of the estimates and standard errors obtained using the case-crossover approach for the primary case

groups were comparable with those from our previously published time-series analysis for cardiovascular visits (15). The

largest degree of inconsistency was observed for congestive

heart failure and for ozone across the outcomes, and this

inconsistency was minor. Congestive heart failure is the

smallest of the cardiovascular disease subgroups, and ozone

was not measured throughout the year (measured April

through October); the smaller number of observations in

these models may make the results more prone to random

error compared with the other subgroups or pollutants with

more observations. As discussed by Kunzli and Schindler

(37), case-crossover methods may reduce the power to detect

associations compared with time-series studies, because

there may be less variability in the difference between air

pollution concentrations on event and control days compared

with using the full distributions of air pollution values as in

time-series methodology. Additionally, case-crossover and

time-series methods differ in the way the models control

long-term time trends. The case-crossover analysis inherently controls for long-term time trends when a sufficiently

small referent time period is used (18, 19, 38); therefore, the

differences observed for the ozone models may indicate that

there is some residual confounding by time in the time-series

results. However, the differences are not extensive enough

to alter the conclusions drawn from the results. The overall

similarity of the time-series and case-crossover estimates provides evidence of the robustness of our results.

This study took advantage of a large database of emergency department visits to examine the issue of increased susceptibility among persons with underlying conditions. The

results provide further evidence of increased susceptibility

to adverse cardiovascular events in relation to air pollution

in persons with comorbid hypertension, diabetes, and COPD.

ACKNOWLEDGMENTS

This work was supported by grant W03253-07 from the

Electric Power Research Institute, research assistance agreement R82921301-0 from the US Environmental Protection

Agency, and grant R01ES11294 from the National Institute

632 Peel et al.

Am J Epidemiol 2007;165:625–633

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of Environmental Health Sciences, National Institutes of

Health.

The authors are grateful to the participating hospitals,

whose staff members devoted many hours of time as a

public service.

Conflict of interest: none declared.

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