Research | Article

There is substantial epidemiologic literature

indicating a link between air pollution and

cardiovascular morbidity and mortality. This

includes not only studies of episodic pollution

such as occurred during 1930 in the Meuse

Valley (Firket 1931), 1948 in Pennsylvania

(Shrenk et al. 1949), and 1952 in London

(Ministry of Health 1954) but also of the generally low concentrations found in urban

areas. Studies have been carried out in North

America (Burnett et al. 1997a, 1997b; Morris

et al. 1995; Morris and Naumova 1998;

Schwartz 1999; Schwartz and Morris 1995;

Zanobetti et al. 2000), in Western Europe

(Atkinson et al. 1999; Ballester et al. 2001;

Diaz et al. 2001; Hoek et al. 2001; Prescott et

al. 1998), in Tokyo, Japan (Piver et al. 1999),

and in Hong Kong, China (Wong et al. 1999).

Most of these studies showed a predominant

effect of particulates and carbon monoxide on

cardiovascular admissions (Ballester et al.

2001; Burnett et al. 1997b; Morris et al.

1995; Morris and Naumova 1998; Schwartz

1999; Schwartz and Morris 1995; Wong et

al. 1999). However, the Tokyo study (Piver

et al. 1999) suggests an independent effect of

nitrogen dioxide. Furthermore, the studies in

Canada (Burnett et al. 1997a) and Spain

(Diaz et al. 2001) demonstrated consistent

effects for sulfur dioxide and/or ozone on cardiovascular hospital admissions. Several

reports have addressed the issue of weather

and mortality; extreme temperatures have

been associated with increased daily mortality

in numerous regions of the world (Braga et al.

2001; Kunst et al. 1993). Mortality has also

been observed to increase during periods of 3

or more days of unusual temperatures during

summer or winter, showing that temperature

variability is an important determinant of

human health effects (Braga et al. 2001,

2002; Saez et al. 1995).

It has been suggested that weather and

temperature may modify the effects of air pollution on health both at high temperatures

(Katsouyanni et al. 1993) and low temperatures (Morris and Naumova 1998). The

Intergovernmental Panel on Climate Change

(IPCC; 1996) has projected that atmospheric

concentrations of carbon dioxide could double in the next 50–100 years. A doubling of

atmospheric concentrations of CO2 could

result in an increase in average global surface

air temperatures of 1–3°C because of the

greenhouse effect. In addition, because

approximately 65% of atmospheric CO2

comes from combustion of fossil fuels,

increasing concentrations of CO2 could also

be accompanied by increasing concentrations

of other air pollutants, particularly in large

urban areas. An increase in surface air temperatures could accompany a greater frequency

and duration of heat waves. According to the

IPCC (1996), the frequency of extremely hot

days in temperate climates approximately

doubles for every 2–3°C increase in temperature during the average summer. Because heat

waves often occur in large metropolitan areas

during warm summer months, these large

cities could experience an increase in the incidence of heat-related morbidity and mortality

(McMichael et al. 1996). To address this specific issue, we focused our research on the

months of the year when the frequency of

exposure to high daily maximum temperatures (Tmax) and high air pollutant concentrations would be the greatest. The present study

focuses on the months of July and August

(1993–1997) in Denver, Colorado.

Studies have documented associations

between daily variations in air pollution and

cardiovascular deaths and hospital admissions

(Dockery et al. 2000). Mechanisms for the

effects of air pollution on cardiovascular mortality and morbidity may include changes in

blood coagulability (Seaton et al. 1995) and

changes in the nervous system control of the

heart, possibly leading to arrhythmias (Peters

et al. 2000). Mechanisms linking temperature

to cardiovascular mortality and morbidity

have also been postulated. The blood viscosity

and cholesterol levels have been found to

increase with high temperatures (Keatinge et

al. 1986), whereas blood pressure and fibrinogen levels increase during winter, although

outdoor temperature does not seem to determine the seasonal variation in fibrinogen (van

der Bom et al. 1997). In many epidemiology

studies with respiratory and cardiovascular diseases, regression models include adjustments

for seasonal climate variability to isolate the

contributions of air pollution on the daily

number of deaths or hospital admissions for

these diseases. In contrast to using seasonal

adjustments for the entire year, we focused on

the months of July and August, partially to

avoid potential bias in the type of seasonal

adjustment to be used and partially to focus

on short time-frame changes in temperature.

Temperature, Air Pollution, and Hospitalization for Cardiovascular Diseases

among Elderly People in Denver

Petra J. M. Koken,1 Warren T. Piver,1 Frank Ye,1 Anne Elixhauser,2 Lola M. Olsen,3 and Christopher J. Portier1

1National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA; 2Agency for Healthcare Research and

Quality, Rockville, Maryland, USA; 3National Aeronautics and Space Administration, Greenbelt, Maryland, USA

Address correspondence to P.J.M. Koken, National

Institute of Environmental Health Sciences, MD

A3-06, PO Box 12233, Research Triangle Park, NC

27709 USA. Telephone: (919) 541-2271. Fax: (919)

541-1479. E-mail: koken@niehs.nih.gov

We are grateful for financial support for this project

from NASA, Order S-10152-X.

The authors declare they have no conflict of interest.

Received 27 August 2002; accepted 19 May 2003.

Daily measures of maximum temperature, particulate matter ≤ 10 μm in aerodynamic diameter

(PM10), and gaseous pollution (ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide) were

collected in Denver, Colorado, in July and August between 1993 and 1997. We then compared

these exposures with concurrent data on the number of daily hospital admissions for cardiovascular

diseases in men and women > 65 years of age. Generalized linear models, assuming a Poisson error

structure for the selected cardiovascular disease hospital admissions, were constructed to evaluate

the associations with air pollution and temperature. After adjusting the admission data for yearly

trends, day-of-week effects, ambient maximum temperature, and dew point temperature, we studied the associations of the pollutants in single-pollutant models with lag times of 0–4 days. The

results suggest that O3 is associated with an increase in the risk of hospitalization for acute myocardial infarction, coronary atherosclerosis, and pulmonary heart disease. SO2 appears to be related to

increased hospital stays for cardiac dysrhythmias, and CO is significantly associated with congestive

heart failure. No association was found between particulate matter or NO2 and any of the health

outcomes. Males tend to have higher numbers of hospital admissions than do females for all of the

selected cardiovascular diseases, except for congestive heart failure. Higher temperatures appear to

be an important factor in increasing the frequency of hospitalization for acute myocardial infarction

and congestive heart failure, and are associated with a decrease in the frequency of visits for coronary atherosclerosis and pulmonary heart disease. Key words: acute myocardial infarction, air pollution, cardiac dysrhythmias, cardiovascular diseases, CO, congestive heart failure, coronary

atherosclerosis, generalized estimating equations, NO2, O3, PM10, Poisson regression, pulmonary

heart disease, SO2, temperature. Environ Health Perspect 111:1312–1317 (2003).

doi:10.1289/ehp.5957 available via http://dx.doi.org/ [Online 19 May 2003]

1312 VOLUME 111 | NUMBER 10 | August 2003 • Environmental Health Perspectives
Materials and Methods

Setting. Denver once was among the most

polluted cities in the United States, with a

brown cloud as a constant reminder of its air

pollution woes. The city regularly exceeded the

federal air pollution standards for 200

days/year, every year in the late 1970s. Air pollution in Denver mainly derives from motor

vehicle exhaust emissions, with industrial pollution playing a minor role. Denver’s air pollution levels are also exacerbated by temperature

inversions resulting from Denver’s geographic

location. During a temperature inversion, a

warm, less-dense inversion layer of air overlies

colder, denser air, forming a “lid” that traps

pollutants below it. In Denver the trapping

effect is even more pronounced because long

periods of calm weather and light winds trap

pollutants against nearby mountains. The local

government has undertaken sizable efforts to

reduce air pollution; the city has now complied

with Clean Air Act regulations for particulates,

CO, and O3 (U.S. EPA 2001) and has cut its

pollution dramatically. Nonetheless, the

remaining levels of pollutants may continue to

have health effects, even at their improved levels. In this study, we defined Denver as all of

Denver County.

Hospital admission data. The numbers of

daily hospital admissions to all 11 hospitals in

Denver County were obtained for the months

of July and August during 1993–1997. July

and August are of particular interest because

these are the months with highest temperatures, combined with relatively high levels of

outdoor activity during which exposure would

be maximized. The data were provided by the

Agency for Healthcare Research and Quality

(AHRQ; Rockville, MD). The AHRQ maintains state-specific hospital discharge databases as part of the Healthcare Cost and

Utilization Project (HCUP 2001). Data elements included admission date, age, sex, and

discharge diagnoses. The study population was

restricted to men and women > 65 years of age

admitted to the hospitals with a primary discharge diagnosis of the following causes [coded

using International Classification of Diseases, 9th

Revision-CM (ICD-9-CM); Centers for

Medicare & Medicaid Services 2003]: acute

myocardial infarction (ICD-9-CM 410.00–

410.92), coronary atherosclerosis (ICD-9-CM

414.00–414.05), pulmonary heart disease

(ICD-9-CM 416.0–416.9), cardiac dysrhythmias (ICD-9-CM 427.0–427.9), and congestive heart failure (ICD-9-CM 428.0). These

particular diseases were chosen because they

include a mixture of acute and chronic cardiovascular diseases. Admissions resulting from

viral infections (e.g., influenza) and chronic

pulmonary dysfunctions (e.g., embolisms,

hypertension, thrombosis, aneurysms) were

excluded. Observations were available for all

diseases on all the days of the study period

(310 total days). The unit of analysis was the

daily number of admissions for males and

females > 65 years of age per 10,000 residents.

The numbers of residents in Denver per sex

and age for the years 1993–1997 were derived

from the Population Estimates Program of the

U.S. Census Bureau (2000). From 1993 to

1997, the total population of males and

females > 65 years of age has declined from

about 64,000 to about 60,000. The percentage

of males and females in the > 65 age group was

38% and 62%, respectively.

Environmental data. Air pollution data

were extracted from the U.S. Environmental

Protection Agency (EPA) Aerometric

Information Retrieval System (AIRS) (U.S.

EPA 2002). Daily concentrations of the U.S.

EPA’s criteria air pollutants were obtained

from all of the monitoring stations in Denver

County. These pollutants included NO2, SO2,

O3, CO, and particulate matter ≤ 10 μm in

aerodynamic diameter (PM10). The PM10 level

was collected over a 24-hr period, whereas

gaseous air pollutants were measured hourly.

For each day, the 24-hr arithmetic average

reading was determined for each station, and

then an arithmetic average was calculated over

all stations. During July and August of

1993–1997, three monitoring stations measured PM10 in Denver. PM10 measurements

were available for 298 (96%) of the possible

days during the study periods (July–August

1993–1997). O3 was monitored at two stations during the study periods, and data were

available for 310 (100%) of the days. NO2 and

SO2 were measured at one site in all 5 years,

with 303 (98%) and 310 (100%) of the days

available, respectively. CO was monitored at

three stations from 1993 to 1995 and four stations in 1996 and 1997; data were available on

all days during the study periods.

Daily 24-hr meteorologic measurements

such as Tmax and dew point temperature

(DPT) were provided by the National Climate

Data Center (NCDC 2002). During July and

August of 1993–1997, three measurement stations were located in Denver County. These

meteorologic measurements were available on

all days during the study period and were averaged over all stations.

Modeling approach. We used general linear models (GLMs; McCullagh and Nelder

1989) to analyze the frequency of hospital

admissions for a given day as a function of

environmental exposures. Poisson error structure was assumed because hospital admissions

are generally rare events. In a classic Poisson

regression, the model assumes

log [E(Yi

)] = βXi, [1]

where Xi is the matrix of covariates on day i,

Yi is the morbidity count on day i, and E

denotes the expected value. Unlike the normal

distribution, which is completely determined

by its two parameters, the mean μ and the variance σ2, the relationship between the mean

and variance for a Poisson variate is a fixed

relationship. Given a Poisson distribution with

parameter λ, this relationship is μ = σ2 = λ.

In the present study, explicit adjustments

for seasonal patterns were not necessary

because we used only data for July and August

from each year. In addition, the daily number

of hospital admissions for each disease and the

daily levels of each environmental variable in

July and August within any given year were

assumed to be independent of daily disease

counts and environmental levels from the previous years. Although the hospital admission

and environmental data are treated as independent from year to year, within a 2-month

period in a given year the number of hospital

admissions and the levels of the environmental

variables for a given day could be correlated

with the data for each of these variables from

previous days. To account for potential delays

in disease incidence after important exposures,

lag times of 1–4 days for each of the environmental variables were included as additional

model covariates. Model covariates were also

centered using their means. Besides an intercept term and indicator variables for sex (0 =

female, 1 = male), day of the week, and year of

study, all GLM models included an offset or

normalizing factor to account for population

changes during 1993–1997. The application

of an offset is a computational procedure to

treat the population size (PS) as a reference

value and does not require an additional parameter in the Poisson regression:

log [E(Yi

)] = log(PS) βXi . [2]

Parameters in the Poisson regression models

were estimated using the generalized estimating equations (GEEs) of Liang and Zeger

(1986) to account for the possibility of

overdispersion and serial correlation (Lipsitz

et al. 1994; Schwartz 1993). Overdispersion

refers to the increase in the variance of the

distribution of the disease counts that can

occur when the underlying population is not

homogeneous with respect to risk of morbidity. Serial correlation refers to a situation

where the daily number of hospital admissions on 2 days close to each other in time is

correlated. The GEE for estimating β is an

extension of the independence estimating

equation for correlated data. The covariance

matrix of Yi is modeled as

V = φA1/2R(α)A1/2, [3]

where A is an n × n diagonal matrix with the

variance of Yi as the diagonal element, R(α) is

the n × n working correlation matrix that is

fully specified by the vector of parameters α,

Environmental Health Perspectives • VOLUME 111 | NUMBER 10 | August 2003 1313

Article | Temperature, air pollution, and hospitalization among Denver elderly
and φ is the overdispersion parameter (Liang

and Zeger 1986; SAS/STAT Software 1997).

The working correlation structure for daily

number of hospital admissions within a year

was assumed to be autoregressive with the

following structure:

corr(Yi,Yi ́) = α|i–i ́|

. [4]

α is estimated empirically from the data, and

φ is estimated from the residual χ2 using the

method of McCullagh and Nelder (1989).

Missing values were treated as being missing

completely at random and were dropped

from the analyses, but appropriate care was

taken to ensure that autoregressive parameters

were estimated with the proper lags.

Before adding the air pollution variables to

the models, the effects of the Tmax and DPT

were investigated. For each cardiovascular disease, the daily admissions per 10,000 population were regressed against the Tmax and DPT

with various lag times from 0 to 4 days. The

lags for Tmax and DPT that minimized the

deviance were kept fixed for the subsequent

analyses incorporating the effect of the air pollutants. Residuals of each model were examined

to check whether there were discernible patterns and autocorrelation by means of residual

plots and partial autocorrelation function plots,

respectively (Schwartz et al. 1996). Once we

determined the optimal model for temperaturerelated effects on hospital admissions (core

model), we examined the association between

exposure to one air pollutant and daily numbers

of hospital admissions per 10,000 population.

Each air pollutant was entered linearly (with a

log link function) into the regression, and lags

between 0 and 4 days were examined. Because

of the large number of pollutants and lags

tested, emphasis was placed on the consistency

of associations across lags and measures, and

only the more highly significant findings are

discussed. To examine the effect of a period of

time with consistently above-median temperatures, the same analyses were carried out containing a variable describing the number of

days in a row when maximum daily temperatures were greater than or equal to its 50th percentile. Additional analyses were conducted for

each sex separately but resulted in insufficient

sample sizes and are not reported here. Other

strategies for model choice (e.g., forward,

backward elimination) and autocorrelation

formulations (exchangeable, unstructured) did

not significantly alter the results and are not

presented here.

We used PROC GENMOD (SAS System

for Windows, Release 8.00; SAS Institute,

Cary, NC) in the analysis.

Results

Table 1 shows the mean and variance of the

daily numbers of hospital admissions for the

cardiovascular diseases per 10,000 population

in Denver during the study period. The highest mean daily rate of hospital admissions was

observed in males for coronary atherosclerosis

(0.74/10,000) and in females for congestive

heart failure (0.52/10,000).

Figure 1 shows the annual daily average

number of hospital admissions for cardiovascular diseases per 10,000 population for males

and females. In general, there appears to be a

greater incidence for males than for females,

and for most of the diseases a slight increase

in daily average number of hospital admissions per 10,000 population was observed

during 1993–1997. As a result, the models

for each disease include classification variables

for annual trends and for sex.

Table 2 contains the summary statistics of

the environmental data in Denver during the

study period. Levels of pollutants were generally low if examined in light of U.S. EPA

National Ambient Air Quality Standards

(U.S. EPA 2001). All of the average pollutant

levels would have easily met the standards.

Table 3 shows the correlations among the

air pollution and weather variables. O3 presented positive correlation with Tmax, negative

correlations with other environmental variables, and almost no correlation with PM10

(0.03). It showed the strongest correlation

with Tmax (0.41), and CO (–0.40). NO2 presented high positive correlations with CO

(0.73), PM10 (0.56), and SO2 (0.46). The

correlations observed for O3 can be explained

by the fact that O3 is a summertime pollutant

caused by the reaction of volatile organic

compounds, CO, and NO2 in the presence of

sunlight. The correlations seen in Table 3

between PM10, NO2, and SO2 probably exist

because secondary particles are formed from

gaseous precursors, including the SOx and

NOx. No consistent trends over the 5 years of

this study were seen in the average daily concentrations of the environmental variables.

The single-pollutant GLMs were evaluated

based on sequentially identifying factors that

significantly reduce the residual error. Table 4

summarizes the results of the single-pollutant

analysis for the different cardiovascular diseases

in the > 65-year-old population of Denver for

up to 4 lag days. The most statistically significant association from the single-pollutant measures investigated, whether positive or negative,

is presented. The coefficients from Table 4 can

be interpreted in terms of increased daily hospital admissions. For example, the model for

coronary atherosclerosis results in a statistically

significant 2-day lag effect of O3 on daily coronary atherosclerosis hospital admissions. An

increase in the daily average level of O3 from

20 ppb (25th percentile) to 29.7 ppb (75th

percentile; Table 2) is statistically associated

with an increase in daily hospital admissions

for coronary atherosclerosis of (e0.012(29.7–20.0) –

1) × 100% = 12.3% with a 95% confidence

interval (CI) of 3.96–21.4%. The percentage

change in risk and the 95% CI associated with

a 25–75th percentile increase in Tmax, DPT,

and/or pollutant measure are reported together

with the p-value in Table 4. The estimates for

the overdispersion factor for the individual

models (not shown) were nearly equal to 1.0,

so adjustment of the standard errors was not

necessary.

The results indicate that Tmax at either lag

0 or 1 has a significant association with all

cardiovascular diseases except for cardiac dysrhythmias. Tmax is positively associated

(increasing Tmax leads to increasing hospital

admissions) with both acute myocardial

infarction (p < 0.01) and congestive heart failure (p = 0.02). In contrast, the Tmax is negatively associated with the observed number of

hospital admissions for coronary atherosclerosis (p < 0.01) and pulmonary heart disease

1314 VOLUME 111 | NUMBER 10 | August 2003 • Environmental Health Perspectives

Table 1. Means and variances of the daily numbers of hospital admissions for males and females > 65

years of age per 10,000 population in Denver, July–August 1993–1997.

Males Females

Disease Mean Variance Mean Variance

Acute myocardial infarction 0.51 0.20 0.31 0.08

Coronary atherosclerosis 0.74 0.36 0.40 0.10

Pulmonary heart disease 0.08 0.03 0.06 0.02

Cardiac dysrhythmias 0.36 0.14 0.29 0.08

Congestive heart failure 0.48 0.19 0.52 0.15

Figure 1. Annual daily average number of hospital

admissions for cardiovascular diseases per 10,000

population for males (M) and females (F) > 65 years

of age in Denver, July–August 1993–1997.

▼

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

1993

Acute myocardial infarction, M

Acute myocardial infarction, F

Coronary atherosclerosis, M

Coronary atherosclerosis, F

Cardiac dysrhythmias, M

Cardiac dysrhythmias, F

Congestive heart failure, M

Congestive heart failure, F

Pulmonary heart disease, M

Pulmonary heart disease, F

1995 1996 1997

Year

Admissions per 10,000

◆

▲

▼

■

●

◆

▲

▼

■

●

■

■

■ ■

■

■ ■ ■

▲ ▲

▲

▲

▲

▲ ▲

▲ ● ●

●

● ● ●

● ●

◆

◆

◆ ◆

◆

◆

◆

▼ ▼

▼ ▼

▼ ▼ ▼

■

■

▲

▲

●

●

◆

◆

▼

▼

1994

Article | Koken et al.
(p < 0.01). A 25–75th percentile increase in

Tmax (from 83 to 93.5°F) is associated with an

18, 13, 28, and 13% change in risk of hospitalization for acute myocardial infarction,

coronary atherosclerosis, pulmonary heart disease, and congestive heart failure, respectively.

Tmax has no significant association with cardiac dysrhythmia hospital admissions

(p > 0.05). A 1-day lag DPT association was

observed for coronary atherosclerosis (negative, p < 0.01) and for congestive heart failure

(positive, p < 0.01). A 25–75th percentile

increase in DPT (from 44.2 to 53.3°F) is

associated with a 9 and 16% change in risk of

hospitalization for coronary atherosclerosis

and congestive heart failure, respectively.

Levels of O3 are positively associated with

the number of coronary atherosclerosis (lag 2,

p < 0.01) and pulmonary heart disease (lag 1,

p < 0.01) hospital admissions and negatively

associated with the number of acute myocardial

infarction hospital admissions (lag 0, p < 0.01).

A 25–75th percentile increase in average daily

O3 concentrations (from 20 to 29.7 ppb) is

associated with an 18, 12, and 21% change in

risk of hospitalization for acute myocardial

infarction, coronary atherosclerosis, and pulmonary heart disease, respectively. Marginal

positive associations are seen for current-day

SO2 concentrations with hospital admissions

for cardiac dysrhythmias (p = 0.055) and for

the level of CO with congestive heart failure

(lag 3, p = 0.046). A 25–75th percentile

increase in average daily SO2 concentrations

(from 3.8 to 7.2 ppb) is associated with a 9%

change in risk of hospitalization for cardiac dysrhythmias. An increase from 0.8 to 1.1 ppm in

average daily CO is associated with an 11%

change in risk of hospitalization for congestive

heart failure. No significant associations were

found between particulate matter or NO2 for

any lag day and any of the health outcomes.

Sex is an important risk factor in all of the

selected cardiovascular diseases, except for

congestive heart failure. Males had higher

numbers of hospital admissions than did

females (p = 0.01). When exposed to both

higher temperatures and O3, males are associated with a 68, 82, and 17% increased risk of

hospitalization for acute myocardial infarction,

coronary atherosclerosis, and pulmonary heart

disease, respectively. Also, males have a 23%

increased risk of hospitalization for cardiac

dysrhythmias compared with women.

Inclusion of a variable describing the

number of days in a row when maximum

daily temperatures were greater than or equal

to its 50th percentile (88.5°F; Table 2) had

no effect on the model parameter estimates.

Discussion and Conclusions

The purpose of this study was to determine

if exposures to higher temperatures and air

pollutant concentrations were significantly

associated with hospital admissions for cardiovascular diseases. Hospital admissions represent morbidity data, rather than mortality

data, and represent a more immediate

response to higher temperatures and/or air

pollutant concentrations. Studies of morbidity effects can strengthen the consistency of

findings regarding the biologic plausibility of

mortality effects of temperature and air pollution. This study was conducted during

July–August, a period of time when outdoor

levels of air pollution likely are a better reflection of actual exposure, given that individuals

spend more time outdoors in summer than

in winter, and the time when temperatures

are usually the highest of the year.

From the results, it is clear that there is heterogeneity in the association of individual

components of air pollution and temperature

with the disease end points. No association was

found between particulate matter or NO2 and

any of the health outcomes. A large number of

daily time-series studies have reported associations between particulate matter and hospitalizations. Most of these studies have focused on

respiratory hospital admissions rather than cardiovascular diseases and do not address an issue

similar to the one studied in this study. Some

recent studies have reported associations

between particulate matter and hospitalizations

for cardiovascular diseases (Schwartz 1999;

Schwartz and Morris 1995), and others have

not (Burnett et al. 1997a). Burnett et al.

(1997a) could not identify particulate mass as

an independent risk factor beyond that attributable to climate and gaseous air pollution.

All of these studies have analyzed full-year

data, whereas the present study focused on

July–August only. A seasonal particulate matter

effect might exist, as was seen in a study of

Cook County, Illinois (Styer et al. 1995) in

which a significant effect of particulate matter

on cardiovascular mortality numbers was

found in spring and fall, but no significant

effect was found in winter and summer.

The significant associations between

July–August concentrations of CO and specific

cardiovascular diseases are confirmed in a large

number of year-round studies (Ballester et al.

2001; Burnett et al. 1997b; Morris et al. 1995;

Morris and Naumova 1998; Schwartz 1999;

Schwartz and Morris 1995). Morris et al.

(1995) found that CO showed a consistent, significant association with hospital admissions for

congestive heart failure. Burnett et al. (1997b)

demonstrated a similar effect in investigation of

10 Canadian cities. The limited data from laboratory studies on the combined effects of air

pollution with either heat stress or cold stress

seem to support the possibility of enhanced toxicity of CO associated with extreme temperatures (Yang et al. 1988). Studies in Chicago,

Illinois (Morris and Naumova 1998), and

Tucson, Arizona (Schwartz 1997), failed to

demonstrate an increase in the effect of CO at

high temperatures. It is possible that the cut

point for the high temperature range was not

sufficiently high in the Chicago study, whereas

the summer CO levels were not high enough in

the Tucson study.

We found a significant negative association of O3 with hospital admissions for acute

myocardial infarction and a positive association for coronary atherosclerosis and pulmonary heart disease. Only three studies that

we reviewed showed evidence of a consistent

association between O3 and cardiovascular

admissions. Prescott et al. (1998) found that

cardiovascular admissions for patients ≥ 65

years of age had a significant (negative) association with O3. Burnett et al. (1997a) showed

that O3 was most strongly associated with

Environmental Health Perspectives • VOLUME 111 | NUMBER 10 | August 2003 1315

Table 2. Summary statistics for daily levels of environmental variables in Denver, July–August 1993–1997.

Percentile

Variable Source No. Mean Minimum 25th 50th 75th Maximum SD

NO2 (ppb) AIRS 303 32.7 13.0 28.0 33.2 37.9 51.3 7.24

SO2 (ppb) AIRS 310 5.7 0.4 3.8 5.3 7.2 18.9 2.94

O3 (ppb) AIRS 310 25.0 5.4 20.0 25.2 29.7 40.2 6.61

CO (ppm) AIRS 310 0.9 0.3 0.8 0.9 1.1 1.6 0.27

PM10 (μg/m3) AIRS 298 24.2 7.0 20.0 24.0 28.0 51.6 6.25

Tmax (°F) NCDC 310 87.4 62.0 83.0 88.5 92.5 99.0 7.37

DPT (°F) NCDC 310 48.6 26.8 44.2 49.3 53.3 62.4 6.23

Data from U.S. EPA (2002) and NCDC (2002).

Table 3. Pearson correlation coefficients of environmental variables for Denver, July–August 1993–1997.

Tmax DPT NO2 SO2 O3 CO PM10

Tmax 1.00 –0.42 0.34 0.26 0.41 0.11 0.38

DPT — 1.00 –0.12 –0.15 –0.26 0.01* –0.24

NO2 — — 1.00 0.46 –0.30 0.73 0.56

SO2 — — — 1.00 –0.10 0.21 0.36

O3 — — — — 1.00 –0.40 0.03*

CO — — — — — 1.00 0.25

PM10 — — — — — — 1.00

*p > 0.05.

Article | Temperature, air pollution, and hospitalization among Denver elderly
1316 VOLUME 111 | NUMBER 10 | August 2003 • Environmental Health Perspectives

hospitalization for cardiac diseases in Toronto

in the summer. In Hong Kong, Wong et al.

(1999) found a significant relation of O3 with

hospital admissions for cardiovascular diseases.

However, most of the reviewed studies do not

suggest an impact of O3 on the number of hospitalizations attributable to cardiovascular disease (Ballester et al. 2001; Morris et al. 1995;

Morris and Naumova 1998; Schwartz 1997;

Schwartz and Morris 1995).

It is noteworthy to mention the association between SO2 and cardiac dysrhythmias.

The physiopathologic mechanisms of SO2

causing cardiovascular damage are insufficiently understood, and more research is

needed in this area. Only two studies that we

reviewed showed SO2 as being positively associated with daily hospital admissions for cardiac diseases (Burnett et al. 1997a; Diaz et al.

2001). It is possible that, in this case, SO2 is

serving as a general indicator of air pollution

exposure rather than a direct causal initiator

of cardiovascular damage.

Sex appeared to be an important risk factor in all of the selected cardiovascular diseases except for congestive heart failure. Males

tend to have higher numbers of hospital

admissions than do females.

Higher temperatures a) appear to be an

important factor in increasing the frequency of

hospitalization for acute myocardial infarction

and congestive heart failure; b) are associated

with a decrease in the frequency of visits for

coronary atherosclerosis and pulmonary heart

disease; and c) have no significant effect on

visits for cardiac dysrhythmias. It is possible

that patients with chronic cardiovascular conditions (coronary atherosclerosis and pulmonary heart disease) avoid outdoor exposures

during periods of peak heat, resulting in the

negative association. In contrast, exposures to

higher air pollutant concentrations (except for

particulate matter and NO2) appear to have a

positive effect on the number of hospital

admissions for cardiovascular diseases.

According to Bernard et al. (2001), climate

change may affect exposures to air pollutants

by a) affecting weather and thereby local and

regional pollution concentrations; b) affecting

anthropogenic emissions, including adaptive

responses involving increased fuel combustion

for power generation; c) affecting natural

sources of air pollutant emissions; and d)

changing the distribution and types of aeroallergens. According to Bernard et al. (2001), it

is expected that, in the future, the pollutants

of concern are likely to remain, as now, particulate matter—both from its primary sources

and from the secondarily formed particles—

and photochemical pollution with O3.

Emissions of SOx and NOx due to power generation might increase, but control technologies are available and should be able to control

such emissions.

The present study is limited because it uses

environmental monitoring data to represent

ambient concentrations. The fact that different pollutants have not been measured at the

same monitoring stations (e.g., NO2 was measured only at one site, and other pollutants at

three or four sites) can affect the estimated

correlations between the pollutants. Besides

this, environmental monitoring data do not

necessarily represent individual exposures.

This could result in associations between

exposures and hospital admissions that are

spurious. Also, measurement errors resulting

from differences between the population-average exposure and ambient levels cannot be

avoided. However, this kind of measurement

error is likely to cause a bias toward the null

and underestimates the pollutant effects

(Zeger et al. 2000). Many individual risk factors, such as smoking, diet, cholesterol level,

and obesity, either do not vary significantly

over time or vary slowly. In addition, there is

no reason to believe that daily variations in the

individual risk factors are correlated with daily

changes in air pollution; therefore, they are

unlikely to be confounding factors in this

study (Schwartz 1997). It is possible that with

accurate, individual exposures, the major associations seen in this study may not be all of

the associations that are important.

Care should be taken with drawing conclusions about the impact of temperature on morbidity because the estimated adverse health

effects from exposures to higher temperatures

and air pollutants are small. Uncertainties

inherent in the analyses go far beyond the sampling variability that is captured by the standard error of the confidence interval. These

uncertainties arise from model misspecification, omitted covariates, and errors in the measurement of covariates considered in the

models. Care should also be taken in comparing different studies because discrepancies may

reflect regional differences in pollution mixture, climate, flora and fauna, period of data

collection, measurement methods of air pollutants, or medical practice patterns. Time series

do not give precise information on the relationship between air pollution and health for a

particular individual. However, they provide

valuable information on the overall health

impact of air pollution as measured at fixed-site

monitoring stations.

In summary, the results of this study in

Denver suggest that O3 increases the risk of

hospitalization for acute myocardial infarction,

coronary atherosclerosis, and pulmonary heart

disease. SO2 appears to be related to increased

hospital stays for cardiac dysrhythmias, and

CO is significantly associated with congestive

heart failure hospitalization. No association

was found between particulate matter or NO2

and hospitalization for any condition. Males

had more hospital admissions for cardiovascular diseases than did females. Higher temperatures appear to be an important factor in

increasing the frequency of hospitalization for

acute myocardial infarction and congestive

heart failure. However, higher temperatures are

associated with a decrease in the frequency of

visits for coronary atherosclerosis and pulmonary heart disease and have no significant

effect on admissions for cardiac dysrhythmias.

In contrast, exposures to higher air pollutant

concentrations (except for particulate matter

and NO2), even at levels that meet federal air

quality standards, appear to have an effect of

increasing the number of hospital admissions

for cardiovascular diseases as a whole.

REFERENCES

Atkinson RW, Bremner SA, Anderson HR, Strachan DP, Bland

JM, de Leon AP. 1999. Short-term associations between

emergency hospital admissions for respiratory and cardiovascular disease and outdoor air pollution in London. Arch

Environ Health 54:398–411.

Ballester F, Tenias JM, Perez-Hoyos S. 2001. Air pollution and

emergency hospital admissions for cardiovascular diseases

in Valencia, Spain. J Epidemiol Community Health 55:57–65.

Table 4. Summary of results for single-pollutant analysis of hospital admissions for cardiovascular diseases for males and females > 65 years of age in Denver, July–August 1993–1997.

Model Percent change in hospital

Disease covariate Single-day lag β (SE) admissions (95% CI) p-Value

Acute myocardial Tmax 0 0.017 (0.007) 17.5 (2.9–34.3) 0.009

infarction O3 0 –0.020 (0.006) –17.6 (–26.7––7.5) 0.001

Male sex — 0.517 (0.061) 67.7 (48.4–89.5) < 0.0001

Coronary Tmax 1 –0.014 (0.004) –12.5 (–18.9––5.5) 0.001

atherosclerosis DPT 1 –0.010 (0.003) –8.7 (–13.6––3.6) 0.003

O3 2 0.012 (0.004) 12.3 (4.0–21.4) 0.004

Male sex — 0.601 (0.038) 82.4 (69.0–96.8) < 0.0001

Pulmonary heart Tmax 0 –0.035 (0.008) –28.3 (–38.4––16.5) < 0.0001

disease O3 1 0.020 (0.008) 21.4 (4.0–41.8) 0.009

Male sex — 0.160 (0.062) 17.4 (3.7–32.8) 0.010

Cardiac SO2 0 0.025 (0.013) 8.9 (–0.34–18.93) 0.055

dysrhythmias Male sex — 0.203 (0.028) 22.5 (15.8–29.6) < 0.0001

Congestive heart Tmax 1 0.013 (0.005) 13.2 (2.9–24.4) 0.016

failure DPT 1 0.016 (0.004) 15.7 (7.6–24.4) < 0.0001

CO 3 0.332 (0.166) 10.5 (0.1–22.0) 0.046

CI, confidence interval. The percentage change in the number of admissions for a 25–75th percentile change in each pollutant

(or for males compared with females) is presented at its most statistically significant single lag.

Article | Koken et al.
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Bernard SM, Samet JM, Grambsch A, Ebi KL, Romieu I. 2001.

The potential impacts of climate variability and change on

air pollution-related health effects in the United States.

Environ Health Perspect 109:199–209.

Braga ALF, Zanobetti A, Schwartz J. 2001. The time course of

weather related deaths. Epidemiology 12:662–667.

———. 2002. The effect of weather on respiratory and cardiovascular deaths in 12 U.S. cities. Environ Health Perspect

110:859–863.

Burnett RT, Cakmak S, Brook JR, Krewski D. 1997a. The role of

particulate size and chemistry in the association between

summertime ambient air pollution and hospitalization for

cardiorespiratory diseases. Environ Health Perspect

105:614–620.

Burnett RT, Dales RE, Brook JR, Raizenne ME, Krewski D.

1997b. Associations between ambient carbon monoxide

levels and hospitalizations for congestive heart failure in

the elderly in 10 Canadian cities. Epidemiology 8:162–167.

Centers for Medicare & Medicaid Services. 2003. ICD-9-CM

Coordination and Maintenance Committee. Available:

http://cms.hhs.gov/paymentsystems/icd9 [accessed 23

June 2003].

Diaz J, Alberdi JC, Pajares MS, Lopez C, Lopez R, Lage MB, et al.

2001. A model for forecasting emergency hospital admissions: effect of environmental variables. J Environ Health

64:9–15.

Dockery DW, Pope CA, Speizer FE. 2000. Effects of particulate

air pollution exposure. In: Particle-Lung Interactions (Gehr

P, Heyder J, eds). New York:Marcel Dekker, 671–703.

Firket M. 1931. The cause of the symptoms found in the Meuse

Valley during the fog of December, 1930. Bull Acad Roy

Med Belg 11:683–741.

HCUP 2001. Health Care Cost and Utilization Project 1988–2000.

A Federal-State-Industry Partnership in Health Data.

Rockville, MD:Agency for Healthcare Research and

Quality. Available: http://www.ahrq.gov/data/hcup/hcuppkt.htm [accessed 28 August 2002]. 

Hoek G, Brunekreef B, Fischer P, van Wijnen J. 2001. The association between air pollution and heart failure, arrhythmia,

embolism, thrombosis, and other cardiovascular causes of

death in a time series study. Epidemiology 12:355–357.

IPCC. 1996. Climate Change 1995. The Science of Climate

Change. Cambridge, UK:Cambridge University Press.

Katsouyanni K, Pantazopoulou A, Touloumi G, Tselepidaki I,

Moustris K, Asimakopoulos D, et al. 1993. Evidence for

interaction between air-pollution and high temperature in

the causation of excess mortality. Arch Environ Health

48:235–242.

Keatinge WR, Coleshaw SR, Easton JC, Cotter F, Mattock MB,

Chelliah R. 1986. Increased platelet and red cell counts,

blood viscosity, and plasma cholesterol levels during heat

stress, and mortality from coronary and cerebral thrombosis.

Am J Med 81:795–800.

Kunst AE, Looman CW, Mackenbach JP. 1993. Outdoor air temperature and mortality in The Netherlands: a time-series

analysis. Am J Epidemiol 137:331–341.

Liang KY, Zeger SL. 1986. Longitudinal data-analysis using generalized linear-models. Biometrika 73:13–22.

Lipsitz SR, Fitzmaurice GM, Orav EJ, Laird NM. 1994.

Performance of generalized estimating equations in practical situations. Biometrics 50:270–278.

McCullagh P, Nelder JA. 1989. Generalized Linear Models.

London:Chapman and Hall.

McMichael AJ, Ando M, Carcavallo R, Epstein P, Haines A,

Jendritsky G, et al. 1996. Climate Change and Human Health.

Geneva:World Health Organization.

Ministry of Health. 1954. Mortality and Morbidity during the

London Fog of December 1952. Reports on Public Health

and Medical Subjects no. 95. London:HMSO.

Morris RD, Naumova EN. 1998. Carbon monoxide and hospital

admissions for congestive heart failure: evidence of an

increased effect at low temperatures. Environ Health

Perspect 106:649–653.

Morris RD, Naumova EN, Munasinghe RL. 1995. Ambient air

pollution hospitalization for congestive heart failure

among elderly people in seven United States cities. Am J

Public Health 85:1361–1365.

NCDC 2002. Online Climate Data. Asheville, NC:National Climate

Data Center. Available: http://www.ncdc.noaa.gov/ol/

climate/climatedata.html [accessed 28 August 2002].

Peters A, Liu E, Verrier RL, Schwartz J, Gold DR, Mittleman M, et

al. 2000. Air pollution and incidence of cardiac arhythmia.

Epidemiology 11:11–17.

Piver WT, Ando M, Ye F, Portier CJ. 1999. Temperature and air

pollution as risk factors for heat stroke in Tokyo, July and

August 1980–1995. Environ Health Perspect 107:911–916.

Prescott GJ, Cohen GR, Elton RA, Fowkes FG, Agius RM. 1998.

Urban air pollution and cardiopulmonary ill health: a 14.5

year time series study. Occup Environ Med 55:697–704.

Saez M, Sunyer J, Castellsague J, Murillo C, Anto JM. 1995.

Relationship between weather temperature and mortality:

a time series analysis approach in Barcelona. Int J

Epidemiol 24:576–582.

SAS/STAT Software. 1997. The GENMOD procedure. In:

Changes and Enhancements through Release 6.12. Cary,

NC:SAS Institute Inc, 247–348.

Schwartz J. 1993. Air-pollution and daily mortality in

Birmingham, Alabama. Am J Epidemiol 137:1136–1147.

———. 1997. Air pollution and hospital admissions for cardiovascular disease in Tucson. Epidemiology 8:371–377.

———. 1999. Air pollution and hospital admissions for heart

disease in eight U.S. counties. Epidemiology 10:17–22.

Schwartz J, Morris R. 1995. Air-pollution and hospital admissions for cardiovascular disease in Detroit, Michigan. Am

J Epidemiol 142:23–35.

Schwartz J, Spix C, Touloumi G, Bacharova L, Barumamdzadeh T,

leTertre A, et al. 1996. Methodological issues in studies of air

pollution and daily counts of deaths or hospital admissions. J

Epidemiol Community Health 50:S3–S11.

Seaton A, MacNee W, Donaldson K, Godden D. 1995.

Particulate air pollution and acute health effects. Lancet

345:176–178.

Shrenk HM, Heiman H, Clayton GD. 1949. Air Pollution in

Donara, PA: Epidemiology of the Unusual Smog Episode of

October 1948. Preliminary Report no. 306. Washington,

DC:U.S. Public Health Service.

Styer P, McMillan N, Gao F, Davis J, Sacks J. 1995. Effect of

outdoor airborne particulate matter on daily death counts.

Environ Health Perspect 103:490–497.

U.S. Census Bureau 2000. Population Estimates for Counties by

Age and Sex: Annual Time Series July 1, 1990 to July 1,

1999. Washington, DC:Population Estimates Program,

Population Division. Available: http://www.census.gov/

population/estimates/county/cas/cas08.txt [accessed 28

August 2002].

U.S. EPA. 2001. National Ambient Air Quality Standards

(NAAQS). Research Triangle Park, NC:U.S. Environmental

Protection Agency. Available: http://www.epa.gov/airs/

criteria.html [accessed 28 August 2002].

———. 2002. Aerometric Information Retrieval System.

Research Triangle Park, NC:U.S. Environmental Protection

Agency. Available: http://www.epa.gov/air/data/index.html

[accessed 28 August 2002].

van der Bom JG, de Maat MPM, Bots ML, Hofman A, Kluft C,

Grobbee DE. 1997. Seasonal variation in fibrinogen in the

Rotterdam study. Thromb Haemost 78:1059–1062.

Wong TW, Lau TS, Yu TS, Neller A, Wong SL, Tam W, et al. 1999.

Air pollution and hospital admissions for cardiovascular diseases in Hong Kong. Occup Environ Med 56:679–683.

Yang L, Zhang W, He HZ, Zhang GG. 1988. Experimental studies

of combined effects of high temperature and carbon

monoxide. J Tongji Med Univ 8:60–65.

Zanobetti A, Schwartz J, Dockery DW. 2000. Airborne particles

are a risk factor for hospital admissions for heart and lung

disease. Environ Health Perspect 108:1071–1077.

Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J, Dockery

D, et al. 2000. Exposure measurement error in time–series

studies of air pollution: concepts and consequence. Environ

Health Perspect 108:419–426.

Article | Temperature, air pollution, and hospitalization among Denver elderly