Environmental Health Perspectives • VOLUME 109 | NUMBER 4 | April 2001 355

Effects of Temperature and Air Pollutants on Cardiovascular and Respiratory

Diseases for Males and Females Older than 65 Years of Age in Tokyo, July

and August 1980–1995

Frank Ye,1 Warren T. Piver,1 Mitsuru Ando,2 and Christopher J. Portier1

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

Studies, Tsukuba, Japan

Previous studies have examined exposures to

daily maximum temperatures and air pollutant concentrations during the warm summer

months of July and August in Tokyo and

their effect on the daily number of hospital

emergency transports per million for heat

stroke (1) and three cerebral vascular diseases:

cerebral hemorrhage, cerebral infarction, and

cerebral ischemia (2). Both studies indicated

that males and females > 65 years of age had

the highest daily number of heat stroke and

cerebral vascular disease hospital emergency

transports per million of any age group to

four Tokyo city hospitals. For heat stroke,

exposures to daily maximum temperatures

(Tmax) and concentrations of nitrogen dioxide were associated with the daily number of

hospital emergency transports per million

residents. Exposures to higher Tmax were

associated with a decrease in the daily number of hospital emergency transports per million residents for cerebral hemorrhage. For

cerebral infarction, hospital emergency transports per million were associated with

increasing daily average NO2 concentrations.

For cerebral ischemia, exposures to Tmax and

daily average ozone concentrations were associated with the daily hospital emergency

transports per million residents.

For these four diseases, it was evident that

increasing or decreasing numbers of hospital

emergency transports per million were not

associated with the same set of temperature

and air pollutant variables. Therefore, additional studies to examine hospital emergency

transports for other diseases as functions of

daily maximum temperatures and air pollutant concentrations needed to be conducted

on a disease-by-disease basis.

The studies on cardiovascular and respiratory diseases in Tokyo were undertaken

because in the next 50–100 years, there could

be a doubling of atmospheric concentrations

of carbon dioxide (3). This could raise surface temperatures year-round by 1–3°C and

result in a greater frequency and longer duration of heat waves during warmer months of

the year not only in Tokyo but in many

other major urban areas worldwide. Also the

number of people > 65 years of age in Tokyo

is increasing (4), and this population appears

to be most vulnerable to higher temperatures

and higher air pollutant concentrations. 

The months of July and August in

Tokyo are when daily maximum temperatures are the highest and when many air pollutants have high concentrations because of

atmospheric chemical and photochemical

reactions and meteorological conditions that

cause air pollutant concentrations to be high

at ground level, especially in large, urban

areas. Therefore, months of the year were

selected when the frequency of exposures to

both higher daily maximum temperatures

and air pollutant concentrations would be

the greatest.

The covariates that were used to determine if temperature or air quality may be

associated with each cardiovascular and respiratory disease were Tmax and air pollutant

concentrations for NO2, O3, sulfur dioxide,

carbon monoxide, and particles with aerodynamic diameters ≤ 10 μm (PM10). We chose

Tmax temperatures over average daily temperatures (Tav), and minimum daily temperatures (Tmin), because Tmax temperatures

represented the maximum levels of heat stress

that could occur on a daily basis. Tmax was

also selected over the heat index because Tmax

can be more directly associated with core

body temperatures. The heat index is not a

temperature and is calculated from a complex

polynomial expression for temperature and

relative humidity that makes it difficult to

separate the temperature contribution from

the relative humidity contribution. The cardiovascular diseases that were examined and

their International Classification of Disease, 9th

Revision (ICD-9) codes included hypertension

(ICD-9 codes 401–405), myocardial infarction (ICD-9 code 410), angina (ICD-9 code

413), and cardiac insufficiency (ICD-9 code

428). The respiratory diseases included acute

bronchitis (ICD-9 code 466), pneumonia

(ICD-9 code 486), chronic bronchitis (ICD9 code 491), and asthma (ICD-9 code 493).

There is an extensive literature about the

association between daily mortality and hospital admissions for cardiovascular and respiratory diseases and daily changes in air

pollutant concentrations and climate variables (5–36). This literature was important in

selecting variables that may affect hospital

emergency transports in Tokyo. Exposures to

air pollutants have been associated with damage to lung tissues and have been shown to

have significant effects on pulmonary function (37–42). Because of the interrelationship

of the cardiac and pulmonary systems, damage to lung tissue is an important factor not

only in many respiratory diseases, but in

many cardiovascular diseases as well. 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. The

Address correspondence to W. Piver, National

Institute of Environmental Health Sciences, MD

EC-14, P.O. Box 12233, Research Triangle Park,

NC 27709 USA. Telephone: (919) 541-3471. Fax:

(919) 541-0144. E-mail: piver@niehs.nih.gov

Received 19 July 2000; accepted 7 November

2000.

Articles

We studied exposures to higher daily maximum temperatures and concentrations of air pollutants

in Tokyo during the summer months of July and August from 1980 to 1995 and their effects on

hospital emergency transports for cardiovascular and respiratory diseases for males and females

> 65 years of age. Cardiovascular diseases were angina, cardiac insufficiency, hypertension, and

myocardial infarction. Respiratory diseases were asthma, acute and chronic bronchitis, and pneumonia. Except for pneumonia, daily maximum temperatures were not associated with hospital

emergency transports. Increasing daily maximum temperatures, however, were associated with

decreased hospital emergency transports for hypertension. Concentrations of nitrogen dioxide or

particulate matter ≤ 10 μm, however, were associated with daily hospital emergency transports for

angina, cardiac insufficiency, myocardial infarction, asthma, acute and chronic bronchitis, and

pneumonia. For cardiac insufficiency, hypertension, myocardial infarction, asthma, chronic bronchitis, and pneumonia, the expected daily number of emergency transports per million were

greater for males than for females. For angina and acute bronchitis, there were no differences for

the expected daily numbers of emergency transports per million between males and females. Key

words: air pollutants, cardiovascular disease, elderly, respiratory disease, temperature. Environ

Health Perspect 109:355–359 (2001). [Online 13 March 2001]

http://ehpnet1.niehs.nih.gov/docs/2001/109p355-359ye/abstract.html
focus of this study, however, was on the

months of July and August. We selected

these months to obtain an indication of how

prolonged exposures to both higher temperatures and air pollutant concentrations could

affect hospital emergency transports for a

wide variety of diseases.

Data Characteristics and

Model Development

We obtained data on hospital emergency

transports to four Tokyo city hospitals for

each of the four cardiovascular and respiratory

diseases for the summer months of July and

August, 1980–1995, for males and females

> 65 years of age from the Tokyo Emergency

Office. In all cases, if a record of transport had

been entered into the data collection system

for the Tokyo Emergency Office, the patient

was transported by an emergency vehicle to

the hospital and admitted for the diagnosed

disease. The diagnosis was made by the

attending physician in the emergency room

and was based on the symptoms that the

patient presented at the time of arrival.

The daily number of hospital emergency

transports per million residents for the four

cardiovascular and respiratory diseases were

determined from population data provided

by the Ministry of Health and Welfare (4).

Because information was not available on

the percentage of males and females in this

age group from 1980 to 1995, we assumed

that 50% was male and 50% was female.

From 1980 to 1995, the percentage of the

population within the city limits of Tokyo

of males and females > 65 years of age has

increased from 7.7% (894,586) in 1980 to

13.2% (1,553,772) in 1995, even though

the total population has remained relatively

constant at 11.8 million.

To determine if there were annual trends

in the daily number of hospital emergency

transports per million for July and August,

we calculated the annual daily average number of cardiovascular disease hospital emergency transports per million for each disease

for males and females (Figure 1). The data

indicated that the annual daily average numbers of angina and cardiac insufficiency hospital emergency transports per million

increased for both males and females in July

and August from 1980 to 1995, whereas the

annual daily average number of hypertension

hospital emergency transports per million

appeared to decrease for males and females.

There did not appear to be much change in

the annual daily average number of myocardial infarction hospital emergency transports

per million from 1980 to 1995. However,

there did appear to be significant differences

in response between males and females for all

diseases. From these observations, we concluded that classification variables were

needed in the regression model for each cardiovascular disease to account for differences

between sexes and annual changes in the July

and August number of emergency transports

per million.

In a similar manner, we calculated the

annual daily average number of hospital

emergency transports per million for July and

August for each respiratory disease (Figure 2).

The annual daily average number of pneumonia and asthma hospital emergency transports per million appeared to increase for

both males and females from 1980 to 1995.

The annual daily average number of hospital

emergency transports per million for acute

and chronic bronchitis, however, appeared to

be unchanged from 1980 to 1995. There did

appear to be significant differences in

response between males and females for all

diseases. As with the cardiovascular diseases,

this behavior indicated that classification

variables were needed in the regression models for each respiratory disease to account for

differences between sexes and for annual

changes in the July and August number of

emergency transports per million.

For the daily number of emergency transports for each cardiovascular and respiratory

disease for July and August, 1980–1995, calculations of means and variances of their

density functions for each disease are given in

Table 1. For the most part, these calculations

indicated that the density functions for each

disease could be assumed to be Poisson distributed. However, the means and variances

were not equal for the distributions for cardiac insufficiency, asthma, and pneumonia.

As a result, there may be overdispersion in

the distributions for all diseases that will need

to be evaluated.

We obtained daily average concentrations

of air pollutants and climate variables for

Tokyo from the Japan Environment Agency.

Summary statistics for each of the variables

used in this analysis for July and August,

1980–1995 are given in Table 2. Table 3

contains the Pearson correlation coefficients

among air-quality variables and Tmax. For the

study periods, these results indicate possible

correlations between NO2 and PM10, NO2

and CO, and PM10 and CO. The daily average concentrations of NO2 and CO and

Tmax temperatures increased slightly during

this time, indicating that yearly trends in

model covariates would need to be taken into

account by model calculations.

Analysis Methods

We fit generalized linear models (GLMs) to

hospital emergency transport data for each

disease to determine which covariates were

significantly associated with each of the four

cardiovascular and four respiratory diseases

(43). A GLM was used because daily numbers of hospital emergency transports per

million for each disease were count data.

Because only data for emergency transports

for July and August were used, confounding

Articles • Ye et al.

356 VOLUME 109 | NUMBER 4 | April 2001 • Environmental Health Perspectives

Figure 2. Daily average number of respiratory disease hospital emergency

transports per million for males (M) and females (F) > 65 years of age in Tokyo,

July–August, 1980–1995.

Figure 1. Daily average number of cardiovascular disease hospital emergency

transports per million for males (M) and females (F) > 65 years of age in Tokyo,

July–August, 1980–1995.

7

6

5

4

3

2

1

1980 1982 1984 1986 1988 1990 1992 1994

Year

Daily average hospital emergency

transports/million

Angina M

Angina F

Cardiac insufficiency M

Cardiac insufficiency F

Hypertension M

Hypertension F

Myocardial infraction M

Myocardial infraction F

6

5

4

3

2

1

0

1980 1982 1984 1986 1988 1990 1992 1994

Year

Daily average hospital emergency

transports/million

Asthma M

Asthma F

Pneumonia M

Pneumonia F

Acute bronchitis M

Acute bronchitis F

Chronic bronchitis M

Chronic bronchitis F
effects from seasonal variability were likely to

be minimal. In addition, because only data

from July and August were being used in

this study, it was assumed that the daily

number of hospital emergency transports per

million for each disease and the daily data

for each covariate within any given year were

independent of daily disease count and daily

covariate data from previous years.

Within each 2-month period in each year,

however, the daily number of hospital emergency transports and the daily maximum

temperature and daily average air pollutant

concentration data for a given day can be correlated with the data for each of these variables from previous days. To account for

autocorrelations in the temperature and air

quality variables, we included lag times of 1–4

days for each of these covariates as additional

model variables. Generalized estimating equations that accounted for serial correlations in

the daily number of emergency transports per

million data for each disease were used to estimate values for the coefficients of each model

covariate (44). The correlation structure for

the covariance matrix of these generalized estimating equations was assumed to be autoregressive. When an exchangeable correlation

structure was used, model parameter estimates

were nearly the same as those estimates given

by the autoregressive structure of the covariance matrix. Based on the data given in

Figures 1 and 2, classification variables were

also included in the models for each disease to

account for sex and for unexpected annual

trends in the daily number of hospital emergency transports per million and annual

trends in Tmax and air pollutant concentrations. We used PROC GENMOD from SAS

software (45) to model the number of hospital emergency transports per million for each

cardiovascular and respiratory disease for

males and females for July and August from

1980 to 1995 and to provide parameter estimates for model covariates.

The model for the expected daily number

of emergency transports per million for each

cardiovascular and respiratory disease (ET) for

males and females > 65 years of age is given as

ln(ET) = Xβ, [1]

where X is a matrix that includes a column of

1s for the intercept; classification variable

columns for sex and for annual trends;

columns for the daily values for Tmax and/or

daily average concentrations for each air pollutant variable with lag times of 1–4 days;

columns for interaction effects between pairs

of model covariates as indicated from the correlation analysis given in Table 3; and β is

the vector of model coefficients for each

model covariate. Population changes from

1980 to 1995 have been included as an offset

or normalizing factor in this model so that

the expected daily number of hospital emergency transports are expressed as the expected

daily number of hospital emergency transports per million. This makes it possible to

include changes in population for each 2-

month interval from 1980 to 1995.

To determine which covariates were significant for each disease, we constructed an

initial series of models for each of the individual covariates and their lag effects. These

initial models for each disease also contained

classification variables for sex and annual

trends. After each round of model fitting,

insignificant model variables were eliminated

as indicated by their significance probabilities or p-values, and a reduced model was

constructed for the next round of calculations. This process was repeated until the

most significant model covariates and classification variables for each disease were identified. At this point variables that included

interactions between model covariates and

covariates and classification variables were

added to the model to determine if they

were significant. In the final models, the

effects of overdispersion in the count data

for each disease were negligible.

Results and Discussion

Table 4 provides coefficient estimates for significant model covariates, 95% confidence

intervals for these estimates, and significance

probabilities (the column designated as

Pr > |Z|) for model covariates and classification variables that were significant contributing factors for each cardiovascular and

respiratory disease. Z is the coefficient estimate for a model covariate divided by its

standard error. For the most part, same-day

exposures to either Tmax or an air pollutant

variable were significantly associated with

each disease. For chronic bronchitis, however, PM10 with a lag time of 2 days was the

most statistically significant model covariate.

When interaction variables were included in

the models for each disease, their impacts

were negligible. Also for each disease there

was a classification variable for sex that indicated diseases for which there were differences in response between males and females.

For cardiac insufficiency, hypertension,

myocardial infarction, asthma, chronic bronchitis, and pneumonia, the expected daily

number of emergency transports per million

were greater for males than for females. For

angina and acute bronchitis, there were no

Articles • Effects of temperature and air quality on cardiovascular diseases

Environmental Health Perspectives • VOLUME 109 | NUMBER 4 | April 2001 357

Table 1. Cardiovascular and respiratory diseases studied: means and variances of the daily number of

hospital emergency transports for males and females > 65 years of age in Tokyo July–August, 1980–1995.

Males Females

Disease (ICD-9 code) Mean Variance Mean Variance

Cardiovascular diseases

Angina (413) 1.79 2.03 1.88 2.25

Cardiac insufficiency (428) 2.21 2.65 2.56 3.70

Hypertension (401–405) 0.96 0.97 1.54 1.61

Myocardial infarction (410) 1.93 2.05 1.35 1.43

Respiratory diseases

Acute bronchitis (466) 0.21 0.23 0.19 0.20

Asthma (493) 1.28 1.55 1.16 1.41

Chronic bronchitis (491) 0.11 0.12 0.07 0.07

Pneumonia (486) 2.33 3.75 1.86 3.14

The population in Tokyo of males and females > 65 years of age increased from 0.895 million in 1980 to 1.544 million in

1995. The total population remained relatively constant at 11.8 million from 1980 to 1995. About 50% of the population is

assumed to be male and 50% is assumed to be female.

Table 2. Summary statistics for Tmax and concentrations of NO2, O3, PM10, SO2, and CO in Tokyo, July–August,

1980–1995.

Variable No. of observations Minimum Maximum Mean (SD)

Tmax (°C) 992 17.3 36.5 28.9 (3.8)

NO2 (ppb) 988 5.3 72.2 25.4 (11.4)

O3 (ppb) 990 0.05 59.4 13.9 (9.8)

PM10 (μg/m3) 902 7.3 185.4 46.0 (27.1)

SO2 (ppb) 948 0.6 28.8 7.7 (4.8)

CO (ppb) 972 41.7 2354.2 667.2 (314.9)

Table 3. Pearson correlation coefficients for Tmax and air pollution variables.

Variable Tmax NO2 O3 PM10 SO2 CO

Tmax 1.000 0.021 0.313 0.122 0.078 0.066

NO2 – 1.000 0.183 0.643 0.333 0.759

O3 – – 1.000 0.376 0.259 0.202

PM10 – – – 1.000 0.306 0.754

SO2 – – – – 1.000 0.389

CO – – – – – 1.000
differences for the expected daily numbers of

emergency transports per million between

males and females. Tmax was a significant

model covariate only for pneumonia.

Concentrations of NO2 were significant

model covariates for angina, cardiac insufficiency, myocardial infarction, and acute

bronchitis. Concentrations of PM10 were significant model covariates for asthma, chronic

bronchitis, and pneumonia. For hypertension, the daily expected number of hospital

emergency transports per million decreased

as daily Tmax increased.

Many studies (3,25,27–34) have shown

correlations between hospital admissions for

cardiovascular diseases and CO concentrations. However, the associations between the

four cardiovascular diseases and concentrations of CO in these studies for July and

August in Tokyo do not appear to be very

strong. A possible explanation for this observation may be that daily average concentrations of CO during the summer months

from 1980 to 1995 in Tokyo were not high

compared to other cities where correlations

between CO concentrations and cardiovascular diseases were much stronger. The

mean CO concentration for Tokyo during

the study period was 0.67 ppm (SD = 0.31

ppm). Morris et al. (5), in their study of

seven U.S. cities, found average year-round

mean concentrations 3–9 times higher than

those observed in the present study. Because

of the lower mean CO concentration in

Tokyo, exposures to CO appear to have

minimal impact on the daily number of hospital emergency transports for the four cardiovascular diseases that were studied.

For this group of cardiovascular and respiratory diseases, even though conditions

were chosen so that daily temperatures

would be at their maximum values, exposures to concentrations of NO2 and PM10

during the summer months of July and

August in Tokyo appeared to be much more

significant model covariates for many of

these diseases than were exposures to daily

maximum temperatures. However, Tmax was

a contributing factor for pneumonia.

It would be incorrect to conclude, however, that higher temperatures that may

occur as a result of climate change would not

be a significant covariate for many of these

diseases. Any time increased cardiac output

is required to return core body temperatures

to normal ranges and there are people with

cardiovascular and respiratory systems that

are not functioning at optimal levels because

of age or preexisting disease, there is an

increased probability of emergency transport

for a particular cardiovascular or respiratory

disease as daily maximum temperatures

increase. Future modeling work for these

diseases will examine the year-round

response to temperature and concentrations

of air pollutants and will require the use of

generalized additive models (14,46).

As with the heat stroke and cerebral vascular disease studies, it is important to note

that the model covariates were not the same

for each disease. This is not a surprising result

because the physiologic and molecular mechanisms for each disease are different, even

though cardiovascular and respiratory functions are highly interconnected. Even so, the

results of this study strongly suggest that each

disease should be considered separately rather

than grouped together. It should be noted

that possible factors that could be confounders in these studies are the smoking

habits of this age group of males and females.

Smoking habits and the presence of chronic

cardiovascular and respiratory diseases, however, should not change from day to day.

Therefore, these existing conditions should

not be confounding factors in this study (31).

Finally, it is well known that hypertension is

a precursor for congestive heart failure (cardiac insufficiency in our nomenclature), but

it was not possible to account for the possible

interrelationships between these two diseases

because data on blood pressure at the time of

emergency transport and hospital admission

for individual patients were not available. 

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358 VOLUME 109 | NUMBER 4 | April 2001 • Environmental Health Perspectives

Table 4. Model covariates for cardiovascular and respiratory diseases for males and females > 65 years

of age in Tokyo, July–August, 1980–1995.

Model Coefficient 95% Confidence limits

Disease covariatea estimate (SE) Lower Upper Z-Valueb Pr > |Z|

c

Cardiovascular diseases

Angina NO2 0.007 (0.001) 0.004 0.009 4.8 0.0000

Cardiac insufficiency N02 0.006 (0.002) 0.003 0.01 3.54 0.0004

Male –0.152 (0.03) –0.209 –0.095 –5.26 0.0000

Hypertension Tmax –0.014 (0.005) –0.02 –0.004 –2.8 0.0056

Male –0.44 (0.04) –0.52 –0.36 –11.0 0.0000

Myocardial infarction NO2 0.006 (0.002) 0.003 0.01 3.56 0.0004

Male 0.374 (0.03) 0.31 0.43 12.1 0.0000

Respiratory diseases

Asthma PM10 0.003 (0.001) 0.001 0.004 3.32 0.0009

Male 0.115 (0.03) 0.06 0.17 4.1 0.0000

Acute bronchitis NO2 0.014 (0.005) 0.004 0.024 2.67 0.0075

Chronic bronchitis PM10-2 0.006 (0.002) 0.001 0.01 2.6 0.0102

Male 0.483 (0.1) 0.28 0.69 4.63 0.0000

Pneumonia Tmax 0.038 (0.007) 0.02 0.05 5.24 0.0000

PM10 0.003 (0.001) 0.002 0.005 4.3 0.0000

Male 0.226 (0.02) 0.19 0.26 13.0 0.0000

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Articles • Effects of temperature and air quality on cardiovascular diseases

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