Hospital Admissions for Cardiac and - NCBI

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Antti Pbnkii, MD, and Mikko Virtanen, MSc. Introduction .... owing to the strategy of sampling, and nitric oxide ..... production, car traffic, and a steel mill have been ...
Low-Level Air Pollution and Hospital Admissions for Cardiac and Cerebrovascular Diseases in Helsinki

Antti Pbnkii, MD, and Mikko Virtanen, MSc

Introduction

Materials and Methods

Ambient air pollutants are known to morbidity and mortality from respiratorv infections and to exacerbate symptoms of chronic obstructive pulmonary diseases. Pollutants have also been shown to increase the risk of death from cardiovascular and cerebrovascular dis-

Air Pollutants and Meteorologic Variables

increase

-a:

eases, sometimes perhaps even more than the risk of death from respiratory causes.'-3 This increased risk of death has been due to high concentra tions of pollutants, as in the London smog episodes of the 1 950s and 1 960s and in the Ruhr area of Germany in 1962 and 1985.1 24' However, recent evidence from Athens and from various parts of the United States suggests that particulates,

significantly ..

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pe-, ...... ..

even at rather low concentrations, may

increase the risk of death by exacerbating cardiovascular and cerebrovascular diseases.3'6-2 This observation is important. because the number of people exposed to low-level air pollution is very high, and even a slight increase in the relative risk of death results in a large number of ..premature deaths. Although the effects of ambient air pollutants on the risk of death from ischemic cardiac and cerebrovascular disr;9 *?.2-eases are fairly well documented, the effects on the exacerbations of symptoms of these diseases are less well known. We therefore conducted a longitudinal investigation to determine whether low-level ambient air pollutants are associated with hospital admissions pertaining to ischemic cardiac and cerebrovascular diseases in Helsinki, Finland. The study investigated .38t. -: the relationships between daily counts of hospital admissions and ambient air sulfur dioxide, nitric oxide, nitrogen dioxide, ozonc, and total suspended particulate concentrations; tempcrature; and relative humidity.

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Air pollution measurements are con-

ducted in Helsinki by district municipal authorities. Sulfur dioxide is measured hourly by coulometric instruments at four automatic monitoring stations, nitric oxide and nitrogen dioxide are measured by chemiluminescence at two stations, and ozone is measured by ultraviolet absorption at one station. Total suspended particulates are collected by high-volume samplers at six stations, four of which collect samples every second day and two of which collect samples every third day. At one weather station, measurements of temperature and relative humidity are recorded hourly. The total number of stations is eight (four in the center of the town and four in the suburbs); these stations range in height from 2 m to 10 m. The sites of these stations have been selected to represent the actual exposure of the population to ambient air pollutants. The daily mean conccntrations of each pollutant at the various stations during the study period (1987 through 1989) were used in the analysis. Helsinki City covers a relatively small area, 185 km2 (the population of Helsinki was 488 604 in 1987, 491 148 in 1988, and 491 777 in 1989). The main sources of air pollutants are energy production by coalfired and oil-fired power plants, road traffic, and, to a small extent, industrialization (Figure 1). In 1987, the total emission The authors are with the Helsinki Citv Center of the Environment. Helsinki. Finland. Requests for reprints should be sent to Antti Ponka. MD. Helsinki City Center of the Environment. Sturenkatu 25. 00510 Helsinki, Finland. This paper was accepted March 22. 1996.

American Journal of Public Health 1273

PonkA and Virtanen

made by specialists. Case records are compiled for each patient occupying a hospital bed or visiting an outpatient department. The ICD-9 code number for each patient is recorded by the physician after the episode requiring hospitalization when the results of all tests and examinations are available. The case records, as well as the diagnoses, are always seen by a senior specialist who is not directly responsible for treatment but who checks that the diagnostic criteria are valid. If the patient had more than one of the diagnoses just mentioned, only that which primarily led to hospitalization was used in this analysis.

Statistical Methods

FIGURE 1-Energy production plants, major roads, air pollution measurement stations, and residential areas in Helsinki.

of nitrogen dioxide was 17 600 tons, of which 32% was derived from traffic, 67% from energy production, and 1% from industries. The monitoring stations measuring nitrogen dioxide are situated on the main streets. At street level, 60% to 80% of the nitrogen dioxide level is derived from road traffic. The total emission of sulfur dioxide was 22500 tons, 93% ofwhich derived from energy production, 2% from traffic, and 5% from industries. The stacks of the power plants are 100 to 150 m high, while the exhaust gases from cars spread out at street level. Thus, nitrogen dioxide can be taken as an indicator of pollution due to traffic, and sulfur dioxide can be taken as an indicator of pollution due to energy production.

Incidence ofAdmissionsforlschemic Cardiac and Cerebrovascular Symptoms The data concerning hospital admissions for exacerbations of symptoms of ischemic cardiac and cerebrovascular diseases were obtained from the register kept of all periods of these illnesses requiring hospitalization. Quality control was achieved by checking diagnoses after every episode of illness, as described later. The register contains information on the dates of hospitalization and on the diag1274 American Journal of Public Health

and ages of the patients. The data all of the municipal hospitals and Helsinki University Central Hospital, which together treat practically all patients with ischemic cardiac and cerebrovascular diseases requiring hospitalization. The study included only the residents of Helsinki. Those patients who were admitted to emergency wards, and thus needed more effective treatment, were also subsequently treated in bed wards but were studied as a separate group. Diagnoses were based on the Intemational Classification of Diseases (ninth edition; ICD-9), published by the World Health Organization. Those included were ischemic cardiac diseases (ICD-9 codes 410 through 414) and cerebrovascular diseases (ICD-9 codes 430 through 438). The following diagnoses were analyzed separately: acute myocardial infarctions (ICD-9 code 410), long-term transient ischemic myocardial attacks (ICD-9 code 411), and short-term transient ischemic myocardial attacks (ICD-9 code 413), as well as cerebral ischemia due to occlusion of extracerebral vessels (ICD-9 code 433) or cerebral vessels (ICD-9 code 434) and transient ischemic cerebral attacks (ICD-9 code 435). Patients with acute appendicitis (ICD-9 codes 540 through 543) were selected as controls. All diagnoses were noses cover

Analytical methodology. The daily numbers of admissions were not normally distributed but skewed, with a low mean and a long tail. Such cases are usually modeled with the Poisson regression,13'14 which was also used in our analysis. The explanatory variables used were the concentrations of nitric oxide, nitrogen dioxide, sulfur dioxide, ozone, and total suspended particulates, along with temperature and relative humidity. For each pollutant, we studied the relation between the daily mean value of the average concentrations of each pollutant at the various stations and the number of admissions on the same day, as well as the number of admissions after lags of 1 to 7 days, because the effects of pollution and weather on admissions may be delayed. All of the concentrations of pollutants were skewed, with some high values. These values seemed to dominate the regression, and logarithmic transformations were used as a means of avoiding this difficulty. Because the number of admission categories was high, the association between any pollutant and a given admission category was tested by comparing models with and without all pollutants and weather variables. Both models included the basic secular variables (e.g., weekday dummies). If the result of such a test was significant, the modeling was repeated after omitting the nonsignificant pollutants and weather variables. In all phases of the modeling, if a pollutant was included in the model, all of its lagged values up to a lag of 7 days were included. The significance of associations between pollutants and admissions was tested by comparing the final model including the pollutant (or pollutants) with a model not including it. The evaluations were made in two different ways. First, all pollutants September 1996, Vol. 86, No. 9

Pollution and Cardiac Diseases

were included in the model simultaneously. Second, the pollutants were included one by one and evaluated separately. Missing values. The study period comprised 1096 days. Information about the concentration of total suspended particulates was lacking for 375 days, owing to the strategy of sampling, and nitric oxide, nitrogen dioxide, and sulfur dioxide values were lacking for 16, 13, and 4 days, respectively. However, the frequencies of admissions during the days with missing observations were similar to those of other days. Thus, the effect of the missing observations was probably small. Testing was done by comparing the numbers of admissions on days with missing values and on days with observed values. The missing values were imputed by means of the standard regression technique. In this method, the independent variables of the analysis in question are divided into two groups, one with missing data and the other without. First, each variable in the first group is used as a dependent variable and the variables in the second group are used as independent variables, and then the missing values are replaced with predictions from this regression. Next, the regression is extended as follows: in each regression, all of the other variables in the first group are added to the list of independent variables. The predictions from this second regression are then used to replace the missing values. In addition to the pollutants and weather variables, the imputation models included year and weekday dummies, as well as trends.15 After the actual modeling, the residuals were studied graphically to determine whether there were any differences between the imputed and nonimputed values. No clear patterns were detected. Temporalpattems and time series. The number of admissions varied seasonally and also in shorter cycles among the different weekdays. The secular trends and seasonal variation were controlled through the use of dummy variables. The number of admissions during the winter was taken as the baseline because, in every year of the study, admissions were most frequent during that period. The years were divided into four seasons: summer (June through August), autumn (September through November), winter (December through February), and spring (March through May). The number of admissions was highest on Mondays and considerably smaller on weekends, which

September 1996, Vol. 86, No. 9

TABLE 1-Numbers of Admissions for lschemic Cardiac and Cerebrovascular Diseases and the Significance of Their Overall Relations to Pollutants No. Admissions

Diagnosis

in 3-Year Period

All ischemic cardiac diseases (ICD-9 codes 410-414) All admissions 12 664 7 005 ER admissions Myocardial infarction (ICD-9 code 41 0) 4 501 All admissions 2 677 ER admissions Transient long-term ischemic attack (ICD-9 code 41 1) 1 670 All admissions 1 062 ER admissions Transient short-term ischemic attack (ICD-9 code 413) 2 134 All admissions 1 087 ER admissions Cerebrovascular diseases (ICD-9 codes 430-438) 7 232 All admissions ER admissions 3 737 Cerebral ischemia due to occlusion of extracerebral vessels (ICD-9 code 433) 254 All admissions 97 ER admissions Cerebral ischemia due to occlusion of cerebral vessels (ICD-9 code 434) 2 521 All admissions 1 230 ER admissions Transient ischemic cerebral attacks (ICD-9 code 435) 976 All admissions 563 ER admissions 2 280 Appendicitis (ICD-9 codes

Daily No. Admissions Mean

Range

Deviance

P

11.6 6.4

1-28 0-19

86.7 86.0

.0053 .0061

4.1 2.5

0-12 0-9

68.6 58.9

.1201

1.5 1.0

0-7 0-6

75.4 75.7

.0432 .0411

2.0 1.0

0-12 0-6

70.7 78.4

.0895 .0257

6.6 3.4

0-18 0-11

66.5 74.8

.1684 .0478

0.2 0.1

0-3 0-2

49.7 63.2

.7114 .237

2.3 1.1

0-8 0-5

52.7 58.2

.600 .396

0.9 0.5 2.1

0-5

49.7 60.0 41.3

.712 .475 .928

0-4 0-4

.3702

540-543) Note. ER

=

emergency room.

is probably explained by delay in seeking treatment. The effect of this day-of-theweek variation was controlled by using the dummy variables, with Monday as a baseline. The cyclic nature of the admissions was also checked up to the fourth order of sine and cosine terms. Because these fluctuations were found to be nonsignificant, they were not included in the final model. Cold weather has been considered to be associated with the symptoms of ischemic cardiac and cerebrovascular diseases.1618 In addition, severe infectious diseases such as influenza are more

harmful for people with preexisting disease than for healthy persons. Therefore, these two variables had to be controlled. The effect of weather was controlled by using a smoothing technique, and the effect of influenza epidemics was controlled by using multiple dummy variables. Influenza was classified as epidemic when the weekly count of influenza diagnoses in Helsinki was more than 50. The incidence of cardiovascular disease is decreasing in Finland, and this declining trend was seen in the present study. The predicted time series plot, residual time series plots, and periodograms were used to determine the appropriateAmerican Journal of Public Health 1275

Ponkai and Virtanen TABLE 2-Mean Daily Concentrations of Ambient Air Pollutants and Mean Values TABLE 2-Mean Daily Concentrations of Ambient Air Pollutants and Mean Values of Meteorologic Variables in Helsinki: 1987 through 1989

Sulfur dioxide, ,ug/m3 Nitrogen dioxide, ,ug/m3 Nitric oxide, ,ug/m3 Ozone, ,ug/m3 Total suspended particulates, ,ug/m3 Mean temperature, °C Relative humidity, %

Mean

Range

SD

19 39 91 22 76 5.4 83

0.2-95 4-170

12.6 16.2 61.0 13.1 51.6 9.3 12.0

7-467 0-90 6-414 -37.0-26.4 37-100

tive effect of the pollutants. Because the true size of the risk group was unknown, the former effect was assumed to be nonsignificant and was omitted from the models. Such an omission could have caused additional problems if a significant pollutant had been omitted from the model. Therefore, the autocorrelation pattern of the residuals was examined closely to detect such a possibility.

Results Number ofAdmissions

TABLE 3-Poisson Regression Models for Associations between Hospital Admissions for Ischemic Cardiac Disease (ICD-9 410-414) and Pollutants in Helsinki ER Admissions

All Admissions Parameter Intercept Year: 1988 Year: 1989 Tuesday Wednesday Thursday Friday Saturday Sunday

Spring Summer Autumn Influenza epidemic NO, log, no lag NO, log, lag = 1 day NO, log, lag = 2 days NO, log, lag = 3 days NO, log, lag = 4 days NO, log, lag = 5 days NO, log, lag = 6 days NO, log, lag = 7 days 03, log, no lag 03, log, lag = 1 day 03, log, lag = 2 days 03, log, lag = 3 days 03, log, lag = 4 days 03, log, lag = 5 days 03, log, lag = 6 days 03, log, lag = 7 days Deviance Pearson X2 df

Estimate

SE

P

Estimate

SE

P

2.308 0.006 0.084 -0.055 -0.127 -0.118 -0.263 -0.578 -0.421 0.060 -0.020 0.005 0.020 0.005 0.097 -0.038 0.011 -0.009 0.002 -0.018 0.009 -0.074 0.097 -0.034 0.011 0.032 0.004 -0.008 -0.008

0.228 0.023 0.028 0.038 0.044 0.044 0.044 0.049 0.044 0.027 0.028 0.027 0.040 0.022 0.023 0.022 0.023 0.023 0.023 0.022 0.022 0.028 0.032 0.033 0.033 0.032 0.032 0.032 0.028 1173.5 1151.0 1060