1 Environmental and Occupational Health Division, Institute for Risk Assessment Sciences, University of Utrecht, Utrecht, the Netherlands.
2 Institute of Epidemiology, National Research Center for Health and Environment (GSF), Neuherberg, Germany.
3 Unit of Environmental Epidemiology, National Public Health Institute, Kuopio, Finland.
4 Department of Environmental Medicine, Municipal Health Service Amsterdam, Amsterdam, the Netherlands.
5 Institute for Inhalation Biology, National Research Center for Health and Environment (GSF), Neuherberg, Germany.
6 Department of Physics, University of Helsinki, Helsinki, Finland.
Received for publication December 10, 2001; accepted for publication October 14, 2002.
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ABSTRACT |
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aged; air pollution; cardiovascular diseases; coronary disease; environmental exposure; particle size; pathological conditions, signs and symptoms; respiration disorders
Abbreviations: Abbreviations: CI, confidence interval; CPC, condensation particle counter; NC, number concentration; NC0.010.1, number concentrations of particles with a size range of 0.010.1 µm; PM10, mass concentration of particles less than 10 µm in diameter; PM2.5, mass concentration of particles less than 2.5 µm in diameter; ULTRA, Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air.
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INTRODUCTION |
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In most epidemiologic studies, particulate matter has been characterized as the mass concentration of particles less than 10 µm in diameter (PM10). However, it is not clear which fraction of particulate matter is responsible for the observed health effects. In addition, a plausible mechanism for cardiovascular effects cannot yet be established.
One hypothesis is that ultrafine particles (smaller than 0.1 µm in diameter) deposited in the alveoli lead to increased blood coagulation. This mechanism operates either via pulmonary inflammation or via a direct action of those ultrafine particles on red blood cells, leading to the sequestration of erythrocytes (3, 4). An alternative hypothesis is that the cardiovascular effects are caused by alteration of the autonomic control of the heart. This theory is supported by epidemiologic studies on heart rate, heart rate variability, and arrhythmia. Peters et al. (6) found increases in heart rate in association with air pollution (5), and in another study they found increased arrhythmias on high air pollution days among patients with implanted cardioverter defibrillators. In three recent US studies (79), changes in heart rate and heart rate variability were found among elderly subjects on days with elevated levels of particulate matter. These findings are supported by the results of several toxicologic studies. Changes in respiratory and cardiac parameters, such as an increase in the S-T segment, were observed in dogs with coronary occlusion and rats after exposure to particulate matter (10, 11).
Because of these studies, one would anticipate that air pollution would be associated with cardiac symptoms as well. However, to our knowledge, there have been no published articles from panel studies reporting on the association between cardiovascular symptoms and air pollution, except for one older study (12).
The aim of the ULTRA Study (Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air) was to investigate the effects of different fractions of particulate matter (mass and number concentration) and gaseous air pollutants on the cardiovascular system in panels of elderly subjects with a history of coronary heart disease. In this paper, we report the effects of air pollution on the presence of cardiorespiratory symptoms.
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MATERIALS AND METHODS |
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In Amsterdam, the panelists were recruited through distribution of screening questionnaires in homes for the elderly (senior residences). Letters were distributed in residential areas populated by a relatively high number of elderly persons, and an advertisement was placed in a local newspaper after an initially low response. Finally, additional subjects were recruited via the Department of Cardiology of the Academic Medical Center. In Erfurt, the study population was recruited through a local cardiologist. In Helsinki, subjects were recruited through the placement of advertisements in the journal of a Finnish Heart Association patient organization and a local newspaper. Furthermore, informational letters were distributed to members of the heart association who had the postal code of the study area in Helsinki and to gymnastic groups affiliated with the association. Written informed consent was obtained from each subject. The study protocol was approved by a medical ethical committee at each study center. Detailed information on the study methods has been reported elsewhere (13) and is available at the ULTRA Study website (http://www.ktl.fi/ultra).
Symptom diary
Subjects were instructed to report the presence of several selected cardiovascular and respiratory symptoms in the daily diary (13). Respiratory symptoms were selected from previous diary studies (14, 15). The cardiac symptoms were selected on the basis of angina pectoris quality-of-life questionnaires (16). The symptoms included in the diary were: chest pain, chest pain at physical exertion, shortness of breath, feeling tired or weak, tripping or racing heart, cold hands or feet, cough, phlegm, being awakened by breathing problems, wheezing, and common cold or flu and fever; symptoms were reported as absent (0), mild (1), or moderate/severe (2). In addition, subjects were asked about the avoidance of physically demanding activities because of symptoms (hereafter called avoidance of activities), as well as the question, "How would you rate your overall health today?". This perceived health could be reported in five grades, ranging from "very bad" to "good." Finally, the use of respiratory or cardiovascular medication could be reported (13). During each clinic visit, the completed diary was checked and replaced by a new diary. Assistants at each center manually keyed in the diary data. To ensure data quality, checks on impossible values and decision rules for coding were applied, as described in the ULTRA manual (13).
Air pollution exposure
In each city, concentrations of ambient air pollutants were measured at a fixed monitoring site representing urban background levels. Size distributions of particle number concentrations were measured with aerosol spectrometers. Previous side-by-side measurements showed that these spectrometers were comparable (1719). Number concentrations (NC) in different size classes were determined to form one size class for particles with a size range of 0.010.1 µm, referred to as ultrafine particles (NC0.010.1). A condensation particle counter (CPC) with a lower detection limit of 0.007 µm (TSI 3022A; TSI, Inc., St. Paul, Minnesota) was used at all centers to measure continuously the total number concentration of particles (hereafter called CPC count). Mass concentration of particles less than 2.5 µm in diameter (PM2.5) was measured with Harvard impactors (20). All air pollution measurements were taken according to standard operating procedures that are described in the ULTRA manual (13).
PM10 measurements, as well as measures of gaseous air pollution (nitrogen oxide, nitrogen dioxide, carbon monoxide, sulfur dioxide, and ozone), were obtained from the National Quality Monitoring Network, which is operated by the National Institute of Public Health and the Environment (21) in the Netherlands. In Finland, these data came from the network measurement site of the Helsinki Metropolitan Area Council. In Germany, carbon monoxide data were collected by the state (Thüringer Landesanstalt für Umwelt). All other measurements were performed by the National Research Center for Health and Environment at the ULTRA site. All variables were transformed to 24-hour means, noon-to-noon, to coincide with the PM2.5 measurements.
Confounder data
Hourly data on ambient temperature, relative humidity, and atmospheric pressure were obtained from national meteorologic network sites in or near the study area (the Royal Dutch Meteorological Institute in Amsterdam, the Thüringer Landesanstalt für Umwelt und Geologie in Erfurt, and the Helsinki Metropolitan Area Council in Helsinki). Data on the incidence of influenza-like illness were obtained per center from a sentinel system. Regional influenza data were used in both the Netherlands (cases/10,000 in northwestern Netherlands) (22) and Finland (total number of influenza cases reported in Helsinki). In Germany, regional respiratory infection data (weekly counts of patients in Thuringia who had to stay home because of acute respiratory infection) were used. Data on influenza-like illness were recoded from the weekly registrations into 0- to 6-day and 7- to 13-day averages.
Data analysis
All symptom variables were recoded as binary variables, with 0 representing no symptoms and 1 representing moderate/severe symptoms. Chest pain and chest pain upon physical exertion were interpreted as mutually exclusive symptoms by the subjects, especially in Finland and the Netherlands. Therefore, chest pain was recoded to 1 (present) when chest pain upon physical exertion was reported. Perceived health was recoded as 0 (good, quite good, or average) versus 1 (bad or very bad). We analyzed all symptoms separately instead of grouping them, since each symptom might be a reflection of a different mechanistic response. We analyzed the daily occurrence of a symptom when that symptom was reported to be absent on the previous day (incident symptom). We also analyzed the occurrence of symptoms irrespective of the occurrence of symptoms on the previous day (prevalent symptoms). In the diary, subjects only recorded "as needed" medication. Medication use was recoded as 0 (no medication) versus 1 (intake of one or more doses) by medication group on the basis of Anatomical-Therapeutical-Chemical codes (table 1).
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Logistic regression with an indicator variable for each subject was used to obtain center-specific effect estimates controlled for individual differences in frequency of symptom reporting. A confounder model was built for each symptom and for each center separately, without air pollution in the model. All models contained data on time trend, temperature, relative humidity, and ambient pressure independently of the direction of the association, as well as indicator variables for day of the week and subject. Influenza was only included when a relation in the expected direction appeared in the exploratory analyses. For temperature and relative humidity, lags of 0, 1, 2, and 3 days were evaluated. The shapes and lags of the covariates were explored using LOESS functions (23). A linear term was compared with nonparametric functions with spans that ranged from 0.3 to 1 for trend and from 0.6 to 1 for other covariates, with steps of 0.1. In principle, the model with the lowest Akaikes Information Criterion was selected. However, a less detailed model was selected when the response-trend plot showed too much detail (patterns with a period of less than 1 month) or if, at lags from 1 day to 10 days, consistent negative autocorrelation coefficients occurred in the residuals. Model-building started with long-term confounders (time trend) and moved towards short-term confounders (weather).
The partial autocorrelation plot of the prevalence of symptoms showed autocorrelation from the previous 2 days. Therefore, an autoregressive structure for the previous 2 days was included in the model. No autocorrelation was present for incidence.
Effect estimates are expressed as odds ratios for an increase of 10 µg/m3 in PM2.5, 10,000 particles/cm3 for NC0.010.1, 0.25 mg/m3 for carbon monoxide, and 15 µg/m3 for nitrogen dioxide, based on the interquartile ranges of the air pollution levels at the three centers. To obtain combined effect estimates, we used the inverse of the variances of the center-specific estimates as weights to calculate weighted means of the panel-specific slopes. The heterogeneity of effect estimates between centers was tested with a 2 test (24). Sensitivity analyses changing time trend and temperature function, excluding high air pollution days, high influenza days, and high temperature days, and adding pollen counts were carried out to evaluate robustness of the effect estimates. In addition, two-pollutant models were explored. All analyses were performed with S-PLUS 2000 (25).
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RESULTS |
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The dates of the study periods were as follows: in Amsterdam, from November 2, 1998, to June 18, 1999 (229 days); in Erfurt, from October 12, 1998, to April 4, 1999 (171 days); and in Helsinki, from November 2, 1998, to April 30, 1999 (175 days). Because of recruitment problems, we extended the study period towards the summer in the Netherlands to obtain a sufficient number of observations. In total, 6,228, 7,491, and 7,828 observations were obtained in Amsterdam, Erfurt, and Helsinki, respectively. For some of the observations (12.5 percent in Amsterdam, 5.9 percent in Erfurt, and 9.1 percent in Helsinki), there were one or more missing values because a subject reported having been out of town for more than 8 hours that day or failed to complete the diary. In both Amsterdam (1.8 percent) and Helsinki (0.9 percent), the item on which data were missing most frequently was the avoidance of activities. All other symptoms had less than 0.6 percent missing values, except for perceived health in Amsterdam (1.6 percent). In Germany, none of the symptoms had missing values.
In general, the incidence and prevalence of cardiovascular symptoms were lowest in Amsterdam and highest in Erfurt (table 2). There were small differences in the incidence of the respiratory symptoms coughing and wheezing, whereas the incidence of phlegm was highest in Amsterdam. Symptoms were weakly correlated, supporting the analysis of individual symptoms. All kappa values were below 0.4, except for chest pain during exercise and shortness of breath in Erfurt ( = 0.429).
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Prevalence analyses
Patterns similar to those in the combined incidence analyses were observed for the symptoms chest pain, wheezing, coughing, phlegm (data not shown), and chest pain upon physical exertion (table 5). The combined odds ratios for PM2.5 and phlegm ranged from 1.05 (95 percent CI: 0.98, 1.12) for lag 0 to 1.15 (95 percent CI: 1.06, 1.25) for the 5-day average, with no indication of heterogeneity between the three centers. No association with NC0.010.1 was found. In contrast to the incidence analysis, the prevalence of shortness of breath was not associated with any of the air pollutants (table 5). Moreover, for some lags there was highly significant heterogeneity between the centers. Being awakened by breathing problems was consistently associated with PM2.5, with no indication of heterogeneity between the centers (table 5). Perceived health was associated with PM2.5, carbon monoxide, and nitrogen dioxide, especially at the 2- and 3-day lags and the 5-day average (table 5). However, these results were fairly heterogeneous, except those for carbon monoxide. Avoidance of activities was homogeneously associated with NC0.010.1 but was not clearly associated with PM2.5, carbon monoxide, or nitrogen dioxide.
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Sensitivity analyses showed that effect estimates for incident symptoms were generally stable across different models. For phlegm only, substantial changes in the effect estimates occurred after exclusion of days with PM2.5 concentrations above the 98th percentile (5 days in Amsterdam, 4 days in Erfurt and Helsinki). Combined effect estimates were 0.97, 1.00, 1.03, 1.08, and 1.07 for PM2.5 lags of 0, 1, 2, and 3 days and the 5-day average, respectively. Although none of these odds ratios were statistically significant, odds ratios were still elevated for the 3-day lag and the 5-day average. This suggests that most, but not all, of the association was due to the highest PM2.5 concentrations.
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DISCUSSION |
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Associations of PM2.5 with shortness of breath, phlegm, being awakened by breathing problems, and avoidance of activities are not likely to have been due to chance, since associations were consistent across the different lag times evaluated. Furthermore, odds ratios for incidence were mainly homogeneous across centers. Odds ratios for prevalence were more often heterogeneous, which makes combined effect estimates more difficult to interpret. It is unlikely that the study design or data handling caused these differences, since all of the centers used standard operating procedures for the fieldwork and data management. Differences in age, sex, and lifestyle between subjects do not confound the results in panel studies. The use of fixed-effects models permitted each subject to serve as his or her own control, ruling out confounding. Medication use was stable for the study population during the study period, and the use of "as needed" medication showed no association with air pollution. All associations were adjusted for potentially confounding factors such as long-term time trends, respiratory infections, and meteorologic variables. A sensitivity analysis indicated that the estimates from the models were robust.
Apart from phlegm, respiratory symptoms (wheeze and cough) showed little association with air pollution, which may have been related to the composition of the panels (cardiovascular patients). Shortness of breath and being awakened by breathing problems have been considered respiratory symptoms in previous panel studies of asthmatic subjects or subjects with chronic obstructive pulmonary disease. It is plausible that in the current study, with cardiovascular patients, the presence of these symptoms was largely determined by cardiovascular conditions. This probably also holds for the avoidance of activities and perceived health.
Although chest pain showed no relation with air pollution, other cardiovascular symptoms such as shortness of breath, being awakened by breathing problems, and avoidance of activities were related to PM2.5. One explanation for this might be that some patients have difficulty describing their angina as pain, feeling it more as a faint constriction in the chest (26). At the same time, reduced cardiac output leads to prolonged dyspnea even with minor exertion, which may influence a subjects decision to avoid physical activities. It is common for patients with left heart failure to be awakened from sleep by severe dyspnea resulting from pulmonary congestion (26). The observed associations were generally stronger for the higher lags, which contrasts with studies that showed daily and even hourly effects on the cardiovascular system (79). However, these studies examined variations in heart rhythm, and it is not obvious that mild changes in the heart are immediately reflected by symptoms.
One older panel study reported on the relation between air pollution and cardiovascular symptoms (12). In that study, symptoms of heart disease were related to total particulate levels, but no corrections for trend or other covariates were made. In a controlled exposure study, in which both heart disease patients and controls were exposed to ozone while walking on a treadmill, the onset of angina pain was not related to ozone exposure (27). The authors suggested that this could have been caused by the symptom-limited exercise of the patients (27).
In contrast with some previous epidemiologic studies (28, 29) and several toxicologic studies (3032), we found more consistent associations with PM2.5 than with NC0.010.1. Three Finnish studies found that respiratory health was not more strongly associated with ultrafine particles than with fine particles (3335). Although few studies used NC0.010.1 as an exposure variable, the levels of NC0.010.1 were comparable to those of earlier studies (28). Thus, low levels of exposure cannot be the explanation for the weak associations found in this study. One possibility is that the use of concentrations in outdoor air measured at a fixed site is not a good proxy measure for the personal exposure of subjects, who spend most of their time indoors. This may be especially true for elderly subjects. For PM2.5, there is evidence that ambient concentrations correlate well with the personal exposures of children (36) and the personal exposures of elderly adults in the current study in Amsterdam and Helsinki (20). Similar information is currently lacking for NC0.010.1, but the short lifetime of NC0.010.1 suggests that this correlation may be lower. Koponen et al. (37) showed that the indoor:outdoor ratio for particles smaller than 90 nm was approximately 0.2, but this was measured in an office building that had an effective filtration system, in contrast to most homes.
Associations of NC0.010.1 and CPC count with the different symptoms were generally of the same magnitude and significance. This is consistent with the notion that ultrafine particles dominate outdoor particle number concentrations. This implies that in future epidemiologic studies, the less complicated CPC count could be used to study the effects of ultrafine number counts. However, the mean NC0.010.1:CPC count ratio differed for Amsterdam (0.71), Helsinki (0.75), and Erfurt (0.89), which emphasizes the need for calibration of the two instruments per study site.
In conclusion, these results from the ULTRA Study indicate that some cardiovascular and respiratory symptoms are related to levels of particulate air pollution among medicated subjects with coronary heart disease. The results indicate that the associations are stronger for PM2.5 than for ultrafine particles and gaseous air pollutants.
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ACKNOWLEDGMENTS |
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The authors gratefully acknowledge the contribution of the following persons and institutions to the fieldwork: Helsinki, FinlandDr. Aadu Mirme, Dr. Gintautas Buzorius, Ismo Koponen, Marko Vallius, Sami Penttinen, Kati Oravisjärvi, Annalea Lohila, Anita Tyrväinen, the Helsinki Metropolitan Area Council (Päivi Aarnio and Tarja Koskentalo), and the Finnish Heart Association; Erfurt, GermanyGabi Wölke, Dr. Martina Stadeler, Regina Müller, Cornelia Engel, Dr. Thomas Tuch, Sabine Koett, Klaus Koschine, and Mike Pitz; Amsterdam, the NetherlandsDr. Andrey Khlystov, Gerard Kos, Carolien Mommers, Marloes Jongeneel, Boukje de Wit, Isabella van Schothorst, Veronique van den Beuken, Marieke Oldenwening, Dr. Nicole Janssen, Jean Pierre van Mulken, and the Department of Environmental Medicine, Municipal Health Service Amsterdam (Drs. Saskia van der Zee and Willem Roemer).
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NOTES |
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REFERENCES |
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