Original Article: Air Pollution
Traffic-Related Air Pollution in Relation to Incidence and Prognosis of Coronary Heart Disease
Rosenlund, Mats*†‡; Picciotto, Sally*; Forastiere, Francesco*; Stafoggia, Massimo*; Perucci, Carlo A.*
From the *Department of Epidemiology, Rome E Local Health Authority, Rome, Italy; †Institute of Environmental Medicine, Karolinska Institutet; and ‡Department of Occupational and Environmental Health, Stockholm County Council, Stockholm, Sweden.
Submitted 27 January 2007; accepted 21 September 2007.
Supported by intramural funding.
Correspondence: Francesco Forastiere, Department of Epidemiology, Rome E Local Health Authority, Via di S. Costanza, 53, 00198 Rome, Italy. E-mail: firstname.lastname@example.org.
Background: Long-term air pollution exposure is associated with increased mortality, but the association with incidence of fatal and nonfatal coronary heart disease is less certain. Moreover, it is unknown how chronic exposure to air pollution affects prognosis among survivors of a first coronary event. This study evaluated the association between long-term traffic-related air pollution exposure and incidence of nonfatal and fatal coronary events, as well as subsequent hospital readmission and mortality among myocardial infarction survivors.
Methods: The study population comprised all residents of Rome aged 35–84 years during the period 1998–2000. Residential nitrogen dioxide (NO2) exposure as a marker of traffic pollution was assessed by a land-use regression model in 1995–1996 (R2 = 0.69). A total of 11,167 incident coronary events were observed (4654 fatal, including 3598 out-of-hospital coronary deaths, and 6513 nonfatal). The cohort of 6513 survivors was followed 4.0–7.5 years for readmission or mortality, starting 28 days from the date of first event. Relative risks per 10 μg/m3 of NO2 exposure, adjusted for age, sex, and socioeconomic status, were calculated by Poisson regression (population-based incidence) and Cox regression (cohort analysis).
Results: The relative risk for incidence in coronary events per 10 μg/m3 of NO2 was 1.03 (95% confidence interval = 1.00–1.07). Stronger associations were found for fatal cases (1.07; 1.02–1.12) and out-of-hospital deaths (1.08; 1.02–1.13). Using NO2 exposure at the time of the first event, there was no association of air pollution exposure with either subsequent hospital readmission or mortality among survivors of the first coronary event.
Conclusions: Long-term air pollution exposure increases the risk of coronary heart disease, particularly fatal events. Hospital readmission or subsequent mortality among survivors was not associated with traffic air pollution.
Numerous time-series studies have demonstrated that day-to-day variations in outdoor air pollution concentrations are associated with daily variations in total and cause-specific mortality.1–5 Large cohort studies have also shown that long-term annual exposure to urban air pollution increases the risk of cardiopulmonary death.6–13 Furthermore, a reduction of ambient air pollution has been associated with lower mortality rates in urban areas.14 Certain groups of the population may experience a particularly high mortality risk from air pollution exposure due to an underlying cardiovascular disease, diabetes, or excess exposure as a result of their socioeconomic status (SES).15
Several plausible mechanistic pathways that may be of importance for the observed association between short-term air pollution exposure and coronary heart disease have been proposed.16 These are related either to enhanced coagulation, thrombosis, and systemic inflammation17–23 or to altered cardiac autonomic function and increased susceptibility of arrhythmia, as indicated by changes in heart rate variability.24–33 The biologic mechanisms behind the association between long-term air pollution exposure and mortality in coronary heart disease are less well characterized, although both animal and human studies point to accelerated atherosclerosis as a result of chronic air pollution exposure.34,35 However, recent epidemiologic data suggest that long-term air pollution exposure may be associated specifically with fatal events rather than overall incidence or nonfatal coronary heart disease,36,37 implying that short-term effects such as arrhythmia may be of special importance. Because previous cohort studies have focused exclusively on mortality, little is known regarding the association between long-term air pollution exposure and incidence or nonfatal coronary heart disease. Thus, the relative importance of long-term and short-term effects is still an open question.
Daily variations in exposure to urban air pollution have been associated with the risk of hospital cardiac readmissions among myocardial infarction survivors in 5 European cities,38 but to our knowledge, there are no data on longer-term hospital readmission outcomes following nonfatal coronary events related to chronic air pollution exposure.
The aim of this study was to examine the association between long-term residential traffic-related air pollution exposure and the risk of fatal and nonfatal first-time coronary events in the population of Rome. A second aim was to evaluate subsequent mortality or hospital readmission among the cohort of survivors of a primary coronary event in relation to their residential exposure to urban-traffic-generated air pollution.
The data used in this study have been described in detail elsewhere.39–41 The study population comprises all residents of Rome aged 35–84 years. Cases who experienced their first coronary event during 1998–2000 were found using 3 data sources: the regional cause-of-death registry, the regional hospital-discharge registry, and the municipal population registry. The regional cause-of-death registry contains individual causes of death, coded according to the International Classification of Diseases, 9th Revision (ICD-9), for all residents of the Lazio region where Rome is situated. The regional hospital-discharge registry covers all public and private hospitals in Lazio. The high data quality of this registry is maintained by specific training programs for personnel (physicians and administrators) periodically provided at each hospital to improve the quality of coding, and routine data quality checks by the regional health authority of all hospitals in the region.42 Information on each hospital discharge includes patient characteristics, diagnoses, and surgical interventions by ICD-9 codes. The municipal population registry maintains records on all official residents of Rome, including vital status and dates of birth and death. Fiscal codes were used as identifiers in linkages of these records, based on name, birth date, and birth place for each resident. These codes are present or calculable from data present in all government records including the registries mentioned above.
Cases of a first-time acute coronary event were divided in categories according to whether they died within 28 days (fatal cases)—either outside of hospital (out-of-hospital deaths) or in hospital (fatal hospitalizations)—or survived more than 28 days (nonfatal hospitalizations). Out-of-hospital deaths were retrieved from the regional cause-of-death registry and defined as deaths with ICD-9 codes 410–414. Fatal and nonfatal hospitalizations were retrieved by linking the regional cause-of-death registry with the regional hospital-discharge registry and defined as a principle diagnosis of acute myocardial infarction (ICD-9: 410). Predefined exclusion criteria, described in a previous publication,39 were applied to avoid duplication of hospitalized subjects, recent previous infarctions, and doubtful or otherwise concurrent coronary diagnoses. In brief, subjects who were excluded as out-of-hospital deaths were those who had been hospitalized in the preceding 28 days, deaths occurring in a hospital, and deaths immediately preceded by a hospitalization from causes other than ischemic heart disease. Excluded hospitalized patients were those discharged alive after fewer than 3 days, an indication that acute myocardial infarction was ruled out, or those with a secondary diagnosis of previous infarction (ICD-9: 412). All subjects with a prior hospitalization for acute myocardial infarction or previous infarction in the calendar year of the index event or in the 3 preceding years were also excluded.
Comorbidities noted in previous hospitalizations during the 3 years before the event were identified for each subject by linkage of the aforementioned hospital records. These included diabetes (ICD-9: 250), hypertension (ICD-9: 401–405), chronic obstructive pulmonary disease (COPD) (ICD-9: 490–496), peripheral vascular disease (ICD-9: 440.2), angina/other chronic ischemic heart disease (ICD-9: 413–414), conduction disorders (ICD-9: 426), arrhythmia (ICD-9: 427), and heart failure (ICD-9: 428). Description of medical history prior to the incidence of the coronary event is given in a previous publication.39 For the present study, we present the percentage of subjects with selected prior comorbidities according to estimated air pollution exposure to evaluate possible confounding by risk factors related to these diagnoses.
Those surviving at least 28 days were followed up for mortality up to June 30, 2005, and for cardiac readmissions up to December 31, 2004 (minimum of 4.0 years after the index event, maximum 7.5 years), by the same record linkage procedure. Cardiac readmission was defined as hospital readmission for a new acute myocardial infarction (ICD-9: 410), hospital admission for angina/other acute ischemic heart disease (ICD-9: 411, 413), arrhythmia (ICD-9: 427), or congestive heart failure (ICD-9: 428).
To assign an area-based SES to each subject, data from the closest population census, in 2001, were used to characterize the geographic distribution of the population according to administrative census blocks. The total number of residents of Rome was 2,516,666 in 2001. We considered 5376 census blocks (average 468 inhabitants per census block). The population aged 35–84 years, used as the denominator in this study, was 1,487,223. Following the same technique previously used for 1991 census data,43 each subject's SES was derived from a deprivation index based on multiple characteristics of the census block of residence from 2001 census data, including distribution of education level, occupation, unemployment rate, family size, crowding, proportion of dwellings rented/owned, and immigration. The city distribution of this index was divided into 4 categories from the least deprived to the most deprived, which thus constitutes the SES variable used in this study.
Air Pollution Assessment
Exposure to traffic-related air pollution for each resident in Rome was represented by the estimated nitrogen dioxide (NO2) concentration in the subject's census block of residence in 1995–1996. The NO2 concentration was estimated for each of the 5376 census blocks by a land-use regression model described in detail elsewhere.44 This model was built by assessing the association between variables describing land use and traffic information and the NO2 concentration measured at 68 sites in Rome. Three Palmes tubes measured outdoor pollution simultaneously at each site over three 7-day periods in June 1995, November 1995, and March 1996, and the site-specific mean annual level was calculated. The most important predicting variables were the geographic location according to the administrative zones (R2 = 0.57), distance to busy streets (R2 = 0.22), size of the census block (R2 = 0.26), the inverse population density (R2 = 0.28), and altitude (R2 = 0.01), resulting in a multiple model determination coefficient (R2) of 0.69. As specified in our previous work,44 we have used a hybrid version of that model where altitude and distance to busy roads were replaced by emission data of benzene (R2 = 0.69). The correlation between observed and predicted NO2 concentrations from the land-use regression model was 0.77 and the mean prediction error from cross validation was 1.13 μg/m3, which was small in comparison with the range of NO2 concentrations across all measurement sites (24–73 μg/m3). Census blocks with extreme values of input data for the prediction model (ie, inverse population and size of the census block) were excluded to avoid extrapolation of predicted NO2 beyond the reasonable range. This affected only 11 cases.
In all, 12,207 incident coronary events were found in the Rome population among persons aged 35–84 years over the 3-year period.39 However, after exclusions due to inconsistencies in the follow-up registry (2 cases), no information or inconsistencies in the census block of residence (713 cases), census blocks that were not assigned a SES level (254 cases), and census blocks that were not assigned an estimated NO2 due to extreme input data for air pollution prediction modeling (11 cases), a total of 11,167 cases remained.
Incidence rates (total events and fatal and nonfatal cases) in the Rome population among persons aged 35–84 years were calculated using the total number of person-years of observation as the denominator. Crude and adjusted relative risks (RRs) and 95% confidence intervals (CIs) were estimated by Poisson models for a linear increase in NO2 exposure of 10 μg/m3. Adjusted incidence models included age (10-year age classes), sex, and SES (4 categories). In addition, categorical analyses of NO2 exposure in quintiles were performed to evaluate any nonlinearity in estimated effects. Tests for nonlinearity using generalized additive models (GAM) demonstrated no evidence of nonlinear associations (not presented).
Within the hospitalized cohort, separate associations of estimated NO2 levels and several outcomes were examined. Follow-up for hospital readmission or mortality among survivors was analyzed with Cox proportional hazards models adjusted for age, sex, SES, starting from 28 days after myocardial infarction. Because long-term clinical prognosis of cardiac patients depends on the existing disease burden of the patient, the analyses of readmission and mortality follow-up were also adjusted for comorbidities during the 3 preceding years (diabetes, hypertension, COPD, peripheral vascular disease, dysrhythmia, angina, conduction disorder, heart failure, angina/other ischemic heart disease). However, including comorbidities in the Cox model did not change the effect estimates.
To account for spatial autocorrelation due to possible geographic clustering of mortality, we adjusted estimates of variance for clustering in census block and used robust estimates. In addition, the residuals of the regression models were checked and Moran's index was calculated for the presence of a residual spatial pattern where the weight for each pair of points was the inverse of the distance between the 2 centroids of the census blocks.45 It is an index ranging from −1 (perfect negative correlation) to 1 (perfect positive correlation), where values close to 0 represent independence among locations. Possible effect modification was analyzed by interaction terms between the pollution variable and sex, age, and SES. The analyses were performed with Stata version 8.0 (StataCorp, College Station, TX) and with ArcView version 9.2 (ESRI, Redlands, CA).
The distribution of person-years and acute coronary events according to sex, age, area-based SES, and estimated residential NO2 exposure is provided in Table 1. Most of the coronary events that occurred in the Rome population among persons aged 35–84 years during 1998–2000 were nonfatal, accounting for 6513 of the events. However, 4654 fatal events occurred, of which 3598 were out-of-hospital deaths and 1056 were hospitalizations followed by death within 28 days. The overall proportion of coronary events was higher in men than in women, and increased with age. Women accounted for a higher proportion of fatal events than they did of nonfatal cases. A larger proportion of the census blocks with high estimated NO2 concentration also had a high area-based SES, where 30% and 40% of the census blocks with estimated NO2 above 60 μg/m3 were classified as high or midhigh, compared with only 5% and 15% of those below 20 μg/m3, respectively (data not shown). The correlation between SES and estimated NO2 at the census block level was −0.36.
Table 2 provides additional characterization of the cases according to air pollution exposure, by displaying the comorbidities diagnosed in the 3 preceding years for fatal and nonfatal cases according to categories of estimated NO2 exposure. Fatal cases with previous diabetes, hypertension or other ischemic heart diseases were evenly distributed over the air pollution categories, while the proportion of fatal cases with prior COPD, dysrhythmia or heart failure was lower in the highest category of exposure. For nonfatal events, there was a similar tendency for COPD, but the opposite for heart failure and other ischemic heart disease, presenting the highest proportion of cases in the top exposure category.
Table 3 reports RRs (crude and adjusted) of first coronary events overall and by case outcome categories, including out-of-hospital deaths, fatal hospitalizations, and nonfatal hospitalizations, associated with air pollution exposure. Estimates adjusted for age and sex suggested an association only for fatal cases and out-of-hospital deaths. After adjustment for age, sex, and area-based SES, the analyses suggested an increased risk from air pollution in all case groups, although the positive effect appeared to be most pronounced among fatal cases and especially out-of-hospital deaths, with a RR of 1.07 (95% CI = 1.02–1.12) and 1.08 (1.02–1.13), respectively. Rescaling the exposure variable to the difference between the 5th and 95th percentile (33.0–60.1 μg/m3) resulted in an adjusted RR of 1.20 (1.06–1.36) for fatal cases and 1.22 (1.06–1.39) for out-of-hospital death.
Figure 1 shows the results in each case group according to categories of estimated NO2 exposure and the linear slope per 10 μg/m3 increase in NO2 exposure. There was no evidence of nonlinearity, although the top category of exposure shifted the slope downwards in all analyses.
The NO2 values themselves were spatially autocorrelated, as Moran's index for the exposure variable was 0.241 (standard error = 0.00044). However, the check for between-census blocks spatial autocorrelation of the residuals from the final regression model adjusting for age, sex, and SES showed that the Moran's index was only 0.0032 (standard error = 0.00043). Although the index remained statistically significant at the 0.05 level, the modest levels observed were not expected to have a substantial effect on model results. Analyses of possible interaction between air pollution exposure and coronary events did not indicate any difference in effect for men and women, in different age groups, or according to SES (data not shown).
Out of 6513 survivors of the first coronary event, a total of 2438 subjects were readmitted to a hospital for any cardiac event, and 1802 subjects died during the follow-up period. The analysis of the follow-up of the nonfatal cases did not indicate any association between air pollution exposure and either mortality or subsequent hospital readmission in cardiac-related diagnoses (Table 4). All estimates were close to or below 1.0.
The results of this study suggest that residential traffic-related air pollution exposure increases the risk of coronary heart disease, in particular fatal outcomes. The strongest association appeared among out-of-hospital deaths. However, there was no indication that air pollution exposure prior to the primary event would affect long-term prognosis in terms of subsequent mortality or hospital cardiac readmission among nonfatal cases several years later.
Our results are largely in accordance with previous cohort studies reporting an association between long-term air pollution exposure and cardiopulmonary mortality.6–13 The exposure was assessed by a land-use regression model that could explain almost 70% of the geographical distribution of traffic-related air pollution in the study region. We used NO2 because it is a good marker of the traffic-generated pollution mixture, and because we had readily available measurements from which to build a model. These measurements had a spatial distribution that was large enough to allow the construction of a geographic model to predict pollution levels throughout the whole study area. Other pollutants, in particular particles, may be responsible for the effects rather than NO2 in itself. Although similar modeling techniques have been used previously,9 most other studies have used measurements from centrally located urban air pollution monitors to assess the air pollution exposure in cohort studies. Using central monitors to assess individual long-term air pollution exposure does not take into account small-scale, within-city variation in pollution concentrations, which may cause up to a 3-fold underestimation of mortality risks due to air pollution.46 Land-use regression models with similar determination coefficients, such as the one in the present study, have demonstrated promising performance for assessment of the small-scale spatial distribution of traffic-related air pollution within urban areas.47,48 Thus, our exposure assessment has some advantages compared with previous cohort studies in which central monitoring data were used. Internal cross-validation of the regression model demonstrated good agreement between measured and predicted concentrations both with and without emission data. Nevertheless, for the validity of the exposure assessment it is important to consider in this study the temporal difference between the exposure assessment in 1995–1996, the first coronary events in 1998–2000, and the subsequent readmission or mortality follow-up until 2004–2005. Only air pollution exposure prior to the primary event was examined, which might be a reason for the null-effects on readmission or mortality follow-up. However, time trends display very small changes in annual average NO2 concentrations in the study region during 1995–2003.44 An additional reason for concern is that we could not examine patients' mobility within the city after the coronary event.
Air pollution may bring about its harmful effects gradually during the life course of an exposed individual, or act via mechanisms with a shorter time scale caused by a severe form of ischemia, thus resulting in more immediate coronary death. Our results suggest stronger associations among fatal than nonfatal coronary events, which is supported by previous findings.36,37,41 We found the strongest association among out-of-hospital coronary deaths, which also accords with previous findings.36,40 This may imply that the mechanisms related to short-term effects (eg, arrhythmia) could be of special importance. Despite the objective to assess long-term effects of air pollution on coronary incidence, these results may thus also depend on short-term effects. Time-series studies have repeatedly reported associations between daily variations in mortality and air pollution concentrations,1–5 while cohort studies demonstrate increased mortality risks from annual average air pollution levels for people living in different geographic areas.6–13 However, because no investigation has been able to separate the effects of short- and long-term air pollution exposure, the question remains open as to whether variations in air pollution with time or geography, or possibly both, are responsible for the increased mortality risks. Residents of Rome who have high air pollution exposure are probably also those who have a large variability in short-term exposure, whereas those who live in areas with low exposure on the geographical scale are expected also to experience a lower gradient in short-term exposure. In a case-crossover analysis of the same dataset, the risk for out-of-hospital coronary death per interquartile range (IQR) increase (IQR=18.5 μg/m3) in short-term air pollution exposure was 2.9% (95% CI =−2.8 to 8.9) associated with NO2 and 6.1% (0.6–11.9) associated with particles less than 10 μm in size (PM10) the same day or the day before (IQR = 29.7 μg/m3).40 This might be compared with 6.7% (0.2–11.5) for an IQR increase in long-term NO2 exposure (IQR = 8.9 μg/m3) in the present study. Thus, the results are likely to be affected by short-term effects and it was not possible to disentangle adequately the importance of short- or long-term exposure, because there is still no time-series data for different small geographic areas in the region.
Daily variations in air pollution exposure have been linked to hospital cardiac readmissions among myocardial infarction survivors38—a finding that was not supported by our data on long-term exposure. In addition, we did not find any association between air pollution and subsequent mortality on the hospitalized nonfatal coronary events several years afterward. Although patients with a previous myocardial infarction constitute a group that might be more susceptible to an increased risk of mortality from air pollution, it may be hard to investigate because postmyocardial infarction patients generally are being treated for their disease, which modulates their susceptibility toward potentially harmful agents like air pollution.49 Selection bias is another possible explanation, because the frailer cases may have been those whose incident coronary event was fatal. Patients surviving a coronary event several years may also be less vulnerable or biologically susceptible to air pollution in general, because the mortality risk decreases over time following an acute event when the disease has been stabilized. Such selection processes may thus constitute reasons for the negative results on the mortality follow-up of nonfatal cases. However, pollution exposures following the primary event were not examined or adjusted for, and this is the most likely explanation for the lack of significant findings.
Our subjects were enrolled and followed up using population registries, and the results are thus also prone to potential bias related to the quality of diagnosis. However, validity studies of the diagnoses in hospital discharge registries of Rome demonstrate high sensitivity and confirmation rate of hospitalization for myocardial infarction.42,50,51 In addition, any such bias would probably affect patients equally regardless of their air pollution exposure and thus contribute to an underestimation of any positive associations.
Although it was possible to adjust for the individual risk factors age, sex, and area-based SES, other important risk factors or covariates which may have confounded the results were not available in this study. Most importantly, no data on individual smoking habits were available. However, smoking is correlated with SES and affluent people live in areas with higher air pollution concentrations in Rome,52 so adjustment for SES might also in part control for the confounding effect of smoking. In addition, several of the comorbidities considered in this study are strongly correlated with smoking—particularly COPD, as most cases are caused by smoking. Because cases living in areas with high air pollution exposure have a lower rate of COPD and other diseases expected to be correlated with smoking (as indicated by Table 2) adjusting for smoking could, in theory, result in increased effect estimates, in which case the associations found here would be underestimated. In a random sample of 9488 residents of Rome aged 25–59 years, who completed a self-administered questionnaire on respiratory health and various risk factors including smoking in 1995, no association was found between smoking status and NO2 concentrations at the address of residence (Cesaroni G, unpublished data). Nevertheless, similar to another recent publication,53 confounding from smoking or other individual risk factors could not be properly evaluated in this study because it was based on administrative registry data.
In conclusion, we found an association between long-term residential air pollution exposure and the risk of coronary heart disease. In particular, fatal events and especially out-of-hospital deaths were related to air pollution exposure. However, we found no indication that air pollution exposure before the coronary event would affect long-term prognosis in terms of mortality or hospital readmissions among nonfatal cases several years later. The effect of additional pollution exposures after the primary event should be further evaluated.
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