Several variables exerted positive confounding in analyses of MI risks related to air pollution exposure (eg, smoking), and risk estimates for NO2 were reduced after adjustment (Fig. 2). Additional adjustment for heating by including SO2 in the model tended to increase the estimates of the traffic-generated pollutants, whereas the SO2 estimate decreased (data not shown). The OR for fatal MI associated with a difference from the 5th to the 95th percentile of the 30-year average NO2 exposure increased to 1.64 (95% CI = 0.90–3.01) when adjusted for SO2. The corresponding SO2 estimate changed to 0.87 (0.45–1.69). After adjustment for heating-related SO2, the estimates for fatal MI was 1.21 (0.95–1.54) for CO and 1.40 (0.86–2.26) for PM10.
Analysis in different time windows did not indicate substantially different associations than the 30-year average exposure. However, because of low mobility of the population across the study period, the power to detect any such temporal patterns was low; for example, the correlation between the 30-year average NO2 exposure level and the levels during each of the 3 decades was between 0.85 and 0.95.
For subjects who had ever lived at an address that was assigned a street canyon contribution to the air pollution level, the adjusted OR for MI was 1.23 (0.85–1.78) and 1.40 (0.78–2.52) for fatal MI, suggesting that a dichotomous classification of residency close to hot spot streets might capture much of the long-term risk associated with traffic-generated air pollution. The adjusted OR for fatal MI associated with ever residency at such hot spot streets with additional adjustment for SO2 was 1.58 (0.86–2.90).
This study did not indicate an association between long-term air pollution exposure and overall incidence or nonfatal MI. However, the results suggest an increased risk of fatal MI associated with 30 years of residential exposure to air pollution, especially for out-of-hospital death. Our results are in accordance with previous cohort studies reporting an association between long-term air pollution exposure and cardiovascular mortality.9,11,12,50,51 We used traffic-generated NO2, CO, and PM10 as surrogates for the pollution mixture from road traffic, and we used SO2 as an indicator of air pollution from residential heating. The importance of the traffic pollutant increased in multipollutant models that incorporated both a traffic-related component and the marker for heating, suggesting an association primarily between long-term traffic-generated air pollution exposure and fatal MI.
Because the aim of this study was to investigate the regional source-specific contribution of air pollution in relation to MI, we did not assess the total levels of these pollutants. Focusing on the total levels by adding the long-range transported fraction and contributions from other regional sources would not notably change the spatial contrast in the study. According to measurements from monitors throughout the region, the annual average regional background level of NO2 during 2000 was 3 μg/m3, the urban background was 20 μg/m3, and the inner-city street level was 45 μg/m3.52 The CO levels were 200 μg/m3, 300 μg/m3, and 950 μg/m3, respectively; for PM10, they were 12 μg/m3, 17 μg/m3, 40 μg/m3; and for SO2, they were 0.5 μg/m3, 2 μg/m3, and 2 μg/m3.
We assessed exposure using residential address information from different sources, detailed historical emission data, dispersion modeling, and GIS techniques. This method was developed for the purposes of quantifying annual long-term air pollution exposure in a previously reported case–control study on lung cancer36,37; it has been improved to now allow for a resolution up to 25 m grids. The exposure was assessed without knowledge of case–control status, making differential misclassification of exposure unlikely. Nondifferential misclassification of residential exposure, however, is likely to occur and may have contributed to the overall null effects. Such bias could result from incorrect address information, errors in geographic location of addresses, or inexact dispersion calculations, especially for calculated levels far back in time, because of less reliable emission data. However, the address information was collected from several partly overlapping sources, the exact geographic location could not be found for only 9% of the addresses using both automatic and manual procedures, and the dispersion calculations have been calibrated and improved thoroughly over the last decade. We considered only residential data, thus ignoring outdoor air pollution at other locations, including the workplace. The resulting imprecision in the exposure assessment might further have attenuated any associations. Data quality may be of special concern regarding proxy reporting of historical addresses for fatal cases and for years long before infarction, which would lead to underestimation of the effect, although parish office and tax authority registers were used to minimize such bias.
The participation rate was 75% overall and not systematically different across geographic areas, speaking against exposure-related selection bias. In addition, the missing exposure information due to years with incomplete address data might have reduced exposure contrast, because replacement of missing data was based on the annual specific mean among the controls47; however, this issue applies to only 4% of all exposure years. To reduce this potential problem, we excluded subjects with more than 5 years of unknown address history.
The exposure assessment in our study has several advantages compared with previously reported cohort studies of long-term exposure to air pollution. In some earlier studies, the exposure was assessed for groups of individuals residing in the same city or close to the same pollution monitor and thus more crude.9,13,50,51 Other previous studies used less accurate modeling approaches such as the distance to major roads11 or much larger exposure grids.12
Other strengths of our study include a high reliability of case identification and good quality of diagnosis using several sources of information to ascertain the MI cases together with application of common diagnostic criteria and a comparatively high autopsy rate.38 Thus, potential misclassification of disease or bias due to nonresponse was minimized. It is unlikely that the identification or diagnosis of MI would be different in areas with higher air pollution levels; it is also unlikely that people in such areas would have differential rates of study participation or that they would systematically respond differently on questions regarding their lifestyle and habits. In addition, potential confounding from a large set of exposures and cardiovascular risk factors was considered.
Comparison of estimates with other mortality studies is difficult, because we have modeled historical source-specific levels at the subject's home address rather than using measurements of total ambient concentrations at a few sites at study baseline, or other modeling approaches using distance to major streets or total concentrations. Nevertheless, the OR for fatal MI associated with an increase in traffic-generated NOx of 10 μg/m3 in our study (1.06 [95% CI = 0.99–1.13]) might be compared with a risk ratio for ischemic heart disease death associated with a 10-μg/m3 increase in exposure to NOx in the cohort of Norwegian men (1.08 [95% CI = 1.03–1.12]), where monitored concentrations and emission data were used to calculate dispersion in kilometer grids.12 The Dutch cohort study showed a relative risk of 1.81 (95% CI = 0.98–3.34) for cardiopulmonary mortality associated with a change in NO2 concentration from the 5th to the 95th percentile from both urban background and local contribution, which was rounded to 30 μg/m3 and thus of equal magnitude as in our study, whereas the result was 1.54 (0.81–2.92) when considering only the background concentration.11 Our estimate for fatal MI associated with the same increase in traffic-generated NO2 was an OR of 1.51 (0.96–2.37). Our results are also in accordance with 2 U.S. cohort studies. The Six Cities study reported a relative risk for the most polluted compared with the least polluted city of 1.37 (95% CI = 1.11–1.68) for cardiopulmonary mortality, corresponding to a change in NO2 of approximately 30 μg/m3 (6 to 22 ppb).9 The relative risk of cardiopulmonary mortality comparing the least polluted with the most polluted city in the American Cancer Society study was 1.31 (95% CI = 1.17–1.46), equivalent to an increase of approximately 24 μg/m3 ofPM2.5.10
Possible biologic mechanisms for an association between long-term air pollution exposure and cardiovascular disease are primarily systemic inflammation (as indicated by increased levels of inflammatory markers in the blood),22–24 progression of atherosclerosis,53 and altered cardiac function by changes in heart rate and blood pressure.25–32 Our results suggest the strongest association among those who died outside of the hospital, which implies that sudden death might be of special importance in relation to long-term air pollution exposure. Persons who die of MI before reaching a hospital may have had an underlying heart disease to which chronic air pollution exposure might have contributed.7,21 However, it is not possible to assess etiologic mechanisms in detail in our study.
Fine-particle emissions from traffic have been proposed as the agent responsible for the increased cardiovascular risk from ambient air pollution.2 Although we modeled particulate matter for all addresses in our study, the long-term assessment of exposure to particulate matter assumed constant levels during 1960 to 2000 due to lack of historical measurements and past emission data for particles. This makes our long-term exposure estimates for particles somewhat less valid. However, the high correlations among the main traffic indicators NO2, CO, and PM10 and the strong similarities in the estimated effects suggest that the gaseous pollutants probably are good proxies for particulate exposure. In general, the risk estimates for CO seemed to be more precise than those for NO2, although these were indicators of the same source, ie, traffic. Because CO is a more direct primary pollutant from traffic, it has a steeper geographic gradient, which was reflected by a greater contribution for street canyons and thus larger contrast in exposure. Nevertheless, the source-specific pollutants are spatially correlated, and the results for the single traffic pollutants should be regarded as different estimates of the association between traffic emissions and MI.
There was a suggestion of effect modification by sex, smoking, education, and diabetes. Potential effect modification by smoking and educational level has also been reported previously.35,50 However, the only covariate demonstrating statistically significant effect modification in the reanalysis of the 2 previous U.S. cohorts was education, showing a decreasing risk with increasing education,35 contrary to our findings. This inconsistency may be due to differences between the countries in air pollution exposure in socioeconomic groups, because socioeconomically privileged people in our study tend to live in the city center and certain suburbs in close vicinity of the city, whereas another demographic situation may be present in U.S. cities.
We adjusted for important covariates, but residual confounding and potential influence from other unmeasured factors cannot be ruled out. Our results did not appear to be sensitive to different analytic approaches. Furthermore, we found that living close to major hot spot roads that are narrow and have dense traffic might be a good proxy for the risk of MI from long-term traffic-generated urban air pollution. Similar results have been reported by Hoek and coworkers11 showing high-risk estimates for fatal cardiovascular disease among people living close major roads in The Netherlands.
In conclusion, the results from this study did not indicate any association between long-term ambient air pollution exposure and MI incidence, but they provide some support for an association between long-term air pollution exposure and cardiovascular mortality.
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Figure. No Caption Available.