The important harmful characteristics of air pollution are still unclear. The main candidates include chemical composition, especially carbon compounds and heavy metals,^6,7 or high number of ultrafine particles (<0.1 μm).⁸ Epidemiological studies have suggested that crustal material may have less of an effect on health than combustion particles.^9,10 Traffic related combustion particles have been associated with increased mortality in cohort studies.¹¹ Recently, short term exposure to traffic has been associated with the triggering of onset of myocardial infarction.¹²

The present study was conducted as part of the HEAPSS (Health Effects of Air Pollution among Susceptible Sub‐populations) project, aimed at evaluating the effects of air pollution on cardiovascular diseases in five European cities. We have previously shown combustion originating air pollutants to be associated with fatal, non‐hospitalised coronary events in Rome, Italy.¹³ In the HEAPSS cohort of AMI survivors, we have found an association between ambient air pollution and increased risk of cardiac hospital readmissions.¹⁴ The aim of the present study was to analyse the associations between traffic related air pollution and hospitalisation for first AMI, the event responsible for the individual's inclusion into the HEAPSS cohort. CO and NO₂ were included in the study as indicators of traffic related air pollution, while particle number concentration (PNC) was included both as an indicator of traffic related air pollution and by merit of possible direct causal association with AMI, since it is a good proxy for ultrafine particle concentration.¹⁵ There is a general lack of long measurement series of PNC. In the current study, we modelled PNC retrospectively.^16,17

Modelled particle number concentrations were clearly highest in Barcelona, followed by Rome (table 2​2).). CO and NO₂ levels and warm season O₃ levels were highest in Rome. CO levels, as well as PM₁₀ levels, were lowest in the far northern cities Helsinki and Stockholm.

Modelled PNC correlated most strongly with NO₂ and CO in Augsburg, CO in Barcelona and Rome, and NO₂ in Helsinki and Stockholm (table 3​3).

When we pooled the results from all the five cities together, we found at 0 day lag a suggestive association of CO and PNC with first AMI hospitalisations (table 4​4).). When we restricted analyses to the three cities where large HDRs were used, we found marginally stronger associations of CO and PNC with AMI. The effects were homogeneous between cities at lag 0. Pooled estimates at 1 and 3 day lags for CO and PNC were heterogeneous. Results for maximum 8 h CO and 1 h NO₂ did not differ from the results obtained using the daily averages (data not shown). Ozone was not associated with AMI, and including ozone in the same model with PNC did not affect the results for either. Thus, stratified analyses have been conducted without ozone. In a two‐pollutant model including PNC and PM₁₀, pooled rate ratios (95% CI) for PNC remained essentially the same in the five cities, but decreased for PM₁₀ to 0.999 (0.990–1.009) at lag 0, and increased to 1.006 (0.996–1.015) at lag 3.

The heterogeneity in the pooled estimates was mainly caused by differences between cities in lag times. The lag structure for PNC in the five cities has been plotted in figure 1​1.. PNC seemed to have an immediate effect in Rome, data of which drove the pooled results at 0 day lag. In Helsinki, the effects were most pronounced at lag 1 and in Stockholm at lag 3. PNC had no clear association with AMI in the two cities with AMI registers, where the smaller data set resulted in larger confidence intervals.

In the sensitivity analyses, only the exclusion of extreme concentrations had any effect on the associations of PNC and CO with first AMI. The pooled rate ratio (five cities) for PNC at 0 day lag decreased to 1.002 (95% CI 0.991 to 1.014), and confidence intervals for the association of CO with AMI got wider: 95% CI 0.984 to 1.030 (RR=1.007). When we pooled results of only the three cities with HDRs, the association between PNC and AMI at 0 day lag became weaker, 1.005 (95% CI 0.990 to 1.021), whereas the association for CO got clearly stronger, 1.042 (95% CI 1.009 to 1.077), after exclusion of extreme values.

In Helsinki, Rome, and Stockholm, there were altogether 5158 fatal AMI hospitalisations. Among persons below 75 years of age, 12.8% of the hospitalisations were fatal, whereas 33.1% of them were fatal among persons aged 75 or above. AMI was consistently associated with PNC and CO among fatal AMI cases below 75 years of age (table 5​5).). In the stratum, there was evidence also on the associations of PM₁₀ and NO₂ with AMI, and any pooled results were no longer significantly heterogeneous. Figure 2​2 illustrates how the stratification affected the lag structure for PNC and consequently decreased heterogeneity between cities. Among non‐fatal cases aged 75 or above, we observed associations of the same day PNC and CO levels with AMI (table 5​5).

The five cities in our study reflect different demographics, climates, and air pollution sources encountered within the European Union. In the current study, the focus was on air pollutants originating mainly from traffic in urban environments. The pooled results of the five cities suggest a weak association of CO and PNC with AMI. However, in Augsburg and Barcelona, CO and PNC were not associated with AMI. This is probably due to lower power for city‐specific analyses in these cities. In the three cities with large HDRs, there was considerable heterogeneity in results. However, the heterogeneity was reduced in stratified analyses leading to higher pooled effect estimates and providing further support for the association between the pollutants and AMI.

Many recent studies support the importance of traffic and other combustion processes as the major source of harmful particulate matter.^9,10 CO and PNC both originate from combustion processes, mainly traffic in most urban environments. It is known that exposure to CO may aggravate myocardial ischaemia by reducing the oxygen carrying capacity of blood by binding to haemoglobin, and occupational studies have suggested an association between ischaemic heart disease mortality and CO exposure.²⁷ However, given the very low ambient levels of CO in especially Stockholm and Helsinki a causal role of CO is not likely in the current study, but CO has probably acted as a proxy for traffic related particulate pollution. It has been shown that outdoor CO levels may serve as surrogates for exposure to PM_2.5, and may be poorly correlated with CO exposures.²⁸ On the other hand, PNC might be also causally related to AMI.⁸

Our findings are in line with the few smaller scale studies that have evaluated associations between occurrence of AMI and traffic related air pollution. D'Ippoliti et al found CO and NO₂ to be positively associated with AMI hospitalisations in Rome, but the strongest association was observed between total suspended particles and AMI.³ Peters et al found daily levels of both particles and gaseous pollutants to be associated with the onset of non‐fatal AMI in Boston.⁴ In a recent similar study in Augsburg,²⁹ the results for daily air pollution concentrations were essentially the same. PNC was included in the latter study, but no association with AMI was found.

In our study the effects of PM₁₀ were less clear than those of CO and PNC. The percentage increase in AMI hospitalisations for a 10 μg/m³ increase in PM₁₀ was also lower than in a recent large study on AMI in the US: 0.3% versus 0.65%.⁵ However, especially in stratified analyses PM₁₀ seemed to have an effect in the current study, too. It should be noted that the use of a few central outdoor monitors makes the comparison of effect estimates of pollutants from different sources difficult. It is likely that also in our study, for example, the spatial variability in PM₁₀ levels has been different from the variability of more obviously traffic related pollutants like PNC and CO. However, in time series analyses the spatial variability in pollutant levels is less of a problem than possible poor temporal correlation between sites. For PM_2.5 it has been shown that the levels of traffic originating mass might be very different at different sites, but still longitudinally correlating.³⁰ We have shown in the HEAPSS study fair correlation between two monitoring sites for PNC in Barcelona, Rome, and Stockholm.¹⁶ In Helsinki, rather high longitudinal correlations have been previously reported for several monitoring sites for PNC.³¹ Part of the exposure to traffic originating pollutants obviously occurs while outdoors and in traffic. As the study population consisted mainly of elderly persons, the time spent outdoors has probably been lower than for general population.

We found PNC and CO to be more strongly associated with AMI among fatal cases below 75 years of age than in the whole data. In this category, there was also some evidence of PM₁₀ and NO₂ effects. In the higher age group, we observed air pollution effects mainly among non‐fatal cases. Persons 75 years of age within the fatal category may be so frail that the effects of air pollution are masked by factors related, for example, to multiple comorbidities. It should also be noted that the fraction of fatal AMIs never reaching the hospital is dependent on age.^32,33 Within the HEAPSS study, we have observed strong associations of out‐of‐hospital coronary deaths with PNC, PM₁₀, and CO in Rome.¹³

We observed higher pooled effect estimates for air pollution during warm season than during cold season. This is consistent with a recent large American mortality study,³⁴ and could be due to higher exposure or better exposure assessment during the warm season because of larger proportion of time spent outdoors and higher ventilation rates. On the other hand, seasonal differences in air pollution mixture may also affect the effect estimates, and could explain the seasonal differences in lag structure. Taken together, our two types of stratified analyses demonstrate how straightforward pooling of effect estimates in multicity studies may conceal inherent differences between cities in climate, air pollution mixture, and population. One potentially important effect modifier is medication—for example, the use of statins and beta‐blockers,^35,36 which might differ between cities. Unfortunately, HDRs do not include data on medication.

Recently, an association between ozone and AMI was reported in Toulouse, but no association between AMI and NO₂ or SO₂.³⁷ Thus, we decided to study the possible effects of ozone in addition to the effects of closely traffic related pollutants. However, we could not confirm the finding, as O₃ was found not to be associated with AMI.

An obvious limitation in our study is the use of two different types of registers for the collection of AMI data. Nevertheless, the different practices of AMI diagnosis and registration are probably a minor limitation. Instead, the upper age limit of the AMI registers may be more problematic since it caused not only different age structure, but also different sex structure as compared to the cities with administrative hospital databases. In any case, the small number of cases alone might explain the lack of associations in Augsburg and Barcelona.

Particle number concentrations were not measured directly, but estimated using other pollutants and meteorology. We have demonstrated that the estimated concentrations correlate well with measured concentrations.¹⁷ However, due to uncertainties associated with retrospective modelling in general, the effect estimates of CO and PNC are not directly comparable. In the future, when more measurement data on the smallest fractions of particles accumulate in the five study centres and elsewhere, measured PNC can be used.