Recent multi-city studies in both the United States and Europe have confirmed that air pollution is associated with daily mortality and hospitalization for cardiac and respiratory diseases.1-4 Although time-series investigations have focused on the respiratory system as the most important target of the effects of air pollution,5 recent evidence indicates the key importance of air pollution effects on the cardiovascular system, in particular ischemic heart diseases. Alterations in the control of the autonomous nervous system, as well as inflammatory mechanisms involving plaque rupture and clot formation, have been hypothesized as the possible physiological pathways linking particulate matter (PM) exposure with heart effects.6,7 An association of air pollution (particulate matter measures as well as nitrogen dioxide [NO2] and carbon monoxide [CO]) in the preceding 2 days with increased risk of ST-segment depression during an exercise test (an electrocardiogram [ECG] indicator of myocardial ischemia) has been recently reported among subjects with coronary heart diseases.8 However, a limited number of epidemiologic studies have evaluated the association between current levels of air pollution and the occurrence of acute myocardial infarction (MI) with both positive9-11 and nonpositive results.12
Recent studies have addressed the issue of susceptibility to the effects of air pollution.13-15 The basic research question for myocardial infarction is whether the classic risk factors for the disease (gender, age, smoking, blood pressure, diabetes, and cholesterol level) act as effect modifiers. Zanobetti16 has recently indicated that patients with diabetes might be at increased risk of particulate matter-associated cardiovascular events. In addition, specific cardiac factors, such as secondary diagnoses of congestive heart failure or of arrhythmia among people with ischemic heart diseases, have been suggested as indicators of susceptibility to the effects of air pollutants.11
We used a case-crossover approach, originally developed to study triggers of myocardial infarction,17 to evaluate the relation between daily indicators of air quality and hospitalizations for acute MI in Rome, where the main source of pollution is vehicular traffic. We considered various characteristics of the patients as possible effect modifiers.
The Lazio Hospital Information System maintains records of all hospital admissions occurring in the region, which encompasses Rome and its surroundings. The system covers 96% of both public and private hospitals. We identified 8812 records of patients (aged 18+ y) residing in Rome who were admitted to a hospital in the city from January 1995 to June 1997 with an acute MI (principal diagnosis, International Classification of Diseases, 9th ed. [ICD-9]: 410). Several exclusions were made to increase the specificity of our case definition. To limit the study to first events, we excluded records of 506 patients who were hospitalized with an acute MI in the 6 months preceding their index hospitalization, using a record-linkage procedure within the hospital database and eliminating cases with an ICD-9 code 412 (previous infarction). We also excluded 616 patients transferred from other hospitals presumably outside Rome, 554 patients with probable or possible miscoding of MI diagnosis (discharged alive after a hospital stay of less than 5 days, consistent with a ruled-out MI), 41 patients with a diagnosis of trauma (ICD-9: 800-959), 6 patients who had important surgical operations in the same hospital admission, and 10 duplicate records. For the 986 patients who were transferred to another hospital after the index hospitalization, a single-record file was created to summarize information about diagnoses and procedures of the 2 admissions. We then excluded 548 patients who were transferred to a second hospital within 5 days after the index admission and discharged with a non-MI diagnosis. Thus, a total of 6531 patients were included in the present analysis.
The following individual information was considered from the hospital database: sex, age, season of hospitalization (cold: October-March; warm: April-September), and up to 4 diagnoses (ICD-9 codes). From the secondary diagnoses, we classified subjects according to both diabetes (ICD-9: 250) and hypertension (401-405) as contributory factors to the admission. Heart conduction disorders (426), cardiac dysrhythmia (427, excluding 427.1, 427.2, and 427.5), and heart failure (428) indicated cardiac severity.
We conducted a separate study to evaluate the accuracy of diagnoses within the hospital database. A random sample of clinical records was retrieved for 390 patients hospitalized with acute MI and examined (with no indication of the final diagnosis) by 3 expert clinicians. Based on a positive ECG, laboratory enzyme criteria, and typical pain, the diagnosis of acute MI was confirmed in 96% of the cases. Although the secondary diagnoses coded in the hospital database were rather poor at detecting common comorbidities reported on the clinical records (sensitivity: diabetes, 29%; hypertension, 56%), the positive predictive values for the conditions reported were relatively high (diabetes, 83%; hypertension, 86%).
Air Pollution and Weather Data
Our air pollution data have been described in detail elsewhere.18,19 Briefly, we collected data on sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) from 5 monitoring stations located in densely populated areas in the center of the city. Total suspended particulates (TSPs) were continuously monitored in 3 stations using a low-volume air sampler (flow rate at 25 L/min) with an open-face inlet and Beta-ray atomic absorption. Parallel measurement of particulate matter of a diameter less than 10 μm (PM10) (gravimetric method) and total suspended particulate showed a ratio between the 2 ranging from 0.7-0.8. We did not consider ozone data because they were available only from monitors located in the urban area near traffic; those ozone concentrations are strongly affected by the scavenger role of primary pollutants and do not reflect daily variation in population exposure. For all pollutants, 24-hour values were considered. Imputation of missing data for a specific day and a specific monitor was done using regression models based on the other monitors’ values. For each day, we averaged the data from the available monitors to compute a city mean. Mean daily temperature (Celsius) and relative humidity (percent) were available from the weather station located in the urban area. Barometric pressure (hPa) was available from the city airport.
We used the time-stratified approach20 for the case-crossover analysis. A stratification of time into separate months was made to select referent days as the days falling on the same day of the week within the same month as the index day. Conditional logistic regression analysis was fitted to the data to calculate odds ratios (OR) and 95% confidence intervals (CI). The case-crossover approach controls for long-term trends, seasonality, and day of the week, whereas adjustment by meteorologic variables was done in the logistic model. We selected a 1-day lag for temperature (both a linear and a quadratic term) and relative humidity because they had the best combined fit using the Akaike Information Criteria, ie, −2 (log-likelihood) +2 (estimated parameters). Barometric pressure did not improve the fit and was not considered in the final model.
We considered a priori the average of the same and the previous 2 days (cumulative 0- to 2-day lag) as the lag structure for the 4 pollutants; corroborative analysis was conducted with single-day lags. To examine the shape of the exposure–response, we first calculated adjusted ORs for the quartiles (0- to 2-day lag) of each pollutant. The quartiles were also included as ordinal variables in the logistic regression to test for trend (Wald test). To evaluate the linear association (on a logistic scale), we then entered single pollutants as linear terms into the model and estimated the effects at cumulative 0- to 2-day lag and at single-day lags 0, 1, 2, 3, and 4. The results of the analysis were expressed as an OR based on an increment in exposure corresponding to 10 μg/m3 (1 mg/m3 for CO).
We examined effect modification in persons with and without a specific condition by introducing interaction terms in the model. We evaluated effect modification only for cumulative 0- to 2-day lag. Homogeneity of the coefficients across categories of various factors was evaluated by using a likelihood ratio test (χ2).
Table 1 summarizes the environmental variables during the cold (October-March) and the warm (April-September) seasons and provides the Spearman correlation coefficients. TSP and NO2 did not follow a seasonal pattern and were not correlated with temperature; SO2 and CO tended to be higher in winter than in summer. Barometric pressure was moderately correlated with all the pollutants. There was a certain degree of collinearity among the pollutants, especially between SO2 and CO (r = 0.57), and between TSP and both CO (r = 0.35) and NO2 (r = 0.35).
Table 2 describes the main characteristics of the 6531 patients included in the analysis; subjects were more likely to be men and over age 65. Hypertension, arrhythmia, and diabetes were the conditions most frequently reported.
The associations between quartiles of each pollutant (at 0- to 2-day lag) and acute MI are illustrated in Table 3. A steady trend with increasing concentration was detected for TSP and NO2 and, to a lesser extent, for CO. For SO2, only the fourth quartile had a somewhat elevated OR.
On the basis of these results, we evaluated the linear effect of the pollutants (at different lags) for all pollutants except SO2. Table 4 shows the ORs associated with 10 μg/m3 for TSP and NO2, and 1 mg/m3 for CO, at the lags from 0 to 4, as well as the cumulative 0- to 2-day lag. The OR was 1.028 (95% CI = 1.005–1.052) for TSP and 1.044 (95% CI = 1.000-1.089) for CO at the cumulative 0- to 2-day lag. The results of the single-day lags support the a priori lag structure (an increased OR at lag 0) for TSP and NO2, whereas no effect was present at the 3- and 4-day lags for any of the pollutants. We also ran 2-pollutant models evaluating the linear terms for the 0- to 2-day lags. The ORs were all slightly reduced (with confidence interval including 1.0), but an effect still remained for TSP (OR = 1.022, 95% CI = 0.999–1.047, and OR = 1.025, 95% CI = 0.978–1.075, for TSP and CO, respectively; OR = 1.023, 95% CI = 0.999–1.049, and OR = 1.013, 95% CI = 0.999–1.046 for TSP and NO2, respectively).
Table 5 shows the effects of the pollutants in the subgroups together with the χ2 for the interaction terms. Women had higher risk estimates than men for all the pollutants. The effect estimates increased with age (for TSP and NO2), and they were larger during the warm period. Heart conduction disorder was the only health condition for which an effect modification was consistently suggested for all 3 of the pollutants. The results of the subgroup analyses were similar when specific age strata were explored (data not shown).
The study suggests an effect of traffic-derived air pollutants in Rome on hospitalization for MI. Although an association was found for TSP, NO2, and CO, the strongest and most consistent effect for TSP suggests a direct effect of particles. Although TSP is an imperfect measure of fine particulate matter, both NO2 and CO are good markers of traffic-related emissions. In addition, as NO2 is converted to nitrates, it also contributes to fine particle mass. Like for most time-series studies, the interpretation of the results of multiple pollutant models is difficult given the high correlation among pollutants originating from the same source. We think the 3 pollutants are acting as surrogates for personal exposure to fine particles from mobile sources as a recent report from the United States has indicated.21
Our results are in line with previous indications from mortality and morbidity studies. Rossi et al.22 analyzed the dataset of mortality for the period 1980–1989 in Milan, Italy. An association of TSP with MI (1.0% increase) was found when considering the mean values measured 3 and 4 days before death. In a time-series analysis of cause-specific mortality in The Netherlands, deaths resulting from heart failure, arrhythmia, cerebrovascular causes, and causes related to embolism and thrombosis were more strongly associated with air pollution than were cardiovascular deaths in general.23 Poloniecki et al.,9 in a time-series analysis of emergency hospitalizations in London, found a close association between concentration of black smoke and myocardial infarction. Five percent of the acute MIs in London were attributed to air pollution. Assuming a scaling factor of 0.75 between TSP and PM10 in Rome, our OR of 1.028 (at 0- to 2-day lag) (ie, 2.8% increase per 10 μg/m3 of TSP) can be translated into a 3.7% increase per 10 μg/m3 of PM10. This estimate is higher than what has been reported in previous time-series studies but lower than a recent case-crossover study of 772 patients from the Boston area.10 In this study, the risk of acute myocardial infarction was related to the PM concentration measured 2 hours, as well as 24 hours, before the onset; the OR associated with an increase of 30 μg/m3 PM10 was 1.66 (95% CI = 1.11–2.49). Another case-crossover study,12 however, performed in the Seattle area failed to find an association between several air pollutants and death from out-of-hospital primary cardiac arrest.
Exposure to inhaled particles might increase the level of blood coagulability and modify the adhesive properties of red blood cells, thus leading to an increased risk of ischemic damage in individuals with poor coronary circulation.24 Peters et al.25 found that exposure to air pollution was associated with substantially increased risk of high C-reactive protein levels. The elevation in C-reactive protein underlines the important role of the increase in inflammatory mediators,26 because elevation in markers of inflammation predicts outcomes of patients with ischemic heart diseases.27 The oxidative stress from particles could also alter the sympathetic and parasympathetic tone that affects heart rate and heart rate variability.28 Air pollution has been associated in epidemiologic studies with increased heart rate29 and with decreased heart rate variability.30 Decreased heart rate variability is associated with a higher risk of arrhythmic events and cardiac arrest after MI.31 In a study conducted in Boston among patients with implanted cardioverter defibrillators, PM2.5 (as well as NO2 and CO) exposure have been associated with increased risk of defibrillator discharges.32 Finally, particles might act as endocrine/paracrine modulators increasing the circulating levels of endothelins, which are powerful vasoconstrictors.13,33
The issue of sensitivity to air pollution (the level of response as a function of individual biologic and clinical factors) is a research priority, but few steps have been taken to explore this issue.13,16,34 Effect modification by age in our data, with a stronger effect among the elderly, is not surprising given similar results already observed in other time-series studies on air pollution and daily mortality.34 A stronger effect of PM10 on mortality during the warm season compared with the cold period has also been observed in European studies,2,35 including in Rome.18 Some hypotheses include higher population exposure in the warm season, changes in the particulate matter’s composition during summer leading to strong toxic effects, or a selection of more vulnerable geriatrics in the city during the summer. We did not find diabetes or hypertension as effect modifiers; risk estimates for acute MI were similar among those with and without the conditions, and stratification by age did not alter the results. Other research has suggested that patients with diabetes might be at increased risk of cardiovascular admissions as a result of air pollution16; there is a need for further research on this topic.
The cardiac conditions we have considered are common chronic disorders, especially in the elderly, but they can also be complications of MI.36 There is a temporal issue, but it is not possible from the hospital discharge abstracts to evaluate whether these conditions precede or complicate the acute MI episode, because information on the sequencing of clinical diagnoses is not available. In a reabstract study of people who were admitted for an acute MI,37,38 65% of complete atrioventricular blocks and 67% of congestive heart failure occurred before the admission, whereas they appeared during the hospitalization for the other cases. Therefore, the effect modification we found for people with heart conduction disorders might have several interpretations in addition to perhaps being a chance finding. People with atrioventricular blocks might be more sensitive to particles’ effects and are at increased risk of acute MI. Alternatively, atrioventricular blocks might more often complicate MI associated with air pollution. Given the link between particles and alterations of the sympathetic/parasympathetic balance, this is another area for further research.
The case-crossover approach has been recently applied to air pollution epidemiology and provides a framework to avoid certain types of bias resulting from confounding by seasonality or long-term trends in air pollutants. However, the method is vulnerable to problems of selection of the control period.39,40 The time-stratified approach proposed by Levy et al.20 seems to offer the least biased strategy. The case-crossover method is particularly suitable for exploration of the characteristics of subjects and their susceptibility factors, but a loss in statistical power is present when compared with Poisson regression. On the other hand, the effect estimates (and standard errors) from time-series analysis using Poisson regression are sensitive to the model choice.41,42
We were able to examine hospital records in detail to limit the study to first events and to exclude potentially misclassified acute MIs and posttraumatic or postsurgery episodes probably not attributable to air pollution. These procedures might have increased the possibility of finding an association. However, as a limitation of the study, it should be recognized that our particle measurements were only proxies of the true PM10 concentration, and measurements of fine particles (PM2.5) were not available. We were unable to explore all the main risk factors for ischemic heart disease; in particular, data on individual smoking habit were unavailable. The characterization of the individual comorbidities from the secondary diagnoses was not optimal given that the sensitivity of the hospital discharge records for secondary diagnoses is generally low.37,43 In fact, the prevalence of diabetes and hypertension among our subjects was lower than what is generally reported.44 For example, hypertension occurred in 41% of the patients with acute MI studied in Boston,10 in 43% of patients investigated in Spain,45 and in 25% of the subjects in this study; the corresponding figures for diabetes are 19%, 22%, and 15%. Although the positive predictive values of these conditions reported in the discharge abstracts are generally high, as suggested from this and other validity studies,37,38 the low sensitivity might have precluded us from finding effect modification, especially for diabetes. Finally, hospital admissions for acute MI represent only a fraction of all coronary events, fatal and nonfatal, in the population.46 Because acute fatal events are often not hospitalized, future studies on air pollution and MI should evaluate the entire population burden of this condition.
We thank Margaret Huber and Patrizia Compagnucci for their editorial assistance.
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