1 Department of Environmental Health, Harvard School of Public Health, Boston, MA
2 Department of Epidemiology, Harvard School of Public Health, Boston, MA
3 Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
4 Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
5 Tufts-New England Medical Center, Tufts University, Boston, MA
6 Department of Biostatistics, Harvard School of Public Health, Boston, MA
7 Department of Biostatistical Science, Dana-Farber Cancer Institute, Boston, MA
Correspondence to Dr. Douglas W. Dockery, Harvard School of Public Health, Landmark Building, Suite 415 West, P.O. Box 15677, 401 Park Drive, Boston, MA 02215 (e-mail: ddockery{at}hsph.harvard.edu).
Received for publication December 6, 2004. Accepted for publication February 16, 2005.
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ABSTRACT |
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air pollution; arrhythmias; heart arrest; tachycardia, ventricular; ventricular fibrillation
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INTRODUCTION |
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Associations with pollutant concentrations of shorter duration have been observed in a few studies. In Boston, Massachusetts, increased risk of myocardial infarction was associated with mean air pollution concentrations in the 2 hours preceding onset and independently within the 48 hours preceding onset (17). Heart rate variability was reduced in association with mean particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) in the previous 34 hours in a panel of elderly subjects (18
) and in an occupationally exposed cohort (19
, 20
) in Boston.
Implantable cardioverter defibrillator (ICD) devices continuously monitor for ventricular arrhythmias and record the date and time of each detected arrhythmia. They also record an electrogram and beat-to-beat intervals immediately preceding, during, and after any such event. In the event of a life-threatening arrhythmia, the ICD can automatically administer pacing or a therapeutic shock to restore normal rhythm. These devices have been shown to be effective in the prevention of sudden cardiac death in high-risk patients (2123
).
Previous studies of ICD-detected ventricular arrhythmias or discharges have found positive but inconsistent associations with daily ambient air pollution concentrations (2428
). A pilot study of 100 ICD patients in Boston found an association between ICD-recorded discharges and mean nitrogen dioxide concentration in the previous 2 days (24
). Subjects with frequent events (10 or more during 3 years of follow-up) experienced increased ICD discharges associated with carbon monoxide, nitrogen dioxide, PM2.5, and black carbon (24
). A follow-up study of confirmed ventricular arrhythmias in approximately 200 Boston ICD patients found increased risks associated with 2-calendar-day mean levels of nitrogen dioxide, PM2.5, black carbon, carbon monoxide, ozone, and sulfur dioxide (25
, 26
). A study of 50 patients in Vancouver, Canada (27
, 28
), found no consistent relations between days with ICD discharges and daily ambient air pollution. All of these studies examined the association of calendar-day mean air pollution with ICD-detected arrhythmias on the same calendar day and previous calendar days but did not take advantage of the more precise time definition available from the ICD. Using the Boston study population noted above (25
, 26
), we evaluated the association between confirmed ventricular arrhythmias and short-term pollution concentrations, with specific interest in pollution levels less than 24 hours before the arrhythmia.
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MATERIALS AND METHODS |
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Outcome and clinical data
For each ICD-recorded episode of arrhythmia, the date, time, and intracardiac electrogram was recovered from the ICD. An electrophysiologist blinded to air pollution levels classified the arrhythmia according to preset criteria, including onset rate, regularity, QRS morphology during and prior to the episode, and response to therapy. In the few cases in which the patient had experienced a large number of ICD-detected episodes since the last clinic visit, such that early electrograms in the ICD memory were overwritten, arrhythmias were classified on the basis of beat-to-beat intervals (R-R intervals). Arrhythmias were classified as ventricular tachycardia, ventricular fibrillation, sinus tachycardia, atrial flutter, atrial fibrillation, supraventricular tachycardia (i.e., when the chamber was identified as the atrium but the specific arrhythmia type was not determined), or noise/oversensing. Ventricular tachycardia and ventricular fibrillation (both sustained and nonsustained) were classified as ventricular arrhythmias. All arrhythmias originating outside the ventricle, as well as events classified as noise/oversensing, were excluded. An episode was defined as a new arrhythmic event if there had been a period of at least 60 minutes since the previous event. Residence zip code, date of birth, race/ethnicity, clinic visit dates, and prescribed medications (beta blockers, other antiarrhythmic medications, and digoxin) were abstracted from patients' records. The Harvard School of Public Health Human Subjects Committee and the New England Medical Center Institutional Review Board approved this study.
Air pollution and weather data
Hourly barometric pressure, temperature, and dew point measurements were made at Logan International Airport and extracted from National Weather Service records (EarthInfo, Inc., Boulder, Colorado). Hourly ambient concentrations of gaseous criteria air pollutants in the greater Boston area were obtained from the Massachusetts Department of Environmental Protection. Nitrogen dioxide, sulfur dioxide, and ozone were measured consistently at six sites during this period. Carbon monoxide was measured at four urban sites established to monitor possible violations of the National Ambient Air Quality Standards. For each hour, we calculated a mean gaseous pollutant concentration using all reporting monitors for that hour. If one monitor was missing a value for an hour, any difference in the mean might reflect a change in the monitors used to compute the mean, rather than a change in pollutant concentration. To address this, we used the method proposed by Schwartz (29), which takes into account the yearly means and standardized deviations of individual monitors when computing averages across sites.
Concentrations of PM2.5 were measured using a tapered element oscillating microbalance (model 1400A; Rupprecht and Patashnick, East Greenbush, New York) in South Boston (5 km east of the Harvard School of Public Health) from April 1, 1995, to January 20, 1998, and at the Harvard School of Public Health from March 16, 1999, to July 31, 2002. Tapered element oscillating microbalance measurements were corrected for loss of volatile mass using a season-specific correction factor (30
), based on collocated 24-hour gravimetric PM2.5 measurements. Black carbon was measured hourly by an Aethalometer (model 8021; McGee Scientific, Berkeley, California) in South Boston from April 1, 1995, to March 29, 1997, and at the Harvard School of Public Health from October 15, 1999, to July 31, 2002.
Statistical analysis
The association of air pollution with ventricular arrhythmias was analyzed using a case-crossover design (31), which has previously been used to study triggers of acute cardiovascular events (17
, 28
, 32
38
). In this design, each subject contributes information as a case during the event periods and as a matched control during nonevent times. Because case periods and their matched control periods are derived from the same person and a conditional analysis is conducted, non-time-varying confounders such as underlying medical conditions and long-term smoking history are controlled by design. Variables that may be related to both air pollution and the occurrence of ventricular arrhythmias that vary over time (e.g., season and meteorologic conditions) are possible confounders.
Case periods were defined by the time each confirmed arrhythmic event began, rounded to the nearest hour. Control periods (34 per case) were selected by matching on weekday and hour of the day within the same calendar month (39). Hourly pollution concentrations and weather conditions were then matched to the case and control time periods for analysis.
Moving average air pollution concentrations
Based on the previous finding for cardiovascular events, we examined associations for the period between 3 and 48 hours prior to ICD-detected ventricular arrhythmias. We estimated the association of moving average air pollutant concentration in specific time periods prior to the arrhythmia (lags of 02, 06, 023, and 047 hours) for each pollutant (PM2.5, black carbon, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide). Conditional logistic regression analyses including a pollutant moving average and natural splines (3 df) for the mean temperature, dew point, and barometric pressure in the previous 24 hours were run separately for each pollutant moving average. Our models assumed that each outcome was independent, but many subjects had more than one event. If those subjects had different susceptibilities to air pollution on the basis of their clinical history and genetic characteristics, this would have induced correlation in our errors. Therefore, we included a frailty term (40) for each subject (akin to a random intercept) in all models. Odds ratios and 95 percent confidence intervals are presented for an interquartile range increase in mean concentration for each pollutant and averaging time. To assess the stability of pollutant-specific risk estimates after adjustment for other pollutant concentrations, we created two-pollutant models using 24-hour moving averages.
Sensitivity analyses
The moving average models assumed that each of the hour-specific concentrations within the interval of the moving average had the same effect. We investigated whether relaxing that assumption and not putting any constraints on the shape of the lagged effect across the 24-hour period before the arrhythmia changed our findings and inference. Using the same conditional logistic regression models as those described above, we created unconstrained distributed lag models (41), replacing the 24-hour moving average term with 24 terms for the individual lag hours (lags 023). From each model, we used the exponentiated sum of the lag hours' regression coefficients times the interquartile range increase in 24-hour moving average concentration to estimate the cumulative risk for that time period. We estimated standard errors and 95 percent confidence intervals from the covariance matrix. We then compared results from these models with those from the moving average models.
Previous analyses have assessed associations of ICD-detected arrhythmias with calendar-day (midnight to midnight) air pollution concentrations (2428
). For comparison, we conducted the same conditional logistic regression analysis as described above but replaced the 24-hour moving average with the mean air pollution concentration for the calendar day of the event, and then again with the mean air pollution concentration for the previous calendar day. We then compared the odds ratios and 95 percent confidence intervals from these models with those from the model assessing a 24-hour moving average. All odds ratios and 95 percent confidence intervals were scaled to the same interquartile range increase.
Exposure response
We stratified each case and control's mean air pollution concentrations into quintiles. We then used the same conditional logistic regression model as that described above, replacing the 24-hour moving average term with indicator variables for each quintile of 24-hour pollutant moving average concentration. We performed a test for trend using the same model but replacing the indicator variables with an ordinal variable (quintile's median value).
Effect modification by patient and event characteristics
We separately examined modification of the 24-hour mean PM2.5 association by gender, race (White vs. non-White), age (<65 years vs. 65 years), low preimplantation ejection fraction (<25 percent vs.
25 percent), prescribed beta blockers, digoxin, and other antiarrhythmic medications (i.e., amiodarone, mexiletine, quinidine, or sotalol). For each characteristic, we added an interaction term to the conditional logistic regression model with the 24-hour mean PM2.5 concentration (lags 023). Since each subject's prescription drug use could change over the course of follow-up, each event/case's prescription drugs were defined by a report of prescription at the subject's most recent clinic follow-up visit. We also assessed effect modification by recent ventricular arrhythmias, defined as a ventricular arrhythmia within 72 hours of each case and control period. We included both an interaction term and a main-effect term for those recent ventricular arrhythmias in the conditional logistic regression model described above.
We used SAS software (version 8.2; SAS Institute, Inc., Cary, North Carolina) to construct all data sets and to calculate descriptive statistics. We used S-Plus software (version 6.2; Insightful, Inc., Seattle, Washington) for all modeling.
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RESULTS |
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Subjects with confirmed ventricular arrhythmias were predominantly White males with an average age of 64 years (range, 1990 years) (table 1). At their first clinic visit, 68 percent of patients were prescribed beta blockers, 53 percent digoxin, and 25 percent other antiarrhythmic medications (i.e., amiodarone, mexiletine, quinidine, or sotalol). Eight subjects (10 percent) were not prescribed any of these medications. For 58 percent of the ventricular arrhythmias, subjects were prescribed beta blockers at the follow-up visit immediately preceding the arrhythmia. For 56 percent of the ventricular arrhythmias, subjects were prescribed digoxin, and for 55 percent, they were prescribed other antiarrhythmic medications. Coronary artery disease was the predominant implantation diagnosis. Sixty-five percent had an ejection fraction less than 35 percent, while 27 percent had an ejection fraction less than 25 percent (table 1).
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Descriptive data on Boston's air pollution during the study period, measured hourly and daily, are shown in table 2. Hourly mean PM2.5, nitrogen dioxide, carbon monoxide, sulfur dioxide, and black carbon concentrations were highest in the early morning (67 a.m.), while levels of nitrogen dioxide and carbon monoxide had additional early evening peaks (47 p.m.). Hourly mean ozone concentrations were highest at midday (1 p.m.). Mean pollutant concentrations were higher on weekdays than on weekends for all pollutants but ozone, which was higher on weekends than on weekdays.
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The estimated odds ratio for the 24-hour moving average PM2.5 concentration was substantially larger than the odds ratios for the mean PM2.5 concentration on the same calendar day (OR = 1.08, 95 percent CI: 0.94, 1.24) and the previous calendar day (OR = 1.12, 95 percent CI: 0.97, 1.28). Similarly, for ozone, the odds ratio for the 24-hour moving average concentration was larger than the estimated odds ratios for the same-calendar-day concentration (OR = 0.96, 95 percent CI: 0.80, 1.15) and the previous-calendar-day concentration (OR = 0.97, 95 percent CI: 0.81, 1.16).
Exposure response
We assessed the exposure-response relationship for the association between ventricular arrhythmias and quintiles of 24-hour moving average PM2.5 and ozone concentration (figure 1). Risk generally increased with quintiles of PM2.5 and ozone, but the test for trend was statistically significant only for PM2.5.
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DISCUSSION |
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Our findings are stronger than those reported in an analysis of these same patients using calendar-day mean pollution concentrations (25, 26
). In those analyses, we reported smaller increases in risk of 216 percent associated with each interquartile range increase (using the same interquartile range increase as in this analysis) in mean pollutant concentrations for the day of the arrhythmia and the day before the arrhythmia. Our results are stronger than those reported by Peters et al. (24
), who found an 8 percent increased risk of an ICD discharge associated with daily mean nitrogen dioxide and a 5 percent increase in risk associated with carbon monoxide (both scaled to the same increment in air pollution used here). Our findings differ from those of Vedal et al. (27
) and Rich et al. (28
), who found no consistently increased risk associated with pollutants among all study subjects in Vancouver.
Our finding of greater risk associated with pollutants for cases with a prior ventricular arrhythmia within 72 hours, using 24-hour moving averages, is consistent with our previous analysis using 2-calendar-day mean concentrations (25, 26
). It is also consistent with the effects shown for patients with frequent events (defined as 10 or more events during 3 years of follow-up) in the Boston pilot study (25
). One of the few positive associations seen in the Vancouver study was for 2-day-lagged sulfur dioxide in the 16 subjects with two or more events per year (28
). Our finding is consistent with this result as well, suggesting that air pollution's effects may be greatest in the presence of an electrically unstable substrate.
There are several potential explanations for the stronger estimated associations with the moving average data as compared with calendar-day data. First, the case-crossover design and conditional analysis used in the current study controlled for season, time trends, and weekday (and any interaction between them) by design, thereby eliminating any residual confounding present in the previous analyses, where these factors were controlled through modeling. Second, in this analysis, onset of ventricular arrhythmia was defined by the start time recorded by the ICD device, and ambient air pollution concentrations for the 24 hours before onset were then analyzed. In analyses based on the calendar day of the ventricular arrhythmia, the timing/matching of the air pollution concentrations prior to the arrhythmia is not as well defined. The calendar-day mean ambient air pollution level could be mismatched to the 24 hours before the arrhythmia by as much as 24 hours and on average by 12 hours, assuming a uniform distribution of arrhythmias throughout the day. If the etiologically important pollutant concentrations lie within the 24 hours prior to the arrhythmia, this can be an important source of exposure misclassification. Such misclassification should be nondifferential with respect to the arrhythmia and should lead to underestimates of risk. Both reasons may explain the much weaker associations observed in our own (2426
) and other (27
, 28
) earlier studies, which assessed the association of ICD-detected arrhythmias and calendar-day mean air pollution.
The finding of associations with 24-hour mean ambient air pollution but not with shorter time periods could imply a cumulative effect across the previous 24 hours. Alternatively, these associations could reflect the net effect of a single hour of ambient air pollution distributed over the succeeding 24 hours. However, this study design did not allow us to differentiate these two interpretations of the data.
These findings suggest that ambient air pollution is associated with an increased incidence of ventricular arrhythmias among patients with ICDs. Furthermore, those subjects experiencing clusters of ventricular arrhythmias in time (here within 72 hours) may be particularly susceptible to the effects of air pollution. Understanding the underlying mechanism by which air pollutants affect cardiovascular morbidity and mortality is crucial to further defining susceptible populations. However, in order to do so, we first must understand the time periods of highest risk and the air pollutants responsible for that risk.
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ACKNOWLEDGMENTS |
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The authors appreciate the work of the fellows and researchers who abstracted the implantable cardioverter defibrillator data, including Jeff Baliff, Emerson Liu, Lynn McClellan, Chris Freed, Chris Hu, and Robert Hulefeld. David Bush provided important guidance in the quality assurance review. Jim Sullivan, Mark Davey, and George Allen were important resources regarding the air pollution data.
The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, the National Institutes of Health, the EPA, or the Health Effects Institute.
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References |
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