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Changes in triggering of ST-elevation myocardial infarction by particulate air pollution in Monroe County, New York over time: a case-crossover study

Environmental Health volume 18, Article number: 82 (2019) Cite this article

Abstract

Background

Previous studies have reported that fine particle (PM2.5) concentrations triggered ST elevation myocardial infarctions (STEMI). In Rochester, NY, multiple air quality policies and economic changes/influences from 2008 to 2013 led to decreased concentrations of PM2.5 and its major constituents (SO42−, NO3−, elemental and primary organic carbon). This study examined whether the rate of STEMI associated with increased ambient gaseous and PM component concentrations was different AFTER these air quality policies and economic changes (2014–2016), compared to DURING (2008–2013) and BEFORE these polices and changes (2005–2007).

Methods

Using 921 STEMIs treated at the University of Rochester Medical Center (2005–2016) and a case-crossover design, we examined whether the rate of STEMI associated with increased PM2.5, ultrafine particles (UFP, < 100 nm), accumulation mode particles (AMP, 100-500 nm), black carbon, SO2, CO, and O3 concentrations in the previous 1–72 h was modified by the time period related to these pollutant source changes (BEFORE, DURING, AFTER).

Results

Each interquartile range (3702 particles/cm3) increase in UFP concentration in the previous 1 h was associated with a 12% (95% CI = 3%, 22%) increase in the rate of STEMI. The effect size was larger in the AFTER period (26%) than the DURING (5%) or BEFORE periods (9%). There were similar patterns for black carbon and SO2.

Conclusions

An increased rate of STEMI associated with UFP and other pollutant concentrations was higher in the AFTER period compared to the BEFORE and DURING periods. This may be due to changes in PM composition (e.g. higher secondary organic carbon and particle bound reactive oxygen species) following these air quality policies and economic changes.

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Introduction

Over the past decade, policy initiatives to improve air quality have been implemented nationwide and in New York State [1]. These measures included the lowering of the sulfur content of diesel and home heating fuels, use of particle regenerative traps, and nitrogen oxide controls on new heavy-duty diesel vehicles. Additionally, several actions to reduce sulfur dioxide (SO2) and nitrogen oxide (NOx) emissions from power plants in upwind source areas have also been enacted (i.e. Cross-State Air Pollution Rule). Furthermore, economic changes have also led to changes in emissions [1, 2]. The economic drivers include the recession of 2008 that resulted in generally decreased economic activity and thus lower emissions, and a dramatic decline in the price of natural gas that displaced coal as a fuel for electricity generation.

These policy initiatives and economic drivers resulted in changes in the concentrations and composition of air pollutants measured in Rochester, NY [1,2,3]. Long-term trend analyses from 2001/2002 to 2015/2016 in Rochester showed decreases in concentrations of particulate matter with aerodynamic diameter < 2.5 μm (PM2.5), accumulation mode particles (AMP, 100–500 nm), ultrafine particles (UFP, ≤ 100 nm), black carbon (BC), SO2, and carbon monoxide (CO), while the concentration of ozone (O3) showed a slight upward trend [4]. The reduction in PM2.5 concentrations was largely attributed to substantial decreases in sulfate and nitrate (− 65% and − 37%, respectively, from 2001 to 2015) [1], which are secondary PM species formed from the oxidation of precursor gases SO2 and NOx. These pollutants have been previously associated with adverse cardiovascular outcomes [5,6,7,8,9,10].

Short-term increases in PM2.5 concentrations in the previous hours and days have been repeatedly associated with the triggering of myocardial infarction [8, 11,12,13,14,15,16,17], including prior Rochester studies reporting triggering of ST segment elevation myocardial infarction (STEMI) by short-term increases in ambient PM2.5 concentrations in the previous hour [11, 12]. Furthermore, Rich et al. reported a higher rate of MI associated with increased PM2.5 concentrations when the PM2.5 mixtures had high mass fractions of secondary PM species (sulfate, nitrate, and/or organics), compared to when the mixtures had low mass fractions of secondary PM species [18]. This result suggests that PM2.5 high in secondary species, formed through active, oxidative atmospheric photochemistry, may be more likely to trigger myocardial infarctions than primary fine particles [18]. Given these findings, reduced concentrations of secondary PM species sulfate and nitrate following the policy initiatives described above may result in a lower rate of STEMI per unit mass of PM, compared to periods when PM contained a higher proportion of secondary PM.

Using data on patients with a STEMI treated at the University of Rochester Medical Center (URMC) and air pollutant concentrations measured in Rochester, we examined whether the rate of STEMI associated with increased PM (PM2.5, UFP, and AMP) concentrations in the previous few hours and days was modified by periods defined based on the time of pollutant source changes following these environmental policies. We hypothesized that increases in the rate of STEMI associated with the same incremental increase in PM concentrations would be smaller AFTER these pollutant changes occurred (2014–2016), compared to BEFORE (2005–2007), and DURING this period (2008–2013). We also explored effect modifications by periods (BEFORE, DURING, and AFTER) of the rate of STEMI associated with concentrations of BC and gaseous pollutants SO2, O3, and CO.

Methods

Study population

We included all patients treated at the Cardiac Catheterization Laboratory (Cath Lab) at URMC in Rochester, NY for STEMI between January 1, 2005 and December 31, 2016, who resided within 15 miles of the pollution monitoring station in Rochester. If a patient experienced multiple STEMIs during the study period, we only included STEMI that occurred at least 72 h after a previous STEMI, resulting in 921 STEMI events in 912 patients available for analyses. Patients’ demographic and clinical characteristics were also obtained. This study was approved by the University of Rochester Medical Center Research Subjects Review Board. Informed consent was not required since data were past events and the research could not affect treatment, there was no contact with study subjects, and all results are presented in aggregate only.

Outcome assessments

American College of Cardiology (ACC)/American Heart Association (AHA) guidelines were used at the time of Cath Lab admission to diagnose STEMI [19]. Specifically, STEMI was defined as ST segment elevation on the presenting electrocardiogram (ECG) of > 1 mm in ≥2 contiguous precordial leads, or in ≥2 adjacent limb leads, or new or presumed new left bundle branch block in the presence of angina or angina equivalent. ECG criteria are both necessary and sufficient for the diagnosis of STEMI in a patient deemed by the treating physician to have symptoms consistent with cardiac chest pain. Symptom onset time (date and hour) was self-reported by each patient (or kin if the patient was unable to communicate) upon arrival to the URMC Cath Lab.

Air pollution and meteorology measurements

The New York State Department of Environmental Conservation operates a routine air quality monitoring site in Rochester, New York, where PM 2.5 mass concentrations, SO2, O3, and CO were measured continuously throughout the study period (2005–2016). However, the CO and SO2 monitors were replaced near the end of 2010 with higher sensitivity models. The measured CO concentrations from 2007 to the installation of the new monitor were generally close to the instrument’s detection limit (0.2 ppm). This site operated a single-wavelength (880 nm) aethalometer (Magee Scientific, Berkeley, CA, USA) to measure black carbon (BC) until mid-2008, and a two-wavelength unit (370 and 880 nm) from July 2008 to the present. Particle number size distributions were measured using a Scanning Mobility Particle Sizer (SMPS, TSI, Inc., Shoreview, MN). The concentrations were aggregated into several size groups including UFP (≤ 100 nm) and AMP (100–500 nm). The UFP were further divided into UFP11-50 nm (marker for nucleation and spark ignition vehicle emissions) [20] and UFP50-100 nm (marker of diesel vehicle emissions and residential wood burning; Aitken mode) [21,22,23]. These data have been presented and their trends analyzed in several recent studies [3, 24]. NOx data were not available for the entire study period and thus were not included in our study. Temperature and relative humidity data measured at the Rochester International Airport were obtained from the National Climate Data Center. Hourly means of each pollutant and weather variables were used in all statistical analyses described below.

Study design

We used a time-stratified case-crossover design to estimate the rate of STEMI associated with air pollutant concentrations [25, 26]. This design contrasts pollutant concentrations immediately before the acute STEMI (case-period) to other time periods when the patient did not have an acute STEMI (control period). Case periods of this study were defined as the 1, 3, 12, 24, 48, and 72-h periods prior to the time of STEMI symptom onset, with control periods (3–4 per case, depending on the number of days in calendar month) matched by calendar year, month, weekday, and hour of the day. Since these case and control periods were separated by 1 week, there was no overlap in the air pollutant concentrations included in the case and control periods. Further, since the case and control periods were from the same patient, time-invariant confounders such as age, gender, and chronic comorbidities were controlled by design. Factors that varied between the case and control periods (e.g., temperature and relative humidity) were potential confounders included in our models.

Statistical analysis

First, we computed the average concentrations of each pollutant (PM2.5, AMP, UFP, UFP 11-50 nm, UFP 50-100 nm, BC, SO2, O3, and CO) in the 1 (lag hour 0), 3 (lag hours 0–2), 12 (lag hours 0–11), 24 (lag hours 0–23), 48 (lag hours 0–47), and 72 (lag hours 0–71) hours prior to each symptom onset (i.e. case) time and its matched control time. If the time of symptom onset of a STEMI event was estimated to be in the first 29 min of the hour (e.g. 12:28), then lag hour 0 was defined as the previous hour (i.e. 11:00–11:59). If the STEMI symptom onset time was estimated to be in the 30th minute or after (e.g. 12:49), then lag hour 0 was defined as that same hour (i.e. 12:00–12:59). We then regressed the case-control status (i.e., case = 1, control = 0) against the mean PM2.5 concentration at lag hour 0 of the case and control periods, using conditional logistic regression models stratified by each case-control set, adjusting for mean temperature and relative humidity during the same lag hours. We used Akaike’s information criterion to select the optimal functional form (natural spline with 2, 3, 4, or 5 degrees of freedom versus 1 degree of freedom/linear) for these two covariates. One degree of freedom (linear) was selected for both variables and used in the model. We also estimated the rate of STEMI associated with each interquartile (IQR) increase in the average PM2.5 concentration at longer lag hours (lag hours 0–2, 0–11, 0–23, 0–47, and 0–71) in separate models in the same manner. We then repeated these analyses for the other pollutants (AMP, UFP, UFP 11-50 nm, UFP 50-100 nm, BC, SO2, O3, CO). Three sensitivity analyses were performed. First, we examined whether the exposure-response functions (i.e. relative rates in the model described above) were linear or non-linear, by comparing AIC from the model described above, to models where the rate of STEMI associated with increased pollutant concentration was modeled using a natural spline with 2, 3, 4, or 5 degrees of freedom. Second, we examined whether the pollutant/STEMI association changed after further adjustment for holiday in the model. Third, we examined whether the pollutant/STEMI association was modified by age (< 65 vs. ≥65 years), sex (male vs. female), and history of smoking, diabetes, dyslipidemia, and heart failure (yes vs. no) in separate models by adding an interaction term (e.g. UFP *male) to the model described above. The IQRs used to scale effect estimates were the IQRs of the pollutant specific lag times, from the control periods during the entire study years (2005–2016). Since the correlation among data resulting from multiple STEMI events contributed by the same patient is likely negligible (921 events contributed by 912 patients), we did not account for within-subject correlation in our analyses.

To examine whether the rate of STEMI associated with increased PM2.5 concentration in the previous hour (lag hour 0) was modified by the period of pollutant source changes (BEFORE [2005–2007], DURING [2008–2013], and AFTER [2014–2016]), we added two interaction terms of ‘period’ (a categorical variable with three levels) and PM2.5 concentration to the model of main effect analysis described above. We then repeated this analysis for other lag hours (lag hours 0–2, 0–11, 0–23, 0–47, and 0–71) and the other pollutants (AMP, UFP, UFP 11-50 nm, UFP 50-100 nm, BC, SO2, O3, CO). To account for the unbalanced distribution of patients’ age across the three periods, in sensitivity analysis, we added an interaction term between pollutant concentration and age (< 65 years vs. ≥65 years) to the model of effect modification by period described above. We then repeated this analysis for all patient characteristics with unbalanced distributions across the three periods (sex, smoking, diabetes, dyslipidemia, heart failure). Analyses were performed using SAS (version 9.4; SAS institute Inc., Cary, NC) and R (version 3.0.1, R foundation for statistical computing, Vienna, Austria, splines package). A p < 0.05 was used to define statistical significance.

Results

Most STEMI patients were male (71%) and non-Hispanic white (83%), and were on average 63 years of age (standard deviation: ± 14 years) (Table 1). Thirty-one percent of patients were smokers. Patients in the BEFORE period were generally older, less often male, and less often smokers compared to patients in the DURING and AFTER periods. The prevalence of a prior history of coronary artery bypass grafting (CABG), diabetes, and heart failure was higher in patients in the BEFORE period, than other periods. However, the prevalence of dyslipidemia was lower in patients in the BEFORE period (Table 1).

Table 1 Characteristics of study subjects, by period
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There were substantial decreases in the concentrations of almost all pollutants during the study period except for O3 (Table 2). From the BEFORE to the AFTER period, the median concentration of PM2.5 decreased by ~ 30%, AMP by ~ 40%, UFP by ~ 45%, BC and CO by ~ 50%, and SO2 by ~ 90%. However, the median O3 concentration increased by ~ 30% from the BEFORE period to the DURING and AFTER periods. Further description of these changes in Rochester and across New York State has been provided previously [1,2,3]. Pearson correlations between hourly pollutants concentrations are shown in Additional file 1: Table S1. PM2.5 was moderately correlated with AMP (r = 0.62–0.69), but poorly correlated with UFP (r = 0.12–0.22) in all years. AMP was moderately correlated with UFP in the BEFORE (r = 0.53) and DURING (r = 0.57) period, but the correlation was weaker in the AFTER period (r = 0.29).

Table 2 Distribution of hourly pollutant concentrations and weather characteristics (lag hour 0 of control periods) by period
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Over all periods, estimates of the relative rate of STEMI associated with increased PM2.5 and AMP concentrations were all close to 1.0 and not statistically significant (Table 3). However, increased rates of STEMI were associated with increased UFP and UFP 11-50 nm concentrations in the previous 1 (lag hour 0) and 3 h (lag hours 0–2). IQR increases in UFP concentrations in the previous 1 and 3 h were associated with 12% (95%CI: 3%, 22%) and 11% (95%CI: 2%, 22%) increases in the rate of STEMI, respectively. Similar to UFP, IQR increases in the particle number concentrations of UFP 11-50 nm (nucleation mode and spark ignition vehicle emissions [20,21,22]) at lag hour 0 and lag hours 0–2 were associated with 13% (95%CI: 4%, 22%) and 12% (95%CI: 3%, 21%) increases in the rate of STEMI, respectively. However, increases in UFP 50-100 nm (diesel vehicle emissions and residential wood burning; Aitken mode [20,21,22]) concentrations were not significantly associated with increases in the rate of STEMI, with effect estimates for all lag hours slightly greater than 1.0.

Table 3 Rate of STEMI associated with each interquartile range increase in pollutant concentration (2005–2016)
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IQR increases in BC concentrations (lag hour 0: 9%, 95%CI: 1%, 18%; lag hours 0–2: 9%, 95%CI: 0%, 18%) and CO concentrations (lag hour 0: 15%, 95%CI: 2%, 29%; lag hours 0–2: 13%, 95%CI: 0%, 28%) had similar patterns as UFP. Increased SO2 concentrations were associated with increased rates of STEMI in the previous 3 to 72 h, with statistically significant increases in the previous 3 to 24 h. Relative rates ranged from 1.14 to 1.18 in the previous 12 to 72 h. The rates of STEMI associated with each IQR increase in O3 concentrations in the previous 1 to 72 h were all less than 1.0 (OR = 0.87–0.92), but none were statistically significant. These results were essentially the same after additional adjustment for holiday (Additional file 1: Table S2). The rate of STEMI associated with increased UFP concentrations in the previous hour was not substantially different by age, sex, smoking, diabetes, dyslipidemia, and heart failure (Additional file 1: Table S3), although the relative rate was larger in males than females (1.16, 95%CI: 1.05–1.29 in males vs. 1.04, 95%CI: 0.89–1.21 in females). Further, there was no evidence of a non-linear UFP concentration-response function, as the rate of STEMI associated with increased UFP in the previous hour (linear function, 1 df) had the lowest AIC (2168.9) compared to models using a natural spline with 2 df (AIC = 2170.7), 3 df (2171.9), 4 df (AIC = 2173.7), or 5 df (AIC = 2174.9).

Increases in the rate of STEMI associated with increased concentrations of UFP, UFP 11-50 nm, BC, and SO2 were larger in the AFTER period compared to those in the BEFORE and DURING periods (Fig. 1; Table 4). In the previous hour (lag hour with the largest UFP relative rate in Table 3), each IQR (3702 particles/cm3) increase in UFP concentration was associated with 9% (95%CI: − 4%, 23%), 5% (95%CI: − 12%, 24%), and 26% (95%CI: 7%, 48%) increases in the rate of STEMI, in the BEFORE, DURING, and AFTER periods, respectively. This pattern remained even when separately including interaction terms between air pollution and age, sex, smoking, diabetes, dyslipidemia, and heart failure in the model (Additional file 1: Table S4). There were similar patterns for UFP in the previous 3 h, UFP 11-50 nm in the previous hour and previous 3 h, BC in the previous hour, and SO2 in the previous 1 to 24 h. However, the pattern for CO relative rates was different. Each IQR (0.23 ppm) increase in CO concentration at lag hour 0 was associated with 9% (− 6 to 26%), 31% (2 to 68%), and 29% (− 15 to 96%) increases in the rate of STEMI, in the BEFORE, DURING, and AFTER periods, respectively, showing similarly larger relative rates in the DURING and AFTER periods. We did not observe a pattern of effect modification by period for the associations between STEMI and PM2.5, UFP 50-100 nm, AMP, or O3.

Fig. 1
figure 1

Rate of STEMI associated with each interquartile range increase in pollutant concentration, by time period. For each pollutant, the lag hour with the largest main effect that was statistically significant was selected for presentation. Error bars are 95% confidence intervals

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Table 4 Rate of STEMI associated with each interquartile range increase in pollutant concentration in the Before, During, and After periods
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Discussion

In Rochester, NY from 2005 to 2016, increased rates of STEMI were associated with increased concentrations of UFP, UFP 11-50 nm, BC, and CO in the previous 1 to 3 h, as well as increased SO2 concentration in the previous 3 to 72 h. Further, we found larger rates of STEMI associated with UFP, UFP11-50 nm, BC, and SO2 in the period after air quality policy implementation and economic changes (AFTER period: 2014–2016) compared to the periods BEFORE (2005–2007) or DURING (2008–2013) these changes. Given concomitant changes in pollutant composition over the three periods, this difference in relative rate by period may reflect differential triggering of STEMI by specific PM constituents.

In our previous studies, Evans et al. reported a 17% increase in the rate of STEMI associated with each 7.1 μ g/m3 increase in PM2.5 concentrations in the previous hour using STEMI from 2007 to 2012 and subjects living in Monroe County, NY [11], and Gardner et al. reported a 18% increased rate associated with each 7.1 μg/m3 increase in PM 2.5 in the previous hour using STEMI from 2007 to 2010 and subjects living within 15 miles of the Rochester monitoring station [12]. When using data from 2005 to 2016, but the same analysis methods for subjects living within 15 miles of the monitoring station, we found only a 3% (− 5 to 12%) increased rate of STEMI associated with each 7.1 μg/m3 increase in PM2.5 in the previous hour. Differences in PM composition between the study periods and differences in study populations may explain, in part, this discrepancy. Most, but not all other case-crossover studies also reported increased risks/odds/rates of STEMI associated with short-term increases in PM2.5 concentrations [13,14,15,16,17, 27]. However, most of these studies did not have symptom onset time and thus were not able to examine triggering of MI by increased pollutant concentrations in the previous few hours, perhaps missing an etiologically important time window. Most of these studies observed the largest increased rate of STEMI associated with pollutant concentrations in the concurrent or previous day, with excess relative rate estimates (associated with each 10 μg/m3 increase in PM2.5 concentration) ranging from 5 to 15% [13, 14, 16, 17].

In contrast with the lack of findings for PM2.5, we found a 12% increase in the rate of STEMI associated with each 3702 particles/cm3 increase in UFP concentration in the previous hour. Analyses of different UFP size fractions (11-50 nm and 50-100 nm) suggested that this observed increase in STEMI rate associated with UFPs was primarily driven by UFP 11-50 nm, which are mostly gasoline emissions and nucleation mode particles [20, 22]. UFPs comprise a large particle number concentration, but contribute little mass to PM2.5 [28, 29]. In the present study, PM2.5 mass concentration and UFP particle number concentration were poorly correlated (r = 0.21). Both UFPs and PM2.5 have been associated with adverse cardiovascular responses including systematic inflammation, oxidative stress, endothelial dysfunction, thrombosis and coagulation, and autonomic dysfunction [7, 10, 30, 31]. However, compared to larger particle fractions (e.g. AMP), UFPs deposit more deeply into the lung and may even escape clearance mechanisms and translocate into the circulatory system [32, 33]. Therefore, UFPs may exert adverse health effects via pathophysiological pathways different from PM2.5 and AMP [30].

Consistent with a meta-analysis of air pollutants and MI by Mustafic et al. [8], we observed increased rates of STEMI associated with increased CO and SO2 concentrations, but not O3. Our findings of an increased rate of STEMI associated with increased concentrations of BC in the previous hour (Excess Rate = 9%, 95% CI: 1%, 18%; per 0.44 μg/m3) and previous 3 h (Excess Rate = 9%, 95% CI: 0%, 18%; per 0.43 μg/m3) are generally consistent with a study by Zanobetti and Schwartz that reported an 8.3% (95% CI: 0.2%, 15.8%) increase in the risk of hospitalization for MI associated with each 1.7 μg/m3 increase in BC concentrations in the previous 2 days in Boston, MA from 1995 to 1999 [34].

An interesting finding of our study is the pattern of larger rates of STEMI associated with increased concentrations of UFP, UFP 11-50 nm, BC, and SO2 in the period AFTER pollutant source and economic changes compared to the periods BEFORE or DURING these changes, which is contradictory to our a priori hypothesis. However, this is consistent with our other work conducted in New York State using hospital admissions of NY residents living near PM2.5 monitoring stations in Buffalo, Albany, Rochester, and New York City during the same study period of 2005–2016 [35]. In that analysis, we observed a greater excess rate of ischemic heart disease hospital admissions associated with increased PM2.5 concentrations in the previous 2 days in the AFTER period (2.8%; 2014–2016) than in the DURING (0.6%; 2008–2013) or BEFORE (0.8%; 2005–2007) periods [35]. Our finding of a greater excess rate of STEMI associated with increased UFP concentrations in the previous hour in the AFTER period (26%) compared to the DURING (5%) and BEFORE (9%) periods, is similar. Together, these findings may reflect the same change in PM composition and differential triggering of acute cardiovascular events by one or more PM components and/or PM mixtures. We suspect that secondary organic compounds and particle-bound reactive oxygen species (ROS) may play a role in the stronger adverse effect of these pollutants (i.e., UFP, UFP 11-50 nm, BC, SO2,) after economic changes and policy initiatives that reduced SO2 and NOx emissions. It is also possible that this lower relative rate estimated in the AFTER period was attributable to non-linearity of the exposure-response function. However, this seems unlikely, since our sensitivity analyses of the rate of STEMI associated with UFP in the previous hour suggested a linear (1 df) exposure-response function fit the data best.

Concentrations of sulfate and nitrate have decreased from 2005 to 2016 across Rochester and New York State following air quality policy initiatives and economic changes [1, 36]. However, Squizzato et al. and Zhang et al. reported that over this same period, secondary organic carbon increased across New York State [35, 37]. These decreases in SO2 and NOx emissions may have led to an increased oxidation rate of volatile organic compounds by hydroxyl radicals that would have otherwise reacted with the pollutant gases [37, 38]. The oxidation of SO2 and NO2 forms strong acid, but oxidation of organic compounds forms secondary organic aerosol and produces ROS such as peroxy radicals and peroxide compounds [39]. ROS along with the secondary organics can deposit on particles (particle-bound ROS), which can then be inhaled leading to oxidative stress [40]. Oxidative stress and associated inflammation are hypothesized to be key mechanisms underlying the adverse cardiovascular health effect associated with air pollutants [7]. Although it is well known that ROS can be formed in situ after particles are deposited in the respiratory tract, little attention has been focused on particle-bound ROS [39, 40]. We suspect that PM high in secondary organics and ROS may be more toxic than PM high in secondary sulfate and nitrate in triggering MI.

Furthermore, recent source apportionment analyses across New York State including Rochester suggest that although PM2.5 concentrations and contributions from most major source types were decreasing, spark-ignition vehicle emissions were increasing [4, 37]. Rich et al. (2019) found increased rates of cardiovascular hospitalizations associated with increased concentrations of spark ignition vehicles and diesel emissions in the previous few days [41]. The correlation coefficient between secondary organic carbon and the spark-ignition vehicle contribution to PM2.5 mass in Rochester was 0.73, suggesting that the increased formation of secondary organic carbon in the AFTER period may be associated with spark-ignition automotive emissions. This result is consistent with our findings that the UFP 11–50 nm fraction (marker for nucleation and spark ignition vehicle emissions [23]) was the primary contributor for the increased rate of STEMI associated with UFP. Diesel emissions also contribute to nucleation mode particles [23]. UFP 50–100 nm fraction (a larger mode indicator of diesel vehicle emissions) was not associated with increased rates of STEMI. Taken together, the observed stronger effect of UFP and BC on the rate of STEMI in the AFTER period may be explained by increased formation of secondary organics and ROS in the atmosphere, in part from spark-ignition vehicle emissions, and the subsequent increase in particle-bound ROS. These findings suggest that although the concentrations of PM have decreased following policy initiatives and economic changes, changing composition of PM may make the same dose of PM (e.g. each 3702 particles/cm3 of UFP) more toxic in triggering STEMI.

SO2 also had a stronger effect in the AFTER period, which may represent the influence of the remaining emissions from diesel traffic near the monitoring site including heavy-duty diesel traffic on the major roads and the adjacent diesel trains. Although on-road diesel fuel went to ultralow sulfur in 2006 and non-road diesel sold in NYS went to ultralow in 2012, out-of-state non-road diesel such as that used in diesel railroad engines was not ultralow sulfur until 2014. There would also be an influence from upwind coal-fired power plants in Dunkirk, NY (shutdown December 31, 2015) and Tonawanda, NY (Huntley Generating Station closed March 1, 2016). Thus, SO2 may be a surrogate for fresh secondary organic aerosol being produced in the upwind domain and transported with the SO2. CO had a stronger influence in the DURING and AFTER periods. CO could be a surrogate for spark-ignitions vehicles and residential wood combustion. Squizzato et al. [37] reported that car registrations increased in Monroe County after 2010. However, this also may be a result of a more sensitive CO monitor starting in 2011, resulting in reduced exposure misclassification and less downward bias, and larger relative rates in the DURING and AFTER periods. Health effect studies based on apportioned source contributions are needed to further assess these associations.

This study had several strengths, including a large sample size and multiple years of exposure data, resulting in increased statistical power. Additionally, we used symptom onset time, estimated by the patient and treating physician, to define the start time of each STEMI event, likely resulting in less exposure error than previous studies that used only hospital arrival date/time [8, 13,14,15,16,17, 27]. Further, it allowed us to examine whether the rate of STEMI increased a few hours after increases in ambient pollutant concentrations, rather than just on or after one or more days.

However, our study also had several limitations. First, we used air pollution measurements from a central monitoring station to assign exposure to all subjects living within a 15-mile radius of the site, regardless of the specific distance from the monitoring station to subject’s residence and/or where they worked and spent time. However, this exposure error is likely to be non-differential with regard to case and control periods and thus, should result only in underestimates of the relative rate of STEMI. The magnitude of this measurement error and underestimation may vary for different pollutants. UFP is thought to be more spatially variable than PM2.5 and AMP, and our previous work observed high spatial heterogeneity in BC across the Rochester area [42]. Although UFP and BC may be subject to greater exposure error, we observed increased rates of STEMI associated with these two pollutants. The true effect estimates in the absence of exposure measurement error might be even larger.

Second, the three time periods BEFORE (2005–2007), DURING (2008–2013), and AFTER (2014–2016) were selected based on times of emission changes. However, for many of the policies and emission changes, there are no well-defined time windows for the change because the interventions were implemented over time. For example, emission standards are only for new vehicles, but we do not know how quickly people buy new cars and trucks and take old vehicles out of service. We determined the best approach was to select groups of years where there were moderately constant or specifically changing emissions. As is shown in Additional file 1: Figure S1 from Squizzato et al. [2], SO2 emissions from coal-fired power plants in western New York were relatively high before 2008, and since 2008 it dropped substantially and remained at a relatively low level after 2013. The differences in air pollution concentrations and compositions among the 3 periods were analyzed and reported in Masiol et al. [4], Zhang et al. [35], and Croft et al. [43]. In the present study, the BEFORE, DURING, and AFTER period is only a proxy for the time of air quality policy implementation/intervention or economic change.

Third, patients in the three periods were perhaps different regarding several clinical characteristics associated with an increased rate of MI [44]. STEMI patients included in the BEFORE period were older and had a higher prevalence of diabetes and heart failure than patients in the AFTER period. By contrast, STEMI patients in the AFTER period were more often male, smokers, and more often had dyslipidemia. Although these characteristics may be related to susceptibility to acute effects of short-term air pollution exposure, our sensitivity analyses show that the pattern of larger increased rate of STEMI associated with increased UFP concentrations at lag hour 0 in the AFTER period compared to the BEFORE and DURING periods remained when also adjusting for interactions between air pollutant and age, sex, smoking, diabetes, dyslipidemia, and heart failure in separate models (Additional file 1: Table S4), suggesting these differences in period-specific effect estimates were not likely due to differences in the characteristics of the STEMI patients by period and interactions between those characteristics and air pollutant concentrations. Furthermore, as discussed above, the pattern of a larger increased rate of STEMI associated with increased UFP in the AFTER period in the present study is similar to the larger rate of ischemic heart disease hospital admissions associated with increased PM2.5 in the AFTER period reported by Zhang et al. across New York State [35]. This correspondence suggests that difference in study population by period may not be the sole reason for our observed period-specific effects. It is also of note that since the proportion of missing data on clinical characteristics was large in the BEFORE period (18% missing), we may not have accurately compared STEMI patient characteristics between periods.

Fourth, we did not have data on influenza in our study subjects or influenza epidemics occurring during the study period, and thus did not adjust for this variable in our analyses. However, influenza may have been a mediator of the association between air pollution and STEMI, rather than a confounder, as several studies have reported triggering of respiratory infection by short term increases in air pollutant concentrations [43, 45,46,47,48], and others have suggested that influenza may trigger acute myocardial infarction [49,50,51]. Finally, although our inference was primarily made by considering the pattern of STEMI response to pollutants across multiple lag times and not just whether each was statistically significant, type I error in the assessment of the main effect of air pollution on STEMI might be inflated due to multiple comparisons.

Conclusions

In summary, increased rates of STEMI associated with increased UFP, UFP-11-50 nm, BC, and SO2 concentrations were higher in the AFTER period (2014–2016) compared to the BEFORE (2005–2007) and DURING (2008–2013) periods. This may be due to changes in PM composition (i.e. higher secondary organic carbon and particle-bound ROS) following multiple air quality policies and economic changes. The sources and the role of secondary organics and particle-bound ROS in triggering MI need to be further studied.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available since it contains patient health information.

Abbreviations

AMP:

Accumulation mode particle

BC:

Black carbon

CABG:

Coronary Artery Bypass Grafting

Cath Lab:

Catheterization Laboratory

CO:

Carbon monoxide

IQR:

Interquartile range

NOx:

Nitrogen oxide

O3 :

Ozone

OR:

Odds ratio

PM:

Particulate matter

SO2 :

Sulfur dioxide

STEMI:

ST elevation myocardial infarctions

UFP:

Ultrafine particle

URMC:

University of Rochester Medical Center

References

  1. Emami F, Masiol M, Hopke PK. Air pollution at Rochester, NY: long-term trends and multivariate analysis of upwind SO2 source impacts. Sci Total Environ. 2018;612:1506–15.

    Article  CAS  Google Scholar 

  2. Squizzato S, Masiol M, Rich DQ, Hopke PK. PM2.5 and gaseous pollutants in New York State during 2005–2016: Spatial variability, temporal trends, and economic influences. Atmos Environ. 2018;183:209–24.

    Article  CAS  Google Scholar 

  3. Masiol M, Squizzato S, Chalupa DC, Utell MJ, Rich DQ, Hopke PK. Long-term trends in submicron particle concentrations in a metropolitan area of the northeastern United States. Sci Total Environ. 2018;633:59–70.

    Article  CAS  Google Scholar 

  4. Masiol M, Squizzato S, Rich DQ, Hopke PK. Long-term trends (2005–2016) of source apportioned PM2.5 across New York state. Atmos Environ. 2019;201:110–20.

    Article  CAS  Google Scholar 

  5. Bourdrel T, Bind M-A, Béjot Y, Morel O, Argacha J-F. Cardiovascular effects of air pollution. Arch Cardiovasc Dis. 2017;110(11):634–42.

    Article  Google Scholar 

  6. Brook RD. Cardiovascular effects of air pollution. Clin Sci (London, England : 1979). 2008;115(6):175–87.

    Article  CAS  Google Scholar 

  7. Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation. 2010;121(21):2331–78.

    Article  CAS  Google Scholar 

  8. Mustafic H, Jabre P, Caussin C, Murad MH, Escolano S, Tafflet M, Perier MC, Marijon E, Vernerey D, Empana JP, et al. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. JAMA. 2012;307(7):713–21.

    Article  CAS  Google Scholar 

  9. Rich DQ, Kipen HM, Huang W, Wang G, Wang Y, Zhu P, Ohman-Strickland P, Hu M, Philipp C, Diehl SR, et al. Association between changes in air pollution levels during the Beijing Olympics and biomarkers of inflammation and thrombosis in healthy young adults. Jama. 2012;307(19):2068–78.

    Article  CAS  Google Scholar 

  10. Rich DQ, Zareba W, Beckett W, Hopke PK, Oakes D, Frampton MW, Bisognano J, Chalupa D, Bausch J, O'Shea K, et al. Are ambient ultrafine, accumulation mode, and fine particles associated with adverse cardiac responses in patients undergoing cardiac rehabilitation? Environ Health Perspect. 2012;120(8):1162–9.

    Article  Google Scholar 

  11. Evans KA, Hopke PK, Utell MJ, Kane C, Thurston SW, Ling FS, Chalupa D, Rich DQ. Triggering of ST-elevation myocardial infarction by ambient wood smoke and other particulate and gaseous pollutants. J Expo Sci Environ Epidemiol. 2017;27(2):198–206.

    Article  CAS  Google Scholar 

  12. Gardner B, Ling F, Hopke PK, Frampton MW, Utell MJ, Zareba W, Cameron SJ, Chalupa D, Kane C, Kulandhaisamy S, et al. Ambient fine particulate air pollution triggers ST-elevation myocardial infarction, but not non-ST elevation myocardial infarction: a case-crossover study. Part Fibre Toxicol. 2014;11:1.

    Article  Google Scholar 

  13. Akbarzadeh MA, Khaheshi I, Sharifi A, Yousefi N, Naderian M, Namazi MH, Safi M, Vakili H, Saadat H, Alipour Parsa S, et al. The association between exposure to air pollutants including PM10, PM2.5, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide concentration and the relative risk of developing STEMI: a case-crossover design. Environ Res. 2018;161:299–303.

    Article  CAS  Google Scholar 

  14. Argacha JF, Collart P, Wauters A, Kayaert P, Lochy S, Schoors D, Sonck J, de Vos T, Forton M, Brasseur O, et al. Air pollution and ST-elevation myocardial infarction: a case-crossover study of the Belgian STEMI registry 2009-2013. Int J Cardiol. 2016;223:300–5.

    Article  CAS  Google Scholar 

  15. Liu H, Tian Y, Cao Y, Song J, Huang C, Xiang X, Li M, Hu Y. Fine particulate air pollution and hospital admissions and readmissions for acute myocardial infarction in 26 Chinese cities. Chemosphere. 2018;192:282–8.

    Article  CAS  Google Scholar 

  16. Pope CA, Muhlestein JB, Anderson JL, Cannon JB, Hales NM, Meredith KG, Le V, Horne BD. Short-Term Exposure to Fine Particulate Matter Air Pollution Is Preferentially Associated With the Risk of ST-Segment Elevation Acute Coronary Events. J Am Heart Assoc. 2015;4(12):e002506.

  17. Zhang Q, Qi W, Yao W, Wang M, Chen Y, Zhou Y. Ambient particulate matter (PM2.5/PM10) exposure and emergency department visits for acute myocardial infarction in Chaoyang District, Beijing, China during 2014: A case-crossover study. J Epidemiol. 2016;26(10):538–45.

    Article  Google Scholar 

  18. Rich DQ, Özkaynak H, Crooks J, Baxter L, Burke J, Ohman-Strickland P, Thevenet-Morrison K, Kipen HM, Zhang J, Kostis JB, et al. The triggering of myocardial infarction by fine particles is enhanced when particles are enriched in secondary species. Environ Sci Technol. 2013;47(16). https://doi.org/10.1021/es4027248.

    Article  CAS  Google Scholar 

  19. O'Gara PT, Kushner FG, Ascheim DD, Casey DE Jr, Chung MK, de Lemos JA, Ettinger SM, Fang JC, Fesmire FM, Franklin BA, et al. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines. Circulation. 2013;127(4):e362–425.

    Google Scholar 

  20. Kittelson DB, Watts WF, Johnson JP, Schauer JJ, Lawson DR. On-road and laboratory evaluation of combustion aerosols—Part 2:: Summary of spark ignition engine results. J Aerosol Sci. 2006;37(8):931–49.

    Article  CAS  Google Scholar 

  21. Kittelson DB, Watts WF, Johnson JP. On-road and laboratory evaluation of combustion aerosols—Part1: summary of diesel engine results. J Aerosol Sci. 2006;37(8):913–30.

    Article  CAS  Google Scholar 

  22. Kasumba J, Hopke PK, Chalupa DC, Utell MJ. Comparison of sources of submicron particle number concentrations measured at two sites in Rochester, NY. Sci Total Environ. 2009;407(18):5071–84.

    Article  CAS  Google Scholar 

  23. Kittelson DB. Engines and nanoparticles: a review. J Aerosol Sci. 1998;29(5):575–88.

    Article  CAS  Google Scholar 

  24. Masiol M, Squizzato S, Cheng M-D, Rich DQ, Hopke PK. Differential probability functions for investigating long-term changes in local and regional air pollution sources. Aerosol Air Qual Res. 2019;19(4):724–36.

    Article  Google Scholar 

  25. Levy D, Lumley T, Sheppard L, Kaufman J, Checkoway H. Referent selection in case-crossover analyses of acute health effects of air pollution. Epidemiology. 2001;12(2):186–92.

    Article  CAS  Google Scholar 

  26. Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–53.

    Article  CAS  Google Scholar 

  27. Butland BK, Atkinson RW, Milojevic A, Heal MR, Doherty RM, Armstrong BG, MacKenzie IA, Vieno M, Lin C, Wilkinson P. Myocardial infarction, ST-elevation and non-ST-elevation myocardial infarction and modelled daily pollution concentrations: a case-crossover analysis of MINAP data. Open Heart. 2016;3(2):e000429.

    Article  Google Scholar 

  28. Jeong C-H, Hopke PK, Chalupa D, Utell M. Characteristics of nucleation and growth events of ultrafine particles measured in Rochester, NY. Environ Sci Technol. 2004;38(7):1933–40.

    Article  CAS  Google Scholar 

  29. Oberdorster G, Oberdorster E, Oberdorster J. Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environ Health Perspect. 2005;113(7):823–39.

    Article  CAS  Google Scholar 

  30. Gong J, Zhu T, Kipen H, Wang G, Hu M, Guo Q, Ohman-Strickland P, Lu SE, Wang Y, Zhu P, et al. Comparisons of ultrafine and fine particles in their associations with biomarkers reflecting physiological pathways. Environ Sci Technol. 2014;48(9):5264–73.

    Article  CAS  Google Scholar 

  31. Wang M, Utell MJ, Schneider A, Zareba W, Frampton MW, Oakes D, Hopke PK, Wiltshire J, Kane C, Peters A, et al. Does total antioxidant capacity modify adverse cardiac responses associated with ambient ultrafine, accumulation mode, and fine particles in patients undergoing cardiac rehabilitation? Environ Res. 2016;149:15–22.

    Article  CAS  Google Scholar 

  32. Geiser M, Rothen-Rutishauser B, Kapp N, Schurch S, Kreyling W, Schulz H, Semmler M, Im Hof V, Heyder J, Gehr P. Ultrafine particles cross cellular membranes by nonphagocytic mechanisms in lungs and in cultured cells. Environ Health Perspect. 2005;113(11):1555–60.

    Article  Google Scholar 

  33. Moller W, Felten K, Sommerer K, Scheuch G, Meyer G, Meyer P, Haussinger K, Kreyling WG. Deposition, retention, and translocation of ultrafine particles from the central airways and lung periphery. Am J Respir Crit Care Med. 2008;177(4):426–32.

    Article  Google Scholar 

  34. Zanobetti A, Schwartz J. Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health. 2006;60(10):890–5.

    Article  Google Scholar 

  35. Zhang W, Lin S, Hopke PK, Thurston SW, van Wijngaarden E, Croft D, Squizzato S, Masiol M, Rich DQ. Triggering of cardiovascular hospital admissions by fine particle concentrations in New York state: before, during, and after implementation of multiple environmental policies and a recession. Environ Pollut. 2018;242:1404–16.

    Article  CAS  Google Scholar 

  36. Rattigan OV, Civerolo KL, Felton HD, Schwab JJ, Demerjian KL. Long term trends in New York: PM2.5 mass and particle components. Aerosol Air Qual Res. 2016;16(5):1191–205.

    Article  CAS  Google Scholar 

  37. Squizzato S, Masiol M, Rich DQ, Hopke PK. A long-term source apportionment of PM2.5 in New York state during 2005–2016. Atmos Environ. 2018;192:35–47.

    Article  CAS  Google Scholar 

  38. Zhao Y, Saleh R, Saliba G, Presto AA, Gordon TD, Drozd GT, Goldstein AH, Donahue NM, Robinson AL. Reducing secondary organic aerosol formation from gasoline vehicle exhaust. Proc Natl Acad Sci. 2017;114(27):6984–9.

    Article  CAS  Google Scholar 

  39. Hopke PK. Reactive Ambient Particles. In: Nadadur SS, Hollingsworth JW, editors. Air Pollution and Health Effects. London: Springer London; 2015. p. 1–24.

    Google Scholar 

  40. Hopke PK. New directions: reactive particles as a source of human health effects. Atmos Environ. 2008;42(13):3192–4.

    Article  CAS  Google Scholar 

  41. Rich DQ, Zhang W, Lin S, Squizzato S, Thurston SW, van Wijngaarden E, Croft D, Masiol M, Hopke PK. Triggering of cardiovascular hospital admissions by source specific fine particle concentrations in urban centers of New York state. Environ Int. 2019;126:387–94.

    Article  CAS  Google Scholar 

  42. Wang Y, Hopke PK, Utell MJ. Urban-scale spatial-temporal variability of black carbon and winter residential wood combustion particles. Aerosol Air Qual Res. 2011;11(5):473–81.

    Article  CAS  Google Scholar 

  43. Croft DP, Zhang W, Lin S, Thurston SW, Hopke PK, Masiol M, Squizzato S, van Wijngaarden E, Utell MJ, Rich DQ. The association between respiratory infection and air pollution in the setting of air quality policy and economic change. Ann Am Thorac Soc. 2019;16(3):321–30.

    Google Scholar 

  44. D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117(6):743–53.

    Article  Google Scholar 

  45. Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect. 2017;125(1):97–103.

    Article  CAS  Google Scholar 

  46. Pirozzi CS, Jones BE, VanDerslice JA, Zhang Y, Paine R 3rd, Dean NC. Short-term air pollution and incident pneumonia. A case-crossover study. Ann Am Thorac Soc. 2018;15(4):449–59.

    Article  Google Scholar 

  47. Horne BD, Joy EA, Hofmann MG, Gesteland PH, Cannon JB, Lefler JS, Blagev DP, Korgenski EK, Torosyan N, Hansen GI, et al. Short-term elevation of fine particulate matter air pollution and acute lower respiratory infection. Am J Respir Crit Care Med. 2018;198(6):759–66.

    Article  Google Scholar 

  48. Ciencewicki J, Jaspers I. Air pollution and respiratory viral infection. Inhal Toxicol. 2007;19(14):1135–46.

    Article  CAS  Google Scholar 

  49. Kwong JC, Schwartz KL, Campitelli MA, Chung H, Crowcroft NS, Karnauchow T, Katz K, Ko DT, McGeer AJ, McNally D, et al. Acute myocardial infarction after laboratory-confirmed influenza infection. N Engl J Med. 2018;378(4):345–53.

    Article  Google Scholar 

  50. Warren-Gash C, Hayward AC, Hemingway H, Denaxas S, Thomas SL, Timmis AD, Whitaker H, Smeeth L. Influenza infection and risk of acute myocardial infarction in England and Wales: a CALIBER self-controlled case series study. J Infect Dis. 2012;206(11):1652–9.

    Article  Google Scholar 

  51. Warren-Gash C, Smeeth L, Hayward AC. Influenza as a trigger for acute myocardial infarction or death from cardiovascular disease: a systematic review. Lancet Infect Dis. 2009;9(10):601–10.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Melanie Robinson for her assistance in initial data acquisition and management.

Funding

This work was supported by contract 59802 and 59800 from the New York State Energy Research and Development Authority, and grant P30 ES01247 from the National Institutes of Environmental Health Sciences. Scott Cameron was supported by grant K08HL128856 from the National Heart, Lung, and Blood Institute and training grant T32 HL066988–1 from the National Institute of Health. Daniel Croft was supported by training grant T32 HL066988–1 from the National Institute of Health.

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Authors and Affiliations

  1. Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA

    Meng Wang, Philip K. Hopke, Mauro Masiol, Edwin van Wijngaarden, Stefania Squizzato, Kelly Thevenet-Morrison & David Q. Rich

  2. Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA

    Philip K. Hopke, Mauro Masiol & Stefania Squizzato

  3. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    Sally W. Thurston

  4. Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA

    Scott Cameron, Frederick Ling, Daniel Croft & David Q. Rich

  5. Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA

    Edwin van Wijngaarden, David Chalupa & David Q. Rich

  6. Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, 265 Crittenden Boulevard, CU 420644, Rochester, NY, 14642, USA

    David Q. Rich

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  1. Meng Wang
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Contributions

MW, PKH, and DQR drafted the manuscript. PKH, SC, FL, MM, SS, KTM, and D Chalupa collected and processed the health and air pollution data. MW, PKH, DQR, EvW, D Croft, MM, and SS analyzed and interpreted the data. MM, SWT, SC, FL, EvW, D Croft, SS, KTW, and D Chalupa contributed to critical revision of the manuscript. All authors read and approved the final manuscript.

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Correspondence to David Q. Rich.

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Wang, M., Hopke, P.K., Masiol, M. et al. Changes in triggering of ST-elevation myocardial infarction by particulate air pollution in Monroe County, New York over time: a case-crossover study. Environ Health 18, 82 (2019). https://doi.org/10.1186/s12940-019-0521-3

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Environmental Health

ISSN: 1476-069X