Original Article| Volume 32, ISSUE 1, P79-89, January 2023

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# Effects of Air Pollutant Exposure on Acute Myocardial Infarction

Published:November 22, 2022

### Background

Air pollution is a consequence of industrial development that is exacerbated as a result of population growth, and urbanisation.

### Aim

The goal of the study is to investigate the effects of air pollution on the number of cases of acute myocardial infarction (AMI) according to gender using the Zero-inflated Poisson Regression model in Hamadan, Iran.

### Methods

The study used an ecological design, and data collected from March 2016 to September 2020 in Hamadan were included. The intended response was the number of cases of AMI recorded in the investigated period. The time lag of the pollutants was used to investigate the effect of air pollution on the number of AMIs.

### Results

The number of AMI recorded for men and women was 1,195 and 553, respectively. The average age (±SD) for men and women was 64.60 (±12.27) and 70.98 (±11.79) years, respectively. According to the air quality index in Hamadan, the values of particulate matter < 2.5 μm (PM2.5), SO2, O3, and CO were below moderate levels. Also, according to NO2 and particulate matter between 25 μm–10 μm (PM10), the air quality index of Hamadan was in the very unhealthy mode just for 2 and 3 days, respectively. The O3 and NO2 are significant positive effects on AMI among men. But, PM2.5, PM10, and SO2 are negative impacts on hospitalisation in men due to AMI. For women, PM2.5 and O3 had positive effects on AMI. But, NO2 and PM10 had a significant negative impact on hospitalisation in women during different time lags.

### Conclusions

The results of the study showed that if the analyses are based on gender, the responses to pollutants are different and hence the stratified analysis is important.

## Background

Industrial development and technological advancement have introduced various achievements for human life. However, sometimes unwanted and harmful waste is released into the environment as a result of advancement and the implementation of industrial development programs, which can have quite negative effects on the environment [
• Mostafavi S.
• Safikhani H.
• Zamani J.
Experimental investigation of air pollution in saveh city and presenting the related solutions to reduce it.
]. Air pollution is a consequence of industrial development that is exacerbated as a result of population growth, urbanisation, and the increased consumption of fossil fuels [
• Mostafavi S.
• Safikhani H.
• Zamani J.
Experimental investigation of air pollution in saveh city and presenting the related solutions to reduce it.
]. Today, air pollution in different urban centres around the world is one of the major environmental issues resulting from the industrial civilisation of human beings and is considered a significant risk factor for human health [
• Glinianaia S.V.
• Rankin J.
• Bell R.
• Pless-Mulloli T.
• Howel D.
Particulate air pollution and fetal health: a systematic review of the epidemiologic evidence.
,
• Šrám R.
• Binková B.
• Dejmek J.
• Bobak M.
Ambient air pollution and pregnancy outcomes: a review of the literature.
]. In examining the effects of air pollution on the occurrence of diseases, two groups of pollution are considered. Outdoor pollution and indoor pollution. The aim of this study was to investigate the effect of outdoor air pollution on the number of hospitalisations of heart patients due to acute myocardial infarction (AMI); indoor air pollution was not the subject of this study. Common sources of outdoor air pollution are emissions caused by combustion processes from motor vehicles, solid fuel burning, and industry. The most common ambient air pollutants include suspended particles, ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). Also, air pollution is divided into primary and secondary pollutants. Primary pollutants are materials that are directly released from their sources into the air and include CO, NO2, SO2, particulates, and lead. Secondary pollutants are produced through the transformation made in the atmosphere, and O3 is classified in this group [
• Mostafavi S.
• Safikhani H.
• Zamani J.
Experimental investigation of air pollution in saveh city and presenting the related solutions to reduce it.
].
Acute myocardial infarction (with code ICD10: I21.9) is myocardial necrosis that occurs as a result of acute coronary artery occlusion. Symptoms include chest discomfort with or without shortness of breath and nausea. The only way to initially treat an acute myocardial infarction is to open the blocked artery. Currently, the approved and standard method for the treatment of acute myocardial infarction is to perform angiography to identify the involved vessel and remove its blockage by removing the blood clot through angioplasty. After initial treatment and opening of the blocked vessel, patients must be hospitalised for 3 to 5 days to prevent acute complications of acute myocardial infarction. This hospitalisation was in the critical care unit (CCU) department for the first days, and after ensuring that the patient's condition is stable, the treatment can be continued outside the CCU department. The standard incidence of AMI in Iran is 73.3 per 100,000 people and it varies significantly from 24.5 to 152.5 per 100,000 people in 31 provinces of Iran. The adjusted incidence rate of AMI in Hamadan is 57.6 per 100,000 people [
• Soori H.
• Mehrabi Y.
• Samavat T.
• Khaledifar A.
Incidence of acute myocardial infarction in Islamic Republic of Iran: a study using national registry data in 2012.
].
Many risk factors are important for the development of MI, including lifestyle, environmental factors, psychosocial factors and genetic factors [
• Zhan C.
• Shi M.
• Wu R.
• He H.
• Liu X.
• Shen B.
MIRKB: a myocardial infarction risk knowledge base.
]. Recent epidemiological studies have shown that short-exposure to particulate matter <2.5 μm (PM2.5) and NO2 led to an increase in the risk of AMI [
• Masoudipoor N.
• Frouzandeh S.
Modeling of the relationship between the environmental air pollution, clinical risk factors, and hospital mortality due to myocardial infarction in Isfahan, Iran.
,
• Yu Y.
• Yao S.
• Dong H.
Short-term effects of ambient air pollutants and myocardial infarction in Changzhou, China.
]. Also, O3 is an important risk factor in hospitalisation due to AMI [
• Masoudipoor N.
• Frouzandeh S.
Modeling of the relationship between the environmental air pollution, clinical risk factors, and hospital mortality due to myocardial infarction in Isfahan, Iran.
]. Studies show that short-term exposure to moderate to severe air pollution is associated with an increased risk of AMI [
• Wang X.
• Zhang X.
• Zhuang S.
• Luo Y.
• Kang S.
• Liu Y.
Short-term effects of air pollution on acute myocardial infarctions in Shanghai, China, 2013-2014.
].
Nevertheless, the association between air pollution and AMI is still debated. Some studies have shown a correlation [
• Braga A.
• Zanobetti A.
• Schwartz J.
The lag structure between particulate air pollution and respiratory and cardiovascular deaths in 10 US cities.
,
• Koken P.
• Piver W.
• Ye F.
• Elixhauser A.
• Olsen L.
• Portier C.
Temperature, air pollution, and hospitalization for cardiovascular diseases among elderly people in Denver.
]; other studies have found either no correlation [
• Barnett A.
• Williams G.
• Schwartz J.
• Best T.
• Neller A.
• Petroeschevsky A.
• et al.
The effects of air pollution on hospitalizations for cardiovascular disease in elderly people in Australian and New Zealand cities.
,
• Berglind N.
• Ljungman P.
• Möller J.
• Hallqvist J.
• Nyberg F.
• Rosenqvist M.
• et al.
Air pollution exposure—a trigger for myocardial infarction?.
], or only related to selected pollutants [
• Cendon S.
• Pereira L.
• Braga A.
• Conceição G.
• Cury Junior A.
• Romaldini H.
• et al.
Air pollution effects on myocardial infarction.
,
• Linn W.
• Szlachcic Y.
• Gong J.H.
• Kinney P.
• Berhane K.
Air pollution and daily hospital admissions in metropolitan Los Angeles.
]. Thus, the goal of the current study is to investigate the effects of air pollution (SO2, NO2, CO, O3, particulate matter between 25 μm–10 μm [PM10], PM2.5) on the number of AMI according to gender using the Zero-inflated Poisson (ZIP) Regression model in Hamadan, in western Iran.

## Material and Methods

The current study was approved by the Ethics Committee of Hamadan University of Medical Sciences with the code IR.UMSHA.REC.1399.459. To conduct research and evaluate its results, there are two steps:
• 1.
Is there a relationship between exposure and the risk of disease? To achieve this goal, the following assessments are carried out:
• a.
Examining group attributes: ecological research.
• b.
Examining individual characteristics: case-control and cohort.
• 2.
If there is an association, then we examine whether there is a possibility of causation.
The first step to determining the existence of a relationship is to use ecological research. This type of research can be a guide for studies that help clarify the causal relationship. If the results of ecological studies indicate the existence of a relationship, the next goal may be to design a study to investigate the relationship between the exposure and the desired response. The importance of these studies is that they can reveal relationships between individuals and society [
• Gordis L.
Epidemiology.
]. In ecological studies, the unit of analysis is populations or groups of people and measurements are population measures. In these studies, individual level exposure is not considered. One of the advantages of these studies is the generation of hypotheses for further investigation at the individual level.
Therefore, this study used an ecological design, and data collected from March 2016 to September 2020 in Hamadan were included. The AMI and pollution data were collected from Farshchian Cardiovascular Subspecialty Medical Center, and the Department of Environment in Hamadan, respectively. Farshchian cardiovascular medical research and training hospital is the only specialised cardiology hospital in Hamadan city, and where all cardiac patients in Hamadan city are referred. People who lived in Hamadan for at least 3 years until 2016 were considered for inclusion. In this study, 5-year daily data on AMI and air pollution were extracted in the form of a total population sampling. Therefore, it can be said that the obtained results can be generalised to the city of Hamadan.
The air pollutants included O3, CO, NO2, SO2, PM10, and PM2.5, and the 24-hour average of these pollutants was extracted from Hamadan Environmental Organization. Also, the information of patients who were admitted to Farshchian Hospital in Hamadan due to AMI between March 2016 and September 2020 was extracted daily. In fact, for each day, data included the 24-hour average of air pollutants and the number of hospitalisations due to AMI.

Hamadan province is located in the longitude 340°47 and E360°49 and the latitude 580°33 and N480°35 in the west of Iran. It has a 3,000-year history and a population of around 1.75 million people. Its climate is semi-arid, and the annual rainfall is around 300 mm. The rains begin from October and continue until May and reach their apex in November and February. The average monthly temperature ranges between -4C° and 25C°, and the average annual temperature is 11C° [
• Jalali M.
• Khanlari Z.
Environmental contamination of Zn, Cd, Ni, Cu, and Pb from industrial areas in Hamadan Province, western Iran.
].

## Statistical Analysis

In the current study, the intended response was the number of AMI recorded. As is evident in Figure 1, the majority of responses (number of AMIs) were zero among men (49.2%) and women (71.9%). Therefore, the Vuong test was implemented to compare the Zero-inflated Poisson and the Poisson Regression models. Based on the Vuong test, it was found that the Zero-inflated Poisson Model is more fit to the data (for both men and women, p<0.001). Ordinary models such as the Poisson regression result in inaccurate estimates when a large number of zeros is present. Thus, the Zero-inflated Poisson Regression was used to solve this problem and make more accurate estimates. For the analysis of data, we used the Zero-inflated Poisson (ZIP) model. In the ZIP model, the yis are responses and independent in the following manner [
• Hedeker D.
• Gibbons R.
Longitudinal data analysis.
,
• Lambert D.
Zero-Inflated poisson regression, with an application to defects in manufacturing.
]:
$yi∼0$ with probability πi
$yi∼poisson(λi)$ with probability (1-πi)
so that,
$p(yi=0)=πi+(1−πi)e−λi$

$p(yi=k)=(1−πi)e−λiλikk!K=1,2,…$

The independent variables related to the Poisson and logistics sections of the ZIP model can be either similar or dissimilar. If similar variables affect λi and πi, and if πi is written as a function of λi, then:
$logit(πi)=−τx'iβ$

$logit(λi)=x'iβ$

Coefficients provided by the zero-inflated Poisson model were transformed into incidence risk ratio (IRR), using the Poisson regression coefficient. The variables considered in the current study included day, month, the 24-hour average of pollutants including NO2, CO, SO2, O3, and particulates smaller than 2.5 and 10 microns (PM2.5, PM10). The time lag of the pollutants was used to investigate the effect of air pollution on the number of AMIs. In the current study, the effect of single-day (days 0 to 7) and cumulative lags (i.e., lag 0-7, lag 0-5, lag 0-2) were investigated. The time lag is defined as the time interval between exposure to air pollution and hospitalisation due to acute myocardial infarction. The time lag is considered because the effect of exposure to pollutants is observed not only on the same day but also days after exposure to pollution. For example, lag 2 means how much the effects of air pollution 2 days ago are on today's hospitalisation due to AMI. In the cumulative log, the effects of air pollution over the past few days on the number of hospitalisations due to AMI were investigated. For example, lag 0–2 means how much the effects of air pollution over the previous 2 days are on today's hospitalisation due to AMI.
Since the amount of pollutants had not been recorded for some days, the na.interpolation function in the R software used and the missing data were imputed using the linear interpolation argument. In addition, stratified analysis is applied to investigate the effect of air pollution on the subgroups divided according to gender. The analyses were conducted using the SPSS 26 (IBM Corp., Armonk, NY, USA) and R 4.0.2 (Foundation for Statistical Computing, Vienna, Austria) with Nonnest2, imputeTS, and Hmisc packages.

## Results

### Statistics and Information on AMI

From March 2016 to September 2020, 1,748 AMIs had been recorded in Hamadan. The number of AMIs recorded for men and women was 1,195 and 553, respectively. The average age (±SD) for patients is 67.16 (12.42) years. The average age (±SD) for men and women was 64.60 (±12.27) and 70.98 (±11.79) years, respectively. The descriptive statistics related to air pollutants have been presented in Table 1. The maximum number of recorded daily incidences of AMI in Farshchian Cardiovascular Subspecialty Medical Center was five for men, three for women (Figure 1), and seven for total. The total average of daily AMI was 1.06, being 0.73 for men and 0.34 for women (Figure 1).
Table 1Characteristics of air pollutants in Hamadan during March 2016 – September 2020.
VariablesMeanSEMinimumPercentileMaximum
25%Median75%
O3 ppb16.720.224.2011.5615.4219.3666.00
CO ppm13.980.210.2310.9516.1918.3053.59
NO2 ppb53.251.021.5815.1844.5484.70259.43
SO2 ppb12.630.250.169.3211.9217.04112.13
PM10 ug/m336.711.200.421.302.1770.26323.41
PM2.5 ug/m324.200.263.2916.9922.8629.9890.97
Abbreviations: PM, particulate matter; ppb, parts per billion; ppm, parts per million; O3, ozone; CO, carbon monoxide; NO2, nitrogen dioxide; SO2, sulfur dioxide.

### Statistics and Information on Air Pollution

During the 1,647 days that were investigated in the current study, the mean and median of CO were 13.98 ppm and 16.19 ppm, respectively. In addition, the mean and median obtained for nitrogen dioxide were 53.25 ppb and 44.54 ppb, respectively (Table 1). According to the air quality index in Hamadan, the presence of PM2.5, SO2, O3, and CO was below moderate levels. The air quality index in Hamadan showed that in terms of NO2 and PM10, the air was unhealthy for 20 and 26 days, respectively. The records of meteorology stations showed that according to NO2 and PM10, the air quality index of Hamadan was in the very unhealthy mode only for 2 and 3 days, respectively.
The change pattern of air pollutants from 2016 to 2020 is shown in Figure 2. A decreasing trend can be observed in terms of O3 (p-value=0.006), PM2.5 (p-value=0.0005), and PM10 (p-value=0.0005) from 2016 to 2020, and it was found to be significant using the Cochran-Armitage test. However, the trend of change for CO (p-value=0.0005), NO2 (p-value=0.0005), and SO2 (p-value=0.0005) was increasing during the same period, and it was found significant using the Cochran-Armitage test.

### Relationship Between AMI and Air Pollution

The results of fitting the Zero-inflated Poisson Regression model according to each gender and time lags were as follows:

#### Effects of air pollution on AMI in men

In the “count” component of the Zero-inflated Poisson model, the O3 and NO2 are significant positive effects on AMI among men (that is, as O3 and NO2 increased, so did the incidence of AMI). But, PM2.5, PM10, and SO2 are negative impacts on hospitalisation in men due to AMI. The results of the study showed that the impact of O3 on AMI in men occurs only 2 days before hospitalisation. A one-unit increase in O3 increased the possibility of AMI among men by 0.09%. The effect of increased NO2 on AMI in men appeared after 5 days. This result can be seen in single (5, 6, 7) and cumulative (0-7) time lags (Table 2). PM2.5, PM10, and SO2 have a negative impact on AMI in men. It means that a one-unit increase of PM2.5 or PM10 or SO2 decreased the possibility of AMI among men.
Table 2The fit of the model for AMI among men in single (0 to 7 days) and cumulative (0-2, 0-5, 0-7) lags.
VariablesCountZero-Inflation
BetaSEP-valueBetaSEP-value
Lag 0O3-0.0040.0050.412-0.5220.3380.123
CO-0.0110.0060.064-0.1610.1590.313
NO20.0010.0010.3400.4180.0210.042
Significant at the level of 0.05.
PM10-0.0010.0020.4980.0340.0140.016
Significant at the level of 0.05.
PM2.50.0020.0040.525-0.0630.0560.262
SO2-0.0060.0040.157-0.0300.0590.612
Month-0.0040.0110.678-0.3170.1630.052
Day-0.0830.0720.246
Lag 1O30.0020.0040.642-0.2840.1520.061
CO-0.0040.0050.3920.0350.0890.690
NO2-0.0000.0010.900-0.0140.0200.463
PM10-0.0020.0010.016
Significant at the level of 0.05.
-0.0170.0240.486
PM2.50.0050.0040.160-0.0190.0510.714
SO2-0.0050.0040.2200.0270.0290.342
Month0.0110.0100.2940.0940.1280.466
Day-0.3490.1180.003
Significant at the level of 0.01.
Lag 2O30.0090.0040.040
Significant at the level of 0.05.
0.2330.1360.086
CO0.0000.0050.9410.0150.0930.876
NO2-0.0010.0010.656-0.0110.0200.573
PM10-0.0020.0010.077-0.1180.0760.120
PM2.50.0010.0040.8480.0550.0540.302
SO20.0000.0000.9900.0810.0360.026
Significant at the level of 0.05.
Month0.0020.0110.0870.7000.4050.084
Day-0.2240.0920.015
Significant at the level of 0.05.
Lag 3O30.0030.0040.475-0.2400.1460.101
CO-0.0010.0050.9770.0110.0730.882
NO20.0010.0010.909-0.0060.0120.591
PM10-0.0010.0010.196-0.0100.0160.535
PM2.5-0.0050.0040.2300.0090.0410.825
SO2-0.0040.0040.3200.0130.0250.601
Month0.0180.0110.0850.1570.1490.293
Day-0.2460.0940.009
Significant at the level of 0.01.
Lag 4O30.0020.0040.629-0.2750.1380.046
Significant at the level of 0.05.
CO-0.0010.0050.908-0.0320.0750.669
NO20.0010.0010.5780.0020.0110.843
PM10-0.0010.0010.697-0.0090.0130.457
PM2.5-0.0060.0040.0940.0240.0390.544
SO2-0.0030.0040.510-0.0020.0260.939
Month0.0140.0100.1880.0770.1560.623
Day-0.3230.1430.024
Significant at the level of 0.05.
Lag 5O30.0050.0040.2370.5470.2730.045
Significant at the level of 0.05.
CO-0.0070.0050.146-0.8300.4490.064
NO20.0030.0010.005
Significant at the level of 0.01.
0.2250.1070.036
Significant at the level of 0.05.
PM100.0010.0010.1590.0970.0480.045
Significant at the level of 0.05.
PM2.5-0.0110.0040.005
Significant at the level of 0.01.
-0.6700.3400.049
Significant at the level of 0.05.
SO2-0.0060.0040.1080.0590.0690.395
Month0.0120.0100.2271.6060.8810.068
Day0.8530.3580.017
Significant at the level of 0.05.
Lag 6O30.0050.0040.244-0.1990.1550.200
CO-0.0030.0050.6080.0890.0710.209
NO20.0020.0010.050
Significant at the level of 0.05.
-0.0110.0150.472
PM10-0.0010.0010.334-0.0100.0150.516
PM2.5-0.0020.0040.6650.0160.0500.749
SO2-0.0090.0050.040
Significant at the level of 0.05.
0.0110.0460.820
Month0.0210.0110.0530.1830.2210.408
Day-0.2510.0970.009
Significant at the level of 0.01.
Lag 7O30.0050.0040.21311.6299.6180.227
CO-0.0090.0050.063-8.3688.2990.313
NO20.0040.0010.000
Significant at the level of 0.001.
4.5873.8220.230
PM100.0010.0010.1502.8702.3780.227
PM2.5-0.0100.0040.006
Significant at the level of 0.01.
-8.1056.6680.224
SO2-0.0080.0040.0512.7452.3240.237
Month0.0120.0100.22227.79123.4230.235
Day11.2269.4080.233
Lag 0-2O30.0010.0050.757-0.3790.1900.046
Significant at the level of 0.05.
CO-0.0040.0050.466-0.0320.0690.647
NO2-0.0010.0010.557-0.0030.0160.827
PM10-0.0030.0010.010
Significant at the level of 0.05.
-0.0020.0140.883
PM2.50.0040.0040.283-0.0160.0430.708
SO2-0.0040.0040.3190.0120.0350.746
Month0.0140.0100.1690.0840.1130.453
Day-0.0050.0040.184-0.2770.0810.0005
Significant at the level of 0.001.
Lag 0-5O30.0050.0050.3000.4070.1620.012
Significant at the level of 0.05.
CO-0.0050.0050.3470.5430.2550.034
Significant at the level of 0.05.
NO20.0020.0010.1190.0950.0390.015
Significant at the level of 0.05.
PM100.0010.0010.7820.2020.0850.018
Significant at the level of 0.05.
PM2.5-0.0090.0050.051-0.6060.2780.029
Significant at the level of 0.05.
SO2-0.0060.0050.2220.2700.1150.019
Significant at the level of 0.05.
Month0.0110.0100.2850.6760.4240.110
Day0.0050.0030.1250.6040.2320.009
Significant at the level of 0.01.
Lag 0-7O30.0060.0050.2310.3840.1420.007
Significant at the level of 0.01.
CO-0.0080.0060.1360.3720.1910.052
NO20.0040.0010.011
Significant at the level of 0.05.
0.1160.0540.032
Significant at the level of 0.05.
PM100.0010.0010.4310.1710.0730.020
Significant at the level of 0.05.
PM2.5-0.0120.0050.017
Significant at the level of 0.05.
-0.4980.2360.035
Significant at the level of 0.05.
SO2-0.0080.0050.1090.2950.1610.067
Month0.0130.0100.1990.0040.7690.996
Day0.0050.0030.1450.5310.1850.004
Significant at the level of 0.01.
Table 2 shows the fitting results of the zero-inflated Poisson regression model for men. The results of this model include two parts, zero-inflated and count. Each model is fitted in single lags (0,1,2,3,4,5,6,7) and cumulative lags 0-2, 0-5, 0-7. Lag 0 means how much the effect of exposure to pollution on the same day on the number of AMI. A time lag of 0-2 means how much the effect of exposure to air pollution from the previous 2 days before a day affects the number of AMI on that day.
Abbreviations: AMI, acute myocardial infarction; PM, particulate matter; CO, carbon monoxide; O3, ozone; SO2, sulfur dioxide; NO2, nitrogen dioxide.
Significant at the level of 0.05.
∗∗ Significant at the level of 0.01.
∗∗∗ Significant at the level of 0.001.

#### Effects of air pollution on AMI in women

For women, PM2.5 and O3 had “positive” effects on AMI (i.e., as PM2.5 and O3 increased, so did AMI) (Table 3). But, NO2 and PM10 had a significant negative impact on hospitalisation in women during different time lags. PM10 and NO2 have the greatest negative impact on hospitalisation in women due to AMI in lag time 2 day. A one-unit increase in PM10 or NO2 decreased the possibility of AMI women by about 0.06% (IRR=0.994). PM2.5 and O3 have the greatest impact on hospitalisation in women due to AMI in lag times 2 and 3.
Table 3The fit of the model for AMI among women in single (0 to 7 days) and cumulative (0-2, 0-5, 0-7) lags.
VariablesCountZero-Inflation
BetaSEP-valueBetaSEP-value
Lag 0O3-0.0000.0060.983-19.45726.9520.470
CO-0.0060.0070.410-107.615123.9580.358
NO2-0.0020.0020.3072.8424.0000.477
PM10-0.0020.0010.202-1.7522.8600.540
PM2.50.0060.0050.21716.76825.9930.519
SO20.0020.0050.6630.3830.8040.634
Month-0.0000.0140.970-5.96712.4220.631
Day-10.49916.7470.531
Lag 1O3-0.0050.0070.492-0.3440.1830.060
CO-0.0050.0080.501-0.0700.0880.422
NO2-0.0050.0020.007
Significant at the level of 0.01.
-0.1330.0680.052
PM10-0.0040.0020.009
Significant at the level of 0.01.
-0.0310.0260.226
PM2.50.0080.0050.1240.0830.0620.179
SO20.0030.0050.6370.0590.0450.192
Month-0.0110.0180.499-0.4540.2250.043
Significant at the level of 0.05.
Day-0.0770.0670.251
Lag 2O30.0030.0060.591-29.1946161.980.996
CO-0.0050.0070.470-28.3616948.8850.997
NO2-0.0060.0020.000
Significant at the level of 0.001.
-14.8643431.0880.997
PM10-0.0060.0020.000
Significant at the level of 0.001.
-8.4951770.5660.996
PM2.50.0150.0050.004
Significant at the level of 0.01.
25.1224938.6770.996
SO20.0010.0050.7693.353909.4000.997
Month0.0050.0140.71741.82710115.5500.997
Day3.76039.3280.924
Lag 3O30.0130.0060.027
Significant at the level of 0.05.
5.6184.8860.250
CO0.0040.0070.558-11.33710.8200.295
NO2-0.0050.0020.002
Significant at the level of 0.01.
-1.9101.8240.295
PM10-0.0040.0020.010
Significant at the level of 0.05.
-0.6270.5750.275
PM2.50.0110.0060.047
Significant at the level of 0.05.
7.3336.5340.262
SO20.0060.0050.165-8.3158.2310.312
Month0.0100.0150.48325.61423.3750.252
Day-7.4586.7570.270
Lag 4O30.0100.0070.1690.0660.0840.432
CO0.0010.0090.967-0.0860.0690.213
NO2-0.0040.0020.052-0.0230.0260.378
PM10-0.0030.0020.111-0.0140.0110.225
PM2.50.0080.0070.2550.1150.0740.124
SO20.0040.0050.397-0.0440.0620.471
Month0.0140.0180.4300.2830.3270.387
Day-0.1600.1970.418
Lag 5O30.0090.0070.2150.0430.0520.409
CO0.0010.0080.888-0.1160.0790.142
NO2-0.0050.0020.014
Significant at the level of 0.05.
-0.0070.0160.672
PM10-0.0050.0020.006
Significant at the level of 0.01.
-0.0240.0220.284
PM2.50.0120.0080.1520.0930.0460.042
Significant at the level of 0.05.
SO20.0010.0050.919-0.1190.1000.234
Month0.0140.0180.4310.2550.2400.289
Day-0.1780.1600.267
Lag 6O30.0100.0060.0806.02912.4170.627
CO0.0050.0070.4532.10110.9740.848
NO2-0.0050.0020.004
Significant at the level of 0.01.
-2.4435.3050.645
PM10-0.0040.0020.016
Significant at the level of 0.05.
-1.6902.5350.505
PM2.50.0040.0050.4542.3084.5240.610
SO20.0030.0050.509-6.40734.7310.854
Month0.0100.0140.45013.19620.4310.518
Day-26.04748.9690.595
Lag 7O30.0100.0060.0784.32085.3490.960
CO0.0020.0070.797-6.601125.5300.958
NO2-0.0030.0020.081-0.70635.0840.984
PM10-0.0030.0020.033
Significant at the level of 0.05.
-1.38435.7440.969
PM2.50.0100.0050.0746.616144.2650.963
SO2-0.0010.0050.872-3.96073.6730.957
Month0.0090.0150.54915.215304.4840.960
Day-14.730293.8640.960
Lag 0-2O30.0110.0060.0769.9729.1490.276
CO-0.0010.0070.901-29.33627.5420.287
NO2-0.0030.0020.0703.9183.5880.275
PM10-0.0040.0020.034
Significant at the level of 0.05.
1.0150.9830.302
PM2.50.0090.0060.1184.4004.1980.295
SO20.0010.0050.800-38.89236.4030.285
Month0.0100.0150.49839.19036.1950.279
Day0.0030.0050.526-9.0648.4330.282
Lag 0-5O30.0120.0060.0688.780100.9900.931
CO-0.0010.0080.960-31.758439.7550.942
NO2-0.0070.0020.002
Significant at the level of 0.01.
-6.48346.9450.890
PM10-0.0060.0020.001
Significant at the level of 0.001.
-8.05678.8990.919
PM2.50.0150.0070.029
Significant at the level of 0.05.
40.785383.3180.915
SO20.0030.0050.525-4.908328.5410.988
Month0.0100.0150.51932.183398.7500.936
Day0.0020.0050.735-19.156197.5070.923
Lag 0-7O30.0090.0060.15312.015135.7210.929
CO0.0010.0080.956-20.141264.6890.939
NO2-0.0070.0020.001
Significant at the level of 0.01.
-4.85735.9880.893
PM10-0.0060.0020.001
Significant at the level of 0.01.
-9.13993.5140.922
PM2.50.0110.0070.12927.535314.1040.930
SO20.0040.0060.495-3.70752.8560.944
Month0.0140.0150.36158.291755.4240.938
Day0.0010.0050.881-21.049133.6350.875
Abbreviations: AMI, acute myocardial infarction; PM, particulate matter; CO, carbon monoxide; O3, ozone; SO2, sulfur dioxide; NO2, nitrogen dioxide.
Significant at the level of 0.05.
∗∗ Significant at the level of 0.01.
∗∗∗ Significant at the level of 0.001.
The fit of the Poisson regression model with the zero-inflated regression model was compared using the Vuong test. Vuong's test showed the goodness of fit of a zero-inflated regression model. This test shows the validity and reliability of the model. Also, the information in Tables 2 and 3 has been fitted to check the validity of the model results. In this study, 5-year daily data on AMI and air pollution were extracted in the form of a total population sampling. Therefore, it can be said that the obtained results can be generalised to the city of Hamadan.

## Discussion

The goal of this study was to investigate the effects of air pollutants on the number of AMI among men and women. In the current study, only outdoor pollution was taken into consideration. The results of the study show that the effects of pollutants on the incidence of AMI are different between men and women.
Ozone has a positive effect on hospitalisations due to AMI in men and women. With increasing amount of ozone, the number of hospitalisations due to AMI increases. PM10 has an inverse relationship with hospitalisation due to AMI in both genders. In other words, the higher the PM10, the less the hospitalisation of patients. The effect of PM2.5 is positive in women and negative in men, and the effect of NO2 is negative in women and positive in men. SO2 has a significant negative effect on the hospitalisation of men only in time lag 6. The effect of CO is not significant in any of the time lags.
The impact of ozone on AMI was found to be significant in both genders. The amount of ozone within 3 days before hospitalisation was found to have a significant impact on women’s and men’s AMI. In the study by Ruidavets, the short-term exposure to O3 within 1-2 days before hospitalisation was found to have a significant impact on the incidence of AMI among middle-aged people without any record of cardiac illness [
• Ruidavets J.
• Cournot M.
• Giroux M.
• Meybeck M.
• Ferrières J.
Ozone air pollution is associated with acute myocardial infarction.
].
The source of tropospheric ozone is involving photochemical reactions oxides of nitrogen (NOx) which often contain NO2 and NO and volatile organic compounds (VOCs). In the absence or low concentration of VOCs or CO, ozone reaches steady-state concentration depending on the intensity of sunlight, air temperature, and the ratio of NO2 to NO concentration. In these conditions, a NO2 molecule is converted to an O3 molecule and a NO molecule [
• Zhang J.
• Wei Y.
• Fang Z.
Ozone pollution: a major health hazard worldwide.
]. The half-life of ozone at 20°C temperature is 3 days [
• Miller F.
• Silva C.
• Brandão T.
A review on ozone-based treatments for fruit and vegetables preservation.
]. In this study, the effect of ozone during the 3 days on AMI was effective in both genders and after 3 days its effect disappeared.
The effect of exposure to NO2 on the number of hospitalisation of men and women due to AMI was different. The effect of NO2 on men’s hospitalisation was positive and become evident in a few days. In a meta-analysis conducted by Mustafic, the impact of NO2 on myocardial infarction was found to be positive [
• Mustafić H.
• Jabre P.
• Caussin C.
• Escolano S.
• Tafflet M.
• et al.
Main air pollutants and myocardial infarction: a systematic review and meta-analysis.
]. Increasing the amount of NO2 increases the number of daily myocardial infarctions on men.
But, NO2 was negatively associated with daily AMI admissions in women. In the study conducted by Yongquan, increasing NO2 10 mg/m3 was found to have a significant impact on MI during lags 4, 0–5, and 0–6 [
• Yu Y.
• Yao S.
• Dong H.
Short-term effects of ambient air pollutants and myocardial infarction in Changzhou, China.
]. Findings in the current study and the study by Yongquan showed that NO2 has a protective impact on the number of daily myocardial infarctions. Increasing the amount of NO2 reduces the number of daily myocardial infarctions.
Increasing PM10 has shown to have a significant negative impact on the number of women and men’s AMI. This negative effect in women lasts up to a week, but in men, it exists only in the early days and then the effect disappears.
The findings of the study by Yongquan indicated the significant negative impact of PM10 on an AMI in 0–2 cumulative lag. In this study and the study by Yongquan, it was observed that increasing PM10 reduced the number of hospitalisation due to AMI.
Increasing PM2.5 within 2 and 3 days before hospitalisation showed a significant positive impact on women’s hospitalisation. The results of a meta-analysis conducted on 34 studies in 2012 showed the significant positive impact of PM2.5 on MI [
• Mustafić H.
• Jabre P.
• Caussin C.
• Escolano S.
• Tafflet M.
• et al.
Main air pollutants and myocardial infarction: a systematic review and meta-analysis.
]. In addition, the study by Wang showed a significant positive relationship between PM2.5 and MI [
• Wang X.
• Zhang X.
• Zhuang S.
• Luo Y.
• Kang S.
• Liu Y.
Short-term effects of air pollution on acute myocardial infarctions in Shanghai, China, 2013-2014.
]. In our study, increasing PM2.5 increases the rate of women’s hospitalisation due to AMI. But, PM2.5 was negatively associated with daily AMI admissions on men in lag 5 and lag 7 days. The study by Liao showed that between 1-year mean PM2.5 exposure and an increase in the risk of AMI was not observed significant association.
The significant negative impact of SO2 on the number of AMI in men was evident, but the impact of SO2 on women’s AMI was not significant. The estimation of risk in the study conducted by Yongquan indicated the protective effect of SO2 on MI in lags 4, 0–5, 0–6, and 0–7 [
• Yu Y.
• Yao S.
• Dong H.
Short-term effects of ambient air pollutants and myocardial infarction in Changzhou, China.
]. The relation between SO2 and MI was found to be protective in the study by Wang, which is not significant [
• Wang X.
• Zhang X.
• Zhuang S.
• Luo Y.
• Kang S.
• Liu Y.
Short-term effects of air pollution on acute myocardial infarctions in Shanghai, China, 2013-2014.
].
The impact of CO on men’s and women’s AMI was not significant. The results of the Bhaskaran study showed little evidence of the harmful effects of dioxide on AMI. Indeed, a protective effect of CO on AMI was observed over 1–72 hours.
Perhaps the protective impact of the pollutants on the rate of AMI is ignoring the confounding variables. They are variables that inverse or nullify the relation between AMI and air pollutants.
Several possible mechanisms have been proposed for the relation between pollutants and AMI. The first potential mechanism is inflammation [
• Ayres J.
Cardiovascular disease and air pollution: a report by the Committee on the Medical Effects of Air Pollutants.
,
• Pope Cr
• Hansen M.
• Long R.
• Nielsen K.
• Eatough D.
• Wilson W.
• et al.
Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects.
]. Studies have shown that exposure to air pollution increases the level of inflammatory markers such as the c-reactive protein [
• Bräuner E.
• Møller P.
• Barregard L.
• Dragsted L.
• Glasius M.
• Wåhlin P.
• et al.
Exposure to ambient concentrations of particulate air pollution does not influence vascular function or inflammatory pathways in young healthy individuals.
]. The second mechanism is the unnatural regulation of the cardiac autonomic system [
• Ayres J.
Cardiovascular disease and air pollution: a report by the Committee on the Medical Effects of Air Pollutants.
]. Several observational studies have reported the relation between high levels of air pollution and increased heart rate, and reduced variability of the heart rate [
• Pope Cr
• Hansen M.
• Long R.
• Nielsen K.
• Eatough D.
• Wilson W.
• et al.
Ambient particulate air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly subjects.
,
• Pope Cr
• Verrier R.
• Lovett E.
• Larson A.
• Raizenne M.
• Kanner R.
• et al.
Heart rate variability associated with particulate air pollution.
]. The next mechanism is the increased viscosity of blood due to air pollution [
• Peters A.
• Döring A.
• Wichmann H.
• Koenig W.
Increased plasma viscosity during an air pollution episode: a link to mortality?.
]. This can enhance the formation of thrombosis [
• Lucking A.
• Lundback M.
• Mills N.
• Faratian D.
• Barath S.
• Pourazar J.
• et al.
Diesel exhaust inhalation increases thrombus formation in man.
], accelerate atherosclerosis, and weaken the stability of atherosclerotic plaques [
• Mustafić H.
• Jabre P.
• Caussin C.
• Escolano S.
• Tafflet M.
• et al.
Main air pollutants and myocardial infarction: a systematic review and meta-analysis.
]. In the fourth mechanism, air pollutants may increase the rate of vasoconstriction in cases such as endothelin [
• Bouthillier L.
• Vincent R.
• Goegan P.
• Bjarnason S.
• Stewart M.
• et al.
Acute effects of inhaled urban particles and ozone: lung morphology, macrophage activity, and plasma endothelin-1.
]. In addition, mechanisms such as the direct induction of cardiac ischaemia are elicited by vasospasm or direct arrhythmogenesis [
• Liao D.
• Whitsel E.
• Duan Y.
• Lin H.
• Quibrera P.
• Smith R.
• et al.
Ambient particulate air pollution and ectopy--the environmental epidemiology of arrhythmogenesis in Women's Health Initiative Study, 1999-2004.
]. Of course, these findings have been obtained in studies conducted on women [
• Mustafić H.
• Jabre P.
• Caussin C.
• Escolano S.
• Tafflet M.
• et al.
Main air pollutants and myocardial infarction: a systematic review and meta-analysis.
]. All these findings obtained in experimental studies support the hypothesis that exposure to air pollution may increase the rate of myocardial infarction (MI) through several mechanisms [
• Mustafić H.
• Jabre P.
• Caussin C.
• Escolano S.
• Tafflet M.
• et al.
Main air pollutants and myocardial infarction: a systematic review and meta-analysis.
].
Based on the previous studies, increased rate of inflammatory markers and heartbeat, reduced heart variability, discharge of implanted cardioverter defibrillator, changes in homeostasis, inappropriate clot formation, defect in clot formation impact of air pollutants including SO2, NO2, O3, PM10, PM2.5 on increase the rate of ST-elevation myocardial infarction (STMI) [
• Shahrbaf M.
• Mahjoob M.
• Khahesh l.
• Barkhordari E.
• et al.
The role of air pollution on ST-elevation myocardial infarction: a narrative mini review.
]. Also, it seems that air pollutants, particularly PM2.5, enhance the activity of platelets and fibrinogens, reduce the rate of endogenous thrombolysis, and stimulate the formation of platelets [
• Soleimani A.
• Hosseini S.
• Jafari-Koshki T.
• Rahimi M.
• et al.
Correlation between air pollution and hospitalization due to myocardial infarction.
].
Limitations of the study included lack of access to information regarding the humidity and temperature of Hamadan, missing data for some days within the scope of the study, and lack of clinical information on the patients experiencing AMI. In ecological studies, comparisons and tests are not at the individual level, so the effect of some confounders cannot be controlled at the individual level. Of course, it is not necessary. Also, these types of studies can be a guide to conduct investigations that will help to clarify the causal relationship, but alone cannot determine the cause of the disease.

## Recommendations

The results of the study can help to better identify the effect of air pollutants on human cardiovascular health and to try to eliminate or reduce the sources of production of these pollutants. It is also suggested the relationship between air pollution and AMI should be investigated in different cities with different levels of pollution.

## Conclusion

Based on the obtained results in the current study, the impact of ozone on AMI was found to be significant and positive for both genders. The effects of PM2.5 in women and NO2 in men were found to be significant and positive, and this meant that they are among the risk factors for severe AMI in Hamadan. In the addition, the effects of SO2 and CO in men were found to be negative, while the impact of PM10 was found to be negative in both genders. This implied the protective effects of these pollutants against AMI.
The results of the study showed that if the analyses are based on gender, the responses to pollutants are different and hence the stratified analysis is important.

None.

## Disclosure Statement

No potential conflict of interest was reported by the authors.

## Acknowledgments

We would like to appreciate the Vice-Chancellor for Research and Technology of the Hamadan University of Medical Sciences for supporting this work.

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