Effect of air pollution on onset of acute coronary syndrome in susceptible subgroups

Qorbani, Mostafa; Yunesian, Masud; Fotouhi, Akbar; Zeraati, Hojjat; Sadeghian, Saeed

doi:10.26719/2012.18.6.550

Cited by 20 publications

(12 citation statements)

References 34 publications

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“…To evaluate the short-term effects of air pollution exposure, some studies took into account different lag patterns using single-day lags from lag 0 to 7 days. 6,9,22 Other studies have used both single-day lag and cumulative lag pattern. 7,8 Nevertheless, most of the included studies use a singleday lag pattern.…”

Section: Quantitative Data Synthesismentioning

confidence: 99%

“…4,6,7,9,10,12,21,22,[24][25][26] The air pollutant concentrations were selected from the monitoring station nearest to the individual's residence or were calculated using the average concentrations in a region where there were >1 monitoring stations. Four studies assessed the air pollution exposure using complex dispersion models or land use regression models that included various databases, including traffic, meteorology, roadway geometry, vehicle emission, air quality monitoring data, and land use.…”

Section: Assessment Of Air Pollution Exposure Measurementsmentioning

confidence: 99%

“…[6][7][8][9]21,22 Of these studies, 5 were case-crossover studies and one was time-series study. Two studies were conducted in China, 2 in Canada, and 2 in Brazil and Iran.…”

Section: Associations Between Short-term Exposure To Air Pollutants Amentioning

confidence: 99%

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Associations of Short-Term and Long-Term Exposure to Ambient Air Pollutants With Hypertension

et al. 2016

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C ardiovascular disease is the leading cause of death in the world. It is responsible for ≈30% of all deaths or ≈17.5 million people in 2012.1 As the major risk factor for cardiovascular disease, hypertension has been identified as the most important cause of disability and the leading risk factor for death globally and causes ≈16.5% of all deaths.2 The causes of hypertension are complex and are related to genetic factors, lifestyle, diet structure, and environmental factors, including air pollution.3 Since the 1990s, many epidemiological studies have investigated the associations between air pollution exposure and hypertension. However, the results remain controversial. Some studies have shown an association, 4-8 whereas other studies have found either no association or an association only for selected pollutants or limitation based on short-term or long-term exposure.9-12 These inconsistent and controversial results indicate the need to quantitatively synthesize and interpret the available evidence to provide more explicit information for policy decisions and clinical use. Meta-analysis is the most commonly used statistical technique to quantitatively synthesize results from ≥2 separate studies. 13 To our knowledge, there is only one meta-analysis that summarized the association between exposure to traffic-related air pollution and hypertension.14 However, this study included only cohort studies conducted in the Europe and mainly focused on the traffic-related air pollutants, which might limit the external validity of their findings. Therefore, more such meta-analyses are urgently needed.The previous published studies on hypertension and air pollution can be broadly divided into 2 categories: short-term and long-term studies. The former estimate the acute effects of air pollution exposure and mostly include time-series analyses over a few days. The latter evaluate the chronic effects of air pollution, such as cohort survival analyses over years of exposure. 15The short-term and long-term effects of air pollution may have Abstract-Hypertension is a major disease of burden worldwide. Previous studies have indicated that air pollution might be a risk factor for hypertension, but the results were controversial. To fill this gap, we performed a meta-analysis of epidemiological studies to investigate the associations of short-term and long-term exposure to ambient air pollutants with hypertension. We searched all of the studies published before September 1, 2015, on the associations of ozone (O 3 ), carbon monoxide (CO), nitrogen oxide (NO 2 and NO X ), sulfur dioxide (SO 2 ), and particulate matter (PM 10 and PM 2.5 ) with hypertension in the English electronic databases. A pooled odds ratio (OR) for hypertension in association with each 10 μg/m 3 increase in air pollutant was calculated by a random-effects model (for studies with significant heterogeneity) or a fixed-effect model (for studies without significant heterogeneity). A total of 17 studies examining the effects of short-term (n=6) and long-term exposure ...

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Section: Quantitative Data Synthesismentioning

confidence: 99%

Section: Assessment Of Air Pollution Exposure Measurementsmentioning

confidence: 99%

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Associations of Short-Term and Long-Term Exposure to Ambient Air Pollutants With Hypertension

et al. 2016

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“…Moreover, a significant correlation between CO and particulate matter of median aerometric diameter \10 lm (PM 10 ) concentrations with hospital admissions for cardiovascular disease was reported from Kermanshah Province in the western part of Iran (Khamutian et al 2014). In another study, a significant association was reported between the risks of acute coronary syndrome and elevated concentrations of CO in Tehran (Qorbani et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, conditional or mixed model logistic regressions, accounting for correlation between case and control records, are commonly used to model the relation between air pollution and meteorological data with adverse health effects (Chan and Ng 2011;Chang et al 2005;Chen et al 2010;Gleason et al 2014;Guo et al 2009;Hansen et al 2012;Kan and Chen 2003;Lee et al 2008;Li et al 2011;Lin et al 2013;Qorbani et al 2012;Tobías et al 2011;Villeneuve et al 2012;Wendt et al 2014;Wong et al 2014;Yang et al 2004). However, the artificial neural networks (ANNs) were also used for modeling in the present study.…”

Section: Introductionmentioning

confidence: 99%

Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Shakerkhatibi

Dianat

Jafarabadi

et al. 2015

Int. J. Environ. Sci. Technol.

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This study was conducted to evaluate the relationship between air pollutants (including nitrogen oxides [NO, NO 2 ]) and hospital admissions for cardiovascular and respiratory diseases. The study had a case-crossover design which was conducted in Tabriz, Iran. Daily hospital admissions and air quality data from March 2009 to March 2011 were analyzed using the artificial neural networks (ANNs) and conditional logistic regression modeling. The results showed significant associations between gaseous air pollutants including NO 2 , O 3 , and NO and hospital admissions for cardiovascular disease. Gaseous air pollutants of NO 2 , NO, and CO were associated with hospital admissions for chronic obstructive pulmonary disease, while PM 10 was associated with hospitalizations due to respiratory infections. PM 10 and O 3 were also associated with asthmatic hospital admissions. There was no significant association between SO 2 and studied health outcomes. Comparing the results of logistic regressions and ANNs confirmed the optimality of the ANNs for detection of the best predictors of hospital admissions caused by air pollution. Further research is required to investigate the effects of seasonal variations on air pollution-related health outcomes.

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Air Pollution and Health Outcomes in the Eastern Mediterranean Region: Knowledge and Research Gaps and Need

Khader¹

2021

Handbook of Healthcare in the Arab World

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Effect of air pollution on onset of acute coronary syndrome in susceptible subgroups

Cited by 20 publications

References 34 publications

Associations of Short-Term and Long-Term Exposure to Ambient Air Pollutants With Hypertension

Associations of Short-Term and Long-Term Exposure to Ambient Air Pollutants With Hypertension

Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

Air Pollution and Health Outcomes in the Eastern Mediterranean Region: Knowledge and Research Gaps and Need

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