Exposure to urban nitrogen dioxide pollution and the risk of myocardial infarction

Gražulevičienė, Regina; Marozienė, Ligita; Dulskienė, Virginija; Malinauskienė, Vilija; Azaraviciene, Ada; Laurinavičienė, Dovilė; Jankauskienė, Konstancija

doi:10.5271/sjweh.797

Cited by 43 publications

(28 citation statements)

References 26 publications

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“…5 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 A further seven studies looked at the longer-term effects of air pollution on MI risk (table 4). 29 30 31 32 33 34 35…”

Section: Resultsmentioning

confidence: 99%

“…Two case–control studies found detrimental effects of long-term exposure to traffic, both for a directly estimated traffic exposure based on (road length × traffic density) as measured near the place of residence (OR = 1.04, 1.02 to 1.07 per 794 vehicle-km),32 and for NO 2 exposure classified by residential district (OR = 1.43, 1.07 to 1.35 for regions with “high” versus “low” NO 2 levels) 34. The latter effect was reported to be stronger in older people (OR = 2.07, 1.28 to 3.35 for those aged 55–64 years).…”

Section: Resultsmentioning

confidence: 99%

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Effects of air pollution on the incidence of myocardial infarction

Bhaskaran

Hajat

Haines

et al. 2009

Heart

128

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Context: Short-term fluctuations in air pollution have been associated with changes in both overall and cardiovascular mortality. Objective: To consider the effects of air pollution on myocardial infarction (MI) risk by systematically reviewing studies looking at this specific outcome. Data sources: Medline, Embase and TOXNET publication databases, as well as reference lists and the websites of relevant public organisations. Study selection: Studies presenting original data with MI as a specific outcome and one or more of the following as an exposure of interest were included: particulate matter (PM), black carbon/black smoke, ozone, carbon monoxide, nitrogen oxides, sulphur dioxide and traffic exposure. Data extraction: The effects of each pollutant on risk of MI, including effect sizes and confidence intervals, were recorded where possible. Methodological details were also extracted including study population, location and setting, ascertainment of MI events, adjustment for potential confounders and consideration of lagged effects. Results: 26 studies were identified: 19 looked at the shortterm effects of pollution on a daily timescale; the remaining 7 at longer-term effects. A proportion of studies reported statistically significant detrimental effects of PM with diameter ,2.5 mm (3/5 studies, risk increase estimates ranging from 5 to 17% per 10 mg/m 3 increase), PM ,10 mm (3/10, 0.7-11% per 10 mg/m 3 ), CO (6/14, 2-4% per ppm), SO 2 (6/13, effect estimates on varied scales) and NO 2 (6/13, 1-9% per 10 ppb). Increasing ozone levels were associated with a reduction in MI risk in 3/12 studies. A number of differences in location, population and demographics and study methodology between studies were identified that might have affected results. Conclusion: There is some evidence that short-term fluctuations in air pollution affect the risk of MI. However, further studies are needed to clarify the nature of these effects and identify vulnerable populations and individuals.

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“…5 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 A further seven studies looked at the longer-term effects of air pollution on MI risk (table 4). 29 30 31 32 33 34 35…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Effects of air pollution on the incidence of myocardial infarction

Bhaskaran

Hajat

Haines

et al. 2009

Heart

128

View full text Add to dashboard Cite

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“…The association with exposure to traffic-related PM 10 was also not apparent 12. A second population-based case–control study conducted in male residents of Kaunas, Lithuania (AMI cases: n = 448) observed an OR for first AMI of 1.17 (95% CI 1.01 to 1.35) per intertertile change in annual average NO 2 measured at monitors located in each residential district of the city 31. Evidence of a positive association between short-term exposure to ambient air pollution and the occurrence of AMI is more consistent 32 33 34…”

Section: Discussionmentioning

confidence: 96%

Traffic particles and occurrence of acute myocardial infarction: a case–control analysis

Tonne

Yanosky²,

Gryparis

et al. 2009

Occup Environ Med

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Objectives:We modelled exposure to traffic particles using a latent variable approach and investigated whether long-term exposure to traffic particles is associated with an increase in the occurrence of acute myocardial infarction (AMI) using data from a population-based coronary disease registry.Methods:Cases of individually validated AMI were identified between 1995 and 2003 as part of the Worcester Heart Attack Study. Population controls were selected from Massachusetts, USA, resident lists. NO2 and PM2.5 filter absorbance were measured at 36 locations throughout the study area. The air pollution data were used to estimate exposure to traffic particles using a semiparametric latent variable regression model. Conditional logistic models were used to estimate the association between exposure to traffic particles and occurrence of AMI.Results:Modelled exposure to traffic particles was highest near the city of Worcester. Cases of AMI were more exposed to traffic and traffic particles compared to controls. An interquartile range increase in modelled traffic particles was associated with a 10% (95% CI 4% to 16%) increase in the odds of AMI. Accounting for spatial dependence at the census tract, but not block group, scale substantially attenuated this association.Conclusions:These results provide some support for an association between long-term exposure to traffic particles and risk of AMI. The results were sensitive to the scale selected for the analysis of spatial dependence, an issue that requires further investigation. The latent variable model captured variation in exposure, although on a relatively large spatial scale.

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“…The relationship between A and B is then examined for these extreme scoring persons (eg, using logistic regression analysis or an analysis of variance). For example, a study on the association between exposure to urban nitrogen dioxide pollution and the risk of myocardial infarction may contrast patients being treated for first-time myocardial infarction to controls reporting no chest pain complaints during a first interview; those without myocardial infarction who reported any chest pain complaints are thus excluded from the study (7). Similarly, one may examine the effects of environmental annoyance on performance only for people scoring at the extreme ends of an environmental annoyance scale (8).…”

Section: Study 1: the Extreme-groups Gamementioning

confidence: 99%