Short-term effects of ambient air pollutants and myocardial infarction in Changzhou, China

Abstract

Ambient air pollution had been shown strongly associated with cardiovascular diseases. However, the association between air pollution and myocardial infarction (MI) is inconsistent. In the present study, we conducted a time-series study to investigate the association between air pollution and MI. Daily air pollutants, weather data, and MI data were collected from January 2015 to December 2016 in Changzhou, China. Generalized linear model (GLM) was used to assess the immediate effects of air pollutants (PM, PM, NO, SO, and O) on MI. We identified a total of 5545 cases for MI, and a 10-μg/m increment in concentrations of PM and PM was associated with respective increases of 1.636% (95% confidence interval [CI] 0.537-2.740%) and 0.805% (95% CI 0.037-1.574%) for daily MI with 2-day cumulative effects. The associations were more robust among males and in the warm season versus the cold one. No significant effect was found in SO, NO, or O. This study suggested that short-term exposure to PM and PM was associated with the increased MI risks. Our results might be useful for the primary prevention of MI exacerbated by air pollutants.

The contribution of PM2.5 to cardiovascular disease in China

“…In addition, the study suggested that more AMIs occurred during 6:00–18:00 when people were more likely to be outdoors and during November, December, and January when the pollution level was high. The study was consistent with that of Yu et al ( 2018 ) conducted in Guangzhou province, in which it was found that a 10-μg/m 3 increment in concentrations of PM 2.5 was associated with an increase of 1.636% (95% CI 0.537–2.740%) in AMI.…”

“…The study was consistent with that of Yu et al ( 2018 ) conducted in Guangzhou province, in which it was found that a 10-μg/m 3 increment in concentrations of PM 2.5 was associated with an increase of 1.636% (95% CI 0.537–2.740%) in AMI.…”

Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity

“…Thirteen studies evaluated the shape of the exposureresponse relationship between NO 2 and IHD morbidity by examining the association by quantile of NO 2 [43,48,50,110], plotting the association using a non-linear function of NO 2 [76,80,94,98,103,106,107,111], or testing the significance of the difference between linear and non-linear models [41]. Of these, eight studies found a linear association [41,43,50,80,94,98,106,107], in some instances only in subsets of the data by age [50] or season [80], while three found evidence of a threshold [76,103,110], although the available evidence is insufficient to identify a precise threshold value.…”

“…Of these, eight studies found a linear association [41,43,50,80,94,98,106,107], in some instances only in subsets of the data by age [50] or season [80], while three found evidence of a threshold [76,103,110], although the available evidence is insufficient to identify a precise threshold value. Two studies reported no association between NO 2 and MI risk, based on analysis by quantiles [48], and a plot using a non-linear function of NO 2 [111]. An additional casecrossover study not included in pooled estimates because it characterized exposure using fixed increment/ decrement thresholds rather than a linear term, found an apparently linear association between rapid changes in NO 2 concentration and odds of MI [115].…”

Evaluation of LJ1-01 Nighttime Light Imagery for Estimating Monthly PM2.5 Concentration: A Comparison With NPP-VIIRS Nighttime Light Data

“…Different statistical models have been proposed to establish quantitative relationships for AOD-PM2.5, e.g., linear mixed effects (LME) models [18], generalized linear regression (GLM) [19], generalized additive models (GAM) [20], and geographically weighted regression (GWR) models [10], the geographically and temporarily weighted regression model (GTWR) [17], the two step models [21]. A GWR model adopts a local regression model to embed the spatial position of data into the regression parameters, and it uses the local weighted least square method to estimate the point-by-point parameters.…”