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Effects of particulate air pollution on cardiovascular health: a population health risk assessment.

Feng J, Yang W - PLoS ONE (2012)

Bottom Line: There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23-1.43) and 1.15 (1.07-1.22) times per 10 µg/m(3) increase in PM(2.5) and PM(10) respectively in the LCR analyses.After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations.These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity.

View Article: PubMed Central - PubMed

Affiliation: School of Community Health Sciences, University of Nevada, Reno, Nevada, United States of America.

ABSTRACT
Particulate matter (PM) air pollution is increasingly recognized as an important and modifiable risk factor for adverse health outcomes including cardiovascular disease (CVD). However, there are still gaps regarding large population risk assessment. Results from the nationwide Behavioral Risk Factor Surveillance System (BRFSS) were used along with air quality monitoring measurements to implement a systematic evaluation of PM-related CVD risks at the national and regional scales. CVD status and individual-level risk factors were collected from more than 500,000 BRFSS respondents across 2,231 contiguous U.S. counties for 2007 and 2009. Chronic exposures to PM pollutants were estimated with spatial modeling from measurement data. CVD outcomes attributable to PM pollutants were assessed by mixed-effects logistic regression and latent class regression (LCR), with adjustment for multicausality. There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23-1.43) and 1.15 (1.07-1.22) times per 10 µg/m(3) increase in PM(2.5) and PM(10) respectively in the LCR analyses. After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations. These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity.

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Related in: MedlinePlus

Estimated background PM10 and PM2.5 concentrations (µg/m3) across contiguous U.S. counties.A—PM10 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, exponential covariance, lag distance = 125 km, nugget = 0.037, range = 1,538 km, partial sill = 0.083; B—PM2.5 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, spherical covariance, lag distance = 170 km, nugget = 0.014, range = 1,687 km, partial sill = 0.066.
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pone-0033385-g002: Estimated background PM10 and PM2.5 concentrations (µg/m3) across contiguous U.S. counties.A—PM10 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, exponential covariance, lag distance = 125 km, nugget = 0.037, range = 1,538 km, partial sill = 0.083; B—PM2.5 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, spherical covariance, lag distance = 170 km, nugget = 0.014, range = 1,687 km, partial sill = 0.066.

Mentions: Exposure estimation results showed that the chosen kriging models did not extrapolate much beyond the range of measured concentrations; however, estimated values have a markedly lower standard deviation (Table 2). Such discrepancies possibly arose from monitor placement bias as they tend to lie in urban, more polluted areas, whereas the modeled concentrations utilized measurements from neighboring samples to provide full coverages across measurement units. As such, they may give “smoothed” spatial patterns of pollution levels and underestimate exposure gradients. Figure 2 shows the median background PM concentrations across contiguous U.S. counties for the selected time window, based on the optimal modeling methods (defined as those with the lowest RMSE values). Interpolated PM surfaces were similar for the preferred kriging models, as indicated by the high correlations (>0.95) between estimates assessed with the different models. This suggests that the background PM pollution landscape for the study region and time frame was unlikely to change greatly depending on the choice of the optimal spatial interpolation model. Figure S1 in the Supporting Information provides graphical comparisons between measured concentrations at the study sites and predicted values by the chosen kriging methods.


Effects of particulate air pollution on cardiovascular health: a population health risk assessment.

Feng J, Yang W - PLoS ONE (2012)

Estimated background PM10 and PM2.5 concentrations (µg/m3) across contiguous U.S. counties.A—PM10 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, exponential covariance, lag distance = 125 km, nugget = 0.037, range = 1,538 km, partial sill = 0.083; B—PM2.5 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, spherical covariance, lag distance = 170 km, nugget = 0.014, range = 1,687 km, partial sill = 0.066.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3303831&req=5

pone-0033385-g002: Estimated background PM10 and PM2.5 concentrations (µg/m3) across contiguous U.S. counties.A—PM10 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, exponential covariance, lag distance = 125 km, nugget = 0.037, range = 1,538 km, partial sill = 0.083; B—PM2.5 yearly median concentrations (averaging 1999–2005), assessed with ordinary kriging, spherical covariance, lag distance = 170 km, nugget = 0.014, range = 1,687 km, partial sill = 0.066.
Mentions: Exposure estimation results showed that the chosen kriging models did not extrapolate much beyond the range of measured concentrations; however, estimated values have a markedly lower standard deviation (Table 2). Such discrepancies possibly arose from monitor placement bias as they tend to lie in urban, more polluted areas, whereas the modeled concentrations utilized measurements from neighboring samples to provide full coverages across measurement units. As such, they may give “smoothed” spatial patterns of pollution levels and underestimate exposure gradients. Figure 2 shows the median background PM concentrations across contiguous U.S. counties for the selected time window, based on the optimal modeling methods (defined as those with the lowest RMSE values). Interpolated PM surfaces were similar for the preferred kriging models, as indicated by the high correlations (>0.95) between estimates assessed with the different models. This suggests that the background PM pollution landscape for the study region and time frame was unlikely to change greatly depending on the choice of the optimal spatial interpolation model. Figure S1 in the Supporting Information provides graphical comparisons between measured concentrations at the study sites and predicted values by the chosen kriging methods.

Bottom Line: There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23-1.43) and 1.15 (1.07-1.22) times per 10 µg/m(3) increase in PM(2.5) and PM(10) respectively in the LCR analyses.After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations.These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity.

View Article: PubMed Central - PubMed

Affiliation: School of Community Health Sciences, University of Nevada, Reno, Nevada, United States of America.

ABSTRACT
Particulate matter (PM) air pollution is increasingly recognized as an important and modifiable risk factor for adverse health outcomes including cardiovascular disease (CVD). However, there are still gaps regarding large population risk assessment. Results from the nationwide Behavioral Risk Factor Surveillance System (BRFSS) were used along with air quality monitoring measurements to implement a systematic evaluation of PM-related CVD risks at the national and regional scales. CVD status and individual-level risk factors were collected from more than 500,000 BRFSS respondents across 2,231 contiguous U.S. counties for 2007 and 2009. Chronic exposures to PM pollutants were estimated with spatial modeling from measurement data. CVD outcomes attributable to PM pollutants were assessed by mixed-effects logistic regression and latent class regression (LCR), with adjustment for multicausality. There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23-1.43) and 1.15 (1.07-1.22) times per 10 µg/m(3) increase in PM(2.5) and PM(10) respectively in the LCR analyses. After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations. These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity.

Show MeSH
Related in: MedlinePlus