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Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

Pearce JL, Waller LA, Mulholland JA, Sarnat SE, Strickland MJ, Chang HH, Tolbert PE - Environ Health (2015)

Bottom Line: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed).We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, 135 Cannon Street, Charleston, SC, 29422, United States. pearcejo@musc.edu.

ABSTRACT

Background: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

Objective: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

Methods: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

Results: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the .

Conclusions: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

No MeSH data available.


Related in: MedlinePlus

Results from sensitivity analysis of number of categories. Panel a presents a Principal Component Analysis biplot of the multipollutant data in our study. Vectors depict the primary modes of variation in the data (i.e., loading weights) and circles reflect estimated Rate Ratios (RR) using Poisson regression for SOM generated multipollutant profiles from a range of classifications (categories n = 2:20) found to be significantly associated with our outcome (p < 0.05). Panel b presents the distribution of rate ratio estimates for each SOM classification. Categories found significant (p < 0.05) are colored black, and to indicate estimate stability, size of the symbol is inversely proportional to the estimated standard error (SE). Dashed lines reflect quartiles for the distribution of RRs across all classifications
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Fig5: Results from sensitivity analysis of number of categories. Panel a presents a Principal Component Analysis biplot of the multipollutant data in our study. Vectors depict the primary modes of variation in the data (i.e., loading weights) and circles reflect estimated Rate Ratios (RR) using Poisson regression for SOM generated multipollutant profiles from a range of classifications (categories n = 2:20) found to be significantly associated with our outcome (p < 0.05). Panel b presents the distribution of rate ratio estimates for each SOM classification. Categories found significant (p < 0.05) are colored black, and to indicate estimate stability, size of the symbol is inversely proportional to the estimated standard error (SE). Dashed lines reflect quartiles for the distribution of RRs across all classifications

Mentions: Results from our epidemiologic analysis with multiple SOMs revealed that RRs for multipollutant day types were somewhat sensitive to variations of class number (Fig. 5); however, based on the properties of the classifications a certain degree of variability was expected. By overlaying the SOM-derived chemical profiles found to be significantly associated with pediatric asthma from all classifications derived using a range of categories from 2 to 20 we are able to see if identification of similar types of days resulting in associations with adverse health was captured by multiple categorizations of our data (Fig. 5a). We see a general clustering of profiles significantly associated with asthma morbidity, a feature that suggests that profiles capturing similar features in the air quality data resulted in similar associations with asthma morbidity.Fig. 5


Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications.

Pearce JL, Waller LA, Mulholland JA, Sarnat SE, Strickland MJ, Chang HH, Tolbert PE - Environ Health (2015)

Results from sensitivity analysis of number of categories. Panel a presents a Principal Component Analysis biplot of the multipollutant data in our study. Vectors depict the primary modes of variation in the data (i.e., loading weights) and circles reflect estimated Rate Ratios (RR) using Poisson regression for SOM generated multipollutant profiles from a range of classifications (categories n = 2:20) found to be significantly associated with our outcome (p < 0.05). Panel b presents the distribution of rate ratio estimates for each SOM classification. Categories found significant (p < 0.05) are colored black, and to indicate estimate stability, size of the symbol is inversely proportional to the estimated standard error (SE). Dashed lines reflect quartiles for the distribution of RRs across all classifications
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4477305&req=5

Fig5: Results from sensitivity analysis of number of categories. Panel a presents a Principal Component Analysis biplot of the multipollutant data in our study. Vectors depict the primary modes of variation in the data (i.e., loading weights) and circles reflect estimated Rate Ratios (RR) using Poisson regression for SOM generated multipollutant profiles from a range of classifications (categories n = 2:20) found to be significantly associated with our outcome (p < 0.05). Panel b presents the distribution of rate ratio estimates for each SOM classification. Categories found significant (p < 0.05) are colored black, and to indicate estimate stability, size of the symbol is inversely proportional to the estimated standard error (SE). Dashed lines reflect quartiles for the distribution of RRs across all classifications
Mentions: Results from our epidemiologic analysis with multiple SOMs revealed that RRs for multipollutant day types were somewhat sensitive to variations of class number (Fig. 5); however, based on the properties of the classifications a certain degree of variability was expected. By overlaying the SOM-derived chemical profiles found to be significantly associated with pediatric asthma from all classifications derived using a range of categories from 2 to 20 we are able to see if identification of similar types of days resulting in associations with adverse health was captured by multiple categorizations of our data (Fig. 5a). We see a general clustering of profiles significantly associated with asthma morbidity, a feature that suggests that profiles capturing similar features in the air quality data resulted in similar associations with asthma morbidity.Fig. 5

Bottom Line: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed).We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, 135 Cannon Street, Charleston, SC, 29422, United States. pearcejo@musc.edu.

ABSTRACT

Background: Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

Objective: Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

Methods: First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

Results: Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the .

Conclusions: We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

No MeSH data available.


Related in: MedlinePlus