Associations between urban air pollution and pediatric asthma control in El Paso, Texas

STOTEN-14206; No of Pages 10 Science of the Total Environment xxx (2013) xxx–xxx

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Associations between urban air pollution and pediatric asthma control in El Paso, Texas Jennifer E. Zora a, b, Stefanie Ebelt Sarnat a, Amit U. Raysoni a, Brent A. Johnson c, Wen-Whai Li d, Roby Greenwald a, Fernando Holguin e, Thomas H. Stock f, Jeremy A. Sarnat a,⁎ a

Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA Emory University School of Medicine, Atlanta, GA 30322, USA Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA d Department of Civil Engineering, University of Texas at El Paso, El Paso, TX 79968, USA e Division of Pulmonary, Allergy, and CCM, University of Pittsburgh Medical Center, Pittsburgh, PA 15224, USA f Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77225, USA b c

H I G H L I G H T S ► ► ► ► ►

We examined weekly associations between asthma control scores and air pollution. The Asthma Control Questionnaire (ACQ) was used as a metric of asthma control. We found positive, albeit non-significant associations for many pollutants. Significant associations existed among subjects taking inhaled corticosteroids. The ACQ can reflect biologically relevant changes in control from poor air quality.

a r t i c l e

i n f o

Article history: Received 8 February 2012 Received in revised form 19 November 2012 Accepted 19 November 2012 Available online xxxx Keywords: Air pollution Traffic Asthma control Children Asthma Control Questionnaire Panel study

a b s t r a c t Exposure to traffic-related pollutants poses a serious health threat to residents of major urban centers around the world. In El Paso, Texas, this problem is exacerbated by the region's arid weather, frequent temperature inversions, heavy border traffic, and an aged, poorly maintained vehicle fleet. The impact of exposure to traffic pollution, particularly on children with asthma, is poorly understood. Tracking the environmental health burden related to traffic pollution in El Paso is difficult, especially within school microenvironments, because of the lack of sensitive environmental health indicator data. The Asthma Control Questionnaire (ACQ) is a survey tool for the measurement of overall asthma control, yet has not previously been considered as an outcome in air pollution health effect research. We conducted a repeated measure panel study to examine weekly associations between ACQ scores and trafficand non-traffic air pollutants among asthmatic schoolchildren in El Paso. In the main one- and two-pollutant epidemiologic models, we found non-significant, albeit suggestive, positive associations between ACQ scores and respirable particulate matter (PM10), coarse particulate matter (PM10–2.5), fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), benzene, toluene, and ozone (O3). Notably, associations were stronger and significant for some subgroups, in particular among subjects taking daily inhaled corticosteroids. This pattern may indicate heightened immune system response in more severe asthmatics, those with worse asthma “control” and higher ACQ scores at baseline. If the ACQ is appropriately used in the context of air pollution studies, it could reflect clinically measurable and biologically relevant changes in lung function and asthma symptoms that result from poor air quality and may increase our understanding of how air pollution influences asthma exacerbation. © 2012 Published by Elsevier B.V.

1. Introduction In 2009, 7.1 million children within the United States (9.6% of individuals 0–17 years of age) were estimated to suffer from asthma ⁎ Corresponding author at: Department of Environmental Health, Rollins School of Public Health, 1518 Clifton Road, Room 2029, Atlanta, GA 30322, USA. Tel.: +1 404 712 9725 (work); fax: +1 404 727 8744. E-mail address: [email protected] (J.A. Sarnat).

(Akinbami et al., 2011), a chronic inflammatory disease of the airways with the potential for acute worsening of symptoms in response to environmental exposures. Short-term increases in traffic-related air pollutants such as fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC), benzene, and toluene have been associated with a range of health responses including increased adverse respiratory symptoms (Delfino et al., 2003a; Escamilla-Nunez et al., 2008; Gent et al., 2009; Mann et al., 2010; Ostro et al., 2001; Spira-Cohen et al., 2011), emergency room visits and hospital admissions (Barnett et al.,

0048-9697/$ – see front matter © 2012 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

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J.E. Zora et al. / Science of the Total Environment xxx (2013) xxx–xxx

2005; Friedman et al., 2001; Halonen et al., 2008; Hirshon et al., 2008; Strickland et al., 2010; Tolbert et al., 2000; White et al., 1994), and decreased lung function (Barraza-Villarreal et al., 2008; Dales et al., 2009; Delfino et al., 2003b, 2008; Liu et al., 2009). In addition, residential proximity to roadways and heavy traffic has been associated with decreased lung function (Holguin et al., 2007; Rosenlund et al., 2009) and increased hospital utilization (Chang et al., 2009; Wilhelm et al., 2008) among children with asthma. Among asthmatics, socioeconomic status (SES) (Gold and Wright, 2005; Goodman et al., 1998; Meng et al., 2008; Neidell, 2004; Ray et al., 1998), atopy (Mann et al., 2010), and inhaled corticosteroid (ICS) use (Delfino et al., 1998) have also been shown to modify air pollution-related outcomes. The Paso del Norte (PdN) region along the United States–Mexico border has several unique geographic and demographic features that make it an important region for the investigation of traffic pollution and asthma response among children in North America. It was estimated that 10 million passenger cars and over 700,000 trucks passed through the portal city of El Paso into Mexico in 2010 (U.S. Department of Transportation RaITA, Bureau of Transportation Statistics, 2010). In addition, elevated sunlight intensities, sustained temperature inversions, an older, poorly maintained vehicle fleet, and infrequent rainfall all contribute to poor urban air quality within the greater El Paso area (Li et al., 1999, 2001; Parks et al., 2002). Previous investigations (Hart et al., 1999), including two studies conducted by our group (Sarnat et al., 2012; Li et al., 2011), have linked short-term exposures to various air pollutants and respiratory health among asthmatic children in the PdN region. In our 2008 study (Sarnat et al., 2012) of 58 asthmatic schoolchildren living in El Paso and Ciudad Juarez, Mexico, we reported interquartile range (IQR) increases in PM2.5, PM10, PM10–2.5, NO2, and BC to be positively and significantly associated with exhaled nitric oxide (eNO), a biomarker of pulmonary inflammation in asthmatics. In a follow-up study conducted in 2010 (Li et al., 2011), we examined associations between pollution and a broader range of potential biological response, including inflammation, medication usage, symptom reporting and lung function in 38 asthmatic children from two El Paso elementary schools. As with our 2008 study, we found associations between levels of several traffic-related outdoor air pollutants and eNO, including particulate BC and gas phase benzene. Outdoor benzene levels, along with other volatile organic compounds (VOCs) were also negatively and significantly associated with decrements in lung function, expressed as forced expiratory volume in 1 s (FEV1). We did not observe any associations between the pollutants and self-reported symptoms in either of the studies. Although previous studies have implicated components of traffic pollution with asthma response in children, finding clear associations within the epidemiological results has been challenging given the diverse range of pollutants and biological responses examined. Most studies of air pollution and asthma typically examine associations in a univariate or two-pollutant setting as predictors of a single, specific response class (i.e., either lung function or symptom reporting, for example). Examining aggregate response across a range of health endpoints, however, may offer novel insight into potential causal agents and asthma etiology. In our 2010 study, we administered the Asthma Control Questionnaire (ACQ) to each of our subjects on weekly basis for a 13 week study period (Li et al., 2011). The ACQ is a seven question survey tool intended for use in clinical settings to assess differences in asthma status related to treatment therapy, efficacy of treatment, or response to treatment. It was initially developed for adults (Juniper et al., 1999), but has been subsequently validated for use among children 6–16 years of age (Juniper et al., 2010). ACQ questions score respiratory symptoms (4 questions), activity limitation (1 question), use of short-acting beta agonist (1 question) over the previous week, as well as the percent predicted FEV1 at the time of the questionnaire based on age, gender, race, and height. Higher individual ACQ scores are thought to represent reduced asthma

“control” that may warrant increased short-term or long-term medical intervention. Asthma control as a health outcome, assessed through the ACQ, represents a health metric that, to our knowledge has not been previously used in air pollution studies either in children or adults. Moreover, modeling ACQ as an outcome provides a unique means of assessing a range of acute asthmatic responses, including symptom occurrence, medication usage, and lung function simultaneously instead of separately as has been done previously. Additionally, the ACQ is a clinically relevant tool that allows for quantification of asthma status in a consistent and repeatable manner. In this analysis, we examine associations between ACQ scores and weekly traffic- and non-traffic related air pollutants among asthmatic children in El Paso, and address the potential implications of these findings for understanding pollution-derived asthma risk. 2. Material and methods 2.1. Study overview This study was conducted in El Paso, Texas from March to June, 2010 at two elementary schools (Li et al., 2011), both of which participated in the 2008 study (Sarnat et al., 2012). School 1 was located in a “high traffic” area within 300 ft of principal arterial or high-service, capacity-controlled access roadways, with heavy truck traffic. School 2 was located in a “low traffic” area adjacent to local surface streets exclusively. The study consisted of weekly repeated measurements of health outcomes, air pollution, and meteorology over a 13 week study period, which spanned from the spring to early summertime. Baseline data (related to asthma medication use, symptoms, activity limitation, prior emergency room visits and hospital admissions) was collected from parents on March 5, 2010. Outdoor pollutant measurements and meteorological data were aggregated for the weeks leading up to the Fridays March 12, 2010 to June 4, 2010 (data for several pollutants were not collected during the week of spring break for both schools and during the final collection week). Weekly health outcome sampling occurred on these Fridays at each school during that time period (data not collected during the week of spring break for both schools). The protocol for this study was approved by the International Review Board of Emory University prior to subject recruitment and data collection. 2.2. Subject recruitment At each school, children were recruited to participate in the study through school nurses. A legal guardian for each child provided written consent; children greater than or equal to 11 years of age provided written assent, while younger children provided verbal assent. Consent and assent forms were provided in both English and Spanish. Eligibility criteria included age between 6 and 12, a physician diagnosis of asthma, no other lung disease or major illness, a non-smoking household, and residence proximal to their school. Among the 38 subjects who completed the study protocol, one subject from School 1 was excluded from the current analysis due to missing information related to ACQ scoring, and one subject from School 2 was excluded due to a lack of data regarding current asthma medication use. The current analysis is based on19 children who attended School 1 and 17 children who attended School 2. 2.3. Exposure and meteorological measurements Air pollutants, including size resolved particles, gases, and speciated volatile organic compounds were measured for 96-hour Mondays– Fridays outdoors and indoors at each school as a metric of weekly pollutant exposures preceding the Friday survey date each week. Concentrations in the PdN region have been shown to vary by school

Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

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(Raysoni et al., 2011), and outdoor and indoor measurements of these pollutants are thought to correlate fairly well with corresponding personal exposures (Janssen et al., 1997). The current analysis focuses only on outdoor measurements of select pollutants, including PM10–2.5, PM2.5, PM10, BC, NO2, benzene, and toluene. BC, NO2, benzene and toluene were specifically chosen as indicators of motor vehicle-related emissions. Samplers for these measured particles and gases were located on the roof or in a fenced area next to each school and measured 96-hour integrated pollutant concentrations each week. Monitoring equipment was placed between 8:30 and 11:30 on Monday mornings and taken down at the same time on Friday mornings to coincide with Friday health measurements. PM2.5 and PM10–2.5 mass were measured gravimetrically using Harvard cascade impactors (Demokritou et al., 2002), with a single 37 mm Teflon filter (Pall Life Sciences, Ann Arbor, MI) and a polyurethane foam substrate, respectively. All gravimetric analyses of particle filters were conducted at the University of Texas at El Paso (UTEP) (Raysoni et al., 2011). The black carbon loading on particle filters was estimated by measuring surface reflectance using a Digital Smoke Stain Reflectometer Model EEL 43D (Diffusion Systems Ltd., London, UK) (Raysoni et al., 2011). VOC samples were measured using passive badge samplers (3M 3500 Organic Vapor Monitor, 3M, St. Paul, MN). Gas chromatography/mass spectrometry (GC/MS) analysis was performed using a HP 6890 Series GC with a 5975B mass selective detector (Agilent Technologies, Santa Clara, CA). NO2 and SO2 samples were obtained using Ogawa passive samplers (Ogawa & Co, Pompano Beach, FL). All sampler extraction and analytical procedures have been described in detail elsewhere (Li et al., 2011). NO2 measurements were obtained using passive badge samplers with a single cellulose filter coated with triethanolamine (Ogawa and Company, Pompano Beach, FLA). Samplers were prepared at the Harvard School of Public Health (Boston, MA) and were placed in the field from Monday through Friday for a 96-hour sampling session. Loaded samplers were stored at 4 °C at the UTEP Air Quality Laboratory prior to shipment back to the Harvard School of Public Health analysis. NO2 was extracted and quantified using ion chromatography analysis (Ogawa, 1997) in parts per billion (ppb) by volume. Ozone (O3) (in ppb), temperature (in °F), and relative humidity (%) were collected from Continuous Air Monitoring Station (CAMS)-41 in El Paso, operated by the Texas Commission on Environmental Quality. Hourly values were aggregated to produce 96-hour averages that paralleled the Monday–Friday pollutant measurements. We chose the CAMS-41 site a priori for these data due to its central location between School 1 and School 2. 2.4. Health outcome measurement Health outcomes were collected at the school sites consistent with methods described in other studies (Kim et al., 2004; Zhao et al., 2008) as well as in our 2008 study (Sarnat et al., 2012). At the start of the study, a baseline questionnaire was administered to each child and guardian to obtain information on health status (including current allergies), insurance status, baseline medication usage, and home characteristics. Each Friday during the study (except for spring break), children underwent pulmonary function testing and reported symptoms and use of asthma medications. 2.4.1. Lung function testing The percent predicted lung function for each subject was assessed for use in the 7th question of the ACQ. Spirometers (EasyOne; NDD Medical Technologies, Andover, MA) were used with disposable spirettes through which the subjects exhaled and inhaled. English– Spanish bilingual coaches were available as needed. For the ACQ, lung function was estimated based on FEV1. The best effort was determined using maximum FEV1, and the percent predicted FEV1 was determined using age, height, gender, and race as suggested by the

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American Thoracic Society based on the National Health and Nutrition Examination Survey III (NHANES III) data (Hankinson et al., 1999; Miller et al., 2005). 2.4.2. Asthma Control Questionnaires In addition to lung function measurements, subjects answered questions about symptoms and medication use per the first 6 questions of the ACQ (Juniper et al., 2006, 1999). English–Spanish interpretation was available as needed. ACQ scores for each subject were calculated as the mean of the sum of the individual ACQ question scores, which included 5 questions relating to respiratory symptoms secondary to asthma (nighttime awakenings, morning symptoms, activity limitation, shortness of breath, wheezing), 1 question regarding the use of short-acting bronchodilators (SABA), and 1 question incorporating a scoring of the FEV1 percent predicted value. Individual question scores on the ACQ are scaled from 0 to 6 based on severity of symptoms, and the overall score is the mean of the 7 questions. Therefore, the minimum overall ACQ score is 0.0 for well-controlled asthma and the maximum score is 6.0 for poorly-controlled asthma. The lowest clinically relevant score for the ACQ among asthmatic children has been shown to be 0.53 ± 0.45 (Juniper et al., 2010). 2.5. Statistical analysis Descriptive statistics and Spearman's correlation analyses (rs) were conducted to assess central tendency and linear associations among the variable distributions. Linear mixed effects models (PROC MIXED, SAS v9.3) were used to examine associations between weekly asthma control scores and preceding 96-hour average air pollution levels. In these analyses, pollutants were modeled as fixed effects, subjects as random effects, and within-subject errors were modeled using an exchangeable correlation structure. Covariates in the models included 96-hour average relative humidity and temperature as linear terms, and school as a categorical variable (Sarnat et al., 2012; Spira-Cohen et al., 2011). We considered the robustness of our results by using transformation models that allow for more flexibility for non-normal error distributions. To assess potential effect modification of the air pollution–asthma control associations, analyses were stratified by allergic phenotype (defined as allergic to aeroallergens or food: yes/no), use of government-sponsored insurance status [defined as Medicaid coverage at some point in the past year (yes/no)] as a marker of socioeconomic status, current daily use of inhaled corticosteroids (yes/no), use of oral corticosteroids during the previous 3 months (yes/no), weight status (obese versus non-obese), and school. Formal tests of interaction were also conducted using product terms with air pollution. We ran two-pollutant modeling of the PM metrics with one of NO2, benzene, toluene, or O3 to assess for potential co-pollutant confounding. To compare the magnitude of effect across different pollutants, effect estimates were scaled to IQR increases in pollutant concentrations determined from the distribution of all measurements. Sensitivity analysis was also performed to assess the effects of individual subjects on overall trends using Cook's D statistics. 3. Results Table 1 presents the baseline characteristics of the study subjects, which ranged from 6 to 11 years of age with an overall mean age of 9.3 [standard deviation (SD) +/− 1.5] years. The mean age of subjects at School 1 [8.8 (+/− 1.7) years] was significantly lower than that at School 2 [9.9 (+/− 1.1) years; p-value = 0.02 for School 1 versus School 2]. Overall, 33.3% of the subjects were females; 66.6% were males. Of those whose parents reported their race, 28 (75%) of individuals were of Hispanic ethnicity; 1 (2.8%) was African-American; and 5 (19.4%) were Caucasian race/ethnicity. At School 1, 9 subjects (47.4%)

Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

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Table 1 Subject demographic and baseline characteristics. Characteristic

Overall (n = 36)

School 1 (n = 19)

School 2 (n = 17)

p-Value difference b/t schools

Age (years as a whole number)a Height (in.)a Genderb Female Male Raceb Mexican Other Hispanic African American/Black American Caucasian Mixed race(s) Weight category b,c Healthy weight Overweight Obese Parent with asthmab Sibling with asthmab Caretaker (parent or sitter) smokingb Medicaid coverage (at some point past year)b Allergic phenotype (aeroallergens or food)b Inhaled corticosteroid use (current daily)b SABA use (for symptoms or daily)b Asthma control (over past 3 months)b Asthma symptoms with exercise Asthma symptoms at night Oral steroid use Emergency Room visit (for asthma)

9.33 ± 1.53 55.83 ± 4.29

8.79 ± 1.65 55.00 ± 4.77

9.94 ± 1.14 55.76 ± 3.58

0.02 0.22

12 (33.3) 24 (66.7)

7 (36.8) 12 (63.2)

5 (29.4) 12 (70.6)

0.64

22 (61.1) 6 (16.7) 1 (2.8) 6 (16.7) 1 (2.8)

12 (63.2) 6 (31.6) 0 (0.00) 1(5.3) 0 (0.00)

10 (58.8) 0 (0.00) 1(5.9) 5 (29.4) 1(5.9)

0.24

22 (61.1) 5 (13.9) 9 (25.0) 13 (36.1) 10 (27.8) 5 (13.9) 18 (50.0) 17 (47.2) 12 (33.3) 24 (66.7)

9 (47.4) 2 (10.5) 8 (42.1) 9 (47.4) 6 (31.6) 2 (10.5) 14 (73.7) 8 (42.1) 7 (36.8) 14 (73.7)

13 (76.5) 3 (17.6) 1 (5.9) 4 (23.5) 4 (23.5) 3 (17.7) 4 (23.5) 9 (52.9) 5 (29.4) 10 (58.8)

0.02 0.14 0.60 0.54 0.003 0.54 0.64 0.35

22 (61.1) 17 (47.2) 16 (44.4) 11 (30.6)

12 (63.2) 9 (47.4) 8 (42.1) 7 (36.8)

10 (58.8) 8 (47.2) 8 (47.2) 4 (25.5)

0.79 0.99 0.77 0.39

Abbreviations: SABA = short-acting beta-agonist. a Mean ± standard deviation. b Number of subjects (%). c Weight classifications are determined by Centers for Disease Control and Prevention guidelines for pediatric weights (“healthy” weight greater than 5th and less than 85th percentile of body mass index for age, “overweight” is greater than the 85th and less than the 95th percentile of body mass index for age, and “obese” is greater than or equal to the 95th percentile of body mass index for age).

had a healthy weight, 2 (10.5%) were overweight, and 8 (42.1%) were obese per the Centers for Disease Control and Prevention weight categories for children and adolescents. At School 2, 13 (76.5%) subjects had a healthy weight, 3 (17.6) were overweight, and 1 (5.9%) was obese per the CDC guidelines. Overall, the distribution by weight category differed significantly by school, with a greater number of obese children at School 1 (p-value = 0.02 for School 1 versus School 2). Additional baseline factors related to SES and asthmatic type included government sponsored insurance (during the past year) for 14 subjects (73.7%) at School 1 compared to 4 subjects (23.5%) at School 2 (p-value = 0.003). From School 1, 8 subjects (42.1%) had allergies to either food or aeroallergens, and from School 2, 9 subjects (52.9%) suffered from allergies per parental report. Inhaled corticosteroids as a marker of more severe asthma were used by 7 (36.8%) from School 1 and 5 (29.4%) from School 2. Oral steroids were used during the past 3 months by 8 subjects from both School 1 and School 2 (42.1 and 47.2% respectively). From School 1, 7 (36.8%) had gone to the ER for asthma during the past 3 months, while 4 (25.5%) from School 2 had utilized the ER during the same time period. 3.1. Pollutant concentrations Air pollution and meteorological data are presented in Table 2 by measurement location and overall (calculated as the average of the two school-based measurements each week). Overall PM10–2.5 levels ranged between 2.6 and 41.2 μg/m3 (IQR=14.8) and PM2.5 ranged between 4.0 and 24.9 μg/m3 (IQR=5.7). Levels of NO2 ranged from 1.2 to 16.2 micrograms per cubic meter of air (μg/m3, IQR=5.5). Benzene levels overall ranged from 0.2 to 2.4 μg/m3 (IQR=0.8), and toluene levels overall ranged from 0.2 to 8.2 μg/m3 (IQR=2.4). Levels of O3 ranged from 20.1 to 39.6 ppb with an IQR of 8.7. Temperatures over the course of the study ranged from 49.1 to 82.9 °F with an IQR of 15.9. Relative humidity ranged from 12.3 to 42.7% with an IQR of 21.5. PM values were

consistently higher at School 1 compared to School 2. Levels of NO2, benzene, and toluene were also substantially higher at School 1 compared to School 2. This difference in traffic-related pollutant concentrations between the schools was expected given that School 1 is located closer to major highways, with higher traffic density compared to School 2. At School 1, the concentrations of traffic-related pollutants BC, NO2, benzene, and toluene were all significantly and positively inter-correlated (rs ≥ 0.51) (Table 3). Patterns of correlation among the pollutants were different at School 2, in that correlations among PM10, PM10–2.5, and PM2.5 were all high (rs > 0.87), and correlations among the traffic-related pollutants were weaker (r b 0.53). At both schools, the PM pollutants (PM10, PM10–2.5, PM2.5) showed weak negative correlations with the traffic-related pollutants. Moderate to strong correlations in pollutant concentrations between the schools were found for all pollutants, except for PM10–25 (rs = 0.09); strongest correlations were found for PM2.5 (rs = 0.89). 3.2. ACQ scores A total of 386 Asthma Control Questionnaires were completed during the study, with 7 to 12 repeated measures per subject (average of 10.7 ACQ scores per subject). Overall, the mean ACQ score for the study subjects was 0.8 (+/−0.6) with a minimum score of 0.0 and a maximum score of 3.3 (Table 4). For School 1 (N=210), the mean ACQ score (0.9 (+/−0.7)) was significantly higher than that at School 2 (N=176; mean ACQ score=0.6 (+/−0.5); pb 0.0001). Subject-specific ACQ summary statistics are listed in the Supplementary material (Table S1). 3.3. Epidemiologic associations For the single-pollutant models, associations between the ACQ scores and 96-hour pollutant concentrations per IQR increase were all positive (albeit non-significant) (Table 5). With the exception of

Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

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Table 2 Air pollution and meteorology summary statistics overall and by measurement location.a Pollutant (unit)

N

Mean

SD

Minimum

Maximum

IQR

PM10 (μg/m3) School 1 School 2 PM10–2.5 (μg/m3) School 1 School 2 PM2.5 (μg/m3) School 1 School 2 BC (μg/m3) School 1 School 2 NO2 (ppb) School 1 School 2 Benzene (μg/m3) School 1 School 2 Toluene (μg/m3) School 1 School 2 O3 (ppb) CAMS-41 Temperature (°C) CAMS-41b Relative humidity (%) CAMS-41b

21 11 10 21 11 10 21 11 10 21 11 10 26 13 13 26 13 13 26 13 13

28.2 35.2 20.4 16.2 21.4 10.5 11.9 13.8 9.9 0.3 0.5 0.2 6.4 9.3 3.4 1.0 1.5 0.5 2.6 4.1 1.1

15.2 12.1 14.9 10.8 8.0 10.9 4.9 4.4 4.8 0.3 0.3 0.2 3.9 3.2 1.7 0.6 0.5 0.2 2.0 1.8 0.5

6.6 16.7 6.6 2.6 9.6 2.6 4.0 7.1 4.0 0.0 0.2 0.0 1.2 5.6 1.2 0.2 0.8 0.2 0.2 1.8 0.2

66.1 66.1 59.3 41.2 41.2 40.8 24.9 24.9 18.5 0.9 0.9 0.7 16.2 16.2 7.5 2.4 2.4 0.8 8.2 8.2 2.4

19.7 9.6 11.7 14.8 7.2 5.0 5.7 2.0 6.7 0.4 0.5 0.2 5.5 2.1 1.6 0.8 0.7 0.3 2.4 1.9 0.6

12

31.7

6.0

20.1

39.6

8.7

12

68.0

10.5

49.1

82.6

15.9

12

25.2

11.2

12.3

42.7

21.5

Abbreviations: N = number; SD = standard deviation; IQR = interquartile range; μg/m3 = microgram/meter3; ppb = parts per billion; μg/m3 = micrograms per cubic meter of air; PM2.5 = particulate matter with aerodynamic diameter less than 2.5 μm (fine PM); PM10–2.5 = particulate matter with aerodynamic diameter between 2.5 and 10 μm (coarse PM); PM10 = particulate matter with aerodynamic diameter less than 10 μm; NO2 = nitrogen dioxide; BC = black carbon; O3 = ozone; C = Celcius. a Pollutant, temperature, and humidity measurements are 96-hour averages from Monday through Friday. b Measured at central monitoring station, CAMS-41 = continuous air monitoring station #41.

NO2 and O3, most models indicated an approximate 0.03 unit increase in ACQ score for each IQR increase in pollutant concentration. Particularly for the PM pollutants, results for each pollutant were similar whether examined in a single- or a two-pollutant setting, suggesting that co-pollutant confounding did not drive the main associations for these pollutants. (Results of two-pollutant models are included for comparison as Supplementary material). Associations between ACQ score and selected pollutants were also examined by subgroups of allergic (vs. non-allergic), governmentsponsored insurance (i.e., Medicaid over the past year versus not), as

an indicator of socio-economic status, daily inhaled corticosteroids (vs. not), recent oral steroid (in the past 3 months vs. none) (Table 6). While none of these factors were significant effect modifiers in formal tests of interaction, some patterns of associations across strata were notable. For the traffic-related pollutants, associations were stronger in allergic subjects than non-allergic subjects, with marginally-significant associations for allergic subjects with BC and NO2. Patterns of association by daily ICS use were mixed among the pollutants, but strong associations in ICS users were found for the traffic-related VOCs, benzene (effect estimate = 0.18 per IQR increase, p-value = 0.01) and toluene

Table 3 Spearman correlations among school outdoor and ambient environmental concentrations (N = 10–13).a School 1

School 1

School 2

CAMS-41

PM10 PM10–2.5 PM2.5 BC NO2 Benz Tol PM10 PM10–2.5 PM2.5 BC NO2 Benz Tol O3 T RH

School 2

CAMS-41

PM10

PM10–2.5

PM2.5

BC

NO2

Benz

Tol

PM10

PM10–2.5

PM2.5

BC

NO2

Benz

Tol

O3

T

RH

1.00 0.96 0.68 −0.25 −0.12 −0.22 −0.04 0.74 0.26 0.52 −0.41 −0.22 −0.11 0.05 0.62 −0.06 −0.40

1.00 0.53 −0.2 −0.10 −0.19 0.01 0.25 0.09 0.30 −0.55 −0.18 −0.15 0.11 0.67 −0.12 −0.50

1.00 −0.02 −0.33 −0.02 0.10 0.84 0.68 0.89 0.13 −0.45 0.07 0.20 0.47 0.45 −0.30

1.00 0.56 0.88 0.87 −0.10 −0.25 −0.01 0.60 0.29 0.05 0.69 −0.51 0.20 −0.19

1.00 0.71 0.71 −0.26 −0.50 −0.20 0.26 0.59 0.37 0.16 −0.46 −0.15 −0.10

1.00 0.91 −0.07 −0.22 0.02 0.49 0.36 0.70 0.64 −0.36 0.17 −0.25

1.00 0.07 −0.08 0.15 0.26 0.40 0.53 0.77 −0.29 0.29 −0.41

1.00 0.92 0.99 0.09 −0.21 0.20 0.28 0.31 0.60 −0.27

1.00 0.87 −0.03 −0.26 0.08 0.21 0.36 0.70 −0.32

1.00 0.15 −0.19 0.25 0.33 0.28 0.56 −0.28

1.00 0.02 0.38 0.04 −0.53 −0.32 0.14

1.00 0.34 0.20 −0.58 −0.42 0.24

1.00 0.53 −0.30 0.06 −0.05

1.0 −0.15 0.37 −0.39

1.00 0.20 −0.49

1.00 −0.64

1.00

Abbreviations: PM2.5 = particulate matter with aerodynamic diameter less than 2.5 μm (fine PM); PM10–2.5 = particulate matter with aerodynamic diameter between 2.5 and 10 μm (coarse PM); PM10 = particulate matter with aerodynamic diameter less than 10 μm; NO2 = nitrogen dioxide; BC = black carbon; Benz = benzene; Tol = toluene. a p-Value less than 0.05 in bold indicates significant correlation.

Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

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Table 4 Overall Asthma Control Questionnaire score summary statistics.a Overall

Subgroup Allergic Medicaidc Daily ICS Recent OSd

Yes No Yes No Yes No Yes No

School 1

School 2

N

Meanb

SD

Min

Max

N

Meanb

SD

Min

Max

N

Meanb

SD

Min

Max

386 184 202 195 191 129 257 169 217

0.8 0.8 0.8 0.7 0.8 0.6 0.9 0.7 0.8

0.6 0.6 0.7 0.6 0.6 0.4 0.7 0.7 0.6

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.3 2.4 3.3 3.3 3.0 2.1 3.3 3.3 3.0

210 86 124 156 54 77 133 85 125

0.9 1.0 0.8 0.8 1.2 0.6 1.1 0.9 0.9

0.7 0.7 0.7 0.6 0.7 0.4 0.8 0.8 0.6

0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 0.0

3.3 2.4 3.3 3.3 2.6 2.1 3.3 3.3 2.4

176 98 78 39 137 52 124 84 92

0.6 0.5 0.7 0.5 0.6 0.5 0.7 0.5 0.7

0.5 0.3 0.7 0.2 0.5 0.3 0.5 0.4 0.6

0.0 0.1 0.0 0.3 0.0 0.1 0.0 0.0 0.1

3.0 1.6 3.0 1.1 3.0 1.6 3.0 2.0 3.0

Abbreviations: N = number; SD = standard deviation; Min = minimum; Max = maximum; OS = oral steroid. a Score equals mean of sum of total question score, with higher scores indicating less control. b Mean of ACQ scores. c Medicaid coverage for child was used for some amount of time during the last year. d Signifies one or more oral steroid uses during last 3 months.

(effect estimate = 0.12 per IQR increase, p-value= 0.05). Among subjects having utilized Medicaid during the previous year, associations were consistently stronger, albeit non-significant, than for those subjects not having utilized Medicaid. For all pollutants, subjects without recent oral steroids use showed stronger associations than those with recent oral steroids, with marginally-significant associations found with PM10, PM10–2.5, and PM2.5 for those without recent oral steroid use. Stratification by weight status and by school did show any consistent or notable patterns of associations across strata (results not presented). 3.4. Sensitivity analysis To examine the sensitivity of our overall results to individual subjects' response, four subjects with the highest Cook's D effects by pollutant were individually removed from the analyses and the resulting model estimates are presented as Supplementary material. Exclusion of these subjects generally did not alter the interpretation of the single-pollutant model results, with these models generally showing the same trend of non-significant, albeit consistently positive associations between ACQ score and each pollutant. Two subjects, however, did impact the magnitude of observed effects. For example, removal of subject 30 from School 2 weakened the overall effect estimate substantially for PM2.5, PM10, PM10–2.5, and BC. Removal of subject 9 from School 1 notably strengthened the overall effect estimate for BC, benzene, and toluene, although results remained non-significant. In general, the results presented in Section 3 were robust to transformations

of the outcome and, hence, not overly sensitive to the normal distribution. 4. Discussion This is the first analysis, to our knowledge, examining associations between short-term changes in urban air pollution and corresponding asthma control as measured by a questionnaire that incorporates both reports of symptoms and objective lung function measurement. In the main single- and two-pollutant epidemiologic models, we found non-significant, albeit suggestive, positive associations between ACQ scores and PM10, PM10–2.5, PM2.5, BC, NO2, benzene, toluene, and ozone. All eight of the primary epidemiologic models showed positive associations between the pollutants and the ACQ scores, which is a trend that challenges an explanation of randomness in the results. Based on previous studies that have shown effects of air pollution on respiratory symptoms (Delfino et al., 2003a; Delfino et al., 2006; Escamilla-Nunez et al., 2008; Gent et al., 2009; Mann et al., 2010; Ostro et al., 2001; Spira-Cohen et al., 2011) and lung function (Barraza-Villarreal et al., 2008; Dales et al., 2009; Delfino et al., 2003b; Liu et al., 2009; O'Connor et al., 2008), our observation of positive associations when modeling the overall ACQ score was expected given that this score includes measurements of both subjective asthma symptoms and objective lung function measurements. These findings are also consistent with our previous results from a pediatric asthma panel study in the PdN region. In our initial 2008 study (Sarnat et al., 2012), we reported IQR increases of a similar range of

Table 5 Associations between Asthma Control Questionnaire score and 96-hour integrated pollutant concentrations.a Pollutant used in model

IQRb

N

Δ in ACQ score with IQR increase in pollutant (95% CI)c

p-Valued

PM10 PM10–2.5 PM2.5 BC NO2 Benzene Toluene Ozone (C-41)

19.7 14.8 5.7 0.4 5.5 0.8 2.4 8.7

303 303 303 303 352 352 352 432

0.0365 (−0.0349, 0.0350 (−0.0414, 0.0352 (−0.0268, 0.0387 (−0.0561, 0.0096 (−0.1345, 0.0313 (−0.0684, 0.0249 (−0.0621, 0.0060 (−0.0887,

0.315 0.368 0.264 0.422 0.896 0.538 0.574 0.901

0.1080) 0.1115) 0.0972) 0.1334) 0.1537) 0.1309) 0.1118) 0.1007)

Abbreviations: PM2.5 = particulate matter with aerodynamic diameter less than 2.5 μm (fine PM); PM10–2.5 = particulate matter with aerodynamic diameter between 2.5 and 10 μm (coarse PM); PM10 = particulate matter with aerodynamic diameter less than 10 μm; NO2 = nitrogen dioxide; BC = black carbon; IQR = interquartile range; N = number of observations used in model for analysis; ACQ = Asthma Control Questionnaire. C-41 = continuous air monitoring station (CAMS)-#41. a Using mixed effects modeling with repeated week and random subject effect, each model controls for 96-hour averaged relative humidity, 96-hour averaged temperature, and school. b IQR is in μg/m3 for PM2.5, PM10, PM10–2.5, and BC; IQR is in ppb for NO2 and ozone; IQR is in μg/m3 for benzene and toluene. IQR (PM2.5, PM10, PM10–2.5, BC, NO2, benzene, toluene) is average from measurement overall, including School 1 and School 2. The IQR value for O3 is taken from measurements at CAMS-41 site only. c Δ in ACQ score and 95% CI derived by multiplication of effect estimate, its lower bound, and its upper bounds by the IQR. d p-Value from t-test solution for fixed effects.

Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067

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Table 6 Associations between Asthma Control Questionnaire score and selected pollutants with subgroup analysis.a Pollutant (IQR)b

Subgroup

PM10 (19.7)

Allergic Medicaide Daily ICS Recent oral steroidf

PM10–2.5 (14.8)

Allergic Medicaide Daily ICS Recent oral steroide

PM2.5 (5.7)

Allergic Medicaide Daily ICS Recent oral steroidf

BC (0.4)

Allergic Medicaide Daily ICS Recent oral steroidf

NO2 (5.5)

Allergic Medicaide Daily ICS Recent oral steroidf

Benzene (0.8)

Allergic Medicaide Daily ICS Recent oral steroidf

Toluene (2.4)

Allergic Medicaide Daily ICS Recent oral steroidf

Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No

N

Δ in ACQ score with IQR increase in pollutant (95% CI)c

p-Valued

143 160 158 145 102 201 133 170 143 160 158 145 102 201 133 170 143 160 158 145 102 201 133 170 143 160 158 145 102 201 133 170 168 184 179 173 117 235 154 198 168 184 179 173 117 235 154 198 168 184 179 173 117 235 154 198

0.0297 0.0497 0.0495 0.0163 −0.0270 0.0694 −0.0305 0.0911 0.0233 0.0542 0.0524 0.0130 −0.0374 0.0708 −0.0351 0.0930 0.0379 0.0373 0.0419 0.0212 −0.0075 0.0589 −0.0198 0.0776 0.1160 −0.0235 0.0729 −0.0391 0.0851 0.0090 −0.0529 0.1164 0.1528 −0.0797 0.0554 −0.1132 −0.0470 0.0320 −0.0338 0.0405 0.0780 −0.0070 0.0746 −0.0694 0.1749 −0.0500 −0.0232 0.0707 0.0767 −0.0142 0.0643 −0.0750 0.1203 −0.0321 −0.0093 0.0507

0.497 0.384 0.329 0.758 0.623 0.142 0.533 0.081 0.616 0.380 0.360 0.812 0.528 0.159 0.503 0.097 0.324 0.445 0.307 0.667 0.873 0.155 0.645 0.085 0.057 0.745 0.228 0.626 0.243 0.885 0.417 0.094 0.098 0.465 0.513 0.388 0.652 0.743 0.744 0.692 0.216 0.927 0.195 0.464 0.014 0.458 0.741 0.324 0.167 0.830 0.190 0.384 0.052 0.588 0.879 0.419

(−0.0565, 0.1159) (−0.0630, 0.1624) (−0.0504, 0.1496) (−0.0883, 0.1210) (−0.1353, 0.0815) (−0.0234, 0.1623) (−0.1277, 0.0664) (−0.0114, 0.1937) (−0.0682, 0.1148) (−0.0675, 0.1760) (−0.0605, 0.1653) (−0.0944, 0.1204) (−0.1548, 0.0800) (−0.0280, 0.1696) (−0.1387, 0.0685) (−0.0170, 0.2032) (−0.0380, 0.1138) (−0.0591, 0.1337) (−0.0388, 0.1226) (−0.0763, 0.1187) (−0.1004, 0.0854) (−0.0224, 0.1397) (−0.1051, 0.0654) (−0.0108, 0.1660) (−0.0033, 0.2354) (−0.1660, 0.1190) (−0.0460, 0.1917) (−0.1973, 0.1191) (−0.0588, 0.2289) (−0.1140, 0.1320) (−0.1816, 0.0757) (−0.0200, 0.2528) (−0.0287, 0.3343) (−0.2948, 0.1354) (−0.1114, 0.2224) (−0.3718, 0.1453) (−0.2534, 0.1593) (−0.1601, 0.2240) (−0.2380, 0.1703) (−0.1612, 0.2421) (−0.0461, 0.2021) (−0.1578, 0.1438) (−0.0387, 0.1880) (−0.2562, 0.1175) (0.0357, 0.3141) (−0.1826, 0.0826) (−0.1617, 0.1154) (−0.0703, 0.2117) (−0.0325, 0.1858) (−0.1448, 0.1164) (−0.0322, 0.1609) (−0.2446, 0.0948) (−0.0008, 0.2414) (−0.1489, 0.0846) (−0.1299, 0.1113) (−0.0727, 0.1742)

Abbreviations: PM2.5 = particulate matter with aerodynamic diameter less than 2.5 μm (fine PM); PM10–2.5 = particulate matter with aerodynamic diameter between 2.5 and 10 μm (coarse PM); PM10 = particulate matter with aerodynamic diameter less than 10 μm; NO2 = nitrogen dioxide; BC = black carbon; IQR = interquartile range; N = number of observations used in analysis; ACQ = Asthma Control Questionnaire. a Using mixed effects modeling with repeated week and random subject effect, each model controls for 96-hour averaged relative humidity, 96-hour averaged temperature, and school. b IQR is in μg/m3 for PM2.5, PM10, PM10–2.5, and BC; IQR is in ppb for NO2 and ozone; IQR is in μg/m3 for benzene and toluene. IQR (PM2.5, PM10, PM10–2.5, BC, NO2, benzene, toluene) is average from measurement overall, including School 1 and School 2. The IQR value for O3 is taken from measurements at CAMS-41 site only. c Δ in ACQ score and 95% CI are derived by multiplication of the effect estimate, its lower bound, and its upper bound by the IQR. d p-Value from t-test solution for fixed effects. e Medicaid coverage for child was used for some amount of time during the last year. f Signifies oral steroid use during the last 3 months. Bold values indicate significance at pb0.10.

urban air pollutants, including PM2.5, PM10, PM10–2.5, NO2, and BC to be significantly associated with increased eNO. The ACQ has been shown to correlate significantly with changes in eNO in a study of adults with asthma (Farah et al., 2011b). The lack of statistical significance in the associations for these main models in the current analysis may be due to several factors.

Air pollution panel studies almost always reflect a brief snapshot in space and time of a potential exposure–response relationship. It is possible that the pollutant exposure levels and variability occurring during this study period were not sufficient to elicit a statisticallydetectable response in ACQ. It is possible that the study period conducted during the late spring/early summer did not capture the

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time period during which associations might be strongest such as might occur in the late summer (e.g., when ozone concentrations are higher). Indeed, a previous study involving ACQ variability in a pediatric asthma cohort (Juniper et al., 2010) found that the lowest clinically relevant and detectable change in ACQ to be 0.52 ± 0.45, which is considerably higher than mean air pollution-related ACQ changes in the current panel scaled to our measured pollutant IQR's. Based on our empirical epidemiologic results (Table 5), however, we would predict that BC scaled per 2 μg/m 3 increment (i.e., around four times the IQR), would result in a corresponding increase of approximately 0.5 in mean ACQ. This range in concentrations, while higher than that observed in the current study, is plausible in many urban environments, especially in cities throughout the developing world where aging diesel engine truck traffic continues to predominate as a central pollutant source (Huang et al., 2012; Lin et al., 2011). It is worth also noting that we previously reported significant associations between increased concentrations of a number of pollutants, specifically the traffic-related VOCs and reduced FEV1 in this panel (Li et al., 2011). Moreover, among the seven ACQ questions, the question pertaining to FEV1 is the only one where an acute physiological response is directly measured. It is possible that the current lack of significance between the pollutants and the broader ACQ metric is indicative of a lagged response between acute, measureable changes in lung physiology (i.e., expressed as reduced FEV1) and a child experiencing increased symptoms that may also necessitate pharmacological intervention. Future analyses examining alternative lag structures between the pollutants and the ACQ scores may help elucidate whether the various components of asthma control respond differentially. In addition, other questionnaires related to asthma control in children have been validated (Liu et al., 2007), which could potentially be more sensitive to changes in child asthma status using picture-based response options, and may provide additional opportunity for study of this subject. While the pollutant-only models were largely non-significant, strengths of association in the subgroup analyses varied. Notably, associations were stronger for some subgroups, in particular among subjects taking daily ICS. Among these subjects, benzene was shown to predict increases in corresponding ACQ scores, reflecting significant decrements in asthma control (p = 0.014). A marginally significant association was also found between toluene and ACQ scores (p = 0.052). Other near-significant associations were noted among children without recent oral steroid exposure. It is important to note, however, that some associations were negative (although not significant) for some strata and some pollutants. This pattern of association related to steroid use (both inhaled or oral) may reflect distinct asthma subject qualities, related to heightened immune system response in more severe asthmatics or those with more poorly controlled asthma at baseline. Since children with more severe asthma are prescribed daily ICS, it is plausible that air pollutant exposures lead to greater acute exacerbation in these subjects, consistent with the trends we observed. This pattern, however, did not extend to children reporting recent oral steroid use. It is possible that the use of daily ICS more reliably reflects persons with chronic severe asthma, than the use of oral corticosteroids (used for very short term treatment of acute, severe asthma exacerbation). Moreover, daily ICS may not dampen immune response in the same way, therefore allowing pollutant exposures to influence asthma control among these asthmatics with increased severity at baseline. It has previously been shown that children not taking inhaled corticosteroids had increased eNO in response to pollutant exposure (Koenig et al., 2003), but the mechanism leading to eNO production and the outcomes measured by the ACQ may be differently affected by immunosuppressive steroids. Other subgroup findings pertained to allergic phenotype and SES/ access to care as measured by use of government-sponsored insurance during the past year. Associations were not significant for those having used government sponsored insurance during the

previous year, although they were consistently more positive than results from children who did not use government insurance. These findings compare to previous studies that have found stronger associations between air pollutants and exacerbation of asthma among low SES subjects (Gold and Wright, 2005; Goodman et al., 1998; Meng et al., 2008; Neidell, 2004; Ray et al., 1998). In those who were not defined as having an allergic phenotype, associations were also negative with NO2, benzene, and toluene. This may point to reduced sensitivity to air pollutants as air irritants among those who are not allergic as has also been shown previously (Delfino et al., 2004; Mann et al., 2010). An ACQ score for an individual may reflect a variety of factors both directly and indirectly related to the asthma disease process (Bacon et al., 2009; Barros et al., 2011; Bousquet et al., 2011; Chhabra and Chhabra, 2011; de Groot et al., 2012; Farah et al., 2011a; Giraud et al., 2011; Hermosa et al., 2010; Lavoie et al., 2006; Schumann et al., 2012; Walter et al., 2008; Wisnivesky et al., 2010). For example, the ACQ score for a given individual may provide an indication of his/her access or adherence to medications, education related to the disease (Hermosa et al., 2010), current medication (Bousquet et al., 2011; Schumann et al., 2012), exposure to illness (Walter et al., 2008), and/or allergic status (de Groot et al., 2012). The ACQ may also signal appropriate prescription of medications based on level of asthma severity (Schumann et al., 2012) and appropriate medication and equipment use (Giraud et al., 2011). Both short-term and long-term indoor and outdoor environmental exposures and propensity to an allergic response would be expected to influence the ACQ score, and ACQ scores have also been shown to differ among persons based on body mass index (Barros et al., 2011; Farah et al., 2011a). Moreover the ACQ might reflect certain unintended factors, such as socioeconomic status (Bacon et al., 2009), stress exposure (Wisnivesky et al., 2010), mood status (Lavoie et al., 2006), or quality of parenting. In the case of using the ACQ score in pediatrics, the score reflects not just the child's approach to managing the disease, but also that of the parent. ACQ scores are also dependent on the person answering the questions in terms of their understanding of the questions and personal perception of asthma symptom severity, and this perception has been shown to differ by gender (Chhabra and Chhabra, 2011). Since an ACQ score represents a multitude of factors that may affect asthma control for a given individual, it was important to use a longitudinal design to assess the impact of short-term changes in air quality. This design allowed for each individual to serve as his/her own control throughout the length of the study, and allowed for the analysis of changes in ACQ scores within each individual in relation to changes in air pollutant levels, rather than a comparison of scores between subjects. If an individual person had reduced access to medications or consistently used improper form when using medications that resulted in little inhaled medication, then this person may present with more “severe” asthma at baseline and during exacerbation that would reflect poor asthma “control.” This person might therefore start at a higher ACQ score at baseline and then change more with air pollutant exposures or change at the same rate as another person with a baseline at a lower score. Limitations of this study exist related to our analytical methods. First, the use of the a priori 96-hour time window of exposure, relative humidity, and temperature may not accurately reflect the interaction and effects of air pollutants on asthma. In addition, the use of outdoor monitoring at school-based sites is superior to region-based monitoring, but perhaps inferior to more costly personal monitoring. It should be noted that findings from our previous epidemiologic analyses using corresponding indoor pollutant concentrations did not substantially affect overall results interpretation (Li et al., 2011). Medication use was also not controlled during the study, and changes in medications used either added or discontinued may have led to unaccounted within-subject in response. Exposure to household environmental tobacco smoke (ETS) was assessed, qualitatively, via self-reported questionnaire data exclusively.

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While the responses indicate generally limited ETS exposures during the course of an individual exposure week, it is possible albeit unlikely that this potential risk factor contributed to increased Type 2 errors in the epidemiologic model results. It is also possible that the children who participated in the study misunderstood aspect of the ACQ questions or could have received unbalanced coaching, which might have unfairly strengthened effect estimates for these individuals. It is possible that the inclusion of additional subjects in the study could have provided more power to observe significant associations. Additional limitations relate to the study in the context of sensitivity analysis which suggests that certain individuals might have influenced the results. This limitation must be considered particularly in the interpretation of the borderline significant effect estimates among children who had not recently used oral steroids and PM pollutants, since this group contained an influential subject who would have strengthened the effect estimate (Subject 30 from School 2) and also did not contain one that would have weakened the estimate (Subject 9 from School 1). Although associations were stronger among those who used Medicaid during the previous year compared to those who did not, the inclusion of an influential subject in the non-Medicaid group (Subject 30 from School 2) might have also contributed to the lack of observed significance among subjects with Medicaid status over the past year as has been seen in other studies (Gold and Wright, 2005; Goodman et al., 1998; Meng et al., 2008; Neidell, 2004). It is also possible that Medicaid status also does not appropriately reflect socioeconomic status in general. 5. Conclusion The ACQ was developed as a clinical tool to assess the need for treatment, the efficacy of treatment, or the response to treatment. Physicians typically would use an ACQ score or individual questions to increase or potentially decrease asthma medications as needed based on the scoring of asthma symptoms, lung function decrement, or perceived activity limitation. One of the main aims of the current analysis was to determine whether the ACQ is a sensitive, aggregate environmental health indicator that reflects changes in air quality, in particular traffic-related air pollutants, that have previously been shown to impact asthma among children in the PdN region (Hart et al., 1999; Li et al., 2011; Sarnat et al., 2012). We believe that the current findings strongly warrant additional studies with larger sample sizes that might be able to detect significant changes in ACQ scores based on pollutant levels that might exist. Pronounced differences in association by medication use, specifically ICS, also provide insight into potential mechanistic drivers of acute asthma control. Since asthma exacerbation leads to poor quality of life for children and their families, as well as a high economic burden for society (Gold and Wright, 2005; Lozano et al., 1997; Szefler et al., 2011), this issue remains paramount. If the ACQ is appropriately used in the context of air pollution studies, it could reflect clinically measurable and biologically relevant changes in lung function and asthma symptoms that result from poor air quality and could increase our understanding of how air pollution influences asthma exacerbation. If the adverse health effect of air pollution can be more precisely quantified, public-health related instructions to avoid certain pollutant exposures that might be clinically harmful to asthmatic children might also be further delineated. Environmental regulations to reduce air pollutant emissions or allowable ambient levels might also be changed and/or improved. This would be especially important in high traffic regions like PdN and cities like El Paso that are home to a sizable, potentially-sensitive pediatric population. Acknowledgments The authors would like to express their gratitude to the El Paso Independent School District and the Ysleta Independent School District, the principals, nurses and the students who participated in

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this research project. We thank Teresa Montoya-Sosa, Joseph Pinon, Mario Lopez, Daniel Perez, Michael Bevilacqua, and Veronica Guerrero for the help in the field. Finally, we would like to acknowledge and pay tribute to the life of Dr. Gerald Keeler, a colleague, teacher and friend. Appendix A. Supplementary data Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.scitotenv.2012.11.067. References Akinbami LJ, Moorman JE, Liu X. Asthma prevalence, health care use, and mortality: United States, 2005–2009. Natl Health Stat Report 2011:1-14. Bacon SL, Bouchard A, Loucks EB, Lavoie KL. Individual-level socioeconomic status is associated with worse asthma morbidity in patients with asthma. Respir Res 2009;10:125. Barnett AG, Williams GM, Schwartz J, Neller AH, Best TL, Petroeschevsky AL, et al. Air pollution and child respiratory health: a case-crossover study in Australia and New Zealand. Am J Respir Crit Care Med 2005;171:1272–8. 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Please cite this article as: Zora JE, et al, Associations between urban air pollution and pediatric asthma control in El Paso, Texas, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2012.11.067