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Final Report: Asthma Indices Associated with Ambient Submicron Particles and Formaldehyde in Ambient Air Pollution
EPA Grant Number: R825275Title: Asthma Indices Associated with Ambient Submicron Particles and Formaldehyde in Ambient Air Pollution
Investigators: Fennelly, Kevin P. , Anderson, Larry G. , Bartelson, Becki
Institution: National Jewish Medical and Research Center
EPA Project Officer: Katz, Stacey
Project Period: December 1, 1996 through November 30, 1997
Project Amount: $178,865
RFA: Air Quality (1996)
Research Category: Air Quality and Air Toxics
Description:
Objective:The objectives of this research project were to:
· Test the hypothesis that increased asthma symptoms, inhaled bronchodilator medication use, and decreased lung function associated with ambient submicron particle counts are higher than with larger particles and with gravimetric measures due to the biological importance of surface area;
· Determine the correlation between particle count and gravimetric concentration of both particulate and gaseous pollutants in the Denver urban aerosol;
· Test the hypothesis that visibility reduction as measured by light extinction is similarly associated with adverse asthma outcomes because visibility reduction is largely due to airborne particles;
· Test the hypothesis that ambient formaldehyde and acetaldehyde concentrations are associated with asthma outcomes, because they are known to be products of motor vehicle exhaust and to provoke asthma in some individuals; and
· Compare current associations between asthma outcomes and air pollution to those found in two previous studies in Denver in 1979, and in 1987-1988.
Summary/Accomplishments (Outputs/Outcomes):This section describes the models and their parameters, model results, and comparisons.
Models and Parameters. The primary outcome variables of this study were: (1) daily total asthma symptom score; (2) number of bronchodilator medication puffs used daily; and (3) evening peak expiratory flow rates daily. The study was conducted in central Denver, CO, from December 1, 1997, to February 28, 2001. The Air Pollution Control Division (APCD) of the Colorado Department of Health and the Environment provided the air pollution data that was collected from the continuous air monitoring program (CAMP) monitor in Denver. These included daily measures of carbon monoxide (CO), ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ambient temperature, and relative humidity. Barometric pressure data measured at the Denver International Airport were obtained from the National Climatic Data Center in Asheville, NC. Particulate matter (PM) concentrations were obtained hourly at the CAMP monitor using the beta gauge method for PM10, and daily PM2.5 concentrations were measured nearby at the Denver Visitors' Center monitoring site. Ambient formaldehyde and acetaldehyde concentrations were collected every four hours. Particle counts were collected hourly at the same site using a laser aerosol spectrometer (Las-X, PMS, Inc., Boulder, CO). All hourly and four-hour data were averaged to provide a daily mean value. We also obtained hourly light extinction data from the air pollution control device (APCD), and these data were converted to daily means. These data are collected using an Optec LPV-2 transmissometer positioned between the Federal and Denver Educational Senior Citizens, Incorporated (DESCI) Buildings in downtown Denver.
This research project, utilized a longitudinal analysis method. We used generalized estimating equations with a random subject effect, and an auto regression of first order within subject error structure, assuming a Poisson distribution, to determine the strength of the associations of individual pollutants with the asthma outcome measures. Normally distributed errors were assumed for peak expiratory flow rates and total symptom score measures. All models were adjusted for month, temperature, relative humidity, and barometric pressure, to account for possible confounding by time and meteorological conditions. Lag effects of the pollutant were examined by including one-, two-, three-, four-, and five-day lags in the models. Lag terms were removed in descending order starting with the five-day lag if not significant. We did not include multiple pollutants in our models to avoid problems of multicollinearity. Light extinction data can be strongly affected by relative humidity above 60 percent; therefore, we analyzed models in which these data were restricted to days on which the relative humidity was below 60 percent.
Normalized z-scores for the asthma indices were calculated for each subject. Cluster analyses of the z-scores were used to identify high and low asthma index days. The intent was to provide potential collaborators with days in which particle filters could be analyzed to determine if there were differences among high versus low index days.
Because we used multiple models in this study, it is appropriate to correct for multiple comparisons. We arbitrarily designated as "significant" results with a p <0.001, and as a "trend towards significant" results with a "p" approximately = 0.01.
Forty-five subjects were recruited from central Denver, and 40 subjects completed the study. Three subjects discontinued after the run-in phase, and two discontinued data collection because of the inability to comply with the protocol. There were 28 (70 percent) women and 12 (30 percent) men, with a median age of 30 (range 21-45). The racial and ethnic distribution was 36 (90 percent) White, 1 (2.5 percent) African-American, 2 (5 percent) White-Hispanic, and 1 (2.5 percent) Asian. They were well-educated with a median of 16 years of education (range 12-23). Twenty-six (55 percent) had household pets, and 33 (83 percent) reported increased symptoms around pets. Twenty-eight (70 percent) reported having had allergy skin tests done, and only 2 of these reported no skin test reactions. Twelve (30 percent) were former smokers. The median time reported outdoors was 0.5 hour (range 0-2 hours). Fourteen (35 percent) were using inhaled steroid medication regularly. One subject required a brief oral steroid medication burst and taper during the protocol for an exacerbation associated with an acute respiratory infection. There were no adverse events.
The group demonstrated bronchial hyperresponsiveness (BHR) to methacholine, with an initial provocative concentration causing a 20 percent decrease in the FEV1 (PC20FEV1) of 1.3 mg/ml (geometric mean; 0.67, 2.5 (95 percent confidence interval (CI))). (A normal response to methacholine is considered to be a PC20FEV1 of 8 mg/ml or greater.) There was a statistically significant increase in the PC20FEV1 to 1.96 mg/ml (1.1, 3.5 (95 percent CI)) after the 16-week study period. The initial methacholine challenge data was correlated with mean variability of peak expiratory flow rate (PEFR) (r = 0.43; p = 0.009) and mean variability of FEV1 (0.34; p = 0.04). The median amplitude percent mean variability of PEFR was 6.7 percent (range 2.8-26.3 percent) and of FEV1 was 7.5 percent (range 3.3-20.2 percent). There was no difference in variability of FEV1 or of PEFR among those subjects who used or did not use inhaled steroids. However, there was a trend towards increased BHR (i.e., lower PC20FEV1) among those using inhaled steroids (0.62 versus 1.87 mg/ml; p = 0.08). The median exhaled nitric oxide (NO) was 46.73 ppb (range 11.4-184.3); (geometric mean 44.57 ppb; 31.72, 62.63 (95 percent CI)). Exhaled NO was not significantly associated with bronchial hyperresponsiveness to methacholine or to diurnal variability of FEV1 or PEFR. Exhaled NO was significantly lower in those using inhaled steroid medication versus those not using such medication (32.7 versus 73.9 ppb; p = 0.04).
Median daily PM10 concentration was 31.7 :g/m3, and the median daily carbon monoxide concentration was 1.46 parts per million (ppm). PM10 correlated well with CO (r = 0.83), NO (r = 0.82), acetaldehyde (r = 0.79), PM2.5 (r = 0.78), formaldehyde (r = 0.74), NO2 (r = 0.69), and total submicron particle counts (r = 0.67 for both) (all p <0.0001). Light extinction, a measure of visibility degradation, was highly associated with relative humidity (r = 0.82), but also with PM2.5 (r = 0.68) and particle counts (r = 0.58) (all p <0.001) more than with PM10 (r = 0.32; p <0.05) or the pollutant gases. There were strong correlations between CO and NO (r = 0.97), between formaldehyde and acetaldehyde (r = 0.94), between CO and acetaldehyde (r = 0.88), between CO and formaldehyde (r = 0.83), and between CO and NO2 (r = 0.80) (all p <0.0001).
The normalized z-scores for all asthma symptoms correlated well with the z-scores for wheeze alone (r = 0.70; p <0.0001). There was a weaker correlation between the z-scores for asthma symptoms and the number of inhaled bronchodilator puffs (r = 0.25; p = 0.002). The z-score for the change in peak expiratory flow rate between morning and evening also weakly correlated with both asthma symptoms (r = 0.24; p = 0.003) and with bronchodilator use (r = 0.29; p = 0.0004).
Cluster analyses indicated that there were 4 clusters of days when the mean z-score for the symptom wheezing was elevated from baseline and that there were 3 clusters of days when these z-scores were below the baseline. There were 2 of the same clusters of days with elevated z-scores for bronchodilator use, and 2 of the same clusters with decreased z-scores.
Mixed models controlling for possible confounding variables using single pollutants were analyzed for each of the following asthma outcomes: symptom scores, inhaled bronchodilator use, and peak expiratory flow rates in the evening.
Model Results. In this section of results, we summarize the models that were "statistically significant" with p <0.001 and those that showed a "trend towards statistically significant" with a p-value of approximately 0.01. All odds ratios (OR) presented will be for the interquartile range (IQ) to facilitate comparisons among different pollutants. Among the models with the total asthma symptom score as the outcome, none were statistically significant at p <0.01. There was a trend towards significance between symptoms and light extinction mean and maxima in a one-day lag model (OR = 1.12, (1.01:1.25) (95 percent (CI)) p = 0.037; OR = 1.08 (1.003:1.15); p = 0.042). Among the models with bronchodilator use as the outcome variable, only the one-day lag models with both mean and maximum light extinction as the predictor variable were statistically significant with a modest magnitude (OR = 1.02 (1.01:1.03); p = 0.0003; OR = 1.04; (1.02:1.06); p = 0.0004, respectively). There was also an association with a trend towards significance for the 4-day lag model for the maximum light extinction limited to low-humidity days (OR = 1.33 (1.06:1.65); p = 0.012). Despite these findings associated with visibility, there were no significant associations between symptoms and any of the other particulate measures or the gaseous pollutants.
There were associations observed between bronchodilator use and 3-day lag models for both mean and maximum formaldehyde concentrations (OR = 1.04 (1.01:1.07); p = 0.008; OR = 1.04 (1.01:1.07); p = 0.009) as well as both mean and maximum acetaldehyde concentrations (OR = 1.04; (1.004:1.11); p = 0.011; OR = 1.04; (1.01:1.07); p = 0.012). Bronchodilator use was similarly associated with mean CO concentrations in the 3-day lag model (OR = 1.05; (1.01:1.10); p = 0.012), and the 3-day model with maximum CO had a similar association, although not statistically significant (OR = 1.05; (1.004:1.11); p = 0.034).
Mean NO2 concentrations were negatively associated with same-day bronchodilator use with a trend towards significance (OR = 0.96; (0.93:0.99); p = 0.016), but there were no significant associations with maximum concentrations. Similarly, there was a negative association between bronchodilator use and maximum SO2 concentrations in a 4-day lag model with a trend toward significance (OR = 0.95 (0.92:0.99); p = 0.011). However, there were no significant associations observed with mean SO2 in the models.
There were trends towards statistically significant negative associations between bronchodilator use and submicron particle count daily means (OR = 0.95 (0.90:0.99); p = 0.008), and daily maxima (OR = 0.94 (0.90:0.99); p = 0.02). Supramicron particle counts were also negatively associated with bronchodilator use with similar magnitudes but with less statistical significance.
In the models with evening PEFR as the outcome, there were no statistically significant associations observed at the a-level of <0.001. Mean formaldehyde concentrations were associated with a 3 L/min decrease in the PEFR in the 1-day lag model (p = 0.016), but no similar associations were observed using formaldehyde maximum or the acetaldehyde concentrations. Mean nitrogen dioxide concentrations were associated with a 4 L/min decrease in PEFR (p = 0.009) in a 1-day lag model, but no similar associations were observed for the maximal concentrations. There were trends towards statistical significance in the 4-day lag model with light extinction as the predictor, but the change in sign of the association for the lag 3 variable suggests that these may be spurious results.
In this panel study of 40 adults with asthma living in Denver during the winter of 1997-1998, the most statistically significant associations were between bronchodilator medication use and light extinction (i.e., visibility) data in a 1-day lag model, using both the daily mean and maximum. There was a modest magnitude of this association, with approximately 2-4 percent increased use of bronchodilator medicine associated with the interquartile range of light extinction. When the dataset was restricted to days of relative humidity <60 percent, the magnitude of the association between light extinction and bronchodilator use increased in 3- and 4-day lag models for the mean and maximum values, respectively. This suggests a 17-33 percent increase in bronchodilator use, and the statistical significance decreased toward a trend level. There was a trend towards statistical significance of asthma symptoms with light extinction in a 1-day lag model, with an 8-12 percent increase associated with the interquartile range of the daily maximum and mean. There were no consistent associations between light extinction and changes in lung function.
We also found consistent associations between bronchodilator use and both means and maxima of acetaldehyde, formaldehyde, and CO. There was an approximate 4-5 percent increase in bronchodilator use associated with the interquartile ranges of these pollutants in a 3-day lag model, with a trend towards significance. Small decreases in evening PEFR with a 1-day lag were associated with the interquartile ranges of daily mean of formaldehyde (3 L/min) and nitrogen dioxide (4 L/min). There were no significant associations of asthma symptoms with any of the gaseous pollutants.
Despite the associations between asthma symptoms and bronchodilator use with light extinction, we found negative (or "protective") associations between bronchodilator use and both particle count measures (i.e., "submicron" being <1.0 micron in diameter and supramicron being >1.0 micron in diameter) with a trend towards statistical significance, suggesting approximately a 5 percent decrease in bronchodilator use associated with the interquartile range. There were no statistically significant associations between PM10 or PM2.5 and bronchodilator use, although some might consider a borderline trend towards significance with PM2.5 (p = 0.069). There were no associations between the gravimetric or count measures of particles, asthma symptoms, or PEFR.
These mixed results are difficult to interpret, but they suggest that light extinction may be a marker of an air pollutant exposure that provokes asthma symptoms and medication use. These data suggest that the air pollution mix or a specific component or components of the mix containing CO, formaldehyde, and acetaldehyde may be causing airway inflammation with a delayed effect among these asthmatic subjects. Since these three gases are components of motor vehicle exhaust, this is the most likely source of this air pollution mix. These findings would be consistent with cross-sectional studies that have found asthma symptoms associated with living or working in proximity to motor vehicle traffic. A potential explanation for the consistency of the light extinction and gaseous pollutant models is that light extinction on days of low humidity may also reflect this pollutant mix, possibly because of the effects of ultrafine or specific particles not adequately accounted for by the particulate measures we used in this study. An alternative explanation might be that light extinction is associated with an unknown meteorological condition that provokes asthma, but we are unsure of any data that might suggest this.
"Brown Cloud" is a local term that refers to the urban air pollution in the Denver metropolitan area. It continues to raise concerns among residents, policy makers, and public health practitioners. In the winter, particles containing carbon species averaged 53 percent of the PM2.5, and 75 percent of these carbonaceous particles were produced by motor vehicle exhaust. Ammonium nitrate accounted for 21 percent of the PM2.5 mass, and dust accounted for less than 10 percent. The investigators suggested that future work include determining the relationships between PM2.5 concentrations and visual air quality using the data collected during the study period. Given the associations we found with light extinction, we support the need for such research. We obtained data that was collected at the CAMP monitor from January and early February 1997, the year prior to our study. Light extinction was most highly correlated with sulfates (Spearman r = 0.82; p <0.0001) among the pollutants in these data, suggesting that light extinction might be a surrogate marker for sulfate concentrations.
It is biologically plausible to implicate both acetaldehyde and formaldehyde as possible causative agents in the air pollution associated with the observed increased-use bronchodilator. Acetaldehyde has been used as an agent in clinical bronchial provocation studies of asthmatics, but in much higher concentrations than ambient levels. The role of formaldehyde as a causative agent of asthma is still controversial. Studies of asthmatic subjects exposed to 3 ppm of formaldehyde vapor while exercising for short periods have shown no bronchoconstriction, but 55 minutes of exposure decrements in lung function were found in another study. Chronic indoor exposures were associated with decreased PEFR in both normal and asthmatic children, with a 22 percent decrease in the PEFR associated with 60 ppb of formaldehyde. Increased rates of asthma were found in children from homes with formaldehyde levels of 60-120 ppb. (Note that the range of ambient formaldehyde measured during our study was 13-105 ppb, with a mean of 47.6 ppb). The mode of exposure may be critically important to the airway response. In carefully controlled exposure studies, two subjects who did not respond to formaldehyde gas exposure developed asthmatic responses with exposure to formaldehyde resin dust, and one subject responded to both the gas and the dust. Other investigators had previously reported a case of one patient who had an asthmatic response to bronchial challenge with formaldehyde foam dust but not to 3 ppm of formaldehyde vapor. Thus, it may be important to consider interactions between ambient particles and aldehyde gases in future studies.
Although these data suggest a potential biological mechanism to explain our findings, we cannot infer causality, due to the epidemiological nature of our study. In spite of the consistency among the associations between bronchodilator use, aldehydes, and CO, we must caution against over-interpretation of these data. The models, which include these gases as predictors, only trend towards statistical significance by our criteria, and the magnitudes are very small. The opposite sign and small magnitude of the coefficients on the same day and the 1-day lag, conflict with the observed lag effect.
We should be cautious in over-interpreting these data. We also are concerned about the over-interpretation of the small magnitudes of the associations found in this population of young adults with mild asthma. This may be a signal that it would be much larger in a more susceptible population (e.g., young children with severe asthma or elderly persons with chronic obstructive pulmonary disease).
We observed few associations of statistical significance between asthma symptoms and the exposure variables. The weather variables were associated with asthma symptoms in our base models, suggesting that these outcomes were not insensitive. We suspect that we had poor statistical power to detect asthma symptoms.
The associations between PEFR and exposure variables that inclined towards statistical significance were not consistent; we also have difficulty interpreting these data in a meaningful way. The variability of the PEFR and its correlation with BHR suggest the consistency and validity of the PEFR measures; we doubt that the data are faulty to explain the lack of associations.
Bronchodilator use seemed to provide the best signal for a response to air pollutant exposures in this study. Interestingly, it was poorly correlated with symptoms (r = 0.25) and PEFR changes (r = 0.29), and the latter were weakly correlated with asthma symptoms (r = 0.24; p = 0.003). Although we expected better correlation among these outcome variables, these findings are consistent with other reports in the literature.
In spite of intense study, the best markers of asthma disease status remain controversial. In a recent study of marker treatment failure in asthma clinical trials, no one measure was found to estimate disease severity. In another study of 67 adults with chronic asthma, symptoms were not well-correlated with either airway obstruction determined by the forced expiratory volume in one second (r = 0.143) or the PEFR (r = 0.38). Health-related quality of life in asthma has been independently associated with both symptoms and rescue bronchodilator use, but not with lung function measures. Bronchodilator use may have been a sensitive marker of asthma response due to our use of the Doser™ microelectronic monitoring device, which has been previously validated. Increased monitoring of bronchodilator use is not likely an explanation of the lack of associations found with PEFR, because we also used an electronic spirometer to confirm that subjects were collecting their PEFR data as planned. Future research is necessary to assess the optimal outcome measures and methods in assessing asthma responses to environmental exposures.
The negative associations observed between bronchodilator use and particle count data were unexpected and remain unexplained. These measures have not been standardized or validated; there may have been a systematic error in our technical methods. Although not statistically significant, the similar magnitude in the relative risk associated with the gravimetric measures suggests that these particle measures were associated with the outcome in a similar way. The submicron and total particle counts surprisingly correlated poorly with light extinction (r = 0.58), and only moderately correlated well with PM2.5 (r = 0.84), with PM10 (0.67), formaldehyde (r = 0.68), and acetaldehyde (0.81). Perhaps they are simply measure component(s) of air pollution that do not provoke asthma. Another possibility is that there is a beneficial health effect of low doses of some air pollutants. This has been suggested regarding the therapeutic use of NO as a pulmonary vasodilator. However, this seems unlikely given the high correlation of NO with the other pollutants associated with motor vehicle exhaust. A similar explanation is "hormesis" (i.e., a positive effect at low doses, possibly due to homeostatic mechanisms, and toxic effects at high doses). Further research is necessary to progress beyond such speculation.
Comparisons
Comparison to other studies of particle counts and asthma. There are few data on the role of particle size in the response of asthmatics to ambient air pollution. Peters, et al., found that both fine and ultrafine particles were associated with a decrease of PEFR and an increase of symptoms among 27 adults with a history of asthma in Erfurt, Eastern Germany. The ultrafine fraction showing a stronger effect than PM10 on decreases in evening PEFR, but both were significantly associated with symptoms. The major source of air pollution in Erfurt is coal burning, and the median PM10 during the study period was 59 µg/m3, which is 1.86 times larger than the median in our study. The maximum PM10 concentration was 247 µg/m3; 2.28 times larger than the maximum of 108.4 µg/m3 in our study. We were only able to count particles greater than 0.1 micron in diameter. In Erfurt, there were approximately 5,000 particles/cm3, or 5 x 109 particles/m3, in this size range, compared to approximately 1 x 109 particles/m3 in our study. This is a five-fold difference. Thus, the differences between our study and that of Peters, et al., might be explained by differences in both quantity of air pollution and in its composition.
Another study, by Pekkanen, et al., 1997, studied asthmatic children in the small city of Kuopio where the main source of particular air pollution was from motor vehicle traffic. This airshed is probably more similar to Denver than that of Erfurt. The mean PM10 concentrations during the study period was 18 µg/m3 with an IQ range of 10-23 µg/m3, which was lower than our study mean of 36.8 µg/m3 with an IQ range of 21.3-48.3 µg/m3. However, there was a mean of 2,105 particles/cm3, or 2.1 x106/m3, in the size range of 0.1 to 1.0 microns. This is approximately twice the mean concentration in our study. There was also a larger interquartile range of 1,200 compared with 474 in our study. They found that both the concentrations of ultrafine particles and PM10 were associated with variations in PEFR. However, these analyses were not robust; when the 5 days with unusually high levels of pollution were excluded from the analyses, there were no longer any statistically significant associations. These days were scattered throughout the study period, suggesting that the observed associations are real and that there may be a threshold at which asthmatic responses can be detected. Unfortunately, the data on the concentrations of particle counts and PM10 on the excluded days was not provided. The concentrations of CO were lower in the Finnish study, with a mean of 0.52 ppm compared to 1.62 ppm in the current Denver study. Sulfur dioxide concentrations were also lower, with a mean concentration of 0.002 ppm compared to 0.007 in Denver. The correlation between PM10 and submicron particle counts was similar (r = 0.68-0.69 in Kuopio and r = 0.67 in Denver). However, CO was more highly correlated with PM10 in Denver (r = 0.83) compared to Kuopio (r = 0.50), suggesting that there may be differences in air pollution composition.
A major limitation of our study is the lack of data on ultrafine particle concentrations. Because we and previous investigators in Denver saw few associations between asthma and PM2.5, and because both studies discussed found associations with PM10, as well as ultrafine particles, it may be unlikely that effects from the ultrafine fraction will be found. However, the atmospheric chemistry may be quite different among airsheds, and Denver may be unusual given its altitude and arid climate. The role of ultrafine particle and health effects as well as visibility in Denver should be considered for future research.
We have noted qualitative differences in the time-series plots of PM10 concentrations in the Utah Valley and in Erfurt, Germany, when compared to those of our work in Denver. It appears that PM10 excursions last much longer in these areas, whereas high air pollutant concentrations in Denver may last for only hours. Thus, we suggest further research regarding the duration of exposure to pollutants in relation to observed health effects. Denver may provide an excellent "natural laboratory" for short-term exposures in contrast to areas with more prolonged exposures, such as the Utah Valley.
Comparison to previous studies of air pollution and asthma in Denver. Perry, et al., had performed a similar study of a panel of 41 adult subjects with asthma in Denver from January through March 1979. They followed asthma outcome measures similar to those in our study obtained twice daily: asthma symptoms, bronchodilator use, and peak expiratory flow rates. They found an association between fine nitrate concentrations, asthma symptoms, and bronchodilator use, with p-values of 0.023 and 0.025, respectively, but the coefficients were not provided. There were no corrections for their multiple comparisons. There were no associations between these outcome measures and PM2.5, "coarse mass" (PM2.5-PM15), CO, sulfates, or SO2. Summary statistics are not provided for the entire period of the study; it is not possible to compare the two studies with the same measure. In the 1979 study, the monthly diurnal PM2.5 data ranged from 11.5 to 36.5 µg/m3. Carbon monoxide concentrations ranged from 1.3 to 7.9 ppm. The daily PM2.5 concentrations during our 1997-1998 study had a mean of 15.7 µg/m3, with a range of 3.5 to 51.5 µg/m3, and CO concentrations had a mean of 1.62, with a range of 0.63 to 4.0 ppm. Both sources of data suggest that carbon monoxide and particulate concentrations were substantially lower in 1998.
Ostro, et al., obtained daily symptom and medication use data on 207 adult subjects with asthma during the winter of 1987-1988. Hydrogen ion concentrations were associated with increased shortness of breath and cough, and sulfate concentrations were associated with shortness of breath (all at p <0.05). However, there were no significant associations between the asthma outcome measures, nitrates, or PM2.5. The mean PM2.5 concentrations over the 47 days of collection was 21.97 µg/m3 higher than the mean of 15.7 µg/m3 during our 1997-1998 study. Unfortunately, no gaseous pollutant data were collected except for SO2, which was correlated with PM2.5 (r = 0.64) as it was in our study period (r = 0.62). Both particulate and CO concentrations decreased substantially between 1988 and 1998. Relative humidity correlated weakly with PM2.5 (r = 0.40), as it was in our study (r = 0.52). Ostro, et al., eliminated relative humidity from their models because they did not find that it related to their outcome measures, but we retained it in our models. Weaknesses of the 1987-1988 study also included that: (1) it was done during a clean air campaign in which the electric utilities alternated between burning coal and natural gas every 15 days; (2) only half of the subjects completed their diaries daily; and (3) no CO data were reported.
We may not have found a positive association between particulate air pollution and asthma because of the lower concentrations of particulates since these previous studies in Denver (i.e., concentrations are now below a critical threshold of effect). However, neither of these had significant associations with PM2.5. The composition of the particulate pollution has reportedly changed significantly since 1987. At that time, wood-burning and meat-cooking during the winter contributed approximately 35 percent of the observed PM2.5 mass. Since then, the Colorado Department of Public Health and Environment established a program to restrict wood burning. In the Northern Front Range Air Quality Study in 1996-1997, wood-burning emissions contributed only 5 percent to the PM2.5. The air pollution composition in Denver may differ from that of other cities in which air pollution has been more highly associated with asthma, due to the arid climate and high altitude.
The medical care of asthma has changed since the previous Denver asthma studies, especially regarding the availability and use of inhaled steroid medications. Fourteen (35 percent) of our subjects were using inhaled steroid medication, which might have stunted the response to air pollution. However, Ostro, et al., did not find such an effect in another Southern California population of children using a different method. Among our subjects, exhaled NO was decreased among those using inhaled steroids, but bronchial hyperresponsiveness was the same. A weakness of our analysis is the failure to study the effect of inhaled steroid use.
To our knowledge, this is the first study to assess the association between asthma outcomes and ambient aldehydes, as well as visibility reduction measured by light extinction. We used several unique methods in this study. First, the Doser™ monitoring device aided subjects in the recall of how many times they activated their metered dose inhalers on each day of the study. We also used the AirWatch™ pocket spirometer to confirm that the subjects were complying with the performance of their PEFR maneuvers. If they did not download their data, our research assistant called them with a reminder. Some subjects were not compliant with the time of day at which they were instructed to obtain their data. Such behavior of these young adults might explain the lack of a sensitive signal of PEFRs when compared to studies of young children, who may be on more similar time schedules. This could be a study for future research.
We were able to enlist the collaboration of colleagues at the National Jewish Center, which enabled us to add the use of the Doser™, as well as the exhaled NO measurements. We also were able to obtain funding and assistance from the General Clinical Research Center of the University of Colorado/National Jewish Center, which provided research nursing assistance for the questionnaires and initial spirometry as well as the rooms for confidential interviews. The American Lung Association of Colorado funded another ancillary study that enabled us to obtain light extinction and financial support data to analyze.
One problem with most epidemiological studies is the inability to determine the exact days research subjects might have had asthma responses to (an) exposure(s). We performed cluster analyses to obtain stored particle filters on days when the normalized scores for outcomes of interest were significantly above or below baseline. Similar cluster analyses can be done for covariates to help guide the selection of the most appropriate days. This may allow for new approaches to understanding the physical or chemical nature of particles associated with health effects.
One potential limitation in any panel study such as this is a lack of statistical power due to low sample size. Although 40 subjects is a larger study population than many panel studies, we had planned to enroll 60. However, we learned that there are multiple investigators in Denver performing clinical research studies on new therapies for asthma, and subject reimbursement is often more attractive for such studies. After we received funding, there was considerable interest in the role of ultrafine particles. Unfortunately, we were unable to borrow or buy a condensation nuclei counter to determine ultrafine concentrations limitation in the timely completion of our analyses. The loss of biostatistics personnel from the project at the National Jewish Medical and Research Center was a limitation in the timely completion of our analyses. The lack of inclusion of inhaled steroid use and of NO in our multivariate models was a limitation in our analysis.
In summary, our data did not support the hypothesis that submicron particle counts are more highly associated with asthma indices than gravimetric measures in Denver. In fact, particle counts were negatively associated with bronchodilator use, but there were no associations with asthma symptoms or peak flow rates. We observed a modest but statistically significant association between bronchodilator use for asthma and visibility degradation as measured by light extinction. There was a suggestion of asthma symptoms associated with light extinction as well. There also were statistical trends for correlations between bronchodilator use and pollutants associated with motor vehicle exhaust; namely CO2, formaldehyde, and acetaldehyde with a 3-4 day lag effect. Only formaldehyde and NO2 were associated with small decreases in PEFR. Because these gases are components of motor vehicle exhaust, and their concentrations were correlated, it is likely that the major source of this pollution mix is motor vehicle exhaust.
Although these aldehydes might be causative agents in provoking asthmatic responses, they may also be surrogate markers of a pollutant mix containing other causative agent(s). Further research is needed to confirm our findings, especially among more susceptible populations. A larger study population will be needed to improve statistical power, and we suggest the inclusion of daily exposure data on sulfates and other chemical species. Novel markers of asthma disease activity, such as exhaled carbon monoxide, sputum inflammatory markers, and others could be used in conjunction with controlled exposure studies to assess the subacute effects of low-dose ambient exposures. Because these data implicate motor vehicle exhaust as the source of the pollution mix associated with asthma, it would seem reasonable for policymakers to seriously consider modes of transportation with less reliance on motor vehicles for growing metropolitan areas such as Denver. These data can be used to educate our leaders as well as the public that visibility and public health are indeed linked.
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Supplemental Keywords:asthma, formaldehyde, particulate matter, visibility, air pollution, carbon monoxide, particulate matter, PM2.5, PM10, health effects, ambient air, nitric oxide, susceptibility, aldehydes, nitrogen dioxide, particle counts, oxygenated fuels, acetaldehyde, sulfur dioxide, light extinction, Denver, Colorado, CO. , Air, Geographic Area, Scientific Discipline, Health, RFA, Risk Assessments, Epidemiology, air toxics, Atmospheric Sciences, EPA Region, particulate matter, Allergens/Asthma, mobile sources, State, tropospheric ozone, aerosols, oxidants, asthma indices, exposure and effects, Acute health effects, ambient air quality, cardiovascular vulnerability, coronary artery disease, inhalation, lungs, urban air, ambient air, cardiopulmonary mechanisms, cardiotoxicity, cardiopulmonary responses, lung inflammation, air quality models, chronic health effects, human health effects, particulates, respiratory, Region 8, air pollution, airborne pollutants, atmospheric chemistry, inhaled, exposure, aldehyde emissions, motor vehicle emissions, atmospheric transport, human exposure, particle size, Colorado (CO), airway inflammation
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