1 Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand.
2 Department of Epidemiology, School of Public Health, University of North Carolina-Chapel Hill, CB-7435 UNC-CH, Chapel Hill NC 275997435 USA.
3 College of Public Health, Chulalongkorn University, Floor 10th, Building 3, Soi Chula 62, Payathai Road, Bangkok 10330, Thailand.
4 Faculty of Medicine, Chulalongkorn University, Rama 4 Road, Bangkok 10330, Thailand.
Wichai Aekplakorn, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand. E-mail address: rawap{at}mahidol.ac.th
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Abstract |
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Methods The associations of daily exposure to SO2 and particulate matter 10 µm in diameter (PM10) with pulmonary function were examined in 175 asthmatic and non-asthmatic children aged 614 years who resided near a coal-fired power plant in Thailand. Each child performed daily pulmonary function tests during the 61-day study period. General linear mixed models were used to estimate the association of air pollution and pulmonary function controlling for time, temperature, co-pollutants, and autocorrelation.
Results In the asthmatic children, a daily increase in SO2 was associated with negligible declines in pulmonary function, but a small negative association was found between PM10 and pulmonary function. A 10-µg/m3 increment was associated with changes in the highest forced vital capacity (FVC) (6.3 ml, 95% CI: 9.8, 2.8), forced expiratory volume at 1 second (FEV1) (-6.0 ml, 95% CI: -9.2, 2.7), peak expiratory flow rate (PEFR) (-18.9 ml.sec-1, 95% CI: -28.5, -9.3) and forced expiratory flow 25 to 75% of the FVC (FEF25-75%) (-3.7 ml.sec-1, 95% CI: -10.9, 3.5). No consistent associations between air pollution and pulmonary function were found for non-asthmatic children.
Conclusion Declines in pulmonary function among asthmatic children were associated with increases in particulate air pollution, rather than with increases in SO2.
Accepted 14 May 2003
Epidemiological studies have shown a short-term, reversible decline of lung function among children in response to air pollution.15 Inhalable particles have been frequently shown to be associated with adverse health effects, but epidemiological studies of short-term exposure and respiratory health still provide inconclusive evidence of an independent effect of sulphur dioxide (SO2) on the health of children. Some studies demonstrated pulmonary function changes and an increase in respiratory symptoms,69 whereas other studies failed to show an adverse effect.1013
Epidemiological studies on the independent relationship between SO2 and respiratory effects are typically limited by the high correlation between SO2 and particulate air pollution. Most previous panel studies on the acute effect of short-term air pollution exposure on respiratory health were conducted in cold weather countries and were subject to some limitations on exposure assessment. In cold countries, children usually spend a large portion of the winter indoors, and individual exposure estimation based on outdoor fixed-site ambient air concentrations might overestimate personal exposure.14 Studies of short-term effects of air pollution in tropical countries where the climate, population characteristics, and environmental conditions differ from Western countries are still limited. This study presents an evaluation of the association of short-term exposure to increased daily ambient SO2 with daily pulmonary function changes among children in Maemoh, Thailand.
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Methods |
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Parents were asked to complete a respiratory symptom questionnaire modified from a World Health Organization questionnaire for children.15 Children were considered to be suspected cases of asthma if the parents gave a positive response to the following question: Has your child had attacks of shortness of breath with wheezing during the past year? Suspected asthmatic children were examined by a physician. Consequently, children who had shortness of breath and wheezing consistent with asthma were classified as asthmatic by the physician if they reported that the symptoms were relieved by bronchodilator medication. Of the 98 asthmatic children identified, 88 participated in the study. To include a group of non-asthmatic children, 98 children were randomly selected from children who had no history of any chronic diseases and asthma and 96 of them participated.
Pulmonary function testing
Data were collected in the field by teams that included nurses trained in coaching pulmonary function test manoeuvres. Pulmonary function testing was conducted in the afternoon at a meeting place in each village. Participants were shown a videotape of study procedures on the first day. For the first week, each child was instructed and the manoeuvre was demonstrated. The pulmonary function data were collected for 61 days from 1 October 1997 to 30 November 1997 at 3 p.m. to 5 p.m. each day.
Based on the recommendation for standardization of spirometry by the American Thoracic Society (ATS), 1994,16 spirometry was performed while standing, without a nose clip, using a Pneumotach spirometer (S&M Instruments, USA) coupled with automatic data acquisition software in a laptop computer. A minimum of five and a maximum of eight blows were performed. From a minimum of three valid expiratory manoeuvres, the highest forced vital capacity (FVC), forced expiratory volume at 1 second (FEV1), peak expiratory flow rate (PEFR), and mean forced expiratory flow during the middle half of the FVC (FEF 2575%) were selected. A manoeuvre was considered acceptable when it met the ATS criteria. The results of the computerized spirometry were checked for acceptability and reproducibility on site. Later, the stored volumetime spirograms were validated by a pulmonologist.
Ambient air monitoring and meteorological data
Daily outdoor air pollution data were obtained from the air-monitoring network in the area around the power plant. SO2 concentrations were monitored by the Electric Generating Authority of Thailand at three sites (SP, SM, and HF) in the study area. The 24-hour average SO2 concentrations were measured by photometer at all monitoring stations. Particulate matter 10 µm in diameter (PM10) concentrations were determined gravimetrically, using Hi-Volume samplers at SP and HF. SO2 data were available on most of the days; however, the PM10 data from the Hi-Volume sampler were available on 58 days at SP and 53 days at HF, which accounted for 95.1% and 86.9% of the total study days, respectively, and days with missing pollution data were excluded from the analysis. Nitrogen dioxide (NO2) and ozone were undetectable most of the time, therefore they were not included in the analysis. Meteorological data including temperature, wind speed, and wind direction were obtained from each monitoring station. For wind direction, a variable indicating the number of hours in a day that participants residences were downwind from the power plant was created. Daily relative humidity and dew point were obtained from measurements at the airport approximately 30 km southwest of the study area. The daily mean level of each variable at the outdoor fixed air monitoring data corresponding to the subjects village was assigned as daily personal exposure.
Statistical analysis
Descriptive statistics for the continuous outcome variables, FVC, FEV1, PEFR, and FEF2575% were examined for normality of distribution and outliers and correlations among outcome variables, temperature, humidity, and air pollution were evaluated.
Because of repeated measurements resulting in potentially correlated outcomes for each subject, we used general linear mixed models to analyse the relationship between pulmonary function and air pollution. The individual subject was the unit of analysis. In separate models, the pulmonary function parameters FVC, FEV1, PEFR, and FEF2575% were regressed as dependent variables on the independent variables of pollutant concentration, time, temperature, height, gender, and village.
Base models were created to control the effect of time trends, temperature, weekday, personal characteristics of height and gender as fixed effects, and indicator variables of village. For personal characteristics, models including height and gender appeared to be adequate as age and weight were highly correlated with height and did not improve fit. Day of study was included in the models to control for time trend. An indicator of weekend was also considered to control for the weekend effect. Both model fit and biological plausibility were considered in developing a base model for each lung function parameter. For example, the final best-fit base models for FVC and FEV1 included linear terms for day of study, same day temperature, height, and gender. Using maximum or minimum temperature lagged by one day did not improve the model and had less effect on pulmonary function than the effect of same-day mean temperature. Relative humidity or a binary indicator of humidity (relative humidity >72%) and a binary variable for hot days (daily average temperature >25°C) were also considered.
The goodness of fit for the base models and for appropriate covariance structures for within-subject observation and accounting for autocorrelation was determined by examination of Akaikes Information Criterion (AIC). We found that models with compound symmetry (CS) structure provided the best fit for all the pulmonary function parameters.
In addition to the air pollutant variables, we also evaluated the potential effects of wind speed and direction but these variables were not strongly associated with pulmonary function and were not included in the final models.
We added daily average pollutant concentrations to the models as continuous variables. Analyses initially examined exposures on the same day; cumulative effects were also considered by including SO2 with lags of 13 days and averaged over 3 (mean of lags 02 days) to 5 (mean of lags 04 days) days. We computed the change in pulmonary function for a 10-µg/m3 change in each pollutant. The two-pollutant models were examined using the optimal air pollution time span from the single pollutant models.
Additional analysis was done focusing on the decrement in FEV1 on each day for each child from his or her median FEV1. A decrement of >10% in FEV1 was considered a meaningful adverse effect.1719 A binary variable indicating whether the decrement in FEV1 on each day was larger than 10% of the childs median FEV1 was created. The association between air pollutants and decrement >10% in FEV1 was evaluated by logistic regression based on generalized estimating equations (GEE) with a working correlation matrix of first order autocorrelation.
To eliminate possible training effects from repeated testing, we excluded the first week of pulmonary function data from the analysis. Residual analysis was performed to examine outliers, and sensitivity analyses excluding outliers or observations with low SO2 were also performed. Participants who performed pulmonary function tests for fewer than 15 days were excluded from the analysis. The statistical analysis procedure was performed using SAS (version 8.1)
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Results |
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Association of pulmonary function and SO2
In asthmatic children, all pulmonary function parameters had small declines associated with increases in SO2 concentrations in the one-pollutant model. The modest associations were weakened after controlling for particulate air pollution in the two-pollutant models (Table 3). When the absolute changes were translated into percentage changes relative to the population mean, the changes in daily lung function for a 10-µg/m3 increase of SO2 among asthmatics were: FVC of -0.05%, FEV1 of -0.04%, PEFR of -0.07%, and FEF2575% of -0.04%. Non-asthmatic children appeared to be less susceptible to SO2.
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For the asthmatic group, the FVC and FEV1 showed no changes when SO2 increased from the first to the second level (Figure 1). When SO2 increased to the third level, the FVC and FEV1 decreased by 34.0 ml (95% CI: -59.4, -10.1) and 25.4 ml (95% CI: -47.7, -2.98) respectively after adjusting for PM10. Smaller effects were observed for the highest level, and there was no substantial change in PEFR or FEF2575% as SO2 increased (data not shown). No pattern of a doseresponse relationship with FVC, FEV1, PEFR, or FEF2575% was observed among non-asthmatics.
Association of pulmonary function and particulate matter
Table 3 shows negative mean changes in pulmonary function associated with PM10 when the absolute changes were translated into percentage changes relative to the population means. After adjusting for SO2, time, and temperature, among asthmatics a 10-µg/m3 increase in PM10 was associated with changes in FVC of -0.33%, FEV1 of -0.36 %, PEFR of -0.42%, and FEF25 - 75% of -0.17%. For non-asthmatics, no consistent association of PM10 with pulmonary functions was observed. The effect of particulate matter did not materially change in the two-pollutant models adjusted for SO2. Additional analyses were conducted to evaluate the effects of wind direction and wind speed by including these variables in the models as potential predictors; however, no substantial changes in estimates were observed.
Table 4 shows the cumulative effect of PM10 exposure. For asthmatic children, small negative associations with exposure to PM10 were observed at 1- to 3-day lags, and with 3- to 5-day average exposures. However, most of the CI included null value. Among non-asthmatic children, no consistent associations were observed for lagged PM10 exposure.
The effect of PM10 was evaluated at different quartiles similar to SO2. The categorized boundaries for PM10 were: <26.51 (1), 26.5230.79 (2), 30.8037.85 (3), and >37.85 µg/m3 (4). Using the lowest concentration as the reference category, for asthmatics, all the lung function parameters showed slightly negative changes as the level of PM10 increased (data not shown). As an example, the effect on FEV1 is shown in Figure 2. No pattern of consistent relationships were observed among non-asthmatics.
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Table 5 shows the association between daily FEV1 decrement >10% and air pollution. For asthmatics, the rate of decrements >10% had a modest positive association with SO2 and PM10, but the association was weaker for SO2 and the CI included null value. No associations were found between air pollution and daily decrement of FEV1 in the non-asthmatic group.
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Discussion |
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The concentrations of pollutants observed in the present study were relatively low, possibly due to recent measures to mitigate S02 emissions, including the installation of scrubbers and the use of low-sulphur coal. Excluding days with extremely low SO2 concentrations (2.62 µg/m3) from the analysis did not substantially change the results.
Particulate air pollution was considered primarily as a confounding factor for the association between SO2 and pulmonary function. A major concern for evaluating the separate effects of SO2 and particulate air pollution is that they may be highly correlated, but that was not the case in the present study.
Experimental studies21 report reduction in FEV1 by up to 12.8% in asthmatic adults after 5-minute exposures to SO2 at concentrations much higher than we observed in ambient air. However, observational epidemiological studies show susceptibility to the effects of air pollution at lower concentrations, and this might be due to the individual differences in susceptibility, the nature of the air pollution mixture, and other environmental conditions. The results of the present study are consistent with some others which show a modest negative association between pulmonary function and SO2.5,9,22,23
The association of particulate air pollution and pulmonary function found in this study is also consistent with other studies in the US and Europe. A meta-analysis24 of panel studies reported a combined effect estimate, calculated for a 10-µg/m3 increase in PM10 (daily mean), of a weight average of 0.15% decrease in FEV1 and of 0.08% decrease in PEFR. A study that combined data from five panel studies also reported a 0.07% PEFR decrement for a 10-µg/m3 increase in PM10.17
There were some limitations in the present study. Since the air pollution levels in the study area were relatively low and the study period was rather short, it provided few days with high pollution levels. The smaller number of days with higher air pollution could affect the power of the study, as reflected by the relatively wide CI. This small number of days with high levels of SO2 during the study period might also partly contribute to the lack of association of SO2 with pulmonary function, because it might not reach a threshold for the effect of SO2 to be detectable.
If missing pulmonary function tests due to illness on certain days were related to high air pollution the estimated changes might be biased. However, in the present study, the proportion of children missing pulmonary function tests on each day (either asthmatic or non-asthmatic) were not correlated with air pollution concentrations. There were 7 days of missing data for PM10 measurement at HF station, but they were distributed randomly.
The present study did not incorporate the proportion of time each child spent indoors and outdoors, but this estimation is necessary only when indoor and outdoor concentrations are different. Unlike those in the Western countries, the daily temperatures were consistently moderate and the houses in the study areas have no air conditioning systems, so that indoor air is well mixed with outdoor air. A study of air pollution in Bangkok reported that the day-to-day fluctuations in indoor particulate matter concentration are well correlated with outdoor particulate matter concentration at nearby outdoor locations and stationary monitoring stations.25 Spatial variability is likely to be minimal, because the areas of the villages under study are small and the participants reside within approximately 7 km of the monitoring stations. As a result of the preceding factors, data from fixed, outdoor monitors should be a reasonable surrogate for individual exposure.
Another limitation is related to the measurement of other pollutants derived from SO2. Although this study focused on the association of SO2 and PM10 with pulmonary function, a few studies recently found an association with sulphate particles (SO4) or with particle strong acidity.17,26 These data on SO4 and particle strong acidity were not available in the air monitoring system of the study area. In other studies, SO2 was found to be correlated with SO4 (r = 0.70) and particle strong acidity (r = 0.51), so SO2 concentration should serve as a partial surrogate for its other derivatives.
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Conclusion |
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KEY MESSAGES
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Acknowledgments |
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References |
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