1 Environmental Health Directorate, Health Canada, Ottawa, Ontario, Canada.
2 Meteorological Service of Canada, Environment Canada, Downsview, Ontario, Canada.
3 Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
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ABSTRACT |
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air pollution; child; hospitalization; ozone; respiratory tract diseases
Abbreviations:
ICD-9, International Classification of Diseases, Ninth Revision; PM2.5, particulate matter 2.5 µ in diameter; PM10-2.5, particulate matter >2.5 µ and
10 µ in diameter; ppb, parts per billion; TSP, total suspended particulate matter.
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INTRODUCTION |
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To further examine the hypothesis that young children are at risk for acute respiratory effects because of short-term exposure to ambient ozone, we examined the daily number of emergency or urgent hospital admissions for croup, pneumonia, asthma, and acute bronchitis/bronchiolitis between 1980 and 1994 in the greater Toronto area. Concurrent data on ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, and estimated daily values of fine (PM2.5, particulate matter whose average aerometric diameter is 2.5 µ) and coarse (PM10-2.5, particulate matter whose average aerometric diameter is >2.5 µ and
10 µ) particulate matter also were obtained, along with weather variables. The statistical association between daily fluctuations in ambient ozone levels and corresponding changes in daily hospital admission rates were examined, after adjustment for temporal trends in admissions, weather factors, and exposure to other pollutants. To examine the hypothesis that hospital admission practices, access to health care, or statistical analysis methods unduly influenced the observed association between respiratory admissions and air pollution, we also studied the association between ozone and emergency or urgent admissions for gastroenteritis, a disease expected to be unrelated to air pollution levels. In our study, gastroenteritis was the second leading cause of hospitalization for children less than 2 years of age.
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MATERIALS AND METHODS |
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Daily values of particulate mass were available during only the MayAugust period in 1992, 1993, and 1994. The association between these data and hospitalization has been reported previously (1). These values were estimated from daily values of total suspended particulate matter (TSP) and sulfates obtained at the downtown monitoring site by using high-volume samplers and from daily average values of the coefficient of haze (a measurement of visibility interference in the atmosphere) monitored at the same site. To predict daily measures of particulate mass, these three time series were then compared with PM2.5 or PM10-2.5 mass concentrations obtained from co-located dichotomous samplers operating on a 6-day sampling schedule from 1984 to 1991. Separate prediction equations were developed for the AprilSeptember and OctoberMarch time periods. Sulfates and the coefficient of haze were sufficient to predict PM2.5(R2 = 0.80); however, TSP did not provide any additional predictive power beyond that given by these two variables. Sulfates were a stronger predictor of PM2.5 during the AprilSeptember period compared with the OctoberMarch period, with the coefficient of haze displaying the opposite pattern. TSP was the only significant predictor of PM10-2.5 (R2 = 0.63). The daily maximum and minimum temperatures and the daily average relative humidity were obtained from the Pearson International Airport, located northwest of metropolitan Toronto.
Hospital discharge records for residents of the study area were obtained from the Ontario Ministry of Health for all hospitals located in the study area. Only those admissions to acute care, active treatment hospitals considered emergent or urgent were selected. Both planned admissions and transfers from other institutions were excluded. The number of daily admissions from January 1, 1980, to December 31, 1994, of children less than 2 years of age was abstracted for cases in which the principal reason for hospitalization was asthma (International Classification of Diseases, Ninth Revision (ICD-9) code 493), acute bronchitis/bronchiolitis (ICD-9 code 466), croup (ICD-9 code 464.4), or pneumonia (ICD-9 codes 480486). The number of daily admissions for each of the four disease groups was then consolidated to form a single time series of total respiratory admissions.
It is conceivable that the association between air pollution and hospital admissions could be due, in part, to health care delivery practices (i.e., people are admitted more often to a hospital for any cause on high-pollution days compared with low-pollution days). To examine this issue, we also abstracted the daily number of admissions for gastroenteritis (ICD-9 code 558) in children less than 2 years of age, the second single leading cause of hospitalization in this age group after respiratory problems. We did not expect that air pollution would be associated with gastroenteritis.
The purpose of our analysis was to relate air pollution levels to hospital admissions on a temporal basis. However, a number of other factors influence the temporal variation in both admissions and air pollution. Respiratory admission rates vary by time of year; the highest and lowest mean daily admission rates occur in February (8.23 admissions/day) and August (2.01 admissions/day), respectively, which may in part be due to increased infection during the months in which people spend a greater amount of time indoors (23). Daily admission rates also vary by day of the week and are highest on Monday and lowest on the weekend. Air pollution levels in Toronto also vary by season and day of the week (22
). Finally, weather factors, such as temperature and humidity, are predictors of both admissions and air pollution.
Variation in hospital admissions should not be attributed to variation in air pollution if the former is due, in whole or in part, to common, but possibly unrelated temporal cycles and weather factors. We adjusted the time series of hospital admissions for temporal trends by using a LOESS (24) nonparametric smoothed representation of day of study with a 70-day span (figure 1) and for day-of-the-week effects. We also adjusted the admission series for the influences of weather. The effects of weather were modeled by using LOESS smoothing functions (span of 50 percent) of daily maximum temperature, daily minimum temperature, and daily average relative humidity, which were recorded on the date of admission and 1 and 2 days prior to admission. To identify the smallest number of weather variables required to predict admissions, we used a forward inclusion, stepwise regression procedure to select a minimally sufficient set of weather predictors. Akaike's Information Criterion was used, which is a function of the residual deviance and the degrees of freedom of the model.
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The length of the span was selected to minimize autocorrelation in the residuals. We considered spans ranging from 30 days to 150 days in 10-day increments. For each span, Bartlett's statistic (27) was used to test the residuals for the presence of white noise. A span of 70 days yielded the least evidence that the residuals were correlated serially (p = 0.08).
We chose to relate temporally filtered weather and air pollution values to temporally filtered daily number of admissions (denoted by preadjustment). Therefore, we removed the potentially confounding temporal cycles in all time series prior to linking them. Residuals from these temporally filtered series were then compared.
An alternate statistical approach, denoted by coadjustment, is to simultaneously regress unfiltered air pollution and weather data on daily admission counts, adjusting for temporal trends in the admission time series only and using a smoothed function of day of study. Differences in the results between these two methods suggest that there is additional information in the time series data relating air pollution and admissions that is not captured in the high-frequency or day-to-day cycles in the respective series. The preadjusted method removes all non-high-frequency information in the data. The coadjusted approach can leave some residual confounding in the cycles between admissions and air pollution. Ozone levels are highest in the summer, while respiratory admissions peak in the winter months. When the coadjusted approach is used, the cycles in these two time series compete to predict admissions. Thus, some of the midfrequency or seasonal cycle in admissions may be captured by a corresponding, but contrary cycle in air pollution, thus deflating the air pollution effect. If the cycles are in unison, such as occurs with particulate matter and respiratory admissions, the effect estimates will be biased upward.
The preadjustment approach controls for such confounding. Estimates of the air pollution effect based on the preadjustment approach reflect the true association between short-term (day-to-day) variation in air pollution and admissions, while estimates based on the coadjustment method reflect the sum of short-term and residual mid-term concentrations. Compared with the short-term concentration signal, the residual mid-term cyclic effect on admissions is subject to greater potential confounding because of other temporally varying risk factors. We thus recommend use of the preadjustment method, which better controls for potentially confounding temporal cycles than does the coadjustment technique. However, for comparison purposes, this paper presents results of both approaches, since most previous time series studies of air pollution and hospital admissions have used the coadjustment approach.
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RESULTS |
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Of the nine weather variables examined (daily minimum temperature, daily maximum temperature, and relative humidity, all recorded on the day of and 1 and 2 days prior to the date of admission), relative humidity recorded on the date of admission was sufficient to explain day-to-day variations in admission rates for respiratory problems based on a forward inclusion, stepwise regression analysis in which the Akaike Information Criterion was used as the inclusion criterion. Consequently, relative humidity with no time lag was used in all further analyses to represent the effects of weather.
The percentage increase in the daily number of respiratory admissions associated with a change in ozone levels from 0 to 45.2 parts per billion (ppb) (the mean concentration in this study) recorded on the day of admission and up to 5 days prior to admission is shown in table 2, by time of year (MayAugust, SeptemberApril, and January December). The association between ozone and admissions in the SeptemberApril period was weak (-1.29 < t ratios < 0.05 for all time lags considered). In the MayAugust period, the association was much stronger for ozone levels recorded on the day of admission and 1, 2, 3, and 4 days prior to admission. Thus, ozone was positively associated with increased hospital admissions for several days after the date of exposure.
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The association between daily admissions for gastroenteritis and summertime ozone levels was determined for time lags of 05 days. None of the corresponding t ratios was greater than 2. Ozone concentrations recorded 5 days prior to admission displayed the strongest association with gastroenteritis both in terms of the largest predicted percentage increase in admissions (8.5 percent) and the largest t ratio (1.58). On the basis of this analysis, it appears that neither hospital admission patterns in general nor statistical methods of analysis were responsible for the observed association between ozone and hospital admissions for respiratory problems.
The sensitivity of the ozone association with admissions to the specification of the weather model was examined by adjusting the ozone effect for either daily maximum temperature (a 35.1 percent increase in admissions associated with ozone) or daily minimum temperature (a 41.5 percent increase) recorded on the date of admission. The ozone association with admissions did not seem sensitive to the form of the weather model specification.
We also examined the hypothesis that exposure should precede response by regressing hospital admissions lagged 15 days on ozone concentrations. The percentage increase in hospital admissions attributable to a 45.2 ppb increase in the filtered daily 1-hour maximum ozone level ranged from -3.9 percent (t ratio = -1.10) at lag 3 days to 2.2 percent (t ratio = 0.59) at lag 1 day. We concluded from this analysis that there is no evidence to suggest that hospital admissions are a predictor of ozone and that the positive and statistically significant associations we observed with ozone levels preceding hospital admissions were not due to some unmeasured factor that covaried with ozone within a time period encompassing the date of exposure.
Standard errors, t ratios, and confidence intervals characterize uncertainty in the estimates of model parameters. However, these measures do not describe uncertainty regarding the form of the model. One approach to examining model uncertainty is to determine the increase in daily admission rates associated with ozone levels for each year of observation. If the model specification is correct, the variation in effect estimates among years should equal the uncertainty in estimating the effect over all years. A positive association was observed between the 5-day moving average of the filtered daily 1-hour maximum ozone concentrations and the filtered admissions for 13 of the 15 years examined (not in 1980 or 1993). The standard error of the percentage increase in admissions among years was 7.40 percent, a value slightly higher than the standard error based on the model-based analysis of all available data (6.25 percent). This finding suggests that most of the uncertainty in estimating the association between ozone and hospitalizations could be attributed to uncertainty in parameter estimation rather than to uncertainty in the form of statistical model.
To compare the effects of ozone on respiratory diseases in young children and adults, we regressed the 5-day moving average of filtered ozone concentrations against filtered respiratory hospital admissions defined by ICD-9 codes 490496, 480486, 466, and 464.4 for those patients aged 18 years or more. An average of 13.5 admissions/day occurred in the MayAugust period for this group of diseases in adults. While the diagnoses of croup and bronchiolitis are essentially restricted to young children, adults were admitted for chronic bronchitis, emphysema, and chronic airway obstruction at a higher rate. A 45.2 ppb increase in ozone was associated with a 19.0 percent (95 percent confidence interval: 12.2 percent, 26.2 percent) increase in admissions, a value much lower than that observed for respiratory problems in children less than 2 years of age (34.8 percent).
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DISCUSSION |
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Neither the method of determining population exposure to ozone nor the statistical methods of analyzing the data was likely to induce an artifactual association. (If this were the case, we also would have observed an association between ozone and hospitalization for gastroenteritis.)
Air pollution effects on health may not be characterized fully if the association is described by a single day, even if that day is selected so that the lag time yields the largest relative risk. Ozone levels recorded on the day prior to the admission date were associated with the largest percentage increase in daily admissions (14.2 percent). However, the percentage increase in admissions over a 5-day period was much greater (34.8 percent), suggesting a temporal distribution of effects in the population of young children possibly due to host factors such as presence of respiratory disease, acute symptoms, and parental management of symptom episodes. Parental willingness to visit the hospital may depend on the child's history of respiratory or general health problems, the severity of the specific symptomatic episode, and the parent's use of hospital emergency services as a method of treatment. A time-distributed ozone association with nonaccidental mortality was also observed in Philadelphia, Pennsylvania (28).
As part of a study to identify host factors influencing the effects of urban air pollution on cardiorespiratory disease, we collected information from patients who visited the two emergency departments in Saint John, New Brunswick, Canada (29). Each patient who presented to the emergency department for a lung or heart problem was interviewed. Information was ascertained on the time of onset of the earliest symptoms and the time of onset of the symptoms most responsible for the current visit. Seventy-seven percent of visits for respiratory problems in children less than 2 years of age occurred within 2 days of the onset of the symptom most responsible for the current visit, with 75 percent of visits occurring within 5 days of the earliest symptom. The time-lag distribution of ozone effects on hospital admissions observed in the current study clearly overlaps the distributions of both the most responsible and the earliest symptom in the Saint John study of emergency room visits, indicating that ozone could play a role in initiating respiratory problems in young children and exacerbating preexisting problems. These data also indicate that the time-lag distribution of ozone effects used in the current study was adequate.
If other unmeasured factors were in fact responsible for the time-distributed association between ozone and hospital admissions, it is likely that an equally strong association would be detected for ozone levels recorded after the date of hospitalization (i.e., response precedes exposure). Since respiratory admissions did not predict ozone levels (i.e., response only followed exposure), it is unlikely that some other unmeasured factor that temporally covaries with ozone was the sole cause of the observed ozone effect.
The effect of ozone was insensitive to adjustment for other ambient air pollutants, with the adjusted increase in admissions for a 45.2 ppb increase in the 5-day moving average ozone concentrations ranging from 29.4 to 33.2 percent. However, since all other air pollutants examined were associated positively with hospitalization rates, we cannot rule out the possibility that ozone may act as a marker for the summertime atmospheric mix in Toronto. On the basis of our current analysis, however, ozone is the most important agent within the mix.
Regarding hospital admissions associated with a 42.5 ppb increase in ozone, we observed a 31.3 percent (t ratio = 2.79) increase for asthma, a 45.3 percent (t ratio = 2.61) increase for croup, a 45.7 percent (t ratio = 3.01) increase for acute bronchitis/bronchiolitis, and a 23.3 percent (t ratio = 1.42) increase for pneumonia. Ozone affects diseases of the lower and more peripheral respiratory tract, such as asthma, chronic obstructive lung disease, and pneumonia (30). The relation between croup and ozone indicates that this pollutant, at ambient concentrations, is also a risk factor for clinically significant disease of the high tracheobronchial region.
The relation between croup and ozone is biologically plausible. Inhaled ozone is absorbed by the surface liquid lining the airways. Toxicity is a function of concentration and duration of exposure. Within hours of exposure, there is increased ICAM-1 expression on mouse tracheobronchial epithelium and transepithelial migration of neutrophils into the mucosa (31). In animal studies, exposure causes morphologic injury along the entire respiratory tract (32
), with loss of epithelial cell cilia, injury to type 1 cells, and bronchiolitis with edema and inflammatory cell infiltration (33
). We also noted that children's nasal lavage neutrophils were increased on higher- versus lower-ozone days (34
).
Evidence of upper airway involvement comes from several sources. At least 50 percent of ozone is absorbed in the nasal passages and upper airways (35). In a study by McBride et al., 18 adults were submitted to nasal lavage following experimental ozone exposure (36
). The number of white blood cells increased only in the 10 asthmatic subjects both immediately after exposure to 240 ppb and 24 hours later. No changes were evident in 1-second forced expiratory volume, suggesting that upper airway inflammation occurs at lower ozone levels than those causing measurable changes in lung function.
While a number of studies have examined effects of air pollution on children, fewer studies have examined effects specifically in young children. In a study in Montreal, Canada, an increase of 4 nmol/m3 in the 24-hour average concentration of hydrogen ion (the study mean) was associated with a 5.0 percent increase (95 percent confidence interval: 0.4 percent, 9.6 percent) in daily emergency room visits for respiratory illnesses among children less than 2 years of age (37). Much larger effects were generally noted in association with other pollutants for persons aged 65 years or more. In a study in Hong Kong, elevated TSP concentrations were associated with hospital admissions in the group aged 14 years but not in older children or adults (38
). We previously reported a stronger association of respiratory hospital admissions with ozone and sulfates in children aged 2 years or less (a 15 percent increase in admissions attributed to increases of 50 ppb in daily maximum ozone and 5.3 µg/m3 in daily average sulfate) than in older children or adults (13
). In a study in Utah, respiratory hospital admissions were related to monthly PM10 levels and the status of a local steel mill (operating or not) (39
). Significant associations were observed for admissions of both preschool-aged children and the total population in relation to air pollution, although effects were not consistently higher in the preschool population.
Two studies have specifically examined the effects of air pollution on hospital admissions for croup. No association with monthly mean or peak concentrations of sulfur dioxide or smoke was found regarding hospital admissions in Zagreb, Croatia (40). A significant association of daily average TSP, nitrogen dioxide, and sulfur dioxide with cases of croup reported by hospitals and pediatricians was found in several German cities (41
). Specifically, changes in TSP and nitrogen dioxide from 10 to 70 µg/m3 were associated with increases of 27 and 28 percent, respectively, in the number of cases of croup. No association was found in this study between any pollutant and cases of obstructive bronchitis, which the authors indicated may have included some cases of bronchiolitis.
Only a few studies of air pollution and daily mortality have specifically examined effects in infants and children. In a study in Sao Paulo, Brazil, statistically significant associations were found between nitrogen oxides and daily mortality in children less than 5 years of age (30 percent increase, 95 percent confidence interval: 17 percent, 43 percent) for a 127 ppb increment in nitrogen oxides (the mean 24-hour average concentration observed in the study) (42). However, no associations with carbon monoxide, sulfur dioxide, nitrogen oxides, ozone, or PM10 were detected. In a Mexico City study, no statistically significant effect of ozone on daily mortality in children less than 5 years of age (or for deaths of persons of all ages or those older than age 65 years) could be detected when TSP was included as a covariate (43
). Significant effects of TSP were reported for persons of all ages and those older than age 65 years, but no results were reported for children. Mortality from sudden infant death syndrome per 1,000 livebirths was three to five times higher on days with the lowest versus highest levels of visibility in Taiwan, China (44
).
Carbon monoxide showed the strongest association with respiratory hospital admissions after adjustment for the effects of ozone compared with the other air pollutants examined. The biologic link between carbon monoxide and exacerbation of respiratory problems in young children is not clear. We are not aware of any biologic mechanisms that would explain such a result; however, similar findings have been reported. For example, carbon monoxide was a predictor of respiratory admissions for asthma and obstructive lung diseases for persons of all ages in Toronto (45) and for asthma admissions in Seattle, Washington (46
). The largest source of carbon monoxide in Toronto is gasoline-powered motor vehicles (47
); carbon monoxide consists of several pollutants, including ultrafine particles. Thus, it could act as an index for automobile combustion pollution in general, and the effect could be caused by other unmeasured ambient air pollutants. We also noted the similarity in the ordering of air pollution effects: ozone > carbon monoxide > particulate matter > sulfur dioxide in the present study, the earlier study in Toronto (45
), and the study in Seattle (46
).
We also examined the sensitivity of our results to the method of statistical analysis used. The daily number of admissions increased by 37.2 percent (t ratio = 6.24) for a 45.2 ppb increase in the 5-day moving average of the daily 1-hour maximum ozone levels in the summertime based on the coadjustment model. Here, temporal trends in admissions and weather were modeled simultaneously with ozone. This percentage is similar to that based on using the preadjustment approach (34.5 percent), in which temporal trends in admissions and weather effects are removed from the admissions time series prior to linking with a prefiltered ozone series in which temporal trends in ozone levels have been removed. However, an 8.1 percent decrease (t ratio = -3.46) in admissions was associated with an increase in ozone for data based on the entire year and the coadjustment method. This result is clearly different from that obtained by using the preadjustment approach (a 16.1 percent increase) and may have occurred because there were strong and conflicting seasonal cycles in both admissions and ozone. The coadjustment approach allows the admissions and ozone cycles to compete to predict admissions.
The stronger admission cycle apparently dominated the modeling of temporal effects, resulting in a negative association with ozone. The preadjustment method removed these temporal cycles prior to linking the two time series together, thus eliminating any potential confounding between the cycle in admissions and the conflicting cycle in ozone. Restricting the analysis to the MayAugust period mitigated this confounding problem, since the cycles were much weaker during the summer months. We recommend that if the coadjustment method is used, a season-specific analysis should be conducted to minimize residual confounding due to coincident (conflicting) temporal cycles in the health and environmental variables. On the basis of the analyses described in this paper, we conclude that summertime urban air pollution, especially ozone, increases the risk that children less than 2 years of age will be hospitalized for respiratory disease.
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NOTES |
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REFERENCES |
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