Air Pollution and Disability Days in Toronto: Results from the National Population Health Survey

Environmental Research Section A 89, 210}219 (2002) doi:10.1006/enrs.2002.4373

Air Pollution and Disability Days in Toronto: Results from the National Population Health Survey David M. Stieb,* Marc Smith-Doiron,* Jeffrey R. Brook,- Richard T. Burnett,* Tom Dann,? Alexandre Mamedov,- and Yue ChenA *Healthy Environments and Consumer Safety Branch, Health Canada; -Meteorological Service of Canada, Environment Canada; ?Environmental Technology Centre, Environment Canada; and ADepartment of Epidemiology and Community Medicine, University of Ottawa Received October 3, 2001

the analysis of readily available large administrative datasets on health outcomes such as mortality and hospital admission, which have made it possible to detect subtle air pollution effects. Fewer studies have looked at other health outcomes, and in particular, only two studies have examined the association between air pollution exposure and restricted activity days (Ostro, 1987; Portney and Mullahy, 1986). These studies demonstrated effects of air pollution at the then higher levels of air pollution observed in North America and did not re8ect more recent advances in the analysis of time series data of this kind, such as the use of methods of controlling for seasonal cycles in exposure and response and for accounting for potentially nonlinear, confounding effects of weather. In this study, we attempted to address these issues by looking at the association between relatively lower-level exposures seen in Toronto, Canada during the 1990s and applying current methods to control for confounding by seasonal cycles and weather.

The inBuence of air pollution on disability days in Toronto during the period 1994+1999 was examined using data from Canada’s National Population Health Survey. A model of disability days (the sum of days spent in bed and days when the respondent cut down on usual activities) during the 2 weeks prior to the interview was constructed by sequentially examining the inBuence of time period, personal characteristics, weather, and air pollution. After adjusting for these other factors, only the effects of carbon monoxide and particulate matter of median diameter less than 2.5
m (PM2.5) were statistically signiAcant (respectively, 30.8% (95% CI 1.2 +69.0) and 21.9% (95% CI 3.8 +43.0) increase in disability days for a change in concentration equal to the interquartile range of the 2-week average pollutant concentration). PM2.5 was more strongly associated with disability days in the warm season. Results of multipollutant models were difAcult to interpret in that effect sizes appeared to be inBuenced by covariation among pollutants. With the exception of warm season results for PM2.5 , Andings were not sensitive to alternative analytical approaches. While these results are suggestive of signiAcant effects of the urban air pollution mix at relatively low ambient concentrations, the precise contribution of individual pollutants could not be determined.  2002 Elsevier Science (USA)

METHODS

We utilized data from Canada’s National Population Health Survey (NPHS), which has been described in detail elsewhere (Tambay and Catlin, 1995). Brie8y, this is a large-scale nationally representative survey on general health status which was initiated in 1994 and is repeated every 2 years. For the purposes of the current analysis we utilized data from the 7rst three cycles (1994/1995, 1996/1997, and 1998/1999), which included both longitudinal and cross-sectional samples. Thus the data that we analyzed included up to three observations on some individuals (i.e., one in each survey cycle). Core survey content includes information on personal

INTRODUCTION

Over the past 10 years, scores of epidemiological studies linking air pollution with a variety of acute health effects ranging from premature mortality to small changes in lung function have been published (CEPA, 1999a,b; USEPA, 1996; WHO, 1999). The preponderance of evidence in this area comes from 210 0013-9351/02 $35.00  2002 Elsevier Science (USA) All rights reserved.

AIR POLLUTION AND DISABILITY DAYS IN TORONTO

characteristics, health-related behaviors such as smoking, and a number of general health measures, including the occurrence of days spent in bed and days when the respondent cut down on usual activities, during the 2 weeks prior to the interview. Our primary analysis utilized data from the single individual selected from each household who completed a more detailed interview, including a broad spectrum of personal characteristics relevant to health status. Those characteristics considered in our analysis were gender, self-reported presence of chronic disease, age, smoking habits, education, employment, type of occupation (dichotomized into service and trade), income, and the health utilities index. The latter is a multiattribute index of health status which has been widely used to quantify health state utilities for the purpose of cost}utility analysis (Feeny et al., 1995). It is measured on a 0 to 1 scale,with 0 representing death and 1 denoting perfect health. We extracted the ‘‘number of bed days in the past 2 weeks’’ and ‘‘number of cut down days in the past 2 weeks’’ from the 1994}1995, 1996}1997, and 1997}1998 NPHS, for residents of the City of Toronto (Census Division 20). As sensitivity analyses, we also examined the greater Toronto area (areas in both Census Metropolitan Area 535 and one of Census Divisions 18, 19, 20, 21, or 24). Air pollution data were obtained from the National Air Pollution Surveillance system. We obtained data on carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), particulate matter of median aerometric diameter less than 10 and 2.5
m (PM10 and PM2.5 , respectively), and sulfate from PM2.5. Daily data were available for pollutant gases, while particulate matter was measured on an every sixth day sampling schedule. CO, NO2 , O3 , and SO2 were measured using ‘‘reference methods’’ or ‘‘equivalent methods’’ as designated by the United States Environmental Protection Agency. CO was measured using nondispersive infrared spectrometry, NO2 using chemiluminesence, O3 using chemiluminesence/ultraviolet photometry, and SO2 using coulometry/ultraviolet 8uorescence. PM2.5 and PM10 were measured gravimetrically using dichotomous samplers. When data from more than one monitoring site were available, they were averaged. Aeroallergen data (pollen grains and fungal spores) were collected at a single site using rotation impaction sampling equipment operating at 2400 rpm set to collect 1 min from every 10-min period over a 24-h interval. The site was initially located in midtown Toronto, but was moved in 1996 to a location northwest of the study area. Concentrations of individual pollen grains and fungal spores were

211

FIG. 1. Location of monitoring sites within Toronto (Census Division 20).

collapsed into the following classes for analysis: Ascomycetes, Basidiomycetes, Deuteromycetes, small round spores (less than 8}10
m), ferns, grasses, trees, and weeds. In addition to these groups, individual Basidiomycetes (Ganoderma) and Deuteromycetes (Alternaria, Cladosporium, Epiccocum) species were examined. Weather data from Pearson International Airport located northwest of the study area included fog, maximum humidex, maximum 3-h pressure change, 24-h pressure change, precipitation, mean/minimum/maximum temperature, dewpoint temperature, and relative humidity. Locations of the monitoring sites are shown in Fig. 1. Because bed days were infrequently reported, we employed a single outcome measure, disability days, equal to the sum of bed and cut down days. We constructed a model of disability days by sequentially examining the in8uence of four groups of independent variables: time period, personal characteristics, weather, and air pollution. In the 7rst step, we de7ned variables for each period of NPHS data collection, to control for differences in the probability of reporting bed and cut down days by time period (e.g., winter versus summer periods). As a sensitivity analysis, rather than employing a model term for each period we pre7ltered both the exposure and the response series by taking the ratio of actual versus predicted disability days and the difference between actual and predicted environmental variables, where predicted values were the average for each period. While it has become customary to employ nonparametric smoothing functions to control for these effects (Schwartz et al., 1996), the discontinuous nature of data collection (data were collected in waves lasting a minimum of 13 days interspersed with similar or longer periods with no

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data) prevented us from using this approach. In the second and third steps, backward and forward stepwise regression procedures were used to select variables representing personal characteristics and weather variables, respectively. Weather variables entered as nonparametric smooth functions;locally optimal estimating and smoothing scatterplots (LOESS) (Cleveland and Devlin, 1988), with a span of 50%;to allow for 8exibility in modeling nonlinear effects of weather. The model with the lowest Akaike’s Information Criterion (AIC) was chosen at each step. The AIC is the deviance penalized for the number of parameters being estimated (Hastie and Tibshirani, 1990). In the 7nal step, air pollution variables were added 7rst singly, and then a backward stepwise procedure was employed to construct a parsimonious multipollutant model. Weather, air pollution, and aeroallergen variables were introduced into the model as 2-week averages, lagged 0, 7, and 14 days. In the case of air pollution variables, 2-week averages were based on 24-h average concentrations. Regression analysis was carried out using the Generalized Additive Model procedure in S-Plus (Hastie and Tibshirani, 1990). Disability days were treated as Poisson distributed. Results for the lag associated with the largest t ratio, positive or negative, are reported. The per-

centage increase in the health outcome is provided for the interquartile range of the 2-week average air pollution concentration. The latter more appropriately re8ects air pollution burden of illness given the limited variability in 2-week average concentrations relative to daily values. RESULTS

Data were available from 5309 interviews. Tables 1 and 2 present descriptive statistics on the variables used in the analysis. Mean disability days per period are shown in Fig. 2. There is evidence of a seasonal cycle, with the highest reporting of disability days generally occurring in late winter or early spring. The outlier observed for the period January/February 1995 may re8ect the small number of observations (n"14) during this period. Table 3 presents results of single-pollutant models for analyses of the entire year and May to September. Only the effects of CO (entire year) and PM2.5 (entire year and warm season) were statistically signi7cant. Results are shown graphically in Fig. 3, by substituting a spline function of the pollution variable in place of the linear term. The smoothing parameter was selected by S-plus. In all cases, the shape of the concentration}response appears

TABLE 1 Descriptive Summary of Disability Days and Personal Characteristics, 1994+1999 Variable

Units

Mean/percent

SD

95th %ile

Max

0.73 0.48 0.24

2.63 2.04 1.37

5.0 3.0 1.0

14.0 14.0 14.0

3.4 19.5

20.0 79.0

24.0 97.0

9.6 15.5 4.3 0.19

25.0 53.0 32.4 1.0

99.0 71.0 55.3 1.0

Disability days Cut down days Bed days

Mean Mean Mean

Self-reported chronic disease Percentage female Educationa Age Household income

Percentage yes Percentage female Mean years Mean years Median category [$ (Canadian)]

Employedb Type of jobb

Percentage yes Service Trade Percentage yes Percentage yes

59.2 82.1 17.9 26.2 24.0

Mean Mean Mean Mean

16.4 21.6 24.5 0.89

Regular smoker in household Respondent daily or occasional smokera Number cigarettes smoked dailya Years smokeda Body Mass Indexc Health Utilities Index a

Age 512 years. Age 515 years. c Age 520 years. b

55.1 54.6 14.3 43.3 40,000}49,999

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AIR POLLUTION AND DISABILITY DAYS IN TORONTO

TABLE 2 Descriptive Summary of Environmental Variables, 1994+1999, 2-Week Averages Variable

Units

CO NO2 O3 SO2 PM10 PM25 Coarse fraction PM10 }PM2.5 SO4 from PM2.5

ppm ppb ppb ppb
g/m3
g/m3
g/m3
g/m3

CO NO2 O3 SO2 PM10 PM25 Coarse fraction PM10-PM2.5 SO4 from PM2.5

ppm ppb ppb ppb
g/m3
g/m3
g/m3
g/m3

Ascomycetes Basidiomycetes Ganoderma Deuteromycetes Alternaria Cladosporium Epicoccum Small round spores Ferns Grasses Trees Weeds

Spores/m3 Spores/m3 Spores/m3 Spores/m3 Spores/m3 Spores/m3 Spores/m3 Spores/m3 Pollen grains/m3 Pollen grains/m3 Pollen grains/m3 Pollen grains/m3

n

Mean

All year 2192 1.0 2192 25.7 2192 19.2 2192 4.5 2145 23.6 2145 12.9 2145 10.8 1985 3.2 May to September 919 0.9 919 25.4 919 25.7 919 4.0 919 26.4 919 14.2 919 12.2 859 4.2 919 919 919 919 919 919 919 919 919 919 919 919

compatible with a linear, no-threshold relationship, although the relationship at the extremes of pollutant concentration are uncertain. The probability of the F statistic comparing the 7t of models with linear terms versus spline smoothers of air pollution concentration was nonsigni7cant (P'0.05), supporting a linear relationship, in all cases other than the effect of PM2.5 in the entire year analysis. Substituting the natural log of PM2.5 resulted in a slightly stronger effect of similar magnitude to that based on a linear air pollution term. Although, in single-pollutant models for the entire year, the effect of PM2.5 was statistically stronger than that of CO, PM2.5 dropped out of the multipollutant model, regardless of whether it entered as a linear or a log term, while CO persisted through the stepwise process and its effect increased in magnitude and statistical signi7cance (Table 4). NO2 and SO2 also persisted in the multipollutant model, but were associated with weak, negative effects. In the warm season multipollutant model, both CO and

234.8 339.3 181.8 1425.4 53.2 1064.3 14.0 32.6 0.5 8.1 64.0 24.6

SD

95th %ile

Maximum

0.2 3.2 7.6 1.4 7.8 5.1 4.4 2.2

1.3 31.4 32.2 6.9 37.8 21.5 18.4 8.2

1.6 38.4 38.4 10.0 56.3 35.3 29.5 14.2

0.2 3.0 5.3 1.3 7.1 5.0 3.6 2.8

1.2 31.1 34.2 5.8 39.5 22.9 18.8 10.3

1.5 35.0 38.4 9.0 56.3 35.3 25.8 14.2

145.0 334.0 207.4 1228.9 65.8 956.6 17.4 48.7 1.0 11.8 124.0 39.6

486.5 1018.9 627.5 3685.0 204.5 2936.4 51.8 141.5 2.7 29.7 337.6 113.2

996.1 1697.9 891.1 6950.1 341.8 5928.9 108.1 219.9 5.7 69.7 646.5 231.4

PM2.5 persisted through the selection process, but the effect of PM2.5 was smaller in magnitude and statistical signi7cance, while NO2 and SO2 were associated with strong, negative effects, greater both in magnitude and in statistical signi7cance than in single pollutant models. Effects of aeroallergens were weak and inconsistent and are therefore not presented. These results are available from the authors upon request. Effects of personal characteristics variables are also shown in Table 4. The presence of a chronic health condition and the health utilities index were both very strongly associated with disability days in both the entire year and the warm season analyses. The effect sizes for these factors observed in multipollutant models were typical of those seen in singlepollutant models. Correlations among the pollutants which appeared together in multipollutant models are shown in Table 5. CO was positively correlated with NO2 and to a lesser degree with PM2.5 (more strongly in

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STIEB ET AL.

FIG. 2.

Mean disability days per period.

summer than all year in both cases), while it was positively correlated with SO2 all year but negatively and not signi7cantly in the summer. NO2 was positively correlated with SO2 and to a lesser degree with PM2.5 (more strongly all year than in summer in both cases). SO2 and PM2.5 were positively correlated both all year and in the summer period.

We also examined whether the effects of CO and PM2.5 were sensitive to alternative forms of temporal adjustment and examination of the much larger greater Toronto area. As shown in Fig. 4, with the exception of PM2.5 in the warm season, precision of estimates was not sensitive to type of analysis, precision being greater for PM2.5 than for CO and for

TABLE 3 Percentage Increase in Disability Days for Interquartile Range, by Pollutant and Season, Single-Pollutant Modelsa Percentage increase Season All year May to September All year May to September All year May to September All year May to September All year May to September All year All year May to September All year May to September All year May to September

Pollutant CO CO NO2 NO2 O3 O3 SO2 SO2 PM10 PM10 PM2.5 ln(PM2.5) PM2.5 PM2.5}10 PM2.5}10 Fine SO4 Fine SO4

Units

Lag (days)

Interquartile range

ppm ppm ppb ppb ppb ppb ppb ppb
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3

0 0 14 14 7 7 0 0 0 0 0 0 0 0 7 0 0

0.2 0.2 4.1 4.3 12.5 7.5 1.9 2.1 9.7 8.4 6.6 6.6 5.7 5.7 4.8 2.0 3.2

Point estimate 30.8 52.3 !5.1 !5.6 !28.6 !21.7 !9.5 !41.5 14.5 23.3 21.9 20.6b 39.3 !6.6 7.1 7.4 26.4

95% CI 1.2 !12.9 !18.8 !21.9 !60.0 !51.7 !30.1 !67.1 !5.3 !5.0 3.8 5.0 7.9 !20.9 !19.2 !4.9 !3.9

69.0 166.4 11.0 14.0 27.4 26.9 17.1 4.1 38.4 59.9 43.0 38.5 79.8 10.4 42.1 21.2 66.3

P value 0.040 0.140 0.513 0.547 0.254 0.320 0.448 0.068 0.161 0.115 0.015 0.008 0.011 0.426 0.632 0.250 0.094

a All models include terms for LOESS smooths of 2-week average mean relative humidity, lag 7 days, and 2-week average maximum 3-h pressure change, lag 14 days. b Calculated for a change in concentration equal to the interquartile range (6.6), evaluated at the mean (12.9), i.e., percentage increase"e[
*(ln19.5!ln12.9)]!1.

AIR POLLUTION AND DISABILITY DAYS IN TORONTO

215

FIG. 3. Disability days versus spline function of pollutant concentration for entire year (top) and warm season (bottom).

year round versus warm season analyses. Effect size estimates for PM2.5 appeared to be more sensitive than those for CO. There were no cases for other pollutants where signi7cant positive effects were observed with pre7ltering when they were not observed in our base analysis. DISCUSSION

Our study illustrates the utility of data from a general health survey in constructing a time series of an acute health outcome for the purpose of examining adverse effects of air pollution. These data have the advantage of constituting a relatively large sample which is representative, by design, of the general population. They also include detailed information on other major determinants of health and disability, including the presence of chronic health conditions, behaviors such as smoking, and social environment which is not routinely available for

administrative data sets. However, these data did not permit us to isolate disability days due to cardiorespiratory causes which one would expect to be more likely to be associated with air pollution exposure. We found that in single-pollutant analyses of both the entire year and the warm season, PM2.5 was consistently associated with disability days. This is consistent with earlier results from similar data in the United States (Ostro, 1987), despite a nearly twofold higher average concentration of PM2.5 in the Ostro study. In this study, an increase of approximately 6% in restricted activity days (RADs) was observed for a change equal to the interquartile range in our study, in 2-week average concentration of PM2.5 lagged 2 weeks. In the present study, CO was associated with disability days in the entire year analysis, but not as strongly as PM2.5 . Another earlier study found a consistent association between ozone and respiratory restricted activity days

216

STIEB ET AL.

TABLE 4 Percentage Increase in Disability Days, by Season, multipollutant Modelsa Percentage increase Variable

Lag

Units

CO NO2 SO2 Chronic condition Gender Age Health Utilities Index

0 14 0

ppm ppb ppb Yes"1 Female"1 Years 0 to 1 scale

CO NO2 SO2 PM2.5 Chronic condition Gender Age Health Utilities Index

0 14 0 0

x

Entire year 0.2 4.1 1.9 1 1 1 !0.1 May to September 0.2 4.3 2.1 5.7 Yes"1 1 Female"1 1 Years 1 0 to 1 scale !0.1

Point estimate

95% CI

P value

36.6 !10.8 !18.2 105.5 12.1 !0.4 26.3

5.3 !24.4 !38.0 63.0 !7.1 !0.9 30.1

77.2 5.2 7.9 159.0 35.3 0.1 22.6

0.0187 0.1735 0.1545 0.0000 0.2335 0.1112 0.0000

66.3 !33.2 !73.2 29.7 110.0 10.9 0.0 28.9

!15.6 !48.2 !87.0 !3.5 44.4 !17.0 !0.8 34.6

227.7 !13.9 !44.4 74.3 205.4 48.2 0.8 23.4

0.1419 0.0019 0.0004 0.0843 0.0001 0.4831 0.9378 0.0000

a All models include terms for LOESS smooths of 2-week average mean relative humidity, lag 7 days, and 2-week average maximum 3-h pressure change, lag 14 days.

(RRADs) (Portney and Mullahy, 1986), at an average ozone concentration approximately twice that observed in our study. As indicated earlier, unlike this study, we were not able to isolate restricted activity days due to respiratory causes, which could have accounted for our inability to detect an ozone effect. No consistent effect of sulfate was observed in the Portney and Mullahy (1986) study, despite an average concentration approximately threefold higher than that observed in our study. Effects in their study were observed for a variety of exposure metrics for ozone, including unlagged 2-week averages, as in our study, and annual average concentrations. The effect size varied depending on assumptions about the shape of the concentration response, but was estimated at up to a 14% increase in respiratory restricted activity days for a change in 2-week average ozone concentration equivalent to the interquartile range observed in the current study. The signi7cantly larger effect sizes observed in our study may re8ect better control for temporal cycles and weather than was employed in the earlier studies. Ostro (1987) employed dummy variables for quarter, while both previous studies included linear terms for weather variables. A logarithmic concentration response function could also account for a higher slope at the lower air pollution concentrations observed in our study, and we did in fact observe a slightly improved 7t using a logarithmic form in the case of the effect of PM2.5 in the all year analysis. Relative to

the epidemiologic literature in general, the relatively large effect sizes could also re8ect our utilization of 2-week average pollutant concentrations, which would capture a much larger cumulative effect than single-day or even 2- or 3-day average concentrations traditionally employed in other studies. An effect size of approximately 35.0% excess summertime respiratory hospital admissions was observed in a recent study of children under 2 years of age in Toronto, based on a 5-day average concentration (Burnett et al., 2001). Our results from multipollutant analyses were dif7cult to interpret. The CO effect appeared to be more robust to the inclusion of other pollutants than that of PM2.5 , which could re8ect the fact that 2-week average concentrations of PM2.5 would have been derived from fewer daily measurements because of the every sixth day sampling frequency. Strong negative effects of NO2 and SO2 in the warm season multipollutant model are most likely attributable to confounding among these pollutants, CO, and PM2.5 , as re8ected in the signi7cant correlations among them. Portney and Mullahy (1986) found a signi7cant ozone effect when sulfate was also included in the model and reported that inclusion of other pollutants did not in8uence the magnitude of the ozone effect. However, they did not contrast these results with the effect of ozone in a single-pollutant model. Ostro (1987) reported only single-pollutant results for PM2.5. Interestingly, in an analysis of the

AIR POLLUTION AND DISABILITY DAYS IN TORONTO

217

TABLE 5 Correlations among CO, NO2 , SO2, and PM2.5 EAll Year (above Diagonals) and May to September (below Diagonals) CO

NO2

SO2

CO 0.30 (0.0001) 0.22 (0.0001) 0.41 (0.0001) 0.41 (0.0001) NO2 SO2 !0.01 (0.73) 0.21 (0.0001) 0.17 (0.0001) 0.14 (0.0001) 0.18 (0.0001) PM2.5

PM2.5 0. 06 (0.004) 0.27 (0.0001) 0.18 (0.0001)

FIG. 4. Sensitivity analysis of effects of CO and PM2.5.

association between air pollution and daily mortality from internal causes in Toronto during the period 1980}1994, CO and total suspended particulate matter were selected using a stepwise procedure as best representing the effect of the Toronto air pollution mix (Burnett et al., 1998). CO and particulate matter therefore appear to be a common thread at least in these two analyses of the adverse health effects of air pollution in Toronto. A number of direct observational studies have documented 7ndings related to those observed here. In two small human chamber studies involving both asthmatic and nonasthmatic subjects, controlled exposures to PM2.5 concentrated from Toronto air, up to 150
g/m3, with or without exposure to ozone, were not associated with symptoms, but there was a signi7cant effect on sputum neutrophils (Urch et al., 2001; Petrovic et al., 2000). In a panel study of school children in Mexico City, ozone, PM2.5 , and PM10 were associated with morning reporting of phlegm, but not with reporting of cough or cold symptoms (Gold et al., 1999). These pollutants were also predictors of reduced morning peak 8ow in this study. A reanalysis of three panel studies revealed similar 7ndings, and it was observed that PM2.5 was more strongly associated with these outcomes than measures of coarse particles (Schwartz and Neas, 2000). In a study of exposure to vehicle emissions in a traf7c tunnel, asthmatics were found to have stronger reactions to inhaled allergens after sitting in a parked vehicle inside the tunnel when exposed to more than 300
g/m3 of NO2 and weaker reactions when exposed to more than 100
g/m3 of PM2.5(Svartengren et al., 2000) PM2.5 was also associated with incidence of cough in a panel study of children with chronic respiratory symptoms in Finland (Tittanen et al., 1999). It is well recognized that 7xed-site air pollution monitors more effectively represent personal exposure for some pollutants than for others (Suh et al., 1992; Liu et al., 1995; Brauer et al., 1989; Stieb et al.,

1998; Ozkaynak et al., 1996). However, a community-level time series analysis involves examining the relationship between the counts of events occurring in the community and the average exposure of the community. We elected to represent exposure as an average among all available monitoring sites to re8ect mobility of individuals within the community. While this clearly results in exposure misclassi7cation (Ozkaynak et al., 1986), this would also occur with other mapping schemes such as matching individuals to the monitor closest to their home, since this would not re8ect exposures experienced outside their home area, when at work, or during other activities. Exposure misclassi7cation is likely to result in a reduced signal to noise ratio, leading to a reduction in the apparent magnitude and precision of estimates of the association between air pollution and health outcome. Our inability to detect consistent effects of aeroallergens may relate to the nonspeci7c nature of our outcome measure, in that it would include disability days due to numerous nonallergic conditions. Portney and Mullahy (1986) noted that the effects of pollens were assessed in their study, but they did not report speci7c results. Exposure misclassi7cation is equally relevant to aeroallergens and to conventional air pollutants, although there are some data to suggest that temporal variability in personal exposure is approximated reasonably well by 7xed monitoring sites, at least for some allergens (Riediker et al., 2000). Overall, however, the lack of apparent association with aeroallergens is more likely to be attributable to the nonspeci7city of the outcome than to exposure misclassi7cation. Estimates of the effects of personal characteristics were similar to those observed in the two earlier studies. In particular, the presence of a chronic condition was associated with a statistically signi7cant effect, similar in magnitude to that observed in the current study. There were approximately 2- and 1.6-fold increases in reporting of RRADs and RADs

218

STIEB ET AL.

in the Portney and Mullahy and Ostro studies, respectively, in relation to chronic disease, although the variable in the former was de7ned as any limitations in activity due to a chronic condition. The effects of gender and age were not signi7cant in our analysis, although they persisted through the stepwise selection procedure, while the remaining personal characteristics variables, education, income, personal and household smoking, employment, and body mass index were not selected. Effects of these and other variables differed in the two earlier studies. Race and income were the only other signi7cant predictors in the Portney and Mullahy study, while age, gender, education, income, race, marital status, and employment status were all associated with signi7cant effects in the Ostro study. We hypothesize that in our study the health utilities index variable may have captured the effects of some of these other variables on general health status and, in turn, their in8uence on the reporting of disability days. With the exception of warm season results for PM2.5, our results were not sensitive to alternative adjustment for temporal cycles or consideration of a larger geographic area. While we did not account for repeated measurements on subjects from the longitudinal portion of the sample, when we restricted our analysis to a single survey cycle, our results were not altered appreciably. It has recently been observed that estimates of air pollution effects from time series studies may be more sensitive than previously thought to methods used to simultaneously adjust for the effects of temporal cycles and weather (Greenbaum 2002). Effect estimates appear to be particularly sensitive to inclusion of non-parametric terms for both time and weather. This was not the case in our base analysis, since we were constrained to use indicator functions for time because of the nature of the health outcome data. Nonetheless, we did conduct a sensitivity analysis by using parametric natural spline functions of weather variables rather than nonparametric smooths as we used in our base analysis. We found that effect estimates were unchanged.

at relatively low ambient concentrations, the precise contribution of individual pollutants could not be determined conclusively. ACKNOWLEDGMENTS This project was supported by Health Canada and the Program of Energy Research and Development, Natural Resources Canada. Aeroallergen data were provided by Aerobiology Research Laboratories. Collection of survey data was carried out in accordance with the institutional guidelines of Statistics Canada.

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CONCLUSION

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