Validity of Annoyance Scores for Estimation of Long Term Air Pollution Exposure in Epidemiologic Studies

The Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA)

Lucy Oglesby1, Nino Künzli1, Christian Monn2, Christian Schindler1, Ursula Ackermann-Liebrich1, Philippe Leuenberger3 and the SAPALDIA Team4

1 Institute of Social and Preventive Medicine, University of Basel, Basel, Switzerland.
2 Institute for Hygiene and Applied Physiology, Federal Institute of Technology, Zürich, Switzerland.
3 Division of Pneumology, University of Lausanne, Lausanne, Switzerland.
4 Members of the SAPALDIA Team are listed in the Acknowledgments.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In air pollution epidemiology, estimates of long term exposure are often based on measurements made at one fixed site monitor per area. This may lead to exposure misclassification. The present paper validates a questionnaire-based indicator of ambient air pollution levels and its applicability to assess their within-area variability. Within the framework of the SAPALDIA (Swiss Study on Air Pollution and Lung Diseases in Adults) cross-sectional study (1991), 9,651 participants reported their level of annoyance caused by air pollution on an 11-point scale. This subjective measure was compared with annual mean concentrations of particulate matter less than 10 µm in diameter (PM10) and nitrogen dioxide. The impact of individual factors on reported scores was evaluated. Nitrogen dioxide concentrations at home outdoors (measured in 1993), smoking, workplace dust exposure, and respiratory symptoms were found to be predictors of individual annoyance scores. Regression of population mean annoyance scores against annual mean PM10 and nitrogen dioxide concentrations (measured in 1993 and 1991, respectively) across areas showed a linear relation and strong correlations (r > 0.85). Analysis within areas yielded consistent results. The observed associations between subjective and objective air pollution exposure estimates suggest that population mean scores, but not individual scores, may serve as a simple tool for grading air quality within areas. Reported annoyance due to air pollution should be considered an indicator for a complex environmental condition and thus might be used for evaluating the implementation of environmental policies.

air pollutants; environmental exposure; nitrogen dioxide; public health; self-assessment (psychology); stress

Abbreviations: FEV1, forced expiratory volume in 1 second; PM10, particulate matter less than 10 µm in diameter; SAPALDIA, Swiss Study on Air Pollution and Lung Diseases in Adults


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Assessment of long term effects of air pollution on human health strongly depends on epidemiologic studies. Whereas long term health outcomes such as mortality (1Go) or lung function (2Go) may be accurately assessed, exposure assignment has inherent limitations in long term air pollution studies. Exposure estimates are usually based on ambient air pollution levels measured at fixed monitoring sites. More recent approaches, such as methods for microenvironmental and personal monitoring, are feasible for assessing short term exposure in small population samples (3Go) but not for monitoring individual air pollution exposure in large population samples or over longer time periods. Therefore, long term effects of air pollution exposure are often investigated in studies where health outcome and important covariates are measured on the individual level while exposure is assigned on a group or "ecologic" level (4Go). The resulting nondifferential exposure misclassification in these studies (5Go) might be reduced if the within-area variability of air pollution levels could be assessed. However, air pollution levels, which are normally measured by some indicator of total ambient air pollution such as particulate matter less than 10 µm in diameter (PM10) or nitrogen dioxide (6Go), are mostly not available for subareas or neighborhoods, and the health effect assessment relies on an ambient air pollution gradient between study areas (4Go). Thus, the question arises as to whether some questionnaire-based estimate may be a useful surrogate for assessing the within-area variability of ambient air quality.

The objective of this analysis was to validate an indicator for the complex mixture of air pollutants with questionnaire-based assessment of air quality by study subjects. Apart from its impact on objective health measures (7Go), air pollution can also be a source of annoyance. However, annoyance may also depend on individual characteristics of persons, such as their sensitivity, health status, attitude toward traffic or other sources of air pollution, or satisfaction with their living area. Furthermore, the degree of annoyance may vary with the actual context (work–leisure time; day–night). Annoyance due to environmental stressors impairs well-being and is recognized by the World Health Organization as an adverse health effect per se.

Several studies have quantified the relation between the perception of air quality and measured air pollution levels and found consistent associations between reported annoyance due to air pollution and ambient air pollution (8GoGoGoGo–12Go). Data from the SAPALDIA (Swiss Study on Air Pollution and Lung Diseases in Adults) cross-sectional study (2Go, 13Go) offered us the opportunity to further evaluate whether annoyance scores may serve as an indicator for long term exposure to air pollution.

All SAPALDIA participants reported their annoyance due to air pollution at home. On the other hand, objective measures of air pollution (annual mean values) were available for each area (PM10, nitrogen dioxide), for neighborhoods within areas (nitrogen dioxide), and, for a subsample of SAPALDIA participants, for home outdoors (nitrogen dioxide). Thus, SAPALDIA allowed us to address the following questions with a large, representative population:

The validity and limitations of such an indirect and simplistic but inexpensive exposure assessment tool in air pollution epidemiology will be discussed below.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Study population
The SAPALDIA cross-sectional study was conducted in 1991. It included 9,651 adult participants aged 18–60 years (49 percent males) from random samples drawn in each area. The method of the SAPALDIA cross-sectional study has been described in detail elsewhere (13Go). The study protocol consisted of a detailed interview and extensive health examinations. The questionnaire included the assessment of annoyance due to air pollution on an 11-point Annoyance Scale. Eight study areas in Switzerland, representing different conditions of climate, weather, and air pollution, were selected (14Go). They covered three major categories of areas common in Switzerland: urban and suburban areas (Geneva, Basel, Lugano, and Aarau), rural areas (Payerne and Wald), and alpine areas (Davos and Montana).

Measurements of air pollutants
For the present investigation, three sources of air quality data were used.

Individual-level data. For a subsample (n = 560) of participants from the SAPALDIA diary study (1992–1993), nitrogen dioxide levels had been determined with passive samplers (Palmes tubes) (15Go) placed outside their homes in 1993 (16Go). These subjects had been randomly selected from the eight study areas and from the neighborhoods within areas. Based on three measurement periods of 4 weeks each (one monitor per week), annual mean levels of home outdoor nitrogen dioxide concentrations could be estimated for 400 subjects in this subsample (17Go).

Across-area variability. Annual mean PM10 and nitrogen dioxide levels were determined at one fixed monitoring site of each study area. For nitrogen dioxide, data from 1991 were used; for PM10 (with no data available in 1991), 1993 data were used (2Go, 18Go). PM10 level was determined using sharp-cut, low-volume cascade impactors, and nitrogen dioxide level was determined with chemiluminescence (14Go).

Within-area variability. Within each area, annual mean nitrogen dioxide concentrations were estimated for 6–13 subareas (neighborhoods), based on passive sampler measurements made in 1991 (19Go). These neighborhoods, which ranged from 0.5 km to 4 km in diameter, were quite homogeneous in terms of topography, type of buildings, construction density, and traffic volume.

Measurements of annoyance
Study subjects self-assessed their degree of annoyance on an 11-point scale referred to as the Annoyance Scale. Possible scores on the scale range from 0 (no disturbance at all) to 10 (intolerable disturbance). The Annoyance Scale has previously been applied for the assessment of annoyance caused by ambient air pollution (9GoGo–11Go). During the SAPALDIA computer-assisted personal interview, a picture of the Annoyance Scale in the form of a thermometer was presented and the participant was asked, "How much are you annoyed by outdoor air pollution (from traffic and industry) at your actual home, if you keep the windows open?" The participant indicated his or her score and the interviewer directly entered it, rounded to the nearest integer.

Descriptive analysis
Distribution parameters (mean, median, percentiles) for the Annoyance Scale scores were calculated for all areas. In addition, mean subpopulation scores were computed for all neighborhoods. Furthermore, three annoyance categories (low = 0–3, medium = 4–7, high = 8–10) were defined, consistent with previous studies (10Go, 11Go). Their distribution was determined for areas and for neighborhoods within areas. From a public health and regulatory perspective, the percentage of subjects scoring high, i.e., those who are considerably impaired in their well-being, is of particular interest.

Regression analysis: annoyance levels versus objective air pollution measures
Annoyance Scale scores (individual scores, population mean scores) and scores indicating a high level of annoyance (scores of 8–10) were considered as dependent variables, and objective measures of air pollution exposure were considered as independent variables. The validity of the Annoyance Scale as a surrogate measure for long term air pollution exposure was assessed using three different approaches.

Individual perceptions. In a first approach, individual Annoyance Scale scores were regressed against estimates of individual annual mean levels of nitrogen dioxide at home outdoors, based on passive sampler measurements. This approach included 400 individuals who had all data available. For multivariate regression, the following factors were tested as potential predictors of reported levels of annoyance: demographic and socioeconomic factors (sex, age, nationality, and educational level); smoking status (never, former, or current smoker); the presence of respiratory symptoms and conditions (wheezing without cold, tightness in chest, attacks of shortness of breath, attacks of cough, constant phlegm (day and night), shortness of breath while walking, and asthma); low lung function (values lower than the fifth percentile of reference values (20Go) for forced expiratory volume in 1 second (FEV1), mid-expiratory flow (FEF25–75), and FEV1/forced vital capacity); and indoor exposures (environmental tobacco smoke at home and at work and workplace exposure to dust, gas, vapors, aerosol, smells, and noise). In multivariate logistic regression analysis, a "high annoyance" rating (scores of 8–10 on the Annoyance Scale) was considered as an outcome measure.

Population means across areas. In a second approach, population mean scores of reported annoyance were calculated for the eight study areas and were regressed against annual mean PM10 and nitrogen dioxide values obtained at the fixed site monitor in each area. First, all SAPALDIA participants were included. In a second step, population mean scores were recalculated based only on subjects (n = 1,351) living in the neighborhood where the fixed monitoring site of an area was located.

Subpopulation means within areas. In a third approach, population mean scores were calculated for each neighborhood and were regressed against estimated annual mean nitrogen dioxide levels (based on measurements made with passive samplers) in the respective neighborhoods.

The fit of the models was evaluated, and the underlying distributional assumptions were examined. Statistical analysis was carried out with the Stata software package (21Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Table 1 shows the characteristics of the cross-sectional study population of SAPALDIA. Table 2 shows annual mean PM10 and nitrogen dioxide values, as well as population mean annoyance scores for the eight SAPALDIA study areas. In all areas, the attributed scores ranged from 0 to 10. However, score distributions were clearly different between study areas. In the alpine and rural areas, with a median of zero, only a few subjects reported a high level of annoyance (in Montana, the 75th percentile point was 1; in Wald, it was 3; in Davos, 5), whereas in urban areas the median level was 4 (Basel, Geneva) or 5 (Lugano) and the 75th percentile points were greater than 7.


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TABLE 1. Characteristics (%) of the population of the SAPALDIA* cross-sectional study (sex, age, education, and nationality{dagger}), 1991

 

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TABLE 2. Objective and subjective measures of ambient air pollution levels in SAPALDIA* study areas

 
Individual perceptions
The correlation of the individual, subjective annoyance scores with estimates of annual mean levels of nitrogen dioxide at home outdoors (n = 400) was rather low (r = 0.36), indicating a wide range of individual perceptions at a given level of ambient air quality (figure 1). In univariate linear regression analysis, nitrogen dioxide level at home outdoors explained 12.6 percent of the Annoyance Scale score variability, with an average increase of 0.92 points per 10 µg per m3 of nitrogen dioxide (table 3, crude estimate). The multivariate model presented in table 3 included all further covariates found to have an impact on subjective annoyance grading. Independent of the nitrogen dioxide level, current smokers scored 1.0 points lower (p = 0.01), whereas workplace exposure to dust led to 1.1-point higher scores (p = 0.007). Respiratory health was also found to be a predictor of individual scoring: On average, subjects affected by shortness of breath scored 1.3 points higher (p = 0.013) and those suffering from constant phlegm scored 1.2 points higher (p = 0.045). Average scores of subjects with asthma were 1.3 points lower (p = 0.017). Since, in the alpine areas with low air pollution levels, the prevalence of asthma was significantly higher compared with the other areas (16.5 percent vs. 7.6 percent, {chi}2 = 0.004), we conducted a sensitivity analysis excluding the alpine areas. This resulted in only minor changes in the estimates, with a somewhat reduced effect of asthma (ß = -1.12, p = 0.11). Whereas women tended to score approximately 0.5 points higher than men in all tested models, age, nationality, and educational level could not be identified as significant predictors and/or confounders. In the multivariate model, a 10-µg per m3 increment in nitrogen dioxide level at home outdoors was associated with an average increase in annoyance score of 0.87 points, similar to the crude estimate of 0.92.



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FIGURE 1. Scatterplot of individual Annoyance Scale scores versus estimated nitrogen dioxide (NO2) levels at home outdoors (annual means) based on passive sampler measurements, Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA) subsample (n = 400), 1991.

 

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TABLE 3. Coefficients from linear (Annoyance Scale scores) and logistic (high levels of annoyance) regression models (individual level): SAPALDIA* diary study, 1992–1993

 
Investigation of the model for potential interactions between nitrogen dioxide and the other covariates revealed workplace dust exposure to be an effect modifier. In stratified analysis, persons exposed to dust at work were found to be more sensitive air pollution "graders" (ß = 1.2 per 10 µg/m3 of nitrogen dioxide) than the nonexposed (ß = 0.74). Furthermore, interactions of workplace dust exposure with sex and with phlegm were observed. Men scored 1.7 points higher when exposed to dust at work, whereas for women such exposure had no significant impact on annoyance scoring (ß = 0.4, p = 0.48).

The multivariate model presented explains 22 percent of the variance in individual annoyance scores. Nitrogen dioxide level at home outdoors has the highest share and contributes 7.5 percent, followed by area, with 3.1 percent. The other predictors explain between 0.5 percent (phlegm) and 1.5 percent (workplace dust exposure) of the variance.

The assumptions of homoscedasticity, independence, and normality of residuals were found to be met. Omitting two influential observations (Annoyance Scale score = 0, nitrogen dioxide level >78 µg/m3) slightly increased both the observed effects and the total explained variability.

Similar results were obtained from the multivariate logistic regression analysis using the "high annoyance" rating (scores of 8–10 on the Annoyance Scale) as the outcome measure (table 3). The fit of the model was found to be satisfactory according to the Hosmer-Lemeshow {chi}2 test (8 df).

Population means across areas
Figures 2 and 3 show the regression lines between population mean annoyance scores and annual mean PM10 and nitrogen dioxide levels across areas. When all participants in the cross-sectional survey were included for calculation of the population mean Annoyance Scale scores, correlation between measured air pollution levels and subjective assessment of air pollution was high (PM10: r = 0.85; nitrogen dioxide: r = 0.88) (figure 2).



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FIGURE 2. Mean Annoyance Scale scores (bars, 95% confidence interval) among all participants, according to annual mean levels of particulate matter less than 10 µm in diameter (PM10) and nitrogen dioxide (NO2) (measured at fixed monitoring sites), for eight areas in the Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA), 1991 and 1993.

 


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FIGURE 3. Mean Annoyance Scale scores (bars, 95% confidence interval) among participants living close to the fixed monitoring sites (FMS), according to annual mean levels of particulate matter less than 10 µm in diameter (PM10) and nitrogen dioxide (NO2), for eight areas in the Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA), 1991 and 1993. B1, Basel-St. Johann; B2, Basel-Feldbergstrasse; W, Wald; D, Davos; L, Lugano; M, Montana; P, Payerne; Aa, Aarau; G, Geneva.

 
In a next step, only participants living close (in the same neighborhood) to the fixed monitoring site of a study area were selected for calculation of mean annoyance scores (figure 3). Correlation between objective and subjective air pollution measures was higher than it was with all participants (PM10: r = 0.92; nitrogen dioxide: r = 0.93). In addition, the change associated with a 10-µg/m3 increment in PM10 or nitrogen dioxide level was higher for participants living close to the fixed monitor than for all participants (PM10: 1.90 vs. 1.31; nitrogen dioxide: 0.84 vs. 0.64).

Regression between the percentage of highly annoyed subjects and mean concentrations of PM10 and nitrogen dioxide showed similar associations. Again, restricting the analysis to participants living close to the monitoring site strengthened the relation.

Subpopulation means within areas
For each area, neighborhood mean annoyance scores were regressed against corresponding estimated annual mean nitrogen dioxide values based on passive sampler measurements (table 4). Correlation was high in urban areas (0.90–0.97), intermediate in suburban and rural areas and Davos (0.6–0.7), and low in Montana (0.08). Overall correlation including all neighborhoods (n = 81) was high as well (r = 0.88). Common slope models were found to appropriately fit the data; however, an individual intercept model provided a better fit of the data than did a simple model with common intercepts and common slopes.


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TABLE 4. Estimated effects (population level) of annual neighborhood mean nitrogen dioxide levels* on neighborhood mean Annoyance Scale scores within each SAPALDIA{dagger} area and across all areas

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Association between population mean scores and measured air pollution levels
We have shown that, although individuals rate annoyance due to air pollution very differently, given the same objective level of air pollution, population mean annoyance scores are strongly correlated with annual mean air pollution levels. In fact, across areas, the average rating of persons living close to the fixed site monitors showed even stronger associations with the nitrogen dioxide measures. The same conclusion holds for the percentage of subjects reporting a high level of annoyance. Within areas, people living in the same neighborhood had, on average, a subjective rating scheme of air pollution which was correlated with the corresponding neighborhood levels. These observations support the hypothesis that reported annoyance is a function of true exposure, though it is distorted by subjective factors, which are apparently leveled out when population average scores are used.

Except for Montana, where only a small range of air pollution levels was observed, estimated slopes varied only slightly between areas (0.6–1.4) and were consistent with the adjusted estimate for nitrogen dioxide (0.9) in the multivariate model based on individual data. The heterogeneity of intercepts between areas suggests that, apart from individual characteristics, local and possibly cultural factors may influence the relation between objective and subjective measures of ambient air pollution. Evidence for some cultural impact could also be found in the multivariate model: In some areas, subjects tended to score significantly lower compared with the reference area, Basel. When area was replaced with a dichotomous variable, "Swiss German-speaking areas," as opposed to the French- and Italian-speaking parts of Switzerland, the Swiss German speakers tended to score 0.6 points higher.

The contrast between our individual-level and grouped scores analysis is corroborated by recent reports on poor individual cross-sectional correlation between personal exposure and ambient air pollution levels, as opposed to high longitudinal correlation within subjects (22Go, 23Go). A large number of individual factors have an impact on both personal exposure and individual annoyance scores. These are not taken into account in the (crude) cross-sectional analysis, resulting in poor correlations. Whereas individual factors affecting personal exposure may be explained to a large extent by indoor sources and exposure-relevant activities, predictors of individual annoyance are to a large extent subjective and may not easily be accessible through questionnaires. However, the work of Janssen et al. (22Go, 23Go) supports the use of ambient air pollution levels as exposure estimates in air pollution epidemiology and, indirectly, the applicability of grouped annoyance scores, which correlate well with measured ambient levels.

Consistency with similar studies
A small-scale urban study carried out in the city of Zurich, Switzerland, in 1995 (9Go) investigated the associations between measured levels of ambient air pollution (8-week average of PM10 and nitrogen dioxide levels) and traffic noise on the one hand and degree of annoyance, assessed with the Annoyance Scale, on the other hand at four sites with different traffic densities. In a postal survey, 324 subjects living in houses adjacent to the fixed monitoring sites assessed their level of annoyance due to air pollution on the Annoyance Scale. The results of the Zurich study are consistent with the present findings. In fact, even stronger correlations between measured and self-reported data (PM10: r = 0.97; nitrogen dioxide: r = 0.96) and steeper slopes (ß = 1.6 per 10 µg/m3 of nitrogen dioxide) were observed. In a study based on random population samples in 55 urban areas in Sweden, Forsberg et al. (8Go) used the frequency of annoyance due to traffic exhaust for quantifying the relation between subjective and objective measures of air pollution. Similarly, they found a significant correlation (r = 0.56) between the percentage of subjects reporting being annoyed daily or almost daily by traffic exhaust in their residential area and measured nitrogen dioxide levels (6-month averages) at nearby fixed monitoring sites. In another study (24Go), where similar annoyance scores (a seven-point bipolar semantic differential scale) were applied to the assessment of disturbance due to smoke, fumes, and odors from road traffic in selected population groups, median scores ranging from 0 to 4 were observed in neighborhoods across the United Kingdom, but the association with measured air pollution levels was not reported.

Validity of annoyance scores
The validity of an exposure estimate (x) refers to the agreement between this measured exposure surrogate (x) and the true exposure (z) (25Go). Because the true exposure (z) of study subjects could not be obtained, we validated population mean Annoyance Scale scores against measured ambient air pollution levels. The observed high correlation, especially within areas (r >= 0.60, except for Montana), suggests that population mean Annoyance Scale scores could be valid indicators for ambient air pollution.

However, the underlying mechanism of the "exposure-response" relation, observed in both SAPALDIA and the Zurich study, is not fully understood. Is olfactory perception of air quality a main factor, or is the reported level of annoyance due to air pollution at home determined by traffic-related noise or visible traffic volume on adjacent streets? In SAPALDIA and the Zurich study, annoyance due to traffic noise, in addition to air pollution, was also assessed with the Annoyance Scale. Correlation coefficients between population mean scores for annoyance due to air pollution and annoyance due to traffic noise (SAPALDIA: r = 0.90; Zurich study: r = 0.96), as well as between mean PM10 concentrations and annoyance due to traffic noise (SAPALDIA: r = 0.87; Zurich study: r = 0.99), were high and significant. These findings are plausible, given the obvious association between traffic-related noise and air pollution. However, other factors, such as home environment (green space, construction density), might act as confounders.

Possible role and limitations of annoyance scores
Our findings suggest that population mean annoyance scores do reflect gradients of air pollution levels between and within areas in Switzerland. On the other hand, a high level of annoyance indicates an impairment of well-being. This ambiguity of annoyance relating to both exposure and health outcome opens up two main fields of application: 1) exposure estimation in semi-individual studies and 2) evaluation of environmental policies.

In semi-individual studies, within-area variability of air pollution levels could be assessed using neighborhood population mean Annoyance Scale scores, and thus the establishment of expensive monitoring networks within areas may not be needed. In questionnaire surveys, the marginal costs for this additional measure would be very low. The fact that the association between annoyance scores and air pollution levels across areas was improved when analysis was restricted to subjects living close to the monitors suggests that assigning exposure estimates based on neighborhood mean scores to subjects living in those neighborhoods would mitigate nondifferential exposure misclassification (5Go). However, the annoyance scores could not replace personal exposure measurements, because of the high between-person variability of annoyance rating, which could only partly be explained by smoking status, respiratory health status, and workplace exposure. Moreover, the observed interactions illustrate the complexity of individual scoring, and it will hardly be possible to use individual scores as exposure estimates, even when adjusting for known confounders. Forsberg et al. (8Go) also reported individual characteristics (female sex, asthma, lack of access to a car) to have a significant impact on annoyance reporting. We found some evidence for reporting bias, as subjects affected by respiratory symptoms scored higher given the same nitrogen dioxide level. The anomalous finding that asthmatics scored lower, on average, may partially be explained by the fact that in Davos, 44 percent of the asthmatics stated that they had moved there because of the positive effect of the alpine climate on their asthma (13Go, 26Go). In fact, only in alpine areas did asthmatics score lower than nonasthmatics (mean score = 0.8 vs. 2.4; p (t test) = 0.02), whereas in the other areas no difference could be observed (mean score = 3.7 vs. 3.8; p = 0.42). In general, the emerging problem for the semi-individual study design of using an exposure measure which might be related to the health outcome of interest may be solved by excluding subjects with positive health outcomes when calculating the grouped scores assigned to all participants in a neighborhood.

The observed exposure-response relation should not be extrapolated to areas with considerably higher pollution levels than those encountered in Switzerland, because of the limited range of the Annoyance Scale. Therefore, the question of whether our findings hold in general should be evaluated in international studies including areas covering a large range of air pollution levels and cultural settings. Furthermore, in this analysis, the Annoyance Scale was evaluated in the context of long term exposure to air pollution, estimated by recent annual mean levels. The validity of the Annoyance Scale for short term exposure assessment remains to be evaluated.

The aspect of health outcome might be a disadvantage for the use of annoyance scores as an exposure estimate. From a public health and regulatory perspective, however, the health relevance of annoyance may be its real strength, since it relates to a broader understanding of environmental quality. Annoyance lacks pollutant-specific information, and therefore it cannot be used for etiologic inference or direct pollutant-oriented air quality management decisions. However, the focus on single pollutants which may allow implementation of effective risk management strategies may fall short from a broader public health perspective. Annoyance due to air pollution, by default, is an indicator for a complex environmental condition. Through the addition of the Annoyance Scale question to regular nationwide population surveys, the change over time in the prevalence of highly annoyed subjects might be monitored and the successful implementation of environmental policy strategies evaluated.


    ACKNOWLEDGMENTS
 
This work was supported by the Swiss Federal Institute of Technology (Zürich, Switzerland) and the Swiss National Science Foundation (grant 32 048922.96). SAPALDIA is part of Swiss National Research Program 26A, supported by the Swiss National Science Foundation (grant 4026-28099) and the Federal Office of Education and Science. SAPALDIA Basel is part of the European Respiratory Survey.

The SAPALDIA Team: Study Director—P. Leuenberger (pneumology); Program Director—U. Ackermann-Liebrich (epidemiology); air pollution monitoring—P. Alean and C. Monn; allergology—K. Blaser and B. Wüthrich; epidemiology—N. Künzli, B. W. Martin, and E. Zemp; local assistants—C. Bron, M. Brutsche, S. Elsasser, P. Guldimann, P. Hufschmid, J. C. Luthy, and A. Radaelli; meteorology—C. Defila; occupational medicine—H. Keller-Wossidlo; palynology—A. G. Peeters; pneumology—G. Bolognini, J. P. Bongard, O. Brändli, P. Braun, G. Domenighetti, W. Karrer, R. Keller, T. G. Medici, A. P. Perruchoud, G. Solari, M. H. Schöni, J. M. Tschopp, B. Villiger, and J. P. Zellweger; statistics—L. Grize, C. Schindler, and J. Schwartz.

The SAPALDIA Team acknowledges the help of the many people who made the study possible. Logistic and financial support was received from Swiss cantonal governments (Basel-Stadt, Genf, Graubünden, Tessin, Waadt, Wallis, and Zürich) and from the cantonal offices for air hygiene measurements. The Federal Office for Forest and Environment supported the quality control of air hygiene measurements and the PM10 measurements. Further financial contributions were received from the Swiss Society of Pulmonology, the Lega Ticinese Contro la Tuberculosi e le Malattie Pulmonari, and various cantonal offices of public health.

The study could not have been carried out without the help of the field-workers of the local medical teams: Aarau—C. Persoz-Borer, C. Wettstein, G. Giger, H. Grob-Stalder, J. Lohmüller, K. Häfeli, and U. Rippstein; Basel—V. Fluri, M. Herrous, G. Imboden, and L. Joos; Davos—K. D'Alberti and A. Sönnichsen; Genf—I. Barbey, K. Gegere, and N. Penay; Lugano—M. Astone, E. Haechler, E. Riesen, and B. Viscardi; Montana—Dr. C. Hollenstein, E. Borgeat, and I. Clivaz; Payerne—S. Menétrey-Jaques, C. Gilomen-Pages, and M. C. Collaud; Wald—B. Salzmann, V. Kienast, H. Astone, V. Keller, and C. Schwalm. During the entire study, the team received advice from Dr. Frank Speizer, Harvard Medical School (Cambridge, Massachusetts), for which they are very grateful.


    NOTES
 
Reprint requests to Lucy Oglesby, Institute of Social and Preventive Medicine, University of Basel, Steinengraben 49, CH-4051 Basel, Switzerland (e-mail: Lucy.Oglesby{at}unibas.ch).


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Dockery DW, Pope AC III, Xu X, et al. An association between air pollution and mortality in six U.S. cities. N Engl J Med 1993;329:1753–9.[Abstract/Free Full Text]
  2. Ackermann-Liebrich U, Leuenberger P, Schwartz J, et al. Lung function and long term exposure to air pollutants in Switzerland. Study on Air Pollution and Lung Diseases in Adults (SAPALDIA) Team. Am J Respir Crit Care Med 1997;155:122–9.[Abstract]
  3. Jantunen MJ, Hänninen O, Katsouyanni K, et al. Air pollution exposure in European cities: the "EXPOLIS" Study. J Expo Anal Environ Epidemiol 1998;8:495–518.[ISI]
  4. Künzli N, Tager IB. The semi-individual study in air pollution epidemiology: a valid design as compared to ecologic studies. Environ Health Perspect 1997;105:1078–83.[ISI][Medline]
  5. Steenland K, Deddens JA. Design and analysis of studies in environmental epidemiology. In: Steenland K, Savitz DA, eds. Topics in environmental epidemiology. New York, NY: Oxford University Press, 1997:9–27.
  6. Künzli N, Braun-Fährlander C, Rapp R, et al. Air pollution and health—causal criteria in environmental epidemiology. (In German). Schweiz Med Wochenschr 1997;127:1334–44.[ISI][Medline]
  7. Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society. Health effects of outdoor air pollution. Am J Respir Crit Care Med 1996;153:3–50.[Abstract]
  8. Forsberg B, Stjernberg N, Wall S. People can detect poor air quality well below guideline concentrations: a prevalence study of annoyance reactions and air pollution from traffic. Occup Environ Med 1997;54:44–8.[Abstract]
  9. Oglesby L. Atembarer Schwebestaub (PM10) in der Stadt Zürich: Messungen von Luftschadstoffen und Lärm sowie Beurteilung durch die Anwohnerinnen und Anwohner an verschieden stark verkehrsbelasteten Standorten in der Stadt Zürich. (Master's thesis). Zürich, Switzerland: Swiss Federal Institute of Technology, 1995.
  10. Wanner HU, Wehrli B, Nemecek J, et al. Burdens due to noise and air pollution in residents of areas near streets with heavy traffic. (In German). Soz Praventivmed 1977;22:108–15.[ISI][Medline]
  11. Hangartner M. Bewertung von Geruchsbelästigungen. Staub-Reinhaltung der Luft 1988;48:81–5.
  12. Hangartner M, Wanner HU. Bewertung von Belästigungen durch den Motorfahrzeugverkehr. Forum Städte-Hyg 1983;34:285–7.
  13. Martin BW, Ackermann-Liebrich U, Leuenberger P, et al. SAPALDIA: methods and participation in the cross-sectional part of the Swiss Study on Air Pollution and Lung Diseases in Adults. Soz Praventivmed 1997;42:67–84.[ISI][Medline]
  14. Monn C, Brändli O, Schäppi G, et al. Particulate matter < 10 µm (PM10) and total suspended particulates (TSP) in urban, rural and alpine air in Switzerland. Atmosph Environ 1995;29:2565–73.[ISI]
  15. Burri PA. Passivsammler für die Messung von Stickstoffdioxid- und Schwefeldioxidimmissionen. (Doctoral thesis). Zürich, Switzerland: Swiss Federal Institute of Technology, 1991.
  16. Monn C, Brändli O, Schindler C, et al. Personal exposure to nitrogen dioxide in Switzerland. SAPALDIA Team (Swiss Study on Air Pollution and Lung Diseases in Adults). Sci Total Environ 1998;215:243–51.[ISI][Medline]
  17. Schindler C, Ackermann-Liebrich U, Leuenberger P, et al. Associations between lung function and estimated average exposure to NO2 in eight areas of Switzerland. The SAPALDIA Team (Swiss Study of Air Pollution and Lung Diseases in Adults). Epidemiology 1998;9:405–11.[ISI][Medline]
  18. Zemp E, Elsasser S, Schindler C, et al. Long-term ambient air pollution and respiratory symptoms in adults (SAPALDIA study). Am J Respir Crit Care Med 1999;159:1257–66.[Abstract/Free Full Text]
  19. Hegner H, Monn C, Stahel W, et al. Small scale spatial variability of nitrogen dioxide. (In German). Gesundheits- und Umwelttechnik 1996;4:3–6.
  20. Brändli O, Schindler C, Künzli N, et al. Lung function in healthy never smoking adults: reference values and lower limits of normal of a Swiss population. Thorax 1996;51:277–83.[Abstract]
  21. Stata Corporation. Stata statistical software: release 6.0. College Station, TX: Stata Corporation, 1999.
  22. Janssen NA, Hoek G, Harssema H, et al. Childhood exposure to PM10: relation between personal, classroom, and outdoor concentrations. Occup Environ Med 1997;54:888–94.[Abstract]
  23. Janssen NA, Hoek G, Brunekreef B, et al. Personal sampling of particles in adults: relation among personal, indoor, and outdoor air concentrations. Am J Epidemiol 1998;147:537–47.[Abstract]
  24. Williams ID, McCrae IS. Road traffic nuisance in residential and commercial areas. Sci Total Environ 1995;169:75–82.[ISI][Medline]
  25. Armstrong BK, White E, Saracci R. Principles of exposure measurement in epidemiology. (Monographs in epidemiology and biostatistics, vol 21). New York, NY: Oxford University Press, 1994:49–77.
  26. Künzli N. The SAPALDIA Study—no results? (Editorial). Soz Praventivmed 1997;42:131–2.[ISI][Medline]
Received for publication November 10, 1998. Accepted for publication December 3, 1999.