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Urban air pollution and health: an ecological study of chronic rhinosinusitis in Cologne, Germany
Health & Place 8 (2002) 129–139
Urban air pollution and health: an ecological study of chronic rhinosinusitis in Cologne, Germany$ Christof Wolf* Research Institute for Sociology, Greinstr. 2, 50939 Koln . (Cologne), Germany Received 25 April 2001; received in revised form 17 October 2001; accepted 31 October 2001
Abstract The study investigates the association between outdoor air pollution and chronic rhinosinusitis (CRS) in Cologne, Germany. First, using their addresses, all patients treated for CRS at the ENT Department of the University Hospital between 1990 and 1999 were assigned to one of the 85 city districts. Second, indicators of air pollution (SO2, NOx, TSP) were linked to these areas. Third, to control for socioeconomic confounding, data reflecting the socioeconomic and demographic composition of these districts were collected. Regression analyses reveal weak but consistent statistical effects of pollution on the prevalence of CRS in those parts of the city with air pollution levels above average. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Chronic rhinosinusitis; Outdoor air pollution; Ecological epidemiology
Introduction Chronic rhinosinusitis (CRS) is one of the most prevalent chronic diseases today and is associated with high health care costs (Krouse, 1999). Besides individual risk factors, such as smoking, allergies, anatomic conditions and other inherited dispositions, it is hypothesized that air quality is related to the development of CRS. The study presented here evaluates the strength of the association between outdoor air pollution and CRS in Cologne, Germany. Previous research on the relationship between outdoor air pollution and CRS shows mixed results. Luttmann et al. (1994) examined the effect of air quality on respiratory symptoms by comparing schoolchildren from an industrial German city (Mannheim) with schoolchildren from two rural areas. This comparison was carried out in 1977, 1979 and 1985. One result of $
This article is dedicated to Prof. Dr. Walter Kristof, Ph.D. who stimulated my interest in statistics through his enthusiasm as a teacher. *Tel.: +49-221-470-4397; fax: +49-221-470-5180. E-mail address: [email protected] (C. Wolf).
their study was that bronchitis, inflamed throat with fever and sinusitis was more prevalent in children living in the urban than in the rural location. In addition, their research showed that the relative risk for developing a respiratory disease in the city compared to the countryside decreased over time, reflecting the reduction in the level of sulfur dioxide (SO2) pollution. A similar study conducted in Brazil (Sih, 1999) demonstrated that children living in an urban environment were more likely to be affected by rhinitis, sinusitis and upper respiratory infections in general than children living in rural locations. On the other hand, an epidemiological study in Korea (Min et al., 1996) found no difference between rural and urban areas with regard to the prevalence of chronic sinusitis. Yet the authors report on differences between regions which they attribute to the level of economic activity and crowding. In their review of research on the effects of particulate air pollution on acute respiratory conditions, Dockery and Pope (1994) report that upper respiratory symptoms and exposure to particulate pollution are only weakly related. Although there are several studies reporting statistical relationships between (acute as well as chronic) sinusitis and outdoor air pollution, especially for children, the
1353-8292/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 3 - 8 2 9 2 ( 0 1 ) 0 0 0 4 0 - 5
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C. Wolf / Health & Place 8 (2002) 129–139
evidence is anything but conclusive. This study aims to help fill this gap by investigating the hypothesis that there is a positive correlation between the prevalence of severe forms of CRS and air pollution.
Data and methods The site of the study The city of CologneFthe site of the research reported hereFis located in the German state of North Rhine, Westphalia on the river Rhine. It has a total area of 405 km2 (156 mile2) and a population of approximately 1 million inhabitants. With temperatures ranging from a low of 8.61C to a high of 33.51C (16.7–92.31F), a yearly mean of 11.41C (52.5.1F) and 768.6 mm (20.3 in) precipitation Cologne enjoys temperate climatic conditions.1 In Cologne, as in developed countries, in general, emissions of SO2, CO and dustFmeasured as total suspended particulate (TSP) matterFand thus air pollution, were reduced in the last 20 years (Holman, 1999; Williams, 1999). Emissions of other pollutants like O3 (ozone), CO2 (carbon dioxide) and NO2 (nitrogen dioxide) have increased until the middle of the 1990s and were only reduced in the last few years. The mean values (98 percentile in parentheses) for 1998 were 10 (32) mg/ m3 SO2, 1.5 (3.8) mg/m3 CO, 27 (91) mg/m3 O3, 25 (77) mg/m3 dust (TSP), 39 (61) mg/m3 NO2 and 389 ppm CO2. Although, objectively these figures are far below the officially set critical values, air pollution is still the most pressing environmental problem for the population. Asked for the most important environmental issues, almost 90% of the population names air pollution, leaving other environmental problems, such as water quality or waste disposal, far behind (Statistical Yearbook of City of Cologne 1996/1997: 237).
identified. These patients were assigned to the 85 administrative city districts according to their place of residence at the time of surgery. This was possible in 97.8% of the cases which yields N ¼ 1435 patients for the analysis. Next, the cases in each district were counted separately for 13 age groups and age specific rates were computed. Finally, the age specific rates were weighted according to the mean age distribution of the population in the city and these weighted rates were aggregated within the districts. This age-standardized rate is the dependent variable of our analysis and will also be referred to as patient rate or patient density. A closer inspection of the distribution of this variable reveals that one of the city districts is an extreme outlier having a rate that is over 6 times the standard deviation above the mean rate. This case was excluded from the following analyses, leaving 84 city districts. Fig. 1 shows the means and standard deviations of the age specific rates. It can be seen that the probability of undergoing surgery for CRS increases during the lifespan until it reaches a maximum for persons aged 55–59.
Independent variables Assessing outdoor air pollution on a small-scale basis is costly and therefore data referring to relatively small areas are seldom available. Fortunately, it was possible to collect several indicators of outdoor air pollution from existing sources. First, there was a program monitoring air quality in the Rhine and Ruhr area closely on a scale of 1 km2. From this source, we obtained data on the level of SO2 and dust fall (TSP)Fdata for the means and 98 percentiles, respectivelyFfor almost the whole city area for each year between 1983 and 1988; whereas these indicators are the result of actual measurement, the next indicator of air 30
Cologne is divided into 85 administrative districts with between 744 and over 40,000 inhabitants, i.e. a median of 9475 (1998). In the study presented here, these districts are the unit of analysis and all measures are aggregated to these units.
25
Dependent variable
Patients per 100,000
Units of analysis
20 15 10 5
From the patient register of the Otorhinolaryngology Department of the University of Cologne, those patients living in Cologne and treated for CRS with functional endoscopic sinus surgery between 1990 and 1999 were 1
These figures are mean values for the years 1990–1998; these and the following figures are taken from the city’s statistical yearbook of 1999.
0 0
10
20
30
40
50
60
70
80
Age at surgery
Fig. 1. Mean age specific rates of patients with CRS undergoing surgery in the ENT Department of the University Hospital, Cologne. Lines represent 95% confidence intervals of means, N ¼ 84; outlying district excluded.
C. Wolf / Health & Place 8 (2002) 129–139
quality is based on a simulation study. This project simulates the spatial distribution of NOx in Cologne on the basis of emission data taking into account several meteorological parameters such as the speed and direction of wind (Brucher, . 1997; Brucher . et al., 2000). From this study, estimates of the means and 98 percentiles of NOx pollution for the city region in 1997 were obtained. All these indicators of outdoor air pollution (SO2, dust fall, NOx) were aggregated to the level of the 85 city districts.2 Finally, the indicators were combined to form a single factor of air quality.3 It should be noted again that the level of outdoor air pollution within the city is comparably low, far below the critical values set by the German TA-Luft. Since the distribution of air quality in a city is correlated with the demographic and socioeconomic characteristics of its population one has to control for these factors (Jolley et al., 1992; Heinrich et al., 1998). Data were, therefore, collected which reflected the demographic and socioeconomic composition of the city districts. Using social areas analysis (Shevky and Bell, 1955), two factors were extracted from the data:4 social status and family status. City districts with low unemployment rates, high proportions of white collar and civil service employees, and much living space per inhabitant gain high scores on the social status indicator. High family status, on the other hand, is marked by high proportions of children and youth, low proportions of one-person households and low proportions of divorced men and women. These two dimensions are slightly correlated, r ¼ 20:22: Finally, two further control variables were constructed: residential stability of the districts between 1990 and 1998 and distance between the University Hospital and the city districts, reflecting the likelihood for choosing the ENT Department at the university rather than some other hospital. Means, standard deviations and correlations for all variables are listed in Table 5 in the appendix. Methods The first step of our analysis was to map our data using a geographical information system (Briggs, 1997). By visualizing the spatial variation of the data, this approach gives a clearer understanding of the data. By comparing different regions within the same map or by comparing the spatial distributions of different vari2
In the process of aggregating these indicators only data points located in buildup areas were considered. 3 Technically, this factor is the first component of a principal component analysis explaining 47% of the total variation in air quality. 4 The third factor, ethnic segregation, identified in American cities usually is not found in German cities.
131
ables, a GIS approach is also useful for formulating hypotheses of relationships underlying the data. In a second step, we tested our hypothesis estimating several models using standard OLS regression techniques.
Results The age-standardized rates of patients per 100,000 inhabitants per year who underwent surgery in the 1990s is shown in Fig. 2. This variable was categorized into five classes of the same size (quintiles) ranging from low to high rates. Districts with low rates of patients are concentrated in the eastern and partly in the southern part of the city. City districts marked by high rates of patients are disproportionately often located to the west of the Rhine. Especially, inner city sections seem to be affected. The spatial distribution of social status within the city can be found in Fig. 3. Again this variable is categorized into quintiles. The vast majority of the lowest status neighborhoods are clustered in two parts of the city. The larger one is located east of the river Rhine and is characterized by older buildings and a formerly high level of industrial enterprises which were mostly closed down during the last decades. The second cluster is located in the northwest of the city. In this area, the city’s largest complex of high-rise apartment buildings built in the 1960s is located. In both clusters, the proportion of foreigners, the relative number of welfare recipients and unemployed persons is rather high. The city districts with the highest social status are more dispersed though most of them are located at the border of the city. The distribution of the family status throughout the city shows a concentric pattern with inner city districts displaying low levels of family status, while areas further away from the center show higher values, i.e. have a higher proportion of children and smaller proportions of one-person households (see Fig. 4). Finally, the spatial distribution of air pollution is depicted in Fig. 5.5 This variable was split into six categories; three below and three above the average pollution level. The map shows a clear pattern in which the city districts with higher levels of air pollution are located in the city center, whereas those with cleaner air are located towards the margins of the city. A visual inspection of the four maps may lead to the conclusion that there is an association between the variables. First, social status and air pollution seem to correlate negatively: on the one hand, most of the districts where air quality is poor are located in the inner city and this coincides with moderate values for the 5 The frequency distribution of districts over the 6 classes of air pollution can be found in Table 4 of the appendix.
C. Wolf / Health & Place 8 (2002) 129–139
132
Patients per 10,000 p.a. low Median high Map: CRC 419, Project C3 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 2. Age-standardized patient rate, 1990–1999.
social status measure. On the other hand, those districts with better than average air quality are located towards the city borders, thus in areas with higher proportions of better off households. There are, however, several exceptions to this rule, the northwest, for example, deviates from this pattern as a cluster of low-status districts with the best air quality. The correlation between social status and air quality is only moderate (r ¼ 20:28), although, it is statistically significant at the 1% level. With r ¼ 20:60; the correlation between family status and air pollution is much stronger, indicating that families with children tend to reside in those parts of the city where air quality is good. When comparing Figs. 2 and 5, an association between the patient rate and air pollution seems
obvious. Particularly, the higher values of the inner city districts compared to the lower rates of some of the districts further away from the center are striking. However, as the correlation coefficient (r ¼ 20:04) indicates there is no linear relationship between these variables (see Table 1). Indeed, the rate of patients does not seem to be associated with any of the indicators taken into account in our analysis. Regressing the patient rates6 on air pollution while controlling for the other variables listed in Table 1 also fails to show any effect.
6 In the analyses presented here, we use the natural logarithm of the patient rates and the distance measure.
C. Wolf / Health & Place 8 (2002) 129–139
133
Social Status low Median high Map: CRC 419, Project C3 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 3. Social status in Cologne (factor score, see text).
As further statistical analyses reveals, the picture changes when those parts of the city where the level of air pollution is below average and those where it is above average are analyzed separately.7 In a separate examination of these two groups, a statistically significant correlation coefficient between patient rate and
7 Forty-seven districts have levels of air pollution which are below the average, accordingly 38 districts have above average pollution levels. Although this second group is smaller than the first, it almost houses two thirds of the populationF646,000 compared to only 361,000 in the 47 cleaner districts (the excluded district belongs to this latter group).
the air quality in the group of districts in which the air is less clean was obtained (see last column of Table 1). A multivariate analysis supports this finding. In the group of city districts where air pollution is below average the model does not account for any variation in the patient ratesFthe adjusted R2 value even becomes negative, indicating that the model explains nothing at all (see Table 2). Of course, this is reasonable: in these parts of the city CRS cannot be caused by air pollution since there is (almost) none. Instead other factorsF e.g. indoor air pollution, behavioral and innate differencesFhave to account for the variation in these districts. In the remaining districts which are marked by air pollution levels above average there is a
C. Wolf / Health & Place 8 (2002) 129–139
134
Family Status low Median
high
Map: CRC 419, Project C3 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 4. Family status in Cologne (factor score, see text).
significantFalthough weakFpositive correlation of r ¼ 0:3 between air quality and patient rates.8 Thus, in those parts of the city where the level of pollution surpasses a certain threshold, we observe the positive association we expected to find. This relationship persists in a multivariate regression model where the confounding factors introduced above are controlled (see Table 2). As expected, the distance between the districts and the hospital is a strong predictor for patient density, thus, in districts close to the clinic patient rates tend to be higher than in those further away. Likewise, the family status of 8
For the full correlation matrix see Table 5 in the appendix.
the areas has a statistical significant effect on the observed patient rates. Although on the individual level patient rates are highest for persons aged 55–59 at the aggregate level those districts marked by high proportions of children and low divorce rates show higher rates of persons treated for CRS.9 The social status and the residential stability of the areas have no effect on the proportion of patients. Finally, in this multivariate model the effect of air pollution on the patient rate is small but substantial. The standardized regression 9 Here, we have a good example that it is not wise to transfer aggregate relationships directly to the individual level.
C. Wolf / Health & Place 8 (2002) 129–139
135
Outdoor Air Polution high
Mean
Map: CRC 419, Project C3
low 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 5. Pattern of air pollution in Cologne (factor score; see text).
coefficient even exceeds the bivariate correlation and reaches significance at the 5% level. Thus, under the condition that air pollution is above average, higher levels of pollution are associated with higher levels of CRS. The fit of the model can be improved slightly by dropping the variables of social status and residential stability which have no significant effect on the dependent variable (see model II in Table 2).10
10 These models were extensively tested for non-linearity and interaction effects. The simple linear model presented here seems to be the most satisfactory, though, this might be due to the small sample size.
Of course, we would be more confident about the relationship between air pollution and chronic sinusitis if more data were available. Fortunately, an additional test of the relationship between air pollution and the chronic condition was possible. As was pointed out earlier, the level of air pollution has generally declined during the 1990s. Therefore, it can be assumed that the relationship between air quality and CRS should weaken during this period. To test this hypothesis, two age-standardized patient rates were calculated; the first referring to the years 1990–1994 and the second referring to the years 1995–1999. As Table 3 shows this hypothesis cannot be refuted. During the early 1990s, air pollution and CRS are
C. Wolf / Health & Place 8 (2002) 129–139
136
Table 1 Correlation of patient rates with independent variables by level of air pollution Total (N ¼ 84)
0.025 0.139 0.071 0.222* 0.030
Air pollution Social status Family status Distance to hospital Stable population in % a
Districts with air pollution y
For details see text and Table 5 in the appendix;
a
below average (N ¼ 46)
above average (N ¼ 38)
0.132 0.037 0.101 0.001 0.167
0.304+ 0.290+ 0.375* 0.569** 0.187
+
po0:10; *po0:05; **po0:01:
Table 2 OLS regressions with patient rate as dependent variable (standardized coefficients) Districts with air pollution y below average (N ¼ 46)
Air pollution Family status Distance to hospital Social status Stability Adj. R2
above average (N ¼ 38)
I
II
I
II
0.082 0.013 0.062 0.069 0.148 0.081
0.135 0.071 0.087
0.384* 0.450+ 0.828** 0.009 0.008 0.333
0.382* 0.446+ 0.832**
0.045
0.372
+
po0:10; *po0:05; **po0:01:
Table 3 OLS regressions with patient rate as dependent variableFonly districts in which air pollution is above average (N ¼ 38) Patient rate y 1990–1994
Constant Air pollution Family status Distance to hospital Adj. R2
1995–1999
b
B
b
B
3.458 0.235 0.109 0.348 0.291
0.427* 0.265 0.596*
4.657 0.124 0.177 0.446 0.356
0.265 0.508* 0.904**
b: unstandardized regression weights; B: standardized regression weights;
associated moderately but consistently, even after controlling for other factors.11 In the second half of the decade, the unstandardized regression coefficient is 11
We only present the results for model II (see Table 2) here because it also fits these data best. We reach the same conclusions when the full model (model I in Table 2) is estimated. With regard to the districts with below average levels of air pollution the results are also unchanged: none of the factors considered here has an influence on the dependent variable.
+
po0:10; *po0:05; **po0:01:
considerably smaller and ceases to reach statistical significance. Hence, a robust positive association between air pollution and the relative frequency of people treated for CRS by the University Hospital in Cologne can be observed for the beginning of the last decade of the past millennium. An association that weakens as the level of air pollution declines during the end of this decade. According to these results, the possibility that chronic rhinosinusitis is influenced by the level of air pollution cannot be ruled out.
C. Wolf / Health & Place 8 (2002) 129–139
Conclusions As is the case with most ecological studies in epidemiology, the data have several limitations that should be considered when trying to understand the results presented above (Morgenstern, 1995). First, the data on air pollution is limited with regard to the time points and the pollutants available. Outdoor air pollutionFat least with respect to the classical pollutants SO2, NOx and TSPFhas considerably declined in the past decades. It may not be these pollutants anymore that have major adverse effects contributing to the development of CRS. Instead other pollutants may have gained importance or will become more important in the future. Nevertheless, the observed systematic spatial variation of chronic sinusitis points to the fact that environmental factors play a role in the etiology of CRS. Second, no information on the level of exposure of patients to outdoor air pollution was available. Neither their length of residence nor the amount of time they spent outdoors and in the district they live in is known. Third, though the University Hospital enjoys an excellent reputation and, as the map shows, attracts patients throughout the city, the possibility that the University Hospital does not treat the same proportion of all people with CRS in each district cannot be dismissed. Controlling for the residential stability of the districts and their distance to the University Hospital, respectively, may not suffice to rule out possible distortions in the data. It is hoped that these problems can be overcome in the next steps of the project. Presently, register data from other Hospitals are collected and combined with the current database. This may allow more effective selection control in the future.12 These limitations notwithstanding, the analyses show a clear result: in those parts of the city where the level of air pollution is above average, a positive correlation between patient density and air pollution that remains stable under multivariate controls can be found. Thus, the original hypothesis can be refined to state that the relationship between severe forms of CRS treated by functional endoscopic surgery and the quality of outdoor air is non-linear. A positive association between CRS and air pollution can be observed where the level of pollution extends beyond a certain, but rather low,
threshold. Where this is not the case an association between air quality and CRS cannot be found. Acknowledgements The author is a member of the Collaborative Research Center 419 (CRC 419) at the University of Cologne: ‘Environmental Problems of an Industrial Conurbation; Scientific Solution Strategies and Socio-Economic Implications’. Without the help of my collaborators, this paper would never have been written. I am especially indebted to Michael Damm, MD and Prof. Hans E. Eckel, MD from the ENT Department of the University Hospital and to Christoph Kassel. Mr. Damm coordinated the collection of patient data and answered all my questions concerning chronic rhinosinusitis and its treatment. Mr. Kassel was responsible for integrating the different data in a single database, he ascertained that the patients were assigned to the correct districts, and he prepared the maps presented in this paper. The maps were finalized by Gert Heider who did not lose his patience when asked to modify them yet another time. Part of the patient data was collected by Arzu Karakoc, Gesine Rassek, Anke Sieb and Ayan Yildiz. Another part was made available through the support of Mr. Eckel and the administration of the University Hospital. Prof. Dr. Michael Kerschgens, Dr. Wenzel Br.ucher and their colleagues shared their simulation data on NOx pollution with us. I am also grateful to the Statistical Office of the city of Cologne for making administrative data available. My thanks belongs to all these persons and organizations as well as to the anonymous reviewers. Finally, I have the pleasure to acknowledge my great debt to Prof. Dr. Jurgen . Friedrichs who helped in more ways than possibly could be listed here. As always the responsibility for the shortcomings of this article lies by the author alone. This research was made possible by the financial support from the German Research Foundation. Appendix A Air pollution in 6 groups and means, standard deviations and correlations of measures are shown in Tables 4 and 5. Table 4 Air pollution in 6 groups f
12
In a joint project of the ENT Department of the University Hospital and the Research Institute for Sociology at the University of Cologne we collect individual level data from patients with CRS related to several areas of interest, i.e. length of residence, cytological characteristics of epithelial cells, allergy status, quality of indoor air, psychological dispositions, a wide range of behavioral aspects, etc. These data will give us the opportunity to learn more about the individual level factors that contribute to CRS.
137
Low
%
16 15 16
18.8 17.6 18.8
13 13 12
15.3 15.3 14.1
districts with clean air ’ Mean
High
# of districts 85 100.0
districts with less clean air
138
C. Wolf / Health & Place 8 (2002) 129–139
Table 5 Means, standard deviations and correlations of measures Total (N ¼ 84)
PR
AP
Patient ratio (logged)FPR
0.82 0.30
0.025
0.139
0.03 0.97
0.281**
Air pollutionFAP Social statusFSOC
SOC
0.02 0.99
FAM
DIST
0.071
0.222*
0.030
0.599**
0.549**
0.452**
0.160
0.118
0.04 0.93
Family statusFFAM Distance to hospital (logged)FDIST
0.463**
8.86 0.59
0.359** 75.46 5.31
0.85 0.30
0.132 0.70 0.44
Air pollutionFAP Social statusFSOC
0.037
0.101
0.001
0.102
0.477**
0.427**
0.36 0.94
0.274+
0.154
Family statusFFAM
0.31 0.81
Distance to hospital (logged)FDIST
Air pollutionFAP
0.167 0.424** 0.235
0.389**
0.368*
9.13 0.38
0.065
StabilityFSTAB Air pollution > mean (N=38) Patient ratio (logged)FPR
0.287**
0.676**
StabilityFSTAB Air pollution o mean (N=46) Patient ratio (logged)FPR
STAB
77.18 4.61 0.78 0.31
0.304+
0.290+
0.375*
0.569**
0.187
0.91 0.67
0.025
0.519**
0.184
0.212
0.504**
0.604**
Social statusFSOC
0.39 0.89
0.47 0.89
Family statusFFAM Distance to hospital (logged)FDIST
0.748**
0.370*
8.53 0.63
0.321*
StabilityFSTAB
73.38 5.40
Means and standard deviations in main diagonal; correlations in off-diagonal;
References Briggs, D., 1997. Using geographical information systems to link environment and health data. In: Corval!an, C., Nurminen, M., Pastides, H. (Eds.), Linkage Methods for Environmental Health Analysis. Office of Global and Integrated Environmental Health, World Health Organization, Geneva, pp. 125–153. Brucher, . W., 1997. Numerische Studien zum Mehrfachnesting . mit einem nicht-hydrostatischen Modell. Koln: Mitteilungen aus dem Institut fur . Geophysik und Meteorologie, Heft . 119, Universit.at zu Koln. Brucher, . W., Kessler, C., Kerschgens, M.J., Ebel, A., 2000. Simulation of traffic-induced air pollution on regional to local scales. Atmospheric Environment 34, 4675–4681.
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+
po0:10; *po0:05; **po0:01:
Dockery, D.W., Pope, C.A., 1994. Acute respiratory effects of particulate air pollution. Annual Review of Public Health 15, 107–132. Heinrich, J., Mielck, A., Sch.afer, I., Mey, W., 1998. Soziale Ungleichheit und umweltbedingte Erkrankungen in Deutschland. ecomed, Landsberg. Holman, C., 1999. Sources of air pollution. In: Holgate, S.T., Samet, J.M., Koren, H.S., Maynard, R.L. (Eds.), Air Pollution and Health. Academic Press, San Diego, pp. 115–148. Jolley, D.J., Jarman, B., Elliot, P., 1992. Socio-economic confounding. In: Elliot, P., Cuzick, J., English, D., Stern, R. (Eds.), Geographical and Environmental Epidemiology. Oxford University Press, Oxford, pp. 115–124.
C. Wolf / Health & Place 8 (2002) 129–139 Krouse, J.H., 1999. Introduction to sinus disease. Part I: anatomy and physiology. ORL Head and Neck Nursing 17, 7–12. . . Luttmann, H., Gromping, U., Kreienbrock, L., Treiber-Klotzer, C., Wolf-Ostermann, K., Wichmann, H.E., 1994. Kohortenstudie zu Atemwegserkrankungen und Lungenfunktion bei Schulkindern in Sudwestdeutschland. . Teil 2: Regionale Einflusse . auf Atemwegserkrankungen in Mannheim und im Raum Freiburg. Zentralblatt Hygiene und Umweltmedizin 196, 114–138. Min, Y.G., Jung, H.W., Kim, H.S., Park, S.K., Yoo, K.Y., 1996. Prevalence and risk factors of chronic sinusitis in Korea: results of a nationwide survey. European Archive of Otorhinolaryngology 253, 435–439.
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Morgenstern, H., 1995. Ecological studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health 16, 61–81. Shevky, E., Bell, W., 1955. Social Area Analysis. Theory, Illustrative Application and Computational Procedures. Stanford University Press, Stanford. Sih, T., 1999. Correlation between respiratory alterations and respiratory diseases due to urban pollution. International Journal of Pediatric Otorhinolaryngology 49, S261–S267. Williams, M.L., 1999. Patterns of air pollution in developed countries. In: Holgate, S.T., Samet, J.M., Koren, H.S., Maynard, R.L. (Eds.), Air Pollution and Health. Academic Press, San Diego, pp. 83–104.
Urban air pollution and health: an ecological study of chronic rhinosinusitis in Cologne, Germany$ Christof Wolf* Research Institute for Sociology, Greinstr. 2, 50939 Koln . (Cologne), Germany Received 25 April 2001; received in revised form 17 October 2001; accepted 31 October 2001
Abstract The study investigates the association between outdoor air pollution and chronic rhinosinusitis (CRS) in Cologne, Germany. First, using their addresses, all patients treated for CRS at the ENT Department of the University Hospital between 1990 and 1999 were assigned to one of the 85 city districts. Second, indicators of air pollution (SO2, NOx, TSP) were linked to these areas. Third, to control for socioeconomic confounding, data reflecting the socioeconomic and demographic composition of these districts were collected. Regression analyses reveal weak but consistent statistical effects of pollution on the prevalence of CRS in those parts of the city with air pollution levels above average. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Chronic rhinosinusitis; Outdoor air pollution; Ecological epidemiology
Introduction Chronic rhinosinusitis (CRS) is one of the most prevalent chronic diseases today and is associated with high health care costs (Krouse, 1999). Besides individual risk factors, such as smoking, allergies, anatomic conditions and other inherited dispositions, it is hypothesized that air quality is related to the development of CRS. The study presented here evaluates the strength of the association between outdoor air pollution and CRS in Cologne, Germany. Previous research on the relationship between outdoor air pollution and CRS shows mixed results. Luttmann et al. (1994) examined the effect of air quality on respiratory symptoms by comparing schoolchildren from an industrial German city (Mannheim) with schoolchildren from two rural areas. This comparison was carried out in 1977, 1979 and 1985. One result of $
This article is dedicated to Prof. Dr. Walter Kristof, Ph.D. who stimulated my interest in statistics through his enthusiasm as a teacher. *Tel.: +49-221-470-4397; fax: +49-221-470-5180. E-mail address: [email protected] (C. Wolf).
their study was that bronchitis, inflamed throat with fever and sinusitis was more prevalent in children living in the urban than in the rural location. In addition, their research showed that the relative risk for developing a respiratory disease in the city compared to the countryside decreased over time, reflecting the reduction in the level of sulfur dioxide (SO2) pollution. A similar study conducted in Brazil (Sih, 1999) demonstrated that children living in an urban environment were more likely to be affected by rhinitis, sinusitis and upper respiratory infections in general than children living in rural locations. On the other hand, an epidemiological study in Korea (Min et al., 1996) found no difference between rural and urban areas with regard to the prevalence of chronic sinusitis. Yet the authors report on differences between regions which they attribute to the level of economic activity and crowding. In their review of research on the effects of particulate air pollution on acute respiratory conditions, Dockery and Pope (1994) report that upper respiratory symptoms and exposure to particulate pollution are only weakly related. Although there are several studies reporting statistical relationships between (acute as well as chronic) sinusitis and outdoor air pollution, especially for children, the
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evidence is anything but conclusive. This study aims to help fill this gap by investigating the hypothesis that there is a positive correlation between the prevalence of severe forms of CRS and air pollution.
Data and methods The site of the study The city of CologneFthe site of the research reported hereFis located in the German state of North Rhine, Westphalia on the river Rhine. It has a total area of 405 km2 (156 mile2) and a population of approximately 1 million inhabitants. With temperatures ranging from a low of 8.61C to a high of 33.51C (16.7–92.31F), a yearly mean of 11.41C (52.5.1F) and 768.6 mm (20.3 in) precipitation Cologne enjoys temperate climatic conditions.1 In Cologne, as in developed countries, in general, emissions of SO2, CO and dustFmeasured as total suspended particulate (TSP) matterFand thus air pollution, were reduced in the last 20 years (Holman, 1999; Williams, 1999). Emissions of other pollutants like O3 (ozone), CO2 (carbon dioxide) and NO2 (nitrogen dioxide) have increased until the middle of the 1990s and were only reduced in the last few years. The mean values (98 percentile in parentheses) for 1998 were 10 (32) mg/ m3 SO2, 1.5 (3.8) mg/m3 CO, 27 (91) mg/m3 O3, 25 (77) mg/m3 dust (TSP), 39 (61) mg/m3 NO2 and 389 ppm CO2. Although, objectively these figures are far below the officially set critical values, air pollution is still the most pressing environmental problem for the population. Asked for the most important environmental issues, almost 90% of the population names air pollution, leaving other environmental problems, such as water quality or waste disposal, far behind (Statistical Yearbook of City of Cologne 1996/1997: 237).
identified. These patients were assigned to the 85 administrative city districts according to their place of residence at the time of surgery. This was possible in 97.8% of the cases which yields N ¼ 1435 patients for the analysis. Next, the cases in each district were counted separately for 13 age groups and age specific rates were computed. Finally, the age specific rates were weighted according to the mean age distribution of the population in the city and these weighted rates were aggregated within the districts. This age-standardized rate is the dependent variable of our analysis and will also be referred to as patient rate or patient density. A closer inspection of the distribution of this variable reveals that one of the city districts is an extreme outlier having a rate that is over 6 times the standard deviation above the mean rate. This case was excluded from the following analyses, leaving 84 city districts. Fig. 1 shows the means and standard deviations of the age specific rates. It can be seen that the probability of undergoing surgery for CRS increases during the lifespan until it reaches a maximum for persons aged 55–59.
Independent variables Assessing outdoor air pollution on a small-scale basis is costly and therefore data referring to relatively small areas are seldom available. Fortunately, it was possible to collect several indicators of outdoor air pollution from existing sources. First, there was a program monitoring air quality in the Rhine and Ruhr area closely on a scale of 1 km2. From this source, we obtained data on the level of SO2 and dust fall (TSP)Fdata for the means and 98 percentiles, respectivelyFfor almost the whole city area for each year between 1983 and 1988; whereas these indicators are the result of actual measurement, the next indicator of air 30
Cologne is divided into 85 administrative districts with between 744 and over 40,000 inhabitants, i.e. a median of 9475 (1998). In the study presented here, these districts are the unit of analysis and all measures are aggregated to these units.
25
Dependent variable
Patients per 100,000
Units of analysis
20 15 10 5
From the patient register of the Otorhinolaryngology Department of the University of Cologne, those patients living in Cologne and treated for CRS with functional endoscopic sinus surgery between 1990 and 1999 were 1
These figures are mean values for the years 1990–1998; these and the following figures are taken from the city’s statistical yearbook of 1999.
0 0
10
20
30
40
50
60
70
80
Age at surgery
Fig. 1. Mean age specific rates of patients with CRS undergoing surgery in the ENT Department of the University Hospital, Cologne. Lines represent 95% confidence intervals of means, N ¼ 84; outlying district excluded.
C. Wolf / Health & Place 8 (2002) 129–139
quality is based on a simulation study. This project simulates the spatial distribution of NOx in Cologne on the basis of emission data taking into account several meteorological parameters such as the speed and direction of wind (Brucher, . 1997; Brucher . et al., 2000). From this study, estimates of the means and 98 percentiles of NOx pollution for the city region in 1997 were obtained. All these indicators of outdoor air pollution (SO2, dust fall, NOx) were aggregated to the level of the 85 city districts.2 Finally, the indicators were combined to form a single factor of air quality.3 It should be noted again that the level of outdoor air pollution within the city is comparably low, far below the critical values set by the German TA-Luft. Since the distribution of air quality in a city is correlated with the demographic and socioeconomic characteristics of its population one has to control for these factors (Jolley et al., 1992; Heinrich et al., 1998). Data were, therefore, collected which reflected the demographic and socioeconomic composition of the city districts. Using social areas analysis (Shevky and Bell, 1955), two factors were extracted from the data:4 social status and family status. City districts with low unemployment rates, high proportions of white collar and civil service employees, and much living space per inhabitant gain high scores on the social status indicator. High family status, on the other hand, is marked by high proportions of children and youth, low proportions of one-person households and low proportions of divorced men and women. These two dimensions are slightly correlated, r ¼ 20:22: Finally, two further control variables were constructed: residential stability of the districts between 1990 and 1998 and distance between the University Hospital and the city districts, reflecting the likelihood for choosing the ENT Department at the university rather than some other hospital. Means, standard deviations and correlations for all variables are listed in Table 5 in the appendix. Methods The first step of our analysis was to map our data using a geographical information system (Briggs, 1997). By visualizing the spatial variation of the data, this approach gives a clearer understanding of the data. By comparing different regions within the same map or by comparing the spatial distributions of different vari2
In the process of aggregating these indicators only data points located in buildup areas were considered. 3 Technically, this factor is the first component of a principal component analysis explaining 47% of the total variation in air quality. 4 The third factor, ethnic segregation, identified in American cities usually is not found in German cities.
131
ables, a GIS approach is also useful for formulating hypotheses of relationships underlying the data. In a second step, we tested our hypothesis estimating several models using standard OLS regression techniques.
Results The age-standardized rates of patients per 100,000 inhabitants per year who underwent surgery in the 1990s is shown in Fig. 2. This variable was categorized into five classes of the same size (quintiles) ranging from low to high rates. Districts with low rates of patients are concentrated in the eastern and partly in the southern part of the city. City districts marked by high rates of patients are disproportionately often located to the west of the Rhine. Especially, inner city sections seem to be affected. The spatial distribution of social status within the city can be found in Fig. 3. Again this variable is categorized into quintiles. The vast majority of the lowest status neighborhoods are clustered in two parts of the city. The larger one is located east of the river Rhine and is characterized by older buildings and a formerly high level of industrial enterprises which were mostly closed down during the last decades. The second cluster is located in the northwest of the city. In this area, the city’s largest complex of high-rise apartment buildings built in the 1960s is located. In both clusters, the proportion of foreigners, the relative number of welfare recipients and unemployed persons is rather high. The city districts with the highest social status are more dispersed though most of them are located at the border of the city. The distribution of the family status throughout the city shows a concentric pattern with inner city districts displaying low levels of family status, while areas further away from the center show higher values, i.e. have a higher proportion of children and smaller proportions of one-person households (see Fig. 4). Finally, the spatial distribution of air pollution is depicted in Fig. 5.5 This variable was split into six categories; three below and three above the average pollution level. The map shows a clear pattern in which the city districts with higher levels of air pollution are located in the city center, whereas those with cleaner air are located towards the margins of the city. A visual inspection of the four maps may lead to the conclusion that there is an association between the variables. First, social status and air pollution seem to correlate negatively: on the one hand, most of the districts where air quality is poor are located in the inner city and this coincides with moderate values for the 5 The frequency distribution of districts over the 6 classes of air pollution can be found in Table 4 of the appendix.
C. Wolf / Health & Place 8 (2002) 129–139
132
Patients per 10,000 p.a. low Median high Map: CRC 419, Project C3 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 2. Age-standardized patient rate, 1990–1999.
social status measure. On the other hand, those districts with better than average air quality are located towards the city borders, thus in areas with higher proportions of better off households. There are, however, several exceptions to this rule, the northwest, for example, deviates from this pattern as a cluster of low-status districts with the best air quality. The correlation between social status and air quality is only moderate (r ¼ 20:28), although, it is statistically significant at the 1% level. With r ¼ 20:60; the correlation between family status and air pollution is much stronger, indicating that families with children tend to reside in those parts of the city where air quality is good. When comparing Figs. 2 and 5, an association between the patient rate and air pollution seems
obvious. Particularly, the higher values of the inner city districts compared to the lower rates of some of the districts further away from the center are striking. However, as the correlation coefficient (r ¼ 20:04) indicates there is no linear relationship between these variables (see Table 1). Indeed, the rate of patients does not seem to be associated with any of the indicators taken into account in our analysis. Regressing the patient rates6 on air pollution while controlling for the other variables listed in Table 1 also fails to show any effect.
6 In the analyses presented here, we use the natural logarithm of the patient rates and the distance measure.
C. Wolf / Health & Place 8 (2002) 129–139
133
Social Status low Median high Map: CRC 419, Project C3 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 3. Social status in Cologne (factor score, see text).
As further statistical analyses reveals, the picture changes when those parts of the city where the level of air pollution is below average and those where it is above average are analyzed separately.7 In a separate examination of these two groups, a statistically significant correlation coefficient between patient rate and
7 Forty-seven districts have levels of air pollution which are below the average, accordingly 38 districts have above average pollution levels. Although this second group is smaller than the first, it almost houses two thirds of the populationF646,000 compared to only 361,000 in the 47 cleaner districts (the excluded district belongs to this latter group).
the air quality in the group of districts in which the air is less clean was obtained (see last column of Table 1). A multivariate analysis supports this finding. In the group of city districts where air pollution is below average the model does not account for any variation in the patient ratesFthe adjusted R2 value even becomes negative, indicating that the model explains nothing at all (see Table 2). Of course, this is reasonable: in these parts of the city CRS cannot be caused by air pollution since there is (almost) none. Instead other factorsF e.g. indoor air pollution, behavioral and innate differencesFhave to account for the variation in these districts. In the remaining districts which are marked by air pollution levels above average there is a
C. Wolf / Health & Place 8 (2002) 129–139
134
Family Status low Median
high
Map: CRC 419, Project C3 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 4. Family status in Cologne (factor score, see text).
significantFalthough weakFpositive correlation of r ¼ 0:3 between air quality and patient rates.8 Thus, in those parts of the city where the level of pollution surpasses a certain threshold, we observe the positive association we expected to find. This relationship persists in a multivariate regression model where the confounding factors introduced above are controlled (see Table 2). As expected, the distance between the districts and the hospital is a strong predictor for patient density, thus, in districts close to the clinic patient rates tend to be higher than in those further away. Likewise, the family status of 8
For the full correlation matrix see Table 5 in the appendix.
the areas has a statistical significant effect on the observed patient rates. Although on the individual level patient rates are highest for persons aged 55–59 at the aggregate level those districts marked by high proportions of children and low divorce rates show higher rates of persons treated for CRS.9 The social status and the residential stability of the areas have no effect on the proportion of patients. Finally, in this multivariate model the effect of air pollution on the patient rate is small but substantial. The standardized regression 9 Here, we have a good example that it is not wise to transfer aggregate relationships directly to the individual level.
C. Wolf / Health & Place 8 (2002) 129–139
135
Outdoor Air Polution high
Mean
Map: CRC 419, Project C3
low 0
2000
4000 Meters
Sources of Data: Statistical Office, City of Cologne
Fig. 5. Pattern of air pollution in Cologne (factor score; see text).
coefficient even exceeds the bivariate correlation and reaches significance at the 5% level. Thus, under the condition that air pollution is above average, higher levels of pollution are associated with higher levels of CRS. The fit of the model can be improved slightly by dropping the variables of social status and residential stability which have no significant effect on the dependent variable (see model II in Table 2).10
10 These models were extensively tested for non-linearity and interaction effects. The simple linear model presented here seems to be the most satisfactory, though, this might be due to the small sample size.
Of course, we would be more confident about the relationship between air pollution and chronic sinusitis if more data were available. Fortunately, an additional test of the relationship between air pollution and the chronic condition was possible. As was pointed out earlier, the level of air pollution has generally declined during the 1990s. Therefore, it can be assumed that the relationship between air quality and CRS should weaken during this period. To test this hypothesis, two age-standardized patient rates were calculated; the first referring to the years 1990–1994 and the second referring to the years 1995–1999. As Table 3 shows this hypothesis cannot be refuted. During the early 1990s, air pollution and CRS are
C. Wolf / Health & Place 8 (2002) 129–139
136
Table 1 Correlation of patient rates with independent variables by level of air pollution Total (N ¼ 84)
0.025 0.139 0.071 0.222* 0.030
Air pollution Social status Family status Distance to hospital Stable population in % a
Districts with air pollution y
For details see text and Table 5 in the appendix;
a
below average (N ¼ 46)
above average (N ¼ 38)
0.132 0.037 0.101 0.001 0.167
0.304+ 0.290+ 0.375* 0.569** 0.187
+
po0:10; *po0:05; **po0:01:
Table 2 OLS regressions with patient rate as dependent variable (standardized coefficients) Districts with air pollution y below average (N ¼ 46)
Air pollution Family status Distance to hospital Social status Stability Adj. R2
above average (N ¼ 38)
I
II
I
II
0.082 0.013 0.062 0.069 0.148 0.081
0.135 0.071 0.087
0.384* 0.450+ 0.828** 0.009 0.008 0.333
0.382* 0.446+ 0.832**
0.045
0.372
+
po0:10; *po0:05; **po0:01:
Table 3 OLS regressions with patient rate as dependent variableFonly districts in which air pollution is above average (N ¼ 38) Patient rate y 1990–1994
Constant Air pollution Family status Distance to hospital Adj. R2
1995–1999
b
B
b
B
3.458 0.235 0.109 0.348 0.291
0.427* 0.265 0.596*
4.657 0.124 0.177 0.446 0.356
0.265 0.508* 0.904**
b: unstandardized regression weights; B: standardized regression weights;
associated moderately but consistently, even after controlling for other factors.11 In the second half of the decade, the unstandardized regression coefficient is 11
We only present the results for model II (see Table 2) here because it also fits these data best. We reach the same conclusions when the full model (model I in Table 2) is estimated. With regard to the districts with below average levels of air pollution the results are also unchanged: none of the factors considered here has an influence on the dependent variable.
+
po0:10; *po0:05; **po0:01:
considerably smaller and ceases to reach statistical significance. Hence, a robust positive association between air pollution and the relative frequency of people treated for CRS by the University Hospital in Cologne can be observed for the beginning of the last decade of the past millennium. An association that weakens as the level of air pollution declines during the end of this decade. According to these results, the possibility that chronic rhinosinusitis is influenced by the level of air pollution cannot be ruled out.
C. Wolf / Health & Place 8 (2002) 129–139
Conclusions As is the case with most ecological studies in epidemiology, the data have several limitations that should be considered when trying to understand the results presented above (Morgenstern, 1995). First, the data on air pollution is limited with regard to the time points and the pollutants available. Outdoor air pollutionFat least with respect to the classical pollutants SO2, NOx and TSPFhas considerably declined in the past decades. It may not be these pollutants anymore that have major adverse effects contributing to the development of CRS. Instead other pollutants may have gained importance or will become more important in the future. Nevertheless, the observed systematic spatial variation of chronic sinusitis points to the fact that environmental factors play a role in the etiology of CRS. Second, no information on the level of exposure of patients to outdoor air pollution was available. Neither their length of residence nor the amount of time they spent outdoors and in the district they live in is known. Third, though the University Hospital enjoys an excellent reputation and, as the map shows, attracts patients throughout the city, the possibility that the University Hospital does not treat the same proportion of all people with CRS in each district cannot be dismissed. Controlling for the residential stability of the districts and their distance to the University Hospital, respectively, may not suffice to rule out possible distortions in the data. It is hoped that these problems can be overcome in the next steps of the project. Presently, register data from other Hospitals are collected and combined with the current database. This may allow more effective selection control in the future.12 These limitations notwithstanding, the analyses show a clear result: in those parts of the city where the level of air pollution is above average, a positive correlation between patient density and air pollution that remains stable under multivariate controls can be found. Thus, the original hypothesis can be refined to state that the relationship between severe forms of CRS treated by functional endoscopic surgery and the quality of outdoor air is non-linear. A positive association between CRS and air pollution can be observed where the level of pollution extends beyond a certain, but rather low,
threshold. Where this is not the case an association between air quality and CRS cannot be found. Acknowledgements The author is a member of the Collaborative Research Center 419 (CRC 419) at the University of Cologne: ‘Environmental Problems of an Industrial Conurbation; Scientific Solution Strategies and Socio-Economic Implications’. Without the help of my collaborators, this paper would never have been written. I am especially indebted to Michael Damm, MD and Prof. Hans E. Eckel, MD from the ENT Department of the University Hospital and to Christoph Kassel. Mr. Damm coordinated the collection of patient data and answered all my questions concerning chronic rhinosinusitis and its treatment. Mr. Kassel was responsible for integrating the different data in a single database, he ascertained that the patients were assigned to the correct districts, and he prepared the maps presented in this paper. The maps were finalized by Gert Heider who did not lose his patience when asked to modify them yet another time. Part of the patient data was collected by Arzu Karakoc, Gesine Rassek, Anke Sieb and Ayan Yildiz. Another part was made available through the support of Mr. Eckel and the administration of the University Hospital. Prof. Dr. Michael Kerschgens, Dr. Wenzel Br.ucher and their colleagues shared their simulation data on NOx pollution with us. I am also grateful to the Statistical Office of the city of Cologne for making administrative data available. My thanks belongs to all these persons and organizations as well as to the anonymous reviewers. Finally, I have the pleasure to acknowledge my great debt to Prof. Dr. Jurgen . Friedrichs who helped in more ways than possibly could be listed here. As always the responsibility for the shortcomings of this article lies by the author alone. This research was made possible by the financial support from the German Research Foundation. Appendix A Air pollution in 6 groups and means, standard deviations and correlations of measures are shown in Tables 4 and 5. Table 4 Air pollution in 6 groups f
12
In a joint project of the ENT Department of the University Hospital and the Research Institute for Sociology at the University of Cologne we collect individual level data from patients with CRS related to several areas of interest, i.e. length of residence, cytological characteristics of epithelial cells, allergy status, quality of indoor air, psychological dispositions, a wide range of behavioral aspects, etc. These data will give us the opportunity to learn more about the individual level factors that contribute to CRS.
137
Low
%
16 15 16
18.8 17.6 18.8
13 13 12
15.3 15.3 14.1
districts with clean air ’ Mean
High
# of districts 85 100.0
districts with less clean air
138
C. Wolf / Health & Place 8 (2002) 129–139
Table 5 Means, standard deviations and correlations of measures Total (N ¼ 84)
PR
AP
Patient ratio (logged)FPR
0.82 0.30
0.025
0.139
0.03 0.97
0.281**
Air pollutionFAP Social statusFSOC
SOC
0.02 0.99
FAM
DIST
0.071
0.222*
0.030
0.599**
0.549**
0.452**
0.160
0.118
0.04 0.93
Family statusFFAM Distance to hospital (logged)FDIST
0.463**
8.86 0.59
0.359** 75.46 5.31
0.85 0.30
0.132 0.70 0.44
Air pollutionFAP Social statusFSOC
0.037
0.101
0.001
0.102
0.477**
0.427**
0.36 0.94
0.274+
0.154
Family statusFFAM
0.31 0.81
Distance to hospital (logged)FDIST
Air pollutionFAP
0.167 0.424** 0.235
0.389**
0.368*
9.13 0.38
0.065
StabilityFSTAB Air pollution > mean (N=38) Patient ratio (logged)FPR
0.287**
0.676**
StabilityFSTAB Air pollution o mean (N=46) Patient ratio (logged)FPR
STAB
77.18 4.61 0.78 0.31
0.304+
0.290+
0.375*
0.569**
0.187
0.91 0.67
0.025
0.519**
0.184
0.212
0.504**
0.604**
Social statusFSOC
0.39 0.89
0.47 0.89
Family statusFFAM Distance to hospital (logged)FDIST
0.748**
0.370*
8.53 0.63
0.321*
StabilityFSTAB
73.38 5.40
Means and standard deviations in main diagonal; correlations in off-diagonal;
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