- Email: [email protected]
Short-term associations between air pollution and respiratory drug sales
ARTICLE IN PRESS
Environmental Research 95 (2004) 43–52
Short-term associations between air pollution and respiratory drug sales$ Alexandre Pitard,a, Abdelkrim Zeghnoun,b Annabelle Courseaux,a Jackie Lamberty,c Ve´ronique Delmas,d Jean Luc Fossard,e and Herve´ Villeta b
a Observatoire Re´gional de la Sante´ de Haute-Normandie, 57, Avenue de Bretagne, Rouen 76 100, France De´partement d’Epide´miologie et de Sante´ Publique, Centre Hospitalier Universitaire de Rouen, 1 Rue de Germont, 76 000 Rouen, France c East Sussex Brighton and Hove Health Authority, 36-38 Friar Walk, Lewes, East Sussex BN7 2PB, UK d Re´seau de Surveillance de la Qualite´ de l’Air, Air Normand, 21, Avenue de la Porte des Champs, Rouen 76 100, France e Union Re´gionale des Caisses d’Assurance Maladie, 13, Rue Pierre Gilles de Gennes, Mont Saint Aignan 76 137, France
Received 10 March 2003; received in revised form 11 August 2003; accepted 29 August 2003
Abstract Study objective: This research was implemented to assess the effect of air pollution on anti-asthmatic, bronchodilator, and cough and cold preparation sales in the city of Rouen (France) based on the Upper Normandy Regional Union of Health Insurance Offices database and the Air Quality Monitoring Network database. Design: An ecological time-series analysis was performed for a period of 2 years (July 1998–June 2000). Generalized additive model yields to relative risks and 95% confidence interval (CI) estimates were also carried out. Main results: The 10-day cumulative effect of a 10-mg/m3 black smoke increase was significantly associated with a 6.2% (95% CI, 2.4–10.1%) increase in the sales of anti-asthmatics and bronchodilators and to a 9.2% (95% CI, 5.9–12.6%) increase in the sales of cough and cold preparation for children aged under 15 years. The cumulative effect of a 10-mg/m3 increase in SO2 was associated with an 11.8% (95% CI, 6.7–17.1%) increase in cough and cold preparation sales for children aged under 15 years. The cumulative effect of 10-mg/m3 increase in NO2 was associated with an 13.6% (95% CI, 8–18.3%) increase in cough and cold preparation sales for children under 15 years of age. Conclusions: The results of this study suggest that an increase in drugs sales was directly related to air pollutant concentration increases in the city of Rouen (France). r 2003 Elsevier Inc. All rights reserved. Keywords: Respiratory drug sales; Sulfur dioxide; Nitrogen dioxide; Black smoke; Time series
1. Introduction In environmental epidemiology, time series of pollutants are often related to health indicators (mortality, respiratory, and cardiovascular hospital admissions) to evaluate the response of populations to the pollutant exposures encountered in the course of their normal daily life. In the past decade numerous studies have reported associations between day-to-day fluctuations in air pollution and day-to-day fluctuations in these health events (American Thoracic Society, 1996; Bates, 1996; Dab et al., 1996; Dockery and Pope, 1994; Eilstein et al., $ This study was supported by a grant from the European Union as a part of the INTERREG II project and was conducted according to CNIL (Commission Nationale de l’Informatique et des Liberte´s) guidelines. Corresponding author. Fax: +33-2-32-18-07-51. E-mail address: [email protected] (A. Pitard).
0013-9351/$ - see front matter r 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.envres.2003.08.006
2001; Katsouyanni et al., 1996a, 1996b, 1997; Medina et al., 1997; Michelozzi et al., 1998; Pope et al., 1995; Samet et al., 2000; Schenker, 1993; Utell and Samet, 1993; Thurston, 1996; Touloumi et al., 1997; Wilson and Spengler, 1996; Zeghnoun et al., 2001a, 2001b; Zmirou et al., 1996). Three ecological time series were carried out in the upper Normandy region. In a mortality study Zeghnoun et al. (2001a), examined the short-term effects of ambient air pollution on mortality in two French cities, Rouen and Le Havre. In Rouen, O3 was associated with an increase in total mortality [relative risk (RR)=1.041 (1.006–1.078)]. Daily variations in SO2 were also associated with an increase in respiratory mortality [RR=1.082 (1.004, 1.166)]. An increase in cardiovascular mortality was also observed with NO2 [RR=1.061 (1.015–1.11)]. In the city of Le Havre, SO2 was associated with an increase in cardiovascular mortality
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A. Pitard et al. / Environmental Research 95 (2004) 43–52
[RR=1.03(1.08–1.05.)]. For PM13 (particles with aerodynamic particle diameters of less than 13 mm) a RR of 1.062 (1.001–1.128) was observed. A study of emergency health services data in Rouen (Hautemanie`re et al., 2000) evaluated the feasibility of using both emergency phone calls and medical interventions related to ambulatory emergency services for a local epidemiological surveillance of health effects due to air pollution. A correlation was observed between ambulatory service activity for cardiovascular diseases and the daily variations of both SO2 [RR=1.008 (1.001–1.016) for an increase of 10 mg/m3] and NO2 [RR=1.018 (1.008– 1.030) for an increase of 10 mg/m3]. To our knowledge, there has been only one temporal ecological study aimed at evaluating air pollution effects on respiratory drug sales (Zeghnoun et al., 1999). Daily drug sales time series were constructed using a pharmacists’ network established in 1993 in the city of Le Havre. The network comprises approximately 40 pharmacies. The study evaluated ambulatory respiratory drug sales data as health indicators for the shortterm effects of ambient air pollution. Daily respiratory drug sales were cross-referenced with daily ambient air concentrations of SO2, NO2, and black smoke (BS). Respiratory drug sales were associated with the pollutants studied with lags varying from 1 to 9 days. This study assesses the following points: (1) drug sales as a health indicator contain information about daily incident cases, (2) drug sales levels are very sensitive indicators, allowing temporal ecological study of a small area, and (3) this indicator can easily be constructed for different types or subsets of drugs, as well as different population groups (defined by sex or age groups). Unfortunately, no age-related results are available, as the collection of data by the Le Havre network ceased in 2000. The pharmacists’ network carried out another study to assess tap water quality and anti-diarrheal drug sales (Beaudeau et al., 1999). These two studies, based on the data collected by the pharmacists network, provided the basic theoretical framework of our study.
2. Materials 2.1. Population The study includes all of the respiratory drugs that were provided to the inhabitants of the city of Rouen, which has 106,592 inhabitants (1999 population census). This population accounts for 27.7% of the total population of the greater Rouen area. 2.2. Study period The study covers the period from 1 July 1998 to 30 June 2000.
2.3. Study area The greater Rouen area is located 120 km north west of Paris and is located in a river basin. The River Seine runs through the city, which is bordered by steep hills to the north and two valleys to the east; the Cailly and Robec valleys. In the south a gentle slope rises to a plateau. Weak winds and temperature inversion phenomena (cold air in the lower part of the atmospheric stratum) combined with the location of Rouen in a basin prevent pollutants from dispersing. The greater Rouen area mainly receives southwest winds, which bring clouds of pollution emanating from industrial chimneys. During the fall-out, air pollutant levels are high, short, and localized. Low-speed southwest winds promote air stagnation and air cooling. In combination with temperature-inversion phenomena, these winds produce increasing air pollution. Morning fogs are frequent and the annual mean of foggy days is one of the highest in France (88 days). Summers are quite cool, however, the temperature is steady, with moderate daily variations. Maximum and minimum temperatures are 14.4 C and 6.2 C, respectively. May, June, and July are usually sunnier than August. There is a major industrial complex in the suburb of Rouen along the Seine River. A refinery located at Petit Couronnes is one of the largest petrochemical refineries in France. Rouen harbor is the primary European river port for cereal exports. The main industries are the automobile industry, metallurgy, the chemical fertilizer industry, paper manufacturing, and electrical and electronic industries. 2.4. Environmental data Since 1974, a permanent automated air pollution network has been operating in the city of Rouen and has been managed by an association called ‘‘Air Normand’’. Air pollutants permanently under study are sulfur dioxide (SO2), nitrogen dioxide (NO2), and black smoke. To study the impact of air pollution in the city of Rouen, only the two stations not directly exposed to main roads or to industrial sources of pollution were selected. One of these two ambient stations is located on the right bank of the Seine River and the other on the left bank. The period ranging from 1 July 1998 to 30 June 2000 corresponds to 732 days of measurements. These two stations provided levels of SO2, measured by ultraviolet fluorescence, and levels of NO2, measured by chemiluminescence. BS was measured using reflectrometric techniques at only the left bank station. Daily means were computed based on 24-h values. The correlation of pollutant levels measured at each station was 0.76 and 0.62 for SO2 and NO2, respectively. The population exposure was calculated as the average of the values from these two stations, as in several previous
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epidemiological studies (Katsouyanni et al., 1996b; Moolgavkar et al., 1997; Schwartz, 1997). There were no missing data for NO2 and SO2 concentrations and only 6% of the data were missing for BS. We restricted the study to the city of Rouen and not to the greater Rouen area in order to define a homogeneous air pollutant exposure. The air pollution network Air Normand also carries out meteorological monitoring (temperature and relative humidity).
Table 1 Anti-asthma and COPD products and cough and cold preparations Classes according to EPhMRA
Anatomical classification guidelines
R3 R3A1 R3A2 R3B2 R3C1
Anti-asthma and COPD products b2 stimulants; inhalant b2 stimulants; systemic Xanthines; systemic Nonsteroidal respiratory antiinflammatories; inhalant Nonsteroidal respiratory antiinflammatories; systemic Corticoids; inhalant Anti-cholinergics; plain, and combinations with b2 stimulants; inhalant All other anti-asthma and COPD products; systemic Cough and cold preparations Cold preparations without anti-infectives Cough/cold preparations with antiinfectives Expectorants Plain anti-tussives Anti-tussives in combinations Other cough and cold preparations
R3C2
2.5. Drug sales The recently installed system whereby pharmacists electronically transmit data relating to the reimbursement of medications to the National Health Insurance Offices permits access to useful data for medical cost analyses. This transmission also provides an opportunity to study relationships between air pollution and respiratory drug sales because the data are collected daily and are continuously transmitted to a database. Drugs are defined according to the Anatomical Therapeutic Chemical (ATC) classification provided by European Pharmaceutical Marketing Research Association (EPhMRA, 2001). This international classification, established in 1971, was developed by EPhMRA and the Pharmaceutical Business Intelligence and Research Group (PBIRG). Its aim is to standardize chemical substance classification in order to allow international comparisons, especially in the field of drug usage. In the ATC classification, drugs are classified in different subsets at several levels. Drugs are classified according to the targeted organ or group of organs (first level), the therapeutic indication (second level), the pharmacological effects (third and fourth levels), and the chemical characteristics (fifth level). The substances are related to one unique ATC code that corresponds to their primary use. Two classes of drugs were included in this study, antiasthma and COPD (chronic obstructive pulmonary disease) products and cough and cold preparations. These types of drugs are those considered to be the most likely to be related to air pollution (Table 1).
3. Methods The approach is based on the method designed by the Air Pollution and Health, a European Approach project (APHEA) (Katsouyanni et al., 1996a) and in the nine French cities project (Que´nel et al., 1999). Statistical methods are related to the time-series theory and more specifically to the use of generalized additive models (GAM) applied to time-series (Hastie and Tibshirani, 1990, 1995). The generalized additive model postulates that: p X gðE½yt Þ ¼ gðmt Þ ¼ a þ fi ðxit Þ ¼ Zðxt Þ; ð1Þ i¼1
45
R3D1 R3G1 R3X2 R5 R5A R5B R5C RO5D1 RO5D2 RO5F
where g is the link function. In this paper the response variable yt denotes drug sales. Because these are count data, they have typically been modelled as a Poisson process (g=log) (Pope and Schwartz, 1996). a is the intercept and fi is the unspecified function (nonparametric) describing the relationship between the transformed mean response variable and the predictor xit : Here, Zðxt Þ is an additive predictor. As for a generalized linear model (McCullagh and Nelder, 1983), a relationship between the variance of yt and the expected value mt can be specified. The GAM model we used is slightly different from model (1), as it combines a nonparametric predictor and a linear predictor: ln ðE½yt Þ ¼ gðmt Þ ¼ a þ
k X j¼1
djt xjt þ
p X
fi ðxit Þ ¼ Zðxt Þ;
i¼1
ð2Þ where xj is a predictor included in the model in a linear form and xi is a predictor included in the model by a nonparametric smoothing function. The second term of Pp the GAM model, f ðx i¼1 i i Þ; is the sum of nonparametric functions producing the non-parametric smoothing of the response variable. This nonparametric smoothing can be performed with the LOESS method (or LOWESS, locally weighted scatter plot smoothing). LOESS, originally proposed by Cleveland (1979) and further developed by Cleveland and Devlin (1988), specifically describes a method known as locally weighted polynomial regression. It combines much of the simplicity of linear least-squares regression with the flexibility of nonlinear regression. It does this by fitting
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simple models to localized subsets of the data to build up a function that describes the determining part of the variation in the data, point by point. One of the chief attractions of this method is that the data analyst is not required to specify a function of any form to fit a model to the data. Lags of up to 10 days were included in the analysis for each pollutant. Unlagged and lagged values of the same variable in the model generate colinearity. The presence of multicolinearity can lead to unstable solutions for d (although the sum of estimates d# j is a nonbiased estimate P of d). A way to decrease the effects of multicolinearity is to reduce the number of parameters to estimate by assuming a particular configuration of the parameters d: Almon assumed that all the dj lie on a curve of an sthorder polynomial in order to obtain a reasonably flexible distribution of lags and less parameters to estimate (even if the number of lagged values is large) (Almon, 1962). Therefore, in this study all of the models have been constrained and a polynomial of degree 3 was retained for air pollutant coefficients. This temporal ecological study takes into account daily fluctuations in confounding factors. These factors exhibit a covariation with explanatory variables (air pollutants) and with health indicators. The construction of the statistical model then takes into consideration, step by step:
The method includes the nonparametric smoothing in a Poisson regression. A feature of Poisson regression is that the regression coefficient can be interpreted as an estimate of the logarithm of the relative risk of an increase of the corresponding variable of one unit, adjusted for the other predictors in the model. To allow for an easier interpretation of these continuous scale variables, the exponential of the regression coefficients multiplied by a predefined increase (i.e., 10 mg/m3) have been calculated. For lags of up to 10 days, results were expressed as the relative risks and 95% confidence interval for an increase of 10 mg/m3 of each unlagged and lagged pollutant concentration. We also provided the estimates of the cumulative effect for an increase of 10 mg/m3 of pollutant concentration. This cumulative effect is basically derived from the sum of the coefficients estimates associated with each lag of the pollutants. GAM models are designed to model the total drug sales and drug sales by age group (0–14, 15–64, 65–74, and over 75 years). The goodness-of-fit criterion is the residual partial autocorrelation, the aim being to obtain a white noise process as the final residual (absence of residual correlation). Analyses were performed using S-Plus software for Windows (S-plus 2000, Insightful Corp., 2002, Toulouse, France).
4. Results *
*
*
*
*
Days of the week: traffic and industrial exhausts emissions are lower on Saturdays and Sundays. At the same time, drug sales are lower on Saturdays and Sundays (pharmacies are closed on Sunday). Days of the week were introduced as dummy variables. Trend: the covariation of predictors and health indicators over a period of many years should be taken into account in the analysis. Seasonal variations and trends are taken into account in the analysis with the nonparametric smoothing LOESS on the time variable t: Influenza epidemics: such data were provided by a physicians’ network and were obtained from a database available on the website Sentiweb (URL: http://www.b3e.jussieu.fr/sentiweb/). The effects of influenza epidemics on drug sales must be taken into account in the model as explanatory variables because of the potential influence of any influenza epidemic on drug sales and the variability of their incidence from year to year. Meteorological variables: temperature and humidity can modify levels of drug sales. Temperature and relative humidity data were introduced in the model with the nonparametric smoothing. Holidays: they can modify drug sales and must be included in the analysis. The start and end of holidays were obtained at the following web site: URL: http:// www.education.gouv.fr. Holidays were introduced as dummy variables.
Anti-asthmatic and COPD products sales and cough and cold preparations sales represented 33 and 67%, respectively, of the total respiratory drug sales in the city of Rouen. The daily mean numbers of anti-asthmatic and COPD product boxes sold and of cough and cold preparations boxes sold were 157 and 321, respectively. Table 2 shows descriptive statistics of the daily mean drug sales by age group. Drug sales were close to 0 on Sundays, with a maximum value on Tuesdays. Timeseries plots of daily counts of drug sales are illustrated in Figs. 1 and 2. Figs. 1 and 2 clearly show a strong seasonal pattern, with higher drug sales in the winter. There is also a visible daily fluctuation with no drug sales on Sundays. Table 3 shows descriptive statistics of air pollutants. The missing data were estimated using linear interpolation. The daily time series (Figs. 3–5) of air pollutants exhibit winter peaks and summer minima. The correlation matrix (Table 4) shows a high correlation between NO2 and BS. Correlation coefficients between temperature and air pollutants are negative, and correlation coefficients between relative humidity and pollutants are positive. Associations with different lags in total drug sales are presented in Tables 5 and 6. For the anti-asthma and COPD products (Table 5), associations were found for lags ranging from 5 to 7 days with NO2 and for lags ranging from 1 to 7 days with BS. The highest relative risks were
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Table 2 Characteristics of drug sales by age group
Minimum First quartile Mean Median Third quartile Maximum Standard error
R3
R3cl1 (0–14)
R3cl2 (15–64)
R3cl3 (65–74)
R3cl4 (+75)
R5
R5cl1 (0–14)
R5cl2 (15–64)
R5cl3 (65–74)
R5cl4 (+75)
0 132 157.3 177 212 325 80.6
0 13 25.4 26 36 90 16.7
0 64 78.4 87 105 173 41.2
0 15 25.2 27 36 78 15.6
0 17 28.2 29 40 101 17.8
0 174 321.4 330 463 1073 205.4
0 43 101.4 107 150 329 67.2
0 80 155.6 153 222 695 107.8
0 15 29.6 29 43 98 20.3
0 19 34.7 34 49 115 23.2
R3, anti-asthmatics and COPD drugs; R5, cough and cold preparations. 90
350
80
300
70 60
µg/m3
200
50 40 30
150
20
100
10 j-00
a-00
m-00
m-00
j-00
f-00
d-99
n-99
s-99
o-99
j-99
a-99
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a-99
m-99
m-99
j-99
f-99
d-98
n-98
s-98
j-98
o-98
0
50
a-98
number of boxes
250
j-00
a-00
m-00
m-00
j-00
f-00
d-99
n-99
s-99
o-99
j-99
a-99
j-99
a-99
m-99
j-99
f-99 m-99
d-98
n-98
s-98
o-98
j-98
a-98
0
Fig. 3. Time-series plot of daily SO2 concentrations in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
Fig. 1. Time-series plot of daily sales of anti-asthmatic and COPD boxes in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
120 100
1200
j-00
m-00
a-00
m-00
f-00
j-00
d-99
n-99
s-99
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m-99
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0 f-99
400
j-99
20
d-98
600
n-98
40
s-98
800
o-98
60
j-98
number of boxes
1000
a-98
µg/m3
80
200
Fig. 4. Time-series plot of daily NO2 concentrations in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.). j-00
m-00
a-00
m-00
f-00
j-00
d-99
n-99
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m-99
f-99 m-99
j-99
d-98
n-98
s-98
o-98
j-98
a-98
0
Fig. 2. Time-series plot of daily sales of cough and cold preparation boxes in Rouen, France from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
Table 3 Air pollutant descriptive statistics
Minimum First quartile Mean Median Third quartile Maximum Standard error Missing data
SO2
NO2
BS
0.5 8.5 16.07 14 21 81 10.5 0
12.00 31.5 39.15 38 46.5 100.5 12 0
2.00 8.00 16.7 13.0 20 126 13.3 6%
observed for 6- and 7-day lags with NO2 and for lags ranging from 2 to 5 days with BS. There was no association between SO2 and anti-asthmatic and COPD drug sales. For the cough and cold preparations (Table 6), associations were found with a 9-day lag with SO2, with lags ranging from 3 to 9 days with NO2, and with lags ranging from 1 to 9 days for BS. The highest relative risks were observed for the 6- and 7-day lags with NO2 and for the 5-day lag with BS. For the age group analysis, GAM models, for which at least 1 of the 11 estimated relative risks associated with the pollutants (corresponding to the unlagged and lagged concentration of a pollutant) was significant, are presented in Tables 7–9. For children (0–14 years) and anti-asthma and COPD products (Table 7), associations were found with lags ranging from 7 to 9 days with NO2
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48 140 120
µg/m3
100 80 60 40 20 j-00
a-00
m-00
m-00
j-00
f-00
d-99
n-99
s-99
o-99
j-99
a-99
j-99
a-99
m-99
m-99
j-99
f-99
d-98
n-98
s-98
o-98
j-98
a-98
0
Fig. 5. Time-series plot of daily black smoke concentrations in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
Table 4 Correlations between environmental data
SO2 NO2 BS Temperature Humidity
SO2
NO2
BS
Temperature
Humidity
1.00 0.46 0.39 0.28 0.17
1.00 0.61 0.54 0.25
1.00 0.31 0.25
1.00 0.41
1.00
and with lags ranging from 6 to 9 days for BS. The highest relative risks were observed for the 8-day lag with NO2 and for the 9-day lag with BS. No association was found between SO2 and anti-asthmatic and COPD drug sales, whatever the lag and the age group considered. For children (0–14 years) and cough and cold preparations (Table 7), associations were found for lags ranging from 2 to 9 days with SO2, NO2, and BS. The highest relative risks were observed for the 3- and 4-day lags with SO2, for the 4- and 5-day lags with NO2, and for the 6- and 7-day lags with BS. For people of 15 to 64 years of age and anti-asthma and COPD products (Table 8), associations were found for 5- and 6-day lags with NO2 and the highest relative risk was observed for the 6-day lag. For people aged 15–64 years and cough and cold preparations (Table 8), associations were found for the 1-, 8- and 9-day lags with SO2, for lags ranging from 5 to 9 days for NO2, and for 6- and 8-day lags for BS. The highest relative risks were observed for the 9-day lag with SO2, for the 7-day lag with NO2, and for the 6- and 8-day lags with BS.
Table 5 Estimated relative risksa and 95% confidence intervals (CI) for anti-asthma and COPD drug sales associated with daily means of SO2, NO2, and BS Lag in days
SO2 RR (95% CI)
NO2 RR (95% CI)
BS RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
1.000 1.001 1.002 1.002 1.002 1.003 1.002 1.002 1.001 1.000 0.998
1.004 1.001 1.001 1.002 1.004 1.006 1.007 1.007 1.004 0.998 0.988
1.003 1.006 1.008 1.008 1.008 1.008 1.006 1.004 1.003 1.001 0.999
a b
(0.987–1.013) (0.994–1.008) (0.995–1.009) (0.995–1.009) (0.996–1.009) (0.997–1.008) (0.996–1.009) (0.995–1.009) (0.994–1.008) (0.993–1.007) (0.984–1.012)
(0.991–1.016) (0.994–1.008) (0.994–1.007) (0.995–1.008) (0.998–1.009) (1.001–1.011)b (1.002–1.012)b (1.001–1.013)b (0.998–1.010) (0.992–1.004) (0.976–1.000)
(0.993–1.013) (1.001–1.011)b (1.002–1.013)b (1.003–1.014)b (1.004–1.013)b (1.004–1.012)b (1.002–1.011)b (1.000–1.009)b (0.998–1.007) (0.995–1.006) (0.988–1.010)
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. Po0:05:
Table 6 Estimated relative risksa and 95% confidence intervals (CI) for cough and cold preparation sales associated with daily means of SO2, NO2, and BS Lag in days
SO2 RR (95% CI)
NO2 RR (95% CI)
BS RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
0.999 1.001 1.002 1.002 1.002 1.002 1.002 1.002 1.003 1.004 1.007
0.998 1.000 1.002 1.005 1.007 1.009 1.010 1.010 1.008 1.005 1.001
1.005 1.007 1.008 1.010 1.010 1.011 1.010 1.009 1.007 1.004 1.001
a b
(0.990–1.008) (0.996–1.005) (0.997–1.006) (0.997–1.007) (0.998–1.006) (0.998–1.005) (0.998–1.006) (0.997–1.007) (0.998–1.007) (1.000–1.009)b (0.998–1.016)
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. Po0:05:
(0.990–1.007) (0.996–1.005) (0.998–1.007) (1.001–1.009)b (1.004–1.011)b (1.006–1.012)b (1.006–1.013)b (1.006–1.014)b (1.005–1.012)b (1.002–1.009)b (0.993–1.009)
(0.999–1.011) (1.003–1.010)b (1.005–1.012)b (1.006–1.013)b (1.008–1.013)b (1.008–1.013)b (1.008–1.013)b (1.006–1.012)b (1.004–1.010)b (1.001–1.008)b (0.994–1.008)
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Table 7 Estimated relative risksa and 95% confidence intervals (CI) of drug salesb for children (0–14) years of age associated with daily means of SO2, NO2, and BS Lag in days
NO2 R3 RR (95% CI)
BS R3 RR (95% CI)
SO2 R5 RR (95% CI)
NO2 R5 RR (95% CI)
BS R5 RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
0.997 0.997 0.999 1.000 1.003 1.005 1.008 1.009 1.010 1.009 1.007
1.002 1.002 1.002 1.003 1.004 1.005 1.006 1.007 1.008 1.009 1.010
0.998 1.006 1.012 1.014 1.014 1.013 1.011 1.010 1.009 1.011 1.015
1.001 1.007 1.012 1.014 1.015 1.015 1.014 1.013 1.011 1.010 1.010
1.002 1.004 1.006 1.008 1.011 1.012 1.013 1.013 1.011 1.007 1.002
(0.978–1.017) (0.987–1.008) (0.988–1.009) (0.990–1.011) (0.994–1.012) (0.998–1.013) (0.999–1.016) (1.000–1.019)c (1.001–1.019)c (1.000–1.019)c (0.988–1.027)
(0.988–1.018) (0.995–1.010) (0.995–1.010) (0.995–1.011) (0.997–1.011) (0.999–1.011) (1.000–1.013)c (1.000–1.015)c (1.001–1.015)c (1.002–1.017)c (0.994–1.026)
(0.982–1.014) (0.998–1.015) (1.003–1.020)c (1.005–1.023)c (1.006–1.022)c (1.006–1.020)c (1.004–1.019)c (1.001–1.018)c (1.001–1.018)c (1.002–1.019)c (0.998–1.031)
(0.985–1.017) (0.999–1.016) (1.004–1.019)c (1.006–1.022)c (1.008–1.022)c (1.009–1.021)c (1.007–1.021)c (1.005–1.021)c (1.004–1.019)c (1.003–1.018)c (0.995–1.025)
(0.991–1.014) (0.998–1.010) (1.000–1.012)c (1.002–1.015)c (1.005–1.016)c (1.007–1.017)c (1.008–1.018)c (1.007–1.018)c (1.005–1.016)c (1.001–1.013)c (0.989–1.015)
a
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. R3, anti-asthma and COPD products, R5, cough and cold preparations. c Po0:05: b
Table 8 Estimated relative risksa and 95% confidence intervals (CI) of drugb sales for people aged between 15 and 64 years associated with daily mean of SO2, NO2, and BS Lag in days
NO2 R3 RR (95% CI)
SO2 R5 RR (95% CI)
NO2 R5 RR (95% CI)
BS R5 RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
1.005 1.002 1.002 1.003 1.006 1.008 1.010 1.009 1.006 0.998 0.986
1.008 1.005 1.003 1.002 1.002 1.002 1.003 1.004 1.005 1.006 1.006
0.999 0.998 0.999 1.001 1.004 1.006 1.008 1.009 1.008 1.004 0.997
1.000 0.999 1.000 1.000 1.001 1.002 1.003 1.003 1.003 1.000 0.998
(0.985–1.026) (0.991–1.013) (0.992–1.012) (0.993–1.013) (0.997–1.014) (1.000–1.016)c (1.001–1.018)c (0.999–1.019) (0.996–1.015) (0.989–1.008) (0.967–1.005)
(0.998–1.018) (1.000–1.010)c (0.998–1.008) (0.996–1.007) (0.997–1.006) (0.998–1.006) (0.998–1.008) (0.999–1.009) (1.000–1.010)c (1.001–1.011)c (0.996–1.017)
(0.990–1.009) (0.993–1.003) (0.994–1.003) (0.996–1.005) (0.999–1.008) (1.003–1.010)c (1.004–1.013)c (1.005–1.014)c (1.004–1.013)c (1.000–1.009)c (0.988–1.006)
(0.993–1.007) (0.996–1.003) (0.996–1.003) (0.997–1.004) (0.998–1.005) (0.999–1.005) (1.000–1.006)c (0.969–1.038) (1.000–1.006)c (0.997–1.004) (0.990–1.005)
a
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. R3, anti-asthma and COPD products, R5, cough and cold preparations. c Po0:05: b
Table 9 Estimated relative risksa and 95% confidence intervals (CI) of cough and cold preparation drug sales for people older than 75 years associated with daily means of SO2 and NO2 Lag in days
SO2 RR (95% CI)
NO2 RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
1.015 1.003 0.997 0.996 0.999 1.003 1.008 1.011 1.012 1.007 0.997
0.995 0.984 0.981 0.985 0.992 1.000 1.008 1.011 1.009 0.998 0.976
a
(0.997–1.034) (0.993–1.012) (0.987–1.007) (0.986–1.006) (0.990–1.008) (0.995–1.011) (0.999–1.017) (1.002–1.021)b (1.002–1.021)b (0.998–1.017) (0.978–1.016)
(0.978–1.012) (0.975–0.993)b (0.972–0.990)b (0.976–0.994)b (0.984–1.000)b (0.993–1.007) (1.000–1.015)b (1.003–1.020)b (1.001–1.017)b (0.990–1.006) (0.960–0.992)b
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. b Po0:05:
No association was found between SO2, NO2, and BS and anti-asthma and COPD products sales for people older than 64 years of age. No association was found between SO2, NO2, and BS and cough and cold preparations sales for people aged between 65 and 74 years. For people older than 75 years of age and cough and cold preparations (Table 9), associations were found for the 7- and 8-day lags with SO2. The highest relative risk was observed for the 8-day lag with SO2. For NO2, there were significant relative risks smaller than 1 for the 1- to 4-day and 10-day lags and a significant relative risk greater than 1 for the lags ranging from 6 to 8 days. No association was found between BS and cough and cold preparations for people older than 75 years of age. Tables 10 and 11 present the cumulative effect of pollutant increase for the three pollutants and the age groups. The cumulative effect of a 10-mg/m3 BS increase over 10 days was significantly associated with a 6.2%
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increase of anti-asthmatic and COPD drug sales for children (0–14 years of age). BS was not associated with anti-asthmatic and COPD drug sales for the three other age groups. There were no associations between SO2, NO2, and anti-asthmatic and COPD drug sales. The effect of a 10-mg/m3 SO2 increase over 10 days was significantly associated with
The effect of a 10-mg/m3 NO2 increase over 10 days was significantly associated with
(1) an 11.8% increase of cough and cold preparation sales for children (0–14 years) and, (2) a 4.6% increase of cough and cold preparation sales for people between 15 and 64 years of age.
The effect of a 10-mg/m3 BS increase over 10 days was significantly associated with a 9.2% increase of cough and cold preparation sales for children (0–14 years). The effect of a 10-mg/m3 NO2 increase over 10 days was significantly associated with a 6.1% decrease of cough and cold preparation sales for people older than 75 years of age.
Table 10 Estimated relative risks and 95% confidence intervals (CI) for antiasthma and COPD drug sales associated with a cumulative effect of a 10-mg/m3 concentration increase over 10 days Age in years
Pollutant
Relative risk (95% CI)
All
SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS
1.012 1.020 1.054 1.040 1.046 1.062 1.011 1.035 0.988 0.955 0.983 1.013 1.008 0.950 0.999
0–14
15–64
65–74
+75
a
(0.974–1.051) (0.986–1.054) (1.029–1.080)a (0.982–1.102) (0.991–1.104) (1.024–1.101)a (0.951–1.075) (0.979–1.093) (0.954–1.022) (0.896–1.015) (0.930–1.038) (0.972–1.055) (0.921–1.104) (0.871–1.037) (0.935–1.068)
Po0:05:
Table 11 Estimated relative risks and 95% confidence intervals (CI) for cough and cold preparation drug sales associated with a cumulative effect of a 10-mg/m3 concentration increase over 10 days Age in years
Pollutant
Relative risk (95% CI)
All
SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS
1.024 1.057 1.086 1.118 1.136 1.092 1.046 1.035 1.008 0.985 0.987 1.001 1.048 0.939 0.988
0–14
15–64
65–74
+75
a
Po0:05:
(0.998–1.050) (1.033–1.084)a (1.069–1.103)a (1.067–1.171)a (1.080–1.183)a (1.059–1.126)a (1.015–1.078)a (1.006–1.064)a (0.989–1.028) (0.912–1.065) (0.921–1.059) (0.954–1.050) (0.993–1.106) (0.893–0.987)a (0.954–1.022)
(1) a 13.6% increase of cough and cold preparation sales for children (0–14 years) and (2) a 3.5% increase of cough and cold preparation sales for people between 15 and 64 years of age.
5. Discussion This study highlights a significant positive effect of pollutants (SO2, NO2, BS) on anti-asthmatic and COPD product and on cough and cold preparation drug sales. Furthermore, this study shows that it is possible to construct a drug sales time series based on the Regional Union of Health Insurance Offices (RUHIO) database. Drug sales can serve as a useful health indicator that can provide information on daily variations under conditions that do not require medical care and do not involve serious cases (death, hospital admission). This study is based not on a one-time data collection but on health data routinely collected. In France, routinely and continuously collected morbidity data sources are rare, especially those covering the whole country. The RUHIO database has broadened the data set available for epidemiological purposes, although it was initially created for economic management. However, this database does not include nonreimbursable drugs. Such data can only be collected by specific recording, as in the Le Havre city experiment (Zeghnoun et al., 1999). This indicator is very sensitive and allows temporal ecological studies in small areas because the number of sales is elevated. A great advantage of the respiratory drug sales indicator, over mortality and hospital admissions data is that it is possible to set up an epidemiological time-series study in a very small geographical area. The average daily death count, whatever the reason, is three for the city of Rouen. The drug sales indicator is 159-fold higher than the mortality indicator. This is very important, because the smaller the average daily indicator count, the wider the confidence intervals. Therefore, for a given period the statistical power of an ecological study based on respiratory drug sales is higher than the one based on such standard health indicators as mortality or hospital admissions. This indicator can easily be constructed for
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different types or subsets of drugs and can be adapted to the general population or specific groups (children, the elderly). For the period ranging from June 30, 1998, to July 1, 2000, particulates were not collected by the stations in the city of Rouen; therefore, we were not able to explore the impact of particulates on respiratory drug sales. In this study we chose to alleviate multicolinearity by using polynomial distributed lag models. In this theoretical approach, ozone pollutant levels present a problem because the ozone concentrations are widely contrasted between the winter and summer periods. This pollutant is virtually absent in winter because hours of sunshine are rare. This fact generates gap periods in ozone time series and does not permit the use of the polynomial distributed lag model, which includes lagged and unlagged ozone values. Therefore, we decided not to present ozone as a specific model for the sake of comparability with the three other pollutant models. We assumed that all individuals were equally and evenly exposed to urban air pollution (occupational and indoor personal exposures being either marginal or constant over time). The heterogeneity of the pollution levels in different area of Rouen was not taken into account in our study. Only the daily data of persons affiliated with general insurance systems are included in this study. However, because only the city of Rouen was taken into account, there were not many people with agricultural insurance and thus the selection bias was not considered significant. People in the professional community, which represents a nonnegligible number of people in the town, are not collected by RUHIO. There are significant relative risks smaller than 1 for the 1- to 4-day and 10-day lags and significant relative risk greater than 1 for the 6- to 8-day lag. This combination of relative risks greater or smaller than 1 produces a cumulative risk smaller than 1. Indeed, the NO2 protective effect could be generated by the fact that older persons seldom leave their accommodations and therefore are less exposed to outdoor pollution compared to younger persons. Some patients with chronic respiratory diseases like asthma and COPD have their own stocks of medicine that they renew regularly by purchasing additional drugs. However, this is less true in the case of cough and cold preparations, which explains why in our study the pollutant associations were higher with cough and cold preparations than with anti-asthmatic and bronchodilator drugs. The respiratory drugs used in the study from Zeghnoun et al. (1999) were defined by the International pharmaceutical codes and correspond to the mucolytic and anti-cough medications. In fact, these drugs are all
51
included in the cough and cold preparations class of the EPhMRA classification. Thus, we were able to compare the results from Zeghnoun et al., and those obtained for the cough and cold preparation sales in our study. The drug sales data used by Zeghnoun et al., were not exhaustive, as they were related to only some of the pharmacies of Le Havre city. On the contrary, in our study the sales were for all of the pharmacies in the city of Rouen. Zeghnoun et al., explored lags of up to 14 days to assess the effects of the pollutants on respiratory drug sales. These authors chose to use just one lagged value of the pollutant concentration in a Poisson regression model. However, any effect of the pollution exposure on health outcomes ranges over several days. As it is more reliable to include the concurrent and lagged pollutant measures simultaneously in distributed lag models, we chose to use a GAM model including a polynomial distributed lag model with lags of up to 10 days. The principal advantage of this model is that it allowed us to calculate, in addition to the relative risks associated with each lag, the relative risk associated with the cumulative effect over 10 days. The results of these two studies exhibit discrepancies but also similarities. Indeed, in the Zeghnoun et al., study associations were found for lags ranging from 1 to 9 days. According to the short-term response, respiratory drug sales were associated with BS and NO2 for a 1-day lag. Moreover, associations were also found for lags of between 6 and 9 days. The highest relative risks were observed for an 8-day lag with BS and NO2. For SO2, the strongest association was observed for a 9-day lag. In our study associations are found for lags ranging from 1 to 9 days. Associations were found for a 9-day lag with SO2, for lags ranging from 3 to 9 days with NO2, and for lags ranging from 1 to 9 days for BS. The highest relative risks were observed for the 6- and 7-day lags with NO2 and for the 5-day lag with BS. The RUHIO database is a 2-year moving database, hampering retrospective studies using data that are more than 2 years old. It is theoretically possible, but difficult, to obtain access to archived RUHIO data. It is desirable for research teams wanting to perform analyses linking drug sales and air pollution to sign agreements with RUHIO to collect data. On the basis of these results, one can consider configuring analyses by subsets of drugs, assessments of pollutant effects over a longer period, and the use of multipollutant models. Therefore, this study should open an encouraging avenue for similar studies, considering the easy implementation. Based on the results of this study, the authors conclude that an increase in drug sales was directly related to air pollutant concentration increases in the city of Rouen, France.
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Acknowledgment The authors thank Richard Medeiros, the Medical Editor of Rouen University Hospital–Rouen, France, for his valuable help editing the manuscript.
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Short-term associations between air pollution and respiratory drug sales$ Alexandre Pitard,a, Abdelkrim Zeghnoun,b Annabelle Courseaux,a Jackie Lamberty,c Ve´ronique Delmas,d Jean Luc Fossard,e and Herve´ Villeta b
a Observatoire Re´gional de la Sante´ de Haute-Normandie, 57, Avenue de Bretagne, Rouen 76 100, France De´partement d’Epide´miologie et de Sante´ Publique, Centre Hospitalier Universitaire de Rouen, 1 Rue de Germont, 76 000 Rouen, France c East Sussex Brighton and Hove Health Authority, 36-38 Friar Walk, Lewes, East Sussex BN7 2PB, UK d Re´seau de Surveillance de la Qualite´ de l’Air, Air Normand, 21, Avenue de la Porte des Champs, Rouen 76 100, France e Union Re´gionale des Caisses d’Assurance Maladie, 13, Rue Pierre Gilles de Gennes, Mont Saint Aignan 76 137, France
Received 10 March 2003; received in revised form 11 August 2003; accepted 29 August 2003
Abstract Study objective: This research was implemented to assess the effect of air pollution on anti-asthmatic, bronchodilator, and cough and cold preparation sales in the city of Rouen (France) based on the Upper Normandy Regional Union of Health Insurance Offices database and the Air Quality Monitoring Network database. Design: An ecological time-series analysis was performed for a period of 2 years (July 1998–June 2000). Generalized additive model yields to relative risks and 95% confidence interval (CI) estimates were also carried out. Main results: The 10-day cumulative effect of a 10-mg/m3 black smoke increase was significantly associated with a 6.2% (95% CI, 2.4–10.1%) increase in the sales of anti-asthmatics and bronchodilators and to a 9.2% (95% CI, 5.9–12.6%) increase in the sales of cough and cold preparation for children aged under 15 years. The cumulative effect of a 10-mg/m3 increase in SO2 was associated with an 11.8% (95% CI, 6.7–17.1%) increase in cough and cold preparation sales for children aged under 15 years. The cumulative effect of 10-mg/m3 increase in NO2 was associated with an 13.6% (95% CI, 8–18.3%) increase in cough and cold preparation sales for children under 15 years of age. Conclusions: The results of this study suggest that an increase in drugs sales was directly related to air pollutant concentration increases in the city of Rouen (France). r 2003 Elsevier Inc. All rights reserved. Keywords: Respiratory drug sales; Sulfur dioxide; Nitrogen dioxide; Black smoke; Time series
1. Introduction In environmental epidemiology, time series of pollutants are often related to health indicators (mortality, respiratory, and cardiovascular hospital admissions) to evaluate the response of populations to the pollutant exposures encountered in the course of their normal daily life. In the past decade numerous studies have reported associations between day-to-day fluctuations in air pollution and day-to-day fluctuations in these health events (American Thoracic Society, 1996; Bates, 1996; Dab et al., 1996; Dockery and Pope, 1994; Eilstein et al., $ This study was supported by a grant from the European Union as a part of the INTERREG II project and was conducted according to CNIL (Commission Nationale de l’Informatique et des Liberte´s) guidelines. Corresponding author. Fax: +33-2-32-18-07-51. E-mail address: [email protected] (A. Pitard).
0013-9351/$ - see front matter r 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.envres.2003.08.006
2001; Katsouyanni et al., 1996a, 1996b, 1997; Medina et al., 1997; Michelozzi et al., 1998; Pope et al., 1995; Samet et al., 2000; Schenker, 1993; Utell and Samet, 1993; Thurston, 1996; Touloumi et al., 1997; Wilson and Spengler, 1996; Zeghnoun et al., 2001a, 2001b; Zmirou et al., 1996). Three ecological time series were carried out in the upper Normandy region. In a mortality study Zeghnoun et al. (2001a), examined the short-term effects of ambient air pollution on mortality in two French cities, Rouen and Le Havre. In Rouen, O3 was associated with an increase in total mortality [relative risk (RR)=1.041 (1.006–1.078)]. Daily variations in SO2 were also associated with an increase in respiratory mortality [RR=1.082 (1.004, 1.166)]. An increase in cardiovascular mortality was also observed with NO2 [RR=1.061 (1.015–1.11)]. In the city of Le Havre, SO2 was associated with an increase in cardiovascular mortality
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[RR=1.03(1.08–1.05.)]. For PM13 (particles with aerodynamic particle diameters of less than 13 mm) a RR of 1.062 (1.001–1.128) was observed. A study of emergency health services data in Rouen (Hautemanie`re et al., 2000) evaluated the feasibility of using both emergency phone calls and medical interventions related to ambulatory emergency services for a local epidemiological surveillance of health effects due to air pollution. A correlation was observed between ambulatory service activity for cardiovascular diseases and the daily variations of both SO2 [RR=1.008 (1.001–1.016) for an increase of 10 mg/m3] and NO2 [RR=1.018 (1.008– 1.030) for an increase of 10 mg/m3]. To our knowledge, there has been only one temporal ecological study aimed at evaluating air pollution effects on respiratory drug sales (Zeghnoun et al., 1999). Daily drug sales time series were constructed using a pharmacists’ network established in 1993 in the city of Le Havre. The network comprises approximately 40 pharmacies. The study evaluated ambulatory respiratory drug sales data as health indicators for the shortterm effects of ambient air pollution. Daily respiratory drug sales were cross-referenced with daily ambient air concentrations of SO2, NO2, and black smoke (BS). Respiratory drug sales were associated with the pollutants studied with lags varying from 1 to 9 days. This study assesses the following points: (1) drug sales as a health indicator contain information about daily incident cases, (2) drug sales levels are very sensitive indicators, allowing temporal ecological study of a small area, and (3) this indicator can easily be constructed for different types or subsets of drugs, as well as different population groups (defined by sex or age groups). Unfortunately, no age-related results are available, as the collection of data by the Le Havre network ceased in 2000. The pharmacists’ network carried out another study to assess tap water quality and anti-diarrheal drug sales (Beaudeau et al., 1999). These two studies, based on the data collected by the pharmacists network, provided the basic theoretical framework of our study.
2. Materials 2.1. Population The study includes all of the respiratory drugs that were provided to the inhabitants of the city of Rouen, which has 106,592 inhabitants (1999 population census). This population accounts for 27.7% of the total population of the greater Rouen area. 2.2. Study period The study covers the period from 1 July 1998 to 30 June 2000.
2.3. Study area The greater Rouen area is located 120 km north west of Paris and is located in a river basin. The River Seine runs through the city, which is bordered by steep hills to the north and two valleys to the east; the Cailly and Robec valleys. In the south a gentle slope rises to a plateau. Weak winds and temperature inversion phenomena (cold air in the lower part of the atmospheric stratum) combined with the location of Rouen in a basin prevent pollutants from dispersing. The greater Rouen area mainly receives southwest winds, which bring clouds of pollution emanating from industrial chimneys. During the fall-out, air pollutant levels are high, short, and localized. Low-speed southwest winds promote air stagnation and air cooling. In combination with temperature-inversion phenomena, these winds produce increasing air pollution. Morning fogs are frequent and the annual mean of foggy days is one of the highest in France (88 days). Summers are quite cool, however, the temperature is steady, with moderate daily variations. Maximum and minimum temperatures are 14.4 C and 6.2 C, respectively. May, June, and July are usually sunnier than August. There is a major industrial complex in the suburb of Rouen along the Seine River. A refinery located at Petit Couronnes is one of the largest petrochemical refineries in France. Rouen harbor is the primary European river port for cereal exports. The main industries are the automobile industry, metallurgy, the chemical fertilizer industry, paper manufacturing, and electrical and electronic industries. 2.4. Environmental data Since 1974, a permanent automated air pollution network has been operating in the city of Rouen and has been managed by an association called ‘‘Air Normand’’. Air pollutants permanently under study are sulfur dioxide (SO2), nitrogen dioxide (NO2), and black smoke. To study the impact of air pollution in the city of Rouen, only the two stations not directly exposed to main roads or to industrial sources of pollution were selected. One of these two ambient stations is located on the right bank of the Seine River and the other on the left bank. The period ranging from 1 July 1998 to 30 June 2000 corresponds to 732 days of measurements. These two stations provided levels of SO2, measured by ultraviolet fluorescence, and levels of NO2, measured by chemiluminescence. BS was measured using reflectrometric techniques at only the left bank station. Daily means were computed based on 24-h values. The correlation of pollutant levels measured at each station was 0.76 and 0.62 for SO2 and NO2, respectively. The population exposure was calculated as the average of the values from these two stations, as in several previous
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epidemiological studies (Katsouyanni et al., 1996b; Moolgavkar et al., 1997; Schwartz, 1997). There were no missing data for NO2 and SO2 concentrations and only 6% of the data were missing for BS. We restricted the study to the city of Rouen and not to the greater Rouen area in order to define a homogeneous air pollutant exposure. The air pollution network Air Normand also carries out meteorological monitoring (temperature and relative humidity).
Table 1 Anti-asthma and COPD products and cough and cold preparations Classes according to EPhMRA
Anatomical classification guidelines
R3 R3A1 R3A2 R3B2 R3C1
Anti-asthma and COPD products b2 stimulants; inhalant b2 stimulants; systemic Xanthines; systemic Nonsteroidal respiratory antiinflammatories; inhalant Nonsteroidal respiratory antiinflammatories; systemic Corticoids; inhalant Anti-cholinergics; plain, and combinations with b2 stimulants; inhalant All other anti-asthma and COPD products; systemic Cough and cold preparations Cold preparations without anti-infectives Cough/cold preparations with antiinfectives Expectorants Plain anti-tussives Anti-tussives in combinations Other cough and cold preparations
R3C2
2.5. Drug sales The recently installed system whereby pharmacists electronically transmit data relating to the reimbursement of medications to the National Health Insurance Offices permits access to useful data for medical cost analyses. This transmission also provides an opportunity to study relationships between air pollution and respiratory drug sales because the data are collected daily and are continuously transmitted to a database. Drugs are defined according to the Anatomical Therapeutic Chemical (ATC) classification provided by European Pharmaceutical Marketing Research Association (EPhMRA, 2001). This international classification, established in 1971, was developed by EPhMRA and the Pharmaceutical Business Intelligence and Research Group (PBIRG). Its aim is to standardize chemical substance classification in order to allow international comparisons, especially in the field of drug usage. In the ATC classification, drugs are classified in different subsets at several levels. Drugs are classified according to the targeted organ or group of organs (first level), the therapeutic indication (second level), the pharmacological effects (third and fourth levels), and the chemical characteristics (fifth level). The substances are related to one unique ATC code that corresponds to their primary use. Two classes of drugs were included in this study, antiasthma and COPD (chronic obstructive pulmonary disease) products and cough and cold preparations. These types of drugs are those considered to be the most likely to be related to air pollution (Table 1).
3. Methods The approach is based on the method designed by the Air Pollution and Health, a European Approach project (APHEA) (Katsouyanni et al., 1996a) and in the nine French cities project (Que´nel et al., 1999). Statistical methods are related to the time-series theory and more specifically to the use of generalized additive models (GAM) applied to time-series (Hastie and Tibshirani, 1990, 1995). The generalized additive model postulates that: p X gðE½yt Þ ¼ gðmt Þ ¼ a þ fi ðxit Þ ¼ Zðxt Þ; ð1Þ i¼1
45
R3D1 R3G1 R3X2 R5 R5A R5B R5C RO5D1 RO5D2 RO5F
where g is the link function. In this paper the response variable yt denotes drug sales. Because these are count data, they have typically been modelled as a Poisson process (g=log) (Pope and Schwartz, 1996). a is the intercept and fi is the unspecified function (nonparametric) describing the relationship between the transformed mean response variable and the predictor xit : Here, Zðxt Þ is an additive predictor. As for a generalized linear model (McCullagh and Nelder, 1983), a relationship between the variance of yt and the expected value mt can be specified. The GAM model we used is slightly different from model (1), as it combines a nonparametric predictor and a linear predictor: ln ðE½yt Þ ¼ gðmt Þ ¼ a þ
k X j¼1
djt xjt þ
p X
fi ðxit Þ ¼ Zðxt Þ;
i¼1
ð2Þ where xj is a predictor included in the model in a linear form and xi is a predictor included in the model by a nonparametric smoothing function. The second term of Pp the GAM model, f ðx i¼1 i i Þ; is the sum of nonparametric functions producing the non-parametric smoothing of the response variable. This nonparametric smoothing can be performed with the LOESS method (or LOWESS, locally weighted scatter plot smoothing). LOESS, originally proposed by Cleveland (1979) and further developed by Cleveland and Devlin (1988), specifically describes a method known as locally weighted polynomial regression. It combines much of the simplicity of linear least-squares regression with the flexibility of nonlinear regression. It does this by fitting
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A. Pitard et al. / Environmental Research 95 (2004) 43–52
simple models to localized subsets of the data to build up a function that describes the determining part of the variation in the data, point by point. One of the chief attractions of this method is that the data analyst is not required to specify a function of any form to fit a model to the data. Lags of up to 10 days were included in the analysis for each pollutant. Unlagged and lagged values of the same variable in the model generate colinearity. The presence of multicolinearity can lead to unstable solutions for d (although the sum of estimates d# j is a nonbiased estimate P of d). A way to decrease the effects of multicolinearity is to reduce the number of parameters to estimate by assuming a particular configuration of the parameters d: Almon assumed that all the dj lie on a curve of an sthorder polynomial in order to obtain a reasonably flexible distribution of lags and less parameters to estimate (even if the number of lagged values is large) (Almon, 1962). Therefore, in this study all of the models have been constrained and a polynomial of degree 3 was retained for air pollutant coefficients. This temporal ecological study takes into account daily fluctuations in confounding factors. These factors exhibit a covariation with explanatory variables (air pollutants) and with health indicators. The construction of the statistical model then takes into consideration, step by step:
The method includes the nonparametric smoothing in a Poisson regression. A feature of Poisson regression is that the regression coefficient can be interpreted as an estimate of the logarithm of the relative risk of an increase of the corresponding variable of one unit, adjusted for the other predictors in the model. To allow for an easier interpretation of these continuous scale variables, the exponential of the regression coefficients multiplied by a predefined increase (i.e., 10 mg/m3) have been calculated. For lags of up to 10 days, results were expressed as the relative risks and 95% confidence interval for an increase of 10 mg/m3 of each unlagged and lagged pollutant concentration. We also provided the estimates of the cumulative effect for an increase of 10 mg/m3 of pollutant concentration. This cumulative effect is basically derived from the sum of the coefficients estimates associated with each lag of the pollutants. GAM models are designed to model the total drug sales and drug sales by age group (0–14, 15–64, 65–74, and over 75 years). The goodness-of-fit criterion is the residual partial autocorrelation, the aim being to obtain a white noise process as the final residual (absence of residual correlation). Analyses were performed using S-Plus software for Windows (S-plus 2000, Insightful Corp., 2002, Toulouse, France).
4. Results *
*
*
*
*
Days of the week: traffic and industrial exhausts emissions are lower on Saturdays and Sundays. At the same time, drug sales are lower on Saturdays and Sundays (pharmacies are closed on Sunday). Days of the week were introduced as dummy variables. Trend: the covariation of predictors and health indicators over a period of many years should be taken into account in the analysis. Seasonal variations and trends are taken into account in the analysis with the nonparametric smoothing LOESS on the time variable t: Influenza epidemics: such data were provided by a physicians’ network and were obtained from a database available on the website Sentiweb (URL: http://www.b3e.jussieu.fr/sentiweb/). The effects of influenza epidemics on drug sales must be taken into account in the model as explanatory variables because of the potential influence of any influenza epidemic on drug sales and the variability of their incidence from year to year. Meteorological variables: temperature and humidity can modify levels of drug sales. Temperature and relative humidity data were introduced in the model with the nonparametric smoothing. Holidays: they can modify drug sales and must be included in the analysis. The start and end of holidays were obtained at the following web site: URL: http:// www.education.gouv.fr. Holidays were introduced as dummy variables.
Anti-asthmatic and COPD products sales and cough and cold preparations sales represented 33 and 67%, respectively, of the total respiratory drug sales in the city of Rouen. The daily mean numbers of anti-asthmatic and COPD product boxes sold and of cough and cold preparations boxes sold were 157 and 321, respectively. Table 2 shows descriptive statistics of the daily mean drug sales by age group. Drug sales were close to 0 on Sundays, with a maximum value on Tuesdays. Timeseries plots of daily counts of drug sales are illustrated in Figs. 1 and 2. Figs. 1 and 2 clearly show a strong seasonal pattern, with higher drug sales in the winter. There is also a visible daily fluctuation with no drug sales on Sundays. Table 3 shows descriptive statistics of air pollutants. The missing data were estimated using linear interpolation. The daily time series (Figs. 3–5) of air pollutants exhibit winter peaks and summer minima. The correlation matrix (Table 4) shows a high correlation between NO2 and BS. Correlation coefficients between temperature and air pollutants are negative, and correlation coefficients between relative humidity and pollutants are positive. Associations with different lags in total drug sales are presented in Tables 5 and 6. For the anti-asthma and COPD products (Table 5), associations were found for lags ranging from 5 to 7 days with NO2 and for lags ranging from 1 to 7 days with BS. The highest relative risks were
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47
Table 2 Characteristics of drug sales by age group
Minimum First quartile Mean Median Third quartile Maximum Standard error
R3
R3cl1 (0–14)
R3cl2 (15–64)
R3cl3 (65–74)
R3cl4 (+75)
R5
R5cl1 (0–14)
R5cl2 (15–64)
R5cl3 (65–74)
R5cl4 (+75)
0 132 157.3 177 212 325 80.6
0 13 25.4 26 36 90 16.7
0 64 78.4 87 105 173 41.2
0 15 25.2 27 36 78 15.6
0 17 28.2 29 40 101 17.8
0 174 321.4 330 463 1073 205.4
0 43 101.4 107 150 329 67.2
0 80 155.6 153 222 695 107.8
0 15 29.6 29 43 98 20.3
0 19 34.7 34 49 115 23.2
R3, anti-asthmatics and COPD drugs; R5, cough and cold preparations. 90
350
80
300
70 60
µg/m3
200
50 40 30
150
20
100
10 j-00
a-00
m-00
m-00
j-00
f-00
d-99
n-99
s-99
o-99
j-99
a-99
j-99
a-99
m-99
m-99
j-99
f-99
d-98
n-98
s-98
j-98
o-98
0
50
a-98
number of boxes
250
j-00
a-00
m-00
m-00
j-00
f-00
d-99
n-99
s-99
o-99
j-99
a-99
j-99
a-99
m-99
j-99
f-99 m-99
d-98
n-98
s-98
o-98
j-98
a-98
0
Fig. 3. Time-series plot of daily SO2 concentrations in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
Fig. 1. Time-series plot of daily sales of anti-asthmatic and COPD boxes in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
120 100
1200
j-00
m-00
a-00
m-00
f-00
j-00
d-99
n-99
s-99
o-99
a-99
j-99
j-99
m-99
a-99
m-99
0 f-99
400
j-99
20
d-98
600
n-98
40
s-98
800
o-98
60
j-98
number of boxes
1000
a-98
µg/m3
80
200
Fig. 4. Time-series plot of daily NO2 concentrations in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.). j-00
m-00
a-00
m-00
f-00
j-00
d-99
n-99
s-99
o-99
a-99
j-99
j-99
a-99
m-99
f-99 m-99
j-99
d-98
n-98
s-98
o-98
j-98
a-98
0
Fig. 2. Time-series plot of daily sales of cough and cold preparation boxes in Rouen, France from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
Table 3 Air pollutant descriptive statistics
Minimum First quartile Mean Median Third quartile Maximum Standard error Missing data
SO2
NO2
BS
0.5 8.5 16.07 14 21 81 10.5 0
12.00 31.5 39.15 38 46.5 100.5 12 0
2.00 8.00 16.7 13.0 20 126 13.3 6%
observed for 6- and 7-day lags with NO2 and for lags ranging from 2 to 5 days with BS. There was no association between SO2 and anti-asthmatic and COPD drug sales. For the cough and cold preparations (Table 6), associations were found with a 9-day lag with SO2, with lags ranging from 3 to 9 days with NO2, and with lags ranging from 1 to 9 days for BS. The highest relative risks were observed for the 6- and 7-day lags with NO2 and for the 5-day lag with BS. For the age group analysis, GAM models, for which at least 1 of the 11 estimated relative risks associated with the pollutants (corresponding to the unlagged and lagged concentration of a pollutant) was significant, are presented in Tables 7–9. For children (0–14 years) and anti-asthma and COPD products (Table 7), associations were found with lags ranging from 7 to 9 days with NO2
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48 140 120
µg/m3
100 80 60 40 20 j-00
a-00
m-00
m-00
j-00
f-00
d-99
n-99
s-99
o-99
j-99
a-99
j-99
a-99
m-99
m-99
j-99
f-99
d-98
n-98
s-98
o-98
j-98
a-98
0
Fig. 5. Time-series plot of daily black smoke concentrations in Rouen, France, from 1 July 1998 to 30 June 2000 (s; September; o, October; etc.).
Table 4 Correlations between environmental data
SO2 NO2 BS Temperature Humidity
SO2
NO2
BS
Temperature
Humidity
1.00 0.46 0.39 0.28 0.17
1.00 0.61 0.54 0.25
1.00 0.31 0.25
1.00 0.41
1.00
and with lags ranging from 6 to 9 days for BS. The highest relative risks were observed for the 8-day lag with NO2 and for the 9-day lag with BS. No association was found between SO2 and anti-asthmatic and COPD drug sales, whatever the lag and the age group considered. For children (0–14 years) and cough and cold preparations (Table 7), associations were found for lags ranging from 2 to 9 days with SO2, NO2, and BS. The highest relative risks were observed for the 3- and 4-day lags with SO2, for the 4- and 5-day lags with NO2, and for the 6- and 7-day lags with BS. For people of 15 to 64 years of age and anti-asthma and COPD products (Table 8), associations were found for 5- and 6-day lags with NO2 and the highest relative risk was observed for the 6-day lag. For people aged 15–64 years and cough and cold preparations (Table 8), associations were found for the 1-, 8- and 9-day lags with SO2, for lags ranging from 5 to 9 days for NO2, and for 6- and 8-day lags for BS. The highest relative risks were observed for the 9-day lag with SO2, for the 7-day lag with NO2, and for the 6- and 8-day lags with BS.
Table 5 Estimated relative risksa and 95% confidence intervals (CI) for anti-asthma and COPD drug sales associated with daily means of SO2, NO2, and BS Lag in days
SO2 RR (95% CI)
NO2 RR (95% CI)
BS RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
1.000 1.001 1.002 1.002 1.002 1.003 1.002 1.002 1.001 1.000 0.998
1.004 1.001 1.001 1.002 1.004 1.006 1.007 1.007 1.004 0.998 0.988
1.003 1.006 1.008 1.008 1.008 1.008 1.006 1.004 1.003 1.001 0.999
a b
(0.987–1.013) (0.994–1.008) (0.995–1.009) (0.995–1.009) (0.996–1.009) (0.997–1.008) (0.996–1.009) (0.995–1.009) (0.994–1.008) (0.993–1.007) (0.984–1.012)
(0.991–1.016) (0.994–1.008) (0.994–1.007) (0.995–1.008) (0.998–1.009) (1.001–1.011)b (1.002–1.012)b (1.001–1.013)b (0.998–1.010) (0.992–1.004) (0.976–1.000)
(0.993–1.013) (1.001–1.011)b (1.002–1.013)b (1.003–1.014)b (1.004–1.013)b (1.004–1.012)b (1.002–1.011)b (1.000–1.009)b (0.998–1.007) (0.995–1.006) (0.988–1.010)
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. Po0:05:
Table 6 Estimated relative risksa and 95% confidence intervals (CI) for cough and cold preparation sales associated with daily means of SO2, NO2, and BS Lag in days
SO2 RR (95% CI)
NO2 RR (95% CI)
BS RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
0.999 1.001 1.002 1.002 1.002 1.002 1.002 1.002 1.003 1.004 1.007
0.998 1.000 1.002 1.005 1.007 1.009 1.010 1.010 1.008 1.005 1.001
1.005 1.007 1.008 1.010 1.010 1.011 1.010 1.009 1.007 1.004 1.001
a b
(0.990–1.008) (0.996–1.005) (0.997–1.006) (0.997–1.007) (0.998–1.006) (0.998–1.005) (0.998–1.006) (0.997–1.007) (0.998–1.007) (1.000–1.009)b (0.998–1.016)
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. Po0:05:
(0.990–1.007) (0.996–1.005) (0.998–1.007) (1.001–1.009)b (1.004–1.011)b (1.006–1.012)b (1.006–1.013)b (1.006–1.014)b (1.005–1.012)b (1.002–1.009)b (0.993–1.009)
(0.999–1.011) (1.003–1.010)b (1.005–1.012)b (1.006–1.013)b (1.008–1.013)b (1.008–1.013)b (1.008–1.013)b (1.006–1.012)b (1.004–1.010)b (1.001–1.008)b (0.994–1.008)
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Table 7 Estimated relative risksa and 95% confidence intervals (CI) of drug salesb for children (0–14) years of age associated with daily means of SO2, NO2, and BS Lag in days
NO2 R3 RR (95% CI)
BS R3 RR (95% CI)
SO2 R5 RR (95% CI)
NO2 R5 RR (95% CI)
BS R5 RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
0.997 0.997 0.999 1.000 1.003 1.005 1.008 1.009 1.010 1.009 1.007
1.002 1.002 1.002 1.003 1.004 1.005 1.006 1.007 1.008 1.009 1.010
0.998 1.006 1.012 1.014 1.014 1.013 1.011 1.010 1.009 1.011 1.015
1.001 1.007 1.012 1.014 1.015 1.015 1.014 1.013 1.011 1.010 1.010
1.002 1.004 1.006 1.008 1.011 1.012 1.013 1.013 1.011 1.007 1.002
(0.978–1.017) (0.987–1.008) (0.988–1.009) (0.990–1.011) (0.994–1.012) (0.998–1.013) (0.999–1.016) (1.000–1.019)c (1.001–1.019)c (1.000–1.019)c (0.988–1.027)
(0.988–1.018) (0.995–1.010) (0.995–1.010) (0.995–1.011) (0.997–1.011) (0.999–1.011) (1.000–1.013)c (1.000–1.015)c (1.001–1.015)c (1.002–1.017)c (0.994–1.026)
(0.982–1.014) (0.998–1.015) (1.003–1.020)c (1.005–1.023)c (1.006–1.022)c (1.006–1.020)c (1.004–1.019)c (1.001–1.018)c (1.001–1.018)c (1.002–1.019)c (0.998–1.031)
(0.985–1.017) (0.999–1.016) (1.004–1.019)c (1.006–1.022)c (1.008–1.022)c (1.009–1.021)c (1.007–1.021)c (1.005–1.021)c (1.004–1.019)c (1.003–1.018)c (0.995–1.025)
(0.991–1.014) (0.998–1.010) (1.000–1.012)c (1.002–1.015)c (1.005–1.016)c (1.007–1.017)c (1.008–1.018)c (1.007–1.018)c (1.005–1.016)c (1.001–1.013)c (0.989–1.015)
a
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. R3, anti-asthma and COPD products, R5, cough and cold preparations. c Po0:05: b
Table 8 Estimated relative risksa and 95% confidence intervals (CI) of drugb sales for people aged between 15 and 64 years associated with daily mean of SO2, NO2, and BS Lag in days
NO2 R3 RR (95% CI)
SO2 R5 RR (95% CI)
NO2 R5 RR (95% CI)
BS R5 RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
1.005 1.002 1.002 1.003 1.006 1.008 1.010 1.009 1.006 0.998 0.986
1.008 1.005 1.003 1.002 1.002 1.002 1.003 1.004 1.005 1.006 1.006
0.999 0.998 0.999 1.001 1.004 1.006 1.008 1.009 1.008 1.004 0.997
1.000 0.999 1.000 1.000 1.001 1.002 1.003 1.003 1.003 1.000 0.998
(0.985–1.026) (0.991–1.013) (0.992–1.012) (0.993–1.013) (0.997–1.014) (1.000–1.016)c (1.001–1.018)c (0.999–1.019) (0.996–1.015) (0.989–1.008) (0.967–1.005)
(0.998–1.018) (1.000–1.010)c (0.998–1.008) (0.996–1.007) (0.997–1.006) (0.998–1.006) (0.998–1.008) (0.999–1.009) (1.000–1.010)c (1.001–1.011)c (0.996–1.017)
(0.990–1.009) (0.993–1.003) (0.994–1.003) (0.996–1.005) (0.999–1.008) (1.003–1.010)c (1.004–1.013)c (1.005–1.014)c (1.004–1.013)c (1.000–1.009)c (0.988–1.006)
(0.993–1.007) (0.996–1.003) (0.996–1.003) (0.997–1.004) (0.998–1.005) (0.999–1.005) (1.000–1.006)c (0.969–1.038) (1.000–1.006)c (0.997–1.004) (0.990–1.005)
a
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. R3, anti-asthma and COPD products, R5, cough and cold preparations. c Po0:05: b
Table 9 Estimated relative risksa and 95% confidence intervals (CI) of cough and cold preparation drug sales for people older than 75 years associated with daily means of SO2 and NO2 Lag in days
SO2 RR (95% CI)
NO2 RR (95% CI)
0 1 2 3 4 5 6 7 8 9 10
1.015 1.003 0.997 0.996 0.999 1.003 1.008 1.011 1.012 1.007 0.997
0.995 0.984 0.981 0.985 0.992 1.000 1.008 1.011 1.009 0.998 0.976
a
(0.997–1.034) (0.993–1.012) (0.987–1.007) (0.986–1.006) (0.990–1.008) (0.995–1.011) (0.999–1.017) (1.002–1.021)b (1.002–1.021)b (0.998–1.017) (0.978–1.016)
(0.978–1.012) (0.975–0.993)b (0.972–0.990)b (0.976–0.994)b (0.984–1.000)b (0.993–1.007) (1.000–1.015)b (1.003–1.020)b (1.001–1.017)b (0.990–1.006) (0.960–0.992)b
Relative risks (RR) were estimated for each lag and for an increase of 10 mg/m3. b Po0:05:
No association was found between SO2, NO2, and BS and anti-asthma and COPD products sales for people older than 64 years of age. No association was found between SO2, NO2, and BS and cough and cold preparations sales for people aged between 65 and 74 years. For people older than 75 years of age and cough and cold preparations (Table 9), associations were found for the 7- and 8-day lags with SO2. The highest relative risk was observed for the 8-day lag with SO2. For NO2, there were significant relative risks smaller than 1 for the 1- to 4-day and 10-day lags and a significant relative risk greater than 1 for the lags ranging from 6 to 8 days. No association was found between BS and cough and cold preparations for people older than 75 years of age. Tables 10 and 11 present the cumulative effect of pollutant increase for the three pollutants and the age groups. The cumulative effect of a 10-mg/m3 BS increase over 10 days was significantly associated with a 6.2%
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increase of anti-asthmatic and COPD drug sales for children (0–14 years of age). BS was not associated with anti-asthmatic and COPD drug sales for the three other age groups. There were no associations between SO2, NO2, and anti-asthmatic and COPD drug sales. The effect of a 10-mg/m3 SO2 increase over 10 days was significantly associated with
The effect of a 10-mg/m3 NO2 increase over 10 days was significantly associated with
(1) an 11.8% increase of cough and cold preparation sales for children (0–14 years) and, (2) a 4.6% increase of cough and cold preparation sales for people between 15 and 64 years of age.
The effect of a 10-mg/m3 BS increase over 10 days was significantly associated with a 9.2% increase of cough and cold preparation sales for children (0–14 years). The effect of a 10-mg/m3 NO2 increase over 10 days was significantly associated with a 6.1% decrease of cough and cold preparation sales for people older than 75 years of age.
Table 10 Estimated relative risks and 95% confidence intervals (CI) for antiasthma and COPD drug sales associated with a cumulative effect of a 10-mg/m3 concentration increase over 10 days Age in years
Pollutant
Relative risk (95% CI)
All
SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS
1.012 1.020 1.054 1.040 1.046 1.062 1.011 1.035 0.988 0.955 0.983 1.013 1.008 0.950 0.999
0–14
15–64
65–74
+75
a
(0.974–1.051) (0.986–1.054) (1.029–1.080)a (0.982–1.102) (0.991–1.104) (1.024–1.101)a (0.951–1.075) (0.979–1.093) (0.954–1.022) (0.896–1.015) (0.930–1.038) (0.972–1.055) (0.921–1.104) (0.871–1.037) (0.935–1.068)
Po0:05:
Table 11 Estimated relative risks and 95% confidence intervals (CI) for cough and cold preparation drug sales associated with a cumulative effect of a 10-mg/m3 concentration increase over 10 days Age in years
Pollutant
Relative risk (95% CI)
All
SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS SO2 NO2 BS
1.024 1.057 1.086 1.118 1.136 1.092 1.046 1.035 1.008 0.985 0.987 1.001 1.048 0.939 0.988
0–14
15–64
65–74
+75
a
Po0:05:
(0.998–1.050) (1.033–1.084)a (1.069–1.103)a (1.067–1.171)a (1.080–1.183)a (1.059–1.126)a (1.015–1.078)a (1.006–1.064)a (0.989–1.028) (0.912–1.065) (0.921–1.059) (0.954–1.050) (0.993–1.106) (0.893–0.987)a (0.954–1.022)
(1) a 13.6% increase of cough and cold preparation sales for children (0–14 years) and (2) a 3.5% increase of cough and cold preparation sales for people between 15 and 64 years of age.
5. Discussion This study highlights a significant positive effect of pollutants (SO2, NO2, BS) on anti-asthmatic and COPD product and on cough and cold preparation drug sales. Furthermore, this study shows that it is possible to construct a drug sales time series based on the Regional Union of Health Insurance Offices (RUHIO) database. Drug sales can serve as a useful health indicator that can provide information on daily variations under conditions that do not require medical care and do not involve serious cases (death, hospital admission). This study is based not on a one-time data collection but on health data routinely collected. In France, routinely and continuously collected morbidity data sources are rare, especially those covering the whole country. The RUHIO database has broadened the data set available for epidemiological purposes, although it was initially created for economic management. However, this database does not include nonreimbursable drugs. Such data can only be collected by specific recording, as in the Le Havre city experiment (Zeghnoun et al., 1999). This indicator is very sensitive and allows temporal ecological studies in small areas because the number of sales is elevated. A great advantage of the respiratory drug sales indicator, over mortality and hospital admissions data is that it is possible to set up an epidemiological time-series study in a very small geographical area. The average daily death count, whatever the reason, is three for the city of Rouen. The drug sales indicator is 159-fold higher than the mortality indicator. This is very important, because the smaller the average daily indicator count, the wider the confidence intervals. Therefore, for a given period the statistical power of an ecological study based on respiratory drug sales is higher than the one based on such standard health indicators as mortality or hospital admissions. This indicator can easily be constructed for
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different types or subsets of drugs and can be adapted to the general population or specific groups (children, the elderly). For the period ranging from June 30, 1998, to July 1, 2000, particulates were not collected by the stations in the city of Rouen; therefore, we were not able to explore the impact of particulates on respiratory drug sales. In this study we chose to alleviate multicolinearity by using polynomial distributed lag models. In this theoretical approach, ozone pollutant levels present a problem because the ozone concentrations are widely contrasted between the winter and summer periods. This pollutant is virtually absent in winter because hours of sunshine are rare. This fact generates gap periods in ozone time series and does not permit the use of the polynomial distributed lag model, which includes lagged and unlagged ozone values. Therefore, we decided not to present ozone as a specific model for the sake of comparability with the three other pollutant models. We assumed that all individuals were equally and evenly exposed to urban air pollution (occupational and indoor personal exposures being either marginal or constant over time). The heterogeneity of the pollution levels in different area of Rouen was not taken into account in our study. Only the daily data of persons affiliated with general insurance systems are included in this study. However, because only the city of Rouen was taken into account, there were not many people with agricultural insurance and thus the selection bias was not considered significant. People in the professional community, which represents a nonnegligible number of people in the town, are not collected by RUHIO. There are significant relative risks smaller than 1 for the 1- to 4-day and 10-day lags and significant relative risk greater than 1 for the 6- to 8-day lag. This combination of relative risks greater or smaller than 1 produces a cumulative risk smaller than 1. Indeed, the NO2 protective effect could be generated by the fact that older persons seldom leave their accommodations and therefore are less exposed to outdoor pollution compared to younger persons. Some patients with chronic respiratory diseases like asthma and COPD have their own stocks of medicine that they renew regularly by purchasing additional drugs. However, this is less true in the case of cough and cold preparations, which explains why in our study the pollutant associations were higher with cough and cold preparations than with anti-asthmatic and bronchodilator drugs. The respiratory drugs used in the study from Zeghnoun et al. (1999) were defined by the International pharmaceutical codes and correspond to the mucolytic and anti-cough medications. In fact, these drugs are all
51
included in the cough and cold preparations class of the EPhMRA classification. Thus, we were able to compare the results from Zeghnoun et al., and those obtained for the cough and cold preparation sales in our study. The drug sales data used by Zeghnoun et al., were not exhaustive, as they were related to only some of the pharmacies of Le Havre city. On the contrary, in our study the sales were for all of the pharmacies in the city of Rouen. Zeghnoun et al., explored lags of up to 14 days to assess the effects of the pollutants on respiratory drug sales. These authors chose to use just one lagged value of the pollutant concentration in a Poisson regression model. However, any effect of the pollution exposure on health outcomes ranges over several days. As it is more reliable to include the concurrent and lagged pollutant measures simultaneously in distributed lag models, we chose to use a GAM model including a polynomial distributed lag model with lags of up to 10 days. The principal advantage of this model is that it allowed us to calculate, in addition to the relative risks associated with each lag, the relative risk associated with the cumulative effect over 10 days. The results of these two studies exhibit discrepancies but also similarities. Indeed, in the Zeghnoun et al., study associations were found for lags ranging from 1 to 9 days. According to the short-term response, respiratory drug sales were associated with BS and NO2 for a 1-day lag. Moreover, associations were also found for lags of between 6 and 9 days. The highest relative risks were observed for an 8-day lag with BS and NO2. For SO2, the strongest association was observed for a 9-day lag. In our study associations are found for lags ranging from 1 to 9 days. Associations were found for a 9-day lag with SO2, for lags ranging from 3 to 9 days with NO2, and for lags ranging from 1 to 9 days for BS. The highest relative risks were observed for the 6- and 7-day lags with NO2 and for the 5-day lag with BS. The RUHIO database is a 2-year moving database, hampering retrospective studies using data that are more than 2 years old. It is theoretically possible, but difficult, to obtain access to archived RUHIO data. It is desirable for research teams wanting to perform analyses linking drug sales and air pollution to sign agreements with RUHIO to collect data. On the basis of these results, one can consider configuring analyses by subsets of drugs, assessments of pollutant effects over a longer period, and the use of multipollutant models. Therefore, this study should open an encouraging avenue for similar studies, considering the easy implementation. Based on the results of this study, the authors conclude that an increase in drug sales was directly related to air pollutant concentration increases in the city of Rouen, France.
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Acknowledgment The authors thank Richard Medeiros, the Medical Editor of Rouen University Hospital–Rouen, France, for his valuable help editing the manuscript.
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