Influence of Socioeconomic Deprivation on the Relation Between Air Pollution and β-Agonist Sales for Asthma

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CHEST

Original Research ASTHMA

Influence of Socioeconomic Deprivation on the Relation Between Air Pollution and p-Agonist Sales for Asthma* Olivier Laurent, PhD; Gaelle Pedrono, MSc; Laurent Filleul, PhD; Claire Segala, MD; Agnes Lefraac, PhD; Charles Schillinger, MSc; Emmanuel Riviere, MSc; and Denis Bard, MD

Background: Air pollution biggers asthma attacks hours to days after exposure. It remains unclear whether socioeconomic deprivation modulates these effects. Investigation of these interactions requires adequate statistical power, obtainable by using either a sufficient number of observations or very sensitive indicators of asthma attacks. Using a small-area temporal ecologic approach, we studied the short-term relations between ambient air pollution and sales of short-acting p.agonist (SABA) drugs, a frequent and specific treatment for control of asthma attacks in children and young adults, and then tested the influence of deprivation on these relations. Methods: The study took place in Strasbourg, France in 2004. Health insurance funds provided data on 15,121 SABA sales for patients aged 0 to 39 years. Deprivation was estimated hy small geographic areas using an index constructed from census data. Daily average ambient concentrations of particulate matter (particles with an aerodynamic diameter < 10 f.UD [PMlCJ), nitrogen dioxide (N02), and ozone (03) were modeled on a small-area level. Adjusted case-crossover models were used for statistical analysis. Heaul",: Increased of 10 Jl.p 3 in ambient PM lO , N02 , and 0 3 concentrations were associated, respectively, with increases of 1.5% (95% con8denceinterval [CIl, 4 to 11.2%), 8.4% (95% CI, 5 to 11.9%), and 1% (95% CI, - 0.3 to 2.2%) in SABA sales. Deprivation had no influence on these relations. Conclusion: The associations observed are consistent with those reported by studies focusing on SABA use. similar studies in other settings shouldcon6rm whether the lack ofinteraction with deprivation is due to specific local conditions. (CHEST 2009; 135:717-723) Key words: air pollution; asthma; bronchodilators; exarerbations; socioeconomic deprivation Abbreviations: CI = confidenceinterval;ED = emergency department; N02 = nitrogen dioxide; 03 = ozone; PM\l) = particles with an aerodynamic diameter < 10 urn, SABA= Short-acting l3-agonist; SES = socioeconomic status; SMA = Stmsbourg

metropolitan area

T he relation between air pollution and the onset of asthma attacks is solidly established.' Epidemiclogic studies report short-term associations (latency of several hours to several days) between exposure to

"From LERES (Drs. Laurent and Bard), Ecole des Hautes Etudes en Sante Publique, Rennes; SEPIA Sante (Ms. Pedrono and Dr. Segala), Baud; CIRE Aquitaine (Dr. Filleul), Institut de Veille Sanitaire, Bordeaux; Departement Sante Environnernent (Dr. Lefranc), Institut de Veille Sanitaire, Saint Maurice; Association pour la Surveillance et l'Etude de la Pollution Atmospherique en Alsace (Mr. Schillinger and Mr. Riviere), Schiltigbeirn. The work was performed at the French School of Public Health (Ecole des Hautes Etudes en Sante Publique). This proposal was funded by the French National Research Agency (A~enc:e Nationale de la Recherche) grant ANR SEST 00057 05. The authors have no conflict of interest to disclose. www.chestjoumal.org

air pollutants (particulate matter, nitrogen dioxide [N02 ] , sulfur dioxide, and ozone [03]) and various asthma attack indicators routinely available: hospitalization.f emergency department (ED) visits," calls to mobile emergency medical service networks," and Manuscript received June 29, 2008; revision accepted September 25,2008. Reproduction of thisarticle is prohibited without written permission from the American College of Chest Physicians (www.chestjoumal, orglrnisc!re,Prints.shtml).

C~ to: Dr. Denis Bard, Laboratoire d'Etude et de Recherche en Environnement et Sante (LERES), Ecole des Routes Etudes en Sante Publique, Au du PrLeonBernard, F-35043 Rermes cedex, France; e-mail: denis.barrl@ehespfr

DOl: lO.13781chest.08-1604

CHEST/135/3/ MARCH, 2009

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visits to doctors' offices for asthma attacks." Associations are also reported with indicators measured directly in individuals with asthma: respiratory symptoms and consumption of short-acting ~agonist (SABA) drugs.6 - 7 Socioeconomicstatus (SES) is also associated with asthmamorbidity. In particular,severeformsof asthma are more common among disadvantaged populations," Risk factors for exacerbations (eg, passive smoking," psychosocial stress,'? cockroach allergens;'! and suboptimal compliance with anti-inflammatory medication's) are generally more common among people with asthma and low SES than their better-offcounterparts. We thus hypothesize that they are more susceptible to asthma attacks when they face additional environmental insults, such as ambient air pollution. 13 Nonetheless, the few studiesI 3-18 that have tested this hypothesis have produced divergent results. This may be because the distribution according to SES of factors modulating the relations between air pollution and asthma morbidity differs between the study settings or because of statistical fluctuations. Statistical fluctuations may be reduced by using more sensitive indicators of asthmamorbidity than in previous studies (calls to mobile emergency medical service networks.P hospitalizations.P or ED visits-") or by considering more numerous observations. Studying the sales of SABA drugs meets both of these conditions. In France, health insurance fund databases routinely record each time these drugs are dispensed, that is, each time a prescription is filled or refilled. Naureckas et alI9 showed that SABA sales are valuable indicators of asthma morbidity and its temporal variability. These sales are especially good predictors of the risk of ED visits and hospitalization for asthma attacks in the days immediately afterwards. 19 Moreover, they generally reflect less severe morbidity states than those requiring ED visits or hospitalizations and are therefore very sensitive indicators. Finally, they are very common: likely to involve large numbers of individuals, even within small geographic units and short observation periods.19 .20 While SABA drugs are the treatment of choice for asthma attacks." they are also prescribed for acute exacerbation of COPD.22 Nevertheless, this disease is rare in people < 40 years old23; within this age group, SABA prescriptions and sales are therefore veryspecific ' for asthma attacks. Accordingly, this work examined the following among people < 40 years old: (1) short-term relations betweenambient air pollution and SABA sales, and (2) potential influence of SES on these relations. MATERIALS AND METHODS

Backgroundand Unit of Observation The study area is the Strasbourg metropolitan area (SMA) in eastern France. At the last census, it was home to approximately 718

450,000 inhabitants, 261,000 < 40 years old. The geographic unit of observation used is the finest for which socioeconomic data are routinely available in France: the residential L'EnSUS block (Ilots Regroupes pour l'Infonnation Statistique in French, 2,500 inhabitants on average). SMA has 190 census blocks.

Data SABA Sales: The data about SABAsales to people living in tilt' SMA during 2004 came from four health insurance funds: the regional union of health insurance funds (Union Regionale des Caisses d'Assuranee Maladie [URCAM], for salaried workers and very low-income families), agricultural social insurance fund (Mutualite Sociale Agricole [MSA], for fanners and similar workers), the fund for self-employed workers (Regime Social des Independents [RSI]), and the Lorraine students' insurance fund (Mutuelle Generale des Estudiants de Lorraine [MGEL]). These funds cover> 90% of the local population.w In 2004, these funds either paid or reimbursed (fully or partially) all SABA sales prescribed by a physician. All records of SABA sales in phannacies were extracted from the databases of these four insurance funds by requests for code R3A4 of the European Pharmaceutical Market Research Association nomenclature.w The following information was furnished for each sale: date, age group (0 to 9. 10 to 19. 20 to 39 years), sex, and census block of residence of the person for which SABA were prescribed. All sales were combined into a Single file for analysis. The data collection methods were defined according to the French Data Protection Authority regulation. Neither Institutional Review Board approval nor patient consent were required, Ambient Air Pollution: The hourly mean concentrations of particles with an aerodynamic diameter < 10 IJ.m (PM IU ) , NO z• and 0 3 were modeled at the resolution of the census block, for all of 2004 and for the last 10 days of 2003 with the Atmospheric Dispersion Modelling System (ADMS) urban gaussian dispersion modeI.26 Its input indicators included an inventory of pollutant emissions.s? meteorologic data (wind direction and speed. temperature and cloudiness), and measurements of background levels of ambient pollution. A limitation of the model was the estimation of Monin-Obukhov length from cloudiness measurements, but the correlation coefficients for the modeled and measured ambient concentrations were high (0.73 for PM IU• 0.87 for N02• and 0.84 for 0 3 ) , Potential Confounding Factors: Meteo France (the French meteorologic service) furnished daily meteorologic data (each day's minimum, mean and maximum temperature and atmospheric pressure, and mean relative humidity) for the SMA. The national network of uerobiologtcal surveillance provided daily pollen counts for the SMA.28 Weekly counts of influenza cases for the region of Strasbourg (A1sace) came from the INSERM Sentinelles network.29 Statistical Analysis The associations between air pollution and SABA sales were studied bya case-crossover approach. This approach, appropriate for studying the acute effects of exposure that varies rapidly over time, consists of comparing the exposure of a subject immediately preceding the onset of a health event to his or her exposnrp in periods during which the health event did not (!Ccnr.:·' By using a time-stratified approach, we defined the control periods to be the same dayof the week (eg, Monday)as the day of the SABA purchase during the same month." This allowed us to control by matchingthe important variationsassociatedwith day of the week (eg, SABA sales are rare on Saturdays and nearly nonexistent on Sundays). The statistical analysis used conditional logisticregression. OriginalResearch

Basic Model: Associations between SABA sales and pollutant concentrations were tested for different time lags. We tested the influence of pollution indicators (mean pollutant 24-h ooncentration for PM lO and N0 2 , maximum B-h moving averages for 03), related to the day of the sale (lag 0), and then of the same indicators: (1) related to each of the 1 to 10 days preceding the sale, considered individually (lag 1 to 10); and (2) averaged on the day of the sale and the 1 to 10 days preceding it (lag 0 to 1, to 0 to 10). These associations were adjusted for relative humidity (lag o to 7), minimum daily temperature (lag 0 to 7), minimum daily atmospheric pressure (lag 0 to 7) and daily pollen counts (lag 0 to 4), influenza epidemics, vacation periods, and holidays. Test of Interaction According to SES: To characterize the SES of the SMA census blocks, we used the index designed by Havard et al, described elsewhere.w built from 52 socioeconomic variabies that reflect different dimensions of deprivation (income, educational level, job, housing characteristics). Principal component analysis identified an axis that explained 66% of the inertia of the initial variables and serves as the socioeconomic index. 32 To test the influenceofSES on the relations between SABAsales and air pollution, the SMA census blocks were classified into five strata mrresponding to quintiles of the index's distribution. Associations between SABA sales and pollutant concentrations were measured within each stratum for the combinations of lags for which the highest associations were ohserved in the basic model. Statisticalsoftware (SAS vS.2; SAS Institute; Cary, NC) was used to conduct all analyses. RESULTS

Table 1 presents the distribution of SABA sales and ambient pollutant concentrations for the entire SMA and for the five socioeconomically different strata. In 2004, SABA drugs were dispensed on 15,121 separate occasions for subjects aged 0 to 39 years. The number of sales per inhabitant slightlyincreased from the least to the most deprived stratum.F The mean ambient pollutant concentrations over the study period were 20.8 !J.Wm3 for PM lO (range, 1.2 to 106.3 !J.Wm3), 35.0 !J.Wm3 for N02 (range, 6.4 to 84.3 !J.Wm3), and 58.7 !J.Wm3 for 0 3 (range, 2.6 to 220.0 !J.Wm3).

Basic Model Figure 1 presents the odds ratios for the individual lags for subjects aged 0 to 39 years. For all three pollutants, odds ratios greater than one were observed for lags from 4 to 10 days. For PM lO and N02 , most of the odds ratios were statistically Significant (p < 0.05) for lags from 5 to 10 days. For a lag of 2 days, odds ratios significantly lower than one were observed for all three pollutants. The structure of the lags reported in Figure 2 did not change when the analyses were stratified by smaller age subgroups (0 to 19 years, 20 to 39 years). No association was observed between SABA sales and the mean pollutant concentrations of the day of the sale and the 1 to 10 preceding days (lags 0 to 1 to 0 to 10). On the basis of the associations reported in Figure 2, the lags kept for the subsequent analyses were lag 4 to 7 (mean concentrations of days 4, 5, 6, and 7 for PM lO , lag 4 to 10 for N02 , and lag 4 to 6 for 03)' A www.chestjoumal.org

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10 JLglm 3 increase in ambient concentrations of PM lO , N02 , and 0 3 for these lags was associatedwith increases of 7.5% (95% confidence interval [CI], 4 to 11.2%), 8.4% (95% CI, 5 to 11.9%), and 1 (95% CI, - 0.3 to 2.2%), respectively, in SABA sales. Interactions According to SES

Figure 2 presents the odds ratios for the five different socioeconomic strata for the optimal lags 720

mentioned above for all subjects aged 0 to 39 years. We observed no trend toward an increase or decrease of these odds ratios according to SES or when we stratified the analyses by smaller age subgroups (0 to 19 years, 20 to 39 years). DISCUSSION

We observed positive associations between ambient concentrations of atmospheric pollutants and Original Reeearch

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FIGURE 2. Interactions by socioeconomic level in those aged < 40 years: associations for the five different strata of socioeconomic levels. * Stratum 1 is the most advantaged and stratum 5 the most disadvantaged. Associations were estimated for "optimal" lags defined according to the associations reported for individual lags. see Figure 1. For example, for PM lO among those younger than 40 years, PM lO 4-7 corresponds to the mean concentrations for day D4. D5. D6 and D7.

SABA sales for subjects < 40 years old. These are expressed with latency periods of 4 to 10 days and do not tend to increase or decrease according to SES.

Strengths and Limitations of the Study This study is the first to examine the relations between exposure to urban air pollution and SABA www.chestjoumal.org

sales. The use of this indicator, obtained from the four primary French health insurance funds. allowed us to cover > 90% of the local population and to capture the entire range of SES in the SMA. People not covered by these funds are mainly employees of various sectors once publicly owned (railway. electricity, gas), with jobs ranging CHEST/135/3/ MARCH, 2009

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from manual workers to administrators and mainly in the middle classes. The large number of SABA sales allowed us to measure their associations with air pollution modeled by small areas. This resolution is particularly pertinent for studying this risk factor because its spatial distribution varies strongly within urban areas. 13 These large numbers also allowed us to test the existence of interactions by neighborhood SES with satisfactory statistical power. The event analyzed is the patient's purchase of one (or sometimes more) box of drugs, and not a quantity of active ingredient delivered or really inhaled. Naureckas et all g nonetheless showed that this indicator is a good predictor of the risk of ED visits and of hospitalization for asthma attacks in the days immediately afterwards. These purchases generally reflect asthma morbidity less severe than that requiring hospitalization or emergency treatment. 24 A large portion of SABA sales anticipate the respiratory disorders the drugs are intended to treat. These sales, which need not be associated in time with asthma, add some "noise" to the data. Noise is standard in ecologic studies of the short-term health effects of air pollution because of the influence of unmeasured competing factors (other than air pollution) on the temporal distribution of the health outcomes studied. Nevertheless, if the signal-tonoise ratio is sufficiently high, the effect specifically due to air pollution can be observed. In our study, despite the additional noise due to anticipatory sales, this ratio appears to be high enough to detect statistically Significant associations. These associations are consistent with those reported by most panel studies 3 ,6 - 7 that have investigated the relation between air pollution and SABA consumption.

Delayed Responses The associations observed involved latency periods of 4 to 10 days, an order of magnitude similar to the delayed responses observed in two earlier temporal ecologic studies of the relation between air pollution and drug sales (mucolytic and antitussive agents,33 "cough and cold preparations," and all types of anti-COPD/antiasthma drugs 20 ) . This is probably because the latency periods between exposure to air pollution and drug purchases result from a mixed process involving both pathophysiologic response and management of medicine supplies. The literature3 •6 ,7,34 shows that in people with asthma, air pollution induces respiratory disorders expressed by increased SABA consumption, with low latency periods (several hours 34 to several days6.7) . This increased consumption requires a successivebut not necessarily immediate-replenishment of 722

the SABA supply. Several days of delay, possibly marked by a return to a normal rhythm of use, may pass before the purchase, which may explain the particularly long lags (up to 10 days) observed. For lag 2, we observed odds ratios significantlyless than one. Other studies similar to ours, but in other settings and with different methods, report similar findings. Zeghnoun et al33 also observed low associations for lag 2, especially for mucolytic and antitussive drugs. Moreover, in a panel study of SABA consumption, Rabinovitch et al34 also observed relative risks less than one for the same lag. Von Klot et al? report comparable observations specifically for lag 1. No satisfactory explanation has yet been found for these observations.

Interactions by SES SES did not influence the relation between SABA sales and ambient air pollution. This is consistent with the results observed in SMA for an indicator of more severe asthma morbidity: interventions of mobile emergency medical services for asthma attaeks.P These results do not rule out the possibility that SES might be an interaction factor in other settings, for the distribution according to SES of other factors that might modulate the relations between air pollution and asthma morbidity may well differ between countries or even cities. In conclusion, we observed statistically significant associations between air-pollutant concentrations and SABA sales for children, adolescents, and young adults. These results are consistent with those of panels of asthma patients and their SABA consumption, although expressed here with longer time lags. Our results support the usefulness of SABAsales for the analysis of relations between asthma morbidity and air pollution. The associations observed do not appear to be influenced by SES, but studies in other settings should attempt to confirm this lack of interaction. ACKNOWLEDGMENT: The authors thank all the organizations that kindly provided the data used in these analyses: URCAM Alsace (e5£ecially Benoit Wollbrett), RSI Alsace (especially Katia Bischoff), MSA Alsace (especially Herve Hunold), MGEL (especially Gerard Rey), Meteo France, National Network of Aerobiologic Surveillance. and the INSERM Sentinelles Network. The authors also thank Professor Frederic de Blay for providing opinion as a specialist in pulmonary medicine. and Dr, Fabienne Wachet for providing expertise on drug supply and repayment systems. Finally, the authors thank Jo Ann Calm for editorial assistance.

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