# Association between air pollution levels and drug sales for allergies in 63 million people in metropolitan France in 2013.

Marwan El Homsi, Stéphane Sclison, Domitille Huguet, Boris Dessimond, Julie Prud'Homme, Augustin Collette, Lk Tanno, Isabella Annesi-maesano

<figure><img src="image_1.png" /></figure>

## To cite this version:

Marwan El Homsi, Stéphane Sclison, Domitille Huguet, Boris Dessimond, Julie Prud'Homme, et al.  
Association between air pollution levels and drug sales for allergies in 63 million people in metropolitan  
France in 2013. BSACI - WAO 2022, Apr 2022, Edimbourg, United Kingdom.  
hal-03787340

HAL Id: hal-03787340

https://hal.science/hal-03787340

Submitted on 24 Sep 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

# IDESP Association between air pollution levels and drug sales for allergies in 63 million people in metropolitan France in 2013

<figure><img src="image_4.png" /></figure>

Inserm

M. El Homsi<fnref n="1" />*, S. Sclison², D. Huguet², B. Dessimond<fnref n="1" />, J. Prud'homme<fnref n="1" />, A. Collette³, L. Kase Tanno<fnref n="1" />,⁴, I. Annesi-Maesano<fnref n="1" />

## Introduction:

* Air pollution is a major public health problem because it is ubiquitous and the cause of several adverse health effects.

* The World Health Organization (WHO) estimated in 2016 that 91% of the world's population lived below the air quality thresholds set by their guidelines.

* The study of drug sales data provides a new way to assess the health impacts of air pollution.

## Aims:

To assess the **short-term associations** between particulate matter less than or equal to a diameter of 10 micrometers (PM₁₀) and 2.5 micrometers (PM₂.₅), nitrogen dioxide (NO₂) with the sale of medication against **allergies** (R06) in **metropolitan France** in 2013, on a population of **63 million inhabitants** exposed to air pollution.

<figure><img src="image_5.png" /></figure>

## Methods:

We created a database from the data of each **French department** in **2013**. We obtained **relative risks** from a Quasi-Poisson regression with a **Generalized Additive Model (GAM)**. We also added smoothing functions to fit the models on seasonal data.

<figure><img src="image_6.png" /></figure>

**Unadjusted models with one pollutant (univariate):**

$$y(\text{R06 sales}) = \beta_{1}(\text{PM}_{2.5}) + \text{intercept}$$

$$y(\text{R06 sales}) = \beta_{1}(\text{PM}_{10}) + \text{intercept}$$

$$y(\text{R06 sales}) = \beta_{1}(\text{NO}_{2}) + \text{intercept}$$

**Adjusted models with one pollutant (multivariate):**

$$y(\text{R06 sales}) = \beta_{1}(\text{PM}_{2.5}) + s(\text{confounding factors}^{*}) + \text{intercept}$$

$$y(\text{R06 sales}) = \beta_{1}(\text{PM}_{10}) + s(\text{confounding factors}) + \text{intercept}$$

$$y(\text{R06 sales}) = \beta_{1}(\text{NO}_{2}) + s(\text{confounding factors}) + \text{intercept}$$

**Adjusted bipollutant models (multivariate):**

$$y(\text{R06 sales}) = \beta_{1}(\text{NO}_{2}) + \beta_{2}(\text{PM}_{2.5}) + s(\text{confounding factors}) + \text{intercept}$$

$$y(\text{R06 sales}) = \beta_{1}(\text{NO}_{2}) + \beta_{2}(\text{PM}_{10}) + s(\text{confounding factors}) + \text{intercept}$$

Where $\beta_i$ represent a coefficient of exposure and $s_i$ represent a smoothing spline function.

* Confusions factors are the Tobacco consumption, mean temperature, relative humidity.

## Results:

A

<figure><img src="image_7.png" /></figure>

B

<table><thead><tr><th rowspan="2"></th><th colspan="3">Unadjusted models with one pollutant</th><th colspan="3">Adjusted models with one pollutant **</th></tr><tr><th>RR</th><th>Confidence Interval (95%)</th><th>p-value</th><th>RRa</th><th>Confidence Interval (95%)</th><th>p-value</th></tr></thead><tbody><tr><td>R06*</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td>1.027</td><td>1.019 - 1.036</td><td>&lt;0.001</td><td>1.039</td><td>1.029 - 1.050</td><td>&lt;0.001</td></tr><tr><td>NO<sub>2</sub></td><td>1.032</td><td>1.028 - 1.036</td><td>&lt;0.001</td><td>1.034</td><td>1.028 - 1.039</td><td>&lt;0.001</td></tr><tr><td>PM<sub>10</sub></td><td>1.036</td><td>1.028 - 1.043</td><td>&lt;0.001</td><td>1.043</td><td>1.034 - 1.051</td><td>&lt;0.001</td></tr></tbody></table>

<table><thead><tr><th rowspan="2"></th><th colspan="3">Adjusted bipollutant model ** with PM<sub>2.5</sub></th><th colspan="3">Adjusted bipollutant model ** with PM<sub>10</sub></th></tr><tr><th>RRa</th><th>Confidence Interval (95%)</th><th>p-value</th><th>RRa</th><th>Confidence Interval (95%)</th><th>p-value</th></tr></thead><tbody><tr><td>R06*</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>2.5</sub></td><td>1.007</td><td>0.996 - 1.019</td><td>0.2</td><td>PM<sub>10</sub></td><td>1.021</td><td>1.012 - 1.031</td><td>&lt;0.001</td></tr><tr><td>+ NO<sub>2</sub></td><td>1.031</td><td>1.025 - 1.038</td><td>&lt;0.001</td><td>+ NO<sub>2</sub></td><td>1.024</td><td>1.018 - 1.030</td><td>&lt;0.001</td></tr></tbody></table>

* Drugs are classified according to the Anatomical Therapeutic Chemical (ATC) Classification System: R06

(Antihistamines for systemic use).

** Models are fitted to mean temperature, relative humidity and crude lung cancer rate.

## Conclusions and perspectives:

Our study confirms the presence of a **short-term association** between NO₂, and PM₁₀ and the **sale of anti-allergy drugs** thus **confirming** previous observations on the **impact of air pollution** on **allergies**.

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<footnote n="1">¹INSERM, Université de Montpellier et IDESP, 34090 Montpellier, France ; ²IQVIA, 92400 Courbevoie, France ; ³Institut National de l'Environnement Industriel et des Risques (INERIS), 60550 Verneuil-en-Halatte, France ; ⁴CHU de Montpellier, 34295 Montpellier, France</footnote>