The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA—disseminated and implemented in over 70 countries globally—is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.

Ambient air pollution is a leading environmental risk factor and its broad spectrum of adverse health effects includes a decrease in lung function. Socioeconomic status (SES) is known to be associated with both air pollution exposure and respiratory function. This study assesses the role of SES either as confounder or effect modifier of the association between ambient air pollution and lung function. Cross-sectional data from three European multicenter adult cohorts were pooled to assess factors associated with lung function, including annual means of home outdoor NO₂. Pre-bronchodilator lung function was measured according to the ATS-criteria. Multiple mixed linear models with random intercepts for study areas were used. Three different factors (education, occupation and neighborhood unemployment rate) were considered to represent SES. NO₂ exposure was negatively associated with lung function. Occupation and neighborhood unemployment rates were not associated with lung function. However, the inclusion of the SES-variable education improved the models and the air pollution-lung function associations got slightly stronger. NO₂ associations with lung function were not substantially modified by SES-variables. In this multicenter European study we could show that SES plays a role as a confounder in the association of ambient NO₂ exposure with lung function.

The association between air pollution and rhinitis is not well established. The aim of this longitudinal analysis was to study the association between modeled air pollution at the subjects' home addresses and self-reported incidence of rhinitis. We used data from 1533 adults from two multicentre cohorts' studies (EGEA and ECRHS). Rhinitis incidence was defined as reporting rhinitis at the second follow-up (2011 to 2013) but not at the first follow-up (2000 to 2007). Annual exposure to NO2, PM10 and PM2.5 at the participants' home addresses was estimated using land-use regression models developed by the ESCAPE project for the 2009-2010 period. Incidence rate ratios (IRR) were computed using Poisson regression. Pooled analysis, analyses by city and meta-regression testing for heterogeneity were carried out. No association between long-term air pollution exposure and incidence of rhinitis was found (adjusted IRR (aIRR) for an increase of 10 μg·m-3 of NO2: 1.00 [0.91-1.09], for an increase of 5 μg·m-3 of PM2.5: 0.88 [0.73-1.04]). Similar results were found in the two-pollutant model (aIRR for an increase of 10 μg·m-3 of NO2: 1.01 [0.87-1.17], for an increase of 5 μg·m-3 of PM2.5: 0.87 [0.68-1.08]). Results differed depending on the city, but no regional pattern emerged for any of the pollutants. This study did not find any consistent evidence of an association between long-term air pollution and incident rhinitis.

The classification of rhinitis in adults is missing in epidemiological studies. To identify phenotypes of adult rhinitis using an unsupervised approach (data-driven) compared with a classical hypothesis-driven approach. 983 adults of the French Epidemiological Study on the Genetics and Environment of Asthma (EGEA) were studied. Self-reported symptoms related to rhinitis such as nasal symptoms, hay fever, sinusitis, conjunctivitis, and sensitivities to different triggers (dust, animals, hay/flowers, cold air…) were used. Allergic sensitization was defined by at least one positive skin prick test to 12 aeroallergens. Mixture model was used to cluster participants, independently in those without (Asthma-, n = 582) and with asthma (Asthma+, n = 401). Three clusters were identified in both groups: 1) Cluster A (55% in Asthma-, and 22% in Asthma+) mainly characterized by the absence of nasal symptoms, 2) Cluster B (23% in Asthma-, 36% in Asthma+) mainly characterized by nasal symptoms all over the year, sinusitis and a low prevalence of positive skin prick tests, and 3) Cluster C (22% in Asthma-, 42% in Asthma+) mainly characterized by a peak of nasal symptoms during spring, a high prevalence of positive skin prick tests and a high report of hay fever, allergic rhinitis and conjunctivitis. The highest rate of polysensitization (80%) was found in participants with comorbid asthma and allergic rhinitis. This cluster analysis highlighted three clusters of rhinitis with similar characteristics than those known by clinicians but differing according to allergic sensitization, and this whatever the asthma status. These clusters could be easily rebuilt using a small number of variables.

Background/aim Little is known about the effects of outdoor air pollution on severity of rhinitis. The objective is to assess the association between modelled PM₁₀, PM_2.5 and NO₂ concentrations and severity of rhinitis in two multicenter European cohorts on respiratory health (EGEA and ECRHS). Methods 1603 adults with data on air pollution and on severity of at least one rhinitis symptom were included. Annual exposure to NO₂, PM₁₀ and PM_2.5 was estimated at participants’ residential address using land use regression models thanks to the ESCAPE project. Severity of rhinitis was defined in two ways: according to the importance of the disturbance due to four symptoms of rhinitis (runny nose, blocked nose, sneezing and itchy nose) categorised in 3 groups: no (reference), mild or high severity, using an overall score of severity including disturbances to all symptoms, varying from 0 to 12. Polytomous logistic or linear regression was used, and the city was further included as a random effect. Adjusted odds ratio are presented for an increase of 10 µg.m⁻³ of NO₂ and 5 µg.m⁻³ of PM_2.5. Results The 1603 adults (mean age=52.5 years, 45% men, 73% from ECRHS) from 17 cities had a median[Q1-Q3] score of severity of 4[2–6]. Exposure to air pollution was associated with an increased score of severity of rhinitis (aOR[95% CI] for NO₂: 1.13 [1.03–1.25], for PM_2.5: 1.85 [1.47–2.33]). Exposure to NO₂ was also associated with an increased severity of blocked nose (aOR for NO₂: 1.17 [1.06–1.30] for mild and 1.21 [1.10–1.33] for high severity) and similarly with runny nose, but not with itchy nose or sneezing. Exposure to PM_2.5 or PM₁₀ was associated with mild severity for blocked nose and with high severity for all symptoms (aOR for blocked nose for PM_2.5: 1.41 [1.06–1.88] for mild and 1.91 [1.46–2.51] for high severity). Similar results were found when considering city as a random effect. Conclusion Severity of rhinitis and particularly blocked nose symptoms are associated with air pollution exposure.