Chronic effects of air pollution on lung function after lung transplantation in the Systems prediction of Chronic Lung Allograft Dysfunction (SysCLAD) study.

An irreversible loss in lung function limits the long-term success in lung transplantation. We evaluated the role of chronic exposure to ambient air pollution on lung function levels in lung transplant recipients (LTRs).The lung function of 520 LTRs from the Cohort in Lung Transplantation (COLT) study was measured every 6 months. The levels of air pollutants (nitrogen dioxide (NO2), particulate matter with an aerodynamic cut-off diameter of x µm (PMx) and ozone (O3)) at the patients' home address were averaged in the 12 months before each spirometry test. The effects of air pollutants on forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) in % predicted were estimated using mixed linear regressions. We assessed the effect modification of macrolide antibiotics in this relationship.Increased 12-month levels of pollutants were associated with lower levels of FVC % pred (-2.56%, 95% CI -3.86--1.25 for 5 µg·m-3 of PM10; -0.75%, 95% CI -1.38--0.12 for 2 µg·m-3 of PM2.5 and -2.58%, 95% CI -4.63--0.53 for 10 µg·m-3 of NO2). In patients not taking macrolides, the deleterious association between PM and FVC tended to be stronger and PM10 was associated with lower FEV1Our study suggests a deleterious effect of chronic exposure to air pollutants on lung function levels in LTRs, which might be modified with macrolides.

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data.

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR2: 0.33-0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.

Is atmospheric pollution exposure during pregnancy associated with individual and contextual characteristics? A nationwide study in France.

BACKGROUND: Exposure to atmospheric pollutants is a danger for the health of pregnant mother and children. Our objective was to identify individual (socioeconomic and behavioural) and contextual factors associated with atmospheric pollution pregnancy exposure at the nationwide level. METHOD: Among 14 921 women from the French nationwide ELFE (French Longitudinal Study of Children) mother-child cohort recruited in 2011, outdoor exposure levels of PM2.5, PM10 (particulate matter <2.5 µm and <10 µm in diameter) and NO2 (nitrogen dioxide) were estimated at the pregnancy home address from a dispersion model with 1 km resolution. We used classification and regression trees (CART) and linear regression to characterise the association of atmospheric pollutants with individual (maternal age, body mass index, parity, education level, relationship status, smoking status) and contextual (European Deprivation Index, urbanisation level) factors. RESULTS: Patterns of associations were globally similar across pollutants. For the CART approach, the highest tertile of exposure included mainly women not in a relationship living in urban and socially deprived areas, with lower education level. Linear regression models identified different determinants of atmospheric pollutants exposure according to the residential urbanisation level. In urban areas, atmospheric pollutants exposure increased with social deprivation, while in rural areas a U-shaped relationship was observed. CONCLUSION: We highlighted social inequalities in atmospheric pollutants exposure according to contextual characteristics such as urbanisation level and social deprivation and also according to individual characteristics such as education, being in a relationship and smoking status. In French urban areas, pregnant women from the most deprived neighbourhoods were those most exposed to health-threatening atmospheric pollutants.