Spatial variability in temperature exists within metropolitan areas but very few studies have investigated intra-urban differentiation in the temperature-mortality effects. We investigated whether local characteristics of 42 Municipalities within the Greater Athens Area lead to modified temperature effects on mortality and if effect modifiers can be identified. Generalized Estimating Equations models were used to assess the effect of high ambient temperature on the total and cause-specific daily number of deaths and meta-regression to investigate effect modification. We found significant effects of daily temperature increases on all-cause, cardiovascular, and respiratory mortality (e.g., for all ages 4.16% (95% CI: 3.73,4.60%) per 1 °C increase in daily temperature (lags 0–3). Heterogeneity in the effect estimates between Municipalities was observed in several outcomes and environmental and socio-economic effect modifying variables were identified, such as % area coverage of buildings, length of roads/km², population density, % unemployed, % born outside the EU countries and mean daily temperature. To further examine the role of temperature, we alternatively used modelled temperature per Municipality and calculated the effects. We found that heterogeneity was reduced but not eliminated. It appears that there are socioeconomic status and environmental determinants of the magnitude of heat-related effects on mortality, which are detected with some consistency and should be further investigated.

Land use regression (LUR) and dispersion/chemical transport models (D/CTMs) are frequently applied to predict exposure to air pollution concentrations at a fine scale for use in epidemiological studies. Moreover, the use of satellite aerosol optical depth data has been a key predictor especially for particulate matter pollution and when studying large populations. Within the STEAM project we present a hybrid spatio-temporal modeling framework by (a) incorporating predictions from dispersion modeling of nitrogen dioxide (NO₂), ozone (O₃) and particulate matter with an aerodynamic diameter equal or less than 10 μm (PM10) and less than 2.5 μm (PM2.5) into a spatio-temporal LUR model; and (b) combining the predictions LUR and dispersion modeling and additionally, only for PM2.5, from an ensemble machine learning approach using a generalized additive model (GAM). We used air pollution measurements from 2009 to 2013 from 62 fixed monitoring sites for O3, 115 for particles and up to 130 for NO₂, obtained from the dense network in the Greater London Area, UK. We assessed all models following a 10-fold cross validation (10-fold CV) procedure. The hybrid models performed better compared to separate LUR models. Incorporation of the dispersion estimates in the LUR models as a predictor, improved the LUR model fit: CV-R² increased to 0.76 from 0.71 for NO₂, to 0.79 from 0.57 for PM10, to 0.81 to 0.66 for PM2.5 and to 0.75 from 0.62 for O₃. The CV-R² obtained from the hybrid GAM framework was also increased compared to separate LUR models (CV-R² = 0.80 for NO₂, 0.76 for PM10, 0.79 for PM2.5 and 0.75 for O₃). Our study supports the combined use of different air pollution exposure assessment methods in a single modeling framework to improve the accuracy of spatio-temporal predictions for subsequent use in epidemiological studies.

Background: Although the associations of outdoor air pollution exposure with mortality and hospital admissions are well established, few previous studies have reported on primary care clinical and prescribing data. We assessed the associations of short and long-term pollutant exposures with General Practitioner respiratory consultations and inhaler prescriptions. Methods: Daily primary care data, for 2009–2013, were obtained from Lambeth DataNet (LDN), an anonymised dataset containing coded data from all patients (1.2 million) registered at general practices in Lambeth, an inner-city south London borough. Counts of respiratory consultations and inhaler prescriptions by day and Lower Super Output Area (LSOA) of residence were constructed. We developed models for predicting daily PM_2.5, PM₁₀, NO₂ and O₃ per LSOA. We used spatio-temporal mixed effects zero inflated negative binomial models to investigate the simultaneous short- and long-term effects of exposure to pollutants on the number of events. Results: The mean concentrations of NO₂, PM₁₀, PM_2.5 and O₃ over the study period were 50.7, 21.2, 15.6, and 49.9 μg/m³ respectively, with all pollutants except NO₂ having much larger temporal rather than spatial variability. Following short-term exposure increases to PM₁₀, NO₂ and PM_2.5 the number of consultations and inhaler prescriptions were found to increase, especially for PM₁₀ exposure in children which was associated with increases in daily respiratory consultations of 3.4% and inhaler prescriptions of 0.8%, per PM₁₀ interquartile range (IQR) increase. Associations further increased after adjustment for weekly average exposures, rising to 6.1 and 1.2%, respectively, for weekly average PM₁₀ exposure. In contrast, a short-term increase in O₃ exposure was associated with decreased number of respiratory consultations. No association was found between long-term exposures to PM₁₀, PM_2.5 and NO₂ and number of respiratory consultations. Long-term exposure to NO₂ was associated with an increase (8%) in preventer inhaler prescriptions only. Conclusions: We found increases in the daily number of GP respiratory consultations and inhaler prescriptions following short-term increases in exposure to NO₂, PM₁₀ and PM_2.5. These associations are more pronounced in children and persist for at least a week. The association with long term exposure to NO₂ and preventer inhaler prescriptions indicates likely increased chronic respiratory morbidity.