Using spatio-temporal land use regression models to address spatial variation in air pollution concentrations in time series studies

Abstract

Time series studies are used to assess the effects of short-term exposures to PM₁₀ and NO₂ on mortality using an integrated pollutant series taken to characterize exposure over a large area. We propose using spatio-temporal land use regression (LUR) models by smaller geographical sectors within an area of interest to account for spatial variability in these studies. Based on model-estimated time series, we conducted a case-crossover analysis for each sub-sector within two larger areas of interest (Athens and Thessaloniki, Greece) separately to investigate heterogeneity and provide combined results if appropriate. As sensitivity analysis, we compared the case-crossover method to classical time series analysis and also to using fixed site measurements only. For PM₁₀ exposures in Athens, we found consistent adverse effects which were larger when using spatio-temporal LUR modeled concentrations (total mortality RR 2.55 and 95% CI − 0.30 to 5.39) compared to measurements (RR 0.36 and 95% CI − 0.21 to 0.93). For NO₂, we found a similar magnitude in the effects, when using measurements from fixed sites (RR 0.81 and 95% CI 0.39 to 1.22) and modeled levels (RR 0.71 and 95% CI 0.14 to 1.28). Analysis by geographical sector did not add information over a unified analysis for the whole area. The effect estimates using classical Poisson regression time series yielded consistently smaller size effects compared to the case-crossover method. Our analysis demonstrates the potential of using spatio-temporal models in time series analysis for short-term air pollution effects to account for spatial variability in addition to the temporal.

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