Bayer-Oglesby, Lucy
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Characterization of source-specific air pollution exposure for a large population-based Swiss cohort (SAPALDIA)
Background: Although the dispersion model approach has been used in some epidemiologic studies to examine health effects of traffic-specific air pollution, no study has evaluated the model predictions vigorously. Methods: We evaluated total and traffic-specific particulate matter < 10 and < 2.5 microm in aero-dynamic diameter (PM(10), PM(2.5)), nitrogren dioxide, and nitrogen oxide concentrations predicted by Gaussian dispersion models against fixed-site measurements at different locations, including traffic-impacted, urban-background, and alpine settings between and across cities. The model predictions were then used to estimate individual subjects' historical and cumulative exposures with a temporal trend model. Results: Modeled PM(10) and NO(2) predicted at least 55% and 72% of the variability of the measured PM(10) and NO(2), respectively. Traffic-specific pollution estimates correlated with the NO(x) measurements (R(2) >or=0.77) for background sites but not for traffic sites. Regional background PM(10) accounted for most PM(10) mass in all cities. Whereas traffic PM(10) accounted for < 20% of the total PM(10), it varied significantly within cities. The modeling error for PM(10) was similar within and between cities. Traffic NO(x) accounted for the majority of NO(x) mass in urban areas, whereas background NO(x) accounted for the majority of NO(x) in rural areas. The within-city NO(2) modeling error was larger than that between cities. Conclusions: The dispersion model predicted well the total PM(10), NO(x), and NO(2) and traffic-specific pollution at background sites. However, the model underpredicted traffic NO(x) and NO(2) at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM(10) are suitable for examining individual exposures and health effects within and between cities.
Soziale Ungleichheit und Covid-19. Schwere Covid-19-Verläufe mit Spitalaufenthalt und der Einfluss von Demografie, Vorerkrankung, Sozialstatus und Expositionsrisiken
Das Bulletin untersucht soziale Ungleichheiten in der Covid-19-Pandemie. Auf Basis einer Datenverknüpfung zwischen der Strukturerhebung (SE) und der Medizinischen Statistik (MS) wird untersucht, welche Teile der Schweizer Bevölkerung in den Pandemiejahren 2020 und 2021 ein erhöhtes Risiko für einen Spitalaufenthalt wegen Covid-19 aufwiesen und welche Rolle dabei die soziale Lage, die Wohnsituation und der Beruf spielten. Ausserdem thematisiert das Bulletin die Rolle schwerer Vorerkrankungen und zeigt auf, wie sich die sozialen Ungleichheiten bei schweren Covid-19-Verläufen mit der Zugänglichkeit von Impfungen verändert haben.