Prädiktoren der Inanspruchnahme von kardiovaskulären und respiratorischen Notfallaufnahmen – welchen Einfluss hat die Umwelt?

Anne Caroline Krefis*, Jana Fischereit, Peter Hoffmann, Christina Sorbe, Hans Pinnschmidt, Matthias Augustin, Jobst Augustin

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review


    Aim There has been an increasing number of emergency department (ED) visits recently. It is unclear whether, in addition to a shift in services from the outpatient to the inpatient sector, other causes, (e. g. environmental factors), play a role. The aim was to investigate associations between the number of cardiovascular and respiratory ED visits and environmental variables. Methods Highly correlated environmental data were subjected to a principal component analysis. By using cross-correlation functions, environmental variables with time lags that showed the highest correlation with the number of ED visits were taken into consideration in the UNIANOVA analysis model, together with, among others, the day of the week and interaction terms. Results The final regression model explained 47% of the variation in respiratory ED visits demonstrating main effects for Mondays (B=10.69; p<0.001). Season showed significant effects with highest ED visits in autumn. No direct associations between environmental variables and number of respiratory ED visits were found. The results for the cardiovascular outcome were less expressive (R2=0.20). Again, the day of the week had the main effect on cardiovascular ED visits (p<0.001). Conclusions The results suggest that weekdays had the main effect on ED visits. In future, we will collect and analyze environmental data at the micro level to achieve a higher model quality and better interpretability.
    Original languageGerman
    JournalDas Gesundheitswesen
    Publication statusPublished - 2019


    • Temporal analyses
    • Respiratory and cardiovascular emergency department visits
    • Temporal risk factors
    • Environmental risk factors
    • Categorical principal components analysis

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