Selected article for: "confidence interval and important factor"

Author: Temte, J. L.; Meiman, J. G.; Gangnon, R. E.
Title: School sessions are correlated with seasonal outbreaks of medically attended respiratory infections: electronic health record time series analysis, Wisconsin 2004–2011
  • Cord-id: oafg4a4u
  • Document date: 2019_3_8
  • ID: oafg4a4u
    Snippet: Increased social contact within school settings is thought to be an important factor in seasonal outbreaks of acute respiratory infection (ARI). To better understand the degree of impact, we analysed electronic health records and compared risks of respiratory infections within communities while schools were in session and out-of-session. A time series analysis of weekly respiratory infection diagnoses from 28 family medicine clinics in Wisconsin showed that people under the age of 65 experienced
    Document: Increased social contact within school settings is thought to be an important factor in seasonal outbreaks of acute respiratory infection (ARI). To better understand the degree of impact, we analysed electronic health records and compared risks of respiratory infections within communities while schools were in session and out-of-session. A time series analysis of weekly respiratory infection diagnoses from 28 family medicine clinics in Wisconsin showed that people under the age of 65 experienced an increased risk of ARI when schools were in session. For children aged 5–17 years, the risk ratio for the first week of a school session was 1.12 (95% confidence interval (CI) 0.93–1.34), the second week of a session was 1.39 (95% CI 1.15–1.68) and more than 2 weeks into a session was 1.43 (95% CI 1.20–1.71). Less significant increased risk ratios were also observed in young children (0–4 years) and adults (18–64 years). These results were obtained after modelling for baseline seasonal variations in disease prevalence and controlling for short-term changes in ambient temperature and relative humidity. Understanding the mechanisms of seasonality make it easier to predict outbreaks and launch timely public health interventions.

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