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Traffic-Related High Sleep Disturbance in the LIFE-Adult Cohort Study: A Comparison to the WHO Exposure-Response-Curves.

Melanie SchubertKarla Romero StarkeJulia GerlachMatthias ReuschePauline KabothWolfram SchmidtDieter FriedemannJanice HegewaldHajo ZeebAndrea E ZuelkeSteffi G Riedel-HellerAndreas Seidler
Published in: International journal of environmental research and public health (2023)
Sleep is negatively affected by environmental noise. In the present study, we investigated self-reported high sleep disturbances (being "highly sleep disturbed"-HSD) from road traffic (primary and secondary road networks), rail (train and tram) and air traffic noise in the LIFE-Adult cohort study in Leipzig, Germany. For this, we used exposure data from 2012 and outcome data of Wave 2 (collected during 2018-2021). HSD was determined and defined according to internationally standardized norms. The highest risk for transportation noise-related HSD was found for aircraft noise: the odds ratio (OR) was 19.66, 95% CI 11.47-33.71 per 10 dB increase in L night . For road and rail traffic, similar risk estimates were observed (road: OR = 2.86, 95% CI 1.92-4.28; rail: OR = 2.67, 95% CI 2.03-3.50 per 10 dB L night increase). Further, we compared our exposure-risk curves with the curves of the WHO environmental noise guidelines for the European region. The proportion of individuals with HSD for a given noise level was lower for rail traffic but higher for aircraft noise in the LIFE study than in the WHO curves. For road traffic, curves are not directly comparable because we also included the secondary road network. The results of our study add to the body of evidence for increased health risks by traffic noise. Moreover, the results indicate that aircraft noise is particularly harmful to health. We recommend reconsidering threshold values for nightly aircraft exposure.
Keyphrases
  • air pollution
  • sleep quality
  • physical activity
  • healthcare
  • public health
  • mass spectrometry
  • depressive symptoms
  • high resolution
  • big data
  • drug induced
  • health information