Login / Signup

Association between short-term ambient air pollution exposure and depression outpatient visits in cold seasons: a time-series analysis in northwestern China.

Yu-Meng ZhouShu-Jie AnEn-Jie TangChen XuYi CaoXiao-Ling LiuChun-Yan YaoHua XiaoQian ZhangFeng LiuYa-Fei LiAi-Ling JiTong-Jian Cai
Published in: Journal of toxicology and environmental health. Part A (2021)
Depression is known to be one of the most common mental disorders raising global concerns. However, evidence regarding the association between short-term air pollution exposure and risk of development of depression is limited. The aim of this was to assess the relationship between short-term ambient air pollution exposure and depression in outpatient visits in Xi'an, a northwestern Chinese metropolis. Data for air pollutants including particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) levels from October 1, 2010 to December 31, 2013 and number of daily depression outpatient visits (92,387 in total) were collected. A time-series quasi-Poisson regression model was adopted to determine the association between short-term air pollutant concentrations and frequency of outpatient visits for depression with different lag models. Consequently, 10 μg/m3 increase of SO2 and NO2 levels corresponded to significant elevation in number of outpatient-visits for depression on concurrent days (lag 0), and this relationship appeared stronger in cool seasons (October to March). However, the association of PM10 was only significant in males aged 30-50 at lag 0. Evidence indicated that short-term exposure to ambient air pollutants especially in cool seasons might be associated with increased risk of outpatient visits for depression.
Keyphrases
  • air pollution
  • particulate matter
  • depressive symptoms
  • sleep quality
  • lung function
  • machine learning
  • cystic fibrosis
  • electronic health record
  • deep learning
  • big data
  • polycyclic aromatic hydrocarbons