Wayne R Lawrence1,2, Mo Yang1, Chuan Zhang1, Ru-Qing Liu1, Shao Lin2, Si-Quan Wang3, Yimin Liu4, Huimin Ma5, Duo-Hong Chen6, Xiao-Wen Zeng1, Bo-Yi Yang1, Li-Wen Hu1, Steve Hung Lam Yim7, Guang-Hui Dong1. 1. Department of Preventive Medicine, Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, School of Public Health, Sun Yat-sen University, Guangzhou, China. 2. Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, NY. 3. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA. 4. Laboratory of Occupational Environment and Health Effects, Guangzhou Key Medical Discipline of Occupational Health Guardianship, Guangzhou Prevention and Treatment Center for Occupational Diseases, Guangzhou No.12 Hospital, Guangzhou, China. 5. State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China. 6. Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou, China. 7. Department of Geography and Resource Management, Stanley Ho Big Data Decision Analytics Research Centre, Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin N.T., Hong Kong, China.
Abstract
Study Objectives: There is limited knowledge regarding the effects of air pollution on sleep disorders, particularly in children. The aim of this study is to investigate this association in Chinese children. Methods: During 2012-2013, 59754 children aged 2-17 years were randomly selected from 27 districts in seven northeastern Chinese cities. All participants' sleep was evaluated with the Sleep Disturbance Scale for Children. Four year average concentrations of pollutants were calculated for particles with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm (PM2.5) from a spatial statistical model, and ≤10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) from monitoring stations. To examine the effects, two-level regression analysis was used, controlling for covariates. Results: We observed that sleep disorder was generally associated with all air pollutants, with the highest odds among PM1 exposure for male (odds ratio [OR] 1.55; 95% confidence interval [95% CI] 1.36-1.76) and female (OR 1.50; 95% CI 1.30-1.72) children. The overall strongest association with sleep disorder symptom was exposure to PM1 and Disorders of Excessive Somnolence (OR 1.43; 95% CI 1.30-1.58). PM1 and PM2.5 were strongly associated with all sleep disorder symptoms in females (ORs ranged for PM1 from 1.19 to 1.49; and PM2.5 1.18 to 1.44). The association between air pollutants and total sleep score was generally greater in female than in male children. Conclusions: Our findings suggest that exposure to air pollutants increases the odds of sleep disorder in children and point to the need to make reducing exposure to air pollutants a public health priority.
Study Objectives: There is limited knowledge regarding the effects of air pollution on sleep disorders, particularly in children. The aim of this study is to investigate this association in Chinese children. Methods: During 2012-2013, 59754 children aged 2-17 years were randomly selected from 27 districts in seven northeastern Chinese cities. All participants' sleep was evaluated with the Sleep Disturbance Scale for Children. Four year average concentrations of pollutants were calculated for particles with an aerodynamic diameter of ≤1 µm (PM1), ≤2.5 µm (PM2.5) from a spatial statistical model, and ≤10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) from monitoring stations. To examine the effects, two-level regression analysis was used, controlling for covariates. Results: We observed that sleep disorder was generally associated with all air pollutants, with the highest odds among PM1 exposure for male (odds ratio [OR] 1.55; 95% confidence interval [95% CI] 1.36-1.76) and female (OR 1.50; 95% CI 1.30-1.72) children. The overall strongest association with sleep disorder symptom was exposure to PM1 and Disorders of Excessive Somnolence (OR 1.43; 95% CI 1.30-1.58). PM1 and PM2.5 were strongly associated with all sleep disorder symptoms in females (ORs ranged for PM1 from 1.19 to 1.49; and PM2.5 1.18 to 1.44). The association between air pollutants and total sleep score was generally greater in female than in male children. Conclusions: Our findings suggest that exposure to air pollutants increases the odds of sleep disorder in children and point to the need to make reducing exposure to air pollutants a public health priority.
Authors: Hongjun Yu; Panpan Chen; Shelby Paige Gordon; Miao Yu; Yangyang Wang Journal: Int J Environ Res Public Health Date: 2019-09-11 Impact factor: 3.390
Authors: S Elavsky; V Jandačková; L Knapová; V Vašendová; M Sebera; B Kaštovská; D Blaschová; J Kühnová; R Cimler; D Vilímek; T Bosek; J Koenig; D Jandačka Journal: BMC Public Health Date: 2021-01-12 Impact factor: 3.295
Authors: Alexandra Ursache; R Gabriela Barajas-Gonzalez; Samrachana Adhikari; Dimitra Kamboukos; Laurie M Brotman; Spring Dawson-McClure Journal: SSM Popul Health Date: 2022-02-26