Ambient air pollution and depression: A systematic review with meta-analysis up to 2019

Shu-Jun Fan; Joachim Heinrich; Michael S Bloom; Tian-Yu Zhao; Tong-Xing Shi; Wen-Ru Feng; Yi Sun; Ji-Chuan Shen; Zhi-Cong Yang; Bo-Yi Yang; Guang-Hui Dong

doi:10.1016/j.scitotenv.2019.134721

Ambient air pollution and depression: A systematic review with meta-analysis up to 2019

Sci Total Environ. 2020 Jan 20:701:134721. doi: 10.1016/j.scitotenv.2019.134721. Epub 2019 Oct 31.

Authors

Affiliations

¹ Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China.
² Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraβe 1, 80336 Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziessenstaβe 1, 80336 Munich, Germany; Allergy and Lung Health Unit, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
³ Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY 12144, USA.
⁴ Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraβe 1, 80336 Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziessenstaβe 1, 80336 Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany.
⁵ Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China. Electronic address: yangzc@gzcdc.org.cn.
⁶ Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: yangby23@mail.sysu.edu.cn.
⁷ Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: donggh5@mail.sysu.edu.cn.

PMID: 31715478
DOI: 10.1016/j.scitotenv.2019.134721

Abstract

Although epidemiological studies have evaluated the associations of ambient air pollution with depression, the results remained mixed. To clarify the nature of the association, we performed a comprehensive systematic review and meta-analysis with the Inverse Variance Heterogeneity (IVhet) model to estimate the effect of ambient air pollution on depression. Three English and four Chinese databases were searched for epidemiologic studies investigating associations of ambient particulate (diameter ≤ 2.5 μm (PM_2.5), ≤10 μm (PM₁₀)) and gaseous (nitric oxide (NO), nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂) and ozone (O₃)) air pollutants with depression. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were calculated to evaluate the strength of the associations. We identified 22 eligible studies from 10 countries of the world. Under the IVhet model, per 10 µg/m³ increase in long-term exposure to PM_2.5 (OR: 1.12, 95% CI: 0.97-1.29, I²: 51.6), PM₁₀ (OR: 1.04, 95% CI: 0.88-1.25, I²: 85.7), and NO₂ (OR: 1.05, 95% CI: 0.83-1.34, I²: 83.6), as well as short-term exposure to PM_2.5 (OR: 1.01, 95% CI: 0.99-1.04, I²: 51.6), PM₁₀ (OR: 1.01, 95% CI: 0.98-1.04, I²: 86.7), SO₂ (OR: 1.03, 95% CI: 0.99-1.07, I²: 71.2), and O₃ (OR: 1.01, 95% CI: 0.99-1.03, I²: 82.2) was not significantly associated with depression. However, we observed significant association between short-term NO₂ exposure (per 10 µg/m³ increase) and depression (OR: 1.02, 95% CI: 1.00-1.04, I²: 65.4). However, the heterogeneity was high for all of the pooled estimates, which reduced credibility of the cumulative evidence. Additionally, publication bias was detected for six of eight meta-estimates. In conclusion, short-term exposure to NO₂, but not other air pollutants, was significantly associated with depression. Given the limitations, a larger meta-analysis incorporating future well-designed longitudinal studies, and investigations into potential biologic mechanisms, will be necessary for a more definitive result.

Keywords: Depressive disorder; Gaseous pollutants; Inverse Variance Heterogeneity (IVhet) model; Particulate matter; Random effects model.

Publication types

Meta-Analysis
Review
Systematic Review

MeSH terms

Air Pollution / statistics & numerical data*
Depression / epidemiology*
Environmental Exposure / statistics & numerical data*
Female
Humans
Male