Air pollution exposure and depression: A comprehensive updated systematic review and meta-analysis.

Elisa Borroni, Angela Cecilia Pesatori, Valentina Bollati, Massimiliano Buoli, Michele Carugno

Journal: Environmental pollution (Barking, Essex : 1987) 2021;292(Pt A):118245

PMID: 34600062

Abstract

We provide a comprehensive and updated systematic review and meta-analysis of the association between air pollution exposure and depression, searching PubMed, Embase, and Web of Sciences for relevant articles published up to May 2021, and eventually including 39 studies. Meta-analyses were performed separately according to pollutant type [particulate matter with diameter ≤10 μm (PM) and ≤2.5 μm (PM), nitrogen dioxide (NO), sulfur dioxide (SO), ozone (O), and carbon monoxide (CO)] and exposure duration [short- (<30 days) and long-term (≥30 days)]. Test for homogeneity based on Cochran's Q and I statistics were calculated and the restricted maximum likelihood (REML) random effect model was applied. We assessed overall quality of pooled estimates, influence of single studies on the meta-analytic estimates, sources of between-study heterogeneity, and publication bias. We observed an increased risk of depression associated with long-term exposure to PM (relative risk: 1.074, 95% confidence interval: 1.021-1.129) and NO (1.037, 1.011-1.064), and with short-term exposure to PM (1.009, 1.006-1.012), PM (1.009, 1.007-1.011), NO (1.022, 1.012-1.033), SO (1.024, 1.010-1.037), O (1.011, 0.997-1.026), and CO (1.062, 1.020-1.105). The publication bias affecting half of the investigated associations and the high heterogeneity characterizing most of the meta-analytic estimates partly prevent to draw very firm conclusions. On the other hand, the coherence of all the estimates after excluding single studies in the sensitivity analysis supports the soundness of our results. This especially applies to the association between PM and depression, strengthened by the absence of heterogeneity and of relevant publication bias in both long- and short-term exposure studies. Should further investigations be designed, they should involve large sample sizes, well-defined diagnostic criteria for depression, and thorough control of potential confounding factors. Finally, studies dedicated to the comprehension of the mechanisms underlying the association between air pollution and depression remain necessary.

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Address: Department of Clinical Sciences and Community Health, University of Milan, via san Barnaba 8, 20122, Milan, Italy.; Department of Clinical Sciences and Community Health, University of Milan, via san Barnaba 8, 20122, Milan, Italy; Epidemiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via san Barnaba 8, 20122, Milan, Italy. Electronic address: [email protected].; Department of Pathophysiology and Transplantation, University of Milan, Via Francesco Sforza 35, 20122, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122, Milan, Italy.; Department of Clinical Sciences and Community Health, University of Milan, via san Barnaba 8, 20122, Milan, Italy; Epidemiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via san Barnaba 8, 20122, Milan, Italy.
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