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Meta-Analysis
. 2022 Feb 25;19(5):2696.
doi: 10.3390/ijerph19052696.

Association between Noise Annoyance and Mental Health Outcomes: A Systematic Review and Meta-Analysis

Xiangpu Gong  1   2 , Benjamin Fenech  3 , Claire Blackmore  1 , Yingxin Chen  1 , Georgia Rodgers  3 , John Gulliver  1   2 , Anna L Hansell  1   2
Affiliations
Meta-Analysis

Association between Noise Annoyance and Mental Health Outcomes: A Systematic Review and Meta-Analysis

Xiangpu Gong et al. Int J Environ Res Public Health. .

Abstract

To date, most studies of noise and mental health have focused on noise exposure rather than noise annoyance. The purpose of this systematic review and meta-analysis was to evaluate whether the available evidence supports an adverse association between noise annoyance and mental health problems in people. We carried out a literature search of Web of Science, PubMed, Scopus, PsycINFO, and conference proceedings published between 2000 and 2022. Thirteen papers met the inclusion criteria. We conducted meta-analyses of noise annoyance in relation to depression, anxiety, and general mental health. In the meta-analyses, we found that depression was approximately 1.23 times greater in those who were highly noise-annoyed (N = 8 studies). We found an approximately 55% higher risk of anxiety (N = 6) in highly noise-annoyed people. For general mental health (N = 5), highly annoyed participants had an almost 119% increased risk of mental health problems as assessed by Short Form (SF) or General Household Questionnaires (GHQ), but with high heterogeneity and risk of publication bias. In conclusion, findings are suggestive of a potential link between noise annoyance and poorer mental health based on a small number of studies. More evidence is needed to confirm these findings.

Keywords: anxiety disorder; depression; environmental and neighborhood noise; general mental health; mental health; noise annoyance; traffic noise.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Summary of the risk of bias assessments.
Figure A2
Figure A2
Risk of bias assessment.
Figure A3
Figure A3
Leave-one-out analysis for identifying outliers in depression studies. Note: weights are from random-effects model.
Figure A4
Figure A4
Leave-one-out analysis for identifying outliers in anxiety studies. Note: weights are from random-effects model.
Figure A5
Figure A5
Leave-one-out analysis for identifying outliers in general mental health studies. Note: weights are from random-effects model.
Figure A6
Figure A6
Funnel plot—depression.
Figure A7
Figure A7
Funnel plot—anxiety disorder.
Figure A8
Figure A8
Funnel plot—general mental health.
Figure 1
Figure 1
PRISMA flow chart showing number of papers identified [20].
Figure 2
Figure 2
Forest plot displaying the link between high noise annoyance and depression. Note: weights and between-subgroup heterogeneity text are from random-effects model.
Figure 3
Figure 3
Forest plot displaying the link between high noise annoyance and anxiety disorder. Note: weights and between-subgroup heterogeneity text are from random-effects model.
Figure 4
Figure 4
Forest plot displaying the link between high noise annoyance and general mental health. Note: weights and between-subgroup heterogeneity text are from random-effects model.
See this image and copyright information in PMC

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