Road traffic noise and registry based use of sleep medication

Evandt, Jorunn; Oftedal, Bente; Krog, Norun Hjertager; Nafstad, Per; Schwarze, Per E.; Skovlund, Eva; Houthuijs, Danny; Aasvang, Gunn Marit

doi:10.1186/s12940-017-0330-5

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…After removing duplicate records, we screened the titles and abstracts of the remaining 907 records and further excluded 877 that were deemed irrelevant, leaving us with 30 full texts for in-depth review. One of them [22] did not report results for anxiolytic or antidepressant use, rather it had combined psychotropic medication use as the outcome; the outcome in the study by Bocquier et al [21] was combined anxiolytic–hypnotic medication intake; and others had general psychological distress [23,51], quality of life [52,53], or sleep/hypnotic medication use [54,55] as the outcome. In two studies, the exposure considered was noise annoyance [56,57].…”

Section: Resultsmentioning

confidence: 99%

Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis

Dzhambov

Lercher

2019

IJERPH

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Unlike other World Health Organization evidence reviews, the systematic review on mental disorders could not provide a quantitative estimate of the effect of environmental noise. With that in mind, we aimed to update it with additional studies published through to 18 August 2019 in order to allow for a formal meta-analysis of the association of residential road traffic noise with anxiety and depression. The quality effects and random effects estimators were used for meta-analysis and the robustness of findings was tested in several sensitivity analyses. Ten studies were included in the qualitative synthesis, from which we extracted 15 estimates for depression (n = 1,201,168) and five for anxiety (n = 372,079). Almost all studies were cross-sectional and the risk of bias in them was generally high. We found 4% (95% CI: −3%, 11%) higher odds of depression and 12% (95% CI: −4%, 30%) of anxiety associated with a 10 dB(A) increase in day–evening–night noise level (Lden). Both models suffered from moderate heterogeneity (55% and 54%), but there was evidence of publication bias only in the depression model. These findings were robust with no evidence of study-level moderators. A sensitivity analysis on an alternative set of categorically-reported estimates supported a linear relationship between Lden and depression. Taking into account an overall quality assessment for the included studies, we conclude that there is evidence of “very low” quality that increasing exposure to road traffic noise may be associated with depression and anxiety.

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Section: Resultsmentioning

confidence: 99%

Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis

Dzhambov

Lercher

2019

IJERPH

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“…Causal diagrams were available for 144 (62%) articles. Sixty (42% of those with one or more diagrams available) included the diagram in the manuscript directly, 81 (56%) in supplementary online material, one (<1%) provided the diagram on request (Bliddal et al 2016) 22 one (<1%) provided a link to the diagram on www.dagitty.net (Evandt et al 2017A), 23 and one (<1%) referred to a diagram from a previous publication (Buchner & Rehfuess 2015). 24 No diagram was available for 90 (38%) articles, including four (1%) articles that referred to supplementary online materials that were unavailable, one article where the diagram was missing from supplementary material, one (<1%) where the printed figure was incorrect, and one (<1%) that provided invalid weblinks to www.dagitty.net .…”

Section: Resultsmentioning

confidence: 99%

Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations

Tennant

Harrison

Murray

et al. 2019

Preprint

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BACKGROUND: Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require adjustment when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. METHODS: Original health research articles published during 1999-2017 mentioning "directed acyclic graphs" or similar or citing DAGitty were identified from Scopus, Web of Science, Medline, and Embase. Data were extracted on the reporting of: estimands, DAGs, and adjustment sets, alongside the characteristics of each article's largest DAG. RESULTS: A total of 234 articles were identified that reported using DAGs. A fifth (n=48, 21%) reported their target estimand(s) and half (n=115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n=144, 62%) made at least one DAG available. Diagrams varied in size but averaged 12 nodes (IQR: 9-16, range: 3-28) and 29 arcs (IQR: 19-42, range: 3-99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31-67, range: 12-100). 37% (n=53) of the DAGs included unobserved variables, 17% (n=25) included super-nodes (i.e. nodes containing more than one variable, and a 34% (n=49) were arranged so the constituent arcs flowed in a consistent direction. CONCLUSIONS: There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlight some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

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“…Recent studies have attempted to extend the noise sources to include traffic noise from roads and railways as well [ 31 , 32 , 33 , 34 , 35 ]. In particular, much research has investigated road traffic noise in different contexts and its correlation with population’s health and wellbeing [ 36 , 37 ]. Studies have shown that higher exposure to road traffic noise could significantly increase annoyance and have harmful effects on people’s sleep quality, wellbeing and other health outcomes [ 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing

Kwan

et al. 2018

IJERPH

100

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With rapid urbanization and increase in car ownership, ambient noise pollution resulting from diversified sources (e.g., road traffic, railway, commercial services) has become a severe environmental problem in the populated areas in China. However, research on the spatial variation of noise pollution and its potential effects on urban residents’ mental health has to date been quite scarce in developing countries like China. Using a health survey conducted in Beijing in 2017, we for the first time investigated the spatial distributions of multiple noise pollution perceived by residents in Beijing, including road traffic noise, railway (or subway) noise, commercial noise, and housing renovation (or construction) noise. Our results indicate that there is geographic variability in noise pollution at the neighborhood scale, and road traffic and housing renovation/construction are the principal sources of noise pollution in Beijing. We then employed Bayesian multilevel logistic models to examine the associations between diversified noise pollution and urban residents’ mental health symptoms, including anxiety, stress, fatigue, headache, and sleep disturbance, while controlling for a wide range of confounding factors such as socio-demographics, objective built environment characteristics, social environment and geographic context. The results show that perceived higher noise-pollution exposure is significantly associated with worse mental health, while physical environment variables seem to contribute little to variations in self-reported mental disorders, except for proximity to the main road. Social factors or socio-demographic attributes, such as age and income, are significant covariates of urban residents’ mental health, while the social environment (i.e., community attachment) and housing satisfaction are significantly correlated with anxiety and stress. This study provides empirical evidence on the noise-health relationships in the Chinese context and sheds light on the policy implications for environmental pollution mitigation and healthy city development in China.

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Road traffic noise and registry based use of sleep medication

Cited by 9 publications

References 41 publications

Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis

Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis

Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations

A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing

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