DÄ internationalArchive14/2019Aircraft Noise and the Risk of Stroke

Original article

Aircraft Noise and the Risk of Stroke

A systematic review and meta-analysis

Dtsch Arztebl Int 2019; 116: 237-44. DOI: 10.3238/arztebl.2019.0237

Weihofen, V M; Hegewald, J; Euler, U; Schlattmann, P; Zeeb, H; Seidler, A

Background: There have been many individual studies on the question whether aircraft noise is a risk factor for stroke, but until now there has not been any summary of the current state of the evidence of adequately high methodological quality.

Methods: In a systematic review and meta-analysis (PROSPERO registry number CRD42013006004), we evaluated the relation between address-based aircraft noise exposure and the incidence of stroke. A systematic literature search was performed in the MEDLINE, EMBASE, and BIOSIS databases including publications up to August 2017. Two of the authors, working independently of each other, screened the titles, abstracts, and full texts for eligible articles and evaluated the quality of the included studies on a three-level scale. The change of risk per 10 dB increase in the weighted mean aircraft noise level (LDEN) was calculated. LDEN is a noise level indicator with additional weighting of evening and nighttime noise.

Results: Of the nine studies that met the inclusion criteria, seven were suitable for inclusion in the meta-analysis. The result of the meta-analysis indicated a relative stroke risk of 1.013 (95% confidence interval, [0.998; 1.028]) per 10 dB increase in LDEN, corresponding with an estimated 1.3% increase in the risk of stroke for each additional 10 dB of aircraft noise. The underlying studies were of poor to medium quality. The analyses of the studies included adjustments for various combinations of confounders, including age, sex, ethnicity, and socioeconomic status.

Conclusion: The present meta-analysis indicates that aircraft noise increases the risk of stroke, even if the overall finding just fails to reach statistical significance. The differing measures of exposure in the included studies, the lack of differentiation between ischemic and hemorrhagic stroke, and the lack of consideration of maximum noise levels are all factors that may have led to a marked underestimation of the risk of stroke.

LNSLNS

The health effects of noise are a highly relevant problem for the population. The World Health Organization (WHO) estimates that in the European Union and Western Europe the number of healthy life years lost due to environmental noise exceeds one million (disability-adjusted life years, DALYs) (1). Environmental noise-related sleep disturbances are presumably the greatest problem with more than 900 000 DALYs; the WHO report estimates that noise-related ischemic heart disease accounts for 61 000 DALYs. The WHO report does not provide a corresponding estimate for cerebrovascular events. Furthermore, the various individual sources of noise are not considered separately in the WHO report.

The German Federal Environmental Agency set as an average target to reduce noise pollution to 55 dB during the day and 45 dB during nighttime to prevent considerable annoyance (e1). Based on noise mapping information (e2), 8.7 million people in Germany are exposed to road noise of day–evening–night noise levels (LDEN) of more than 55 dB. For 6.4 million people, an LDEN of 55 dB is exceeded by rail traffic and for 0.8 million by air traffic. According to a representative survey from 2016 (e3), 76% of the German population feels disturbed or annoyed by road noise, and the same applies to 44% and 34% for aircraft noise and rail traffic noise, respectively.

Pathophysiologically, the cardiovascular effects of noise have been attributed to an activation of the autonomous nervous system with subsequent release of stress hormones (norepinephrine, epinephrine, cortisol); most of the supporting data are from experimental studies. These neuroendocrinological mechanisms can trigger or promote abnormal processes, such as increases in blood pressure and insulin resistance (e4). Disturbed sleep at night with subsequent daytime sleepiness and impaired regenerative processes may be of special importance in chronic stress reactions caused by noise pollution (e5, e6). While a causal relationship between noise and cardiovascular morbidity appears biologically plausible, it remains unclear whether the postulated effects can cause a measurable increase in stroke incidence that is also potentially detectable at the population level.

There is no current systematic review of the evidence on the relationship between aircraft noise and stroke incidence available at present. The design and methodological quality of the available individual studies are as heterogeneous as their results. Both cohort studies (for example [2]) and cross-sectional studies (for example [3]) have been reported. Some studies evaluate noise exposure as a continuous variable, other studies as a categorical variable with various intervals and cut-offs. In some studies, the endpoints were recorded as ICD-coded (ICD, International Classification of Diseases and Related Health Problems) hospital diagnoses or deaths determined by physicians (for example [4, 5]), while other studies rely solely on patient-reported information (for example [3]). Reviewing the individual studies identified mostly statistically non-significant risk estimates. The aim of this systematic review is to present the currently available evidence on the aircraft noise-related stroke risk.

Methods

Research question and inclusion criteria

This systematic review addresses the question whether noise due to civil air traffic or the perceived annoyance caused by it has an effect on the risk of fatal or non-fatal stroke (cerebrovascular accident) in humans. The research question was operationalized by means of a detailed a priori specification of the population, exposure and assessed endpoints. The inclusion and exclusion criteria are detailed in Table 1.

Inclusion and exclusion criteria
Inclusion and exclusion criteria
Table 1
Inclusion and exclusion criteria

Search strategy

We performed a systematic electronic literature search in the MEDLINE (publications from 1947), EMBASE (publications from 1974) and BIOSIS (publications from 1969) bibliographic databases up to 31 August 2017, and complemented this with a manual search. Only original studies with an available abstract were included. No language limitations were imposed. With regard to study design, the search included cohort studies, case-control studies, cross-sectional studies, and ecological studies.

For the database search, the search strategy was adapted to the respective database, and is shown in eBox 1. In addition, a manual search was conducted in references of included publications, narrative reviews and key publications, as well as an online search using the citation-tracking function of the Google Scholar search engine (6, 7).

Search strategy in MEDLINE (via PubMed)
Search strategy in MEDLINE (via PubMed)
eBox 1
Search strategy in MEDLINE (via PubMed)

Literature screening

The screening of the titles, abstracts and full-text articles of the publications identified was performed by two authors independently of each other (VMW, AS); in case of diverging judgment, a third person (UE) was consulted. The reasons for excluding full-text articles were documented for each publication not included.

Data extraction

The data of the included studies were entered into an extraction table created a priori. The authors responsible for screening the literature (VMW, AS) performed the data extraction. Differences were discussed in consensus conferences. In case of incomplete data, the authors of the corresponding publication were contacted and asked to provide additional information.

Quality evaluation

The study quality was also assessed by two authors (VMW, AS) independently of each other, using an instrument which had been developed based on SIGN (Scottish Intercollegiate Guidelines Network 2004) and CASP (Critical Appraisal Skills Program 2004/2006) and which had been successfully used in several earlier reviews (among others in [8–13]). The instrument was adapted to the aircraft noise topic. The assessment included a summary quality rating on a three-step scale (++, +, −). In cases where the potential effect of methodological weaknesses on the core results of the study appeared to be significant, the respective studies were classified as “of low methodological quality (−)”.

Data synthesis and statistical analysis

In preparation of the quantitative analysis, data were processed and converted. This included, among others, transformation of noise metrics of all studies included in the meta-analysis to the average noise level LDEN. LDEN is the weighted day-evening-night level with an extra 5 dB being added to noise in the evening hours and 10 dB to the nighttime hours. For the transformation, the conversion rules of the WHO working paper by Brink (14) were used. These rules are based on aircraft noise measurements. An overview of the noise metrics and their definitions in provided in eTable 1.

Noise metrics and their definitions*1
Noise metrics and their definitions*1
eTable 1
Noise metrics and their definitions*1

The statistical software Stata (version 14.1) was used to determine the potential dose-response relationships from the studies’ results and for the meta-analysis conducted using the random-effects model.

In the core analysis, the linear change of the effect estimate with each increase in the LDEN noise level by 10 dB was assessed. For this, the data of the studies with continuous exposure measurements—after transformation to LDEN, if necessary—were directly integrated into the model. In studies with categorical noise intervals and various corresponding risk estimates, the change in risk for each 10 dB increase in aircraft noise levels was first calculated using the Stata function glst (method of generalized least squares [16]). For this, the risk estimates reported in the studies were allocated to the mean dB of the respective exposure category. If it was not possible to specify the covariance matrix, the Stata vwls procedure was used instead. Next, the studies were pooled using a random-effects model (Stata metan [15]).

In order to assess the impact of individual studies on the pooled effect estimate, a sensitivity analysis was conducted excluding one study at a time from the meta-analysis (leave-one-out method) (17).

Further details on the methodology are provided in eBox 2.

Results

Study selection

The flowchart (eFigure) shows the literature selection process. Twenty-two publications (25, 1834, e14), containing data of altogether 9 studies, met the inclusion criteria. The extracted study data with the characteristics of the included studies/publications are listed in eTable 2.

Forest plot with the included risk estimates of the individual studies
Forest plot with the included risk estimates of the individual studies
Figure
Forest plot with the included risk estimates of the individual studies
Flowchart of the selection process (based on the PRISMA Statement [e7])
Flowchart of the selection process (based on the PRISMA Statement [e7])
eFigure
Flowchart of the selection process (based on the PRISMA Statement [e7])

Results of the individual included studies

The results of the included individual studies are summarized in Table 2. For studies with categorical measurements of aircraft noise, the risk estimates are allocated to their corresponding intervals; for studies with continuous exposure data, reference intervals are reported in dB. The described noise metrics and effect estimates are the original data and have not yet been converted.

The included studies apply a cohort approach (2, 4, 20, 23), a case-control approach (32), a cross-sectional design (3, 18, 34) or an ecological design with case-control approach (19). The majority of the studies are secondary data analyses (2, 4, 1820, 23, 32). The observation periods studied vary between two (20) and eight (2) years. With the exception of the study by Wiens (34), data of women and men are evaluated in the studies. The included age groups are very heterogeneous between the individual studies. Frequently, the lower limit is set at 30 to 45 years (2, 3, 23, 32, 34), presumably because the endpoint “stroke“ would only be very sporadically observed in younger age groups.

Synthesis of the results

Seven of the 9 included studies (with 20 related publications) were included in the meta-analysis. The meta-analysis yielded a relative stroke risk of 1.013 (95% confidence interval [0.998; 1.028]) with each increase in the noise level LDEN by 10 dB. This corresponds to a risk increase of 1.3% with each 10 dB increase. Formally, the significance level is not reached. However, the result is so close to the significance threshold that an actual effect seems likely. The results of the core analysis are shown in the Figure. With a heterogeneity measure I2 of 0%, no formal indication of heterogeneity of the included studies was found.

In the leave-one-out sensitivity analysis, pooled effect estimates between 1.010 and 1.016 were found, corresponding to a risk increase of 1.0 to 1.6% with each 10 dB increase in the aircraft noise level LDEN (Table 3). Only when excluding the largest study, the NORAH study on health risks (35), a statistically significant risk increase of 1.6% with each increase in LDEN by 10 dB (risk ratio [RR] = 1.016; [1.001; 1.032]) was found. This applies to studies with good quality ratings as well as for studies rated as low-quality studies in our review. Separate analysis of the cohort studies also found no substantially different effect estimate. The NORAH study on health risks reported no positive association between average 24-hour sound levels and the diagnosis of stroke; however, this study shows a statistically significant increased stroke risk of 7% when nighttime maximal levels exceed 50 dB (NAT6) and average 24-hour noise levels are below 40 dB (odds ratio [OR]: 1.07; [1.02; 1.13]).

Results of the leave-one-out analysis
Results of the leave-one-out analysis
Table 3
Results of the leave-one-out analysis

Further information about the results is provided in eBox 3.

Supplementary information about the results
Supplementary information about the results
eBox 3
Supplementary information about the results

Discussion

Our systematic review with meta-analysis found a statistically nonsignificant increase in stroke risk of 1.3% with each increase of the weighted aircraft noise level LDEN by 10 dB. If the largest study (the NORAH study) is excluded, this association is statistically significant.

Strengths and limitations

Special features of this review include the a priori defined and published procedure and the comprehensive systematic search of the literature.

A literature search update for the period from September 2017 to November 2018 performed in the PubMed database yielded 35 hits. These were screened by 2 authors (VMW, AS) independently of each other. No new publications meeting the inclusion criteria were identified in the process.

It is generally possible that confounding and temporality could have biased the risk estimation. The 3 methodologically sound cohort studies included (2, 4, 23) found risk increases between 1.3% (2) and 9.2% (4) with each 10 dB increase in aircraft noise level. Thus, it seems to be rather unlikely that the risk was underestimated due to the above mentioned methodological aspects. The—in comparison to the pooled risk estimates of all studies—equal or higher risk estimates of the methodologically sound cohort studies also suggest that no relevant publication bias was present. Another important limitation is that the actual exposure of the subjects can deviate from the exposure assumed in the studies because the latter is based on the place of residence, but presumably many study participants spend much of their time elsewhere.

A large proportion of the studies is based on collections of secondary data which may harbor a higher risk of systematic bias, because these data were not collected directly from the subjects and not generated for research purposes. On the other hand, analyses of secondary data can counteract various biases: Selection mechanisms affecting the recruitment of research participants (selection bias) are usually negligible and recall bias can generally be avoided.

Comparison with the results of other studies

The meta-analysis of a Bulgarian working group (e16), investigating the association between various types of traffic noise and stroke has several issues related to the selection of the included studies. Among other things, the results of Gan et al. (23) were only analyzed collectively in the “mixed noise” category. Depending on the model used, this meta-analysis arrived at a risk estimate of 1.04 or 1.05 for each 10 dB increase in noise levels.

The meta-analysis published by Vienneau et al. (36) included 4 aircraft noise studies with stroke as the endpoint (4, 5, 18, 23). However, the aircraft-noise risk estimates were not reported separately from the risk estimates for other types of traffic noise. The Swiss working group found a pooled overall effect estimate of 1.014 ([0.96; 1.066]) with each increase in LDEN by 10 dB. This result is very close to the result of our meta-analysis.

The 2018 WHO review on the association between traffic noise and cardiovascular disease (37, 38) reported a pooled risk estimate with each increase in LDEN by 10 dB for the stroke risk due to aircraft noise for:

  • prevalence studies of 1.02 ([0.80; 1.28]),
  • incidence studies of 1.05 ([0.96; 1.15]),
  • mortality studies of 1.07 ([0.98; 1.17]).

However, the search period is limited to January 2000 until August 2015 and thus does not include older (for example, Frerichs et al. [20]) and, most importantly, newer analyses (for example, the NORAH study on health risks [32]).

The systematic review by Vienneau et al. (36, e12) and the WHO review (37, 38) allow a comparison between the traffic noise–related risk estimates for the “stroke” outcome with the corresponding risk estimates for the “ischemic heart disease” outcome . In both systematic reviews, the risk estimates for stroke are considerably lower compared to those for ischemic heart disease. At least to some extent, this could be explained by the fact that the diagnosis “stroke” includes both the ischemic and the hemorrhagic stroke. These two types of stroke can differ in their etiology (e17, e18). Thus, the separate analyses of the NORAH study on health risks indicated potential differences with regard to the traffic noise–related risks between hemorrhagic and ischemic stroke (e19).

Overall, our systematic review with meta-analysis provided evidence in support of an association between aircraft noise and the occurrence of stroke. Although the risk increase did not reach statistical significance when all identified studies were included in the analysis, the NORAH study on health risks—with the lowest risk estimates of all included studies—indicated that stroke risks may not be adequately described based solely on average sound levels. Accordingly, the NORAH study showed an association between the diagnosis of stroke and maximum aircraft noise levels at night. In recent years/decades, night flight restrictions, or even bans on night flights, have been introduced at many airports. A comparatively lower proportion of night flights in the NORAH study on health risks compared to several older studies may explain why the increased stroke risks observed with maximum nighttime aircraft noise levels are not apparent with the weighted average sound levels. Consequently, future studies on stroke risks associated with aircraft noise should not be limited to average sound levels, but should also take maximum nighttime levels into account.

Even if all uncertainties are taken into account, significantly less strokes are caused by aircraft noise compared to unhealthy lifestyle patterns. Based on the INTERSTROKE study by O´Donnell et al. (39), approximately 39% of strokes can be attributed to hypertension and 36% to being overweight. Even if all people were exposed to very high levels of aircraft noise (60 dB), less than 3% of all strokes could be attributed to aircraft noise, based on a 1.3% risk increase with every 10 dB increase in aircraft noise (the lifetime prevalence of stroke is 2,9% [40]).

Although the cerebrovascular accidents which could be prevented by noise-reducing measures only account for a comparatively small proportion of all strokes, the authors of this review believe that traffic noise-related health risks are important for the health of the population. Unlike the exposure to lifestyle factors, it is hardly possible for individuals to change their exposure to traffic noise. Consequently, effective noise reduction is a social responsibility.

Conclusion

All in all, our systematic review with meta-analysis indicates that there is an association between aircraft noise and the occurrence of stroke. Especially differences between the individual studies and in some cases inaccurate exposure estimations, the lack of differentiation between ischemic and hemorrhagic stroke, as well as the failure to take into account maximum levels could have resulted in a significant underestimation of the stroke risk. Given the large number of individuals exposed to environmental noise and the high prevalence of stroke in the population, more studies with improved methodology should be conducted. This would further strengthen the scientific evidence that serves as the foundation for effective noise protection—and thus effective health protection—of the population.

Financial support

This review was financed by the Institute and Policlinic for Occupational and Social Medicine (IPAS), Faculty of Medicine Carl Gustav Carus, Technical University of Dresden, Germany, without additional external funding.

Acknowledgement

Our special thanks go to our librarian Soja Nazarov for her continuous technical support and her unceasing efforts in searching and retrieving documents. We also wish to thank Prof. Jochen Schmitt, one of the original initiators of this review. We would furthermore like to express our heartfelt thanks to all study authors who answered to our request for additional information to their publications and so supported us: Prof. Michael Brauer (23), Dr. Andrew Correia (18), Prof. Wenqi Gan (23), Dr. Rebecca Ghosh (4), Dr. Hind Sbihi (23), Prof. Martin Röösli (2), and the team of the NORAH study on health risks (32).

Conflict of interest statement
Prof. Seidler received reimbursement of congress fee and travel expenses for the “International Conference Active Noise Protection“ as well as study support (third party funding) from the State of Hesse (Umwelt- und Nachbarschaftshaus GmbH) and the German Federal Environment Agency. He received lecture fees from the Klinik Henningsdorf, the University of Mainz (Robert-Müller lecture) and the Lärmkontor GmbH.

The remaining authors declare no conflicts of interest.

Manuscript received on: 1 October 2018; revised version accepted on 6 February 2019

Translated from the original German by Ralf Thoene, MD.

Corresponding author
Dr. rer. medic. Verena Weihofen, MPH
Institut und Poliklinik für Arbeits- und Sozialmedizin,
Technische Universität Dresden, Medizinische Fakultät
Fetscherstr. 74
01307 Dresden, Germany
Verena_Maria.Weihofen@tu-dresden.de

Supplementary material
For eReferences please refer to:
www.aerzteblatt-international.de/ref1419

eBoxes, eTables, eFigure:
www.aerzteblatt-international.de/19m0237

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e13.
Seidler A, Wagner M, Schubert M, et al.: Aircraft, road and railway traffic noise as risk factors for heart failure and hypertensive heart disease–a case-control study based on secondary data. Int J Hyg Environ Health 2016; 219: 749–58 CrossRef MEDLINE
e14.
Babisch W, Houthuijis D, Kwekkeboom J, et al.: HYENA-Hypertension and exposure to noise near airports. A European study on health effects of aircraft noise. INTER-NOISE and NOISE-CON Congress and Conference Proceedings: Institute of Noise Control Engineering 2005; p. 1376–85.
e15.
Ulm K: A simple method to calculate the confidence interval of a standardized mortality ratio (SMR). Am J Epidemiol 1990; 131: 373–5 CrossRef
e16.
Dzhambov AM, Dimitrova DD: Exposure-response relationship between traffic noise and the risk of stroke: a systematic review with meta-analysis. Arh Hig Rada Toksikol 2016; 67: 136–51 MEDLINE
e17.
O‘Donnell MJ, Xavier D, Liu L, et al.: Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet 2010; 376: 112–23 CrossRef
e18.
Blomstrand A, Blomstrand C, Ariai N, Bengtsson C, Bjorkelund C: Stroke incidence and association with risk factors in women: a 32-year follow-up of the Prospective Population Study of Women in Gothenburg. BMJ Open 2014; 4: e005173 CrossRef MEDLINE PubMed Central
e19.
Seidler A, Steputat A, Drössler S, et al.: Determinanten und Auswirkungen von Informationsüberflutung am Arbeitsplatz. Zentralblatt für Arbeitsmedizin, Arbeitsschutz und Ergonomie 2018; 68: 12–26 CrossRef
Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Institute and Policlinic for Occupational and Social Medicine (IPAS), Dresden, Germany: Dr. rer. medic. Verena Maria Weihofen, MPH; Dr. rer. biol. hum. Janice Hegewald, M. Sc.; Dr. med. Ulrike Euler, M. Sc.; Prof. Dr. med. Andreas Seidler, M.P.H.
Jena University Hospital, Department of Statistics, Informatics and Documentation, Jena, Germany: Prof. Dr. med. habil. Peter Schlattmann, M. Sc.
Leibniz Institute for Prevention Research and Epidemiology – BIPS GmbH, Bremen, Germany:
Prof. Dr. med. Hajo Zeeb, M. Sc.
University of Bremen, Health Sciences Bremen, Bremen, Germany: Prof. Dr. med. Hajo Zeeb, M. Sc.
Forest plot with the included risk estimates of the individual studies
Forest plot with the included risk estimates of the individual studies
Figure
Forest plot with the included risk estimates of the individual studies
Key messages
Inclusion and exclusion criteria
Inclusion and exclusion criteria
Table 1
Inclusion and exclusion criteria
Results of the leave-one-out analysis
Results of the leave-one-out analysis
Table 3
Results of the leave-one-out analysis
Search strategy in MEDLINE (via PubMed)
Search strategy in MEDLINE (via PubMed)
eBox 1
Search strategy in MEDLINE (via PubMed)
Supplementary information about the results
Supplementary information about the results
eBox 3
Supplementary information about the results
Flowchart of the selection process (based on the PRISMA Statement [e7])
Flowchart of the selection process (based on the PRISMA Statement [e7])
eFigure
Flowchart of the selection process (based on the PRISMA Statement [e7])
Noise metrics and their definitions*1
Noise metrics and their definitions*1
eTable 1
Noise metrics and their definitions*1
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e12.Vienneau D, Schindler C, Perez L, Probst-Hensch N, Roosli M: The relationship between transportation noise exposure and ischemic heart disease: a meta-analysis. Environ Res 2015; 138: 372–80 CrossRef MEDLINE
e13.Seidler A, Wagner M, Schubert M, et al.: Aircraft, road and railway traffic noise as risk factors for heart failure and hypertensive heart disease–a case-control study based on secondary data. Int J Hyg Environ Health 2016; 219: 749–58 CrossRef MEDLINE
e14.Babisch W, Houthuijis D, Kwekkeboom J, et al.: HYENA-Hypertension and exposure to noise near airports. A European study on health effects of aircraft noise. INTER-NOISE and NOISE-CON Congress and Conference Proceedings: Institute of Noise Control Engineering 2005; p. 1376–85.
e15.Ulm K: A simple method to calculate the confidence interval of a standardized mortality ratio (SMR). Am J Epidemiol 1990; 131: 373–5 CrossRef
e16.Dzhambov AM, Dimitrova DD: Exposure-response relationship between traffic noise and the risk of stroke: a systematic review with meta-analysis. Arh Hig Rada Toksikol 2016; 67: 136–51 MEDLINE
e17.O‘Donnell MJ, Xavier D, Liu L, et al.: Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet 2010; 376: 112–23 CrossRef
e18.Blomstrand A, Blomstrand C, Ariai N, Bengtsson C, Bjorkelund C: Stroke incidence and association with risk factors in women: a 32-year follow-up of the Prospective Population Study of Women in Gothenburg. BMJ Open 2014; 4: e005173 CrossRef MEDLINE PubMed Central
e19.Seidler A, Steputat A, Drössler S, et al.: Determinanten und Auswirkungen von Informationsüberflutung am Arbeitsplatz. Zentralblatt für Arbeitsmedizin, Arbeitsschutz und Ergonomie 2018; 68: 12–26 CrossRef