Original article
Myocardial Infarction Risk Due to Aircraft, Road, and Rail Traffic Noise: Results of a Case–Control Study Based on Secondary Data
Results of a case–control study based on secondary data
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Background: Traffic noise can induce stress reactions that have effects on the cardiovascular system. The exposure–risk relationship between aircraft, road, and rail traffic noise and myocardial infarction is currently unknown.
Method: 19 632 patients from the Rhine-Main region of Germany who were diagnosed with myocardial infarction in the years 2006–2010 were compared with 834 734 control subjects. The assignment of persons to groups was performed on the basis of billing and prescription data from three statutory health insurance carriers. The exposure of all insurees to aircraft, road, and rail traffic noise in 2005 was determined from their residence addresses. As estimators of risk, odds ratios (OR) were calculated by logistic regression analysis, with adjustment for age, sex, regional social status variables, and individual social status (if available). The evaluation was performed on the basis of the continuous 24-hour noise level and the categorized noise level (in 5 decibel classes).
Results: The linear model revealed a statistically significant risk increase due to road noise (2.8% per 10 dB rise, 95% confidence interval [1.2; 4.5]) and railroad noise (2.3% per 10 dB rise [0.5; 4.2]), but not airplane noise. Airplane noise levels of 60 dB and above were associated with a higher risk of myocardial infarction (OR 1.42 [0.62; 3.25]). This higher risk is statistically significant if the analysis is restricted to patients who had died of myocardial infarction by 2014/2015 (OR 2.70 [1.08; 6.74]. In this subgroup, the risk estimators for all three types of traffic noise were of comparable magnitude (3.2% to 3.9% per 10 dB rise in noise level).
Conclusion: In this study, a substantial proportion of the population was exposed to traffic noise levels that were associated with an albeit small increase in the risk of myocardial infarction. These findings underscore the importance of effective traffic noise prevention.
Traffic noise can trigger complex psychological and physiological stress reactions. In terms of the effects of traffic noise on the cardiovascular system, activation of the sympathetic nervous system is regarded as the chief mechanism, along with activation of the hypothalamus–pituitary–adrenal axis (1). The World Health Organization (WHO) estimates that in the western part of Europe at least 1 million disability-adjusted life years (DALYs) are lost due to diseases induced by traffic noise (2). “Disability-adjusted life years” means the total number of life years lost due to premature death and life years spent with a disease-related disability; severe disability is weighted more heavily in the calculation than mild disability.
Several studies have investigated the relationship between traffic noise and cardiovascular disease. In a recently published systematic review with meta-analysis, Vienneau and colleagues (3) analyzed three studies of the relationship between aircraft noise and ischemic heart disease including myocardial infarction (4–6). In their results, they calculated a pooled relative risk of 1.06 (95% confidence interval [95% CI]: [1.04–1.08]) per 10 dB Lden aircraft or road traffic noise. “Lden” is the term used to refer to the weighted day–evening–night sound level, where 5 dB is added to the evening noise and 10 dB to the night-time noise. In the systematic review by Vienneau et al. (3), different average sound level indicators were converted to the Lden: for example, 1.5 dB were added to the 24-hour continuous noise level in order to estimate the Lden.
By comparison, very little is known about the effects of rail traffic noise. In a Swedish study, Eriksson and colleagues found a statistically insignificant correlation between rail traffic noise and a self-reported diagnosis of coronary heart disease (7).
At present the exact nature of the exposure–risk relationship between aircraft, road, and rail traffic noise and defined cardiovascular diseases such as myocardial infarction (MI) remains obscure. The case–control study presented here should help filling this gap. The full scientific report is available on the internet (8).
Method
Full details of the methods used are given in eBox 1.
Study area and study population
The study area consisted of the administrative district of Darmstadt, the cities of Mainz and Worms, and the rural districts of Mainz-Bingen and Alzey-Worms (Figure). The study population consisted of all persons over the age of 40 who were insured with one of three large state health insurers in the study area (n = 1 026 670).
Noise exposure in the study area
The engineering company Möhler und Partner Ingenieure AG provided address-specific external noise level data for aircraft, road, and rail traffic noise in the study area for 2005 (9). Traffic noise levels were calculated in accordance with current German regulations (10–12). Further details on traffic noise calculation are given in eBox 2. To aid interpretation of the traffic noise levels, Table 1 gives some examples of sound sources together with their respective noise levels in decibels (dB).
Linking the diagnostic data to the noise data
Participating insurers delivered pseudonymized billing information to the evaluation center in Dresden, and this information was used for the selection of cases with MI and control cases without MI. Linking of the noise data to the address data of the insured persons was carried out by an external trust center. Noise data were successfully linked to address data for 95.5% of the insured persons (n = 907 736).
Myocardial infarction cases (n = 19 632)
In order best assess incident cases and facilitate the exclusion of prevalent cases in this study based on secondary data, patients were included as cases only if they had an acute MI (ICD-10: I21; Table 2) coded during the reporting period of 2006 to 2010—that is, after the measured exposure (13).
Controls (n = 834 734)
The study control subjects consisted of all insured persons without a diagnosis of MI (including a confirmed outpatient diagnosis) during the relevant reporting period of 2005 to 2010, who were at least 40 years old in 2010 or in the year in which their insurance ceased, and who were insured for at least four consecutive 3-month periods (quarterly insurance periods) during the entire reporting period.
Statistical analysis
Logistic regression analysis was used to calculate odds ratios (OR) with 95% confidence intervals as effect estimates of the relative disease risks. In the analysis of categorized noise levels (5 dB categories), people exposed to a 24-hour continuous noise level <40 dB were assigned to the reference category; for aircraft noise, people exposed to a 24-hour continuous noise level <40 dB and a night-time maximum ≥ 50 dB were removed from the reference category and analyzed in a category of their own. In another analysis, only people who had not been exposed to any traffic noise ≥ 40 dB were placed in the reference category. In addition, the continuous noise levels were included as a linear term in the logistic regression analyses.
A subgroup analysis included only patients with MI (diagnosed 2006–2010) who had died by 2014/2015 (irrespective of cause of death). The different modes of traffic noise were always assessed in separate models. In an additional analysis, the three different modes of traffic noise were included together in one logistic regression model.
Confounding factors
Age, sex, and social status were included as confounding variables in the statistical analysis. For all cases and controls, the (city or rural district-related) SGB-II rate (percentage of population under the age of 65 years receiving social welfare payments; SGB, German Social Code, Sozialgesetzbuch) was included in the logistic regression model as an aggregate social status variable. Where the five-character occupation code was available, additional adjustment was made for the individual’s education (available for 29.4% of the study population) and occupational classification according to Blossfeld (14) (available for 32.4%). A subanalysis included only persons whose individual social status (individual educational level and/or occupation) was known.
Results
eTable 1 lists the characteristics of cases and controls. The population-related higher incidence of MI in men is reflected in the fact that 56.5% of the MI patients are male, compared with only 43.4% of the control subjects. Patients with a diagnosis of MI (median age 74 years, interquartile range 65 to 82 years) are, as would be expected, on average older than control subjects (median age 60 years, interquartile range 48 to 72 years). Of the MI patients whose occupation is known, 23% had graduated from university or finished high school, versus 17% of control subjects. Regional SGB-II rates are similar for MI patients and control subjects.
Relationship between aircraft noise and MI
Up to an aircraft noise level of 55 dB, the effect sizes are around 1 (Table 3). The odds ratio (OR) rises to 1.42 (95% CI: [0.62; 3.25]) in the highest noise level category of >60 dB, but does not achieve statistical significance because of small case numbers. For people exposed to a night-time maximum noise level of over 50 dB with a 24-hour continuous noise level of <40 dB, the OR is 1.05 (95% CI: [0.98; 1.11]). If the 24-hour continuous noise level is included in the logistic regression model as a continuous variable, no statistically significant risk estimates are found.
In all night-time periods the effect sizes are around 1 in the noise level categories up to <50 dB. In higher noise level categories, the increases in risk are not significant. If individual hours are analyzed, a statistically significantly increased risk of MI is shown only for the time between 5.00 and 6.00 a.m. with an aircraft noise level between 55 and <60 dB (OR: 1.25; 95% CI: [1.05; 1.48]); the corresponding OR for the time between 6.00 and 7.00 a.m. is of borderline statistical significance (OR: 1.12; 95% CI: [1.00; 1.25]). Analysis of the night-time maximum noise level shows a non-significantly increased risk of MI for noise between 70 and 80 dB (OR: 1.07; 95% CI: [0.96; 1.19]).
Relationship between road traffic noise and MI
Increased risk estimates can be seen starting from a road traffic noise level of 55 dB: the OR reaches statistical significance at a noise level between 60 dB and <65 dB (OR: 1.09; 95% CI: [1.02; 1.16]); the highest OR of 1.13 (95% CI: [1.00; 1.27]) is found with a 24-hour continuous noise level ≥ 70 dB. When the 24-hour continuous noise level is included as a continuous variable in the logistic regression model, a statistically significant risk increase of 2.8% per 10 dB road traffic noise is seen. Looking at the night-time hours between 10 p.m. and 6 a.m., the risk of MI increases when road traffic noise increases above 50 dB (statistically significant in some cases).
Relationship between rail traffic and MI
For rail traffic, in the 50 to <55 dB category there is a statistically borderline significantly raised OR of 1.05 (95% CI: [1.00; 1.10]); in the 55 to <60 dB category the OR is 1.04 (95% CI: [0.97; 1.12]); while in the highest sound level category, 70 dB and upwards, the OR is 1.16 (95% CI: [0.93; 1.46]). When the 24-hour continuous noise level is included as a continuous variable in the logistic regression model, a statistically significant risk increase of 2.3% is seen per 10 dB increase in rail traffic noise. Considering the night-time hours from 10 p.m. to 6 a.m, the ORs begin to rise notably at noise levels of ≥ 60 dB (OR: 1.10; 95% CI: [1.01; 1.20]).
Esimated risks of fatal MI related to traffic noise
In the interval between their first diagnosis (2006 to 2010) and the selection of insurees to be invited by the insurers to take part in a supplementary survey (2014/2015), about 53% of the MI patients died. If only these cases are included in the analysis (eTable 2), a statistically significant OR of 2.70 (95% CI: [1.08; 6.74]) for aircraft noise with a 24-hour continuous noise level of ≥ 60 dB is observed. In all analyses, higher risk estimates are given for all three modes of traffic noise when the case group is restricted to MI patients who died than when all MI patients are included: thus, in the linear model a risk increase of 3.2% (95% CI: [–5.6; 7.1]) is seen per 10 dB increase in the level of aircraft noise, an increase of 3.9% (95% CI: [1.6; 6.3]) per 10 dB increase in the level of road traffic noise, and an increase of 3.8% (95% CI: 1.2; 6.4]) per 10 dB increase in the level of rail traffic noise.
Restricting the reference category to people without noise exposure ≥ 40 dB
If the reference category consists only of people exposed to traffic noise no higher than 40 dB (eTable 3), this leads to some increase in estimated risk in the individual noise categories. In the analyses where traffic noise is included as a continuous variable with the reference category restricted to people without noise exposure ≥ 40 dB, a slight rise in OR per 10 dB rail traffic noise is seen (from 2.3% without this restriction on the reference category to 3.2%). The increase is smaller in relation to road traffic noise (from 2.8% to 3.3%), while for aircraft noise the continuous model shows no change in OR.
Restricting the analysis to people of known individual social status
If only those people whose individual social status is known are included in the analysis, this subanalysis tends to show a rise in estimated risk for all three modes of traffic noise (eTable 4).
Age-stratified analysis
If persons under 65 years of age and those aged 65 and over are regarded separately, no uniform effect is shown: in the younger people, the estimated risks for road traffic noise are slightly higher, while for the older people the estimated risks for aircraft noise are slightly higher (but without statistical significance) (eTable 5).
Simultaneous inclusion of all three modes of traffic noise in the logistic regression model
For each of the three modes of traffic noise, the estimated risks for MI do not change much after adjustment for the other two modes of noise: the linear model continues to show no statistically significant change of risk for aircraft noise, while for road and rail traffic noise statistically significant risk increases of 2.8% and 2.5% per 10 dB are found, respectively.
Discussion
The results of this case–control study based on secondary data, suggest a relationship between exposure to traffic noise and the occurrence of a myocardial infarction. The risk indicators tend to be more pronounced for road and rail traffic noise than for aircraft noise. If the case group is restricted to MI patients who died, the extent of the increase in risk per 10 dB rise in noise level is similar across the three modes of traffic noise.
A limitation of the subanalysis of cases of MI with fatal outcome is that the cause of death could not be identified from the data provided by the insurers. A methodological strength of this case–control study is that it takes account of newly occurring inpatient and outpatient diagnoses of MI with fatal outcome. However, because of the relatively short pre-observation period, it cannot be ruled out that a previous infarction event had occurred at some time in the past, especially for diagnoses in the earlier years. An estimate of the validity of this case–control study is provided in eBox 3.
If only persons whose individual social status is known are included in the analysis (36% of the study population), the estimated risks rise for all three modes of traffic noise. This result suggests that the traffic-noise-related increases in risk cannot be explained by insufficient accounting for social status as a confounding factor. The insurers’ data include no information about lifestyle- and occupation-related risk factors. However, for the case group of patients with heart failure, bias due to unknown or residual confounding was largely ruled out in an additional in-depth survey of about 8500 insured persons (8).
The acoustic input data in this study are of high quality and take account of a variety of different mean and maximum noise level indicators. Even with 24-hour continuous noise levels <40 dB, many people were found who were briefly exposed to night-time aircraft noise events that were much louder than this. These night-time aircraft noise events can also be linked with health effects, particularly those relating to disturbed sleep. The examination of people exposed to maximum night-time noise levels of ≥ 50 dB as a separate exposure group helps to assess this possible etiological pathway.
The findings of our case–control study based on secondary data essentially agree with other published results. However, there are not many other studies investigating different sources of traffic noise within in the same study. In their systematic review of ischemic heart disease, Vienneau and colleagues showed a 6% increase in risk per 10 dB aircraft noise and a 4% increase in risk per 10 dB road traffic noise (start level: 50 dB Lden corresponding to a 24-hour continuous noise level of about 48.5 dB) (3). We calculated risks of slightly below 4% per 10 dB traffic noise (start level: 24-hour continuous noise level of 35 dB) for a fatal MI, and in some cases much lower risks for a newly diagnosed MI. Unlike our study, Vienneau et al. did not find a higher risk of fatal as compared to nonfatal ischemic heart disease (3). However, these authors included the entire group of all patients with ischemic heart disease in their systematic review – whereas MI as investigated in our study is only a subgroup of all ischemic heart disease, although one that carries relatively high mortality.
No direct comparison is possible between our results and those achieved by Greiser and Greiser for the area around the Cologne Bonn Airport: these latter authors included two interaction terms in their evaluation model, both of which included aircraft noise (15, 16). In that sense the main effects of the aircraft noise cannot be directly derived from their study. We did not consider any interaction terms in our models, because additional stratified analyses did not indicate any substantial effect modification due to age.
Summary
Our case–control study allows, for the first time, direct comparison of MI risk estimates for aircraft and road and rail traffic noises on the basis of a very large data set from health insurers. For all three modes of traffic noise investigated, relationships were found with a diagnosis of MI, although the association tends to be more pronounced for road and rail traffic noise than for aircraft noise. It is possible that the continuous noise level is less well suited to represent aircraft-noise-related MI risks than it is to represent health risks related to, in particular, road traffic noise.
It must also be taken into consideration that comparatively few persons in this study were exposed to aircraft noise ≥ 55 dB (1.9% of controls for aircraft noise compared with 26.4% for road traffic noise and 7.1% for rail traffic noise). With aircraft noise, unlike road and rail traffic noise, a continuous noise level above 65 dB did not occur. This means that the marked increase in risk associated with (very) high road and rail traffic noise levels can of course not be seen in relation to aircraft noise, and estimates of the exposure–risk relationship for aircraft noise are altogether more uncertain. The comparably high estimated risks for all three modes of traffic noise in the subgroup of those who died—well above the respective estimated risks in the study population as a whole—suggest that traffic noise may affect not just the onset, but also the course of a MI. In our opinion, this is an area requiring more research.
A large proportion of the population is exposed to levels of traffic noise that our case–control study indicates to be associated with increased—if only slightly increased—risks of MI. For this reason, effective control of traffic noise is a matter of great importance.
Acknowledgments
We are grateful to Dr. Eva Haufe and Prof. Dr. Jochen Schmitt, MPH, for their collaboration in the design of this study. We are especially grateful to Peter Ihle of the PMV Research Group (PMV-Forschungsgruppe) and Ursel Prote of the Leibniz Institute for Prevention Research and Epidemiology (BIPS) and to the participating insurance providers. Warmest thanks also go to Prof. Dr. Wolfgang Hoffmann, MPH, and all other members of the scientific advisory group for their constructive support and commitment.
Decisions of Ethics Committees and Data Protection Officers
The statement by the Ethics Committee of the Faculty of Medicine at the University of Dresden (reference number: EK328102012; 21 February 2013 and 22 April 2014) was taken into account in carrying out this study. The study design was also submitted to the Federal Commissioner for Data Protection and Freedom of Information (reference number: III-320/010#0011; response dated 11 June 2012) and to the Data Protection Officers of the participating federal states of Hessen (reference number: 43.60-we; response dated 13 March 2012; amendment notification 7 February 2014) and Rhineland-Pfalz (reference number: 6.08.22.002; response dated 7 May 2012; amendment notification 4 February 2014). These authorities confirm that this research project complies with data protection regulations.
Financial support
This study was financially supported by the Gemeinnützige Umwelthaus GmbH (Environment & Community Center) in Kelsterbach.
Conflict of interest statement
The authors declare that no conflict of interest exists.
Manuscript received on 3 November 2015, revised version accepted on
9 May 2016.
Corresponding author:
Prof. Dr. med. Andreas Seidler, MPH
Institut und Poliklinik für Arbeits- und Sozialmedizin
Technische Universität Dresden
Medizinische Fakultät
Fetscherstr. 74, 01307 Dresden
ArbSozPH@mailbox.tu-dresden.de
@Supplementary material
eBox, eTable:
www.aerzteblatt-international.de/16m0407
Sekundärdatenbasierte Fallkontrollstudie mit vertiefender Befragung. NORAH (Noise-related annoyance, cognition and health): Verkehrslärmwirkungen im Flughafenumfeld. Endbericht, Band 6. 2015. www.laermstudie.de/fileadmin/files/Laermstudie/Krankheitsrisiken_Wiss_Ergebnisbericht.pdf (last accessed on 5 March 2016).
Laermstudie/Akustik_Wiss_Ergebnisbericht.pdf (last accessed on 5 March 2016).
Justus Liebig University, Gießen: Dr. agr. Pons-Kühnemann
Otto-von-Guericke University Magdeburg: Dr. rer. biol. hum. Swart
Leibniz Institute for Prevention Research and Epidemiology—BIPS GmbH, Bremen:
Prof. Dr. med. Zeeb, MSc
| 1. | Kraus U, Schneider A, Breitner S, et al.: Individual daytime noise exposure during routine activities and heart rate variability in adults: a repeated measures study. Environ Health Perspect 2013; 121: 607–12 MEDLINE PubMed Central |
| 2. | WHO: Burden of disease from environmental noise: quantification of healthy life years lost in Europe. Bonn: WHO: European Centre for Environment and Health 2010. www.euro.who.int/__data/assets/pdf_file/0008/136466/e94888.pdf (last accessed on 12 October 2015). |
| 3. | Vienneau D, Schindler C, Perez L, Probst-Hensch N, Röösli M: The relationship between transportation noise exposure and ischemic heart disease: A meta-analysis. Environ Res 2015; 138: 372–80 CrossRef MEDLINE |
| 4. | Huss A, Spoerri A, Egger M, Röösli M: Aircraft noise, air pollution, and mortality from myocardial infarction. Epidemiology 2010; 21: 829–36 CrossRef MEDLINE |
| 5. | Hansell A, Blangiardo M, Fortunato L, Floud S, et al.: Aircraft noise and cardiovascular disease near Heathrow airport in London: small area study. BMJ 2013; 347: f5432 CrossRef MEDLINE |
| 6. | Correia AW, Peters JL, Levy JI, Melly S, Dominici F: Residential exposure to aircraft noise and hospital admissions for cardiovascular diseases: multi-airport retrospective study. BMJ 2013; 347: f5561 CrossRef MEDLINE PubMed Central |
| 7. | Eriksson C, Nilsson ME, Willers SM, Gidhagen L, Bellander T, Pershagen G: Traffic noise and cardiovascular health in Sweden: the roadside study. Noise and Health 2012; 14: 140–7 CrossRef MEDLINE |
| 8. | Seidler A, Wagner M, Schubert M, Dröge P, Hegewald J: Sekundärdatenbasierte Fallkontrollstudie mit vertiefender Befragung. NORAH (Noise-related annoyance, cognition and health): Verkehrslärmwirkungen im Flughafenumfeld. Endbericht, Band 6. 2015. www.laermstudie.de/fileadmin/files/Laermstudie/Krankheitsrisiken_Wiss_Ergebnisbericht.pdf (last accessed on 5 March 2016). |
| 9. | Möhler U, Liepert M, Mühlbacher M, et al.: Erfassung der Verkehrsgeräuschexposition. Dokumentation zur Berechnung der akustischen Daten in NORAH. NORAH (Noise-related annoyance, cognition and health): Verkehrslärmwirkungen im Flughafenumfeld. Endbericht, Band 2. 2015. www.laermstudie.de/fileadmin/files/ Laermstudie/Akustik_Wiss_Ergebnisbericht.pdf (last accessed on 5 March 2016). |
| 10. | Bundesregierung: Erste Verordnung über die Datenerfassung und das Berechnungsverfahren für die Festsetzung von Lärmschutzbereichen vom 27. Dezember 2008 (1. FlugLSV). Anlage 2: Anleitung zur Berechnung von Lärmschutzbereichen (AzB). Bundesanzeiger 2008; 195a, 2. |
| 11. | Bundesregierung: Verordnung zur Durchführung des Bundes-Immissionsschutzgesetzes (Verordnung über die Lärmkartierung) vom 6. März 2006 (34. BImSchV (2006). Anlage 2: Vorläufige Berechnungsmethode für den Umgebungslärm an Schienenwegen (VBUSch) vom 22. Mai 2006. Bundesanzeiger 2006a; 154a, 6. |
| 12. | Bundesregierung: Verordnung zur Durchführung des Bundes-Immissionsschutzgesetzes (Verordnung über die Lärmkartierung) vom 6. März 2006 (34. BImSchV (2006). Anlage 3: Vorläufige Berechnungsmethode für den Umgebungslärm an Straßen (VBUS) vom 22. Mai 2006. Bundesanzeiger 2006b; 154a, 6. |
| 13. | Schubert I, Ihle P, Köster I: Interne Validierung von Diagnosen in GKV-Routinedaten: Konzeption mit Beispielen und Falldefinition. Das Gesundheitswesen 2010; 72: 316–22 CrossRef MEDLINE |
| 14. | Blossfeld HP. Bildungsexpansion und Berufschancen. Frankfurt: Campus 1985. |
| 15. | Greiser E, Greiser C: Risikofaktor nächtlicher Fluglärm: Abschlussbericht über eine Fall-Kontroll-Studie zu kardiovaskulären und psychischen Erkrankungen im Umfeld des Flughafens Köln-Bonn. Umweltbundesamt 2010a. www.umweltbundesamt.de/sites/default/files/medien/461/publikationen/3774.pdf (last accessed on 12 October 2015). |
| 16. | Greiser E, Greiser C. Risikofaktor nächtlicher Fluglärm: Abschlussbericht über eine Fall-Kontroll-Studie zu kardiovaskulären und psychischen Erkrankungen im Umfeld des Flughafens Köln-Bonn. Anlagenband: Umweltbundesamt 2010b. www.umweltbundesamt.de/sites/default/files/medien/461/publikationen/3775.pdf (last accessed on 12 October 2015). |
