Cardiovascular effects of environmental noise: Research in Austria

Peter Lercher, Dick Botteldooren, Ulrich Widmann, Ulrich Uhrner, Ewald Kammeringer
2011 Noise and Health  
Cardiovascular effects of noise rank second in terms of DALYs after annoyance. Although research during the past decade has consolidated the available data base the most recent meta-analysis still shows wide confidence intervals -indicating imprecise information for public health risk assessment. The alpine area of the Tyrol in the Austrian part of the Alps has experienced a massive increase in car and heavy goods traffic (road and rail) during the last 35 years. Over the past 25 years small,
more » ... t 25 years small, middle and large sized epidemiologic health surveys have been conducted -mostly within the framework of environmental health impact assessments. By design these studies have emphasized a contextually driven environmental stress perspective where the of adverse health effects by noise are studied in the broader framework of environmental health, susceptibility and coping. Furthermore, innovative exposure assessment strategies were implemented. This paper reviews existing knowledge from those studies over time, presents exposure-response curves with and without interaction assessment based on standardized re-analyses and discusses it in the light of past and current cardio-vascular noise effects research. The findings support relevant moderation by age, gender and family history in nearly all studies and suggest a strong need for consideration of non-linearity in exposure-response analyses. On the other hand, air pollution did not play a relevant role as moderator for the noise-hypertension or the noiseangina pectoris relationship. Finally, different noise modeling procedures can introduce variation in exposure response curves with substantive consequences for public health risk assessment of noise exposure. 3 Keywords Traffic noise, blood pressure, hypertension, angina pectoris, noise, exposure-response relationship, effect modification effects has not yet been established. Thus, the sampled population experience in the studies may differ in terms of the cumulative time to the effect and reflect only the different power to detect effects apart from the power provided by sample size. This paper aims at sharing and integrating the existing knowledge from the Tyrol studies with a wider audience. Firstly, to make analyses available, which have not yet been published -or if so -not in English. Secondly, to summarize the main results observed over a 25 years time period. Thirdly, to add re-analyses based on the existing datasets which contribute to some of the still pertinent questions in cardio-vascular noise effects research. For this purpose 6 updated models were created to further evaluate interaction effects and to gain deeper insight into the meaning of effect modifiers over time. Methods Area, sample selection and recruitment Both areas of investigation, the Unterinntal and the Wipptal, are located along the most important European North-South-access route for heavy goods over the Brenner Pass. The heavy goods traffic over the Brenner has tripled within the last 25 years and the fraction of goods moved onto the road has substantially increased (up to 2/3). The areas consist of small towns and villages with a mix of industrial, small businesses, tourist and agricultural activities. The primary noise sources are highway and railway traffic. In addition, densely trafficked main roads are of importance. These road link the villages and towns and act as access roads to the highway. Over the years sampling strategies have been refined. In the early studies all people of representative villages of a certain age range (25-65 yrs or 25-75 yrs) were approached by interviewers. In the later studies a basic phone survey (15-20 minutes) was conducted based on a stratified, random sampling strategy. The address base was typically stratified using GIS (Geographic information system) data, based on fixed distances to the major traffic sources (railway, highway, main road), leaving a common "background area" outside major traffic activities and an area with exposure to more than one traffic source ("mixed traffic area"). From these five areas, households were randomly selected and replaced in case of non-participation. Entry selection criteria were age range, sufficient hearing and language proficiency and residency of at least one year at the current address. The participation was higher in the earlier (around 60 %) and lower in the most recent surveys (around 40 %). 2001; Öttl et al. 2005). Each run was weighted due to its meteorological classification and frequency. Thereafter, annual, summer and winter means were calculated by post processing and weighting the numerous dispersion calculations. Noise exposure assessment Within the ALPNAP study the simulation results were compared with 7 air quality stations located in the Inn Valley. The background values within this study were height corrected according to Seinfeld & Pandis (1997) . Calculated NO2 and PM10 values for each of the participant's home were assigned by GIS. Questionnaire information The questionnaire covered socio-demographic data, housing, satisfaction with the environment, general noise annoyance, attitudes toward transportation, interference of activities, coping with noise, occupational exposures, lifestyle, reported sensitivities, health status, prevalent diseases and intake of medications. The telephone interview took about 15-20 minutes to complete. Education was measured in 5 grades (basic, skilled labour, vocational school, A-level, University degree). The last two grades were combined in the category "higher education". Noise sensitivity was asked with a 5-point Likert-type question. "High sensitivity" was defined by the two upper points on the scale (4 and 5). Health status was judged on a standard 5-grade scale (1 to 5). The three poorest grades were combined as "less than good" in the analysis. Active and emotional coping was assessed by a sum score based on 13 items (Botteldooren & Lercher, 2004) . The area characteristic (urban, suburban and rural) was defined by residential pattern and community size. Statistical analysis The statistical analyses were carried out with "R" version 2.10.1 (R Development Core Team, 2009). Exposure-effect curves were calculated with extended logistic or ordinary least square regression methods using restricted cubic spline functions to accommodate for non-linear components in the fit if appropriate (Harrell, 2001) . In the results section the p-values are reported for both the linear ("lin") and non-linear ("nlin") estimates. The non-parametric
doi:10.4103/1463-1741.80160 pmid:21537108 fatcat:gebiawrrcvgj3f25lt3d5sluem