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Abstract

Reviews have suggested that road noise exposure is associated with high blood pressure (hypertension). No reliable exposure-response relationship is as yet available. A meta-analysis was carried out in order to derive a quantitative exposure-response relationship between the exposure to road traffic noise and the prevalence of hypertension, and to gain some insight into the sources of heterogeneity among study results. Twenty-seven observational studies published between 1970 and 2010 in English, German or Dutch, were evaluated. Finally, the results of 24 studies were included into the data aggregation. Road traffic noise was positively and significantly associated with hypertension: Data aggregation revealed an odds ratio (OR) of 1.034 [95% confidence interval (CI) 1.011-1.056] per 5 dB(A) increase of the 16 h average road traffic noise level (LAeq16hr) [range 45-75 dB(A)]. Important sources of heterogeneity were the age and sex of the population under study, the way exposure was ascertained, and the noise reference level used. Also the way noise was treated in the statistical model and the minimum years of residence of the population under study, gave an explanation of the observed heterogeneity. No definite conclusions can be drawn about the threshold value for the relationship between road traffic noise and the prevalence of hypertension. Based on the meta-analysis, a quantitative relationship is derived that can be used for health impact assessment. The results of this meta-analysis are consistent with a slight increase of cardiovascular disease risk in populations exposed to transportation noise.

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Most cited references 38

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Global burden of hypertension: analysis of worldwide data

P. Kearney, M Whelton, K. Reynolds … (2005)

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Annoyance from transportation noise: relationships with exposure metrics DNL and DENL and their confidence intervals.

H M Miedema, C Oudshoorn (2001)

We present a model of the distribution of noise annoyance with the mean varying as a function of the noise exposure. Day-night level (DNL) and day-evening-night level (DENL) were used as noise descriptors. Because the entire annoyance distribution has been modeled, any annoyance measure that summarizes this distribution can be calculated from the model. We fitted the model to data from noise annoyance studies for aircraft, road traffic, and railways separately. Polynomial approximations of relationships implied by the model for the combinations of the following exposure and annoyance measures are presented: DNL or DENL, and percentage "highly annoyed" (cutoff at 72 on a scale of 0-100), percentage "annoyed" (cutoff at 50 on a scale of 0-100), or percentage (at least) "a little annoyed" (cutoff at 28 on a scale of 0-100). These approximations are very good, and they are easier to use for practical calculations than the model itself, because the model involves a normal distribution. Our results are based on the same data set that was used earlier to establish relationships between DNL and percentage highly annoyed. In this paper we provide better estimates of the confidence intervals due to the improved model of the relationship between annoyance and noise exposure. Moreover, relationships using descriptors other than DNL and percentage highly annoyed, which are presented here, have not been established earlier on the basis of a large dataset.

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Meta-analysis of continuous outcomes combining individual patient data and aggregate data.

Richard Riley, Paul Lambert, Jan Staessen … (2008)

Meta-analysis of individual patient data (IPD) is the gold-standard for synthesizing evidence across clinical studies. However, for some studies IPD may not be available and only aggregate data (AD), such as a treatment effect estimate and its standard error, may be obtained. In this situation, methods for combining IPD and AD are important to utilize all the available evidence. In this paper, we develop and assess a range of statistical methods for combining IPD and AD in meta-analysis of continuous outcomes from randomized controlled trials. The methods take either a one-step or a two-step approach. The latter is simple, with IPD reduced to AD so that standard AD meta-analysis techniques can be employed. The one-step approach is more complex but offers a flexible framework to include both patient-level and trial-level parameters. It uses a dummy variable to distinguish IPD trials from AD trials and to constrain which parameters the AD trials estimate. We show that this is important when assessing how patient-level covariates modify treatment effect, as aggregate-level relationships across trials are subject to ecological bias and confounding. We thus develop models to separate within-trial and across-trials treatment-covariate interactions; this ensures that only IPD trials estimate the former, whilst both IPD and AD trials estimate the latter in addition to the pooled treatment effect and any between-study heterogeneity. Extension to multiple correlated outcomes is also considered. Ten IPD trials in hypertension, with blood pressure the continuous outcome of interest, are used to assess the models and identify the benefits of utilizing AD alongside IPD. Copyright (c) 2007 John Wiley & Sons, Ltd.