Environmental noise exposure and population health: a cross-sectional study in the Province of Rome

Abstract

Background The aim of the present study is to analyse the relationship between exposure to environmental noise and population health-perceived status in a cross-sectional setting in Italy. Methods A cross-sectional study was carried out, in a population in the Province of Rome (Castelnuovo di Porto), in order to evaluate the auditory impact of road and railway infrastructures on population quality of life (QOL). Environmental noise due to road and railway traffic was assessed by phonometric samples during 1 week. Health status of participants was assessed using the Italian version of the Short-Form 36 item questionnaire (SF-36). Differences between groups in the SF-36 scales were tested using the Mann-Whitney test. Furthermore, we conducted a multiple linear regression analysis in order to assess the influence on the SF-36 scales (dependent variable) of the explanatory variables age (continuous variable), gender (dichotomous variable with males as reference group), educational level (low level as reference group), hours spent at home (continuous variable) and acoustic exposure level (low level as reference group). Results One hundred and fifty-nine people entered into the study (81 males and 78 females, mean age 41.28 years). We found a significantly lower (worse) Mental Health Scale score in those highly exposed to environmental noise (level over 65 dBA) (inverse association), both at univariate (mean scores: 66.4 vs 68.9) and multivariate analysis (beta=− 4.779; p<0.05). Conclusions The use of the SF-36 questionnaire for measuring the association between physical and social factors in the built environment and mental well-being is very recent. Our study, to our knowledge, is the first attempt in Italy to find a relationship between environmental noise exposure and health status, measured with a standard questionnaire, in a specific population. The results for the Mental Health Scale of SF-36 are particularly interesting, as we found an inverse association with level of environmental noise exposure.

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Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

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Strategic environmental noise mapping: Methodological issues concerning the implementation of the EU Environmental Noise Directive and their policy implications

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