Search

Background: Noise has been related to several cardiovascular diseases (CVDs) such as coronary heart disease and to their risk factors such as hypertension, but associations with stroke remain under-researched, even if CVD likely share similar pathophysiologic mechanisms.Aim: The objective of the study was to examine the association between long-term residential exposure to total environmental noise and stroke incidence in Montreal, Canada.Materials and Methods: We created an open cohort of adults aged ≥45years, free of stroke before entering the cohort for the years 2000 to 2014 with health administrative data. Residential total environmental noise levels were estimated with land use regression (LUR) models. Incident stroke was based on hospital admissions. Cox hazard models with age as the time axis and time-varying exposures were used to estimate associations, which were adjusted for material deprivation, year, nitrogen dioxide, stratified for sex, and indirectly adjusted for smoking.Results: There were 9,072,492 person-years of follow-up with 47% men; 26,741 developed stroke (21,402 ischemic; 4947 hemorrhagic; 392 had both). LUR total noise level acoustic equivalent for 24 hours (LAeq24h) ranged 44 to 79 dBA. The adjusted hazard ratio (HR) for stroke (all types), for a 10-dBA increase in LAeq24h, was 1.06 [95% confidence interval (CI): 1.03-1.09]. The LAeq24h was associated with ischemic (HR per 10 dBA: 1.08; 95% CI: 1.04-1.12) but not hemorrhagic stroke (HR per 10 dBA: 0.97; 95% CI: 0.90-1.04).Conclusion: The results suggest that total environmental noise is associated with incident stroke, which is consistent with studies on transportation noise and other CVD. [ABSTRACT FROM AUTHOR]

We investigated whether hypertension may be a mediator in the pathway linking environmental noise exposure to incident MI and stroke. Separately for MI and stroke, we built two population-based cohorts from linked health administrative data. Participants were residents of Montreal (Canada) between 2000 and 2014, aged 45 years and older who were free of hypertension and MI or stroke at time of entry. MI, stroke and hypertension were ascertained from validated case definitions. Residential long-term environmental noise exposure, expressed as the annual mean level acoustic equivalent 24 h (L Aeq24h), was estimated from a land use regression model. We performed mediation analysis based on the potential outcomes framework. We used a Cox proportional hazards model for the exposure-outcome model and a logistic regression for the exposure-mediator model. In sensitivity analysis we applied a marginal structural approach to estimate the natural direct and indirect effects. Each cohort included approximately 900 000 individuals, with 26 647 incident cases of MI and 16 656 incident cases of stroke. 36% of incident MI and 40% of incident stokes had previously developed hypertension. The estimated total effect per interquartile range increase (from 55.0 to 60.5 dB A) in the annual mean L Aeq24h was 1.073 (95% confidence interval (CI): 1.070–1.077) for both MI for stroke. We found no evidence of exposure-mediator interaction for both outcomes. The relationships between environmental noise and MI and stroke was not mediated by hypertension. This population-based cohort study suggests that the main route by which environmental noise exposure may cause MI or stroke is not through hypertension. • We performed causal mediation analysis based on the potential outcomes framework. • Hypertension does not mediate the effect of environmental noise on MI and stroke. • Acting on hypertension (e.g. with medication) will not prevent MI/stroke from noise. [ABSTRACT FROM AUTHOR]

The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g. , vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e. , mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was −0.03 dB (A) , 2.67 dB (A) , and 3.36 dB (A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available. • Traffic noise surfaces were simulated by a propagation model. • Residuals between traffic noise and total environmental noise were estimated by RF. • Total environmental noise was predicted by the hybrid propagation-RF approach. • Closer-to-reality noise levels was simulated by the hybrid model. [ABSTRACT FROM AUTHOR]