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Background The prevalence of diabetes is on rise worldwide and environmental factors are being increasingly recognized to be involved in this rise. An emerging body of evidence has evaluated the impact of long-term exposure to noise on diabetes mellitus, highlighting the need to synthesize this evidence. Objectives To systematically review and conduct meta-analysis of the available evidence on the association between long-term exposure to transport and occupational noise exposure and diabetes mellitus. Methods Selected databases were searched for available evidence published till September 13th, 2017 following MOOSE guidelines. The quality of articles was assessed using the Newcastle–Ottawa Scale. Random effects meta-analysis was applied to abstract combined estimates for diabetes mellitus per 5 dB increase in noise exposure. We evaluated the heterogeneity applying Cochran's Q test and quantified it using I 2 statistic. Meta-regressions were conducted to identify sources of heterogeneity. Publication bias was evaluated using funnel plot and Egger's test. Results Fifteen studies met our inclusion criteria of which nine including five prospective cohorts, two cross-sectional and two case-control studies with a total number of 444460 adult participants and 17430 diabetes mellitus cases included in meta-analyses. We observed a 6% (95% confidence interval (CI): 3%, 9%) increase in the risk of diabetes mellitus per 5 dB increase in noise exposure regardless of its source. Source-specific analyses were suggestive for stronger associations for air traffic noise (combined odds ratio: 1.17; 95% CI: 1.06, 1.29 per 5 dB increase in exposure) flowed by road traffic noise (combined odds ratio: 1.07; 95% CI: 1.02, 1.12). We observed some indications of publication bias; however the findings were robust after trim and fill test. Meta-regression analyses showed that the adjustment in general, and not specifically related to air pollution, could predict the between-study heterogeneity in reported associations. Conclusions The results indicate an increased risk of diabetes mellitus associated with noise exposure, mainly related to air and road traffic. [ABSTRACT FROM AUTHOR]

Abstract: Background: Increasing evidence suggests that ultrafine particles (UFPs) may contribute to cardiorespiratory morbidity. We examined the relationship between near road UFPs and several traffic and built environment factors to identify predictors that may be used to estimate exposures in population-based studies. Black carbon (BC) was also examined. Methods: Data were collected on up to 6 occasions at 73 sites in Montreal, Canada over 6-week period during summer, 2012. After excluding highly correlated variables, road width, truck ratio (trucks/total traffic), building height, land zoning parameters, and meteorological factors were evaluated. Random-effect models were used to estimate percent changes in UFP and BC concentrations with interquartile changes in each candidate predictor adjusted for meteorological factors. Results: Mean pollutant concentrations varied substantially across sites (UFP range: 1977–94, 798particles/cm3; BC range: 29–9460ng/m3). After adjusting for meteorology, interquartile increases in road width (14%, 95% CI: 0, 30), building height (13%, 95% CI: 5, 22), and truck ratio (13%, 95% CI: 3, 23) were the most important predictors of mean UFP concentrations. Road width (28%, 95% CI: 9, 51) and industrial zoning (18%, 95% CI: 2, 37) were the strongest predictors of maximum UFP concentrations. Industrial zoning (35%, 95% CI: 9, 67) was the strongest predictor of BC. Conclusions: A number of traffic and built environmental factors were identified as important predictors of near road UFP and BC concentrations. Exposure models incorporating these factors may be useful in evaluating the health effects of traffic related air pollution. [Copyright &y& Elsevier]

Traffic-generated air pollution and noise have both been linked to cardiovascular morbidity. Since traffic is a shared source, there is potential for correlated exposures that may lead to confounding in epidemiologic studies. As part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), 2-week NO and NO2 concentrations were measured at up to 105 locations, selected primarily to characterize gradients near major roads, in each of 9 US communities. We measured 5-min A-weighted equivalent continuous sound pressure levels (L eq) and ultrafine particle (UFP) counts at a subset of these NO/NO2 monitoring locations in Chicago, IL (N=69 in December 2006; N=36 in April 2007) and Riverside County, CA (N=46 in April 2007). L eq and UFP were measured during non-“rush hour” periods (10:00–16:00) to maximize comparability between measurements. We evaluated roadway proximity exposure surrogates in relation to the measured levels, estimated noise–air pollution correlation coefficients, and evaluated the impact of regional-scale pollution gradients, wind direction, and roadway proximity on the correlations. Five-minute L eq measurements in December 2006 and April 2007 were highly correlated (r=0.84), and measurements made at different times of day were similar (coefficients of variation: 0.5–13%), indicating that 5-min measurements are representative of long-term L eq. Binary and continuous roadway proximity metrics characterized L eq as well or better than NO or NO2. We found strong regional-scale gradients in NO and NO2, particularly in Chicago, but only weak regional-scale gradients in L eq and UFP. L eq was most consistently correlated with NO, but the correlations were moderate (0.20–0.60). After removing the influence of regional-scale gradients the correlations generally increased (L eq–NO: r=0.49–0.62), and correlations downwind of major roads (L eq–NO: r=0.53–0.74) were consistently higher than those upwind (0.35–0.65). There was not a consistent effect of roadway proximity on the correlations. In conclusion, roadway proximity variables are not unique exposure surrogates in studies of endpoints hypothesized to be related to both air pollution and noise. Moderate correlations between traffic-generated air pollution and noise suggest the possibility of confounding, which might be minimized by considering regional pollution gradients and/or prevailing wind direction(s) in epidemiologic studies. [Copyright &y& Elsevier]

Few studies investigate the impact of air pollution on the leading cause of infant morbidity, acute bronchiolitis. We investigated the influence of PM2.5 and other metrics of traffic-derived air pollution exposure using a matched case–control dataset derived from 1997 to 2003 birth and infant hospitalization records from the Puget Sound Region, Washington State. Mean daily PM2.5 exposure for 7, 30, 60 and lifetime days before case bronchiolitis hospitalization date were derived from community monitors. A regional land use regression model of NO2 was applied to characterize subject''s exposure in the month prior to case hospitalization and lifetime average before hospitalization. Subject''s residential proximity within 150m of highways, major roadways, and truck routes was also assigned. We evaluated 2604 (83%) cases and 23,354 (85%) controls with information allowing adjustment for mother''s education, mother''s smoking during pregnancy, and infant race/ethnicity. Effect estimates derived from conditional logistic regression revealed very modest increased risk and were not statistically significant for any of the exposure metrics in fully adjusted models. Overall, risk estimates were stronger when restricted to bronchiolitis cases attributed to respiratory syncytial virus (RSV) versus unspecified and for longer exposure windows. The adjusted odds ratio (ORadj) and 95% confidence interval per 10mcg/m3 increase in lifetime PM2.5 was 1.14, 0.88–1.46 for RSV bronchiolitis hospitalization. This risk was also elevated for infants who resided within 150m of a highway (ORadj 1.17, 0.95–1.44). This study supports a developing hypothesis that there may be a modest increased risk of bronchiolitis attributable to chronic traffic-derived particulate matter exposure particularly for infants born just before or during peak RSV season. Future studies are needed that can investigate threshold effects and capture larger variability in spatial contrasts among populations of infants. [Copyright &y& Elsevier]