Long-term exposure to transportation noise and air pollution in relation to incident diabetes in the SAPALDIA study

Sex-specific associations of environmental exposures with prevalent diabetes and obesity - Results from the KORA Fit study

Promising evidence suggests a link between environmental factors, particularly air pollution, and diabetes and obesity. However, it is still unclear whether men and women are equally susceptible to environmental exposures. Therefore, we aimed to assess sex-specific long-term effects of environmental exposures on metabolic diseases. We analyzed cross-sectional data from 3,034 participants (53.7% female, aged 53-74 years) from the KORA Fit study (2018/19), a German population-based cohort. Environmental exposures, including annual averages of air pollutants [nitrogen oxides (NO2, NOx), ozone, particulate matter of different diameters (PM10, PMcoarse, PM2.5), PM2.5abs, particle number concentration], air temperature and surrounding greenness, were assessed at participants' residences. We evaluated sex-specific associations of environmental exposures with prevalent diabetes, obesity, body-mass-index (BMI) and waist circumference using logistic or linear regression models with an interaction term for sex, adjusted for age, lifestyle factors and education. Further effect modification, in particular by urbanization, was assessed in sex-stratified analyses. Higher annual averages of air pollution, air temperature and greenness at residence were associated with diabetes prevalence in men (NO2: Odds Ratio (OR) per interquartile range increase in exposure: 1.49 [95% confidence interval (CI): 1.13, 1.95], air temperature: OR: 1.48 [95%-CI: 1.15, 1.90]; greenness: OR: 0.78 [95%-CI: 0.59, 1.01]) but not in women. Conversely, higher levels of air pollution, temperature and lack of greenness were associated with lower obesity prevalence and BMI in women. After including an interaction term for urbanization, only higher greenness was associated with higher BMI in rural women, whereas higher air pollution was associated with higher BMI in urban men. To conclude, we observed sex-specific associations of environmental exposures with metabolic diseases. An additional interaction between environmental exposures and urbanization on obesity suggests a higher susceptibility to air pollution among urban men, and higher susceptibility to greenness among rural women, which needs corroboration in future studies.

Long-term exposure to transportation noise and diabetes mellitus mortality: a national cohort study and updated meta-analysis

BACKGROUND

Long-term exposure to transportation noise is related to cardio-metabolic diseases, with more recent evidence also showing associations with diabetes mellitus (DM) incidence. This study aimed to evaluate the association between transportation noise and DM mortality within the Swiss National Cohort.

METHODS

During 15 years of follow-up (2001-2015; 4.14 million adults), over 72,000 DM deaths were accrued. Source-specific noise was calculated at residential locations, considering moving history. Multi-exposure, time-varying Cox regression was used to derive hazard ratios (HR, and 95%-confidence intervals). Models included road traffic, railway and aircraft noise, air pollution, and individual and area-level covariates including socio-economic position. Analyses included exposure-response modelling, effect modification, and a subset analysis around airports. The main findings were integrated into meta-analyses with published studies on mortality and incidence (separately and combined).

RESULTS

HRs were 1.06 (1.05, 1.07), 1.02 (1.01, 1.03) and 1.01 (0.99, 1.02) per 10 dB day evening-night level (Lden) road traffic, railway and aircraft noise, respectively (adjusted model, including NO2). Splines suggested a threshold for road traffic noise (~ 46 dB Lden, well below the 53 dB Lden WHO guideline level), but not railway noise. Substituting for PM2.5, or including deaths with type 1 DM hardly changed the associations. HRs were higher for males compared to females, and in younger compared to older adults. Focusing only on type 1 DM showed an independent association with road traffic noise. Meta-analysis was only possible for road traffic noise in relation to mortality (1.08 [0.99, 1.18] per 10 dB, n = 4), with the point estimate broadly similar to that for incidence (1.07 [1.05, 1.09] per 10 dB, n = 10). Combining incidence and mortality studies indicated positive associations for each source, strongest for road traffic noise (1.07 [1.05, 1.08], 1.02 [1.01, 1.03], and 1.02 [1.00, 1.03] per 10 dB road traffic [n = 14], railway [n = 5] and aircraft noise [n = 5], respectively).

CONCLUSIONS

This study provides new evidence that transportation noise is associated with diabetes mortality. With the growing evidence and large disease burden, DM should be viewed as an important outcome in the noise and health discussion.

Transportation Noise Pollution and Cardiovascular Health

Epidemiological studies have found that transportation noise increases the risk for cardiovascular morbidity and mortality, with solid evidence for ischemic heart disease, heart failure, and stroke. According to the World Health Organization, at least 1.6 million healthy life years are lost annually from traffic-related noise in Western Europe. Traffic noise at night causes fragmentation and shortening of sleep, elevation of stress hormone levels, and increased oxidative stress in the vasculature and the brain. These factors can promote vascular (endothelial) dysfunction, inflammation, and arterial hypertension, thus elevating cardiovascular risk. The present review focusses on the indirect, nonauditory cardiovascular health effects of noise. We provide an updated overview of epidemiological research on the effects of transportation noise on cardiovascular risk factors and disease, and mechanistic insights based on the latest clinical and experimental studies and propose new risk markers to address noise-induced cardiovascular effects in the general population. We will discuss the potential effects of noise on vascular dysfunction, oxidative stress, and inflammation in humans and animals. We will elaborately explain the underlying pathomechanisms by alterations of gene networks, epigenetic pathways, circadian rhythm, signal transduction along the neuronal-cardiovascular axis, and metabolism. We will describe current and future noise mitigation strategies. Finally, we will conduct an overall evaluation of the status of the current evidence of noise as a significant cardiovascular risk factor.

The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.

A review of air pollution as a driver of cardiovascular disease risk across the diabetes spectrum

The prevalence of diabetes is estimated to reach almost 630 million cases worldwide by the year 2045; of current and projected cases, over 90% are type 2 diabetes. Air pollution exposure has been implicated in the onset and progression of diabetes. Increased exposure to fine particulate matter air pollution (PM2.5) is associated with increases in blood glucose and glycated hemoglobin (HbA1c) across the glycemic spectrum, including normoglycemia, prediabetes, and all forms of diabetes. Air pollution exposure is a driver of cardiovascular disease onset and exacerbation and can increase cardiovascular risk among those with diabetes. In this review, we summarize the literature describing the relationships between air pollution exposure, diabetes and cardiovascular disease, highlighting how airborne pollutants can disrupt glucose homeostasis. We discuss how air pollution and diabetes, via shared mechanisms leading to endothelial dysfunction, drive increased cardiovascular disease risk. We identify portable air cleaners as potentially useful tools to prevent adverse cardiovascular outcomes due to air pollution exposure across the diabetes spectrum, while emphasizing the need for further study in this particular population. Given the enormity of the health and financial impacts of air pollution exposure on patients with diabetes, a greater understanding of the interventions to reduce cardiovascular risk in this population is needed.

Health position paper and redox perspectives - Disease burden by transportation noise

Transportation noise is a ubiquitous urban exposure. In 2018, the World Health Organization concluded that chronic exposure to road traffic noise is a risk factor for ischemic heart disease. In contrast, they concluded that the quality of evidence for a link to other diseases was very low to moderate. Since then, several studies on the impact of noise on various diseases have been published. Also, studies investigating the mechanistic pathways underlying noise-induced health effects are emerging. We review the current evidence regarding effects of noise on health and the related disease-mechanisms. Several high-quality cohort studies consistently found road traffic noise to be associated with a higher risk of ischemic heart disease, heart failure, diabetes, and all-cause mortality. Furthermore, recent studies have indicated that road traffic and railway noise may increase the risk of diseases not commonly investigated in an environmental noise context, including breast cancer, dementia, and tinnitus. The harmful effects of noise are related to activation of a physiological stress response and nighttime sleep disturbance. Oxidative stress and inflammation downstream of stress hormone signaling and dysregulated circadian rhythms are identified as major disease-relevant pathomechanistic drivers. We discuss the role of reactive oxygen species and present results from antioxidant interventions. Lastly, we provide an overview of oxidative stress markers and adverse redox processes reported for noise-exposed animals and humans. This position paper summarizes all available epidemiological, clinical, and preclinical evidence of transportation noise as an important environmental risk factor for public health and discusses its implications on the population level.

Associations between PM2.5 and O3 exposures and new onset type 2 diabetes in regional and national samples in the United States

BACKGROUND

Exposure to particulate matter ≤2.5 μm in diameter (PM2.5) and ozone (O3) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM2.5 and O3 across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment.

METHODS

Data were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003-2016 (REGARDS) and 2008-2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM2.5 and O3.

RESULTS

In two pollutant models, associations of PM2.5, and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m3 of PM2.5. Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type.

CONCLUSIONS

Associations between PM2.5, O3 and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations.

Aircraft and road traffic noise, insulin resistance, and diabetes: The role of neighborhood socioeconomic status in San Diego County

Evidence linking traffic noise to insulin resistance and diabetes is limited and unanswered questions remain regarding the potential effect modification by neighborhood socioeconomic status (nSES). We aimed to assess socioeconomic inequalities in noise exposure, whether road and aircraft noise exposures were associated with insulin resistance or diabetes, and whether nSES modified these relationships. Among the Community of Mine Study in San Diego County, road and aircraft noise exposure at enrollment was calculated based on the static (participant's administrative boundary, and circular buffer around participant homes), and dynamic (mobility data by global positioning system, GPS) spatio-temporal aggregation methods. Associations of noise with insulin resistance (HOMA-IR) or type 2 diabetes (T2DM) were quantified using generalized estimating equation models adjusted for sex, age, ethnicity, individual income, and air pollution (nitrogen dioxide) exposure. Additive interaction between noise and nSES was assessed. Among 573 participants (mean age 58.7 y), participants living in low nSES were exposed to higher levels of aircraft and road noise using noise level at the census tract, circular buffer, or Kernel Density Estimation (KDE) of GPS data. Participants exposed to road noise greater or equal to the median (53 dB(A)) at the census tract and living in low nSES had an increased level of insulin resistance (β = 0.15, 95%CI: -0.04, 0.34) and higher odds of T2DM (Odds Ratio = 2.34, 95%CI: 1.12, 4.90). A positive additive interaction was found as participants living in low nSES had higher odds of T2DM. The impact of noise exposure on insulin resistance and T2DM differs substantially by nSES. Public health benefits of reducing exposure to road or aircraft noise would be larger in individuals living in low nSES.

Too Loud to Handle? Transportation Noise and Cardiovascular Disease

The World Health Organization reported that more than 1.6 million healthy life-years are lost yearly from traffic-related noise in western Europe. In addition, the number of studies on health side effects in response to traffic noise is steadily growing, mainly cardiovascular disease, such as acute and chronic ischemic heart disease, heart failure, arrhythmia, and stroke. Pathophysiologically nighttime noise has been shown to cause sleep disturbances, including too short sleep periods and frequent interruption of sleep leading to an increase in the levels of circulating stress hormones and subsequently to a significant increase in the production of reactive oxygen species (oxidative stress) and inflammation in the vasculature and the brain. The consequence is arterial hypertension and vascular (endothelial) dysfunction, which might increase the risk of cardiovascular disease. With the present review, we give an overview of the "so-called" nonauditory cardiovascular health effects of noise, which have been proposed to be responsible for the future development of cardiovascular disease. We present epidemiological evidence but also evidence provided by translational human and experimental noise studies. Finally, we discuss manoeuvres to mitigate noise effectively.

Long-Term Exposure to Traffic-Related Air Pollution and Diabetes: A Systematic Review and Meta-Analysis

Objectives: We report results of a systematic review on the health effects of long-term traffic-related air pollution (TRAP) and diabetes in the adult population. Methods: An expert Panel appointed by the Health Effects Institute conducted this systematic review. We searched the PubMed and LUDOK databases for epidemiological studies from 1980 to July 2019. TRAP was defined based on a comprehensive protocol. Random-effects meta-analyses were performed. Confidence assessments were based on a modified Office for Health Assessment and Translation (OHAT) approach, complemented with a broader narrative synthesis. We extended our interpretation to include evidence published up to May 2022. Results: We considered 21 studies on diabetes. All meta-analytic estimates indicated higher diabetes risks with higher exposure. Exposure to NO₂ was associated with higher diabetes prevalence (RR 1.09; 95% CI: 1.02; 1.17 per 10 μg/m³), but less pronounced for diabetes incidence (RR 1.04; 95% CI: 0.96; 1.13 per 10 μg/m³). The overall confidence in the evidence was rated moderate, strengthened by the addition of 5 recently published studies. Conclusion: There was moderate evidence for an association of long-term TRAP exposure with diabetes.

Mediation of metabolic syndrome in the association between long-term co-exposure to road traffic noise, air pollution and incident type 2 diabetes

OBJECTIVES

Recent studies have linked exposure to road traffic noise or air pollution with incident type 2 diabetes (T2D), but investigation on their co-exposure was limited and underlying mechanisms remain unclear. We hypothesized that long-term co-exposure to road traffic noise and air pollution increases the risk of incident T2D via the development of metabolic syndrome (MetS).

METHODS

This prospective study included 390,834 participants in UK Biobank. Cumulative risk index (CRI), the health-based weighted levels of multiple exposures, was applied to characterize the co-exposure to 24-hour road traffic noise (L_den), particulate matter with aerodynamic diameter ≤ 2.5 µm (PM_2.5), and nitrogen dioxide (NO₂). L_den was modeled by the Common Noise Assessment Methods in Europe and air pollutant levels were measured by the Land Use Regression model at participants' residential addresses. Incident T2D was ascertained through linkages to inpatient hospital records. MetS was defined by five (central obesity, triglycerides, HDL cholesterol, glucose, and blood pressure) or six factors (C-reactive protein additionally). Cox proportional hazard models were used to assess the association between environmental exposures and incident T2D, and mediation analyses were applied to investigate the role of MetS.

RESULTS

After a median of 10.9 years of follow-up, 13,214 (3.4%) incident T2D cases were ascertained. The exposure to L_den, PM_2.5, and NO₂, as well as their co-exposure, were significantly associated with an elevated risk of incident T2D, with HRs of 1.03 (95%CI: 1.00, 1.05) per 3.5 dB(A) increase in L_den, 1.05 (95%CI: 1.01, 1.10) per 1.3 μg/m³ increase in PM_2.5, 1.07 (95%CI: 1.02, 1.11) per 9.8 μg/m³ increase in NO₂, and 1.06 (95%CI: 1.02, 1.09) per interquartile range increase in CRI. MetS significantly mediated 43.5%- 54.7% of the CRI-T2D relationship.

CONCLUSIONS

Long-term co-exposure to road traffic noise and air pollution is associated with an elevated risk of incident T2D, which may partly be mediated by MetS.

Burden of disease due to transportation noise in the Nordic countries

BACKGROUND

Environmental noise is of increasing concern for public health. Quantification of associated health impacts is important for regulation and preventive strategies.

AIM

To estimate the burden of disease (BoD) due to road traffic and railway noise in four Nordic countries and their capitals, in terms of DALYs (Disability-Adjusted Life Years), using comparable input data across countries.

METHOD

Road traffic and railway noise exposure were obtained from the noise mapping conducted according to the Environmental Noise Directive (END) as well as nationwide noise exposure assessments for Denmark and Norway. Noise annoyance, sleep disturbance and ischaemic heart disease were included as the main health outcomes, using exposure-response functions from the WHO, 2018 systematic review. Additional analyses included stroke and type 2 diabetes. Country-specific DALY rates from the Global Burden of Disease (GBD) study were used as health input data.

RESULTS

Comparable exposure data were not available on a national level for the Nordic countries, only for capital cities. The DALY rates for the capitals ranged from 329 to 485 DALYs/100,000 for road traffic noise and 44 to 146 DALY/100,000 for railway noise. Moreover, the DALY estimates for road traffic noise increased with up to 17% upon inclusion of stroke and diabetes. DALY estimates based on nationwide noise data were 51 and 133% higher than the END-based estimates, for Norway and Denmark, respectively.

CONCLUSION

Further harmonization of noise exposure data is required for between-country comparisons. Moreover, nationwide noise models indicate that DALY estimates based on END considerably underestimate national BoD due to transportation noise. The health-related burden of traffic noise was comparable to that of air pollution, an established risk factor for disease in the GBD framework. Inclusion of environmental noise as a risk factor in the GBD is strongly encouraged.

The association between road traffic noise and type 2 diabetes: a systematic review and meta-analysis of cohort studies

The association between road traffic noise and type 2 diabetes (T2DM) was inconsistent. To address this, we have synthesized available cohort studies about their association by meta-analysis. PubMed, Web of Science, EBSCO, Cochrane Library, EMBASE, and Scopus databases were searched up to July 2022. The Quality-effect model (QE) was used to incorporate the results of included studies. The possibility of publication bias was assessed by the Doi plots and Luis Furuya-Kanamori index. Sensitivity analyses included leave-one-out meta-analysis, subgroup meta-analysis, and meta-regressions. The Recommendations for Assessment, Development, and Evaluation (GRADE) guidelines were conducted to evaluate the overall quality of evidence. Eight cohort studies with 4,989,846 participants and 416,799 diabetes cases were included. Based on the fully adjusted models from 8 cohort studies (10 estimates; Lden range ≈ 15-98.5 dB(A)), we found "high" evidence of RR per 10 dB(A) = 1.07 (1.05, 1.10), high heterogeneity (I2 = 0.91%, p < 0.001), and high publication bias (LKF index = 4.55). Sensitivity analyses showed stable model results, and the GRADE assessment suggested the current overall quality of evidence is high. Comprehensive evidence from cohort studies supports that increasing exposure to road traffic noise may be associated with higher risk of T2DM.

How Do Road Traffic Noise and Residential Greenness Correlate with Noise Annoyance and Long-Term Stress? Protocol and Pilot Study for a Large Field Survey with a Cross-Sectional Design

Urban areas are continuously growing, and densification is a frequent strategy to limit urban expansion. This generally entails a loss of green spaces (GSs) and an increase in noise pollution, which has negative effects on health. Within the research project RESTORE (Restorative potential of green spaces in noise-polluted environments), an extended cross-sectional field study in the city of Zurich, Switzerland, is conducted. The aim is to assess the relationship between noise annoyance and stress (self-perceived and physiological) as well as their association with road traffic noise and GSs. A representative stratified sample of participants from more than 5000 inhabitants will be contacted to complete an online survey. In addition to the self-reported stress identified by the questionnaire, hair cortisol and cortisone probes from a subsample of participants will be obtained to determine physiological stress. Participants are selected according to their dwelling location using a spatial analysis to determine exposure to different road traffic noise levels and access to GSs. Further, characteristics of individuals as well as acoustical and non-acoustical attributes of GSs are accounted for. This paper presents the study protocol and reports the first results of a pilot study to test the feasibility of the protocol.

From good sleep to health and to quality of life – a path analysis of determinants of sleep quality of working adults in Abu Dhabi

Background

Sleep quality has significant impacts on many aspects of quality of life. Therefore, identifying the association of sleep quality with that quality of life domains could lead to deeper insights for social policymakers and professionals to enhance their understanding of the lives of Abu Dhabi working adults. This research focuses on sleep quality among working people in Abu Dhabi. The direct and indirect associations of sleep quality with various quality-of-life domains such as income and housing, physical and mental health, sport and activities, eating habits and obesity, work-life balance, online hours, and social connections are investigated and discussed.

Methods

Data were drawn from 36,515 full-time employees in both public, and private sectors, obtained from the third Abu Dhabi Quality of Life (QoL) survey aimed to cover all community members using online platforms. Informed by international research on sleep quality, preliminary investigation using correlation analysis and simple regression identified many well-being variables deemed necessary for inclusion in the path model. Path analysis was then performed.

Results

The final path model produced excellent fit measures. The significant variables directly associated with sleep quality included sleeping hours, social connection, satisfaction with income, satisfaction with residence, subjective physical and mental health, income satisfaction, satisfaction with the surrounding environment, frequency of eating healthy food, work-life balance, and online time. In addition, most variables recorded an indirect association to sleep quality through subjective health.

Conclusions

The importance of multidimensional linkages between many well-being factors influencing sleep quality and subjective health is underscored.

Dose-response association between transportation noise exposure and type 2 diabetes: A systematic review and meta-analysis of prospective cohort studies

AIMS

To examine the longitudinal association between transportation noise exposure (road traffic, aircraft, and railway noise) and T2D in a meta-analysis.

MATERIALS AND METHODS

We systematically searched PubMed, Embase, Scopus, Cochrane, and Web of Science published up to February 2022. The GRADE approach was used to evaluate the study quality, and the pooled effect estimate was calculated by the fixed-effects model or the random-effects model.

RESULTS

We included 10 prospective studies with a total of 4,994,171 participants and 417,332 T2D cases in the meta-analysis. According to the Navigation guide, 8 studies out of 10 were rated as having a probably high or high risk of bias. For road noise, the pooled relative risk (RR) per 10 dB higher L_den for developing T2D was 1.06 (95% CI:1.03, 1.09) with high heterogeneity (I²  = 90.1%, p < 0.001). Similar associations were also observed in aircraft and railway noise: the pooled RR were separately were: 1.01 (1.00, 1.01) and 1.02 (1.01, 1.03) separately. A 'dose-response' analysis found a similar linear association between road noise exposure and the risk of T2D.

CONCLUSIONS

An overall 6% increase in the risk of T2D per 10 dB increase in road exposure was observed. Further studies are needed to confirm our findings, especially for aircraft and railway noise, and to identify the mechanisms involved.

Long-term exposure to transportation noise and risk of type 2 diabetes: A cohort study

BACKGROUND

Some studies have found transportation noise to be associated with higher diabetes risk. This includes studies based on millions of participants, relying entirely on register-based confounder adjustment, which raises concern about residual lifestyle confounding. We aimed to investigate associations between noise and type 2 diabetes (T2D), including investigation of effects of increasing confounder adjustment for register-data and lifestyle.

METHODS

In a cohort of 286,151 participants randomly selected across Denmark in 2010-2013 and followed up until 2017, we identified 7574 incident T2D cases. Based on residential address-history for all participants linked with exposure assessment of high spatial resolution, we calculated 10-year time-weighted mean road and railway noise at the most (LdenMax) and least (LdenMin) exposed façades and air pollution (PM_2.5). We used Cox models to calculate hazard ratios (HR) with increasing adjustment for individual- and area-level register-based sociodemographic covariates, self-reported lifestyle and air pollution.

RESULTS

We found that a 10 dB increase in 10-year mean road LdenMin was associated with HRs (95% CI) of 1.06 (1.02-1.10) after adjustment for age, sex and year, 1.08 (1.04-1.13) after further adjustment for register-based sociodemographic covariates, 1.07 (1.03-1.12) after further lifestyle adjustment (e.g. smoking, diet and alcohol) and 1.06 (1.02-1.11) after further PM_2.5 adjustment. For road LdenMax, the corresponding HRs were 1.07 (1.04-1.10), 1.05 (1.02-1.08), 1.04 (1.01-1.07) and 1.03 (1.00-1.06). Railway noise was associated with HRs of 1.04 (0.98-1.11) for LdenMax and 1.02 (0.92-1.12) for LdenMin after adjustment for sociodemographic and lifestyle covariates and PM_2.5.

CONCLUSIONS

Long-term exposure to road traffic noise was associated with T2D, which together with previous literature indicates that T2D should be considered when calculating health impacts of noise. After sociodemographic adjustment, further lifestyle adjustment only changed HRs slightly, suggesting that large register-based studies with adjustment for key sociodemographic covariates can produce reliable results.

Investigating the effect of workplace noise exposure on cardiovascular disease risk factors in a power plant industry: A case-control study

BACKGROUND

Occupational noise exposure is a significant health problem. In addition to hearing impairments, noise as a stressor may cause cardiovascular problems.

OBJECTIVE

This study aimed to investigate the effect of exposure to workplace noise on cardiovascular disease risk factors.

METHODS

This case-control study was conducted in 2021 in a power plant in Iran. In this study, the health status of 406 employees in both exposed (n = 203) and non-exposed (n = 203) to noise groups was examined for cardiovascular disease risk factors. Also, the trend of changes in the studied variables from 2012 to 2020 in exposed employees was examined. Data were collected from participants' annual physical examinations and occupational noise exposure measurements. To measure the noise in the present study, the KIMO-DB300 noise level meter was used. Data were analyzed in SPSS-26 software.

RESULTS

The results revealed that mean fasting blood sugar (FBS), triglyceride, liver enzyme (SGOT), blood pressure, and body mass index were significantly different in the two groups (p-value<0.05). There was no significant difference in the mean of creatinine, cholesterol, and liver enzyme (SGPT) between the two groups (p-value>0.05). In the exposed group, the mean of all studied variables except diastolic blood pressure was statistically different during the study years (p-value<0.05).

CONCLUSION

This study demonstrates that exposure to noise above the permissible level can affect the cardiovascular disease risk factors, so it is recommended to apply engineering and management measures like using Hearing Conservation Programme (HCP) to reduce the risk of these diseases with periodically assessing the health status of employees and timely diagnosis.

The impact of long-term exposure to PM10, SO2, O3, NO2, and CO on incident dysglycemia: a population-based cohort study

To examine the associations between long-term exposure to five major air pollutants including SO₂, PM₁₀, O₃, NO₂, and CO_, and incident dysglycemia, impaired fasting glucose (IFG), and diabetes, separately. A total of 4254 (1720 men) normoglycemic individuals aged 20-69 years at baseline were followed from 2001 to 2018 every 3 years. To measure the long-term hazards of air pollutants for incident dysglycemia, the Weibull proportional hazards models for every 10-unit increment adjusted for diabetes risk factors were fitted. The air pollutants were put in the models in the form of averages of 1-, 2-, and 3-year periods. During a median follow-up of 12.2 years, we observed 1780 dysglycemia events. In contrast to NO₂, the increase in SO₂, O₃, and PM₁₀ levels were significantly associated with a higher risk of dysglycemia and IFG in all time spans excluding PM₁₀ at 2 years. The largest hazard ratios for incident dysglycemia and IFG were attributable to PM₁₀ in 3 years (2.20 (95% CI 1.67, 2.89) and 2.08 (1.55, 2.80), respectively). Moreover, exposure to all the pollutants except NO₂ in 1 year (0.89 (0.80, 0.98)) had no significant associations with incident diabetes. There was a signal that younger (< 45 years) and never-smoker individuals were more predispose to dysglycemic effects of air pollution (all P for interactions > 0.03). Our findings suggested that long-term exposure to air pollution increased incident dysglycemia risk, the effect which was mainly attributable to IFG status.

Air pollution, road traffic noise and lack of greenness and risk of type 2 diabetes: A multi-exposure prospective study covering Denmark

OBJECTIVE

Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective.

METHODS

We estimated 5-year residential mean exposure to fine particles (PM_2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO₂) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates.

RESULTS

In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO₂ remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO₂, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149).

CONCLUSIONS

Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

Environmental noise-induced cardiovascular, metabolic and mental health disorders: a brief review

BACKGROUND

Environmental noise is a pervasive pollutant that is one of the greatest environmental threats to mental, physiological and psychological well-being and has a significant global health burden associated with it. Many epidemiological studies indicate long-term relationship of noise pollution with wide range of metabolic, cardio-vascular and respiratory disorders and diseases.

OBJECTIVE

The goal of this study was to thoroughly analyse available literature on public health implications and various underlying biological mechanisms associated with ambient noise exposure, taking into account both objective and subjective measures of noise exposure.

METHODS

A search of literature for review on environmental noise and associated cardiovascular, mental health and metabolic implications on human health was done using Web of Science, Google Scholar and PubMed databases.

DISCUSSION

Experimental studies indicate that noise exposure leads to endocrine effects, increased incidence of diabetes, impairment of cognitive performance, sleep disturbance and annoyance. Epidemiological evidence indicates that high levels of noise, particularly at night, may cause arterial hypertension and endothelial dysfunction due to higher level of stress hormones and oxidative stress. An increased incidence of cardio-vascular diseases like myocardial infarction, heart rate, ischemic heart disease, stroke and heart failure is associated with noise-induced mental stress. Furthermore, psychological and mental health issues like anxiety and depression are also related with exposure to noise pollution.

CONCLUSION

This article summarises a comprehensive and systematic knowledge established in recent noise research with the spotlight on cardiovascular, metabolic and mental health disorders of environmental noise, providing unique understanding into underlying mechanisms.

Health effects of exposure to sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide between 1980 and 2019: A systematic review and meta-analysis

The burden of disease attributed to the indoor exposure to sulfur dioxide (SO₂ ), nitrogen dioxide (NO₂ ), ozone (O₃ ), and carbon monoxide (CO) is not clear, and the quantitative concentration-response relationship is a prerequisite. This is a systematic review to summarize the quantitative concentration-response relationships by screening and analyzing the polled effects of population-based epidemiological studies. After collecting literature published between 1980 and 2019, a total of 19 health outcomes in 101 studies with 182 health risk estimates were recruited. By meta-analysis, the leave-one-out sensitivity analysis and Egger's test for publication bias, the robust and reliable effects were found for SO₂ (per 10 μg/m³ ) with chronic obstructive pulmonary diseases (COPD) (pooled relative risks [RRs] 1.016, 95% CI: 1.012-1.021) and cardiovascular diseases (CVD) (RR 1.012, 95%CI: 007-1.018), respectively. NO₂ (per 10 μg/m³ ) had the pooled RRs for childhood asthma, preterm birth, lung cancer, diabetes, and COPD by 1.134 (1.084-1.186), 1.079 (1.007-1.157), 1.055 (1.010-1.101), 1.019 (1.009-1.029), and 1.016 (1.012-1.120), respectively. CO (per 1 mg/m³ ) was significantly associated with Parkinson's disease (RR 1.574, 95% CI: 1.069-2.317) and CVD (RR 1.024, 95% CI: 1.011-1.038). No robust effects were observed for O₃ . This study provided evidence and basis for further estimation of the health burden attributable to the four gaseous pollutants.

Road Traffic Noise, Obesity, and the Risk of Incident Type 2 Diabetes: A Cohort Study in UK Biobank

Objectives: To assess the association of road traffic noise exposure with Type 2 Diabetes (T2D) risk, and to explore the potential moderation effect of obesity. Methods: A total of 305,969 participants from the UK Biobank Cohort - an open access cohort of 500,000 participants recruited in the United Kingdom (UK) between 2006 and 2010 - were included in the study. A Cox proportional hazard model was fitted to assess the association between road traffic noise exposure and T2D. Results: A total of 19,303 participants were diagnosed with T2D during the 11.9-year median follow-up period. For every 10 dB increase in road traffic noise, there was a 4% increase in T2D risk (HR = 1.04, 95%CI: 1.01, 1.07). Besides, a significant positive interaction was observed between obesity and road traffic noise (P interaction <0.001) for the risk of T2D. The association of road traffic noise with T2D was stronger in overweight and obese participants (HR = 1.04, 95% CI: 1.01-1.08), but not significant among lean ones (HR = 0.96, 95% CI: 0.86-1.07). Conclusion: Our study observed a longitudinal association of road traffic noise exposure with T2D risk, which was stronger among overweight and obese individuals than the lean ones.

Railway noise and diabetes among residents living close to the railways in Västra Götaland, Sweden: Cross-sectional mediation analysis on obesity indicators

Railway noise is expected to increase in Europe and Sweden as well, following recommendations for a more sustainable transportation mode. This forecasted increase might lead to higher level of noise exposure, higher population exposure and potentially increased night-time exposure. Evidence supports an effect of transportation noise on several health outcomes, including metabolic conditions such as diabetes. However, few studies were directed to railway noise; present studies on railway noise and diabetes so far show ambiguous results while some studies report an association between railway noise and obesity. The aim of this study is to investigate the relationship between railway noise and diabetes prevalence and to assess whether obesity might be a mediator in this association. The study population (N = 5381) was randomly selected from residents living within 1 km of a trafficked railway in Västra Götaland, Sweden. Survey data was combined with modelled exposures and health register data (ICD10 codes for diabetes). The study uses a cross sectional design, logistic regression analysis and a counterfactual mediation analysis. We found an increase in the prevalence of diabetes associated with the exposure to railway noise: OR = 1.33 per 10 dB increase L_den (95% CI 1.09-1.63) accounting for sociodemographic and lifestyle factors. BMI and waist circumference partially mediate the association between railway noise and diabetes. This is the first study to report an association between railway noise and diabetes in Sweden. BMI and waist circumference seem to be a potential mediators in this association. Still, future studies should further explore the mechanisms from noise to diabetes, considering different pathways in relation to obesity but also exploring other potential mediators.

Cerebral consequences of environmental noise exposure

The importance of noise exposure as a major environmental determinant of public health is being increasingly recognized. While in recent years a large body evidence has emerged linking environmental noise exposure mainly to cardiovascular disease, much less is known concerning the adverse health effects of noise on the brain and associated neuropsychiatric outcomes. Despite being a relatively new area of investigation, indeed, mounting research and conclusive evidence demonstrate that exposure to noise, primarily from traffic sources, may affect the central nervous system and brain, thereby contributing to an increased risk of neuropsychiatric disorders such as stroke, dementia and cognitive decline, neurodevelopmental disorders, depression, and anxiety disorder. On a mechanistic level, a significant number of studies suggest the involvement of reactive oxygen species/oxidative stress and inflammatory pathways, among others, to fundamentally drive the adverse brain health effects of noise exposure. This in-depth review on the cerebral consequences of environmental noise exposure aims to contribute to the associated research needs by evaluating current findings from human and animal studies. From a public health perspective, these findings may also help to reinforce efforts promoting adequate mitigation strategies and preventive measures to lower the societal consequences of unhealthy environments.

Evaluation of associations between estimates of particulate matter exposure and new onset type 2 diabetes in the REGARDS cohort

BACKGROUND

Studies of PM_2.5 and type 2 diabetes employ differing methods for exposure assignment, which could explain inconsistencies in this growing literature. We hypothesized associations between PM_2.5 and new onset type 2 diabetes would differ by PM_2.5 exposure data source, duration, and community type.

METHODS

We identified participants of the US-based REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort who were free of diabetes at baseline (2003-2007); were geocoded at their residence; and had follow-up diabetes information. We assigned PM_2.5 exposure estimates to participants for periods of 1 year prior to baseline using three data sources, and 2 years prior to baseline for two of these data sources. We evaluated adjusted odds of new onset diabetes per 5 µg/m³ increases in PM_2.5 using generalized estimating equations with a binomial distribution and logit link, stratified by community type.

RESULTS

Among 11,208 participants, 1,409 (12.6%) had diabetes at follow-up. We observed no associations between PM_2.5 and diabetes in higher and lower density urban communities, but within suburban/small town and rural communities, increases of 5 µg/m³ PM_2.5 for 2 years (Downscaler model) were associated with diabetes (OR [95% CI] = 1.65 [1.09, 2.51], 1.56 [1.03, 2.36], respectively). Associations were consistent in direction and magnitude for all three PM_2.5 sources evaluated.

SIGNIFICANCE

1- and 2-year durations of PM_2.5 exposure estimates were associated with higher odds of incident diabetes in suburban/small town and rural communities, regardless of exposure data source. Associations within urban communities might be obfuscated by place-based confounding.

Long-Term Exposure to Transportation Noise and Risk for Type 2 Diabetes in a Nationwide Cohort Study from Denmark

BACKGROUND

Epidemiologic studies have linked transportation noise to increased morbidity and mortality, particularly for cardiovascular outcomes. However, studies investigating metabolic outcomes such as diabetes are limited and have focused only on noise exposures estimated for the loudest residential façade.

OBJECTIVES

We aimed to examine the influence of long-term residential exposure to transportation noise at the loudest and quietest residential façades and the risk for type 2 diabetes.

METHODS

Road traffic and railway noise exposures (Lden) at the most and least exposed façades were estimated for all dwellings in Denmark during 1990-2017. Aircraft noise was estimated in 5-dB categories. Ten-year time-weighted mean noise exposures were estimated for 3.56 million individuals ≥35 years of age. From 2000 to 2017, 233,912 incident cases of type 2 diabetes were identified using hospital and prescription registries, with a mean follow-up of 12.9 y. We used Cox proportional hazards models adjusting for individual- and area-level covariates and long-term residential air pollution. The population-attributable fraction (PAF) was also computed.

RESULTS

Hazard ratios (HRs) and 95% confidence intervals (CIs) for type 2 diabetes in association with 10-dB increases in 10-y mean road traffic noise at the most and least exposed façades, respectively, were 1.05 (95% CI: 1.04, 1.05) and 1.09 (95% CI: 1.08, 1.10). Following subsequent adjustment for fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter] (10-y mean), the HRs (CIs) were 1.03 (95% CI: 1.03, 1.04) and 1.08 (95% CI: 1.07, 1.09), respectively. For railway noise, the HRs per 10-dB increase in 10-y mean exposure were 1.03 (95% CI: 1.02, 1.04) and 1.02 (95% CI: 1.01, 1.04) for the most and least exposed façades, respectively. Categorical models supported a linear exposure-outcome relationship for road traffic noise and, to a lesser extent, for railway noise. Aircraft noise >45 dB was associated with a 1-4% higher likelihood of type 2 diabetes compared with those who were unexposed. We found road traffic and railway noise associated with a PAF of 8.5% and 1.4%, respectively, of the diabetes cases.

DISCUSSION

Long-term exposure to road, railway, and possibly aircraft traffic noise was associated with an increased risk of type 2 diabetes in a nationwide cohort of Danish adults. Our findings suggest that diabetes should be included when estimating the burden of disease due to transportation noise. https://doi.org/10.1289/EHP9146.

Poor sleep quality is associated with worse self-rated health in long sleep duration but not short sleep duration

Unhealthy sleep duration, either short or long, is associated with worse health and central subjective dimensions of sleep and health such as fatigue. It has been argued that the link between sleep duration and health may depend on the quality of the slept hours, and on its functional impact (ie, fatigue). The present study therefore assessed whether the relationship between last night's sleep duration and general self-rated health (SRH) differs as a function of sleep quality, and secondly, whether current fatigue and sleep quality are factors linking sleep duration and SRH. The present cross-sectional dataset involved 1304 individuals (57% female, M_age = 28.8, range 18-79). Participants completed surveys for general SRH, previous night's sleep duration and sleep quality, and current fatigue. Results showed the expected inverted U-shaped (ie, quadratic) relation between last night's sleep duration and SRH and a linear relation between last night's sleep quality and SRH. However, long sleep duration was only associated with poorer SRH in individuals who also reported poor sleep quality. Further, the quadratic relationship between sleep duration and SRH was partially mediated by fatigue and sleep quality. The results of this multi-study analysis suggest that SRH is particularly poor in those who slept both long and with poor quality the night before, while good sleep quality may protect those with a long sleep duration from poor SRH. Thus, last night's long sleep does not seem to be associated with poor subjective health unless it is coupled with poor sleep quality. Furthermore, fatigue and sleep quality are potential pathways linking short and long sleep duration with SRH. Different dimensions of sleep interact in their association with health, and future research will benefit from an integrative approach.

Urban Noise Exposure and Cardiometabolic Diseases: An Exploratory Cross-Sectional Study in Lisbon

Introduction: Urban noise pollution has been associated with an increased risk of developing metabolic syndrome. Nevertheless, existing observational studies relating to noise exposure and metabolic syndrome are based on non-generalizable cohorts. Lisbon remains a noisy city where this association has not been evaluated, and for this reason, we studied the relationship between exposure to urban noise and the prevalence of type 2 diabetes mellitus, obesity, and hypertension. Methods: Diurnal, evening and nocturnal noise emission levels were obtained for each street in the city from the Lisbon noise map. After allocation of all roads to the respective parish of Lisbon, the noise emission for each parish was averaged for each day period. The number of adult patients with type 2 diabetes mellitus, obesity and hypertension in 2014, 2015 and 2016 in each parish of Lisbon was obtained from the Regional Health Administration of Lisbon and Tagus Valley. Prevalence as a percentage of the population was determined using the number of residents in each parish determined in the 2011 population census. Spearman’s non-parametric correlation coefficient was used due to the non-normal distribution of the variables, at the 5% significance level (α = 0.05). Results: No correlations were found between daytime, afternoon or night-time noise exposure and the prevalence of type 2 diabetes mellitus, obesity or hypertension, although correlations were found between the cardiometabolic variables. Nevertheless, noise levels in Lisbon were above the legally established limit and the World Health Organization guidelines for environmental noise exposure in the European region. Conclusion: Our results do not agree with previous studies and should be faced as preliminary due to a strong biological plausibility for an association between noise exposure and cardiometabolic diseases and to encourage further studies, with longitudinal cohorts.

The Swiss Tropical and Public Health Institute: Past, present and future

Compared internationally, the history of the Swiss Tropical and Public Health Institute (Swiss TPH) is unusual. Founded in 1944, at a time of utmost isolation, it was a response to specific needs of the government of Switzerland during the Second World War. In 1943, the Swiss Federal Council approached universities in Switzerland and asked them to submit project proposal that had the potential to mitigate possible post-war unemployment and threatening economic isolation. Members of the University of Basel proposed to establish a Swiss Tropical Institute (today: Swiss TPH). With its harbour at the River Rhine, Basel was an important international transport hub. The city was and still is the headquarters of important pharmaceutical companies, such as Novartis Pharma AG and F. Hoffmann-La Roche AG, which were looking for new markets overseas. Last but not least, scientific expeditions to Africa were rather common in the 19^th and the beginning of the 20^th century for members of Basel's bourgeoisie. Initially, Swiss TPH focused primarily on basic research into diseases of poverty, but over the years it has developed into an important player in public, international and global health. This article sees the development of the institute as a reflection of the visions of its directors from the founder Professor Rudolf Geigy to Professor Jürg Utzinger, who is the current Swiss TPH director. It includes interviews with the four latest of them, discussing their experiences and attempts to adapt the institute to an ever changing global environment. From these lessons learnt we hope to gain insights that could be relevant for today's leaders of scientific institutes; foster public-private partnerships and contribute to solve some of the most pressing global health challenges.

Residential exposure to transportation noise in Denmark and incidence of dementia: national cohort study

OBJECTIVE

To investigate the association between long term residential exposure to road traffic and railway noise and risk of incident dementia.

DESIGN

Nationwide prospective register based cohort study.

SETTING

Denmark.

PARTICIPANTS

1 938 994 adults aged ≥60 years living in Denmark between 1 January 2004 and 31 December 2017.

MAIN OUTCOME MEASURES

Incident cases of all cause dementia and dementia subtypes (Alzheimer's disease, vascular dementia, and Parkinson's disease related dementia), identified from national hospital and prescription registries.

RESULTS

The study population included 103 500 participants with incident dementia, and of those, 31 219 received a diagnosis of Alzheimer's disease, 8664 of vascular dementia, and 2192 of Parkinson's disease related dementia. Using Cox regression models, 10 year mean exposure to road traffic and railway noise at the most (L_denmax) and least (L_denmin) exposed façades of buildings were associated with a higher risk of all cause dementia. These associations showed a general pattern of higher hazard ratios with higher noise exposure, but with a levelling off or even small declines in risk at higher noise levels. In subtype analyses, both road traffic noise and railway noise were associated with a higher risk of Alzheimer's disease, with hazard ratios of 1.16 (95% confidence interval 1.11 to 1.22) for road L_denmax ≥65 dB compared with <45 dB, 1.27 (1.22 to 1.34) for road L_denmin ≥55 dB compared with <40 dB, 1.16 (1.10 to 1.23) for railway L_denmax ≥60 dB compared with <40 dB, and 1.24 (1.17 to 1.30) for railway L_denmin ≥50 dB compared with <40 dB. Road traffic, but not railway, noise was associated with an increased risk of vascular dementia. Results indicated associations between road traffic L_denmin and Parkinson's disease related dementia.

CONCLUSIONS

This nationwide cohort study found transportation noise to be associated with a higher risk of all cause dementia and dementia subtypes, especially Alzheimer's disease.

Mortality and Morbidity Effects of Long-Term Exposure to Low-Level PM2.5, BC, NO2, and O3: An Analysis of European Cohorts in the ELAPSE Project

INTRODUCTION

Epidemiological cohort studies have consistently found associations between long-term exposure to outdoor air pollution and a range of morbidity and mortality endpoints. Recent evaluations by the World Health Organization and the Global Burden of Disease study have suggested that these associations may be nonlinear and may persist at very low concentrations. Studies conducted in North America in particular have suggested that associations with mortality persisted at concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) well below current air quality standards and guidelines. The uncertainty about the shape of the concentration-response function at the low end of the concentration distribution, related to the scarcity of observations in the lowest range, was the basis of the current project. Previous studies have focused on PM2.5, but increasingly associations with nitrogen dioxide (NO2) are being reported, particularly in studies that accounted for the fine spatial scale variation of NO2. Very few studies have evaluated the effects of long-term exposure to low concentrations of ozone (O3). Health effects of black carbon (BC), representing primary combustion particles, have not been studied in most large cohort studies of PM2.5. Cohort studies assessing health effects of particle composition, including elements from nontailpipe traffic emissions (iron, copper, and zinc) and secondary aerosol (sulfur) have been few in number and reported inconsistent results. The overall objective of our study was to investigate the shape of the relationship between long-term exposure to four pollutants (PM2.5, NO2, BC, and O3) and four broad health effect categories using a number of different methods to characterize the concentration-response function (i.e., linear, nonlinear, or threshold). The four health effect categories were (1) natural- and cause-specific mortality including cardiovascular and nonmalignant as well as malignant respiratory and diabetes mortality; and morbidity measured as (2) coronary and cerebrovascular events; (3) lung cancer incidence; and (4) asthma and chronic obstructive pulmonary disease (COPD) incidence. We additionally assessed health effects of PM2.5 composition, specifically the copper, iron, zinc, and sulfur content of PM2,5.

METHODS

We focused on analyses of health effects of air pollutants at low concentrations, defined as less than current European Union (EU) Limit Values, U.S. Environmental Protection Agency (U.S. EPA), National Ambient Air Quality Standards (NAAQS), and/or World Health Organization (WHO) Air Quality Guideline values for PM2.5, NO2, and O3. We address the health effects at low air pollution levels by performing new analyses within selected cohorts of the ESCAPE study (European Study of Cohorts for Air Pollution Effects; Beelen et al. 2014a) and within seven very large European administrative cohorts. By combining well-characterized ESCAPE cohorts and large administrative cohorts in one study the strengths and weaknesses of each approach can be addressed. The large administrative cohorts are more representative of national or citywide populations, have higher statistical power, and can efficiently control for area-level confounders, but have fewer possibilities to control for individual-level confounders. The ESCAPE cohorts have detailed information on individual confounders, as well as country-specific information on area-level confounding. The data from the seven included ESCAPE cohorts and one additional non-ESCAPE cohort have been pooled and analyzed centrally. More than 300,000 adults were included in the pooled cohort from existing cohorts in Sweden, Denmark, Germany, the Netherlands, Austria, France, and Italy. Data from the administrative cohorts have been analyzed locally, without transfer to a central database. Privacy regulations prevented transfer of data from administrative cohorts to a central database. More than 28 million adults were included from national administrative cohorts in Belgium, Denmark, England, the Netherlands, Norway, and Switzerland as well as an administrative cohort in Rome, Italy. We developed central exposure assessment using Europewide hybrid land use regression (LUR) models, which incorporated European routine monitoring data for PM2.5, NO2, and O3, and ESCAPE monitoring data for BC and PM2.5 composition, land use, and traffic data supplemented with satellite observations and chemical transport model estimates. For all pollutants, we assessed exposure at a fine spatial scale, 100 × 100 m grids. These models have been applied to individual addresses of all cohorts including the administrative cohorts. In sensitivity analyses, we applied the PM2.5 models developed within the companion HEI-funded Canadian MAPLE study (Brauer et al. 2019) and O3 exposures on a larger spatial scale for comparison with previous studies. Identification of outcomes included linkage with mortality, cancer incidence, hospital discharge registries, and physician-based adjudication of cases. We analyzed natural-cause, cardiovascular, ischemic heart disease, stroke, diabetes, cardiometabolic, respiratory, and COPD mortality. We also analyzed lung cancer incidence, incidence of coronary and cerebrovascular events, and incidence of asthma and COPD (pooled cohort only). We applied the Cox proportional hazard model with increasing control for individual- and area-level covariates to analyze the associations between air pollution and mortality and/or morbidity for both the pooled cohort and the individual administrative cohorts. Age was used as the timescale because of evidence that this results in better adjustment for potential confounding by age. Censoring occurred at the time of the event of interest, death from other causes, emigration, loss to follow-up for other reasons, or at the end of follow-up, whichever came first. A priori we specified three confounder models, following the modeling methods of the ESCAPE study. Model 1 included only age (time axis), sex (as strata), and calendar year of enrollment. Model 2 added individual-level variables that were consistently available in the cohorts contributing to the pooled cohort or all variables available in the administrative cohorts, respectively. Model 3 further added area-level socioeconomic status (SES) variables. A priori model 3 was selected as the main model. All analyses in the pooled cohort were stratified by subcohort. All analyses in the administrative cohorts accounted for clustering of the data in neighborhoods by adjusting the variance of the effect estimates. The main exposure variable we analyzed was derived from the Europewide hybrid models based on 2010 monitoring data. Sensitivity analyses were conducted using earlier time periods, time-varying exposure analyses, local exposure models, and the PM2.5 models from the Canadian MAPLE project. We first specified linear single-pollutant models. Two-pollutant models were specified for all combinations of the four main pollutants. Two-pollutant models for particle composition were analyzed with PM2.5 and NO2 as the second pollutant. We then investigated the shape of the concentration-response function using natural splines with two, three, and four degrees of freedom; penalized splines with the degrees of freedom determined by the algorithm and shape-constrained health impact functions (SCHIF) using confounder model 3. Additionally, we specified linear models in subsets of the concentration range, defined by removing concentrations above a certain value from the analysis, such as for PM2.5 25 μg/m3 (EU limit value), 20, 15, 12 μg/m3 (U.S. EPA National Ambient Air Quality Standard), and 10 μg/m3 (WHO Air Quality Guideline value). Finally, threshold models were evaluated to investigate whether the associations persisted below specific concentration values. For PM2.5, we evaluated 10, 7.5, and 5 μg/m3 as potential thresholds. Performance of threshold models versus the corresponding no-threshold linear model were evaluated using the Akaike information criterion (AIC).

RESULTS

In the pooled cohort, virtually all subjects in 2010 had PM2.5 and NO2 annual average exposures below the EU limit values (25 μg/m3 and 40 μg/m3, respectively). More than 50,000 had a residential PM2.5 exposure below the U.S. EPA NAAQS (12 μg/m3). More than 25,000 subjects had a residential PM2.5 exposure below the WHO guideline (10 μg/m3). We found significant positive associations between PM2.5, NO2, and BC and natural-cause, respiratory, cardiovascular, and diabetes mortality. In our main model, the hazard ratios (HRs) (95% [confidence interval] CI) were 1.13 (CI = 1.11, 1.16) for an increase of 5 μg/m3 PM2.5, 1.09 (CI = 1.07, 1.10) for an increase of 10 μg/m3 NO2, and 1.08 (CI = 1.06, 1.10) for an increase of 0.5 × 10-5/m BC for natural-cause mortality. The highest HRs were found for diabetes mortality. Associations with O3 were negative, both in the fine spatial scale of the main ELAPSE model and in large spatial scale exposure models. For PM2.5, NO2, and BC, we generally observed a supralinear association with steeper slopes at low exposures and no evidence of a concentration below which no association was found. Subset analyses further confirmed that these associations remained at low levels: below 10 μg/m3 for PM2.5 and 20 μg/m3 for NO2. HRs were similar to the full cohort HRs for subjects with exposures below the EU limit values for PM2.5 and NO2, the U.S. NAAQS values for PM2.5, and the WHO guidelines for PM2.5 and NO2. The mortality associations were robust to alternative specifications of exposure, including different time periods, PM2.5 from the MAPLE project, and estimates from the local ESCAPE model. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. HRs in two-pollutant models were attenuated but remained elevated and statistically significant for PM2.5 and NO2. In two-pollutant models of PM2.5 and NO2 HRs for natural-cause mortality were 1.08 (CI = 1.05, 1.11) for PM2.5 and 1.05 (CI = 1.03, 1.07) for NO2. Associations with O3 were attenuated but remained negative in two-pollutant models with NO2, BC, and PM2.5. We found significant positive associations between PM2.5, NO2, and BC and incidence of stroke and asthma and COPD hospital admissions. Furthermore, NO2 was significantly related to acute coronary heart disease and PM2.5 was significantly related to lung cancer incidence. We generally observed linear to supralinear associations with no evidence of a threshold, with the exception of the association between NO2 and acute coronary heart disease, which was sublinear. Subset analyses documented that associations remained even with PM2.5 below 20 μg/m3 and possibly 12 μg/m3. Associations remained even when NO2 was below 30 μg/m3 and in some cases 20 μg/m3. In two-pollutant models, NO2 was most consistently associated with acute coronary heart disease, stroke, asthma, and COPD hospital admissions. PM2.5 was not associated with these outcomes in two-pollutant models with NO2. PM2.5 was the only pollutant that was associated with lung cancer incidence in two-pollutant models. Associations with O3 were negative though generally not statistically significant. In the administrative cohorts, virtually all subjects in 2010 had PM2.5 and NO2 annual average exposures below the EU limit values. More than 3.9 million subjects had a residential PM2.5 exposure below the U.S. EPA NAAQS (12 μg/m3) and more than 1.9 million had residential PM2.5 exposures below the WHO guideline (10 μg/m3). We found significant positive associations between PM2.5, NO2, and BC and natural-cause, respiratory, cardiovascular, and lung cancer mortality, with moderate to high heterogeneity between cohorts. We found positive but statistically nonsignificant associations with diabetes mortality. In our main model meta-analysis, the HRs (95% CI) for natural-cause mortality were 1.05 (CI = 1.02, 1.09) for an increase of 5 μg/m3 PM2.5, 1.04 (CI = 1.02, 1.07) for an increase of 10 μg/m3 NO2, and 1.04 (CI = 1.02, 1.06) for an increase of 0.5 × 10-5/m BC, and 0.95 (CI = 0.93, 0.98) for an increase of 10 μg/m3 O3. The shape of the concentration-response functions differed between cohorts, though the associations were generally linear to supralinear, with no indication of a level below which no associations were found. Subset analyses documented that these associations remained at low levels: below 10 μg/m3 for PM2.5 and 20 μg/m3 for NO2. BC and NO2 remained significantly associated with mortality in two-pollutant models with PM2.5 and O3. The PM2.5 HR attenuated to unity in a two-pollutant model with NO2. The negative O3 association was attenuated to unity and became nonsignificant. The mortality associations were robust to alternative specifications of exposure, including time-varying exposure analyses. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. Effect estimates in the youngest participants (<65 years at baseline) were much larger than in the elderly (>65 years at baseline). Effect estimates obtained with the ELAPSE PM2.5 model did not differ from the MAPLE PM2.5 model on average, but in individual cohorts, substantial differences were found.

CONCLUSIONS

Long-term exposure to PM2.5, NO2, and BC was positively associated with natural-cause and cause-specific mortality in the pooled cohort and the administrative cohorts. Associations were found well below current limit values and guidelines for PM2.5 and NO2. Associations tended to be supralinear, with steeper slopes at low exposures with no indication of a threshold. Two-pollutant models documented the importance of characterizing the ambient mixture with both NO2 and PM2.5. We mostly found negative associations with O3. In two-pollutant models with NO2, the negative associations with O3 were attenuated to essentially unity in the mortality analysis of the administrative cohorts and the incidence analyses in the pooled cohort. In the mortality analysis of the pooled cohort, significant negative associations with O3 remained in two-pollutant models. Long-term exposure to PM2.5, NO2, and BC was also positively associated with morbidity outcomes in the pooled cohort. For stroke, asthma, and COPD, positive associations were found for PM2.5, NO2, and BC. For acute coronary heart disease, an increased HR was observed for NO2. For lung cancer, an increased HR was found only for PM2.5. Associations mostly showed steeper slopes at low exposures with no indication of a threshold.

Transportation noise pollution and cardiovascular disease

Epidemiological studies have found that transportation noise increases the risk of cardiovascular morbidity and mortality, with high-quality evidence for ischaemic heart disease. According to the WHO, ≥1.6 million healthy life-years are lost annually from traffic-related noise in Western Europe. Traffic noise at night causes fragmentation and shortening of sleep, elevation of stress hormone levels, and increased oxidative stress in the vasculature and the brain. These factors can promote vascular dysfunction, inflammation and hypertension, thereby elevating the risk of cardiovascular disease. In this Review, we focus on the indirect, non-auditory cardiovascular health effects of transportation noise. We provide an updated overview of epidemiological research on the effects of transportation noise on cardiovascular risk factors and disease, discuss the mechanistic insights from the latest clinical and experimental studies, and propose new risk markers to address noise-induced cardiovascular effects in the general population. We also explain, in detail, the potential effects of noise on alterations of gene networks, epigenetic pathways, gut microbiota, circadian rhythm, signal transduction along the neuronal-cardiovascular axis, oxidative stress, inflammation and metabolism. Lastly, we describe current and future noise-mitigation strategies and evaluate the status of the existing evidence on noise as a cardiovascular risk factor.

A neurobiological link between transportation noise exposure and metabolic disease in humans

BACKGROUND

Chronic transportation noise exposure associates with cardiovascular events through a link involving heightened stress-associated neurobiological activity (as amygdalar metabolic activity, AmygA) on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT). Increased AmygA also associates with greater visceral adipose tissue (VAT) and type 2 diabetes mellitus (DM). While relationships between noise exposure and VAT and DM have been reported, the underlying mechanisms remain incompletely understood. We tested whether: (1) transportation noise exposure associates with greater (a) baseline and gains in VAT and (b) DM risk, and (2) heightened AmygA partially mediates the link between noise exposure and these metabolic diseases.

METHODS

VAT was measured in a retrospective cohort (N = 403) who underwent clinical ¹⁸F-FDG-PET/CT. AmygA was measured in those with brain imaging (N = 238). Follow-up VAT was remeasured on available imaging (N = 67). Among individuals (N = 224) without baseline DM, incident DM was adjudicated over 2 years from clinical records. Noise (24-h average) was modeled at each individual's home address. Linear regression, survival, and mediation analyses were employed.

RESULTS

Higher noise exposure (upper tertile vs. others) associated with greater: baseline VAT (standardized β [95% confidence interval (CI)]= 0.230 [0.021, 0.438], p = 0.031), gains in VAT (0.686 [0.185, 1.187], p = 0.008 adjusted for baseline VAT), and DM (hazard ratio [95% CI]=2.429 [1.031, 5.719], p = 0.042). The paths of: ↑noise exposure→↑AmygA→↑baseline VAT and ↑noise exposure→↑AmygA→↑subsequent DM were significant (p < 0.05).

CONCLUSIONS

Increased transportation noise exposure associates with greater VAT and DM. This relationship is partially mediated by stress-associated neurobiological activity. These findings suggest altered neurobiology contributes to noise exposure's link to metabolic diseases.

Land use regression modelling of community noise in São Paulo, Brazil

Noise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of São Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice for one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (L_den) and night sound levels (L_night). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source. Noise exposure levels in the study area were high with a site average L_den of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average L_night of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R² of 0.56 for L_den and 0.63 for L_night in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (L_den) or 25 m (L_night) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available.

Road traffic noise and cardiovascular disease risk factors in UK Biobank

AIMS

The aim of this study was to investigate the cross-sectional associations of modelled residential road traffic noise with cardiovascular disease risk factors [systolic (SBP) and diastolic blood pressure (DBP), C-reactive protein, triglycerides, glycated haemoglobin, and self-reported hypertension] in UK Biobank.

METHODS AND RESULTS

The UK Biobank recruited 502 651 individuals aged 40-69 years across the UK during 2006-10. Road traffic noise (Lden and Lnight) exposure for 2009 was estimated at baseline address using a simplified version of the Common Noise Assessment Methods model. We used multivariable linear and logistic regression models, adjusting for age, sex, body mass index (BMI), smoking, alcohol intake, area- and individual-level deprivation, season of blood draw, length of time at residence, and nitrogen dioxide (main model), in an analytical sample size of over 370 000 participants. Exposure to road-traffic Lden >65 dB[A], as compared to ≤55 dB[A], was associated with 0.77% [95% confidence interval (CI) 0.60%, 0.95%], 0.49% (95% CI 0.32%, 0.65%), 0.79% (95% CI 0.11%, 1.47%), and 0.12% (95% CI -0.04%, 0.28%) higher SBP, DBP, triglycerides, and glycated haemoglobin, respectively. Removing BMI from the main model yielded significant positive associations with all five markers with elevated percent changes. The associations with SBP or DBP did not appear to be impacted by hypertension medication while a positive association with prevalent self-reported hypertension was seen in the non-medicated group who exposed to a Lden level of 60-65 dB[A] (odds ratio 1.07, 95% CI 1.00, 1.15).

CONCLUSION

Exposure to road traffic noise >65 dB[A], independent of nitrogen dioxide, was associated with small but adverse changes in blood pressure and cardiovascular biochemistry.

Occupational noise exposure and the prevalence of dyslipidemia in a cross-sectional study

BACKGROUND

Occupational noise exposure was related to cardiovascular disease, of which dyslipidemia was an important inducement. This study investigated the relationship between occupational noise exposure and dyslipidemia.

METHODS

Four hundred ninety-two occupational noise-exposed workers and 664 non-exposed workers were recruited to conduct environmental noise tests and personal occupational physical examinations. A lasso-logistic regression model was used to estimate the relative risk of dyslipidemia. A restricted cubic spline was used to estimate the association between noise exposure years and dyslipidemia after adjusting for potential confounding factors.

RESULTS

A crude association was observed between the occupational noise exposure (75-85 dB(A)) and dyslipidemia. After adjusting for confounding factors, there was a non-linear relationship between noise exposure years and dyslipidemia (P for non-linearity =0.01). Workers exposed to 75-85 dB(A) for 11 to 24.5 years had a higher risk of dyslipidemia than non-exposed workers.

CONCLUSIONS

A positive and non-linear exposure-response relationship was found in workers exposed to 75-85 dB(A) whose exposure years were between 11 and 24.5. Workers had the highest risk of dyslipidemia when exposed for 13.5 years.

Insomnia associated with traffic noise and proximity to traffic-a cross-sectional study of the Respiratory Health in Northern Europe III population

STUDY OBJECTIVES

Exposure to traffic noise increases the risk of sleeping disturbance, but little is known about the effect of traffic-related air pollution on insomnia symptoms. We aimed to investigate the separate associations of self-reported proximity to traffic and traffic noise with insomnia.

METHODS

This is a cross-sectional study of the population included in the Respiratory Health in Northern Europe study, consisting of randomly selected men and women born between 1945 and 1973, from 7 Northern European centers. Hearing traffic noise in the bedroom, bedroom window proximity to traffic, and insomnia symptoms were self-reported. Bedroom window proximity to traffic was used as a surrogate for exposure to traffic-related air pollution. The following insomnia symptoms were assessed: difficulty initiating sleep, difficulty maintaining sleep, and early morning awakening.

RESULTS

A total of 12,963 individuals was included. Traffic noise was positively associated with all three insomnia symptoms: difficulty initiating sleep (odds ratio [OR] = 3.54; 95% confidence interval [CI]: 1.85, 6.76), difficulty maintaining sleep (OR = 2.95; 95% CI: 1.62, 5.37), and early morning awakening (OR = 3.25; 95% CI: 1.97, 5.37). Proximity to traffic without disturbing noise was associated with difficulty initiating sleep (OR = 1.62; 95% CI: 1.45, 1.82).

CONCLUSIONS

This study adds further support to the identification of traffic noise as a risk factor for insomnia. Proximity to traffic without being exposed to noise was associated with an increased risk of difficulty initiating sleep. Our findings indicate that insomnia may be associated with both traffic noise and traffic-related air pollution.

Associations of long-term exposure to ambient nitrogen dioxide with indicators of diabetes and dyslipidemia in China: A nationwide analysis

BACKGROUND

The associations between ambient NO2 and diabetes and dyslipidemia have been controversial, and data is especially lacking in developing countries.

OBJECTIVE

This study aimed to assess the associations of long-term exposure to NO2 with diabetes and dyslipidemia in China.

METHODS

We conducted a cross-sectional study including 13,013 participants from the China Health and Retirement Longitudinal Study (CHRLS). The annual average concentrations of NO2 were estimated based on the residential addresses of participants. We applied logistic regression models to evaluate the associations of NO2 with diabetes and dyslipidemia, and linear regression models to assess the associations with blood biomarkers.

RESULTS

A total of 1933 diabetes cases (14.85%) and 1935 (14.87%) dyslipidemia cases were identified. Significant associations were observed between NO2 and risk of diabetes and dyslipidemia independent of PM2.5 and O3. For an interquartile range (IQR) increase in NO2 (12.39 μg/m3), we observed a 13% [odds ratio (OR): 1.13; 95% confidence interval (CI): 1.01, 1.26] increased risk of diabetes, 1.48% (95%CI: 0.51%, 2.46%) increase in glucose, 0.74% (95%CI: 0.19%, 1.29%) increase in glycosylated hemoglobin (HbA1c), 17% (OR: 1.17; 95% CI: 1.05, 1.31) increased risk of dyslipidemia, 4.62% (95%CI: 2.49%, 6.79%) increase in triglyceride, and a decrease of 2.96% (95%CI: 2.13%, 3.79%) in high-density lipoprotein. The associations of NO2 with glucose disorders were stronger among smokers.

CONCLUSIONS

Our study indicated long-term exposure to NO2 might contribute to the development of diabetes and dyslipidemia, and the associations were potentially independent of O3 and PM2.5.

Does exposure to noise pollution influence the incidence and severity of COVID-19?

Research that analyzes the effect of different environmental factors on the impact of COVID-19 focus primarily on meteorological variables such as humidity and temperature or on air pollution variables. However, noise pollution is also a relevant environmental factor that contributes to the worsening of chronic cardiovascular diseases and even diabetes. This study analyzes the role of short-term noise pollution levels on the incidence and severity of cases of COVID-19 in Madrid from February 1 to May 31, 2020. The following variables were used in the study: daily noise levels averaged over 14 days; daily incidence rates, average cumulative incidence over 14 days; hospital admissions, Intensive Care Unit (ICU) admissions and mortality due to COVID-19. We controlled for the effect of the pollutants PM10 and NO2 as well as for variables related to seasonality and autoregressive nature. GLM models with Poisson regressions were carried out using significant variable selection (p < 0.05) to calculate attributable RR. The results of the modeling using a single variable show that the levels of noise (leq24 h) were related to the incidence rate, the rate of hospital admissions, the ICU admissions and the rate of average cumulative incidence over 14 days. These associations presented lags, and the first association was with incidence (lag 7 and lag 10), then with hospital admissions (lag 17) and finally ICU admissions (lag 22). There was no association with deaths due to COVID-19. In the results of the models that included PM10, NO2, Leq24 h and the control variables simultaneously, we observed that only Leq24 h went on to become a part of the models using COVID-19 variables, including the 14-day average cumulative incidence. These results show that noise pollution is an important environmental variable that is relevant in relation to the incidence and severity of COVID-19 in the Province of Madrid.

Occupational Noise Exposure and Diabetes Risk

Introduction

Noise is one of the most common worldwide environmental pollutants, especially in occupational fields. As a stressor, it affects not only the ear but also the entire body. Its physiological and psychological impacts have been well established in many conditions such as cardiovascular diseases. However, there is a dearth of evidence regarding diabetes risk related to noises.

Aim

To evaluate the relationship between occupational exposure to noise and the risk of developing diabetes.

Methods

This is a cross-sectional analytical study enrolling two groups of 151 workers each. The first group (noise exposed group: EG) included the employees of a Tunisian power plant, who worked during the day shift and had a permanent position. The second group (unexposed to noise group: NEG) included workers assigned to two academic institutions, who were randomly selected in the Occupational Medicine Department of the Farhat Hached University Hospital in Sousse, during periodical fitness to work visits. Both populations (exposed and unexposed) were matched by age and gender. Data collection was based on a preestablished questionnaire, a physical examination, a biological assessment, and a sonometric study.

Results

The mean equivalent continuous sound level was 89 dB for the EG and 44.6 dB for the NEG. Diabetes was diagnosed in 24 workers from EG (15.9%) and 14 workers from NEG (9.3%), with no statistically significant difference (p=0.08). After multiple binary logistic regression, including variables of interest, noise did not appear to be associated with diabetes.

Conclusion

Our results did not reveal a higher risk of developing diabetes in workers exposed to noise. Further studies assessing both level and duration of noise exposure are needed before any definitive conclusion.

Traffic-related Noise Exposure and Late-life Dementia and Cognitive Impairment in Mexican-Americans

BACKGROUND

Recently, it has been suggested that environmental exposures from traffic sources including noise may play a role in cognitive impairment in the elderly. The objective of the study was to investigate the association between local traffic-related noise pollution and incident dementia or cognitive impairment without dementia (CIND) during a 10-year follow-up period.

METHODS

1612 Mexican-American participants from the Sacramento Area Latino Study on Aging (SALSA) were followed every 12-15 months via home visits from 1998 to 2007. We used the SoundPLAN software package to estimate noise originating from local traffic with the input of Annual Average Daily Traffic (AADT) data from Metropolitan Planning Organizations (MPO) based on geocoded residential addresses at baseline (1998-1999). We estimated the risks of incident dementia or CIND from 24-hour and nighttime noise exposure using Cox proportional hazard models.

RESULTS

During the follow-up, we identified 159 incident dementia or CIND cases in total. Per 11.6 dB (interquartile range width) increase in 24-hour noise, the hazard of developing dementia or CIND increased (hazard ratio = 1.3 [1.0, 1.6]) during follow-up; estimates were slightly lower (hazard ratio = 1.2 [0.97, 1.6]) when adjusting for modeled local air pollution exposure from traffic sources. Overall, the risk of dementia/CIND was elevated when 24-hour and nighttime noise were higher than 75 and 65 dB respectively. See video Abstract: http://links.lww.com/EDE/B728.

CONCLUSIONS

In our study, traffic-related noise exposure was associated with increased risk of dementia or CIND in elderly Mexican-Americans. Future studies taking into account other noise sources and occupational noise exposure before retirement are needed.

Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data-Driven and Spatial Methods

To control and prevent the risk of diabetes, diabetes studies have identified the need to better understand and evaluate the associations between influencing indicators and the prevalence of diabetes. One constraint has been that influencing indicators have been selected mainly based on subjective judgment and tested using traditional statistical modeling methods. We proposed a framework new to diabetes studies using data-driven and spatial methods to identify the most significant influential determinants of diabetes automatically and estimated their relationships. We used data from diabetes mellitus patients' health insurance records in Shandong province, China, and collected influencing indicators of diabetes prevalence at the county level in the sociodemographic, economic, education, and geographical environment domains. We specified a framework to identify automatically the most influential determinants of diabetes, and then established the relationship between these selected influencing indicators and diabetes prevalence. Our autocorrelation results showed that the diabetes prevalence in 12 Shandong cities was significantly clustered (Moran's I = 0.328, p < 0.01). In total, 17 significant influencing indicators were selected by executing binary linear regressions and lasso regressions. The spatial error regressions in different subgroups were subject to different diabetes indicators. Some positive indicators existed significantly like per capita fruit production and other indicators correlated with diabetes prevalence negatively like the proportion of green space. Diabetes prevalence was mainly subjected to the joint effects of influencing indicators. This framework can help public health officials to inform the implementation of improved treatment and policies to attenuate diabetes diseases.

The Current and Forecasted Status of Type 2 Diabetes in the Arab Society of Israel

BACKGROUND

Diabetes mellitus (DM) is considered one of the main causes of mortality, morbidity, and health care expenditures. Effectively treating this disease is of crucial importance and imposes a global challenge. The incidence of Type 2 DM (T2DM) is rapidly rising in both developing and developed countries. The Arab community in Israel is a distinct ethnic group with unique characteristics. Recently, this community has undergone major changes in its lifestyle, adopting the Westernized one, which could have caused an increase in the T2DM incidence rate.

OBJECTIVE

This review aims to shed light on various studies undertaken to explore the prevalence of diabetes and determine its current status in the Arab society of Israel, resting on previous and current data. It is presented to highlight the status of diabetes globally and to focus on its current situation in the Arab society of Israel, attempting to forecast its direction in the upcoming decade.

METHODS

Data were obtained from our previous comprehensive socio-economic and health crosssectional surveys for successive periods from 2004 to 2017. These surveys were conducted on the Arab society of Israel by the Galilee Society.

RESULTS

Our results showed a progressive increase in the prevalence of T2DM from 3.4% to 7.6% in the Arab society of Israel. This trend is expected to continue rising in the coming decade, and based on our predictions, may exceed 12% in 2030.

CONCLUSION

Substantial and practical health-related actions must be initiated to prevent an increasing number of adults from developing diabetes and its complications.

Introduction to Environmental Harmful Factors

In this Chapter, we systematically and comprehensively described various environmental harmful factors. They were classified into four aspects: physical factors, chemical factors, biological factors, and physiological and psychological stress factors. Their classification, modes of presence, toxicity and carcinogenicity, routes of exposure to human and toxic effects on the female reproductive health were introduced. It is expected that the exposure routes could be controlled and eliminated, and the pathogenic mechanism of environmental harmful factors should be investigated and explained to protect female reproductive health.

Estimating the health benefits associated with a speed limit reduction to thirty kilometres per hour: A health impact assessment of noise and road traffic crashes for the Swiss city of Lausanne

Reductions of speed limits for road traffic are effective in reducing casualties, and are also increasingly promoted as an effective way to reduce noise exposure. The aim of this study was to estimate the health benefits of the implementation of 30 km/h speed limits in the city of Lausanne (136'077 inhabitants) under different scenarios addressing exposure to noise and road crashes. The study followed a standard methodology for quantitative health impact assessments to derive the number of attributable cases in relation to relevant outcomes. We compared a reference scenario (without any 30 km/h speed limits) to the current situation with partial speed limits and additional scenarios with further implementation of 30 km/h speed limits, including a whole city scenario. Compared to the reference scenario, noise reduction due to the current speed limit situation was estimated to annually prevent 1 cardiovascular death, 72 hospital admissions from cardiovascular disease, 17 incident diabetes cases, 1'127 individuals being highly annoyed and 918 individuals reporting sleep disturbances from noise. Health benefits from a reduction in road traffic crashes were less pronounced (1 severe injury and 4 minor injuries). The whole city speed reduction scenario more than doubled the annual benefits, and was the only scenario that contributed to a reduction in mortality from road traffic crashes (one death per two years). Implementing 30 km/h speed limits in a city yields health benefits due to reduction in road traffic crashes and noise exposure. We found that the benefit from noise reduction was more relevant than safety benefits.

Incidence of depression in relation to transportation noise exposure and noise annoyance in the SAPALDIA study

Prospective evidence on the risk of depression in relation to transportation noise exposure and noise annoyance is limited and mixed. We aimed to investigate the associations of long-term exposure to source-specific transportation noise and noise annoyance with incidence of depression in the SAPALDIA (Swiss cohort study on air pollution and lung and heart diseases in adults) cohort. We investigated 4,581 SAPALDIA participants without depression in the year 2001/2002. Corresponding one-year mean road, railway and aircraft day-evening-night noise (Lden) was calculated at the most exposed façade of the participants' residential floors, and transportation noise annoyance was assessed on an 11-point scale. Incident cases of depression were identified in 2010/2011, and comprised participants reporting physician diagnosis, intake of antidepressant medication or having a short form-36 mental health score < 50. We used robust Poisson regressions to estimate the mutually adjusted relative risks (RR) and 95% confidence intervals (CI) of depression, independent of traffic-related air pollution and other potential confounders. Incidence of depression was 11 cases per 1,000 person-years. In single exposure models, we observed positive but in part, statistically non-significant associations (per 10 dB) of road traffic Lden [RR: 1.06 (0.93, 1.22)] and aircraft Lden [RR: 1.19 (0.93, 1.53)], and (per 1-point difference) of noise annoyance [RR: 1.05 (1.02, 1.08)] with depression risk. In multi-exposure model, noise annoyance effect remained unchanged, with weaker effects of road traffic Lden [(RR: 1.02 (0.89, 1.17)] and aircraft Lden [(RR: 1.17 (0.90, 1.50)]. However, there were statistically significant indirect effects of road traffic Lden [(β: 0.02 (0.01, 0.03)] and aircraft Lden [β: 0.01 (0.002, 0.02)] via noise annoyance. There were no associations with railway Lden in the single and multi-exposure models [(RR_{both models}: 0.88 (0.75, 1.03)]. We made similar findings among 2,885 non-movers, where the effect modification and cumulative risks were more distinct. Noise annoyance effect in non-movers was stronger among the insufficiently active (RR: 1.09; 95%CI: 1.02, 1.17; p_interaction = 0.07) and those with daytime sleepiness [RR: 1.07 (1.02, 1.12); p_interaction = 0.008]. Cumulative risks of Lden in non-movers showed additive tendencies for the linear cumulative risk [(RR_{per 10dB of combined sources}: 1.31 (0.90, 1.91)] and the categorical cumulative risk [(RR_{triple- vs. zero-source ≥45 dB}: 2.29 (1.02, 5.14)], and remained stable to noise annoyance. Transportation noise level and noise annoyance may jointly and independently influence the risk of depression. Combined long-term exposures to noise level seems to be most detrimental, largely acting via annoyance. The moderation of noise annoyance effect by daytime sleepiness and physical activity further contribute to clarifying the involved mechanisms. More evidence is needed to confirm these findings for effective public health control of depression and noise exposure burden.

Incidence of depression in relation to transportation noise exposure and noise annoyance in the SAPALDIA study

Prospective evidence on the risk of depression in relation to transportation noise exposure and noise annoyance is limited and mixed. We aimed to investigate the associations of long-term exposure to source-specific transportation noise and noise annoyance with incidence of depression in the SAPALDIA (Swiss cohort study on air pollution and lung and heart diseases in adults) cohort. We investigated 4,581 SAPALDIA participants without depression in the year 2001/2002. Corresponding one-year mean road, railway and aircraft day-evening-night noise (Lden) was calculated at the most exposed façade of the participants' residential floors, and transportation noise annoyance was assessed on an 11-point scale. Incident cases of depression were identified in 2010/2011, and comprised participants reporting physician diagnosis, intake of antidepressant medication or having a short form-36 mental health score < 50. We used robust Poisson regressions to estimate the mutually adjusted relative risks (RR) and 95% confidence intervals (CI) of depression, independent of traffic-related air pollution and other potential confounders. Incidence of depression was 11 cases per 1,000 person-years. In single exposure models, we observed positive but in part, statistically non-significant associations (per 10 dB) of road traffic Lden [RR: 1.06 (0.93, 1.22)] and aircraft Lden [RR: 1.19 (0.93, 1.53)], and (per 1-point difference) of noise annoyance [RR: 1.05 (1.02, 1.08)] with depression risk. In multi-exposure model, noise annoyance effect remained unchanged, with weaker effects of road traffic Lden [(RR: 1.02 (0.89, 1.17)] and aircraft Lden [(RR: 1.17 (0.90, 1.50)]. However, there were statistically significant indirect effects of road traffic Lden [(β: 0.02 (0.01, 0.03)] and aircraft Lden [β: 0.01 (0.002, 0.02)] via noise annoyance. There were no associations with railway Lden in the single and multi-exposure models [(RR_{both models}: 0.88 (0.75, 1.03)]. We made similar findings among 2,885 non-movers, where the effect modification and cumulative risks were more distinct. Noise annoyance effect in non-movers was stronger among the insufficiently active (RR: 1.09; 95%CI: 1.02, 1.17; p_interaction = 0.07) and those with daytime sleepiness [RR: 1.07 (1.02, 1.12); p_interaction = 0.008]. Cumulative risks of Lden in non-movers showed additive tendencies for the linear cumulative risk [(RR_{per 10dB of combined sources}: 1.31 (0.90, 1.91)] and the categorical cumulative risk [(RR_{triple- vs. zero-source ≥45 dB}: 2.29 (1.02, 5.14)], and remained stable to noise annoyance. Transportation noise level and noise annoyance may jointly and independently influence the risk of depression. Combined long-term exposures to noise level seems to be most detrimental, largely acting via annoyance. The moderation of noise annoyance effect by daytime sleepiness and physical activity further contribute to clarifying the involved mechanisms. More evidence is needed to confirm these findings for effective public health control of depression and noise exposure burden.