Search

Urban areas are constantly growing, and densification is a common strategy to limit settlement expansion. However, this leads to loss of green spaces (GSs) and increasing noise pollution, which is detrimental to public health. Within a research project that aims at elucidating the stress-easing potential of green spaces in noise-polluted environments and the mediating and/or moderating role of annoyance, an extended cross-sectional field study will be performed in the city of Zurich, Switzerland. In total, 5000 participants will be contacted in three waves, during which an online survey will be carried out followed by a visit to a subsample at home to collect hair cortisol probes. Participants of this study were selected according to the characteristics of their residences (stratified by accessibility to GSs and noise exposure). Further, traits of individuals in addition to acoustic and non-acoustic attributes of GSs are accounted. Thus, the association of noise annoyance, perceived stress and long-term physiological stress responses among residents exposed to different road traffic noise levels and with different grades of access to GSs are studied. In this contribution, the study protocol and first results of a pilot study with 256 participants will be shown.

Background: Sleep problems show associations with negative outcomes in both physical and mental health in adolescents, but the associations may be reciprocal. We aimed to assess bidirectional associations between sleep problems and mental\ud health symptoms including behavioural difficulties (internalising and externalising difficulties) and low health‐related quality of life (HRQoL). \ud Methods: A total of 6616 adolescents (52.4% females) across Greater London completed baseline assessments when they were aged 11–12 years, and 3803 of them (57.2% females) completed follow‐up assessments at aged 13–15 years. Weekday and weekend sleep duration were derived from self‐reported bedtime, sleep onset latency and wake time. Sleep disturbance was assessed using a standardized sleep disturbance scale. Internalising and externalising difficulties were assessed using subscales of the Strength and Difficulties Questionnaire. HRQoL was assessed using the KIDSCREEN‐10 questionnaire. Cross‐lagged structural equation\ud modelling was used with multiple imputation to examine bidirectional associations between sleep problems and mental health symptoms. Results: Females had greater internalising difficulties, worse HRQoL and more sleep disturbance than males. Persistent insufficient weekday and weekend sleep, and sleep disturbance (i.e., at both baseline and follow‐up) were associated with internalising and externalising difficulties and low HRQoL at follow‐up (ORs ranged from 1.53 to 3.63). Persistent externalising difficulties and low HRQoL were also associated with insufficient weekend sleep and sleep disturbance at follow‐up (ORs ranged from 1.68 to 4.25). Using continuous variables, we found bidirectional associations between weekday sleep duration and HRQoL, weekend sleep duration and externalising score, sleep quality and internalising score, and sleep quality and HRQoL. The association magnitudes were mostly similar in the two directions.\ud Conclusions: Our study showed bidirectional associations between sleep problems and mental health symptoms during adolescence, indicating that early intervention and treatment on the first‐occurring symptom may prevent the development of \ud subsequent problems.

Background Death from cardiovascular diseases (CVD) has been associated with transportation noise. This nationwide cohort, with state-of-the-art exposure assessment, evaluates these associations by noise source. Methods Road traffic, railway and aircraft noise for 2001 and 2011 were linked to 4.1 million adults in the Swiss National Cohort, accounting for address history. Mean noise exposure in 5-year periods was calculated. Time-varying Cox regression models, with age as timescale, were applied to all and cause-specific cardiovascular causes of death. Models included all three noise sources plus PM2.5, adjusted for individual and spatial covariates. Nighttime noise events for all sources combined (expressed as intermittency ratio or number of events) were considered in sensitivity analyses. Absolute excess risk was calculated by multiplying deaths/100,000 person-years by the excess risk (hazard ratio-1) within each age/sex group. Results During a 15-year follow-up, there were 277,506 CVD and 34,200 myocardial infarction (MI) deaths. Associations (hazard ratio; 95%-CIs) for road traffic, railway and aircraft noise and CVD mortality were 1.029 (1.024–1.034), 1.013 (1.010–1.017), and 1.003 (0.996–1.010) per 10 dB Lden, respectively. Associations for MI mortality were a respective 1.043 (1.029–1.058), 1.020 (1.010–1.030) and 1.040 (1.020–1.060) per 10 dB Lden. Blood pressure-related, ischemic heart disease, and all stroke mortality were significantly associated with road traffic and railway noise, while ischemic stroke mortality was associated with aircraft noise. Associations were mostly linear, often starting below 40 dB Lden for road traffic and railway noise. Higher levels of noise intermittency were also independently associated with each outcome. While the absolute number of deaths attributed to noise increased with age, the hazard ratios declined with age. Relative and absolute risk was higher in males compared to females. Conclusion Independent of air pollution, transportation noise exposure is associated with all and cause-specific CVD mortality, with effects starting below current guideline limits.

AIMS; : It is unclear whether night-time noise events, including from aeroplanes, could trigger a cardiovascular death. In this study, we investigate the potential acute effects of aircraft noise on mortality and the specific role of different night-time exposure windows by means of a case-crossover study design.; METHODS AND RESULTS; : We selected 24 886 cases of death from cardiovascular disease (CVD) from the Swiss National Cohort around Zurich Airport between 2000 and 2015. For night-time deaths, exposure levels 2 h preceding death were significantly associated with mortality for all causes of CVD [OR = 1.44 (1.03-2.04) for the highest exposure group (LAeq > 50 dB vs.

Objective To assess short term mortality risks and excess mortality associated with exposure to ozone in several cities worldwide. Design Two stage time series analysis. Setting 406 cities in 20 countries, with overlapping periods between 1985 and 2015, collected from the database of Multi-City Multi-Country Collaborative Research Network. Population Deaths for all causes or for external causes only registered in each city within the study period. Main outcome measures Daily total mortality (all or non-external causes only). Results A total of 45 165 171 deaths were analysed in the 406 cities. On average, a 10 μg/m 3 increase in ozone during the current and previous day was associated with an overall relative risk of mortality of 1.0018 (95% confidence interval 1.0012 to 1.0024). Some heterogeneity was found across countries, with estimates ranging from greater than 1.0020 in the United Kingdom, South Africa, Estonia, and Canada to less than 1.0008 in Mexico and Spain. Short term excess mortality in association with exposure to ozone higher than maximum background levels (70 μg/m 3) was 0.26% (95% confidence interval 0.24% to 0.28%), corresponding to 8203 annual excess deaths (95% confidence interval 3525 to 12 840) across the 406 cities studied. The excess remained at 0.20% (0.18% to 0.22%) when restricting to days above the WHO guideline (100 μg/m 3), corresponding to 6262 annual excess deaths (1413 to 11 065). Above more lenient thresholds for air quality standards in Europe, America, and China, excess mortality was 0.14%, 0.09%, and 0.05%, respectively. Conclusions Results suggest that ozone related mortality could be potentially reduced under stricter air quality standards. These findings have relevance for the implementation of efficient clean air interventions and mitigation strategies designed within national and international climate policies. © Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to.

The Study of Cognition, Adolescents and Mobile Phones (SCAMP) is a prospective secondary school-based cohort study established to investigate whether use of mobile phones and other wireless devices that emit radio-frequency electromagnetic fields (RF-EMF) is associated with cognitive, behavioural, educational, physical and mental health outcomes during adolescence. Specifically, the principal aim is to discern whether any observed associations may be due to: (i) RF-EMF exposure from mobile phones; (ii) a combination of various RF-EMF sources (e.g. digital enhanced cordless technology phones or wireless internet); or (iii) other behavioural reasons associated with technology use for communication and entertainment, irrespective of exposure to RF-EMF.

Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO₂ and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015⁻2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO₂ and PM2.5 were 22.1 μg/m³ and 10.2 μg/m³, respectively. The NO₂ models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R²). The PM2.5 annual models had lower explanatory power (R² = 0.36, 0.29, and 0.29). The best predictors for NO₂ were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO₂ can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO₂ and PM2.5 seasonal exposure estimates and maps for further health studies.

International audience; ELFSTAT project, founded by the French ANSES (2015-2019, Grant agreement n. 2015/1/202), aims at characterizing children's exposure to extremely low frequency magnetic fields (ELF-MF) in real exposure scenarios using stochastic approaches. In this paper, a stochastic approach for extracting information from dataset of recorded ELF-MF signals is presented. The aim is to obtain a better characterization and description of the phenomenon and to investigate on possible correlations or different features between various data subsets.

Introduction ELFSTAT project, founded by the French ANSES (2015-2019, Grant agreement n. 2015/1/202), aims at characterizing children's exposure to Extremely Low Frequency Magnetic Fields (ELF-MF) in real exposure scenarios using stochastic approaches. The present paper gives details about the first step of the project: this step aims at developing stochastic models to model personal exposure from a dataset of recorded ELF-MF signals. These recordings are coming from the ARIMMORA project [1]: 331 children has worn an EMDEX and their exposition were measured during about 3 days. The stochastic models will then be used to construct realistic simulations of ELF-MF time series. Figure 1 gives an example of ELF-MF 24 hours-signal. The majority of ELF-MF time series are characterized by abrupt changes in structure, such as sudden jumps in mean level or in dispersion around a mean level. The developed model consists in detecting these changes and in modeling the signal between two consecutive changes by a stationary process. These stationary processes are described by parametric models. We chose Auto-Regressive model because of the possibility of characterizing mean effects, variance effects and time-correlation effects with a weak number of parameters. The full-model is obtained by modeling the distribution of the parameters from the whole dataset of 331 recordings. Figure 2 gives a schematic of the full approach. Data sources The 331 individuals of the database are distributed according the geographical location (Switzerland or Italy) and the time season (Winter or Summer), as shown in the following Table: For each individual, we consider a full day of measurements (0h-24h). As the interval between two successive observations is 30 seconds, 2880 values per recording are considered. Change-point detection and segment modeling We consider changepoints (or equivalently breakpoints) to be the time points that divide a data set into distinct homogeneous segments (or equivalently stationary segments); the boundaries of segments can be interpreted as changes in the physical system. The problem of changepoints estimation has attracted significant attention and is required in a number of applications including financial data [2], climate data [3], biomedical data [4] and signal processing [5]. Different authors propose various approaches to the problem of changepoints detection or segmentation of time series. This issue is thoroughly surveyed in [6], where different methods are proposed and an exhaustive list of references is given. Some authors study the estimation of a single changepoint problem [7], while others extend it to multiple changepoints problem. In this last case, the number of changepoints is unknown and it is a challenge to jointly estimate the number of changepoints, their location, and also provide an estimation of the model representing each segment. In this work we consider the [2] procedure because of its ability to jointly estimate the number of changepoints, their location and the AR model parameter of each segment. To resume, the segmentation aims to: (1) find the periods of stability and homogeneity in the behaviour of the time series; (2) identify the locations of change, called changepoints; (3) represent the regularities and features of each segment (estimate the model of each segment by a parameter set like changepoints location, segment amplitude, segment duration, segment regularity). Figure 1 gives the segmentation results for the given example. Statistical characterization of model parameters In order to produce a completely model of the time series, the marginal and joint probability distributions of all model parameters are required. The set of parameters related to piecewise autoregressive model are, at time t: duration ?t and coefficient of the AR model (amplitude or mean ?t, noise variance ?t2 and the autoregressive coefficients ?t). These 4 parameters can be modeled separately (whether by a parametric probability distribution or not) as well as jointly (rather in a nonparametric way because of the difficulty to find a multidimensional probability distribution that efficiently models the parameters set). Figure 3 shows that parameters are correlated and thus cannot be modeled separately. We used the Multivariate Kernel Density Estimation [9] that is a nonparametric and classical method. The advantage is that once we have an estimate of this density, we can simulate realistic realizations of these parameters. The figure 3 also gives the Multivariate KDE contours « Congrès National de Radioprotection » SFRP - LILLE - 7, 8 & 9 juin 2017 and 5000 simulations of these parameters via the Multivariate KDE. Concerning the fitted distributions, first result shows no significant difference between the summer database and the winter database. Finally, in the figure 4, we present an example simulated of ELF-MF exposure time series based in piecewise AR model.