Does night-time aircraft noise trigger mortality? A case-crossover study on 24 886 cardiovascular deaths

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. <20 dB)]. Most consistent associations were observed for ischaemic heart diseases, myocardial infarction, heart failure, and arrhythmia. Association were more pronounced for females (P = 0.02) and for people living in areas with low road and railway background noise (P = 0.01) and in buildings constructed before 1970 (P = 0.36). We calculated a population attributable fraction of 3% in our study population.; CONCLUSION; : Our findings suggest that night-time aircraft noise can trigger acute cardiovascular mortality. The association was similar to that previously observed for long-term aircraft noise exposure

The association of road traffic noise with cognition in adolescents: a cohort study in Switzerland

Environmental noise exposure has been shown to affect children's cognition, but the concept of cognition is multifaceted, and studies on associations with noise are still inconclusive and fragmented. We studied cognitive change within one year in 882 adolescents aged 10-17 years in response to road traffic noise exposure. Participants filled in a comprehensive questionnaire and underwent cognitive testing twice at an interval of one year. Figural and verbal memory was measured with the Intelligenz-Struktur-Test (IST), and concentration accuracy and constancy were measured with FAKT-II and d2 test. Exposure to noise and other environmental stressors were modelled for school and home location at baseline. Missing data was addressed with multiple imputation. Cross-sectional multilevel analyses and longitudinal change score analyses were performed. In cross-sectional analyses, figural memory was significantly reduced by -0.27 (95%CI -0.49,-0.04) units per 10 dB road traffic noise increase at home (L(den)). Longitudinal analyses showed a significant reduction of concentration constancy Z-scores between baseline and follow-up by -0.13 (95%CI -0.25, 0.00) per 10 dB road traffic noise at home (L(den)). Our study indicates that road traffic noise at home reduces cognitive performance in adolescents. Larger cohorts with longer follow-up time are needed to confirm these results

Is high prevalence of Echinococcus multilocularis in wild and domestic animals associated with disease incidence in humans?

We investigated a focus of highly endemic Echinococcus multilocularis infection to assess persistence of high endemicity in rural rodents, explore potential for parasite transmission to domestic carnivores, and assess (serologically) putative exposure versus infection frequency in inhabitants of the region. From spring 1993 to spring 1998, the prevalence of E. multilocularis in rodents was 9% to 39% for Arvicola terrestris and 10% to 21% for Microtus arvalis. From June 1996 to October 1997, 6 (7%) of 86 feral dogs and 1 of 33 cats living close to the region tested positive for intestinal E. multilocularis infection. Testing included egg detection by coproscopy, antigen detection by enzyme-linked immunosorbent assay (ELISA), and specific parasite DNA amplification by polymerase chain reaction. Thus, the presence of infected domestic carnivores can increase E. multilocularis exposure risk in humans. A seroepidemiologic survey of 2,943 blood donors in the area used specific Em2-ELISA. Comparative statistical analyses of seroprevalence and clinical incidence showed an increase in Em2-seroprevalence from 1986 and 1996-97 but no increase in clinical incidence of alveolar hydatid disease

Explorative assessment of the temperature-mortality association to support health-based heat-warning thresholds: a national case-crossover study in Switzerland

Defining health-based thresholds for effective heat warnings is crucial for climate change adaptation strategies. Translating the non-linear function between heat and health effects into an effective threshold for heat warnings to protect the population is a challenge. We present a systematic analysis of heat indicators in relation to mortality. We applied distributed lag non-linear models in an individual-level case-crossover design to assess the effects of heat on mortality in Switzerland during the warm season from 2003 to 2016 for three temperature metrics (daily mean, maximum, and minimum temperature), and various threshold temperatures and heatwave definitions. Individual death records with information on residential address from the Swiss National Cohort were linked to high-resolution temperature estimates from 100 m resolution maps. Moderate (90th percentile) to extreme thresholds (99.5th percentile) of the three temperature metrics implied a significant increase in mortality (5 to 38%) in respect of the median warm-season temperature. Effects of the threshold temperatures on mortality were similar across the seven major regions in Switzerland. Heatwave duration did not modify the effect when considering delayed effects up to 7 days. This nationally representative study, accounting for small-scale exposure variability, suggests that the national heat-warning system should focus on heatwave intensity rather than duration. While a different heat-warning indicator may be appropriate in other countries, our evaluation framework is transferable to any country

Mutual effects of fine particulate matter, nitrogen dioxide, and fireworks on cause-specific acute cardiovascular mortality: a case-crossover study in communities affected by aircraft noise

Ambient air pollution is the leading cause of environmental mortality and morbidity worldwide. However, the individual contributions to acute mortality of traffic-related air pollutants such as nitrogen dioxide (NO2) and fine particulate matter (PM2.5) are still debated. We conducted a time-stratified case-crossover study for a population located around Zurich airport in Switzerland, including 24,886 adult cardiovascular deaths from the Swiss National Cohort. We estimated the risk of cause-specific cardiovascular mortality associated with daily NO2 and PM2.5 concentrations at home using distributed lag models up to 7 days preceding death, adjusted for daily temperature, precipitation, acute night-time aircraft noise, firework celebrations, and holidays. Cardiovascular mortality was associated with NO2, whereas the association with PM2.5 disappeared upon adjustment for NO2. The strongest association was observed between NO2 and ischemic stroke mortality (odds ratio = 1.55 per 10 mug/m(3), 95% confidence intervals = 1.20-2.00). Cause-specific mortality analyses showed differences in terms of delayed effect: odds ratios were highest at 1-3 days after exposure for most outcomes but at lags of 3-5 days for heart failure. Individual vulnerabilities to NO2 associated cardiovascular mortality also varied by cause of death, possibly highlighting the role of different behaviours and risk factors in the most susceptible groups. The risk of cardiovascular mortality was also increased on firework days and after public holidays, independent from NO2 and PM2.5 concentrations. This study confirms the association between ambient NO2, as a marker for primary emissions, and acute cardiovascular mortality in a specific setting around a major airport. Future research should clarify the role of additional air pollutants including ultra-fine particles on cardiovascular diseases to inform most efficient control measures

Comparing methods to impute missing daily ground-level PM10 concentrations between 2010-2017 in South Africa

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete

The role of extreme temperature in cause-specific acute cardiovascular mortality in Switzerland: a case-crossover study

Since the 2003 heatwave in Europe, evidence has been rapidly increasing on the association between extreme temperature and all-cause mortality. Little is known, however, about cause-specific cardiovascular mortality, effect modification by air pollution and aircraft noise, and which population groups are the most vulnerable to extreme temperature. We conducted a time-stratified case-crossover study in Zurich, Switzerland, including all adult cardiovascular deaths between 2000 and 2015 with precise individual exposure estimates at home location. We estimated the risk of 24,884 cardiovascular deaths associated with heat and cold using distributed non-linear lag models. We investigated potential effect modification of temperature-related mortality by fine particles, nitrogen dioxide, and night-time aircraft noise and performed stratified analyses across individual and social characteristics. We found increased risk of mortality for heat (odds ratio OR = 1.28 [95% confidence interval: 1.11-1.49] for 99th percentile of daily Tmean (24 degrees C) versus optimum temperature at 20 degrees C) and cold (OR = 1.15 [0.95-1.39], 5th percentile of daily Tmean (-3 degrees C) versus optimum temperature at 20 degrees C). Heat-related mortality was particularly strong for myocardial infarctions and hypertension related deaths, and among older women (>75 years). Analysis of effect modification also indicated that older women with lower socio-economic position and education are at higher risk for heat-related mortality. PM2.5 increased the risk of heat-related mortality for heart failure, but not all-cause cardiovascular mortality. This study provides useful information for preventing cause-specific cardiovascular temperature-related mortality in moderate climate zones comparable to Switzerland

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM10) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM10 data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM10 concentrations at 1 km x 1 km spatial resolution across the four provinces. An out-of-bag R(2) of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM10 with a spatial CV R(2) of 0.48 and temporal CV R(2) of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data