Heat-related cardiovascular morbidity and mortality in Switzerland: a clinical perspective

AIMS: Previous studies found increased cardiovascular mortality during hot days, while emergency hospital admissions were decreasing. We explored potential underlying reasons by analysing clinically similar cardiovascular disease groups taking into account primary, underlying and immediate causes of death. METHODS AND RESULTS: We assessed associations of daytime maximum temperature in relation to cardiovascular deaths and emergency hospital admissions between 1998 and 2016 in Switzerland. We applied conditional quasi-Poisson models with non-linear distributed lag functions to estimate relative risks (RRs) of daily cardiovascular mortality and morbidity for temperature increases from the median (22 degrees C) to the 98th percentile (32 degrees C) of the warm season temperature distribution with 10 days of lag. Cardiovascular mortality (n = 163,856) increased for total cardiovascular disease (RR 1.13, 95% confidence interval [CI] 1.08-1.19) and the disease groups hypertension (1.18, 1.02-1.38), arrhythmia (1.29, 1.08-1.55), heart failure (1.22, 1.05-1.43) and stroke of unknown origin (1.20, 1.02-1.4). In contrast, emergency hospital admissions (n = 447,577) decreased for total cardiovascular disease (0.91, 0.88-0.94), hypertension (0.72, 0.64-0.81), heart failure (0.83, 0.76-0.9) and myocardial infarction (0.88, 0.82-0.95). Opposing heat effects were most pronounced for disease groups associated with diuretic and antihypertensive drug use, with the age group >/=75 years at highest risk. CONCLUSIONS: Volume depletion and vasodilation from heat stress plausibly explain the risk reduction of heat-related emergency hospital admissions for hypertension and heart failure. Since primary cause of death mostly refers to the underlying chronic disease, the seemingly paradoxical heat-related mortality increase can plausibly be explained by an exacerbation of heat effects by antihypertensive and diuretic drugs. Clinical guidelines should consider recommending strict therapy monitoring of such medication during heatwaves, particularly in the elderly

Estimation of heat-attributable mortality using the cross-validated best temperature metric in Switzerland and South Korea

This study presents a novel method for estimating the heat-attributable fractions (HAF) based on the cross-validated best temperature metric. We analyzed the association of eight temperature metrics (mean, maximum, minimum temperature, maximum temperature during daytime, minimum temperature during nighttime, and mean, maximum, and minimum apparent temperature) with mortality and performed the cross-validation method to select the best model in selected cities of Switzerland and South Korea from May to September of 1995-2015. It was observed that HAF estimated using different metrics varied by 2.69-4.09% in eight cities of Switzerland and by 0.61-0.90% in six cities of South Korea. Based on the cross-validation method, mean temperature was estimated to be the best metric, and it revealed that the HAF of Switzerland and South Korea were 3.29% and 0.72%, respectively. Furthermore, estimates of HAF were improved by selecting the best city-specific model for each city, that is, 3.34% for Switzerland and 0.78% for South Korea. To the best of our knowledge, this study is the first to observe the uncertainty of HAF estimation originated from the selection of temperature metric and to present the HAF estimation based on the cross-validation method

Particle number and mass exposure concentrations by commuter transport modes in Milan, Italy

There is increasing awareness amongst the general public about exposure to atmospheric pollution while travelling in urban areas especially when taking active travelling modes such as walking and cycling. This study presents a comparative investigation of ultrafine particles (UFP), PM10, PM2.5, PM1 exposure levels associated with four transport modes (i.e., walking, cycling, car, and subway) in the city of Milan measured by means of portable instruments. Significant differences in particle exposure between transport modes were found. The subway mode was characterized by the highest PM mass concentrations: PM10, PM2.5, PM1 subway levels were respectively about 2-4-3 times higher than those of the car and open air active modes (i.e. cycling and walking). Conversely, these latter modes displayed the highest UFP levels about 2 to 3 times higher than the subway and car modes, highlighting the influence of direct traffic emissions. The car mode (closed windows, air conditioning and air recirculation on) reported the lowest PM and UFP concentration levels. In particular, the open-air/car average concentration ratio varied from about 2 for UFP up to 4 for PM1 and 6 for PM10 and PM2.5, showing differences that increase with increasing particle size. This work points out that active mode travelling in Milan city centre in summertime results in higher exposure levels than the car mode. Walkers’ and cyclists’ exposure levels is expected to be even higher during wintertime, due to the higher ambient PM and UFP concentration. Interventions intended to re-design the urban mobility should therefore include dedicated routes in order to limit their exposure to PM and UFP by increasing their distance from road traffic

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

The air and viruses we breathe: assessing the effect the PM2.5 air pollutant has on the burden of COVID-19

Evidence suggests an association between air pollutant exposure and worse outcomes for respiratory viral diseases, like COVID-19. However, does breathing polluted air over many years affect the susceptibility to SARS-CoV-2 infection or severity of COVID-19 disease, and how intense are these effects? As climate change intensifies, air pollutant levels may rise, which might further affect the burden of respiratory viral diseases. We assessed the effect of increasing exposure to PM2.5 (particulate matter ≤ 2.5 microns in diameter) on SARS-CoV-2 susceptibility or COVID-19 severity and projected the impact on infections and hospitalisations over two years. Simulations in a hypothetical, representative population show that if exposure affects severity, then hospital admissions are projected to increase by 5-10% for a one-unit exposure increase. However, if exposure affects susceptibility, then infections would increase with the potential for onward transmission and hospital admissions could increase by over 60%. Implications of this study highlight the importance of considering this potential additional health and health system burden as part of strategic planning to mitigate and respond to changing air pollution levels. It is also important to better understand at which point PM2.5 exposure affects SARS-CoV-2 infection through to COVID-19 disease progression, to enable improved protection and better support of those most vulnerabl

№ 107. Додаткове свідчення Миколи Чехівського від 27 вересня 1929 р.

In the headwater catchments of the main Asian rivers, glaciohydrological models are a useful tool to anticipate impacts of climatic changes. However, the reliability of their projections strongly depends on the quality and quantity of data that are available for parameter estimation, model calibration and validation, as well as on the accuracy of climate change projections. In this study the physically oriented, glaciohydrological model TOPKAPI-ETH is used to simulate future changes in snow, glacier, and runoff from the Hunza River Basin in northern Pakistan. Three key sources of model uncertainty in future runoff projections are compared: model parameters, climate projections, and natural climate variability. A novel approach, applicable also to ungauged catchments, is used to determine which model parameters and model components significantly affect the overall model uncertainty. We show that the model is capable of reproducing streamflow and glacier mass balances, but that all analyzed sources of uncertainty significantly affect the reliability of future projections, and that their effect is variable in time and in space. The effect of parametric uncertainty often exceeds the impact of climate uncertainty and natural climate variability, especially in heavily glacierized subcatchments. The results of the uncertainty analysis allow detailed recommendations on network design and the timing and location of field measurements, which could efficiently help to reduce model uncertainty in the future