Effect of season and weather on pediatric emergency department use

It is commonly believed that emergency department (ED) use is affected by extreme weather. To test this hypothesis, data concerning use of a pediatric ED during three seasonally diverse months was analyzed in the light of Weather Bureau information concerning daily conditions during the study months. Seven measures of extreme weather were defined: 1) extreme cold (daily high temperature less than or equal to 25 degrees F); 2) extreme heat (daily high temperature greater than or equal to 88 degrees F); 3) unusual cold (winter) with departure from normal of mean temperature less than -10 degrees F; 4) unusual heat (summer) with departure from normal of mean temperature greater than 10 degrees F; 5) precipitation greater than or equal to 0.25 inches (in water-equivalent inches); 6) stormy (thunderstorm, hail, ice, or blowing snow); 7) snow-covered (greater than or equal to 6 inches of snow on the ground). Seasonal use patterns were examined and the proportion of days with each weather factor was compared with the proportion of visits on days with the factor. The data indicate 1) season has a major affect on ED use because it affects prevalence of disease and injury; 2) extremely cold and stormy conditions significant reductions in ED use of approximately 5-20%; 3) 80-95% of expected visits are made on days with very bad weather. The data indicate that weather is a minor factor in determining ED use.     

The influence of the weather and the phase of the moon on post-operative nausea and vomiting

BACKGROUND: Post-operative nausea and vomiting (PONV) is believed and previously reported to be influenced by the weather and the phase of the moon. We therefore determined the effects of specific and general weather patterns as well as the lunar phase on PONV in adults undergoing balanced inhalation anaesthesia. METHODS: The incidence of PONV was prospectively evaluated in 1801 patients undergoing elective surgical, urologic and head and neck procedures. Air temperature, barometric pressure, air water vapour pressure and the general weather situation were obtained from the National Weather Institute in Germany on the days of surgery. Corresponding categories of temperature, pressure, vapour pressure and their day-to-day changes, the general weather situation and the phase of the moon were used to group the patient data. The differences between the proportion of patients having PONV and the proportion predicted to have PONV according to their calculated risk were determined for each category. Further, bivariate and multivariate testing was applied. RESULTS: Within 24 h after anaesthesia, PONV occurred in 555 of the patients (31%). There was no correlation between weather conditions and PONV occurrence or between the phase of the moon and PONV occurrence. Even when corrected for the patients' risk and other potentially confounding factors in multivariate logistic regression analysis, no statistically significant impact of the hypothesized factors could be detected. CONCLUSION: These data suggest that neither the weather nor the phases of the moon have any clinically relevant effect on the incidence of PONV after general anaesthesia.     

Seizure frequency and bioelectric brain activity in epileptic patients in stable and unstable atmospheric pressure and temperature in different seasons of the year--a preliminary report

Background and purposeAn epileptic seizure is a sum of exogenous and endogenous factors affecting an epileptic focus. The aim of the study was to examine the influence of changes in atmospheric pressure and temperature on the increase in the frequency of seizures and changes in EEG in epileptic patients.Material and methodsThe study included 30 epileptic patients (aged 19–54) reporting the influence of changes in weather conditions on the increase in the frequency of seizures for at least 2 years. EEG was performed twice each season at the time of stable and unstable weather conditions.ResultsIn stable and unstable weather conditions, epileptic changes in EEG were most often found in winter (in 43.3% and 63.3% of patients, respectively). Unstable weather conditions increased the proportion of patients with epileptic changes in EEG also in the other seasons. Unstable weather conditions caused an increase in the frequency of seizures in 40% of patients in spring, 43.3% in autumn, 40% in winter and in approximately 7% in summer.ConclusionsIn spring, autumn and winter, unstable weather conditions cause an increase in the frequency of seizures in almost half of the epileptic patients but only in 7% in summer. The increase in frequency of seizures in unstable weather conditions did not correspond in all patients with increase of changes in EEG. The higher proportion of epileptic patients with changes in EEG in unstable weather conditions in all seasons suggests an impact of these conditions on subclinical seizure discharges in this period.     

气温骤降条件下炎性细胞因子、基因表达与卒中发病的关系

除受传统危险因素影响之外,卒中发病还与气温骤降有关.对绝大多数地区的研究发现,卒中与冷天气关系密切,后者通过多种机制影响卒中的发病,如血管调节机制的季节变化、血液成分的改变以及对感染因素的影响等.研究证实,炎症凶子在动脉粥样硬化和卒中发病中起着重要作用,而寒冷应激对卒中患者基凶表达产生的影响也町能起着重要作用.文章概述了气温骤降与卒中发病的关系和机制.     

气温骤降条件下兴奋性氨基酸与卒中发病的关系

气象因与脑血管病的发病关系密切,气温急剧下降町显著增高卒中发病风险.寒冷天气促进卒中发病的机制有许多,而兴奋性氨皋酸过度释放与卒中风险增高有一定关系.文章对气温骤降与卒中的关系、气温骤降导致兴奋性氨基酸增加的可能机制以及兴奋性氨基酸诱发卒中的机制做了综述.     

Olas de calor,temperatura ambiente y riesgo de infarto de miocardio: un estudio ecológico en la Comunidad de Madrid

Introduction and objectivesEpisodes of extreme heat are associated with increased morbidity and mortality in chronically-ill patients but there is a need to clearly establish the relationship between extreme heat and myocardial infarction. The aim of this study was to analyze the relationship between the incidence of ST-segment elevation myocardial infarction (STEMI) and maximum temperature, in particular during heat wave alert periods (HWAP).MethodsThe population studied consisted of confirmed STEMI cases registered in the Infarction Code of the Community of Madrid between June 2013 and June 2017. Incidence rate ratios (IRR) adjusted for trend and seasonality and 95%CI were estimated using time series regression models.ResultsA total of 6465 cases of STEMI were included; 212 cases occurred during the 66-day period of HWAP and 1816 cases during the nonalert summer period (IRR, 1.14; 95%CI, 0.96-1.35). The minimum incidence rate was observed at the maximum temperature of 18 °C. Warmer temperatures were not associated with a higher incidence (IRR,1.03; 95%CI, 0.76-1.41), whereas colder temperatures were significantly associated with an increased risk (IRR, 1.25; 95%CI, 1.02-1.54). No effect modification was observed by age or sex.ConclusionsWe did not find an increased risk of STEMI during the 66 days of HWAP in the Community of Madrid between June 2013 and June 2017. However, an increased risk was found during colder temperatures. No extra health resources for STEMI management are required during periods of extreme heat, but should be considered during periods of cold weather.     

Discharge Planning in the Era of Climate Change

We are living in an era of climate change, which has a tremendous impact on the health of our patients. Therefore, radiological nurses should be aware of and address climate change–related problems that impact patient health, such as heat, air quality, drought, wildfires, increased precipitation, and extreme weather. This article highlights the concerns and consequences of climate change on patients discharged from interventional radiological and other outpatient settings. Recommendations for discharge planning are provided to support, protect, and promote the health of patients in radiological services.     

Forecast-based attribution of a winter heatwave within the limit of predictability

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model–based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The “storyline” attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event that was predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019.

Attribution of extreme weather events is a relatively young field of research within climate science. However, it has expanded rapidly from its conceptual introduction (1) over the past 20 y; it now has an annual special issue in The Bulletin of the American Meteorological Society (2). Extreme event attribution is of particular importance for communicating the impacts of climate change to the public (3, 4), since the changing frequency of extreme weather events due to climate change is an impact that is physically experienced by society. As a result of this rapid expansion, there now exist a large number of different methodologies for carrying out an event attribution (5). Many of these rely on large ensembles of climate model simulations, the credibility of which has been questioned by recent studies (6–8). A particular issue is the dynamical response of the atmosphere to external forcing, which is highly uncertain within these models (9). As attribution studies try to provide quicker results, with an operational system a clear aim, it is vital that any such system provides trustworthy results. In this study we propose a “forecast-based” attribution methodology using medium-range weather forecasts that could provide several key advantages over traditional climate model-based approaches. First, if an event is predictable within a forecasting system, we know that that system is capable of accurately representing the event. Second, we know that any attribution performed is unequivocally an attribution of the specific event that occurred, unlike in unconditioned climate model simulations. Finally, weather forecasts are run routinely by many different national and research centers. The models used are generally state of the art and extensively verified. We propose that the attribution community could and should take advantage of the massive amount of resources that are put into these forecasts by developing methodologies that use the same type of simulation. Ideally, the experiments required for attribution with forecast models would be able to be run with little additional effort on top of the routine weather forecasts; in this way they might provide a rapid operational attribution system. We discuss these ideas further throughout the text.There have been several studies that propose or perform methodologies related to the forecast-based attribution demonstrated here. Hoerling et al. (10) used two seasonal forecast ensembles to examine the predictability of the 2011 Texas drought/heatwave within a comprehensive attribution analysis involving several different types of climate simulation. Meredith et al. (11) used a triply nested convection-permitting regional forecast model to investigate the role of historical sea surface temperature (SST) warming within an extreme precipitation event. They conditioned their analysis on the large-scale dynamics of the event through nudging in the outermost domain. More recently, Van Garderen et al. (12) employed spectrally nudged simulations to assess the contribution of human influence on the climate over the 20th century on the 2003 European and 2010 Russian heatwaves. Possibly the most similar studies to the one presented here are a series of studies by Hope et al. (13–15). They used a seasonal forecast model to assess anthropogenic CO₂ contributions to record-breaking heat and fire weather in Australia. Two more similar studies carried out forecast-based hurricane attribution studies (16, 17). Tropical cyclones are a natural candidate for forecast-based methodologies due to the high model resolution required to represent them accurately, if at all. A final distinct, but related study is Hannart et al. (18), which proposes the use of data assimilation for detection and attribution (DADA). They suggest that operational causal attribution statements could be made in a computationally efficient manner using the kind of data assimilation procedure carried out by weather centers (to initialize forecasts) to compute the likelihood of a particular weather event under different forcings (these would be observed and estimated preindustrial forcings for conventional attribution). Our forecast-based framework differs from these other studies in several regards. First, we use a state-of-the-art forecast model to perform the attribution analysis of the event in question, rather than to solely assess the predictability of the event. We use free-running coupled ocean-atmosphere global integrations here, allowing the predictable component at initialization to dynamically condition the ensemble, as opposed to nudging our simulations toward the dynamics of the event, using nested regional simulations or using the highly observationally constrained output of data assimilation procedures. A final key difference is that here we present an attribution of the direct radiative effect of CO₂ in isolation, although we hope that our approach could be extended in the future to provide an estimate of the full anthropogenic contribution to extreme weather events as in these other studies. We argue that the relative simplicity in the validation, setup, and conditioning of our simulations is desirable from an operational attribution perspective and flexible across many different types of extreme event.We begin by introducing the chosen case study, the 2019 February heatwave in Europe, describing its synoptic characteristics and formally defining the event quantitatively. We then demonstrate the predictability of the event within the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system, showing that this operational weather forecast was able to capture both the dynamical and thermodynamical features of the event. In perturbed CO₂ forecasts, we outline the experiments we have performed to quantitatively determine the direct CO₂ contribution to the heatwave. We then provide quantitative results from these experiments and finally conclude with a discussion of the strengths and potential issues of our forecast-based attribution methodology, including our proposed directions for further work.     

Effect of weather variables on the incidence of trauma: A retrospective study at a single tertiary hospital center for 4 years

The occurrence of trauma is associated with various factors, including weather. We aimed to elucidate the relationship between local weather factors and the incidence of trauma to effectively manage and treat patients in a community setting. A retrospective study was conducted at a single center from January 2016 to December 2019. The study participants were trauma patients in the Cheongju area where the regional trauma center is located. Weather data including average daily temperature (°C), rainfall duration (hours), amount of rainfall (mm), average relative humidity (%), wind speed (m/s), and total sunlight hours per day were collected. One-way analysis of variance, correlation analysis, and linear regression analysis were performed. The average age of the participants (n = 3352) was 52.69 years. As regards seasonal difference in the incidence of trauma, there were more patients in spring than in winter (2.42/day vs 2.06/day, P = .05). The highest number of average daily trauma incidents occurred from April to June, and the difference between this value and that from January to February was significant (F = 2.20, P = .01). According to the distributed lag nonlinear model (DLnM), the relative risk is greater than 1 when the mean temperature is high (>15°C) compared to when the temperature is low (<15°C). The trauma patient prevalence was the highest at high wind speed (4.5 m/s). When the total amount of sunlight was long (>Ref. 8 hours), the trauma patient prevalence was relatively higher than the median value (lag = 0). DLnM analysis results showed that the relative risk of trauma patients increased as the amount of precipitation increased, and the incidence of trauma increased when the relative humidity was 40% to 50%. Multiple linear regression analysis revealed that high average daily temperatures and long average daily total sunlight hours resulted in an increased incidence of trauma (F = 6.605, P < .001). An increase in temperature, an increase in the daily sunlight hours, an increase in rainfall, high wind speed, and relative humidity of 40% to 50% are associated with a relatively high risk of trauma.     

Influence of Hazy Weather on Patient Presentation with Respiratory Diseases in Beijing,China

Background: Chronic respiratory disease is an important factor for development of lung cancer. To explorethe influence of hazy weather on respiratory diseases and its variation the present study was conducted. Materialsand Methods: Data from air pollution surveillance from January to October 2014 and case records of visitingpatients in the 263th Hospital of Chinese PLA in the corresponding period were collected to analyze the relevancebetween different degrees of air pollution (hazy weather) and the number of visiting patients in Department ofRespiratory Disease. Results: Air quality index (AQI) of hazy weather had significantly positive association withparticulate matter 2.5 (PM2.5) and the number of patients with 5 kinds of respiratory diseases i and differentpollutants had distinct influences on various respiratory diseases. Conclusions: The degree of air pollution inBeijing City is in close association with the number of patients with respiratory diseases, in which PM2.5 andSO2 are in more significant influences on all respiratory diseases. This could have essential implications for lungcancer development in China.