Dataset Information

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

ABSTRACT: Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year.Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC).A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models.Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.

SUBMITTER: Roislien J

PROVIDER: S-EPMC5806884 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

Røislien Jo J Søvik Signe S Eken Torsten T

PloS one 20180209 2

<h4>Background</h4>Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent t ...[more]

PMID: 29425210

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Project description:BackgroundThe survival of coronaviruses are influenced by weather conditions and seasonal coronaviruses are more common in winter months. We examine the seasonality of respiratory infections in England and Wales and the associations between weather parameters and seasonal coronavirus cases.MethodsRespiratory virus disease data for England and Wales between 1989 and 2019 was extracted from the Second-Generation Surveillance System (SGSS) database used for routine surveillance. Seasonal coronaviruses from 2012 to 2019 were compared to daily average weather parameters for the period before the patient's specimen date with a range of lag periods.ResultsThe seasonal distribution of 985,524 viral infections in England and Wales (1989-2019) showed coronavirus infections had a similar seasonal distribution to influenza A and bocavirus, with a winter peak between weeks 2 to 8. Ninety percent of infections occurred where the daily mean ambient temperatures were below 10 °C; where daily average global radiation exceeded 500 kJ/m2/h; where sunshine was less than 5 h per day; or where relative humidity was above 80%. Coronavirus infections were significantly more common where daily average global radiation was under 300 kJ/m2/h (OR 4.3; CI 3.9-4.6; p < 0.001); where average relative humidity was over 84% (OR 1.9; CI 3.9-4.6; p < 0.001); where average air temperature was below 10 °C (OR 6.7; CI 6.1-7.3; p < 0.001) or where sunshine was below 4 h (OR 2.4; CI 2.2-2.6; p < 0.001) when compared to the distribution of weather values for the same time period. Seasonal coronavirus infections in children under 3 years old were more frequent at the start of an annual epidemic than at the end, suggesting that the size of the susceptible child population may be important in the annual cycle.ConclusionsThe dynamics of seasonal coronaviruses reflect immunological, weather, social and travel drivers of infection. Evidence from studies on different coronaviruses suggest that low temperature and low radiation/sunlight favour survival. This implies a seasonal increase in SARS-CoV-2 may occur in the UK and countries with a similar climate as a result of an increase in the R0 associated with reduced temperatures and solar radiation. Increased measures to reduce transmission will need to be introduced in winter months for COVID-19.

Project description:AbstractThe 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.

Dataset Information

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

Publications

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

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