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      Does Temperature Modify the Effects of Rain and Snow Precipitation on Road Traffic Injuries?

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          Abstract

          Background

          There are few data on the interaction between temperature and snow and rain precipitation, although they could interact in their effects on road traffic injuries.

          Methods

          The integrated database of the Korea Road Traffic Authority was used to calculate the daily frequency of road traffic injuries in Seoul. Weather data included rain and snow precipitation, temperature, pressure, and fog from May 2007 to December 2011. Precipitation of rain and snow were divided into nine and six temperature range categories, respectively. The interactive effects of temperature and rain and snow precipitation on road traffic injuries were analyzed using a generalized additive model with a Poisson distribution.

          Results

          The risk of road traffic injuries during snow increased when the temperature was below freezing. Road traffic injuries increased by 6.6% when it was snowing and above 0°C, whereas they increased by 15% when it was snowing and at or below 0°C. In terms of heavy rain precipitation, moderate temperatures were related to an increased prevalence of injuries. When the temperature was 0–20°C, we found a 12% increase in road traffic injuries, whereas it increased by 8.5% and 6.8% when it was <0°C and >20°C, respectively. The interactive effect was consistent across the traffic accident subtypes.

          Conclusions

          The effect of adverse weather conditions on road traffic injuries differed depending on the temperature. More road traffic injuries were related to rain precipitation when the temperature was moderate and to snow when it was below freezing.

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          Most cited references 29

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          Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.

          Reliable and timely information on the leading causes of death in populations, and how these are changing, is a crucial input into health policy debates. In the Global Burden of Diseases, Injuries, and Risk Factors Study 2010 (GBD 2010), we aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex. We attempted to identify all available data on causes of death for 187 countries from 1980 to 2010 from vital registration, verbal autopsy, mortality surveillance, censuses, surveys, hospitals, police records, and mortuaries. We assessed data quality for completeness, diagnostic accuracy, missing data, stochastic variations, and probable causes of death. We applied six different modelling strategies to estimate cause-specific mortality trends depending on the strength of the data. For 133 causes and three special aggregates we used the Cause of Death Ensemble model (CODEm) approach, which uses four families of statistical models testing a large set of different models using different permutations of covariates. Model ensembles were developed from these component models. We assessed model performance with rigorous out-of-sample testing of prediction error and the validity of 95% UIs. For 13 causes with low observed numbers of deaths, we developed negative binomial models with plausible covariates. For 27 causes for which death is rare, we modelled the higher level cause in the cause hierarchy of the GBD 2010 and then allocated deaths across component causes proportionately, estimated from all available data in the database. For selected causes (African trypanosomiasis, congenital syphilis, whooping cough, measles, typhoid and parathyroid, leishmaniasis, acute hepatitis E, and HIV/AIDS), we used natural history models based on information on incidence, prevalence, and case-fatality. We separately estimated cause fractions by aetiology for diarrhoea, lower respiratory infections, and meningitis, as well as disaggregations by subcause for chronic kidney disease, maternal disorders, cirrhosis, and liver cancer. For deaths due to collective violence and natural disasters, we used mortality shock regressions. For every cause, we estimated 95% UIs that captured both parameter estimation uncertainty and uncertainty due to model specification where CODEm was used. We constrained cause-specific fractions within every age-sex group to sum to total mortality based on draws from the uncertainty distributions. In 2010, there were 52·8 million deaths globally. At the most aggregate level, communicable, maternal, neonatal, and nutritional causes were 24·9% of deaths worldwide in 2010, down from 15·9 million (34·1%) of 46·5 million in 1990. This decrease was largely due to decreases in mortality from diarrhoeal disease (from 2·5 to 1·4 million), lower respiratory infections (from 3·4 to 2·8 million), neonatal disorders (from 3·1 to 2·2 million), measles (from 0·63 to 0·13 million), and tetanus (from 0·27 to 0·06 million). Deaths from HIV/AIDS increased from 0·30 million in 1990 to 1·5 million in 2010, reaching a peak of 1·7 million in 2006. Malaria mortality also rose by an estimated 19·9% since 1990 to 1·17 million deaths in 2010. Tuberculosis killed 1·2 million people in 2010. Deaths from non-communicable diseases rose by just under 8 million between 1990 and 2010, accounting for two of every three deaths (34·5 million) worldwide by 2010. 8 million people died from cancer in 2010, 38% more than two decades ago; of these, 1·5 million (19%) were from trachea, bronchus, and lung cancer. Ischaemic heart disease and stroke collectively killed 12·9 million people in 2010, or one in four deaths worldwide, compared with one in five in 1990; 1·3 million deaths were due to diabetes, twice as many as in 1990. The fraction of global deaths due to injuries (5·1 million deaths) was marginally higher in 2010 (9·6%) compared with two decades earlier (8·8%). This was driven by a 46% rise in deaths worldwide due to road traffic accidents (1·3 million in 2010) and a rise in deaths from falls. Ischaemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lower respiratory infections, lung cancer, and HIV/AIDS were the leading causes of death in 2010. Ischaemic heart disease, lower respiratory infections, stroke, diarrhoeal disease, malaria, and HIV/AIDS were the leading causes of years of life lost due to premature mortality (YLLs) in 2010, similar to what was estimated for 1990, except for HIV/AIDS and preterm birth complications. YLLs from lower respiratory infections and diarrhoea decreased by 45-54% since 1990; ischaemic heart disease and stroke YLLs increased by 17-28%. Regional variations in leading causes of death were substantial. Communicable, maternal, neonatal, and nutritional causes still accounted for 76% of premature mortality in sub-Saharan Africa in 2010. Age standardised death rates from some key disorders rose (HIV/AIDS, Alzheimer's disease, diabetes mellitus, and chronic kidney disease in particular), but for most diseases, death rates fell in the past two decades; including major vascular diseases, COPD, most forms of cancer, liver cirrhosis, and maternal disorders. For other conditions, notably malaria, prostate cancer, and injuries, little change was noted. Population growth, increased average age of the world's population, and largely decreasing age-specific, sex-specific, and cause-specific death rates combine to drive a broad shift from communicable, maternal, neonatal, and nutritional causes towards non-communicable diseases. Nevertheless, communicable, maternal, neonatal, and nutritional causes remain the dominant causes of YLLs in sub-Saharan Africa. Overlaid on this general pattern of the epidemiological transition, marked regional variation exists in many causes, such as interpersonal violence, suicide, liver cancer, diabetes, cirrhosis, Chagas disease, African trypanosomiasis, melanoma, and others. Regional heterogeneity highlights the importance of sound epidemiological assessments of the causes of death on a regular basis. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts.

            Road accident counts are influenced by random variation as well as by various systematic, causal factors. To study these issues, a four-country, segmented data base has been compiled, each segment consisting of monthly accident counts, along with candidate explanatory factors, in the various counties (provinces) of Denmark, Finland, Norway, or Sweden. Using a generalized Poisson regression model, we are able to decompose the variation in accident counts into parts attributable to randomness, exposure, weather, daylight, or changing reporting routines and speed limits. To this purpose, a set of specialized goodness-of-fit measures have been developed, taking explicit account of the inevitable amount of random variation that would be present in any set of accident counts, no matter how well known the accident generating Poisson process. Pure randomness is seen to "explain" a major part of the variation in smaller accident counts (e.g. fatal accidents per county per month), while exposure is the dominant systematic determinant. The relationship between exposure and injury accidents appears to be almost proportional, while it is less than proportional in the case of fatal accidents or death victims. Together, randomness and exposure account for 80% to 90% of the observable variation in our data sets. A surprisingly large share of the variation in road casualty counts is thus explicable in terms of factors not ordinarily within the realm of traffic safety policy. In view of this observation, it may seem unlikely that very substantial reductions in the accident toll can be achieved without a decrease in the one most important systematic determinant: the traffic volume.
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              • Record: found
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              • Article: not found

              Effects of snowfalls on motor vehicle collisions, injuries, and fatalities.

              We estimated the effects of snowfalls on US traffic crash rates between 1975 and 2000.
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                Author and article information

                Journal
                J Epidemiol
                J Epidemiol
                JE
                Journal of Epidemiology
                Japan Epidemiological Association
                0917-5040
                1349-9092
                5 August 2015
                13 June 2015
                2015
                : 25
                : 8
                : 544-552
                Affiliations
                [1 ]Department of Social and Preventive Medicine, Inha University School of Medicine, Incheon, Republic of Korea
                [2 ]Department of Preventive Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
                [3 ]Department of Epidemiology and Biostatistics, Seoul National University, Seoul, Republic of Korea
                [4 ]Institute of Environmental Medicine, Seoul National University of Medical Research Center, Seoul National University, Seoul, Republic of Korea
                [5 ]Department of Preventive Medicine, School of Medicine, Seoul National University, Seoul, Republic of Korea
                Author notes
                Address for correspondence. Hyesook Park, MD, PhD, Department of Preventive Medicine, School of Medicine, Ewha Womans University, 1071, Anyangcheon-ro, Yangcheon-ku, Seoul 158-710, Republic of Korea (e-mail: hpark@ 123456ewha.ac.kr ).
                Article
                JE20140244
                10.2188/jea.JE20140244
                4517993
                26073021
                © 2015 Won-Kyung Lee et al.

                This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Funding
                Funded by: INHA UNIVERSITY RESITY Research Grant
                Award ID: INHA-48964-1
                Funded by: the Korean Health Technology R&D Project, Ministry of Health & Welfare
                Award ID: HI13C0729
                Categories
                Original Article
                Environment

                precipitation, injuries, snow, traffic accidents, weather

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