PRECIPITATION AND FATAL MOTOR VEHICLE CRASHES: Continental Analysis with High-Resolution Radar Data.
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SUBMITTER: Stevens SE
PROVIDER: S-EPMC10248718 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
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PRECIPITATION AND FATAL MOTOR VEHICLE CRASHES: Continental Analysis with High-Resolution Radar Data.
Stevens Scott E SE Schreck Carl J CJ Saha Shubhayu S Bell Jesse E JE Kunkel Kenneth E KE
Bulletin of the American Meteorological Society 20190801 8
Precipitation, even at light intensity, contributes a significant risk of fatal motor vehicle crashes across the United States, at nearly all times of day, and in all seasons. ...[more]
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Project description:BackgroundIn Japan, introduction of severe drunk-driving penalties and a lower blood alcohol concentration (BAC) limit in June 2002 was followed by a substantial reduction in fatal alcohol-related crashes. However, previous research suggests that this reduction started before the legal amendments. The causes of the decrease have not been studied in detail.MethodsMonthly police data on fatal road traffic crashes from January 1995 to August 2006 were analyzed using a joinpoint regression model to identify change-points in the trends of the proportion of drunk-driving among drivers primarily responsible for fatal crashes. We analyzed the data by BAC level (≥0.5 or <0.5 mg/ml), then conducted analyses stratified by vehicle type (car or motorcycle) and age group (<45 or ≥45 years) only for the proportion of those with a BAC of 0.5 mg/ml or higher.ResultsAmong all drivers, the proportion of those with a BAC of 0.5 mg/ml or higher and those with a BAC greater than 0 but less than 0.5 mg/ml showed a change-point from increase to decrease in February 2000 and in May 2002, respectively. The proportion of those with a BAC of 0.5 mg/ml or higher showed a change-point from increase to decrease in October 1999 among car drivers and in April 2000 among drivers younger than 45 years. There was no change-point among motorcyclists. A change-point from no trend to a decrease in January 2002 was observed among those 45 years or older.ConclusionsThe change-point identified around the end of 1999 to the start of 2000 suggests that a high-profile fatal crash in November 1999, which drew media attention and provoked public debate, triggered subsequent changes in drunk-driving behavior.
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Project description:ObjectiveWhile attention has been given to how legalization of recreational cannabis affects traffic crash rates, there was been limited research on how cannabis affects pedestrians involved in traffic crashes. This study examined the association between cannabis legalization (medical, recreational use, and recreational sales) and fatal motor vehicle crash rates (both pedestrian-involved and total fatal crashes).MethodsWe used crash data from the Fatality Analysis Reporting System (FARS) to calculate monthly rates of fatal motor vehicle crashes and fatal pedestrian-involved crashes per 100,000 people from 1991 to 2018. Changes in monthly crash rates in three states that had legalized cannabis (Colorado, Washington, and Oregon) were compared to matched control states using segmented regression with autoregressive terms.ResultsWe found no significant differences in pedestrian-involved fatal motor vehicle crashes between legalized cannabis states and control states following medical or recreational cannabis legalization. Washington and Oregon saw immediate decreases in all fatal crashes (-4.15 and -6.60) following medical cannabis legalization. Colorado showed an increase in trend for all fatal crashes after recreational cannabis legalization and the beginning of sales (0.15 and 0.18 monthly fatal crashes per 100,000 people).ConclusionsOverall findings do not suggest an elevated risk of total or pedestrian-involved fatal motor vehicle crashes associated with cannabis legalization.
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Project description:BackgroundMotor vehicle crashes are a leading cause of serious trauma during pregnancy, but little is known about their relationships with pregnancy outcomes.PurposeTo estimate the association between motor vehicle crashes and adverse pregnancy outcomes.MethodsA retrospective cohort study of 878,546 pregnant women, aged 16-46 years, who delivered a singleton infant in North Carolina from 2001 to 2008. Pregnant drivers in crashes were identified by probabilistic linkage of vital records and crash reports. Poisson regression modeled the association among crashes, vehicle safety features, and adverse pregnancy outcomes. Analyses were conducted in 2012.ResultsIn 2001-2008, 2.9% of pregnant North Carolina women were drivers in one or more crashes. After a single crash, compared to not being in a crash, pregnant drivers had slightly elevated rates of preterm birth (adjusted rate ratio [aRR]=1.23, 95% CI=1.19, 1.28); placental abruption (aRR=1.34, 95% CI=1.15, 1.56); and premature rupture of the membranes (PROM; aRR=1.32, 95% CI=1.21, 1.43). Following a second or subsequent crash, pregnant drivers had more highly elevated rates of preterm birth (aRR=1.54, 95% CI=1.24, 1.90); stillbirth (aRR=4.82, 95% CI=2.85, 8.14); placental abruption (aRR=2.97, 95% CI=1.60, 5.53); and PROM (aRR=1.95, 95% CI=1.27, 2.99). Stillbirth rates were elevated following crashes involving unbelted pregnant drivers (aRR=2.77, 95% CI=1.22, 6.28) compared to belted pregnant drivers.ConclusionsCrashes while driving during pregnancy were associated with elevated rates of adverse pregnancy outcomes, and multiple crashes were associated with even higher rates of adverse pregnancy outcomes. Crashes were especially harmful if drivers were unbelted.
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Project description:We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 timesteps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section at 5 min sampling rate, covering an area of 240 km of diameter at 500 m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validate TAASRAD19 as a benchmark for nowcasting methods by introducing a TrajGRU deep learning model to forecast reflectivity, and a procedure based on the UMAP dimensionality reduction algorithm for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available on GitHub ( https://github.com/MPBA/TAASRAD19 ) for study replication and reproducibility.
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