Zachary Dezman1, Luciano de Andrade2, Joao Ricardo Vissoci3, Deena El-Gabri4, Abree Johnson5, Jon Mark Hirshon6, Catherine A Staton7. 1. Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States; National Study Center for Trauma and Emergency Medical Services, University of Maryland School of Medicine, Baltimore, Maryland, United States. 2. State University of Maringa/UEM, Maringa, Brazil. 3. Division of Global Neurosurgery and Neurosciences, Duke University, Durham, North Carolina, United States; Duke Global Health Institute, Duke University, Durham, North Carolina, United States. 4. Duke Global Health Institute, Duke University, Durham, North Carolina, United States. 5. National Study Center for Trauma and Emergency Medical Services, University of Maryland School of Medicine, Baltimore, Maryland, United States. 6. Duke Global Health Institute, Duke University, Durham, North Carolina, United States; Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States; National Study Center for Trauma and Emergency Medical Services, University of Maryland School of Medicine, Baltimore, Maryland, United States. 7. Division of Global Neurosurgery and Neurosciences, Duke University, Durham, North Carolina, United States; Duke Global Health Institute, Duke University, Durham, North Carolina, United States; Duke Emergency Medicine, Duke University Medical Center, Durham, North Carolina, United States. Electronic address: catherine.staton@duke.edu.
Abstract
INTRODUCTION: Road traffic injuries are a leading killer of youth (aged 15-29) and are projected to be the 7th leading cause of death by 2030. To better understand road traffic crash locations and characteristics in the city of Baltimore, we used police and census data, to describe the epidemiology, hotspots, and modifiable risk factors involved to guide further interventions. MATERIALS AND METHODS: Data on all crashes in Baltimore City from 2009 to 2013 were made available from the Maryland Automated Accident Reporting System. Socioeconomic data collected by the US CENSUS 2010 were obtained. A time series analysis was conducted using an ARIMA model. We analyzed the geographical distribution of traffic crashes and hotspots using exploratory spatial data analysis and spatial autocorrelation. Spatial regression was performed to evaluate the impact of socioeconomic indicators on hotspots. RESULTS: In Baltimore City, between 2009 and 2013, there were a total of 100,110 crashes reported, with 1% of crashes considered severe. Of all crashes, 7% involved vulnerable road users and 12% had elderly or youth involvement. Reasons for crashes included: distracted driving (31%), speeding (6%), and alcohol or drug use (5%). After 2010, we observed an increasing trend in all crashes especially from March to June. Distracted driving then youth and elderly drivers were consistently the highest risk factors over time. Multivariate spatial regression model including socioeconomic indicators and controlling for age, gender and population size did not show a distinct predictor of crashes explaining only 20% of the road crash variability, indicating crashes are not geographically explained by socioeconomic indicators alone. CONCLUSION: In Baltimore City, road traffic crashes occurred predominantly in the high density center of the city, involved distracted driving and extremes of age with an increase in crashes from March to June. There was no association between socioeconomic variables where crashes occurred and hotspots. In depth analysis of how modifiable risk factors are impacted by geospatial characteristics and the built environment is warranted in Baltimore to tailor interventions.
INTRODUCTION: Road traffic injuries are a leading killer of youth (aged 15-29) and are projected to be the 7th leading cause of death by 2030. To better understand road traffic crash locations and characteristics in the city of Baltimore, we used police and census data, to describe the epidemiology, hotspots, and modifiable risk factors involved to guide further interventions. MATERIALS AND METHODS: Data on all crashes in Baltimore City from 2009 to 2013 were made available from the Maryland Automated Accident Reporting System. Socioeconomic data collected by the US CENSUS 2010 were obtained. A time series analysis was conducted using an ARIMA model. We analyzed the geographical distribution of traffic crashes and hotspots using exploratory spatial data analysis and spatial autocorrelation. Spatial regression was performed to evaluate the impact of socioeconomic indicators on hotspots. RESULTS: In Baltimore City, between 2009 and 2013, there were a total of 100,110 crashes reported, with 1% of crashes considered severe. Of all crashes, 7% involved vulnerable road users and 12% had elderly or youth involvement. Reasons for crashes included: distracted driving (31%), speeding (6%), and alcohol or drug use (5%). After 2010, we observed an increasing trend in all crashes especially from March to June. Distracted driving then youth and elderly drivers were consistently the highest risk factors over time. Multivariate spatial regression model including socioeconomic indicators and controlling for age, gender and population size did not show a distinct predictor of crashes explaining only 20% of the road crash variability, indicating crashes are not geographically explained by socioeconomic indicators alone. CONCLUSION: In Baltimore City, road traffic crashes occurred predominantly in the high density center of the city, involved distracted driving and extremes of age with an increase in crashes from March to June. There was no association between socioeconomic variables where crashes occurred and hotspots. In depth analysis of how modifiable risk factors are impacted by geospatial characteristics and the built environment is warranted in Baltimore to tailor interventions.
Authors: Luciano de Andrade; João Ricardo Nickenig Vissoci; Clarissa Garcia Rodrigues; Karen Finato; Elias Carvalho; Ricardo Pietrobon; Eniuce Menezes de Souza; Oscar Kenji Nihei; Catherine Lynch; Maria Dalva de Barros Carvalho Journal: PLoS One Date: 2014-01-30 Impact factor: 3.240
Authors: David Watson; Blair Benton; Elizabeth Ablah; Kelly Lightwine; Ronda Lusk; Hayrettin Okut; Thuy Bui; James M Haan Journal: Kans J Med Date: 2021-01-21