Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: A geospatial analysis of five years of police crash and census data.
BACKGROUND
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.


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.


CONCLUSIONS
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.
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.
Zachary
Z
Dezman
State University of Maringa/UEM, Maringa, Brazil.
Luciano
L
de Andrade
Division of Global Neurosurgery and Neurosciences, Duke University, Durham, North Carolina, United States; Duke Global Health Institute, Duke University, Durham, North Carolina, United States.
Joao Ricardo
JR
Vissoci
Duke Global Health Institute, Duke University, Durham, North Carolina, United States.
Deena
D
El-Gabri
National Study Center for Trauma and Emergency Medical Services, University of Maryland School of Medicine, Baltimore, Maryland, United States.
Abree
A
Johnson
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.
Jon Mark
JM
Hirshon
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.
Catherine A
CA
Staton
D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000367:Age Factors; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001336:Automobiles; D015142:Baltimore / Q000453:epidemiology; D019352:Censuses; D003430:Cross-Sectional Studies; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D009049:Motorcycles; D016495:Police; D012307:Risk Factors; D017678:Sex Distribution; D012959:Socioeconomic Factors; D062206:Spatial Analysis; D019966:Substance-Related Disorders / Q000453:epidemiology*; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000517:prevention & control; D055815:Young Adult
Geographic information system; Hotspot analysis; Road traffic crash