Practical Application of Traffic Records

1. Practical Application of Traffic Records Barbara M. Stepanski, MPH; Janace Pierce, MS; Margaret Lutz, BA; Dale Cooper; Leslie Upledger Ray, MA, MPPA; Edward Castillo, PhD, MPH; Alan Smith, MPH

2. Background • There is a wealth of traffic record data now available • The real usefulness is in sharing this with the community, so responsive and targeted interventions/programs may be developed

3. Background • Patient record databases used to develop injury prevention campaigns • Prehospital patient records/run sheets • EMS Surveillance • Trauma Registry • Medical Examiner • State-Wide Integrated Traffic Records (SWITRS) • Child Safety Seat Inspection

4. Background • Prehospital, Trauma and ME records contain patient related data • Demographics -Injury and medical • Location of incident -Transport logistics • SWITRS records provide incident level data • Type of incident (vehicle type, location etc.) • Number of people involved, especially those not injured • Information on party at fault, contributing factors and violations • Incident specifics (road/weather conditions, speed) • All critical in developing effective injury prevention campaigns

5. Example 1Emergency Medical and Injury Atlas • Designed to visually display tabular data using choropleth mapping • Overall county and by regions • Contained information on injured patients by incident category (MV, MC, pedestrian) • Prehospital • Paramedic/EMT response • Trauma Registry • Severe injury • ME • Deaths

7. Research Question • Atlas identified the central San Diego community as having the highest rate of pedestrian injuries – why? • Community felt it was due to high number of drunk pedestrians • Investigators used SWITRS data to determine • Party at Fault Demographics • Victim Demographics • Causality and Violation • Incident location/logistics

12. Conclusions • Contrary to community opinion pedestrian crashes were rarely due to PUI (pedestrian under the influence) • Most common violations were failure to yield (driver turning right and looking left) and crossing outside of a crosswalk (jaywalking) • Community refocused pedestrian injury prevention campaign to emphasize these issues

13. Example 2Pediatric Pedestrian Injuries • One out of every seven injury calls for paramedic assistance to a child under age 15 was for pedestrian related injury • With these high numbers investigators questioned the logistics associated with such incidents • It was hypothesized that hours in which children travel to and from school would have an increased number of pedestrian injury crashes

14. Research Questions • How does prehospital data compare with SWITRS data for child pedestrian crashes? • How are child pedestrian injuries distributed • Demographically (age, gender, race/ethnicity) • Temporally (Day/night, day of week) • Geographically • What were the actions of the driver and the pedestrian leading up to the crash?

15. Pedestrian Action by Age Source: County of San Diego Health and Human Services Agency, Division of Emergency Medical Services, SWITRS database, FY 96/97 – 99/00

16. Pediatric PedestrianWhere and When? Mid-City SRA • The highest incidence • 99/100,000 for Prehospital • 135 /100,000 for SWITRS • Hours of Day – high incidence of injuries occurred during school travel times (7-9am and 2-6pm) • 45/100,000 for Prehospital • 66/100,000 for SWITRS

18. What Were They Doing? • 52% of the children injured were crossing the street outside of a crosswalk • The proportion struck while crossing outside of a crosswalk decreased with increasing age (57% aged 0-4; 59% aged 5-9; 42% aged 10-14) • 26% of the children injured were crossing the street at a crosswalk (55% were aged 10-14) • Contrary to common belief, only 4 (<1%) of the 1234 students were injured while approaching or leaving a school bus

19. Results • Most (46% of the Prehospital and 51% of the SWITRS) cases occurred in the hours children travel to and from school, which is 25% of the weekday hours. • Mid City area made up 6.8% of the county’s population under age 15, but accounted for 14% of the Prehospital and 15% of the SWITRS pedestrian incidents in this age group. • Victims were 60% male in Prehospital, 59% SWITRS

20. Conclusions • At the county level, the distribution of pedestrian injuries mirrors population density patterns. • Childhood pedestrian injuries tend to cluster in time according to when children may be walking to or from school. • School bus associated injuries do not contribute to pedestrian injuries to the degree previously thought. • Most childhood pedestrian injuries can be attributed to unsafe street-crossing behavior by the child.

21. Example 3 Elderly Drivers • Concerns • Physical limitations • Possibly higher crash rates • More severe injuries due to fragility of aging bodies • Proposed license restrictions • Reverse graduated licensing

22. Elderly DriversPopulation • Nationally, 70+ age group increased 2.1 times faster than the total population from 1989 to 1998 • Locally, the population over 55 is expected to more than double by 2020 • The number of drivers and collisions are expected to increase proportionally

23. Elderly DriversResearch Questions • What is the crash rate in elderly? • Compared with other age groups • Severity • What factors contribute to crashes involving elderly drivers? • What impact do older drivers have on the overall population?

24. SWITRSPercent of Drivers at Fault by Age* % *Drivers at fault/Drivers in injury collisions Source: SWITRS Database, FY 99/00

25. SWITRSRate (per Licensed Drivers) of At-Fault Collision by Age* *Drivers at fault/Licensed drivers Source: SWITRS Database, FY 99/00

26. Conclusions • Crash rates and at-fault rates are lower in seniors than previously thought • Crash rates of 55-64 and 65-74 are comparable to middle-aged drivers • Highest crash and fault rates in youngest drivers (16-24) • Percent at fault comparable to 16-24 year-olds

27. Conclusions (cont.) • Results question the proposed restrictions on elderly drivers, which may be detrimental to their independence • Results support the need to focus safe driving interventions on young drivers

28. Example 4 Child Safety Seat Studies • Prehospital data provides child safety seat use rates for those children injured in a crash • SWITRS records provide child safety seat use rates for those children involved in injury crashes, violations, faults • Seat inspections provide child safety seat misuse rates for those inspected

29. Child Safety Seat Studies - Results • Prehospital setting • 8% of children 0-9 seen by paramedics/EMTs are due to MV crashes • 50% 0-4 YO are not restrained in a child seat • 11% of 0-4 YO are completely unrestrained • 20% of 5-9 YO are completely unrestrained • Child passengers of impaired drivers were significantly (p=.03) less likely to wear active restraints than passengers in crashes where the other driver was at fault. • CSS Inspections • No inspection events occurred in the most densely populated 0-9 year old population areas • Majority of participants traveled up to 15 miles to attend an event

30. Why Are Traffic Records Important to Public Health? • Contain critical information on the event • How it happened? • Who was involved? • What they did/last action? • Who was at fault? • What were the contributing factors? • This information allows public health to develop prevention campaigns that target the event not the injured victim

31. Acknowledgements The investigators would like to thank Buckle Up San Diego San Diego Safe Communities 2000 San Diego Safe Kids Coalition San Diego County Prehospital Agencies, Hospitals, and Paramedics/EMTs For more information please contact: Barbara M. Stepanski, MPH County of San Diego HHSA Division of Emergency Medical Services 6255 Mission Gorge Road San Diego, CA 92120 phone - (619) 285-6429 Barbara.Stepanski@sdcounty.ca.gov