1. “Banggaan sa daan, paano na si Juan?” A Case Control Study on the Exposure Factors Leading to the Occurrence of Vehicular Accident-Related Injuries Agustin RD, Aranjuez KB, Magat JL, Maglaque JA, Ocampo TC, Parco MD, Regalado AJ, Serrano KF, Tan JT, Tanbonliong BH

2. Introduction In many developing countries like the Philippines: 1.Road traffic accidents were the most significant cause of injuries, ranking eleventh among the most important causes of lost years of healthy life. 2. One hospital bed in ten is occupied by an accident victim. Traffic accidents are a major cause of severe injuries in most countries.

3. 3. One study estimates that PhP 3.5 Million is lost per fatal road accident showing a clear picture of their economic effects. 4. Social cost or pain, grief, and suffering are valued at PhP 506,450 per fatal accident. Estimation of socio-economic cost of road accidents in Metro Manila. Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3183 - 3198, 2005

4. Conceptual Framework Driver Exposure to factors -Environmental Factors daylight dawn/dusk night: lit night: unlit dry road surface wet road surface -Vehicular Factors Motorcycle motor tricycle Car Jeepney Taxi Bus truck rigid truck arctic Van others -Driver Error driving too fast Inattentiveness bad overtaking driving too close disobeying traffic signs/ lights others Positive for Vehicular Accident Related Injury (VARIs) Negative for Vehicular Accident Related Injury (VARIs) Vehicular Accident:

5. Statement of the Problem Are there certain factors that lead to the acquisition of vehicular accident related injuries (VARIs)? If so, what are these exposure factors?

6. Hypothesis If the driver is exposed to specific kinds of environmental, vehicular or driver factors prior to the accident then there is an increased odds of acquiring VARIs.

7. Research Objectives 1.Identify exposures involved with the occurrence of VARIs. 2.Identify the prevalence of each exposure. 3.Identify the strength of association between VARIs and each exposure (via Odds Ratio Analysis). 4.Rank each exposure according to degree of prevalence. 5.Rank each exposure according to strength of association with VARIs (via Odds Ratio).

8. Significance of the Study To help concerned authorities formulate necessary interventions which would help decrease the economic cost of accidents and possibly improve the future quality of life of drivers, commuters and private vehicle owners.

9. METHODS

10. Case Control Study Occurrence of a Vehicular Accident Positive for VARIs Negative for VARIs Identify the exposure/s suspected of causing VARIs

11. Data Collection

13. RESULTS

14. Overall Descriptive Statistics Sampling size = 2246 Controls = 2104 Cases = 142 o Minor injuries = 128 o Major injuries = 1 o Serious injuries = 13

15. Frequency according to Month MONTH No injury (CONTROL) With injury (CASE) Total Percent and Rank January16014174 7.7 (9) February14515160 7.1 (11) March17114185 8.3 (7.5) April1638171 7.6 (10) May17213185 8.3 (7.5) June19415209 9.3 (2) July19513208 9.2 (3.5) August18212194 8.6 (6) September19611207 9.2 (3.5) October1888196 8.7 (5) November21513228 10.1 (1) December1236129 5.7 (12)

16. Frequency according to Location Commonwealth13971191516 EDSA70723730 LOCATIONNo injury (CONTROL) With injury (CASE) Total

17. Objectives addressed Identify exposures involved in occurrence of VARIs Obtain prevalence and rank exposures according to prevalence Obtain strength of association and rank exposures according to strength of association

18. Objective: obtain prevalence and rank each exposure according to prevalence Motorcycle38911295.7 (6) Motor Tricycle6280.4 (10) Car8403087038.7 (1) Jeepney13941436.4 (5) Taxi171180.8 (9) Van21662229.9 (4) Bus410541518.5 (2) Truck (arctic)340 1.5 (8) Truck (rigid)11311145.1 (7) Other291229313.0 (3) VEHICLE TYPENo injury (CONTROL) With injury (CASE) TotalPercent and rank

19. HUMAN ERRORNo injury (CONTROL) With injury (CASE) TotalPercent and rank Driving too fast136181546.8 (3) Inattentive100181185.2 (4) Bad overtaking100 0.4 (5.5) Driving too close2981731514.0 (2) Fatigue/ Asleep3030.1 (9) none153987162672.4 (1) other9090.4 (5.5) bad turning6170.3 (7) no signal3140.2 (8)

20. No injury (CONTROL) With injury (CASE) TotalPercent and rank AMBIENT LIGHT daylight166792175978.3 (1) dawn/dusk767833.7 ()3 night (lit)2873632314.4 (3) night (unlit)747813.6 (4) SURFACE CONDITION Dry1770121189184.1 (1) Wet3342135515.8 (2)

21. Objective: Obtain and rank the strength of association between exposures and VARIs Fourteen (14) Non-significant exposures Motor tricycle, Jeepney, Taxi, Truck (arctic) Bad overtaking, Driving too close, Fatigue/asleep, Other, Bad turning, No signal Dawn/dusk, night (unlit) Dry, Wet surface conditions Eleven (11) Significant exposures Motorcycle, Car, Van, Bus, Truck (Rigid,) Other vehicle type (i.e., wagon) Inattentiveness, driving too fast, no human error (“none”) Daylight, night (lit) Significant: p<0.05 Not significant: p>0.05

22. ExposureORp-value Type of Association Protective/Injuring Motorcycle97.010.00PositiveInjuring Inattentive (human error)2.910.00PositiveInjuring Driving too fast (human error) 2.210.00PositiveInjuring Night (lit)2.150.00PositiveInjuring None (human error)0.580.00NegativeProtective Daylight0.480.00NegativeProtective Car0.400.00NegativeProtective Van0.390.03NegativeProtective Bus0.150.00NegativeProtective Truck Rigid0.120.02NegativeProtective Other (vehicle)0.090.00NegativeProtective

23. DISCUSSION

24. Discussion Vehicle-Type and VARIs o Motorcycle  Risk Factor (97 Times)  Had the most number of cases among the vehicle types  Higher rate of fatal accidents  Less stable than a 4-wheeled automobile o Protective factors:  Car  Bus  Van  Truck

25. Discussion Human Error and VARIs o Driving too Fast  As seen in the literature, this is also the human error that is most associated with the risk of VARIs  Literature cited that in Metro Manila, overspeeding is the top cause of vehicular accidents (Bonabente, 2006, Libres, 2008).  Ex. In EDSA, public utility vehicles keep on competing for passengers

26. Discussion Human Error and VARIs o Inattentiveness  Also reported in the literature as a major cause of accidents  One reason why it can cause VARIs is that, as the literature shows, when one is inattentive, it is usually brings about another human error (ex. overspeeding) (Bonabente, 2006)

27. Discussion Human Error and VARIs o None  Protective factor

28. Discussion Ambient Light and VARIs o Daylight  Protective factor  As what the literature reports, dimly-lit roads are more prone to accidents. (Tandoc, 2007) o Night (lit)  Risk Factor  People are confident about lit areas and take less precaution

29. Discussion Surface Condition and VARIs o No significant results o Dry  Suppose to assume that it is safer because the road is not slippery, and yet, majority of the reports were in dry roads

30. Discussion Surface Condition and VARIs o Wet  Expected to be a risk factor  People take more precaution  Lesser traffic enforcers to report Perhaps, there are other factors that are more associated with the risk of VARIs. In the literature, they looked at fog and smoke as major environmental factors

31. Other notable findings Based on descriptive statistics: o On the month where most vehicular accidents occur—can be correlated with volume  Literature shows that number of accidents is more common in areas with the greatest volume of cars o Commonwealth has more accidents with VARIs than EDSA

32. CONCLUSION AND RECOMMENDATIONS

33. Conclusion ExposureProtective/Injuring MotorcycleInjuring Inattentive (human error)Injuring Driving too fast (human error) Injuring Night (lit)Injuring None (human error)Protective DaylightProtective CarProtective VanProtective BusProtective Truck RigidProtective Other (vehicle)Protective

34. Limitations Limitations encountered in this study: o Based heavily on traffic reports  Limited resources to check the validity of the reports o Inter-rater reliability ? o Recall Bias? o No specifics about injury

35. Recommendations For future researchers: o Consider other variables o Consider other measurement tools o Compare other similar roads, considering other conditions o Perhaps there is a need for a new measuring tool for the traffic enforcers

36. THANK YOU! DRIVE SAFELY!