“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

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. 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

Conceptual FrameworkDriver 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:

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?

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.

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).

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.

METHODS

Case Control Study

Occurrence of a Vehicular Accident

Positive for VARIs

Negative for VARIs

Identify the exposure/s suspected of causing VARIs

Data Collection

RESULTS

Overall Descriptive Statistics

• Sampling size = 2246• Controls = 2104• Cases = 142

o Minor injuries = 128o Major injuries = 1o Serious injuries = 13

Frequency according to Month

MONTH No injury(CONTROL)

With injury(CASE)

Total Percent and Rank

January 160 14 174 7.7 (9)

February 145 15 160 7.1 (11)

March 171 14 185 8.3 (7.5)

April 163 8 171 7.6 (10)

May 172 13 185 8.3 (7.5)

June 194 15 209 9.3 (2)

July 195 13 208 9.2 (3.5)

August 182 12 194 8.6 (6)

September 196 11 207 9.2 (3.5)

October 188 8 196 8.7 (5)

November 215 13 228 10.1 (1)

December 123 6 129 5.7 (12)

Frequency according to Location

Commonwealth 1397 119 1516EDSA 707 23 730

LOCATION No injury(CONTROL)

With injury(CASE)

Total

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

Objective: obtain prevalence and rank each exposure according to prevalence

Motorcycle 38 91 129 5.7 (6)Motor Tricycle 6 2 8 0.4 (10)Car 840 30 870 38.7 (1)Jeepney 139 4 143 6.4 (5)Taxi 17 1 18 0.8 (9)Van 216 6 222 9.9 (4)Bus 410 5 415 18.5 (2)Truck (arctic) 34 0 34 1.5 (8)Truck (rigid) 113 1 114 5.1 (7)Other 291 2 293 13.0 (3)

VEHICLE TYPE No injury(CONTROL)

With injury(CASE)

Total Percent and rank

HUMAN ERROR No injury(CONTROL)

With injury(CASE)

Total Percent and rank

Driving too fast 136 18 154 6.8 (3)

Inattentive 100 18 118 5.2 (4)

Bad overtaking 10 0 10 0.4 (5.5)

Driving too close 298 17 315 14.0 (2)

Fatigue/ Asleep 3 0 3 0.1 (9)

none 1539 87 1626 72.4 (1)

other 9 0 9 0.4 (5.5)

bad turning 6 1 7 0.3 (7)

no signal 3 1 4 0.2 (8)

No injury(CONTROL)

With injury(CASE)

Total Percent and rank

AMBIENT LIGHT

daylight 1667 92 1759 78.3 (1)

dawn/dusk 76 7 83 3.7 ()3night (lit) 287 36 323 14.4 (3)

night (unlit) 74 7 81 3.6 (4)SURFACE

CONDITIONDry 1770 121 1891 84.1 (1)

Wet 334 21 355 15.8 (2)

Objective: Obtain and rank the strength of association between exposures and VARIs

Significant: p<0.05Not significant: p>0.05

Exposure OR p-value Type of Association

Protective/Injuring

Motorcycle 97.01 0.00 Positive InjuringInattentive (human error) 2.91 0.00 Positive InjuringDriving too fast (human

error)2.21 0.00 Positive Injuring

Night (lit) 2.15 0.00 Positive InjuringNone (human error) 0.58 0.00 Negative Protective

Daylight 0.48 0.00 Negative ProtectiveCar 0.40 0.00 Negative ProtectiveVan 0.39 0.03 Negative ProtectiveBus 0.15 0.00 Negative Protective

Truck Rigid 0.12 0.02 Negative ProtectiveOther (vehicle) 0.09 0.00 Negative Protective

DISCUSSION

DiscussionVehicle-Type and VARIs

oMotorcycleRisk Factor (97 Times)Had the most number of cases among the vehicle typesHigher rate of fatal accidentsLess stable than a 4-wheeled automobile

oProtective factors:CarBusVanTruck

DiscussionHuman Error and VARIs

o Driving too FastAs seen in the literature, this is also the human error that is most associated with the risk of VARIsLiterature 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

Discussion Human Error and VARIs

oInattentivenessAlso 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)

DiscussionHuman Error and VARIs

oNoneProtective factor

DiscussionAmbient Light and VARIs

oDaylightProtective factor As what the literature reports, dimly-lit roads are more prone to accidents. (Tandoc, 2007)

oNight (lit) Risk Factor People are confident about lit areas and take less precaution

DiscussionSurface Condition and VARIs

oNo significant resultsoDry

Suppose to assume that it is safer because the road is not slippery, and yet, majority of the reports were in dry roads

Discussion Surface Condition and VARIsoWet

Expected to be a risk factorPeople take more precautionLesser 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

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

CONCLUSION AND RECOMMENDATIONS

Conclusion Exposure Protective/Injuring

Motorcycle InjuringInattentive (human error) InjuringDriving too fast (human

error)Injuring

Night (lit) InjuringNone (human error) Protective

Daylight ProtectiveCar ProtectiveVan ProtectiveBus Protective

Truck Rigid ProtectiveOther (vehicle) Protective

Limitations

Limitations encountered in this study:oBased 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

Recommendations

For future researchers:oConsider other variablesoConsider other measurement toolsoCompare other similar roads, considering other conditions

oPerhaps there is a need for a new measuring tool for the traffic enforcers

THANK YOU!

DRIVE SAFELY!