International Journal of Injury Control and Safety Promotion
ISSN: 1745-7300 (Print) 1745-7319 (Online) Journal homepage: http://www.tandfonline.com/loi/nics20
Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach Gamze Ozel Kadilar To cite this article: Gamze Ozel Kadilar (2014): Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach, International Journal of Injury Control and Safety Promotion, DOI: 10.1080/17457300.2014.942323 To link to this article: http://dx.doi.org/10.1080/17457300.2014.942323
Published online: 04 Aug 2014.
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Date: 14 September 2015, At: 12:21
International Journal of Injury Control and Safety Promotion, 2014 http://dx.doi.org/10.1080/17457300.2014.942323
Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach Gamze Ozel Kadilar* Department of Statistics, Hacettepe University, Ankara, Turkey
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(Received 11 August 2013; accepted 3 July 2014) The aim of the study is to examine the factors that appear to have a higher potential for serious injury or death of drivers in traffic accidents in Turkey, such as collision type, roadway surface, vehicle speed, alcohol/drug use, and restraint use. Driver crash severity is the dependent variable of this study with two categories, fatal and non-fatal. Due to the binary nature of the dependent variable, a conditional logistic regression analysis was found suitable. Of the 16 independent variables obtained from Turkish police accident reports, 11 variables were found most significantly associated with driver crash severity. They are age, education level, restraint use, roadway condition, roadway type, time of day, collision location, collision type, number and direction of vehicles, vehicle speed, and alcohol/drug use. This study found that belted drivers aged 1825 years involving two vehicles travelling in the same direction, in an urban area, during the daytime, and on an avenue or a street have better chances of survival in traffic accidents. Keywords: conditional logistic regression; driver crash severity; matched case-control design; traffic crash
Introduction Turkey has a significant problem with high levels of traffic, which exceeds normal figures due to various factors such as transportation policies, socio-cultural and economic characteristics. In terms of traffic-related deaths, the Traffic Research Committee of Turkish Grand National Assembly (January 2001) report states that deaths in traffic accidents exceeded the number of deaths from terrorism. The report further states that ‘Although we lost 30 thousand citizens due to terrorism in the last 15 years, approximately 100 thousand people died in traffic accidents in the last 10 years’. The traffic accidents are increasing in Turkey but the causes of the traffic accidents are not identified well. According to the report of the Turkish Statistical Institute (Turkstat, 2012), traffic accidents were caused mainly by driver fault and collision characteristics. However, though driver faults and collision characteristics are at the top of the list of causes in Turkey, other factors cannot be ignored. Traffic accidents also occur due to other factors such as, types of roadways, environment, and vehicles. Therefore, some prediction models for the traffic accidents are developed to represent the effects of these factors. As driver factors such as age, gender, education level, alcohol/drug use, and restraint use are considered as the most effective parameters, many researchers have used these as the parameters in their models (Bonneson & McCoy, 1993; Elander, West, &
*Email: [email protected] Ó 2014 Taylor & Francis
French, 1993; Sumer, 2003). Age, gender, education level, alcohol/drug and restraint use of a driver have also been considered as risk factors affecting driver crash severity in many studies (Gebers & Peck, 2003; Lui & Marchbanks, 1990). In all the accidents, male drivers usually exhibited a higher degree of probability to become involved in serious or fatal accidents (Valent et al., 2002). Low education level is another important reason of traffic accidents (Bishai & Hyder, 2004). Alcohol and drugs are important risk factors for traffic injuries since alcohol reduces alertness, interferes with judgement, and impairs vision. Most drugs that affect the central nervous system may have the potential to impair driving ability. Seat belts are used for protecting drivers from severe injuries and fatalities during traffic accident (Elvik & Vaa, 2004). To obtain more reasonable traffic accident predictions, roadway geometry elements and environment conditions have been also included in some traffic accident prediction models (Bedard, Gordon, Stones, & Hirdes, 2002; Li, Kim, & Nitz, 1999). Besides, weather plays a key role in traffic accidents, due to slippery roadways, bad visibility, and other adverse weather conditions. Several studies implicated adverse weather conditions as a significant factor in causing driver crash severity (Laverty, Kelly, Flynn, & Rotton, 1992; Valent et al., 2002). However, a few have examined these factors as well as vehicle characteristics and collision type as factors that cause driver crash
Downloaded by [University of Cambridge] at 12:21 14 September 2015
2
G.O. Kadilar
severity (Azmani, Rusli, Ismail, & Hashim, 2005; Chang & Wang, 2006; Golob & Recker, 2003). Turkey has higher crash rates when compared with countries that have been more widely studied (e.g., US). Based on the safety culture, Turkey most likely has different crash characteristics. Although similar studies on driver crash severity exist, these have not been done for Turkey earlier. Driver crash severity studies have been generally based only on descriptive statistics and no statistical model has been developed for Turkish driver crash severity thus far. The main objective of this study therefore, is to identify the factors that influence driver crash severity using traffic accident data from Turkey. To achieve this goal, a matched case-control study is conducted, as it is a statistically powerful method of assessing driver crash severity, particularly addressing questions on the effects of driver, collision, roadway/environment, and vehicle characteristics. To analyse the matched case-control study, ordinary and conditional logistic regression analyses are used. Early readable references on the application of the conditional logistic regression analysis to matched casecontrol designs are Breslow, Day, Halvorsen, Prentice, and Sabai (1978), Holford, White, and Kelsey (1978), and Breslow and Day (1980). This technique is also discussed by Hosmer and Lemeshow (2000). In traffic crash research works, matched case-control designs are used to estimate crash involvement risks and they produce comparability for some important predictors of the risk of death, such as collision type, roadway surface, driver’s age, etc. Note that vehicle type is commonly used as a matching variable in traffic crash research works. Crandall, Olson, and Sklar (2001), Rueda-Domingo et al. (2004), and Bunn, Slavova, Struttmann, and Browning (2005) applied the conditional logistic regression analysis to crash data. Crandall et al. (2001) used this analysis to show the independent effect of airbags and seat belts in reducing driver mortality. Rueda-Domingo et al. (2004), using the conditional logistic regression model, found that drivers were less likely to cause a car collision between two or more cars resulting in injury or death when they were accompanied by passengers, regardless of the driver or passenger characteristics. Conditional logistic regression model was performed by Bunn et al. (2005) and it was found that driver age of 5060 years, sleepiness/fatigue, distraction/inattention, and non-use of safety belts increased the odds of a collision being fatal. However, conditional logistic regression analysis has never been applied to Turkish driver crash severity. The paper is organised as follows: First, an overview of the conditional logistic regression model is presented, after a description of the Turkish traffic accident data and study variables. Next, descriptive statistics of these variables and the results of the conditional logistic model are
examined and discussed. Finally, some concluding comments are presented.
Methods Data source This study employed case study reports of 73,216 traffic accidents recorded in Turkey by The Department of Traffic Training and Research of the General Directorate of Security Affairs of Turkey, where drivers had been involved during 20112012. As some data were missing, 65,536 traffic accidents were examined and the factors influencing the chances of the drivers surviving or not, were determined.
Study variables Several variables may affect the risk of driver crash severity in the event of a traffic accident. In this study, these variables included driver, roadway/environment, collision, and vehicle characteristics. Driver characteristics were determined as age (17, 1825, 2635, 3645, 4655, 56 years), gender, education level (primary, secondary, high, higher), alcohol/ drug use (no alcohol/no drug, alcohol under influence, drug under influence, alcohol/drug under influence, had been drinking), and restraint use (none, shoulder, lap, lap C shoulder, unknown). Roadway/environment characteristics were categorised as roadway surface [concrete, asphalt, parquet, others (stabilised, dirt road)], roadway condition [dry, wet, snowy, icy, others (muddy, dusty)], roadway type [avenue, street, superhighway, state highway, province road, others (village road, front of school)], weather condition (sunny, cloudy, foggy, rainy, snowy), and time of day (daytime, night-time, twilight). Collision characteristics included collision location (urban, rural), collision type [front, rear-end, sideswipe, stationary vehicle, stationary object, pedestrian, tumbling, derailment, and others (falling down from vehicle, going out from roadway)], number and direction of vehicles (single vehicle, two vehicles travelling in the same direction, two vehicles in the opposite direction, two vehicles in the adjacent direction, multiple vehicles), and vehicle speed in kph (mph) [69)]. Vehicle characteristics were categorised as vehicle type [bicycle, motorcycle, car, minibus, pickup/van, truck, bus, tractor, others (wrecker, tanker, train, and military vehicle)], vehicle age (1959, 19601969, 19701979, 19801989, 19901999, 20002008), and airbag (deployed/not deployed). In this study, all these variables included in the conditional logistic model were believed to have an effect on
International Journal of Injury Control and Safety Promotion
Downloaded by [University of Cambridge] at 12:21 14 September 2015
the dependent variable, which was defined as driver crash severity, distinguished as ‘fatal’ or ‘non-fatal’. Conditional logistic regression analysis and matched case-control design Conditional logistic regression analysis works in nearly the same way as ordinary logistic regression analysis, except it is required to specify which individuals belong to which matched set (e.g., which pair) or stratum. Let ðYi1 ; Yi2 Þ, i ¼ 1; . . .; n denote the ith pair of subjects, where Yi1 represents a case subject (1, if a driver dies in the traffic accident) and Yi2 is a control subject (0, otherwise). When the binary dependent variable has p explanatory variables for the matched case-control design, the conditional logistic regression model is given by logit½PðYit ¼ 1Þ ¼ ai þ b1 x1 it þ b2 x2 it þ þ bp xpit (1) where xpit denotes the value of independent variable p for subject t in pair i, t ¼ 1; 2 and PðYit ¼ 1Þ represents the probability for Yit of being 1. After the estimation of parameters b in Equation (1), all interpretations are assessed on the basis of reference categories of the independent variables. The signs of the parameter estimates indicate the direction of change in the probability of belonging to category coded as ‘1’ in the dependent variable. While positive parameter estimates imply an increase in the probability of belonging to category coded ‘1’, negative parameter estimates indicate a decrease. To determine the effect of each independent variable on the driver fatalities, the odds ratio (OR) is used in the conditional logistic regression analysis. If all other variables are maintained constant in Equation (1), the OR for an independent variable Xi can be estimated as expðbi Þ. When Xi increases by one unit, with all factors remaining constant, the odds increases by a factor expðbi Þ and ranges from 0 to positive infinity. It indicates the relative amount by which the odds of the outcome increases ðOR > 1Þ or decreases ðOR < 1Þ when the value of the corresponding independent variable increases by one unit (Cummings, McKnight, & Weiss, 2003). Results In this study, cases ðn ¼ 739Þ were fatal traffic crashes. Controls ðn ¼ 64; 797Þ were non-fatal traffic crashes. Cases and controls were matched on vehicle type to account for potential confounders at a ratio of m casen controls. Note that (m:n) matching refers to the situation in which there is a varying number of cases and controls in the matched sets. (m:n) matching design was used in order to increase the precision of the OR estimates.
3
Table 1. Driver characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatal Driver characteristics Age (years) 17 1825 2635 3645 4655 56 Gender Male Female Education level Primary Secondary High Higher Alcohol/drug use Alcohol under influence Drug under influence Alcohol/drug under influence Had been drinking No alcohol/no drug Restraint use None Shoulder Lap Lap C shoulder Unknown
Controls (n D 64, 797) non-fatal
n
(%)
n
(%)
32 83 213 210 146 55
(4.3) (11.2) (28.8) (28.4) (19.8) (7.4)
2063 12,652 21,214 16,689 8692 3487
(3.2) (19.5) (32.7) (25.8) (13.4) (5.4)
725 14
(98.1) (1.9)
62,337 2460
(96.2) (3.8)
532 72 82 53
(72.0) (9.7) (11.1) (7.2)
34,150 9293 13,761 7593
(52.7) (14.3) (21.2) (11.7)
560
(75.8)
26,068
(40.2)
72 23
(9.7) (3.1)
1095 2937
(1.7) (4.5)
37 47
(5.0) (6.4)
14,972 19,725
(23.1) (30.4)
543 97 66 30 3
(73.5) (13.1) (8.9) (4.1) (0.4)
29,360 19,892 8520 6440 15
(45.3) (30.7) (13.1) (9.9) (0.9)
Descriptive statistics of fatal and non-fatal accidents Age, gender, education level, alcohol/drug use, and restraint use were considered as factors of the driver effects in the traffic accidents. Descriptive driver characteristics of fatal and non-fatal accidents in Turkey are provided in Table 1. Traffic accidents are a major problem among 2635-year-old drivers in Turkey. 28.8% of the fatal accidents involve 2635-year-old drivers, whereas the corresponding rate is 32.7% for the non-fatal accidents. The vast majority of drivers who got into a fatal or non-fatal crash were men (Table 1). Education is another vital factor in traffic safety, as it is in many fields. In this study, most (72%) of the drivers had graduated from primary school who were involved in fatal crashes. Similarly, 52.7% of the drivers had graduated from primary schools who were involved in non-fatal crashes. The percentage of the drivers under the influence of alcohol is
Downloaded by [University of Cambridge] at 12:21 14 September 2015
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G.O. Kadilar
75.8% in the fatal accidents, whereas 40.2% of the drivers were under the influence of alcohol in the non-fatal accidents. The percentage of non-belted drivers was 73.5% for the fatal accidents in Turkey. The roadway/environment conditions could play significant roles in traffic accidents and they include all types of non-driver-related factors such as, lighting conditions, roadway characteristics, weather conditions, etc. Roadway/environment statistics of fatal and non-fatal accidents in Turkey are presented in Table 2. Fatal accidents occurred on asphalted roads (97.7%), on a dry surface (80.5%), on state highway (66.8%), in sunny weather (73.6%), and during the daytime (50.6%). Similarly, nonfatal accidents occurred on asphalted roads (95.2%), on a dry surface (83.0%), on an avenue (53.6%), in sunny weather (77.5%), and during the daytime (65.8%). The collision characteristics of fatal and non-fatal accidents in Turkey are shown in Table 3. More non-fatal
Table 2. Roadway/environment characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatal Roadway/environment characteristics Roadway surface Concrete Asphalt Parquet Others Roadway condition Dry Wet Snowy Icy Others Roadway type Avenue Street Superhighway State highway Province road Others Weather condition Sunny Cloudy Foggy Rainy Snowy Time of day Daytime Night-time Twilight
n
(%)
Controls (n D 64, 797) non-fatal n
(%)
6 722 2 9
(0.8) (97.7) (0.3) (1.2)
1001 61,709 1416 671
(1.5) (95.2) (2.2) (1.0)
595 131 5 7 1
(80.5) (17.7) (0.7) (0.9) (0.1)
53,759 9499 700 701 138
(83.0) (14.6) (1.1) (1.1) (0.2)
148 13 45 494 17 22
(20.0) (1.8) (6.1) (66.8) (2.3) (3.0)
36,488 4491 1945 19,576 1212 1085
(56.3) (6.9) (3.0) (30.2) (1.9) (1.7)
544 92 10 81 12
(73.6) (12.4) (1.4) (11.0) (1.6)
50,220 8060 457 5287 773
(77.5) (12.4) (0.7) (8.2) (1.2)
374 322 43
(50.6) (43.6) (5.8)
42,667 19,944 2186
(65.8) (30.8) (3.4)
Table 3. Collision characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatal Collision characteristics Collision location Urban Rural Collision type Front Rear-end Sideswipe Stationary vehicle Stationary object Pedestrian Tumbling Derailment Others Number and direction of vehicles Single vehicle Two vehicles (same direction) Two vehicles (opp. direction) Two vehicles (adj. direction) Multiple vehicles Vehicle speed in kph (mph) 69)
Controls (n D 64, 797) non-fatal
n
(%)
n
(%)
277 462
(37.5) (62.5)
49,330 15,467
(76.1) (23.9)
213 86 150 18 70 6 52 140 4
(28.8) (11.6) (20.3) (2.4) (9.5) (0.8) (7.0) (18.9) (0.5)
7294 9155 22,840 1517 3547 11,545 3234 5279 386
(11.3) (14.1) (35.2) (2.3) (5.5) (17.8) (5.0) (8.1) (0.6)
257 152
(34.8) (20.6)
22,974 16,872
(35.5) (26.0)
208
(28.1)
11,578
(17.9)
42
(5.7)
7703
(11.9)
80
(10.8)
5670
(8.8)
18 71 124 526
(2.4) (9.6) (16.8) (71.2)
5953 11,348 19,984 27,512
(9.2) (17.5) (30.8) (42.5)
traffic accidents occurred in the urban areas (76.1%) than rural areas (23.9%), whereas most of the fatal accidents occurred in rural areas (62.5%) (Table 3). Front collisions represented the most frequent collision type in fatal accidents, accounting for 213 fatalities (28.8%). On the other hand, sideswipe collisions (35.2%) were one of the more frequently occurring types of non-fatal crashes in Turkey and such collisions could be injurious for drivers. The results indicate that a large proportion of fatal (34.8%) and non-fatal (35.5%) accidents involved single-vehicle crashes. In this study, 42.5% of the drivers who were involved in non-fatal accidents exceeded 111 kph and 71.2% of the drivers who were involved in fatal accidents exceeded 111 kph. Vehicle characteristics are presented in Table 4. Traffic accidents mostly involved cars for fatal (52.2%) and non-fatal (53.5%) accidents. In this study, vehicle type
International Journal of Injury Control and Safety Promotion Table 4. Vehicle characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatals
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Vehicle characteristics Vehicle type Bicycle Motorcycle Car Minibus Pickup/van Truck Bus Tractor Others Vehicle age 1959 19601969 19701979 19801989 19901999 20002008 Airbag Not deployed/unknown Deployed
Controls (n D 64, 797) non-fatals
n
(%)
n
(%)
47 72 386 17 50 96 16 18 37
(6.4) (9.7) (52.2) (2.3) (6.8) (13.0) (2.2) (2.4) (5.0)
1685 6017 34,692 3668 7211 5600 3023 836 2065
(2.6) (9.3) (53.5) (5.7) (11.1) (8.6) (4.7) (1.3) (3.2)
2 4 23 60 248 405
(0.3) (0.5) (3.1) (8.1) (33.6) (54.8)
26 131 2081 5444 22,018 35,097
(
ISSN: 1745-7300 (Print) 1745-7319 (Online) Journal homepage: http://www.tandfonline.com/loi/nics20
Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach Gamze Ozel Kadilar To cite this article: Gamze Ozel Kadilar (2014): Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach, International Journal of Injury Control and Safety Promotion, DOI: 10.1080/17457300.2014.942323 To link to this article: http://dx.doi.org/10.1080/17457300.2014.942323
Published online: 04 Aug 2014.
Submit your article to this journal
Article views: 31
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=nics20 Download by: [University of Cambridge]
Date: 14 September 2015, At: 12:21
International Journal of Injury Control and Safety Promotion, 2014 http://dx.doi.org/10.1080/17457300.2014.942323
Effect of driver, roadway, collision, and vehicle characteristics on crash severity: a conditional logistic regression approach Gamze Ozel Kadilar* Department of Statistics, Hacettepe University, Ankara, Turkey
Downloaded by [University of Cambridge] at 12:21 14 September 2015
(Received 11 August 2013; accepted 3 July 2014) The aim of the study is to examine the factors that appear to have a higher potential for serious injury or death of drivers in traffic accidents in Turkey, such as collision type, roadway surface, vehicle speed, alcohol/drug use, and restraint use. Driver crash severity is the dependent variable of this study with two categories, fatal and non-fatal. Due to the binary nature of the dependent variable, a conditional logistic regression analysis was found suitable. Of the 16 independent variables obtained from Turkish police accident reports, 11 variables were found most significantly associated with driver crash severity. They are age, education level, restraint use, roadway condition, roadway type, time of day, collision location, collision type, number and direction of vehicles, vehicle speed, and alcohol/drug use. This study found that belted drivers aged 1825 years involving two vehicles travelling in the same direction, in an urban area, during the daytime, and on an avenue or a street have better chances of survival in traffic accidents. Keywords: conditional logistic regression; driver crash severity; matched case-control design; traffic crash
Introduction Turkey has a significant problem with high levels of traffic, which exceeds normal figures due to various factors such as transportation policies, socio-cultural and economic characteristics. In terms of traffic-related deaths, the Traffic Research Committee of Turkish Grand National Assembly (January 2001) report states that deaths in traffic accidents exceeded the number of deaths from terrorism. The report further states that ‘Although we lost 30 thousand citizens due to terrorism in the last 15 years, approximately 100 thousand people died in traffic accidents in the last 10 years’. The traffic accidents are increasing in Turkey but the causes of the traffic accidents are not identified well. According to the report of the Turkish Statistical Institute (Turkstat, 2012), traffic accidents were caused mainly by driver fault and collision characteristics. However, though driver faults and collision characteristics are at the top of the list of causes in Turkey, other factors cannot be ignored. Traffic accidents also occur due to other factors such as, types of roadways, environment, and vehicles. Therefore, some prediction models for the traffic accidents are developed to represent the effects of these factors. As driver factors such as age, gender, education level, alcohol/drug use, and restraint use are considered as the most effective parameters, many researchers have used these as the parameters in their models (Bonneson & McCoy, 1993; Elander, West, &
*Email: [email protected] Ó 2014 Taylor & Francis
French, 1993; Sumer, 2003). Age, gender, education level, alcohol/drug and restraint use of a driver have also been considered as risk factors affecting driver crash severity in many studies (Gebers & Peck, 2003; Lui & Marchbanks, 1990). In all the accidents, male drivers usually exhibited a higher degree of probability to become involved in serious or fatal accidents (Valent et al., 2002). Low education level is another important reason of traffic accidents (Bishai & Hyder, 2004). Alcohol and drugs are important risk factors for traffic injuries since alcohol reduces alertness, interferes with judgement, and impairs vision. Most drugs that affect the central nervous system may have the potential to impair driving ability. Seat belts are used for protecting drivers from severe injuries and fatalities during traffic accident (Elvik & Vaa, 2004). To obtain more reasonable traffic accident predictions, roadway geometry elements and environment conditions have been also included in some traffic accident prediction models (Bedard, Gordon, Stones, & Hirdes, 2002; Li, Kim, & Nitz, 1999). Besides, weather plays a key role in traffic accidents, due to slippery roadways, bad visibility, and other adverse weather conditions. Several studies implicated adverse weather conditions as a significant factor in causing driver crash severity (Laverty, Kelly, Flynn, & Rotton, 1992; Valent et al., 2002). However, a few have examined these factors as well as vehicle characteristics and collision type as factors that cause driver crash
Downloaded by [University of Cambridge] at 12:21 14 September 2015
2
G.O. Kadilar
severity (Azmani, Rusli, Ismail, & Hashim, 2005; Chang & Wang, 2006; Golob & Recker, 2003). Turkey has higher crash rates when compared with countries that have been more widely studied (e.g., US). Based on the safety culture, Turkey most likely has different crash characteristics. Although similar studies on driver crash severity exist, these have not been done for Turkey earlier. Driver crash severity studies have been generally based only on descriptive statistics and no statistical model has been developed for Turkish driver crash severity thus far. The main objective of this study therefore, is to identify the factors that influence driver crash severity using traffic accident data from Turkey. To achieve this goal, a matched case-control study is conducted, as it is a statistically powerful method of assessing driver crash severity, particularly addressing questions on the effects of driver, collision, roadway/environment, and vehicle characteristics. To analyse the matched case-control study, ordinary and conditional logistic regression analyses are used. Early readable references on the application of the conditional logistic regression analysis to matched casecontrol designs are Breslow, Day, Halvorsen, Prentice, and Sabai (1978), Holford, White, and Kelsey (1978), and Breslow and Day (1980). This technique is also discussed by Hosmer and Lemeshow (2000). In traffic crash research works, matched case-control designs are used to estimate crash involvement risks and they produce comparability for some important predictors of the risk of death, such as collision type, roadway surface, driver’s age, etc. Note that vehicle type is commonly used as a matching variable in traffic crash research works. Crandall, Olson, and Sklar (2001), Rueda-Domingo et al. (2004), and Bunn, Slavova, Struttmann, and Browning (2005) applied the conditional logistic regression analysis to crash data. Crandall et al. (2001) used this analysis to show the independent effect of airbags and seat belts in reducing driver mortality. Rueda-Domingo et al. (2004), using the conditional logistic regression model, found that drivers were less likely to cause a car collision between two or more cars resulting in injury or death when they were accompanied by passengers, regardless of the driver or passenger characteristics. Conditional logistic regression model was performed by Bunn et al. (2005) and it was found that driver age of 5060 years, sleepiness/fatigue, distraction/inattention, and non-use of safety belts increased the odds of a collision being fatal. However, conditional logistic regression analysis has never been applied to Turkish driver crash severity. The paper is organised as follows: First, an overview of the conditional logistic regression model is presented, after a description of the Turkish traffic accident data and study variables. Next, descriptive statistics of these variables and the results of the conditional logistic model are
examined and discussed. Finally, some concluding comments are presented.
Methods Data source This study employed case study reports of 73,216 traffic accidents recorded in Turkey by The Department of Traffic Training and Research of the General Directorate of Security Affairs of Turkey, where drivers had been involved during 20112012. As some data were missing, 65,536 traffic accidents were examined and the factors influencing the chances of the drivers surviving or not, were determined.
Study variables Several variables may affect the risk of driver crash severity in the event of a traffic accident. In this study, these variables included driver, roadway/environment, collision, and vehicle characteristics. Driver characteristics were determined as age (17, 1825, 2635, 3645, 4655, 56 years), gender, education level (primary, secondary, high, higher), alcohol/ drug use (no alcohol/no drug, alcohol under influence, drug under influence, alcohol/drug under influence, had been drinking), and restraint use (none, shoulder, lap, lap C shoulder, unknown). Roadway/environment characteristics were categorised as roadway surface [concrete, asphalt, parquet, others (stabilised, dirt road)], roadway condition [dry, wet, snowy, icy, others (muddy, dusty)], roadway type [avenue, street, superhighway, state highway, province road, others (village road, front of school)], weather condition (sunny, cloudy, foggy, rainy, snowy), and time of day (daytime, night-time, twilight). Collision characteristics included collision location (urban, rural), collision type [front, rear-end, sideswipe, stationary vehicle, stationary object, pedestrian, tumbling, derailment, and others (falling down from vehicle, going out from roadway)], number and direction of vehicles (single vehicle, two vehicles travelling in the same direction, two vehicles in the opposite direction, two vehicles in the adjacent direction, multiple vehicles), and vehicle speed in kph (mph) [69)]. Vehicle characteristics were categorised as vehicle type [bicycle, motorcycle, car, minibus, pickup/van, truck, bus, tractor, others (wrecker, tanker, train, and military vehicle)], vehicle age (1959, 19601969, 19701979, 19801989, 19901999, 20002008), and airbag (deployed/not deployed). In this study, all these variables included in the conditional logistic model were believed to have an effect on
International Journal of Injury Control and Safety Promotion
Downloaded by [University of Cambridge] at 12:21 14 September 2015
the dependent variable, which was defined as driver crash severity, distinguished as ‘fatal’ or ‘non-fatal’. Conditional logistic regression analysis and matched case-control design Conditional logistic regression analysis works in nearly the same way as ordinary logistic regression analysis, except it is required to specify which individuals belong to which matched set (e.g., which pair) or stratum. Let ðYi1 ; Yi2 Þ, i ¼ 1; . . .; n denote the ith pair of subjects, where Yi1 represents a case subject (1, if a driver dies in the traffic accident) and Yi2 is a control subject (0, otherwise). When the binary dependent variable has p explanatory variables for the matched case-control design, the conditional logistic regression model is given by logit½PðYit ¼ 1Þ ¼ ai þ b1 x1 it þ b2 x2 it þ þ bp xpit (1) where xpit denotes the value of independent variable p for subject t in pair i, t ¼ 1; 2 and PðYit ¼ 1Þ represents the probability for Yit of being 1. After the estimation of parameters b in Equation (1), all interpretations are assessed on the basis of reference categories of the independent variables. The signs of the parameter estimates indicate the direction of change in the probability of belonging to category coded as ‘1’ in the dependent variable. While positive parameter estimates imply an increase in the probability of belonging to category coded ‘1’, negative parameter estimates indicate a decrease. To determine the effect of each independent variable on the driver fatalities, the odds ratio (OR) is used in the conditional logistic regression analysis. If all other variables are maintained constant in Equation (1), the OR for an independent variable Xi can be estimated as expðbi Þ. When Xi increases by one unit, with all factors remaining constant, the odds increases by a factor expðbi Þ and ranges from 0 to positive infinity. It indicates the relative amount by which the odds of the outcome increases ðOR > 1Þ or decreases ðOR < 1Þ when the value of the corresponding independent variable increases by one unit (Cummings, McKnight, & Weiss, 2003). Results In this study, cases ðn ¼ 739Þ were fatal traffic crashes. Controls ðn ¼ 64; 797Þ were non-fatal traffic crashes. Cases and controls were matched on vehicle type to account for potential confounders at a ratio of m casen controls. Note that (m:n) matching refers to the situation in which there is a varying number of cases and controls in the matched sets. (m:n) matching design was used in order to increase the precision of the OR estimates.
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Table 1. Driver characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatal Driver characteristics Age (years) 17 1825 2635 3645 4655 56 Gender Male Female Education level Primary Secondary High Higher Alcohol/drug use Alcohol under influence Drug under influence Alcohol/drug under influence Had been drinking No alcohol/no drug Restraint use None Shoulder Lap Lap C shoulder Unknown
Controls (n D 64, 797) non-fatal
n
(%)
n
(%)
32 83 213 210 146 55
(4.3) (11.2) (28.8) (28.4) (19.8) (7.4)
2063 12,652 21,214 16,689 8692 3487
(3.2) (19.5) (32.7) (25.8) (13.4) (5.4)
725 14
(98.1) (1.9)
62,337 2460
(96.2) (3.8)
532 72 82 53
(72.0) (9.7) (11.1) (7.2)
34,150 9293 13,761 7593
(52.7) (14.3) (21.2) (11.7)
560
(75.8)
26,068
(40.2)
72 23
(9.7) (3.1)
1095 2937
(1.7) (4.5)
37 47
(5.0) (6.4)
14,972 19,725
(23.1) (30.4)
543 97 66 30 3
(73.5) (13.1) (8.9) (4.1) (0.4)
29,360 19,892 8520 6440 15
(45.3) (30.7) (13.1) (9.9) (0.9)
Descriptive statistics of fatal and non-fatal accidents Age, gender, education level, alcohol/drug use, and restraint use were considered as factors of the driver effects in the traffic accidents. Descriptive driver characteristics of fatal and non-fatal accidents in Turkey are provided in Table 1. Traffic accidents are a major problem among 2635-year-old drivers in Turkey. 28.8% of the fatal accidents involve 2635-year-old drivers, whereas the corresponding rate is 32.7% for the non-fatal accidents. The vast majority of drivers who got into a fatal or non-fatal crash were men (Table 1). Education is another vital factor in traffic safety, as it is in many fields. In this study, most (72%) of the drivers had graduated from primary school who were involved in fatal crashes. Similarly, 52.7% of the drivers had graduated from primary schools who were involved in non-fatal crashes. The percentage of the drivers under the influence of alcohol is
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G.O. Kadilar
75.8% in the fatal accidents, whereas 40.2% of the drivers were under the influence of alcohol in the non-fatal accidents. The percentage of non-belted drivers was 73.5% for the fatal accidents in Turkey. The roadway/environment conditions could play significant roles in traffic accidents and they include all types of non-driver-related factors such as, lighting conditions, roadway characteristics, weather conditions, etc. Roadway/environment statistics of fatal and non-fatal accidents in Turkey are presented in Table 2. Fatal accidents occurred on asphalted roads (97.7%), on a dry surface (80.5%), on state highway (66.8%), in sunny weather (73.6%), and during the daytime (50.6%). Similarly, nonfatal accidents occurred on asphalted roads (95.2%), on a dry surface (83.0%), on an avenue (53.6%), in sunny weather (77.5%), and during the daytime (65.8%). The collision characteristics of fatal and non-fatal accidents in Turkey are shown in Table 3. More non-fatal
Table 2. Roadway/environment characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatal Roadway/environment characteristics Roadway surface Concrete Asphalt Parquet Others Roadway condition Dry Wet Snowy Icy Others Roadway type Avenue Street Superhighway State highway Province road Others Weather condition Sunny Cloudy Foggy Rainy Snowy Time of day Daytime Night-time Twilight
n
(%)
Controls (n D 64, 797) non-fatal n
(%)
6 722 2 9
(0.8) (97.7) (0.3) (1.2)
1001 61,709 1416 671
(1.5) (95.2) (2.2) (1.0)
595 131 5 7 1
(80.5) (17.7) (0.7) (0.9) (0.1)
53,759 9499 700 701 138
(83.0) (14.6) (1.1) (1.1) (0.2)
148 13 45 494 17 22
(20.0) (1.8) (6.1) (66.8) (2.3) (3.0)
36,488 4491 1945 19,576 1212 1085
(56.3) (6.9) (3.0) (30.2) (1.9) (1.7)
544 92 10 81 12
(73.6) (12.4) (1.4) (11.0) (1.6)
50,220 8060 457 5287 773
(77.5) (12.4) (0.7) (8.2) (1.2)
374 322 43
(50.6) (43.6) (5.8)
42,667 19,944 2186
(65.8) (30.8) (3.4)
Table 3. Collision characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatal Collision characteristics Collision location Urban Rural Collision type Front Rear-end Sideswipe Stationary vehicle Stationary object Pedestrian Tumbling Derailment Others Number and direction of vehicles Single vehicle Two vehicles (same direction) Two vehicles (opp. direction) Two vehicles (adj. direction) Multiple vehicles Vehicle speed in kph (mph) 69)
Controls (n D 64, 797) non-fatal
n
(%)
n
(%)
277 462
(37.5) (62.5)
49,330 15,467
(76.1) (23.9)
213 86 150 18 70 6 52 140 4
(28.8) (11.6) (20.3) (2.4) (9.5) (0.8) (7.0) (18.9) (0.5)
7294 9155 22,840 1517 3547 11,545 3234 5279 386
(11.3) (14.1) (35.2) (2.3) (5.5) (17.8) (5.0) (8.1) (0.6)
257 152
(34.8) (20.6)
22,974 16,872
(35.5) (26.0)
208
(28.1)
11,578
(17.9)
42
(5.7)
7703
(11.9)
80
(10.8)
5670
(8.8)
18 71 124 526
(2.4) (9.6) (16.8) (71.2)
5953 11,348 19,984 27,512
(9.2) (17.5) (30.8) (42.5)
traffic accidents occurred in the urban areas (76.1%) than rural areas (23.9%), whereas most of the fatal accidents occurred in rural areas (62.5%) (Table 3). Front collisions represented the most frequent collision type in fatal accidents, accounting for 213 fatalities (28.8%). On the other hand, sideswipe collisions (35.2%) were one of the more frequently occurring types of non-fatal crashes in Turkey and such collisions could be injurious for drivers. The results indicate that a large proportion of fatal (34.8%) and non-fatal (35.5%) accidents involved single-vehicle crashes. In this study, 42.5% of the drivers who were involved in non-fatal accidents exceeded 111 kph and 71.2% of the drivers who were involved in fatal accidents exceeded 111 kph. Vehicle characteristics are presented in Table 4. Traffic accidents mostly involved cars for fatal (52.2%) and non-fatal (53.5%) accidents. In this study, vehicle type
International Journal of Injury Control and Safety Promotion Table 4. Vehicle characteristics of fatal and non-fatal accidents in Turkey. Cases (n D 739) fatals
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Vehicle characteristics Vehicle type Bicycle Motorcycle Car Minibus Pickup/van Truck Bus Tractor Others Vehicle age 1959 19601969 19701979 19801989 19901999 20002008 Airbag Not deployed/unknown Deployed
Controls (n D 64, 797) non-fatals
n
(%)
n
(%)
47 72 386 17 50 96 16 18 37
(6.4) (9.7) (52.2) (2.3) (6.8) (13.0) (2.2) (2.4) (5.0)
1685 6017 34,692 3668 7211 5600 3023 836 2065
(2.6) (9.3) (53.5) (5.7) (11.1) (8.6) (4.7) (1.3) (3.2)
2 4 23 60 248 405
(0.3) (0.5) (3.1) (8.1) (33.6) (54.8)
26 131 2081 5444 22,018 35,097
(
