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Age group differences in collision risk
Journal of Safety Research 41 (2010) 445–449
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Journal of Safety Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j s r
Age group differences in collision risk Robert E. Mann a,b,⁎, Gina Stoduto a, Jennifer Butters a, Anca Ialomiteanu a, Paul Boase c, Mark Asbridge d, Mary Chipman b, Christine M. Wickens a a
Social and Epidemiological Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada Transport Canada, Ottawa, ON, Canada d Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada b c
a r t i c l e
i n f o
Article history: Received 22 October 2009 Received in revised form 7 July 2010 Accepted 3 August 2010 Available online 16 September 2010 Keywords: Motor vehicle collision Driver age Drinking driving Heavy alcohol use Cannabis use Driving exposure Population survey
a b s t r a c t Introduction: The purpose of the current study was to examine differences in factors associated with selfreported collision involvement of three age groups of drivers based on a large representative sample of Ontario adults. Method: This study was based on data from the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults 18 years and older from 2002 to 2005. Three age groups were examined: 18-34 (n = 1,294), 35-54 (n = 2,428), and 55+ (n = 1,576). For each age group sample, a logistic regression analysis was conducted of self-reported collision involvement in the last 12 months by risk factor measures of driving exposure (kilometers driven in a typical week, driving is stressful, and driving on busy roads), consuming five or more drinks of alcohol on one occasion (past 12 months), cannabis use (lifetime, and past 12 months), and driving after drinking among drinkers (past 12 months), controlling for demographics (gender, region, income, and marital status). Results: The study identified differences in factors associated with self-reported collision involvement of the three age groups of adult drivers. The logistic regression model for the youngest group revealed that drivers who reported that driving was stressful at least some of the time, drank five or more drinks on an occasion, and drove after drinking had an increased risk of collision involvement. For the middle age group, those who reported using cannabis in the last 12 months had significantly increased odds of reporting collision involvement. None of the risk factor measures showed significant associations with collision risk for older drivers (aged 55+). Impact: The results suggest potential areas for intervention and new directions for future research. © 2010 National Safety Council and Elsevier Ltd. All rights reserved.
1. Introduction The skills and abilities needed, and available, for safe driving vary with age. The factors underlying these changes may include biological, psychological, and social development processes, cultural and subcultural differences, and differing experience with the driving task, among others (Gregersen & Bjurulf, 1996; Mayhew, Simpson, Singhal, & Desmond, 2006). Many studies have revealed age-related differences in collision rates and factors associated with collision risk. Perhaps the largest body of research has examined collision rates and risk factors among young drivers. It is clear that young drivers have collision rates higher than those seen among older drivers (Mayhew, Simpson, & Pak, 2003; Williams, 2006). Factors contributing to the higher collision rates and risk among this group include driving inexperience, age-related risk-taking and other developmental factors, difficult or challenging road conditions, and use of alcohol and cannabis (Asbridge, Poulin, &
⁎ Corresponding author. Centre for Addiction and Mental Health, 33 Russell Street, Toronto, ON, M5S 2S1, Canada. Tel.: +1 416 535 8501x4496; fax: +1 416 595 6899. E-mail address: [email protected] (R.E. Mann).
Donato, 2005; Jonah, 1997; Mayhew & Simpson, 1990; Vingilis & Wilk, 2008; Williams). Identification of these risk factors has led to the development of graduated driver's licensing programs that aim to control exposure to these risk factors during the period of acquisition of driving skills (Boase & Tasca, 1998; Mann et al., 1997), and these programs appear to have been successful in reducing risk behaviors and collision rates among young and novice drivers (Begg, Stephenson, Alsop, & Langley, 2001; Chen, Baker, & Li, 2006; Mann et al., 1997; Mayhew et al., 2003; Zhao et al., 2006). Older or senior drivers may also experience an elevation in collision risk, particularly per mile or kilometer traveled (Braver & Trempel, 2004; Eberhard, 2008; Langford, Koppel, McCarthy, & Srinivasan, 2008). These increases may be due to the maturation process in which skills and abilities potentially decline with age, specific medical conditions such as Alzheimer's disease, and factors such as increased medication use (e.g., Caird, Edwards, Creaser, & Horrey, 2005). Although several measures have been suggested to address potential collision risks experienced by senior drivers, such as more aggressive monitoring of driving skills by licensing authorities, the value of these measures has been debated (e.g., Bedard, Weaver, Darzins, & Porter, 2008; Eberhard, 2008).
0022-4375/$ – see front matter © 2010 National Safety Council and Elsevier Ltd. All rights reserved. doi:10.1016/j.jsr.2010.08.004
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R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449
While administrative databases provide important information on the characteristics of collisions and the environmental conditions when collisions have occurred, other investigators have begun to examine collision and injury risk based on population survey data, which allows for the consideration of social and psychological factors as contributors to collision risk. Based on the longitudinal dataset of the National Population Health Survey, Vingilis and Wilk (2008) examined age group differences in the importance of several risk factors: sociodemographic variables, binge drinking, health status, psychological distress, and medication use. Their path analysis revealed that binge drinking was the only significant predictor of subsequent motor vehicle collision (MVC) injury for the youngest group (12-29.9 year olds). Medication use was the only significant predictor of subsequent MVC injury for the middle age group (30-59.9 year olds), and no variables were found to be significant risk factors among older people (60-85 year olds). We report here a comparison of demographic, behavioral, and driving-related correlates of self-reported collision involvement for three age groups of drivers: those aged 18-34 years, 35-54 years, and 55 years and older. 2. Method
single item: ‘During the past 12 months, have you driven a motor vehicle after having two or more drinks in the previous hour?’ (Reported among respondents who had consumed alcohol at least once in the past 12 months, coded yes = 1, no = 0). 2.4. Analyses The results in the current study are based on “valid” responses (n's) such that missing data (i.e., “don't know” responses and refusals) were excluded from analyses. SPSS 15.0 software was employed for all analyses. The percentages reported are based on the weighted sample size and are considered representative for the population surveyed. Data on prevalence of collision involvement by independent variables were examined through chi-square and t-test analyses. For each age group, a logistic regression analysis was conducted of collision risk by driving exposure, heavy alcohol use, cannabis use, and driving after drinking, controlling for demographic characteristics. The Wald statistic was used to indicate statistically significant estimates (odds ratios) of relative risk of self-reported collision involvement. Due to listwise deletion, logistic regression models were based on reduced samples. Model fit was evaluated using the model chi-square (Hosmer & Lemeshow, 2000; Pampel, 2000).
2.1. Sample 3. Results This study was based on data from the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults 18 years and older, conducted by the Centre for Addiction and Mental Health and administered by the Institute for Social Research at York University. The CAMH Monitor is a regionally stratified survey consisting of independent monthly samples with approximately 200 completions each (response rate 56% to 61%; see Ialomiteanu & Adlaf, 2006 for sampling design details). Since 2002, the CAMH Monitor has included an item used to measure collision involvement; thus, for the current study, data from 2002 to 2005 were merged into one dataset of respondents who had valid data for driving-related measures. The data were then split into three samples: 18-34 year olds (n = 1,294), 35-54 year olds (n = 2,428), and 55 years of age or older (n = 1,576). 2.2. Dependent variable The key outcome measure was collision involvement: ‘During the past 12 months, how often, if at all, were you involved in an accident or collision involving any kind of damage or injury to you or another person or vehicle while you were driving?’ (coded yes = 1, no = 0). 2.3. Independent variables Demographic variables included gender (coded 1 = male, 0 = female), region (comprised of six regions in Ontario: Toronto, Central East, Central West, West, East, North), income (b$30,000, $30,000-49,999, $50,000-79,999, $80,000+, not stated), and marital status (coded 0 = not married, 1 = married or common law). Driving exposure was assessed by measures of driving distance, stress, and exposure to busy roads. Respondents were asked to indicate how much they drive in a typical week (number of kilometers, continuous variable, range 1-7,000). They were also asked how much of their driving is stressful and how much of their driving is on busy roads (coded: 0 = none of the time, 1 = at least some of the time). Heavy alcohol use was measured with a single item: ‘About how often during the past 12 months would you say you had five or more drinks at the same sitting or occasion?” (coded never = 0, at least once = 1). Cannabis use was assessed by two items: ‘Have you ever in your lifetime used marijuana or hash?’ and ‘How many times, if any, have you used marijuana or hash during the past twelve months?’ (coded yes = 1, no = 0). Driving after drinking was measured by a
Table 1 presents self-reported collision involvement for each age group by demographic characteristics, driving exposure measures, substance use measures, and driving after drinking. Overall the prevalence of collision involvement was highest for the youngest (18-34 years) age group (12.5%), with 35-54 year olds and 55+ year olds having similar collision involvement (6.0% and 6.5%, respectively). Demographic variables found to be significantly related to collision involvement varied between age groups. For the youngest group (18-34 year olds), collision involvement differed significantly by marital status. Collision involvement was higher for those not married (15.5%) compared to married or common law (8.3%). For 3554 year olds, a marginally significant association was found between collision involvement and income level (p = .06), with the highest rates of collision involvement reported by those earning less than $30,000 (7.6%) and those earning $80,000+ (7.4%), and the lowest rates reported by middle income earners (3.9% for $30,000 - $49,999 and 5.3% for $50,000 - $79,999). For those aged 55+ years, collision involvement differed significantly by gender (males 7.9% vs. females 4.9%) and by region (highest for Toronto 10.7%, and lowest for West 2.4% and North 3.9%). The driving exposure measures significantly associated with collision involvement did not vary much by age group. For the youngest group, a marginally significant association was found between number of kilometers driven in a typical week and collision involvement in the last year (p = .07). For all age groups, collision involvement was significantly associated with driving stress. Collision involvement was more common for those who reported that driving is stressful at least some of the time compared to those who did not. The substance use measures significantly associated with collision involvement varied by age group. For the youngest group, respondents reporting consumption of five or more drinks on one occasion, and use of cannabis in the last 12 months were significantly more likely to have been involved in a collision. For the 35 to 54 year olds, the two cannabis use measures were significantly associated with collision involvement. For the oldest age group, respondents reporting cannabis use in the last 12 months were significantly more likely to have been involved in a collision. For this same age group, reported use of cannabis during one's lifetime was marginally significant (p = .054). Driving after drinking was significantly related to collision involvement for the youngest group only. About one fifth of young
R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449 Table 1 Collision involvement for three age groups (18-34, 35-54, 55+) by demographic characteristics, driving exposure, heavy drinking, cannabis use, and drinking driving measures: Ontario CAMH Monitor, 2002-2005. Age 18-34 Collision N TOTAL Gender Male Female Region Toronto Central East Central West West East North Income b b$30,000 $30,000-49,999 $50,000-79,999 $80,000+ Not stated Marital Status Not married/partner Married/partner Kilometers Driven typical week mean (SD) Yes collision No collision Driving is Stressful None of the time At least some of the time Driving on Busy Roads None of the time At least some of the time Five + Drinks last 12 months Never At least once Cannabis Use lifetime No Yes Cannabis Use last 12 months No Yes Driving after Drinking No Yes
Age 35-54 a
Collision
Yes (%)
1294
N
12.5
Yes (%)
2428
6.0
Age 55+ a
Collisiona N
Yes (%)
1576
6.5 *
644 650
13.4 11.3
1227 1201
5.9 6.0
751 825
7.9 4.9
190 220 223 243 204 214
15.0 10.4 14.2 11.6 11.3 8.5
311 399 470 395 400 453
167 257 222 270 292 368
10.7 6.1 7.2 2.4 6.4 3.9
170 237 346 405 136
14.7 8.2 12.2 14.4 10.9 *** 15.5 8.3 p = .07
196 374 633 959 266
6.1 6.4 6.3 4.1 6.8 5.5 p = .06 7.6 3.9 5.3 7.4 4.1
321 310 324 302 318
6.6 4.3 8.1 6.8 6.2
650 1771
6.5 5.9
519 1043
7.7 6.2
144
*
692 598
148
508 780
398.9 (512.2) 329.4 (497.9) * 9.8 13.8
1014 1404
381.7 (372.9) 348.9 (545.0) * 4.7 6.7
57 1237
12.1 12.4
184 2244
1234 1184
1146
867 696
272.6 (435.3) 241.1 (426.2) * 5.2 7.8
4.4 6.1
185 1391
3.5 6.8
5.7 6.4
1125 443
5.9 7.7 p = .054
4.6 7.5 ***
1302 268
5.9 9.3
2167 254
5.4 10.6
1546 28
6.3 17.2
1933 204
6.2 6.6
1224 73
6.8 8.7
2284
91 1485
*** 440 850
8.6 14.5
** 581 707
12.3 12.6
1156 1251
* 961 327 998 163
11.4 15.6 *** 11.8 21.1
Notes: a At least once in the last 12 months. significance: * p b .05, ** p b .01, *** p b .001.
b
*
Canadian dollars. Chi-square statistical
drinking drivers were involved in a collision (21.1%) compared to 11.8% of non-drinking drivers. Table 2 presents logistic regression models of collision risk for each age group, controlling for demographic characteristics. Odds ratios greater than one indicate an increased risk of self-reported collision involvement in the past 12 months and odds ratios less than one indicate a decreased risk of collision involvement. The goodness of fit statistics (Hosmer & Lemeshow) for the models suggests they are good fits to the data. The logistic regression model for the youngest group revealed that stressful driving, heavy drinking, and driving after drinking were significantly associated with risk of collision involvement. Drivers reporting that driving is stressful had an increased risk of collision involvement (OR = 1.47) compared to those who never found driving to
447
be stressful. Those reporting consuming five or more drinks on one occasion had significantly increased odds of reporting collision involvement (OR = 1.61). Also, younger drivers reporting driving after drinking were roughly twice as likely (OR = 1.81) to indicate collision involvement in comparison to those who did not drive after drinking. The logistic regression model for the middle age group revealed that those who reported having used cannabis within the last 12 months had significantly increased odds of reporting collision involvement (OR = 1.90). Indeed, the likelihood of reporting a collision was almost twice as great among those reporting past year cannabis use. The logistic regression for the 55+ age group revealed that none of the risk factor variables were significantly associated with collision risk. The only measure found to have a significant relationship with collision involvement was the demographic indicator: region. Those who lived in the West region of the province had much lower odds of reporting collision involvement (OR = 0.13) compared to those in Toronto. 4. Discussion 4.1. Limitations It is important to point out some limitations of this study. First, given that self-report survey methods were used, the data may have been subject to estimation errors (e.g., kilometers traveled) or social desirability biases. However, at least in the latter case, such biased responding would only serve to weaken the strength of the study's findings. Second, as this study was based on cross-sectional survey data, it cannot be determined whether non-respondents would have responded the same way as those who participated in this study. However, given that other research has demonstrated that nonrespondents in studies of substance use and driving behavior are likely to be heavier substance users (Mann et al., 2003), it seems probable that any bias introduced by non-response would be a conservative one. Third, the cross-sectional nature of this research means that no conclusions about causal relationships can be drawn. Finally, it cannot be determined from the present data whether or not any of the collisions reported by those who drove after drinking actually involved impaired driving. Nonetheless, based on the results of the current study, it is clear that different factors are associated with collision involvement in different age groups. 4.2. Interpretation Overall, the prevalence of collision involvement was lowest for those 55+ years of age (i.e., about one half of the percentage of the youngest group). This is consistent with research on MVC injury risk in Canada that reveals persons aged 60–85 years have the lowest percentage of reported MVC injuries (Vingilis & Wilk, 2008). Even so, collisions remain an important source of death and injuries among seniors. It is interesting that the particular risk factors included in these analyses did not significantly influence collision occurrence for older respondents, similar to the findings of Vingilis and Wilk. Thus, further research to determine what factors do affect older driver collisions is needed. We live in an aging society and therefore in anticipation of the demographic changes that lie ahead, future research should focus specifically on identifying factors associated with collisions among this segment of the population. Some evidence for regional variation in the likelihood of collisions was observed for the youngest and oldest groups. The relationship between driving location and collisions presents an interesting area of future investigation. For example, detailed investigation of the driving environment of those regions associated with a reduced likelihood of collisions may provide important information that could be used to improve driving conditions in high collision risk regions. Several risk factors that are significantly associated with the increased likelihood of collisions among the youngest respondents
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Table 2 Logistic regression models of collision involvement risk for three age groups (18-34, 35-54, 55+): Ontario CAMH Monitor, 2002-2005. Collision Involvement
a
Age 18-34 (N = 1140) OR Control Variables: Gender (ref. = female) Region (ref. = Toronto) Central East Central West West East North Income c (ref. = b$30,000) $30,000-49,999 $50,000-79,999 $80,000+ Not stated Marital Status (ref. = not married/partner) Risk Factor Variables: Kilometers Driven - typical week Driving is Stressful (ref. = none of the time) Driving on Busy Roads (ref. = none of the time) Five + Drinks - last 12 months (ref. = no) Cannabis Use - lifetime (ref. = no) Cannabis Use - last 12 months (ref. = no) Driving after Drinking (ref. = no) Constant Hosmer & Lemeshow test Notes: a At least once in the last 12 months.
b
b
Age 35-54 (N = 2094) 95%CI
OR
Age 55+ (N = 1264) 95%CI
OR
95%CI
1.61
(.94, 2.74)
.84
(.60, 1.20)
.81
(.55, 1.20)
.59* .87 .62 .65 .45
(.36, .98) (.55, 1.36) (.36, 1.08) (.36, 1.17) (.19, 1.07)
1.12 .97 .77 1.17 .77
(.63, 1.97) (.55, 1.70) (.38, 1.56) (.62, 2.19) (.33, 1.79)
.48* .72 .79 .52 .58**
(.24, .94) (.40, 1.28) (.46, 1.36) (.25, 1.10) (.40, .84)
.40 .68 .93 .50 .99
(.15, 1.08) (.30, 1.54) (.42, 2.02) (.18, 1.39) (.60, 1.63)
.58 1.12 .74 .79 .75
(.23, 1.46) (.50, 2.49) (.32, 1.74) (.34, 1.88) (.42, 1.37)
(1.00, 1.00) (.86, 1.97) (.42, 2.23) (.69, 1.49) (.87, 2.01) (1.13, 3.18) (.53, 1.90)
1.00 1.46 1.18 1.00 1.39 1.18 1.12 .09*** 4.26(8df) p = .83
(1.00, 1.00) (.89, 2.41) (.44, 3.13) (.60, 1.66) (.76, 2.54) (.32, 4.36) (.43, 2.95)
1.00 1.47* .82 1.61* .74 1.32 1.81** .22* 8.66(8df) p = .37
(1.00, 1.00) (1.02, 2.13) (.32, 2.10) (1.05, 2.47) (.48, 1.13) (.85, 2.06) (1.18, 2.77)
1.00 1.30 .97 1.01 1.32 1.90* 1.01 .07*** 2.01(8df) p = .98
.48 .73 .13** .58 .39
(.24, .98) (.37, 1.42) (.03, .48) (.29, 1.19) (.15, 1.03)
Odds Ratio. c Canadian dollars. ref. = reference category. Wald statistical significance: * p b .05, ** p b .01, *** p b .001.
were identified. A significant relationship was found between perceptions of driving as stressful and the likelihood of collisions among the respondents in this age group. Because of the cross-sectional nature of the research, it is possible that past-year collision experience may have influenced reported driving stress and thus we cannot assume that more stressful driving results in collisions. Nonetheless, assuming that congestion on roadways, inconsiderate driver behavior, and other factors that contribute to the stress of driving will only continue to intensify, it is worth considering how we can make driving less stressful for younger drivers. Perhaps young drivers need more supervised ‘road’ time in order to provide them with greater experience in dealing with the stressors associated with driving. Also, driver training programs could provide additional education that addresses driving stress. The observation that heavy drinking (among the youngest respondents) and past year cannabis use (among the middle aged group) increases the risk of collisions underscores the well-known link between alcohol and collision risk (Borkenstein, Crowther, Shumate, Ziel, & Zylman, 1964; Mann et al., 2001) and also is consistent with research showing a link between cannabis use and collision risk (Asbridge et al., 2005; Mann et al., 2007). Our results may reflect impairing effects of these substances on driving behavior, but they may also be highlighting a general risk taking tendency in some individuals. Specifically, it may be that those who partake in potentially risky behaviors regarding substance use also engage in risky driving. Jessor's (1987) Problem Behavior Theory suggests that drinking and driving is related to involvement in general problem behavior, particularly pointing to correlations between drinking driving and risky driving, alcohol use, and drug use.
educating young people about the hazards of heavy drinking, drug use, and drinking and driving. It seems that the legislative measures enacted to deter drinking and driving, although effective overall, may be less effective among those young people who may be engaged in a problem behavior lifestyle. Prevention efforts aimed at a constellation of risky behavior engaged in by younger people is warranted. Driving will likely remain a primary mode of transportation and the complexities associated with navigating roadways may possibly intensify. This study has identified differences in factors associated with the self-reported collision involvement of three age groups of adult drivers. The results suggest potential areas for intervention and new directions for future research. 4.4. Impact on Industry No impact on industry is anticipated. Acknowledgements This research was supported by a grant from AUTO21, a member of the Networks of Centres of Excellence program which is administered and funded by the Natural Sciences and Engineering Research Council, the Canadian Institutes of Health Research, and the Social Sciences and Humanities Research Council, in partnership with Industry Canada. The authors also acknowledge ongoing funding support from the Ontario Ministry of Health and Long-Term Care. Funders had no involvement in the design, data collection, analysis, writing or decision to submit the paper for publication. The views expressed here do not necessarily reflect those of AUTO21 or the Ministry of Health and Long Term Care.
4.3. Conclusions This study supports findings of previous research (Vingilis & Wilk, 2008) and suggests that further investigation of risk factors as they relate to age groups is needed. Our results highlight the value of comparative research that assesses collision risk factors in different age groups. This study continues to illustrate the importance of
References Asbridge, M., Poulin, C., & Donato, A. (2005). Driving under the influence of cannabis and motor vehicle collision risk: Evidence from adolescents in Atlantic Canada. Accident Analysis and Prevention, 37, 1025−1034. Bedard, M., Weaver, B., Darzins, P., & Porter, M. M. (2008). Predicting driving performance in older adults: we are not there yet! Traffic Injury Prevention, 9, 336−341.
R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449 Begg, D. J., Stephenson, S., Alsop, J., & Langley, J. (2001). Impact of graduated driver licensing restrictions on crashes involving young drivers in New Zealand. Injury Prevention, 7, 292−296. Boase, P., & Tasca, L. (1998). Graduated licensing system evaluation: Interim evaluation. Toronto, ON: Ontario Ministry of Transportation. Borkenstein, R. F., Crowther, R. F., Shumate, R. P., Ziel, W. B., & Zylman, R. (1964). The role of the drinking driver in traffic accidents. Bloomington, IN: Department of Police Administration. Braver, E. R., & Trempel, R. E. (2004). Are older drivers actually at higher risk of involvement in collisions resulting in deaths or non-fatal injuries among their passengers and other road users? Injury Prevention, 10, 27−32. Caird, J. K., Edwards, C. J., Creaser, J. I., & Horrey, W. J. (2005). Older driver failures of attention at intersections: using change blindness methods to assess turn decision accuracy. Human Factors, 47, 235−249. Chen, L. H., Baker, S. P., & Li, G. (2006). Graduated driver licensing programs and fatal crashes of 16-year-old drivers: a national evaluation. Pediatrics, 118, 56−62. Eberhard, J. (2008). Older drivers' "high per-mile crash involvement": the implications for licensing authorities. Traffic Injury Prevention, 9, 284−290. Gregersen, N. P., & Bjurulf, P. (1996). Young novice drivers: towards a model of their accident involvement. Accident Analysis and Prevention, 28, 229−241. Hosmer, D. W., Jr., & Lemeshow, S. (2000). Applied Logistic Regression, 2nd edition. New York: John Wiley and Sons. Ialomiteanu, A., & Adlaf, E. M. (2006). CAMH Monitor 2005: Technical guide.Toronto, ON: Centre for Addiction and Mental Health available at: www.camh.net/ Research/Areas_of_research/Population_Life_Course_Studies/CAMH_Monitor/CM2005_TechDoc.pdf Jessor, R. (1987). Risky driving and adolescent problem behavior: An extension of problem-behavior theory. Alcohol, Drugs and Driving, 3(3–4), 1−11. Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident Analysis and Prevention, 29, 651−665. Langford, J., Koppel, S., McCarthy, D., & Srinivasan, S. (2008). In defence of the 'lowmileage bias'. Accident Analysis and Prevention, 40, 1996−1999. Mann, R. E., Adlaf, E., Zhao, J., Stoduto, G., Ialomiteanu, A., Smart, R. G., & Asbridge, M. (2007). Cannabis use and self-reported collisions in a representative sample of adult drivers. Journal of Safety Research, 38, 669−674. Mann, R. E., Macdonald, S., Chipman, M. L., Adlaf, E. M., Anglin-Bodrug, K., & Zhao, J. (2003). Identifying possible sources of bias introduced in traffic safety research: Comparison of blind linkage with volunteer clinical samples. In D. R. Mayhew, & C. Dussault (Eds.), Alcohol, drugs and traffic safety - T'2002: Proceedings of the 16th International Conference on Alcohol, Drugs and Traffic Safety (pp. 275−280). Montreal: Société de l'Assurance Automobile du Québec. Mann, R. E., Stoduto, G., Anglin, L., Pavic, B., Fallon, F., Lauzon, R., & Amitay, O. A. (1997). Graduated licensing in Ontario: Impact of the 0 BAL provision on adolescents’ drinkingdriving. In C. Mercier-Guyon (Ed.), Alcohol, drugs and traffic safety - T'97: Proceedings of the 14th International Conference on Alcohol, Drugs and Traffic Safety (pp. 1055−1060). Annecy, France: Centre d'Etudes et de Researches en Medecine du Trafic. Mann, R. E., Stoduto, G., Macdonald, S., Shaikh, A., Bondy, S., & Jonah, B. (2001). The effects of introducing or lowering legal per se blood alcohol limits for driving: An international review. Accident Analysis and Prevention, 33, 61−75. Mayhew, D. R., & Simpson, H. M. (1990). New to the road. Young drivers and novice drivers: Similar problems and solutions? Ottawa, ON: Traffic Injury Research Foundation. Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis and Prevention, 25, 683−691. Mayhew, D. R., Simpson, H. M., Singhal, D., & Desmond, K. (2006). Reducing the crash risk for young drivers. Washington, DC: American Automobile Association Foundation for Traffic Safety. Pampel, F. C. (2000). Logistic Regression: A Primer. Sage University Paper series on Quantitative Applications in Social Sciences, 07-132. CA: Sage: Thousand Oaks. Vingilis, E., & Wilk, P. (2008). The effects of health status, distress, alcohol and medicinal drug use on subsequent motor vehicle injuries. Accident Analysis and Prevention, 40, 1901−1907. Williams, A. F. (2006). Young driver risk factors: successful and unsuccessful approaches for dealing with them and an agenda for the future. Injury Prevention, 12(Suppl. 1), i4−i8. Zhao, J., Mann, R. E., Chipman, M., Adlaf, E., Stoduto, G., & Smart, R. G. (2006). The impact of driver education on self-reported collisions among young drivers with a graduated license. Accident Analysis and Prevention, 38, 35−42.
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Robert Mann has a Ph.D. in Psychology from the University of Waterloo. He is a Senior Scientist in the Public Health and Regulatory Policy Section of the Social and Epidemiological Research Department at the Centre for Addiction and Mental Health and an Associate Professor in the Dalla Lana School of Public Health at the University of Toronto. He has 30 years of research experience on alcohol and drug impaired driving, including legislative policies and assessing the impact of policies, prevention programs and remedial activities. Gina Stoduto is a Research Coordinator in the Social and Epidemiological Research Department at the Centre for Addiction and Mental Health. She obtained her B.Sc. in Psychology, and M.Ed. in Applied Psychology from the University of Toronto. For 20 years she has been involved in traffic safety research focusing on alcohol and drug impaired driving and road rage, and in research on the development of legislative policies and evaluations of the impact of policies and prevention and remedial programs. Jennifer Butters is an Affiliate Scientist with the Centre for Addiction and Mental Health. Her research examines automobile use and injury, with a particular emphasis on mental health, substance use and aggression in the context of driving. She is also interested in the intersection of drug use, violence and mental health among youth. She has served as a co-investigator on several research projects and was awarded the Edie Yolles Award for Dissertation Excellence from the Department of Sociology at the University of Toronto. Anca Ialomiteanu is a Research Coordinator in the Social and Epidemiological Research Department at the Centre for Addiction and Mental Health. She has an M.A. in Information Studies from the University of Bucharest, Romania, and over 20 years experience in population survey research. She is the project coordinator of the CAMH Monitor, an annual monitoring study of alcohol and drug use and other health behaviors of Ontario adults. She was also involved in the design and analysis of the 2004 Canadian Addiction Survey and the 2008 Canadian Alcohol and Drug Use Monitoring Survey, two major national surveys of attitudes, beliefs, and use of alcohol and other drugs. Paul Boase is Chief of the Road Safety & Motor Vehicle Regulation Directorate's Road Users’ Division with Transport Canada. He obtained a B.A. in Sociology/Psychology at York University in 1982, and an M.A. in Psychology from the University of Toronto in 1983. Over the past twenty-five years, he has worked as a researcher and statistician in traffic safety. Mark Asbridge is an Assistant Professor in the Department of Community Health and Epidemiology, Dalhousie University. Mark completed his Ph.D. in Sociology and Addiction Studies at the University of Toronto and a post-doctoral fellowship at the Centre for Addiction and Mental Health in Toronto. Mark's research program examines the areas of addictions, public and population health, injury prevention and emergency medicine, and public policy. Mary Chipman is Professor Emeritus at the Dalla Lana School of Public Health, University of Toronto. Professor Chipman has worked for many years in medical statistics and epidemiology, with a particular interest in the epidemiology of traffic crashes and the effectiveness of various preventive measures. Christine Wickens is a Post-Doctoral Fellow at the Centre for Addiction and Mental Health. She obtained her H.B.Sc. in Psychology/Sociology from the University of Toronto and her M.A. and Ph.D. in Social and Personality Psychology from York University. Her primary research interests address the psychology of driver behavior including driver stress, aggression, and impaired driving.
Contents lists available at ScienceDirect
Journal of Safety Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j s r
Age group differences in collision risk Robert E. Mann a,b,⁎, Gina Stoduto a, Jennifer Butters a, Anca Ialomiteanu a, Paul Boase c, Mark Asbridge d, Mary Chipman b, Christine M. Wickens a a
Social and Epidemiological Research Department, Centre for Addiction and Mental Health, Toronto, ON, Canada Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada Transport Canada, Ottawa, ON, Canada d Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada b c
a r t i c l e
i n f o
Article history: Received 22 October 2009 Received in revised form 7 July 2010 Accepted 3 August 2010 Available online 16 September 2010 Keywords: Motor vehicle collision Driver age Drinking driving Heavy alcohol use Cannabis use Driving exposure Population survey
a b s t r a c t Introduction: The purpose of the current study was to examine differences in factors associated with selfreported collision involvement of three age groups of drivers based on a large representative sample of Ontario adults. Method: This study was based on data from the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults 18 years and older from 2002 to 2005. Three age groups were examined: 18-34 (n = 1,294), 35-54 (n = 2,428), and 55+ (n = 1,576). For each age group sample, a logistic regression analysis was conducted of self-reported collision involvement in the last 12 months by risk factor measures of driving exposure (kilometers driven in a typical week, driving is stressful, and driving on busy roads), consuming five or more drinks of alcohol on one occasion (past 12 months), cannabis use (lifetime, and past 12 months), and driving after drinking among drinkers (past 12 months), controlling for demographics (gender, region, income, and marital status). Results: The study identified differences in factors associated with self-reported collision involvement of the three age groups of adult drivers. The logistic regression model for the youngest group revealed that drivers who reported that driving was stressful at least some of the time, drank five or more drinks on an occasion, and drove after drinking had an increased risk of collision involvement. For the middle age group, those who reported using cannabis in the last 12 months had significantly increased odds of reporting collision involvement. None of the risk factor measures showed significant associations with collision risk for older drivers (aged 55+). Impact: The results suggest potential areas for intervention and new directions for future research. © 2010 National Safety Council and Elsevier Ltd. All rights reserved.
1. Introduction The skills and abilities needed, and available, for safe driving vary with age. The factors underlying these changes may include biological, psychological, and social development processes, cultural and subcultural differences, and differing experience with the driving task, among others (Gregersen & Bjurulf, 1996; Mayhew, Simpson, Singhal, & Desmond, 2006). Many studies have revealed age-related differences in collision rates and factors associated with collision risk. Perhaps the largest body of research has examined collision rates and risk factors among young drivers. It is clear that young drivers have collision rates higher than those seen among older drivers (Mayhew, Simpson, & Pak, 2003; Williams, 2006). Factors contributing to the higher collision rates and risk among this group include driving inexperience, age-related risk-taking and other developmental factors, difficult or challenging road conditions, and use of alcohol and cannabis (Asbridge, Poulin, &
⁎ Corresponding author. Centre for Addiction and Mental Health, 33 Russell Street, Toronto, ON, M5S 2S1, Canada. Tel.: +1 416 535 8501x4496; fax: +1 416 595 6899. E-mail address: [email protected] (R.E. Mann).
Donato, 2005; Jonah, 1997; Mayhew & Simpson, 1990; Vingilis & Wilk, 2008; Williams). Identification of these risk factors has led to the development of graduated driver's licensing programs that aim to control exposure to these risk factors during the period of acquisition of driving skills (Boase & Tasca, 1998; Mann et al., 1997), and these programs appear to have been successful in reducing risk behaviors and collision rates among young and novice drivers (Begg, Stephenson, Alsop, & Langley, 2001; Chen, Baker, & Li, 2006; Mann et al., 1997; Mayhew et al., 2003; Zhao et al., 2006). Older or senior drivers may also experience an elevation in collision risk, particularly per mile or kilometer traveled (Braver & Trempel, 2004; Eberhard, 2008; Langford, Koppel, McCarthy, & Srinivasan, 2008). These increases may be due to the maturation process in which skills and abilities potentially decline with age, specific medical conditions such as Alzheimer's disease, and factors such as increased medication use (e.g., Caird, Edwards, Creaser, & Horrey, 2005). Although several measures have been suggested to address potential collision risks experienced by senior drivers, such as more aggressive monitoring of driving skills by licensing authorities, the value of these measures has been debated (e.g., Bedard, Weaver, Darzins, & Porter, 2008; Eberhard, 2008).
0022-4375/$ – see front matter © 2010 National Safety Council and Elsevier Ltd. All rights reserved. doi:10.1016/j.jsr.2010.08.004
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R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449
While administrative databases provide important information on the characteristics of collisions and the environmental conditions when collisions have occurred, other investigators have begun to examine collision and injury risk based on population survey data, which allows for the consideration of social and psychological factors as contributors to collision risk. Based on the longitudinal dataset of the National Population Health Survey, Vingilis and Wilk (2008) examined age group differences in the importance of several risk factors: sociodemographic variables, binge drinking, health status, psychological distress, and medication use. Their path analysis revealed that binge drinking was the only significant predictor of subsequent motor vehicle collision (MVC) injury for the youngest group (12-29.9 year olds). Medication use was the only significant predictor of subsequent MVC injury for the middle age group (30-59.9 year olds), and no variables were found to be significant risk factors among older people (60-85 year olds). We report here a comparison of demographic, behavioral, and driving-related correlates of self-reported collision involvement for three age groups of drivers: those aged 18-34 years, 35-54 years, and 55 years and older. 2. Method
single item: ‘During the past 12 months, have you driven a motor vehicle after having two or more drinks in the previous hour?’ (Reported among respondents who had consumed alcohol at least once in the past 12 months, coded yes = 1, no = 0). 2.4. Analyses The results in the current study are based on “valid” responses (n's) such that missing data (i.e., “don't know” responses and refusals) were excluded from analyses. SPSS 15.0 software was employed for all analyses. The percentages reported are based on the weighted sample size and are considered representative for the population surveyed. Data on prevalence of collision involvement by independent variables were examined through chi-square and t-test analyses. For each age group, a logistic regression analysis was conducted of collision risk by driving exposure, heavy alcohol use, cannabis use, and driving after drinking, controlling for demographic characteristics. The Wald statistic was used to indicate statistically significant estimates (odds ratios) of relative risk of self-reported collision involvement. Due to listwise deletion, logistic regression models were based on reduced samples. Model fit was evaluated using the model chi-square (Hosmer & Lemeshow, 2000; Pampel, 2000).
2.1. Sample 3. Results This study was based on data from the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults 18 years and older, conducted by the Centre for Addiction and Mental Health and administered by the Institute for Social Research at York University. The CAMH Monitor is a regionally stratified survey consisting of independent monthly samples with approximately 200 completions each (response rate 56% to 61%; see Ialomiteanu & Adlaf, 2006 for sampling design details). Since 2002, the CAMH Monitor has included an item used to measure collision involvement; thus, for the current study, data from 2002 to 2005 were merged into one dataset of respondents who had valid data for driving-related measures. The data were then split into three samples: 18-34 year olds (n = 1,294), 35-54 year olds (n = 2,428), and 55 years of age or older (n = 1,576). 2.2. Dependent variable The key outcome measure was collision involvement: ‘During the past 12 months, how often, if at all, were you involved in an accident or collision involving any kind of damage or injury to you or another person or vehicle while you were driving?’ (coded yes = 1, no = 0). 2.3. Independent variables Demographic variables included gender (coded 1 = male, 0 = female), region (comprised of six regions in Ontario: Toronto, Central East, Central West, West, East, North), income (b$30,000, $30,000-49,999, $50,000-79,999, $80,000+, not stated), and marital status (coded 0 = not married, 1 = married or common law). Driving exposure was assessed by measures of driving distance, stress, and exposure to busy roads. Respondents were asked to indicate how much they drive in a typical week (number of kilometers, continuous variable, range 1-7,000). They were also asked how much of their driving is stressful and how much of their driving is on busy roads (coded: 0 = none of the time, 1 = at least some of the time). Heavy alcohol use was measured with a single item: ‘About how often during the past 12 months would you say you had five or more drinks at the same sitting or occasion?” (coded never = 0, at least once = 1). Cannabis use was assessed by two items: ‘Have you ever in your lifetime used marijuana or hash?’ and ‘How many times, if any, have you used marijuana or hash during the past twelve months?’ (coded yes = 1, no = 0). Driving after drinking was measured by a
Table 1 presents self-reported collision involvement for each age group by demographic characteristics, driving exposure measures, substance use measures, and driving after drinking. Overall the prevalence of collision involvement was highest for the youngest (18-34 years) age group (12.5%), with 35-54 year olds and 55+ year olds having similar collision involvement (6.0% and 6.5%, respectively). Demographic variables found to be significantly related to collision involvement varied between age groups. For the youngest group (18-34 year olds), collision involvement differed significantly by marital status. Collision involvement was higher for those not married (15.5%) compared to married or common law (8.3%). For 3554 year olds, a marginally significant association was found between collision involvement and income level (p = .06), with the highest rates of collision involvement reported by those earning less than $30,000 (7.6%) and those earning $80,000+ (7.4%), and the lowest rates reported by middle income earners (3.9% for $30,000 - $49,999 and 5.3% for $50,000 - $79,999). For those aged 55+ years, collision involvement differed significantly by gender (males 7.9% vs. females 4.9%) and by region (highest for Toronto 10.7%, and lowest for West 2.4% and North 3.9%). The driving exposure measures significantly associated with collision involvement did not vary much by age group. For the youngest group, a marginally significant association was found between number of kilometers driven in a typical week and collision involvement in the last year (p = .07). For all age groups, collision involvement was significantly associated with driving stress. Collision involvement was more common for those who reported that driving is stressful at least some of the time compared to those who did not. The substance use measures significantly associated with collision involvement varied by age group. For the youngest group, respondents reporting consumption of five or more drinks on one occasion, and use of cannabis in the last 12 months were significantly more likely to have been involved in a collision. For the 35 to 54 year olds, the two cannabis use measures were significantly associated with collision involvement. For the oldest age group, respondents reporting cannabis use in the last 12 months were significantly more likely to have been involved in a collision. For this same age group, reported use of cannabis during one's lifetime was marginally significant (p = .054). Driving after drinking was significantly related to collision involvement for the youngest group only. About one fifth of young
R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449 Table 1 Collision involvement for three age groups (18-34, 35-54, 55+) by demographic characteristics, driving exposure, heavy drinking, cannabis use, and drinking driving measures: Ontario CAMH Monitor, 2002-2005. Age 18-34 Collision N TOTAL Gender Male Female Region Toronto Central East Central West West East North Income b b$30,000 $30,000-49,999 $50,000-79,999 $80,000+ Not stated Marital Status Not married/partner Married/partner Kilometers Driven typical week mean (SD) Yes collision No collision Driving is Stressful None of the time At least some of the time Driving on Busy Roads None of the time At least some of the time Five + Drinks last 12 months Never At least once Cannabis Use lifetime No Yes Cannabis Use last 12 months No Yes Driving after Drinking No Yes
Age 35-54 a
Collision
Yes (%)
1294
N
12.5
Yes (%)
2428
6.0
Age 55+ a
Collisiona N
Yes (%)
1576
6.5 *
644 650
13.4 11.3
1227 1201
5.9 6.0
751 825
7.9 4.9
190 220 223 243 204 214
15.0 10.4 14.2 11.6 11.3 8.5
311 399 470 395 400 453
167 257 222 270 292 368
10.7 6.1 7.2 2.4 6.4 3.9
170 237 346 405 136
14.7 8.2 12.2 14.4 10.9 *** 15.5 8.3 p = .07
196 374 633 959 266
6.1 6.4 6.3 4.1 6.8 5.5 p = .06 7.6 3.9 5.3 7.4 4.1
321 310 324 302 318
6.6 4.3 8.1 6.8 6.2
650 1771
6.5 5.9
519 1043
7.7 6.2
144
*
692 598
148
508 780
398.9 (512.2) 329.4 (497.9) * 9.8 13.8
1014 1404
381.7 (372.9) 348.9 (545.0) * 4.7 6.7
57 1237
12.1 12.4
184 2244
1234 1184
1146
867 696
272.6 (435.3) 241.1 (426.2) * 5.2 7.8
4.4 6.1
185 1391
3.5 6.8
5.7 6.4
1125 443
5.9 7.7 p = .054
4.6 7.5 ***
1302 268
5.9 9.3
2167 254
5.4 10.6
1546 28
6.3 17.2
1933 204
6.2 6.6
1224 73
6.8 8.7
2284
91 1485
*** 440 850
8.6 14.5
** 581 707
12.3 12.6
1156 1251
* 961 327 998 163
11.4 15.6 *** 11.8 21.1
Notes: a At least once in the last 12 months. significance: * p b .05, ** p b .01, *** p b .001.
b
*
Canadian dollars. Chi-square statistical
drinking drivers were involved in a collision (21.1%) compared to 11.8% of non-drinking drivers. Table 2 presents logistic regression models of collision risk for each age group, controlling for demographic characteristics. Odds ratios greater than one indicate an increased risk of self-reported collision involvement in the past 12 months and odds ratios less than one indicate a decreased risk of collision involvement. The goodness of fit statistics (Hosmer & Lemeshow) for the models suggests they are good fits to the data. The logistic regression model for the youngest group revealed that stressful driving, heavy drinking, and driving after drinking were significantly associated with risk of collision involvement. Drivers reporting that driving is stressful had an increased risk of collision involvement (OR = 1.47) compared to those who never found driving to
447
be stressful. Those reporting consuming five or more drinks on one occasion had significantly increased odds of reporting collision involvement (OR = 1.61). Also, younger drivers reporting driving after drinking were roughly twice as likely (OR = 1.81) to indicate collision involvement in comparison to those who did not drive after drinking. The logistic regression model for the middle age group revealed that those who reported having used cannabis within the last 12 months had significantly increased odds of reporting collision involvement (OR = 1.90). Indeed, the likelihood of reporting a collision was almost twice as great among those reporting past year cannabis use. The logistic regression for the 55+ age group revealed that none of the risk factor variables were significantly associated with collision risk. The only measure found to have a significant relationship with collision involvement was the demographic indicator: region. Those who lived in the West region of the province had much lower odds of reporting collision involvement (OR = 0.13) compared to those in Toronto. 4. Discussion 4.1. Limitations It is important to point out some limitations of this study. First, given that self-report survey methods were used, the data may have been subject to estimation errors (e.g., kilometers traveled) or social desirability biases. However, at least in the latter case, such biased responding would only serve to weaken the strength of the study's findings. Second, as this study was based on cross-sectional survey data, it cannot be determined whether non-respondents would have responded the same way as those who participated in this study. However, given that other research has demonstrated that nonrespondents in studies of substance use and driving behavior are likely to be heavier substance users (Mann et al., 2003), it seems probable that any bias introduced by non-response would be a conservative one. Third, the cross-sectional nature of this research means that no conclusions about causal relationships can be drawn. Finally, it cannot be determined from the present data whether or not any of the collisions reported by those who drove after drinking actually involved impaired driving. Nonetheless, based on the results of the current study, it is clear that different factors are associated with collision involvement in different age groups. 4.2. Interpretation Overall, the prevalence of collision involvement was lowest for those 55+ years of age (i.e., about one half of the percentage of the youngest group). This is consistent with research on MVC injury risk in Canada that reveals persons aged 60–85 years have the lowest percentage of reported MVC injuries (Vingilis & Wilk, 2008). Even so, collisions remain an important source of death and injuries among seniors. It is interesting that the particular risk factors included in these analyses did not significantly influence collision occurrence for older respondents, similar to the findings of Vingilis and Wilk. Thus, further research to determine what factors do affect older driver collisions is needed. We live in an aging society and therefore in anticipation of the demographic changes that lie ahead, future research should focus specifically on identifying factors associated with collisions among this segment of the population. Some evidence for regional variation in the likelihood of collisions was observed for the youngest and oldest groups. The relationship between driving location and collisions presents an interesting area of future investigation. For example, detailed investigation of the driving environment of those regions associated with a reduced likelihood of collisions may provide important information that could be used to improve driving conditions in high collision risk regions. Several risk factors that are significantly associated with the increased likelihood of collisions among the youngest respondents
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R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449
Table 2 Logistic regression models of collision involvement risk for three age groups (18-34, 35-54, 55+): Ontario CAMH Monitor, 2002-2005. Collision Involvement
a
Age 18-34 (N = 1140) OR Control Variables: Gender (ref. = female) Region (ref. = Toronto) Central East Central West West East North Income c (ref. = b$30,000) $30,000-49,999 $50,000-79,999 $80,000+ Not stated Marital Status (ref. = not married/partner) Risk Factor Variables: Kilometers Driven - typical week Driving is Stressful (ref. = none of the time) Driving on Busy Roads (ref. = none of the time) Five + Drinks - last 12 months (ref. = no) Cannabis Use - lifetime (ref. = no) Cannabis Use - last 12 months (ref. = no) Driving after Drinking (ref. = no) Constant Hosmer & Lemeshow test Notes: a At least once in the last 12 months.
b
b
Age 35-54 (N = 2094) 95%CI
OR
Age 55+ (N = 1264) 95%CI
OR
95%CI
1.61
(.94, 2.74)
.84
(.60, 1.20)
.81
(.55, 1.20)
.59* .87 .62 .65 .45
(.36, .98) (.55, 1.36) (.36, 1.08) (.36, 1.17) (.19, 1.07)
1.12 .97 .77 1.17 .77
(.63, 1.97) (.55, 1.70) (.38, 1.56) (.62, 2.19) (.33, 1.79)
.48* .72 .79 .52 .58**
(.24, .94) (.40, 1.28) (.46, 1.36) (.25, 1.10) (.40, .84)
.40 .68 .93 .50 .99
(.15, 1.08) (.30, 1.54) (.42, 2.02) (.18, 1.39) (.60, 1.63)
.58 1.12 .74 .79 .75
(.23, 1.46) (.50, 2.49) (.32, 1.74) (.34, 1.88) (.42, 1.37)
(1.00, 1.00) (.86, 1.97) (.42, 2.23) (.69, 1.49) (.87, 2.01) (1.13, 3.18) (.53, 1.90)
1.00 1.46 1.18 1.00 1.39 1.18 1.12 .09*** 4.26(8df) p = .83
(1.00, 1.00) (.89, 2.41) (.44, 3.13) (.60, 1.66) (.76, 2.54) (.32, 4.36) (.43, 2.95)
1.00 1.47* .82 1.61* .74 1.32 1.81** .22* 8.66(8df) p = .37
(1.00, 1.00) (1.02, 2.13) (.32, 2.10) (1.05, 2.47) (.48, 1.13) (.85, 2.06) (1.18, 2.77)
1.00 1.30 .97 1.01 1.32 1.90* 1.01 .07*** 2.01(8df) p = .98
.48 .73 .13** .58 .39
(.24, .98) (.37, 1.42) (.03, .48) (.29, 1.19) (.15, 1.03)
Odds Ratio. c Canadian dollars. ref. = reference category. Wald statistical significance: * p b .05, ** p b .01, *** p b .001.
were identified. A significant relationship was found between perceptions of driving as stressful and the likelihood of collisions among the respondents in this age group. Because of the cross-sectional nature of the research, it is possible that past-year collision experience may have influenced reported driving stress and thus we cannot assume that more stressful driving results in collisions. Nonetheless, assuming that congestion on roadways, inconsiderate driver behavior, and other factors that contribute to the stress of driving will only continue to intensify, it is worth considering how we can make driving less stressful for younger drivers. Perhaps young drivers need more supervised ‘road’ time in order to provide them with greater experience in dealing with the stressors associated with driving. Also, driver training programs could provide additional education that addresses driving stress. The observation that heavy drinking (among the youngest respondents) and past year cannabis use (among the middle aged group) increases the risk of collisions underscores the well-known link between alcohol and collision risk (Borkenstein, Crowther, Shumate, Ziel, & Zylman, 1964; Mann et al., 2001) and also is consistent with research showing a link between cannabis use and collision risk (Asbridge et al., 2005; Mann et al., 2007). Our results may reflect impairing effects of these substances on driving behavior, but they may also be highlighting a general risk taking tendency in some individuals. Specifically, it may be that those who partake in potentially risky behaviors regarding substance use also engage in risky driving. Jessor's (1987) Problem Behavior Theory suggests that drinking and driving is related to involvement in general problem behavior, particularly pointing to correlations between drinking driving and risky driving, alcohol use, and drug use.
educating young people about the hazards of heavy drinking, drug use, and drinking and driving. It seems that the legislative measures enacted to deter drinking and driving, although effective overall, may be less effective among those young people who may be engaged in a problem behavior lifestyle. Prevention efforts aimed at a constellation of risky behavior engaged in by younger people is warranted. Driving will likely remain a primary mode of transportation and the complexities associated with navigating roadways may possibly intensify. This study has identified differences in factors associated with the self-reported collision involvement of three age groups of adult drivers. The results suggest potential areas for intervention and new directions for future research. 4.4. Impact on Industry No impact on industry is anticipated. Acknowledgements This research was supported by a grant from AUTO21, a member of the Networks of Centres of Excellence program which is administered and funded by the Natural Sciences and Engineering Research Council, the Canadian Institutes of Health Research, and the Social Sciences and Humanities Research Council, in partnership with Industry Canada. The authors also acknowledge ongoing funding support from the Ontario Ministry of Health and Long-Term Care. Funders had no involvement in the design, data collection, analysis, writing or decision to submit the paper for publication. The views expressed here do not necessarily reflect those of AUTO21 or the Ministry of Health and Long Term Care.
4.3. Conclusions This study supports findings of previous research (Vingilis & Wilk, 2008) and suggests that further investigation of risk factors as they relate to age groups is needed. Our results highlight the value of comparative research that assesses collision risk factors in different age groups. This study continues to illustrate the importance of
References Asbridge, M., Poulin, C., & Donato, A. (2005). Driving under the influence of cannabis and motor vehicle collision risk: Evidence from adolescents in Atlantic Canada. Accident Analysis and Prevention, 37, 1025−1034. Bedard, M., Weaver, B., Darzins, P., & Porter, M. M. (2008). Predicting driving performance in older adults: we are not there yet! Traffic Injury Prevention, 9, 336−341.
R.E. Mann et al. / Journal of Safety Research 41 (2010) 445–449 Begg, D. J., Stephenson, S., Alsop, J., & Langley, J. (2001). Impact of graduated driver licensing restrictions on crashes involving young drivers in New Zealand. Injury Prevention, 7, 292−296. Boase, P., & Tasca, L. (1998). Graduated licensing system evaluation: Interim evaluation. Toronto, ON: Ontario Ministry of Transportation. Borkenstein, R. F., Crowther, R. F., Shumate, R. P., Ziel, W. B., & Zylman, R. (1964). The role of the drinking driver in traffic accidents. Bloomington, IN: Department of Police Administration. Braver, E. R., & Trempel, R. E. (2004). Are older drivers actually at higher risk of involvement in collisions resulting in deaths or non-fatal injuries among their passengers and other road users? Injury Prevention, 10, 27−32. Caird, J. K., Edwards, C. J., Creaser, J. I., & Horrey, W. J. (2005). Older driver failures of attention at intersections: using change blindness methods to assess turn decision accuracy. Human Factors, 47, 235−249. Chen, L. H., Baker, S. P., & Li, G. (2006). Graduated driver licensing programs and fatal crashes of 16-year-old drivers: a national evaluation. Pediatrics, 118, 56−62. Eberhard, J. (2008). Older drivers' "high per-mile crash involvement": the implications for licensing authorities. Traffic Injury Prevention, 9, 284−290. Gregersen, N. P., & Bjurulf, P. (1996). Young novice drivers: towards a model of their accident involvement. Accident Analysis and Prevention, 28, 229−241. Hosmer, D. W., Jr., & Lemeshow, S. (2000). Applied Logistic Regression, 2nd edition. New York: John Wiley and Sons. Ialomiteanu, A., & Adlaf, E. M. (2006). CAMH Monitor 2005: Technical guide.Toronto, ON: Centre for Addiction and Mental Health available at: www.camh.net/ Research/Areas_of_research/Population_Life_Course_Studies/CAMH_Monitor/CM2005_TechDoc.pdf Jessor, R. (1987). Risky driving and adolescent problem behavior: An extension of problem-behavior theory. Alcohol, Drugs and Driving, 3(3–4), 1−11. Jonah, B. A. (1997). Sensation seeking and risky driving: A review and synthesis of the literature. Accident Analysis and Prevention, 29, 651−665. Langford, J., Koppel, S., McCarthy, D., & Srinivasan, S. (2008). In defence of the 'lowmileage bias'. Accident Analysis and Prevention, 40, 1996−1999. Mann, R. E., Adlaf, E., Zhao, J., Stoduto, G., Ialomiteanu, A., Smart, R. G., & Asbridge, M. (2007). Cannabis use and self-reported collisions in a representative sample of adult drivers. Journal of Safety Research, 38, 669−674. Mann, R. E., Macdonald, S., Chipman, M. L., Adlaf, E. M., Anglin-Bodrug, K., & Zhao, J. (2003). Identifying possible sources of bias introduced in traffic safety research: Comparison of blind linkage with volunteer clinical samples. In D. R. Mayhew, & C. Dussault (Eds.), Alcohol, drugs and traffic safety - T'2002: Proceedings of the 16th International Conference on Alcohol, Drugs and Traffic Safety (pp. 275−280). Montreal: Société de l'Assurance Automobile du Québec. Mann, R. E., Stoduto, G., Anglin, L., Pavic, B., Fallon, F., Lauzon, R., & Amitay, O. A. (1997). Graduated licensing in Ontario: Impact of the 0 BAL provision on adolescents’ drinkingdriving. In C. Mercier-Guyon (Ed.), Alcohol, drugs and traffic safety - T'97: Proceedings of the 14th International Conference on Alcohol, Drugs and Traffic Safety (pp. 1055−1060). Annecy, France: Centre d'Etudes et de Researches en Medecine du Trafic. Mann, R. E., Stoduto, G., Macdonald, S., Shaikh, A., Bondy, S., & Jonah, B. (2001). The effects of introducing or lowering legal per se blood alcohol limits for driving: An international review. Accident Analysis and Prevention, 33, 61−75. Mayhew, D. R., & Simpson, H. M. (1990). New to the road. Young drivers and novice drivers: Similar problems and solutions? Ottawa, ON: Traffic Injury Research Foundation. Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis and Prevention, 25, 683−691. Mayhew, D. R., Simpson, H. M., Singhal, D., & Desmond, K. (2006). Reducing the crash risk for young drivers. Washington, DC: American Automobile Association Foundation for Traffic Safety. Pampel, F. C. (2000). Logistic Regression: A Primer. Sage University Paper series on Quantitative Applications in Social Sciences, 07-132. CA: Sage: Thousand Oaks. Vingilis, E., & Wilk, P. (2008). The effects of health status, distress, alcohol and medicinal drug use on subsequent motor vehicle injuries. Accident Analysis and Prevention, 40, 1901−1907. Williams, A. F. (2006). Young driver risk factors: successful and unsuccessful approaches for dealing with them and an agenda for the future. Injury Prevention, 12(Suppl. 1), i4−i8. Zhao, J., Mann, R. E., Chipman, M., Adlaf, E., Stoduto, G., & Smart, R. G. (2006). The impact of driver education on self-reported collisions among young drivers with a graduated license. Accident Analysis and Prevention, 38, 35−42.
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Robert Mann has a Ph.D. in Psychology from the University of Waterloo. He is a Senior Scientist in the Public Health and Regulatory Policy Section of the Social and Epidemiological Research Department at the Centre for Addiction and Mental Health and an Associate Professor in the Dalla Lana School of Public Health at the University of Toronto. He has 30 years of research experience on alcohol and drug impaired driving, including legislative policies and assessing the impact of policies, prevention programs and remedial activities. Gina Stoduto is a Research Coordinator in the Social and Epidemiological Research Department at the Centre for Addiction and Mental Health. She obtained her B.Sc. in Psychology, and M.Ed. in Applied Psychology from the University of Toronto. For 20 years she has been involved in traffic safety research focusing on alcohol and drug impaired driving and road rage, and in research on the development of legislative policies and evaluations of the impact of policies and prevention and remedial programs. Jennifer Butters is an Affiliate Scientist with the Centre for Addiction and Mental Health. Her research examines automobile use and injury, with a particular emphasis on mental health, substance use and aggression in the context of driving. She is also interested in the intersection of drug use, violence and mental health among youth. She has served as a co-investigator on several research projects and was awarded the Edie Yolles Award for Dissertation Excellence from the Department of Sociology at the University of Toronto. Anca Ialomiteanu is a Research Coordinator in the Social and Epidemiological Research Department at the Centre for Addiction and Mental Health. She has an M.A. in Information Studies from the University of Bucharest, Romania, and over 20 years experience in population survey research. She is the project coordinator of the CAMH Monitor, an annual monitoring study of alcohol and drug use and other health behaviors of Ontario adults. She was also involved in the design and analysis of the 2004 Canadian Addiction Survey and the 2008 Canadian Alcohol and Drug Use Monitoring Survey, two major national surveys of attitudes, beliefs, and use of alcohol and other drugs. Paul Boase is Chief of the Road Safety & Motor Vehicle Regulation Directorate's Road Users’ Division with Transport Canada. He obtained a B.A. in Sociology/Psychology at York University in 1982, and an M.A. in Psychology from the University of Toronto in 1983. Over the past twenty-five years, he has worked as a researcher and statistician in traffic safety. Mark Asbridge is an Assistant Professor in the Department of Community Health and Epidemiology, Dalhousie University. Mark completed his Ph.D. in Sociology and Addiction Studies at the University of Toronto and a post-doctoral fellowship at the Centre for Addiction and Mental Health in Toronto. Mark's research program examines the areas of addictions, public and population health, injury prevention and emergency medicine, and public policy. Mary Chipman is Professor Emeritus at the Dalla Lana School of Public Health, University of Toronto. Professor Chipman has worked for many years in medical statistics and epidemiology, with a particular interest in the epidemiology of traffic crashes and the effectiveness of various preventive measures. Christine Wickens is a Post-Doctoral Fellow at the Centre for Addiction and Mental Health. She obtained her H.B.Sc. in Psychology/Sociology from the University of Toronto and her M.A. and Ph.D. in Social and Personality Psychology from York University. Her primary research interests address the psychology of driver behavior including driver stress, aggression, and impaired driving.