DRIVE is a prospective cohort study of a large (≥20 000) nonrepresentative sample of newly licensed young drivers in New South Wales, Australia.1 Its objective is to identify risk and protective factors for motor vehicle crashes (MVCs) (or crashes) by observing subgroup differences over time. A previously observed protective factor was participation in the New South Wales Health Reduce Risk Increase Student Knowledge (RRISK) resilience program.2 RRISK brings together grade 11 students from diverse schools for a 1-day, whole-of-community seminar that is preceded and followed by multiple peer-led teacher and parent support activities. It addresses common youth risks, including alcohol and drug use, partying, and driver and passenger risks. The focus is on empowering students to personalize learning into awareness of their own and their friends’ risk propensities, contributing factors, and how to minimize these, including forward planning and back-up strategies. The current research aims were to explore whether crash differences by program participation persisted over 13 years and to explore any differences by crash type, including severity.
Methods
Detailed DRIVE methods are reported elsewhere.1,3 Briefly, all drivers aged 17 to 24 in New South Wales, Australia, on their first independent (postlearner) license during 2003–2004 were invited to complete a detailed online questionnaire, which included RRISK participation, and gave permission to access their police-recorded MVC records in future years. Survey and crash data to 2016 were linked by an independent body and were provided deidentified (ethics approvals: Aboriginal Health & Medical Research Council, University of New South Wales, and New South Wales Population & Health Services; reference: HREC/16/CIPHS/9).
Univariate and multivariate negative binomial regression models were analyzed, with the number of MVCs of legally permitted vehicles as the primary outcome and MVC police codes for wet road surface, dark lighting, and severity (no injury versus minor injury to fatal) as secondary outcomes (other MVC types were too infrequent to include). We imputed missing values in the survey data using chained equations with 30 imputation cycles. Table 1 includes baseline survey variables considered as potential confounders, with time in study (from survey completion to either December 31, 2016, or [all cause] date of death) included as an offset. Analyses were performed by using Stata 15 (Stata Corp, College Station, TX).
Variable . | Program Participation . | Total,a No. (%) . | |
---|---|---|---|
No, n (%) . | Yes, n (%) . | ||
Sex | |||
Female | 10 133 (54.1) | 277 (51.3) | 10 410 (54.1) |
Male | 8583 (45.9) | 263 (48.7) | 8846 (45.9) |
Age, y | |||
17 | 9096 (48.6) | 350 (64.8) | 9446 (49.1) |
18–19 | 6977 (37.3) | 173 (32.0) | 7150 (37.1) |
20–25 | 2643 (14.1) | 17 (3.1) | 2660 (13.8) |
Country of birth | |||
Australia and New Zealand | 16 012 (87.1) | 511 (95.0) | 16 523 (87.3) |
Other | 2368 (12.9) | 27 (5.0) | 2395 (12.7) |
Area-level SES | |||
Least disadvantaged | 4829 (25.8) | 36 (6.7) | 4865 (25.3) |
Second quartile | 4719 (25.2) | 98 (18.1) | 4817 (25.0) |
Third quartile | 4678 (25.0) | 285 (52.8) | 4963 (25.8) |
Most disadvantaged | 4490 (24.0) | 121 (22.4) | 4611 (23.9) |
Remoteness | |||
Metropolitan | 14 330 (76.6) | 86 (15.9) | 14 416 (74.9) |
Inner regional | 3562 (19.0) | 437 (80.9) | 3999 (20.8) |
Outer regional or remote | 824 (4.4) | 17 (3.1) | 841 (4.4) |
Driver test attempts | |||
1 | 12 125 (65.0) | 384 (71.2) | 12 509 (65.2) |
2 | 4477 (24.0) | 114 (21.2) | 4591 (23.9) |
≥3 | 2056 (11.0) | 41 (7.6) | 2097 (10.9) |
Time on postlearner license, y | |||
<1 | 7050 (37.8) | 254 (47.1) | 7304 (38.1) |
1–1.5 | 6677 (35.8) | 217 (40.3) | 6894 (35.9) |
>1.5 | 4919 (26.4) | 68 (12.6) | 4987 (26.0) |
Crash before study | |||
No | 18 117 (96.8) | 523 (96.9) | 18 640 (96.8) |
Yes | 599 (3.2) | 17 (3.1) | 616 (3.2) |
Professional training, h | |||
0 | 3353 (17.9) | 76 (14.1) | 3429 (17.8) |
1–4 | 5315 (28.4) | 257 (47.6) | 5572 (28.9) |
5–8 | 3926 (21.0) | 123 (22.8) | 4049 (21.0) |
≥9 | 6122 (32.7) | 84 (15.6) | 6206 (32.2) |
Self-rated driving ability compared with other drivers | |||
Much better | 3363 (18.3) | 80 (15.0) | 3443 (18.3) |
Better | 8019 (43.8) | 235 (44.2) | 8254 (43.8) |
Same | 6628 (36.2) | 207 (38.9) | 6835 (36.2) |
Worse or much worse | 318 (1.7) | 10 (1.9) | 328 (1.7) |
Marijuana smoking in last 12 mo | |||
Never | 15 801 (86.3) | 418 (78.9) | 16 219 (86.1) |
Once a month or less | 1912 (10.4) | 82 (15.5) | 1994 (10.6) |
2–4 times a month | 355 (1.9) | 15 (2.8) | 370 (2.0) |
2–3 or ≥4 times per week | 244 (1.3) | 15 (2.8) | 259 (1.4) |
Use of other drugs in last 12 mo | |||
Never | 17 042 (93.2) | 479 (90.7) | 17 521 (93.2) |
Once a month or less | 933 (5.1) | 38 (7.2) | 971 (5.2) |
2–4 times a month or more | 302 (1.7) | 11 (2.1) | 313 (1.7) |
Alcohol audit summary score | |||
0–6 | 15 909 (86.7) | 450 (84.9) | 16 359 (86.7) |
≥7 | 2439 (13.3) | 80 (15.1) | 2519 (13.3) |
Self-harm | |||
No | 16 540 (92.2) | 454 (89.4) | 16 994 (92.2) |
Yes | 1392 (7.8) | 54 (10.6) | 1446 (7.8) |
Risk taking | |||
Low | 6276 (34.6) | 178 (34.0) | 6454 (34.5) |
Medium | 5927 (32.6) | 191 (36.5) | 6118 (32.7) |
High | 5955 (32.8) | 154 (29.4) | 6109 (32.7) |
Risk perception | |||
Low | 5568 (30.8) | 196 (37.5) | 5764 (31.0) |
Medium | 5688 (31.5) | 138 (26.4) | 5826 (31.4) |
High | 6800 (37.7) | 189 (36.1) | 6989 (37.6) |
Sensation score | |||
Low | 5746 (31.8) | 143 (27.7) | 5889 (31.7) |
Medium | 5865 (32.4) | 176 (34.1) | 6041 (32.5) |
High | 6463 (35.8) | 197 (38.2) | 6660 (35.8) |
Average weekly driving, h | |||
0–2 | 3757 (20.1) | 120 (22.2) | 3877 (20.1) |
3–5 | 5914 (31.6) | 196 (36.3) | 6110 (31.7) |
6–9 | 3014 (16.1) | 86 (15.9) | 3100 (16.1) |
≥10 | 6031 (32.2) | 138 (25.6) | 6169 (32.0) |
Any crash | |||
None | 14 885 (79.5) | 466 (86.3) | 16 557 (79.6) |
1 | 3247 (17.3) | 64 (11.9) | 3596 (17.3) |
≥2 | 584 (3.1) | 10 (1.9) | 653 (3.1) |
Crash on wet road surface | |||
None | 17 827 (95.3) | 521 (96.5) | 19 814 (95.2) |
≥1 | 889 (4.7) | 19 (3.5) | 992 (4.8) |
Crash in dark lighting | |||
None | 17 569 (93.9) | 524 (97.0) | 19 552 (94.0) |
≥1 | 1147 (6.1) | 16 (3.0) | 1254 (6.0) |
Crash resulting in injury | |||
None | 16 931 (90.5) | 506 (93.7) | 18 819 (90.4) |
≥1 | 1785 (9.5) | 34 (6.3) | 1987 (9.6) |
Variable . | Program Participation . | Total,a No. (%) . | |
---|---|---|---|
No, n (%) . | Yes, n (%) . | ||
Sex | |||
Female | 10 133 (54.1) | 277 (51.3) | 10 410 (54.1) |
Male | 8583 (45.9) | 263 (48.7) | 8846 (45.9) |
Age, y | |||
17 | 9096 (48.6) | 350 (64.8) | 9446 (49.1) |
18–19 | 6977 (37.3) | 173 (32.0) | 7150 (37.1) |
20–25 | 2643 (14.1) | 17 (3.1) | 2660 (13.8) |
Country of birth | |||
Australia and New Zealand | 16 012 (87.1) | 511 (95.0) | 16 523 (87.3) |
Other | 2368 (12.9) | 27 (5.0) | 2395 (12.7) |
Area-level SES | |||
Least disadvantaged | 4829 (25.8) | 36 (6.7) | 4865 (25.3) |
Second quartile | 4719 (25.2) | 98 (18.1) | 4817 (25.0) |
Third quartile | 4678 (25.0) | 285 (52.8) | 4963 (25.8) |
Most disadvantaged | 4490 (24.0) | 121 (22.4) | 4611 (23.9) |
Remoteness | |||
Metropolitan | 14 330 (76.6) | 86 (15.9) | 14 416 (74.9) |
Inner regional | 3562 (19.0) | 437 (80.9) | 3999 (20.8) |
Outer regional or remote | 824 (4.4) | 17 (3.1) | 841 (4.4) |
Driver test attempts | |||
1 | 12 125 (65.0) | 384 (71.2) | 12 509 (65.2) |
2 | 4477 (24.0) | 114 (21.2) | 4591 (23.9) |
≥3 | 2056 (11.0) | 41 (7.6) | 2097 (10.9) |
Time on postlearner license, y | |||
<1 | 7050 (37.8) | 254 (47.1) | 7304 (38.1) |
1–1.5 | 6677 (35.8) | 217 (40.3) | 6894 (35.9) |
>1.5 | 4919 (26.4) | 68 (12.6) | 4987 (26.0) |
Crash before study | |||
No | 18 117 (96.8) | 523 (96.9) | 18 640 (96.8) |
Yes | 599 (3.2) | 17 (3.1) | 616 (3.2) |
Professional training, h | |||
0 | 3353 (17.9) | 76 (14.1) | 3429 (17.8) |
1–4 | 5315 (28.4) | 257 (47.6) | 5572 (28.9) |
5–8 | 3926 (21.0) | 123 (22.8) | 4049 (21.0) |
≥9 | 6122 (32.7) | 84 (15.6) | 6206 (32.2) |
Self-rated driving ability compared with other drivers | |||
Much better | 3363 (18.3) | 80 (15.0) | 3443 (18.3) |
Better | 8019 (43.8) | 235 (44.2) | 8254 (43.8) |
Same | 6628 (36.2) | 207 (38.9) | 6835 (36.2) |
Worse or much worse | 318 (1.7) | 10 (1.9) | 328 (1.7) |
Marijuana smoking in last 12 mo | |||
Never | 15 801 (86.3) | 418 (78.9) | 16 219 (86.1) |
Once a month or less | 1912 (10.4) | 82 (15.5) | 1994 (10.6) |
2–4 times a month | 355 (1.9) | 15 (2.8) | 370 (2.0) |
2–3 or ≥4 times per week | 244 (1.3) | 15 (2.8) | 259 (1.4) |
Use of other drugs in last 12 mo | |||
Never | 17 042 (93.2) | 479 (90.7) | 17 521 (93.2) |
Once a month or less | 933 (5.1) | 38 (7.2) | 971 (5.2) |
2–4 times a month or more | 302 (1.7) | 11 (2.1) | 313 (1.7) |
Alcohol audit summary score | |||
0–6 | 15 909 (86.7) | 450 (84.9) | 16 359 (86.7) |
≥7 | 2439 (13.3) | 80 (15.1) | 2519 (13.3) |
Self-harm | |||
No | 16 540 (92.2) | 454 (89.4) | 16 994 (92.2) |
Yes | 1392 (7.8) | 54 (10.6) | 1446 (7.8) |
Risk taking | |||
Low | 6276 (34.6) | 178 (34.0) | 6454 (34.5) |
Medium | 5927 (32.6) | 191 (36.5) | 6118 (32.7) |
High | 5955 (32.8) | 154 (29.4) | 6109 (32.7) |
Risk perception | |||
Low | 5568 (30.8) | 196 (37.5) | 5764 (31.0) |
Medium | 5688 (31.5) | 138 (26.4) | 5826 (31.4) |
High | 6800 (37.7) | 189 (36.1) | 6989 (37.6) |
Sensation score | |||
Low | 5746 (31.8) | 143 (27.7) | 5889 (31.7) |
Medium | 5865 (32.4) | 176 (34.1) | 6041 (32.5) |
High | 6463 (35.8) | 197 (38.2) | 6660 (35.8) |
Average weekly driving, h | |||
0–2 | 3757 (20.1) | 120 (22.2) | 3877 (20.1) |
3–5 | 5914 (31.6) | 196 (36.3) | 6110 (31.7) |
6–9 | 3014 (16.1) | 86 (15.9) | 3100 (16.1) |
≥10 | 6031 (32.2) | 138 (25.6) | 6169 (32.0) |
Any crash | |||
None | 14 885 (79.5) | 466 (86.3) | 16 557 (79.6) |
1 | 3247 (17.3) | 64 (11.9) | 3596 (17.3) |
≥2 | 584 (3.1) | 10 (1.9) | 653 (3.1) |
Crash on wet road surface | |||
None | 17 827 (95.3) | 521 (96.5) | 19 814 (95.2) |
≥1 | 889 (4.7) | 19 (3.5) | 992 (4.8) |
Crash in dark lighting | |||
None | 17 569 (93.9) | 524 (97.0) | 19 552 (94.0) |
≥1 | 1147 (6.1) | 16 (3.0) | 1254 (6.0) |
Crash resulting in injury | |||
None | 16 931 (90.5) | 506 (93.7) | 18 819 (90.4) |
≥1 | 1785 (9.5) | 34 (6.3) | 1987 (9.6) |
SES, socioeconomic status.
Missing not shown.
Results
The cohort comprised 20 806 participants; 54.1% of the cohort was female (Table 1). Other demographics differed by group, with more program participants aged 17 and living in a regional and disadvantaged area, reflecting program delivery locations. Overall, 20.4% of the cohort had at least 1 crash as a driver, including 4.7% crashes on wet roads, 6.1% crashes in dark lighting, and 9.5% crashes resulting in injury.
Table 2 summarizes resulting rate ratios. Unadjusted models revealed lower risk for program participants than nonparticipants for all crashes, crashes in dark lighting, and injury crashes but not crashes on wet roads. When adjusted for confounders, injury crashes were no longer statistically different; however, program participants were 24% less likely to have had any crash and 42% less likely to crash in darkness than nonparticipants.
Outcome . | Program Participation . | |
---|---|---|
No (Reference) . | Yes, Rate Ratio (95% CI) . | |
Any crash | ||
Unadjusted model | 1 | 0.66 (0.52–0.83) |
Adjusted model | 1 | 0.76 (0.60–0.96) |
Crash on wet road surface | ||
Unadjusted model | 1 | 0.74 (0.47–1.17) |
Adjusted model | 1 | 0.81 (0.51–1.31) |
Crash in dark lighting | ||
Unadjusted model | 1 | 0.48 (0.29–0.79) |
Adjusted model | 1 | 0.58 (0.35–0.96) |
Crash resulting in injury | ||
Unadjusted model | 1 | 0.67 (0.48–0.95) |
Adjusted model | 1 | 0.79 (0.55–1.13) |
Outcome . | Program Participation . | |
---|---|---|
No (Reference) . | Yes, Rate Ratio (95% CI) . | |
Any crash | ||
Unadjusted model | 1 | 0.66 (0.52–0.83) |
Adjusted model | 1 | 0.76 (0.60–0.96) |
Crash on wet road surface | ||
Unadjusted model | 1 | 0.74 (0.47–1.17) |
Adjusted model | 1 | 0.81 (0.51–1.31) |
Crash in dark lighting | ||
Unadjusted model | 1 | 0.48 (0.29–0.79) |
Adjusted model | 1 | 0.58 (0.35–0.96) |
Crash resulting in injury | ||
Unadjusted model | 1 | 0.67 (0.48–0.95) |
Adjusted model | 1 | 0.79 (0.55–1.13) |
Regression models were adjusted for all variables in Table 1, including imputed missing values. This included age, lessons with a professional driving training instructor, risk taking score, risk perception score, sensation seeking score, and average weekly driving as continuous variables and sex, country of birth, area-level socioeconomic status, geographical remoteness, number of attempts on driving test, length on learner license, crash before study, self-rated driving ability, cannabis smoking, use of other drugs, and alcohol audit summary score as categorical variables. CI, confidence interval.
Discussion
In this prospective study, rich baseline information on known confounders and access to statewide records were available for a large cohort of drivers for up to 13 years. Even when known confounders were accounted for, a lower risk of crash persisted for resilience program participants, including crashes in darkness. Injury crashes indicated similar positive findings but were not statistically different after adjustment for confounders.
Limitations of the observational study design, including potential selection bias (more by schools than individuals) and complex changes in baseline measures over time, such as crash risk exposures and lifestyle changes (including cessation of driving), albeit unlikely influenced by program participation, have been reported previously.2,3 At-fault status was unknown; however, driver errors contribute to the majority of young novice crashes.4 Regardless, positive outcomes persisted, strongly indicating that setting up youth to be safer, responsible drivers early has real potential to reduce their lifetime risk of a crash that is significant enough to warrant police attendance. Research increasingly identifies that road risks cluster with other youth risks and that addressing these collectively and early (including at predriving age) is likely to have better outcomes than addressing them in isolation.5–7
High-quality evaluations of school-based road safety initiatives are limited or treat any driver education program as equal.8,9 This contributes to overriding beliefs that driver education does not work rather than that good programs need to be identified from the proliferation of traditional programs. Positive outcomes are evident for more nuanced initiatives.10
With road crashes persisting as a leading cause of preventable deaths and serious injuries, the current research warrants increased efforts to improve the evidence base and thereby the quality of youth road safety initiatives to set them up for a lifetime of safer driving.
Dr Senserrick conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Möller conceptualized and designed the study, supervised data collection, conducted the initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Dr Rogers conceptualized and designed the study, critically reviewed the analyses, and reviewed and revised the manuscript; Drs Cullen and Ivers conceptualized and designed the study and critically reviewed the manuscript for important intellectual content; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
FUNDING: Relinkage of the DRIVE study was funded by the National Roads and Motorists’ Association (NRMA)–Australian Capital Territory Road Safety Trust. The original DRIVE study was funded by the National Health and Medical Research Council (NHMRC) of Australia, the Roads and Traffic Authority of New South Wales, NRMA Motoring and Services, the NRMA–Australian Capital Territory Road Safety Trust, New South Wales Health, and the Motor Accidents Authority of New South Wales. Dr Ivers was funded by an NHMRC Senior Research Fellowship (grant APP1136430), and Dr Cullen was funded by an NHMRC Early Career Fellowship (grant APP1158223). This research was supported by the Centre of Research Excellence: Driving Global Investment in Adolescent Health, which is funded by the NHMRC (grant APP1171981), as well as by the Wellbeing Health & Youth Centre of Research Excellence in Adolescent Health, which is funded by the NHMRC (grant APP1134894). Funding bodies had no role in the design and conduct of the study.
References
Competing Interests
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
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