OBJECTIVES:

To compare monthly rates of specific types of crashes, violations, and license suspensions over the first years of licensure for drivers with and without attention-deficit/hyperactivity disorder (ADHD).

METHODS:

We identified patients of New Jersey primary care locations of the Children’s Hospital of Philadelphia who were born in 1987–1997, were New Jersey residents, had their last primary care visit at age ≥12 years, and acquired a driver’s license (N = 14 936). Electronic health records were linked to New Jersey’s licensing, crash, and violation databases. ADHD diagnosis was based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes. We calculated monthly per-driver rates of crashes (at fault, alcohol related, nighttime, and with peers), violations, and suspensions. Adjusted rate ratios were estimated by using repeated-measures Poisson regression.

RESULTS:

Crash rates were higher for drivers with ADHD regardless of licensing age and, in particular, during the first month of licensure (adjusted rate ratio: 1.62 [95% confidence interval: 1.18−2.23]). They also experienced higher rates of specific crash types: their 4-year rate of alcohol-related crashes was 2.1 times that of drivers without ADHD. Finally, drivers with ADHD had higher rates of moving violations (for speeding, seat belt nonuse, and electronic equipment use) and suspensions. In the first year of driving, the rate of alcohol and/or drug violations was 3.6 times higher for adolescents with ADHD.

CONCLUSIONS:

Adolescents with ADHD are at particularly high crash risk in their initial months of licensure, and engagement in preventable risky driving behaviors may contribute to this elevated risk. Comprehensive preventive approaches that extend beyond current recommendations are critically needed.

What’s Known on This Subject:

In recent studies, it has been established that crash risk is higher among adolescents with attention-deficit/hyperactivity disorder. However, we know little about behavioral mechanisms underlying this risk, and no previous studies have examined risk during the newly licensed period.

This longitudinal study suggests that increased engagement in risky driving behaviors may be an important factor underlying elevated crash risk among adolescent drivers with attention-deficit/hyperactivity disorder. Findings highlight the critical need to develop comprehensive preventive approaches that extend beyond current recommendations.

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood disorder characterized by excessive levels of hyperactivity and impulsivity and/or inattention.1 For the majority of children, ADHD persists into adolescence, when many become licensed to drive.2,3 Skills that are critical in driving, including executive functioning, are frequently impaired in individuals with ADHD.4,5 Indeed, licensing rates among adolescents with ADHD are lower than those among other adolescents.6

Additionally, research on the specific behavioral mechanisms underlying this elevated crash risk is lacking, limiting the ability to develop evidence-based prevention efforts for novice drivers with ADHD. Examining specific crash types (including single-vehicle crashes, crashes involving alcohol, at-fault crashes, and crashes occurring at night or with peers [2 high-risk driving conditions restricted for newly licensed adolescents under GDL14]), may provide critical insights on driving behaviors that are known to increase the likelihood of crashes or crash-related injuries and may be responsive to targeted intervention. In addition, authors of several previous studies have assessed traffic violations and license suspensions (both frequently used as proxies for risky driving) as well as self-reported risky behaviors (eg, drinking and driving) among these adolescents.7,9,11 However, these studies revealed inconsistent findings and involved samples that were either small or more severely affected.9,10 Moreover, drivers with ADHD have been found to overestimate their driving competence,15 challenging the validity of self-reported measures and highlighting a critical need for studies in which objective traffic safety data are used.

To address these knowledge gaps, we conducted a large retrospective cohort study to compare monthly rates of overall and specific crash types, violations, and suspensions over the initial 4 years of licensure for adolescent drivers with and without ADHD; we hypothesized that rates would be higher among those with ADHD. To do this, we established a cohort of primary care patients at Children’s Hospital of Philadelphia (CHOP) and leveraged a unique linkage of electronic health records (EHRs) and statewide traffic data.

Subjects were identified from the 6 New Jersey primary care practices within the CHOP network, which serves a socioeconomically, ethnically, and racially diverse population. Full details on the study’s design are available elsewhere.6 Briefly, we queried CHOP’s EHR database to select individuals who (1) were born in 1987–1997; (2) were patients at a New Jersey CHOP primary care practice; and (3), to establish New Jersey residency, had a CHOP network visit as a New Jersey resident within 4 years of becoming age eligible to drive (at 16 years) and maintained a New Jersey address through their last CHOP visit. We identified a total of 19 588 individuals. We then excluded individuals with a diagnosed intellectual disability (n = 73); individuals with only 1 primary care visit (n = 676), to minimize ADHD misclassification; and individuals who had their last primary care visit before age 12 years (n = 317), to ensure that individuals were seen at an old enough age to confirm ADHD status.1 The underlying cohort included 18 522 patients. We limited this study to adolescents who obtained an intermediate (initial) driver’s license during the study period and had at least 1 month of post-licensure follow-up (n = 14 936; see Fig 1).

FIGURE 1

Flowchart revealing selection of study cohort. Gray boxes reveal individuals who were excluded from the study.

FIGURE 1

Flowchart revealing selection of study cohort. Gray boxes reveal individuals who were excluded from the study.

Close modal

We classified subjects as having ADHD if their EHR indicated a 314.x International Classification of Diseases, Ninth Revision, Clinical Modification code either at a CHOP network visit or on the list of known conditions. A total of 1769 subjects were identified; 94% were classified on the basis of visits. We conducted a formal internal validation study of this classification scheme (sensitivity = 0.96; specificity = 0.98).16

Details on the process and validation of data linkages are available in previous publications.6,17 Briefly, we obtained records for individuals who received a New Jersey license through December 2014 from the New Jersey Motor Vehicle Commission; data included exact dates of licensure, license suspensions and restorations, and traffic violations issued. We also obtained data on all police-reported crashes in New Jersey from January 2004 to December 2014. We conducted a hierarchical deterministic linkage; 98% of New Jersey drivers involved in a crash linked to a licensing record. We then used similar methods to link this licensing-crash database with CHOP EHR data. We estimated the true-match rate (true matches divided by original matches) to be 99.95% and the false nonmatch rate (true matches not found) to be 1.5%.

All subjects acquired an intermediate license in New Jersey, where the minimum licensure age is 17 years. In New Jersey, intermediate drivers licensed at age <21 years can drive independently (without adult supervision) but are restricted from driving between 11:01 pm and 4:59 am, while using electronic equipment, or with >1 peer passenger for the first year. They are also prohibited from driving with any detectable amount of alcohol in their system.18 Notably, New Jersey drivers age <21 years cannot plea bargain a point-carrying moving violation.

Primary crash outcomes for adolescent drivers were defined a priori and included (1) all crashes, reportable to police if an injury or >$500 in property damage occurred19; (2) injury crashes, in which at least 1 person had a moderate or greater severity injury (noted on crash report); (3) at-fault crashes, defined in previous work as those with a crash-contributing driver action (eg, inattention or unsafe speed)20; (4) night crashes, including crashes that occurred during New Jersey’s restricted 11:01 pm to 4:59 am period (late) and between 9:00 pm and 11:00 pm (early), a period also identified to be higher risk21; (5) passenger crashes, previously defined as crashes with only passengers aged 14 to 20 years (peer) or ≥2 passengers of any age (multiple)22; (6) single-vehicle crashes, the majority of which have been shown to involve speeding or traveling too fast for conditions23; and (7) alcohol-related crashes, in which the driver was issued a violation for alcohol use or noted on the crash report to have a blood alcohol content level of ≥0.01 for drivers age <21 years and ≥0.08 for drivers age ≥21 years. We calculated monthly rates per 10 000 driver-months; the numerator was the number of crashes among validly licensed drivers, and person-time was calculated by summing for all drivers the proportion of the month that the driver had a valid license. Average monthly rates for the first 12 and 48 months were estimated. Follow-up time concluded at 48 months after licensure, at death, or at end of the study period, whichever occurred first. Using similar methods, we calculated the monthly rate of violations issued for (1) all traffic offenses; (2) point-carrying moving violations, which include (a) speeding, (b) careless driving, (c) alcohol and/or drug use, and (d) electronic equipment use; (3) seat belt nonuse; and, (4) for intermediate drivers age <21 years, violation of GDL restrictions (eg, nighttime or passenger restrictions). Finally, exact periods of license suspension were identified; suspension rates were calculated as the number of days in which a driver had a suspended license per year of follow-up since the date of licensure. Demographic variables were ascertained from the EHR. Two co-occurring conditions that may affect crash risk were categorized via International Classification of Diseases, Ninth Revision, Clinical Modification codes: seizure disorder (345.x) and disruptive behavior disorder (DBD) (312.x or 313.81). DBD was further verified through EHR review by study authors (T.J.P. and B.E.Y.) and trained abstractors; confirmation included ≥3 visits for DBD, independent sources (eg, provider notes), or symptoms consistent with DBD. We used 2010 Census Gazetteer Files and 2007−2011 American Community Survey data to categorize residence zip code at last visit into quintiles of population density and median household income, respectively.24,25 We compared bivariate distributions of demographic and clinical characteristics among drivers with and without ADHD using χ2 and Wilcoxon rank-sum tests. Crash and violation risk within the first 12 and 48 months of licensure were compared by using χ2 statistics; estimates were restricted to drivers followed to the specified post-licensure month. We estimated adjusted rate ratios (adjRRs) and 95% confidence intervals (CIs) using generalized estimating equation models with a log link (ie, Poisson distribution). For models, we accounted for correlation within individual drivers using an independent correlation structure. Potential covariates were chosen a priori on the basis of known or suspected association with ADHD (or its diagnosis) and outcomes, including sex, race and/or ethnicity, insurance payer, DBD and seizure disorders, licensing age (17 years 0 months, 17 years 1 month−17 years 11 months, 18 years, and ≥19 years), primary care practice, and birth year. Models also included indicators for zip code–level household income and population density as well as linear and quadratic terms for month since licensure to control for temporal trends. Analyses of GDL violations were restricted to drivers licensed at age <21 years and only through the first year of licensure. For rarer outcomes (alcohol-related crashes, violations for alcohol and/or drug use [12 months after licensure], and licensure suspension), fully adjusted models failed to converge; thus, models were adjusted only for sex and licensing age. Analyses were conducted in SAS version 9.4 (SAS Institute, Inc, Cary, NC). This study was approved by the CHOP Institutional Review Board. Twelve percent of the cohort had a diagnosis of ADHD (Table 1). The majority of subjects were long-term CHOP primary care patients and were last seen at a median age of 18.1 years. Drivers with ADHD were licensed a median of 3.6 months later than those without ADHD and were more likely to be male and non-Hispanic white. TABLE 1 Demographic and Clinical Characteristics of Study Cohort; Comparing Drivers With and Without ADHD ADHD Status Overall (N = 14 936)ADHD (n = 1769)No ADHD (n = 13 167)P (ADHD Versus No ADHD) Age at licensure, median (IQR), y 17.1 (17.0–17.6) 17.3 (17.0–18.0) 17.0 (17.0–17.6) <.001 Follow-up time, median (IQR), mo 48 (31–48) 48 (28–48) 48 (32–48) <.001 Age at last primary care visit, median (IQR), y 18.1 (16.5–19.1) 18.3 (17.1–19.6) 18.1 (16.4–19.1) <.001 No. CHOP primary care visits, median (IQR) 21 (11–34) 27 (16–42) 20 (11–33) <.001 Sex, n (%) <.001 Female 7480 (50.1) 495 (28.0) 6985 (53.0) Male 7456 (49.9) 1274 (72.0) 6182 (47.0) Race and/or ethnicity, n (%) <.001 Non-Hispanic white 9620 (64.4) 1317 (74.4) 8303 (63.1) Non-Hispanic black or African American 2176 (14.6) 205 (11.6) 1971 (15.0) Non-Hispanic other 2754 (18.4) 208 (11.8) 2546 (19.3) Hispanic 386 (2.6) 39 (2.2) 347 (2.6) Payer at last visit, n (%) <.001 Private 13 802 (92.4) 1681 (95.0) 12 121 (92.1) Medicaid or self-pay 437 (2.9) 42 (2.4) 395 (3.0) Not recorded or not billed 697 (4.7) 46 (2.6) 651 (4.9) DBD, n (%)a <.001 No 14 389 (96.3) 1506 (85.1) 12 883 (97.8) Yes 547 (3.7) 263 (14.9) 284 (2.2) Seizure disorder, n (%) <.001 No 14 727 (98.6) 1710 (96.7) 13 017 (98.9) Yes 209 (1.4) 59 (3.3) 150 (1.1) Neighborhood income, n (%),$    <.001
≤57 226 2714 (18.2) 298 (16.8) 2416 (18.3)
57 227–72 857 5530 (37.0) 598 (33.8) 4932 (37.5)
72 858–87 222 3521 (23.6) 458 (25.9) 3063 (23.3)
87 223–105 888 2451 (16.4) 305 (17.2) 2146 (16.3)
≥105 889 696 (4.7) 110 (6.2) 586 (4.5)
Unknown 24 (0.2) 0 (0) 24 (0.2)
Neighborhood population density, n (%), population per square mile    .14
≤408 1811 (12.1) 236 (13.3) 1575 (12.0)
409–1223 3950 (26.4) 472 (26.7) 3478 (26.4)
1224–2615 5685 (38.1) 673 (38.0) 5012 (38.1)
2616–4876 3204 (21.5) 349 (19.7) 2855 (21.7)
≥4877 271 (1.8) 39 (2.2) 232 (1.8)
Unknown 15 (0.1) 0 (0) 15 (0.1)
Age at licensure, median (IQR), y 17.1 (17.0–17.6) 17.3 (17.0–18.0) 17.0 (17.0–17.6) <.001
Follow-up time, median (IQR), mo 48 (31–48) 48 (28–48) 48 (32–48) <.001
Age at last primary care visit, median (IQR), y 18.1 (16.5–19.1) 18.3 (17.1–19.6) 18.1 (16.4–19.1) <.001
No. CHOP primary care visits, median (IQR) 21 (11–34) 27 (16–42) 20 (11–33) <.001
Sex, n (%)    <.001
Female 7480 (50.1) 495 (28.0) 6985 (53.0)
Male 7456 (49.9) 1274 (72.0) 6182 (47.0)
Race and/or ethnicity, n (%)    <.001
Non-Hispanic white 9620 (64.4) 1317 (74.4) 8303 (63.1)
Non-Hispanic black or African American 2176 (14.6) 205 (11.6) 1971 (15.0)
Non-Hispanic other 2754 (18.4) 208 (11.8) 2546 (19.3)
Hispanic 386 (2.6) 39 (2.2) 347 (2.6)
Payer at last visit, n (%)    <.001
Private 13 802 (92.4) 1681 (95.0) 12 121 (92.1)
Medicaid or self-pay 437 (2.9) 42 (2.4) 395 (3.0)
Not recorded or not billed 697 (4.7) 46 (2.6) 651 (4.9)
DBD, n (%)a    <.001
No 14 389 (96.3) 1506 (85.1) 12 883 (97.8)
Yes 547 (3.7) 263 (14.9) 284 (2.2)
Seizure disorder, n (%)    <.001
No 14 727 (98.6) 1710 (96.7) 13 017 (98.9)
Yes 209 (1.4) 59 (3.3) 150 (1.1)
Neighborhood income, n (%), \$    <.001
≤57 226 2714 (18.2) 298 (16.8) 2416 (18.3)
57 227–72 857 5530 (37.0) 598 (33.8) 4932 (37.5)
72 858–87 222 3521 (23.6) 458 (25.9) 3063 (23.3)
87 223–105 888 2451 (16.4) 305 (17.2) 2146 (16.3)
≥105 889 696 (4.7) 110 (6.2) 586 (4.5)
Unknown 24 (0.2) 0 (0) 24 (0.2)
Neighborhood population density, n (%), population per square mile    .14
≤408 1811 (12.1) 236 (13.3) 1575 (12.0)
409–1223 3950 (26.4) 472 (26.7) 3478 (26.4)
1224–2615 5685 (38.1) 673 (38.0) 5012 (38.1)
2616–4876 3204 (21.5) 349 (19.7) 2855 (21.7)
≥4877 271 (1.8) 39 (2.2) 232 (1.8)
Unknown 15 (0.1) 0 (0) 15 (0.1)

IQR, interquartile range.

a

DBD includes conduct disorder and oppositional defiant disorder.

TABLE 2

Risk of Crash Involvement, Traffic Violations, and License Suspension Within 12 and 48 Months After Licensure; Comparing Drivers With and Without ADHD

Within 12 Months Post-LicensureWithin 48 Months Post-Licensure
No. Drivers (%)No. Drivers (%)No. Drivers (%)No. Drivers (%)
Crashes
All crashes 316 (19.8) 1951 (16.2) <.001 461 (46.8) 2943 (36.4) <.001
At fault 259 (16.3) 1456 (12.1) <.001 370 (37.6) 2130 (26.3) <.001
Peer passenger 113 (7.1) 664 (5.5) .01 148 (15.0) 877 (10.8) <.001
Single vehicle 57 (3.6) 312 (2.6) .02 108 (11.0) 559 (6.9) <.001
Multiple passenger 47 (3.0) 224 (1.9) .003 60 (6.1) 355 (4.4) .02
Early night 29 (1.8) 172 (1.4) .22 41 (4.2) 298 (3.7) .45
Injury 28 (1.8) 114 (0.9) .003 42 (4.3) 219 (2.7) .006
Late night 22 (1.4) 106 (0.9) .05 55 (5.6) 261 (3.2) <.001
Alcohol related 5 (0.3) 11 (0.1) .01 12 (1.2) 47 (0.6) .02
Violations
All violations 567 (35.6) 3053 (25.3) <.001 721 (73.2) 4703 (58.1) <.001
Moving violations 427 (26.8) 2247 (18.6) <.001 619 (62.8) 3898 (48.2) <.001
Careless driving 225 (14.1) 1193 (9.9) <.001 387 (39.3) 2094 (25.9) <.001
Speeding 135 (8.5) 663 (5.5) <.001 299 (30.4) 1750 (21.6) <.001
Electronic equipment use 20 (1.3) 104 (0.9) .12 76 (7.7) 385 (4.8) <.001
Alcohol and/or drug use 17 (1.1) 31 (0.3) <.001 35 (3.6) 175 (2.2) .006
Seat belt nonuse 105 (6.6) 461 (3.8) <.001 229 (23.2) 1334 (16.5) <.001
GDL restrictionsa 68 (4.4) 343 (2.9) .001 — — —
License suspension 45 (2.8) 171 (1.4) <.001 168 (17.1) 813 (10.1) <.001
Within 12 Months Post-LicensureWithin 48 Months Post-Licensure
No. Drivers (%)No. Drivers (%)No. Drivers (%)No. Drivers (%)
Crashes
All crashes 316 (19.8) 1951 (16.2) <.001 461 (46.8) 2943 (36.4) <.001
At fault 259 (16.3) 1456 (12.1) <.001 370 (37.6) 2130 (26.3) <.001
Peer passenger 113 (7.1) 664 (5.5) .01 148 (15.0) 877 (10.8) <.001
Single vehicle 57 (3.6) 312 (2.6) .02 108 (11.0) 559 (6.9) <.001
Multiple passenger 47 (3.0) 224 (1.9) .003 60 (6.1) 355 (4.4) .02
Early night 29 (1.8) 172 (1.4) .22 41 (4.2) 298 (3.7) .45
Injury 28 (1.8) 114 (0.9) .003 42 (4.3) 219 (2.7) .006
Late night 22 (1.4) 106 (0.9) .05 55 (5.6) 261 (3.2) <.001
Alcohol related 5 (0.3) 11 (0.1) .01 12 (1.2) 47 (0.6) .02
Violations
All violations 567 (35.6) 3053 (25.3) <.001 721 (73.2) 4703 (58.1) <.001
Moving violations 427 (26.8) 2247 (18.6) <.001 619 (62.8) 3898 (48.2) <.001
Careless driving 225 (14.1) 1193 (9.9) <.001 387 (39.3) 2094 (25.9) <.001
Speeding 135 (8.5) 663 (5.5) <.001 299 (30.4) 1750 (21.6) <.001
Electronic equipment use 20 (1.3) 104 (0.9) .12 76 (7.7) 385 (4.8) <.001
Alcohol and/or drug use 17 (1.1) 31 (0.3) <.001 35 (3.6) 175 (2.2) .006
Seat belt nonuse 105 (6.6) 461 (3.8) <.001 229 (23.2) 1334 (16.5) <.001
GDL restrictionsa 68 (4.4) 343 (2.9) .001 — — —
License suspension 45 (2.8) 171 (1.4) <.001 168 (17.1) 813 (10.1) <.001

Risk is estimated among drivers who were followed to the specified post-licensure month. —, not applicable.

a

Risk of GDL violations (eg, nighttime or passenger restrictions) was limited to drivers licensed before age 21 y (ADHD: n = 1563; no ADHD: n = 11 920) and estimated only for the first 12 mo after licensure.

FIGURE 2

A, Monthly observed rate per 10 000 driver-months of crash involvement. B, Monthly observed rate per 10 000 driver-months of moving violations. Drivers with and without ADHD were compared over the first 4 years of licensure. Purple lines indicate drivers with ADHD and orange lines indicate drivers without ADHD.

FIGURE 2

A, Monthly observed rate per 10 000 driver-months of crash involvement. B, Monthly observed rate per 10 000 driver-months of moving violations. Drivers with and without ADHD were compared over the first 4 years of licensure. Purple lines indicate drivers with ADHD and orange lines indicate drivers without ADHD.

Close modal
FIGURE 3

AdjRRs and 95% CIs for crash outcomes; comparing drivers with and without ADHD. Dots indicate the estimated adjRR, and lines indicate the width of the 95% CI from repeated-measures Poisson regression models. Purple dots and lines compare outcomes for 12 months after licensure. Orange dots and lines compare outcomes for 48 months after licensure.

FIGURE 3

AdjRRs and 95% CIs for crash outcomes; comparing drivers with and without ADHD. Dots indicate the estimated adjRR, and lines indicate the width of the 95% CI from repeated-measures Poisson regression models. Purple dots and lines compare outcomes for 12 months after licensure. Orange dots and lines compare outcomes for 48 months after licensure.

Close modal

Drivers with ADHD also experienced higher rates of crash subtypes (crashes involving passengers and at-fault, single-vehicle, injury, and alcohol-related crashes; Fig 3, Supplemental Table 3). For example, in the first 48 months after licensure, drivers with ADHD had a 62% higher rate of injury crashes (95% CI: 1.23−2.14) and a 109% higher rate of alcohol-related crashes (95% CI: 1.16−3.76). Notably, these 2 outcomes were rare events; within the 48-month study period, 4.3% of young drivers with ADHD were involved in an injury crash, and 1.2% were involved in an alcohol-related crash (Table 2).

FIGURE 4

AdjRRs and 95% CIs for violation and license suspension outcomes; comparing drivers with and without ADHD. Dots indicate the estimated adjRR, and lines indicate the width of the 95% CI from repeated-measures Poisson regression models. Purple dots and lines compare outcomes for 12 months after licensure. Orange dots and lines compare outcomes for 48 months after licensure.

FIGURE 4

AdjRRs and 95% CIs for violation and license suspension outcomes; comparing drivers with and without ADHD. Dots indicate the estimated adjRR, and lines indicate the width of the 95% CI from repeated-measures Poisson regression models. Purple dots and lines compare outcomes for 12 months after licensure. Orange dots and lines compare outcomes for 48 months after licensure.

Close modal

With this study, we provide the first longitudinal assessment of crash, violation, and suspension risk among adolescent drivers with ADHD. Findings indicate that adolescent drivers with ADHD have a moderately increased crash risk, a finding consistent with several recent population-based studies of adolescent and adult drivers with ADHD12,13 but lower than estimates in early small studies of adolescents more severely affected.11 In addition, with our study, we uniquely identify the early licensure period as a time of particularly high risk and the potential contribution of preventable factors to increased risk. These findings both offer important implications for families and for professionals working with them and highlight the need to develop comprehensive preventive approaches to reduce these adolescents’ crash risk.

Adolescent drivers with ADHD are at particularly high crash risk in their initial months of licensure, and engagement in preventable risky driving behaviors likely underlies this increased risk. Prospective studies to objectively measure risky driving behaviors among novice drivers with ADHD and examine the extent to which these behaviors mediate driving outcomes are vital to inform prevention strategies. The development of comprehensive preventive approaches to reduce crash risk is critically needed.

Dr Curry conceptualized and designed the study, drafted the initial manuscript, and critically reviewed and revised the manuscript; Dr Metzger led data collection and analysis and critically reviewed and revised the manuscript; Mrs Carey and Drs Power and Yerys participated in data analysis and interpretation and critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development at the National Institutes of Health (awards R01HD079398 and R21HD092850; principal investigator: Dr Curry). Funded by the National Institutes of Health (NIH).

We thank the New Jersey Department of Transportation, the New Jersey Motor Vehicle Commission, the New Jersey Office of Information Technology, and Children’s Hospital of Philadelphia Department for Biomedical Informatics for their assistance in providing data. We thank Melissa R. Pfeiffer, MPH, for her work on the New Jersey Traffic Safety Outcomes data warehouse. Drs Curry and Metzger had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

attention-deficit/hyperactivity disorder

•

•
• CHOP

•
• CI

confidence interval

•
• DBD

disruptive behavior disorder

•
• EHR

electronic health record

•
• GDL

1
American Psychiatric Association
.
Diagnostic and Statistical Manual of Mental Disorders (DSM-5)
. 5th ed.
Washington, DC
:
American Psychiatric Association
;
2013
2
Mannuzza
S
,
Klein
RG
.
Long-term prognosis in attention-deficit/hyperactivity disorder.
Child Adolesc Psychiatr Clin N Am
.
2000
;
9
(
3
):
711
726
[PubMed]
3
Winston
FK
,
Senserrick
TM
.
Inj Prev
.
2006
;
12
(
suppl 1
):
i1
i3
[PubMed]
4
Barkley
RA
ed.
Attention-Deficit Hyperactivity Disorder: A Handbook for Diagnosis and Treatment
. 4th ed.
New York, NY
:
The Guilford Press
;
2014
5
Willcutt
EG
,
Doyle
AE
,
Nigg
JT
,
Faraone
SV
,
Pennington
BF
.
Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review.
Biol Psychiatry
.
2005
;
57
(
11
):
1336
1346
[PubMed]
6
Curry
AE
,
Metzger
KB
,
Pfeiffer
MR
,
Elliott
MR
,
Winston
FK
,
Power
TJ
.
Motor vehicle crash risk among adolescents and young adults with attention-deficit/hyperactivity disorder.
JAMA Pediatr
.
2017
;
171
(
8
):
756
763
[PubMed]
7
Woodward
LJ
,
Fergusson
DM
,
Horwood
LJ
.
Driving outcomes of young people with attentional difficulties in adolescence.
.
2000
;
39
(
5
):
627
634
[PubMed]
8
Barkley
RA
,
Murphy
KR
,
Kwasnik
D
.
Motor vehicle driving competencies and risks in teens and young adults with attention deficit hyperactivity disorder.
Pediatrics
.
1996
;
98
(
6, pt 1
):
1089
1095
[PubMed]
9
S
,
Langley
JD
,
McGee
R
,
Williams
SM
,
Begg
DJ
,
Reeder
AI
.
Inattentive and hyperactive behaviors and driving offenses in adolescence.
.
1997
;
36
(
4
):
515
522
[PubMed]
10
Thompson
AL
,
Molina
BS
,
Pelham
W
Jr
,
Gnagy
EM
.
J Pediatr Psychol
.
2007
;
32
(
7
):
745
759
[PubMed]
11
Barkley
RA
,
Guevremont
DC
,
Anastopoulos
,
DuPaul
GJ
,
Shelton
TL
.
Driving-related risks and outcomes of attention deficit hyperactivity disorder in adolescents and young adults: a 3- to 5-year follow-up survey.
Pediatrics
.
1993
;
92
(
2
):
212
218
[PubMed]
12
Redelmeier
DA
,
Chan
WK
,
Lu
H
.
Road trauma in teenage male youth with childhood disruptive behavior disorders: a population based analysis.
PLoS Med
.
2010
;
7
(
11
):
e1000369
[PubMed]
13
Chang
Z
,
Lichtenstein
P
,
D’Onofrio
BM
,
Sjölander
A
,
H
.
Serious transport accidents in adults with attention-deficit/hyperactivity disorder and the effect of medication: a population-based study.
JAMA Psychiatry
.
2014
;
71
(
3
):
319
325
[PubMed]
14
Williams
AF
.
Graduated driver licensing (GDL) in the United States in 2016: a literature review and commentary.
J Safety Res
.
2017
;
63
:
29
41
[PubMed]
15
Fabiano
GA
,
Schatz
NK
,
Hulme
KF
, et al
.
Positive bias in teenage drivers with ADHD within a simulated driving task.
J Atten Disord
.
2018
;
22
(
12
):
1150
1157
[PubMed]
16
Gruschow
SM
,
Yerys
BE
,
Power
TJ
,
Durbin
DR
,
Curry
AE
.
Validation of the use of electronic health records for classification of ADHD status [published online ahead of print October 1, 2016].
J Atten Disord
.
[PubMed]
17
Curry
AE
,
Pfeiffer
MR
,
Localio
R
,
Durbin
DR
.
Graduated driver licensing decal law: effect on young probationary drivers [published correction appears in Am J Prev Med. 2014;47(1):103].
Am J Prev Med
.
2013
;
44
(
1
):
1
7
[PubMed]
18
New Jersey Motor Vehicle Commission
19
New Jersey Motor Vehicle Commission
. NJTR-1 form field manual. 2011. Available at: www.state.nj.us/transportation/refdata/accident/pdf/NJTR-1Field_Manual.pdf. Accessed May 1, 2015
20
Curry
AE
,
Pfeiffer
MR
,
Myers
RK
,
Durbin
DR
,
Elliott
MR
.
Statistical implications of using moving violations to determine crash responsibility in young driver crashes.
Accid Anal Prev
.
2014
;
65
:
28
35
[PubMed]
21
Mayhew
D
,
Williams
A
,
Pashley
C
;
Traffic Injury Research Foundation
. A new GDL framework: evidence base to integrate novice driver strategies. Available at: http://trid.trb.org/view.aspx?id=1358954. Accessed February 16, 2016
22
Curry
AE
,
Metzger
KB
,
Williams
AF
,
Tefft
BC
.
Comparison of older and younger novice driver crash rates: informing the need for extended Graduated Driver Licensing restrictions.
Accid Anal Prev
.
2017
;
108
:
66
73
[PubMed]
23
Carney
C
,
McGehee
DV
,
Harland
K
,
Weiss
M
,
Raby
M
. Using naturalistic driving data to assess the prevalence of environmental factors and driver behaviors in teen driver crashes. 2015. Available at: https://www.aaafoundation.org/using-naturalistic-driving-data-assess-prevalence-environmental-factors-and-driver-behaviors-teen. Accessed April 6, 2015
24
United States Census Bureau
. 2010 census gazetteer files. 2010. Available at: www.census.gov/geo/maps-data/data/gazetteer2010.html. Accessed April 5, 2018
25
United States Census Bureau
. ACS demographic and housing estimates: 2007-2011 American Community Survey 5-year estimates. Available at: http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_11_5YR_DP05&prodType=table. Accessed April 5, 2018
26
Molina
BS
,
Pelham
WE
Jr
.
Childhood predictors of adolescent substance use in a longitudinal study of children with ADHD.
J Abnorm Psychol
.
2003
;
112
(
3
):
497
507
[PubMed]
27
Sarver
DE
,
McCart
MR
,
Sheidow
AJ
,
Letourneau
EJ
.
ADHD and risky sexual behavior in adolescents: conduct problems and substance use as mediators of risk.
J Child Psychol Psychiatry
.
2014
;
55
(
12
):
1345
1353
[PubMed]
28
Surman
CBH
,
Fried
R
,
Rhodewalt
L
,
Boland
H
.
Do pharmaceuticals improve driving in individuals with ADHD? A review of the literature and evidence for clinical practice.
CNS Drugs
.
2017
;
31
(
10
):
857
866
29
Chang
Z
,
Quinn
PD
,
Hur
K
, et al
.
Association between medication use for attention-deficit/hyperactivity disorder and risk of motor vehicle crashes.
JAMA Psychiatry
.
2017
;
74
(
6
):
597
603
[PubMed]
30
31
Humphreys
KL
,
Eng
T
,
Lee
SS
.
Stimulant medication and substance use outcomes: a meta-analysis.
JAMA Psychiatry
.
2013
;
70
(
7
):
740
749
[PubMed]
32
Molina
BS
,
Hinshaw
SP
,
Swanson
JM
, et al;
MTA Cooperative Group
.
The MTA at 8 years: prospective follow-up of children treated for combined-type ADHD in a multisite study.
.
2009
;
48
(
5
):
484
500
[PubMed]
33
Evans
SW
,
Owens
JS
,
Wymbs
BT
,
Ray
AR
.
Evidence-based psychosocial treatments for children and adolescents with attention deficit/hyperactivity disorder.
.
2018
;
47
(
2
):
157
198
[PubMed]
34
Shoham
R
,
Sonuga-Barke
EJ
,
Aloni
H
,
Yaniv
I
,
Pollak
Y
.
ADHD-associated risk taking is linked to exaggerated views of the benefits of positive outcomes.
Sci Rep
.
2016
;
6
:
34833
[PubMed]
35
Pollak
Y
,
Oz
A
,
Neventsal
O
,
Rabi
O
,
Kitrossky
L
,
Maeir
A
.
Do adolescents with attention-deficit/hyperactivity disorder show risk seeking? Disentangling probabilistic decision making by equalizing the favorability of alternatives.
J Abnorm Psychol
.
2016
;
125
(
3
):
387
398
[PubMed]
36
Institute of Medicine
.
Initial National Priorities for Comparative Effectiveness Research
.
Washington, DC
:
;
2009
37
Chapman
EA
,
Masten
SV
,
Browning
KK
.
Crash and traffic violation rates before and after licensure for novice California drivers subject to different driver licensing requirements.
J Safety Res
.
2014
;
50
:
125
138
[PubMed]
38
Curry
AE
,
Pfeiffer
MR
,
Durbin
DR
,
Elliott
MR
.
Young driver crash rates by licensing age, driving experience, and license phase.
Accid Anal Prev
.
2015
;
80
:
243
250
[PubMed]
39
Curry
AE
,
Foss
RD
,
Williams
AF
.
Graduated driver licensing for older novice drivers: critical analysis of the issues.
Am J Prev Med
.
2017
;
53
(
6
):
923
927
[PubMed]
40
Cherkasova
M
,
Sulla
EM
,
Dalena
KL
,
Pondé
MP
,
Hechtman
L
.
Developmental course of attention deficit hyperactivity disorder and its predictors.
.
2013
;
22
(
1
):
47
54
[PubMed]
41
Barkley
RA
,
Murphy
KR
,
Dupaul
GI
,
Bush
T
.
Driving in young adults with attention deficit hyperactivity disorder: knowledge, performance, adverse outcomes, and the role of executive functioning.
J Int Neuropsychol Soc
.
2002
;
8
(
5
):
655
672
[PubMed]
42
Klauer
C
,
Ollendick
T
,
Ankem
G
,
Dingus
T
. Improving driving safety for teenagers with attention deficit and hyperactivity disorder (ADHD). Available at: https://vtechworks.lib.vt.edu/bitstream/handle/10919/79137/ADHD STSCE Report_Final.pdf?sequence=1&isAllowed=y. Accessed February 28, 2018
43
Barkley
RA
,
Fischer
M
,
Smallish
L
,
Fletcher
K
.
Young adult outcome of hyperactive children: adaptive functioning in major life activities.
.
2006
;
45
(
2
):
192
202
[PubMed]
44
Kuriyan
AB
,
Pelham
WE
Jr
,
Molina
BS
, et al
.
J Abnorm Child Psychol
.
2013
;
41
(
1
):
27
41
[PubMed]
45
Centers for Disease Control and Prevention
. State-based prevalence data of parent reported ADHD diagnosis by a health care provider. Available at: https://www.cdc.gov/ncbddd/adhd/prevalence.html. Accessed May 11, 2018

Competing Interests

POTENTIAL CONFLICT OF INTEREST: Dr Yerys was part of a single consultation meeting with Aevi Genomic Medicine about development of a novel treatment of attention-deficit/hyperactivity disorder; the other 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.