Motor Vehicle Crash Risk Among Adolescents and Young Adults With Attention-Deficit/Hyperactivity Disorder

Key Points

Questions  What is the association between attention-deficit/hyperactivity disorder (ADHD) and crash involvement among adolescent and young adult drivers, and does this vary by sex, licensing age, and/or being prescribed ADHD medication?

Findings  In this cohort study of 2479 adolescents and young adults with ADHD and 15 865 without ADHD, the crash hazard among newly licensed drivers with ADHD was 36% higher. Hazard ratios persisted over licensure and did not vary by sex, licensing age, or medication.

Meaning  Crash risk is heightened for young drivers with ADHD; research is critically needed to identify the specific mechanisms by which ADHD influences crash risk to develop effective countermeasures.

Importance  Attention-deficit/hyperactivity disorder (ADHD) often persists into adolescence, when motor vehicle crash risk peaks. We know little about when adolescents with ADHD get licensed and, once they do, the extent to which they have increased crash risk compared with adolescents without ADHD.

Objectives  To examine the association between ADHD and both driver licensing and crash involvement and whether it varies by sex, licensing age, and/or being prescribed ADHD medication at licensure.

Design, Setting, and Participants  This retrospective cohort study was conducted at 6 primary care practices of the Children’s Hospital of Philadelphia, a large pediatric health care network in southeastern Pennsylvania and southern New Jersey. Using electronic health records, we defined a cohort of 2479 adolescents and young adults with ADHD and 15 865 without ADHD who were (1) born from 1987 to 1997; (2) residents of New Jersey and patients at 1 of 6 New Jersey primary care practices at age 12 years or older; and (3) age-eligible to obtain a driver’s license from 2004 through 2014. Electronic health records data were then linked with New Jersey’s statewide driver licensing and crash databases for 2004 through 2014.

Main Outcomes and Measures  Acquisition of a driver’s license and first involvement as a driver in a police-reported crash. Survival analysis was used to estimate adjusted hazard ratios for licensing and crash outcomes through age 25 years.

Results  The median age of individuals at the end of the study was 22.2 years (interquartile range, 19.7-24.8). Compared with individuals without ADHD, the licensing probability of individuals with ADHD 6 months after eligibility was 35% lower (for males: adjusted hazard ratio, 0.65; 95% CI, 0.61-0.70; females: adjusted hazard ratio, 0.64; 95% CI, 0.58-0.70). Among individuals with a driver’s license, 764 of 1785 with ADHD (42.8%) and 4715 of 13 221 without ADHD (35.7%) crashed during the study period. The adjusted risk for first crash among licensed drivers with ADHD was 1.36 times higher than for those without ADHD (95% CI, 1.25-1.48) and did not vary by sex, licensing age, or over time. Only 129 individuals with ADHD (12.1%) were prescribed medication in the 30 days before licensure.

Conclusions and Relevance  Adolescents with ADHD get licensed less often and at an older age. Once licensed, this cohort has a greater risk of crashing. Additional research is needed to understand the specific mechanisms by which ADHD influences crash risk.

Attention-deficit/hyperactivity disorder (ADHD), a common childhood disorder,^1-3 often persists into adolescence,⁴ a period in which many individuals get licensed to drive.⁵ Along with benefits of mobility and independence, driving increases the risk of a motor vehicle crash, the leading cause of death among US adolescents.⁶ Of particular concern is that defining symptoms of ADHD—inattention, hyperactivity, and impulsivity—have been linked to unsafe driving behaviors,^7,8 creating the potential for heightened adverse health risk for adolescents with ADHD.

Previous studies revealed more driving errors and unsafe driving behaviors (in simulator-based and naturalistic studies)^7,9,10 and an increased crash risk among those with ADHD,^11-15 with a 1993 study citing a 4-fold increase.¹⁵ However, previous crash studies^11-15 have substantial methodological limitations: inclusion of small samples from specialty clinics; self- or parent-reported driving outcomes with long recall periods; lack of adjustment for important confounders (eg, disruptive behavioral disorder and driving experience); and almost entirely samples of male drivers. Additionally, all but 1 study¹⁴ were conducted before 2001 when distractions, particularly from handheld and in-vehicle electronic devices, were less prevalent and before Graduated Driver Licensing systems, which increased the minimum driving age and introduced an intermediate licensing phase to restrict exposure to hazardous conditions, were widely implemented. Thus, we know little about when adolescents with ADHD get licensed and, once they do, the extent to which they are at increased crash risk.

To our knowledge, we conducted the first longitudinal study specifically designed to examine crash risk among adolescents and young adults with community-identified ADHD over their initial years of licensure. Specifically, we linked electronic health records (EHRs) to state traffic safety databases for a cohort of more than 18 000 primary care patients of the Children’s Hospital of Philadelphia (CHOP) health care network. We also determined whether the ADHD-crash association varies by sex, licensing age, and/or being prescribed ADHD medication given that ADHD prevalence and crash risk both vary by sex, there are substantial variations in crash risk by licensing age,^16,17 and ADHD medication may improve driving behavior.^18,19

Individuals for this retrospective cohort study were identified from the 6 New Jersey CHOP primary care practices within CHOP’s network, which encompasses more than 50 locations in Pennsylvania and New Jersey. Clinicians manage all aspects of clinical care using a linked EHR system, which was fully implemented in New Jersey primary care by 2005. We queried CHOP’s EHR database to select individuals who (1) were born from 1987 to 1997; (2) were patients at a New Jersey primary care practice; and (3) had a CHOP network visit (to any location) as a New Jersey resident within 4 years of becoming eligible for their learner’s permit at age 16 years and maintained a New Jersey address through their last network visit to establish New Jersey residency. We identified 19 588 individuals. We excluded 73 individuals with an intellectual disability given that this essentially removed them from the pool of eligible drivers; intellectual disability was defined as the presence of an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis code beginning with 317, 318, or 319 either at a CHOP visit or on the list of known conditions (ie, problem list). Additionally, we limited our population to individuals who had 2 or more primary care visits to minimize ADHD misclassification and those who had their last primary care visit at age 12 years or older to ensure individuals were seen by a primary care clinician at an age old enough to confirm ADHD status (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition] criteria call for ADHD symptom onset by age 12 years).²⁰ The study cohort included 18 522 primary care patients (Figure 1). This study was approved by CHOP’s institutional review board, and 1 of the authors (K.B.M.) analyzed the data. Consent was waived by the institutional review board because the study included linking of 2 databases, with a total of over 10 million records.

Individuals were classified as having ADHD if their EHR indicated an ICD-9-CM code beginning with 314 either at a CHOP network visit or on their problem list; 2503 individuals with ADHD were identified. We formally validated this algorithm using manual review of EHRs; estimated sensitivity was 0.96, and specificity was 0.98.²¹ For 2021 of 2503 (80.7%) of individuals, we found an independent source in the EHR confirming ADHD status (eg, letters, medication or test results). Of 2503 individuals, 2357 with ADHD (94.2%) were classified based on visit-level ICD-9-CM codes.

We obtained records for all individuals who received a New Jersey driver’s license through December 2014 from the New Jersey Motor Vehicle Commission’s Licensing Database, including exact dates of birth and licensure. The minimum licensure age in New Jersey is 17 years. Detailed data on police-reported crashes occurring in New Jersey from 2004 to 2014 were obtained from the New Jersey Department of Transportation’s Crash Database. These databases were linked in a previous study.²² In total, 98% of New Jersey drivers who were involved in crashes were linked to a licensing record. The linkage process was validated, and we concluded that the process was conducted with high quality.

Data from the EHR for a larger cohort of CHOP patients who were New Jersey residents were individually linked with licensing-crash data via a hierarchical deterministic linkage. Data elements included date of birth, full name, sex, zip code, city, and street number of residence. Based on a hand review of 3348 records, we estimated the true match rate (ie, true matches/original matches) to be 99.95%. The false nonmatch rate (ie, true matches that were not found), assessed by searching for the 5 most likely potential matches for a sample of 474 unmatched individuals, was estimated to be 1.5%.

Main outcome measures were time to acquisition of a New Jersey driver’s license and first involvement as a driver in a New Jersey police-reported crash. A crash is reportable if it results in an injury or more than $500 in property damage.²³

Sex, date of birth, race/ethnicity, and insurance payor at last visit were determined from the EHR. We used ICD-9-CM codes to determine the presence of seizure disorder (345x) and disruptive behavior disorders (conduct disorder [312x] and oppositional defiant disorder [313.81]). For each participant with ADHD, we determined from the EHR whether an ADHD medication was prescribed by a CHOP clinician within 12 months of licensure (a proxy for ADHD severity) and within 30 days of licensure (given that ADHD stimulant medications prescriptions cover 30 days and cannot be refilled). We used the 2010 US Census Gazetteer Files and 2007 to 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

Analyses were restricted to 2479 individuals with ADHD and 15 865 individuals without ADHD who had at least 1 full month of follow-up after becoming age-eligible for licensure (Figure 1). Kaplan-Meier survival curves were used to estimate cumulative probability of licensure and crash involvement over time; the log-rank test was used to compare differences. Multivariable Cox regression models were used to estimate adjusted hazard ratios (aHRs). The proportionality assumption was assessed using Kolmogorov-type supremum tests and by considering the interaction of ADHD status with follow-up time. The association between ADHD and time to licensure was observed to vary temporally in males (supremum test: P < .001), but not females (P = .10); thus, to account for time dependence among males only, final Cox models that used linear and quadratic terms determined to provide the best model fit by likelihood ratio tests. The association between ADHD and time to first crash did not vary over time (P = .40). Adjusted models include potential confounders chosen a priori based on known or suspected associations with ADHD (or its clinical diagnosis) and driving outcomes including sex, race/ethnicity, insurance payor, disruptive behavior and seizure disorders, licensure age (for crash outcome), primary care practice, birth year, and zip code–level income and population density. Multiplicative interaction was assessed using likelihood ratio tests. We examined the association between medication and crashes among the 1036 licensed drivers with ADHD who had a CHOP network visit in the year before licensure. We compared crash hazards in the 2 years after licensure for 3 groups: those not prescribed ADHD medication within the year before licensure; those prescribed medication in the year before licensure but not in the 30 days before licensure; and those prescribed medication within 30 days of licensure. Estimates were considered significant at the .05 level, and hypothesis tests were 2-sided. Analyses were conducted in SAS, version 9.3 (SAS Institute Inc).

Individuals had a median of 19 primary care visits (interquartile range [IQR], 10-33), had their last visit at a median age of 18.0 years (IQR, 16.0-18.9), and were a median age of 22.2 years (IQR, 19.7-24.8) at the end of the study period (Table 1). Individuals with ADHD had a median of 2 primary care visits (IQR, 1-5) that included an ADHD-specific ICD-9-CM code. Table 1 shows comparisons of relevant characteristics.

Overall, 222 of 691 females with ADHD (32.1%) and 3874 of 8323 females without ADHD (46.5%) were licensed in their first month of eligibility, and 311 of 391 (79.5%) and 4502 of 5210 (86.4%), respectively, were licensed by age 21 years. Table 2 shows the unadjusted cumulative probability of licensure by select ages. Licensing rates among males were lower in ADHD and non-ADHD groups but followed a similar pattern. Unadjusted curves of time to licensure by ADHD status and sex are shown in Figure 2. Among males, adjusted Cox models indicated that differences in licensure between ADHD and non-ADHD groups diminished over time (by age 17.5 years: aHR, 0.65; 95% CI, 0.61-0.70 vs by age 19 years: aHR, 1.00; 95% CI, 0.87-1.14). Among females, the difference remained constant over time (aHR, 0.64; 95% CI, 0.58-0.70). Among individuals ultimately licensed, females with ADHD were older at licensure than females without ADHD (median age, 17.2 years; IQR, 17.0-17.8 years vs 17.0 years; IQR, 17.0-17.5 years; P < .001) and males with ADHD were older than males without ADHD (median age, 17.4 years; IQR, 17.0-18.0 years vs 17.1 years; IQR, 17.0-17.6 years; P < .001).

Crash analyses were limited to those licensed: 1785 individuals with ADHD and 13 221 without ADHD. Of these, 764 with ADHD (42.8%) and 4715 without ADHD (35.7%) crashed by the end of the study period. Unadjusted cumulative probabilities of crash involvement by select postlicensure months are shown in Table 2. Figure 3 shows the proportion of licensed drivers who remained crash-free over time by ADHD status and sex; both males and females with ADHD were more likely to crash earlier and at higher frequencies than those without ADHD. In adjusted Cox models, the overall aHR for crash risk among adolescent drivers with ADHD was 1.36 (95% CI, 1.25-1.48). Further, there was no evidence that the ADHD-crash relationship varied on the multiplicative scale by licensing age (χ² = 0.26; P = .97) or sex (χ² = 2.05; P = .15). The crash hazard among males with ADHD was 1.42 times higher (95% CI, 1.28-1.56) than among males without ADHD and 1.25 times higher (95% CI, 1.08-1.45) for females with ADHD than females without ADHD.

Of 1063 licensed individuals with ADHD and a CHOP network visit in the year before licensure, 267 (25.1%) were prescribed ADHD medication in that year and 129 (12.1%) were prescribed ADHD medication by a CHOP clinician in the 30 days before licensure. There were no significant differences in 2-year postlicensure risk among the 3 groups compared: medication in the previous year but not just before licensure vs none (aHR, 0.95; 95% CI, 0.64-1.41); medication just before licensure vs none (aHR, 1.17; 95% CI, 0.80-1.71); and medication just before licensure vs in the previous year but not just before licensure (aHR, 1.24; 95% CI, 0.78-1.97).

We conducted sensitivity analyses by reclassifying 482 individuals with ADHD who did not have independent confirming evidence of ADHD into the non-ADHD group. Results did not meaningfully change; estimated aHRs for crash were attenuated by less than 1%. We also reweighted the time variable in Cox regression models to assess potential bias owing to differences in driving exposure (mileage) and estimated that individuals with ADHD would have to drive a mean of 48% more miles than those without ADHD before the 95% CIs of aHRs included 1.0.

To our knowledge, this is the first longitudinal study to estimate licensing probability and crash likelihood throughout adolescence and young adulthood among individuals with ADHD identified in a community setting. Results indicate that individuals with ADHD are licensed somewhat less and later than those without ADHD. Further, licensed individuals with ADHD experienced an estimated 36% increase in the risk of first crash involvement than those without ADHD, an increase that exists regardless of licensure age and persists into early adulthood. Only 12.1% of individuals with ADHD were prescribed medication by a CHOP clinician in the 30 days before licensure; medication status at licensure did not significantly alter crash risk.

Using a rigorous epidemiologic design, this study overcame limitations of previous studies by including objective measures of license and crash occurrence over an extended period, accounting for sex, licensing age, driving experience, comorbid conditions, and other potential confounders and increasing generalizability to the general population of adolescents with ADHD via use of a community-identified cohort. In 1993, a small clinic-based investigation of this topic suggested “an almost fourfold increase in the average frequency of [crash involvement]” for adolescents with ADHD,¹⁵ a finding frequently cited in other studies and media.^7,26,27 However, it is likely that internal and/or external validity were compromised in that and other previous studies given potential of confounding and limited generalizability owing to comparing specialized clinic-based ADHD samples to conveniently sampled comparison groups; no or inadequate control for driving experience and/or comorbid conditions; small sample sizes; and lack of females.^11-15 Additionally, these studies assessed crash outcomes via self-report surveys with long recall periods.^11-14 This may be of particular concern among those with ADHD given findings of “positive illusory bias,” or overestimation of performance and abilities, in driving and other domains.^28,29 A 2006 meta-analysis of self-report driving studies among adolescent and young adult drivers estimated a risk ratio for crash involvement of 1.88 (95% CI, 1.42-2.50)³⁰; a 2014 meta-analysis that included adult studies but accounted for driving exposure estimated a risk ratio of 1.23 (95% CI, 1.04-1.46).³¹ Two population-based studies of crash-related hospital visits published in 2010 and 2014 estimated a 34% to 47% increased risk among adolescent male drivers with ADHD and adult drivers with ADHD.^32,33

The large population included in this study made it, to our knowledge, the first sufficiently powered to examine how the effect of ADHD on crash rates varied by both sex and licensing age. We did not find evidence that HRs varied significantly by either factor. However, studies^34-37 among community samples of children with ADHD have suggested that symptom profiles may differ in ways that would affect driving. Boys may experience greater hyperactivity, impulsivity, and externalizing problems, and girls may experience more internalizing problems (eg, anxiety, depression) and potentially higher levels of inattention.^34-37 Future research should examine male and female drivers separately to uncover potential sex-based mechanisms underlying increased crash risk among adolescent drivers with ADHD. In addition, although initial crash risk is generally lower for adolescents licensed later,¹⁶ we found no evidence that crash risk differs multiplicatively by licensing age for adolescents and young adults with and without ADHD. More in-depth analyses should be conducted to determine why adolescents with ADHD get licensed later and whether the potential benefits of later licensure might be enhanced by extending the intermediate phase of Graduated Driver Licensing, which lowers crash risk by restricting exposure to high-risk situations.³⁸

Study results demonstrate the need to provide guidance for families around reducing the elevated crash risk among young drivers with ADHD,³⁹ yet foundational research and evidence-based recommendations are lacking. Several efficacy studies, primarily using driving simulators, indicate that ADHD medication improves driving behavior.^18,19 Further, a prior study found that medication statistically significantly reduced the risk of serious transport crashes in male but not female patients with ADHD.³³ Our study found that prescription rates at licensure were low and did not reveal a difference in crash risk among those prescribed medication at licensure. Notably, however, we were not able to capture prescriptions from non-CHOP clinicians, and sample size was limited. The real-world effectiveness of medication may be attenuated because of known inconsistent adherence among adolescents and failure to use medication, which is generally short-acting, when driving (eg, after school).^40-43 On-road crash studies that capture medication use more precisely than ordered or dispensed prescriptions and at the time of the crash are critically needed. Other studies have explored aspects of driver training and parental monitoring with some early suggestion of benefit,^44,45 but none have demonstrated clear positive effects on objective measures of risky driving or crashes. Additional work is critically needed to provide clinicians with evidence-based guidance on appropriate age of licensure and specific practice guidelines to reduce crashes in this population (including training, monitoring, and medication use).

There are several limitations of this study. Diagnoses relied on assessment by primary care clinicians rather than rigorous testing using criterion standard as set by Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition); however, we validated ADHD diagnosis codes with high sensitivity and specificity, and most individuals continued to have primary care visits after becoming eligible for licensure.²¹ Given that our sample includes individuals who were identified before adolescence, a certain percentage may not have impairments owing to ADHD symptoms by the time of licensure⁴⁶; thus, this sample more appropriately reflects individuals with a lifetime history of ADHD. Additionally, we did not directly measure driving exposure. Young adults with ADHD reported driving more in 2 of 3 previous studies that assessed mileage.^11,12,47 However, these assessments involved surveys of usual driving patterns at the time of survey, potentially years after crash occurrence, or total mileage driven in the 3 to 5 years since licensure and may be subject to positive illusory bias. Our analyses inherently accounted for time since licensure, which has been used as a proxy for exposure and itself is a critically important variable given rapid declines in crash rates over the first few years of driving. Adolescents and young adults with ADHD may also be less likely to attend college, and hence move out of state after high school, than their counterparts without ADHD^48,49; however, significant associations were constant over time and include the period just after licensure when most individuals were still in high school. Finally, there may be limits to generalizability. New Jersey has the oldest licensing age in the United States and is highly urbanized. Further, the prevalence of ADHD in our primary care cohort (14%) was somewhat higher than US-based estimates,⁵⁰ perhaps reflecting parental decisions to seek care within the CHOP network.

Adolescents and young adults with ADHD experience an estimated 36% higher motor vehicle crash risk than their counterparts without ADHD regardless of licensure age or sex; risk persists over the first few years of licensure. Few individuals with ADHD had active ADHD prescriptions at the time of licensure. Future research is needed to examine parent and clinician management of licensure decisions and crash risk among patients with ADHD, elucidate sex-specific mechanisms by which ADHD influences crash risk to develop countermeasures, and examine real-world effectiveness of medication use and detrimental effects of distractions.

Accepted for Publication: March 8, 2017.

Corresponding Author: Allison E. Curry, PhD, MPH, Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, Roberts Pediatric Research Building, 2716 South St, 13 Floor, Philadelphia, PA 19146 (currya@email.chop.edu).

Published Online: June 12, 2017. doi:10.1001/jamapediatrics.2017.0910

Author Contributions: 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.

Study concept and design: Curry, Metzger, Winston, Power.

Acquisition, analysis, or interpretation of data: Curry, Metzger, Pfeiffer, Elliott, Winston.

Drafting of the manuscript: Curry, Metzger, Elliott.

Critical revision of the manuscript for important intellectual content: Metzger, Pfeiffer, Winston, Power.

Statistical analysis: Curry, Metzger, Pfeiffer, Elliott.

Obtained funding: Curry.

Administrative, technical, or material support: Pfeiffer, Winston.

Study supervision: Curry, Elliott.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by grant 1R01HD079398 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development at the National Institutes of Health (Dr Curry).

Role of the Funder/Sponsor: The sponsor had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Additional Contributions: We thank Meghan Kirk, MS, and Sayaka Ogawa, BS, from the Children’s Hospital of Philadelphia, for their help in coordinating and formatting the manuscript; Christine Norris, BA, from the Children’s Hospital of Philadelphia for her editorial guidance; and the New Jersey Department of Transportation, New Jersey Motor Vehicle Commission, and New Jersey Office of Information Technology for their assistance in providing data. Ms Ogawa was a research assistant funded on the R01 grant from the National Institutes of Health. Ms Kirk and Ms Norris did not receive compensation.