Medication Use for ADHD and the Risk of Driving Citations and Crashes Among Teenage Drivers: A Population-Based Cohort Study

Abstract

Objectives: To evaluate the real-world effectiveness of ADHD medications on adverse driving outcomes in teenage drivers with ADHD. Method: We retrospectively followed 15- to 20-year-old ADHD patients with valid driver’s license to compare the risk for crashes and citations between periods with and without ADHD medication use, using Florida Medicaid records linked to Department of Motor Vehicles data from 1999 to 2004. Patient-level demographic, clinical, and driver licensing characteristics as well as county-level crash and traffic statistics were adjusted in Cox models. Results: A total of 2,049 patients had 67 crashes and 319 citations. Adjusted hazard ratios comparing ADHD medication use versus no use were 1.22 (95% confidence interval [CI] = [0.66, 1.90]) and 0.89 (95% CI = [0.69, 1.13]) for crashes and citations, respectively. Conclusion: Our study showed no evidence that ADHD medication use was associated with a reduced risk of adverse driving outcomes among teenage drivers enrolled in Medicaid programs. Limitations in interpreting this finding are presented.

Keywords

cohort study adolescent ADHD pharmacological treatment driving safety epidemiology

Introduction

Road traffic accidents are a major public health issue in the United States and around the world. Among 6.5 million police-reported motor vehicle crashes in the United States in 2017, more than a quarter resulted in personal injury, and about 37,000 people were killed (National Highway Traffic Safety Administration, 2019). Notably, teenage drivers (15–20 years) are disproportionately overrepresented in road traffic accidents. They had the highest rates of crash and fatal crash involvement among all age groups, after accounting for both their share in the driving population and the amount of driving, consistently during the last two decades (Tefft, 2012).

Various age-related and inexperience-related factors contribute to the excessive risk of crashes among teenage drivers. Attention-deficit/hyperactivity disorder (ADHD), a prevalent chronic condition in this age group, is one of them (Bates et al., 2014; Williams, 2006). Characterized by deficiencies in attention, aggression and impulsivity, increased risk taking, and commonly coexisting conduct disorder and substance use, symptoms and risky behaviors of teenagers with ADHD can adversely affect driving (Barkley, 2004; Merkel et al., 2016; Pliszka, 2003; Richards et al., 2006; Vingilis et al., 2014). Various studies suggest drivers with this condition are more likely to have deficient driving skills, receive traffic citations, and be involved in traffic accidents (Barkley et al., 1996; Curry et al., 2019; Fischer et al., 2007; Vingilis et al., 2014). A meta-analysis of 16 studies reported a 36% increase in the driving accident risk associated with ADHD diagnosis (Vaa, 2014).

In light of the importance of driving in modern society and negative driving outcomes associated with ADHD, it is imperative to identify effective intervention strategies that can reduce driving impairment and adverse outcomes. Several randomized controlled trials have documented improved driving performance from pharmacological treatment (stimulants or atomoxetine). Characterized by small sample size and mostly conducted in driving simulators or other highly controlled environments, the generalizability of these study findings to more realistic driving conditions and their implications in terms of accident risk remain unclear (Gobbo & Louza, 2014). Among all reviewed evidence, only five studies enrolled teenage drivers (D. J. Cox et al., 2006, 2008; D. J. Cox, Humphrey, et al., 2004; D. J. Cox, Merkel, et al., 2004; Mikami et al., 2009).

Two large observational studies have evaluated the association between ADHD medication use and the risk of severe motor vehicle crashes in adults (≥18 years) with ADHD in Sweden and the United States using large administrative claims databases (Chang et al., 2014, 2017). Although both studies suggested that use of ADHD medications may lower risk, neither incorporated information on actual driving permits or driving experience. This information is particularly relevant to the evaluation of adverse driving outcomes in teenagers because of the small proportion of licensed drivers and their relative inexperience as beginners. Furthermore, the authors used external causes of injury diagnostic codes to ascertain motor vehicle traffic accidents, which has not been validated as proxy for crashes (Bowman & Aitken, 2011). Their outcome definitions identified a mixed group of drivers, passengers, and pedestrians who may get injured from motor vehicle crashes where the interpretation of a composite medication effect was ambiguous.

Given the pressing traffic safety issue among teenage drivers, the scarcity of evidence on ADHD treatment effectiveness on driving performance and adverse driving outcomes in this age group, and limitations of previous studies, we conducted a population-based cohort study of teenage drivers in Florida to further evaluate the effect of ADHD medication use on the risk of adverse driving outcomes, including driving citations and crashes identified from official driving records.

Method

Data Source

Our study took advantage of a retrospective population-based cohort of ADHD patients established from the Medicaid Analytic eXtract (MAX) files for a larger federally funded project that aimed to assess the comparative safety of stimulants in youth with ADHD (Winterstein et al., 2012). We extracted a subcohort of enrollees aged 15 to 20 years, covered under the Medicaid fee-for-service program in Florida between January 1999 and June 2004. The data sets contained information on patients’ demographics, diagnoses and procedures performed during outpatient visits or inpatient stays, and outpatient filled prescription drugs. To assure that patients had legal permission to drive, we linked our cohort to the driver’s license database maintained by the Florida Department of Highway Safety and Motor Vehicles (DMV) and identified all records with a noncommercial vehicle operating license (Class E) that were active during the study period (Figure 1). This study was approved by the University of Florida Institutional Review Board.

Figure 1.

Flowchart of study cohort assembly.

Study Population

During the study period, patients were eligible if they (a) were 15 to 20 years old; (b) had an active ADHD diagnosis, defined by having at least one inpatient or outpatient encounter with ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification) diagnostic code for ADHD (314.xx) that had occurred within 1 year; (c) had at least 6-month continuous enrollment to assess baseline demographic and clinical characteristics; and (d) had a valid Florida noncommercial Class E or Class E-Learner driver’s license with a valid Florida city and zip code in the licensee’s registered address.

We started to follow patients on the earliest time (index date) when all above criteria were met until the occurrence of study outcomes, patients’ 21st birthday, end of Medicaid enrollment, more than 365 days since the last ADHD diagnosis, expiration, or suspension of their driver’s license, or death, whichever came first.

Study Outcomes

For each eligible patient, we searched the linked DMV database to identify records and their dates of occurrence for two study outcomes: vehicle crashes while driving and citations for “active driving” violations (i.e., violations most directly relate to the activity of driving, see Supplemental Appendix Table S1 for details). “Passive driving” citations were excluded in our definition which included standing violations (e.g., parking violations), equipment violations (e.g., broken tail-light), or administrative violations (e.g., no proof of insurance). Only crashes with a “history description” data field indicating investigation by the police or Florida Highway Patrol were considered as valid crash events.

Definition of Exposure

We defined ADHD medication exposure based on prescription fill claims for methylphenidate, mixed amphetamine salts, and atomoxetine. The exposure began on the day of dispensing and ended based on the recorded dispensed days’ supply plus a 25% grace period to incorporate residual supply because of drug holidays such as weekends (Cooper et al., 2011; Winterstein et al., 2012). A new dispensing was set to override previous assignments of exposure without adjustments for potential overlaps between time periods that were covered by previous dispending. Our previous studies indicate that about 85% of claims were for a 30-day supply, resulting in a 38-day exposure period for such claims (Winterstein et al., 2012).

Covariates

Covariates that were summarized via a baseline exposure propensity score included sex, race/ethnicity, age at index date, reasons for Medicaid eligibility at index date (i.e., foster care, poverty, disability, and recipient of cash assistance), length of time since being issued a valid Florida driver’s license (>1 year), having a learner’s permit, and presence of an in- or outpatient diagnosis of oppositional defiant/conduct disorders (ODD/OD, ICD-9-CM codes: 312–312.29, 312.4x–312.9x, 312.30, 312.34, 312.35, 313, 313.8, 313.81, and 313.9x), substance use disorder (291.xx–292.xx, 303.xx–305.xx), diabetes (250.xx), or epilepsy (345.xx) during the 6-month baseline period before the index date. Also included in the propensity score were several county-level variables ascertained from the Florida Department of Transportation (DOT) for the year 2002, including the total number of citations per licensed driver, crashes per licensed driver, citations for driving under the influence (DUI) per licensed driver, total number of miles traveled per miles of road, and the total population. We allocated the county for each patient based on the zip code in the MAX file.

Data Analysis

To control for potential confounders, we first used mixed effect logistic regression model to estimate the propensity score (i.e., the predicted probability of receiving ADHD treatment vs. no treatment during follow-up) based on above-listed baseline covariates with county modeled as a random effect. Person-time with ADHD medication treatment and person-time without treatment were then compared using Cox proportional hazards regression, in which we estimated the hazard ratio (HR) for ADHD medication exposure after adjusting for the propensity score at baseline and several important time-varying covariates: age, licensed driver status >1 year, learner’s permit, substance use disorder, and exposure to alpha-agonists, anticonvulsants, antidepressants, antipsychotics, anxiolytics, and lithium. Covariates that changed the exposure HR by less than 10% were removed from the final model to save the degree of freedom because they were unlikely to substantially confound the relationship between exposure and outcome. To explore the value of adjusting for additional human and environmental determinants of traffic accidents from the DMV/DOT database, we conducted a separate analysis without using this information (i.e., driver’s licensure status and county-level characteristics). All analyses were conducted with SAS Version 9.3 (Cary, NC).

Results

Study Cohort and Patient Characteristics

A total of 2,049 patients met the inclusion criteria and were followed for 3,969 years. Of these, 1,113 (53.5%) were exposed to ADHD medications during follow-up (Figure 1), with 582, 570, and 132 patients ever being exposed to amphetamine, methylphenidate, and atomoxetine. Included patients were, on average, about 18 years old with about three quarters being male. At the index date, 47.1% of patients had held a valid driver’s license for more than 1 year, and 13.1% were using a leaner’s permit.

Compared with unexposed patients (Table 1), patients who were ever exposed to ADHD medication were more likely to be White, eligible for Medicaid due to poverty, have a valid driver’s license for more than 1 year, and were less likely to have ODD/CD. During follow-up, treated patients were more likely to receive other psychotropic medications including alpha-agonists, antidepressants, and antipsychotics.

Table 1.

Cohort Characteristics Between Patients Who Are Ever Exposed and Not Exposed to ADHD Medications.

Characteristics	No exposureN = 936	Some exposure to ADHD medicationN = 1,113
Age at index date	17.9 ± 1.2	17.6 ± 1.2
Male	723 (77.2)	851 (76.5)
Race
White	556 (57.1%)	810 (68.2%)
Black	204 (20.9%)	195 (16.4%)
Hispanic	174 (17.9%)	119 (10%)
Other	40 (4.3%)	63 (5.7%)
Foster care	178 (19.0%)	154 (13.8)
Cash assistance	482 (51.5)	601 (54.0)
Disability	351 (37.5)	391 (35.1)
Poverty	664 (70.9)	877 (78.8)
Valid driver’s license >1 year at index date	375 (40.1%)	592 (53.2)
Holding a learner’s permit at index date	121 (12.9)	148 (13.3)
Diabetes	8 (0.9)	7 (0.6)
Epilepsy	19 (2.0)	20 (1.8)
ODD/CD	115 (12.3)	94 (8.5)
Substance abuse disorder	35 (3.7)	43 (3.9)
Other psychotropic use during follow-up
Any	350 (37.4)	587 (52.7)
Alpha-agonists	36 (3.9)	141 (12.7)
Anticonvulsants	127 (13.6)	147 (13.2)
Antidepressants	261 (27.9)	399 (35.9)
Antipsychotics	136 (14.5)	195 (17.5)
Anxiolytics	69 (7.4)	80 (7.2)
Lithium	19 (2.0)	22 (2.0)
Citations/licensed drivers
1st Quintile	151 (16.1%)	281 (25.3%)
2nd Quintile	145 (15.5%)	196 (17.6%)
3rd Quintile	221 (23.6%)	264 (23.7%)
4th Quintile	341 (36.4%)	299 (26.9%)
5th Quintile	78 (8.3%)	73 (6.6%)
Crashes/licensed drivers
1st Quintile	142 (15.2%)	190 (17.1%)
2nd Quintile	132 (14.1%)	197 (17.7%)
3rd Quintile	140 (15.0%)	161 (14.5%)
4th Quintile	142 (15.2%)	209 (18.8%)
5th Quintile	380 (40.6%)	356 (32.0%)
DUIs/licensed drivers
1st Quintile	325 (34.7%)	284 (25.5%)
2nd Quintile	182 (19.4%)	228 (20.5%)
3rd Quintile	160 (17.1%)	246 (22.1%)
4th Quintile	151 (16.1%)	199 (17.9%)
5th Quintile	118 (12.6%)	156 (14.0%)
Miles traveled/miles road
1st Quintile	35 (3.74%)	57 (5.1%)
2nd Quintile	71 (7.6%)	96 (8.6%)
3rd Quintile	176 (18.8%)	252 (22.6%)
4th Quintile	191 (20.4%)	254 (22.8%)
5th Quintile	463 (49.5%)	454 (40.8%)
County population
1st Quintile	24 (2.6%)	47 (4.2%)
2nd Quintile	71 (7.6%)	84 (7.6%)
3rd Quintile	131 (14.0%)	183 (16.4%)
4th Quintile	184 (19.7%)	276 (24.8%)
5th Quintile	256 (56.2%)	523 (47.0%)

Note. ADHD = attention-deficit/hyperactivity disorder; ODD/CD = oppositional defiant disorder/conduct disorder; DUI = driving under the influence.

We also noted differences between exposed and unexposed patients with regard to county-level characteristics. Specifically, unexposed patients were more likely to reside in counties with larger population, high travel mobility (miles traveled/miles road), and high crash and citation rates, while being less likely to reside in counties with high DUI rates.

Risks of Adverse Driving Outcomes

As shown in Table 2, during follow-up, 67 patients had a crash (3.3 per 100 patient-years) and 319 received a citation (16.7 per 100 patient-years). Although periods of ADHD medication use had lower crude rates of crashes (3.0 vs. 3.4 crashes per 100 person-years, unadjusted HR = 0.87, 95% confidence interval [CI] = [0.52, 1.45]), the adjusted HR did not suggest any treatment effect (adjusted HR = 1.22, 95% CI = [0.66, 1.90]). Analysis without using DMV/DOT information generated an estimate in-between (partially adjusted HR = 0.95, 95% CI = [0.58, 1.56]).

Table 2.

Risk of Driving Crashes and Citations Associated With ADHD Medication Use.

Variable	No. of events	No. of follow-up years	Events per 100 person-years	Crude HR (95% CI)	Partially adjusted HR (95% CI) without using DMV/DOT variables	Fully adjusted HR (95% CI)
Crashes
All patients	67	2,059.9	3.3	—	—	—
Unexposed	45	1,319.4	3.4	Reference	Reference	Reference
Exposed	22	740.6	3.0	0.87 [0.52, 1.45]	0.95 [0.58, 1.56]	1.22^a [0.66, 1.90]
Citations
All patients	319	1,909.4	16.7	—	—	—
Unexposed	223	1,212.9	18.4	Reference	Reference	Reference
Exposed	96	696.6	13.8	0.75 [0.59, 0.95]	0.94 [0.74, 1.19]	0.89^b [0.69, 1.13]

Note. ADHD = attention-deficit/hyperactivity disorder; HR = hazard ratio; CI = confidence interval; DMV = Department of Highway Safety and Motor Vehicles; DOT = Department of Transportation.

Adjusted for baseline propensity score and time-varying covariates: antidepressant use and substance use disorder. ^bAdjusted for baseline propensity score and time-varying covariates: age, antidepressant use, and licensed driver status >1 year.

The crude rate for citations was also lower during periods of ADHD medication use (13.8 vs. 18.4 citations per 100 person-years; crude HR = 0.75, 95% CI = [0.59, 0.95]), but this effect was likewise attenuated in the adjusted analysis (adjusted HR = 0.89, 95% CI = [0.69, 1.13]). Analysis without using DMV/DOT information yielded a HR closer to null (partially adjusted HR = 0.94, 95% CI = [0.74, 1.19]).

Discussion

To our knowledge, this is the first real-world, population-based study that characterized the incidence rate of and the effectiveness of ADHD medications on driving citations and crashes among teenage drivers with ADHD. Although crude analyses suggested potential protective effects, we did not observe statistically significant changes in the risk of adverse driving outcomes associated with ADHD medication use after adjusting for several important patient- and environment-level risk factors.

To date, there is limited evidence on benefits of ADHD medications on the driving performance of teenage drivers. Two small studies in driving simulators found improved computer-rated driving behaviors associated with long-acting methylphenidate use among adolescent drivers (D. J. Cox et al., 2006; D. J. Cox, Merkel, et al., 2004). These findings may have limited generalizability to real-world situations because, without bearing any risk, participants may drive with less engagement (Gobbo & Louza, 2014). Another study documented reduced inattentive but not impulsive driving errors among 12 male adolescents when using long-acting methylphenidate, assessed by a blinded rater over a standard 16-mile road course (D. J. Cox, Humphrey, et al., 2004). Our results are consistent with a prospective cohort study that used various continuous monitoring techniques to capture the routine driving experience of 275 licensed drivers with ADHD across six U.S. cities. In that study, treated ADHD patients (N = 57) had a slightly increased but not statistically significant crash risk compared with nontreated patients (incident rate ratio = 1.33, 95% CI = [0.85, 2.08]; Aduen et al., 2018). With less than a quarter of patients less than 21 years of age, it is unclear whether results are generalizable to teenage drivers. Finally, our study findings are inconsistent with the reports from two large cohort studies in adults (Chang et al., 2014, 2017), which found treatment benefit when using diagnostic codes for driving-related injury from administrative claims data as proxy for crashes.

Several reasons may explain our finding of no significant beneficial treatment effect among teenagers. First, the effectiveness of medications may be attenuated by the well-known inconsistent adherence in this age group (Charach & Fernandez, 2013). Driving activities may occur more frequently during weekends and school vacations, when patients may have opted to not use stimulants. Second, use of short-acting medications is ineffective in improving driving performance at later times of a day, when the medication effect has worn off and driving activities are more frequent (i.e., after school; D. J. Cox, Merkel, et al., 2004). Adolescents and parents may prefer short-acting over long-acting medications to target behavior and learning at school (Charach & Gajaria, 2008; Pelham et al., 2017). Future studies with larger sample size should further examine the treatment effect among long-acting medication users. Third, for holders of learner’s permits or drivers less than 18, driving is restricted or has to be accompanied by a licensed driver above 21 years, which may attenuate ADHD-related driving risk.

On the contrary, previous positive findings may have been prone to information bias and residual confounding, which were addressed by several unique methodologic features of our study. First, we created a linkage between official driving records and Medicaid claims data which allowed for an accurate determination of the population at risk (i.e., patients with valid driver’s license status) and outcomes of interest. No driving indicated by no valid driver’s license would be highly protective of accidents and such effect would be pronounced in studies without supplemental DMV data as many teenagers don’t have driver’s licenses (Sivak & Schoettle, 2016). Patients with more severe ADHD symptoms, potentially indicated by active use of treatment, may not have driving permits, thus being protected from crashes. Likewise, using official driving records to ascertain crashes and citations is more reliable, when compared with reliance on self-reporting or use of unvalidated claims-based algorithms to capture crash-related injuries.

Second, we controlled for additional environment- and human-level confounders that were unmeasured in previous studies. Although human factors are considered the most important contributor to motor vehicle crashes, environment and road factors play a role as well (U.S. General Accounting Office, 2003). Importantly, the baseline risk for traffic accidents differs by geographic region due to differences in traffic density, road behaviors, and traffic mobility (La Torre et al., 2007; van Beeck et al., 1991), as does the probability for ADHD treatment (E. R. Cox et al., 2003). In this study, we found untreated patients were more likely to live in counties with higher traffic density and higher crash and citation rates, and were less experienced, all pointing to a higher baseline risk. Further adjusting for these differences moved the HR point estimate for crashes from 0.95 to 1.22, which highlights the importance of accounting for geographic differences in studies of medication use and driving safety using large health care databases.

During follow-up, we observed an incidence rate of 3.3 crashes per 100 person-years, which is almost 12 times higher compared with an incidence rate of 0.27 emergency room visits with injuries from motor vehicle crashes per 100 person-years in an U.S. cohort study of adults with ADHD (Chang et al., 2017). While these numbers are not directly comparable due to different outcome definitions used, the high background risk reconfirms the need to tackle road safety issues for teenage drivers with ADHD. Future studies should continue to explore the value of pharmacological, cognitive, behavioral, family, educational, and technical interventions in the real-world setting to provide evidence-based guidance for daily clinical practice (Aduen et al., 2019; El Farouki et al., 2014).

This study is subject to a number of limitations that are inherent in the data source used. While sampling from a large population of all Medicaid recipients in Florida, our study was not powered to adequately assess the effect of ADHD treatment on crashes. However, the evaluation of citations resulting from poor driving behavior, which had narrower confidence intervals, corroborated our findings. With a focus on the Florida Medicaid population, generalizations of our findings to other patient groups should be made with caution, because the more complex clinical and social challenges may modify treatment effect. While limited evidence has been published on this topic, one study suggests socioeconomic status did not predict response to stimulants (Thomson & Varley, 1998).

In addition, using data from 1999 to 2004, our findings may not be generalizable to more recent years as prescribing patterns of ADHD medication have changed and driver assistance technologies have become more prevalent and advanced. However, updates of our data set with more recent years of Florida DMV data were not feasible due to increased state-level privacy rules. We encourage researchers to consider such linkage in future driving safety studies to ensure proper definition of at-risk populations and outcomes, and proper adjustment of environment-level confounders. Finally, prescription claims reflect the dispensing data and cannot determine whether and when medications are taken. While found more accurate than self-report or prescribing data (Gnjidic et al., 2017), some misclassification of treatment may have occurred, which would have biased effect estimates to the null.

Conclusion

In this sample of publicly insured teenage drivers, our study found no evidence that ADHD medication use can reduce the risk of crashes and citations. Future studies with larger sample size, proper definition of at-risk population, valid outcome ascertainment, and rigorous confounding control are needed to further elucidate the real-world effectiveness of ADHD treatment on driving performance among teenagers.

Supplemental Material

Supplemental_Material – Supplemental material for Medication Use for ADHD and the Risk of Driving Citations and Crashes Among Teenage Drivers: A Population-Based Cohort Study

Supplemental material, Supplemental_Material for Medication Use for ADHD and the Risk of Driving Citations and Crashes Among Teenage Drivers: A Population-Based Cohort Study by Almut G. Winterstein, Yan Li, Tobias Gerhard, Stephan Linden and Jonathan J. Shuster in Journal of Attention Disorders

Footnotes

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: T.G. reports grants from the National Institute of Mental Health during the conduct of the study; grants and personal fees from Bristol-Myers Sqibb; personal fees from Merck, Pfizer, Lilly, and IntraCellular Therapies outside the submitted work. The remaining authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded in part by grant R01-HS0185606 from the Agency of Healthcare Research and Quality (AHRQ). AHRQ had no role in the design, conduct, or reporting of the study or in the decision to submit the manuscript for publication.

ORCID iD

Yan Li

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Almut G. Winterstein, PhD, is a professor and the chair of the Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, and the director of Center for Drug Evaluation and Safety at the University of Florida. She is interested in postmarketing evaluations of drugs in pediatrics and perinatal care, infectious disease and psychiatry, and the evaluation and improvement of quality surrounding medication use using real-world data.

Yan Li, PhD, was a student in the Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, when the work in this article was done. He is currently an ORISE fellow at the U.S. Food and Drug Administration. He is interested in examining the utilization, safe, and effectiveness of medical products using real-world data.

Tobias Gerhard, PhD, is an associate professor in the Department of Pharmacy Practice, College of Pharmacy, and the director of Center for Pharmacoepidemiology and Treatment Sciences at Rutgers University. He is interested in epidemiological methods and their applications in the analysis of large administrative databases.

Stephan Linden, PhD, was a student in the Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, when the work in this article was done. He currently works at the Global Health Economics and Outcomes Research, Boehringer Ingelheim.

Jonathan J. Shuster, PhD, is a professor emeritus in the Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida.

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