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Research Article                                                                                                                                        Open Access

## Attention-Deficit/Hyperactivity Disorder, Autistic Traits, and Substance Use Among Missouri Adolescents

Richard C. Mulligan, Angela M. Reiersen*, Alexandre A. Todorov

Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

*Corresponding author: reiersea@psychiatry.wustl.edu

## Abstract

Background: Although existing literature demonstrates the association of attention-deficit/hyperactivity disorder (ADHD) with  both  substance  use  (SU)  and  autism  spectrum  disorder  (ASD),  few  studies  have  examined  rates  of  SU  among adolescents  with  elevated  ASD  symptoms,  with  or  without  comorbid  ADHD.  Clinic-based  studies  suggest  a  possible protective effect of ASD against SU, but this has not been confirmed in population-based studies.

Objective: We examined alcohol, tobacco, and drug use in adolescents with either ADHD, elevated autistic traits, or both as compared with controls.

Methods: Subjects (N = 2937) who were 13 to 17 years old from a Missouri population-based large sibship sample were assessed for ADHD, autistic traits, and SU with the use of parent-report questionnaires. The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition ADHD symptom criterion (Criterion A) was applied to the Strengths and Weaknesses of ADHD-symptoms  and  Normal-behavior  (SWAN)  questionnaire  item  responses  to  determine  ADHD  diagnosis.  The presence of elevated autistic traits was defined as a raw Social Responsiveness Scale (SRS) score of 62 (95 th  percentile for this sample) or higher. SU was determined with the use of three items from the Child Behavior Checklist (CBCL). Statistical methods used included logistic and fractional polynomial regression.

Results: As  compared with controls, adolescents with ADHD were at increased risk for alcohol, tobacco, and drug use whether or not they had elevated autistic traits. Adolescents with elevated autistic traits were at significantly increased risk for drug use other than alcohol and tobacco, even if they did not have ADHD. Among those with raw SRS scores in the range of about 20 (normal) to 80 (consistent with mild to moderate ASD), adolescents with ADHD had higher levels of SU than  control  individuals  with  similar  levels  of  autistic  traits.  However,  strong  conclusions  cannot  be  drawn  regarding individuals with very low or very high SRS scores as a result of sparse data.

Conclusions: This study confirms previous research showing an increased risk of SU among adolescents with ADHD. It also provides new information indicating that adolescents with high levels of autistic traits are at elevated risk for alcohol and tobacco use if they have comorbid ADHD; in addition, they may be at high risk for other drug use, even if they do not have comorbid ADHD. Therefore, it should not be assumed that adolescents with mild to moderate ASD have a low risk of SU, especially if ADHD is also present.

Keywords: ADHD, autism, substance use, tobacco, alcohol

## Introduction:

Previous literature has strongly suggested that diagnosis of attention-deficit/hyperactivity disorder (ADHD) may place adolescents at elevated risk for the initiation of substance use (SU) and the development of substance use disorder (SUD) (1-3). For example, a recent meta-analysis of 27 prospective  longitudinal  studies  that  examined  the association between childhood ADHD and lifetime SU and SUD found that a childhood diagnosis of ADHD was associated with a greater likelihood of using  nicotine  or  another  substance  within  one's lifetime  and  also  with  developing  a  disorder  of abuse or a dependence involving nicotine, alcohol, marijuana, cocaine, or another substance (4). Consistent with this view is the fact that epidemiological  studies  also  suggest  that  ADHD and  SUD  frequently  co-occur  within  the  general population  (5).  Although  the  exact  reason  for  this association  remains  unclear  at  the  time  of  this writing,  proposed  explanatory  mechanisms  include psychosocial concerns (e.g., poor  coping  styles, inaccurate expectancies associated with use, limited knowledge  of  related  health  effects);  cognitive  or executive function issues (e.g., dysfunction of cognitive abilities related  to  impulsive  behavior, such as reward delay aversion, reward/punishment sensitivity,  inattention,  and  inhibitory  control);  and genetic or neurobiological concerns (e.g., genetically mediated  monoaminergic  neurotransmitter  system dysfunction in frontostriatal brain regions) (6).

Although ADHD and autistic traits also co-occur in  the  general  population  at  a  rate  greater  than would be expected by chance (7), few studies have examined  the  risk  for  SU  among  adolescents  with elevated levels of autistic traits. One  study  of children and adolescents (N = 414; mean age, 16.3 years; standard deviation, 4.6 years)  who  were identified  through  a  Danish  historic  birth  cohort reported that autism spectrum disorder (ASD) diagnosis  may  be  associated  with  reduced  risk  for alcohol-related  disorders  as  compared  with  those individuals without a diagnosis of ASD (8); however,  the  prevalence  of  SU  (and  non-alcoholrelated  SUDs) was not addressed. With the use of data obtained from a medical chart review, another study suggested that adolescents with ASD (N = 97; mean age, 14.3 years; standard deviation, 1.7 years) had a lower prevalence of using drugs, alcohol, or both as compared with patients with other psychiatric  diagnoses  (3%  vs.  17%,  respectively). When  individuals  with  ASD  did  use  drugs or alcohol, they co-presented with symptoms of ADHD (9). Although this study was among the first to  discuss  the  co-presentation  of  ADHD,  autistic traits, and the use of psychoactive substances, it did not  comment on the extent  to which ADHD and ASD  traits  were  specifically  related  to  the  use  of alcohol, tobacco, and other drugs among adolescents.

The  current  study  examined  whether  alcohol, tobacco, and drug use in adolescents with ADHD, autistic  traits,  or  both  was  elevated  as  compared with  controls  in  a  non-clinical,  population-based sample.  It  was  hypothesized  that  adolescents  with high  levels  of  autistic  traits  would  have  relatively low  levels  of  SU,  likely  because  they  have  less opportunity  for  the  initiation  of  SU  as  a  result  of having fewer social contacts and decreased access to drugs as compared with typically developing individuals (10). However, given past research, which indicates that individuals with the combination  of  ASD  and  ADHD  may  be  at increased risk for SU (9), we expected that adolescents  with  ADHD  would  show  increased rates of SU even if they presented with moderately elevated levels of autistic traits.

## Methods:

Sample

The study  subjects  were  members  of  the  Missouri large sibship sample (MO-BIGSIBS), which consists of  families  with four  or more children born in the state  of  Missouri  (as  determined  through  a  birth records database) (11). Because a later phase of the parent study would require DNA from the parents and  their  children,  families  were  only  screened  if both  biological  parents  were  available  (i.e.,  neither parent could be deceased or incarcerated). Families were excluded at the birth records review or tracking level if they were known to have twins (to ensure the availability of twins for other studies), if they had a level of difficulty with English that would otherwise prevent English-language telephone screening,  or  if  one  of  the  parents  was  deceased. Individual offspring were excluded during the parent-report  telephone  screening  if  the  child  was adopted  or  not  a  full sibling or if the  parent reported  that  the  child  had  a  diagnosis  of  autism, major  hearing  impairment,  or  a  significant  medical illness such as cancer, Down's syndrome, or intellectual disability. Because the original recruitment  protocol  did  not  allow  interviewers  to ask  specifically  about  medical  diagnoses,  individual children were only excluded if the parent volunteered information about a child being diagnosed with any of these conditions (11).

Informed consent was obtained from respondent parents before the initial screening interview. Parents  were  informed  that  phase  1  of  the  study would  include  an  initial telephone  interview in addition to the completion of questionnaires regarding their children's behaviors.  They  were additionally informed that they might be contacted in the future to participate in additional assessments,  depending  on  the  answers  that  they provided in response to the initial telephone screening.  The  results  of  the  current  article  are based only on phase 1 parent-report screening data. The Washington University Human Research Protection Office reviewed and approved the study's protocols, including the consenting procedures.

The  total  MO-BIGSIBS  sample  included  22,581 offspring  with  complete  data  regarding  sex,  age  at screening,  and  ADHD  screening  interview  items (11).  Analyses  for  the  current  study  made  use  of information  obtained  for  an  adolescent  subset  of the sample (i.e., those 13 to 17 years old) for whom we had complete data regarding their sex, age, and lifetime ADHD  symptoms assessed by the screening interview plus questionnaire items required for this analysis (N = 2937). Of the 2937 subjects, 98% were Caucasian, and 51% were male.

## Measures

The best informant parent, according to the parents' opinions (usually the mother), completed behavioral rating  questionnaires  regarding  offspring  behavior, including the Strengths and Weaknesses of ADHDsymptoms and Normal-behavior (SWAN) scale, the Social  Responsiveness  Scale  (SRS),  and  the  Child Behavior Checklist (CBCL).

The  SWAN  scale  contains  18  items  that  assess Diagnostic  and  Statistical  Manual  of  Mental  Disorders, Fourth Edition (DSM-IV) ADHD  Criterion A symptoms,  each  on  the  basis  of  a  7-point  rating scale  (i.e.,  -3  to  +3)  (12).  Positive  scores  indicate problem  behaviors,  and  negative  scores  suggest better-than-average behaviors. Items from the SWAN scale were used to define ADHD diagnosis at time of assessment  based  on  the  symptom criterion  (Criterion  A)  of  the  DSM-IV  (13),  which lists the 18 symptoms of ADHD and describes the threshold number of symptoms required for diagnosis. SWAN-based  ADHD  symptoms  were counted as present if the parent rated the child at a level  of  1  or  higher,  which  is  a  cutoff  that  has previously been shown to correspond with the 90 th to 95th percentile for each item (11). Although the strict  application  of  full  DSM-IV  criteria  was  not possible  given  a  lack  of  information  regarding  age of  onset  (Criterion  B),  multiple  settings  (Criterion C), current impairment caused by ADHD symptoms (Criterion D), and disorders that exclude an  ADHD diagnosis (Criterion  E),  our method  of estimating  DSM-IV  ADHD  diagnosis  produced ADHD  prevalence estimates for the state of Missouri that are similar to those of the Centers for Disease Control and Prevention (13).

The SRS is a 65-item questionnaire that is sensitive to autistic traits (14-16). It shows moderate to  high  correlation with  algorithm scores from the Autism Diagnostic Interview-Revised (14,16), a structured psychiatric interview designed to establish  a  diagnosis  of  ASD  (17).  It  also  shows moderate  correlation  with  the  Autism  Diagnostic Observation Schedule (16), a semi-structured observational  assessment  instrument  designed  to elicit  criterion  symptoms  related  to  autism  (18).  In the  current  study,  the  presence  of  a  high  level  of autistic traits was defined by a raw SRS score of at least 62. This corresponded to an autistic trait score that is more severe than that of 95% of the present sample and to a clinically significant level of autistic traits associated with at least mild to moderate levels of  interference  with  everyday  social  interactions  in both  male  and  female  participants  (male  T-score, 61;  female  T-score,  64),  according  to  the  recently updated  SRS-2  manual  (15).  Although  clinically relevant SRS T-scores are calculated on the basis of comparison  with  same-sex  peers,  the  use  of  an identical threshold for males and females may better estimate  the  absolute  level  of  autistic  traits  (7,19) because  DSM-IV  criteria  do  not  delineate sexspecific  differences  in  ASD  symptomatology.  As such,  we  chose  to  use  a  single  raw  SRS  score threshold for both sexes.

The CBCL is a well-validated measure of psychopathology that covers several problem behaviors  of  interest,  including  three  items  related to SU (20): 'Drinks alcohol without parents' approval,' 'Smokes, chews, or sniffs tobacco,' and 'Uses drugs for non-medical purposes (don't include  alcohol or  tobacco).'  In  the  current  study, these items were used to measure alcohol, tobacco, and other drug use, respectively. These items were rated on a 3-point scale, with 0 meaning 'Not True (as  far  as  you  know)',  1  meaning  'Somewhat  or Sometimes  True'  (assumed  to  indicate  'some'  or moderate use), and 2 meaning 'Very True or Often True'  (assumed  to  indicate  a  frequent  or  'high' level of use).

## Data analyses

The  analyses were conducted  with  the use of STATA 12 data analysis and statistical software (21). In Table 1, the prevalences of 'some,' 'high,' and 'any'  (either  'some'  or  'high')  use  of  alcohol, tobacco, and other drugs are reported for adolescents with ADHD only (ADHD+SRS-), high SRS  score  only  (ADHD-SRS+),  both  ADHD  and high SRS score (ADHD+SRS+),  and  controls (ADHD-SRS-).  To  address  concerns  regarding  a lack  of  power  to  fully  examine  'some'  versus 'high'  use,  CBCL  items  were  dichotomized  to indicate  'no'  use  versus  'any'  use  for  the  main analysis. Logistic regression analyses were then applied to investigate whether the relevant diagnostic  groups  were  at  elevated  risk  for  'any' use  of  alcohol,  tobacco,  or  other  drugs  during adolescence  (Table  2).  Logistic  regression  models included diagnostic group (with ADHD-SRS- as the comparison  class),  sex,  and  age  as  independent variables. Standard errors were adjusted to account for  family  clustering  by  using  the 'Cluster'  option available in STATA 12 (21). With the use of each of the  three  CBCL  SU  items,  standardized  scores (mean, 0; SD, 1) were then created to measure SU severity. The three standardized SU items were also combined to create a score that indicated overall SU severity. For  this overall  SU  severity  scale, the average  inter-item  correlation  was  0.45,  and  the Substance Use Score Alcohol Use (standardized)

Tobacco Use (standardized)

scale  reliability  coefficient  (Cronbach's  alpha)  was 0.71.  Fractional  polynomial  regression  plots  were used to estimate the mean values of the standardized  SU  scores  across  a  range  of  autistic trait  severity  as  measured  by  the  SRS  (Figures  1 through 4). These analyses were performed separately for adolescents with and without ADHD.

TABLE 1. Prevalence of reported alcohol, tobacco, and other drug use in adolescents between the ages of 13 and 17 years separated by attentiondeficit/hyperactivity disorder diagnosis and Social Responsiveness Scale scores (N = 2937)

| Alcohol Use          | 'Some' Use     | 'High' Use    | 'Any' Use      |
|----------------------|----------------|---------------|----------------|
| ADHD-SRS- (n = 2586) | 5.5% (n = 142) | 0.5% (n = 13) | 6.0% (n = 155) |
| ADHD-SRS+ (n = 61)   | 6.6% (n = 4)   | 1.6% (n = 1)  | 8.2% (n = 5)   |
| ADHD+SRS- (n = 207)  | 15.0% (n = 31) | 2.9% (n = 6)  | 17.9% (n = 37) |
| ADHD+SRS+ (n = 83)   | 10.8% (n = 9)  | 3.6% (n = 3)  | 14.4% (n = 12) |
| Tobacco Use          | 'Some' Use     | 'High' Use    | 'Any' Use      |
| ADHD-SRS- (n = 2586) | 1.2% (n = 31)  | 0.7% (n = 18) | 1.9% (n = 49)  |
| ADHD-SRS+ (n = 61)   | 3.3% (n = 2)   | 3.3% (n = 2)  | 6.6% (n = 4)   |
| ADHD+SRS- (n = 207)  | 6.8% (n = 14)  | 4.3% (n = 9)  | 11.1% (n = 23) |
| ADHD+SRS+ (n = 83)   | 6.0% (n = 5)   | 9.6% (n = 8)  | 15.6% (n = 13) |
| Other Drug Use       | 'Some' Use     | 'High' Use    | 'Any' Use      |
| ADHD-SRS- (n = 2586) | 0.9% (n = 23)  | 0.2% (n = 5)  | 1.1% (n = 28)  |
| ADHD-SRS+ (n = 61)   | 4.9% (n = 3)   | 3.3% (n = 2)  | 8.2% (n = 5)   |
| ADHD+SRS- (n = 207)  | 6.3% (n = 13)  | 1.0% (n = 2)  | 7.3% (n = 15)  |
| ADHD+SRS+ (n = 83)   | 9.6% (n = 8)   | 2.4% (n = 2)  | 12.0% (n = 10) |

ADHD, Attention-deficit/hyperactivity disorder diagnosis; SRS, Social Responsiveness Scale elevated score; +, present; -, absent

TABLE 2. Summary of logistic regression analyses predicting alcohol, tobacco, and other drug use (N = 2937)

| Predictor of Alcohol Use    | Odds Ratio   | P Value   | 95% Confidence Interval   |
|-----------------------------|--------------|-----------|---------------------------|
| ADHD-SRS+                   | 1.06         | .912      | 0.38 to 2.92              |
| ADHD+SRS-                   | 4.37         | <.001     | 2.88 to 6.44              |
| ADHD+SRS+                   | 2.44         | .010      | 1.24 to 4.81              |
| Male gender                 | 1.11         | .525      | 0.81 to 1.51              |
| Age                         | 2.38         | <.001     | 2.08 to 2.72              |
| Predictor of                | Odds         | P Value   | 95% Confidence            |
| Tobacco Use                 | Ratio        |           | Interval                  |
| ADHD-SRS+                   | 2.70         | .090      | 0.86 to 8.55              |
| ADHD+SRS-                   | 6.47         | <.001     | 3.79 to 11.05             |
| ADHD+SRS+                   | 8.35         | <.001     | 4.19 to 16.65             |
| Male gender                 | 1.98         | .007      | 1.20 to 3.26              |
| Age                         | 2.04         | <.001     | 1.68 to 2.47              |
| Predictor of Other Drug Use | Odds Ratio   | P Value   | 95% Confidence Interval   |
| ADHD-SRS+                   | 6.91         | <.001     | 2.41 to 19.84             |
| ADHD+SRS-                   | 8.07         | <.001     | 4.13 to 15.80             |
| ADHD+SRS+                   | 11.96        | <.001     | 5.49 to 26.04             |
| Male gender                 | 1.28         | .422      | 0.70 to 2.35              |
| Age                         | 1.99         | <.001     | 1.59 to 2.49              |

ADHD, Attention-deficit/hyperactivity disorder diagnosis; SRS, Social Responsiveness Scale elevated score; +, present; -, absent. Bold type indicates statistical significance (p<.05)

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FIGURE  1. Fractional  polynomial  regression  plot showing  estimated  mean substance  use  score  (includes  alcohol,  tobacco,  and  other  drugs;  score standardized with mean = 0, standard deviation = 1) across the range of Social Responsiveness Scale raw scores. Red, Attention-deficit/hyperactivity disorder (ADHD) diagnosis; blue, no ADHD diagnosis. Total N = 2937 (290 with ADHD)

FIGURE  2. Fractional  polynomial  regression  plot  showing  estimated  mean alcohol use score (standardized with mean = 0, standard deviation = 1) across the range of Social Responsiveness Scale raw scores. Red, Attentiondeficit/hyperactivity  disorder  (ADHD)  diagnosis; blue, no  ADHD  diagnosis. Total N = 2937 (290 with ADHD)

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FIGURE  3. Fractional  polynomial  regression  plot  showing  estimated  mean tobacco use score (standardized with mean = 0, standard deviation = 1) across the range of Social Responsiveness Scale raw scores. Red, Attentiondeficit/hyperactivity  disorder  (ADHD)  diagnosis; blue, no  ADHD  diagnosis. Total N = 2937 (290 with ADHD)

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Drug Use (standardized)

FIGURE  4. Fractional  polynomial  regression  plot  showing  estimated  mean 'other drug' use score (standardized with mean = 0, standard deviation = 1) across  the  range  of  Social  Responsiveness  Scale  raw  scores. Red, Attentiondeficit/hyperactivity  disorder  (ADHD)  diagnosis; blue, no  ADHD  diagnosis. Total N = 2937 (290 with ADHD)

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## Results

Table  1  shows  the  prevalence  of  alcohol,  tobacco, and  other  drug  use  in  each  of  the  subject  groups. Logistic  regression  analyses  (see  Table  2)  showed that, as compared with the control group (ADHDSRS-),  adolescents  with  ADHD  were  at  elevated risk  for  alcohol,  tobacco,  and drug use  whether or not they had autistic traits above the 95th percentile. Adolescents  with  elevated  autistic  traits  were  not clearly at risk for alcohol or tobacco use unless they also had ADHD, but individuals with high levels of autistic traits were at significantly increased risk for 'other drug' use as compared with controls, regardless of whether they had ADHD. For alcohol use,  the  highest  odds  ratio (0R,  4.37) was  seen  for the ADHD+SRS- group. However, for tobacco and other drugs, ORs were highest for the ADHD+SRS+ group (8.35 and 11.96, respectively). There  were  no  clear  differences  in  SU  risk  when comparing the ADHD+SRS+  group  with the ADHD+SRS-  and  ADHD-SRS+  groups,  because the odds ratio confidence intervals overlapped. Males  were  more  likely  to  use  tobacco,  but  sex effects  were  not  statistically  significant  for  alcohol and  drug  use.  Trends  observed  in  the  fractional polynomial regression plots (see Figures 1 through 4) suggested that adolescents with the combination of ADHD and very high SRS scores (i.e., raw SRS scores of approximately ≥100) may have lower SU as compared with those with moderate SRS scores (i.e.,  raw scores in the range of 40 to 80). In those without ADHD, mean SU scores (especially in the case  of  'other  drugs')  continued  to  trend  upward with increasing SRS scores, even among individuals with the highest levels of autistic traits (see Figures

1 through 4). It is unclear whether this is partly an artifact  related  to  the  small  number  of  individuals without ADHD who have very high SRS scores.

## Discussion

Adolescents  with  ADHD  are  at  elevated  risk  for alcohol,  tobacco,  and  other  drug  use  regardless  of co-occurring autistic traits. Adolescents with autistic traits  may  have  elevated  risk  for  drug  use  (other than alcohol and tobacco), even if they do not have ADHD. However, these findings must be considered within the context of several limitations of this study.

Given the non-specificity of the wording regarding the  'other  drugs'  item, it is unclear whether the reported use of 'other drugs' refers to the  use  of  street  drugs,  the  non-medical  use  of prescription  or  over-the-counter  pharmaceuticals, or some combination of these. Because this study is based on cross-sectional data, the direction of causality  is  also  unclear.  For  example,  the  use  of cannabis could actually cause social withdrawal symptoms  that  may  increase  the  level  of  autistic traits as measured by the SRS.

Information regarding the age of onset of ADHD symptoms  and  the  current  functional  impairments related to these ADHD  symptoms was not available, thereby making it impossible to establish a strict  DSM-IV  diagnosis  of  ADHD.  As  such,  it  is possible  that  our  estimate  of  the  prevalence  of  a current diagnosis of ADHD in Missouri adolescents (9.9%)  could  have  been  higher  than  what  would otherwise  have  been  observed  if  strict  DSM-IV ADHD  criteria  had  been  applied.  Nevertheless, other studies that have recently attempted to estimate  the  prevalence  of  a  current  diagnosis  of ADHD  in  adolescent  samples  from  the  United States have reported estimates somewhat consistent with  our  own.  For  example,  in  a  parent-reportbased  national  Centers  for  Disease  Control  and Prevention survey that examined the prevalence of ADHD among American children and adolescents between the ages of 4 and 17 years in 2007 (i.e., the National Survey of Children's Health), 9.3% of the parents reported that their adolescent children had a current  diagnosis  of  ADHD  when  their  children were  between  the  ages  of  15  and  17  years,  and 10.8%  of  parents  in  Missouri  reported  that  their children had lifetime histories that included diagnosis  with  ADHD  (22).  Thus,  as  discussed  in previous  reports  (11,13),  it  may  be  possible  to generate  reliable  estimates  of  the  prevalence  of ADHD  through  the  application  of  the  DSM-IV symptom  criterion  (Criterion  A)  to  SWAN  items even  when  information regarding  the  age  of  onset or functional impairment is absent.

Our  sample  was  composed  of  adolescents  from stable families with four or more children. Families with  parents  who  were  incarcerated  for  criminal behavior were excluded from participation. Responses on CBCL items related to SU were based on  parental  report  as  opposed  to  self-report,  and the prevalence of ADHD and high levels of autistic traits is expected to be low within a general population  sample  such  as  our  own.  All  of  these factors  may  have  contributed  to  the  small  number of participants who were identified as using substances of abuse in our study. Other studies that have  examined  the  prevalence  of  SU  in  Missouribased samples of adolescents have reported a higher prevalence  of  SU.  For  example,  in  a  national  selfreport-based Centers for Disease Control and Prevention survey of adolescents in the ninth through  twelfth  grades  that  was  performed  in  the United States in 2009 (i.e., the High School Youth Risk  Behavior  Survey),  39.3%  of  Missouri-based adolescents reported that they had had at least one drink of alcohol on at least one day during the 30 days before the survey was administered, and 70.5% reported that they had ever had at least one drink of alcohol  in  their  lifetime  (23).  In  this  same  survey, 25.5% of the sample reported that they had either smoked cigarettes,  cigars,  or  cigarillos  (little  cigars) or used chewing tobacco, snuff, or dip on at least 1 day during the 30 days before the survey, and 46.5% reported that they had ever tried cigarette smoking in  their  lifetime  (even  just  one  or  two  puffs).  In addition, 20.6% of the sample reported having used marijuana  one  or  more  times  during  the  30  days before the survey, and 34.9% reported that they had ever  used  marijuana  one  or  more  times  in  their lifetime.  In  the  present  study,  we  observed  that 7.1% of parents of stable families with four or more children  in  Missouri  indicated  that  their  children between the ages of 13 and 17 years had consumed alcohol  without  parental  approval;  3.0%  of  the parents endorsed the item that asked whether their children  had  smoked,  chewed,  or  sniffed  tobacco, and  2.0%  endorsed  the  item  that  asked  whether their  children  used  drugs  other  than  alcohol  or tobacco for non-medical purposes. The discrepancies  between  these  estimates  and  those provided  by  other  studies  raise  the  concern  that parents may not have been fully aware of the their children's SU or that the prevalence of SU in stable Missouri families with four or more children that do not include parents who have been incarcerated for criminal behavior could be lower than what may be observed in the general population. Additional work that examines larger samples and that involves more detailed measures of SU behaviors across time will be needed to further explore the complex relationship between ADHD, autistic traits, and SU disorders.

Despite these limitations, it should be noted that the purpose of the present study  was not to estimate the prevalence of  ADHD  or  SU  in Missouri adolescents but rather to determine whether the level of risk for SU was elevated by the presence  of  clinically  relevant  levels  of  ADHD symptoms  and  autistic  traits.  In  this  regard,  it  is remarkable that significant associations were found. In  another  recently  published  study  that  examined the risk for self-reported regular SU in a populationrepresentative  sample  of  adult  Australian  twins  (N = 3080), clinically relevant levels of  ADHD symptoms  and  autistic  trait  scores  were  not  only associated  with  elevated  levels  of  regular  smoking and cannabis but were also associated with the risk of dependence on these substances. Although those individuals  with  high  autistic  trait  scores  were  less likely  to  report  drinking  alcohol  to  intoxication, after  the  initiation  of  the  regular  use  of  alcohol, these individuals were at elevated risk for developing  alcohol  dependence,  even  when  the influence of  ADHD  was  controlled  (24). This finding highlights the importance of examining the risk  for  SU  in  individuals  with  clinically  relevant symptoms  of  ADHD  and  autistic  traits.  These individuals may not only be at risk for SU, but they may  also be at risk for  dependence  on  these substances after the initiation of their use.

## Clinical Significance

Mental  health  providers  should  not  assume  that adolescents with mild to moderate ASD are at low risk  for  SU  (25),  especially  if  they  have  coexisting ADHD.  Monitoring for SU is important for individuals with ADHD or ASD and particularly in those who exhibit symptoms of both disorders. At this time, is it unclear whether SU treatment should be modified in any way for individuals with ASD or autistic traits, so this topic is also worthy of further investigation.

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## Acknowledgements

This work was supported by grant numbers MH067921, MH080287, and MH083823 from the National Institute of Mental Health and by a grant from the McDonnell Center for Systems Neuroscience. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

## Disclosures

All  authors  have  received  research  grant  support from  the  National  Institutes  of  Health.  AMR  has received grant support from the McDonnell Center for Systems Neuroscience and the McDonnell Center  for  Cellular  and  Molecular  Neurobiology, travel  support  and  speaker's  honorarium  support from the American Academy of Child & Adolescent Psychiatry,  and  travel  support  from  the  Child  and Adolescent Psychiatric Department, Region Zealand  (a  hospital  in  Denmark),  to  attend  and speak  at  a  scientific  meeting.  None  of  the  authors have financial relationships with commercial industry.