Psychopathology in Youngsters with Autism Spectrum Disorders

Home , Autism, Autism spectrum, Specific social phobia

PSYCHOPATHOLOGY IN YOUNGSTERS WITH AUTISM SPECTRUM DISORDERS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By Andrea N Witwer, M.A. * * * * *

The Ohio State University 2009

Dissertation Committee:

Dr Luc Lecavalier, Adviser

Dr. Michael G. Aman

Dr. Mary Fristad

Dr. David Hammer

APPROVED BY

______ADVISER GRADUATE PROGRAM IN PSYCHOLOGY

ABSTRACT

The primary purpose of this study was to examine the reliability and validity of the Children’s Interview for Psychiatric Syndromes-Parent Version (P-ChIPS).

Reliability of the P-ChIPS was examined through interrater reliability (i.e., degree of agreement between raters) and internal consistency analyses. Concordant validity was explored by examining the agreement between the P-ChIPS and the Child and Adolescent

Symptom Inventory (CASI). Convergent validity was examined by measuring the relationship between P-ChIPS-derived diagnoses and Nisonger Child Behavior Checklist

(NCBRF) problem behavior and prosocial subscales. The impact of IQ, language, and age on these analyses were also examined. The second purpose of this study was to elucidate the clinical picture of psychiatric disorders in this population. This was done by examining the rates of symptoms and disorders, the presence of subsyndromal diagnoses, and behavioral equivalents. Parents of 61 children and adolescents (mean age 11.22±

3.80; range 6-17) with autism, Asperger’s disorder, and PDD-NOS were interviewed with the P-ChIPS and Autism Diagnostic Interview-Revised (ADI-R) and completed the CASI and NCBRF. The youngsters were administered the Stanford-Binet V IQ test. Interrater reliability kappa values were largely in the good to excellent range. Internal consistency values were good for ADHD, ODD, Social Phobia, Depression and Mania (.89-.86), but below acceptable values for Obsessions (.30) and Compulsions (.65). Concordance

between the P-ChIPS and the CASI was fair (i.e., .41 < k < .57) for the majority of

disorders. Percent overall agreement for most disorders was good with values at or above

70%, lending support to the concordant validity of the P-ChIPS. P-ChIPS derived diagnoses as a whole converged as expected with related NCBRF subscales.

Subsyndromal analyses suggested that some modifications may be needed to diagnostic criteria cutoffs. Behavioral equivalent analyses were largely nonsignificant. The P-

ChIPS appears to be appropriate for this population although some modifications may be

necessary for those without language or IQ less than 70.

iii

Dedicated to my family.

ACKNOWLEDGMENT

I would like to thank the many people who have provided support throughout the

completion of this dissertation. First, I would like to thank Dr Luc Lecavalier, my

advisor, for his guidance throughout the development, implementation, and analysis of

this study. He provided thoughtful feedback while also allowing me independence to

complete the project the way I saw fit. I have learned an immeasurable amount from him

throughout graduate school and for that I will always be grateful.

I am grateful to my family for their love and unwavering support throughout this process and my graduate years. Without all of you this would not have been possible.

I would also like to thank the many families who took time out of their hectic

lives to participate in the study. Without them this project would not have been possible.

This research was supported in part by a grant from the Ohio Department of

Mental Health and the Ohio State University Alumni Grant for Graduate Research and

Scholarship.

v VITA

October 29, 1978…………………………………………… Born Wadsworth, Ohio

2001…………………………………………………………B.S Psychology, Ohio State University

2001-2003…………………………………………………..Clinical Research Coordinator Ohio State University

2003-2004………………………………………………….. University Fellow, Ohio State University

2004-2005…………………………………………………...Graduate Teaching Associate Ohio State University

2005…………………………………………………………..M.A. Psychology, The Ohio State University

2005-2009…………………………………………………….Graduate Associate, Ohio State University

PUBLICATIONS Aman, M.G., Arnold, L.E., Ramadan, Y, Witwer, A.N., & Lindsay, R., et al. (2005). Randomized, controlled, crossover trial of methylphenidate in pervasive developmental disorders with hyperactivity. Archives of General Psychiatry, 62, 1266-1274.

Witwer, A. & Lecavalier, L. (2005). Treatment incidence and patterns in children and adolescents with autism spectrum disorders. Journal of Child and Adolescent Psychopharmacology, 15, 671-681.

Arnold, L.E., Aman, M.G., Cook, A.M., Witwer, A.N., Hall, K., Thompson, S., & Ramadan, Y. (2006). Atomoxetine for hyperactivity in autistic spectrum disorders: Placebo-controlled crossover pilot trial. Archives of General Psychiatry, 45, 1196- 205.

vi Witwer, A.N. & Lecavalier, L. (2007). Autism screening tools: An evaluation of the Social Communication Questionnaire and the Developmental Behaviour Checklist- Autism Screening Algorithm. Journal of Intellectual & Developmental Disability, 32, 197-188.

Witwer, A.N. & Lecavalier, L. (2007). A response to John Taffe’s commentary on an evaluation of the SCQ and DBC-ASA. Journal of Intellectual & Developmental Disability, 32, 189.

Posey D.J., Aman M.G., McCracken J.T., Scahill L., Tierney E., Arnold L.E., Vitiello B., Chuang S.Z., Davies M., Ramadan Y., Witwer A., Swiezy N.B., Cronin P., Shah B., Carroll D.H., Young C., Wheeler C., & McDougle C.J. (2007). Positive effects of methylphenidate on inattention and hyperactivity in pervasive developmental disorders: An analysis of secondary measures. Biological Psychiatry, 61, 538-544.

Witwer, A.N. & Lecavalier, L. (2008). Psychopathology in children with intellectual disability: Risk markers and correlates. Journal of Mental Health Research in Intellectual Disabilities, 1, 75–96.

Witwer, A.N. & Lecavalier, L. (2008). Examining the validity of autism spectrum disorder subtypes. Journal of Autism and Developmental Disorders, 38, 1611-1624.

Jahromi, L.B., Kasari, C.L., McCracken, J.T., Lee, L.S-Y., Aman, M.G., McDougle, C.J., Scahill, L., Tierney, E., Arnold, L.E., Vitiello, B., Ritz, L., Witwer, A., Kustan, E., Ghuman, J., & Posey, D. (2009). Positive effects of methylphenidate on social communication and self-regulation in children with pervasive developmental disorders and hyperactivity. Journal of Autism and Developmental Disorders, 39, 395–404.

FIELDS OF STUDY Major Field: Psychology Specialization: Intellectual and Developmental Disabilities

vii TABLE OF CONTENTS

Page Abstract ...... ii

Acknowledgments...... v

Vita ...... vi

List of Tables...... ix

Chapters:

1. Introduction...... 1

2. Method ...... 20

3. Results...... 31

4. Discussion...... 69

References ...... 93

Appendices:

Appendix A: Demographic Form……………………………………………………104

Appendix B: Consent Form………………………………………………………….108

Appendix C: Release of Information……………………………………………….112

Appendix D: Assent Form………………………………………………………...…114

viii LIST OF TABLES

Table Page 1 Child and Informant characteristics………………………………..…………….25

2 Interrater agreement on the P-ChIPS (n=30)…………………………………….32

3 Interrater Agreement based on IQ………………………………………………..34

4 Interrater Agreement based on Age…………………………………………...…35

5 Internal Consistency of P-ChIPS items by diagnostic category (N=61)……...... 36

6 Agreement between CASI and P-ChIPS (N=61)………..……………………....38

7 Impact of IQ on Agreement between CASI and P-ChIPS…………………….…39

8 Agreement between CASI and P-ChIPS based on language (N=61)…………....40

9 Agreement between the CASI and P-ChIPS Based on Age……………………..41

10 NCBRF Means and Standard Deviation by Diagnostic Categorization………....43

11 Correlations between NCBRF subscales and P-CHIPS symptom counts……….46

12 Rates of children meeting P-ChIPS diagnostic criteria (N=61)………………….47

13 Rates of children with ID and without ID meeting P-ChIPS diagnostic criteria (n=58)…………………………………………………………………………….48

14 Rates of those with and without language meeting P-ChIPS diagnostic criteria...49

15 Rates of those above and below 12 years of age meeting P-ChIPS diagnostic criteria………………………………………………………...………………….50

16 Frequency of ADHD Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age…………………………………………………….…………..53

17 Frequency of ODD Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age………………………………………………..……………….55

18 Frequency of Conduct Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age………………………………………………...………………56

19 Frequency of Specific/Social Phobia Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age…………...……………………………57

20 Frequency of Separation Anxiety Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age……………………………………...…...……58

21 Frequency/Percentage of Generalized Anxiety and Obsessive Compulsive Disorder Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age………………………………………………………………...…………59

22 Frequency/Percentage of Major Depressive Disorder/Dysthymia Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age………………....60

23 Frequency/Percentage of Mania Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and …………………………………………..………..63

24 Rates of Individuals with Symptoms Endorsed, Impairment Noted and Meeting Full Diagnostic criteria ……...…………………………………………………..66

CHAPTER 1

INTRODUCTION

Psychopathology (i.e., psychiatric and behavioral problems) in children and adolescents with Autism Spectrum Disorders (ASDs) has received increased attention in recent years (e.g., Gadow, DeVincent, Pomeroy, & Azizian, 2004; Gadow, DeVincent,

Pomeroy, & Azizian, 2005; deBruin, Ferdinand, Meester, de Nijs, & Verheij, 2007;

Simonoff, Pickles, Charman, Chandler, Loucas, & Baird, 2008; Tonge & Einfeld, 2003).

Research has shown that youngsters with ASDs present with high rates of behavior and emotional problems, including tantrums, mood swings, aggression, self-injury, and irritability (Lecavalier, 2006; Tonge & Einfeld, 2003). Psychiatric disorders are also quite prevalent; the most commonly-reported are disruptive behavior, mood, and anxiety disorders (deBruin et al., 2007; Kim, Szatmari, Bryson, Striener, & Wilson, 2000; Leyfer et al., 2006; Muris, Steerneman, Merckelbach, Holdrinet, & Meesters, 1998; Simonoff et al., 2008; Szatmari, Bartolucci, & Bremner, 1989; Wozniak et al., 1997).

Psychopathology impacts not only youngsters but those around them and society.

For instance, it has been linked to significant increases in parent/teacher stress

(Lecavalier, Leone, & Wiltz, 2006) and parental depression/anxiety (Hastings & Brown,

2002). It often requires behavioral or pharmacological treatment. Impairment can be severe enough to require more intensive supports and restrictive school placement than a youngster’s intellectual level alone would predict. Since 1995, agencies such as the

Department of Health and Human Services and the National Institute of Health have

made ASDs research a priority, recognizing the significant impact they have on the

individuals, those around them, and society. From 1995 to 2001, research funding for

autism quintupled from $11 to $56 million annually (Newschaffer & Curran, 2003) and continues to increase.

Despite the recent increased interest in psychopathology, the taxonomy of psychiatric disorders in ASDs remains largely unstudied (Lecavalier and Gadow, 2008).

It is unclear if behaviors and symptoms of those with ASDs are features of Diagnostic and Statistical Manual-Fourth Edition (DSM-IV; American Psychiatric Association,

2000) defined psychiatric disorders or separate clusters of behavior which appears similar but are really part of the ASD diathesis (i.e., a phenocopy of psychiatric disorders).

Attention Deficit Hyperactivity Disorder (ADHD) illustrates this point well. The DSM-

IV instructs that ADHD is not to be diagnosed in those with ASDs presumably because it is considered a phenocopy rather than a separate disorder. Despite this, clinicians often diagnose it in individuals with ASDs. Additionally, a study found that children with

ASDs present differentially with DSM-IV ADHD subtype symptoms (Gadow,

DeVincent, Pomeroy, 2006). ADHD symptoms have also been the subject of pharmacological trials (Posey, Aman, McCracken, Scahill, Tierney et al., 2007). It is

clear that ADHD symptoms differentially impact a subset of children with ASDs. The

lack of clarity and discrepancy between current nosology and clinical/research realities

hampers research on assessment, etiology, and treatment.

Potential Obstacles in Taxonomic Research

Difficulties with diagnostic assessment due to language impairments and

intellectual disability (ID) make research in this area complex. In typically-developing

youngsters, clinicians rely to a certain extent on children’s verbal reports of experiences

and emotions. Individuals with ASDs, by definition, have qualitative impairments in

receptive and expressive language (American Psychiatric Association, 2000), with a

significant proportion of them nonverbal. Even mild impairments in language can make

discussion of abstract concepts and subtle differences in emotion difficult to detect and

assess (Fletcher, Loschen, Stavrakaki, & First, 2007). The lack of speech in some with

ASDs makes it difficult to determine the presence of many DSM-IV symptoms (Einfeld

& Aman, 1995). The high prevalence of ID in individuals with autism also complicates

assessment. The intellectual limitations of some individual with ASDs may lead these

individuals to have difficulty understanding and expressing the more complex cognitive

phenomena that occur in some conditions (e.g., anxiety disorders; Cooray, Gabriel, &

Gaus, 2007; Findlay & Lyons, 2001; Fletcher, et al., 2007).

Two consequences of these obstacles seen in the ID population are diagnostic

overshadowing (Reiss, Levitan, & Szysko, 1982) and the reliance on behavioral

equivalents. In diagnostic overshadowing, an individual’s unusual behavior is attributed by the clinician to developmental or social delays. The developmental disability (e.g., ID or ASD) has the potential to overshadow other psychiatric diagnoses in the eyes of

clinicians and researchers, thereby being attributed to the developmental disability. This

phenomenon has received virtually no attention in the ASD field, but may be present.

Lack of language and intellectual impairment often require evaluators to rely on

third parties for basic information on the behavior and emotions of individuals with ID

and ASDs. Because of this, many within the ID field use diagnostic equivalents (Hurley,

Levitas, Lecavalier, & Pary, 2007). They equate DSM-IV criteria with proposed

alternatives that are compatible with the individual’s limited communication and

cognitive delays. These equivalents are based on observable behaviors. Guidelines for

incorporating behavioral equivalents into psychiatric diagnosis in ID have emerged

(Fletcher, et al., 2007; Royal College of Psychiatrists, 2001). Some have suggested that

behavioral equivalents for depression include property destruction, aggression, self-

injury, spitting, yelling, refusing preferred activities, lost response to reinforcers, and

stealing or obsessing about food (Charlot, Fox, Silka, Hurley, Lowry et al., 2007).

Examples for mania include loud inappropriate laughing, excessive giddiness, more

talkative or more noisemaking/screaming (Charlot et al., 2007).

There is a paucity of research on these symptoms or behavioral equivalents

(McBrien, 2003). There has been some limited research in the ID field on behavioral

equivalents for depression. Three studies evaluated the relationship between behavioral

equivalents and DSM-IV/ICD-10 derived diagnoses of depression and found

relationships between depression and aggression (Marston, Perry, & Roy, 1997; Reiss &

Rojahn, 1993), self-injury (Marston, et al., 1997), stereotypy (Matson, et al., 1999) and

screaming (Marston, et al., 1997). Marston et al. found that those with moderate ID diagnosed with depression were more likely to have self-injurious behavior than those without the diagnosis. Those with severe/profound ID and depression were more likely to

engage in aggression, screaming, and self-injurious behavior. Using the Diagnostic

Assessment for the Severely Handicapped Scale to assess behavioral equivalents, Matson

et al. (1999) found that 40% of those diagnosed with depression exhibited tantrums,

stereotypy, and echolalia. Rates were not reported for those not diagnosed with

depression. In contrast to the results of these studies, Tsiouris, Mann, Patti, and Sturmey

(2003) found that depressed and non-depressed individuals did not differ on items

assessing potential behavioral equivalents (i.e., self-injury, aggression, screaming) of

depression as measured by the Clinical Behavior Checklist for Persons with Intellectual

Disability.

These studies evaluated behavioral equivalents against DSM-IV/ICD-10

diagnoses. They all used standard checklists to assess behavioral equivalents. However, it

is not clear specifically how clinicians derived the depression diagnosis. In all three

studies, diagnostic criteria appear to have been assessed via unstructured interview. It is possible that the clinician used parent/caregiver reports of aggression, screaming, and

self-injury to arrive at the original diagnosis. This illustrates the potential for circularity

in the behavioral equivalents domain.

In contrast to these studies, Reiss and Rojahn (1993) used a rating scale to assess

both depression and behavioral equivalents. Using the Reiss Screen for Maladaptive

Behavior and the Reiss Scales for Children’s Dual Diagnosis, they found that children

and adults meeting the instruments’ criteria for depression exhibited a four-fold increase

in scores of aggression compared to those not meeting criteria. Depression items assessed

irritability, anxiety, sadness, energy level, eating, sleeping, stress, and loss of enjoyment.

In this case, diagnoses were derived in a systematic manner and most symptoms assessed are consistent with DSM-IV criteria for major depression. However, some symptoms

assessed by the Reiss instruments are not directly associated with depression criteria

(e.g., anxiety and stress). Additionally, the psychiatric status of the comparison group

was not reported. It is not clear if those in the comparison group were free of any psychiatric disorder or if they had other psychiatric disorders (e.g., GAD). Therefore, it is

impossible based on their results to determine if aggression was a specific marker for

depression, or an indicator of general dysfunction. Finally, the instrument relies on caregivers' interpretation of items and the individual’s behavior, with no clinician input.

In addition to inconsistent assessment methods, sample characteristics of these studies vary widely. Some samples included a small number of individuals with autism, others did not report diagnoses. One study included children and adults with mild to profound ID (Reiss & Rojahn, 1993). Another study limited their sample to adults with severe to profound ID (Matson, et al., 1999).

Equivocal results have led to disagreement on how behavioral equivalents should be applied to DSM-IV disorders in individuals with ID. Some propose using substitute criteria for fear of missing cases due to the individuals’ inability to report emotions

(McBrien, 2003). Others suggest adding these as criteria in addition to the established

DSM-IV criteria. Alternatively, some suggest that behavior problems are non-specific indicators of distress and dysfunction and should be used as indicators of potential underlying psychopathology rather than as a characteristic or criterion for specific

diagnoses (Tsiaris et al., 2003). As with diagnostic overshadowing, behavioral

equivalents in ASDs have received very little attention from researchers. Systematic

research with standardized clinician-completed instruments is needed within the field of

ID, as well as in ASDs, before definitive conclusions can be drawn.

Clearly, the obstacles of assessment complicate psychopathology research in

ASDs and lead to confusion among clinicians and researchers. In a 2000 survey, field experts reported that they felt they could only sometimes reliability diagnose psychiatric disorders in those with ID (Rush & Frances, 2000). Despite research in this area, a clear diagnostic procedure still has not been agreed upon for those with ID or ASDs. Cognitive and language impairments may render some diagnostic criteria not applicable or impossible to assess. Behavioral equivalents and diagnostic overshadowing have the potential to impact ASD psychopathology research as they have the ID field. However, there is a lack of research addressing these topics in ASDs. Many ASD studies have appeared to side-step the problem by including only high-functioning individuals (i.e.,

IQ>70) in their samples (e.g., Kim et al., 2000; Leyfer et al., 2006; Verte et al., 2006).

Assessment concerns highlight the importance of including those with ID in ASD psychiatric research and the necessity for standardized measurement.

Moving Toward Valid Assessment and Taxonomy

A reliable and valid taxonomy of psychopathology facilitates research by summarizing symptoms under a category, allowing for research on its clinical features, etiology, treatment, and prognosis (Sturmey, 1998). Support for such taxonomy in ASDs is lacking. Different researchers and clinicians evaluating a child with an ASD must be able to make reliable psychiatric diagnoses. However, currently a mish-mash of both

DSM-IV disorders and behavior problems are used interchangeably to characterize those with ASDs, both clinically and in research (Lecavalier & Gadow, 2008).

The reliability and validity of current DSM-IV criteria need to be examined in

ASDs. This process will be extremely complex and will likely require multiple research

strategies (Gadow, et al., 2006). Robins and Guze (1970) proposed a process to facilitate

valid classification which included the following phases: clinical description, laboratory, follow-up, and family studies. The first step described consists of defining the clinical picture. This includes describing the hallmark and associated features. After the clinical picture is elucidated, differences on external factors, such as treatment response, family functioning, and adaptive behavior would provide further support for the validity of the classification system. Follow-up studies examining marked differences in outcome, and family/genetic studies could provide further evidence for the validity of respective disorders. Applying Robins and Guze’s model, the first step toward valid psychopathology taxonomy for ASDs will be to elucidate the clinical description of disorders.

An important aspect of describing the clinical picture is the ability of professionals to apply criteria reliably in light of the many obstacles discussed above.

Reliability is the precision and lack of distortion of measurement (Kerlinger & Lee, 2000) and is a necessary pre-requisite for a diagnostic classification system to be useful in research (Cantwell, 1996). Researchers cannot have an accurate clinical picture of a disorder or examine its relationship with external variables if they cannot measure them reliably. The wide range of assessment methods used currently in the ASD field make reliability among different clinicians and researchers difficult to achieve.

ASD Psychopathology Research

Some researchers have examined psychiatric symptoms/diagnoses in ASDs

(deBruin et al., 2007; Gadow et al., 2004; Gadow et al., 2005; Ghaziuddin, Weidmer-

Mikhail & Ghaziuddin, 1998; Kim et al., 2000; Leyfer et al., 2006; Muris, et al., 1998;

Simonoff et al., 2008; Szatmari, et al.,1998; Wozniak et al., 1997). The majority of these studies have been clinical reports examining the presence of psychiatric disorders

(Gadow et al., 2004; Gadow et al., 2005; Ghaziuddin, Weidmer-Mikhail & Ghaziuddin,

1998; Muris et al., 1998; Simonoff et al., 2008; Szatmari et al., 1989; Verte et al., 2006) and their subtypes (Gadow et al., 2006). These studies report a wide range of prevalence rates (i.e., ADHD: 17-74%, Bipolar Disorder: 2-27%, Major depression/Dysthymia: .9-

37%, Generalized anxiety disorder (GAD): 2-13%, Obsessive Compulsive Disorder

(OCD): 1-46%, Oppositional Defiant Disorder (ODD): 1-30%, Separation anxiety: .5-

13%, and Specific phobia: 8.5-44%).

The inconsistency of assessment methods may be contributing to the wide range of psychiatric disorder rates reported by studies. Some researchers have modified criteria to make them more applicable to ASDs (Leyfer et al., 2006), adding in behavioral equivalents and excluding questions when not applicable due to intellectual impairment.

These modifications are usually not explicitly described, making replication and comparisons difficult. Others have used existing measures such as the Diagnostic

Interview Schedule of Children (DISC; Costello et al., 1984). the Schedule for Affective

Disorders and Schizophrenia for School-Age Children (K-SADS; Puig-Antich &

Chambers, 1978) and the Child and Adolescent Psychiatric Assessment (CAPA; Costello,

Mustillo, Erkanli, Keeler, & Angold, 2003). However, it is not clear how symptoms

requiring language and insight are assessed in those with cognitive and language

impairments with these instruments. The difficulties with assessment noted above highlight the many potential places for error in this process.

Some researchers use empirically-derived and validated dimensional measures of

emotional and behavior problems such as the Nisonger Child Behavior Rating Form

(NCBRF; Aman, Tasse, Rojahn, & Hammer, 1996), Developmental Behaviour Checklist

(DBC; Einfeld & Tonge, 1992, 2002) and Child Behavior Checklist (CBCL; Achenbach

& Rescorla (2001) in ID samples. These instruments offer some advantages, but they are not based on DSM-IV criteria. Also, the content among them varies. One can surmise what types of questions might be included in the subscales, but the specific symptoms assessed differ and areas of impairment are not always clear. This makes comparisons among studies difficult.

Little has been done to examine the reliability and validity of existing DSM-IV

criteria in ASDs. Clinical report of the presence of psychiatric disorders is an important

research topic. However, it is important to know how psychiatric disorders manifest in

ASDs before drawing conclusions from these types of studies. There has been little

attention to the specific symptoms exhibited by those with ASDs. For instance, it is not clear how feelings of worthlessness and guilt associated with depression or worry associated with the anxiety disorders manifest in this population. It is not clear how researchers apply this type of criteria when dealing with nonverbal children or those with significant cognitive impairments. This is largely because most study samples have consisted of high-functioning (non-ID) individuals with ASD (Kim et al., 2000; Leyfer et al., 2006; Verte et al., 2006) and have therefore bypassed these concerns.

Studies have taken initial steps to examine the validity and reliability of DSM-IV

criteria. Some have examined specific disorders (i.e., Gadow, DeVincent, & Pomeroy,

2006; Gadow, DeVincent, Drabick, 2008; Wozniak et al., 1997) and others have widened

their investigation to include multiple disorders (Lecavalier, Gadow, DeVincent, and

Edwards, 2008; Leyfer et al., 2006).

Gadow et al. (2008) examined differences among those with ASD who had

ADHD and ODD, ODD only, ADHD only, and neither diagnosis. Those with both

ADHD and ODD had the most comorbid disorders and medication use. Those with ODD

were more likely than those without ODD (ADHD-only or neither diagnosis) to have

comorbid disorders. The symptom patterns and differential relationships found in these

studies provide tentative support for the validity of ADHD and ODD in ASDs. Gadow et

al. (2006) found tentative support for ADHD subtypes in 278 children with ASDs. They

found that those with ADHD subtypes were markedly different from one another in terms

of comorbid disorders, medication, and home environment variables. Patterns were

similar to those observed in typically-developing children. There was a greater

association of oppositional and aggressive behavior with hyperactive and inattentive

symptoms and greater home adversity in the ADHD-combined type.

Wozniak et al (1997) examined the reliability and validity of mania in ASDs

using the K-SADS paired with expert assessment. Interrater reliability was high (kappa

coefficient = .91) for the three psychiatrists. Nine percent of their pool of psychiatric

referrals had an ASD. Twenty-one percent of those with ASD also met criteria for mania.

They compared children with ASD and mania (n=14) to those with ASD-only (n=38) and

mania-only (n=114). Children with both ASD and mania had significantly higher rates of

MDD, anxiety disorders, CD, ODD and psychosis than children with ASD without

mania. The rates of disorders in those with ASD and mania were similar to manic

children without an ASD diagnosis. Their study provides initial support for the validity of

mania in children with ASD.

Simonoff et al., 2008 examined the prevalence rates of mood, disruptive behavior and anxiety disorders and their relationship to risk factors in a population-based sample of 112-ten to fourteen-year-olds using the Child and Adolescent Psychiatric Assessment

(CASI; Gadow & Sprafkin, 1994, 2002). Seventy percent had at least one comorbid

psychiatric disorder. Most common disorders were social anxiety disorder (29%), ADHD

(28%), and ODD (28%). They found no relationship between psychiatric disorders and

external factors such as autism severity, IQ, adaptive behavior, or parental characteristics.

Lecavalier, et al. (2008) examined the internal construct validity of the DSM-IV

with confirmatory factor analysis. Largely, their analyses supported the validity of

ADHD, ODD, CD, GAD, and Depression as diagnostic categories in school age children with ASDs (n=494). Indices of fit were not significantly different than those observed in typically developing children. Of note, indices of fit improved in the subsample of children with IQ ≥70.

Assessment of Psychiatric Disorders

Researchers thus far have used a number of different methods to identify

symptoms of DSM-IV disorders and their validity (i.e., unstructured/structured clinical

interviews and ratings scales) and have shown that these methods have potential utility in

studying the clinical presentation of psychopathology in youngsters with ASDs. The most

common assessment methods used in research are structured interviews and rating scales.

Unstructured clinical interviews can be unreliable when assigning individuals to diagnostic categories due to their lack of clarity on decision rules, the operation of confirmatory biases, and other potential judgment errors (Mash & Terdal, 1997). Parent- and teacher-completed DSM-IV-based rating scales are often used as a guide for parent interviews, allowing for efficiency and comprehensiveness during the interview. They also sometimes offer the advantage of a severity scale. An example of this is the Child and Adolescent Symptom Inventory (CASI; Gadow & Sprafkin, 1994, 2002) used in the

Gadow et al. studies detailed above.

Structured interviews are considered the gold standard for research assessment of

psychiatric disorders. They systematically assess symptoms of individual disorders by

using specifically-worded questions and explicit criteria to assess responses (Teare,

Fristad, Weller, Weller, & Salmon, 1998). This reduces clinical judgment and error and

increases reliability (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000).

A small number of structured interviews assessing existing DSM-IV criteria have

been used in ASD samples, but little attention has been paid to their reliability and

validity in this population. Research is needed to examine the validity of instruments in

this population. Some of the items and/or scoring algorithms may not be valid or optimal.

One study (Leyfer et al., 2006) has examined the criterion validity and reliability

of a DSM-IV-based interview and examined some issues related to the validity of

psychiatric disorders in ASDs. Leyfer et al. assessed the lifetime presence of mood,

disruptive behavior, and anxiety disorders in 109 autistic children 5-17 years old with a

modified version of the Schedule for Affective Disorders and Schizophrenia for School-

Age Children (K-SADS; Puig-Antich & Chambers, 1978), the Autism Comorbidity

Interview (ACI). Two-thirds of the sample had a full scale IQ ≥70. They modified the K-

SADS to include descriptions of how disorders are typically manifested in autism and added questions assessing observable features (i.e., behavioral equivalents) believed to occur commonly with specific psychiatric disorders [these modifications were not detailed, and the tool is not publicly available at the time of this writing]. Criterion validity was established by examining agreement between psychiatric treatment and the

ACI results. Concurrent validity was established with high correlations between the impairment score for compulsions on the ACI and the Autism Diagnostic Interview-

Revised (ADI-R) Repetitive Behavior domain. Inter-rater reliability was high for MDD,

OCD, and ADHD. Test-retest of lifetime diagnosis was calculated over a two to six-year period with values ranging from .61-.75.

Leyfer et al. also explored the presence of subsyndromal disorders. Attention toward subthreshold diagnoses is important. It is possible that some individuals may have impairments related to a disorder and benefit from treatment, but not meet full criteria due to the inability of clinicians to assess certain symptoms. In their study, the subsyndromal category identified children who were impaired and could benefit from treatment, but fell just short of full diagnostic criteria. The most common subsybdromal disorders were ADHD (25%), MDD (14%), Separation Anxiety Disorder (7%), and OCD

(6%).

The specific criteria used by Leyfer et al. to derive subsyndromal categories (i.e., how many symptoms were required) were not described. They did define impairment.

Episodic syndromes (e.g., MDD) were considered impairing if they were associated with significant impairment that was in addition to baseline level of impairment. Non-episodic

disorders (ADHD) were considered impairing if they were associated with impairment above and beyond impairment associated with the core social, communication, and repetitive features of ASDs. However, they did not outline how this was presented to the informant or address the ability of informants to separate impairment due to ASD symptoms from those of comorbid disorders.

The Leyfer et al. (2006) study took important steps by examining the reliability and validity of a structured interview. How psychiatric disorders manifest themselves in those with ASDs is an important aspect of psychopathology research in ASDs and is at the heart of its taxonomy. Research is needed to examine if unmodified DSM-IV criteria are applicable and can be assessed reliability in those with ASDs. Any subsequent modifications to symptom descriptions or criteria should be supported by data and listed explicitly, allowing for replication. As with other studies, the Leyfer et al. (2006) sample included a large proportion of high-functioning individuals (68% with Full Scale

IQs>70). Clearly IQ has the potential to impact the validity of assessment. Future research needs to include children with ID and examine how factors such as IQ and language influence validity and reliability. It is likely that ASDs are too heterogeneous in terms of language and intellectual functioning for any single set of criteria to be applied, but research needs to address the impact of these variables before any conclusions are drawn. The Leyfer et al. study also highlights the need to study those who fall short of diagnostic criteria (subsyndromal) but have significant impairment associated with disorder symptoms. Specific criteria for subsyndromal disorders and the method used to measure impairment should be clearly delineated to allow for replication.

Children’s Interview for Psychiatric Syndromes-Parent Version (P-ChIPS)

A structured interview with some advantages over those used thus far is the

Children’s Interview for Psychiatric Syndromes-Parent Version (P-ChIPS; Fristad, Teare,

Weller, Weller, & Salmon, 1998). It is a structured interview designed to assess psychopathology concisely according to DSM-IV criteria in clinical and epidemiological research with children and adolescents 6 to 18 years old. Fristad et al. (1998) reported a sensitivity of .87 and a specificity of .76 across disorders, and an average P-ChIPS- clinician kappa coefficient of .49. These psychometric properties are comparable to those of other structured interviews. Among the advantages are short administration time, clear operational representations of DSM-IV criteria, limited training requirements, and a condensed scoring sheet rather than a booklet (Teare et al., 1998). The P-ChIPS’ characteristics suggest it might have promise for use in ASD psychiatric research. First, however, research on its reliability and validity in ASDs is needed.

Current Study

The current study examined the reliability and validity of the P-ChIPS. Reliability of the P-ChIPS was examined through interrater reliability (i.e., degree of agreement between raters) and internal consistency analyses. Concordant validity was explored by examining the agreement between the P-ChIPS (a clinician-completed measure) and the

CASI (a parent-completed measure of psychiatric disorders) (Gadow, 1997). The impact of IQ, language, and age on interrater reliability and concordance were also examined.

Convergent validity was examined by measuring the relationship between P-ChIPS- derived diagnoses and NCBRF problem behavior and prosocial subscales.

The second purpose of this study was to elucidate the clinical picture of psychiatric disorders in this population. This was done by first examining the rate of

diagnoses and symptoms endorsed, and exploring the impact of IQ, language, and age on

frequency of endorsement. Second the presence of subsyndromal diagnoses and the

impact of IQ and language were explored. Frequency counts were taken of those who

were subsyndromal (i.e., had symptoms of the disorder and related impairment, but fell

short of full criteria by one or two symptoms) and explored how these varied based on

IQ, language, and age. Finally, the validity of behavioral equivalents was explored.

Behavioral equivalent analyses were done by measuring the association between

internalizing disorder diagnoses and specific behavior problems as assessed by the

NCBRF. The NCBRF includes a number of concrete items that assess prosocial and

problem behaviors. This endeavor corresponds to the first step of Robins and Guze’s

(1970) process, clinical description.

This study was the first to examine unmodified DSM-IV criteria in individuals

with ASDs and to explore systematically the impact of language, IQ, and age on the

validity of the P-ChIPS, a diagnostic instrument based on the DSM-IV. This study also

built on subsyndromal and behavioral equivalent research. Only one other study has

examined subthreshold diagnoses and the criteria used were not published. This is the

first study to define clearly the term subsyndromal and the implications for classification

in ASDs. Previous behavioral equivalence studies have used unstructured interviews to

assess DSM-IV criteria and have focused on ID samples, only including a small

proportion of individuals with ASDs. Here, criteria were assessed systematically by using

a structured interview in an ASD sample.

Research Questions and Hypotheses

Primary Aim: Examine the reliability and validity of the P-ChIPS

This study examined the Inter-rater reliability between independent coders of the

P-ChIPS. It was hypothesized that inter-rater reliability would be good (i.e., kappa values

and ICC correlation coefficients above .60). It was hypothesized that reliability would be

lower for those with ID and without expressive language.

Internal consistency of individual disorders was measured. P-ChIPS internal

consistency was hypothesized to be good to excellent for externalizing disorders and fair

to good for internalizing disorders.

Concordant validity was examined by measuring the level of agreement between

the P-ChIPS diagnoses and the CASI. It was hypothesized that values would be adequate,

but lower than those found in non-ASD samples. The impact of subject characteristics

(i.e., IQ, language, and age) on agreement was also explored. It was hypothesized that

kappa statistics would be lower for those with ID and without expressive language.

Convergent validity was examined by correlating P-ChIPS and NCBRF subscale

scores. It was hypothesized that relationships would be manifested by differential

correlations across subscales. Relationships would be strongest/significant for those

symptoms that rely more of parent observation of overt behaviors (e.g., symptoms of

disruptive behavior disorders) than for those that require insight into internal states (e.g., symptoms of anxiety disorders).

Secondary Aim: Elucidation of Clinical Picture

First, frequency counts of those meeting diagnostic criteria for disorders and symptoms endorsed were examined. An important aspect of this was to examine the impact of IQ, language, and age on rates of disorders and symptoms endorsed. This provides valuable information about the symptoms and their applicability in children with

ASDs. It was hypothesized that items requiring language would be endorsed at a lower frequency. IQ was hypothesized to impact significantly on internalizing disorder symptoms.

Frequency counts were also derived for those who were: 1) above proposed cutoffs (symptom, duration, and impairment criteria met), 2) were above symptom count criteria 3) fell short of proposed cutoffs by one or two symptoms and impairment was reported (subsyndromal), and 4) those who had at least one impairing symptom. It was hypothesized that a significant number of symptoms would not be present in those with

IQ and language impairments leading to larger proportion of those with ID being subsyndromal.

This study also explored the distribution of specific behaviors (self-injury, aggression, and stereotypy) among those above the clinical cutoffs for P-ChIPS-derived internalizing disorders. Differential relationships between diagnoses and behavior problems would provide support for the notion of behavioral equivalents. It was hypothesized that specific behavior problems would not be associated with specific diagnoses. This would indicate that behavioral equivalents are better conceptualized as an indication of overall impairment, rather than as specific diagnostic indicators.

CHAPTER 2

METHOD

Instruments

The Children’s Interview for Psychiatric Symptoms-Parent Version (P-ChIPS)

(Weller, Weller, Teare, & Fristad, 1999) is a structured interview designed to assess

psychopathology according to DSM-IV criteria in children and adolescents aged 6 to 18

years. It assesses 20 DSM-IV disorders including ADHD, ODD, Conduct Disorder (CD),

substance abuse, anxiety disorders (phobias, separation anxiety, OCD, acute stress

disorder, post traumatic stress disorder, and generalized anxiety disorder) eating disorders

(anorexia nervosa and bulimia), mood disorders (depression, dysthymia, mania, and

hypomania), schizophrenia/psychosis, and elimination disorders. It also assesses

psychosocial stressors. Symptoms are assessed in a yes/no format. Within each diagnostic

section cardinal questions are asked. If a respondent answers “no” to a certain number of

questions the rest of the questions in that section may be skipped. Onset and duration

information are obtained for each disorder. Impairment is assessed with questions asking

if symptoms cause problems at home, school, or with other children. It has good

psychometric properties and offers the advantage of a shorter administration time (i.e., one hour; Weller, Weller, Fristad, Rooney, & Schecter, 2000). Individual meet criteria for a disorder if they have the minimum symptoms and duration requirements and impairment is reported.

Rather than just relying on the cardinal questions, all questions in each mood, anxiety, and disruptive behavior disorder were asked. Current (i.e., last month) symptoms and the presence/duration of impairment were assessed. Items requiring the child to have functional communication were not administered to nonverbal children. For Depression, this included the following Sections: Guilt, Hopelessness, and Morbid Thoughts (except items assessing suicide attempts). For Separation Anxiety and Generalized Anxiety, this included items asking the parent to report on child worry. For OCD these included items requiring parents to report on child’s thoughts. For Mania/Hypomania this includes items asking parents to report if the child felt he or she had special abilities and racing thoughts.

The Child and Adolescent Symptom Inventory (CASI; Gadow & Sprafkin, 1994,

2002) is a DSM-IV referenced rating scale for children aged 5-17 years. It is available in both parent and teacher form. Subscales include the most common childhood disorders;

ADHD, ODD, CD, GAD, Separation Anxiety Disorder, Social Phobia, MDD, Dysthymic disorder, and the triad of ASD symptoms. Items are rated on a 4-point Likert scale ranging from 0 (Never) to 3 (Very Often). Items can be scored two different ways, symptoms count (categorical) and symptom severity (dimensional) scores. Studies indicate good internal consistency, test-retest reliability, and construct validity (Gadow &

Sprafkin, 2004, 2002). They have been used in a number of studies of behavior problems in children with ASDs.

The Nisonger Child Behavior Rating Form (NCBRF: Aman, et al., 1996) is a rating scale developed specifically for children with ID. It was adapted from the Child

Behavior Rating Form by modifying the instructions, rewording questions to make them more concrete and adding behavior problems (e.g., self injury and stereotypy). The

NCBRF offers the advantage of assessing social competence in addition to problem

behavior. The prosocial section contains two subscales, Compliant/Calm and

Adaptive/Social. The problem behavior section contains the following six subscales:

Conduct Problems, Insecure/Anxious, Hyperactive, Self-injury/Stereotypic, Self-

isolated/Ritualistic, and Overly Sensitive. Its subscales had a median alpha value of .85

(with values ranging from .77 to .93), indicating acceptable internal consistency. It had a high level of correspondence with the Aberrant Behavior Checklist (median correlation

of .72), providing evidence for its external validity. Subsequent studies have also found

the psychometric properties of the NCBRF to be sound and norms are also available for

children with ASDs (with and without ID) (Lecavalier, Aman, Hammer, Stoica, &

Mathews, 2004).

Specific NCBRF items were used to assess the presence of behavioral

equivalents. The item Physically harms or hurts self on purpose was selected to assess

self-injury. The following items were used to assess stereotypy: Rocks body or head back

and forth repetitively; Repeatedly flaps or waves hands, fingers, or objects; and Engages

in meaningless, repetitive body movements. The following items were used to assess

aggression: Knowingly destroys property; and Physically attacks people. The behavioral equivalent Temper tantrums was also used.

The Autism Diagnostic Interview-Revised (Rutter, LeCouteur, & Lord, 2003) is a semi-structured interview for caregivers of children and adults for whom autism or ASD are possible diagnoses. It contains 93 questions, 34 of which are used in the diagnostic algorithm that is based on DSM-IV autism criteria. Most questions are scored as 0 (no definite behavior of the type identified); 1 (behavior of the type specified probably

present but defining criteria not fully met); or 2 (definite abnormal behavior of the type described in the definition and coding). A score of 3 is used to indicate extreme severity.

To avoid placing undue weight on some items, all 3s are converted to 2s when scoring the algorithm. Scores of 7 (abnormality not of the type associated with autism) and 8 (not applicable) can also be used. Most items are scored for their most abnormal manifestation between the ages of four and five years, but some are scored ‘‘ever’’ and others are scored only during particular age periods. The algorithm specifies that a score of 8 on the communication domain is necessary to meet criteria for autism (7 for non-verbal individuals). A minimum of ten on the Social and three on the Restricted Repetitive behavior domains are also necessary. Studies have found sensitivity, specificity, and internal consistency to be high (Lord, Rutter, & LeCouteur, 1994; Lord et al. (1997) and good interrater reliability (e.g., Lord, et al., 1994; Lord et al., 1997). The algorithm items were administered to confirm ASD diagnosis and to assess the presence of functional language.

The Stanford-Binet-V (Roid, 2003) is an individually-administered standardized intelligence test for individuals age 2 years through adulthood. It provides full scale, verbal, and nonverbal IQ scores and a Brief IQ. These scores have a mean of 100 and a standard deviation of 15. It has strong psychometric properties and the ability to measure intellectual functioning reliably at lower levels of functioning. A Brief IQ score was obtained on children who did not have another IQ test done within the last year. A

Nonverbal IQ score was obtained from children without conversational language.

A standard Demographic Form (see Appendix A) was used to collect information

on the child and informant. This form contained questions concerning the child’s date of

birth, gender, ASD diagnosis, psychiatric diagnoses, and treatments used. Informants

were asked their age, relationship to the child, and highest level of education.

Participants

Table 1 lists child and informant characteristics in detail. A total of 61 children

and adolescents (mean=11.2 ± 3.8 years; range 6-17) with an ASD were assessed. Eighty-

two percent (n=50) of the sample was male and 77% (n=47) were Caucasian. IQ scores

ranged from 42 to 150 with a mean of 68.4 (sd =23.3). IQs broke down as follows:

IQ≥85, 13 (22.4%); IQ 70-84 (Borderline), 9 (15.5%); IQ 69-55 (Mild), 18 (31.0%); IQ

54-40 (Moderate), 18 (31.0%). The sample contained 16 children with parent-reported diagnoses of Asperger’s Disorder, 17 with autism, and 26 with PDD-NOS. Mean ADI-R diagnostic algorithm scores were as follows: Social, 20.4 (sd = 5.3); Repetitive Behavior,

6.7 (sd=2.6). Communication (nonverbal group), 12.5 (sd=3.6); Communication (verbal group), 15.0 (sd = 4.8). Fourteen (23%) of the 61 children did not have conversational language based on the ADI-R. Thirty-six were receiving treatment from a psychiatrist

and 12 from a developmental pediatrician. Psychotropic medications are listed in Table 1.

Informant ages ranged from 25 to 56 with a mean of 40.4 years (sd = 3.4 years).

Ninety-seven percent (n=59) of informants were mothers. A large proportion of the 55

informants who provided information on their education had college or higher (n=29,

52.7%).

Participant Characteristics Mean (SD)/n, (%) Children Age (in years) 11.2 (3.8) Gender (n, % male) 50 (82%) Race (n, % Caucasian) 47 (77%)

IQ (n=58) 68.4 (23.3) IQ≥85 13 (22.4%) IQ 70-84 (Borderline) 9 (15.5%) IQ 69-55 (Mild) 18 (31.0%) IQ 54-40 (Moderate) 18 (31.0%)

ADI-R Diagnostic Algorithm Scores ADI-R Social 20.4 (5.3) ADI-R Communication (nonverbal ; n=14) 12.5 (3.6) ADI-R Communication (verbal ; n=47) 15.0 (4.8) ADI-R Repetitive 6.7 (2.6)

Medication (n, %) Antipsychotic 37 (60.7%) ADHD medication 25 (41.0%) Beta blocker 28 (32.8) Mood stabilizer 13 (21.3%) SSRI 12 (19.7%) Anxiolytic 3 (4.9%) Atypical antidepressant 3 (4.9%) Antiepileptic 3 (4.9%)

Informants Age (years) 40.4 (3.4) Relationship (n, % mother) 59 (97%)

Education (n, %) Attended High School 1 (1.8%) Graduated High School 6 (10.9%) Attended College 19 (34.5%) Graduated College 27 (49.1%) Graduate/Professional School 2 (3.6%) Table 1: Child and Informant characteristics.

Procedure

The P-ChIPS has been validated in youngsters 6 -17. Therefore, this was the age inclusion criteria for the current study. Additional inclusion criteria were as follows: a.) previous diagnosis of an ASD confirmed by the Autism Diagnostic Interview–Revised

(ADI-R), b.) presence of significant emotional/behavior problems requiring behavior/ psychiatric treatment or special school placement, c.) IQ >40; and d.) parent/guardian fluent in English. Since the ADI-R does not have specific criteria for Asperger’s disorder or PDD NOS, confirming these diagnoses was done by requiring those with Asperger’s disorder to be above cutoffs on the Social and Restricted Repetitive behavior domains and those with PDD-NOS to be above cutoff on the Social and one of the other two domains. This is consistent with DSM-IV criteria and has been done in previous studies

(Posey et al., 2007). The parent/caregivers of the children served as informants.

Children were recruited from Nisonger Child Behavior Support Service, Nisonger

Dual diagnosis clinic, local psychiatrists’ offices, local support groups, listservs for parents of children with ASDs, ASD schools/classrooms, and the Nisonger Autism

Clinic. These agencies/clinics posted advertisements in their offices and distributed fliers to their clients. Previous Nisonger psychiatric research participants who gave permission to be contacted for future studies were recruited with the help of the research staff.

The appointment(s) took place either in the family home or in the Nisonger

Center depending on parent availability/convenience. The child’s parent/guardian signed

a consent form (Appendix B) and a release of information (Appendix C) so that

psychological/psychiatric reports could be reviewed for psychiatric diagnoses and IQ

testing if available. Children over the age of 12 years signed an assent form (Appendix D)

if deemed appropriate by the child’s parent. First, the ADI-R diagnostic algorithm was

completed to confirm ASD diagnoses. Next, parents completed the CASI and NCBRF in

a random order to avoid bias in parent responses. The rating scales were administered

before the structured interview because this is typically the order of assessments

conducted in applied settings. Then the P-ChIPs was administered. IQ tests were

administered at various times depending on the parent’s schedule and child’s behavior.

Assessments were administered by the author who is trained and research certified on the

ADI-R and the P-ChIPS. Children were administered the Stanford Binet Intelligence

Scales-V. This was done if the participant did not have another IQ test completed within the last year. Blindness was protected by not inquiring about diagnoses and treatment until after the interview and assessments were completed. The assessments took approximately 4-5 hours and were split into multiple visits when necessary (i.e., due to parents’ schedule or stress experienced during the interview process). Families received a summary of interview results and were compensated for their time.

After the assessment, the child’s psychiatrist, pediatrician, or psychologist were contacted for diagnostic information when applicable. Two to three follow-up calls were made to practitioners who did not respond after the first request. Thirty-six provided the requested diagnostic information. Due to the low response rate and the unreliable reporting of diagnoses (i.e., just reporting superordinate categories) clinical psychiatric diagnoses were not used in the analyses. Three children did not have IQ scores, because parent/guardians did not return calls to schedule the testing. Therefore, 58 children were used for analyses involving IQ scores.

In an effort to measure interrater reliability, P-ChIPS interviews were recorded so that a second rater could review the interview and code parent responses. The interrater

reliability coder was trained and research reliable on the P-ChIPS. Interrater reliability

was calculated for 30 randomly-selected taped interviews.

Statistical Analyses

Validation of P-ChIPS. Kuder-Richardson analyses were used to examine the

internal consistency of each diagnostic algorithm. Interrater reliability and concordant

validity between the P-ChIPS and CASI were evaluated with standard or a low-base-rate

kappa, Intraclass Correlation Coefficient (ICC), and percent overall agreement. These

multiple methods were used, because there is no one best statistical procedure when

assessing low base rate conditions (Verducci, Mack, & DeGroot, 1988).

Although kappa is the standard measure of interrater reliability, it can be

influenced by base rates, making it unreliable when prevalence rates are extremely low

(Spitnagel & Helzer, 1985). Therefore, rare kappa (Kx; Verducci, Mack, DeGroot, 1988)

was used for those disorders with less than 15% endorsement on both instruments. This

included Obsessions, Compulsions, OCD, MDD, dysthymia, and Mania. In subanalyses

(e.g., nonverbal versus verbal) rare kappa was used for some additional disorders whose

base rates were lower in the subsamples of interest. This is indicated in tables 2, 3, 4, 6, 7,

8, and 9. Low base rate kappa also has statistical limitations (e.g., it can not be calculated

in cases with 100% agreement by both measures/raters). Therefore, percent overall

agreement was also reported. ICC was used to measure association between severity

scores. For the P-ChIPS, which does not provide severity scores, a simple count of

number of symptoms was conducted. Values were calculated for superordinate domains

(disruptive behavior, depression, mania, and, anxiety disorders) and specific diagnoses.

Z-test comparisons were used to compare kappa coefficients across different IQ (IQ≥

70/IQ<70), language (presence/absence), and age (12 ≥ age/age<12), groups.

Convergent validity was examined by measuring the association between

diagnoses and NCBRF subscales. This was a two-step process. First, Mann-Whitney U

tests were used to measure the NCBRF score differences of those above and below P-

ChIPS diagnostic cutoffs. Mann-Whitney was used rather than ANOVA due to the

uneven diagnostic groups and variances. Second, P-ChIPS severity scores (described

above) were correlated with NCBRF subscales using Pearson correlations.

Kappa and internal consistency values were interpreted based on the values

recommended by Cichetti (1994). Kappa and ICC values were interpreted as follows:

Below .40 poor, between.40-.59 fair; between .60-.74 good; and above .75 excellent.

Internal consistency values below .70 were considered unacceptable; between .70 and .79

to be fair; between .80 and .89 to be good; and those at and above .90 to be excellent.

Correlations were interpreted based on Cohen’s (1988) guidelines which are as follows:

correlations greater than .50 was large, between .50-.30 moderate, and between .30-.10

small. Due to the large number of comparisons, the p-value was set at .01 for all analyses.

In tables, the notation “n/a” was used when kappa coefficients could not be calculated

due to 100% agreement or in cases with cell counts equaling zero.

Elucidation of Clinical Picture. Frequency counts of those meeting diagnostic

criteria for disorders and symptoms endorsed were conducted. Chi square was used to

examine the relationship between symptom frequency and IQ (IQ≥70/IQ<70), language

(presence/absence of conversational language), and age (age<12 yrs/age≥ 12 yrs).

Fisher’s exact test was used for analyses when expected cell counts were less than 5.

Second, frequency counts of those who 1.) were above proposed cutoffs, 2.) were above symptom count criteria, 3.) fell short of proposed cutoffs by one or two symptoms and impairment was reported (subsyndromal), and 4.) had at least one impairing symptom were conducted. An individual was considered subsyndromal if he or she fell short of cutoffs by one or two symptoms of the disorder and the parent reported significant impairment. Chi square was used to examine the relationship between subsyndromal status and IQ, language, and age.

Chi square analyses were conducted to examine the distribution of behavioral equivalents among those above the clinical P-ChIPS cutoffs for internalizing disorders.

NCBRF scores were dichotomized for analyses of symptom presence such that a score of

0 or 1 was coded as a 0; a score of 2 or 3 was coded as a 1. Associations were examined between diagnoses and the NCBRF items listed above.

CHAPTER 3

RESULTS

Interrater Reliability Table 2 lists Kappa coefficients, ICCs, and overall agreement values for the two raters on diagnoses. Kappa statistics ranged from .47-.91 for the disruptive behavior disorders, indicating fair to excellent agreement. Overall agreement ranged from 53-97% and ICCs ranged from .86-.97. Kappa statistics for the mood disorders ranged from .41-

.43, indicating fair agreement. Overall agreement was 80% for all three mood disorders and ICCs ranged from .56 to .78. Agreement for the anxiety disorders ranged from .79 to

.84, indicating excellent agreement. Overall agreement ranged from 61.7%-100%. ICCs ranged from .89-1.00. Rare kappa could not be calculated for obsessions due to 100% rater agreement.

66.60% overall agreement COMP=compulsions; COMP=compulsions; e; 2 5 53.30% 4 80.00% 1 83.30% 01 96.70% 83.30% 11 90.00% 21 61.67% 93.30% 3 96.70% 80.00% 2 80.00% 2 86.70% 2 90.00% 0 93.30% 4 86.70% 5 70.00% 0 96.70% 1 76.70% no Rater 1/ yes Rater 2 1 2 10 no Rater 2 Rater no yes Rater 1/ activity Disorder-Combinedactivity Typ 5 er-Hyperactive Type; Type; er-Hyperactive no Rater 2 no Rater 1/ Rater no 2 23 4 24 27 22 1 4 722700100% 3 2012800100% 53 25 1 232 26 0 0 222 0 3 22 4 3 18 4 5 18 6 15 1122 15 5 0 22 6 ralized Anxiety Disorder; PHO=Specific Phobia; Disorder; PHO=Specific OBB=obsessions; Anxiety ralized yes Rater2 yes Rater 1/ ) is indicated with footnote; b rare kappa cannot be calculated for disorders with 100% agreement; agreement; 100% with disorders for calculated be cannot kappa b rare footnote; with indicated is ) x ADHD-C=Attention Deficit Hyper ADHD-C=Attention Deficit MDD=Major Depression Disorder;MAN=Mania DYS=dysthymia; ** * on Deficit Hyperactivity Disord Hyperactivity on Deficit ** ** ** ** ** ** ** ** ** .56 ICC .89 .84 .96 .78 .97 .92 .92 .99 .89 1.00 ** *** ** ** ** ** ** .07 .94** 6 10 9 .12 .60 .71 .84 .91** .94** 2 27 1 .83 .47 n/a .46 n/a .42 .41.42 .39.43*** 3 21 2 .79 .82 .73 .73** .86** 11 15 2 .44 0.2 kappa kappa ac a Disorder a a a ab b a MAN Any Mania ADHD-I ADHD-C PHO OCD SEP ANX Any Mood DYS Hypomania Any ADHD GAD OBB ADHD-H. COMP PHO SOC MDD Any Disruptive ODD Any Anxiety CD *p< .01Standard a *p< **p<.001; kappa used except where rare kappa(K SOC PHO=Social Phobia; SEP ANX=Separation Disorder; Anxiety ODD=Oppositional Defiant Disorder CD=Conduct Disorder; GAD= Gene Disorder; GAD= CD=Conduct Disorder Defiant ODD=Oppositional ADHD-I=Attention Deficit Hyperactivity Disorder-Inattentive Hyperactivity ADHD-I=Attention Deficit Type; c Hypomania severity score is same as Mania.;ADHD-H=Attenti as scoreseverity is same c Hypomania Table agreement 2: on Interrater the P-ChIPS (n=30).

Table 3 shows kappa coefficients, ICCs, and percent agreement values according

to IQ (≥70 or < 70). Table 4 shows kappa coefficients, ICCs, and percent agreement values according to age. There were no significant differences between groups on kappa values. Kappa could not be calculated for neither ADHD-H in the IQ>70 group due to

100% agreement nor for SEP ANX for those with IQ<70 because it was a constant. Due to 100% agreement, kappa could not be calculated for OCD, obsessions, compulsions, and separation anxiety in the age analyses. The impact of language on interrater reliability could not be calculated due to the small number of nonverbal participants.

overall overall agreement no/no yes/yes 70 (n=9) ≥ ICC .90** 1 8 100% n/a 0.36 0 5 55.60% IQ kappa kappa overall overall agreement no/no ) is indicated with footnote; b rare kappa cannot be calculated for for calculated be cannot kappa b rare footnote; with indicated is ) x 2 14 80.0% 0.47 n/a 0 8 88.90% 3 11 70.0% .73* 2 6 88.90% yes/yes ICC n/an/a .82**n/a ** .94 .96** 1 1 0 19 19 20 100.0% 100.0% 100.0% .44 n/a .93** .95** 1 1 7 8 88.90% 100% .47.46 .93** .91** 0 2 18 17.61 90.0% 95.0% .94** 2 .31 .85** n/a .97** 4 16 0.46 90.0% 4.41 5 .70**.32 .82*** 88.90% .43 2 1.41 100% 2 .57* .90** 5 17 14 0 95.0% 4 80.0% 15 100% 75.0% 0.61 0.44 0.73 1 0.4 7 2 88.90% 6 88.90% IQ<70 (n=20) IQ<70 kappa kappa c ab a ab *p< .01; a Standard*p< **p<.001; kappa used except rare kappa where (K disorders with 100% agreement; c Hypomania severity score is same as Mania. as same is score severity Hypomania c agreement; 100% with disorders a a ab ab a COMP OCD SOC PHOSEP ANXAny MoodMDD .74 n/a .98** .38 .91** 4 3 14 16 2 90.0% 95.0% 14 1.00 80.0% .83** 1.00** .88** 1 0.00 0 8 9 100% 1 100% 4 55.60% Disorder based on IQ. Agreement 3: Interrater Table ADHD-H ADHD-CAny DisruptODDCDAny Anxiety* .12GAD .74**PHO .97**OBB 14 .69** .88* .94** 6 4 .70* .84** 14 5 8 .89** .69** 5 6 55.0% 9 6 90.0% 95.0%Any Mania 11MAN 85.0% n/a 2Hypomania 85.0% 1.00** 1.00 .87** .95** 40.0% 7 .77* 0 1.00 .91** 3 2 0.73 76 5 .98** 77.80% 5 2 55.60% 2 5 88.90% 100% 88.90% 4 100% DYS Any ADHDADHD-I .25 10 3 65.0% n/a 4 0 44.40%

overall overall 94.10% 70.60% 76.50% agreement no/no 1 11 70.60% 8 8 94.10% 3 10 76.50% 11 5 ICC yes/yes ≥ 12 (n=17) Age n/a .95** 1 16 100% n/a .79** 1 16 100% 0.110.19 .57* .61* 1 1 11 12 0.41 .43* 2 12 82.30% 0.43 a 0 13 76.50% 0.680.06 .97** .92** 3 20.87 12 .95** 8 88.20% 5 58.80% 11 94.10% 0.44 0.87 .95** 11 5 94.10% 0.47 .86** 1 15 94.10% 0.74 .91**0.45 5 .94** 10 2 88.20% 14 94.10% 0.87 .94** 2 14 94.10% -0.05 .88** .86** kappa 1.00** .88** 3 14 100% 1.00** 0.98 3 14 100% 100% overall overall 84.60% 53.80% 0.27 8 5 76.50% agreement ) is indicated with footnote; b rare kappa cannot be calculated for for calculated be cannot kappa b rare footnote; with is indicated ) x no/no 2 10 92.30% 2 8 76.90% 2 9 3 10 11 1 92.30% ICC yes/yes Age<12 (n=13) Age<12 1 .93** 11 2 100% 1 .93** 2 11 100% n/a 7 0 n/a .86** 1 12 100% n/an/a 1 .96** 1 1 12 12 100% 100% n/a 0.54 11 0 84.60% .46.41 .95** .85** 1.43 2 .79** 11 9 0 92.30% 84.60% 10 76.90% .26 .97** 1.53 .73**.52 9 6 76.90% 4 76.90% n/a .92**n/a 1 12 100% kappa .43*** n/a n/a .98** 0 13 100% ac ab ab *p< .01; *p< **p<.001; a Standard kappa used exceptwhere rare kappa (K disorderswith 100% agreement; c Hypomania severity scoreas is Mania. same a ab ab a a Man DYS ManiaAny .45 MDD SOC PHO SEP ANX Any Mood .58 1.00** .58 2 9 84.60% Disorder Any ADHD Table 4: Interrater Agreement based on Age. ADHD-H COMP ADHD-I ADHD-C DisruptAny n/a .63 .94**Any Anxiety 4 2OCD 46.20% ODD CD Hypomania GAD OBB PHO

35 Internal Consistency Table 5 lists Kuder-Richardson values for diagnostic categories within the P-

ChIPS. Internal consistency was good for ADHD-Combined subtype (.89), ADHD-

Inattentive subtype (.84), ADHD-Hyperactive subtype (.80), ODD (.82), Social Phobia

(.88), Depression (.83), and Mania (.86). Internal consistency values were fair for

Specific Phobia (.74) and Separation Anxiety (.70). Levels were below acceptable standards for Conduct Disorder (.65), GAD (.65), OCD (.68), OCD-Compulsions (.65), and OCD-Obsessions (.30).

Diagnostic categories Kuder-Richardson 20 value ADHD-Comb .89 ADHD-I .84 ADHD-H .80 ODD .82 CD .65 PHO .74 SOC PHO .88 SEP ANX .70 GAD .65 COMP .65 OBB .30 OCD .68 Depression .83 Maniaa .86 a Hypomania items are same as Mania.

Table 5 Internal Consistency of P-ChIPS items by diagnostic category (N=61)

Concordance P-CHIPS and CASI

Table 6 lists kappa coefficients, ICCs, and percent overall agreement of the P-

ChIPS and CASI for the disruptive behavior, mood, and anxiety disorders. ADHD Kappa and ICC statistics indicated poor to fair agreement ranging from .24-.47 and .37-.41, respectively. Overall agreement ranged from 65% to 92%. Kappa statistics were.44 and

.57 for ODD and CD, and ICCs were .45 and .46, respectively, indicating fair agreement.

Overall agreement was 74% for ODD and 79% for CD. Kappa statistics for the MDD and dysthymia were .44 and .42 and ICCs were .24 and .32, indicating fair to poor agreement.

Overall agreement was 84% for MDD and 77% for Dysthymia. The kappa statistic for

Mania (.42) and ICC were in the fair range. Overall agreement was 62%. Agreement for the anxiety disorders was poor to moderate (.22

80.30% overall agreement overall % 2 91.80% 583 83.60% 77.10% 61.50% 6 80.30% 6 80.30% 31 2 65.60% 73.80% 7 78.70% 1822 67.20% 60.70% 11 75.40% 11 77.10% 10 78.70% 11 68.90% 23 59.00% yes CASI no PChIPS/ PChIPS/ no 3 2 2 5 5 2 4 6 6 3 6 3 8 2 14 18 11 no CASI no yes PChIPS/ ) is indicated with footnote; ) is indicated x 9 55 37 33 47 44 15 40 46 42 13 17 11 40 24 36 13 no CASI no no PChIPS/ PChIPS/ no 7 38 23 yes CASI yes PChIPS/ 12 on CASI (n=26). 12 on CASI ≥ .30* .41** 6 0.47 .37* 1 0.410.36 0.18 0.240.44 4 0.42 4 0.42 0.24 .32* .57* 4 3 1 .35* .38** 23 0.22 .32* 3 .37* .37* 7 0.27 .43** 33 .44** .45** 32 .53** .57** .46** 24 .50** 0.13 12 .001; a Standard exceptrare kappa (K kappa used where a a a b a a a a .01; **p ≤ Disorder kappa ICC ≤ b Mania ratings only available for children for children available only b Mania ratings Any ADHDADHD-H .40** 43 6 11 1 80.30% ADHD-I DYS *p COMP SOC PHO SEP ANX Any Mood .41** MDD ODD ADHD-C Any Disruptive CD Any Anxiety GAD .25* OBB PHO Table 6: Agreement between CASI and P-ChIPS. (N=61) CASI between 6: Agreement Table MAN

Table 7 splits kappa and percent agreement values according to IQ. The available z-values comparing kappas of those with IQs above and below 70 were as follows:

ADHD-Inattentive, z= 0.36; ADHD-Combined, z=0.59; ODD, z=1.19; CD, z=0.30;

GAD, z=1.66; Phobia, z=0.29, and Social Phobia, z=0.37. None of the values were significant for a two-tailed test at the .01 level.

IQ<70 (n=36) IQ≥70 (n=22) yes/ no/ overall yes/ no/ overall Disorder kappa yes no agreement kappa yes no agreement Any ADHD .52** 23 6 80.5% n/a 17 0 81.0% ADHD-I .22 2 26 77.8% .32 3 13 72.7% ADHD-H a .47 0 32 88.9% n/a 0 21 95.5% ADHD-C .38* 13 11 66.7% .24* 10 4 63.6% Any Disrupt. .60** 23 7 80.5% .34 13 3 72.7% ODD .53** 20 8 77.8% .24 10 4 63.6% CD .61** 15 14 80.6% .55* 9 8 77.3% Any Anxiety .26 11 10 58.3% .41 14 3 77.2% GAD .24 2 26 77.8% .64 9 9 81.8% PHO .25 18 6 66.7% .17 13 2 68.2% OBB a .41 1 25 72.2% .33 3 11 63.6% COMP a .36 1 20 58.3% .35 2 12 63.6% SOC PHO .38* 4 24 77.8% .48* 3 15 81.8% SEP ANX .30 2 28 83.3% n/a 0 17 77.3% Any Mood .72** 5 28 91.7% .17 2 13 68.2% MDD a .45 2 29 86.1% .44 2 17 86.4% DYS a .44 3 28 86.1% .41 0 15 68.2% a *p≤.01; **p≤.001 Standard kappa used except where rare kappa (Kx) is indicated with footnote. Table 7: Impact of IQ on Agreement between CASI and P-ChIPS.

Table 8 lists kappa and overall agreement according to language group. The available z-values for those with and without language were as follows: ADHD-Combined, z=2.77;

ODD, z=0.31; CD, z=0.55; Phobia, z=0.02. Only ADHD-Combined was significant for a two-tailed test at the .01 significance level. Kappa coefficients could not be calculated for

GAD, OCD-obsessions Separation Anxiety disorder and Depression because both 39

instruments agreed (i.e., all nonverbal participants screened negative for these disorders).

Rare Kappa could not be calculated for MDD, or dysthymia due to 100% rater agreement in nonverbal group.

No Language (n=14) Language Present (n=47) yes/ no/ overall yes/ no/ overall Disorder kappa yes/ no agreement kappa yes no agreement AnyADHD .39 7 3 71.4% .37** 36 3 83.0% ADHD-I a .39 0 9 64.3% .40 6 31 64.9% ADHD-H a .46 0 12 85.7% .48 1 43 77.2% ADHD-C -.07 1 4 35.7% .49** 22 13 61.4% Any Disruptive .46 4 6 71.4% .46** 34 5 83.0% ODD .43 3 7 71.4% .35* 29 6 61.4% CD .66* 3 9 85.7% .53** 21 15 63.2% Any Anxiety .08 1 5 42.9% .38** 26 8 72.3% GAD n/a 0 14 100.0% .43* 12 22 59.6% PHO .25 6 3 64.3% .26 27 6 57.9% OBB n/a 0 14 100.0% .33 4 23 47.4% COMP .33 0 7 50.0% .35 4 26 52.6% SOC PHO a .46 1 12 92.9% .37 6 28 59.6% SEP ANX n/a 0 13 92.9% .21 3 33 63.2% Any Mood n/a 0 14 100.0% .36 7 28 74.5% MDD a n/a 0 14 100.0% .41 1 39 70.2% DYS a n/a 0 14 100.0% .40 9 28 64.9% *p≤.01; **p≤.001; a Standard kappa used except where rare kappa (Kx) is indicated; b rare kappa cannot be calculated for disorders with 100% agreement. Table 8: Agreement between CASI and P-ChIPS based on language (n=61).

Table 9 lists the kappa and overall agreement values according to age group. The available z-values comparing kappas of those older and younger than 12 years, 11 months were as follows: ADHD-Combined, z=0.07; ODD, z=0.78; CD, z=0.42; GAD, z=0.99;

Phobia, z=2.39 (p=.01); and Social Phobia, z=0.82. Only Phobia was significant for a two-tailed test at the .01 level.

Age<12 (n=35) Age ≥ 12 (n=26) yes/ no/ overall yes/ no/ overall Disorder kappa yes/ no agreement kappa yes no agreement Any ADHD .18 27 1 80.0% .54* 16 5 84.0% ADHD-I .02 1 24 71.4% .54* 5 16 84.0% ADHD-H a .48 1 32 94.3% .47 0 23 92.0% ADHD-C .27 18 5 65.7% .28 5 12 68.0% Any Disruptive .53** 26 4 85.7% .48* 7 12 73.1% ODD .50* 22 6 80.0% .37* 10 7 68.0% CD .52* 17 10 77.1% .59* 7 14 84.0% Any Anxiety .39* 18 7 71.4% .27 9 6 57.7% GAD .56** 8 20 80.0% .42 4 16 80.0% PHO .05 21 2 65.7% .44 12 7 76.0% OBB a .36 3 20 65.7% .40 1 17 72.0% COMP a .36 2 20 62.9% .33 2 13 60.0% Soc Pho .31 4 22 74.3% .45* 3 18 84.0% Sep Anx .29 3 24 77.1% n/a 0 22 88.0% Any Mood .29 3 24 77.1% .57* 4 18 88.0% MDD a .44 2 28 85.7% .42 2 19 84.0% DYS a .42 1 25 74.3% .42 2 19 84.0% a *p< .01 **p<.001; Standard kappa used except where rare kappa (Kx) is indicated with footnote. Table 9: Agreement between the CASI and P-ChIPS Based on Age.

Convergence with NCBRF

Table 10 lists mean NCBRF subscale scores and standard deviations for those

above and below cutoff for P-ChIPS derived disorders. Those above the cutoff for

ADHD-Inattentive, Specific Phobia, Social Phobia, Mania, and Hypomania did not differ

significantly on any of the NCBRF subscales compared to those below the cutoff. Those

who screened positive for ADHD-Hyperactive had significantly lower scores on the

prosocial subscale, Adaptive/Social (Mann-Whitney U =9.5, p = .003). Those who screened positive for ADHD-Combined type had significantly higher NCBRF

Hyperactivity scores (Mann-Whitney U = 220.5, p = .004). Those who screened positive for ODD had significantly higher scores on the Conduct Problems (Mann-Whitney U = 41

94.5, p <.001) and Insecure/Anxious subscales (Mann-Whitney U =158.0, p =.002).

Those who screened positive for CD had significantly higher scores on the Conduct

Problems (Mann-Whitney U = 240.5, p = .001) and significantly lower scores on the

Compliant/Calm subscale (Mann-Whitney U = 235.5, p =.002). Those who screened

positive for Separation Anxiety (Mann-Whitney U = 95.5, p = .005) and GAD (Mann-

Whitney U =163.5, p = .002) scored higher on the Insecure/Anxious subscale: Those who

screened positive for OCD-Compulsions scored significantly higher on the

Insecure/Anxious (Mann-Whitney U =60.5, p = .009) and Self-Isolated subscales (Mann-

Whitney U =39.5, p = .001). Those who screened positive for MDD also scored higher on the Insecure/Anxious subscale (Mann-Whitney U =53.0, p <.001). Those who screened positive for Dysthymia scored significantly lower on the Compliant/Calm subscale

(Mann-Whitney U =87.5, p = .016), and higher on the Insecure/Anxious subscale (Mann-

Whitney U =88.5, p =.008).

Overly Sens Overly .8) (3.5) 7.11 .7) (3.5) 7.14 .85 (6.5) 5 .70 (4.1) Iso/Ritual Iso/Ritual (4.4) (4 7.37 .11 (4.2) (4 7.39 SIB/Stereo 40 (6.8) 3.85 (4.1) 7 .10 (6.80) 4.82 Hyper Insec/Anx 15.80 (8.9)* 16.00 (6.6) 5.39 (4.5)14.50 (8.9) 7.46 (4.5) 16.17 (6.0) 7.72 (3.5) 5.20 (4.5) 7.00 (3.9) 7.73 (3.2) * * (11.8) 13.61 (9.5) (7.0) 15.90 5 (11.8) 13.80 (9.32) 15 (12.8) 10.8 (8.5) 11. CondProb CondProb viation by Diagnostic Categorization. Diagnostic viation by .001 Comp/Calm ≤ **p .01; ≤ Adapt/Soc No ODDCD 5.13 (2.7)No CD 7.67 (1.3)GAD 10.27 (5.8) 5.17 (2.3)No GAD 4.33 (1.4) 7.33 (7.2) 7.76 (2.0) 6.37 (1.3)*PHO 4.69 (1.8) 13.07 (7.3) 4.93 (2.1) 26.27 (11.4)* No PHO 16.71 (10.4) 3.00 (3.1) 7.27 (1.8) 12.97 (9.6) 6.36 (1.8)OBB 14.41 (7.6) 8.07 (6.4) 5.37 (2.3) 21.74 (12.41) 4.45 (1.6) 20.40 (10.2) 11.74 (8.8)No OBB 4.42 (4.18) 19.80 (8.1)* 5.47 (3.2) 7.47 (2.0) 15.70 (7.1) 6.85 (1.7) 14.00 (6.0) 8.19 (5.9)COMP 5.33 (4.6) 4.77 (1.9) 3.20 (2.9) 4.50 (2.4) 17.90 (10.5) 23.12 (12.2)No COMP 6.61 (3.8) 11.40 (8.8) 14.85 (9.3) 7.00 (4.7) 7.11 (1.8) 9.47 (5.5) 6.50(1.4) 13.70 (7.1) 4.64 (1.8) 16.05 (6.7)OCD 6.67 (2.6) 6.72 (3.4) 21.60 (11.6) 8.53 (3.6) 4.40 (4.5)No OCD 19.67 (14.6) 5.00 (4.3) 7.06 (1.8) 13.25 (9.2) 7.00 (1.5) 18.0 (9.6) 6.85 (4.2) 15.53 (7.1) 7.98 (5.4) 20.96 (11.7) 25.50 (13.3) 4.73 (1.8) 13.0(3.8) 5.00 (3.6) 12.73 (9.0) 4.76 (4.2) 22.83 (6.4)* 5.60 (3.4) 7.93 (3.4) 14.00 (6.7) 7.10 (1.8) 15.42 (6.9) 6.33 (2.1) 5.17 (5.9) 7.35 (4.69) 5.17 (6.1) 4.76 (4.1) 21.62 (11.9) 7.15 (3.6) 17.33 (11.4) 10.0 (7.4) 13.29 (9.2) 22.00 (8.2) 14.33 (5.5)* 6.87 (4.4) 8.67 (2.7) 7.33 (2.9) 15.41 (6.9) 12.67 (4.5) 7.00 (3.6) 4.97 (4.3) 1.67 (1.5) 7.24 (4.6) 14.67 (8.3) 8.00 (26) 7.12 (3.6) NCBRF NCBRF Diagnosis ADHD-INo ADHD-I (1.9) 4.73 4.80 (2.1) 7.00 (1.8) 7.40 (1.8) 21.37 21.60 (12.6) 14.3 (8.2) 12.1 (4.9) 3.20 (4.6) 8.70 (6.3) 7.4 (3.7) Table 10: NCBRF Means and Standard De ADHD-HNo ADHD-H (1.8)* 4.89 2.00 (1.0) 7.10 (1.8) 6.00 (1.0) 21.47 20.50 (14.7) 12.5 (9.1) 17.75 (8.1) 4.50 (3.1) 11.00 (8.1) 7.50 (5.2) ADHD-C No ADHD-C 4.75 (1.6) (2.5) 4.72 ODD 6.85 7.50 (1.8) (1.7) 17.4 23.37 (11.0) 15.14 (9.3) 4.61 (1.5) 17.17(6.1)* 6.84 (1.9) 5.27 (4.4) 25.04 (11.02) 7.49 (4.2) 7.88 (3.1) *Mann-Whiney test significant p significant test *Mann-Whiney

43 Overly Sens Overly Iso/Ritual SIB/Stereo Hyper Insec/Anx CondProb CondProb Comp/Calm Adapt/Soc Adapt/Soc Table 10 Continued NCBRF Diagnosis SOC PHO No SOC PHO (1.8) 4.80 4.44 (2.4)SEP ANX (1.9) 7.16 6.44 (1.3) SEP ANXNo (2.0) 4.90 (11.8) 21.98 3.89 (1.1) 18.50 (12.2) (9.7) 13.88 MDD 12.90 (6.6)(1.8) 7.26 5.89 (1.7) (7.0) 15.86 MDDNo 12.3 (5.6)(12.0) 20.82 (4.3) 4.75 24.78 (10.9)(9.1) 12.37 5.10 (4.3)DYS 21.56 (5.1)* (1.9) 4.72 4.89 (1.8) (4.7) 7.24 17.89 (4.2) (7.1) 14.82 DYSNo 9.50 (6.1)(1.7) 7.26 (4.2) 4.56 6.22 5.89 (2.1)(4.8) (3.7) 7.35 6.20 (2.8) (11.9) 20.48 (2.0) 4.75 (4.9) 7.69 (.7) 4.75 26.78 7.11(10.7) (5.7)MAN(8.4) 11.87 24.44(6.0)* MANNo (1.8) 7.27 (3.5) 6.77 19.33 (7.4) 9.44 (6.6)(3.2) 14.58 (1.1)* 5.63 3.78 (4.5) (3.2) 4.98 (11.48) 20.02 (10.5) 30.63 (9.0) 12.55 (1.9) 4.84 (2.0) 4.22 (6.9)* 21.50 Hypomani(4.6) 6.98 11.22 (6.2) (7.1) 14.96 (5.1) 17.38 (1.8) 7.18 (1.9) 6.33 HyomaniaNo (4.2) 4.77 10.44(2.4) (5.3) 5.00 (3.4) 6.60 (1.9) 4.80 (2.1) 4.44 (12.1) 20.81 (10.2) 24.89 (5.2) 7.43 (3.7) 8.75 (9.3) 13.12 (1.9) 7.22 (8.7) 17.22 (1.1) 6.11 (7.1) 15.12 (3.5) 6.71 (5.1) 16.22 (2.2) 10.13 (11.1) 20.83 (15.6) 24.78 (4.4) 4.87 (8.8) 13.56 (4.1) 4.44 (12.2) 14.67 (6.7) 19.67 (6.6) 14.52 (4.8) 7.23 (6.0) 9.78 (2.0) 3.78 (4.6) 4.98 (3.7) 7.15 (2.9) 7.22 (4.2) 9.44 (5.1) 7.29 (3.4) 8.22 (3.6) 6.98

Table 11 lists correlations between NCBRF subscales and P-ChIPS symptom

counts. Large correlations were noted between the NCBRF Conduct problems subscale

and the disruptive behavior disorders, ODD (.70) and CD (.60). Small to medium

correlations were noted between the Insecure/Anxious subscale and Anxiety (.15-.36) and

mood disorders (.39-.40). Medium correlations were noted between ADHD subtypes and

the Insecure/Anxious (.38-.47), Hyperactive (.44-.54), and Overly sensitive subscales

(.44-.51). Small to medium correlations were noted between the Overly Sensitive subscale and Separation anxiety (.44), Phobia (.35), and GAD (.29).

NCBRF Soc Calm CP Insec Hyp SIB Iso Sens P-ChIPS ADHD ADHD-I -.17 -.17 .34* .38* .44* .05 .14 .44** ADHD-H -.29 -.45** .49** .46** .53** .17 .03 .48** ADHD-C -.26 -.35* .46** .47** .54** .13 .09 .51**

Disruptive ODD -.08 - .41* .70** .67** .24 .24 .11 .51** CD -.29 -.50** .60** .17 .17 .17 -.15 .13

Anxiety GAD .16 -.04 .02 .36* -.17 -.12 .19 .29 PHO -.10 -.11 .14 .23 .11 .04 .18 .35* OBB -.10 -.04 -.05 .15 -.11 .03 .16 .02 COMP -.01 .17 .12 .33* -.06 .03 .45** .14 SOC PHO -.35* -.15 .07 .26 -.13 .21 .24 .15 SEP ANX -.06 -.13 .24 .41* .13 .11 -.01 .44**

Mood MDD .08 -.06 .06 .39* -.08 .07 .01 .20 DYS .09 -.06 .09 .43* -.01 .11 .05 .24 MAN -.12 -.39* .40* .40* .30* .11 .24 .32 *p< .01 **p<.001 Table 11: Correlations between NCBRF subscales and P-CHIPS symptom counts.

Secondary aims: Elucidation of Clinical Picture

Distribution of Diagnoses and Symptoms

Diagnoses Frequency and Impact of IQ, language and age.

Table 12 lists frequencies of diagnoses for the entire sample. The most frequent

diagnoses were ODD, ADHD, and specific phobia.

Disorder n (%) ADHD-Comb 41 (67.2 %) ADHD-I 10 (16.4 %) ADHD-H 5 (8.2 %) ODD 46 (75.4%) CD 30 (49.2%) PHO 41 (67.2 %) SOC PHO 10 (16.4 %) SEP ANX 9 (14.8 %) GAD 15 (24.6 %) OCD 3 (4.9 %) COMP 6 (9.8 %) OBB 6 (9.8 %) MDD 9 (14.8 %) DYS 8 (13.1 %) MAN 9 (14.8 %) Hypomania 9 (14.8 %) Table 12: Rates of children meeting P-ChIPS diagnostic criteria (N=61).

Tables 13, 14, and 15, break diagnostic frequency down by IQ, language, and age,

respectively. Table 13 shows that those with ID were less likely to exceed cutoffs for

GAD (χ2= 12.95, p <.001). Chi Square/Fischer exact results examining the impact of IQ

were not significant for the following disorders: ADHD-Inattentive (Fischer exact, p =

.21), ADHD-Hyperactive (Fischer exact, p = .23), ADHD-Combined (χ2= .23, p = .63),

ODD (χ2= .19, p = .76), CD (χ2= .04, p = .84), Specific Phobia (χ2= .01, p = .91), Social

Phobia (Fischer exact, p = .30), Separation Anxiety (Fischer exact, p = .65), Compulsions

(Fischer exact, p = .28), Obsessions (Fischer exact, p = .14), OCD (Fischer exact, p =

.05), MDD (Fischer exact, p = .21), Dysthymia (Fischer exact, p = .65), and Mania

(Fischer exact, p = .47).

No ID ID n (%) n (%) Disorder n = 22 n=36 ADHD-H 0 3 (8.3) ADHD-I 5(22.7) 4(11.1) ADHD-Comb 16 (72.7) 24 (66.7) ODD 16 (72.7) 28 (77.8) CD 11(50.0) 19(52.8) PHO 15 (68.2) 24 (67.2) SOC PHO 5 (22.7) 5(13.9) SEP ANX 3 (13.6) 5 (13.9) GAD 11 (50.0) 3 (8.3)** OBB 4 (18.2) 2 (5.6) COMP 3 (13.6) 2 (5.6) OCD 3 (13.6) 0 MDD 5 (22.7) 4 (11.1) DYS 3 (13.6) 5 (13.9) MANIA 4 (18.2) 5 (13.9) Hypomania 3 (13.6) 6 (16.7) **p<.001 Table 13: Rates of children with ID and without ID meeting P-ChIPS diagnostic criteria (n=58).

Table 14 shows that verbal children were significantly more likely to be diagnosed with ODD (Fischer exact, p = .01) and GAD (Fischer exact, p = .01). Chi square/Fischer exact results examining the impact of language were not significant for the following disorders: ADHD-Inattentive (Fischer exact, p = .06), ADHD-Hyperactive

(Fischer exact, p = .66), ADHD-Combined (Fischer exact, p = .52), ODD (χ2 = .19, p =

.76), CD (χ2= 1.32, p = .25), Specific Phobia (Fischer exact, p = .52), Social Phobia

(Fischer exact, p =.27), Separation Anxiety (Fischer exact, p = .08), Compulsions 48

(Fischer exact, p = .19), Obsessions (Fischer exact, p = .19), OCD (Fischer exact, p =

.45), MDD (Fischer exact, p = .08), Dysthymia (Fischer exact, p = .11), and Mania

(Fischer exact, p = .66).

No Language Language n (%) n (%) Disorder n = 14 n=47 ADHD-H 1 (7.1) 3(6.4) ADHD-I 0 10 (21.3) ADHD-Comb 9 (64.3) 32 (68.1) ODD 7 (50.0) 39 (83.0)* CD 5 (35.7) 25 (53.2) PHO 9 (64.3) 32 (68.1) SOC PHO 1 (7.1) 9 (19.1) SEP ANX 0 9 (19.1) GAD 0 15 (31.9)* OBB 0 6 (12.8) COMP 0 6 (12.8) OCD 0 3 (6.4) MDD 0 9 (19.1) DYS 0 5 (13.9) MAN 2 (14.3) 7 (4.9) HYPO 3 (21.4) 6 (12.8) *p<.01 Table 14: Rates of those with and without language meeting P-ChIPS diagnostic criteria (N=61).

Table 15 shows that children under 12 were more likely to be diagnosed with

ADHD-Combined type (χ2= 9.12, p =.003) than their older counterparts. Chi

Square/Fischer exact results examining the impact of age were not significant for the following disorders: ADHD-Inattentive (Fischer exact, p=.19), ADHD-Hyperactive

(Fischer exact, p =.57), ODD (χ2= .13, p = .72), CD (χ2= 3.85, p = .05), Specific Phobia

(χ2= .66, p = .42), Social Phobia (Fischer exact, p = .30), Separation Anxiety (Fischer

exact, p = .41), GAD (χ2= .06, p = .81), Compulsions (Fischer exact, p = .49), Obsessions

(Fischer exact, p = .18), OCD (Fischer exact, p = .61), MDD (Fischer exact, p = .59),

Dysthymia (Fischer exact, p = .53), and Mania (Fischer exact, p = .59).

<12 >12 n (%) n (%) Disorder (n=35) (n=26) ADHD-Comb 29(82.9) 12 (26.2)* ADHD-I 4 (11.4) 6 (23.1) ADHD-H 2 (5.7) 12 (46.2) ODD 27 (77.1) 19 (73.1) CD 21 (60.0) 9 (34.6) PHO 25 (71.4) 16 (61.5) SOC PHO 7 (20.0) 3 (11.5) SEP ANX 6 (17.1) 3 (11.5) GAD 9 (25.7) 6 (23.1) OCD 2 (5.7) 1 (3.8) COMP 4 (11.4) 2 (7.7) OBB 5 (14.3) 1 (3.8) MDD 5 (14.3) 4 (15.4) DYS 5 (14.3) 3 (11.5) Mania 5 (14.3) 4 (15.4) Hypomania 6 (17.1) 3 (11.5) *p<.01 Table 15: Rates of those above and below 12 years of age meeting P-ChIPS diagnostic criteria.

Symptom Frequency Tables 16-23 list diagnostic/symptom frequencies for the entire sample and those

broken up by IQ (≥70 or <70), Language (presence/absence of conversational language), and age (<12 or 12 or older) for the seven diagnostic categories. In the text below, test statistics and p-values are provided for those items analyzed with Chi Square; all others were analyzed with Fischer’s exact test and were significant at the .01 level.

Variation by IQ. The following ADHD symptom frequencies were inversely

related to IQ: Pushes into groups (χ2=7.86, p =.005) and Pushes ahead in line (χ2= 4.16, p

= .04). The ADHD symptom, Blurts out answers (χ2= 6.00, p = .01), was directly related

to IQ. The Phobia symptoms, Fear keeps up at night (χ2= 4.16, p = .04) and Fear keep

from with school (χ2= 10.50, p = .001) were directly related to IQ. Symptoms directly

related to IQ for GAD included: Worry more than others (χ2= 10.89, p = .001), Hard to

relax when worries (χ2= 7.32, p = .007), and Trouble letting go of worry (χ2= 10.50, p =

.001). Only one depression symptom was directly related to IQ, Depressed every day

(Fischer exact, p = .001).

Variation by Language. The following ADHD symptom frequencies were

inversely related to the presence of language: Teachers say forgetful (χ2 = 9.60, p = .002),

Talking too much (χ2 = 6.00, p = .01), and Blurts out answers (χ2= 17.39, p <.001). ODD

symptoms included: Argues with parents (χ2 = 15.44, p < .001), Argues with teachers (χ2

= 7.23, p = .01), Bugs others (χ2 = 6.17, p = .01), Blames others for mistakes (χ2 = 11.89, p = .001), Angry a lot of time (χ2 = 11.00, p =.001), and Gets even (χ2 = 7.90, p = .01).

The Phobia symptom, Fear keep from school (χ2 = 9.35, p = .002), was also directly related. The following GAD symptoms were directly related to language: Hard to relax

when worries (χ2 = 9.70, p = .002), and Trouble letting go of worry (χ2 = 10.15, p = .001).

OCD symptoms included: Symptoms are a problem at home, school (Fischer exact, p =

.003), and Time spent interferes with routine/ >1 hour (Fischer exact, p = .01). The

depression symptom, Gets into arguments (χ2= 14.58, p <.001) and the mania symptom,

Talks way too much/loudly (χ2= 6.28, p = .01), was also directly related.

Variation by Age. The frequency of the following ADHD symptoms were inversely related to age: Not finish schoolwork (χ2 = 9.88, p = .002), Trouble staying seated (χ2 =17.86, p < .001), get in trouble for getting out of chair (χ2 =12.32, p < .001),

Gets in trouble for running/climbing (χ2 =12.34, p < .001), and Always on the go (χ2

=6.09, p =.01). The mania symptom, Gets hurts because not careful (χ2 = 15.32, p <.001) was also negatively associated with age.

12 15.0 (57.7) 21.0 (80.8) (n=26) 12.0 (46.2) 12 (46.2) (34.6) 9 (23.1) 6 13 (50.0) ≥ * ** ** ** * 2 (5.7) 12 (46.2) 29 (82.9) 34 (97.1) 30 (85.7) (n=35) 27 (77.1) 31 (88.6) 17.0 (65.4) 33 (94.3) <12 (n=47) 32 (68.1) 22 (62.9) 13.0 (50.0) 37 (78.7) (78.7) 37 (51.1) 24 (66.0) 31 (68.6) 24 (80.0) 28 37 (78.7) 28 (80.0) 15.0 (57.7) * 1 (7.1) 3 (6.4) No LangNo (n=14) Lang 6 (42.9) 6 (42.9) 3 (21.4) mple, by IQ, language, and age. language, IQ, mple, by 3 (8.3) 70 IQ<70 ≥ 0 (n=22) (n=36) s and Symptoms Endorsed: Entire sa s and Symptoms Entire Sample IQ 10 (16.4)10 5(22.7) 4(11.1) 0 (21.3) 10 4 (11.4) 6 (23.1) 41 (67.2)41 16 (72.7) 24 (66.7) 9 (64.3) (68.1) 32 (82.9) 29 (26.2) 12 5 (8.2) (n=61) I .01; **p<.001. ≤ p ADHD-H 1a. attention to to detail attention 1a. mistakes careless 1b. (90.2) 55 (72.1) 44 21 (95.5) 18(81.8) 31 (86.1) 23(63.9) (92.9) 13 8(57.1) (89.4) 42 36(76.6) (91.4) 32 (88.5) 23.0 2. keeping mind keeping 2. listen not 3a. 3b teacher not listen (90.2) 55 finishing trouble 4a. 37 (61.7) school not finish 4b. (88.5) 54 (88.5) 54 (70) 42 19 (86.4) 14 (63.6) 33 (91.7) 22 (100.0) 20 (90.9) 20 (55.6) 30 (83.3) 7 (77.3) 32 (88.9) (92.9) 13 24 (66.7) 7 (50.0) (85.7) 12 (89.4) 42 (78.6) 11 (89.4) 42 30 (63.8) (91.5) 44 (71.4) 10 (68.1) 32 (91.4) 32 24 (68.6) (94.2) 33 (84.6) 22.0 13.0 (50.0) (84.6) 22.0 Disorder/ ADHD-Comb of ADHD Diagnose 16: Frequency Table 4c. one thing/ another thing/ one 4c. (78.7) 48 19 (86.4) 36 (100.0) (78.6) 11 out chair getting 2b. running/climbing3. (59.0) 36 loud too 4a. (49.2) 30 quietly play hard 4b. go the on 5. (43.3) 26 14 (63.6) turn out talk 6a 13 (59.1) (67.2) 41 19 (52.8) 17 (47.2) 9 (40.9) (50.8) 31 (67.2) 41 14 (63.6) 16 (44.4) 6 (42.9) 6 (42.9) 25 (69.4) (63.8) 9 (40.9) 30 16 (72.7) 3 (21.4) 24 (66.7) 21 (58.3) 8 (57.1) (48.9) 23 (70.2) 33 (71.4) 10 5 (35.7) (48.6) 17 (55.3) 26 (74.3) 26 9 (34.6) (57.7) 15 (54.3) 19 (46.2) 12 5. trouble organizing trouble 5. (71.7) 43 19 (86.4) 23 (63.9) 6. avoid homework avoid 6. things lose 7. (67.2) 41 sticking trouble 8a. daydreams8b. (88.5) 54 things forget 9a. (57.4) 35 forgetful9b. 17 (77.3) (57.4) 35 22 (61.1) (68.9) 42 21 (95.5) 15 (68.2) 31 (86.1) (57.4) 35 19 (52.8) 13 (59.1) (85.7) 12 19 (86.4) 19 (52.8) (61.7) 29 22 (61.1) (85.7) 12 18 (81.8) 5 (35.7) (89.4) 42 16 (44.4) 7 (50.0) (74.3) 26 (63.8) 30 3 (21.4) (57.7) 15.0 (59.6) 28 (91.4) 32 (74.5) 35 (65.7) 23 (84.6) 22.0 (46.2) 12.0 (65.7) 23 (74.3) 26 (46.2) 12.0 (61.5) 16.0 1a. told to sit still hands/feet move 1b. in seat trouble 2a. 43 (70.5) (78.7) 48 (73.8) 45 16 (72.7) 19 (86.4) 24(66.7) 26 (72.2) 17 (77.3) 26 (72.2) (78.6) 11 10 (71.4) (78.7) 37 33 (70.2) (78.6) 11 (72.3) 34 (85.7) 30 28 (80.0) (69.2) 18 15 (57.7) ADHD- Symptom *

12 ≥ 21 (44.7)21 (37.1) 13 9 (34.6) * 1 (7.1) 26 (72.2) 7 (50.0) (66.0) 31 (68.6) 24 (53.8) 14 16 (44.4) 1 (7.1)***21 (58.3) (70.2) 33 8 (57.1) (65.7) 23 (42.3) 11 (46.8) 22 (54.3) 19 (42.3) 11 * )* 70 IQ<70 Lang No Lang <12 ≥ 8 (36.4 10 (45.5) (45.5) 10 17 (77.3) 17 (n=61) (n=22) (n=36) (n=14) (n=47) (n=35) (n=26) Entire Sample IQ .01; **p<.001. ≤ p 8b. trouble waiting trouble 8b. in barge 9a. (83.3) 50 into groups push 9b. (49.1) 30 (47.5) 29 17 (77.3) 31 (86.1) 9 (40.9) (78.6) 11 20 (55.6) (83.0) 39 6 (42.9) (88.6) 31 (48.9) 23 (73.1) 19 (57.1) 20 9 (34.6) 6b. talk too much too talk 6b. (36.7) 22 8 (36.4) 13 (36.1) 8a. push ahead 38 (62.3) interrupt9c. (96.5) 55 17 (77.3) 35 (97.2) (92.9) 13 (89.4) 42 (91.4) 32 (88.5) 23 7. blurt answers 34 (55.7) Table 16 ContinuedTable 16 *

Disorder Entire Sample IQ≥70 IQ<70 No Lang Lang <12 ≥12 Symptom (n=61) (n=22) (n=36) (n=14) (n=47) (n=35) (n=26) ODD 46 (75.4) 16 (72.7) 28 (77.8) 7 (50.0) 39 (83.0) 27 (77.1) 19 (73.1) 1a. lose temper 56 (91.8) 20 (90.9) 33 (91.7) 13 (92.9) 43 (91.5) 33 (94.3) 23 (88.5) 1b. throw tantrums 40 (65.6) 12 (54.5) 26 (72.2) 6 (42.9) 34 (72.3) 24 (68.6) 16 (61.5) 2a. talk back parents 46 (75.4) 18 (81.8) 26 (72.2) 5 (35.7) ** 41 (87.2) 27 (77.1) 19 (73.1) 2b. talk back teachers 23 (37.7) 9 (40.9) 12 (33.3) 1 (7.1) * 22 (46.8) 14 (40.0) 9 (34.6) 3a. break rules-home 46 (75.4) 15 (68.2) 28 (77.8) 10 (71.4) 36 (76.6) 30 (85.7) 16 (61.5) 3b. break rules-school 32 (52.5) 11 (50.0) 19 (52.8) 7 (50.0) 25 (53.2) 17 (48.6) 15 (57.7) 3c. refuse to do 48 (78.7) 17 (77.3) 30 (83.3) 9 (64.3) 39 (83.0) 29 (82.9) 19 (73.1) 3d. not do what told 49 (80.3) 17 (77.3) 29 (80.6) 9 (64.3) 40 (85.1) 31 (88.6) 18 (69.2) 4. bug others 35 (57.4) 13 (59.1) 20 (55.6) 4 (28.6) * 31 (66.0) 23 (65.7) 12 (46.2) 5a. blame others 29 (47.5) 13 (59.1) 16 (44.4) 1 (7.1)** 28 (59.6) 18 (51.4) 11 (42.3) 5b. blame others 25 (41.7) 11 (50.0) 13 (36.1) 2 (14.3) ** 23 (48.9) 15 (42.9) 10 (38.5) 6. easy to make mad 46 (75.4) 17 (77.3) 27 (75.0) 7 (50.0) 39 (83.0) 28 (80.0) 18 (69.2) 7. angry a lot 23 (37.7) 8 (36.4) 13 (36.1) 0 ** 23 (48.9) 14 (40.0) 9 (34.6) 8. get even 24 (39.3) 8(36.4) 16 (44.4) 1 (7.1) * 23 (48.9) 15 (42.9) 9 (34.6) 55 *p<.01; **p<.001

Table 17: Frequency of ODD Diagnoses and Symptoms Endorsed: Entire sample, by IQ, language, and age.

12 (n=26) ≥ (n=35) (n=47) 22 (46.8)22 (45.7) 16 8 (30.8) 18 (38.3)18 (34.3) 12 7 (26.9) 26 (55.3)26 (54.3) 19 10 (38.5) 16 (34.0)16 (45.7) 16 9 (34.6) 2 (14.3) 1 (7.1) 3 (21.4) 9 (64.3) 70 IQ<70 Lang No Lang <12 .1) 0 0 2 (4.3) (2.9) 1 1 (3.8) ≥ 30 (49.2%) 11 (50.0) 19 (52.8) 5 (35.7) 25 (53.2) 21 (60.0) 9 (34.6) 1. steals2a. lie 2b. con others 16 (26.2%) 24 (39.3%) 21 (34.4%) 7 (31.8) 10 (45.5) 9 (40.9) 14 (38.9) 8 (22.2) 11 (30.6) 5 (35.7) n/a 11 (23.4) 21 (44.7) 12 (34.3) 4 (15.4) 12 (34.3) 9 (34.6) Disorder/ Entire Sample IQ Disorder Conduct Symptom (n=61) (n=22) (n=36) (n=14) 3. broken into broken 3. school4. skipped out late5. stay 0 (1.6%) 1 6. run away bullya child 7a. 2 (3.3%)7b. threaten others (21.3%) 13 0 19 (31.1%) 0 2 (9 0 3 (13.6) 5 (22.7) 1 (2.8) (27.8) 10 14 (38.9) 0 0 3 (21.4) 0 (21.3) 10 0 1 (2.1) 0 (25.7) 9 4 (15.4) 0 0 (2.9) 1 0 0 0 0 0 0 8a. starts fights starts 8a. fights for 8b. trouble (47.5%) 29 (16.4%) 10 (59.1) 13 2 (9.1) (44.4) 16 8 (22.2) 1 (7.1) 9 (19.1) (20.0) 7 3 (11.5) 9. used a a weapon9. used other/fight hurt 10a. (8.2%) 5 (13.1%) 8 no reason hurt 10b. force11. take by (24.6%) 15 12. damaged property 25 (41.0%) 17 (27.9%) 2 (9.1) 1 (4.5) 4 (18.2) 9 (40.9) 5 (13.9) 4 (11.1) (30.6) 11 5 (22.7) 15 (41.7) 12 (33.3) 2 (14.3) 3 (21.4) 2 (14.3) 6 (12.8) (25.5) 12 5 (35.7) 3 (6.4) 12 (25.5) (28.6) 10 (14.3) 5 (11.4) 4 5 (19.2) 13 (37.1) 3 (11.5) 1 (3.8) 4 (15.4) 13. set on fire animal14. hurt 15a. other’s privates 3 (5.1%) 3 (4.9%) other forced 15b. (11.5%) 7 0 age. and language, IQ, by sample, Entire Endorsed: Symptoms and Diagnoses Conduct of 18: Frequency Table 2 (9.1) 0 2 (9.1) 1 (2.8) 5 (13.9) 3 (8.3) 0 1 (7.1) 1 (7.1) 0 0 6 (12.8) 2 (4.3) (11.4) 4 3 (6.4) 3 (8.6) 3 (11.5) 0 0 2 (5.7) 0 1 (3.8) 0 0

12 19 (73.1) 21 (80.8) 7 (26.9) (n=26) ≥ 33 (94.3) 33 (94.3) 34 (97.1) 34 (97.1) 14 (40.0) (n=35) (n=47) (n=14) 70 IQ<70 Lang No Lang <12 ≥ (n=22) (n=36) 41 (67.2)41 (68.2) 15 24 (67.2) 9 (64.3) (68.1) 32 (16.4)10 25 (71.4)(61.5) 16 (22.7) 5 5 (13.9) 1 (7.1)(19.1) 9 7 (20.0) 3 (11.5) mptom (n=61) y 3b. pass out pass 3b. interfere:sleep4ai. interfere:school4aii. (36.1) 22 (42.6) 26 4aiii.interfere:activity (54.1) 33 (16.7) 10 interfere:other4aiv. (54.1) 33 uncomfortable4b. (54.5) 12 (68.2) 15 scared more 5a. (21.3) 13 (68.2) 15 (18.2) 4 silly5b. think 10 (27.7) 9 (25.0) Phobia Soc (68.3) 41 16 (44.4) (54.4) 12 5 (13.89) 2 (3.3) 19 (52.7)(13.6) 3 2 (14.3) 1 (7.1) 4 (28.6) (68.2) 15 9 (25.00) 0 (42.6) 20 7 (50.0) 24 (66.67) (61.7) 29 (53.2) 25 (4.5) 1 (55.3) 26 1 (7.1) 14 (40.0) (21.3) 10 8 (57.1) 1 (2.78) 21 (60.0) 19 (54.3) 8 (30.8) (25.5) 12 (70.2) 33 (46.2) 12 7 (26.9) 21 (60.0) 7 (20.0)(46.2) 12 n/a 8 (22.9) 3 (11.5) 23 (65.7) 5 (19.2) (69.2) 18 (4.3) 2 1 (2.9) 1 (3.8) 2a. can’t move can’t 2a. cling/cry2b. from away stay 3a. (42.6) 26 (85.2) 52 (77.0) 47 (40.9) 9 (90.9) 20 (72.7) 16 15 (41.7) 29 (80.56) 29 (80.6) 11 (78.6) 3 (21.4) 11 (78.6) (87.2) 41 (48.9) 23 (76.6) 36 uncomfortable2. to avoid try 3a. (36.4) 20 awful feel 3b. 16 (45.7) 29 (82.9) sleep Interfere 4ai. (30.9) 17 (38.5) 10 school4aii.Interfere (9.4) 5 (69.2) 18 (16.7) 9 (11.3) 6 activity Inter. 4aiii. (40.9) 9 (15.1) 8 other4aiv. Interfere (27.3) 6 (1.9) 1 10 (27.78) uncomfortable4b. 5. silly/senseless (11.5) 7 (18.2) 4 (18.2) 4 (4.5) 1 10 (27.78) (1.9) 1 (18.2) 4 1 (7.1) 1 (2.78) 5 (13.89) 5 (13.89)(4.5) 1 1 (7.1) 4 (11.11) (40.4) 19 (13.6) 3 0 1 (7.1) (34.0) 16 n/a 4 (11.11)(4.5) 1 n/a 0 15 (42.9)(17.0) 8 0 5 (19.2) 12 (34.3)(12.8) 6 (10.6) 5 n/a 5 (19.2) (17.0) 8 0 5 (14.3)(14.9) 7 1 (2.9) 4 (11.4) 4 (15.4) n/a 6 (17.1) 1 (3.8) 5 (19.2) (2.1) 1 2 (5.7) 2 (7.7) (2.1) 1 5 (19.2) 0 1 (2.9) 1 (3.8) 0 1. very scared very 1. (90.2) 55 (95.5) 21 31 (86.1) 12 (85.7) (91.5) 43 people afraid 1a. perform afraid 1b. (9.8) 6 (34.4) 21 (45.5) 10 (9.1) 2 10 (27.78) 4 (11.11) 1 (7.1) 1 (7.1) (42.6) 20 (10.6) 5 4 (11.4) 2 (7.7) Disorder/ Disorder/ Sample Entire IQ PHOBIA age. and language, IQ, by sample, Endorsed:Entire Symptoms and Diagnoses Phobia Specific/Social of Frequency 19: Table S *p<.01; **p<.001 *p<.01;

12 ≥ (n=26) 0 1(2.9) 1 (3.8) 2 (5.7) 0 1 (2.9) 1 (3.8) (n=35) <12 0 7 (14.9)7 (14.3) 5 (7.7) 2 7 7 (14.9) 6 (17.1) 1 (3.8) 2 (4.3) 2 (4.3) 9 (19.1)9 (17.1) 6 (11.5) 3 (n=47) 0 No LangNo Lang (n=14) 0 0 70 IQ<70 ≥ 2 (9.1) 3 (8.33) n/a 6 (12.8) 5 (14.3) 1 (3.8) 1 (4.5) 1 (2.78) 0 1(4.5) 1 (2.78) n/a 2 (4.3) 2 (9.1)2 (8.33) 3 n/a (12.8) 6 (14.3) 5 (3.8) 1 1 (4.5) 1 (2.78) 0 (n=22) (n=36) 0 9 (14.8)9 (13.6) 3 (13.9) 5 0 (n=61) Entire Sample IQ Disorder/ Sep Anxiety age. and language, IQ, by sample, Entire Endorsed: Symptoms and Diagnoses Anxiety Separation of 20: Frequency Table Symptom 4a. interfere school 2 (3.3) 1a. cry parent leaves parent cry 1a. (24.6) 15 home fit to stay 1b. parent re: worry 2a. 7 (11.5) away when (23.0) 14 2b.worry (19.7) 12 self re: 3.worry 4 (18.2) 3 (13.6) school refuse 4b. (27.78) 10 2 (3.3) 7 (31.8) home alone afraid 5a. 6 (27.3) (11.5) 7 0 4 (11.11) parent follow 5b. 5 (13.89) 4 (11.11) 4 (28.6) problems sleep 6a. (21.3) 13 problems sleep 6b. (19.7) 12 (23.4) 11 dreams separation 7a. 1 (7.1)(18.2) 4 (29.5) 18 6 (9.8) n/a n/a (8.33) 3 4 (18.2) 6 (12.8) 5 (22.7) 10 (28.6) 5 (22.7) (29.8) 14 7 (19.44) 5 (19.2) (25.5) 12 7 (19.44) (33.33) 12 0 6 (17.1) 8 (22.9) 1 (7.1) 8 (22.9) 1 (3.8) 1 (7.1) 6 (42.9) 6 (23.1) (25.5) 12 4 (15.4) (23.4) 11 (25.5) 12 7 (20.0) 9 (25.7) 13 (37.1) 6 (23.1) 5 (19.2) 3 (11.5) 7b. dreams-losing dreams-losing 7b. (9.8) 6 8a. stomach/headache8a. 7 (11.5) 4 (18.2) 2 (5.56) 0 8b. sick w/o parent 2 (3.3) *p<.01; **p<.001 *p<.01;

12 0 ≥ 0 5 (14.3) 2 (7.7) 2 (5.7) (3.8) 1 3 (8.6) (3.8) 1 <12 (n=35) (n=26) 6 (17.1) 6 (17.1) 1 (2.9) 3 (6.4) 6 (12.8) 5 (14.3) (3.8) 1 5 (10.6) 3 (8.6) (7.7) 2 15 (31.9) 9 (25.7) (23.1) 6 6 (12.8) 4 (11.4) (7.7) (2 29 (61.7) 18 (51.4) (50.0) 13 18 (38.3) 13 (37.1) (19.2) 5 17 (36.2) 10 (28.6) (30.8) 8 37 (78.7) 22 (62.9) (84.6) 22 * ** No LangNo Lang 0 0 0 0 (n=14) (n=47) 1 (7.1) 1 (7.1) 2 (14.3) 0 7 (50.0) 7 (50.0) n/a 1 (2.1) (2.78) 0 (5.56) n/a 5 (10.6) 3 (8.6) (7.7) 2 5(13.89) 13 (36.11) (25.00)9 1 (7.1)** 26 (55.3) 16 (45.7) (42.3) 11 1 (2.78) n/a 4 (8.5) ** * ** 70 IQ<70 ≥ (4.5) 0 3 (13.6) 3 (13.6) 1 2 (9.1) 2 3 (13.6) 0 4 (18.2) (5.6) 2 1 (n=22) (n=36) 3 (13.6) (5.6) 2 15 (68.2) 16 (72.7) 2 (9.1) (11.11) 4 n/a 6 (12.8) Entire Sample IQ (n=61) 18 (29.5) 18 (29.5) 12 (54.5) 15 (24.6) 11 (50.0) (8.3) 3 3 (4.9) 6 (9.8) 6 (9.8) tom p m y GAD Ia. Feel anxious Feel Ia. interferenceIb. 13 (21.7) 18 (30.0) 7 (31.8) 9 (40.9) (16.67) 6 9 (25.00) n/a 13 (27.7) 10 (28.6) (11.5) 3 1. worry more 1. worry 3. thoughts own 7 (11.9) 4 (18.2) 3 (8.33) n/a 0 1c. feel has to do has feel 1c. awaygo make try 2a. 7 (11.7) no there pretend 2b. 7 (11.7)2c. do something 4 (6.6) 5 (8.2) 5 (22.7) 4 (18.2) (5.56) 2 (8.33) 3 n/a n/a 7 (14.9) 7 (14.9) 6 (17.1) 6 (17.1) (3.8) 1 (3.8) 1 OCD-Compulsions 1a. bothersome ideas bothersome 1a. 14 (23.0) in head pictures 1b. 6 (9.7) 5 (22.7) (25.00) 9 1 (7.1) 13 (27.7) 7 (20.0) (26.9) 7 Disorder/ to do have 3. OCD-Obsessions 10 (16.4) 5 (22.7) (11.11) 4 n/aTable 21: of Frequency/Percentage Generalized Anxiety and Obsessive Compulsive Disorder Diagnoses age. and language, IQ, by sample, Entire Endorsed: Symptoms and 10 (21.3) 8 (22.9) (7.7) 2 2b. trouble letting go letting trouble 2b. 27 (44.3) spent Time Ic. 15 (25.0) 6 (27.3) (22.22) 8 0* 15 (31.9) 10 (28.6) (19.2) 5 3a. edgy3a. easily tired 3b. blank mind 3c. 3d. cranky 23 (38.3) 38 (63.3) 40 (66.7) 44 (73.3) 9 (40.9) 15 (68.2) 17 (77.3) (30.56) 11 (63.89) 23 (55.56) 20 15 (68.2) 5 (35.7) 27 (75.00) 7 (50.0) 6 (42.9) 18 (38.3) 31 (66.0) 34 (72.3) 11 (31.4) 21 (60.0) 25 (71.4) (46.2) 12 (65.4) 17 (57.7) 15 2. hard to relax 31 (50.8) 1. say/do over&over say/do 1. better makes 2a 21 (34.4) happen might bad 2b. 1 (1.6) 5 (8.2) 8 (36.4) (33.33) 12 5 (35.7) 16 (34.0) 12 (34.3) (34.6) 9 S 3e. muscles tight muscles 3e. sleeping trouble 3f. OCD 27 (45) 35 (59.3) 15 (68.2) 12 (54.5) (47.22) 17 (36.11) 13 6 (42.9) 4 (28.6) 29 (61.7) 23 (48.9) 20 (57.1) 17 (48.6) (57.7) 15 (38.5) 10 *p<.01; **p<.001

12 ≥ (n=35) (n=26) <12 9 (19.1)9 5 (13.9) (14.3) 5 5 (14.3)(15.4) 4 3 (11.5) 33 (70.2)33 (57.1) 20 15 (57.7) ** 2 (14.3) No LangNo Lang (n=14) (n=47) 11) 7 (50.0) 29 (61.7) 20 (57.1) 16 (61.5) 4 (11.11) 1 (7.1) (27.7) 13 8 (22.9) 6 (23.1) 07 (19.44) 0 3 (21.4) (29.8) 14 3 (6.4) (37.1) 13 4 (15.4) 1 (2.9) 2(7.7) ** Disorder/Dysthymia Diagnoses and Symptoms Endorsed: Endorsed: and Symptoms Diagnoses Disorder/Dysthymia 70 IQ<70 ≥ 9 (40.9) 3 (13.6) 10 (45.5) (n=22) (n=36) Entire Sample IQ 9 (14.8)9 8 (13.1)(22.7) 5 (13.6) 3 (11.1) 4 5 (13.9) 0 0 (n=61) Dysthymia Dysphoric Mood depressedA1a. day every A1b. (47.5) 29 (29.8) 14 (54.5) 12 16 (44.44) 7 (50.0) (46.8) 22 (42.9) 15 14 (53.8) A1c. most of day of most A1c. A2a. cranky argumentsA2bi. (16.4) 10 (58.3) 35 27 (44.3)(22.7) 5 (13.89) 5 (68.2) 15 12 (54.5) 18 (50.00) 15 (41.67)(7.1) 1 5 (35.7) (19.1) 9 22 (46.8) (17.1) 6 15 (42.9)(15.4) 4 12 (46.2) Entire sample, by IQ, language, and age. and age. language, IQ, Entire sample, by Disorder/ Depressive Major of Frequency/Percentage 22: Table A2bii. Yell, cry Yell, A2bii. A2biii. fights dayA2c. every day of most A2d. (71.7) 43 Loss of Interest B1a. no activities 36 (60.0) 20 (35.7)B1b. loss enjoyment (18.9) 10 B2a. can’t have fun (72.7) 16 B2b. nothingfun 1 (1.6) 6 (9.8) 25 (69.44) 13 (59.1) 8 (36.4) 14 (23.0) (27.3) 6 22 (61. 11 (30.56) 3 (4.9) 7 (50.0) 4 (11.11) 0 2 (9.1) 6 (27.3) (76.6) 36 2 (14.3) 0 4 (11.11) 7 (19.44) 18 (38.3) 1 (2.78) (68.6) 24 19 (73.1) 3 (21.4) 2 (14.3) (21.3) 10 13 (37.1) 3 (6.4) 12 (25.5) 0 7 (26.9) 7 (20.0) 9 (25.7) 3 (11.5) 3 (8.6) 1 (2.1) 5 (19.2) 3 (11.5) 0 1 (3.8) B2c.lost interestAppetite Changes C1a. not eatingC1b. lost weight 6 (9.8)C1c.too clothes big C2a. moreeating (27.9) 17 C2b. gained weight (3.3) 2 2 (3.3)(18.2) 4 14 (23.3) 14 (23.3) 2(5.56) (4.5) 1 (4.5) 1 4 (18.2) 2 (9.1)(2.78) 1 1 (2.78) 0 9 (25.00) 11 (30.56) 0 0 2 (14.3) 4 (28.6) 6 (12.8) 12 (25.5) 10 (21.3) (4.3) 2 2 (4.3) 3 (8.6) 7 (20.0) 5 (14.3) 3(11.5) 7 (26.9) 9 (34.6) (2.9) 1 1 (2.9)(3.8) 1 1 (3.8) MDD Symptom *p<.01; ***p<.001

12 ≥ 11 (42.3) 11 5 (14.3) 10 10 (27.78)4 (11.11) n/a n/a (48.9) 23 (25.5) 12 (40.0) 14 9 (34.6) 8 (22.9) 4 (15.4) 70 IQ<70 Lang No Lang <12 ≥ 13 (59.1) 8 (36.4) Entire Sample IQ Table 22 ContinuedTable 22 D2b. sleep more sleep D2b. Changes Psychomotor sit can’t E1a. moving keep E1b. (16.9) 10 E1c. wring hands clothes rub/pull E1d. (38.3) 23 to do time long E2a. (35.0) 21 (18.2) 4 (41.7) 25 E2b. hard to do things 10 (16.7)Low Energy (60.0) 36 34 (59.6) 6 (16.67)F1a. lack of energy (36.4) 8 (36.4) 8 F1b. push to do things (50.0) 11 3 (13.6) easily tired F1c. 15 (41.67) 19 (31.7) 30 (50.0) (72.7) 16 12 (33.33) 11 (30.56) 2 (14.3)F1d. sits around 12 (54.5) 6 (16.67)Guilt 19 (52.78) 21 (58.33) 8 (17.0) 6 (42.9)G1a. bad self thought (40.7) 24 4 (28.6) 5 (35.7)G1b. 8 (36.4) down on self 11 (50.0) 18 (29.5) 3 (21.4) (36.2) 17 23 (38.3) 8 (57.1)G1c. feel no good (36.2) 17 6 (42.9) 17 (42.6) 20 (47.22) 10 (27.78) self hate G1d. 5 (14.3) 7 (14.9) (59.6) 28 23 (38.3) (45.5) 10 a lotG2a. guilty 28 (59.6) 16 (27.1) punish think G2b. 6 (27.3) (37.1) 13 5 (19.2) 12 (54.5) 13 (36.11) 5 (35.7) (37.1) 13 2 (14.3) (37.1) 13 10 (38.5) 11 (30.56) 11 (30.56) (60.0) 21 8 (30.8) (20.0) 12 12 (46.2) 25 (53.2) 4 (11.4) 7 (11.7) 20 17 (57.1) (36.2) 5 (8.2) 4 (28.6) 15 (57.7) 10 (45.5) 14 (53.8) 6 (23.1) n/a 4 (28.6) 6 (16.67) (42.6) 20 16 (45.7) 10 (28.6) 3 (13.6) 14 (29.8) (9.1) 14 2 (53.8) 9 (34.6) 23 (48.9) 4 (11.11) n/a (34.3) 12 3 (8.33) 12 (46.2) 8 (22.9) 14 (40.0) 16 (34.0) n/a 10 (38.5) 9 (34.6) n/a 7 (14.9) 10 (28.6) 5 (10.6) 6 (23.1) 4 (11.4) 3 (8.6) 3 (11.5) 2 (7.7) Sleep Changes D1a. asleeplonger nightin wake D1b. awakening early D1c. (n=61) 24 (40.7)D2a. naps (41.7) 25 (41.7) 25 11 (50.0) (n=22) (36.4) 8 (36.4) 8 12 (33.33) 16 (26.2) 16 (44.44) (n=36) 4 (28.6) 5 (35.7) 3 (13.6) 20 (42.6) (n=14) (4 15 5 (35.7) (42.6) 20 12 (33.33) (n=47) 16 (45.7) 4 (28.6) (45.7) 16 8 (30.8) 9 (34.6) 12 (25.5) (n=35) (4 20 (45.7) 16 9 (34.6) (n=26) *p<.01; ***p<.001

12 2 (7.7) ≥ 1 (2.9)1 (3.8) 1 2 (5.7)2 0 0 (n=35) (n=26) 1 (2.9) 2(7.7) 2 (5.7) 0 3 3 (8.6) 1 (3.8) 2 (4.3) 2 5 (10.6) 1 (2.9) 4(15.4) (n=14) (n=47) 2 (5.56)2 n/a (4.3) 2 2 (5.56)2 0 2 (5.56) n/a 2 (4.3) 70 IQ<70 Lang No Lang <12 ≥ 0 0 (n=22) (n=36) 0 1 (4.5)1 2(5.56) n/a 3 (6.4) 1 (4.5)1 4 (11.11) n/a 6 (12.8) 4 (11.4) 2 (7.7) 1 (4.5)1 1(2.78) n/a 2 (4.3) 2 (9.1)2 3(8.33) 0 1 (4.5)1 5(13.89) n/a 6 (12.8) 3 (8.6) 3(11.5) 2 (9.1) 2 (8.33) 3 n/a 5 (10.6) 3 (8.6)(7.7) 2 1 (4.5)1 3 (8.33) n/a 4 (8.5) 2 (3.3) 2 Entire Sample IQ 3 (4.9) 2 (3.3) rades droppedrades 5 (8.2) . forget things 23 (38.3) 10 (45.5) 12 (33.33) 4 (28.6) 19 (40.4) 15 (42.9) 8 (30.8) problem deciding 20 (33.3) 8 (36.4) 10 (27.78) 3 (21.4) 17 (36.2) 12 (34.3) 8 (30.8) rbid/Suicidal Thoughts rbid/Suicidal . life not worth not life . (3.3) 2 . thought of death of thought . 6 (9.8) . attempted . . won’t get betterwon’t get . (8.2) 5 .01; ***p<.001 . not good future good . not 2 (3.3) c. attention trouble attention c. not listeningd. (50.0) 30 (36.7) 22 (63.6) 14 15 (41.67) (36.4) 8 12 (33.33) 7 (50.0) (48.9) 23 5 (35.7) (36.2) 17 (45.7) 16 14 (53.8) (28.6) 10 12 (46.2) a. slowedmind a. (22.0) 13 (27.3) 6 7 (19.44) 1 (7.1) (25.5) 12 8 (22.9) 5 (19.2) paired Concentrationpaired (n=61) b. dead peoplewish deada. 10 (16.7) 6 (9.8) 2 (9.1) 7 (19.44) n/a 10 (21.3) 4 (11.4) 6 (23.1) c. thought suicide 4 (6.6) d. planned c. nohope 1a I1b I1 Mo J1a J1 J2 J2b J2 J2 J2e Im H1 H1b H1 H1 H1e. g H2. I Table 22 ContinuedTable 22 Hopelessness *p<

12 5 (19.2) 4 (15.4) ≥ ** 5 (14.3) 4 (15.4) 17 (48.6) 23 (65.7) (n=35) (n=26) <12 15 (31.9) 13 (37.1) 11 (42.3) 28 (59.6) 21 (60.0) 10 (38.5) * 3 (21.4) 9 (64.3) No Lang Lang (n=14) (n=47) 15 (41.7) 70 IQ<70 ≥ 15 (68.2) 15 (68.2) (n=22) (n=36) 2 (9.1)2 (8.33) 3 n/a (10.6) 5 1 (2.9) (15.4) 4 Entire SampleEntire IQ 9 (14.8) 4 (18.2) 4 (13.9) 2 (14.3) 7 (4.9) (n=61) Mania B3b. speech worries speech B3b. too much talk B3c. 8 (13.1) 31 (50.8) 1 (4.5) 7 (19.4) 1 (7.1) 7 (14.9) 3 (8.6) 5 (19.2) A1b. for no reason too highA1c. 17 (27.4) irritableA2. very abilities special B1a. 5 (8.2) better than otherB1b. 38 (62.3) 17 (27.4) 7 (11.5) sleep not need B2a. 6 (27.3) less sleep B2b. 18 (29.0) 11 (30.6) hours ok w/ less B2c. 14 (63.6) 5 (22.7) 12 (19.7) fast speak B3a. 3 (13.6) 16 (19.7) 22 (62.9) 12 (33.3) 5 (22.7) 5 (35.7) 4 (11.1) 22 (36.1) 2 (9.1) 13 (36.1) 12 (25.5) 4 (18.2) 9 (64.3) 5 (35.7) 10 (27.8) n/a 12 (33.3) 28 (59.6) 10 (45.5) 12 (25.5) 5 (35.7) 9 (25.7) 11 (30.6) 4 (28.6) 13 (27.7) 8 (30.8) 5 (35.7) 20 (57.1) 6 (12.8) 11 (31.4) 8 (17.0) 18 (69.2) 3 (21.4) 11 (23.4) 6 (23.1) 9 (25.7) 19 (40.4) 3 (8.6) 9 (34.6) 8 (22.9) 11 (31.4) 4 (15.4) 5 (19.2) 4 (15.4) B3d. can’t understand can’t B3d. 16 (26.2) thoughts racing B4a. 13 (21.0) fast thoughtsB4b. minds speededB4c. 13 (21.3) distractedB5. 5 (22.7) 15 (24.6) do moreB6a. 8 (36.4) 11 (30.6) energy more B6b. up keep can’t B6c. 26 (42.6) 7 (31.8) 8 (22.2) 8 (36.4) 21 (34.4) active more B6d. 16 (26.2) 15 (24.6) pacingB6e. 6 (16.7) 4 (28.6) 6 (16.7) trouble more B7a. 10 (16.7) 10 (45.5) away give n/aB7b. 12 (25.5) 6 (27.3) 6 (27.3) 29 (46.8) not carefulB7c. 4 (18.2) 16 (44.4) 26 (42.6) n/a 15 (41.7) n/a 10 (27.8) 9 (14.8) 2 (9.1) 11 (30.6) 9 (25.7) 15 (31.9) 27 (44.3) 12 (54.5) 5 (35.7) 8 (22.2) 12 (25.5) 7 (26.9) 8 (36.4) 6 (42.9) 14 (29.8) 16 (44.4) 4 (28.6) 3 (21.4) 21 (44.7) 2 (9.1) 8 (22.9) 10 (45.5) 17 (47.2) 15 (31.9) 12 (25.5) 12 (25.5) 17 (47.2) 7 (20.0) 1 (7.1) 8 (30.8) 6 (16.7) 8 (22.9) 4 (28.6) 16 (45.7) 6 (23.1) 5 (35.7) 11 (31.4) 7 (26.9) 9 (19.1) 25 (53.2) 10 (38.5) 10 (28.6) 11 (31.4) 6 (42.9) 10 (38.5) 21 (44.7) 1 (7.1) 6 (23.1) 4 (15.4) 21 (44.7) 18 (51.4) 3 (8.6) 8 (17.0) 13 (37.1) 11 (42.3) 7 (26.9) 13 (50.0) 5 (14.3) 4 (15.4) Disorder/ A1a. very very good very very A1a. 24 (39.3) 7 (31.8) 17 (47.2) B7d. sex interestB7d. C1. Interfere Endorsed. Symptoms and Diagnoses Mania of Table 23: Frequency/Percentage 8 (14.0) 33 (55.0) 1 (4.5) 12 (54.5) 5 (13.9) 20 (55.6) 3 (21.4) 5 (35.7) 5 (10.6) 28 (59.6) 5 (14.3) 23 (65.7) 10 (38.5) 3 (11.5) Symptom

Subsyndromal

Table 24 lists frequency counts of those who: 1) were above proposed cutoffs

(symptoms, duration, and impairment criteria met), 2) were above symptom count criteria, 3) fell short of proposed cutoffs by one or two symptoms but impairment was reported (subsyndromal), and 4) had at least one impairing symptom. Chi square analyses were used to examine the relationship between subsyndromal frequency and IQ and language. Significant IQ effects were noted for GAD (Chi Square=7.04, p =.008). Those with ID were more likely to be subsyndromal for GAD (n=10) versus meeting full criteria

(n=3). Those without ID were more likely to meet full GAD criteria (n=11) than to be subsyndromal (n=4). Chi Square/Fischer exact results examining the impact of IQ were not significant for the following disorders: ADHD-Inattentive (Fischer exact, p = .41),

ADHD-Hyperactive (Fischer exact, p = .12), ADHD-Combined (Fischer exact, p =.21),

ODD (Fischer exact, p = .57), CD (Fischer exact, p =.42), Social Phobia (Fischer exact, p = .20), Separation Anxiety (Fischer exact, p=.57),Compulsions (Fischer exact, p = .25),

Obsessions (Fischer exact, p = .21), OCD (Fischer exact, p = .30), MDD (Fischer exact, p = .24), Dysthymia (Fischer exact, p = .43), and Mania (Fischer exact, p = .78). No subjects were subsyndromal for specific phobia, so it was not included in the analyses.

The only significant effect noted for language was ODD (Fischer exact, p < .001).

Children who were verbal (n=42) were more likely to meet full ODD criteria (n=38) than those who were nonverbal (4 of 11 met criteria). Those who were nonverbal were more likely to be subsyndromal (n=7 vs. 3 for verbal children). Chi Square/Fischer exact results examining the impact of IQ were not significant for the following disorders:

ADHD-Inattentive (Fischer exact, p = .14), ADHD-Hyperactive (Fischer exact, p = .71),

ADHD-Combined (Fischer exact, p = .70 ), CD (Fischer exact, p = .60), Social Phobia

(Fischer exact, p =.77), Separation Anxiety (Fischer exact, p = .18), GAD (Fischer

exact, p = .05), Compulsions (Fischer exact, p =.07), OCD (Fischer exact, p =.58),

MDD (Fischer exact, p =.11), Dysthymia (Fischer exact, p =.11), Mania (Fischer exact, p = .38). No nonverbal individuals met criteria, or were subsyndromal, for obsessions.

Therefore, significance testing could not be conducted. Zero subjects were subsyndromal

for specific phobia, so it was not included in the analyses.

Symptoms Impairment Symptoms 1-2 Symptom Met Criteria DisorderADHD-IADHD-HADHD-CombODDCD ReportedPHO(100) 61 (100) 61 SOC PHO 61(100) SEP ANX Reported 55 (90.2)GAD (90.2) 55 OBB (90.2) 55 (100) 61 short of criteriaCOMP Criteria Met 9 (14.8)(41.0) 25 OCD(82.0) 50 for Disorder (9.8) 6 (86.9) 53 MDD(60.7) (8.2) 5 37 (88.5) 54 DYS(8.2) 5 MANIA (23.0) 14 (54.1) 33 (68.9) 42 (67.2) 41 HYPOMANIA (26.2) 16 (16.4) 10 (34.4) 21 (67.2) 41 (16.4) 10 (29.5) 18 (4.9) 3 Table 24:Rates of Individuals with Noted Endorsed, Symptoms and Impairment Meeting Diagnostic Full criteria(N=61). 4 (6.6) (4.9) 3 (47.5) 29 0 (49.2) 30 (11.5) (16.4) 7 10 (67.2) 41 (98.4)(95.1) 60 58 (78.7) 48 (21.3) 13 (23.0) 14 (95.1) 58 (98.4) 60 (23.0) 14 (18.0) 11 (31.1) 19 (57.4) 35 (75.4) 46 (21.3) 13 (60.7) 37 (57.4) 35 (11.5) 7 (13.1) 8 (57.4) 35 37 (60.7) (70.5) 43 (16.4) 10 (50.0) 30 (26.2) 16 (4.9) 3 9 (14.8) (8.2) 5 (24.6) 15 (11.5) 7 (9.8) 6 (3.3) 2 21(34.4) (67.2) 41 (24.6) 15 (4.9) 3 (21.3) 13 6 (9.8) (18.0) 11 6 (9.8) 8 (13.1) (41.0) 25 9 (14.8) 3 (4.9) 9 (14.8) 8 (13.1) 9 (14.8)

Behavioral equivalence. Analyses only indicated two associations among

behavioral equivalents and P-ChIPS internalizing diagnoses. The item “Physically attacks people” was associated with hypomania (Fischer exact, p = .05) and the item “Engages in meaningless body movements” was associated with MDD (Fischer exact, p = .01). The items “Knowingly destroys property,” “Repeatedly flaps or waves hands, fingers, or objects,” “Physically harms or hurts self on purpose,” and “Engages in temper tantrums,” were not associated with any internalizing diagnoses.

67 CHAPTER 4

DISCUSSION

Primary Aims: Validation of P-ChIPS

Interrater Reliability

Reliability is a prerequisite for establishing the validity of diagnostic instruments

and psychiatric disorders in ASDs. It implies precision and a lack of distortion in the

diagnostic process (Kerlinger & Lee, 2000). Therefore, examining the reliability of the

DSM criteria as measured by the P-ChIPS was the first objective of this study.

The majority of interrater reliability kappa values were in the fair to excellent range. A

lower kappa coefficient was noted for ADHD-Combined type. A large proportion of the

sample (67%) met criteria for this disorder, so results are not due to a low base rate.

Overall agreement for ADHD-C was only fair (53%) despite an ICC value in the

excellent range (.94). In this case, the low kappa value may indicate a discrepancy in the

diagnostic decision-making process. P-ChIPS ADHD-Combined criteria require six

Inattentive and six Hyperactive symptoms. So, one rater who endorses five Inattentive

and six Hyperactive symptoms would “disagree” with another rater whose ratings are

above both cutoffs (i.e., six of both). This would still result in a high ICC. This suggests

that rater disagreement may be due to discrepancies on only a few symptoms.

68 Surprisingly, no IQ effects were noted in interrater reliability results. Both raters

had extensive experience with individuals with ASD and ID, which no doubt aided in

achieving high levels of agreement. An example of where this made a difference is in

distinguishing repetitive behavior/stereotypy associated with ASDs from OCD-

compulsions. Results may differ among individuals with less understanding of autism and

less experience interviewing parents of children with developmental disabilities.

Internal Consistency

Internal Consistency values ranged from poor to good (.30-.89). It was

hypothesized that diagnoses with more overt, observable behaviors would have the higher

internal consistency values. This was true for ADHD subtypes and ODD, with values

ranging from .80-.89. The exception was CD (i.e., .65), whose internal consistency value

was lower than that of a number of internalizing disorders. A careful examination of the

CD items and the rate at which each was endorsed by the sample can explain this

discrepancy. Symptoms more likely to be endorsed were those with aggressive behavior

or property damage. Rule violations were endorsed for very few individuals. The lower

internal consistency might reflect that different subcategories of CD (aggressive, property

destruction, deceitfulness) were differentially endorsed.

Internal consistency values were higher than expected for MDD. While MDD is

an internalizing disorder, the P-ChIPS asks a number of questions about observable behavior. This paired with the high number of items in the MDD section (i.e., 56) may have led to higher-than-expected values.

Lower internal consistency values for GAD and OCD were expected. This study modified the interview procedure by asking all the questions rather than just cardinal questions. Therefore, if parents reported that the child did not worry or had trouble letting go of worry, they were still asked if there were times their child exhibited the physical symptoms of GAD (e.g., was edgy). Inspection of symptom distributions shows that a number of the youngsters exhibited the physical symptoms of GAD without parents reporting worry. OCD was similar. While parents reported that 34% said or did things over and over, very few (2-16%) reported on the corresponding cognitive symptoms of

OCD.

Concordance P-ChIPS and CASI-4

Kappa and ICC indicated fair concordance between the P-ChIPS and the CASI

(i.e., .41 < kappa < .57) for the majority of disorders. Highest values were noted for

Disruptive disorders (.57) and GAD (.50). Higher kappa values for the disruptive

behavior disorders are consistent with the study hypothesis. However, it was not expected

that GAD would have a higher kappa than ADHD and ODD. The hypothesis was based

on the assumption that internalizing disorders would be more difficult to assess reliably.

The difficulty assessing anxiety disorders probably led to under-identification by both

instruments. It is probable that both instruments are not sensitive to anxiety as exhibited

by those with lower IQs. The noticeable impact of IQ on GAD rates may help to explain

this. While not statistically significant because of sample size, GAD values were higher

in those with IQ≥70 (i.e., k = .64) compared with those with IQ < 70 (i.e., k =.24). The higher kappa value might indicate that in higher functioning children (the majority of

those diagnosed with GAD) it is clear when anxiety is present and impairing. It is not as

clear in lower-functioning children, who therefore were not identified by either

instrument.

Lower concordance in ADHD was not expected. However, a similar pattern was also noted in the Fristad et al. (1998) study. The number of observable behaviors present in the ADHD items makes this finding hard to reconcile. Potentially, the issue is with parents recognizing these particular behaviors as causing a problem. A number of items also do not merely assess the presence of a symptom, but also ask whether the child gets in trouble for the behavior. It is possible that parents compensated such that they did not recognize how impairing the symptom was or that the child did not get in trouble for the behavior because parents were addressing more severe and pervasive behavioral issues associated with the ASD.

The anxiety disorders (with the exception of GAD) had lower kappa values than those reported in non-ASD samples, as was hypothesized. However, kappa values for the majority of disorders were comparable to those reported in non-ASD samples. Percent agreement for most disorders was good with values at or above 70%. This suggests that they P-ChIPS may be appropriate for use in this sample. However, care should be given when administering the anxiety disorder subtests, and some modification may be needed to the ADHD questions.

Convergence with NCBRF

The NCBRF is an empirically-derived dimensional measure of emotional and behavior problems. A number of the subscales have content that overlaps with psychiatric disorders, but subscales were derived from statistical analyses rather than DSM diagnostic criteria. Therefore, I expected some significant associations, but not overwhelming agreement. This was the case with correlations. The only large correlations were between the NCBRF Conduct Problems subscale and P-ChIPS CD and

ODD. This is not surprising, as the Conduct Problems subscale contains a number of items that overlap with DSM criteria (e.g., Argues with others, Knowingly destroys

property).

P-ChIPS derived diagnoses as a whole converged as expected with related

NCBRF subscales in the Mann-Whiney U analyses. An ADHD-Combined diagnosis was

associated with higher NCBRF Hyperactivity scores. Those with ODD and CD had

significantly higher scores on the Conduct Problems subscale. Higher scores on the

Insecure/Anxious subscale were associated with diagnoses of GAD, separation anxiety,

and OCD. Those with OCD-Compulsions scored significantly higher on the

Insecure/Anxious and Self-Isolated/Ritualistic subscales but did not score higher on the

Self-injury/Stereotypic subscale. This suggests that the P-ChIPS did correctly distinguish

stereotypic behavior from compulsions associated with OCD. Depression is commonly

linked to anxiety, so it is not surprising also that those with MDD and dysthymia scored

higher on the Insecure/Anxious subscale.

Not expected was the association between the Insecure/Anxious subscale and

ADHD-Hyperactivity/ ODD. The hyperactive symptoms may be a manifestation of

physical anxiety symptoms. The relationship between ODD and the Insecure/Anxious

subscale may be due to the Insecure/Anxious subscale’s content. In this study ODD was

more likely in verbal children. A large proportion of the NCBRF’s items have content

which requires the child have language (e.g., Feels others are against him/her).

The convergence and divergence with the NCBRF, an instrument validated in

ASD samples (with and without ID; Lecavalier, Aman, Hammer, Stoica, & Mathews,

2004), provide evidence of the validity of the P-ChIPS in ASDs,

P-ChIPS Face Validity

While administering the P-ChIPS, a number of face and content validity

challenges came to light. This included broader concerns such as question wording and

assessment of impairment, as well as problems with the actual symptoms assessed. The

latter is more relevant to the larger issues of describing the clinical picture of psychiatric disorders and will be discussed below. A few P-ChIPS questions seemed inappropriate for the majority of the ASD sample. One example was asking if grades dropped/were affected by a symptom. Most children are assessed according to IEP goals rather than grades. A slight modification to this question to include IEP goals would make it applicable to a wider range of children. Additionally, as noted earlier, a number of

ADHD questions ask if the child gets in trouble for a behavior. Many of the parents stated that the behavior occurred at a high rate (e.g., running and climbing), but that their

child did not get in trouble for it. In this sample a more appropriate question might be one

that assesses the presence, frequency, and severity of behavior rather than the

consequence parents give for the behavior. Additionally, the conduct disorder symptom,

stealing, may require slight modification. While not endorsed by a large proportion of the

sample, 26% (n=16) of parents responded that their child did steal. However, only 15%

(n=9) responded that the child understood personal property when asked. Consideration

should be given to modifying this question to clarify the child’s understanding and

intention.

A more pervasive content issue is the P-ChIPS’s assessment of impairment. At

the end of the assessment of every disorder the interviewer is to ask if the problems cause

trouble at home, school, or with other children. This question was problematic for a

number of different reasons. For instance, impairment defined as causing trouble to other children is not always applicable for these children. This question is difficult to interpret because social deficits are a core feature of ASDs. Parents had difficultly responding to this question and asking it seemed to decrease the face validity of the instrument. More problematic was parents’ overall ability to assess the impairment caused by symptoms of a particular disorder. This was sometimes due to modifications parents and teachers had in place for the child. Common responses indicated that the symptoms did not cause problems because of supports (e.g., one-on-one aide) that parents and teachers had in place. Also, it appeared as though a type of diagnostic overshadowing occurred. Parents seemed to see most of the symptoms as secondary to the autism. This was especially true for the lower functioning children. They have made so many accommodations for their child that often they did not even realize they were doing it anymore. For example, when asked if her child had difficulty finishing schoolwork one mother said no. Later she told the interviewer that her child had a one-on-one aide at school. Without the aide the child would not be able to attend to schoolwork long enough to finish it, but with the aide he was able to. Parents also stated that symptoms were not problematic because they avoided situations where the child would typically have difficulties. For example, some parents avoided placing demands on a child that might result in a temper tantrum. Clearly the symptoms have impacted the families, yet they answer negatively to questions of impairment. Consideration should be given to modifying the wording of this question in

ASD samples.

Secondary aims: Elucidation of Clinical Picture

Distribution of Diagnoses and Symptoms

Examining the distribution of endorsed symptoms can provide important

information about the P-ChIPS as well as help to define the clinical picture of psychiatric

disorders. Looking at differential rates of diagnoses and symptoms among different

groups of children (based on IQ, language skills, or age) can provide information on

which populations this instrument would be most appropriate and potentially suggest

certain modifications. If a symptom is endorsed by no or very few individuals, it may

suggest that 1) it is not present in ASDs, 2) not appropriate for this population , or 3) the

P-ChIPS question is not written in a manner which makes it sensitive to the manifestation

of the symptom. Differential endorsement based solely on subject characteristics such as

IQ, language skills, or age can provide further information on the validity of the

instrument and the DSM criteria.

The first step is to look at the rates of symptoms and disorders in this population

as a whole, then consider the effects of subject characteristics, and finally scrutinize symptom frequencies to determine if they might help to explain observed phenomena.

Over two-thirds of the sample met full criteria for ODD (75%), ADHD-Combined type

(67%), and specific phobia (67%). Also common were CD (49%) and GAD (25%).

Previous studies have found specific phobia and ADHD to be common disorders in this population, with rates around 30-40% (Leyfer et al., 2006; Simonoff et al., 2008; deBruin et al., 2007). deBruin et al. and Simonoff et al. also found that ODD was common in their sample. Leyfer et al. did not report rates of CD, but those reported by deBruin et al.

(10%) and Simonoff et al. (3.2%) are markedly lower than those found in this study

(49%). The frequency rates of most disorders in the current study are higher than those reported in previous studies. In the current study, children were recruited mainly from clinics, rather than in the community. It is expected that rates would be higher in this clinically-referred sample. When comparing the rates to a study with another clinically- referred sample (Gadow et al., 2005) rates were more similar. However, ODD and

ADHD rates were still higher in this current study compared with Gadow et al.’s clinical sample. It is possible that rates may reflect a lower specificity in the P-ChIPS compared with other instruments. It may also be a reflection of the subject characteristics of the current sample. A large proportion of individuals in this study were referred from behavioral or psychiatric clinics that primarily treated behaviors associated with externalizing (rather than internalizing) disorders. Examining symptom frequency and the impact of IQ, language, and age might help to explain discrepancies.

Disruptive Behavior Disorders

Attention Deficit Hyperactivity Disorder. The frequency of ADHD was not impacted by IQ or language. Children under 12 were more likely to meet criteria for

ADHD-Combined type. This is consistent with patterns in the typically-developing and

ID literature (Witwer & Lecavalier, 2008). Endorsed ADHD items were distributed among all symptoms, with at least one-third of the sample meeting criteria for all symptoms. Those with lower IQ were more likely to Push their way into groups and Push ahead in line. Seventy-two percent of those with IQ <70 were noted to Push ahead in line. Consideration should be given to refining or excluding these items, as they may reflect an interaction of low IQ paired with impaired social understanding often present in

ASDs rather than symptoms of ADHD. This may be adversely impacting the specificity of the P-ChIPS. Also of similar concern is the item Interrupts others, which was endorsed by 97% of the IQ <70 sample.

Oppositional Defiant Disorder. Age and IQ did not impact the frequency of ODD, but those who were verbal were more likely to meet criteria for the disorder. Within the

ODD criteria, a high percentage of the sample was reported to lose their temper (92%) or have big temper tantrums often (66%). Over 50% of the sample endorsed the following items: Talk back, Broke rules, Refuse to do what told to do, Bug others, and Easily made mad. Other symptoms were endorsed by at least one-third of the sample. As with ADHD, children with ASDs exhibited all symptoms with relatively even distribution. Language impacted items as would be expected. Those with language were more likely to be rated positively on the following symptoms: Argues with parents, Argues with teachers, Bugs others, and Blames others for mistakes. Consideration should be given to modifying

DSM criteria when applying them to nonverbal individuals. If the P-ChIPS is to be used

in nonverbal individuals, these items might need to be refined or omitted and the criteria

cut-off number may need to be amended.

Conduct Disorder. The rate of CD did not vary according to IQ or language.

Younger children were more likely to meet criteria for CD. IQ, language, and age had

minimal impact on symptoms frequency. CD symptoms were not as evenly distributed as

they were for ADHD and ODD. The majority of individuals appear to fall into the

subgroups of 1.) aggressive conduct that threatens physical harm to others, 2)

nonagressive conduct that causes property damage, and 3) deceitfulness. Diagnoses

appear to be based largely on items measuring impulsive, disruptive behavior (trouble for

fights, damaged property). Over 40% of parent reported that their children got into

physical fights and damaged property. DSM diagnostic criteria only require one more

symptom in addition to these two for a diagnosis of CD. Noticeably less common were

serious violation of rules. These symptoms were endorsed by less than 10% of the sample

(i.e., Broken into car or building, Skipped school, Stay out late, Run away, and sexual

predator behavior). These are typical CD symptoms, but require higher social

intelligence, planning, and independence. They were relatively uncommon in this sample.

This may be more of an issue of the validity of CD in ASDs rather than a limitation of the

P-ChIPS. Further consideration should be given to the expression of CD in ASDs. The

question is whether CD symptoms truly reflect the presence of CD, the expression of a mood disorder, or the individual’s inability to appropriately respond to social situations.

Anxiety Disorders

Generalized Anxiety Disorder. Consistent with hypotheses, those with IQ<70 and no language were less likely to have a diagnosis of GAD. The associated physical symptoms (e.g., feel edgy, have trouble sleeping) were endorsed across groups. The separation occurred with questions requiring the ability of the child to voice worry. Fifty- five percent of those with IQs ≥70 worried more than other children their age, compared with only 14% of those with IQs < 70. Interestingly, 36% of parents of children with IQs

< 70 reported that their children had difficulty calming or relaxing when worrying.

Because both of these symptoms are required by the P-ChIPS, only 8% of children with

IQs < 70 were diagnosed according to the P-ChIPS with GAD. This is compared to 50% of those with IQs > 70. Consideration could be given to revising the cardinal questions such that either symptom is necessary rather than both in this population. Ten parents

(28%) of those with IQs < 70 reported impairment from GAD symptoms but only 8% were identified with the P-ChIPS. Also at issue here is how of the diagnostic criteria of

GAD are interpreted. The P-ChIPS asks if the child “worry more than other kids his/her age (for example about things coming up in the future such as school, small mistakes he/she made in the past, taking a test, or seeing the dentist) The DSM-IV-TR criteria are more vague about the nature of worries (events or activities). This may be more appropriate for children on the autism spectrum rather than the examples given in the P-

ChIPS item which appear to require an individual have some sense of social awareness and intelligence to recognize symptoms and report them.

Specific Phobia. A high rate of phobias in this sample is consistent with other

studies. However, the rate in this study, 67.5% is much higher than that of the Leyfer et al

(2006) study (i.e., 40%). This may reflect the fact that the sample was largely clinically

referred and subjects were required to have significant behavioral/psychiatric problems.

Also, the wording of the P-ChIPS often made if difficult to assess the severity of fear and impact. At issue here is that children with ASDs largely did not appear to discuss fears.

All supposition regarding impairment is based on observable behavior rather than voiced fears. Symptoms requiring children be able to communicate fears were associated with

IQ and language. The very first phobia question relies on the informants’ interpretation of child’s mood as being fearful/anxious. This can be problematic. For example, some parents reported that their child had a fear of crowds. However, upon further probing, it became apparent that children would go off on their own when in a crowd−a behavior associated with social deficits rather than fearful avoidance. Others reported a fear of loud noises. Often children with ASDs are sensitive to loud noises. Is sensitivity to noise the same as fear? Some parents interpreted it in this manner. Some criteria become problematic as well. For instance, a child who throws a tantrum in the doctor’s office before a hypodermic injection and has to be held down would meet criteria for phobias.

In this population it is nearly impossible for most children to assess if the thought of the injection interferes with school or other daily routines, but it does interfere with doctors’ visits (i.e., meets criteria for item assessing interference with other activities). This means that the child exhibiting the behaviors above would meet criteria for a phobia. Often this interviewer finished this section not convinced that the child truly had a phobia, despite

meeting P-ChIPS criteria.

Social Phobia and Separation Anxiety. These disorders and their symptoms were

not impacted by IQ, language, or age. Nonverbal individuals exhibited very few to none

of these symptoms. Insufficient power probably accounts for the lack of significant

results in this case. Only 10 individuals (16%) met criteria for social phobia and 9 (15%)

for separation anxiety disorder which is markedly lower than the rate of 29% found in the

Simonoff et al. (2008) population-based study. This is counter to what would be expected when comparing a clinical to a population-based sample. This could be due to different instrument content, a different conceptualization of fear (versus lack of interest in social

interaction), or a function of the IQs of the samples. Unfortunately, Simonoff et al. did

not detail their interview process or the mean and range of IQs, so it is impossible to

determine the cause of the discrepancy.

Obsessive-Compulsive Disorder. The rate of OCD was not impacted by IQ, language, or age. Surprisingly, symptom endorsement was not impacted by IQ. One would have expected those with higher IQs to be more likely to express why they engaged in compulsions. Apparently, expressing these ideas is difficult for most children with ASDs regardless of age or IQ. The rate of OCD in this clinically-referred sample was significantly lower (5%) than that of the Leyfer et al (2006) study (37%). The results of this study were more in line with that of deBruin et al (2007) (6%) and Simonoff et al

(2008) (8.2%). Leyfer et al. modified their criteria which may have affected the specificity of their instrument. Unfortunately, they did not state how criteria were modified, making comparisons difficult.

Examination of the symptom frequencies provides a possible explanation to the

discrepancy between the Leyfer et al. (2006) study and the current one. In the current

study, 34% of the sample exhibited the symptom of having to say or do something over

and over. This is similar to the rate of OCD in the Leyfer et al. (2006) sample. However,

only 8-16% of those in this study met the other two necessary criteria for compulsions.

Further, only 10% of the sample met criteria for obsessions. This suggests that Leyfer et

al. may have modified their criteria for OCD. What is not clear is if their sample was

exhibiting OCD, or rather just the compulsions associated with an ASD diagnosis. The

DSM-IV-TR does not address this differential diagnosis. Very little has been done in the

field to delineate this distinction. This is an important issue, because lack of a clear

understanding of the distinction between repetitive behaviors and a diagnosis of OCD has the potential to impact negatively research and treatment in children in ASDs with OCD and/or repetitive behaviors.

Mood Disorders

Major Depressive Disorder and Dysthymia. MDD and Dysthymia rates did not

differ by IQ, language, or age. The most common symptoms were those that addressed

behaviors related to tantrums (i.e., crying, yelling, and aggression toward property) and

sleep disturbance. Also common were psychomotor changes that reflect ADHD symptoms or noncompliance. These are symptoms that are commonly present in those with ASD regardless of the presence of a mood disorder (Lecavalier et al., 2006;

Hoffman, Sweeney, Gilliam, & Lopez-Wagner, 2006). An important distinction appears to be in regard to symptom frequency and duration. While 48% were reported to be depressed, crying or moody, only 30% were said to do this every day or every other day

and only for 16% did it last most of the day. This is an important diagnostic distinction.

Those with ASD had episodes of tantrums and moodiness but such that it appeared to be episodic throughout the day. The DSM and the P-ChIPS application of its diagnostic criteria appear to be successful in making this distinction when used in children with

ASDs. Consideration should be given to modifying the instrument so it emphasizes not only duration as assessed at the end of the disorder’s section (how many days/months?) but also for various symptoms assessed (e.g., Section A: 1b and 1c: does it occur all day and almost every day?). This will provide a more prominent prompt for interviewers so that they do not skim over these important questions.

Manic Episode and Hypomania. The frequency of manic episodes and hypomania

did not differ according to IQ, language, or age. The mania symptoms Elevated mood and

Very irritable were quite common in this sample, occurring in 39% and 62% respectively. Other common symptoms were Talks too much (51%), “More active than usual (e.g., pacing) (43%), Gets hurt because not careful (44%), Distracted more by little things (43%), Gets in trouble more than usual (47%). Sleep disturbance occurred in about one-fifth to one-third of the sample. Examination of these symptoms leads to more question of the validity of the use of these particular symptoms. They may not be appropriate in individuals with ASDs. It is not clear if the children were exhibiting symptoms of a comorbid disorder (i.e., bipolar), or rather symptoms associated commonly with autism. Most frequently endorsed symptoms are related to the associated features of autism. For instance, inappropriate emotionality (giddiness for no reason) is used diagnostically on ADI-R. Tantrums and irritability are quite common in these children, as are sleep disturbances (Lecavalier et al., 2006; Hoffman, Sweeney, Gilliam,

& Lopez-Wagner, 2006).

The distinction may again come down to duration. Despite the high rate of symptom endorsement, very few children met duration for manic episode (15%) or hypomania (15%). Symptoms appear to be episodic rather than occurring at a high rate over a defined amount of time (e.g., two weeks). The P-ChIPS and DSM are successful in making this distinction based on duration. The results of this study suggest that the instrument be modified to specifically assess episodic change in mood, sleep, language, and emotionality (change from baseline) rather than chronicity associated with ASDs.

Across disorders, those with IQ <70 had fewer reported symptoms than those with

IQ ≥70. The exception to this was those disorders with more overt behaviors. This was also the case for observable MDD symptoms (e.g., weight gain and sleep disturbance).

This may suggest that the number of symptoms required to meet diagnostic criteria be lowered in lower functioning children.

Subsyndromal

Comparing those who are subsyndromal to those meeting full diagnostic criteria is important to elucidate the clinical picture of psychiatric disorders. The DSM takes dimensional phenomena and applies criteria/cutoffs to make them categorical. It is not clear that the same cutoffs/criteria apply for those with ASD, or if they should be modified based on language and IQ. As illustrated above, many times the existing criteria are inappropriate for children with ASDs. Children with ASDs were unlikely to exhibit symptoms requiring them to voice thoughts of guilt, hopelessness, and death associated with depression. Very few reported thoughts of grandiosity associated with Mania and fewer yet exhibited the serious rule violations of CD. Additionally, analyses indicated that those with lower IQs were more likely to be subsyndromal for GAD, falling short of criteria by one or two symptoms. Nonverbal individuals were more likely to be subsyndromal for ODD. This poses the question, should individuals with and without

ASD be required to have the same number of symptoms to meet criteria for a disorder?

Children may benefit from treatment, yet fall just short of diagnostic criteria. Arguments such as these provide support for further examination of subsyndromal disorders.

Researchers can apply the Robins and Guze (1970) model to examine

subsyndromal questions. Looking at the pattern of external variables could help to

differentiate (or not) these children from those with and without the disorder. If children

classified as subsyndromal have different patterns on external variables such as response

to treatment, course, and patterns of comorbidity than those meeting criteria, than it is

possible that they should not be classified as having the disorder. However, if patterns are

similar, this could suggest that criteria should be modified to include these individuals.

Behavioral equivalence

This study also examined behavioral equivalents which have the potential to

provide a clearer picture of psychiatric disorders in this population. Behavioral equivalence analyses were largely nonsignificant. The items Knowingly destroys

property, Repeatedly flaps or waves hands, fingers, or objects, and Physically harms or

hurts self on purpose, and Tantrums, were not associated with any internalizing diagnoses. One stereotypy item, “Engages in meaningless body movements” was associated with MDD. This may provide some support for behavioral equivalents.

However, other stereotypy and self-injury items were not associated with MDD. Further, both correlational and Mann-Whitney U analyses failed to find a relationship between

NCBRF Self-injury/Stereotypy subscale and MDD/dysthymia. The few significant findings may also be a result of Type 1 error, as this study entailed a large number of analyses. The nominal results of the current study are in contrast to the finding of Reiss and Rojahn (1993) and Matson et al, (1999). Reiss and Rojahn found a four-fold increase in aggression among those with depression. Matson et al. found that 40% of those diagnosed with depression had tantrums and stereotypy. However, Reiss and Rojahn as

well as Matson et al. did not report the characteristic of their non-depressed subjects. It is

not clear if the behavioral equivalents were able to distinguish among those with different psychiatric disorders (Depression versus GAD) or if behavioral equivalent rates were higher because those in the comparison group did not have any psychiatric disorder. The

current study sample had a high rate of psychiatric disorders. It is possible that while

comparing those with depression to those with no psychiatric disorders, the

differentiation by behavioral equivalents is obvious. It may become muddled when

comparing behavioral equivalents among individuals with other emotional and behavior

problems. In this sample, potential behavioral equivalents might just be indicators of overall impairment rather than specific disorders. Of course, it may be argued that this study was underpowered for such analyses. However, it is more likely that the use of behavioral equivalents is not as clear- cut as purported by some.

Limitations

Limitations of this study are primarily related to instruments used and sample size

and characteristics. The original study design included concordance analyses with

psychiatric diagnoses obtained by outside professionals. Replicating this would have

allowed for a direct study comparison. However, obstacles made this difficult in the

current study and ultimately led to a change in study analyses. First, response rate of

psychiatrists was low (despite repeated attempts at obtaining responses). Second, even

when responses were obtained, there was a large heterogeneity in diagnostic reporting.

Some treating doctors reported comorbid psychiatric diagnoses. Others reported only the

ASD diagnosis and no comorbid disorders. Some of the latter doctors may have been

displaying diagnostic overshadowing, or they may have believed that their patients were

exhibiting phenocopies of disorders, rather than true psychiatric disorders. The

heterogeneity in reporting illustrates the need for a valid assessment procedure and

classification system. An ideal design would have been to have psychiatrists familiar with

ASDs diagnosing the children and providing rationales for their diagnosis. However this

was not financially feasible for this study.

The discrepant results between the CASI and the P-ChIPS are at least partially

due to the CASI’s reliance on parental judgment rather than a trained interviewer. The

difficulty parents of children with ASDs experienced in interpreting questions, even when

administered by an interviewer, has been discussed above. Future studies might benefit

from including another structured interview for comparison. Potentially, Leyfer et al.’s

(2006) instrument, the Autism Comorbidity Index, might be available for use in future

studies.

The sample size and characteristics also limited the analyses of this study. A

larger sample size would have given more power to IQ, language, and age analyses. This

was especially true for the language analyses which were underpowered. Future studies

should seek to include more nonverbal individuals and larger sample sizes. While this

study sought to recruit participants from a wide range of treatment venues (i.e.,

behavioral intervention clinic, psychiatrists, school-based programs) it is not clear that

this is a representative sample of all ASD youngsters with significant emotional and behavioral problems. Presumably those in the behavioral intervention program were referred largely for disruptive behaviors. It is possible that those with internalizing disorders were underrepresented in this sample.

Another limitation of this study was that it did not examine the relationship between psychiatric symptoms and disorders and autism symptoms. This was limited by the instrument used. ADI-R social and communication diagnostic domain totals, in large part, are based on the age of 4-5 years and questions assessing current symptoms vary widely by age. What would be of interest is the relationship between current autism symptoms and psychiatric disorders. Consideration should be given to using an instrument which assesses the same symptoms across age groups in future studies to examine this relationship which might provide important additional information.

Implications

This study has a number of implications for the use of the P-ChIPS in children with ASDs. First, the P-ChIPS appears largely to be appropriate in this sample with small modifications. These include modifying some question content, an emphasis on the episodic nature of symptoms, and changes in the assessment of impairment.

Consideration should be given to modifying questions with social content (e.g., ADHD and GAD). Some disorders may require modifications when being used in nonverbal individuals. It is important that the duration elements of the P-ChIPS be emphasized.

While there is overlap between ASD symptoms and other psychiatric disorders, the P-

ChIPS requirement of duration provides a needed distinction for a number of disorders

(i.e., MDD, dysthymia, and mania). Changing the P-ChIPS assessment of impairment should also be considered. This might include modifying the question so that it is more sensitive to impairment in this population. That may entail including specific examples such as, Do these problems cause trouble at home (e.g., cause you to avoid situations)? and Do these symptoms cause problems at school (e.g., require modifications/supports

such as a one-on-one aide). Consideration should also be given to improving the face validity of the instrument by excluding the question, Do these problems cause problems with other kids?

This study highlights broader issues related to the assessment of psychiatric disorders in this population. In order to study the etiology, treatment and prognosis of psychiatric disorders we have to be able to identify and describe them reliably.

Structured interviews, such as the P-ChIPS, will be useful in the endeavor. However, this study illustrates that certain modifications may be necessary. Researchers ought not to use existing instruments without carefully considering question content and how it is applied to those with ASDs. This is especially true in those who are nonverbal and/or with lower IQs.

Directions for Future Research

The P-ChIPS will be a useful instrument in this population, but more research on its reliability and validity are needed. Future studies should examine the P-ChIPs concordance with psychiatric diagnoses to further validate the P-ChIPS. When conducting these studies, consideration should be given toward modifying the P-ChIPS criteria as suggested above. Any modifications should be carefully documented to allow for replication. Future research should also strive to include larger samples, which would provide more power and allow for a more-detailed examination of subject characteristics.

This study began the process outlined by Robins and Guze (1970), examining the clinical picture of psychiatric disorders in ASDs. The examination of symptom content raised questions as to the validity of some psychiatric disorder criteria in ASDs. Further attention should be given to OCD and mania and their overlap with symptoms of ASDs.

The questions raised by this study about the validity of CD in this population also deserve further study. Additionally, examining differences between subsyndromal individuals and those above syndrome cutoffs on external variables will aid in elucidating the clinical picture.

This study only began to explore the clinical picture of psychiatric disorders in

ASDs. Further research is needed to continue the steps outlined by Robins and Guze.

Some studies have begun to examine the relationship between disorders (e.g.,

ADHD/ODD) and a limited number of external variables (e.g., adaptive and family functioning). Research on a wider breadth of disorders and more external variables is needed. Further, follow-up studies examining differential outcome and genetic studies will be essential to this process.

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Appendix A: Demographic Form

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Demographic Information Today’s Date__ __/__ __/______

1. Child’s Date of Birth __ __ / __ __ / ______2. Child’s Gender Male (1) Female (2)

m m d d y y y y

3. Child’s Race/Ethnicity White, Non-Hispanic (1) Black, Non-Hispanic (2) Hispanic (3) Asian or Pacific Islander (4) Other(5)______

4a. Is your child CURRENTLY diagnosed with any of the following? (please check one) Asperger’s Disorder (1) Autism (2) Pervasive Developmental Disorder-Not Otherwise Specified (3) Other (4)______

4b. At what age did he/she obtain this diagnosis? ______/___ 4c. Who diagnosed your child?______Years Months

5. Please list any psychiatric disorders with which your child is current diagnosed:

6. Please list any previous psychiatric diagnoses with which your child has been diagnosed:

______

7. Please list any behavioral/psychological treatment your child is receiving: Treatment: Purpose/Diagnosis:

______

9. Please list any medications your child is currently taking to control his/her emotions and behavior (e.g., Prozac, Ritalin, Risperdal, etc ). Medication/Dose: Purpose/Diagnosis: ______

______

10. Please list any other complimentary and/or alternative treatment your child is receiving (e.g., vitamins, minerals).

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Treatment: Purpose/Diagnosis:

______

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______PARENT/GUARDIAN INFORMATION

1. Your Age______

2. Your Relationship to child Mother (1) Guardian (3) Father (2) Other (4) ______

3. What is your highest level of education? Attended High School (1) Graduated High School (2) Attended College (3) Graduated College (4) Professional/Graduate School (5) Other (6)______

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Appendix B: Consent Form

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The Ohio State University Consent to Participate in Research

PSYCHOPATHOLOGY IN YOUNGSTERS WITH Study Title: AUTISM SPECTRUM DISORDERS Researcher: Luc Lecavalier, PhD and Andrea Witwer, M.A.

Sponsor: None

This is a consent form for research participation. It contains important information about this study and what to expect if you decide to participate. Your participation is voluntary. Please consider the information carefully. Feel free to ask questions before making your decision whether or not to participate. If you decide to participate, you will be asked to sign this form and will receive a copy of the form. Purpose: The primary purpose of the current study will be to examine psychiatric symptoms as assessed by a structured interview in youngsters with Autism Spectrum Disorders (ASDs). A secondary purpose of this study will be to look at the relationship between psychiatric disorders and other factors.

Procedures/Tasks: The study assessments will take place either at your family home or in the Nisonger Center, depending on your availability/convenience. The measures include two rating scales that you will complete and two investigator-administered interviews regarding your child behavior. Your child will be administered a Brief IQ test if he/she has not had another IQ test completed within the last year. You will also be asked to sign a release of information so that psychological/psychiatric reports can be reviewed for previous diagnoses and IQ testing, if applicable. After the assessment, the child psychiatrist or psychologist will be contacted for other pertinent information.

With your permission, the structured psychiatric interview portion will be videotaped. You agree to be videotaped You do not agree to be videotaped

Duration: You may exit the study at any time. If you decide to stop participating in the study, there will be no penalty to you, and you will not lose any benefits to which you are otherwise entitled. Your decision will not affect your future relationship with The Ohio State University. The assessment should take approximately 4-5 hours and can be split into two visits if necessary.

Risks and Benefits: Personal information will be collected during the interview. However, all information will be locked and kept confidential. There is a small likelihood that you or your child will experience psychological stress when completing study

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measures. The frequency of this experience is very small and the potential severity of the discomfort is minimal.

There are direct benefits to you as well as to society. You will receive a summary of the assessment results, which you are free to share with professionals working with your child. Studying the presentation of psychiatric symptoms in youngsters with ASD will lead to better diagnostic practices. Valid assessment methods can then be used to further research on the causes and treatment of psychiatric disorders in children with ASDs.

Confidentiality: Efforts will be made to keep your study-related information confidential. However, there may be circumstances where this information must be released. For example, personal information regarding your participation in this study may be disclosed if required by state law. Also, your records may be reviewed by the following groups (as applicable to the research):  Office for Human Research Protections or other federal, state, or international regulatory agencies;  The Ohio State University Institutional Review Board or Office of Responsible Research Practices;  The sponsor, if any, or agency (including the Food and Drug Administration for FDA-regulated research) supporting the study.

Incentives: You will be mailed a summary of the results of the assessment and a check for $50 for your time after completing the study. The check will be made payable to you, the parent/guardian. If you choose to withdraw from the study before its completion your payment amount will be based on the portion of the study you’ve completed.”

By law, payments to subjects are considered taxable income. If you are an OSU employee, any compensation you receive as a result of participating in the study will be made through the payroll system and applicable taxes will be deducted.

Participant Rights: You may refuse to participate in this study without penalty or loss of benefits to which you are otherwise entitled. If you are a student or employee at Ohio State, your decision will not affect your grades or employment status.

If you choose to participate in the study, you may discontinue participation at any time without penalty or loss of benefits. By signing this form, you do not give up any personal legal rights you may have as a participant in this study.

An Institutional Review Board responsible for human subjects research at The Ohio State University reviewed this research project and found it to be acceptable, according to applicable state and federal regulations and University policies designed to protect the rights and welfare of participants in research.

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Contacts and Questions: For questions, concerns, or complaints about the study you may contact Andrea Witwer (247-8028) or Luc Lecavalier, PhD (292-2378).

For questions about your rights as a participant in this study or to discuss other study- related concerns or complaints with someone who is not part of the research team, you may contact Ms. Sandra Meadows in the Office of Responsible Research Practices at 1- 800-678-6251.

If you are injured as a result of participating in this study or for questions about a study- related injury, you may contact Andrea Witwer (247-8028) or Luc Lecavalier, PhD (292-2378).

Signing the consent form

I have read (or someone has read to me) this form and I am aware that I am being asked to participate in a research study. I have had the opportunity to ask questions and have had them answered to my satisfaction. I voluntarily agree to participate in this study. I also am aware that by signing this form I am also giving permission for my child to participate in this study.

I am not giving up any legal rights by signing this form. I will be given a copy of this form.

Printed name of subject Signature of subject

AM/PM Date and time

Printed name of person authorized to consent for subject Signature of person authorized to consent for subject (when applicable) (when applicable)

AM/PM Relationship to the subject Date and time

Investigator/Research Staff

I have explained the research to the participant or his/her representative before requesting the signature(s) above. There are no blanks in this document. A copy of this form has been given to the participant or his/her representative.

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Appendix C: Release of Information

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AUTHORIZATION FOR RELEASE/EXCHANGE OF MEDICAL INFORMATION TO THE NISONGER CENTER

Consumer Name: Date of Birth: Consumer ID #: Phone Number:

I authorize Nisonger Center for Mental Retardation and Developmental Disabilities at The Ohio State University to:

 REQUEST INFORMATION FROM: Name:

Purpose of Disclosure: Participation in Research Study Dates of Service: Information to be Disclosed: Current Psychiatric diagnoses and recent psychological testing

I hereby authorize the treatment facility indicated above and its employees to release the designated information contained in my consumer record or designated record set. I understand and acknowledge that this authorization extends to all or part of the information designated above, which may include treatment for physical and mental illness, alcohol and/or drug abuse, and/or AIDS (Acquired Immunodeficiency Syndrome), and/or may include results of an HIV test or the fact that an HIV test was performed. Information in the form of audio, photo, or video has been designated above, if applicable. A separate authorization is required for the release of psychotherapy notes. I expressly consent to the release of information designated above. This authorization is valid for 60 days*, unless revoked by my written notice provided said notice is received prior to release of the above designated information. The revocation of this authorization is effective except as indicated in The Nisonger Center Notice of Privacy Practices. Information released by this authorization may no longer be protected by federal privacy rules, such as HIPAA. I understand that The Nisonger Center cannot condition my treatment or payment for health care on this Authorization unless the treatment is research-related or the care was provided solely to provide information for a third party.

*  I wish to waive the 60-day validation period and grant a validation period of 1 year from the date signed below.

Signature of Consumer or Person Authorized to Consent Date Signed

Relationship, if not the consumer

Witness (Optional) Date Signed

For records covered by 42 CFR Part 2: This information has been disclosed to you from records protected by Federal Confidentiality Rules. The Federal Rules Prohibit you from making any further disclosure of this information unless further disclosure is expressly permitted by the written consent of the person to whom it pertains or as otherwise permitted by 42 CFR Part 2. A general authorization for the release of medical or other information is not sufficient for this purpose. The Federal Rules restrict any use of information to criminally investigate or prosecute any alcohol or drug abuse client.

If you have questions regarding release of information from The Nisonger Center, please call Sherry Feinstein, Clinic Manager, at (614) 247-7190.

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Appendix D: Assent Form

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The Ohio State University Assent to Participate in Research

PSYCHOPATHOLOGY IN YOUNGSTERS WITH Study Title: AUTISM SPECTRUM DISORDERS Researcher: Luc Lecavalier, PhD and Andrea Witwer, M.A.

Sponsor: None

 You are being asked to be in a research study. Studies are done to find better ways to treat people or to understand things better.  This form will tell you about the study to help you decide whether or not you want to participate.  You should ask any questions you have before making up your mind. You can think about it and discuss it with your family or friends before you decide.  It is okay to say “No” if you don’t want to be in the study. If you say “Yes” you can change your mind and quit being in the study at any time without getting in trouble.  If you decide you want to be in the study, an adult (usually a parent) will also need to give permission for you to be in the study.

1. What is this study about? This study will look at the measurement of emotional and behavioral problems in kids with autism spectrum disorders (ASDs).

2. What will I need to do if I am in this study? You will be asked to complete an intelligence measure.

3. How long will I be in the study? The measure should only take 30 minutes to 1 hour.

4. Can I stop being in the study? You may stop being in the study at any time.

5. What bad things might happen to me if I am in the study? You may get a little stressed during the intelligence test.

6. What good things might happen to me if I am in the study? Your parents will be given a summary of the test results which they can share with those helping you.

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7. Will I be given anything for being in this study? No.

8. Who can I talk to about the study?

For questions about the study you may contact Andrea Witwer (247-8028) or Luc Lecavalier, PhD (292-2378).

To discuss other study-related questions with someone who is not part of the research team, you may contact Ms. Sandra Meadows in the Office of Responsible Research Practices at 1-800-678-6251.

Signing the assent form

I have read (or someone has read to me) this form. I have had a chance to ask questions before making up my mind. I want to be in this research study.

AM/PM Signature or printed name of subject Date and time

Investigator/Research Staff

I have explained the research to the participant before requesting the signature above. There are no blanks in this document. A copy of this form has been given to the participant or his/her representative.

Printed name of person obtaining assent Signature of person obtaining assent

AM/PM Date and time

This form must be accompanied by an IRB approved parental permission form signed by a parent/guardian.

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