Correlates of Suicide-‐Related Behaviors Among

Correlates of Suicide-Related Behaviors among Children Ages Six to Twelve

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of

Philosophy in the Graduate School of The Ohio State University

Molly S. Martinez, M.A.

Graduate Program in Psychology

The Ohio State University

2013

Dissertation Committee:

Mary A. Fristad, PhD, Advisor

Steven Beck, PhD

Jennifer Cheavens, PhD

Molly S. Martinez

2013

Abstract

Research has shown young children do contemplate and attempt suicide and are considered an under-studied population. Studies have identified risk factors for suicide-related behaviors among children across several domains; however, few studies have examined how risk factors from disparate domains interact to increase or decrease risk for suicide attempt in children.

The Longitudinal Assessment of Manic Symptoms (LAMS) study (Findling et al., 2010; Horwitz et al., 2010) has collected a wealth of data on a child (ages 6 to 12) community mental health sample enriched for elevated symptoms of mania. Current or past suicidal acts were present for 57 (8.4%) of the 678 participants in the LAMS study for whom SRB status could be determined. For the current project, data from the LAMS study were analyzed in a multiple logistic regression model-building procedure. First, five independent domain-specific models (i.e., demographic variables, psychiatric family history, child psychopathology, psychosocial factors, and stressful life events) were constructed to better understand correlates of suicide-related behaviors among children in the sample. Subsequently, an integrated model of the combined influence of these factors was developed. ii

Covariates that were highly associated with SRB in the domain-specific models were as follows: Demographic variables—age at baseline (OR=1.31) and having both biological parents as primary and secondary caregiver (OR=.51); Family history variables—having a parent attempt suicide (OR=2.71); Child psychopathology variables—suicidal ideation (OR=20.41), tobacco use (OR=3.98), and anhedonia (OR=2.01); Psychosocial varialbes—changing schools for reasons other than normal progression (OR=2.06), quality of parent-child relationship

(OR=1.36), and ever having an academic tutor (OR=.30); Stressful life events— knowing someone who had recently tried to hurt him/herself (OR=3.24), mother or father recently remarrying (OR=3.23), ending a close friendship (OR=2.90), increased fighting with parents (OR=2.86), and having a friend who recently died

(OR=.27).

A comprehensive model that combined factors across all five domains resulted in a model that included three covariates: ever having experienced significant suicidal ideation (OR=31.27), parent report of the child’s mother or father recently remarrying (OR=1.87), and ever having an academic tutor (OR=.22).

Understanding the complex interrelation of factors that coalesce to increase and/or decrease risk for SRB in children with the goal of establishing a developmentally sensitive biopsychosocial model of SRB is worthy of future research.

iii

Dedication

Dedicated to my daughter, Cora.

Acknowledgments

This dissertation would not have been possible without the many families who participated in the LAMS study and the researchers dedicated to this project— thank you all. I would also like to thank my committee, for the time, effort, and patience provided during the process of drafting and completing this dissertation.

Special thanks to Dr. Fristad, for your encouragement, understanding, and guidance and to Dr. Beck, who has been a role model, coach, and my greatest support throughout graduate school. I would like to acknowledge and thank the Lilly

Foundation, which provided me with numerous scholarships over the past six years.

The financial support was key to completing my course of study. Much love and gratitude goes to my family and friends who have believed in me, encouraged me, and commiserated with me over the years, and whom I know celebrate this accomplishment with me. I would especially like to thank my mother, Sandra, my stepfather, David, my sister, Amber, and brother-in-law, Fabio, for their constant faith, motivational ingenuity, and their readiness to renew my internal resources when they were depleted. Finally, and most profoundly, I would like to thank my husband, Manuel, without whose love, patience, understanding, support, and encouragement I may have never completed my first year of graduate school, much less this dissertation and doctoral degree. v

Vita

January 23, 1976…………………………….……...Born – Indianapolis, IN

1998……………………………………………...……....B.A., English, Indiana University

1999 – 2000………………………………...………....Behavioral Intervention Specialist Seabrook Elementary, Seabrook, NH

2004……………………………………………..……….M.A., English, Indiana University

2005…………………………………………..………….B.A., Psychology, Indiana University

2005 – 2006…………………………….……………..Research Study Coordinator Department of Psychiatry The Ohio State University

2006 – 2011…………………………………………...Graduate Research Associate Departments of Psychiatry & Psychology The Ohio State University

2008……………………………………………………...M.A., Psychology The Ohio State University

2011 – 2012…………………………………………...Graduate Teaching Associate Department of Psychology The Ohio State University

2012 – 2013…………………………………………..Internship, Clinincal Child Psychology Ann & Robert H. Lurie Children’s Hospital of Chicago

PUBLICATIONS

Martinez, M., & Fristad, M. (2013). Conversion from bipolar disorder not otherwise

specified (BP-NOS) to bipolar I or II in youth with family history as a

predictor of conversion, Journal of Affective Disorders, 148(2–3), 431-434.

Snodgrass, M., & Vlachos-Weber, I. (2006). “Which one of us is truly crazy?”: Pop

psychology and the discourse of sanity and normativity in The Simpsons. In A.

Brown & C. Logan (Eds.), D’Oh: The Psychology of the Simpsons (pp. 37-48).

Dallas, TX: Ben Bella Books.

FIELDS OF STUDY

Major field: Psychology Clinical Child & Adolescent Psychology Track

vii

Table of Contents

Abstract ...... ii Dedication ...... iv Acknowledgments ...... v Vita ...... vi Table of Contents ...... viii List of Tables ...... x Introduction ...... 1 Suicide Terminology ...... 2 Historical Context of Research on Child and Adolescent Suicide ...... 5 Scope of the Problem ...... 8 Developmental Considerations ...... 11 Suicide Risk Assessment Scales for Young Children ...... 18 Correlates and Risk Factors for Suicide-Related Behaviors in Children ...... 22 Summary ...... 55 Study Aims and Hypotheses ...... 56 Method ...... 58 Screening and Enrollment Procedures ...... 59 Participants ...... 61 Instruments ...... 61 Rater Training and Reliability Procedures ...... 72 Statistical Analyses ...... 73 Results ...... 80 Descriptive Statistics ...... 80 Hypotheses 1 through 6 ...... 81 Hypothesis 1 ...... 82 Hypothesis 2 ...... 83 Hypothesis 3 ...... 84 Hypothesis 4 ...... 85 Hypothesis 5 ...... 87 Hypothesis 6 ...... 88 Discussion ...... 90 viii

Appendix: Tables ...... 103 References ...... 146

List of Tables

Table 1. Definitions of suicide-related communications and behavior………………...103

Table 2. Conversion of previous suicide terminology into revised terms………...... 105

Table 3. Two conventional mnemonic devices for assessing suicide risk in adulthood……………………………………………………………………………………....…..106

Table 4. The Adapted-SAD PERSONS Scale for Children and Adolescents…………..107

Table 5. Percentage of high school students who engaged in self-reported suicidal ideation, planning, or attempts by sex and grade – United States Youth Risk Behavior Survey, 2009……………………………………………………………………….108

Table 6. Longitudinal Assessment of Manic Symptoms (LAMS) study inclusion and exclusion criteria……………………………………………………………………………….109

Table 7. Instruments administered at the LAMS study baseline assessment………110

Table 8. Description of items on KSADS-PL-W “Suicidal Acts – Severity” and “Suicidal Acts – Medical Lethality”………………………………………………………112

Table 9. Rules for coding suicide-related behavior……………………………………………113

Table 10. Independent variables considered in model-building…………………………114

Table 11. Distribution of suicide-related behaviors in the sample by highest severity (current or past) and baseline age………………………………………………………..123

Table 12. Example descriptions of suicide-related behaviors in the sample………..124

Table 13. Characteristics of the sample: Comparing children with suicide-related behaviors to those without……………………………………………………………….127

Table 14. Results of univariate logistic regression analyses………………………………131

Table 15. Correlation matrix for significant child psychopathology variables in univariate analyses………………………………………………………………………….135

Table 16. Correlation matrix for significant stressful life events variables in univariate analyses………………………………………………………………………….139

Table 17. Results of Hypothesis 1-6: Final models of covariates that are correlated with increased likelihood of suicide-related behavior………………………...... 142

Table 18. Two-by-two contingency table for the relationship between suicidal ideation and suicide-related behavior…………………………………..143

Table 19. Two-by-two contingency table for the relationship between parent-report of a parent remarrying and suicide-related behavior………………….144

Table 20. Two-by-two contingency table for the relationship between having an academic tutor and suicide-related behavior…………………………..145

Introduction

Suicide-related behavior among young people has been of increasing concern in recent decades (Pfeffer, 2000; Wagner, 2009). While suicide is the eleventh leading cause of death among all ages in the United States (Xu et al., 2010), it is the third leading cause of death for youth ages 12 to 19 (Miniño, 2010), and the fifth leading cause of death among children ages 5 to 14 (Xu et al., 2010). An average of

16,375 youth ages 12 to 19 died each year from 1999 to 2006; 11% of these were suicides (Miniño, 2010). The death rate from suicide among youth ages 15 to 24 years in the United States ranged from 9.7 to 10.3 per 100,000 people in the standard population during the years 1999 to 2007 (Xu et al., 2010). In a study of

1,547 suicidal children and controls ages 10 to 21, the risk of completed suicide was greatest two years after the index attempt (Otto, 1972), a statistic that underscores the importance of identifying initial suicide attempts in order to intervene prior to a possible subsequent completed suicide.

In addition to the tragic deaths by suicide among youth, non-fatal suicide attempts and suicide-related ideation are leading causes of psychiatric hospitalization among children and adolescents, and constitute severe and often chronic impairment (Asarnow et al., 2008; Herba et al., 2007; Pfeffer, Conte,

Plutchik, & Jerrett, 1979). Furthermore, suicide-related ideation is more common among young people than previously thought, with some recent estimates ranging 1

from 11 to 21% of non-clinical child and adolescent samples (Andrews &

Lewinsohn, 1992; Dunn, Goodrow, Givens, & Austin, 2008; Eaton et al., 2010; Pfeffer,

Zuckerman, Plutchik, & Mizruchi, 1984).

Research dedicated to understanding teen suicide has exploded in recent years, but less attention has been paid to suicide-related ideation and attempts among pre-adolescent children. While it is rare, very young children can and do contemplate suicide and attempt to end their own lives. Suicide-related behaviors among children between the ages of 6 and 12 are the focus of the current research project. Improved understanding of the factors associated with suicide-related behaviors among these “school-age” or “latency age” children will fill a gap in our understanding of the phenomenology of youth suicide. Additionally, a theoretical model that conceptualizes the impact of correlates of suicide-related behavior across various domains (i.e., demographic variables, psychiatric family history, child psychological factors, psychosocial functioning, and stressful life events) in a single model will complement the existing literature, which currently consists primarily of studies that examine smaller constellations of risk factors within a single domain.

Suicide Terminology

Progress in the field of suicidology has been hampered by inconsistent and unclear terminology, making it imperative to clarify from the start the nomenclature used in the current study. In an effort to establish a standard nomenclature for the study of suicide, a National Institute of Mental Heath work group was formed in the

1990s with the goal of improving communication among researchers, clinicians, 2

public health officials, and policy makers across multiple disciplines by settling on a standard, well-defined vocabulary for suicide and suicide-related behaviors. In a

1996 article, the work group articulated the confusion created in treatment, research, and public health communication by the lack of clearly defined terms, and summarized a proposed nomenclature to be used in medical records, official documents, and publications (O’Carroll et al., 1996). Although not the first proposed set of definitions, their proposal was unique in that the authors differentiated their goal of establishing a nomenclature from the related, but much more nuanced and detailed task of creating a classification system for suicidal thoughts and behaviors.

The article, entitled “Beyond the Tower of Babel: A Nomenclature for Suicidology,” received much attention and feedback from other experts.

Although the proposed nomenclature was adopted by the American

Psychiatric Association in its practice guidelines for the assessment and treatment of suicide-related behaviors (American Psychiatric Association, 2003), it was not widely accepted and applied (Silverman, Berman, Sanddal, O’Carroll, & Joiner,

2007a). Eleven years later, a revision to the proposed nomenclature was published in a two-part series (“Rebuilding the Tower of Babel” Parts 1 and 2) based on the work of the Denver Veterans Administration VISN 19 Mental Illness Research,

Education, and Clinical Care (MIRECC) Nomenclature Workgroup, which included several authors from the original NIMH workgroup (Silverman et al., 2007a;

Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007b). As much as possible, the terminology used in the current research follows these revised recommendations.

The revised terms and definitions are outlined in Table 1 and a conversion from old to new terminology (adapted from Silverman et al, 2007b, Table 4, pg. 268), is presented in Table 2.

Silverman and colleagues include a broad spectrum of thoughts and behaviors in the suicide nomenclature. The first of the two articles outlining the revised lexicon explains the background, rationale, and methodology behind their selection of terms (Silverman et al., 2007a). Given that most suicide-related behaviors are not fatal, careful consideration was given to factors such as agency, intent, and lethality of the outcome in determining which behaviors to include in the nomenclature and how to discriminate and define them. In the child literature,

Pfeffer has also acknowledged the full spectrum of suicide-related behavior includes suicide-related ideation and self-harm (Pfeffer, 1981). Pfeffer’s conceptualization of suicide-related behaviors among children as “thoughts and/or actions that if fully carried out may lead to serious self-injury or death” is useful to keep in mind when applying the revised nomenclature to the thoughts and behaviors of children

(Pfeffer, 1981, p. 154).

Silverman and colleagues note they “had the most difficulty with the terms suicidal threat, suicidal gesture, and suicidal plan” (Silverman et al., 2007b, p. 265).

Ultimately, they chose to eliminate the term “suicidal gesture” due to the negative connotation and implication of manipulative intent this term has acquired. The authors first distinguished suicide-related communications (which do not involve self-inflicted injurious behavior) from suicide-related behaviors (which do involve

self-inflected injurious or potentially injurious behavior). Suicide-related communications include suicide threats (conceptualized as “a form of a coping communication to regain control or attachment,” pg. 267) and plans. Suicide-related communications can be further clarified by the individual’s intent to carry out the threat or plan: “Type I” indicates no intent to carry out the plan, “Type II” indicates undetermined intent to act, and “Type III” indicates at least some intent to act upon the threat.

Silverman and colleagues’ nomenclature subdivides terminology for suicide- related behaviors by intent to die: the term “self-harm” indicates no intent to die,

“suicide attempt” indicates at least some intent to die, and “undetermined suicide- related behavior” describes cases in which the individual’s intent to die is unknown or unclear. Each of these three terms can be further described by the resulting presence or absence of injury: “Type I” indicates no injury, “Type II” indicates some degree of injury. (The authors chose not to attempt to specify degree of injury due to the lack of an agreed upon definition of lethality or measure for it.) Events that result in fatality have their own terms, depending on the evidence of intent:

“suicide,” “self-inflicted unintentional death,” and “self-inflicted death with undetermined intent.” Full definitions of each term are presented in Table 1.

Historical Context of Research on Child and Adolescent Suicide

Scientific attention to the issue of suicide among young people dates back over a century. In the early 1900s, Stanley Hall brought attention to the fact that more than 300 youth in Italy between the ages of 10 and 15 committed suicide (Hall, 5

1904, pp. 378–380). In the 1930s, case studies of children ages 6 to 13 who were hospitalized for suicide attempts at Bellevue Hospital Psychiatric Division were published and presented (Bender & Schilder, 1937; Bromberg & Schilder, 1934;

Zilboorg, 1937). For the next thirty years, coverage of the topic consisted exclusively of infrequent references in the research literature, such as these.

The 1960s marked the beginning of a steep incline in suicide among young people in the United States that lasted three decades (AACAP Communications

Department, 2001). Suicide prevention and crisis intervention services increased in the 1960s and 1970s, but little scientific research on factors associated with suicide in youth emerged (AACAP Communications Department, 2001). Many studies of suicide-related behavior among children were heavily influenced by psychoanalytic theory and/or relied on case studies and anecdotal evidence (e.g., (Ackerly, 1967;

Morrison & Collier, 1969). Notable exceptions include Ulff Otto’s study of 1,547 suicide attempters under age 20 in Sweden (Otto, 1966, 1972) and Paulson and colleagues’ (Paulson, Stone, & Sposto, 1978) follow-up report on 662 children ages

12 and younger who were seen at the University of California at Los Angeles (UCLA)

Neuropsychiatric Institute from 1970 to 1974. The suicide rate among white, male adolescents had more than doubled by the early 1980s, causing public alarm and a burgeoning of the research on suicide among people under age 19 (AACAP

Communications Department, 2001). Since that time, understanding suicide and suicide-related behavior in adolescence has been a top priority for researchers.

Despite the fact that the rate of suicide among prepubertal children rose

120% between 1980 and 1992 in the United States (Roche et al., 2005), the scientific community has been slower to embrace research into pre-adolescent suicide. This is likely because suicide among prepubertal children continues to be a relatively rare phenomenon. However, a small number of researchers have been dedicated to understanding self-harm and suicide-related behaviors and communications in children ages 6 to 12 (i.e., “latency age” or “school-age” children). Cynthia Pfeffer has been one of the most prolific researchers dedicated to understanding suicide-related ideation, communications, and behaviors in latency- aged children. Her work has examined inpatient (Pfeffer et al., 1979), outpatient

(Pfeffer, Conte, Plutchik, & Jerrett, 1980), and nonclinical (Pfeffer, Plutchik, Mizruchi,

& Lipkins, 1986; Pfeffer et al., 1984; Pfeffer, 1989) samples. She and her colleagues have worked to develop one of the few reliable and valid scales for measuring suicide-related ideation, communications, and behavior suitable for children under

12 (Pfeffer, Jiang, & Kakuma, 2000).

Perihan and Stuart Rosenthal also paved the way for consideration of suicide-related behavior in very young children through their research on preschool children who were psychiatrically hospitalized for suicidal thoughts and behaviors (Rosenthal, Rosenthal, Doherty, & Santora, 1986; Rosenthal & Rosenthal,

1984). More recently, others have drawn attention to the need for more research and awareness about suicide attempts in young children (Tishler & Meltzer, 2004;

Tishler et al., 2007; Tishler, 1980). The combined efforts of these and other

researchers have lead to more acknowledgement of the problem of suicide in pre- adolescent children.

Scope of the Problem

Data from international sources indicate completed suicides among children under 12 are rare worldwide. In 1996, only 302 children ages 5 to 14 were confirmed to have died by suicide in the United States (World Health Organization,

1999). The rate of completed suicide among children ages 5 to 14 in the United

States ranged from 0.5 to 0.7 per 100,000 people in the standard population from

1999 to 2007 (Hoyert, Heron, Murphy, & Kung, 2006; Xu et al., 2010). Similar findings have been reported internationally. Statistics Canada, Canada’s central bureau for collecting, analyzing, and publishing national statistics, reports 160 children ages 10 to 14 and one child under age 10 completed suicide in Canada between 2001 and 2005 (Government of Canada, 2010). In Brazil, the suicide rates for children ages 10 to 14 ranged from 0.4 to 0.6 per 100,000 during the period from

1980 to 2006 (Lovisi, Santos, Legay, Abelha, & Valencia, 2009). In New Zealand, 61 children 15 and younger died by suicide from 1989 to 1998 (Beautrais, 2001).

Although these numbers are small, it is important to keep in mind that deaths of any kind are also very rare among this age group. Data from the National Vital

Statistics System (NVSS) indicate the children ages 5 to 14 have the lowest death rate in the US of all eleven age groups (Xu et al., 2010). The rate of death among children ages 5 to 14 from all causes was 15.3 per 100,000 in 2007 in the US—the rarity of death among this age group is put in perspective when considering the rate 8

of death for all ages was 803.6 per 100,000 (Xu et al., 2010). In 2007, suicide was the fifth leading cause of death among children ages 5 to 14 in the US, ranking only behind accidents, malignant neoplasms, homicide, and diseases of the heart (Xu et al., 2010).

Some research indicates suicide may be a significantly under-reported cause of death among children for various reasons, including a reluctance to label a child’s death as “suicide” for the sake of the family. One study in London examined the coroner records for all deaths of those under age 20 from 1980 to 1996 and found the true youth suicide rate was likely three times higher than had been reported

(Madge & Harvey, 1999).

Compared to completed suicides, there is a much higher rate of contemplated, threatened, and/or attempted suicide among children and adolescents. Data on prevalence rates of suicide attempts in children under age 12 are hard to come by, but one author estimates the rate of suicide-related ideation among prepubertal youth to be 14, 500 per 100, 000 (Jellinek, 2006). Pfeffer and colleagues (Pfeffer et al., 1980) examined a sample of 39 children under 12 who were receiving outpatient psychiatric care and found 33% reported suicide-related ideation, threats, or attempts. A publication from the Great Smoky Mountains

Study—a longitudinal study of the epidemiology of psychiatric disorders and need for psychiatric services among youth in North Carolina—reported 3-month prevalence rates of suicide-related ideations, communications, and behaviors among 9 to 16 year-olds: 0.99% of the sample experienced a desire to die; 0.69%

endorsed suicide-related ideation; and 0.31% attempted suicide (Foley et al., 2006).

Children age 12 and under made up 29.3% of participants in the Great Smoky

Mountain Study who reported a desire to die, 26% of those with suicide-related ideations, 8.4% of those who made a suicide plan, and 16.7% of those who attempted suicide (Foley et al., 2006).

By way of contrast, research in adolescent samples is more readily available through organizations such as the national Youth Risk Behavior Surveillance System

(YRBSS). The YRBSS has conducted a biannual survey of health risk behaviors (i.e., the Youth Risk Behavior Survey; YRBS) among high school students in the United

States since 1991 (Brener et al., 2004). Based on results from the 2009 YRBS

(N=16,410 students from 158 schools in all 50 states and the District of Columbia), in the year leading up to the survey, 13.8% (95% CI: 13.1 – 14.6%) of high school students seriously considered suicide, 10.9% (95% CI: 10.0 – 11.8%) planned how they would attempt suicide, 6.3% (95% CI: 5.7 – 7.0%) attempted suicide one or more times, and 1.9% (95% CI: 1.6 – 2.3%) made a suicide attempt that resulted in injury, poisoning, or overdose serious enough to be treated by a doctor or nurse

(Center for Disease Control and Prevention, n.d.; Eaton et al., 2010).

It is important to keep in mind that the data from the YRBS are limited by the fact that it is a school-based survey. Because of this, the YRBS does not represent youth who do not attend school due to homeschooling, delinquency, drop-out, or other reasons. Some of the most seriously mentally ill youth may be too impaired to attend school, and therefore would not be represented in such a sample. Research

has found out-of-school youth to be more likely to engage in risky health behaviors

(Center for Disease Control and Prevention, 1994), underscoring the potential that

YRBS data under-represents true suicidality rates among US high-school-age youth.

Furthermore, missing data may impact the statistic about suicide attempt resulting in serious injury, as analyses of the 2003 YRBS data indicated this was the most commonly skipped item on the survey, at a rate of 15.5% missing responses (Brener et al., 2004).

While suicide-related communications and behaviors are much more commonly studied in older adolescent populations, researchers are beginning to expand the age range of their studies to include younger children. Although not currently conducted at the national level, middle school version of the YRBS has been widely used in research. One study of 10,273 students attending 10 public middle schools (sixth to eighth grade) in rural Tennessee found 28.3% of females and 18.5% of males endorsed suicide-related ideation in the past year, 18.8% of females and 12.8% of males had made a plan to commit suicide, and 12.6% of females and 7.4% of males had attempted to kill themselves (Dunn et al., 2008).

These higher numbers of reported suicidal ideations, plans, and attempts in the younger age group (middle school vs. high school) underscore the importance of improving our understanding of younger youth at risk for suicide-related behaviors.

Developmental Considerations

In addition to the confusion caused by unclear terminology, another issue that has delayed research on suicide-related behaviors in children has been the 11

belief that children are not developmentally capable of understanding and, therefore, enacting suicide. In fact, in a recent search of dictionary definitions on the word “suicide,” the Merriam-Webster online dictionary defined suicide as: “the act or an instance of taking one's own life voluntarily and intentionally especially by a person of years of discretion [emphasis added] and of sound mind” (“Suicide,” 2011).

Children’s concepts of death. The question of whether or not young people are able to comprehend the finality of death and the consequences of their actions leads some to doubt that children know what they are doing when they make suicide threats and attempts. After all, can a child be said to have attempted suicide if s/he does not really understand death to be a final state? Maria Nagy conducted some of the earliest and best-known work on children’s concepts of death (Nagy,

1959). Subsequent research on this topic has attempted to capture the development of children’s understanding of death in a Piagetian framework, with progression through the stages of cognitive development accounting for gains in understanding different aspects of death (Stambrook & Parker, 1987). The concept of death is divided into three generally agreed-upon component concepts: universality of death

(i.e., all living things will eventually die), irreversibility of death (i.e., once a living thing dies, it cannot be brought back to life), and nonfunctionality of death (i.e., all life-defining functions stop when a living thing dies) (Speece & Brent, 1992). While the preponderance of evidence suggests children do progress in their understanding of death with age, the process is neither unified nor linear. The literature is consistent in demonstrating children first understand death as a temporary and

reversible state similar to sleep, but there is little consensus about the age by which a mature concept of death is attained or the developmental process through which it is acquired (Stambrook & Parker, 1987).

There is evidence to suggest that the majority of children ages 6 to 12 have a fundamental understanding of at least two of the three basic components of the concept death (i.e., universality, irreversibility, and/or nonfunctionality). In an interview-based study of 91 kindergarten through third-graders (ages 5 to 10),

Speece and Brent (Speece & Brent, 1992) found each component concept had been mastered by at least 50% of participants in each grade level, with the universality of death being understood by 92% of the total sample, irreversibility understood by

69%, and nonfunctionality understood by 60% (Speece & Brent, 1992).

Environmental factors influencing the development of the concept of death have included socioeconomic status (Tallmer, Formanek, & Tallmer, 1974), religious up-bringing (Candy-Gibbs, Sharp, & Petrun, 1984), personal experience with death

(e.g., having lost a parent, a loved one, or a pet; (Kane, 1979), and personal experience coping with childhood terminal illness (Jay, Green, Johnson, Caldwell, &

Nitschke, 1987). Many of these factors are correlated with a child’s understanding of death. For example, Tallmer, Formanek, and Tallmer (1974) found that children from lower socioeconomic status households tended to develop a mature concept of death earlier than those from middle class backgrounds. The authors’ hypothesized life experience, including possible exposure to danger in impoverished neighborhoods, may account for this unusual reversal of the general rule that

children in more enriched environments develop at a faster rate than disadvantaged youth. Environmental influences on the development of the concept of death have been understudied, as past research has strongly emphasized age and cognitive development as primary dependent variables. Environmental factors such as these may account for the variability and inconsistency in chronological and cognitive developmental models.

One line of research relevant to the study of suicide-related behavior in children concerns whether or not children who engage in suicide-related ideation and behaviors have less mature concepts of death compared to their same-age peers. Pfeffer’s investigated this question in three studies, with mixed findings: one study found psychiatric inpatients ages 6 to 12 had an increased tendency to view death as a temporary, pleasant state, which was correlated with increasing lethality of their suicide-related behavior (Pfeffer et al., 1979); two other studies did not find evidence of this association (Pfeffer et al., 1980; Pfeffer, Solomon, Plutchik, Mizruchi,

& Weiner, 1982). In a study of psychiatric outpatient children ages 30 months to 5 years, Rosenthal and Rosenthal (1984) found children’s concept of death was related to suicide-related ideation in that those children who expressed suicidal intent related to being reunited with a loved one thought of death as reversible, while those who expressed an intent to escape from perceived intolerable circumstances viewed death as irreversible (Rosenthal & Rosenthal, 1984).

In the end, while on one hand it may be important to understand on a theoretical level whether or not a child who harms or kills himself truly understands

the function of his behavior, on the other hand, the presence or absence of such a philosophical understanding does not determine whether or not a child does real, physical harm to himself.

Theories of child suicide-related behaviors. Comprehensive theories of why adults and adolescents attempt and commit suicide abound, but few theoretical models exist for suicide-related behaviors in children. An early theory of suicide- related behaviors in adolescents posited that adolescents who attempt suicide are acting out perceived parent desire to be rid of the child (Sabbath, 1969). This

“expendable child” hypothesis was proposed based on three case studies, but was later supported by an empirical study of 40 adolescents and young adults, ages 13 to

24 (Woznica & Shapiro, 1990). The theory fits neatly with Joiner’s interpersonal- psychological theory of suicide-related behavior that perceived burdensomeness and a lack of sense of belonging are two crucial factors that contribute to suicide- related behavior in adults (Joiner et al., 2009). Joiner hypothesizes perceived burdensomeness and social alienation activate a desire to die, and the capability to act on that desire is acquired via habituation to “painful and/or fearsome” experiences (i.e., self-harm and non-lethal suicide attempts).

Marsha Linehan’s biosocial theory of the development of borderline personality disorder has been applied to suicidal adolescents (Miller, Rathus, &

Linehan, 2007) and is the basis for dialectical behavior therapy. Linehan’s biosocial theory proposes that suicide-related behavior (and other traits associated with borderline personality disorder) emerge as the result of a transactional process that

occurs between the individual and his/her environment: biological factors that leave a person vulnerable to emotion dysregulation interact with the early life experience of an invalidating environment (i.e., the tendency for important others to negate or respond erratically to the individual’s private, and especially emotional, experience) resulting in that person never learning to adaptively regulate emotion, tolerate distress, trust his/her own emotional responses, and effectively problem- solve in social and emotional situations. Additionally, this model accounts for the dramatic vacillations between ignoring one’s emotions completely and the extreme behavioral displays (e.g., self-harm and suicide attempts) common in people with borderline personality disorder, hypothesizing the latter are effective (albeit, maladaptive) help-seeking behaviors that the individual has learned (Miller et al.,

2007).

Apter and colleagues (1995) posited two pathways to suicide-related behavior in adolescents: 1) a direct path from depression and a desire to die, and 2) an indirect path from impulsivity to aggressive/violent behavior and, subsequently, to suicide-related behavior. These two paths were replicated in a study of boys ages

6 to 12 admitted to a psychiatric inpatient unit (Greening et al., 2008). This model is closely related to, but divergent from, the adult diathesis-stress model of suicide, which posits that it is the combination of impulsive aggression (the diathesis) in individuals with depression and other psychiatric illness (the stressor) that put them at risk for suicide-related behavior (J. J. Mann, Waternaux, Haas, & Malone,

1999). David Brent has pointed out that impulsive aggression and depression are

not necessarily orthogonal constructs; the former may predispose an individual to the latter (either directly or through an increased risk for life events that lead to depression), or early-onset depression may interfere with the development of the emotion-regulation skills everyone needs to curb natural impulsive-aggressive tendencies (D. A. Brent, 2009).

Bridge and colleagues (2006) have posited one of the more contemporary and comprehensive models of adolescent suicide attempts and behavior, which draws from the above-mentioned theories. In Bridge and colleagues’ formulation, suicide-related behaviors result from the interaction of a variety of socio-cultural, developmental, psychiatric, psychological, and family environmental factors. This last model is the basis for the current project, which aims to pull together disparate literatures and analyze the empirical evidence for such a comprehensive model in latency-age children.

Method of suicide attempt. A final developmental consideration is that suicide-related behaviors may look very different in childhood than in adulthood.

Some children do attempt suicide by using direct and violent methods, similar to adults and adolescents. Similar to completed suicide in adulthood, the most common methods used in completed youth suicides are firearms, hanging, and poisoning

(Evans & Farberow, 1988; Hawton, 1986; Pfeffer, 2000). However, especially in younger children, unconventional and ill-conceived methods are frequently employed. For example, case studies have described young children who threaten to jump from heights, run into traffic, or attempt to strangle themselves (Ackerly,

1967; Morrison & Collier, 1969; Pfeffer et al., 1982). Fortunately, these methods are often unsuccessful, and leave parents and professionals wondering whether or not a true suicide attempt had taken place. Children who have a desire to die may enact behaviors with the intent to die, but may lack an understanding of the lethality of their chosen method. A young child who jumps out the window of his single-story home with the intent of killing himself may not grasp that a fall from such a low height is unlikely to result in death, but this behavior would still meet criteria for

“suicide attempt” under the nomenclature system outlined previously. When there are such questions about the child’s intent to die, the category of “undetermined suicide-related behavior” is useful in identifying children who may be a danger to themselves.

Suicide Risk Assessment Scales for Young Children

Research on the risk factors for suicide-related behavior has led to the development of various tools for suicide risk assessment. In fact, in the proposed revisions to the Diagnostic and Statistical Manual-5th Edition (DSM-5) published online on May 13, 2010, one such tool was presented for consideration to be included in DSM-5 (Mood Disorder Work Group, 2010). Most suicide risk assessment instruments have been developed based on research from adult and adolescent populations; very few have been designed specifically for use with children under 12. In a 1997 review of the available instruments for suicide risk assessment, Range and Knott (Range & Knott, 1997) identified two instruments appropriate for use in children under 12: The Suicide Behavior Questionnaire for 18

Children (SBQ-C) (Cotton & Range, 1993) and The Fairy Tales Test (Orbach,

Feshbach, Carlson, Glaubman, & Gross, 1983). Subsequently, the Scale for Suicidal

Ideation (Allan, Kashani, Dahlmeier, Taghizadeh, & Reid, 1997); the Child Suicide

Potential Scales (CSPS) (Pfeffer et al., 1979, 1980; Pfeffer, 1986); the Child-

Adolescent Suicide Potential Index (CASPI) (Pfeffer, Jiang, et al., 2000); the Risk of

Suicide Questionnaire (RSQ) (Horowitz et al., 2001), and the Child Suicide Risk

Assessment (CSRA) (Larzelere et al., 2004) have been published. These instruments vary in their comprehensiveness, time needed for the assessment, and psychometric rigor.

With the exception of the Fairy Tales Test, which is a semi-projective measure of Orbach’s four attitudes toward life and death (attraction to life, attraction to death, repulsion from death, repulsion from life), the above-mentioned suicide risk assessment instruments measure different constellations of risk factors for suicide-related behaviors. The SSI and SBQ-C emphasize suicide-related ideations as the primary risk factor to be assessed, to the exclusion of other important factors, such as previous suicide attempts, depressive mood, poor peer relations, and recent loss. Similarly, the RSQ is a 14-item structured interview that focuses mostly on suicide-related ideations and behaviors that are current or in the recent past (only the last four items address recent loss, psychosocial stress, alcohol and drug use, and whether or not the young person has known someone who has committed suicide) (Horowitz et al., 2001). The RSQ was designed to help emergency department personnel to screen and triage potentially suicidal children

and adolescents; by design it is concise, which leaves it lacking in the depth required for a more thorough assessment instrument.

The most comprehensive assessment tool available for suicide risk assessment in children is Pfeffer’s extensive battery, the CSPS (Pfeffer et al., 1979,

1980; Pfeffer, 1986). The CSPS covers eight domains: severity of suicidal behavior, precipitating events/environmental stressors, family background, recent affect and behavior, past affect and behavior, concept of death, ego functioning, and ego defense mechanisms. The CSPS was designed as a semi-structured interview for administration to children and their parents. While the CSPS is comprehensive, it requires two-hours to administer, making it onerous and impractical in clinical settings. It is also somewhat outdated and heavily influenced by psychodynamic theory; the CSPS may be over-inclusive in assessment of some factors that are not empirically supported risk factors for suicide attempt (e.g., ego functioning and ego defense mechanisms). To address the need for a user-friendly screening instrument,

Pfeffer and colleagues developed the CASPI by extracting items from the CSPS that had been empirically shown to be associated with risk for suicide-related behaviors in children and adolescents and converting these into a self-report format (Pfeffer,

Jiang, et al., 2000). The domains covered by the 30-item CASPI include: psychiatric symptoms, suicide-related ideations and behavior, family discord and psychopathology, and social stress.

Finally, the CRSA is an 18-item, brief structured interview. Items are written to elicit yes/no answers, and probing follow-up questions are asked in response to

high-risk answers (Larzelere et al., 2004). Items on the CRSA load onto three factors: worsening depression, perceived lack of support, and concept of death as escape. Of the brief screening assessments available for suicide risk assessment in children ages 6 to 12, the CRSA and the CASPI have the strongest psychometric properties, and there are advantages and disadvantages to each (Larzelere et al., 2004; Pfeffer,

Jiang, et al., 2000). Both of these scales were designed to screen for imminent threat of suicide attempt rather than to gain a more comprehensive understanding of the accumulation of risk factors that may put a child at long-term risk for suicide- related behaviors.

Research that has emerged in the past two decades has yet to be integrated into a comprehensive, but succinct structured interview or self-/parent-report form applicable to children under 12. This may be partially due to the fact that researchers who study correlates and risk factors of child and adolescent suicide- related behavior tend to concentrate their research on specific domains (e.g., family factors, psychological factors) or even one discrete factor (e.g., bullying, bipolar disorder) rather than exploring the “big picture” of how all of these factors interact to put a child at greater or lesser risk. Additionally, advances in understanding adolescent and adult suicide have not been adequately applied to research in young children, and unique factors that may be associated with suicide-related behaviors in young children have not been sufficiently examined. While the current study is not a scale construction project, a better understanding of the correlates to suicide-

related behavior outlined below could contribute to the eventual development of a better, more comprehensive suicide risk assessment instrument for young children.

Correlates and Risk Factors for Suicide-Related Behaviors in Children

Suicide-related behavior at any age is too complex to be reduced to a simple one-to-one causal pathway. The existing evidence indicates that risk for suicide- related behavior in children is best conceptualized as multi-factorial. It is important to distinguish between “correlates” of suicide-related behavior and “risk factors” for suicide-related behavior. Although neither term necessarily indicates a causal relationship, a temporal relationship is implied in the term “risk factor.” In order for something to be considered a risk factor, it must be present prior to the outcome of interest and be associated with a higher likelihood for that outcome. Research methodology must, therefore, be in place to measure the presence of the risk factor prior to the incidence of suicide-related behavior. Longitudinal, prospective studies are best designed to do this, but these are uncommon. Rather, many more studies assess the presence of factors correlated with suicide-related behavior in children who have already made a suicide attempt or have been hospitalized for suicide- related ideations and behaviors. While these studies commonly claim to be assessing “risk factors” for suicide-related behavior, it is more accurate to say they measure “correlates” of suicide-related behavior.

Correlates of and risk factors for suicide-related behaviors have been identified across many domains, including psychological, social, demographic, academic, and family factors. There is no consensus or definitive list of risk factors 22

for suicide-related behavior. Acronyms, such as SAD PERSONS (Patterson, Dohn,

Bird, & Patterson, 1983) and IS PATH WARM? (American Association of Suicidology,

2011) have been useful mnemonic device for mental health care professionals to use for ease of remembering these and other risk factors when assessing for client suicide risk (see Table 3 for the explanation of these acronyms). Additionally, the

Mood Disorders Work Group for DSM-5 identified the following as important indicators of suicide risk: any history of suicide attempt, any history of mental illness, any history of physical or sexual abuse, history of aggression, chronic severe pain, family history of suicide-related behavior, recent significant loss, recent psychiatric hospitalization, recent diagnosis with first Axis I or II disorder, recent increase in alcohol or drug use, recent worsening of depressive symptoms, current preoccupation with or plans for suicide, current psychomotor agitation or marked anxiety, current feelings of hopelessness, and living alone or lack of social support

(Mood Disorder Work Group, 2010).

The SAD PERSONS Scale was adapted for children and adolescents in 1996 with a few minor changes (Juhnke, 1996). The Adapted-SAD PERSONS Scale (A-SPS) indicates the following factors are suggestive of increased risk for suicide-related behavior in children and adolescents: male sex; age 15 years and older; depression or affective disorder; previous suicide attempt; ethanol or drug abuse; rational thinking loss (i.e., psychosis; which can be the result of a physical or psychological disorder); social supports lacking; organized plan for suicide; negligent parenting, significant family stressors, or suicide-related behavior modeling by parents or

siblings; and school problems (aggressive behaviors or experiencing humiliation)

(see also Table 4). While the A-SPS takes into account more domains than the suicide risk assessment scales previously mentioned and is easy to remember, there is no information about the reliability, validity, or other psychometric properties of it as an assessment instrument. Additionally, it offers only a basic guideline for aspects of the child’s intra- and interpersonal functioning to consider.

A brief review of the important correlates and risk factors for suicide-related behavior across multiple domains in youth is presented below. Because so little of the available research on risk factors for suicide has been done with children ages 6 to 12, this review borrows from the adolescent literature to complement or fill gaps in the research conducted in latency-age child samples.

Demographic factors. Socioeconomic status (SES), age, sex, and ethnic background are important to consider when assessing the influence of demographic variables on risk of suicide-related behavior in children. While SES has been an important variable in adult suicide research, it has not been confirmed in child samples. One study examining the impact of SES as a risk factor for suicide-related behavior found it to be non-significant (Pfeffer et al., 1986), however, this study was limited in that it only assessed the SES of the children’s fathers, several of whom were absent or not involved with the child.

More boys than girls commit suicide (gender ratio 3:1), but the gender disparity is smaller among 5- to 14-year-olds than any other age group (Pfeffer,

2000). While child inpatient samples have frequently been comprised of more males

than females, boys have not been found to be higher than girls on the severity spectrum of suicide-related behavior (Pfeffer et al., 1982). In fact, in one study of

175 consecutive inpatient admissions of children ages 6 to 13 (mean age 11.1)—in which over two-thirds (n=122, 69.7%) were boys—girls were more than twice as likely to have attempted suicide (Nock & Kazdin, 2002). This sex difference was significant only among children above the median age of 11.25 years, however

(Nock & Kazdin, 2002). The finding from the adult and adolescent literature that females more commonly attempt suicide, while males make more lethal suicide attempts (Lewinsohn, Rohde, & Seeley, 1996) has been neither confirmed nor rejected by research in samples of children under 12. There is evidence, however, that suicide-related ideation is more common among girls, whereas serious suicide attempts are more common among boys (Pfeffer et al., 1986).

Generally speaking, older children and young adolescents are more at risk for suicide-related behavior than younger children (Beautrais, 2001). For example,

Algorta and colleagues (2011) found that children and adolescents (ages 5 to 18) with bipolar disorder who attempted suicide were older than non-attempters. One retrospective study of college students found an increase in risk for the first instance of suicide-related ideation began at age 9 (10% of the sample), increased sharply between age 12 (25%) and 16 (60%), and subsequently leveled off with approximately 75% of respondents reporting they had seriously considered suicide by age 22 (Bolger, Downey, Walker, & Steininger, 1989).

However, other research has found no increase in risk during latency-age.

For example, in the above-mentioned study by Nock and Kazdin (Nock & Kazdin,

2002), the researchers found no age difference between children with suicide- related ideation, children who had made an attempt, and those who had neither. In a prospective, longitudinal study of children (age at study entry: 8 to 13), Kovacs and colleagues (Kovacs, Goldston, & Gatsonis, 1993) found no increase in suicide-related ideation over time in the sample, but the rate of suicide attempts did increase with age.

Data on the sex and grade breakdown of suicide attempts among adolescents from the 2009 YRBS are presented in Table 5. Some group differences and general trends are apparent in these data. Females were more likely than males to report a history of suicide-related ideation (17.4% females, 10.5% males), suicide plan

(13.2% female, 8.6% male), and suicide attempt (8.1% females, 4.6% males), and seeking treatment after an attempt (2.3% females, 1.6% males) (Eaton et al., 2010).

Only one sex-by-grade statistic was higher among males than females: 12th-grade males reported a higher rate of treatment required after a suicide attempt than 12th- grade girls. While males in all grade levels reported similar rates of suicide-related ideation, suicide plan, suicide attempt, and seeking medical treatment after an attempt, among females there was a general trend of decreasing prevalence of suicide-related ideation and behaviors in more advanced grade levels. Most notably, for 12th grade females, there was a statistically significant reduction in reported

rates in all four areas of suicide-related ideations and behaviors, evidenced by non- overlapping confidence intervals.

Concerning differences among ethnic groups, in the 5- to 14-year-old age range, rates of suicide are highest for Whites, followed by Hispanics, and then Blacks

(Peters, Kochanek, & Murphy, 1998; Pfeffer, 2000). In the adolescent research, data from the 2009 YRBS revealed more Hispanic students reported having seriously considered suicide (15.4%), made a suicide plan (12.2%), and made a suicide attempt (8.1%) than White (13.1%, 10.3%, and 5.0%, respectively) and Black

(13.0%, 9.8%, and 7.9%, respectively) students (Eaton et al., 2010). Hispanic females were particularly at risk; 20.2% reported having seriously considered suicide, 15.4% reported making a plan, and 11.1% reported attempting suicide in the past year. The rate of suicide attempts among Black students (7.9%) was higher than that of White students (5.0%).

Psychiatric family history. Gathering data on psychiatric family history is an essential part of any psychological assessment in children for many reasons: it is useful in validating psychiatric illness, understanding the child’s family environment, and predicting the course of the child’s illness and his/her treatment outcome. Family history of suicide and suicide attempt, particularly among parents, has been associated with child suicide-related ideation and behavior. Pfeffer and colleagues found children who were hospitalized for a suicide attempt were more likely to have a first-degree relative (especially a mother) who had attempted suicide; similar rates were reported for children hospitalized for suicide-related

ideation (Pfeffer, Normandin, & Kakuma, 1994). In another study, Pfeffer and colleagues (1979) found children ages 12 and under who were hospitalized for suicide-related behavior had parents who were more depressed and exhibited more suicide-related ideation and behavior than parents of non-suicidal child psychiatric inpatients. Likewise, the adolescent literature indicates those who had a family member attempt suicide in the past 12 months were at risk for a number of negative health-related outcomes, including suicide-related ideation and attempt (Cerel &

Roberts, 2005). Additionally, suicide and suicide attempts were more prevalent among first- and second-degree relatives of adolescents (ages 13 to 20) who completed suicide than community controls, even after controlling for increased rates of Axis I psychiatric disorders among relatives (Brent, Bridge, Johnson, &

Connolly, 1996).

The impact of completed parental suicide may have a particularly significant impact on children, but published research on this topic is sparse. In a comprehensive review of the literature, Kuramoto and colleagues (Kuramoto, Brent,

& Wilcox, 2009) found only nine independently published research studies on the outcome of children who had a parent complete suicide. Of these nine, two were studies conducted using samples of adults who had experienced the suicide of a parent in their childhood (Avrami, 2005; Roy, 1983). Two studies (Agerbo,

Nordentoft, & Mortensen, 2002; Roy, 1983) found an increased risk for suicide- related behavior in children whose parents completed suicide, while two (Cerel,

Fristad, Weller, & Weller, 1999; Pfeffer, Karus, et al., 2000) did not. Roy (1983)

found the age of the child when his/her parent completed suicide made a difference: children under age 20 when their parent completed suicide were at increased risk for suicide-related behavior later in life, while those who were older had no additional risk. Several studies found children of parents who completed suicide had increase psychiatric problems such as depression, anxiety, bipolar disorder, and excessive guilt, shame, and anger (Avrami, 2005; J. Cerel et al., 1999; J. Cerel, Fristad,

Weller, & Weller, 2000; Pfeffer, Karus, et al., 2000; Shepherd & Barraclough, 1976;

Tsuchiya, Agerbo, & Mortensen, 2005); while two studies (A. C. Brown, Sandler,

Tein, Liu, & Haine, 2007; Grossman, Clark, Gross, Halstead, & Pennington, 1995) found no significant differences in emotional and behavioral outcomes of children who lost a parent to suicide compared with those whose parent died of other causes.

A recent study that compared offspring of suicide decedents with offspring of parents who died as a result of accidents or other causes found only child and adolescent offspring of suicide victims were at an increased risk for completing suicide; there was no increased risk of death by suicide for older offspring of suicide victims and offspring of parents who died from other causes (Wilcox et al., 2010). All offspring of suicide decedents did have an increased risk for suicide attempts, whereas the offspring of parents who died from accidents and other causes did not

(Wilcox et al., 2010). In a study of offspring of depressed parents, offspring who were exposed to a parental suicide attempt were four times more likely to have a lifetime suicide attempt than those who were not exposed to a parental attempt suicide (Burke et al., 2010). The exposure to a suicide attempt did not always pre-

date the offspring’s suicide attempt, indicating this relationship cannot be fully explained by modeling and imitation.

Some research has found the gender of the parent who commits suicide may matter. Agerbo and colleagues (Agerbo et al., 2002) found, both maternal and paternal suicide put children at a significantly increased risk for suicide-related behavior, but the risk was significantly higher when it was the child’s mother rather than father who had committed suicide. Maternal, but not paternal, suicide was associated with hospitalization for suicide attempt in youth under 17 years of age in one study (S. J. Kuramoto et al., 2010).

The adolescent and adult literature has found a significant correlation between suicide-related behavior and the presence of affective disorders, substance abuse, violence, and antisocial personality disorder among parents and siblings (e.g.,

(D. A. Brent et al., 2003; Goldstein TR, 2012; C. King, Kerr, Passarelli, Foster, &

Merchant, 2010). The relationship between parental psychopathology and offspring suicide-related behavior may be mediated by parental suicide-related behavior, however. Brent and colleagues (2002) found the prevalence of mood disorders among offspring (mean age: 20 ±12.3) of parents with mood disorders was the same regardless of whether their parent had attempted suicide or not, but offspring of mood-disordered parents who had made a suicide attempt were significantly more likely to attempt suicide than offspring of mood-disordered parents who had not attempted suicide.

Familial loading of suicide attempts may also play an important role. In a reanalysis of the data, Brent and colleagues (2003) found children of parents who attempted suicide and who had a sibling (i.e., the child’s aunt or uncle) concordant for suicide attempt were more likely to attempt suicide themselves. The children also had a younger age of onset of suicide attempt than parents with siblings discordant for suicide attempt. One recent study comparing children and adolescents who presented with suicide-related ideations, communications, and attempts found children had more family history of depression than adolescents

(Sarkar et al., 2010). The relationship between parental psychopathology and suicide-related behavior in childhood is still emerging, however. In the adult literature, familial transmission of depression and suicide-related behavior are believed to be overlapping, but distinct (McGirr et al., 2009). Similarly, no studies have demonstrated conferred risk for childhood suicide-related behavior based on parental psychiatric diagnosis in the absence of parental suicide-related behavior since Pfeffer pointed out the need for more research in this area almost two decades ago (Pfeffer et al., 1994).

Researchers have begun to look for specific genetic biomarkers to better understand the mechanism through which suicide-related behavior is inherited

(e.g., Joiner, Johnson, & Soderstrom, 2002; Kamali, Oquendo, & Mann, 2001). One mechanism that has been proposed is a peripheral serotonin profile that maintains a lower-than-average tryptophan level in the blood (Pfeffer, 2000; Roy, Rylander, &

Sarchiapone, 1997). Another proposed mechanism is fewer presynaptic serotonin

transporter sites in the ventromedial prefrontal cortex and fewer noradrenergic neurons in the locus coeruleus and brainstem that may comprise a neurobiological diathesis for suicide (J. John Mann, 2003). These studies are beyond the scope of the current project, however.

Child psychopathology and other psychological factors. Psychiatric disorders and symptoms are the most commonly researched risk factors in children and adolescents with suicide-related ideations and behaviors (Wagner, 2009).

Research has found an association between independent disorders and suicide- related behaviors as well as increased risk when more than one disorder is present.

For example, Shafii and colleagues (1988) studied 21 young suicide completers

(ages 11 to 19), and found 95% had at least one diagnosable psychiatric disorder and 81% had more than one disorder. Goldstein and colleagues found, among their sample of bipolar youth (ages 7-17), comorbid panic disorder and substance abuse disorder were significantly associated with suicide attempts (T. R. Goldstein et al.,

2005). Groholt, Ekeberg, and Haldorsen (2006) found comorbid diagnoses predicted repeated suicide attempts in an adolescent sample. In an adult sample, Beautrais and colleagues (1996) found more than half of the attempters in the sample had more than one diagnosis, and the odds of making a suicide attempt were 89 times greater for those with two or more diagnoses than for those without a diagnosis.

Below is a brief review of the major findings related to individual diagnoses and symptoms.

Depression. The presence of significant depressive symptoms has been identified as the strongest correlate of suicide-related ideation and behavior in samples of children ages 13 and under (Asarnow, Tompson, & Goldstein, 1994;

Asarnow, 1992; Goldstein TR, 2012; Greening et al., 2008; Marciano & Kazdin, 1994;

O’Leary et al., 2006; Pfeffer et al., 1979, 1980, 1986) and mixed samples of children and adolescents (Algorta et al., 2011; Foley et al., 2006; Gould et al., 1998; Berit

Groholt, Ekeberg, Wichstrom, & Haldorsen, 1997; Kovacs et al., 1993). Most recently, severity of depressive symptoms was found to be associated with risk for suicide attempt in a sample of youth with early-onset bipolar disorder (Goldstein

TR, 2012).

The duration of depressive symptoms may play a role in the risk for suicide- related behavior. Thirty-eight percent of the 581 suicidal children and adolescents in one study had been depressed three months or longer before attempting suicide

(Otto, 1966). Mattsson and colleagues (Otto, 1966) found 40% of suicidal children and only 13% of nonsuicidal psychiatric patients were depressed for one month or longer prior to being seen by a professional, making duration of untreated depression the key distinguishing feature between the two groups (Mattsson et al.,

1969). Ryan and colleagues (1987) found adolescents who were depressed for two years or longer had more suicide-related ideation and attempts than those with shorter depressive episodes.

Of the depressive symptoms associated with suicide-related ideations and behaviors, special attention has been paid to “cognitive risk factors” such as

hopelessness and feelings of worthlessness (Goldston et al., 2001; Berit Groholt et al., 2006; Kashani, Reid, & Rosenberg, 1989). Pfeffer and colleagues (Pfeffer et al.,

1979) studied 58 child (ages 6 to 12) psychiatric inpatients, and found children with suicide-related ideations and behaviors had significantly more feelings of hopelessness and worthlessness than nonsuicidal children. One study of hopelessness in a child sample found it to be more highly correlated with suicidal intent than with depression (Kazdin, French, Unis, Esveldt-Dawson, & Sherick,

1983). On the contrary, another study found that hopelessness and low self-esteem did not contribute to differentiating child suicide ideators and attempters from those without suicide-related ideation and attempts once other depressive symptoms were considered (Marciano & Kazdin, 1994).

Bipolar disorder. Bipolar disorder and mixed mood episodes are strongly related to suicide in the adult literature (Goodwin & Jamison, 1990). Research has found this to also be true for bipolar youth (Algorta et al., 2011; T. R. Goldstein et al.,

2005). Among youth (ages 7 to 17) with bipolar disorder in the Course and Outcome of Bipolar Youth (COBY) study, approximately one-third of the sample experienced a suicide attempt (T. R. Goldstein et al., 2005). In a study comparing adolescents hospitalized for suicide-related ideation or attempts with adolescent suicide completers, a diagnosis of bipolar disorder in the suicide completers was one of the few key factors (along with comorbidity, lack of mental health treatment, and the presence of a firearm in the home) that significantly differentiated the two groups

(D. A. Brent et al., 1988).

The presence of a mixed episode may constitute a specific risk for suicide attempt. Suicide attempts were associated with mixed episodes, psychotic symptoms, family history of suicide attempt, psychiatric hospitalization, self-harm, history of physical and/or sexual abuse, panic disorder, and substance use disorder in the COBY study (T. R. Goldstein et al., 2005). Algorta and colleagues (2011) also found that youth (ages 5 to 18) diagnosed with bipolar spectrum disorder who met criteria for the proposed DSM-5 criteria for mixed mood specifier (i.e., presence of subthreshold manic and depressive symptoms; (“Mixed Features Specifier,

Proposed Revision, APA, DSM-5,” 2011) had more lifetime suicide-related ideation and suicide attempts than those who did not. Dilsaver and colleagues (2005) found mixed mood episodes significantly predicted suicide-related ideation and behavior in female, but not male, adolescent inpatients.

Anxiety disorder. The American Association of Suicidology has identified anxiety as a key factor to look for in assessing risk for suicide (American Association of Suicidology, 2011). One group of researchers has identified a subgroup of children who experience significant symptoms of anxiety associated with suicide- related ideations, coining the term “anxious suicidality” to describe their condition

(Allan, Kashani, Dahlmeier, Beck, & Reid, 1998). Gould and colleagues (1998) also found anxiety independently increased the risk for suicide-related ideation and attempts in children and adolescents (ages 9 to 17). In a sample of 1,829 youth (ages

10 to 18) incarcerated in a juvenile detention center in Chicago, recent suicide attempts were most common among those with major depression and generalized

anxiety disorder (Abram et al., 2008). However, the Great Smoky Mountains Study found anxiety (generalized anxiety disorder, in particular) was only associated with suicide-related ideation, behavior, and attempts when it was comorbid with a diagnosis of depression (Foley et al., 2006). Likewise, Strauss and colleagues (2000) found no remarkable differences in suicide-related ideations and behaviors among a sample of 1,979 youth (ages 5 to 19) with and without anxiety disorders when analyzed both by specific diagnosis and by diagnostic category. Of note, separation anxiety disorder was actually less common in children under age 15 who attempted suicide compared to same-age non-attempters (Strauss et al., 2000).

Boden and colleagues found the presence of an anxiety disorder increased the risk for suicide-related ideation and attempts among 16 to 25 year olds, even when adjusted for co-occurring anxiety disorders, other disorders (i.e., major depressive disorder [MDD], conduct/antisocial personality disorder, and substance use disorders), stressful life events, and unemployment (Boden, Fergusson, &

Horwood, 2007). Participants with two or more anxiety disorders were almost 6 times more likely to experience suicidal ideation, and almost 4 times more likely to have a suicide attempt than those without an anxiety disorder (Boden et al., 2007).

Beautrais and colleagues (Beautrais et al., 1996) found the presence of an anxiety disorder significantly increased the risk of suicide attempt in adult males, but not females. Findings from the Baltimore Epidemiological Catchment Area Follow-up

Study demonstrated the presence of an anxiety disorder at baseline significantly increased the risk of a suicide attempt over the course of the next 13 years, even

when controlling for all other baseline psychiatric disorders and sociodemographic variables (Bolton et al., 2008). On the other hand, in a national survey of over 10,000 respondents in Australian, anxiety was associated with suicide-related ideation but not suicide attempts (Pirkis, Burgess, & Dunt, 2000).

Disruptive behavior disorders. Angry, impulsive, and acting-out behaviors have been identified as correlates of suicide-related behavior in children and young adolescents since the 1950s (Despert, 1952). In a meta-analysis of the literature on completed suicides among 10 to 29-year-olds, Fleischmann and colleagues (2005) found disruptive behavior disorders to be the third most common diagnostic category assigned to young suicide completers (with mood disorders and substance abuse disorders ranking first and second, respectively).

In a review of the literature on adolescent suicide-related behavior, Bridge and colleagues (2006) list conduct disorder as one of the primary independent risk factors for suicide attempt. Conduct disorder is particularly predictive of suicide attempt (Kovacs et al., 1993) and completed suicide (Shafii et al., 1988) when it is comorbid with an affective disorder in child and adolescent samples. Apter and colleagues (1988) studied 140 consecutive adolescent psychiatric inpatients ages 12 to 17 (mean age 14 years, 6 months; 57% male) and found suicide-related ideation and behavior ratings were highest among youth diagnosed with conduct disorder; these ratings were significantly higher than the ratings of depressed youth. The authors conclude that aggression and impulsivity are two clinical characteristics of conduct disorder patients that may account for this elevation in suicide-related

ideation and behavior (Apter et al., 1988). They hypothesized two pathways to suicide-related behavior: 1) a desire to die (associated with depression), and 2) a desire to “not be here anymore” (a more impulsive, failed coping mechanism). A later study on a similar population replicated the finding that adolescent psychiatric inpatients with conduct disorder were at the highest risk for suicide-related behaviors, and found aggressive and violent behaviors were strongly associated with suicide-related behavior but not depressive symptoms (Apter et al., 1995).

Findings linking conduct disorder and suicide-related behaviors are mixed, however. Disruptive behavior and conduct disorder symptoms (e.g., truancy, fire- setting, impulsivity, fighting, stealing, defiance) did not differentiate youth (ages 5 to

14) hospitalized for suicide attempt from those admitted for non-suicidal depression and other reasons (Cohen-Sandler et al., 1982a). Additionally, the relationship between conduct disorder and suicide-related behavior may be moderated by age and sex. Beautrais and colleagues (1996) found a nonsignificant association between suicide attempts and conduct disorders (including antisocial personality disorder) in adult males, but not females. Brent and colleagues (1999) found conduct disorder to be more common and to represent more of an increased risk for suicide-related behavior only among males and older youth. Finally, findings from the National Longitudinal Study of Adolescent Health (M. P. Thompson, Ho, &

Kingree, 2007) indicate a clear relationship between delinquency and suicide- related ideations and attempts at one-year follow-up, which was stronger for girls

and significant even after controlling for depression, self-esteem, problem drinking, impulsivity, and religiosity.

Attention-deficit/hyperactivity disorder. Less research has been conducted on the relationship between attention-deficit/hyperactivity disorder (ADHD) and suicide-related behaviors in children, and findings have been mixed. Recently, researchers have found that adults who were diagnosed with ADHD as children were more likely to commit suicide in adulthood (Barbaresi et al., 2013). Chronis-

Tuscano and colleagues (2010) found a diagnosis of ADHD at age 4 to 6 predicted depression, suicide-related ideations and attempts by age 18. More specifically, the authors found children with ADHD – Combined Type were at risk for depression, suicide-related ideations, and suicide attempts; those with ADHD – Predominantly

Hyperactive/Impulsive were at increased risk for suicide attempts only; and children with ADHD – Predominantly Inattentive Type were at increased risk for depression but not suicide-related ideation or attempts. Cox proportional hazards ratios for the three ADHD subtypes and the comparison group indicate children with ADHD – Predominantly Inattentive Type were less likely than the comparison group to attempt suicide over the follow-up period, but the significance of this relationship was not reported (Chronis-Tuscano et al., 2010).

In another study of youth (ages 7 to 17) with bipolar spectrum disorders, comorbid ADHD was actually associated with a reduced risk for suicide attempt, although subtypes of ADHD were not reported (T. R. Goldstein et al., 2005).

Taken together, these studies underscore the importance of examining ADHD subtypes in relation to suicide risk. They introduce the possibility that ADHD (or a subtype of it, namely Inattentive Type) may actually be a protective factor against suicide attempts in children and adolescents. The notion that forgetfulness and distractibility may be adaptive if what is being cognitively “lost” is attention to risk factors for suicide attempt (e.g., stressful life events, family dysfunction; see below) is a novel one.

Adjustment disorder. Adjustment disorders have been associated with increased risk for suicide-related ideation and behavior (Yudofsky & Gabbard,

2011). In a study comparing adolescent (ages 12 to 19) psychiatric inpatients,

Goldston and colleagues (Goldston et al., 1998) found first-time suicide attempters had higher rates of adjustment disorder than repeat attempters, those with past

(but not current) attempts, and those with no history of suicide attempt.

Impulsivity. Beyond psychiatric diagnoses, a great deal of empirical evidence suggests there is an increased risk for suicide conferred via impulsive traits (D. A.

Brent et al., 2003; J. John Mann, 2003; McGirr et al., 2009). David Brent, one of the most prolific thought leaders in the current literature on adolescent and young adult suicide, advocates examining impulsive aggression as a possible endophenotype linking genetics with both psychiatric disorders and suicide-related behavior (D. A.

Brent, 2009).

In the child literature, as previously mentioned, Apter and colleagues (1995) observed that conduct disordered children seemed to be acting on an urge to flee

their immediate situation rather than an underlying desire to end their lives, and hypothesized that the impulsive nature of the disorder was the crux of the problem.

Javdani and colleagues (2011) found impulsivity uniquely contributed to suicide- related attempts and behaviors, above the influence of depression in adolescents. In a study of children and adolescents with bipolar disorder, Papolos and colleagues

(2005) found aggression and impulsivity were associated with suicide-related communications independent of dysphoric mood. In the previously mentioned study of offspring of parent suicide-attempters who had siblings concordant or discordant for suicide attempt, the authors found the relationship between sibling- concordant parents and suicide attempts in offspring was mediated by the higher level of impulsive aggression in the sibling-concordant group (D. A. Brent et al.,

2003). The same study found higher rates of impulsive aggression were linked to earlier age of onset of suicide-related behavior in offspring.

Given that impulsivity is a hallmark in many (but not all) children with disruptive behavior disorders, the unique contribution of impulsivity (or, more specifically, impulsive aggression) to risk for suicide attempt may explain the mixed findings in the research linking disruptive behavior disorders to suicide-related behaviors in children. If impulsivity were indeed the crucial risk factor, the findings in studies that examine the relationship of conduct disorder to suicide risk would be confounded by the failure to distinguish impulsive conduct-disordered youth from those who are more callous, unemotional, and premeditated in their antisocial behavior. Likewise, studies that group together children with all three subtypes of

ADHD (Predominantly Hyperactive/Impulsive, Predominantly Inattentive, and

Combined) would be watered down by the inclusion of predominantly inattentive children.

Another way of examining the impact of impulsivity on suicidal acts is to consider the increased risk for completed suicide when there is a lethal method readily available. Brent and colleagues (1999) found having a firearm in the home was associated with more attributable risk for suicide than psychopathology in children and adolescents under age 16. Among youth older than 16, the presence of a firearm in the home increased the risk of suicide by three times for those with psychopathology, but it increased by 30 times for those without psychopathology

(D. Brent & Bridge, 2003). These statistics suggest the availability of a lethal method may increase the risk for impulsive suicide attempts.

Suicide-related ideation. While research has shown almost 90% of older adolescents who attempt suicide also experienced suicide-related ideations

(Andrews & Lewinsohn, 1992; Lewinsohn et al., 1996), this may not be the case for younger children. A study of all completed suicides among children and adolescents under age 19 in Norway between 1990 and 1992 found children under age 15 less frequently expressed suicide-related ideation prior to their attempt (7%) compared to older adolescents (33%) (B. Groholt, Ekeberg, Wichstrom, & Haldorsen, 1998).

This finding further suggests suicide-related behavior in many young children may not be premeditated, but rather an impulsive act. An alternative possibility is that young children do experience suicide-related ideations, but are less likely to report

having them.

The children who do report suicide-related ideation may be experiencing a more stable phenomenon than previously thought, which may put them at increased risk for suicide attempt. Results from a prospective, longitudinal study following

1,022 Dutch children age 11 or younger over the course of 10 to 14 years found children who presented with suicide-related ideation in childhood were more likely to have suicide-related ideations in adulthood (Herba et al., 2007). These children were also more likely to have made a suicide attempt later in life (Herba et al.,

2007). Similarly, Kovacs and colleagues (1993) followed a sample of 8- to 13-year- olds over the course of 6 to 9 years and found that suicide-related ideations remained relatively stable over time, while suicide attempts rose from 9% at baseline to 24% by the average age of 17. The authors point out that 16 to 30% of the children with suicide-related ideations at baseline actually went on to attempt it by age 17 (Kovacs et al., 1993).

On the other hand, Birmaher and colleagues (2004) found suicide-related ideation at intake did not predict suicide attempt or ideation among depressed prepubertal children (Tanner stage I or II; ages 7.5 to 12.5 years) at follow-up (54.5

± 27.1 months) the way it did for depressed adolescents (Tanner stage III to V; ages

11.5 to 16 years).

Previous suicide attempts. Research on adolescent suicide attempts indicates one of the strongest predictors of current suicide attempt is a past history of suicide attempt. It is well-documented that previous suicide attempts put adolescents at a

substantially increased risk for repeated attempts (e.g., D. A. Brent, Perper, et al.,

1993; D. A. Brent et al., 2002; Bridge et al., 2006; Goldston et al., 1999; Miranda et al., 2008; Rudd, Joiner, & Rajab, 1996; Shaffer, 1988). In younger samples, however, this finding does not necessarily hold. In a study of 31 completed suicides in young adolescents (ages 12 to 15) between 1962 and 1968, Shaffer (1974) found 46% had documented previous suicidal behavior. On the other hand, in a study of the 61 children and young adolescents under age 15 who had died by suicide over a 10- year period in New Zealand, Beautrais (2001) found only 13% had made a previous attempt.

One logical explanation for the lower rate of previous attempts among children and young adolescents when compared with older adolescents and adults is the age difference: no matter how many lifetime suicide attempts an individual makes, there is always a first attempt. The earlier in life we study suicide attempters, the more likely we will be to capture these first attempts. The youngest suicide attempters, who are of interest in the current study, may be attempting for the first of what will be many attempts.

Psychosis. Lifetime psychosis was a significant predictor of suicide attempt in a sample of youth (ages 7 to 17) with bipolar spectrum disorders (T. R. Goldstein et al., 2005). Asarnow, Tompson, and Goldstein (1994) found 38% of a sample of children ages 7 to 14 with child-onset schizophrenia had made a suicide attempt; an additional 38% of the sample had suicide-related ideations. In a more recent study of 10- to 18-year-olds presenting to the emergency department for suicide-related

ideations and attempts, Asarnow and colleagues (2008) found 30% of these youth fell in the clinical range on the Thought Problems scale (e.g., “hears voices”) of the

Child Behavior Checklist (Achenbach, 1991), even though acute psychosis was an exclusion criteria for the study.

Substance use and abuse. The adolescent literature has found a strong association between suicide-related behavior and alcohol and substance use and abuse (Dunn et al., 2008; Epstein & Spirito, 2009, 2010; Garrison, McKeown, Valois,

& Vincent, 1993; B. I. Goldstein et al., 2009; Berit Groholt et al., 1997; Hoberman &

Garfinkel, 1988; R. A. King et al., 2001; Kosky, Silburn, & Zubrick, 1990; Marttunen,

Aro, Henriksson, & Lönnqvist, 1991; Pfeffer, Newcorn, Kaplan, Mizruchi, & Plutchik,

1988; Rosenberg, Jankowski, Sengupta, Wolfe, & Wolford, 2005; Shaffer et al., 1996;

Shaw et al., 2005; Spirito & Esposito-Smythers, 2006; Wu et al., 2004). Because the prevalence of alcohol and substance use and abuse is very low in children under age

12, the relationship has not been studied extensively in a younger population. One study of 8-year-olds who had experienced maltreatment or were at risk for maltreatment found children who had tried substances were four times more likely to report suicide-related ideation (R. Thompson et al., 2005). Middle school students who indicated on the YRBS study that they had initiated smoking cigarettes, drinking alcohol, or using marijuana, cocaine, inhalants were more likely to endorse suicide-related ideation, having made a suicide plan, or having attempted suicide in the past year (Dunn et al., 2008). Likewise, smoking, marijuana use, and alcohol intoxication were associated with increased risk for suicide-related ideation and

suicide attempts in a sample of youth ages 9 to 17 (King et al. 2001). Early use of alcohol and drugs may also be evidence of other environmental factors (such as low parental monitoring) that combine with later substance abuse problems to contribute to an increased risk for suicide-related behaviors. Although no research as found a direct relationship between suicide-related behaviors and their early experimentation with alcohol and drugs in a sample of children under 12, this is worthy of study.

Previous self-harm. While there is a clear link between self-harm and suicide attempt in the adult literature, findings in the child literature are mixed. A history of self-harm was a significant predictor of suicide attempt in one youth sample (ages 7 to 17) with bipolar spectrum disorder (T. R. Goldstein et al., 2005). However, Cohen-

Sandler and colleagues (1982a) found that non-suicidal self-injurious behavior (e.g., head-banging, self-biting) did not differentiate suicide attempters from depressed non-suicidal and other child psychiatric inpatients (ages 5 to 14).

Sleep disturbance. A study of Taiwanese junior and senior high school students found that youth who slept less than 6 hours per night were more likely than average (6 to 8 hours/night) or long (>8 hours/night) sleepers to engage in a variety of risky behaviors, including suicide-related ideation and attempts (Yen,

King, & Tang, 2010). In an Australian sample of youth (mean age 12.9), those with suicide-related ideations and attempts had elevated rates of sleep disorders (Kosky et al., 1990). In a US sample, both insomnia and hypersomnia were more common among adolescent suicide completers compared with matched community controls,

and distinguished suicide completers even when severity of depressive symptoms was taken into account (Goldstein, Bridge, & D. A. Brent, 2008). Insomnia has been associated with suicide attempts in adult populations as well (Carli et al., 2011; Li,

Lam, Yu, Zhang, & Wing, 2010).

Antidepressant medication. Much attention has been paid to the black-box warning about the possibility of increased risk for suicide-related ideations and behaviors in children and adolescents who take antidepressant medication. A meta- analysis of antidepressant medication in youth under age 19 indicated no significant increase in pooled risk for suicide-related ideations and behaviors among youth with MDD, obsessive-compulsive disorder (OCD), and non-OCD anxiety disorders

(Bridge et al., 2007). The authors concluded that the benefits of antidepressant medication in this population outweigh any potential for elevated risk of suicide- related ideations and behaviors.

Psychosocial functioning.

Family factors. Pfeffer argues that familial factors are crucial to the risk of suicide-related ideations and behaviors in children and young adolescents (Pfeffer,

2000). This line of research dates back to Sabbath’s (1969) “expendable child” theory of suicide in young people. Sabbath articulates his argument by presenting three case histories of adolescents who attempted suicide. In each case, the family dynamic was such that the adolescents received messages from their parents that they were not wanted and the family would be better off without them. These messages were both direct (e.g., one mother repeatedly told her daughter to “drop

dead”) and indirect (e.g., parents not responding to suicide-related communications, one father told the doctor at a psychiatric inpatient facility to discharge his daughter in order to “get rid of the bad apple” and “not ruin it for everyone else”). Ultimately,

Sabbath argues, the youth in each case was acting out a conscious or unconscious parental desire to be rid of their child.

Sabbath’s hypothesis was based on extreme cases of family dysfunction.

Family stress may negatively impact a child and influence him/her to consider, threaten, or attempt suicide in more subtle ways. Two developmental features of childhood are: 1) children are dependent upon adults for their basic needs and survival, and 2) children have an egocentric perception of the world prior to reaching the formal operational stage of cognitive development (around age 11)

(Pfeffer, 2000). When caretakers are not able to meet the needs of the child, or they make decisions that negatively impact the child’s sense of security, children are adversely affected. It is difficult for children to understand that problems in the home are not always directly related to them. Children may perceive themselves to be responsible for conflict and problems in the family and find it difficult to differentiate their own response to family stress from a sense of culpability (Pfeffer,

2000). Finally, children’s emotion regulation and problem-solving skills are not fully developed and suicide-related ideations and behavior may emerge as a maladaptive attempt to escape or solve the problem as they see it.

Hawton and Harriss (2008) found 77% of children under age 15 who presented to the hospital for treatment of suicide-related behaviors and attempts

(95.8% involved overdosing on a substance), reported recent problems involving the child’s relationships with family members. This was, by far, the most commonly reported recent difficulty. More recently, Consoli and colleagues (2013) found that negative relationship(s) between an adolescent and one or both parents and high parental conflict were both significantly correlated with adolescent suicide-related behavior and depression. Lower family cohesion was one of the factors that predicted suicide attempts in the Treatment of Adolescent Suicide Attempters

(TASA) study (D. A. Brent et al., 2009). Family discord differentiated suicide-related ideators from attempters in another study (Kosky et al., 1990).

Beyond the parent-child relationship, relationships with siblings may also be important. Lower levels of support and understanding from siblings were reported by adolescents at high risk for suicide attempts compared to low-risk groups (E. J. de

Wilde, Kienhorst, Diekstra, & Wolters, 1994).

Recent research has attempted to understand the bidirectional influence of child and adolescent suicidal behavior on the parent-child relationship and overall family functioning. Algorta and colleagues (Algorta et al., 2011) found suicide- related ideations and/or attempt made a significant, unique contribution to the regression model predicting family functioning along with the covariates of age, race, and gender. The authors speculated that suicide-related behavior might not simply be a result of negative family functioning, but also a contributing factor to the dysfunction.

Similarly, Cohen-Sandler and colleagues found inpatients (ages 5 to 14) hospitalized for suicide attempt were more likely to be living with at least one biological parent than non-suicidal depressed inpatients, and other inpatients

(Cohen-Sandler et al., 1982a). However, at 5-month to 3-year follow-up, children who had made an attempt were much more likely to be removed from living with one or both parents than those who were hospitalized for depression or other reasons (Cohen-Sandler, Berman, & King, 1982b). If young suicide attempters were simply responding to their disrupted, chaotic environments prior to their attempt and hospitalization, these children would have likely been removed from the home at the same rate as the depressed non-suicidal and other inpatients. While reasons for removal of the suicidal child from the home after hospitalization may be correlated with the higher stressful life events suicide attempters experienced prior to hospitalization, it is reasonable to postulate that the act of attempting suicide was disruptive enough to precipitate the child’s removal.

Peer factors. In a study of children and young adolescents presenting to the hospital for treatment of suicide-related behavior, the second most commonly reported recent problem involved peer relationships, with problems reported by

38.9% of youth (Hawton & Harriss, 2008). Negative peer relationships, particularly the experience of being bullied, have been associated with suicide-related behaviors in several studies. The relationship between being bullied and suicide-related behaviors may be especially important in childhood. One study found more children than adolescents who presented with suicide-related communications, attempts,

ideations, and behaviors reported having been recently bullied (Sarkar et al., 2010).

In another study, both traditional bullying and cyberbullying—or bullying over the

Internet—was associated with suicide-related thoughts and suicide attempts in a sample of almost 2,000 middle-schoolers (Hinduja & Patchin, 2010). Interestingly, this relationship was true for both the victims and, to a lesser extent, the persons doing the bullying. Klomek and colleagues also found both victims and bullies to be at increased risk for depression, suicide attempts, and suicide-related ideation.

Recent research suggests the impact of childhood bullying may be life long.

Roeger and colleagues (2010) found a significant relationship between suicide- related ideations in adulthood and the experience of having been bullied in childhood, even when controlling for depression and socioeconomic variables.

School factors. Difficulties at school ranked a close third in most frequent areas of recent difficulty reported by youth under age 15 presenting for treatment of suicide-related behavior, with 37.9% of children and adolescent endorsing difficulty in academics (Hawton & Harriss, 2008). Kaplan and colleagues (1999) found poor academic functioning to be one of the risk factors most frequently predictive of suicide-related ideations and behaviors in a sample of adolescents ages 12 to 18. In one study, a factor distinguishing adolescent suicide attempters from both depressed and psychologically healthy comparison groups was that the suicide attempters were more likely to have had to repeat a course in school (E. de Wilde,

Kienhorst, Diekstra, & Wolters, 1992).

Finally, some research suggests the particular type of psychosocial dysfunction may not be important, but rather that there are significant problems in at least one domain (i.e., home, school, or with peers) may constitute elevated risk for suicide-related behavior. For example, in one study, Pfeffer and colleagues administered a social adjustment scale that measured psychosocial functioning at home, school, with peers, and in recreational activities. Rather than analyzing the impact of each of these, the authors collapsed the data and ran their analyses based on the highest domain score (with higher scores indicating more dysfunction). They found that poor social adjustment was the most significant risk factor for a subsequent suicide attempt 6 to 8 years after initial hospitalization for suicide- related ideations and behaviors among children ages 4 to 14 (Pfeffer et al., 1993).

While some nuance is lost in this type of analysis, findings underscore the importance of looking at psychosocial functioning as a broad category.

Stressful life events. Stressful life events impact children in much the same way as family dysfunction. In fact, the two categories overlap a great deal, because family dysfunction is inherently a stressful life event, and many stressful life events occur within the context of the family. A separate category for stressful life events is warranted in order to emphasize certain types of stressful life events that have been found to particularly relevant to suicide risk and to capture the important cumulative effect of stressful life events across various domains of functioning. The relationship between total stressful life events (both lifetime and in the previous

year) and suicide attempts has been empirically demonstrated (Horesh, Nachshoni,

Wolmer, & Toren, 2009).

Early, chronic stress has been associated with child suicide-related behaviors

(Pfeffer et al., 1993). Cohen-Sandler and colleagues (Cohen-Sandler et al., 1982a) found increasing rates of stress across the lifetime (from infancy to present), especially in early and later childhood and in the 12 months prior to hospitalization, significantly differentiated child and adolescent (ages 5 to 14) inpatients admitted for suicide attempt from non-suicidally depressed and other inpatients. Specifically, those who had been hospitalized for a suicide attempt were more likely to have experienced the loss of a parent due to parental death, separation, or divorce, than those hospitalized for non-suicidal depression or other reasons. A follow-up of this study found these three groups did not differ on a measure of post-discharge stressful life events, suggesting the suicidal inpatients may have attempted suicide as a maladaptive coping response to escape a stressful situation (Cohen-Sandler et al., 1982b).

Parental divorce or separation is a significant stressful life event that may play a role in childhood suicide-related behavior. Stanley and Barter (1970) found equal rates of parental divorce or separation among suicidal and nonsuicidal adolescents, but noted the divorce/separation more commonly occurred before age

12 for the suicidal group. Parental divorce during childhood has been related to suicide-related ideations years later when that child was in his/her own adulthood

(Fuller-Thomson & Dalton, 2011; Preidt, 2011).

Frequent moves and changes in household is another life stressor that has been associated with suicide-related behavior in youth. Adolescents designated

“high risk” for attempting suicide had more lifetime changes in living situation than a psychologically healthy comparison group (E. J. de Wilde et al., 1994).

Both physical and sexual abuse in childhood and adolescence has been associated with increased rates of suicide-related behaviors (E. J. de Wilde et al.,

1994). In a review of the literature related to childhood sexual abuse and suicide- related behaviors, Maniglio (2010) considered evidence from 177 studies and concluded child sexual abuse is a significant, but non-specific risk factor for suicide attempts and self-harm later in life. One group of authors theorizes the mechanism by which childhood abuse increases the risk for suicide attempts later in life is via a decrease in neurobiological processes responsible for behavioral inhibition, and subsequent increase in impulsivity (Braquehais, Oquendo, Baca-Garcia, & Sher,

2010). Both the Treatment of Adolescents with Depression Study (TADS) and the

Treatment of Adolescent Suicide Attempters (TASA) study found a history of sexual abuse was significantly associated with baseline suicide-related ideations and behaviors (D. A. Brent et al., 2009; Lewis et al., 2010). Kienhorst and colleagues

(1992) found adolescent suicide attempters were more likely to have experienced sexual abuse than adolescents who were depressed.

In one study comparing children who had been physically abused with neglected and non-abused/neglected children, children who were abused committed more self-destructive behavior than children in either of the other

groups (Green, 1978). In another study comparing physically abused adolescents with a non-abused comparison group, there was an indirect relationship between abuse and risk for suicide-related ideations and behaviors (Kaplan et al., 1999). The authors found that the abused adolescents were more likely to be exposed to other more direct risk factors, including lack of family cohesion, depressive disorder, and poor academic functioning, which put them at risk for suicide-related ideations and behaviors.

Summary

Suicide-related behaviors among children ages six to 12 are uncommon, but serious events that are worthy of scientific study. To assist in clarity of communication and accuracy of reporting relevant statistics, it is important that the language used to describe and define suicide-related thoughts, communications, and behaviors moves toward uniformity and standardization. Developmental factors must be taken into consideration in order to understand the ways in which the phenomenon of suicide in childhood may be similar or different from adult suicide.

Recent theories from adult and adolescent research point to transactions between an individual (i.e., biological, psychological) and his/her environment (i.e., interpersonal, social) that contribute to increased risk for SRB. Although previous research on SRBs in children under age 12 is limited, past research in adolescent samples has provided a good starting point for understanding possible correlates and risk factors for SRB in a younger sample. The domains that are likely associated 55

with risk for SRB in children include: demographic factors; psychiatric family history; child psychopathology; psychosocial functioning at home, school, and among peers; and stressful life events. To date, the preponderance of published research has focused on factors within a single domain, while studies that examine the interactions of variables across multiple domains are much less common.

Study Aims and Hypotheses

The aim of the current study was to identify biological, psychological, and social factors associated with suicide-related behavior in children in a community mental health sample enriched for elevated symptoms of mania. The data provided a rich and rare opportunity to explore a multitude of factors across various domains that have been previously identified as correlates of suicide-related behavior in youth. Characteristics of children who have experienced suicide-related behavior were explored within five domains: 1) demographic factors; 2) psychiatric family history; 3) child psychopathology; 4) psychosocial functioning; and 5) stressful life events. A model building procedure was conducted to identify the variables within each domain that most effectively discriminated children who had a suicide-related behavior from those who did not. Each of these models was compared to the naïve model; or a model that predicts SRB group membership by chance without consideration of any of the predictor variables. After the five distinct domain models were developed, an integrated model of the combined influence of factors across the domains was explored. The following hypotheses guided this procedure:

Hypothesis 1. The final model of demographic factors will discriminate children with suicide-related behaviors from those without better than the naïve model.

Hypothesis 2. The final model of psychiatric family history will discriminate children with suicide-related behaviors from those without better than the naïve model.

Hypothesis 3. The final model of variables related to child psychopathology will discriminate children with suicide-related behaviors from those without better than the naïve model.

Hypothesis 4. The final model of psychosocial functioning (at home, at school, and with peers) will discriminate children with suicide-related behaviors from those without better than the naïve model.

Hypothesis 5. The final model of stressful life events will discriminate children with suicide-related behaviors from those without better than the naïve model.

Hypothesis 6. The integrated model of demographic factors, psychiatric family history, child psychopathology, psychosocial functioning in the family, psychosocial functioning with peers, psychosocial functioning at school, and stressful life events will discriminate children with suicide-related behaviors from those without better than the naïve model.

Method

The current study analyzes a subset of data from the Longitudinal

Assessment of Manic Symptoms (LAMS) Study. The LAMS Study is a multicenter study being conducted at four sites: Case Western Reserve University (Coordinating

Principle Investigator [PI]: Robert Findling, M.D.); Western Psychiatric Institute and

Clinic (PI: Boris Birmaher, M.D.); Cincinnati Children’s Hospital Medical

Center/University of Cincinnati (PI: Robert Kowatch, M.D.); and The Ohio State

University (PI: Mary Fristad, Ph.D.). Approval to conduct research was granted by the Institutional Review Boards affiliated with each of these sites. The LAMS Study is an ongoing, prospective, longitudinal study of an epidemiologically ascertained sample of children with elevated symptoms of mania (ESM+) and a community mental health comparison group of children without elevated symptoms of mania

(ESM-). The primary aims of the LAMS study are to 1) estimate the prevalence of

ESM+ among children ages six to 12 who present to community mental health clinics; 2) follow ESM+ children over time to document the evolution of their psychiatric symptoms, 3) identify factors associated with poor functional outcome in children with baseline ESM+. Study design (Horwitz et al., 2010), and

characteristics of the baseline sample (Findling et al., 2010) have been reported, and are described below.

Screening and Enrollment Procedures

Screening. Children ages 6 years 0 months, to 12 years 11 months who presented for an intake appointment for clinical care at 9 outpatient mental health clinics (five in Columbus, Ohio; two in Cleveland, Ohio; one in Cincinnati, Ohio; and one in Pittsburgh, Pennsylvania) were considered for screening. To be eligible, both parent and child were required to be fluent in English and the adult accompanying the child at the clinic was required to be a parent or legal guardian (other relatives, friends, or temporary custodians accompanying the child were not able to consent for participation in the screening). Families were excluded from participation in screening if: (1) the child had received mental health treatment at the site or its affiliates in the past 12 months, (2) the child, the child’s siblings, or any other child in the household had participated in this protocol, or (3) if the child has already been approached to participate in LAMS screening. These provisions were in place to prevent potential confounding of data due to screening the same child twice or over-representing any one family in the LAMS Study. Table 6 presents the screen inclusion and exclusion criteria.

Once it was confirmed that inclusion and exclusion criteria for screening were met, parents were asked to complete a demographic questionnaire and a 10- item parent-report of manic symptoms in the identified child that served as the screening tool for the study (the screening questionnaire, the Parent-Completed 59

General Behavior Inventory 10-Item Mania Scale [PGBI-10M], is described in detail in the Instruments section, below).

Baseline enrollment. Participants were invited to enroll in the LAMS baseline assessment if their score on the PGBI-10M indicated the presence of ESM+.

Additionally, ESM- participants were recruited as part of a comparison group on a rolling basis using a minimization procedure. Each study site periodically selected one ESM- participant for every ten consecutive ESM+ participants enrolled (for study sites with a lower volume of ESM+ enrollment, one ESM- participant was chosen for every five ESM+ participants enrolled). These comparison participants were randomly chosen every three to four weeks from among the pool of ESM- participants who were similar in age (within 2 years), sex, race/ethnicity, and type of insurance (i.e., Medicaid, private insurance, or self-pay) as the “modal” ESM+ participant enrolled since the last ESM- selection. If a selected ESM- participant declined to enroll, that participant was replaced. The goal of this strategy for comparison group selection was to minimize the differences between the ESM+ and

ESM- groups, and is a commonly accepted procedure in clinical trials where randomization to groups is impossible and stratification is implausible (Pocock &

Simon, 1975).

At the baseline assessment, eligible children and their parents completed an initial assessment to ascertain psychiatric diagnosis, current mood symptom severity, psychiatric family history, a brief measure of intelligence, psychosocial functioning, quality of life, stressful life events, treatment utilization, and

demographic information. Modes of assessment included semi-structured interview with parent and child (separately), parent and child self-report questionnaires, and psychological testing. Children and parents had the option to have self-report measures read to them if they preferred for any reason. Unless participants met exclusion criteria for the longitudinal portion of the study, they were invited to return every six months for abbreviated follow-up assessments. Exclusion criteria for the longitudinal portion of the study were: (1) having manic symptoms due to general medical condition, (2) consensus diagnosis of a pervasive developmental disorder (PDD) other than Asperger’s Syndrome or PDD-NOS, and (3) intellectual and adaptive functioning measured to be two standard deviations below the mean.

The rationale behind these exclusion criteria was to avoid confounding factors that could adversely affect the interpretability of LAMS study results (see Table 6).

Participants

A total of 707 children between the ages of 6 and 12 were enrolled across sites. One parent or guardian was selected as the primary informant for the duration of the longitudinal study.

Instruments

Table 7 provides a complete list of the measures administered as part of the

LAMS battery. Full descriptions of the assessment instruments relevant to the current study are described below.

Parent General Behavior Inventory 10-Item Mania Scale (PGBI-10M). The

PGBI-10M is a 10-item, empirically validated, parent-completed scale that was used as the screening instrument to identify children ages 6 to 12 with ESM. The PGBI-

10M was developed from the 73-item Parent General Behavior Inventory (PGBI;

(Findling et al., 2002; Youngstrom et al., 2004; Youngstrom, Findling, Danielson, &

Calabrese, 2001; Youngstrom, Frazier, Demeter, Calabrese, & Findling, 2008) by extracting the ten items that were shown to best discriminate bipolar disorder from other diagnoses. The PGBI-10M has been shown to have excellent reliability (alpha =

.92) (Youngstrom et al., 2008).

A score of 12 or above on the PGBI-10M was chosen as the cut-off score for a child to be considered ESM+ based on the positive predictive power (PPP) of this score. In an outpatient medical clinic, 38% of children being seen who scored 12 or higher on the PGBI-10M were subsequently diagnosed with bipolar disorder

(sensitivity=.64, specificity=.88) (Youngstrom et al., 2008). This proportion of participants meeting versus not meeting criteria for a formal BP diagnosis was considered ideal for the overall goals of the study.

Wechsler Abbreviated Scales of Intelligence (WASI) (The Psychological

Corporation, 1999). The WASI is a brief standardized intelligence test that provides estimates of verbal and nonverbal cognitive ability in individuals aged 6 to 89. The

WASI was used as an objective rubric with which to screen out participants whose low cognitive ability and intellectual functioning may have impeded the reliable collection of data. The two-subtest form was administered at baseline to determine

whether or not each child met the intelligence criterion required for a longitudinal follow-up (i.e., FSIQ of 70 or higher). While the WASI is not intended to take the place of more comprehensive testing for purposes such as disability determination or access to educational services, it was designed to be used as a screening tool and an efficient estimate of intelligence for research purposes, making it ideal for use in the LAMS study.

Scores from the Vocabulary and Matrix Reasoning subtests of the WASI can converted to a standardized measure of general intelligence, the two-subtest full- scale IQ (FSIQ-2). Items on the WASI subtests are different from, but follow the same format as, their parallel subtests on the Wechsler Intelligence Scales for Children,

Third Edition (WISC-III) and Wechsler Adult Intelligence Scales, Third Edition

(WAIS-III). For the 1,100 children in the standardization sample, internal consistency reliability was high for the Vocabulary (0.89) and Matrix Reasoning

(0.92) subtests as well as the FSIQ-2 (0.93). Test-retest reliability for the FSIQ-2 in the child sample was 0.85, with scores on the retest being consistently higher, presumably due to practice effects. The WASI FSIQ-2 was found to have good concurrent validity (0.82) relative to the WISC-III FSIQ.

Subsequent research comparing WASI FSIQ scores to those derived from the

Wechsler Intelligence Scales for Children, Fourth Edition (WISC-IV; (Adaki, 2010) found the WASI tended to be an overestimate of FSIQ in adults and children and should be interpreted with caution. The implication of this research is that using the

WASI FSIQ as a screening instrument is more likely to result errors of over-inclusion

of participants with low intellectual function than in errors of over-exclusion, which is acceptable given the aims of the LAMS study.

Scales of Independent Behavior – Revised (SIB-R) (Bruininks, Woodcock,

Weatherman, & Hill, 1996). The SIB-R is a measure of adaptive functioning that can be administered as a questionnaire or in interview format to an adult caregiver of an individual (infant through adult) with or without developmental disabilities. The

SIB-R is divided into six domains of adaptive functioning: motor skills, social interaction, communication skills, personal living sills, community living skills, and problem behavior. Strong internal consistency has been demonstrated with split- half reliabilities ranging from 0.70 to 0.94. The SIB-R was also found to have good test-retest reliability (0.83 to 0.97) and inter-rater reliability (0.82 to 0.95). The SIB-

R has been found to have convergent validity (0.83) with the Vineland Adaptive

Behavior Scale (Middleton, Keene, & Brown, 1990)

For the LAMS study, the SIB-R was administered as an interview to parents of children who scored below 70 on the WASI FSIQ, but who were suspected to not have mental retardation based on other information such as educational history, interviewer observation, or factors associated with WASI administration that may have resulted in unreliable or invalid test results (e.g., child test anxiety, oppositional behavior). If the score on the SIB-R was 70 or higher and other information suggests the child did not have mental retardation, the family was invited to participate in the longitudinal portion of the study.

Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age

Children – Present and Lifetime Version with additional items from the WASH-U-

KSADS (K-SADS-PL-W). The Kiddie Schedule for Affective Disorders and

Schizophrenia for School-Age Children (KSADS) is the gold standard semi- structured interview for assessing psychiatric disorders in children and adolescents.

There are many versions of the KSADS in use that have been adapted for numerous studies of child psychiatric disorders. Differences in versions of the KSADS include, but are not limited to, the addition of symptom questions, screen items, onset and offset dates for symptoms, global versus diagnosis-specific impairment ratings, and timeframe (i.e., present, last week, last two weeks, lifetime) for rating diagnoses

(Kaufman et al., 1997).

Each participant and his/her parent or caregiver were interviewed separately using the KSADS – Present and Lifetime Version (K-SADS-PL). The K-

SADS-PL consists of screen and supplemental questions to thoroughly assess current and lifetime presence of DSM-IV disorders most commonly found in children (Kaufman et al., 1997). The KSADS-PL has demonstrated concurrent validity with other measures of childhood psychopathology including the Child

Behavior Checklist (Achenbach & Edelbrock, 1983), the Conners Abbreviated

Questionnaire – Parent version (Conners & Barkley, 1985), the Screen for Child

Anxiety-Related Emotional Disorders (Birmaher et al., 1997), the Children’s

Depression Inventory (Kovacs, 1985), and the Beck Depression Inventory (Beck,

Ward, Mendelson, Mock, & Erbaugh, 1961). KSADS-PL was demonstrated to have

high inter-rater agreement on the scoring of screens and diagnoses (range: 93% to

100% agreement) (Kaufman et al., 1997). Test-retest reliability coefficients varied by diagnosis, but ranged from good (κ=.63 to .67) for diagnoses of PTSD and ADHD to excellent (κ = .77 to 1.00) for diagnoses of major depressive disorder, bipolar disorder, generalized anxiety disorder, conduct, and oppositional defiant disorder.

For the purposes of the LAMS Study, the K-SADS-PL was supplemented with additional questions to probe for prebubertal manic symptoms from the

Washington University K-SADS (WASH-U-K-SADS; (Geller et al., 2001; Geller,

Warner, Williams, & Zimerman, 1998). The kappa statistics for all manic symptoms in the WASH-U-KSADS are between 0.82 and 1.00, which demonstrates excellent reliability (Geller et al., 2001). Because the instrument used for the purposes the

LAMS Study consists of the K-SADS-PL with supplemental items based on the

WASH-U-KADS, it is referred to as the K-SADS-PL-W.

Additionally, because the KSADS in its original form lacked questions related to PDD, PDD screening items and a PDD supplement were added to the KSADS by a team of investigators, interviewers, and consultants in the field of developmental disabilities. Questions in the PDD screen and supplement correspond to the four

PDD symptom categories: Stereotyped Behaviors, Communication, Social

Interaction, and Abnormal Language. The KSADS PDD items were the basis for determining a diagnosis of PDD (and subsequent exclusion from the study of any child meeting criteria for a PDD diagnosis other than Asperger’s Syndrome or PDD-

NOS at baseline).

For the purposes of the LAMS study, presence and severity of symptoms was gathered using the KSADS-PL-W from parent and child separately. The interviewer decided on a summary score for each symptom that most accurately captured the child’s presentation. Current and worst lifetime diagnoses for each disorder were established by consensus between the interviewer and a doctoral level, licensed clinician shortly after the baseline interview.

Weissman Family History Screen (FHS) (Weissman et al., 2000). The FHS was used to collect information on 15 psychiatric disorders and suicidal behavior in informants and their first- and second-degree relatives of the child participant.

First-degree relatives include the child’s biological parents and full siblings; second- degree relatives include the child’s half-siblings, aunts, uncles, and grandparents.

Sensitivity and specificity of proband report of family members’ psychiatric disorders on the FHS (compared to self-report by the relative) ranges widely depending on the disorder being assessed. Sensitivity of the FHS ranges 9.8% for simple phobia to 87% for suicide attempts, with a median sensitivity of 71.1%.

Specificity of the FHS ranges from 68.0% for any diagnosis to 99.3% for drug dependence, with a median specificity of 89.4%. For the purposes of the current study, it is important to note the FHS has demonstrates its highest sensitivity (87%) and specificity (97.2%) in assessing suicide attempts among relatives. Test-retest reliability across 15 months resulted in a median kappas ranging from 0.30 for simple phobia to 0.68 for any diagnosis. The 15-month test-retest reliability for suicide attempt was 0.67, which is adequate. For the purposes of the current

analysis, the FHS was used to identify suicide attempts among first- and second- degree relatives of the child participant and was used as a predictor variable in analyzing the relevant hypotheses.

For the purposes of the current analysis, only parent psychiatric family history was considered.

Stressful Life Events Schedule (SLES) (Williamson et al., 2003). The SLES was originally developed as an interview, but has been adapted for use as a self-report instrument. In the LAMS study, it was administered as a child self-report (SLES-C) and parent proxy-report about the child (SLES-P). The SLES provides information on the occurrence and perceived impairment caused by stressful events experienced by the child in the past six months. The SLES is comprised of a list of stressful life events that the rater may endorse or skip. For each item endorsed, the rater must indicate using a five-point Likert scale the extent to which the child was affected by the event. Items on the SLES include questions about the child’s living situation (e.g., “Has anyone moved in or out of your home?”; “Do you feel unsafe at home or in your neighborhood due to gangs or violence?”); the relationships among family members (e.g., “Do your parents often fight or argue?”; “Have you been fighting with your parents more?”) and other stressful life events (e.g., “Has a close friend or family member died?”; “Have you been really sick?”). There are blank spaces at the end of the scale where the parent or child may write their own stressful life event and rate the impact on the child.

The SLES was developed to assess the presence of stressful life events in a way that was more informative than standard respondent-based checklists, but less time- and cost-intensive than the Bedford College Life Event and Difficulty Schedule

(LEDS; G. W. Brown, Sklair, Harris, & Birley, 1973). The SLES was also applicable to a wider age range than the LEDS. The SLES and the LEDS were found to have high concurrent validity for specific severe events (κ=0.77). Additionally, the SLES and a commonly used stressful life events checklist, the Life Event Checklist, had high interclass correlations for total number of stressful life events (ICC=0.83); however, concurrence for specific events ranged from fair (κ=0.47) for non-severe events to moderate (κ=0.61) for severe events. There was a high degree of parent-child agreement about severe events (κ=0.73), but only moderate agreement about all events (κ=0.48). Test-retest reliability (κ=0.68) of the SLES was found to be substantial (Williamson et al., 2003).

For the current analyses, specific stressful life events demonstrated by past research to be associated with suicide-related behaviors and items that identify dysfunction at home, school, and with peers were analyzed individually. Child

(SLES-C) and parent (SLES-P) ratings were analyzed separately.

Psychosocial Schedule of the Adolescent Longitudinal Interval Follow-up

Evaluation (A-LIFE) (M. Keller, 1993). The A-LIFE is a semi-structured interview developed to provide a comprehensive week-by-week assessment of psychiatric symptoms and psychosocial course and outcome in youth 6 to 18 years of age. The

A-LIFE was adapted from Longitudinal Interval Follow-up Evaluation (LIFE), a

measure designed for adults (M. B. Keller, Lavori, & Friedman, 1987). Confirmatory factor analysis of the psychosocial functioning schedule of the LIFE suggests it measures a single construct (Leon et al., 2000). Internal consistency (α=0.78 to

0.84) and inter-rater reliability (ICC=0.94 to 0.99) for the LIFE were shown to be very good (Leon et al., 2000).

The Psychosocial Schedule of the A-LIFE was administered as measure of psychosocial functioning for the LAMS study. Seven distinct areas of psychosocial functioning were addressed by the A-LIFE: 1) relationship with primary caregiver,

2) relationship with secondary caregiver, 3) relationship with siblings, 4) relationship with other significant adult, 5) relationship with peers, 6) school and academic functioning, 7) recreational activities. Additionally, respondents were asked to provide a rating of the child’s global functioning. The A-LIFE interview was conducted with the parent and child separately, resulting in unique parent and child scores for each item. Ratings for both current (past two weeks) and the past six months were assigned. Parent and child scores for each area of functioning were rated on a five-point Likert scale, with high scores reflecting poor functioning. The interviewer determined current and past summary scores for each item based on parent and child ratings. Additionally, the interviewer assigned a current and past rating for overall functioning, independent of the child and parent perception.

For the current analyses, A-LIFE summary scores were analyzed in two ways: the current score and the mean of the past and current scores were considered separately.

Clinical Global Impression Scale -Severity (CGI-S). The CGI (NIMH, 1985) was developed as a scale to assess severity and improvement of symptoms over time.

For the purposes of the LAMS study, only the CGI Severity ratings were made.

Ratings were determined by the interviewer and confirmed by a licensed clinician at the consensus diagnosis meeting. Three severity ratings were made at baseline: severity of manic symptoms, severity of depressive symptoms, and severity of overall illness. Ratings were made on a 7-point Likert scale with a score of “1” indicating no illness and a score of “7” indicating the child is among the most severely ill.

Demographics Questionnaire. Information about the primary and secondary in-home and out-of-home caretakers was gathered using a demographics questionnaire developed by the LAMS team. In the event that biological parents were not the primary or secondary in- or out-of-home caregivers, a supplemental questionnaire was administered to gather demographic information about the child’s biological parents. This questionnaire was administered as a structured interview with the parent. Information about the caregivers’ age, sex, ethnic/racial background, marital status, level of education, occupation, employment status, and annual income were gathered. Additionally, information about the child was gathered including: main source of healthcare coverage, number of times the child has moved in his/her lifetime, number of changes in schools (for reasons other than normal progression), number of psychiatric placements (hospitalizations, residential/day treatment), number of out-of-home placements (e.g., detention

center, foster care), and history of academic services (ever repeated a grade, received a tutor, placement in special education class, or behavioral intervention).

Rater Training and Reliability Procedures

At study start-up, the investigators and project coordinators from all sites met for a two-day didactic training on the LAMS protocol. Interviewers who were hired after study start-up received formal training by the study coordinator at his/her site. Didactic training consisted of a review of good interviewing techniques, a discussion of the development, structure and function of the KSADS-PL-W, an item-by-item review of the battery of instruments, and sample interviews. Upon completion of didactic training, all interviewers sat in on three or more complete baseline interviews in order to observe and rate along with an experienced interviewer (interviewers who were present for study start-up, rated along to taped interviews). Interviewer trainees were required to match seven of eight diagnostic categories (i.e., BP spectrum, depressive spectrum, ADHD spectrum, disruptive behavior disorder spectrum, psychotic disorders spectrum, anxiety disorders spectrum, substance abuse spectrum, and adjustment disorders) on the K-SADS-PL-

W, and obtain satisfactory item-level weighted kappas (κ≥ 0.40) for the Childhood

Depression Rating Scale – Revised (CDRS-R), the Young Mania Rating Scale (YMRS), and KSADS-PL-W. Upon reliably rating along with three interviews (see criteria for reliability below), each interviewer was then observed for three additional complete baseline interviews by an experienced interviewer who rated along. Training was

complete when the interviewer trainee successfully met reliability criteria on three observed interviews.

All interviewers were required to tape two interviews per year. Twice a year, two tapes were chosen from the taped assessments across sites and all interviewers were required to watch and rate along with these assessments. Inter- rater reliability was calculated for the K-SADS-PL-W, CDRS-R, and YMRS from these ratings. Intra-class correlations (ICC) were calculated based on the item-level responses of the group on the CDRS-R and YMRS. The ICC score must be ≥ 0.70. In addition, item-level weighted kappas will be calculated for the KSADS-PL-W, CDRS-

R, and YMRS. The item-level weighted kappas must be ≥ 0.40 (Cicchetti et al., 2006).

In addition, the KSADS-PL-W diagnoses must match on 7 of the 8 diagnostic categories listed above. If these criteria were not met, any interviewer who was an outlier was identified and re-trained (if necessary). Conference calls were scheduled to discuss IRR procedures and ratings as necessary.

Statistical Analyses

Outcome variable. The outcome variable of the current study is suicide- related behavior, as defined in the literature review (see also Table 1). The K-SADS-

PL-W captures information about suicide-related behavior on two items: 1) Suicidal

Acts – Seriousness and 2) Suicidal Acts – Medical Lethality. Of note, many suicide scales have separate items that measure “seriousness” (i.e., how likely the action was to result in death) and “intent” (i.e., how committed to the goal of suicide the individual was). There is no separate rating measuring intent to die in the K-SADS- 73

PL-W; the Seriousness item on the K-SADS includes language that indicates various degrees of both suicidal intent and seriousness. The item descriptions, sample questions, and anchors for these items are presented in Table 8.

Ratings on the Seriousness and Lethality items are made on a 7-point Likert scale, with “0” indicating “No attempt” and the highest rating being “6” indicating

“Extreme.” A rating of “1” on both scales is indicative of a type I suicide-related behavior, such as a child who holds a knife to his neck and says, “I’m going to kill myself!” but does not actually cut him/herself. If a rating on one of these scales is scored above “0,” both scales should be scored above “0”; however, the ratings on the two scales are not always identical. That is, a child may demonstrate serious intent to kill him/herself and take a very serious action toward this end, but his/her actions result in no harm. Likewise, a child may demonstrate ambivalent intent and with actions that are not serious, but end up lethally harming him/herself. For example, a child who ran into traffic screaming, “I want to die!” after an argument with his mother may qualify for a high rating on the Seriousness scale, but a low rating on the Lethality scale if he was not actually hit by a car or harmed in any way.

The inverse is also possible. For example, a child who, in front of his family and friends, accidentally fell out of a second floor window after threatening to jump may have caused serious harm to himself, resulting in a high Medical Lethality rating, but his questionable intent would indicate a lower score on the Seriousness scale.

For the current study, the data represented by these two items were collapsed and coded as a binary variable. Any child with a score of “1” or higher for

their current or past rating on either the Suicidal Acts – Seriousness or Suicidal Acts

– Lethality scale was coded suicide-related behavior positive (SRB+; or SRB coded

“1”), indicating the presence of at least one suicide-related behavior; those who have

“0” on both scales were coded suicide-related behavior negative (SRB-; or SRB coded “0”), indicating no reported history of suicide-related behavior. These outcome ratings were verified by cross-checking the narrative summaries as well as other data points representing suicide-related behavior in the LAMS data set.

Careful consideration was given to participants with scores of “1” on the Suicidal

Acts – Seriousness and Lethality items. This was due to the fact that for all other symptom items in the KSADS a “1” rating indicated “not present.” Given the high potential for rater error on these items, only those participants with KSADS Suicidal

Acts ratings of “1” for whom a suicide gesture was confirmed by supporting evidence were included in the sample. In the event of conflicting data (i.e., the

KSADS Suicidal Acts items indicated presence/absence of SRB and supporting evidence indicated otherwise), the rules outlined in Table 9 were followed to correctly code the outcome as present (SRB=1), absent (SRB=0), or missing

(participant excluded from the sample).

Independent variables. The selection of diagnoses, symptoms, demographic factors, psychosocial functioning factors, and stressful life events that were explored as independent variables was guided by previous research. These independent variables, along with the instrument that was used to capture these data points, and the scale by which they were measured are listed in Table 10.

Logistic regression. A multi-step model building processes using logistic regression was conducted to construct statistical models that best represent the relationship between suicidal acts in the sample and the independent variables that were considered for each hypothesis. Logistic regression models perform best when there are at least ten outcomes of interest (in this case, SRB+ participants) per covariate included in the model (Peduzzi, Concato, Kemper, Holford, & Feinstein,

1996). For this reason, careful consideration was paid to the number of covariates in any one model relative to the number of outcomes of interest for that model.

Furthermore, careful consideration was also given to preserving the sample size for each model in order to preserve the number of outcomes of interest for each model.

For this reason, an a priori decision was made not to eliminate participants from all analyses based on the fact that they had missing data for one or more variables.

Rather, participants with missing data were only excluded from the analyses that involved the variables for which that participant had missing data. The consequence of this decision is that the ability to directly compare the different models is limited given that the sample size of (i.e., the participants included in) each model varies.

Odds ratios (OR) were calculated as part of the logistic regression procedure.

In logistic regression, ORs function as an effect size statistic and are used as a measure of the impact of the constellation of independent variables in each model.

A forward stepwise procedure was conducted to determine the preliminary main effects model and a purposeful selection procedure was conducted for all

subsequent steps. The steps in this process were as follows (Hosmer & Lemeshow,

2000):

1. Univariate analyses explored the relationship between each independent

variable and the outcome variable (i.e., suicide-related behavior).

2. Covariates were selected as candidates in a multivariate model based on the

significance level of each variable’s Wald statistic (p≤.25).

3. Covariates were removed from the model based on the Wald statistic of that

variable within the multivariate model (p≥.10).

4. Steps 2 and 3 were repeated until only covariates that were significant

remained in the model. At the completion of this step, a “preliminary main

effects model” was identified.

5. Correlations between the variables in the preliminary main effects model

were then considered. If any two covariates were highly correlated (.30 ≥ r ≤

-.30) within the model, the covariate with the least significant Wald statistic

was removed to avoid confounding of the results due to colinearity.

6. A model-fitting procedure was conducted in which covariates were added

back into the model one at a time to see if the constellation of covariates in

the model changed the significance of any variable that had previously been

eliminated or not considered. Covariates previously removed from the semi-

final model were reconsidered for inclusion based on the following criteria:

a) Covariates whose Wald statistic was now significant (p<0.10) were

included.

b) If the addition of a covariate caused a substantial change (>20%, with

a minimum change to the coefficient of .20) to the coefficients of any

of the other variables without a substantial inflation of the standard

error, the covariate was included.

c) If the covariate that was added back to the semi-final model was

highly correlated (.30 ≥ r ≤ -.30) with any of the other variables in the

semi-final model, it was excluded.

7. Steps 6a through 6c were repeated until the model contained only

statistically significant, not highly correlated variables. At the end of step 7, a

“main effects model” was established.

8. Clinically plausible interactions were considered. Because inclusion of an

interaction term in a logistic regression model renders the main effects of the

interacting covariates uninterpretable (Hosmer & Lemeshow, 2000), a very

conservative, cautious approach to interaction terms was employed. If an

interaction was determined to be clinically important to consider, the effect

was calculated and inclusion in the model was determined using the

procedures outlined above. At the completion of this step, the “preliminary

final model” was established.

9. Linearity in the logit was assessed for any continuous variables in the

preliminary final model.

10. Goodness-of-fit of the preliminary final model was assessed using the

Hosmer-Lemeshow goodness-of-fit test (in which p<.10 would signify the

model lacks goodness-of-fit). The model that met criteria for goodness-of-fit

was considered the final model.

Once the final models in each domain (hypotheses 1 through 5) were established, an integrated model of variables across these domains (hypothesis 6) was constructed via the procedure outlined above using only the covariates identified in the final models for each domain. As all of the covariates under consideration in this hypothesis had been previously identified as significant, the criteria for inclusion and removal from the model were more stringent (include if p<.05; exclude if p≥.01). The a priori decision was made to forego a validation sample for the final models due to the limited number of SRB+ participants in the overall sample.

Results

Descriptive Statistics

The sample for the current study included 678 participants for whom SRB status could be determined; 29 participants were excluded from the overall LAMS sample (N=707) due to missing or conflicting and irreconcilable data regarding their

SRB status. Current or past suicidal acts were present for 57 (8.4%) of the 678 participants in the current sample (SRB+, n=57). All 29 participants excluded from the sample were excluded due to having KSADS Suicidal Acts – Seriousness and

Lethality scores of “1” (i.e., suicide gesture with no harm to self) but no other evidence in the KSADS or narrative report confirming the presence of a suicide- related behavior. Four participants were included in the SRB+ group despite having

KSADS Suicidal Acts items scored “0” because the safety items at the back of the

KSADS and the participants’ narrative reports presented convincing evidence of a suicide-related behavior. Table 9 presents the rules for coding a participant SRB+ or

SRB- as well as rules for excluding participants based on missing or conflicting data.

Table 11 presents a distribution of baseline age by the highest KSADS

Suicidal Acts severity ratings for the 57 participants in the SRB+ group. This table illustrates that the majority of suicide related behaviors in the sample were on the

lower end of the spectrum of Seriousness and Lethality and these severity ratings were relatively evenly distributed across the age range. Example narrative descriptions of suicide-related behaviors present in the sample are presented in

Table 12.

There were no statistically significant differences between the SRB+ and

SRB- groups in terms of screen status (i.e., ESM+ or ESM-); enrollment site; sex; race; ethnicity; insurance type; primary caretaker; parental family history of completed suicide; or lifetime diagnosis of anxiety disorder, disruptive behavior disorder, adjustment disorder, psychotic disorder, or elimination disorder.

However, it is noteworthy that while Hispanic participants made up only 4.3% of the sample, they comprised 8.8% of the SRB+ group and only 3.9% of the SRB- group. The SRB+ group was significantly older than the SRB- group; more likely to have a parent who had attempted suicide; more likely to have a lifetime diagnosis of bipolar spectrum disorder, depressive disorder, and ADHD; more likely to have a lifetime history of psychiatric hospitalization; more likely to have been prescribed psychotropic medication in his/her lifetime; and had an increased likelihood of being prescribed a greater number of psychotropic medications at baseline. Table

13 presents a summary of these sample characteristics.

Hypotheses 1 through 6

Covariates considered in the models for hypotheses 1 through 5 (i.e., the separate models for the domains of demographics, psychiatric family history, child psychopathology, child psychosocial functioning, and stressful life events) are listed 81

in Table 10. Results of univariate analsyses are listed in Table 14. The correlation matrices for the significant univariates from the two domains with the highest number of covariates considered—psychopathology and stressful life events—are presented in Tables 15 and 16, respectively. Results of model building based on hypotheses 1 through 6 are listed in Table 17. Covariates considered in the model for hypothesis 6 (i.e., the overall model which includes variables from all domains) were the covariates retained in the final models for each domain (hypotheses 1 through 5; see Table 17).

Hypothesis 1

The final model of demographic factors will correlate with the presence of suicide-related behaviors better than the naïve model. Model building for hypothesis

1 considered eight covariates, which are outlined in Table 10. After participants were excluded for having missing data for one or more of the relevant covariates, sample size for the model was N=676, with 57 participants in the SRB+ group. The final model of demographic factors for hypothesis 1 included two significant covariates: age at baseline and presence of both biological parents and caregivers for the child. This model of demographic variables indicated that being older was positively correlated with suicide-related behavior while having both biological parents present as primary and secondary caregivers was negatively correlated with SRB status. The odds of being in the SRB+ group increased at a rate of approximately 30% for every year older a participant was (OR=1.31), while having both biological parents as primary/secondary caregivers halved the odds of being 82

SRB+ (OR=.51). Table 17 presents the logistic regression statistics for this model. No interaction effects between covariates in the semi-final model were deemed clinically plausible; therefore, none were calculated for consideration in the final model. No continuous variables were included in the final model, therefore testing for linearity in the logit was not applicable. The Hosmer-Lemeshow goodness-of-fit statistic (p=.15) was not significant at the a priori α-level set at .10, indicating the model was a good fit.

Hypothesis 2

The final model of psychiatric family history will predict suicide-related behaviors better than the naïve model. Model building for hypothesis 2 considered four covariates: parent psychiatric history of attempted suicide, depression, psychosis/schizophrenia, and ADHD (see also Table 10). The variable representing completed parent suicide was omitted from consideration due to insufficient variability in the data (n=5; 0.7%). Parent psychiatric history of mania/bipolar disorder was also eliminated from consideration due to the high rate of missing data for this variable (n=62; 9%). Of note, there was an exceptionally high rate of parental depression (n=402; 62.9%) in the sample, while variability in the data for the covariates representing parental suicide attempt (n=154; 23.2%), psychosis

(n=75; 11.3%), and ADHD (n=181; 27.3%) was more consistent with expectations.

After participants were excluded for having missing data for one or more of the included covariates, the sample size for the model was N=664, with 54 participants in the SRB+ group. The final model of psychiatric family history factors 83

for hypothesis 2 included one significant covariate: history of one or both parents with a suicide attempt. A second covariate—one or both parents with a history of psychosis—was significant in the preliminary main effects model, but was excluded from the final model because it was higly correlated (r=.37) with parent history of suicide attempt. This final model of psychiatric family history variables indicated that having a parent with a history of suicide attempt was positively correlated with suicide-related behavior. Participants who had one or both parents attempt suicide in the past nearly tripled the odds of being SRB+ (OR=2.71). Table 17 presents the logistic regression statistics for this model. With only one variable in the final model, interaction effects did not apply. No continuous variables were included in the final model, therefore testing for linearity in the logit was not applicable. The Hosmer-

Lemeshow goodness-of-fit statistic could not be calculated with zero degrees of freedom (i.e., a single variable); however, over-fitting was not a concern given there were 54 outcomes of interest and only one variable in the model.

Hypothesis 3

The final model of variables related to child psychopathology will correlate with the presence of suicide-related behaviors better than the naïve model. Model building for hypothesis 3 considered 49 covariates, which are outlined in Table 10.

The variables representing regular alcohol and substance use were omitted from consideration due to insufficient variability in the data. After participants were excluded due to missing data for one or more of the relevant covariates, the sample size for the final model was N=669, with 55 participants in the SRB+ group. The final 84

model of child psychopathology factors for hypothesis 3 included three significant covariates: ever having experienced threshold (i.e., “mild” or higher) suicidal ideation, ever having experienced threshold anhedonia (i.e., “mild” or higher), and ever having used tobacco regularly. This final model of child psychopathology variables indicated that having experienced significant suicidal ideation, regular tobacco use, or anhedonia was positively correlated with suicide-related behavior.

The odds of being in the SRB+ group were more than 20 times higher for participants who had experienced suicidal ideation (OR=20.41). In addition, a history of regular tobacco use (OR=3.98) or anhedonia (OR=2.01) nearly quadrupled and doubled the odds of being in the SRB+ group, respectively. Table 17 presents the logistic regression statistics for this model. No interaction effects between covariates in the semi-final model were deemed clinically plausible; therefore, none were calculated for consideration in the final model. No continuous variables were included in the final model, therefore testing for linearity in the logit was not applicable. The Hosmer-Lemeshow goodness-of-fit statistic (p=.84) was not significant at the a priori α-level set at .10, indicating the model was a good fit.

Hypothesis 4

The final model of psychosocial functioning (at home, at school, and with peers) will correlate with the presence of suicide-related behaviors better than the naïve model. Model building for hypothesis 4 considered 19 covariates, which are outlined in Table 10. After participants were excluded for having missing data for one or more of the relevant covariates, the sample size for the model was N=673, with 56 85

participants in the SRB+ group. The final model of psychosocial factors for hypothesis 3 included three significant covariates: a history of having changed schools for reasons other than normal progression, poorer relationship between the child and primary caregiver, and ever having an academic tutor. A fourth covariate—lifetime number of moves a child has experienced—was significant in the preliminary main effects model, but was excluded from the final model because it was higly correlated (r=.30) with another significant covariate: having changed schools for reasons other than normal progression. The final model of psychosocial variables indicated the odds of being in the SRB+ group doubled if a child had ever changed schools (OR=2.06) and increased by approximately 36% (OR=1.36) for every point increase in ALIFE parent-child relationship score (higher scores on the

ALIFE indicate poorer psychosocial functioning). On the other hand, having ever had an academic tutor decreased the odds of being in the SRB+ group by approximately

30% (OR=.295). Table 17 presents the logistic regression statistics for this model.

No interaction effects between covariates in the semi-final model were deemed clinically plausible; therefore, none were calculated for consideration in the final model. No continuous variables were included in the final model, therefore testing for linearity in the logit was not applicable. The Hosmer-Lemeshow goodness-of-fit statistic (p=.23) was not significant at the a priori α-level set at .10, indicating the model was a good fit.

Hypothesis 5

The final model of stressful life events will correlate with the presence of suicide-related behaviors better than the naïve model. Model building for hypothesis

5 considered 35 covariates, which are outlined in Table 10. After participants were excluded for having missing data for one or more of the relevant covariates, the sample size for the model was N=654, with 54 participants in the SRB+ group. The final model of stressful life events for hypothesis 5 included five significant covariates: child report of fighting more with parent, parent/caregiver report on the

SLES of recent remarriage, child report on the SLES of knowing someone who recently tried to hurt him/herslf, parent/caregiver report on the SLES of the child ending a friendship recently, and child report of having a friend who recently died.

Two covariates in the final model—child report on the SLES of knowing someone who recently tried to hurt him/herself and child report of having a friend who recently died—were negatively correlated with one another (r=-.26), but not at the exclusion criterion level established a priori (.30 ≥ r ≤ -.30). This final model of stressful life event variables indicated the odds of being in the SRB+ group were approximately three times higher for participants who reported knowing someone who tried to hurt him/herself (OR=3.24) and those who reported increased arguing with parents (OR=2.86). Those whose parent/caregivers reported one of the child’s parents had recently remarried (OR=3.23) or the child recently ended a friendship

(OR=2.90) were also approximately three times more likely to be in the SRB+ group.

In addition, child report of recently having a friend who died slightly decreased the

odds of that child being in the SRB+ group (OR=.27). Table 17 presents the logistic regression statistics for this model. No interaction effects between covariates in the semi-final model were deemed clinically plausible; therefore, none were calculated for consideration in the final model. No continuous variables were included in the final model, therefore testing for linearity in the logit was not applicable. The

Hosmer-Lemeshow goodness-of-fit statistic (p=.87) was not significant at the a priori α-level set at .10, indicating the model was a good fit.

Hypothesis 6

The integrated model of demographic factors, psychiatric family history, child psychopathology, psychosocial functioning in the family, psychosocial functioning with peers, psychosocial functioning at school, and stressful life events will correlate with the presence of suicide-related behaviors better than the naïve model. Model building for hypothesis 6 considered 14 covariates (the covariates that were significant in the previous five models) across all five domains, which are outlined in Table 17.

After participants were excluded for having missing data for one or more of the relevant covariates, sample size for the model was N=647, with 53 participants in the SRB+ group. The final model for this hypothesis included three significant covariates: child ever experiencing suicidal ideation at threshold or higher, parent report on the SLES that the child’s parent had recently remarried, and children who had ever had an academic tutor. One of these covariates—ever having experienced significant suicidal ideation—dominated the model; the odds of being in the SRB+ group increase 30-fold (OR=31.27) when lifetime suicidal thoughts were endorsed. 88

Parent/caregiver-report that the child’s parent had recently remarried almost doubled the odds of being in the SRB+ group (OR=1.87) and participants who had ever received services from an academic tutor were slightly less likely to be in the

SRB+ group than those who had never been tutored (OR=.22). Table 17 presents the logistic regression statistics for this model. Tables 18 through 20 present the two- by-two contingency tables for each of the three independent variables in the model and SRB status. No interaction effects between covariates in the semi-final model were deemed clinically plausible; therefore, none were calculated for consideration in the final model. No continuous variables were included in the final model, therefore testing for linearity in the logit was not applicable. The Hosmer-

Lemeshow goodness-of-fit statistic (p=.42) was not significant at the a priori α-level set at .10, indicating the model was a good fit.

Discussion

In the overall model of covariates across the five domains, three domains were represented by significant covariates that were retained: child psychopathology, stressful life events, and psychosocial factors. Not surprisingly, lifetime suicidal ideation (OR=31.27) took the lead role, followed distantly by parent report of the child’s parent recently remarrying (OR=1.87), and history of ever having received an academic tutor (OR=0.22). The fact that no single domain area dominated the overall model with multiple covariates remaining significant underscores the unique and multifactorial nature of SRB as well as the importance of further research that looks across domains for variables that may, together, contribute to increased likelihood of SRB.

In addition to the overall model, several variables were identified as being significantly positively correlated with SRB+ status in the domain-specific logistic regression models constructed from the current sample: age, having at least one parent who attempted suicide, suicidal ideation, tobacco use, anhedonia, having changed schools for reasons other than normal progression, a poor interpersonal relationship between the child and his/her primary caregiver, child report of knowing someone who tried to hurt him/herself, child report of increased arguing

with parents, parent report that the child’s parent had recently remarried, and parent report that the child recently ended a friendship. In addition, a few factors were significantly negatively correlated with SRB+ status: having both biological parents active in the child’s life as primary and secondary caretakers, ever having an academic tutor, and child report of having a friend who had recently died. Although many of these factors fell out of the overall model, they are worth considering individually. It is important to remember that this study is not designed to identify risk and protective factors given that a temporal relationship (i.e., risk factor identified prior to presence of SRB) cannot be established in the current dataset.

Nonetheless, further consideration of the clinical significance of these relationships is warranted.

Of the demographic variables considered in this study, age was the only covariate that was significantly positively correlated with SRB+ status in the model of demographic variables. As previous studies have found, older children in this study were more likely to be SRB+; each year older constituted a 30% increase in odds that the child would be SRB+. From a developmental perspective and given the previous research that has shown a higher incidence and prevelance of suicide in adolescents and young adults compared to children, this is not a surprising finding.

However, the younger age range of the current sample (ages 6 to 12 years) provides new information about just how young the risk for SRB may begin and an approximate rate in which this risk may increase with each year of life. Interestingly, once age was considered in a model that included covariates from other domains, it

was no longer significantly correlated with SRB status. This may be due—in full or in part—to the fact that the psychopathology, stressful life events, and psychosocial factors that confer increased risk for SRB are correlated with older age of the child.

Although the current study was not designed to examine the trend of increased severity of SRB with age, the distribution of SRB severity ratings (Table

11) suggests the majority of SRBs in this sample of children ages 6 to 12 were rated on the lower end of the Seriousness severity rating scale (42 of 53 were rated “3 –

Ambivalent” or less severe; 49 of 53 were rated “4 – Serious” or less severe) and

Lethality severity rating scale (49 of 53 were rated “3 – Mild” or less severel 52 of 53 were rated “4 – Moderate” or less severe) on a Likert scale of 1 to 6 and severity ratings were missing for 4 participants on both scales. While research comparing the seriousness and lethality of SRBs in a sample of children and adolescents would be necessary to accurately compare the differences in degree of severity of SRBs, this qualitative data suggests younger children’s attempts may be less severe than adolescents, in general. This may help to explain why research has tended to focus

SRB and suicide attempts in adolescents rather than in younger age groups.

However, as seen in Table 11, exceptions certainly exist. Two of the most serious and lethal SRBs in the current sample were by children who were 8 and 9 years old.

Furthermore, children ages 6 through 8 are represented in each of the severity categories, as are children at the upper end of the age range. While older children more likely to be in the SRB+ group, age may or may not have any bearing on the severity of the SRB.

Failure to find any significant differences in SRB status among ethnic/racial groups in this study may have been a matter of low power given the relatively low prevalence of minorities in the sample. However, it is clinically significant to note that the percentage of SRB+ status among Hispanic participants was double the percentage of Hispanic participants in the sample. The fact that this finding was not significant in this sample may indicate that the cultural or ethnic factors that have been found to confer risk in previous studies of Hispanic adolescent females have not yet been activated in this younger age group. This question is worthy of future study.

The demographic variable negatively correlated with SRB+ status was the presence of both biological parents as primary and secondary caregiver for the child. While the current study is limited in the extent to which this finding can be interpreted, many plausible explanations can be hypothesized. Two caregivers present and consistent in a child’s life is likely to offer a greater amount support— financially, materially, emotionally, etc.—and to decrease stress in the home and on the child. On the other hand, it is possible that it is not the presence of both biological parents that may foster resilience to SRB, but the absence of a biological parent that confers additional risk. That is, perhaps the reason that one or both biological parent is absent—parent suicide, death, psychopathology, substance abuse, criminal behavior—belies some factor associated with increased risk for SRB.

In fact, consistent with previous research, the result of the parent history of psychopathology model does suggest that parent suicide attempt may be associated

with similar behaivors in offspring: the odds of a child being in the SRB+ group nearly tripled (OR=2.71) if the child had one or more parents with a suicide attempt.

Parent completed suicide was not statistically significant in the model, likely due to low prevelance in the sample. Similarly, the high rate of parental depression may, in part, have contributed to the lack of statistical significance in the model. However, it is also possible that this sample accurately reflects prevalence rates in parents of children with significant emotional and/or behavioral dysregulation (i.e., the sample used in this study), and that these very high and very low prevalence rates contribute to the lack of specificity of parental depression and parental completed suicide as predictors of offspring SRB. This result is consistent with previous research suggesting that familial transmission of depression and suicide-related behavior are overlapping, but distinct (Brent et al., 2002; McGirr et al., 2009).

The model of child psychopathology factors indicated that specific symptoms were more highly associated with SRB+ status than any diagnosis—no diagnosis variable was significant in the final model, whereas three symptom variables were.

By far, lifetime history of suicidal ideation was the psychological symptom most highly correlated with SRB+ status—the odds of being in the SRB+ group were 20 times greater for participants who endorsed lifetime suicidal ideation at threshold

(i.e., “Mild”) or higher. Although the design of the study does not allow for examination of a temporal relationship between the suicidal ideation and SRB in this sample, logic and previous research concludes that suicidal ideation typically comes before SRB. This suggests that SRBs in young children may be more

contemplated and less impulsive than hypothesized. Furthermore, unlike the

Norwegian study that relied on retrospective report from parents and others after a completed youth suicide (B. Groholt et al., 1998), the current study depends on child-report of his/her own experience, which may or may not have been previously reported to parents. The different findings underscore the importance of asking children directly about suicide-related ideation and the need to take it seriously if and when it is endorsed.

Interestingly, the other two covariates that were retained as significant in the model of child psychopathology factors were lifetime tobacco use (OR=3.98) and lifetime anhedonia (OR=2.01). Tobacco use has been identified in previous studies as a risk factor for suicide-related ideation and behavior. The mechanism for this association is hypothesized to be low parental monitoring of child behavior and other environmental factors. Tobacco use has also been identified as an early risk factor for other substance use. In this sample, there were too few participants that endorsed any regular alcohol or drug use to include these factors as covariates in the model (an expected finding, given the young age of the sample). The results of this study do suggest that tobacco use begins earlier than substance use. Given that the correlation between tobacco use and SRB is already present prior to the onset of substance use, it could be that a third variable present in early childhood is independently responsible for risk of tobacco use, substance use, and SRB. It is also possible that some early risk factor serves as a catalyst for the accumulation of risk

factors (i.e., first tobacco and later substance use), which cumulatively add up to increased risk for SRB over time.

The fact that anhedonia, rather than depressed mood, stood out as the hallmark depressive symptom in the model of child psychopathology was somewhat surprising given no previous studies had identified anhedonia as a hallmark of SRB.

Depressed or sad mood is usually thought to be the defining characteristic of depression, but it is important to remember that depression sometimes manifests primarily as the loss of ability to enjoy activities that are typically pleasurable.

Further research is warranted into the relationship between anhedonia and SRBs in children.

Psychosocial factors that were associated with increased likelihood of being

SRB+ included changing schools and the child having a poor relationship with his/her primary caregiver. These findings are consistent with previous research. In the present study, changing schools edged out moving residence as the factor most associated with SRB+ status, but the two were highly correlated. Further research comparing the psychological and behavioral consequences of unanticipated changes in schools with changes in residence that do not require a school change may help parse the two factors apart. More interesting was the finding that having ever had an academic tutor was negatively correlated with SRB+ status. This was unexpected given the assumption that requiring a tutor indicates a child is struggling in school, and school failure and/or stress was hypothesized to be positively correlated with a history of SRB among children. Quite the contrary, this finding suggests that

receiving additional academic services is associated with a better SRB outcome.

Perhaps rethinking the role of academic struggles is in order: rather than understanding challenges in school as inherently stressful and fraught with associated problems, it may be that it is not the academic struggle itself that is the problem, but the absence of needed services that predisposes a child to subsequent risk. Should this finding be replicated and supported in future studies, the new perspective it brings to an age-old problem has the potential to reframe policy and de-stigmatize learning disabilities.

Both parent and child report of certain stressful life events were significantly correlated with SRB status. Interestingly, there was no overlap of parents and children reporting the same stressful life event. On the contrary, the factors endorsed that were significant per parent report were completely different from the factors children reported. This indicates two important considerations: 1) parents and children interpret and report stressful life events quite differently, and 2) both parent and child interpretation and report of stressful life events are important.

While there is some utility it trying to determine which report is accurate when conflicting reports are received, at the same time, when it comes to retrospective reporting (particularly by young children) the child’s perception of the event may be as important or even more important than what is historically accurate.

Interestingly, four of the five significant covarites in the stressful life events model had odds ratios of approximately 3 (range of ORs: 2.86 to 3.24 for parent report of parent remarriage, parent report of child ending friendship, child report of

knowing someone who tried to hurt him/herself, and child report of increased arguing with parents). The one stressful life event that was negatively correlated with SRB+ group membership was child report of having a friend who had recently died (OR=0.27). The child’s parent recently remarrying and the child ending a friendship were the parent-reported stressful life events associated with increased likelihood of the child being SRB+. Both of these have a good deal of face validity, at least, for the degree of stress and change they introduce into the lives of school-age children. Child-reported factors associated with increased likelihood of SRB+ group membership included knowing someone who had recently tried to hurt him/herself and increased arguing with parents. Both of these child-reported factors are supported by previous research that does not rely on retrospective child-report of events, which lends credibility to the finding.

Limitations. A major limitation in all work on SRBs among children is that suicide and suicide-related behaviors are extremely rare events in this population.

Prospectively following a sample of children with the goal of gathering data that will help predict which children will have an SRB and which will not requires very large samples and, as in the current study, will likely result in a small SRB+ group. The small size of the SRB+ group in the current sample (n=57) poses inherent limits to the generalizability of the findings.

Another limitation of the current study is that it was not designed to establish temporal relationships among all of the variables examined. For this reason, interpretation of the results is limited to understanding the relationships

between significant covariates and the dependent variable (i.e., SRB status) as correlations rather than risk or protective factors.

Furthermore, despite the large overall sample size, the relatively low prevalence of SRBs in the sample lead to decisions that influenced statistical procedures and interpretability. First, the a priori decision to preserve sample size and power by not eliminating all participants with missing data for any variable of interest meant that the sample of partcipants included was unique for each model.

Because some participants were excluded from certain models due to missing data, but included in other models for which they had no data missing for the relevant covariates, each model has a slightly different sample size and is composed of a slightly different combination of participants. For this reason, the models cannot be statistically compared to one another in terms of which provides the most comprehensive explanation SRBs in the sample.

Although a validation sample in which to re-run and test the final models that were derived would have been ideal, this would have meant splitting the original sample in half (for split-half validation) or examining small subsets of the sample at a time (for cross-validation and bootstrapping methods). Any of these approaches would have greatly reduced the number of variables that could have been considered in each model. Given the exploratory nature of the current study, the decision to forego a validation sample in order to consider more covariates was most in line with the aims of the study. While a validation sample may be the ideal statistical approach in much larger samples with a higher number of outcomes of

interests (i.e., SRB+ participants), goodness-of-fit testing and other steps taken to test model validity (e.g., checking for linearity in the logit) were appropriate and sufficient for the current study.

In addition, the conservative decision to only consider clinically plausible interaction terms in the interest of preserving that ability to interpret main effects in the logistic regression modesl did come at a cost: it is possible that some unpredictable but meaningful interactions were undetected in the models.

Finally, missing data in the family history variable representing parent psychiatric history of mania affected enough participants that the cost of including this variable in the model of psychiatric family history variables outweighed the possible benefits of including it. Unfortunately, the decision to exclude parental history of mania from the family psychiatric history model means the study is unable to comment on how parental mania may or may not be associated with SRB status in children ages six to 12.

Future Directions. The results of the current study are a solid first step in understanding the ways in which factors across domains are associated with increased or decreased likelihood of childhood SRB. Previous research and theory guided the selection of variables in the current study, and the findings of this study should be used to guide variable selection and theory development in future research. Several suggestions for future research have already been mentioned above. These include investigating the relationship between increasing age and possible increasing seriousness/lethality of SRBs among youth, comparing

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prevalence of SRBs in different ethnic groups (which particular attention to the

Hispanic population), gathering prospective data in such way as to establish a temporal relationship between independent and outcome variables in order to make predictions and establish causality, further exploring the role anhedonia may play in SRBs among youth, understanding how children who get certain services they require (e.g., tutoring) may be at reduced risk for negative outcomes such as

SRB, and further exploration of differences between parent and child report of stressful life events.

Furthermore, ongoing prospective analysis of the data in the longitudinal

LAMS study is needed. Follow-up data from the LAMS study should be analyzed to see if the variables that were significant at baseline continue to be significant as the sample ages. This will reveal whether or not the baseline SRBs in the current sample

(which are predominantly low in seriousness and lethality) will predict more serious/lethal suicide attempts and/or behaviors in these participants as they get older.

Additionally, large child samples should be seriously considered for future studies of SRB. Ongoing large-scale studies such as the YRBS are encouraged to continue the movement to include younger age groups by developing an elementary school version of the survey. Sample sizes that studies like the YRBS are able to amass will be crucial for continuing efforts to build models that may be able to predict which youth are at greatest risk for SRB. Furthermore, these studies would

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be well served to assess independent variables within multiple domain areas, and break the trend of focusing on only variables in a single domain.

Understanding the complex interrelation of factors that coalesce to increase and/or decrease risk for SRB in children ages six to 12 is in its infancy. Replicating the findings of the current study will be one step in the right direction. Exploring a developmentally sensitive biopsychosocial theory of SRB among children in large sample studies would significantly contribute to improved understanding of this serious public health problem.

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Appendix: Tables

Table 1. Definitions of suicide-related communications and behavior presented by The Denver Veterans Administration VISN 19 Mental Illness Research, Education, and Clinical Care (MIRECC) Nomenclature Workgroup (Silverman et al., 2007b).

Suicide-Related Communications – “Any interpersonal act of imparting, conveying, or transmitting thoughts, wishes, desires, or intent for which there is evidence (either explicit or implicit) that the act of communication is not itself a self-inflicted behavior or self-injurious.” Includes “verbal and nonverbal communications that may have suicidal intent but have no injurious outcome.” (pg. 268)

Suicide Threat – “any interpersonal action, verbal or nonverbal, without a direct self-injurious component, that a reasonable person would interpret as communicating or suggesting that suicidal behavior might occur in the near future.” (pg. 268)

Suicide Plan – “a proposed method of carrying out a design that will lead to a potentially self-injurious outcome; a systematic formulation of a program of action that has the potential for resulting in self-injury.”

Suicide-related communications can be further specified by presence/absence of intent to carry out the threat or plan: Type I – No intent Type II – Undetermined intent Type III – Some intent

Suicide–Related Behaviors – “a self-inflicted, potentially injurious behavior for which there is evidence (either explicit or implicit) either that: (a) the person wished to use the appearance of intending to kill himself/herself in order to attain some other end; or (b) the person intended at some undetermined or some known degree to kill himself/herself. Suicide-related behaviors can result in no injuries, injuries, or death. Suicide-related behaviors comprise self-harm, self-inflicted unintentional death, undetermined suicide-related behaviors, self- inflicted death with undetermined intent, suicide attempt, and suicide.” (pg 272)

(continued)

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Table 1. (continued)

Self-Harm (no intent to die) – “a self-inflicted, potentially injurious behavior for which there is evidence (either implicit or explicit) that the person did not intend to kill himself/herself (i.e., had no intent to die). Persons engage in self- harm behaviors when they wish to use the appearance of intending to kill themselves in order to attain some other end (e.g., to seek help, to punish others, to receive attention, or to regulate negative mood). Self-harm may result in no injuries, injuries, or death.” (pg. 272-273)

Undetermined Suicide-Related Behavior (undetermined degree of suicidal intent) – “a self-inflicted, potentially injurious behavior where intent is unknown. For example, if a person is unable to admit positively to the intent to die, due to being unconscious, under the influence of alcohol or other drugs (and therefore cognitively impaired), psychotic, delusional, demented, dissociated, disoriented, delirious, or in another state of altered consciousness; or is reluctant to admit positively to the intent to die due to other psychological states, we categorize the self-injurious behavior as Undetermined Suicide-Related Behavior.” (pg. 273)

Suicide Attempt (some degree of suicidal intent) – “a self-inflicted, potentially injurious behavior with a nonfatal outcome for which there is evidence (either explicit or implicit) of intent to die. A Suicide Attempt may result in no injuries, injuries, or death.” (pg. 273)

Non-fatal outcomes of suicide-related behavior can be further specified as:

Type I - No injury Type II – Non-fatal injury

Fatal outcomes of suicide-related behavior are specified using the following terms:

Self-Inflicted Unintentional Death – Self-harm resulting in death

Self-Inflicted Death with Undetermined Intent – Self-inflicted injury resulting in death, when intent to died cannot be determined

Suicide – Suicide attempt resulting in death

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Table 2. Conversion of previous suicide terminology into revised terms (adapted from Silverman et al, 2007b).

Previous Term Revised Term

Suicidality Suicide-Related Behaviors

Suicidal Behaviors Suicide-Related Behaviors Suicide-Related Ideations Suicide-Related Communications

Suicidal Thoughts Suicide-Related Ideations Suicidal Ideation

Suicide Threat Suicide Threat, Types I–III Instrumental Suicide-Related Behavior Suicide Gesture

Suicide Plan Suicide Plan, Types I–III Suicide Gesture

Intentional Self-Harm Self-Harm, Types I–II Intentional Self-Injury Deliberate Self-Harm Nonsuicidal Self-Injury

Parasuicide Suicide Attempt, Types I–II Suicide Attempt

Accidental Suicide Self-Inflicted Unintentional Death

Completed Suicide Suicide

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Table 3. Two conventional mnemonic devices for assessing suicide risk in adulthood.

SAD PERSONS IS PATH WARM? (Patterson et al., 1983) (American Association of Suicidology, 2011)

S – Sex (male) I – Ideation

A – Age (older) S – Substance abuse

D – Depression P – Purposelessness

P – Previous suicide attempt A – Anxiety

E – Ethanol abuse T – Trapped

R – Rational thinking loss H – Hopelessness (psychosis)

S – Social supports lacking W – Withdrawal

O – Organized plan for suicide A – Anger

N – No spouse R – Recklessness

S – Sickness M – Mood changes

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Table 4. The Adapted-SAD PERSONS Scale for Children and Adolescents (Juhnke, 1996).

The Adapted-SAD PERSONS Scale

S – Sex (male)

A – Age (older than 15 years)

D – Depression or affective disorder

P – Previous suicide attempt

E – Ethanol or drug abuse

R – Rational thinking loss (i.e., psychosis) from physical or psychological disorder

S – Social supports lacking

O – Organized plan for suicide

N – Negligent parenting, significant family stressors, or suicide-related behavior modeling by parents or siblings

S – School problems (aggressive behaviors or experiencing humiliation)

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Table 5. Percentage of high school students who engaged in self-reported suicidal ideation, planning, or attempts by sex and grade – United States Youth Risk Behavior Survey, 2009.

Suicidal 9th Grade 10th Grade 11th Grade 12th Grade All grades Behavior % (CI) % (CI) % (CI) % (CI) % (CI)

Seriously Total: 14.8 (13.3-16.4) Total: 13.4 (12.3-14.7) Total: 14.5 (12.9-16.3) Total: 12.1 (10.7-13.5) Total: 13.8 (13.1-14.6) Considered F: 20.3 (18.4-22.3) F: 17.2 (15.2-19.5) F: 17.8 (15.6-20.2) F: 13.6 (12.0-15.5) F: 17.4 (16.5-18.4) Suicide M: 10.9 (8.3-12.2) M: 10.0 (8.4-11.8) M: 11.4 (9.4-13.8) M: 10.5 (8.6-12.7) M: 10.5 (9.4-11.6)

Made a Total: 10.8 (9.5-12.3) Total: 11.7 (10.6-12.9) Total: 11.3 (9.8-13.1) Total: 9.2 (7.9-10.7) Total: 10.9 (10.0-11.8) Suicide F: 14.9 (13.4-16.5) F: 14.3 (12.8-16.1) F: 13.4 (11.5-15.6) F: 9.6 (8.1-11.3) F: 13.2 (12.4-14.1) Plan M: 7.3 (5.8-9.1) M: 9.3 (7.7-11.2) M: 9.4 (7.2-12.1) M: 8.8 (7.1-10.8) M: 8.6 (7.4-10.0)

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Attempted Total: 7.3 (6.2-8.5) Total: 6.9 (5.8-8.2) Total: 6.3 (5.3-7.3) Total: 4.2 (3.4-5.2) Total: 6.3 (5.7-7.0) Suicide F: 10.3 (8.7-12.2) F: 8.8 (7.2-10.7) F: 7.8 (6.5-9.3) F: 4.6 (3.6-6.0) F: 8.1 (7.2-9.0) M: 4.5 (3.4-6.0) M: 5.2 (3.7-7.2) M: 4.7 (3.4-6.6) M: 3.8 (2.8-5.2) M: 4.6 (3.9-5.5)

Required Total: 2.1 (1.6-2.7) Total: 2.2 (1.6-2.9) Total: 2.1 (1.5-3.0) Total: 1.2 (0.8-1.7) Total: 1.9 (1.6-2.3) Medical F: 2.8 (2.1-3.7) F: 2.3 (1.6-3.4) F: 2.6 (1.7-4.0) F: 1.0 (0.7-1.6) F: 2.3 (1.8-2.8) Attention M 1.4 (0.9-2.2) M: 2.0 (1.2-3.2) M: 1.7 (1.0-2.9) M: 1.4 (0.8-2.3) M: 1.6 (1.2-2.1) After Suicide Attempt CI=95% Confidence Interval, F=Female, M=Male Data from (Eaton et al., 2010)

Table 6. Longitudinal Assessment of Manic Symptoms (LAMS) study inclusion and exclusion criteria.

Inclusion Criteria for Screening 1. Children age 6 years 0 months, to 12 years 11 months old. 2. Both the child and parent must speak English. 3. The child must be accompanied by his/her parent/guardian.

Exclusion Criteria for Screening 1. The child has received mental health treatment at the site or its affiliates in the past 12 months. 2. The child or any other child in the household or any biological half or full sibling has participated in this protocol. 3. The child has already been approached to participate in LAMS screening.

Inclusion Criterion for Baseline Assessment 1. Scoring above 12 on the Parent General Behavior Inventory Short Form (PGBI-10M) OR scoring below 12 on the PGBI-10M and being one of the randomly chosen to be part of the ESM- comparison group.

Exclusion Criteria for Longitudinal Portion of LAMS 1. Participants with substantial evidence of a Pervasive Developmental Disorder (PDD) other than Asperger’s Syndrome or PDD NOS, as determined by the PDD screen and supplement of the K-SADS-PL-W and confirmed by a study clinician (either by history or by mental status exam). 2. Participants with symptoms of mania that may be attributable to a general medical condition (e.g., thyrotoxicosis), or secondary to use of medications (e.g., corticosteroids) based on the general medical history that is obtained at the intake interview. 3. Participants with a global IQ less than 70 (determined using two subtests of the Wechsler Abbreviated Scales of Intelligence [WASI]) AND significant maladaptive behavior as determined by: 1) clinical manifestations and/or academic placement consistent with impaired intellectual functioning OR 2) a standard score less than 70 on the Scales of Independent Behavior (SIB-R)

109

Table 7. Instruments administered at the LAMS study baseline assessment.

Instrument Abbreviation Age Range Interview with Both Parent and Child Separately Kiddie Schedule for Affective Disorders KSADS-PL-W + PDD All and Schizophrenia for School-Age Children – Present and Lifetime Episode, with mood items from the WASH-U K-SADS and additional questions about PDD added Child Depression Rating Scale - Revised CDRS-R All Young Mania Rating Scale YMRS All Longitudinal Interval Follow-Up A-LIFE All Evaluation – Adolescent Version Irritability, Depression, Anxiety Scale IDA All Interview with Child Only Wechsler Abbreviated Scales of WASI All Intelligence Interview with Parent Only Service Assessment for Children and SACA All Adolescents Medication Code Form Med Code Form All Weissman Family History Screen FHS All Demographics Questionnaire (with Demo All Biological Parents Supplement) Scales of Independent Behavior - Revised SIB-R* All Parent Self-Report Questionnaires General Behavior Inventory (Parent Self- P-GBI All Report) Parent Stress Survey PSS All Parent Proxy-Report Questionnaires re: Child Medical History Form Med Hx All Parent General Behavior Inventory – P-GBI-SF All Short Form Child and Adolescent Symptom Inventory, CASI-4 Parent All 4th Edition – Parent Version Screen for Anxiety Related Emotional SCARED-P All Disorders – Parent-Report Stressful Life Events Scale – Parent SLES-P All Proxy-Report (continued)

110

Table 7. (continued)

Instrument Abbreviation Age Range Parent Proxy-Report Questionnaires re: Child Children’s Quality of Life Questionnaire – KIDDY KINDL-P 6 -7 Parent Proxy-Report KID KINDL-P 8-12 Peterson Pubertal Scale PPS 6 – 9

Child Self-Report Questionnaires Stressful Life Events Scale – Child SLES-C All Self-Report Children’s Quality of Life Questionnaire – KIDDY KINDL 6-7 Child Self-Report KID KINDL 8-12 Screen for Anxiety Related Emotional SCARED-C All Disorders – Child Self-Report Youth Risk Behavior Survey – Middle YRBS-MS 10-12 School Version Peterson Pubertal Scale PPS 10-12 Interviewer Completed Scales Clinical Global Impressions Scale - Severity CGI-S All Children’s Global Assessment Scale C-GAS All Teacher Proxy-Report Questionnaires re: Child Child and Adolescent Symptom Inventory, CASI-4 Teacher All 4th Edition – Parent Version Teacher General Behavior Inventory – T-GBI-SF All Short Form WASH-U K-SADS = Washington University K-SADS; PDD = pervasive developmental disorders *The SIB-R was only administered to a select group of participants, as explained in the Instruments section

111

Table 8. Description of items on KSADS-PL-W “Suicidal Acts – Severity” and “Suicidal Acts – Medical Lethality”.

Item Description Rating Anchors Suicidal Acts- Judge the seriousness of suicidal intent 0 = No attempt Seriousness as expressed in his suicidal acts like: 1 = Obviously no Likelihood of being rescued; intent, purely precautions against discovery; actions manipulative to gain help during or after attempt; gestures degree of planning; apparent purpose 2 = Not sure or only of the attempt (manipulative or truly minimal intent suicidal intent). 3 = Definite but very ambivalent Sample questions: 4 = Serious How did you try to kill yourself? 5 = Very serious Was anybody in the room? 6 = Extreme (every In the apartment? expectation of Did you tell them in advance? death). How were you found? Did you really want to die? Did you ask for any help after you did it? Suicidal Acts- Actual medical threat to life or physical 0 = No attempt Medical condition following the most serious 1 = No danger (e.g., Lethality suicidal act. Take into account the no effects, held method, impaired consciousness at pills in hand) time of being rescued, seriousness of 2 = Minimal (e.g., physical injury, toxicity of ingested scratch on wrist) material, reversibility, amount of time 3 = Mild (e.g., took needed for complete recovery and how ten aspirins, much medical treatment needed. mild gastritis) 4 = Moderate (e.g., Sample questions: took ten seconals, How close were you to dying after your brief loss of (most serious suicidal act)? consciousness) 5 = Severe (e.g., cut throat, hanging) 6 = Extreme (e.g., respiratory arrest, prolonged coma)

112

Table 9. Rules for coding suicide-related behavior.

1. Consider Suicidal Acts-Seriousness and Suicidal Acts-Lethality items in KSADS 1a. If scored "2" or higher for current or past SA-Seriousness or SA- Lethality, SRB_Outcome coded "1" for present 1b. If score is "1", check supporting evidence. (supporting evidence includes presence of a date of suicidal act in KSADS, back of KSADS safety items for Suicide Gesture or Suicide Attempt coded "Y", or anything in the narrative report or handwritten KSADS notes indicating possible suicidal gesture or attempt) 1bi. If any supporting evidence suggests KSADS suicide items score of "1" is accurate, SRB coded "1" 1bii. If no supporting evidence suggests KSADS suicide items score of "1" is accurate, SRB coded "9" for missing; assumed a coding error 2. Consider date of suicide items in KSADS and back of KSADS safety items for Suicide Gesture and/or Attempt 2a. If there is a recorded date of a suicide item coded in KSADS and the KSADS SA-Seriousness and SA-Lethality items were marked "0", check in KSADS handwritten notes and narrative summary to confirm presence/absence of suicide gesture/attempt. 2ai. If suicide-related behavior is convincingly confirmed in narrative summary or handwritten notes in KSADS, mark SRB_Outcome as "1" (convincing evidence includes hospitalization or description of suicide gesture/attempt) 2aii. If an explanation for the date being listed in error is discovered, code SRB_Outcome as "0" consistent with KSADS SA- Seriousness/Lethality items 2aiii. If there are no comments in KSADS handwritten notes or the narrative summary that convincingly makes the case for presence or absence of SRB, code SRB_Outcome as "9” 2b. If KSADS safety items for Suicide Gesture or Suicide Attempt are marked "Y" (present) and SA-Seriousness and SA-Lethality marked "0", confirm in KSADS handwritten notes and narrative summary. 2bi. If suicide gesture or attempt is convincingly confirmed in narrative summary or handwritten notes in KSADS, mark SRB_Outcome as "1" (convincing evidence includes hospitalization or description of suicide gesture/attempt) 2bii. If there are no comments in KSADS handwritten notes or the narrative summary that convincingly makes the case for presence of SRB, SRB_Outcome coded "0" (KSADS Depression section scores take precedence over back of KSADS safety item KSADS=Kiddie Schedule for Affective Disorders and Schizophrenia; SA=suicidal acts; SRB=suicide-related behavior 113

Table 10. Independent variables considered in model-building.

Variable Name Item Source Values Demographic Variables Sexscreen Is the child male or female? Demo 0 = male 1 = female Baseage What is the child’s age at baseline? Demo Continuous White To what racial group does the child Demo 0 = Non-White belong? 1 = White Hispsc To what ethnic group does the child Demo 0 = Hisp/Lat belong? 1 = Other Medicaid Does the child have Medicaid or some Demo 0 = Medicaid other form of insurance or healthcare 1 = Other coverage (e.g., self-pay)? BioMomCaregiver Is the biological mother the primary Demo 0 = no caregiver for the child? 1 = yes BothBioParents Are the child’s biological mother and Demo 0 = no father the primary and secondary 1 = yes caregivers? SecondaryCaregiver Does the child have a secondary in-home Demo 0 = no caretaker? 1 = yes Psychiatric Family History AnyParAttempt Has [the child’s biological mother and/or FHS 0 = no father] ever made a suicide attempt? 1 = yes AnyParSuicidea Did [the child’s biological mother and/or FHS 0 = no father] actually kill him or her self? 1 = yes AnyParDep Without including times of physical illness FHS 0 = no or mourning after a death has [the child’s 1 = yes biological mother and/or father] had a period of two weeks or more when they felt sad, blue or depressed? Very tired…or not cared about their usual activities? To the point it caused significant problems? AnyParMania Has [the child’s biological mother and/or FHS 0 = no father] had a period of feeling “high as a 1 = yes kite” or over-active/talkative for two days or more? Not because of drugs/alcohol? To the point it caused significant AnyParADHD problems? FHS 0 = no When they were younger did [the child’s 1 = yes biological mother and/or father] have difficulty focusing in school, following directions, or sitting still? To the point it caused significant problems? (continued) 114

Table 10. (continued)

Variable Name Item Source Values Psychiatric Family History AnyParPsychosis Has [the child’s biological mother and/or FHS 0 = no father] ever seen or heard anything other 1 = yes people cannot see or hear? Ever experienced hallucinations or delusions? Child Psychopathology Diagnosis Determined by consensus diagnosis based KSADS 0 = present AnyBP_Ever on KSADS ratings. Unique variables were 1 = absent AnyBP_curr created to indicate the presence or AnyDep_Ever absence of each diagnostic category under AnyDep_curr consideration. Both lifetime and current AnyAnx_Ever diagnosis were considered as separate AnyAnx_curr variables. The unique variable names and AnyADHD_Ever descriptions were as follows: AnyADHD_curr • AnyBP = any bipolar spectrum AnyDBD_Ever disorder AnyDBD_curr • Any Dep = any depressive disorder AnyAdj_Ever • AnyAnx = any anxiety disorder AnyAdj_curr • AnyADHD = any ADHD AnyPsy_Ever • AnyDBD = any conduct, ODD, or DBD- AnyPsy_curr NOS • AnyAdj = any adjustment disorder • AnyPsy = any psychotic disorder Symptoms SI_Ever Preoccupation with desire to die (passive KSADS Worst suicidal ideation) or thoughts of suicide Lifetimeb: SI_curr (active suicidal ideation). Both the worst 0 = no lifetime rating (i.e., whether or not the 1 = yes child ever experienced the symptom at threshold of “Mild” or higher) and current Current: symptom rating (i.e., rating for the past Likert scale two weeks) were considered. 1 – 6c NSSI_Ever Presence of self-mutilation or other self- KSADS Worst damaging acts done without intent of lifetime: NSSI_curr killing oneself. Both the worst lifetime 0 = no rating (i.e., whether or not the child ever 1 = yes experienced the symptom at threshold of “Moderate” or higher) and current Current: symptom rating (i.e., rating for the past Likert scale two weeks) were considered. 1 – 6 (continued)

115

Table 10. (continued)

Variable Name Item Source Values Child Psychopathology (cont’d) MI_Ever Recurrent thoughts of death (not just fear KSADS Worst of dying and not including suicidal lifetime: MI_curr ideation). Both the worst lifetime rating 0 = no (i.e., whether or not the child ever 1 = yes experienced the symptom at threshold of “Mild” or higher) and current symptom Current: rating (i.e., rating for the past two weeks) Likert were considered. scale 1 – 6

DepMood_Ever Subjective feelings of depression or verbal KSADS Worst complaints of feeling depressed, sad, blue, lifetime: DepMood_curr gloomy, very unhappy, down, empty, or 0 = no feeling like crying. Does not include 1 = yes discouragement, pessimism, worthlessness, anhedonia, hopelessness, Current: suicidal thoughts, depressed appearance, Likert or feelings of anxiety or tension. Both the scale worst lifetime rating (i.e., whether or not 1 – 6 the child ever experienced the symptom at threshold of “Moderate” or higher) and current symptom rating (i.e., rating for the past two weeks) were considered. Anhedonia_Ever Apathy, low motivation, or boredom; loss KSADS Worst of ability to enjoy and/or loss of interest in lifetime: Anhedonia_curr activities that are normally enjoyed. Both 0 = no the worst lifetime rating (i.e., whether or 1 = yes not the child ever experienced the symptom at threshold of “Mild” or higher) Current: and current symptom rating (i.e., rating Likert for the past two weeks) were considered. scale 1 – 6 IrritDep_Ever In the context of depressed mood or a Worst depressive episode, the subjective feeling lifetime: IrritDep_curr of irritability, anger, crankiness, bad 0 = no temper… whether expressed overtly or 1 = yes not. Do not rate here if irritability is due to mania or disruptive disorders. Both the Current: worst lifetime rating (i.e., whether or not Likert the child ever experienced the symptom at scale threshold of “Moderate” or higher) and 1 – 6 current symptom rating (i.e., rating for the past two weeks) were considered. (continued) 116

Table 10. (continued)

Variable Name Item Source Values Child Psychopathology (cont’d) IrritManic_Ever In the context of elevated mood or a manic Worst episode, the subjective feeling of irritability, lifetime: IrritManic_curr anger, crankiness, bad temper…whether 0 = no expressed overtly or not. Do not rate here if 1 = yes irritability is due to depression or disruptive disorders. Both the worst lifetime rating Current: (i.e., whether or not the child ever Likert scale experienced the symptom at threshold of 1 – 6 “Moderate” or higher) and current symptom rating (i.e., rating for the past two weeks) were considered. Worthless_Ever Negative self-image including feelings of KSADS Worst inadequacy, inferiority, failure, lifetime: Worthless_curr worthlessness, self-deprecation, and self- 0 = no belittling, regardless of how “realistic” the 1 = yes negative self-evaluation is. Both the worst lifetime rating (i.e., whether or not the child Current: ever experienced the symptom at threshold Likert scale of “Moderate” or higher) and current 1 – 6 symptom rating (i.e., rating for the past two weeks) were considered. Hopeless_Ever Negative outlook toward the future KSADS Worst regarding life and current problems. This lifetime: Hopeless_curr item refers to ideational content and not 0 = no feelings. Both the worst lifetime rating (i.e., 1 = yes whether or not the child ever experienced the symptom at threshold of “Mild” or Current: higher) and current symptom rating (i.e., Likert scale rating for the past two weeks) were 1 – 6 considered. SocWithdr_Ever Decreased frequency of contact and depth of Worst involvement with family members, friends, lifetime: SocWithdr_curr and other social situations compared to 0 = no usual before onset of illness. Both the worst 1 = yes lifetime rating (i.e., whether or not the child ever experienced the symptom at threshold Current: of “Mild” or higher) and current symptom Likert scale rating (i.e., rating for the past two weeks) 1 – 6 were considered. (continued)

117

Table 10. (continued)

Variable Name Item Source Values Child Psychopathology (cont’d) Insomnia_Ever Sleep difficulty involving initial, middle, KSADS Worst and terminal insomnia; difficulty getting lifetime: Insomnia_curr to sleep or staying asleep. Not rated if the 0 = no child feels no need for sleep. 1 = yes Developmental norms for number of hours sleep per night and subjective Current: sense of lost sleep are taken into Likert consideration. Both the worst lifetime scale rating (i.e., whether or not the child ever 1 – 6 experienced the symptom at threshold of “Mild” or higher) and current symptom rating (i.e., rating for the past two weeks) were considered. Hypersom_Ever Increased need for sleep, sleeping more KSADS Worst than usual. Not rated if nighttime sleep lifetime: Hypersom_curr plus daytime sleep (compensatory 0 = no napping) = developmental norm. Both 1 = yes the worst lifetime rating (i.e., whether or Current: not the child ever experienced the Likert symptom at threshold of “Moderate” or scale higher) and current symptom rating (i.e., 1 – 6 rating for the past two weeks) were considered. DecreaseSleep_Ever Less need for sleep than usual in order to KSADS Worst feel rested. Both the worst lifetime rating lifetime: DecreaseSleep_curr (i.e., whether or not the child ever 0 = no experienced the symptom at threshold of 1 = yes “Mild” or higher) and current symptom Current: rating (i.e., rating for the past two weeks) Likert were considered. scale 1 – 6 PsychoticSx_Ever Current or past experience of KSADS 0 = no hallucinations, delusions, disorganized 1 = yes thinking/sentence incoherence, and/or derailment at threshold or higher. Impulsivity_Ever Child acts before s/he thinks, and this has KSADS 0 = no caused him/her difficulty at home, 1 = yes school, and/or with peers. Current or past at threshold or higher. (continued)

118

Table 10. (continued)

Variable Name Item Source Values Child Psychopathology (cont’d) InitiatesFight_Ever Child starts fights with peers and/or KSADS 0 = no adults. Current or past at threshold or 1 = yes higher. Bullies_Ever Child bullies, threatens, or intimidates KSADS 0 = no others. Current or past at threshold or 1 = yes higher. TobaccoUse_Ever Child has ever used tobacco products KSADS 0 = no regularly. Current or past at threshold or 1 = yes higher. AlcUse_Evera Child has ever used alcohol regularly. KSADS 0 = no Current or past at threshold or higher. 1 = yes SubUse_Evera Child has ever regularly used cannabis, KSADS 0 = no stimulants, sedatives, hypnotics, 1 = yes anxiolytics, cocaine, opioids, PCP, hallucinogens, solvents, inhalants, or other drugs for recreational or other non- prescribed use. Current or past at threshold or higher. CGIMania_WST Clinical Global Impression – Mania, CGI Likert scale Worst. Interviewer rating of severity of 1 – 7d worst lifetime manic symptoms; confirmed at consensus diagnosis meeting. CGIDep_WST Clinical Global Impression – Depression, CGI Likert scale Worst. Interviewer rating of severity of 1 – 7 worst lifetime depressive symptoms; confirmed at consensus diagnosis meeting. CGIoverall_WST Clinical Global Impression – Overall, CGI Likert scale Worst. Interviewer rating of severity of 1 – 7 worst psychiatric lifetime symptoms; confirmed at consensus diagnosis meeting. CGAS_baseline Children’s Global Assessment Scale – CGAS Continuous Rating of overall general functioning, (1-100) independent of psychiatric diagnoses.

(continued)

119

Table 10. (continued)

Variable Name Item Sourc Values e Psychosocial Functioning ALIFE_PosAdultRel Variable computed based on the A-LIFE 0 = no presence or absence of a summary 1 = yes rating of “outstanding” or “good” for the relationship between the child and at least one adult inquired about on the A-LIFE (i.e., primary caregiver, secondary caregiver, or other significant adult) ALIFEsum_primcare_ Both current ratings and mean of past A-LIFE Likert scale MEAN and current summary score ratings of 1 – 5e the relationship between the child and ALIFEsum_primcare_ his/her primary caregiver were curr considered ALIFEsum_peers_MEA Both current and mean of past and A-LIFE Likert scale N current summary score ratings of the 1 – 5 relationship between the child and ALIFEsum_peers_curr his/her peers were considered ALIFEsum_schl_MEAN Both current and mean of past and A-LIFE Likert scale current summary score ratings of the 1 – 5 ALIFEsum_schl_curr child’s functioning at school, including academic performance and behavior were considered ALIFEsum_rec_MEAN Both current and mean of past and A-LIFE Likert scale current summary score ratings of the 1 – 5 ALIFEsum_rec_curr child’s access to and psychosocial functioning when engaged in recreational activities were considered ALIFEsum_GSA_MEAN Interviewer-rated summary score of A-LIFE Likert scale chlid’s global social adjustment (i.e., 1 – 5 ALIFEsum_GSA_curr overall psychosocial functioning at home, with peers, at school, and in leisure activities). Both current and mean of past and current summary score ratings were considered. FSIQ-2 Full scale IQ score (from two subtests) WASI Continuous (continued)

120

Table 10. (continued)

Variable Name Item Source Values Psychosocial Functioning (cont’d) RepeatedGrade Has the child ever repeated a grade? Demo 0 = no 1 = yes AcademicTutor Has the child received an academic Demo 0 = no tutor? 1 = yes BehaviorIntervention Has the child ever had special Demo 0 = no educational class placement or 1 = yes behavioral intervention (other than gifted & talented placement)? ChangeSchls Has your child had to change schools Demo 0 = no due to moving, school problems, or for 1 = yes any other reasons other than normal progression through schools? ThreePlusSchool Variable computed from ChangedSchls Demo 0 = no variable, dichotomizing children who 1 = yes had attending 3 schools or more from those who had not NumberMoves How many times has your child moved? Demo Continuous FourPlusMoves Variable computed from NumberMoves Demo 0 = no variable, dichotomizing children who 1 = yes had moved 4 or more times in their lives from those who had not Stressful Life Events PhysAbuse_ever Has the child ever been physically KSADS 0 = no abused? 1 = yes SexAbuse_ever Has the child ever been sexually KSADS 0 = no abused? 1 = yes AnyAbuse_ever Computed from WitnessDomViol_ever KSADS 0 = no and PhysAbuse_ever variables; if either 1 = yes item was coded “1” for present, this item was coded “1” for present. WitnessDomViol_ever Has the child ever witnessed domestic KSADS 0 = no violence? 1 = yes AnyTrauma_ever Ever experienced a traumatic event KSADS 0 = no (last item on screening section for 1 = yes PTSD in the KSADS-PL-W)? SLES_60C_sexabuse “[Child] was sexually hurt or touched in SLES-C 0 = no SLES_11P_sexabuse private parts.” SLES-P 1 = yes SLES_31C_physabuse “[Child] was hurt or punched by SLES-C 0 = no SLES_45P_physabuse someone.” SLES-P 1 = yes (continued)

121

Table 10. (continued)

Variable Name Item Source Values Stressful Life Events (cont’d) SLES_50C_hurtself “[Child’s] close friends or family tried SLES-C 0 = no SLES_66P_hurtself to hurt themselves.” SLES-P 1 = yes SLES_11C_divorce “[Child’s] parents got divorced or SLES-C 0 = no SLES_15P_divorce separated.” SLES-P 1 = yes SLES_18P_remarry “[Child’s mom or dad] remarried.” SLES-C 0 = no SLES_13C_remarry SLES-P 1 = yes SLES_3C_domv “[Child’s] parents hit each other SLES-C 0 = no SLES_5P_domv (fight).” SLES-P 1 = yes

SLES_18C_frnddied “A close friend [of the child] died.” SLES-C 0 = no SLES_25P_frnddied SLES-P 1 = yes SLES_26C_deathfam “A close relative [of the child] died.” SLES-C 0 = no SLES_37P_deathfam SLES-P 1 = yes SLES_25C_fightwpar “[Child] was fighting more with SLES-C 0 = no SLES_35P_fightwpar parents.” SLES-P 1 = yes SLES_49C_classmate “[Child] had problems being liked by SLES-C 0 = no SLES_65P_classmate school friends.” SLES-P 1 = yes SLES_38C_fightwfrnd “[Child] fought with a good friend.” SLES-C 0 = no SLES_53P_fightwfrnd SLES-P 1 = yes SLES_37C_endfrnd “[Child] stopped talking to a good SLES-C 0 = no SLES_52P_endfrnd friend.” SLES-P 1 = yes SLES_58C_fightschl “[Child] fought with someone at SLES-C 0 = no SLES_75P_fightschl school.” SLES-P 1 = yes SLES_34C_bullied “[Child] was bullied at school or in SLES-C 0 = no SLES_49P_bullied neighborhood.” SLES-P 1 = yes SLES_1C_grades “[Child] had trouble with grades or SLES-C 0 = no SLES_1P_grades schoolwork.” SLES-P 1 = yes ADHD=attention deficit/hyperactivity disorder; A-LIFE=Longitudinal Interval Follow-Up Evaluation – Adolescent Version; C-GAS=Children’s Global Assessment Scale; CGI-S=Clinical Global Impression – Severity; DBD=disruptive behavior disorder; Demo=Demographics Questionairre; FHS=Weissman Family History Screen; Hisp/Lat=Hispanic/Latino; KSADS=Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children – Present and Lifetime Episode, with additional mood items from the WASH-U K-SADS and screen questions for pervasive developmental disorder added; NOS=not otherwise specified; ODD=oppositional defiant disorder; SLES-C=Stressful Life Events Scale – Child Version; SLES-P=Stressful Life Events Scale – Parent Version; WASI=Wechsler Abbreviated Scales of Intelligence. aInsufficient variability in this variables resulted in it being excluded from consideration in the model building process. bFor “Ever” symptoms, current and past ratings on the KSADS-PL-W were collapsed and the highest of the two ratings was considered. If the participant had a rating at threshold or higher (as determined by scale developers), the symptom was coded “1” for present; if not, the symptom was coded “0” for absent. cLikert scale 1-6 ratings: 1=not present, 2=slight, 3=mild, 4=moderate, 5=severe, 6=extreme. dLikert scale 1-7 ratings: 1=normal, 2=borderline, 3=mild, 4=moderate, 5=marked, 6=severe, 7=among the most extremely ill. eLikert scale 1-5 ratings: 1=outstanding, 2=good, 3=mild difficulty with relationship or area of functioning, 4=moderate difficulty with relationship or area of functioning, 5=severe difficulty with relationship or area of functioning. 122

Table 11. Distribution of suicide-related behaviors in the sample by highest severity (current or past) and baseline age.

KSADS Suicidal Acts Age at baseline – Seriousness n mean years (range) Rating

1 – Gesture 15 10.21 (6.33 – 12.43)

2 – Minimal Intent 15 9.8 (7.12 – 12.97)

3 – Ambivalent 12 10.81 (7.6 – 12.43)

4 – Serious 7 11.24 (7.85 – 12.96)

5 – Very Serious 3 11.29 (8.91 – 12.99)

6 – Extreme 1 9.25

KSADS Suicidal Acts Age at baseline – Lethality Rating n mean years (range)

1 – No Harm 31 10.31 (6.33 – 12.97)

2 – Minimal Harm 13 10.32 (7.12 – 12.69)

3 – Mild 5 10.94 (7.85 – 13.0)

4 – Moderate 3 11.39 (10.58 – 12.99)

5 – Severe 1 8.91

6 – Extreme 0 --

Missing KSADS 4 9.08 (6.06 – 12.9) Suicidal Acts Ratings

Total 57 10.32 (6.06 – 13.0)

KSADS=Kiddie Schedule for Affective Disorders and Schizophrenia

123

Table 12. Example descriptions of suicide-related behaviors in the sample.

BL KSADS KSADS Description Age SA-S SA-L 7.78 2 1 “…three weeks ago child started to present more acute symptoms with clear suicidal ideation, with poor structured plans as jumping from a window (he doesn’t have access to tall window), holding his breath (he tried to choke himself with his hands, no marks, he didn’t pass out), jumping to the ocean into a shark mouth, additionally he is irritable, angry, upset and he is out of control.” 7.85 4 3 “Suicidal thinking and behavior – was suddenly and impulsively disappointed with her coach…she was so angry that when she went home after the game she decided out of the blue to take a plastic bag put it over her head to get suffocated and then jumped into the laundry basket, as soon she was gasping for air she fought her way out…her father has made 4 attempts and they talk openly about suicide in that home…” 8.69 2 2 “Since he started the fire and they lost the house and his brothers blame him sometimes in the last 2 weeks he had expressed he will be better off dead or he should choke himself and holds his throat for 30 seconds until can’t hold breath anymore.” 8.91 5 5 “He was hospitalized…[1 year ago] when he tried to kill himself (hanging)” 9.32 1 1 “[Mother] reported [child] has displayed suicidal gestures approximately twelve times since age four. She stated that [child] yells ‘I’m going to kill myself’ during times of extreme irritability and frustration. During these episodes, [child] reportedly gets a knife in the kitchen and hold it pointed toward his chest. [Mother] reported, however, that [child] holds the knife as far away from himself as possible, because ‘he’d never hurt himself’ and ‘he’s really afraid of pain.’ Ms. Z reported Y has never caused injury to himself during these times, but the most recent occurrence of these behaviors was approximately one week prior to this interview.” (continued) 124

Table 12. (continued)

BL KSADS KSADS Description Age SA-S SA-L 10.07 3 2 “[The patient] has had past suicidal ideation with past gesture of strangling herself with a telephone cord, as well as thoughts about hurting herself deliberately (breaking her wrist by twisting it)” 10.61 4 4 “Since age 6 – puts plastic bag on head. September was last time. Two past suicide attempts…[put] belt around his neck in his room. Stated, ‘I wish I were dead.’ Dad had to call police. Hopitalized at age 8. Tried to get knives. Says, ‘I want to kill you mom, grandma,’ a couple times week.” 11.71 1 1 “The subject denied suicidal ideation but his mother reported that he recently sat on the window ledge of a second-story window in the family’s home and threatened to jump and has also tightened belts around his neck out of anger in the past. His mother reported that he does not talk about suicide and that she didn’t feel he has ever had any intent to kill himself.” “Patient’s mother reported a past suicide attempt 11.96 5 2 in which [child] attempted to run in front of a moving vehicle. She was hospitalized, treated, and released following the attempt. Patient’s mother also reported past suicidal and homicidal ideation in which the patient made statements about wanting to hurt herself and her mother with scissors.” 12.01 3 1 “When queried, mom reported that during his 2 month depression [2 years ago] it got so bad that he had gotten a belt around his neck and was ‘trying to get it tied up to something’ when they found him. While he was not hurt, they took it seriously and have monitored him closely.” (continued)

125

Table 12. (continued)

BL KSADS KSADS Description Age SA-S SA-L 12.24 4 3 “In the past [the patient] has been hospitalized for two suicide attempts. [The patient’s] second suicide attempt was in response to auditory hallucinations that told her to kill herself… [the patient] took pills – Sudafed. Threatened to stab self in heart with scissors…The first [attempt] was at age 8…the second was at age 11.” 12.35 3 1 “It was found that patient has been hospitalized three times since January this year for suicidal thoughts, aggression toward his mother when he threatened to stab her with a knife, and a suicide attempt most recently in April when he ran out into traffic…Patient has also previously hit himself with objects and hit his head on a pole. No serious injury.” 12.86 4 1 “[Mother] reported that Y has been taken to the emergency room and admitted overnight approximately 15-20 times in his life, always for suicidal ideation and gestures…[was recently admitted] for saying that he wanted to kill himself and placing a lamp cord around his neck…was admitted [7 months ago] for three days… over concerns regarding suicidal ideation.” 12.99 5 4 “He was hospitalized…for approximately 2 weeks for suicide attempt. (He was severely dehydrated after 3 weeks without drinking any type of fluids.)” BL=Baseline, KSADS=Kiddie Schedule for Affective Disorders and Schizophrenia, SA-S=Suicidal Acts-Seriousness score, SA-L=Suicidal Acts-Lethality score

126

Table 13. Characteristics of the sample: Comparing children with suicide-related behaviors to those without.

Study-Related Factors Total SRB- SRB+ χ2(df)

Total sample, n (%) 678 621 (91.7) 57 (8.3) --

Screen status, ESM+, n (%) 596 544 (87.6) 52 (91.2) χ2(1)=.646, p=.421

χ2(3)=3.005, Site p=.391

Case Western Reserve, n (%) 165 (24.3) 147 (23.7) 18 (13.9)

Cincinnati Children’s Hospital, 164 (24.2) 154 (24.8) 10 (13.8) n (%)

Ohio State University, n (%) 179 (26.4) 166 (26.7) 13 (22.8)

Pittsburgh – Western Psychiatric 170 (25.1) 154 (24.8) 16 (28.1) Research Institute, n (%) SRB- SRB+ Total (n=621), (n=57), χ2(df) or t Demographics N=678 91.7% 8.3%

Age, mean years ± SD 9.4 ± 1.9 9.3 ± 1.9 10.3 ± 2.1 t=1.778, p=.000*

Sex, male, n (%) 461 (68.0) 420 (67.6) 41 (71.9) χ2(1)= 443, p=.506

Race, White, n (%) 431 (63.6) 395 (63.6) 36 (63.2) χ2(1)=.005, p=.946

Ethnicity, Hispanic, n (%) 29 (4.3) 24 (3.9) 5 (8.8) χ2(1)=3.071, p=.08 (continued)

127

Table 13. (continued)

Demographics Total SRB- SRB+ χ2(df) or t

Race/Ethnicity χ2(3)=3.875, p=.275

White non-Hispanic, n (%) 415 (61.2) 382 (61.5) 33 (57.9)

Black non-Hispanic, n (%) 177 (26.1) 161 (25.9) 16 (28.1)

29 (4.3) 24 (3.9) 5 (8.8) Hispanic, n (%)

57 (8.4) 54 (8.7) 3 (5.3) Other, n (%)

Primary caretaker, Biological 548 (80.9) 502 (81.0) 46 (80.7) χ2(1)=.002, Mother, n (%) p=.961

Insurance type, Medicaid, n (%) 358 (52.8) 322 (51.9) 36 (63.2) χ2(1)=2.678, p=.102

χ2(df) Total SRB- SRB+ Family History of Attempted/Completed Suicide

2 Parent with attempted or χ (1)=12.41 completed suicide [N=664], n 154 (23.2) 131 (21.5) 23 (42.6) 9, (%) p=.000*

✣Fisher’s: Parent with completed suicide 5 (0.7) 5 (0.8) 0 [N=678], n (%) p=1.00 χ2(df) or Lifetime Diagnostic Fisher’s Total SRB- SRB+ Information Exact p- value

Dx any bipolar spectrum 152 (22.4) 130 (20.9) 22 (38.6) χ2(1)=9.364, disorder, p=.002* n (%)

Dx any depressive disorder, n 143 (21.1) 123 (19.8) 20 (35.1) χ2(1)=7.325, (%) p=.007* (continued)

128

Table 13. (continued)

Lifetime Diagnostic Total SRB- SRB+ χ2(df) Information (cont’d)

Dx any anxiety disorder, n (%) 232 (34.2) 215 (34.6) 17 (29.8) χ2(1)=.534, p=.465

Dx any ADHD, n (%) 521 (76.8) 486 (78.3) 35 (61.4) χ2(1)=8.338, p=.004*

Dx any DBD, n (%) 352 (51.9) 326 (52.5) 26 (45.6) χ2(1)=.991, p=.320

Dx any adjustment disorder, 26 (3.8) 25 (4.0) 1 (1.8) χ2(1)=.730, n (%) p=.393

Dx any psychotic disorder, 16 (2.4) 14 (2.3) 2 (3.5) χ2(1)=.356 n (%) p=.550

Dx any elimination disorder, 194 (28.6) 176 (28.3) 18 (31.6) χ2(1)=.268, n (%) p=.605

Dx any substance abuse 0 0 0 -- disorder, n (%)

Lifetime treatment history Total SRB- SRB+ χ2(df) or t

Psychiatric hospitalization in 63 (9.3) 36 (5.8) 27 (47.4) χ2(1)=107.045 lifetime, yes, n (%) p=.000*

Ever prescribed psychotropic 481 (70.9) 434 (69.9) 47 (82.5) χ2(1)=4.001, medication, yes, n (%) p=.045*

Number of medications at 1.05 ± 1.07 1.02 ± 1.03 1.40 ± t=13.56, baseline, mean ± SD 1.37 p=.000*

(continued)

129

Table 13. (continued)

SRB- SRB+ Total Overall Functioning (n=621), (n=57), t N=677 91.7% 8.3%

CGAS score (current), 54.56 ± 54.73 ± 52.67 ± t=.565, mean ± SD 10.35 10.29 10.98 p=.453

✣Fisher’s Exact was used when a cell size of <5 was present. *p≤.05 ADHD=attention deficit/hyperactivity disorder, CGAS=childhood global assessment scale, DBD=disruptive behavior disorder, df=degrees of freedom, Dx=diagnosis, SD=standard deviation, SRB-=no lifetime suicide-related behavior reported at baseline, SRB+=one or more lifetime suicide-related behaviors reported at baseline.

130

Table 14. Results of univariate logistic regression analyses.

Variable Name p-value N Demographic Variables Sexscreen .506 678 Baseage .000* 678 White .946 678 Hispsc .089 678 Medicaid .104 678 BioMomCaregiver .961 677 BothBioParents .024* 676 SecondaryCaregiver .624 678 Psychiatric Family History Variables AnyParAttempt .001* 664 AnyParSuicide .999 678 AnyParDep .053 639 AnyParADHD .587 664 AnyParPsychosis .001* 665 Child Psychopathology Variables AnyBP_Ever .003* 678 AnyBP_curr .003* 678 AnyDep_Ever .008* 678 AnyDep_curr .006* 678 AnyAnx_Ever .466 678 AnyAnx_curr .374 678 AnyADHD_Ever .005* 678 AnyADHD_curr .003* 678 AnyDBD_Ever .321 678 AnyDBD_curr .381 678 AnyAdj_Ever .407 678 AnyAdj_curr .999 678 AnyPsy_Ever .554 678 AnyPsy_cur .492 678 SI_Ever .000* 676 SI_curr .000* 676 NSSI_Ever .000* 673 NSSI_curr .000* 673 MI_Ever .000* 677 MI_curr .000* 677 DepMood_Ever .000* 676 DepMood_curr .000* 676 (continued) 131

Table 14. (continued) Variable Name p-value N Child Psychopathology Variables (cont’d) Anhedonia_Ever .000* 676 Anhedonia_curr .007* 676 IrritDep_Ever .000* 677 IrritDep_curr .001* 677 IrritManic_Ever .008* 677 IrritManic_curr .184 677 Worthless_Ever .000* 677 Worthless_curr .000* 677 Hopeless_Ever .000* 674 Hopeless_curr .003* 674 SocWithdr_Ever .002* 676 SocWithdr_curr .010* 676 Insomnia_Ever .333 677 Insomnia_curr .463 677 Hypersom_Ever .001* 671 Hypersom_curr .041* 671 DecreaseSleep_Ever .039* 675 DecreaseSleep_curr .710 675 PsychoticSx_Ever .010* 677 Impulsivity_Ever .506 673 InitiatesFight_Ever .323 676 Bullies_Ever .209 673 TobaccoUse_Ever .002* 672 CGIMania_WST .000* 678 CGIDep_WST .000* 678 CGIoverall_WST .000* 678 CGAS_baseline .150 677 Psychosocial Functioning Variables ALIFE_PosAdultRel .215 676 ALIFEsum_primcare_MEAN .042* 676 ALIFEsum_primcare_curr .129 676 ALIFEsum_peers_MEAN .953 674 ALIFEsum_peers_cur .931 674 ALIFEsum_schl_MEAN .855 625 ALIFEsum_schl_curr .827 628 ALIFEsum_rec_MEAN .690 643 ALIFEsum_rec_curr .568 644 ALIFEsum_GSA_MEAN .235 676 ALIFEsum_GSA_curr .736 676 (continued) 132

Table 14. (continued)

Variable Name p-value N Psychosocial Functioning Variables (cont’d) FSIQ-2 .852 674 RepeatedGrade .612 675 AcademicTutor .003* 675 BehaviorIntervention .673 674 ChangeSchls .009* 677 ThreePlusSchool .175 677 NumberMoves .010* 670 FourPlusMoves .015* 670 Stressful Life Events Variables PhysAbuse_ever .116 676 SexAbuse_ever .015* 676 AnyAbuse_ever .029* 676 WitnessDomViol_ever .244 676 AnyTrauma_ever .314 676 SLES_60C_sexabuse .740 665 SLES_11P_sexabuse .014* 666 SLES_31C_physabuse .424 664 SLES_45P_physabuse .915 663 SLES_50C_hurtself .007* 665 SLES_66P_hurtself .878 666 SLES_11C_divorce .555 666 SLES_15P_divorce .925 666 SLES_18P_remarry .001* 663 SLES_13C_remarry .037* 665 SLES_3C_domv .553 666 SLES_5P_domv .494 666 SLES_18C_frnddied .323 665 SLES_25P_frnddied .999 663 SLES_26C_deathfam .858 664 SLES_37P_deathfam .753 663 SLES_25C_fightwpar .001* 663 SLES_35P_fightwpar .182 663 SLES_49C_classmate .192 665 SLES_65P_classmate .041* 666 SLES_38C_fightwfrnd .103 664 SLES_53P_fightwfrnd .015* 663 SLES_37C_endfrnd .034* 664 (continued) 133

Table 14. (continued)

Variable Name p-value N Stressful Life Events Variables (cont’d) SLES_52P_endfrnd .030* 663 SLES_58C_fightschl .221 664 SLES_75P_fightschl .715 666 SLES_34C_bullied .017* 664 SLES_49P_bullied .091 663 SLES_1C_grades .310 666 SLES_1P_grades .316 666 *p≤.05; See Table 10 for explanation of variable names.

134

Table 15. Correlation matrix for significant child psychopathology variables in univariate analyses.

Variable 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1. AnyBP r = -.25 -.05 .19 .14 .23 .20 .16 .20 .30 .20 .12 .18 .16 .31 .17 .05 .83 .35 .29 _Ever p = .00* .21 .00* .00* .00* .00* .00* .00* .00* .00* .00* .00* .00* .00* .00* .23 .00* .00* .00* N=678 678 676 673 677 676 676 677 677 677 674 676 671 675 677 672 678 678 678 2. -.01 .18 .08 .18 .39 .38 .23 -.13 .34 .34 .29 .11 -.08 -.06 .01 -.20 .50 .21

AnyDep * * * * * * * * * * * * * * * _Ever .84 .00 .04 .00 .00 .00 .00 .00 .00 .00 .00 .01 .05 .10 .76 .00 .00 .00 678 676 673 677 676 676 677 677 677 674 676 671 675 677 672 678 678 678 3. Any -.14 -.08 -.05 -.12 -.12 -.06 .05 -.10 -.11 -.06 -.08 -.02 -.08 -.00 -.03 -.10 .01

135 ADHD * * * * * * * * * _Ever .00 .05 .19 .00 .00 .11 .23 .01 .01 .11 .05 .57 .05 .92 .49 .01 .77 676 673 677 676 676 677 677 677 674 676 671 675 677 672 678 678 678

4. .26 .57 .23 .18 .20 .09 .34 .29 .25 .12 .07 .09 .08 .21 .40 .25

SI_Ever * * * * * * * * * * * * * * * .00 .00 .00 .00 .00 .02 .00 .00 .00 .00 .07 .02 .05 .00 .00 .00 672 676 675 675 675 675 676 673 675 670 673 675 670 676 676 676 5. NSSI .24 .15 .09 .15 .00 .14 .08 .14 .08 .05 .08 .08 .13 .23 .18 _Ever * * * * * * * * * * * * * .00 .00 .02 .00 .91 .00 .04 .00 .05 .28 .04 .03 .00 .00 .00 673 672 672 672 672 673 670 672 667 670 672 667 673 673 673 (continued)

Table 15. (continued)

Variable 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 6. .28 .27 .25 .13 .41 .28 .27 .18 .09 .17 .10 .28 .45 .25

MI_Ever * * * * * * * * * * * * * * .00 .00 .00 .00 .00 .00 .00 .00 .02 .00 .01 .00 .00 .00 676 676 676 676 677 674 676 671 674 676 671 677 677 677 7. .44 .30 .01 .41 .39 .44 .27 .04 .10 .06 .18 .62 .31

DepMood * * * * * * * * * * _Ever .00 .00 .83 .00 .00 .00 .00 .31 .01 .11 .00 .00 .00 675 675 675 676 673 675 670 673 675 670 676 676 676

136 8. .31 .13 .41 .39 .43 .26 .13 .09 .06 .17 .56 .22

Anhedonia * * * * * * * * * * * _Ever .00 .00 .00 .00 .00 .00 .00 .02 .13 .00 .00 .00

675 675 676 673 675 670 673 675 670 676 676 676 9. IrritDep .26 .31 .22 .23 .05 .08 .03 .06 .22 .42 .17

_Ever * * * * * * * * .00 .00 .00 .00 .17 .04 .39 .12 .00 .00 .00 676 676 673 675 670 674 676 671 677 677 677 10. .11 .03 .10 .01 .31 -.01 -.01 .38 .14 .09

IrritManic * * * * * * _Ever .00 .46 .01 .88 .00 .80 .88 .00 .00 .02 676 673 675 670 674 676 671 677 677 677 (continued)

Table 15. (continued)

Variable 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 11. .38 .33 .19 .08 .05 .08 .22 .54 .24 Worthless * * * * * * * * _Ever .00 .00 .00 .04 .18 .04 .00 .00 .00 674 676 671 674 676 671 677 677 677

12. .36 .22 .06 .06 .03 .12 .47 .22

Hopeless * * * * * _Ever .00 .00 .15 .14 .45 .00 .00 .00 673 669 671 673 668 674 674 674 13. .20 .05 .03 .07 .15 .51 .26 137 SocWithdr * * * * _Ever .00 .19 .50 .06 .00 .00 .00

670 673 675 670 676 676 676

14. .05 .02 .10 .16 .28 .13

Hypersom * * * * _Ever .21 .66 .01 .00 .00 .00 668 670 665 671 671 671 15. .07 .00 .36 .14 .15

Decrease * * * Sleep .08 .92 .00 .00 .00 _Ever 674 669 675 675 675 (continued)

Table 15. (continued)

Variable 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 16. -.02 .22 .12 .19 Psychotic

Sx_Ever * * * .58 .00 .00 .00 671 677 677 677 17. .05 .07 .04 Tobacco _Ever .20 .08 .30 672 672 672 18. .41 .37 138 CGIMania .00* .00*

_WST

678 678 19. .48

CGIDep * _WST .00 678 20. CGI overall _WST

*p≤.05; r=Pearson correlation (2-tailed); For explanation of variable names, see Table 10.

Table 16. Correlation matrix for significant stressful life events variables in univariate analyses.

Variable 2 3 4 5 6 7 8 9 10 11 12 1. SexAbuse r=.72 .49 .06 .08 .05 .05 -.00 .00 .05 .07 .12 _Ever p=.00* .00* .14 .04* .18 .20 .92 .96 .23 .06 .00* N=676 664 663 661 663 661 664 661 662 661 662 2. AnyAbuse .38 .07 .07 .06 .02 .06 .04 .04 .03 .08 _Ever * * .00 .09 .09 .12 .63 .13 .32 .31 .52 .03 664 663 661 663 661 664 661 662 661 662 139 3. SLES-11P .10 .14 .09 .00 .04 .02 .08 .03 .07

_sexabuse * * * .01 .00 .02 1.00 .35 .56 .06 .47 .09

659 663 659 657 666 663 658 663 658 4. SLES-50C .10 .16 .13 .02 .02 .22 -.00 .09 _hurtself * * * * * .01 .00 .00 .68 .60 .00 .93 .02 656 665 663 659 656 664 656 664 5. SLES-18P .25 .04 -.06 .03 .01 -.01 .08 _remarry * * .00 .35 .11 .47 .77 .78 .03 656 654 663 663 655 663 655 (continued)

Table 16. (continued)

Variable 2 3 4 5 6 7 8 9 10 11 12 6. SLES-13P .01 -.01 .04 .05 .01 .03 _remarry .83 .80 .27 .25 .71 .41 663 659 656 664 656 664 7. SLES-25C .03 .08 .16 -.01 .20 _fightwpar * * * .39 .04 .00 .86 .00 657 654 663 654 663 8. SLES-65P .18 .07 .13 .11

140 _classmate * * * .00 .10 .00 .01

663 658 663 658

9. SLES-53P .11 .36 .09 _fightwfrnd * * * .01 .00 .02 655 663 655 10. SLES-37C .15 .18 _endfrnd * * .00 .00 655 664 (continued)

Table 16. (continued)

Variable 2 3 4 5 6 7 8 9 10 11 12 11. SLES-52P .07 _endfrnd .06 655 12. SLES-34C _bullied

141 *p≤.05; r=Pearson correlation (2-tailed); For explanation of variable names, see Table 10.

Table 17. Results of Hypothesis 1-6: Final models of covariates that are correlated with increased likelihood of suicide-related behavior.

Variable B SE Wald p-value OR

Hypothesis 1: Demographic factors, N=676, SRB+=57 Baseage .272 .075 12.28 .000 1.31 BothBioParents -.680 .292 5.42 .020 .51 Hypothesis 2: Psychiatric family history, N=664, SRB+=54 AnyParAttempt .998 .292 11.66 .001 2.71 Hypothesis 3: Child psychopathology, N=669, SRB+=55 SI_Ever 3.016 .347 75.56 .000 20.41 TobaccoUse_Ever 1.381 .645 4.59 .032 3.98 Anhedonia_Ever .699 .347 4.58 .032 2.01 Hypothesis 4: Psychosocial factors (home, school, peers), N=673, SRB+=56 ChangeSchls .723 .293 6.067 .014 2.060 ALIFE_primcare_MEAN .306 .152 4.048 .044 1.358 AcademicTutor -1.220 .416 8.604 .003 .295 Hypothesis 5: Stressful life events, N=654, SRB+=54 SLES_50C_hurtself 1.176 .440 7.153 .007 3.241 SLES_18P_remarry 1.171 .377 9.663 .002 3.225 SLES_52P_endfrnd 1.063 .435 5.982 .014 2.896 SLES_25C_fightwpar 1.050 .303 11.974 .001 2.857 SLES_18C_frnddied -1.314 .675 3.792 .051 .269 Hypothesis 6: Overall model, N=647, SRB+=53 SI_Ever 3.443 .375 84.435 .000 31.27 SLES_18P_remarry 1.533 .464 10.911 .001 1.87 AcademicTutor -1.510 .471 10.280 .001 .221 OR=odds ratio; SE=standard error; see Table 10 for explanation of variable names.

142

Table 18. Two-by-two contingency table for the relationship between suicidal ideation and suicide-related behavior (χ2=145.25, p=.000).

SI- SI+ Total SRB- 544 75 619 (87.9%) (12.1%) SRB+ 14 43 57 (24.6%) (75.4%) Total 558 118 676 (82.5%) (17.5%) SI=suicidal ideation, SRB=suicide-related behavior, -=absent, +=present.

143

Table 19. Two-by-two contingency table for the relationship between parent- report of a parent remarrying and suicide-related behavior (χ2=11.06, p=.001).

Parent did not Parent remarried Total remarry SRB- 558 51 609 (91.6%) (8.4%) SRB+ 42 12 54 (77.8%) (22.2%) Total 600 63 663 (90.5%) (9.5%) SRB=suicide-related behavior, -=absent, +=present.

144

Table 20. Two-by-two contingency table for the relationship between having an academic tutor and suicide-related behavior (χ2=9.5, p=.002).

No academic tutor Academic tutor Total SRB- 421 197 618 (68.1%) (31.9%) SRB+ 50 7 57 (87.7%) (12.3%) Total 471 204 675 (69.8%) (30.2) SRB=suicide-related behavior, -=absent, +=present.

145

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