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Bullying and psychotic symptoms in youth with bipolar disorder
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Bullying and psychotic symptoms in youth with bipolar disorder Jandira Rahmeier Acosta , Diego Librenza-Garcia , Devon Watts , ´ Ana Paula Franciscoa , Franco Zortea , Bruno Raffa , Andre´ Kohmann , Fabiana Eloisa Mugnol , Gledis Lisiane Motta , Silza´ Tramontina , Ives Cavalcante Passos PII: DOI: Reference:
S0165-0327(19)30719-0 https://doi.org/10.1016/j.jad.2019.11.101 JAD 11348
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Journal of Affective Disorders
Received date: Revised date: Accepted date:
20 March 2019 27 September 2019 21 November 2019
Please cite this article as: Jandira Rahmeier Acosta , Diego Librenza-Garcia , Devon Watts , ´ Ana Paula Franciscoa , Franco Zortea , Bruno Raffa , Andre´ Kohmann , Fabiana Eloisa Mugnol , Gledis Lisiane Motta , Silza´ Tramontina , Ives Cavalcante Passos , Bullying and psychotic symptoms in youth with bipolar disorder, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.11.101
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HIGHLIGHTS
Study participants (N = 64) included those who had a lifetime history of psychotic symptoms (n = 21; 32.8%), and those who reported being bullied (n = 24; 37.5%).
A lifetime history of psychotic symptoms was associated with bullying, suicidal behavior, low socioeconomic status, and higher Clinical Global Impression-Severity (CGI-S) scale scores.
Only bullying and suicidal behavior remained significant after adjusting for potential confounders.
In a supplementary analysis, using a supervised machine learning model, the most relevant predictive variables in differentiating participants with vs without psychotic symptoms were bullying, CGI-S scale scores, and suicidal behavior.
Study limitations include the small sample size, use of a cross-sectional design, and the generalizability of findings beyond the outpatient clinical sample.
Bullying and psychotic symptoms in youth with bipolar disorder
Jandira Rahmeier Acosta, MD a,b; Diego Librenza-Garcia, MD b,c Devon Wattsc , Ana Paula Franciscoa,b,c, Franco Zórteaa; MD; Bruno Raffa MD a,b, André Kohmann MDa; Fabiana Eloisa Mugnol, MDa; Gledis Lisiane Motta, MDa; Silzá Tramontina, MD, PhDa; Ives Cavalcante Passos, MD, PhDd
a. Program for Children and Adolescents with Bipolar Disorder (ProCAB), Division of Child and Adolescent Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil. b. Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento – Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil; c. Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada. d. Laboratory of Molecular Psychiatry and Bipolar Disorder Program, HCPA, UFRGS, Porto Alegre, RS, Brazil.
ABSTRACT Background: Childhood trauma is associated with psychosis in adults with bipolar disorder (BD). Although bullying represents a widespread form of childhood trauma, no studies thus far have investigated the association of bullying and psychosis in pediatric bipolar disorder (PBD). We aim to examine the association between psychosis in PBD with bullying victimization. Methods: We included 64 children and adolescents (age± mean= 12±3.43) outpatients with BD spectrum disorders. Psychiatric diagnoses were assessed with the semi- structured interview Schedule for Affective Disorders and Schizophrenia for School Age Children-Present
and Lifetime (KSADS-PL) version with additional depression and manic symptom items derived from the Washington University in St. Louis Kiddie Schedule for Affective Disorders (WASH-UKSADS). Bullying, demographic, and clinical variables were assessed during the clinical interview. Results: A lifetime history of psychotic symptoms was associated with bullying (p=0.002), suicidal behavior (p=0.006), low socioeconomic status (p=0.04), and severity of PBD (p=0.02). Only bullying (OR=7.3; 95%CI= 2-32) and suicidal behavior (OR=7.6; 95%CI=1.5–47.8) remained significant after adjustment for confounders. In a supplementary analysis, we developed a model using supervised machine learning to identify the most relevant variables that differentiated participants with psychotic symptoms, which included bullying, Clinical Global Impression-Severity scale (CGI-S), and suicidal behavior (accuracy=75%, [p=0.03]; sensitivity=77.91%; specificity=69.05%; area under the curve [AUC]= 0.86). Limitations: Small sample, cross-sectional design, and generalizability of findings beyond the outpatient clinical sample. Conclusions: Findings underscore the importance of assessing bullying in PBD participants. Future longitudinal studies with larger samples are needed to replicate our findings and determine causality.
INTRODUCTION According to epidemiological data from several countries, pediatric bipolar disorder (PBD) has an overall prevalence of 3.9% across the spectrum of the disorder (van Meter et al., 2019) and previous reports suggest that pediatric onset of bipolar disorder (BD) presents with a more severe course of illness than BD that manifests in adulthood (Duffy et al., 2017); Kapczinski et al., 2017; Leverich et al., 2007). Furthermore, psychotic symptoms such as hallucinations and delusions are associated with pernicious outcomes (Birmaher et al., 2006; Geller et al., 2004), and are commonly observed in pediatric cases of BD. Indeed, psychotic symptoms have been reported in approximately 42% of PBD participants (Kowatch et al., 2005). Similarly, in a cross-sectional study comprising 226 participants with PBD, the presence of psychotic symptoms was associated with an increased number of mood episodes, psychiatric hospitalizations, psychiatric comorbidities, functional impairment, and a family history of psychosis (Hua et al., 2011). In recent years, an increasing number of studies have investigated the association between psychotic symptoms and childhood trauma (Etain et al., 2017; Romero et al., 2009; van Bergen et al., 2018). For instance, in a meta-analysis of 30 studies, childhood trauma was associated with unfavorable clinical outcomes in BD, such as a higher prevalence of psychosis, greater mood recurrence and severity, early onset, suicide attempts, and psychiatric comorbidities (Agnew-Blais and Danese, 2016). A cross-sectional study in PBD reported a twofold risk of psychotic symptoms among individuals with a history of physical and/or sexual abuse (Romero et al., 2009). Most studies of trauma in BD have focused on sexual, physical, or emotional abuse, and/or neglect. Although increasing research has focused on bullying as a traumatic event (van Dam et al., 2012), no studies thus far have evaluated the association between bullying and psychotic symptoms in BD. Bullying can be defined as an aggressive act, where there is an imbalance of power; that is, the victim cannot defend him/herself, with some element of repetition (Sourander et al., 2007). Birth cohort studies have indicated that bullying victimization at school is a risk factor for psychotic symptoms in both early adolescence (Kelleher et al., 2013; Schreier et al., 2009) and adulthood (Catone et al., 2015; Lereya et al., 2015) among the general population. However, the majority of epidemiological studies focus on subthreshold psychotic experiences that are not necessarily associated with a formal psychotic episode diagnosis. In clinical
samples, despite the higher prevalence of being bullied among participants, relative to controls, the association between bullying and psychotic symptoms remains unclear. In a nationwide birth cohort study of 5034 Finnish children, bullying victimization at 8 years old was associated with psychosis in adulthood. When controlling for childhood psychiatric symptoms, the association with psychosis was no longer significant (Sourander et al., 2016). In another study comprising 508 psychiatric inpatient adolescents, victims of bullying showed two to three times the prevalence of psychotic disorders relative to bullies or bully-victims (i.e. a person who is both a bully and a victim of bullying), but the association was not statistically significant (Luukkonen et al., 2010). Conversely, a longitudinal study found that youth with Attention Deficit Hyperactivity Disorder (ADHD) who were bullied at the age of 10 had a twofold risk of psychotic experiences at the age of 12 (Hennig et al., 2016). In the present study, we aim to assess the association between psychosis and bullying victimization in a sample of outpatients with PBD. We hypothesize that PBD participants with a lifetime history of psychosis will show higher rates of bullying, compared to participants without a history of psychosis. We also examined the association between clinical and demographic variables and psychosis. Clinical variables were selected based on previous studies with positive findings on psychosis in BD, such as anxiety disorders, rapid cycling, and suicidal behavior (Caetano et al., 2006; Hua et al., 2011). Considering the association previously found between psychotic symptoms and bullying in ADHD participants, we also examined this potential relationship (Hennig et al., 2016). Additionally, in a supplementary analysis, we developed a machine learning model to identify the most relevant variables to predict a risk of lifetime psychosis among participants with PBD.
METHODS
Participants The present cross-sectional study included 64 children and adolescents with BD from the outpatient Program for Children and Adolescents with Bipolar Disorder (ProCAB) of the Hospital de Clínicas of Porto Alegre, Brazil, between January 2012 and November 2017.
Participants were included if they were between 5 - 17 years old with a diagnosis of BD I, II, or not otherwise specified (NOS), based on the text revision of the fourth edition Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria. Participants were excluded if they had a clinical diagnosis of schizophrenia, pervasive developmental disorder, active substance abuse at intake, or if they were unable to answer the questionnaires.
Procedures The institutional ethics committee approved the study protocol. Parental written informed consent and verbal assent were obtained from the children. Diagnostic assessment relied on a screening interview based on DSM-IV criteria administered by trained investigators. All diagnoses, including rapid cycling, were confirmed with the semi-structured interview Schedule for Affective Disorders and Schizophrenia for School Age Children-Present and Lifetime version (KSADS- PL) (Kaufman et al., 1997). Additional depression and manic symptom items were derived from the Washington University in St. Louis Kiddie Schedule for Affective Disorders (Wash-U-KSADS). This version offers the advantage of focused diagnosis in pre- pubertal and early adolescent manifestations of mania with and without rapid cycling (Geller et al., 2001). Data was collected from the caregivers and participants, and it was reviewed and confirmed by a trained child psychiatrist.
2.2. Measures
Psychosis K-SADS-PL-W was used to assess psychiatric diagnosis and the presence of lifetime psychosis at intake. K-SADS-PL rates hallucinations and delusions on a scale from 1 to 3 (1 = absent, 2 = suspected or likely, 3 = definite). Participants who were classified as “definite” in current or previous hallucinations or delusions were determined to have lifetime psychosis. Bullying
Bullying victimization was assessed through a clinical interview by trained psychiatrists, which involved asking participants if they had experienced psychological or physical bullying more than once in their life. This question was asked to the participants and caregivers in the screening interview. A positive response from either the caregiver or child was taken as evidence of a history of bullying victimization. When the participant and caregiver had conflicting answers of being bullied or when they did not fully understand the concept of bullying, more explanations and examples of bullying victimization were provided by the evaluator. Moreover, participants and caregivers were asked to provide examples of the conflicting situations considered as bullying. Based on this information, the evaluator had the final decision.
Suicidal behavior Lifetime suicidal behavior was evaluated at intake using the Columbia-Suicide Severity Rating Scale (C-SSRS) screen version, which assesses the presence of suicidal behavior using five questions. We considered suicidal behavior to be present if “yes” was answered to any of following 5 items: 1) Preparatory acts or suicidal behavior, 2) Actual attempt, 3) Interrupted attempt, 4) Aborted attempt, or 5) Suicide.
Socio-demographics and clinical characteristics Demographic and clinical characteristics of the disorder such as sex, ethnicity, age, and socioeconomic status (SES) were collected in the screening interview. Family history of psychiatric disorders in first degree relatives was collected with a clinical interview. We included in the analysis family history of BD; we did not include psychotic disorders due the small number of cases. The Clinical Global Impression-Severity (CGI-S) scale was used to determine BD symptom severity at the time of assessment. SES was assessed using a validated Brazilian questionnaire, the Brazilian Association of Research Companies questionnaire (http://www.abep.org/criterio-brasil). This scale assesses acquisition of material assets, level of education of the head of household, and access to public services; classifying families into five distinct socioeconomic groups (A, B, C, D or E). In
the present study, groups A/B and C/D had similar socioeconomic characteristics, and therefore, classes were grouped in two categories, Middle-High (A, B) and Middle-Low (C, D). There was no family included in the study within the lowest level (E). Statistical analysis Statistical analyses were conducted using the program R software 1.0.136 (https://www.R-project.org/). Descriptive analyses were reported as means (standard deviations) or absolute and relative frequencies. We divided participants into two groups: those with a lifetime history of psychosis, and those without a history of psychosis. We used the chi-square or Student t tests to analyze demographic, clinical, and school related variables between these two groups. Any comparisons in the bivariate model that were significant at p < 0.1 were entered in multiple logistic regression analyses with a history of psychosis as the dependent variable. All statistics were two-tailed, and significance was set at p-value ≤0.05. Odds ratios (OR) and confidence intervals (CI) were computed.
Machine learning analysis Linear associations between risk factors and clinical outcomes are important in understanding chronic diseases, however, traditional statistical approaches explore the linear relationship between variables at a group-level (Bzdok et al., 2018). Machine learning approaches can be used in problems that are difficult to address using traditional statistics and have increasingly been used in psychiatry. This is because machine learning models can assume a complex relationship between variables, including non-linear patterns, and allow us to make individualized predictions (Dwyer et al., 2018). As such, machine learning may facilitate predictive models that can be used in clinical practice (Passos et al., 2016; Passos and Mwangi, 2018; Sartori et al., 2018; Wu et al., 2017). In the present study, we built a supervised machine learning model to predict psychosis among participants with PBD. The predictors included in the machine learning model were the same sociodemographic and clinical characteristics used in the classical bivariate analyses. The machine learning analysis was conducted using R software (Version R 3.3.1) and R Studio (Version 0.99.902), using the package CARET. The CARET package (short
for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: 1) data splitting; 2) preprocessing; 3) feature selection model tuning using resampling; 4) variable importance estimation; 5) as well as other functionality. It is worth mentioning that the caret package contains numerous tools for developing predictive models using the rich set of models available in R. There are many different modeling functions in R. Some have different syntax for model training and/or prediction. The package started off as a way to provide a uniform interface for the functions themselves, as well as a way to standardize common tasks (such parameter tuning and variable importance). Missing data was inputted after multiple imputations (m=5, maxit=50, seed=500) with the R package mice (Buuren and Groothuis- Oudshoorn, 2011). We used a naive bayes classifier with a high-density kernel to train the model. Naive bayes classifiers are a family of probabilistic algorithms based on the Bayes' theorem, using the maximum a posteriori (MAP) decision rule. We used leave-one-out cross validation (LOOCV), ROC (receiver operating characteristic) and AUC (area under the curve) to estimate model performance, as well as accuracy and kappa. LOOCV consists of training the algorithm with all participants but one, testing the model on the omitted participant, then repeating the process until all participants were used during algorithm testing at least once. This approach ensures that our model is being tested in an “unseen” dataset in order to avoid overfitting (Librenza-Garcia et al., 2017). LOOCV has become the standard for estimating model performance for studies with small sample sizes (Vehtari et al., 2017).
RESULTS A total of 64 participants were included in the present study, of which 32.8% (n=21) had a history of psychosis. Of those, 71.4% (n=15) reported hallucinations and 52.4% (n=11) delusions. Table 1 shows the sociodemographic characteristics and prevalence of bullying in the nonpsychotic and psychotic PBD participants. Clinical characteristics are presented in Table 2. In the bivariate analyses, psychosis was associated with Middle-Low SES (p=0.04); higher scores in CGI- S, which indicate greater symptom severity of BD (p=0.02); suicidal behavior (p=0.006); and being bullied (p=0.002). No differences were found between
nonpsychotic and psychotic participants for the other variables assessed, however there was a statistical trend toward an association between psychosis and caucasian ethnicity (p=0.06). In the psychotic PBD group, 35,7% (n=5) of the participants with a lifetime history of bullying victimization also had a lifetime history of suicidal behavior. In the non-psychotic PBD group, none of the participants with a lifetime history of bullying victimization reported suicidal behavior. We also compared the frequencies of suicidal behavior in bullying (n=25) and non-bullying (n=39) participants. The prevalence of suicidal behavior among those with history of bullying victimization was 20% (n=5). Of those, 100% had history of psychosis. The prevalence of suicidal behavior in the non-bullying group was 17.9% (n=7), with 42.8% (n=3) reporting also psychosis. However, due to the small number of participants in the sample, it was not possible to analyze the difference between groups. Considering the positive relationship between bullying and psychosis, and inability to infer causation due to the cross-sectional design, we also performed an analysis comparing those with and without a history of bullying victimization. Our objective was to analyze whether SES, severity of PBD, and suicidal behavior were also associated with bullying. SES (X² = 0.30, p=0.58), CGI-S scale (t=-1.36, p=0.12), and suicidal behavior (X²= 0.04, p= 0.84) were not statistically significant between bullying and non-bullying participants.
Multivariate analysis We computed logistic regression analyses, with psychotic symptoms as the dependent variable, and clinical variables significant at p < 0.1 from the bivariate analyses entered as the independent variables, as listed in Table 3. We included bullying, suicidal behavior, SES, ethnicity, and severity of the disorder as independent variables. Only bullying and suicidal behavior remained associated with lifetime psychotic symptoms in PBD.
Predictive model of lifetime psychosis The Naive Bayes algorithm differentiated participants with psychosis in PBD from those without psychosis in PBD with 75% accuracy (CI95%=6.66-8.23, p=0.03, 77.91%
sensitivity, 69.05% specificity). The other performance measures are listed in Table 4. Figure 1 demonstrates the variable importance for predicted probability of lifetime psychosis. The most relevant predictive variables in differentiating participants with psychosis were bullying, CGI-S scale, and suicidal behavior. The AUC for the model was 0.86 and the ROC curve for the model is represented in Figure 2.
DISCUSSION To our knowledge, this is the first study to investigate the relationship between psychosis and bullying victimization in children and adolescents with BD. Psychosis was associated with bullying, suicidal behavior, low SES, and symptom severity of BD. In the multivariate analysis, only bullying and suicidal behavior remained significant. In the machine learning model, the most relevant predictive variables in differentiating the participants with psychosis were bullying, BD symptom severity, and suicidal behavior. It is important to mention that machine learning and most traditional statistics are rooted in different mathematical contexts, and largely judge evidence of an effect in different ways. Therefore, an effect that is observed to be statistically significant using a p-value will not necessarily yield a high predictive accuracy in new independent data, and vice versa (Bzdok et al., 2018). Classical statistical analysis allows us to compute a quantitative measure of confidence that a discovered relationship describes a 'true' effect that is unlikely the result of statistical noise. By contrast, machine learning uses general- purpose learning algorithms to find patterns and provide predictions in a dataset. Given that we observed similar results using both traditional statistical and machine learning methods, this lends support to the notion that we observed a true effect in our study. The positive association between psychosis and bullying victimization in PBD participants is in accordance with prior cross-sectional and longitudinal studies in youth among community samples (Arseneault et al., 2011; Kelleher et al., 2013; Schreier et al., 2009; Singham et al., 2017). However, among clinical studies, the results are inconclusive (van Dam et al., 2012), and a majority of the participants included had psychotic disorders (Bebbington et al., 2004; Trotta et al., 2013). In a cross-sectional study in adults, prior repeated peer victimization was reported more frequently by participants with a psychotic disorder, as
compared to those without a psychotic disorder, but the effect disappeared after adjusting for other negative life events (Bebbington et al., 2004). In another cross-sectional study with adults, participants with psychotic disorders reported more bullying victimization, when compared to controls. This association remained significant after adjustments only in those with schizophrenia-spectrum disorders but did not remain significant in those with affective disorders. However, the negative finding may be related to the small sample size in the affective group (Trotta et al., 2013). Psychological and affective mechanisms may be involved in the association between psychosis with bullying victimization and suicidal behavior. Affective dysregulation and impulsivity/hostility are associated with childhood trauma in BD (Aas et al., 2017; Etain et al., 2017), and appears to mediate the association between childhood trauma and risk of a more severe clinical trajectory, including suicide attempts (Etain et al., 2017) and psychosis (Fisher et al., 2013; Myin-Germeys and van Os, 2007; Winsper et al., 2017). In a community sample study, children with higher levels of dysregulated behaviour were prone to the development of Borderline Personality Disorder symptoms, depressive symptoms, and psychosis in early adolescence, when exposed to bullying victimization (Winsper et al., 2017). Traumatic experiences resulting in maladaptive cognitive schemas characterized by negative beliefs about self, world, and others, as well as an increased suspiciousness of others, may lead to the development of paranoid delusions (Read et al., 2005). Children who are victims of bullying may also be vulnerable to a ‘victim schema’ that demonstrate an implicit cognitive association of themselves as victims and develop an attributional bias whereby victims may inaccurately attribute hostile intent in social situations perceived as threatening (Rosen et al., 2007). In addition, Fisher et al. (2013) evidenced that the association between bullying victimization and psychosis was mediated by believing that the outcome of events in one’s life are external forces beyond their control. Alternatively, being at risk for psychosis might affect the perception of being bullied. For instance, individuals with paranoia may experience real and overestimated threat as bullying (Jack and Egan, 2018). There is also evidence that some individuals with BD have a poor genetic susceptibility to environmental stress (Aas et al., 2016), such as bullying victimization during critical years of development in childhood or adolescence. This poor stress response coupled with prior victimization may not only make the individual more vulnerable to further bullying, but also
precipitate a more pernicious manifestation of the disorder, including psychosis. Moreover, internalizing and externalizing behaviors that emerge during the prodromal phases of psychosis may contribute to youth vulnerable to bullying victimization (Arseneault et al., 2011; Singham et al., 2017). Genetic factors appear to influence these behaviors (Marceau et al., 2013; Schoeler et al., 2019), and genetic influences can be accounted for over two thirds of individual differences in bullying victimization (Ball et al., 2008). For instance, Schoeler et al. (2019) evidenced that genetic predisposition for certain externalizing and internalizing behaviors were associated with bullying, and Shakoor et al. (2015) found that individuals vulnerable to bullying are also phenotypically susceptible to paranoia via a shared genetic propensity(Shakoor et al., 2015). Factors beyond the individual may also influence the relationship between psychosis and bullying victimization, including other types of childhood trauma (Bowes et al., 2009), family environment (Jansen et al., 2011), and SES (Bowes et al., 2009a; Jansen et al., 2011). In our study, low SES was also associated with psychosis, but this association did not remain after logistic regression. BD participants with a history of childhood trauma may be less likely to have protective factors, such as family and social support, increasing the vulnerability to the negative long-term effects of the trauma (Aas et al., 2016). The statistical association identified between psychosis and suicidal behavior in PBD is in accordance with previous cross-sectional studies of children and adolescents with BD (Caetano et al., 2006; Goldstein et al., 2005) Conversely, Goldstein et al. (2012) performed a longitudinal study and observed no relationship between history of psychosis at intake with suicide attempts at 5-year follow-up. On the other hand, community based-studies reported an association between psychosis and an elevated risk of suicidal ideation and behavior (Bromet et al., 2017; Kelleher et al., 2012). In the study from Bromet et al. (2017), psychosis was associated with an increased 2-fold risk (after adjusting for psychiatric disorders) of subsequent suicidal ideation and behavior across all life stages, with the strongest association observed in individuals 12 years and younger. Kelleher et al. (2012) reported an increased risk for suicidal behavior in the general adolescent population and in adolescents with (nonpsychotic) psychiatric disorder. In our study, around one third of the participants with both psychosis and bullying victimization reported suicidal behavior. No participant without psychosis with history bullying victimization reported suicidal behavior. After separating the
sample into participants with and without a history of bullying victimization, we found no statistical association between suicidal behavior and bullying victimization, contrary to many studies (Bang and Park, 2017; Copeland et al., 2013; Geoffroy et al., 2018). For example, adolescents with a history of bullying victimization were more likely to be diagnosed with depression, psychosis, and suicide attempts in a cross-sectional study. The association between bullying and suicide attempts remained statistically significant even after adjusting for depression and psychosis (Bang and Park, 2017). Our study presents with some limitations. First, data was collected retrospectively, and recall bias is possible. Moreover, children might have under-reported bullying or suicidal behavior due to feelings of shame and self-blame. The young age of our participants may also account for some occasional misreporting that may have also occurred. We evaluated bullying victimization in a clinical interview with the participants and parents. No previously validated instrument was used to assess this. When participants did not fully understand the concept of bullying, the evaluator provided further examples and a more detailed explanation. However, the definition of bullying is partly subjective, and it may be increased without a validated instrument, which could lead to over-reporting or under-reporting (Gromann et al., 2013). Second, the small sample size restricted the statistical power of the study. Given the limited power, a lack of statistical significance with other variables does not mean that the covariates cannot have any confounding or mediating effect. Besides that, the small sample size in the different categories of interest increases the risk for making Type I errors. Therefore, our findings need to be interpreted carefully. Third, since this is a cross-sectional study, the possibility of reverse causality cannot be discounted. We are unaware if bullying victimization or suicidal behavior are associated with later onset of psychosis in PBD or whether psychosis in PBD tends to occur before bullying victimization or before suicidal behavior. It is also possible that psychosis and bullying may both emerge during a prodromal phase of the PBD. However, studies demonstrated that there is a bidirectional relationship between childhood trauma and psychotic experiences, with each independently predicting the other (Kelleher et al., 2013; Mcgrath et al., 2017), and psychosis may predict suicidal behavior even before meeting criteria for psychiatric disorders (Bromet et al., 2017).
Fourth, we also did not investigate the possible subtypes, intensity, or frequency of bullying. A dose–response relationship between frequency and intensity of bullying victimization with levels of psychotic symptoms have been reported. (Arseneault et al., 2011; Kelleher et al., 2013). We did not investigate other types of childhood traumas that are also associated with psychosis in BD, such as sexual, physical, or emotional abuse (Agnew-Blais and Danese, 2016; Etain et al., 2017; Romero et al., 2009; van Bergen et al., 2018). The occurrence of additional negative childhood traumas in this period appears to intensify the risk of later developing psychosis (Mcgrath et al., 2017). Besides that, our study was carried out in an outpatient clinic of a tertiary hospital, so it may be different depending on the context in which the evaluation takes place. Despite these limitations, our study has several strengths. This is the first study to investigate the association of psychosis and bullying in PBD participants. The prevalence of psychosis, suicidal behavior, and bullying in our sample were similar to those reported in the literature. We used two different analytic approaches in the study, classical statistics and machine learning techniques. Additionally, we assessed for some potential confounding effects (i.e. SES, family history of BD, and comorbidities). We used validated instruments to assess psychiatric diagnosis and suicidal behavior in children and adolescents. Nonetheless, the inclusion of participants at all levels of symptom severity, treatment, and phase of illness allows for a broader generalization in outpatient settings. Bullying victimization may be viewed not only as a cause of psychosis but may also in part represent a “symptom” of pre-existing vulnerabilities (Singham et al., 2017). The integration of the school in the treatment of PBD may impact positively in the outcomes of the disorder, even if psychosis precedes bullying victimization. A community sample study reported a decrease in psychosis in 3 months after the school bullying had ceased (Kelleher et al., 2013). School bullying programs focusing on potential groups of vulnerable children and adolescents, as those with PBD, instead of only those who bully, may reduce the burden of bullying victimization. Programs at school focusing on negative cognitions and in increasing resilience among victims of bullying is one example (Arseneault, 2018). The role of family support is also important to minimize the negative effects of bullying victimization (Bowes et al., 2009). Parent support for child autonomy may also contribute to reduce bullying during early school years (Rajendran et al., 2016).
In summary, in our study psychosis was associated with bullying victimization and suicidal behavior in children and adolescents with BD. Future longitudinal studies including a larger sample, other potential confounding, or mediating factors could help to better understand the association between psychosis and bullying in PBD.
Table 1. Sociodemographic and bullying characteristics comparing nonpsychotic and psychotic PBD participants
All sample
Nonpsychotic PBD
(n=64)
(n=43)
Variable
N (%)
N (%)
Age (mean±SD)
12 (±3.43)
11.63 (± 3.38)
Sex (female)
28 (43.7)
Ethnicity (Caucasian)
Psychotic PBD (n=21) Effect size
p-value
12.76 (± 3.49)
t = -1.23
0.225
16 (37.2)
12 (57.1)
x² = 2.27
0.131
49 (76.5)
30 (69.7)
19 (90.4)
x² = 3.37
0.066
Middle-low SES
28 (43.7)
15 (34.8)
13 (61.9)
x²= 4.19
0.04*
Bullying
25 (39)
11 (25.6)
14 (66,6%)
x²= 10.00
0.001**
N (%)
PBD = pediatric bipolar disorder; SD= standard deviations; SES = Socioeconomic status. * p <.0.05; ** p < 0.01
Table 2. Clinical characteristics comparing nonpsychotic and psychotic PBD participants
All sample Variables (n=64)
Nonpsychotic PBD (n=43) N (%)
Psychotic PBD Effect size (n=21)
p-value
N (%) Family History of BD
N (%)
27 (42.2)
21 (48.8)
6 (28.6)
X²= 2.38
0.12
39 (60.9)
24 (55.8)
15 (71.4)
4 (6.2)
4 (9.3)
0
X² = 2.69
0.26
21 (32.8)
15 (34.9)
6 (28.6)
Rapid cycling
26 (40.6)
15 (34.9)
11 (52.4)
Anxiety disorders
32 (50)
21 (48.8)
11 (52.4)
X²= 0.07
0.79
ADHD
41 (64.1)
30 (69.8)
11 (52.4)
X²= 1.85
0.17
Suicidal Behavior
12 (18.7)
4 (9.3)
8 (38.1)
X²= 7.68
0.005**
CGI-S (mean±SD)
3.64 (±1.43)
3.35 (±1.34)
4.24 (± 1.45)
t = -2.36
0.023*
BD type Type I Type II NOS X² = 1.79 0.18
PBD = pediatric bipolar disorder; NOS= not otherwise specified; ADHD= Attention Deficit Hyperactivity Disorder; CGI-S = Clinical Global Impression-Severity scale; SD= standard deviation. * p <.0.05; ** p < 0.01.
Table 3. Logistic Regression model with psychosis as the dependent variable in PBD participants
Variable
z-value
P value
OR (adjusted)
95%CI
Bullying
2.807
0.005**
7.4
1.9 – 34.6
Suicidal behavior
2.158
0.03*
6.6
1.3 – 41.7
Middle-low SES
1.633
0.1
3
0.8 – 12.4
Ethnicity (Caucasian)
1.412
0.15
3.7
0.7 – 2.1
CGI-S
0.989
0.32
1.27
0.8 - 2
SES = Socioeconomic status; CGI-S = Clinical Global Impression-Severity scale CI = confidence interval; OR = odds ratio; p <.0.05; ** p < 0.01 p value for the logistic regression model = 0.00012
Table 4. Performance measures of Naïve Bayes algorithm on differentiating nonpsychotic and psychotic Pediatric Bipolar Disorder participants
Naive bayes
Sensitivity
Specificity
PPV
NPV
77.91%
69.05%
83.75%
60.42%
PPV = positive predictive value; NPV = negative predictive value
Balanced accuracy 73.48%
ADHD= Attention Deficit Hyperactivity Disorder; CGI-S = Clinical Global Impression-Severity scale
Figure 1. Variable importance for predicted probability of lifetime psychosis among participants with Pediatric Bipolar Disorder based on the Naive bayes algorithm.
Figure 2. Receiver operating characteristic (ROC) curve for predicted probability of lifetime psychosis among participants with Pediatric Bipolar Disorder based on the Naive bayes algorithm. We used leaveone-out cross-validation.
Author Statement We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We further confirm that any aspect of the work covered in this manuscript that has involved either experimental animals or human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from [email protected]
Contributors All authors contributed to the final version of the manuscript and have approved the final article. Their individual contribution to the article were:
Jandira Rahmeier Acosta: conception and design, acquisition of data, drafting of manuscript, critical revision, statics analyses.
Franco Zortea: conception of the idea, construct the data bank and statics analyses.
Diego Librenza-Garcia: machine learning analyses, drafting of manuscript, critical revision.
Devon Watts: drafting of manuscript, critical revision
Ana Paula Francisco: acquisition of data, drafting of manuscript, critical revision.
Bruno Raffa: acquisition of data, critical revision
Gledis Lisiane Motta: conception of the idea, acquisition of data, critical revision
André Kohmann: acquisition of data, critical revision
Fabiana Eloisa Mugnol: acquisition of data, critical revision.
Silza Tramontina: Supervision of the collection of the data from the patients, critical revision and drafting of manuscript.
Ives Cavalcante Passos: conception and design of the idea, supervision of the article organization and the data collection, critical revision and drafting of manuscript.
Declarations of interest: Ives Cavalcante Passos has received consulting fees from Torrent/Omnifarma. He has received research grants from CNPq/CAPES. Ana Paula Francisco has been receiving a scholarship that is partially financed by Shire. The remaining authors does not have competing interests to declare.
Funding: ProCAB was partially supported by FIPE/HCPA (Fund of Incentive to Research at Hospital de Clínicas de Porto Alegre).
Acknowledgements: None
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Bullying and psychotic symptoms in youth with bipolar disorder Jandira Rahmeier Acosta , Diego Librenza-Garcia , Devon Watts , ´ Ana Paula Franciscoa , Franco Zortea , Bruno Raffa , Andre´ Kohmann , Fabiana Eloisa Mugnol , Gledis Lisiane Motta , Silza´ Tramontina , Ives Cavalcante Passos PII: DOI: Reference:
S0165-0327(19)30719-0 https://doi.org/10.1016/j.jad.2019.11.101 JAD 11348
To appear in:
Journal of Affective Disorders
Received date: Revised date: Accepted date:
20 March 2019 27 September 2019 21 November 2019
Please cite this article as: Jandira Rahmeier Acosta , Diego Librenza-Garcia , Devon Watts , ´ Ana Paula Franciscoa , Franco Zortea , Bruno Raffa , Andre´ Kohmann , Fabiana Eloisa Mugnol , Gledis Lisiane Motta , Silza´ Tramontina , Ives Cavalcante Passos , Bullying and psychotic symptoms in youth with bipolar disorder, Journal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.11.101
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HIGHLIGHTS
Study participants (N = 64) included those who had a lifetime history of psychotic symptoms (n = 21; 32.8%), and those who reported being bullied (n = 24; 37.5%).
A lifetime history of psychotic symptoms was associated with bullying, suicidal behavior, low socioeconomic status, and higher Clinical Global Impression-Severity (CGI-S) scale scores.
Only bullying and suicidal behavior remained significant after adjusting for potential confounders.
In a supplementary analysis, using a supervised machine learning model, the most relevant predictive variables in differentiating participants with vs without psychotic symptoms were bullying, CGI-S scale scores, and suicidal behavior.
Study limitations include the small sample size, use of a cross-sectional design, and the generalizability of findings beyond the outpatient clinical sample.
Bullying and psychotic symptoms in youth with bipolar disorder
Jandira Rahmeier Acosta, MD a,b; Diego Librenza-Garcia, MD b,c Devon Wattsc , Ana Paula Franciscoa,b,c, Franco Zórteaa; MD; Bruno Raffa MD a,b, André Kohmann MDa; Fabiana Eloisa Mugnol, MDa; Gledis Lisiane Motta, MDa; Silzá Tramontina, MD, PhDa; Ives Cavalcante Passos, MD, PhDd
a. Program for Children and Adolescents with Bipolar Disorder (ProCAB), Division of Child and Adolescent Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil. b. Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento – Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil; c. Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada. d. Laboratory of Molecular Psychiatry and Bipolar Disorder Program, HCPA, UFRGS, Porto Alegre, RS, Brazil.
ABSTRACT Background: Childhood trauma is associated with psychosis in adults with bipolar disorder (BD). Although bullying represents a widespread form of childhood trauma, no studies thus far have investigated the association of bullying and psychosis in pediatric bipolar disorder (PBD). We aim to examine the association between psychosis in PBD with bullying victimization. Methods: We included 64 children and adolescents (age± mean= 12±3.43) outpatients with BD spectrum disorders. Psychiatric diagnoses were assessed with the semi- structured interview Schedule for Affective Disorders and Schizophrenia for School Age Children-Present
and Lifetime (KSADS-PL) version with additional depression and manic symptom items derived from the Washington University in St. Louis Kiddie Schedule for Affective Disorders (WASH-UKSADS). Bullying, demographic, and clinical variables were assessed during the clinical interview. Results: A lifetime history of psychotic symptoms was associated with bullying (p=0.002), suicidal behavior (p=0.006), low socioeconomic status (p=0.04), and severity of PBD (p=0.02). Only bullying (OR=7.3; 95%CI= 2-32) and suicidal behavior (OR=7.6; 95%CI=1.5–47.8) remained significant after adjustment for confounders. In a supplementary analysis, we developed a model using supervised machine learning to identify the most relevant variables that differentiated participants with psychotic symptoms, which included bullying, Clinical Global Impression-Severity scale (CGI-S), and suicidal behavior (accuracy=75%, [p=0.03]; sensitivity=77.91%; specificity=69.05%; area under the curve [AUC]= 0.86). Limitations: Small sample, cross-sectional design, and generalizability of findings beyond the outpatient clinical sample. Conclusions: Findings underscore the importance of assessing bullying in PBD participants. Future longitudinal studies with larger samples are needed to replicate our findings and determine causality.
INTRODUCTION According to epidemiological data from several countries, pediatric bipolar disorder (PBD) has an overall prevalence of 3.9% across the spectrum of the disorder (van Meter et al., 2019) and previous reports suggest that pediatric onset of bipolar disorder (BD) presents with a more severe course of illness than BD that manifests in adulthood (Duffy et al., 2017); Kapczinski et al., 2017; Leverich et al., 2007). Furthermore, psychotic symptoms such as hallucinations and delusions are associated with pernicious outcomes (Birmaher et al., 2006; Geller et al., 2004), and are commonly observed in pediatric cases of BD. Indeed, psychotic symptoms have been reported in approximately 42% of PBD participants (Kowatch et al., 2005). Similarly, in a cross-sectional study comprising 226 participants with PBD, the presence of psychotic symptoms was associated with an increased number of mood episodes, psychiatric hospitalizations, psychiatric comorbidities, functional impairment, and a family history of psychosis (Hua et al., 2011). In recent years, an increasing number of studies have investigated the association between psychotic symptoms and childhood trauma (Etain et al., 2017; Romero et al., 2009; van Bergen et al., 2018). For instance, in a meta-analysis of 30 studies, childhood trauma was associated with unfavorable clinical outcomes in BD, such as a higher prevalence of psychosis, greater mood recurrence and severity, early onset, suicide attempts, and psychiatric comorbidities (Agnew-Blais and Danese, 2016). A cross-sectional study in PBD reported a twofold risk of psychotic symptoms among individuals with a history of physical and/or sexual abuse (Romero et al., 2009). Most studies of trauma in BD have focused on sexual, physical, or emotional abuse, and/or neglect. Although increasing research has focused on bullying as a traumatic event (van Dam et al., 2012), no studies thus far have evaluated the association between bullying and psychotic symptoms in BD. Bullying can be defined as an aggressive act, where there is an imbalance of power; that is, the victim cannot defend him/herself, with some element of repetition (Sourander et al., 2007). Birth cohort studies have indicated that bullying victimization at school is a risk factor for psychotic symptoms in both early adolescence (Kelleher et al., 2013; Schreier et al., 2009) and adulthood (Catone et al., 2015; Lereya et al., 2015) among the general population. However, the majority of epidemiological studies focus on subthreshold psychotic experiences that are not necessarily associated with a formal psychotic episode diagnosis. In clinical
samples, despite the higher prevalence of being bullied among participants, relative to controls, the association between bullying and psychotic symptoms remains unclear. In a nationwide birth cohort study of 5034 Finnish children, bullying victimization at 8 years old was associated with psychosis in adulthood. When controlling for childhood psychiatric symptoms, the association with psychosis was no longer significant (Sourander et al., 2016). In another study comprising 508 psychiatric inpatient adolescents, victims of bullying showed two to three times the prevalence of psychotic disorders relative to bullies or bully-victims (i.e. a person who is both a bully and a victim of bullying), but the association was not statistically significant (Luukkonen et al., 2010). Conversely, a longitudinal study found that youth with Attention Deficit Hyperactivity Disorder (ADHD) who were bullied at the age of 10 had a twofold risk of psychotic experiences at the age of 12 (Hennig et al., 2016). In the present study, we aim to assess the association between psychosis and bullying victimization in a sample of outpatients with PBD. We hypothesize that PBD participants with a lifetime history of psychosis will show higher rates of bullying, compared to participants without a history of psychosis. We also examined the association between clinical and demographic variables and psychosis. Clinical variables were selected based on previous studies with positive findings on psychosis in BD, such as anxiety disorders, rapid cycling, and suicidal behavior (Caetano et al., 2006; Hua et al., 2011). Considering the association previously found between psychotic symptoms and bullying in ADHD participants, we also examined this potential relationship (Hennig et al., 2016). Additionally, in a supplementary analysis, we developed a machine learning model to identify the most relevant variables to predict a risk of lifetime psychosis among participants with PBD.
METHODS
Participants The present cross-sectional study included 64 children and adolescents with BD from the outpatient Program for Children and Adolescents with Bipolar Disorder (ProCAB) of the Hospital de Clínicas of Porto Alegre, Brazil, between January 2012 and November 2017.
Participants were included if they were between 5 - 17 years old with a diagnosis of BD I, II, or not otherwise specified (NOS), based on the text revision of the fourth edition Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria. Participants were excluded if they had a clinical diagnosis of schizophrenia, pervasive developmental disorder, active substance abuse at intake, or if they were unable to answer the questionnaires.
Procedures The institutional ethics committee approved the study protocol. Parental written informed consent and verbal assent were obtained from the children. Diagnostic assessment relied on a screening interview based on DSM-IV criteria administered by trained investigators. All diagnoses, including rapid cycling, were confirmed with the semi-structured interview Schedule for Affective Disorders and Schizophrenia for School Age Children-Present and Lifetime version (KSADS- PL) (Kaufman et al., 1997). Additional depression and manic symptom items were derived from the Washington University in St. Louis Kiddie Schedule for Affective Disorders (Wash-U-KSADS). This version offers the advantage of focused diagnosis in pre- pubertal and early adolescent manifestations of mania with and without rapid cycling (Geller et al., 2001). Data was collected from the caregivers and participants, and it was reviewed and confirmed by a trained child psychiatrist.
2.2. Measures
Psychosis K-SADS-PL-W was used to assess psychiatric diagnosis and the presence of lifetime psychosis at intake. K-SADS-PL rates hallucinations and delusions on a scale from 1 to 3 (1 = absent, 2 = suspected or likely, 3 = definite). Participants who were classified as “definite” in current or previous hallucinations or delusions were determined to have lifetime psychosis. Bullying
Bullying victimization was assessed through a clinical interview by trained psychiatrists, which involved asking participants if they had experienced psychological or physical bullying more than once in their life. This question was asked to the participants and caregivers in the screening interview. A positive response from either the caregiver or child was taken as evidence of a history of bullying victimization. When the participant and caregiver had conflicting answers of being bullied or when they did not fully understand the concept of bullying, more explanations and examples of bullying victimization were provided by the evaluator. Moreover, participants and caregivers were asked to provide examples of the conflicting situations considered as bullying. Based on this information, the evaluator had the final decision.
Suicidal behavior Lifetime suicidal behavior was evaluated at intake using the Columbia-Suicide Severity Rating Scale (C-SSRS) screen version, which assesses the presence of suicidal behavior using five questions. We considered suicidal behavior to be present if “yes” was answered to any of following 5 items: 1) Preparatory acts or suicidal behavior, 2) Actual attempt, 3) Interrupted attempt, 4) Aborted attempt, or 5) Suicide.
Socio-demographics and clinical characteristics Demographic and clinical characteristics of the disorder such as sex, ethnicity, age, and socioeconomic status (SES) were collected in the screening interview. Family history of psychiatric disorders in first degree relatives was collected with a clinical interview. We included in the analysis family history of BD; we did not include psychotic disorders due the small number of cases. The Clinical Global Impression-Severity (CGI-S) scale was used to determine BD symptom severity at the time of assessment. SES was assessed using a validated Brazilian questionnaire, the Brazilian Association of Research Companies questionnaire (http://www.abep.org/criterio-brasil). This scale assesses acquisition of material assets, level of education of the head of household, and access to public services; classifying families into five distinct socioeconomic groups (A, B, C, D or E). In
the present study, groups A/B and C/D had similar socioeconomic characteristics, and therefore, classes were grouped in two categories, Middle-High (A, B) and Middle-Low (C, D). There was no family included in the study within the lowest level (E). Statistical analysis Statistical analyses were conducted using the program R software 1.0.136 (https://www.R-project.org/). Descriptive analyses were reported as means (standard deviations) or absolute and relative frequencies. We divided participants into two groups: those with a lifetime history of psychosis, and those without a history of psychosis. We used the chi-square or Student t tests to analyze demographic, clinical, and school related variables between these two groups. Any comparisons in the bivariate model that were significant at p < 0.1 were entered in multiple logistic regression analyses with a history of psychosis as the dependent variable. All statistics were two-tailed, and significance was set at p-value ≤0.05. Odds ratios (OR) and confidence intervals (CI) were computed.
Machine learning analysis Linear associations between risk factors and clinical outcomes are important in understanding chronic diseases, however, traditional statistical approaches explore the linear relationship between variables at a group-level (Bzdok et al., 2018). Machine learning approaches can be used in problems that are difficult to address using traditional statistics and have increasingly been used in psychiatry. This is because machine learning models can assume a complex relationship between variables, including non-linear patterns, and allow us to make individualized predictions (Dwyer et al., 2018). As such, machine learning may facilitate predictive models that can be used in clinical practice (Passos et al., 2016; Passos and Mwangi, 2018; Sartori et al., 2018; Wu et al., 2017). In the present study, we built a supervised machine learning model to predict psychosis among participants with PBD. The predictors included in the machine learning model were the same sociodemographic and clinical characteristics used in the classical bivariate analyses. The machine learning analysis was conducted using R software (Version R 3.3.1) and R Studio (Version 0.99.902), using the package CARET. The CARET package (short
for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: 1) data splitting; 2) preprocessing; 3) feature selection model tuning using resampling; 4) variable importance estimation; 5) as well as other functionality. It is worth mentioning that the caret package contains numerous tools for developing predictive models using the rich set of models available in R. There are many different modeling functions in R. Some have different syntax for model training and/or prediction. The package started off as a way to provide a uniform interface for the functions themselves, as well as a way to standardize common tasks (such parameter tuning and variable importance). Missing data was inputted after multiple imputations (m=5, maxit=50, seed=500) with the R package mice (Buuren and Groothuis- Oudshoorn, 2011). We used a naive bayes classifier with a high-density kernel to train the model. Naive bayes classifiers are a family of probabilistic algorithms based on the Bayes' theorem, using the maximum a posteriori (MAP) decision rule. We used leave-one-out cross validation (LOOCV), ROC (receiver operating characteristic) and AUC (area under the curve) to estimate model performance, as well as accuracy and kappa. LOOCV consists of training the algorithm with all participants but one, testing the model on the omitted participant, then repeating the process until all participants were used during algorithm testing at least once. This approach ensures that our model is being tested in an “unseen” dataset in order to avoid overfitting (Librenza-Garcia et al., 2017). LOOCV has become the standard for estimating model performance for studies with small sample sizes (Vehtari et al., 2017).
RESULTS A total of 64 participants were included in the present study, of which 32.8% (n=21) had a history of psychosis. Of those, 71.4% (n=15) reported hallucinations and 52.4% (n=11) delusions. Table 1 shows the sociodemographic characteristics and prevalence of bullying in the nonpsychotic and psychotic PBD participants. Clinical characteristics are presented in Table 2. In the bivariate analyses, psychosis was associated with Middle-Low SES (p=0.04); higher scores in CGI- S, which indicate greater symptom severity of BD (p=0.02); suicidal behavior (p=0.006); and being bullied (p=0.002). No differences were found between
nonpsychotic and psychotic participants for the other variables assessed, however there was a statistical trend toward an association between psychosis and caucasian ethnicity (p=0.06). In the psychotic PBD group, 35,7% (n=5) of the participants with a lifetime history of bullying victimization also had a lifetime history of suicidal behavior. In the non-psychotic PBD group, none of the participants with a lifetime history of bullying victimization reported suicidal behavior. We also compared the frequencies of suicidal behavior in bullying (n=25) and non-bullying (n=39) participants. The prevalence of suicidal behavior among those with history of bullying victimization was 20% (n=5). Of those, 100% had history of psychosis. The prevalence of suicidal behavior in the non-bullying group was 17.9% (n=7), with 42.8% (n=3) reporting also psychosis. However, due to the small number of participants in the sample, it was not possible to analyze the difference between groups. Considering the positive relationship between bullying and psychosis, and inability to infer causation due to the cross-sectional design, we also performed an analysis comparing those with and without a history of bullying victimization. Our objective was to analyze whether SES, severity of PBD, and suicidal behavior were also associated with bullying. SES (X² = 0.30, p=0.58), CGI-S scale (t=-1.36, p=0.12), and suicidal behavior (X²= 0.04, p= 0.84) were not statistically significant between bullying and non-bullying participants.
Multivariate analysis We computed logistic regression analyses, with psychotic symptoms as the dependent variable, and clinical variables significant at p < 0.1 from the bivariate analyses entered as the independent variables, as listed in Table 3. We included bullying, suicidal behavior, SES, ethnicity, and severity of the disorder as independent variables. Only bullying and suicidal behavior remained associated with lifetime psychotic symptoms in PBD.
Predictive model of lifetime psychosis The Naive Bayes algorithm differentiated participants with psychosis in PBD from those without psychosis in PBD with 75% accuracy (CI95%=6.66-8.23, p=0.03, 77.91%
sensitivity, 69.05% specificity). The other performance measures are listed in Table 4. Figure 1 demonstrates the variable importance for predicted probability of lifetime psychosis. The most relevant predictive variables in differentiating participants with psychosis were bullying, CGI-S scale, and suicidal behavior. The AUC for the model was 0.86 and the ROC curve for the model is represented in Figure 2.
DISCUSSION To our knowledge, this is the first study to investigate the relationship between psychosis and bullying victimization in children and adolescents with BD. Psychosis was associated with bullying, suicidal behavior, low SES, and symptom severity of BD. In the multivariate analysis, only bullying and suicidal behavior remained significant. In the machine learning model, the most relevant predictive variables in differentiating the participants with psychosis were bullying, BD symptom severity, and suicidal behavior. It is important to mention that machine learning and most traditional statistics are rooted in different mathematical contexts, and largely judge evidence of an effect in different ways. Therefore, an effect that is observed to be statistically significant using a p-value will not necessarily yield a high predictive accuracy in new independent data, and vice versa (Bzdok et al., 2018). Classical statistical analysis allows us to compute a quantitative measure of confidence that a discovered relationship describes a 'true' effect that is unlikely the result of statistical noise. By contrast, machine learning uses general- purpose learning algorithms to find patterns and provide predictions in a dataset. Given that we observed similar results using both traditional statistical and machine learning methods, this lends support to the notion that we observed a true effect in our study. The positive association between psychosis and bullying victimization in PBD participants is in accordance with prior cross-sectional and longitudinal studies in youth among community samples (Arseneault et al., 2011; Kelleher et al., 2013; Schreier et al., 2009; Singham et al., 2017). However, among clinical studies, the results are inconclusive (van Dam et al., 2012), and a majority of the participants included had psychotic disorders (Bebbington et al., 2004; Trotta et al., 2013). In a cross-sectional study in adults, prior repeated peer victimization was reported more frequently by participants with a psychotic disorder, as
compared to those without a psychotic disorder, but the effect disappeared after adjusting for other negative life events (Bebbington et al., 2004). In another cross-sectional study with adults, participants with psychotic disorders reported more bullying victimization, when compared to controls. This association remained significant after adjustments only in those with schizophrenia-spectrum disorders but did not remain significant in those with affective disorders. However, the negative finding may be related to the small sample size in the affective group (Trotta et al., 2013). Psychological and affective mechanisms may be involved in the association between psychosis with bullying victimization and suicidal behavior. Affective dysregulation and impulsivity/hostility are associated with childhood trauma in BD (Aas et al., 2017; Etain et al., 2017), and appears to mediate the association between childhood trauma and risk of a more severe clinical trajectory, including suicide attempts (Etain et al., 2017) and psychosis (Fisher et al., 2013; Myin-Germeys and van Os, 2007; Winsper et al., 2017). In a community sample study, children with higher levels of dysregulated behaviour were prone to the development of Borderline Personality Disorder symptoms, depressive symptoms, and psychosis in early adolescence, when exposed to bullying victimization (Winsper et al., 2017). Traumatic experiences resulting in maladaptive cognitive schemas characterized by negative beliefs about self, world, and others, as well as an increased suspiciousness of others, may lead to the development of paranoid delusions (Read et al., 2005). Children who are victims of bullying may also be vulnerable to a ‘victim schema’ that demonstrate an implicit cognitive association of themselves as victims and develop an attributional bias whereby victims may inaccurately attribute hostile intent in social situations perceived as threatening (Rosen et al., 2007). In addition, Fisher et al. (2013) evidenced that the association between bullying victimization and psychosis was mediated by believing that the outcome of events in one’s life are external forces beyond their control. Alternatively, being at risk for psychosis might affect the perception of being bullied. For instance, individuals with paranoia may experience real and overestimated threat as bullying (Jack and Egan, 2018). There is also evidence that some individuals with BD have a poor genetic susceptibility to environmental stress (Aas et al., 2016), such as bullying victimization during critical years of development in childhood or adolescence. This poor stress response coupled with prior victimization may not only make the individual more vulnerable to further bullying, but also
precipitate a more pernicious manifestation of the disorder, including psychosis. Moreover, internalizing and externalizing behaviors that emerge during the prodromal phases of psychosis may contribute to youth vulnerable to bullying victimization (Arseneault et al., 2011; Singham et al., 2017). Genetic factors appear to influence these behaviors (Marceau et al., 2013; Schoeler et al., 2019), and genetic influences can be accounted for over two thirds of individual differences in bullying victimization (Ball et al., 2008). For instance, Schoeler et al. (2019) evidenced that genetic predisposition for certain externalizing and internalizing behaviors were associated with bullying, and Shakoor et al. (2015) found that individuals vulnerable to bullying are also phenotypically susceptible to paranoia via a shared genetic propensity(Shakoor et al., 2015). Factors beyond the individual may also influence the relationship between psychosis and bullying victimization, including other types of childhood trauma (Bowes et al., 2009), family environment (Jansen et al., 2011), and SES (Bowes et al., 2009a; Jansen et al., 2011). In our study, low SES was also associated with psychosis, but this association did not remain after logistic regression. BD participants with a history of childhood trauma may be less likely to have protective factors, such as family and social support, increasing the vulnerability to the negative long-term effects of the trauma (Aas et al., 2016). The statistical association identified between psychosis and suicidal behavior in PBD is in accordance with previous cross-sectional studies of children and adolescents with BD (Caetano et al., 2006; Goldstein et al., 2005) Conversely, Goldstein et al. (2012) performed a longitudinal study and observed no relationship between history of psychosis at intake with suicide attempts at 5-year follow-up. On the other hand, community based-studies reported an association between psychosis and an elevated risk of suicidal ideation and behavior (Bromet et al., 2017; Kelleher et al., 2012). In the study from Bromet et al. (2017), psychosis was associated with an increased 2-fold risk (after adjusting for psychiatric disorders) of subsequent suicidal ideation and behavior across all life stages, with the strongest association observed in individuals 12 years and younger. Kelleher et al. (2012) reported an increased risk for suicidal behavior in the general adolescent population and in adolescents with (nonpsychotic) psychiatric disorder. In our study, around one third of the participants with both psychosis and bullying victimization reported suicidal behavior. No participant without psychosis with history bullying victimization reported suicidal behavior. After separating the
sample into participants with and without a history of bullying victimization, we found no statistical association between suicidal behavior and bullying victimization, contrary to many studies (Bang and Park, 2017; Copeland et al., 2013; Geoffroy et al., 2018). For example, adolescents with a history of bullying victimization were more likely to be diagnosed with depression, psychosis, and suicide attempts in a cross-sectional study. The association between bullying and suicide attempts remained statistically significant even after adjusting for depression and psychosis (Bang and Park, 2017). Our study presents with some limitations. First, data was collected retrospectively, and recall bias is possible. Moreover, children might have under-reported bullying or suicidal behavior due to feelings of shame and self-blame. The young age of our participants may also account for some occasional misreporting that may have also occurred. We evaluated bullying victimization in a clinical interview with the participants and parents. No previously validated instrument was used to assess this. When participants did not fully understand the concept of bullying, the evaluator provided further examples and a more detailed explanation. However, the definition of bullying is partly subjective, and it may be increased without a validated instrument, which could lead to over-reporting or under-reporting (Gromann et al., 2013). Second, the small sample size restricted the statistical power of the study. Given the limited power, a lack of statistical significance with other variables does not mean that the covariates cannot have any confounding or mediating effect. Besides that, the small sample size in the different categories of interest increases the risk for making Type I errors. Therefore, our findings need to be interpreted carefully. Third, since this is a cross-sectional study, the possibility of reverse causality cannot be discounted. We are unaware if bullying victimization or suicidal behavior are associated with later onset of psychosis in PBD or whether psychosis in PBD tends to occur before bullying victimization or before suicidal behavior. It is also possible that psychosis and bullying may both emerge during a prodromal phase of the PBD. However, studies demonstrated that there is a bidirectional relationship between childhood trauma and psychotic experiences, with each independently predicting the other (Kelleher et al., 2013; Mcgrath et al., 2017), and psychosis may predict suicidal behavior even before meeting criteria for psychiatric disorders (Bromet et al., 2017).
Fourth, we also did not investigate the possible subtypes, intensity, or frequency of bullying. A dose–response relationship between frequency and intensity of bullying victimization with levels of psychotic symptoms have been reported. (Arseneault et al., 2011; Kelleher et al., 2013). We did not investigate other types of childhood traumas that are also associated with psychosis in BD, such as sexual, physical, or emotional abuse (Agnew-Blais and Danese, 2016; Etain et al., 2017; Romero et al., 2009; van Bergen et al., 2018). The occurrence of additional negative childhood traumas in this period appears to intensify the risk of later developing psychosis (Mcgrath et al., 2017). Besides that, our study was carried out in an outpatient clinic of a tertiary hospital, so it may be different depending on the context in which the evaluation takes place. Despite these limitations, our study has several strengths. This is the first study to investigate the association of psychosis and bullying in PBD participants. The prevalence of psychosis, suicidal behavior, and bullying in our sample were similar to those reported in the literature. We used two different analytic approaches in the study, classical statistics and machine learning techniques. Additionally, we assessed for some potential confounding effects (i.e. SES, family history of BD, and comorbidities). We used validated instruments to assess psychiatric diagnosis and suicidal behavior in children and adolescents. Nonetheless, the inclusion of participants at all levels of symptom severity, treatment, and phase of illness allows for a broader generalization in outpatient settings. Bullying victimization may be viewed not only as a cause of psychosis but may also in part represent a “symptom” of pre-existing vulnerabilities (Singham et al., 2017). The integration of the school in the treatment of PBD may impact positively in the outcomes of the disorder, even if psychosis precedes bullying victimization. A community sample study reported a decrease in psychosis in 3 months after the school bullying had ceased (Kelleher et al., 2013). School bullying programs focusing on potential groups of vulnerable children and adolescents, as those with PBD, instead of only those who bully, may reduce the burden of bullying victimization. Programs at school focusing on negative cognitions and in increasing resilience among victims of bullying is one example (Arseneault, 2018). The role of family support is also important to minimize the negative effects of bullying victimization (Bowes et al., 2009). Parent support for child autonomy may also contribute to reduce bullying during early school years (Rajendran et al., 2016).
In summary, in our study psychosis was associated with bullying victimization and suicidal behavior in children and adolescents with BD. Future longitudinal studies including a larger sample, other potential confounding, or mediating factors could help to better understand the association between psychosis and bullying in PBD.
Table 1. Sociodemographic and bullying characteristics comparing nonpsychotic and psychotic PBD participants
All sample
Nonpsychotic PBD
(n=64)
(n=43)
Variable
N (%)
N (%)
Age (mean±SD)
12 (±3.43)
11.63 (± 3.38)
Sex (female)
28 (43.7)
Ethnicity (Caucasian)
Psychotic PBD (n=21) Effect size
p-value
12.76 (± 3.49)
t = -1.23
0.225
16 (37.2)
12 (57.1)
x² = 2.27
0.131
49 (76.5)
30 (69.7)
19 (90.4)
x² = 3.37
0.066
Middle-low SES
28 (43.7)
15 (34.8)
13 (61.9)
x²= 4.19
0.04*
Bullying
25 (39)
11 (25.6)
14 (66,6%)
x²= 10.00
0.001**
N (%)
PBD = pediatric bipolar disorder; SD= standard deviations; SES = Socioeconomic status. * p <.0.05; ** p < 0.01
Table 2. Clinical characteristics comparing nonpsychotic and psychotic PBD participants
All sample Variables (n=64)
Nonpsychotic PBD (n=43) N (%)
Psychotic PBD Effect size (n=21)
p-value
N (%) Family History of BD
N (%)
27 (42.2)
21 (48.8)
6 (28.6)
X²= 2.38
0.12
39 (60.9)
24 (55.8)
15 (71.4)
4 (6.2)
4 (9.3)
0
X² = 2.69
0.26
21 (32.8)
15 (34.9)
6 (28.6)
Rapid cycling
26 (40.6)
15 (34.9)
11 (52.4)
Anxiety disorders
32 (50)
21 (48.8)
11 (52.4)
X²= 0.07
0.79
ADHD
41 (64.1)
30 (69.8)
11 (52.4)
X²= 1.85
0.17
Suicidal Behavior
12 (18.7)
4 (9.3)
8 (38.1)
X²= 7.68
0.005**
CGI-S (mean±SD)
3.64 (±1.43)
3.35 (±1.34)
4.24 (± 1.45)
t = -2.36
0.023*
BD type Type I Type II NOS X² = 1.79 0.18
PBD = pediatric bipolar disorder; NOS= not otherwise specified; ADHD= Attention Deficit Hyperactivity Disorder; CGI-S = Clinical Global Impression-Severity scale; SD= standard deviation. * p <.0.05; ** p < 0.01.
Table 3. Logistic Regression model with psychosis as the dependent variable in PBD participants
Variable
z-value
P value
OR (adjusted)
95%CI
Bullying
2.807
0.005**
7.4
1.9 – 34.6
Suicidal behavior
2.158
0.03*
6.6
1.3 – 41.7
Middle-low SES
1.633
0.1
3
0.8 – 12.4
Ethnicity (Caucasian)
1.412
0.15
3.7
0.7 – 2.1
CGI-S
0.989
0.32
1.27
0.8 - 2
SES = Socioeconomic status; CGI-S = Clinical Global Impression-Severity scale CI = confidence interval; OR = odds ratio; p <.0.05; ** p < 0.01 p value for the logistic regression model = 0.00012
Table 4. Performance measures of Naïve Bayes algorithm on differentiating nonpsychotic and psychotic Pediatric Bipolar Disorder participants
Naive bayes
Sensitivity
Specificity
PPV
NPV
77.91%
69.05%
83.75%
60.42%
PPV = positive predictive value; NPV = negative predictive value
Balanced accuracy 73.48%
ADHD= Attention Deficit Hyperactivity Disorder; CGI-S = Clinical Global Impression-Severity scale
Figure 1. Variable importance for predicted probability of lifetime psychosis among participants with Pediatric Bipolar Disorder based on the Naive bayes algorithm.
Figure 2. Receiver operating characteristic (ROC) curve for predicted probability of lifetime psychosis among participants with Pediatric Bipolar Disorder based on the Naive bayes algorithm. We used leaveone-out cross-validation.
Author Statement We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We further confirm that any aspect of the work covered in this manuscript that has involved either experimental animals or human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from [email protected]
Contributors All authors contributed to the final version of the manuscript and have approved the final article. Their individual contribution to the article were:
Jandira Rahmeier Acosta: conception and design, acquisition of data, drafting of manuscript, critical revision, statics analyses.
Franco Zortea: conception of the idea, construct the data bank and statics analyses.
Diego Librenza-Garcia: machine learning analyses, drafting of manuscript, critical revision.
Devon Watts: drafting of manuscript, critical revision
Ana Paula Francisco: acquisition of data, drafting of manuscript, critical revision.
Bruno Raffa: acquisition of data, critical revision
Gledis Lisiane Motta: conception of the idea, acquisition of data, critical revision
André Kohmann: acquisition of data, critical revision
Fabiana Eloisa Mugnol: acquisition of data, critical revision.
Silza Tramontina: Supervision of the collection of the data from the patients, critical revision and drafting of manuscript.
Ives Cavalcante Passos: conception and design of the idea, supervision of the article organization and the data collection, critical revision and drafting of manuscript.
Declarations of interest: Ives Cavalcante Passos has received consulting fees from Torrent/Omnifarma. He has received research grants from CNPq/CAPES. Ana Paula Francisco has been receiving a scholarship that is partially financed by Shire. The remaining authors does not have competing interests to declare.
Funding: ProCAB was partially supported by FIPE/HCPA (Fund of Incentive to Research at Hospital de Clínicas de Porto Alegre).
Acknowledgements: None
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