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Discovery of Comorbid Psychiatric Conditions among Youth Detainees in Juvenile Justice System using Clinical Data

1Department of Health Management and Informatics, University of Missouri, Columbia, Missouri, United States 2 Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh 3Center for Biomedical Informatics, School of Medicine, University of Missouri, Columbia, Missouri, United States 4 Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, United States 5Department of Electrical and Computer Science, University of Missouri, Columbia, Missouri, United States 6Department of Psychiatry, School of Medicine, University of Missouri, Columbia, Missouri, United States


Background and Significance
Many youths under 18 years of age in the United States are incarcerated in the juvenile justice system (JJS) residential facilities. According to the census data of 2016, approximately 46,000 youth juvenile offenders were housed in 1,772 residential placements. 1 Mental health disorders are highly prevalent among these incarcerated youth, and the prevalence has been consistently higher than those within general population. 2 In previous studies on juveniles, estimates reveal that approximately 50 to 75% of youth encountering JJS are diagnosed with at least one diagnosable mental health disorder. [3][4][5] Numerous comprehensive studies have indicated that mental health disorders like, depressive disorders (DDs) (major depression, persistent depression, and manic episodes), schizophrenia spectrum disorders (SSD) (psychotic disorders), anxiety disorders (AD) (panic, separation anxiety, and generalized anxiety), obsessive-compulsive related disorder, trauma and stressor related disorders (posttraumatic stress disorder), disruptive behavior disorders (conduct and oppositional defiant disorder), and neurodevelopment disorders (NDD) (attention-deficit hyperactivity disorder [ADHD]), and substance use disorders are commonly found among these incarcerated youth. [6][7][8] There is growing evidence that commonly found mental disorders are associated with increase in risks for youth engaging in aggressive behaviors. 8,9 Thus, addressing the mental health conditions of the incarcerated youth is important in considering treatment response, and lack of such treatment in the residential facilities can lead in worsening effect on offending behavior and delinquency.
The coexistence of more than one mental health disorders in a patient, regardless of causality or chronology is called comorbidity. Comorbidity is common among adolescents with mental health disorders, and nearly two-thirds of the juvenile youth meet the criteria for two or more diagnosable disorders. 6,7,[10][11][12] Additionally, there is evidence of association of co-presence of conduct disorder and attention-deficit/hyperactivity disorder among adolescence with chronic offending behavior. 8,13,14 Comorbidity can often lead to increased complexity of treatment as compared with single disorder. This emphasizes the need for different levels of mental health care with varying effective treatment options for the incarcerated youth addressing the varied co-occurrence pattern among them. Knowledge on comorbidity as evidenced from reliable epidemiologic and clinical data can thus play important role in developing effective individualized treatment for delinquent youth. 11,[15][16][17][18] The studies that have examined the prevalence of mental health disorders among the juvenile offenders used data obtained through survey studies to identify the relevant cooccurrence pattern for two diagnoses reporting through prevalence and odds ratios (OR). 7,8,12,19,20 However, the co-occurrence pattern among three or more psychiatric conditions has not been explored on delinquent youth population in any available literature. In this paper, we have focused on identifying the comorbid patterns of mental disorders using data mining techniques on clinical assessment data from incarcerated youth from state of Missouri.
Association rule mining (ARM) have been proven to be useful for mining clinical data for co-occurrence analysis. For example, ARM was applied for identifying co-occurrence patterns in prescription drugs, and disease comorbidity patterns of ADHD, schizophrenia, hyperprolactinemia and diabetes mellitus type 2, and borderline personality disorder using insurance claims and clinical datasets. [21][22][23][24][25][26][27][28] Applying data mining algorithms to explore the co-occurrence of psychiatric disorders is a novel approach for the incarcerated population, which led to the main objective of this paper.

Study Objective
The main objective of this study was to discover the comorbid psychiatric conditions among the youth detainees within the state of Missouri serving under the JJS. In this study, ARM was used to find the prevalent combination of psychiatric disorders, and to identify the co-occurrence patterns among the comorbid disorders.

Population Selection and Data Source
The analysis of this study was done using clinical data on 613 patients from age 11 to 17 years from the residential placements across Missouri serving under the Division of Youth Services (DYS) within the period 2013 to 2017. The youth patients in the Missouri DYS residential centers received psychiatric care by the board-certified child and adolescent psychiatry (CAP) specialists through a telemedicine network established with University of Missouri Department of Psychiatry (MUPC). The delinquent youth patients received complete psychiatric assessment in their first visit by the MUPC CAP specialists. The clinical notes of the patients were collected from the electronic health record (EHR), and mined to extract the psychiatric assessments of each patient during their visits using a semiautomated process. ►Fig. 1 illustrates the process applied to extract the assessments for a patient X. The notes were first parsed into unique segments, each part was then coded manually to identify the disorders. The psychiatric disorders were mapped to the Diagnostic and Statistical Manual (DSM-5) of Mental Disorders classes. 29 The team for data collection included a graduate student and a professor of informatics, and training was provided to the graduate student prior to the process. One team member executed the mapping of unique segments to specific disorders, and mapping of specific disorders to DSM-5 classes, while another team member overviewed the mappings for double-checking. This led to the presence of 11 distinct psychiatric disorder classes such as, AD, bipolar and related disorders (BD), DD, disruptive, impulse-control and conduct disorders (CD), neurocognitive disorders, NDD, obsessivecompulsive and related disorders, schizophrenia spectrum disorders (SSD), sleep-wake disorders, substance-related or -addicted disorders (SUD) and trauma-and stressor-related disorders (TSD). ►Table 1 shows the frequency distribution of the specific disorders under the broad DSM-5 classes for psychiatric disorders.

Measure Comorbidity and Comparison by Demographics
The comorbidity for each DYS youth patient was computed by summing up the number of specific disorders diagnosed for each patient. The frequency distribution of the total diagnosed disorders was compared by the demographic variables like gender and race. The comorbidity was further categorized into two groups: exactly one disorder and two or more disorders, and the frequency distribution for this is also compared by gender and races. The comorbidity among the most common DSM-5 categories SUD, NDD, CD, and DD with other DSM-5 categories were examined through prevalence ratios and OR. Additionally, prevalence ratios were compared with understand the presence of common specific disorders among the youth patients belonging to the four broad DSM-5 categories.
Descriptive statistics were calculated to examine demographics (race and gender). Statistical tests like Z-test for proportion were performed to compare the proportions between the two groups of comorbidities across demographics. The p-values less than 0.05 were set to evaluate statistical significance for OR and Z-tests for proportions. All tests were two-tailed.

Data Mining Approach
The main aspect of data analyses in this paper is based on the application of the data mining technique known as ARM. The concepts of ARM were first introduced for mining transaction databases to find the most frequent items and to generate significant associations between them. 30 The clinical assessment data were converted into two types: one using the specific disorders, and the other using the DSM-5 broad classes, merged from the specific disorders. ARM algorithms were applied on each of the datasets. For the purpose of mining, each patient data was considered as a "transaction" in each of the database, and each disorder was considered as an "item." ►Fig. 2 shows a sample of the table To perform analysis on the psychiatric assessment datasets R (version 3.5.1, R Core Team) 31 was used, and the library "arules 32 " was applied to examine the datasets to identify combinations of disorders and their patterns of comorbidity. This study utilized the apriori 33 algorithm, which is the best-known ARM algorithm, to determine rules of the comorbidities among the mental disorders. ARM considers all combinations of psychiatric disorders to identify the combinations of disorders that occur together more often than would expect by chance only. For example, 11 identified DSM-5 categories for psychiatric disorders would result into a total of 2 11 possible combinations of disorders that need to be considered. In apriori setting, the association rules between A and B are expressed as A ! B, where A and B are both disorders, and can be defined as follows: "if disorder A exists, disorder B coexists." The rules are evaluated according to the values of measures like "support" and "confidence." The support indicates the number of patients with disorder A and B among all patients. The confidence of a rule A ! B shows the percentage of patients with disorder B among patients with disorder A. This measure is comparable to conditional probability of B given A: , which means what is the probability of occurrence of B given that A is known to have occurred. Thus, to capture the interpretation of "confidence" measure as chances of one to be present conditional upon another, we would rename it as "conditional prevalence." The value of confidence will change with change in the denominator, and thus, the rules A ! B and B ! A may or may not imply the same. To limit the number of item sets consisting of combinations of disorders, a minimum threshold of support (prevalence) and confidence (conditional prevalence) can be provided.

Results
Among the 613 patients, there are 77 (12.6%) females and 533 (86.9%) males (with three patients with missing gender information), and 292 (47.6%), and 282 (46%) patients with white and black races, respectively. From the clinical diagnoses of the 613 youth detainees in the DYS facilities, 11 broad DSM-5 classes of psychiatric disorders is identified with 46 specific disorders under those categories. The total number of specific disorders is computed for each patient, which reflects the co-presence of multiple disorders among the DYS youth. ►Fig. 3 shows the frequency distribution of total diagnosed disorders versus gender and race variables. Female youth patients are seen to have greater proportions for multiple disorders as compared with that of male youths. Moreover, white youths are seen to have more significant percentages for higher diagnosed disorder number as compared with black youths. ►Table 2 provides a frequency distribution categorizing the total diagnosed disorders into two groups: exactly one disorder and two or more disorders versus gender and race. Evidently, significantly more males and females have two or more specific disorders, and more white youths than black youths have two or more diagnosed disorders in this population. This shows that the youth patients in the DYS facilities are commonly diagnosed with multiple disorders that need to be addressed for treatment simultaneously From ►Table 1 we can see that among the broad DSM-5 categories, disruptive, impulse-control, and CD, have the maximum prevalence with 393 (64.1%) youth patients, followed by substance-related or addicted disorders (SUD) with n . Among the patients diagnosed with NDD (n ¼ 329), CD and SUD occurred for 65.7% (n ¼ 216) and 53.5% (n ¼ 176) of the cases, respectively; however, their OR are not significant (p-values > 0.05). TSD and AD co-occurred with NDD approximately 15% cases with OR of less than 1 (p-values < 0.05), respectively. CD and SUD co-occurred with DD (n ¼ 333) 66.7% (n ¼ 222) and 58% (n ¼ 193) of the cases, respectively; however, with not significant OR (p-value > 0.05).
►Table 4 provides the association rules obtained from ARM technique applied to the broad categories of the psychiatric    Abbreviations: AD, anxiety disorder; BD, bipolar and related disorder; CD, conduct disorder; DD, depressive disorder; NDD, neurodevelopment disorder; SUD, substance-related or addicted disorder; SWD, sleep-wake disorder; TSD, trauma-and stressor-related disorder. a p-Values less than 0.05 are considered to be significant.

Discussion
This study has focused on utilizing a data mining technique called ARM to analyze clinical assessment data from the population of youth juvenile detainees in Missouri DYS custody. In ARM, "confidence" represents the probability of the predicted event given the co-occurrence of other event(s) and "support" provides how frequently a set of events co-occur in the database. This study used analogical terms like "conditional prevalence" to determine the chances of diagnosis of one psychiatric disorder conditional upon co-presence of other psychiatric disorder(s), and "prevalence" as a measure of how frequently the comorbid psychiatric disorders co-occur. Interest measures like OR and p-values obtained from Chi-square test of association were used to determine which associations are strong and significant. This data mining approach is novel specific to this population as no other literature has been found to identify the comorbidity among more than two psychiatric conditions for the juvenile youth population using advanced data mining algorithms. Moreover, the use of clinical records from EHR in our study rather than interview questionnaires, such as the Diagnostic Interview Schedule for Children (DISC), to identify the psychiatric conditions is believed to provide better reliable insight into the real-world scenario of comorbidity. Survey questionnaires like DISC are not inclusive of many psychiatric conditions, 35 and thus, it can be claimed that clinical assessments done through regular doctor visits through a standard of care can capture a broader spectrum of disorders compared with any questionnaire-based assessment results. The prevalence of psychiatric disorders and the comorbidities found in the general youth population of the United States is comparable to that of the Missouri DYS youth detainees. Among the general youth population, CD (74.2%, OR: 3.4), ADHD (63.6%, OR: 3.0), DD (52.7%, OR: 5.6), AD (24.6%, OR: 4.6), and TSD (50.6, OR: 2.9) are most commonly found to coexist with SUD. 36 Another study estimated 23.1% of general youth with SUD to have ADHD and other NDD diagnoses, 37 which is almost half to the prevalence of ADHD among SUD youth in our study (n [%] ¼176 [49.2]). One study estimated that 45 to 50% of youth in general with ADHD are also diagnosed with CD, which is less than what we found in our sample of DYS youth (n [%] ¼ 216 [65.7]). However, juvenile facilities often have inadequate treatment facilities for the youth detainees with no access to CAP specialists, 20,38-40 which implies limited efficacy in designing treatment for co-occurring psychiatric conditions for this population. Hence, we can infer that though comorbidity is a significant problem among the youth jail detainees com-pared with that of the general youth population across the country, the existing lack of access to proper care for the youth detainees can lead to increase future recidivism and delinquency.
The study on incarcerated adults by Abram et al (1991) found that the vast majority of detainees met the criteria for alcohol disorders, drug disorders, or antisocial personality disorder. 10 In our study, substance-related or addicted disorders (SUD) account for 58.4% of the cases with 94.5% CUD diagnosis. However, youth detainees from our study are diagnosed with conduct related disorders and DDs like ADHD in more significant percentage than adult detainees. Our results from this study are more aligned compared with the existing literature on adolescents in juvenile detention. A meta-regression analysis of 25 studies of adolescents in juvenile detention and correctional facilities found that CD was the most common of the studied disorders with similar prevalence of slightly over 50% across sexes. 2 Interestingly, the meta-analysis study showed that among the 13 surveys of 14,639 adolescents, approximately 21.6% were diagnosed with ADHD. Washburn et al study 7 on 1,715 arrested and detained youths over age 13 estimated diagnoses of CD to be 38% and SUD to be 51 to 55%. In the Teplin et al study 12 of 1,829 detained youths aged 10 to 18, SUD was diagnosed between 46 and 50%, and CD was diagnosed between 37 and 40%. The DYS youth showed a greater prevalence of CD (64.1%) and SUD (58.4%), while approximately twice for ADHD (50.4%) as compared with these studies on youth detainees.
Abram et al 17 assessed comorbidity of psychiatric diagnoses of 1,829 youth detainee participants utilizing the DISC Version 2.3 for disorder assessment and DSM-III-R for categorization. For example, they have merged disorders like ADHD, ODD, and CD into one category and named it ADHD  Using the ARM approach for data analysis provides insight into the conditional effects of comorbid psychiatric disorders. The association rules from ARM may answer the questions "if a patient has diagnoses of A, how likely it is for the patient to be diagnosed with B as well? Is there any change in the likeliness of the patient being diagnosed with B if the patient has diagnoses of both A and C?" The ARM results showed that the odds for youth to be diagnosed with SUD range from 1.6 to 2.3 for youth patients diagnosed with CD, AD, DD, and their combinations. Also, the odds for youth to be diagnosed with CD are higher with a stronger association for higher-order combinations of SUD, DD, and NDD. The ideal scenario for assessing of any of these disorders in a patient should include substantial consideration in delineating the symptoms and history before eliminating any of the potential comorbid conditions. Our results showed that the odds of CUD are very high with a strong association to co-occur with anxiety disorder, the odds of which increases when anxiety is combined with conduct disorder and when combined with AUD. Treating anxiety disorder with pharmacotherapy should receive careful consideration in choosing medications that are not likely to contribute to potentially adverse interactions with drugs and alcohol. 41 Moreover, cognitive-behavioral therapy (CBT) has been highly effective against AD; however, CBT alone is not sufficient for patients with anxiety and substance use disorders. 42,43 CBT can be beneficial if emphasized after controlling substance use because of the anxiety associated with the therapy may worsen substance use symptoms. 44 DYS youth patients diagnosed with DDs like mood and major DDs combined with ADHD or CD showed higher odds of substance use disorders, like cannabis use. Cases like this where multiple disorders are present could be very challenging for providing treatment. Clinical data suggest that DDs can contribute to cannabis use, and there are very few studies to suggest a significant treatment effect of any pharmacotherapy on such comorbid patients. However, there is also a concern in selecting appropriate medications to reduce ADHD symptoms in the presence of substance abuse because of the risks of abusing ADHD medications.
Additionally, there is evidence that the association between ADHD and SUD becomes more robust in the presence of conduct disorder, 45 which is also proven from our results. Integrated treatment therapies like integrated co-occurring treatment model, functional family therapy, family integrative transition, and multisystemic therapy have shown to be effective for juveniles with behavioral-related disorders (CD), substance-related disorders (SUD) in reducing recidivism, and delinquency. 8 However, more research is needed to substantiate the effectiveness of integrated treatment plans for youth juveniles diagnosed with multiple comorbid conditions.
Moreover, there is evidence that the co-presence of multiple psychiatric disorders among the youth detainees can result in excessive psychotropic medications in their treatment. Such concomitant drug usages are associated with increased vulnerability to adverse drug interactions, risk of excessive dosing, risk of having premetabolic syndrome, and early death. [46][47][48] Thus, we can suggest that the comorbid relations identified from our study can be utilized in designing a specific treatment regimen for youth with common diagnostic profiles.
However, one limitation of this data mining technique is that the ARM outcomes do not provide any causalities or directional effects regarding the psychiatric disorders. Thus, exactly what disorder was responsible for the onset of another disorder cannot be obtained from such analysis. Despite this, the findings of this study have usefulness in determining the best treatment strategies for these youth.

Conclusion
This study has found comorbidity to be prevalent among the youth patients of the MO DYS facilities receiving psychiatric services through the MUPC telemedicine network. Psychiatric disorders related to substance use, conduct, mood, anxiety, and ADHD have been found to overlap and have some conditional likelihood of co-occurring with one another.

Clinical Relevance Statement
The combination of psychiatric disorders can increase the complexity of treatment interventions, more likely to be intractable to traditional treatment, and subsequently cause treatment failures. Moreover, treating multiple psychiatric disorders simultaneously can be challenging as pharmacotherapy can effectively reduce symptoms from some disorders while worsening the effect on others. 45,49 This implies that knowing of the conditional prevalence of disorders and the interplay of symptoms can help the psychiatrists determine the best treatment for the youth detainees with multiple disorders.

Protection of Human and Animal Subjects
The study was approved by University of XXX Institutional Review Board.

Funding
None.

Conflict of Interest
None declared.