Brief History of Checkmate Plus Symptom Inventories

The Early Years

 

The initial spark of inspiration that ultimately led to a career-long program of research into the development of practical, cost-effective, clinical assessment tools for the diagnosis and medical management of mental health disorders occurred in the early 1970s and emanated from personal experiences as a special education teacher of children with diverse disabilities.  At that time, although there were numerous well-validated measures of cognitive and academic performance, this was much less the case for child mental health problems, particularly assessments linked to specific diagnostic criteria. This situation made it very difficult for caregivers interested in child welfare to communicate with one another in a standardized way. To better understand the extent of the problem and identify potential solutions, I decided to conduct two large-scale, epidemiologic studies into practices associated with the pharmacologic treatment of childhood psychiatric and seizure disorders among children receiving special education services (Gadow, 1977, 1981; Sprague & Gadow, 1976). These studies focused, in part, on data-based procedures used in diagnosis and assessment of response to treatment as well as disagreements in the perceptions of caregivers over child behavior and therapeutic improvement (i.e., informant discrepancy), which eventually became an important concern among professionals involved in the care of children with disabilities. Data collection for these studies required development of an early prototype of what was to later become the Checkmate Plus Symptom Inventories.

 

One of the major findings from these studies was the limited use of standardized assessment instruments in clinical management (Gadow, 1982a, 1982b, 1983). To help remedy this situation, the next logical step was the development of a practical assessment battery to address these needs, and this became the objective of an ambitious program of research in the 1980’s. At this time, the two primary categories of mental health assessments were structured psychiatric interviews based on consensus-driven characterizations of psychiatric syndromes (medical or categorical model) and behavior rating scales developed from factor analyses of a pool of items that reflected common problem behaviors (dimensional model).

 

On the simplest level, the practicality of structured interviews was challenged on the grounds they were much too expensive to administer and took too much time, and critics were critical of their inherent, tenuous notions about what constituted a disorder.  Behavior rating scales were criticized for being easily biased; their factor structure lacked footing in the biological sciences; and their items did not match well with diagnostic criteria. Regardless of one’s preferences, it remains the case that the wellspring of good intentions that led to the development of these assessments also generated an occasionally vituperative controversy over relative superiority that remains unresolved. Although there are clear advantages and disadvantages to each approach, the real word imposed a compromise strategy that ultimately incorporated aspects of each, though for different reasons and different objectives, and these psychometric hybrids remain the dominant force in everyday clinical settings to this day. 

 

Conceptual Model: 1980’s

 

The primary conceptual framework for the diagnosis and treatment of mental health disorders in the United States is the Diagnostic and Statistical Manual of Mental Disorders (DSM), and in the early 1980s, the third edition was current (American Psychiatric Association, 1980). The first hybrid measure to eventually become a Checkmate Plus Symptom Inventory was a behavior rating scale based on the symptoms of disruptive behaviors disorders, which was soon followed in 1986 by a more broadband scale that incorporated the symptoms of anxiety, depression, and autism (Stony Brook Child Symptom Inventory-3) that was developed by myself and Dr. Joyce Sprafkin, a collaboration that lead to additional iterations. The Child Symptom Inventory-3 was revised shortly thereafter with publication of the 1987 version of the DSM (DSM-III-R). Owing to their inherent simplicity and readability and self-evident functionality, these Symptom Inventories, which were widely disseminated, soon became a valuable clinical assessment tool in a busy child and adolescent psychiatry outpatient clinic as well as a useful teaching aide for clinicians in training.

 

Clinical considerations. Development of Checkmate Plus Symptom Inventories required sensitization to several practical considerations. Perhaps the single most important factor for children referred for clinical evaluation of a mental health problem is that they can (and often do) behave very differently in different situations (e.g., home vs. school vs. doctor’s office), a phenomenon known by various names, one of which is situation specificity (Gadow & Drabick, 2012). This of course would mean that reports from the child’s parent could differ in important ways from the report of their teacher (Gadow, 1977) or doctor (Sleator & Ullmann, 1981), sometimes referred to as informant discrepancy, which has important clinical implications. To address this situation, it was necessary to create versions or methods of assessment that facilitated the capture of information from multiple sources. For example, it makes little sense to put symptom items in a teacher rating scale that only parents are likely to observe or have any direct knowledge.

 

In addition to informant discrepancy, several other considerations shaped the design and development of the Checkmate Plus Symptom Inventories. Symptom statements had to be phrased in such a way as to be understandable to lay people. Further, they had to be parsimonious yet fleshed-out enough to adequately capture their meaning, which was occasionally a challenge. I also believed that measures used to facilitate diagnosis offer advantages when used to evaluate response to treatment, particularly when the treatment is directed toward symptom reduction (which is, of course, not always the case).

 

Research. Unquestionably one of the most important motivations for the development of the Symptom Inventories was their application in research. It was my belief that practical, cost-effective symptom inventories would be of great value to researchers who were not able, for whatever reason, to administer structured psychiatric interviews. In other words, the availability of such measures would facilitate the further validation of DSM-defined psychiatric syndromes by enabling the involvement of an even larger number of researchers in this effort.

 

Expansion: The 1990’s

 

The 1990’s witnessed considerable expansion of the Symptom Inventories, both in breath and scope. For many disorders (but certainly not all), the DSM does not articulate specific, age-related, diagnostic rules; nevertheless, data collected in the 1980’s indicated age differences in the relative prevalence and severity of specific symptoms (as well as gender differences). What to do? One thing that could be done to address these age-related differences would be to create inventories that reflected these patterns of pathology. Thus, symptom inventories were developed for four different age groups: early childhood, elementary school age, secondary school age, and adults.

 

Dimensional model. Perhaps the most radical departure from conventional practice was to incorporate a dimensional model into Checkmate Plus Symptom Inventories (with appropriate caveats about score interpretation) as there were a number of clinical scenarios where such information might prove helpful, not the least of which was DSM-IV’s Not Otherwise Specified (in DSM-5, Other Specified) diagnostic classifications. For example, the person being evaluated may not meet conventional symptom count criteria for a specific disorder but nevertheless receive a T score of 70 and be clinically impaired. To achieve this end, normative data were collected. These data permitted the generation of T scores and cutoffs based on standard deviations. Review of these data indicated important gender differences in symptom severity for some (but certainly not all) disorders, thus revealing yet another application of a dimensional model of symptom severity. In other words, exclusive reliance on gender-neutral diagnostic algorithms could introduce bias in clinical decision making. 

 

Impairment. One thing missing from most DSM-referenced symptom rating scales is the assessment of impairment, a critical criterion in the DSM diagnostic algorithms. To address this need, the Symptom Inventories were amended to include an impairment question for each multi-item symptom category, and in keeping with the DSM, impairment is categorical (impaired: yes, no) and pertains specifically to symptom-induced impairment (Gadow, Kaat, & Lecavalier, 2013; Kaat, Gadow, & Lecavalier, 2013). As was the case for symptom severity and symptom cutoff scores, the person being evaluated may not meet full symptom count criteria for a specific disorder but nevertheless be impaired (possibly Other Specified), or vice versa (i.e., symptomatic but not impaired; potentially at risk).

 

Response to treatment. As previously noted, one application for which Checkmate Plus Symptom Inventories were ideally suited was the evaluation of response to treatment and monitoring longer-term clinical care. Owing to commonly expressed concerns about time constraints, treatment response and clinical progress measures generally contain fewer items than their diagnostically-oriented counterparts. Three such Checkmate Plus measures are the ADHD-Symptom Checklist-4, the CASI Progress Monitor, and the ADHD School Observation Code. Although brevity has its virtues, it is not always possible to know á priori the true breadth of therapeutic improvement with highly co-morbid treatment populations as is illustrated by the use of the Child and Adolescent Symptom Inventory in a controlled clinical trial of children with severe aggression (Arnold, Gadow, Farmer, et al., 2015; Gadow,  Arnold, Molina, et al., 2015). For more examples of treatment-response studies click the Research button and follow the link to Research Bibliography.

 

Current Developments: 2010 and Beyond

 

In 2013, American Psychiatric Association published the fifth edition of the DSM (DSM-5). Although there was a bit of a kerfuffle over some of the changes, for the most part they had relatively little impact on the Symptom Inventories as changes to the DSM were for the most part minor. In cases where updates were deemed to be warranted, DSM-5 versions are available.

Multicultural research. As part of an international effort to promote research into DSM-defined syndromes and foster data-driven clinical practices, Checkmate Plus Symptom Inventories continue to be translated into native languages from countries around the world. Recent representative examples from 2019-2020 are Brazil with translation into Portuguese (Gracia, Lara, Ottoni, et al., 2020); Egypt with translation into Egyptian Arabic (Sakhr, Hassan, & Desoky, 2020); India with translation into Hindi (Pandey, Gupta, Upadhyay, et al., 2020); Iran with translation into Persian (Farsi) (Ghadampour, Khodarahimi, Bougar, et al., 2020); Mexico with translation into Latin American Spanish (Haack, Araujo, Meza, et al., 2020); Netherlands with translation into Dutch (van den Bedem, Dockrell, van Alphen, et al., 2020); Norway with translation into Norwegian (Overgaard, Oerbeck, Friis, et al., 2019); Nepal with translation into Nepali (Dhakal, Niraula, Sharma, et al. 2019), East Africa with multiple translations into Luganda, one of the tribal languages of Uganda (Mpango, Ssembajjwe, Muyingo, et al., 2020; Mpango, Kinyanda, Rukundo, et a., 2017), and so forth. For more examples click the Research button and follow the link to Research Bibliography.

Empirically-derived scoring algorithms and subscales. In keeping with the spirit of hybrid assessment tools, efforts are being made to use DSM symptomatology to develop and validate empirically-derived subscales as well as empirically-generated scoring algorithms. Much of this research involves the Child and Adolescent Symptom Inventory (CASI). Representative examples of empirically-derived subscales are the CASI Autism Spectrum Disorder informant-specific scales (DeVincent, & Gadow, 2009; DeVincent, Gadow, Strong, et al., 2008; Gadow, Schwartz, DeVincent, et al., 2008), CASI Oppositional Defiant Disorder subscales (Drabick & Gadow, 2012); CASI Autism Anxiety scale (Sukhodolsky, Scahill, Gadow, et al., 2008), CASI Social Anhedonia scale (Gadow & Garman, 2020) and CASI Emotion Dysregulation scale (Johnstone, Leung, Srikanth, et al., 2020). In addition, work continues on empirically-derived scoring algorithms for existing CASI DSM-referenced subscales such as childhood psychosis (Rizvi, Salcedo, Youngstrom, et al., 2019), depression (Salcedo, Chen, Youngstrom, et al., 2018), and bipolar spectrum (Ong, Youngstrom, Chua, et al., 2017).

 

Online scoring and assessment services. Checkmate Plus continues to explore ways to improve its clinical assessment tools and expand its applications into telemedicine.  The most ambitious effort in that direction is the development of online scoring, administration, and data upload systems.

 

 

References

 

            American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.

            American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author.

            American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

            Arnold, L., Gadow, K.D., Farmer, C.A., Findling, R.L., Bukstein, O., Molina, B.S.G., et al. (2015).  Comorbid anxiety and social avoidance in TOSCA: Response to adding risperidone to stimulant & parent training; Mediation of disruptive symptom response. Journal of Child and Adolescent Psychopharmacology, 25,203-212. DOI: 10. 1089/cap.2014.0104

            DeVincent, C.J., & Gadow, K.D. (2009). Relative clinical utility of three Child Symptom Inventory-4 scoring algorithms for differentiating children with autism spectrum disorder versus attention-deficit hyperactivity disorder. Autism Research, 2, 312-321.  doi:10.1002/aur.106

            DeVincent, C., Gadow, K.D., Strong, G., Schwartz, J., & Cuva, S. (2008). Screening for autism spectrum disorder with the Early Childhood Inventory-4. Journal of Developmental and Behavioral Pediatrics, 29, 1-10. DOI: 10.1097/DBP.0b013e3181943595.

            Dhakal, S., Niraula, S., Sharma, N.P., Sthapit, S., Bennett, E., Vaswani, A., Pandey, R., Kumari, V., & Lau, J.Y.F. (2019). History of abuse and neglect and their associations with mental health in rescued child labourers in Nepal. Australian and New Zealand Journal of Psychiatry, 53(12), 1199-1207. DOI: 10.1177/0004867419853882.

            Drabick, D.A.G., & Gadow, K.D. (2012). Deconstructing oppositional defiant disorder: Clinic-based evidence for an anger/irritability phenotype. Journal of the American Academy of Child and Adolescent Psychiatry, 51, 384-393. DOI: 10.1016/j.jaac.2012.01.010.

            Gadow, K.D. (1977). Psychotropic and antiepileptic drug treatment in early childhood special education.  Champaign, IL: Institute for Child Behavior and Development.  (ERIC Document Reproduction Service No. ED 162 494)

            Gadow, K.D. (1981).  Drug therapy for hyperactivity: Treatment procedures in natural settings.  In K.D. Gadow & J. Loney (Eds.), Psychosocial aspects of drug treatment for hyperactivity (pp. 325‑378).  Boulder, CO:  Westview Press.

            Gadow, K.D. (1982a).  School involvement in pharmacotherapy for behavior disorders.  Journal of Special Education, 16, 385‑399. DOI: 10.1177/002246698201600403.

            Gadow, K.D. (1982b).  School involvement in the treatment of seizure disorders.  Epilepsia, 23, 215‑224. DOI: 10.1111/j.1528-1157.1982.tb05069.x

            Gadow, K.D. (1983).  Pharmacotherapy for behavior disorder: Typical treatment practices.  Clinical Pediatrics, 22, 48‑53. DOI: 10.1177/000992288302200106.

            Gadow, K.D., Arnold, L.E., Molina, B.S.G., Findling R.L., Bukstein, O.G., Brown, N.V., et al.  (2014). Risperidone added to parent training + stimulant medication: Effects on attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, and peer aggression. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 948-959.

            Gadow, K.D., & Drabick, D.A.G. (2012). Anger and irritability symptoms among youth with ODD: Cross-informant versus source-exclusive syndromes. Journal of Abnormal Child Psychology, 40, 1073-1085. DOI: 10.1007/s10802-012-9637-4.

            Gadow, K.D., Kaat, A.J., & Lecavalier, L. (2013). Relation of symptom-induced impairment with other illness parameters in clinic-referred youth. Journal of Child Psychology and Psychiatry, 54, 1198-1207. DOI: 10.1111/jcpp.12077.

            Gadow K.D., & Garman, H. (2020). Social anhedonia in children and adolescents with autism spectrum disorder and psychiatry referrals. Journal of Clinical Child and Adolescent Psychology, 49(2). DOI: 10.1080/15374416.2018.1539912.

            Gadow, K.D., Schwartz, J., DeVincent, C., Strong, G., & Cuva, S. (2008). Clinical utility of autism spectrum disorder scoring algorithms for the Child Symptom Inventory. Journal of Autism and Developmental Disorders, 38, 419-427. DOI: 10.1007/s10803-007-0408-y.

            Ghadampour, E., Khodarahimi, S., Bougar, M.R., & Nahaboo, S. (2020). Single mothers' attachment styles and personality influences on child psychopathology. (2020). American Journal of Family Therapy. Early Access: JAN 2020. DOI: 10.1080/01926187.2020.1716870

            Gracia, D.F.K., Lara, D.R., Ottoni, G.D., & de Araujo, R.M.F. (2020). Analysis of association between temperament and psychological symptoms using the Affective and Emotional Composite Temperament (AFECT) model: An internet-based survey. Journal of Affective Disorders, 264, 446-454. DOI: 10.1016/j.jad.2019.11.073.

            Haack, L.M., Araujo, E.A., Meza, J., Friedman, L.M., Spiess, M., Beltran, D.K.A., Delucchi, K., Herladez, A.M., & Pfiffner, L. (2020). Can school mental health providers deliver psychosocial treatment improving youth attention and behavior in Mexico? A pilot randomized controlled trial of CLS-FUERTE. Journal of Attention Disorders, Early Access: SEP 2020. DOI: 10.1177/1087054720959698.

            Johnstone, J.M., Leung, B., Srikanth, P., Hatsu, I., Perez, P., Tost, G., Gracious, B., Aman, M., Gadow, K.D., Fielding, R., Bukstein, O., & Arnold, L.A. (2020). Development of a composite primary outcome score for children with ADHD and emotional dysregulation. Journal of Child and Adolescent Psychopharmacology, (30)3, 1-7. DOI: 10.1089/cap.2019.0179.

            Kaat, A.J., Gadow, K.D., & Lecavalier, L. (2013). Psychiatric symptom impairment in children with autism spectrum disorders. Journal of Abnormal Child Psychology, 41, 959-969. DOI: 10.1007/s10802-013-9739-7.

            Mpango, R.S., Ssembajjwe, W., Muyingo, S.K., Gadow, K.D., Patel, V., & Kinyanda, E. (2020). Adaptation and validation of a brief DSM-5 based psychiatric rating scale for childhood and adolescent mental health in Uganda: The Child and Adolescent Symptom Inventory-Progress Monitor (CASI-PM). Vulnerable Children and Youth Studies, 15(2), 144-154. DOI: 10.1080/17450128.2019.1686672.

            Mpango, R.S., Kinyanda, E., Rukundo, G.Z., Gadow, K.D., & Patel, K. (2017). Cross-cultural adaptation of the Child and Adolescent Symptom Inventory-5 (CASI-5) for use in central and south-western Uganda: the CHAKA project. Tropical Doctor, 47(4), 347-354. DOI: 10.1177/0049475517724688.

            Ong, M.L., Youngstrom, E.A., Chua, J.J.X., Halverson, T.F., Horwitz, S.M., Storfer-Isser, A., Frazier, T.W., Fristad, M.A., Arnold, L.E., Phillips, M.L., … LAMS Grp (2017). Comparing the CASI-4R and the PGBI-10 M for differentiating bipolar spectrum disorders from other outpatient diagnoses in youth. Journal of Abnormal Child Psychology, 45(3), 611-623.

            Overgaard, K.R., Oerbeck, B., Friis, S., Biele, G., Pripp, A.H., Aase, H., & Zeiner, P. (2019). Screening with an ADHD-specific rating scale in preschoolers: a cross-cultural comparison of the Early Childhood Inventory-4. Psychological Assessment, 31(8), 985-994. DOI: 10.1037/pas0000722.

            Pandey, R., Gupta, S., Upadhyay, A., Gupta, R.P., Shukla, M., Mishra, R.C., Arya, Y.K., Singh, T., Niraula, S., Lau, J.Y.F. et al. (2020). Childhood maltreatment and its mental health consequences among Indian adolescents with a history of child work. Australian and New Zealand Journal of Psychiatry, Article Number: 0004867420909524. Early Access: MAR 2020. DOI: 10.1177/0004867420909524,

            Rizvi, S. H., Salcedo, S., Youngstrom, E. A., Freeman, L. K., Gadow, K. D., Fristad, M. A., Birmaher, B., Kowatch, R. A., Horwitz, S. M., Frazier, T. W., Arnold, L. E., Taylor, H. G., & Findling, R. L. (2019). Diagnostic accuracy of CASI-4R psychosis subscale for children evaluated in outpatient clinics. Journal of Clinical Child and Adolescent Psychology, 48(4), 610-621. DOI: 10.1080/15374416.2017.1410824.

            Sakhr, H.M., Hassan, M.H., & Desoky, T. (2020). Possible associations of disturbed neurometals and ammonia with glycaemic control in type 1 diabetic children with attention deficit hyperactivity disorder. Biological Trace Element Research. Early Access: FEB 2020. DOI: 10.1007/s12011-020-02063-5

            Salcedo, S., Chen, Y. L., Youngstrom, E. A., Fristad, M. A., Gadow, K. D., Horwitz, S. M., Frazier, T. W., Arnold, L. E., Phillips, M. L., Birmaher, B., Kowatch, R. A., & Findling, R. L. (2018). Diagnostic efficiency of the Child and Adolescent Symptom Inventory (CASI-4R) depression subscales for identifying youth mood disorders. Journal of Clinical Child and Adolescent Psychology, 47(5), 832-846. DOI: 10.1080/15374416.2017.1280807

            Sleator, E.K., & Ullmann, R.K. (1981). Can the physician diagnose hyperactivity in the office? Pediatrics, 67(1),13-17.

            Sprague, R.L., & Gadow, K.D. (1976).  The role of the teacher in drug treatment.  School Review, 85(1), 109‑120. DOI: 10.1086/443317

            Sukhodolsky, D.G., Scahill, L., Gadow, K.D., Eugene, A.L., Aman, M.G., McDougle, C.J., McCracken, J.T., Tierney, E., White, S.W., Lecavalier, L., & Vitiello, B. (2008). Parent-rated anxiety symptoms in children with pervasive developmental disorders: Frequency and association with core autism symptoms and cognitive functioning. Journal of Abnormal Child Psychology, 36, 117-128. DOI: 10.1007/s10802-007-9165-9.

            van den Bedem, N.P., Dockrell, J.E., van Alphen, P.M., & Rieffe, C. (2020). Emotional competence mediates the relationship between communication problems and reactive externalizing problems in children with and without developmental language disorder: a longitudinal study. International Journal of Environmental Research and Public Health, 17(16), Article Number: 6008. DOI: 10.3390/ijerph17166008.