Advances in Neurodevelopmental Disorders https://doi.org/10.1007/s41252-018-0078-0
ORIGINAL PAPER
Psychotropic Medication Prescription for Autism: Data Sources for Decision Making Chrystal Jansz Rieken 1
&
Annette K. Griffith 2 & Jacqueline Huscroft D’Angelo 3 & Tyler Re 2
# Springer Nature Switzerland AG 2018
Abstract Currently, there are two medications (i.e., risperidone and aripiprazole) with sufficient established evidence for effectively treating related symptoms (i.e., irritability and hyperactivity) of autism spectrum disorder (ASD). A recent study on the prevalence of psychotropic medication treatment for children with ASD in the USA found that approximately 65% had filled a prescription for at least one psychotropic medication, 35% filled prescriptions across two medication classes, and 15% filled prescriptions across three or more classes concurrently. While these numbers inform rates of psychotropic medication use among this population, little is known about the prescribing practices of practitioners treating ASD symptoms in pediatric populations. There are currently no known empirical studies examining the prescribing practitioner’s perspective and, as a result, it is not known how prescribers are making psychotropic medication management decisions or what factors may influence those decisions. Therefore, the current study sought to evaluate the degree to which prescribers rated the importance of several sources of information when prescribing and managing psychotropic medication for the treatment of core and secondary symptoms of ASD. Keywords Autism . Pediatric . Psychotropic . Medication . Prescribing
The core symptoms of autism spectrum disorder (ASD) required for diagnosis include persistent deficits in social communication and social interaction across multiple contexts, and restricted, repetitive patterns of behavior, interests, or activities (DSM-5; American Psychiatric Association 2013). In addition to these core symptoms, many individuals with ASD exhibit one or more related (i.e., secondary) symptoms which may include hyperactivity, inattention, problems with eating and sleeping, aggression, self-injurious behaviors, destruction, and/or compulsive behaviors (Bradley et al. 2011; Paul et al. 2015; Rivard et al. 2016). In fact, research has consistently demonstrated that high rates of related symptoms are common among individuals with ASD (Bradley et al. 2011; Gray et al. 2012; Paul et al. 2015). While there remains no cure for ASD, one treatment increasing in
* Chrystal Jansz Rieken
[email protected] 1
Texas Tech University, Lubbock, TX, USA
2
The Chicago School of Professional Psychology, Chicago, IL, USA
3
Texas Christian University, Fort Worth, TX, USA
use is psychotropic medication (Madden et al. 2017). Currently, there are two medications (i.e., risperidone and aripiprazole) with sufficient established evidence for effectively treating specific related symptoms (i.e., irritability and hyperactivity) that have been approved by the Food and Drug Administration (FDA). No medications have shown conclusively effective for the core symptoms of ASD (Siegel and Beaulieu 2011). The use of psychotropic medications by individuals with ASD has remained controversial, especially for children, due to the limited research that has been conducted to determine the safety and efficacy for this population (Barnard-Brak et al. 2016; Bertelli et al. 2016; Murray et al. 2014; Park et al. 2016). As stated above, only two psychotropic medications have been approved for use with individuals with ASD for the treatment of related symptoms (i.e., irritability specifically); however, a number of different psychotropic medications are often prescribed off label, with particularly high rates of antipsychotics, antidepressants, and stimulants (Braüner et al. 2016; Freeman and McIntosh 2009; Murray et al. 2014; Park et al. 2016; Poling et al. 2017). Within the field of child and adolescent psychiatry, prescribing off-label is seen as Bethical, appropriate and consistent with general medical practice^ (AACAP 2012, p. 11). While this might be the case
Adv Neurodev Disord
traditionally, and even safe and effective in some instances, the dangers of polypharmacy in pediatric populations are considerable, with the occurrence of several reported negative outcomes including death (Zonfrillo et al. 2005). A recent study on the prevalence of psychotropic medication treatment for children with ASD in the USA found that approximately 65% had filled a prescription for at least one psychotropic medication, 35% filled prescriptions across two medication classes, and 15% filled prescriptions across three or more classes concurrently (Spencer et al. 2013). These high rates of polypharmacy remain consistent with previous literature reporting similar findings within this population (Bramble 2007; Shireman et al. 2005). More recent literature has demonstrated that rates of polypharmacy remain high for this population (Poling et al. 2017). While these numbers inform rates of psychotropic medication use, little is known about the prescribing practices of practitioners treating ASD symptoms in pediatric populations. The AACAP guidelines for psychotropic medication use for preschool-aged children, regardless of specific diagnosis, advise that medications be used in instances of moderate to severe symptoms and functional impairment where there is a high risk of injury to self/others or worsening family dysfunction, and after other non-pharmacological interventions have failed; however, a more systematic process for psychotropic medication management is not included. These guidelines have been criticized as insufficient for guiding the prescription process (Frazier et al. 2011; Murray et al. 2014). In the absence of more systematic guidelines, it can be assumed that prescribers rely on clinical judgment when making psychotropic medication decisions (Aarons 2005; Hoagwood et al. 2001). Literature on the accuracy of clinical judgment suggests that there is reason to be concerned about the appropriateness of using judgment alone as a source of information, especially considering the dangers involved in prescribing and monitoring effectiveness of psychotropic medication in children (Hatfield and Ogles 2006). This may be particularly true in cases where medications without FDA approval are prescribed to children (i.e., prescribing Boff label^), where danger and risk are increased. Other sources of information readily available to prescribers, such as medical tests (e.g., blood work) are appropriate for measuring certain dimensions of medication effects (i.e., therapeutic levels); however, they are not appropriate for measuring the effects of psychotropic medication on ASD symptoms (e.g., behavioral challenges). Finally, standardized measures (e.g., Clinical Global Impressions Scale (CGI; Guy 1976), Aberrant Behavior Checklist (ABC; Aman et al. 1985), and Children’s Yale Brown Obsessive Compulsive Scale (CYBOCS; Scahill et al. 1997)) are commonly used in medication trials and published efficacy studies; however, it is not known if prescribers use these measures, or other forms of quantifiable data, when overseeing medical
treatment for children with ASD. In fact, literature in other fields suggests that their use in clinical practice is not common (Garland et al. 2003; Grove and Meehl 1996). Due to physiological differences and possible differences in the ways that psychiatric disorders are manifested in this population, without prescribing guidelines, it is unclear if psychotropic medications for children, youth, and adults with ASD require the same dosing regimens, have the same levels of effect, or have the same safety profiles as they do for typically developing adults (Barnard-Brak et al. 2016; Haw and Stubbs 2005). This may make it difficult for prescribers to make informed decisions about risks and benefits when making psychotropic medication management decisions (Myers 2008). Several recent studies have emerged describing patient characteristics and variables related to medication use in this population (e.g., Lake et al. 2017; Lopez De-Fede et al. 2014; Rosenberg et al. 2010); however, these sources of information (e.g., pharmacy records, Medicaid claims) better inform prescriptions filled than the prescribing practices, specifically. How prescribers come to the decision to prescribe, and what information they use to monitor and judge effectiveness, or to make medication changes, will impact the course of treatment the patient receives. There are currently no empirical studies examining the prescribing practices of practitioners for clients with ASD, and as a result, it is not known how prescribers are making psychotropic medication management decisions or what factors may influence those decisions. Therefore, the current study sought to evaluate the degree to which prescribers rated the importance of several sources of information when prescribing and managing psychotropic medication for the treatment of core and secondary symptoms of ASD. Specifically, the aim of this study was to examine the influence of factors such as current or previous use of psychotropic medications, current or previous participation in psychosocial or behavioral interventions, availability of psychosocial or behavioral interventions, quantified and/or anecdotal information from caregivers, and quantified and/or anecdotal information from other professionals across three key points in service (i.e., at time of prescription, when monitoring effectiveness, when making a medication or dosage change).
Method Participants Practitioners of any specialty who could prescribe psychotropic medication to children with ASD according to their medical license were invited to participate in the study. The decision to include a range of providers was made because the literature on medication use indicates that medications are prescribed by practitioners with varying areas of specialty
Adv Neurodev Disord
(e.g., psychiatrists, neurologists, developmental pediatricians; Rosenberg et al. 2010). Fifty-four surveys were started. Data from four surveys were excluded based on respondent type, as they could not prescribe medication (PhD only—3; parent only—1). Of the remaining 50 surveys, 23 (46%) respondents completed at least half of the survey. Gender, race, and medical training information were provided by 27 (54%) respondents; 14 (52%) were female, 24 (89%) were Caucasian (89%), and 25 (93%) reported receiving specific instruction in treating ASD with medication as part of their medical training. Thirty participants reported the geographic locations in which practice; 13 (43%) reported practicing in the Western region of the USA, followed by the Southeast (20%), Southwest (17%), Northeast (13%), and Midwest (7%).
Procedure Third-party organizations were identified to assist in recruiting. These organizations included medical/clinical settings (e.g., where patients are seen and treated) and practice organizations (e.g., American Pediatric Society) or administration settings (e.g., Chairman of Pediatrics in medical departments at universities and training hospitals). Each organization was provided a digital copy of study information letter and a link to the survey. The recruiting organizations were asked to distribute the study information letter and survey link on behalf of the researchers using their professional contacts, websites, and social media platforms. Because of this method of recruitment and dissemination, the number of emails or referrals to the survey that were distributed by the organizations and clinical settings contacted is unknown; however, results of other web-based surveys suggest that 25 to 43% of individuals who viewed the recruitment information may have responded (e.g., Blydenburg and Diller 2016; Hughes et al. 2017). The first 20 respondents who completed the survey were offered compensation in the form of a $25 Amazon® gift card delivered to them through e-mail from the Amazon® website.
Measure A survey was developed in Qualtrics ®, an online program. Survey items were initially developed based on current literature in the fields of ASD and medication, and tested with input from various professionals with experience with pediatric populations with ASD, as well as measurement. Experts included behavior analysts, a pediatric neurologist, family practice physician, parent of a child with ASD, and a statistician. Scaled questions were used to evaluate which types of information prescribers rated as least-to-most important when making psychotropic medication treatment decision for their pediatric patients with ASD. Using a 3-point scale (1 = very/
most important, 2 = somewhat important, 3 = not/least important), prescribers were asked to rate the importance each of 12 different types of information to their decision-making process. On this scale, a rating closer to 1 represented a higher level of importance for that variable. This can be interpreted to mean that variables with a rating closer to 1 have more influence on prescriber decisions about medication.
Data Analysis Following closure of the survey, all data were transferred from Qualtrics® to SPSS®, a statistical software used for data collection, analysis, and reporting. Data for the 27 completed surveys were included in the analysis presented here. Once data were entered into SPSS®, they were inspected to ensure that the data transfer was accurate (e.g., there were no missing or incorrectly transferred data). A series of analyses were conducted to analyze the data. First, descriptive analyses (e.g., means and standard deviations) were conducted for each item to identify the most and least influential informational variables at each measurement period. Second, data were analyzed using the Friedman test (Friedman 1937). The Friedman test is the non-parametric version of the one-way, repeated measures ANOVA and appropriate for ordinal (e.g., very important, somewhat important, not important) data. The test is used to compare group means when participants are measured on three or more different times. The test indicates if there is a statistically significant difference between mean scores of related groups across measurement times (Lund and Lund 2013). When a statistically significant result was found using the Friedman test, a Wilcoxon signed-rank test was used for post hoc comparisons using the same Bonferroni adjustment for significance, to reduce the likelihood of a type I error. Finally, Cronbach’s alpha was selected to measure the internal consistency of the items in the practitioner-level scale.
Results Descriptive data can be found in Table 1. Mean ratings of importance were ranked for each item in the scale at each service time evaluated (i.e., at initial time of prescription). To look for patterns, data were analyzed to identify variables rated as least and most important across service times. Specifically, data were reviewed to identify the three most (i.e., mean score closer to 1) and least (i.e., mean score closer to 3) important variables at each service time. Rank ordering the variables by mean score of importance from most-to-least important showed that no source of information (e.g., anecdotal report and formal assessment scores) was consistently rated in the top 3 most important variables across the three service times. Four variables were rated in the top 3 most important
Adv Neurodev Disord Table 1 Mean ratings of level of importance of treatment data sources at the time of prescription, monitoring medication effectiveness, making dosage change or terminating medication, and Friedman’s chi-square Initial prescription
Information variables Overall Anecdotal information from parents
Monitoring effectiveness
Dosage change/termination
Friedman
M
SD
n
M
SD
n
M
SD
n
χ2
1.64 1.74
0.34 0.656
25 27
1.64 1.96
0.31 0.706
23 23
1.63 1.79
0.35 0.658
22 24
1.92 1.8
1.96 1.68 1.75
0.693 0.548 0.585
28 28 28
1.69 1.46 1.68
0.679 0.508 0.557
26 26 25
1.87 1.54 1.67
0.626 0.509 0.702
23 24 24
0.933 2 0.839
Anecdotal information from other professionals/service providers Quantified information from parents (e.g., rate, frequency of behaviors) Quantified information from other professionals/service providers (e.g., rate, frequency of behaviors) Formal scores from published outcome measures/standardized measures (other than diagnosis information) Other psychotropic medications (past) Other psychotropic medications (current)
1.86
0.581
29
1.96
0.662
26
1.84
0.624
25
0.955
1.39 1.64
0.629 0.731
28 28
1.79 1.56
0.779 0.651
24 25
1.71 1.58
0.806 0.584
24 24
6.465* 1.105
Other non-psychotropic medications (current) Psychosocial/behavioral treatment (past) Psychosocial/behavioral treatment (current) Progress in psychosocial/behavioral treatments
1.67 1.68 1.46 1.7
0.832 0.67 0.806 0.669
27 28 26 27
1.6 1.65 1.68 1.54
0.577 0.629 0.627 0.721
25 26 25 24
1.71 1.57 1.45 1.42
0.624 0.662 0.671 0.584
24 23 22 24
0.378 0.844 1.902 3.707
Availability of psychosocial/behavioral treatments
1.57
0.69
28
1.39
0.49
23
1.67
0.702
24
2.526
Note. Items were rated on a reversed 3-point Likert scale (1 = very important, 2 = somewhat important, 3 = not important) *p < 0.05
variables across two measurement times: if psychosocial/ behavioral treatment were available to the patient (at time of initial prescription and when monitoring treatment effectiveness), if the patient was currently receiving psychosocial/behavioral treatment (at time of initial prescription and when making a dosage change or terminating treatment), patient’s progress in psychosocial/ behavioral treatment (when monitoring medication effectiveness or when making a dosage change or terminating treatment), and quantified information from parents (when monitoring medication effectiveness or when making a dosage change or terminating treatment). When looking at the variables rated as least important (i.e., score closer to 3), information from formal, published outcome measures (other than those used for diagnosis of ASD) was rated as the second-to-least important variable. Two other variables were rated within the three least important variables across two measurement times: anecdotal information from other professionals or service providers (at time of initial prescription and when making a dosage change or terminating treatment), and anecdotal information from parents (when monitoring effectiveness and when making a dosage change or terminating treatment). Moving from the individual item level to the overall level, there was almost no change in level of importance across the three measurement times. At time of initial prescription, and when monitoring medication effectiveness, the overall mean rating of importance for all items was 1.63. When making
dosage changes or deciding to terminate a medication, the overall mean rating was 1.64. Although the 3-point scale did not allow for a wide distribution, all variables had a mean score of < 2 (i.e., Bsomewhat important^). This means that the average rating for each item always fell within the Bvery important^ range. Overall, it does not appear that there is much variability in the level of importance practitioners place on different sources of treatment information; variables seem to be equally important when initially diagnosing, monitoring effectiveness, and making dosage changes or terminating medication treatment. Statistical analysis did suggest one change in importance that was not obvious from visual inspection of the data. Based on the chi-squared statistic from the Friedman test, there was a statistically significant difference in the level of importance of the item, Bother psychotropic medications taken in the past^ across measurement times, χ2(2) = 6.465, p = 0.039. Post hoc analyses were conducted and the Wilcoxon signed-rank tests along with a Bonferroni correction to examine the relationship between measurement times at the construct level for this item. No pair-wise comparisons were significant at the Bonferroni-corrected significance level. This means that when this item was compared in three measurement combinations (initial monitoring, initial change, monitor change), no significant difference was found at the p < 0.017 level. This result means that the original significant finding was not likely the result of an actual change in importance over time.
Adv Neurodev Disord
Cronbach’s alpha reliability coefficient for items measuring the influence of different sources of data was 0.915 (n = 36 items), indicating an excellent level of internal consistency for this scale/construct (George and Mallery 2003). This means that items in the scale were measuring treatment information variables, and not something else.
Discussion Despite awareness that treating ASD in pediatric populations with psychotropic medications is common, and the growing body of literature addressing the effectiveness of psychotropic medication as a treatment for ASD, a considerable practice gap remains. Currently, very little is known about prescribing practices seen for this population. No known study has previously examined the context in which prescribing occurs from the prescriber’s perspective, and the types of information that affect prescribing decisions in ASD. This study served as an initial investigation on the prescribing practices for psychotropic medication for practitioners serving children with ASD. One promising finding was that variables related to non-medical interventions were rated as one of the top 3 most important variables across two measurement times: if psychosocial/ behavioral treatments were available to the patient (at time of initial prescription), if the patient was currently receiving psychosocial/behavioral treatment (at time of initial prescription and when making a dosage change or terminating treatment), and patient progress in psychosocial/behavioral treatment (when making a dosage change or terminating treatment). This means that, across all measurement times, prescribers rated the inclusion of information about the patients’ psychosocial or behavioral treatments as having high importance. The American Association of Child and Adolescent Psychiatry (AACAP 2009) guidelines state that only after a trial of evidence-based psychosocial/behavioral treatment has failed or is inaccessible should psychotropic medication be prescribed to children, regardless of diagnosis. These outcomes suggest that prescribers may be taking guidelines into consideration or, even if unaware of guidelines, they are considering the same information identified by the AACAP as relevant. It should be noted, however, that although these data are promising, they cannot support the conclusion that prescribers are requiring that non-medical interventions first be attempted. That prescribers in this sample rated psychosocial/ behavioral treatment progress information as important does conflict with respondents’ lower rating of quantified and anecdotal data from other providers and formal scores from published outcome measures (not including diagnostic tools). These variables were rated as being the least important to prescribers across the three measurement times. These low
ratings are conflicting not only with the higher rating of progress in other treatment settings but also with AACAP (2009) guidelines dictating the use of a standardized method of monitoring medication effectiveness. It is not clear how prescribers can evaluate progress without such anecdotal or formal progress information. The variable rated with the highest level of importance was past psychotropic medication use, indicating that prescribers are referring to the patient’s medication history. This is a positive finding in light of the high rates and dangers of polypharmacy in this population.
Limitations and Future Research Directions A methodological limitation of this study was that prescribers were not asked to rate the importance of their own clinical judgment as a source of data when making prescribing decisions and choosing effectiveness data. This would have been a reasonable item to include in this survey, considering the lower value placed on formal information or on information from other treating professionals or parents. There is reason to be concerned about the prevalence and appropriateness of clinical judgment as a source of primary information, especially in the absence of formal assessment, considering the literature on the accuracy of clinical judgment in isolation (Aarons 2005; Hoagwood et al. 2001) and the dangers of medication for this population. This is particularly true in cases where medications without FDA approval (i.e., off label) are prescribed, because in those cases prescribers do not have clinical trials or effectiveness studies to guide practice. In the prescription of psychotropic medication in young children, inaccurate judgment has the potential to result in detrimental outcomes (Zonfrillo et al. 2005). Therefore, future research should investigate the value placed on prevalence and accuracy of clinical judgment in the prescription and management of psychotropic medication in ASD a discussion of the level of importance cannot be interpreted to infer a level of actual influence and impact on practice. Future studies should also investigate the actual use of various sources of information across the service times identified. Prescribers might endorse an item as being very important but that item may actually have little impact on prescribing and monitoring trends in actual practice. Such could be the case when prescribers know and agree that behavioral interventions are important and effective but do not necessarily encourage caregivers to pursue this line of treatment. If this was found to be the case, it would be beneficial to identify barriers preventing prescribers from actual use and reliance on various sources of information they rate as important or useful. For example, the prescriber might be aware of psychosocial and behavioral evidence-based intervention for ASD, and value the knowledge of their clients’ participation in treatment, but decide that the immediate need for medication
Adv Neurodev Disord
outweighs information from those sources. If other ASDtreatment professions could learn from this information, how to be more helpful to prescribing physicians, perhaps the practice could move towards a higher emphasis on nonpharmacological treatments where warranted, reducing the pediatric patients’ exposure to risk and increasing access to other evidence-based treatments. Finally, in an effort to minimize response effort and increase response rates, a 3-point Likert scale was used. Using a 3-point scale could have prompted respondents regarding what was important based on the listed items. This, coupled with the small sample size, limited the interpretability of the data. Future investigations should use the standard 5-point Likert scale to allow for more variability in responses.
Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interest. Human and Animal Rights and Informed Consent This research involved human participants, and was approved by the Institutional Review Board at the site at which it was conducted. Informed consent was obtained from all participants.
References Aarons, G. A. (2005). Measuring provider attitudes towards evidencebased practice: Consideration of organizational context and individual differences. Child and Adolescent Psychiatric Clinics, 14(2), 255–267. https://doi.org/10.1016/j.chc.2004.04.008. Aman, M. G., Singh, N. N., Stewart, A. W., & Field, C. J. (1985). The aberrant behavior checklist: A behavior rating scale for the assessment of treatment effects. American Journal of Mental Deficiency, 89(5), 485–491 Retrieved from http://psycnet.apa.org/record/198519035-001. American Academy of Child and Adolescent Psychiatry. (2009). Practice parameter on the use of psychotropic medication in children and adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 48(9), 961–973. https://doi.org/10.1097/ CHI.0b013e3181ae0a08. American Academy of Child and Adolescent Psychiatry (2012). A Guide for Public Child Serving Agencies on Psychotropic Medications for Children and Adolescents. Retrieved from http://www.aacap.org/ App_Themes/AACAP/docs/press/guide_for_community_child_ serving_agencies_on_psychotropic_medications_for_children_ and_adolescents_2012.pdf. Accessed 26 Feb 2015. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. Barnard-Brak, L., Davis, T. N., Schmidt, M., & Richman, D. M. (2016). Effects associated with on- and off-label stimulant treatment of core autism and ADHD symptoms exhibited by children with autism spectrum disorder. Developmental Neurorehabilitation, 19(1), 46– 53 http://dx.doi.org.tcsedsystem.idm.oclc.org/10.3109/17518423. 2014.904949. Bertelli, M. O., Rossi, M., Keller, R., & Lassi, S. (2016). Update on psychopharmacology for autism spectrum disorders. Advances in Mental Health and Intellectual Disabilities, 10(1), 6–26. https:// doi.org/10.1108/AMHID-10-2015-0049.
Blydenburg, D. M., & Diller, J. A. (2016). Evaluating components of behavior-analytic training programs. Behavior Analysis in Practice, 9(2), 179–183. https://doi.org/10.1007/s40617-0160123-2. Bradley, E. A., Ames, C. S., & Bolton, P. F. (2011). Psychiatric conditions and behavioral problems in adolescents with intellectual disabilities: Correlates with autism. Canadian Journal of Psychiatry, 56(2), 102–109. https://doi.org/10.1177/070674371105600205. Bramble, D. (2007). Psychotropic drug prescribing in child and adolescent learning disability psychiatry. Journal of Psychopharmacology, 21(5), 486–491. https://doi.org/10.1177/0269881106075642. Braüner, J. V., Johansen, L. M., Roesbjerg, T., & Pagsberg, A. K. (2016). Off-label prescription of psychopharmacological drugs in child and adolescent psychiatry. Journal of Clinical Psychopharmacology, 36(5), 500–507 http://dx.doi.org.tcsedsystem.idm.oclc.org/10. 1097/JCP.0000000000000559. Frazier, T.W., Shattuck, P.T., Carter Narendorf, S., Cooper, B.P., Wagner, M. & Spitznagel, E.L. (2011). Prevalence and correlates of psychotropic medication use in adolescents with an autism spectrum disorder with and without caregiver-reported attention-deficit hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology, 21(6), 571–579. Freeman, E., & McIntosh, D. E. (2009). Current practice in psychopharmacology for children and adolescents with autism spectrum disorders. Psychology in the Schools, 46, 905–909. https://doi.org/10. 1002/pits.20431. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701. Garland, A. F., Kruse, M., & Aarons, G. A. (2003). Clinicians and outcomes measurement: What’s the use? Journal of Behavioral Health Services & Research, 30(4), 393–405. https://doi.org/10.1007/ BF02287427. George, D., & Mallery, M. (2003). Using SPSS for Windows step by step: a simple guide and reference. Boston, MA: Allyn & Bacon. Gray, K., Keating, C., Taffe, J., Brereton, A., Einfeld, S., & Tonge, B. (2012). Trajectory of behavior and emotional problems in autism. American Journal on Intellectual and Developmental Disabilities, 117(2), 121–133. https://doi.org/10.1352/1944-7588-117-2.121. Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency in informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical –statistical controversy. Psychology, Public Policy, and Law, 2(2), 293–323. https://doi. org/10.1037/1076-8971.2.2.293. Guy, W. (Ed.). (1976). Clinical Global Impressions Scale. Rockville: National Institute of Mental Health. Hatfield, D. R., & Ogles, B. M. (2006). The influence of outcomes in assessing client change and treatment decisions. Journal of Clinical Psychology, 62(3), 325–337. https://doi.org/10.1002/jclp.20235. Haw, C., & Stubbs, J. (2005). A survey of off-label prescribing for inpatients with mild intellectual disability and mental illness. Journal of Intellectual Disability Research, 49(11), 858–864. https://doi.org/ 10.1111/j.1365-2788.2005.00723.x. Hoagwood, K., Burns, B. J., Kiser, L., Ringeisen, H., & Schoenwald, S. K. (2001). Evidence-based practice in child and adolescent mental health services. Psychiatric Services, 52(9), 1179–1189. https://doi. org/10.1176/appi.ps.52.9.1179. Hughes, S., Narendorf, S., & Lacasse, J. R. (2017). A national survey of graduate education in psychopharmacology: Advancing the social work perspective on psychiatric medication. Journal of Social Work Educations, 53(3), 424–434. https://doi.org/10.1080/10437797. 2016.1272513. Lake, J. A., Denton, D., Lunsky, Y., Shui, A. M., Veenstra-VanderWeele, J., & Anagnostou, E. (2017). Medical conditions and demographic, service, and clinical factors associated with atypical antipsychotic medication use among children with an autism spectrum disorder.
Adv Neurodev Disord Journal of Autism and Developmental Disorders, 47(5), 1391– 1402. https://doi.org/10.1007/s10803-017-3058-8. Lopez De-Fede, A., Vyavaharkar, M., & Bellinger, J. D. (2014). Antipsychotic prescriptions for children aged 5 years or younger; do we need policy oversight standards? SAGE Open, 4(4). https:// doi.org/10.1177/2158244014555116. Lund, A. & Lund, M. (2013). Laerd Statistics Retrieved from https:// statistics.laerd.com/aboutus.php. Madden, J. M., Lakoma, M. D., Lynch, F. L., Rusinak, D., Owen-Smith, A. A., Coleman, K. J., Quinn, V. P., Yau, V. M., Qian, Y. X., & Croen, L. A. (2017). Psychotropic medication use among insured children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 47(1), 144–154. https://doi.org/10.1007/ s10803-016-2946-7. Murray, M. L., Hsia, Y., Glaser, K., Simonoff, E., Murphy, D. G., Asherson, P. J., Eklund, H., & Wong, I. C. J. (2014). Pharmacological treatments prescribed to people with autism spectrum disorder in primary health care. Psychopharmacology, 231(6), 1011–1021 http://dx.doi.org.tcsedsystem.idm.oclc.org/10.1007/ s00213-013-3140-7. Myers, S. M. (2008). Psychopharmacologic approaches to challenging behaviors in individuals with autism. In B. K. Shapiro & P. J. Accardo (Eds.), Autism frontiers: Clinical issues and innovations (pp. 153–175). Baltimore: P. H. Brookes. Park, S. Y., Cervesi, C., Galling, B., Molteni, S., Walyzada, F., Ameis, S. H., et al. (2016). Antipsychotic use trends in youth with autism spectrum disorder and/or intellectual disability: A meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 55(6), 456–468 http://dx.doi.org.tcsedsystem.idm.oclc. org/10.1016/j.jaac.2016.03.012. Paul, A. R., Mckechanie, A. G., Johnstone, E. C., Cunningham Owens, D. G., & Stanfield, A. C. (2015). Brief report: The association of autistic traits and behavioral patterns in adolescents receiving special educational assistance. Journal of Autism and Developmental Disorders, 45(9), 3055–3060 http://dx.doi.org.tcsedsystem.idm. oclc.org/10.1007/s10803-015-2445-2. Poling, A. D., Ehrhardt, K., & Li, A. (2017). Pharmacotherapy. In J. L. Matson (Ed.), Handbook of treatments for autism (pp. 459476). New York: Springer. Rivard, M., Forget, J., Giroux, N., Mello, C., Kerr, K., & Regli, G. (2016). Observation of socially appropriate and inappropriate behaviors among children with autism spectrum disorder during an
early behavioral intervention program. Journal on Developmental Disabilities, 22(1), 52–67 Retrieved from https://tcsedsystem.idm. oclc.org/login?url=http://search.proquest.com.tcsedsystem.idm. oclc.org/docview/1823375716?accountid=34120. Rosenberg, R., Mandell, D. S., Farmer, J. E., Kiely Law, J., Marvin, A. R., & Law, P. A. (2010). Psychotropic medication use among children with autism spectrum disorders enrolled in a national registry, 2007-2008. Journal of Autism and Developmental Disorders, 40(3), 342–351. https://doi.org/10.1007/s10803-009-0878-1. Scahill, L., Riddle, M. A., McSwiggin-Hardin, M., Ort, S. I., King, R. A., Goodman, W. K., et al. (1997). Children’s Yale-Brown obsessive compulsive scale: Reliability and validity. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 844–852. https:// doi.org/10.1097/00004583-199706000-00023. Shireman, T. I., Reichard, A., & Rigler, S. K. (2005). Psychotropic medication use among Kansas medicaid youths with disabilities. Journal of Child and Adolescent Psychopharmacology, 15(1), 107–115. https://doi.org/10.1089/cap.2005.15.107. Siegel, M., & Beaulieu, A. A. (2011). Psychotropic medications in children with autism spectrum disorders: A systematic review and synthesis for evidence-based practice. Journal of Autism and Developmental Disorders, 42(8), 1592–1605. https://doi.org/10. 1007/s10803-011-1399-2. Spencer, D., Marshall, J., Post, B., Kulakodlu, M., Newschaffer, C., Dennen, T., Azocar, F., & Jain, A. (2013). Psychotropic medication use and polypharmacy in children with autism spectrum disorders. Pediatrics, 132(5), 833–840. https://doi.org/10.1542/ peds.2012-3774. Zonfrillo, M. R., Penn, J. V., & Leonard, H. L. (2005). Pediatric psychotropic polypharmacy. Psychiatry, 2(8), 14–19.
Chrystal Jansz Rieken is now at The Chicago School of Professional Psychology Jacqueline Huscroft D’Angelo is now at The University of NebraskaLincoln Tyler Re is now at The Chicago School of Professional Psychology The authors would like to thank Wesley H. Dotson and Stacy L. Carter for their contributions.
