Adherence to Antipsychotic Adverse Effect Monitoring Among a Referred Sample of Children with Intellectual Disabilities

Abstract

Objectives:

Despite frequent use of antipsychotic medications to target severe behavioral problems among children with intellectual disabilities (ID), there is little information as to the extent to which adverse effect monitoring is in place for this population. The aim of this pilot study was to determine the extent to which monitoring for adverse effects was documented in health records of a cohort of children with ID who had been prescribed antipsychotic medication.

Methods:

Data were available on all children referred to a mental health clinic at a children's hospital in Canada who had ID and behavioral difficulties with intake appointments between September 1, 2016 and November 30, 2017. Charts of all those on antipsychotic medications were reviewed for a 12-week period to determine the extent to which adverse effect monitoring was documented using the parameters stipulated by the Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in Children (CAMESA), including laboratory, anthropometric, and neurological measures.

Results:

The database was composed of 47 patients of whom 25 were on antipsychotics (56% boys; mean age 13 [SD 3] years). The most commonly used antipsychotic was risperidone (48%). The extent of adherence to the guidelines was (1) 96% for weight, height, and body mass index; (2) 84% for extrapyramidal symptom screening; (3) 80% for blood pressure; (4) 64% for abdominal girth and liver enzymes; (5) 60% for fasting plasma glucose; and (6) 56% for fasting lipids. Only 20% had all core recommended parameters documented.

Conclusions:

There were significant gaps in adverse effect monitoring in this cohort. Examination of variation in larger samples from multiple clinical services are required to determine the extent of this quality care gap. Several barriers to adherence are proposed with suggested solutions.

Introduction

There has been a marked increase in the use of antipsychotic medications in children and adolescents for the past three decades in North America, primarily second-generation antipsychotics (SGAs) (Pringsheim et al. 2011a; Harrison et al. 2012; Olfson et al. 2012; Patten et al. 2012; Hales et al. 2018). Recent data suggest a possible leveling off of the increase, at least in Canada (Pringsheim et al. 2018). Persons with autism spectrum disorder (ASD) and intellectual disabilities (ID) are an additional overlapping vulnerable population in which the use of antipsychotic medication can be substantial and higher than other populations (Hsia et al. 2014; Schubart et al. 2014; Park et al. 2016; Madden et al. 2017).

SGAs are associated with multiple adverse side effects. A recent meta-analysis of children and young adults confirmed the relationship of SGAs with increased weight gain, body mass index (BMI), triglyceride levels, extrapyramidal symptoms (EPS), sedation, and somnolence (Pillay et al. 2018). These same adverse effects have been reported in persons with ASD and/or ID taking SGAs (Zuddas et al. 2000; Luby et al. 2006; Cohen et al. 2013; Wink et al. 2014; Yoon et al. 2016). Of particular concerns is onset of these adverse effects at younger ages and in populations that may have greater difficulty in being able to report adverse effects or to advocate for changes in response to adverse effects. In addition, weight gain secondary to the use of SGAs may be more severe in persons with ASD and ID (Hellings et al. 2001).

To address the risk of adverse effects from SGAs, different groups have developed guidelines for adverse effect monitoring. One example specific to children and adolescents is the Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in Children (CAMESA) guidelines. The CAMESA group synthesized evidence-based recommendations for monitoring safety, as well as management recommendations for metabolic and neurological complications associated with the use of SGAs (Ho et al. 2011; Pringsheim et al. 2011b, 2011c). Other examples include the Practice Parameter for the Use of Atypical Antipsychotic Medications in Children and Adolescents by the American Academy of Child and Adolescent Psychiatry (Findling et al. 2013), as well as the 2004 guidelines developed by the American Diabetes Association and others (American Diabetes Association et al. 2004).

Despite the availability of these guidelines, there have been few reports of use of, and adherence to, these guidelines in child and adolescent populations. Two studies were found that used the CAMESA guidelines. One prospectively followed 35 children with Tourette's syndrome and/or attention-deficit/hyperactivity disorder newly prescribed SGAs in Canada; however, that study focused on the extent of emergence of adverse effects rather than adherence to the guideline per se (Pringsheim et al. 2014). A second conducted a retrospective chart review of children and adolescents who had accessed an outpatient psychiatry service in Canada and were prescribed antipsychotics (n = 294) (Coughlin et al. 2018a). Within this sample, none received metabolic monitoring that was fully adherent to the recommended CAMESA guidelines. At baseline, before antipsychotic initiation, only 25% of children and 23% of adolescents had any recommended monitoring documented, with only 24% and 22%, respectively, having any follow-up monitoring within a 1 year window (Coughlin et al. 2018a). Having a greater number of psychiatric appointments and a prescription for another medication (in addition to the SGA) were related to greater metabolic monitoring documentation for the adolescent subgroup, but not the child subgroup (Coughlin et al. 2018a). No studies were found that reported on the use of the CAMESA guidelines for persons with ASD or ID.

There are also a few relevant reports from US populations. For example, glucose and lipid screening were identified for 32% and 13%, respectively, in a retrospective cohort study of children and youth within the period of 30 days before and 180 days after having started an SGA (n = 5370) (Morrato et al. 2010). Significant predictors of glucose and lipid screening in this study included the child being older, having “serious” mental illness, and having mental disorder comorbidities (Morrato et al. 2010). In another US study of youth, only 11% had their serum glucose (or HbA1c) assessed 90 days before and 3 days after dispensing a new SGA (n = 16,304) (Raebel et al. 2014).

A few US studies have also considered compliance with a broader range of recommended tests. For example, in a study of children and adolescents newly started on SGAs, only 0.1% were found to have all baseline (defined as 84 days before and 14 days after, being started on an SGA) and follow-up (defined as 84 days after being started on an SGA or after the date of baseline testing) monitoring, including fasting triglyceride, fasting blood glucose, blood pressure, and weight measurement (n = 1023) (Delate et al. 2014). Among a sample of persons with developmental disabilities (children and adults) taking antipsychotics (mostly SGAs) and enrolled in an integrated primary and psychiatric care clinic, the following rates were identified within a 1-year period: 99% for weight, 98% for blood pressure, 93% for blood glucose/HbA1c, and 89% for lipid profile (n = 499) (Ruiz et al. 2016). Within a retrospective cohort of children aged 5 years and under newly initiated on an SGA, only 5% had all recommended baseline monitoring (lipid profile, blood glucose, blood pressure, and weight measurements) and none had all follow-up measures within a subsequent 1-year period (n = 40) (Kauffman et al. 2017).

The aim of this study was to determine the extent to which a cohort of children with ID who were on antipsychotic medications had documentation of recommended adverse-effect monitoring. It was anticipated that adherence rates might be low given potential challenges completing some of the recommendations in youth with ID, some who may not have the cognitive capacity to understand and comply with some of the tests (e.g., cooperating with an examination of extrapyramidal side effects) and/or the challenge if the youth presented with aggression. Information derived from this project may identify the type and extent of care gaps that may be addressed through quality improvement.

Methods

Setting

This study was based in a clinic at a children's hospital in a city in Canada, which receives all referrals of children and youth to the hospital's outpatient mental health service who have identified ID. All required physician referrals, either external to the hospital (e.g., family doctor and community pediatrician) or internal to the hospital (e.g., neurologist). All services were within the public health system in Canada and as such there were no direct costs to the patient and family for physician visits or laboratory measures.

Sample

Eligibility for this study included that the patient (1) had an intake assessment in the aforementioned clinic between September 1, 2016 and November 30, 2017 and (2) was already taking at least one fixed dose (i.e., not PRN) of an antipsychotic medication at the time of the intake assessment.

Procedures

The study was a secondary data analysis of a data set created on all new intakes to the clinic within the aforementioned time period and which contained basic demographics, as well as extracted data from the health records on medication use and adverse effect monitoring documentation. The data set had been created as part of a quality improvement project using a retrospective chart review examining each subject's initial assessment report, subsequent clinical progress notes, as well as documented blood work on the hospital's electronic medical records system for a period of 12 weeks after the date of the intake assessment. The extracted data points were in line with those stipulated in the CAMESA guidelines (Pringsheim et al. 2011c) specifically: (1) anthropometrics (weight, height, BMI, and waist circumference), (2) blood pressure, (3) screening for EPS (operationalized as documentation of the presence or absences of any EPS symptoms), and (4) blood work (fasting glucose, lipid profile, aspartate aminotransferase [AST], alanine aminotransferase [ALT], and prolactin). Additional recommended blood work is dependent on antipsychotic type (e.g., thyroid-stimulating hormone if quetiapine used; fasting insulin if using quetiapine, olanzapine, or risperidone). Any explicit documentation of these variables within the 12-week period was coded as item completed.

The CAMESA guideline stipulates that the aforementioned information be obtained at baseline before initiation of an SGA and then at set follow-up points if the patient stays on the SGA (Pringsheim et al. 2011c). Given that this is a cohort of youth who were already on antipsychotic medication at the time of assessment and information on whether CAMESA guideline variables were obtained at baseline (before this clinic's involvement) were rarely available, the modification of guideline adherence in this study was, therefore, whether the clinic documented adverse effect monitoring within a 12-week window from the point of clinic intake.

The Aberrant Behavior Checklist (ABC) is a caregiver completed symptom checklist designed specifically for persons with ID and has strong psychometric properties (Aman et al. 1985a, 1985b) This instrument is completed by parent/guardian as part of the intake process of this clinic; however, its inclusion in the intake process started at a point later than the start of the study inclusion period; therefore, ABC scores are only available for a subset of the sample.

Exploratory analysis was conducted to determine whether any of the available and relevant clinical characteristics predicted monitoring adherence. It was hypothesized that greater adherence may be attained for children who were (1) younger, (2) did not have comorbid ASD, (3) lived full time at home, and (4) had lower ABC irritability scores, given that each of these variables might index children for whom it might be easier to complete the recommended measures. Greater adherence may also be related to number of psychotropic medications given presumed higher adverse effect risk with more medications. A single adherence composite measure was created based on a summation of one point each for (1) BMI, (2) EPS, (3) BP, (4) waist circumference, (5) any blood work, and (6) complete blood work. Given the small sample size and exploratory nature of the analysis, any statistical test showing a positive trend based on p < 0.1 was flagged.

Ethics

This study was approved by the ethics board of the participating children's hospital.

Results

Of the 47 new assessments in the participating clinic within the study time period, 25 (53%) were on antipsychotic medications at the time of their initial assessment in this clinic. Of the 25, comorbid ASD was documented for 56% (in addition to their ID, which was required to enter the clinic). Mean age of this group was 13 (SD 3) years, and 14 (56%) were boys. Most lived full time at home (72%), with the remaining either having partial or full placements outside of the home (e.g., group homes). A subset (n = 17) had scores from parent/guardian completed ABC. The mean value on the irritability subscale was 25 (SD 12), with 11 (65%) scoring ≥18 (a cut point used for admission to the RUPP risperidone study) (McCracken et al. 2002).

Only four different SGAs were used, with risperidone being the most frequent (48%) (Table 1). No first-generation antipsychotics were used. A majority of the sample was also taking other nonantipsychotic psychotropic medications. Weigh, height, and BMI were the clinical variables with the most frequent documented completion rate among the recommended clinical monitoring variables, with waist circumference the least frequent. Completion rates for laboratory variables were overall lower than clinical variables, ranging from 52% to 64%. Only 20% had all core values documented.

Table 1.

Frequency Distribution of Medication Use and Adverse Effect Monitoring (n = 25)

Psychotropic medication use	% (n)
Antipsychotics^a
Risperidone	48 (12)
Aripiprazole	28 (7)
Quetiapine	20 (5)
Olanzapine	16 (4)
Hypnotics^b	36 (9)
Antidepressants	28 (7)
Stimulants	28 (7)
Alpha-2 adrenergic agents	24 (6)
Mood stabilizer/anticonvulsant	16 (4)
Benzodiazepines	16 (4)
Mu receptor antagonist	4 (1)
Number of total psychotropic medications
1	24 (6)
2	28 (7)
3	20 (5)
4+	28 (7)
Completion rate for variables recommended by the monitoring guideline
Clinical variables
Weight, height, BMI	96 (24)
Assessment for EPS	84 (21)
Blood pressure	80 (20)
Waist circumference	64 (16)
Laboratory variables^c,d
Liver enzymes^e	64 (16)
Fasting glucose	60 (15)
Lipid profile	56 (14)
Prolactin	52 (13)
All above variables	20 (5)

Three patients were on two antipsychotic combinations: olanzapine–quetiapine, risperidone–quetiapine, and aripiprazole–quetiapine.

Melatonin or trazodone.

TSH was available for 4 of 5 (80%) patients on quetiapine.

Serum insulin was available for 3 of 19 (16%) patients on quetiapine, olanzapine, or risperidone.

Alanine aminotransferase and aspartate aminotransferase.

None of the hypothesized predictors demonstrated a positive statistical trend in relationship to the composite adherence measure (Table 2).

Table 2.

Relationship Between Patient Characteristics and a Composite Measure of Adverse Effect Monitoring Adherence (n = 25)

	Adherence composite ^a
Predictor variable	Mean (SD)
Comorbid autism
No	4.2 (1.3)
Yes	4.4 (1.4) NS
Lives at home full time
Yes	4.2 (1.6)
No	4.6 (0.5) NS
	Spearman correlation
Age (years)	0.23 NS
Number of medications	0.32 NS
Irritability^b	0.16 NS

Adherence composite composed of one point each for (1) BMI, (2) EPS, (3) BP, (4) waist circumference, (5) any blood work, and (6) complete blood work, with a resulting range from 0 to 6.

Irritability subscale of the ABC was only available for n = 17.

NS, not significant.

Discussion

Substantial gaps in adverse effect monitoring for SGAs were identified in a specialized clinic serving a vulnerable population. Given the variability in study designs (e.g., time periods considered), it is not possible to directly contrast findings with other studies. For example, some studies report on the availability of baseline values before the start of an SGA, as recommended in some guidelines. These were not available in this study given its focus on a clinic referred sample already on SGAs. The findings from this study may, therefore, be more applicable to comparisons with specialty clinics receiving persons already having started SGAs.

Although direct contrasts are problematic, comparisons of patterns across studies may be informative. Similar to the other published study using the CAMESA guideline, weight was the most commonly documented of the recommended tests (Coughlin et al. 2018a). However, that study reported that only 15% had documented weight at baseline and only 18% in the 1 year follow-up period (Coughlin et al. 2018a), whereas 96% of this study's cohort had a documented weight within a 12-week period of clinic contact. As noted in the introduction, a high value (99%) for weight documentation was also reported in the medical records of a cohort of persons with developmental disabilities enrolled in a specialized clinic within a 1-year period (Ruiz et al. 2016). Although some of the referenced studies also reported on height, very few reported documentation of BMI (Honey et al. 2013 was one exception), a more critical value than weight alone. Furthermore, none reported on documentation of waist circumference. Waist circumference is recommended as it is an independent predictor of obesity-related health risks beyond BMI (Janssen et al. 2004).

The rate of screening for laboratory variables was lower than that of clinical variables in our study. This is consistent with other studies (Honey et al. 2013; Delate et al. 2014; Ruiz et al. 2016). The coverage rates identified in this study were higher than several in the literature with the exception of values of 93% and 89% for fasting serum glucose and lipids, respectively, in the study by Ruiz et al. (2016), although that study considered a 1-year time period. Of note, none of the aforementioned studies reported on the rates of screening for liver function enzymes and serum insulin. This may signify that these additional laboratory variables are not consistently recognized as a standard of care when screening for SGA adverse effects.

There were no reports of the extent of EPS screening in the reported studies on SGA adverse effect monitoring that would facilitate comparisons. This may in part be a function of ease of extraction of quantitative values that may have specified sections in electronic health record such as weight and laboratory values in contrast to information on a physical examination that may be in text format. However, some electronic health records may incorporate tick boxes for the presence or absence of specific symptoms (e.g., akathisia). This information gap may be particularly concerning for persons with ASD and ID who may be less able to communicate such side effects to their health care provider.

Although not a focus of this study, there may be a number of barriers related to these gaps in care. Ideas were generated in discussions with staff about these results as well as consideration of ideas and initiatives described in the literature. These are summarized in Table 3 with suggested ideas to reduce or overcome these barriers

Table 3.

Factors That May Have Contributed to Gaps in Guideline Adherence with Proposed Remedial Strategies

Agent	Factor	Possible remedial strategies
Patient	Patient not willing and/or able to cooperate with procedures (this may or may not involve safety risk to patient or provider through manifestations such as physical aggression)	Behavior interventions to increase compliance (e.g., positively reinforcing tolerance to incremental exposure to blood pressure cuff)
		Obtain measures in less distressing environment (e.g., parent weighing at home vs. in clinic)
		Restraint procedures to obtain blood work (e.g., within a medical day unit with adequate staff support; with papoose associated with dental examination)
		Coordinate procedures if and when patient under sedation for other medical procedure (e.g., blood work obtained while undergoing dental surgery)
Parent/guardian	Not following up with taking patient for recommended blood work and/or not routinely bringing patient to appointments^a	Explore barriers (e.g., if need safety support in obtaining blood work, consider aforementioned recommendations)
		Consider changes in clinic space and processes so child and family able to participate more fully (e.g., availability of distractors, additional staffing supports, and meeting in safer spaces)
		Provide systematic parent reminders for missing tests
	Drop out from clinic^a	Providing psychoeducation to parents around the clinical significance of adherence with adverse effects monitoring as well as attending follow-up appointments.
	Drop out from clinic^a	Make prescription renewal of antipsychotic medication contingent on obtaining monitoring measures unless documented reason why this is not possible or feasible
Physician and allied health professionals	Errors of omission	Automatic reminders of deficiencies in care generated within electronic records
	Errors of omission	Use of checklists^b
	Lack of awareness of what is needed or associated skills	Education (e.g., relevance of including waist circumference in set of anthropometric values^c,d; on guidelines and guideline implementation^b,e)
	Not believing that obtaining a measure is realistic/feasible^f	If concern is related to patient aggression or noncompliance, consider the possible remedial strategies listed in the section above labeled Patient.
		If issues related to expensive/limited physician time, consider increased role for allied health professionals^e,f
Health system	Lack of systematic feedback on measurement deficits	Periodic audits to track quality of care
	Lack of systematic feedback on measurement deficits	Implement automatic reminders within electronic health record system tied to prescriptions of antipsychotics
	Lack of consensus on needed tests	Additional evidence reviews and consensus meetings to refine core required tests (e.g., variable opinion as to whether fasting insulin required^c)
	Clarity as to who is responsible (e.g., specialist vs. primary care)^g	Development of an accountability framework

Additional cost barriers may occur in jurisdictions where families face upfront costs for physician visits and laboratory costs.

Featherston et al. (2018).

Pringsheim et al. (2011c).

Janssen et al. (2004).

Ronsley et al. (2012).

Coughlin et al. (2018b).

Ronsley et al. (2011).

This study has a number of limitations. The sample size was small and comprised subjects from a single specialized clinic in a tertiary care center. This may affect the generalizability of the results to children and adolescents with ID treated in other settings by other health care providers such as family physicians. The small sample size, along with having access to only a few clinical characteristics in the database, also limited the ability to explore and detect predictors of monitoring adherence. In particular, a positive relationship between number of medications and adherence might be found in a larger sample given the relatively large positive correlation found in this study. In addition, the data were extracted from the available information through a hospital's electronic medical record; as such, blood work obtained at outside laboratories and not included in the hospital's electronic records would not have been captured by our study, leading to potential underestimations of the rates of adherence. Furthermore, this study only investigated monitoring in a window of time, rather than before SGA initiation and at set follow-up points as recommended by various guidelines. Also, adherence was only considered specific to CAMESA recommended variables and not additional variables recommended by other guidelines (e.g., documentation of personal and family history of obesity, diabetes, hypertension, or cardiovascular disease) (American Diabetes Association et al. 2004). This study also did not investigate the reasons behind, and justification for, SGA use, a dimension that requires ongoing study to avoid inappropriate use of SGAs in this population.

Conclusion

Although this clinic may have had higher performance on the extent of adverse effect monitoring than reported in some other studies, there were still important quality care gaps that may place this vulnerable population at risk for undetected emerging health problems. Future studies should include samples that are larger and capture a greater diversity of clinic types for better generalizability to the target population, as well as to examine variation across service types (e.g., primary versus specialized services). In addition, studies should also investigate predictors of adherence to guideline-based monitoring. Finally, future studies should further investigate the effect of various quality improvement interventions aimed at raising the bar of quality of care for this population in concert with determining the extent of appropriateness of SGA use to scrutinize risk-benefit balances.

Clinical Significance

This study identified gaps in the extent to which children and adolescents with ID who were on second-generation antipsychotic medications had medical record documentation that they were monitored for potential adverse effects as stipulated in existing clinical practice guidelines. Identifying gaps in care is essential to drive quality improvement initiatives and to encourage research into intervention evaluations to determine effective strategies for improving practice guideline adherence.

Footnotes

Disclosures

The first author has no disclosures. The second author received travel expenses and an honorarium from an unrestricted educational grant from Shire for a pediatric lecture on fetal alcohol spectrum disorder given in 2017 unrelated to this article.

References

Aman

, Singh

, Stewart

, Field

: Psychometric Characteristics of the Aberrant Behavior Checklist.”. Am J Ment Defic, 89:492–502, 1985a.

Aman

, Singh

, Stewart

, Field

: The Aberrant Behavior Checklist: A Behavior Rating Scale for the Assessment of Treatment Effects. Am J Ment Defic, 89:485–491, 1985b.

American Diabetes

Association

, American Psychiatric

Association

, American Association of Clinical

Endocrinologists

, and North American Association for the Study of

Obesity

: Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes. Diabetes Care, 27:596–601, 2004.

Cohen

, Raffin

, Canitano

, Bodeau

, Bonnot

, Périsse

, Consoli

, Laurent

: Risperidone or Aripiprazole in Children and Adolescents with Autism and/or Intellectual Disability: A Bayesian Meta-Analysis of Efficacy and Secondary Effects. Res Autism Spect Dis, 7:167–175, 2013.

Coughlin

, Goldie

, Tranmer

, Khalid-Khan

, Tregunno

: Patient, Treatment, and Health Care Utilization Variables Associated with Adherence to Metabolic Monitoring Practices in Children and Adolescents Taking Second-Generation Antipsychotics. Can J Psychiatry, 63:240–249, 2018a.

Coughlin

, Goldie

, Tregunno

, Tranmer

, Kanellos-Sutton

, Khalid-Khan

: Enhancing Metabolic Monitoring for Children and Adolescents Using Second-Generation Antipsychotics. Int J Ment Health Nurs, 27:1188–1198, 2018b.

Delate

, Kauffman

, Botts

, Wong

, Gaughan

: Metabolic Monitoring in Commercially Insured Pediatric Patients Newly Initiated to Take a Second-Generation Antipsychotic. JAMA Pediatr, 168:679, 2014.

Featherston

, Dihigo

, Gilder

: Improving Adherence to Atypical Antipsychotic Agent Screening Guidelines in Pediatric Patients: A Quality Improvement Project Within an Integrated Community Mental Health Setting. J Am Psychiatr Nurses Assoc, 24:352–359, 2018.

Findling

, Drury

, Jensen

, Rapoport

, Bukstein

, Walter

, Benson

, Chrisman

, Farchione

, Hamilton

, Keable

, Kinlan

, Schoettle

, Siegel

, Stock

: Practice Parameters for the use of Atypical Antipsychotic Medication in Children and Adolescents. Am Acad Child Adolesc Psychiatry, 52:976–990, 2013.

10.

Hales

, Kit

, Gu

, Ogden

: Trends in Prescription Medication Use Among Children and Adolescents—United States, 1999–2014. JAMA, 319:2009–2020, 2018.

11.

Harrison

, Cluxton-Keller

, Gross

, Farrington

, Moffet

: Antipsychotic Medication Prescribing Trends in Children and Adolescents. J Pediatr Health Care, 26:139–145, 2012.

12.

Hellings

, Zarcone

, Crandall

, Wallace

, Schroeder

: Weight Gain in a Controlled Study of Risperidone in Children, Adolescents and Adults with Mental Retardation and Autism. J Child Adolesc Psychopharmacol, 11:229–238, 2001.

13.

, Panagiotopoulos

, McCrindle

, Grisaru

, Pringsheim

: Management Recommendations for Metabolic Complications Associated with Second-Generation Antipsychotic Use in Children and Youth. Paediatr Child Health, 16:575–580, 2011.

14.

Honey

, Ramos

, Brahm

: Evaluation of monitoring for metabolic effects in children treated with second generation antipsychotics in a pediatric clinic. J Pediatr Pharmacol Ther, 18:292–297, 2013.

15.

Hsia

, Wong

AYS

, Murphy

DGM

, Simonoff

, Buitelaar

, Wong

ICK

: Psychopharmacological Prescriptions for People with Autism Spectrum Disorder (ASD): A Multinational Study. Psychopharmacol, 231:999–1009, 2014.

16.

Janssen

, Katzmarzyk

, Ross

: Waist Circumference and Not Body Mass Index Explains Obesity-Related Health Risk. Am J Clin Nutr, 79:379–384. 2004.

17.

Kauffman

, Delate

, Botts

: Metabolic monitoring in children 5 years of age and younger prescribed second-generation antipsychotic medications. Ment Health Clin, 7:1–6. 2017.

18.

Luby

, Mrakotsky

, Stalets

, Belden

, Heffelfinger

, Williams

, Spitznagel

: Risperidone in Preschool Children with Autistic Spectrum Disorders: An Investigation of Safety and Efficacy. J Child Adolesc Psychopharmacol, 16:575–587. 2006.

19.

Madden

, Lakoma

, Lynch

, Rusinak

, Owen-Smith

, Coleman

, Quinn

, Yau

, Qian

, Croen

: Psychotropic Medication Use among Insured Children with Autism Spectrum Disorder. J Autism Dev Disord, 47:144–154. 2017.

20.

McCracken

, McGough

, Shah

, Cronin

, Hong

, Aman

, Arnold

, Lindsay

, Nash

, Hollway

, McDougle

, Posey

, Swiezy

, Kohn

, Scahill

, Martin

, Koenig

, Volkmar

, Carroll

, Lancor

, Tierney

, Ghuman

, Gonzalez

, Grados

, Vitiello

, Ritz

, Davies

, Robinson

, McMahon

: Risperidone in Children with Autism and Serious Behavioral Problems. N Engl J Med, 347:314–321, 2002.

21.

Morrato

, Nicol

, Maahs

, Druss

, Hartung

, Valuck

, Campagna

, Newcomer

: Metabolic Screening in Children Receiving Antipsychotic Drug Treatment. Arch Pediatr Adolesc Med, 164:344–351, 2010.

22.

Olfson

, Blanco

, Liu

, Wang

, Correll

: National Trends in the Office-Based Treatment of Children, Adolescents, and Adults With Antipsychotics. Arch Gen Psychiatry, 69:1247–1256, 2012.

23.

Park

, Cervesi

, Galling

, Molteni

, Walyzada

, Ameis

, Gerhard

, Olfson

, Correll

: Antipsychotic Use Trends in Youth With Autism Spectrum Disorder and/or Intellectual Disability: A Meta-Analysis. J Am Acad Child Adolesc Psychiatry, 55:456–468, 2016.

24.

Patten , Scott

, Waqar

Waheed

, Lauren

Bresee

: A Review of Pharmacoepidemiologic Studies of Antipsychotic Use in Children and Adolescents. Can J Psychiatry, 57:717–721, 2012.

25.

Pillay

, Boylan

, Newton

, Hartling

, Vandermeer

, Nuspl

, MacGregor

, Featherstone

, Carrey

: Harms of Antipsychotics in Children and Young Adults: A Systematic Review Update. Can J Psychiatry, 63:661–678, 2018.

26.

Pringsheim

, Hammer

, McLennan

, Sarna

: Monitoring Antipsychotic Safety in Children with Tourette Syndrome and ADHD: A Prospective Study. Movement Disord, 29:1094, 2014.

27.

Pringsheim

, Stewart

, Chan

, Tehrani

: The Pharmacoepidemiology of Psychotropic Medication Use in Canadian Children from 2012 to 2016. New Research Poster 2.11. 65th Annual Meeting of the American Academy of Child & Adolescent Psychiatry, Seattle, Oct 22–27. 2018. (Abstract)

28.

Pringsheim

, Lam

, Patten

: The Pharmacoepidemiology of Antipsychotic Medications for Canadian Children and Adolescents: 2005–2009. J Child Adolesc Psychopharmacol, 21:537–543, 2011a.

29.

Pringsheim

, Doja

, Belanger

, Patten

: Treatment Recommendations for Extrapyramidal Side Effects Associated with Second-Generation Antipsychotic Use in Children and Youth. Paediatr Child Health, 16:590–598, 2011b.

30.

Pringsheim

, Panagiotopoulos

, Davidson

, Ho

, The Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in Children (CAMESA) guideline Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in Children (CAMESA) guideline group: Evidence-Based Recommendations for Monitoring Safety of Second-Generation Antipsychotics in Children and Youth. Paediatr Child Health, 16:581–589, 2011c.

31.

Raebel

, Penfold

, McMahon

, Reichman

, Shetterly

, Goodrich

, Andrade

, Correll

, Gerhard

: Adherence to Guidelines for Glucose Assessment in Starting Second-Generation Antipsychotics. Pediatrics, 134:e1308–e1314, 2014.

32.

Ronsley

, Raghuram

, Davidson

, Panagiotopoulos

: Barriers and Facilitators to Implementation of a Metabolic Monitoring Protocol in Hospital and Community Settings for Second-Generation Antipsychotic-Treated Youth. J Can Acad Child Adolesc Psychiatry, 20:134–141, 2011.

33.

Ronsley

, Rayter

, Smith

, Davidson

, Panagiotopoulos

: Metabolic Monitoring Training Program Implementation in the Community Setting Was Associated with Improved Monitoring in Second-Generation Antipsychotic-Treated Children. Can J Psychiatry, 57:292–299, 2012.

34.

Ruiz

, Damron

, Jones

, Weedon

, Carbone

, Bakian

, Bilder

: Antipsychotic use and metabolic monitoring in individuals with developmental disabilities served in a medicaid medical home. J Autism Dev Disord, 46:1887–1894, 2016.

35.

Schubart

, Camacho

, Leslie

: Psychotropic medication trends among children and adolescents with autism spectrum disorder in the medicaid program. Autism, 18:631–637, 2014.

36.

Wink

, Early

, Schaefer

, Pottenger

, Horn

, McDougle

, Erickson

: Body Mass Index Change in Autism Spectrum Disorders: Comparison of Treatment with Risperidone and Aripiprazole. J Child Adolesc Psychopharmacol, 24:78–82, 2014.

37.

Yoon

, Wink

, Pedapati

, Horn

, Erickson

: Weight gain effects of second-generation antipsychotic treatment in autism spectrum disorder. J Child Adolesc Psychopharmacol, 26:822–827, 2016.

38.

Zuddas

, Di Martino

, Muglia

, Cianchetti

: Long-term risperidone for pervasive developmental disorder: efficacy, tolerability, and discontinuation. J Child Adolesc Psychopharmacol, 10:79–90, 2000.