Rates and Predictors of Adherence to Psychotropic Medications in Children with Autism Spectrum Disorders

ORIGINAL PAPER

Rates and Predictors of Adherence to Psychotropic Medicationsin Children with Autism Spectrum Disorders

Sarah L. Logan Laura Carpenter R. Scott Leslie

Kelly S. Hunt Elizabeth Garrett-Mayer Jane Charles

Joyce S. Nicholas

Springer Science+Business Media New York 2014

Abstract Medication adherence in children is poor, par-

ticularly among those with chronic or mental health dis-

orders. However, adherence has not been fully assessed in

autism spectrum disorders (ASDs). The validated propor-

tion of days covered method was used to quantify adher-

ence to psychotropic medication in Medicaid-eligible

children who met diagnostic criteria for ASD between

2000 and 2008 (N = 628). Among children prescribed

attention deficit hyperactivity disorder (ADHD) medica-

tions, antidepressants, or antipsychotics, 44, 40 and 52 %

were adherent respectively. Aggressive behaviors and

abnormalities in eating, drinking, and/or sleeping, co-

occurring ADHD, and the Medication Regimen Complex-

ity Index were the most significant predictors of adherence

rather than demographics or core deficits of ASD. Identi-

fying barriers to adherence in ASD may ultimately lead to

improved treatment outcomes.

Keywords Psychotropics Treatment adherence Publichealth surveillance Autism

Introduction

In the United States, 1 in 88 children meet diagnostic criteria

for an autism spectrum disorder (ASD) (CDC 2012). Though

no medication has FDA-approval for use across the spectrum

for any indication (Myers 2007; Myers et al. 2007) the anti-

psychotics aripiprizole and risperidone are approved for

children with autistic disorder for aggression and self-injuri-

ous behavior (SIB) (FDA 2006, 2009). Additionally, off-label

prescribing of psychotropics, including antidepressants,

attention deficit hyperactivity disorder (ADHD) medications,

mood stabilizers, anxiolytics, sedatives or hypnotics (Myers

2007), anticholinergics and Alzheimers medications (Ni-

colson et al. 2006; Webb 2010; Handen et al. 2011) occurs in

over half of medicated children (Volkmar and Wiesner 2009)

to treat hyperactivity, impulsivity, inattention, sleep prob-

lems, irritability, and aggression (Coury 2011).

Along with a rise in ASD-related treatment research

(Dawson 2013; OARC 2012), there is a lack of consistency

among hundreds of study findings (Wink and Erickson

2010; McPheeter et al. 2011; Warren et al. 2011). Multiple

reviews of ASD-specific treatments highlight the lack of an

adherence measure among studies, which could possibly

limit accurate and appropriate understandings (Hack and

Chow 2001; McPheeter et al. 2011; Warren et al. 2011;

Coury et al. 2012; Taylor et al. 2012), as adherence may

have severe implications on study interpretations and in

some instances may explain inconclusive or null findings

(Friedman et al. 2010).

Poor adherence also increases risks of unnecessary medi-

cations, contributes to healthcare costs (WHO 2003), and is

S. L. Logan K. S. Hunt E. Garrett-Mayer J. S. NicholasDepartment of Public Health Sciences, College of Medicine,

Medical University of South Carolina, 135 Cannon St. Ste 301,

Charleston, SC 29425, USA

S. L. Logan (&)Medical University of South Carolina, 176 Croghan Spur Rd.

Ste. 104, Charleston, SC 29407, USA

e-mail: [email protected]; [email protected]

L. Carpenter J. CharlesDepartment of Pediatrics, College of Medicine, Medical

University of South Carolina, 135 Rutledge Ave MSC 567,

Charleston, SC 29425-5670, USA

R. S. Leslie

University of California, San Diego, 9500 Gilman Dr., La Jolla,

San Diego, CA 92093, USA

123

J Autism Dev Disord

DOI 10.1007/s10803-014-2156-0

linked to poor outcomes in chronic childhood conditions

including ADHD (Chacko et al. 2010), depression (Thompson

et al. 2000), schizophrenia (Canas et al. 2013), epilepsy

(Mitchell et al. 2000; Faught et al. 2008) bipolar and other

mood disorders (Drotar et al. 2007; Dean et al. 2011). Because

ASD shares characteristics with these disorders (e.g., dynamic

symptom presentation and high rates of co-occurring condi-

tions) (Matson and Nebel-Schwalm 2007; Ming et al. 2008;

Simonoff et al. 2008; Williams et al. 2008; Joshi et al. 2010;

Levy et al. 2010), examining predictors of adherence in those

conditions (e.g., gender, race, residency, Medicaid-eligibility

category, co-occurring conditions, specific treatments or

regimen characteristics, and knowledge of the condition)

(Ciechanowski et al. 2000; WHO 2003, 2005; George et al.

2004; Patel et al. 2005; Gau et al. 2006; Akincigil et al. 2007;

Hudson et al. 2007; Lazaratou et al. 2007; Ming et al. 2008;

Visser et al. 2007; Gau et al. 2008; Simonoff et al. 2008; Joshi

et al. 2010; Levy et al. 2010; Sleath et al. 2010; Fontanella

et al. 2011) may provide insight to adherence in ASD.

To date, only one study has assessed medication adher-

ence in children with ASD; that study used a survey with

less than a 20 % response rate among members of the

Autism Society of Minnesota (Moore and Symons 2009).

Thus a more complete assessment of psychotropic medica-

tion adherence in ASD is warranted. The purpose of this

study was to quantify medication adherence to three of the

most commonly prescribed psychotropic classes in ASD:

antipsychotics, antidepressants, and ADHD medications

(Logan et al. 2012) using the validated proportion of days

covered (PDC) method (Karve et al. 2009a, b) as the main

outcome. Though many adherence calculations exist, the

PDC is the optimal method in complex conditions where

multiple drug classes are commonly prescribed (Martin et al.

2009). In short, the PDC is a proportion ranging from 0 to 1

that captures the number of days of drug coverage of any

drug from each class of interest during a given time period.

The proportion can be expressed as a percent or dichoto-

mized as adherent versus nonadherent (Karve 2009b). We

identified predictors of adherence using the .80 cut-point by

examining variables related to the child, condition, and

medication regimen. To our knowledge, this is the first study

to systematically quantify psychotropic adherence among

children who meet diagnostic criteria for ASD using a val-

idated adherence measure and data not reliant on parent

report, voluntary participation, or diagnostic status.

Methods

Study Participants

This study included 629 Medicaid-eligible children in

South Carolina who were identified between 2000 and

2008 at 8 years of age by a population-based surveillance

system as having met diagnostic criteria for an ASD based

on the Diagnostic and Statistical Manual of Mental Dis-

orders, Fourth Edition, Text Revision criteria (DSM-IV,

TR) (Association 2000), including autistic disorder, As-

perger disorder, or PDD-NOS. Details of the surveillance

methodology including surveillance area characteristics,

case definition, ascertainment, and quality control have

been published (Nicholas et al. 2008, 2009, 2012; Rice

et al. 2010; Logan et al. 2012) and are briefly outlined

below.

Data Sources

The Centers for Disease Control and Prevention (CDC)-

sponsored South Carolina Autism and Developmental

Disabilities Monitoring (SCADDM) Network is an active,

on-going, population-based surveillance system that serves

as an invaluable resource capable of providing extensive

child-level data to assess healthcare use in ASD (Yeargin-

Allsopp et al. 2003; Durkin et al. 2008; King et al. 2008;

Nicholas et al. 2008, 2009; Van Narden Braun et al. 2008;

Yeargin-Allsopp et al. 2008; Bilder et al. 2009; Mandell

et al. 2009; Pinborough-Zimmerman et al. 2009; Shattuck

et al. 2009; Wiggins et al. 2009; Durkin 2010; Giarelli et al.

2010; Kalkbrenner et al. 2010; Levy et al. 2010; Powell

et al. 2010). Since 2000, the network has documented ASD

prevalence using consistent methodology at multiple health

and educational sources to identify previously diagnosed

and undiagnosed children who meet surveillance diagnos-

tic criteria. That is, all possible cases of ASD are identified

based on key words in medical or education records and

undergo a full review process; no child is assumed to be a

case until study definition of case-status is met, regardless

of previous diagnoses. Hence, the methodology does not

depend on administrative coding that could limit the

completeness or generalizability.

Surveillance data were linked to SCs Medicaid Man-

agement Information System (MMIS) Eligibility and

MMIS Pharmacy Claims Files using unique identifiers

common to both datasets to avoid case duplication result-

ing in a large, high quality, de-identified database. Eligi-

bility and pharmacy data were recorded for each child for

the year identified and 1 year prior; that is, all pharmacy

claims and refills for each medication were recorded for a

2 year period for each child beginning January 1st of the

prior to the year identified by the surveillance network

through December 31st of the surveillance year. Table 1

provides a summary of the data sources.

All regulatory approvals were granted for data collection

by the Medical University of South Carolinas IRB and all

confidentiality procedures were followed. Protected Health

Information (PHI) collected initially was removed after

J Autism Dev Disord

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Medicaids internal review and data linkage, as has been

done in previous studies (Logan et al. 2012).

Variables

Covariates

Child characteristics were race, gender, surveillance year,

Medicaid eligibility category, and urban versus rural resi-

dency as defined by the South Carolina Department of

Commerce (Census; Bunch 2008). Condition related fac-

tors were indicators of emotional and behavioral problems

based on SC ADDMs 12 ASD-associated aberrant

behaviors (e.g., hyperactivity, aggression, eating or sleep-

ing problems), and 12 DSM-IV diagnostic criteria. A full

list of behaviors and diagnostic criteria can be found in

Appendix 1. Mental health disorders were included

based on primary or secondary ICD-9 codes 290.xx to

319.xx in each childs Medicaid file.

We used a modified version of the Medication Regimen

Complexity Index (MRCI) (George et al. 2004) to measure

medication regimen complexity after a review of the reli-

ability and validity of various indices (Kelley 1988; Muir

et al. 2001; Dilorio et al. 2003; George et al. 2004; Martin

et al. 2007; Dean et al. 2011). The MRCI is based on the

sum of three sections each representing an aspect of

complexity: dosage form (tablet, liquid, patch, etc.), fre-

quency (number of total pills (or doses if liquid) per day),

and additional instructions (e.g., take on an empty stom-

ach). For example, an MRCI score of 2.0 represents one

tablet taken once per day (one point for tablet form and one

point for taking once per day). The more complex the

dosage form, the higher the score; there is no maximum

score established. Liquid dosage forms taken once per day

would have a score of 3.0 (2 points for the liquid form and

1 point for the frequency). Liquid doses that required

dilution and taken once per day would have a score of 4.0.

A score for each medication prescribed during the 2-year

period was calculated and summed. Strict guidelines were

set to determine implausible versus extreme values based

on standard clinical judgement and maximum recom-

mended daily doses in the Manual of Clinical Psycho-

pharmacology, 7th Edition (Schatzberg et al. 2010), and

extreme values were eliminated from the calculation.

Ultimately, 42 of the 12,623 medication observations

(\1 %) exceeded the extreme threshold and wereexcluded.

Psychotropic Medications

Three of the most commonly prescribed psychotropics in

ASD (antipsychotics (e.g., aripiprazole, and risperidone);

antidepressants [e.g., citalopram and sertraline); and

ADHD medications (i.e., stimulants and non-stimulants)]

(Mandell et al. 2008; Logan et al. 2012) were identified

using the Specific Therapeutic Class (STC) code. We

grouped medications into these classes based on what has

been done in the literature (Logan et al. 2012; Mire et al.

2013; Zito et al. 2008). A dichotomous variable indicated if

each child had a claim for each psychotropic class any time

during the study period, defined as the year identified and

1 year prior (i.e., at age 7 or 8 years of age). We also

recorded the total number of different psychotropic classes

prescribed over the study period. A full list of medications

by class can be found in Appendix 2.

Adherence Measure

The International Society for Pharmacoeconomics and

Outcomes Research (ISPOR) Medication Compliance and

Persistence Work Group defines adherence as the extent

that a person acts in accordance with the prescribed interval

and dose of a dosing regimen (Cramer et al. 2007) and is

expressed as a proportion of the total number of days a

medication from a class is available during a given time

period. While other adherence measures exist (Karve et al.

2009a, b), the main outcome of this study was the more

conservative adherence measurement method, the PDC.

This method considers refills of varying days supply and

quantity supply to calculate the proportion of days covered

by medication during each childs two-year measurement

Table 1 Data sources and time periods from which data wereobtained

Source Information obtained Study

year

Time period

extracted

SCADDM

network

Gender, race, aberrant

behaviors, DSM-IV

criteria, diagnostic

history

SY2000 Birth through

Dec. 31st of

surveillance

year

SY2002

SY2004

SY2006

SY2008

MMIS

eligibility

file

Individual eligibility

status, county of

residence

SY2000 1999/2000

SY2002 2001/2002

SY2004 2003/2004

SY2006 2005/2006

SY2008 2007/2008

MMIS

pharmacy

file

Dispense date, drug code,

therapeutic class, drug

name, dosage, quantity,

days supply, refills

SY2000 1999/2000

SY2002 2001/2002

SY2004 2003/2004

SY2006 2005/2006

SY2008 2007/2008

SCADDM South Carolina autism and developmental disabilities

monitoring (SCADDM) network, SY study year, DSM-IV diagnostic

and statistical manual of mental disorders, 4th edition, MMIS Med-

icaid management information system

J Autism Dev Disord

123

period. The PDC was measured from first claim in the

childs two-year measurement period to the end of the

measurement period (Leslie 2008). This proportion can be

fit to different situations with longer days of supply per

claim or when multiple medications are prescribed (Leslie

2008). The lower the value of the PDC as a continuous

measure, the poorer the adherence, ranging from 0 to 1.

Though there is no universal cut-point to discriminate

adherent versus non-adherent (Karve 2009b; Martin et al.

2009), we used the standard cut-point of C.80 PDC that has

been used in several studies (Karve et al. 2008, 2009a, b;

Karve 2009b; Fontanella et al. 2011; Farley et al. 2012).

Thus, we defined adherence as a binary variable defined by

a PDC less than .80 (non-adherent) vs. greater than or equal

to .80 (adherent).

Statistical Analysis

Goals of the analysis were to describe and determine pre-

dictors of adherence to three of the most commonly pre-

scribed psychotropics to children with ASD. Separate

bivariate and then multivariable logistic regression models

were fit with adherence as the binary outcome for ADHD

medications, antidepressants, and antipsychotics. To

determine the best method of operationalizing these vari-

ables, each model was assessed for collinearity between

predictors using the Tolerance and Variance Inflation

Factor (TOL/VIF) method (Allison 2001), removing the

variable associated with the largest VIF, examining the

predictive ability of each model using the area under the

receiver operator characteristic (ROC) curve, Area Under

the Curve (AUC) (Hastie et al. 2009). Statistical signifi-

cance was .05 for the final models and analyses were

performed using SAS version 9.2 and R version 2.12.0.

Results

Characteristics of the Study Population

There were 629 children who were identified by the SC

ADDM Network as meeting criteria for an ASD between

2000 and 2008 included in the study; 81 % were male, 31 %

were White, 25 % Black, and 44 % Other race (including

Hispanic, Asian/Pacific Islander, and 1 missing value),

31 % were rural residents, 74 % were Medicaid eligible due

to disability, 24 % income, and 10 % foster care. Charac-

teristics of the study population are shown in Table 2.

The most common aberrant behavior was hyperactivity

(n = 533, 85 %) (Table 2), and the most common

co-occurring conditions by diagnostic codes in Medicaid

Table 2 Characteristics of the study population, N = 629

Variable Total

Demographics

Male 510 (81 %)

Race

White 193 (31 %)

Black 160 (25 %)

Othera 276 (44 %)

Surveillance year identified

2000 119 (19 %)

2002 103 (16 %)

2004 92 (15 %)

2006 129 (21 %)

2008 186 (30 %)

Rural residency (vs. urban) 193 (31 %)

Eligibility category

Disability 465 (74 %)

Income 152 (24 %)

Foster care 10 (2 %)

Aberrant behaviors as evidenced in SC ADDM records

Temper tantrums 363 (58 %)

Abnormal sensory issues 309 (49 %)

Mood abnormalities 375 (60 %)

Argumentative behavior 347 (55 %)

Aggressive behavior 311 (49 %)

Eating, drinking, sleeping abnormalities 340 (54 %)

Abnormal cognitive development 334 (53 %)

Delayed motor milestones/clumsiness 393 (62 %)

Abnormal responses to fear 234 (37 %)

Hyperactivity, impulsivity 533 (85 %)

Self-injurious behavior 233 (37 %)

DSM-IV ASD diagnostic criteria

Nonverbal behavior 541 (86 %)

Poor peer relationships 456 (73 %)

Lack of spontaneous seeking 376 (60 %)

Poor emotional reciprocity 545 (87 %)

Spoken language deficits 593 (94 %)

Conversational deficits 533 (85 %)

Repetitive language 469 (75 %)

Abnormal imaginative play 400 (64 %)

Restricted interests 387 (62 %)

Abnormal routines/rituals 486 (77 %)

Stereotyped motor mannerisms 459 (73 %)

Abnormal preoccupation with parts of objects 376 (60 %)

ASD autism spectrum disorder, SC ADDM South Carolina autism and

developmental disabilities monitoring network, DSM-IV Diagnostic

and statistical manual of mental disordersa Other race includes non-Hispanic white, Asian/Pacific Islander and

1 missing value

J Autism Dev Disord

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claims files were communication disorder (n = 410, 65 %),

intellectual disability (n = 396, 63 %), and ADHD

(n = 242, 38 %). The MRCI score ranged from 2 to 409 (SD

85.3), with a mean/median of 83.6/55.0. Medical conditions

and medication related variables are shown in Table 3.

Adherence Rates

Of children with a claim for each class, 124 of 281 (44 %)

were adherent to ADHD medications, 43 of 108 (40 %)

were adherent to antidepressants, and 53 of 102 (52 %)

were adherent to antipsychotics. In other words, 44, 40, and

52 % of children who were prescribed an ADHD medi-

cation, antidepressant, or antipsychotic continued to refill

their medications such that medication would be available

at least 80 % of the time between the first medication fill

and the end of the study period. Phrased differently,

between 40 and 52 % of children prescribed one of these

psychotropic classes would have missed more than 1.5

doses per week over a 1 month time period.

Predictors of ADHD Medication Adherence

Initially, several demographic variables, aberrant behav-

iors, co-occurring conditions, and medication related vari-

ables were associated with ADHD adherence at the .10

level. However, after adjusting for these covariates in the

final model, the only significant predictors of adherence to

ADHD medications were a diagnosis of ADHD in Med-

icaid claim files, and the MRCI. Specifically, children with

a diagnosis of ADHD had 4.2 times higher odds of being

adherent to ADHD medication compared to those without

the diagnosis (OR 4.2, 95 % CI 1.8, 10.0), and the higher

the MRCI, the better adherence to ADHD medications (for

every 20-point increase in MRCI score, the odds of

adherence increased by 60 %) (OR 1.6, 95 % CI 1.4, 1.8).

These results are summarized in Table 4.

Predictors of Antidepressant Adherence

In the initial simple logistic regression analyses, there were no

demographic variable that neared significance (B.10) with

antidepressant adherence. However, from SC ADDM records

the aberrant behaviors of aggression and seizure-like activity/

staring spells, the co-occurring conditions in Medicaid claims

files of any PDD, and mood disorder, and medication-related

variables in Medicaid pharmacy claim files of having a pre-

scription claim for any anxiolytic or sedative hypnotic, and the

modified MRCI score were significantly associated at the .10

level and included in a multiple logistic regression. Results of

the final model showed that after adjusting for these covari-

ates, the only significant predictors of adherence to antide-

pressants were the aberrant behavior aggression, a

prescription for a sedative or hypnotic, and the MRCI score.

Specifically, children with documented aggressive behaviors

had .21 times lower odds of being adherent to antidepressants

compared to children without documented aggressive

behaviors (OR .21, 95 % CI .08, .58). Children who had been

prescribed a sedative or hypnotic during the study period had

.12 times lower odds of being adherent to antidepressant

medications compared to those without prescription claims

for sedatives or hypnotics, and the higher the MRCI, the better

adherence to antidepressant medications (i.e. for every

20-point increase in MRCI score, the odds of adherence

increased by 20 %). These results are summarized in Table 5.

Predictors of Antipsychotic Adherence

Initial single logistic regression analyses suggested there were

no demographic variables associated with antipsychotic

adherence, and the only aberrant behavior in SC ADDM

Table 3 Medical conditions and medication use among the studypopulation

Co-occurring conditionsa

Any mental health related disorder 588 (93 %)

Adjustment disorder 21 (3 %)

Anxiety disorder 61 (10 %)

Any PDD 381 (61 %)

Communication disorder 410 (65 %)

Conduct disorder/ODD 88 (14 %)

Developmental disability 145 (23 %)

Epilepsy 86 (14 %)

Intellectual disability 396 (63 %)

Learning disability 172 (27 %)

Mood disorder 23 (4 %)

Other mental health disorderb 103 (16 %)

Other psychotropic classes

None 252 (59 %)

Any psychotropic 377 (60 %)

1 class 171 (27 %)

2 classes 109 (17 %)

C3 classes 97 (15 %)

MRCI score

Mean, median 83.6, 55.0

Range, SD 2409, 85.3

PDD pervasive developmental disorder, ODD oppositional defiant

disorder, MRCI medication regimen complexity indexa Co-occurring conditions identified by diagnostic codes in Medicaid

filesb Other mental health disorders include delirium, dementia (n = 3),

motor skills disorder (n = 5), elimination disorder (n = 9), separa-

tion anxiety (n = 3), emotional disorder not otherwise specified

(n = 2), catatonic disorder (n = 3), schizophrenia (n = 1), psycho-

genic disorder (n = 2), sleep disorder (n = 15), and somatoform

disorder (n = 2)

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Table 4 Simple logistic and multivariable logistic regression results of attention deficit hyperactivity disorder (ADHD) medication adherence

Variable Simple logistic

regression

Multiple logistic regression:

Model 1aMultiple logistic regression:

Model 2b

OR (95 % CI) p OR (95 % CI) p OR (95 % CI) p

Demographics

Gender (ref = Male) .65 (.34, 1.3) .21

Race (ref = White) .74

Black .77 (.40, 1.5)

Otherc .91 (.53, 1.6)

Residency (ref = urban) .64 (.38, 1.1) .10 .95 (.44, 2.1) .91

Surveillance year (ref = 2000) .28

2002 1.4 (.57, 3.2)

2004 1.7 (.73, 4.2)

2006 1.9 (.84, 4.5)

2008 2.3 (1.0, 5.0)

Eligibility category (ref = Disability) .10 .10 .26

Income .66 (.35, 1.2) .50 (.19, 1.3) .53 (.23, 1.3)

Foster care 6.0 (.69, 52.5) 7.1 (.48, 104) 2.2 (.20, 23.1)

Aberrant conditions in SC ADDM records

Aggression 1.4 (.84, 2.3) .21

Argumentative, oppositional, defiant, destructive 2.2 (1.3, 3.7) \.01 1.3 (.62, 2.7) .48 Abnormal development of cognitive skills .93 (.58, 1.5) .76

Delayed motor milestones/motor clumsiness .91 (.56, 1.5) .72

Abnormalities in eating, drinking, or sleeping 1.1 (.69, 1.9) .62

Abnormal response to fear 1.2 (.74, 1.92) .48

Hyperactivity, impulsivity 2.0 (.81, 5.1) .13

Abnormalities in mood or affect 1.2 (.71, 2.0) .53

Seizure-like activity/staring spells 1.3 (.79, 2.1) .31

Self-injurious behavior 1.6 (1.0, 2.6) .04 .75 (.37, 1.6) .45

Sensory issues 1.2 (.75, 1.9) .43

Temper tantrums 1.4 (.84, 2.3) .21

Diagnostic criteria as evidenced in SC ADDM records

Nonverbal behavior 1.1 (.52, 2.3) .83

Peer relationships .92 (.55, 1.6) .76

Spontaneous seeking .92 (.57, 1.5) .72

Emotional reciprocity 1.1 (.52, 2.2) .87

Spoken language .69 (.26, 1.8) .45

Conversational deficits .94 (.49, 1.8) .85

Repetitive language .71 (.40, 1.2) .23

Imaginative play 1.1 (.66, 1.8) .78

Restricted interests 1.42 (.86, 2.3) .18

Routines and rituals .75 (.40, 1.4) .38

Stereotyped motor mannerisms .82 (.48, 1.5) .46

Preoccupied by parts of objects 1.0 (.62, 1.7) .96

Co-occurring conditions by ICD9 code in Medicaid claims files

ADHD 4.0 (2.1, 7.7) \.01 3.9 (1.5, 10.4) .01 4.2 (1.8, 10.0) .01Adjustment disorder 1.3 (.40, 4.1) .68

Anxiety disorder .96 (.50, 1.8) .89

Any PDD 1.6 (.97, 2.7) .07 1.1 (.51, 2.5) .76

Communication disorder .99 (.59, 1.7) .97

J Autism Dev Disord

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records associated (at the .10 level or less) was a documented

abnormality in eating, drinking, or sleeping. No DSM-IV

ASD-specific diagnostic criteria were associated with anti-

psychotic adherence. The only other variable to remain sig-

nificant was the MRCI; for every 20-point increase in MRCI

score, the odds of adherence to antipsychotic medications

were 20 % higher. These results are summarized in Table 6.

Discussion

This is the first study to systematically assess adherence

to commonly prescribed psychotropic medications in ASD

using a validated outcome measure, and identify predic-

tors of adherence using the strength of a population-based

surveillance network in conjunction with a robust phar-

macy claims database spanning from 2000 to 2008. We

dichotomized adherence using the standard .80 cut-point

(i.e. an indication that medication was available at a

minimum of 80 % of the time from the first prescription

claim to the end of the study period). Results showed that

44, 40, and 52 % of children with claims for an ADHD

medication, antidepressant, or antipsychotic were consid-

ered adherent at this threshold. Though low, these results

fall within ranges that have been reported in similar

pediatric populations who were prescribed psychotropics

Table 4 continued


regression

Multiple logistic regression:

Model 1aMultiple logistic regression:

Model 2b


Conduct disorder/ODD 2.2 (1.3, 3.7) .01 .67 (.26, 1.7) .41

Developmental disability 1.2 (.69, 2.0) .54

Epilepsy 1.1 (.58, 1.9) .85

Intellectual disability .89 (.54, 1.5) .66

Learning disability .84 (.51, 1.4) .51

Mood disorder 5.7 (1.8, 17.4) \.01 6.3 (1.2, 32.6) .03 2.8 (.69, 11.5) .15Other mental health disorderg 1.7 (.94, 3.0) .08 .77 (.31, 1.9) .68

Medication related variables

Anticholinergic 1.7 (.38, 7.8) .49

Antidepressant 3.1 (1.8, 5.2) \.01 1.3 (.44, 4.0) .62 Anxiolytic 1.1 (.53, 2.2) .84

Antipsychotic 3.8 (2.2, 6.5) \.01 1.0 (.30, 3.5) .97 Mood stabilizer 1.7 (.99, 3.0) .05 .43 (.13, 1.5) .18

Sedative or hypnotic .72 (.35, 1.5) .38

Total # psychotropic classes prescribed (ref = 0/1)f \.01 .31 2 classes 2.1 (1.2, 3.8) .60 (.21, 1.8)

3 or more classes 3.9 (2.1, 7.0) .22 (.03, 1.6)

MRCIh 1.6 (1.5, 1.8) \.01 1.9 (1.6, 2.2) .01 1.6 (1.4, 1.8) \.01

OR odds ratio, 95 % CI 95 % confidence interval, ASD autism spectrum disorder, SC ADDM South Carolina autism developmental and

disabilities monitoring network, ADHD attention deficit hyperactivity disorder, PDD pervasive developmental disorder, ODD oppositional

defiant disordera Model 1 includes covariates significantly associated at the .10 level in simple logistic regressionb Model 2 includes covariates that remained significant at the 10 level in Model 1c Other race includes Hispanic, Asian/Pacific Islander, and 1 unknownd Community ASD diagnosis reflects if a child had evidence of a previous ASD diagnosis in their education or medical records as collected and

recorded by the surveillance networke ASD-related special education services include other health impairment (n = 47), preschool child with a disability (n = 2), speech delay

(n = 20), and developmentally disabled not otherwise specified (n = 2)f Any other special education category includes deaf-blindness, hearing impairment, mental retardation, multiple disabilities, orthopedic

impairment, traumatic brain injury, and visual impairmentg Other mental health disorders include delirium, dementia (n = 3), motor skills disorder (n = 5), elimination disorder (n = 9), separation

anxiety (n = 3), emotional disorder not otherwise specified (n = 2), catatonic disorder (n = 3), schizophrenia (n = 1), psychogenic disorder

(n = 2), sleep disorder (n = 15), and somatoform disorder (n = 2)h MRCI represents a 20-point change in score

J Autism Dev Disord

123

(Hamrin et al. 2010; Fontanella et al. 2011; Marcus

2011).

Regardless of different methodology, sample sizes, and

time periods, our findings share similarities and differences

to previous studies. The single adherence study to date of

children with ASD was a parent survey comparing adher-

ence to behavioral versus medication interventions (Moore

and Symons 2009). While medication adherence was

higher than behavioral (mean 84 vs. 76 %), we found much

lower medication adherence rates. This may be due in part

to participation bias (response rate of \20 % in the pre-vious study) (Hansen et al. 2010), and the parent reported

adherence which may overestimate true adherence (Hack

and Chow 2001).

Our similar adherence rates by gender and race are unlike

those of Fontanella et al. (2011) who found that among a

Medicaid-eligible population of children with depression,

males were better adherent to antidepressants (56 %) than

females (48 %), and non-Hispanic whites (53 %) were

better adherent than minorities (41 %) (Fontanella et al.

2011). Other studies (Bussing et al. 2003; Chavira et al.

2003) have also found that Black children had lower

adherence to antidepressants compared to White children.

On the other hand, Patel et al. (2005) found better adherence

rates among Black children with bipolar disorder who were

prescribed antipsychotics, yet another study found lower

adherence to antipsychotics and antidepressants in minori-

ties youths with schizophrenia, and no gender differences

(Sleath et al. 2010). Stone et al. (2001) found no significant

associations between adherence and race or gender (Stone

et al. 2001), while Akincigil et al. (2007) found that

regardless of race, gender, or disorder, youths in lower

income categories were less likely to be adherent.

Unfortunately, only 2 % (n = 10) of the study popula-

tion were Medicaid-eligible due to foster care and most

were eligible because of their disability (74 %, n = 465)

rather than income which limited our ability to make

inferences across categories. Like Moore and Symons

(2009) we did not find that residency was related to

adherence in the final models. South Carolina is a state

with significant special education resource challenges and

limited access to behavioral interventions particularly in

rural areas (Shah 2011; Courrege 2012). As ASD becomes

more prevalent in South Carolina and across the country

(Akincigil et al. 2007; Census 2013) these challenges may

force increased reliance on pharmacotherapy, which may

impact adherence across socioeconomic classes or Medic-

aid-eligibility categories. Of note, this study was not

powered to detect such differences. Given the uncertain

role of race, gender, eligibility category, and residency on

adherence, future investigation in this area is warranted.

The World Health Organization recognizes that families

with more knowledge about their condition(s) or

medication(s) may adhere better to recommended inter-

ventions (WHO 2003, 2005), though we did not see this in

the present study. Between 2000 and 2008, awareness of

ASD and symptom-specific treatments, and the availability

(FDA 2006, 2008, 2009; Donohue et al. 2007; Hansen et al.

2010; CDC 2012) and use of psychotropics have increased

(Aman et al. 2005; dosReis et al. 2005; Esbensen et al.

2009) that could have impacted adherence (Arney et al.

2012) but does not explain why in the current study neither

knowledge of the disorder or birth-year cohort was not an

important predictor of adherence to any classes of interest

in the final models.

The MRCI (George et al. 2004) was significantly asso-

ciated to all three classes of medications. It is possible that

the more complex the medication regimen, the more dis-

abled the child and in need of more medications, and a

responsible adult is likely in charge of medication admin-

istration. A complex clinical presentation could be difficult

to treat resulting in frequent medication augmentations or

switching that could inflate the MRCI. This variable adds

more information to the overall clinical picture of a child

with ASD than would a 30-day window of overlapping

prescriptions. Regardless, the positive association of the

MRCI with adherence was not expected. As this is the first

study to use this index in ASD, the poorly understood role

of medication regimen is worthy of further exploration.

Limitations and Strengths

There were several limitations in the current study. The

study population was entirely a Medicaid-eligible popula-

tion. However, South Carolina has the Katie Beckett

Waiver program which allows children to qualify for

Medicaid due to their disability regardless of income,

which could explain the high percentage of cases with

Medicaid eligibility (72 % of all ASD surveillance cases

from 2000 to 2008) and the similarities between those with

and without Medicaid data available. Moreover, similar

adherence rates between Medicaid versus non-Medicaid

populations have been shown (Richardson et al. 2004),

therefore the impact of this limitation is likely minimal.

All participants in this study resided in one state (South

Carolina), which may limit the generalizability. A limita-

tion of using surveillance and administrative data is a lack

of interaction with patients or caregivers to understand how

attitudes and beliefs about medication impact medication

use and adherence (Hamrin et al. 2010), as skeptical atti-

tudes regarding a diagnosis or treatment (Olaniyan et al.

2007) have been linked to poor adherence (Horne and

Weinman 1999; Gupta and Horne 2001; Horne et al. 2001;

Petrie and Wessely 2002; Donohue et al. 2004). However

this limitation is likely minimal based on reports of the

strength of pharmacy claims based adherence studies

J Autism Dev Disord

123

Table 5 Simple and multivariable logistic regression results of antidepressant adherence


regression

Multiple logistic

regression: Model 1aMultiple logistic

regression: Model 2b


Demographics

Gender (ref = Male) .89 (.30, 2.7) .84


Black .44 (.13, 1.6)

Otherc .96 (.42, 2.2)

Residency (ref = urban) .65 (.25, 1.7) .36


2002 1.4 (.41, 4.8)

2004 2.3 (.56, 9.4)

2006 .67 (.15, 3.0)

2008 1.5 (.45, 5.2)

Community diagnosisd 1.3 (.57, 3.1) .52

Eligibility category (ref = Disability) .11

Income .38 (.14, 1.0)

Foster care 2.5 (.22, 28.9)

Special education category (ref = Autism-specific) .91

ASD-relatede 1.1 (.35, 3.4)

Other special educationf .73 (.27, 2.0)

None .83 (.26, 2.6)

Aberrant conditions in SC ADDM records

Aggression .29 (.12, .67) \.01 .22 (.07, .65) .01 .21 (.08, .58) \.01Argumentative, oppositional, defiant, destructive .51 (.22, 1.2) .11

Abnormal development of cognitive skills .99 (.45, 2.2) .97

Delayed motor milestones/clumsiness .90 (.41, 2.0) .78

Abnormalities in eating, sleeping .72 (.31, 1.6) .43

Abnormal response to fear .56 (.26, 1.2) .15

Hyperactivity, impulsivity 2.9 (.58, 14.3) .20

Abnormalities in mood/affect 1.2 (.49, 2.7) .75

Seizure-like activity/staring spells .43 (.18, 1.0) .05 .43 (.14, 1.3) .14

Self-injurious behavior 1.4 (.66, 3.1) .36

Sensory issues .65 (.29, 1.4) .28

Temper tantrums .99 (.42, 2.3) .99

Diagnostic criteria as evidenced in SC ADDM records

Social deficits

Nonverbal behavior .63 (.19, 2.1) .45

Peer relationships .99 (.40, 2.5) .98

Spontaneous seeking 1.5 (.66, 3.2) .36

Emotional reciprocity .62 (.20, 1.9) .41



Repetitive language .92 (.39, 2.2) .84


Restricted interests .99 (.43, 2.3) .98

Routines and rituals 1.2 (.32, 4.3) .81

Stereotyped motor mannerisms 1.1 (.41, 2.9) .86

Preoccupation with parts of objects .73 (.32, 1.7) .47

J Autism Dev Disord

123

compared to other methods such as pill counts or parent

report (Karve et al. 2008). We were not able to assess rea-

sons why children who met diagnostic criteria did not have

a previous diagnosis, or why children with aberrant

behaviors or co-occurring conditions did not have medica-

tion claims. This is an important area for future research.

Another limitation inherent in using this type of data is that

not all of the factors can be assessed that may be associated

with adherence in other disorders such as the type of pro-

vider, global severity of illness, duration of illness, time of

day that medications were taken (though we did account for

number of doses per day in the MRCI), and family history.

Table 5 continued


regression

Multiple logistic

regression: Model 1aMultiple logistic

regression: Model 2b


Co-occurring conditions by diagnostic code in Medicaid claims files

ADHD .96 (.43, 2.2) .91

Adjustment disorder 1.6 (.30, 8.1) .60

Anxiety disorder 2.1 (.85, 5.3) .11

Any PDD 2.5 (.94, 6.4) .07 1.5 (.48, 4.8) .48

Communication disorder .96 (.43, 2.2) .91

Conduct disorder/ODD 1.1 (.48, 2.5) .84

Developmental disability .88 (.35, 2.3) .79

Epilepsy .60 (.22, 1.6) .30

Intellectual disability 1.4 (.61, 3.3) .42

Learning disability 1.4 (.61, 3.5) .41

Mood disorder .27 (.06, 1.3) .10 .22 (.03, 1.5) .12

Other mental health disorderg .75 (.30, 1.9) .54

Other psychotropic classes prescribed (ref = no)

Anticholinergic .74 (.13, 4.3) .74

ADHD medication 1.0 (.40, 2.7) .94

Anxiolytic 2.5 (.87, 7.2) .09 2.7 (.72, 10.3) .14

Antipsychotic 1.4 (.62, 3.0) .45

Mood stabilizer .59 (.25, 1.4) .22

Sedative or hypnotic .18 (.04, .83) .03 .14 (.02, .89) .04 .12 (.02, .74) .02

Total psychotropic classes prescribed (ref = 1 or none) .98

2 classes 1.1 (.30, 4.2)

3 or more classes 1.0 (.30, 3.6)

MRCI scoreh 1.10 (1.02, 1.19) .02 1.22 (1.09, 1.36) \.01 1.20 (1.08, 1.33) \.01

OR odds ratio, 95 % CI 95 % confidence interval, ASD autism spectrum disorder, SC ADDM South Carolina autism developmental and

disabilities monitoring network, ADHD attention deficit hyperactivity disorder, PDD pervasive developmental disorder, ODD oppositional

defiant disorder, MRCI medication regimen complexity indexa Model 1 includes covariates significantly associated at the .10 level in the simple logistic regressionb Model 2 includes covariates that remained significant at the 10 level in Model 1c Other race includes Hispanic, Asian/Pacific Islander, and 1 unknownd Community ASD diagnosis reflects if a child had evidence of a previous ASD diagnosis in their education or medical records as collected and

recorded by the surveillance networke ASD-related special education services include other health impairment (n = 47), preschool child with a disability (n = 2), speech delay

(n = 20), and developmentally disabled not otherwise specified (n = 2)f Other special education includes deaf/blindness, hearing impairment, mental retardation, multiple disabilities, orthopedic impairment, trau-

matic brain injury, and visual impairmentg Other mental health disorders include delirium, dementia (n = 3), motor skills disorder (n = 5), elimination disorder (n = 9), separation


(n = 2), sleep disorder (n = 15), and somatoform disorder (n = 2)h MRCI represents a 20-point change in score

J Autism Dev Disord

123

Lastly, this study operated on the assumption that one

would not continue refilling without the intention of taking

a medication, though we cannot confirm that prescriptions

filled were actually consumed. However, the use of phar-

macy claims for measuring medication adherence has been

validated via patient reports, pill counts, questionnaires,

and interviews (Karve et al. 2008; Karve 2009b; Martin

et al. 2009).

Despite these limitations, there were many strengths.

First, data were drawn from a population-based surveil-

lance network not reliant on previous diagnosis, increas-

ing the completeness of the study population. Second,

case criteria was constant across time and location among

children of the same age and did not rely on either parent

report or diagnostic coding in claims-records; as more

than half of children who meet diagnostic criteria have

never received a diagnosis (CDC 2012). Though the study

was limited to Medicaid-eligible children, this subgroup

was similar to the entire SC ADDM cohort, including

ratio of males and females. We were also able to examine

multiple eligibility categories. Lastly, we used a validated

adherence measure as the main outcome. Though there

are multiple ways to calculate adherence, each have

advantages and disadvantages; while the Medication

Possession Ratio (MPR) is less complex to compute, it is

better suited to single drug regimens rather than situations

where inter- or intra-class drug use is common (Karve

et al. 2008, 2009a, b; Leslie 2008; Martin et al. 2009).

The PDC however, is considered the best method to

calculate adherence in complex conditions where multiple

medications use and switching are common, which can be

due to availability, tolerability, or prescribing practices

(George et al. 2004; Karve et al. 2008, 2009a, b; Leslie

et al. 2008; Martin et al. 2009; Dean et al. 2011). During

our study period (2000 to 2008), many medications

increased in popularity or gained FDA approval for use in

conditions that share features of targeted behaviors in

ASD (e.g., non-stimulant ADHD medications or atypical

antipsychotics for irritability). In a situation where, for

example, an older typical antipsychotic was not tolerated

or effective and was replaced with a newer medication in

the same class (e.g., the atypical antipsychotics), the MPR

would erroneously over-estimated adherence (Martin et al.

2009). The PDC assesses daily medication coverage in a

given period to a medications class, yielding a more

conservative adherence estimate and is the preferred

method by the Centers for Medicare & Medicaid Services

(CMS) (CMS 2011), Pharmacy Quality Alliance (PQA)

(Nau 2012), and National Committee for Quality Assur-

ance (NCQA) (NCQA 2011). While other studies of

pediatric psychotropic use have looked at current rates

and limited time periods (Safer et al. 2004; Radigan et al.

2005; Oswald and Sonenklar 2007; Esbensen et al. 2009),

we felt that examining adherence at age 7 and 8 years,

and across multiple birth-year cohorts in relation to child,

condition, and medication related variables was important

to capture patterns over time and show the complexity in

prescribing practices in complicated disorders such as

ASD (Oswald and Sonenklar 2007).

Though there is no universal cut-point for acceptable

adherence rates for psychotropics in ASD, this study pro-

vides a framework to determine such value. In conditions

such as hypertension, a PDC of .80 is considered accept-

able while other disorders (e.g., antiretroviral treatments in

HIV) have a higher standard of .90 PDC. Given the

mechanism of action of psychotropic medications, a high

PDC is likely to be important to produce beneficial psy-

chotropic treatment effects; a delayed onset of treatment

effect for antidepressants and antipsychotics has been

noted in the literature, and may lead to poorer adherence

(Meltzer et al. 2014; Tylee 2007), whereas benefits of

ADHD medications typically occur rapidly. Persistence,

defined as the duration of time from initiation to discon-

tinuation of therapy (Leslie 2008) may be a particularly

interesting future focus area in this field, as many medi-

cations require a minimum amount of consistent treatment

to exert a behavior effect (Schatzberg et al. 2010). There-

fore, future studies should include additional cut-points and

different adherence measures.

It is interesting to note that, though not statistically

significant, medications indicated for externalizing behav-

iors (antipsychotics and ADHD medications) had a higher

PDC than internalizing behaviors (antidepressants). A

possible explanation is that externalizing behaviors are

more obvious and troublesome to caregivers. It is possible

that a diagnosis of ADHD was needed to continue treat-

ment with ADHD medication, or that in children without

an actual ADHD diagnosis (and thus possibly not meeting

diagnostic criteria), ADHD medication used off-label was

not beneficial, therefore treatment stopped (lowering the

PDC).

As the number of clinical trials in ASD increase, doc-

umenting adherence rates with validated measures is

essential for proper study interpretations. A recent review

of ASD interventions confirmed that available evidence for

specific treatment approaches in this population is limited

and emphasized a need for improved quality control mea-

sures in clinical trials, in particular measures of adherence

(Taylor et al. 2012); this transparency may enable

improved understandings of reported outcome measures

(Dove et al. 2012).

Significance

Adherence rates to ADHD medications, antidepressants,

and antipsychotics were less than or near 50 %. Phrased

J Autism Dev Disord

123

Table 6 Simple and multivariable logistic regression results of antipsychotic adherence

Variable Simple logistic regression Multiple logistic regression

OR (95 % CI) p OR (95 % CI) p

Demographics

Gender (ref = Male) .57 (.19, 1.7) .32


Black 1.1 (.37, 3.4)

Othera .93 (.37, 2.3)

Residency (ref = urban) .97 (.43, 2.2) .95


2002 1.3 (.34, 4.7)

2004 .94 (.21, 4.3)

2006 2.3 (.57, 8.8)

2008 2.6 (.73, 9.2)

Eligibility category (ref = Disability) .96

Income 1.2 (.34, 4.2)

Foster care (0, [?)Aberrant conditions in SC ADDM records

Aggression 1.1 (.44, 2.8) .83

Argumentative, oppositional, defiant, destructive 1.2 (.49, 3.1) .65

Abnormal development of cognitive skills 1.4 (.64, 3.1) .39

Delayed motor milestones/motor clumsiness 1.7 (.77, 3.9) .18

Abnormalities in eating, drinking, or sleeping .49 (.21, 1.1) .10 .38 (.15, .96) .04

Abnormal response to fear 1.4 (.64, 3.1) .40

Hyperactivity, impulsivity .85 (.22, 3.4) .82

Abnormalities in mood or affect 1.2 (.49, 3.1) .65

Seizure-like activity or staring spells 1.0 (.47, 2.3) .94

Self-injurious behavior 1.9 (.84, 4.2) .13

Sensory issues 1.3 (.57, 2.7) .57

Temper tantrums 1.0 (.42, 2.4) .99

DSM-IV diagnostic criteria as evidenced in SC ADDM records

Nonverbal behavior .92 (.29, 2.9) .88

Peer relationships 1.1 (.43, 2.8) .84

Spontaneous seeking .88 (.39, 2.0) .75

Emotional reciprocity 1.1 (.33, 3.6) .88



Repetitive language 1.2 (.52, 2.9) .64


Restricted interests 1.1 (.51, 2.5) .76

Routines and rituals 2.2 (.67, 7.0) .20

Stereotyped motor mannerisms .67 (.25, 1.8) .42

Preoccupation with parts of objects 1.2 (.54, 2.8) .62

Co-occurring conditions by diagnostic codes in Medicaid claims files

ADHD 1.4 (.57, 3.2) .49

Adjustment disorder .92 (.18, 4.8) .92

Anxiety disorder 1.3 (.41, 4.0) .68

Any PDD 1.5 (.62, 3.8) .36

Communication disorder 2.6 (1.1, 6.0) .03 2.1 (.83, 5.3) .12

Conduct disorder/ODD 1.2 (.55, 2.7) .62

J Autism Dev Disord

123

differently, roughly half of all children prescribed a psy-

chotropic in this study would have missed more than 1.5

doses per week. Though this study has laid groundwork

for understanding predictors of adherence in ASD, as

future studies continue to explore this area, a better

understanding of associated factors may lead to inter-

ventions that improve adherence among those at risk for

poor adherence. Lastly, regardless of advances in bio-

medical science and treatment approaches (WHO and

Development 2001), medication is less likely to be

effective if not taken properly.

Acknowledgments Support for this grant was provided by theCenters for Disease Control and Prevention (CDC) Sponsored Autism

and Developmental Disabilities Monitoring Network (CDC-RFA-

DD06-601 and CDC-RFA-DD10-1002). Additional collaboration for

this project came from the Commissioner of the Department of Health

and Environmental Control (DHEC), who has designated the South

Carolina Autism and Developmental Disabilities Monitoring

(SCADDM) team as a bona fide agent of the Health Department to

conduct surveillance of autism spectrum disorders. Content is the

responsibility of the authors and does not necessarily represent the

views of the funding agencies. The authors would like to acknowl-

edge the South Carolina Budget & Control Board, Office of Research

& Statistics, and Lydia King, PhD, Heather Kirby, and Wally Altman

Table 6 continued

Variable Simple logistic regression Multiple logistic regression

OR (95 % CI) p OR (95 % CI) p

Developmental disability 1.7 (.68, 4.2) .26

Epilepsy .81 (.34, 2.0) .64

Intellectual disability 1.6 (.60, 4.2) .35

Learning disability 1.9 (.76, 4.9) .17

Mood disorder 2.3 (.66, 8.0) .19

Other mental health disordere 1.6 (.62, 4.1) .33

Medication related variables

Other psychotropic classes prescribed


ADHD medication .77 (.29, 2.0) .59

Anxiolytic .91 (.33, 2.5) .85

Antidepressant .81 (.37, 1.8) .59


ADHD medication .77 (.29, 2.0) .59

Anxiolytic .91 (.33, 2.5) .85

Antidepressant .81 (.37, 1.8) .59

Mood stabilizer .60 (.28, 1.4) .26

Sedative or hypnotic .79 (.28, 2.2) .66

Total # psychotropic classes prescribed (ref = 1 or none) .63

2 classes 1.2 (.30, 5.0)

3 or more classes .78 (.22, 2.8)

MRCI scoref 1.15 (1.05, 1.27) \.01 1.15 (1.05, 1.27) \.01

OR odds ratio, CI confidence interval, ASD autism spectrum disorder, SC ADDM South Carolina autism and developmental disabilities

monitoring network, ADHD attention deficit hyperactivity disorder, PDD pervasive developmental disorder, ODD oppositional defiant disorder,

MRCI medication regimen complexity indexa Other race includes Hispanic, Asian/Pacific Islander, and 1 unknownb Community ASD diagnosis reflects if a child had evidence of a previous ASD diagnosis in their education or medical records as collected and

recorded by the surveillance networkc ASD-related special education services include other health impairment (n = 47), preschool child with a disability (n = 2), speech delay

(n = 20), and developmentally disabled not otherwise specified (n = 2)d Any other special education category includes deaf-blindness, hearing impairment, mental retardation, multiple disabilities, orthopedic

impairment, traumatic brain injury, and visual impairmente Other mental health disorders include delirium, dementia (n = 3), motor skills disorder (n = 5), elimination disorder (n = 9), separation


(n = 2), sleep disorder (n = 15), and somatoform disorder (n = 2)f MRCI represents a 20-point change in score

J Autism Dev Disord

123

for providing exceptional technical assistance for this project. This

manuscript was prepared as part of the first authors dissertation.

Appendix 1

See Table 7.

Appendix 2

See Table 8.

Table 7 ASD-associated aberrant behaviors and diagnostic criteria

Variable Definition

Aberrant behaviors

Hyperactivity, attention

problems

Delayed motor

functioning

Abnormality in mood/

affect

Abnormality in eating/

drinking/sleeping

Temper tantrums

Argumentative,

oppositional, defiant

Aggression

Odd response to

sensory stimuli

Self-injurious

behaviour

Lack of fear or

excessive fear

Staring spells/seizure-

like activity

A dichotomous variable for each AF.

Indicates that the clinician who

reviewed the SCADDM record believed

sufficient evidence existed to

confidently say this child either did or

did not exhibit the behaviour,

considering intensity, frequency, and

differences across settings.

DSM-IV criteria

Deficits in nonverbal

behaviors

Poor peer relationships

Failure to share interest

with others

Poor social/emotional

reciprocity

Delayed spoken

language

Conversational deficits

Repetitive language

Deficits in imaginative

play

Restricted interests

Routines and rituals

Stereotyped

mannerisms

Preoccupation with

parts of objects

A dichotomous variable for each

criterion. Indicates that the clinician

who reviewed the SCADDM record

believed sufficient evidence existed to

confidently say this child either did or

did not exhibit the behaviour,

considering intensity, frequency, and

differences across settings.

SCADDM South Carolina autism and developmental disabilities

monitoring (SCADDM) network, DSM-IV diagnostic and statistical

manual for mental disorders-4th edition

Table 8 Classification of psychotropic medications

Generic name Trade name

Antipsychotics

Aripiprazole Abilify

Chlorpromazine Thorazine

Clozapine Clozaril

Fluphenazine fluphenazine

Haloperidol Haldol

Iloperidone Fanapt

Loxapine Loxitane

Molindone Moban

Olanzapine Zyprexa

Paliperidone Invega

Perphenazine perphenazine

Pimozide Orap

Quetiapine Seroquel

Risperidone Risperdal

Thioridazine Mellaril

Thiothixene Navane

Trifluoperazine Stelazine

Ziprasidone Geodon

Mood stabilizers/anticonvulsants

Carbamazepine Tegretol

Divalproex sodium, (valproic acid) Depakote

Ethosuximide Zarontin

Felbamate Felbatol

Gabapentin Neurontin

Lamotrigine Lamictal

Levetiracetam Keppra

Lithium carbonate Eskalith

Lithium citrate lithium citrate

Methsuximide Celontin

Oxcarbazepine Trileptal

Phenytoin Dilantin

Primidone Mysoline

Tiagabine Gabitril

Topiramate Topamax

Zonisamide Zonegran

Antidepressants

Amitriptyline Elavil

Amoxapine Asendin

Bupropion Wellbutrin

Citalopram Celexa

Clomipramine Anafranil

Desipramine Norpramin

J Autism Dev Disord

123

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Table 8 continued


Desvenlafaxine Pristiq

Doxepin Sinequan

Duloxetine Cymbalta

Escitalopram Lexapro

Fluoxetine Prozac

Fluoxetine Sarafem

Fluvoxamine Luvox

Imipramine Tofranil

Imipramine pamoate Tofranil-PM

Isocarboxazid Marplan

Maprotiline Ludiomil

Mirtazapine Remeron

Nortriptyline Aventyl, Pamelor

Paroxetine Paxil

Phenelzine Nardil

Protriptyline Vivactil

Selegiline Emsam

Sertraline Zoloft

Tranylcypromine Parnate

Trazodone Desyrel

Trimipramine Surmontil

Venlafaxine Effexor

Anti-anxiety medications (including benzodiazepines)

Alprazolam Xanax

Buspirone BuSpar

Chlordiazepoxide Librium

Clonazepam Klonopin

Clorazepate Tranxene

Diazepam Valium

Lorazepam Ativan

Midazolam Versed

Oxazepam Serax

Restoril Temazepam

Triazolam Halcion

Stimulant ADHD medications

Amphetamine Adderall

Amphetamine (ER) Adderall XR

Dexmethylphenidate

dexmethylphenidate (ER)

dextroamphetamine

Lisdexamfetamine dimesylate

Methamphetamine

Methylphenidate

Methylphenidate (long-acting)

Methylphenidate

Non-stimulant ADHD medications

Atomoxetine Strattera

Clonidine Catapres, Kapvay

Table 8 continued


Guanfacine Intinuiv, Tenex

propranolol hydrochloride Inderal

Sedatives and hypnotics

Chloral hydrate Somnote

Ezcopiclone Lunesta

Hydroxyzine pamoate Vistaril

Phenobarbital Solfoton

Ramelteon Rozerem

Anti-Parkinson/anti-dementia medications

Donepezil Aricept

Galantamine Razadyne

Memantine Namenda

Amantadine Symmetrel

Benztropine Cogentin

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Documents

Rates and Predictors of Adherence to Psychotropic Medications in Children with Autism Spectrum Disorders