School Psychology Quarterly 2015, Vol. 30, No. 3, 443– 455

© 2014 American Psychological Association 1045-3830/15/$12.00 http://dx.doi.org/10.1037/spq0000105

Psychopharmacological Treatment Among Adolescents With Disabilities: Prevalence and Predictors in a Nationally Representative Sample Amanda L. Sullivan and Shanna Sadeh

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University of Minnesota Little is known about psychopharmacological treatment among adolescents with educational disabilities. This study (a) describes pharmacotherapy among adolescents who received special education, and (b) examines the relations to adolescents’ disability type and sociodemographic characteristics. The sample was 9,230 adolescents who participated in the National Longitudinal Transition Study 2, a nationally representative study of students with disabilities. Descriptive statistics and logistic regression were used to estimate prevalence and predictors of pharmacotherapy. During the study period, 18.14% of adolescents received pharmacotherapy with 11.75% receiving monopharmacy, 6.39% receiving polypharmacy, and 5.86% simultaneously receiving multiple classes of medications. Stimulants and antidepressants were the most commonly used classes of psychotropic medication. After adjusting for sociodemographics, pharmacotherapy was highest among adolescents with other health impairments, emotional disturbance, and autism. Disability type, race/ethnicity, marital status, head of household education, urbanicity, and private insurance type were significant predictors of polypharmacy. Overall, these results indicated rates of psychopharmacological treatment exceeded those in the general population and disparities across sociodemographic groups existed. Implications for research and practice are discussed. Keywords: disabilities, psychopharmacology, special education

School psychology researchers have long been aware that differential rates of mental health service utilization are related to individuals’ sociodemographic characteristics and may be driven by contextual factors independent of students’ needs (e.g., Power et al., 2005). Whether patterns of differential access extend to psychopharmacological treatment among youth is of growing concern given that nationally ap-

This article was published Online First December 22, 2014. Amanda L. Sullivan and Shanna Sadeh, Department of Educational Psychology and Special Education, College of Education & Human Development, University of Minnesota. The content of this article is solely the responsibility of the authors and does not represent the views of the Robert Wood Johnson Foundation. This research was supported by a grant from the Robert Wood Johnson Foundation (New Connections Award 69589), awarded to Dr. Amanda L. Sullivan. We are grateful for the support. Correspondence concerning this article should be addressed to Amanda L. Sullivan, 56 East River Road, Minneapolis, MN 55455. E-mail: [email protected]

proximately 7% of adolescents use at least one psychotropic medication (Olfson, He, & Merikangas, 2013). School psychologists need to understand better treatment disparities relevant to school psychological services because such treatments may influence students’ behavioral and educational needs (Abrams, Flood, & Phelps, 2006). Unfortunately, missing from this discourse is empirical evidence of the prevalence of pharmacotherapy among students with disabilities, who represent 11% of the student population (Data Accountability Center, 2011) and may be the primary recipients of school psychological services in many school settings. The purpose of this study was to describe prevalence and sociodemographic predictors of psychopharmacological treatment, or pharmacotherapy, among adolescents with educational disabilities. Three kinds of pharmacotherapy were examined: monopharmacy (i.e., using a single medication); polypharmacy (i.e., simultaneously using two or more medications; e.g., Effexor and Lexapro); and multiclass pharmacotherapy (i.e., simultaneously using two or

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more medications from at least two different medication classes; e.g., Ritalin and Prozac). All multiclass treatment is a form of polypharmacy, but not all polypharmacy is multiclass treatment. The latter is presumed to indicate greater severity of needs and potential drug effects. Because psychotropic medication has a strong evidence base for a number of mental health disorders (Walkup & American Academy of Child and Adolescent Psychiatry [AACAP], 2009), pharmacotherapy is common practice for youth with and without diagnosed psychiatric disorders (Olfson, Blanco, Liu, Moreno, & Laje, 2006). Recent estimates from a nationally representative sample of adolescents treated from 2002 through 2004 indicated that 7% of adolescents use at least one psychotropic medication (Olfson, He, & Merikangas, 2013), and one fifth of treated adolescents receive multiple medications (Comer, Olfson, & Mojtabai, 2010). Evidence for polypharmacy, particularly multiclass treatment, is limited, so actual practice may exceed both approved and recommended uses and understanding of the medications’ effects (e.g., AACAP, 2001, 2009; Comer, Olfson, & Mojtabai, 2010). Disparities in pharmacotherapy may indicate differential access to services warranting greater exploration into the nature and implications of such differences. Understanding of the relations of sociodemographics to polypharmacy, particularly in youth subpopulations, is limited. Generally, pharmacotherapy is positively related to older age, male gender, White race/ethnicity, higher income, and poorer health (e.g., Leslie et al., 2003; Merikangas et al., 2011). Certain populations, such as youth in foster care (Rubin et al., 2012) and juvenile justice systems (Lyons et al., 2013), experience higher rates of pharmacotherapy and polypharmacy than the general population. Little is known about the use of pharmacotherapy among adolescents in other contexts, such as adolescents with educational disabilities, or the relations to sociodemographics, because the existing literature is constrained by limited representativeness and small sample sizes (e.g., children with autism seen in outpatient visits, Gerhard, Chavez, Olfson, & Crystal, 2009; only children with ADHD, Graves & Serpell, 2013; 89 students with ED from five self-contained classrooms, Mattison, 1999).

This study examined the prevalence and sociodemographic predictors of pharmacotherapy, both the number and classes of psychotropic medications, among adolescents with educational disabilities using a large nationally representative sample to describe the nature and extent of pharmacotherapy in this unique subpopulation. Significant differences in treatment by disability category and sociodemographics were expected. This study used the National Longitudinal Transition Study 2 (NLTS-2), a study of adolescents who were in high school receiving special education in 2000. Although this sample is not recent, it provides the most comprehensive data available on students with disabilities and can be compared with current estimates of pharmacotherapy and differential treatment in the general population (Merikangas et al., 2013), as well as other adolescent subpopulations based on other large-scale studies conducted at the same time (e.g., Olfson et al., 2013). This study adds to the literature by estimating population-based rates of mono- and polypharmacy and multiclass treatment among this unique population and describing relations to disability and sociodemographic characteristics. Method Data and Sample Data were drawn from the NLTS-2, a nationally representative longitudinal study sponsored by the U.S. Department of Education (http:// www.nlts.org). The NLTS-2 dataset includes information about 11,280 adolescents with disabilities who were 13 to 16 years old and in high school in 2000 (U.S. Department of Education, n.d.). Complex sampling procedures were used and weights were developed to correct for design effects and attrition and to allow for population estimates; these processes are explained in detail elsewhere (SRI International, 2008; Valdes et al., 2006; Wagner, Kutash, Duchnowski, & Epstein, 2005). All analyses presented here used these weights. Unweighted frequencies are presented for descriptive purposes only; as such, all parameters can be inferred to the population of U.S. adolescents who received special education in 2000. The analytic sample was derived from 9,230 students participating in the initial wave of data

PHARMACOTHERAPY IN ADOLESCENTS WITH DISABILITIES

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collection for whom complete parent interviews were reported. Table 1 presents the unweighted frequency counts and weighted proportions of the population for sociodemographic characteristics. The mean age was 15.8 years (SD ⫽ 1.21 years).

Table 1 Sociodemographic Characteristics of the NLTS-2 (Unweighted n ⫽ 9,230; Weighted n ⫽ 1,433,346) Characteristics Primary disability Speech impairment Learning disability Intellectual disability Emotional disturbance Other health impairment Multiple disabilities Autism Other Sex Female Male Racea White, non-Hispanic Hispanic African American Asian/Pacific Islander Marital statusb Married/cohabitating Previously married Never married Head of household education ⬍High school High school graduate Some college B.A. or higher Household income ⬎$25,000 $25,001 to $50,000 ⬍$50,000 Insurance Private Government None Urbanicity Suburban Urban Rural

Unweighted Weighted n %

SE

590 600 610 560 680 650 730 2,210

3.9 61.3 12.3 11.3 4.9 1.9 0.8 3.7

0.38 1.23 0.78 0.75 0.49 0.16 0.05 0.17

4,340 2,290

32.5 67.5

1.34 1.34

4,280 820 1,320 130

64.5 13.2 19.9 1.2

4.41 2.28 2.49 0.34

4,530 1,420 480

70.0 22.4 7.6

2.42 1.54 1.31

1,050 2,080 1,570 1,490

19.7 39.9 24.3 16.1

1.34 1.50 1.29 1.26

1,970 1,860 2,280

33.6 30.4 36.0

2.45 1.31 2.29

4,180 1,900 370

68.6 25.0 6.4

2.31 2.26 0.70

2,970 2,230 530

53.5 32.1 14.4

4.59 5.30 2.00

Note. Per licensee requirements of the Institute for Education Sciences for users of the NLTS-2, all frequencies were rounded to the nearest 10. a Race coded as other (n ⫽ 70) not reported. b Marital status coded as other (n ⬍ 10) not reported.

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Procedures The design, procedures, and measures of the NLTS-2 have been described in detail elsewhere (SRI International, 2000; Wagner et al., 2005). Participants’ parents provided informed consent. Data were collected via school records reviews and questionnaires administered to parents. All measures were administered by trained professional staff of SRI International (2000). During each wave of data collection, parents completed computer-assisted telephone interviews addressing participant and family characteristics, health conditions and treatments, behavior, and school experiences. Paper questionnaires were provided to participants who could not participate by phone. The present study used data drawn primarily from the parent interview items addressing (a) the sociodemographic characteristics of the adolescent and family, and (b) the adolescent’s receipt of medications for psychological or psychiatric difficulties. This study was approved by the university’s institutional review board and approved for dissemination by the Institute for Education Sciences. Measurements Medication treatment. Medication usage was assessed using a structured parent interview. Parents were asked whether their children were currently taking “prescription medicine that controls [his] attention, behavior, or activity level, or changes [his] mood” (SRI International, 2001) and if so, to provide the brand or generic name of those medications. Prior researchers have found through reverse recordcheck studies that parents tend to be reliable reporters of their children’s recent medication usage, which is what was asked in the NLTS-2 (Bussing, Mason, Leon, & Sinha, 2003; Pless & Pless, 1995). Interviewers coded these responses using a list of 88 psychotropic medications. If the medication was not listed, then it was coded by medication class. This study used the National Institute of Mental Health (NIHM, 2012) medication classification system to create a categorical measurement of medication class (anxiolytics, antidepressant, mood stabilizer, stimulant, anticonvulsant, antipsychotic, other). Although the NIMH placed lithium in the “Mood Stabilizing and Anticonvulsant Medica-

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tions” category, it was coded as a mood stabilizer in this study. Polypharmacy was assessed with a categorical measurement for number of medications received (0, 1, ⱖ2). Multiclass treatment was assessed with a categorical measure of the number of classes of medications received (0, 1, ⱖ2). Disability measurement. School records were used to identify students with disabilities. Disabilities were categorized by types defined under the Individuals with Disabilities Education Act (2004). For this analysis, these included learning (LD), intellectual (ID), emotional/behavioral (EBD), autism, other health impairment (OHI), multiple disabilities with severe cognitive impairment (MD), and speechlanguage impairments (SLI). Disability categories too small for individual consideration when weighted (i.e., traumatic brain injuries, hearing impairments, visual impairments, deaf-blindness, and orthopedic impairments) were classified as “other.” Sociodemographic measurements. Sociodemographic variables obtained from the parent interview were assessed with a continuous measure of age (13 to 18 years), a dichotomous indicator for female sex, and categorical measurements for marital status (never married, married/cohabiting, previously married), household income (⬍$25,000, $25,001 to $50,000, or ⬎$50,000), insurance status (private, government, none), and race/ethnicity (non-Hispanic white, Hispanic, African American, Asian/ Pacific Islander).

correlates simultaneously; adjusted odds ratios (AORs) and the Wald F statistics, an indicator of the significance of the factors, are presented.

Analyses

Prevalence of Pharmacotherapy by Class

All statistical analyses were performed using the SPSS Complex Samples Module to accommodate the complex sampling design via weighting and to provide population estimates (SPSS version 16.0). The analysis of patterns in psychotropic medication use among adolescents with special education disabilities was conducted in two phases. First, cross-tabulations were used to estimate prevalence of pharmacotherapy. Next, multivariate binary logistic regression models were used to estimate the relations of any pharmacotherapy, mono- and polypharmacy, and multiclass treatment to adolescents’ disability and sociodemographic characteristics, adjusting for age. The models included all disability and sociodemographic

Table 3 presents the prevalence rates for pharmacotherapy by disability. During the study period, stimulants were the most commonly received psychotropic medication among students with disabilities (11.65%), followed by antidepressants (6.53%). This general pattern was observed across all disability categories except autism, where antidepressants (22.82%) and antipsychotics (18.11%) were used more frequently than stimulants (14.37%). Use of stimulant medications was most common among adolescents with OHI (33.96%) and EBD (22.71%). Antidepressants were most commonly used by students with autism, OHI, and EBD. Nearly 10% of students with EBD were also prescribed antipsychotics and anti-

Results Background Characteristics Table 1 presents background characteristics for the unweighted analytic sample and population estimates. The majority of adolescents with disabilities were identified with LD (61.29%); ID and EBD affected 11% and 12%, respectively. Adolescents with disabilities were primarily male (67.51%) and White (64.48%). Prevalence of Pharmacotherapy Among Adolescents With Disabilities Table 2 presents the prevalence of pharmacotherapy. Among adolescents with disabilities, 18.14% used at least one psychotropic medication. Pharmacotherapy was least frequent among adolescents with SLI and LD. Monopharmacy (11.75%) was nearly twice as common as polypharmacy (6.39%). Pharmacotherapy was most prevalent among adolescents with OHI (44.64%), whereas polypharmacy was most frequent among students with autism (23.65%) or EBD (22.38%). Multiclass treatment was also common among adolescents with special education needs (5.86%), particularly autism (23.19%), EBD (21.03%), OHI (14.91%), and multiple disabilities (13.29%).

PHARMACOTHERAPY IN ADOLESCENTS WITH DISABILITIES

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Table 2 Prevalence of Monopharmacy, Polypharmacy, and Multiclass Treatment Among Adolescents With Disabilities

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Any pharmacotherapy

Monopharmacy

Multiclass treatment

Polypharmacy

Disability

%

(SE)

%

(SE)

%

(SE)

%

(SE)

Any disabilities Speech impairment Learning disability Intellectual disability Emotional disturbance Other health impairment Multiple disabilities Autism Other

18.14 10.29 12.03 18.33 40.62 44.64 26.96 42.46 14.75

(1.07) (1.47) (1.48) (1.79) (2.78) (1.92) (2.42) (2.36) (1.41)

11.75 6.87 9.69 10.74 18.24 29.22 13.40 18.81 9.31

(0.97) (1.26) (1.38) (1.51) (2.47) (1.94) (2.31) (2.20) (1.20)

6.39 3.42 2.34 7.60 22.38 15.42 13.57 23.65 5.44

(0.55) (0.84) (0.67) (1.18) (2.39) (1.59) (1.68) (2.18) (0.61)

5.86 3.42 1.86 7.26 21.03 14.91 13.29 23.19 5.09

(0.52) (0.84) (0.57) (1.18) (2.35) (1.55) (1.62) (2.17) (0.59)

convulsants. Among multiply disabled students, use of antipsychotic medications was also frequent (7.63%). Correlates of Mono- and Polypharmacy Disability and sociodemographic correlates of any pharmacotherapy, monopharmacy, polypharmacy, and multiclass treatment are presented in Table 4. In all of the models for frequency of any pharmacotherapy, the combination of disability and sociodemographic characteristics correctly predicted the outcome in approximately 83% to 94% of cases. Across models, pharmacotherapy was generally related to disability, race, and insurance status. When simultaneously controlling for age and sociodemographic characteristics, pharmacotherapy was most common among adolescents with OHI (AOR ⫽ 7.84, 95% CI ⫽ 5.36, 11.45). Likelihood of pharmacotherapy was also elevated among adolescents with EBD (AOR ⫽ 7.64, 95% CI ⫽ 4.99, 11.69), autism (AOR ⫽ 6.06, 95% CI ⫽ 3.98, 9.24), multiple disabilities (AOR ⫽ 3.41, 95% CI ⫽ 2.16, 5.39), and ID (AOR ⫽ 2.54, 95% CI ⫽ 1.65, 3.92). Race (p ⱕ .001), insurance status (p ⱕ .01), and age and marital status (p ⱕ .05) were significant predictors of pharmacotherapy. Racial minorities were significantly less likely to receive pharmacotherapy than their White peers. Students who had no insurance were also significantly less likely to receive pharmacotherapy than students with private insurance were (AOR ⫽ 0.26, 95% CI ⫽ 0.12, 0.59). Receipt of monopharmacy also related to disability, with students with OHI presenting the

highest likelihood of taking a single psychotropic medication (AOR ⫽ 7.84, 95% CI ⫽ 4.99, 12.33). Monopharmacy was significantly related to students’ age, disability, and race (p ⱕ .001), parents’ marital status (p ⱕ .01), and insurance type (p ⱕ .05). Students’ likelihood of polypharmacy varied by disability. Students with EBD had the highest likelihood of receiving multiple medications (AOR ⫽ 11.66, 95% CI ⫽ 5.63, 24.16), followed by students with autism (AOR ⫽ 8.33, 95% CI ⫽ 4.08, 17.03), OHI (AOR ⫽ 5.47, 95% CI ⫽ 2.61, 11.45), and multiple disabilities (AOR ⫽ 4.36, 95% CI ⫽ 2.10, 9.06). Polypharmacy was also predicted by students’ race, parents’ marital status and education, insurance, and urbanicity of the residence. Among students of similar ages, adolescents of parents with less than a college degree were less likely to receive polypharmacy than adolescents whose parents received at least a college degree. Those on government insurance were more likely (AOR ⫽ 1.85, 95% CI ⫽ 1.15, 2.99) to receive polypharmacy than adolescents on private insurance. Multiclass treatment was also related to disability, race, and insurance status (p ⱕ .001) and education level and urbanicity (p ⱕ .01). After adjusting for sociodemographics, students with EBD were most likely to receive multiclass polypharmacy (AOR ⫽ 11.17, 95% CI ⫽ 5.41, 23.04). Racial minority students were significantly less likely than White students to receive multiclass polypharmacy when disability and sociodemographics were adjusted. Students on government insurance (AOR ⫽ 1.87, 95% CI ⫽

Anxiolytics includes only benzodiazepines, buspirone, and beta blockers. Antidepressants include SSRI, SNRI, MAOI, and tricyclic antidepressants that may also have been used to treat anxiety. b Other medications included clonidine, guanfacine, medications for insomnia and allergies, unspecified antidepressant/anxiolytics, and “other medication.”

a

(SE)

(0.20) (0.44) (0.17) (0.85) (1.02) (0.89) (1.03) (0.98) (0.28)

%

(0.31) (0.46) (0.39) (1.11) (1.37) (1.11) (1.21) (1.28) (0.53)

(SE) %

3.01 0.82 0.94 4.53 10.74 6.62 7.94 8.37 2.87 (0.29) (0.57) (0.35) (0.91) (1.37) (0.92) (0.90) (1.67) (0.28)

(SE) %

2.62 1.59 0.67 3.89 9.83 4.64 7.63 18.11 1.75 (0.13) (0.00) (0.08) (0.22) (1.05) (0.58) (0.15) (0.43) (0.24)

(SE) %

0.58 0.00 0.08 0.37 3.48 1.46 0.22 0.91 0.57 (0.33) (0.00) (0.00) (0.20) (0.69) (1.35) (0.80) (2.75) (0.84)

(SE) %

0.45 0.00 0.00 0.48 1.38 2.41 1.57 4.97 1.39 (0.68) (1.09) (0.88) (0.98) (2.52) (1.38) (1.50) (2.10) (0.68)

(SE) %

6.53 4.82 3.79 5.89 18.71 13.40 9.40 22.82 5.45

(SE)

(0.91) (1.17) (1.32) (1.37) (2.31) (1.83) (2.35) (1.80) (1.10)

%

11.65 5.97 8.73 9.61 22.71 33.96 14.40 14.37 7.96

(SE)

(1.07) (1.47) (1.48) (1.79) (2.78) (1.92) (2.42) (2.36) (1.41)

%

81.86 89.71 87.97 81.67 59.38 55.36 73.03 57.54 85.25

Disability

Any disabilities Speech impairment Learning disability Intellectual disability Emotional disturbance Other health impairment Multiple disabilities Autism Other

Antipsychotics Anticonvulsants Mood stabilizers Anxiolyticsa Antidepressants Stimulants None

Table 3 Prevalence of Psychotropic Medication Use by Class Among Students With Disabilities

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1.57 0.80 0.24 3.20 5.22 4.29 5.44 6.92 1.47

SULLIVAN AND SADEH

Otherb

448

1.16, 3.02) were more likely to receive multiclass polypharmacy when adjusting for all other variables. Correlates of Multiclass Polypharmacy Disability and sociodemographic correlates of psychotropic medication class usage are presented in Table 5. The models for medication type correctly classified between 89% (stimulants) and 99% (other) of cases. Because mood stabilizers and anxiolytics were used infrequently, the models for these classes could not be tested. Adjusting for sociodemographics, medication classes were consistently predicted by disability type and race. Students with OHI had the highest rates of stimulant use (AOR ⫽ 9.95, 95% CI ⫽ 6.13, 16.15); students with autism had the highest rates of antidepressants (AOR ⫽ 4.41, 95% CI ⫽ 2.57, 7.57) and antipsychotics (AOR ⫽ 28.32, 95% CI ⫽ 7.98, 100.43). White adolescents were more likely than racial minority students to use each class of medications except anticonvulsants for which Asian/Pacific Islanders had a higher likelihood of use (AOR ⫽ 4.11, 95% CI ⫽ 1.24, 13.60). Students on government insurance were more likely to use antidepressants (AOR ⫽ 1.46, 95% CI ⫽ 0.81, 2.65), anticonvulsants (AOR ⫽ 2.83, 95% CI ⫽ 1.66, 4.84), antipsychotics (AOR ⫽ 2.10, 95% CI ⫽ 0.93, 4.71), and other medications (AOR ⫽ 1.40, 95% CI ⫽ 0.53, 3.65). Males were significantly more likely than females to receive stimulants (AOR ⫽ 1.94, 95% CI ⫽ 1.19, 3.17), but gender did not have a statistically significant relationship to other classes of psychotropic medications. Discussion The results indicate that nearly 1 in 5 adolescents with disabilities received pharmacotherapy, extending earlier studies showing increasing pharmacotherapy and coprescribing in adolescents generally during a similar time (Olfson et al., 2013). Adolescents with disabilities received pharmacotherapy at rates nearly two and a half times higher than the general population. These rates also exceeded those of other subpopulations studied, including adolescents with diagnosed mental disorders (Merikangas et al., 2013) and youth in foster care and juvenile justice (Lyons et al., 2013). Multiclass

Primary disability Speech impairment Learning disability Intellectual disability Emotional disturbance Other health impairment Multiple disabilities Autism Other Age Sex Female Male Race White, non-Hispanic Hispanic African American Asian/Pacific Islander Marital status Married/cohabitating Previously married Never married Head of household education ⬍high school High school graduate Some college B.A. or higher

Correlate

— 0.87–1.83

— 0.17–0.72 0.14–0.36 0.38–1.59

— 0.92–2.29 1.15–4.53 1.11 0.51–1.71 0.51–1.15 0.48–1.12 —

— 1.26

— 0.35 0.22 0.78

— 1.45 2.29

0.93 0.77 0.74 —

— 0.83–2.15 1.65–3.92 4.99–11.69 5.36–11.45 2.16–5.39 3.98–9.24 1.05–2.36 0.74–0.98

95% CI

— 1.34 2.54 7.64 7.84 3.41 6.06 1.57 0.85

AOR

3.45‡

10.02ⴱ

4.92‡ 1.56

45.82ⴱ

Wald F

Any pharmacotherapy

1.24 0.98 0.72 —

— 2.04 2.78

— 0.35 0.20 0.59

— 1.35

— 1.47 2.26 4.74 7.84 2.75 4.17 1.53 0.77

AOR

— 1.19–3.52 1.18–6.55 1.09 0.59–2.62 0.59–1.62 0.42–1.24 —

— 0.15–0.87 0.11–0.37 0.18–1.96

— 0.86–2.12

— 0.85–2.56 1.31–3.91 2.85–7.89 4.99–12.33 1.49–5.07 2.51–6.92 0.94–2.49 0.66–0.89

95% CI

Monopharmacy

4.65‡

7.04ⴱ

11.53† 1.67

28.86ⴱ

Wald F

0.48 0.48 0.79 —

— 0.60 1.26

— 0.32 0.32 1.14

— 1.04

— 0.92 2.90 11.66 5.47 4.36 8.33 1.62 1.09

AOR

— 0.41–0.89 0.43–3.67 3.21‡ 0.23–1.01 0.30–0.79 0.46–1.35 —

— 0.18–0.58 0.17–0.60 0.23–5.69

— 0.62–1.75

— 0.38–2.27 1.41–5.97 5.63–24.16 2.61–11.45 2.10–9.06 4.08–17.03 0.80–3.28 0.88–1.37

95% CI

Polypharmacy

Table 4 Correlates of Mono- and Polypharmacy, and Multiclass Treatment Among Adolescents With Disabilities

3.36‡

5.74ⴱ

0.64 0.02

21.87ⴱ

Wald F

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0.54 0.46 0.89 —

— 0.67 1.43

— 0.35 0.34 0.15

— 1.28

— 0.68 3.00 11.17 5.26 4.45 7.97 1.53 1.05

AOR

8.44ⴱ

0.20 1.41

21.47ⴱ

Wald F

2.19 — 0.45–1.00 0.48–4.22 5.06† 0.24–1.18 0.29–0.71 0.52–1.54 — (table continues)

— 0.19–0.63 0.18–0.65 0.06–0.38

— 0.85–1.92

— 0.27–1.69 1.46–6.15 5.41–23.04 2.51–11.00 2.15–9.23 3.92–16.20 0.75–3.12 0.84–1.31

95% CI

Multiclass treatment

PHARMACOTHERAPY IN ADOLESCENTS WITH DISABILITIES 449

5.14†

12.02ⴱ

— 0.71–1.83 0.29–0.81 5.97†

— 1.14 0.49

— 1.87 0.32

14.05ⴱ

— 0.72–1.69 0.29–0.78 — 1.10 0.47 1.53 — 0.47–1.73 0.91–2.23 — 0.90 1.42 p ⱕ .001.

— 0.89–1.87 0.43–1.29 — 1.29 0.74

p ⱕ .05. ‡

p ⱕ .01. †

Household income ⬍$25,000 $25,001 to $50,000 ⬎$50,000 Insurance Private Government None Urbanicity Suburban Urban Rural

Correlate

Table 4 (continued)

ⴱ

— 0.64–1.97 0.12–0.59 — 1.12 0.26

2.00

0.57–1.59 0.52–1.27 — 0.95 0.82 —

0.42

6.43†

— 0.86 0.26

0.90 0.65 —

— 0.43–1.71 0.10–0.73

0.49–1.63 0.38–1.10 —

1.30

3.40‡

— 1.85 0.27

1.14 1.37 —

— 1.15–2.99 0.12–0.63

0.64–2.03 0.74–2.52 —

0.56

0.94 1.09 —

— 1.16–3.02 0.13–0.75

0.53–1.69 0.66–1.79 —

95% CI 95% CI 95% CI Wald F 95% CI AOR

Any pharmacotherapy

AOR

Monopharmacy

Wald F

AOR

Polypharmacy

Wald F

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AOR

Multiclass treatment

0.17

SULLIVAN AND SADEH Wald F

450

polypharmacy has been estimated at 20% among those receiving pharmacotherapy in other research (Comer et al., 2010); here it was approximately 33%, and among adolescents classified with autism or EBD, it was more than 55%. Use of stimulants, antidepressants, mood stabilizers, and antipsychotics were two to five times more common among adolescents with disabilities than in the general population (Olfson et al., 2013). As expected, significant sociodemographic differences in treatment were observed. Pharmacotherapy was consistently predicted by disability, race/ethnicity, and insurance status as demonstrated elsewhere (Leslie et al., 2003). Gender and socioeconomic variables were predictive of stimulant usage but not other classes of medications, which differentiates adolescents with disabilities from prior findings (Comer et al., 2010; Cooper et al., 2006; Curtis et al., 2005; Leslie et al., 2003; Olfson et al., 2006). These results show that private insurance was more strongly associated than public insurance with pharmacotherapy, and receipt of specific classes of medication, contradicting previous research (Comer et al., 2010; Olfson et al., 2006) with some exceptions (Leslie et al., 2003). Students in the OHI, EBD, and autism categories had the highest rates of pharmacotherapy. This is likely attributable to the fact that students with ADHD (Schnoes, Reid, Wagner, & Marder, 2006) and epilepsy (Wodrich & Spencer, 2007) largely constitute the OHI category. For students with EBD, rates of pharmacotherapy were triple that of adolescents with diagnosed mental disorders (Merikangas et al., 2013), but were consistent with one early smallscale study (Mattison, 1999). These rates may reflect the tendency of schools to identify only the most severely impaired youth with emotional difficulties as having an emotional disability (AACAP, 1998), and warrants additional consideration to conceptualize appropriate assessment and treatment. Pharmacotherapy rates for autism were lower than previous estimates of outpatient office-based physician care (Gerhard et al., 2009). These results may be attributable to a variety of causes (e.g., the severity of symptomology among adolescents with autism who receive special education, accessibility of mental health therapy, providers’ reliance on medication over other nonpharmaceutical treat-

Primary disability Speech impairment Learning disability Intellectual disability Emotional disturbance Other health impairment Multiple disabilities Autism Other Age Sex Female Male Race White, non-Hispanic Hispanic African American Asian/Pacific Islander Marital status Married/cohabitating Previously married Never married Head of household education ⬍High school High school graduate Some college B.A. or higher Household income ⬍$25,000 $25,001 to $50,000 ⬎$50,000

Correlate

— 1.41 3.26

1.25

0.63–2.63

— 0.44–1.08 0.22–0.87

1.11 0.59–1.98

0.87–1.95 0.89–2.08 —

0.63–1.77 0.79–1.91 —

1.29

— 0.69 0.44

1.08

1.30 1.36 —

1.06 1.22 —

0.42

3.45‡

10.02

— 1.39–5.96 2.81–7.24

— 2.88 4.52

ⴱ

— 0.55–1.14

— 0.79

4.92‡ 1.56

0.09–0.19 0.19–0.46 0.11–0.25 0.42–0.95 1.02–1.36

0.13 0.29 0.17 0.64 1.18

0.87 0.87 —

0.85 0.65 —

0.29

— 0.40 0.24

— 1.94

9.95 3.52 2.70 1.64 0.65

5.18

0.09–0.20

— 1.64 2.33

AOR

0.13

45.82ⴱ

Wald F

— 0.46–1.21 0.26–0.61

95% CI

— 0.75 0.39

AOR

None

26.19ⴱ

Wald F

0.48–1.60 0.55–1.36 —

0.52–1.39 0.39–1.10 —

1.72 0.60–2.58

— 0.76–2.60 1.52–6.99

0.09–0.96

0.22

4.70†

6.13–16.15 1.88–6.61 1.58–4.62 0.98–2.75 0.57–0.75 34.07ⴱ 7.07† — 1.19–3.17 6.01ⴱ — 1.52–1.06 0.13–0.44

3.06–8.79

— 0.91–2.93 1.32–4.11

95% CI

Stimulants

1.03 0.88 —

0.69 0.83 —

0.75

— 1.25 1.19

0.08

— 0.17 0.23

— 0.77

2.45 1.58 4.41 0.76 1.27

4.36

— 0.64 0.94

AOR

0.57–1.84 0.45–1.74 —

0.40–1.19 0.47–1.46 —

0.64 0.36–1.57

— 0.79–1.98 0.41–3.47

0.01–0.40

— 0.86–0.35 0.10–0.50

— 0.46–1.29

1.44–4.17 0.89–2.80 2.57–7.57 0.47–1.22 1.01–1.61

2.31–8.24

— 0.32–1.29 0.49–1.82

95% CI

0.12

0.52

8.87

ⴱ

4.15‡ 0.98

17.65ⴱ

Wald F

Antidepressants

4.11 1.24–13.60

— — 0.27 0.13–0.55 0.42 0.18–0.96

0.21–0.80 0.71–3.19 —

0.99 0.47–2.06 0.85 0.37–1.93 — —

0.31 0.15–0.62 0.75 0.34–1.62 — —

0.23 0.10–0.54

0.52

1.38–74.82 1.54–86.46 1.60–83.33 0.69–37.29 0.84–1.74

— — 1.51 0.84–2.70

10.16 11.52 11.55 5.09 1.21

29.30 3.92–218.79

6.62ⴱ 0.18–1.00

†

95% CI

— — 2.80 0.35–22.15 9.52 1.27–71.48

AOR

— — 0.92 0.50–1.68 1.00 0.20–5.13

0.49

3.22

1.29 0.10

21.19ⴱ

Wald F

Anticonvulsants

— 0.71–2.19 0.54–4.46

0.07–0.62

— 0.24–1.35 0.20–1.29

— 0.60–2.05

1.58–22.12 3.08–37.97 7.98–100.43 0.70–8.05 0.86–1.74

5.17–74.39

— 0.28–7.11 1.80–26.90

95% CI

Antipsychotics

0.68 0..28–1.63 0.71 0.33–1.50 — —

0.41 1.50 —

0.43

— 1.25 1.55

0.21

— 0.57 0.51

— 1.11

5.91 10.82 28.32 2.37 1.22

19.60

— 1.41 6.96

AOR

Table 5 Correlates of Psychotropic Medication Use by Class Among Adolescents With Disabilities

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0.17

5.9ⴱ

0.04

6.89

ⴱ

1.08 1.90

7.95ⴱ

Wald F

1.18 0.69 —

0.48 1.05 —

0.68

— 0.59 0.20

0.05

— 0.87 0.40

— 0.57

15.67 18.84 21.75 4.52 1.26

23.36

— 0.69 13.14

AOR

1.51

3.75‡

5.55ⴱ

3.83‡ 3.05

9.87ⴱ

Wald F

0.78 0.40–3.46 0.32–1.45 — (table continues)

0.22–1.05 0.42–2.62 —

0.24–1.91

— 0.28–1.24 0.06–0.65

0.01–0.41

— 0.33–2.30 0.18–0.86

— 0.30–1.07

2.04–120.23 2.47–143.71 3.00–158.85 0.59–34.40 1.00–1.60

2.99–182.26

— 0.05–10.44 1.67–103.28

95% CI

Other

PHARMACOTHERAPY IN ADOLESCENTS WITH DISABILITIES 451

0.80 — 1.64 1.38

Limitations

— 0.77 0.45 — 0.81 0.58 — 0.77 1.02 — 1.44 0.70

p ⱕ .001.

— 0.54–1.12 0.77–2.35 — 0.78 1.35

ⴱ

— 0.51–1.56 1.69–8.64 — 0.89 3.82

These findings should be interpreted in the context of several limitations. First, the analysis was limited to medications reported by parents, not adolescents’ medical records that may more accurately represent prescription practices. Second, because disability information was drawn from school records, psychiatric diagnoses and specific symptom severity were unavailable. The disability categories represented broad constructs based on federal education regulations and likely have considerable within group differences. Third, analyses of anticonvulsant use may have included medications used to control seizures, as was likely the case in a small, but perhaps not negligible, number of adolescents, thus overestimating use as mood stabilizers. More broadly, in relying on NIMH (2012) classifications of medications, the coding may not have reflected the prescribing physicians’ intended use. Finally, the data were drawn from adolescents during the 2000 –2001 academic year, so they may not represent current usage, more recently developed medications, or changes in psychiatric practice that may influence pharmacotherapy among students with educational disabilities. These factors should be explored in future research. Further, prevalence of educational disabilities, particularly LD, OHI, and autism, has changed since data collection (U.S. Department of Education, 2012) and may have implications for rates of pharmacotherapy among students with disabilities. However, current estimates of pharmacotherapy in large scale or nationally representative samples were drawn from a similar period, so results can be compared with current knowledge about treatment in the general population and other youth subpopulations (e.g., Merikangas et al., 2013; Olfson et al., 2013). Though not ideal, this study presents the best available data on pharmacotherapy among students with disabilities. Implications for Research and Practice

‡

p ⱕ .01. †

Insurance Private Government None Urbanicity ⬎Suburban Urban Rural

p ⱕ .05.

95% CI

2.00

6.43†

— 0.82 0.28

— 0.95–2.20 0.35–1.39

— 0.43–1.56 0.10–0.78

2.78

2.96‡

— 1.46 0.22

— 0.39–1.52 0.66–1.59

— 0.81–2.65 0.09–0.56

0.34

7.27ⴱ

— 2.10 0.25

— 0.46–1.44 0.26–1.30

— 0.93–4.71 0.06–1.13

0.94

4.28†

— 2.83 0.87

— 0.46–1.30 0.18–1.12

— 1.66–4.84 0.38–2.02

1.66

7.53ⴱ

— 1.40 0.43

— 0.74–3.61 0.61–3.15

— 0.53–3.65 0.09–2.03

95% CI 95% CI 95% CI 95% CI

ments) that should be explored in future research.

AOR Correlate

Table 5 (continued)

None

Wald F

AOR

Stimulants

Wald F

AOR

95% CI

Wald F

Antidepressants

AOR

Antipsychotics

Wald F

AOR

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Anticonvulsants

Wald F

AOR

Other

1.11

SULLIVAN AND SADEH Wald F

452

Although few special education categories featured emotional or behavioral criteria, pharmacotherapy was common across all categories. Consequently, there is value in developing com-

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PHARMACOTHERAPY IN ADOLESCENTS WITH DISABILITIES

mon profiles or clusters of comorbidities and resultant coprescribing trends among adolescents with disabilities. These findings underscore the need for systematic evaluations of multiple medication and multiclass regimens, and off-label usage (Field & Boat, 2012) to elucidate issues of efficacy, safety, and tolerability, both in the short- and long-term. The high rate of pharmacotherapy here may be attributable to several factors: elevated need for pharmacotherapy, emphasis on reduction of disruptive behaviors, pressure to control behavior, reduced concern for polypharmacy, or limited access to therapeutic interventions (Comer et al., 2010; Rubin, 2013). Research is needed to understand the mental health needs and treatment outcomes of adolescents with disabilities, and potential sociodemographic differences in treatment access to clarify whether the present findings indicate differential need or disparities in access. Likewise, future research should explore the extent to which specific behaviors or nonmedical services are related to pharmacotherapy and sociodemographic disparities thereof. Given indications here of practices counter to research-based practice or professional standards (e.g., high rates of polypharmacy; AACAP, 2009), the determinants and effects off-label prescriptive practices and implications for nonmedical services should be considered. This is especially important given the added challenges inherent in polypharmacy, particularly with children and adolescents, where such practices may require an extended process of monitoring and consultation with parents, educators, and other service providers to understand fully the effects on students’ behavior and affect. These findings have implications for school psychologists working in schools. At the most basic level, the elevated prevalence of pharmacotherapy among students with disabilities underscores the importance of basic psychopharmacology knowledge to effective assessment, intervention, and collaboration. All students receiving special education are legally entitled to individualized evaluations and education plans, but these plans are likely incomplete without consideration of students’ mental health, resultant medication treatment, and the likely effects thereof on cognitive and social-behavioral functioning in the school environment. School psychologists should gather data on students’ phar-

453

macotherapy when engaging in assessment and intervention to ensure that the consequences of psychotropic medication usage (i.e., intended and unintended effects) are considered. School psychologists need to be aware of the potentially considerable mental health needs of adolescents with disabilities and work to foster interdisciplinary collaboration (e.g., with school nurses or students’ physicians) in assessment and treatment planning. Consultation is especially important because most educational professionals involved in school-based assessment and treatment of adolescents with disabilities lack formal training in psychopharmacology (Roberts, Floress, & Ellis, 2009). School psychologists may facilitate involvement of physicians and psychiatrists in school-based intervention planning by ensuring that these individuals are invited into the process. The information gathered by school psychologists can support physicians’ efforts to engage in appropriate medication management and monitoring. Although the context of contemporary psychiatric practice may have reduced medical providers’ reliance on direct therapies or consultation, school psychologists can encourage a bidirectional flow of information between the medical and educational contexts and encourage thorough consideration of nonmedical interventions. Relatedly, as advocates for students’ best interests, school psychologists’ pharmacological knowledge is essential to their efforts to advocate for research-based practice in and out of schools. Few adolescents with disabilities, even those with EBD, receive school-based mental health services or behavioral interventions as part of their individualized educational plans (Wagner et al., 2005), which may exacerbate efforts to manage behavior pharmacologically. School psychologists can help to rectify this shortcoming by promoting comprehensive, individualized intervention plans designed to address the constellation of difficulties experienced by any given student. They may also help educate families about evidence-based behavioral interventions that may complement or supplant pharmacotherapy outside of schools. In addition, school psychologists can encourage parents’ self-efficacy in dealing with outside providers who manage pharmacotherapy. As advocates of equity, school psychologists should help create choices for students for man-

454

SULLIVAN AND SADEH

aging mental health problems (Power et al., 2005) and challenge systemic and idiosyncratic factors that contribute to differential rates of pharmacotherapy based on sociodemographic variables.

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