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The American Journal of Managed Care September 2014
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Impact of Atypical Antipsychotic Use Among Adolescents With Attention-Deficit/Hyperactivity Disorder
Vanja Sikirica, PharmD, MPH; Steven R. Pliszka, MD; Keith A. Betts, PhD; Paul Hodgkins, PhD, MSc; Thomas M. Samuelson, BA; Jipan Xie, MD, PhD; M. Haim Erder, PhD; Ryan S. Dammerman, MD, PhD; Brigitte
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Gerry Oster, PhD, and A. Mark Fendrick, MD
Targeting High-Risk Employees May Reduce Cardiovascular Racial Disparities
James F. Burke, MD, MS; Sandeep Vijan, MD; Lynette A. Chekan, MBA; Ted M. Makowiec, MBA; Laurita Thomas, MEd; and Lewis B. Morgenstern, MD
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Seth Joseph, MBA; Max Sow, MBA; Michael F. Furukawa, PhD; Steven Posnack, MS, MHS; and Mary Ann Chaffee, MS, MA
Out-of-Plan Medication in Medicare Part D
Pamela N. Roberto, MPP, and Bruce Stuart, PhD
New Thinking on Clinical Utility: Hard Lessons for Molecular Diagnostics
John W. Peabody, MD, PhD, DTM&H, FACP; Riti Shimkhada, PhD; Kuo B. Tong, MS; and Matthew B. Zubiller, MBA
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Shivan J. Mehta, MD, MBA; and Scott Manaker, MD, PhD
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Harn-Shen Chen, MD, PhD; Tzu-En Wu, MD; and Chin-Sung Kuo, MD
Characteristics Driving Higher Diabetes-Related Hospitalization Charges in Pennsylvania
Zhen-qiang Ma, MD, MPH, MS, and Monica A. Fisher, PhD, DDS, MS, MPH
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James C. Robinson, PhD, and Timothy T. Brown, PhD
Effects of a Population-Based Diabetes Management Program in Singapore
Woan Shin Tan, BSocSc, MSocSc; Yew Yoong Ding, MBBS, FRCP, MPH; Wu Christine Xia, BS(IT); and Bee Hoon Heng, MBBS, MSc
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Lindsey Jeanne Leininger, PhD; Donna Friedsam, MPH; Kristen Voskuil, MA; and Thomas DeLeire, PhD
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Impact of Atypical Antipsychotic Use Among Adolescents With Attention-Deficit/Hyperactivity Disorder

Vanja Sikirica, PharmD, MPH; Steven R. Pliszka, MD; Keith A. Betts, PhD; Paul Hodgkins, PhD, MSc; Thomas M. Samuelson, BA; Jipan Xie, MD, PhD; M. Haim Erder, PhD; Ryan S. Dammerman, MD, PhD; Brigitte
A retrospective study of the treatment patterns and economic outcomes associated with off-label atypical antipsychotic use in the treatment of adolescents with attention-deficit/hyperactivity disorder.
Table 1 compares baseline characteristics between the 2 cohorts before and after matching. Prior to matching, several baseline characteristics were significantly different between the 2 cohorts. After matching, the differences in baseline demographic characteristics, resource utilization, and costs (with the exception of drug costs and total costs per patient) were not significantly different, indicating balanced baseline characteristics between the 2 cohorts.

In the post matching AAP cohort, 42.4% used risperidone followed by aripiprazole (27.2%), quetiapine (22.0%), olanzapine (4.7%), ziprasidone (3.3%), and paliperidone (0.4%) as the respective index drug; this distribution was similar to that observed in the pre-matching AAP cohort. In the pre-matching non-antipsychotic cohort, 72.5% of all index drugs were stimulants. After matching, 89.3% of all index drugs were non-stimulants (atomoxetine, 52.2%; clonidine IR, 31.1%; guanfacine IR, 6.0%).

Treatment Patterns

During the 12-month period, persistence to the index drug was low in both the AAP and non-antipsychotic cohorts, with the non-antipsychotic cohort being more likely to discontinue the index drug (12-month KM: 74.9% vs 81.2%; HR = 0.85; 95% CI, 0.76-0.94) (Figure 2A). Overall, patients were more likely to augment the index drug in the AAP cohort compared with the nonantipsychotic cohort (12-month KM: 27.7% vs 15.5%; HR = 2.56; 95% CI, 1.90 3.46) (Figure 2B). The rate of switching was not statistically different between the 2 cohorts (12-month KM: 11.6% vs 9.7%; HR = 1.40; 95% CI, 0.90-2.20) (Figure 2C).

Healthcare Utilization

In the 12-month study period, a significantly higher proportion of patients in the AAP cohort had at least 1 inpatient visit compared with the non-antipsychotic cohort (8.6% vs 3.3%, P <.001) (Table 2). Patients in the AAP cohort experienced an average of 0.13 inpatient visits compared with 0.05 inpatient visits in the non-antipsychotic cohort (IRR = 2.45; 95% CI, 1.73-3.48). The majority of inpatient visits (84.3% in the AAP cohort and 65.9% in the non-antipsychotic cohort) were MH related. Patients in the AAP cohort experienced an average of 0.11 MH-related inpatient visits compared with 0.03 inpatient visits in the nonantipsychotic cohort (IRR = 3.14; 95% CI, 2.07-4.77).

The AAP cohort had a significantly higher proportion of patients having at least 1 ED visit—both all-cause (28.2% vs 19.9%, P <.001) and MH-related (7.9% vs 4.5%, P = .004)—compared with the non-antipsychotic cohort. The average number of all-cause ED events per patient was also significantly higher in the AAP cohort (0.39 vs 0.31, IRR = 1.27; 95% CI, 1.08-1.49). The average number of MH-related ED visits per patient was not statistically different between the 2 cohorts (0.095 vs 0.069, IRR = 1.37; 95% CI, 0.98-1.92).

Almost all patients had at least 1 outpatient visit (98.4% in both cohorts), while the number of patients with at least 1 MH-related outpatient visit was higher in the AAP cohort (94.7% vs 90.8%, P = .002). Patients in the AAP cohort also had more outpatient visits during the study period compared with non-antipsychotic patients. (All-cause: 14.82 vs 13.19; IRR = 1.12; 95% CI, 1.10-1.15. MH-related: 9.34 vs 7.51; IRR = 1.24; 95% CI, 1.20-1.29.)

Healthcare Costs

Overall, the AAP cohort had higher mean healthcare costs compared with the non-antipsychotic cohort for both all-cause ($7936 vs $6195, P <.001) and MH-related services ($5739 vs $3216, P <.001) (Table 3). Mean drug costs were significantly higher in the AAP cohort for both all-cause ($4314 vs $2884, P <.001) and MH-related ($3856 vs $2016, P <.001) pharmacy claims. The total cost for the index drug was also higher in the AAP cohort ($1718 vs $632, P <.001).

Patients in the AAP cohort had higher all-cause and MH-related medical costs compared with the non-antipsychotic cohort (all-cause: $3622 vs $3311, P = .002; MHrelated: $1883 vs $1199, P <.001). Medical costs across all categories (inpatient, outpatient, and ED) were higher in the AAP cohort, with the differences in inpatient costs (all-cause: $853 vs $707, P <.001; MH-related: $644 vs $268, P <.001) and ED costs (all-cause: $239 vs $159, P = .001; MH-related: $46 vs $25, P = .010) being statistically different. The majority of the medical costs for both cohorts were associated with outpatient visits but only MH-related costs were significantly different (all-cause: $2530 vs $2444, P = .190; MH-related: $1193 vs $907, P <.001).


To our knowledge, this is the first study to evaluate the association between AAP treatment and medical resource utilization and costs for adolescents with ADHD. Our study found that 55% of adolescent ADHD patients with at least 1 prescription for an AAP did not have any psychiatric diagnoses for which AAPs are commonly prescribed. This result is consistent with previous studies of prescribing patterns for ADHD, and highlights the fact that the high prevalence of AAP use in this population warrants a careful assessment of the clinical, medical, and economic impact of such utilization.9,13-16,20,23,24 This study showed that even after controlling for baseline differences between the 2 treatment cohorts, adolescents with ADHD using AAPs were more likely to have greater resource utilization and healthcare costs compared with those in the non-AAP cohort.

Although there are documented differences between children and adolescents in the developmental presentation and trajectory of ADHD, the study findings are consistent with a companion investigation that studied a sample of children (aged 6-12 years) with ADHD. These 2 studies demonstrate that AAPs prescribed for ADHD present a substantial incremental burden to US payers compared with non-antipsychotics.20 Much of the incremental burden was related to higher MH-related utilization and costs. Although matched to MH-related ICD-9-CM codes, the exact reasons for the increased resource utilization and higher medical costs among AAP users cannot be elucidated from the administrative claims data; it is possible that they may be related to inadequate ADHD symptom control, the known side effects of AAPs, or other unknown causes.2,25-30 Additional research is warranted to determine the specific causes of treatment pattern differences, higher utilization, and higher costs among this population of ADHD patients.

Previous studies have noted gender differences in the presentation and treatment of ADHD, with males being diagnosed at a rate approximately 3 times that of females.30-32 In the present study, significantly fewer females were treated with AAPs compared with non-antipsychotic treatments before matching (27.2% vs 32.7%, P <.001) (Table 1). These differences were controlled for by including gender within the propensity score match. Future analyses may provide more insight into the role gender plays regarding off-label treatment with AAPs for ADHD.

In addition, this study found that a significant number of patients in the pre-match AAP cohort used the index drug to augment their existing drug therapy (81.0% vs 36.4% in the non-antipsychotic cohort, P <.001) (Table 1). Recent studies have outlined concerns that arise with such polypharmacy involving stimulants and AAPs, due to potential interactions that may affect dopamine regulation or induce metabolic syndrome and its sequelae.33-35

The off-label use of treatments not supported by adequate clinical and economic evidence may pose a substantial economic burden to payers. The need for such evidence is especially pronounced in regard to ADHD, for which several FDA-approved therapies with demonstrated effectiveness exist, including treatments indicated as mono- and combination therapy. A third-party payer could initiate a drug utilization review for off-label treatment of ADHD, which could help managed care organizations better understand the reasons that AAPs are being prescribed, and potentially improve the quality of care for adolescents with ADHD.


There are inherent limitations to using administrative claims data to determine whether AAP prescriptions were used in the treatment of ADHD. Specifically, clinical disease severity measures are not available, and the precise intent of the treating physician cannot be inferred. To minimize these limitations, stringent selection criteria were imposed, and the common indications for the use of AAPs were excluded. However, AAPs may have been used for psychiatric disorders not recorded in the database, possibly due to stigma associated with psychiatric diagnoses or the lack of available codes. Conversely, the stringency of the comorbidities list could have excluded patients with comorbid psychiatric disorders who were prescribed the AAP as a treatment for ADHD (and not their comorbid disorder), which may cause these results to be less generalizable to the entire ADHD population.36 Additionally, it is possible that AAPs were being prescribed for other off-label indications such as depression, conduct disorders, or oppositional defiant disorder. However, these comorbidities were included in the propensity score matching, and there were no statistically significant differences between the 2 cohorts in the frequency of these diagnoses. While all relevant available variables were included in the propensity score model, potential unobserved confounders may exist that are related to the prescribing of the AAP but are not available in the data set. It is also possible that the decision to prescribe AAPs on-label versus off-label may have been influenced by formulary status or other policies.

Propensity score matching was used to create balanced and comparable cohorts. However, after the propensity score match, differences in drug costs and total costs at baseline remained. The results of the propensity score match are only generalizable to the population included in the match. The final match excluded 302 AAP patients who could not be matched with representative non-antipsychotic patients. These patients had higher rates of medical resource utilization and costs at baseline, thus the results of the current study may underestimate the association between off-label AAP use and medical resource utilization and costs.

Finally, because these analyses covered only the first 12 months following the index date, the results of this investigation cannot be extrapolated to provide insight on the longer-term economic implications of off-label AAP treatment of ADHD. Indeed, the results may understate the actual effect of such treatments, as many of the complications associated with AAPs are chronic and the entirety of their effects on costs and utilization may not be observed within the study period. Future research of the long-term health outcomes and economic implications of off-label AAP use is justified.


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