Comparative treatment patterns, resource utilization, and costs in stimulant-treated children with ADHD who require subsequent pharmacotherapy with atypical antipsychotics versus non-antipsychotics.

BACKGROUND: Although not indicated for attention-deficit/hyperactivity disorder (ADHD), atypical antipsychotics (AAPs) are commonly prescribed for children with ADHD. The treatment patterns, resource utilization, and costs associated with AAPs relative to non-antipsychotic medications have not been evaluated for children with ADHD.  OBJECTIVE: To compare treatment patterns, resource utilization, and costs to U.S. third party payers between stimulant-treated ADHD children who switch to or augment their stimulant treatment with AAPs (risperidone, aripiprazole, quetiapine, olanzapine, ziprasidone, paliperidone, and clozapine) compared with non-antipsychotic medications (atomoxetine, clonidine immediate-release (IR), guanfacine IR, dexmethylphenidate, mixed amphetamine salts, methylphenidate, lisdexamfetamine, and dextroamphetamine). METHODS: Patients with at least one ADHD diagnosis (ICD-9-CM codes 314.00 or 314.01) and at least one stimulant medication claim between January 1, 2005 and December 31, 2009, were identified from a large U.S. commercial medical/pharmacy claims database. Patients were classified into the AAP cohort if they had a claim for an AAP following a stimulant fill or into the non-antipsychotic cohort if they had a claim for a non-antipsychotic medication after a stimulant fill and no AAP claims. The index date was defined as the date of the first fill of the AAP or a randomly selected eligible non-antipsychotic medication. Patients were eligible for inclusion if they were aged 6-12 as of the index date and had at least 18 months of continuous eligibility. Patients were excluded if they had a psychiatric diagnosis for which AAPs were approved by the U.S. Food and Drug Administration (FDA) or commonly used. Patients in the non-antipsychotic group were matched 1:1 to patients in the AAP group using a propensity score generated from a logistic regression that included demographics, treatments, resource utilization, and comorbidities during the 6 months prior to the index date. All outcomes were measured during the 12 months following the index date. Treatment patterns were compared using Kaplan-Meier (KM) estimates and Cox proportional hazards models. Annual resource utilization was compared using McNemar's test and Poisson regression. Costs were estimated from the perspective of U.S. third-party payers and were adjusted to 2010 dollars using the medical component of the Consumer Price Index. Both all-cause and mental health-related costs were examined and compared using Wilcoxon signed-rank tests.  RESULTS: Of the 22,622 patients with ADHD identified to have used AAPs after a stimulant, 15,664 (69%) patients did not have a psychiatric diagnosis for which AAPs were FDA-indicated or commonly used. Among the 84,558 patients using non-antipsychotics after a stimulant, 81,397 (96%) did not have such psychiatric diagnoses. A total of 2,127 children in the AAP cohort and 16,508 children in the non-antipsychotic cohort met all of the study inclusion criteria. After propensity score matching, 1,857 children (358 switchers and 1,499 augmenters) were included in each of the matched cohorts. The baseline characteristics were well balanced between the matched cohorts. In the 12 months post-index date, children treated with AAPs were more likely to experience switching (KM: 17.2% vs. 10.4% at 12 months; HR = 1.75) and augmentation (KM: 43.4% vs. 22.4% at 12 months; HR = 2.62) than the non-antipsychotic group (both P less than 0.001). Rates of discontinuation were similar between groups (KM: 71.8% vs. 71.7% at 12 months; HR = 0.98, P = 0.600). The AAP cohort also had higher mean numbers of hospitalizations, emergency room visits, and outpatient visits (0.08 vs. 0.03, 0.34 vs. 0.25, 14.1 vs. 12.7 per patient, respectively; event rate ratios = 2.61, 1.33, and 1.11, respectively; all P less than 0.001). The AAP group also incurred higher all-cause mean medical, prescription drug, and total health care costs compared with the non-antipsychotic group ($3,090 vs. $2,238; $3,844 vs. $2,509; $6,934 vs. $4,748, respectively; all P less than 0.001). Patients in the AAP group also incurred higher mean total, medical, and drug costs related to mental health ($5,057 vs. $2,859; $1,555 vs. $964; $3,502 vs. $1,895, respectively; all P less than 0.001). CONCLUSIONS: Stimulant-treated children with ADHD who switched to or augmented with AAPs versus non-antipsychotics had significantly greater rates of subsequent augmentation and health care resource utilization as well as higher total health care costs. Further research and/or drug utilization reviews may be warranted to fully evaluate the clinical and economic outcomes of pediatric ADHD patients who are receiving AAPs.

Estimation of utilities in attention-deficit hyperactivity disorder for economic evaluations.

BACKGROUND: Attempts to estimate the cost effectiveness of attention-deficit hyperactivity disorder (ADHD) treatments in the past have relied on classifying ADHD patients as responders or non-responders to treatment. Responder status has been associated with a small gain in health-related quality of life (HR-QOL) [or utility, as measured using the generic QOL measure EQ-5D] of 0.06 (on a scale from 0 being dead to 1.0 being full health). OBJECTIVES: The goal of the present study was to develop and validate several ADHD-related health states, and to estimate utility values measured amongst the general public for those states and to re-estimate utility values associated with responder status. METHODS: Detailed qualitative interview data were collected from 20 young ADHD patients to characterize their HR-QOL. In addition, item-by-item clinical and HR-QOL data from a clinical trial were used to define and describe four health states (normal; borderline to mildly ill; moderately to markedly ill; and severely ill). ADHD experts assessed the content validity of the descriptions. The states were rated by 100 members of the UK general public using the time trade-off (TTO) interview and visual analog scale. Statistical mapping was also undertaken to estimate Clinical Global Impression-Improvement (CGI-I) utilities (i.e. response status) from Clinical Global Impression-Severity (CGI-S) defined states. The mapping work estimated changes in utilities from study baseline to last visit for patients with a CGI-I score of ≤ 2 or ≤ 3. RESULTS: The validity of the four health states developed in this study was supported by in-depth interviews with ADHD experts and patients, and clinical trial data. TTO-derived utilities for the four health states ranged from 0.839 (CGI-S state 'normal') to 0.444 (CGI-S state 'severely ill'). From the mapping work, the change in utility for treatment responders was 0.19 for patients with a CGI-I score of ≤ 2 and 0.15 for patients with a CGI-I score of ≤ 3. CONCLUSIONS: The present study provides utilities for different severity levels of ADHD estimated in a TTO study. This approach provides a more granular assessment of the impact of ADHD on HR-QOL than binary approaches employed in previous economic analyses. Change in utility for responders and non-responders at different levels of CGI-I was estimated, and thus these utilities may be used to compare health gains of different ADHD interventions.