Atypical Antipsychotic Utilization: Emerging Trends in Indiana

Medicaid Children and Teens

Professor of Psychiatry Indiana University School of Medicine

MMAC Consultant

Andrew W. Goddard, M.D.

Collaborators IU Medicaid Medical Advisory

Cabinet (MMAC) Steve Downs, MD Kristin Hendrix, PhD Katie Schwartz, JD Matt Aalsma, PhD Maria Finnell, MD Kathleen Unroe, MD Brownsyne Tucker Edmonds, MD Nancy Swigonski, MD Marc Rosenman, MD Wendy Morrison, PhD

Indiana OMPP

Emily Hancock, PharmD

Dave Lambert, JD

IU Regenstrief Institute

Bert Ambeuhl, MS

IU Biostatistics

James Slaven, MS

Context for the Presentation • OMPP requested that MMAC conduct a local replication

and extension of the 16-state AHRQ-Rutgers Study of atypical antipsychotic (AAP) use in IN Medicaid children and teens.

• Initial report to MHQAC 10/2011:

— AAP utilization rates in IN Medicaid FFS youth (2.3%) were similar to AHRQ-Rutgers findings, but IN utilization rates for foster children were greater (17%);

— AHRQ-Rutgers dataset not well defined with respect to FFS, RBMC, and unique cases.

Agenda • Overview of atypical antipsychotics

(AAPs); position in the marketplace • Rationale for current Indiana-OMPP

study extension • Methods • Overview of Current Cost/Utilization

Findings • Conclusions/Next Steps

Currently Available AAPs

Dopamine Serotonin Muscarinic Histamine Adrenergic Agent D1 D2 D4 5-HT1A 5-HT2A 5-HT2C M1 H1 Alpha1A Alpha2A

Aripiprazole - +++ + +++ +++ ++ - ++ ++ ++ Asenapine +++ +++ +++ +++ ++++ ++++ - +++ +++ +++ Clozapine + + ++ + +++ +++ +++ +++ +++ ++

Iloperidone + +++ ++ ++ +++ ++ - ++ ++++ + Lurasidone + +++ ++ +++ +++ + - - ++ ++ Olanzapine ++ ++ ++ - +++ +++ +++ +++ + +

Paliperidone ++ +++ ++ ++ ++++ ++ - ++ +++ +++ Quetiapine + + - + + - ++ +++ ++ - Risperidone + +++ +++ + ++++ ++ - ++ +++ ++ Ziprasidone ++ +++ ++ ++ ++++ ++ - ++ ++ + Binding affinities expressed as plus (+) sign based on Ki (nM) from published sources. Very high (++++): Ki < 1, High (+++): Ki < 10, Moderate (++): Ki < 100, Low (+): Ki < 1000, Very low (-): Ki < 10,000

Dosing/administration

FDA-approved indications for atypical antipsychotics

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Schizophrenia X X X X X X X X X X Bipolar disorder X X X X X X Acute agitation with above disorders X X X Irritability with autistic disorders X X Adjunct in major depressive disorder X X X

AAPs by FDA Indications

Abilify autism approval 2010

OMPP Study Rationale 1. Examine a time frame beyond the original

AHRQ-Rutgers study (i.e., 2004-2011) as new AAPs have come on the market since 2007.

2. Determine impact on AAP utilization and cost curves of OMPP program/policy changes (i.e., CareSelect-2007, SmartPA-2009/10, Pharmacy Benefits consolidation-2009).

Autism Spectrum Disorder - Medicaid Expenditures by Year

diagnosis_yr total total_paid per patient 2002 2200 $37,282,827 $16,947 2003 2707 $48,104,661 $17,770 2004 3133 $55,648,620 $17,762 2005 3464 $58,557,103 $16,904 2006 3826 $71,934,624 $18,802 2007 4618 $75,506,226 $16,350 2008 5139 $87,882,148 $17,101 2009 6081 $102,664,498 $16,883 2010 4946 $78,267,143 $15,824 2011 5053 $84,287,493 $16,681

Between the ages of 1-22 years

Study Methods

• Sample: — all children and teens in IN Medicaid for at

least one month between January 2004 through June 2012 (~ 1.4 million cases)

— AHRQ-Rutgers inclusion criteria: FFS status for at least 1mth, <19 yo, non-duals

• Variables of interest: — counts and rates of AAP utilization — PMPM cost of AAPs and Abilify

Number of IN Medicaid Children (<19yo)

Number of Medicaid Children with Any Claim ($>0) for Any AAP (by year)

Number of Medicaid Children with Any Claim ($>0) for Any AAP (by year)

Yearly Rate of Medicaid Children with Any Claim ($>0) for Any AAP

Number of Medicaid Children with Any Claim ($>0) for Any AAP (by month)

January 2004 April 2009 June 2012

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Average Monthly Cost to Medicaid for AAPs, Per AAP User Jan. 2004 - June 2012

Cost_AAP

Cost_Abilify

Cost_Total

http://insendems.wordpress.com/2011/07/22/budget-briefs-medicaid-budget-breakdown/

1.8 M members in 2011; 5053 with ASD = 0.3% of enrollees have Autism

January 2004 June 2012

Patient*Months with Atypical Antipsychotics not including Aripiprazole N=49,677 children ever in the 8 ½ years

12

9 8 7 6 5 4 3

2 1 or less

ln(total cost of the atypical(s))

in 8½ years

January 2004 June 2012

Patient*Months with Aripiprazole N=26,011 children ever in the 8 ½ years

11 9

8

7

6 5 4 3 or less

ln(total cost of the atypical)

in 8½ years

2004 2005 2006 2007 2008 2009 2010 2011 2012

Total Number of Foster Children

( > 6 months in foster care during calendar yr )

4763 5390 5306 5448 5973 6200 6018 5719 4988

Number of Foster Children with AAP Claim

(during calendar yr) 396 489 544 643 817 893 885 823 652

8.31% 9.07% 10.25% 11.80% 13.68% 14.40% 14.71% 14.39% 13.07%

AAP Use in Medicaid Foster Children

AAP Use in Medicaid Children ≤ 5 Years Old

2004 2005 2006 2007 2008 2009 2010 2011 2012

Number FFS children ≤ 5 y.o.

337 307 349 373 372 301 286 304 136

Number Non-FFS children ≤ 5

398 394 344 361 470 575 581 521 315

Conclusions • Similarity to original AHRQ-Rutgers findings

• Utilization patterns: FFS & non FFS AAP utilization is fairly flat (2-3%),

despite non-FFS population growth after 2007

• Cost trends: Not adjusted. Average Abilify per member per month spending increases after 2007-8. Other AAP spending stabilizes by early 2009.

• Foster children demonstrate a higher AAP exposure rate, when compared to non-foster children, as expected. Their AAP utilization rates were fairly constant over the study period (~8-15%).

• FFS children under 5 years old experience some AAP exposure, though utilization rates did not appear to grow over time.

Limitations • Claims data do not provide information

about individual patient status, patient needs, and individual provider decision processes.

• Minimal access to clinical safety data (e.g.,

basic info such as weight, vitals).

• Evaluation of “clinically appropriate” prescribing calls for either a chart-review study or, ideally, a prospective F/U study.

Next Steps: follow-up analyses • Analysis of AAP use by mental health diagnosis.

• Analysis of AAP use by other demographic

variables (e.g. race, Medicaid aid category, age range).

• Preliminary safety analyses (e.g. rates of diabetes in AAP utilizers versus non-utilizers).

• Regression analyses to more rigorously assess impact of OMPP program/policy updates on costs.