Investigators: Eric Slade 1,2 Lisa Dixon 1,2 Marcia Valenstein 3,4 John McCarthy 3,4

1. Assessing MHICM: Program Effects on Mental Health Care Utilization and Costs*,***Funding from HSR&D grant IIR 06-115 and the VISN5 MIRECC**Data provided by the following VA research centers: SMITREC, NEPEC, and HERC Investigators: Eric Slade1,2 Lisa Dixon1,2 Marcia Valenstein3,4 John McCarthy3,4 Analysts: Stephanie Visnic3, Rose Ignacio3, Deborah Welsh3, Lan Li1,2 1VISN 5 Mental Illness Research and Education Clinical Center, Baltimore 2University of Maryland School of Medicine 3VA Serious Mental Illness Treatment Research and Evaluation Center, Ann Arbor 4University of Michigan School of Medicine

2. The MHICM Program • A psychiatric “hospital without walls” • Uses the Assertive Community Treatment (ACT) model. • Similar in staffing intensity to inpatient psychiatric care • Team-based mobile care • Small caseloads per team member • Team available 24/7 • Care is comprehensive • Improves patients’ quality of life and satisfaction with care, and reduces their inpatient utilization • 1980’s VA randomized trials of Intensive Psychiatric Community Care (IPCC) (Rosenheck et al., 1995 & 1998)

3. MHICM

4. The MHICM Program • Formally implemented as MHICM in FY2000 • Implemented “high hospital use” entry criterion • Rapid growth FY00 FY07 Teams 46 100 Clients 2,655 7,609 Cost $14.5 mil. $46 mil. • <10% of eligible veterans have enrolled • MHICM programs require a max. client-staff ratio of 12 to 1

5. Objectives • Estimate the effects of MHICM on mental health services utilization during the first 12 months following clients’ first enrollment in MHICM • psychiatric inpatient days of stay • partial hospital program days • other outpatient mental health days • Assess the VA cost consequences of the MHICM program

6. MHICM’s Net Impact on Psych Costs B Hypothesized > 0 0 C A < 0 Higher r Lower Inpatient Psychiatric Utilization in the Prior Year Net Impact ($)

7. Declining VA psychiatric inpatient use may reduce savings achieved with MHICM FY00 FY07 %Δ LOS (Days) 15.0 11.4 -24 Bed Census 4,106 2,958 -28 Bed Days/Veteran 22.0 16.8 -24 • Trend in VA Psychiatric Inpatient Use

8. Expected Cost Consequences of MHICM $ VA Trend in Inpatient Psych Days Per Patient Net Savings 0 Net Costs Net Savings from MHICM ($) Time

9. Study Design Retrospective observational design Sample: MHICM-eligible VA patients in FY01 to FY04 “Intervention”: Enrollment in MHICM “Comparison”: Usual care Follow-up period: The 12-month period following either MHICM initiation or becoming MHICM-eligible

10. Study Timeline Enrolled MHICM Enrollees Months -12 0 +12 “High Hospital Use” Eligible MHICM Eligible Non-Enrollees Months -12 0 +12 “High Hospital Use”

11. Sample Data sources • VA National Psychosis Registry (SMITREC) • VA MHICM enrollment data archive (NEPEC) • VA HERC Average Costs data archive Inclusion criteria • Schizophrenia or bipolar disorder diagnosis • Residence within 60 miles of a VA hospital • Recent history of “high hospital use” • Inpatient psychiatric utilization of >30 days or ≥3 stays in the past 12 months

12. Sample 2,102 new MHICM clients 25,630 MHICM-eligible non-enrollees

13. Estimation Potential selection bias Enrollment into MHICM could be related to severity of illness or need for MHICM

14. Estimation • Want to estimate E(y|x,M), where: yi = α0 + α1’xi + δMi + ui. • δ is the average effect of MHICM on study outcome y. • E(u)=0, Cov(M,u)=0 are key assumptions of model. • Initiation into MHICM services: Pr(Mi =1) = F(β0 + β1’zi + vi) • If Cov(v,u) ≠ 0: Cov(M,u)≠ 0, and regression estimates of δ will be biased and inconsistent.

15. Estimation Propensity score one-to-one matching was used to “balance” the sample on observable characteristics z If selection into MHICM is correlated with unmeasured confounders, i.e., E(y|z,M,v) ≠ E(y|z,M) propensity score matching will not alleviate bias →Method of “instrumental variables” was used to further minimize selection bias But, there is another complication…

17. Estimation • IV methods with non-linear outcomes required modification of the model • Terza, Basu, Rathouz, J. Health Econ, 2008 • Estimate: • Obtain: • Estimate:

18. Estimation IV model requires that z include at least one variable that is not in x These “instruments” must be correlated with M but not with y conditional on M For tests of these assumptions, see Baum et. al., Stata Journal, 7(4), 2007. Instruments: distance to the nearest MHICM team and whether a MHICM team was onsite at the VA hospital where client had last psych inpatient stay

19. Estimation Used two-part generalized linear model (GLM) P(y) > 0 (vs. 0) modeled as a normally distributed binary random variable E(y | y > 0) modeled as a gamma distributed random variable with a “log link” ln{ E(y) } = α0 + α1’xi + δMi where y is gamma distributed. To calculate averages, used: E(y) = P(y>0)×E(y|y>0)

20. One-to-One Matching Pre-matching 2,102 new MHICM clients 25,630 MHICM-eligible non-enrollees Post-matching 2,102 new MHICM clients 2,102 MHICM-eligible non-enrollees

26. Net savings from MHICM during the 1st year of enrollment HIGHER LOWER Inpatient Use in the Year Prior to MHICM Average effect Clients' Inpatient Psych Days of Stay 1 Yr Prior to MHICM

27. Implications • MHICM is a cost-effective program • However, financial savings from MHICM have decreased • Future expansions should continue to focus on the disabled • Enrollment in MHICM increases subsequent use of partial hospitalization program services • Unclear whether this effect is desirable • Thousands of MHICM-eligible VA patients are not enrolled in MHICM • Persons who are homeless, have concurrent substance use conditions, and reside further away from MHICM teams may have less access than others

28. Future Work What happens to utilization/costs in the second year of MHICM? What predicts disengagement from MHICM? Does fidelity to the ACT model matter?

29. Thank you! Contact Information: Eric Slade VISN5 Capitol Network MIRECC Baltimore, Maryland Eric.Slade@va.gov 410-706-2490