The Effect of Privatization of
Inpatient Psychiatric Resources
on Jail Inmate Population
Jangho Yoon, PhD
UC Berkeley School of Public Health
June 30, 2009
AcademyHealth ARM
Funding: NIMH
Motivation
o Point-in-time prevalence of severe mental
disorders
o 7-11% in jails or prisons
o < 2% in the general population.
o Why are jails and prisons over-populated
with persons with severe mental disorders?
o The effect of privatized inpatient psychiatry
has rarely been a subject of scrutiny.
Study Aim
o Examine whether privatized inpatient
psychiatric resources increases the size of
jail populations
o Measures of privatization: Market concentration
oNonprofit vs. for-profit.
Jail Population Growth
o Nationwide trend in average daily jail inmates per
100,000 persons, 1985-98
220
200
180
Jail inmates/
160
100K
140
120
100
85
86
87
88
89
90
91
92
93
Year
Sources: Census of Jails; Annual Survey of Jails.
94
95
96
97
98
Inpatient Privatization
o Nationwide changes in market concentration of public,
nonprofit, and for-profit psychiatric beds, 1985-98
100%
90%
80%
70%
Public
67%
45%
60%
50%
40%
Nonprofit
30%
20%
23%
41%
10%
0%
For-profit
10%
85
86
87
88
89
90
91
92
Year
14%
93
94
95
96
97
98
Source: AHA Survey of Hospitals.
Link between Privatization and
Jail Population Growth
o Hospital ownership
 Different objectives and incentives
 Treatment of symptoms
 Jail incarceration.
o For-profit: Treat profitable patients
o Lead to the growth of jail populations.
o Nonprofit: Value patient well-being but
may avoid severe patients
o Effect is not clear.
Methods
o Data
o State panel data for 45 states and DC, 85-98 (n = 644)
o Sources
o
o
o
o
o
o
o
o
o
Survey of Jails & Census of Jails
American Hospital Association
NRI
American Medical Association
Uniform Crime Reporting Program
CJEE Extract
US Census Bureau
Bureau of Economic Analysis
Bureau of Labor Statistics
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o s:
o t:
State
Year
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o Dependent variable: ln(J)
o ln(average daily jail inmates/100,000)
o Sources: Annual Survey of Jails & Census of jails
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o Main independent variables: R
o Nonprofit market share =
Nonprofit psychiatric beds
Total psychiatric beds
o For-profit market share =
For-profit psychiatric beds
Total psychiatric beds
o Source: American Hospital Association
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o Covariates (X)
o Mental health/substance abuse (MH/SA) resources
o Per-capita psychiatric beds
o Per-capita community MH/SA expenditures
o Per-capita SA treatment beds
o Per-capita psychiatrists
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o Covariates (X)
o Mental health financing & welfare
o Mental health insurance mandates (=1 if mandated)
o % residents on Medicaid
o % residents on TANF/AFDC
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o Covariates (X)
o Criminal justice policies and practices
o Clearance rates (= total arrests/total reported crime)
o Tough sentencing (= daily jail inmates/ total arrests)
o Crime rates
o Per-capita police
o Concealed handgun laws
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o Covariates (X)
o Demographic and socio-economic factors
o % male
o % African-American
o % metropolitan residents
o Age categories
o Poverty rate
o Unemployment rate
o Per-capita income
Methods
o Empirical model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o S:
o T:
State fixed effects
Year fixed effects
Methods
o Instrumental Variables (IV)
o Instrumented: R (market share variable)
o Instruments: Determinants of privatization
o # of public psychiatric beds (1-year lagged)
o % chain hospitals
o # medical/surgical beds
o Two-stage IV/GMM estimators
o Adjusted for heteroskedasticity and AR(1)
Results
o Marginal effect of the market share of psychiatric
beds on jail inmates by ownership status
Key Explanatory Variable
(Instrumented):
Marginal Effect
Model 1
Model 2
Nonprofit
Share
For-profit
Share
1.8%
2.5%*
Notes. IV/GMM estimators. Control for all covariates as well as fixed effects.
* p < 0.05.
Results
o Robustness: Marginal effect of market share
Panel A: Remove outliers
• 5 most populous states
• 5 states with greatest increases in
jail population
• 5 states with greatest increases in
market share by ownership
Panel B: Functional form
• Unlogged dependent variable
Notes. IV/GMM estimators. * p < 0.05; ** p < 0.01
Model 1
Nonprofit
Model 2
For-profit
2.0%*
1.8%*
1.4%
2.7%*
7.6%
3.0%**
1.8
2.6*
Conclusion
o A greater for-profit market concentration leads to
jail population growth
o Magnitude is large: From 1985 to 1998, a 4
percentage-point increase in for-profit market share
accounts for a 16% increase in the jail inmate
population.
o A greater nonprofit market share does not appear
to increase jail inmate population.
Policy Implications
o The ownership mix of inpatient psychiatric
resources is an important agenda item for
mental health policy.
o The collaboration between the mental health
and jail systems should be continued and
expanded.
o Jail incarceration of mentally-ill persons as a
indicator for mental health system performance.
Thank You!
E-mail: yoonjangho@gmail.com
Appendix: Jail Population
Growth
o Potential determinants
o Changes in demographics and socio-economics
o Criminal justice practices
o Policing effectiveness; tough sentencing; crime
o Changes to the mental health system
o Managed care1
o Insurance coverage (e.g., Medicaid)2
o Insufficient public mental health resources3
o Privatized inpatient psychiatry: NOT tested yet!
1 Domino
et al. 2004; 2 Morrissey et al. 2007; 3 Palermo et al. 1991; Yoon 2007; Raphael and Stoll 2009.
Appendix: Determinants of
Privatization
o Market niche for inpatient psychiatric services due
to public inpatient reductions
o Awareness of unmet need
o Expansion of insurance coverage  Demand
o Reduced stigmatization of psychiatric treatment
o Mental health practitioner growth
o Financing: Medicare prospective payment and bed
conversion
o State regulations
o Managed care, LRA, lawsuits (1990’s)
References: Dowart and Schlesinger (1988); Geller (2006).
Appendix: IV Tests (1)
o Prospective instruments
Tested instruments
Valid?
o
CON regulations (1/0)
No
o
% hospitals with formal contract with MCO
No
o
# public psychiatric beds (1-year lagged)
Yes
o
% chain hospitals
Yes
o
# medical/surgical beds
Yes
Appendix: IV Tests (2)
o Two conditions for valid IV
o Condition 1: Non-zero correlation between the
instrumented and instrumental variables (Strength of IVs)
o Condition 2: Exclusion restrictions
o Econometric tests employed
o Condition 1
o Individual t test, individual LR test, joint F-test, & joint LR test
o Test statistics should be significant.
o Condition 2
o J test (jointly) & C test (individually)
o Test statistics must be insignificant.
Appendix: IV Tests (3)
o First-stage results and IV tests for Cond. 1
Model 1
Model 2
Instrumented
Nonprofit
For-profit
Public bedt-1
-.31**
-.35***
Public bed2t-1
.0097**
.0081**
Medical/surgical bed
n/a
.0019*
Chain hospital (%)
.24*
n/a
R2
.90
.87
Note. Regressions include all covariates as well as fixed effects. * p < 0.05; ** p < 0.01; *** p < 0.001.
Appendix: IV Tests (4)
o IV tests of Conditions 1 & 2
Model 1
Model 2
Nonprofit
For-profit
F statistic
5.4***
9.4***
Anderson LR statistic
15.8***
42.8***
1.1
1.7
Instrumented
Joint significance (Cond. 1)
Joint exclusion restrictions (Cond. 2)
Hansen J statistic
* p < 0.05; ** p < 0.01; *** p < 0.001.
Appendix: IV Tests (5)
o IV tests of Condition 2 (Exclusion Restrictions)
Instrumented
Model 1
Model 2
Nonprofit
For-profit
Individual exclusion restrictions ( Sargan C statistic)
Public bedt-1
1.0
0.1
Public bed2t-1
0.5
0.2
Medical/surgical bed
n/a
1.2
Chain hospital (%)
1.1
n/a
* p < 0.05; ** p < 0.01; *** p < 0.001
Appendix: Main Results
o Selected variables
Model 1
Model 2
-0.005*
-0.009***
Mandate
-0.300***
-0.259***
Clearance rate
0.035***
0.031***
Tough sentencing
0.259***
0.239***
Handgun
-0.192**
-0.133*
Male
0.284**
0.247**
Metropolitan
0.032***
0.031***
Age 20-24
0.112***
0.148***
Psychiatric Bed
* p < 0.05; ** p < 0.01; *** p < 0.001
Appendix: Empirical Model
ln(J)st = α ⋅ Rst + β ⋅ ∑ X st + Ss + Tt + εst
o
o
o
o
s:
t:
ln(J):
R:
o X:
o S:
o Y:
State
Year
Natural logarithm of average daily jail inmates
Market composition of nonprofit or
for-profit psychiatric beds (instrumented)
Covariates
State fixed effects
Year fixed effects