effects of managed care on southern youths behavioral ...€¦ · cent) (heflinger and saunders,...

Children and adolescents’ access toMedicaid-financed behavioral health ser-vices was examined over 8 years inTennessee (managed care) and Mississippi(fee-for-service [FFS]) using logistic regres-sion. Managed care reduced access to behav-ioral care overall, overnight services (e.g.,inpatient), and specialty outpatient services.Managed care also restricted the relative useof overnight and specialty outpatient for chil-dren and adolescents. However, managedcare had pronounced effects on use of casemanagement services. We also document dif-ferences in access and mix of behavioral ser-vices used over time by race, sex, age, andMedicaid enrollment category.

INTRODUCTION

Since the early 1990s, States have workedat varying speeds to incorporate the princi-ples and practices of managed care for theirMedicaid populations. The hope has beenthat by changing the patterns of service uti-lization to emphasize preventive care andreduce providers’ financial incentives toextend treatment unnecessarily, Stateswould achieve improvements in health atlower cost. The lingering concern in man-aged care systems is that firms or doctorsaccrue cost savings for any service with-held, regardless of its benefit to consumers.

Concurrently, the Nation witnessed agreat expansion in the volume of healthcare data collected and a reduction in thecost of data storage and processing. Thisreduction in research costs has providedhope that statistical analysis of policychanges will protect patient safety and pro-vide a means of evaluating the conse-quences of system changes like the shift tomanaged care. Thus, efforts like theHealth Plan Employer Data and Infor-mation Set (HEDIS®) program by theNational Committee on Quality Assurance(NCQA) have emerged as a means ofusing existing data to monitor health insur-ance systems.

This article evaluates the effects of aswitch to managed behavioral health care inTennessee’s Medicaid Program, focusing onthe experiences of children between 4-17years of age, in contrast to a State whichremained FFS, Mississippi. The analysesadjust for other demographic characteristicsthat might explain variation over time. Ourwork represents an important contributionfor several reasons. First, few State-level dataanalyses have examined behavioral healthservices, and fewer still have focused on chil-dren and adolescents. Second, the length ofthe time series for this analysis is 8 years.Prior studies have tended to use at most 3years, and typically only 1 or 2. This longertime perspective allows investigation of long-term trends in the Medicaid system. Third,our data cover the time period during whichmanaged care started in earnest throughSection 1115 waivers from the former HealthCare Financing Administration.

HEALTH CARE FINANCING REVIEW/Fall 2004/Volume 26, Number 1 23

Effects of Managed Care on Southern Youths’ BehavioralServices Use

Robert C. Saunders, M.P.P. and Craig Anne Heflinger, Ph.D.

The authors are with Vanderbilt University. The research in thisarticle was funded by the Substance Abuse and Mental HealthAdministration under contract numbers UR7-TI11304 and IKD1-TI112328 and by the National Institute on Drug Abuse under con-tract number RO1-DA12982. The statements expressed in thisarticle are those of the authors and do not necessarily reflect theviews or policies of Vanderbilt University, Substance Abuse andMental Health Administration, National Institute on Drug Abuse,or the Centers for Medicare & Medicaid Services (CMS).

BACKGROUND

Two decades of research on Medicaidhave used claims, encounter, and enroll-ment data to examine the effects of changesin policy on service utilization and costs oftreatment. A large portion of this workfocused specifically on risk-adjustment andcapitation ratesetting, a natural pathbecause managed care was the dominantpolicy change of the past two decades anddeveloping a budget-neutral yet profit-mak-ing set of capitation rates is the fundamen-tal cost problem for States and managedcare firms, respectively. Another focus ofresearch with these data has been patternsof utilization among Medicaid enrollees.One branch has followed the economics lit-erature and interest in demand for medicaland mental health services (Manning et al.,1987), while another assessed so-called per-formance indicators or benchmarks for ser-vice utilization as a basis for assuring quali-ty of care and contract enforcement. Groupslike the NCQA, which produces theHEDIS® measures (National Committee forQuality Assurance, 2003), and theChildren’s Mental Health BenchmarkingProject (Perlman et al., 1999) examine dif-ferences in the rates of common servicesand assess patterns of service use in rela-tion to standards of care (e.g., use of outpa-tient services following a hospital dis-charge). Similar efforts are supportedthrough Federal agencies (Mental HealthStatistics Improvement Program, 1996;Garnick et al., 2002). While the ultimateresearch interests differ across these areas,common to them are fundamental questionsabout the probability of using servicesamong different populations.

Hutchinson and Foster (2002) reviewedthe Medicaid managed behavioral healthcare literature as it pertains to children andconcluded, with respect to service use out-comes, that managed care raised overall

access (probability of service use), reduceduse of inpatient services, increased use ofcase management care, and had ambigu-ous effects on the use of outpatient ser-vices. Much of the research they identifiedcomes from the Massachusetts MedicaidProgram (Dickey et al., 2001; Callahan etal., 1995), with additional results fromNorth Carolina (Burns et al., 1999) andColorado (Catalano et al., 2000). TheGeneral Accounting Office (U.S. GeneralAccounting Office, 1999) found similarresults for carve-out programs in fourStates, while more recent work examiningNebraska’s behavioral health carve out(Bouchery and Harwood, 2003) and theIowa (McCarty and Argeriou, 2003) andMaryland (Ettner, et al., 2003) managedsubstance abuse programs followed thetrends identified by Hutchinson and Foster.

TennCare, Tennessee’s statewide man-aged care Medicaid 1115 waiver program,has received particular scrutiny, predomi-nantly addressing medical care for specifictypes of diseases or services. However, nostudies of TennCare have examined patternsof access and use among children with emo-tional and behavioral problems using datafrom the TennCare system. This study goesbeyond existing research in the area of man-aged Medicaid research by examining alonger time series of youth in Medicaid thanthe studies reviewed by Hutchinson andFoster and the General Accounting Office.As a result we are able to examine longer-term trends in how each State’s Medicaidsystem uses behavioral health services andmanaged care’s effect on those trends.

METHODS

Design

Our analysis employs a quasi-experi-mental interrupted time-series non-equiva-lent no treatment control group design

24 HEALTH CARE FINANCING REVIEW/Fall 2004/Volume 26, Number 1

(Cook, Shadish, and Campbell, 2002) toassess the effect of managed care on mea-sures of access to behavioral health ser-vices generally and to inpatient, specialtyoutpatient, and case management servicesin particular. We also focus on the effect ofmanaged care on the mix of services chil-dren receive. The experimental conditionis Medicaid managed care, representedhere by Tennessee’s TennCare program.Prior to January 1994, Tennessee operateda FFS Medicaid Program. From January1994 to June 1996, TennCare operated astatewide full-risk capitated managed careprogram for all medical services under aSection 1115 waiver. Private sector behav-ioral health service providers were part ofthe capitation plan, but most behavioralhealth services in this time period, particu-larly State mental hospitals and communityhealth centers, remained FFS. Beginningin FY 1997, TennCare carved out behav-ioral health services on a capitated basis tospecialty behavioral health organizations.Because these transitions were universaland without an implementation timelag(e.g., switching a few counties at a time),we used data from Mississippi, which oper-ated a statewide FFS system for its behav-ioral health care throughout the time period.

The principal benefits of this design arethe longitudinal nature of the study and thepresence of both pre- and post-interventionmeasures on the outcomes of interest.However, conclusions drawn from theanalysis necessarily must be tempered bythe limits of such a design. Use ofMississippi as a comparison group presup-poses that the populations of the two Statesand the operations of their system weresimilar enough to permit attribution ofchanges over time to managed care andthat no other State-specific interventionsaffected the program or its enrollees.Mississippi was selected initially because itwas the only State in the southeast with a

FFS system that could promise timely dataaccess during the observation period.Table 1 shows the behavioral health accessrates for both States, and access in the ini-tial year, FY 1994, is comparable in eachState. Additional analysis of inpatientadmissions shows the States had similarinitial lengths of stay: Mississippi averaged25.3 days with a median of 21; Tennesseeaveraged 27.4, with a median of 16. Censusdata for Mississippi and Tennessee showthe States have comparable childhoodpoverty levels (19 versus 18 percent,respectively), childhood Medicaid enroll-ment (35 versus 38 percent), and percentof children ages 5-15 with disabilities (6versus 6 percent). Also, survey data onsamples from these States show similarrates of serious emotional disturbance inthe Medicaid population (22 versus 26 per-cent) (Heflinger and Saunders, Forthcom-ing 2005). In addition, our analyses controlfor observable pre-existing differences inthe populations in the analysis. We willnote one policy difference between theStates in terms of case management in theDiscussion section that we treat as part ofTennessee’s managed care intervention.

Data

The analysis focuses on youth betweenage 4-17 years of age with service use andenrollment data between July 1993 andJune 2001 (FYs 1994-2001). Enrollmentand claims or encounter data come fromthe Bureau of TennCare and theMississippi Division of Medicaid. Theanalysis restricted the claims/encountersused to those with a mental health or sub-stance-related diagnosis (ICD-9-CM code290 to 315 in the primary or secondarydiagnosis). We further excluded recordswith a primary or secondary diagnosis ofdevelopmental delay (ICD-9-CM codes 316to 319). Both systems use American



Tab

le 1

An

nu

al N

um

ber

of

Med

icai

d E

nro

llees

an

d B

ehav

iora

l Hea

lth

Ser

vice

Use

rs,A

ge

4-17

,by

Sta

te F

isca

l Yea

r:19

94-2

001

Mis

siss

ippi

Tenn

esse

eN

umbe

r of

N

umbe

r of

N

umbe

r of

N

umbe

r of

N

umbe

r of

N

umbe

r of

N

umbe

r of

B

ehav

iora

l N

umbe

r of

S

peci

alty

Cas

e N

umbe

r of

B

ehav

iora

l N

umbe

r of

S

peci

alty

C

ase

Med

icai

d S

ervi

ce

Ove

rnig

ht

Out

patie

nt

Man

agem

ent

Med

icai

d S

ervi

ce

Ove

rnig

ht

Out

patie

nt

Man

agem

ent

Sta

te F

isca

l Yea

r (J

uly-

June

)E

nrol

lees

Use

rsU

sers

Use

rsU

sers

Enr

olle

esU

sers

Use

rsU

sers

Use

rs

1994

188,

631

14,7

251,

414

11,4

774,

516

352,

703

25,8

813,

171

16,6

941,

813

1995

190,

172

16,1

161,

922

12,1

795,

626

398,

047

28,4

473,

567

17,5

731,

050

1996

189,

298

16,8

011,

945

12,4

866,

236

409,

270

33,9

894,

120

20,9

4167

119

9718

5,82

216

,766

1,83

012

,529

6,78

842

3,24

632

,975

2,30

118

,682

2,77

519

9817

8,03

816

,537

2,05

312

,567

7,55

944

0,41

133

,877

2,10

019

,026

4,24

519

9918

2,51

217

,503

2,04

813

,399

8,01

246

2,50

638

,561

2,28

022

,189

7,07

720

0020

5,61

018

,942

2,09

913

,938

8,08

246

9,69

241

,082

2,42

123

,916

9,50

620

0124

2,16

221

,103

2,52

114

,796

8,31

4 48

2,46

633

,681

2,01

419

,625

9,96

0

SO

UR

CE

:Sau

nder

s, R

.C.,

Van

derb

ilt U

nive

rsity

:Cal

cula

tions

and

est

imat

es b

ased

on

clai

ms/

enco

unte

r an

d en

rollm

ent

data

fro

m B

urea

u of

Ten

nCar

e an

d M

issi

ssip

pi D

ivis

ion

of M

edic

aid,

200

3.

Hospital Association uniform billing rev-enue codes, (1992 revision) for hospital-based records and current procedural ter-minology (CPT) or HCFA common proce-dure coding system (HCPCS) codes forprofessional services.

The Bureau of TennCare receives allencounters from participating managedcare organizations and behavioral healthorganizations. A State audit has document-ed the completeness of the data systems(Division of State Audit, 2002), and furtheranalysis details the completeness and accu-racy of those data for behavioral services(Saunders and Heflinger, 2003). The pri-mary limitation in the TennCare data withrespect to children and youth with emo-tional and behavioral problems is a gap ininpatient services reported between April1995 and June 1996; however, those differ-ences tended to affect lengths of stayrather than the probability of using ser-vices. The Mississippi data are nearly iden-tical in structure and layout to theTennessee data. Other researchers haveused Mississippi’s data system for analysis(Adams, Bronstein, and Raskind-Booth,2002).

Lastly, we have excluded servicesfinanced through a Medicaid set-aside program for youth in State custody inTennessee, which affects the presence oftheir residential and case management ser-vices in the TennCare data. Tennesseeallowed youth in State custody to beenrolled in managed care firms, but thefirms were not responsible for providingresidential or case management servicesfor these youth (Comptroller of theTreasury, 2002). The State continued tofinance these services on a FFS basis. Webelieve this exclusion is appropriatebecause the managed care companies donot determine State custody status for chil-dren, inhibiting dumping responses. Also,we allow children in State custody to con-

tribute other services within each year(e.g., a child in State custody who uses out-patient behavioral health services; a childuses inpatient care while still living athome before entering custody). Finally,omitting these services maximizes the con-trast between the States on the interven-tion variable, managed care. We will notethe effects of this omission when dis-cussing the results.

Analysis

We identified claims or encounters asrelating to a behavioral health servicethrough a combination of diagnostic codesand procedure codes and classified theminto research service categories such asinpatient, individual therapy, and case man-agement. These methods are described indetail in Saunders and Heflinger (2003).For this analysis, we focused on overnightsettings (inpatient, residential, and inpa-tient or residential detoxification), special-ty outpatient services (individual therapy,partial hospitalization/day treatment,group therapy, family therapy), and casemanagement.1

Our analysis focuses on two key proba-bility measures common to performancemeasurement and used in econometricestimates of demand for services. The first,access, measures the annual probability ofusing behavioral health services for indi-viduals in the population.2 This can bethought of as the unconditional probabilityof behavioral service use and is like thefirst-part of multi-part models of demandfor services (i.e., whether a person receives


1 This is only a portion of the types of behavioral services. Wehave focused on the most common specialty sector treatmentservices, meaning that we do not count primary care visits forbehavioral health purposes, crisis and respite services, or med-ication management for example.2 Access means different things to different researchers. A com-mon alternative is a kind of potential to use services, and thoseresearchers study availability of providers within an area, havinga usual source of care or assigned primary care provider, or dis-tance to providers.

a service). We also examine the uncondi-tional probability of using each of our threetypes of services. That is, given that one isenrolled in Medicaid in a year, what is theprobability of receiving an overnight ser-vice? The effects of covariates on suchprobabilities would be of actuarial benefitfor ratesetting.

Service mix measures refer to the condi-tional probability of using a particular typeof service. That is, given that one receivesany behavioral health service, what is theprobability of using services of a giventype. Just as the multi-part models ofdemand for services would estimate aprobability model for whether a behavioralservice user uses inpatient services or not,so we estimate the probability of using aninpatient service, as well as the separateconditional probabilities for specialty out-patient care and case management. We callthem mix measures because they give asense of the relative mix of services usedby a person in treatment. If the inpatientmix (the proportion of users who receiveinpatient care) rises we know that it isbeing used as a greater component of treat-ment for the population.3 This allows exam-ination of how the service system com-bines resources for patients seeking treat-ment of behavioral health disorders.

For the overall access analysis, we clas-sified each person enrolled in Medicaidusing a dummy variable for whether theperson received any behavioral health ser-vices in a year for each of the 8 years ofdata. The unit of analysis is the person-year. We then pooled the person-yearrecords for our two States and estimated alogistic regression for the probability ofusing any service, adjusting for the addi-

tional covariates described later. For theaccess analyses by type of service, we fol-lowed a similar method, classifying individ-uals as receiving services of a given type(i.e., overnight, specialty outpatient, casemanagement) within the year or not andestimating a logistic regression for each ofthose probability measures. For the ser-vice mix analyses, we restricted the pool ofindividuals to those who used a behavioralhealth service within the year. Amongthese individuals, we created a dummyvariable for each type of service, classifiedthe youth according to whether a serviceof that type was used in that year, and esti-mated a logistic regression for each ofthose measures.

Our covariates include a dummy vari-able equal to 1 for Tennessee youth and 0for Mississippi youth and a pre-post man-aged care dummy variable set equal to 1for the years of managed care inTennessee and 0 otherwise. Because man-aged behavioral health care occurs only inTennessee in this study, we could not inter-act State with managed care; in essence,the managed care dummy is an interactionwith the State dummy. The models useHuber-White adjusted standard errors forsignificance levels and hypothesis testingabout the covariates.

For each outcome measure, we con-trolled for youths’ race, sex, age, andMedicaid enrollment category. Becausevery few (<1 percent) minority enrolleesare other than black enrollees, wedichotomized race as white versus minori-ty, with white as the reference category.Sex has the usual dichotomy with male asthe reference. We dropped individuals forwhom we did not have a legitimate valuefor race or sex. We divided youth by agecategories into 4-11 year olds and 12-17year olds with younger children as the ref-erence.


3 However, this increase might reflect severity differences overtime, changes in the service system, or both. For our analyses,and nearly all system-level studies, outcomes and severity dataare not routinely collected as part of claims and encounter datasystems, so this is difficult to assess.

The States share several Medicaidenrollment categories: the disabled (SSI),Aid to Families with Dependent Children/Targeted Assistance for Needy Families(AFDC/TANF), Foster Care, and otherpoverty-related expansion categories.Prior to TennCare, Tennessee picked upthe Medically Needy/Spend Down optionand carried that forward under TennCare.As part of TennCare, the State added unin-sured children (i.e., youth without privatecoverage through a parent’s employer) anduninsurable children (i.e., youth with pre-existing condition exclusions). Each Statealso had a small number of youth enrolledin State-specific categories that did not fitinto the previously mentioned groups; weexcluded these youth from the analysis.SSI served as the reference category in theanalysis because there is likely morehomogeneity across States in terms oftheir SSI population than in their poverty-related populations due to the heterogene-ity in welfare program generosity.

We included a linear time trend (YEAR),coding the first (SFY 1994) as 0 and incre-menting subsequent years accordingly(SFY 1995 is YEAR=1, SFY 1996 is YEAR=2,etc.). This variable represents the effect ofbeing enrolled in Medicaid in a particularFY on the probability that a child uses abehavioral health service (or a particulartype of behavioral service).

A major concern for the analysis iswhether the two States are sufficientlycomparable to pool their data and estimatean effect of managed care. If the Stateshave different structural determinants ofservice use, then we would expect differ-ent coefficients for the regressors usingonly the Tennessee or only the Mississippidata. We assessed the appropriateness ofpooling using the Chow test (Greene,1997). To foreshadow the results, theChow test rejects the null hypothesis foreach analysis, indicating the States are dif-

ferent; however, we present both pooledand separate models by State to look at themagnitude of the managed care effect.

RESULTS

Table 1 presents the size of the cohortsin each State for the study period.Although Tennessee has larger enrolledand treated populations, a greater percent-age of Mississippi enrollees use behavioralhealth services (Figure 1). Table 2describes the population of enrollees andbehavioral health service users in eachState in terms of the independent variablesfor our models for SFY 2000 (the resultsare similar for all years).

In terms of enrollees, the States are com-parable in the proportion of youth who arefemale, teenagers, and enrolled throughthe AFDC/TANF program. The major dif-ferences are in the greater representationof minorities in Mississippi reflecting itsgreater proportion of black residents (41versus 19 percent) in Census 2000. The dif-ferences in enrollment categories are dueto the larger number of options available inTennessee. Despite these differences, wesee similar changes between the enrolledand treated population in the distributionfor these variables. In both States, the pro-portion who are female, minority, orenrolled through other poverty programsdeclines, while the proportion who areteens, on SSI, or foster care rises.

The rest of this section discusses themanaged care effect observed in the over-all access probability and then the accessand mix probabilities for overnight, spe-cialty outpatient, and case managementservices. Because the magnitudes anddirections of effects for the other covari-ates are similar across models, we will con-clude this section by presenting results forthe other covariates together.


Overall Access

The odds of using a behavioral healthservice within a year under managed carewere about 6 percent less than in FFS dur-ing the time period of our data (Table 3).Figure 1 shows the predicted probabilitiesfor each State, evaluated at the means ofthe covariates. In terms of marginal proba-bility, this implies a reduction in the proba-bility of behavioral health service use of0.004 (Figure 1), or about 4 treated individ-uals per 1,000 enrollees. However, lookingat the Tennessee-only model, managedcare led to a slight improvement in theodds of using a service. Thus, managedcare may have improved access in Tennessee,but not enough to offset improvementsthat occurred in Mississippi’s FFS system.

Overnight Services

Managed care reduced the odds of usingovernight care such as inpatient or resi-dential treatment by 56 percent (Table 4).The access reduction implied by this ratiois also 0.004 (Figure 2), which, consideringthe probability of using overnight servicesis less than 0.01, is fairly large. Figure 2shows the large reduction in overnight ser-vice use concurrent with the implementa-tion of Tennessee’s behavioral healthcarve-out. Among youth in treatment, too,managed care reduced the role ofovernight services in treatment by overone-half. The odds of using overnight careconditional on any behavioral service usewas 0.47, which corresponds to a probabil-ity reduction of 0.046.


Pre

dic

ted

Pro

bab

ility

of

Use

(P

erce

nt)

Fiscal Year

Mississippi Access

Tennessee Access

10

8

6

4

2

01994 1995 1996 1997 1998 1999 2000 2001

SOURCE: Saunders, R.C., Vanderbilt University: Data from Bureau of TennCare and the MississippiDivision of Medicaid, 2003.

Figure 1

Annual Probability Behavioral Health Services, Access, Age 4-17, by State Fiscal Years: 1994-2001

Specialty Outpatient Services

The odds of a child within the Medicaidpopulation using specialty outpatient careare 15 percent less under managed care(Table 5). This corresponds to a reductionin the probability of using specialty outpa-tient services of 0.008 (Figure 3). Even

among youth in treatment, youth in man-aged care have a reduced likelihood ofusing these services with an odds ratio of0.915. In probability terms, this translatesto a reduction of 0.022. Within Tennessee,we see a statistically insignificant decreasein access, but significant reduction in themix of specialty outpatient among youth in


Table 2

Demographic Characteristics of Enrollees and Behavioral Service Users, by State Fiscal Year: 2000

Mississippi TennesseeEnrollees Behavioral Health Enrollees Behavioral Health

Category N=205,610 Users N=18,942 N=469,692 Users N=205,610

PercentDemographicFemale 50.7 38.1 49.3 37.3Minority 74.9 62.9 37.8 24.7

Age4-11Years 63.0 51.8 62.4 51.912-17 Years 37.0 48.2 37.6 48.1

Medicaid EnrollmentSSI 8.2 24.8 4.8 14.5AFDC/TANF 25.5 25.7 29.5 27.7Other Poverty 65.5 47.0 21.2 15.1Foster Care 0.5 1.8 2.3 10.4Medically Needy N/A N/A 7.5 5.8Uninsurable N/A N/A 1.7 1.9Uninsured N/A N/A 33.0 24.6

NOTES: SSI is supplemental security income. AFDC is Aid to Families with Dependent Children. TANF is Temporary Assistance to Needy Families.NA is not applicable.

SOURCE: Saunders, R.C., Vanderbilt University: Calculations and estimates based on claims/encounter and enrollment data from Bureau ofTennCare and Mississippi Division of Medicaid, 2003.

Table 3

Logistic Regression Estimates for Access to Behavioral Health Services, Age 4-17, by StateFiscal Years: 1994-2001

Pooled Mississippi TennesseeOdds Robust Odds Robust Odds Robust

Access Analysis Ratio SE P>|z| Ratio SE P>|z| Ratio SE P>|z|

Access VariableFemale 0.653 0.002 0.000 0.665 0.004 0.000 0.647 0.003 0.000Minority 0.505 0.002 0.000 0.568 0.003 0.000 0.469 0.002 0.000Age 12-17 1.244 0.004 0.000 1.264 0.007 0.000 1.229 0.005 0.000Year 1.039 0.001 0.000 1.048 0.001 0.000 1.021 0.002 0.000Managed Care 0.938 0.005 0.000 N/A N/A N/A 1.010 0.008 0.000Tennessee 0.691 0.004 0.000 N/A N/A N/A N/A N/A N/AEnrollment1

Foster Care 1.530 0.011 0.000 1.473 0.028 0.000 1.479 0.012 0.000AFDC/TANF 0.352 0.002 0.000 0.374 0.003 0.000 0.335 0.002 0.000Other Poverty 0.235 0.001 0.000 0.257 0.002 0.000 0.214 0.002 0.000Uninsurable 0.313 0.005 0.000 N/A N/A N/A 0.294 0.005 0.000Uninsured 0.232 0.001 0.000 N/A N/A N/A 0.220 0.002 0.000Medically Needy 0.245 0.002 0.000 N/A N/A N/A 0.231 0.002 0.0001 Supplemental security income is the reference category.

NOTES: SE is standard error. AFDC is Aid to Families with Dependent Children. TANF is Temporary Assistance to Needy Families. NA is not applicable.

SOURCE: Saunders, R.C., Vanderbilt University: Calculations and estimates based on claim/encounter and enrollment data from Bureau of TennCareand Mississippi Division of Medicaid, 2003.

treatment. Figure 3 shows the slight dip in both access and mix of this service coin-ciding with the behavioral health carve-outbeginning in FY 1997.

Case Management Services

We see the first positive effect of man-aged care on access in case management(Table 6). Youth in managed care had a211-percent greater chance of using thisservice, an increase in probability of 0.05(Figure 4). The effect is even larger whenlooking at those in treatment, where youthhad 0.266 greater probability of using casemanagement. In the Tennessee-only

model, the effect is smaller than in thepooled results, but still large and positive.Figure 4 shows the large increase in theconditional probability of using this service(mix), and a less pronounced increase inaccess, beginning in FY 1997.

Effects of Other Covariates

In the overall access results (Table 3) aswell as the separate estimates by type ofservice (Table 4-6), female and minorityyouth were less likely to access behavioralhealth services overall and by type of ser-vice, while teens had a greater likelihood ofaccessing these services. These results


Table 4

Logistic Regression Estimates for Access and Mix of Overnight Services, Age 4-17, by StateFiscal Years: 1994-2001




Foster Care 1.462 0.027 0.000 2.232 0.079 0.000 1.248 0.027 0.000AFDC/TANF 0.249 0.003 0.000 0.273 0.005 0.000 0.225 0.004 0.000Other Poverty 0.129 0.002 0.000 0.150 0.003 0.000 0.101 0.003 0.000Uninsurable 0.312 0.014 0.000 N/A N/A N/A 0.294 0.013 0.000Uninsured 0.139 0.003 0.000 N/A N/A N/A 0.131 0.003 0.000Medically Needy 0.183 0.005 0.000 N/A N/A N/A 0.172 0.005 0.000


Mix Analysis Ratio SE P>|z| Ratio SE P>|z| Ratio SE P>|z|

Access VariableFemale 0.960 0.011 0.000 0.907 0.016 0.000 0.995 0.014 0.724Minority 0.788 0.010 0.000 0.601 0.010 0.000 1.032 0.017 0.049Age 12-17 2.403 0.028 0.000 2.593 0.048 0.000 2.320 0.035 0.000Year 1.006 0.003 0.041 1.015 0.004 0.000 0.974 0.006 0.000Managed care 0.475 0.009 0.000 N/A N/A N/A 0.542 0.015 0.000Tennessee 1.015 0.018 0.394 N/A N/A N/A N/A N/A N/AEnrollment1



SOURCE: Saunders, R.C., Vanderbilt University: Calculations and estimates based on claims/encounter and enrollment data from Bureau ofTennCare and Mississippi Division of Medicaid, 2003.

held for both the pooled and separate esti-mates by State. The same pattern can alsobe seen in the mix of overnight services.However, the pattern does not hold for themix of specialty outpatient care, as minori-ty and female youth both have a greaterchance of using this service once they arein treatment. Minority youth also have agreater chance of using case managementonce in treatment. The effects of being ateen on the mix of specialty outpatient andcase management move closer to a unitaryodds ratio, reflecting differences betweenthe States as Mississippi was more likely touse this service for younger children whileTennessee was more likely to use it withteens.

Looking at the effects of the differentenrollment categories, youth in foster carehad the greatest chance of accessing ser-

vices overall and by service type, relativeto youth on SSI. However, after enteringtreatment, children in the foster care cate-gory were less likely than children on SSIto use each of these services. One way tothink of this is: A random child pulled fromthe roster of foster care Medicaid enrolleesis more likely to use any of these servicesthan one enrolled through SSI, but whenwe cut the roster to look at only those intreatment, the typical SSI enrollee has abetter chance of using each type of service.

Youth in the AFDC/TANF and otherpoverty categories in both States were lesslikely than youth on SSI to access servicesoverall and by type of service. This resultholds for the separate by-State models. Theodds ratios are similar in magnitude acrossservice type, and the most poor youth(AFDC/TANF) have a consistently higher


Pre

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ted

Pro

bab

ility

of

Use

(P

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nt)

Fiscal Year

Mississippi Access

Tennessee Access

Mississippi Mix

Tennessee Mix

1994 1995 1996 1997 1998 1999 2000 2001

12

10

6

4

2

0

8

NOTE: The cells have decimals to several places, but are rounded so the y-axis does not have decimals.

SOURCE: Saunders, R.C., Vanderbilt University: Data from the Bureau of TennCare and MississippiDivision of Medicaid, 2003.

Figure 2

Annual Predicted Probability of Overnight Services, Access and Mix, Age 4-17, by State Fiscal Years: 1994-2001

chance of receiving a service than theother poverty category. These results alsohold in the mix analyses for outpatient andcase management services, though theirdifferences from SSI are of smaller magni-tude (i.e., closer to 1.0).

The Tennessee-specific categories aresignificantly less likely to access servicesacross types of service, and in the accessanalyses, they are more comparable inmagnitude to the poverty categories thanSSI or foster care. However, among serviceusers, the uninsured category usesovernight services at a rate comparable toSSI and only slightly less than SSI for spe-

cialty outpatient. The pattern of coefficientsamong these categories matches what onemight expect. The uninsurable likely havepoor health status, since their health statusmade them uninsurable in the first place,so they have the smallest difference fromthe SSI group, which consists of disabledyouth. In fact, entry into an overnight facil-ity was sometimes the mechanism forbecoming enrolled in TennCare as an unin-surable. The medically needy/spend downgroup also has identified health problems,but they more closely resemble the pover-ty categories in service use, perhapsbecause the reduction in household wealth


Table 5

Logistic Regression Estimates for Access and Mix of Specialty Outpatient Services, Age 4-17, byState Fiscal Years: 1994-2001



Access VariableFemale 0.717 0.003 0.000 0.677 0.004 0.000 0.745 0.004 0Minority 0.573 0.002 0.000 0.649 0.004 0.000 0.521 0.003 0Age 12-17 1.318 0.005 0.000 1.234 0.008 0.000 1.366 0.007 0Year 1.039 0.001 0.000 1.042 0.001 0.000 1.027 0.002 0Managed Care 0.847 0.006 0.000 N/A N/A N/A 0.890 0.009 0.188Tennessee 0.593 0.004 0.000 N/A N/A N/A N/A N/A N/AEnrollment1

Foster Care 1.867 0.015 0.000 1.571 0.033 0.000 1.764 0.017 0AFDC/TANF 0.355 0.002 0.000 0.386 0.003 0.000 0.324 0.003 0Other Poverty 0.222 0.001 0.000 0.249 0.002 0.000 0.189 0.002 0Uninsurable 0.300 0.006 0.000 N/A N/A N/A 0.270 0.006 0Uninsured 0.220 0.002 0.000 N/A N/A N/A 0.201 0.002 0Medically Needy 0.236 0.003 0.000 N/A N/A N/A 0.213 0.002 0







qualifies one for this category. And, finally,the uninsured most resemble the povertygroups as they simply lack access toemployer-sponsored coverage and cannotafford individual coverage. We tested thehypothesis that the coefficients for thesethree enrollment categories were equal to1.0; for all the analyses, because of thelarge sample sizes, we reject the nullhypothesis.

Each State experienced positive annualgrowth in behavioral service access (withthe exception of Tennessee’s overnightservices), all else equal. For the most partMississippi’s FFS system had a fastergrowth rate in access to these services(i.e., increased odds). In terms of servicemix, the annual growth rate is negligibleexcept for case management.

DISCUSSION

The results provide important informa-tion about the patterns of service utiliza-tion among children in the Medicaid popu-lation and the effects of managed care onthese patterns. The logistic regressionsoffer evidence that managed care not onlyreduces access to behavioral services over-all and both access to and mix of inpatientservices, where the effect is most pro-nounced and prior research indicates itshould be found (Hutchinson and Foster,2002), but it also may lead to reductions inspecialty outpatient services as well. Thedecline due to managed care is seen notonly in the access rate among enrollees,but also in the role of specialty outpatientservices in the mix of services childrenreceived. This pattern is consistent with


Pre

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Pro

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Use

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Mississippi Access

Tennessee Access

Mississippi Mix

Tennessee Mix

1994 1995 1996 1997 1998 1999 2000 2001

50

40

20

10

5

0

30

45

35

15

25

SOURCE: Saunders, R.C., Vanderbilt University: Calculations and estimates based on claims/ encounterand enrollment data from Bureau of TennCare and Mississippi Division of Medicaid, 2003.

Figure 3

Annual Predicted Probability of Specialty Outpatient Services, Access and Mix, Age 4-17, byState Fiscal Years: 1994-2001

prior work studying this population (Saundersand Heflinger, 2003) and is due in part tolarge decreases in partial hospitalization,an expensive outpatient alternative to inpa-tient care, and steady decreases in individ-ual therapy.

Nevertheless, the news is not all bad formanaged care. Tennessee experienced sig-nificant positive increases in case manage-ment services, due in large part to specialdirectives from the Bureau of TennCare tothe behavioral health organizations toincrease case management and increasesin financial incentives to the firms begin-ning in FY 1998 for this service. Of course,such large increases tend to be more

believable when the base rate for this ser-vice was so low initially—there was noplace to go but up. We attribute this effectto managed care because one of the impor-tant aspects of the contracting process isthe State’s ability to exert market power, inthis case by enforcing contractual serviceobligations.

Looking at the demographic characteris-tics, there is a consistent pattern of lowerbehavioral health care access amongminority and female youth and greateraccess for teens across all of the servicecategories investigated. Disparities inaccess to behavioral health services forracial/ethnic minorities are of concern


Table 6

Logistic Regression Estimates for Access and Mix of Case Management Services, Age 4-17, byState Fiscal Years: 1994-2001




Foster Care 0.558 0.011 0.000 0.970 0.032 0.356 0.361 0.009 0.000AFDC/TANF 0.314 0.003 0.000 0.353 0.004 0.000 0.249 0.003 0.000Other Poverty 0.174 0.002 0.000 0.211 0.002 0.000 0.118 0.002 0.000Uninsurable 0.247 0.010 0.000 N/A N/A N/A 0.198 0.008 0.000Uninsured 0.150 0.002 0.000 N/A N/A N/A 0.113 0.002 0.000Medically Needy 0.162 0.004 0.000 N/A N/A N/A 0.128 0.003 0.000





NOTES: AFDC is Aid to Families with Dependent Children. TANF is Temporary Assistance to Needy Families. NA is not applicable.


nationally (Agency for Healthcare Researchand Quality, 2003; U.S. Department ofHealth and Human Services, 2001). Blackand other minority populations have hadlower rates of access to health care in gen-eral (LaVeist, 2002) and specifically to men-tal health services (Mayberry, Mili, andOfili, 2002; Padgett et al., 1994; President’sNew Freedom Commission, 2003). Thegreater identification of conduct disordersand other externalizing problems amongmales may ease entry to treatment formales, although such sex differences arenarrowing. Also, male adolescents havebeen documented to use more overnightservices (Halfon, Berkowitz, and Klee,1992; Hoagwood and Cunningham, 1992)and concerns have been raised that thismay be due, in part, to lack of resources toserve them in the community (Heflinger,

Simpkins, and Foster, 2002). It is similarlycommon for the treatment system to serveteens at a greater rate than primary andmiddle school-aged children (Cuffe et al.,2001).

While disparities between groups large-ly persist when we look at children in treat-ment, we notice a few important differ-ences. First, the magnitude of the dispari-ties decreases (i.e., moves closer to anodds-ratio of 1.0), suggesting that much ofthe disparity in observed use is likely dueto barriers to treatment initiation ratherthan differences in how they are treatedafter entering the system. However, insome instances we see a reversal in the dis-parity: for example, minority youth in treat-ment in both States were more likely to beusing case management services thantheir white counterparts, a pattern that


Pre

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Mississippi Access

Tennessee Access

Mississippi Mix

Tennessee Mix

1994 1995 1996 1997 1998 1999 2000 2001

35

20

10

5

0

30

15

25

SOURCE: Saunders, R.C., Vanderbilt University: Data from the Bureau of TennCare and MississippiDivision of Medicaid, 2003.

Figure 4

Annual Predicted Probability of Case Management Services, Access and Mix, Age 4-17, by StateFiscal Years: 1994-2001

largely holds for specialty outpatient careas well. Some alternative hypotheses mightexplain these changes with respect to race,particularly for case management. Onepossibility is minority and white youth whohave sought treatment differ in their sever-ity levels. If minorities delay entry to treat-ment or face greater barriers to treatment,this may exacerbate their illness andrequire use of more services and requirecase management. However, if minoritieswere more functionally impaired once theyentered treatment, we might expect higherrates of the overnight services comparedwith white minorities, and that was not thecase. Another possibility is complicationsin responding to minorities’ (or white) cul-tural patterns of service use. If minoritieswere more likely to use services in multi-ple systems, then case management mightbe reserved to handle these additionalcomplexities. Alternatively, the systemmay have expectations of parental involve-ment in treatment such that parents bearthe burdens of treatment coordination.Studies on this Medicaid population (Kang,Brannan, and Heflinger, Forthcoming2005) and other samples of caregivers(Guarnaccia and Parra, 1996; Horwitz andReinhardt, 1994; Stueve, Vine, andStreuning, 1997) have found that blackfamily members tend to report lower levelsof caregiver strain (i.e., burden of care).Perhaps black families are targeted for thistype of assistance while white families areexpected (by providers or themselves) tobe more self-sufficient in their servicecoordination. Balsa and McGuire (2003)have presented a similar argument withrespect to health disparities. However, wecannot rule out (or in) these alternativescompletely with the present data becausesystematic, standardized outcomes dataare not routinely collected among this pop-ulation.

In the Medicaid categories, we saw aconsistent pattern of lower access tobehavioral health services among youthand greater access of foster care youth rel-ative to youth on SSI. Given the disabilitiesof youth on SSI and even greater behav-ioral health issues involved with State cus-tody placements that result in youth beingin the foster care eligibility category, thisaccess gradient is not too surprising. Wealso saw that the magnitude of effect forthese variables in the mix analysesbrought the odds-ratios closer to 1.0. Thislikely implies that youth in treatment tendto receive similar services; any financialsavings to the firms most likely accruethrough reductions in volume of service.The primary exception in these patternswas for foster care youth in overnight set-tings. This relates to our note in theMethods section about the set-aside pro-gram that placed these services outsidethe management purview of the contrac-tors. The lower rate for foster care youthlikely reflects the fact these children canobtain those services through other financ-ing arrangements.

An important observation for Tennesseeis the patterns of access and mix in theexpansion enrollment categories (i.e., theuninsured and uninsurable) and the med-ically needy spend down category. Interms of access, children in these threeenrollment categories made use of behav-ioral health services at relatively low rates,comparable to youth in the poverty-relatedcategories. Once in treatment, however,their utilization rates for these servicesmoved closer to the rates of SSI, just likewe saw with children in the poverty cate-gories. One of the major concerns in theTennCare program has been the cost ofenrollment expansions to cover youth whoare otherwise uninsurable or uninsured.This suggests their contribution to costs


may not be entirely a matter of adverseselection by enrollees or dumping byemployer-sponsored plans, at least withrespect to children and behavioral healthservices. So, if additional capitationrequirements were necessary to financecare for these youth, the additional shareattributable to behavioral health problemsin this subpopulation is likely to be small.

Examination of the time trend variable,which expresses the annual rate of change inaccess and mix of behavioral services, revealsaccess to behavioral services generallyimproved over time in both States, but by agreater amount in Mississippi than inTennessee. The trend to increased behavioralcare is consistent with an increased focus inthe mental health field on providing compre-hensive, community-based behavioral healthservices during the 1990s (Fox et al., 1992).

CONCLUSION

This study has presented importantinformation on the effect that a switch toMedicaid managed care in one SouthernState had on patterns of access to behav-ioral health services and their relative useby youth in treatment. The analyses docu-ment important ways in which managedcare has shifted the relative use ofresources and the services that youth intreatment receive.

An important limitation in most assess-ments of service utilization patterns andcost analyses is the lack of outcome data.Without outcomes data it is difficult tomake population-level inferences about theappropriateness of behavioral service uti-lization or changes to utilization, financingarrangements, enrollment expansions orother policy interventions. This would bean important improvement to State datasystems, monitoring efforts, and so-calledreport card systems. In addition, Stateagencies that contract with private man-

aged care companies under 1115 or 1915waivers are also responsible for monitoringand oversight. Standardized performancemeasures could be made publicly availableand on a regular and timely basis, allowingpolicymakers, consumers, and health ser-vices researchers to evaluate how well thesystem, whether under a waiver or not, ismeeting its goals. Without monitoring,those charged with contract enforcementor treatment responsibilities cannot knowwhether problems exist within the system.NCQA has advocated a similar position(National Committee for Quality Assurance,2002), and English and colleagues (1998)emphasized the need for monitoring inmanaged care.

In the present study, we saw the role thatTennessee’s efforts in case managementplayed in increasing this service. In addi-tion to stepping up their own data monitor-ing efforts, the results of such analysismight provide opportunities to develop tar-geted 1915 waivers to address the needs ofparticular subgroups or modify practicepatterns for particular services. InTennessee for example, the clustering ofminorities in its major urban areas mightafford special opportunities and efficien-cies in providing treatment and preventiveservices to this population. This might alsomean placing greater emphasis on recruit-ing minority providers and increasing cul-tural-awareness among existing practition-ers. Alternatively, both States have largerural populations, and if service utilizationdifferences reflected problems in providernetworks (whether managed care or FFS)and travel costs (monetary and timewise)States may find opportunities to addressunmet needs for the rural poor.

Regardless of how States respond,whether and how to respond should beinformed by data analysis. While Medicaiddata are far from perfect for evaluating ser-vices, and in many instances state data


systems have problems, one way toimprove them is to create demand for theby-products of these data.

ACKNOWLEDGEMNTS

The authors wish to thank GlennJennings, formerly of the Bureau ofTennCare and Kristi Plotner of theMississippi Division of Medicaid for theirassistance and support in the preparationof this article.

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Reprint Requests: Robert C. Saunders, Vanderbilt University,230 Appleton Place, P.O. Box 90 Peabody College, Nashville, TN37203. E-mail: [email protected]


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