an evaluation of freestanding alcoholism treatment for medicare recipients

Addiction (1993) 88, 53-67

RESEARCH REPORT

An evaluation of freestanding alcoholismtreatment for Medicare recipients

ANNIE LO & ALBERT WOODWARD

Tlje Substance Abuse and Menial Health Services Administration, The US Public Health

Service, Rockville, Maryland USA

AbstractT7ie Health Care Financing Administration (HCFA) and the National Institute on Alcohol Abuse andAlcoholism (NIAAA) conducted a demonstration between 1982 and 1985 to test the feasibility of providingpayments for alcoholism Treatment services to Medicare and Medicaid recipients in specially selectedfreestanding facilities. This study of the Medicare part of the demonstration answers two questions: dofreestanding facilities save money for Medicare and do their patients have lower health care utilizationfollowing initiation of treatment than patients treated in hospital-based facilities? The statistical methodologyIS a logit and cluster approach. The analysis begins with a logistic regression model to predict the probabilityof patients seeking alcoholism treatment in either the demonstration (freestanding facility) or hospital-basedcohort. The statistically significant variables from logit analysis are then used to form clusters. 'The healthexpenditure of freestanding and hospital patients are compared within homogeneous clusters. This study showsthat the number of admissions, the average length of stay, and the average monthly health expendituresfollowing the start of treatment are lower for the group treated in the freestanding facilities. The conclusionis that for some persons with alcohol problems, treatment in freestanding facilities is less costly and leads tolower subsequent health care utilization than treatment in hospitals.

IntroductionThe Alcoholism Treatment DemonstrationIn 1980 the Health Care Financing Administra-tion (HCFA) and the National Institute onAlcohol Abuse and Alcoholism (NIAAA) in con-junction with six states designed a 4-yearAlcoholism Treatment Demonstration Project.The Demonstration began in 1982 and ended in

Ms Lo coauthored the paper while on the staff of the Wash-ington Consulting Group; Dr Woodward is on the staff of theOffice of Applied Studies, Substance Abuse and Mental HealthServices Administration (SAMHSA). Views contained in thispaper may not necessarily reflect the official policy or position ofthe SAMHSA or any other part of the US Department of Healthand Human Services.

1985. The six states that participated in theDemonstration were Connecticut, Illinois, Mich-igan, New Jersey, New York, and Oklahoma.There were 111 facilities selected on the basisof state and federal licensing and certificationstandards.

HCFA and NIAAA sought to test the feasi-bility of providing payments for alcoholismtreatment services to Medicare recipients in spe-cially selected freestanding facilities. Thesefacilities offer inpatient detoxification and reha-bilitation services and outpatient rehabilitationservices. Although the freestanding facilitiesoffered outpatient detoxification, there were few

53

54 Annie Lo & Albert Woodward

cases of outpatient detoxification of patients par-ticipating in the Demonstration. The two federalagencies sought to ascenain the amount of costsavings in expanding Medicare and Medicaidbenefits, which include less costly options suchas inpatient and outpatient facilities 'standingfree' from hospitals. Under the auspices of thisDemonstration, HCFA and the states waived theregulations against participation in Medicare andMedicaid hy such providers.

Each recipient was evaluated at entry into theDemonstration and received an alcoholism diag-nosis grouped into one of four principaldiagnostic categories: alcohol psychosis (ICD291.0-291.9); acute intoxication (ICD 303.0);alcohol dependence- and other unspecified (ICD303.9); and alcohol abuse (ICD 305.0). Servicesprovided to recipients were uniform across thesix states and facilities. These services wereequivalent to, if not more liberal than, thoseprovided under Medicare. Such outpatient ser-vices under the Demonstration included up to45 outpatient visits, whereas Medicare only cov-ered S500 of these outpatient services. Patientswere to be referred to other providers for non-alcoholism medical care as appropriate.

Research overviewThis study is a comparison analysis in whichDemonstration (freestanding) and hospital treat-ment settings are compared on the basis of totalhealth care expenditures following the initiationof treatment. This evaluation of freestandingalcoholism treatment centers considers twoquestions: do they save money for Medicare; anddo their patients have the same or lower healthcare use following the initiation of treatmentthan patients treated in hospital-based facilities?TTie hypothesis in this study is to test whether ornot treatment at freestanding facilities producescost savings or cost-ofFsets in comparison withtreatment at hospital-based facilities.

The main outcome measure is total Medicarehealth expenditures following the initiation ofalcoholism treatment for the recipient. Healthexpenditures include in- and outpatient hospital,nursing home, home health, physician, drugsand equipment, and all other care for whichMedicare pays. Health expenditures are definedin this study as alcoholism treatment and anyother health care from the start of treatmentuntil either the patient died or reached the end of

the study period. The measure of expendituressuggests that if freestanding facilities are as effec-tive as hospital-based facilities, then, on average,a population treated in freestanding facilitiesshould have aggregate post-treatment healthexpenditures at least equal to those of a com-parable population treated in hospital-basedfacilities. Health services research on insuredpopulations with alcohol problems has shown adecline in use following treatment, althoughthe decline is less dramatic with older alcoholicpopulations.'

This study measures two utilization outcomessubsequent to treatment: the number of in-patient admissions with an alcohol diagnosis;and the average length of stay. These measuresare partial, indirect indicators of recovery follow-ing treatment. The study also presents anindirect outcome measure, the percent ofpatients in the study population who died.

Analytic methodsData used for the study and data preparationMost data were extracted from HCFA files.Three HCFA patient claims files were used; (1)the Demonstration (DEMO) file for claims foralcoholism treatment at participating freestand-ing facilities; (2) the Medicare Automated DataRetrieval Systems (MADRS) files, beginning incalendar year 1984, of all claims for all recipientsfor all services (physician office, drug and medi-cal equipment claims are aggregated by providerfor each recipient each year); and (3) the Medi-care Provider Analysis and Review (MEDPAR)of all inpatient claims for all recipients (only 20%sample for calendar years 1980 through 1983).Data on organizational and structural character-istics of treatment facilities were obtained fromtwo files: the HCFA Provider of Service (POS)Master file; and the National Drug and Alco-holism Treatment Utilization Survey(NDATUS) of 1982 conducted by the AlcoholDrug Abuse and Mental Health Administration.

Claims data were aggregated to create patientlevel statistics used in the analysis. These admin-istrative data bases are a comprehensive sourceof information in health services research, al-though they are not as complete in detail aspatient medical records." These statistics includethe number of inpatient admissions with an alco-hol diagnosis, average length of stay per year,and average monthly health expenditures follow-

An evaluation of freestanding alcoholism treatment 55

OctJan Jul Dec

1982 1983 1984 1985Treatment infreestandingfacilities

1986 1987 1988

Study period-

Demonstrationbegins

Collection ofData Ends on31/12/1987

(1) In 20% of both the hospital and the freestanding groups, hospitalizationsfot alcohol diagnoses were tracked for calendar years 1980-1983. (2) Claimsdata were collected through the first half o( 1988 to make sure thai all claimsthrough the end of 1987 were properly adjusted. This served as a check on thef^ADRS file, which only has tinal claims.

Figure 1. Study period of longitudinal analysis of health care costs.

ing the start of treatment. TTic three claims datafiles were merged by the Medicare 11 digitpatient identification code called the Health In-surance Claim Number. Matching of these fileswas about 97%. Data were checked for logic andconsistency, including reference to the masterMedicare entitlement and demographic informa-tion file. Health Insurance Skeleton Write-off(HISKEWj. Data on facilities from the NDA-TUS and POS files were merged with claims filedata to create aggregated provider data at thepatient level.

The dates for selecting Medicare recipientswho began alcoholism treatment were in theperiod beginning January I, 1984 through July31, 1985. This analysis period was chosen forthe study because complete MADRS data wereavailable staning January 1, 1984, and theDemonstration ended on July 31, 1985. Figure 1illustrates the time period of the Demonstrationand the data collection period used in this study.Up to 4 years of health care expenditures werecollected for Medicare recipients who began ini-tial alcoholism treatment in the first quarter of1984. Medicare recipients who died at any pointfrom January 1, 1984 through the last month for

which claims data were collected were includedin the study.

Medicare provides relatively comprehensivecoverage in comparison with publicly financedcoverage and many private, employer-paidhealth insurance plans. The MADRS data basedoes not include recipients' out-of-pocketexpenses. Out-of-pocket costs incurred by Medi-care recipients, although increasing in recentyears, have been in general a small part of recip-ients' income and are not a barrier to utilization. "*Medicare recipients are unlikely to lose theireligibility, even if they are disabled; therefore, thestudy cohorts did not have to be adjusted forthose who lost eligibility, that is, dropouts. Theanalysis was performed on cost variables in termsof 1988 constant dollars.

This study is setting-specific as distinct frommodality-specific because data on modalities ofcare within hospital-based facilities are incom-plete. The freestanding facilities participating inthe Demonstration were required to reportwhether treatment was inpatient detoxificationor rehabilitation or outpatient rehabilitation(outpatient detoxification was rarely provided).Medicare claims do not always contain accurate


detoxification or rehabilitation procedure codes;therefore, there are no data to compare free-standing and hospital settings by modality.

Data limitations of the Medicare administra-tive claims files include the lack of historical dataon patients' alcoholism condition prior to, andpatients' health status and alcohol conditionfollowing, the initiation of treatment. TheDemonstration was initially designed to includean interview of patients in the two settings toobtain data on prior alcoholism history andpatterns of consumption. Unfortunately, theseinterviewers were not conducted because ofbudget constraints. As a result, the study cannotdetermine whether patients in hospital-basedfacilities had more serious problems with alcoholthan freestanding patients. This study overcomesthe lack of pre-treatment alcohol status in panby using the 1980-1983 Medicare file to analyzealcoholism conditions of patients prior to thestudy period.

Selection of study populationsThe study has two main patient groups orcohorts; a Demonstration of freestanding group,and an alcohol-treated hospital group. Patientstreated for alcoholism are classified into the free-standing group or the hospital-treated group onthe basis of where they received most of theirtreatment. Only five Medicare recipientsreceived most of their treatment in outpatienthospitals, which is expected given Medicare'slimited reimbursement of outpatient alcoholismtreatment. These patients were not included inthe analysis. The cohort classification is based onthe assumption that the setting in which patientsreceived most of their treatment is likely to havethe most impact on outcome as measured bytotal health care expenditures, average length ofstay, and readmission for alcoholism.

The Demonstration or freestanding group iscomprised of all Medicare recipients whoreceived most of their alcoholism treatment inDemonstration freestanding facilities during thestudy period. The hospital-treated group is com-prised of all Medicare recipients who receivedmost of their alcoholism treatment in those hos-pitals in the same counties as the freestandingfacilities. They also had in their initial inpatienttreatment a first-listed alcoholism diagnosis ofthose used in the Demonstration.

In the first five tables of this study there are

statistics for two other groups, an alcohol-relatedhospital group, and a non-alcoholism hospitalgroup. The alcohol-related group is comprised ofrandomly selected Medicare recipients who hada second-listed alcoholism diagnosis or alcohol-related diagnosis and who were treated inhospitals in the same countries as the freestand-ing facilities. The non-alcoholism group iscomprised of randomly selected Medicare recipi-ents who had no alcohol diagnoses and who weretreated in hospitals in the same counties as thefreestanding facilities. These two groups are in-cluded as benchmarks for comparing statistics onthe freestanding and hospital alcoholism treat-ment groups.

Study designThis study is an observational comparison studywithout randomization, in which Medicare pa-tients have the option to receive treatment froma freestanding facility or the hospital. At the startof treatment during the study period. Medicarepatients in either a freestanding facility or in ahospital were also free to switch providers. Thus,the cohons may not be directly comparable dueto systematic differences in several aspects. Afrequently used approach to identify and adjustfor prc-treatment differences between experi-mental and control groups in natural designstudies is the use of one-to-one matching on thebasis of comparative population attributes. Themethod is to match each patient in one groupwith a patient in the comprehensive group bythose variables that affect outcome. A commonproblem in this approach is the loss of patientswho cannot be matched from the experimentalgroup.

A method of controlling for systematic differ-ences and to circumvent the problem ofone-to-one matching is to group patients intoclasses based on observed characteristics, apply-ing the technique of the propensity score.* It is aprocedure that first apphes logistic regression toidentity the probability of patients' status in theDemonstration or the hospital group based onpatient demographics, diagnostic variables, andtreatment provider variables. In theory, patientswith similar probabilities of seeking treatment inthe Demonstration group will have similar co-variate values. The same theory holds forpatients in hospitals. Thus, the propensity scorepredicts the likelihood of patients seeking alco-


Table 1. Inpatient characteristics

Characteristics

TotalDeathMedicare status asstart of treatmentAged

DisabledAge

Less than 3535-6465 and over

SexMaleFemale

RaceWhiteBlackOther

Diagnosis (ICD-9-CM code)291.0-291.9303.0303.9305.0Other/blank

State of residence wheretreatment started

ConnecticutIllinoisMichiganNew JerseyNew YorkOklahomaOther/unknown

Demonstration

66866 (9.88%)

308 (46.11%)359 (53.74%)

83 (12.43%)325 (48.65%)260 (38.92%)

563 (84.28%)105 (15.72%)

558 (83.53%)91 (13.62%)9 (1.35%)

110 (16.47%)130 (19.46%)365 (54.64%)63 (9.43%)0 (0.00%)

118 (17.66%)53 (7.93%)

118 (17.66%)224 (33.53%)

68 (10.81%.)45 (6.74%)42 (6.29%)

Alcohol-treated

53048 (9.06%)

249 (46.98%)281 (53.02%)

62 (11.70%)256 (48.30%)212 (40.00%)

414 (78.11%)116 (21.89%)

454 (85.66%)68 (12.83%)

2 (0.38%)

55 (10.38%)118 (22.26%)301 (56.79%)

56 (10.75%)0 (0.00%)

89 (16.79%)17 (3.21%)

153 (28.87%)114 (21.51%)74 (13.96%)31 (5.85%)52 (9.81%)

Hospital

Alcohol-related

1009322 (31.91%)

694 (68.78%)308 (30.53%)

32 (3.17%)302 (29.93%)675 (66.90%)

634 (62.83"/u)375 (37.17%)

859 (85.13%)106 (10.51%)20 (1.98%)

20 (1.98%)36 (3.57%)

281 (27.85%)61 (6.05%)

61! (60.56%)

49 (4.86%)233 (23.09%)259 (25.67%)120 (11.89%)210 (20.81%)88 (8.72%)50 (4.96%)

Non-alcoholism

721172

565156

29139553

355366

6166411

0000

721

4315916556

123120

55

(23.86%)

(78.36%)(21.64%)

(4.02%)(19.28%)(76.70%)

(49.24%)(50.76%)

(85.44%)(8.88%)(1.53%)

(0.00%)(0.00%)(0.00%)(0.00%)

(100.00%)

(5.96%)(22.05%)(22.88%)(7.77%)

(17.06%)(16.64%)

(7.63%)

The Demonstration (freestanding) and the alcohol-treated hospital group are grouped by facility where most ofinpatient treatment received.

holism treatment in the Demonstration or in thehospital, and it is used to assign patients to theDemonstration or the hospital group.

The logistic regression technique is used inthis study to identity a set of statisticallysignificant variables. Applying the significantvariables, cluster analysis was used to defitiesimilar groups (or bomogeneous clusters) of pa-tients who were in the freestanding and hospitalgroups.^ ' The total average health expendituresof Demonstration and hospital patients werethen compared within the homogeneous clusters.The comparison of health expenditures by clus-ters between the freestanding Demonstrationand the hospital groups is largely free of self-selection bias, which is adjusted for in the logitanalysis. Logit-cluster analysis is, in sum, a

technique to adjust for self-selection bias. An-other technique to adjust for bias with thisDemonstration data is to probit self-selectionleast squares model.*

FindingsThere are five tables for univariate statistics forthe Demonstration group, the hospital (alcohol-treated) group, and the two other comparisongroups (alcohol-related and non-alcoholism);these are discussed in the following five parts ofthis section. The findings of the logit-clusteranalysis for the Demonstration and tbe alcoboi-treated hospital groups are presented last in thissection; they are the support for the hypothesis.


Patient characteristicsTable 1 shows the patient's sex, age on July 1,1982, race, and at the start of treatment thepatient's Medicare status, primary diagnosis, andstate of residence. There were 668 patientsclassified in the Demonstration group, 530 pa-tients in the alcohol-treated hospital group, 1009in the alcohol-related hospital group, and 721randomly selected from the non-alcoholismgroup.

Tests of proportions show that demographicdistributions of the Demonstration and thealcohol-treated hospital groups are alike. TTieMedicare population in our study is predomi-nantly male, consists of about 85% whitepatients, and has alcohol dependence (ICD303.9) as the most commonly reported diagno-sis. Tlieir distributions are only statisticallydifferent at the 0.05 significance level in thefollowing patient characteristics: sex, alcoholpsychosis (ICD 291.0-291.9), the residencestates of Illinois, Michigan, New Jersey, andNew York.

The demographic distributions of the alcohol-related and the non-alcoholism hospital groupsare similar to each other but different from theDemonstration and the alcohoi-treated hospitalgroups. These two groups have substantiallymore aged inpatients and higher death rates thanthose of the Demonstration and the alcohol-treated groups.

Provider characteristicsThe term provider in Table 2 refers to the firstfacility where an inpatient received treatment.For the alcohol-related and the non-alcoholismgroups, the first facilities are mostly hospitals.Table 2 includes the descriptive statistics on theprovider state, ownership, provider type, bedsize, and staff size.

More than half of the facilities from which thealcohol-treated group first received treatment areshort stay hospitals, and almost all the facilitiesfrom which the alcohol-related and the non-alco-holism groups first received treatment are alsoshort stay hospitals. In contrast and as expected,most patients who are classified in the Demon-stration group received their first treatment atfreestanding facilities. Statistics for the groupsshow that between 70 and 80% of the facilitiesare non-profit. In terms of bed and staff size, thealcohol-related and the non-alcoholism groups

received their first treatment in larger facilities:the atcohol-treated group went to medium-sizedfacilities, and the Demonstration group went tosmaller providers.

Table 2 also shows the distribution of patientsby states where they began treatment. A third ofall Demonstration patients were in New Jerseyfacihties for their first treatment. Over 30% ofthe alcohol-treated hospital patients, 26% of thealcohol-related, and 22% of the non-alcoholismgroups received their first treatment in Michiganfacilities. These distributions of the providerstates are similar to those of the residence states.That is, patients tend to select a facility withinthe state of their residence. A chi-squared testindicates that the residence state and providerstates are strongly related, with p-value less than0.0001.

Average monthly expendituresAverage monthly expenditures are shown inTable 3. The Demonstration has the lowestaverage monthly expenditure of S546, which aresignificantly lower than the other three groups atthe 0.05 level (S921 for the alcohol-treatedgroups, SI059 for the non-alcoholism group,and SI672 for the alcohol-related group). TheWilks test was used to compare the expendituresmeans of categories within each of the variablesin Table 3 at the 0.05 significance level.** Resultsindicate that the average monthly expenditure ofmales and females are significantly different. Av-erage monthly expenditures of black patients areon average higher than those of white patients,except in the alcohol-treated group. Racial dis-parity in expenditures is significant in all groupsexcept the Demonstration group.

Patients with alcohol dependence (ICD 303.9)generally experience higher average expendituresthan other diagnoses, except in the Demonstra-tion group. The Demonstration expenditures inalcohol abuse (ICD 305.0) are slightly, but notsignificantly, higher than alcohol dependence(ICD 303.9) at the 0.05 level. The lowest aver-age expenditures of the Demonstration and thealcohol-treated hospital groups are found inpatients with alcohol psychosis (ICD 291.0-291.9). Expenditures of the alcohol-relatedgroup appear to be lowest in patients with acuteintoxication (ICD 303.0).

Tlie highest average monthly expenditures ofthe Demonstration and the alcohol-treated


Table 2. Provider characteristics

Hospital

Characteristic DemonstrationAlcohol-treated

Alcohol-related

Non-attoholism

TotalStare of provider

ConnecticutIllinoisMichiganNew JerseyNew YorkOklahomaOther/unknown

OwnershipGovernmentNon-profitProfitUnknown

Provider typeFree-standingLong StayShort StayPsychiatricOtherUnknown

Bed size0 (outpatient)1-25

26-100101-250251-500500 +Unknown

Staff size1-50

51-100101-500501-1000

1001-15001501-25002500 +Unknown

668

13057

123223

514836

41473

20134

4980

3040

136

15186

188129

651336

42549

S65

131

161

(19.46%)(8.53%)

(18.41%)(33.38%)(7.63%)(7.19%)(5.39%)

(6.14%)(70.81%)(2.99%)

(20.06%)

(74.55%)(0.00%)(4.49%)(0.60%)(0.00%)

(20.36%)

(22.60%)(12.87%)(28.14%)(19.31%)

(9.73%)(1.95%)(5.39%)

(63.62%)(7.34%)(1.20%)(0.90%)(0.75%)(1.95%)(0.15%)

(24.10%)

530

10621

164117

603428

102385

439

1771

28132

039

422698

1101339 328

16416566564

1001055

(20.00%)(3.96%)

(30.94%)(22.08%)(11.32%)

(6.42%)(5.28%)

(19.25%)(72.64%)(0.75%)(7.36%)

(33.40%)(0.19%)

(53.02%)(6.04%)(0.00%)(7.36%)

(7.92%)(4.91%)

(18.49%)(20.75%)(25.09%)(17.55%)

(5.28%)

(30.94%)(3.02%)

(10.57%)(12.26%)(12.08%)(18.87%)(1.89%)

(10.37%)

1009

58244261121219

9016

178796

332

02

999512

07

215439222124

2

1538

428254107

9862

7

(5.75%)(24.18%)(25.87%)(11.99%)(21.70%)(8.92%)(1.59%)

(17.64%)(78.89%)(3.27%)(0.20%)

(0.00%)(0.20%)

(99.01%)(0.50%)(0.10%)(0.20%)

(0.00%)(0.69%)

(21.31%)(43.51%)(22.00%)(12.29%)(0.20%)

(1.49%)(3.77%)

(42.42%)(25.17%)(10.60%)(9.71%)

(61.40%)(0.70%)

721

43157161

5012111772

172518

283

04

70212

03

012

2093061256 6

3

2056

368134

525926

6

(5.96%)(21.78%)(22.33%)

(6.93%)(16.78%)(16.23%)

(9.99%)

(23.86%)(71.84%)(3.88%)(0.42%)

(0.00%)(0.55%)

(97.36%)(1.66%)(0.00%)(0.42%)

(0.00%)Cl .66%)

(28.99%)(42.44%)(17.34%)(9.15%)(0.42%)

(2.77%)(7.77%)

(51.04%)(18.59%)

(7.21%)(8.18%)(3.61%)(0.84%)

The Demonstration (freestanding) and the alcohol-treated hospital group are grouped by facilitywhere the most of inpatieni treatment received.

groups are observed in facilities in New Jersey(S636) and Illinois (SI491), respectively. In boththe alcohol-related and the non-alcoholismgroups, expenditures rank highest in New York.The lowest expenditures of the Demonstrationgroup are in facilities in Connecticut, and thelowest expenditures of the other groups arefound in Oklahoma facilities.

Expenditures of the disabled are higher thanthe aged in the Demonstration and the alcohol-treated groups, though the difference is notsignificant in the Demonstration group. An op-

posite pattern is found in the alcohol-related andthe non-alcoholism hospital groups, in whichexpenditures of the aged are significantly higherthan those of the disabled.

Alcoholism records prior to 1984 are availablefrom the 1980-83 MEDPAR file for about 6%of the inpatients in this study. Patients withprevious alcoholism records have averagemonthly expenditures lower than the overall av-erage. This finding suggests that expenditures forpatients with longer alcoholism treatment historydecline over time.

60 Annie La &= Albert Woodward

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To compare the average monthly expendituresbetween the Demonstration and the alcohol-treated groups, non-parametric statisticalmethods arc used that are not based on distribu-tional assumptions. Results reveal that theexpenditures of the Demonstration group aresignificantly lower than the alcohol-treated groupin the following subgroups: sex (male); race(white, black); age; Medicare status (aged, dis-abled); the presence of previous alcoholismrecords; all diagnoses except alcohol abuse (ICD305.0); and all provider states except New Yorkand Oklahoma.

Average length of stayFrom Table 4, the shortest average length of stay(ALOS) per year is observed in the Demonstra-tion group with 16 days, and is significantlyshorter than the alcohol-treated group with 22days. The ALOS differences in sex, race, andMedicare status are not statistically significant.Though not significant, the ALOS of males isapproximately 35'/<) longer than that of femalesin the three hospital (i.e. non-Demonstration)groups. Disabled patients have longer ALOSthan the aged. Geographically, alcohol-treatedhospital patients have the longest ALOS in Illi-nois facilities; and patients from the other groupshave the longest ALOS in New York facilities.Within the principal alcohol diagnosis,significant differences are found between alcoholpsychosis (ICD 291.0-291.9) (12 days) andalcohol abuse (ICD 305.0) (20 days) and theaverage in the Demonstration group, betweenalcohol psychosis (18 days) and acute intoxica-tion (ICD 303.0) (26 days) in the alcohol-treated group. In the alcohol-related group,acute intoxication (9 days) is significantly lowerthan alcohol dependence (ICD 303.9), alcoholabuse, and other ICDs.

Comparing the Demonstration group with thealcohol-treated hospital group using non-para-metric statistical methods, the ALOS of theDemonstration is significantly lower than that ofthe hospital at the 0.05 level in the followingpatient attributes: male, white, alcohol psychosis(ICD 291.0-291.9), acute intoxication (ICD303.0), alcohol dependence (ICD 303.9),provider states of Connecticut, Illinois, Michi-gan, and New Jersey, age, and Medicare status.

Correlation analysis demonstrates that expen-ditures and length of stay are highly correlated.

The Spearman correlation coefficient of 0.8 rein-forces the results displayed in Tables 3 and 4,such that lower expenditures are accompaniedby shorter length of stay in the Demonstrationgroup, and the relatively higher expenditure inthe alcohol-treated hospital group are accompa-nied by longer length of stay. This suggests thatcost and utilization are positively related, as ex-pected. This correlation may be the result of lessintensive treatment in freestanding facilities, per-haps through fewer and shoner inpatient staysand more outpatient treatment than hospitalfacilities. This conjecture follows from the struc-ture and organization of the two types offacilities. If freestanding facilities rely more onoutpatient treatment, the average length of stayper year is likely to be lower (less reliance oninpatient care) and health expenditures also willbe lower (for alcohol treatment outpatient care ischeaper than inpatient care, which lowers totalexpenditures).

Admission patternsTable 5 shows that on average the Demonstra-tion group has 1.01 admissions per year and issignificantly lower than the average in the alco-hol-treated hospital group of 2.43 admissions peryear. Both the Demonstration and the alcohol-treated groups indicate that males generallyexceed females in the number of admissions byabout 35%. The data also indicate that admis-sions of white patients per year are higher thanblack patients, and that on average patients withalcohol dependence (ICD 303.9) have thesmallest number of admissions. Table 5 alsoshows that the disabled are admitted more fre-quently than the aged. By comparing the meansof groups within each variable, the Wilks testshows that all group means are not statisticallydifferent at the 0.05 level. Further non-paramet-ric statistical analysis indicates that the averagenumber of admissions of the Demonstrationgroup is significantly lower than the alcohol-treated hospital group at the 0,05 level in everysubgroup> except in Oklahoma facilities wherepatients received their first treatment.

Logit analysis resultsThe logit analysis specifies statistically significantvariables. The model initially contained variablesfor gender, race, age, the state where treatment


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Table 6. Significant logistic regression findings for the demonstration and the hospital group

Variable

SexPrimary alcohol diagnosis:ClCD-9-CM-Code

303.9305.0

Provider stateMichiganNew JerseyNew YorkOwnership

Provider typeFreestanding

Bed size

Estimatedlog odds

0.79

-0.55-0.94

-0.850.62

-0.93-4.20

3.22-0.001

Standarderror oflog odds

0.21

0.200.29

0.210.210.290.38

0.240.001

Estimatedoddsratios

2.20

0.580.39

0.431.860.390.02

25.03

95'yo Confidence intervalfor odds ratios

Iflwer

1.47

0.390.22

0.281.230.220.01

15.51

Upper

3.29

0.860.69

0.652.810.690.03

40.38

was begun, diagnosis, and characteristics of theproviders. To reduce the number of independentvariables, a multiple logistic regression modelwith backward elimination was used. Statisticallysignificant variables resulting from the multiplelogistic regression analysis are shown in Table 6.TTie following variables are significant in predict-ing the probability that a patient receivestreatment in the Demonstration: sex, alcoholdependence (ICD 303.9), alcohol abuse (ICD305.0), provider states of Michigan, New Jersey,and New York, type of ownership, freestandingfacilities, and bed size.

Table 6 presents the estimated odds ratios forthe variables that are significantly different fromzero at the 0.05 level, and their corresponding95% confidence intervals. The estimated oddsratios indicate that a male alcoholic inpatient istwice as likely to receive treatment in theDemonstration as a female, holding all othervariables constant.'"'" In addition, a patient withalcohol dependence (ICD 303.9) is 1.7 times aslikely to select a hospital as patients having otherdiagnoses. Similarly, patients with alcohol abuse(ICD 305.0) are 2.6 times as likely to select thehospital as patients with other diagnoses. Tlieestimated odds ratios show that New Jerseyproviders are 1.9 times more likely to be aDemonstration facility as providers in otherstates. New York providers are 2.6 times as likelyto be a hospital, and Michigan providers are 2.3times as likely to be a hospital.

The negative value of the estimator for bedsize from the logistic regression indicates that asthe number of beds increases, the probability of

a patient being in a Demonstration facility de-creases. This is in agreement with the data inTable 1, which shows that on average freestand-ing facilities generally have fewer beds thanhospitals.

A critical part of the logistic regression proce-dure is the test of the goodness of fit; in thisstudy a number of test statistics are examined.''First, the R statistic for measuring the predictiveability of the model indicates that the statisticallysignificant independent variables shown in Table6 explain 60% of the variance in the dependentvariable. Second, the overall correct classificationfor the model is 82.2%, the proportion of trueDemonstration patients who are predicted to beDemonstration patients is 92.2%, and the pro-portion of correct classification of hospitalpatients is 69.8%. Third, the plot of the cumula-tive true proportions against the cumulativepredictive probabilities reveals that the data fitthe logit model. Finally, the Hosmer statistic is4.61, with 8 degrees of freedom.'"'" The abovediagnostic tests all indicate high goodness of fitof the data to the logistic curve.

Cluster analysis resultsA preliminary cluster analysis was performedusing the significant provider and patient vari-ables fi-om logistic regression. Results indicatedthat clusters would not be defined distinctly.With the provider variables removed, however,clusters became more distinct using the smallerset of patient variables. These patient variablesare sex, residence state where first treatment


Table 7. Comparison of the demonstration and the alcohol-treated hospital group clusters generated from density linkagemethod

Number of inpatientsAverage monthly

health expenditureSex (%)

MaleFemale

Diagnosis (ICD-9-CM code) (%)291.0-291.9303.0303.9305.0

Residence state wheretreatment started (%)ConnecticutIllinoisMichiganNew JerseyNew YorkOklahoma

Provider ownership (%)Government, non-profitProfit

Provider type (%)Free standingLong stay, PsychiatricShort stay

Average bed size

Cluster 1Demo

385

570.26

84.6815.32

12.4719.2258.709.61

0.0013.770.00

56.8817.6611.69

73.5126.49

71.1724.94

3.90103

Hospital

231

1041.01

73.5926.41

10.8219.9158.8710.39

0.006.490.00

48.4831.6013.42

91.778.23

31.1711.2657.58

301

Cluster 2Demo

116

563.68

83.6216.38

0.0041.3856.90

1.72

0.000.00

100.000.000.000.00

64.6635.34

67.2426.72

6.0389

Hospital

152

972.10

81.5818.42

5.2623.6859.8711.18

0.000.00

100.000.000.000.00

94.085.92

35.535.26

59.21234

Cluster 3Demo

116

422.43

83.6216.38

43.971.72

40.5213.79

100.000.000.000.000.000.00

99.140.86

93.102.594.31

63

Hospital

89

697.40

77.5322.47

19.1022.4744.9413.48

100.000.000.000.000.000.00

98.881.12

38.2021.3540.45

244

The Demonstration (freestanding) and the alcohol-treated hospital group are grouped by facility where most ofinpatient treatment received.

occurred, and first treatment alcohol diagnosis.In place of the provider state, residence state isused in the final cluster analysis.

Employing the ^th-nearest-neighbor densitylinkage method with varying values of k, thenumber of modes remains constant at 3 when kranges from 98 through at least 135. Thus the,^th-nearest-neighbor cluster analysis stronglysuggests forming three clusters. Within each ofthe three homogeneous clusters, the averagemonthly expenditures between the Demonstra-tion and the alcohol-treated hospital groups arecompared statistically; their descriptive statisticsare displayed in Table 7. The key finding is thatin all three clusters non-parametric statisticaltests indicate that the expenditures of theDemonstration are significantly lower than thoseof the hospital at the 0.01 level.

Both the Demonstration and the alcohol-treated hospital groups contain a substantiallyhigher percent of males than females andpatients with alcohol dependence (ICD 303.9).

Most facilities in which patients first receivedtreatment are government owned. The govern-ment to profit ratio in the alcohol-treatedhospital group is higher than in the Demon-stration group for clusters 1 and 2. As expected,the majority of the facilities for first treatment ofthe Demonstration group are freestanding,whereas most facilities for first treatment ofthe hospital group are short stay. Also, theaverage number of beds in the hospital groupis about three times higher than the Demonstra-tion group, and is highly significant at the 0.01level.

With respect to the residence state, mostDemonstration and hospital patients in cluster 1lived in New Jersey when their treatment began.In clusters 2 and 3, all patients resided in Mich-igan and Connecticut, respectively. Thus, it ispossible to infer the average monthly healthexpenditures for patients living in Michigan areS972, and $697 for Connecticut residents. Thisfinding is in congruence with the results shown


in Table 3. As noted above, the distributions ofall characteristics are very similar among thethree clusters. This finding is unexpected, as itsuggests that residence state can be the drivingfactor to form distinct clusters of patients.

DiscussionThis study has two distinctive features. First, itfocuses on a Medicare alcoholic population,which has not been well studied. Although thispopulation is a small part of the total Medicarepopulation, it is probably proportionately morecostly to treat, because its individuals' use ofhealth services is likely to be greater than that ofthe remaining Medicare population.""'' Second,it follows patient health expenditures over severalyears, from 1984 to 1988. Data taken fromMedicare claims files permit analysis of compre-hensive diagnostic, cost, and services utilizationdata.

The most significant finding of this study isthat average health expenditures are lower forthe patient group treated in freestanding facili-ties, when compared with those treated inhospital-based facilities using logit-cluster analy-sis (Table 7). This finding is supported by that ofanother paper on the same two populations us-ing an econometric technique to correct forselection bias.*

One desired outcome at the outset of alco-holism treatment is a decline in the recurringneed for alcoholism treatment and an improve-ment in general health. As general healthimproves following the start of treatment, gen-eral health expenditures decline even allowingfor the costs of alcoholism treatment itself.' To-tal Medicare health expenditures following thestart of alcoholism treatment are thus an indica-tor of overall health status.'^

Health utilization cost savings, however, arenot a sufficient criterion for selecting freestand-ing facilities over hospital programs. Theoutcomes of treatment in the freestanding facili-ties are just as, if not more, important forMedicare recipients as the costs associated withthat treatment. This study has no data on healthstatus outcomes, but instead examines two uti-lization outcomes after initial treatment, namelythe annual average number of inpatient readmis-sions with a first-labelled alcoholism diagnosisand the average length on inpatient stay foralcoholism treatment (Tables 4 and 5). These

two outcomes measure the continuing severity ofalcohol problems after alcoholism treatment hasbeen started. Both these measures have lowervalues for patients treated in freestanding facili-ties than in hospitals. The findings of this studyare consistent with other evaluations of theDemonstration.^'" Thus, the conclusion of thisstudy is that for some patients with alcohol prob-lems, treatment in freestanding facilities is lesscostly and leads to less recurrent treatment thantreatment in hospitals.

The reasons for the lower health care costsamong the group of Demonstration patients canbe divided into three categories; patient charac-teristics not accounted for in the logit-clusteranalysis, provider variables such as treatmentpractices, and the environmental and locationconsiderations stemming from state regulation,reimbursement, and organization of treatment.Findings from the tables and the logit-clusteranalysis offer some insights into these reasons.

The logit-cluster model accounted for suchpatient characteristics as sex, age, and initialalcohol diagnosis. These variables may be in-sufficient to account for all the differencesbetween the hospital and the freestanding groups(alcohol beverage use may not correlate well withhospitalization, however).'^ Freestanding pa-tients may not have on average as severe orserious alcohol problems as the hospital patients.The diagnostic data available for this study donot rule out this possibility, nor do they suggestthat it is a strong possibility. The averagemonthly health expenditures following the startof treatment in Table 3 are highest for the alco-hol-related group. This may be explained if thisgroup is much sicker than either the freestandingor hospital groups (note that the death rate forthe alcohol-related group and the non-alcoholhospitalized groups are much, much higher thanthe freestanding or hospital-treated groups).This finding is consistent with other recent re-search on untreated alcoholics covered by healthinsurance.'

Provider practice patterns and other providervariables may explain some of the differences inexpenditures. The findings from this study sug-gest some obvious differences between thefreestanding and hospital groups, the formerwith more outpatient care, shorter lengths ofstay, lower staff to patients ratios, and smallersized facilities (see Table 2). Logit analysis vari-ables in Table 6 that are significant are size and


type of facility ownership. The size variables areindicators of economies of scale," but there is noreason to expect them to affect differentially thehealth care expenditures following the start oftreatment. The presence of a specialized alco-holism treatment unit in a hospital did notclearly affect the analysis, although such unitscould be expected to have more of an impact onoutcomes than general hospital treatment.

The scope of the study is limited to six states.Inferences can be drawn to the nation only to theextent that these six states are representative ofthe nation. However, results from the clusteranalysis indicate that the variable state plays adominant role in separating patients into clus-ters: cluster 2 consists entirely of Michiganpatients and cluster 3 all Connecticut. Cautionmust therefore be taken when drawing inferencesto the nation, as regulations and policies mightvary from state to state. New York and NewJersey had waivers from HCFA for diagnosis-related group prospective payment, but neitherstate was critical in the formation of the clusters,as were Michigan and Connecticut. Further, andas already noted, the study does not precludethat patients who select treatment in freestand-ing facilities differ in some important andunmeasured ways from those choosing hospitals.There is no reason to suppose, however, that thisprocess of self-selection is unique to patients inthe six states.

The Demonstration was not designed to be arandom assignment study. Random assignmentof patients to the two settings to be compared ispreferred by the scientific community for assess-ing the value of treatment interventions in thetwo settings. It has the advantage over retrospec-tive method of eliminating possible bias incomparing the Medicare patient groups treatedat the two settings. ""^^ Literature reviews, how-ever, have found settings alternative to theinpatient hospital setting to be as cost-effectiveas the traditional setting. " ^ Although randomassignment is an advantage in cost-effectivenessstudies, it is often difficult to do and does notalways represent the real world. ''• ''• ' In thisstudy statistical methods are used to reduce self-selection bias to the maximum extent feasiblegiven the data available. Even with these statisti-cal methods, there is no way to correct for thedifference between the Demonstration situationand the real world: it is possible that the free-standing facilities performed 'optimally' under

the Demonstration, and that the differences inaverage monthly expenditures were at theirgreatest.

The design of the Demonstration and the col-lection of data partially limit certain studyconclusions but suggest policy directions. Froma program or policy perspective, it is clear thatfreestanding facilities do have the desired effecton the outcome most desired by Medicare: re-ductions in the total health expenditures and inrecurrent alcoholism treatment. The study didnot examine the issue of induced demand ofMedicare patients with alcohol problems enter-ing treatment where they would otherwise notseek treatment. For the Medicare program, thecosts of patients getting treated for alcoholismwho would otherwise not seek treatment shouldbe less than the costs of subsequent illnessfollovsfing from untreated alcohol problems. Inother words, there is a possible cost-off-set fromtreatment, which was not measured in this studyand which should be considered against induceddemand. Tables 1 and 3 indicate that the studysample of Medicare patients with alcohol-relatedconditions had health expenditures higher thanany other group, is older on average, and had thehighest mortality percent. In combination thesedata suggest that this group may have been un-treated or been unsuccessfully treated. Perhaps ifpatients in the group could have been induced toget treatment. Medicare program costs wouldnot have been as high. Also, the study did notexamine the impact on expected provider behav-ior, although Medicare capital reimbursement isunlikely to lead to growth in freestanding facili-ties if they were included under Medicare. "' ^

This study finds that Medicare could lowercosts while maintaining certain indirect out-comes if it allowed beneficiaries a choice of thesetting in which to get treatment. A recent Insti-tute of Medicine report recommends furtherresearch into " . . . discovering the costs, effec-tiveness, and responsiveness to insurancecoverage of the various treatment strategies nowin use and the matching strategy . . ." [ref. 24, p.462]. This study suggests avenues of furtherresearch in the area of cost-effectiveness, whiledemonstrating that Medicare program cost sav-ings have been attained.

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an evaluation of freestanding alcoholism treatment for medicare recipients

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