State Variationsin Medicare Expenditures

Robert L. Kane, MD, and Bruce Friedman, MPH

IntroductionThe combination of the predicted

imminent insolvency of the MedicareHospital Insurance Trust Fund and pres-sures to balance the federal government'sbudget has led to a variety of proposals fordramatically reducing Medicare expendi-

2*. ;tures. Both individual strategies and com-W. ...:x:-,prehensive reform packages have been

proposed. The strategies cover a widezS-i_i.. .. .......

*,^!.<i: ,: .-' rangef of opationn.,;°i.,,.,..:i.s_ J - A.i ..&.::' ..= i... :e.. ...:::.. :.. Substantial variation in Medicare

14 f8w '.15 : :.expenditures per enrollee by geographicregion has been recognized almost since

_the program began.5 This variation hasbeen identified for all manner of sizes ofgeographic regions, ranging fromUS census regions to hospital service

- 4 : ;areas,5-13 despite Medicare's being a,..h....,.

.......... federal program with a uniform benefit.i .i..'........ i.. j. >.-a -'istructure and having, for the past decade,

.--S-<4w*;. a nationwide rate-setting and paymentmechanism for its largest single outlay,short-stay hospital inpatient care.

The lack of equity in Medicareexpenditures per enrollee across geo-graphic regions and the absence of propos-als to achieve greater equity are somewhat

i-2- .surprising, given the historical roots ofMedicare. Medicare was established toaddress the great inequities in access tohealth care that existed between differentgroups of elderly persons on the basis ofincome, race, rural residence, and age.'4"5

4 i In part because many disparities had

3971$7611S disappeared by the late 1970s,'5 the focusof equity concerns shifted to the uninsured

: : ::::: : : z ~~~~~~~~~~~~~~~~~1 6younger than age 65, especially unin-sured pregnant women and children.While equity between Blacks and Whites

- -- of all ages has remained "a matter ofcontinuing concem, 17 the issue of equitybetween Black and White Medicare ben-eficiies has reemerged.-

While some of the recent Medicarereform proposals acknowledge the widedifferences in Medicare spending acrossthe country, none propose steps to achievemore uniform distributions of Medicarespending. Simple across-the-board cuts inMedicare, whether directed at providersof care or beneficiaries, fail to addressgeographic disparities in spending. Beforea suitable strategy for Medicare costcontrol can be developed, the variation inMedicare payments must be better under-stood. This paper examines the nature andextent of the variation in Medicare expen-ditures by state and offers some sugges-tions for incorporating these findings intonew policies.

Our results are presented in threeparts. First, we present information onhow Medicare expenditures per enrollee,utilization per user, and unit payment varyby major service category for the stateswith the lowest and highest amount ineach category. Second, we present theresults of regression analyses2' we con-ducted to examine the impact of thosefactors we expected to influence Medicarespending per enrollee. Third, we compareeach state's actual level of spending withthe predicted level, adjusting for theindependent variables in our model.

MethodsInformation on Medicare spending

per enrollee, utilization (both the propor-tion of enrollees using a given service and

The authors are with the Division of HealthServices Research and Policy, University ofMinnesota School of Public Health, Minneapolis.

Requests for reprints should be sent toRobert L. Kane, MD, Division of Health ServicesResearch and Policy, University of MinnesotaSchool of Public Health, D-35 1 Mayo (Box 197),420 Delaware St SE, Minneapolis, MN 55455.

This paper was accepted June 6, 1997.

-~~~~~~~~~~~~~~~~~................ American Joumal of Public Health 1611

Kane and Friedman

the average number of services used peruser), and average payment per unit ofservice by state were taken directly fromthe Medicare and Medicaid StatisticalSupplement issued by the Health CareFinancing Administration (HCFA).22 Aver-age payment per service unit for hospitalday, skilled nursing facility day, andphysician and supplier visit were calcu-lated from data in the Supplement.

Conceptual Model, Hypotheses,and Data Sources

Our conceptual model includes vari-ables for demand (sociodemographic andhealth insurance) and supply (health careresources), as well as variables to controlfor price and population size. The candi-date variables were derived from a reviewof studies that used Medicare spendingper enrollee as the dependent vari-able.9"10'23-26 Only two of these studies23'25used the state as the unit of observation.The following independent variables weredemand variable candidates: age, gender,race/ethnicity, urban vs rural area, mortal-ity, morbidity, Medicare supplemental(medigap) insurance, and Medicaid. Sup-ply variable candidates included hospitalbed supply, physician supply, primarycare physician rate, nursing home bedsupply, Medicare health maintenance orga-nization (HMO) market share, and Medi-care physician assignment rate.

We hypothesized that lower Medi-care expenditures per enrollee would beassociated with a higher proportion amongthe elderly of persons aged 85 andolder,'0'23 women,22 Blacks,9"10'23 and His-panics.24 We also expected that lowerspending per enrollee would be related toa higher proportion of primary carepractitioners among all physicians,9"0more nursing beds per 1000 populationaged 65 and older,9 0 and a higherproportion of the aged Medicare popula-tion enrolled in HMOs.910'26 Further, wehypothesized that higher expenditures perenrollee would be related to larger propor-tions of the elderly residing in urban (asopposed to rural) areas,22'24'25 a higherproportion of the Medicare populationwith Medicare supplemental insurance,26a larger Medicare assignment rate on thepart of physicians,25 a higher state mortal-ity rate for the elderly,9"10 worse healthstatus among the aged,24 more hospitalbeds per 1000 population,9"10'23 and morephysicians per 1000 population.9"10

Although no previous Medicare re-gional variation studies have examined

any effect of Medicaid related specificallyto the elderly, we hypothesized a substitu-tion effect in which Medicaid substitutesfor Medicare spending. Because higheraverage price should be associated withhigher expenditures per enrollee,'023-25the Medicare Wage Index was included tocontrol for price differences across states.Larger state population should be associ-ated with higher expenditures per en-rollee.26

The dependent variable in our regres-sion analyses, Medicare expenditures perenrollee in 1992, was obtained from theMedicare and Medicaid Statistical Supple-ment22 and excludes payments both toMedicare HMOs and to Medicare benefi-ciaries enrolled in Medicare HMOs(Charles Helbing, Health Care FinancingAdministration, personal communica-tion). In calculating this variable, HCFAused data for both disabled enrolleesunder age 65 and elderly enrollees aged65 and older. Table 1 presents the mean,standard deviation, range, and data sourcefor each independent variable.

Our decisions on what independentvariables to include in regression analyseswere complicated by the small number ofobservations (n = 50 states). A rule ofthumb that many follow is that one shouldhave at least five observations for eachindependent variable in an equation.Strictly following this rule would meanthat we would have had to omit from ouranalysis a considerable number of poten-tial independent variables that have beenfound to be statistically significant inMedicare regional variation studies. Exclu-sion of variables could result in significantomitted-variable bias. On the other hand,the use of a large number of independentvariables with a very small sample canresult in overparameterization.

Model BuildingWe thus approached model building

from two extremes. We developed twomodels: a "large" model based on thepremise that omitted-variable bias is ofgreater concern than overparameteriza-tion, and a "small" model based on theopposite premise. In developing the largemodel we ran three regression analyses:one using all 17 supply and demandvariables, a demand-side regression with11 variables, and a supply-side equationwith 9 variables. All three equationsincluded the Medicare Wage Index andthe population aged 65 and older tocontrol for health care price and popula-tion size. We then omitted the 7 variables

that were not statistically significant atP < .10 or better in all of the equations inwhich they appeared. This resulted in alarge model with 11 variables.

In developing the small model, weused three criteria: level of correlationbetween variables, possible endogeneityof variables, and possible simultaneitybetween variables. All variables that had acorrelation of about .60 or higher, werelikely to be endogenous (that is, notpredetermined or lagged endogenous), orwere likely to have a simultaneous rela-tionship with other variables were ex-cluded from the model. Application ofthese criteria resulted in a small modelwith nine variables.

Interaction EffectsWe also examined possible interac-

tion effects. We hypothesized that ifinteractions would be present and statisti-cally significant, they would occur eitherbetween the demand variables for age 85or older, gender, race/ethnicity, and urban/rural residence, or between these demandvariables and supply variables for hospitalbeds, physicians, primary care practitio-ners, and nursing home beds. We addedeach interaction term to the large andsmall main effects models and thenremoved it before adding the next interac-tion. All interaction terms that werestatistically significant at P < .10 or betterwere added to each large and small maineffects model.

The same approach was used toconstruct separate demand-side and sup-ply-side equations for the large and smallmodels. We explored the possibility ofinteraction terms for the demand-sidemodels as described above.

Statistical AnalysisAll of the regression analyses were

performed with Stata 4.0.27 We employedweighted least squares with the Huber-White consistent estimator,28'29 using thenumber of elderly residents in each stateas the weights. This step ensured that agedresidents in each state were equallyrepresented in the analyses. The standarderrors, t statistics, P values, and confi-dence intervals reported were calculatedwith the Huber-White consistent estima-tor to address heteroscedasticity andensure that the inferences drawn werecorrect.

We employed a number of statisticaltests to examine whether regression analy-sis assumptions held for each regression

1612 American Journal of Public Health October 1997, Vol. 87, No. 10

Medicare Expenditures

model. Linearity of the model was testedwith a Pregibon (link) test30 and a

semiparametric test.31 The normality ofstudentized residuals was examined withthe Shapiro-Wilk test32 and the D'Agostinotest.33 Potential outliers were identifiedthrough the use of several outlier tests.3436A number of formal heteroscedasticitytests were used.3740

ResultsExpenditures, Utilization,and Unit Price

Medicare expenditures per enrolleevaried considerably by state in 1992,ranging from a low of $2157 in Hawaii toa high of $4430 in Maryland.22

The average expenditure per Medi-care enrollee for each type of service can

be viewed as the result of three compo-nents: the number of enrollees per 1000who use a given service, the averagenumber of services used per person by the

users of a service, and the average

Medicare payment per unit of service.That is,

expenditures/1000 enrollees

= (users/1000 enrollees)

X (units of use/user)

X (average payment/unit of use)

This formula suggests the employment ofcoefficients of variation to examine eachof the three components for each servicecategory.

As shown in Table 2, the variation inexpenditures for posthospital care (i.e.,care in home health agencies, skillednursing facilities, or both) is substantiallygreater than that for hospital inpatientcare, hospital outpatient care, and physi-cian services (including supplier ser-

vices). The pattern of expenditures variesby category. In the case of hospitaldischarges, data are reported only in termsof discharges, not as actual counts of

unduplicated cases. (Specific data on

hospital outpatient use per user andpayment per unit were not available.)

Hawaii has the lowest level ofMedicare expenditures per enrollee forboth hospital inpatient care and posthospi-tal care (expressed as the sum of homehealth agency and skilled nursing facilitypayments). Maryland has the highest levelfor both hospital inpatient and outpatientservices. In Tennessee, Medicare paysmore than 11 times more per enrollee forhome health care than it does in SouthDakota. Skilled nursing facility expendi-tures per enrollee are nearly seven timeshigher in California than in Maine.

For average expenditures per en-

rollee, the coefficient of variation is muchhigher for posthospital care than forhospital and physician care. This is alsothe case for each of the three componentsof spending per enrollee: users per 1000enrollees, service units per user, and

average payment per unit.

American Journal of Public Health 1613

TABLE 1-Independent Variables Used to Estimate Medicare Expenditures per Enrollee by State

Variable Mean SD Minimum Maximum Source

Demand variables% Population aged 65+ y who are aged 85+ y .1014 .0167 .046 .135 Calculated from Hardwick et al.51% Population aged 65+ y who are female .5914 .0225 .52 .62 Calculated from Byerly52% Population aged 65+ y who are Black .0654 .0733 .0003 .2833 Schick & Schick53% Population aged 65+ y who are Hispanic .0242 .0438 .0015 .259 Schick & Schick53% Population aged 65+ y who live in urban .6301 .2295 .227 1.000

areasMortality rate (per 100 000) of population 4894 339.0 3640 5391 CDC Wonderaged 65+ y

% Population aged 65+ y in fair or poor health .3021 .0629 .211 .442 Special analysis by CDC54% Population aged 65+ y with Medicare .6936 .0766 .519 .818 Jensen et al.55

supplemental policy% Population aged 65+ y enrolled in Medicaid .1048 .0404 0 .212 Calculated from Congressional Research

Service56 and Hardwick et al.51

Supply variablesNo. community hospital beds per 100 000 372.9 101.0 218 696 Morgan et al.57

populationNo. nonfederal physicians per 100 000 185.2 42.81 120 300 Morgan et al.57

population% Physicians who are primary care .3544 .0266 .29 .44 Calculated from Roback et al.58

practitionersNo. nursing home beds per 1000 population 51.85 17.10 15.1 82.0 Morgan et al.57aged 65+ y

% Medicare population enrolled in Medicare .0487 .0672 0 .275 Calculated from Health Care FinancingHMOs Administration22

Medicare assignment rate .8588 .1133 .50 .99 Health Care Financing Administration22

Control variablesMedicare Wage Index .9480 .1198 .71 1.26 Prospective Payment Assessment

Commission59Population aged 65+ y 623 280 665 792 22 369 3 135 552 Honor6O

Note. HMO = health maintenance organization.

October 1997, Vol. 87, No. 10

Kane and Friedman

Medicare expenditures per enrolleeby state are driven more by use rates per

enrollee or amount of service use per user

than by Medicare payment per unit ofservice.

There is considerable variation inaverage payment per discharge for inpa-tient hospital care, although Medicare'sProspective Payment System has estab-lished a national payment rate (adjustedfor various factors) for each diagnosis-related group. (Medicare also has a

national payment system for physicianservices, the Resource Based RelativeValue Scale, but this was not in effect longenough before 1992 to have had much ofan effect on the Medicare spendingexamined in this paper.) When we dividedthe average payment per discharge by theaverage length of stay to estimate theaverage cost per day, we again found largevariation.

Part of the variation in both averagepayment per discharge and payment per

day is due to the many adjustments madeto the national Prospective Payment Sys-tem payment for each diagnosis-relatedgroup. In 1992 these adjustments includedmodifications for geographic region, localhospital wage rate, the Medicare Case-Mix Index, graduate medical education,patients with very long stays or especiallyexpensive hospitalizations, having a highproportion of poor patients, and being a

sole community hospital.4'

Factors Explaining Differencesin Expenditures

The large and small supply anddemand models are considerably differentin terms of independent variables, ex-

plained variance, and the variables thatare statistically significant. Table 3 presentsthese data. We believe that the discrepan-cies between the two models are due to

omitted variables, and that the largemodel is more likely to be accurate

because the signs of its significant vari-

ables match those of previous studies

much more closely than do those of the

small model. Thus, the subsequent Re-

sults and Discussion sections of this paperrefer to the large model.

Seven independent variables (per-centage of elderly aged 85 or older,percentage of elderly women, percentageof Hispanic elderly, self-rated health

status of the elderly, percentage of elderlywith medigap coverage, percentage of

elderly receiving Medicaid, and per capitaphysician supply) were not statisticallysignificant in the initial large model and

were omitted from subsequent models.

Table 3 shows the P values of the

weighted least squares regression analysisfor this initial large model as well as the

P values for three additional models: a

demand model, a supply model, and a

supply and demand model with interac-

tion terms.The supply and demand model with

interaction terms explains 85% of the

1614 American Journal of Public Health

TABLE 2-Variation in Medicare Expenditures, Utilization, and Unit Payment per Enrollee, by Service and State: 1992

Ratio of CoefficientLow (State) High (State) High to Low State of Variation

Average expenditures per enrollee, $Hospital inpatienta 1193 (Hawaii) 2450 (Maryland) 2.05:1 .160Hospital outpatient 187 (Nevada) 471 (Maryland) 2.52:1 .195Physicianb 510 (Minnesota) 1322 (Florida) 2.59:1 .205Home health agency (HHA) 52 (South Dakota) 595 (Tennessee) 11.44:1 .548Skilled nursing facility (SNF) 24 (Maine) 165 (California) 6.88:1 .397Combined HHA/SNFC 102 (Hawaii) 666 (Tennessee) 6.53:1 .400

Users/services per 1000 enrolleesHospital inpatient discharges 216 (Hawaii) 407 (Mississippi) 1.88:1 .148Hospital outpatient service users 282 (Hawaii) 538 (Idaho) 1.91:1 .132Physician service users 667 (Hawaii) 943 (Iowa) 1.41:1 .073Home health agency users 24 (Hawaii) 125 (Michigan) 5.21:1 .314Skilled nursing facility admissions 8 (Maine) 52 (Minnesota) 6.50:1 .381Combined HHA/SNF users/admissions 34 (Hawaii) 142 (Mississippi) 4.18:1 .229

Average service use per userHospital inpatient days per discharge 5.6 (Oregon) 12.4 (New York) 2.21:1 .169Physician services 22 (Vermont) 44 (Florida) 2.00:1 .154Home health agency visits 27 (South Dakota) 100 (Tennessee) 3.70:1 .366Skilled nursing facility covered days per admission 16 (Iowa) 42 (New York) 2.62:1 .216

Average Medicare payment per unit of service, $Hospital inpatient discharge 4726 (Mississippi) 8027 (Alaska) 1.70:1 .142Hospital inpatient day 569 (Maine) 1216 (Alaska) 2.14:1 .195Physician 28 (Colorado) 45 (Alaska) 1.61:1 .100Home health agency 32 (Vermont) 111 (Alaska) 3.47:1 .233Skilled nursing facility 63 (Michigan) 250 (Louisiana) 3.97:1 .342

Note. Data are presented for high and low states only; the District of Columbia was not included in the analysis.aHospital inpatient data are for short-stay hospitals only.blncludes supplier services.CHHA and SNF were combined since postacute hospital care consists of both of these services, and they can serve as substitutes for one another.One or the other of the two services may be emphasized in particular states.

October 1997, Vol. 87, No. 10

Medicare Expenditures

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Kane and Friedman

variance and includes 12 independentvariables: three demand variables (percent-age of population aged 65 or older whoare Black, percentage of population aged65 or older who live in urban areas, andmortality rate of population aged 65 orolder), five supply variables (hospital bedsper 100 000 total population, physiciansper 100 000 total population, percentageof physicians who are primary carepractitioners, percentage of populationaged 65 or older who are enrolled inMedicare HMOs, and Medicare physicianassignment rate), two interaction terms(percentage of Black elderly aged 65 orolder and nursing home beds per 1000persons aged 65 or older, and percentageof urban elderly aged 65 or older andpercentage of primary care physicians),and two control variables (the MedicareWage Index and population aged 65 orolder).

Four variables had significantly posi-tive coefficients, as we had hypothesized.Higher Medicare expenditures per en-rollee were associated with a largerproportion of the elderly living in urban(as opposed to rural) areas (P = .001), alarger per capita hospital bed supply (P =.005), a higher aged mortality rate (P =.012), and a larger Medicare physicianassignment rate (P = .026). These find-ings all confirm results reported in earlierstudies.9"10'2325

The most significant variable, theMedicare HMO penetration rate(P = .000), had a negative coefficient(i.e., higher HMO enrollment rates wereassociated with lower spending). Thisfinding is as hypothesized and confirmsWelch's recent report.26

The sign of one variable, the propor-tion of physicians who are primary carepractitioners, was opposite to the hypoth-esized direction (P = .042). Having ahigher percentage of primary care physi-cians related to higher expenditures iscontradictory to previous reports.9" 0'26However, the positive relationship be-tween primary care physician rate andexpenditures is due to the presence of theinteraction term between percentage ofurban elderly and percentage of physi-cians in primary care. The sign forpercentage of primary care physicians isnegative and strongly significant (P =

.010) when the interaction term is omittedfrom the regression.

Two interaction terms were signifi-cant. The interaction between percentageof Black elderly and nursing home supplyis positive (P = .012), that is, the combi-nation of a higher proportion of Black

elderly and a larger nursing home bedsupply results in more Medicare spend-ing. The presence of the interaction causesboth percentage of Black elderly andnursing home bed supply to be nonsignifi-cant. Both variables are significant in theabsence of the interaction (P = .003 andP = .075, respectively). The interactionbetween urban elderly and percentage ofprimary care physicians is negative(P = .005). A higher proportion of urbanelderly and a larger percentage of primarycare physicians combine to lower Medi-care spending.

Patterns ofExpendituresamong States

We next used the large supply anddemand model with interaction terms toadjust each state's average total Medicareexpenditures per enrollee and comparedthe adjusted level with the actual level for1992. Employing this regression modelcontrols for differences in both sociodemo-graphic and health care resource variablesacross the states. A large number of stateshave considerably higher or lower expen-ditures than our model predicts. The firstcolumn for each state in Figure 1 presentsthis information.

One can argue that Medicare spend-ing should not be based on health caresystem variables, such as number ofphysicians or hospital beds per capita. Thesecond column in Figure 1 presents thedifferences between actual and predictedMedicare expenditures per enrollee foreach state calculated with only sociodemo-graphic (demand) variables.

Applying this restriction has consid-erable impact. Not only do the rank orderand effect sizes change for most states, butonly half of the 10 highest- and lowest-spending states remain in the samecategory. Five of the 10 states that havethe highest expenditures when the largesupply and demand model with interac-tions is used to make the predictions areno longer high spenders when the largedemand model is used. Likewise, 5 of thelowest-spending states can no longer beclassified as low-expenditure states.

According to predictions based onsociodemographic (demand) variablesonly, five states (Montana, Idaho, Mary-land, Wyoming, and North Dakota) re-ceive more than $400 per enrollee peryear more than predicted, while fourstates (South Carolina, Oregon, Minne-sota, and Virginia) receive more than $400less than predicted.

DiscussionGreat variation occurs in Medicare

expenditures per enrollee across the states.In 1992 Medicare spent more than twiceas much per person in the state with thehighest expenditures per enrollee, Mary-land ($4430), than in that with the lowest,Hawaii ($2157). This variation is presentnot only for total expenditures but foreach of the major categories of Medicareservices. Variation is greater for postacutecare than for hospital and physicianservices for total expenditures, number ofusers per 1000 enrollees, service units peruser, and average payment per serviceunit.

The regression model we used maybe criticized on two counts. First, thenumber of independent variables (12) issomewhat large for the number ofobserva-tions (49). Second, the model includesvariables that some may argue are endog-enous or have a simultaneous relationshipwith the dependent variable (Medicareexpenditures per enrollee). The best argu-ments for simultaneity can be made forelderly mortality rate and Medicare HMOpenetration rate. Indeed, Hadley has re-ported that Medicare expenditures perenrollee affect the elderly mortality ratefor aggregates of counties.24'42 Further,Rossiter and Adamache used a two-equation simultaneous equation systemmodel in which Medicare expendituresper enrollee and Medicare HMO penetra-tion rate were the dependent (endog-enous) variables.'0 However, other thanthese studies, there is little evidence that asimultaneous relationship is the properone for these two variables.

To address these shortcomings, wedeveloped a model that omitted poten-tially endogenous variables, factors thatmight have a simultaneous relationshipwith the dependent variable, and variablescorrelated with other included variables atabout .60 or higher. The results from thisconservative model differ considerablyfrom the results reported here. In particu-lar, the adjusted R2 is only .59, comparedwith .85 for the reported model. Webelieve that the differences are due toomitted variables. Using the more conser-vative model required us to leave outvariables measuring the very old, urban/rural residence, mortality rate, healthstatus, medigap insurance, Medicaid,Medicare HMO penetration rate, andMedicare physician assignment rate. Allof these (or similar variables) have beenfound to be statistically significant in

1616 American Journal of Public Health October 1997, Vol. 87, No. 10

Medicare Expendiures

studies of regional variation in Medicarethat employed regions smaller than states.

There are legitimate reasons fordifferences in Medicare spending by state,including differences in sociodemo-graphic factors (such as mortality rate),but we found that substantial variationremains after these differences are con-trolled. Health system characteristics (forexample, hospital bed supply) account fordifferences in Medicare spending, but it isnot clear that Medicare should supportthese discrepancies.

There are probably several reasonsfor the discrepancies that remain afterdifferences in sociodemographic andhealth care system characteristics arecontrolled. First, our model is not perfect.There may be omitted variables, it ispossible that endogeneity bias exists, andno doubt measurement error is present.The model could also be improved by theavailability of better state-level healthstatus measures. Some of the variation isdue to randomness.43 Finally, some of thediscrepancies are probably associated withvariations in practice style.44

Medicare spending per enrollee bystate is driven to some degree by total percapita health care expenditures by state.We did not include a variable for this forthe simple reason that specific informa-tion was not available on per capitaexpenditures for the fee-for-service sectorby state for 1992. A related issue is costshifting. Some states may have higher orlower expenditures because of cost shift-ing from Medicare to other payers.Maryland is a very high-cost state bothbefore and after adjustments, partly be-cause Medicare pays 108% of Marylandhospital costs. This is the only state forwhich this overpayment occurs.41

The coefficients in our model mayalso be biased because of nonadjustmentfor most of the Medicare ProspectivePayment System hospital payment adjust-ers, for example, graduate medical educa-tion and being the sole communityhospital. These variables are not availableby state of residence of the Medicareenrollee. The extent of any bias isunknown. There may also be bias fromthe fact that the dependent variable wascalculated by HCFA, which used datafrom both disabled enrollees under age 65and elderly enrollees aged 65 and older,while most of our explanatory variablesdescribe the age 65 and older population.

Our study improves on the twoprevious studies of regional variation inMedicare expenditures in which the statewas the unit of observation23,25 by includ-

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Note. The dark bars indicate predicted expenditures calculated using the "large" supply anddemand model with interaction terms (see Table 3). The white bars indicate predictedexpenditures calculated using the "large" demand model (see Table 3).

FIGURE 1-Difference between actual and predicted annual Medicareexpenditures per enrollee by state, in dollars, 1992.

ing a number of important variablesabsent from these studies-specifically,measures of Hispanic elderly, mortality,health status, medigap insurance,Medicaid, and Medicare HMO marketpenetration.

Health status may be only weaklysignificant in our study because of themeasure we used, which was the propor-

tion of the elderly in a state who reportedtheir health as fair or poor. Althoughperceived health status has been shown tobe a good predictor of subsequent mortal-ity,45 it does not explain the variance inhealth expenditures as well as morecomprehensive measures of health statusdo.46 The self-perceived health statusvariable may also be overwhelmed by the

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Kane and Friedman

mortality rate variable. On the other hand,there may simply be no relationshipbetween morbidity and Medicare expendi-tures per enrollee, as Wennberg argues isthe case for population hospitalizationrates.47

The significant relationship we foundbetween elderly mortality rate and level ofMedicare spending confirms earlier find-ings by Rossiter and Adamache9"0 anddeserves more exploration. While Medi-care expenditures have not been linked tolongevity per se,48 there is a well-established pattern of high Medicarespending during the last year of life.49 Onthe other hand, to the extent that areciprocal relationship accounts for thestrong role of mortality in predictingexpenditures, one might argue for remov-ing this variable from the regressionanalysis.

An important result is the signifi-cance of the two interaction terms. Ahigher proportion of Black elderly and alarger nursing home bed supply appar-ently combine to raise Medicare spend-ing, while the combination of a higherproportion of urban elderly and a largerpercentage of primary care physiciansseems to result in lower expenditures. It isinteresting that the interactions that aresignificant are those between the demandand supply sides. Because no previousMedicare regional variation studies haveincluded interaction terms, the interactionfindings require confirmation.

Policy Implications

Because variation across states inMedicare expenditures per enrollee formost service categories (hospital inpa-tient, physician, etc.) is driven more byvolume of services per 1000 enrollees andnumber of service units per user than byaverage payment per unit of service,Medicare's Prospective Payment Systemfor hospital inpatient care and its Re-source Based Relative Value Scale forphysician service payment have limitedimpact on reducing variation. New strate-gies that focus on proportions of users andamount of service use per user arerequired.

Substantial variation in Medicarespending per enrollee across states is aproblem. Equity principles suggest thatafter differences in sociodemographic andother demand factors are controlled,spending per enrollee per state should besimilar. While the relationship betweenamount of Medicare spending and theoutcomes of Medicare enrollees is gener-

ally unclear, more Medicare spendingdoes not necessarily result in betteroutcomes.13 However, Hadley has re-ported that more Medicare spending isassociated with lower mortality.24'42

Reducing the variation in spendingacross geographic areas to the point wherethe highest-expenditure areas conformmore closely to the lowest-expenditureareas could help control Medicare costsmore fairly than across-the-board actions.The use of sociodemographic factors isdesirable because they are outside thehealth care system's control. The system'sdistortions in numbers of hospital beds percapita and the like should not be rewardedin terms of reimbursement levels. At leastone proposal has called for allocatingnational spending goals by state afteradjusting for factors related to need anddemand.50 We would go a step further byusing the estimated excess cost as thebasis for such allocation corrections.

Other changes in the Medicare pro-gram should be made first. Some are justcosmetic, but they have strong politicalimport. Two activities that have little to dowith Medicare per se but have used it as apayment vehicle should be separated fromMedicare to allow the public to appreciatethe real costs of the latter. These activi-ties-supporting graduate medical educa-tion and helping hospitals that servedisproportionate shares of poor people-are important, but support for theseprograms should not be confused withMedicare. Even worse, when these costsare included in the amount of Medicareexpenditures used to calculate rates formanaged care, the distortion is exacer-bated.

Many hopes for controlling Medi-care costs have been pinned on managedcare. What many fail to recognize is thatthe method for calculating Medicarepayments to managed care organizationsrelies on the pattem of fee-for-servicefunding and hence preserves geographicdisparities.

Before sweeping cuts are made inMedicare, more attention needs to befocused on the current disproportionatedistribution of expenditures across states.Across-the-board approaches are too blunt.Targeted efforts are preferable becausethey will ensure that those states that havelower Medicare spending (after adjust-ment for sociodemographic differences)will be treated less harshly than those thathave been more profligate. El

AcknowledgmentsThe authors thank Emma Frazier for analyzingdata from the CDC national survey on per-ceived health status specifically for the age 65and older group, and Charles Helbing forproviding information on HCFA data. Theresponsibility for the accuracy of the analysisrests with the authors.

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