Increasing Medicare Enrollment in HMOs: The Need for Capitation Rates Adjusted for Health Status

Excellus Health Plan, Inc.

Increasing Medicare Enrollment in HMOs: The Need for Capitation Rates Adjusted for HealthStatusAuthor(s): J. William Thomas, Richard Lichtenstein, Leon Wyszewianski and S. E. BerkiSource: Inquiry, Vol. 20, No. 3 (Fall 1983), pp. 227-239Published by: Excellus Health Plan, Inc.Stable URL: http://www.jstor.org/stable/29771573 .

Accessed: 28/06/2014 18:09

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected].

.

Excellus Health Plan, Inc. is collaborating with JSTOR to digitize, preserve and extend access to Inquiry.

http://www.jstor.org

This content downloaded from 46.243.173.196 on Sat, 28 Jun 2014 18:09:28 PMAll use subject to JSTOR Terms and Conditions

http://www.jstor.org/action/showPublisher?publisherCode=excellus

http://www.jstor.org/stable/29771573?origin=JSTOR-pdf

http://www.jstor.org/page/info/about/policies/terms.jsp


J. William Thomas Richard Lichtenstein Leon Wyszewianski S. E. Berki

Increasing Medicare Enrollment in HMOs: The Need for Capitation Rates Adjusted for Health Status

Although the federal government has sought to increase enrollment of Medicare beneficiaries in HMOs, at the end of 1981 less than 2% were HMO members. Of these, only two-tenths of 1% were enrolled under the type of risk-sharing contracts

characteristic of HMOs. HMOs might have greater incentives to market to

Medicare beneficiaries if a factor that adjusts for health status could be incorporated into the capitation formula. This paper develops such a factor using

measures based on prior-year utilization, perceived health status, and functional health status.

It is widely agreed that the cost of the Medicare

program could be substantially reduced, with? out reducing benefits, if beneficiary enrollment in health maintenance organizations (HMOs)

were increased. Yet fewer than 2% of Medicare beneficiaries now receive their services from

HMOs,1 which is less than half the proportion, 4.5%, for the general population.2 Medicare thus continues to be underrepresented among

HMO members despite decade-long legisla? tive and administrative efforts by the federal

government to take advantage of the cost-sav?

ing features of this form of health service de?

livery.

In the absence of active marketing efforts by HMOs, the elderly are not likely to leave the fee-for-service system in favor of a new and unfamiliar approach to receiving medical care. But current provisions of the Social Security Act provide no financial incentives to encour?

age HMOs to attempt to increase the number

of elderly members in their plans. HMOs tra?

ditionally enroll members on a group basis, thereby reducing marketing costs and ensuring that the high costs of caring for sicker enrollees are pooled with the low costs associated with healthier enrollees. Because HMOs would have to enroll most Medicare beneficiaries on an individual basis, the actuarial security that

group enrollment affords would be jeopard? ized.

Current reimbursement mechanisms give HMOs a choice between two undesirable pay? ment alternatives for Medicare enrollees. One is for the HMO to be paid on the basis of

retrospectively determined costs. This re?

quires the plans to keep individual patient ac? counts and to generate bills for services pro? vided, thus forcing HMOs, which are distinctive because they operate on the basis of prepayment and capitation, to behave as if

they were fee-for-service providers.

/. William Thomas, Ph.D., Richard Lichtenstein, Ph.D., and Leon Wyszewianski, Ph.D., are Assistant

Professors; S. E. Berki, M.A., is Professor. All are in the Department of Medical Care Organization, School

of Public Health, University of Michigan, Ann Arbor, MI 48109. This paper was prepared with support from the Health Care Financing Administration under Grant 18-P-98179/5-01. The opinions expressed are those of the authors and should not be construed as representing the opinions of any U.S. government agency.

Inquiry 20: 227-239 (Fall 1983). ? 1983 Blue Cross and Blue Shield Association. 227

0046-9580/83/2003-0227$ 1.25



Inquiry/Volume XX, Fall 1983

The other payment alternative, by contrast, is consistent with the principles that govern usual HMO operations. It provides for capi? tation payments to be made on behalf of Medi? care enrollees, with payments determined on the basis of average health care costs for Medi? care beneficiaries in the fee-for-service sector in the HMO's service area, adjusted for age, sex, and other factors. However, computation of the capitation rate, known as the adjusted average per capita cost (AAPCC), has been faulted because it affords HMOs little protec? tion against adverse selection, that is, against the disproportionate enrollment of sick or high risk beneficiaries. With beneficiaries enrolling on an individual basis, adverse selection could cause HMOs to lose money on Medicare en?

rollees, since capitation revenues would be in?

adequate to cover their higher than average costs.3

Although HMO policies toward enrolling the

elderly are influenced by a number of complex issues,4 one dimension is clear: Appropriate financial incentives must be developed if HMOs are to be encouraged to participate more

extensively in serving Medicare beneficiaries on a risk-sharing basis. Given that most Medi? care beneficiaries must enroll individually, capitation rates should be designed to offer HMOs more protection against the financial

consequences of adverse selection. The factors

currently used in the calculation of the AAPCC do not provide that protection, and it is rec?

ognized that additional adjustments are re?

quired before capitation rates will reflect ac?

curately the quantities and costs of services that must be provided to enrolled benefici? aries. Adjusting the AAPCC for differences in

beneficiary health status is considered to rep? resent the most likely means of accomplishing this objective, but such an adjustment has not been made because no suitable health status measure has been identified.

In this paper we examine the feasibility of

incorporating a health status adjustment into calculation of the AAPCC. First we review the evolution of Medicare policies toward HMOs and describe Medicare's current capitation ar?

rangements for risk sharing with HMOs. Next, we discuss methodologies for assessing health status and identify three types of measures that

appear promising for adjusting the AAPCC. In

the final section we discuss the implications of Medicare devising a more effective policy for

paying risk-sharing HMOs.

Medicare Policy and Health Maintenance Organizations

The Social Security Act amendments that es? tablished Medicare and Medicaid (Titles XVIII and XIX of P.L. 89-79) became law in 1965.

Despite evidence then available that prepaid group practices (PGPs)?which later came to be known as health maintenance organiza? tions?might provide high-quality, compre? hensive health services at costs lower than achievable in the fee-for-service system,5 this

legislation made no provision for enrolling Medicare beneficiaries in prepaid plans.

The payment provisions for PGPs under Section 1833(a)(1) of the Social Security Act were based on principles developed for hos?

pitals and physicians and did not allow use of the prepayment approach of prospectively de?

termined, all-inclusive capitation rates. In?

stead, they required retroactive cost-based

reimbursement, limited to those services ac?

tually provided by the PGP itself.6 Medicare Part A payments for hospital costs were sep? arated from Part B payments and were made

directly to hospitals rather than to the PGP.

Thus, under these provisions PGPs, which achieved lower costs primarily through re? duced hospitalization rates, were denied the control over hospital expenditures they needed to realize savings. They were also required to

keep accounts and to present bills to patients as if they were operating on a fee-for-service basis, but most PGPs had neither the capa? bility nor the inclination to do so. The law therefore provided no incentives for PGPs to seek Medicare enrollees, and in 1970 only 34

plans participated in Medicare and only 282,000 beneficiaries (less than 1.4% of all

Medicare beneficiaries) were enrolled in such

plans.7

In 1973 Congress passed the Health Main? tenance Organization Act to encourage the cre? ation of more HMOs (which include PGPs as one important category). Among the chief rea? sons for passing the HMO Act was the poten? tial of HMOs for providing less costly yet com?

prehensive and accessible care. At about the same time that debate on the HMO Act was

228



concluding, Congress passed the 1972 amend? ments to the Social Security Act. Consistent with the intent of the HMO Act, these amend? ments included Section 1876, designed spe?

cifically to encourage HMOs to enroll Medi? care beneficiaries.

By 1972 the disincentives HMOs faced in

enrolling Medicare beneficiaries had been rec?

ognized, and Section 1876 allowed the large, well-established plans to accept capitation payments for Medicare enrollees on an at-risk

basis, paralleling their typical arrangements for non-Medicare enrollees. Final regulations gov? erning implementation of Section 1876, how?

ever, were promulgated only in 1977, and the rules and requirements included in those reg? ulations still posed obstacles to at-risk enroll? ment of Medicare beneficiaries. In particular two aspects of the new law made at-risk par? ticipation unlikely: the capitation formula and the requirement for open enrollment.

The capitation formula, described in the next

section, assumes that all beneficiaries in a giv? en community having the same institutional status and age, sex, and welfare characteristics

will require approximately the same level of health expenditures. Because these beneficiary characteristics do not adequately reflect dif? ferences in prospective utilization, however, the formula is unlikely to result in capitation payments that correspond closely to actual costs of care. In addition, all HMOs serving Medi? care beneficiaries were required to hold an an? nual open-enrollment period during which any Medicare beneficiary in the community could

join. The combination of the open-enrollment requirement and the capitation formula left HMOs participating on an at-risk basis par? ticularly vulnerable to adverse selection. At risk HMOs would have to enroll even the high? est-risk beneficiaries on an individual basis, yet would be reimbursed only at a level rep? resenting the average cost for the Medicare beneficiaries in their communities.

To date only one HMO, Group Health of

Puget Sound, has elected to participate on an at-risk basis.8 All other HMOs have chosen to serve Medicare beneficiaries on a fee-for-ser

vice basis, to enroll them under a cost contract alternative permitted under Section 1876, or not to enroll them at all. The Section 1876

option allows Medicare to reimburse HMOs

Increasing Medicare Enrollment in HMOs

based on a retroactive determination of the costs of Part A and Part B services actually utilized. Thus, of the 41 plans with Medicare members enrolled under Section 1876 con? tracts as of December 1981, 40 were serving them on a retrospective, cost-based reimburse?

ment basis rather than the prepayment prin? ciples characteristic of HMOs.9

By the end of 1981, of the 10.3 million HMO members in the United States, only 23,475, or

approximately two-tenths of 1%, were Medi? care beneficiaries enrolled under the at-risk

provisions of Section 1876.10 Although HMOs listed another 397,000 Medicare members, these beneficiaries were enrolled under cost reimbursement or temporary experimental al? ternatives.

The Adjusted Average Per Capita Cost

The AAPCC was intended to introduce an ele? ment of risk into the reimbursement of HMOs. It was designed to represent the amount, de? termined retrospectively, that Medicare would expect to pay for Medicare HMO enrollees had

they received their care in the fee-for-service

system. Under legislation in effect through September 1983, Medicare sets the capitation rate for HMO enrollees equal to the AAPCC. If the risk-sharing HMO is able to provide services to its enrolled Medicare beneficiaries at a cost lower than the AAPCC, it is permitted to keep 50% of the difference. But the maxi?

mum amount of these savings that the HMO can retain is limited to 10% of the AAPCC, even if the HMO's costs per beneficiary are

substantially lower than the area average. This limitation on savings, often criticized as a dis? incentive to expanded risk-sharing participa? tion by HMOs, will be modified effective Oc? tober 1, 1983, through provisions of the Tax

Equity and Fiscal Responsibility Act of 1982

(TEFRA). As prescribed by the new act, HMOs con?

tracting under the risk-reimbursement alter? native of Section 1876 will receive capitation payments equal to 95% of the AAPCC. If this is greater than the HMO's adjusted commu?

nity rate (ACR) ?the rate that the HMO, using its standard actuarial procedures, would have

charged the beneficiaries had they not been under Medicare?the HMO is required to "re? turn" the overpayment to enrolled benefici

229




aries in the form of lower supplementary pre? miums or expanded benefit coverage. But to the extent that HMO costs are less than the

ACR, because of efficient operation or for any other reason, the HMO is free to retain the entire amount of such savings. In the Medicare reimbursement demonstration projects in which this approach has been tested, the HMOs' ACR has tended to match the level

represented by 95% of the AAPCC,11 so that in effect the previous limitation on retention of savings no longer applies.

Although TEFRA is thus likely to lessen or remove one important disincentive, an even

greater deterrent to at-risk enrollment of Medi? care beneficiaries in HMOs remains: the cal? culation of the AAPCC itself. The AAPCC was created because of concerns by Congress that HMOs might reap excessive profits by "skim?

ming," that is, enrolling the more healthy ben? eficiaries while charging the government the costs associated with the sicker ones. It was believed that if costs incurred by the HMO Medicare beneficiaries could be linked to costs incurred by non-HMO beneficiaries and if sav?

ings accruing to the HMO could be limited, windfall profits would be avoided.

To calculate the AAPCC for an HMO, the Office of the Actuary of the Health Care Fi?

nancing Administration (HCFA) first deter? mines the U.S. per capita cost for all Medicare beneficiaries. This cost is multiplied by the age, sex, and geographic cost indexes that corre?

spond to the counties in which an HMO op? erates. The resulting figure represents Medi? care's expected per capita cost for beneficiaries in the HMO's service area. In the final step, the service area per capita cost is adjusted to reflect the age, sex, welfare, and institutional status of beneficiary members of the HMO.12

Data used by HCFA to make these adjust? ments have been criticized as being outdated and inaccurate.13 More important, however, it

is generally agreed that the set of adjusting variables currently used to calculate the AAPCC is inadequate and that additional variables must be included if the adjusted rate is to approximate with acceptable precision the

expected cost of serving defined groups of ben? eficiaries.14 The single best adjusting factor for the AAPCC would likely be a measure of ben?

eficiary health status.15 Such an adjustment

would yield more accurate estimates of costs and would thereby, it is believed, alleviate HMO concerns about the consequences of ad? verse selection as well as governmental con? cerns about skimming.

Although the 1982 amendments to the So? cial Security Act specifically provide for inclu? sion of additional, actuarially sound factors into the AAPCC, the likelihood that Medicare

will incorporate a health status factor, at least in the near future, has been considered small, since no generally acceptable method exists for

directly measuring health status.16 As a con?

sequence, recent AAPCC research has focused on approaches for determining health status

indirectly, through beneficiaries' utilization of services in previous years.17 As described in the next section, however, even though no cur?

rently known direct health status measure would be satisfactory in all situations, mea? sures do exist that might prove satisfactory in terms of the more narrow requirements of the

AAPCC.

Health Status Measures

For 15 years, epidemiological and health ser? vices researchers have worked toward devel?

opment of a measure of individual health sta? tus. If a general measure of health status were

available, it could be used to determine the nature and degree of change in an individual's health state over time and to compare relative health among individuals and among popu? lations. A measure able to support reliably these two activities could then be used for any of the functions that Ware et al. identify as mo? tivations for constructing health status mea? sures:18

? assessing the efficiency or effectiveness of

specific medical interventions ? assessing the quality of care ? understanding the causes and consequences

of differences in health ? estimating the health needs of populations ? improving clinical decisions

A variety of health status measures have been

proposed by researchers. Although several have been tested and shown to be suitable in well defined situations, no single measure has prov? en sufficiently robust to serve all or even most of the functions listed above. The relevant

230



characteristics of a measure depend on the pur? poses for which it is to be employed, and no

single measure possesses characteristics that are desirable in all situations. Measures suit? able for assessing the effectiveness of medical

interventions, for instance, must be sensitive

enough to detect small changes in patient health

state, whereas the cost of acquiring data to estimate the health care needs of populations

may be a more important criterion than sen?

sitivity. In the context of adjusting the AAPCC, the

purpose of a health status measure is not to assess the impact of health services on health or to provide a fine-grained characterization of the health of the Medicare population. Rather, the objective is to predict the cost of services required by Medicare beneficiaries. The principal criterion for a health status fac? tor in the AAPCC is thus its validity as a pre? dictor of health services utilization. For the measure to be operationally useful, it also must

possess these additional characteristics:

? It must be reliable. ? It must not be subject to manipulation by

providers or others who might benefit from

higher capitation rates. ? Data required to determine the health sta?

tus of individual beneficiaries must be ob? tainable without undue expenditure of time or money by HCFA or by beneficiaries.

None of the currently available methods for

assessing health status was developed for the

purpose of predicting utilization. As a conse? quence, few health status measures have been tested in those terms and little information on their effectiveness as predictors exists. Fur?

thermore, many of the available measures re?

quire clinicians or trained interviewers to col? lect patient data, an approach that is unlikely to be feasible in wide-scale application for

Medicare.

Although two categories of measures, per? ceived health status and functional health sta? tus, do appear to be generally satisfactory in terms of the criteria defined above, recent ef? forts at improving the AAPCC have focused less on direct measures of health status and

more on surrogate measures derived from ben? eficiaries' use and costs in previous years. These efforts are reviewed next.


Prior Utilization and Costs

Arguments for incorporating a health status

adjustment in the AAPCC are based on the

assumption that Medicare beneficiaries who suffer poorer than average health will use more

medical services and incur proportionally more

expenses than other beneficiaries. If this as?

sumption is correct?and, as discussed below, there is considerable evidence that it is?and if relative health status remains reasonably sta? ble over short periods of time for the Medicare

population, then the quantity of services used

by a beneficiary in one year should be predic? tive of the quantity used the following year. Thus, prior-year utilization or costs could be

used, instead of a direct measure of beneficiary health status, to adjust the AAPCC, in a fash? ion similar to the experience rating method of

setting premiums widely employed by insur? ance companies.

Several recent research efforts provide sup? port for this approach. In the prospective Man? itoba Longitudinal Study on Aging, Roos and

Shapiro19 observed consistent first- and sec?

ond-year utilization patterns in a sample of 4,500 randomly selected elderly persons. With number of annual physician office visits ag? gregated into five categories (0, 1-2, 3-6, 7

13, and 14+ visits), 85% of the elderly re? mained in the same category for both years or moved to an adjacent category in the second year. Number of visits was correlated at r =

.64 between the two years, and a similar al?

though weaker pattern was observed for in

patient utilization. Anderson and Knickman20 used Medicare

claims data for a sample of 200,000 beneficia? ries to investigate temporal patterns of utili? zation and reimbursable cost. They found that beneficiaries hospitalized in 1974 were twice as likely as others in the sample to be hospi? talized in 1975. Heavy utilizers in 1974, those

with more than $ 10,000 in reimbursable costs, were 20 times more likely than others in the

sample to incur costs over $10,000 in 1975.

Eggers,21 also using Medicare claims data, ana?

lyzed relationships between 1978 and 1979 medical care costs for a sample of 13,000 bene? ficiaries and found that 45% used no reim? bursable services in 1978 and two-thirds used no services in 1979. When beneficiaries' reim

231




bursement cost for 1979 was regressed on age, sex, and 1978 reimbursement level, the stan? dardized beta coefficient for the 1978 reim? bursement variable was five times as great as that of either age or sex (R2 for the equation

was .05). Similar year-to-year patterns of uti? lization for Medicare beneficiaries have also been reported by McCall and Wai.22

To study the feasibility of incorporating prior-utilization information in the AAPCC, Anderson et al.23 analyzed 1974-1977 claims data for a random sample of beneficiaries from Los Angeles County. They tested five alter? native regression models, with the least de? tailed of these corresponding to the current AAPCC adjusting factors (age, sex, and welfare

status). The most detailed model included, in addition to these demographic variables, mea? sures for categorizing beneficiaries' amount and

type of use in the prior year. For this model, Anderson et al. developed a list of hospital discharge diagnoses that were associated with

high costs in subsequent years, for example, cancers, heart conditions, and certain other chronic diseases. Each beneficiary's prior-year (1974) utilization was then coded to indicate the following: whether any utilization oc?

curred; if so, whether any hospitalization oc?

curred; and if so, whether a secondary diag? nosis was present, whether the primary diagnosis was on the high-cost list, and wheth? er total hospital days exceeded 20. When Anderson et al. tested each of the

models in an analysis of reimbursement im?

plications of adverse selection, they observed that the least detailed model, corresponding to current AAPCC adjusting factors, could result in overpayments to the HMO of as much as 84% or underpayments of as much as 68%, depending on the mix of beneficiaries choosing to enroll. By contrast, with their most detailed

model, the ratio of Medicare payments to HMO costs was stable under all patient mix scenarios

investigated. Thus, in terms of the criteria defined above,

prior-year utilization holds promise as an ad?

justing factor for the AAPCC. The measure's

validity as a predictor of future utilization has been demonstrated in several studies. Because data required for adjusting the AAPCC would be derived from Medicare claims records, the

reliability of the data should not be a problem.

Extra efforts to obtain data would in general be unnecessary, and fraudulent manipulation of the data would be unlikely.

Although difficulties in implementing prior year utilization as an AAPCC adjustment are not great, some potential problems do exist. One is that prior-year claims information would not be available for beneficiaries who have just turned 65 and thus are newly eligible for the program. An alternative measure, per? haps based on one of the methods described below for assessing health status directly, would be necessary for new beneficiaries. Also, be? cause of delays in filing and processing Medi? care claims, complete data typically are not available from Medicare intermediaries for 90 to 180 days after services have been provided.

As a result, AAPCC adjustments might have to be based on utilization data that are two

years old instead of data from the previous year. Anderson and Knickmairs study,24 how? ever, suggests that the utilization level in one

year is still a good predictor of future utiliza?

tion, even as far ahead as three years. An additional problem is that beneficiary

utilization, or reported utilization, is poten? tially manipulatable by HMO personnel. Be? cause a beneficiary's current utilization rate would determine his or her capitation level for the subsequent year, the HMO would have less incentive to discourage unnecessary utilization and conceivably could attempt to promote ex? tra services.25 Furthermore, although it is un?

likely that fraudulent reporting of beneficiary utilization would be increased by incorporat? ing prior-year utilization in the AAPCC, some

reporting bias might occur similar to the "DRG

creep" phenomenon observed with case mix

adjusted hospital reimbursement.26

Perceived Health Status

A second alternative for incorporating health status into the AAPCC is a factor based on beneficiaries' subjective perceptions of their health. Perceived health status, sometimes termed "self-rated health" or "self-reported health," is a measure typically based on an individual's response to a question such as

"Compared to other persons your age, would you say your health is excellent, good, fair, or

poor?" Although perceived health status has been widely used and studied in epidemiolog

232



ical, gerontological, and health services re?

search, it often has been treated as "a conve?

nient but sometimes questionable substitute for objective health status or an indicator of

general well-being."27 This view has recently begun to change, however, since results from an increasing number of studies suggest that

perceived health status is one of the best, and in some cases the single best, predictor of sev?

eral important aspects of patient care-seeking behavior.28

The validity of perceived health status as a

predictor of health services utilization has been well established in the literature. In several

studies, patient ratings of perceived health sta?

tus have been shown to be more strongly re?

lated to reported number of physician visits

than physicians' objective ratings of the pa? tients' health.29 Using a dichotomized measure

of perceived health status, Linn and Linn30

found that elderly subjects (age 65-74 years) who reported poor health averaged signifi?

cantly more physician visits in the prior six

months than those reporting good health (7.02 visits versus 3.00). The same pattern held for

hospital days: 5.00 days for subjects who re?

ported their health status as poor versus 2.37

days for those reporting their health status as

good. Similar results were obtained for very

elderly subjects (age 75 and older) for both

physician visits and hospital days. Perhaps the most convincing evidence of the

relationship between perceived health status

and utilization comes from the Manitoba Lon?

gitudinal Study on Aging. Using attitudinal data

obtained from interviews with the 4,500 el?

derly persons in their sample and data from

the Canadian National Health Service describ?

ing subjects' utilization during the 12-month

period subsequent to the interviews, Roos and

Shapiro31 observed significant differences in

average number of physician visits (4.4, 5.0,

6.5, and 7.5 visits per year for those reporting

excellent, good, fair, and poor health, respec?

tively) and significant differences in average number of hospital days (2.3,3.4,6.2, and 10.5

days for subjects reporting excellent, good, fair, and poor health, respectively). Furthermore,

age and sex adjustment did not change any of

the utilization figures by more than .8 days or

visits.

In these studies, responses to a single ques


Figure 1. Ambulatory use versus perceived health status, all respondents (/V= 418)

tion formed the basis for determining a per? son's perceived health status. Yet, as a rule, attitudinal measures consisting of summated scores constructed from responses to multiple

questions generally provide greater score vari?

ability and greater reliability and validity than

single-item measures.32 Thus a multiitem

measure of perceived health status might prove to be an even better predictor of health services

use than the single-question measure em?

ployed in these studies. Such a multiitem measure was constructed

using interview data collected by Berki et al.33

to investigate factors influencing respondents' choice of health plan. Responses to the follow?

ing nine Likert-scaled questions were used to

construct a summated measure of perceived health status:

1 Compared to other persons your age, how

is your health? Would you say your health

is excellent, good, fair, or poor? 2 How satisfied are you with the health that

runs in your family? 3 How satisfied are you with the way you usu?

ally feel physically? 4 How satisfied are you with your resistance

to illness? 5 How satisfied are you with your physical

ability to do things you need to do?

6 How satisfied are you with your physical fitness?

7 How satisfied are you with your ability to

get around outdoors?

233




8 How satisfied are you with your physical ability to do things you want to do?

9 How satisfied are you with your ability to be as active as others your age?

The response to questions 2-9 were "ex?

tremely satisfied," "somewhat satisfied," "somewhat dissatisfied," or "extremely dis? satisfied."

The curve shown in Figure 1 was obtained

by calculating the average number of ambu?

latory visits for persons at each point on the

perceived health status scale (standardized to a range of 0-10) and plotting the averages against the perceived health status score.

Several reasons have been suggested to ex?

plain the relationship between perceived health status and utilization. One possibility is that perceived health status may reflect "a prescient understanding of subtle biological and phys? iological changes that lead one to perceive one's own health . . . more correctly than objectively assessed health status."34 In that view the pa? tient has access to a greater variety of data relevant to determining health status than does a physician or other second party.35 It is also

likely that self-rated health captures many of the subjective estimates related to illness and the motivations that, in the Health Belief

Model of Becker et al.,36 are seen as central to the explanation and prediction of individual health- and illness-related behaviors. Thus, the patient who thinks of himself or herself as less healthy might use more health services pre? cisely because of this self-perception. Also, the association between perceived health status and utilization of health services may arise from the influence of one or more unmeasured, con?

founding variables. For example, Mossey and Shapiro37 suggest that maintenance of positive health habits such as getting adequate exercise and not smoking might lead to more positive self-ratings of health and also to less morbid? ity.

Currently available research results are in? sufficient to determine which of these expla? nations, if any, is correct. The reason for the relationship, however, is less important than the conclusion that the measure is a valid pre? dictor of utilization and thus satisfies one of the criteria for an AAPCC adjusting factor. Perceived health status also appears satisfac

tory with respect to reliability and ease of administration. Interrater reliability is not an

issue, since only the beneficiary's own percep? tions are being measured. For intrarater reli?

ability, Ware et al.38 concluded after reviewing 39 studies of general health perceptions that

self-ratings are both reliable and reproducible. Unlike data on prior utilization, however, data for measuring perceived health status are not available as a by-product of claims processing. These data, however, can be obtained cheaply and easily from individuals through responses to simple attitudinal questions.

As discussed earlier, any measure included in the AAPCC should not be subject to ma?

nipulation by providers. Yet perceived health status is based on individuals' self-percep? tions, and these perceptions could be influ? enced by subtle or even direct suggestions from

physicians or other medical professionals. Fur?

thermore, monitoring for such manipulation would be difficult. Perceived health status is an attitudinal measure, and there is no simple method for determining whether beneficiaries

actually view their health differently from how

they report it. Thus, although satisfactory with respect to the three criteria listed earlier, per? ceived health status cannot be considered en?

tirely satisfactory for inclusion in the AAPCC until procedures can be devised to prevent the

possibility of biased reporting by beneficiaries.

Functional Health Status Measures

Functional health status may represent a suit? able alternative to perceived health status and prior utilization for adjusting the AAPCC. The interest in measuring the functional health sta? tus of a population is not so much in "the literal details of people's medical conditions ... as in the behavioral consequences" of such conditions and in people's "physical capacity for role fulfillment and social participation."39

Measures designed specifically to assess the functional health of the elderly generally fall into two groups: those that evaluate the indi? vidual's ability to perform activities of daily living (ADL) and those that assess the capacity to perform the "instrumental activities of daily living" (IADL). Many of the measures of the functional

health status of the elderly focus on the ability to perform personal care activities, or activi

234



ties of daily living, which include such tasks as personal feeding, dressing, and bathing. Such measures are designed to allow clinicians to evaluate a patient's need for home care, nurs?

ing home care, or other long-term care ser? vices. In addition, these measures have been used to evaluate patients' progress in treat?

ment, the effectiveness of care given in a va?

riety of settings by a range of providers,40 and, in one instance, to predict health services uti? lization by the elderly.41 Among measures of this type are Katz's Index of Activities of Daily Living,42 Linn's Rapid Disability Rating Scale,43 and Duke University's Multidimen? sional Functional Assessment, known as the OARS Methodology-Physical ADL.44

IADL measures focus on activities that are more complex than the personal care activities included in ADL scales and encompass such tasks as cooking, cleaning house, and walking up stairs. Principal examples of IADL mea? sures include the IADL portions of the PACE II scale, the OARS Methodology, the Phila?

delphia Geriatric Center Instrumental Role Maintenance Scale,45 and the Rosow-Breslau Functional Health Status Test,46 which is based on the following questions:

1 Is there any physical condition, illness, or health problem that bothers you now? A. No B. Yes

2 Which of these things are you still healthy enough to do without help? A. Heavy work around the house, like

shoveling snow or washing walls. B. [Men] Work at a full-time job. C. Walk half a mile (about eight ordinary

blocks). D. Go out to a movie, to church or a meet?

ing, or to visit friends. E. Walk up and down stairs to the second

floor.

3 Which of these statements fits you best? A. I cannot work (keep house) at all now

because of my health. B. I have to limit some of the work or other

things that I do. C. I am not limited in any of my activities.

Although both ADL and IADL measures have been used widely in studies of the elderly, little information has been provided on the


validity of these measures as predictors of uti? lization. Results that are available, however, suggest that both are related to patients' use of services. In one study involving noninstitu tionalized elderly subjects, Branch et al.47 found an adaptation of Katz's Index of Activities of

Daily Living to be a significant predictor of

hospital days, home care services, outpatient speech therapy, rehabilitation therapy, and

counseling services, but not of physician visits.

They also developed an IADL measure con?

sisting of four items from the Rosow-Breslau Functional Health Status Test. This measure

proved to be a significant predictor of hospital days and physician visits, but not of other out?

patient services and home care services.

Reliability, one of the criteria for AAPCC

adjusting factors, has been extensively tested and well established for both ADL and IADL

types of measures.48 In terms of ease of data

collection, most IADL scales are satisfactory, whereas most ADL scales are not. Typically, IADL scales have been developed for use in

population surveys, and many such scales are in a format designed for self-administration.

Most ADL scales, on the other hand, are used in clinical settings and must be administered

by clinicians or trained interviewers.49 ADL measures based on respondent self-reports, however, have been used recently in popula? tion surveys as well.50

Although health status data obtained through patient self-reports involve lower costs and less data collection time than comparable data from other sources, such information is subject to bias or manipulation by providers of care. As with perceived health status, assessments of functional capabilities reflect a patient's atti?

tudes, and these assessments can be influ?

enced, perhaps intentionally, by physicians and others. Unlike perceived health status, how?

ever, functional scales are not completely sub?

jective. Patient responses to most questions concerning functional ability can be verified, and it may be possible to detect patterns of biased or false reporting and thus reduce or control manipulation.

Health Status Measures: Summary

No available method for measuring health sta? tus is completely satisfactory in terms of all of the criteria we defined for factors used in ad

235




justing the AAPCC. Nevertheless, each of the three approaches described in this section pos? sesses most of the required characteristics. All of the measures are satisfactory in terms of

reliability and, with some exceptions, in terms of ease of data acquisition. Although the pre? dictive validity of functional health status measures is not as yet conclusive, measures

based on both prior-year utilization and per? ceived health status have been shown to be

good predictors of future utilization.

Special data collection procedures must be devised to include either perceived or func? tional health status in the AAPCC. Extra data

must be obtained directly from beneficiaries, and therefore additional costs will be incurred to collect and process the information. Data for a prior-utilization-based adjustment are

available from existing Medicare claims rec?

ords, but because prior-year utilization data do not exist for newly eligible beneficiaries, alternative arrangements or adjustments would be necessary for this group.

One of the principal operational issues as?

sociated with adjusting the AAPCC, regardless of the measure chosen, is the means by which

provider bias or manipulation of measurement data can be prevented or controlled. For per? ceived health status, this represents a partic? ularly difficult problem. Patients' perceptions, actual or reported, could be subject to influ? ence by comments or suggestions from pro? viders, and it is unlikely that such manipula? tion could be detected easily. Although providers might also be able to influence pa? tient self-reports for functional health status, both ADL and IADL data can be verified, and thus manipulation can be controlled. For mea? sures based on prior-utilization data, the bias issue has a different dimension. Here the con? cern is less with providers manipulating pa? tients into misrepresenting attitudes or func? tional abilities than it is with providers changing their own behavior to increase future

capitation payments. To move patients into more lucrative utilization categories, some de?

gree of unnecessary utilization might be pro? moted, or providers might alter the manner in which diagnostic and utilization data are re?

ported.

Although predictive validity has been well established for both prior utilization and per

ceived health status and has been suggested for functional health status, no comparisons of the three approaches have as yet been performed.

No study has examined all of these methods as predictors of utilization for the same pop? ulation group, and therefore it is not known at this time which of the three would be most effective for inclusion in the AAPCC. If such a study were to show functional or perceived health status measures to be superior to prior utilization as predictors, the significance of the

improvement, both to HMOs and to Medi?

care, would have to be weighed against the additional costs of obtaining the required health status data. Further, for each of the measures,

procedures will have to be devised to monitor and control unnecessary utilization and to minimize manipulation of data. Although it is

likely that adequate procedures could be de?

veloped, their relative cost, complexity, and effectiveness would represent important con? siderations in choosing among the measures. Because of such trade-offs, a combination of two or more of the health status approaches

might be necessary to obtain more discrimi?

nating predictions of utilization while ensuring adequate control of bias.

Discussion and Conclusion

The incentives embedded in payment policies have pervasive effects. It is provider behavior, which to a significant extent is elicited by ex?

pected financial rewards and penalties, that de? termines the degree to which equity in con? sumer access exists. In systems where providers and patients themselves do not establish prices, but rather third parties, it is particularly im?

portant that payment policies reflect desired social goals. Agreement on the definition of

appropriate or desirable social goals is clearly difficult and continuously changing. Neverthe? less there is little disagreement that it is desir? able both to reduce costs without sacrificing quality and to improve access without increas?

ing costs.

From this perspective, current Medicare

policies toward HMOs must be judged eco?

nomically inefficient in that they discourage utilization of a less expensive mode of health care delivery, and inequitable in that benefi? ciaries are unable to join organizations that

236



might offer improved access to care. This in?

equity is particularly ironic because the orig? inal Title XVIII legislation was intended to

promote access for Medicare beneficiaries to the health care system.

Although no consensus exists on the exact factors that induce individuals to join HMOs, the research literature51 and the increases in

market share of HMOs52 both indicate that substantial numbers of people do enroll when

given the opportunity. The principal enroll? ment mechanism for HMOs is the dual- or

multiple-choice option presented to employed groups, a mechanism that almost by definition excludes Medicare beneficiaries. Furthermore, it is unlikely that in the foreseeable future these beneficiaries will be able to join HMOs on a

group basis. Although senior citizen clubs and associations of retired persons are increasing in popularity, the great majority of the elderly do not belong to these or other types of formal

organizations. Thus, for most Medicare bene?

ficiaries, access to HMO membership is and will continue to be limited to individual en? rollment.

If Medicare beneficiaries are to have access to HMOs, Medicare payment provisions must include incentives to encourage HMOs to mar? ket specifically to this population. But, as de? scribed earlier, Section 1876 of the Social Se?

curity Act still provides no such incentives. HMOs that might otherwise be willing to ex?

pand their Medicare enrollment must accept retrospective cost reimbursement, which in? volves excessive record keeping and reporting inconsistent with HMOs' traditional operating practices, or they must accept a capitation ar?

rangement that exposes them to risks of sig? nificant financial losses.

In the absence of a capitation mechanism that adequately compensates for differences in individuals' expected utilization, HMOs will continue to avoid enrolling beneficiaries on an individual basis.53 In effect, they will avoid

enrolling Medicare beneficiaries at all, and they will continue to reject Medicare's risk-sharing option. Considering the financial incentives

deriving from Section 1876 and associated reg? ulations, this represents rational economic be? havior by HMOs.

These incentives would be significantly al


tered, however, if a health status adjustment were to be incorporated into the AAPCC. The

adjusting factor would reduce HMO concerns about the consequences of adverse selection. Because Medicare capitation would then ap? proximate an experience-rated formula, a par?

ticipating HMO "might even find high-risk people especially desirable because of the larg? er potential savings it could offer relative to the conventional system."54

The scope of legislative initiatives over the

past decade and the number of reimbursement

experiments currently under way are evidence of the strong interest of Congress and HCFA in increasing Medicare enrollment in HMOs.55

Indeed, through the TEFRA legislation, Con?

gress has attempted to further that objective by again amending Social Security Act pro? visions that pertain to risk-sharing HMOs. Given HMO concerns about the financial con?

sequences of adverse selection, it is unlikely that these changes will by themselves have

much effect. If, however, a health status ad?

justment were to be incorporated into the

AAPCC, such concerns should be reduced and

many HMOs would then have adequate in? centive both to participate in Medicare on an at-risk basis and to expand their Medicare en? rollment.

Promoting expansion of beneficiary enroll? ment in HMOs represents a potentially effec? tive and politically appealing option for con?

trolling Medicare program costs. It is

particularly attractive compared with other al?

ternatives, such as further cuts in program ben? efits or radical reformulations like the voucher

systems that have been proposed. But it de?

pends on implementation of a pricing mech? anism that 1) provides sufficient financial in? centives to encourage entry by HMOs into the

Medicare market, 2) enables HMOs to control

high risks resulting from adverse self-selection, 3) limits government program cost ceilings to some defined percentile of fee-for-service costs for comparable populations, and 4) encourages

Medicare beneficiaries, possibly by reducing out-of-pocket costs or increasing benefits or

both, to choose the HMO option. A reformu? lation of the AAPCC to adjust for health status can be the foundation of such an incentive

pricing scheme.

237




Notes and References

1 J. Hetherington, Medicare Enrollment in HMOs, 1981, HMO Industry Report Series, vol. 4 (Excelsior, MN:

InterStudy, 1982). 2 InterStudy, National HMO Census, June 30, 1981

(Excelsior, MN: InterStudy, 1981). 3 The financial effects of adverse selection are illustrated

in J. J. Anderson, A. L. Resnick, and P. M. Gertman, Prediction of Subsequent Year Reimbursement Using the Medicare History File (Boston: Boston University

Medical Center, University Health Policy Consor?

tium, January 1982). 4 S. Trieger, T. W. Galblum, and G. Riley, "HMOs:

Issues and Alternatives for Medicare and Medicaid," Health Care Financing Issues, HCFA Publication 03107 (Washington, DC: HCFA, 1982).

5 A. Donabedian, A Review of Some Experiences With

Prepaid Group Practice (Ann Arbor: University of

Michigan, Bureau of Public Health Economics, 1965). 6 J. M. Feder, Medicare: The Politics of Federal Health

Insurance (Lexington, MA: Lexington Books, 1977), p. 84.

7 M. Corbin and A. Krute, "Some Aspects of Medicare

Experience With Group-Practice Prepayment Plans," Social Security Bulletin 38 (1975): 3-11.

8 Hetherington (note 1). 9 This excludes the 16 so-called group practice prepay?

ment plans that enroll Medicare beneficiaries on a cost

basis, which, under Section 1833(a)(1) of the Social

Security Act, are not counted as HMOs. In addition, HCFA is currently sponsoring several prospective cap? itation demonstration projects in HMOs to determine the effectiveness of alternative risk-sharing arrange?

ments under Medicare. For initial observations from these demonstrations, see T. W. Galblum and S. Trie?

ger, "Demonstrations of Alternate Delivery Systems Under Medicare and Medicaid," Health Care Fi?

nancing Review 3 (March 1982): 1-12. 10 Hetherington (note 1). 11 Galblum and Trieger (note 9). 12 P. Eggers and R. Prihoda, "Pre-Enrollment Reim?

bursement Patterns of Medicare Beneficiaries Enrolled in 'At Risk' HMOs," Health Care Financing Review 4 (September 1982): 55-74.

13 Trieger et al. (note 4). 14 See, e.g., H. S. Luft, J. Feder, J. Holahan, and K. D.

Lennox, "Health Maintenance Organizations," in Na? tional Health Insurance: Conflicting Goals and Policy Choices, ed. J. Feder, J. Holahan, and T. Marmor

(Washington, DC: Urban Institute, 1980); Eggers and Prihoda (note 12); and Trieger et al. (note 4).

15 G. Anderson and J. R. Knickman, "Patterns of Ex?

penditures Among High Utilizers of Medical Care Ser? vices: The Experience of Medicare Beneficiaries from 1974 to 1977" (paper presented at the annual meeting

of the American Public Health Association, Montreal, November 1982). See also Galblum and Trieger (note 9).

16 Trieger et al. (note 4). 17 P. Eggers, "Analysis of the Relationship Between

Reimbursements in One Year and Reimbursements in a Subsequent Year," Working Paper OR-33 (Wash? ington, DC: Health Care Financing Administration,

Office of Research, 1981); Anderson and Knickman

(note 15); Anderson et al. (note 3). 18 J. E. Ware, R. H. Brook, A. R. Davies, and K. N. Lohr,

"Choosing Measures of Health Status for Individuals

in General Populations," American Journal of Public Health 71 (1981): 620-625.

19 N. P. Roos and E. Shapiro, "The Manitoba Longitu? dinal Study on Aging: Preliminary Findings on Health Care Utilization by the Elderly," Medical Care 19

(1981): 644-657. 20 Anderson and Knickman (note 15). 21 Eggers (note 17). 22 N. McCall and H. S. Wai, "An Analysis of the Use of

Medicare Services by the Continuously Enrolled Aged" (Stanford, CA: Stanford Research Institute, 1981).

23 Anderson et al. (note 3). 24 Anderson and Knickman (note 15). 25 Provider behavior in this instance would be analogous

to that of hospitals under total budget prospective reimbursement. See K. G. Bauer, "Hospital Rate Set?

ting?This Way to Salvation?" Milbank Memorial Fund Quarterly 55 (1977): 117-158.

26 D. W. Simborg, "DRG Creep: A New Hospital-Ac? quired Disease," New England Journal of Medicine 304 (June 25, 1981): 1602-1604.

27 J. M. Mossey and E. Shapiro, "Self-Rated Health: A Predictor of Mortality Among the Elderly," American Journal of Public Health 12 (1982): 800-808.

28 Ibid.; Roos and Shapiro (note 19); B. S. Linn and M. W. Linn, "Objective and Self-Assessed Health in the Old and Very Old," Social Science and Medicine 14A

(1980): 311-315; T. F. Garrity, "Vocational Adjust? ment After First Myocardial Infarction: Comparative Assessment of Several Variables Suggested in the Lit?

erature," Social Science Medicine 7 (1973): 705-717; G. L. Maddox and E. B. Douglas, "Self Assessment of Health: A Longitudinal Study of Elderly Subjects," Journal of Health and Social Behavior 14 (1973): 87 93.

29 Maddox and Douglas (note 28); H. J. Friedsam and H. W. Martin, "A Comparison of Self and Physicians' Health Ratings in an Older Population," Journal of Health and Human Behavior 4 (1963): 179-183; G. F. Strieb, E. A. Suchman, and B. S. Phillips, "An Anal?

ysis of the Validity of Health Questionnaires," Social Forces 36 (1958): 223-232.

30 Linn and Linn (note 28). 31 Roos and Shapiro (note 19). 32 J. E. Ware, A. Davies-Avery, and A. L. Stewart, "The

Measurement and Meaning of Patient Satisfaction," Health and Medical Care Services Review 1 (1978): 1

15.

33 S. E. Berki, M. Ashcraft, R. Penchansky, and R. S.

Fortus, "Enrollment Choice in a Multi-HMO Setting: The Roles of Health Risk, Financial Vulnerability, and Access to Care," Medical Care 15 (1977): 95-114.

34 Mossey and Shapiro (note 27). 35 Friedsam and Martin (note 29). 36 M. Becker et al., "Selected Psychosocial Models and

Correlates of Individual Health Related Behaviors," Medical Care 15, suppl. (1977): 27-46.

37 Mossey and Shapiro (note 27). 38 J. E. Ware, A. Davies-Avery, and C. A. Donald, Con?

ceptualization and Measurement of Health for Adults in the Health Insurance Study, vol. 5, General Health

Perceptions (Santa Monica, CA: Rand Corp., 1978). 39 I. Rosow and N. Breslau, "A Guttman Health Scale

for the Aged," Journal of Gerontology 21 (1966): 556 559.

238




40 R. A. Kane and R. L. Kane, Assessing the Elderly (Lexington, MA: Lexington Books, 1981).

41 L. Branch, A. Jette, C. Evashwich, M. Poalansky, G.

Rowe, and P. Diehr, "Toward Understanding Elders' Health Service Utilization," Journal of Community Health 1 (1981): 80-92.

42 S. Katz, A. B. Ford, R. W. Moskowitz, B. A. Jackson, and M. W. Jaffe, "Studies of Illness in the Aged," Journal of the American Medical Association 185

(1963): 914-919. 43 M. W. Linn, "A Rapid Disability Rating Scale," Jour?

nal of the American Geriatric Society 15 (1967): 211 214.

44 Kane and Kane (note 40). 45 Ibid. 46 Rosow and Breslau (note 39). 47 Branch et al. (note 41). 48 Linn and Linn (note 28). 49 Kane and Kane (note 40).

50 A. M. Jette and L. G. Branch, "The Framingham Dis?

ability Study, II: Physical Disability Among the Aging," American Journal of Public Health 1\ (1981): 1211

1216; Branch et al. (note 41). 51 S. E. Berki and M. L. F. Ashcraft, "HMO Enrollment:

Who Joins What and Why: A Review of the Litera?

ture," Milbank Memorial Fund Quarterly 58 (1980): 588-632.

52 J. K. Iglehart, "The Future of HMOs," New England Journal of Medicine 307 (1982): 451-456.

53 Galblum and Trieger (note 9). 54 H. S. Luft, "Health Maintenance Organizations and

the Rationing of Medical Care," Milbank Memorial Fund Quarterly 60 (1982): 268-306.

5 5 Health Care Financing Administration, "Research and Demonstrations in Health Care Financing, 1980

1981," Health Care Financing Issues, HCFA Publi? cation 03144 (Washington, DC: HCFA, 1982); Trieger et al. (note 4).

239



Documents

Increasing Medicare Enrollment in HMOs: The Need for Capitation Rates Adjusted for Health Status