N O V E M B E R 2 0 0 0

R E P O R T T O T H E C O N G R E S S

Improving Risk Adjustment

in Medicare
N O V E M B E R 2 0 0 0

R E P O R T T O T H E C O N G R E S S

Improving Risk Adjustment

in Medicare

1730 K Street, NW • Suite 800 • Washington, DC 20006

(202) 653-7220 • Fax: (202) 653-7238 • www.medpac.gov
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Table of contents

Executive summary ......................................................................................... xi

Chapters

1 Overview of risk adjustment in Medicare+Choice ..................................... 1

Why risk adjust? ................................................................................................................................. 3

Risk adjustment framework ...............................................................................................................8

Cost considerations for risk adjustment .............................................................................................10

2 Risk adjustment in Medicare+Choice ......................................................... 13

How well do risk adjustment models work? ...................................................................................... 15

Move to multiple-site risk adjustment ................................................................................................17

Using multiple years of diagnoses ...................................................................................................... 19

Proper treatment of data lags ............................................................................................................20

Evaluate the use of partial capitation ................................................................................................. 21

Five questions related to PIP-DCG model ........................................................................................22

3 Risk adjustment for special populations .................................................... 25

Frail elderly beneficiaries.................................................................................................................... 27

Payments for beneficiaries with end-stage renal disease .................................................................. 33

Appendix: Analysis of questions related to risk adjustment using

the principal inpatient diagnostic cost group model ........................................ 41

Commission members .....................................................................................63

Commission staff ............................................................................................. 65

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Tables

2 Risk adjustment in Medicare+Choice

2-1 Predictive ratios for demographic, principal inpatient diagnostic cost group, and

 diagnostic cost group hierarchical condition category risk adjustment models ......................... 16

3 Risk adjustment for special populations

3-1 Predictive ratios for alternative risk adjustment models, by functional status subgroup ........... 28

3-2 Managed care programs for frail elderly ..................................................................................31

3-3 Demographic information on Medicare programs for the frail elderly, 1999............................. 32

Appendix

A-1 Relation between per capita costs and per capita payments for Medicare+Choice

 plans, 1999 ................................................................................................................................. 46

A-2 Stability of Medicare+Choice plans’ per capita risk scores under the

 principal inpatient diagnostic cost group risk adjustment model, 1998–1999 ............................48

A-3 Stability of Medicare+Choice plans’ per capita risk scores under the

 principal inpatient diagnostic cost group risk adjustment model,

 excluding new enrollees and disenrollees, 1998–1999 ..............................................................49

A-4 Examining Medicare+Choice enrollees’ costs in the year prior to enrollment, 1997 ................ 52

A-5 Mortality rate of Medicare+Choice enrollees relative to traditional Medicare

 beneficiaries, 1998 .....................................................................................................................55

A-6 Average length of Medicare+Choice (risk plan) enrollment,

 February 1996–February 2000..................................................................................................56

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Figures

2 Risk adjustment in Medicare+Choice

2-1 Lag between year of diagnoses and predicted costs ................................................................. 20

Appendix

A-1 Impact of excluding discretionary hospitalizations from the principal inpatient

 diagnostic cost group model ....................................................................................................... 58

A-2 Impact of excluding one-day hospitalizations from the principal inpatient

 diagnostic cost group model ......................................................................................................58

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Executive summary

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Executive summary

xi

Risk adjustment of payments to Medicare+Choice organizations is needed to pay them fairly for the

people they enroll. Paying fairly means adjusting the payments plans receive to take into account the

predictable variability of health care costs among enrollees. In general, risk adjustment is intended to put

plans on an equal footing so they can compete on the basis of the benefits and services they offer and

not on whether they attract high- or low-cost enrollees.

The Congress asked MedPAC for recommendations to improve risk adjustment, for information on a

specific risk adjustment model, and for recommendations on what to do about payments for specialized

plans for the frail elderly. This report gives our response to each of these issues and discusses two

additional issues: risk adjustment in Medicare+Choice for the frail elderly and for beneficiaries with endstage renal disease.

Risk adjustment in Medicare+Choice

The Balanced Budget Act of 1997 directed the Health Care Financing Administration (HCFA) to

replace the existing system of risk adjustment—which relied solely on demographic factors—with one

that took enrollees’ health status into account. HCFA began phasing in payments based on the new

model, which measures enrollees’ health status using diagnoses from inpatient hospitalizations, in 2000.

Although the new model improves on the demographic system in terms of predicting the costliness of

plans’ enrollees, it could be improved. Accordingly, we make several recommendations in this report to

do so. We also address several questions posed to MedPAC by the Congress regarding the new

system.

One improvement to the system that HCFA has proposed is to move to a multiple-site model capable

of using diagnoses from inpatient, outpatient, and physician encounters. Implementing this model will not

be without cost to HCFA and to plans, but collecting data from multiple sites would allow Medicare to

take into account costly diagnoses that do not result in a hospitalization.

Recommendation 2A: MedPAC supports HCFA’s intention to implement a multiple-site

risk adjustment system in 2004 because diagnoses from multiple sites of care yield better

measures of resource needs among beneficiaries. The system should be phased in to

allow plans time to adjust to the effect on payments and to allow HCFA time to adjust for

changes in providers’ coding behavior.

As HCFA implements the proposed multiple-site risk adjustment system, it should consider three

additional steps. Two are primarily technical; one looks at payments to Medicare+Choice plans more

broadly.

Recommendation 2B: HCFA should develop a multiple-site risk adjustment model

capable of using more than one year of diagnosis data.
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E xe c u t i ve s u m m a r y

Recommendation 2C: HCFA should evaluate modeling risk adjustment so that the time

lag between the diagnosis data and the cost data the agency uses to estimate payment

weights matches the lag between the diagnosis data and the payment.

Using two or more years of data would provide a more complete picture of beneficiaries’ health status

and thus allow more accurate payment for chronic conditions. Analysis of the additional data would

entail some costs on the part of HCFA, but no new data would be required of plans. Conforming the

lags between the risk adjustment model and payments could also increase the accuracy of payments.

The third step for HCFA to consider involves supplementing capitation payments with provisions to

increase or decrease payments based on enrollees’ actual use of services. Such a system would be

analogous to what is now done in some of Medicare’s prospective payment systems.

Recommendation 2D: HCFA should evaluate using partial capitation as the basis for a

payment system for Medicare+Choice plans.

Partial capitation would reduce plans’ risks of large losses, as well as their chance to earn large profits.

It would also lower the risk of beneficiaries getting too little care under a capitated system.

Risk adjustment for special populations

The Congress asked MedPAC to look at payments for beneficiaries in specialized plans for the frail

elderly. We also looked at the frail elderly population in Medicare+Choice more generally and at

another special population, beneficiaries with end-stage renal disease (ESRD). Each of these situations

presents unique problems for risk adjustment systems and the goal of paying plans fairly. In regard to

frail elderly populations we recommend the following:

Recommendation 3A: As it evaluates multiple-site risk adjustment models, HCFA also

should evaluate and report to the Congress on alternatives—including hybrid risk

adjustment systems and partial capitation—to increase payment accuracy for frail elderly

beneficiaries.

Current and proposed encounter-based risk adjustment systems are known to underpredict costs for

frail elderly beneficiaries. Other methods that bring in data on functional status or service use might help

improve payment accuracy. In addition, because specialized plans for frail beneficiaries also differ in the

benefits they offer, we recommend continuing current payment methods for these plans until methods

that account for their benefits and population characteristics are developed.

Recommendation 3B: The current method of payment for specialized plans should be

used until a cost-effective and accurate alternative is developed that accounts for both

the frailty of people enrolled in and differences in the benefits offered by those plans.

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Finally, beneficiaries with ESRD have much higher expected costs than the overall Medicare population.

In addition, the factors that affect their costs are known and differ from factors affecting costs in the

overall population.

Recommendation 3C: The Congress should instruct the Secretary to implement a riskadjusted method to pay for ESRD beneficiaries in Medicare+Choice using factors known

to affect their costs.

In summary, HCFA has proceeded fairly far down the road to adjusting payments to plans based on

encounter data from multiple sites of care. We endorse that general direction with certain refinements to

improve payment accuracy. At the same time, the agency should continue to look at alternatives.

Moreover, until a model that predicts well for all populations is developed, the current model should not

be imposed on specialized plans for the frail elderly where harm might be done by an inappropriate

payment method or on the ESRD population where a better alternative might exist. ■

xiii
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Overview of risk adjustment

in Medicare+Choice

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1C H A P T E R
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The Balanced Budget Act of 1997 (BBA) directed the Secretary of Health and Human Services to

begin making payments to Medicare+Choice (M+C) plans on January 1, 2000 using a system that

accounts for differences in health status among enrollees. This requirement was motivated by concerns

about the limitations of the prior payment system, with its so-called demographic model, which adjusts

capitation payments for enrollees’ demographic characteristics including age, sex, basis for entitlement

(aged or disabled), and whether or not they work, are institutionalized, or are eligible for Medicaid

benefits.

As a first step in meeting the BBA requirement, the Health Care Financing Administration (HCFA)

began phasing in a new risk adjustment model on January 1, 2000. This principal inpatient diagnostic

cost group (PIP-DCG) model measures enrollees’ health status using their demographic characteristics

and principal diagnoses from hospital inpatient stays (if any) during the previous year. HCFA plans to

use the PIP-DCG model only temporarily, intending to replace it with a multiple-site model in 2004 that

takes into account diagnoses from physician and hospital outpatient visits as well as hospital inpatient

visits.

For 2000 and 2001, the Balanced Budget Refinement Act of 1999 (BBRA) mandated that the new risk

adjustment model apply to 10 percent of the payment for M+C plans, and that the remaining 90 percent

be based on the demographic model. For 2002, the portion of the payment adjusted using diagnostic

data rises to 20 percent. Under HCFA’s phase in schedule, by 2003 the risk adjusted portion would

increase to 80 percent and 2004 would see full implementation of the multiple-site model. However,

proposed but not enacted legislation might change that schedule. How risk adjusted payments are

phased in will have significant impact on payment to plans and on the benefits expected from risk

adjustment.

The BBRA also mandated that MedPAC recommend improvements to risk adjustment in

Medicare+Choice in general, report on risk adjustment and payment for the frail elderly in specialized

plans, and answer some specific questions about the PIP-DCG risk adjustment model. This report is

our response to that mandate. The remainder of this chapter provides an overview of risk adjustment,

Chapter 2 explains our recommendations for improving risk adjustment in Medicare+Choice, and

Chapter 3 discusses special populations in Medicare+Choice and the challenge of risk adjustment and

payment for the frail elderly and beneficiaries with end-stage renal disease. An appendix provides our

detailed analysis of the specific questions on the PIP-DCG model.

Why risk adjust?

Risk adjustment systems attempt to pay health plans fairly for the members they enroll. In the Medicare

context, payments to plans that participate in Medicare+Choice are risk adjusted so that the plans are

paid fairly for the particular population of Medicare beneficiaries that they enroll. Before discussing risk

adjustment systems in Medicare and how well they work, we first review what they are and how they

are intended to work.
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4 O v e r v i e w o f ri s k ad j u s t m e n t i n M e d i c a r e + C h o i c e

What is a risk adjustment system?

In the M+C program, monthly payments are made to plans that contract to provide health care for

Medicare beneficiaries. In the contracts, the plans stipulate the benefits they will provide to enrollees for

the payments specified in HCFAs’s payment schedule, which specifies a base payment for Medicare

beneficiaries by county of residence. Historically, the base payment was 95 percent of the cost of an

average beneficiary in traditional fee-for-service Medicare in a county.1 Although plans could be paid

this average amount for each enrollee, they are not because (1) yearly costs for beneficiaries vary

widely, and (2) the variation in costs per beneficiary are to some extent predictable. Payments to plans

have always been adjusted to try to account for the predictable variability of costs. A risk adjustment

system takes the predictable variability of costs into account by calculating how much payments should

depart from the base rate for the set of beneficiaries enrolled in a plan.

How much do costs vary? In 1997, the average cost to Medicare of a beneficiary in the traditional feefor-service program was about $5,300, but many beneficiaries cost the program very little or nothing at

all, and 6 percent of beneficiaries served cost the program more than $25,000 each (HCFA 1999).

If a health care plan enrolled beneficiaries who mirrored the overall beneficiary population exactly, and

the plan were paid $5,300 for each beneficiary enrolled, the plan would be paid fairly overall even

though costs for individual beneficiaries varied from the average. If instead the plan had enrolled only

beneficiaries known to have little or no costs, the plan would be overpaid if it were paid $5,300 per

enrollee. Conversely, if the plan had enrolled only beneficiaries known to have high costs, it would be

underpaid if it were paid $5,300 per enrollee.

Risk adjustment attempts to pay plans more fairly by adjusting the payments the plan receives to match

the expected costliness of the actual mix of beneficiaries enrolled. For example, if we expect a

beneficiary will have very low health care expenses, we might adjust the average payment of $5,300

down to $2,000. If we expect the beneficiary to have very high expenses, we might adjust the average

payment upward to $20,000. The difficulty is in determining at the beginning of a year which

beneficiaries will have low expenses and which will have high expenses during that year. There is no way

of precisely predicting costs for a particular beneficiary in a particular year; however, the premise of risk

adjustment is that for a large group of beneficiaries with particular characteristics, a reasonable estimate

can be made of what that group’s costs will be relative to the average of all beneficiaries.2

1 The Congress in the BBA, recognizing that the system for determining base payment rates resulted in widely varying rates among counties, modified

the system to decrease differences and guarantee a minimum or floor rate. Therefore, current base rates may differ significantly from 95 percent of

the average cost for beneficiaries in traditional Medicare in a county.

2 For a particular beneficiary in a particular year there is no way to predict health care costs exactly because of the random nature of high-cost events.

For example, an apparently healthy person might have a heart attack in any given year that could not have been predicted. These random events give

rise to unpredictable variations in individuals’ heath care costs. In large populations, however, the overall prevalence of random occurrences can be

predicted and are referred to as the insurance risk for the population.
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Different approaches for grouping beneficiaries and predicting those groups’ health care expenses give

rise to different risk adjustment systems. For example, one might observe that as people get older, their

health care costs increase. A simple risk adjustment system would group beneficiaries by age, and the

effect of age on health care costs could then be estimated using data for particular age cohorts. In fact,

a system much like that (with the addition of several other demographic variables) was used by HCFA

to adjust payments to the Medicare risk health maintenance organizations (HMOs) that preceded the

M+C program and is still being used in combination with other systems as they are being phased in. This

demographic system does not do a very good job of adjusting group payments relative to the average,

however, because it explains only about 1 percent of the variation observed in health care costs. Other

factors, such as how sick people are at the beginning of the year, have a greater effect on health care

costs during the year than the factors in the demographic system.

A system that takes into account more of the factors that affect future health care costs can predict

those costs better and would be a better way of adjusting payments. Factors used in a risk adjustment

system must be observable, such as age or hospitalizations. Conversely, some hidden genetic defect,

although it might have a tremendous affect on future costs, is not observable and cannot be used in a

risk adjustment system to place beneficiaries in groups. In addition to being observable in principle, the

factor also must be captured in the data available for running the risk adjustment system. Age has been

routinely available in administrative data systems, but hospitalizations for plan enrollees have not.

The observability of the factors is both essential for running a risk adjustment system and why risk

adjustment is needed at all. If the population mix joining M+C plans exactly mirrored the overall

population mix in Medicare, then the costs of the population in the plans would vary just as costs vary in

the overall population, and no risk adjustment would be needed. Simply paying the average would be

fair. But if the population mix is not the same as the average, and the factors necessitating risk

adjustment are observable either to the plans or to beneficiaries, then risk adjustment is needed.

If plans can observe that certain groups of beneficiaries will have predictably lower costs, they have an

incentive to attract those beneficiaries and reduce their costs below average. Similarly, plans could try to

avoid groups of beneficiaries they expect to have higher costs. For example, if payment did not vary by

age, plans might try to attract younger enrollees and avoid older ones.

If factors are observable to beneficiaries they might self select; for example, people without serious

medical problems might not be as concerned about having broader choice of physicians as people with

serious medical conditions and, hence, might be more likely to join managed care plans. If factors are

observable to either plans or beneficiaries, the result might be a different population mix in plans than in

traditional Medicare. Whether this selection is favorable or unfavorable, risk adjustment is necessary to

pay plans fairly.
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6 O v e r v i e w o f ri s k ad j u s t m e n t i n M e d i c a r e + C h o i c e

Problems with the demographic model

The demographic system, which still determines 90 percent of the monthly capitation payments to M+C

plans, pays flat rates for all enrollees within a demographic category, reflecting average expected service

use for that group. The demographic system, however, accounts for only a small portion of the

predictable variation in costliness among enrollees. Hence, Medicare systematically underpays for

enrollees who have serious illnesses and overpays for healthy enrollees. These systematic payment

errors create several problems. First, paying plans too little for sick beneficiaries creates incentives to

avoid groups of beneficiaries who have or are likely to develop serious illnesses and conversely, to

attract healthy enrollees. Further, payment errors may discourage plans from developing effective care

management programs for serious acute and chronic conditions because doing so might attract more

high-cost beneficiaries. Finally, systematic payment errors may cause Medicare to overpay or underpay

in the aggregate if the health status of beneficiaries who enroll in M+C plans differs from the average.

Evidence shows that overpayment due to systematic payment errors has occurred. The General

Accounting Office (GAO 2000), Riley et al. (1996), and Hill et al. (1992) all find Medicare paid more

under the demographic system than the program would have paid if managed care enrollees were in

traditional Medicare. This overpayment reflects a combination of favorable selection (enrollment of

relatively low-cost beneficiaries) in Medicare managed care and the failure of the demographic system

to adjust payments downward adequately for low-cost beneficiaries.

Whether the favorable selection into plans reflects intentional efforts to attract low-cost beneficiaries or

simply that those beneficiaries find managed care more attractive than do higher-cost beneficiaries is not

an issue; favorable selection in Medicare+Choice does occur (PPRC 1996, MedPAC 1998, and

MedPAC’s latest results, which are presented in the appendix) and the demographic system does

overpay for healthy beneficiaries.

What risk adjustment is designed to do

Risk adjustment is designed to reduce systematic payment errors by making payments reflect

beneficiaries’ expected costs more accurately, and in so doing reduce or eliminate the problems related

to these errors. Effective risk adjustment allows plans to compete on the basis of benefits and services

they offer rather than benefit from attracting favorable risks. Also, under effective risk adjustment, plans

might be more likely to develop and market care management programs for beneficiaries with serious

conditions. Finally, effective risk adjustment would reduce overpayments to plans in the aggregate.
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Risk adjustment will not eliminate overpayments and underpayments to plans that result from base rates

being too high or too low. The base rate—called the M+C county payment rate—was originally set at

95 percent of the expected cost of a beneficiary of average demographic status in traditional Medicare

in that county. However, in many counties the base rate is now a legislatively determined “floor” rate. In

other counties, it is the1997 rate increased by 2 percent a year, while in a third group of counties the

base rate is a blend of updated local rates from 1997 and an average national rate. If these base rates

are higher than average fee-for-service costs, which is true by definition in the floor counties, then

payments will be too high; that is program costs will be higher than they would have been if the

beneficiaries stayed in traditional Medicare. If base rates are lower than expected costs, for example if

fee-for-service payments have been rising at more than 2 percent a year in counties at the 2 percent

limit, payments may be lower than average fee-for-service payments. Risk adjustment will not correct

these problems with the base rate nor is it intended to.

Risk adjustment is also not intended to account for differences in benefit packages. Payments are

adjusted relative to the program cost of beneficiaries in traditional fee-for-service Medicare with its

specified benefits. If plans have different benefit packages that are more costly, the difference should not

be captured through a risk adjustment system, which is meant to predict the expected costs of people as

if they were in the traditional fee-for-service program.

If Medicare set base rates equal to average fee-for-service spending, a perfect risk adjustment system

would match payments to enrollees’ expected costs in the traditional program. The risk adjustment

system would account for all the factors associated with predictable variation in beneficiaries’ costs, but

would not adjust for random variation in costs. For example, because a person who has uncontrolled

high blood pressure is at a higher risk for a stroke than a person with low blood pressure, such a person

would be predicted to have higher costs. Payments under a perfect risk adjuster would reflect that. The

actual costs of the person with high blood pressure depend, in part, on whether the beneficiary has a

stroke that year, which depends in part on the luck of the draw. Payments under a perfect risk adjuster

for the particular beneficiary would not reflect such random, high-cost occurrences—which are part of

insurance risk—but total payments for the plan should.

How much of the total variation in costs is random is not known. One estimate is that at least 20-25

percent of the variation in costs is predictable (Newhouse et al. 1997). If that is correct, then the 1

percent of total variation explained by the demographic system leaves substantial room for

improvement. In comparison, the PIP-DCG system is estimated to account for about 5 to 6 percent of

the variation in total costs, making it a big improvement over the demographic system, even though it

only explains about one-quarter of the predictable variation.3

 The hierarchical coexisting conditions and

clinical risk group models HCFA is considering, which rely on data from multiple treatment sites, explain

about 11 percent, perhaps half the predictable variation.

Although these multiple-site models are an improvement over the demographic system and should be

pursued, the Commission also encourages HCFA to evaluate other risk adjusters because it is doubtful

the multiple-site models explain enough variation to reduce the incentive to risk select to negligible levels.

In addition, it appears these models will have large data collection costs, and it is not clear whether their

additional benefit will be worth the cost.

3 If 25% is predictable, then 6/25 equals approximately 1/4.
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8 O v e r v i e w o f ri s k ad j u s t m e n t i n M e d i c a r e + C h o i c e

Impact of risk adjustment on aggregate payments

HCFA estimates that full implementation of the PIP-DCG model would have reduced payments to

M+C plans in 2000 by 5.9 percent (about $2.2 billion). Plans and members of the Congress have

argued that the aggregate payment reduction from risk adjustment should not occur, claiming that risk

adjustment should be budget neutral. The purpose of risk adjustment, however, is to raise or lower

payments to account for the predictable differences in beneficiaries’ costs. If M+C enrollees are

healthier than average and the PIP-DCG system reflects beneficiaries’ expected resource use more

accurately than the demographic system, then implementing the PIP-DCG should reduce aggregate

payments (MedPAC 1999).

Concerns about aggregate payments should be addressed by considering whether M+C base payment

rates are adequate. As previously discussed, the Congress has legislated base payment rates. The

correct level for these rates in aggregate is a separate decision from the risk adjustment decision. For

any level of base payment rate, risk adjustment should make resulting payments relatively more accurate

in terms of the expected costs of beneficiaries. If the Congress wants to establish an aggregate payment

target for M+C plans, that decision should be made by changing the base rates. Making risk adjustment

budget neutral is essentially an argument for increasing base payment rates and legislating that a certain

amount of money should go to M+C plans. For example, if the Congress wished to make risk

adjustment using PIP-DCGs budget neutral, base rates could be increased 5.9 percent to offset the

expected decrease resulting from fully risk adjusted payments.

Risk adjustment framework

The specific approach taken to risk adjust payments to health plans depends on decisions made as to

the appropriate unit of adjustment, what health status factors should be taken into account, and how to

collect the needed data.

Unit of adjustment

Two general approaches could be followed for risk-adjusting payments to M+C plans. The approach

currently used adjusts payment to plans based on the health status of the individual beneficiaries they

enroll. The other would adjust payments based on plan-level measures of health status.

Under the first approach, payments are made on behalf of each individual beneficiary enrolled in the

plan, based on the individual’s health status in a base period. An advantage of this approach is that

payments can follow individual beneficiaries as they move between the traditional Medicare program

and the M+C program, or between M+C plans. Another advantage is that if payments are accurate,

then plans will not have incentives to avoid any particular groups of beneficiaries. Also, HCFA is further

along in modeling individual-based measures than in modeling plan-based measures.
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Under the second approach, the sum of the payments for all of the beneficiaries enrolled in a plan could

be adjusted based on data collected at the plan level during a base period. Using plan-level data could

impose a smaller data collection burden on plans than collecting individual encounters if sample survey

data were deemed sufficient. (Some would say survey data are not sufficient because of doubts

concerning the validity and reliability of survey data as well as their ability to explain variation in

costliness.) Conversely, the data collection burden on HCFA might increase if HCFA also had to collect

survey data on the fee-for-service population in each county to use a survey-based approach.

Additionally, changes in the risk status of the plan population resulting from enrollment of higher or lower

risk individuals during the year would not be reflected in payments. However, plan-level measures could

influence plan-level behavior.

Health status factors

Health status can be measured in several ways. Demographic factors—such as age and sex—have been

used in the M+C program as proxies for health status. Demographic factors are also used in the

commercial health insurance sector to set premiums along with underwriting and experience data when

those data are available and their use is allowed. While there is dissatisfaction with relying on

demographic factors alone in the M+C program, demographic characteristics remain an important

component in current risk-adjustment modeling.

Another potential source for health status measures is surveys of beneficiaries regarding their health

status. Researchers have found that measures of self-reported health status are useful in predicting future

health care spending, particularly in certain populations such as the frail elderly (see Chapter 3 for more

information). Models based on survey data would require sampling populations in both M+C and the

traditional program to set comparison measures. Surveys are not currently used for payment purposes in

part because policymakers fear that these measures could be manipulated by those receiving payments.

One strong predictor of future healthcare spending is past healthcare spending on behalf of an individual.

While measuring spending for beneficiaries in traditional Medicare is straightforward, doing the same for

beneficiaries enrolled in M+C plans is more challenging. For example, should spending for services not

covered in the traditional Medicare benefit count if it substitutes for care that is in the Medicare

package? What prices should be used for services delivered by M+C plans? (The model that HCFA

uses to estimate incremental costs is based on the fee schedules and cost limits used by traditional

Medicare. Plans may negotiate different fees.) In addition to measurement challenges, some

policymakers are concerned that using past spending can lead to incentives similar to those under a

cost-based payment system where higher spending is rewarded with higher payments.

The current risk-adjustment model used to pay M+C plans measures health status partially by using

diagnoses recorded during Medicare-covered inpatient hospital stays. HCFA is now moving to include

diagnoses recorded during outpatient and physician encounters as well. While the possibility of data

manipulation to increase payments exists, many analysts believe that diagnoses data are less susceptible

to manipulation than plan-reported cost or self-reported health status measures.
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O v e r v i e w o f ri s k ad j u s t m e n t i n M e d i c a r e + C h o i c e

Data collection tools

Several different tools could be used to gather data on beneficiaries’ health status. Some data are

already collected by Medicare and therefore would involve no additional burden or costs. For example,

HCFA routinely collects demographic data on all beneficiaries to determine Medicare eligibility. The

agency also collects encounter data, including diagnoses, to pay claims for beneficiaries enrolled in

traditional Medicare. Other data collection efforts have been initiated for the express purpose of moving

to a risk-adjustment system based on the diagnoses presented on encounter data. For example, HCFA

has been collecting data from all inpatient hospital encounters and will shortly begin collecting data from

hospital outpatient and physician encounters for M+C enrollees. These new sources of data entail new

costs.

In addition to administrative, claims, and encounter data, HCFA has access to survey data that could be

used in risk-adjustment systems. Every year, the Medicare Current Beneficiary Survey is administered

to 12,000 Medicare beneficiaries. The survey asks questions about respondents’ health status, use of

services, and healthcare spending. Also, HCFA administers the Health Outcomes Survey to 1,000

enrollees of each M+C plan. This survey includes health status questions. These surveys provide useful

information, but it is information that is not routinely collected by health plans.

Cost considerations for risk adjustment

As with any other change to the Medicare program, developing and operating a new risk adjustment

system is costly. Those costs have to be weighed against the potential benefits of the new system to

reach a reasoned judgement about the advisability of making the change.

The benefits of a new risk adjustment system—better access for some higher-cost beneficiaries, fairer

payment for M+C plans, and reducing overpayments by the program—have been discussed above. In

the following section we consider development costs and operating costs for a risk adjustment system

based on encounter data. (Other risk adjustment systems, such as those based on survey data, would

have costs in similar categories but a different set of detailed cost elements.) We do not quantify costs,

but discuss the array of possible costs that need to be considered.

Development costs

Development costs—principally the one-time costs of developing the system to be put in place—are

incurred by both HCFA and the plans. HCFA’s development costs include those to develop the risk

adjustment method itself, to communicate the new system requirements to the plans, and to develop

systems to receive and process the data supplied by the plans. These costs have, for the most part,

already been incurred by HCFA for the PIP-DCG model and have been incurred to a lesser extent for

the multi-site models.

10
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 11

Development costs for private entities in the M+C program include those to develop systems to collect

and process data, transmit them to HCFA, and retransmit in response to HCFA requests for

clarification. Collection costs include the costs of renegotiating provider contracts to include encounter

data submission, if necessary; communicating data standards to providers; and developing the internal

systems to collect, edit and process the data from providers. These costs may vary widely depending on

the entities’ contractual relationship with the providers. For example, if plans contract on a fee-forservice basis, much of the data and means to collect and process it could already be available. If instead

providers operate under capitation, there may be no pre-existing means to collect the data.4 Reporting

encounters with those physicians may require developing new systems and procedures and renegotiating

contracts. Many of these costs may already have been incurred at this point for entities already in the

program. If providers have not submitted encounter or claims data they too may have to develop new

systems.

Operating costs

HCFA, plans, and providers face recurring costs to maintain automated systems, as well as for data

origination, collection, and transmittal including rectifying encounter data problems such as duplicate

encounters. HCFA also faces the recurring costs to calculate risk scores for both fee-for-service and

M+C beneficiaries, calculate payments, and update and maintain the risk adjustment system itself, as

well as costs for physician and M+C plan education and training efforts.

Cost impact

The impact on private sector entities of development and operating costs will vary. For example, the

fixed cost of developing a data system may be high, which would represent more of a burden for a plan

with few beneficiaries than for a large plan. Where costs are related to the number of beneficiaries, such

as data transmittal costs, the burden is more even across large and small plans. Burden would also be

less related to plan size if costs were shifted to providers. High fixed costs may be more of a burden for

local plans than for those plans affiliated with large, national M+C organizations; that burden might

discourage new entrants and encourage exit of small, unaffiliated plans. Understanding how costs are

distributed is crucial to understanding the burden associated with a new risk adjustment system and the

incentives and changes in behavior it might create.

A comprehensive assessment of the costs and benefits of risk adjustment would require estimating the

overall costs of the program, delineating the burdens on the different entities, and examining alternative

methods for achieving the same benefit. For example, if risk adjusted payments were limited to 10

percent of the total payment and never fully phased in, the benefit would be very limited and the cost

may not seem commensurate. In that case, other approaches to more accurate payment such as

reassessing the base payments could be more cost effective. ■

4 Recent research shows that three-fourths of Medicare HMOs use capitation to pay primary care physicians (MedPAC 2000).
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O v e r v i e w o f ri s k ad j u s t m e n t i n M e d i c a r e + C h o i c e

References

12

General Accounting Office. Medicare+Choice: payments exceed cost of fee-for-service benefits,

adding billions to spending, No. HEHS-00-161. Washington (DC), GAO. August 2000.

Health Care Financing Administration, Department of Health and Human Services. Medicare and

Medicaid statistical supplement, 1999, Health Care Financing Review, Pub. No. 03417. Washington

(DC), HCFA. November 1999.

Hill J, Brown R, Chu D, Bergeron J. The impact of the Medicare risk program on the use of services

and costs to Medicare, No. 7786-5111. Princeton (NJ), Mathematica Policy Research, Inc. 1992.

Medicare Payment Advisory Commission. Health plans’ selection and payment of health care providers

1999, contract research series, No. 00-2. Washington (DC), MedPAC. May 2000.

Medicare Payment Advisory Commission. MedPAC comment on HCFA’s risk adjustment proposal.

Washington (DC), MedPAC. September 1, 1999.

Newhouse JP, Buntin MB, Chapman JD. Risk adjustment and Medicare: taking a closer look, Health

Affairs. September/October 1997, Vol. 16, No. 5, p. 26–43.

Riley G, Tudor C, Chiang Y, Ingber M. Health status of Medicare enrollees in HMOs and fee-forservice in 1994, Health Care Financing Review. Summer 1996, Vol. 17, No. 4, p. 65–76.
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Risk adjustment

in Medicare+Choice

..................................................................................................................

2C H A P T E R
R E C O M M E N D A T I O N S

2A MedPAC supports HCFA’s intention to implement a multiple-site risk adjustment

system in 2004 because diagnoses from multiple sites of care yield better measures

of resource needs among beneficiaries. The system should be phased in to allow

plans time to adjust to the effect on payments and to allow HCFA time to adjust for

changes in providers’ coding behavior.

2B HCFA should develop a multiple-site risk adjustment model capable of using more

than one year of diagnosis data.

2C HCFA should evaluate modeling risk adjustment so that the time lag between the

diagnosis data and the cost data the agency uses to estimate payment weights

matches the lag between the diagnosis data and the payment.

2D HCFA should evaluate using partial capitation as the basis for a payment system for

Medicare+Choice plans.

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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 15

The Commission believes the benefits of risk adjustment in Medicare+Choice (M+C) make it worth

pursuing. As a result, MedPAC has supported risk adjustment (MedPAC 1998, MedPAC 1999a, and

MedPAC March 1999b) and continues to support it to ensure that plans are paid fairly and to help

ensure beneficiaries’ access to the M+C program. The principal inpatient diagnostic cost group (PIPDCG) model is a move in the right direction, capturing a much greater fraction of the predictable

differences in enrollees’ costliness than the demographic model. However, policymakers have concerns

about the future of risk adjustment and where it is headed. The Balanced Budget Refinement Act of

1999 (BBRA) reflected these concerns in requiring MedPAC to recommend ways to improve risk

adjustment in Medicare+Choice.

How well do risk adjustment models work?

Research shows risk adjustment models that use diagnoses from individual-level encounter data perform

better than the demographic system. HCFA has focused much of its research on risk adjustment in

examining the PIP-DCG model and the diagnostic cost group hierarchical condition category (DCG/

HCC) model. As described in Chapter 1, the PIP-DCG model uses the principal diagnoses from

hospital inpatient stays in one year to predict costs in the next year. The DCG/HCC model uses

diagnoses from multiple sites (physician visits and hospital inpatient and outpatient encounters) to predict

costs, and is a model HCFA is considering for 2004. The main benefit of these models over the

demographic model is that they pay more accurately for the expected costs of plan enrollees.

Payment accuracy can be evaluated using a predictive ratio, which relates the costs to the Medicare

program predicted by a risk adjustment method for a group of people to the actual costs to the program

observed for that group.1

 A predictive ratio of 1.0 indicates predicted costs equal actual costs; that is,

costs are accurately predicted on average. A value less than 1.0 indicates the risk adjustment system

predicts lower costs than actually occurred. Conversely, a ratio greater than 1.0 indicates predicted

costs are greater on average than actual costs.

Research by Pope and colleagues (1999 and 2000) presented in Table 2-1 indicates that a model

similar to Medicare’s demographic system generally has worse predictive ratios than the PIP-DCG and

the DCG/HCC models.2 For example, when Pope and colleagues divided their analytic sample into

total costliness quintiles, the demographic model had a predictive ratio of 0.47 for beneficiaries in the

fifth quintile (representing the highest-cost beneficiaries). In contrast, the PIP-DCG model had a

predictive ratio of 0.75, and the DCG/HCC had a predictive ratio of 0.86. These results indicate that

the PIP-DCG model predicts costs for sick beneficiaries much better than the demographic system, and

the DCG/HCC does an even better job.

1 Actual costs are defined to be costs to the traditional fee-for-service Medicare program. In other words, risk adjustment systems are not attempting to

predict the cost that M+C plans would incur. Accurate payment is defined in terms of fee-for-service costs because a goal of the M+C program is to

take advantage of the efficiencies private sector plans may achieve while ensuring that the Medicare program is no worse off. Where plans are more

efficient, plans are expected to share the surplus from that efficiency by increasing benefits. The program is no worse off because plans are paid what

they would have been paid for beneficiaries if they had stayed in traditional Medicare.

2 The actual demographic system used for risk adjustment bases predicted costs on beneficiaries’ age, sex, Medicaid status, and institutional (nursing

home) status. The model used in Pope et al. (1999) differs from Medicare’s demographic model in that it does not use institutional status to predict

costs. Also, it does use whether an elderly beneficiary was ever eligible for Medicare on the basis of a disability. Finally, Pope’s model is based on a

regression prediction, not the AAPCC rate cell approach.
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Ri s k a d j u s t m e n t in M e d i c a r e+Choice

First (lowest) 2.57 2.09 1.23

Second 1.88 1.54 1.23

Third 1.35 1.10 1.14

Fourth 0.96 0.84 1.02

Fifth (highest) 0.47 0.75 0.86

Cost quintile (base year) Demographic model PIP-DCG DCG/HCC

Predictive ratios for demographic, principal inpatient diagnostic cost

group, and diagnostic cost group hierarchical condition category risk

adjustment models (values closer to 1.0 indicate better prediction)

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TABLE

2-1

Note: PIP-DCG (principal inpatient diagnostic cost group), DCG/HCC (diagnostic cost group hierarchical condition category). Base year indicates the year

prior to the year in which costs are predicted. For the demographic and PIP-DCG models, the base year is 1995, for the DCG-HCC model, the base

year is 1996. Data for the demographic and PIP-DCG results are from the 1995 and 1996 5 percent standard analytic files of Medicare

beneficiaries. Data for the DCG/HCC results are from the 1996 and 1997 5 percent standard analytic files of Medicare beneficiaries.

Source: Pope et al. 1999 and Pope et al. 2000.

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16

The PIP-DCG and DCG/HCC models are more accurate because both models account for the impact

of serious conditions on beneficiaries’ expected costs, whereas the demographic system does not.

Consider, for example, two beneficiaries: a 72-year-old male hospitalized with lymphatic cancer in the

previous year and a 72-year-old male who had no conditions diagnosed in the previous year. The

predicted cost of the beneficiary with lymphatic cancer under the PIP-DCG is $30,059, but the

predicted cost of the healthy beneficiary is $3,596. In contrast, the demographic system yields the same

predicted cost for both beneficiaries: $4,623.

The PIP-DCG and DCG/HCC models also predict costs for healthy beneficiaries better than the

demographic model does. For beneficiaries in the first quintile (representing the lowest-cost

beneficiaries), the predictive ratios of the demographic, the PIP-DCG, and the DCG/HCC models were

2.57, 2.09, and 1.23, respectively.

Although the PIP-DCG is better than the demographic system at predicting costs for low-cost

beneficiaries, it does not do as well as the DCG/HCC. The DCG/HCC performs better because it does

not treat beneficiaries with serious conditions who were not hospitalized the previous year as if they

were healthy. For example, PIP-DCG predicts a 72-year-old male with paraplegia but no hospital stays

in the previous year to cost $3,596, the same as a 72-year-old male with no conditions in the previous

year. In contrast, based on diagnostic information from physician and outpatient hospital claims, the

DCG/HCC predicts the paraplegic to cost $8,478 and the beneficiary in good health to cost $1,854.

Our discussion focuses on the predictive ratios of high- and low-cost beneficiaries because it is among

these groups that incentives for selection are greatest under the demographic model. To the extent that a

risk adjustment model accurately predicts costs for those groups, incentives to attract low-cost

beneficiaries and avoid high-cost beneficiaries diminish. Although the incentives are not completely

eliminated by the PIP-DCG or DCG/HCC models, the PIP-DCG would reduce them, and the DCG/

HCC would reduce them more.
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Move to multiple-site risk adjustment

Although the PIP-DCG model improves on the demographic adjuster, MedPAC recommends that

HCFA continue its efforts to move to a multiple-site risk adjustment system in 2004.

RECOMMENDATION 2A

MedPAC supports HCFA’s intention to implement a multiple-site risk adjustment

system in 2004 because diagnoses from multiple sites of care yield better

measures of resource needs among beneficiaries. The system should be phased in

to allow plans time to adjust to the effect on payments and to allow HCFA time to

adjust for changes in providers’ coding behavior.

The primary shortcoming of the PIP-DCG is its failure to account for conditions treated in ambulatory

settings that predict higher future costs. This shortcoming results in systematic overpayments for healthy

beneficiaries and underpayments for beneficiaries with serious conditions who were not hospitalized in

the previous year. Also, the PIP-DCG system penalizes plans that are effective in substituting

ambulatory care for more expensive hospital inpatient care.

Compared with the PIP-DCG model, a multiple-site system will pay more accurately for beneficiaries’

expected costs, thus reducing the problem of overpaying for healthy enrollees and underpaying for

enrollees with serious conditions. It also will remove disincentives associated with substituting

ambulatory care for inpatient care.

A multiple-site model requires much more data than does the PIP-DCG, raising some cost concerns.

Although some of the costs associated with developing a multiple-site model and data collection and

processing systems to support it have already been incurred, collecting and processing the data will

continue to challenge HCFA, plans, and providers. Therefore, the cost of overcoming those challenges

and the extent to which they present barriers to entering the program should be monitored as should the

ongoing costs and benefits.

HCFA should phase in multiple-site risk adjustment

Experience with the transition from the demographic model to the PIP-DCG model should help plans

adjust to the change from PIP-DCGs to a multiple-site model, but at least some plans are likely to see

substantial changes in payment. HCFA calculates payments to M+C plans as the product of an index of

enrollees’ health status (risk scores), which is calculated using the risk adjustment model, and county

base payment rates. Moving from the PIP-DCG model to a multiple-site model can change risk scores

substantially: this change affects payments.
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18 Ri s k a d j u s t m e n t in M e d i c a r e+Choice

For example, a woman in the 65-69 age group with no conditions would have a risk score of 0.45

under the PIP-DCG model, but only 0.23 under the DCG/HCC model, a 49 percent decline. Risk

scores also can increase substantially by moving from the PIP-DCG to the DCG/HCC model,

especially for beneficiaries with serious conditions who had no inpatient stays in the previous year. For

example, a 72-year-old man with diabetes and congestive heart failure who did not receive hospital

inpatient care would have a PIP-DCG risk score of 0.71, but a DCG/HCC risk score of 1.05. Also,

under multiple-site risk adjustment the risk scores of many more beneficiaries will be affected by

diagnoses from health system encounters. Only 12 percent of traditional program beneficiaries have

hospitalizations that would affect their risk scores under the PIP-DCG model, but 57 percent of them

have health system encounters that would affect their risk scores under the DCG/HCC model (HCFA

2000).

The impact on a plan’s aggregate payments of moving from the PIP-DCG model to a multiple-site

model will not be as extreme as the change in risk scores indicated in these examples because plans will

get higher payments for some enrollees and lower payments for others. Still, there is potential for large

changes in payments at the plan level, and some plans will likely see such changes.

Large changes in risk scores could also affect the county base payment rates. HCFA determines base

rates as the maximum of three possibilities: a blend of a local payment rate and a national rate; a floor

(minimum) rate; or a 2 percent increase from the previous year’s payment rate. When HCFA makes the

switch from the PIP-DCG model to multiple-site risk adjustment, some counties could see large

changes in local payment rates if the average risk score for fee-for-service beneficiaries changes

substantially.3

 Large changes in local rates would cause large changes in blended rates and ultimately in

payments.

The effect of multiple-site risk adjustment on payments is important because the benefits offered and

premiums charged by plans are closely tied to payments. In the most extreme cases, plans facing

payment cuts could withdraw from Medicare+Choice, disrupting coverage for their enrollees. In

contrast, plans that see large payment increases could provide more benefits or develop better systems

of care for beneficiaries with serious conditions.

Transition from the PIP-DCG system to multiple-site risk adjustment could also create problems for

HCFA. In particular, while the agency will likely use diagnosis data from traditional Medicare to

determine payment weights, M+C providers may code diagnoses differently and more completely when

payments depend on how they code. For example, M+C providers may code permanent conditions

such as renal failure more persistently than traditional Medicare providers. This difference in coding

between traditional Medicare and Medicare+Choice could raise payments beyond what HCFA

expects, causing payment weights for some health status categories to be higher than the expected

resource use of M+C enrollees assigned to them. HCFA may want to adjust payment weights or base

payment rates if coding differences occur.

3 For each county, the local payment rate used in the blended rate is the per capita cost of the traditional program beneficiaries in the county divided by

the average risk score of these beneficiaries. If there is a substantial difference between a county’s average risk score under the PIP-DCG model and

its risk score under multiple-site risk adjustment, replacing the PIP-DCG model with a multiple-site model will have a large impact on the county’s local

payment rate.
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Because of its potential impact on payments and the potential difference in coding between traditional

Medicare and M+C providers, MedPAC suggests that HCFA phase in multiple-site risk adjustment.

HCFA has not made it clear whether it intends to do that, nor has a phase-in been enacted legislatively.

The Commission believes a phase-in would allow plans and HCFA time to address these issues before

payments are adjusted exclusively by a multiple-site model.

Using multiple years of diagnoses

Using more than one year of diagnosis data in a risk adjustment system presumably would improve how

well the system predicts beneficiaries’ costs, as some persistent conditions affect beneficiaries’ health

and health care costs over time. Using multiple years of diagnosis data would entail some additional

costs for HCFA, but should not require additional data or collection efforts by plans.

In addition to making payments more accurate, using multiple years of diagnosis data could make

payments more stable as well, which would mean more stable revenue streams to plans. If HCFA uses

only one year of diagnosis data in multiple-site risk adjustment, payments would be unstable if M+C

providers code permanent conditions with the same lack of persistence displayed by providers in

traditional Medicare (MedPAC 1998). Using multiple years of diagnosis data would help solve the

problem of unstable payments by carrying diagnoses of permanent conditions over several years.

RECOMMENDATION 2B

HCFA should develop a multiple-site risk adjustment model capable of using more

than one year of diagnosis data.

Using more than one year of diagnosis data presents three challenges for HCFA. First, the agency

would have to track beneficiaries’ diagnoses over several years. Second, HCFA would have to account

for the fact that costs associated with some conditions diminish over time, but increase for other

conditions. For example, payments for beneficiaries who were diagnosed with a condition one year ago

should reflect the expected costs of those beneficiaries in the year after diagnosis, while payments for

beneficiaries diagnosed two years ago should reflect the typically different expected costs of care two

years following diagnosis. Third, HCFA would have to determine how many years of diagnoses to

include. On the one hand, each additional year of diagnosis data gives more information about

beneficiaries’ health. On the other hand, the incremental improvement in payment accuracy provided by

each additional year is smaller than the previous year. Also, each additional year may make the risk

adjustment system more cumbersome.

Finally, using more than one year of diagnosis data would increase the number of beneficiaries whose

health status was measured with more than one condition. MedPAC believes HCFA should consider

how beneficiaries’ medical conditions interact to affect costs. For example, the resource needs of a

beneficiary who has diabetes and suffers a heart attack may be greater than the combined resource

needs of a person who has a heart attack and another who has diabetes. MedPAC recognizes that

addressing the interaction of conditions could make risk adjustment so complicated that it becomes

unwieldy. Nevertheless, the Commission encourages HCFA to at least explore this issue.
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20 Ri s k a d j u s t m e n t in M e d i c a r e+Choice

Proper treatment of data lags

When HCFA developed the PIP-DCG model, the agency grouped beneficiaries into health status

categories on the basis of their diagnoses from hospital inpatient stays in 1995. HCFA estimated a

payment weight (expected cost) using cost data from 1996. There was no time separation or lag

between the end of the year of diagnosis data and the beginning of the year of cost data HCFA used to

estimate payment weights.

In contrast, when HCFA applies the payment weights to determine payments, there is a six-month lag

between the end of the year of diagnosis data and the beginning of the actual payment year because

plans and the agency need time to collect and evaluate data to determine diagnoses. For example,

HCFA will use hospital inpatient diagnoses from July 1999 through June 2000 to risk adjust payments in

2001 (Figure 2-1). The agency could have established a policy of using July 1999-June 2000 diagnosis

data to set payments in the first few months of 2001 and then making retroactive adjustments to those

payments when complete diagnosis data from January 2000-December 2000 become available. HCFA

gave plans the choice of this option, which would adjust payments more accurately for enrollees’

expected costs, or payments based on diagnosis data with a six-month lag, which gives plans more

certainty because there is no retroactive adjustment of payments. Plans indicated a preference for the

latter method.

HCFA’s use of lagged diagnosis data to determine payments and non-lagged data to estimate payment

weights could potentially decrease the accuracy of the risk adjustment system. We recommend that

HCFA examine whether such a decrease has occurred.

 diagnoses predicted costs

 1995 1996

Lag between year of diagnoses and predicted costs FIGURE

2-1

 1999 2000 2001 2002

Payment weights developed

Payment weights applied

12 months Predicted

diagnoses Lag costs aaaa aaaaaaaaaaaaaaa

RECOMMENDATION 2C

HCFA should evaluate modeling risk adjustment so that the time lag between the

diagnosis data and the cost data the agency uses to estimate payment weights

matches the lag between the diagnosis data and the payment.

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To evaluate the effect of the lag on the accuracy of the risk adjustment systems under consideration, we

propose the following: recalculate payment weights, reassign beneficiaries to health status categories,

reevaluate the predictive power of the model, and assess the impact on aggregate payments using

diagnoses from a time period 18 months to 6 months prior to the start of the year of cost data.

Take the PIP-DCG model as an example. As explained above, HCFA placed beneficiaries in diagnostic

cost groups based on their diagnoses from hospital inpatient stays in 1995. It then developed payment

weights by associating beneficiaries in these cost groups with their actual costs in 1996. In the

experiment we propose, HCFA would repeat this procedure but associate diagnosis data from July

1994 through June 1995 with cost data from 1996. It would then determine the proportion of

beneficiaries in each cost group, payment weights, the predictive ratios, the percent of variation

explained by the model, and the effect on plans’ payments. Finally, these values would be compared

with those obtained previously.

We have reason to expect that the experiment might show a substantial difference in at least one of these

measures: percent of variation in costs explained. For example, Averill et al. (1999) used a multi-site

model (the clinical risk group model) to calculate the difference between the variation in beneficiaries’

costs explained by non-lagged diagnosis data versus the variation explained by data lagged six months.

They estimated that the variation in beneficiaries’ costs explained by the model dropped from 10.1

percent with non-lagged diagnosis data to 8.4 percent with data lagged six months. Our own preliminary

analysis using the DCG/HCC model shows similar results.4

 HCFA should determine how much the

variation explained by the PIP-DCG and the multiple-site risk adjustment models under consideration

drops when using lagged diagnosis data. If the drop is substantial, the benefits of the risk adjustment

systems may be less than reported thus far.

If HCFA decides to reconcile the time lag inconsistency, the agency has two options. First, as discussed

above, the agency could use lagged diagnosis data to set payments for the first few months of each year,

then make retroactive adjustments to payments when a complete set of non-lagged diagnosis data

becomes available. Second, the agency could estimate payment weights with lagged diagnosis data and

use diagnosis data with the same time lag to set payments. If HCFA uses this latter option, it must be

careful not to make the lag too long, as diagnosis data become a less effective tool for predicting

beneficiaries’ costs as the lag increases.

Evaluate the use of partial capitation

A further step to improve payment accuracy would supplement capitation payment with provisions to

increase or decrease payments based on beneficiaries’ actual use of services (Newhouse 1998).

Conceptually similar payments are made in the inpatient prospective payment system (PPS) for outlier

cases that consume exceptionally high levels of resources, payments to PPS-exempt hospitals and the

transitional stop-loss provisions in the outpatient PPS.

4 We used software from DxCG Inc. in our analysis.
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Ri s k a d j u s t m e n t in M e d i c a r e+Choice

HCFA should evaluate using partial capitation as the basis for a payment system

for Medicare+Choice plans.

For example, suppose plans were paid 50 percent based on Medicare allowed costs and 50 percent

based on capitated rates. Payments for the cost portion could be based on a fee schedule or

reimbursement of cost. If payments were based on a Medicare fee schedule, plans would be paid 50

percent of the fee schedule amount for each covered service provided. If instead the cost portion were

based on pure cost reimbursement, plans would receive 50 percent of their allowable Medicare costs,

presumably calculated from something like a cost-report or plans’ adjusted community rate (ACR)

submissions. Regardless of which method was used to determine the cost portion, plans would also get

paid 50 percent of the monthly capitated payment for each enrollee.

Partially-capitated payments would reduce plans’ risk of suffering large losses and their chance to make

large profits. By reducing the risk from enrolling high-cost beneficiaries and the gain from enrolling lowcost beneficiaries, partial capitation would reduce incentives to risk select. The lower risk of large losses

might also encourage greater plan participation.

Partial capitation might also lower the risk of beneficiaries getting too little care under a capitated

payment system. Since partial capitation would provide some payment for additional services provided,

it would reduce incentives that plans or sub-capitated providers might have to limit the amount of care

provided.

At the same time, however, partial capitation would increase the risk to Medicare and beneficiaries of

excessive resource use and cost. Implementation would require development of appropriate fee

schedules and collection of encounter information on units of service provided by plans, or it would

require Medicare to reimburse plans based on cost reports or ACR submissions that are not currently

adequate for payment purposes. Also, under some circumstances plans may choose to treat

beneficiaries by using services that are not covered by Medicare, such as prescription drugs. Under

those circumstances, how would plans be paid for the use of legitimate non-covered services? Because

of the many operational uncertainties, the Commission recommends only that HCFA evaluate using

partial capitation, not that HCFA adopt it as a payment method.

Five questions related to PIP-DCG model

As HCFA moves forward on risk adjustment, policymakers have continued to express concern about

the PIP-DCG model, even though it improves upon the demographic system. Some of these concerns

are manifested in five questions that the BBRA directed MedPAC to investigate. These questions and a

summary of the Commission’s analysis follow:

• How well do risk scores from the PIP-DCG risk adjustment model correlate with M+C plans’ per

capita Medicare costs? We found that plans’ per capita payments (risk adjusted) explain a fairly

large fraction of the variation in plans’ per capita costs—46 to 48 percent, depending on how one

measures costs.

RECOMMENDATION 2D

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• Under the PIP-DCG model, how stable are M+C plans’ per capita risk scores from year to year?

And what is the potential for substantial changes in risk scores for small plans? From September

1998 to September 1999, the average absolute percentage change in plans’ per capita risk scores

was 4.4 percent. Nearly one-third of plans had small absolute changes (less than 2 percent), while

33 percent had large absolute changes (at least 5 percent). Compared with all plans, plans with

fewer than 5,000 enrollees had a higher average absolute change in per capita risk scores, were

less likely to have small changes, and more likely to have large changes. These results reflect two

factors that affect plans’ risk scores over time: changes in risk scores for the given population and

changes in enrolled populations.

• Is there still evidence that M+C enrollees are less costly at their time of enrollment, on average,

than are beneficiaries who remain in fee-for-service Medicare? The average cost in 1997 of

beneficiaries who enrolled in M+C plans during 1998 was 23 percent lower than the average cost

in 1997 of beneficiaries who remained in traditional Medicare in 1998. The risk factors in the PIPDCG model account for only 8 percentage points of this 23 percent difference.

• Is there still evidence that beneficiaries who voluntarily leave M+C plans are more costly at their

time of disenrollment, on average, than are beneficiaries who remain in traditional Medicare? After

adjusting for age, sex, and Medicaid differences, our results show beneficiaries who voluntarily

disenrolled from Medicare risk plans in 1997 were slightly less costly in 1998, on average, than

beneficiaries who did not enroll in Medicare+Choice.

• How does HCFA’s decision to exclude “discretionary” hospitalizations affect the PIP-DCG

model? Excluding discretionary hospitalizations from the PIP-DCG model increases payments to

M+C plans by an average of 1.4 percent. Conversely, HCFA’s decision to exclude hospitalizations

of fewer than two days decreases payments by an average of 0.7 percent.

The third and fourth questions address the issue of whether plans benefit from favorable selection

(enrollment of relatively low-cost beneficiaries). However, these two questions focus on beneficiaries

before and after they enroll in M+C plans, and ignore the period of enrollment. Examining costliness

during beneficiaries’ M+C enrollment would be informative, but there is very little cost information on

M+C enrollees. To address this, MedPAC examined mortality rates to compare the health status of

beneficiaries in the M+C and traditional programs.

The Commission found that in 1998 the mortality rate for M+C enrollees was 15 percent lower than the

rate for beneficiaries in traditional Medicare, after controlling for differences in age, sex, and Medicaid

enrollment. However, the difference in mortality rate decreases as length of M+C enrollment increases,

controlling for age, sex, and Medicaid differences. Beneficiaries enrolled in Medicare+Choice for less

than one year had a 21 percent lower rate than traditional Medicare beneficiaries, but beneficiaries

enrolled in Medicare+Choice for more than 5 years had a rate 11 percent lower.

For a more detailed discussion of our analysis of the five BBRA questions and the additional analysis of

mortality rates, see the appendix. ■
.......................................................................................................................................................................................................................................................................................................

24 Ri s k a d j u s t m e n t in M e d i c a r e+Choice

References

Averill RF, Goldfield NI, Eisenhandler J, et al. Development and evaluation of clinical risk groups

(CRGs). Wallingford (CT), 3M Health Information Systems. April 1999.

Health Care Financing Administration, Department of Health and Human Services. Presentation at

encounter data training session. Baltimore (MD), HCFA. March 15, 2000.

Medicare Payment Advisory Commission. MedPAC comment on HCFA’s risk adjustment proposal.

Washington (DC), MedPAC. September 1, 1999a.

Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy.

Washington (DC), MedPAC. March 1999b.

Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy, volume I:

recommendations. Washington (DC), MedPAC. March 1998.

Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy, volume

II: analytic papers. Washington (DC), MedPAC. March 1998.

Newhouse JP. Risk adjustment: where are we now? Inquiry. Summer 1998, Vol. 35, No. 2, p. 122–

131.

Pope GC, Ellis RP, Ash AS, et al. Diagnostic cost group hierarchical condition category models for

Medicare risk adjustment. Waltham (MA), Health Economics Research, Inc. July 31, 2000.

Pope GC, Liu C, Ellis RP, et al. Principal inpatient diagnostic cost group models for Medicare risk

adjustment. Waltham (MA), Health Economics Research, Inc. February 24, 1999.
..................................................................................................................

Risk adjustment

for special populations

..................................................................................................................

3C H A P T E R
R E C O M M E N D A T I O N S

3A As it evaluates multiple-site risk adjustment models, HCFA also should evaluate and

report to the Congress on alternatives—including hybrid risk adjustment systems and

partial capitation—to increase payment accuracy for frail elderly beneficiaries.

3B The current method of payment for specialized plans should be used until a costeffective and accurate alternative is developed that accounts for both the frailty of

people enrolled in and differences in the benefits offered by those plans.

3C The Congress should instruct the Secretary to implement a risk-adjusted method to

pay for ESRD beneficiaries in Medicare+Choice using factors known to affect their

costs.

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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 27

Medicare+Choice (M+C) payment methods need to be refined for two special populations that have

higher costs than other beneficiaries—the frail elderly and those with end-stage renal disease (ESRD)—

if the Medicare program is to ensure that payments for them reflect their expected costs. Such

refinement would help ensure that plans do not have incentives to select against beneficiaries or stint on

their care. Payments for special populations should be adjusted for factors known to affect their costs.

To account for the additional costs of frail elderly beneficiaries, the Congress and the Secretary should

consider augmenting risk adjusters with functional status data or moving toward partial capitation. For

beneficiaries with ESRD, readily available demographic and clinical data would better account for

beneficiaries’ predictable costs than do current M+C payment methods. In addition, it may be desirable

to supplement capitated payments with payments based on actual services delivered to help ensure

beneficiaries’ access to quality care.

A very small number of frail elderly beneficiaries are enrolled in plans that specialize in caring for such

people, and the Congress requested that MedPAC study the methods used to pay for their care.

Because these specialized plans generally are required to offer benefits in addition to those in the basic

Medicare benefit package, plans should be paid using existing payment methods, separate from the

general M+C payment system. This will ensure that the frail elderly enrolled in these plans will continue

to have access to high-quality care.

Frail elderly beneficiaries

There is no standard definition of frail elderly Medicare beneficiaries. Beneficiaries who are aged and

have limitations performing activities of daily living (ADLs) have higher expected costs than other

Medicare beneficiaries (Gruenberg et al. 1996, Gruenberg et al. 1999, Liu et al. 1997, Pope et al.

1998, Riley 2000, Veazie et al. 1999). ADLs include bathing, dressing, mobility, transferring, toileting,

and eating. In 1996, almost 9 percent of elderly Medicare beneficiaries had difficulty performing five or

more ADLs. Congress is concerned about the payment for frail elderly beneficiaries and asked

MedPAC to study risk adjustment for payments to specialized plans that serve these beneficiaries.

A good risk adjustment model should help ensure that Medicare payments for frail elderly beneficiaries

reflect their expected costs so that plans do not have strong incentives to select against them or stint on

their care. In the following discussion, we first consider risk adjustment for frail elderly. Next, we discuss

risk adjustment for frail elderly in specialized plans.
.......................................................................................................................................................................................................................................................................................................

28 R i s k a d j u s t m e n t f o r s p e c i a l p o p u l a t i o n s

Risk adjustment for frail elderly

Current and contemplated risk adjustment models, which rely on encounter data to predict differences

in beneficiaries’ costs, probably will not adequately account for the higher health care use of frail

elderly beneficiaries. Several studies have examined use of the principal inpatient diagnostic cost group

(PIP-DCG) and hierarchical coexisting conditions (HCC) models with frail elderly populations and

consistently found that these models underpredict Medicare spending for such people (Esposito

2000, Gruenberg et al. 1999, McCall and Korb1998, Pope et al. 1998, Smith 2000).1 For

beneficiaries with high levels of disability—having difficulty with five or six ADLs or needing help with

three or more ADLs—PIP-DCGs underpredict costs by about 30 percent. This underprediction is not

surprising because the PIP-DCG model relies heavily on encounter information for inpatient care to

predict costs, while frail beneficiaries are heavy users of care in other settings. However, the HCCs,

using diagnoses from multiple sites of care, also underpredict costs for these groups by about 12

percent (see Table 3-1).

1 Here and elsewhere we use spending to refer to resource use as measured by fee-for-service Medicare payments.

2 The predictive ratio relates the total costs to the Medicare program predicted by a risk adjustment method for a group of people to the actual costs to

the program observed for that group. A predictive ratio of 1.0 indicates that costs are accurately predicted, on average, because predicted costs

equal actual costs. A value less than 1.0 indicates the method predicts lower costs than actually occurred while a value greater than 1.0 indicates

predicted costs are greater on average than actual costs.

For the frail elderly, hybrid risk adjusters perform better than adjusters based on encounter data alone.

For example, for elderly beneficiaries who receive help with three or more ADLs, the HCC predictive

ratio of 0.88 improves to 1.00 with the addition of survey information on functional status.2

 Hybrid risk

adjusters use information collected by surveys of beneficiaries or plans in addition to encounter data.

Survey data may include data on functional status, health status, and presence of chronic conditions.

Functional status information describes beneficiaries’ performance or performance capacity. ADLs and

instrumental activities of daily living (IADLs) are the most common measures of functional status. IADLs

include telephone use, shopping, money management, light housework, heavy housework, and meal

preparation. In measuring limitations, ADLs can be considered individually, summarized in an index, or

used together with IADLs. An individual may be defined as having an ADL limitation if he or she either

has difficulty or needs help with performance.

 Modified Survey PIP-DCG PIP-DCG DCG-HCC DCG-HCC

Functional status demographic and survey and survey

Predictive ratios for alternative risk adjustment models,

by functional status subgroup

(Values closer to 1.0 indicate better prediction)

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

3-1

1–2 ADL limitations 0.80 1.03 0.85 1.03 0.90 1.03

3–4 ADL limitations 0.67 0.94 0.74 0.94 0.85 0.95

5–6 ADL limitations 0.61 1.08 0.72 1.06 0.88 1.08

Elderly helped with

 3+ ADLs 0.58 0.97 0.70 0.96 0.88 1.00

Risk adjustment models

Note: PIP-DCG (principal inpatient diagnostic cost group), HCC (hierarchical condition category), ADL (activity of daily living). ADL limitations were measured

by report of difficulty in performing activity. Modified demographic model includes age and sex. Data are from the 1992 and 1993 Medicare

Current Beneficiary Surveys.

Source: Pope et al 1998.

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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0

The Medicare Health Outcomes Survey (HOS), formerly the Health of Seniors Survey, is

administered to a sample of beneficiaries in every Medicare+Choice plan. The core of the

survey is the Short-Form Health Survey (SF-36), a standardized health status survey

designed for use with diverse populations. HCFA augments the SF-36 with questions on the

presence of selected chronic conditions, respondent health, and demographic characteristics. Using

answers from a subset of questions (the SF-12 instrument questions), it is possible to calculate

physical and mental function scores for use in risk adjustment.

29

Any decision to use functional and health status data from surveys in risk adjustment for the frail elderly

must address several problems:

Medicare Health Outcomes Survey

• Low survey response rates, high use of proxies, and problems of recall when data are collected

directly from beneficiaries.

• Costs associated with collecting survey data not included in administrative record systems.

• Potential for data manipulation to increase payment when data are collected from plans or

providers. Also, survey data may be difficult to audit.

The federal government has considerable experience collecting data on functional status for purposes

other than risk adjustment. It collects such information in a number of ways for use in ongoing program

management. In addition to the Medicare Health Outcomes Survey (HOS) for Medicare+Choice

enrollees (see text box), these include the Resident Assessment Instrument and Minimum Data Set for

residents of skilled nursing facilities and the Outcome and Assessment Information Set instrument for

users of home health services.

The HOS is a self-administered survey. It is conducted by mail with telephone follow-up. HCFA

annually surveys 1,000 members of each Medicare+Choice plan (or all members for small plans).

Each sample drawn by HCFA is resurveyed after 2 years. Because the survey is already used on a

routine basis for Medicare+Choice enrollees, the additional cost of collecting data for risk

adjustment would be minimal. However, a sample of the beneficiary population in the traditional

fee-for-service program would also have to be surveyed and that additional cost would have to be

considered.

HOS response rates are good overall. In tests with frail elderly beneficiaries, response rates were

lower and a majority of surveys were completed by proxy. Responses for individual questions

(item response rates) are uniformly high. Assuming payment based on response does not alter the

response, current sample size, response rates, and item response rates allow calculation of

meaningful, statistically valid risk scores by plan (Pope et al. 2000).

As noted above, the HOS is built on the SF-36, for which extensive reliability information is

available. Research by the developers of the SF-36 shows high reliability for all scales and nearly

identical reliability results for elderly and non-elderly respondents (McHorney et al. 1994). The

HCFA contractor developing and testing a version of HOS for the fee-for-service population

reports reliability comparable to that reported for the SF-36 in other populations (McCall et al.

2000). Private contractors charge plans about $12 per fielded survey for the HOS. With an

average response rate of 60 percent for the 1998 survey, the total cost per completed survey was

about $20. ■
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30 R i s k a d j u s t m e n t f o r s p e c i a l p o p u l a t i o n s

It may be possible to collect functional status data for Medicare risk adjustment from administrative data

systems in the foreseeable future. It might still be necessary to require additional encounter data to

obtain functional information for M+C beneficiaries. The National Committee on Vital and Health

Statistics, an advisory committee to the Department of Health and Human Services, recommended in

1996 that functional status be included in a set of core data elements for the national health data system

(NCVHS 1996). (It noted, however, that no standard definitions are available yet.) The World Health

Organization (WHO) is revising the International Classification of Impairments, Disabilities, and

Handicaps and several nations are testing it. WHO expects to present the resulting system for approval

by the World Health Assembly in May 2001, after which individual nations may adopt it as they

previously adopted the WHO-developed ICD-9 and are expected to adopt the ICD-10 diagnostic

classification system.

Whatever the source of functional status data, using them in conjunction with contemplated risk

adjustment systems could significantly improve payment accuracy for frail elderly beneficiaries and in

doing so help preserve their access to care in M+C plans. Therefore, the Commission believes that

HCFA should support further research to evaluate hybrid risk adjustment models to improve payment

accuracy for special populations. A hybrid model based on surveys from a sample of enrollees could

potentially be used to adjust aggregate payments at the plan level based on the mix of beneficiaries

enrolled.

A possible complement or alternative to risk adjustment based on functional status is partial capitation.

As discussed in Chapter 2, with this approach capitation payments are supplemented with payments

based on beneficiaries’ use of services. Additional payments could be based on a fee schedule or

reimbursement of cost.

RECOMMENDATION 3A

As it evaluates multiple-site risk adjustment models, HCFA also should evaluate

and report to the Congress on alternatives—including hybrid risk adjustment

systems and partial capitation—to increase payment accuracy for frail elderly

beneficiaries.

Frail elderly in specialized plans

Medicare has developed several specialized plans to test methods of care for frail elderly beneficiaries

(Table 3-2). These include Social Health Maintenance Organizations, EverCare, and several programs

for dual eligibles (not shown in the table). One program, the On Lok demonstration, has been

incorporated into the general Medicare program as the Program of All-Inclusive Care for the Elderly

(PACE), an option for all qualifying beneficiaries. These programs differ in the numbers and the

characteristics of beneficiaries enrolled (Table 3-3). Medicare has used special payment provisions for

care in specialized plans, in part because the benefits in these programs are different.

In the Balanced Budget Refinement Act of 1999, the Congress required MedPAC to comment on the

development of a payment methodology for frail elderly beneficiaries enrolled in M+C under a

specialized program for the frail elderly.
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 31

Program objectives

Feature S/HMO PACE EverCare

Managed care programs for frail elderly

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

3-2

Note: S/HMO (Social Health Maintenance Organization), PACE (Program of All-Inclusive Care for the Elderly), BBA (Balanced Budget Act of 1997), NHC

(nursing home certifiable).

Source: MedPAC 1999.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................

Provide an expanded managed

care benefits package to

prolong independence of frail

elderly in community.

Integration with primary care

not successful; recommended

changes led to S/HMO II.

Base rate is 100/95 x

Medicare+Choice rate.

S/HMO I gets adjuster for

NHC enrollees and reduced

adjusters for others. S/HMO II

uses a multivariate formula.

All Medicare benefits,

expanded benefits, and longterm care benefits. Outpatient

drugs are covered.

Same requirements as

Medicare+Choice, but

beneficiaries under age 65

excluded from S/HMO I.

S/HMOs initially limited

participation of frail

beneficiaries.

3 S/HMO I, 1 S/HMO II

Integrate delivery and financing

of primary, acute, and longterm care services for a frail

elderly population.

Cost savings to Medicare,

reduced use of institutionalized

care.

Base rate is Medicare+Choice

rate. PACE gets base rate

times 2.39 frailty adjuster for

each enrollee.

Provide better primary care to

nursing home residents.

HCFA independent

evaluation findings

Payment methods

Benefits

Eligibility requirements

Number of sites

Characteristics

of sponsors

First year of operation

All medical and long-term care

benefits covered through

pooled Medicare, Medicaid,

and private capitation

payments. Outpatient drugs are

covered.

Enrollees must meet state

criteria for nursing home

certifiability and be age 55 or

older.

26

HMOs and long-term care

providers.

In process, expected

completion 2001.

Base rate is 93/95 x

Medicare+Choice rate.

EverCare gets institutionalized

adjusters for each enrollee.

Similar packages to

Medicare+Choice plans but no

drug coverage.

Nursing home residency.

6 under demonstration, 3

Medicare+Choice

subcontractors.

National HMO corporation—

United HealthCare.

Most are freestanding,

community-based provider

entities; several sponsored by

providers that own HMOs.

1971 (On Lok)

1990 (PACE)

1985 (S/HMO I)

1996 (S/HMO II)

1994
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32 R i s k a d j u s t m e n t f o r s p e c i a l p o p u l a t i o n s

Specialized plans treating large numbers of frail elderly may have greater difficulties than conventional

M+C plans with the PIP-DCG interim risk adjustment system. Frail beneficiaries are more costly than

Medicare beneficiaries in general and, as discussed above, the PIP-DCG model does not explain their

costs well. Because specialized plans currently receive higher payments than those received by M+C

plans, they may be adversely affected if payments are risk adjusted. In addition, many have less

sophisticated information systems than larger M+C plans and may have greater difficulty reporting

encounter data.

Specialized plans generally are required to offer benefits in addition to those in Medicare’s basic benefit

package. Payment based on regular M+C methods would not account for the costs of these benefits.

When M+C plans offer additional or supplemental benefits not covered in the regular benefit package,

such as prescription drugs, they cover the costs of these benefits with savings from providing Medicarecovered services more efficiently or with income from beneficiaries’ premiums. Conceptually,

specialized plans could use the same approach, but this probably would not be feasible for PACE and

EverCare, as enrollees are predominantly Medicaid eligible. For these plans, continuing additional

benefits would require additional Medicaid funding.

Category S/HMO PACE EverCare Medicare+Choice Fee-for-service

Demographic information on Medicare programs for the frail elderly, 1999

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

3-3

Number of enrollees 79,166 6,026 10,617 6.5 million 32.4 million

Annual increase in enrollees 37.5% 22.7% 121.4% 23.7% 0.3%

(1994–1999)

Age distribution (in percent)

<65 6 4 1 12 18

65–74 46 24 12 52 42

75–84 36 39 36 29 30

85+ 12 33 51 7 11

Mortality rate by age

(in percent)

<65 2 6 19 2 3

65–74 3 11 21 2 3

75–84 7 13 28 5 6

85+ 15 21 39 13 17

Note: S/HMO (Social Health Maintenance Organization), PACE (Program of All-Inclusive Care for the Elderly).

Source: MedPAC analysis of data from the Health Care Financing Administration Group Health Plan Master File, Denominator File, and Medicare Prepaid

Health Care Plans Monthly Report.

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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 33

Structural features of specialized plans also give reason for concern when considering basing their

payment on risk-adjusted Medicare+Choice rates. Mainstream Medicare+Choice plans enroll a broad

spectrum of beneficiaries and can offset high spending on some beneficiaries with low spending on

others. Because payment rates are designed to cover expected costs, they should cover plans’ overall

average costs. By contrast, some specialized plans have disproportionate numbers of costly members

and may not be able to offset losses on some members with gains on others.3

The small number of enrollees in some specialized plans makes these plans vulnerable to the inaccurate

payment associated with inadequate risk adjustment. PACE sites in particular are typically very small. In

1999 half had enrollment of 111 or fewer with one reporting 35 members (National PACE Association

1999). Very small plans may have average costs that differ greatly from the national average in years

when they have even a few high-cost members. Even with accurate, risk-adjusted capitation rates, they

could experience losses.

RECOMMENDATION 3B

3 Members of Social Health Maintenance Organizations have characteristics similar to beneficiaries in Medicare+Choice plans and original Medicare.

The current method of payment for specialized plans should be used until a costeffective and accurate alternative is developed that accounts for both the frailty of

people enrolled in and differences in the benefits offered by those plans.

The Social Health Maintenance Organization and EverCare demonstrations and the PACE program are

not Medicare+Choice plans. Instead, they were designed to provide benefits that differ from those of

traditional Medicare and Medicare+Choice. It would be inappropriate to pay them as

Medicare+Choice plans unless the Congress and the Secretary chose to change their designs or to

adopt new methods for financing the different benefits. Financing options include Medicaid payments,

beneficiary premium payments, or savings by plans on delivery of the basic benefit package. The

Commission believes that refining risk adjusters for payment for all frail elderly should take precedence

over developing adjusters solely for use with these small plans with their unique missions.

Payments for beneficiaries with end-stage renal disease

Beneficiaries with ESRD are another group with higher expected Medicare costs than other

beneficiaries. A payment method that recognizes known differences in the costs of beneficiaries with

ESRD should help ensure that plans do not have incentives to select against these beneficiaries or stint

on their care.

Medicare’s payment for the 20,000 beneficiaries with ESRD in Medicare+Choice is not adjusted for

factors known to influence costs. Rather, M+C plans in a state are paid 95 percent of the statewide

average adjusted per capita cost of caring for ESRD beneficiaries under traditional Medicare.

Consequently, plans may be underpaid to the extent that a greater proportion of their members with

ESRD have characteristics associated with higher spending levels compared with the state-wide ESRD

population, or overpaid if the reverse is true. An analysis from HCFA showed that a greater proportion

of ESRD beneficiaries who were in risk-based plans had characteristics associated with higher

spending—being older and having diabetes as their cause of ESRD—than ESRD beneficiaries in

traditional Medicare (HCFA 1999). Average Medicare payments for ESRD beneficiaries 75 years and

older ($56,000) and for beneficiaries with diabetes as the cause of ESRD ($53,000) were about 25

percent more than the 2001 national average of local payment rates ($42,000) (USRDS 2000).
.......................................................................................................................................................................................................................................................................................................

R i s k a d j u s t m e n t f o r s p e c i a l p o p u l a t i o n s

Partly because of concerns that Medicare does not use a payment method that recognizes known

differences in beneficiaries’ costs, the Congress has not removed the statutory bar that prohibits

beneficiaries with ESRD from enrolling in M+C.4 In June 2000, HCFA clarified its M+C eligibility

policy by asserting that ESRD beneficiaries with functioning kidney transplants are no longer considered

to have ESRD for M+C eligibility purposes and can enroll in Medicare+Choice plans if they meet all

other eligibility requirements (HCFA 2000). Although they cannot enroll in Medicare+Choice,

beneficiaries with ESRD requiring maintenance dialysis who were members of a plan before their ESRD

diagnosis can continue to be enrolled in plans with a Medicare contract.

The Congress did not specifically require MedPAC to study risk adjustment for payments to plans for

beneficiaries with ESRD. However, the Commission believes that M+C payment issues and options for

this vulnerable group of beneficiaries should be considered when studying risk adjustment alternatives

for the M+C program.

Risk adjustment for beneficiaries with ESRD

Currently, all M+C plans in a state are paid 95 percent of the statewide average adjusted per capita

cost of caring for ESRD beneficiaries under traditional Medicare. Payment is not adjusted for factors

known to affect the costs of ESRD care.

MedPAC considered whether Medicare should use a payment method guided by information about

ESRD treatment methods or whether the program should use the same general methods developed to

pay plans for non-ESRD beneficiaries. In evaluating these options, the Commission was guided by the

ability of each system to predict the costs of care for beneficiaries with ESRD and by implementation

issues, including data availability, reliability, and the potential to manipulate information to increase

payment.

RECOMMENDATION 3C

The Congress should instruct the Secretary to implement a risk-adjusted method to

pay for ESRD beneficiaries in Medicare+Choice using factors known to affect their

costs.

In the short-term, a capitated payment method should be implemented as soon as possible that includes

only beneficiaries with ESRD and adjusts for factors known to affect costs. MedPAC believes that such

a method will better account for the variability of treatment costs than either the current payment method

for ESRD beneficiaries in Medicare+Choice or the general M+C payment methods that are currently in

use. This recommendation is based on substantial research showing that ESRD beneficiaries are

expensive to treat and their costs are variable but to some extent predictable (Farley et al. 1996, Lewin

Group 2000, USRDS 2000). It is also based on the lack of evidence demonstrating that general M+C

payment methods can predict the costs of beneficiaries with ESRD as well as ESRD-specific methods

can.

4 Two sections of the Social Security Act bar ESRD beneficiaries from enrolling in managed care: 1851(a) and 1876(d). Until recently, all ESRD

beneficiaries were barred by statute from enrolling in Medicare+Choice, unless they were enrolled before ESRD diagnosis. Effective June 2000,

HCFA is permitting ESRD beneficiaries with functioning kidney transplants to enroll in Medicare+Choice if they meet all other eligibility requirements.

34
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 35

ESRD beneficiaries cost about seven times more than average Medicare beneficiaries. Beneficiaries’

renal treatment modality—maintenance dialysis or kidney transplantation—is the most important

predictor of ESRD costs. Data from the U.S. Renal Data System (USRDS)5 show the wide range in

annualized Medicare spending for each renal treatment modality—from $10,000 for beneficiaries with a

functioning kidney transplant (graft) to $49,000 for beneficiaries with a failed kidney graft, $51,000 for

beneficiaries requiring maintenance dialysis, and $93,000 for beneficiaries undergoing kidney

transplantation (USRDS 2000). Additionally, certain clinical characteristics have been shown to affect

ESRD costs. Payments are higher for beneficiaries who have diabetes as the cause of ESRD than for

beneficiaries who do not, and payments increase with duration of ESRD (Farley et al. 1996).

MedPAC’s key concern about including beneficiaries with ESRD in the general M+C risk adjustment

system is the ability of the system to set payments accurately for this relatively small cohort of

beneficiaries with chronic, end-stage, expensive illness. Although no published studies have evaluated

the effectiveness of the general M+C risk-adjustment models to predict the costs of ESRD

beneficiaries, examination of the demographic risk-adjusters suggests that they will not predict costs as

accurately as risk-adjusters in an ESRD-specific model. For example, the demographic risk-adjusters

used in the PIP-DCG model predict higher spending for men over age 65, compared to women in that

age group. In contrast, average annual Medicare payments for women older than 65 years who have

ESRD are higher than payments for men in that age group by about 7 percent (USRDS 2000).

MedPAC believes that the additional costs incurred by Medicare in developing and maintaining an

ESRD-specific system and by health plans in understanding the system are warranted, given the unique

needs of this vulnerable population and the need to minimize plans’ incentive to either risk select or stint

on the services they provide. Like non-ESRD beneficiaries, beneficiaries with ESRD are entitled to

receive the full array of Medicare covered services and Medicare’s payment policies should facilitate

this.6

In the long term, MedPAC encourages the Secretary to explore and evaluate methods to modify the

general M+C payment system to account for the variability of ESRD treatment costs. For example, a

payment category might be developed that is specific to ESRD beneficiaries and adjusts payments for

renal treatment modality and other factors known to affect ESRD costs. The Secretary will need to

consider the pros and cons of establishing distinct, risk-adjusted payment rates for a specific illness and

its treatments.

Until there is evidence suggesting that the general M+C risk-adjustment models predict the costs of

ESRD beneficiaries as well as ESRD-specific models, MedPAC believes that Medicare should use a

payment method designed to reflect the treatment options, clinical processes, and variations in costs

associated with ESRD. Such a method should better ensure that the unique needs of this vulnerable

population are being met by health plans.

5 The USRDS is operated by the National Institute of Diabetes and Digestive and Kidney Diseases with support from HCFA. It collects, analyzes, and

distributes information on the costs of ESRD care, the incidence and prevalence of treated ESRD, treatment patterns, causes of death, patient survival,

and rates of hospitalization.

6 The 1972 amendments to the Social Security Act extended Medicare benefits for all Medicare-covered services to people of all ages whose

physicians certify that they have permanent loss of kidney function that would be fatal unless they receive either maintenance dialysis or a kidney

transplant. To qualify for the ESRD program, individuals must be fully or currently insured under Social Security or Railroad Retirement programs,

entitled to monthly benefits under one of these programs, or the spouse or dependent child of an eligible patient.
.......................................................................................................................................................................................................................................................................................................

36 R i s k a d j u s t m e n t f o r s p e c i a l p o p u l a t i o n s

Implementation issues Development of the payment method should be guided by HCFA’s experience in

implementing the risk-adjusted payment method developed for its ESRD Managed Care Demonstration

Project.7 For this demonstration, HCFA adjusts monthly capitated payments separately for

beneficiaries requiring dialysis and beneficiaries with a functioning or failed kidney graft by age (<20

years, 20–64 years, and ≥65 years) and whether diabetes was the cause of ESRD. Kidney transplant

procedures are paid for outside the capitated payments as lump-sum payments to the plans over a

three-month period.

In designing an ESRD-specific method, the Secretary will need to consider which factors to use to

adjust payments. As discussed above, certain demographic and clinical characteristics of ESRD patients

have been shown to affect their costs. In addition to the three factors (age, underlying cause of ESRD,

and renal treatment modality) HCFA used to adjust capitated payments for its ESRD demonstration

project, other factors have also been shown to affect ESRD costs, including basis of Medicare

entitlement, gender, duration of ESRD, functional status, and the presence of selected comorbidities

(Beddhu et al. 2000, Farley et al. 1996, Lewin Group 2000). ESRD-specific models that include all of

these factors have higher explanatory power than models using fewer predictors (Farley et al. 1996,

Lewin Group 2000). However, the increased explanatory power given by a large set of adjusters needs

to be weighed against the administrative complexity and costs incurred in implementing the payment

method as well as its ability to be easily understood by providers. For administrative simplicity, a smaller

set of adjusters might be advisable.

The Secretary will need to consider the availability of data when selecting factors to adjust capitated

payments. One important source of data about the demographic and clinical characteristics of ESRD

beneficiaries is HCFA’s Medical Evidence Form (HCFA-2728). HCFA requires that providers

complete this form within 45 days of an ESRD diagnosis, as it is used to establish Medicare eligibility for

individuals who previously were not Medicare entitled, reclassify previously eligible Medicare

beneficiaries as ESRD, and provide demographic and diagnostic information on all new ESRD patients

regardless of Medicare entitlement. This form collects information on the date of first ESRD service, the

primary disease causing ESRD, the presence of comorbid conditions, values of laboratory indices, use

of renal treatment modalities, and patients’ employment status at the start of ESRD.

HCFA will have to pay attention to the possibility of gaming in any risk adjustment system. Incentives to

increase the number of beneficiaries with a characteristic associated with higher payment rates may be

high. Gaming may occur if the Secretary continues to use the underlying cause of ESRD, obtained from

HCFA’s Medical Evidence Form, as a risk adjuster. However, the potential for gaming using this

characteristic and other data obtained from HCFA’s administrative databases is less than with other

sources of data because the information is more easily amenable to audit.

7 In its ESRD Managed Care Demonstration Project, HCFA is studying whether access to and quality of care can be enhanced by managed care. The

demonstration is evaluating a number of issues: 1) whether integrated acute and chronic care services and case management improve ESRD

beneficiaries’ health outcomes, 2) whether capitation rates reflecting ESRD beneficiaries’ treatment needs increase the probability of kidney transplant,

and 3) whether additional benefits, such as transportation and nutritional services, are cost-effective. Demonstration services are currently being

provided to 1,623 beneficiaries for three years at three sites, with a completion date of fall 2001. The effectiveness of the program will then be

evaluated by a contractor.
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 37

Another issue that the Secretary will need to consider is whether to combine risk adjustment for

beneficiaries with ESRD with payments based in part on actual services used. Partial capitation would

complement risk adjustment and could be especially useful in situations where costs are expensive and

variable. As noted previously, HCFA’s ESRD demonstration project pays participating sites separate

payments to cover the costs of kidney transplantation. By contrast, for the 20,000 ESRD beneficiaries

currently enrolled in Medicare+Choice, plans receive no additional payment for the costs of kidney

transplantation.

We encourage the Secretary to evaluate the use of partial capitation together with risk-adjusted

payments for beneficiaries with ESRD. One group of beneficiaries with very high spending levels is

those undergoing kidney transplantation. Because it offers beneficiaries a better quality of life and has

been found to be more cost effective than maintenance dialysis, kidney transplantation is the preferred

renal treatment modality (Eggers 1992). If payments for transplantation were included in either the

dialysis or the post-transplant capitated payment, health plans might be discouraged from promoting

kidney transplants because they would receive payments for the high-cost procedure only gradually

over time. Plans with beneficiaries who disenrolled, died, or lost Medicare eligibility soon after

transplantation would be underpaid. Additionally, including payments for the transplant procedure with

post-transplant capitated payments is problematic because the period for plans to recoup their costs is

limited. ESRD beneficiaries under age 65 with a functioning graft lose their Medicare entitlement three

years following the transplant, unless they qualify for benefits based on disability.

Partial capitation also may be beneficial because costs of care vary for other groups of beneficiaries with

ESRD, not just those who receive transplants. The USRDS showed that spending for dialysis

beneficiaries in the 90th percentile was about 2.5 times higher than for those at the median and that

about 12 percent of dialysis beneficiaries had four or more hospitalizations during the course of a year

(USRDS 1999, USRDS 2000). Farley and colleagues (1996) showed that an ESRD-specific payment

method that combined risk-adjustment with partial capitation is more accurate than a model not using

any form of partial capitation. As suggested in the previous chapter, some form of partial capitation

might reduce the risk of plans suffering large losses and the chance for plans to make large profits.

The Secretary will also need to consider whether payments should be administered on a concurrent

basis, changing during the calendar year both when dialysis beneficiaries receive a kidney transplant and

no longer require dialysis and when beneficiaries with a kidney transplant experience a graft failure and

require dialysis. The payment system used for HCFA’s ESRD demonstration project was implemented

in such as fashion, and HCFA should consider its experience in administering the demonstration project

when evaluating the use of such a system for M+C. A concurrent system might present administrative

challenges in collecting information on beneficiaries’ renal treatment modality and changing payments

during the course of the year. An alternative is to design a prospective system, such as the interim PIPDCG system, with separate payments calculated on an “intention-to-treat” basis for caring for

beneficiaries on dialysis and for beneficiaries with a kidney graft . Intention-to-treat methods base

payment on the treatment modality furnished to beneficiaries at one particular point in time, such as the

start of the year.
.......................................................................................................................................................................................................................................................................................................

38 R i s k a d j u s t m e n t f o r s p e c i a l p o p u l a t i o n s

Finally, because of the potential deleterious effect on the access to care for ESRD beneficiaries, the

Commission reiterates its March 2000 recommendation that the Congress lift the bar prohibiting

beneficiaries with ESRD from enrolling in Medicare+Choice once a risk-adjusted payment system and a

system to report on the quality of dialysis care are implemented (MedPAC 2000). Beneficiaries with

ESRD face significant out-of-pocket expenses (about $10,000 per beneficiary in traditional Medicare)

and prohibiting them from enrolling in M+C plans may have a detrimental effect on access, especially for

low-income beneficiaries and those unable to obtain supplemental insurance. The Commission remains

concerned that ESRD beneficiaries do not have the same freedom of choice to enroll in M+C plans that

other beneficiaries do. ■
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 39

References

Beddhu S, Bruns FJ, Saul M, Seddon P, Zeidel ML. A simple comorbidity scale predicts clinical

outcomes and costs in dialysis patients, American Journal of Medicine. June 1, 2000, Vol. 108, No. 8,

p. 609–613.

Eggers P. Comparison of treatment costs between dialysis and transplantation, Seminars in Nephrology.

May 1992, Vol. 12, No. 3, p. 284–289.

Esposito A, HCFA Office of Strategic Planning. Update on the development of M&C risk adjustment

methodology. Baltimore (MD), meeting of the National Chronic Care Consortium. March 31, 2000.

Farley DO, Carter GM, Kallich JD, Lucas TW, Spritzer KL. Modified capitation and treatment

incentives for end-stage renal disease, Health Care Financing Review. Spring 1996, Vol. 17, No. 3, p.

129–142.

Gruenberg L, Kaganova E, Hornbrook MC. Improving the AAPCC with health-status measures from

the MCBS, Health Care Financing Review. 1996, Vol. 17, No. 3, p. 59–75.

Gruenberg L, Silva A, Corazzini K, Malone J. An examination of the impact of the proposed new

Medicare capitation methods on programs for the frail elderly. Cambridge (MA), The Long-Term Care

Data Institute. January 1999.

Health Care Financing Administration. Operational policy letter #122. Washington (DC), HCFA. June

22, 2000.

Health Care Financing Administration. Comparison of demographic and selected intermediate outcome

measures for health maintenance organization (HMO) and fee-for-service in-center hemodialysis

patients, Supplemental Report #1, 1998 ESRD Core Indicators Project. Washington (DC), HCFA.

February 1999.

Lewin Group. Capitation models for ESRD: methodology and results. Falls Church (VA), the Lewin

Group. January 2000.

Liu K, Wall S, Wissoker D. Disability and Medicare costs of elderly persons, Milbank Quarterly. 1997,

Vol. 75, No. 4, p. 461–493.

McCall NT, Pope GC, Griggs M, Adamache KW, Dayhoff DA, Caswell C, Smith K. Research and

analytic support for implementing performance measurement in Medicare fee for service. Second annual

report, Contract No. 500-95-0058, Task Order No. 2. Waltham (MA), Health Economics Research,

Inc. January 6, 2000. Available at http://www.hcfa.gov/quality/docs/.
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McCall N, Korb J. Risk adjustment for dually eligible beneficiaries using long-term care, Health Care

Financing Review. Winter 1998, Vol. 20, No. 2, p.71–90.

McHorney CA, Ware JE, Lu JF, Sherbourne CD. The MOS 36-item short-form health survey (SF36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups, Medical

Care. 1994, Vol. 32, No. 1, p.40–66.

Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy.

Washington (DC), MedPAC. March 2000.

Medicare Payment Advisory Commission. Report to the Congress: selected Medicare issues.

Washington (DC), MedPAC. June 1999.

National Committee on Vital and Health Statistics. Core health data elements report, NCVHS, August

1996. Available at: http://www.ncvhs.hhs.gov/ncvhsr1.htm. Accessed October 2000.

National PACE Association. PACE profile 1999. San Francisco, PACE. October 1999.

Pope GC, Adamache KW, Walsh EG, Khandker RK. Evaluating alternative risk adjusters for

Medicare, No. 17-C-90316/1. Waltham (MA), Center for Health Economics Research. 1998.

Pope GC, Walsh EG, Gilman B. Analysis of risk adjustment data from the Health Outcomes Survey:

final report, Contract No. 500-97-0443. Waltham (MA), Health Economics Research, Inc. August 25,

2000.

Riley GF. Risk adjustment for health plans disproportionately enrolling frail Medicare beneficiaries,

Health Care Financing Review. Spring 2000, Vol. 21, No. 3, p. 135–148.

Smith KD, Brown RS, Khan N, Mathematic Policy Research. Risk adjustment for dual eligibles in

Colorado. Los Angeles (CA), Association for Health Services Research 17th Annual Meeting,

Research to action: shaping the health system in the new millennium. June 2000.

United States Renal Data System. USRDS 2000 annual data report. Bethesda (MD), National Institute

of Diabetes and Digestive and Kidney Diseases. June 2000.

United States Renal Data System. USRDS 1999 annual data report. Bethesda (MD), National Institute

of Diabetes and Digestive and Kidney Diseases. April 1999.

Veazie P, Manning W, Kane R. Medicare risk adjustment using health status indicators, HCFA Contract

No. 500-93-0033, Minneapolis (MN), University of Minnesota, 1999.
..................................................................................................................

Analysis of questions related

to risk adjustment using

the principal inpatient diagnostic

cost group model

..................................................................................................................

A P P E N D I X
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 43

In this appendix, we analyze five questions related to the principal inpatient diagnostic cost group (PIPDCG) model for which the Congress requested answers from MedPAC in the BBRA. We also examine

a sixth question regarding mortality rates. The presentation includes discussion of the Commission’s

analytic methods, findings, and caveats about the findings, when appropriate. The six questions

answered include:

• How well do risk scores from the PIP-DCG model explain variation in Medicare+Choice (M+C)

plans’ per capita Medicare costs, as reported in their adjusted community rate filings?

• Under the PIP-DCG model, how stable are M+C plans’ per capita risk scores from year to year?

And what is the potential for substantial changes in risk scores for small plans?

• Is there still evidence that M+C enrollees are less costly at their time of enrollment, on average,

than are beneficiaries who remain in fee-for-service Medicare?

• Is there still evidence that beneficiaries who voluntarily leave M+C are more costly at their time of

disenrollment, on average, than are beneficiaries who remain in traditional Medicare?

• Is there evidence that Medicare+Choice enrollees have lower mortality rates than beneficiaries in

traditional Medicare?

• How much does HCFA’s decision to exclude “discretionary” hospitalizations affect the PIP-DCG

model?

To provide context for our analysis of these questions, we begin by summarizing the important details of

how HCFA determines payments in the PIP-DCG model. HCFA first groups enrollees into health status

categories according to these risk factors:1

• age,

• sex,

• Medicaid status in the previous year,

• original eligibility for Medicare by reason of disability (for aged beneficiaries), and

• principal diagnoses from their hospital inpatient stays, if any, in the previous year.

In 2002, HCFA will begin making extra payments in recognition of the costs of successful outpatient

care for congestive heart failure. HCFA will make these payments to M+C plans that meet quality

indicator thresholds. These extra payments will be made to qualifying M+C plans in 2002 and 2003

only.

1 A working aged adjustment also may apply.
.......................................................................................................................................................................................................................................................................................................

44 A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

HCFA currently uses the risk factors listed above to determine beneficiaries’ risk scores, which it then

multiplies by base payment rates to compute payments. To determine risk scores, HCFA sums the

expected costliness associated with enrollees’ risk factors and divides that sum by the national average

expected costliness. Consider, for example, a 73-year-old man who was eligible for Medicaid and

hospitalized for prostate cancer the previous year. The expected costliness in 1996 dollars for his age

and sex is $3,598, his Medicaid status would add $2,330, and his prostate cancer would add $2,333.

Therefore, his total expected costliness is $8,261. The national average costliness was $5,100 in 1996,

so his risk score would be 1.62 ($8,261 divided by $5,100). If the county base payment rate was $500

per month, then HCFA would pay the plan $500 x 1.62 = $810 per month for this beneficiary.

Now we turn to the six questions we analyzed.

First question: How well do risk scores from the PIP-DCG model explain variation in

Medicare+Choice plans’ per capita Medicare costs, as reported in their adjusted

community rate filings?

Each year, M+C plans submit adjusted community rate (ACR) filings, which include benefit and

premium proposals for the upcoming calendar year. The ACR filings also show plans’ estimated costs

for the coming year, which are based on M+C plans’ actual costs from two years earlier. For example,

in July 2000 plans submitted ACRs with estimated costs for 2001 based on plans’ actual costs from

1999. We used the 1999 actual cost data in this analysis.

ACRs are the only source of data on plans’ costs, but they have serious shortcomings. First, there likely

is wide variation in the methods plans use to determine costs of providing Medicare covered services.

For example, plans generally serve both the Medicare population and the non-aged working population.

For their ACRs, plans must estimate what proportion of their total costs is attributable to Medicare

enrollees. They likely use a variety of methods to make this estimation, so what the ACR cost data

measure probably varies as well. Second, there is probably wide variation across plans in measuring

administrative costs because HCFA has not provided plans with firm guidelines on how to determine

them. Because of these shortcomings, we caution about the reliability of results based on these data.

We also caution that analyzing cost data for plans participating in 1999 may not be appropriate for

characterizing plans that intend to participate in Medicare+Choice in 2001. Many of the plans

participating in Medicare+Choice in 1999 have since withdrawn, and they may have had costs that

were high relative to payments (or risk scores). (The ACR data do not allow us to observe the

relationship between costs and risk scores for plans that have withdrawn from the program). Therefore,

the data may provide an inaccurate representation of how well currently participating plans’ risk scores

correlate with their costs.
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 45

Method

Our analysis of this question deviated slightly from the BBRA directive because we measured how well

plans’ per capita payments—rather than risk scores—from the PIP-DCG model explain their per capita

costs. We chose to use payments rather than risk scores because payments and costs reflect regional

differences in input prices and practice patterns, whereas risk scores do not. Therefore, payments are

on a more level playing field with costs than are risk scores (and are ultimately what matters).

Our analytic method consists of regressing per capita 1999 costs that plans report in their 2001 ACRs

against their per capita payments from the PIP-DCG model.2 We estimated two simple equations in

which per capita payment was the only variable used to explain per capita costs. First, we estimated

how well plans’ per capita payments from the PIP-DCG model explain their per capita medical care

costs under the basic benefit package in traditional Medicare (Model 1). Model 2 is identical to the first,

except we replaced medical care costs with total costs, which include administrative costs.

An important statistic produced by these regressions is the coefficient on per capita payments, which

indicates the average increase in per capita costs for each dollar increase in per capita payments. A

value of this statistic equal to 1.0 would indicate that, on average, two plans receiving different per

capita payments have per capita costs that differ by the same amount.3 A value less than 1.0 would

mean that the margin between payments and costs tends to be larger for plans with relatively high

payments. Conversely, a value greater than 1.0 indicates the margin between payments and costs tends

to be larger for plans with low payments.

However, even if a one-dollar increase in payments corresponds to a one-dollar increase in costs, on

average, individual plans could have substantial differences between their per capita payments and their

per capita costs. Therefore, it is useful to know how efficiently plans’ per capita payments match their

per capita costs, or, in statistical terminology, how much of the variation in per capita costs is explained

by per capita payments. The R2 statistic measures the variation explained. The higher the R2 value, the

more of the variation in costs is explained by payments.

Results

Results from estimation of Model 1 show that plans’ per capita medical costs increase by an average of

$0.56 for each dollar increase in per capita payments (Table A-1). The R2 from this regression is 0.48,

indicating per capita payments at the plan level explain about 48 percent of the variation in per capita

medical care costs at the plan level.4 Results from Model 2 indicate that per capita total costs increase

$0.67 for each dollar increase in payments, and that per capita payments explain about 46 percent of

the variation in total costs.

2 The regression line estimated was: per capita costs = A + B x per capita payments + E.

3 Although the BBRA directed MedPAC to investigate how well payments explain the variation in costs, this statistic tells us nothing about variation

explained. However, this statistic still provides valuable information because it tells us whether per capita costs and payments differ systematically with

plans’ per capita payments.

4 These R2 values are much higher than those in the main text because we are comparing plan-level rather than individual level figures. It is the figures

in the text using individual level data that indicate plan incentives to select individuals.
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46 A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

The results from Model 1 and Model 2 indicate that per capita payments explain a fair amount of

variation in per capita costs, but per capita costs increase by much less than one dollar for each onedollar increase in per capita payments. At least two factors could be contributing to the latter

relationship. First, the PIP-DCG model could do a poor job of reflecting beneficiaries’ relative

costliness. Second, plans that serve counties with relatively high per capita costs in traditional Medicare

may have greater opportunity to reduce their costs below the traditional Medicare costs in their counties

than do plans that serve counties with relatively low traditional Medicare costs. This results in larger

differences between per capita payments and costs for plans in counties with high traditional program

costs because payments to plans depend heavily on traditional Medicare per capita costs in the counties

they serve.

We used regression analysis to confirm that plans in counties with high traditional Medicare costs tend to

have larger differences between their per capita payments and per capita costs than do plans in counties

with lower traditional Medicare costs. For each plan, we calculated the difference between the plan’s

per capita payments and per capita medical costs and regressed this difference against the average

traditional Medicare costs in the counties the plan serves.5 Results show that this difference increases by

an average of $0.35 for each dollar increase in the average costs in the counties served.

We also considered whether the low rate of increase in per capita costs relative to per capita payments

may reflect large plans having lower per capita costs than small plans, perhaps because fixed costs are

fairly constant across plans. To analyze this possibility, we performed two regressions: plans’ per capita

medical costs against their numbers of enrollees, and plans’ per capita total costs against the numbers of

enrollees. Both regressions show a weak relationship between per capita costs and their number of

enrollees, so we reject this possibility as a factor contributing to the low rate of increase in per capita

costs.

Medical costs and PIP-DCG model payments (Model 1) Medical costs and PIP-DCG model payments (Model 1) $0.56 48%

Medical costs and demographic model payments (Model 1) Medical costs and demographic model payments (Model 1) 0.50 45

Total costs and PIP-DCG model payments (Model 2) otal costs and PIP-DCG model payments (Model 2) 0.67 46

Total costs and demographic model payments (Model 2) otal costs and demographic model payments (Model 2) 0.60 44

Relationship variables Cost increase for each Variation in costs

(plan per capita amounts) dollar of payment increase explained by payments

Relation between per capita costs and per capita payments

for Medicare+Choice plans, 1999

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

A-1

Note: PIP-DCG (principal inpatient diagnostic cost group risk adjustment model). Results are based on a sample of 151 Medicare+Choice plans. Medical

costs include plans’ costs for the basic Medicare benefit package. Total costs include medical and administrative costs. Model 1 shows the correlation

between plans’ per capita payments and their per capita medical costs. Model 2 shows the correlation between plans’ per capita payments and their

per capita total costs.

Source: MedPAC analysis of Medicare+Choice encounter data and 1999 cost data from adjusted community rate filings for 2001.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................

5 For each M+C plan, we calculated average county costs as a weighted average of the traditional program costs in the counties the plan serves. The

weight for each county is the fraction of the plan’s enrollees in that county.
.......................................................................................................................................................................................................................................................................................................

I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 47

Correlation of demographic payments and costs

Over the 2000-2004 period, HCFA will phase in the PIP-DCG model in place of the demographic

model. How much better is the PIP-DCG model?

To answer this in the context of the first question, we re-estimated the regressions for Model 1 and

Model 2 but replaced per capita payments from the PIP-DCG model with per capita payments from

the demographic model. The results from the re-estimations make it clear that the PIP-DCG model

provides a small improvement at the plan level (Table A-1). Under the demographic model, per capita

medical costs increase by $0.50 for each dollar increase in per capita payments, and payments explain

about 45 percent of the variation in medical costs. Also, total costs increase by $0.60 for each dollar in

payments from the demographic model, and the variation explained is 44 percent.

This last exercise shows that compared to the PIP-DCG model a one-dollar increase in payment under

the demographic system corresponds to a little smaller increase in costs. Also, the demographic system

explains only a little less of the variation in plans’ costs than does the PIP-DCG system. These small

differences between the PIP-DCG and demographic systems are to be expected because the set of

beneficiaries in plans is not random and under-represents high-cost beneficiaries for which the PIPDCG model is a better predictor. Therefore, this test is not a good one for comparing how well costs

correlate with payments under the two models.

Second question: Under the PIP-DCG model, how stable are Medicare+Choice

plans’ per capita risk scores from year to year? And what is the potential for

substantial changes in risk scores for small plans?

Because HCFA uses risk scores from the PIP-DCG model to risk adjust payments to M+C plans,

instability in plans’ per capita risk scores over time will likely cause instability in their per capita

payments. Small plans are especially vulnerable because large changes in payments for only a few

enrollees could drastically affect total revenue.

Some might argue that an issue more important than stability of payments is stability of payments relative

to costs. This argument is based on the belief that the purpose of risk adjustment is to pay plans

accurately for the expected cost of their enrollees. If the expected costliness of a plan’s enrollees

increases substantially from one year to the next, an effective risk adjuster would cause a similar change

in the plan’s average payments. In this case, stable payments would be detrimental to the plan’s financial

health. Others might disagree with this perspective, arguing that stable payments are always preferable

because they enable plans to charge stable premiums, which helps reduce the beneficiaries’ uncertainties

about their future health care costs.
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48 A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

Method and results

We examined the stability of M+C plans’ per capita risk scores by looking at the distribution of the

absolute value of the percentage change in plans’ per capita risk scores from September 1998 to

September 1999. This examination revealed per capita risk scores are not stable, with an average

absolute change of 4.4 percent. We also found that 15 percent of plans had very small changes (1

percent or less), 31.1 percent had small changes (2 percent or less), 33 percent had large changes

(more than 5 percent), and 8.1 percent had very large changes (more than 10 percent) (Table A-2).

Much of the change in per capita risk scores was in the upward direction. About 66 percent of plans

had higher per capita risk scores in 1999 than in 1998.

Small plans show less stability Investigation of the potential for substantial changes in per capita risk

scores for small M+C plans required a somewhat arbitrary decision on the definition of a small plan. We

chose to define them as having fewer than 5,000 enrollees in September 1998. Per capita risk scores

for this group were less stable than for all plans in the sample, as expected. The average absolute

percentage change was higher for small plans, at 5.8 percent. Also, small plans were less likely to have

small changes, as only 10.2 percent of them had an absolute percentage change of 1 percent or less,

and 24.5 percent had a change of 2 percent or less. Finally, small plans were more likely to have had

large absolute percentage changes; 46.9 percent of them had absolute changes of greater than 5

percent, and 13.3 percent had changes of more than 10 percent (Table A-2).

Whether small plans face a high potential for large changes in per capita risk scores cannot be

determined with certainty because there is no clear definition of a “high” potential. Nevertheless, we

believe the data show that small plans are quite likely to have large changes in per capita risk scores.

Average absolute percentage change 4.4% 5.8%

Percent of plans with change ≤ 1 percent 15.0 10.2

Percent of plans with change ≤ 2 percent 31.1 24.5

Percent of plans with change > 5 percent 33.0 46.9

Percent of plans with change > 10 percent 8.1 13.3

Plan size

Statistic All plans < 5,000 enrollees

Stability of Medicare+Choice plans per capita risk scores

under the principal inpatient diagnostic cost group

risk adjustment model, 19981999

TABLE

A-2

Note: Results are based on a sample of 273 for “all plans” and 98 for plans with fewer than 5,000 enrollees.

Source: MedPAC analysis of September 1998 and September 1999 Medicare+Choice encounter data collected by HCFA.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................

.......................................................................................................................
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 49

Higher stability in demographic model To provide a point of comparison for the stability of risk scores,

we examined the stability of payments under the demographic model.6 These payments show more

stability than risk scores. For example, plans’ per capita payments from the demographic model from

September 1998 to September 1999 had an average absolute percentage change in payments over that

period of 3.5 percent. Also, only 14.3 percent of plans had absolute changes of greater than 5 percent,

and 2.2 percent had changes of more than 10 percent. It may appear that payments from the

demographic system are less likely to have small absolute changes because only 5.9 percent had

absolute changes of less than 1 percent and 16.8 percent had changes of less than 2 percent. However,

nearly all plans received 2 percent updates in their base payment rates from 1998 to 1999, so it is not

surprising that a relatively small percentage of plans had demographic payments that changed by less

than 2 percent.

Controlling for new enrollees and disenrollees

The year-to-year changes in per capita risk scores provide a measure of the stability of plans’ revenue.

However, they do not represent the stability strictly attributable to the PIP-DCG model because they

also include the effects of enrollment changes between September 1998 and September 1999. For

example, even if risk scores from the PIP-DCG model are perfectly stable, enrollment and disenrollment

of beneficiaries between September 1998 and September 1999 could have caused their plans’ per

capita risk scores to change over that period.

We found that excluding new enrollees and disenrollees from the analysis reduced the stability of plans’

per capita risk scores. For all plans, the average of their absolute percentage change increased to 7.3

percent. Also, the percentage of plans with absolute percentage changes of 1 percent or less decreased

to 7.3 percent, and the percentage with changes of 2 percent or less was only 12.1 percent. Moreover,

69.2 percent of plans had absolute percentage changes of more than 5 percent, and 21.6 percent had

changes of more than 10 percent (Table A-3).

6 In our comparison of the PIP-DCG to the demographic system, we chose to use payments from the demographic system rather than demographic

scores. We did this because each beneficiary has two demographic scores—one for Part A services and one for Part B services—so comparing

stability of PIP-DCG risk scores to stability of demographic payments is more straightforward.

Average absolute percentage change 7.3%

Percent of plans with change ≤ 1 percent 7.3

Percent of plans with change ≤ 2 percent 12.1

Percent of plans with change > 5 percent 69.2

Percent of plans with change > 10 percent 21.6

Statistic

Stability of Medicare+Choice plans’ per capita risk scores

under the principal inpatient diagnostic cost group model

after removing new enrollees and disenrollees, 1998–1999

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

A-3

Note: Results are based on a sample of 273.

Source: MedPAC analysis of September 1998 and September 1999 Medicare+Choice encounter data collected by HCFA.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................
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A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

Why are risk scores more likely to change when new enrollees and disenrollees are excluded? All else

being equal, the risk score for a given set of enrollees will rise over time as those enrollees age. And, if

enough of a plan’s enrollment changes age category, the change in the risk score may be large. New

enrollees, however, tend to have lower risk scores (mainly because they are younger) and offset the

impact that aging of continuing enrollees has. (The opposite is true for disenrollees, but there are many

fewer of them.) Thus, when new enrollees and disenrollees are excluded from the analysis, stability falls.

Factors underlying risk score increase

The fact that per capita risk scores increased from September 1998 to September 1999 for 66 percent

of plans raises the question: why did per capita risk scores increase for most plans? Of the variables

used to determine risk scores, age and health status categories have the most influence. Hence, the

overall increase in plans’ per capita risk scores was probably caused by higher average ages, more

costly health status categories, or both.

Examination of the data shows the prevalence of higher per capita risk scores does not appear to be

due to beneficiaries being in more costly health status categories. The PIP-DCG model assigns each

beneficiary to 1 of 16 numbered health status categories called PIP-DCGs, in which higher numbers

reflect higher risk scores.7 Data from plans that participated in Medicare+Choice in both September

1998 and September 1999 show that 53.7 percent had lower per capita PIP-DCG numbers in

September 1999 than in September 1998. Also, we found that the average of plans’ per capita PIPDCGs declined from 4.62 to 4.58 over that period.

The tendency for plans’ per capita risk scores to increase over the September 1998-September 1999

period depends heavily on how the per capita age of their enrollees changed relative to the per capita

PIP-DCG numbers. Most plans that had risk score declines from September 1998 to September 1999

had relatively large decreases in per capita PIP-DCGs. Conversely, plans that had risk score increases

from September 1998 to September 1999 either had relatively small decreases in PIP-DCGs that were

more than offset by higher per capita age, or they had increases in both per capita PIP-DCGs and age.

Most plans—80.2 percent—had higher per capita age in 1999 than in 1998, which explains why per

capita risk scores increased for a strong majority of plans.

Third question: Is there still evidence that Medicare+Choice enrollees are less costly

at their time of enrollment, on average, than are beneficiaries who remain in feefor-service Medicare?

Previous studies—MedPAC (1998) and PPRC (1996)—indicate that enrollees of Medicare risk plans

were much less costly just prior to enrollment than were beneficiaries who stayed in traditional

Medicare. Also, MedPAC (1998) determined that the PIP-DCG model does not adequately adjust

payments downward to reflect the lower costliness of those enrollees. M+C plans are generally similar

to Medicare risk plans, so the 1998 MedPAC study suggests that M+C plans still benefit from

favorable selection (enrollment of low-cost beneficiaries) even if payments are risk adjusted by a PIPDCG model.

7 The 16 PIP-DCGs are numbered 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 20, 23, 26, and 29.

50
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 51

The data used in these studies are somewhat dated, and some have argued that, on average, M+C

enrollees are no longer much more healthy at their time of enrollment than are beneficiaries in traditional

Medicare. In response, the Congress requested MedPAC to use the most recent data available to test

this assertion.

Method

In satisfying the Congressional request, we used a sample of beneficiaries enrolled in Part A and Part B

of traditional Medicare for all of 1997. We divided this sample into two groups— those who stayed in

traditional Medicare throughout their 1998 entitlement (traditional Medicare group) and those who

enrolled in Medicare+Choice in 1998 (M+C group)—and compared the average program costliness in

1997 for the two groups. We also used DxCG, Inc. software to determine average 1997 payments

under the PIP-DCG model for both groups. The difference in average payments between groups

indicates how much of the difference in costs is due to risk factors in the PIP-DCG model: age, sex,

Medicaid status in the previous year, eligibility for Medicare by reason of disability (for aged

beneficiaries), and principal diagnoses from hospital inpatient stays in the previous year.

Determining 1997 payments under the PIP-DCG model requires data on beneficiaries’ diagnoses from

hospital inpatient stays from July 1995 through June 1996. Therefore, the analytic sample includes

people continuously enrolled in Part A and Part B of traditional Medicare from July 1995 through

December 1997. We excluded from the traditional Medicare group beneficiaries who did not live in a

county with at least one M+C plan in 1998, because plans have located predominantly in counties with

relatively high per capita costs in traditional Medicare and failure to make this adjustment would

introduce a downward bias in the costs of traditional Medicare beneficiaries.

We also modified the traditional Medicare group by controlling for a potential bias in the (often) high

costs related to death. To be in the M+C group, a beneficiary had to survive until enrolling in an M+C

plan in 1998, but traditional Medicare beneficiaries only had to survive until January 1, 1998—a

generally less stringent survival criterion. Some of the traditional Medicare beneficiaries who died in

1998 could have enrolled in Medicare+Choice if they had lived longer. However, their 1997 costs

related to their deaths are included in the average costs of the traditional Medicare group, so there is an

upward bias on the often high costs just before death in the traditional Medicare group.

To correct the problem, we calculated the percentage of the M+C group that enrolled in an M+C plan

in each month of 1998. Based on these percentages, we randomly assigned a “pseudo” M+C

enrollment month in 1998 to each traditional Medicare beneficiary. The probability of being assigned to

a month is equal to the percentage of actual M+C enrollments that took place in that month. For

example, in the M+C group, 9 percent of enrollments in 1998 took place in March, so a traditional

program beneficiary had a 9 percent chance of having March as their pseudo-enrollment month. Finally,

we eliminated traditional Medicare beneficiaries who died before their pseudo-enrollment month, which

gives the traditional Medicare group the same survival criterion as the M+C group.
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52 A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

Results

In 1997, the ratio of average program costs of the M+C group to the average program costs of the

traditional Medicare group was 0.77. This indicates that the M+C group was 23 percent less costly in

the calendar year before their M+C enrollment than the traditional Medicare group. Further, the ratio of

average PIP-DCG payments for the M+C group to the average PIP-DCG payments for the traditional

Medicare group is 0.92, which means that average PIP-DCG payments are 8 percent lower for the

M+C group compared to the traditional Medicare group (Table A-4).

These results imply that the risk factors included in the PIP-DCG model (previously listed) account for

just 8 percentage points of the 23 percentage-point difference in average costs between the two groups.

To the extent that M+C plans continue to experience favorable selection of new enrollees, they benefit

significantly because the PIP-DCG model does not adequately adjust payments to reflect the relatively

low cost of those enrollees.

Some of the difference in average costs between the two groups may be due to a smaller percentage of

the M+C group relative to the traditional Medicare group having supplemental insurance coverage such

as Medigap before enrolling.8 Previous MedPAC analysis indicates that in 1998 26 percent of first-year

Medicare+Choice enrollees did not have supplemental coverage in the previous year. At the same time,

13 percent of beneficiaries who lived in a county with at least one M+C plan and remained in the

traditional program in 1998 were without supplemental coverage in 1997 (MedPAC 2000). We do not

believe, however, this difference in supplemental coverage could explain all of the difference in costs

between these two groups, even after controlling for differences in risk.

The results presented here differ from a previous analysis (MedPAC 1998) that used a similar method

and reported a ratio of average program costs for the M+C group to the traditional Medicare group of

0.72. The discrepancy between studies makes it appear that the degree of favorable selection in

Medicare+Choice has declined, but the difference is because the 1998 analysis did not include an M+C

pseudo-enrollment adjustment for the traditional Medicare group. Indeed, after we re-calculated the

average costliness of the traditional Medicare group without the pseudo-enrollment adjustment, the

costliness of the M+C group relative to the traditional Medicare group declined to 0.72, the same as we

found before.

Actual costs 0.77

Payments from PIP-DCG model 0.92

 Ratio, average for new M+C enrollees

to average for traditional

Measure of cost or payment Medicare beneficiaries

Examining Medicare+Choice enrollees’ costs

in the year prior to enrollment, 1997

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

A-4

Note: M+C (Medicare+Choice), PIP-DCG (principal inpatient diagnostic cost group). All beneficiaries analyzed were in Part A and Part B of traditional

Medicare from July 1995 through December 1997. Traditional Medicare beneficiaries stayed in traditional Medicare in 1998, but M+C enrollees

joined a Medicare managed care plan in 1998.

Source: MedPAC analysis of 1995 through 1998 5 percent standard analytic files of Medicare beneficiaries.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................

8 Supplemental coverage—which includes Medigap, coverage through a former employer, Medicaid, and other public health insurance programs—is

generally thought to induce higher spending.
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 53

Finally, some would argue that our results overstate the degree of favorable selection because although

new enrollees in Medicare+Choice are lower cost, they trend towards the average cost in traditional

Medicare over time. This “regression toward the mean” has most recently been argued by the General

Accounting Office (GAO 2000). Later, we present an analysis showing that M+C enrollees have a

lower mortality rate than traditional Medicare beneficiaries, but the difference narrows as length of M+C

enrollment increases, which supports the concept of regression toward the mean.

Fourth question: Is there still evidence that beneficiaries who voluntarily leave

Medicare+Choice are more costly at their time of disenrollment, on average, than

are beneficiaries who remain in traditional Medicare?

This question overlaps with the third in the sense that the purpose is to search for evidence of favorable

selection in Medicare+Choice. In this case, MedPAC was directed to find out if M+C plans benefit

because relatively costly enrollees leave. We do not believe, however, that the results from this analysis

should have much influence because the rate of voluntary disenrollment from Medicare+Choice is quite

low.

Method

The sample we used for this analysis consists of two distinct groups. One group—the traditional

Medicare group—began its Medicare entitlement during or before 1997, was enrolled in Part A and

Part B of the traditional program throughout 1997 entitlement, and maintained that status throughout

1998 entitlement. The other group—the risk plan group—left Medicare risk plans some time in 1997,

enrolled in Part A and Part B of traditional Medicare, and maintained that status throughout 1998

entitlement. We eliminated people from the traditional Medicare group if they did not reside in a county

with at least one risk plan in 1997 because most risk plans were established in counties with relatively

high per capita costs in traditional Medicare and failure to make this adjustment would introduce a

downward bias in the costs of traditional Medicare beneficiaries.

To determine whether beneficiaries who disenrolled from Medicare risk plans were costly in relation to

the traditional Medicare group, we compared the average 1998 program costs of the risk plan group

with the average 1998 program costs of the traditional Medicare group. We also compared average

1998 payments from the demographic system, but excluded adjustments for institutional status.9 The

difference in average payments between groups indicates how much of the difference in costs is due to

differences in age, sex, and Medicaid status.

Results

There is little difference in 1998 program costliness between the two groups. The ratio of average

costliness of the risk plan group to that of the traditional Medicare group is 1.02. Further, the average

adjusted payments in 1998 for the risk plan group is only 5 percent higher than the average for the

traditional Medicare group. These results suggest M+C plans actually experience slight adverse

selection on the disenrollment of beneficiaries. However, the infrequency of voluntary disenrollment

makes the impact of the adverse selection small.

9 We could not adjust for institutional status because that information was not available for traditional Medicare beneficiaries on the enrollment and

claims files we used in this analysis. This method for estimating payments is basically the same as that used by PPRC (1996).
.......................................................................................................................................................................................................................................................................................................

54 A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

Our results differ substantially from a previous analysis (PPRC 1996) that also examined the costliness

of Medicare risk plan disenrollees. Results from that study showed that risk plan disenrollees were much

more costly than traditional program beneficiaries. However, two differences in method exist.

First, PPRC examined disenrollees’ costliness in the six months immediately after leaving a risk plan, but

MedPAC analyzed costliness in the calendar year after disenrollment. MedPAC’s method means that

disenrollees in its sample had to survive from the time they left their risk plans in 1997 through at least

January 1998. The disenrollees in PPRC’s sample did not have to meet that survival criterion, so

MedPAC’s sample could be healthier, on average. This would be especially important if it was common

for risk plan enrollees to leave their plans shortly before death, which is a plausible scenario. Second,

the PPRC sample had to be enrolled in traditional Medicare at least three months before enrolling in a

risk plan, but beneficiaries in MedPAC’s sample could have enrolled in a risk plan as soon as they were

eligible for Medicare. This is important because MedPAC analysis reveals that in July 1997, 16.9

percent of beneficiaries in Medicare risk plans enrolled in their current plan at age 65. The next most

common age for enrollment was 66, accounting for 7.7 percent of enrollees. The people who enrolled at

age 65 without first being in traditional Medicare for at least three months would not be included in the

PPRC’s disenrollee sample, but they would be part of MedPAC’s analysis. Hence, MedPAC’s sample

may have a lower average age.

Fifth question: Is there evidence that Medicare+Choice enrollees have lower

mortality rates than beneficiaries in traditional Medicare?

Analysis of the third and fourth questions shed some light on the degree of favorable selection in

Medicare+Choice, but an obvious shortcoming of both analyses is that they do not focus on the

costliness of M+C enrollees while enrolled in the M+C program. The reason for analyzing the costliness

of M+C enrollees before enrollment into Medicare+Choice (as in the third question) and after

disenrollment from Medicare+Choice (as in the fourth question) is quite practical: there are scant data

on beneficiaries’ costliness while enrolled in Medicare+Choice.

Despite the scarcity of cost data, other variables can be used to measure the health status of M+C

enrollees during their period of enrollment relative to beneficiaries in traditional Medicare. In this case,

we compared the mortality rates of these two groups.10

Method

The sample we used in this analysis consists of beneficiaries enrolled in Medicare at the start of 1998

and maintained that enrollment throughout 1998 or until death in that year. We compared the mortality

rates of two groups from this sample. One group was enrolled in traditional Medicare on January 1,

1998 (traditional Medicare group); the other was enrolled in Medicare+Choice on January 1, 1998

(M+C group). We controlled for differences in age, sex, and 1998 Medicaid status between groups

because these three variables are strong indicators of mortality risk.

10 We show later in this section that there is a lower mortality rate in Medicare+Choice. It is plausible that the lower mortality rate in Medicare+Choice

could be due to better access to services or quality care. However, the difference between Medicare+Choice enrollees and traditional Medicare

beneficiaries gets smaller as length of M+C enrollment increases. This narrowing of the difference in mortality rates between groups reduces the

credibility of the notion that it could be due to M+C enrollees receiving better health care.
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0 55

Earlier, we discussed regression toward the mean: M+C enrollees are much healthier than traditional

program beneficiaries when they first enroll, but the difference in health status between these two groups

narrows as length of M+C enrollment increases. Although there is evidence supporting regression

toward the mean, we did our own analysis of this issue by computing the mortality rates for beneficiaries

with different lengths of tenure in Medicare+Choice.

Results

After adjusting for differences in age, sex, and Medicaid status, the ratio of the mortality rate of the

M+C group to that for the traditional Medicare group was 0.85 in 1998; in other words, the M+C

group had a mortality rate 15 percent lower than that of the traditional Medicare group, indicating

favorable selection in Medicare+Choice.

We also calculated the mortality rate of each M+C plan relative to the average mortality rate for all

plans. The standard deviation of these relative mortality rates was a fairly high 0.2, which could be an

indication of differences between plans in terms of favorable selection.

Finally, our analysis of mortality rates shows the difference in morality rates between M+C enrollees and

Medicare beneficiaries narrows the longer M+C enrollees participate in the M+C program. After

controlling for age, sex, and Medicaid differences, we found the mortality rate of beneficiaries enrolled

in Medicare+Choice for one year or less was 21 percent lower than the mortality rate of beneficiaries in

traditional Medicare. In contrast, the mortality rate of beneficiaries enrolled in Medicare+Choice for

more than 5 years was just 11 percent lower than the mortality rate of beneficiaries in traditional

Medicare (Table A-5).

All enrollees 15% lower

1 year or less 21% lower

1–2 years 17% lower

2–3 years 15% lower

3–4 years 13% lower

4–5 years 10% lower

More than 5 years 11% lower

Mortality rate relative to traditional

Length of Medicare+Choice enrollment Medicare beneficiaries

Mortality rate of Medicare+Choice enrollees

relative to traditional Medicare beneficiaries, 1998

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

A-5

Source: MedPAC analysis of 1998 Medicare 5 percent denominator file and group health plan master file.

 ....................................................................................................................................................................................................................................................................................................................................................................

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56 A n a l y s i s o f q u e s t i o n s re l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

Results from analysis of this question combined with those from the third question suggest there is

favorable selection in Medicare+Choice, especially for enrollees who have been enrolled a short time.

However, combining the results from analysis of this question and the fourth question provides evidence

that the difference in costliness between M+C enrollees and traditional program beneficiaries narrows

the longer that beneficiaries are enrolled in Medicare+Choice. This supports previous analyses that have

suggested the presence of regression toward the mean.

Regression toward the mean could be an indication that favorable selection would become less of an

issue in Medicare+Choice if enrollment levels stabilize because stable enrollment would probably result

in increased average length of enrollment. Table A-6 shows the effect of stable and unstable periods of

enrollment on average length of M+C enrollment. From February 1996 to February 1997, a period of

rapidly increasing M+C enrollment, average length of M+C enrollment declined from 35.3 months to

34.7 months. But from February 1999 to February 2000, a period of flat M+C enrollment, average

length of enrollment increased from 39.4 months to 46.0 months.

Sixth question: How much does HCFA’s decision to exclude “discretionary”

hospitalizations affect the PIP-DCG model?

Before HCFA began using the PIP-DCG model, the agency was concerned about incentives plans

might have to “game” the system by hospitalizing beneficiaries for conditions they previously had not. To

counteract this incentive, HCFA put two restrictions on the hospitalizations it counts to determine

payments under the PIP-DCG model. First, HCFA does not count hospital inpatient stays it considers

to be discretionary, which it defines as those with principal diagnoses that:

• represent only minor or transitory diseases or disorders,

• are rarely the main cause of an inpatient stay, or

• are vague or ambiguous.

Second, HCFA does not count hospital inpatient stays shorter than two days.

February 2000 46.0

February 1999 39.4

February 1998 35.8

February 1997 34.7

February 1996 35.3

Average length of enrollment

Month and year (in months)

Average length of Medicare+Choice (risk plan) enrollment,

February 1996—February 2000

 ..................................................................................................................................................................................................................................................................................................................................................................

TABLE

A-6

Source: MedPAC analysis of Medicare group health plan master file.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................
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Clearly, these exclusions affect per capita payments to plans, but it is unclear whether payments increase

or decrease. When HCFA developed the PIP-DCG model, the agency grouped beneficiaries into

health status categories and determined an expected costliness for each. HCFA’s decision not to count

discretionary or short hospitalizations required the agency to group beneficiaries with such

hospitalizations into the base category, which includes beneficiaries who had no hospital inpatient stays.

On the one hand, this increases the costliness of the base category and thus increases payments for

beneficiaries with no inpatient stays. On the other hand, these exclusions reduce the number of enrollees

for whom payments are adjusted upward due to hospitalizations. Therefore, one cannot determine

whether these exclusions will increase or decrease payments without doing a numerical analysis. In fact,

it is entirely possible for one exclusion to increase payments and for the other to decrease payments.

Method and results

We would like to determine the combined effect of these two exclusions on plans’ per capita payments,

which would require comparing payments from the PIP-DCG model with payments from a risk

adjustment model that is identical, but without these exclusions. However, the latter risk adjustment

model has not been developed. As a compromise, we performed two separate analyses: one calculated

the effect on per capita payments of excluding discretionary hospitalizations, and the other computed the

impact of excluding hospital stays of less than two days.

Effect of excluding discretionary hospitalizations We used a three-step method to estimate the effect on

payments to M+C plans of excluding discretionary hospitalizations. First, we calculated the payments

that plans would receive from an alternative risk adjustment model developed by HCFA. This

alternative model is identical to the PIP-DCG model, except the alternative model does not use two risk

factors used by the PIP-DCG model—Medicaid status and ever eligible for Medicare because of

disability—nor does it exclude hospital stays of less than two days. Second, we determined payments

that plans would receive from a second alternative risk adjustment model developed by HCFA. This

model is identical to the first alternative, except it does not exclude discretionary hospitalizations. Third,

we computed the difference in payments that plans would receive under these two alternative risk

adjustment models.11

Because the only distinction between these two alternative models is whether they exclude discretionary

hospitalizations, the difference in payments between the two models reveals the effect of this exclusion

on payments. Excluding discretionary hospitalizations increases per capita payments to 93 percent of

plans, with an average increase across plans of 1.4 percent (Figure A-1).

Effect of excluding hospitalizations shorter than two days To determine the effect of excluding

hospitalizations lasting less than two days, we used two more risk adjustment models developed by

HCFA in a method that is otherwise identical to the one used to estimate the impact of excluding

discretionary hospitalizations. One of these risk adjustment models is simply the PIP-DCG model. The

other is identical to the PIP-DCG model, except it does not exclude hospitalizations shorter than two

days (Pope et al. 1999). Because these models are identical except for whether they exclude short

hospital stays, differences in payments between models reveals the impact of excluding such stays.

11 The two alternative risk adjustment models are described in detail in Pope et al. (1999).
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A n a l y s i s o f q u e s t i o n s r e l a t e d t o r is k a d j u s t m e n t u si n g t h e P I P- D CG m o d e l

–5+ –5 to –4 –4 to –3 –3 to –2 –2 to –1 –1 to 0 0 to 1 1 to 2 2 to 3 3 to 4 4 to 5 5+

Impact of excluding discretionary hospitalizations

from the principal inpatient diagnostic cost group model

FIGURE

A-1

120

100

80

60

40

20

0

Number of plans

Percentage change in payment

Note: Distribution includes 326 Medicare+Choice plans.

Source: MedPAC analysis of September 1998 Medicare+Choice encounter data.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................

–5+ –5 to –4 –4 to –3 –3 to –2 –2 to –1 –1 to 0 0 to 1 1+

Impact of excluding one-day hospitalizations

from the principal inpatient diagnostic cost group model

FIGURE

A-2

225

200

175

150

125

100

75

50

25

0

Number of plans

Percentage change in payment

Note: Distribution includes 326 Medicare+Choice plans.

Source: MedPAC analysis of September 1998 Medicare+Choice encounter data.

 ....................................................................................................................................................................................................................................................................................................................................................................

 ..................................................................................................................................................................................................................................................................................................................................................................

58
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I m p r o v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 2 0 0 0

The exclusion of inpatient stays shorter than two days reduces payments in 89 percent of plans and

results in an average decrease across plans of 0.7 percent (Figure A-2).

Caveat about results

We caution readers about the results in Figures A-1 and A-2 because there are two important issues we

could not address. The first is how much more frequently plans would hospitalize enrollees if HCFA did

not add these exclusions to the PIP-DCG model. If plans would hospitalize more frequently without the

exclusions, then payments without exclusions would be higher than what we estimate. The second issue

we could not address is how county base payment rates would change if HCFA had implemented risk

adjustment without either of these exclusions. If we had calculated the effect on base payment rates, our

results may have been a little different because payments to plans are the product of their enrollees’ risk

scores times a base payment rate. ■

59
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General Accounting Office. Medicare+Choice: payments exceed cost of fee-for-service benefits,

adding billions to spending, No. HEHS-00-161. Washington (DC), GAO. August 2000.

Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy.

Washington (DC), MedPAC. March 2000.

Medicare Payment Advisory Commission. Report to the Congress: Medicare payment policy, volume

II: analytic papers. Washington (DC), MedPAC. March 1998.

Physician Payment Review Commission. Annual report to Congress. Washington (DC), PPRC. June

1996.

Pope GC, Liu C, Ellis RP, et al. Principal inpatient diagnostic cost group models for Medicare risk

adjustment. Waltham (MA), Health Economics Research, Inc. February 24, 1999.

References

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I m p ro v i n g R i s k A d j u s t m e n t i n M e d i c a r e • N o v e m b e r 20 0 0

Term expires April 2001

Spencer Johnson

Michigan Health and Hospital

Association

Lansing, MI

Joseph P. Newhouse, Ph.D.

Alice Rosenblatt, F.S.A, M.A.A.A.

Wellpoint Health Networks

Thousand Oaks, CA

John W. Rowe, M.D.

Aetna US Healthcare

Hartford, CT

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Term expires April 2002

Beatrice S. Braun, M.D.

AARP

Spring Hill, FL

Floyd D. Loop, M.D.

The Cleveland Clinic Foundation

Cleveland, OH

Janet G. Newport

PacifiCare Health Systems

Santa Ana, CA

Carol Raphael

Visiting Nurse Service of New York

New York, NY

Mary K. Wakefield, Ph.D.

College of Nursing and Health

Science

George Mason University

Fairfax, VA

Commission members

Gail R. Wilensky, Ph.D., Chair

Project HOPE

Center for Health Affairs

Bethesda, MD

Joseph P. Newhouse, Ph.D., Vice chair

Harvard University

Boston, MA

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63

Term expires April 2003

Autry O.V. “Pete” DeBusk

DeRoyal

Powell, TN

Glenn M. Hackbarth, J.D.

The Bard Group

Newton, MA

Alan R. Nelson, M.D.

American College of Physicians -

American Society of Internal Medicine

Washington, DC

Robert D. Reischauer, Ph.D.

The Urban Institute

Washington, DC

David A. Smith

AFL-CIO

Washington, DC

Ray E. Stowers, D.O.

Oklahoma State University College of

Osteopathic Medicine

Tulsa, OK

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Analysts

Matthew Barry, M.P.A.

Sharon L. Bee, M.S.

David V. Glass, M.S.

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Craig Lisk, M.S.

Mary B. Mazanec, M.D., J.D.

Anne Mutti, M.P.A.

Nancy Ray, M.S.

Deborah Walter, M.P.A.

Raymond Wedgeworth, M.A.

Chantal Worzala, Ph.D.

Daniel Zabinski, Ph.D.

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Murray N. Ross, Ph.D.

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Lu Zawistowich, Sc.D.

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Jennifer Jenson, M.P.H., M.P.P.

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Helaine I. Fingold, J.D.

General counsel

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Jack Ashby, M.H.A.

Elizabeth Docteur, M.S.

Scott Harrison, Ph.D.

Kevin J. Hayes, Ph.D.

Sally Kaplan, Ph.D.

Julian H. Pettengill, M.A.

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Reda Broadnax, B.S.

Wylene Carlyle

Diane E. Ellison

Plinie (Ann) Johnson

Cheron McCrae

Cynthia Wilson

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