The Role and Future of Priv

Medicare Health Plans*

Austin Frakt (VA, BU)

Steve Pizer (VA, BU)

Roger Feldman (UMN)

*Supported by VA HSR&D and the Robert Woods Johnson Founda

Health Care Financing and Organization (HCFO) Initiative

Un

Mi

Recent Papers

• Frakt, Pizer, Feldman, “A Multiproduct Entry M

Private Private Medicare Medicare Health Plans,” J. of Econ. & M

Strat., submitted Nov. 2009.

• Frakt, Pizer, Feldman, “Payment Rates and Med

P ir vate Fee for S i” erv ce,” H lh ea t Care Fi i nanc ng 

30(3), Spring 2009.

• Pizer SD, Frakt AB, and Feldman R, “Nothing fo

Something? ...,” International Journal of Health

Finance and Economics, 9(1) (March 2009):59‐

• Frakt, Pizer, and Hendricks Hendricks, “Controlling Controlling Prescr

Prescr

Drug Costs ...,” J. of Health Politics, Policy and L

33(6) (December 2008).
VA Context

• This is work from studies of the behavio

firms offering offering private private Medicare Medicare plans.

• We (at the VA) care because:

– V t e erans enroll in such plans at the same r

non‐veterans (23% in comprehensive planin drug‐only plans).1 g y p )

– >50% of VA patients are Medicare benefic– Dual use is common. 2

• Medicare is the context in which elderly

veterans make health care decisions.

1 Shen Y, et al. (2003). Med Care Res Rev 60(2):253‐267.2 Research findings from the VA Medicare data merge initiative (XVA 69‐001). (2003). VIR
Elderly Medicare Medicare Beneficiaries Beneficiaries 

Frakt, Pizer, and Hendricks, “Controlling Prescription Drug Costs: Regulation and of Interest Interest Groups in Medicare Medicare and the Veterans Veterans Health Administration Administration ”, Journa

Journa

Health Politics, Policy and Law, 33(6) (December 2008).
Roadmap

• Medicare private plans: enrollment & O• Policy context

• Thinking about firms ( p descriptive result

[with very important data discussion]

• Multi‐product product entry model: background• Multi‐product entry model: results and • M lti u ‐prod ct product entry model: methods methods de

de

• Conclusion
Private Medicare Plans: Enrol

Plan Type

Num. (%) of

Medicare Comme

Beneficiaries

Local coordinated care plans (CCPs) 7.2M (16%) Mostly HMOs

Private fee for service ( ) PFFS 2.3M ( ) 5% Less generous

Prescription drug plans (PDPs) 17.5M (39%) Drug only, donu

2009 figures. These are not mutually exclusive! PFFS + PDP is per

• CCP + PFFS = MA (Medicare Advantage)

• Traditional Traditional FFS Medicare Medicare (34 8M. (77%)) 

program’s “public option.”

• A few other small plan types account account fo

enrollees (1.5%).
Private Medicare Plans: OOP 

Cost Sharing Type

Mean of Non‐Zero (% $

CCP PFFS 

Non‐drug premium $68 (68%) $56 (39%) 

Primary care doc. visit copay $11 (33%) $21 (3%) 

Drug premium $25 (66%) $30 (17%) 

Drug deductible $283 (90%) $233 (66%) $

Mean preferred drug copay $33 (0%) $35 (0%) 

2009 figures. Enrollment weighted.

• In general, CCPs offer the best deal.
Policy Context

Frakt, Pizer, Feldman, “Payment Rates and Medicare Private Fee for Service,” Health Review, 30(3), Spring 2009.
Thinking About Firms: The Fo

Num. Types Offered Number of Firms Percent of Firm

One plan type 151 75%

Two plan types 32 16%

All three plan types 16 8%

TOTALS 199 100%

2007‐2009 data. Plan types need not be offered in the same year or 

• Firms offering all three plan types repre

89% of PDP enrollment, 84% of PFFS

enrollment, and half of CCP enrollment.
Important Data Discussion 

• Years? 2007‐2009.• What s’ in? Firms offering offering all three prod

prod

not necessarily at the same time or plac

• What about them? County‐level entry 

product type by each firm in each year.

• What is entry? > 0 5%. market penetrat

penetrat

State A State B

PFFS PFFS

PDP

CCP PDP

CCP
Important Data Discussion 

• Unit of Obs.? Year‐County‐Firm.• Key variables? variables? CCP entry, PFFS entry, P• Risk set? All counties, less those in state

where firm never had entry of any prod

prod

State A State B

PFFS PFFS

PDP

CCP PDPCCP

In risk set. Not in risk set.

50% of counties at risk for entry.
Thinking About Firms: The W

sPercent of counties in entry risk set ent

Firm

Pct. counties

at risk for

entry

Percent of counties in entry risk set ent

None

CCP

only

PDP

only

PFFS

only

CCP& PDP

only

CCP&

PFFS only

PCP

Aetna 100 38 0 57 0 2 

BCBS of FL 2 1 0 63 0 16 

BCBS of MI 3 0 0 0 1 0 

BCBS of SC 7 14 0 39 5 0 

Bravo Health 56 64 0 35 0 1 

Cigna 100 36 0 64 0 0 

Health Care 100 13 0 40 2 1 

Geisinger Health System 4 77 11 1 2 3 

Health Net 100 57 0 38 1 1 

Highmark 4 1 0 36 0 58 

Humana 100 1 0 23 0 1 

UnitedHealth Group 100 2 0 61 0 7 

Universal American 100 3 0 56 0 0 

Med. Cntr. 4 56 7 24 0 12 

Wellcare Health Plans 100 11 0 68 0 2 

Wellpoint 100 12 0 62 1 3 

Join
Thinking About Firms: The T

• Aggregate the prior table.

– Take all year‐county‐firm combinations combinations.

– Toss state‐firms with no business to get r

– How many have no products? That’s the 

entry below.

Firm

counties

isk for

ntry

Percent of counties in entry risk set ent

ne CPnly DP nly

FS nly & PDP nly P &

only 

Pct.c

atre

NoCConPDon

PFon

CCP&On

CCP

PFFS

All firms in sample 61 22 0 51 0 2 0 

Joint e
Roadmap

• Medicare private plans: enrollment & O• Policy context

• Thinking about firms ( p descriptive result

[with very important data discussion]

• Multi‐product product entry model: background• Multi‐product entry model: results and • M lti u ‐prod ct product entry model: methods methods de

de

• Conclusion
Recent Papers

• Frakt, Pizer, Feldman, “A Multiproduct Entry M

for Private Private Medicare Medicare Health Plans,”

J. of Econ

Manag. Strat., submitted Nov. 2009.

• Frakt, Pizer, Feldman, “Payment Rates and M

Private Fee for Service,” Health Care FinancinReview, 30(3), Spring 2009.

• Pizer SD, Frakt AB, and Feldman Feldman

R, “Nothing Nothing f

Something? ...,” International Journal of Heal

Finance and Economics, 9(1) (March 2009):5

• F kt ra , Pizer, an

d H dik en

d

ric

ks, “C t lli “Con

trolling P 

resc

Drug Costs ...,” J. of Health Politics, Policy and 

33(6) (December 2008).
Objectives

• To estimate estimate a model of multi‐product product e

across the three main types of private

Medicare Medicare plans: CCPs, PFFS plans, PDPs

• To discover the relation between the M

Advantage (MA) payment payment rate, the

administratively‐determined payment 

and PFFS plans, and entry decisions decisions.
Health Care Reform

• Recall MA payments payments are 14% above FFS 

• House Leadership Bill (passed 11‐7‐09):

Payments Payments to MA plans will be reduced reduced t

100% of FFS levels by 2013.

• Senate L d hi ea ership Bill ( d passe 12‐23‐09

Beginning in 2012 with 4‐year phase‐in

plans will sub i m ts bids wi ht payment b 

a

the average of plan bids in each market
Prior Literature

• Ca ey wl , Ce e h rn w a dn Mc aug L hlin (J S, EM 

estimated the level of government paymenecessary to induce various levels of partiby private Medicare plans in counties from 

to 2001.

• Other papers, mostly using earlier d t a a, fo

firms’ decisions to participate in Medicare

• Frakt, Pizer, and Feldman Feldman (HCFA Review, 20

estimated a model of PFFS participation b& county from 2001‐2008.
Our Contribution

• With the exception exception of our recent paper, no 

study has examined private plan participatMedicare during the period when PFFS pla

have existed.

• Since 2006, a new type of plan – the presc

presc

drug plan – has been offered but no study 

examined PDP p p articipation in Medicare.

• No study has looked at firms’ joint decisio

enter local markets with different types of 
Firms’ Joint‐Entry Decisions Decisions 

• Inter‐product competition.

– MA payment rate is a key variable in our m

– Higher MA payment rate should lead to gr

plan partii i c pat on.

– Effect of MA payment rate on PDP particip

co p e m l x:

• MA plans may offer prescription drug coverag

least one CCP plan must cover Rx.

• Beneficiaries Beneficiaries cannot hold both CCP and PDP.

• We expect, therefore, that higher MA paymen

‘crowd out’ PDP plans.
Firms’ Joint‐Entry Decisions Decisions 

• Firms may share some costs across the th

product types.

– Advertising and marketing costs are an examp– PFFS and PDP plans can be j i tl o n y h ld e .

– We expect joint marketing of those plan type

cost‐saving.

• Shared costs would create economies of 

among products.

• We d ’ on t expli i l c t y estimate the fi ’ rm s m

product cost function but we use an esti

method that is sensitive sensitive to economies economies of 
Recall Descriptive Results

nties

for y

Percent of counties in entry risk set ent

DP

ly

Recall Descriptive Results

Firm

Pct. coun

at risk

entry

None

CCP

only

PDP

only

PFFS

only

CCP&P

D

Only

CCP&

PFFS on

l

All firms in sample 61 22 0 51 0 2 0 

Joint e

• How will entry patterns change with MA

payment rate?

• C lli ontrolling for cost and d d eman factors, 

strong correlation of entry between pairs 

plan types?
Recall Data Details

• Same deal as before (year‐county‐firm

meaningf l u entry, drop state‐firms with 

activity).

State A State B

PFFS PFFS

PDP

CCP PDP

CCP

In risk set. Not in risk set.

50% of counties at risk for entry.
Data Details (Variables)

Variable Description (Years) Mean (SD) [Min, Max] Source

Entry

CCP entry i di t n ca or (2007‐2009) 0 04 . (0 20) . [0 00 . , 1 00] . CMS

PDP entry indicator (2007‐2009) 0.78 (0.42) [0.00, 1.00] CMS

PFFS entry indicator (2007‐2009) 0.25 (0.44) [0.00, 1.00] CMS

Payment

Benchmark payment rate (2007‐2009) 769 (76) [692, 1366] CMS

Cost

Average FFS cost (2007‐2009) 659 (81) [436, 1285] CMS

Proportion of elderly 75+ years old (2000) 0.47 (0.05) [0.21, 0.62] CMS

General practitioners per capita (2006) 0.0003 (0.000) [0.00, 0.00] ARF

Hospital beds per capita (2005) 0.004 (0.005) [0.00, 0.00] ARF

Rural county (2003) 0.30 (0.46) [0.00, 1.00] ARF

Urban county (2003) 0.35 (0.48) [0.00, 1.00] ARF

Monthly drug Medigap premium (2005) 235.82 (34.00) [180, 466] ‐

Monthly non‐drug Medigap prem. (2005) 136.33 (25.09) [103, 263] ‐

Aged/disabled risk score (2006) 0.96 (0.07) [0.70, 1.35] CMS

Demand

Proportion Proportion elderly elderly in poverty poverty (1999) 0 12 . (0 06) . [0 00 . , 0 48] . ARF

Per capita income in thousands (2005) 27.09 (7.01) [0.00, 93.0] ARF

Prop. 25+ pop. w/ HS diploma (2000) 0.77 (0.09) [0.35, 0.97] ARF

Prop. 25+ pop. w/ 4+ yrs col. (2000) 0.16 (0.08) [0.05, 0.64] ARF

Prop. workers manufacturing (2000) 0.16 (0.09) [0.00, 0.49] ARF

Prop. workers white collar ( ) 2000 0.52 ( ) 0.08 [ , 0.31 0.84] ARF

Prop. workers construction (2000) 0.08 (0.02) [0.02, 0.23] ARF
Multivariate Probit Mode

Yi = β 1iBenchmark + β 2iCost + β 3iDem

i i

i i i i

Year Firm and State FE

Y Benchmark Cost Dem

β ε

β β β

+

+ + 

( , , ) 4

1 2 3

• Yi = indicators for CCP, PFFS, PDP entry

• We estimate estimate the model with multivariate multivariate probit

probit

allows the εiterms to be correlated across equa

• These correlations will capture economies of sco

among products but also will pick up common

unmeasured demand and cost variables
Key Results

Variable CCP Entry PFFS Entry PDP En

Benchmark 0.27*** 0.41*** ‐0.18*

(0.00096) (0.065) (‐0.02

FFS Cost ‐0.063**

(‐0.00022)

‐0.0078***

(‐0.12)

0.068*

(0.008

rho PDP‐PFFS = 0.42, CCP‐PDP = 0.30, CCP‐PFFS = 0

(Marginal effects) at the mean Benchmark = $769, mean FFS Cost 

** = si ifi t gnificant at .01, *** = si ifi t gnificant at .001

• Common unmeasured demand factors could explain 

PFFS residual correlation.

• Most likely unmeasured cost variable, network deve

applies only to CCPs, exp g laining small CCP‐PFFS residcorrelation.
Simulation

Baseline Simulated Entry b l l 

Plan Type Baseline Entry

Probability

Simulated Entry

Probability after

Payment Cut

Absolute

Change

Rela

Cha

CCP 14.0% 11.8% ‐2.2 ‐15

PDP 89.6% 90.6% 1.0 1.1

PFFS 30.0% 25.5% ‐4.5 ‐15

Simul d ate changes in prob bl a i ity of entry in response to paymen

of one standard deviation in the 2009 benchmark‐mean FFS cost

difference ($58). All results beneficiary weighted.
Comments

• A s u ated imulated pay e t m n cut of $58 (o en s.d. 

difference between the benchmark and FFwould lead to 15% reduction in CCP and Pentry and modest increase in PDP entry.

• Congress is contemplating larger cuts.

• We did not calculate welfare effects from 

changes, although our previous work (Piz

Frakt, and Feldman Feldman, 2007 IJHFE) suggests suggests 

higher MA plan payments were an inefficito increase beneficiaries’ consumer surplu
Limitations

• Our resu tsl app yl o y nl to firms t at h o e ed ff r 

three plan types sometime between 2007 

2009.

• During this period MA payment rates were 

above FFS costs, so simulating the effect o

M dPAC’ MedPAC’s recommend ti a on ( b di d (embodied in 

House Leadership Bill) is outside the range 

data.

• We don’t directly model economies of sco

among plan types.
Follow‐Up

Austin Frakt

Health Economist

Health Care Finance and Economics

VA Boston Health Care System

Austin.Frakt@va.gov