Consumer Driven Health Plans:

Does Theory Follow Practice? Stephen T Parente, Ph.D.

Associate Professor of Finance and Director, Medical Industry Leadership

InstituteUniversity of Minnesota, Carlson School of

Management

Sponsored by the Robert Wood Johnson Foundation’s Health Care Financing &

Organization Initiative (HCFO), the U.S. Department of Health and Human Services and

Pfizer

Presentation Overview• What is (or at least what we see and model) a

consumer directed health plan?– General introduction and preliminary research

findings• Graphic conceptual model of consumer behavior

– CDHP cost-sharing design creates a budget constraint with 2 kinks

– Contrast with ‘standard’ health insurance that uses coinsurance or deductible

• So is there a difference?• New Findings: HSA Selection & Health Reform

‘Classic’ CDHP Model – HRA

Definity Definity HealthHealthCareCare

AdvantageAdvantage

Web- and Web- and Phone-Phone-Based Based ToolsTools

Health ToolsHealth Toolsand Resourcesand Resources

Health Tools and Resources• Care management

program• Internet enabled

Health Coverage• Preventive care covered

100%• Annual deductible• Expenses beyond the

HRA

Health Reimbursement Account (HRA)• Employer allocates HRA1

• Member directs HRA• Roll over at year-end • Apply toward deductible2

Annual Annual DeductibleDeductible

Prev

enti

ve C

are

100%

Prev

enti

ve C

are

100%

Health Health CoverageCoverage

Annu

al

Ded

uctib

le

1 Employer selects which expense apply toward the Health Coverage annual deductible.2 Paid out of employer’s general assets.

HRAHRA

$$

CDHP Version 2.0: The Health Savings Account

(HSA)HSAs legislated in MMA 2003.Pretty similar to Definity Health HRA Design exceptthe consumers owns the account.

Annual Annual DeductibleDeductible

Preventive Care

Preventive Care

100%100%

Health Health CoverageCoverage

Annu

al

Ded

uctib

le

HSAHSA

$$

Questions Addressed from Previous Peer-Reviewed Academic

Research• Do CDHPs (in the form of HRAs) have national appeal?

– Yes. In almost every major market, when introduced, take-up exceeded 5% of employees offered (range 4% to 85%).

• Do CDHPs always have favorable selection?– No. While there is some evidence of initial favorable selection in one

employer, it does not persist. (Parente, Feldman, Christianson, 2004)• Do CDHPs have different effects on cost & utilization compared to

other plans?– Yes. Results depend on benefit generosity. Long run costs are not

less with a generous plan. (Parente, Feldman, Christianson, 2004). For less generous plans, preliminary evidence suggest reduction in rate of increase.

– Biggest cost impact on pharmacy (least cost increase – Parente, Feldman, Chen, 2007). Little impact on utilization.

• Are HSAs a viable approach to addressing the problem of the uninsured?– Yes. But it is still more a political economy question of budgetary

priority. Reductions range from 3 million to 25 million newly insured with federal costs as high as $100 billion per year. (Feldman, Parente, Abraham, 2005).

What We Don’t Know?• Do Consumers Respond to the Actual Financial

Incentives of a CDHP design?– Incentive #1 – Variation in the Price of Medical Care

• Depends on:Contract (single, family)Cost-sharing components (deductible, co-insurance, actual

accountTransparency of price Ability to shop for better price

– Incentive #2 – Save resources in possible for later use• Depends on:

Health statusIncome & wealthRisk aversionPreventive care availability and generosity

Graphic Conceptual Models: CDHP, (C)oinsurance anda (D)eductible Health Plan

Goods

Medical Care

HRA Deductible

d

a b

f Co-Insurance Budgetc

e Deductible Budget

CDHP Budget

Region 1:a to b

Region 2:b to c

Region 3:c to d

Predicted Spending by Budget Region

  Region 1 – predicted spending less than employer contribution to HRA

Region 2 – predicted spending above HRA but below deductible

Region 3 – predicted spending above deductible

D-plan lowestC-planC-plan and CDHP higher with uncertain order

D-plan = CDHP < C-plan

D-plan = CDHP = C-plan

Data to Test Hypotheses• Large employer added a CDHP to

previously-offered PPO and POS Plans in 2001

• Quasi-experimental pre/post design• We selected 3 cohorts of workers

continuously employed from 2000-2003:– Always in PPO– Always in POS– PPO or POS in 2000, switched to CDHP in 2001

and stayed in CDHP 2002 and 2003

Plan CharacteristicsPLAN CHARACTERISTIC

CDHP POS and PPO

Employer HRA contribution

$1,000 single $1,500 2-person $2,000 family

Not applicable

Deductible $1,500 single $2,250 2-person $3,000 family

None

Coinsurance/Co-pay None $15 office visit co-pay $100 inpatient co-pay

Rx coverage Same as other covered services

$10 generic $20 formulary brand $30 non-formulary brand

Preventive Care 100% covered 100% covered Stop-loss limit $500 single

$750 2-person $1,000 family

$1,500 person (POS) $3,000 family (POS) $1,000 person (PPO) $2,000 family (PPO)

Empirical Model – Step 1

• Predict employee’s 2000 spending region on the basis of cohort, contract-level, and employee demographic data– Cohort stands in for unmeasured

variables that affect spending– Control for health status using

indicators for 34 ‘adjusted diagnostic groups’ (Starfield and Weiner, 1991)

Predicted 2000 Spending Regions by Cohort

COHORT

NUMBER of OBS.

PROBABILITY OF REGION

CDHP 429 1 0.548 2 0.118 3 0.333 POS 1,249 1 0.473 2 0.126 3 0.401 PPO 1,025 1 0.465 2 0.135 3 0.400

2001-2003 Cost Models – Step 2

• We estimated 2-part models for total $, physician $, Rx $, and proportion of Rx $ on brand-name drugs

• 1st part = probit analysis of any $• 2nd part = log($ $>0)• Models include predicted region x Cohort• Will present ‘key’ results• ALL RESULTS COMPARED to PPO

OPTION

Total Expenditure PROBIT CONDITIONAL ln(TOTAL

EXPENDITURE) VARIABLE COEF. SE CHI-

SQUARE Pr > CHI-SQUARE

COEFF. SE t-VALUE

Pr > t

POS x REGION2

0.6373 0.2808 5.1499 0.0232 0.42986 0.07023 6.12 <.0001

POS x REGION3

1.1411 0.28 16.6112 <.0001 0.65593 0.04124 15.91 <.0001

CDHP x REGION1

-0.2248 0.1067 4.4411 0.0351 -0.11645 0.05238 -2.22 0.0262

CDHP x REGION2

NA NA NA NA 0.58771 0.12028 4.89 <.0001

CDHP x REGION3

NA NA NA NA 0.76523 0.06473 11.82 <.0001

Regressions control for year, age, male, income, covered lives, FSA use, concurrent ‘health shock’; omitted category = POS x REGION1

Translation: CDHP cohorts uses less of any medical or pharmacy in the account phase only. This leads to an 11.6% reduction in expenditures compared to a PPO. Once all cost-sharing is satisfied, CDHP members have 76% higher expenditures then PPO.

Physician Expenditure PROBIT CONDITIONAL ln(PHYSICIAN

EXPENDITURE) VARIABLE COEF. SE CHI-

SQUARE Pr > CHI-SQUARE

COEFF. SE t-VALUE

Pr > t

POS x REGION2 0.2155 0.2096 1.0575 0.3038 0.33135 0.062 5.34

<.0001

POS x REGION3 1.2256 0.2759 19.7412 <.0001 0.56323 0.03625 15.54

<.0001

CDHP x REGION1 -0.3139 0.1 9.8515 0.0017 -0.02513 0.04642 -0.54 0.5883 CDHP x REGION2 NA NA NA NA 0.5407 0.1056 5.12

<.0001

CDHP x REGION3 3.8598 83.4919 0.0021 0.9631 0.67332 0.0569 11.83

<.0001

Regressions control for year, age, male, income, covered lives, FSA use, concurrent ‘health shock’; omitted category = POS x REGION1

Translation: People use less of any physician services in the account phase, but not enough to effect expenditures.

Rx Expenditure PROBIT CONDITIONAL ln(PHARMACY

EXPENDITURE) VARIABLE COEF. SE CHI-

SQUARE Pr > CHI-SQUARE

COEFF. SE t-VALUE

Pr > t

POS x REGION2 0.6052 0.1467 17.0323 <.0001 0.4581 0.09006 5.09

<.0001

POS x REGION3 0.809 0.0978 68.4763 <.0001 0.74921 0.05297 14.14

<.0001

CDHP x REGION1 -0.2011 0.0714 7.9363 0.0048 -0.35918 0.07034 -5.11

<.0001

CDHP x REGION2 1.2198 0.4054 9.0515 0.0026 0.23713 0.1518 1.56 0.1183 CDHP x REGION3 0.4822 0.1516 10.1168 0.0015 0.66084 0.08266 7.99

<.0001

Regressions control for year, age, male, income, covered lives, FSA use, concurrent ‘health shock’; omitted category = POS x REGION1

Translation: CDHP cohorts uses less of any pharmacy in the account phase only. This leads to an 35.9% reduction in Rx expenditures compared to a PPO. Once all cost-sharing is satisfied, CDHP members have 66% higher Rx expenditures then PPO.

Brand Name Rx ProportionVARIABLE COEFFICIENT SE t-VALUE Pr > t POS x REGION2 0.07377 0.01747 4.22 <.0001 POS x REGION3 0.02545 0.01028 2.48 0.0133 CDHP x REGION1 0.07243 0.01365 5.31 <.0001 CDHP x REGION2 0.15826 0.02945 5.37 <.0001 CDHP x REGION3 0.11147 0.01604 6.95 <.0001

Regressions control for year, age, male, income, covered lives, FSA use, concurrent ‘health shock’; omitted category = POS x REGION1

Translation: CDHP cohort has a higher probability of any brand name drug use in all expenditure regions compared to PPO.

Summary of Findings (1)• CDHP enrollees predicted to be ‘low

spenders’ consistently spent less in following years than a comparison group with conventional cost sharing– This difference was found in all probit

equations and for cases with positive total expenditure and Rx expenditure

• This finding is striking because CDHP enrollees had no cost-sharing in this region– HRA account provides insurance against future

expenses

Summary (2)• CDHP enrollees predicted to be in Region 2 or

3 spent more than the comparison POS group– This finding is similar to our previous cohort study

in 2001 and 2002 (Parente, Feldman, Christianson, 2004)

– CHDP enrollees in Region 3 have used their accounts and face no cost-sharing at the margin no incentive to conserve on medical care

• The maximum out-of-pocket limit is too low– Problem could be addressed by raising the limit

and introducing modest coinsurance above the limit

Graphic Conceptual Models: REVISED CDHP, (C)oinsurance anda (D)eductible Health Plan

Goods

Medical Care

HRA Deductible

d

a b

f Co-Insurance Budgetc

e Deductible Budget

CDHP Budget

Region 1:a to b

Region 2:b to c

Region 3:c to d

Medical Care Price

CDHP Demand

High Deductible Demand

“But what do you have that is current?”

What Happens When You Can Choose between an HSA, an

HRA, an HMO, a PPO, EPO or a POS plan?

2006 Plan Choice Year, 2005 Risk Data

Study Setting• Employer with many different plan design

offers in 2006 including:– CDHP: HSA, HRA High, HRA Not-High– PPO, POS, EPO, 1 or 2 HMOs in some locations

• Non-retiree analysis only.• Employees live in all 50 states. Over 100

employees in 22 states.• Health risk (including measure of chronic

illness) based on 2005 pharmacy claims data.

Plan Design Attributes• Four contract types:

– Single– 2 Person– Adult + Child– Family

• CDHP Design – HRA High: Coinsurance at 5%, Smaller donut– HRA Low: Coinsurance at 10%, Larger donut– HSA – More out of pocket risk

• Non-CDHP Design: Moderate coinsurance (average 10%)

Attributes of Plan ChoosersPlan Designs Age % Female Risk Ratio

All Plans 45.8 26.9% 1.00EPO - Exclusive Provider Organization 44.9 31.0% 1.16Primary HMO 43.5 28.2% 0.48Secondary HMO 45.1 27.3% 0.91HRA High 46.9 29.4% 1.24HRA Low 41.5 22.9% 0.73HSA w/High Deductible 40.3 18.6% 0.57POS - Point of Service 47.4 23.6% 1.22PPO - Preferred Provider Organization 46.2 27.2% 0.71

Notes: • 2006 Plan choice data• Risk ratio based on computation from 2005 pharmacy data• Primary HMO Rx data may be under-represented

HSA Take Up – 2006

2.7-5.6%

1.4 – 2.6%

<1.4%

Take-up

Data based on 1 large employer representing ~50,000 covered lives with HSA initial year offering in 2006.

CDHP Take Up – 2006

Data based on 1 large employer representing ~50,000 covered lives with HSA initial year offering in 2006 along with low and high HRAs.

11-39%

7.5 – 10%

<7.5%

Take-up

HSA/PPO Risk Ratio

Data based on 1 large employer representing ~50,000 covered lives with HSA initial year offering in 2006.

1.0-2.6

0.75 – 0.99

<0.75

HSA/PPO Ratio

Risk Score based 2005 Claims data analysis using RxRisk

HRA High/PPO Risk Ratio

Data based on 1 large employer representing ~50,000 covered lives with HSA initial year offering in 2006.

1.0-3.7

0.75 – 0.99

<0.75

HSA/PPO Ratio

Risk Score based 2005 Claims data analysis using RxRisk

Plan Choice Regression Results

From Conditional Logistic Regression – 8 possible choices

Notes: • All results a regression coefficients• Red results are significant at the .05 level

Age Gender FamilyChroni

c IncomeHMO Bricks

Lite 2.500 -0.171 -0.383 -1.234 -0.011HMO Bricks 0.881 -0.309 0.218 -0.100 -0.009HRA High 0.212 0.097 0.380 0.182 0.013HRA Low -3.244 -0.385 -0.032 -0.458 0.013HSA -4.112 -0.691 -0.118 -0.779 0.017POS 1.327 -0.389 0.175 0.199 0.005PPO 2.539 -0.313 2.228 -1.403 -0.008

Rank of Association Between Plans and Person Attributes

From Conditional Logistic Regression – 8 possible choicesAge

Female Family

Chronic Income

HMO Bricks Lite 2 3 8 7 8HMO Bricks 4 4 3 4 7HRA High 5* 1 2 2 3HRA Low 7 6 6* 5 2HSA 8 8 7* 6 1POS 3 7 4 1 4PPO 1 5 1 8 6EPO 6 2 5 3 5Notes: • 1 is highest rank (most association), 8 is least rank• *results are NOT significant at the .05 level

Plan Price Elasticity ResultsFrom Conditional Logistic Regression – 8 possible choices

Premium Account DeductibleHMO Bricks Lite -0.021 0.000 -0.019HMO Bricks -0.015 0.000 -0.005HRA High -0.156 0.758 -0.436HRA Low -0.119 0.801 -1.383HSA -0.098 0.802 -2.307POS -0.088 0.000 -0.363PPO -0.130 0.000 -0.397EPO -0.049 0.000 0.000

Summary of HSA Choice when HRA and PPO are

Also Choices• Risk-splitting between HRA and HSA• Clearly an issue of benefit design.• Selection not only limited to HSAs.

Favorable selection goes to the HMOs too.• Is the risk segmentation of value? Is too

difficult to fix short of full-replacement?• Next big question: Do HSAs have

better/neutral outcomes and satisfaction, adjusted for risk?

Thank You!For more information on our

research, please visit:www.ehealthplan.org

Stephen T. Parente, Ph.D., M.P.H., M.S.Associate Professor, Department of Finance

Director, Medical Industry Leadership InstituteCarlson School of Management

University of Minnesota321 19th Ave. South, Room 3-122

Minneapolis, MN 55455612-624-1391 (v), sparente@csom.umn.edu

http://www.tc.um.edu/~paren010

EpiloguePolicy Proposal Simulation

President Bush’s 2007 State of the Union Health Insurance Proposal

Impact from ARCOLA* model

*Adjusted Risk Choice & Outcomes Legislative Assessment (ARCOLA) Model

What Does ARCOLA Do?• Models national health plan take-up of policy

proposals in the individual and group markets. Unique combination of attributes.– Uses MEPS for simulation weights.– Choices based on 4 large employers claims, salary,

demographics and plan choices.– Includes CDHP choice data in model.– Risk-adjustment (Hopkins ACGs) included in model to

predict both individual and group market premiums. Model is iterative.

– Can identify premium elasticity response of policy options be specific plan choices and the uninsured.

Data Sources• Health plan choice data from 3 large employers

participating in a Robert Wood Johnson Foundation funded study on CDHPs– Employee premium, deductible, coinsurance, worker’s

age, gender, wage income, single/family coverage• 2001 Medical Expenditure Panel Survey (MEPS)

– Household Component: All adults age 19-64 not enrolled in public insurance programs and not full-time students during Round 1• Demographic, employment, and health insurance information

– Linked Insurance Component: Subset of workers offered employer coverage and their plan choices• Plan type, premiums, contributions, coinsurance, copayments and

deductibles• eHealthinsurance.com

– Individual HSA plan information

Previous Work:2004 State of the Union

Estimates

Most Recent Simulation• Using a micro-simulation model, we

predicted the effect of 2007 SOTU on health insurance take-up and costs– Background: Our model predicted the take-up

of HSA plans in the individual market quite accurately (Parente, Feldman et al, 2005)

– Population: adults aged 19-64 who are not students, not covered by public insurance, and not eligible for coverage under someone else’s ESI policy

– Baseline uninsurance: 27.3 million people• Hold onto your hats…

SOTU 2007• A tax deduction of $7,500/$15,000 – but

you have to have health insurance to get the deduction

• Health insurance premiums will be taxable (equal tax treatment of individual and ESI (employer sponsored, a.k.a. group, premiums)

• Complicated incentives created by SOTU cannot be modeled by existing economics studies

Results• Uninsurance is reduced by 76% to

under ten million people. • Annual cost of $200+ billion:

– $104 billion subsidy to the individual market

– $256 billion subsidy to the ESI market with offsetting tax recovery of $149 billionSource: Steve Parente and Roger Feldman, ‘ARCOLA’

simulation model, SParente@csom.umn.edu and feldm002@umn.edu

Impact of Current ProposalBaseline MMA HSA take-up compared to 2007 SOTU Deduction Proposal

2009 Dollar Estimates

New MMA SOTU 2009 SOTU 2009 SOTU 2009 SOTU 2009 SOTU-MMAIndividual Market Population Population Subsidy Tax Recovery Total Impact DeltaHSA 3,156,133 12,809,458 $44,261,347,424 $0 $44,261,347,424 9,653,326PPO High 37,591 2,600 $12,377,483 $0 $12,377,483 -34,991PPO Low 6,046,777 16,292,898 $59,638,531,244 $0 $59,638,531,244 10,246,122PPO Medium 232,105 126,856 $634,353,122 $0 $634,353,122 -105,249Uninsured 27,305,770 7,507,584 $0 $0 $0 -19,798,186

Group Market HMO 18,757,940 10,447,058 $12,764,147,135 $22,969,906,326 -$10,205,759,191 -8,310,883HRA 2,205,781 4,489,622 $11,719,790,456 $9,286,790,819 $2,432,999,637 2,283,840Employer-sponsored HSA 77,465 697,442 $3,091,933,512 $1,152,406,309 $1,939,527,202 619,977Opt-out HSA 34,863 1,575,665 $4,369,179,114 $1,346,706,807 $3,022,472,308 1,540,802Opt-out PPO Low 59,002 33,762,757 $141,974,293,898 $28,313,788,067 $113,660,505,831 33,703,755PPO High 8,421,022 117,841 $8,302,002 $698,483,517 -$690,181,515 -8,303,181PPO Low 981,114 9,505,821 $34,707,028,622 $16,545,818,966 $18,161,209,656 8,524,707PPO Medium 38,390,473 22,869,075 $47,904,214,757 $69,430,430,506 -$21,526,215,750 -15,521,398Turned Down - Other Private 8,187,222 540,104 $0 $0 $0 -7,647,118Turned Down - No insurance 6,431,778 606,386 $0 $0 $0 -5,825,392Turned Down - Other Insurance 223,786 16,310 $0 $0 $0 -207,476Turned Down - Public Insurance 960,560 91,503 $0 $0 $0 -869,058

Total Subsidy: $361,085,498,767 $149,744,331,317 $211,341,167,450

Why?• Tax subsidy is quite large, even for low-

income workers• Individuals are sensitive to the prices of

different types of health insurance:– Individual HSA policies will increase from 3.1 to 10

million and low-option PPOs from 6 to 19.4 million– The subsidy covers the full cost of these policies

for many people• The ESI market is not hollowed out, but

expensive PPO plans will disappear

Subsidy cost per person, individual market, by income

$2,041

$3,736 $3,813$4,069

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

$4,500

0-25 % 25-50 % 50-75 % 75-100 %Income Percentile

Ten Year Impact

$0

$50,000,000,000

$100,000,000,000

$150,000,000,000

$200,000,000,000

$250,000,000,000

$300,000,000,000

$350,000,000,000

$400,000,000,000

$450,000,000,000

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Subsidy

Tax Recovery

Total Expenditure

Ten Year ImpactYear Uninsured Subsidy Tax Recovery

Total Federal Expenditure

Uninsured Marginal Cost

2005 33,737,548 0 0 02009 8,113,971 $361,085,498,767 $149,744,331,317 $211,341,167,450 $8,2482010 8,680,632 $365,255,876,588 $145,594,064,495 $219,661,812,093 $8,7672011 9,426,261 $372,407,906,863 $139,077,492,747 $233,330,414,116 $9,5982012 10,315,767 $379,677,254,743 $132,602,225,280 $247,075,029,463 $10,5492013 11,260,807 $386,624,661,086 $128,200,537,097 $258,424,123,989 $11,4972014 12,199,164 $393,326,495,911 $127,500,982,033 $265,825,513,878 $12,3422015 13,136,886 $398,732,137,297 $130,537,573,661 $268,194,563,636 $13,0192016 14,091,659 $402,349,641,271 $136,140,941,746 $266,208,699,525 $13,5502017 15,081,622 $404,966,335,238 $142,886,446,405 $262,079,888,832 $14,0482018 16,110,446 $407,611,633,007 $150,140,520,683 $257,471,112,324 $14,607

$3,872,037,440,771 $1,382,425,115,463 $2,489,612,325,308

Summary of 2007 SOTU Effect

• Could be the most comprehensive US health insurance market proposal ever on both the tax treatment of insurance AND reducing the uninsured by 82% to less than 10 million.

Summary of HSA Choice when HRA and PPO are

Also Choices• Risk-splitting between HRA and

HSA• Clearly an issue of benefit design.• Is the risk segmentation of value?

Is too difficult to fix short of full-replacement?

Thank You!For more information on our

research, please visit:www.ehealthplan.org

Stephen T. Parente, Ph.D., M.P.H., M.S.Associate Professor, Department of Finance

Director, Medical Industry Leadership InstituteCarlson School of Management

University of Minnesota321 19th Ave. South, Room 3-122

Minneapolis, MN 55455612-624-1391 (v), sparente@csom.umn.edu

http://www.tc.um.edu/~paren010