Is Consumerism at Odds with Prevention?

The indirect effects of consumer-directed health plans on preventive service utilizationStephen T Parente, University of MinnesotaGiridhar Mallya, University of PennsylvaniaCraig Pollack, University of PennsylvaniaDaniel Polsky, University of PennsylvaniaRoger Feldman, University of MinnesotaWilliam McGuire

Presentation at the American Society of Health Economics, June 24, 2008

Sponsored by the Robert Wood Johnson Foundation’s Health Care Financing & Organization Initiative (HCFO) and the U.S. Department of Health and Human Services

Presentation Overview

Background Research Question Research Setting Empirical Approach Caveats Results Discussion

Consumer Driven Health Plans

Annual Annual DeductibleDeductible

Annual Annual DeductibleDeductible

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Health Health CoverageCoverage

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HSA/HRAHSA/HRAHSA/HRAHSA/HRA

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• Consumer owns the Health Savings/Reimbursement Account (HSA/HRA)

• Unused $$ roll-over at year end• HSA must be purchased with

complementary high deductible health plan (HDHP)

• Can be purchased by consumers in the state-regulated individual or small group markets.

• Employers provide HSAs/HRAs as part of their benefits package.

• Money deposited in HSAs is tax-advantaged.

• For HSA, unused $$ at 59 years of age can be used for medical care and retirement.

• For HSA, early withdrawal penalty for any use other than healthcare.

Previous Literature Rowe, et al (2008), compare preventive care rates in

CDHP and a PPO settings managed by Aetna – No difference in prevention.

Mallya, et al (2007): compare preventive care rates in CDHP and traditional plan in one employer: In CDHPs -fewer preventive care visits; more pap smears.

Busch, et al (2006), Alcoa mandated high-deductible coverage for a subset of employees. Found no significant difference in preventive care use.

Wharam, et al (2008), total replacement study. Little change in cancer screening. Closest to this study.

Research Questions Primary: What is the impact of

consumer driven health plans on preventive care?

Secondary: What factors affect the utilization of preventive care when offered in a total health plan replacement setting with a CDHP?

Conceptual Model

Health benefit design affects the demand for medical care, including preventive services.

Increased patient copayment acts as price increase in medical care demand.

Can be empirically tested by the evaluation of a reduced form expression of the demand for medical care in a CDHP total replacement with higher cost sharing.

Data to Address Research Questions

Four Large employers with over 50,000 covered lives.

Medical & Pharmacy claims and enrollment data or two years: pre and post implementation of CDHP design.

The employers had a full replacement of their PPO/POS plan designs with a CDHP design.

Two of four employers adopted CDHP design later in 2006, the rest in 2005.

Continuously enrolled sample for two years.

Econometric ApproachUse a two part model to complete a

Difference-in-difference estimate of the effect of expenditure on of a CDHP total replacement.

Evaluate probability of getting any preventive care use for a set of specific measures: Any preventive care visit Colonoscopy screening age 40 to 64 Mammography screening, women aged 40 to 64 Cervical cancer screening, women aged 24 to 64

Use firm-specific interaction with the second year of adoption to identify the impact of CDHP total replacement.

Caveats

Unlike previous work, we can not control for the impact on income.

There is unexplained market level variation. Have considered using state-effects as a correction.

Early results.

Attributes of Individuals w/Coverage at Baseline from 4 Firms

Variable Firm 1 Firm 2 Firm 3 Firm 4

Age (years) 29.626 30.810 34.118 33.928Female=1, else Female=0 0.567 0.461 0.527 0.439Baseline Illness Count 2.846 2.111 3.406 2.472Catastrophic Shock=1, else 0 0.216 0.181 0.268 0.234Enrollee is subscriber=1, else 0 0.451 0.707 0.375 0.445Enrollee is spouse=1, else 0 0.195 0.101 0.252 0.258Enrollee is dependent=1, else 0 0.353 0.193 0.373 0.295

Observations (total=61,438) 40,976 16,534 2,464 1,464

Descriptive Statistics of Prevention Services

Variable Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 Year 1 Year 2

Preventive Care

Preventive Visit in Year=1, else 0 26% 26% 15% 13% *** 34% 33% 14% 14%

Cervical Cancer Screening in Year=1, else 0 51% 46% *** 45% 41% *** 70% 64% ** 39% 40%

Mammography Visit in Year=1, else 0 56% 52% *** 38% 35% 70% 65% * 52% 46%

Colonoscopy Visit in Year=1, else 0 20% 19% * 17% 15% 23% 22% 18% 11% **

Statistical Significance*** p<=.001, ** p<=.01, *P<=.05

Firm 1 Firm 2 Firm 3 Firm 4

Descriptive Statistics of Expenditures

Variable Year 1 Year 2 Year 1 Year 2 Year 1 Year 2 Year 1 Year 2

Expenditures

Total Expenditures 2,906$ 2,909$ 2,097$ 1,883$ 3,209$ 3,686$ * 2,520$ 2,335$

Total Medical Expenditures 2,341$ 2,299$ 1,789$ 1,584$ 2,170$ 2,628$ * 1,970$ 1,820$

Consumer Medical Expenditures 334$ 557$ *** 544$ 455$ *** 105$ 378$ *** 339$ 546$ ***

Total Pharmacy Expenditures 565$ 611$ *** 308$ 300$ 1,039$ 1,057$ 550$ 514$

Consumer Pharmacy Expenditures 178$ 186$ *** 156$ 122$ *** 55$ 282$ *** 185$ 237$ ***

Statistical Significance*** p<=.001, ** p<=.01, *P<=.05

Firm 1 Firm 2 Firm 3 Firm 4

Summary of CDHP Impact – by Firm – for Expenditures and Prevention

Total Expenditures

Total Medical Expenditures

Consumer Medical

ExpendituresTotal Pharmacy

Expenditures

Consumer Pharmacy

ExpendituresFirm 1 1.28% -1.32% 99.24% 9.79% 5.40%Firm 2 -4.11% -4.35% -7.22% 3.99% -6.62%Firm 3 3.57% 8.20% 141.45% -19.93% 114.91%Firm 4 -0.94% 0.93% 80.51% -1.42% 15.72%

Preventive Visit

ProbabilityColonoscopy

Probability

Cervical Cancer Screening Probability

Mammography Screening Probability

Firm 1 -0.24% -0.99% -3.28% -5.39%Firm 2 -2.78% -1.48% -2.94% -3.98%Firm 3 -0.95% -2.20% -11.93% -2.29%Firm 4 -0.66% -6.69% -8.78% 3.10%

Total Replacement Employer

Total Replacement Employer

CDHP Replacement Effect:Total Expenditures

Coefficients T-Stat Pr > |t|Intercept 5.2453 273.76 <.0001Age (years) 0.0160 38.89 <.0001Female=1, else 0 0.1351 16.66 <.0001Baseline Illness Count 0.3117 175.66 <.0001Catastrophic Shock=1, else 0 0.5976 62.68 <.0001Year 2=1, else Year 1 0.0128 1.39 0.1657Later Sample=1, else 0 -0.0398 -0.54 0.5874Firm 2=1, else 0 -0.2587 -19.61 <.0001Firm 3=1, else 0 0.0753 0.97 0.3343Firm 4=1, else 0 -0.0674 -1.18 0.2369Firm 2 & Year 2 interaction -0.0539 -2.91 0.0036Firm 3 & Year 2 interaction 0.0229 0.28 0.7815Firm 4 & Year 2 interaction -0.0222 -0.33 0.7434Enrollee is spouse=1, else 0 0.0617 5.66 <.0001Enrollee is dependent=1, else 0 -0.1523 -10.03 <.0001

Second Year ImpactFirm 1 1.28%Firm 2 -4.11%Firm 3 3.57%Firm 4 -0.94%

Adjusted R-Square 0.384

CDHP Replacement Effect:Total Medical Expenditures

Coefficients T-Stat Pr > |t|Intercept 5.2399 272.06 <.0001Age (years) 0.0081 19.68 <.0001Female=1, else 0 0.1416 17.44 <.0001Baseline Illness Count 0.2831 158.90 <.0001Catastrophic Shock=1, else 0 0.7077 75.65 <.0001Year 2=1, else Year 1 -0.0132 -1.43 0.1516Later Sample=1, else 0 -0.0075 -0.10 0.9189Firm 2=1, else 0 -0.0987 -7.43 <.0001Firm 3=1, else 0 -0.1128 -1.44 0.1505Firm 4=1, else 0 -0.0199 -0.35 0.7281Firm 2 & Year 2 interaction -0.0303 -1.62 0.106Firm 3 & Year 2 interaction 0.0952 1.15 0.2502Firm 4 & Year 2 interaction 0.0225 0.33 0.7415Enrollee is spouse=1, else 0 0.0642 5.88 <.0001Enrollee is dependent=1, else 0 -0.2523 -16.53 <.0001

Second Year ImpactFirm 1 -1.32%Firm 2 -4.35%Firm 3 8.20%Firm 4 0.93%

Adjusted R-Square 0.348

CDHP Replacement Effect:Consumer OOP Medical Expenditures

Coefficients T-Stat Pr > |t|Intercept 4.0551 228.93 <.0001Age (years) 0.0047 12.47 <.0001Female=1, else 0 0.0571 7.60 <.0001Baseline Illness Count 0.2244 137.19 <.0001Catastrophic Shock=1, else 0 0.5548 65.61 <.0001Year 2=1, else Year 1 0.9924 116.05 <.0001Later Sample=1, else 0 0.0013 0.02 0.9842Firm 2=1, else 0 1.0133 83.79 <.0001Firm 3=1, else 0 -1.0161 -14.41 <.0001Firm 4=1, else 0 0.2383 4.62 <.0001Firm 2 & Year 2 interaction -1.0646 -62.58 <.0001Firm 3 & Year 2 interaction 0.4221 5.61 <.0001Firm 4 & Year 2 interaction -0.1873 -3.04 0.0024Enrollee is spouse=1, else 0 0.0215 2.15 0.0319Enrollee is dependent=1, else 0 -0.2886 -20.59 <.0001

Second Year ImpactFirm 1 99.24%Firm 2 -7.22%Firm 3 141.45%Firm 4 80.51%

Adjusted R-Square 0.377

CDHP Replacement Effect:Total Pharmacy Expenditures

Coefficients T-Stat Pr > |t|Intercept 3.3627 131.73 <.0001Age (years) 0.0376 70.17 <.0001Female=1, else 0 0.0459 4.18 <.0001Baseline Illness Count 0.2296 99.41 <.0001Catastrophic Shock=1, else 0 -0.0966 -7.69 <.0001Year 2=1, else Year 1 0.0979 7.97 <.0001Later Sample=1, else 0 -0.1814 -1.84 0.0658Firm 2=1, else 0 -0.4840 -26.94 <.0001Firm 3=1, else 0 0.5472 5.24 <.0001Firm 4=1, else 0 -0.1162 -1.52 0.1291Firm 2 & Year 2 interaction -0.0580 -2.29 0.022Firm 3 & Year 2 interaction -0.2972 -2.70 0.0069Firm 4 & Year 2 interaction -0.1121 -1.23 0.2174Enrollee is spouse=1, else 0 -0.0091 -0.63 0.5257Enrollee is dependent=1, else 0 0.2454 12.10 <.0001

Second Year ImpactFirm 1 9.79%Firm 2 3.99%Firm 3 -19.93%Firm 4 -1.42%

Adjusted R-Square 0.256

CDHP Replacement Effect:Consumer OOP Pharmacy Expenditures

Coefficients T-Stat Pr > |t|Intercept 3.0338 144.17 <.0001Age (years) 0.0300 67.92 <.0001Female=1, else 0 0.0987 10.90 <.0001Baseline Illness Count 0.1895 99.43 <.0001Catastrophic Shock=1, else 0 -0.0873 -8.42 <.0001Year 2=1, else Year 1 0.0540 5.33 <.0001Later Sample=1, else 0 -0.1125 -1.39 0.1654Firm 2=1, else 0 0.0325 2.20 0.0281Firm 3=1, else 0 -0.8478 -9.85 <.0001Firm 4=1, else 0 0.0846 1.34 0.1795Firm 2 & Year 2 interaction -0.1202 -5.76 <.0001Firm 3 & Year 2 interaction 1.0951 12.01 <.0001Firm 4 & Year 2 interaction 0.1033 1.37 0.1692Enrollee is spouse=1, else 0 -0.0359 -3.05 0.0023Enrollee is dependent=1, else 0 0.0981 5.87 <.0001

Second Year ImpactFirm 1 5.40%Firm 2 -6.62%Firm 3 114.91%Firm 4 15.72%

Adjusted R-Square 0.253

CDHP Replacement Effect:Probability of Any Preventive Visits

Coefficients T-Stat Pr > |t|Intercept 0.1082 21.08 <.0001Age (years) 0.0016 14.05 <.0001Female=1, else 0 0.1911 86.77 <.0001Baseline Illness Count 0.0311 63.79 <.0001Catastrophic Shock=1, else 0 -0.0376 -13.31 <.0001Year 2=1, else Year 1 -0.0024 -0.91 0.3612Later Sample=1, else 0 -0.0031 -0.16 0.876Firm 2=1, else 0 -0.1030 -29.45 <.0001Firm 3=1, else 0 0.0720 3.41 0.0006Firm 4=1, else 0 -0.0928 -6.12 <.0001Firm 2 & Year 2 interaction -0.0254 -5.28 <.0001Firm 3 & Year 2 interaction -0.0071 -0.32 0.7512Firm 4 & Year 2 interaction -0.0042 -0.24 0.8141Enrollee is spouse=1, else 0 -0.0311 -10.31 <.0001Enrollee is dependent=1, else 0 -0.2226 -54.76 <.0001

Second Year ImpactFirm 1 -0.24%Firm 2 -2.78%Firm 3 -0.95%Firm 4 -0.66%

Adjusted R-Square 0.220

CDHP Replacement Effect:Probability of Colonoscopy Screening

Coefficients T-Stat Pr > |t|Intercept -0.2750 -15.96 <.0001Age (years) 0.0080 23.18 <.0001Female=1, else 0 0.0266 5.80 <.0001Baseline Illness Count 0.0203 23.36 <.0001Catastrophic Shock=1, else 0 -0.0167 -3.33 0.0009Year 2=1, else Year 1 -0.0099 -1.98 0.0482Later Sample=1, else 0 -0.0091 -0.27 0.7893Firm 2=1, else 0 -0.0235 -3.11 0.0019Firm 3=1, else 0 0.0093 0.26 0.7971Firm 4=1, else 0 -0.0214 -0.82 0.4144Firm 2 & Year 2 interaction -0.0049 -0.45 0.6497Firm 3 & Year 2 interaction -0.0122 -0.32 0.7501Firm 4 & Year 2 interaction -0.0570 -1.82 0.0684Enrollee is spouse=1, else 0 -0.0026 -0.53 0.5989Enrollee is dependent=1, else 0 -0.0376 -1.69 0.0911

Second Year ImpactFirm 1 -0.99%Firm 2 -1.48%Firm 3 -2.20%Firm 4 -6.69%

Adjusted R-Square 0.043

CDHP Replacement Effect:Probability of Mammography Screening

Coefficients T-Stat Pr > |t|Intercept 0.2531 8.81 <.0001Age (years) 0.0041 7.01 <.0001Baseline Illness Count 0.0275 19.93 <.0001Catastrophic Shock=1, else 0 -0.0355 -4.21 <.0001Year 2=1, else Year 1 -0.0328 -4.03 <.0001Later Sample=1, else 0 -0.0635 -1.01 0.3146Firm 2=1, else 0 -0.1693 -13.01 <.0001Firm 3=1, else 0 0.1773 2.67 0.0077Firm 4=1, else 0 -0.0144 -0.30 0.7631Firm 2 & Year 2 interaction 0.0034 0.18 0.8539Firm 3 & Year 2 interaction -0.0865 -1.24 0.2139Firm 4 & Year 2 interaction -0.0550 -0.97 0.3323Enrollee is spouse=1, else 0 0.0113 1.23 0.22Enrollee is dependent=1, else 0 -0.0848 -1.68 0.0929

Second Year ImpactFirm 1 -3.28%Firm 2 -2.94%Firm 3 -11.93%Firm 4 -8.78%

Adjusted R-Square 0.048

CDHP Replacement Effect:Probability of Cervical Cancer Screening

Coefficients T-Stat Pr > |t|Intercept 0.6648 55.47 <.0001Age (years) -0.0060 -22.06 <.0001Baseline Illness Count 0.0266 25.62 <.0001Catastrophic Shock=1, else 0 -0.0593 -9.54 <.0001Year 2=1, else Year 1 -0.0539 -9.22 <.0001Later Sample=1, else 0 0.0380 0.71 0.48Firm 2=1, else 0 -0.0637 -7.26 <.0001Firm 3=1, else 0 0.1686 2.97 0.003Firm 4=1, else 0 -0.1169 -2.85 0.0044Firm 2 & Year 2 interaction 0.0142 1.13 0.2573Firm 3 & Year 2 interaction 0.0310 0.52 0.6014Firm 4 & Year 2 interaction 0.0849 1.74 0.0819Enrollee is spouse=1, else 0 0.0089 1.39 0.1652Enrollee is dependent=1, else 0 -0.0650 -2.01 0.0449

Second Year ImpactFirm 1 -5.39%Firm 2 -3.98%Firm 3 -2.29%Firm 4 3.10%

Adjusted R-Square 0.037

Summary of CDHP Impact – by Firm – for Expenditures and Prevention

Total Expenditures

Total Medical Expenditures

Consumer Medical

ExpendituresTotal Pharmacy

Expenditures

Consumer Pharmacy

ExpendituresFirm 1 1.28% -1.32% 99.24% 9.79% 5.40%Firm 2 -4.11% -4.35% -7.22% 3.99% -6.62%Firm 3 3.57% 8.20% 141.45% -19.93% 114.91%Firm 4 -0.94% 0.93% 80.51% -1.42% 15.72%

Preventive Visit

ProbabilityColonoscopy

Probability

Cervical Cancer Screening Probability

Mammography Screening Probability

Firm 1 -0.24% -0.99% -3.28% -5.39%Firm 2 -2.78% -1.48% -2.94% -3.98%Firm 3 -0.95% -2.20% -11.93% -2.29%Firm 4 -0.66% -6.69% -8.78% 3.10%

Total Replacement Employer

Total Replacement Employer

Summary of Empirical Findings Total replacement with CDHPs achieves a level of

cost savings not seen in previous empirical studies where consumers had other plan choices.

Significant increases in consumer expenditures found in some firms.

General decrease or neutral affect on prevention. Few of the changes in preventive care measures were statistically significant.

At best consumerism affects prevention in a neutral fashion. At worse, consumers use prevention less.

Irony is that prevention was covered at 100% reimbursement with no cost-sharing in all of the firms.

Next Steps

Get more precise firms specific affects beyond a linear probability model.

Address selection more completely.Bootstrap correct standard errors for

interaction affects on expenditure and utilization. Prior work has shown the bootstrapped significance is not as significant as the non bootstrapped method.

Try to find firms with second and third year post replacement affects

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 FinanceDirector, Medical Industry Leadership InstituteCarlson School of ManagementUniversity of Minnesota321 19th Ave. South, Room 3-122Minneapolis, MN 55455612-624-1391 (v)sparente@csom.umn.eduhttp://www.tc.um.edu/~paren010