Reduction of Medicaid Expenditures from State Prescription Programs in

Illinois and Wisconsin

Donald S Shepard, PhD* Desiree Koh, * Cindy Thomas, PhD*Grant Ritter, PhD* Daniel Gilden,+ William Stason, MD,MS*

Christine Bishop, PhD*

*Brandeis University; +JEN Associates

Supported by the Centers for Medicare & Medicaid Services under Contract No. CMS 500-00-0031/T.O. #2 to Brandeis

University

AcademyHealth Annual Research Meeting,June 8-10, 2008

22

Framework

Prescription coverage

Better use of drugs

and medical services

Maintain health Lower

nursing home use

Less Medicaid

entry

33

Past research

Rector (2004), Safran (2005), Leung (2005) – About 30% of low income people skip some prescribed medications

Soumerai et al. (1991) –Limiting drugs to vulnerable population increased nursing home admissions

Gilman (2004) – Members of Prescription Assistance Programs (PAP) skip fewer doses than comparable controls

Shepard (2006) – SeniorCare halved risk of skimping

Leung (2005) – Risk is related to individual characteristics

44

Program background In mid-2002, Illinois and Wisconsin initiated

“SeniorCare” (SC) pharmacy assistance programs (PAPs) that provide low-income persons aged 65+ with publicly funded prescription drug assistance.

Maximum co-payments per prescription are generally $4 in IL and $15 in WI.

Enrollees with incomes up to 200% of the federal poverty limit (FPL) are funded under a Medicaid waiver designed to help seniors improve prescription drug use, maintain health and reduce financial vulnerability due to prescription costs.

55

Three strata studied 68,292 Wisconsin members, who were all

new enrollees (1,189 interviewed), 121,000 Illinois members previously in

Circuit Breaker, a limited PAP that excluded mental health and gastro-intestinal drugs and automatically rolled over into SC (termed ‘IL rollovers, 374 interviewed);

47,782 Illinois members not previously in this PAP (termed ‘IL new,’ 664 interviewed).

66

Skimping by Income Group

77

Study Design for Medicaid Analysis

Ohio served as the comparison state. Using Medicare claims and zip codes,

matched Illinois and Wisconsin enrollees exactly on demographic and disease categories to similar Ohio Medicare beneficiaries.

Used propensity scores to match closely on disease severity and socio-economic characteristics based on census information and Social Security benefits.

88

Population Studied

Needed precise matching on income for examining Medicaid entry

Limited this analysis to buy-in Medicare beneficiaries in the three states Received subsidies for Medicare premiums

and deductibles Qualified Medicare Beneficiaries, QMB Special Low Income Medicare Beneficiaries,

SLMB We matched 7,699 Illinois and 1,798

Wisconsin buy-in beneficiaries to comparable buy-in Ohio controls.

99

Research Objective:Evaluate First Year Impacts on

Nursing home entry Medicaid entry Medicaid expenditures

1010

Approach

Descriptive analysis Multivariate analysis

1111

Nursing Home Entry among Wisconsin Buy-in Beneficiaries and Matched Ohio Controls

1212

Medicaid Entry amongWisconsin Buy-in Beneficiaries and

Matched Ohio Controls

1313

Hazard Function for Nursing Home Entry, Wisconsin, part 1

Variable Parameter Estimat

e

Standard Erro

r

Statistical Signifi-cance

Hazard Rati

o

Inpatient 0-3 Months of Index 0.858 0.289 0.003 2.357

Home Health 0-3 Months of Index 0.055 0.544 0.919 1.057

SNF 0-3 Months of Index 0.179 0.824 0.828 1.196

2001 JAI Morbidity Score 0.097 0.065 0.136 1.102

2001 Indicator for a Arthritis diagnosis 0.013 0.243 0.957 1.013

2001 Indicator for a Chronic heart disease diagnosis

-0.244 0.251 0.332 0.784

2001 Indicator for a Congestive heart failure diagnosis

0.364 0.309 0.238 1.439

2001 Indicator for a COPD diagnosis -0.393 0.272 0.149 0.675

2001 Indicator for a Cerebrovascular disease diagnosis

0.205 0.332 0.536 1.228

2001 Indicator for a Diabetes diagnosis -0.284 0.261 0.277 0.753

1414

Hazard Function for Nursing Home Entry, Wisconsin, part 2

Variable Parameter Estimat

e

Standard Erro

r

Statistical Signifi-cance

Hazard Rati

o

SSA Dept Count=1; SSA Pym (in 1,000s) 0.016 0.037 0.670 1.016

SSA Dept Count>1 -3.186 3.131 0.309 0.041

SSA Dep Count>1 * SSA Pymt (in 1,000s) 0.212 0.218 0.331 1.237

% Census Block: Income $0-$10,000 -0.267 1.233 0.829 0.766

% Census Block: Income $10,000-$20,000 -0.214 1.157 0.853 0.807

% Census Block: Income $20,000-$30,000 0.745 1.234 0.546 2.107

% Census Block: Income $30,000-$40,000 -2.037 1.498 0.174 0.130

% Census Block: Income >$40,000 0.952 1.132 0.400 2.591

% Census Block: HMO Participant -0.295 0.224 0.188 0.745

State Rx Enrollee -0.658 0.217 0.002 0.518

1515

Adjusted nursing home entry (SC/OH)

0.550.72

0.52

0.00

0.20

0.40

0.60

0.80

1.00

IL Rollover IL New WI-All

Enrollment group

Adj

. haz

ard

rate

1616

Crude risk ratio forMedicaid entry (SC/OH)

1.60

0.94

0.51

0.00

0.50

1.00

1.50

2.00

IL Rollover IL New WI-All

Enrollment group

Ris

k ra

tio

1717

Relative spending per entrant (SC/OH)

0.26 0.28

0.38

0.00

0.10

0.20

0.30

0.40

0.50

IL Rollover IL New WI-All

Enrollment group

Ris

k r

ati

o

1818

Relative spending per enrollee (SC/OH)

0.41

0.260.19

0.00

0.10

0.20

0.30

0.40

0.50

IL Rollover IL New WI-All

Enrollment group

Ris

k r

ati

o

1919

Illinois summary Due to preexisting PAP, SeniorCare did not reduce

Medicaid entry, but did reduce nursing home entry and spending.

Cumulative rate of nursing home entry of Illinois SeniorCare buy-in beneficiaries (2.4%) was half the rate of the matched Ohio controls (4.4%).

Medicaid spending over the first year when averaged over all Illinois buy-in SeniorCare members (with standard errors of the mean) was $631 ($26) vs. $1,605 ($83) for matched buy-in Ohio controls

Per enrollee savings $974 ($87) or 61%. Savings in Illinois did not quite equal the state’s share of

first-year program costs per enrollee year ($1,394).

2020

Wisconsin summary

SeniorCare buy-in enrollees had half the rate Medicaid entry in the first year (11%) than matched Ohio controls (22%)

Wisconsin SC had half the rate of nursing home entry (2.2%) compared to Ohio controls (4.5%)

Had $1,190 ($163) or 81% lower Medicaid spending per buy-in enrollee.

Wisconsin savings on buy-ins were greater than the state’s share of first-year program costs per enrollee year ($1,032).

2121

Extrapolation possible?

Question: Do the data allow examining impacts on nursing home and Medicaid for all SeniorCare enrollees?

Answer: No Why not? Ascertainment of income

2222

Medicaid entry by family incomeMedicaid entry by family income, marital status, and state

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Marital status, state, and family income quintile (1 = poorest)

% E

ntr

y in

to M

edic

aid

.

1, Poorest 2 3 4 5, Richest

1, Poorest 17.9% 12.0% 8.6% 5.9%

2 11.5% 5.4% 6.7% 3.3%

3 5.6% 3.6% 4.6% 2.9%

4 3.7% 2.9% 3.2% 2.4%

5, Richest 2.3% 2.1% 2.1% 1.6%

Unmarrried, IL Marrried, IL Unmarrried, WI Marrried, WI

2323

Approximate probability of Medicaid entry

Illustrative model of Medicaid entry as a function of family income (assuming income threshold of $8000)

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

10 15 20 25 30

Family income ($1000)

Med

icai

d e

ntr

y p

rob

abili

ty in

on

e ye

ar

Unmarried, IL (elasticity=-0.9)

Married, IL (elasticity=-1.9)

Unmarried, WI (elasticity=-0.9)

Married, WI (elasticity=-1.4)

2424

Household incomes by neighborhood

Distribution of household incomes in neighborhood

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

State, group, and maximum household income in stratum

Cu

mu

lati

ve %

of

hou

seh

old

s .

>$40,000 29% 27% 26% 29%

<$40,000 13% 13% 14% 13%

<$30,000 19% 20% 20% 21%

<$20,000 24% 25% 26% 24%

<$10,000 14% 14% 13% 12%

IL Enrollees, median $26,100

OH (matched to IL), median $25,300

WI Enrollees, median $25,300

OH (matched to WI), median $26,400

2525

Neighborhood: weak predictor of family income

Family income by marital status, neighborhood income and state

0%

5%

10%

15%

20%

25%

30%

Neighborhood income

% o

f fa

mil

ies

in lo

wes

t q

uin

tile

Missing 1, Poorest 2 3 4 5, Richest

Missing 24.5% 26.8% 25.8% 26.4%

1, Poorest 24.3% 22.8% 23.3% 21.4%

2 19.9% 19.2% 20.8% 23.2%

3 19.7% 19.1% 20.4% 21.1%

4 17.0% 16.8% 18.6% 17.1%

5, Richest 17.6% 18.3% 15.1% 15.5%

Unmarried, IL Married, IL Unmarried, WI Married, WI

2626

Unsuccessful extrapolation beyond buy-ins

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Follow-up Months

Pro

po

rtio

n N

ot

En

teri

ng

Med

icai

d

Illinois SeniorCare

Ohio Comparisons

IL:Rollover Enrollees

IL:Later Month Enrollees

2727

Conclusions

50% reductions in skimping applied to all SeniorCare enrollees

Comparable declines in nursing home entry among buy-ins.

First year savings in buy-in population not quite enough to pay for the program costs in Illinois

These savings were more than sufficient in Wisconsin.

Prescription drug coverage for vulnerable populations pays off with less nursing home entry and lower costs.

2828

Limitations

Differences in nursing home and Medicaid policies among states could confound interpretation

2929

Strength: Consistent improvements in

Self reported behavior (skimping) Costly services (nursing home entry) Medicaid expenditures

3030

Research implications

Observations and natural experiments very powerful.

Must understand and control for selection effects.

Stay within the data.

3131

Policy implications

Enrollment of needy elders in both states benefited from outreach, straightforward design, and federal subsidies that extended to 200% of the FPL.

These findings show the value of completing “coverage” with access to prescription drugs.

3232

Thank you

Shepard@brandeis.edu