impact of a voucher program on consumer choices of personal assistance providers: urban-rural...

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Impact of a Voucher Program on Consumer Choices of Personal Assistance Providers: Urban-Rural Differences Hongdao Meng, Ph.D., Stony Brook University Brenda Wamsley, Ph.D., West Virginia State University

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Impact of a Voucher Program on Consumer Choices of

Personal Assistance Providers: Urban-Rural Differences

Impact of a Voucher Program on Consumer Choices of

Personal Assistance Providers: Urban-Rural Differences

Hongdao Meng, Ph.D., Stony Brook University

Brenda Wamsley, Ph.D., West Virginia State University

Acknowledgments Acknowledgments

• Funding agency: Centers for Medicare and Medicaid Services (# 95-C-90467/2-01)

• Monroe County Long Term Care Program, Inc., Rochester, NY.

• Center for Aging and Healthcare in WV, Inc., Parkersburg, WV.

BackgroundBackground

• Personal Assistance Services (PAS) help people with long-term care needs to live independently in their homes.

• PAS is delivered via:– “Agency-directed” model (ADM)– “Consumer-directed” model (CDM)

BackgroundBackground

• Benefits of CDM:– Flexibility– Autonomy– Potential cost-savings– Expanded care worker pool

• Concerns of CDM:– Cognitive impairment– Hiring of family members– Quality assurance

ObjectivesObjectives

• To examine the impact of a voucher program on consumer choices of PAS providers (ADM or CDM).

• To assess urban-rural differences in these choices.

Study DesignStudy Design

• Randomized controlled trial:– Control group: regular Medicare– Nurse group: health promotion nurse home

visit– Voucher group: $200 monthly PAS benefits– Combination group: Nurse + Voucher

SampleSample

• 1605 participants who meet the inclusion criteria of this study:– Community-dwelling (NY, WV, OH) – Medicare Parts A and B enrollee, and– 2+ ADLs or 3+ IADLs, and– Had prior health services use (ER, hospital,

NH, or home care)

DataData

• Baseline assessment data – Socio-demographics– Health and functional status– Prior health services use

• PAS utilization data over two years– Personal care aide – Home health aide – Respite care

Analytical StrategiesAnalytical Strategies

• Descriptive statistics

• Multivariate logistic regression adjusting for the following co-variates:– Age, gender, education, income, MediGap,

Medicaid, caregiver status– ADLs, IADLs, Cognitive Performance Scale– Prior health services use

Baseline Descriptive ResultsBaseline Descriptive Results

• Mean age 77– 27% age 85+

• 69% female• 96% White• 33% income < $10k• 73% had caregiver • 38% lived alone• 11% had Medicaid• 3% had LTC

insurance

• Mean # of ADLs 2.3• Mean # of IADLs 3.5• Mean # of chronic

conditions: 4.4• Prior service use:

– Hospital 63%– ER 23%– Nursing home

10%– Home care 52%

PAS Use Over Two YearsPAS Use Over Two Years

% with any use

19%

21%

21%

38%ADM onlyCDM onlyBothNeither

PAS Use, Control GroupPAS Use, Control Group

% with any use

16%

22%

18%

44% ADM only

CDM only

BothNeither

PAS Use, Voucher GroupPAS Use, Voucher Group

% with any use

21%

21%

24%

33%

ADM only

CDM only

BothNeither

Odds Ratio for PAS Use, by Provider Type

Odds Ratio for PAS Use, by Provider Type

1.74

1.28

0

0.5

1

1.5

2

ADM CDM

Adjusted for covariates

Odds Ratio for PAS Use, by Provider Type and Urban-Rural

Odds Ratio for PAS Use, by Provider Type and Urban-Rural

**: p < 0.01, adjusted for covariates

Summary of Key FindingsSummary of Key Findings

• Overall, the voucher program increased the probabilities of using both types of PAS providers.

• In urban areas, the voucher effect is primarily on the use of agency-employed workers.

• In rural areas, the voucher effect is primarily on the use of privately-hired workers.

LimitationsLimitations

• Generalizability

• Definition of rural

Policy ImplicationPolicy Implication

• A voucher program for PAS promotes its use moderately.

• Consumer behavior under the voucher program differs substantially between urban and rural participants.

• The availability of agency and private workers may

explain part of the observed differences.

• These differences should be taken into account in promoting the use of consumer-directed models.