Impact of Eligibility Reform on the Demand for VHA Services

by Medicare Eligible Veterans

Yvonne Jonk, PhDRoger Feldman, PhDBryan Dowd, PhDDiane Cowper-Ripley, PhD

Heidi O’Connor, MS Andrea Cutting, MATamara Schult, MPH

Funded by VA HSRD

IIR 01-164

Presentation

Introduction Objectives Hypotheses Research Design Methods Results Conclusions Policy Implications

Introduction

Mid 90’s – VHA administrative changes: Changes in eligibility guidelines Decentralization of administrative operations Formation of Veterans Integrated Service Networks

(VISNs) Incentives for shifting inpatient to outpatient care Creation of Community Based Outpatient Clinics

(CBOCs) Adoption of the Veterans Equitable Research

Allocation (VERA) system

Introduction

1996 Veterans’ Health Care Eligibility Reform Act1996 Veterans’ Health Care Eligibility Reform Act Prior to 1996, Service Connected (SC) & low

income Cat A vets were eligible for services Cat C Non-Service Connected Means Tested

(NSC-MT) veterans were considered eligible for inpatient care on a first come, first serve basis depending on capacity limitations– Outpatient and pharmaceutical follow up care– Care deemed necessary to avoid a hospitalization

Introduction

1996 Veterans’ Health Care Eligibility Reform Act1996 Veterans’ Health Care Eligibility Reform Act

After reforms were fully implemented in 1998: All veterans regardless of SC status or income,

were entitled to a uniform benefits package Depending on SC and financial status, some

veterans pay co-payments Expect to see impact on utilization of outpatient

and prescription services

Objectives

1) Analyze the impact of the

Veterans Health Administration’s (VHA)

1996 eligibility reforms

on

Medicare-eligible veterans’

health care utilization and cost

2) Factors influencing demand for medical care

Hypotheses

Main Hypotheses:1) After the reforms, Medicare eligible NSC-MT vets

increased their use of VHA services2) NSC-MT vets decreased their utilization of

Medicare IP and OP services

Secondary focus: Address factors influencing demand for VHA/Medicare

Socioeconomic, health, distance traveled

Research Design

Observational study Sample:

– Nationally representative sample of 10,838 non-institutionalized veterans who were Medicare beneficiaries from 1992-2002

Data: – Medicare Current Beneficiary Survey (MCBS)– Medicare claims data– VHA administrative data

Research Design

Medicare Current Beneficiary Survey (MCBS)– Nationally representative sample of Medicare

eligible population– Rotating panel, in panel for 4 years– Rich dataset: comprehensive information on

socioeconomic, health and functional status, health insurance, health care utilization and costs

Research Design – Data

Because VHA does not bill for services, all VHA costs found within the MCBS were imputed

Ideally, we wanted to validate the self-reported utilization data as well as CMS’ imputed cost estimates for VHA users using VHA administrative datasets

Research Design - Data

Table 1. Available VHA Data

Type Care Pre/Post Utilization Cost

Inpatient

(IP)

Pre ‘98

Post ‘98

PTF

NPCD

CDR Categorical

HERC IP AC/CDR

Outpatient

(OP)

Pre ‘98

Post ‘98

OPC

NPCD

NA

HERC OP AC

Prescrip-tions (Rx)

Pre ‘98

Post ‘98

NA

PBM

NA

PBMPTF = Patient Treatment File, CDR = Cost Distribution Report, NPCD = National Patient Care Database, HERC = Health Economics Resource Center, AC = Average Cost, OPC = Outpatient Care, PBM = Pharmacy Benefits Management

Research Design – Data Issues

In general, MCBS self-reported VHA utilization data tended to be (consistently) underreported relative to that found in the administrative datasets

Very difficult to match self-reported VHA utilization to that found in the VHA datasets

– Dates are off– Within MCBS, we don’t know what the patient came in for– Discrepancies betw/ patient’s def’n of a VHA OP visit and

admin def’n (by day or by stop code)– Discrepancies betw/ patient’s def’n of Rx and admin def’n

Research Design – Inpatient Data

For FY99 onward, we found large discrepancies between CMS’s imputed cost estimates for VHA IP hospitalizations and HERC IP AC estimates

For each VA user, we replaced all of their IP utilization and cost data with VHA and HERC categorical costing data over years 92-02

Research Design – Outpatient Data

Using VHA OP data for FY97 onward, we found consistent underreporting of MCBS self-report OP event data

Distribution of annual HERC AC OP data were consistent with what we found in MCBS’ imputed cost estimates

Research Design – Prescriptions

Using VA’s PBM data for FY99 onward, we found consistent underreporting of MCBS self-reported Rx’s

Distribution of annual PBM costing data were consistent with what we found in MCBS’ imputed cost estimates

Research Design – Data Issues

To Summarize: Consistent underreporting with self-report Don’t have all VHA administrative data for ‘92-’02 Self-reported data facilitates analyzing impact of

eligibility reform over all years ‘92-’02

Used the “best” data available: VHA IP utilization and cost measures (big $tx) MCBS self reported OP and Rx utilization and CMS

imputed cost estimates

Methods - “Difference in Differences”

Goal: Disentangle the impact of the eligibility expansions from the rest of the administrative changes taking place

Identify experimental and control groups:– Both face same secular trends – effect of factors unrelated to the

intervention and common to both groups– Experimental group also experiences effect of the intervention

Difference in changes in the dependent variable (e.g. % use VHA) from pre to post-intervention betw/ 2 groups isolates the effect of intervention from secular trend

Methods - “Difference in Differences”

Control group (SC low income)– Service Connected (SC) – Low income (below VHA means test thresholds)

Experimental group (NSC-MT) – Non-Service Connected (NSC)– Means Tested (above VHA means test thresholds)

Methods - “Difference in Differences”

Table 2. Matrix of Utilization Rates (Uxx)

Change in Eligibility

Impact of Eligibility Reforms Pre 98 Post 98

Control (SC low income) U00 U01

Experimental (NSC-MT) U10 U11

U11 – U10 : ignores fact that other admin changes utilizationU01 – U00 = impact of other admin changes on the control group

DD: (U11 – U10) – (U01 – U00) = pure measure of effect

Methods – Regression Model

Figure 1. Illustrating the DD Model

Utilization, Cost

1992 2002 Time (years)1998

Control group

NSC-MT

Methods – Regression Model

Y = α + β1 NSC-MT + β2 NSC-MT x POST YR + β3 YR + β4 X + β5 VISN + ε

Where:

NSC-MT = binary variable, 1 for the experimental groupYR = vector of year dummy variablesPOST YR = vector of binary variables, 1 for years ‘98–‘02NSC-MT x POST YR = vector of interaction terms for

experimental group and the post year variablesX = vector of additional variables (socio-economic, health)VISN = vector of binary variables indicating the VISN (21

VISNs) from which the subject received care.

Methods – Regression Model

Figure 1. Illustrating the DD Model

Utilization, Cost

1992 2002 Time (years)1998

+ 1

Interaction term = experimental effect

Control group

NSC-MT

Methods – Regression Model

Y = α + β1 NSC-MT + β2 NSC-MT x POST YR + β3 YR + β4 X + β5 VISN + ε

Because utilization variables have a large proportion of observations at zero, we used two part models to analyze the factors influencing the use of VHA (Medicare) services:

1) Y = Probability of use

2) Y = Level of use for those who used services

Methods – MV Probit Model

Decision to use VHA and/or Medicare services are not made independently of each other

Modeling the use/no use of VHA (Medicare) services involved estimating a set of 5 equations simultaneously:

VHA IP, VHA OP, VHA Rx,

Medicare IP, Medicare OP

Using the multivariate probit model in STATA

Methods – SUR Model

Similarly, for those with positive utilization of services within the VHA and/or Medicare sectors, the number of times patients come in may depend on how many times they use services in the other sector.

Thus the method of Seemingly Unrelated Regressions (SUR) was used to estimate the impact of eligibility reforms and other factors on the level of use.

Because these count data are highly skewed, we used a log transformation on the dependent variable.

In all models, the unit of observation was a person (calendar) year.

Methods – Variables

Demographic variables: Gender (male) Age (<65, 65-75, 75+) Race (white) Marital status (married) Education (some college, college grad, ref = no

college) Income (in $10,000 increments) Family size (one to five or more)

Methods – Variables

Measures of Health Status: VHA SC disability (1 = Yes, 0 = No) SC Rating 0-100% General health status

– (1 = good, very good, or excellent, 0 = fair or poor)

Chronic conditions – heart condition, hypertension, stroke, cancer

(including skin), diabetes, arthritis, lung disease, Alzheimer’s, and mental illness

Methods – Variables

Measures of Health Status: Activities of Daily Living (ADLs)

– (0-6, higher is lower health status)

Independent Activities of Daily Living (IADLs) – (0-6, higher is lower health status)

Smoking – smoke now, ever smoked

Died in any given year

Methods

Sample weighted to reflect complex survey design using STATA (v9)

Results are generalizable to entire Medicare eligible population

Research received IRB approval from both the UMN and VA

Results – Multivariate Probit

N = 27,730 person yrs (10,838 unique)

VHA OP

MCare OP

VHA Rx

VHA IP

MCare IP

NSCMT -0.476 0.208 -0.430 -0.373 -0.062

NSCMT98 0.099 -0.090 0.057 0.218 -0.002

NSCMT99 0.121 -0.029 0.038 -0.085 0.045

NSCMT00 0.051 -0.024 0.089 0.247 0.151

NSCMT01 0.143 0.041 0.279 0.042 0.097

NSCMT02 0.277 -0.135 0.316 -0.186 -0.005

Table 3. Primary Results for the Multivariate Probit Model

NSCMT = Non-Service Connected Means Tested, MCare = Medicare

Results – Multivariate Probit

Dependent Variable Significant Years/Signs

VA OP 2001 (+) 2002 (+)

Medicare OP 2002 (-)

VA Rx 2001 (+) 2002 (+)

VA IP

Medicare IP 2000(+)The eligibility expansions:• increased the probability of NSC-MT veterans using VA OP & Rx’s. • decreased the probability of using Medicare outpatient care.• increased the probability of using Medicare IP services

Table 4. Summary of Primary Results

Results - Seemingly Unrelated Regression

N = 1,670 person yrs VHA OP MCare OP VHA Rx

NSCMT -0.214 0.086 0.113

NSCMT98 -0.044 0.399 -0.588

NSCMT99 0.097 -0.091 -0.153

NSCMT00 0.142 0.436 -0.328

NSCMT01 -0.016 0.288 -0.359

NSCMT02 0.081 0.270 -0.097

Table 5. Primary Results for Seemingly Unrelated Regressions (SUR) for Positive Use

NSCMT = Non-Service Connected Means Tested, MCare = Medicare

Results – Conditional Use Equations

• SUR results indicated only 2 significant effects: • Among those who used, the number of Rx’s decreased in 1998 and 2001 (n = 1,670 person yrs)

• Separate regressions for users showed • negative VHA Rx effect in 1998 (n=3,531 person yrs)

• learning curve• positive Medicare OP effects in ’00 & ’02 (n=21,022)• positive VHA OP effect in 2000 (n=3,143)

• Separate regressions for VHA inpatient and Medicare inpatient use showed no significant effects.

Discussion

• Eligibility reforms resulted in NSC-MT veterans using more VHA OP and Rx services than the control group

• Since veterans must see a VHA provider in order to receive a Rx, expected to see an increase in both VHA OP and Rx’s (i.e. they are complements)

• Demand for VHA OP services may be driven by veterans’ demand for Rx’s

• Since NSC-MT also decreased their tendency to use Medicare OP services VHA & Medicare OP = substitutes

• Effects of reforms not realized for a few years after the reforms were fully implemented learning curve

Discussion

• Since NSC-MT veterans could use the VHA for IP services prior to the reforms (limited only by capacity constraints), we didn’t expect to see an effect on VHA IP services (and we didn’t).

• Consistent with the literature, distance from VHA facilities posed a significant barrier to using VHA services.

• Likely due to the availability of mail order Rxs, ↑’g distance didn’t reduce the number of Rx’s filled, while it significantly reduced the number of VHA OP visits.

Discussion - VISNs

• Inclusion of VISN DV’s controlled for differing regional capacity constraints.• VHA treated as a homogenous provider of services • Organization of care and timely policy implementation may vary by VISN• We tested whether the treatment effects differed by VISN by including the interaction of VISN*Post*NSC-MT. • Found treatment effect was concentrated in a few large VISNs. However, the sample sizes were too small for these results to have much power. • Thus, we reported the average treatment effect over all of the VISNs.

Conclusion

• Medicare eligible veterans consider the VHA an important provider source, especially for services not well covered by Medicare during the study time period.

• As the veteran population continues to age, an increasing percentage of veterans will be dually eligible for VHA and Medicare services, and will continue to challenge VHA’s budget.

Policy Implications - Normative

• Providing both VHA and Medicare coverage for Medicare eligible veterans essentially duplicates federal spending on health care.

• How does the federal government want veterans to access these two systems?

• Should we level the playing field in terms of the coordination of benefits provided by these two programs?

• Given the implementation of Medicare Part D in 2006, this is a particularly relevant issue. Many veterans now have the option of obtaining Rx’s through Medicare & the VHA.

Questions?

Thanks for Participating!

HERC Cyber SeminarWed April 25th, 1-2 CST

MVPROBIT RUN:

VAOPUSE MCOPUSE NATLVAIPUSE MCIPUSE VAPMUSE_NEGNSCMT -0.476 0.208 -0.373 -0.062 -0.430NSCMT98 0.099 -0.090 0.218 -0.002 0.057NSCMT99 0.121 -0.029 -0.085 0.045 0.038NSCMT00 0.051 -0.024 0.247 0.151 0.089NSCMT01 0.143 0.041 0.042 0.097 0.279NSCMT02 0.277 -0.135 -0.186 -0.005 0.316DEATH -0.367 0.033 0.361 0.020 0.309DISTA_B -0.162 0.665 -0.198 0.120 -0.063FEMALE -0.252 0.090 0.063 -0.031 -0.144UNDER65 0.217 -0.306 0.095 -0.241 0.168AGE6575 0.101 -0.194 0.015 -0.140 0.083WHITE -0.404 0.205 -0.379 0.035 -0.358MARRIED -0.023 0.085 -0.219 -0.026 -0.018NEWINCOM_IC -0.013 0.000 -0.006 -0.005 -0.013SOMCOLL -0.087 0.012 -0.128 -0.009 -0.043COLLGRAD -0.142 0.158 -0.307 0.025 -0.143FAMILY3 -0.023 -0.074 -0.040 -0.003 -0.002SCDISAB 0.461 0.044 0.244 -0.040 0.386SCRATING 0.011 -0.007 0.008 -0.001 0.012EXVGGH -0.114 -0.090 -0.185 -0.264 -0.151HEARTALL 0.109 0.236 0.147 0.360 0.171HBP_ALL 0.194 0.162 0.042 0.087 0.230STROK_ALL 0.130 -0.003 0.128 0.149 0.099ALL_CANCER~N -0.013 0.222 0.048 0.140 -0.005DIAB_ALL 0.128 0.066 0.091 0.108 0.125ARTHALL_2 0.104 0.059 0.045 0.016 0.121LUNG_ALL 0.099 0.173 0.037 0.186 0.102ALZH_ALL -0.027 -0.074 -0.113 -0.232 -0.158MIALL_2 0.265 0.020 0.313 0.053 0.278ADL -0.021 -0.001 0.017 0.039 -0.021IADL 0.024 0.038 0.050 0.064 0.038EVERSMOK2 0.007 0.042 -0.057 0.039 0.014SMOKNOW2 0.020 -0.148 0.146 -0.089 0.000_CONS -0.996 0.206 -1.788 -1.213 -1.149

SUR RUN:

LOG_CTVA_OPA LOG_CTMC_OPA LOG_CTVA_OPA LOG_CTMC_OPA LOG_CTVA_PM

NSCMT -0.171 -0.005 -0.214 0.086 0.113NSCMT98 -0.142 0.514 -0.044 0.399 -0.588NSCMT99 -0.039 0.156 0.097 -0.091 -0.153NSCMT00 0.129 0.488 0.142 0.436 -0.328NSCMT01 0.005 0.348 -0.016 0.288 -0.359NSCMT02 0.058 0.307 0.081 0.270 -0.097DEATH -0.334 0.259 -0.254 0.043 -0.340DISTA_B -0.456 -0.003 -0.496 0.030 -0.075FEMALE 0.209 -0.170 0.188 -0.184 0.135UNDER65 0.210 -0.339 0.258 -0.357 0.223AGE6575 0.101 -0.328 0.107 -0.344 0.006WHITE -0.012 -0.076 -0.029 -0.061 0.133MARRIED -0.028 0.052 -0.025 0.023 0.075NEWINCOM_IC -0.009 0.041 -0.019 0.041 -0.019SOMCOLL -0.115 0.158 -0.106 0.154 -0.071COLLGRAD -0.122 0.249 -0.025 0.184 -0.071FAMILY3 -0.001 -0.040 -0.010 -0.039 0.062SCDISAB 0.061 -0.029 0.068 -0.042 -0.104SCRATING 0.004 0.001 0.004 0.002 0.007EXVGGH -0.061 -0.179 -0.016 -0.163 -0.168HEARTALL -0.007 0.286 -0.035 0.294 0.183HBP_ALL -0.032 0.136 -0.082 0.120 0.307STROK_ALL 0.027 -0.050 0.029 -0.035 -0.001ALL_CANCER~N 0.040 0.201 0.060 0.221 0.107DIAB_ALL 0.167 0.086 0.203 0.076 0.278ARTHALL_2 0.019 0.035 0.009 0.017 0.032LUNG_ALL 0.126 0.149 0.096 0.128 0.306ALZH_ALL -0.238 -0.237 -0.227 -0.188 -0.327MIALL_2 0.295 0.038 0.284 0.021 0.209ADL 0.051 0.011 0.075 0.015 0.015IADL 0.006 0.036 -0.011 0.018 0.038EVERSMOK2 -0.139 0.218 -0.105 0.215 0.017SMOKNOW2 0.154 -0.242 0.216 -0.180 -0.069_CONS 1.315 1.956 1.454 2.023 1.831

With VA OP and MC OP With VA OP, MC OP and VA RX