# Presentation: 779615

## Impact of Eligibility Reform on the Demand for VHA Services by Medicare Eligible Veterans

- Yvonne Jonk, PhD
- Roger Feldman, PhD
- Bryan Dowd, PhD
- Diane Cowper-Ripley, PhD

- Heidi O’Connor, MS
- Andrea Cutting, MA
- Tamara Schult, MPH

*Funded by VA HSRD*
*IIR 01-164*

## Introduction
Objectives
Hypotheses
Research Design
Methods
Results
Conclusions 
Policy Implications

- Introduction
- Objectives
- Hypotheses
- Research Design
- Methods
- Results
- Conclusions
- Policy Implications

## 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

- 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

## 1996 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

*1996 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

## 1996 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

*1996 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

## Analyze the impact of the 
Veterans Health Administration’s (VHA) 
1996 eligibility reforms 
on 
Medicare-eligible veterans’ 
health care utilization and cost

Factors influencing demand for medical care

- Analyze the impact of the
- Veterans Health Administration’s (VHA)
- 1996 eligibility reforms
- on
- Medicare-eligible veterans’
- health care utilization and cost
- Factors influencing demand for medical care

## Main Hypotheses:
After the reforms, Medicare eligible NSC-MT vets increased their use of VHA services
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

- _Main Hypotheses_:
- After the reforms, Medicare eligible NSC-MT vets increased their use of VHA services
- 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

## 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

- 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

## 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

- 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

## 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

- 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

## Table 1. Available VHA 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
PBM |

*PTF = 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*

## 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

- 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

## 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

- 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

## 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

- 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

## 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

- 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

## 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

- 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

## 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

**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

## 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)

- 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)

## Table 2. Matrix of Utilization Rates (Uxx)

- 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   utilization
- U01 – U00 = impact of other admin changes on the control group
- 	DD: (U11 – U10) – (U01 – U00) = pure measure of effect

## Figure 1. Illustrating the DD Model

- Time (years)

- 1998

- Control group

- NSC-MT

## 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 group
YR = vector of year dummy variables
POST YR = vector of binary variables, 1 for years ‘98–‘02
NSC-MT x POST YR = vector of interaction terms for  experimental group and the post year variables
X = vector of additional variables (socio-economic, health)
VISN = vector of binary variables indicating the VISN (21 VISNs) from which the subject received care.  

- 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 group
- YR = vector of year dummy variables
- POST YR = vector of binary variables, 1 for years ‘98–‘02
- NSC-MT x POST YR = vector of interaction terms for  experimental group and the post year variables
- X = vector of additional variables (socio-economic, health)
- VISN = vector of binary variables indicating the VISN (21 VISNs) from which the subject received care.

## Figure 1. Illustrating the DD Model

- Time (years)

- 1998

- 

-  + 1

- Interaction term = experimental effect

- Control group

- NSC-MT

## 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:
Y = Probability of use
Y = Level of use for those who used services

- 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:
- Y = Probability of use
- Y = Level of use for those who used services

## 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

- 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

## 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.

- 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.

## 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)

- 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)

## 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

- 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

## 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

- 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

## 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

- 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

| 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

-  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.

-  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

-  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.

-  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.

-  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.

-  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 Seminar
- Wed April 25th, 1-2 CST

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## SUR RUN: