Fredric D. Wolinsky
Iowa City VAMC
The University of Iowa
Li Liu
Suzanne E. Bentler
The University of Iowa
AcademyHealth
June 30, 2009
Acknowledgements
 We appreciate the contributions to our prior papers in BMC Health
Services Research (2006;6:131 and 2007;7:70) by:
 Thomas Miller
 Hyonggin An
 Paul Brezinski
 Thomas Vaughn
 Gary Rosenthal
 We gratefully acknowledge the National Institutes of Health for
supporting this work with grants
 R01-AG022913
 R03-AG027741
 R21-AG030333
 R21-AG031307
 R21-AT004578
Disclaimer
 Neither the National Institutes of Health nor the Veterans
Administration had any role in the design of the study, the analyses
conducted, or the interpretation and presentation of the results.
 The opinions expressed here today are those of the authors and do
not necessarily reflect those of any of the funding, academic or
governmental institutions involved.
Approvals
 Our research was approved by all applicable oversight groups,
specifically including:
 the AHEAD restricted data access board (#2003-006; 2-20-2003),
 the University of Iowa IRB (#200303008; 03-24-2003; and all
successful annual reviews), and
 the Center for Medicare and Medicaid Services
(DUA #14807; 3-3-2005).
Background
 About 10 million veterans are eligible to use both the Veterans
Health Administration (VHA) and the private health care delivery
systems due to their combined veteran and Medicare entitlements
 This phenomenon is known as dual use
 Dual use can have positive and negative implications:
 On the positive side, it provides veterans with access to more sources
and sites of health care and to a greater diversity of health care product
lines
 On the negative side, it may decrease the likelihood that veterans
receive continuously coordinated care, because that care is received
from multiple health professionals in two distinct and disarticulated
delivery systems
Etiologic Mechanism
 Older adults, whether veterans or not, have multiple chronic
conditions that are best addressed with central management and
coordination of services
 When older adults receive services from several different providers
who are not centrally managed and coordinated, adverse processes
may result in:
 needless replication of tests and procedures
 diagnostic and treatment delays
 decreased monitoring effectiveness
 increased likelihood of medical errors
 contraindicated and competing regimens
 Those processes begin a cascade that results in preventable
hospitalizations (ACSCs), increased costs, and greater mortality risk
How Big is the Dual Use Problem?
 Hard to say, because the estimates range from 10% to 68%
 One GAO report says that among Medicare-eligible veterans who used
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any health care services, 81% used Medicare only, 9% used only the VA,
and 10% used both systems
Fisher and Welch reported that 52% of all Medicare eligible VA patients
filed at least one Medicare benefit claim within a single year
Another GAO report suggests that 54% of Medicare eligible veterans
were dual users
VIReC has concluded that although 90% of older VA patients were
enrolled in Medicare, 22% used only VA services, 30% used only
Medicare services, and 43% used services from both sources
Note that the lowest estimated prevalence rate is from the only
population-based study, while the highest are from VA user samples
Preliminary Studies
 Previously we estimated both the prevalence of dual use and its
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potential adversity among older men using the Survey on Assets and
Health Dynamics among the Oldest Old (AHEAD)
Baseline interviews were conducted in 1993-1994 with 7,447
respondents > 70 years old, with an 80.4% response rate
Re-interviews were conducted in 1995, 1998, 2000, 2002, 2004,
2006, and 2008, but we only used the baseline interview data
Of the 2,911 AHEAD men, our prior analyses were restricted to the
1,566 (53.8%) who were self-respondents and whose survey data
could be linked to the UNICON LLC release of their Medicare claims
We used an indirect measure of dual use based on the concordance
of self-reported vs. claims-based hospitalizations in the 12 months
prior to each man’s baseline interview
Preliminary Studies…
 Over-reporting existed when the self-reported number of
hospitalizations exceeded the claims-based number
 Veteran status was self-reported
 The cross-classification of over-reporting and veteran status was
used to identify four distinct groups:
 Veterans who over-reported (the implied dual users group; N=96)
 Veterans who accurately reported (N=792)
 Non-veterans who over-reported (N=60)
 Non-veterans who accurately reported (the reference group; N=574)
 Using this approach, 10.8% of the veterans were “dual users”,
which is remarkably equivalent to the only prior population-based
estimate, which came from the GAO
Preliminary Studies…
 We then used National Death Index (NDI) data to model the effect of
dual use on mortality through 2002 (50.3% overall, but 64.9% of dual
users vs. 49.3% of all other men)
 Covariates included in the proportional hazards regression models
were age, race, education, income, wealth, self-rated health, ADLs,
IADLs, cancer, diabetes, heart disease, lung disease, stroke,
cognitive status, CES-D symptoms, prior self-reported
hospitalization, a propensity score variable for selection bias, and a
binary marker for having any ACSC hospital admission
 Model building and estimation followed standard guidelines
The Four Comparison
PreliminaryAdjusted
Studies…95% Confidence
Groups, and Any ACSC
Hazards
Intervals
Admission
Ratios
(Mortality)
Veterans Who Over-Reported
(Dual Users)
1.561**
1.120 – 2.177
Veterans Who Accurately
Reported
1.168
0.982 – 1.389
Non-Veterans Who OverReported
0.775
0.529 – 1.136
Non-Veterans Who Accurately
Reported (Reference Group)
1.000
Any ACSC Admission
1.874***
1.552 – 2.262
Limitations of Our Preliminary Studies
 Although these results showed that veterans who were dual users of
the VA and Medicare systems had a 56% increased risk of mortality,
net of the intervening (mediating) risk associated with subsequent
hospitalizations for ACSCs, that analysis had several key limitations:
 Limited linkage to Medicare claims
 NDI vital status determination only through 2002
 Relatively small sample size with selection bias
 Inability to adjust for managed care participation
New Claims Data Linkage
 In late 2007, we received a much improved Medicare claims linkage
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from 1991-2005 from ACUMEN LLC, which included all of the SAF
files, including the denominator file, which contains death dates, and
allowed us to exclude men in managed care at baseline, or to censor
them at entry into managed care post-baseline
Effective sample size of self-respondents linked to their Medicare
claims increased to 2,133 (74%) vs. 1,522 (53%) previously
We were able to exclude those in managed care at baseline, and to
censor those who entered managed care post-baseline
Prevalence of dual use remains the same, around 10-11%
Three more years of vital status determination using Medicare
denominator file rather than the NDI
Ability to determine actual Medicare payments
Mortality Determination and Analysis
 Vital status was taken from the denominator files
 The denominator files indicate on a monthly basis whether the
Medicare beneficiary is alive or dead
 Because the exact day of the month of death is not given, we coded
the number of days from each participant’s baseline AHEAD
interview to their death as the actual number of days up to the first
day of the month in which decedent status was indicated in the
denominator file, plus 15 days
 We used multivariable proportional hazards regression to model time
to death
 Proportional hazards model development and evaluation followed
standard procedures
ACSC Determination and Analysis
 ACSC status was taken from the inpatient files
 We used the AHRQ Guide to Prevention Quality Indicators:
Ambulatory Care Sensitive Conditions codes for the 16 PQIs,
including:
 Chronic Obstructive Pulmonary Disease
 Dehydration
 Bacterial Pneumonia
 Urinary Tract Infection
 Uncontrolled Diabetes
 Hypertension
 Once again, we used multivariable proportional hazards regression
to model time to the first post-baseline ACSC hospitalization for each
AHEAD man
Medicare Payment Determination
and Analysis
 Medicare payment amounts were taken from the inpatient,
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outpatient, and carrier SAF files
We first summed across all three SAF files to determine total actual
post-baseline Medicare payments
For norming purposes, we then calculated the number of years of
post-baseline surveillance for each man
Within each strata of truncated years of exposure, we calculated the
90th percentile of total actual Medicare payments, and created status
and date markers for men who within their surveillance strata
reached the 90th percentile
Finally, we used multivariable hazards regression to model time to
reaching the 90th percentile for each AHEAD man
The Four
Comparison
Groups
Adjusted
Adjusted
Preliminary
Studies…
Hazards
Hazards
Adjusted
Hazards Ratios
for the 90th
Payment
Percentile
Ratios for
Mortality
Ratios for
ACSCs
Veterans Who OverReported (Dual
Users)
1.34+
1.38+
0.83
Veterans Who
Accurately Reported
1.15
0.98
0.81
Non-Veterans Who
Over-Reported
(Reference Group)
1.00
1.00
1.00
Non-Veterans Who
Accurately Reported
1.08
1.15
0.94
Discussion
 Why is there a difference (only “trend” [p < .10] evidence for mortality
and ACSC hospitalizations) between our prior results and those that
we have shown here today?
 One issue is the UNICON vs. ACUMEN Medicare claims data linkage
 Differences in sample size (one-third larger) and selection bias (less)
 Ability to exclude those in managed care at the outset, and upon entrance to it
 Another issue is the NDI vs. Medicare vital status determination
 All Medicare claims have vital status information (less selection bias), but the
comparative validity is not yet well established
 It could also be the longer length of follow-up and the reduced value of more distal
dual use information (baseline only)
 But there is no effect in the ACUMEN claims on Medical payments,
although further modeling work is needed here
Implications
 Although not statistically significant (only trend or p < .10 evidence),
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our indirect measure of dual use suggests increased mortality risk
We are, however, still relying on an indirect measure of dual use
based on discordance between self-reports and claims-reports of
hospitalizations in the year prior to baseline
Therefore, we urge the VA to facilitate and support linkage of the
AHEAD survey and Medicare claims data to the VHA electronic
health record
In addition to allowing a direct assessment of dual use, this would
allow us to calculate a time dependent dual use marker
And the resulting data set would be, by far, the best ever to address
the dual use issue from a population perspective