Is Veteran User Status an
Independent Risk Factor for Mortality
After Private Sector CABG?
William B. Weeks, MD, MBA
Dorothy A. Bazos, Ph.D.
David M. Bott, Ph.D.
Stacey L. Campbell, MPH
Edward L. Hannan, Ph.D.
Michael J. Racz, MA
Stephen M. Wright, Ph.D.
Elliott S. Fisher, MD, MPH
This work was funded by the HSR&D Grant ACC 01-117-1
(Utilization, System of Care, and Outcome of CABG in New York Veterans)
•Good morning – I am David Bott
•Before I describe this study I’d like to acknowledge my colleagues
William Weeks, the Study PI, from White River Junction VA and
Dartmouth College
Dotty Bazos, from White River Junction VA
Stacey Campbell, VHA Rhode Island
Ed Hannan, Albany School of Public Health
Mike Racz, the NY State Department of Health
Stephen Wright, the VHA Office of Quality and Performance
Washington DC
and Elliott Fisher from Dartmouth Medical School.
•I’d also like to acknowledge HSR&D as our funding source.
•The research that I will summarize addresses the question: Is
Veteran “User Status” an Independent Risk Factor for Mortality
After Private Sector CABG ?
1
Background
• Improving the quality of care for VHA patients
with Coronary Artery Disease is a priority.
• Prior research has reported worse risk-adjusted
outcomes for veterans following AMI and CABG.
• Concerns remain that previous studies may not
have adequately accounted for the increased
burden of illness of veterans who rely on the VHA
health care system for their care.
Improving the quality of care for VHA patients with Coronary
Artery Disease is a priority.
•AMI is a leading cause of morbidity and mortality among veterans and
•The VHA has been working to improve the level of care for their heart
patients by examining the quality, cost, access, and outcomes of care
related to heart disease.
Prior research has reported worse risk-adjusted outcomes for
veterans following AMI and CABG
•For example the 2003 study published by Rosenthal, Sarrazin and
Hannan reported higher adjusted mortality rates for VA patients
compared to those who had CABG in the private sector.
However, Concerns remain that previous studies may not have
adequately accounted for the increased burden of illness of veterans
who rely on the VHA health care system for their care.
•It could be possible that “veteran status” in and of itself puts one at
greater risk for poor outcomes compared to other patients.
2
We know that many veterans who receive care in the VHA
(VHA users) obtain bypass surgery in the private sector...
Study Question One
• Are VHA users who obtain CABG in the
private sector sicker than non-VHA users?
Study Question Two
• Is being a VHA user an independent risk
factor for mortality following CABG in the
private sector?
•Because we know that many veterans who receive care in the VHA
(VHA users) obtain bypass surgery in the private sector...
•We went to the private sector - to New York State which has an
excellent cardiac database from which we could identify both VHAusers and non-VHA users- to address the following research
questions:
•Study Question One: Are VHA users sicker than non-VHA users
who obtain CABG in the private sector?
•Study Question Two: Is being a VHA user an independent risk factor
for mortality following CABG in the private sector?
NOTE:
For example Wright and Petersen found that VA users who were
hospitalized for AMI in 1996 – 70% were initially admitted to a
Medicare financed facility.
And in the study of utilization that we are doing (1997-1999) we are
finding that about 80% of VA-users go to private sector hospitals for
CABG
3
Data Sources
(Study period: 1997-1999)
• VHA
– Administrative and Enrollment files
• New York State Department of Health
– Cardiac Surgery Reporting System (CSRS)
•We used the VHA administrative and enrollment files to identify
Veterans who had (a) New York State resident ZIP Codes and (b)
had used VHA services within 2 years prior to or during the study
years (1997, 1998, 1999).
•Veteran SSN, DOB, and Gender was used to link this file to the
New York State Department of Health Cardiac Surgery Reporting
System (CSRS)
•CSRS is a registry which consists of detailed clinical information
about every patient undergoing CABG in NY State. It contains
variables on patient demographics, clinical risk factors including
comorbidities, admission, surgery, and discharge dates as well as
discharge status.
NOTE:
CSRS allows for public reporting of hospital and surgeon risk-adjusted
mortality rates for CABG.
Development of the registry began in 1989 with eh first formal public
release (1989-1992) of
data occurred in December 1992.
4
Methods
• Cohort study
• Study population
–
–
–
–
Males only
Isolated CABG (no other heart or vascular surgery)
Private sector hospitals located in New York State
Discharged January 1, 1997 – December 31, 1999
• Comparison groups
– VHA users
– non-VHA users
• Outcome
– In-hospital mortality after CABG
•This is a cohort study
•Of a study population that included:
• Only males
• Who had Isolated CABG: defined as Coronary Artery Bypass
Surgery (CABG) when no other major heart surgery, such as
valves or aneurysms, are performed during the same admission.
•In a private sector hospital: defined as Non-VHA hospitals (so
this does include NY State and City run facilities).
•Who were discharged after CABG between January 1, 1997 –
December 31, 1999
We identified two patient groups within the CSRS cardiac
database:
•VHA Users: who we defined as : US veterans who were residents of
NY State during the calendar years of 1997-1999. Who were enrolled
in and had used the VHA health system during calendar years of 19951999.
•Non-VHA Users:All other patients .
The outcome of interest was: Patient in-hospital death subsequent to
CABG surgery during the same admission.
5
Methods
• Statistical Methods
– Logistic regression models
• To determine expected mortality risk for each participant
• To determine whether VHA user status is an independent risk
factor for mortality
• Risk Factors – defined by CSRS
–
–
–
–
Demographics
Comorbidity (e.g. diabetes, vascular disease)
Disease Severity
Models included hospital effects
• This allows us to account for possibility that VHA patients
received care in higher or lower quality hospitals
•Statistical Methods
Logistic regression models
To determine expected mortality risk for each participant
To determine whether VHA user status is an independent
risk factor for mortality
•the Risk Factors that were included in our models were those that
have been defined by CSRS
as being predictive of bypass surgery mortality. Included in the model
were measures of
•Patient Demographics
•Comorbidity, for example diabetes, and vascular disease
•and Disease Severity.
•Our models also included hospital effects –
which allowed us to account for the possibility that VHA patients
received care in higher or lower quality hospitals
•recall, we’re interested in the characteristics of the veterans themselves
6
Results – Study Population
Male Population Having Isolated CABG
in NY private sector cardiac facilities 1997-1999
Male VHA and non-VHA users
n = 40,728
VHA user
n = 3,009
non-VHA user
n = 37,719
Deaths = 67
Deaths = 670
•Total Study population of 40,728 males who had isolated CABG in
NY private sector hospitals during the study period.
•Of those – 3,009 were identified as VHA users and 37,719 were
identified as non-VHA users.
•During the study period 67 in-hospitals deaths after CABG during the
CABG admission were reported for VHA users and 670 deaths for
non-VHA users.
NOTE:
* 70.24% of CABGS in CSRS system were isolated (01.01.97-12.31.99)
VA Users made up 7% of the Study Population
Observed Mortality Rates:
•VHA- user: 67/3009 = 2.23
•non-VHA user: 670/37719 = 1.78
7
Are VHA users sicker?
Male Patients Having Risk Factor
Risk Factor
Demographics
Age <45 years
Age 45-64 years
Age 65 + years
% VHA user % non-VHA user
p-value
(n=3,009)
(n=37,719)
0.3
21.0
78.6
3.2
43.6
53.1
<0.0001
Comorbidity
Stroke
9.8
6.1
<0.0001
Aortoiliac Disease
6.7
5.0
<0.0001
20.2
15.1
<0.0001
COPD
Calcified Aorta
Diabetes
Renal CR < 2.5
7.2
5.5
0.0002
31.0
26.9
<0.0001
2.3
1.7
0.0372
•One method for assessing whether VHA users are sicker than other
patients is to compare the percent of each population having the risk
factor of interest.
•This table provides a descriptive analysis of the demographic and
comorbid risk factors that were used in our regression models where
there was a significant difference between the two patient populations.
Some of the risk factors of the model did not differ, thus we do not
report them here.
•For our patient populations we found that VHA users were
significantly older and sicker compared to non-VHA users.
•For example 78.6% of VHA users were 65 or older compared to
53% of non-VHA users
•And significantly more VHA users had stoke, aortoiliac disease,
COPD, calcified aorta, diabetes and renal disease.
Notes Only
•Other factors in the model but not reported..........
•There was no difference in % having hepatic disease (.1%) or dialysis
(1.1%).
•Race is not included in the NY model because it has not been found to
be a significant predictor of CABG mortality
•We excluded gender from the model because we are comparing all
8
Are VHA users sicker?
Male Patients Having Risk Factor
Risk Factors
% VHA user % non-VHA user
(n=3,009)
(n=37,719)
p-value
Disease Severity
Ejection Fraction (missing)
Ejection Fraction < 20
Ejection Fraction 20-29
Ejection Fraction 30-39
Ejection Fraction 40+
3.7
1.7
8.1
16.0
70.7
2.8
2.0
7.6
14.6
73.0
MI < 6 hrs
MI 6-23 hrs
MI 1- 7days
MI 7 or more days
No previous MI
Left Main Disease
0.8
0.9
13.6
42.8
41.8
26.9
0.9
0.9
14.2
39.2
44.2
24.8
Previous Operations
10.0
6.6
0.0031
0.0352
0.0011
<0.0001
•This table summarizes the two patient populations in regard to cardiac
disease severity.
•Here we find that more VHA users had lower ejection fractions
(about 30% had EF below 40 compared to 27% for non-VHA users)
•and that more VHA users had previous MIs (58%) compared to
(56%), Left Main Disease and Previous Heart Operations (10%
compared to 6.6%).
Notes Only
Other Risk Factors in the model ( but not reported in the table) were
equally likely to be found in both groups:
They are the risk factors associated with emergent or urgent surgery
(unstable, shock, CPR and Arrhythmia).
9
VHA users are sicker than others...
Mortality Rate per 100 population
VHA user
non-VHA User
3
2.5
2
1.5
1
2.33
1.77
0.5
0
Expected Mortality Rates
•A second method for assessing whether VHA users are sicker than
other patients is to use a statistical model to predict the expected
mortality rates for each patient group taking all of the risk factors of
interest into account.
•After calculating our logistic regression model we found the
Expected Mortality Rates for VHA users to be considerably higher
(2.33 deaths per 100 population) compared to non-VHA users(1.77
per 100 population).
•Thus, we interpreted this finding to means that VHA users are
indeed sicker than non-VHA users.
Notes:
Expected Mortality rates are calculated using a logistic regression
model .The dependent variable is discharge status (alive/dead)
Predicted probabilities of death are calculated for each patient. These
probabilities are then summed for each patient group and divided by the
number of patients in that group.
CI for EMR: VHA user (1.81, 2.85) and non-VHA user (1.64, 1.90).
10
Veteran user status is not an independent
risk factor for CABG mortality
Multiple Logistic Regression Model
Adjusted
Odds Ratio
95% CI
p-value
VHA user
0.947
(0.726, 1.234) 0.6853
•Finally, to assess whether “veteran user status” was an
independent risk factor for CABG mortality we calculated the riskadjusted odds of mortality for VHA users relative to non-VHA users
using logistic regression ( the standard method of controlling
simultaneously for observed covariates. (from Petersens paper))
•Our findings – of an adjusted Odds Ratio of .947 - indicates that
being a VHA user is NOT an independent risk factor for mortality
and that the odds of dying after CABG for VHA users compared to
non-VHA users was slightly lower but not statistically not different.
11
Limitations
• Only looked at CABG Surgery.
• Only looked at male VHA users who had
private sector CABG – male veterans
having CABG in the VHA could still be
sicker.
There were two major limitations to this study:
•First we only looked at CABG surgery, thus we cannot say if we
would get similar results for other cardiac surgeries.
•Second – We only looked at male VHA users who had private sector
CABG – male veterans having CABG in the VHA could still be a sicker
group of patients
compared to these patients.
•Thus, there might STILL be something about being a Veteran that
is not accounted for in these models. It may well
be that Vets who stay in the VA and get their care there are different in
ways that we have not accounted for.
12
Conclusions
• Male veterans having CABG in private sector
facilities in NY, who are users of the VHA system
tend to be sicker than male non-VHA users.
• VHA user status is NOT an independent risk factor
for CABG mortality in the private sector.
• VHA users do as well as other male patients in
regard to in-hospital mortality associated with
CABG.
•In summary - given our findings we have concluded that:
•Male veterans having CABG in private sector facilities in NY, who
are users of the VHA system, tend to be sicker than male non-VHA
users.
•That VHA user status in NOT an independent risk factor for CABG
mortality in the private sector
•And that Adequate risk adjustment should allow fair comparisons of
VHA to non-VHA care
13
Implications
• Opportunities exist for VHA to improve
CABG outcomes by coordinating where
CABG occurs in the private sector
(hospitals with lower mortality rates).
• Adequate risk adjustment should allow fair
comparisons of VHA to non-VHA care for
veterans.
Notes
We think that the Rosenthal Study might have been well adjusted
And it might just might be that Veterans who obtain CABG in the VHA
DO WORSE not because they are sicker but possibly for other factors
including hospital and surgeon volume etc.
14