Strategic planning to reduce the burden of stroke among veterans: using simulation modeling to inform decision making.
Abstract
BACKGROUND AND PURPOSE: Reducing the burden of stroke is a priority for the Veterans
Affairs Health System, reflected by the creation of the Veterans Affairs Stroke Quality
Enhancement Research Initiative. To inform the initiative's strategic planning, we
estimated the relative population-level impact and efficiency of distinct approaches
to improving stroke care in the US Veteran population to inform policy and practice.
METHODS: A System Dynamics stroke model of the Veteran population was constructed
to evaluate the relative impact of 15 intervention scenarios including both broad
and targeted primary and secondary prevention and acute care/rehabilitation on cumulative
(20 years) outcomes including quality-adjusted life years (QALYs) gained, strokes
prevented, stroke fatalities prevented, and the number-needed-to-treat per QALY gained.
RESULTS: At the population level, a broad hypertension control effort yielded the
largest increase in QALYs (35,517), followed by targeted prevention addressing hypertension
and anticoagulation among Veterans with prior cardiovascular disease (27,856) and
hypertension control among diabetics (23,100). Adjusting QALYs gained by the number
of Veterans needed to treat, thrombolytic therapy with tissue-type plasminogen activator
was most efficient, needing 3.1 Veterans to be treated per QALY gained. This was followed
by rehabilitation (3.9) and targeted prevention addressing hypertension and anticoagulation
among those with prior cardiovascular disease (5.1). Probabilistic sensitivity analysis
showed that the ranking of interventions was robust to uncertainty in input parameter
values. CONCLUSIONS: Prevention strategies tend to have larger population impacts,
though interventions targeting specific high-risk groups tend to be more efficient
in terms of number-needed-to-treat per QALY gained.
Type
Journal articleSubject
Veteranscomparative effectiveness research
computer simulation
health planning
stroke
Adult
Calibration
Computer Simulation
Cost of Illness
Decision Making
Health Planning
Humans
Quality-Adjusted Life Years
Risk Factors
Sensitivity and Specificity
Stroke
Stroke Rehabilitation
Systems Theory
Uncertainty
United States
United States Department of Veterans Affairs
Veterans
Veterans Health
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https://hdl.handle.net/10161/13105Published Version (Please cite this version)
10.1161/STROKEAHA.114.004694Publication Info
Lich, Kristen Hassmiller; Tian, Yuan; Beadles, Christopher A; Williams, Linda S; Bravata,
Dawn M; Cheng, Eric M; ... Matchar, David B (2014). Strategic planning to reduce the burden of stroke among veterans: using simulation
modeling to inform decision making. Stroke, 45(7). pp. 2078-2084. 10.1161/STROKEAHA.114.004694. Retrieved from https://hdl.handle.net/10161/13105.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Hayden Barry Bosworth
Professor in Population Health Sciences
Dr. Bosworth is a health services researcher and Associate Director of the Center
for Health Services Research in Primary Care at the Durham VA Medical Center. He is
also a Professor of Medicine, Psychiatry, and Nursing at Duke University Medical Center
and Adjunct Professor in Health Policy and Administration at the School of Public
Health at the University of North Carolina at Chapel Hill. His research interests
comprise three overarching areas of research: 1) clinical research that provide
David Bruce Matchar
Professor of Medicine
My research relates to clinical practice improvement - from the development of clinical
policies to their implementation in real world clinical settings. Most recently my
major content focus has been cerebrovascular disease. Other major clinical areas in
which I work include the range of disabling neurological conditions, cardiovascular
disease, and cancer prevention. Notable features of my work are: (1) reliance on
analytic strategies such as meta-analysis, simulation, decision analy
Alphabetical list of authors with Scholars@Duke profiles.

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