Kristen Hassmiller Lich1, Yuan Tian2, Christopher A Beadles2, Linda S Williams2, Dawn M Bravata2, Eric M Cheng2, Hayden B Bosworth2, Jack B Homer2, David B Matchar2. 1. From the Department of Health Policy and Management, The University of North Carolina, Chapel Hill (K.H.L.); Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore (Y.T., D.B.M.); Center for Health Services Research in Primary Care, Durham VAMC, NC (C.A.B., H.B.B.); VHA Health Services Research and Development Stroke Quality Enhancement Research Initiative, Indianapolis, IN (L.S.W., D.M.B.); VHA Health Services Research and Development Center of Excellence on Implementing Evidence-Based Practice, Richard L. Roudebush VHA Medical Center, Indianapolis, IN (L.S.W. D.M.B.); Regenstrief Institute, Indianapolis, IN (L.S.W., D.M.B.); Department of Veterans Affairs, Veterans Engineering Resource Center, VA-Center for Applied Systems Engineering, Indianapolis, IN (L.S.W.); Department of Internal Medicine, Indiana University School of Medicine, Indianapolis (D.M.B.); Department of Neurology, VA Greater Los Angeles Healthcare System, CA (E.M.C.); Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles (E.M.C.); Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC (H.B.B., D.B.M.); Homer Consulting, Voorhees, NJ (J.B.H.); and Duke Clinical Research Institute, Duke University, Durham, NC (D.B.M.). klich@unc.edu. 2. From the Department of Health Policy and Management, The University of North Carolina, Chapel Hill (K.H.L.); Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore (Y.T., D.B.M.); Center for Health Services Research in Primary Care, Durham VAMC, NC (C.A.B., H.B.B.); VHA Health Services Research and Development Stroke Quality Enhancement Research Initiative, Indianapolis, IN (L.S.W., D.M.B.); VHA Health Services Research and Development Center of Excellence on Implementing Evidence-Based Practice, Richard L. Roudebush VHA Medical Center, Indianapolis, IN (L.S.W. D.M.B.); Regenstrief Institute, Indianapolis, IN (L.S.W., D.M.B.); Department of Veterans Affairs, Veterans Engineering Resource Center, VA-Center for Applied Systems Engineering, Indianapolis, IN (L.S.W.); Department of Internal Medicine, Indiana University School of Medicine, Indianapolis (D.M.B.); Department of Neurology, VA Greater Los Angeles Healthcare System, CA (E.M.C.); Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles (E.M.C.); Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC (H.B.B., D.B.M.); Homer Consulting, Voorhees, NJ (J.B.H.); and Duke Clinical Research Institute, Duke University, Durham, NC (D.B.M.).
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.
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.
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