Kevin P Cohoon1, Cynthia L Leibson2, Jeanine E Ransom3, Aneel A Ashrani4, Myung S Park5, Tanya M Petterson3, Kirsten Hall Long6, Kent R Bailey3, John A Heit7. 1. Division of Cardiovascular Diseases and Gonda Vascular Center, Department of Internal Medicine, Mayo Clinic, Rochester, MN. 2. Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN. 3. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN. 4. Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN. 5. Division of Trauma, Critical Care and General Surgery, Department of Surgery, Mayo Clinic, Rochester, MN. 6. K Long Health Economics Consulting LLC, St. Paul, MN. 7. Division of Cardiovascular Diseases and Gonda Vascular Center, Department of Internal Medicine, Mayo Clinic, Rochester, MN; Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN; Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN. Electronic address: heit.john@mayo.edu.
Abstract
BACKGROUND: We estimated medical costs attributable to venous thromboembolism (VTE) among patients currently or recently hospitalized for major operation. METHODS: Using Rochester Epidemiology Project resources, we identified all Olmsted County, MN, residents with objectively diagnosed incident VTE within 92 days of hospitalization for major operation during an 18-year period, 1988-2005 (n = 355). One Olmsted County resident hospitalized for major operation without VTE was matched to each case on event date (±1 year), type of operation, duration of previous medical history, and active cancer status. Subjects were followed in Rochester Epidemiology Project provider-linked billing data for standardized, inflation-adjusted direct medical costs from 1 year before index (case's VTE event date and control's matched date) to earliest of death, emigration, or December 31, 2011. We used generalized linear modeling to predict costs for cases and controls and used bootstrapping methods to assess uncertainty and significance of mean adjusted cost differences. RESULTS: Adjusted mean predicted costs were more than 1.5-fold greater for cases ($55,956) than for controls ($32,718) (P ≤ .001) from index to up to 5 years postindex. Cost differences between cases and controls were greatest within the first 3 months after index (mean difference = $12,381). Costs were greater for cases than controls (mean difference = $10,797) from 3 months to up to 5 years postindex and together accounted for about half of the overall cost difference. CONCLUSION: VTE during or after recent hospitalization for major operation contributes a substantial economic burden; VTE-attributable costs are greatest in the initial 3 months but persist for up to 5 years.
BACKGROUND: We estimated medical costs attributable to venous thromboembolism (VTE) among patients currently or recently hospitalized for major operation. METHODS: Using Rochester Epidemiology Project resources, we identified all Olmsted County, MN, residents with objectively diagnosed incident VTE within 92 days of hospitalization for major operation during an 18-year period, 1988-2005 (n = 355). One Olmsted County resident hospitalized for major operation without VTE was matched to each case on event date (±1 year), type of operation, duration of previous medical history, and active cancer status. Subjects were followed in Rochester Epidemiology Project provider-linked billing data for standardized, inflation-adjusted direct medical costs from 1 year before index (case's VTE event date and control's matched date) to earliest of death, emigration, or December 31, 2011. We used generalized linear modeling to predict costs for cases and controls and used bootstrapping methods to assess uncertainty and significance of mean adjusted cost differences. RESULTS: Adjusted mean predicted costs were more than 1.5-fold greater for cases ($55,956) than for controls ($32,718) (P ≤ .001) from index to up to 5 years postindex. Cost differences between cases and controls were greatest within the first 3 months after index (mean difference = $12,381). Costs were greater for cases than controls (mean difference = $10,797) from 3 months to up to 5 years postindex and together accounted for about half of the overall cost difference. CONCLUSION:VTE during or after recent hospitalization for major operation contributes a substantial economic burden; VTE-attributable costs are greatest in the initial 3 months but persist for up to 5 years.
Authors: Cynthia L Leibson; Allen W Brown; Kirsten Hall Long; Jeanine E Ransom; Jay Mandrekar; Turner M Osler; James F Malec Journal: J Neurotrauma Date: 2012-04-26 Impact factor: 5.269
Authors: S E Gabriel; A N A Tosteson; C L Leibson; C S Crowson; G R Pond; C S Hammond; L J Melton Journal: Osteoporos Int Date: 2002 Impact factor: 4.507
Authors: John A Heit; W Michael O'Fallon; Tanya M Petterson; Christine M Lohse; Marc D Silverstein; David N Mohr; L Joseph Melton Journal: Arch Intern Med Date: 2002-06-10
Authors: J P Newhouse; W G Manning; C N Morris; L L Orr; N Duan; E B Keeler; A Leibowitz; K H Marquis; M S Marquis; C E Phelps; R H Brook Journal: N Engl J Med Date: 1981-12-17 Impact factor: 91.245
Authors: Kevin P Cohoon; Cynthia L Leibson; Jeanine E Ransom; Aneel A Ashrani; Tanya M Petterson; Kirsten Hall Long; Kent R Bailey; Johm A Heit Journal: Am J Manag Care Date: 2015-04-01 Impact factor: 2.229
Authors: Kevin P Cohoon; Jeanine E Ransom; Cynthia L Leibson; Aneel A Ashrani; Tanya M Petterson; Kirsten Hall Long; Kent R Bailey; John A Heit Journal: Am J Med Date: 2016-03-21 Impact factor: 4.965
Authors: Sue L Visscher; James M Naessens; Barbara P Yawn; Megan S Reinalda; Stephanie S Anderson; Bijan J Borah Journal: BMC Health Serv Res Date: 2017-06-12 Impact factor: 2.655
Authors: Dalia M Dawoud; David Wonderling; Jessica Glen; Sedina Lewis; Xavier L Griffin; Beverley J Hunt; Gerard Stansby; Michael Reed; Nigel Rossiter; Jagjot Kaur Chahal; Carlos Sharpin; Peter Barry Journal: Front Pharmacol Date: 2018-11-27 Impact factor: 5.810