Nancy L Keating1,2, Haiden A Huskamp1, Deborah Schrag3, John M McWilliams1,2, Barbara J McNeil1, Bruce E Landon1,4, Michael E Chernew1, Sharon-Lise T Normand1,5. 1. Department of Health Care Policy, Harvard Medical School. 2. Division of General Internal Medicine. 3. Brigham and Women's Hospital, Department of Medical Oncology, Dana-Farber Cancer Institute. 4. Beth Israel Deaconess Medical Center, Division of Primary Care and General Internal Medicine. 5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Abstract
BACKGROUND: Technological advances can improve care and outcomes but are a primary driver of health care spending growth. Understanding diffusion and use of new oncology therapies is important, given substantial increases in prices and spending on such treatments. OBJECTIVES: Examine diffusion of bevacizumab, a novel (in 2004) and high-priced biologic cancer therapy, among US oncology practices during 2005-2012 and assess variation in use across practices. RESEARCH DESIGN: Population-based observational study. SETTING: A total of 2329 US practices providing cancer chemotherapy. PARTICIPANTS: Random 20% sample of 236,304 Medicare fee-for-service beneficiaries aged above 65 years in 2004-2012 undergoing infused chemotherapy for cancer. MEASURES: Diffusion of bevacizumab (cumulative time to first use and 10% use) in practices, variation in use across practices overall and by higher versus lower-value use. We used hierarchical models with practice random effects to estimate the between-practice variation in the probability of receiving bevacizumab and to identify factors associated with use. RESULTS: We observed relatively rapid diffusion of bevacizumab, particularly in independent practices and larger versus smaller practices. We observed substantial variation in use; the adjusted odds ratio (95% confidence interval) of bevacizumab use was 2.90 higher (2.73-3.08) for practices 1 SD above versus one standard deviation below the mean. Variation was less for higher-value [odds ratio=2.72 (2.56-2.89)] than lower-value uses [odds ratio=3.61 (3.21-4.06)]. CONCLUSIONS: Use of bevacizumab varied widely across oncology practices, particularly for lower-value indications. These findings suggest that interventions targeted to practices have potential for decreasing low-value use of high-cost cancer therapies.
BACKGROUND: Technological advances can improve care and outcomes but are a primary driver of health care spending growth. Understanding diffusion and use of new oncology therapies is important, given substantial increases in prices and spending on such treatments. OBJECTIVES: Examine diffusion of bevacizumab, a novel (in 2004) and high-priced biologic cancer therapy, among US oncology practices during 2005-2012 and assess variation in use across practices. RESEARCH DESIGN: Population-based observational study. SETTING: A total of 2329 US practices providing cancer chemotherapy. PARTICIPANTS: Random 20% sample of 236,304 Medicare fee-for-service beneficiaries aged above 65 years in 2004-2012 undergoing infused chemotherapy for cancer. MEASURES: Diffusion of bevacizumab (cumulative time to first use and 10% use) in practices, variation in use across practices overall and by higher versus lower-value use. We used hierarchical models with practice random effects to estimate the between-practice variation in the probability of receiving bevacizumab and to identify factors associated with use. RESULTS: We observed relatively rapid diffusion of bevacizumab, particularly in independent practices and larger versus smaller practices. We observed substantial variation in use; the adjusted odds ratio (95% confidence interval) of bevacizumab use was 2.90 higher (2.73-3.08) for practices 1 SD above versus one standard deviation below the mean. Variation was less for higher-value [odds ratio=2.72 (2.56-2.89)] than lower-value uses [odds ratio=3.61 (3.21-4.06)]. CONCLUSIONS: Use of bevacizumab varied widely across oncology practices, particularly for lower-value indications. These findings suggest that interventions targeted to practices have potential for decreasing low-value use of high-cost cancer therapies.
Authors: Xavier Pivot; Andreas Schneeweiss; Shailendra Verma; Christoph Thomssen; José Luis Passos-Coelho; Giovanni Benedetti; Eva Ciruelos; Roger von Moos; Hong-Tai Chang; Anja-Alexandra Duenne; David W Miles Journal: Eur J Cancer Date: 2011-07-15 Impact factor: 9.162
Authors: Blase N Polite; Jeffery C Ward; John V Cox; Roscoe F Morton; John Hennessy; Ray D Page; Rena M Conti Journal: J Oncol Pract Date: 2014-11 Impact factor: 3.840
Authors: Rena M Conti; Arielle C Bernstein; Victoria M Villaflor; Richard L Schilsky; Meredith B Rosenthal; Peter B Bach Journal: J Clin Oncol Date: 2013-02-19 Impact factor: 44.544
Authors: Herbert Hurwitz; Louis Fehrenbacher; William Novotny; Thomas Cartwright; John Hainsworth; William Heim; Jordan Berlin; Ari Baron; Susan Griffing; Eric Holmgren; Napoleone Ferrara; Gwen Fyfe; Beth Rogers; Robert Ross; Fairooz Kabbinavar Journal: N Engl J Med Date: 2004-06-03 Impact factor: 91.245
Authors: Gabriel A Brooks; Ling Li; Hajime Uno; Michael J Hassett; Bruce E Landon; Deborah Schrag Journal: Health Aff (Millwood) Date: 2014-10 Impact factor: 6.301
Authors: Hannah T Neprash; Michael E Chernew; Andrew L Hicks; Teresa Gibson; J Michael McWilliams Journal: JAMA Intern Med Date: 2015-12 Impact factor: 21.873
Authors: Nancy L Keating; Shalini Jhatakia; Gabriel A Brooks; Amanda S Tripp; Inna Cintina; Mary Beth Landrum; Qing Zheng; Thomas J Christian; Roberta Glass; Van Doren Hsu; Colleen M Kummet; Susannah Woodman; Carol Simon; Andrea Hassol Journal: JAMA Date: 2021-11-09 Impact factor: 56.272
Authors: Marieke J Krimphove; Karl H Tully; David F Friedlander; Maya Marchese; Praful Ravi; Stuart R Lipsitz; Kerry L Kilbridge; Adam S Kibel; Luis A Kluth; Patrick A Ott; Toni K Choueiri; Quoc-Dien Trinh Journal: J Immunother Cancer Date: 2019-11-07 Impact factor: 13.751