Dae Hyun Kim1,2,3, Elisabetta Patorno1, Ajinkya Pawar1, Hemin Lee1, Sebastian Schneeweiss1, Robert J Glynn1. 1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 2. Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts. 3. Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Abstract
BACKGROUND: There has been increasing effort to measure frailty in the U.S. Medicare data. The performance of claims-based frailty measures has not been compared. METHODS: This cross-sectional study included 3,097 community-dwelling fee-for-service Medicare beneficiaries (mean age 75.6 years) who participated in the 2008 Health and Retirement Study examination. Four claims-based frailty measures developed by Davidoff, Faurot, Segal, and Kim were compared against frailty phenotype, a deficit-accumulation frailty index (FI), and activities of daily living (ADL) dependence using Spearman correlation coefficients and C-statistics. RESULTS: Claims-based frailty measures were positively associated with frailty phenotype (prevalence in ≤10th vs >90th percentile: 8.0% vs 41.3% for Davidoff; 5.9% vs 53.1% for Faurot; 3.3% vs 48.0% for Segal; 2.9% vs 51.0% for Kim) and FI (mean in ≤10th vs >90th percentile: 0.17 vs 0.33 for Davidoff; 0.13 vs 0.37 for Faurot; 0.12 vs 0.31 for Segal; 0.10 vs 0.37 for Kim). The age and sex-adjusted C-statistics for frailty phenotype for Davidoff, Faurot, Segal, and Kim indices were 0.73, 0.74, 0.73, and 0.78, respectively, and partial correlation coefficients with FI were 0.18, 0.32, 0.26, and 0.55, respectively. The results for ADL dependence were similar (prevalence in ≤10th vs >90th percentile: 3.7% vs 50.5% for Davidoff; 2.3% vs 55.0% for Faurot; 3.0% vs 38.3% for Segal; 2.3% vs 50.8% for Kim). The age and sex-adjusted C-statistics for the indices were 0.79, 0.80, 0.74, and 0.81, respectively. CONCLUSIONS: The choice of a claims-based frailty measure can influence the identification of older adults with frailty and disability in Medicare data.
BACKGROUND: There has been increasing effort to measure frailty in the U.S. Medicare data. The performance of claims-based frailty measures has not been compared. METHODS: This cross-sectional study included 3,097 community-dwelling fee-for-service Medicare beneficiaries (mean age 75.6 years) who participated in the 2008 Health and Retirement Study examination. Four claims-based frailty measures developed by Davidoff, Faurot, Segal, and Kim were compared against frailty phenotype, a deficit-accumulation frailty index (FI), and activities of daily living (ADL) dependence using Spearman correlation coefficients and C-statistics. RESULTS: Claims-based frailty measures were positively associated with frailty phenotype (prevalence in ≤10th vs >90th percentile: 8.0% vs 41.3% for Davidoff; 5.9% vs 53.1% for Faurot; 3.3% vs 48.0% for Segal; 2.9% vs 51.0% for Kim) and FI (mean in ≤10th vs >90th percentile: 0.17 vs 0.33 for Davidoff; 0.13 vs 0.37 for Faurot; 0.12 vs 0.31 for Segal; 0.10 vs 0.37 for Kim). The age and sex-adjusted C-statistics for frailty phenotype for Davidoff, Faurot, Segal, and Kim indices were 0.73, 0.74, 0.73, and 0.78, respectively, and partial correlation coefficients with FI were 0.18, 0.32, 0.26, and 0.55, respectively. The results for ADL dependence were similar (prevalence in ≤10th vs >90th percentile: 3.7% vs 50.5% for Davidoff; 2.3% vs 55.0% for Faurot; 3.0% vs 38.3% for Segal; 2.3% vs 50.8% for Kim). The age and sex-adjusted C-statistics for the indices were 0.79, 0.80, 0.74, and 0.81, respectively. CONCLUSIONS: The choice of a claims-based frailty measure can influence the identification of older adults with frailty and disability in Medicare data.
Authors: Carmen C Cuthbertson; Anna Kucharska-Newton; Keturah R Faurot; Til Stürmer; Michele Jonsson Funk; Priya Palta; B Gwen Windham; Sydney Thai; Jennifer L Lund Journal: Epidemiology Date: 2018-07 Impact factor: 4.822
Authors: Dae Hyun Kim; Robert J Glynn; Jerry Avorn; Lewis A Lipsitz; Kenneth Rockwood; Ajinkya Pawar; Sebastian Schneeweiss Journal: J Gerontol A Biol Sci Med Sci Date: 2019-07-12 Impact factor: 6.053
Authors: L P Fried; C M Tangen; J Walston; A B Newman; C Hirsch; J Gottdiener; T Seeman; R Tracy; W J Kop; G Burke; M A McBurnie Journal: J Gerontol A Biol Sci Med Sci Date: 2001-03 Impact factor: 6.053
Authors: Brian J Buta; Jeremy D Walston; Job G Godino; Minsun Park; Rita R Kalyani; Qian-Li Xue; Karen Bandeen-Roche; Ravi Varadhan Journal: Ageing Res Rev Date: 2015-12-07 Impact factor: 10.895
Authors: Nancy L Schoenborn; Amanda L Blackford; Corinne E Joshu; Cynthia M Boyd; Ravi Varadhan Journal: J Am Geriatr Soc Date: 2021-09-18 Impact factor: 5.562
Authors: Prajakta P Masurkar; Satabdi Chatterjee; Jeffrey T Sherer; Hua Chen; Michael L Johnson; Rajender R Aparasu Journal: Drugs Aging Date: 2022-06-06 Impact factor: 4.271
Authors: Katherine C Lee; Jocelyn Streid; Dan Sturgeon; Stuart Lipsitz; Joel S Weissman; Ronnie A Rosenthal; Dae H Kim; Susan L Mitchell; Zara Cooper Journal: J Am Geriatr Soc Date: 2020-02-11 Impact factor: 5.562
Authors: Brianne L Olivieri-Mui; Sandra M Shi; Ellen P McCarthy; Daniel Habtemariam; Dae H Kim Journal: J Am Geriatr Soc Date: 2020-11-25 Impact factor: 5.562
Authors: Jeremy Louissaint; Susan L Murphy; Christopher J Sonnenday; Anna S Lok; Elliot B Tapper Journal: Liver Transpl Date: 2021-07-31 Impact factor: 5.799
Authors: David Cheng; Clark DuMontier; Cenk Yildirim; Brian Charest; Chelsea E Hawley; Min Zhuo; Julie M Paik; Enzo Yaksic; J Michael Gaziano; Nhan Do; Mary Brophy; Kelly Cho; Dae H Kim; Jane A Driver; Nathanael R Fillmore; Ariela R Orkaby Journal: J Gerontol A Biol Sci Med Sci Date: 2021-06-14 Impact factor: 6.053
Authors: Lauren Gilstrap; Andrea M Austin; Barbara Gladders; Parag Goyal; A James O'Malley; Amber Barnato; Anna N A Tosteson; Jonathan S Skinner Journal: J Am Geriatr Soc Date: 2021-06-15 Impact factor: 7.538