Matteo Cesari1, Nadege Costa2, Emiel O Hoogendijk3, Bruno Vellas4, Marco Canevelli5, Mario Ulises Pérez-Zepeda6. 1. Gérontopôle, Centre Hospitalier Universitaire de Toulouse, Toulouse, France; Université de Toulouse III Paul Sabatier, Toulouse, France. Electronic address: macesari@gmail.com. 2. Département d'Information Médicale, Centre Hospitalier Universitaire de Toulouse, Toulouse, France. 3. Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. 4. Gérontopôle, Centre Hospitalier Universitaire de Toulouse, Toulouse, France; Université de Toulouse III Paul Sabatier, Toulouse, France. 5. Memory Clinic, Department of Neurology and Psychiatry, "Sapienza" University, Rome, Italy. 6. Instituto Nacional de Geriatria, Mexico City, Mexico.
Abstract
BACKGROUND: The Frailty Index (FI), proposed by Rockwood and Mitniski, measures the deficits accumulation occurring with aging, and can be generated from the results of a comprehensive clinical assessment. Its construct (based on pure arithmetical assumptions) may represent a unique feature for supporting unbiased comparisons among clinical facilities/services. OBJECTIVE: To propose an example depicting how the FI may support health economic evaluations and provide insights for public health. DESIGN: Observational study. SETTING: Nine nursing homes participating in the "Incidence of pNeumonia and related ConseqUences in nursing home Residents" (INCUR) study. SUBJECTS: A sample of 345 older persons living in nursing homes. METHODS: A 30-item FI was generated from clinical data retrieved from medical charts. Health care expenditures that occurred over 12 months of follow-up for each participant were obtained from the Caisse Primaire d'Assurance Maladie. Descriptive analyses describing the relationships between the FI of residents with the annual health care expenditures according to nursing home are presented. RESULTS: Mean age of the study sample was 86.0 (SD 7.9) years. The median annual cost per patient was 27,717.75 (interquartile range, IQR 25,917.60-32,118.02) Euros. The median FI was 0.33 (IQR 0.27-0.43). Results are graphically presented to highlight clinical and economic differences across nursing homes, so as to identify potential discrepancies between clinical burden and consumed resources. CONCLUSIONS: In this article, an example on how the FI may support health economic analyses and promote an improved allocation of healthcare resources is presented.
BACKGROUND: The Frailty Index (FI), proposed by Rockwood and Mitniski, measures the deficits accumulation occurring with aging, and can be generated from the results of a comprehensive clinical assessment. Its construct (based on pure arithmetical assumptions) may represent a unique feature for supporting unbiased comparisons among clinical facilities/services. OBJECTIVE: To propose an example depicting how the FI may support health economic evaluations and provide insights for public health. DESIGN: Observational study. SETTING: Nine nursing homes participating in the "Incidence of pNeumonia and related ConseqUences in nursing home Residents" (INCUR) study. SUBJECTS: A sample of 345 older persons living in nursing homes. METHODS: A 30-item FI was generated from clinical data retrieved from medical charts. Health care expenditures that occurred over 12 months of follow-up for each participant were obtained from the Caisse Primaire d'Assurance Maladie. Descriptive analyses describing the relationships between the FI of residents with the annual health care expenditures according to nursing home are presented. RESULTS: Mean age of the study sample was 86.0 (SD 7.9) years. The median annual cost per patient was 27,717.75 (interquartile range, IQR 25,917.60-32,118.02) Euros. The median FI was 0.33 (IQR 0.27-0.43). Results are graphically presented to highlight clinical and economic differences across nursing homes, so as to identify potential discrepancies between clinical burden and consumed resources. CONCLUSIONS: In this article, an example on how the FI may support health economic analyses and promote an improved allocation of healthcare resources is presented.
Authors: Jonathan F Easton; Christopher R Stephens; Heriberto Román-Sicilia; Matteo Cesari; Mario Ulises Pérez-Zepeda Journal: Exp Gerontol Date: 2018-05-26 Impact factor: 4.032
Authors: Guillermo Salinas-Escudero; María Fernanda Carrillo-Vega; Carmen García-Peña; Silvia Martínez-Valverde; Luis David Jácome-Maldonado; Matteo Cesari; Mario Ulises Pérez-Zepeda Journal: J Appl Gerontol Date: 2021-06-28
Authors: Mario Ulises Pérez-Zepeda; Matteo Cesari; María Fernanda Carrillo-Vega; Guillermo Salinas-Escudero; Pamela Tella-Vega; Carmen García-Peña Journal: Biomed Res Int Date: 2017-04-19 Impact factor: 3.411
Authors: Eva María Andrés-Esteban; Manuel Quintana-Diaz; Karen Lizzette Ramírez-Cervantes; Irene Benayas-Peña; Alberto Silva-Obregón; Rosa Magallón-Botaya; Ivan Santolalla-Arnedo; Raúl Juárez-Vela; Vicente Gea-Caballero Journal: PeerJ Date: 2021-04-13 Impact factor: 2.984
Authors: Marco Canevelli; Matteo Cesari; Francesca Remiddi; Alessandro Trebbastoni; Federica Quarata; Carlo Vico; Carlo de Lena; Giuseppe Bruno Journal: Front Aging Neurosci Date: 2017-02-24 Impact factor: 5.750
Authors: Angeline Price; Fenella Barlow-Pay; Siobhan Duffy; Lyndsay Pearce; Arturo Vilches-Moraga; Susan Moug; Terry Quinn; Michael Stechman; Philip Braude; Emma Mitchell; Phyo Kyaw Myint; Alessia Verduri; Kathryn McCarthy; Ben Carter; Jonathan Hewitt Journal: BMJ Open Date: 2020-09-29 Impact factor: 2.692