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George LS, Duberstein PR, Keating NL, Bates B, Bhagianadh D, Lin H, Saraiya B, Goel S, Akincigil A. Estimating oncologist variability in prescribing systemic cancer therapies to patients in the last 30 days of life. Cancer 2024; 130:3757-3767. [PMID: 39077884 DOI: 10.1002/cncr.35488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/12/2024] [Accepted: 06/14/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION Clinical guidelines and quality improvement initiatives have identified reducing the use of end-of-life cancer therapies as an opportunity to improve care. We examined the extent to which oncologists differed in prescribing systemic therapies in the last 30 days of life. METHODS Using Surveillance, Epidemiology, and End Results-Medicare data, we identified patients who died of cancer from 2012 to 2017 (N = 17,609), their treating oncologists (N = 960), and the corresponding physician practice (N = 388). We used multilevel models to estimate oncologists' rates of providing cancer therapy for patients in their last 30 days of life, adjusted for patient characteristics and practice variation. RESULTS Patients' median age at the time of death was 74 years (interquartile range, 69-79); patients had lung (62%), colorectal (17%), breast (13%), and prostate (8%) cancers. We observed substantial variation across oncologists in their adjusted rate of treating patients in the last 30 days of life: oncologists in the 95th percentile exhibited a 45% adjusted rate of treatment, versus 17% among the 5th percentile. A patient treated by an oncologist with a high end-of-life prescribing behavior (top quartile), compared to an oncologist with a low prescribing behavior (bottom quartile), had more than four times greater odds of receiving end-of-life cancer therapy (OR, 4.42; 95% CI, 4.00-4.89). CONCLUSIONS Oncologists show substantial variation in end-of-life prescribing behavior. Future research should examine why some oncologists more often continue systemic therapy at the end of life than others, the consequences of this for patient and care outcomes, and whether interventions shaping oncologist decision-making can reduce overuse of end-of-life cancer therapies.
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Affiliation(s)
| | | | | | | | | | - Haiqun Lin
- Rutgers University, New Brunswick, New Jersey, USA
| | | | - Sanjay Goel
- Rutgers University, New Brunswick, New Jersey, USA
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Keeney T, Miller A, Gilissen J, Coombs LA, Ritchie CS, McCarthy EP. Identification of older adults with Alzheimer's and related dementias among patients newly diagnosed with cancer: A comparison of methodological approaches. J Geriatr Oncol 2024; 15:101842. [PMID: 39122573 PMCID: PMC11411497 DOI: 10.1016/j.jgo.2024.101842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 07/10/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
INTRODUCTION Research efforts to characterize and evaluate care delivery and outcomes for older adults with cancer and comorbid dementia are limited by varied methods used to classify Alzheimer's disease and related dementias (ADRD). The purpose of this study is to evaluate differences in demographic, clinical, and cancer characteristics of people newly diagnosed with cancer and concomitant dementia comparing two common methods to identify ADRD using administrative claims data. MATERIALS AND METHODS We conducted a retrospective cohort study using Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Our sample included adults aged 66 years and older with a first primary diagnosis of lung or colorectal cancer between 2011 and 2017. For each cancer diagnosis, we constructed analytical cohorts using the Center for Medicare and Medicaid Services' Chronic Condition Warehouse (CCW) flag and the Bynum-Standard one- and three-year algorithms to capture diagnosed ADRD. We estimated ADRD prevalence using the algorithms and compared Bynum and CCW cohorts on demographic, clinical, and cancer characteristics at cancer diagnosis and survival for lung and colorectal cancer separately. RESULTS Among older adults with lung cancer, ADRD prevalence was 4.7% with the one-year Bynum, 6.5% with the three-year Bynum, and 12.5% using the CCW flag. In the colorectal cohort, ADRD prevalence was 5.6% with the one-year Bynum, 7.6% with the three-year Bynum, and 14.1% with the CCW flag. Demographic characteristics were similar across ADRD cohorts. The Bynum cohorts identified higher proportions of individuals with moderate to severe dementia (13.8% and 11.2% versus 7.1% CCW in lung cancer; 13.1% and 10.6% versus 6.8% CCW in colorectal cancer), higher frailty rates (27.4% and 22.7% versus 15.0% CCW in lung cancer; 26.4% and 22.3% versus 14.8% CCW in colorectal cancer). Median survival was lower for the Bynum cohorts compared to the CCW, regardless of cancer type. DISCUSSION Findings demonstrate that ADRD prevalence and certain clinical characteristics vary based on dementia ascertainment method and observation period used to classify individuals with ADRD. Considering differences in the cohorts of registry cases generated by the identification method used is essential when interpreting findings related to treatment, utilization, and outcomes within and across cancer cohorts.
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Affiliation(s)
- Tamra Keeney
- Mongan Institute Center for Aging and Serious Illness, Massachusetts General Hospital, Boston, MA, United States of America; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.
| | - Angela Miller
- Mongan Institute Center for Aging and Serious Illness, Massachusetts General Hospital, Boston, MA, United States of America
| | - Joni Gilissen
- End-of-Life Care Research Group, Department of Family Medicine & Chronic Care, Vrije Universiteit Brussel, Brussels, Belgium; Department Public Health and Primary Care, Universiteit Gent, Ghent, Belgium; Research Centre Care in Connection, Karel de Grote University of Applied Sciences and Arts, Antwerp, Belgium
| | - Lorinda A Coombs
- University of North Carolina Chapel Hill School of Nursing, Chapel Hill, NC, United States of America; Lineberger Comprehensive Cancer Center, Chapel Hill, NC, United States of America
| | - Christine S Ritchie
- Mongan Institute Center for Aging and Serious Illness, Massachusetts General Hospital, Boston, MA, United States of America; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Ellen P McCarthy
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, United States of America; Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
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Zafari A, Mehdizadeh P, Bahadori M, Dopeykar N, Teymourzadeh E, Ravangard R. Estimating the Costs of End-of-Life Care in Patients With Advanced Cancer From the Perspective of an Insurance Organization: A Cross-Sectional Study in Iran. Value Health Reg Issues 2023; 41:7-14. [PMID: 38154367 DOI: 10.1016/j.vhri.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/28/2023] [Accepted: 02/28/2023] [Indexed: 12/30/2023]
Abstract
OBJECTIVES Cancers are significant medical conditions that contribute to the rising costs of healthcare systems and chronic diseases. This study aimed to estimate the average costs of medical services provided to patients with advanced cancers at the end of life (EOL). METHODS We analyzed data from the Sata insurance claim database and the Health Information System of Baqiyatallah hospital in Iran. The study included all adult decedents who had advanced cancer without comorbidities, died between March 2020 and September 2020, and had a history of hospitalization in the hospital. We calculated the average total cost of healthcare services per patient during the EOL period, including both cancer-related and noncancer-related costs. RESULTS A total of 220 patients met the inclusion criteria. The average duration of the EOL period for these patients was 178 days, with an average total cost of $8278 (SD $5698) for men and $9396 (SD $6593) for women. Cancer-related costs accounted for 64.42% of the total costs, including inpatient and outpatient services. Among these costs, hospitalization was the primary cost driver and had the greatest impact on EOL costs. This observation was supported by the multiple linear regression model, which suggested that hospitalization in the final days of life could potentially drive costs in these patients. Notably, no specialized palliative care was provided to the patients included in this study. CONCLUSIONS The results demonstrate that there is a significant rise in costs of care in patients receiving routine cancer care rather than optimized EOL care.
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Affiliation(s)
- Ali Zafari
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Iran, Tehran Province, Tehran
| | - Parisa Mehdizadeh
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Iran, Tehran Province, Tehran
| | - Mohammadkarim Bahadori
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Iran, Tehran Province, Tehran.
| | - Nooredin Dopeykar
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Iran, Tehran Province, Tehran
| | - Ehsan Teymourzadeh
- Health Management Research Center, Baqiyatallah University of Medical Sciences, Iran, Tehran Province, Tehran
| | - Ramin Ravangard
- Health Human Resources Research Centre, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Iran, Fars Province, Shiraz
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Depoorter V, Vanschoenbeek K, Decoster L, Silversmit G, Debruyne PR, De Groof I, Bron D, Cornélis F, Luce S, Focan C, Verschaeve V, Debugne G, Langenaeken C, Van Den Bulck H, Goeminne JC, Teurfs W, Jerusalem G, Schrijvers D, Petit B, Rasschaert M, Praet JP, Vandenborre K, De Schutter H, Milisen K, Flamaing J, Kenis C, Verdoodt F, Wildiers H. End-of-Life Care in the Last Three Months before Death in Older Patients with Cancer in Belgium: A Large Retrospective Cohort Study Using Data Linkage. Cancers (Basel) 2023; 15:3349. [PMID: 37444458 DOI: 10.3390/cancers15133349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 07/15/2023] Open
Abstract
This study aims to describe end-of-life (EOL) care in older patients with cancer and investigate the association between geriatric assessment (GA) results and specialized palliative care (SPC) use. Older patients with a new cancer diagnosis (2009-2015) originally included in a previous multicentric study were selected if they died before the end of follow-up (2019). At the time of cancer diagnosis, patients underwent geriatric screening with Geriatric 8 (G8) followed by GA in case of a G8 score ≤14/17. These data were linked to the cancer registry and healthcare reimbursement data for follow-up. EOL care was assessed in the last three months before death, and associations were analyzed using logistic regression. A total of 3546 deceased older patients with cancer with a median age of 79 years at diagnosis were included. Breast, colon, and lung cancer were the most common diagnoses. In the last three months of life, 76.3% were hospitalized, 49.1% had an emergency department visit, and 43.5% received SPC. In total, 55.0% died in the hospital (38.5% in a non-palliative care unit and 16.4% in a palliative care unit). In multivariable analyses, functional and cognitive impairment at cancer diagnosis was associated with less SPC. Further research on optimizing EOL healthcare utilization and broadening access to SPC is needed.
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Affiliation(s)
| | | | - Lore Decoster
- Department of Medical Oncology, Oncologisch Centrum, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, 1090 Brussels, Belgium
| | - Geert Silversmit
- Research Department, Belgian Cancer Registry, 1210 Brussels, Belgium
| | - Philip R Debruyne
- Division of Medical Oncology, Kortrijk Cancer Centre, AZ Groeninge, 8500 Kortrijk, Belgium
- School of Life Sciences, Medical Technology Research Centre (MTRC), Anglia Ruskin University, Cambridge CB1 1PT, UK
- School of Nursing & Midwifery, University of Plymouth, Plymouth PL4 8AA, UK
| | - Inge De Groof
- Department of Geriatric Medicine, Iridium Cancer Network Antwerp, Sint-Augustinus, 2610 Wilrijk, Belgium
| | - Dominique Bron
- Department of Hematology, ULB-Institute Jules Bordet, 1070 Brussels, Belgium
| | - Frank Cornélis
- Department of Medical Oncology, Cliniques Universitaires Saint-Luc-UCLouvain, 1200 Brussels, Belgium
| | - Sylvie Luce
- Department Medical Oncology, University Hospital Erasme, Université Libre de Bruxelles ULB, 1000 Brussels, Belgium
| | - Christian Focan
- Department of Oncology, Groupe Santé CHC-Liège, Clinique CHC-MontLégia, 4000 Liège, Belgium
| | - Vincent Verschaeve
- Department of Medical Oncology, GHDC Grand Hôpital de Charleroi, 6000 Charleroi, Belgium
| | - Gwenaëlle Debugne
- Department of Geriatric Medicine, Centre Hospitalier de Mouscron, 7700 Mouscron, Belgium
| | | | | | | | - Wesley Teurfs
- Department Medical Oncology, ZNA Stuivenberg, 2060 Antwerp, Belgium
| | - Guy Jerusalem
- Department of Medical Oncology, Centre Hospitalier Universitaire Sart Tilman, Liège University, 4000 Liège, Belgium
| | - Dirk Schrijvers
- Department of Medical Oncology, ZNA Middelheim, 2020 Antwerp, Belgium
| | - Bénédicte Petit
- Department of Medical Oncology, Centre Hospitalier Jolimont, 7100 La Louvière, Belgium
| | - Marika Rasschaert
- Department of Medical Oncology, University Hospital Antwerp, 2650 Edegem, Belgium
| | - Jean-Philippe Praet
- Department of Geriatric Medicine, CHU St-Pierre, Free Universities Brussels, 1000 Brussels, Belgium
| | | | | | - Koen Milisen
- Academic Centre for Nursing and Midwifery, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
- Department of Geriatric Medicine, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Johan Flamaing
- Department of Geriatric Medicine, University Hospitals Leuven, 3000 Leuven, Belgium
- Gerontology and Geriatrics, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
| | - Cindy Kenis
- Academic Centre for Nursing and Midwifery, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
- Department of Geriatric Medicine, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of General Medical Oncology, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Freija Verdoodt
- Research Department, Belgian Cancer Registry, 1210 Brussels, Belgium
| | - Hans Wildiers
- Department of Oncology, KU Leuven, 3000 Leuven, Belgium
- Department of General Medical Oncology, University Hospitals Leuven, 3000 Leuven, Belgium
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