Siran M Koroukian1, Nicholas K Schiltz2, David F Warner3, Charles W Given4, Mark Schluchter5, Cynthia Owusu6, Nathan A Berger6. 1. Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States; Case Comprehensive Cancer Center, Cleveland, Ohio, United States; Population Health and Outcomes Research Core, Clinical and Translational Science Collaborative, Case Western Reserve University, Cleveland, Ohio, United States. Electronic address: skoroukian@case.edu. 2. Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States; Population Health and Outcomes Research Core, Clinical and Translational Science Collaborative, Case Western Reserve University, Cleveland, Ohio, United States. 3. Department of Sociology, University of Nebraska-Lincoln, Lincoln, Nebraska. 4. Department of Family Medicine, Michigan State University, East Lansing, Michigan, United States. 5. Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States; Case Comprehensive Cancer Center, Cleveland, Ohio, United States. 6. Case Comprehensive Cancer Center, Cleveland, Ohio, United States; Department of Medicine, Division of Hematology/Oncology, University Hospitals of Cleveland, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States.
Abstract
OBJECTIVE: Most prior studies on aggressive end-of-life care in older patients with cancer have accounted for social determinants of health (e.g., race, income, and education), but rarely for multimoribidity (MM). In this study, we examine the association between end-of-life care and each of the social determinants of health and MM, hypothesizing that higher MM is associated with less aggressive care. METHODS: From the linked 1991-2008 Health and Retirement Study, Medicare data, and the National Death Index, we identified fee-for-service patients age ≥66years who died from cancer (n=835). MM was defined as the occurrence or co-occurrence of chronic conditions, functional limitations, and/or geriatric syndromes. Aggressive care was based on claims-derived measures of receipt of cancer-directed treatment in the last two weeks of life; admission to the hospital and/or emergency department (ED) within the last month; and in-hospital death. We also identified patients enrolled in hospice. In multivariable logistic regression models, we analyzed the associations of interest, adjusting for potential confounders. RESULTS: While 61.2% of the patients enrolled in hospice, 24.6% underwent cancer-directed treatment; 55.1% were admitted to the hospital and/or ED; and 21.7% died in the hospital. We observed a U-shaped distribution between income and in-hospital death. Chronic conditions and geriatric syndromes were associated with some outcomes, but not with others. CONCLUSIONS: To improve quality end-of-life care and curtail costs incurred by dying patients, relevant interventions need to account for social determinants of health and MM in a nuanced fashion.
OBJECTIVE: Most prior studies on aggressive end-of-life care in older patients with cancer have accounted for social determinants of health (e.g., race, income, and education), but rarely for multimoribidity (MM). In this study, we examine the association between end-of-life care and each of the social determinants of health and MM, hypothesizing that higher MM is associated with less aggressive care. METHODS: From the linked 1991-2008 Health and Retirement Study, Medicare data, and the National Death Index, we identified fee-for-service patients age ≥66years who died from cancer (n=835). MM was defined as the occurrence or co-occurrence of chronic conditions, functional limitations, and/or geriatric syndromes. Aggressive care was based on claims-derived measures of receipt of cancer-directed treatment in the last two weeks of life; admission to the hospital and/or emergency department (ED) within the last month; and in-hospital death. We also identified patients enrolled in hospice. In multivariable logistic regression models, we analyzed the associations of interest, adjusting for potential confounders. RESULTS: While 61.2% of the patients enrolled in hospice, 24.6% underwent cancer-directed treatment; 55.1% were admitted to the hospital and/or ED; and 21.7% died in the hospital. We observed a U-shaped distribution between income and in-hospital death. Chronic conditions and geriatric syndromes were associated with some outcomes, but not with others. CONCLUSIONS: To improve quality end-of-life care and curtail costs incurred by dying patients, relevant interventions need to account for social determinants of health and MM in a nuanced fashion.
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