Shi-Yi Wang1, Jane Hall2, Craig E Pollack2, Kerin Adelson1, Amy J Davidoff1, Jessica B Long2, Cary P Gross1. 1. From the Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, Connecticut; Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center and Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Section of Medical Oncology, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Department of Health Policy and Management, Yale University School of Public Health, New Haven, Connecticut; and Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut. From the Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, Connecticut; Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center and Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Section of Medical Oncology, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Department of Health Policy and Management, Yale University School of Public Health, New Haven, Connecticut; and Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut. 2. From the Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, Connecticut; Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center and Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland; Section of Medical Oncology, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; Department of Health Policy and Management, Yale University School of Public Health, New Haven, Connecticut; and Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut.
Abstract
BACKGROUND: The purpose of this study was to examine the extent to which patterns of intensive end-of-life care explain geographic variation in end-of-life care expenditures among cancer decedents. METHODS: Using the SEER-Medicare database, we identified 90,465 decedents who were diagnosed with cancer in 2004-2011. Measures of intensive end-of-life care included chemotherapy received within 14 days of death; more than 1 emergency department visit, more than 1 hospitalization, or 1 or more intensive care unit (ICU) admissions within 30 days of death; in-hospital death; and hospice enrollment less than 3 days before death. Using hierarchical generalized linear models, we estimated risk-adjusted expenditures in the last month of life for each hospital referral region and identified key contributors to variation in expenditures. RESULTS: The mean expenditure per cancer decedent in the last month of life was $10,800, ranging from $8,300 to $15,400 in the lowest and highest expenditure quintile areas, respectively. There was considerable variation in the percentage of decedents receiving intensive end-of-life care intervention, with 41.7% of decedents receiving intensive care in the lowest quintile of expenditures versus 57.9% in the highest quintile. Regional patterns of late chemotherapy or late hospice use explained only approximately 1% of the expenditure difference between the highest and lowest quintile areas. In contrast, the proportion of decedents who had ICU admissions within 30 days of death was a major driver of variation, explaining 37.6% of the expenditure difference. CONCLUSIONS: Promoting appropriate end-of-life care has the potential to reduce geographic variation in end-of-life care expenditures.
BACKGROUND: The purpose of this study was to examine the extent to which patterns of intensive end-of-life care explain geographic variation in end-of-life care expenditures among cancer decedents. METHODS: Using the SEER-Medicare database, we identified 90,465 decedents who were diagnosed with cancer in 2004-2011. Measures of intensive end-of-life care included chemotherapy received within 14 days of death; more than 1 emergency department visit, more than 1 hospitalization, or 1 or more intensive care unit (ICU) admissions within 30 days of death; in-hospital death; and hospice enrollment less than 3 days before death. Using hierarchical generalized linear models, we estimated risk-adjusted expenditures in the last month of life for each hospital referral region and identified key contributors to variation in expenditures. RESULTS: The mean expenditure per cancer decedent in the last month of life was $10,800, ranging from $8,300 to $15,400 in the lowest and highest expenditure quintile areas, respectively. There was considerable variation in the percentage of decedents receiving intensive end-of-life care intervention, with 41.7% of decedents receiving intensive care in the lowest quintile of expenditures versus 57.9% in the highest quintile. Regional patterns of late chemotherapy or late hospice use explained only approximately 1% of the expenditure difference between the highest and lowest quintile areas. In contrast, the proportion of decedents who had ICU admissions within 30 days of death was a major driver of variation, explaining 37.6% of the expenditure difference. CONCLUSIONS: Promoting appropriate end-of-life care has the potential to reduce geographic variation in end-of-life care expenditures.
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