Lihua Li1,2,3, Liangyuan Hu1,2,3, Jiayi Ji1,2,3, Karen Mckendrick4, Jaison Moreno4, Amy S Kelley4, Madhu Mazumdar1,2,3, Melissa Aldridge4. 1. Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 2. Institute for Healthcare Delivery Science, Mount Sinai Health System, New York, New York, USA. 3. Tisch Cancer Institute, New York, New York, USA. 4. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Abstract
BACKGROUND: To identify and rank the importance of key determinants of end-of-life (EOL) health care costs, and to understand how the key factors impact different percentiles of the distribution of health care costs. METHOD: We applied a principled, machine learning-based variable selection algorithm, using Quantile Regression Forests, to identify key determinants for predicting the 10th (low), 50th (median), and 90th (high) quantiles of EOL health care costs, including costs paid for by Medicare, Medicaid, Medicare Health Maintenance Organizations (HMOs), private HMOs, and patient's out-of-pocket expenditures. RESULTS: Our sample included 7 539 Medicare beneficiaries who died between 2002 and 2017. The 10th, 50th, and 90th quantiles of EOL health care cost are $5 244, $35 466, and $87 241, respectively. Regional characteristics, specifically, the EOL-Expenditure Index, a measure for regional variation in Medicare spending driven by physician practice, and the number of total specialists in the hospital referral region were the top 2 influential determinants for predicting the 50th and 90th quantiles of EOL costs but were not determinants of the 10th quantile. Black race and Hispanic ethnicity were associated with lower EOL health care costs among decedents with lower total EOL health care costs but were associated with higher costs among decedents with the highest total EOL health care costs. CONCLUSIONS: Factors associated with EOL health care costs varied across different percentiles of the cost distribution. Regional characteristics and decedent race/ethnicity exemplified factors that did not impact EOL costs uniformly across its distribution, suggesting the need to use a "higher-resolution" analysis for examining the association between risk factors and health care costs.
BACKGROUND: To identify and rank the importance of key determinants of end-of-life (EOL) health care costs, and to understand how the key factors impact different percentiles of the distribution of health care costs. METHOD: We applied a principled, machine learning-based variable selection algorithm, using Quantile Regression Forests, to identify key determinants for predicting the 10th (low), 50th (median), and 90th (high) quantiles of EOL health care costs, including costs paid for by Medicare, Medicaid, Medicare Health Maintenance Organizations (HMOs), private HMOs, and patient's out-of-pocket expenditures. RESULTS: Our sample included 7 539 Medicare beneficiaries who died between 2002 and 2017. The 10th, 50th, and 90th quantiles of EOL health care cost are $5 244, $35 466, and $87 241, respectively. Regional characteristics, specifically, the EOL-Expenditure Index, a measure for regional variation in Medicare spending driven by physician practice, and the number of total specialists in the hospital referral region were the top 2 influential determinants for predicting the 50th and 90th quantiles of EOL costs but were not determinants of the 10th quantile. Black race and Hispanic ethnicity were associated with lower EOL health care costs among decedents with lower total EOL health care costs but were associated with higher costs among decedents with the highest total EOL health care costs. CONCLUSIONS: Factors associated with EOL health care costs varied across different percentiles of the cost distribution. Regional characteristics and decedent race/ethnicity exemplified factors that did not impact EOL costs uniformly across its distribution, suggesting the need to use a "higher-resolution" analysis for examining the association between risk factors and health care costs.
Authors: Elliott S Fisher; David E Wennberg; Thérèse A Stukel; Daniel J Gottlieb; F L Lucas; Etoile L Pinder Journal: Ann Intern Med Date: 2003-02-18 Impact factor: 25.391
Authors: Peter May; Melissa M Garrido; J Brian Cassel; Amy S Kelley; Diane E Meier; Charles Normand; Lee Stefanis; Thomas J Smith; R Sean Morrison Journal: Health Aff (Millwood) Date: 2016-01 Impact factor: 6.301