Associations between end-of-life cancer care patterns and medicare expenditures

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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.

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Wang, S. Y., Hall, J., Pollack, C. E., Adelson, K., Davidoff, A. J., Long, J. B., & Gross, C. P. (2016). Associations between end-of-life cancer care patterns and medicare expenditures. JNCCN Journal of the National Comprehensive Cancer Network, 14(8), 1001–1008. https://doi.org/10.6004/jnccn.2016.0107

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