Background: Health care utilization among decedents is increasingly used as a measure of health care efficiency, but decedent-based measures may be biased estimates of care received by “dying” patients.
Objective: To develop and validate new measures of hospital “end-of-life” treatment intensity.
Research Design: Retrospective cohort study using Pennsylvania Health Care Cost Containment Council (PHC4) discharge data (April 2001–March 2005) and Centers for Medicare and Medicaid Services (CMS) data (January 1999–December 2003).
Subjects: Patients 65 and older admitted to 174 Pennsylvania acute care hospitals.
Measures: Hospital-specific standardized ratios of intensive care unit (ICU) and life-sustaining treatment (LST) use among terminal admissions (decedents) and admissions with a high probability of dying, and spending and use of hospitals, ICUs, and physicians among patients in their last 6 months of life.
Results: There was marked between-hospital variation in the use of the ICU and LSTs among decedents and admissions with high probability of dying. All hospital decedent and high probability of dying measures were highly correlated (P < 00001). In principal components factor analysis, all 4 of the last-6-months cohort-based measures, the decedent and high-risk admission-based ICU measures, and 8 of the 12 decedent and high probability of dying LST measures loaded onto a single factor, explaining 42% of the variation in the data.
Conclusions: Hospitals’ end-of-life intensity varies in the use of specific life-sustaining treatments that are somewhat emblematic of aggressive end-of-life care. End-of-life intensity is a relatively stable hospital attribute that is robust to multiple measurement approaches.
From the *Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; †Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; ‡Department of Critical Care Medicine, The CRISMA (Clinical Research, Investigation, and Systems Modeling of Acute Illness) Laboratory, University of Pittsburgh, Pittsburgh, Pennsylvania; §Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; and ¶Department of Economics, University of Michigan, Ann Arbor, Michigan.
Supported by NIH grant K08 AG021921 (to A.E.B. PI), with additional support from P01 AG019783 (to Jonathan S. Skinner PI) and 1UL1 RR024153 (Steven E. Reis PI).
Reprints: Amber E. Barnato, MD, MPH, MS, Center for Research on Health Care, 200 Meyran Avenue, Suite 200, Pittsburgh, PA 15213. E-mail: email@example.com.
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