Address correspondence to Orna Intrator, Ph.D., Associate Professor (Research), Center for Gerontology and Health Care Research, Brown University, PO Box G-S121-6, Providence, RI 02912; e-mail: email@example.com. Orna Intrator, Ph.D., Health Research Scientist, VA HSR&D REAP, Providence, RI. Jeffrey Hiris, M.A., Senior Systems Analyst, Susan C. Miller, Ph.D., Associate Professor (Research), Vincent Mor, Ph.D., Professor, are with the Center for Gerontology and Health Care Research, Brown University, Providence, RI. Katherine Berg, Ph.D., P.T., Associate Professor and Chair, is with the Department of Physical Therapy, University of Toronto, Toronto, ON, Canada.
The Residential History File: Studying Nursing Home Residents' Long-Term Care Histories*
Article first published online: 28 OCT 2010
© Health Research and Educational Trust
Health Services Research
Volume 46, Issue 1p1, pages 120–137, February 2011
How to Cite
Intrator, O., Hiris, J., Berg, K., Miller, S. C. and Mor, V. (2011), The Residential History File: Studying Nursing Home Residents' Long-Term Care Histories. Health Services Research, 46: 120–137. doi: 10.1111/j.1475-6773.2010.01194.x
*The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.
- Issue published online: 3 JAN 2011
- Article first published online: 28 OCT 2010
- Minimum Data Set (MDS);
- transitions in care settings;
- linking administrative files;
- tracking health care utilization
Objective. To construct a data tool, the Residential History File (RHF), that summarizes information from Medicare claims and nursing home (NH) Minimum Data Set (MDS) assessments to track people through health care locations, including non-Medicare-paid NH stays.
Data Sources. Online Survey of Certification and Reporting (OSCAR) data for 202 free-standing NHs, Medicare Denominator, claims (parts A and B), and MDS assessments for 60,984 people who were present in one of these NHs in 2006.
Methods. The algorithm creating the RHF is outlined and the RHF for the study data are used to describe place of death. The identification of residents in NHs is compared with the reports in OSCAR and part B claims.
Principal Findings. The RHF correctly identified 84.8 percent of part B claims with place-of-service in NH, and it identified 18.3 less residents on average than reported in the OSCAR on the day of the survey. The RHF indicated that 17.5 percent non-Medicare NH decedents were transferred to the hospital to die versus 45.6 percent skilled nursing facility decedents.
Conclusions. The population-based design of the RHF makes it possible to conduct policy-relevant research to examine the variation in the rate and type of health care transitions across the United States.