Medicaid, Medicare, and the Michigan Tumor Registry: A Linkage Strategy

Cathy J. Bradley, Charles W. Given, Zhehui Luo (), Caralee Roberts, Glenn Copeland and Beth A. Virnig
Additional contact information
Cathy J. Bradley: Virginia Commonwealth University, Department of Health Administration and Massey Cancer Center, Grant House, Richmond, VA, cjbradley@vcu.edu
Charles W. Given: Department of Family Practice and Michigan State Univ. East Lansing
Caralee Roberts: Roberts Research Associates, East Lansing, MI
Glenn Copeland: Michigan Cancer Surveillance Program, Michigan Department of Community Health, Lansing
Beth A. Virnig: Department of Health Services Research and Policy, School of Public Health, University of Minnesota, Minneapolis

Medical Decision Making, 2007, vol. 27, issue 4, 352-363

Abstract: The study of health outcomes and the reduction in health disparities is at the forefront of the nation's health care agenda. A theme in the disparities literature is the call for a data infrastructure that can track progress toward goals aimed at reducing differences in health outcomes. This article describes a strategy for linking Medicaid, Medicare, and Michigan Tumor Registry data for the purposes of studying disparities in cancer diagnosis, quality of care, and survival. The authors review their procedures for ensuring that a correct match between files occurred and offer guidance for merging and assessing the quality of these complex linked data sets. A cohort of 113,604 subjects (90%) from a population of 125,900 subjects was correctly linked from the Michigan Tumor Registry to Medicare and Medicaid files. Using probabilistic and deterministic methods, the prediction rate of the Medicaid match to the Michigan Tumor Registry was 93%. Approximately 13% of the subjects were dually eligible for Medicare and Medicaid. An expansive data set reflecting the Medicare and Medicaid medical service utilization and outcomes for a cohort of individuals age 65 years and older when diagnosed with cancer was created. This data set serves as a cornerstone of a health outcomes data infrastructure. The methodology described may serve as a model for other researchers seeking to create a similar data set in their state.

Keywords: Key words: cancer; Medicare; Medicaid; administrative data. (Med Decis Making 2007; 27:352—363) (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:27:y:2007:i:4:p:352-363

DOI: 10.1177/0272989X07302129

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