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Identifying Chronic Conditions in Medicare Claims Data: Evaluating the Chronic Condition Data Warehouse Algorithm

Authors

  • Yelena Gorina,

    1. Centers for Disease Control and Prevention, National Center for Health Statistics, Office of Analysis and Epidemiology, Hyattsville, MD
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  • Ellen A. Kramarow

    1. Centers for Disease Control and Prevention, National Center for Health Statistics, Office of Analysis and Epidemiology, 3311 Toledo Road, Room 6332, Hyattsville, MD 20782
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    • Address correspondence to Ellen A. Kramarow, Ph.D., Centers for Disease Control and Prevention, National Center for Health Statistics, Office of Analysis and Epidemiology, 3311 Toledo Road, Room 6332, Hyattsville, MD 20782; e-mail: EKramarow@cdc.gov. Yelena Gorina, M.S., M.P.H., is with the Centers for Disease Control and Prevention, National Center for Health Statistics, Office of Analysis and Epidemiology, Hyattsville, MD.


Abstract

Objective. To examine the strengths and limitations of the Center for Medicare and Medicaid Services' Chronic Condition Data Warehouse (CCW) algorithm for identifying chronic conditions in older persons from Medicare beneficiary data.

Data Sources. Records from participants of the NHANES I Epidemiologic Follow-up Study (NHEFS 1971–1992) linked to Medicare claims data from 1991 to 2000.

Study Design. We estimated the percent of preexisting cases of chronic conditions correctly identified by the CCW algorithm during its reference period and the number of years of claims data necessary to find a preexisting condition.

Principal Findings. The CCW algorithm identified 69 percent of preexisting diabetes cases but only 17 percent of preexisting arthritis cases. Cases identified by the CCW are a mix of preexisting and newly diagnosed conditions.

Conclusions. The prevalence of conditions needing less frequent health care utilization (e.g., arthritis) may be underestimated by the CCW algorithm. The CCW reference periods may not be sufficient for all analytic purposes.

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