Use of a Medical Records Linkage System to Enumerate a Dynamic Population Over Time: The Rochester Epidemiology Project
Author(s) -
Jennifer L. St. Sauver,
Brandon R. Grossardt,
Barbara P. Yawn,
L. Joseph Melton,
Walter A. Rocca
Publication year - 2011
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwq482
Subject(s) - census , rochester epidemiology project , medical record , epidemiology , population , random digit dialing , record linkage , demography , medicine , public health , gerontology , family medicine , geography , environmental health , nursing , population based study , pathology , sociology , radiology
The Rochester Epidemiology Project (REP) is a unique research infrastructure in which the medical records of virtually all persons residing in Olmsted County, Minnesota, for over 40 years have been linked and archived. In the present article, the authors describe how the REP links medical records from multiple health care institutions to specific individuals and how residency is confirmed over time. Additionally, the authors provide evidence for the validity of the REP Census enumeration. Between 1966 and 2008, 1,145,856 medical records were linked to 486,564 individuals in the REP. The REP Census was found to be valid when compared with a list of residents obtained from random digit dialing, a list of residents of nursing homes and senior citizen complexes, a commercial list of residents, and a manual review of records. In addition, the REP Census counts were comparable to those of 4 decennial US censuses (e.g., it included 104.1% of 1970 and 102.7% of 2000 census counts). The duration for which each person was captured in the system varied greatly by age and calendar year; however, the duration was typically substantial. Comprehensive medical records linkage systems like the REP can be used to maintain a continuously updated census and to provide an optimal sampling framework for epidemiologic studies.
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