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Methods Inf Med 1989; 28(02): 86-91
DOI: 10.1055/s-0038-1635550
Original Article
Schattauer GmbH

Discriminating Powers of Partial Agreements of Names for Linking Personal Records

Part I: The Logical Basis
H. B. Newcombe
1   Consultant, Deep River, Ontario, Canada
,
M. E. Fair
2   Occupational and Environmental Health Research Unit, Vital Statistics and Health Status Section, Health Division, Statistics Canada, Ottawa, Canada
,
P. Lalonde
2   Occupational and Environmental Health Research Unit, Vital Statistics and Health Status Section, Health Division, Statistics Canada, Ottawa, Canada
› Author Affiliations
Further Information

Publication History

Publication Date:
19 February 2018 (online)

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Abstract:

Machines have difficulty when using people’s names to link medical and other records pertaining to the same individuals because of nicknames, ethnic synonyms, truncations, misspellings and typographical errors. Present algorithms used to compute the discriminating powers (or ODDS) associated with partial agreements of names are based, inappropriately, on the degrees of outward similarity alone. They are particularly ineffective in dealing with names that look alike but are unrelated, and with related names that have little apparent similarity. A fundamentally different rationale is, therefore, proposed which, like the human mind, assesses the relatedness of two alternative forms of a name in terms of how often they are used, interchangeably in practice. This must be taken into account if the associated discriminating powers (ODDS) are to be correctly computed. A way of implementing this more precise approach is described and illustrated, using the given names on linked records from an earlier epidemiological study. This first study of two describes the logical basis for record linkage, a second one the empirical test.

Key-words:

Record Linkage - Probabilistic Linkage - Name Comparisons - Partial Agreements - Value Specific Odds

* Available from Ted Hill, Research and General Systems Subdivision, Systems Development Division, Rm 2405, Main Building, Statistics Canada, Tunney’s Pasture, Ottawa, Canada KIA OT6.


** Available from Howard B. Newcombe, P. O. Box 135, Deep River, Ontario, Canada KOJ IPO.


 

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