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Activity Number: 360 - Contributed Poster Presentations: ENAR
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #330814
Title: Probabilistic or Deterministic Data Linkage? Experience from Linking Cancer Registry Data with Health Claims Data
Author(s): Bin Huang* and Quan Chen
Companies: University of Kentucky and University of Kentucky
Keywords: Data linkage; Cancer Registry; Health Claims; Probabilistic linkage; Deterministic linkage

Background: Both probabilistic and deterministic data linkages have been utilized in linking cancer registry data with Heath administrative claims data. However, these approaches have rarely been compared. We examined the characteristics of both approaches and calculated sensitivity and specificity for various scenarios. Methods: Many-to-Many and One-to-Many linkage were performed and manual review were conducted. A final set of true matches was identified and then used to calculate related statistics for both the probabilistic and the deterministic approach. Simulations were conducted to examine how missing SSN will impact the results. Results: Little difference were found in true matches between Many-to-Many matching and One-to-Many matching. Deterministic matching provided similar results. When percentages of SSN increase, quality of linkage for both approaches decreased, especially for the deterministic approach. Discussion: Since it is challenging to acquire permissions to conduct the manual review process, deterministic or combination of both approaches without manual review may be most useful.

Authors who are presenting talks have a * after their name.

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