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Background: Electronic data linkage is increasingly being used as a tool for public health professionals, both in regards to enhanced service coordination and delivery, and in conducting large-scale epidemiological research. However, in many cases, a common pre-existing ID number does not exist across different datasets, and common identifiers, such as names or dates of birth, which could be used to match records may be known to contain errors or even legitimate differences over time. In such situations, probabilistic matching, which does not require that all identifying fields exactly agree in order for one to conclude that two records belong to the same individual, can be a valuable tool for improving data linkage. However, probabilistic matching is computationally complex and demanding, and not well understood by many who may wish to apply it in their work. Therefore, the purpose of this paper is to provide an overview of one approach to probabilistic matching, including the step-by-step procedures involved in the calculation of indices corresponding to the likelihood that two records are a correct match. In addition, the use of multiple iterative protocols, in which several different matching strategies are used in order to maximize the number of linked records, is discussed. Finally, issues related to de-duplication and verification of internal-consistency in the linked data set are also reviewed.
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