Accuracy of probabilistic linkage using the enhanced matching system for public health and epidemiological studies

Robert W. Aldridge, Kunju Shaji, Andrew C. Hayward, Ibrahim Abubakar

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


Background: The Enhanced Matching System (EMS) is a probabilistic record linkage program developed by the tuberculosis section at Public Health England to match data for individuals across two datasets. This paper outlines how EMS works and investigates its accuracy for linkage across public health datasets. Methods: EMS is a configurable Microsoft SQL Server database program. To examine the accuracy of EMS, two public health databases were matched using National Health Service (NHS) numbers as a gold standard unique identifier. Probabilistic linkage was then performed on the same two datasets without inclusion of NHS number. Sensitivity analyses were carried out to examine the effect of varying matching process parameters. Results: Exact matching using NHS number between two datasets (containing 5931 and 1759 records) identified 1071 matched pairs. EMS probabilistic linkage identified 1068 record pairs. The sensitivity of probabilistic linkage was calculated as 99.5% (95%CI: 98.9, 99.8), specificity 100.0% (95%CI: 99.9,100.0), positive predictive value 99.8% (95%CI: 99.3,100.0), and negative predictive value 99.9% (95%CI: 99.8,100.0). Probabilistic matching was most accurate when including address variables and using the automatically generated threshold for determining links with manual review. Conclusion: With the establishment of national electronic datasets across health and social care, EMS enables previously unanswerable research questions to be tackled with confidence in the

Original languageEnglish
Article numbere0136179
JournalPLoS ONE
Issue number8
Publication statusPublished - 24 Aug 2015

Bibliographical note

Publisher Copyright:
© 2015 Aldridge et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Copyright 2015 Elsevier B.V., All rights reserved.


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