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Licensed Unlicensed Requires Authentication Published by De Gruyter December 2, 2010

Probabilistic Record Linkage of Infection Records and Death Registrations: A Tool to Strengthen Surveillance

Nicola Potz, David Powell, Theresa L Lamagni, Richard Pebody, David Bridger and Georgia Duckworth

An important element for many infectious disease surveillance programmes is their capacity to monitor not only the incidence of infection, but also the associated mortality. The ability to monitor post-infection mortality is dependent on outcome information being collected through the surveillance reports, or on infections being precisely specified on death certificates. For many infectious diseases, neither of these sources provides a reliable source of this information, so a method for linking infection and death registration data is needed. Given that surveillance data often lacks a unique patient identifier, a probabilistic record linkage method was developed to reliably bring together large-scale data sources to identify deaths following infection. The method was developed using Streptococcus pneumonia infection records but with wider applicability to other infectious disease surveillance programmes. Evaluation of the mechanism was undertaken by tracing patients through a central health service database. Results of the evaluation showed a positive predictive value of 97.7-99.8% for correctly identifying deaths following infection, and a negative predictive value of 90.2-98.0%. The successful application of probabilistic matching to link infections and death registrations paves the way for a new era in infectious disease surveillance in the UK, with its potential application to augment a wide array of ongoing surveillance programmes with information on patient outcome.

Published Online: 2010-12-2

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