Literature DB >> 15778797

Decision analysis for the assessment of a record linkage procedure: application to a perinatal network.

C Quantin1, C Binquet, F A Allaert, B Cornet, R Pattisina, G Leteuff, C Ferdynus, J B Gouyon.   

Abstract

OBJECTIVES: According to European legislation, we must develop computer software allowing the linkage of medical records previously rendered anonymous. Some of them, like AUTOMATCH, are used in daily practice either to gather medical files in epidemiologic studies or for clinical purpose. In the first situation, the aim is to avoid homonymous errors, and in the second one, synonymous errors. The objective of this work is to study the effect of different parameters (number of identification variables, phonetic treatments of names, direct or probabilistic linkage procedure) on the reliability of the linkage in order to determine which strategy is the best according to the purpose of the linkage.
METHODS: The assessment of the Burgundy Perinatal Network requires the linking of discharge abstracts of mothers and neonates, collected in all the hospitals of the region. Those data are used to compare direct and probabilistic linkage, using different parameterization strategies.
RESULTS: If the linkage has to be performed in real time, so that no validation of indecisions generated by probabilistic linkage is possible, probabilistic linkage using three variables without any phonetic treatment seems to be the most appropriate approach, combined with a direct linkage using four variables applied to non-conclusive links. If a validation of indecisions is possible in an epidemiological study, probabilistic linkage using five variables, with a phonetic treatment adapted to the local language has to be preferred. For medical purpose, it should be combined with a direct linkage with four or five variables.
CONCLUSION: This paper reveals that the time and money available to manage indecision as well as the purpose of the linkage are of paramount importance for choosing a linkage strategy.

Mesh:

Year:  2005        PMID: 15778797

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  8 in total

1.  Probabilistic master lists: integration of patient records from different databases when unique patient identifier is missing.

Authors:  Farrokh Alemi; Francisco Loaiza; Jee Vang
Journal:  Health Care Manag Sci       Date:  2007-02

2.  Ignoring dependency between linking variables and its impact on the outcome of probabilistic record linkage studies.

Authors:  Miranda Tromp; Nora Méray; Anita C J Ravelli; Johannes B Reitsma; Gouke J Bonsel
Journal:  J Am Med Inform Assoc       Date:  2008-06-25       Impact factor: 4.497

3.  Medical record: systematic centralization versus secure on demand aggregation.

Authors:  Catherine Quantin; David-Olivier Jaquet-Chiffelle; Gouenou Coatrieux; Eric Benzenine; Bertrand Auverlot; François-André Allaert
Journal:  BMC Med Inform Decis Mak       Date:  2011-03-22       Impact factor: 2.796

4.  CANDIDATE: A tool for generating anonymous participant-linking IDs in multi-session studies.

Authors:  Frode Eika Sandnes
Journal:  PLoS One       Date:  2021-12-15       Impact factor: 3.240

5.  Privacy-preserving record linkage using Bloom filters.

Authors:  Rainer Schnell; Tobias Bachteler; Jörg Reiher
Journal:  BMC Med Inform Decis Mak       Date:  2009-08-25       Impact factor: 2.796

6.  How accurate is the reporting of stroke in hospital discharge data? A pilot validation study using a population-based stroke registry as control.

Authors:  Corine Aboa-Eboulé; Dominique Mengue; Eric Benzenine; Marc Hommel; Maurice Giroud; Yannick Béjot; Catherine Quantin
Journal:  J Neurol       Date:  2012-10-18       Impact factor: 4.849

7.  Using discharge abstracts to evaluate a regional perinatal network: assessment of the linkage procedure of anonymous data.

Authors:  Catherine Quantin; Béatrice Gouyon; Paul Avillach; Cyril Ferdynus; Paul Sagot; Jean-Bernard Gouyon
Journal:  Int J Telemed Appl       Date:  2008-12-23

8.  Securizing data linkage in french public statistics.

Authors:  Maxence Guesdon; Eric Benzenine; Kamel Gadouche; Catherine Quantin
Journal:  BMC Med Inform Decis Mak       Date:  2016-10-06       Impact factor: 2.796

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.