A new hybrid record linkage process to make epidemiological databases interoperable: application to the GEMO and GENEPSO studies involving BRCA1 and BRCA2 mutation carriers
Authors: Jiao Yue , Lesueur Fabienne , Azencott Chloé-Agathe , Laurent Maïté , Mebirouk Noura , Laborde Lilian , Beauvallet Juana , Dondon Marie-Gabrielle , Eon-Marchais Séverine , Laugé Anthony ,   GEMO Study Collaborators, Boutry-Kryza Nadia , Calender Alain , Giraud Sophie , Léone Mélanie , Bressac-de-Paillerets Brigitte , Caron Olivier , Guillaud-Bataille Marine , Bignon Yves-Jean , Uhrhammer Nancy , Bonadona Valérie , Lasset Christine , Berthet Pascaline , Castera Laurent , Vaur Dominique , Bourdon Violaine , Noguès Catherine , Noguchi Tetsuro , Popovici Cornel , Remenieras Audrey , Sobol Hagay , Coupier Isabelle , Harmand Pierre-Olivier , Pujol Pascal , Vilquin Paul , Dumont Aurélie , Révillion Françoise , Muller Danièle , Barouk-Simonet Emmanuelle , Bonnet Françoise , Bubien Virginie , Longy Michel , Sévenet Nicolas , Gladieff Laurence , Guimbaud Rosine , Feillel Viviane , Toulas Christine , Dreyfus Hélène , Leroux Dominique , Peysselon Magalie , Rebischung Christine , Baurand Amandine , Bertolone Geoffrey , Coron Fanny , Faivre Laurence , Goussot Vincent , Jacquot Caroline , Sawka Caroline , Kientz Caroline , Lebrun Marine , Prieur Fabienne , Fert-Ferrer Sandra , Mari Véronique , Vénat-Bouvet Laurence , Bézieau Stéphane , Delnatte Capucine , Mortemousque Isabelle , Coulet Florence , Soubrier Florent , Warcoin Mathilde , Bronner Myriam , Lizard Sarab , Sokolowska Johanna , Collonge-Rame Marie-Agnès , Damette Alexandre , Gesta Paul , Lallaoui Hakima , Chiesa Jean , Molina-Gomes Denise , Ingster Olivier , Manouvrier-Hanu Sylvie , Lejeune Sophie ,   GENEPSO Study Collaborators, Noguès Catherine , Laborde Lilian , Pontois Pauline , Stoppa-Lyonnet Dominique , Gauthier-Villars Marion , Buecher Bruno , Caron Olivier , Mouret-Fourme Emmanuelle , Fricker Jean-Pierre , Lasset Christine , Bonadona Valérie , Berthet Pascaline , Faivre Laurence , Luporsi Elisabeth , Frénay Marc , Gladieff Laurence , Gesta Paul , Sobol Hagay , Eisinger François , Moretta Jessica , Longy Michel , Dugast Catherine , Colas Chrystelle , Soubrier Florent , Coupier Isabelle , Pujol Pascal , Lortholary Alain , Vennin Philippe , Adenis Claude , Nguyen Tan Dat , Delnatte Capucine , Rossi Annick , Tinat Julie , Tennevet Isabelle , Limacher Jean-Marc , Maugard Christine , Bignon Yves-Jean , Demange Liliane , Dreyfus Hélène , Cohen-Haguenauer Odile , Gilbert Brigitte , Leroux Dominique , Zattara-Cannoni Hélène , Noguès Catherine , Andrieu Nadine , Stoppa-Lyonnet Dominique , Caputo Sandrine M. 
Published: July 2021

Background

Linking independent sources of data describing the same individuals enable innovative epidemiological and health studies but require a robust record linkage approach. We describe a hybrid record linkage process to link databases from two independent ongoing French national studies, GEMO (Genetic Modifiers of BRCA1 and BRCA2), which focuses on the identification of genetic factors modifying cancer risk of BRCA1 and BRCA2 mutation carriers, and GENEPSO (prospective cohort of BRCAx mutation carriers), which focuses on environmental and lifestyle risk factors.

Methods

To identify as many as possible of the individuals participating in the two studies but not registered by a shared identifier, we combined probabilistic record linkage (PRL) and supervised machine learning (ML). This approach (named “PRL + ML”) combined together the candidate matches identified by both approaches. We built the ML model using the gold standard on a first version of the two databases as a training dataset. This gold standard was obtained from PRL-derived matches verified by an exhaustive manual review. Results

The Random Forest (RF) algorithm showed a highest recall (0.985) among six widely used ML algorithms: RF, Bagged trees, AdaBoost, Support Vector Machine, Neural Network.

Therefore, RF was selected to build the ML model since our goal was to identify the maximum number of true matches. Our combined linkage PRL + ML showed a higher recall (range 0.988–0.992) than either PRL (range 0.916–0.991) or ML (0.981) alone. It identified 1995 individuals participating in both GEMO (6375 participants) and GENEPSO (4925 participants).

Conclusions

Our hybrid linkage process represents an efficient tool for linking GEMO and GENEPSO. It may be generalizable to other epidemiological studies involving other databases and registries.