A Generalized Fellegi-Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems

TL;DR

This paper introduces a probabilistic framework extending Fellegi-Sunter theory for linking multiple data files without unique identifiers, demonstrated on homicide records and evaluated through simulations.

Contribution

It generalizes Fellegi-Sunter for multiple record linkage, incorporating transitivity and using a mixture model with EM algorithm for improved accuracy.

Findings

Method performs well on homicide data integration

Effective under measurement error scenarios

Proves optimality of record pattern classification

Abstract

We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record-systems need to be integrated for posterior analysis. Our method generalizes the Fellegi-Sunter theory for linking records from two datafiles and its modern implementations. The multiple record linkage goal is to classify the record K-tuples coming from K datafiles according to the different matching patterns. Our method incorporates the transitivity of agreement in the computation of the data used to model matching probabilities. We use a mixture model to fit matching probabilities via maximum likelihood using the EM algorithm. We present a method to decide the record K-tuples membership to the subsets of matching patterns and we prove its optimality. We apply our method to the integration of three Colombian homicide record systems and we perform a simulation study in order to explore the performance of the method under measurement error and different scenarios. The proposed method works well and opens some directions for future research.