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Generating synthetic identifiers to support development and evaluation of data linkage methods.

Joseph Lam1, Andy Boyd2, Robin Linacre3

  • 1Population, Policy & Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom.

International Journal of Population Data Science
|December 2, 2024
PubMed
Summary

Generating synthetic identifiers for large cohort studies improves data linkage methods. This approach creates realistic datasets for evaluating linkage accuracy without privacy concerns, aiding in selecting appropriate strategies.

Keywords:
ALSPACKeywords record linkagedata linkagelinkage evaluationsynthetic datasynthetic identifiers

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Area of Science:

  • Health Informatics
  • Data Science
  • Biostatistics

Background:

  • Developing and evaluating data linkage methods is hindered by limited access to personal identifiers.
  • Synthetic identifiers offer a privacy-preserving solution for creating training datasets.
  • Such datasets can inform the selection of optimal data linkage strategies.

Purpose of the Study:

  • To develop and demonstrate a framework for generating synthetic identifier datasets.
  • To support the development and evaluation of data linkage methods.
  • To assess if replicating attribute-identifier associations enhances synthetic data utility for linkage error assessment.

Main Methods:

  • Determined steps for generating synthetic identifiers mirroring real-world data collection.
  • Created synthetic versions of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort data.
  • Evaluated synthetic data utility for assessing linkage quality, including false and missed matches.

Main Results:

  • Observed 18% surname and 12% forename disagreement in ALSPAC identifiers between collection points.
  • Disagreement rates varied by maternal age and ethnic group.
  • Synthetic data accurately estimated linkage quality metrics, with improved utility when incorporating attribute associations.

Conclusions:

  • Replicating dependencies between attributes, identifiers, and their errors enables realistic synthetic data generation.
  • This synthetic data facilitates robust evaluation of data linkage methods.
  • The framework supports informed choices for data linkage strategies in research settings.