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Comparing Methods for Record Linkage for Public Health Action: Matching Algorithm Validation Study.

Tigran Avoundjian1,2, Julia C Dombrowski1,2,3, Matthew R Golden1,2,3

  • 1Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States.

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|May 1, 2020
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Summary
This summary is machine-generated.

Probabilistic record linkage algorithms, like beta record linkage (BRL), identify more true matches than deterministic methods, improving public health intervention reach and reducing inequities. These advanced methods are crucial for accurate data matching in public health surveillance.

Keywords:
data managementmedical record linkagepublic health practicepublic health surveillance

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Area of Science:

  • Public Health Informatics
  • Biostatistics
  • Data Science

Background:

  • Public health departments utilize record linkage for interventions, but lack guidance.
  • Deterministic algorithms often miss true matches, leading to missed intervention opportunities and health inequities.

Purpose of the Study:

  • To compare the performance of commonly used record linkage algorithms in public health.
  • Evaluate deterministic and probabilistic algorithms using simulations and a real-world HIV/syphilis dataset.

Main Methods:

  • Compared five deterministic and two probabilistic record linkage algorithms (fastLink, BRL).
  • Used simulations with varying data errors and overlap, and a real-world dataset from King County, Washington.
  • Calculated recall and precision for each algorithm, alongside computation time.

Main Results:

  • Probabilistic algorithms (BRL, fastLink) maintained high recall across data quality levels.
  • Deterministic algorithms struggled with low data quality but were faster.
  • In a real-world scenario, BRL achieved 100% recall and 99.0% precision.

Conclusions:

  • Probabilistic record linkage maximizes true matches, enhancing public health intervention coverage.
  • These algorithms reduce intervention gaps and improve the reach of public health actions.
  • Choosing the right algorithm is crucial for effective public health surveillance and equity.