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Linkability measures to assess the data characteristics for record linkage.

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|September 20, 2024
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Summary

Evaluating data linkability before record linkage (RL) improves data quality. This study used linkability measures and anomaly detection to identify data fitness issues, preventing wasted resources on poor linkage results.

Keywords:
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Area of Science:

  • Data Science
  • Health Informatics
  • Biostatistics

Background:

  • Accurate record linkage (RL) is crucial for consolidating patient data from disparate sources.
  • Poor data quality can lead to inefficient resource allocation and suboptimal linkage outcomes.
  • Evaluating data linkability is essential to enhance the effectiveness of RL.

Purpose of the Study:

  • To systematically evaluate data linkability to improve the effectiveness of record linkage.
  • To identify data fitness-for-use issues before initiating the RL process.

Main Methods:

  • Developed and applied data fitness (linkability) measures, assessing variable availability, discriminatory power, and distribution.
  • Utilized the isolation forest algorithm to detect anomalous linkability values across 188 sites.
  • Conducted manual data review to understand the root causes of identified anomalies.

Main Results:

  • Calculated 20,680 linkability metrics for 11 potential linkage variables (LVs) across 188 sites.
  • Identified variations in completeness for LVs like Social Security Number, while others (e.g., name, DOB, sex) showed low missingness.
  • Uncovered issues such as identical LV values, placeholder values masking missing data, and orphan records.

Conclusions:

  • Variable fitness for RL depends on availability and discriminatory power for unique individual identification.
  • Awareness of placeholder values and data anomalies is critical for selecting appropriate variables and methods to optimize RL.
  • Employing linkability measures and anomaly detection aids in identifying and addressing fitness-for-use issues, ensuring high-quality linkage outcomes.