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Related Experiment Video

Updated: Aug 15, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Implementation and validation of a probabilistic linkage method for population databases without identification

Amado D Quezada-Sánchez1, Iván Espín-Arellano1, Evangelina Morales-Carmona1

  • 1Center for Evaluation and Surveys Research, National Institute of Public Health. Av. Universidad 655 Col. Sta. María Ahuacatitlán. C.P. 62100. Cuernavaca, Morelos, Mexico.

Heliyon
|December 30, 2022
PubMed
Summary
This summary is machine-generated.

Linking health records using the Fellegi-Sunter method creates administrative cohorts for public health research. This approach demonstrated high accuracy in identifying unique individuals across different data sources.

Keywords:
AlgorithmBlockingHospital dischargeInformation systemsMortalityProbabilityRecord linkage

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Informatics

Background:

  • Linking records from disparate sources enables administrative cohort creation.
  • This offers an alternative to traditional cohort studies for longitudinal analysis.
  • Such linkage is crucial for advancing public health knowledge.

Purpose of the Study:

  • To implement and evaluate the Fellegi-Sunter probabilistic linkage method.
  • To assess linkage performance using Mexican hospital discharge and mortality data.
  • To determine the effectiveness of different blocking strategies and classification methods.

Main Methods:

  • The Fellegi-Sunter probabilistic linkage method was applied.
  • Data were split into training (25%) and validation (75%) samples.
  • Blocking schemes (e.g., trigrams of full name) and pair classification were evaluated for sensitivity and positive predictive value.

Main Results:

  • A blocking scheme using trigrams of the full name achieved 95.76% pairs completeness and 99.9996% complexity reduction.
  • The linkage achieved 90.72% sensitivity and 97.10% positive predictive value in the validation sample.
  • These metrics slightly improved with clerical review compared to automatic classification.

Conclusions:

  • The implemented linkage algorithm demonstrates good performance in sensitivity and positive predictive value.
  • This method is suitable for building administrative cohorts from health information systems.
  • It facilitates epidemiological analysis of populations within these systems.