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Quantifying emergent drug-resistant TB using laboratory data and record-linkage methods.

C H Rhea¹, F Maruri^2,3, Y Ghebrekristos^4,5

¹Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, USA.

|July 14, 2026

Summary

Accurate patient data linkage is crucial for tracking drug-resistant tuberculosis (TB). Custom approximate linkage methods improved the identification of individuals with rifampicin-resistant TB who developed fluoroquinolone resistance.

Keywords:

fluoroquinolone resistance linking patient results rifampicin-resistant TB tuberculosis

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Published on: May 19, 2020

Area of Science:

Public Health
Infectious Disease Epidemiology
Biostatistics

Background:

Linking laboratory results to patient records is essential for identifying drug-resistant tuberculosis (TB).
Emerging resistance patterns require robust data management strategies for effective surveillance.

Purpose of the Study:

To evaluate the proportion of individuals with rifampicin-resistant TB (RR-TB) who developed fluoroquinolone resistance.
To compare the effectiveness of different data matching methods for patient record linkage in TB surveillance.

Main Methods:

Utilized laboratory data from the National Health Laboratory Service (NHLS) in South Africa (2008-2015).
Employed four patient data linkage methods: exact matching (patient number, demographic details) and custom approximate matching algorithms.
Conducted manual review of linked patient records to validate matches.

Main Results:

Estimates for fluoroquinolone resistance development ranged from 4% (exact match) to 8% (approximate match).
Custom iterative approximate linkage (Method D) correctly identified 98% of patients who developed fluoroquinolone resistance.
A total of 688 unique patients developing fluoroquinolone resistance were identified, with 598 confirmed by manual review.

Conclusions:

Customised approximate patient linkage approaches significantly enhance the accuracy of identifying individuals with drug-resistant TB.
Improved data linkage methods are vital for precise surveillance of antimicrobial resistance in TB.