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Diagnosing Pulmonary Tuberculosis with the Xpert MTB/RIF Test
Published on: April 9, 2012
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Surveillance for TB drug resistance using routine rapid diagnostic testing data: Methodological development and
Sarah E Baum1, Daniele M Pelissari2, Fernanda Dockhorn Costa2
1Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Plos Computational Biology
|December 23, 2024
Summary
Accurate drug-resistant tuberculosis surveillance is crucial. This study presents a statistical method to correct for biased rapid diagnostic test data, providing more reliable estimates of rifampicin resistance in TB cases.
Area of Science:
- Epidemiology
- Infectious Diseases
- Public Health
Background:
- Accurate surveillance of drug-resistant tuberculosis (TB) is essential for effective control.
- Traditional methods for estimating rifampicin resistance rely on resource-intensive surveys.
- Routine use of rapid diagnostic tests (RDTs) may yield biased resistance estimates due to non-representative patient selection.
Purpose of the Study:
- To develop and validate a statistical method to correct for biases in RDT data.
- To obtain unbiased estimates of rifampicin resistance among new and previously treated TB cases.
- To improve the timeliness and accuracy of drug-resistant TB surveillance.
Main Methods:
- Developed a hierarchical generalized additive regression model.
- Applied statistical corrections to model rifampicin resistance using routine Xpert MTB/RIF data.
- Imputed resistance results for untested individuals using model outputs.
- Utilized 2017-2023 case-level data from over 800,000 patients in Brazil.
Main Results:
- Modeled estimates showed substantially higher rifampicin resistance prevalence than naive estimates.
- Estimated prevalence was 28-44% higher for new cases and 2-17% higher for previously treated cases.
- Estimated RR-TB incidence had narrower uncertainty intervals compared to WHO estimates.
- The method proved robust to alternative model specifications.
Conclusions:
- The developed method effectively leverages routine RDT data for unbiased RR-TB prevalence estimation.
- This approach offers a generalizable solution for settings with non-universal TB drug resistance testing.
- Provides more timely and precise estimates crucial for public health interventions.
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