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Background <p>New and effective tools for detecting drug-resistant tuberculosis (DR-TB) include GeneXpert XDR and targeted Next Generation Sequencing (tNGS). However, data on their implementation in high TB-burden settings is limited. We aimed to determine cost-effectiveness of different strategies using GeneXpert XDR or tNGS for DR-TB detection in high TB-burden, low-resource settings.</p> Methods <p>A dynamic simulation model was calibrated to WHO-reported TB data for Philippines and Thailand. Intervention scenarios for expanded diagnostic testing of drug-resistance were simulated for 2025 – 2035. Health benefits were estimated using disability-adjusted life years. Cost-effectiveness was calculated from a health system perspective using country-level TB diagnosis and treatment costs. Analyses include incremental cost-effectiveness ratios (ICERs) and incremental net monetary benefit (INMB).</p> Results <p>Implementing GeneXpert XDR or tNGS for DR-TB detection improves TB health outcomes. Scenarios using GeneXpert XDR are more likely to be cost-effective than scenarios using tNGS. Interventions targeting previously treated cases reduce costs but also reduce health benefits. Testing all TB cases with GeneXpert XDR is cost-effective (Philippines ICER = $1,808, INMB = $210M; Thailand ICER = $5,251, INMB = $26M) with a 1 x GDP willingness-to-pay threshold (WTP). Targeting GeneXpert XDR to previously treated cases is also cost-effective (Philippines ICER = $1,288, INMB = $52M; Thailand ICER = $3,667, INMB = $9.2M) but results in lower INMB. tNGS is cost-effective at higher WTP.</p> Interpretation <p>In high TB-burden countries, GeneXpert XDR is cost-effective as an additional DR-TB diagnostic test. tNGS is not cost-effective for routine clinical DR-TB testing but has potential for application to high-risk populations, especially with introduction of new TB treatment regimens.</p>

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Expanding diagnostic testing for drug-resistant tuberculosis in high burden settings: a cost-effectiveness analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Marya Getchell,
  • John Pastor Ansah,
  • Dodge Lim,
  • Ramon Basilio,
  • Francis Tablizo,
  • Carmen Corpuz,
  • David Matchar

摘要

Background

New and effective tools for detecting drug-resistant tuberculosis (DR-TB) include GeneXpert XDR and targeted Next Generation Sequencing (tNGS). However, data on their implementation in high TB-burden settings is limited. We aimed to determine cost-effectiveness of different strategies using GeneXpert XDR or tNGS for DR-TB detection in high TB-burden, low-resource settings.

Methods

A dynamic simulation model was calibrated to WHO-reported TB data for Philippines and Thailand. Intervention scenarios for expanded diagnostic testing of drug-resistance were simulated for 2025 – 2035. Health benefits were estimated using disability-adjusted life years. Cost-effectiveness was calculated from a health system perspective using country-level TB diagnosis and treatment costs. Analyses include incremental cost-effectiveness ratios (ICERs) and incremental net monetary benefit (INMB).

Results

Implementing GeneXpert XDR or tNGS for DR-TB detection improves TB health outcomes. Scenarios using GeneXpert XDR are more likely to be cost-effective than scenarios using tNGS. Interventions targeting previously treated cases reduce costs but also reduce health benefits. Testing all TB cases with GeneXpert XDR is cost-effective (Philippines ICER = $1,808, INMB = $210M; Thailand ICER = $5,251, INMB = $26M) with a 1 x GDP willingness-to-pay threshold (WTP). Targeting GeneXpert XDR to previously treated cases is also cost-effective (Philippines ICER = $1,288, INMB = $52M; Thailand ICER = $3,667, INMB = $9.2M) but results in lower INMB. tNGS is cost-effective at higher WTP.

Interpretation

In high TB-burden countries, GeneXpert XDR is cost-effective as an additional DR-TB diagnostic test. tNGS is not cost-effective for routine clinical DR-TB testing but has potential for application to high-risk populations, especially with introduction of new TB treatment regimens.