Trade-offs between cost and accuracy in active case-finding for tuberculosis: a dynamic modelling analysis
File(s)
Published version
Author(s)
Cilloni, Lucia
Arinaminpathy, Nimalan
Stagg, Helen
Kranzer, Katharina
Type
Journal Article
Abstract
Background
Active case-finding (ACF) may be valuable in tuberculosis (TB) control, but questions remain about its optimum implementation in different settings. For example, smear microscopy misses up to half of TB cases, yet is cheap, and detects the most infectious TB cases. What, then, is the incremental value of using more sensitive and specific, yet more costly, tests such as Xpert MTB/RIF, in ACF in a high burden setting?
Methods and Findings
We constructed a dynamic transmission model of TB, calibrated to be consistent with an urban slum population in India. We applied this model to compare the potential cost and impact of two hypothetical approaches, following initial symptom screening: (i) ‘moderate accuracy’ testing employing a microscopy-like test (that is, lower cost but also lower accuracy) for bacteriological confirmation and (ii) ‘high accuracy’ testing employing an Xpert-like test (higher-cost but also higher accuracy, while also detecting rifampicin resistance).
Results suggest that ACF using a moderate-accuracy test could in fact cost more overall than using a high-accuracy test. Under an illustrative budget of USD 20 million in a slum population of 2 million, high-accuracy testing would avert 1·14 (95% Bayesian credible intervals 0·75 – 1·99, with p = 0.28) cases relative to each case averted by moderate-accuracy testing. Test specificity is a key driver: high-accuracy testing would be significantly more impactful at the 5% significance level, as long as the high-accuracy test has specificity at least 3 percentage points greater than the moderate-accuracy test. Additional factors promoting the impact of a high-accuracy are that: its ability to detect rifampicin resistance can lead to long-term cost savings in second-line treatment; and its higher sensitivity contributes to the overall cases averted by ACF.
Amongst limitations of this study, our cost model has a narrow focus on the commodity costs of testing and treatment; our estimates should not be taken as indicative of the overall cost of ACF. There remains uncertainty about the true specificity of tests such as smear and Xpert-like tests in ACF, including variations in the accuracy of the reference standard under such conditions.
Conclusions
Our results suggest that cheaper diagnostics do not necessarily translate to less costly ACF, as any savings from the test cost can be strongly outweighed by factors including false-positive TB treatment, reduced sensitivity, and foregone savings in second-line treatment. In resource-limited settings, it is therefore important to take all of these factors into account, when designing cost-effective strategies for ACF.
Active case-finding (ACF) may be valuable in tuberculosis (TB) control, but questions remain about its optimum implementation in different settings. For example, smear microscopy misses up to half of TB cases, yet is cheap, and detects the most infectious TB cases. What, then, is the incremental value of using more sensitive and specific, yet more costly, tests such as Xpert MTB/RIF, in ACF in a high burden setting?
Methods and Findings
We constructed a dynamic transmission model of TB, calibrated to be consistent with an urban slum population in India. We applied this model to compare the potential cost and impact of two hypothetical approaches, following initial symptom screening: (i) ‘moderate accuracy’ testing employing a microscopy-like test (that is, lower cost but also lower accuracy) for bacteriological confirmation and (ii) ‘high accuracy’ testing employing an Xpert-like test (higher-cost but also higher accuracy, while also detecting rifampicin resistance).
Results suggest that ACF using a moderate-accuracy test could in fact cost more overall than using a high-accuracy test. Under an illustrative budget of USD 20 million in a slum population of 2 million, high-accuracy testing would avert 1·14 (95% Bayesian credible intervals 0·75 – 1·99, with p = 0.28) cases relative to each case averted by moderate-accuracy testing. Test specificity is a key driver: high-accuracy testing would be significantly more impactful at the 5% significance level, as long as the high-accuracy test has specificity at least 3 percentage points greater than the moderate-accuracy test. Additional factors promoting the impact of a high-accuracy are that: its ability to detect rifampicin resistance can lead to long-term cost savings in second-line treatment; and its higher sensitivity contributes to the overall cases averted by ACF.
Amongst limitations of this study, our cost model has a narrow focus on the commodity costs of testing and treatment; our estimates should not be taken as indicative of the overall cost of ACF. There remains uncertainty about the true specificity of tests such as smear and Xpert-like tests in ACF, including variations in the accuracy of the reference standard under such conditions.
Conclusions
Our results suggest that cheaper diagnostics do not necessarily translate to less costly ACF, as any savings from the test cost can be strongly outweighed by factors including false-positive TB treatment, reduced sensitivity, and foregone savings in second-line treatment. In resource-limited settings, it is therefore important to take all of these factors into account, when designing cost-effective strategies for ACF.
Date Issued
2020-12-02
Date Acceptance
2020-11-02
Citation
PLoS Medicine, 2020, 17 (12), pp.1-20
ISSN
1549-1277
Publisher
Public Library of Science (PLoS)
Start Page
1
End Page
20
Journal / Book Title
PLoS Medicine
Volume
17
Issue
12
Copyright Statement
© 2020 Cilloni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Sponsor
Medical Research Council
Identifier
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003456
Grant Number
1645544
Subjects
Tuberculosis
Publication Status
Published
Date Publish Online
2020-12-02