Trade-offs between cost and accuracy in active case finding for tuberculosis: A dynamic modelling analysis

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 2 hypothetical approaches following initial symptom screening: (i) ‘moderate accuracy’ testing employing a microscopy-like test (i.e., 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 US$20 million in a slum population of 2 million, high-accuracy testing would avert 1.14 (95% credible interval 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 high-accuracy testing are that (i) its ability to detect rifampicin resistance can lead to long-term cost savings in second-line treatment and (ii) its higher sensitivity contributes to the overall cases averted by ACF. Amongst the 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, relating to 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.

In-home TB Testing Using GeneXpert Edge is Acceptable, Feasible, and Improves the Proportion of Symptomatic Household Contacts Tested for TB: A Proof-of-Concept Study

“…A critical barrier addressed by our study is the challenge of motivating symptomatic household contacts to present to a health facility for evaluation and TB testing, a significant bottleneck of traditional contact investigations that conduct in-home symptom screening and referral for clinic-based TB services [ 19–21 , 38 ]. Previous household contact investigations have reported successful referral rates between 12% and 26% [ 17 , 19 , 20 ]. This initial barrier to clinic presentation can be further compounded by centralization of TB testing at referral laboratories, which requires individuals to return to the clinic to get results, and in some cases, a third time to initiate treatment.…”

“…Unfortunately, both studies continued to rely on passive presentation to these community-based testing sites. To improve case detection, targeted ACF and decentralized molecular testing must be implemented and scaled up [ 16 , 17 ].…”

Cost-effectiveness analysis of tuberculosis detection strategy based on close contact cohort

“…The economic burden under disease status is derived from the "economic burden survey of tuberculosis patients in ve counties and districts of Xinjiang," which is included in the model after discount calculation to the baseline year of the study. Cost of Pre treatment overhead 683.4 (0 ~ 415.9) [20] Lost workday expenses 1253.5 (51 ~ 853.6) [20] Other costs per case of average screening 19.07 (15.2 ~ 22.9) [21] Health outcome…”

A new combination testing methodology to identify accurate and economical point-of-care testing strategies

“…We compare this method to existing benchmarks using data from a national seroprevalence study. [13] Our methodology complements prior literature on the utility of cheap and less accurate tests [22,23]and simple combination testing, which assumes classification heuristics such as any, all or majority. [19][20][21]…”