How can virtual implementation modelling inform the scale-up of new molecular diagnostic tools for tuberculosis?

[摘要] ENGLISH ABSTRACT: The aim of this dissertation was to develop an operational model to explain why theexpected increase in the number of tuberculosis (TB) cases detected was not found inour empirical study, Policy Relevant Outcomes from Validating Evidence on ImpacT(PROVE IT), done in 142 health clinics in Cape Town after the roll-out of a new TBdiagnostic test, Xpert MTB/RIF (Xpert). I then used the model to model the effect ofinterventions to improve the detection of TB and rifampicin resistant (RMP-R) TB.Strategies were modelled to reduce laboratory cost for detecting TB as well as theeffect of introducing a more sensitive molecular diagnostic test, Xpert MTB/RIF Ultra(Ultra), as a replacement for Xpert on the number of TB and RMP-R TB casesdetected.I developed and validated an operational model using a discrete event simulationapproach for the detection of TB and RMP-R TB in a smear/culture-based algorithmand an Xpert-based algorithm using data from published articles as well as from thestep-wedge analysis of the Xpert-based TB diagnostic algorithm in Cape Town(PROVE IT). The model was adapted to incorporate a more sensitive moleculardiagnostic test as a replacement test for Xpert in the Xpert-based algorithm. Allcomparisons between algorithms were conducted with identical populationcharacteristics and adherence to diagnostic algorithms.The empirical study found no increase in the number of TB cases detected (20.9%smear/culture-based and 17.7% with the Xpert-based algorithm) while the operationalmodel, using identical population characteristics and adherence to diagnosticalgorithms (adherence to algorithms as observed from the analysis of routine data inthe empirical study), showed that there were more TB cases detected in the Xpertbasedalgorithm than in the smear/culture-based algorithm (an increase of 13.3%)(Chapter 2). The model indicated that a decrease in background TB prevalence andthe extensive use of culture testing for smear-negative HIV-positive TB cases duringthe smear/culture-based algorithm contributed to not finding an increase in the numberof TB cases detected in the empirical study.When adherence to the diagnostic algorithms was modelled to be 100%, the modelindicated a 95.4% increase in the number of RMP-R TB cases detected in the Xpertbasedalgorithm compared to the smear/culture-based algorithm, while the empirical study showed only a 54% increase (Chapter 3). This difference is attributable to thedifferences in drug susceptibility test (DST) screening strategy between algorithms aswell as poor adherence to diagnostic algorithms. In the smear/culture-based algorithm,only high MDR-TB risk cases are screened for RMP-R pre-treatment compared to allpresumptive TB cases screened for RMP-R with the Xpert-based algorithm. Theempirical study found that the proportion of TB cases with DST undertaken pretreatmentincreased from 42.7% in the smear/culture-based algorithm to 78.9% in the Xpert-based algorithm.The model indicated that for the Xpert-based algorithm compared to the smearbasedalgorithm (with 100% adherence to algorithms), the cost per TB case detectedwould increase by 114% with only a 5.5% increase in the number of TB casesdetected (Chapter 3). Even though the model indicated a small increase in thenumber of TB cases detected, the real benefit of the Xpert-based algorithm is the 95.4% increase in RMP-R TB cases detected with only a 15.8% increase in the costper RMP-R TB case detected (Chapter 3).The model indicated that the best approach to improve the laboratory cost per TB casedetected, would be a combined approach of increasing the TB prevalence amongpresumptive cases tested by using either a triage test or other pre-screeningstrategies, and a reduction in the price of Xpert cartridges (Chapter 4). With an increasein TB prevalence among presumptive cases tested to between 25.9% – 30.8% and theprice of the Xpert cartridge reduced by 50%, the cost per TB case detected wouldrange from US$50 to US$59, a level that is comparable with the cost per TB case detected in the smear/culture-based algorithm (US$48.77) found in the empirical laboratory costing study.Finally, when modelling the use of the not-yet released Xpert MTB/RIF Ultra as areplacement for Xpert MTB/RIF (Chapter 5), the number of TB cases detected wouldincrease by 3.4% and RMP-R TB cases detected by 3.5%. The number of falsepositiveTB cases detected with Ultra would however increase by 166.6%. We couldnot model the cost per TB case and cost per RMP-R TB case diagnosed with Ultra, asthe price is not available yet. Ultra has small benefits over that of Xpert for both thenumber of TB and RMP-R TB cases detected and therefore the cost of introducingUltra would be an important consideration in the decision to implement Ultra. Theintroduction of Ultra poses potential health system and patient related challenges due to the high number of false-positive TB cases detected. Alternative strategies, such asalternative diagnostic algorithms, will have to be considered to find a balance betweenincreased detection of TB cases and unnecessarily starting patients on TB treatmentdue to false positive results.The strengths of the model used in this dissertation are that the model was developedand validated using detailed routine data and information collected with the empiricalstudy on health and laboratory processes in a large number of clinics. The model madea direct comparison between the algorithms taking into account differences inpopulation characteristics and adherence to algorithms. Generalisability of findingsfrom the model and the use of the model for other settings may be limited as the modelwas validated against data from a well-resourced, urban setting, with good health andlaboratory infrastructure and therefore may not reflect reality in other settings, such rural areas.The findings from the studies presented in this dissertation highlight the important rolethat an operation model can play in informing decision makers on the optimal use of anew diagnostic test in an operational setting, even after the rollout of the new test.Operational modelling can therefore be an effective tool to be used to assist the healthdepartment to optimise the way in which tests are currently used and could serve to inform decision makers about the implementation of new, more sensitive, diagnostic tests.

[发布机构] Stellenbosch University