Doctoral Degrees (Paediatrics and Child Health)

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Browsing Doctoral Degrees (Paediatrics and Child Health) by Author "Dunbar, Rory"

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    How can virtual implementation modelling inform the scale-up of new molecular diagnostic tools for tuberculosis?
    (Stellenbosch : Stellenbosch University, 2018-03) Dunbar, Rory; Beyers, Nulda; Langey, Ivor; Naidoo, Pren; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Paediatrics and Child Health.
    ENGLISH ABSTRACT: The aim of this dissertation was to develop an operational model to explain why the expected increase in the number of tuberculosis (TB) cases detected was not found in our 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 TB diagnostic test, Xpert MTB/RIF (Xpert). I then used the model to model the effect of interventions 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 the effect 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 cases detected. I developed and validated an operational model using a discrete event simulation approach for the detection of TB and RMP-R TB in a smear/culture-based algorithm and an Xpert-based algorithm using data from published articles as well as from the step-wedge analysis of the Xpert-based TB diagnostic algorithm in Cape Town (PROVE IT). The model was adapted to incorporate a more sensitive molecular diagnostic test as a replacement test for Xpert in the Xpert-based algorithm. All comparisons between algorithms were conducted with identical population characteristics 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 operational model, using identical population characteristics and adherence to diagnostic algorithms (adherence to algorithms as observed from the analysis of routine data in the empirical study), showed that there were more TB cases detected in the Xpertbased algorithm than in the smear/culture-based algorithm (an increase of 13.3%) (Chapter 2). The model indicated that a decrease in background TB prevalence and the extensive use of culture testing for smear-negative HIV-positive TB cases during the smear/culture-based algorithm contributed to not finding an increase in the number of TB cases detected in the empirical study. When adherence to the diagnostic algorithms was modelled to be 100%, the model indicated a 95.4% increase in the number of RMP-R TB cases detected in the Xpertbased algorithm compared to the smear/culture-based algorithm, while the empirical study showed only a 54% increase (Chapter 3). This difference is attributable to the differences in drug susceptibility test (DST) screening strategy between algorithms as well 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 all presumptive TB cases screened for RMP-R with the Xpert-based algorithm. The empirical study found that the proportion of TB cases with DST undertaken pretreatment increased 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 smearbased algorithm (with 100% adherence to algorithms), the cost per TB case detected would increase by 114% with only a 5.5% increase in the number of TB cases detected (Chapter 3). Even though the model indicated a small increase in the number 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 cost per RMP-R TB case detected (Chapter 3). The model indicated that the best approach to improve the laboratory cost per TB case detected, would be a combined approach of increasing the TB prevalence among presumptive cases tested by using either a triage test or other pre-screening strategies, and a reduction in the price of Xpert cartridges (Chapter 4). With an increase in TB prevalence among presumptive cases tested to between 25.9% – 30.8% and the price of the Xpert cartridge reduced by 50%, the cost per TB case detected would range 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 a replacement for Xpert MTB/RIF (Chapter 5), the number of TB cases detected would increase by 3.4% and RMP-R TB cases detected by 3.5%. The number of falsepositive TB cases detected with Ultra would however increase by 166.6%. We could not model the cost per TB case and cost per RMP-R TB case diagnosed with Ultra, as the price is not available yet. Ultra has small benefits over that of Xpert for both the number of TB and RMP-R TB cases detected and therefore the cost of introducing Ultra would be an important consideration in the decision to implement Ultra. The introduction of Ultra poses potential health system and patient related challenges due to the high number of false-positive TB cases detected. Alternative strategies, such as alternative diagnostic algorithms, will have to be considered to find a balance between increased detection of TB cases and unnecessarily starting patients on TB treatment due to false positive results. The strengths of the model used in this dissertation are that the model was developed and validated using detailed routine data and information collected with the empirical study on health and laboratory processes in a large number of clinics. The model made a direct comparison between the algorithms taking into account differences in population characteristics and adherence to algorithms. Generalisability of findings from the model and the use of the model for other settings may be limited as the model was validated against data from a well-resourced, urban setting, with good health and laboratory 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 role that an operation model can play in informing decision makers on the optimal use of a new 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 health department 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.