An assessment of nucleic acid amplification testing for active mycobacterial infection



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Modelled economic evaluation


The structure of the economic model has been adapted from the cost–utility analyses identified in the literature search (Choi et al. 2013; Hughes et al. 2012) to suit the local context. As clinical management in Australia differs depending on the clinical suspicion of TB, the model will be separated into patients with:

  • a high clinical suspicion of TB (where treatment is initiated based on clinical suspicion)

  • a low clinical suspicion of TB (where treatment decisions are initiated or delayed based on AFB ± NAAT results).

The benefit of NAAT in patients with high clinical suspicion of TB is to identify resistance mutations and initiate appropriate treatment for MDR earlier. In addition to earlier MDR treatment initiation, patients with low clinical suspicion of TB have additional benefits: NAAT may differentiate between TB and NTM infections (who would have been previously treated on the basis of the AFB results alone), and may reduce the delay in treating those with true TB who returned a negative AFB result (who would not have been treated without the availability of NAAT).

The model will take the form of a cost–utility analysis as this enables an assessment of NAAT in the context of the proposed benefits described above, in addition to quantifying the cost and outcome implications of false-positive and false-negative results (for the AFB microscopy ± NAAT alternatives).

A time horizon of 20 months was chosen, although this is longer than previously published cost–utility analyses, to capture all costs and outcomes associated with treatment for all patients, as treatment beyond 1 year is standard in patients with MDR-TB.

A summary of the structure of the economic model is presented in Table 43.

Table 43 Summary of the economic evaluation

Time horizon

20 months

Outcomes

QALYs

Costs

Australian dollars, 2014 prices

Methods used to generate results

Decision tree analysis

Discount rate

5% costs and outcomes accrued beyond 1 year

Software packages used

Microsoft Excel

QALY = quality-adjusted life-year

The structure of the decision tree is presented in Figure 33 (AFB model arm) and Figure 34 (AFB plus NAAT model arm).

Currently, patients with true TB are likely to be mixed across the populations that have a high or low clinical suspicion of TB. The prevalence of TB in each of these patient groups is likely to vary, as it would be expected that those with a high clinical suspicion of TB would have a higher prevalence than those with a low clinical suspicion of TB. These assumptions are used to inform the base-case scenario (‘TB mixed scenario’). However, given the influence of clinical judgement on the treatment management pathways, and the uncertainties associated with estimating the relative mix of patients across these groups (see ‘Prevalence of TB’), the influence of clinical judgment on the cost-effectiveness of NAAT will be explored through the addition of the following scenarios:


  • TB low suspicion scenario: all patients (including all with true TB) are treated as though they have a low clinical suspicion of TB (i.e. clinical judgment is not used as a basis to initiate treatment)

  • Perfect clinical judgment scenario: all patients with true TB are treated as though they have a high clinical suspicion of TB (i.e. clinical judgement is used as a basis to initiate treatment, and it is assumed that this has 100% sensitivity and specificity in identifying TB), and all patients without TB are treated as though they have a low clinical suspicion of TB (i.e. treatment initiation decisions are based on results of AFB ± NAAT)

  • TB high suspicion scenario: all patients are treated as though they have a high clinical suspicion of TB (i.e. treatment is initiated in all patients on the basis of clinical judgment).

These additional scenarios are considered to be extreme cases. NAAT is expected to be most cost-effective in the TB low scenario, as it is associated with more benefits in those considered to have a low clinical suspicion of TB. In contrast, NAAT is also expected to be least cost-effective in the scenarios in which all patients with TB, and with or without TB, respectively, are managed as though they have a high clinical suspicion of TB. In these scenarios treatment initiation decisions are based on clinical judgement, with the benefit of NAAT restricted to identifying drug resistance to initiate an appropriate treatment earlier. In the perfect clinical judgement scenario all true TB-negative patients are treated as though they have a low clinical suspicion of TB, and so treatment decisions are based on the results of AFB ± NAAT and only false-positive patients will receive treatment (determined by the specificity of testing). The relative cost-effectiveness of NAAT between these extreme high and low scenarios is likely to be determined by the relative specificity of NAAT compared with AFB.

The cost-effectiveness of the TB mixed scenario, which is thought best to reflect current practice, is likely to lie between the extreme additional scenarios.


Model assumptions

  • When AFB and NAAT are discordant, the treatment decision is based on NAAT (consistent with PASC protocol)

  • C&S testing (the reference standard) is assumed to be 100% sensitive and specific, as all patients have C&S testing and at the end of 2 months all will have correct diagnosis (i.e. MDR-TB, TB or no TB)

  • To simplify the model structure, rifampicin resistance is used as a surrogate marker of MDR-TB (Lumb 2000), as the majority (37/40) of Australian bacteriologically confirmed cases in 2010 with rifampicin resistance were also MDR (Lumb et al. 2013)

  • Once the decision to initiate or delay treatment has been made, the model assumes there will be no change in treatment until the results of C&S are available; this assumption may favour NAAT, as the earlier initiation of resistant drugs in the comparator arm would reduce the benefit of introducing NAAT

  • Cost and utility penalties associated with the secondary transmission of TB are applied for each index case in the model, but the consequences (cost or health outcome) of further ongoing transmissions (e.g. tertiary transmissions and beyond) are not included in the base-case.

Decision analytic structure of the economic evaluation, comparator (AFB) model arm

Figure 33 Decision analytic structure of the economic evaluation, comparator (AFB) model arm



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