An assessment of nucleic acid amplification testing for active mycobacterial infection
Outputs from the economic evaluation
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- Outcome-state probabilities
- Incremental costs
- Incremental QALYs
- Incremental cost-effectiveness
Outputs from the economic evaluationThe results of the economic evaluation are presented for four scenarios: TB mixed scenario: patients with true TB are spread across treatment populations based on high or low clinical suspicion of TB (best reflective of current practice) TB low-suspicion scenario: all patients (including all with true TB) are treated as though they have a low clinical suspicion of TB—that is, clinical judgment is not used as a basis to initiate treatment but, rather, treatment decisions are based on AFB ± NAAT Perfect clinical judgment scenario: all patients with true TB are treated as per high clinical suspicion—that is, clinical judgement has 100% sensitivity and specificity in identifying TB; and all patients without TB are treated as per low clinical suspicion—that is, treatment decisions are based on AFB ± NAAT TB high-suspicion scenario: all patients are treated as though they have a high clinical suspicion of TB—that is, treatment is initiated in all patients on the basis of clinical judgment. The following summarises the results of the economic evaluation that will be presented: disaggregated by decision tree probabilities, incremental costs and incremental outcomes (TB mixed scenario only); the disaggregated results for additional scenarios are presented in Appendix I for the TB mixed scenario, the incremental cost-effectiveness is presented incorporating costs in a stepped manner (additional scenarios in Appendix I) incremental cost-effectiveness of each of the three additional scenarios sensitivity analyses. Outcome-state probabilitiesThe results of the decision tree analysis are presented in Figure 55 (AFB model arm) and Figure 56 (AFB plus NAAT model arm), Appendix I. The probability at each decision tree terminal is derived from a composite of the prevalence of TB in the tested population, clinical suspicion and test accuracy parameters. The difference between the model arms is the incorporation of NAAT accuracy parameters into the intervention arm. The proportion of patients managed per each outcome state, as derived from the decision tree analysis, is presented in Table 59. Table 59 Outcome state probabilities, TB mixed scenario
Note: The probabilities associated with the correct treatment are highlighted. AFB = acid-fast bacilli; MDR = multidrug-resistant; NAAT = nucleic acid amplification test; TB = tuberculosis The highlighted rows in Table 59 reflect the relevant optimal treatment strategy. NAAT is associated with improvements in each of these strategies, due to improved sensitivity of NAAT in conjunction with AFB (fewer false-negative results) and the ability of NAAT to identify and treat MDR-TB earlier. A net reduction in false-positive results is also observed, due to false-positive results associated with NAAT in AFB true-negative patients being outweighed by AFB false-positive patients correctly identified using NAAT.
Table 60 Breakdown of incremental costs, TB mixed scenario
AEs = adverse events; AFB = acid-fast bacilli; NAAT = nucleic acid amplification test; TB = tuberculosis Incremental QALYsThe incremental costs by outcome health state are derived from the product of the outcome-state probabilities (Table 59) and the overall utility, by outcome state as presented in Table 58. These are presented in Table 61. Overall NAAT is associated with a small incremental QALY gain (0.001), driven by a shift from initially untreated TB (or standard treatment in the case of MDR-TB) to correct treatment. In part the small difference results from the assumption that the correct diagnosis is achieved in all patients after 2 months, and that this 2-month delay in treatment is not assumed to affect patient mortality rates or the severity of disease. Whereas a utility penalty has been applied for the transmission of active TB, none has been applied for the transmission of latent TB. Table 61 Weighted utility by outcome state, TB mixed scenario
Note: The outcomes associated with the correct treatment are highlighted. AFB = acid-fast bacilli; MDR = multidrug-resistant; NAAT = nucleic acid amplification test; TB = tuberculosis Incremental cost-effectivenessThe effect on the ICER of adding each additional group of costs is presented in Table 62 for the TB mixed scenario. Consistent with Table 60, the addition of transmissions and hospitalisation costs contribute most to the final ICER. The addition of the utility penalty for transmissions decreases the ICER further. This effect is consistently observed in the additional scenarios (Table 113, Appendix I). Table 62 Stepped economic evaluation, TB mixed scenario
AEs = adverse events; ICER = incremental cost-effectiveness ratio The incremental cost-effectiveness of NAAT in each of the additional scenarios is presented in Table 63. Table 63 Incremental cost-effectiveness ratios for additional scenarios
AFB = acid-fast bacilli; ICER = incremental cost-effectiveness ratio; NAAT = nucleic acid amplification test NAAT is most cost-effective in the scenario in which all patients are managed as though they have a low clinical suspicion of TB. In this scenario, treatment decisions are driven by the results of AFB ± NAAT. The PASC protocol indicated that when AFB and NAAT results were discordant, treatment decisions would be based on NAAT, and so the introduction of NAAT in this scenario has the greatest ability to change patient management. NAAT is more sensitive than AFB, and this leads to improved management in patients with TB as there are fewer false-negative results. In patients without TB, change in patient management with NAAT occurs in two ways: in patients with an initial AFB (false)-positive result, NAAT may correctly identify no TB (i.e. NAAT may improve patient management), and decreases in the specificity of NAAT lead to the same treatment outcomes as if tested with AFB in patients with an initial AFB (true)-negative result, NAAT may falsely identify TB, and decreases in the specificity of NAAT lead to more false-positive results (i.e. NAAT leads to detrimental patient management). Given that the pooled estimate of AFB specificity is high (98%), false-positive results are likely to be driven by the specificity of NAAT in AFB-negative results. As the pooled estimate for this parameter and that in AFB-positive results in low-incidence countries are also both high (99% and 97%, respectively), the net effect is a reduction in the number of false-positive results. The cost-effectiveness of NAAT is least 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. As the prevalence of MDR-TB in those with TB is approximately 2%, the benefits of NAAT are accrued in a very small proportion of the population tested (0.44%). As NAAT is associated with a net reduction in the number of false-positive results, the perfect clinical suspicion scenario, in which treatment initiation in true TB-negative patients is based on the results of AFB ± NAAT, is more cost-effective than the TB high scenario, in which all patients receive treatment. Given that the TB mixed population is a combination of patients with high and low clinical suspicion of TB, it is unsurprising that the cost-effectiveness of NAAT lies between the respective estimates observed when all suspects are treated as though they have either a low or a high clinical suspicion of TB.
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