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ORIGINAL ARTICLE
Year : 2021 | Volume
: 70
| Issue : 1 | Page : 6-11
Cost-effectiveness of active tuberculosis case finding using sputum Xpert MTB/RIF and acid-fast bacilli tests
Wararat Thatayu, Win Techakehakij
Department of Social Medicine, Lampang Hospital, Amphur Muang, Lampang, Thailand
Correspondence Address:
MD, PdD Win Techakehakij
Department of Social Medicine, Lampang Hospital, Amphur Muang, Lampang 52000
Thailand
Source of Support: None, Conflict of Interest: None
DOI: 10.4103/ejcdt.ejcdt_30_20
Background Active case finding (ACF) of tuberculosis (TB) has been recommended by the WHO for early detection of pulmonary TB. Nonetheless, there is little evidence about the cost-effectiveness of different ACF strategies.
Objective The aim was to assess the cost-effectiveness of ACF using sputum Xpert MTB/RIF and acid-fast bacilli tests.
Materials and methods Economic modeling was employed to assess the effectiveness and costs of three ACF strategies, based on applying Xpert MTB/RIF (‘Xpert only’), acid-fast bacilli (‘AFB only’), or both AFB and Xpert (‘AFB+Xpert’) in the screening protocols. Outcomes included TB case detection rate, lost to follow-up rate, costs, and incremental cost-effectiveness ratios of the strategies. Costs were estimated in US dollars from the societal perspective, with base year 2018.
Results The ‘AFB only’ strategy showed a low case detection rate with higher costs, in relation to other strategies. Compared with the ‘Xpert only’ strategy, a rising rate of lost to follow-up was observed from the ‘AFB+Xpert.’ The case detection rate in the ‘Xpert only’ strategy is approximately twice the rate compared with the ‘AFB+Xpert’ strategy. The costs per TB case detected of the ‘AFB+Xpert’ and the ‘Xpert only’ strategies were $17 778.33 and $8334.47, respectively. The incremental cost-effectiveness ratio of the ‘Xpert only’ strategy was $257.28 per case detected as compared with the ‘AFB+Xpert’ strategy.
Conclusion This study showed that the ‘AFB only’ strategy was dominated, in comparison with other strategies. It should not be recommended in the settings where application of Xpert MTB/RIF is feasible.
Keywords: active case finding, cost-effectiveness, tuberculosis, Xpert MTB/RIF
Tuberculosis (TB) has increasingly become a serious public health problem globally [1]. It was estimated that one-third of the patients worldwide have been underdiagnosed each year [2]. This is because more than half of the patients infected with pulmonary TB do not have any symptoms in the early stage of the disease [3]. This high number of undiagnosed cases not only causes poor treatment outcomes for patients and disease transmission, but the evidence has shown that at least half of the patients with TB who did not receive treatment died within five years [1]. During the past decade, active case finding (ACF) of TB has been proposed for early detection of the disease [3]. ACF is proven to be effective in finding undiagnosed TB among high-risk groups, in comparison with the passive screening strategy [4],[5],[6].
Concerning ACF strategies, WHO has launched screening algorithms for early diagnosis in an attempt to reduce the incidence and mortality rates of TB [3]. As for the screening recommendations, a chest radiography is proposed as the initial screening, followed by either a sputum-smear microscopy, for example, acid-fast bacilli (AFB) test, or a molecular test, for example, Xpert MTB/RIF. Selection of these algorithms depends on the prevalence of TB and the availability of health care resources [3].
Adhering to the WHO recommendation, employing either AFB or Xpert MTB/RIF tests for ACF screening depends upon the availability of resources at the site of interest. Although the Xpert MTB/RIF test is superior to the AFB test in terms of diagnostic accuracy [1], an issue has been raised to whether a strategy to combine AFB and Xpert MTB/RIF tests could increase the screening effectiveness, compared with using either the AFB or Xpert MTB/RIF alone. Studies that compared the diagnostic accuracy of sputum-smear AFB microscopy and Xpert MTB/RIF for TB diagnosis revealed that all positive cases detected by AFB can be identified by Xpert MTB/RIF, whereas Xpert MTB/RIF can also detect some TB-positive cases from those with negative AFB results [7],[8]. This evidence points out that applying the Xpert MTB/RIF test after the AFB test in the ACF strategy could increase TB case detection, especially in the setting where access to the Xpert MTB/RIF test remains a problem.
Despite the promising benefit of integrating both AFB and Xpert MTB/RIF tests into ACF, drawbacks pertaining to the possible reduction of TB case detection owing to the additional step of the screening processes remain. Approximately 80% of the suspected cases were lost to follow-up during each additional step of the screening process [9]. Furthermore, owing to the TB prevalence and a rather low sensitivity of the AFB test, a negative result from sputum-smear test would be expected from most screening participants [3]. This implies that most of the cases will also need to undertake the Xpert MTB/RIF test [9], in which efficiency would eventually become an issue.
These uncertainties raise the question about effectiveness and efficiency if both AFB and Xpert MTB/RIF tests are included in the ACF guideline, compared with using either AFB test or Xpert MTB/RIF alone. However, there is very little evidence about the cost-effectiveness of different ACF strategies [10]. This study aims to estimate the cost-effectiveness of the ACF in patients with suspected pulmonary TB, using the AFB and Xpert MTB/RIF tests, compared with employing either AFB or Xpert MTB/RIF test.
Materials and methodsEpidemiologic data and transitional probabilities
Epidemiologic data and transitional probabilities used in this study came from a previous ACF study of 11 021 high-risk TB population, including elderly, migrant workers, patients with HIV, cancer, chronic obstructive pulmonary disease, diabetes mellitus, and late-staged kidney disease, at Lampang Hospital, Thailand, from October 2017 to April 2018 [9]. The ACF strategy consists of a chest radiography, followed by the sputum AFB test for those with TB suspected results from radiography. Although participants with the positive AFB result were diagnosed as TB, further TB screening with sputum Xpert MTB/RIF test was applied to their negative AFB counterparts. The TB suspected cases with both negative AFB and negative Xpert MTB/RIF results were then sent to consult a medical specialist for further investigation, if appropriate, and making a final decision for TB diagnosis. This research was approved by the Ethics Committee at Lampang Hospital.
Economic modeling for TB screening strategies
To assess the cost-effectiveness of ACF, the economic modeling was employed to compare 3 ACF strategies:
Strategy 1: the ‘AFB+Xpert’ strategy, which is the conventional ACF strategy used at Lampang hospital as described previously.Strategy 2: the ‘AFB only’ strategy, which is similar to the strategy 1 except for the omission of sputum Xpert MTB/RIF test.Strategy 3: the ‘Xpert only’ strategy, which is similar to the strategy 1 except for the omission of sputum AFB test.The summary of 3 ACF strategies is illustrated in [Figure 1].
Estimation of costs and outcome
This study estimated the costs from the societal and health care provider perspectives in US dollars (1 US$=30 Baht). Pertaining to health care provider perspective, direct medical costs of ACF strategies were estimated from the standard cost lists for health technology assessment [11] and the previous research [9]. The direct non-medical costs, such as costs of food, travel, and the productivity loss, were approximated from existing literature [12]. All costs were adjusted to the base year of 2018 using the consumer price indices [13].
Health outcome is the rate of pulmonary TB cases diagnosed from each strategy, which were estimated using the economic modeling. As the duration of the ACF process took approximately a few months, no discounting was applied in the analysis.
Economic evaluation and sensitivity analyses
An economic modeling was constructed to compare the effectiveness and costs of 3 ACF strategies. Incremental cost-effectiveness ratios (ICERs) were calculated by incremental costs divided by incremental TB cases diagnosed. Monte Carlo simulation of 1000 replications was employed to examine the 95% confidence intervals of costs and ICERs. One-way sensitivity analysis was demonstrated using tornado diagrams.
ResultsTransitional probabilities and the values of costs applied in this study are shown in [Table 1].
Concerning the effectiveness of 3 ACF strategies, the number of TB detected cases from the ‘Xpert only’ strategy is approximately twice as many as that found from the ‘AFB+Xpert’ and ‘AFB only’ strategies. Regarding this, a high proportion of lost to follow-up cases, in relation to other ACF strategies, was observed. Moreover, the costs per TB case detected in ‘Xpert only’ strategy are also cheaper than ‘AFB+Xpert’ and ‘AFB only’ strategy, as seen in [Table 2].
[Table 3] exhibits the incremental costs and outcomes of the ‘Xpert only’ and ‘AFB only’ strategies, in comparison with the ‘AFB+Xpert’ scenario. In comparison with the ‘AFB+Xpert’ strategy, TB case detection increases when the ‘Xpert only’ strategy is applied, whereas fewer TB cases are diagnosed in the ‘AFB only’ scenario. The ICERs of the ‘Xpert only’ strategy were ICER $234.27 and $257.28 from the health care provider and societal perspectives, respectively. Demonstration of the one-way sensitivity analyses using Tornado diagrams from the health care provider perspective was as illustrated in [Figure 2]. It is seen that the costs of the Xpert MTB/RIF test hugely affect the change in ICERs.
Table 3 Incremental cost-effectiveness ratios of the ‘Xpert only’ and ‘AFB only’ strategies compared with the ‘AFB+Xpert’ scenario
Figure 2 Tornado diagram from the health care provider and societal perspectives. Discussion To the author’s knowledge, this is the first study that compares the cost-effectiveness of different ACF strategies in the high-prevalence setting. Results from this study revealed that, among the 3 ACF strategies, the ‘AFB only’ strategy yields the lowest number of TB case detection. Moreover, the total costs of screening per case and the average costs per TB case detection of the ‘AFB only’ strategy are higher than that of other strategies. As effectiveness and efficiency of screening are of crucial concern, employing the ‘AFB only’ strategy may not be advised in the setting where access to other screening strategies is feasible.
Evidence of this study clearly demonstrates that using the ‘Xpert only’ strategy has surpassed the ‘AFB+Xpert’ strategy in terms of the TB case detection rate and efficiency. Concerning effectiveness, the case detection rate of the ‘Xpert only’ strategy is more than twice the rate compared with the ‘AFB+Xpert’ strategy. An explanation of the higher effectiveness of the ‘Xpert only’ is owing to the additional step of screening process, which results in the increased number of lost to follow-up cases [9] and thus reduces the screening effectiveness.
Furthermore, although the total costs of screening per case of the ‘Xpert only’ strategy were slightly higher than that of the ‘AFB+Xpert,’ the costs per case detection were far much lower. From the societal perspective, although the average costs of $17 778.33 were expected from the ‘AFB+Xpert’ strategy to find each TB case, only $8334.47 were required to obtain the same outcome in the ‘Xpert only’ scenario. Additionally, considering the setting where the ‘AFB+Xpert’ strategy is the routine ACF, results demonstrated that the additional costs of only $0.27 per case, from replacing the ‘AFB+Xpert’ with ‘Xpert only’ strategy, allows more than a twofold increase in the number of TB case detection. These results point out that the ‘Xpert only’ strategy may be considered the first option for ACF strategy where possible.
Even though there is a clear advantage of the ‘Xpert only’ strategy, issues pertaining to the total costs of screening, availability of the investigation, and increased workload still need to be addressed for consideration before the implementation of such a strategy. Unlike the sputum AFB test, performing the Xpert MTB/RIF requires advanced technology, which still remains challenging because of the high costs and technology scarcity.
Another issue worth noting is regarding the increased workload of specialists in the screening process. It is seen from the results that the TB suspected cases that required specialist consultation have increased in the ‘Xpert only’ strategy, compared with the ‘AFB+Xpert.’ This is a consequence of the reduction in the lost to follow-up rate during the screening process. Regardless of effectiveness and efficiency of the screening, evaluation of the capacity of specialist consultations, together with further investigation, for example, bronchoscopy, is essential to assess the feasibility of the work system before selecting the appropriate ACF strategy.
As illustrated in the sensitivity analysis, costs of the Xpert MTB/RIF are immensely related to the ICER. Reduction of the Xpert MTB/RIF cost could make the ‘Xpert only’ strategy even more cost-effective. Re-evaluation of the cost-effectiveness and budget impact analysis, using the cost information in the setting of interest, is thus suggested to obtain the efficiency information that fits the context.
There are several limitations in this study. First, this study employed the rate of TB case detection as an outcome in the efficiency analysis of TB screening, which is still considered the intermediate outcome. Although it gives an understanding of the comparative ACF strategies to some extent, consequences of the screening, that is, subsequent costs and health outcomes of the detected and undetected cases as a result of TB transmission and delay diagnosis, were not yet included in the analysis. This leaves the knowledge gap for future research to explore.
Second, this study did not include the benefit of Xpert MTB/RIF for detecting multidrug-resistant TB (MDR-TB), which helps clinicians about selecting treatment regimens. This advantage is important particularly in the high-prevalence area of MDR-TB. Further study, integrating this benefit of Xpert MTB/RIF in the analysis, is suggested to gain a better understanding of screening effectiveness.
Acknowledgements
The authors thank Dr Napat Phetkub for comments on the manuscript.
Concept, design, literature search, data acquisition, data analysis, statistical analysis, manuscript preparation, manuscript editing and manuscript review was contributed by all authors.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References
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