Our analysis reconfirms that TB ACF can be a costly undertaking, depending on the target population and the diagnostic strategy used. Therefore, the prioritization of proper target populations and careful selection of cost-effective diagnostic strategies are critical prerequisites for launching any rational ACF activities.
There seem to be several important conditions under which ACF can be cost-effective and, in such circumstances, it can contribute to a significant increase in case detection. First, TB prevalence among a target population is a very important determinant of cost-effectiveness. The higher the TB prevalence among the target population, the more TB cases detected thus contributing to better cost-effectiveness.
Second, using the conventional DOTS approach, symptom screening followed by smear microscopy (Strategy 1) was found to be almost always cost-effective as the unit cost of tests is inexpensive. Although the yield is very low due to low sensitivity, the strategy is always an option for a population with different TB risks.
Third, in designing ACF activities, a critical decision to make is to determine whether all subjects should undergo X-ray screening. The cost per case detected for strategies 3, 4 and 5 was sensitive to the unit cost of X-ray which generally ranges between USD 2 and USD 6, in accordance with the country context. Therefore, it is important to carefully assess the cost-effectiveness of ACF by using accurate local cost information including human resources, as well as infrastructural and logistics expenses. This was part of the reason for providing an online tool for national stakeholders to examine the cost-effectiveness indicators according to their local settings. To be noted, digital X-ray technologies could substantially reduce the unit cost of X-ray (without printing the films), and substantially expand the potential of X-ray screening for ACF.
As with any model analysis, the current approach carries several limitations. The model assumes a number of parameters based on available information including the prevalence of test abnormalities, proportional yields and unit costs. Although we used the best available information for these assumptions, our source of information might be biased towards Asian settings due to the availability of such data. Including data from other parts of the world might be needed as a future development of the model. To address the uncertainly around the assumptions, we conducted sensitivity analysis of the model output. It is important to note that the model output was quite robust against the diagnostic yield assumptions, which support the general observation described above.
The cost information was limited to direct diagnostic costs that represent a part of the minimum necessary costs for ACF activities. In reality, other operational costs such as those involving human resources and logistics should also be considered in any project planning. In view of this, the model output should be used to check whether the proposed diagnostic strategy is worth further consideration or if its diagnostic cost alone is too prohibitive to consider further project planning.
Besides cost per case detected employed in our analysis, there would be other important cost-effectiveness and cost-utility indicators such as deaths averted, secondary cases prevented and disability-adjusted life years saved. All of these are important indicators in the context of TB ACF. For example, preventing secondary infection through early case-finding [20, 21] might be one of the major benefits of ACF in reducing the TB burden in a community. However, assessing these benefit indicators requires many additional assumptions for which information is scarce. Moreover, the evidence base is still insubstantial to support any epidemiological impact of ACF though, theoretically, it is expected to exist.
For the reasons stated above, it is justifiable to limit the scope to the simple but robust cost-effectiveness calculation. Our intention was to support national TB programmes to formulate various ACF initiatives rather than to model the epidemiological impact of ACF. Nevertheless, we believe the model and our online tool can contribute to the debate on conditions required for cost-effective ACF and the selection of diagnostic strategies for different target groups.
TB ACF is not a new intervention. It has been extensively used in many parts of the world, sometimes involving mass radiological screening . As our model shows, these ACF activities can be cost-effective only against the backdrop of high TB prevalence in the society or when targeting a TB high-risk population. This explains the fact that many industrialized countries discontinued mass population screening when the TB prevalence among the general population decreased. However, in countries with a high burden of TB and a well-established basic DOTS programme, there is a renewed interest in ACF as a complementary strategy to increase case detection .
Many studies documented several important benefits of ACF over routine passive case-finding. ACF can detect a substantial portion of undiagnosed TB patients much earlier than passive case-finding while their bacterial load is low [20–23]. Consequently, it also contributes to reducing transmission by shortening the duration of infectiousness [24, 25]. Moreover, ACF would potentially play an important role to address health inequities. ACF can specifically target and benefit vulnerable segments of the population such as the elderly, the poor, and the marginalized [15, 20, 22, 23].
Our online tool provides some essential information on TB ACF that would help national TB programme managers and partners make decisions on priority target populations and cost-effective diagnostic strategies. In the current culture of information technology, the concept of an interactive, online tool for context-specific decision-making is not novel. However, it is also true that public health programmes are not benefiting as much from the full potential of available technologies as the private sector. We aimed at providing a model of an interactive tool that contributes to the national and subnational levels of planning for public health activities. Real-time user experience and feedback will help us further improve the model which may, overtime, initiate an innovative way to build up and refine public health policy guidance.