Ivor Langley

Do high rates of empirical treatment undermine the potential effect of new diagnostic tests for tuberculosis in high-burden settings?

In tuberculosis-endemic settings, patients are often treated empirically, meaning that they are placed on treatment based on clinical symptoms or tests that do not provide a microbiological diagnosis (eg, chest radiography). New tests for tuberculosis, such as the Xpert MTB/RIF assay (Cepheid, Sunnyvale, CA, USA), are being implemented at substantial cost. To inform policy and rationally drive implementation, data are needed for how these tests affect morbidity, mortality, transmission, and population-level tuberculosis burden. If people diagnosed by use of new diagnostics would have received empirical treatment a few days later anyway, then the incremental benefit might be small. Will new diagnostics substantially improve outcomes and disease burden, or simply displace empirical treatment? Will the extent and accuracy of empirical treatment change with the introduction of a new test? In this Personal View, we review emerging data for how empirical treatment is frequently same-day, and might still be the predominant form of treatment in high-burden settings, even after Xpert implementation; and how Xpert might displace so-called true-positive, rather than false-positive, empirical treatment. We suggest types of studies needed to accurately assess the effect of new tuberculosis tests and the role of empirical treatment in real-world settings. Until such questions can be addressed, and empirical treatment is appropriately characterised, we postulate that the estimated population-level effect of new tests such as Xpert might be substantially overestimated.

A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools.

Efforts to stimulate technological innovation in the diagnosis of tuberculosis (TB) have resulted in the recent introduction of several novel diagnostic tools. As these products come to market, policy makers must make difficult decisions about which of the available tools to implement. This choice should depend not only on the test characteristics (e.g., sensitivity and specificity) of the tools, but also on how they will be used within the existing health care infrastructure. Accordingly, policy makers choosing between diagnostic strategies must decide: 1) What is the best combination of tools to select? 2)Who should be tested with the new tools? and 3)Will these tools complement or replace existing diagnostics? The best choice of diagnostic strategy will likely vary between settings with different epidemiology (e.g., levels of TB incidence, human immunodeficiency virus co-infection and drug-resistant TB) and structural and resource constraints (e.g., existing diagnostic pathways, human resources and laboratory capacity). We propose a joint modelling framework that includes a tuberculosis (TB) transmission component (a dynamic epidemiological model) and a health system component (an operational systems model) to support diagnostic strategy decisions. This modelling approach captures the complex feedback loops in this system: new diagnostic strategies alter the demands on and performance of health systems that impact TB transmission dynamics which, in turn, result in further changes to demands on the health system. We demonstrate the use of a simplified model to support the rational choice of a diagnostic strategy based on health systems requirements, patient outcomes and population-level TB impact.Efforts to stimulate technological innovation in the diagnosis of tuberculosis (TB) have resulted in the recent introduction of several novel diagnostic tools. As these products come to market, policy makers must make difficult decisions about which of the available tools to implement. This choice should depend not only on the test characteristics (e.g., sensitivity and specificity) of the tools, but also on how they will be used within the existing health care infrastructure. Accordingly, policy makers choosing between diagnostic strategies must decide: 1) What is the best combination of tools to select? 2)Who should be tested with the new tools? and 3)Will these tools complement or replace existing diagnostics? The best choice of diagnostic strategy will likely vary between settings with different epidemiology (e.g., levels of TB incidence, human immunodeficiency virus co-infection and drug-resistant TB) and structural and resource constraints (e.g., existing diagnostic pathways, human resources and laboratory capacity). We propose a joint modelling framework that includes a tuberculosis (TB) transmission component (a dynamic epidemiological model) and a health system component (an operational systems model) to support diagnostic strategy decisions. This modelling approach captures the complex feedback loops in this system: new diagnostic strategies alter the demands on and performance of health systems that impact TB transmission dynamics which, in turn, result in further changes to demands on the health system. We demonstrate the use of a simplified model to support the rational choice of a diagnostic strategy based on health systems requirements, patient outcomes and population-level TB impact.

Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach

BACKGROUND Several promising new diagnostic methods and algorithms for tuberculosis have been endorsed by WHO. National tuberculosis programmes now face the decision on which methods to implement and where to place them in the diagnostic algorithm. METHODS We used an integrated model to assess the effects of different algorithms of Xpert MTB/RIF and light-emitting diode (LED) fluorescence microscopy in Tanzania. To understand the effects of new diagnostics from the patient, health system, and population perspective, the model incorporated and linked a detailed operational component and a transmission component. The model was designed to represent the operational and epidemiological context of Tanzania and was used to compare the effects and cost-effectiveness of different diagnostic options. FINDINGS Among the diagnostic options considered, we identified three strategies as cost effective in Tanzania. Full scale-up of Xpert would have the greatest population-level effect with the highest incremental cost: 346 000 disability-adjusted life-years (DALYs) averted with an additional cost of US

Making innovations accessible to the poor through implementation research

36·9 million over 10 years. The incremental cost-effectiveness ratio (ICER) of Xpert scale-up (

Modelling the impacts of new diagnostic tools for tuberculosis in developing countries to enhance policy decisions.

169 per DALY averted, 95% credible interval [CrI] 104-265) is below the willingness-to-pay threshold (

Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling.

599) for Tanzania. Same-day LED fluorescence microscopy is the next most effective strategy with an ICER of

Infectious disease and health systems modelling for local decision making to control neglected tropical diseases

45 (95% CrI 25-74), followed by LED fluorescence microscopy with an ICER of

Developments in Impact Assessment of New Diagnostic Algorithms for Tuberculosis Control

29 (6-59). Compared with same-day LED fluorescence microscopy and Xpert full rollout, targeted use of Xpert in presumptive tuberculosis cases with HIV infection, either as an initial diagnostic test or as a follow-on test to microscopy, would produce DALY gains at a higher incremental cost and therefore is dominated in the context of Tanzania. INTERPRETATION For Tanzania, this integrated modelling approach predicts that full rollout of Xpert is a cost-effective option for tuberculosis diagnosis and has the potential to substantially reduce the national tuberculosis burden. It also estimates the substantial level of funding that will need to be mobilised to translate this into clinical practice. This approach could be adapted and replicated in other developing countries to inform rational health policy formulation.

Cost-effectiveness of Xpert MTB/RIF and investing in health care in Africa.

Within countries, poorer populations have greater health needs and less access to good medical care than better-off populations. This is particularly true for tuberculosis (TB), the archetypal disease of poverty. Innovations also tend to become available to better-off populations well before they become available to those who need them the most. In a new era of innovations for TB diagnosis and treatment, it is increasingly important not only to be sure that these innovations can work in terms of accuracy and efficacy, but also that they will work, especially for the poor. We argue that after an innovation or a group of innovations has been endorsed, based on demonstrated accuracy and/or efficacy, introduction into routine practice should proceed through implementation by research. Cluster-randomised pragmatic trials are suited to this approach, and permit the prospective collection of evidence needed for full impact assessment according to a previously published framework. The novel approach of linking transmission modelling with operational modelling provides a methodology for expanding and enhancing the range of evidence, and can be used alongside evidence from pragmatic implementation trials. This evidence from routine practice should then be used to ensure that innovations in TB control are used for positive action for all, and particularly the poor.

Operational modelling to guide implementation and scale-up of diagnostic tests within the health system: exploring opportunities for parasitic disease diagnostics based on example application for tuberculosis

The introduction and scale-up of new tools for the diagnosis of Tuberculosis (TB) in developing countries has the potential to make a huge difference to the lives of millions of people living in poverty. To achieve this, policy makers need the information to make the right decisions about which new tools to implement and where in the diagnostic algorithm to apply them most effectively. These decisions are difficult as the new tools are often expensive to implement and use, and the health system and patient impacts uncertain, particularly in developing countries where there is a high burden of TB. The authors demonstrate that a discrete event simulation model could play a significant part in improving and informing these decisions. The feasibility of linking the discrete event simulation to a dynamic epidemiology model is also explored in order to take account of longer term impacts on the incidence of TB. Results from two diagnostic districts in Tanzania are used to illustrate how the approach could be used to improve decisions.