A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools [State of the art series. Operational research. Number 8 in the series]

Abstract

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.

The impact of new tuberculosis diagnostics on transmission: why context matters

“…A formal cost-effectiveness analysis that takes into account various aspects of impact and the associated costs of a new tool would be valuable to policy-makers. 9,36 In conclusion, our model-based quantitative analysis suggests that novel diagnostic tests can have substantial population-level impact on tuberculosis epidemics, but only in certain contexts or in combination with interventions geared towards promoting earlier health-care seeking after symptom onset to reduce the length of the infectious period. It is therefore critical to consider contextual factors such as the sensitivity of existing tests, the degree of access to tuberculosis care and the average time that transpires before careseeking, in determining when and where novel diagnostics should be scaled up.…”

“…An impact assessment framework that comprehensively evaluates the impact of a new diagnostic at the patient level, the health system level and the population level will serve as a useful guiding tool for policy-makers. 35,36 Our epidemic model follows the standard epidemic model of tuberculosis transmission to evaluate the impact of new diagnostics on disease transmission. Unlike most previous epidemic models, ours includes the patient diagnostic pathway and the operational components of the diagnostic process.…”

Modeling the patient and health system impacts of alternative xpert® MTB/RIF algorithms for the diagnosis of pulmonary tuberculosis in Addis Ababa, Ethiopia

“…It would be expensive, time-consuming and disruptive to the health system to try and answer all these questions through experimentation in the live environment. Mathematical modeling has the potential of addressing the above questions to help guide policy makers in the most optimal and cost-effective use of new diagnostic tools [11, 12]. Modeling can project the effect of rolling out the new algorithm on the number of patients diagnosed, treated and cured.…”

Diagnostic accuracy of three technologies for the diagnosis of multi-drug resistant tuberculosis

“…Se deben tener en cuenta, asimismo, los datos epidemiológicos existentes, el modelo de provisión de servicios de salud, el recurso humano disponible y la infraestructura de los servicios de atención (72). La existencia de estudios hechos en Colombia es un buen antecedente para precisar el alcance diagnóstico de estas pruebas y orientar su uso en nuestro medio (50).…”