Considering equity in priority setting using transmission models: Recommendations and data needs.

OBJECTIVES: Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. METHODS: We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. RESULTS: We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. CONCLUSIONS: Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies.

Budget impact of next-generation sequencing for diagnosis of TB drug resistance in Moldova.

BACKGROUND Diagnosing drug resistance is critical for choosing effective TB treatment regimens. Next-generation sequencing (NGS) represents an alternative approach to conventional phenotypic drug susceptibility testing (pDST) for diagnosing TB drug resistance.METHODS We undertook a budget impact analysis estimating the costs of introduction and routine use of NGS in the Moldovan National TB Programme. We conducted an empirical costing study and collated price and operating characteristics for NGS platforms. We examined multiple NGS scenarios in comparison to the current approach (pDST) for pre-treatment drug resistance testing over 2021-2025.RESULTS Annual testing volume ranged from 912 to 1,926 patients. For the pDST scenario, we estimated total costs of US$362,000 (2021 USD) over the 5-year study period. Total costs for NGS scenarios ranged from US$475,000 to US$1,486,000. Lowest cost NGS options involved targeted sequencing as a replacement for pDST, and excluded individuals diagnosed as RIF-susceptible on Xpert® MTB/RIF. For all NGS scenarios, the majority (55-80%) of costs were devoted to reagent kits. Start-up costs of NGS were small relative to routine costs borne each year.CONCLUSION NGS adoption will require expanded resources compared to conventional pDST. Further work is required to better understand the feasibility of NGS in settings such as Moldova.

An approach for improving the quality of country-level TB modelling.

Mathematical modelling is increasingly used to inform budgeting and strategic decision-making by national TB programmes. Despite the importance of these decisions, there is currently no mechanism to review and confirm the appropriateness of modelling analyses. We have developed a benchmarking, reporting, and review (BRR) approach and accompanying tools to allow constructive review of country-level TB modelling applications. This approach has been piloted in five modelling applications and the results of this study have been used to revise and finalise the approach. The BRR approach consists of 1) quantitative benchmarks against which model assumptions and results can be compared, 2) standardised reporting templates and review criteria, and 3) a multi-stage review process providing feedback to modellers during the application, as well as a summary evaluation after completion. During the pilot, use of the tools prompted important changes in the approaches taken to modelling. The pilot also identified issues beyond the scope of a review mechanism, such as a lack of empirical evidence and capacity constraints. This approach provides independent evaluation of the appropriateness of modelling decisions during the course of an application, allowing meaningful changes to be made before results are used to inform decision-making. The use of these tools can improve the quality and transparency of country-level TB modelling applications.

Determining the value of TB active case-finding: current evidence and methodological considerations.

Active case-finding (ACF) is an important component of the End TB Strategy. However, ACF is resource-intensive, and the economics of ACF are not well-understood. Data on the costs of ACF are limited, with little consistency in the units and methods used to estimate and report costs. Mathematical models to forecast the long-term effects of ACF require empirical measurements of the yield, timing and costs of case detection. Pragmatic trials offer an opportunity to assess the cost-effectiveness of ACF interventions within a 'real-world´ context. However, such analyses generally require early introduction of economic evaluations to enable prospective data collection on resource requirements. Closing the global case-detection gap will require substantial additional resources, including continued investment in innovative technologies. Research is essential to the optimal implementation, cost-effectiveness, and affordability of ACF in high-burden settings. To assess the value of ACF, we must prioritize the collection of high-quality data regarding costs and effectiveness, and link those data to analytical models that are adapted to local settings.

Improving the quality of modelling evidence used for tuberculosis policy evaluation.

Mathematical modelling is commonly used to evaluate policy options for tuberculosis (TB) control in high-burden countries. Although major policy and funding decisions are made based on these analyses, there is concern about the variability of results produced using modelled policy analyses. We discuss new guidance for country-level TB policy modelling. The guidance was developed by the TB Modelling and Analysis Consortium in collaboration with the World Health Organization Global TB Programme, with input from a range of TB stakeholders (funders, modelling groups, country TB programme staff and subject matter experts). The guidance describes principles for country-level TB modelling, as well as good practices for operationalising the principles. The principles cover technical concerns such as model design, parameterisation and validation, as well as approaches for incorporating modelling into country-led policy making and budgeting. For modellers, this guidance suggests approaches to improve the quality and relevance of modelling undertaken to support country-level planning. For non-modellers, this guidance describes considerations for engaging modelling technical assistance, contributing to a modelling exercise and reviewing the results of modelled analyses. If routinely adopted, this guidance should improve the reliability, transparency and usefulness of modelling for country-level TB policy making. However, this guidance will not address all challenges facing modelling, and ongoing work is needed to improve the empirical evidence base for TB policy evaluation and develop stronger mechanisms for validating models. Increasing country ownership of the modelling process remains a challenge, requiring sustained engagement and capacity building.

The impact of migration on tuberculosis in the United States.

Due to greater exposure to Mycobacterium tuberculosis infection before migration, migrants moving to low-incidence settings can experience substantially higher tuberculosis (TB) rates than the native-born population. This review describes the impact of migration on TB epidemiology in the United States, and how the TB burden differs between US-born and non-US-born populations. The United States has a long history of receiving migrants from other parts of the world, and TB among non-US-born individuals now represents the majority of new TB cases. Based on an analysis of TB cases among individuals from the top 30 countries of origin in terms of non-US-born TB burden between 2003 and 2015, we describe how TB risks vary within the non-US-born population according to age, years since entry, entry year, and country of origin. Variation along each of these dimensions is associated with more than 10-fold differences in the risk of developing active TB, and this risk is also positively associated with TB incidence estimates for the country of origin and the composition of the migrant pool in the entry year. Approximately 87 000 lifetime TB cases are predicted for the non-US-born population resident in the United States in 2015, and 5800 lifetime cases for the population entering the United States in 2015.

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

The landscape of diagnostic testing for tuberculosis (TB) is changing rapidly, and stakeholders need urgent guidance on how to develop, deploy and optimize TB diagnostics in a way that maximizes impact and makes best use of available resources. When decisions must be made with only incomplete or preliminary data available, modelling is a useful tool for providing such guidance. Following a meeting of modelers and other key stakeholders organized by the TB Modelling and Analysis Consortium, we propose a conceptual framework for positioning models of TB diagnostics. We use that framework to describe modelling priorities in four key areas: Xpert(®) MTB/RIF scale-up, target product profiles for novel assays, drug susceptibility testing to support new drug regimens, and the improvement of future TB diagnostic models. If we are to maximize the impact and cost-effectiveness of TB diagnostics, these modelling priorities should figure prominently as targets for future research.