Optima TB: A tool to help optimally allocate tuberculosis spending

Abstract

Approximately 85% of tuberculosis (TB) related deaths occur in low- and middle-income countries where health resources are scarce. Effective priority setting is required to maximise the impact of limited budgets. The Optima TB tool has been developed to support analytical capacity and inform evidence-based priority setting processes for TB health benefits package design. This paper outlines the Optima TB framework and how it was applied in Belarus, an upper-middle income country in Eastern Europe with a relatively high burden of TB. Optima TB is a population-based disease transmission model, with programmatic cost functions and an optimisation algorithm. Modelled populations include age-differentiated general populations and higher-risk populations such as people living with HIV. Populations and prospective interventions are defined in consultation with local stakeholders. In partnership with the latter, demographic, epidemiological, programmatic, as well as cost and spending data for these populations and interventions are then collated. An optimisation analysis of TB spending was conducted in Belarus, using program objectives and constraints defined in collaboration with local stakeholders, which included experts, decision makers, funders and organisations involved in service delivery, support and technical assistance. These analyses show that it is possible to improve health impact by redistributing current TB spending in Belarus. Specifically, shifting funding from inpatient- to outpatient-focused care models, and from mass screening to active case finding strategies, could reduce TB prevalence and mortality by up to 45% and 50%, respectively, by 2035. In addition, an optimised allocation of TB spending could lead to a reduction in drug-resistant TB infections by 40% over this period. This would support progress towards national TB targets without additional financial resources. The case study in Belarus demonstrates how reallocations of spending across existing and new interventions could have a substantial impact on TB outcomes. This highlights the potential for Optima TB and similar modelling tools to support evidence-based priority setting.

Impacts of COVID-19-related service disruptions on TB incidence and deaths in Indonesia, Kyrgyzstan, Malawi, Mozambique, and Peru: Implications for national TB responses

“…Optima TB (http://ocds.co/tb/), a mathematical model of TB transmission and disease progression integrated with an economic and program analysis framework [18], was used to conduct these five country analyses. All analyses were led by the respective NTPs, with technical assistance in running the model, in quality assurance, and in interpreting results from the World Bank and the Burnet Institute.…”

Public health benefits of shifting from inpatient to outpatient TB care in Eastern Europe: optimising TB investments in Belarus, the Republic of Moldova, and Romania

“…Mathematical optimisation of TB spending was conducted using Optima TB, a dynamic population-based model of TB transmission fully described in (15) for three countries in Eastern Europe: Belarus, the Republic of Moldova, and Romania. All studies were conducted in collaboration with local stakeholders.…”

“…There is plausibly no clinical benefit of DR TB treatment delivery in hospital for the majority of cases (unless hospitalisation is necessary where clinically indicated for the minority of cases) compared with outpatient primary care. The motivation to transition from inpatient to outpatient DR TB care is to not only save costs for the health system and for patients and their families (including lost income due to hospital stays estimated at 60% of out-of-pocket expenses as reported in a 2014 review in low- and middle-income countries (15)), but also to reduce the risk of nosocomial transmission.…”

How to do (or not to do)… health resource allocations using constrained mathematical optimization

“…OptiDX, ArcGIS and AccessMod), actual decisions are often made by town planners or due to political reasons rather than with rigorous analysis. For the allocation of health-care budgets, our group developed the Optima suite of decision support tools, which includes web-based applications for optimizing budgets for HIV ( Stuart et al ., 2018; Kerr, et al ., 2015 ), tuberculosis ( Goscé et al ., 2021 ) and nutrition ( Pearson et al ., 2018 ).…”