Optima TB: A tool to help optimally allocate tuberculosis spending

Lara Goscé, Gerard J. Abou Jaoude, David J. Kedziora, Clemens Benedikt, Azfar Hussain, Sarah Jarvis, Alena Skrahina, Dzmitry Klimuk, Henadz Hurevich, Feng Zhao, Nicole Fraser-Hurt, Nejma Cheikh, Marelize Gorgens, David J. Wilson, Romesh Abeysuriya, Rowan Martin-Hughes, Sherrie L. Kelly, Anna Roberts, Robyn M. Stuart, Tom PalmerJasmina Panovska-Griffiths, Cliff C. Kerr, David P. Wilson, Hassan Haghparast-Bidgoli, Jolene Skordis, Ibrahim Abubakar

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere1009255
JournalPLoS Computational Biology
Issue number9
Publication statusPublished - Sep 2021
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by the World Bank Group (www.worldbank.org). The Bank team provided input in study design, assisted with data collation efforts, in interpreting and disseminating results, and were involved in the decision to submit this paper for publication. We thank the following people and organisations for their contributions to the study: Irina Oleinik, Hanna Shvanok from the World Bank; Inna Nekrasova, Marina Sachek, Vassily Akulov from the Republican Scientific and Practice Centre for Medical Technologies (RSPC MT); Alena Tkatcheva from the Ministry of Public Health of the Republic of Belarus; Viatcheslav Grankov, Valentin Rusovich from the World Health Organisation Belarus Country Office; David Kokiashvili and George Sakvarelidze from The Global Fund to Fight AIDS, Tuberculosis and Malaria. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the funding agency.

Publisher Copyright:
© 2021 Goscé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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