Optima TB: A tool to help optimally allocate tuberculosis spending.

Goscé, L; Abou Jaoude, GJ; Kedziora, DJ; Benedikt, C; Hussain, A; Jarvis, S; Skrahina, A; Klimuk, D; Hurevich, H; Zhao, F; Fraser-Hurt, N; Cheikh, N; Gorgens, M; Wilson, DJ; Abeysuriya, R; Martin-Hughes, R; Kelly, SL; Roberts, A; Stuart, RM; Palmer, T; Panovska-Griffiths, J; Kerr, CC; Wilson, DP; Haghparast-Bidgoli, H; Skordis, J; Abubakar, I

Optima TB: A tool to help optimally allocate tuberculosis spending.

Goscé, L Abou Jaoude, GJ Kedziora, DJ Benedikt, C Hussain, A Jarvis, S Skrahina, A Klimuk, D Hurevich, H Zhao, F Fraser-Hurt, N Cheikh, N Gorgens, M Wilson, DJ Abeysuriya, R Martin-Hughes, R Kelly, SL Roberts, A Stuart, RM Palmer, T Panovska-Griffiths, J Kerr, CC Wilson, DP Haghparast-Bidgoli, H Skordis, J Abubakar, I

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Publisher:: PUBLIC LIBRARY SCIENCE
Publication Type:: Journal Article
Citation:: PLoS Comput Biol, 2021, 17, (9), pp. e1009255
Issue Date:: 2021-09

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Field	Value	Language
dc.contributor.author	Goscé, L
dc.contributor.author	Abou Jaoude, GJ
dc.contributor.author	Kedziora, DJ
dc.contributor.author	Benedikt, C
dc.contributor.author	Hussain, A
dc.contributor.author	Jarvis, S
dc.contributor.author	Skrahina, A
dc.contributor.author	Klimuk, D
dc.contributor.author	Hurevich, H
dc.contributor.author	Zhao, F
dc.contributor.author	Fraser-Hurt, N
dc.contributor.author	Cheikh, N
dc.contributor.author	Gorgens, M
dc.contributor.author	Wilson, DJ
dc.contributor.author	Abeysuriya, R
dc.contributor.author	Martin-Hughes, R
dc.contributor.author	Kelly, SL
dc.contributor.author	Roberts, A
dc.contributor.author	Stuart, RM
dc.contributor.author	Palmer, T
dc.contributor.author	Panovska-Griffiths, J
dc.contributor.author	Kerr, CC
dc.contributor.author	Wilson, DP
dc.contributor.author	Haghparast-Bidgoli, H
dc.contributor.author	Skordis, J
dc.contributor.author	Abubakar, I
dc.date.accessioned	2022-05-19T03:39:05Z
dc.date.available	2021-07-07
dc.date.available	2022-05-19T03:39:05Z
dc.date.issued	2021-09
dc.identifier.citation	PLoS Comput Biol, 2021, 17, (9), pp. e1009255
dc.identifier.issn	1553-734X
dc.identifier.issn	1553-7358
dc.identifier.uri	http://hdl.handle.net/10453/157523
dc.description.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.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	PUBLIC LIBRARY SCIENCE
dc.relation.ispartof	PLoS Comput Biol
dc.relation.isbasedon	10.1371/journal.pcbi.1009255
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Adolescent
dc.subject.mesh	Adult
dc.subject.mesh	Aged
dc.subject.mesh	Aged, 80 and over
dc.subject.mesh	Algorithms
dc.subject.mesh	Child
dc.subject.mesh	Child, Preschool
dc.subject.mesh	Computational Biology
dc.subject.mesh	Cost-Benefit Analysis
dc.subject.mesh	Female
dc.subject.mesh	Health Care Costs
dc.subject.mesh	Humans
dc.subject.mesh	Infant
dc.subject.mesh	Infant, Newborn
dc.subject.mesh	Male
dc.subject.mesh	Middle Aged
dc.subject.mesh	Models, Biological
dc.subject.mesh	Models, Economic
dc.subject.mesh	Prevalence
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Republic of Belarus
dc.subject.mesh	Resource Allocation
dc.subject.mesh	Software
dc.subject.mesh	Tuberculosis
dc.subject.mesh	Young Adult
dc.subject.mesh	Humans
dc.subject.mesh	Tuberculosis
dc.subject.mesh	Prevalence
dc.subject.mesh	Models, Economic
dc.subject.mesh	Prospective Studies
dc.subject.mesh	Computational Biology
dc.subject.mesh	Algorithms
dc.subject.mesh	Models, Biological
dc.subject.mesh	Resource Allocation
dc.subject.mesh	Software
dc.subject.mesh	Adolescent
dc.subject.mesh	Adult
dc.subject.mesh	Aged
dc.subject.mesh	Aged, 80 and over
dc.subject.mesh	Middle Aged
dc.subject.mesh	Child
dc.subject.mesh	Child, Preschool
dc.subject.mesh	Infant
dc.subject.mesh	Infant, Newborn
dc.subject.mesh	Cost-Benefit Analysis
dc.subject.mesh	Health Care Costs
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Young Adult
dc.subject.mesh	Republic of Belarus
dc.title	Optima TB: A tool to help optimally allocate tuberculosis spending.
dc.type	Journal Article
utslib.citation.volume	17
utslib.location.activity	United States
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access	*
dc.date.updated	2022-05-19T03:39:01Z
pubs.issue	9
pubs.publication-status	Published online
pubs.volume	17
utslib.citation.issue	9

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.

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http://hdl.handle.net/10453/157523