Evidence map›Paper›PMID 40502558›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Optimizing TB policies using the global TB portfolio model: an economic analysis.

Srinath Satyanarayana, Sandip Mandal, Finn McQuaid, Sreenivas Nair, Suvanand Sahu, Nicolas A Menzies, Sedona Sweeney, Rachel Sanders, Ines Garcia Baena, Richard G White and 3 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Srinath SatyanarayanaCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.
Sandip MandalCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.
Finn McQuaidTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Sreenivas NairStop TB Partnership, Geneva, Switzerland.
Suvanand SahuStop TB Partnership, Geneva, Switzerland.
Nicolas A MenziesDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Sedona SweeneyDepartment of Global Health and Development at London School of Hygiene & Tropical Medicine, London, UK.
Rachel SandersCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.
Ines Garcia BaenaWorld Health Organization, Geneva, Switzerland.
Richard G WhiteTB Modelling Group, TB Centre, and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Taghreed AdamWorld Health Organization, Geneva, Switzerland.
Mikaela SmitThe Global Fund, Geneva, Switzerland.
Carel PretoriusCenter for Modeling and Analysis, Avenir Health, Glastonbury, CT, USA.

Funding

Defining drivers of TB transmission in the era of universal ART, and implications for finding the walking wellR01AI147321 · NIAID · LONDON SCH/HYGIENE & TROPICAL MEDICINE · PI GRANT, ALISON · 2019 to 2024
$2.3M
Gates Foundation INV-035506NIAID NIH HHS R01 AI147321Wellcome Trust
6 · The paper itself

Abstract

Rationale: Tuberculosis (TB) remains a global health crisis, disproportionately affecting low- and middle-income countries. Strategic resource allocation is essential to achieving the WHO End TB targets. Existing TB costing tools have limitations in conducting global analyses, prompting the development of a novel model tailored to address these gaps. Methods: We developed a new, open-source TB costing model that simulates detailed TB care cascades comprising steps of screening, diagnosis, treatment, and prevention for those eligible - according to WHO guidelines for 20 distinct population groups. These include 10 groups each from patient-initiated and provider-initiated pathways, capturing variations in pulmonary status, age, HIV/ART status, and drug sensitivity. The model captures the cost of a large-scale vaccine. We demonstrate the model's functionality through a case study that informed the Global Fund's Investment Case for its 8th replenishment (2027-2029). Results: In the case study, the model was first used to estimate the cost of implementing the TB Global Plan 2023 - 2030. This scenario incorporated intervention targets, normative standards of care, and the availability of new TB tools. An optimization routine applied to 29 high-burden countries estimated maximal TB impact under constrained funding scenarios. The results were also used to assess the potential impact and contribution of innovation within the Global Fund's 8th replenishment. Discussion: This new TB costing model offers improved representation of TB care complexity across diverse populations, with enhanced transparency, flexibility, and policy relevance. Its application in global TB strategy analysis highlights its value in informing investment cases and prioritizing interventions for maximal impact under resource constraints.

Identifiers

PMID40502558
PMCPMC12155012

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.