Evidence map›Paper›PMID 40934257›Full record

ArticlePLOS global public health2025

A broader lens on tuberculosis cost-effectiveness analysis: How patient-incurred costs and post-tuberculosis outcomes reshape estimates in a multi-country study.

Ewan M Tomeny, Phuong Bich Tran, Joseph Kazibwe, Laura Rosu, Georgios F Nikolaidis, Rebecca Nightingale, Tom Wingfield, Jamilah Meghji, S Bertel Squire, Eve Worrall

Abstract read
In one paragraph

Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

10 authors.

Ewan M TomenyCentre for Tuberculosis Research, Departments of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.ORCID https://orcid.org/0000-0003-4547-2389
Phuong Bich TranNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
Joseph KazibweDepartment of Clinical Sciences, Lund University, Malmö, Sweden.
Laura RosuCentre for Tuberculosis Research, Departments of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Georgios F NikolaidisMethods and Evidence Generation Department, Centre of Excellence in Evidence Synthesis, IQVIA Ltd, London, United Kingdom.ORCID https://orcid.org/0000-0001-9008-6896
Rebecca NightingaleCentre for Tuberculosis Research, Departments of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Tom WingfieldCentre for Tuberculosis Research, Departments of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Jamilah MeghjiNational Heart and Lung Institute, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-4693-8884
S Bertel SquireCentre for Tuberculosis Research, Departments of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
Eve WorrallCentre for Tuberculosis Research, Departments of Clinical Sciences and International Public Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) remains a major public health challenge, with financial and health impacts extending beyond treatment. Both the perspective adopted in cost-effectiveness analyses-which critically determines choices such as the inclusion of patient-incurred costs-and the extent to which long-term post-treatment considerations are incorporated have important policy implications. This study examines how the choice of timeframe and cost perspectives influence the estimated cost-effectiveness of TB interventions, particularly preventative measures. Using data from 19 WHO TB patient cost surveys and global epidemiological databases, we modelled a hypothetical preventative TB intervention, generating four incremental cost-effectiveness ratios (ICERs) per country under different analytical approaches. These included a conventional timeframe up to treatment completion, an extended timeframe incorporating post-TB effects, and two perspectives that either included or excluded patient-incurred costs. The approach yielding the lowest ICER (societal perspective; extended timeframe) was anchored in the primary analysis to a 1×GDP per capita threshold. Using this benchmark simplified cross-country comparisons and removed the need for health system cost estimates. Sensitivity and scenario analyses explored how threshold values influenced the relative impact of timeframe and costing perspective. ICERs were higher when patient costs were omitted or the post-TB period was excluded, peaking when both were absent. However, across all countries, post-TB considerations had a far greater impact on cost-effectiveness. On average, removing the post-TB period increased ICERs by over 50% (ranging from +19.3% in Ghana to +108% in Mongolia, societal perspective). Including patient-incurred costs increased the likelihood that prevention was cost-effective, particularly in low-GDP settings with lower willingness-to-pay thresholds. However, their impact was minimal above 2×GDP. Our study highlights how narrowly defining the financial and health burden of tuberculosis in cost-effectiveness analyses risks underestimating the benefits of interventions-particularly in lower-GDP countries where the socioeconomic burden of tuberculosis is greatest-which could lead to misguided policy decisions that overlook the full impact of tuberculosis.

Identifiers

PMID40934257
PMCPMC12425205

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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.