Evidence map›Paper›PMID 39666614›Full record

ArticlePLoS medicine2024

Estimating the health and macroeconomic burdens of tuberculosis in India, 2021-2040: A fully integrated modelling study.

Marcus R Keogh-Brown, Tom Sumner, Sedona Sweeney, Anna Vassall, Henning Tarp Jensen

Abstract read
In one paragraph

Article in PLoS medicine, 2024. 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

5 authors.

Marcus R Keogh-BrownDepartment of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-7556-0016
Tom SumnerTB Modelling Group, TB Centre, Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0001-9305-534X
Sedona SweeneyDepartment of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0003-4233-9080
Anna VassallGlobal Health Economics Centre at LSHTM, Department of Global Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Henning Tarp JensenDepartment of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0003-4526-458X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis (TB) imposes a substantial health and economic burden on many populations and countries, but lack of funding has significantly contributed to several countries falling short of global TB reduction targets. Furthermore, existing assessments of the economic impact of TB do not capture the impacts on productivity and economic growth or the pathways by which epidemiology, demography, and the economy interact. Evidence is needed to answer how investment in treatment and control measures may help to mitigate the twin Indian health and macroeconomic burdens of TB over the coming decades. METHODS AND

findingsWe develop a fully integrated dynamic macroeconomic-health-demographic simulation model for India, the country with the largest national TB burden, and use it to estimate the macroeconomic return to investment in TB treatment. Our estimated results indicate that, over 2021 to 2040, the health and macroeconomic burdens of TB in India will include over 62.4m incident cases, 8.1m TB-related deaths and a cumulative gross domestic product (GDP) loss of US$146.4bn. Low-income households will bear larger health and relative economic burdens while larger absolute economic burdens will fall on high-income households. Achieving the World Health Organisation's End TB target of 90% case detection could reduce clinical and demographic disease burdens by 75% to 89% and reduce the macroeconomic burden by US$120.2bn. Developing a 95% effective pan-TB treatment regimen would reduce the same burdens by 25% to 31% and US$35.3bn, respectively, while less effective but immediately achievable scaling-up of existing treatment regimens would reduce burdens by 20% to 25% and US$28.4bn, respectively. If an increase in case detection to 90% could be combined with 95% effective pan-TB treatment, it could reduce clinical and demographic disease burdens by 78% to 91% and reduce the macroeconomic burden by US$124.2bn. In order to develop this complex integrated model framework, some aspects of the epidemiological model were simplified such that the model does not capture, for example, separate modelling of drug susceptible and multidrug-resistant (MDR) cases or separate public/private healthcare provision. However, future iterations of the model could address these limitations.

conclusionsIn this study, we find that even our least effective, but most accessible, revised TB treatment regimen has the potential to generate US$28bn in GDP gains. Clearly, the economic gains of increasing case detection rates and implementing improved TB treatment regimens hinges on both the feasibility and timeframe over which they can be achieved in practice. Nevertheless, the revised TB treatment regimen is readily accessible, and our results therefore demonstrate that there is room for undertaking substantial additional investment in control and treatment of TB in India, in order to reduce the suffering of TB patients while maintaining acceptable provision of resourcing elsewhere in the Indian economy.

Indexed as

Cost of IllnessTuberculosisGross Domestic ProductHumansIndiaModels, Economic

Identifiers

PMID39666614
PMCPMC11637336

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.