Evidence map›Paper›PMID 36925175›Full record

ArticleThe Lancet. Global health2023

The impact of alternative delivery strategies for novel tuberculosis vaccines in low-income and middle-income countries: a modelling study.

Rebecca A Clark, Christinah Mukandavire, Allison Portnoy, Chathika K Weerasuriya, Arminder Deol, Danny Scarponi, Andrew Iskauskas, Roel Bakker, Matthew Quaife, Shelly Malhotra and 8 more

Open access · goldFull text read
In one paragraph

Article in The Lancet. Global health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
15.4field-weighted citation impact, top 1% of its field
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

34 citing papers in PubMed, 56 citations in OpenAlex.

  1. Trial
  2. Article
  3. A global analysis of patterns of tuberculosis exposure and transmission.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  8. Review
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  14. Review
  15. Tuberculosis: An Update for the Clinician.Respirology (Carlton, Vic.) · 2025
    Review
  16. Article
  17. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 6 institutions in 4 countries.

Rebecca A ClarkTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Vaccine Centre, London School of Hygiene & Tropical Medicine, London, UK. Electronic address: rebecca.clark@lshtm.ac.uk.
Christinah MukandavireTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Allison PortnoyCenter for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA.
Chathika K WeerasuriyaTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Arminder DeolTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Danny ScarponiTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Andrew IskauskasDepartment of Mathematical Sciences, Durham University, Durham, UK.
Roel BakkerTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; KNCV Tuberculosis Foundation, The Hague, Netherlands.
Matthew QuaifeTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Shelly MalhotraGlobal Access, IAVI, New York, NY, USA.
Nebiat GebreselassieGlobal TB Programme, World Health Organization, Geneva, Switzerland.
Matteo ZignolGlobal TB Programme, World Health Organization, Geneva, Switzerland.
Raymond C W HutubessyDepartment of Immunization, Vaccines, and Biologicals, World Health Organization, Geneva, Switzerland.
Birgitte GiersingThe Initiative for Vaccine Research, World Health Organization, Geneva, Switzerland.
Mark JitCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Rebecca C HarrisTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Global Medical Evidence Generation for Influenza Vaccines, Sanofi Pasteur, Singapore.
Nicolas A MenziesCenter for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA.
Richard G WhiteTB Modelling Group and TB Centre, London School of Hygiene & Tropical Medicine, London, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
London School of Hygiene & Tropical Medicine · GBWorld Health Organization · CHDurham University · GBHarvard Global Health Institute · USInternational AIDS Vaccine Initiative · USTuBerculosis Vaccine Initiative · NL

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundTuberculosis is a leading infectious cause of death worldwide. Novel vaccines will be required to reach global targets and reverse setbacks resulting from the COVID-19 pandemic. We estimated the impact of novel tuberculosis vaccines in low-income and middle-income countries (LMICs) in several delivery scenarios.

methodsWe calibrated a tuberculosis model to 105 LMICs (accounting for 93% of global incidence). Vaccine scenarios were implemented as the base-case (routine vaccination of those aged 9 years and one-off vaccination for those aged 10 years and older, with country-specific introduction between 2028 and 2047, and 5-year scale-up to target coverage); accelerated scale-up similar to the base-case, but with all countries introducing vaccines in 2025, with instant scale-up; and routine-only (similar to the base-case, but including routine vaccination only). Vaccines were assumed to protect against disease for 10 years, with 50% efficacy.

findingsThe base-case scenario would prevent 44·0 million (95% uncertainty range 37·2-51·6) tuberculosis cases and 5·0 million (4·6-5·4) tuberculosis deaths before 2050, compared with equivalent estimates of cases and deaths that would be predicted to occur before 2050 with no new vaccine introduction (the baseline scenario). The accelerated scale-up scenario would prevent 65·5 million (55·6-76·0) cases and 7·9 million (7·3-8·5) deaths before 2050, relative to baseline. The routine-only scenario would prevent 8·8 million (95% uncertainty range 7·6-10·1) cases and 1·1 million (0·9-1·2) deaths before 2050, relative to baseline.

interpretationOur results suggest novel tuberculosis vaccines could have substantial impact, which will vary depending on delivery strategy. Including a one-off vaccination campaign will be crucial for rapid impact. Accelerated introduction-at a pace similar to that seen for COVID-19 vaccines-would increase the number of lives saved before 2050 by around 60%. Investment is required to support vaccine development, manufacturing, prompt introduction, and scale-up.

fundingWHO (2020/985800-0). TRANSLATIONS: For the French, Spanish, Italian and Dutch translations of the abstract see Supplementary Materials section.

Indexed as

COVID-19TuberculosisTuberculosis VaccinesCOVID-19 VaccinesDeveloping CountriesHumansPandemicsCOVID-19 VaccinesTuberculosis Vaccines

Identifiers

PMID36925175
PMCPMC10030455
OpenAlexW4324131102

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read208
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.