Is neglect of self-clearance biasing TB vaccine impact estimates?
Mathematical modelling
Tuberculosis
Vaccines
Journal
BMJ global health
ISSN: 2059-7908
Titre abrégé: BMJ Glob Health
Pays: England
ID NLM: 101685275
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
11
05
2023
accepted:
13
07
2023
medline:
11
8
2023
pubmed:
10
8
2023
entrez:
9
8
2023
Statut:
ppublish
Résumé
Mathematical modelling has been used extensively to estimate the potential impact of new tuberculosis vaccines, with the majority of existing models assuming that individuals with For both countries, we calibrated a tuberculosis model to a scenario without self-clearance and to various scenarios with self-clearance. To account for the current uncertainty in self-clearance properties, we varied the rate of self-clearance, and the level of protection against reinfection in self-cleared individuals. We introduced potential new vaccines in 2025, exploring vaccines that work in uninfected or infected individuals only, or that are effective regardless of infection status, and modelling scenarios with different levels of vaccine efficacy in self-cleared individuals. We then estimated the relative disease incidence reduction in 2050 for each vaccine compared with the no vaccination scenario. The inclusion of self-clearance increased the estimated relative reductions in incidence in 2050 for vaccines effective only in uninfected individuals, by a maximum of 12% in China and 8% in India. The inclusion of self-clearance increased the estimated impact of vaccines only effective in infected individuals in some scenarios and decreased it in others, by a maximum of 14% in China and 15% in India. As would be expected, the inclusion of self-clearance had minimal impact on estimated reductions in incidence for vaccines that work regardless of infection status. Our work suggests that the neglect of self-clearance in mathematical models of tuberculosis vaccines does not result in substantially biased estimates of tuberculosis vaccine impact. It may, however, mean that we are slightly underestimating the relative advantages of vaccines that work in uninfected individuals only compared with those that work in infected individuals.
Sections du résumé
BACKGROUND
Mathematical modelling has been used extensively to estimate the potential impact of new tuberculosis vaccines, with the majority of existing models assuming that individuals with
METHODS
For both countries, we calibrated a tuberculosis model to a scenario without self-clearance and to various scenarios with self-clearance. To account for the current uncertainty in self-clearance properties, we varied the rate of self-clearance, and the level of protection against reinfection in self-cleared individuals. We introduced potential new vaccines in 2025, exploring vaccines that work in uninfected or infected individuals only, or that are effective regardless of infection status, and modelling scenarios with different levels of vaccine efficacy in self-cleared individuals. We then estimated the relative disease incidence reduction in 2050 for each vaccine compared with the no vaccination scenario.
FINDINGS
The inclusion of self-clearance increased the estimated relative reductions in incidence in 2050 for vaccines effective only in uninfected individuals, by a maximum of 12% in China and 8% in India. The inclusion of self-clearance increased the estimated impact of vaccines only effective in infected individuals in some scenarios and decreased it in others, by a maximum of 14% in China and 15% in India. As would be expected, the inclusion of self-clearance had minimal impact on estimated reductions in incidence for vaccines that work regardless of infection status.
INTERPRETATIONS
Our work suggests that the neglect of self-clearance in mathematical models of tuberculosis vaccines does not result in substantially biased estimates of tuberculosis vaccine impact. It may, however, mean that we are slightly underestimating the relative advantages of vaccines that work in uninfected individuals only compared with those that work in infected individuals.
Identifiants
pubmed: 37558271
pii: bmjgh-2023-012799
doi: 10.1136/bmjgh-2023-012799
pmc: PMC10414120
pii:
doi:
Substances chimiques
Tuberculosis Vaccines
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Bill & Melinda Gates Foundation
ID : INV-001754
Pays : United States
Organisme : Bill & Melinda Gates Foundation
ID : INV-035506
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI147321
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
Références
Clin Infect Dis. 2012 Mar;54(6):784-91
pubmed: 22267721
Sci Transl Med. 2020 Oct 7;12(564):
pubmed: 33028708
BMJ. 2019 Oct 24;367:l5770
pubmed: 31649096
J Intern Med. 2020 Dec;288(6):661-681
pubmed: 33128834
N Engl J Med. 2019 Dec 19;381(25):2429-2439
pubmed: 31661198
N Engl J Med. 2018 Jul 12;379(2):138-149
pubmed: 29996082
Lancet Glob Health. 2019 Feb;7(2):e209-e218
pubmed: 30630775
Lancet. 2014 Jun 14;383(9934):2057-2064
pubmed: 24650955
Lancet Glob Health. 2023 Apr;11(4):e546-e555
pubmed: 36925175
Emerg Infect Dis. 2004 Sep;10(9):1529-35
pubmed: 15498152
Proc Natl Acad Sci U S A. 2014 Oct 28;111(43):15520-5
pubmed: 25288770
Hum Vaccin Immunother. 2016 Nov;12(11):2813-2832
pubmed: 27448625
Proc Biol Sci. 2021 Jan 27;288(1943):20201635
pubmed: 33467995
J Theor Biol. 2019 May 21;469:1-11
pubmed: 30851550
PLoS Med. 2023 Jan 24;20(1):e1004155
pubmed: 36693081
Lancet Glob Health. 2022 Sep;10(9):e1307-e1316
pubmed: 35961354
F1000Res. 2018 Nov 1;7:1732
pubmed: 30613395
Epidemics. 2023 Jun;43:100678
pubmed: 36913805