Use of mathematical modelling to assess the impact of vaccines on antibiotic resistance. Issue 6 (June 2018)

• Record Type: Journal Article
• Authors: Atkins, Katherine E; Lafferty, Erin I; Deeny, Sarah R; Davies, Nicholas G; Robotham, Julie V; Jit, Mark
• Is Part Of: Lancet infectious diseases. Volume 18:Issue 6(2018)
• Page Start: e204
• Page End: e213
• Publication Date: 2018-06
• Subjects: Communicable diseases -- Periodicals; Infection -- Periodicals; Communicable Diseases -- Periodicals; Infection -- Periodicals; Maladies infectieuses -- Périodiques; Infection -- Périodiques; Communicable diseases; Infection; Periodicals; 616.905
• DOI: 10.1016/S1473-3099(17)30478-4
• Languages: English
• ISSNs: 1473-3099

Abstract

Summary: Antibiotic resistance is a major global threat to the provision of safe and effective health care. To control antibiotic resistance, vaccines have been proposed as an essential intervention, complementing improvements in diagnostic testing, antibiotic stewardship, and drug pipelines. The decision to introduce or amend vaccination programmes is routinely based on mathematical modelling. However, few mathematical models address the impact of vaccination on antibiotic resistance. We reviewed the literature using PubMed to identify all studies that used an original mathematical model to quantify the impact of a vaccine on antibiotic resistance transmission within a human population. We reviewed the models from the resulting studies in the context of a new framework to elucidate the pathways through which vaccination might impact antibiotic resistance. We identified eight mathematical modelling studies; the state of the literature highlighted important gaps in our understanding. Notably, studies are limited in the range of pathways represented, their geographical scope, and the vaccine–pathogen combinations assessed. Furthermore, to translate model predictions into public health decision making, more work is needed to understand how model structure and parameterisation affects model predictions and how to embed these predictions within economic frameworks.