The impact of influenza and pneumococcal vaccination on antibiotic use: an updated systematic review and meta-analysis

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Abstract

Background

Vaccination can prevent bacterial and viral infections that could otherwise increase the chances of receiving (unnecessary) antibiotic treatment(s). As a result, vaccination may provide an important public health intervention to control antimicrobial resistance (AMR).

Objectives

Perform a systematic literature review to better understand the impact of influenza, pneumococcal and COVID-19 vaccination on antibiotic use, and to identify differences in effect between world regions and study designs.

Methods

We performed a systematic literature review and meta-analysis which updated previous literature reviews with new data from 1 October 2018 to 1 December 2021. The study focuses on randomised controlled trials (RCTs) and observational studies. Results from the meta-analysis of RCTs were stratified by WHO region and age group. Vote counting based on the direction of effect was applied to synthesize the results of the observational studies.

Results

Most studies are performed in the WHO European Region and the Region of the Americas in high-income countries. RCTs show that the effect of influenza vaccination on the number of antibiotic prescriptions or days of antibiotic use (Ratio of Means (RoM) 0.71, 95% CI 0.62–0.83) is stronger compared to the effect of pneumococcal vaccination (RoM 0.92, 95% CI 0.85–1.00). These studies also confirm a reduction in the proportion of people receiving antibiotics after influenza vaccination (Risk Ratio (RR) 0.63, 95% CI 0.51–0.79). The effect of influenza vaccination in the European and American regions ranged from RoM 0.63 and 0.87 to RR 0.70 and 0.66, respectively. The evidence from observational studies supports these findings but presents a less consistent picture. No COVID-19 studies were identified.

Conclusion

We find that both RCTs and observational studies show that influenza vaccination significantly reduces antibiotic use, while the effect of pneumococcal vaccination is less pronounced. We were unable to study the effect of COVID-19 vaccination and no clear regional patterns were found due to the high heterogeneity between studies. Overall, our data supports the use of influenza vaccination as an important public health intervention to reduce antibiotic use and possibly control AMR.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-023-01272-6.

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The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Matthew J L Page, Joanne E McKenzie, Patrick Bossuyt … (2025)

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

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Rayyan—a web and mobile app for systematic reviews

Mourad Ouzzani, Hossam Hammady, Zbys Fedorowicz … (2017)

Background Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Results Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Conclusions Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

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Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

(2022)

Summary Background Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen–drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. Methods We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen–drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. Findings On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62–6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911–1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9–35·3), and lowest in Australasia, at 6·5 deaths (4·3–9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000–1 270 000) deaths attributable to AMR and 3·57 million (2·62–4·78) deaths associated with AMR in 2019. One pathogen–drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000–100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. Interpretation To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen–drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. Funding Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.

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Author and article information

Contributors

John Paget: J.Paget@nivel.nl

Journal

Journal ID (nlm-ta): Antimicrob Resist Infect Control

Journal ID (iso-abbrev): Antimicrob Resist Infect Control

Title: Antimicrobial Resistance and Infection Control

Publisher: BioMed Central (London )

ISSN (Electronic): 2047-2994

Publication date (Electronic): 14 July 2023

Publication date PMC-release: 14 July 2023

Publication date Collection: 2023

Volume: 12

Electronic Location Identifier: 70

Affiliations

[1 ]GRID grid.416005.6, ISNI 0000 0001 0681 4687, Netherlands Institute for Health Services Research (Nivel), ; Otterstraat 118, 3513 CR Utrecht, The Netherlands

[2 ]ARQ Centre of Expertise for the Impact of Disasters and Crises, Diemen, The Netherlands

[3 ]GRID grid.4830.f, ISNI 0000 0004 0407 1981, Faculty of Behavioural and Social Sciences, , University of Groningen, ; Groningen, The Netherlands

Article

Publisher ID: 1272

DOI: 10.1186/s13756-023-01272-6

PMC ID: 10347879

PubMed ID: 37452389

SO-VID: 8f8cbf9a-dcca-4d5f-9543-45473eb373b4

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 16 February 2023

Date accepted : 4 July 2023

Funding

Funded by: Sanofi, France

Award ID: SSA_2021_055670

Award Recipient : Lotte van Heuvel John Paget Michel Dückers

Custom metadata

ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: vaccination,influenza,pneumococcal disease,antibiotic use,antibiotic prescriptions,antimicrobial resistance,systematic literature review

Data availability:

ScienceOpen disciplines: Infectious disease & Microbiology

Keywords: vaccination, influenza, pneumococcal disease, antibiotic use, antibiotic prescriptions, antimicrobial resistance, systematic literature review

The impact of influenza and pneumococcal vaccination on antibiotic use: an updated systematic review and meta-analysis

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Abstract

Background

Objectives

Methods

Results

Conclusion

Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 48

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Rayyan—a web and mobile app for systematic reviews

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

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