Efficacy of bacterial vaccines to prevent respiratory disease in swine: a systematic review and network meta-analysis

Author:

Sargeant Jan M.ORCID,Deb Bhumika,Bergevin Michele D.,Churchill Katheryn,Dawkins Kaitlyn,Dunn Jennifer,Hu Dapeng,Moody Carly,O'Connor Annette M.ORCID,O'Sullivan Terri L.,Reist Mark,Wang ChongORCID,Wilhelm Barbara,Winder Charlotte B.ORCID

Abstract

AbstractA systematic review and network meta-analysis (MA) was conducted to address the question, ‘What is the efficacy of bacterial vaccines to prevent respiratory disease in swine?’ Four electronic databases and the grey literature were searched to identify clinical trials in healthy swine where at least one intervention arm was a commercially available vaccine for one or more bacterial pathogens associated with respiratory disease in swine, including Mycoplasma hyopneumoniae, Actinobacillus pleuropneumonia, Actinobacillus suis, Bordetella bronchiseptica, Pasteurella multocida, Stretococcus suis, Haemophils parasuis, and Mycoplasma hyorhinis. To be eligible, trials had to measure at least one of the following outcomes: incidence of clinical morbidity, mortality, lung lesions, or total antibiotic use. There were 179 eligible trials identified in 146 publications. Network MA was undertaken for morbidity, mortality, and the presence or absence of non-specific lung lesions. However, there was not a sufficient body of research evaluating the same interventions and outcomes to allow a meaningful synthesis of the comparative efficacy of the vaccines. To build this body of research, additional rigor in trial design and analysis, and detailed reporting of trial methods and results are warranted.

Publisher

Cambridge University Press (CUP)

Subject

Animal Science and Zoology

Reference52 articles.

1. Invited review: Completeness of reporting of experiments: REFLECTing on a year of animal trials in the Journal of Dairy Science

2. R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available at https://www.R-project.org (Accessed 15 April 2019).

3. Randomized controlled trials and challenge trials: design and criterion for validity;Sargeant;Zoonoses and Public Health,2014

4. Dias, S , Welton, NJ , Sutton, AJ and Ades, A (2011) NICE DSU technical support document 2: a generalised linear modelling framework for pairwise and network meta-analysis of randomized controlled trials. National Institute for Health and Clinical Excellence, Technical Support Document in Evidence Synthesis

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