10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Meta-Analysis for the Global Prevalence of Foodborne Pathogens Exhibiting Antibiotic Resistance and Biofilm Formation

      research-article

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Antimicrobial-resistant (AMR) foodborne bacteria causing bacterial infections pose a serious threat to human health. In addition, the ability of some of these bacteria to form biofilms increases the threat level as treatment options may become compromised. The extent of antibiotic resistance and biofilm formation among foodborne pathogens remain uncertain globally due to the lack of systematic reviews. We performed a meta-analysis on the global prevalence of foodborne pathogens exhibiting antibiotic resistance and biofilm formation using the methodology of a Cochrane review by accessing data from the China National Knowledge Infrastructure (CNKI), PubMed, and Web of Science databases between 2010 and 2020. A random effects model of dichotomous variables consisting of antibiotic class, sample source, and foodborne pathogens was completed using data from 332 studies in 36 countries. The results indicated AMR foodborne pathogens has become a worrisome global issue. The prevalence of AMR foodborne pathogens in food samples was greater than 10% and these foodborne pathogens were most resistant to β-lactamase antibiotics with Bacillus cereus being most resistant (94%). The prevalence of AMR foodborne pathogens in human clinical specimens was greater than 19%, and the resistance of these pathogens to the antibiotic class used in this research was high. Independently, the overall biofilm formation rate of foodborne pathogenic bacteria was 90% (95% CI, 68%–96%) and a direct linear relationship between biofilm formation ability and antibiotic resistance was not established. Future investigations should document both AMR and biofilm formation of the foodborne pathogen isolated in samples. The additional information could lead to alternative strategies to reduce the burden cause by AMR foodborne pathogens.

          Related collections

          Most cited references46

          • Record: found
          • Abstract: not found
          • Article: not found

          Measuring inconsistency in meta-analyses.

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Plea for routinely presenting prediction intervals in meta-analysis

              Objectives Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ2 or I2, but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. Design We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009–2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. Results In 72.4% of 479 statistically significant (random-effects p 0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. Conclusions The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                14 June 2022
                2022
                : 13
                : 906490
                Affiliations
                [1] 1College of Food Science and Technology, Shanghai Ocean University , Shanghai, China
                [2] 2Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture and Rural Affairs , Shanghai, China
                [3] 3Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation , Shanghai, China
                Author notes

                Edited by: Qingli Dong, University of Shanghai for Science and Technology, China

                Reviewed by: Andrew Hemmings, University of East Anglia, United Kingdom; Zhijun Liu, Shanghai Fisheries Research Institute, China

                These authors have contributed equally to this work

                This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.906490
                9239547
                f6b74dc2-c449-4d50-be78-f3431caa0084
                Copyright © 2022 Tao, Wu, Zhang, Liu, Tian, Huang, Malakar, Pan and Zhao.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 March 2022
                : 27 April 2022
                Page count
                Figures: 3, Tables: 5, Equations: 0, References: 55, Pages: 11, Words: 7426
                Funding
                Funded by: Shanghai Municipal Education Commission , doi 10.13039/501100003395;
                Award ID: 2017-01-07-00-10-E00056
                Funded by: Program of Shanghai Academic Research Leader , doi 10.13039/501100012247;
                Award ID: 21XD1401200
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                foodborne pathogens,antimicrobial resistance,biofilm,meta-analysis,global prevalence

                Comments

                Comment on this article