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Potential for Meta-Analysis in the Realm of Preharvest Food Safety

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  • Authors: Jan M. Sargeant1, Annette M. O’Connor2
  • Editors: Kalmia Kniel3, Siddhartha Thakur4
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Center for Public Health and Zoonoses and Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1; 2: Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University College of Veterinary Medicine, Ames, IA 50100; 3: Department of Animal and Food Science, University of Delaware, Newark, DE; 4: North Carolina State University, College of Veterinary Medicine, Raleigh, NC
    • Source: microbiolspec September 2016 vol. 4 no. 5 doi:10.1128/microbiolspec.PFS-0004-2014
  • Received 25 August 2014 Accepted 17 December 2015 Published 16 September 2016
  • Jan M. Sargeant, [email protected]
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  • Abstract:

    Meta-analysis, the statistical combination of results from multiple studies, can be used to summarize all of the available research on an intervention, etiology, descriptive, or diagnostic test accuracy question. Meta-analysis should be conducted as a component of a systematic review, to increase transparency in the selection of studies and to incorporate an evaluation of the risk of bias in the individual studies included in the meta-analysis. The process of meta-analysis may include a forest plot to graphically display the study results and the calculation of a weighted average summary effect size. Heterogeneity (differences in the effect size between studies) can be evaluated using formal statistics and the reasons for heterogeneity can be explored using sub-group analysis or meta-regression. Thus, meta-analysis may be a useful methodology for preharvest food safety research to aid in policy or clinical decision-making or to provide input to quantitative risk assessment or other models.

  • Citation: Sargeant J, O’Connor A. 2016. Potential for Meta-Analysis in the Realm of Preharvest Food Safety. Microbiol Spectrum 4(5):PFS-0004-2014. doi:10.1128/microbiolspec.PFS-0004-2014.

Key Concept Ranking

Quantitative Risk Assessment
0.54344976
Food Safety
0.50187594
Bovine Respiratory Disease
0.49470627
0.54344976

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2016-09-16
2019-10-23

Abstract:

Meta-analysis, the statistical combination of results from multiple studies, can be used to summarize all of the available research on an intervention, etiology, descriptive, or diagnostic test accuracy question. Meta-analysis should be conducted as a component of a systematic review, to increase transparency in the selection of studies and to incorporate an evaluation of the risk of bias in the individual studies included in the meta-analysis. The process of meta-analysis may include a forest plot to graphically display the study results and the calculation of a weighted average summary effect size. Heterogeneity (differences in the effect size between studies) can be evaluated using formal statistics and the reasons for heterogeneity can be explored using sub-group analysis or meta-regression. Thus, meta-analysis may be a useful methodology for preharvest food safety research to aid in policy or clinical decision-making or to provide input to quantitative risk assessment or other models.

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Potential for Meta-Analysis in the Realm of Preharvest Food Safety
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Citation: Sargeant J, O’Connor A. 2016. Potential for meta-analysis in the realm of preharvest food safety. microbiolspec 4(5): doi:10.1128/microbiolspec.PFS-0004-2014
/content/microbiolspec/10.1128/microbiolspec.PFS-0004-2014.fig1
FIGURE 1

Example of a risk of bias graph using hypothetical data (created in Revman version 5.2). Each study included in the review has been evaluated for the risk of bias based on the domains shown in this figure. Each row of the figure summarizes the proportion of studies classified as low risk of bias, high risk of bias, or unclear risk of bias for that domain.

microbiolspec/4/5/PFS-0004-2014-fig1_thmb.gif
microbiolspec/4/5/PFS-0004-2014-fig1.gif
Image of FIGURE 1

Click to view

FIGURE 1

Example of a risk of bias graph using hypothetical data (created in Revman version 5.2). Each study included in the review has been evaluated for the risk of bias based on the domains shown in this figure. Each row of the figure summarizes the proportion of studies classified as low risk of bias, high risk of bias, or unclear risk of bias for that domain.

Source: microbiolspec September 2016 vol. 4 no. 5 doi:10.1128/microbiolspec.PFS-0004-2014
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/content/microbiolspec/10.1128/microbiolspec.PFS-0004-2014.fig2
FIGURE 2

Example of a risk of bias summary using hypothetical data (created in Revman version 5.2). The results of the risk of bias assessment for each study for each risk of bias domain are shown, where “+” (green circles) corresponds to a low risk of bias in a specific study for that domain, “-” (red circles) corresponds to a high risk of bias, and “?” (yellow circles) corresponds to an unclear risk of bias.

microbiolspec/4/5/PFS-0004-2014-fig2_thmb.gif
microbiolspec/4/5/PFS-0004-2014-fig2.gif
Image of FIGURE 2

Click to view

FIGURE 2

Example of a risk of bias summary using hypothetical data (created in Revman version 5.2). The results of the risk of bias assessment for each study for each risk of bias domain are shown, where “+” (green circles) corresponds to a low risk of bias in a specific study for that domain, “-” (red circles) corresponds to a high risk of bias, and “?” (yellow circles) corresponds to an unclear risk of bias.

Source: microbiolspec September 2016 vol. 4 no. 5 doi:10.1128/microbiolspec.PFS-0004-2014
Permissions and Reprints Request Permissions
Download as Powerpoint
/content/microbiolspec/10.1128/microbiolspec.PFS-0004-2014.fig3
FIGURE 3

Forest plot illustrating relative risk of retreatment for bovine respiratory disease following treatment with tulathromycin compared to other available antibiotic treatments ( 61 ). Each row corresponds to treatment comparison, with the box representing the relative risk estimate for the comparison and the line corresponding to the 95% confidence interval around that estimate. The size of the box is representative of the relative amount of information contributed for that comparison (study weighting). The vertical line represents the null effect (relative risk of 1).

microbiolspec/4/5/PFS-0004-2014-fig3_thmb.gif
microbiolspec/4/5/PFS-0004-2014-fig3.gif
Image of FIGURE 3

Click to view

FIGURE 3

Forest plot illustrating relative risk of retreatment for bovine respiratory disease following treatment with tulathromycin compared to other available antibiotic treatments ( 61 ). Each row corresponds to treatment comparison, with the box representing the relative risk estimate for the comparison and the line corresponding to the 95% confidence interval around that estimate. The size of the box is representative of the relative amount of information contributed for that comparison (study weighting). The vertical line represents the null effect (relative risk of 1).

Source: microbiolspec September 2016 vol. 4 no. 5 doi:10.1128/microbiolspec.PFS-0004-2014
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Download as Powerpoint

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