Prevalence of Vancomycin-Resistant Enterococcus (VRE) in Poultry in Malaysia: The First Meta-Analysis and Systematic Review

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Abstract

Databases such as PubMed, Scopus and Google Scholar were searched. Data extraction and assessment of study protocol was done by two independent reviewers and the results were reviewed by a third. OpenMeta analyst and comprehensive meta-analysis (CMA) were used for the meta-analysis. The random effect model was used, publication bias and between-study heterogeneity was assessed. Seventeen studies were added to the final meta-analysis. Studies were sampled from 2000–2018 and of the 8684 isolates tested, 2824 were VRE. The pooled prevalence of VRE among poultry in Malaysia was estimated at 24.0% (95% CI; 16.7–33.1%; I2 = 98.14%; p < 0.001). Between-study variability was high (t2 = 0.788; heterogeneity I2 = 98.14% with heterogeneity chi-square (Q) = 858.379, degrees of freedom (df) = 16, and p < 0.001). The funnel plot showed bias which was confirmed by Egger’s test and estimates from the leave-one-out forest plot did not affect the pooled prevalence. Pooled prevalence of VRE in chickens and ducks were 29.2% (CI = 18.8–42.5%) and 11.2%, CI = 9.0–14.0%) respectively. Enterococcus faecalis was reported most with more studies being reported in Peninsular Malaysia Central region and used antibiotic disc diffusion as detection method. Increased surveillance of VRE in poultry in Malaysia is required.

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The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.

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An important task of the clinical microbiology laboratory is the performance of antimicrobial susceptibility testing of significant bacterial isolates. The goals of testing are to detect possible drug resistance in common pathogens and to assure susceptibility to drugs of choice for particular infections. The most widely used testing methods include broth microdilution or rapid automated instrument methods that use commercially marketed materials and devices. Manual methods that provide flexibility and possible cost savings include the disk diffusion and gradient diffusion methods. Each method has strengths and weaknesses, including organisms that may be accurately tested by the method. Some methods provide quantitative results (eg, minimum inhibitory concentration), and all provide qualitative assessments using the categories susceptible, intermediate, or resistant. In general, current testing methods provide accurate detection of common antimicrobial resistance mechanisms. However, newer or emerging mechanisms of resistance require constant vigilance regarding the ability of each test method to accurately detect resistance.