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Review

Systematic Review and Meta-Analysis of Campylobacter Species Contamination in Poultry, Meat, and Processing Environments in South Korea

by
Hyeon Ji Je
1,†,
Saloni Singh
1,†,
Dong Woo Kim
1,
Hyun Seok Hur
1,
Ah Leum Kim
1,
Eun Jin Seo
2 and
Ok Kyung Koo
1,*
1
Department of Food Science & Technology, Chungnam National University, Daejeon 34134, Republic of Korea
2
Agro-Bioproduct Analysis Team, Korea Agriculture Technology Promotion Agency, Iksan 54667, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2023, 11(11), 2722; https://doi.org/10.3390/microorganisms11112722
Submission received: 18 October 2023 / Revised: 3 November 2023 / Accepted: 6 November 2023 / Published: 7 November 2023
(This article belongs to the Special Issue Microbiology and Food Safety)
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Abstract

Campylobacter spp. constitute a significant global threat as a leading cause of foodborne illnesses, with poultry meat as a prominent reservoir for these pathogens. South Korea is known for its diverse poultry consumption habits, and continuous outbreaks make it a matter of concern to perform a meta-analysis to identify the primary source of contamination. This systematic review and meta-analysis aimed to assess and compare the prevalence of Campylobacter in various poultry and meat types while also considering the importance of environmental factors in South Korea. The meta-analysis revealed that duck meat exhibited the highest prevalence of Campylobacter, with a pooled estimate of 70.46% (95% CI: 42.80% to 88.38%), followed by chicken meat at a pooled prevalence of 36.17% (95% CI: 26.44% to 47.91%). Additionally, our analysis highlighted the predominance of C. jejuni and C. coli in South Korea. These findings underscore the importance of implementing rigorous food safety measures and establishing robust surveillance programs in the poultry industry to mitigate the risk of Campylobacter-related foodborne illnesses associated with meat consumption in South Korea.

1. Introduction

Campylobacter is a Gram-negative, spiral-shaped, and microaerophilic pathogen commonly associated with foodborne illnesses. The optimal growth temperature range for Campylobacter spp. is 37–42 °C, which is close to the body temperature of warm-blooded animals [1]. The Campylobacter genus comprises 15 known species, and 12 have been linked to causing diseases in humans [2]. Notably, C. jejuni and C. coli account for over 95% of human Campylobacter infections [3]. Campylobacter infection can lead to long-term complications such as irritable bowel syndrome (IBS), arthritis, and Guillain–Barré Syndrome (GBS). It is estimated that 0.2 to 1.7 per 1000 individuals with diagnosed or undiagnosed Campylobacter infections ultimately develop GBS, accounting for 5–41% of total GBS cases [4].
C. jejuni contamination has emerged as a global concern, as evidenced by a comprehensive epidemiological study conducted by Kaakoush et al., 2015 [5]. The study revealed a concerning increase in cases in North America, Europe, and Australia. Furthermore, data from Africa, Asia, and the Middle East indicated a particularly high prevalence among children [6]. In the United States, the Foodborne Disease Active Surveillance Network (FoodNet) reported an annual incidence of approximately 20 cases per 100,000 individuals [7]. An outbreak of C. jejuni foodborne infection in 2017 in Seoul, South Korea, was associated with cross-contamination through sharing cutting boards and knives with various food items. Notably, chicken was identified as the primary source, and the bacterium was subsequently transferred to other foods, leading to a widespread outbreak [8]. Another study by Yu et al., 2010, indicated an outbreak in a middle school linked to undercooked chicken as the primary source and subsequently transferred to other foods, leading to a widespread outbreak. [9].
The upswing in foodborne Campylobacter infections can be attributed to various intertwined factors. Changes in food production and consumption patterns, including a surge in demand for convenience foods like poultry products, particularly chicken, and a growing tendency to eat out have bolstered Campylobacter infections [10]. This bacterium often contaminates chicken products and can spread through cross-contamination in both domestic and commercial kitchens [11]. The emergence of antibiotic-resistant Campylobacter strains further complicates treatment and prolongs illness [12]. The global movement of food and people facilitates the spread of Campylobacter, leading to sporadic outbreaks and widespread infections [13]. Environmental influences, such as climate change and weather conditions, also affect the prevalence of Campylobacter in the environment, adding to the complexity of addressing this public health challenge [14].
Analyzing the historical data allows health authorities and researchers to gain insights into the epidemiology of the disease, such as identifying high-risk areas, vulnerable populations, and seasonal variations [15]. Campylobacter outbreaks, despite frequent occurrences, have historically been underreported. However, an observable upward trend in their prevalence has become evident. According to the CDC, from 2004 to 2009, an average of 22 outbreaks were officially reported annually. This figure slightly increased to 31 outbreaks from 2010 to 2012 before declining to 29 from 2013 to 2017 [16]. One of the most significant case studies of Campylobacteriosis was in June 2019, when Askøy in Norway was struck by a significant waterborne outbreak, resulting in over 1500 cases of Campylobacteriosis [17]. Another large-scale outbreak was in New Zealand in 2020, stemming from a contaminated water supply, which led to an estimated 8320 cases [18], underscoring the urgency of addressing this issue globally. According to data published by the Ministry of Food and Drug Safety in South Korea, Campylobacter ranks as the third most prevalent food pathogen, following pathogenic E. coli and Salmonella in this decade [19]. Thus, by examining the patterns and trends of past cases, we can identify common factors, potential sources, and contamination pathways associated with Campylobacteriosis. This analysis offers crucial insights into the causes of contamination and transmission pathways, facilitating evidence-based interventions and strategies to control the disease and protect public health.
Meta-analysis with systematic reviews can offer a comprehensive perspective by amalgamating data from numerous studies and identifying knowledge gaps [20]. Systematic review employs a comprehensive and structured approach to synthesize existing research, while meta-analysis employs statistical methods to combine the outcomes of multiple studies, yielding an overall estimate of the effect of an intervention [21]. These methodologies are crucial for conducting a thorough and exhaustive evaluation of the available research on a specific topic by facilitating the consolidation and synthesis of evidence from diverse studies to enhance the statistical power and generalizability of the findings. By providing a robust summary of the available evidence, they support evidence-based decision-making processes and inform policy formulation and implementation [22]. Ultimately, these approaches benefit researchers, policymakers, clinicians, and other stakeholders by offering a reliable and evidence-based foundation for decision making and further investigation. Therefore, investigating these methodologies would be valuable in guiding future research and informing public health policies and interventions to mitigate the burden of Campylobacter-related illness in Korea.
Several studies have been conducted in South Korea to investigate the prevalence of Campylobacter contamination in various poultry and meat products. However, these studies have been limited in scope and have reported conflicting results, potentially because of differences in study design, sampling methods, or laboratory testing procedures. Despite efforts to mitigate Campylobacter infection in meat products by implementing food safety regulations and guidelines for handling and processing, concerns regarding the prevalence of contamination persist [23]. Therefore, gathering and analyzing all available data from previous studies becomes imperative to facilitate further research in this area. This study aims to determine the prevalence of Campylobacter spp. in poultry and meat products in South Korea. This study also aims to consider the environmental conditions under which the products were processed, as these factors may also play a significant role in meat contamination. By conducting a comprehensive analysis of existing studies, this research endeavors to provide a consolidated and robust assessment of the prevalence of Campylobacter contamination in poultry and meat products in South Korea, accounting for relevant environmental factors.

2. Materials and Methods

2.1. Search Strategy

This systematic review strictly adhered to the PRISMA 2020 guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analysis, http://www.prisma-statement.org/, accessed on 14 March 2023). PRISMA 2020 guidelines were specifically employed for “new systematic reviews which included searches of databases and registers only.” The implementation of PRISMA 2020 aimed to uphold high reporting standards and minimize bias in the review’s findings [24]. Thus, we meticulously followed the PRISMA 2020 guidelines to ensure the transparency, reliability, and rigor of our methodology.
In order to compile a comprehensive body of literature, an exhaustive search was conducted across multiple databases. The search encompassed two widely recognized English databases, Web of Science and PubMed. Additionally, to include relevant studies in South Korea, three Korean-language-based databases were explored: DBpia (https://www.dbpia.co.kr/, accessed on 7 March 2023), RISS (http://www.riss.kr/index.do, accessed on 7 March 2023) and ScienceON (https://scienceon.kisti.re.kr/, accessed on 7 March 2023).
The search algorithm used was “Campylobacter” and “Korea”. After retrieving research from each database, the reference management software EndNote 20 (Clarivate Analytics, Boston, MA, USA) was employed to facilitate the de-duplication and screening processes in March 2023.

2.2. Eligibility Criteria

A two-level screening procedure was conducted from March to April 2023: title comprisal and abstract screening (Level 1), followed by full-text screening (Level 2). Various criteria aligned with the study’s specific objectives were carefully considered during the data screening and selection process at the searching stage. The authors (HJ Je, DW Kim, HS Hur, AL Kim, and EJ Seo) independently conducted the selection process, rigorously applying the predetermined criteria to each retrieved article. The data were assembled in a Microsoft Excel sheet, and screening was performed according to the parameters set for exclusion and inclusion criteria. In cases where discrepancies in the selection arose, all authors engaged in constructive discussions to reach a consensus, ensuring a meticulous and unbiased assessment of the data.

2.3. Inclusion Criteria

The inclusion criteria encompassed studies investigating the presence and contamination of Campylobacter in poultry and meat products (chicken, duck, beef, and pork), and contamination by environmental sources (feces, washing water, and equipment). Additionally, articles unrelated to the prevalence, including those centered on antimicrobial research, detection methods, risk analysis, pathogenesis, and other microbiological studies, were excluded. No restrictions were set on the year of publication or the study period; however, articles not in Korean or English were excluded during the initial screening phase. Meticulously and independently, the authors cross-checked each article’s eligibility based on the predefined criteria, ensuring consistency in the selection process. Ultimately, only articles meeting the specific inclusion criteria were considered for this study, and their relevant details were diligently recorded systematically.

2.4. Exclusion Criteria

Exclusion criteria in this study were research articles that did not demonstrate the prevalence of Campylobacter. Additionally, studies focusing on other bacterial contaminations such as other food products, detection methods different from standard methods, antimicrobial research, and abstract-only papers were excluded. The detection methods excluded from this study were detection via PCR and metagenome analysis without any enrichment process. Furthermore, sampling sites outside South Korea and studies published in languages other than Korean or English were also excluded, but no limitation was made regarding publication years.

2.5. Data Extraction

In order to ensure accuracy and reliability, data extraction was carried out by employing a consensus-based approach to minimize the potential for individual bias and enhance the overall quality of the systematic review. Authors (HJ Je, S Singh) extracted data including the sampling period; food type; environmental factors; and the presence of Campylobacter spp., C. jejuni, or C. coli and summarized them in the Microsoft Office Excel software 365, version 2016 (Microsoft Corporation, Redmond, WA, USA). Samples were classified into two groups: food (raw chicken, duck, beef, pork, ham, and meat products) and environmental factors (feces, washing water, and equipment) for meta-analysis.

2.6. Risk of Bias for Quality Assessment

A risk of bias assessment was conducted using a questionnaire approach, with scores calculated based on the answers. Each selected study was evaluated based on specific questions, and scores were assigned accordingly (2 points for “YES,” 0 points for “NO,” and 1 point for “UNSURE”) [25]. The total scores ranged from 0 to 12, with scores ≥9 considered high quality, scores ≥6 considered moderate quality, and less than 6 considered low-quality studies [26,27]. The questions were as follows:
Q1.
Was the research question/objective clearly described and stated?
Q2.
Was the period of study clearly stated?
Q3.
Was the sample population clearly specified?
Q4.
Was the sampling method described in detail?
Q5.
Was the same laboratory method used for all samples in the study?
Q6.
Was the isolation method tested based on a standard bacteriological and/or molecular procedure?

2.7. Data Analysis

Statistical analysis was performed using the Comprehensive Meta-Analysis Software program version 4 (Biostat Inc., Englewood, NJ, USA). The prevalence of Campylobacter and corresponding 95% confidence intervals (CIs) were calculated based on the total number of tested and positive samples. A forest plot was generated to visualize the estimated prevalence and distribution for individual studies and the pooled study estimate within the 95% confidence interval. A random effects model was employed for the meta-analysis, which accounts for expected heterogeneity among the included studies. Heterogeneity levels were assessed using Cochran’s Q statistic and the I-squared (I2) inconsistency index. Heterogeneity levels of I2 were categorized as low (less than 40%), moderate (between 25% and 50%), substantial (between 50% and 90%), and considerable (greater than 75%) heterogeneity [28].
The groups considered for the study included different types of meat, including beef, pork, chicken, and duck. Since environmental factors play an important role in contamination, various factors like feces, equipment, and washing water were also considered. Equipment includes bedding for cattle, chopping boards, drawers, and knives. The data were also divided into specific detection values for C. jejuni and C. coli to find which species had more prevalence. The study also included the detection method of using enrichment and selective media techniques.
Publication bias was evaluated using a funnel plot, which could indicate the asymmetrical distribution of effect sizes and standard errors, suggesting the presence of publication bias. Statistical significance for publication bias was determined using a threshold of p < 0.05 [29,30].

3. Results

3.1. Search Results and Risk of Bias

In this study, a total of 1045 studies were considered from the databases RISS, DBpia, and Science ON in Korean search engines and Web of Science and PubMed in international search engines after duplicate removal (Figure 1). Title and abstract screening was performed thereafter, resulting in 70 full-text articles. After the full-text screening, 31 studies between 1985 to 2020 were considered for further systematic review and meta-analysis (Table 1). The studies considered in the meta-analysis were confirmed as high (22/32) to moderate (10/32) quality studies, with no low (0/32) quality studies using risk of bias assessment (Figure S1).

3.2. Overall Meta-Analysis

The comprehensive meta-analysis considered all the relevant food and environmental factors. Among the 31 studies, the overall pooled prevalence of Campylobacter was 23.38% (95% CI: 16.78–31.58%) (Figure 2 and Figure S2). The analysis showed an I2 value of 98% (p < 0.001), indicating significant variability among the studies (Table 2). When considering the food groups, ducks exhibited the highest prevalence of Campylobacter spp. at 70.46% (95% CI: 42.80–88.38%), followed by chicken with a prevalence rate of 36.17% (95% CI: 26.44–47.19%), pork at 2.10% (95% CI:0.67–6.35%), and beef at 0.99% (95% CI: 0.20–4.71%) (Figure 3 and Figure S3 and Table 2). The analysis also included ham and meat products such as patties, meatballs, and cutlets; however, they did not yield enough studies for meta-analysis.

3.3. Campylobacter Prevalence in Food

Studies examining the prevalence of Campylobacter species in food sources, particularly poultry products, have consistently found C. jejuni to be more prevalent than C. coli [61]. Our study verified these findings, as C. jejuni exhibited higher prevalence rates than C. coli across all samples (Table 3 and Table 4).
Microorganisms 11 02722 g003
Figure 3. Forest plot of each food type for the prevalence of Campylobacter in South Korea: (A) chicken, (B) duck, (C) pork, and (D) beef [3,31,32,34,35,39,41,42,43,46,47,48,49,50,52,53,54,57,59,60].
Figure 3. Forest plot of each food type for the prevalence of Campylobacter in South Korea: (A) chicken, (B) duck, (C) pork, and (D) beef [3,31,32,34,35,39,41,42,43,46,47,48,49,50,52,53,54,57,59,60].
Microorganisms 11 02722 g003

3.4. Environmental Factors Play a Major Role in Contamination

Among the environmental factors considered in this study, feces showed the highest prevalence at 36.33% (95% CI: 22.62–52.68%), followed by wash water at 27.69% (95% CI: 6.0.5–69.47%) and equipment at 4.99% (95% CI: 0.76–26.41%). Duck feces exhibited the highest prevalence of Campylobacter spp., followed by pig and chicken feces. Chilling and chicken wash water also showed high prevalence rates of 60% and 45%, respectively. Among the equipment commonly used, knives showed the highest prevalence at 45% (95% CI: 25.32–63.38%) (Figure 4).

4. Discussion

A World Health Organization (WHO) report states that poultry, including chicken and turkey, is a common source of foodborne pathogens such as Salmonella and Campylobacter [62]. Meat products such as beef and pork are potential sources of Campylobacter contamination [63]. Taremi et al. (2006) found the highest prevalence of Campylobacter in chicken (63%) and beef (10%) [64]. Given that chicken is the most consumed meat worldwide [65], addressing the prevalence and consequences of Campylobacter infections in poultry becomes paramount. Furthermore, Campylobacter prevalence is not confined to poultry and meat; it has been found in vegetables, fruits, and fresh produce at an estimated prevalence of approximately 0.53% [66].
During the screening process, the detection method for Campylobacter was also considered (Table 1). Although specific differences exist in overall protocols for detecting Campylobacter, the methodology was similar in media composition and temperature, which can be excluded from the potential cause of heterogeneity and bias. Components such as amphotericin, sodium bisulfite, sodium pyruvate, and sodium chloride were prevalent across most compositions, with pH levels ranging from 7.2 to 7.4. Additionally, we conducted a risk of bias assessment to determine the quality of the studies considered. Overall, 22 out of 32 studies were classified as high quality, and the remaining nine were moderate quality, without any studies considered low quality (Figure S1).
It is noteworthy that our findings showed a higher prevalence of duck, in contrast to studies conducted in the US (12.5%), UK (50.7%), and Ireland (45.8%) [59]. There could be several factors contributing to a high prevalence of duck, including contamination in duck farms [67], high intestinal concentration, or the protective effects of thicker skin layers [68]. Another explanation is that chicken has recently been the focus of contamination prevention efforts, which may not be the case for ducks [59]. Nevertheless, chicken is still more prevalent than other meat, such as beef and pork. It is important to note the limitations in conducting subgroup analysis due to insufficient study information. For instance, the condition of the meat (sliced or whole) was not consistently specified in the studies, limiting our ability to perform subgroup analysis (Figure 3). The considerable variation in the sample sizes and event rates also posed challenges in conducting subgroup analysis and identifying the sources of high heterogeneity (Figures S2 and S3). Nevertheless, the results provide valuable insights into the prevalence of Campylobacter in poultry and meat, aiding in understanding the trends and high-risk foods.
A study conducted in Brazil also showed that C. jejuni was more prevalent in poultry (28.8%) compared with C. coli (15.6%) [69]. In a Netherlands case study, consuming poultry and undercooked meat was associated with more C. jejuni infections than C. coli infections [69]. Usually, there are more cases found related to C. jejuni, but cases also exist where C. coli surpasses C. jejuni, as a study in Argentina showed that C. coli (59%) was more prevalent than C. jejuni (41%) in slaughterhouse samples [70]. The variation in prevalence between two species could be due to factors such as seasons, geography, and the evolutionary forces of recombination [71,72].
Studying food, its environment, and processing units is crucial for comprehensively understanding pathogen contamination risks. It allows for identifying contamination sources, assessing transmission pathways, evaluating overall risk, and developing effective intervention strategies [73]. A notable example is the 2017 outbreak of C. jejuni in Seoul, Korea, where environmental factors and improper handling were implicated as potential causes [8]. Chai et al., (2008) showed that up to 38.2% of C. jejuni was transferred from vegetables to wash water, up to 47.2% from wash water to cucumbers, and up to 73.3% from cutting boards to cucumbers, highlighting the importance of environmental factors [74]. In Figure 4, the forest plot shows the high prevalence of C. jejuni through handling and equipment sources and contamination through feces. The data in this study (Figure 4) suggest that, given the high prevalence of Campylobacter in environmental sources, there could be high contamination in final food products, which, upon consumption, may pose a threat to public health. Although these results show the high contamination risks, a lack of enough studies puts a limitation on finding the ultimate source.
Campylobacter contamination sources have been the subject of extensive research because of the prevalence of Campylobacter infections worldwide. Poultry, especially chicken and turkey, is a well-documented reservoir of Campylobacter species, with high prevalence rates reported in many countries [75]. Campylobacter colonization in poultry can be attributed to the gut microflora of these birds, which serves as a natural reservoir. Additionally, improper handling, cross-contamination during processing, and the consumption of undercooked poultry products have all been implicated in Campylobacter infections [10].
Moreover, Campylobacter can also contaminate water sources, posing a risk to individuals who consume untreated or contaminated water [76]. The primary sources of Campylobacter contamination in surface water have been identified as wild birds and poultry, although their influence varies based on factors such as the type of water body, the time of year, and the concentrations of local poultry and ruminant populations [77]. Research has revealed that isolates from poultry exhibit a prolonged survival period compared with other sources, suggesting a critical role in the transmission of Campylobacter through water sources [78]. Even in our meta-analysis, river and lake water, chicken wash water, and others revealed a significant amount of positive Campylobacter cases, with C. jejuni being the predominant species. Notably, a study on waterborne-outbreak-associated C. jejuni provided insight into how bacteria originating from cattle manure can infiltrate groundwater, leading to the contamination of water supplies [79]. Understanding these diverse contamination sources is crucial for the prevention and control of Campylobacter infections, and ongoing research seeks to elucidate the complex dynamics involved in Campylobacter transmission.

5. Conclusions

This review comprehensively examined the prevalence of Campylobacter in South Korea in poultry, meat, and environmental contexts. The results highlighted ducks as a high-risk food source, corroborating previous research showing higher antibiotic resistance than chickens. The widespread presence of Campylobacter species across various meat types and processing settings indicates the urgent need for stringent hygiene measures throughout the production chain. The diverse findings emphasize the significance of tailored control strategies in mitigating the risk of Campylobacter contamination in meat products, thereby safeguarding public health and emphasizing the importance of continuous monitoring and intervention efforts in the meat industry. The insights derived from this analysis can serve as a foundation for shaping future strategies in food safety management. By understanding the prevalence and distribution of Campylobacter in meat and processing environments, regulatory bodies and industry stakeholders can design interventions to target specific sources of contamination. This knowledge can guide the development of more effective hygiene protocols, surveillance programs, and risk assessment models, reducing the incidence of foodborne illnesses associated with Campylobacter.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microorganisms11112722/s1: Figure S1: Risk of bias assessment for all included studies presented as the percentage of bias risk for each question. Figure S2: Funnel plot of the overall study for the prevalence of Campylobacter in South Korea. Figure S3: Funnel plot of each food type for the prevalence of Campylobacter in South Korea: (a) chicken, (b) duck, (c) pork, (d) beef.

Author Contributions

H.J.J. and S.S. contributed equally to the systematic review and meta-analysis. H.J.J. contributed to the overall processes and suggested the direction of the meta-analysis. S.S. contributed to data extraction; meta-analysis; and, especially, research writing. The other authors (D.W.K., H.S.H., A.L.K. and E.J.S.) participated in the entire systematic review. O.K.K. contributed to supervising the entire process of this research. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Research Foundation of Korea (NRF-2022R1A4A1033015).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We would like to extend our heartfelt appreciation to the researchers whose invaluable assistance, provided through correspondence, enabled us to access the crucial data necessary for our research.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Young, K.T.; Davis, L.M.; DiRita, V.J. Campylobacter jejuni: Molecular Biology and Pathogenesis. Nat. Rev. Microbiol. 2007, 5, 665–679. [Google Scholar] [CrossRef] [PubMed]
  2. Lastovica, A.J.; Allos, B.M. Clinical significance of Campylobacter and related species other than Campylobacter jejuni and C. coli. In Campylobacter; Nachamkin, I., Szymanski, C.M., Blaser, M.J., Eds.; ASM Press: Washington, DC, USA, 2008; Volume 3, pp. 89–120. [Google Scholar]
  3. Park, S.F. The physiology of Campylobacter species and its relevance to their role as foodborne pathogens. Int. J. Food Microbiol. 2002, 74, 177–188. [Google Scholar] [CrossRef]
  4. Campylobacter (Campylobacteriosis): Information for Health Professionals. Available online: https://www.cdc.gov/Campylobacter/technical.html (accessed on 30 August 2023).
  5. Kaakoush, N.O.; Castaño-Rodríguez, N.; Mitchell, H.M.; Man, S.M. Global epidemiology of Campylobacter infection. Clin. Microbiol. Rev. 2015, 28, 687–720. [Google Scholar] [CrossRef]
  6. Mukherjee, P.; Ramamurthy, T.; Bhattacharya, M.K.; Rajendran, K.; Mukhopadhyay, A.K. Campylobacter jejuni in hospitalized patients with diarrhea, Kolkata, India. Emerg. Infect. Dis. 2013, 19, 1155. [Google Scholar] [CrossRef] [PubMed]
  7. Campylobacter (Campylobacteriosis): Reports of Selected Campylobacter Outbreak Investigation. Available online: https://www.cdc.gov/Campylobacter/outbreaks/outbreaks.html (accessed on 2 July 2023).
  8. Kang, C.R.; Bang, J.H.; Cho, S.I. Campylobacter jejuni foodborne infection associated with cross-contamination: Outbreak in Seoul in 2017. Infect. Chemother. 2019, 51, 21–27. [Google Scholar] [CrossRef] [PubMed]
  9. Yu, J.H.; Kim, N.Y.; Cho, N.G.; Kim, J.H.; Kang, Y.A.; Lee, H.G. Epidemiology of Campylobacter jejuni outbreak in a middle school in Incheon, Korea. J. Korean Med. Sci. 2010, 25, 1595–1600. [Google Scholar] [CrossRef]
  10. Silva, J.; Leite, D.; Fernandes, M.; Mena, C.; Gibbs, P.A.; Teixeira, P. Campylobacter spp. as a foodborne pathogen: A review. Front. Microbiol. 2011, 2, 200. [Google Scholar] [CrossRef]
  11. Luangtongkum, T.; Morishita, T.Y.; Ison, A.J.; Huang, S.; McDermott, P.F.; Zhang, Q. Effect of conventional and organic production practices on the prevalence and antimicrobial resistance of Campylobacter spp. in poultry. Appl. Environ. Microbiol. 2006, 72, 3600–3607. [Google Scholar] [CrossRef]
  12. Hoelzer, K.; Wong, N.; Thomas, J.; Talkington, K.; Jungman, E.; Coukell, A. Antimicrobial drug use in food-producing animals and associated human health risks: What, and how strong, is the evidence? BMC Vet. Res. 2017, 13, 211. [Google Scholar] [CrossRef]
  13. Strachan, N.J.; Gormley, F.J.; Rotariu, O.; Ogden, I.D.; Miller, G.; Dunn, G.M.; Sheppard, K.S.; Dallas, F.J.; Reid, T.S.; Howie, H.; et al. Attribution of Campylobacter infections in northeast Scotland to specific sources by use of multilocus sequence typing. J. Infect. Dis. 2009, 199, 1205–1208. [Google Scholar] [CrossRef]
  14. Lake, I.R.; Colon-Gonzalez, F.J.; Takkinen, J.; Rossi, M.; Sudre, B.; Dias, J.G.; Tavoschi, L.; Joshi, A.; Semenza, J.C.; Nichols, G. Exploring Campylobacter seasonality across Europe using the European surveillance system (TESSy), 2008 to 2016. Eurosurveillance 2019, 24, 1800028. [Google Scholar] [CrossRef] [PubMed]
  15. Cassidy, A.; Myles, J.P.; Liloglou, T.; Duffy, S.W.; Field, J.K. Defining high-risk individuals in a population-based molecular-epidemiological study of lung cancer. Int. J. Oncol. 2006, 28, 1295–1301. [Google Scholar] [CrossRef]
  16. Tack, D.M.; Marder, E.; Griffin, P.M.; Cieslak, P.R.; Dunn, J.; Hurd, S.; Scallan, E.; Lathrop, S.; Muse, A.; Ryan, P.; et al. Preliminary incidence and trends of infections with pathogens transmitted commonly through food—Foodborne Diseases Active Surveillance Network, 10 US sites, 2015–2018. Am. J. Transplant. 2019, 19, 1859–1863. [Google Scholar] [CrossRef]
  17. Hyllestad, S.; Iversen, A.; MacDonald, E.; Amato, E.; Borge, B.Å.; Bøe, A.; Sandvin, A.; Brandal, L.T.; Lyngstad, T.M.; Naseer, U.; et al. Large waterborne Campylobacter outbreak: Use of multiple approaches to investigate contamination of the drinking water supply system, Norway, June 2019. Eurosurveillance 2020, 25, 2000011. [Google Scholar] [CrossRef] [PubMed]
  18. Gilpin, B.J.; Walker, T.; Paine, S.; Sherwood, J.; Mackereth, G.; Wood, T.; Hambling, T.; Hewison, C.; Brounts, A.; Wilson, M.; et al. A large scale waterborne Campylobacteriosis outbreak, Havelock North, New Zealand. J. Infect. 2020, 81, 390–395. [Google Scholar] [CrossRef]
  19. Ministry of Food and Drug Safety: Food and Food Additives Codex, Food Code: Article 8: 4.19: Campylobacter jejuni/coli. Available online: https://www.foodsafetykorea.go.kr/foodcode/01_03.jsp?idx=392 (accessed on 25 July 2023).
  20. Muka, T.; Glisic, M.; Milic, J.; Verhoog, S.; Bohlius, J.; Bramer, W.; Chowdhury, R.; Franco, O.H. A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research. Eur. J. Epidemiol. 2020, 35, 49–60. [Google Scholar] [CrossRef] [PubMed]
  21. Delgado-Rodríguez, M.; Sillero-Arenas, M. Systematic review and meta-analysis. Med. Intensiv. 2018, 42, 444–453. [Google Scholar] [CrossRef]
  22. Siddaway, A.P.; Wood, A.M.; Hedges, L.V. How to do a systematic review: A best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annu. Rev. Psychol. 2019, 70, 747–770. [Google Scholar] [CrossRef]
  23. Lee, J.; Lee, H.; Lee, S.; Kim, S.; Ha, J.; Choi, Y.; Oh, H.; Kim, Y.; Lee, Y.; Yoon, K.S.; et al. Quantitative microbial risk assessment for Campylobacter jejuni in ground meat products in Korea. Food Sci. Anim. Resour. 2019, 39, 565–575. [Google Scholar] [CrossRef]
  24. Rethlefsen, M.L.; Kirtley, S.; Waffenschmidt, S.; Ayala, A.P.; Moher, D.; Page, M.J.; Koffel, J.B. PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Sys. Rev. 2021, 10, 39. [Google Scholar] [CrossRef]
  25. Armijo-Olivo, S.; Stiles, C.R.; Hagen, N.A.; Biondo, P.D.; Cummings, G.G. Assessment of study quality for systematic reviews: A comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: Methodological research. J. Eval. Clin. Pract. 2012, 18, 12–18. [Google Scholar] [CrossRef] [PubMed]
  26. Grooten, W.J.; Tseli, E.; Äng, B.O.; Boersma, K.; Stålnacke, B.M.; Gerdle, B.; Enthoven, P. Elaborating on the assessment of the risk of bias in prognostic studies in pain rehabilitation using QUIPS—Aspects of interrater agreement. Diagn. Progn. Res. 2019, 3, 5. [Google Scholar] [CrossRef]
  27. Rubinstein, S.M.; van Eekelen, R.; Oosterhuis, T.; de Boer, M.R.; Ostelo, R.W.; van Tulder, M.W. The risk of bias and sample size of trials of spinal manipulative therapy for low back and neck pain: Analysis and recommendations. J. Manip. Physiol. Ther. 2014, 37, 523–541. [Google Scholar] [CrossRef] [PubMed][Green Version]
  28. Bashiry, M.; Javanmardi, F.; Sadeghi, E.; Shokri, S.; Hossieni, H.; Oliveira, C.A.; Khaneghah, A.M. The prevalence of aflatoxins in commercial baby food products: A global systematic review, meta-analysis, and risk assessment study. Trends Food Sci. Technol. 2021, 114, 100–115. [Google Scholar] [CrossRef]
  29. Lohmueller, K.E.; Pearce, C.L.; Pike, M.; Lander, E.S.; Hirschhorn, J.N. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet. 2003, 33, 177–182. [Google Scholar] [CrossRef]
  30. Ranjbar, R.; Halaji, M. Epidemiology of Listeria monocytogenes prevalence in foods, animals and human origin from Iran: A systematic review and meta-analysis. BMC Public Health 2018, 18, 1057. [Google Scholar] [CrossRef] [PubMed]
  31. Kang, H.J.; Kim, Y.H.; Suk, J.M.; Lee, S.M.; Kim, J.Y.; Jung, S.C. Prevalence and serovar of food poisoning bacteria in retail fresh, frozen and packed meats. J. Food Hyg. Saf. 1999, 14, 327–332. [Google Scholar]
  32. Kang, H.J.; Kim, Y.H.; Cho, H.H. Isolation of Campylobacter jejuni from chicken. Korean J. Vet. Public Health 1985, 9, 43–47. [Google Scholar]
  33. Kim, N.H.; Chae, H.S.; Kang, Y.I.; Shin, B.W.; Choi, N.H.; Kim, H.B. Prevalence and antimicrobial resistance patterns of Campylobacter jejuni from duck feces. Korean J. Vet. Serv. 2013, 36, 57–60. [Google Scholar] [CrossRef]
  34. Kim, S.M.; Chong, S.J. Prevalence and in vitro antimicrobial activity against Campylobacter jejuni/coli from chickens. Korean J. Clin Lab. Sci. 1996, 28, 20–28. [Google Scholar]
  35. Kim, J.S.; Jeong, N.B.; Kim, B.Y.; Mun, S.; Lim, E.S.; Koo, O.K. Prevalence of Campylobacter in retail raw chickens and their motility phenotypes in South Korea. J. Agri. Life Environ. Sci. 2020, 32, 345–352. [Google Scholar]
  36. Kim, C.K.; Oh, H.S.; Ryeom, K.; Cho, M.K. Contamination and survival of Campylobacter jejuni in river water. Korean J. Limnol. 1986, 19, 39–48. [Google Scholar]
  37. Na, H.; Koh, B.; Park, S.; Kim, Y. Studies on Campylobacter jejuni and Campylobacter coli contamination on broiler carcasses in slaughterhouse. Korean J. Vet. Serv. 2007, 30, 77–84. [Google Scholar]
  38. Park, S.D.; Kim, Y.H.; Koh, B.R.; Kim, C.H.; Yoon, B.C.; Kim, C.K. A study on the contamination level of pathogenic microorganisms in beef distribution stages. Korean J. Vet. Serv. 2002, 25, 117–126. [Google Scholar]
  39. Lee, E.M.; Shin, D.S.; Kwon, M.S.; Lee, S.J. Survey of pathogenic microorganisms contamination of chicken carcasses. Korean J. Vet. Serv. 2015, 38, 167–171. [Google Scholar] [CrossRef][Green Version]
  40. Yang, J.W.; Kim, S.H.; Lee, W.W.; Kim, Y.H. Prevalence of virulence-associated genes and antimicrobial resistance of Campylobacter jejuni from ducks in Gyeongnam Province, Korea. Korean J. Vet. Serv. 2014, 37, 85–96. [Google Scholar] [CrossRef]
  41. Oh, J.S.; Shin, K.S.; Yoon, Y.D.; Park, J.M. Prevalence of Campylobacter jejuni in broilers and chicken processing plants. Korean J. Food Hyg. 1988, 3, 27–36. [Google Scholar]
  42. Woo, Y.K. Microbial hygienic status of poultry meats and eggs collected at the public markets in Seoul and Kyung-gi regions in 1996. Korean J. Microbiol. 2005, 41, 38–46. [Google Scholar]
  43. Woo, Y.K. Survey on the status of microbial contamination of chicken meats collected from poultry processing plants in nationwide. Korean J. Microbiol. 2007, 43, 186–192. [Google Scholar]
  44. Hong, C.H.; Lee, K.H.; Lee, S.M. Microbial change of pork carcass during processing in small size slaughterhouse. Korean J. Vet. Serv. 2002, 25, 31–37. [Google Scholar]
  45. An, J.U.; Ho, H.; Kim, J.; Kim, W.H.; Kim, J.; Lee, S.; Mun, S.H.; Guk, J.H.; Hong, S.; Cho, S. Dairy cattle, a potential reservoir of human Campylobacteriosis: Epidemiological and molecular characterization of Campylobacter jejuni from cattle farms. Front. Microbiol. 2018, 18, 3136. [Google Scholar] [CrossRef] [PubMed]
  46. Cho, J.I.; Joo, I.S.; Choi, J.H.; Jung, K.H.; Choi, E.J.; Lee, S.H.; Hwang, I.G. Prevalence and characterization of foodborne bacteria from meat products in Korea. Food Sci. Biotechnol. 2012, 21, 1257–1261. [Google Scholar] [CrossRef]
  47. Chon, J.W.; Lee, S.K.; Yoon, Y.; Yoon, K.S.; Kwak, H.S.; Joo, I.S.; Seo, K.H. Quantitative prevalence and characterization of Campylobacter from chicken and duck carcasses from poultry slaughterhouses in South Korea. Poult. Sci. 2018, 97, 2909–2916. [Google Scholar] [CrossRef]
  48. Chon, J.W.; Jung, H.I.; Kuk, M.; Lim, J.S.; Seo, K.H.; Kim, S.K. Microbiological evaluation of pork and chicken by-products in South Korea. J. Food Protect. 2016, 79, 715–722. [Google Scholar] [CrossRef] [PubMed]
  49. Han, K.; Jang, S.S.; Choo, E.; Heu, S.; Ryu, S. Prevalence, genetic diversity, and antibiotic resistance patterns of Campylobacter jejuni from retail raw chickens in Korea. Int. J. Food Microbiol. 2007, 114, 50–59. [Google Scholar] [CrossRef]
  50. Hong, J.; Kim, J.M.; Jung, W.K.; Kim, S.H.; Bae, W.; Koo, H.C.; Gil, J.; Kim, M.; Ser, J.; Park, Y.H. Prevalence and antibiotic resistance of Campylobacter spp. isolated from chicken meat, pork, and beef in Korea, from 2001 to 2006. J. Food Protect. 2007, 70, 860–866. [Google Scholar] [CrossRef]
  51. Hong, J.; Lim, S.Y. Microbial contamination in kitchens and refrigerators of Korea households. J. Food Hyg. Saf. 2015, 30, 303–308. [Google Scholar] [CrossRef]
  52. Jeong, J.; Lee, J.; Lee, H.; Lee, S.; Kim, S.; Ha, J.; Yoon, K.S.; Yoon, Y. Quantitative microbial risk assessment for Campylobacter foodborne illness in raw beef offal consumption in South Korea. J. Food Protect. 2017, 80, 609–618. [Google Scholar] [CrossRef]
  53. Kim, J.; Park, H.; Kim, J.; Kim, J.H.; Jung, J.I.; Cho, S.; Ryu, S.; Jeon, B. Comparative analysis of aerotolerance, antibiotic resistance, and virulence gene prevalence in Campylobacter jejuni isolates from retail raw chicken and duck meat in South Korea. Microorganisms 2019, 7, 433. [Google Scholar] [CrossRef]
  54. Kim, H.J.; Kim, J.H.; Kim, Y.I.; Choi, J.S.; Park, M.Y.; Nam, H.M.; Jung, S.C.; Kwon, J.W.; Lee, C.H.; Kim, Y.H.; et al. Prevalence and characterization of Campylobacter spp. isolated from domestic and imported poultry meat in Korea, 2004–2008. Foodborne Pathog. Dis. 2010, 7, 1203–1209. [Google Scholar] [CrossRef]
  55. Kim, S.H.; Park, C.; Lee, E.J.; Bang, W.S.; Kim, Y.J.; Kim, J.S. Biofilm formation of Campylobacter strains isolated from raw chickens and its reduction with DNase I treatment. Food Control 2017, 71, 94–100. [Google Scholar] [CrossRef]
  56. Lee, J.; Ha, J.; Kim, S.; Lee, H.; Lee, S.; Yoon, Y. Quantitative microbial risk assessment for Campylobacter spp. on ham in Korea. Korean J. Food Sci. Anim. Resour. 2015, 35, 674–682. [Google Scholar] [CrossRef]
  57. Lee, J.; Jeong, J.; Lee, H.; Ha, J.; Kim, S.; Choi, Y.; Oh, H.; Seo, K.; Yoon, Y.; Lee, S. Antibiotic susceptibility, genetic diversity, and the presence of toxin producing genes in Campylobacter isolates from poultry. Int. J. Environ. Res. Public Health 2017, 14, 1400. [Google Scholar] [CrossRef] [PubMed]
  58. Choi, M.R.; Kim, S.M.; Kim, S.H.; Choi, W.S.; Kim, Y.K. Prevalence and antimicrobial susceptibility of erythromycin-resistant Campylobacter jejuni and Campylobacter coli isolated from swine. Biomed. Sci. Lett. 2012, 18, 152–159. [Google Scholar]
  59. Wei, B.; Cha, S.Y.; Yoon, R.H.; Kang, M.; Roh, J.H.; Seo, H.S.; Lee, J.A.; Jang, H.K. Prevalence and antimicrobial resistance of Campylobacter spp. isolated from retail chicken and duck meat in South Korea. Food Control 2016, 62, 63–68. [Google Scholar] [CrossRef]
  60. Park, H.J.; Kim, Y.J.; Kim, J.H.; Song, S.W.; Heo, E.J.; Kim, H.J.; Ku, B.K.; Lee, S.W.; Lee, J.Y.; Moon, J.S.; et al. Prevalence of Campylobacter jejuni and Campylobacter coli isolated from domestic and imported meats in Korea, 2005~2009. Korean J. Vet. Public Health 2010, 34, 181–187. [Google Scholar]
  61. Carter, P.E.; McTavish, S.M.; Brooks, H.J.; Campbell, D.; Collins-Emerson, J.M.; Midwinter, A.C.; French, N.P. Novel clonal complexes with an unknown animal reservoir dominate Campylobacter jejuni isolates from river water in New Zealand. Appl. Environ. Microbiol. 2000, 275, 6038–6046. [Google Scholar] [CrossRef]
  62. World Health Organization. Foodborne Disease Outbreaks: Guidelines for Investigation and Control; World Health Organization: Geneva, Switzerland, 2008. [Google Scholar]
  63. European Food Safety Authority. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2017. EFSA J. 2018, 16, 5500. [Google Scholar]
  64. Taremi, M.; Dallal, M.M.; Gachkar, L.; MoezArdalan, S.; Zolfagharian, K.; Zali, M.R. Prevalence and antimicrobial resistance of Campylobacter isolated from retail raw chicken and beef meat, Tehran, Iran. Int. J. Food Microbiol. 2006, 108, 401–403. [Google Scholar] [CrossRef]
  65. Chowdhury, M.A.; Ashrafudoulla, M.; Mevo, S.I.; Mizan, M.F.; Park, S.H.; Ha, S.D. Current and future interventions for improving poultry health and poultry food safety and security: A comprehensive review. Compr. Rev. Food Sci. Food Saf. 2023, 22, 1555–1596. [Google Scholar] [CrossRef]
  66. Mohammadpour, H.; Berizi, E.; Hosseinzadeh, S.; Majlesi, M.; Zare, M. The prevalence of Campylobacter spp. in vegetables, fruits, and fresh produce: A systematic review and meta-analysis. Gut Pathog. 2018, 10, 41. [Google Scholar] [CrossRef] [PubMed]
  67. Wei, B.M.; Cha, S.Y.; Kang, M.; Roh, J.H.; Seo, H.S.; Yoon, R.H.; Jang, H.K. Antimicrobial susceptibility profiles and molecular typing of Campylobacter jejuni and Campylobacter coli isolates from ducks in South Korea. Appl. Environ. Microbiol. 2014, 80, 7604–7610. [Google Scholar] [CrossRef] [PubMed]
  68. Rosenquist, H.; Sommer, H.M.; Nielsen, N.L.; Christensen, B.B. The effect of slaughter operations on the contamination of chicken carcasses with thermotolerant Campylobacter. Int. J. Food Microbiol. 2006, 108, 226–232. [Google Scholar] [CrossRef]
  69. Doorduyn, Y.; Van Den Brandhof, W.E.; Van Duynhoven, Y.T.; Breukink, B.J.; Wagenaar, J.A.; Van Pelt, W. Risk factors for indigenous Campylobacter jejuni and Campylobacter coli infections in The Netherlands: A case-control study. Epidemiol. Infect. 2010, 138, 1391–1404. [Google Scholar] [CrossRef] [PubMed]
  70. Schreyer, M.E.; Olivero, C.R.; Rossler, E.; Soto, L.P.; Frizzo, L.S.; Zimmermann, J.A.; Signorini, M.L.; Virginia, Z.M. Prevalence and antimicrobial resistance of Campylobacter jejuni and C. coli identified in a slaughterhouse in Argentina. Curr. Res. Food Sci. 2022, 5, 590–597. [Google Scholar] [CrossRef] [PubMed]
  71. Jorgensen, F.; Ellis-Iversen, J.; Rushton, S.; Bull, S.A.; Harris, S.A.; Bryan, S.J.; Gonzalez, A.; Humphrey, T.J. Influence of season and geography on Campylobacter jejuni and C. coli subtypes in housed broiler flocks reared in Great Britain. Appl. Environ. Microbiol. 2021, 77, 3741–3748. [Google Scholar] [CrossRef]
  72. Sheppard, S.K.; Maiden, M.C. The evolution of Campylobacter jejuni and Campylobacter coli. Cold Spring Harb. Perspect. Biol. 2015, 7, a018119. [Google Scholar] [CrossRef]
  73. Li, A.M. Ecological determinants of health: Food and environment on human health. Environ. Sci. Pollut. Res. 2017, 24, 9002–9015. [Google Scholar] [CrossRef]
  74. Chai, L.C.; Lee, H.Y.; Ghazali, F.M.; Bakar, F.A.; Malakar, P.K.; Nishibuchi, M.; Nakaguchi, Y.; Radu, S. Simulation of cross-contamination and decontamination of Campylobacter jejuni during handling of contaminated raw vegetables in a domestic kitchen. J. Food Protect. 2008, 71, 2448–2452. [Google Scholar] [CrossRef]
  75. Acheson, D.; Hohmann, E.L. Nontyphoidal salmonellosis. Clin. Infect. Dis. 2001, 32, 263–269. [Google Scholar] [CrossRef]
  76. Vally, H.; Glass, K.; Ford, L.; Hall, G.; Kirk, M.D.; Shadbolt, C.; Veitch, M.; Fullerton, K.E.; Musto, J.; Becker, N. Proportion of illness acquired by foodborne transmission for nine enteric pathogens in Australia: An expert elicitation. Foodborne Pathog. Dis. 2014, 11, 727–733. [Google Scholar] [CrossRef] [PubMed]
  77. Ottesen, A.; Ramachandran, P.; Reed, E.; White, J.R.; Hasan, N.; Subramanian, P.; Ryan, G.; Jarvis, K.; Grim, C.; Daquiqan, N.; et al. Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC Microbiol. 2016, 16, 275. [Google Scholar] [CrossRef] [PubMed]
  78. Whiley, H.; Van den Akker, B.; Giglio, S.; Bentham, R. The role of environmental reservoirs in human campylobacteriosis. Int. J. Environ. Res. Public Health 2013, 10, 5886–5907. [Google Scholar] [CrossRef] [PubMed]
  79. Clark, C.G.; Price, L.; Ahmed, R.; Woodward, D.L.; Melito, P.L.; Rodgers, F.G.; Jamieson, F.; Ciebin, B.; Li, A.; Ellis, A. Characterization of waterborne outbreak–associated Campylobacter jejuni, Walkerton, Ontario. Emerg. Infect. Dis. 2023, 9, 1232. [Google Scholar] [CrossRef]
Microorganisms 11 02722 g001
Figure 1. Flowchart of the study selection process followed by a PRISMA 2020 flow diagram for systematic reviews.
Figure 1. Flowchart of the study selection process followed by a PRISMA 2020 flow diagram for systematic reviews.
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Figure 2. Forest plot of the overall study for the prevalence of Campylobacter in South Korea [23,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60].
Figure 2. Forest plot of the overall study for the prevalence of Campylobacter in South Korea [23,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60].
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Figure 4. Forest plot of prevalence of Campylobacter considering environmental factors and processing environments in South Korea: (A) feces, (B) equipment, (C) wash water [32,33,36,37,40,41,45,51,58].
Figure 4. Forest plot of prevalence of Campylobacter considering environmental factors and processing environments in South Korea: (A) feces, (B) equipment, (C) wash water [32,33,36,37,40,41,45,51,58].
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Table 1. Characteristics of studies with the prevalence of Campylobacter spp. in South Korea.
Table 1. Characteristics of studies with the prevalence of Campylobacter spp. in South Korea.
ReferenceSampling Period
(YYYY. MM)		Sample GroupSampleTotal
Sample Size		Campylobacter spp.
(No. of Positive
Samples)		C. jejuni
(No. of Positive Samples)		C. coli
(No. of Positive Samples)		Detection Methods
Enrichment MediumSelective
Medium
Kang et al., 1999 [31] 1996.03–1998.10FoodBeef3004500VTP-Brucella FBP brothCampy BAP
Beef (frozen)50100
Pork2886800
Pork (frozen)50100
Chicken3007000
Chicken (frozen)50000
Kang et al., 1985 [32]1985.03–1985.05FoodChicken44979790VTP-Brucella FBP brothCampy BAP
EnvironmentFeces of chicken27867670
Kim et al., 2013 [33]2010.09–2010.12EnvironmentFeces of duck4301121120CEBCBFA
Kim and Chong, 1996 [34]1996.01–1996.08FoodChicken31319000-BM
Kim et al., 2020 [35]2015FoodChicken302312112 × BDPA
Chicken (cut)3023184
Kim et al., 1986 [36]-EnvironmentRiver and lake waters48110BMBM
Na et al., 2007 [37] -EnvironmentFeces of chicken80000HBModified CBFA
Chicken wash water20900
Park et al., 2002 [38]2000.05–2000.10FoodBeef145000SCBCBFA
Lee et al., 2015 [39]2013.02–2014.10FoodChicken204301515BDCampylobacter agar base, blood agar
Yang et al., 2014 [40]2009.06–2010.01EnvironmentFeces of duck11799936BBMCCDA-PA, blood agar
Oh et al., 1988 [41]1987.06–1987.09EnvironmentFeces of chicken12041410BBCampy BAP
FoodChicken20990
FoodChicken (frozen)2011110
EnvironmentChilling water2012120
EnvironmentKnife20990
Woo, 2005 [42]1996.03–1996.10FoodChicken251600--
Woo, 2007 [43]2007FoodChicken11543430--
Hong et al., 2002 [44]1997FoodPork2962400-Campy brucella agar
An et al., 2018 [45]2012.08–2013.09EnvironmentFeces of cattle26668680-MCCDA
Bedding sample of cattle32330
Cho et al., 2012 [46]2011.02–2011.10FoodBeef52000BDCBFA
Pork62000
Chicken41000
Chon et al., 2018 [47]2014.06–08, 2014.12–2015.02FoodChicken12038002 × blood-free BDMCCDA
Duck1209300
Chon et al., 2016 [48]2015.01–2015.02FoodPork by-product95500BDMCCDA
Chicken by-product159800
Han et al., 2007 [49]2004.02–2004.09FoodChicken26518110094BDAbeyta–Hunt–Bark agar
Hong et al., 2007 [50]2001.09–2006.04FoodChicken270220140170BDCBFA
Pork250333
Beef250404
Hong and Lim, 2015 [51] -EnvironmentDishcloth50000Modified BDMCCDA
Chopping board50000
Drawer of
Refrigerator		50000
Jeong et al., 2017 [52]-FoodBeef80110-MCCDA + Preston enrichment broth
Kim et al., 2019 [53]2016.12–2017.03 2017.04–06FoodChicken133675129BDPA
Duck61383019
Kim et al., 2010 [54]2004–2008FoodPoultry meat
(domestic)		475375219156PBCBFA
Poultry meat
(imported)		86721717344
Kim et al., 2017 [55]2013.12–2014.03FoodChicken12437002 × BDPA
Lee et al., 2015 [56]-FoodPressed ham with
antimicrobials		80000BDModified CCDA-PA and MCCDA
Pressed hams without
antimicrobials		80000
Fermented–cured hams40000
Lee et al., 2017 [57]2014.06–08, 2014.12–2015.02FoodChicken15215002 × blood-free BDMCCDA
Duck1543000
Lee et al., 2019 [23]-FoodPatties96000-Modified CCDA-PA
Meatballs73000
Cutlets55000
Choi et al., 2012 [58]2010.01EnvironmentFeces of pig100553322-PA
Wei et al., 2016 [59]2013.01–03FoodChicken80474252 × BDMCCDA
Duck5203913
Duck (sliced)5450436
Park et al., 2010 [60]2005–2009FoodBeef (domestic)630110PBCBFA
Pork (domestic)644110
Chicken
(domestic)		60918712562
Duck (domestic)70321814
Beef (imported)711000
Pork (imported)943110
Chicken
(imported)		5461098326
Abbreviations: YYYY= year, MM= month, BD = Bolton broth, MCCDA = modified charcoal cefoperazone deoxycholate agar, CEB = Campylobacter enrichment broth, BB = Brucella broth, PB/PA = Preston broth/Preston Agar, BM = Butzler medium, SCB = Skirrow’s Campylobacter selective broth, HB = Hunt broth, CBFA = Campylobacter blood-free agar, Campy BAP = BD Campylobacter agar + ASB, VTP = vancomycin–trimethoprim–polymyxin B, FBP = Brucella–fructose-1,6-bisphosphate.
Table 2. Meta-analysis results for the overall study and each food type and environment.
Table 2. Meta-analysis results for the overall study and each food type and environment.
Sample TypeNo. of StudiesPooled Prevalence and 95% IntervalI2 (%)p-Value
Pooled Prevalence
(%)		Lower Limit
(%)		Upper Limit
(%)
Overall3123.3816.7831.5898%<0.001
FoodChicken2236.1726.4447.1997%<0.001
Duck670.4642.8088.3896%<0.001
Beef80.990.204.7190%<0.001
Pork82.100.676.3594%<0.001
EnvironmentFeces736.3322.6252.6896%<0.001
Washing water327.696.0569.4786%0.001
Equipment54.990.7626.4184%<0.001
Table 3. Prevalence of C. jejuni and C. coli in duck.
Table 3. Prevalence of C. jejuni and C. coli in duck.
AuthorTotal Sample SizeTotal Positive Samples (%)C. jejuni (%)C. coli (%)
Wei et al., 2016 [59]5252 (100)39 (75.0)13 (25.0)
Wei et al., 2016 [59]5450 (92.6)43 (79.6)6 (11.1)
Park et al., 2010 [60]7032 (45.7)18 (25.7)14 (20.0)
Table 4. Prevalence of C. jejuni and C. coli in chicken.
Table 4. Prevalence of C. jejuni and C. coli in chicken.
AuthorTotal Sample SizeTotal Positive Sample (%)C. jejuni (%)C. coli (%)
Kim et al., 2019 [53]6767 (100)51 (76.1)29 (43.3)
Wei et al., 2016 [59]8047 (58.8)42 (52.5)5 (6.3)
Park et al., 2010 [60]609187 (30.7)125(20.5)62 (10.2)
Park et al., 2010 [60]546109 (20.0)83 (15.2)26 (4.8)
Kang et al., 1985 [32]44979 (17.6)79 (17.6)0 (0.0)
Kim et al., 2020 [35]3023 (76.7)12 (40.0)11 (36.7)
Kim et al., 2020 [35]3022 (73.3)18 (60.0)4 (13.3)
Lee et al., 2015 [56]20430 (14.7)15 (7.4)15 (7.4)
Oh et al., 1988 [41]209 (45.0)9 (45.0)0 (0.0)
Oh et al., 1988 [41]2011 (55.0)11 (55.0)0 (0.0)
Woo, 2007 [43]11543 (37.4)43 (37.4)0 (0.0)
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Je, H.J.; Singh, S.; Kim, D.W.; Hur, H.S.; Kim, A.L.; Seo, E.J.; Koo, O.K. Systematic Review and Meta-Analysis of Campylobacter Species Contamination in Poultry, Meat, and Processing Environments in South Korea. Microorganisms 2023, 11, 2722. https://doi.org/10.3390/microorganisms11112722

AMA Style

Je HJ, Singh S, Kim DW, Hur HS, Kim AL, Seo EJ, Koo OK. Systematic Review and Meta-Analysis of Campylobacter Species Contamination in Poultry, Meat, and Processing Environments in South Korea. Microorganisms. 2023; 11(11):2722. https://doi.org/10.3390/microorganisms11112722

Chicago/Turabian Style

Je, Hyeon Ji, Saloni Singh, Dong Woo Kim, Hyun Seok Hur, Ah Leum Kim, Eun Jin Seo, and Ok Kyung Koo. 2023. "Systematic Review and Meta-Analysis of Campylobacter Species Contamination in Poultry, Meat, and Processing Environments in South Korea" Microorganisms 11, no. 11: 2722. https://doi.org/10.3390/microorganisms11112722

APA Style

Je, H. J., Singh, S., Kim, D. W., Hur, H. S., Kim, A. L., Seo, E. J., & Koo, O. K. (2023). Systematic Review and Meta-Analysis of Campylobacter Species Contamination in Poultry, Meat, and Processing Environments in South Korea. Microorganisms, 11(11), 2722. https://doi.org/10.3390/microorganisms11112722

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