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Prevalence and antimicrobial resistance of Campylobacter species in South Africa: A “One Health” approach using systematic review and meta-analysis

Open AccessPublished:November 03, 2022DOI:https://doi.org/10.1016/j.ijid.2022.10.042

      Highlights

      • Animals had highest pooled prevalence estimate (PPE) of Campylobacter infection.
      • Majority of Campylobacter prevalence studies were conducted in animals.
      • Highest antibiotic resistance PPE by Campylobacter isolates is against clindamycin.
      • Combined multi-drug resistance PPE of Campylobacter isolates is 35.3%.

      Abstract

      Objectives

      This study investigated the prevalence and antibiotic resistance (AR) profiles of Campylobacter spp. isolated from animals, humans and the environment in South Africa based on available published data.

      Methods

      Original articles published from January 1, 1990, to January 1, 2021, were searched from PubMed, ScienceDirect, Google Scholar, Africa Index Medicus, Scopus, and African Journal Online databases. Data were analysed with Comprehensive Meta-Analysis (version 3.0).

      Results

      After screening, articles on animals (n=25), human (n=7), environment (n=3), animals/environment (n=2), and n=1 study from both animals, human and environment, were included in this review. The pooled prevalence estimates (PPE) were 28.8%, 16.4% 28.4% in animals, humans and the environment respectively. The C. jejuni and C. coli species were commonly isolated from humans, animals, and the environment in South Africa. The AR profiles were screened from 2032 Campylobacter spp. with highest PPE of AR observed against clindamycin (76.9%) and clarithromycin (76.5%). Campylobacter isolates tested with the disk diffusion assay and minimum inhibitory concentration methods recorded overall AR prevalence of 35.3% and 37.1% respectively, whilst Multi-Drug Resistance PPE was 35.3%.

      Conclusion

      Regular surveillance of Campylobacter spp. prevalence and its AMR strains is recommended, as well as formulation of a “One Health" approach for better management and control of Campylobacter spp. infection in South Africa.

      Keywords

      Introduction
      Campylobacter is a zoonotic pathogen that causes campylobacteriosis with C. jejuni and C. coli being the commonly isolated species (Karikari et al., 2017). They infect animals such as chickens, cattle, sheep, pigs, birds, reptiles, and crustaceans (Hlashwayo et al., 2021), and cats and dogs (Begum et al., 2015; Koziel et al., 2014; Thépault et al., 2020; Karama et al., 2019). Most of these animals are natural reservoirs for Campylobacter spp. (Paintsil et al., 2022), and offer a significant risk to humans, as the bacteria is shed in livestock waste and water sources (Oporto et al., 2007; Gahamanyi et al., 2020). Since campylobacteriosis outbreaks are infrequent and triggered by cross-contamination, it is difficult to identify the origins of contamination (Facciolà et al., 2017; Lee et al., 2017). Campylobacter jejuni and C. coli have been associated with human disease (Sheppard and Maiden, 2015; Igwaran and Okoh, 2019).
      Findings from a recently published systematic review and meta-analysis found C. jejuni was the most prevalent species in sub–Saharan Africa (Hlashwayo et al., 2021). These findings are in coherence with another systematic review conducted in West Africa in 2022 (Paintsil et al., 2022), whereby C. jejuni was the most recorded species in terms of prevalence compared to C. coli with 52% and 30% respectively. Campylobacter jejuni is the most frequently detected Campylobacter spp. in food and the most common species linked to human campylobacteriosis (Christidis et al., 2016).
      Campylobacteriosis is best managed using antibiotics such as erythromycin, amoxicillin, azithromycin, clarithromycin, tetracycline, and ciprofloxacin (Gahamanyi et al., 2020 Szczepanska et al., 2017; Shobo et al., 2016; Ramatla et al., 2022). Antibiotic resistance (AR) by Campylobacter spp. associated with animal sources has been widely reported globally (Karikari et al., 2017) including sub-Saharan Africa (Paintsil et al., 2022; Gahamanyi et al., 2020). An exception to this has been observed in immune-deficient or immune-suppressed people, where campylobacteriosis does not require antimicrobial therapy, except in severe cases as this disease is normally a self-limiting (Thakur et al., 2010; Guévremontet al., 2006; Gahamanyi et al., 2020).
      Understanding the epidemiology of Campylobacter in animals, humans, and environment in South Africa is critical in the control of infections associated with the pathogen (Nannan et al., 2012; Thobela, 2017). An estimated 3.552 million children under the age of five years die in Africa every year, with diarrhoea (11%) being the leading cause of mortality (Mason et al., 2013). Few systematic review studies have focused on Campylobacter spp. prevalence and AR in Africa and those that have been conducted have focused on Campylobacter in human and food animals including their products (Thomas et al., 2020; Paintsil et al., 2022; Hlashwayo et al., 2021; Hlashwayo et al., 2020; Diriba et al., 2021). However, there is no comprehensive data available to estimate the prevalence of Campylobacter spp. in South Africa. The available study reports that are either limited to a single province (Sithole et al., 2021; Pillay et al., 2020) or to a specific Campylobacter species (Mileng et al. 2021). The main aim of this study was to perform a systematic review and meta-analysis in order to provide a comprehensive prevalence of Campylobacter spp. in South Africa, AR profiles in animals, humans, and environment based on available published data in South Africa.
      Materials and methods
      Study design
      The systematic review protocol was developed and registered with the international prospective register of systematic reviews (PROSPERO CRD42022316070). The study was conducted using the recommended methodology for systematic reviews as outlined by Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines (Page et al., 2021) which have been confirmed on a checklist (Supplementary Table S1). Journal articles published between 1990 and 2021 that reported on Campylobacter in animals, humans, and the environment (vegetables, water, and soil).
      Search strategy
      Studies were searched in five electronic databases, PubMed (https://pubmed.ncbi.nlm.nih.gov/, from 11/11/2021 to 12/11/2021); ScienceDirect (https://www.sciencedirect.com/ from 11/11/2021 to 12/11/2021); Google Scholar (https://scholar.google.com/ from 25/11/2021 to 06/12/2021); Africa Index Medicus (https://indexmedicus.afro.who.int/, 12/01/2022), Scopus (https://www.scopus.com/, 12/01/2022 to 13/01/2022), and African Journal Online (https://www.ajol.info/index.php/ajol/, 12/01/2022). The following keywords were used to search for articles: Campylobacter species OR Campylobacter jejuni OR Campylobacter coli OR antibiotic OR antimicrobial resistance OR drug resistance OR bacterial resistance OR human OR water OR soil OR vegetable OR animal AND South Africa. Subsequently, the titles, abstracts and full article identified and downloaded. The last search was run on 29th of January 2022.
      Inclusion criteria
      Studies included in the meta-analysis were based on the following: (1) journal articles published in English language, (2) studies conducted in South Africa, (3) studies that included author names, publication year, location, total number of isolates, total samples collected, and conditions, (4) studies that reported the proportion of animals, humans and water collected from the environment harbouring Campylobacter spp. as well as their AR profiles within South Africa, (5) the availability of the full texts, (6) as well as studies conducted within the period spanning the year 1990 to the year 2021. The studies that were eligible had the following characteristics: articles focusing on the prevalence of Campylobacter spp. isolated from various animals including cattle, chickens, goats, sheep, ostriches, dogs, turkey, seabirds and pigs, as well as from the environment such as water, slurry and litter in South Africa.
      Exclusion criteria
      Studies were excluded if they were: (1) not published in English, (2) reviews and experimental studies, (3) book chapters were also excluded, (4) studies with no clearly defined number of samples screened, (5) no number of isolates, (6) studies not conducted in South Africa, (7) articles not published between 1990 and 2021.
      Data quality control measures
      Two authors independently used the Joanna Briggs Institute (JBI) Critical Appraisal Tools Checklist 2017 review guideline (Buccheri and Sharifi, 2017) for prevalence studies to verify the methodological soundness of the research articles chosen for quantitative synthesis. The inclusion of studies was based on the assessment criteria score of 5 or above (Supplementary Table S2).
      Data extraction and data collection
      Full versions of potentially relevant articles were obtained to determine eligibility. The data from each paper was compiled independently and placed into a spreadsheet, including author names, publication year, location, total number of isolates, and total samples collected was entered into a spreadsheet (Microsoft Excel® 2013). Text, tables, and figures were used to extract the data. The meta-analysis only included journal articles that dealt with antimicrobial resistance of Campylobacter spp. in South Africa.
      Meta-Analysis
      Only journal papers specific to Campylobacter spp. were included in the meta-analysis. Comprehensive Meta-Analysis (CMA) Version 3.0 program (https://www.meta-analysis.com/) was used to conduct the meta-analysis. To estimate the pooled prevalence for subgroup analysis, the random-effects model was used and the corresponding 95% confidence interval (CI) was calculated. The pooled prevalence estimates (PPE) is arrived at by using the inverse of the sampling variance and a constant variable across the population effects to weigh each study. Higgin's I2 (inverse variance) and Cochran's Q method were used to assess Cochran's heterogeneity (Q) within studies as well as percentage variation in prevalence. Values close to 0% indicate no heterogeneity, low, moderate, or high heterogeneity were defined as I2 values of ≤25%, 50%, or ≥75%, respectively (Monyama et al., 2022). Heterogeneity with a p-value less than 0.05 (P< 0.05) was considered statistically significant. Subgroup analysis was carried out on the study's outcome based on province, diagnostic methods, study year, and Campylobacter species. Subgroup analyses with less than 3 studies were not included in meta-analysis Lastly, if the number of positive Campylobacter spp. reported exceeded the sample size, a prevalence rate of 100% was recorded.
      Publication bias
      An inverted funnel plot was used to ascertain publication bias using the visual eye test, as well as Egger's and Begg's bias indicator tests (Venâncio et al., 2022; Light et al., 1994). The influence of publishing and selection bias was tested using the Begg-Mazumdar bias indicator test (Egger et al., 1997). A funnel plot often displays effect sizes plotted against their standard errors or precisions (the inverse of standard errors). This allowed us to generate the best estimate of the unbiased pooled effect size by producing a funnel plot that included both the observed studies and the imputed studies required to determine the lack of bias.
      Results
      Search results
      The search process yielded a total of 4 473 studies. A total of 2 552 studies were recorded after duplicates were removed and 2489 after study titles and abstracts were reviewed. Sixty-three full text articles were assessed for eligibility resulting in further exclusion of 25 studies for reasons such as repeated data, not investigating Campylobacter, and unclear results presentation (Figure 1). Meta-analysis was based on a total of 38 articles which reported the prevalence of Campylobacter spp., while AR was reported in 17 of the 38 studies (Figure 1). The quality assessment score from the Joanna Briggs Institute (JBI) critical appraisal ranges from 1 to 9. The lowest score was found only on 55.6% (5/9) studies (Supplementary Table S2).
      Figure 1
      Figure 1PRISMA flowchart for systematic review and meta-analysis of Campylobacter species in South Africa from 1990 to 2021.
      Characteristics of eligible studies
      The number of samples per study ranged from 10 to 2400, and all studies were published between 1990 and 2021. Limpopo (n = 7) was observed to have the highest number of studies followed by KwaZulu-Natal (n = 6), North West (n = 5), Gauteng (n = 2), Eastern Cape (n = 4), and Western Cape (n = 2) being the least (Figure 2). Furthermore, one study was conducted in both Eastern Cape and KwaZulu-Natal provinces and another one in both Western Cape and Gauteng provinces. Ten studies were conducted across all the provinces. The prevalence of Campylobacter spp. ranged from 23.6% to 41.8% across the country. Campylobacter jejuni was reported in twenty-seven (n = 27) studies with 1694/9957 (17%) isolates, while C. coli was identified in twenty-four (n = 24) studies with 817/7297 (11%) isolates. The other Campylobacter spp. were detected from 17 studies with 938/8044 (12%) isolates. Disk diffusion assay and minimal inhibitory concentration were the most commonly used methods for evaluating antibiotic-resistant Campylobacter species. Campylobacter spp. were isolates from water, human and animal faecal samples, sheath washes and animal products including milk and meat from chicken, turkey, beef, and pork.
      Figure 2
      Figure 2Map showing the number of published studies on Campylobacter spp. per province. Black circle shows that there were no studies conducted.
      Pooling and heterogeneity of overall prevalence of Campylobacter species. in animals, human and the environment
      Pooled prevalence estimates (PPE) of Campylobacter spp. in animals, human, and the environment as well as summary of the subgroup analysis are shown inTable 2. A total of 137666 samples were reportedly screened, and only 10809 were confirmed as Campylobacter spp. Of 10809 isolates, only 2609 Campylobacter spp. were identified in animals from 25 studies, with a PPE of 28.8% (95% CI: 19.1 – 40.9) (Figure 3). Seven studies were included in the meta-analysis for human isolates, with a PPE of 16.4% (95% CI: 11.8 – 16.4) (Figure 3). The PPE for isolates from the environment was 28.4% (95% CI: 11.9– 53.9) from three studies (Figure 3).
      Table 1Overview of Campylobacter species studies in animals, humans and the environment published from 1990 to 2021 in South Africa.
      IDStudy (Citation)ProvinceDiagnostic MethodSample sizeIsolatesType of samplesSampled host/site
      1Moré et al., 2017Western CapePCR22933SwabsSeabirds
      2Sithole et al., 2021
      = Multidrug resistance (MDR)
      KwaZulu-NatalPCR520378Faecal, litter, and slurryPigs
      3Uaboi-Egbenni et al., 2010
      = Multidrug resistance (MDR)
      LimpopoPCR10090FaecalSheep
      4Uaboi-Egbenni et al., 2012
      = Antibiotic resistance (AR)
      LimpopoDryspot Campylobacter test kit600164FaecalChickens and cattle
      5Uaboi-Egbenni et al., 2011 ALimpopoPCR450115FaecalPigs
      6Uaboi-Egbenni et al., 2011 B
      = Multidrug resistance (MDR)
      LimpopoPCR20058FaecalGoats
      7Mileng et al., 2021
      = Multidrug resistance (MDR)
      South AfricaPCR240026FaecalChicken
      8Bissong and Ateba, 2019South AfricaPCR40824CloacalChicken
      9Igwaran and Okoh, 2020A
      = Multidrug resistance (MDR)
      South AfricaPCR248240Raw meatChicken, turkey, beef, and pork
      10Jonker and Picard, 2012
      = Multidrug resistance (MDR)
      Western Cape and GautengCulture36238ColonsChicken and pigs
      11Bester and Essack, 2008
      = Antibiotic resistance (AR)
      KwaZulu-NatalCulture15577MeatChicken
      12Bester and Essack, 2012KwaZulu-NatalCulture and Biochemical363293CaecalChicken
      13Pillay et al., 2020
      = Multidrug resistance (MDR)
      KwaZulu-NatalPCR257176Litter, faecal and waterChickens
      14Karama et al., 2019GautengPCR481200Faecal swabDogs
      15Madoroba et al., 2011South AfricaPCR199990Sheath washes and sheath scrapingsCattle
      16Njiro et al., 2012GautengPCR1432Sheath washCattle
      17Bartkowiak-Higgo et al., 2006South AfricaPCR10025Liver, and intestineChicken
      18Madoroba et al., 2021South AfricaqPCR1758159Meat and meat productsBovine, ovine, caprine, poultry, and game meat
      19Reddy and Zishiri, 2017KwaZulu-NatalPCR10078FaecalChicken
      20Igwaran and Okoh, 2020BEastern CapeERIC-PCR376376Milk, water, and meatCattle and water
      21Igwaran and Okoh, 2020C
      = Multidrug resistance (MDR)
      Eastern CapePCR128128Water and milkCattle and water
      22Karama et al., 2020
      = Multidrug resistance (MDR)
      GautengPCR537160Faecal swabCattle and calves
      23Van Nierop et al., 2005GautengPCR9934CarcassesChicken
      24Karama et al., 2019South AfricaPCR481200FaecalDogs
      25Kalule et al., 2019Western CapePCR856MeatChicken, beef, and pork
      26Montwedi and Ateba, 2012North WestCulture and API1010MeatChicken
      27Shange et al., 2020South AfricaPCR836206Cloacal swapOstriches
      28Otigbu et al., 2018
      = Multidrug resistance (MDR)
      Eastern CapePCR244120WaterWater
      29Diergaardt et al., 2004South AfricaPCR203WaterWater
      30Ngobese et al., 2020Eastern Cape and KwaZulu-NatalPCR250135FaecalCattle, chickens, goats, sheep, and pigs
      31Samie et al., 2007ALimpopoPCR32264StoolHuman
      32Lastovica, 1996South AfricaBiotyping and Serotyping1211956999BloodHuman
      33Shobo et al., 2016
      = Antibiotic resistance (AR)
      KwaZulu-NatalPCR7272StoolHuman
      34Thobela et al., 2018South AfricaqPCR51262StoolHuman
      35Obi and Bessong, 2002
      = Antibiotic resistance (AR)
      LimpopoCulture and biochemical10012StoolHuman
      36Chukwu et al., 2020
      = Antibiotic resistance (AR)
      North WestqPCR505124StoolHuman
      37Samie et al., 2007B
      = Antibiotic resistance (AR)
      LimpopoPCR565115StoolHuman
      38Chukwu et al., 2019
      = Antibiotic resistance (AR)
      North WestqPCR9220WaterWater
      PCR = polymerase chain reaction, qPCR= real-time polymerase chain reaction, ERIC-PCR= Enterobacterial repetitive intergenic consensus- polymerase chain reaction, API= Application Programming Interface.
      low asterisk = Antibiotic resistance (AR)
      low asterisklow asterisk = Multidrug resistance (MDR)
      Table 2The proportion of Campylobacter species isolated from humans, animals, and the environment, as well as the screening methods used with sample locations.
      Risk factorsNumber of studiesPooled estimatesMeasure of heterogeneityPublication bias
      Sample sizeNumber of positiveI2 % (95%CI)Q ValueI2Q-PBegg and Mazumdar rank P-value
      Overall study
      Human7124094754616.4% (11.8 – 16.4)82.51891.5170.00010.45077
      Environment335614328.4 (53.9 – 11.9)24.57091.8600.00010.13541
      Animal2512627260928.8 (19.1 – 40.9)2237.83498.9280.00010.34567
      Animal/ environment2504504
      Animal/human environment1856
      Study year
      1990 – 200011211956999
      2000 – 20108248445320.0 (13.4− 29.0)147.00194.5580.00010.50000
      2010 – 20212914173358338.6 (27.2 – 51.4)2657.49698.9460.0810.35369
      Diagnostic technique
      PCR2510864215532.1 (21.1 – 45.6)2559.91298.9380.0030.17363
      Culture5136649046.5 (13.9 – 81.3)317.38898.7400.8330.40325
      qPCR4227022115.6 (8.9 – 26.1)85.85796.5060.00010.14532
      Serotyping11211956999
      ERIC-PCR1376376
      Dryspot kit1600164
      Provinces
      KwaZulu-Natal61467107472.9 (62.6 – 81.2)60.24391.7000.00010.5000
      Gauteng2624202
      Eastern Cape499686397.2 (67.7 – 99.8)124.48297.5900.0130.50000
      North West4332318415.1 (3.6 – 45.5)267.15298.5030.0280.50000
      Eastern Cape/KwaZulu-Natal1250135
      Western Cape/Gauteng136238
      Limpopo7233771627.1 (18.9 – 37.2)180.54096.1230.00010.35526
      Western Cape231439
      All provinces10128707776011.3 (7.5 – 16.6)266.82096.6270.00010.39422
      Campylobacter spp.
      C. jejuni279957169461.4 (50.1 – 71.1)732.51096.3140.00010.66710
      C. coli24729781724.3 (19.1 – 31.5)330.83293.0480.00010.47539
      Others17804493854.1 (29.6 – 64.9)478.44196.6560.00010.32954
      Antibiotic resistance methods
      DDA136157168440.8 (22.3 – 62.3)1188.34199.1580.4030.62280
      MIC6115430237.1 (17.4 – 42.4)106.71295.3150.00010.50000
      PCR = polymerase chain reaction; qPCR= real-time polymerase chain reaction; ERIC-PCR= Enterobacterial repetitive intergenic consensus- polymerase chain reaction; DDA= disk diffusion assays; MIC= Minimal inhibitory concentration; I2= inverse variance; Q-p = Cochran's; CI= confidence interval; Measure of heterogeneity= the weighted sum of squared differences between individual study effects and the pooled effect across studies.
      Figure 3
      Figure 3Forest plot showing the pooled prevalence of Campylobacter spp. in A) humans, B) animals and C) the environment from South Africa. The squares show the individual point estimate. The diamond at the base indicates the pooled estimates from the total studies. Favour A = positive effect while B= negative effect.
      Prevalence by diagnostic methods
      Six diagnostic techniques were used to identify Campylobacter spp. (Figure 4). The highest PPE was observed on studies using the traditional culture and isolation technique with 45.6% (95% CI: 13.9 – 81.3) from five studies, followed by conventional PCR with 32.1% (95% CI: 21.0 – 45.6) from twenty-four studies, and lastly by qPCR with 15.6% (95% CI: 8.9 – 26.1) from four studies (Table 2). Due to low number of eligible studies that utilized the API and Campylobacter kit, the data was not computed.
      Figure 4
      Figure 4The pooled prevalence of Campylobacter spp. using different diagnostic techniques.
      Prevalence by study provinces
      The highest prevalence of Campylobacter spp. was reported in the Eastern Cape province (97.2%; 95% CI: 67.7 – 99.8, 4 studies), followed by KwaZulu-Natal provinces (72.9%; 95% CI: 62.6 – 81.2, 6 studies), Limpopo (27.1%; 95% CI: 18.9 – 37.2, 6 studies), North West with (15.1%; 95% CI: 3.6 – 45.5, 5 studies), and all provinces (11.3%; 95% CI: 7.5 – 16.6, 6 studies) (Table 2).
      Prevalence by years of study
      Studies conducted between 2010 and 2021 yielded a high PPE of 38.6% (95% CI: 27.2 – 51.4), from 29 studies with 3583 isolates, followed by eight studies conducted during 2000 – 2010 with a pooled prevalence estimate of 20.0% (95% CI: 13.4− 29.0) from 453 isolates (Table 2). There was only one study published between 1990 to 2000 (Lastovica, 1996).
      Prevalence by Campylobacter species
      Campylobacter spp. was screened for 11057 isolates, of which 1694 were C. jejuni, 817 C. coli and 938 were Campylobacter species. The overall PPE for C. jejuni was 61.4% based on twenty-seven studies (95% CI: 50.1 – 71.5). The C. coli had a PPE of 24.3% (95% CI: 18.6 – 31.1) based on twenty-four studies (Table 2). While other Campylobacter spp. had 54.1% (95% CI: 35.8 – 71.4) based on twenty-eight articles. For uncharacterized Campylobacter spp., a PPE of 54.1% was documented.
      Prevalence of antibiotic resistance in Campylobacter species
      Out of 20 studies subjected to meta-analysis for antibiotic resistance, only 14 used DDA with a PPE of 40.8% (95% CI: 22.3 – 62.3). Minimum inhibitory concentration (MIC) was employed in 6 studies with a PPE of 37.1% (95% CI: 17.4 – 42.4). The heterogeneity estimates of the different AR profile of Campylobacter spp. isolated from animals, human and the environment is shown in Table 2.
      Prevalence based on antibiotic resistance profile
      The Campylobacter spp. antibiotic resistance (AR) profiles were screened from 2032 Campylobacter spp. obtained from 17 studies and the results are summarized in Table 3. A total of ten (n = 10) studies classified as multidrug resistance (MDR), which is defined as resistance to more than two drugs with a PPE of 35.3%. The PPE of clindamycin was 76.9%, followed by clarithromycin 76.5%, doxycycline 67.1%, ampicillin 60.4%, tetracycline 56.3%, erythromycin 49.6%, ciprofloxacin 38.5%, nalidixic acid 35.8%, chloramphenicol 33.3%, imipenem 30.0%, and gentamicin 27.7% (Table 3).
      Table 3Pooled prevalence estimates and 95% CI of antibiotic resistance of Campylobacter spp. isolates from this study.
      Antimicrobial agentsNumber of studiesNumber of isolates% Prevalence (95%CI)I2 (95%CI)P-value
      Tetracycline16109248.3(28.4 – 68.7)0.500
      Chloramphenicol35533.3(15.8 – 57.1)0.148
      Ciprofloxacin1790231.9(18.8 – 48.8)0.200
      Clindamycin560576.8(52.0 – 91.0)0.043
      Nalidixic acid1248435.8(28.1 – 44.3)0.151
      Ampicillin13109552.5(33.9 – 70.4)0.292
      Clarithromycin327476.5(50.2 – 91.4)0.148
      Erythromycin18119444.0(27.8 – 61.6)0.310
      Gentamicin1456822.7(11.4 – 40.1)0.151
      Imipenem420630.0(15.7 −49.8)0.248
      Doxycycline549767.1(36.7 – 87.7)0.312
      MDR1068835.3(20.5 – 53.6)0.124
      MDR = multidrug-resistant
      Other antibiotic resistance patterns included the following: penicillin 2 (12.5%), levofloxacin 2 (12.5%), florfenicol 2 (12.5%), vancomycin 2 (12.5%), metronidazole 1 (6.3%), ceftiofur 1 (6.3%), fosfomycin 1 (6.3%), enrofloxacin 1 (6.3%), tylosin 1 (6.3%), lincomycin 1 (6.3%), methicillin 1 (6.3%), ceftriaxone 1 (6.3%), azithromycin 1 (6.3%), sulfamethoxazole-trimethoprim 1 (6.3%), amoxicillin/clavulanic acid 1 (6.3%) and norfloxacin 1 (6.3%).
      Publication bias
      The Begg and Mazumdar rank correlation test demonstrated no significant publishing bias for practically all parameters except for one antibiotic (Clindamycin), where both asymmetry of the funnel plots and P-value 0.043 indicated considerable bias (Figure 5).
      Figure 5
      Figure 5Funnel plot with 95% confidence limits of the pooled prevalence of the studies conducted on clindamycin.
      Discussion
      This systematic review and meta-analysis produced an overall PPE of 32.0% for Campylobacter spp. from 38 analysed studies in South Africa. This finding is consistent with other systematic reviews that reported that there are few Campylobacter research studies in Africa (Thomas et al., 2020; Paintsil et al., 2022; Hlashwayo et al., 2021; Hlashwayo et al., 2020). The results obtained from this study revealed the PPE of 28.8%, 16.4% and 28.4% of Campylobacter spp. in animals, humans, and the environment respectively. These findings highlight the primary public health risk connected with the presence of Campylobacter spp. in the animal food supply chain, as well as the environment, which may eventually harm humans. The estimated pooled prevalence differed between the nine provinces that reported Campylobacter species in human, animal, and the environment across South Africa.
      In this study, we observed a significant increase of the studies on Campylobacter spp. conducted from 1990 to 2021. The increased number of funds for research might be the possible reasons for this change. Furthermore, this could be due to new and improved detection methods becoming available in recent years (Paintsil et al., 2022).
      Studies from various provinces were included in this systematic review and meta-analysis and most of these studies (76.3%) were carried out between 2010 and 2021, followed by period of the years 2000 to 2010 with 21%, and lastly 1990 to 2000 with 2.6% of the studies that screened a total of 1137852 samples. The Mpumalanga, Northern Cape, and the Free State provinces were not included in the data sets due to the absence of published data on the prevalence of Campylobacter spp. These could be due to lack of resources for research studies, a lack of funding, or the fact that campylobacteriosis is a neglected disease in those provinces as they are resource-poor areas.
      The C. jejuni and C. coli PPE from this study is 61.4% % and 24.3% respectively. This is comparatively higher to similarly reported PPE from reviews conducted in sub-Saharan Africa with 8.3% and 9.9% on gastrointestinal pathogens and humans respectively (Fletcher et al., 2011; Hlashwayo et al., 2021). The disparity could be due to differences in Campylobacter isolates, the study population's makeup, or the microbiological diagnostic methods employed.
      The most sensitive diagnostic techniques are the molecular-based methods, and this meta-analysis used prevalence data from studies that also used these methods to detect Campylobacter spp. Most of the studies (32.1%) in this review used PCR to confirm Campylobacter spp. in animals, humans, and the environment on 10442 samples. A systematic review on Campylobacter spp. in West Africa reported closely similar results (34%) that are consistent with current data (Paintsil et al., 2022). However, our Campylobacter spp. PPE results are higher than those reported from systematic review conducted in Africa as continent (Hlashwayo et al., 2021) and the country Ethiopia (Diriba et al., 2021), with prevalence of 10% and 9.9% respectively. Since Campylobacter spp. can become unviable during transport and processing, the culture-based method can have some limitations (Hlashwayo et al., 2021). Environmental stress during sample transportation and processing can make some Campylobacter spp. viable but not culturable on media (Lv et al., 2020; Paintsil et al., 2022). Hence, some studies make use of culture-based method together with molecular techniques, such as PCR, due to its high sensitivity (Ramatla et al., 2021; Monyama et al., 2022).
      Antimicrobial-resistant (AR) bacteria are a global issue that affects every country worldwide. The AR is a source of concern since it increases the likelihood of treatment failure (Kebede et al., 2017). Development of antimicrobial resistance may be due to misuse in both human disease treatment and in animal husbandry (Kashoma et al., 2015; Hlashwayo et al., 2020). In certain instances, farmers disregard the withdrawal period and recommended dosages, and this leads to antibiotic resistance (Olabode et al., 2017). Additionally, pathogens with resistance can be directly transferred to humans from animals and animal products (Noreen et al., 2020).
      We observed a significant increase in Campylobacter spp. AR prevalence (29.6% to 63.1%) from 1990 to 2021. Antimicrobials are widely used in animal farming for growth promotion and as prophylaxis, which could explain the rising trend (Mengistu et al., 2020; Paintsil et al., 2022). This could also be due to new and improved detection methods (Paintsil et al., 2022), awareness of campylobacteriosis and the availability of resources to conduct research.
      The meta-analyses showed that the PPE of AR by Campylobacter species was higher against clindamycin (76.8%). Resistance against clindamycin is of public concern because it has been reported to possess good in vitro activity against C. jejuni and is used to treat Campylobacter spp. infections in humans (Varela et al., 2007). The AR against clarithromycin had second highest PPE, however, it is not commonly used to treat Campylobacter infections because its maximum inhibitory concentration (MIC90s) is 2-fold higher than of the erythromycin (Gibreel, and Taylor, 2006). Macrolides/lincosamides (clindamycin and erythromycin) are infrequently used to treat respiratory disease and mastitis in dairy cattle (Englen et al., 2007).
      An aminoglycoside like gentamicin can also be used to treat serious systemic infections (Skirrow, and Blaser, 2000; Gibreel, and Taylor, 2006). The AR by Campylobacter spp. PPE against gentamicin was low (27.7%) in this study. This is lower than what was observed in previous studies conducted in Ghana, Japan, Brazil; 47%, 33%, 30% respectively (Karikari et al., 2017; Koga et al., 2017; de Moura et al., 2013), however, is higher than the values reported in Malaysia (4%), and Egypt (0%) (Tang et al., 2016; Abd El-Baky et al., 2014). The differences could also be explained by the countries differing drug administration policies. For treating Campylobacter gastroenteritis, erythromycin is the preferred antibiotic, but ciprofloxacin and tetracycline are alternative drugs (Gibreel, and Taylor, 2006; Hlashwayo et al., 2021). Our review shows that AR PPE against erythromycin is 49.6% among the antibiotics tested. The overuse of erythromycin due to its low risk of side effects could explain the observed high resistance in humans (Paintsil et al., 2022). It is possible that ciprofloxacin-resistant isolates originated in humans, where this antibiotic is routinely used (Kashoma et al., 2015). In this study, the proportion of the public health implication of multidrug resistance (MDR) isolates were observed from ten studies.
      Food safety, zoonotic disease control, laboratory services, neglected tropical diseases, environmental health, and antimicrobial resistance are among the areas of work where a "One Health" approach is particularly relevant, according to the World Health Organization (WHO) (Ramatla et al., 2022). Campylobacteriosis is most often caused by the consumption of contaminated animals and food products of animal origin such as cattle, pigs, ostriches, poultry, and sheep (Chlebicz and Śliżewska, 2018; Del Collo et al., 2017), vegetables are also a frequent vector of transmission. It can also be acquired through direct contact with infected pets, including cats and dogs at home environment (Shane, 2000). Shellfish have also been shown to contain Campylobacter bacteria according to the World Health Organization (WHO) (https://www.who.int/news-room/fact-sheets/detail/campylobacter). This review reported the presence of Campylobacter spp. in both human, animals and as well as environment samples suggesting that the pathogen may be circulating indefinitely.
      Limitations
      There are few limitations to our systematic review and meta-analysis: (a) The search strategy was limited to articles published in English meaning that there may have been articles published in other languages that were overlooked. (b) Because the number of studies from some provinces was limited, the findings may not be the true representative of the rest of the country. (c) In comparison to other provinces, some had more research reports than other. d) There were few studies available on environmental samples, limiting comparisons of the prevalence and resistance profile. (e) Due to the inconsistent data reported within each study, it was not possible to compare resistance patterns by species.
      5. Conclusions
      The results obtained in this study revealed that the C. jejuni and C. coli are commonly isolated from humans, animals, and environment in South Africa. In some provinces, such as Free State, Mpumalanga and Northern Cape, there are significant gaps in surveillance and a lack of published studies on the prevalence of Campylobacter species. These findings revealed a significant incidence of Campylobacter spp. in animals, as opposed to humans and the environment. This is an indication of the primary public health threat posed by the presence of Campylobacter spp. via animal production chain, which can later impact the human population. To the best of our knowledge, this is the first comprehensive study assessing AR of Campylobacter spp. among humans, animals, and the environment in South Africa. For the prevention and control of Campylobacter spp. and its AR in South Africa, regular surveillance of AR strains and early detection of these isolates using phenotypic and genotypic laboratory approaches is recommended. Furthermore, the human and animal health as well as environmental sectors need to formulate singular and deliberate "One Health" approach for management and control of Campylobacter spp. in South Africa.
      Funding
      This study did not receive any specific grant from funding agencies.
      Disclosures
      The authors have no conflict of interests.
      Ethical approval
      Approval was not required.
      Authors’ contributions
      TR, MP, TEO, KEL, and OT conceived and designed the study. TR performed the literature review and extraction of data. TR, MCM and MT analysed and interpreted the data, created figures and tables, and drafted the manuscript. OT, KEL and CB offered mentorship and guidance on antimicrobial resistance. RN, and MBNM offered veterinary expertise. All authors read, commented, and approved the final manuscript.
      Declaration of interests
      The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
      The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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