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Record W3113283693 · doi:10.1186/s12941-020-00405-8

Prevalence of Campylobacter species in human, animal and food of animal origin and their antimicrobial susceptibility in Ethiopia: a systematic review and meta-analysis

2020· review· en· W3113283693 on OpenAlexaboutno aff
Tizazu Zenebe, Niguse Zegeye, Tadesse Eguale

Bibliographic record

VenueAnnals of Clinical Microbiology and Antimicrobials · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsCampylobacterMeta-analysisVeterinary medicineChecklistAntimicrobialBiologyEnvironmental healthFunnel plotPublication biasAntibiotic resistanceMedicineInternal medicineMicrobiologyAntibioticsBacteria

Abstract

fetched live from OpenAlex

Abstract Background Campylobacter jejuniandCampylobacter coliaccounts for most cases of human gastrointestinal infections. The infection occurs through ingestion of contaminated food or water, and direct contact with feces of infected animal or human. Regardless of few local reports ofCampylobacterand its antimicrobial susceptibility profile, there is no comprehensive data that show the burden ofCampylobacterinfection at national level in Ethiopia. This systemic review and meta-analysis aimed to determine the pooled prevalence ofCampylobacterand its resistance patterns in Ethiopia from different sources. Method A comprehensive literature search of PubMed, Google scholar, Science direct and Google engine search was conducted for studies published from 2000 to July 30, 2020 on prevalence and antimicrobial susceptibility ofCampylobacterin human, animal and food. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Checklist. Data from articles was extracted using a standardized data extraction format. The quality of the studies was assessed based on the Newcastle–Ottawa scale. The Q test and I2test statistic were used to test heterogeneity across studies. The Pooled estimate of prevalence ofCampylobacterspecies and its antimicrobial susceptibility profile was computed by a random effects model using STATA 16.0 software. Results were presented in forest plot, tables, funnel plot and figures with 95% confidence interval. Results A total of 291 articles were retrieved initially. The pooled prevalence ofCampylobacter speciesfrom different sources was 10.2% (95% CI 3.79, 16.51). In this meta-analysis, the lowest prevalence was 6.0% whereas the highest prevalence was 72.7%. In the sub-group analysis, the pooled prevalence was similar in Amhara and Oromia region, higher in Gambella and lower in Sidama. Prevalence ofCampylobacterwas higher in animals (14.6%) compared to humans (9%). The pooled antimicrobial resistance rates ofCampylobacter speciesto different antimicrobials ranged from 2.9–100%. Overall, higher rate of resistance was to cephalothin (67.2%), gentamicin (67.2%), and trimethoprim-sulfamethoxazole (33.3%) inCampylobacterisolates from all sources. In isolates from human, resistance to cephalothin was 83% followed by amoxicillin (80%), amoxicillin-clavulnate (36%), trimethoprim-sulfamethpxazole (32%), clindamycin (31%) and ceftriaxone (28%). On the other hand, higher rate of resistance to penicillin (100%), cephalothin (60%), ciprofloxacin (71.2%), and trimethoprim-sulfamethoxazole (39%) was recorded in isolates from animals. Conclusion The present study highlights the burden ofCampylobacter speciesin the country and higher rate of resistance among investigated isolates. Designing appropriate prevention strategies and further local in-depth studies are recommended to establish actual epidemiological burden of the bacteria in the country.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.028
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.034
Bibliometrics0.0140.012
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.296
GPT teacher head0.420
Teacher spread0.124 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2020
Admission routes1
Has abstractyes

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