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Prevalence of foodborne pathogens in ready-to-eat foods: systematic review, meta-analysis and meta-regression

  • Pavel V. Berezhanskiy , Amirhossein Mahmoudizeh and Yadolah Fakhri EMAIL logo
Published/Copyright: September 10, 2025

Abstract

Ready-to-eat (RTE) foods are highly susceptible to microbial contamination, posing significant public health risks. This study aimed to conduct a systematic review, meta-analysis, and meta-regression to evaluate the prevalence of foodborne pathogens in RTE foods, assess trends over time, and examine the impact of the Global Food Security Index (GFSI). The search was conducted across Scopus, Web of Science, Embase, and PubMed until 2025. Studies reporting pathogen prevalence in RTE foods were included. Meta-analysis in defined subgroup and meta-regression assessed the effects of time and GFSI on prevalence trends. Sixty-five papers with 858 datasets comprising a total of 232,760 samples were included in the meta-analysis. Gram-negative pathogens (ES=18.32, 95 % CI: 15.29–21.53) were more prevalent than Gram-positive pathogens (ES=8.08, 95 % CI: 7.17–9.02). The African Region had the highest prevalence (ES=30.02, 95 % CI: 25.75–34.44), while the Americas had the lowest (ES=4.56, 95 % CI: 2.28–7.34). Contamination increased over time (C=0.018, p<0.001) but decreased with GFSI implementation (C=−0.005, p<0.001). RTE foods are highly vulnerable to contamination, particularly by Gram-negative pathogens. Regional disparities highlight the need for improved food safety infrastructure, especially in high-risk areas. Adopting global standards like GFSI and addressing emerging challenges such as antimicrobial resistance and climate change are essential for reducing foodborne illnesses.


Corresponding author: Yadolah Fakhri, Food Health Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Search in databases was conducted by Amirhossein Mahmoudizeh, Yadolah Fakhri; data collection by Amirhossein Mahmoudizeh, Yadolah Fakhri; Data Analysis by Amirhossein Mahmoudizeh, Yadolah Fakhriand the manuscript and editing by Pavel V. Berezhanskiy, Amirhossein Mahmoudizeh, Yadolah Fakhri.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/reveh-2025-0047).


Received: 2025-04-22
Accepted: 2025-08-26
Published Online: 2025-09-10

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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