https://doi.org/10.4081/ijfs.2026.16245
PO17 | MAKING A LONG STORY SHORT (1997-2019): META-ANALYSIS OF VIBRIO PARAHAEMOLYTICUS PREVALENCE IN RAW BIVALVE MUSSELS PRODUCED IN ITALY
Gabriele Spatola1, Anne Thebault2, Alice Giusti1, Francesca Pedonese1, Pauline Kooh2, Vasilis Valdramidis3, Andrea Armani1 | 1Dipartimento di Scienze Veterinarie, Università di Pisa, Italy; 2Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (Anses), Direction de l'Evaluation des Risques, Maisons-Alfort, France; 3Laboratory of Food Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Greece.
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Introduction and Objective. Vibrio spp. is part of the natural microbiota of fresh waters and marine environments. Some species, including V. cholerae, V. parahaemolyticus and V. vulnificus, are foodborne pathogens associated with the consumption of raw or undercooked seafood. In 2024, the European Food Safety Authority (EFSA) identified V. parahaemolyticus as a major cause of vibriosis in the European Union (EU) and estimated a prevalence of 20.89% (95% CI: 11.65–34.58) of pathogenic strains (tdh+ and/or trh+) in bivalve mollusks (BM). Considering that EFSA did not provide detailed occurrence data for individual Member States, this study aimed to perform a meta-analysis to estimate the pooled prevalence of V. parahaemolyticus in raw MB produced in Italy.
Methods. The meta-analysis was conducted using data from the Pathogens-in-Foods (PIF) database, a systematic repository of EU microbiological occurrence data extracted from peer-reviewed articles. PIF was accessed on 13 March 2026 and the “seafood-borne” section was searched by selecting “Vibrio” as the investigated pathogen. Occurence data for the pathogenic genes (i.e. tdh+ and/or trh+) were analyzed when avalaible. Records were restricted to the following FoodClass categories: “Clams, cockles, arkshells”, “Miscellaneous marine molluscs”, “Mussels”, “Oysters” and “Scallops, pectens”. The dataset was filtered to include only studies on samples collected at primary production, from harvesting to post-depuration, in wild and aquaculture production systems. A random-effects meta-analysis was performed using the metafor R package, with study as random effect and logit-transformed prevalence data. Pooled prevalences and 95% confidence intervals (95% CI) were estimated using restricted maximum likelihood (REML). Heterogeneity was assessed using I² (the proportion) and τ² (the variance).
Results. Thirteen Italian studies detecting V. parahaemolyticus in raw BM at primary production were found in PIF databases, for a total of 4,957 samples analyzed between 1996 and 2019. Most samples originated from Sardinia (74.7%; n = 3,703/4,957) followed by the Adriatic Sea (19.1%; n = 949/4,957), meanwhile only 1.1% originated from the Ligurian Sea. The overall pooled prevalence of V. parahaemolyticus in Italian BM at primary production was 10.07% (95% CI: 4.57 - 20.74), with substantial between-study heterogeneity (I² = 96.0%, τ² = 2.10). The pooled prevalence of tdh and/or trh + strains was 13.84 (95% CI: 9.16 - 20.38). At FoodClass level, prevalence estimates were similar for mussels (Mytilus spp.; 10.40%, 95% CI: 3.94 - 24.76) and clams (Veneridae; 11.33%, 95% CI: 3.86 - 28.94), whereas oysters (Magallana gigas) showed a lower pooled prevalence (1.44%, 95% CI: 0.47 - 4.34). Stratification by sampling area and MB species highlighted marked geographical differences. In clams, prevalence was higher in Sardinia (17.88%, 95% CI: 2.79–62.32) than in the Adriatic area, including Veneto, Emilia-Romagna and Marche (11.99%, 95% CI: 3.87–31.55). Conversely, mussels showed higher prevalence in the Adriatic Sea (20.74%, 95% CI: 11.12–35.37) than in Sardinia (7.40%, 95% CI: 0.94–40.29).
Conclusions. These region-stratified estimates differed from the national pooled values, suggesting that local epidemiological conditions influence the occurrence of V. parahaemolyticus in Italian BM. Overall, these findings indicate that national-level data aggregation may not fully capture local epidemiological patterns, underscoring the need for region-specific surveillance, risk assessment, and management strategies.
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