Renata Ivanek

Raw milk consumption is a controllable source of foodborne pathogen exposure in the United States (US), yet existing national estimates of raw milk consumption behaviors are outdated and do not capture emerging factors such as consumers’ trusted information sources, sociopolitical influences, and at-home risk mitigation behaviors. We conducted a nationally representative cross-sectional survey of n=1078 US adults to estimate the prevalence of raw milk consumption and at-home thermal treatment practices and identify predictors of these behaviors. Multivariable logistic regression models were used to evaluate predictors of raw milk consumption and thermal treatment behavior using adjusted odds ratios (aORs). Among 1,075 eligible respondents, 15% consumed raw milk in the last six months, including 5% exclusively and 10% in addition to pasteurized milk. In multivariable analysis, raw milk consumption was independently associated with younger age, cohabitation with high-risk individuals for foodborne infections (including children ≤5 years of age, and elderly, pregnant or immunocompromised individuals) who had consumed raw milk, and perceptions of raw milk taste and safety. Adults aged ≥55 years had lower odds of consumption than those aged 18–24 years (aOR, 0.05; 95% CI, 0.02–0.15), while those living with a high-risk household member who consumed raw milk had higher odds of consumption (aOR, 4.39; 95% CI, 2.69–7.22). Perceiving raw milk to have higher risk of causing illness was associated with lower odds of raw milk consumption (aOR: 0.32, 95% CI: 0.17-0.62) compared to believing the risk to be equal across milk types, and thermal treatment was reported by 38% of exclusive raw milk consumers (n=58) and 47% of consumers of both milk types (n=106). Raw milk consumption in the US appears to be substantially more common than previously reported and shaped by factors such as taste, safety, and household raw milk consumption among high-risk individuals for foodborne infections. The proportion of raw milk consumers already practicing at-home thermal treatment suggests a potential opportunity for risk mitigation.

The surge of highly pathogenic avian influenza A (H5N1) infection in animals across the United States since 2024 has renewed pandemic concern, making public perception a critical determinant of effective risk communication. We analyzed 42,957 Reddit comments (January 2024–December 2025) to characterize the patterns in online discourse during the outbreak. Topic modeling identified themes of public health, pandemic comparisons, and food safety. Sentiment analysis identified fear as the dominant emotional response, while anger was the most event-reactive, correlating with reported cases in cats, poultry, and wild mammals. Autoregressive Integrated Moving Average with Exogenous Regressors (ARIMAX) models with blocked cross-validation identified reported cases in domestic cats and dairy cattle as predictors of weekly Reddit engagement, reducing forecasting error by 31% over baseline. Mammalian cases thus were associated with early-warning signals for public reaction during the 2024–2025 outbreak, offering a one-week window for proactive risk communication.

This scoping review synthesized current evidence on the epidemiology of highly pathogenic avian influenza (HPAI) A(H5N1) clade 2.3.4.4b in U.S. dairy cattle and occupationally exposed workers, and identified potential mitigation strategies. Literature searches were conducted in PubMed, Web of Science, CAB Abstracts, FSTA, and AGRICOLA across three search periods (spanning January 1, 2024–September 30, 2025), supplemented with gray literature. A total of n = 53 peer-reviewed articles and n = 8 gray literature sources met the inclusion criteria. Current evidence suggests that the outbreak likely resulted from a single spillover from wild birds followed by sustained cattle-to-cattle transmission, with the mammary gland serving as a major site of viral replication. Human infections occurred primarily among occupationally exposed workers through direct contact with infected cattle or contaminated milk, with conjunctivitis being the predominant clinical presentation. Consistently recommended mitigation strategies included enhanced surveillance, biosecurity, improved milking hygiene, pre-movement testing of cattle, use of personal protective equipment, and worker education within a One Health framework. However, important knowledge gaps remain regarding the primary route of viral entry into cattle, the relative contribution of respiratory and fomite transmission, viral dissemination beyond the mammary gland, infection risk in beef cattle, and the effectiveness of current mitigation strategies under field conditions. Limited evidence is also available for protecting occupational groups other than dairy farmworkers. Overall, this review highlights the need for coordinated One Health approaches and further research to strengthen evidence-based prevention strategies for both animal and worker health.

As digital tools for modeling microorganisms of food safety concern (foodborne pathogens and indicator microorganisms) become more widespread in food safety decision-making, a comprehensive assessment of existing model alignment with public health and stakeholder needs is lacking. This scoping review systematically characterized published digital tools for food safety microorganism modeling to evaluate their purpose, modeling approach, validation extent, code transparency, and user interface design. We searched six databases (PubMed, Web of Science, FSTA, AGRICOLA, CAB Abstracts, Medline) for peer-reviewed studies on digital tools that model food safety microorganisms in food/agriculture matrices published between 2012 and 2024. We identified 62 eligible studies with code access provided and found that model purpose largely aligned with established public health priorities. Mechanistic models were the dominant modeling approach (48%), followed by data-driven (32%), and hybrid (19%) models. Most included models demonstrated rigorous validation (82%) and were developer-oriented tools (58%) requiring programming or command-line interaction. During full-text screening of 375 articles, we first assessed code transparency, which resulted in the exclusion of 208 studies where the underlying code was not publicly available. Furthermore, among the 62 eligible studies with available code, 21% made their code available only upon request via email. Reporting of model characteristics remained heterogeneous despite the availability of software reporting standards. These findings provide a foundation to guide future research priorities and highlight the need for collaborative efforts among researchers, journals, and funding bodies to improve transparency, reproducibility, and usability for diverse stakeholder needs.

Many types of minimally processed fresh produce, including baby spinach, are sensitive to microbial spoilage and other defects that influence quality and consumer acceptance. However, there is limited understanding of how various growing, processing, and storage practices can influence the composition of bacterial and fungal communities on leafy greens, including the proliferation of spoilage microorganisms. Baby spinach samples obtained from a single supply chain at various stages of the produce supply chain (i.e., after harvest and after commercial washing, packaging, and storage) over 1 year were analyzed using 16S ribosomal RNA and internal transcribed spacer amplicon sequencing to assess changes in the bacterial and fungal microbiota. Our data showed that there were significant differences in the bacterial and fungal community composition of Harvest samples associated with (i) the geographic area where the spinach was grown and (ii) seasonality (i.e., time since the start of the growing season). After commercial washing and during storage, Pseudomonas spp. dominated the bacterial community (>60% average relative abundance [RA], compared to 8.62% average RA on Harvest samples). Analysis at the amplicon sequence variant (ASV) level revealed that the RA of 39 out of 868 Pseudomonas ASVs significantly increased over shelf life, indicating that only select taxa from a particular genus might drive spoilage. Four of the top five fungal genera maintained a high RA across harvest, washing, and postharvest storage, except for one genus ( Vishniacozyma ) that was only represented among the top five in samples after washing. Our data could be used to inform more targeted quality management practices. IMPORTANCE: While spoilage of leafy greens can be influenced by a variety of factors, the limited understanding of how microbial communities change throughout the various stages of the produce supply chain remains a barrier to identifying factors that drive baby spinach spoilage. Additionally, many studies focus solely on the bacterial community, leaving gaps in understanding how the fungal community is impacted by processing and storage conditions. The findings of this study deepen our understanding of how the bacterial and fungal communities on baby spinach are influenced by both (i) preharvest factors (i.e., growing region and seasonality) and (ii) postharvest factors (i.e., commercial washing/packaging and storage over shelf life). This research deepens our understanding of the variation of the microbial composition of leafy greens and specifically suggests the potential value of spoilage mitigation efforts that target specific Pseudomonas taxa.

Voluntary horizontal sharing of confidential food safety data among companies for joint analysis can improve food safety and enhance efficiency and decision-making, especially for rare events. However, despite its potential benefits, such horizontal data sharing in the food industry has lagged, with limited research exploring the reasons for this hesitation. To address this knowledge gap, we conducted in-depth semi-structured interviews with 27 food industry leaders. We found that companies face trade-offs when deciding to share food safety data, weighing risks against benefits. Data sharing decisions were strongly influenced by the level of trust participants had in stakeholders (e.g., industry peers, regulatory bodies, customers) and in data protection measures. Data governance emerged as a solution to concerns stemming from trust, such as loss of control once data is shared. By uncovering the tensions that shape food industry decision-making, the findings of this exploratory study offer timely insights to guide horizontal data sharing initiatives and prioritize areas for future research, towards a collaborative data-driven approach to tackling food safety challenges.

Baseline plasma ACTH concentrations are frequently utilized as part of the diagnostic evaluation of equids when PPID is suspected. Baseline ACTH can be impacted by many factors including time of year, i.e., ACTH has generally been found to be elevated during late summer through early autumn in the northern hemisphere. An understanding of ACTH concentrations in healthy equids over the course of a year is useful for the proper interpretation of concentrations in PPID-suspect animals. Previous studies assessing ACTH concentrations in healthy donkeys (Equus asinus) and hybrids (E. asinus × E. caballus) are limited, often utilizing very small numbers, equids from specific and limited geographical regions, limited timeframes or unspecified donkey types (miniature, standard, or mammoth). We aimed to characterize the seasonal variation in baseline ACTH concentrations in healthy miniature donkeys, standard donkeys and hybrids in the United States (US) and to compare those concentrations across these groups. Following outlier removal, 19 standard donkeys (from California (CA), Massachusetts (MA), New York (NY)), 14 miniature donkeys (CA and NY), and 28 hybrids (Texas (TX) and NY) were utilized for analysis. Samples were collected from each equid twice per month from June to November 2019 and once per month from December 2019 through May 2020. The mean ACTH concentration of all equids was higher from mid-August through the end of October compared to the rest of the year (being the highest in the second half of September with the mean (standard deviation) values of 109.6 (52.6), 134.6 (67.4), and 100.8 (189.6) in standard donkeys, miniature donkeys, and hybrids, respectively). Additionally, ACTH concentrations in hybrids were 23% (95% Confidence Interval (CI): 4–38%) and 51% (95% CI: 36–63%) lower than in standard and miniature donkeys, respectively, from mid-August through October. During the rest of the year, hybrids similarly showed 31% (95% CI: 16–43%) and 30% (95% CI: 15–42%) lower ACTH concentrations compared with standard and miniature donkeys, respectively.