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Objective This paper highlights the occupational risk of Q fever from exposure to raw animal products in the context of multiple notified Q fever cases from 2020 to 2023 linked to four pet food manufacturing facilities in South-East Queensland, Australia. Methods The Queensland Government Notifiable Conditions System was used to identify Q fever cases linked to pet food manufacturing in the Metro North and Gold Coast Hospital and Health Service areas of Brisbane, Australia. Data on each case from routine public health follow-up were collected and descriptively analysed. Results Between 2020 and 2023, 12 confirmed Q fever infections (17% of total cases) were linked to four pet food manufacturing facilities. Eleven cases reported direct or environmental exposure to raw meat and animal products. None were previously vaccinated for Q fever. Conclusion These cases demonstrate the increased risk of Q fever infection as part of the pet food manufacturing process, highlighting an underappreciated preventable occupational risk, which can be mitigated with the use of pre-screening and vaccination of workers. All occupations should conduct workplace-based risk assessments to identify risks such as Q fever to prevent adverse negative health outcomes. [ABSTRACT FROM AUTHOR]

Objectives Food processing facilities represent critical infrastructure that have stayed open during much of the COVID-19 pandemic. Understanding the burden of COVID-19 in this sector is thus important to help reduce the potential for workplace infection in future outbreaks. Methods We undertook a workplace survey in the UK food and drink processing sector and collected information on workplace size, characteristics (e.g. temperature, ventilation), and experience with COVID-19 (e.g. numbers of positive cases). For each site, we calculated COVID-19 case rates per month per 1000 workers. We performed an ecological analysis using negative binomial regression to assess the association between COVID-19 rates and workplace and local risk factors. Results Respondents from 33 companies including 66 individual sites completed the survey. COVID-19 cases were reported from the start of the pandemic up to June 2021. Respondents represented a range of industry subgroups, including grain milling/storage (n = 16), manufacture of malt (n = 14), manufacture of prepared meals (n = 12), manufacture of beverages (n = 8), distilling (n = 5), manufacture of baked goods (n = 5), and other (n = 6), with a total of 15 563 workers across all sites. Average monthly case rates per 1000 workers ranged from 0.9 in distilling to 6.1 in grain milling/storage. Incidence rate ratios were partially attenuated after adjusting for several local and workplace factors, though risks for one subgroup (grain milling/storage) remained elevated. Certain local and workplace characteristics were related to higher infection rates, such as higher deprivation (5 km only), a lower proportion of remote workers, lower proportion of workers in close proximity, and higher numbers of workers overall. Conclusions Our analysis suggests some heterogeneity in the rates of COVID-19 across sectors of the UK food and drink processing industry. Infection rates were associated with deprivation, the proportions of remote workers and workers in close proximity, and the number of workers. [ABSTRACT FROM AUTHOR]

In the late 2010s, artificial intelligence (AI) technologies became complementary to the research areas of food science and nutrition. This review aims to summarize these technological advances by systematically describing the following: the use of AI in other fields (eg, engineering, pharmacy, and medicine); the history of AI in relation to food science and nutrition; the AI technologies currently used in the agricultural and food industries; and some of the important applications of AI in areas such as immunity-boosting foods, dietary assessment, gut microbiome profile analysis, and toxicity prediction of food ingredients. These applications are likely to be in great demand in the near future. This review can provide a starting point for brainstorming and for generating new AI applications in food science and nutrition that have yet to be imagined. [ABSTRACT FROM AUTHOR]

Issue: EU Reg 625/2017 establishes that all Food Business Operator FBO needs to be categorised based on risk. The risk rating is obtained by summing the scores of the non-conformities and risk factors checked. Description of the problem: In the past, various categorisation systems have been used but due to many FBO present in the regional territory, a new system of categorisation rapid and efficient is required. Results: The system classifies the risk of FBO in two phases: the first, based on static criteria (type and volume of production, market areas), in which a default category is established; the second, based on dynamic and technical criteria (provisions and scores of official control, structural and productive features), in which a definitive category is established. When a FBO is set up, it presents a declaration of start-up that included the static criteria: a preliminary risk category is determined. The authorities assign a definitive rating by a complex inspection, during which specific check-lists of the dynamic and technical criteria are completed. Each element is assigned a percentage weight, which contributes to the risk assessment, and so to the frequency of controls. A value >70% means high risk and 1 control per year; a value of 69.9-50% means medium risk and 1 control every 2 year; a value of 59.9- 30% means low risk and 1 control every 3 years, and a value <29.9% indicates a negligible risk and controls on demand. In the period between 2 inspections, the score will be updated by the outcomes of any other official controls. The rating is updated according to favourable or unfavourable outcome. Lessons: In addition to being efficacious and efficient, this new system of categorisation optimises the work of the authorities. Key messages: • This new categorisation system makes risk-based controls simpler and more efficacious. • The project involves various Italian regions and the OECD, thereby enabling official controls to be standardised at the European level. [ABSTRACT FROM AUTHOR]