The European Food Safety Authority (EFSA) provides independent scientific advice to EU risk managers on a wide range of food safety issues and communicates on existing and emerging risks in the food chain. This advice helps to protect consumers, animals and the environment. Data are essential to EFSA’s scientific assessments. EFSA collects data from various sources including scientific literature, biological and chemical monitoring programmes, as well as food consumption and composition databases. EFSA also assesses data from authorisation dossiers for regulated products submitted by industry. To continue delivering the highest value for society, EFSA keeps abreast of new scientific, technological and societal developments. EFSA also engages in partnerships as an essential means to address the growing complexity in science and society, and to better connect and integrate knowledge, data and expertise across sectors. This paper provides insights into EFSA’s data related activities and future perspectives in the following key areas of EFSA’s 2027 strategy: one substance-one assessment, combined exposure to multiple chemicals, environmental risk assessment, new approach methodologies, antimicrobial resistance and risk-benefit assessment. EFSA’s initiatives to integrate societal insights in its risk communication are also described.

Bacterial infection risk in work environments has been extensively reported for healthcare workers, while this risk is rarely researched in other occupations. This study aimed to identify occupational environments in Taiwan’s agricultural and healthcare industries with elevated bacterial infection risks by comparing risks for general bacterial infections and pneumonia. Using labour and health insurance claim data from 3.3 million workers (January 2004–December 2020), a retrospective cohort was constructed to estimate occupational infection risks with Cox regression and the Anderson-Gill extension. Significantly elevated hazard ratios were found for workers in vegetable growing, crop cultivation service, mushroom growing, flower growing, and fruit growing, ranging from 1.13 to 1.39 for general bacterial infections and 1.68 to 3.06 for pneumonia infections. In afforestation and the inland fishing industry, pneumonia risk was significantly elevated with, respectively, 1.87 and 1.21. In the healthcare section, especially workers in residential care services and residential care services for elderly stand out regarding their pneumonia risk, with significant hazard ratios of 3.49 and 1.75. The methods used in this study were proven to be effective in identification of occupation environments at risk and can be used in other settings. These findings call for prioritization of bacterial infection prevention by occupation.

Summary: The aging of the population poses significant challenges in healthcare, necessitating innovative approaches. Advancements in brain imaging and artificial intelligence now allow for characterizing an individual’s state through their brain age,’’ derived from observable brain features. Exploring an individual’s biological age’’ rather than chronological age is becoming crucial to identify relevant clinical indicators and refine risk models for age-related diseases. However, traditional brain age measurement has limitations, focusing solely on brain structure assessment while neglecting functional efficiency.

Our study focuses on developing neurocognitive ages’’ specific to cognitive systems to enhance the precision of decline estimation. Leveraging international (NKI2, ADNI) and Canadian (CIMA- Q, COMPASS-ND) databases with neuroimaging and neuropsychological data from older adults [control subjects with no cognitive impairment (CON): n = 1811; people living with mild cognitive impairment (MCI): n = 1341; with Alzheimer’s disease (AD): n= 513], we predicted individual brain ages within groups. These estimations were enriched with neuropsychological data to generate specific neurocognitive ages. We used longitudinal statistical models to map evolutionary trajectories. Comparing the accuracy of neurocognitive ages to traditional brain ages involved statistical learning techniques and precision measures.

The results demonstrated that neurocognitive age enhances the prediction of individual brain and cognition change trajectories related to aging and dementia. This promising approach could strengthen diagnostic reliability, facilitate early detection of at-risk profiles, and contribute to the emergence of precision gerontology/geriatrics.

Domestic abuse – abusive behaviour perpetrated by an adult towards another adult to whom they are personally connected (e.g. partners, ex-partners or family members) – damages mental health, increases mental health service use and challenges clinical management. Training and guidance for mental health professionals on identifying and responding to patients exposed to domestic abuse are available, but there has been less development of resources for mental health professionals in identifying, assessing and responding to perpetrators of domestic abuse. In this article, we describe a framework for responding to domestic abuse perpetration in clinical settings in general adult mental health services, aimed at improving practice. This could support mental health professionals in sensitive enquiry and assessment for domestic abuse perpetration, and guide appropriate responses, as part of routine training and continuing professional development.

Navigating the complex relationship between violence and psychosis can frequently be challenging. Psychiatrists may find assessing and managing the risk of violence in this context daunting. In their article on the topic, Anderson et al helpfully summarise the role that psychopathology can play in this process. However, although careful elucidation of an individual's experiences may assist in the nuanced formulation of their risk and could offer a specific focus for interventions, the approach has potential shortcomings in certain settings. For some phenomena the link with violence is unclear and it may be constellations of symptoms that are important. Causal pathways are not always linear and there may be important mediators linking psychopathological features to behavioural outcomes. In the resource-limited settings in which many contemporary health services operate, a detailed assessment of psychopathology may be hampered by time or other constraints. Alternative, more scalable solutions may therefore be needed in particular scenarios.

The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.