Listeria monocytogenes (LM) is a health threat worldwide given its high mortality and the growing of high-risk susceptible populations. All hospitalizations with a diagnosis of LM in the National Registry of Hospital Discharges were examined in Spain from 2000 to 2021. A total of 8152 hospital admissions with LM were identified. The mean age was 59.5 years and 48% were immunosuppressed (IS). The rate of LM hospitalizations increased from 5 per 1 million population in 2000 to 8.9 in 2021 (p < 0.001). A foodborne outbreak in Andalusia determined a sharp increase in admissions with LM during 2019. The COVID-19 pandemic and lockdowns were associated with a decrease in LM admissions. The overall in-hospital mortality was 16.7%. The number of deaths in patients hospitalized with LM rose from 7.8 per 100,000 deceased in 2000 to 18 in 2021 (p < 0.001). After adjustment, age >65 years (odds ratio [OR] = 2.16), sepsis (OR = 2.60), meningoencephalitis (OR = 1.72), endocarditis (OR = 2.0), neonatal listeriosis (OR = 2.10) and IS (OR = 2.09) were associated with mortality. The number of patients hospitalized with LM in Spain has increased significantly from 2000 to 2021. The increase in the rate of admissions and deaths was largely driven by the growing proportion of elderly and IS patients.

Immunosuppression (IS) determines a higher risk of disease severity from Listeria monocytogenes (LM) infection. We examined the epidemiology of IS in all patients hospitalized with LM in Spain from 2000 to 2021 in the National Registry of Hospital Discharges. IS was defined by liver disease (LD), diabetes mellitus (DM), chronic kidney disease (CKD), solid organ transplantation (SOT), bone marrow transplantation (BMT), primary immunodeficiencies (ID), systemic autoimmune diseases (SAD), solid organ neoplasms (SON), and hematological neoplasms (HN). Among 8,152 admissions with LM, 48% were IS. There was an increase from 39.5% to 60% during the study period, mainly driven by rises in DM (from 12.6% to 26.2%), SON (from 9.9% to 17.5%), CKD (from 4.4% to 16.3%), HN (from 6.6% to 13.4%), and LD (from 4.9% to 6.6%) (p < 0.001 for all trends). IS fatality rate was higher than in non-IS (22.4% vs 11.3%; OR = 2.09). The proportion of LM patients with IS among LM in-hospital deaths increased from 57.1% in 2000 to 67.95% in 2021 (p < 0.001). Fatality risk differed according to baseline IS condition: LD (OR = 2.42), CKD (OR = 1.49), SON (OR 3.01) and HN (OR 1.45). The prevalence of IS among patients hospitalized with LM in Spain has risen over the past two decades, with a growing impact on fatality rates. These findings should prompt further efforts to prevent and manage properly LM infection.

In this chapter, the authors present how the use of artificial intelligence (AI) can help to identify and reduce the new digital crimes according to hate messages. The appearance of the internet in our lives, at the end of the last century, has meant a great technological advance, providing easier access to a huge volume of information and communication between people. The rise of communication-oriented networks has been such that true digital environments have been created, the so-called social networks, with millions of users all over the planet. This has meant, to a large extent, the modification of our personal relationships, and, unfortunately, the appearance of new ways of sending hate messages. The work presented is aimed at a digital tool built for this purpose for the automatic detection of hate (and non-hate) messages, in Spanish and other non-Anglo-Saxon languages, with AI algorithms, using training data from the Spanish language.

This study explores how low-intensity religious hate becomes normalized on Spanish social media, shaped by multiple power centers (editorial policies, algorithms, and social norms), especially during major news events. An analysis of 53,787 comments on articles from 6 ideologically diverse outlets (March–April 2025) found 3.4% expressed religious hate, mainly anti-Muslim, followed by anti-Catholic and anti-Semitic discourse. Using multiple correspondence analyses and semantic clustering, significant links emerged among hate type, outlet ideology, and comment intensity. The results also revealed connections among geopolitical conflicts, migration crises, and peaks in hostility, showing how such dynamics normalize exclusionary narratives. The study calls for greater editorial transparency, event-sensitive moderation, and stronger cooperation between platforms and newsrooms to reduce algorithm-driven amplification of hate.