The association between motor vehicle accidents (MVAs) and obstructive sleep apnea syndrome (OSAS) has always been quantified as risk of MVAs for individual drivers with OSAS. We evaluated the expected injured patients per year attributable to OSAS-dependent MVAs in a general population. By combining OSAS prevalence and OSAS-dependent MVAs odds ratio, we assessed the population attributable fraction (PAF), an epidemiological tool that can be used to quantify the proportion of road traffic injuries (RTIs) attributable to OSAS. For an apnea hypopnea index >5, the weighed median and combined average of OSAS prevalence were 4.4 (95% confidence interval (CI): 3.7–7.5) and 4.7 (95% CI: 4.2–5.2), respectively; values of risk of OSAS-dependent MVAs were 2.83 (95% CI: 2.72–3.08) and 2.52 (95% CI: 2.07–3.08), respectively. The PAF showed weighed median and combined average values of 6.6 (95% CI: 4.3–9.8) and 7.3% (95% CI: 6.0–13.5), respectively. Our results show that about 7% of RTIs for a population of male drivers involved in MVAs are attributable to OSAS. This value can be used to assess the potential impact, on the reduction of incidence of the motor vehicle injuries, of prevention programs aimed at reducing the number of subjects with an undiagnosed and/or untreated OSAS.

The international scientific literature reports no data on the prevalence and effectiveness of back protector devices (BPD). In Italy, no data have been collected on BPD because their use is not mandatory. To fill this gap, the National Institute of Health implemented a cross-sectional study in collaboration with the National Traffic Police. Accident cases were collected from 1 December 2011 to 25 October 2013. Overall, data from 2104 accidents involving 2319 injured subjects were analysed: 1821 (78.5%) of these were motorcyclists and 498 (21.5%) mopedists. The use of Hard-shell BPD or jackets with airbags in motorcyclists is higher then in moped drivers (16.2% vs 1.3%, P = 0.000). Concerning level of protection, there are no differences between drivers and passengers. In most severely injured motorcyclists (i.e. hospitalized or deceased), the percentage of injuries to the spine was lower (13.6%) among those who used a high-level safety device (hard-shell BPD and/or airbags) and rose to 27.3% among those who used only protective clothing (P = 0.022). When the variables potentially affecting the results of not using a high-safety device were controlled, a bivariate analysis showed that the odds of serious spinal injury were 2.72 times greater (P = 0.049) and a multivariate analysis showed that they were 2.81 times greater (P = 0.012). This study points out that greater use of BPD could reduce the number of injuries to the spinal column resulting from road traffic accidents involving motorized two-wheeled vehicles.

Intimate Partner Violence (IPV) is a major public health problem to be addressed with innovative and interconnecting strategies for ensuring the psychophysical health of the surviving woman. According to the World Health Organization, 27% of women worldwide have experienced physical and sexual IPV in their lifetime. Most of the studies on gender-based violence focus on short-term effects, while long-term effects are often marginally included even though they represent the most serious and complex consequences. The molecular mechanisms underlying stress-related disorders in IPV victims are multiple and include dysregulation of the hypothalamic-pituitary-adrenal axis, inflammatory response, epigenetic modifications, neurotransmitter imbalances, structural changes in the brain, and oxidative stress. This review aims to explore the long-term health consequences of intimate partner violence (IPV), emphasizing the biological and psychological mechanisms underlying stress-related disorders and resilience. By integrating findings from epigenetics, microbiome research, and artificial intelligence (AI)-based data analysis, we highlight novel strategies for mitigating IPV-related trauma and improving recovery pathways. Genome-wide environment interaction studies, enhanced by AI-assisted data analysis, offer a promising public health approach for identifying factors that contribute to stress-related disorders and those that promote resilience, thus guiding more effective prevention and intervention strategies.

Background: Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries (V)vs. non-violence-related injuries (NV) is thus a relevant task to the ends of devising alerting mechanisms to track (and prevent) violence episodes.Methods: We presentViDeS(so dubbed afterViolence Detection System), a system to detect episodes of violence from narrative texts in emergency room reports. It employs a deep neural network for categorizing textual ER reports data, and complements such output by making explicit which elements corroborate the interpretation of the record as reporting about violence-related injuries. To these ends we designed a novel hybrid technique for filling semantic frames that employs distributed representations of terms herein, along with syntactic and semantic information. The system has been validated on real data annotated with two sorts of information: about the presence vs. absence of violence-related injuries, and about some semantic roles that can be interpreted as major cues for violent episodes, such as the agent that committed violence, the victim, the body district involved, etc.. The employed dataset contains over 150K records annotated with class (V,NV) information, and 200 records with finer-grained information on the aforementioned semantic roles.Results: We used data coming from an Italian branch of the EU-Injury Database (EU-IDB) project, compiled by hospital staff. Categorization figures approach full precision and recall for negative cases and.97 precision and.94 recall on positive cases. As regards as the recognition of semantic roles, we recorded an accuracy varying from.28 to.90 according to the semantic roles involved. Moreover, the system allowed unveiling annotation errors committed by hospital staff.Conclusions: Explaining systems’ results, so to make their output more comprehensible and convincing, is today necessary for AI systems. Our proposal is to combine distributed and symbolic (frame-like) representations as a possible answer to such pressing request for interpretability. Although presently focused on the medical domain, the proposed methodology is general and, in principle, it can be extended to further application areas and categorization tasks.