Journal of injury & violence research最新文献

Background: Violence Against Women (VAW) impacts 1 in 3 women worldwide, making it a significant public health problem. Most survivors will seek some form of care at healthcare facilities, often making hospitals a critical point of intervention. Psychosocial support plays a crucial role in the rehabilitation of survivors, helping them navigate the physical, emotional, and psychological consequences of violence. This study sought to assess the experiences of both Healthcare Workers (HCWs) and users of facility-based psychosocial care at a private, not-for-profit hospital in Uganda.

Methods: A qualitative design using in-depth interviews was employed to explore experiences and perspectives of eight survivors and nine healthcare workers at a private not-for-profit hospital in Uganda in 2023.

Results: The psychosocial services included screening, medical treatment, mental health support, referrals, and follow-up care. Key challenges identified were: limited Healthcare worker capacity to provide psychosocial care, inadequate infrastructure to provide safe spaces for care; high loss to follow up rate; and poorly formed networks with other service providers. While survivors appreciated care, findings emphasized the need for enhanced staff training, more tailored support for survivors and awareness creation for response services at the facilities.

Conclusions: While survivors value psychosocial services, gaps remain in staff capacity, infrastructure, visibility, and follow-up. A client-centered approach that protects privacy, enhances training, and strengthens referral networks can make care more responsive, comprehensive, and sustainable for women affected by violence.

Background: Head trauma is a known cause of seizures, with about 10% of patients with moderate or severe injuries experiencing seizure events. Early prevention and control of seizures are critical to limit secondary brain injuries. This study aimed to evaluate the frequency, type, and timing of seizures in patients with traumatic brain injury.

Methods: This analytical-descriptive study included all patients with post-traumatic seizures. Exclusion criteria were pre-existing brain disorders, prior epilepsy, or anticonvulsant use. Data were collected from hospital records using a structured checklist, with incomplete information supplemented by phone contact. Statistical analysis was performed using SPSS with chi-square, Fisher's exact test, and t-tests, considering p less than 0.05 as significant.

Results: Twenty-five patients (mean age 34 years) were studied. Accidents were the most frequent cause in men (54%), while falls predominated in women (100%). Chronic subdural hematoma was the most common brain injury in men, whereas Subarachnoid Hemorrhage (SAH) and SAH with brain contusion were noted in women. Primary seizures were most common in men (63%), while late seizures predominated in women (66%). Tonic-clonic seizures were the most frequent type in both men (95%) and women (66%). Recurrent seizures occurred in 22% of men and 66% of women. A significant association was found between Glasgow Coma Scale (GCS) level and both seizure type and timing (p less than 0.05).

Conclusions: Children and adolescents are more prone to early-onset seizures, whereas adults experience both primary and late seizures. Generalized seizures were predominant (96%), with only 4% being focal. The overall incidence of post-traumatic seizures was 1.26%. These findings high-light the need for targeted monitoring of high-risk patients based on age and consciousness lev-el. Future studies should involve larger multicenter cohorts and explore alternative strategies for preventing both early and late post-traumatic seizures.

Background: Unintentional injuries are a major contributor to morbidity and healthcare burden in early childhood. While falls and fractures are globally recognized as the most common pediatric injuries, region-specific data from primary care emergency settings in Southeast Europe remain scarce. The objective was to investigate the mechanisms, anatomical distribution, and contextual factors of injuries in children aged 0 to 6 years treated in a primary care pediatric service in Bosnia and Herzegovina.

Methods: This retrospective study included ninety-nine children aged 0 to 6 years who presented with injuries to a primary care service between September 2019 and December 2024. Data were collected from medical records and included age, sex, mechanism of injury, type of injury, anatomical site, supervision, home safety, and treatment outcome. Descriptive statistics and chi-square tests were used to analyze associations between demographic variables and injury characteristics. Logistic regression was also applied to examine predictors of fracture occurrence, adjusting for age group and sex.

Results: Falls were the leading cause of injury, accounting for 69.7% of all cases, with the highest number recorded in the 13- to 36-month age group. Fractures were the most frequent injury type, of which 74.4% affected the upper limbs, particularly the radius and humerus. Head injuries were more prevalent among infants, while boys experienced a higher overall injury rate. No statistically significant associations were found between injury occurrence and supervision or home safety, largely due to missing data and limited sample size.

Conclusions: Falls were the predominant cause of injury in early childhood, with upper limb fractures being common, especially among toddlers. While these findings provide important insights for prevention and pediatric emergency care planning in Bosnia and Herzegovina, larger prospective studies are needed to validate and extend these results.

Background: Suicide remains a critical global health crisis that disproportionately targets young adults, and medical students consistently display higher prevalence rates than their non-medical peers. The main aim of this study was investigation of the mediating roles of three intrapersonal factors-perfectionism, difficulties in emotion regulation, and self-disgust-in the relationship between psychological distress and suicidal ideation within in medical students.

Methods: The present study utilized correlational research design with path analysis. A convenience sample of 404 medical students from Kermanshah University of Medical Sciences (KUMS), Iran was selected in the latter half of the 2024-2025 academic year. Data were collected using structured questionnaires, including the Psychological Distress Scale (PDS), Beck Scale for Suicidal Ideation (BSSI), Difficulties in Emotion Regulation Scale (DERS), Frost Multidimensional Perfectionism Scale (FROST-MPS), and Multidimensional Self-Disgust Scale (MSDS). SPSS software version 26 handled descriptive statistics and assumption checks, whereas Amos software performed the structural modeling.

Results: The study found that psychological distress significantly predicted difficulties in emotion regulation (β = 0.370, P less than 0.001), perfectionism (β = 0.426, P less than 0.001), and self-disgust (β = 0.348, P less than 0.001). These variables mediated the relationship between psychological distress and suicidal ideation, with significant indirect effects through perfectionism (indirect effect = 0.094, p less than 0.001), difficulties in emotion regulation (indirect effect = 0.119, P less than 0.001), and self-disgust (indirect effect = 0.096, P less than 0.001). Among the total sample, 148 students (36.6%) were at high risk and 200 (49.5%) at very high risk of suicidal ideation.

Conclusions: There is a strong correlation between suicidal ideation and psychological distress among medical students. The findings highlight the roles of perfectionism, difficulties in emotion regulation, and self-disgust in this relationship. Universities should enhance mental health support and offer interventions targeting these factors to reduce suicide risk.

Background: Traumatic spinal fractures is a serious condition with significant morbidity and mortality, commonly caused by falls. However, data on patient characteristics and epidemiological patterns are limited. This study aims to describe the epidemiological features of fall-related traumatic spinal injuries in Markazi Province, in center of Iran.

Methods: A retrospective cohort study was conducted on 337 hospitalized trauma patients diagnosed with spinal injuries due to falls during 2022-2023. The patients' information including demographic and clinical characteristics were gathered. Mechanism of fall was considered low (less than 3 meters) and high height (more than 3 meters). Data analysis was done with SPSS24 software.

Results: The mean (SD) age of the patients was 48.59 (SD = 17.73) years, with a range from 1 to 94 years. Among the patients, 240 (71.2%) were male, and 246 (73%) sustained injuries from falls at a low height. The variables of age, sex, length of stay, number of injured vertebrae, ICU hospitalization, surgery, level of injury, presence of spinal cord injury, and occupation all showed a significant relationship with the mechanism of the fall (p less than 0.05). Additionally, the level of injury was associated with age, ICU hospitalization, presence of spinal cord injury, in-hospital mortality, length of stay in ICU, and ASIA classification (p less than 0.05).

Conclusions: Our study identifies distinct patterns in fall-related spinal fractures by age and demographics, with high-energy falls linked to greater severity and low-height falls more common in older adults and women. Findings underscore the need for targeted prevention in occupational and domestic settings and suggest that clinical and demographic factors can aid in early identification of high-energy mechanisms.

Background: A Level I trauma center used machine learning algorithms to identify risk factors and patterns in falls among older adults, which constitute our greatest burden of traumatic admissions.

Methods: A retrospective analysis was conducted on 2,391 ground-level fall trauma admissions from 2017-2022 including variables related to demographics, and weather conditions at admission. Supervised learning models were developed to predict older adult vs younger counterpart falls. In this machine learning modality, we generated a Decision Tree, a Support Vector Machine Classifier Algorithm, and a Logistic Regression Model. Unsupervised learning methods uncover patterns or groupings in the dataset of older adult ground-level falls, which consists of 1,742 records from 2017-2022 trauma admissions including comorbidity variables. Unsupervised learning methods of Principal Components Analysis, Hierarchical Clustering, and Market Basket Analysis were employed.

Results: All three supervised models found the female sex as an important variable in predicting older adult falls. Unsupervised learning identified discernible patterns and groupings, revealing that certain weather variables are associated with falls.

Conclusions: These machine learning modalities can shed light on what may be important risk factors for older adult falls and can help to target awareness and outreach.

Background: Timely identification of the need for surgical intervention in traumatic epidural hematoma (tEDH) is critical to optimizing outcomes. This retrospective study aimed to identify predictive factors for surgical intervention in tEDH using machine learning and develop a nomogram to support clinical decision-making.

Methods: In this retrospective study, data from 147 patients with tEDH at a major trauma center in western Iran (2023-2024) were analyzed. Demographic, Clinical, and CT scan data were extracted from medical records. Four machine learning models (Logistic Regression (LR)/ Support Vector Machine (SVM)/ Naive Bayes (NB)/Neural Network (NN)), were developed to predict surgical need. A Random Forest (RF) algorithm identified key predictors, and a nomogram was constructed from the LR model to facilitate individualized risk assessment. Statistical analyses were conducted using R software (version 4.3.2).

Results: In this study, 131 (89.1%) of 147 patients with tEDH were male. Of these, 72 (49%) underwent surgery. The cause of brain trauma was a Motor Vehicle Accident (MVA) in 76 (51.7%) of patients and a fall in 50 (34%) of patients. The mean (±Standard Deviation) age of the patients was 31.47 (±18.27). The initial hematoma volume demonstrated the highest discriminatory power, with an AUC of 0.92 (95% CI: 0.83-1.00) and an accuracy of 0.89 (95% CI: 0.76-0.96). The Glasgow Coma Scale (GCS) score also exhibited strong predictive performance, with an AUC of 0.76 (95% CI: 0.62-0.89) and an accuracy of 0.71 (95% CI: 0.56-0.84). The SVM model demonstrated the highest AUC of 0.96 (95% CI: 0.91-1.00), with sensitivity and specificity values above 90%.

Conclusions: In this study, the novel integration of machine learning with a nomogram offers clinicians a precise, user-friendly tool for rapid decision-making, potentially reducing complications. These findings help surgeons to make more informed clinical decisions by accurately assessing these parameters in the early stages and to identify patients at higher risk for surgical intervention more quickly.

Background: Bangladesh has experienced a mass uprising that resulted in deaths, injuries, and trauma, ended with the ousting of an autocratic government on 05 August 2024. People from all spheres of life have experienced violence and trauma, including children and adolescents.

Objectives: We aimed to report the socio-demography of 89 children killed in the mass uprising in Bangladesh in July-August 2024.

Methods: We extracted the data of this report from a widely circulated Bangla national daily newspaper. We extracted name, age, occupation, place of death, and district of killing.

Results: The mean (SD) age of the deceased was 15.2 (±2.6) years, ranging from 4-17 years. Among the 89 deaths, there were bullet wounds in 79 children, 9 died by burn, and one died by splinter; 56 deaths happened during 18 July- 4 August, and 31 deaths happened after 5 August, 2024; 58 deaths happened in Dhaka, and 31 deaths happened in districts outside Dhaka. Among the adolescents, 42 were students, and 29 were involved in child labor. Deaths happened in 16 districts in Bangladesh.

Conclusions: This analysis revealed that about 90% of the adolescents were killed by bullets, indicating the spectrum of armed conflict. Dhaka was the center of violence that resulted in the killing of adolescents. Local, regional, and international human rights agencies should ensure initiatives to prevent such killings of children during any mass protest elsewhere in the world.

Background: Bangladesh has experienced a mass uprising that resulted in deaths, injuries, and trauma, ended with the ousting of an autocratic government on 05 August 2024. People from all spheres of life have experienced violence and trauma, including children and adolescents.

Objectives: We aimed to report the socio-demography of 89 children killed in the mass uprising in Bangladesh in July-August 2024.

Methods: We extracted the data of this report from a widely circulated Bangla national daily newspaper. We extracted name, age, occupation, place of death, and district of killing.

Results: The mean (SD) age of the deceased was 15.2 (±2.6) years, ranging from 4-17 years. Among the 89 deaths, there were bullet wounds in 79 children, 9 died by burn, and one died by splinter; 56 deaths happened during 18 July- 4 August, and 31 deaths happened after 5 August, 2024; 58 deaths happened in Dhaka, and 31 deaths happened in districts outside Dhaka. Among the adolescents, 42 were students, and 29 were involved in child labor. Deaths happened in 16 districts in Bangladesh.

Conclusions: This analysis revealed that about 90% of the adolescents were killed by bullets, indicating the spectrum of armed conflict. Dhaka was the center of violence that resulted in the killing of adolescents. Local, regional, and international human rights agencies should ensure initiatives to prevent such killings of children during any mass protest elsewhere in the world.

Background: Driver-associated factors are significant contributors to road traffic accidents. Conversely, personality traits are the characteristics and qualities that define an individual's consistent patterns of thoughts, feelings, and behaviors. Driving behavior is influenced by a variety of factors. We hypothesized that different personality traits may affect driving behavior. This study aimed to investigate the relationship between various personality traits and involvement in accidents.

Methods: Drivers with a history of accidents resulting in injuries for which they were at fault were classified as cases, and drivers without a history of an accident in the past year were considered as controls. We assessed the Big Five personality traits among all participants using the NEO Five-Factor Inventory (NEO-FFI) test. Additionally, we collected data on potential determinants of high-risk driving, including age, marital status, education, alcohol consumption, smoking, psychiatric disorders, self-assessment of driving skills, and substance abuse. The NEO-FFI test scores were compared between cases and controls. We employed Partitioning Around Medoids (PAM) to create clusters for each personality trait. Logistic regression was utilized to examine the association between the independent variables and the clusters of personality traits, adjusting for potential confounders such as age, marital status, and education level.

Results: A total of 662 participants, comprising 393 cases and 269 controls, were recruited for the study. The mean score for neuroticism was significantly higher in the case group, while the mean scores for extroversion, agreeableness, and conscientiousness were substantially lower. The mean score for openness to experience did not show a significant difference. The Personality Assessment Model (PAM) identified two clusters for all personality traits, labeled as high and low. In the logistic regression model, high levels of neuroticism (aOR: 2.75, 95%CI: 1.69-4.45) and low levels of conscientiousness (aOR: 0.50, 95%CI: 0.30-0.84) were associated with an increased likelihood of being involved in a car accident.

Conclusions: Drivers involved in severe accidents tended to exhibit higher levels of neuroticism and lower levels of extraversion, conscientiousness, and agreeableness, as measured by the NEO-FFI. Regression analysis revealed that elevated neuroticism and diminished conscientiousness were significantly associated with high-risk driving behaviors. Although assessing personality traits can aid in predicting risky driving, this association is not definitive, and caution should be exercised when generalizing these findings.