Journal of Injury and Violence Research

Violence and trauma towards children and adolescents in the July Mass Uprising (2024) in Bangladesh: a socio-demographic analysis of 89 deaths

S M Yasir Arafat — 2025-10-12

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.

Safety or Risk? Exploring Perceptions of Firearm-Related Risks Among Military Service Members and Civilian Employees at a Military Installation

Makala D. Carrington — 2025-08-09

Background: Firearm suicide ranks among the leading causes of death in the U.S. military, with access to personal firearms significantly elevating the risk of firearm-related injuries and death. In this study, we analyzed perceived risks of firearm access and storage among active-duty military service members and embedded civilians with a firearm at home.

Methods: We conducted an anonymous online survey at a single military installation in the United States. Data were analyzed using logistic regression models across four firearm-related risk factors: suicide, others’ suicide, interpersonal violence, and unintentional shootings.

Results: Of the 324 participants, 50.5% reported a minimum of one firearm at the home. Respondents with a minimum of one firearm at home (vs. those without) were less likely to agree that there was a risk of suicide for themselves (6.0% vs. 16.6%) or others (7.8% vs. 21.8%), interpersonal violence (16.4% vs. 26.9%), or unintentional shootings (27.9% vs. 42.3%). After adjusting for age, gender, race, ethnicity, and living alone, respondents with a firearm at home (vs. those without) were significantly less likely to agree that firearm access increased the risk of suicide for themselves (odds ratio [OR]: 0.20; 95% CI: 0.10, 0.40; p <.001) or others (OR 0.19; 95% CI: 0.10, 0.36; p <.001), interpersonal violence (OR: 0.25; 95% CI: 0.15, 0.43; p <.001), or unintentional shootings (OR: 0.22; 95% CI: 0.13, 0.38; p <.001).

Conclusions: Our findings identify opportunities for strengthening messaging to help service members understand and acknowledge risks surrounding a firearm at home and promote secure firearm storage behaviors.

Keywords: firearm injury, military population, firearm storage practice, firearm access, firearm ownership

Association of personality traits and traffic accident involvement: a multicenter case-control study in Iran

Reza Fereidooni — 2025-10-07

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.

Conclusion: 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.

Application of machine learning to predict and identify factors associated with the need for surgery in traumatic epidural hematoma

Iran Chanideh — 2025-10-29

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%.

Conclusion: 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.

Age and gender distribution of firearm violence in high-income countries: an analysis of data from 1990 to 2019

Moslem Taheri-Soodejani — 2025-07-08

Background: Physical Violence by Firearms (PVF) is a type of violence which is considered a public health challenge in high-income countries. This study is designed to investigate the trend of incidence in these countries among different ages and gender groups, cluster countries based on PVF incidence rates, and analyze changes during the years 1990 to 2019.

Methods: At first, countries were clustered using the K-means algorithm, with the number of clusters determined by the elbow method. The clustering was based on the Euclidean distance of physical violence by Firearms (PVF) incidence rates, and the data were sourced from the Global Burden of Disease (GBD) database. The annual changes in the incidence in each cluster were calculated by means of sex and age groups. A heat map was also used to investigate the trend of firearms violence, and Arc map GIS was employed to provide the geographical incidence distribution of firearms violence by gender in 4-time points of 1990, 2000, 2010 and 2019.

Results: The United States, which was placed alone in a cluster, had the highest incidence changes with an increase of 1.44 cases per 100,000 per year. The highest incidence of violence was among American men aged 20-24, which ranged from 150 to 240 cases per 100,000 people between 1990 and 2019.

Conclusions: The study highlights that access to firearms and related laws are key drivers of the increasing trend of PVF in high-income countries. The clustering of countries revealed distinct patterns of PVF incidence, with the USA showing the highest rates. These findings underscore the need for stricter firearm regulations and targeted interventions, particularly for young men aged 20-24, who are most affected by PVF.