High blood alcohol concentration associated with traumatic brain injury among traffic injury patients during New Year festivals in Thailand

Prevalence of alcohol-impaired driving: a systematic review with a gender-driven approach and meta-analysis of gender differences

BACKGROUND

A growing number of studies investigated the factors that contribute to driving under the influence (DUI) of alcohol in relation to gender. However, a gendered approach of the scientific evidence is missing in the literature. To fill this gap, a gender-driven systematic review on real case studies of the last two decades was performed. In addition to the gender of the drivers involved, major independent variables such as the period of recruitment, the type of drivers recruited, and the geographical area where the study was conducted, were examined. Afterwards, a meta-analysis was performed comparing alcohol-positive rates (APR) between male and female drivers in three subgroups of drivers: those involved in road traffic accidents, those randomly tested on the road, and volunteers.

METHODS

Three databases were searched for eligible studies in October 2023. Real-case studies reporting APR in man and women convicted for DUI of alcohol worldwide were included. Univariate analysis by ANOVA with post-hoc tests identified the independent variables with a significant impact on the dependent variable APR, according to a relationship subsequently investigated by standard multiple linear regression. The meta-analysis of random effects estimates was performed to investigate the change in overall effect size (measured by Cohen's d standardized mean difference test) and 95% confidence interval (CI).

RESULTS

Among papers addressing driver gender, univariate analysis of independent variables revealed a higher Alcohol Positive Rate (APR) in men, particularly in drivers involved in crashes, with a noticeable decrease over time. Analyzing the gender of drivers involved in crashes, the meta-analysis showed that men had a significantly higher APR (30.7%; 95%CI 26.8-35.0) compared to women (13.2%; 95%CI 10.7-16.1). However, in drivers randomly tested, there was no significant difference in APR between genders (2.1% for men and 1.4% for women), while in volunteers, there was a statistically significant difference in APR with 3.4% (95%CI 1.5-7.6) for men and 1.1% (95%CI 0.5-2.7) for women.

CONCLUSION

Despite a progressive decrease in the epidemiological prevalence of alcohol-related DUI over time, this phenomenon remains at worryingly high levels among drivers involved in road traffic accidents in both genders, with a higher prevalence in men. It's important for policymakers, professionals, and scientists to consider gender when planning research, analysis, interventions, and policies related to psychoactive substances, such as alcohol or other licit drugs. Forensic sciences can play a vital role in this regard, enabling a thorough analysis of gender gaps in different populations.

Association Between Lifetime Sexual Violence and Recent Traumatic Brain Injury Among Adults: 2017 Connecticut Behavioral Risk Factor Surveillance System

Sexual violence (SV) is a critical public health problem that is associated with numerous negative health consequences, including immediate- and long-term physical and mental health conditions and health-risk behaviors. Some of these health-risk behaviors (e.g., substance use, unsafe driving practices, poor mental health, lower impulse control, and abnormal brain circuitry) might increase the risk for sustaining a traumatic brain injury (TBI). A TBI causes neurological or neuropsychological changes and may also lead to various symptoms that affect a person's cognition, mobility, behavior, and mental health. Determining if those who have experienced SV are at increased risk of sustaining a TBI in their lifetime is critical given the high prevalence and health impacts of SV, the potential vulnerability to TBI after SV, and the known detrimental effects of TBI. This exploratory study examined data from the 2017 Connecticut behavioral risk factor surveillance system and found that lifetime SV victimization (controlling for age and sex) was associated with increased odds of reporting a recent TBI in the past 12 months (adjusted odds ratio [AOR] = 2.1; 95% confidence interval [CI] [1.03, 4.21]). Further research is needed to better understand how SV history is related to the risk of sustaining a TBI. Healthcare professionals can support patients who experience SV by providing resources to help reduce associated physical and mental health conditions and health-risk behaviors.

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

Background: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Methods: Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002–2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Results: Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Conclusions: Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation.

Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means

In Thailand in 2016, more than 70% of all deaths due to road accidents were motorcyclist deaths. This study uses a correlated random parameters ordered probit model with heterogeneity in means (CRPOPHM) to obtain insight into differences in the significant factors determining the severity of motorcyclist injury between motorcycle crashes in urban and rural roadways, using data on motorcycle crashes in Thailand from 2016 to 2019. Using a rating system for injury severity level from minor injury to severe injury and to fatal injury, a wide range of potential risk factors are considered, including rider characteristics and actions, roadway characteristics, environmental and temporal characteristics, and crash characteristics. The findings indicate that, although some factors are significant in both urban and rural models, factors such as male rider, illegally overtaking, drowsiness, four-lane or wider highway, flush or depressed median, road on slope, weekend, nighttime with light, crash with van or minibus, and rear-ending or sideswiping crash, are significant only in the rural model, whereas the factors barrier median, occurring between 18:00 and 23:59, and striking a passenger car are statistically significant in only the urban model. These findings further suggests that difference in effect of unobserved characteristics could be seen in different crash locations, and splitting the model estimation between both location types could be done to develop effective guidance for policies to mitigate the severity of motorcyclist injuries. In addition, practical policy-related recommendations drawn from the results of the analysis are provided. With respect to methodology, the proposed CRPOPHM method outperforms lower-ordered models in terms of statistical fit and captures unobserved heterogeneity to a greater extent.

Sex, Drugs, and TBI: The Role of Sex in Substance Abuse Related to Traumatic Brain Injuries

Traumatic brain injuries (TBI) are a significant public health problem costing billions of dollars in healthcare costs and lost productivity while simultaneously reducing the quality of life for both patients and caregivers. Substance abuse is closely interconnected with TBI, as intoxicated individuals are at a greater risk of suffering brain injuries, and TBI may serve as a risk factor for the subsequent development of substance use disorders. There are also prominent sex differences in the etiology, epidemiology, and consequences of TBI. For instance, men are more likely to be injured on sporting fields or in auto accidents, while women are disproportionately likely to suffer TBI associated with intimate partner violence. Moreover, while men are much more likely to suffer TBI during late adolescence–young adulthood, sex differences in the incidence of TBI are much less prominent during other developmental epochs. Further, there are prominent sex differences in substance abuse biology; for example, while more men meet diagnostic criteria for substance abuse disorders, women tend to advance from casual use to addiction more quickly. In this paper, we will discuss the emerging clinical and preclinical evidence that these sex differences in TBI and substance abuse interact and may be prominent determinates of long-term outcomes.