Community support programmes can simultaneously improve human and ecosystem health. However, whether and how supported communities maintain these sustainable trajectories during major disruptions remains unclear. We used a mixed-methods approach to document how the COVID-19 pandemic impacted rural communities and protected rainforests in West Kalimantan. We surveyed 1016 households across six villages with non-governmental organisation (NGO)-affiliated health-livelihood support and four nearby unaffiliated villages to understand their pandemic experiences and logging activity. We also independently estimated weekly forest loss in protected rainforests in 28 NGO-affiliated and 698 unaffiliated villages using satellite imagery. The pandemic created an economic shock, whereby 50% of households lost income, struggled with increased costs of basic needs, or both. We expected this shock to increase the amount of logging; however, the average forest loss across West Kalimantan decreased by 39% after the pandemic declaration. This decrease was due to the combined effects of the global timber market crash in early 2020, pandemic travel restrictions, and heavy precipitation in 2020 and 2021. Before the pandemic, the average forest loss was 52% lower in NGO-affiliated villages than in unaffiliated villages, with this difference increasing to 68% during the pandemic. Correspondingly, NGO-affiliated households were more likely to report having alternative sustainable livelihoods, several sources of external support (eg, loans), and access to affordable health care and less likely to report increased spending on basic needs during the pandemic. Health and livelihood support can buffer communities during major disturbances, sustaining progress towards improved human wellbeing and forest conservation. David and Lucile Packard Foundation and the National Geographic Society.
Vision-language and generative models have recently emerged as powerful tools for interpreting multimodal traffic-video data and advancing safety analysis. This paper reviews and integrates progress across foundation vision-language models, multimodal large language models, video-centric temporal reasoning frameworks, and diffusion-based world models, emphasizing how these paradigms enable richer semantic understanding, causal reasoning, and counterfactual safety assessment. We propose a unified taxonomy that maps model families to three application levels across diverse deployment environments-from cloud to onboard systems: network-scale monitoring, event-level crash and near-miss understanding, and generative or counterfactual scenario analysis. Key technical and methodological challenges are identified, including hallucination control, temporal consistency, sim-to-real transfer, and safety alignment with physical and rule-based constraints. The paper synthesizes open research problems and outlines a structured agenda toward grounded, interpretable, and computationally efficient multimodal cognition for real-world traffic safety applications.
Developing accurate traffic injury severity prediction models for cities with limited historical data remains a critical challenge. The primary obstacle stems from the significant spatial heterogeneity of traffic environments, where data-driven models trained on source domains often exhibit poor generalization to target cities with distinct road geometries and crash characteristics. Conventional transfer learning and standard meta-learning algorithms typically struggle to mitigate negative transfer, as they lack explicit mechanisms to decouple domain-specific noise from generalizable safety patterns. To address these limitations, this study proposes the Context-aware Domain Generalization Model-Agnostic Meta-Learning (CaDG-MAML) framework. First, a rigorous domain holdout strategy is incorporated into the meta-training loop, simulating the shift to unseen domains to compel the model to prioritize domain-invariant feature representations. Second, to resolve local heterogeneity, a Context-FiLM (Feature-wise Linear Modulation) module dynamically recalibrates task-specific decision boundaries by modulating intermediate feature maps using aggregated support-set statistics. The proposed framework is evaluated on UK STATS19 two-vehicle crashes (2019-2023) under a 2-way 5-shot setting, with four held-out target cities. Results show that CaDG-MAML achieves the best four-city average performance on AUC, Balanced Accuracy, F1-Severe, F1-Macro, and G-Mean among eight baseline models, with particularly stable advantages on severe-crash-oriented metrics. Paired statistical tests indicate that these gains are generally significant relative to the strongest baseline. Hyperparameter sensitivity and ablation analyses further support robustness and component complementarity. Dynamic SHAP analysis indicates that the framework preserves established risk-related feature groups while recalibrating feature weights across cities to inform tailored safety interventions.
Cannabinoid positivity in motor vehicle collision (MVC) fatalities is a topic of public interest, particularly as cannabis legalization expands. In Connecticut, recreational cannabis legalization in 2021 raised questions about impairment in fatal motor vehicle crashes. A retrospective study using Connecticut Office of the Chief Medical Examiner (CT OCME) data investigated MVC fatalities between 2018 and 2024, providing 3.5 years of data before and after legalization. We examined 1168 driver, 380 pedestrian, and 52 suicide fatalities. For the drivers, cannabinoid positivity increased from 32.8% prelegalization of cannabis to 37.9% postlegalization (P=0.0789). There was a statistically significant increase in cannabinoid-positive motorcyclist deaths following legalization. For pedestrians, cannabinoid positivity increased from 11% prelegalization to 20.6% postlegalization (P=0.016). Multidrug detection was common in cannabinoid-positive cases, most frequently with ethanol. This study demonstrates a rise in cannabinoid positivity in driver and pedestrian MVC fatalities in Connecticut after cannabis legalization. The findings suggest cannabis may play a growing role in MVC fatalities, and/or a demonstration of greater cannabinoid use following legalization. The challenges with interpreting cannabinoid concentrations and the multidrug findings complicate the ability to attribute cannabinoid use to impairment. These data, however, are useful for understanding and responding to evolving drug trends in traffic fatalities.
On January 1, 2024, Nebraska repealed its universal motorcycle helmet law for riders aged ≥21 years with a valid Class M license. We evaluated changes in helmet use, clinical outcomes, and short-term direct institutional costs following repeal. In this multicenter retrospective cohort study, motorcycle crash patients treated at five ACS-verified Level I-III trauma centers in eastern Nebraska before and after repeal were compared by law era and helmet status. Helmet-use trends were assessed using segmented binomial logistic regression. Cost analyses were restricted to patients with positive direct institutional costs using survivor-only and log-transformed models. Among 467 patients (241 pre-repeal, 226 post-repeal), helmet use declined from 84.2% to 20.4% after repeal (p<0.001). Segmented regression demonstrated an immediate reduction in helmet use after repeal (OR 0.17, 95% CI 0.06-0.44; p<0.001), consistent with adjusted individual-level analysis (aOR 0.04, 95% CI 0.03-0.07; p<0.001). Post-repeal, non-helmeted riders had greater unadjusted head-injury burden and more neurosurgical interventions. After adjustment, non-helmeted status remained independently associated with neurosurgical intervention (aOR 3.10, 95% CI 1.03-9.35; p=0.044), but not BIG score ≥2, severe traumatic brain injury composite, or mortality. Adjusted log-transformed analyses showed lower short-term direct institutional costs among non-helmeted riders (cost ratio 0.68, 95% CI 0.54-0.86; p=0.001), likely reflecting differences in injury patterns and procedural utilization rather than reduced economic burden. Nebraska's helmet-law repeal was associated with an immediate and sustained reduction in helmet use. Non-helmeted riders had higher adjusted odds of neurosurgical intervention despite similar adjusted severe brain injury and mortality outcomes. Lower short-term institutional costs should not be interpreted as economic neutrality because they exclude downstream rehabilitation, disability, productivity losses, and societal costs.
Traffic near misses refer to situations in which conflicting vehicles or vulnerable road users (VRUs) are about to crash but take evasive actions to avoid collision. Frequent near misses are strongly associated with crash potential, making them useful proactive safety indicators. Existing trajectory-based near-miss identification methods commonly require continuous tracking and post-processing of road-user trajectories, which can be computationally intensive for real-time applications. This paper presents a proximity-and-speed-based method for instantaneous near-miss identification at signalized intersections. The proposed framework scopes conflict zones by translating TTC/PET-based safety logic into spatial detection zones using vehicle speed, perception-reaction time, deceleration assumptions, and intersection geometry. A near miss is identified when conflicting fast vehicles occupy the scoped conflict zone during the relevant signal phase, allowing isolated event detection rather than continuous pairwise trajectory processing. The framework was tested at a LiDAR-equipped signalized intersection in Salt Lake City, Utah, USA. In a four-hour field evaluation, the algorithm identified 38 instantaneous near-miss events between permissive northbound left-turn vehicles and opposing southbound through vehicles using a 2.0-s post-encroachment time (PET) threshold. All LiDAR-reported events were validated through live monitoring and video review, and manually reviewed PET values were highly correlated with LiDAR-reported PET values. These findings demonstrate that the proposed conflict-zone-based framework can generate movement-specific and phase-specific near-miss events in real time while reducing the need for continuous trajectory processing of all vehicles.
This research aimed to establish a methodological framework for field-testing motorcycle autonomous emergency steering (M-AES) safety function using a specialized prototype demonstrator to identify critical intervention parameters and propose objective metrics for quantifying vehicle stability and human-machine interaction in emergency maneuvers. The study was conducted using a standard street motorcycle equipped with a specifically developed prototype steering actuator capable of delivering controlled steering torques up to 30 Nm. M-AES intervention parameters were those indicated in prior work to be effective in preventing typical crash conditions. A data acquisition system recorded kinematic parameters at 100 Hz, including roll, roll rate, steering angle, and steering torque. The experimental protocol consisted of trials on a closed-to-traffic track testing the intervention of M-AES in different conditions: "Active M-AES" trials, where the rider allowed the system to operate autonomously and "passive M-AES" trials, where the rider's task was to override M-AES action. To quantify the conflict between the rider and the system and the stability of the vehicle in correspondence with M-AES intervention, specific metrics were also developed. First, this study established an experimental approach for the field testing of M-AES, building upon previous research on autonomous emergency systems for motorcycles and existing literature regarding effective crash-avoidance parameters. Field trials were conducted to validate the proposed methodology and the newly introduced metrics. Utilizing the M-AES prototype system, automated lane-change maneuvers were performed, achieving target roll angles of 25-35 deg and roll rates of 50 deg/s. Under "active M-AES" conditions, the vehicle remained stable across all tested speeds (40-60 km/h), with dynamic parameters confirming robust vehicle stability. Results from "passive M-AES" trials demonstrated that the motorcycle remained controllable during the autonomous intervention; riders successfully overrode the system's torque by applying manual steering inputs, confirming the feasibility of rider-initiated overrides. This study established M-AES critical intervention parameters influencing motorcycle stability and objective metrics for quantifying human-machine interaction, providing a technical foundation for future large-scale human subject studies. This is the first testing protocol for evaluating M-AES and riders' reciprocal actions using a prototype demonstrator on a standard motorcycle.
Reckless driving remains a major contributor to road traffic injuries and fatalities in Ghana, posing significant public health and development challenges. This systematic review synthesizes multidisciplinary evidence to examine the multidimensional determinants of reckless driving, its socio-economic and health consequences and global best practices for sustainable reform. Drawing on the theory of planned behaviour (TPB) and the safe systems approach (SSsA), the study integrates behavioural, institutional, infrastructural and economic perspectives. The findings indicate that speeding, dangerous overtaking, weak enforcement credibility, commercial transport incentive structures and inadequate road design collectively sustain high crash rates. Ghana records thousands of road traffic fatalities annually, with substantial productivity losses and economic costs estimated at 3%-5% of gross domestic product (GDP). Vulnerable road users, particularly pedestrians, bear a disproportionate burden of fatalities. Comparative evidence from countries implementing comprehensive Safe Systems reforms demonstrates that sustained reductions in road deaths are achievable through integrated enforcement, infrastructure redesign, licensing modernization and institutional strengthening. The review concludes that reckless driving in Ghana is preventable through coordinated, evidence-based policy action and long-term governance commitment aligned with national development goals.
This study developed a new multibody model that accurately represents the collision behavior of crash test dummies using PC-Crash. The model replicates the shape and weight of an actual dummy. To investigate the influence of joint structures on collision behavior, an additional multibody model was developed to reproduce the joint structure of the actual dummy. These models were applied to analyze occupant behavior in a full-frontal rigid barrier and pedestrian behavior in a vehicle-to-pedestrian impact experiments. A comparison of the multibody model simulations with actual dummy impact experiments revealed that the behavior of the multibody model, which simulates the joint structure of the dummy, closely matched that of the actual dummy. The results indicate that joint structure significantly influences collision behavior, and accurately recreating it improves the precision of crash test dummy collision behavior analysis using PC-Crash.
A merging area is one of the crash-prone locations in a highway network because of frequent lane changing, weaving, and merging behaviors. In Hong Kong, specific lane marking is implemented to guide traffic movements by keeping vehicles in designated lanes, reducing weaving, and channeling traffic. Therefore, conflicting traffic movements can be separated, and overall operational efficiency can be enhanced. However, the effects of traffic channelization through lane marking restrictions on the occurrence of traffic conflicts and associated risks to driving safety are less explored. In this study, the influence of lane marking restriction on traffic conflict risk involving merging traffic is investigated using vehicle trajectory data obtained from unmanned aerial vehicles. In particular, a two-dimensional modified time-to-collision-based indicator is applied to model traffic conflict risk involving lane-changing vehicles at a highway merging area. In addition to lane marking restriction, the influences of confounding factors including vehicle class, vehicle dimensions, and speed and acceleration profiles are also considered. A random parameters logit model with heterogeneity in means is then employed to account for the effects of unobserved heterogeneity. Results indicate that both-side restricted situation significantly reduces traffic conflict risk by limiting all lane-changing behaviors. Additionally, one-side restricted situation also exhibits favorable, even less remarkable, effect on traffic conflict risk. Furthermore, traffic conflict risk decreases for trucks because of the compensatory driving behavior of professional drivers, while the favorable effects of goods vehicles on traffic conflict risk are random. Nevertheless, the random effects of trucks can be moderated by lane change restrictions and lane changing behaviors. Findings should shed light on effective mitigation measures and traffic control strategies that reduce potential crash risk at the highway merging areas.
Two-wheelers, including bicycles and electric scooters (e-scooters), account for a significant portion of road traffic fatalities. Although there are several measures, including alcohol and helmet legislation, mortality rates remain high, specifically in low- and middle-income countries. This systematic review aims to identify and synthesize factors associated with fatal outcomes in bicycle and e-scooter collisions. We conducted a systematic search across PubMed, Scopus, and Web of Science from 2014 to April 2026. The included studies were observational (cohort, case-control, and cross-sectional) and examined fatal bicycle and e-scooter crashes. Data extraction centered on helmet use, substance use involvement, injury patterns, and demographic variables. Study quality was assessed using the Joanna Briggs Institute (JBI) methodology for systematic reviews. Due to substantial heterogeneity in study design, populations, vehicle type, outcome definitions, and reported variables, a meta-analysis was not performed. Instead, findings were synthesized using a narrative approach. A total of 26 studies were included, including data from across the globe. Age, as well as sex distribution, was reported in 23 studies. Helmet use was reported in 14 studies. Injury patterns were described in 18 studies. Toxicological findings were reported in 14 studies. The lack of helmet use appeared to be linked to mortality. Alcohol use, although not frequently analyzed, was also significantly correlated with mortality. Elderly victims of male sex appear to demonstrate the highest mortality numbers. Fatalities from bicycle and e-scooter collisions result from several commonly modifiable behavioral and environmental factors. The establishment of intervention strategies, including helmet laws, substance abuse prevention, infrastructure improvements, and age- and gender-targeted education, is crucial to reducing mortality. Enhanced prehospital care and injury surveillance systems are also mandatory to improve survival outcomes.
Relatively little is known about the relationship between anxiety disorders and fitness-to-drive. This study analysis the characteristics of motor vehicle fatalities, involving drivers with hospital-diagnosed anxiety disorders, as well as the contribution of psychiatric comorbidity to the cause of these fatalities. In our population-based data of all deceased Finnish drivers we compare socio-demographic, driving-related and clinical characteristics of study subjects with anxiety disorders with matched control subjects without any history of hospital treated psychiatric disorders. We also explore whether the cause of death of drivers killed in motor vehicle accidents differs between those with anxiety disorders and comorbid psychiatric disorders. This study was based on OTI (The Finnish Crash Data Institute) data with linkage to two national registers: The Care Register for Health Care and the National Cause of Death Register. The initial study population included 4,930 drivers involved in fatal motor vehicle accidents in Finland between 1990 and 2011. It was confirmed that 93 drivers had received an anxiety disorder diagnosis (ICD-10 codes: F40-44) in the 5 years prior to their collision. We found that drivers with anxiety disorders were more likely to be involved in crashes with suicidal intent (OR = 2.83, 95% CI [1.40, 5.72]), drivers who had previously experienced headaches or insomnia (OR = 4.99, 95% CI [1.34, 18.62]), and drivers who were impaired by alcohol or drugs (OR = 3.92, 95% CI [1.28, 11.98] when DUI was 0.5‰-1.19‰ and OR = 2.01, 95% CI [1.15, 3.52] when DUI was aggravated, BAC 1.2‰ or above). It was notable that the proportion of suicides increased when anxiety disorder occurred simultaneously with depression (20.7%) or other psychiatric disorders (24.0%). In the overall dataset, the most common cause of death was an accident (82.8%). Regarding to accidental deaths, no significant differences were observed between individuals with anxiety disorders and matched control subjects without any history of hospital treated psychiatric disorders. The findings of this study suggest that assessment of suicidal intent may be particularly important when evaluating fitness to drive in patients with anxiety disorders, including those with psychiatric comorbidity, alongside consideration of substance use that may impair driving, and a history of headaches or insomnia.
Safety control for automated vehicles under occluded visibility has emerged as a key research topic. Nevertheless, prior work largely focuses on risk-aware control based on onboard sensing, with limited discussion of cloud-supported proactive safety decision-making and control (CPSC) for occluded scenarios. To address this gap, we analyze the key characteristics of the vehicle-road-cloud integrated control system (VRCICS) and develop a generic and scalable CPSC architecture across different SAE Levels of Driving Automation. Using the lead-vehicle cut-out with occluded hazard exposure (LVCO) as a representative scenario, we propose a cloud-based TTC interaction-matrix risk assessment method that leverages predicted spatiotemporal trajectory interactions to dynamically identify potential collision points and risk targets. A risk-transfer-aware proactive safety decision algorithm is then developed to enable timely risk warnings and adaptive safe-speed planning. Simulation and ablation studies verify risk-transfer identification and adaptive safety control in potential multi-vehicle rear-end crash chains, and highlight the role of vehicle-side safety functions in the hierarchical architecture. Field tests show that conventional AEB collides at 50 km/h (headway level 1) and 60 km/h (headway level 1/2), whereas CPSC avoids collisions with early warning and proactive deceleration (mean≤4m/s2). Moreover, a sensitivity analysis of roadside perception refinement indicates that the false-trigger rate decreases by 72.7% and the collision-avoidance success rate increases by 18.5%. Additionally, Joint experiments across multiple OEMs confirm the system's engineering feasibility, scalability, and robust performance across diverse vehicle platforms. This study provides a practical guidance and insights for VRCICS-based assisted driving applications.
Road injuries are a leading cause of mortality and morbidity worldwide. Years of international efforts have aimed to strengthen policy engagement, including the 2020 UN General Assembly's proclamation of the Second Decade of Action for Road Safety (2021-30), targeting a 50% reduction in road traffic deaths and serious injuries by 2030. The aim of this study is to provide estimates to monitor progress and identify intervention gaps. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2023, we estimated incidence, mortality, and morbidity of road injuries for 204 countries and territories from 1990 to 2023. Four road injury types and 47 nature-of-injury categories were examined. Morbidity and mortality data from clinical records, vital registration, and police reports were harmonised using meta-analytic techniques to ensure consistency and correct for systematic bias. Incidence was modelled with the meta-regression tool Disease Modelling-Meta-Regression version 2.1 and cause-specific mortality with the Cause of Death Ensemble model, both incorporating location-specific covariates to support interpolation. Years of life lived with disability (YLDs) were estimated from the prevalence and severity of the nature of road injury, and years of life lost (YLLs) from the number of cause-specific deaths multiplied by the standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were the sum of YLLs and YLDs. All metrics were calculated with 95% uncertainty intervals (UIs). In 2023, there were 50·9 million (95% UI 46·1-56·1) road injury incident cases, 1·34 million (1·04-1·58) deaths, and 75·3 million (59·8-89·2) DALYs globally. Road injuries were the leading global cause of death among males aged 10-39 years. Between 1990 and 2023, age-standardised incidence decreased by 38·3% (95% UI 36·9-39·7) and mortality decreased by 32·3% (6·1-49·0), but progress varied widely by World Bank income group. Mortality in low-income countries (43·8 [95% UI 31·7-56·0] deaths per 100 000 population) was approximately six times higher than in high-income countries (7·5 [7·1-7·9] deaths per 100 000), despite the high-income countries showing the highest age-standardised incidence rates (858·1 [95% UI 781·9-947·1] cases per 100 000). In the past decade, many countries achieved notable reductions in road injuries, but others, including Ghana and the USA, saw increases. More severe injuries tended to occur in low-income and middle-income countries. Although global incidence, mortality, and DALY rates from road injuries have declined, progress remains uneven, with pronounced disparities across income groups reflecting systemic inadequacies in infrastructure, vehicle standards, enforcement, and post-crash care. Strengthening emergency response, improving road design, enforcing safety measures, and adapting policies to the evolving demographics remain essential. Gates Foundation.
Geometric design critically influences mountainous freeway safety. Undulating terrain forces complex horizontal-vertical alignments that approach design limits, impairing operational safety and increasing crash risks. This necessitates examining how these combined parameters affect safety. However, critical limitations exist in current research and practice: (1) design specification limitations (a lack of empirical crash data validation for specified geometric parameter limits and interactive effects) and (2) methodological shortcomings (qualitative models masking risk heterogeneity on different combined sections). Thus, this study proposed a quantitative crash frequency analysis framework to investigate crash frequency relationships with combined horizontal and vertical curve alignments. Specifically, geometric alignments were classified into four distinct section types: tangent-sag curve (T-SC), tangent-crest curve (T-CC), curve-sag curve (C-SC), and curve-crest curve (C-CC). Using crash, geometric, and operational data from two typical Chinese mountainous freeways (5228 crashes), CatBoost regression models were developed for each section type. Shapley Additive exPlanations analysis and robustness check were then applied to quantify risk trends and identify high-risk thresholds for key geometric parameters. Key findings revealed significantly increased crash frequency associated with specific parameter interactions per section type: (1) T-SC/T-CC: short tangents, steep grade change rates, and small vertical curve radii. (2) C-SC/C-CC: high horizontal radius-to-curve length ratios, large horizontal curve average curvatures, steep grade change rates, and small vertical curve radii. (3) T-SC/C-SC: tangents exert a more significant influence on crash frequencies at sag vertical curves than horizontal curves. (4) T-CC/C-CC: crash risk on crest vertical curves is exacerbated more by horizontal curves than by tangents. These findings provide quantitative reference values and localized support for optimizing geometric design and safety management on mountainous freeways.
Transportation safety remains a critical global concern, with road traffic crashes causing approximately 1 [...].
Insufficient validation of simulation tools and protection methods, as well as inadequate understanding of protection mechanisms, exists in pedestrian-ground contact injury research. This study aimed to evaluate the effectiveness of the PC-Crash pedestrian model in predicting pedestrian motion and ground contact after a vehicle impact. Six cadaver tests were reconstructed while a braking control method was applied to verify the effectiveness of the pedestrian-ground contact injury protection method. The change in pedestrian energy in full and controlled braking cases was compared and analyzed to reveal the mechanism of pedestrian-ground contact injury reduction. The PC-Crash 2014 pedestrian model not only accurately reconstructed the pedestrian kinematic response but also reconstructed pedestrian injury with high accuracy. The mean error of HIC for vehicle and ground contact was only 6.07% and 5.85%, respectively. Reasonably controlling the vehicle braking can reduce ground-related HIC by up to 87.65% on average without increasing vehicle contact injury. The direct reason why controlled braking reduces pedestrian-ground contact injuries is that it changes the pedestrian ground contact mechanism. The essential reason is that the change in ground contact mechanism extends the head buffer time, thus reducing the energy absorbed by the pedestrian's head when it comes into direct contact with the ground. The study's findings not only provide tools for future pedestrian-ground injury research but also improve the understanding of pedestrian-ground contact injury protection methods, which is significant.
Mast cell activation syndrome (MCAS) is characterized by pronounced heterogeneity, fluctuating severity, delayed post-perturbation crashes analogous to those documented in post-exertional malaise, and highly variable clinical trajectories that are poorly explained by mediator-centric diagnostic approaches. Patients with similar symptom profiles or laboratory findings often follow divergent courses, while resting biomarkers frequently fail to correlate with functional capacity or prognosis. Beyond serving as markers of activation, mast-cell mediators influence neural, vascular, epithelial, immune, and autonomic systems, providing a biologically plausible mechanism through which local perturbations may propagate into multisystem instability. Building on a pressure-reserve framework of energetic constraint, this paper proposes a stability-state classification framework for interpreting system behavior and identifying operating regimes. Four operational stability classifications are proposed-recovery-capable, plateau, slow drift, and crash-prone-based on the relationship between energetic reserve and reactive pressure, modulated by the degree of multisystem synchronization. Recovery-capable, plateau, and slow-drift classifications are conceptual regions along a continuous fragility axis, whereas crash-prone behavior is proposed as a threshold-crossing regime characterized by impaired recovery dynamics, hysteresis, and increased susceptibility to instability. Three complementary stability axes are introduced: energetic reserve, dominant pressure domain (dominant ingress), and synchronization. Together, these axes generate characteristic classification signatures that remain observable despite overlapping mediator profiles and fluctuating baseline values. This framework reframes MCAS as a disorder of state-dependent instability rather than mediator burden alone, provides a structured means to interpret heterogeneity, stratify patients prognostically, and track trajectory over time, and offers a foundation for longitudinal monitoring and future validation studies grounded in system dynamics rather than isolated biomarkers.
Mass shooting incidents receive widespread attention and create sudden distress not only within affected communities, but also in populations far from the event. Distraction and acute psychological stress can impair attention and judgment, raising the risk of unintended harms, such as traffic crashes. To assess whether major mass shootings are associated with short-term increases in fatal motor vehicle crashes, as a potential extension of their broader mortality impact. The Fatality Analysis Reporting System, a population-based registry of fatal US motor vehicle crashes, was used to measure traffic fatalities on the days before and after the top 10 deadliest shootings from 2008 through 2023. Using a quasi-experimental event study design that combined the days before and after mass shootings, changes in the mean additional risk of traffic fatalities attributable to mass shootings were estimated. The study team also examined when public internet search interest in "shooting" peaked relative to the mass shooting event, to assess whether the timing of this peak corresponded to changes in fatal motor vehicle crashes. These data were analyzed from January 2024 through April 2024. Dates of 10 major mass shootings. Traffic fatalities. On the day following mass shooting events, public internet search interest in "shooting" peaked and traffic fatalities rose by an average of 14.3%, corresponding to 19.9 (95% CI, 12.7-27.1) additional deaths across the country. Increases in traffic fatalities were consistent across driver age and sex, vehicle occupancy, rurality, and lighting conditions, and persisted when restricted to states outside the affected state and its neighbors. Active shooter events that did not result in fatalities were not accompanied by increases in internet searches for "shooting" and traffic fatalities did not increase significantly, suggesting that public awareness of shooting events, and their impact on driving, differs meaningfully by event severity. In this quasi-experimental analysis of major US mass shootings, major mass shootings were associated with a transient nationwide increase in fatal motor vehicle crashes on the day following the event. The excess fatalities observed on the day after these 10 events were equivalent in magnitude to approximately 75% of the deaths occurring during the events themselves, suggesting that the broader population-level mortality associated with mass shootings may extend beyond individuals who directly experienced the event.
Field triage guidelines exist to inform first responder personnel on criteria to expedite the transfer of motor vehicle collision (MVC) patients to trauma centers; however, these guidelines are minimally informed by the vehicle characteristics and collision characteristics. Given this, we studied associations of the vehicle characteristics and collision characteristics with patient injury and outcomes. We conducted a retrospective study using Michigan State Police crash reports linked with Michigan Trauma Quality Improvement Program collaborative quality initiative data. Univariable analysis was used to identify characteristics predictive of the primary outcome of severe injury, defined as an Injury Severity Score >15 (ISS>15), and multivariable logistic regression analysis was performed across primary and secondary outcomes of ISS>15, mortality, and the need for surgery, intensive care, or transfer to another hospital. There were 13 154 linked cases between the two data sources. This contained 52.3% males with a mean age of 41.7 years (SD 20.7 years). In multivariable logistic regression, the collision characteristics associated with the highest increased odds of ISS>15 were single-vehicle crashes (OR 1.35, p<0.01) and head-on crashes (OR 1.54, p<0.01). Head-on crashes (OR 1.60, p<0.01) and single-vehicle crashes (OR 1.46, p=0.03) were associated with a significantly increased risk of mortality. Head-on crashes (OR 1.49, p<0.01) and single-vehicle crashes (OR 1.37, p<0.01) were associated with the highest odds of operative intervention or intensive care. Several vehicle and collision characteristics are associated with increased odds of severe traumatic injury and death after MVC. The addition of these characteristics to field triage assessments could improve triage decision-making to predict injury severity and the risk of mortality. III, prognostic, epidemiological.