Potential For Safety Improvement Research Articles

Non-secreting IL12 expressing oncolytic adenovirus Ad-TD-nsIL12 in recurrent high-grade glioma: a phase I trial

Malignant glioma is a highly fatal central nervous system malignancy with high recurrence rates. Oncolytic viruses offer potential treatment but need improvement in efficacy and safety. Here we describe a phase I, dose-escalating, single arm trial (ChiCTR2000032402) to study the safety of Ad-TD-nsIL12, an oncolytic adenovirus expressing non-secreting interleukin-12, in patients with recurrent high-grade glioma that connects with the ventricular system. Eight patients received intratumoral treatment via stereotaxis or an Ommaya reservoir, with doses ranging from 5 × 109 to 5 × 1010vp. The primary end point was to determine the maximal tolerated dose. Secondary endpoints included toxicity and anti-tumour ability. Minimal adverse events were observed at doses of 5 × 109 and 1 × 1010vp. Grade 3 seizure was observed in two patients from Cohort 3 (5 × 1010vp). Therefore, the maximum tolerated dose was determined to be 1 × 1010vp. Four patients developed hydrocephalus during follow-up. Among them, symptoms in two patients were relieved after placement of a ventriculo-peritoneal shunt, and the other two only showed ventriculomegaly on MRI scan without neurological deterioration. Complete response (according to Response Assessment in Neuro-Oncology Criteria) in one patient, a partial response in one patient and post-treatment infiltrations of CD4+ and CD8 + T cells into the tumour were documented during this trial. In conclusion, Ad-TD-nsIL12 has demonstrated safety and preliminary efficacy in patients with recurrent high-grade glioma.

Developing equity-aware safety performance functions for identifying hotspots of pedestrian-involved crashes

Crashes are frequently disproportionally observed in disadvantaged areas. Despite the evident disparities in transportation safety, there has been limited exploration of quantitative approaches to incorporating equity considerations into road safety management. This study proposes a novel concept of equity-aware safety performance functions (SPFs), enabling a distinct treatment of equity-related variables such as race and income. Equity-aware SPFs introduce a fairness distance and integrate it into the log-likelihood function of the negative binomial regression as a form of partial lasso regularization. A parameter λ is used to control the importance of the regularization term. Equity-aware SPFs are developed for pedestrian-involved crashes at the census tract level in Virginia, USA, and then employed to compute the potential for safety improvement (PSI), a prevalent metric used in hotspot identification. Results show that equity-aware SPFs can diminish the effects of equity-related variables, including poverty ratio, black ratio, Asian ratio, and the ratio of households without vehicles, on the expected crash frequencies, generating higher PSIs for disadvantaged areas. Based on the results of Wilcoxon signed-rank tests, it is evident that there are significant differences in the rankings of PSIs when equity awareness is considered, especially for disadvantaged areas. This study adds to the literature a new quantitative approach to harmonize equity and effectiveness considerations, empowering more equitable decision-making in safety management, such as allocating resources for safety enhancement.

Characterization of health and safety hazards of deconstruction activities.

Manual deconstruction-an alternative to mechanical demolition-is a crucial technique in the building industry that facilitates a circular economy for the built environment and achieves sustainable development goals (SDGs). However, the current body of literature lacks a thorough grasp of the OHS (occupational health and safety) hazards related to the deconstruction process. The current work closes this scholarly gap by outlining the deconstruction process as phases and sets of activities and highlighting potential health and safety hazards throughout different activities and phases. The study uses numerous case studies and applies a descriptive case study technique to gather and analyze primary data from hundreds of photos and notes from over 40 field visits. The primary data are first used to prepare a comprehensive process map of deconstruction. Six construction safety experts and four industrial hygiene experts were then interviewed to identify and characterize health and safety hazards in images representative of the deconstruction activities. A taxonomy of OHS hazards for deconstruction was developed based on identifying 35 unique safety and 51 unique health hazards. Significant safety hazards in deconstruction activities include "struck-by," "trip, slip, and fall" hazards, and "lack of personal protective equipment." Major health hazards include ergonomic concerns, respiratory diseases, and eye strain. The study highlights the occupational health and safety issues faced by the deconstruction trade with a granularity rarely covered in existing literature. The assessments can be helpful for OHS practitioners to identify potential health and safety improvements across various deconstruction phases.

A National Analysis of Craniosynostosis Demographic and Surgical Trends Over a 10-Year Period.

The pathogenesis of craniosynostosis, characterized by the premature fusion of calvarial sutures, is multifaceted and often the result of an amalgamation of contributing factors. The current study seeks examine the possible contributors to craniosynostosis development and its surgical trends over time. A multicenter/national retrospective cohort study was conducted of patients who underwent surgical repair of craniosynostosis (n=11,279) between 2012 and 2021 identified in the American College of Surgeons National Surgical Quality Improvement Program Pediatric Data File. Main outcome measures included risk factors and trends relating to surgical repair of craniosynostosis. Nationwide reports of craniosynostosis in the NSQIP-P database have increased between 2012 and 2021 by 195%. The prevalence of craniosynostosis per overall cases has remained between 1.0% and 1.3%. There were predominantly more White male patients in the craniosynostosis cohort ( P <0.001). Craniosynostosis patients had significantly greater birth weights, gestational ages, and were less likely to be premature ( P <0.05). Linear regression demonstrated that operative time, anesthesia time, and length of stay significantly decreased over the study period ( P <0.001). This national data analysis highlights trends in craniosynostosis repair indicating potential improvements in safety and patient outcomes over time. While these findings offer insights for health care professionals, caution is warranted in extrapolating beyond the data's scope. Future research should focus on diverse patient populations, compare outcomes across institutions, and employ prospective study designs to enhance the evidence base for craniosynostosis management. These efforts will help refine diagnostic and treatment strategies, potentially leading to better outcomes for patients.

A CNN-Based Framework for Video Analysis and Accident Detection

This research investigates the development and deployment of a Convolutional Neural Network (CNN) model for automatic accident detection in CCTV footage. The ever-increasing reliance on video surveillance necessitates efficient and accurate methods for accident identification. CNNs, with their inherent ability to learn complex spatial relationships within images, are particularly well-suited for this task. This study proposes a CNN architecture that utilizes a pre-trained MobileNetV2 base for feature extraction, followed by a custom classification head tailored to the specific task of accident vs. no accident classification. The model is trained on a dataset of grayscale video frames, achieving an impressive accuracy of 92% on the testing set. This high level of accuracy suggests that CNNs hold significant promise for real-world accident detection applications. Furthermore, to bridge the gap between research and practical implementation, the model is converted to a TensorFlow Lite (TFLite) format for deployment on resource-constrained devices. Additionally, a user-friendly frontend application is developed, empowering users to interact with the model and analyze both images and videos. This user-centric approach broadens the model's accessibility and paves the way for potential improvements in road safety through real-time accident detection.

Temporal trends in outcomes following inpatient transcatheter aortic valve replacement

IntroductionDespite the growing adoption of transcatheter aortic valve replacement (TAVR), there remains a lack of clinical data evaluating procedural safety and discharge practices. AimsThis study aims to investigate if there have been improvements in postoperative clinical outcomes following TAVR. MethodsIn this large-scale, retrospective cohort study, patients who underwent TAVR as an inpatient were identified from 2016 to 2020 using the National Readmissions Database. The primary outcome was temporal trends in the rates of discharge to home. Secondary endpoints assessed annual discharge survival rates, 30-day readmissions, length of stay, and periprocedural cardiac arrest rates. ResultsOver the 5-year study period, a total of 31,621 inpatient TAVR procedures were identified. Of these, 79.2 % of patients were successfully discharged home with home disposition increasing year-over-year from 74.5 % in 2016 to 85.9 % in 2020 (Odds ratio: 2.01; 95 % CI 1.62–2.48, p < 0.001). The mean annual discharge survival rate was 97.7 % which did not change significantly over the 5-year study period (p = 0.551). From 2016 to 2020, 30-day readmissions decreased from 14.0 % to 10.3 %, respectively (p = 0.028). Perioperative cardiac arrest occurred in 1.8 % (n = 579) of cases with rates remaining unchanged during the study (p = 0.674). ConclusionMost TAVR patients are successfully discharged alive and home, with decreasing 30-day readmissions observed over recent years. This data suggests potential improvements in preoperative planning, procedural safety, and postoperative care. Despite perioperative cardiac arrest being associated with high mortality, it remains a relatively rare complication of TAVR.

Identification of Road Black Spots Based on the Sliding Window Optimization and Safety Performance Function Development

The sliding window method is a road network screening approach commonly used for identifying black spots. Previous studies have indicated that the selection of window length significantly impacts the black spot identification process. This research proposes a new method that optimizes the sliding window framework by examining its characteristics. The optimization methodology employed in this study is as follows: Firstly, the road is segmented, and for each segment, different scenarios of window lengths are chosen using the Density-Based Spatial Clustering of Applications with Noise algorithm. Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. Subsequently, the average differences are calculated using the analysis of variance, and the window length with the lowest dispersion of difference values from the mean is identified as the optimal length for each segment. The case study yielded noteworthy results, indicating that the utilization of the sliding window with optimal lengths led to the identification of 122 high-risk black spot-candidates. These points exhibit a higher crash density, effective length, and greater value in quantitative evaluation tests compared to the results obtained using windows with common fixed lengths.

An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study

Road safety is considered one of the fundamental factors for sustainable mobility, which requires establishing effective highway safety management programs and processes. Identifying safety improvement sites (network screening) is a critical step in the state roadway safety programs. The overall effectiveness of these programs largely relies on the robustness of the network screening method in identifying sites with high potential for safety improvements. This study investigates the network screening performance of a new proposed methodology that employs heuristic scoring schemes to account for roadway and roadside characteristics, crash history, and traffic exposure. The new method, originally proposed for use on rural low-volume roads, is simplistic in that it does not require exact and detailed information on traffic and geometric characteristics and can still be applied in the absence of crash data. The performance evaluation is conducted using a sample of 1495 miles of rural two-lane highway segments in Oregon. The effectiveness of the proposed method is assessed using observed crash history and compared to the well-established Empirical Bayes method as a reference. The effect of traffic level on the performance of the proposed method was evaluated using separate analyses for lower and higher traffic volume segments. The study findings indicate that the effectiveness of the proposed method in identifying sites with potential safety improvements is overall high and slightly exceeded that of the more sophisticated and data-intensive Empirical Bayes method. When data were stratified by traffic volume, the proposed method was found to be more effective for lower volume roads; however, the Empirical Bayes method outperformed the proposed method for higher volume roads.

Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models

The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID.

A New Approach for Identifying Safety Improvement Sites on Rural Highways: A Validation Study

The research presented in this paper examines a new proposed approach for identifying safety improvement sites on rural highways. Unlike conventional approaches, the proposed approach does not require crash history, but rather utilizes classified variables for traffic volume, geometric features, and roadside characteristics that do not require access to exact data or extensive technical expertise. The research validates the performance of the proposed approach using field data from a large sample of rural two-lane highway segments in the state of Oregon including traffic, roadway, and crash data. A mathematical model for the prediction of the EB expected number of crashes using multivariate regression analysis is developed and used as the network screening criterion. The model’s independent variables include roadway geometry, roadside characteristics, and traffic exposure, while the dependent variable is the EB expected number of crashes. Using observed crash history as a reference, the performance of the proposed approach was compared to two of the well-established methods in practice, namely, the Empirical Bayes (EB) and the potential for safety improvement (PSI) methods. The study results suggest that by using crash density for highway segments, the performance of the proposed method was lower than that of the EB and PSI methods. This is despite the high R-square value of the predictive model used in the proposed method. However, when using crash frequencies for highway segments, the performance of the proposed method was found comparable to the well-established EB and PSI methods.

Advancing Sustainable Herbal Medicine: Synthesizing Nanoparticles from Medicinal Plants

The crucial challenge in herbal medicine development lies in ensuring the safety, quality, and effectiveness of herbal materials to comply with the regulatory standards in the herbal medicine industry. This research aims to synthesize nano-based herbal medicine by combining two medicinal plants: Bischofia javanica leaves and Phaleria macrocarpa fruits. Through the High-Energy Milling (HEM) process, both materials are transformed into nanoparticles to enhance the solubility and bioavailability of active compounds. The herbal medicine standards are measured through Scanning Electron Microscopy (SEM), Particle Size Analysis (PSA), and Fourier-transform infrared spectroscopy (FTIR) tests. The SEM analysis results indicate optimal dispersion of nanoparticles, while PSA analysis reveals a reduction in particle size and increased homogeneity after nanonization with HEM. FTIR analysis results show various functional groups in nanoparticles, indicating the potential interaction between the combined components. The effectiveness of nanoparticles in the Rattus norvegicus Oral Squamous Cell Carcinoma (OSCC) model demonstrates an increase in lipid profile comparable to the effectiveness of Vitamin C. These findings highlight the potential of combined herbal treatment with nanoparticles produced by HEM, offering better efficacy and potential safety improvement, particularly in addressing lipid metabolism disorders.

Severe Plastic Deformation of Mg–Zn–Zr–Ce Alloys: Advancing Corrosion Resistance and Mechanical Strength for Medical Applications

Magnesium-based alloys hold potential for medical applications, but face challenges like rapid bioresorption and limited mechanical strength during early bone healing. In our study, we present a novel Mg–Zn–Zr–Ce alloy with low cerium content (up to 0.1 wt.% Ce) processed using two severe plastic deformation (SPD) techniques. Through an innovative combination of multiaxial forging and multipass rolling, we have achieved a fine-grained structure with an average grain size of the primary α-Mg phase of 1.0 μm. This refined microstructure exhibits improved mechanical properties, including a substantial increase in yield strength (σYS) from 130 to 240 MPa, while preserving ductility. The alloy’s composition includes α-Mg grains, cerium and zinc hydrides, and intermetallic phases with cerium and zinc elements. Tensile testing of the fine-grained alloy demonstrates an enhancement in yield strength (σYS) to 250 MPa, marking a 2.8-fold improvement over the conventional state (σYS = 90 MPa), with a modest 2-fold reduction in ductility. Crucially, electrochemical tests conducted in physiological solutions highlight substantial advancements in corrosion resistance. The corrosion current was reduced from 14 to 2 μA/cm2, while polarization resistance decreased from 3.1 to 8.1 kΩ∙cm2, underlining the alloy’s enhanced resistance to biodegradation. Our results show that the novel Mg–Zn–Zr–Ce alloy, after combined SPD, demonstrates mitigated bioresorption and enhanced mechanical properties. Our findings highlight the fact that the introduction of this innovative alloy and the application of SPD represent significant steps towards addressing the limitations of magnesium-based alloys for medical implants, offering potential improvements in safety and effectiveness.

A systematic approach to macro-level safety assessment and contributing factors analysis considering traffic crashes and violations

During rapid urbanization and increase in motorization, it becomes particularly important to understand the relationships between traffic safety and risk factors in order to provide targeted improvements and policy recommendations. Violations and police enforcement are key variables, but the endogenous relationship between crashes and violations has made these variables unreliable and has limited their use. To manage this problem, this study developed a systematic approach for the joint modeling of crashes and violations to identify crash and violation hotspots and examine the mechanisms underlying macro-level contributing factors. Socio-economic, road network, public facility, traffic enforcement, and land use intensity data from 115 towns in Suzhou, China, were collected as independent variables. A bivariate negative binomial spatial conditional autoregressive model (BNB-CAR) and the potential for safety improvement (PSI) method were adopted to identify crash-prone and violation-prone areas, and an interpretable machine learning framework was applied to explore the factors’ effects by area. Results showed that the proposed framework was able to accurately identify problem areas and quantify the impact of key factors, which, in Suzhou, were the number of traffic police and their daily patrol time. Considering such enforcement-related information provided important insights into reducing crash and violation frequency; for example, keeping the number of traffic police and daily patrol time under certain thresholds (number of police lower than 11 and patrol time lower than 2.3 h in this sample) was as effective as increasing these numbers for reducing the probability of high-crash and high-violation areas. The proposed approach can help traffic administrators identify the key contributing factors, especially enforcement factors, in crash-prone and violation-prone areas and provide guidelines for improvement.

Machine Learning Improves the Accuracy of Trauma Team Activation Level Assignments in Pediatric Patients

BackgroundThe assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries. MethodsFollowing IRB approval, we retrospectively collected data from the institutional trauma registry and electronic medical record at our Pediatric Trauma Center for all patients (age <18 y) who triggered a trauma team activation (1/2014–12/2021), including: demographics, mechanisms of injury, comorbidities, pre-hospital interventions, numeric variables, and the six “Need for Trauma Intervention (NFTI)” criteria. Three machine learning models (Logistic Regression, Random Forest, Support Vector Machine) were tested 1000 times in separate trials using the union of the Cribari and NFTI metrics as ground-truth (Injury Severity Score >15 or positive for any of 6 NFTI criteria = full activation). Model performance was quantified and compared to emergency department (ED) staff. ResultsED staff had 75% accuracy, an area under the curve (AUC) of 0.73 ± 0.04, and an F1 score of 0.49. The best performing of all machine learning models, the support vector machine, had 80% accuracy, AUC 0.81 ± 4.1e−5, F1 Score 0.80, with less variance compared to other models and ED staff. ConclusionsAll machine learning models outperformed ED staff in all performance metrics. These results suggest that data-driven methods can optimize trauma team activations in the ED, with potential improvements in both patient safety and hospital resource utilization. Type of studyEconomic/Decision Analysis or Modeling Studies. Level of evidenceII.

Searching for the viability of using thorium-based accident-tolerant fuel for VVER-1200

This study explores the feasibility of employing (U, Th)-based accident tolerant fuels (ATFs), specifically (0.8UO2, 0.2ThO2), (0.8UN, 0.2ThN), and (0.8UC, 0.2ThC). The investigation assesses the overall performance of these proposed fuel materials in comparison to the conventional UO2, focusing on deep neutronic and thermal-hydraulic (Th) analyses. Neutronic analysis utilized the MCNPX code, while COMSOL Multiphysics was employed for thermal-hydraulic analysis. The primary objective of this research is to overcome the limitations associated with traditional UO2 fuel by exploring alternative fuel materials that offer advantages in terms of abundance and potential improvements in performance and safety. Given the limited abundance of UO2, long-term sustainable nuclear energy production faces challenges. From a neutronic standpoint, the U–Th based fuels demonstrated remarkable fuel cycle lengths, except (0.8UN, 0.2ThN), which exhibited the minimum fuel cycle length and, consequently, the lowest fuel burn-up. Regarding thermal-hydraulic performance, (0.8UN, 0.2ThN) exhibited outstanding performance with significant margins against fuel melting compared to the other materials. Overall, when considering the integrated performance, the most favourable results were obtained with the use of the (0.8UC, 0.2ThC) fuel configurations. This study contributes valuable insights into the potential benefits of (U, Th)-based ATFs as a promising avenue for enhanced nuclear fuel performance.

Corridor-level network screening and modeling of fatal and serious injury crashes on urban and suburban arterial corridors in Florida

Network screening is a key aspect of the safety management process. The current standard methods in the Highway Safety Manual assume that intersections and roadway segments are independent, even though interactions between sites likely exist. One way to account for these interactions is by studying roadway corridors made of consecutive intersections and segments. This paper developed a roadway corridor definition to identify urban and suburban arterial corridors in Florida with context classification and lane count as defining features. Applying this definition to 10 Florida counties resulted in 1105 corridors with 14,097 fatal or serious injury crashes from 2017 to 2021. Historic crashes and a developed regression model were used to estimate the expected crash frequency for fatal and serious injury crashes with the empirical Bayes method. After performing k-means clustering on expected crash frequency per mile, 25 high-risk corridors were identified. The difference between the expected and predicted crash frequencies (potential for safety improvement) for each high-risk corridor provided a list of priority corridors for safety improvements. Countermeasures focused on speed reduction in suburban corridors would likely lead to the greatest reductions in fatal and serious injury crashes. This corridor approach allows agencies to quickly identify high-risk areas for priority treatment.

Navigated, percutaneous, three-step technique for lumbar and sacral screw placement: a novel, minimally invasive, and maximally safe strategy

BackgroundMinimally invasive spine surgery is a field of active and intense research. Image-guided percutaneous pedicle screw (PPS) placement is a valid alternative to the standard free-hand technique, thanks to technological advancements that provide potential improvement in accuracy and safety. Herein, we describe the clinical results of a surgical technique exploiting integration of neuronavigation and intraoperative neurophysiological monitoring (IONM) for minimally invasive PPS.Materials and MethodsAn intraoperative-computed tomography (CT)-based neuronavigation system was combined with IONM in a three-step technique for PPS. Clinical and radiological data were collected to evaluate the safety and efficacy of the procedure. The accuracy of PPS placement was classified according to the Gertzbein–Robbins scale.ResultsA total of 230 screws were placed in 49 patients. Only two screws were misplaced (0.8%); nevertheless, no clinical sign of radiculopathy was experienced by these patients. The majority of the screws (221, 96.1%) were classified as grade A according to Gertzbein–Robbins scale, seven screws were classified as grade B, one screw was classified as grade D, and one last screw was classified as grade E.ConclusionsThe proposed three-step, navigated, percutaneous procedure offers a safe and accurate alternative to traditional techniques for lumbar and sacral pedicle screw placement.Level of Evidence Level 3.Trial registration Not applicable.

Evaluating Electrolyte-Anode Interface Stability in Sodium All-Solid-State Batteries.

All-solid-state batteries have recently gained considerable attention due to their potential improvements in safety, energy density, and cycle-life compared to conventional liquid electrolyte batteries. Sodium all-solid-state batteries also offer the potential to eliminate costly materials containing lithium, nickel, and cobalt, making them ideal for emerging grid energy storage applications. However, significant work is required to understand the persisting limitations and long-term cyclability of Na all-solid-state-based batteries. In this work, we demonstrate the importance of careful solid electrolyte selection for use against an alloy anode in Na all-solid-state batteries. Three emerging solid electrolyte material classes were chosen for this study: the chloride Na2.25Y0.25Zr0.75Cl6, sulfide Na3PS4, and borohydride Na2(B10H10)0.5(B12H12)0.5. Focused ion beam scanning electron microscopy (FIB-SEM) imaging, X-ray photoelectron spectroscopy (XPS), and electrochemical impedance spectroscopy (EIS) were utilized to characterize the evolution of the anode–electrolyte interface upon electrochemical cycling. The obtained results revealed that the interface stability is determined by both the intrinsic electrochemical stability of the solid electrolyte and the passivating properties of the formed interfacial products. With appropriate material selection for stability at the respective anode and cathode interfaces, stable cycling performance can be achieved for Na all-solid-state batteries.

Hospital management priorities and key factors affecting overall perception of patient safety: a cross-sectional study

Abstract Objective Evaluating a staff's perception of safety culture is a critical factor in hospital management, and the knowledge of value and efficiency in hospitals is still inadequate. This study aimed to investigate the perceptions of safety culture among medical staffs and determine priorities for clear and better management. Methods A cross-sectional survey of 595 medical staff members was conducted at 2 tertiary hospitals in Western China using a hospital survey on patient safety culture (HSOPSC) and its value and efficiency in the hospital. Results The dimensions with a disadvantaged positive response were nonpunitive response to error (44.6%) and staffing (42.0%). Five dimensions can explain 37.7% of the variation in the overall perception of patient safety, and handoffs and transitions are the most important dimensions (standardized coefficients 0.295). Conclusions Hospital managers should pay more attention to nonpunitive management and staffing. Handoffs and transitions are the most important areas of potential improvement in patient safety in hospitals.

Application of Bayesian Space-Time interaction models for Deer-Vehicle crash hotspot identification

The objective of this research was to identify and prioritize deer-vehicle crash (DVC) hotspots using five years of crash data. This study applied Bayesian spatiotemporal models for the identification of the DVC hotspots. The Bayesian spatiotemporal model allows to observe area-specific trends in the DVC data and highlights specific locations where DVC occurrence is deteriorating or improving over time. Census Tracts (CTs) were used as the geographic units to aggregate DVC, land use, and transportation infrastructure related data of Minnesota (MN) for the year 2015 to 2019. Several tests were conducted to evaluate the performance of the hotspot identification methods. The result showed that Type-I spatiotemporal interaction model (Model-2) outperforms other four space–time models in terms of predicting DVC frequency in CTs and hotspot identification performance test measures. Results showed that forest area, vegetation, and wetland percentages were positively associated with DVC frequency, whereas the percentage of developed land use was negatively associated with DVC frequency. The findings of this study suggest that the deer population plays an important role in DVCs, which indicates that deer population management is necessary to minimize the DVC risks. Using the final Type-I spatiotemporal interaction model, 65 “High-High” CTs were identified, where both the posterior mean of the decision parameter (potential for safety improvement) and the area-specific trend were higher. The distribution of the identified hotspots showed that the risk of DVCs was more in suburban areas with mixed land use conditions. These CTs represent high-risk zones, which need immediate safety improvement measures to reduce the DVC risks. As DVC can occur at any roadway segment location, DVC hotspots information is important for safety engineers and policymakers to implement area specific countermeasures.