Public Safety Concern Research Articles

Using advanced computing to improve outcome monitoring in substance use disorder treatment in Alaska

Abstract Issue In Alaska, crime associated with substance misuse is a massive public health and safety concern costing over $2.3 billion annually. Government officials make substantial investments to address this challenge but have little ability to monitor the long-term effectiveness of these interventions. Treatment providers are also stifled by minimal feedback regarding their lasting impact. While government databases exist that contain relevant information related to long-term treatment outcomes, low knowledge transfer continues to occur between these big data systems and treatment providers. Description To determine if emerging database technology can effectively monitor long-term recidivism outcomes, a prototype was implemented by Alaska’s leading treatment provider, Set Free Alaska. The Recidivism Tracking Interface is an innovative database system that strategically connects treatment records with the criminal justice system database. While maintaining high confidentiality and security standards, the first-of-its-kind interface assessed long-term recidivism outcomes of Set Free Alaska’s former clients from 2010 to 2023. Results Of those who successfully completed treatment (n = 970), only 12.06% were convicted of a new crime with an average of 1,167 days between discharge and new criminal charges. The current recidivism rate in Alaska is 66.3%. In addition to recidivism outcomes, the interface also revealed data incredibly useful for informing treatment services and relapse prevention practices. The Recidivism Tracking Interface effectively demonstrated that advanced computing systems can be utilized to monitor long-term outcomes in a manner that was previously unavailable to treatment providers. Lessons In the age of advanced cloud computing and generative AI, treatment providers around the world and their government partners can overcome current knowledge transfer barriers to monitor long-term outcomes in a manner that is automated, inexpensive, and easy to use. Key messages • Advanced computing offers new opportunities to improve long-term outcome monitoring. • Expanding secure access to relevant government databases can ultimately improve treatment outcomes and advance outcome-based funding models.

Modeling motorcycle crash-injury severity utilizing explainable data-driven approaches

ABSTRACT Motorcycle crashes remain a significant public safety concern, requiring diverse analytical approaches to inform countermeasures. This study uses machine learning to analyze injury severity in crashes in Thailand from 2018 to 2020. Traditional and advanced models, including including random forest (RF), support vector machine (SVM), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), and eXtreme gradient boosting (XGBoost), were compared. Hyperparameter tuning via GridSearchCV optimized performance. XGBoost, with a tradeoff score of 105.65%, outperformed other models in predicting severe and fatal injuries. SHapley Additive exPlanations (SHAPs) identified significant risk factors including speeding, drunk driving, two-lane roads, unlit conditions, head-on and truck collisions, and nighttime crashes. Conversely, factors such as barrier medians, flashing traffic signals, sideswipes, rear-end crashes, and wet roads were associated with reduced severity. These findings suggest opportunities for integrated infrastructure improvements and expanded rider training and education programs to address behavioral risks.

Four-year evaluation of drug-impaired driving drug concentrations.

Drug-impaired driving is a significant public health and safety concern in the USA. To help assess current patterns of drug use in drivers, we evaluated 4 years of drug positivity in a large cohort of suspected impaired drivers. Samples collected between January 2017 and December 2020 were tested via a method compliant with the National Safety Council's Alcohol, Drugs, and Impairment Division's Tier I scope of recommended drugs. In 2017, NMS Labs received 17 346 driving under the influence of drugs cases, 17 471 in 2018, 19 050 in 2019, and 16 539 in 2020. The most common drug class detected was cannabinoids in ∼50% of the cases each year. The most common drugs detected over the 4 years were delta-9 tetrahydrocannabinol (delta-9 THC), ethanol, amphetamine/methamphetamine, fentanyl, and alprazolam. Delta-9 THC increased in positivity over the study, having been identified in 45% of cases in 2017, 46% in 2018, 46% in 2019, and 49% in 2020. Ethanol was found in 59% of cases in 2017, 59% in 2018, 61% in 2019, and 53% in 2020. Delta-9 THC and ethanol were the most common drug combination, found together in ∼19% of the cases every year of the study. Statistically significant increases in the average concentration of the following drugs were observed: fentanyl (5.7 ng/mL in 2017 to 9.6 ng/mL in 2020), methamphetamine (301 ng/mL in 2017 to 381 ng/mL in 2020), and delta-9-THC (6.4 ng/mL in 2017 to 7.3 ng/mL in 2020). Other findings included increases in the maximum reported concentrations between 2017 and 2020 for amphetamine (1400 to 2700 ng/mL), methamphetamine (5550 to 13 000 ng/mL), and fentanyl (56 to 310 ng/mL). Statistically significant concentration decreases were noted for several central nervous system depressants, notably prescription benzodiazepines, and several prescription narcotic analgesics.

In the Depths of Wash Water: Isolation of Opportunistic Bacteria from Fresh-Cut Processing Plants.

The fruit and vegetable industry in post-harvest processing plants is characterized by a substantial consumption of water resources. Wash waters may serve as an environment for the periodic or permanent habitation of microorganisms, particularly if biofilm forms on the inner walls of tanks and flushing channels. Despite the implementation of integrated food safety monitoring systems in numerous countries, foodborne pathogens remain a global public health and food safety concern, particularly for minimally processed food products such as vegetables and fruits. This necessitates the importance of studies that will explore wash water quality to safeguard minimally processed food against foodborne pathogen contamination. Therefore, the current study aimed to isolate and identify bacteria contaminating the wash waters of four fresh-cut processing plants (Poland) and to evaluate the phenotypic antibiotic resistance profiles in selected species. Bacteria were isolated using membrane filtration and identified through mass spectrometry, followed by antibiotic susceptibility testing according to EUCAST guidelines. The results revealed that the level of contamination with total aerobic bacteria in the water ranged from 1.30 × 106 cfu/mL to 2.54 × 108 cfu/mL. Among the isolates, opportunistic pathogens including Enterococcus faecalis, Pseudomonas aeruginosa, Klebsiella oxytoca, Klebsiella pneumoniae, Serratia marcescens, and Proteus vulgaris strains were identified. An especially noteworthy result was the identification of cefepime-resistant K. oxytoca isolates. These findings highlight the importance of monitoring the microbial microflora in minimally processed foods and the need for appropriate sanitary control procedures to minimize the risk of pathogen contamination, ensuring that products remain safe and of high quality throughout the supply chain.

Road crash dynamics in Malaysia: Analysis of trends and patterns

Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014–2022.

Evaluating the Crashworthiness of Truck Rear-End Under-Run Bars: A Finite Element Analysis

Rear-end collisions involving trucks can have a direct impact on the safety of vehicle occupants, particularly due to the crashworthiness of the truck's rear-end under-run bar. These types of accidents, which occur between small vehicle and truck, contribute significantly to fatalities and serious injuries, presenting a significant public safety concern. This research paper aims to examine the collision effects on the rear-end under-run bar of a truck and its behaviour when it collides with another vehicle. The study employed the finite element method and simulated the under-run bar and impactor collisions using finite element software. The entire structure of the rear-end under-run bar had been designed, taking into account real parameters. The under-run bar structure was made of steel, and the impactor had a mass of 115kg, which resembled the mass of a motorcycle. A predefined field feature was utilized to apply a velocity of 30km/h to the impactor, which had a cylindrical shape. The element size of the mesh for the under-run bar was determined by using the mesh sensitivity technique. The simulation was tested a few times by using different sizes of elements. It is shown that the force reached a peak value of 28kN at a displacement of 68.25 mm, corresponding to the maximum under-run bar displacement. The data revealed that the energy absorbed increased from time 0.003s to 0.012s, reaching its peak value of 11.08kJ at 0.012s. The results show convincing results and prove the capability of rear-end under-run bar to be simulated and analysed using finite element simulation.

Environment Risk Assessment of Healthcare Waste Using Ensemble Learning Technique-Based EFMEA

Healthcare waste (HCW) affects the sustainable development of the environment greatly. The choice of medical waste treatment methods has become a significant concern for public health and safety due to the rapid surge in the volume and diversity of medical waste. As an important aspect of environmental risk assessment (ERA), HCW risk assessment plays a crucial role in environmental protection to develop sustainability strategies. Failure Mode and Effects Analysis (FMEA) has been extensively utilized in HCW risk assessment in the past few decades. In this paper, we aimed to address the limitations of traditional FMEA while incorporating the benefits of diverse FMEA methods and employ a novel ensemble learning technique-based FMEA method to perform risk assessment of HCW. A real-world case of HCW risk assessment is investigated for the verification of the performance and effectiveness of the ensemble learning technique-based Environment FMEA (EFMEA). Result of the case study shows that this ensemble learning technique-based EFMEA can provide a more reliable assessment result for HCW risk management.

Extending (UTAUT2) to examine the utilization of mobile food-sharing apps and online platforms for the reduction of household food waste

PurposeThe study aimed to investigate the relationship between the intention to avoid food waste (IAFW) and the use of food-sharing technologies, such as internet platforms and mobile applications. The study utilized a model based on the theory of planned behavior (TPB) as an extension of the unified theory of acceptance and use of technology (UTAUT2).Design/methodology/approachAn online platform tool (Prolific), and online self-report questionnaires were used to gather empirical data on 309 individuals. These data were then analyzed using two-step structural equation modeling.FindingsThe model explained 76% of the variance in user adoption of food-sharing mobile applications and internet platforms, supporting seven out of the nine tested hypotheses. Effort expectancy, social influence and IAFW were found to be the significant determinants of the behavioural intention to use food-sharing mobile applications and internet platforms (BITA). IAFW partially mediated the relationship between perceived behavior control and BITA. Age played a moderator role between the adoption of food-sharing mobile applications and internet platforms. However, IAFW did not play a mediating role between environmental concerns and BITA. The facilitating condition construct had an insignificant impact on BITA.Research limitations/implicationsThe current study was affected by some limitations. First, the data may not be considered as statistically representative because they were gathered online. However, the varied sociodemographic backgrounds of the respondents would boost the reliability of the findings. However, it would be prudent to use caution when extrapolating these findings to other contexts and cultures. Second, environmental concerns and perceived behavior control related to the avoidance of food waste behavior, as well as other factors that affect technology acceptance, may alter with time. Data from cross-sections may cause difficulties in following such changes. Thus, we recommend that longitudinal research studies aimed at building on our findings should be conducted. A qualitative study may help gain deeper insights into the relationship between IAFW related behavior and the adoption of various technologies to share leftover food, thereby revealing further details regarding different perspectives held by various respondents.Practical implicationsThe positive relationship between environmental concerns and IAFW underlines the significance of investing in this area to raise social awareness and public concern for environmental safety. Additional initiatives aimed at increasing public concern regarding environmental issues may increase the overall IAFW. Instead of concentrating on a single source pertaining to the avoidance of food waste, the government and policy regulations should focus on regulating and eliminating waste from all sources that generate waste. The adoption of technology to share leftover meals may be influenced by social factors. Increased advertising for food-sharing mobile apps and online platforms may persuade more users to join. Additionally, building additional platforms and mobile apps in these fields with friendlier interactions may improve the cyber environment, making it easier for people to use them. By providing information, tools and assistance to promote the reduction of food wastage, policymakers may create interventions that enhance public perception and behavior toward the reduction of food waste. In conclusion, the findings of our study indicated that the social impact and ease of use are important factors in determining the adoption of food-sharing technology. Cooperation with social influencers, policymakers and developers may lead to the development of user-friendly technology that may improve accessibility to food-sharing technology.Social implicationsIn order to encourage the adoption of food-sharing technology among various age groups, policymakers may create initiatives that take the specific requirements and traits of each group into consideration. Policymakers and governments may also create legislation and regulations that are tailored to guarantee food safety and health security for users of food-sharing technology, such as instructions for handling and storing food as well as safeguards against food fraud and contamination.Originality/valueThis study addressed practical issues related to managing and reducing household food wastage through social sharing via mobile applications and internet platforms. The proposed model, which integrated TPB with UTAUT2 in the context of food wastage and technology acceptance, contributes to the current body of knowledge.

A two-year review of cocaine findings in impaired driving investigations in Ontario, Canada.

Drug-impaired driving is an increasing public safety concern across Canada, particularly due to the demonstrated increase in use of recreational drugs such as cocaine. Cocaine is a central nervous system stimulant drug; however, it can impair an individual's driving ability in both the stimulant and crash phases. Despite the scientific consensus regarding cocaine's potential for driving impairment, there is relatively little information available regarding blood concentrations and associated observations of impairment in suspected impaired drivers. Retrospective data analysis was performed to evaluate suspected impaired driving cases in which cocaine and/or benzoylecgonine were detected alone, or in combination with other drugs, in blood and urine samples submitted to the Toxicology Section of the Centre of Forensic Sciences with incident dates between 2021 and 2022. Cocaine and/or benzoylecgonine were detected in 46% (blood) and 66% (urine) of the total impaired driving samples submitted. In 41 cases where cocaine and/or benzoylecgonine were the only drug finding in blood, concentrations of cocaine and benzoylecgonine ranged from 0.0073 to 0.26 mg/L (mean 0.096 mg/L) and 0.13 to 5.3 mg/L (mean 2.1 mg/L), respectively. Driving observations reported by the arresting officer in cases where cocaine and/or benzoylecgonine were the only drug finding in blood and urine included the driver being involved in a collision, the vehicle leaving the roadway, erratic driving and the driver being asleep at the wheel; observations of drug impairment reported by the drug recognition expert at the time of driver evaluation included abnormal speech patterns, poor balance/incoordination, abnormal body movements and the individual falling asleep. The results provide concentrations of cocaine and benzoylecgonine observed in suspected impaired drivers, insight into observations that may be associated with prior cocaine use and additional information to inform on the effects of cocaine on driving.

Lycopene as a Therapeutic Agent against Aflatoxin B1-Related Toxicity: Mechanistic Insights and Future Directions.

Aflatoxin (AFT) contamination poses a significant global public health and safety concern, prompting widespread apprehension. Of the various AFTs, aflatoxin B1 (AFB1) stands out for its pronounced toxicity and its association with a spectrum of chronic ailments, including cardiovascular disease, neurodegenerative disorders, and cancer. Lycopene, a lipid-soluble natural carotenoid, has emerged as a potential mitigator of the deleterious effects induced by AFB1 exposure, spanning cardiac injury, hepatotoxicity, nephrotoxicity, intestinal damage, and reproductive impairment. This protective mechanism operates by reducing oxidative stress, inflammation, and lipid peroxidation, and activating the mitochondrial apoptotic pathway, facilitating the activation of mitochondrial biogenesis, the endogenous antioxidant system, and the nuclear factor erythroid 2-related factor 2 (Nrf2)/kelch-like ECH-associated protein 1 (KEAP1) and peroxisome proliferator-activated receptor-γ coactivator-1 (PGC-1) pathways, as well as regulating the activities of cytochrome P450 (CYP450) enzymes. This review provides an overview of the protective effects of lycopene against AFB1 exposure-induced toxicity and the underlying molecular mechanisms. Furthermore, it explores the safety profile and potential clinical applications of lycopene. The present review underscores lycopene's potential as a promising detoxification agent against AFB1 exposure, with the intent to stimulate further research and practical utilization in this domain.

A New Approach for Single-Phase-to-Ground Fault Detection in Mid-Voltage Systems Using Low-Voltage Sensors

Single-phase-to-ground fault in low-current grounding systems represents a serious public safety concern. Low-voltage (LV) sensors, with their growing maturity, can now monitor multiple points of the mid-voltage (MV) distribution network. This paper proposes a new method for identifying single-phase-to-ground line faults and locating them using LV sensors deployed on the LV side of distribution transformers. We analyze the characteristics of the negative-sequence signal on the LV side after a single-phase grounding fault occurs on the MV side. The negative-sequence current can distinguish between fault and non-fault lines. By setting the ratio coefficient of negative-sequence voltage and positive-sequence voltage, we can use multi-point collaborative calculation and comparison to determine the section of the fault point. We consider the unbalanced load on the LV side and the special case of a fault point on one end of the line. Through simulation of combined MV and LV distribution systems in MATLAB software and dynamic model experiments, we verify that the proposed method has good robustness and accuracy. Monitoring the status information of the MV distribution network through LV sensors has great potential in practical application and implementation for realizing the fault detection of low-current grounding systems.

Public health perspective of public abattoirs in Nigeria, challenges and solutions

Abattoirs play a vital role in the supply chain of meat in society. However, the processing of meats in these facilities can lead to the spread of pathogenic bacteria and fungi, posing a significant public health and food safety concern. According to a report by the Food and Agriculture Organization of the United Nations, global meat production generates about 20 billion tons of animal waste annually, with developing countries accounting for the largest share of this waste and, a significant portion of which comes from abattoirs. This waste contains high levels of nutrients such as nitrogen and phosphorus, as well as organic matter, pathogens, and other contaminants that can have negative impacts on the environment and human health. In Nigeria, slaughterhouse effluents have been found to be a significant source of environmental pollution, containing food-borne pathogens and greenhouse gases such as carbon dioxide, methane, and nitrous oxide. This is particularly worrisome in a growing country like Nigeria which heavily relies on meat as a source of protein. The lack of standard waste management facilities in many abattoirs in Nigeria further exacerbates the problem, exposing inhabitants to harmful pathogens and gases. Despite these risks, little attention has been paid to curbing the menace of unhygienic abattoir waste disposal. This paper aims to raise awareness of the public health and environmental implications of unhygienic abattoir waste disposal in Nigeria and recommends solutions to this challenge. Specifically, the paper explores the potential for commercializing bio-waste into energy production as a sustainable solution. The wider significance of this work is that it highlights the urgent need to address the challenges of public abattoirs to ensure public health and environmental sustainability. Furthermore, the wider implications of this work are that the findings and recommendations can be adapted to other countries facing similar challenges. Overall, this paper contributes to planetary health by addressing a critical public health and environmental issue and recommending sustainable solutions.

VISION-BASED ACCIDENT IDENTIFICATION IN TRAFFIC VIDEOS USING DEEP LEARNING

Traffic collisions have emerged as a prominent factor contributing to a significant rise in injury-related fatalities and injuries, making them a pressing concern for public safety. Statistics show that an estimated 1.4 million people die in traffic accidents every year and around fifty million are injured. With the increasing population, ensuring road safety has become one of the critical challenges for city administration. In response to this challenge, the field of computer vision has gained prominence, focusing on the development of digital systems capable of processing, analyzing, and interpreting visual data like humans. Recently, a few studies have applied machine learning techniques for accident detection in traffic videos, offering automated solutions for city monitoring. However, these existing models are limited to binary classification, that is, they only determine whether the accident has occurred or not. This research takes a step further by proposing a deep learning-based accident classification model that not only identifies vehicle accidents but also categorizes them based on the type of collision. We employ the transfer learning technique and evaluate five pre-trained models, including DenseNet121, InceptionNetV3, ResNet50, VGG16, and Xception. Our findings demonstrate that VGG16 stands out for the vehicle accident classification task, achieving an impressive accuracy of 96.76%. This research offers a significant contribution to enhance road safety by advancing the capabilities of accident detection and classification systems. The identification of collision types presented in this research empowers safety authorities and organizations to conduct more effective safety analyses serving dual purposes. Firstly, it ensures that emergency response teams and medical personnel are adequately prepared and secondly, it allows insurance companies to make fair determinations of liability and coverage.

Social Engineering in Social Media Networks and Its Impact on Digital and Societal Security in the Sultanate of Oman: An Exploratory Study

Social engineering within social media networks presents a growing concern for public safety in the Sultanate of Oman. The risks associated with such attacks primarily arise from human vulnerabilities rather than technical failures. Consequently, this research conducted an in-depth examination of the ramifications of social engineering and the responses of Omani society to these threats, revealing their significant implications at the national level. Social engineering poses a substantial risk to public security by exploiting users into disclosing sensitive information, thereby necessitating enhanced security protocols to protect personal identities and financial assets. Utilizing a descriptive analytical approach, the study administered a questionnaire to a sampled population, comprising five sections related to demographic profiles, awareness of social engineering, its social and psychological impacts, proficiency in countering such schemes, and the various forms of social engineering attacks. The findings highlighted disparities in Omani societal attitudes towards social engineering, evident across gender, age, educational attainment, and occupational categories. This underscores the need to develop tailored security strategies that account for these variations to enhance protective measures. The study advocates for increasing digital literacy among Omani citizens and developing skills to combat social engineering tactics. These recommendations should form essential components of a cohesive national strategy aimed at strengthening digital security. Moreover, the research emphasizes the importance of exploring technological solutions to address social engineering challenges and enhance digital literacy. It stresses the necessity of collaborative efforts across sectors to cultivate a secure digital environment in the Sultanate of Oman, while highlighting the importance of continuous improvement and the implementation of comprehensive strategies to effectively counter social engineering threats.

Drug driving in Italy. The results of the first roadside drug testing service utilizing on-site confirmatory analysis between 2019 and 2022

BackgroundDrug driving represents a public safety concern, and the size of this issue in Italy is not fully known. Drug testing is composed of two steps: 1) screening and 2) confirmatory analysis. The second step, and the associate medical examination to assess the state of impairment, usually are not performed right after the screening as they require specialized personnel and instrumental equipment that are not historically available at roadblocks. These pitfalls make this process both complicated and time-consuming. MethodsA mobile laboratory was set up in 2019 by the Forensic Lab Service S.r.l. (limited liability company) to improve roadblock timing, planning, as well as to shed light on the extent of the drug driving issue in Italy. Drug screenings were performed using DrugWipe® Saliva testing. Confirmatory analysis was performed on oral fluids by liquid chromatography coupled with tandem mass spectrometry. A dedicated room of the mobile laboratory was also designed for drug driving medical assessment. Result2082 samples were collected during 88 road safety services held in different locations across Italy. In total, 9 % of the tested subjects were positive to both the screening and the confirmatory analysis. The most prevalent illicit drugs found in this study were THC (72 %), followed by cocaine (41 %). Drug drivers were mostly male (93 %) and younger than 30 years of age (58 %). ConclusionsThe prevalence of drivers testing positive for illicit drugs resulted to be higher compared to the results obtained in the DRUID project and to other surveys previously performed in Italy. These data demonstrate the need for control services to improve road safety in regards to drug driving.

Investigating the diurnal effects of on-street population and streetscape physical environment on street theft crime: A machine learning and negative binomial regression approach using street view images

Street theft crime remains a significant public safety concern in China and a central focus of urban crime prevention initiatives. Recent studies have combined street view images and deep learning networks to detect on-street population and streetscape physical environment features, highlighting their significance in comprehending the occurrence of street crime. However, the question of whether on-street population better represents the ‘true risk population of street crime’ compared to previous static (residential) and dynamic (ambient) measures, and whether this advantage persists across different times of the day, remains unexplored. Additionally, the association between streetscape physical environment features and street theft crime, as established in the existing literature, has not been thoroughly examined with respect to temporal variations throughout the day. To address these gaps, this study employed a machine learning model and a simultaneous negative binomial regression model to explore the influence of on-street population and streetscape physical environment on street theft counts. Specifically, we assessed whether their effects exhibit noticeable variations between day and night. Furthermore, we controlled for potential effects of crime attractors, generators, and sociodemographic variables. Our findings demonstrate, firstly, that on-street population extracted from street view images outperforms other measures in assessing the risk population for street theft due to its superior ability to indicate outdoor activities. Moreover, this advantage of on-street population holds true during both daytime and nighttime hours. Secondly, differences in the impact of streetscape physical environment variables and control variables on street theft counts are observed across daytime and nighttime hours. On-street population proves to be a promising alternative for measuring the risk population of street crime victimization, and temporal variations should not be overlooked in future research endeavors.

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

Epidemiological and clinical profile of pediatric hepatitis B virus infections in Wuhan: a retrospective cohort study

BackgroundHepatitis B virus (HBV) remains a substantial public health safety concern drawing considerable attention in China and globally. The detection of HBV serological markers can enable the assessment of HBV infection and replication status in vivo and evaluate the body’s protection against HBV. Therefore, this study aims to identify the epidemiological and clinical characteristics of HBV infection in children to prevent and control HBV infection in Wuhan areas.MethodsWe conducted an extensive retrospective cohort analysis of 115,029 individuals aged 0–18 years who underwent HBV serological markers detection for HBV infection in hospital between 2018 and 2021 using Electrochemiluminescence immunoassay. We generated descriptive statistics and analysed HBV infection’s epidemiological and clinical characteristics between different sex and age groups.ResultsThe overall positive detection rates of HBsAg, HBsAb, HBeAg, HBeAb, and HBcAb in all participants were 0.13%, 79.09%, 0.17%, 2.81%, and 5.82%, respectively. The positive rate of HBeAb and HBcAb in males was significantly lower than that in females (2.64% vs. 3.13%, 5.56% vs. 6.29%) (P < 0.05). Twenty-two distinct HBV serological expression patterns were revealed. Among them, 8 common expression patterns accounted for 99.63%, while the remaining 14 uncommon expression patterns were primarily observed in neonatal patients with HBV infection. There are no significant differences in serological patterns based on sex (P < 0.05). The overall HBV infection detection rate was 5.82% [range 5.68–5.95] and showed a declining yearly trend. The rate in females was higher than that in males 6.29% [6.05, 6.35] vs. 5.56% [5.39, 5.59]. The overall HBV diagnostic rate over 4 years was 0.20% [0.17, 0.22], and the rate declined yearly. The prevalence of acute infection was higher than that of other infection types before 2019, but the incidence of unclassified infection showed a significant upward trend after 2019.ConclusionsWhile the overall HBV infection detection rate in children has decreased year by year, the infection rate remains high in children under one year and between 4 and 18 years. This continued prevalence warrants heightened attention and vigilance.

Multispecies Bacterial Biofilms and Their Evaluation Using Bioreactors.

Pathogenic biofilm formation within food processing industries raises a serious public health and safety concern, and places burdens on the economy. Biofilm formation on equipment surfaces is a rather complex phenomenon, wherein multiple steps are involved in bacterial biofilm formation. In this review we discuss the stages of biofilm formation, the existing literature on the impact of surface properties and shear stress on biofilms, types of bioreactors, and antimicrobial coatings. The review underscores the significance of prioritizing biofilm prevention strategies as a first line of defense, followed by control measures. Utilizing specific biofilm eradication strategies as opposed to a uniform approach is crucial because biofilms exhibit different behavioral outcomes even amongst the same species when the environmental conditions change. This review is geared towards biofilm researchers and food safety experts, and seeks to derive insights into the scope of biofilm formation, prevention, and control. The use of suitable bioreactors is paramount to understanding the mechanisms of biofilm formation. The findings provide useful information to researchers involved in bioreactor selection for biofilm investigation, and food processors in surfaces with novel antimicrobial coatings, which provide minimal bacterial attachment.

Health Professionals and Pharmacists Awareness and Attitude Towards Counterfeit Medicine.

Counterfeit medicine is not only illegal, but it is also an insidious threat and poses serious public health and safety concern. Health professionals can play an important role in campaign against counterfeit drugs by staying vigilant, reporting suspicious products and preventing the distribution of counterfeit medicine. The present study aims to assess the awareness and attitude towards counterfeit medicine among health professionals and pharmacists in Nepal. It was a cross-sectional study conducted among 264 health professionals and pharmacists of Saptari, Nepal. Samples were recruited by quota sampling technique and pretested, self-administered questionnaire were used for collecting data regarding socio-demographic, knowledge and attitude on counterfeit medicine. Descriptive & inferential statistics were used to analyze the data generated. This study found that mean knowledge score of respondent was 12.11±4.3 and only 31.7% (n=39) of the respondents had good knowledge on counterfeit medicine while 44.7% (n=55) showed moderate and 23.6% (n=29) had poor knowledge levels. Nurses, paramedics and pharmacists scored statistically lower than doctors, however there was no significant difference in knowledge between nurses and pharmacists (p< 0.001). Mean attitude score was 3.82 (±0.68) and majority of respondents (85.3%) showed favorable attitude towards counterfeit medicine. Respondents having poor knowledge level had statistically significant unfavorable attitude towards counterfeit medicine Conclusions: The study highlighted the need for counterfeit medicine awareness campaigns and training to enhance the role of health professionals and pharmacists to recognize and report suspicious medicine and prevent counterfeit medicines-associated harms.