Fatality Risk Research Articles

Systematic review and meta-analysis of Tuberculosis and COVID-19 Co-infection: Prevalence, fatality, and treatment considerations.

Tuberculosis (TB) and COVID-19 co-infection poses a significant global health challenge with increased fatality rates and adverse outcomes. However, the existing evidence on the epidemiology and treatment of TB-COVID co-infection remains limited. This updated systematic review aimed to investigate the prevalence, fatality rates, and treatment outcomes of TB-COVID co-infection. A comprehensive search across six electronic databases spanning November 1, 2019, to January 24, 2023, was conducted. The Joanna Briggs Institute Critical Appraisal Checklist assessed risk of bias of included studies, and meta-analysis estimated co-infection fatality rates and relative risk. From 5,095 studies screened, 17 were included. TB-COVID co-infection prevalence was reported in 38 countries or regions, spanning both high and low TB prevalence areas. Prevalence estimates were approximately 0.06% in West Cape Province, South Africa, and 0.02% in California, USA. Treatment approaches for TB-COVID co-infection displayed minimal evolution since 2021. Converging findings from diverse studies underscored increased hospitalization risks, extended recovery periods, and accelerated mortality compared to single COVID-19 cases. The pooled fatality rate among co-infected patients was 7.1% (95%CI: 4.0% ~ 10.8%), slightly lower than previous estimates. In-hospital co-infected patients faced a mean fatality rate of 11.4% (95%CI: 5.6% ~ 18.8%). The pooled relative risk of in-hospital fatality was 0.8 (95% CI, 0.18-3.68) for TB-COVID patients versus single COVID patients. TB-COVID co-infection is increasingly prevalent worldwide, with fatality rates gradually declining but remaining higher than COVID-19 alone. This underscores the urgency of continued research to understand and address the challenges posed by TB-COVID co-infection.

An appraisal of the LQI as an approach to setting target reliabilities in ISO 2394:2015

The idea of coupling economics and safety into one optimization exercise, and hence deriving target reliabilities explicitly from socio-economic consequences of limit state violation, appeared in the JCSS Probabilistic Model Code around the turn of the century; the effort culminated in the form of a Life Quality Index (LQI)-based marginal lifesaving costs (MLSC) methodology in ISO 2394:2015 for determining maximum acceptable failure probabilities, pT, in life safety limit states for civil engineering structures across all nations. Unfortunately, while the methodology does yield adequate levels of safety when applied to structures in the developed countries, the recommended values of pT turn out to be one to two orders of magnitude higher, and unacceptable to every known standard in the world including its own earlier edition (ISO 2394:1998) when applied to structures in the developing world (exemplified by India) with identical functions and expected fatalities. This arises from two shortcomings: (1) an MLSC approach is generally unable to provide an independent constraint on monetary optimization, and (2) the LQI-based measure of MLSC constraint is strongly dependent on a country’s per capita GDP (g) and the resultant pT is effectively governed by the reciprocal of g. This paper recommends continuing with the absolute − not marginal − consequences of violating life safety limit states, and setting constraints on monetary optimization that are consistent with fatality risks from engineering activities more universally accepted to be safe.

Clinical Characteristics and Fatality Risk Factors for Patients with Listeria monocytogenes Infection: A 12-Year Hospital-Based Study in Xi'an, China.

Listeriosis is a severe food-borne disease caused by Listeria monocytogenes infection. The data of listeriosis in Xi'an population are limited. The aim of this study is to evaluate the clinical features and fatality risk factors for listeriosis in three tertiary-care hospitals in Xi'an, China METHODS: The characteristics of demographic data, underlying diseases, clinical manifestations, laboratory indicators, cranial imaging examination, antibiotics therapeutic schemes, and clinical outcomes were collected between 2011 and 2023. Logistic regression analysis was performed. Seventy-one etiologically confirmed listeriosis patients were enrolled, including 12 neonatal and 59 non-neonatal cases. The majority of neonatal listeriosis presented as preterm (50%) and fetal distress (75%). The main clinical manifestations of non-neonatal listeriosis included fever (88%), headache (32%), disorder of consciousness (25%), vomiting (17%), abdominal pain (12%), and convulsions (8%). The fatality rate in neonatal cases was higher than in non-neonatal listeriosis (42 vs. 17%). Although no deaths were reported in maternal listeriosis, only two of 23 patients had an uneventful obstetrical outcome. Five maternal listeriosis delivered culture-positive neonates, three of whom decreased within 1week post-gestation due to severe complications. Twenty-eight cases were neurolisteriosis and 43 cases were bacteremia. Neurolisteriosis had a higher fatality rate compared with bacteremia listeriosis (36 vs. 12%). The main neuroradiological images were cerebral edema/hydrocephalus, intracranial infection, and cerebral hernia. Listeria monocytogenes showed extremely low resistance to ampicillin (two isolates) and penicillin (one isolate). The fatality risk factors were the involvement of the central nervous system, hyperbilirubinemia, and hyponatremia for all enrolled subjects. Hyperuricemia contributed to the elevation of fatality risk in non-neonatal listeriosis. When the patients suffered with symptoms of fever and central nervous system infection, they should be alert to the possibility of listeriosis. Early administration of ampicillin- or penicillin-based therapy might be beneficial for recovery of listeriosis.

Sex differences in prognosis factors in patients with lung cancer: A nationwide retrospective cohort study in Korea.

Large-scale studies elucidating sex differences in factors impacting prognosis and sex-specific prognossis factors scoring in patients with lung cancer are insufficient. The present study aimed to develop a model to predict sex-specific prognosis factors in Korean patients with lung cancer. This nationwide cohort study included 96,255 patients aged ≥19 years diagnosed with lung cancer and underwent Korean National Health Insurance Service health examinations between January 1, 2005 and December 31, 2015 and followed until 2020. Factors associated with prognosis were estimated using multivariable Cox proportional hazards regression analyses, and separate prognosis scores were calculated for male and female patients. The sex-specific risk scoring models were validated with Kaplan-Meier survival curves and c-statistic. During a mean follow-up of 2.8 years, 60.5% of the patients died. In male patients with lung cancer, age ≥ 65 years (24 points) had the highest mortality risk score, followed by chemotherapy in combination with radiotherapy (16 points), chemotherapy (14 points), and radiotherapy (11 points). In female patients with lung cancer, chemotherapy in combination with radiotherapy (19 points) had the highest mortality risk score, followed by chemotherapy (16 points), age ≥ 65 years (13 points), and radiotherapy (13 points). The analysis of patients categorized into three risk groups based on risk scores revealed that the fatality rates within 5 years were 7%, 54%, and 89% in the low-, intermediate-, and high-risk groups for male patients and 3%, 46%, 85% in the low-, intermediate-, and high-risk groups for female patients, respectively. The c-statistic was 0.86 for male patients and 0.85 for female patients. The strongest fatality risk factors in lung cancer were age ≥ 65 years in male patients and chemotherapy in female patients. The present study developed sex-specific prognosis scoring models to predict fatality risk in patients with lung cancer.

Cardiovascular Risk Profile in Master Paralympic Athletes, a High-Risk Undertreated Population: A Cross-Sectional Longitudinal Study.

Recently, European Society of Cardiology (ESC)validated aprediction model to estimate 10-year fatal and non-fatal cardiovascular disease risk (CVDR) in individuals (aged 40-60 years) without previous cardiovascular disease or diabetes(ESC-SCORE2)and to provide indications for treatment. At present, data describing the CVDR in Paralympic athletes (PAs) are scarce and inconsistent. Therefore,we sought to assess the prevalence of risk factors in PAs to estimate their CVDR through SCORE2. Weenrolled 99 PAs aged ≥40 y.o., who participated at 2012-2022 Paralympic Games, competing in 22 different sport disciplines classified according to sport type (power, skills, endurance and mixed) and disabilities: spinal cord injuries (SCI) and non-SCI. CVDR factors,anthropometric measurements and blood samples were collected. Among the 99 PAs (78% males, mean age 45.7±4.7 y.o.), 52.5% had SCI; 54% were dyslipidemic and 23% were smokers. According to ESC-SCORE2, 29% had high and 1% very-high CVDR. Women (compared to men) and endurance (compared to other sport) exhibited better CV profile. SCI showed no differences when compared with non-SCI for CVDR, excepted for a lower HDL and lower exercise performance.None of the dyslipidemic athlete was on pharmacologically treatment, despitethe altered lipid profile had already been detected at younger age. PAs are a selected population, presenting a high CV risk profile, with 30% showing either high or very-high CVDR according to ESC-SCORE2. Dyslipidemiawas the most common risk factor, underestimated and undertreated, emphasizing the need for specific preventive strategies in this special setting of athletes.

An aquaculture risk model to understand the causes and consequences of Atlantic Salmon mass mortality events: A review

AbstractMass mortality events (MMEs) are defined as the death of large numbers of fish over a short period of time. These events can result in catastrophic losses to the Atlantic salmon aquaculture industry and the local economy. However, they are challenging to understand because of their relative infrequency and the high number of potential factors involved. As a result, the causes and consequences of MMEs in Atlantic salmon aquaculture are not well understood. In this study, we developed a structural network of causal risk factors for MMEs for aquaculture and the communities that depend on Atlantic salmon aquaculture. Using the Interpretive Structural Modeling (ISM) technique, we analysed the causes of Atlantic salmon mass mortalities due to environmental (abiotic), biological (biotic) and nutritional risk factors. The consequences of MMEs were also assessed for the occupational health and safety of aquaculture workers and their implications for the livelihoods of local communities. This structural network deepens our understanding of MMEs and points to management actions and interventions that can help mitigate mass mortalities. MMEs are typically not the result of a single risk factor but are caused by the systematic interaction of risk factors related to the environment, fish diseases, feeding/nutrition and cage‐site management. Results also indicate that considerations of health and safety risk, through pre‐ and post‐event risk assessments, may help to minimize workplace injuries and eliminate potential risks of human fatalities. Company and government‐assisted socio‐economic measures could help mitigate post‐mass mortality impacts. Appropriate and timely management actions may help reduce MMEs at Atlantic salmon cage sites and minimize the physical and social vulnerabilities of workers and local communities.

Predicting Accident Severity using Machine Learning

Each year, millions of people are killed in automobile accidents. Predicting the severity of an occurrence allows local authorities to respond quickly and save many lives. Information and data on traffic accidents made available by public organizations can be used to categories these incidents based on their nature and severity, and then attempt to construct predictive models that can be further investigated to identify fatal accident risk factors. The study provides ways to develop a system for determining the severity of accidents. To evaluate the severity of the collision, we use a variety of weather-related characteristics such as temperature, humidity, visibility, and pressure, as well as several other circumstances, such as the presence of a traffic light or a junction. The Select-K-Best features selection algorithms were used to choose the best characteristics from a list of 47. The accuracy of both balanced and unbalanced data was measured, and the balanced data was chosen for further analysis. The balanced dataset is then trained, analyzed, and compared with a number of machine learning approaches, including the Random Forest Classifier (RFC), K closest neighbour (KNN) classifier, and Naive Bayes classifier. The RFC classifier outperformed with an AUC of 95%, whereas the naïve bayes underperformed. Furthermore, the accuracy is improved by using all of the aforementioned methods as base learners for stacking ensemble models with logistic regression as meta learner. This stacking ensemble strategy outperforms RFC with improved precision and accuracy, resulting in an AUC of 96.92%. The results revealed that the model has a 96.92% AUC in predicting accident severity. The work can be extended to investigate the complex correlations between a few key parameters and accident severity.

Spatiotemporal analysis of the association between residential eviction and fatal overdose in Rhode Island

ObjectivePolicy ramifications of the COVID-19 pandemic shape the concurrent housing and overdose crises in the USA. Housing insecurity is a known risk factor for overdose, yet how residential eviction may...

Audio analysis speeding detection techniques based on metaheuristic-optimized machine learning models

Law enforcement plays an important role in road safety across the world. Speed enforcement merits special attention as the correlation between exceeding speed limits and the risk of fatalities and serious injuries is well-known. Methods based on radar and light detection and ranging techniques have established themselves as effective methods for measuring vehicle speeds in real time on roads. However, the price of instruments used for these measurements limits their widespread applicability. This work explored the potential of coupling artificial intelligence and audio analysis for vehicle speed assessment. The potential of deep neural networks and extreme gradient boosting is explored on a recently proposed real-world vehicle speed dataset. Frequency analysis techniques are also applied to help reduce the computational demands of experimentation. Additionally, a modified optimization algorithm is introduced to help select optimal control parameters and improve the performance of the suggested method. Five experiments were executed to demonstrate the potential for detecting the exact vehicle speed using regression techniques as well as utilizing classifications to detect vehicles breaking the speed limit. The proposed methods demonstrated promising potential when applied to this pressing challenge.

Mechanical marvels: robots in carotid artery stenting - a systematic review of effectiveness in neuroendovascular interventions

Aim: Carotid artery disease, or carotid stenosis, is a critical medical condition carrying the risk of stroke and fatality. The underlying pathology involves the accumulation of atheromatous plaques, leading to luminal constriction and disrupted blood flow. Endovascular interventions, such as carotid artery stenting (CAS), aim to restore vessel patency. Robotic assistance in CAS surgeries is rapidly evolving globally, offering precision and fatigue-free capabilities. Despite growing interest, a dedicated systematic review on robotic applications in CAS is notably absent. Methods: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search strategy involving five databases. Data extraction encompassed study origin, patient demographics, procedural details, procedure times, complications, fluoroscopy, and radiation parameters. Results: Over 199 articles were identified from five databases. Seven studies meeting inclusion criteria were analyzed. The predominant robotic system, CorPath GRX, demonstrated advantages such as remote operation, precision, and compatibility with various catheter sizes. Magellan Robotic System, employed in one study, showcased remote-controlled capabilities. These studies involved a total of 44 patients undergoing carotid artery stenosis procedures with robotic assistance. Reported complications included access site conversion, Angio-Seal device failure, and residual stenosis. Mean operation time varied from 34 to 85 min. Conclusions: The integration of robotic assistance in carotid interventions, as demonstrated by the CorPath GRX and Magellan Robotic System, holds significant promise in improving the precision and safety of CAS procedures. However, the limited number of studies, the high risk of bias, and the need for further research and standardization highlight the evolving nature of this technology.

Safety performance of dedicated and preferential bus lanes using multivariate negative binomial models for Bogotá, Colombia

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 –fatalities, injuries, and property damage only– and 30 operational and infrastructure variables grouped into six classes –exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure –logarithm of average annual traffic volumes at the peak hour (LOG_AAPHT) and the percentage of motorcycles, cars, buses, and trucks– the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries.

A Proposal for Sediment Control Countermeasures in Non-Flowing Mountain Streams

In Japan, the heavy rain disaster that occurred in July 2018 revealed that about 70% of the streams affected by debris flows that resulted in human casualties were small, steep mountain streams with a catchment area < 0.05 km2. Generally, many streams that are close to residential houses or roads do not have a constant flow of water and are known to pose a high risk of human fatalities when a debris flow occurs. This study aimed to promote sediment control as debris flow countermeasures in non-flowing mountain streams, utilizing secondary manufactured products (permeable debris flow barriers) with excellent constructability, focusing on the mechanism of sediment outflow from the gaps between a permeable debris flow barrier and mountain stream side banks. The necessity and effectiveness of preventative measures based on preliminary experimental results are presented. When impermeable structures were installed at both ends of the permeable debris flow barrier side, compared to using only a permeable debris flow barrier (covering the entire width with permeable debris flow barriers), we found that the capture function improved significantly, achieving a 200% increase in effectiveness.

Optimization method for offsite consequence analysis by efficient plume segmentation

The speed of offsite consequence analysis is highly important due to the extensive calculations required to handle all the scenarios for a single-unit or multi-unit Level 3 PSA (probabilistic safety assessment). To perform an offsite consequence analysis as part of Level 3 PSA, various input parameters are considered, amongst which certain parameters, such as plume segments, spatial grids, and particle size distributions, have flexible input formats. This study describes the development of an effective optimization method to reduce the analysis time as much as possible while maintaining the accuracy of the offsite consequence analysis results. The effect of plume segmentation on offsite consequence analysis was investigated by observing deviations in analysis results and changes in the required analysis times following changes in plume release. Then a plume segmentation optimization method based on the cumulative release fraction slope was developed to intensively analyze the sections with rapid release and to simplify the analysis for the sections with nonsignificant release. As a result of applying this method, the analysis time was reduced by about 54.5 % compared to the base case, while the resulting health effects showed very small deviations of 0.03 % and 1.77 % for early fatality risk and cancer fatality risk, respectively.

Evaluation and comparison of administrative division-level road traffic safety indices of Egypt, England, Turkey, and the United States

Introduction: There is regional diversity inside countries regarding road safety indices (RSIs), and countries rarely have been compared based on these indicators. Thus, regional RSIs of England, the United States, Egypt, and Turkey were evaluated. Regional data were collected from the statistical center of each country. The adopted regional RSIs include road fatalities, health risk (HR) or fatalities per population, and traffic risk (TR) or fatalities per number of vehicles. The associations between variables were examined using correlation and regression analysis. The spatial distributions of subdivisions were evaluated using Moran's I, the local Moran index. Results: Considerable differences between the countries were observed, including differences in the spatial distribution of regions and associations between RSIs. Significant relationships were detected between road fatality, population, and the number of motor vehicles. Higher exposure rates mean higher fatalities in regions. A robust linear relationship between the HR and TR indices was identified in developed countries. There is a nonlinear and significant association between motorization rates and TR indices of regions, and fatality risk decreases as the motorization rate increases. There is a considerable gap between developed and developing countries regarding regional RSIs, and the transferability of road safety models from one country to another is challenging. Huge hotspots regarding RSIs were observed in Turkey and the United States. The locations of hot spots in terms of the risk indices were identical in the developed countries.

Uranium concentrations and its isotopes in baby food of Iraq

Abstract This study determines uranium concentrations and isotopes (238U, 235U, and 234U) in baby food samples collected from Iraqi markets using solid-state nuclear track detector technical (CR-39). Also, some radiological hazard parameters such as annual committed effective dose (E U) and risk of an excess cancer fatality per million persons (RECFPMP) for the same study samples were calculated using theoretical equations. The samples in the present study were classified into three varieties: cereals and cereal products, vegetables and fruits, and biscuits. The results show that the average uranium concentration (mg/kg or ppm) values in cereals and cereal products, vegetables and fruits, and biscuits were 0.71 ± 0.01, 0.81 ± 0.04, and 0.72 ± 0.031, respectively. At the same time, the average values of 238U, 235U, and 234U in all samples of the present study were 9.08 ±0.17 Bq/kg, 0.42 ± 0.0081 Bq/kg, and 9.33 ± 0.18 Bq/kg, respectively. The total average of E U (mSv/y) due to all uranium isotopes (238U, 235U, and 234U) in cereals and cereal products, vegetables and fruits, and biscuits were 0.042 ± 0.0006, 0.039 ± 0.001, and 0.034 ± 0.001, respectively. Moreover, it was found that the average values of RECFPMP were 0.159 ± 0.002 in cereals and cereal products samples, 0.154 ± 0.007 in vegetables and fruits samples, and 0.130 ± 0.005 in biscuits samples. Uranium concentrations and the total annual committed effective dose in all baby food samples comply with the worldwide limits approved by UNSCEAR, which state safe limitations (1.7 mg/kg) and (0.32 mSv/y), respectively. So, one may conclude that there is no danger of uranium concentrations in the case of baby consumption.

Injury and fatality risks for child pedestrians and cyclists on public roads

BackgroundPedestrians and cyclists are often referred to as “vulnerable road users,” yet most research is focused on fatal crashes. We used fatal and nonfatal crash data to examine risk factors (i.e., relationship to an intersection, urbanicity, crash circumstances, and vehicle type) for police-reported pedestrian and cyclist injuries on public roads among children aged 0–9 and aged 10–19. We also compared risk factors among these two age groups with adults aged 20–29 and aged 30–39.MethodsCrash data were obtained for 2016–2020 from the National Highway Traffic Safety Administration’s Fatality Analysis Reporting System for fatal crash injuries and Crash Report Sampling System for nonfatal crash injuries. We collected data on victim demographics, roadway, and vehicle- and driver-related factors. Descriptive analyses were conducted between and within pedestrian and cyclist victims.ResultsWe analyzed 206,429 pedestrian injuries (36% in children aged 0–19) and 148,828 cyclist injuries (41% in children aged 0–19) from 2016 to 2020. Overall, child pedestrians had lower injury rates than adults, but children aged 10–19 had greater cycling crash rates than adults. Almost half of the pedestrian injuries in children aged 0–9 were “dart-out” injuries (43%). In the majority of the cyclist injuries, children in both age groups failed to yield to vehicles (aged 0–9 = 40% and aged 10–19 = 24%). For children and all ages included in the study, the fatality risk ratio was highest when pedestrians and cyclists were struck by larger vehicles, such as trucks and buses. Further exploration of roadway factors is presented across ages and transportation mode.ConclusionOur findings on child, driver, vehicle, and roadway factors related to fatal and nonfatal pedestrian and cyclist injuries may help to tailor prevention efforts for younger and older children.

A monetary model of global peace and health

BackgroundThis study aims to expand on the concept of peace and health by drawing from Keynes' theory of the economic consequences of peace, in light of the global pandemic experienced in 2020 due to COVID_19.MethodsIn this paper, I will elaborate on the concept of ‘security’, as an indicator of peace in the time of biological shocks, in order to expand the definition of Keynesian precautionary motivation. This puts forth a new monetary policy model developed to make contributions to achieving global peace. In so doing, I will calculate the optimal growth rate of discount rate through utilizing the Global Peace Index (GPI), adjusted by the Case Fatality Risk (CFR) of COVID-19 in a dynamic shopping time monetary model. This analysis is comprised of the top 15 GDP countries as well as the 10 most and least peaceful countries in 2020.ResultsThe results indicate that households in more peaceful and healthy countries tend to hold less money compared to those in less peaceful and healthy countries. Besides, the discount rate needs to be reduced due to the outbreak of COVID-19 and the decrease in the level of peace in the economy.ConclusionInsofar as the imposition of fines through international legal circles on countries with an insignificant health and peace policy will increase the cost of liquidity, other alternative methods of financing will be affor dable for the countries.

Temporal analysis of factors affecting injury severities of expressway rear-end crashes during weekdays and weekends

ABSTRACT High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods.

Enhancing Gym Safety and Workout Efficiency during Squats and Deadlifting Exercises with an IoT based Novel Smart Workout Belt

This paper introduces a smart workout belt specifically tailored for squats and deadlifting exercises and is designed to improve safety and workout effectiveness in gyms. Traditional protocols often overlook cardiac distress and muscle injuries, increasing the risk of fatalities. Our solution integrates sensors like ECG, EMG, and accelerometer into a Node MCU microcontroller ESP32 for real-time monitoring. It detects cardiac issues, muscle fatigue, and unsafe behaviours, alerting users through audible and visual cues. This system aims to enhance gym safety by providing continuous monitoring and timely interventions, reducing exercise-related risks.

Public Health Interventions and Overdose-Related Outcomes Among Persons With Opioid Use Disorder

Given the high number of opioid overdose deaths in the US and the complex epidemiology of opioid use disorder (OUD), systems models can serve as a tool to identify opportunities for public health interventions. To estimate the projected 3-year association between public health interventions and opioid overdose-related outcomes among persons with OUD. This decision analytical model used a simulation model of the estimated US population aged 12 years and older with OUD that was developed and analyzed between January 2019 and December 2023. The model was parameterized and calibrated using 2019 to 2020 data and used to estimate the relative change in outcomes associated with simulated public health interventions implemented between 2021 and 2023. Projected OUD and medications for OUD (MOUD) prevalence in 2023 and number of nonfatal and fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. In a baseline scenario assuming parameters calibrated using 2019 to 2020 data remained constant, the model projected more than 16 million persons with OUD not receiving MOUD treatment and nearly 1.7 million persons receiving MOUD treatment in 2023. Additionally, the model projected over 5 million nonfatal and over 145 000 fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. When simulating combinations of interventions that involved reducing overdose rates by 50%, the model projected decreases of up to 35.2% in nonfatal and 36.6% in fatal opioid-involved overdoses among persons with OUD. Interventions specific to persons with OUD not currently receiving MOUD treatment demonstrated the greatest reduction in numbers of nonfatal and fatal overdoses. Combinations of interventions that increased MOUD initiation and decreased OUD recurrence were projected to reduce OUD prevalence by up to 23.4%, increase MOUD prevalence by up to 137.1%, and reduce nonfatal and fatal opioid-involved overdoses among persons with OUD by 6.7% and 3.5%, respectively. In this decision analytical model study of persons with OUD, findings suggested that expansion of evidence-based interventions that directly reduce the risk of overdose fatality among persons with OUD, such as through harm reduction efforts, could engender the highest reductions in fatal overdoses in the short-term. Interventions aimed at increasing MOUD initiation and retention of persons in treatment projected considerable improvement in MOUD and OUD prevalence but could require a longer time horizon for substantial reductions in opioid-involved overdoses.