Risk Assessment Approach Research Articles

Predictors of incidents during short leave: An analysis of prisoner personal files in the Lower Saxony prison system

Abstract This study aimed to investigate the prediction of incidents during short leave (ISLs) for inmates in the German prison system using static risk and protective factors that had been assessed at the start of the incarceration. Data from the personal files of inmates in Lower Saxony were analysed to examine the prevalence of ISLs and to assess the ability of the Offender Group Reconviction Scale – Version 3 (OGRS 3) to predict ISLs. In addition, random forest models were used to identify potential predictors that could further improve the prediction performance. The results showed that the OGRS 3 had significant predictive validity for drug abuse, late return, and any ISL but not for new offences during short leave. However, the OGRS 3 exhibited problems with false positive rates in its prediction of ISLs. The random forest models did not substantially improve the prediction of any ISL, but they did improve the prediction of drug abuse during short leave. This study highlights the importance of considering dynamic factors and using a more comprehensive approach in risk assessment for ISLs.

A multi-fidelity approach for reliability-based risk assessment of single-vehicle crashes

Road vehicles are highly susceptible to single-vehicle crashes (SVCs) under complex road geometry and inclement weather, which can significantly threaten traffic safety and mobility of the whole traffic system. Most existing studies involve various simplifications and approximations to assess the associated SVC risks promptly, and therefore the assessment accuracy is often compromised. A novel multi-fidelity approach is developed for the reliability-based risk assessment of SVCs to balance the simulation accuracy and efficiency. Specifically, a high-fidelity transient dynamic vehicle model is introduced for a robust estimation of the vehicle dynamics under various driving environments, assisted by a low-fidelity simplified physics-based vehicle model to improve the computational efficiency. Based on the simulations of the two models, a new multi-fidelity improved cross entropy-based importance sampling (MFICE) algorithm is proposed for integrating multi-fidelity information and facilitating accurate and efficient reliability analysis. Five demonstrative cases are studied to evaluate the performance of the proposed approach, including the comparison with existing representative approaches. The results show that the proposed innovative multi-fidelity approach can provide a reliability evaluation of SVCs both accurately and efficiently, with obviously superior performance over typical state-of-the-art counterparts. Therefore, the proposed approach bears great potential on developing proactive and near real-time intelligent traffic operation and management strategies against SVCs in both normal and hazardous conditions.

MCDM-Based Wildfire Risk Assessment: A Case Study on the State of Arizona

The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard’s likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments.

Progression of flood risk assessment in India at a decadal scale: a critical review

Abstract Floods are a recurrent natural phenomenon in India, including perennial occurrences in some parts of the country. Progressively, floods are transformed into flood hazards because of the anthropogenic activities in the flood plains and adjoining catchments, causing injuries, loss of lives, and property damage. Flood hazards, when considered in relation to vulnerability and exposure limits, describe the associated flood risk. This article aims to discuss the progression in flood risk assessment through several government policies and actions in India at a decadal scale from 1951 to 2020. While doing this, some important extreme flood events witnessed in those decades that shaped the perspectives, measures, action plans, and policies in the subsequent years are discussed. The review confirms that with the changing patterns of floods, associated hazards, and risks over the years, improvements in risk assessment approaches are noticeable on dual fronts. Technical advancements in flood risk assessment have corroborated the policy reforms. Albeit these developments, the issues related to the scale of study, data sources and resolutions, climatic variability, urban development, complex population dynamics, and their interrelationships in the context of flood risk need to be resolved with serious efforts. Addressing these issues through multidimensional strategies is imperative to aver robust flood risk assessment.

Spatial risk assessment of maritime transportation in offshore waters of China using machine learning and geospatial big data

Maritime transportation plays a crucial role in global trade and economic development. However, this industry is exposed to various risks (e.g., natural disasters), which can cause significant economic and environmental damage. This study aims to develop a spatial risk assessment approach for maritime transportation using machine learning and geospatial big data to identify potential risks in China's maritime transportation industry. The proposed approach first produces risk maps that reveal significant variability in maritime transportation risks across different regions of China. Then, factor importance analysis identifies wave height, rainfall, and sea surface temperature as the most influential factors affecting navigational safety. Finally, capability indicators are employed to analyze the matching relationship between coastal search and rescue resources and maritime transportation risks. Our study provides valuable references for enhancing maritime emergency response capabilities and protecting marine ecological environments.

Comparative deterministic and probabilistic approaches for assessing the aquatic ecological risk of pesticides in a mixed land use basin: A case study in Uruguay

Environmental concentrations of 25 pesticides in superficial water were employed to conduct an ecological risk assessment (ERA) in a mixed land-use basin utilized as a drinking water source. A deterministic risk assessment (RQ) was utilized to evaluate the chronic risk to aquatic biota, while a probabilistic risk assessment (PRA) approach was applied to assess the acute and chronic risk in the most sensitive species and at the community level. A high risk was identified for insecticides (pyrethroids, organophosphates and organochlorines). RQs ranged from 4.0e−4 (2,4-D) to 105.3 (ethion) considering median concentrations and from 8.0e−4 (2,4-D) to 230 (p,p’-DDT) considering extreme concentrations. Temporal variation in ΣRQs showed the highest risk during spring and summer months, which is related to the crop calendar and land use in the Laguna del Cisne basin. For PRA, the probability of exceeding the hazardous concentration HC5 (5th percentile) was higher for the most sensitive species in chronic exposure, especially for cypermethrin (38.9 %), permethrin (25.6 %), and chlorpyrifos (16.6 %). In the case of acute exposures, the probability of surpassing HC5 was higher for the entire freshwater biota, with the highest values observed for bifenthrin (28.3 %), cypermethrin (25.5 %), permethrin (11.75 %), and ethion (11.1 %). The advantages and disadvantages of PRA for assessing pesticide ecological risk were compared with the conventional deterministic RQs approach, highlighting that PRA offers improvements over the deterministic risk assessment, especially for organophosphate pesticides. Additionally, PRA provides a more comprehensive evaluation of risk for both short and long-term exposure, has the potential to incorporate others available toxicity data (e.g., LD50, Daily Intake), and utilizes different hazardous concentrations, such as HC5, HC10, and HC50. Our findings emphasize the urgent need to establish a national regulatory framework to evaluate and mitigate pesticide risks in aquatic ecosystems, especially in drinking water source like Laguna del Cisne.

Failure probability analysis of hydrogen doped pipelines based on the Bayesian network

Damage from hydrogen, such as hydrogen embrittlement in natural gas pipelines, is a possibility. There are currently lacking ways for calculating the failure of hydrogen doped pipelines quantitatively; instead, research on the safety of them is mostly concentrated on hydrogen compatibility tests and consequence analysis. A method for quantifying hydrogen doped pipelines losses using Bayesian network-based failure probability analysis is presented. The influence of hydrogen on pipeline failure is introduced using the expert scoring method based on the analysis of historical failure data for gas pipelines. The failure probabilities of various components of hydrogen doped pipelines are then quantified by combining fuzzy mathematical theory and hierarchical analysis, which is subsequently utilized as prior probabilities to input into the Bayesian network for quantitative calculation. The results show that the Bayesian network can more accurately quantify the failure probability of hydrogen doped pipelines. They also show that the Bayesian network can provide a practical approach for quantitative risk assessment and safety evaluation, as well as a decision basis for the prevention of hydrogen doping pipeline failure accidents.

Threats to the soil microbiome from nanomaterials: A global meta and machine-learning analysis

Soil is the primary sink for released nanomaterials (NMs), but the understanding of the impacts of NMs on the soil microbiome remains fragmented. Moreover, there is currently lack of systematic approaches to evaluate the microbial ecological risks of NMs. In this study, we conducted a global meta-analysis incorporating 2134 paired observations from 107 publications from 2000 to June 2023 to comprehensively assess the effects of NMs on the soil microbiome. Additionally, we developed a machine-learning approach to predict these impacts and identified key contributing features. The results reveal that NMs have significant negative effects on soil microbial diversity (−0.96%), biomass (−14.01%), activity (−3.39%), and function (−14.44%). The impacts of NMs on fungal diversity were greater than those on bacteria. Compared with carbon NMs, metal NMs have more pronounced negative effects on various soil microbial community metrics, with Ag NMs exhibiting the greatest negative impact. Ag NMs exhibited greater negative effects on microbial function than bulk Ag or Ag+. Nanoscale effects played a pivotal role in these adverse effects. These adverse effects are primarily associated with NM type, size and content. Two machine learning models achieved acceptable prediction accuracy in assessing the impact of NMs on the soil microbial community. This study offers an effective approach for the ecological risk assessment of NMs and provides a scientific foundation for the rational and informed application of NMs in the soil environment.

Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol

Deoxynivalenol (DON) is a mycotoxin frequently observed in cereals and cereal-based foods, with reported toxicological effects including reduced body weight, immunotoxicity and reproductive defects. The European Food Safety Authority used traditional risk assessment approaches to derive a deterministic Tolerable Daily Intake (TDI) of 1 μg/kg-day, however data from human biomarkers studies indicate widespread and variable exposure worldwide, necessitating more sophisticated and advanced methods to quantify population risk. The World Health Organization/International Programme on Chemical Safety (WHO/IPCS) has previously used DON as a case example in replacing the TDI with a probabilistic toxicity value, using default uncertainty and variability distributions to derive the Human Dose corresponding to an effect size M in the Ith percentile of the population (HDMI) for M = 5 % decrease in body weight and I = 1 %. In this study, we extend this case study by incorporating (1) Bayesian modeling approaches, (2) using both in vivo data and in vitro population new approach methods to replace default distributions for interspecies toxicokinetic (TK) differences and intraspecies TK and toxicodynamic (TD) variability, and (3) integrating biomonitoring data and probabilistic dose–response functions to characterize population risk distributions. We first derive an HDMI of 5.5 [1.4–24] μg/kg-day, also using TK modeling to converted the HDMI to Biomonitoring Equivalents, BEMI for comparison with biomonitoring data, with a blood BEMI of 0.53 [0.17–1.6] μg/L and a urinary excretion BEMI of 3.9 [1.0–16] μg/kg-day. We then illustrate how this integrative approach can advance quantitative risk characterization using two human biomonitoring datasets, estimating both the fraction of population with an effect size M ≥ 5 % as well as the distribution of effect sizes. Overall, we demonstrate that integration of Bayesian modeling, human biomonitoring data, and in vitro population-based TD data within the WHO/IPCS probabilistic framework yields more accurate, precise, and comprehensive risk characterization.

SolBeePop: A model of solitary bee populations in agricultural landscapes

Abstract With their large diversity of species, solitary bees are important pollinators of crops and native plant communities in agricultural landscapes. Stressors such as changing landscapes, climate and pesticide exposures may affect populations differently, dependent on each species' ecological traits. We developed a population model for solitary bees, SolBeePop, which can be applied to simulate a variety of species by using species‐specific traits, including the nesting strategies. Species' phenological traits are mechanistically combined with input time series capturing temporal and spatial variability in landscape compositions. Calibration and validation of the model with empirical study data demonstrate that the model can capture realistic dynamics in bee populations. In simulations conducted representing four species, Osmia bicornis, Megachile rotundata, Nomia melanderi and Eucera pruinosa, identical conditions and assumed nesting resource limitations resulted in different population‐level outcomes, indicating the importance of interactions between external factors and species‐specific traits including phenological, survival and reproductive traits. Synthesis and applications. The publicly available model is intended as a tool for the assessment of population‐level outcomes of stressors, for instance, the limitation of floral resources in agricultural landscapes, limitation of nesting habitat and the exposure to pesticides. Realistic landscape scenarios can be tested and available data for one species can be used to estimate outcomes in other solitary bee species, informing conservation plans and risk assessment approaches to support managed and natural populations in the field.

Hemodynamics analysis and computational fluid dynamics simulations to identify potential location of rupture in abdominal aortic aneurysm

Abstract Background Abdominal Aortic Aneurysm (AAA) rupture is the most severe consequence of aneurysmal disease, with an overall death rate of 80%. Early diagnosis and appropriate follow-up and management of AAA will significantly reduce the risk of rupture and mortality. Rupture risk assessment of AAA has a critical role for the early warning. Purpose To Predict of the course of an AAA and determine the rupture risk and location from a biomechanical point of view. AAA hemodynamics were investigated to clarify the relation between the hemodynamic parameters and the AAA rupture. Methods Computed tomography (CT) images of one healthy aorta and 23 patients’ aorta with aneurysms were analyzed. WSS-related hemodynamic parameters of time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), endothelial cell activation potential (ECAP), and relative residence time (RRT) are investigated in patient-specific AAA geometries by performing computational fluid dynamics (CFD) simulations (Figure 1). Patient-specific AAA geometries are identified using medical imaging techniques and realistic boundary conditions are determined using Doppler ultrasonography. Results AAA rupture locations predicted by CFD simulations were in good agreement with the actual rupture locations of retrospective data. The rupture locations had relatively low TAWSS, high OSI, high ECAP, and high RTT levels. High risk of rupture is expected in regions with a local minimum of TAWSS, and a local maximum of OSI, ECAP, and RRT. Conclusions Examining the spatial distribution of the hemodynamic parameters was a powerful approach for rupture risk assessment of AAA. Predicting the rupture location using a single hemodynamic parameter is not feasible, and the accuracy of AAA rupture risk assessment can be improved by using multiple WSS-related parameters by identifying the common intersection regions of low TAWSS, high OSI, high ECAP, and high RRT.Figure 1

Assessing airborne transmission risks in COVID-19 hospitals by systematically monitoring SARS-CoV-2 in the air.

The continuous emergence of highly infectious SARS-CoV-2 variants with the capability of immune evasion demands more effective risk assessment and management approaches for infection prevention and control. Controlling nosocomial transmission is critical in response to the COVID-19 pandemic upon the resumption of day-to-day activities. Here, we systematically analyzed the presence of airborne SARS-CoV-2 in the isolation ward and within the contaminant, emergency, and clean zones of a designated COVID-19 hospital. A total of 359 air samples collected in the clean and emergency zones tested negative for SARS-CoV-2, demonstrating the interruption of airborne transmission in a designated COVID-19 hospital by combining the closed-loop management, unidirectional airflow, and negative pressure of isolation wards. In contrast, some air samples in the corridor outside the isolation wards tested positive for SARS-CoV-2. The leakage of virus-containing air from patient rooms to adjoining areas during rounds and meal delivery sheds light on the need for intensive and widespread virus monitoring in designated COVID-19 hospitals. In conclusion, by systematically monitoring viruses in the air, the study provides a starting point for the standardization of transmission risk management in designated hospitals for the future pandemic of a newly emerged respiratory infectious disease.IMPORTANCERisk management and control of airborne transmission in hospitals is crucial in response to a respiratory virus pandemic. However, the formulation of these infection control measures is often based on epidemiological investigations, which are an indirect way of analyzing the transmission route of viruses. This can lead to careless omissions in infection prevention and control or excessively restrictive measures that increase the burden on healthcare workers. The study provides a starting point for standardizing transmission risk management in designated hospitals by systemically monitoring viruses in the air of typical spaces in COVID-19 hospitals. The negative results of 359 air samples in the clean and emergency zones demonstrated the existing measures to interrupt airborne transmission in a designated COVID-19 hospital. Some positive cases in the corridor of the contaminant zone during rounds and meal delivery highlighted the importance of monitoring airborne viruses for interrupting nosocomial infection.

Probabilistic modelling is superior to deterministic approaches in the human health risk assessment: an example from a tribal stretch in central India

This case drew national attention in 2018. About 100 people died and more than 300 hospitalized in a span of few years in a village of 1200 people in a tribal stretch in central India. Medical teams visiting the area reported severe renal failure and blamed the local eating and drinking habits as causative factors. This human health assessment based on geochemical investigations finds nitrate (NO3−) and fluoride (F−) pollution as well in village’s groundwater. Both deterministic and probabilistic techniques are employed to decipher the contamination pathways and extent of contamination. Source apportionments of NO3− and F− and their relationship with other ions in groundwater are carried out through chemometric modelling. Latent factors controlling the hydrogeochemistry of groundwater too are explored. While hazard quotients (HQ) of the chemical parameters (HQ_{{{text{NO}}_{3}^{ - } }} and HQ_{{{text{F}}^{ - } }}) identify ingestion as the prominent pathway, the calculated risk certainty levels (RCL) of the hazard index (HI) values above unity are compared between the deterministic and probabilistic approaches. Deterministic model overestimates the HI values and magnify the contamination problems. Probabilistic model gives realistic results that stand at infants (HI_{{{text{NO}}_{3}^{ - } }} = 34.03%, HI_{{{text{F}}^{ - } }} = 24.17%) > children (HI_{{{text{NO}}_{3}^{ - } }} = 23.01%, HI_{{{text{F}}^{ - } }} = 10.56%) > teens (HI_{{{text{NO}}_{3}^{ - } }} = 13.17%, HI_{{{text{F}}^{ - } }} = 2.00%) > adults (HI_{{{text{NO}}_{3}^{ - } }} = 11.62%, HI_{{{text{F}}^{ - } }} = 1.25%). Geochemically, about 90% of the samples are controlled by rock-water interaction with Ca2+–Mg2+–HCO3− (~ 56%) as the dominant hydrochemical facies. Chemometric modelling confirms Ca2+, Mg2+, HCO3−, F−, and SO42− to originate from geogenic sources, Cl− and NO3− from anthropogenic inputs and Na+ and K+ from mixed factors. The area needs treated groundwater for human consumption.

A new risk assessment approach for design of hybrid microgrids considering stability issues

AbstractStability issues can significantly increase the risk of hybrid microgrids (HMGs), particularly during island mode operation. The dynamic performance of the system can induce constraints and stability margins that may elevate the loss of load probability. This paper presents a new stability‐oriented risk assessment model that bridges the conventional reliability models, stability, and system risk. The proposed model ensures the risk of the system by considering the redesign or reconfiguration of HMGs to address stability issues. First, the interlinking converters (ICs) DC‐link voltage stability is analysed to determine the acceptable power flow margins in rectifying and inversion mode. Next, the new general risk assessment model is introduced. The results show that the stability margin significantly increases the risk of the HMG, particularly when considering the aging of converters. The study also examines the impact of various load characteristics and ICs with different numbers but the same total size. In some cases, the risk is acceptable for the desired loads, or it can be reduced to an admissible level by reconfiguring the ICs. Finally, the paper demonstrates the effectiveness of the proposed model in the optimal design of HMGs, aiming to guarantee the system's risk.

Predicting an Underlying Clonal Plasma Cell Population in Light-Chain Monoclonal Gammopathy of Undetermined Significance Using Free Light-Chain Ratio

Introduction: The management of light-chain (LC) monoclonal gammopathy of undetermined significance (MGUS) presents inherent challenges as the key diagnostic factor - abnormal free-light chain (FLC) levels - only indirectly implies the presence of an underlying clonal cell population. Moreover, the lack of M protein, used in current risk-stratification models, necessitates a distinct approach for risk assessment in LC-MGUS. Using next-generation flow cytometry (NGF), we assessed clonal plasma cell presence in bone marrow (BM) samples from individuals with LC monoclonal gammopathies. This approach enabled us to evaluate the predictive value of FLC ratio for an underlying clonal plasma cell population in LC-MGUS and its prognostic implications. Aim: To evaluate the association between FLC ratio and the presence of clonal plasma cells in the BM of individuals with LC monoclonal gammopathies as well as the risk of progression. Methods: The study is a part of the Iceland Screens, Treats, or Prevents Multiple Myeloma (iStopMM) study, a population-based screening study for multiple myeloma (MM) precursors and randomized trial of follow-up strategies. We included screened individuals with an abnormal FLC ratio (<0.26 or >1.65) and elevated involved LC (kappa ≥19.4 mg/L or lambda ≥26.3 mg/L) (Freelite, The Binding Site), with no detected M protein. Those with previously known MM or related disorders were excluded. NGF was used to determine clonal plasma cell presence in BM samples. Logistic regression was used to assess the relationship between FLC ratio (used as involved/uninvolved ratio throughout the study) and clonal plasma cell presence. Receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of FLC ratio for clonal plasma cell presence in LC-MGUS and to determine an optimal cutoff value. Kaplan-Meier analysis was used to assess time to progression to more advanced plasma cell disorder for LC-MGUS, stratified by the FLC ratio cutoff. A linear mixed-effects model was used to evaluate the change in FLC ratio (expressed as fold change relative to the screening sample) over time for those with FLC ratio below the cutoff at screening. Results: After screening 75,422 individuals, 1,965 with LC disease were included, with median age of 71 years (range of 42-100 years), and a median FLC ratio of 1.85. Most (95.4%) had involved FLC of type kappa. Clonal plasma cells were identified by NGF in 8/21 (38.1%) LC-MGUS, and all LC-smoldering MM (SMM) (n=21) and LC-MM (n=7) cases. A significant prediction of clonal plasma cell presence was found for FLC ratio (OR=6.47; p=0.012; n=49), supported by ROC curve analysis for LC-MGUS (AUC=0.94; n=21), identifying FLC ratio of 3.15 as optimal cutoff (92% sensitivity, 100% specificity). Of the 254 individuals randomized to active follow-up within the iStopMM study, baseline diagnosis distribution was 223 (87.8%) LC-MGUS, 22 (8.7%) LC-SMM, 7 (2.8%) LC-MM, 1 (0.4%) solitary plasmacytoma, and 1 (0.4%) chronic lymphocytic leukemia. Among these, 123 (representing 88% of the screened study cohort) had FLC ratio <3.15 at screening, thereof 122 (99.2%) with LC-MGUS and 1 (0.8%) with solitary plasmacytoma. No disease progression was observed for individuals with FLC ratio <3.15 (n=100) over a median follow-up of 46 months. Conversely, disease progression occurred in 13/97 (13.4%) LC-MGUS with ≥3.15 FLC ratio (9 to SMM, 3 to MM, and 1 to smoldering Waldenströms macroglobulinemia) during median follow-up of 42 months (p=0.00018 vs FLC ratio <3.15) (Figure). The linear mixed-effects model of 454 repeated FLC from 128 individuals with <3.15 FLC ratio at screening did not indicate significant change in FLC ratio over a median follow-up of 48 months. Conclusions: In this study, we utilized NGF to confirm an underlying clonal plasma cell population in BM of individuals with LC-MGUS and evaluate its association with involved/uninvolved FLC ratio. Clonal plasma cells were detected in a minority of LC-MGUS (38.1%), for which FLC ratio was found to be highly predictive. An FLC ratio cutoff of 3.15 optimally discriminated based on clonal plasma cell presence, and we found no evidence of progressive disease in LC-MGUS below this cutoff during follow-up. Altogether, the more stringent FLC ratio cutoff can assist in determining the likelihood of an underlying clonal plasma cell population in LC-MGUS and may, as such, help to guide the management of LC-MGUS.

A macroplastic vulnerability index for marine mammals, seabirds, and sea turtles in Hawai‘i

Plastic pollution is having devastating consequences for marine organisms across the planet. However, the population level effects of macroplastic pollution remain difficult and costly to quantify. As a result, there is a need for alternative approaches to evaluate species risk to plastic pollution and inform management needs. We apply a trait-based framework for macroplastic pollution to develop a relative vulnerability index—informed by three dimensions: likelihood of exposure, species' sensitivity, and population resilience—for marine mammals, seabirds, and sea turtles found in Hawai‘i. This index ranks 63 study species based on their population level vulnerability to macroplastic pollution, with the highest scoring species being the most vulnerable. Our results indicate that ducks, waders, and noddies with large populations were the least vulnerable to macroplastics, while the most vulnerable were the Hawaiian monk seal, sea turtles, baleen whales, and some albatross and petrel species. This index can inform species in need of population monitoring in Hawai'i, and direct other management priorities (e.g., locations for clean-ups or booms). More broadly, this work exemplifies the value of qualitative risk assessment approaches for better understanding the population level effects of macroplastic pollution and showcases how vulnerability indices can be used to inform management priorities.

Enhanced Category-Based Risk Assessment for Neonatal Early-Onset Sepsis: A Prospective Observational Study

Introduction: Compared with multivariate risk assessment, traditional category-based risk assessment (CRA) approaches for neonatal early-onset sepsis (EOS) screening are usually straightforward to use, do not require electronic devices, but are associated with higher rates of antibiotic use. This study aims to evaluate the performance of a novel enhanced CRA (eCRA) framework on EOS admissions and antibiotic use and to investigate whether a modified version with adjustments in risk factor weighting can allow its performance to match the EOS calculator while remaining easy to implement. Method: This is a prospective, single-center, two-phase observational study. Infants of all gestations delivered in a tertiary hospital in Hong Kong with risk factors or clinical features of EOS were recruited. Phase I: A novel eCRA framework (period 2) was compared with the CDC 2010-based protocol (period 1). Phase II: A modified eCRA framework was compared theoretically with the EOS calculator. EOS-specific admissions and antibiotic use were measured. Results: Phase I: 1,025 at-risk infants were recruited during period 2 and compared with 757 infants of period 1. Admissions and antibiotic use decreased from 45.8% to 29.4% and 41.1% to 28.2%, respectively. Antibiotics among those at-risk but well-appearing infants decreased from 25.3% to 16.3% (p < 0.001 for all). Phase II: antibiotic use was similar (7.3 vs. 6.4%, p = 0.42) between the modified eCRA framework and the EOS calculator. Conclusions: An eCRA framework can effectively and safely provide individualized guidance for EOS screening without the need for tools such as the EOS calculator.

Application of Artificial Neural Network in Predicting Direct Economic Losses Due to Earthquake

Accurately predicting the direct economic losses caused by earthquakes is important for policy makers for disaster budgets. Before a disaster strikes, it is important to consider the public policy costsassociated with disaster relief and recovery. The aim of this study is to provide a risk assessment approach, which can benefit all parties involved. Artificial neural networks are widely used for time seriesforecasting, especially financial forecasting. Therefore, this study proposes a cutting-edge forecastingmethod such as backpropagation neural network (BPNN) and other prediction methods: neural networkautoregressive (NNAR) and ARIMA-GARCH to obtain the best prediction results. This paper appliesinterpolation data to increase the amount of data used. Two interpolations were applied to amplify theoriginal small sample with virtual points, namely cubic splines and further piecewise interpolation using. The results of this study are the cubic spline interpolation is the most effective way to solve thesmall sampling problem to predict direct economic losses due to the Indonesian earthquake and theBPNN method outperforms other traditional methods with an RMSE of 0.024 in the training period and0.174 in the testing period, significantly lower than other methods. The results of this research can beused as reference material for the government in estimating the level of earthquake losses and can beused to develop risk reduction strategies

Improving predoctoral education related to caries risk assessment in adults.

This project examined patterns of adult patient management using a caries risk assessment (CRA) protocol at East Carolina University, School of Dental Medicine. Usage of the CRA protocol from 2014 to 2019 was assessed. Non-operative anti-caries treatments were measured against caries risk status (high, moderate, low, or none). Steps to improve the appropriate management of patients based on caries risk are presented to align with accreditation standards for predoctoral education programs. The CRA protocol is based on the Caries Management by Risk Assessment approach. Risk-based patterns for two non-operative interventions were examined: (1) prescriptions for 0.12% chlorhexidine gluconate (CHX) mouth rinse and (2) prescriptions for 5000 ppm fluoride toothpaste (PreviDent 5000 [PreviDent]). Statistical analyses included chi-square tests and logistic regression. Over the study period only 16.4% of adult patients had completed the CRA form. Among 29,411 patients from nine community sites, treatment rates for PreviDent were 18.7% among high-risk patients, 11.6% for moderate-risk adults, and 6.4% for low-risk adults (p<0.01). Treatment rates for CHX were 23.0%, 22.6%, and 17.1%, respectively (p<0.05). Patients without a CRA status were least likely to receive any anti-caries treatments, indicating that CRA status affects clinical, non-operative care. Patterns for prescription of PreviDent and CHX are consistent with CRA status. Future efforts to improve usage of the CRA protocol using faculty calibration, tracking with quality improvement tools, and reassessment. Training in the community-based educational setting is enhanced through data-based tracking to assure evidence-based decision making.

The Importance of Basal Cell Carcinoma Risk Stratification and Potential Future Pathways.

Basal cell carcinoma (BCC) is the most common human cancer. Although there are surgical and topical treatments available, surgery remains the mainstay of treatment, leading to higher costs. What is needed is an accurate risk assessment of BCC so that treatments can be planned in a patient-centered manner. In this study, we will review the literature about guidelines for the management of BCC and analyze the potential indicators of high-risk BCC. Using this risk assessment approach, we will propose pathways that will be able to optimize treatments more efficiently. This paper presents a perspective from a skin cancer expert and clinic involved in the treatment of both simple and complex cases of BCC. It addresses the key challenges associated with accurate risk stratification prior to any treatment or procedure. Different immunohistochemical and angiogenic markers for high-risk BCC were reviewed in this study. The expression of interleukin-6, vascular endothelial growth factor, and mast cells within BCC correlates with its aggressiveness. Other immunohistochemical markers, such as Cyclin D1 and Bcl-2, also play a significant role-Cyclin D1 is higher in the aggressive BCC, while Bcl-2 is lower in the aggressive BCC, compared to the nonaggressive variants. Based on our research, we will conclude that using immunohistochemical and angiogenic markers for risk assessment and stratification of BCC can help optimize treatment, ensuring that surgical procedures are used only when necessary.