Existing Risk Assessment Tools Research Articles

Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank

ObjectivesDespite rising rates of multimorbidity, existing risk assessment tools are mostly limited to a single outcome of interest. This study tests the feasibility of producing multiple disease risk estimates with...

Risk Assessment Tools from the One Health Perspective: A Narrative Review.

61% of the infections around the world that have emerged to date are zoonotic. Evidence warns that the threat posed by zoonoses is on the rise, and the risk of a new pandemic is higher now than ever. Early identification of risk, populations at risk, and risk of transmission are essential steps towards a prevention, preparation and response to outbreaks. This review aims to look at the tools available for identifying and estimating risks and threats from one health perspective and finally propose a list of indicators which could assess the risk of transmission of disease at the humans, animals and the environment intersection. The databases like PubMed, google scholar, Embase and Scopus were used to extract the relevant articles. A search was carried out using a keyword. A total of 1311 articles were listed initially after the search and reviewed. Out of 1311, only 26 tools which assessed the risk of diseases mainly infectious or were relevant to risk of transmission of any infectious diseases were included in the review. The tools included in this review involve risk assessment at the environmental, animal and human dimensions. The tools are used to evaluate the contamination of the environment due to chemicals or toxins or the risk of transmission of infection due to environmental factors like air contamination, to identify the animal diseases like bovine respiratory disease and foot and mouth disease and to estimate the human health risk at the community or individual levels. Risk assessment tools are an essential part of the prevention of pandemics. These tools are helpful in assessing the risk of transmission of infections either from human to human, between human and animals, between animals and animals and so on. Thus this review gives us an insight into the existing risk assessment tools and the need for a One Health risk assessment tools to prevent outbreaks in future. It also provides a list of factors that can be included in a one health risk assessment tool.

Suitability of risk assessment tools used during the portfolio recommendation process

Orientation: Investors are overwhelmed with Collective Investment Schemes (CIS) portfolio options.Research purpose: The study set out to assess the emerging themes considered essential and prevalent in establishing a risk profile, establishing a framework for risk assessment tools, and evaluating existing risk assessment tools used in the industry.Motivation for the study: In 2020, newly amended legislation confirmed the importance of a risk assessment. This legislation made it mandatory and compulsory for financial planners to conduct a risk profile analysis concerning investment products in South Africa. Consequently, CIS have implemented various risk assessment tools to establish the investor risk profile.Research approach/design and method: Documentary research methodology was employed, where secondary data were analysed qualitatively. Twelve risk assessment tools were collected, and the established framework was used to evaluate existing risk assessment tools.Main findings: A sound theoretical framework for risk assessment tools, which is comprehensive in nature and include demographic factors, risk categorisation, and regulatory factors, was established. The results indicate that current tools used in the industry are inconsistent and do not address all the factors required to establish an investor risk profile.Practical/managerial implications: The study is significant in that it guides the financial services industry into the elements of assessment in establishing an investor risk profile.Contribution/value-add: A risk profile analysis is compulsory for financial planning. Financial planners and policymakers can consider the established framework to implement a standardised, comprehensive, holistic, risk assessment tool.

Gait speed assessment as a prognostic tool for morbidity and mortality in vulnerable older adult patients following vascular surgery

IntroductionPredicting the risk associated with vascular surgery in older adult patients has become increasingly challenging, primarily due to limitations in existing risk assessment tools. This study aimed to evaluate the utility of gait speed, a clinical indicator of frailty, in enhancing the prediction of mortality and morbidity in older adult patients undergoing vascular surgery. MethodsA single-center prospective cohort study was conducted, involving older adult patients undergoing vascular surgery at four tertiary care hospitals between 2021 and 2022. Eligible patients were aged 80 years or older and scheduled for surgical treatment of peripheral arterial disease of the lower limbs (IIb Leriche-Le Fontaine). The primary factor of interest was gait speed, defined as taking more than 6 s to walk 5 meters. The primary outcomes were in-hospital postoperative mortality and major morbidity. ResultsThe cohort comprised 131 patients with a mean age of 82.8 ± 1.4 years, with 34 % being female. Before vascular surgery, 60 patients (46 %) were categorized as slow walkers. Slow walkers were more likely to be female (43 % vs. 25 %, p < 0.03) and diabetic (50 % vs. 28 %, p < 0.01). Among the patients, 30 (23 %) experienced the primary composite outcome of mortality or major morbidity following vascular surgery. After adjusting for the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) Surgical Risk Calculator, slow gait speed independently predicted the composite outcome (odds ratio: 3.05; 95 % confidence interval: 1.23 to 7.54). ConclusionsGait speed is a straightforward and effective test that can help identify a subgroup of frail older adult patients at an elevated and incremental risk of mortality and major morbidity after vascular surgery. While gait speed remains a valuable clinical indicator of frailty, it is important to recognize that the broader context of mobility plays a pivotal role in postoperative outcomes.

Risk assessment for people living with dementia: a systematic review.

This systematic review identified key components of risk assessment for people with dementia, examined attitudes toward risk identification and risk assessment, and appraised existing risk assessment tools. Systematic searches of five databases on two platforms (EBSCO, OVID) and gray literature databases (Open Grey, Base) were conducted. Studies were screened for inclusion based on predetermined eligibility criteria and quality assessed using the Mixed Methods Appraisal Tool. Findings were tabulated and synthesized using thematic synthesis. Our review found people with dementia, their family carers, and healthcare professionals differed in how risk is conceptualized, with views being shaped by media perceptions, personal experiences, socio-cultural influences, dementia knowledge, and dementia severity. We found that mobilization (causing falls inside and getting lost outside) is the most frequently identified risk factor. Our findings show people with dementia are generally risk-tolerant, while healthcare professionals may adopt risk-averse approaches because of organizational requirements. We found factors that disrupt daily routines, living and caring arrangements, medication management, and unclear care pathways contribute toward adverse risk events. We discovered that most studies about risk and risk assessment scales did not consider insight of the person with dementia into risks although this is important for the impact of a risk. No risk instrument identified had sufficient evidence that it was useful. Accurate risk assessment and effective communication strategies that include the perspectives of people with dementia are needed to enable risk-tolerant practice. No risk instrument to date was shown to be widely acceptable and useful in practice.

The devil is in the details: exploring how functionally distinct round goby is among native fish in the Baltic Sea

Understanding the characteristics and conditions that make non-indigenous species (NIS) successful at establishing in recipient communities is a key in determining their potential impacts on native species, as well as to improve management actions such as prevention of future invasions. The round goby (Neogobius melanostomus) is one of the most widespread non-indigenous fish species in the Northern Hemisphere, including the coastal zones of the Baltic Sea. The impacts of round goby in the Baltic Sea are pronounced and multifaceted, yet our knowledge regarding the underlying assembly processes determining its establishment is limited. To overcome this knowledge gap, we applied a trait-based approach to assess the degree of niche overlap and functional (trait) similarity between round goby and native fish species in coastal areas from the Baltic Sea, based on the functional distinctiveness metric. Our results show that round goby is generally quite similar (or not dissimilar) to the native fish of the regional species pool, at least in terms of its overall trait composition. Conversely, round goby demonstrates pronounced differences compared to the native community in its display of parental care and territorial behaviour. Such differences in individual traits could play an important role in round goby’s invasion success in the Baltic Sea, including its interactions with native species (e.g. competition). Our results and their potential implications may be highly relevant for conservation and management if integrated within existing risk assessment tools for biological invasions in order to prioritise and enhance the effectiveness of preventative actions towards the expansion of round goby.

Predictors of In-Hospital Death in Patients With Acute Myocardial Infarction.

Factors such as age, vital signs, renal function, Killip class, cardiac arrest, elevated cardiac biomarker levels, and ST deviation predict survival in patients with acute myocardial infarction (AMI). However, the existing risk assessment tools lack comprehensive consideration of catheter-related factors, and short-term prognostic predictors are unknown. This study aimed to clarify in-hospital prognostic predictors in hospitalized patients with AMI. Five hundred and thirty-six patients who underwent percutaneous coronary intervention (PCI) for AMI were divided into non-survivor (n = 36) and survivor (n = 500) groups. Coronary risk factors, laboratory findings, angiographic findings, and clinical courses were compared between the two groups. Multiple logistic regression was used to analyze in-hospital death in pre- and post-PCI phases. In the pre-PCI phase, multiple logistic regression analysis revealed several predictors of in-hospital death, including systolic blood pressure [odds ratio (OR) = 0.985, p = 0.023)], Killip class ≥2 (OR = 14.051, p <0.001), and chronic kidney disease (OR = 4.859, p = 0.040). In the post-PCI phase, multiple logistic regression analysis revealed additional predictors of in-hospital death, including Killip class ≥2 (OR = 5.982, p = 0.039), presence of lesions in the left main trunk (OR = 51.381, p = 0.044), utilization of intra-aortic balloon pumps and percutaneous cardiopulmonary support (OR = 6.141, p = 0.016), and presence of multi-vessel disease (OR = 6.323, p = 0.022). Predictors of in-hospital death in AMI extend beyond conventional risk factors to include culprit lesions, mechanical support, and multi-vessel disease that manifest post-PCI.

RISK FACTORS FOR ATONIC POSTPARTUM HEMORRHAGE: A SYSTEMATIC REVIEW

ABSTRACT&#x0D; Introduction: Postpartum bleeding occurs in 3-10% of all deliveries. Although numerous causes exist, uterine atony is the most frequent, accounting for about 70% of all instances.&#x0D; Objective: This study aimed to identify and quantify risk variables for atonic postpartum hemorrhage. &#x0D; Methods: The databases PubMed, CINAHL, EMBASE, Web of Science, and ClinicalTrials.gov were searched for English language studies with no date or geographical constraints. Randomized trials, prospective or retrospective cohort studies, and case-control studies of pregnant individuals who suffered atonic postpartum hemorrhage and reported at least one risk factor were included in the studies. This review contained 16 studies out of the 1,977 records evaluated. A qualitative synthesis of research with low and moderate risk of bias rates each risk factor as definite, likely, unclear, or not a risk factor.&#x0D; Results: This research found 47 potential risk factors for atonic postpartum hemorrhage, 15 of which were deemed definite or likely risk factors. The remaining 32 risk factors were unrelated to atonic postpartum hemorrhage or had inconsistent or inconclusive data.&#x0D; Conclusion: A significant proportion of postpartum bleeding occurs without known risk factors. Many risk variables for atonic hemorrhage contained in existing risk assessment tools were confirmed, with past postpartum hemorrhage of any cause, placenta previa, placental abruption, uterine rupture, and multiple pregnancies conferring the largest risk. Hypertension, diabetes, and ethnicity were new risk factors not previously included in risk-assessment systems. In this study, obesity and magnesium were not associated with to atonic postpartum hemorrhage.

Investigation of Intrapartum Parenteral Magnesium Sulfate as an Independent Risk Factor for Postpartum Hemorrhage Using Quantitative Blood Loss Assessment

Magnesium sulfate is used for seizure prophylaxis in preeclampsia and for fetal neuroprotection when delivery is anticipated before 32 weeks of gestation. Existing risk assessment tools for postpartum hemorrhage often identify the use of magnesium sulfate as an intrapartum risk factor. Previous studies examining the association between the use of magnesium sulfate and postpartum hemorrhage have relied largely on qualitative estimates of blood loss rather than quantitative estimates of blood loss. This study aimed to determine whether intrapartum administration of magnesium sulfate is associated with an increased risk of postpartum hemorrhage using a quantitative blood loss assessment via the use of graduated drapes and weight differences in surgical supplies. This case-control study was conducted to test the hypothesis that intrapartum parenteral administration of magnesium sulfate is not independently associated with postpartum hemorrhage. All deliveries at our tertiary-level academic medical center between July 2017 and June 2018 were reviewed. Of note, 2 categories of postpartum hemorrhage were defined: the traditional definition (>500 mL for vaginal delivery and >1000 mL for cesarean delivery) and the contemporary definition (>1000 mL regardless of delivery mode). Statistical analyses using the chi-square test, Fisher exact test, t test, or Wilcoxon rank-sum test were performed to compare the patients who did and did not receive magnesium sulfate concerning the rates of postpartum hemorrhage, pre- and postdelivery hemoglobin level, and rates of blood transfusion. A total of 1318 deliveries were included, with postpartum hemorrhage rates of 12.2% (traditional definition) and 6.2% (contemporary definition). Multivariate logistic regression did not find the use of magnesium sulfate as an independent risk factor by either definition (odds ratio, 1.44 [95% confidence interval, 0.87-2.38] and 1.34 [95% confidence interval, 0.71-2.54]). The only significant independent risk factor was cesarean delivery, by both definitions (odds ratio, 2.71 [95% confidence interval, 1.85-3.98] and 19.34 [95% confidence interval, 8.55-43.72]). In our study population, intrapartum administration of magnesium sulfate was not found to be an independent risk factor for postpartum hemorrhage. Cesarean delivery was determined as an independent risk factor, consistent with previous reports.

Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.

This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD. We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively. The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.

Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.

Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).

Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools.

Simple SummaryOesophago-gastric cancer is one of the commonest cancers worldwide, yet it can be particularly difficult to diagnose given that initial symptoms are often non-specific and routine screening is not available. Cancer risk-assessment tools, which calculate cancer risk based on symptoms and other risk factors present in the primary care record, can aid decisions on referrals for cancer investigations, facilitating earlier diagnosis. Diagnosing common cancers earlier could help improve survival rates. Using UK primary care electronic health record data, we compared five different machine learning techniques for probabilistic classification of cancer patients against a current widely used UK primary care cancer risk-assessment tool. The machine learning algorithms outperformed the current risk-assessment tool, with a higher overall accuracy and an ability to reasonably identify 11–25% more cancer patients. We conclude that machine-learning-based risk-assessment tools could help better identify suitable patients for further investigation and support earlier diagnosis.Oesophago-gastric cancer is difficult to diagnose in the early stages given its typical non-specific initial manifestation. We hypothesise that machine learning can improve upon the diagnostic performance of current primary care risk-assessment tools by using advanced analytical techniques to exploit the wealth of evidence available in the electronic health record. We used a primary care electronic health record dataset derived from the UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic machine learning classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees. Features included basic demographics, symptoms, and lab test results. The Logistic Regression, Support Vector Machine, and Extreme Gradient Boosted Decision Tree models achieved the highest performance in terms of accuracy and AUROC (0.89 accuracy, 0.87 AUROC), outperforming a current UK oesophago-gastric cancer risk-assessment tool (ogRAT). Machine learning also identified more cancer patients than the ogRAT: 11.0% more with little to no effect on false positives, or up to 25.0% more with a slight increase in false positives (for Logistic Regression, results threshold-dependent). Feature contribution estimates and individual prediction explanations indicated clinical relevance. We conclude that machine learning could improve primary care cancer risk-assessment tools, potentially helping clinicians to identify additional cancer cases earlier. This could, in turn, improve survival outcomes.

Sexual Violence Perpetration Against Intimate Partners: Current Progress and Future Directions.

The literature on sexual violence perpetration against an intimate partner is reviewed and synthesized. Intimate partner sexual violence (IPSV) is compared to other forms of interpersonal violence, and the heterogeneity of perpetrators is explored through an examination of proposed taxonomies. This review also addresses the applicability of existing risk assessment tools to IPSV perpetrators and identifies criminogenic needs of particular relevance to IPSV. Recent research suggests the perpetration of IPSV is heterogeneous in nature, and individuals who perpetrate IPSV present with criminogenic needs consistent with exclusively sexual and violent offenders. There is support for existing risk tools to predict recidivism, but they may not encompass all relevant risk factors specific to IPSV offending. Commonality exists with other forms of interpersonal violence; however, the literature indicates that IPSV is complex and does not wholly resemble other sexual or violent offenses. Although further study is needed to fully understand IPSV perpetration, best practices in risk assessment and rehabilitation should employ tools that capture criminogenic needs.

Examining Whether Practical Measures of Neighborhood Characteristics Improve Correctional Risk Assessment Tools

This study examined whether inclusion of a neighborhood domain improved prediction and classification in an existing risk assessment tool. Logistic regression and analysis of covariance (ANCOVA) with random effects were conducted using a sample of individuals under community supervision ( N = 10,548) to determine whether a neighborhood domain improved the predictive validity of the Ohio Risk Assessment System-Community Supervision Tool (ORAS-CST). In five of our six models, inclusion of the neighborhood domain did not significantly improve the predictive validity of the ORAS-CST regardless of whether it was considered as an additive variable or moderator. One model found that the addition of a neighborhood domain improved prediction; however, the relationship was opposite from what was theoretically expected. The findings suggest that individual-level factors remain the most meaningful predictors in correctional risk assessment tools for those under community supervision. Future research is needed on whether neighborhood indicators improve assessment tools for individuals under other forms of supervision.

Outcome prediction of chronic myeloid leukemia (CML) in children.

We evaluated the feasibility of existing risk assessment tools for chronic myeloid leukemia (CML) in children. Fifty-five patients with newly diagnosed CML between 1996 and 2019 were included. Forty-nine patients presented in chronic phase, thirty-six of whom were treated with upfront tyrosine kinase inhibitor (CP-TKI group); one presented in accelerated phase and four in blastic phase. Treatment, survival, responses, and tolerance were evaluated. All patients in the CP-TKI group received imatinib as their first TKI treatment. The 10-year overall survival (OS), progression-free survival (PFS), and event-free survival (EFS) of TKI-treated group was 97%, 91.4%, and 72.3%, respectively. At 60 months, the rates of major molecular response were 81.2% and deep molecular response was 67.5%. The EUTOS long-term survival (ELTS) risk grouping did not predict OS, PFS, or EFS. The IMAFAIL risk groups were correlated with the risk of imatinib failure. Further studies are required to modify the existing risk assessment tools for children.

Network analysis for financial crime risk assessment: the case study of the gambling division in Malta

ABSTRACT The present study aims to support existing risk assessment tools by proposing an innovative network-oriented methodology based on ownership information. The approach involves calculating company-level indicators that are then transformed into red flags and used to rate risk. To this end, we collect data on companies active in the division of gambling and betting activities in Malta, and further combine them with information on enforcement actions imposed on Maltese companies, their beneficial owners, intermediate shareholders and subsidiaries. Correlation analysis and statistical testing were performed to assess the individual relevance of company-level indicators, while machine learning methods were employed to validate the usefulness of the indicators when used collectively. We conclude that the intelligent use of ownership information and proper analysis of ownership networks greatly supports the detection of firms involved in financial crime, hence the recommendation to adopt akin approaches to improve risk assessment strategies.

The Risky-Opportunity Analysis Method (ROAM) to Support Risk-Based Decisions in a Case-Study of Critical Infrastructure Digitization

Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes—Stress and Strain—of the alternatives that are designed to be employed in the new quantitative ROAM approach.

Mapping the Brain to Predict Antisocial Behaviour: New Frontiers in Neurocriminology,‘New’ Challenges for Criminal Justice

Neuroscientific research on the relationship between neurobiology and antisocial behaviour has grown exponentially over the last two decades. One of the most intriguing challenges that has started occupying the minds of scientists and legal scholars is the potential use of neuroscience-based methodology to predict future antisocial behaviour in forensic and justice contexts. While neuroprediction holds the promise of adding predictive value to existing risk assessment tools, its hypothetical use for forensic and justice purposes touches on some specific ethical and legal issues, in particular the threat it poses to offenders’ individual rights and civil liberties under the pretext of enhancing public safety. This article provides some arguments for overcoming these concerns. More importantly, it argues that neuroprediction should be viewed as an instrument to help criminal justice integrate current punitive policies and measures with socio-rehabilitative strategies, which could improve the treatment of offenders at risk without threatening their individual rights.

Risk Assessment—the Key to More Security? Factors, Tools, and Practices in Dealing with Extremist Individuals

In recent years, risk assessments for violent extremism have attracted great interest from both scholars and practitioners, and many assessment tools have been developed. After a critical review of this development, the paper examines differences and similarities between the indicators of various violent extremist risk assessment tools and checklists. Based on an interview study with 34 experts in the field of Counter Violent Extremism and 24 (formerly) radicalized persons from the right-wing radical and Salafi-Jihadist spectrum, risk factors were identified and the findings merged and compared with already existing risk assessment tools. The paper will present results especially regarding the risk signals’ occurrence and applicability in the German context. One key finding is that existing assessment tools insufficiently take into account personal contacts in the radicalization process. Thus, the paper—based on the results of a social network analysis—draws attention to the potential and importance of networks. The paper concludes by outlining the potential of risk assessment, suggesting improvements, and raising awareness of the limits and deficits of these tools. The paper thus scientifically addresses the challenge of more security through efficient risk assessment and management. It offers a list of radicalization process characteristics (ARISNA: Assessment of Radicalized Individuals including Social Network Analysis), which is designed to help users analyze the risk of radicalization based on concrete traits of a person and their environment.

Tools for Assessing Cardiovascular Disease Risk Factors in Underserved Young Adult Populations: A Systematic Review.

Cardiovascular disease (CVD, i.e., disease of the heart and blood vessels) is a major cause of death globally. Current assessment tools use either clinical or non-clinical factors alone or in combination to assess CVD risk. The aim of this review was to critically appraise, compare, and summarize existing non-clinically based tools for assessing CVD risk factors in underserved young adult (18–34-year-old) populations. Two online electronic databases—PubMed and Scopus—were searched to identify existing risk assessment tools, using a combination of CVD-related keywords. The search was limited to articles available in English only and published between January 2008 and January 2019. Of the 10,383 studies initially identified, 67 were eligible. In total, 5 out of the 67 articles assessed CVD risk in underserved young adult populations. A total of 21 distinct CVD risk assessment tools were identified; six of these did not require clinical or laboratory data in their estimation (i.e., non-clinical). The main non-clinically based tools identified were the Heart Disease Fact Questionnaire, the Health Beliefs Related to CVD-Perception measure, the Healthy Eating Opinion Survey, the Perception of Risk of Heart Disease Scale, and the WHO STEPwise approach to chronic disease factor surveillance (i.e., the STEPS instrument).