Prioritize Risk Factors Research Articles

Health Associated Social Care Professionals on a Toddler Protection Referral of an unborn baby: A Short Note

Midwives and children’s services social employees have responsibilities to spot and support pregnant girls wherever there are child protection considerations. Professionals obtain to anticipate the chance of damage and initiate interventions to produce support to families. there's very little analysis on however professionals prioritize risk factors and also the challenges they face in protective unborn babies.

Identifying and prioritizing risk factors involved in motorcyclists' traffic accidents in Tehran

Identifying and prioritizing risk factors involved in motorcyclists' traffic accidents in Tehran

Picture Fuzzy MCDM Approach for Risk Assessment of Railway Infrastructure

To increase the level of safety and prevent significant accidents, it is essential to prioritize risk factors and assess railway infrastructure. The key question is how to identify unsafe railway infrastructure so authorities can undertake safety improvement projects on time. The paper aims to introduce a picture fuzzy group multi-criteria decision-making approach for risk assessment of railway infrastructure. Firstly, picture fuzzy sets are employed for representing and handling risk-related information. Secondly, a picture fuzzy hybrid method based on the direct rating, and Tsallis–Havrda–Charvát entropy is provided to prioritize risk factors. Thirdly, a picture fuzzy measurement of alternatives and ranking according to compromise solution method is developed to rank railway infrastructures. Lastly, the formulated approach is implemented in the Czech Republic context. Two sensitivity analyses verified the high robustness of the formulated approach. The comparative analysis with five state-of-the-art picture fuzzy approaches approved its high reliability. Compared to the state-of-the-art picture fuzzy approaches, the provided three-parametric approach has superior flexibility.

Contribution of AHP method in prioritizing risk factors regarding tunnel project phases

Tunnel projects are consistently regarded to be mostly a complex and risky engineering projects, owing to the features of the projects, since they increasingly achieve high risk and complexity during the execution of the project. this led to the fact that the necessity of risk management understanding and implementing is a crucial quest with the intention to obtain tunnel project objectives considering (time, cost, quality & safety) to avoid time and cost overruns and any other obstacles. it was observed the most affecting objective was Safety (47.3%) by AHP method in which it is actually reasonable as such project of tunnel need a great concern regarding that matter for many reasons for instance the closed construction area, ventilation considerations, blasting, and so on. the second major objective is quality (31.1%) in which as experts always say it has to be maintained as a high quality as possible.

Neutrosophic DEMATEL to Prioritize Risk Factors in Teenage Pregnancy

Neutrosophic DEMATEL to Prioritize Risk Factors in Teenage Pregnancy

Neutrosophic DEMATEL to Prioritize Risk Factors in Teenage Pregnancy

Care during pregnancy is a task of high importance for the health system. In Ecuador, special importance has been given to pregnant teenagers. However, there are various factors that influence pregnancy and in many cases it is not possible to prioritize their care. This research aims to develop a model to prioritize the factors that influence teenage pregnancy. The model operates using the neutrosophic DEMATEL method for the study of cause and effect in complex situations. The method implements as a principle the modeling of neutrality using neutrosophic numbers and allows evaluation through the use of linguistic terms, which is a natural form of evaluation for humans.

Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease.

To elucidate the genetics of coronary artery disease (CAD) in the Japanese population, we conducted a large-scale genome-wide association study of 168,228 individuals of Japanese ancestry (25,892 cases and 142,336 controls) with genotype imputation using a newly developed reference panel of Japanese haplotypes including 1,781 CAD cases and 2,636 controls. We detected eight new susceptibility loci and Japanese-specific rare variants contributing to disease severity and increased cardiovascular mortality. We then conducted a trans-ancestry meta-analysis and discovered 35 additional new loci. Using the meta-analysis results, we derived a polygenic risk score (PRS) for CAD, which outperformed those derived from either Japanese or European genome-wide association studies. The PRS prioritized risk factors among various clinical parameters and segregated individuals with increased risk of long-term cardiovascular mortality. Our data improve the clinical characterization of CAD genetics and suggest the utility of trans-ancestry meta-analysis for PRS derivation in non-European populations.

Musculoskeletal Consequences in Cyber-Addicted Students - Is It Really A Matter of Health? A ROC Curve Analysis for Prioritizing Risk Factors.

Background: The high prevalence and also multiple consequences of addiction to various online content, including online games and social networks, have become a major challenge. The ability to predict musculoskeletal disorders from this addiction can help reveal in students' health status in the near future. The aim of this study was to determine the prevalence of this addiction and the ability to predict neck pain from this matter in students. Study Design: A cross sectional study. Methods: This study was carried out among 665 students. Data collection was performed through three questionnaires on problematic use of online games, social networking addiction, and Nordic musculoskeletal disorders. Data were compared with Chi-square and independent T-test, and the logistic regression model was then presented at a significant level of 0.05. Finally, the area under the receiver operating characteristic (ROC) curve and Discriminant analysis were conducted to clarify associations. Results: The prevalence of Internet-related content addiction was 32.8%. The results showed that addiction to social networks and online games can increase the risk of neck disorder. Also 0.58 area under ROC curve depicted the ability to predict neck pain from this addiction. Conclusions: In students with internet - related content addiction, neck disorder can be predicted. Given the high prevalence of internet addiction in students, it is essential to take immediate and appropriate interventions to avoid the associated adverse effects such as neck problems.

Context-Specific Coordinately Regulatory Network Prioritize Breast Cancer Genetic Risk Factors.

Breast cancer (BC) is one of the most common tumors, leading the causes of cancer death in women. However, the pathogenesis of BC still remains unclear, and the atlas of BC-associated risk factors is far from complete. In this study, we constructed a BC-specific coordinately regulatory network (CRN) to prioritize potential BC-associated protein-coding genes (PCGs) and non-coding RNAs (ncRNAs). We integrated 813 BC sample transcriptome data from The Cancer Genome Atlas (TCGA) and eight types of regulatory relationships to construct BC-specific CRN, including 387 transcription factors (TFs), 174 microRNAs (miRNAs), 407 long non-coding RNAs (lncRNAs), and 905 PCGs. After that, the random walk with restart (RWR) method was performed on the CRN by using the known BC-associated factors as seeds, and potential BC-associated risk factors were prioritized. The leave-one-out cross-validation (LOOCV) was utilized on the BC-specific CRN and achieved an area under the curve (AUC) of 0.92. The performances of common CRN, common protein–protein interaction (PPI) network, and BC-specific PPI network were also evaluated, demonstrating that the context-specific CRN prioritizes BC risk factors. Functional analysis for the top 100-ranked risk factors in the candidate list revealed that these factors were significantly enriched in cancer-related functions and had significant semantic similarity with BC-related gene ontology (GO) terms. Differential expression analysis and survival analysis proved that the prioritized risk factors significantly associated with BC progression and prognosis. In total, we provided a computational method to predict reliable BC-associated risk factors, which would help improve the understanding of the pathology of BC and benefit disease diagnosis and prognosis.

High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease.

BackgroundGenetic variants can be used to prioritize risk factors as potential therapeutic targets via Mendelian randomization (MR). An agnostic statistical framework using Bayesian model averaging (MR-BMA) can disentangle the causal role of correlated risk factors with shared genetic predictors. Here, our objective is to identify lipoprotein measures as mediators between lipid-associated genetic variants and coronary artery disease (CAD) for the purpose of detecting therapeutic targets for CAD.MethodsAs risk factors we consider 30 lipoprotein measures and metabolites derived from a high-throughput metabolomics study including 24 925 participants. We fit multivariable MR models of genetic associations with CAD estimated in 453 595 participants (including 113 937 cases) regressed on genetic associations with the risk factors. MR-BMA assigns to each combination of risk factors a model score quantifying how well the genetic associations with CAD are explained. Risk factors are ranked by their marginal score and selected using false-discovery rate (FDR) criteria. We perform supplementary and sensitivity analyses varying the dataset for genetic associations with CAD.ResultsIn the main analysis, the top combination of risk factors ranked by the model score contains apolipoprotein B (ApoB) only. ApoB is also the highest ranked risk factor with respect to the marginal score (FDR <0.005). Additionally, ApoB is selected in all sensitivity analyses. No other measure of cholesterol or triglyceride is consistently selected otherwise.ConclusionsOur agnostic genetic investigation prioritizes ApoB across all datasets considered, suggesting that ApoB, representing the total number of hepatic-derived lipoprotein particles, is the primary lipid determinant of CAD.

Design and construction risks in BOT type mega transportation projects

Purpose In build-operate-transfer (BOT) transportation projects, design and construction phases are critical in terms of their effect on time and cost overruns. The purpose of this paper is to identify the role of risk factors affecting these phases and their significance level for BOT transportation projects. Design/methodology/approach Design and construction risks were determined and then validated by focus group discussions. Afterwards, an illustrated case study was presented to better understand the effects of determined risks in a BOT mega transportation project. As the last step of the study, the fuzzy analytical hierarchy process method was used to prioritize risk factors. Findings The prominent risk factors were found out as occupational accidents, integration between design and construction phases and excessive design variations. Research limitations/implications Different kinds of BOT transportation projects in different countries might be executed very differently considering specific social, political, economic and other factors. However, the results of the study are important in terms of the specific lessons learned from the case study that can be used as a foundation for developing possible risk mitigation measures. Originality/value Though the risk management of BOT projects has been investigated frequently in the literature, there is a knowledge gap in the quantitative evaluation of risk significance specific to design and construction risks. The prioritization of determined risks with an associated case from a mega transportation project will contribute to the BOT project practitioners about possible challenges in design and construction phases in BOT mega transportation projects.

Prioritizing Key Supply Chain Risks Using the Risk Assessment Matrix and Shannon Fuzzy Entropy — with a Case Study in the Home Appliance Industry

The aim of this study is to present an approach for identifying and prioritizing risk factors of supply chain in the home appliance industry. First, the indexes related to the supply chain risk have been determined by literature review; and then the indexes have been refined by consulting experts; finally, the six main indexes including supplier, manufacturer, customer, environment, distributor and information as well as 32 subsidiary indexes for supply chain risks have been selected. In order to assess the indexes, a fuzzy questionnaire has been developed and distributed to 15 managers and employees of Snowa as one of the Entekhab Industrial Group corporate brands, the main home appliance manufacturing company in Iran. Research population included managers and experts of the company and the analysis approaches included risk-assessment matrix and Shannon fuzzy entropy. Findings indicated that environment, manufacturer and supplier indexes with the weights of 0.105, 0.102 and 0.095, respectively were prioritized as the top three risk factors in product development. Furthermore, the subsidiary indexes of raw material alteration, delay in supply of the demand, work force, after-sale services, competitors and lack of political stability were among the top risk factors of new product development.

Application of logical analysis of data to machinery-related accident prevention based on scarce data

This paper deals with the application of Logical Analysis of Data (LAD) to machinery-related occupational accidents, using belt-conveyor-related accidents as an example. LAD is a pattern recognition and classification approach. It exploits the advancement in information technology and computational power in order to characterize the phenomenon under study. The application of LAD to machinery-related accident prevention is innovative. Ideally, accidents do not occur regularly, and as a result, companies have little data about them. The first objective of this paper is to demonstrate the feasibility of using LAD as an algorithm to characterize a small sample of machinery-related accidents with an adequate average classification accuracy. The second is to show that LAD can be used for prevention of machinery-related accidents. The results indicate that LAD is able to characterize different types of accidents with an average classification accuracy of 72–74%, which is satisfactory when compared with other studies dealing with large amounts of data where such a level of accuracy is considered adequate. The paper shows that the quantitative information provided by LAD about the patterns generated can be used as a logical way to prioritize risk factors. This prioritization helps safety practitioners make decisions regarding safety measures for machines.

Evaluation of Lighting Performance Risk Using Surrogate Model and EnergyPlus

In building retrofit projects, numerous sources of uncertainty about building performance need to be addressed. Deterministic simulations are usually used to estimate the potential performance difference from a baseline building. This approach needs building model calibration. Varying uncertain building parameters could lead to orders of magnitude simulation times. For example, in a lighting retrofit project, uncertainty (e.g., lamp input wattage, and varying weather conditions may negatively affect model calibration and lighting energy savings, which increases the chance of default on performance contract. Therefore, the aim of this paper is to develop a method that can conduct risk analysis based on building model (e.g., building information modelling). A stochastic occupancy model is used to simulate the occupancy pattern. Two lighting control strategies (occupancy and daylight controls) and two luminaire types (LED and fluorescent) are considered. Historical weather data is analyzed using statistical analysis, and the extreme weather conditions are generated to evaluate the impact of weather conditions on the lighting and HVAC energy usage. Then, EnergyPlus is used to simulate the energy usage based on different lamp types, control strategies, occupancy patterns, and weather conditions. A surrogate model is developed by using a small sample size of simulation data to construct an approximation surface to enable fast computing time. This method can evaluate impact of uncertainty of risk factors (e.g., occupancy level, luminaire type, weather) on lighting and HVAC energy usage and lighting electricity demand. This method can also prioritize risk factors based on sensitivity analysis on building performance and help users to choose different lamp types to minimize risks.

Identifying and Analyzing Risks and Responses for Risk Management in Information Technology Outsourcing Projects Under Fuzzy Environment

Due to ever-increasing trend in outsourcing information technology projects in today's competitive world, the risk management in information technology outsourcing (ITO) projects is a challenging issue. Hence, this paper reviews and extracts present corresponding risks by literature review to implement risk management in ITO. After reviewing a number of frameworks in the literatures related to prioritizing of extracted risk factors, a new framework is presented to determine the priority of them. Because of network structure of the proposed framework and multi-dimensional nature of the project risk, the fuzzy analytic network process (fuzzy ANP) is applied to prioritize risk factors. Also, since identifying and prioritizing of risk factors cannot necessarily meet the organization's needs related to the project risk, the ways to respond to these factors are evaluated. For this purpose, responses to the five highest ranked risk factors are considered. Prioritization of responses to these risk factors is done by applying fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) based on four criteria: quality, cost, time, and scope. Results, achieved from experts' judgment, show that the risk factor "Supplier's lack of expertise with an IT operation" is the most significant. Also, the best response for this factor, is "Review of monetary value and volume of suppliers' contracts prior to their selection" according to experts' point of view. In addition, a sensitivity analysis is carried out for validating the results.

Risk Factors of Pre-Eclampsia/Eclampsia and Its Adverse Outcomes in Low- and Middle-Income Countries: A WHO Secondary Analysis

BackgroundPre-eclampsia has an immense adverse impact on maternal and perinatal health especially in low- and middle-income settings. We aimed to estimate the associations between pre-eclampsia/eclampsia and its risk factors, and adverse maternal and perinatal outcomes.MethodsWe performed a secondary analysis of the WHO Global Survey on Maternal and Perinatal Health. The survey was a multi-country, facility-based cross-sectional study. A global sample consisting of 24 countries from three regions and 373 health facilities was obtained via a stratified multi-stage cluster sampling design. Maternal and offspring data were extracted from records using standardized questionnaires. Multi-level logistic regression modelling was conducted with random effects at the individual, facility and country levels.ResultsData for 276,388 mothers and their infants was analysed. The prevalence of pre-eclampsia/eclampsia in the study population was 10,754 (4%). At the individual level, sociodemographic characteristics of maternal age ≥30 years and low educational attainment were significantly associated with higher risk of pre-eclampsia/eclampsia. As for clinical and obstetric variables, high body mass index (BMI), nulliparity (AOR: 2.04; 95%CI 1.92–2.16), absence of antenatal care (AOR: 1.41; 95%CI 1.26–1.57), chronic hypertension (AOR: 7.75; 95%CI 6.77–8.87), gestational diabetes (AOR: 2.00; 95%CI 1.63–2.45), cardiac or renal disease (AOR: 2.38; 95%CI 1.86–3.05), pyelonephritis or urinary tract infection (AOR: 1.13; 95%CI 1.03–1.24) and severe anemia (AOR: 2.98; 95%CI 2.47–3.61) were found to be significant risk factors, while having >8 visits of antenatal care was protective (AOR: 0.90; 95%CI 0.83–0.98). Pre-eclampsia/eclampsia was found to be a significant risk factor for maternal death, perinatal death, preterm birth and low birthweight.ConclusionChronic hypertension, obesity and severe anemia were the highest risk factors of preeclampsia/eclampsia. Implementation of effective interventions prioritizing risk factors, provision of quality health services during pre-pregnancy and during pregnancy for joint efforts in the areas of maternal health are recommended.

Systematic evaluation of environmental and behavioural factors associated with all-cause mortality in the United States National Health and Nutrition Examination Survey

Environmental and behavioural factors are thought to contribute to all-cause mortality. Here, we develop a method to systematically screen and validate the potential independent contributions to all-cause mortality of 249 environmental and behavioural factors in the National Health and Nutrition Examination Survey (NHANES). We used Cox proportional hazards regression to associate 249 factors with all-cause mortality while adjusting for sociodemographic factors on data in the 1999-2000 and 2001-02 surveys (median 5.5 follow-up years). We controlled for multiple comparisons with the false discovery rate (FDR) and validated significant findings in the 2003-04 survey (median 2.8 follow-up years). We selected 249 factors from a set of all possible factors based on their presence in both the 1999-2002 and 2003-04 surveys and linkage with at least 20 deceased participants. We evaluated the correlation pattern of validated factors and built a multivariable model to identify their independent contribution to mortality. We identified seven environmental and behavioural factors associated with all-cause mortality, including serum and urinary cadmium, serum lycopene levels, smoking (3-level factor) and physical activity. In a multivariable model, only physical activity, past smoking, smoking in participant's home and lycopene were independently associated with mortality. These three factors explained 2.1% of the variance of all-cause mortality after adjusting for demographic and socio-economic factors. Our association study suggests that, of the set of 249 factors in NHANES, physical activity, smoking, serum lycopene and serum/urinary cadmium are associated with all-cause mortality as identified in previous studies and after controlling for multiple hypotheses and validation in an independent survey. Whereas other NHANES factors may be associated with mortality, they may require larger cohorts with longer time of follow-up to detect. It is possible to use a systematic association study to prioritize risk factors for further investigation.

Community Epidemiology of Risk and Adolescent Substance Use: Practical Questions for Enhancing Prevention

To promote an effective approach to prevention, the community diagnosis model helps communities systematically assess and prioritize risk factors to guide the selection of preventive interventions. This increasingly widely used model relies primarily on individual-level research that links risk and protective factors to substance use outcomes. I discuss common assumptions in the translation of such research concerning the definition of risk factor elevation; the equivalence, independence, and stability of relations between risk factors and problem behaviors; and community differences in risk factors and risk factor-problem behavior relations. Exploring these assumptions could improve understanding of the relations of risk factors and substance use within and across communities and enhance the efficacy of the community diagnosis model. This approach can also be applied to other areas of public health where individual and community levels of risk and outcomes intersect.

Prioritizing risk factors to identify preventive interventions for economic assessment.

To explore a risk factor approach for identifying preventive interventions that require more in-depth economic assessment, including cost-effectiveness analyses. A three-step approach was employed to: (i)identify the risk factors that contribute most substantially to disability-adjusted life years (DALYs); (ii)re-rank these risk factors based on the availability of effective preventive interventions warranting further cost-effectiveness analysis (and in some instances on evidence from existing cost-effectiveness analyses); and (iii)re-rank these risk factors in accordance with their relative contribution to health inequalities. Health inequalities between the Māori and non-Māori populations in New Zealand were used by way of illustration. Seven of the top 10 risk factors prioritized for research on preventive interventions in New Zealand were also among the 10 risk factors most highly ranked as contributing to DALYs in high-income countries of the World Health Organization's Western Pacific Region. The final list of priority risk factors included tobacco use; alcohol use; high blood pressure; high blood cholesterol; overweight/obesity, and physical inactivity. All of these factors contributed to health inequalities. Effective interventions for preventing all of them are available, and for each risk factor there is at least one documented cost-saving preventive intervention. The straightforward approach to prioritizing risk factors described in this paper may be applicable in many countries, and even in those countries that lack the capacity to perform additional cost-effectiveness analyses, this approach will still make it possible to determine which cost-effective interventions should be implemented in the short run.

Evaluation of Risk Factors in Agriculture: An Application of the Analytical Hierarchical Process (AHP) Methodology

Risk in the agricultural sector has multiple dimensions or factors and prioritization of these can support decision making. On the other hand, knowing the importance of these risk factors for distinct agricultural activities and how they vary according to geographic zone constitutes relevant information for agricultural development. The objective of this study was to prioritize risk factors that are highly relevant for farmers in Central South Chile. The multicriteria Analytical Hierarchical Process (AHP) methodology was used to define a decision structure with four risk factors or criteria: climate, price and direct cost variability, human factor, and commercialization. In general, results obtained showed that there are no important imbalances in the weightings of different risk factors. Price and cost variability was the most important factor (0.30) whereas climate was the least important (0.20). It also confirmed that there are spatial differences in the weightings obtained for the distinct risk factors which determine distinct risk levels for the respective agricultural activities according to geographic region.