Ranking Of Risk Factors Research Articles

Ranking Fuzzy Numbers with Fuzzy Analytical Hierarchy in Risk Assessment

Risk in the construction industry is an important factor which must be considered in every decision. To ensure the smooth operation of any project, construction risk factors need to be investigated and assessed. For this reason, the evaluation of the risk factor is determined using expert opinion. Four main risk categories are used in this study, namely risk on project management, engineering risk, implementation risk, and supplier risk. The purpose of this study is to implement a fuzzy analytical hierarchy process (fuzzy AHP), a method that has been receiving increased interest in decision making situations to determine the relative importance of the criteria used for the decision when calibrating the end of each risk factor stage. A paired comparison was employed for subjective judgment made by experts in order to compute the priority weight vector for each risk item. Results show that the risk on supplier carries the highest weight (0.91), followed by the risk of accidents on site (0.58) and less motivation among the project management team (0.55). The final relative weight at the last level of the hierarchy gives a signal to the decision maker of organization of all the possible impacts of the risks revealed. Hence, the application of fuzzy AHP in ranking risk factors has demonstrated better results and more flexibility in human decision making.

Risk and protective factors for childhood suicidality: a US population-based study

Childhood suicidal ideation and behaviours are poorly understood. We examined correlates of suicidality in a US population-based sample of children participating in the Adolescent Brain and Cognitive Development (ABCD) study. The ABCD study aims to examine trajectories of mental health from childhood to adulthood and collects information on multiple domains, including mental and physical wellbeing, brain imaging, behavioural and cognitive characteristics, and social and family environment. We sought to identify and rank risk and protective factors for childhood suicidal thoughts and behaviours across these multiple domains and evaluate their association with self-agreement and caregiver agreement in reporting suicidality. The ABCD sample comprises a cohort of 11 875 children aged 9-10 years. The sociodemographic factors on which the sample was recruited were age, sex, race, socioeconomic status, and urbanicity. Participants were enrolled at 22 sites, the catchment area of which encompassed over 20% of the entire US population in this age group. Multistage sampling was used to ensure both local randomisation and representativeness of sociodemographic variation of the ABCD sample. The data used in this study were accessed from the ABCD Study Curated Annual Release 2.0. Suicidal thoughts and behaviours (suicidality) in each child were evaluated through independent child and caregiver reports based on the computerized Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5 (KSADS-5). We used bootstrapped logistic regression to quantify the association between suicidal ideation and behaviours, with measures of mental and physical wellbeing, behaviour, cognition, and social and family environment in participants from the ABCD study. Our study sample comprised 7994 unrelated children (mean age 9·9 years [SD 0·5]; 4234 [53%] male participants) with complete data on child-reported and caregiver-reported suicidal ideas and behaviours. Overall, 673 (8·4%) children reported any past or current suicidal ideation, 75 (0·9%) had any past or current suicidal plans, and 107 (1·3%) had any past or current suicidal attempts. According to caregivers, 650 (8·1%) of the children reported any past or current suicidal ideation, 46 (0·6%) reported any past or current suicidal plans, and 39 (0·5%) reported past or current suicidal attempts. However, inter-informant agreement was low (Cohen's κ range 0·0-0·2). Regardless of informant, child psychopathology (odds ratio [OR] 1·7-4·8, 95% CI 1·5-7·4) and child-reported family conflict (OR 1·4-1·8, 95% CI 1·1-2·5) were the most robust risk factors for suicidality. The risk of child-reported suicidality increased with higher weekend screen use time (OR 1·3, 95% CI 1·2-1·7) and reduced with greater parental supervision and positive school involvement (for both OR 0·8, 95% CI 0·7-0·9). Additionally, caregiver-reported suicidality was positively associated with caregiver educational level (OR 1·3, 95% CI 1·1-1·5) and male sex in children (1·5, 1·1-2·0), and inversely associated with the number of household cohabitants (0·8, 0·7-1·0). We identified risk and protective factors that show robust and generalisable associations with childhood suicidality. These factors provide actionable targets for optimising prevention and intervention strategies, support the need to identify and treat psychopathology in school-age children, and underscore the importance of school and family interventions for childhood suicidality. National Institutes of Health.

Risk allocation in public construction projects: the case of Jordan

Proper risk identification, assessment, and allocation can reduce cost and time delay. This study identifies, assesses and allocates risks in public construction projects in Jordan with the goal of reducing the overall costly claims and disputes. Based on extensive literature review, the study identifies 62 risk factors that are grouped under 14 categories. The respondents were asked to estimate the probability, impact and proper allocation of these risk factors. Based on 208 valid responses, the results indicate that the top ranked risk factors are delays in client’s payments, poorly tailored contract forms, competition, delay in approval of permits, default by subcontractors, unclear specifications, material prices fluctuations, different construction standards, change in design, and poor design. The respondents of the study recommend 4 risk factors to be shouldered by owners, five risk factors to be shouldered by contractors, and 42 risk factors to be shared by the contracting parties. The results of the study also help practitioners to allocate risks to the stakeholder who is more capable to assesses, controls and manages these risks. The generated risk priority can be used to prioritize the risks for response planning.

Population attributable fractions for continuously distributed exposures

Abstract When estimating population attributable fractions (PAF), it is common to partition a naturally continuous exposure into a categorical risk factor. While prior risk factor categorization can help estimation and interpretation, it can result in underestimation of the disease burden attributable to the exposure as well as biased comparisons across different exposures and risk factors. Here, we propose sensible PAF estimands for continuous exposures under a potential outcomes framework. In contrast to previous approaches, we incorporate estimation of the minimum risk exposure value (MREV) into our procedures. While for exposures such as tobacco usage, a sensible value of the MREV is known, often it is unknown and needs to be estimated. Second, in the setting that the MREV value is an extreme-value of the exposure lying in the distributional tail, we argue that the natural estimator of PAF may be both statistically biased and highly volatile; instead, we consider a family of modified PAFs which include the natural estimate of PAF as a limit. A graphical comparison of this set of modified PAF for differing risk factors may be a better way to rank risk factors as intervention targets, compared to the standard PAF calculation. Finally, we analyse the bias that may ensue from prior risk factor categorization, examining whether categorization is ever a good idea, and suggest interpretations of categorized-estimands within a causal inference setting.

Identifying the Risk Impact on Cost and Time of the Egyptian Non-Residential Buildings Projects

Construction projects in Egypt were typically influenced by multiple risk factors which have various impacts on both cost and time objectives that lead to project cost overruns and time delays. This paper presents the results of an investigation study concerns with the identification and assessment of risks associated with the Egyptian Non-Residential Buildings Projects (ENRBP). The study explores risk factors probability of occurrence and their effects on cost and time of these projects. A field survey was conducted with professionals in the construction projects in EGYPT for a purpose of collecting the required data of the risk factors characteristics. Based on results analysis, several risk factors were identified and categorized into fifteen risk groups as well as they were prioritized based on their effects on cost and time. A high agreement for ranking risk factors among contractors, consultants and owners was acquired. The list of the major risks included many factors such as: dramatic changes in the materials prices and Adopting direct attribution system rather than tendering and bidding systems. The most expected average increase of the cost overruns and time delays was more than 20% of the project planned budget and schedule. Finally, the analysis and findings showed that risk factors were very close in their impacts on both cost and time of ENRBP.

Decision support framework for the risk ranking of agroforestry biomass power generation projects with picture fuzzy information

With the rapid growth of the global population and economy, energy consumption and demad are increasing sharply. As an essential renewable energy, biomass energy can promote the reform of energy production and consumption. Considering the characteristics of long investment cycle and large investment scale of agroforestry biomass power generation (AFBPG) projects, this study establishes a decision support framework for risk ranking of AFBPG project under picture fuzzy environment. The proposed framework considers not only the fuzziness and uncertainty of decision-making problems but also the decision-makers’ (DMs) psychological behavior. First, given the integrity of information representation, DMs provide risk assessment information expressed with picture fuzzy numbers, and then gives the distance of the picture fuzzy set (PFS) to maximize the PFS information. Second, the entropy weight method is used to compute the objective weight. Third, the VIKOR (Vlse Kriterijumska Optimizacija I Kompromisno Resenje) – TODIM (an acronym in Portuguese for an interactive multi-criteria decision making) method is suggested for ranking risk factors, which reflects the behavioral psychology of DMs. Moreover, the proposed evaluation model is successfully applied in a practical case. The results show that the model is valid for ranking risk factors under picture fuzzy environment. Last but not least, comparison and sensitivity analysis are implemented to verify the effectiveness and applicability of the proposed method and some suggestions for practical application are put forward.

A Hybrid Approach for Dynamic Simulation of Safety Risks in Mega Construction Projects

Mega construction projects (MCPs) are inherently high‐risk and complex. The challenge of safety management for mega construction projects is that safety risk factors constantly change and interact with each other in the long‐term construction period. Few of the prior studies have enabled the prediction of the safety state in a dynamic and connected overview, which is a critical characteristic of safety risks in MCPs. Therefore, a hybrid approach for the dynamic simulation of risk factors is proposed. A three‐stage procedure review of explicit documents, including accident investigation reports and construction standards, was carried out to identify safety risk factors and the causal relationships among them. Subsequently, the likelihood exposure and consequence (LEC) assessment method was applied to define the changes in risk factors over time. A system dynamics (SD) model was established to integrate the interacting risks and simulate the developing trend of the overall safety risk state. Moreover, a sensitivity analysis was provided to rank risk factors and simulate optimal risk mitigation strategies. Finally, the model was applied to the urban rail transit Line 9 project in China as a case study. The results indicated that the proposed hybrid approach performed satisfactorily under complex interrelated risk factors. Therefore, this study provides a practical framework to simulate and predict the safety state dynamically in a timely process for MCPs, either ahead of a project theoretically or during a project with real data.

Determining critical risk factors affecting public‐private partnership waste‐to‐energy incineration projects in China

AbstractAs the most effective way to manage the surge in municipal solid waste (MSW) and global energy issues, waste‐to‐energy (WTE) incineration plants have been actively promoted by the Chinese government, and the public‐private partnership (PPP) procurement model is widely used to improve management efficiency. However, risk events have occurred frequently in this industry due to a lack of practice experience and to poor risk management. To investigate the potential risk factors in PPP WTE incineration projects in China, this paper aims to identify critical risk factors by assessing their severity and occurrence probability. Opinions of industry experts were collected via an empirical questionnaire survey. The respondents were invited to rate a total of 18 risk factors that impact the success of PPP WTE projects as gleaned from previous research. The overall results ranked risk factors based on risk impact and showed that public opposition, government decision making, defective legal and regulatory systems, environmental pollution, lack of supporting infrastructure, and government credit are the top six risk factors affecting the sustainable development of PPP WTE incineration projects. In addition, the causes and consequences of each critical risk factor are comprehensively analyzed. Lastly, practical and managerial implications are analyzed in terms of their management of these critical risks. The originality of this research lies in thoroughly identifying potential implicit risk factors according to the current social background using an empirical questionnaire survey based on case study findings. The results of this study are expected to improve risk management and facilitate the sustainable development of the PPP WTE incineration industry in China.

A Hybrid Fuzzy TOPSIS – Best Worst Method for Risk Prioritization in Megaprojects

Megaprojects are usually complex and in many cases encounter failure in terms of finish late or overspent. This study aims to investigate the critical risk factors behind these projects as well as their priority. Project risk management is a mature research stream. But when focus on megaprojects the amount of research decreases significantly. This research provides a hierarchy of risk structure in Tehran-Rasht railway megaproject and prioritizes the risk factors through a two-phase methodology. This method is a new hybrid MCDM technique consist of group fuzzy TOPSIS and fuzzy Best-Worst Method. BWM is the latest MCDM technique which in this paper, its fuzzy version combined with fuzzy TOPSIS is employed. This research also considers all the project success criteria including time, cost and quality simultaneously and calculates the risk priority Index (RPI) accordingly. The results imply that quality is the most important project success factor and the risk elements with greater impact on project quality, get higher PRI. The identified and ranked risk factors help practitioners and academics to follow the subsequent steps of the risk management process of Iranian transportation megaprojects.

A Goal-driven Risk Management approach for Distributed Agile Development Projects

Software companies are now using Distributed Agile Development (DAD) in order to create high quality solutions, which aligns with the business priorities of lesser time and cost. Although, DAD is beneficial, there are significant risks involved in such projects. In order to minimize the adverse effect of risks on DAD projects, it is imperative to understand, how the risks impact the project performance goals including ‘Time’, ‘Cost’ and ‘Quality’. In this paper we present a goal driven approach for managing risks in DAD projects. This approach of risk management will enable project managers to identify the most important risks with respect to the goal to be achieved and focus on managing those risks first. The study shows that if ‘Time’ is a considered goal for a DAD project, the most important risks that would need consideration are related to requirement management, architecture changes and coordination issues between stakeholders. Similarly, if ‘Quality’ is the primary performance goal in a DAD project, it would be necessary to first deal with risks related to internal and external communication in the organization, team collaboration and requirement documentation availability. The awareness of top ranking risk factors that impact a particular project goal will assist the projects managers to control the risks in a way that the desired project goals can be achieved.

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender

ABSTRACT This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk factors of acute respiratory diseases were identified in Beijing, China. We conducted age and gender stratified deep neural network models in air pollution epidemiology. We ranked risk factors of respiratory diseases in stratified populations and conducted quantitative comparison. People ≥50 years were more sensitive to PM2.5 pollution than <50 years people, especially women ≥50 years. Compared with women, both men ≥50 years and <50 years were more susceptible to PM10. Young women <50 years were more sensitive to general air pollutants such as SO2 and NO2 than <50 years young men. Meteorological factors such as wind speed and precipitation could promote the diffusion of fine particulate matter and general air pollutants (SO2, NO2, etc.), which could help to reduce the incidence of acute respiratory diseases. This study represents a quantitative analysis of environmental health risk factors identification related to acute respiratory diseases based on deep neural network method. The results of this study could help people to improve their awareness of acute respiratory diseases prevention.

Risk Analysis: Fuzzy Cognitive Maps Vs Fault Trees

Restrictions of the method of fault trees in risk analysis problems are analyzed. As an alternative to this method, we consider the possibility to apply fuzzy cognitive maps; this new modelling technique is not sufficiently propagated in the reliability theory. Based on fuzzy cognitive maps, we propose a method to rank risk factors and illustrate it by an example of a man-machine system.

SpillOver: A new tool for ranking the risk of viral spillover to humans using big data

SpillOver: A new tool for ranking the risk of viral spillover to humans using big data

An Evaluation of Risk Factors and their Impact on Road Project

Construction projects are generally known to be susceptible to risks. This results in an inability to meet performance targets. This paper assessed the risk factors encountered in the delivery of road projects in Osun State, Nigeria. A questionnaire survey was used to obtain data from all the 146 construction professionals involved in the construction of the 34 road projects executed in the study area. The professionals comprised of 34 Consultant Civil Engineers, 34 Consultants’ Civil Engineers, 34 Consultant Quantity Surveyors, 34 Consultants’ Quantity Surveyors, one project financier and 9 Project Managers, making a total of 146 respondents. Using two-dimensional scaling, the respondents were asked to provide opinions on risk occurrence and their impacts on road projects. The data collected were analysed using mean ranking analysis and student t-test. A further analysis was carried out using factor analysis to reduce the factors impacting road projects into fewer factor components. The results revealed that the risk factors with high extent of occurrence are in the order of change in scope of work, defective design, error and re-work, change in design, delay in availability of design details and unforeseen adverse conditions. Surprisingly, the top ranking risk factors are not the ones with high impact based on the result of the analysis. Using factor analysis, 9 component factors regarding the extent of risk occurrence were obtained and they accounted for 73.73% of the variance explained while 4- component factors were obtained regarding the impact of risk occurrence and they accounted for 46.16% of the variance explained. The study concluded that professionals involved in the delivery of road projects need to be aware of the significant risk factors impacting road projects and put proactive risk management approach in place to deal with them to avoid surprises.Keywords: Performance, Projects, Risk factors, Risk impact, Road.

Voluntary Disclosure Through the Ranking of Risk Factors in the 10-K

In this study, we examine whether managers rank risk factors and list them in order of their importance in Item 1A of the 10-K. We focus on firms’ credit risk disclosures and where they are positioned in Item 1A. Firms that place the credit risk factor closer to the beginning have lower credit ratings and higher bond spreads. They are also more likely to experience future rating downgrades and bankruptcies. Risk factor position, or rank, is a more robust measure of risk than disclosure characteristics examined in prior studies. We also use firms’ proximity to the investment grade threshold as an exogenous disclosure cost measure and find that firms with ratings near the threshold are more likely to move their credit risk factor to a less prominent position. Our study provides evidence on firms’ voluntary risk disclosure behavior and informs the SEC debate on whether to require risk factor ordering.

On exploring and ranking risk factors of child malnutrition in Bangladesh using multiple classification analysis

BackgroundLogistic regression analysis is widely used to explore the determinants of child malnutrition status mainly for nominal response variable and non-linear relationship of interval-scale anthropometric measure with nominal-scale predictors. Multiple classification analysis relaxes the linearity assumption and additionally prioritizes the predictors. Main objective of the study is to show how does multiple classification analysis perform like linear and logistic regression analyses for exploring and ranking the determinants of child malnutrition.MethodsAnthropometric data of under-5 children are extracted from the 2011 Bangladesh Demographic and Health Survey. The analysis is carried out considering several socio-economic, demographic and environmental explanatory variables. The Height-for-age Z-score is used as the anthropometric measure from which malnutrition status (stunting: below −2.0 Z-score) is identified.ResultsThe fitted multiple classification analysis models show similar results as linear and logistic models. Children age, birth weight and birth interval; mother’s education and nutrition status; household economic status and family size; residential place and regional settings are observed as the significant predictors of both Height-for-age Z-score and stunting. Child, household, and mother level variables have been ranked as the first three significant groups of predictors by multiple classification analysis.ConclusionsDetecting and ranking the determinants of child malnutrition through Multiple classification analysis might help the policy makers in priority-based decision-making.Trial registration“Retrospectively registered”

Using Multi Criteria Evaluation in Forest resource conservation in Ghana: Spatially identifying vulnerable areas

The importance of forest resources to the socio-economic development of countries cannot be over-emphasized. A lot of research has been conducted in areas relating to forest management and protection in Sub-Saharan African. This paper discussed and illustrated the use of Spatial Multicriteria Decision Support System (SMDSS) to identify factors that make forest and game reserves vulnerable to rampant human induced depletion of forest resources by assessing risk factors of slope, roads and settlement data in Ghana. The model structure is aimed at understanding the critical vulnerable factors that threatens the survival of forest resources to deforestation, and draws on data from the Forestry Commission, CERSGIS and the Ghana Statistical Service. This paper incorporates multiple criteria and rank risk factors. In the end spatial maps are produced that identifies vulnerable areas for each ecological zone, so that specific policy interventions can be targeted by government to safeguard same. The research attempts to propose technology in managing scarce forest resources through the use of GIS techniques. It contributes to the discourse on forest management, ecological mapping and inventory of forest resources in Ghana. It provides an information base to tackle the threat of deforestation on a location by location basis. We conclude that spatial information is critical to forest resource management although spatial data is virtually unavailable in most developing countries. There is therefore the need by government to ensure acquisition and access to spatial data if forest resources are to be managed effectively.Keywords—Forest, Conservation, Vulnerability, GIS and RS, SMDSS, Ghana

Risk assessment of bridges under multiple hazards in operation period

Over the past several decades, the number of disasters around the world has been amplified. Moreover, the impacts of disasters on communities have attracted extra attentions, requiring efficient measures of disaster management. Similarly, safety problems of bridges have become public topics since bridges represent essential parts of highway infrastructure that have been exposed to multiple hazards during their life cycle. In this paper, a novel framework of disaster risk assessment is proposed, by combining Fuzzy Analytical Hierarchy Process (FAHP) with fuzzy knowledge representation and fuzzy logic techniques into a single integrated approach. A survey about collapsed bridges in the past few decades has been conducted. From this investigation the potential hazards have been identified. The FAHP approach is applied to ranking risk factors since it is more systematic, accurate and effective than traditional AHP. Risk indicators, the probability of occurrence, the impacts of disasters and disaster consequences are analyzed from expert opinions. The bridge risk is computed by implementing fuzzy logic tools on the risk factors and their parameters. The proposed method is practical and efficient for a quick and reliable multi-hazards risk analysis and assessment of bridges. Based on the level of disaster risks to which the bridge is exposed, an appropriate bridge safety management plan is required to reduce or prevent the effects of the disasters. A case study is examined for a bridge in Tianjin, People’s Republic of China. The results illustrate the applicability of the proposed risk assessment method of bridges under multiple hazards.

A priority health index identifies the top six priority risk and related factors for non-communicable diseases in Brazilian cities.

BackgroundIn Brazil, 72% of all deaths in 2007 were attributable to non-communicable diseases (NCD). We used a risk and related factor based index to prioritize NCD prevention programs in the combined 26 capital cities and the federal district (i.e., Brasilia) of Brazil.MethodsWe used 2006–2011 data (adults) from census and Brazil's surveillance of 12 NCD risk factors and 74 disease group mortality. The risk and related factors were: smoking, physical inactivity, overweight-obesity, low fruits and vegetables intake, binge drinking, insufficient Pap smear screening (women aged 25 to 59 years), insufficient mammography screening (women aged 50 to 69 years), insufficient blood pressure screening, insufficient blood glucose screening, diagnosis of hypercholesterolemia, diagnosis of hypertension and diagnosis of diabetes. We generated six indicators: intervention reduction of the risk factor prevalence, intervention cost per person, prevalence of risk factor, deaths attributable to risk factor, risk factor prevalence trend and ratio of risk factor prevalence between people with and without a high school education. We transformed risk and related factor indicators into priority scores to compute a priority health index (PHI). We implemented sensitivity analysis of PHI by computing it with slightly altered formulas and altering values of indicators under the assumption of bias in their estimation. We ranked risk factors based on PHI values.ResultsWe found one intermediate (i.e., overweight-obesity) and six top risk and related factors priorities for NCD prevention in Brazil's large urban areas: diagnosed hypertension, physical inactivity, blood pressure screening, diagnosed hypercholesterolemia, smoking and binge drinking.ConclusionBrazil has already prioritized the six top priorities (i.e., hypertension, physical inactivity, blood pressure screening, hypercholesterolemia, smoking and binge drinking) and one intermediate priority (i.e., overweight-obesity) for NCD prevention identified in this report. Because effective interventions to reduce disease burden associated with each of the six priority risk factors are available, strategies based on these interventions need to be sustained in order to reduce NCD burden in Brazil. PHI can be used to track NCD prevention and health promotion actions at the local and national level in Brazil and in countries with similar public health surveillance systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-1787-1) contains supplementary material, which is available to authorized users.

Non-specific low back pain: occupational or lifestyle consequences?

Nursing occupation was identified as a risk occupation for the development of low back pain (LBP). The aim of our study was to find out how much occupational factors influence the development of LBP in hospital nursing personnel. Non-experimental approach with a cross-sectional survey and statistical analysis. Nine hundred questionnaires were distributed among nursing personnel, 663 were returned and 659 (73.2 %) were considered for the analysis. Univariate and multivariate statistics for LBP risk was calculated by the binary logistic regression. The χ(2), influence factor, 95 % confidence interval and P value were calculated. Multivariate binary logistic regression was calculated by the Wald method to omit insignificant variables. Not performing exercises represented the highest risk for the development of LBP (OR 2.8, 95 % CI 1.7-4.4; p < 0.001). The second and third ranked risk factors were frequent manual lifting > 10 kg (OR 2.4, 95 % CI 1.5-3.8; p < 0.001) and duration of employment ≥ 19 years (OR 2.4, 95 % CI 1.6-3.7; p < 0.001). The fourth ranked risk factor was better physical condition by frequent recreation and sports, which reduced the risk for the development of LBP (OR 0.4, 95 % CI 0.3-0.7; p = 0.001). Work with the computer ≥ 2 h per day as last significant risk factor also reduced the risk for the development of LBP (OR 0.6, 95 % CI 0.4-0.1; p = 0.049). Risk factors for LBP established in our study (exercises, duration of employment, frequent manual lifting, recreation and sports and work with the computer) are not specifically linked to the working environment of the nursing personnel. Rather than focusing on mechanical causes and direct workload in the development of non-specific LBP, the complex approach to LBP including genetics, psychosocial environment, lifestyle and quality of life is coming more to the fore.