The importance of cross‐disciplinary scientific engagement in the development of quantitative procedures for aggregate safety assessments

Recent guidance on safety monitoring during drug development, issued by regulatory authorities in the United States and European Union, indicate a shift in focus towards aggregate safety monitoring and scientific evaluation of integrated safety data. The call for program‐level reviews of accumulating safety data, including from ongoing studies, provides an opportunity to leverage the scientific expertise and medical judgment of safety management teams with (a) a multidisciplinary approach, (b) quantitative frameworks to measure level of evidence, and (c) assessments that are product‐specific and driven by medical judgment. A multidisciplinary team, regularly reviewing aggregate safety data throughout the development program, is vital not only for early signal detection but also for generating a better understanding of the accumulating data and context needed for decreasing false alarms.     

新时代教师安全素质培养体系的构建

在倡导"生命至上、安全第一"的新时代，安全素质已然成为教师专业素质结构中的重要组成部分。但目前我国中小学教师安全素质水平较低，其主要原因在于校内安全教育的低效与共性化、职后安全培训的不足以及自主学习途径的缺乏。针对当前存在的主要问题，教师安全素质培养路径的优化和培养体系的构建可以从三个方面展开：首先，在职前修订师范教育人才培养标准，重建教师专业素质结构，同时调整安全教育的课程设置，协同第二课堂提升教师安全素质；其次，在职后扩大教师安全培训对象，建立"分层进阶"新模式，对安全考试合格者发放教师安全培训合格证书，调整和完善中小学校岗位安全工作指南；第三，在日常工作期间，逐步改善教师自主学习条件，积极开展安全专题类校本课程研究。     

基于交叉效率DEA与群体共识的区间乘性语言偏好关系群决策

针对区间乘性语言偏好关系群决策问题，提出了一种基于交叉效率DEA和群体共识的群决策方法。首先，提出乘性语言偏好关系导出函数的定义，并构建产出导向的DEA模型，证明了一致性乘性语言偏好关系的DEA效率得分与排序向量之间存在比例关系。在此基础上，建立基于理想值的交叉效率DEA模型，提出乘性语言偏好关系的通用排序方法。同时，基于群体共识建立目标规划模型来计算各语言偏好关系的权重系数。最后，利用Monte Carlo随机模拟的方法对群体语言偏好空间进行统计分析，得到群决策期望排序向量及其可信度。算例分析表明本文方法能够有效的避免信息损失，具有较强的适用性和较高的可信度。     

ISO 45001 and controversial transnational private regulation for occupational health and safety

This article analyses the genesis of the ISO 45001 standard on occupational health and safety, a new initiative of transnational private regulation. The authors draw a picture of controversy from interviews with stakeholders involved in its design, approval and initial dissemination, and from a qualitative content analysis of the internal documentation of the committee responsible for its approval. Like its predecessors relating to environmental management – ISO 14001 – and corporate social responsibility – ISO 26000 – this new standard raises serious concerns among stakeholders given that it deals with substantive political, social and legal issues.     

A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

This article presents a discourse on the incorporation of organizational factors into probabilistic risk assessment (PRA)/probabilistic safety assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this article include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) How significant is the contribution of organizational factors to accidents and incidents? (II) How critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) What theoretical bases are needed for explicit incorporation of organizational factors into PRA? (IV) What methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross-disciplinary studies that have evaluated the effects of organizational factors on safety (not solely PRA-based) to shed more light on future research needs.     

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

Digital crime,trauma, and abuse: Internet safety and cyber risks for adolescents and emerging adults in the 21st century

ABSTRACT

The shift in the technology landscape has altered the technology ecosystem of adolescents and emerging adults in the 21^st century. Yet, with greater use of digital gadgets comes greater mental health risks that technological advancement brings. This study provides a narrative review of contemporary cyber risks faced by adolescents and emerging adults. In particular, this review will cover dangers and effects of cyber-bullying, social media, cyber-dating violence, sextortion, sexting, revenge porn, online dating, catfishing, and scammers, with an emphasis to raise awareness and encourage proactive efforts dedicated to address these social concerns as the digital era continues to evolve.     

Toward an Application Guide for Safety Integrity Level Allocation in Railway Systems

The work in the article presents the development of an application guide based on feedback and comments stemming from various railway actors on their practices of SIL allocation to railway safety‐related functions. The initial generic methodology for SIL allocation has been updated to be applied to railway rolling stock safety‐related functions in order to solve the SIL concept application issues. Various actors dealing with railway SIL allocation problems are the intended target of the methodology; its principles will be summarized in this article with a focus on modifications and precisions made in order to establish a practical guide for railway safety authorities. The methodology is based on the flowchart formalism used in CSM (common safety method) European regulation. It starts with the use of quantitative safety requirements, particularly tolerable hazard rates (THR). THR apportioning rules are applied. On the one hand, the rules are related to classical logical combinations of safety‐related functions preventing hazard occurrence. On the other hand, to take into account technical conditions (last safety weak link, functional dependencies, technological complexity, etc.), specific rules implicitly used in existing practices are defined for readjusting some THR values. SIL allocation process based on apportioned and validated THR values is finally illustrated through the example of “emergency brake” subsystems. Some specific SIL allocation rules are also defined and illustrated.     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.