Mlonte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Most public health risk assessments assume and combine a series of average, conservative, and worst-case values to derive a conservative point estimate of risk. This procedure has major limitations. This paper demonstrates a new methodology for extended uncertainty analyses in public health risk assessments using Monte Carlo techniques. The extended method begins as do some conventional methods--with the preparation of a spreadsheet to estimate exposure and risk. This method, however, continues by modeling key inputs as random variables described by probability density functions (PDFs). Overall, the technique provides a quantitative way to estimate the probability distributions for exposure and health risks within the validity of the model used. As an example, this paper presents a simplified case study for children playing in soils contaminated with benzene and benzo(a)pyrene (BaP).     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

影响川南浅丘区田坎玉米产量的气候条件分析

利用积分回归方法分析气候因子对川南浅丘区田坎玉米产量的影响。结果表明 ,日照、温度及降雨日数是影响田坎玉米产量的主要因子。苗期的高温 ,生育中期光照不足及灌浆成熟期的雨日偏多均不利于提高田坎玉米产量     

Residential Building Codes,Affordability, and Health Protection: A Risk-Tradeoff Approach

Residential building codes intended to promote health and safety may produce unintended countervailing risks by adding to the cost of construction. Higher construction costs increase the price of new homes and may increase health and safety risks through income and stock effects. The income effect arises because households that purchase a new home have less income remaining for spending on other goods that contribute to health and safety. The stock effect arises because suppression of new-home construction leads to slower replacement of less safe housing units. These countervailing risks are not presently considered in code debates. We demonstrate the feasibility of estimating the approximate magnitude of countervailing risks by combining the income effect with three relatively well understood and significant home-health risks. We estimate that a code change that increases the nationwide cost of constructing and maintaining homes by $150 (0.1% of the average cost to build a single-family home) would induce offsetting risks yielding between 2 and 60 premature fatalities or, including morbidity effects, between 20 and 800 lost quality-adjusted life years (both discounted at 3%) each year the code provision remains in effect. To provide a net health benefit, the code change would need to reduce risk by at least this amount. Future research should refine these estimates, incorporate quantitative uncertainty analysis, and apply a full risk-tradeoff approach to real-world case studies of proposed code changes.     

Challenges to the Acceptance of Probabilistic Risk Analysis

This paper discusses a number of the key challenges to the acceptance and application of probabilistic risk analysis (PRA). Those challenges include: (a) the extensive reliance on subjective judgment in PRA, requiring the development of guidance for the use of PRA in risk-informed regulation, and possibly the development of robust or reference prior distributions to minimize the reliance on judgment; and (b) the treatment of human performance in PRA, including not only human error per se but also management and organizational factors more broadly. All of these areas are seen as presenting interesting research challenges at the interface between engineering and other disciplines.     

Assessing Effects on Long-term Survival after Early Termination of Randomized Trials

In this paper we present an approach to using historical control data to augment information from a randomized controlled clinical trial, when it is not possible to continue the control regimen to obtain the most reliable and valid assessment of long term treatment effects. Using an adjustment procedure to the historical control data, we investigate a method of estimating the long term survival function for the clinical trial control group and for evaluating the long term treatment effect. The suggested method is simple to interpret, and particularly motivated in clinical trial settings when ethical considerations preclude the long term follow-up of placebo controls. A simulation study reveals that the bias in parameter estimates that arises in the setting of group sequential monitoring will be attenuated when long term historical control information is used in the proposed manner. Data from the first and second National Wilms' Tumor studies are used to illustrate the method.     

Aalen's Linear Model for Sampled Risk Set Data: A Large Sample Study

Borgan and Langholz (1997) describe a method for estimating the parameter functions in Aalen's linear hazard regression model from sampled risk set data. Using a counting process formulation and the martingale central limit theorem, we provide a study of the asymptotic distributional properties of the estimator. The results are applied to study the efficiencies of the nested case-control and counter-matched designs relative to a full cohort analysis.     

Terrorist Population Dynamics Model

A system that includes a number of terrorist cells is considered. The cells can consist of one or more terrorists. The current number of terrorist cells is further denoted by N(t), where t is a current time counted from any appropriate origin. The objective is to find the evolution of the system in terms of N(t) and some interpretable parameters, such as the initial number of the terrorist cells N0=N(0), the cell disabling rate constant lambda (or the cell half-life t1/2), and the rate of formation of new cells P. The cost-effectiveness analysis, performed in the framework of the model, reveals that the effectiveness of disabling a terrorist cell is getting worse after 2-3 half-lives of a cell, which shows that if the anti-terrorist actions have not reached their goal during that time, the respective policy should be considered for revision, using the risk assessment consideration. Another important issue raised concerns balancing the efforts related to counterterrorism actions inside the system and the efforts protecting its borders. The respective data analysis is suggested and illustrated using simulated data.     

Character-Based Determinants of Trust in Leaders

Trust is a prominent determinant of effective interpersonal relationships, group process, and organizational development. However, for leaders and managers, trust building is often problematic. The aim of this article is to contribute to a growing understanding of the way in which individual managers can develop trust in organizations, particularly those defined by medium to high risk. The article presents a theoretical and empirical analysis of the nature of trust at work. Building on the results of an earlier study, use is made of the facet-theoretical approach to generate a definitional framework of trust, which focuses on the characteristics of the trusted person that contribute to the development of trust. Using items developed on the basis of a mapping sentence, hypotheses regarding the relations between the definitional framework and empirical observations were tested through smallest space analysis (SSA) of data collected from a sample of 398 bank and hospital employees. The results demonstrate strong support for the definitional system and show a clear association with results of prior research that identify perceptions of ability, integrity, fairness, and openness as key determinants of trust. The empirical structure clearly reflects the attitudinal nature of interpersonal trust at work and the items empirically distinguish distinct character-based components of trust.