Nuclear Risk Management on Stage: A Decision Analysis Perspective on the UK's Committee on Radioactive Waste Management

In 2003, the UK government set up a broad-based Committee on radioactive waste management (CoRWM) to look at the UK's policy on radioactive waste management with a view to jumpstarting a stalled policy process. The committee's brief was to come up with a set of recommendations that would protect the public and the environment, and be capable of inspiring public confidence. After consulting widely with the public and stakeholders, and drawing on advice from scientists and other experts, CoRWM arrived at a remarkably well-received set of recommendations. On the basis of our experiences of working on CoRWM's multi-criteria decision analysis of different management options, study of CoRWM documentation, and interviews with committee members, we describe the explicit and implicit principles that guided CoRWM. We also give an account of the process by which CoRWM arrived at its conclusions, covering four phases: framing, shortlisting, option assessment, and integration; and four cross-cutting activities: public and stakeholder engagement (PSE), science and engineering input, ethics and social science input, and learning from overseas practice. We finish by outlining some of the key developments in the UK's radioactive waste management process, which followed on from the publication of CoRWM's report, and present our reflections for the benefit of the risk and decision analysts of future committees that, like CoRWM, are charged with recommending to government on the management of technically complex and risky technologies, drawing on extensive public and stakeholder consultation.     

Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near‐integrated time series, it is shown here that inverse and ill‐posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.     

Optimal Bandwidth Selection in Heteroskedasticity–Autocorrelation Robust Testing

This paper considers studentized tests in time series regressions with nonparametrically autocorrelated errors. The studentization is based on robust standard errors with truncation lag M=bT for some constant b∈(0, 1] and sample size T. It is shown that the nonstandard fixed‐b limit distributions of such nonparametrically studentized tests provide more accurate approximations to the finite sample distributions than the standard small‐b limit distribution. We further show that, for typical economic time series, the optimal bandwidth that minimizes a weighted average of type I and type II errors is larger by an order of magnitude than the bandwidth that minimizes the asymptotic mean squared error of the corresponding long‐run variance estimator. A plug‐in procedure for implementing this optimal bandwidth is suggested and simulations (not reported here) confirm that the new plug‐in procedure works well in finite samples.     

Foster care placement breakdown in the Netherlands and Flanders: Prevalence,precursors, and associated factors

Family foster care is the option of choice for children in need of out‐of‐home care in Flanders and the Netherlands. Foster care is however a vulnerable intervention, and questions can be raised as to its efficacy. Although the literature on placement breakdown has made significant progress during the last years, empirical knowledge regarding breakdown in Flanders and the Netherlands remains scant. Consequently, this study aimed at investigating prevalence and precursors of breakdowns in long‐term foster care, the duration of placement before breakdown, and the association of child and placement characteristics with breakdown. Case files of 271 Dutch and 309 Flemish foster children were analysed with a coding scheme designed for this study. After 6 years, 398 placements had terminated: 169 placements broke down and 229 placements ended positively. Placements broke down mainly because of behavioural problems of the foster child, foster parents' parenting problems, and conflicts between birth and foster parents. Foster children with behavioural problems, older foster children, foster children denied treatment, and foster children in care because of sexual abuse were more at risk of breakdown. Assessing these factors is important when evaluating the appropriateness of a family foster care placement.     

Comparison of multiple imputation and two-phase logistic regression to analyse two-phase case–control studies with rich phase 1: a simulation study

Two-phase case–control studies cope with the problem of confounding by obtaining required additional information for a subset (phase 2) of all individuals (phase 1). Nowadays, studies with rich phase 1 data are available where only few unmeasured confounders need to be obtained in phase 2. The extended conditional maximum likelihood (ECML) approach in two-phase logistic regression is a novel method to analyse such data. Alternatively, two-phase case–control studies can be analysed by multiple imputation (MI), where phase 2 information for individuals included in phase 1 is treated as missing. We conducted a simulation of two-phase studies, where we compared the performance of ECML and MI in typical scenarios with rich phase 1. Regarding exposure effect, MI was less biased and more precise than ECML. Furthermore, ECML was sensitive against misspecification of the participation model. We therefore recommend MI to analyse two-phase case–control studies in situations with rich phase 1 data.     

Screening for Alcohol Problems Among 4-Year Colleges and Universities

Abstract

Objective: To assess the use of alcohol screening tools across US colleges. Participants: Directors of health services at 333 four-year colleges. Methods: An online survey was conducted regarding the use of alcohol screening tools. Schools reporting use of formal tools were further described in terms of 4 tools (AUDIT, CUGE, CAPS, and RAPS) that the authors judged to be the most favorable based on prior empirical comparative studies. Results: Forty-four percent of colleges reported use of at least 1 formal alcohol screening tool and nearly all of these used a tool appropriate for college students. However, less than half of the 44% of colleges that used a screening tool used 1 of the 4 most favorable tools. Conclusions: Continued efforts are needed to encourage colleges to use the most effective available screening tools to identify alcohol-related problems that require intervention among students.     

Examining the Relationships Between Resilience,Mental Health,and Academic Persistence in Undergraduate College Students

Abstract

Objective: In this study, the relationships between measures of interpersonal resilience, intrapersonal resilience, and mental health were examined with respect to academic and social integration, key determinants of academic persistence. Participants: A sample (n = 605) of undergraduate students was recruited from 2 midwestern universities during the 2007–2008 academic year. Methods: Hierarchal (or sequential) regression analysis examined whether the inter- and intrapersonal resilience and mental health measures contributed to explaining variance in the response variables of university cumulative grade point average (GPA) and university sense of belonging. Results: The intrapersonal resilience factors contributed to explaining variance in cumulative GPA in addition to aptitude and achievement. Furthermore, there was a strong statistical correlation between the inter- and intrapersonal resilience factors and mental health. Conclusions: The demands in college are significant and there is a need for more research on the concept of resilience as it relates to college health and academic persistence.     

Reporting Rape in a National Sample of College Women

Abstract

Background: Studies indicate that a small percentage of rapes are reported to law enforcement officials. Research also suggests that rapes perpetrated by a stranger are more likely to be reported and that rapes involving drugs and/or alcohol are less likely to be reported. College women represent a unique and understudied population with regard to reporting rape. Methods: In the current study, the authors interviewed a national sample of 2,000 college women about rape experiences in 2006. Results: Only 11.5% of college women in the sample reported their most recent/only rape experience to authorities, with only 2.7% of rapes involving drugs and/or alcohol reported. Minority status (ie, nonwhite race) was associated with lower likelihood of reporting, whereas sustaining injuries during the rape was associated with increased likelihood of reporting. Discussion: Reporting, particularly for rapes involving drugs and alcohol, is low among college women. Implications for policy are discussed.