Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability

In quantitative uncertainty analysis, it is essential to define rigorously the endpoint or target of the assessment. Two distinctly different approaches using Monte Carlo methods are discussed: (1) the end point is a fixed but unknown value (e.g., the maximally exposed individual, the average individual, or a specific individual) or (2) the end point is an unknown distribution of values (e.g., the variability of exposures among unspecified individuals in the population). In the first case, values are sampled at random from distributions representing various "degrees of belief" about the unknown "fixed" values of the parameters to produce a distribution of model results. The distribution of model results represents a subjective confidence statement about the true but unknown assessment end point. The important input parameters are those that contribute most to the spread in the distribution of the model results. In the second case, Monte Carlo calculations are performed in two dimensions producing numerous alternative representations of the true but unknown distribution. These alternative distributions permit subject confidence statements to be made from two perspectives: (1) for the individual exposure occurring at a specified fractile of the distribution or (2) for the fractile of the distribution associated with a specified level of individual exposure. The relative importance of input parameters will depend on the fractile or exposure level of interest. The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state-of-the-art in assessment modeling.     

The use of survey data to study migration–environment relationships in developing countries: alternative approaches to data collection

Growing interest in the environmental aspects of migration is not matched by research on their interrelationships, due partly to the lack of adequate data sets on the two together. Focusing on the microlevel, we describe the data required to effectively investigate these interrelationships. Data sources are discussed, including information that should be collected, focusing on household surveys and remote sensing. The main section of the paper describes three alternative approaches to data collection: (a) using existing population and environmental data from different sources, illustrated by Burkina Faso; (b) adding questions to a survey developed for another purpose, illustrated for Guatemala using a DHS survey; and (c) designing a new survey specifically to collect both migration and environmental data to investigate interrelationships, illustrated by Ecuador. Methods used and summary findings are described, followed by a discussion of their advantages and limitations. We conclude with recommendations as to effective use of each approach as research on migration?Cenvironment linkages moves forward.     

A Household-Based Distribution-Sensitive Human Development Index: An Empirical Application to Mexico, Nicaragua and Peru

In measuring human development, one of the main concerns relates to the inclusion of a measure that penalizes inequalities in the distribution of achievements across the population. Using indicators from nationally representative household surveys and census data, this paper proposes a straightforward methodology to estimate a household-based distribution-sensitive human development index aggregated through generalized means. The evidence shows that the losses in human development due to inequality reach up 22, 29 and 57% in Mexico, Peru and Nicaragua, respectively. Among dimensions, the loss in the income index reaches up 61% in Nicaragua, while the education index appears as the most sensitive in the case of Mexico and Peru, with a percentage of loss between 38 and 48%. The importance of household-level calculations is highlighted when we compare the indices computed from the entire distribution with those existing indices computed for quintiles of the distribution, which minimizes the losses due to inequality. Overall, the estimations evidence a higher sensitivity of the index to inequality, and therefore an important space for public action to reduce inequality that could involve positive development returns.     

Therapeutic residential care in Spain. Population treated and therapeutic coverage

Therapeutic residential care (TRC) is the name given to specialized children's homes for treating cases with severe emotional and behavioural problems that have been placed in residential care. A recent international review has revealed great diversity in the referral criteria of cases and in the models of intervention carried out. The goal of this study is to describe the population treated in these types of facilities in Spain and the therapeutic coverage given. The sample is made up of 215 young people in children's homes, of whom 93 are in TRC. The cases referred to TRC have been in residential care for less time but have gone through a greater number of placements. These young people also exhibit more problems of drug use, and there is a larger percentage of clinical‐range cases in the Child Behaviour Checklist scales of attention problems and aggressive behaviour. Nevertheless, the results of logistic regression indicate that the only variables that significantly increase the probability of being referred to TRC are drug use and changes of care placements. With regard to therapeutic care, there is a higher percentage in the TRC group receiving psychiatric care, and the sessions are also more frequent.     

On a tobit–Birnbaum–Saunders model with an application to medical data

The tobit model allows a censored response variable to be described by covariates. Its applications cover different areas such as economics, engineering, environment and medicine. A strong assumption of the standard tobit model is that its errors follow a normal distribution. However, not all applications are well modeled by this distribution. Some efforts have relaxed the normality assumption by considering more flexible distributions. Nevertheless, the presence of asymmetry could not be well described by these flexible distributions. A real-world data application of measles vaccine in Haiti is explored, which confirms this asymmetry. We propose a tobit model with errors following a Birnbaum–Saunders (BS) distribution, which is asymmetrical and has shown to be a good alternative for describing medical data. Inference based on the maximum likelihood method and a type of residual are derived for the tobit–BS model. We perform global and local influence diagnostics to assess the sensitivity of the maximum likelihood estimators to atypical cases. A Monte Carlo simulation study is carried out to empirically evaluate the performance of these estimators. We conduct a data analysis for the mentioned application of measles vaccine based on the proposed model with the help of the R software. The results show the good performance of the tobit–BS model.     

Bootstrap tests for time varying cointegration

This article proposes wild and the independent and identically distibuted (i.i.d.) parametric bootstrap implementations of the time-varying cointegration test of Bierens and Martins (2010 Bierens, H. J., Martins, L. F. (2010). Time varying cointegration. Econometric Theory 26:1453–1490.[Crossref], [Web of Science ®] , [Google Scholar]). The bootstrap statistics and the original likelihood ratio test share the same first-order asymptotic null distribution. Monte Carlo results suggest that the bootstrap approximation to the finite-sample distribution is very accurate, in particular for the wild bootstrap case. The tests are applied to study the purchasing power parity hypothesis for twelve Organisation for Economic Cooperation and Development (OECD) countries and we only find evidence of a constant long-term equilibrium for the U.S.–U.K. relationship.     

Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates

Two- or multi-phase study designs are often used in settings involving failure times. In most studies, whether or not certain covariates are measured on an individual depends on their failure time and status. For example, when failures are rare, case–cohort or case–control designs are used to increase the number of failures relative to a random sample of the same size. Another scenario is where certain covariates are expensive to measure, so they are obtained only for selected individuals in a cohort. This paper considers such situations and focuses on cases where we wish to test hypotheses of no association between failure time and expensive covariates. Efficient score tests based on maximum likelihood are developed and shown to have a simple form for a wide class of models and sampling designs. Some numerical comparisons of study designs are presented.     

‘Drink a 12 box before you go’: pre-loading among young people in Aotearoa New Zealand

The practice of pre-loading—drinking large amounts of alcohol rapidly in private spaces prior to socialising in the night-time economy—has come to notice recently in the study of alcohol-related harm, but no studies have explored these phenomena in Aotearoa New Zealand. We used a theoretical framework developed with public health alcohol studies for understanding drinking cultures that conceptualises patterns of behaviours as arising within a dynamic interaction between forces of hedonism, function and control. We report findings from 34 focus groups conducted with 18–25 year olds as part of a project supported by the Marsden Fund, between 2011 and 2012, to investigate drinking cultures among young people. Our thematic analyses of participants’ accounts of pre-loading show that the term is in common use, applying to a range of practices motivated by price of alcohol but influenced by the pleasures of intoxication, the importance of peer processes and certain aspects of the regulatory system. We conclude with a discussion of the usefulness of the framework and the implications of the findings for public health policy that aims to reduce alcohol consumption and the harm that arises from it.