Secular declines in the association between obesity and mortality in the United States

Recent research suggests that rising obesity will restrain future gains in US life expectancy and that obesity is an important contributor to the current shortfall in us longevity compared to other high-income countries. Estimates of the contribution of obesity to current and future national-level mortality patterns are sensitive to estimates of the magnitude of the association between obesity and mortality at the individual level. We assessed secular trends in the obesity/mortality association among cohorts of middle-aged adults between 1948 and 2006 using three long-running US data sources: the Framingham Heart Study, the National Health and Nutrition Examination Survey, and the National Health Interview Survey. We find substantial declines over time in the magnitude of the association between obesity and overall mortality and, in certain instances, cardiovascular-specific mortality. We conclude that estimates of the contribution of obesity to current national-level mortality patterns should take into account recent reductions in the magnitude of the obesity and mortality association.     

Culturally specific treatment for partner-abusive Latino men: a qualitative study to identify and implement program components

Research based on a demographic survey and qualitative interviews of Latino intimate partner violence perpetrators in Southern California forms the basis of a Spanish-language treatment program designed to be culturally appropriate for Latino immigrant men, and piloted for 4 years with their input. Culturally-specific topics emphasized by participants and integrated into the program are: effective parenting skills for men; gender roles; discussion of discrimination towards immigrants and women; immigration and changing gender roles; marital sexual abuse; and spirituality as related to violence prevention. Attention is given to alcohol abuse and childhood trauma. Results suggest the desirability of an empathic and culturally-sensitive approach, without diminishing responsibility. This program was designed to help clinicians refine their skills and effectiveness in working with this rapidly expanding population.     

Some applications of nonlinear and non-Gaussian state–space modelling by means of hidden Markov models

Nonlinear and non-Gaussian state–space models (SSMs) are fitted to different types of time series. The applications include homogeneous and seasonal time series, in particular earthquake counts, polio counts, rainfall occurrence data, glacial varve data and daily returns on a share. The considered SSMs comprise Poisson, Bernoulli, gamma and Student-t distributions at the observation level. Parameter estimations for the SSMs are carried out using a likelihood approximation that is obtained after discretization of the state space. The approximation can be made arbitrarily accurate, and the approximated likelihood is precisely that of a finite-state hidden Markov model (HMM). The proposed method enables us to apply standard HMM techniques. It is easy to implement and can be extended to all kinds of SSMs in a straightforward manner.     

Robust variable selection with application to quality of life research

A large database containing socioeconomic data from 60 communities in Austria and Germany has been built, stemming from 18,000 citizens’ responses to a survey, together with data from official statistical institutes about these communities. This paper describes a procedure for extracting a small set of explanatory variables to explain response variables such as the cognition of quality of life. For better interpretability, the set of explanatory variables needs to be very small and the dependencies among the selected variables need to be low. Due to possible inhomogeneities within the data set, it is further required that the solution is robust to outliers and deviating points. In order to achieve these goals, a robust model selection method, combined with a strategy to reduce the number of selected predictor variables to a necessary minimum, is developed. In addition, this context-sensitive method is applied to obtain responsible factors describing quality of life in communities.     

Robust nonparametric tests for the two-sample location problem

We construct and investigate robust nonparametric tests for the two-sample location problem. A test based on a suitable scaling of the median of the set of differences between the two samples, which is the Hodges-Lehmann shift estimator corresponding to the Wilcoxon two-sample rank test, leads to higher robustness against outliers than the Wilcoxon test itself, while preserving its efficiency under a broad range of distributions. The good performance of the constructed test is investigated under different distributions and outlier configurations and compared to alternatives like the two-sample t-, the Wilcoxon and the median test, as well as to tests based on the difference of the sample medians or the one-sample Hodges-Lehmann estimators.     

Bilanzmanipulation und Managementpräferenzen bezüglich der Strenge des Systems der Rechnungslegungsregulierung

The present paper analyses the political economy of accounting regulation. It develops a game theoretic model of earnings management in a capital market context. Various forms of information asymmetry are examined. The analysis shows that the preferences of managers over the strictness of accounting regulation depend on the nature of the information asymmetry. In a number of instances, managers prefer to be subject to strict regulation. This result contradicts the often heard assertion that managers prefer lax regulation and has implications for regulatory competition.     

Pool‐Point Distribution of Zero‐Inventory Products

We study zero‐inventory production‐distribution systems under pool‐point delivery. The zero‐inventory production and distribution paradigm is supported in a variety of industries in which a product cannot be inventoried because of its short shelf life. The advantages of pool‐point (or hub‐and‐spoke) distribution, explored extensively in the literature, include the efficient use of transportation resources and effective day‐to‐day management of operations. The setting of our analysis is as follows: A production facility (plant) with a finite production rate distributes its single product, which cannot be inventoried, to several pool points. Each pool point may require multiple truckloads to satisfy its customers' demand. A third‐party logistics provider then transports the product to individual customers surrounding each pool point. The production rate can be increased up to a certain limit by incurring additional cost. The delivery of the product is done by identical trucks, each having limited capacity and non‐negligible traveling time between the plant and the pool points. Our objective is to coordinate the production and transportation operations so that the total cost of production and distribution is minimized, while respecting the product lifetime and the delivery capacity constraints. This study attempts to develop intuition into zero‐inventory production‐distribution systems under pool‐point delivery by considering several variants of the above setting. These include multiple trucks, a modifiable production rate, and alternative objectives. Using a combination of theoretical analysis and computational experiments, we gain insights into optimizing the total cost of a production‐delivery plan by understanding the trade‐off between production and transportation.     

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.     

Application of Social Science Knowledge to the Community Organization Field

Community organization graduates of the School of Social Work, University of Washington, from 1966 to 1971, found a ready market for their skills although there was evidence of some slackening of demand and broader diversification of initial employment settings in the last two years. Graduates moved rapidly into administrative and management levels and appeared relatively stable in terms of interagency and geographical mobility. The small number of ethnically identified graduates made meaningful comparisons difficult. Sex, however, was a disadvantaging factor in employability and subsequent status. Of the ninety-one graduates surveyed, 94.5 percent responded Similar follow-up study of graduates in other geographic regions was recommended