Ethnicity, migration and health: A population-based study of 338 refugees from Latin America, 396 nonrefugee immigrants from Finland and 161 from southern Europe and 996 age-, sex- and education-matched controls from Sweden

This article shows the influence of being a refugee from Latin America or a nonrefugee immigrant from southern Europe or Finland on self-reported illness, controlling for social factors and lifestyle. The study population consisted of 338 Latin American refugees, a random sample of 396 Finnish and 161 southern European immigrants and 996 age-, sex- and education-matched Swedish controls. The data were analysed unmatched with logistic regression (multivariate analysis) in main effect models. The strongest independent risk indicator for long-term illness was being a Latin American refugee (estimated odds ratio (OR)=2.96, 95% confidence interval (CI)=2.19–3.82). There was a significant association between being a Latin American refugee and period prevalence, ill health and unsatisfied need for care. Being a southern European or Finnish immigrant was a risk indicator of ill health but was not associated with the other dependent factors. Not feeling secure in daily life was a strong risk indicator for long-term illness and ill health (estimated OR=1.89, 95% CI=1.26–2.76 and OR=3.04, 95% CI= 1.97–4.48) respectively). Being a Latin American refugee was equal in importance to traditional risk factors such as overweight and not taking regular exercise for long-term illness and ill health.     

对林业税费的经济学分析

该文运用经济学的基本工具———供给和需求曲线 ,以商品林为研究对象 ,探讨了林业税费的作用机理和内涵。该文认为商品林生产具有正的外部性 ,市场失灵使生产者将不能提供社会真正所需要的商品林数量。在封闭的市场条件下 ,林业税费会直接成为价格的一部分 ,商品林生产者应当能够获得正常利润 ,与税费高低无关 ,但会使一部分林地退出生产。林业补贴是一种负的林业税收。在开放市场经济条件下 ,国内商品林供应量低于没有林业税收时的商品林供应量 ,更低于考虑社会生态需求时的商品林供应量 ,造成巨大的社会福利损失 ,应减免林业税费、增加林业扶持、取消价格管制。该文进一步研究了与林业税费密切相关的森林采伐限额政策 ,认为对商品林应该完全取消采伐限额政策     

浅谈外语学院教学管理工作

教学管理是教学工作运转的支柱,教学管理的优劣影响着教学质量。该文介绍了外语学院教学管理工作的基本内容,包括教学任务的布置落实、教学检查、教学文件的建设、学籍管理,以及结合学院的实际情况完善考试制度、制定成绩管理细则和考试结果奖励办法等。     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.     

Führung durch Selbstführung

This paper outlines the conceptual bases, strategies, and psychological attributes of leadership by self-leadership. In addition, relations to other theories of leadership are drawn. Dimensions and correlates of self-leadership competence are described and discussed. Various approaches for developing self-leadership competence are discussed as well.     

Computing maximum likelihood estimates from type II doubly censored exponential data

It is well-known that, under Type II double censoring, the maximum likelihood (ML) estimators of the location and scale parameters, θ and δ, of a twoparameter exponential distribution are linear functions of the order statistics. In contrast, when θ is known, theML estimator of δ does not admit a closed form expression. It is shown, however, that theML estimator of the scale parameter exists and is unique. Moreover, it has good large-sample properties. In addition, sharp lower and upper bounds for this estimator are provided, which can serve as starting points for iterative interpolation methods such as regula falsi. Explicit expressions for the expected Fisher information and Cramér-Rao lower bound are also derived. In the Bayesian context, assuming an inverted gamma prior on δ, the uniqueness, boundedness and asymptotics of the highest posterior density estimator of δ can be deduced in a similar way. Finally, an illustrative example is included.     

Bayesian modelling of catch in a north-west Atlantic fishery

Summary. We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.