International migration: demographic and socioeconomic consequences in the United Kingdom and Europe

"This article evaluates the consequences of international migration since World War II, with particular reference to Great Britain. It emphasizes the substantial differences between the origins and responses to immigration in the United Kingdom and that of the rest of Western Europe." It is found that "people of non-European origin from Commonwealth countries have predominated in postwar immigration to the United Kingdom. That migration neutralized the previously dominant pattern of emigration and increased U.K. population by about 3 million people through immigration and higher fertility, with only slight effects upon the age distribution."     

Power, Politics, and the Framing of Environmental Illness

The medical community, along with other government agencies, has created its own frame of environmental illness. This frame has been generally accepted by the American public. In this paper we discuss framing in general and the factors related to how the environmental illness frame has been constructed and maintained. We offer a brief history of the medical institution and illustrate the frame with its definitions of environmental illness. Qualitative data from a study of Oak Ridge, a contaminated community located in Tennessee, are examined to analyze the consequences of challenging the environmental illness frame. Implications for future research are discussed.     

Genotypic performance over environmental arrays: A non-linear grouping protocol

A non-linear model for examining genotypic responses across an array of environments is contrasted with the 'joint regression' formulation, and a rigorous approach to hypothesis testing using the conditional error principle is demonstrated. The model is extended to cater for situations where single straight-line response patterns fail to characterize genotypic behaviors over an environmental array: a combination of two straight lines, with slope in below-average and in above-average environments, is offered as the 1 2 simplest representation of convex and concave patterns. A protocol for classifying genotypes according to the results of hypothesis tests, i.e. H( = ) and H( = = = 1), is 1 2 1 2 presented . A doubly desirable response pattern is convex ( < 1< ), while a doubly 1 2 undesirable pattern is concave ( > 1> ). 1 2     

Sample selection in radiocarbon dating

Archaeologists working on the island of O'ahu, Hawai'i, use radiocarbon dating of samples of organic matter found trapped in fish-pond sediments to help them to learn about the chronology of the construction and use of the aquicultural systems created by the Polynesians. At one particular site, Loko Kuwili, 25 organic samples were obtained and funds were available to date an initial nine. However, on calibration to the calendar scale, the radiocarbon determinations provided date estimates that had very large variances. As a result, major issues of chronology remained unresolved and the archaeologists were faced with the prospect of another expensive programme of radiocarbon dating. This paper presents results of research that tackles the problems associated with selecting samples from those which are still available. Building on considerable recent research that utilizes Markov chain Monte Carlo methods to aid archaeologists in their radiocarbon calibration and interpretation, we adopt the standard Bayesian framework of risk functions, which allows us to assess the optimal samples to be sent for dating. Although rather computer intensive, our algorithms are simple to implement within the Bayesian radiocarbon framework that is already in place and produce results that are capable of direct interpretation by the archaeologists. By dating just three more samples from Loko Kuwili the expected variance on the date of greatest interest could be substantially reduced.     

Spatial variation in risk of disease: a nonparametric binary regression approach

A common problem in environmental epidemiology is the estimation and mapping of spatial variation in disease risk. In this paper we analyse data from the Walsall District Health Authority, UK, concerning the spatial distributions of cancer cases compared with controls sampled from the population register. We formulate the risk estimation problem as a nonparametric binary regression problem and consider two different methods of estimation. The first uses a standard kernel method with a cross-validation criterion for choosing the associated bandwidth parameter. The second uses the framework of the generalized additive model (GAM) which has the advantage that it can allow for additional explanatory variables, but is computationally more demanding. For the Walsall data, we obtain similar results using either the kernel method with controls stratified by age and sex to match the age–sex distribution of the cases or the GAM method with random controls but incorporating age and sex as additional explanatory variables. For cancers of the lung or stomach, the analysis shows highly statistically significant spatial variation in risk. For the less common cancers of the pancreas, the spatial variation in risk is not statistically significant.