A new approach to investigating spatial variations of disease

Summary.  For rare diseases the observed disease count may exhibit extra Poisson variability, particularly in areas with low or sparse populations. Hence the variance of the estimates of disease risk, the standardized mortality ratios, may be highly unstable. This overdispersion must be taken into account otherwise subsequent maps based on standardized mortality ratios will be misleading and, rather than displaying the true spatial pattern of disease risk, the most extreme values will be highlighted. Neighbouring areas tend to exhibit spatial correlation as they may share more similarities than non-neighbouring areas. The need to address overdispersion and spatial correlation has led to the proposal of Bayesian approaches for smoothing estimates of disease risk. We propose a new model for investigating the spatial variation of disease risks in conjunction with an alternative specification for estimates of disease risk in geographical areas—the multivariate Poisson–gamma model. The main advantages of this new model lie in its simplicity and ability to account naturally for overdispersion and spatial auto-correlation. Exact expressions for important quantities such as expectations, variances and covariances can be easily derived.     

Ordering the urban body: professional planning in early twentieth-century Britain

The early twentieth century has been regarded as a crucial period in the development of planning in Britain during which the key elements of the modern profession were put in place. At the time, planners represented their practice as a progressive answer to the environmental legacies of the Victorian era, capable of contributing decisively to the social and political reform of British society. Existing histories dealing with the subject uniformly locate planning within a historical trajectory that emphasizes its links with philanthropic experiment, reformism and the growth of the welfare state. This article scrutinizes such key propositions in order to show some of the ways that planning can be located within an alternative historical trajectory in which questions of orderliness, expert knowledge and government emerge to take priority. It examines the presence of rivalry among early professionals, the methodological commitment to civic surveying and the way planners sought to make use of the interconnections between space and social life to wield power over the environment and the private lives of urban dwellers.     

Modeling of rates over a hierarchical health administrative structure

Identifying the distribution of the incidence rate of a disease over a region is a prediction problem where area‐specific random effects are to be estimated. The authors consider the inclusion of such effects at different levels of a hierarchical health administrative structure. They develop inference procedures for this type of multi‐level model and show that the predicted rates are approximately weighted sums of the crude rates obtained by pooling data on each level of the hierarchy. Their techniques are illustrated using infant mortality data from British Columbia.     

英语概念习语隐喻性及汉译探析

隐喻性是英语概念习语最本质特征。基于英语概念习语隐喻性特征,以概念习语深层跨域标定为切入点,提出跨域标定和习语达意功能的四种关系,并在此基础上探讨英语概念习语汉译原则,构建翻译模式,指出英语概念习语汉译应突出概念习语隐喻性,保持习语文化特色,实现达意功能。     

Multivariate applications of the ash in regression

A simple algorithm for estimating the regression function over the United States is introduced. The approach allows for data obtained from a complicated sampling design, as well as for the inclusion of a few additional covariates. The regression estimates are obtained from an associated probability density estimate, namely the averaged shifted histogram. The algorithm has proven especially successful over a large mesh, say 300 by 200 nodes, in a data rich setting, even on a 486 computer running Splus. We currently run much higher resolution meshes on a Pentium. Commonly available alternative codes including kriging failed to produce useful estimates in this setting.     

Using Concept Mapping to Develop a Statewide Kinship Care Coalition: A Case Study

Increasingly, community coalitions are being developed to address a myriad of social challenges. However, documented research efforts related to planning processes associated with coalition formation are few. This case-study documents the use of concept mapping (CM) to d elineate a conceptual framework for developing a statewide kinship care coalition in one southeastern state. CM is a mixed-method research approach that analyzes qualitative data via nonmetric multidimensional scaling and hierarchical cluster analyses. After a review of relevant literature, this article discusses CM processes associated with this endeavor and examines implications for future use of CM for coalition formation and development.     

The use of concept mapping in measurement development and evaluation: Application and future directions

The past decade has seen an increase of measurement development research in social and health sciences that featured the use of concept mapping as a core technique. The purpose, application, and utility of concept mapping have varied across this emerging literature. Despite the variety of uses and range of outputs, little has been done to critically review how researchers have approached the application of concept mapping in the measurement development and evaluation process. This article focuses on a review of the current state of practice regarding the use of concept mapping as methodological tool in this process. We systematically reviewed 23 scale or measure development and evaluation studies, and detail the application of concept mapping in the context of traditional measurement development and psychometric testing processes. Although several limitations surfaced, we found several strengths in the contemporary application of the method. We determined concept mapping provides (a) a solid method for establishing content validity, (b) facilitates researcher decision-making, (c) insight into target population perspectives that are integrated a priori, and (d) a foundation for analytical and interpretative choices. Based on these results, we outline how concept mapping can be situated in the measurement development and evaluation processes for new instrumentation.     

2MTO,a new mapping tool to achieve lean benefits in high-variety low-volume job shops

This paper proposes the ‘Mapping Tool for Make-To-Order companies’ (2MTO), a new approach to map, analyse and achieve lean benefits in high-variety-low-volume job shops. The focus is, mainly, on the transitional phase from push to pull, when there is a compelling need to acquire a deep understanding of the current state of the system under analysis. Sometimes, in this initial phase, a pure pull approach cannot be developed and a job shop configuration must be maintained. If so, 2MTO allows selecting a suitable hybrid Production Planning and Control system to control Work-In-Process and to limit lead times, so as to quickly achieve lean benefits, without significantly altering the original layout. 2MTO is fully explained making reference to an Italian precision mechanic company, where the application of the method led to a successful implementation of a Workload Control system.     

Evaluation of Bayesian multiple stage estimation under spatial CAR model variants

In this study, an evaluation of Bayesian hierarchical models is made based on simulation scenarios to compare single-stage and multi-stage Bayesian estimations. Simulated datasets of lung cancer disease counts for men aged 65 and older across 44 wards in the London Health Authority were analysed using a range of spatially structured random effect components. The goals of this study are to determine which of these single-stage models perform best given a certain simulating model, how estimation methods (single- vs. multi-stage) compare in yielding posterior estimates of fixed effects in the presence of spatially structured random effects, and finally which of two spatial prior models – the Leroux or ICAR model, perform best in a multi-stage context under different assumptions concerning spatial correlation. Among the fitted single-stage models without covariates, we found that when there is low amount of variability in the distribution of disease counts, the BYM model is relatively robust to misspecification in terms of DIC, while the Leroux model is the least robust to misspecification. When these models were fit to data generated from models with covariates, we found that when there was one set of covariates – either spatially correlated or non-spatially correlated, changing the values of the fixed coefficients affected the ability of either the Leroux or ICAR model to fit the data well in terms of DIC. When there were multiple sets of spatially correlated covariates in the simulating model, however, we could not distinguish the goodness of fit to the data between these single-stage models. We found that the multi-stage modelling process via the Leroux and ICAR models generally reduced the variance of the posterior estimated fixed effects for data generated from models with covariates and a UH term compared to analogous single-stage models. Finally, we found the multi-stage Leroux model compares favourably to the multi-stage ICAR model in terms of DIC. We conclude that the mutli-stage Leroux model should be seriously considered in applications of Bayesian disease mapping when an investigator desires to fit a model with both fixed effects and spatially structured random effects to Poisson count data.     

高新技术园区投入产出绩效的智能评价

投入产出绩效评价是关系着中国各高新技术园区可持续发展的关键问题。由于评价问题的高维复杂性,神经网络方法与其他方法相比具有一定的优越性,但存在着训练准则和分类准则不一致的问题。采用一种区域映射神经网络模型,实现了从区域到区域的映射比区域到点的映射更快、更好,保证了神经网络训练准则与实际分类准则的一致性,具有比前馈式神经网络更快的收敛速度和更高的分类精度。利用区域映射神经网络模型对中国高新技术园区投入产出绩效进行智能评价,取得了较好的效果。