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.     

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.     

Stereotypical Disease Inferences From Gay/Lesbian Versus Heterosexual Voices

Voice is a cue used to categorize speakers as members of social groups, including sexual orientation. We investigate the consequences of such voice-based categorization, showing that people infer stereotype-congruent disease likelihood on the basis of vocal information and without explicit information about the speaker’s sexual orientation. Study 1 and Study 2 reveal that participants attribute diseases to gay/lesbian and heterosexual men and women in line with stereotypes. Gay speakers were more likely to be associated with gay and female diseases, and lesbian speakers with male diseases. These findings demonstrate that likelihood to suffer from diseases is erroneously, but stereotypically, inferred from targets’ vocal information.     

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.     

乘客视角下绿色公交服务质量的模糊综合评价———基于平顶山市的问卷调查

从乘客视角对公交服务质量进行评价,已引起众多研究者和相关管理者的重视,而在环保新时代,还必须增加相对应的绿色环保指标。以主客观联合赋权为基础建立模糊综合评价模型,并对平顶山市公交乘客进行问卷调查,发现：在乘客看来,影响绿色公交服务质量的多种因素中,最重要的是安全、方便、总体服务质量,环保的重要程度超过了舒适与经济。平顶山市绿色公交服务质量的模糊综合评价结果是中等偏上：在准则层和指标层中,准时、总体质量、经济、直达性、行车安全、卫生整洁的评级最高,均达到中等偏上或良好;而快捷、环保、减缓拥挤、公共安全、住所到站点距离得到较低评级。     

音乐声波对人体作用机理探讨——兼谈音乐逍遥气功的实践

本文介绍了作者在音乐与气功相结合方面所作的创造性实践和研究。在特制音乐声波激发下,能很收快到传统气功的功效,使练功者达到强身治病的目的。实践证明音乐逍遥气功对多种慢性病有很好疗效。文章从物理学、生物物理学的角度,以系统论的方法对音乐逍遥气功的机理进行探讨。认为这是自觉利用各种形式、以一定规律动态变化的能量流的一种新的尝试。     

应对“城市病”：英国工业化时期的经历与启示

英国工业化时期的＂城市病＂问题主要因城市人口的急剧增加所致,集中表现为工人住房短缺、环境污染严重、流行疾病蔓延、社会治安混乱。在客观调研的基础上,英国政府在扩大住房供给、改善城市环境、加强社会治安方面采取了诸多措施,并取得一定成效。目前,中国正处于＂城市病＂爆发的高峰期,认识和反思英国的早期经历对我们有镜鉴作用。     

Money, real output, and deficit effects of coffee booms in Colombia

This paper develops a Vector Error Correction (VEC) model and uses the recently developed technique of ‘generalized’ impulse response analysis to test the empirical relationships in the Colombian economy between coffee revenues and a set of macro variables. We find that coffee price (revenue) shocks have exerted an important influence on money growth, inflation, and real exchange rates, and the direction of these effects are in line with some of the predictions of traditional Dutch Disease type models. The major difference between our results and the results of Dutch Disease type models arise in the effects of coffee booms on real output. We find that in the time horizon of 5 years after the boom, real output has increased in response to the effects of the coffee boom. The finding that coffee booms can result in positive long-run output effects is an important finding since it contradicts the traditional conclusion of Dutch Disease type models which envision an adverse long-run effect on output. We also find that the long-run effect of coffee booms is to reduce both current account and government deficits. These results illustrate strikingly that the term “Dutch Disease” is an unfortunately pejorative term that obscures the fact that coffee booms need not be viewed as a “disease” but as an extraordinary opportunity to strengthen internal and external balances.     

深圳正落入“后中等收入陷阱”吗?——对深圳服务业内部结构调整的思考

本文基于威廉·鲍莫尔提出的"鲍莫尔病"概念,对深圳市是否正落入"后中等收入陷阱"进行了验证。在此基础上,结合伦敦、巴黎和旧金山等国外城市的发展经验,提出深圳应优先发展生产性服务业,抑制消费性服务业成本的快速上涨。     

Foreign aid,public investment,and the informal economy

This paper examines the absorption of foreign aid in the presence of formal and informal production. Calibrating a two-sector open economy model to 67 aid-recipient countries for 1990–2019, we show that an increase in foreign aid drives resources into the informal sector, and away from the formal sector. With untied aid, the expansion of the informal sector can lead to an economic contraction through the Dutch Disease effect. An economic expansion with an increase in the share of formal production can be attained by re-allocating existing aid to public investment rather than an increase in the aggregate level of aid.