Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

The analysis of incomplete data using stochastic covariates

In the development of many diseases there are often associated random variables which continuously reflect the progress of a subject towards the final expression of the disease (failure). At any given time these processes, which we call stochastic covariates, may provide information about the current hazard and the remaining time to failure. Likewise, in situations when the specific times of key prior events are not known, such as the time of onset of an occult tumour or the time of infection with HIV-1, it may be possible to identify a stochastic covariate which reveals, indirectly, when the event of interest occurred. The analysis of carcinogenicity trials which involve occult tumours is usually based on the time of death or sacrifice and an indicator of tumour presence for each animal in the experiment. However, the size of an occult tumour observed at the endpoint represents data concerning tumour development which may convey additional information concerning both the tumour incidence rate and the rate of death to which tumour-bearing animals are subject. We develop a stochastic model for tumour growth and suggest different ways in which the effect of this growth on the hazard of failure might be modelled. Using a combined model for tumour growth and additive competing risks of death, we show that if this tumour size information is used, assumptions concerning tumour lethality, the context of observation or multiple sacrifice times are no longer necessary in order to estimate the tumour incidence rate. Parametric estimation based on the method of maximum likelihood is outlined and is applied to simulated data from the combined model. The results of this limited study confirm that use of the stochastic covariate tumour size results in more precise estimation of the incidence rate for occult tumours.     

基于灰色理论的房地产市场需求分析

运用灰色系统理论，建立了基于灰色关联熵的房地产市场需求影响因素分析模型，并用实例进行计算，为房地产市场需求分析提供了一种新方法，从而进一步完善了房地产市场评价体系。     

基于Simpson公式改进的FAGM(1,1)模型及其应用研究

本文分析了传统FAGM（1,1）模型建模过程中存在的误差,提出了一种基于Simpson公式改进的FAGM（1,1）模型。首先,基于分数阶累加生成算子和分数阶累减生成算子建立分数阶FAGM（1,1）模型。其次,利用Simpson积分公式对FAGM（1,1）模型的背景值进行改进,建立SFAGM（1,1）模型。进一步,应用遗传算法确定SFAGM（1,1）模型的最优阶数以提高模型的预测精度。最后,以中国人均GDP为例,对比分析GM（1,1）模型、Simpson改进的GM（1,1）模型（SGM（1,1））、FAGM（1,1）模型、SFAGM（1,1）模型的模拟结果,并对"十三五"时期的人均GDP进行预测,其结果表明SFAGM（1,1）模型比GM（1,1）、SGM（1,1）、FAGM（1,1）在人均GDP的预测方面有更高的精度,"十三五"期间人均GDP年平均增长率为10.64%,到2020年达到83146.97元,是2010年人均GDP的2.69倍,以2010年的人均GDP为基准,到2020年将能够实现翻一番的目标。     

无金属基底吸波结构对TE波的反射

详细分析了ＴＥ极化波以大角度入射到无金属基底的平面多层吸波结构上时的反射特性,导出连接层间电磁场的通用矩阵公式,计算了两层和三层吸波结构的反射系数,并证明了在何种条件下可以获得低的反射率,在此基础上提出了一个简易设计方法。对进气道和进气道内吸波导流板的两层和三层材料设计都获得了良好的特性。     

基于二元关系的产业关联分析方法研究

本文运用二元关系理论和Warshall算法计算产业系统中各产业之间的连通关联关系,为认识产业结构和产业关联状况提供了简便的量化分析方法。结合安徽产业系统的实例,分析了安徽产业结构的特点及产业关联状况。     

我国绿色基础设施的经济价值预算与分析

绿色基础设施是自然的生态基础设施和半自然的市政绿色空间的结合,它遵循自然发展的规律,在改善生态环境和保护生物多样性的同时,也蕴藏着新的经济增长点.本研究尝试构建绿色基础设施经济价值评价指标,测算全国各省的绿色基础设施经济价值(2004-2015),探究其区域分布状况,并进一步运用灰色模型预测2016年、2020年和2025年我国绿色基础设施的经济价值,以期提升人们对绿色基础设施的认识,为在城市规划中建设和完善绿色基础设施提供参考,促进生态环境与经济的良性循环,推动城市可持续发展.     

双向FDI与经济增长的实证研究

从双向FDI的角度分析OFDI和IFDI与经济增长的关系。选取双向FDI对经济增长的3个主要影响因素,分别对双向FDI与GDP、投资、净出口和产业结构的关系进行实证分析。研究发现:双向FDI对于GDP和投资都具有拉动作用;IFDI对投资的影响滞后4期才能产生,对净出口的增长起到正向促进作用;OFDI的扩大不利于净出口的增长;双向FDI与三次产业都存在较强的灰色关联度。据此,提出改善对内对外投资环境、发挥双向FDI对固定资产投资和净出口的促进作用、强化双向FDI对产业结构的调整作用等建议。     

基于灰色系统建模技术的人体疾病早期预测预警研究

传统健康体检主要通过对单次体检指标进行横向比较和静态分析,忽略个体差异,纯粹从指标高低来判断体检者健康状况与身体状态,难以及时发现体检者可能存在的疾病隐患。由于个体体检指标具有样本量小、信息不确定、数据类型异构、影响因素构成复杂等特征,传统以大样本为基础的数学模型均难以适应此类小数据系统的建模要求。为此,通过建立适用于人体主要指标趋势预测的灰色系统模型（简称HIGM（1,1）模型）,实现对人体主要健康指标的动态建模与趋势分析,从而可以掌握体检者未来一段时间身体指标的变化趋势及可能存在的疾病隐患。研究成果对提升体检效果、强化体检意义,促进灰色理论与现实问题的有效对接等方面,均具有积极意义。