大数据生态流行病学理论模型诠释

当今,数字技术作为一种新型的健康决定因素,它改变了社会、经济、城市和家庭,进而又影响了人类健康;人类进入了一个全新的数字虚拟世界。由此,在大数据生态流行病学理论范式中,现实世界生态流行病学模型与虚拟世界生态流行病学模型,组成既相对独立又相互博弈交互的共同体,它们均以表观遗传为中介,在以基因组为核心的多组学因素、现实世界健康决定因素、虚拟世界健康决定因素间相互博弈和相互依赖;由此形成了众多镶嵌层级内健康决定因素交互博弈的生态流行病学新病因论框架。现实世界与虚拟世界两个生态流行病学模型,通过同一数字健康模型,以数字技术作为“双刃剑”角色而发挥作用。     

基于系统动力学方法的我国宏观卫生筹资系统要素分析

依据复杂系统相关理论及系统动力学原理,综合运用系统分析、主体行为分析方法,界定了我国宏观卫生筹资系统要素组成,定性分析了其系统组成、系统结构和系统边界,描述了系统主体行为特征,构建了基于系统动力学方法的我国宏观卫生筹资系统概念模型,为深入认识卫生筹资系统、进一步建立该系统的SD模型提供了理论依据.     

“农村人群就医行为选择”建模设计

目的：建立＂农村人群就医行为选择＂概念模型,为进一步建立系统动力学模型和揭示农村人群就医行为特征提供理论支撑和前期准备。方法：依据复杂适应性系统理论（CAS）,应用系统动力学建模技术与方法,构建＂农村人群就医行为选择＂逻辑模型。结果：分析了系统模型结构、内部主体与外部环境,并在此基础上构建了＂农村人群就医行为选择＂问题模型的概念模型。结论：系统动力学建模方法是揭示农村人群就医行为复杂性的有效工具,模型能有效分析并解释系统主体行为特征,有利于下一步继续构建数学模型。     

系统流行病学

医学大数据、转化医学、精准医学时代为慢性复杂疾病及其病因的研究带来新的契机。如何实现循证医学、科学转化、合理精准是我们目前面临的任务和挑战。系统流行病学是一种进行疾病危险因素风险识别的流行病学方法,是流行病学的新领域,其利用系统生物学、流行病学、计算数学等技术将健康大数据与系统生物学结合起来,在分子、细胞、组织、人群社会行为和生态环境等多水平、多组学上深入研究疾病发生风险的统计学模型,并对未来风险状况进行计算模拟和预警预测。由于数据来源的多样性、复杂性以及大数据的特征,为系统流行病学的设计方法和分析方法提出了新的挑战。本文详细介绍了系统流行病学的理论基础、概念、研究目的、研究内容、研究意义、研究设计、分析方法及其在公共卫生领域的应用。     

我国公共卫生服务系统的系统动力学模型研究

我国公共卫生服务系统（PHs）是一个极其复杂的大系统,研究运用系统动力学建模技术构建我国公共卫生服务系统的系统动力学模型,揭示我国公共卫生系统各子系统的运行规律,为政策干预试验打下基础。     

复杂网络模型在传染病预防控制中的应用

为探讨复杂网络模型在传染病预防控制中的应用,根据复杂网络的基本属性,利用网络模型和常用软件分析传染病传播过程.相对于传统的流行病学方法,复杂网络的理论不仅能描述传染病动态传播过程,也能进行传染病预测.通过复杂网络的理论来研究疾病的传播,能深入理解到网络的拓扑结构对疾病传播有重要影响,从而找到控制疾病传播的更有效的方法.     

人群就医行为模拟在医疗卫生服务系统动力学建模研究中的应用

目的构建医疗卫生服务系统模型的人群就医选择的动力学模块，仿真模拟并分析人群行为对系统结构的影响，探讨系统资源结构演化的动力机制。方法数据主要来自国家三次卫生服务调查资料和国家统计年鉴资料；HDS模型的构建和人群就医行为的模拟使用系统动力学理论与方法。结果构建医疗卫生服务系统动力学模型的人群就医选择模拟功能模块；模型模拟结果符合预期，人群就医选择模型仿真模拟结果具有较好的真实性和稳定性。结论人群就医选择的动力学模块是HDS建模研究的核心内容之一，是应用系统动力学理论和方法进行HDS结构演化模拟的关键步骤，模型的构建将为政策试验组的干预试验提供平台。     

我国宏观卫生筹资系统动力学模型构建

文章在卫生筹资系统概念模型与逻辑模型的基础上,依据复杂系统相关理论,运用系统动力学建模技术,对宏观卫生筹资系统建模研究进行假设,筛选了模型重要变量,确定主要函数关系,构建我国宏观卫生筹资系统动力学模型,为我国卫生筹资体系的改革提供理论依据。     

系统动力学模型在卫生总费用推算中的应用研究

目的:探索系统动力学仿真模型在卫生总费用推算中的应用.方法:依据系统动力学建模原理.采用VensimPLE 5.8b仿真模拟软件建立卫生总费用的系统仿真模型,推算2005-2009年卫生总费用水平并与核算报告值进行对比.结果:系统动力学模型的推算结果优于其他推算方法,非常接近报告值,推算值与报告值的相对平均误差为0.71%.结论:系统动力学模型应用于卫生总费用推算效果良好,可为卫生总费用仿真研究增添一个新工具.     

基于系统动力学的过度医疗问题博弈分析

过度医疗是一个世界性难题,被认为是造成医疗费用上涨过快,卫生资源严重浪费,医患关系不断恶化等结果的主要原因。本文基于演化博弈思想与系统动力学理论,在信息不确定的条件下,对过度医疗问题进行建模分析,揭示过度医疗问题博弈双方的动态特性,利用系统动力学为解决过度医疗问题提供一个定量定性相结合的政策仿真平台,从而为缓解“看病难、看病贵”问题提供可参照的依据,为我国医疗卫生事业的改革提供策略。     

Choosing a future for epidemiology: II. From black box to Chinese boxes and eco-epidemiology.

Part I of this paper traced the evolution of modern epidemiology in terms of three eras, each with its dominant paradigm, culminating in the present era of chronic disease epidemiology with its paradigm, the black box. This paper sees the close of the present era and foresees a new era of eco-epidemiology in which the deployment of a different paradigm will be crucial. Here a paradigm is advocated for the emergent era. Encompassing many levels of organization--molecular and societal as well as individual--this paradigm, termed Chinese boxes, aims to integrate more than a single level in design, analysis, and interpretation. Such a paradigm could sustain and refine a public health-oriented epidemiology. But preventing a decline of creative epidemiology in this new era will require more than a cogent scientific paradigm. Attention will have to be paid to the social processes that foster a cohesive and humane discipline.     

机器学习在子宫内膜异位症诊断中的应用

机器学习是一种多学科交叉下产生的人工智能学科.在大数据时代,从数据挖掘的角度出发,应用机器学习方法,通过在繁复的数据中寻找隐含的信息与规律,是探索子宫内膜异位症(EMs)诊断和预测标准的新契机.利用机器学习挖掘EMs相关数据、构建诊断及预测模型具有可行性,但目前机器学习模型用于EMs辅助诊断尚处于研究阶段.从用于机器学...     

Disentangling the complexity of infectious diseases: Time is ripe to improve the first-line statistical toolbox for epidemiologists

Because many biological processes related to the dynamics of infectious diseases are caused by complex interactions between the environment, the host(s) and the agent(s), the necessity to address the methodological implications of this inherent complexity has recently emerged in epidemiology. Most epidemiologists now acknowledge that most human infectious diseases are likely to have complex dynamics. However, this knowledge still percolates with difficulty in their statistical “modus operandi”. Indeed, for the study of complex systems, the traditional first-line statistical toolbox of epidemiologists (mainly built around the Generalized Linear Model family), despite its undeniable practicality and robustness, has structural limitations deprecating its usefulness. Three major sources of complexity neglected or not taken into account by this first-line statistical toolbox and having deep statistical implications are the multi-level organization of data, the non-linear relationships between variables and the complex interactions between variables. Three promising candidates to incorporate along with traditional tools for a new first-line statistical toolbox more suitable to apprehend these sources of complexity are the generalized linear mixed models, the generalized additive models, and the structural equation models. The aforementioned methodologies have the advantage to be generalizations of GLM models and are relatively easy to implement. Their assimilation and implementation would thus be greatly facilitated for epidemiologists. More globally, this text underlines that an improved use of other methods as such described here compared to traditional ones has to be performed to better understand the complexity challenging epidemiologists every day. This is particularly true in the field of infectious diseases for which major public health challenges will have to be addressed in the coming decades.     

Applying sample survey methods to clinical trials data

This paper outlines the utility of statistical methods for sample surveys in analysing clinical trials data. Sample survey statisticians face a variety of complex data analysis issues deriving from the use of multi-stage probability sampling from finite populations. One such issue is that of clustering of observations at the various stages of sampling. Survey data analysis approaches developed to accommodate clustering in the sample design have more general application to clinical studies in which repeated measures structures are encountered. Situations where these methods are of interest include multi-visit studies where responses are observed at two or more time points for each patient, multi-period cross-over studies, and epidemiological studies for repeated occurrences of adverse events or illnesses. We describe statistical procedures for fitting multiple regression models to sample survey data that are more effective for repeated measures studies with complicated data structures than the more traditional approaches of multivariate repeated measures analysis. In this setting, one can specify a primary sampling unit within which repeated measures have intraclass correlation. This intraclass correlation is taken into account by sample survey regression methods through robust estimates of the standard errors of the regression coefficients. Regression estimates are obtained from model fitting estimation equations which ignore the correlation structure of the data (that is, computing procedures which assume that all observational units are independent or are from simple random samples). The analytic approach is straightforward to apply with logistic models for dichotomous data, proportional odds models for ordinal data, and linear models for continuously scaled data, and results are interpretable in terms of population average parameters. Through the features summarized here, the sample survey regression methods have many similarities to the broader family of methods based on generalized estimating equations (GEE). Sample survey methods for the analysis of time-to-event data have more recently been developed and implemented in the context of finite probability sampling. Given the importance of survival endpoints in late phase studies for drug development, these methods have clear utility in the area of clinical trials data analysis. A brief overview of methods for sample survey data analysis is first provided, followed by motivation for applying these methods to clinical trials data. Examples drawn from three clinical studies are provided to illustrate survey methods for logistic regression, proportional odds regression and proportional hazards regression. Potential problems with the proposed methods and ways of addressing them are discussed.     

Introduction to Mediation Analysis and Examples of Its Application to Real-world Data

Traditional epidemiological assessments, which mainly focused on evaluating the statistical association between two major components-the exposure and outcome-have recently evolved to ascertain the in-between process, which can explain the underlying causal pathway. Mediation analysis has emerged as a compelling method to disentangle the complex nature of these pathways. The statistical method of mediation analysis has evolved from simple regression analysis to causal mediation analysis, and each amendment refined the underlying mathematical theory and required assumptions. This short guide will introduce the basic statistical framework and assumptions of both traditional and modern mediation analyses, providing examples conducted with real-world data.     

Combining several ordinal measures in clinical studies

In medical research, it is rare that a single variable is sufficient to represent all relevant aspects of epidemiological risk, genomic activity, adverse events, or clinical response. Since biological systems tend to be neither linear, nor hierarchical in nature, the assumptions of traditional multivariate statistical methods based on the linear model can often not be justified on theoretical grounds. Establishing concept validity through empirical validation is not only problematic, but also time consuming. This paper proposes the use of u-statistics for scoring multivariate ordinal data and a family of simple non-parametric tests for analysis. The scoring method is demonstrated to be applicable to scoring clinical response profiles in the treatment of psoriasis and then to identifying genomic pathways that best correlate with these profiles.     

Multiblock modelling to assess the overall risk factors for a composite outcome

Research in epidemiology may be concerned with assessing risk factors for complex health issues described by several variables. Moreover, epidemiological data are usually organized in several blocks of variables, consisting of a block of variables to be explained and a large number of explanatory variables organized in meaningful blocks. Usual statistical procedures such as generalized linear models do not allow the explanation of a multivariate outcome, such as a complex disease described by several variables, with a single model. Moreover, it is not easy to take account of the organization of explanatory variables into blocks. Here we propose an innovative method in the multiblock modelling framework, called multiblock redundancy analysis, which is designed to handle most specificities of complex epidemiological data. Overall indices and graphical displays associated with different interpretation levels are proposed. The interest and relevance of multiblock redundancy analysis is illustrated using a dataset pertaining to veterinary epidemiology.     

Use of poisson regression and box-jenkins models to evaluate the short-term effects of environmental noise levels on daily emergency admissions in Madrid,Spain

The relationship between environmental factors and hospital admissions has usually been analysed without taking into account the influence of a factor closely related to traffic in big cities, that is, environmental noise levels. We analysed the relationship between environmental noise and emergency admissions, for all causes and specific causes in Madrid (Spain), for the study period 1995–1997, using two statistical methods for the analysis of epidemiological time series data: Poisson autoregressive models and Box–Jenkins (ARIMA) methodology. Both methods produce a clear association between emergency admissions for all and specific causes and environmental noise levels. We found very similar results from both methods for all and circulatory causes, but slightly different for respiratory causes. Around 5% of all emergency admissions can be attributed to high noise levels, with a lower figure for specific causes. Current levels of environmental noise have a considerable epidemiological impact on emergency admissions in Madrid. A reduction of environmental noise levels could be accompanied by a possible reduction in the number of emergency admissions.