上海市浦东新区发热呼吸道症候群与流感样病例的相关性分析

<正>症状监测是对临床诊断前患者相关的非特异性信息进行监测。相关研究表明,症状监测可应用于公共危机应对,以及大型体育活动与政治集会等大规模人群聚集活动的公共卫生保障[1-2]。浦东新区传染病症状监测预警系统,以2010年上海世博会卫生保障为背景,建立社区的症状监测系统,探索症状监测的早期预警技术。在监测点医院HIS系统嵌入症状监测模块,严格按症状监测要求填写各类症候群信息,数据每晚12∶00通过医院前置机准时推送到卫生     

济南市某高中一起流行性腮腺炎暴发调查

流行性腮腺炎(以下简称流腮)属于法定传染病中的丙类传染病,近年来发病一直保持较高水平[1-2].流腮属呼吸道传染病,其传染性较强,家庭易感接触者续发率可达70％[3],极易在人群密集场所引起暴发.济南市突发公共卫生事件管理信息系统数据显示,校园流腮突发事件每年都有发生.2011年底,济南市通过疾病监测信息系统发现市属某重点高中出现流腮暴发.现将结果报道如下. 1 资料来源与方法 1.1 资料来源 突发公共卫生事件管理信息系统,现场流行病学调查,济南市疾病预防控制中心病毒实验室检测数据.1.2病例搜索与分类医疗机构网络直报结合访谈校医、教师及学生搜索和发现病例,病例诊断及分类标准参照"流行性腮腺炎诊断标准及处理原则"[4].     

严重临床异常病例/事件监测系统与长宁区现有监测系统监测结果的比较研究

目的分析严重临床异常病例/事件监测试运行期间上海市长宁区重点传染病监测结果,为系统的推广使用提供依据。方法分析2014年1-7月间辖区内重点传染病及症状监测系统的结果,与试点医院严重临床异常病例/事件监测结果进行比对。结果试运行期间,通过长宁区区域内现有的传染病、突发公共卫生事件、流感、输入性传染病等各类疾病和症状系统的监测,共检出2例具有公共卫生意义的严重/异常临床个案,其中1例为血清学确诊的流行性出血热病例,1例为可疑的中东呼吸道综合征病例。但因为哨点医院设置、筛查标准等问题,这两例病例均未通过严重临床异常病例/事件监测系统试点医院的系统报告。结论严重临床异常病例/事件监测系统是突发公共卫生事件早期预警的补充手段之一,但在推广应用时需扩大监测范围,并完善筛查标准。     

症状监测在伤寒和副伤寒防治中的应用

WHO一直把伤寒和副伤寒发病列为发展中国家重要的公共卫生问题[1].伤寒和副伤寒的典型临床症状体征主要以持续性高热、玫瑰疹、相对缓脉、肝脾肿大及表情淡漠等为特征,但临床表现逐渐呈不典型化和轻型化,尤其是副伤寒常以持续发热为主要特征.这种早期非特异性的临床表现,如缺乏实验室检测,给临床及时正确诊断带来很大困难,误诊和诊断滞后会导致治疗效果不佳,传染源得不到及时控制,易引起暴发或地方性流行.症状监测(syndromic surveillance)近年在理论和实践方面取得长足的进步与发展,在很多疾病监测中得以运用.公共卫生工作者开始尝试利用症状监测的思想,针对伤寒和副伤寒以持续高热为主的临床特点,来设计发热症状监测系统,以提高伤寒和副伤寒防治水平.现将相关信息与研究进展综述如下.     

非处方药销售监测在早期预警中应用研究进展

症状监测(syndromic surveillance)是根据应对生物恐怖的要求而发展起来的一类新的公共卫生监测方法,数据来源广泛[1],可用于疾病监测和突发公共卫生事件的早期预警[2-3].其中非处方药物(over-the-counter,OTC)销售监测由于具有及时、详细、数据易获得等优点[4-5],越来越受到公共卫生工作者的重视.但国内外对OTC药物销售监测的早期预警意义尚未统一,需要更多的研究进行探讨.现对近年来OTC药物销售监测在公共卫生监测和早期预警中应用的研究进展综述如下.     

传染病监测及其关口前移研究进展

本世纪以来,美国炭疽生物恐怖事件,中国传染性非典型肺炎( SARS)流行、人感染高致病禽流感的出现等公共卫生事件发生后,国内外很多学者都在致力于提高公共卫生早期预警能力的研究.世界卫生组织( WHO)修订了《国际卫生条例》,建立了全球传染病突发预警和应对网络(the Global outbreak Alert and Response Network,GOARN)[1].因此,为了提高公共卫生事件的早期预警,除了完善现行的公共卫生监测体系外,不少新的监测系统应运而生,监测技术和监测方法不断提高,监测重心下移作为早期预警手段,近几年来受到很大关注.本文将传染病监测的方法及其关口前移研究进展进行综述.     

上海市托幼机构缺勤缺课网络直报系统数据质量调查

正托幼机构是学龄前儿童聚集生活和学习的场所,也是传染病聚集发病和暴发的重点场所。做好托幼机构内传染病的早发现、早报告,对传染病防控工作意义重大。症状监测是指系统、持续地收集、分析临床确诊前出现的与疾病暴发相关的信息,以便据此做出公共卫生反应~([1-5])。症状监测作为一种新兴的公共卫生监测手段,有利于地区暴发疫情和其他突发公共卫生事件的早期探测和预警。上海市于2010年建立并于2011学年起正式运行     

加强我国传染病预警的研究与应用

突发公共卫生事件是指突然发生,造成或者可能造成社会公众健康严重损害的传染病疫情、群体性不明原因疾病、食物和职业中毒以及其他严重影响公众健康的事件,其中,重大传染病疫情是突发公共卫生事件的主要类型之一[1].传染病疫情的应急处置包括预防、监测与预警、病例救治、调查与处置和评估等多个环节,开展传染病疫情的监测,及时发现传染病暴发并进行预警是控制传染病疫情的重要前提.     

加强我国传染病预警的研究与应用

突发公共卫生事件是指突然发生,造成或者可能造成社会公众健康严重损害的传染病疫情、群体性不明原因疾病、食物和职业中毒以及其他严重影响公众健康的事件,其中,重大传染病疫情是突发公共卫生事件的主要类型之一[1].传染病疫情的应急处置包括预防、监测与预警、病例救治、调查与处置和评估等多个环节,开展传染病疫情的监测,及时发现传染病暴发并进行预警是控制传染病疫情的重要前提.     

辽宁省急性呼吸系统疾病监测结果分析

急性呼吸系统传染病发病急、传播快,如不及时发现和处理,严重危害人群健康[1,2].辽宁省针对急性呼吸系统传染性疾病监测初步建立了传统公共卫生监测与症状监测相结合的主动监测系统,选择突发公共卫生事件报告的主体医院为哨点,由疾病预防控制中心派专人进驻以实时收集门急诊患者尤其是具有呼吸系统感染表现患者的基本信息,有选择地抽样采集实验室标本,进行相关病原微生物的快速检验.     

应用广义多因子降维法分析数量性状的交互作用

介绍广义多因子降维法(GMDR)在交互作用分析,尤其是数量性状的基因-基因交互作用分析中的应用.文中简述GMDR的原理、基本步骤及其特点,并结合实例说明如何在研究中对GMDR进行应用.GMDR是无模型的交互作用分析方法,能够处理连续型结局变量,还可纳入协变量改善预测准确率,目前已成功应用于尼古丁依赖等疾病的研究.GMDR能够处理多种样本类型和结局变量类型,与其他连续变量交互作用分析方法相比具有一定优势.     

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits

The manifestation of complex traits is influenced by gene–gene and gene–environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.     

Detecting genetic interactions for quantitative traits with U-statistics

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.     

Application of artificial neural network models in occupational safety and health utilizing ordinal variables

Safety professionals and practitioners are always searching for methods to accurately assess the association between exposures and possible occupational disorders or diseases and predict the outcome of any variable. Statistical analysis and logistic regression (LR) in particular are among the most popular tools being used today. Artificial neural network (ANN) models are another method of predicting outcomes, which are gradually finding their way into the safety field. Limited studies have shown that they are capable of predicting outcomes more accurately than LR, but they have been tested either on continuous or on dichotomous variables or combinations of them. The objective of this research was to demonstrate that ANN models can perform better than LR models with data sets comprised of all ordinal variables, which has not been done so far. The data set used in this research was collected from construction workers using the Work Compatibility questionnaire. The data set contained only ordinal variables both as input (exposure) and as output (outcome) variables. LR models and ANN models were constructed using the same data set and the performance of all models was compared by using the log-likelihood ratio. The result of this study showed that ANN models performed significantly better than LR models with a data set of all ordinal variables as well as other types of variables such as dichotomous and continuous.     

A survival tree method for the analysis of discrete event times in clinical and epidemiological studies

Survival trees are a popular alternative to parametric survival modeling when there are interactions between the predictor variables or when the aim is to stratify patients into prognostic subgroups. A limitation of classical survival tree methodology is that most algorithms for tree construction are designed for continuous outcome variables. Hence, classical methods might not be appropriate if failure time data are measured on a discrete time scale (as is often the case in longitudinal studies where data are collected, e.g., quarterly or yearly). To address this issue, we develop a method for discrete survival tree construction. The proposed technique is based on the result that the likelihood of a discrete survival model is equivalent to the likelihood of a regression model for binary outcome data. Hence, we modify tree construction methods for binary outcomes such that they result in optimized partitions for the estimation of discrete hazard functions. By applying the proposed method to data from a randomized trial in patients with filarial lymphedema, we demonstrate how discrete survival trees can be used to identify clinically relevant patient groups with similar survival behavior. Copyright © 2015 John Wiley & Sons, Ltd.     

Discovery of complex pathways from observational data

Unraveling complex interactions has been a challenge in epidemiologic research. We introduce a pathway modeling framework that discovers plausible pathways from observational data, and allows estimation of both the net effect of the pathway and the types of interactions occurring among genetic or environmental risk factors. Each discovered pathway structure links combinations of observed variables through intermediate latent nodes to a final node, the outcome. Biologic knowledge can be readily applied in this framework as a prior on pathway structure to give preference to more biologically plausible models, thereby providing more precise estimation of Bayes factors for pathways of greatest interest by Markov Chain Monte Carlo (MCMC) methods. Data were simulated for binary inputs of which only a subset was involved in different pathway topologies. Our algorithm was then used to recover the pathway from the simulated data. The posterior distributions of inputs, pairwise and higher‐order interactions, and topologies were obtained by MCMC methods. The evidence in favor of a particular pathway or interaction was summarized using Bayes factors. Our method can correctly identify the risk factors and interactions involved in the simulated pathway. We apply our framework to an asthma case–control data set with polymorphisms in 12 genes. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation of the results of a randomized controlled trial: how to define changes between baseline and follow-up

OBJECTIVE: The most common way to evaluate the effect of an intervention is to compare the intervention and nonintervention groups regarding the change in the outcome variable between baseline and follow-up; however, there are many different ways to define "changes." The purpose of this article is to demonstrate how different definitions of "change" used in the analysis can influence the results of a study. STUDY DESIGN AND SETTING: Two different randomized controlled trials were used as examples. RESULTS: The results of the analyses showed that for continuous outcome variables, analysis of covariance seems to be the most appropriate because it corrects for the phenomenon of regression to the mean. For dichotomous outcome variables, multinomial logistic regression analysis with all possible changes over time as outcome seems to be the most appropriate, especially because of its straightforward interpretation. CONCLUSION: A different definition of "change" can lead to different results in the evaluation of the effect of an intervention.     

Assessing smallest detectable change over time in continuous structural outcome measures: application to radiological change in knee osteoarthritis

Interpreting changes in continuous structural outcome measures is a common problem in clinical research and in daily practice. We propose a method for estimating whether difference observed between two successive measures in an individual constitutes a statistically relevant change or a change induced by variability. This statistically relevant change is based on an analysis of reproducibility. The continuous structural outcome measure investigated as an example was joint space width (JSW) measurement on standard X-rays, which is known to be the primary end-point for assessing structural osteoarthritis progression. The results of the present study demonstrate that cutoffs are closely dependent on all sources of variabilities in JSW measurement such as joint positioning, radiographic procedure, and the measurement process itself. Therefore, we suggest to determine cutoffs for each study using a representative sample of the population studied and using the procedures and methods of measurement of the specific study. This approach may easily be extended to other continuous structural outcome measures.     

Semiparametric methods for estimation of a nonlinear exposure‐outcome relationship using instrumental variables with application to Mendelian randomization

Mendelian randomization, the use of genetic variants as instrumental variables (IV), can test for and estimate the causal effect of an exposure on an outcome. Most IV methods assume that the function relating the exposure to the expected value of the outcome (the exposure‐outcome relationship) is linear. However, in practice, this assumption may not hold. Indeed, often the primary question of interest is to assess the shape of this relationship. We present two novel IV methods for investigating the shape of the exposure‐outcome relationship: a fractional polynomial method and a piecewise linear method. We divide the population into strata using the exposure distribution, and estimate a causal effect, referred to as a localized average causal effect (LACE), in each stratum of population. The fractional polynomial method performs metaregression on these LACE estimates. The piecewise linear method estimates a continuous piecewise linear function, the gradient of which is the LACE estimate in each stratum. Both methods were demonstrated in a simulation study to estimate the true exposure‐outcome relationship well, particularly when the relationship was a fractional polynomial (for the fractional polynomial method) or was piecewise linear (for the piecewise linear method). The methods were used to investigate the shape of relationship of body mass index with systolic blood pressure and diastolic blood pressure.