大气污染人群健康流行病学研究中统计学分析方法应用进展

在大气污染人群健康流行病学研究中,统计学分析方法是一项重要的技术手段,相关统计学方法的发展为研究人员更准确地评估环境空气污染对人群健康的影响发挥重要作用。本文综述了大气污染与人群健康流行病学研究中常用的统计学方法,以期为大气污染健康效应研究领域相关研究提供方法学线索和科学参考。     

职业流行病学

职业流行病学是介于流行病学、劳动卫生学、职业病学以及数理统计学之间的边缘学科。职业流行病学作为劳动卫生与职业病学学术发展的主要技术支柱之一,近年发展很快。国内应用流行病学方法研究职业肿瘤、尘肺、乡镇工业职业卫生服务,以及职业中毒的分布等方面作了大量工作,取得了可喜成绩。不能把职业流行病学理解为仅限于研究职业病的流行病学,而应认为是应用流行病学方法研究职业卫生的人群现象,其内容很广泛。职业流行病学是以工业人群为对象,研究职业危害因素对工人健康的影响,描述工人健康和病伤损     

对五部规划教材《流行病学》中“病例对照研究”一词的解释引发的思考

病例对照研究是流行病学的重要研究方法之一。在具体疾病病因研究中发挥重要的作用，是目前原因不明疾病的病因研究的常用研究方法之一，病例对照研究是流行病学教科书里一个非常重要的概念，如何理解和使用这个概念，笔者对改革开放之后由人民卫生出版社出版的《流行病学》五部规划教材进行了学习、研究和对比，同时对一些知识点、文字及有关的问题进行了深入思考，期望引起初学者对正确理解和正确使用病例对照研究给予高度重视。     

协方差分析及其在流行病学研究中的应用

流行病学研究是现代医学研究的重要方法之一,为了得到真实的研究结果,在流行病学研究或推理过程中应注意消除或减少任何使所     

机器学习在公共卫生领域的应用现状

公共卫生问题与人们的健康状况和生命质量密切相关。近年来随着人工智能的发展,机器学习在公共卫生领域得到了更为广泛的应用,机器学习技术可以处理大量的数据,并从中发现模式和趋势,从而帮助公共卫生部门更好地了解公共卫生问题。本文综述了近年来机器学习在公共卫生领域的应用,包括在流行病学研究、公共卫生监测、健康信息处理、公共卫生决策、心理健康等方面中的应用。     

我国老年流行病学研究进展

老年流行病学是运用流行病学原理和方法,研究老年人群健康状态、疾病及其危险因素的分布和预防,并采取干预措施促进老年人群健康的学科。我国人口老龄化和高龄化进程逐渐加深,老年流行病学作为流行病学的重要分支和老年预防医学的骨干学科之一,为实现预防疾病、促进健康的目标发挥着重要作用。本文总结了近年来我国老年流行病学研究的进展,梳理我国老年人的健康分布规律和危险因素等,并对老年流行病学学科发展的趋势进行了展望。     

活动行为时间使用流行病学及其国内研究展望

活动行为时间使用流行病学已成为当前国际公共卫生领域的重要发展前沿之一。本文对时间使用流行病学的学科形成进行概述,并从关键研究问题、数据性质和统计学分析方法3个方面总结活动行为时间使用流行病学的特点。通过梳理国内活动行为时间使用流行病学的研究现状,提出未来应重点开展活动行为时间分配的健康影响效应、活动行为最佳使用时间分配、活动行为使用时间分配模式的分布及其影响因素等领域的研究,同时指出在方法学方面的改进之处。     

现代实用流行病学方法 第四讲 综合评价方法(1)

现代实用流行病学方法第四讲综合评价方法（１）章扬熙评价是管理流行病学的重要内容之一，流行病学方法在卫生管理过程中起着重要作用，流行病学信息是卫生规划、计划、实施和评价的重要依据，流行病学在卫生管理领域中的实践，也促进了现代流行病学的发展。规划与计划、...     

流行病学实验研究发展历史

流行病学实验研究作为流行病学的重要研究方法之一,是通过比较给予干预措施后的实验组人群与对照组人群的结局,从而验证研究假设和考核干预措施效果的一种前瞻性研究方法,又称干预研究、实验流行病学研究等。其中包括临床试验、现场试验和社区干预项目等研究类型。这类研究方法在临床治疗和疾病预防措施的科学评价和筛选、医疗卫生政策、健康教育及诊断技术效果评估等方面起着举足轻重的作用,已被视为评价干预措施有效性的标准方法。但流行病学实验研究在方法学发展和科学性不断完善方面经历了漫长的历程。     

病因流行病学研究方法进展

流行病学是病因学研究的重要方法,病因学研究是流行病学研究的重要领域。由于面临的病因研究问题越来越复杂,促进了病因流行病学研究方法的快速发展。本文从病因概念与模型、混杂因素的控制、交互作用的研究、流行病学与组学研究的结合、分子病理学的概念与发展、病因判断标准这六个方面,对病因流行病学研究方法的进展做一全面总结。     

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...     

Using longitudinal targeted maximum likelihood estimation in complex settings with dynamic interventions

Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, ie, it is doubly robust, and can yield valid inference when used in conjunction with machine learning. It also has the advantage of easy-to-calculate analytic standard errors in contrast to the g-formula, which requires bootstrapping. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings, and we compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies: (1) long follow-up time (30 months); (2) gradually declining sample size; (3) limited support for some intervention rules of interest; (4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods; and (5) consideration of collider bias. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: We show that (1) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (2) machine learning utilized with a small set of simple learners (if more complex ones cannot be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; and (3) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis. We provide guidance for the practical application of LTMLE.     

How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments

Researchers have found that machine learning methods are typically better at prediction than econometric models when the choice environment is stable. We study hospital demand models, and evaluate the relative performance of machine learning algorithms when the choice environment changes substantially due to natural disasters that closed previously available hospitals. While machine learning algorithms outperform traditional econometric models in prediction, the gain they provide shrinks when patients’ choice sets are more profoundly affected. We show that traditional econometric methods provide important additional information when there are major changes in the choice environment.     

基于机器学习模型缺血性脑卒中1年死亡预测效果评价

  目的  对机器学习在医疗和公共卫生研究中的应用方向进行梳理,为制定相应政策和策略提供科学依据。  方法  通过Citespace科研文献可视化工具对Web of Science数据库有关机器学习在医疗和公共卫生中应用的2 014篇文献进行聚类,找到当今的研究热点。  结果  机器学习在医疗和公共卫生中的应用主要为辅助诊疗、监测和预防疾病、机器人辅助手术、药物警戒和研发以及医疗体系质量管理5个方向。  结论  机器学习在医疗和公共卫生相关研究中具有较强的适用性和十分广阔的应用前景,在机器学习模型的帮助下根据医学相关数据做出的诊断和决策,不仅能够为个人带来健康,更有助于国家战略的实现。未来在打破医疗数据孤岛以及机器学习的医学伦理等方向可进一步加强探索。     

Socioeconomic,cultural, and behavioral factors affecting Hispanic health outcomes

Evidence suggests that social and economic factors are important determinants of health. Yet, despite higher porverty rates, less education, and worse access to health care, health outcomes of many Hispanics living in the United States today are equal to, or better than, those of non-Hispanic whites. This paradox is described in the literature as the epidemiological paradox or Hispanic health paradox. In this paper, the authors selectively review data and research supporting the existence of the epidemiological paradox. They find substantial support for the existence of the epidemiological paradox, particularly among Mexican Americans. Census undercounts of Hispanics, misclassification of Hispanic deaths, and emigration of Hispanics do not fully account for the epidemiological paradox. Identifying protective factors underlying the epidemiological paradox, while improving access to care and the economic conditions among Hispanics, are important research and policy implications of this review.     

B[a]P所致神经发育毒性的研究进展

B[a]P（benzo[a]pyrene,B[a]P）是重要的职业和环境污染物之一,它的神经发育毒性与致癌性／致畸性相比虽是一种弱效应,但会对处于发育中的机体造成不良后果,尤其是中枢神经系统对其更为敏感。本课题组以近年来国内外有关B[a]P神经发育毒性的研究为基础,综述其流行病学研究、实验动物研究以及可能的毒性机制等方面研究进展;结合本课题组的已有研究,认为由于B[a]P的神经发育毒性所致学习和记忆损伤应得到广泛关注。     

Epidemiologische Forschung mit den Daten der Krebsregister

In 2007, the German Cancer Aid started a program to promote epidemiological research with data from cancer registries. Within this program, ten projects with various epidemiological research questions were funded. The program was a major boost for the use of data from population-based cancer registries and was an important milestone for cancer epidemiology in Germany.     

Implications from epidemiologic studies on magnetic fields and the risk of childhood leukemia on protection guidelines

The objective of this review is to discuss the impact of findings in epidemiological studies on magnetic fields and the risk of childhood leukemia on the definition of exposure limits. A large number of epidemiological studies have consistently shown an association between the risk of childhood leukemia and residential extremely low-frequency magnetic field exposures. There is virtually no supportive data from experimental research and, so far, no proposed explanation has reached a level beyond speculation. The contradictory results from epidemiological and experimental research may either be due to methodological limitations creating a spurious association in the epidemiological studies or to a failure of experimental research to examine mechanisms relevant in the complex origin of childhood leukemia. Taking this together, the overall evidence is not strong enough to demand a revision of the current guidelines for public protection. Application of precautionary measures may be an option; however, decision-makers should be advised that these measures are often not straightforward and a careful evaluation of a possible benefit needs to be performed for each individual situation. Undoubtedly there are gaps in research, and no substantial contribution for clarification of the apparent inconsistencies emerges from recent studies. However, there are important lessons to learn, either with respect to the etiology of childhood leukemia or with respect to the need for improving epidemiological methods for the identification of presumably weak associations.     

The Wire and Urban Health Education

As urban health has emerged as a distinct field, experts have collaborated to develop models for interdisciplinary education to train health professionals. Interdisciplinary learning is an important yet challenging imperative for urban health education. This paper explores lessons learned from a 2010 speaker series at the Johns Hopkins Bloomberg School of Public Health. The television show, The Wire, was used as a teaching tool to illustrate the context of health disparities in American cities and to explore the complex factors perpetuating urban health outcomes. We suggest that individuals interested in developing interdisciplinary teaching models can learn from both the form and the content of The Wire. As a popular televised serial narrative, The Wire prompts an investigation into the forms and circulation of academic research in a fractured and specialized media landscape. The formal narrative structure of the show provides mental scaffolding from which epidemiological, historical, geographical, anthropological, and other relevant disciplinary learning can build. The Wire encourages critical reflection among public health professionals about the forces that shape public health training, research, and practice and offers creative expansions to existing urban health educational efforts.