现代流行病学创始人:威廉·法尔

威廉·法尔（William Farr，1807-1883），英国流行病学家，医学统计学的奠基人之一，维多利亚时期公共卫生活动领域的杰出人物。他在英国创造了世界上最好的官方生命统计系统，进一步改进了寿命表法，并提出许多流行病学概念，如标化死亡率、患病率、人年和剂量-反应关系等。这一系列工作不仅使他成为生命统计领域的先驱人物，也为流行病学的定量研究打下坚实的理论基础。     

多元统计学的开创者:乔治·乌德尼·尤尔

乔治·乌德尼,尤尔（George Udny Yule,1871-1951）是19世纪未到20世纪中叶最伟大的统计学家之一。他开创了多元冋归与相关分析方法,是现代时间序列分析的创始人。他尊重科学,重视理论与实践相结合,实现了统计学从生物学向社会学的跨越,合理地将统计学拓展至生物学、社会学和经济学等领域,推动了统计学历史的进步。     

具有古典医学思想的现代流行病学家:威廉·巴德

威廉·巴德（William Budd,1811-1880）是19世纪英国著名的流行病学家之一,撰写了很多关于传染病防治的文章。他的主要贡献是阐明了部分流行病具有传染性,如伤寒、霍乱、牛瘟、猩红热、肺结核等,并提出了这些流行病的传播途径以及预防策略,尤其是伤寒和霍乱。这些贡献使他成为流行病学的先驱人物之一,为现代流行病学的微生物理论的发展奠定了基础。     

流行病和统计学的先驱:威廉·奥古斯特·盖伊

19世纪的英国医生威廉·奥古斯特·盖伊是公共卫生史上的一位杰出贡献者,一生都致力于医学事业和人类事业,给后世带来了深远的影响。盖伊最具影响力的工作之一是进行了最早的病例对照研究,在流行病学历史上具有开创性意义。同时,盖伊还在提倡疫苗接种、改善职业人群健康、践行医疗慈善事业、完善表格分析法等方面做出特殊贡献。作为一名著名的内科医生,却在统计学、流行病学上做出重大的突破,令后人十分敬仰。     

英国公共卫生运动的领导者:约翰·西蒙

约翰·西蒙（John Simon,1816-1904）是19世纪英国杰出的卫生官。他以一名医学专家的身份投身于英国公共卫生运动,自创一套公共卫生工作体系,构建国家卫生防疫体系,推动公共卫生立法,开拓了公共卫生领域的科学研究。他超前的公共卫生理念和先进的公共卫生实践,加快了世界公共卫生历史的发展进程。     

临床流行病学之父:皮埃尔·路易斯

19世纪的法国医生皮埃尔·路易斯（Pierre Charles Alexandre Louis,1787-1872）是医学史上的重要人物,他率先运用单纯的观察法和统计分析研究肺结核、伤寒和肺炎等疾病,将"数值计算法"应用于医学,开创了临床流行病学的先河。路易斯是一名临床医生、病理学家,人们记住他的却是一位著名的临床流行病学开拓者。     

莫里斯·弗利德曼与《中国东南部的宗族组织》

有关莫里斯·弗利德曼的生平、履历、业绩和他的学问背景，笔者已在《莫里斯·弗利德曼与＜中国宗族与社会：福建与广东＞》①一文中做了较全面的介绍。出版于 １９５８年的《中国东南部的宗族组织》（以下简称《东南》）一书，是莫里斯·弗利德曼根据自己所掌握的社会人类学方法，以一种整体构想的形式来重新理解旧中国社会的初次尝试；而１９６６年出版的《中国的宗族与社会》（以下简称《中国宗族》），则是遵循着《东南》一书所提出的框架，在实践中对新出现的问题的进一步探索。两书在结构上密切关连，相辅相成。如果勉为区分的话，《…     

林·亨特与新文化史

新文化史运动是20世纪最后20年中西方史学主要的研究趋势，也是整个西方史学长河中的一段重要篇章。美国历史学家林·亨特是这一运动的主要倡导者和领导者之一，她本人不仅在法国大革命史研究领域积极实践新文化史的研究主张，更是先后主编了两部重要的新文化史理论文集——《新文化史》（1989年）及《超越文化转向》（1999年），从而确立起了新文化史最基本的研究范畴与规范。     

氧化苦参碱清除·OH自由基作用及对胸腺嘧啶核苷酸辐射防护作用的ESR研究

本实验利用ＥＳＲ技术研究了氧化苦参碱清除·ＯＨ作用及辐射对胸腺嘧啶核苷酸（５′－ＴＭＰ）间接损伤的防护作用。结果表明：氧化苦参碱有较好的清除·ＯＨ作用，且呈明显量效关系。通过清除·ＯＨ，氧化苦参碱对辐射导致的胸腺嘧啶核苷酸（５′－ＴＭＰ）的间接损伤有显著防护作用     

享受生活 关注健康——乐龄·长寿俱乐部深圳工作站系列活动专题报道

不久前，天津长寿杂志读者俱乐部（乐龄·长寿俱乐部）深圳工作站为会员们举办了健康之旅、茶话会、大型联谊会等系列活动，受到了会员们的欢迎。俱乐部组织会员南澳一日游，让会员们无比开心。南澳岛的青澳湾是沙质细软的缓坡海滩，海水清澈，是天然优良海滨浴场。     

Causality in medicine: getting back to the Hill top

Evidence from randomized controlled trials (RCTs) is almost universally regarded as setting the "gold standard" for medical evidence. Claims that RCTs carry special epistemic weight are often based on the notion that evidence from randomized studies, and only such evidence, can establish that any observed connection between treatment and outcome was caused by the treatment on trial. Any non-randomized trial, on the contrary, inevitably leaves open the possibility that there is some underlying connection independent of receiving the treatment between outcome and one or more differentiating characteristics between those in the experimental and control groups; and hence inevitably leaves open the possibility that treatment and an observed better outcome were "merely correlated" rather than directly causally connected. Here I scrutinize this argument and point towards a more tenable and more modest position by recalling some of the forgotten insights of the RCT pioneer, Austin Bradford Hill.     

A weight of evidence approach to causal inference

ObjectiveThe Bradford Hill criteria are the best available criteria for causal inference. However, there is no information on how the criteria should be weighed and they cannot be combined into one probability estimate for causality. Our objective is to provide an empirical basis for weighing the Bradford Hill criteria and to develop a transparent method to estimate the probability for causality.Study Design and SettingAll 159 agents classified by International Agency for Research of Cancer as category 1 or 2A carcinogens were evaluated by applying the nine Bradford Hill criteria. Discriminant analysis was used to estimate the weights for each of the nine Bradford Hill criteria.ResultsThe discriminant analysis yielded weights for the nine causality criteria. These weights were used to combine the nine criteria into one overall assessment of the probability that an association is causal. The criteria strength, consistency of the association and experimental evidence were the three criteria with the largest impact. The model correctly predicted 130 of the 159 (81.8%) agents.ConclusionThe proposed approach enables using the Bradford Hill criteria in a quantitative manner resulting in a probability estimate of the probability that an association is causal.     

The Bradford Hill considerations on causality: a counterfactual perspective

Bradford Hill's considerations published in 1965 had an enormous influence on attempts to separate causal from non-causal explanations of observed associations. These considerations were often applied as a checklist of criteria, although they were by no means intended to be used in this way by Hill himself. Hill, however, avoided defining explicitly what he meant by "causal effect".     

The GRADE approach and Bradford Hill's criteria for causation

This article describes how the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to grading the quality of evidence and strength of recommendations considers the Bradford Hill criteria for causation and how GRADE may relate to questions in public health. A primary concern in public health is that evidence from non-randomised studies may provide a more adequate or best available measure of a public health strategy's impact, but that such evidence might be graded as lower quality in the GRADE framework. GRADE, however, presents a framework that describes both criteria for assessing the quality of research evidence and the strength of recommendations that includes considerations arising from the Bradford Hill criteria. GRADE places emphasis on recommendations and in assessing quality of evidence; GRADE notes that randomisation is only one of many relevant factors. This article describes how causation may relate to developing recommendations and how the Bradford Hill criteria are considered in GRADE, using examples from the public health literature with a focus on immunisation.     

Reflecting on “A Statistician in Medicine” in 2020

In this commentary, we revisit Sir Austin Bradford Hill's seminal Alfred Watson Memorial Lecture in 1962 through the eyes of two practicing biostatisticians of the current era. We summarize some eternal takeaway messages from Hill's lecture regarding observations and experiments translated through the modern lexicon of causal inference. Finally, we pose a series of questions that we would have liked to pose to Sir Austin Bradford Hill if he were to deliver the lecture in 2020.     

Multicentre clinical trials: Bradford Hill's contributions and some subsequent developments

Most large comparative trials of therapeutic agents are now conducted as multicentre studies. Some of the major studies designed by Bradford Hill were also multicentre, and his methods of design and conduct remain valid today, even though improved methods for analysing the data have appeared subsequently.     

Reflections on the 1962 Paper “The Statistician in Medicine” by Sir Austin Bradford Hill

This article provides reflections on the 1962 paper by Sir Austin Bradford Hill, entitled “The Statistician in Medicine.” It discusses several key takeaways of this paper, including causal inference for big data, reproducibility and replicability in science, and integration of statistics and data science with domain science.     

Clinical trials: Some reflections

The principles and historical development of clinical trials are outlined briefly. Some issues that extend the basic philosophy first put forward by Sir Austin Bradford Hill 40 years ago and that will need consideration in the future application of clinical trials are discussed.     

The missed lessons of Sir Austin Bradford Hill

Austin Bradford Hill's landmark 1965 paper contains several important lessons for the current conduct of epidemiology. Unfortunately, it is almost exclusively cited as the source of the "Bradford-Hill criteria" for inferring causation when association is observed, despite Hill's explicit statement that cause-effect decisions cannot be based on a set of rules. Overlooked are Hill's important lessons about how to make decisions based on epidemiologic evidence. He advised epidemiologists to avoid over-emphasizing statistical significance testing, given the observation that systematic error is often greater than random error. His compelling and intuitive examples point out the need to consider costs and benefits when making decisions about health-promoting interventions. These lessons, which offer ways to dramatically increase the contribution of health science to decision making, are as needed today as they were when Hill presented them.     

Getting causal considerations back on the right track

In their commentary on my paper Phillips and Goodman suggested that counterfactual causality and considerations on causality like those by Bradford Hill are only "guideposts on the road to common sense". I argue that if common sense is understood to mean views that the vast majority of researchers share, Hill's considerations did not lead to common sense in the past – precisely because they are so controversial. If common sense is taken to mean beliefs that are true, then Hill's considerations can only lead to common sense in the simple and well-understood causal systems they apply to. Counterfactuals, however, are largely common sense in the latter meaning.