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《BMC医学》的十五年流行病学研究

Fifteen years of epidemiology in BMC Medicine.

作者信息

Lawlor Deborah A

机构信息

MRC Integrative Epidemiology Unit at the University of Bristol, Population Health Science, Bristol Medical School and Bristol NIHR Biomedical Research Centre, Bristol, UK.

出版信息

BMC Med. 2019 Sep 23;17(1):177. doi: 10.1186/s12916-019-1407-5.

Abstract

BMC Medicine was launched in November 2003 as an open access, open peer-reviewed general medical journal that has a broad remit to publish "outstanding and influential research in all areas of clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities". Here, I discuss the last 15 years of epidemiological research published by BMC Medicine, with a specific focus on how this reflects changes occurring in the field of epidemiology over this period; the impact of 'Big Data'; the reinvigoration of debates about causality; and, as we increasingly work across and with many diverse disciplines, the use of the name 'population health science'. Reviewing all publications from the first volume to the end of 2018, I show that most BMC Medicine papers are epidemiological in nature, and the majority of them are applied epidemiology, with few methodological papers. Good research must address important translational questions that should not be driven by the increasing availability of data, but should take appropriate advantage of it. Over the next 15 years it would be good to see more publications that integrate results from several different methods, each with different sources of bias, in a triangulation framework.

摘要

《BMC医学》于2003年11月创刊,是一本开放获取、同行公开评审的综合性医学期刊,其广泛的宗旨是发表“临床实践、转化医学、医学与健康进展、公共卫生、全球健康、政策以及生物医学和社会医学专业群体感兴趣的一般主题等所有领域的杰出且有影响力的研究”。在此,我将探讨《BMC医学》过去15年发表的流行病学研究,特别关注其如何反映这一时期流行病学领域发生的变化;“大数据”的影响;因果关系辩论的复兴;以及随着我们越来越多地跨学科和与多学科合作,“人群健康科学”这一名称的使用情况。回顾从第一卷到2018年底的所有出版物,我发现《BMC医学》的大多数论文本质上都是流行病学的,其中大多数是应用流行病学,方法学论文很少。优秀的研究必须解决重要的转化问题,这些问题不应由数据可用性的增加所驱动,而应适当利用数据。在未来15年里,希望能看到更多在三角测量框架中整合来自几种不同方法(每种方法都有不同偏差来源)结果的出版物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d05f/6755685/7cd8320951b9/12916_2019_1407_Fig1_HTML.jpg

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