Khoury Muin J, Engelgau Michael, Chambers David A, Mensah George A
Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, USA,
Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA.
Public Health Genomics. 2018;21(5-6):244-250. doi: 10.1159/000501465. Epub 2019 Jul 17.
The field of public health genomics has matured in the past two decades and is beginning to deliver genomic-based interventions for health and health care. In the past few years, the terms precision medicine and precision public health have been used to include information from multiple fields measuring biomarkers as well as environmental and other variables to provide tailored interventions. In the context of public health, "precision" implies delivering the right intervention to the right population at the right time, with the goal of improving health for all. In addition to genomics, precision public health can be driven by "big data" as identified by volume, variety, and variability in biomedical, sociodemographic, environmental, geographic, and other information. Most current big data applications in health are in elucidating pathobiology and tailored drug discovery. We explore how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person, and time. We use selected examples drawn from child health, cardiovascular disease, and cancer to illustrate the promises of precision public health, as well as current methodologic and analytic challenges to big data to fulfill these promises.
在过去二十年中，公共卫生基因组学领域已经成熟，并开始提供基于基因组学的健康和医疗保健干预措施。在过去几年中，精准医学和精准公共卫生这两个术语被用来涵盖来自多个领域的信息，这些领域测量生物标志物以及环境和其他变量，以提供量身定制的干预措施。在公共卫生背景下，“精准”意味着在正确的时间为正确的人群提供正确的干预措施，目标是改善所有人的健康状况。除了基因组学之外，精准公共卫生还可以由“大数据”驱动，这些大数据是由生物医学、社会人口统计学、环境、地理和其他信息中的数量、种类和变异性所确定的。目前大多数健康领域的大数据应用都集中在阐明病理生物学和量身定制药物研发方面。我们探讨大数据和预测分析如何通过改善公共卫生监测和评估，以及通过纳入与地点、人员和时间相关的更广泛信息来促进循证干预措施的采用，从而为精准公共卫生做出贡献。我们使用从儿童健康、心血管疾病和癌症中选取的例子来说明精准公共卫生的前景，以及大数据在实现这些前景方面当前面临的方法学和分析挑战。
