RTI International , Durham, NC , USA.
Front Public Health. 2014 Aug 12;2:97. doi: 10.3389/fpubh.2014.00097. eCollection 2014.
A new concept of within-individual epidemiology termed "myEpi" is introduced. It is argued that traditional epidemiological methods, which are usually applied to populations of humans, can be applicable to a single individual and thus used for self-monitoring and forecasting of "epidemic" outbreaks within an individual. Traditional epidemiology requires that results be generalizable to a predefined population. The key component of myEpi is that a single individual may be viewed as an entire population of events and thus, the analysis should be generalizable to this population. Applications of myEpi are aimed for, but not limited to, the analysis of data collected by individuals with the help of wearable sensors and digital diaries. These data can include physiological measures and records of healthy and risky behaviors (e.g., exercise, sleep, smoking, food consumption, alcohol, and drug use). Although many examples of within-individual epidemiology exist, there is a pressing need for systematic guidance to the analysis and interpretation of intensive individual-level data. myEpi serves this need by adapting statistical methods (e.g., regressions, hierarchical models, survival analysis, agent-based models) to individual-level data.
引入了一种新的个体内流行病学概念,称为“myEpi”。有人认为,传统的流行病学方法通常应用于人群,可以应用于单个个体,从而用于个体内部“流行”爆发的自我监测和预测。传统的流行病学要求结果可以推广到预先定义的人群。myEpi 的关键组成部分是,单个个体可以被视为整个事件人群,因此,分析应该可以推广到这个人群。myEpi 的应用旨在但不限于分析个人在可穿戴传感器和数字日记的帮助下收集的数据。这些数据可以包括生理测量和健康和危险行为的记录(例如,运动、睡眠、吸烟、食物消费、酒精和药物使用)。虽然个体内流行病学有许多例子,但迫切需要对密集的个体水平数据进行系统的分析和解释指导。myEpi 通过将统计方法(例如回归、分层模型、生存分析、基于代理的模型)适应于个体水平的数据来满足这一需求。