Am J Epidemiol. 2022 Nov 19;191(12):2063-2070. doi: 10.1093/aje/kwac115.
In this paper, we propose a framework for thinking through the design and conduct of descriptive epidemiologic studies. A well-defined descriptive question aims to quantify and characterize some feature of the health of a population and must clearly state: 1) the target population, characterized by person and place, and anchored in time; 2) the outcome, event, or health state or characteristic; and 3) the measure of occurrence that will be used to summarize the outcome (e.g., incidence, prevalence, average time to event, etc.). Additionally, 4) any auxiliary variables will be prespecified and their roles as stratification factors (to characterize the outcome distribution) or nuisance variables (to be standardized over) will be stated. We illustrate application of this framework to describe the prevalence of viral suppression on December 31, 2019, among people living with human immunodeficiency virus (HIV) who had been linked to HIV care in the United States. Application of this framework highlights biases that may arise from missing data, especially 1) differences between the target population and the analytical sample; 2) measurement error; 3) competing events, late entries, loss to follow-up, and inappropriate interpretation of the chosen measure of outcome occurrence; and 4) inappropriate adjustment.
在本文中,我们提出了一个用于思考描述性流行病学研究设计和实施的框架。一个定义明确的描述性问题旨在量化和描述人群健康的某些特征,并且必须明确说明:1)目标人群,以人和地点为特征,并固定在时间上;2)结局、事件或健康状态或特征;以及 3)用于总结结局的发生度量(例如,发病率、患病率、平均事件时间等)。此外,4)将预先指定任何辅助变量,并说明它们作为分层因素(用于描述结局分布)或干扰变量(进行标准化)的作用。我们将应用该框架来说明描述 2019 年 12 月 31 日在美国接受艾滋病毒护理的艾滋病毒感染者中病毒抑制的流行率。应用该框架突出了可能由于缺失数据引起的偏差,特别是 1)目标人群和分析样本之间的差异;2)测量误差;3)竞争事件、晚期进入、随访丢失和对所选结局发生度量的不当解释;以及 4)不当调整。