Annis Ann, Freitag Michelle B, Evans Richard R, Wiitala Wyndy L, Burns Jennifer, Raffa Susan D, Spohr Stephanie A, Damschroder Laura J
Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.
College of Nursing, Michigan State University, East Lansing, Michigan, USA.
Obesity (Silver Spring). 2020 Jul;28(7):1205-1214. doi: 10.1002/oby.22790. Epub 2020 Jun 1.
Administrative data are increasingly used in research and evaluation yet lack standardized guidelines for constructing measures using these data. Body weight measures from administrative data serve critical functions of monitoring patient health, evaluating interventions, and informing research. This study aimed to describe the algorithms used by researchers to construct and use weight measures.
A structured, systematic literature review of studies that constructed body weight measures from the Veterans Health Administration was conducted. Key information regarding time frames and time windows of data collection, measure calculations, data cleaning, treatment of missing and outlier weight values, and validation processes was collected.
We identified 39 studies out of 492 nonduplicated records for inclusion. Studies parameterized weight outcomes as change in weight from baseline to follow-up (62%), weight trajectory over time (21%), proportion of participants meeting weight threshold (46%), or multiple methods (28%). Most (90%) reported total time in follow-up and number of time points. Fewer reported time windows (54%), outlier values (51%), missing values (34%), or validation strategies (15%).
A high variability in the operationalization of weight measures was found. Improving methods to construct clinical measures will support transparency and replicability in approaches, guide interpretation of findings, and facilitate comparisons across studies.
行政数据在研究和评估中的应用日益广泛，但在使用这些数据构建测量指标方面缺乏标准化指南。行政数据中的体重测量指标在监测患者健康、评估干预措施和为研究提供信息方面发挥着关键作用。本研究旨在描述研究人员用于构建和使用体重测量指标的算法。
对从退伍军人健康管理局构建体重测量指标的研究进行了结构化、系统的文献综述。收集了有关数据收集的时间框架和时间窗口、测量计算、数据清理、缺失和异常体重值的处理以及验证过程的关键信息。
在492条非重复记录中，我们确定了39项纳入研究。研究将体重结果参数化为从基线到随访的体重变化（62%）、随时间的体重轨迹（21%）、达到体重阈值的参与者比例（46%）或多种方法（28%）。大多数（90%）报告了随访总时间和时间点数。较少有研究报告时间窗口（54%）、异常值（51%）、缺失值（34%）或验证策略（15%）。
发现体重测量指标的操作存在高度变异性。改进构建临床测量指标的方法将支持方法的透明度和可重复性，指导研究结果的解释，并促进不同研究之间的比较。
