Zhang Wei, Tocher Paige, L'Heureux Jacynthe, Sou Julie, Sun Huiying
School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada; Centre for Health Evaluation and Outcome Sciences, Vancouver, BC, Canada.
Centre for Health Evaluation and Outcome Sciences, Vancouver, BC, Canada.
Value Health. 2023 Jan;26(1):123-137. doi: 10.1016/j.jval.2022.06.015. Epub 2022 Aug 9.
This study aimed to conduct a scoping review of randomized controlled trials (RCTs) and investigate which work productivity loss outcomes were measured in these RCTs, how each outcome was measured and analyzed, and how the results for each outcome were presented.
A systematic search was conducted from January 2010 to April 2020 from 2 databases: PubMed and Cochrane Central Register of Controlled Trials. Data on country, study population, disease focus, sample size, work productivity loss outcomes measured (absenteeism, presenteeism, employment status changes), and methods used to measure, report, and analyze each work productivity loss outcome were extracted and analyzed.
We found 435 studies measuring absenteeism or presenteeism, of which 155 studies (35.6%) measured both absenteeism and presenteeism and were included in our final review. Only 9 studies also measured employment status changes. The most used questionnaire was the Work Productivity and Activity Impairment Questionnaire. The analysis of absenteeism and presenteeism data was mostly done using regression models (n = 98, n = 98, respectively) for which a normal distribution was assumed (n = 77, n = 89, respectively). Absenteeism results were most often presented in time whereas presenteeism was commonly presented using a percent scale or score.
There is a lack of consensus on how to measure, analyze, and present work productivity loss outcomes in RCTs published in the past 10 years. The diversity of measurement, analysis, and presentation methods used in RCTs may make comparability challenging. There is a need for guidelines providing recommendations to standardize the comprehensiveness and the appropriateness of methods used to measure, analyze, and report work productivity loss in RCTs.
本研究旨在对随机对照试验(RCT)进行范围综述,并调查这些RCT中测量了哪些工作效率损失结果,每个结果是如何测量和分析的,以及每个结果的结果是如何呈现的。
于2010年1月至2020年4月从两个数据库进行系统检索:PubMed和Cochrane对照试验中央注册库。提取并分析了关于国家、研究人群、疾病重点、样本量、测量的工作效率损失结果(旷工、出勤主义、就业状况变化)以及用于测量、报告和分析每个工作效率损失结果的方法的数据。
我们发现435项研究测量了旷工或出勤主义,其中155项研究(35.6%)同时测量了旷工和出勤主义,并纳入了我们的最终综述。只有9项研究还测量了就业状况变化。最常用的问卷是工作效率和活动障碍问卷。旷工和出勤主义数据的分析大多使用回归模型(分别为n = 98,n = 98),假定为正态分布(分别为n = 77,n = 89)。旷工结果最常以时间呈现,而出勤主义通常以百分比量表或分数呈现。
在过去10年发表的RCT中,对于如何测量、分析和呈现工作效率损失结果缺乏共识。RCT中使用的测量、分析和呈现方法的多样性可能使可比性具有挑战性。需要提供指南,以推荐标准化用于测量、分析和报告RCT中工作效率损失的方法的全面性和适当性。