Prospective cohort data quality assurance and quality control strategy and method: Korea HIV/AIDS Cohort Study

• Published in: Epidemiology and health 2020, 42(0), , pp.1-8
• Main Authors: Kim, Soo Min, Choi, Yunsu, Choi, Bo Youl, Kim, Minjeong, Kim, Sang Il, Choi, Jun Young, Kim, Shin-Woo, Song, Joon Young, Kim, Youn Jeong, Kee, Mee-Kyung, Yoo, Myeongsu, Lee, Jeong Gyu, Park, Bo Young
• Format: Article
• Language: English
• Subjects: Brief Communication; Data Accuracy; HIV Infections; Humans; Prospective Studies; Quality Control; Republic of Korea; Research Design; Surveys and Questionnaires; 예방의학
• ISSN: 2092-7193
• DOI: 10.4178/epih.e2020063

The aim of effective data quality control and management is to minimize the impact of errors on study results by identifying and correcting them. This study presents the results of a data quality control system for the Korea HIV/AIDS Cohort Study that took into account the characteristics of the data. The HIV/AIDS Cohort Study in Korea conducts repeated measurements every 6 months using an electronic survey administered to voluntarily consenting participants and collects data from 21 hospitals. In total, 5,795 sets of data from 1,442 participants were collected from the first investigation in 2006 to 2016. The data refining results of 2015 and 2019 were converted into the data refining rate and compared. The quality control system involved 3 steps at different points in the process, and each step contributed to data quality management and results. By improving data quality control in the pre-phase and the data collection phase, the estimated error value in 2019 was 1,803, reflecting a 53.9% reduction from 2015. Due to improvements in the stage after data collection, the data refining rate was 92.7% in 2019, a 24.21%p increase from 2015. Despite this quality management strategy, errors may still exist at each stage. Logically possible errors for the post-review refining of downloaded data should be actively identified with appropriate consideration of the purpose and epidemiological characteristics of the study data. To improve data quality and reliability, data management strategies should be systematically implemented.