Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System

Jianhua Sun; Yun Cao; Mingyan Hei; Huiqing Sun; Laishuan Wang; Wei Zhou; Xiafang Chen; Siyuan Jiang; Huayan Zhang; Xiaolu Ma; Hui Wu; Xiaoying Li; Yuan Shi; Xinyue Gu; Yanchen Wang; Tongling Yang; Yulan Lu; Wenhao Zhou; Chao Chen; Shoo K Lee; Lizhong Du; Chinese Neonatal Network

doi:10.3389/fped.2021.711200

Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System

Front Pediatr. 2021 Oct 4:9:711200. doi: 10.3389/fped.2021.711200. eCollection 2021.

Authors

Jianhua Sun¹, Yun Cao², Mingyan Hei³, Huiqing Sun⁴, Laishuan Wang², Wei Zhou⁵, Xiafang Chen¹, Siyuan Jiang², Huayan Zhang^{5

6}, Xiaolu Ma⁷, Hui Wu⁸, Xiaoying Li⁹, Yuan Shi¹⁰, Xinyue Gu¹¹, Yanchen Wang¹¹, Tongling Yang², Yulan Lu¹², Wenhao Zhou², Chao Chen², Shoo K Lee^{13

14

15

16

17}, Lizhong Du⁷; Chinese Neonatal Network

Collaborators

Chinese Neonatal Network:
Shoo K Lee, Chao Chen, Lizhong Du, Wenhao Zhou, Yun Cao, Falin Xu, Xiuying Tian, Huayan Zhang, Yong Ji, Zhankui Li, Jingyun Shi, Xindong Xue, Chuanzhong Yang, Dongmei Chen, Sannan Wang, Ling Liu, Xirong Gao, Hui Wu, Changyi Yang, Shuping Han, Ruobing Shan, Hong Jiang, Gang Qiu, Qiufen Wei, Rui Cheng, Wenqing Kang, Mingxia Li, Yiheng Dai, Lili Wang, Jiangqin Liu, Zhenlang Lin, Yuan Shi, Xiuyong Cheng, Jiahua Pan, Qin Zhang, Xing Feng, Qin Zhou, Long Li, Pingyang Chen, Xiaoying Li, Ling Yang, Deyi Zhuang, Yongjun Zhang, Jianhua Sun, Jinxing Feng, Li Li, Xinzhu Lin, Yinping Qiu, Kun Liang, Li Ma, Liping Chen, Liyan Zhang, Hongxia Song, Zhaoqing Yin, Mingyan Hei, Huiwen Huang, Jie Yang, Dong Li, Guofang Ding, Jimei Wang, Qianshen Zhang, Xiaolu Ma

Affiliations

¹ Department of Neonatology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
² Division of Neonatology, Children's Hospital of Fudan University, Shanghai, China.
³ Neonatal Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
⁴ Division of Neonatology, Henan Children's Hospital, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
⁵ Division of Neonatology and Center for Newborn Care, Guangzhou Women and Children's Medical Center, Guangzhou, China.
⁶ Department of Pediatrics, Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
⁷ Division of Neonatology, The Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China.
⁸ Division of Neonatology, The First Bethune Hospital of Jilin University, Changchun, China.
⁹ Division of Neonatology, Qilu Children's Hospital of Shandong University, Ji'nan, China.
¹⁰ Division of Neonatology, Children's Hospital of Chongqing Medical University, Chongqing, China.
¹¹ NHC Key Laboratory of Neonatal Diseases (Fudan University), Children's Hospital of Fudan University, Shanghai, China.
¹² Center for Molecular Medicine, Pediatrics Research Institute, Children's Hospital of Fudan University, Shanghai, China.
¹³ Maternal-Infant Care Research Centre, Mount Sinai Hospital, Toronto, ON, Canada.
¹⁴ Department of Pediatrics, Mount Sinai Hospital, Toronto, ON, Canada.
¹⁵ Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
¹⁶ Department of Obstetrics and Gynecology, University of Toronto, Toronto, ON, Canada.
¹⁷ Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Abstract

Background: The Chinese Neonatal Network (CHNN) is a nationwide neonatal network that aims to improve clinical neonatal care quality and short- and long-term health outcomes of infants. This study aims to assess the quality of the Chinese Neonatal Network database by conducting an internal audit of data extraction. Methods: A data audit was performed by independently replicating the data collection and entry process in all 58 tertiary neonatal intensive care units (NICU) participating in the CHNN. Eighty-eight data elements selected for re-abstraction were classified into three categories (critical, important, less important), and agreement rates for original and re-abstracted data were predefined. Three to five records were randomly selected at each site for re-abstraction, including one short- (0-7 days), two medium- (8-28 days), and two long-stay (more than 28 days) cases. Agreement rates for each data item were calculated for individual NICUs and across the network, respectively. Results: A total of 283 cases and 24,904 data fields were re-abstracted. The agreement rates for original and re-abstracted data elements were 96.1% overall, and 97.2, 94.3, and 96.6% for critical, important, and less important data elements, respectively. Individual site variation for discrepancies ranged between 0.0 and 18.4% for all collected data elements. Conclusion: The completeness, precision, and quality of data in the CHNN database are high, providing assurance for multipurpose use, including health service evaluation, quality improvement, clinical trials, and other research.

Keywords: data quality improvement; internal data audit; neonatal database; neonatal network; preterm infant.