Centralized Data Management in a Multicountry, Multisite Population-based Study. Issue 5 (May 2016) - Rahman, Qazi Sadeq-ur; Islam, Mohammad Shahidul; Hossain, Belal; Hossain, Tanvir; Connor, Nicholas E.; Jaman, Md. Jahiduj; Rahman, Md. Mahmudur; Ahmed, A. S. M. Nawshad Uddin; Ahmed, Imran; Ali, Murtaza; Moin, Syed Mamun Ibne; Mullany, Luke; et al.
Abstract : Background: A centralized data management system was developed for data collection and processing for the Aetiology of Neonatal Infection in South Asia (ANISA) study. ANISA is a longitudinal cohort study involving neonatal infection surveillance and etiology detection in multiple sites in South Asia. The primary goal of designing such a system was to collect and store data from different sites in a standardized way to pool the data for analysis. Methods: We designed the data management system centrally and implemented it to enable data entry at individual sites. This system uses validation rules and audit that reduce errors. The study sites employ a dual data entry method to minimize keystroke errors. They upload collected data weekly to a central server via internet to create a pooled central database. Any inconsistent data identified in the central database are flagged and corrected after discussion with the relevant site. The ANISA Data Coordination Centre in Dhaka provides technical support for operations, maintenance and updating the data management system centrally. Password-protected login identifications and audit trails are maintained for the management system to ensure the integrity and safety of stored data. Conclusion: Centralized management of the ANISA database helps to use common data capture forms (DCFs), adapted to site-specific contextual requirements. DCFs and data entry interfaces allow on-site data entry. This reduces the workload as DCFs do not need to be shipped to a single location for entry. It also improves data quality as all collected data from ANISA goes through the same quality check and cleaning process.