Enabling access to british birth cohort studies: A Secure Web Interface for the NSHD (SWIFT)

Abstract

This paper provides an overview of the new Secure Web Interface for the Medical Research Council (MRC) National Survey of Health and Development (NSHD), which is a webbased data and metadata access system for the longest-running longitudinal study of health in the world [1]. Accessing NSHD metadata and data has often been a challenge for external (non-MRC) users, because the underlying data and metadata formats have changed dramatically in 63 years of operation, and because the processes involved in metadata search and data access were manual and usually required "on-site" access [2]. The design goals of the SWIFT include maintaining confidentiality and privacy of study members, enabling metadata search access to internal (MRC) and external users, facilitating data downloading and extraction from a range of underlying formats, and implementing procedures to ensure compliance with the governance policies of the MRC and the NSHD's governance panel. This paper details some of the challenges and successes of the MRC pathfinder "Data Access Project" to enable access using SWIFT while protecting the study members and their data.

Cohort Profile: Updating the cohort profile for the MRC National Survey of Health and Development: a new clinic-based data collection for ageing research

“…In parallel with the data collection, a secure web-based tool to facilitate working with shared scientific data and resources is being developed. 21 Enquiries should be made to swiftinfo@nshd.mrc.ac.uk …”

Enhancing early detection of cognitive impairment in the criminal justice system: feasibility of a proposed method

“…There is increasing recognition of the potential power of secondary research uses of the data contained in EHRs to transform the quality of life for the population as a whole through advances in knowledge about diseases, illnesses and impairments and how to more effectively treat or even prevent them. However, owing in part to the Value, Velocity, Volume, Veracity and Variety (the five Vs) of big data as well as privacy constraints (Watters et al 2009), sophisticated methods of data extraction and analysis are required. We propose here the potential of a data mining method, NLP, to extract and analyse EHR data.…”