Innovative Infrastructure to Enhance the California Teachers Study
Investigator: Peggy J. Reynolds, PhD, MPH, MA
Sponsor: Beckman Research Inst of City of Hope
Location(s): United States
The California Teachers Study (CTS) has a rich combination of data on lifestyle factors, directly measured environmental exposures, a wide range of cancer and non-cancer clinical endpoints, and a growing biorepository. In this project, we will maintain and grow the CTS by implementing an innovative, secure, cloud- computing based, study-wide data management system (DMS) that integrates, in a single place and in real- time, all existing, current, and future CTS data. In addition to conducting follow-up for endpoints by linking to cancer, mortality, hospitalization databases through our cloud-based DMS, we will also implement a new model of participant follow-up by sending customized questionnaires and establishing secure, multi-purpose participant portals to share information with and engage study participants.
Cancer epidemiology cohorts (CECs) uniquely contribute to cancer research and will play an even larger role in the future as new discoveries, especially in the area of genomics, continue to advance science and improve health. A CEC's infrastructure should enable it to maximize the value of its data and biospecimens, but many common components of current CEC infrastructure-such as how data are collected, managed, analyzed, integrated, and shared-need to improve if CECs are to remain sustainable and productive as the growing opportunities and challenges of Big Data transform biomedical research. In the California Teachers Study (CTS), an ongoing prospective CEC of 133,479 female public school professionals, our novel biobanking project proves that an established CEC can successfully leverage existing cloud computing and integrated mobile technology to collect thousands of high-quality annotated biospecimens more quickly, securely, and cost-effectively. The objectives of this application are to a) extend our innovative approaches to all other CTS infrastructure activities and b) document the efficiency gains of this approach to create a practical blueprint that other studies could consider. This project has four specific aims
1) We will create a comprehensive, secure, cloud data management and analysis system that integrates all CTS data in one cloud-based dedicated Data Mart that gives CTS investigators on-demand access to all existing and future CTS data, analyses, and study information in real-time. This will modernize CTS data management to markedly improve how we store, use, and share the extensive CTS data.
2) We will implement a new data collection model that sends participants personalized questionnaires in the format they choose-web, tablet, smartphone, or paper-and receives clean, analysis-ready data in real-time. This will make it easier for participants to provide data and for us to collect more and better data.
3) We will establish secure portals that permit participants to update their data, view study information, and communicate with their peers and the CTS team. This will transform how the CTS interacts with our participants.
4) We will openly and broadly share our progress with other CECs that are considering adopting similar approaches for their unique infrastructures.
During this 5-year project, we will replace our previous data infrastructure with this new, connected environment; we will continue to collect new exposure and endpoint data from CTS participants but do so in better ways; and we will complete a series of proof-of-concept analytic projects that demonstrate the advantages of this novel approach. Key parts of the research enterprise, such as large-scale genomics and complex claims data, are increasingly cloud-based, and forward-looking CECs should position themselves to connect with and operate in those environments. The CTS is one of the first large-scale CECs to successfully adopt cloud computing and mobile. This application describes our detailed, data-driven, and practical plan to extend our novel achievements to improve CTS core infrastructure and serve as a model for other CECs that wish to adopt similarly innovative infrastructure.