Development of an Automated, Customized Data Report for Ongoing Aberration Detection in Syphilis Surveillance Data

INTRODUCTION: Identifying aberrations in live surveillance data representing before year-end data reconciliation can improve public health response and data quality. We developed an automated, customized data report to communicate potential aberrations to jurisdictions submitting syphilis data to the Centers for Disease Control and Prevention. DEVELOPMENT: Our multiphase approach encompassed: requirements gathering, exploratory data analyses, rapid prototyping, evaluating relative performance of different aberration detection methodologies, and soliciting end-user feedback on the prototype. EVALUATION: The final product encompasses statistical code that generates user-friendly quarterly data reports on live syphilis surveillance data, customized to each US jurisdiction. Data reports include an executive summary highlighting critical issues followed by detailed charts and tables showing anomalies in priority variables. The flexible code includes options for alternative methods, figures, and descriptions. End-users preferred a mix of tables and figures, with simple data representations. DISCUSSION: Evolving epidemics, decentralized data collection, jurisdictions' use of various data systems, and public health workforce shortages are critical challenges for national disease surveillance. Additional technical challenges include setting thresholds for aberrations and jurisdictions' different information needs. End users expressed satisfaction with the prototype, identifying multiple use cases for how jurisdictions with variable morbidity and surveillance capacities could leverage the information for action. CONCLUSION: Our multiphase, user-centered design approach identified challenges in determining and communicating data quality aberrations in a complex data ecosystem. An important consideration for developing such products is balancing complex methodologies with visualizations that are easy to interpret by nonstatistical audiences.