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

Area of Science:

• Public Health Surveillance
• Data Quality Management
• Computational Epidemiology

Background:

• Timely identification of data aberrations in live surveillance is crucial for effective public health response and data integrity.
• The Centers for Disease Control and Prevention (CDC) receives syphilis surveillance data from various US jurisdictions.
• Existing methods for identifying data quality issues may not be sufficient for real-time public health needs.

Purpose of the Study:

• To develop an automated, customized data reporting system to detect and communicate potential aberrations in live syphilis surveillance data.
• To improve the quality and timeliness of public health data submitted to national surveillance systems.
• To provide actionable insights to jurisdictions for data reconciliation and response.

Main Methods:

• A multiphase, user-centered design approach was employed.
• Key phases included requirements gathering, exploratory data analysis, and rapid prototyping.
• Methodologies for aberration detection were evaluated, and end-user feedback was incorporated.

Main Results:

• An automated system generating user-friendly, quarterly data reports for US syphilis surveillance was developed.
• Reports feature an executive summary, detailed charts, and tables highlighting anomalies in priority variables.
• End-users preferred a combination of tables and figures with simple data visualizations.

Conclusions:

• The developed system addresses challenges in identifying and communicating data quality aberrations within complex surveillance ecosystems.
• Balancing sophisticated methodologies with easily interpretable visualizations is key for non-statistical audiences.
• Jurisdictions with varying capacities can utilize these reports for informed action, enhancing disease surveillance efforts.