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Propagating the effects of missing data sources in a surveillance system
I S Painter1, A Harvey, K Peterson
1Foundation for Health Care Quality, Seattle, WA, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|August 13, 2008
Summary
Sporadic missing data in syndromic surveillance systems causes inaccurate health counts. This study introduces a method to track and visualize how these data quality issues impact surveillance system performance.
Area of Science:
- Public Health
- Data Science
- Epidemiology
Background:
- Syndromic surveillance systems are crucial for public health.
- Incomplete data sources can compromise the accuracy of these systems.
- Existing methods may not adequately address the propagation of data quality issues.
Purpose of the Study:
- To investigate the impact of sporadically missing data sources on syndromic surveillance.
- To develop a method for tracking data quality problems within surveillance systems.
- To visualize the propagation of data quality issues through the system.
Main Methods:
- Analyzing data flow within syndromic surveillance systems.
- Quantifying the effects of missing data on counts and algorithms.
- Developing visualization techniques for data quality metrics.
Main Results:
- Missing data sources lead to significant inaccuracies in surveillance counts.
- Detection algorithm performance is demonstrably affected by data gaps.
- The proposed method effectively tracks and visualizes data quality degradation.
Conclusions:
- Addressing data quality issues, specifically missing data, is essential for reliable syndromic surveillance.
- The developed tracking and visualization method can improve system monitoring.
- This work contributes to enhancing the robustness of public health surveillance data.
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