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Activity Number:
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130
- Time Series Data, Trend Analysis, and Repeated Measures
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Government Statistics Section
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Abstract #329738
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Title:
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Use of National Syndromic Surveillance Data to Monitor Weekly Lyme Disease Activity in Four US Regions
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Author(s):
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Hong Zhou* and Michael Coletta and Howard Burkom and Aaron Kite Powell and Ruth Jajosky and Tara Strine
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Companies:
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CDC and CDC and Johns Hopkins Applied Physics Laboratory and CDC and CDC and CDC
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Keywords:
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syndromic surveillance;
disease surveillance;
aberration detection;
Lyme disease
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Abstract:
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The National Syndromic Surveillance Program (NSSP) collects national near real-time patient encounter data to help detect health events and diseases outbreaks. We assessed the term "Lyme" in chief complaint text and diagnosis codes for Lyme disease from emergency visits in four Northeastern and upper Midwest regions, where Lyme disease is most prevalent. We compared findings from the first 40 weeks of 2017 in NSSP to provisional data from the same time period in the National Notifiable Diseases Surveillance System (NNDSS). Weekly case counts from NNDSS were highly correlated (Pearson correlation coefficient 0.86-0.94) with counts from NSSP in all four regions. In three of these regions, adjusting for NNDSS reporting delays by implementing a 2-week lag in NSSP, the correlations were raised to 0.94-0.98. When the C2 algorithm was applied to weekly counts from each system, consecutive alerts began 1-4 weeks earlier in NSSP than in NNDSS in three of the four regions. Monitoring NSSP data gave timelier signals than NNDSS and may be useful at national and local levels to improve situational awareness and enable enhanced response to increased incidence of Lyme and certain other diseases.
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Authors who are presenting talks have a * after their name.
