The Myths and Truths About Comparing Syndromic Data Across Sites

ObjectiveAs the BioSense Platform matures and more sites submit surveillance data, many in the community have voiced concerns about comparing data across sites. Recently, a number of jurisdictions from across the country were asked to provide opioid overdose data to a news agency highlighting the epidemic. Many jurisdictions requested information on how to present syndromic surveillance data from across sites and shared concern about how the data would be interpreted. This round table will address those concerns and explore options for comparing data across sites.IntroductionOne of the more recent successes of NSSP has been the introduction of more robust data quality monitoring and reporting. However, despite the increased insight into data quality, there are still concerns about data sharing and comparisons across sites. For NSSP to be most effective, users need to feel confident in sharing data and making comparisons across sites.DescriptionThis round table session will focus on determining where the real issues are, where the myths are, and how to overcome these challenges. Topics will include data quality issues that will affect comparisons, methods for comparing data across sites, and best practices for sharing data across sites and dealing with interpretation concerns.How the Moderator Intends to Engage the Audience in Discussions on the TopicThe moderator will provide two or three real world examples of comparisons across sites and/or HHS regions. The following questions will guide the discussion:- What are the pros of not comparing data across sites?- What are the cons of not comparing data across sites?- What are the best practices for users to consider when attempting to compare across sites – are there criteria when comparisons should not be allowed?- What development items are suggested to the system to allow the user to better evaluate comparing data across regions?- Even with the potential issues when comparing across sites, are there accepted practices that could help describe the data and/or limitations until some of the issues are better addressed