Accrued – An R Package for Visualizing Data Quality for Aggregate Surveillance Data

Description: 

The utility of specific sources of data for surveillance, and the quality of those data, are an ingoing issue in public health(1). Syndromic surveillance is typically conducted as a secondary use of data collected as part of routine clinical practice, and as such the data can be of high quality for the clinical use but of lower quality for the purpose of surveillance. A major data quality issue with surveillance data is that of timeliness. Data used in surveillance typically arrive as a periodic process, inherently creating a delay in the availability of the data for surveillance purposes. Surveillance data are often collected from multiple sources, each with their own processes and delays, creating a situation where the data available for surveillance are accrued piecemeal.

Objective

This abstract discusses the quality issues identified in using Distribute. From 2006 to 2012, the ISDS ran Distribute (2), a surveillance system for monitoring influenza like illness (ILI) and gastroenteritis (GI) ED visits on a nationwide basis. This system collected counts for ILI, GI and total ED visits, aggregated to the level of jurisdiction. The primary data quality issue faced with the Distribute system was that of timeliness due to accrual lag; variable delays in the receipt of surveillance data from sources by jurisdictions together with variable delays in the reporting of aggregate data from jurisdictions to Distribute resulted in data which accrued over time(3).

Original Publication Year: 
2013
Event/Publication Date: 
December, 2013

August 22, 2018

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