Skip to main content

Lober William

The Distribute project began in 2006 as a distributed, syndromic surveillance demonstration project that networked state and local health departments to share aggregate emergency department-based influenza-like illness (ILI) syndrome data. Preliminary work found that local systems often applied syndrome definitions specific to their regions; these definitions were sometimes trusted and understood better than standardized ones because they allowed for regional variations in idiom and coding and were tailored by departments for their own surveillance needs. Originally, sites were asked to send whatever syndrome definition they had found most useful for monitoring ILI. Places using multiple definitions were asked to send their broader, higher count syndrome. In 2008, sites were asked to send both a broad syndrome, and a narrow syndrome specific to ILI.

 

Objective

To describe the initial phase of the ISDS Distribute project ILI syndrome standardization pilot.

Referenced File
Submitted by hparton on

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project creates some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

To present exploratory tools and methods developed as part of the data quality monitoring of Distribute data, and discuss these tools and their applications with other participants.

Referenced File
Submitted by elamb on

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance (ISDS) for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance (ISDS) for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project create some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

The goal of this session will be to briefly present two methods for comparing aggregate data quality and invite continued discussion on data quality from other surveillance practitioners, and to present the range of data quality results across participating Distribute sites.

Referenced File
Submitted by elamb on

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).

Submitted by knowledge_repo… on

As major disease outbreaks are rare, empirical evaluation of statistical methods for outbreak detection requires the use of modified or completely simulated health event data in addition to real data. Comparisons of different techniques will be more reliable when they are evaluated on the same sets of artificial and real data. To this end, we are developing a toolkit for implementing and evaluating outbreak detection methods and exposing this framework via a web services interface.

Referenced File
Submitted by elamb on

Data consisting of counts or indicators aggregated from multiple sources pose particular problems for data quality monitoring when the users of the aggregate data are blind to the individual sources. This arises when agencies wish to share data but for privacy or contractual reasons are only able to share data at an aggregate level. If the aggregators of the data are unable to guarantee the quality of either the sources of the data or the aggregation process then the quality of the aggregate data may be compromised. This situation arose in the Distribute surveillance system (1). Distribute was a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrated data from existing state and local public health department surveillance systems, and operated from 2006 until mid 2012. Distribute was designed to work solely with aggregated data, with sites providing data aggregated from sources within their jurisdiction, and for which detailed information on the un-aggregated ‘raw’ data was unavailable. Previous work (2) on Distribute data quality identified several issues caused in part by the nature of the system: transient problems due to inconsistent uploads, problems associated with transient or long-term changes in the source make up of the reporting sites and lack of data timeliness due to individual site data accruing over time rather than in batch. Data timeliness was addressed using prediction intervals to assess the reliability of the partially accrued data (3). The types of data quality issues present in the Distribute data are likely to appear to some extent in any aggregate data surveillance system where direct control over the quality of the source data is not possible.

Objective

In this work we present methods for detecting both transient and long-term changes in the source data makeup.

 

Submitted by uysz on

Clinical data captured in electronic health records (EHR) for patient health care could be used for chronic disease surveillance, helping to inform and prioritize interventions at a state or community level. While there has been significant progress in the collection of clinical information such as immunizations for public health purposes, greater attention could be paid to the collection of data on chronic illness. Obesity is a chronic disease that affects over a third of the US adult population1 , making it an important public health concern. Both HL7 v.2.5.12 and Clinical Document Architecture (CDA) messages3 can be used to facilitate the collection of HW EHR data. These standards include anthropometric and demographic information along with the option to transmit behavioral, continuity of care, community resource identification and care plan information. We worked with vendors participating in the Integrating the Healthcare Enterprise initiative (IHE) in developing, testing and showcasing scenarios to facilitate system development, increase the visibility of HW standards and demonstrate potential usages of obesity-related information.

Objective

To demonstrate the feasibility of using healthy weight (HW) IT standards in public health surveillance through the collection and visualization of patient height, weight and behavioral data.

Submitted by teresa.hamby@d… on
Subscribe to Lober William