INTRODUCTION: Syndromic surveillance systems might serve as an early warning to detect outbreaks of infectious diseases and chemical poisoning, including those caused by deliberate release. In England and Wales, data from National Health Service (NHS) Direct, a national telephone health advice service, were used for surveillance of 10 syndromes commonly occurring in the community. OBJECTIVES: The objective of this study was to evaluate NHS Direct syndromic surveillance using the "Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks", published by CDC. METHODS: Quantitative and qualitative assessments were performed. Examination of daily data flow was used to determine the timeliness and data quality. Validity was determined by comparing NHS Direct surveillance with a well-established clinical-based surveillance system using a time series analysis. Semistructured interviews of main stakeholders were conducted to determine usefulness, flexibility, acceptability, portability, stability, and system costs. RESULTS: NHS Direct syndromic surveillance has representative national coverage, provides near real-time recording and data analysis, and can potentially detect high-risk, large-scale events. Direct costs are low and variable costs are unpredictable. Flexibility depends on urgency of the need for change, and portability relies on the existence of infrastructure similar to NHS Direct. Statistically significant correlation exists between NHS Direct surveillance and a surveillance system based on the Royal College of General Practitioners data for influenza-like illness. CONCLUSION: The CDC framework is a useful tool to standardize the evaluation of syndromic surveillance. NHS Direct syndromic surveillance is timely, representative, useful, and acceptable with low marginal costs and borderline flexibility and portability. Cross-correlation time series modeling might represent an appropriate method in the evaluation of syndromic surveillance validity.
INTRODUCTION: Syndromic surveillance systems might serve as an early warning to detect outbreaks of infectious diseases and chemical poisoning, including those caused by deliberate release. In England and Wales, data from National Health Service (NHS) Direct, a national telephone health advice service, were used for surveillance of 10 syndromes commonly occurring in the community. OBJECTIVES: The objective of this study was to evaluate NHS Direct syndromic surveillance using the "Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks", published by CDC. METHODS: Quantitative and qualitative assessments were performed. Examination of daily data flow was used to determine the timeliness and data quality. Validity was determined by comparing NHS Direct surveillance with a well-established clinical-based surveillance system using a time series analysis. Semistructured interviews of main stakeholders were conducted to determine usefulness, flexibility, acceptability, portability, stability, and system costs. RESULTS: NHS Direct syndromic surveillance has representative national coverage, provides near real-time recording and data analysis, and can potentially detect high-risk, large-scale events. Direct costs are low and variable costs are unpredictable. Flexibility depends on urgency of the need for change, and portability relies on the existence of infrastructure similar to NHS Direct. Statistically significant correlation exists between NHS Direct surveillance and a surveillance system based on the Royal College of General Practitioners data for influenza-like illness. CONCLUSION: The CDC framework is a useful tool to standardize the evaluation of syndromic surveillance. NHS Direct syndromic surveillance is timely, representative, useful, and acceptable with low marginal costs and borderline flexibility and portability. Cross-correlation time series modeling might represent an appropriate method in the evaluation of syndromic surveillance validity.
Authors: Brajendra K Singh; Nicholas J Savill; Neil M Ferguson; Chris Robertson; Mark Ej Woolhouse Journal: BMC Public Health Date: 2010-11-24 Impact factor: 3.295
Authors: W Katherine Yih; Kathryn S Teates; Allyson Abrams; Ken Kleinman; Martin Kulldorff; Robert Pinner; Robert Harmon; Stanley Wang; Richard Platt Journal: PLoS One Date: 2009-04-17 Impact factor: 3.240
Authors: Anna Chu; Rachel Savage; Don Willison; Natasha S Crowcroft; Laura C Rosella; Doug Sider; Jason Garay; Ian Gemmill; Anne-Luise Winter; Richard F Davies; Ian Johnson Journal: BMC Public Health Date: 2012-10-30 Impact factor: 3.295