Data from public health surveillance systems can provide meaningful measures of population risks for disease, disability, and death. Analysis and evaluation of these surveillance data help public health practitioners react to important health events in a timely manner both locally and nationally. Aberration detection methods allow the rapid assessment of changes in frequencies and rates of different health outcomes and the characterization of unusual trends or clusters.
Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS) was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS) started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real-time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and transmit information either in real time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC) in China at the county...
Background: The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends. Objective: To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI. Methodology and principal findings Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when −0.2≤ρ≤0.2 and 80% when −0.5≤ρ≤0.5). During the 2008–9 season...
Bioterrorism is not a new threat, but the potential for disastrous outcomes is greater than it has ever been. In order to confront this threat, biosurveillance systems are utilized to provide early warning of health threats, early detection of health events, and situational awareness of disease activity. To date, there is little known about the performance of such biosurveillance systems in comparison to diagnosis capabilities of medical personnel. In this thesis, a discrete event simulation model of an anthrax outbreak is developed in order to analyze the performance of such biosurveillance systems in comparison to medical personnel. This research found the Early Aberration Reporting System C1 statistical algorithm is useful in early event detection of a bioterror attack. Given an exposed population of 1,000 people, the nominal probability that the algorithm signals first is 31.5% and it is 0.3% for an exposed population of 10,000 people. Given an exposed population of 1,000 people, the nominal time it takes for the algorithm to signal is 3.3 days and 0.38 days for an exposed population of 10,000 people.
Fricker, Ronald D.; Hagen, Katie; Barnes, Suan; Fricker, Ronald; Michie, Kristy; Rees, Bryan; Hanni, Krista
Fonte: Escola de Pós-Graduação NavalPublicador: Escola de Pós-Graduação Naval
Relevância na Pesquisa
The Monterey County Health Department (MCHD) in California uses the Early
Aberration Reporting System (EARS) to monitor emergency room and clinic data
for biosurveillance, particularly as an alert system for increases in various types of
The flexibility of the syndrome building process has proven to be the most useful
feature of EARS compared to other biosurveillance tools, but it is also the one
feature most prone to programming errors.
To ameliorate this issue, a collaborative academic/public health partnership was
developed to provide an opportunity to study methods which improve the overall
biosurveillance goals of EARS.
After the terrorist attacks of September 11, 2001, questions developed over how quickly the country could respond if a bioterrorism attack was to occur. "Syndromic surveillance" systems are a relatively new concept that is being implemented and used by public health practitioners to attempt to detect a bioterrorism attack earlier than would be possible using conventional biosurveillance methods. The idea behind using syndromic surveillance is to detect a bioterrorist attack by monitoring potential leading indicators of an outbreak such as absenteeism from work or school, over-the-counter drug sales, or emergency room counts. The Center for Disease Control and Prevention's Early Aberration Reporting System (EARS) is one syndromic surveillance system that is currently in operation around the United States. This thesis compares the performance of three syndromic surveillance detection algorithms, entitled C1, C2, and C3, that are implemented in EARS, versus the CUSUM applied to model-based prediction errors. The CUSUM performed significantly better than the EARS' methods across all of the scenarios evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle)...
Approved for public release; distribution is unlimited; The Monterey County Health Department (MCHD) in California uses the Early Aberration Reporting System (EARS) to monitor emergency room and clinic data for biosurveillance, particularly as an alert system for various types of disease outbreaks. The flexibility of the system has proven to be a very useful feature of EARS; however, little research has been conducted to assess its performance. In this thesis, a quantitative analysis based on modifications to EARS' internal logic and algorithms is assessed. Logic is used as a counting tool for potential cases of outbreak, and the Early Event Detection (EED) algorithms are used to determine whether or not an outbreak is about to occur. The EED methods are compared by assessing their ability to detect the presence of a known H1N1 outbreak in Monterey County. This research found the cumulative sum (CUSUM) detection method to be the most reliable in signaling the H1N1 outbreak, across all combinations of logic explored.; US Navy (USN) author
Statistics, Politics, and Policy, 2, issue 1, article 1.; The article of record as published may be found at http://dx.doi.org/10.2202/2151-7509.1018; The Early Aberration Reporting System (EARS) is used by some local health departments
(LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief
complaint data from local public health clinics, we evaluate how EARS—both the baseline system
distributed by the CDC and two variants implemented by one LHD—perform at locally detecting
the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based
method. We find that the baseline EARS system performed poorly in comparison to one of the
LHD variants and the CUSUM-based method. These results suggest that changes in how
syndromes are defined can substantially improve EARS performance. The results also show that
incorporating algorithms that use more historical data will improve EARS performance for routine
surveillance by local health departments.
Statistics in Medicine, 27, 3407-3429.; The article of record as published may be located at http://dx.doi.org/10.1002/sim.3197; This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are
implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems
versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed
signiﬁcantly better than the EARS’ methods across all of the scenarios we evaluated. These scenarios
consisted of various combinations of large and small background disease incidence rates, seasonal cycles
from large to small (as well as no cycle), daily effects, and various types and levels of random daily
variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic
surveillance systems with an appropriately implemented CUSUM method. Published in 2008 by John
Wiley & Sons, Ltd.