Página 1 dos resultados de 11 itens digitais encontrados em 0.021 segundos

Implementing the centers for disease control and prevention’s Early Aberration Reporting System (EARS): A frontline perspective from the knox county, Tennessee, health department

Lawson, Brian; Fitzhugh, Gene; Hall, Stephanie; Garcia, Melissa; Hutwagner, Lori; Seeman, G. Matthew
Fonte: Springer-Verlag Publicador: Springer-Verlag
Tipo: Artigo de Revista Científica
Publicado em /03/2003 EN
Relevância na Pesquisa
45.73%

The bioterrorism preparedness and response Early Aberration Reporting System (EARS)

Hutwagner, Lori; Thompson, William; Seeman, G. Matthew; Treadwell, Tracee
Fonte: Springer-Verlag Publicador: Springer-Verlag
Tipo: Artigo de Revista Científica
Publicado em /03/2003 EN
Relevância na Pesquisa
55.84%
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.

A nationwide web-based automated system for outbreak early detection and rapid response in China

Yang, Weizhong; Li, Zhongjie; Lan, Yajia; Wang, Jinfeng; Ma, Jiaqi; Jin, Lianmei; Sun, Qiao; Lv, Wei; Lai, Shengjie; Liao, Yilan; Hu, Wenbiao
Fonte: World Health Organization Publicador: World Health Organization
Tipo: Artigo de Revista Científica
Publicado em 08/03/2011 EN
Relevância na Pesquisa
26.21%
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...

Application of change point analysis to daily influenza-like illness emergency department visits

Kass-Hout, Taha A; Xu, Zhiheng; McMurray, Paul; Park, Soyoun; Buckeridge, David L; Brownstein, John S; Finelli, Lyn; Groseclose, Samuel L
Fonte: BMJ Group Publicador: BMJ Group
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
45.84%
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...

Assessing the effectiveness of biosurveillance via discrete event simulation

Dao, Jason H.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado
Relevância na Pesquisa
45.82%
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.

Evaluating Biosurveillance System Performance: Using the Early Aberration Reporting System (EARS) to Detect H1N1 in Monterey County, California (Poster)

Fricker, Ronald D.; Hagen, Katie; Barnes, Suan; Fricker, Ronald; Michie, Kristy; Rees, Bryan; Hanni, Krista
Fonte: Escola de Pós-Graduação Naval Publicador: Escola de Pós-Graduação Naval
Tipo: Poster
Relevância na Pesquisa
86.04%
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 disease. 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.

Assessing the Effectiveness of the Early Aberration Reporting System (EARS) with Application to Bioterrorism (Student Research Briefing)

Fricker, Ronald D.; Hagen, Katie
Fonte: Escola de Pós-Graduação Naval Publicador: Escola de Pós-Graduação Naval
Tipo: Conferência ou Objeto de Conferência
Relevância na Pesquisa
45.73%
Student Research Briefings: Biosurveillance, Defense Threat Deduction Agency, November 2010.

Biological terrorism preparedness evaluating the performance of the Early Aberration Reporting System (EARS) syndromic surveillance algorithms

Hegler, Benjamin L.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xx, 123 p. : ill. ;
Relevância na Pesquisa
86.03%
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)...

Assessing the effectiveness of the Early Aberration Reporting System (EARS) for early event detection of the H1N1 ("SWINE FLU") virus

Hagen, Katie S.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xx, 67 p. ;
Relevância na Pesquisa
86.08%
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

Assessing the Early Aberration Reporting System's Ability to Locally Detect the 2009 Influenza Pandemic

Hagen, Katie S.; Fricker, Ronald D. Jr.; Hanni, Krista D.; Michie, Kristy; Barnes, Susan
Fonte: Escola de Pós-Graduação Naval Publicador: Escola de Pós-Graduação Naval
Tipo: Journal Paper
Relevância na Pesquisa
76.03%
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.

Comparing Syndromic Surveillance Detection Methods: EARS' Versus a CUSUM-Based Methodology

Fricker, Ronald D. Jr.; Hegler, Benjamin L.; Dunfee, David A.
Fonte: Escola de Pós-Graduação Naval Publicador: Escola de Pós-Graduação Naval
Tipo: Journal Paper
Relevância na Pesquisa
86.01%
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.