Early Aberration Reporting System Research Articles

Comparing malaria early detection methods in a declining transmission setting in northwestern Ethiopia

BackgroundDespite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods.MethodsWe used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods.ResultsAll of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity.ConclusionsOf the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

Recent advances in the surveillance for measles: when will these be feasible in Africa?

Measles is a highly contagious viral infection with worldwide prevalence. It is transmitted by airborne route and has an incubation period is 8–12 days. Symptoms include fever, coryza, cough, rash, while signs include exudative conjunctivitis, maculopapular rashes. The population at risk for measles are those less than five years of age especially infants, those above 20 years of age, the immuno-suppressed and immune-compromised patients such as those with HIV/AIDS, those on steroids and chemotherapy, those with severe malnutrition. We aim to describe the different surveillance methods for measles including the traditional and modern methods, their evaluation and analysis. The traditional surveillance for measles includes over the counter sales of antipyretics and cough syrups, data from school absenteeism and school nurses, daily physician office visit, hospital ED visits, hospital admission data, nurse hotline data, emergency medical services and ambulance 911 calls and laboratory test requests. Measles surveillance can also be done using modern methods which can be used at the local, state and regional levels for the detection of measles outbreak. These modern methods include the Electronic Surveillance System for the early notification of community-based epidemics (ESSENCE), the real-time outbreak detection system (RODS), early aberration reporting system (EARS), Redbat and the syndrome reporting information system (SYRIS). Telehealth is another modern method for the surveillance of measles which detects outbreak of measles earlier regardless of how far the patient is from the healthcare centers. The various disease surveillance methods above are effective and beneficial in terms of early detection of outbreaks and situation analysis for measles and are hence recommended for use.

Application of early aberration reporting system (EARS) to seasonal influenza weekly report in Mexico (2007–2014)

Purpose: Seasonal influenza is a serious re-emerging acute infectious diseases with its social and health impact. Understanding its temporal characteristics in different spatial perspectives is a priority in public health and disease surveillance due to its annual recurrent episodes causing significant health burden. However, national and state-level influenza temporal trends are not fully investigated yet. Therefore, we applied EARS methods o describe temporal trends of seasonal influenza in Mexico from 2007 to 2014 in different spatial level. Methods & Materials: The number of confirmed seasonal influenza cases are reported every week at the state level from 2007 to 2014 (except in 2009) in Mexico. We use Early Aberration Reporting System (EARS) methods (C1, C2, and C3 algorithms) to detect the signals from influenza outbreak as temporal trends investigation in two different spatial level: national level and state level. Results: At national level analysis, C1 method could catch up 22 signals, C2 method could catch up 73 signals and C3 method could catch up 116 signals during 2007–2014 seasonal influenza surveillance. At state-level analysis, C1 statistics could catch up total 570 signals in 32 states. Average number of signals from C1 statistics is 17.8 signals/state ranged from 11 to 28. C2 statistics could catch up total 1612 signals in 32 states. The average number of signals C2 statistics is 50.4 signals/state ranged from 31 to 67 signals. C3 statistics could catch up total 3163 signals in 32 states. The average number of signals from C3 statistics is 98.8 signals/state ranged from 72 to 120 signals.Tabled 1Number of C1 SignalsNumber of C2 SignalsNumber of C3 SignalsMinimum (per year)113173Maximum (per year)2867119Average(SD)17.8(4.09)50.4(10.6)88.5(14.6) Open table in a new tab Conclusion: National level surveillance system and its signals may not meet the state level outbreak signals. Public health professionals should understand the difference of seasonal influenza temporal trends. Public health professionals and governments should apply state-level surveillance system to provide better guidelines for seasonal influenza prevention and control.

Evaluation of Early Aberration Reporting System for Dengue Outbreak Detection in Thailand

Thailand is one of the highest-burden countries for dengue infections in the South-East Asia Region of the World Health Organization. The 5-year median is normally used for outbreak detection; however, studies assessing the performance of this indicator against other detection methods are lacking. We, therefore, conducted a descriptive ecological study from a dataset comprised of patient visits to public hospitals for dengue treatment that were reported to the Ministry of Public Health. The aim was to evaluate the performance of an early aberration reporting system (EARS) in detecting dengue outbreaks, compared to using the 5-year median method. During 2003-2015, there were 1,014,201 patient visits and seven reported dengue outbreaks, with the largest occurring in 2013, and six seasonal peaks. The EARS was able to detect all seven dengue outbreaks and six seasonal peaks, including one outbreak that occurred in 2014 which was undetected by the 5-year median. However, EARS cannot provide information on trends, outbreak severity and issues noise signals. Our recommendation was to combine the EARS with the 5-year median method to reduce the number of false positive signals, or use the 5-year median method as a confirmatory tool.

Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study.

BackgroundNew nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort.ObjectiveThis study aimed to build and assess a national ecological NH PH surveillance system (SS).MethodsUsing a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines.ResultsBy extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016).ConclusionsThis study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats.

Comparison of statistical algorithms for syndromic surveillance aberration detection

ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE's syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–2122

A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks.

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt–Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

Development and Application of an Alert System to Detect Cases of Food Poisoning in Japan.

Recent public health concerns regarding commercial food products have increased the need to develop an automated method to detect food product-related health events. We developed and verified a method for the early detection of potentially harmful events caused by commercial food products. We collected data from daily internet-based questionnaires examining the presence or absence of symptoms and information about food purchased by the respondents. Using these data, we developed a method to detect possible health concerns regarding commercialized food products. To achieve this, we combined the signal detection method used in the reporting system of adverse effects of pharmaceutical products and the Early Aberration Reporting System (EARS) used by the United States Centers for Disease Control. Whiteleg shrimp (Litopenaeus vannamei), which had odds ratio and Odds(−) of 8.99 and 4.13, respectively, was identified as a possible causative food product for diarrhea and vomiting. In conclusion, this study demonstrated that food distributors can implement post-marketing monitoring of the safety of food products purchased via the internet.

Using health and demographic surveillance for the early detection of cholera outbreaks: analysis of community- and hospital-based data from Matlab, Bangladesh

BackgroundCholera outbreaks are a continuing problem in Bangladesh, and the timely detection of an outbreak is important for reducing morbidity and mortality. In Matlab, the ongoing Health and Demographic Surveillance System (HDSS) data records symptoms of diarrhea in children under the age of 5 years at the community level. Cholera surveillance in Matlab currently uses hospital-based data.ObjectiveThe objective of this study is to determine whether increases in cholera in Matlab can be detected earlier by using HDSS diarrhea symptom data in a syndromic surveillance analysis, when compared to hospital admissions for cholera.MethodsHDSS diarrhea symptom data and hospital admissions for cholera in children under 5 years of age over a 2-year period were analyzed with the syndromic surveillance statistical program EARS (Early Aberration Reporting System). Dates when significant increases in either symptoms or cholera cases occurred were compared to one another.ResultsThe analysis revealed that there were 43 days over 16 months when the cholera cases or diarrhea symptoms increased significantly. There were 8 months when both data sets detected days with significant increases. In 5 of the 8 months, increases in diarrheal symptoms occurred before increases of cholera cases. The increases in symptoms occurred between 1 and 15 days before the increases in cholera cases.ConclusionsThe results suggest that the HDSS survey data may be able to detect an increase in cholera before an increase in hospital admissions is seen. However, there was no direct link between diarrheal symptom increases and cholera cases, and this, as well as other methodological weaknesses, should be taken into consideration.

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

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

BackgroundThe 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.ObjectiveTo evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI.Methodology and principal findingsData 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, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009–10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states.Conclusions and significanceAs a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems.

Comments on ‘Some methodological issues in biosurveillance’

Rarely does one get to be a participant in the frontier of a new discipline or area of research. But since 9/11 the field of syndromic surveillance has provided public health researchers and practitioners just such an opportunity. Some of the $5 billion in funds awarded by the Centers for Disease Control and Prevention (CDC) during fiscal years 2002– 2007 to states, territories, and several large local jurisdictions to enhance public health capacities to detect, respond to, and recover from bioterrorism events have been allocated to expanding or improving surveillance systems [1]. These expansions have included developing public health capabilities in the area of biosurveillance and its sub-field, syndromic surveillance. The aim of these new systems is to use near ‘real-time’ data and automated tools to detect and characterize outbreaks (natural or intentional) before conventional methods. Several systems have become popular for use by state and local health jurisdictions in conducting these syndromic surveillance activities, including the Early Aberration Reporting System (EARS), BioSense, and the Electronic Surveillance System for the Early Notification of Community Based Epidemics (ESSENCE II) [2--4]. In the initial years of implementing syndromic surveillance systems, state and local health departments used the applications primarily to detect bioterrorism-related events [5]. However, due to developing needs, the public health community has applied biosurveillance more broadly to other situations, including influenza and fire-related illness surveillance, as well as to affirm the absence of outbreaks after a disaster [6]. In Monterey County, California, we have had similar needs and particularly wanted a program that we could use for local purposes. We began using EARS in 2005 because of its flexibility for developing syndrome definitions and applied it to a variety of situations, from daily ongoing surveillance for influenza-like illness to emerging situations such as respiratory syndromes potentially associated with an aerial spraying of pesticide (Monterey County, unpublished data). The field is young, and Fricker (this issue) is timely in his advocacy for the standardization of terms and methods used for biosurveillance and particularly syndromic surveillance. Public health practitioners should support such standardization and have outlined areas for further research and evaluation [7]. An equally pressing issue is the statistical methods that are currently used for biosurveillance. Fricker does an admirable job of summarizing how methodologies from the field of industrial statistical process control have been adapted for use in detecting attacks by terrorists on the health of a population. A comprehensive understanding of bioterrorism is relatively recent in the literature and, for research purposes, the number of modern bioterrorist events that resulted in actual cases have been few [8]. However, there has been an apparent recent increase in the use of biological agents with 40 of 56 confirmed criminal cases and 19 of 27 confirmed terrorist cases in the 20th century occurring in the 1990s [8]. While these provide a relative paucity of events for modeling purposes and for developing effective syndromic surveillance methods, they underscore the necessity of surveillance methods that can be used to improve time to event detection and to help ensure that a bioterrorist attack is detected. This is likely why there appears to be general support in public health for an expansion of syndromic and other public health surveillance systems. The majority of public health jurisdictions surveyed in the United States use some form of syndromic surveillance [5]. Internationally, the World Health Organization revised the International Health Regulations in 2007 to include the concept of syndromic surveillance as part of an expanded traditional disease notification system. May et al. [9] provide a review of its uses in developing nations. There is a national recognition of the potential usefulness and importance for developing syndromic surveillance systems. In December 2009, proposed regulations were released by the Centers for Medicare and Medicaid Services in the United States defining ‘meaningful use’ of electronic health records (EHR). In addition, the Office of the National Coordinator for Health Information Technology released an interim final rule describing the required certification standards

Comparison of the early aberration reporting system (EARS) W2 methods to an adaptive threshold method

In this paper we examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of these methods, which are designed using an empiric recurrence interval (RI), with simulated parametric data. Counts from the Poisson and negative binomial distributions are generated, and used to examine W2 properties. An adaptive threshold monitoring method is introduced based on fitting sample data to the above distributions, then converting the current value to a Z-score through a p-value. We compare the thresholds required to obtain given values of the RI for different sets of parameter values. We then simulate 1-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate that the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the sensitivity analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases and the adaptive threshold methods tend to outperform the W2 methods. Copyright © 2011 John Wiley & Sons, Ltd.

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

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.

Outbreak detection algorithms for seasonal disease data: a case study using ross river virus disease

BackgroundDetection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens.MethodsWe applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms.ResultsThe 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs.ConclusionsThis study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

The 2005 Wellington influenza outbreak: syndromic surveillance of Wellington Hospital Emergency Department activity may have provided early warning

To assess whether the Wellington Emergency Department (ED) Respiratory Syndromic Surveillance System may have provided early warning of the influenza outbreak in Wellington schools during 2005, and as a result might have provided the opportunity for an earlier or more effective public health response. All events of respiratory syndrome, as defined by selected ICD 10 codes, were extracted from Wellington Hospital ED for the dates 1 January 2004 to 31 December 2006, and analysed using the Centers for Disease Control and Prevention (CDC) surveillance program, Early Aberration Reporting System (EARS). Daily events were analysed for total counts and by lifecycle age group. Seven day moving averages of the numbers of events were also calculated. This study indicated that the surveillance system may have provided early warning of a potential respiratory outbreak. Regular exceedance flags were generated nine days prior to the initial notification received by Regional Public Health (RPH). The surveillance system also provided information on the type of illness (respiratory), the groups affected (5-14 year olds), and the progression of the outbreak (peak, end). The surveillance system might have worked by providing early notification of the outbreak. This may have prompted RPH to earlier investigate the potential outbreak and may have led to an earlier response. Surveillance of Emergency Department activity may be useful for early public health response.

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

BackgroundThe automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.MethodsRRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.ResultsWe found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.ConclusionOur retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.

Comparing syndromic surveillance detection methods: EARS' versus a CUSUM‐based methodology

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.

Multifaceted Syndromic Surveillance in a Public Health Department Using the Early Aberration Reporting System

Local health departments concerned with early detection of potential terrorist threats are beginning to explore novel approaches to syndromic surveillance. Using the Early Aberration Reporting System (EARS) developed by the Centers for Disease Control and Prevention, a metropolitan health department in Tennessee and five community partners have agreed to exchange data in order to implement a multifaceted syndromic surveillance system for early detection of a biological attack. This article describes how we used EARS as the foundation for implementing a surveillance system that encompasses a rich variety of data sources. We address technical requirements for operating EARS, recommend staffing and training prerequisites, describe the involvement of our data partners, and provide details related to data transfer and analysis, review, and response protocol. Other health departments may find this information useful as a general model for implementing EARS-based syndromic surveillance systems in their own jurisdictions.

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

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. The Early Aberration Reporting System (EARS) of the Centers for Disease Control and Prevention allows the analysis of public health surveillance data using available aberration detection methods. The primary purpose of EARS is to provide national, state, and local health departments with several alternative aberration detection methods. EARS helps assist local and state health officials to focus limited resources on appropriate activities during epidemiological investigations of important public health events. Finally, EARS allows end users to select validated aberration detection methods and modify sensitivity and specificity thresholds to values considered to be of public health importance by local and state health departments.