Syndromic surveillance and bioterrorism-related epidemics

Automated detection of influenza-like illness using clinical surveillance markers at a Department of Veterans Affairs Medical Center

Background:

Using demographic and clinical measures from emergency department evaluations, we developed an automated surveillance system for influenza-like illness (ILI).

Methods:

We selected a random sample of patients who were seen at the Durham, NC Veterans Affairs Medical Center between May 2002 and October 2009 with fever or a respiratory ICD-9 diagnosis code and divided this into subsets for system development and validation. Comprehensive chart reviews identified patients who met a standard case definition for ILI. Logistic regression models predicting ILI were fit in the development sample. We applied the parameter estimates from these models to the validation sample and evaluated their utility using receiver-operator characteristic analysis.

Results:

The models discriminated ILI very well in the validation sample; the C-statistics were >0.89.

Conclusions:

Risk estimates based on statistical models can be incorporated into electronic medical records systems to assist clinicians and could be used in real-time surveillance for disease outbreaks.

Detection of excess influenza severity: associating respiratory hospitalization and mortality data with reports of influenza-like illness by primary care physicians

OBJECTIVES

We explored whether excesses in influenza severity can be detected by combining respiratory syndromic hospital and mortality data with data on influenza-like illness (ILI) cases obtained from general practitioners.

METHODS

To identify excesses in the severity of influenza infections in the population of the Netherlands between 1999 and 2005, we looked for increases in influenza-associated hospitalizations and mortality that were disproportionate to the number of ILI cases reported by general practitioners. We used generalized estimating equation regression models to associate syndromic hospital and mortality data with ILI surveillance data obtained from general practitioners. Virus isolation and antigenic characterization data were used to interpret the results.

RESULTS

Disproportionate increases in hospitalizations and mortality (relative to ILI cases reported by general practitioners) were identified in 2003/04 during the A/Fujian/411/02(H3N2) drift variant epidemic.

CONCLUSIONS

Combined surveillance of respiratory hospitalizations and mortality and ILI data obtained from general practitioners can capture increases in severe influenza-associated illness that are disproportionate to influenza incidence rates. Therefore, this novel approach should complement traditional seasonal and pandemic influenza surveillance in efforts to detect increases in influenza case fatality rates and percentages of patients hospitalized.

Review of methods for space-time disease surveillance

A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.

Trends and directions of global public health surveillance

Recently, global health and global health surveillance have received unprecedented recognition of their importance because of the newly emerging and reemerging infectious diseases, new cycles of pandemics, and the threats of bioterrorism. The aim of this review is to provide an update of the current state of knowledge on health surveillance in a globalized world. Three key areas will be highlighted in this review: 1) the role of the new International Health Regulations, 2) the emergence of new global health networks for surveillance and bioterrorism, and 3) the reshaping of guidelines for the collection, dissemination, and interventions in global surveillance. A discussion is also presented of the more important challenges of global health surveillance. Global surveillance has been reshaped by important changes in the new International Health Regulations and the rapid development of new global networks for disease surveillance and bioterrorism. These networks provide for the first time at the global scale real-time information about potential outbreaks and epidemics of newly emerging and reemerging infectious diseases. The recent outbreaks of severe acute respiratory syndrome (SARS) and the influenza A (H1N1) pandemic provide evidence of the benefits of the new global monitoring and of the importance of the World Health Organization in its coordinating role in the multilateral response of the global public health community.

Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?

Background

Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.

Methods and Findings

Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999–2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999–2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999–2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).

Conclusions

To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.

Acute diarrheal syndromic surveillance: effects of weather and holidays

OBJECTIVE

In an effort to identify and characterize the environmental factors that affect the number of patients with acute diarrheal (AD) syndrome, we developed and tested two regional surveillance models including holiday and weather information in addition to visitor records, at emergency medical facilities in the Seoul metropolitan area of Korea.

METHODS

With 1,328,686 emergency department visitor records from the National Emergency Department Information system (NEDIS) and the holiday and weather information, two seasonal ARIMA models were constructed: (1) The simple model (only with total patient number), (2) the environmental factor-added model. The stationary R-squared was utilized as an in-sample model goodness-of-fit statistic for the constructed models, and the cumulative mean of the Mean Absolute Percentage Error (MAPE) was used to measure post-sample forecast accuracy over the next 1 month.

RESULTS

The (1,0,1)(0,1,1)7 ARIMA model resulted in an adequate model fit for the daily number of AD patient visits over 12 months for both cases. Among various features, the total number of patient visits was selected as a commonly influential independent variable. Additionally, for the environmental factor-added model, holidays and daily precipitation were selected as features that statistically significantly affected model fitting. Stationary R-squared values were changed in a range of 0.651-0.828 (simple), and 0.805-0.844 (environmental factor-added) with p<0.05. In terms of prediction, the MAPE values changed within 0.090-0.120 and 0.089-0.114, respectively.

CONCLUSION

The environmental factor-added model yielded better MAPE values. Holiday and weather information appear to be crucial for the construction of an accurate syndromic surveillance model for AD, in addition to the visitor and assessment records.

Privacy versus public health: the impact of current confidentiality rules

Public health research and practice often have been facilitated through the evaluation and study of population-based data collected by local, state, and federal governments. However, recent concerns about identify theft, confidentiality, and patient privacy have led to increasingly restrictive policies on data access, often preventing researchers from using these valuable data. We believe that these restrictions, and the research impeded or precluded by their implementation and enforcement, have had a significant negative impact on important public health research. Members of the public health community should challenge these policies through their professional societies and by lobbying legislators and health officials to advocate for changes that establish a more appropriate balance between privacy concerns and the protection of public health.

Sentinel surveillance system for early outbreak detection in Madagascar

Background

Following the outbreak of chikungunya in the Indian Ocean, the Ministry of Health directed the necessary development of an early outbreak detection system. A disease surveillance team including the Institut Pasteur in Madagascar (IPM) was organized to establish a sentinel syndromic-based surveillance system. The system, which was set up in March 2007, transmits patient data on a daily basis from the various voluntary general practitioners throughout the six provinces of the country to the IPM. We describe the challenges and steps involved in developing a sentinel surveillance system and the well-timed information it provides for improving public health decision-making.

Methods

Surveillance was based on data collected from sentinel general practitioners (SGP). The SGPs report the sex, age, visit date and time, and symptoms of each new patient weekly, using forms addressed to the management team. However, the system is original in that SGPs also report data at least once a day, from Monday to Friday (number of fever cases, rapid test confirmed malaria, influenza, arboviral syndromes or diarrhoeal disease), by cellular telephone (encrypted message SMS). Information can also be validated by the management team, by mobile phone. This data transmission costs 120 ariary per day, less than US$1 per month.

Results

In 2008, the sentinel surveillance system included 13 health centers, and identified 5 outbreaks. Of the 218,849 visits to SGPs, 12.2% were related to fever syndromes. Of these 26,669 fever cases, 12.3% were related to Dengue-like fever, 11.1% to Influenza-like illness and 9.7% to malaria cases confirmed by a specific rapid diagnostic test.

Conclusion

The sentinel surveillance system represents the first nationwide real-time-like surveillance system ever established in Madagascar. Our findings should encourage other African countries to develop their own syndromic surveillance systems.

Prompt detection of an outbreak of infectious disease may lead to control measures that limit its impact and help prevent future outbreaks.

Evaluation of alternative respiratory syndromes for specific syndromic surveillance of influenza and respiratory syncytial virus: a time series analysis

BACKGROUND

Syndromic surveillance is increasingly being evaluated for its potential for early warning of increased disease activity in the population. However, interpretation is hampered by the difficulty of attributing a causative pathogen. We described the temporal relationship between laboratory counts of influenza and respiratory syncytial virus (RSV) detection and alternative groupings of Emergency Department (ED) respiratory diagnoses.

METHODS

ED and laboratory data were obtained for the south-eastern area of Sydney, NSW for the period 1 June 2001 - 1 December 2006. Counts of ED visits and laboratory confirmed positive RSV and influenza cases were aggregated by week. Semi-parametric generalized additive models (GAM) were used to determine the association between the incidence of RSV and influenza and the incidence of respiratory syndrome ED presentations while controlling for temporal confounders.

RESULTS

For every additional RSV laboratory count, ED diagnoses of bronchiolitis increased by 3.1% (95%CI: 2.7%-3.5%) in the same week. For every additional influenza laboratory count, ED diagnoses of influenza-like illness increased by 4.7% (95%CI: 4.2%-5.2%) one week earlier.

CONCLUSION

In this study, large increases in ED diagnoses of bronchiolitis and influenza-like illness were independent and proxy indicators for RSV and influenza activity, respectively.

Situational uses of syndromic surveillance

Since 2001, many state and local health departments have implemented automated systems to monitor healthcare use and to promptly identify and track epidemics and other public health threats. In 2007-08, we conducted case studies of selected events with actual or potential public health impacts to determine whether and how health departments and hospitals used these new systems. We interviewed public health and hospital representatives and applied qualitative analysis methods to identify response themes. So-called "syndromic" surveillance methods were most useful in situations with widespread health effects, such as respiratory illness associated with seasonal influenza or exposures to smoke from wildfires. In other instances, such as a tornado or hazardous material exposures, these systems were useful for detecting or monitoring health impacts that affected relatively few people, or they were used to affirm the absence of outbreaks following natural disasters or the detection of a potential pathogen in air samples. Typically, these data supplemented information from traditional sources to provide a timelier or fuller mosaic of community health status, and use was shaped by long-standing contacts between health department and hospital staffs. State or local epidemiologists generally preferred syndromic systems they had developed over the CDC BioSense system, citing lesser familiarity with BioSense and less engagement in its development. Instances when BioSense data were most useful to state officials occurred when analyses and reports were provided by CDC staff. Understanding the uses of surveillance information during such events can inform further investments in surveillance capacity in public health emergency preparedness programs.

Software-engineering challenges of building and deploying reusable problem solvers

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task-method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

Beyond traditional surveillance: applying syndromic surveillance to developing settings--opportunities and challenges

BACKGROUND

All countries need effective disease surveillance systems for early detection of outbreaks. The revised International Health Regulations [IHR], which entered into force for all 194 World Health Organization member states in 2007, have expanded traditional infectious disease notification to include surveillance for public health events of potential international importance, even if the causative agent is not yet known. However, there are no clearly established guidelines for how countries should conduct this surveillance, which types of emerging disease syndromes should be reported, nor any means for enforcement.

DISCUSSION

The commonly established concept of syndromic surveillance in developed regions encompasses the use of pre-diagnostic information in a near real time fashion for further investigation for public health action. Syndromic surveillance is widely used in North America and Europe, and is typically thought of as a highly complex, technology driven automated tool for early detection of outbreaks. Nonetheless, low technology applications of syndromic surveillance are being used worldwide to augment traditional surveillance.

SUMMARY

In this paper, we review examples of these novel applications in the detection of vector-borne diseases, foodborne illness, and sexually transmitted infections. We hope to demonstrate that syndromic surveillance in its basic version is a feasible and effective tool for surveillance in developing countries and may facilitate compliance with the new IHR guidelines.

Temporal Features in Biological Warfare

No matter how prepared a population may be, bioterrorism cannot be prevented: the first clues will always be given by ill people. Temporal analysis applied to this type of scenarios could be an additional tool for limiting disruption among civilians allowing for recognizing typical temporal progression and duration of symptoms in first infected people. We propose the application of a fuzzy temporal reasoning system we have developed for biomedical temporal data analysis in different scenarios after a hypothetical attack. The system is able to handle both qualitative and metric temporal knowledge affected by vagueness and uncertainty, taking into account in this way the vagueness of patients reports expressed in natural language.

Syndromic surveillance: sensitivity and positive predictive value of the case definitions

The aim of the study was to measure the positive predictive value (PPV) and sensitivity of operational case definitions of 13 syndromes in a surveillance system based on the Emergency online database of the Lazio region. The PPVs were calculated using electronic emergency department (ED) medical records and subsequent hospitalizations to ascertain the cases. Sensitivity was calculated using a modified capture–recapture method. The number of cases that fulfilled the case definition criteria in the 2004 database ranged from 27 320 for gastroenteritis to three for haemorrhagic diarrhoea. The PPVs ranged from 99·3 to 20; sepsis, meningitis-like and coma were below 50%. The estimated sensitivity ranged from 90% for coma to 22% for haemorrhagic diarrhoea. Syndromes such as gastroenteritis, where the signs, symptoms, and exposure history provide immediate diagnostic implications fit this surveillance system better than others such as haemorrhagic diarrhoea, where symptoms are not evident and a more precise diagnosis is needed.

Syndromic surveillance for influenzalike illness

Emergency department data are currently being used by several syndromic surveillance systems to identify outbreaks of natural or man-made illnesses, and preliminary results suggest that regular outbreaks might be detected earlier with such data than with traditional reporting. This article summarizes a retrospective study of 5 influenza seasons in Ottawa,Canada; time-series analysis was used to look for an association between consultation to the emergency department for influenzalike illness and the isolation of influenza virus in the community. The population studied included both children and adults consulting to 3 local hospitals. In 4 seasons, visits to the emergency department involving children younger than 5 years consulting mainly for fever and for respiratory symptoms peaked 1 to 4 weeks before the isolation of influenza virus in the community. If monitored regularly for the presence of key symptoms, pediatric hospitals might be efficient and cost-effective sentinels of influenza and of other infectious diseases.

[Syndromic surveillances based on the emergency department]

Due to heightened concerns regarding possible bioterrorist attacks, the Korea Center for Disease Control and Prevention introduced syndromic surveillance systems, which have been run by emergency departments in hospitals throughout Korea since 2002. These systems are designed to identify illness clusters before diagnoses are confirmed and reported to public health agencies, to mobilize a rapid response, and thereby to reduce morbidity and mortality. The Korea Center for Disease Control and Prevention performed drop-in syndromic surveillance successfully during the World Cup Football Games in 2002, the Universiad games in 2004, and the Asian Pacific Economic Cooperation meeting in 2005. In addition, sustainable syndromic surveillance system involving the collaborative efforts of 125 sentinel hospitals has been in operation nationwide since 2002. Because active data collection can bias decisions a physician makes, there is a need to generate an automatic and passive data collection system. Therefore, the Korea Center for Disease Control and Prevention plans to establish computerized automatic data collection systems in the near future. These systems will be used not only for the early detection of bioterrorism but also for more effective public health responses to disease.

Validation of syndromic surveillance for respiratory pathogen activity

The studied respiratory syndromes are suitable for syndromic surveillance because they reflect respiratory pathogen activity patterns

Syndromic surveillance is increasingly used to signal unusual illness events. To validate data-source selection, we retrospectively investigated the extent to which 6 respiratory syndromes (based on different medical registries) reflected respiratory pathogen activity. These syndromes showed higher levels in winter, which corresponded with higher laboratory counts of Streptococcus pneumoniae, respiratory syncytial virus, and influenza virus. Multiple linear regression models indicated that most syndrome variations (up to 86%) can be explained by counts of respiratory pathogens. Absenteeism and pharmacy syndromes might reflect nonrespiratory conditions as well. We also observed systematic syndrome elevations in the fall, which were unexplained by pathogen counts but likely reflected rhinovirus activity. Earliest syndrome elevations were observed in absenteeism data, followed by hospital data (+1 week), pharmacy/general practitioner consultations (+2 weeks), and deaths/laboratory submissions (test requests) (+3 weeks). We conclude that these syndromes can be used for respiratory syndromic surveillance, since they reflect patterns in respiratory pathogen activity.

[Real time epidemiological surveillance within the armed forces: concepts, realities and prospects in France]

BACKGROUND

In 2002, the North Atlantic Treaty Organization took five initiatives in order to enhance the defence capacities against the massive destruction weapons, one of them concerned the development of an interoperable surveillance system, giving in real time some informations permitting early warning to the commanders. Thoughts in France to improve the military surveillance system, methodological constraints and first results are shown.

METHODS

Medical, technological, human and organisational aspects had to be taken into account to develop real time surveillance within the armed forces, and also specific military constraints. In order to evaluate the validity of its methodology, the "Institut de médecine tropicale du service de santé des armées" developed a prototype, set up in French Guyana and which took part in a second time at a multinational exercise.

RESULTS

The "surveillance spatiale des épidémies au sein des forces armées de Guyane" has been set up in 2004, formed by both a recording and an analysis networks. This system permits to provide in real time some dashboards directly operational for the commanders. The exhaustiveness rate has been evaluated at 104%, compared to the traditional surveillance. It permitted three times to detect outbreaks several weeks before the other systems. Some limits have been identified, as the use of personal digitalized assistants. The involvement in a multinational exercise showed the system's efficacy, by detecting two simulated outbreaks, but also its interoperability. In 2006, it has been decided to extend the concept by deploying its second generation within the French armed forces in Djibouti. The "alerte et surveillance en temps réel" disposal permitted to take into account multiple geographical localizations.

CONCLUSION

A real time surveillance system is an essential alarm disposal, however it is only an information tool within the complex activity of piloting the sanitary situation. It must be integrated within the whole situation expertise supports, represented also by medical intelligence, epidemiological investigations and prediction of the epidemiological phenomenon evolution.

Can syndromic thresholds provide early warning of national influenza outbreaks?

BACKGROUND

Influenza incidence thresholds are used to help predict the likely impact of influenza and inform health professionals and the public of current activity. We evaluate the potential of syndromic data (calls to a UK health helpline NHS Direct) to provide early warning of national influenza outbreaks.

METHODS

Time series of NHS Direct calls concerning 'cold/flu' and fever syndromes for England and Wales were compared against influenza-like-illness clinical incidence data and laboratory reports of influenza. Poisson regression models were used to derive NHS Direct thresholds. The early warning potential of thresholds was evaluated retrospectively for 2002-06 and prospectively for winter 2006-07.

RESULTS

NHS Direct 'cold/flu' and fever calls generally rose and peaked at the same time as clinical and laboratory influenza data. We derived a national 'cold/flu' threshold of 1.2% of total calls and a fever (5-14 years) threshold of 9%. An initial lower fever threshold of 7.7% was discarded as it produced false alarms. Thresholds provided 2 weeks advanced warning of seasonal influenza activity during three of the four winters studied retrospectively, and 6 days advance warning during prospective evaluation.

CONCLUSION

Syndromic thresholds based on NHS Direct data provide advance warning of influenza circulating in the community. We recommend that age-group specific thresholds be developed for other clinical influenza surveillance systems in the UK and elsewhere.

Direct cost associated with the development and implementation of a local syndromic surveillance system

OBJECTIVE

Enhancing public health surveillance to include electronic syndromic surveillance systems has received increased attention in recent years. Although cost continually serves as a critical factor in public health decision making, few studies have evaluated direct costs associated with syndromic surveillance systems. In this study, we calculated the direct costs associated with developing and implementing a syndromic surveillance system in Boston, Massachusetts, from the perspective of local, state, and federal governments.

METHODS

Between December 2003 and July 2005, the Boston Public Health Commission (BPHC), in collaboration with the Centers for Disease Control and Prevention (CDC), and the Massachusetts Department of Public Health developed a syndromic surveillance system in which limited demographic and chief complaint data are collected from all Boston acute care emergency departments every 24 hours. Costs were divided into three categories: development, operation, and upgrade. Within these categories, all fixed and variable costs incurred by both BPHC and CDC were assessed, including those associated with development of syndromic surveillance-related city regulations and system enhancements.

RESULTS

The total estimated direct cost of system development and implementation during the study period was $422,899 ($396,716 invested by BPHC and $26,183 invested by CDC). Syndromic system enhancements to improve situational awareness accounted for $74,389.

CONCLUSION

Development, implementation, and operation of a syndromic surveillance system accounted for a relatively small proportion of surveillance costs in a large urban health department. Funding made available for a future cost-benefit analysis, and an assessment of local epidemiologic capacity will help to guide decisions for local health departments. Although not a replacement for traditional surveillance, syndromic surveillance in Boston is an important and relatively inexpensive component of a comprehensive local public health surveillance system.

Evaluating detection of an inhalational anthrax outbreak

Timely detection of an inhalational anthrax outbreak is critical for clinical and public health management. Syndromic surveillance has received considerable investment, but little is known about how it will perform relative to routine clinical case finding for detection of an inhalational anthrax outbreak. We conducted a simulation study to compare clinical case finding with syndromic surveillance for detection of an outbreak of inhalational anthrax. After simulated release of 1 kg of anthrax spores, the proportion of outbreaks detected first by syndromic surveillance was 0.59 at a specificity of 0.9 and 0.28 at a specificity of 0.975. The mean detection benefit of syndromic surveillance was 1.0 day at a specificity of 0.9 and 0.32 days at a specificity of 0.975. When syndromic surveillance was sufficiently sensitive to detect a substantial proportion of outbreaks before clinical case finding, it generated frequent false alarms.

Discernment between deliberate and natural infectious disease outbreaks

Public health authorities should be vigilant to the potential for outbreaks deliberately caused by biological agents (bioterrorism). Such events require a rapid response and incorporation of non-traditional partners for disease investigation and outbreak control. The astute application of infectious disease epidemiological principles can promote an enhanced index of suspicion for such events. We discuss epidemiological indicators that should be considered during outbreak investigations, and also examine their application during bioterrorism incidents, an accidental release of an agent, outbreaks of infections that were alleged to have been deliberately initiated, and a model scenario. The Grunow & Finke epidemiological assessment tool is used to examine these historical events and the model scenario. The results received from this analysis, coupled with an understanding of epidemiological clues to unnatural events, and knowledge of how to manage such events, can aid in the improved response and resolution of epidemics.

Discriminating Inhalational Anthrax From Community-Acquired Pneumonia Using Chest Radiograph Findings and a Clinical Algorithm

BACKGROUND

Limiting the effects of a large-scale bioterrorist anthrax attack will require rapid and accurate detection of the earliest victims. We undertook this study to improve physicians' ability to rapidly detect inhalational anthrax victims.

METHODS

We conducted a case-control study to compare chest radiograph findings from 47 patients from historical inhalational anthrax cases and 188 community-acquired pneumonia control subjects. We then used classification tree analyses to derive an algorithm of chest radiograph findings and clinical characteristics that accurately and explicitly discriminated between inhalational anthrax and community-acquired pneumonia.

RESULTS

Twenty-two of the 47 patients from historical inhalational anthrax cases (46.8%) had reported chest radiograph findings. All 22 case patients (100%) had mediastinal widening, pleural effusion, or both. However, 16 case patients (72.7%) also had infiltrates. In comparison, all 188 community-acquired control subjects had reported chest radiographs. Of these, 127 control subjects (67.6%) had infiltrates, 43 control subjects (22.9%) had pleural effusions, and 15 control subjects (8.0%) had mediastinal widening. A derived algorithm with three predictor variables (chest radiograph finding of mediastinal widening, altered mental status, and elevated hematocrit) is 100% sensitive (95% confidence interval [CI], 73.5 to 100) and 98.3% specific (95% CI, 95.1 to 99.6). The derivation process used 12 patients with inhalational anthrax and 177 control subjects with community-acquired pneumonia who had information available for all three variables.

CONCLUSIONS

There are significant chest radiograph differences between inhalational anthrax and community-acquired pneumonia, but none of the chest radiograph findings are both highly sensitive and highly specific. The derived clinical algorithm can improve physicians' ability to discriminate inhalational anthrax from community-acquired pneumonia, but its utility is limited to previously healthy individuals and its accuracy may be limited by missing values.

Early Outbreak Detection Using an Automated Data Feed of Test Orders from a Veterinary Diagnostic Laboratory

Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.

Pre-spillover prevention of emerging zoonotic diseases: what are the targets and what are the tools?

The uneven standards of surveillance, human- or animal-based, for zoonotic diseases or pathogens maintained and transmitted by wildlife H(R)s, or even domestic species, is a global problem, readily apparent even within the United States, where investment in public health, including surveillance systems, has a long and enviable history. As of 2006, there appears to be little scientific, social, or political consensus that animal-based surveillance for zoonoses merits investment in international infrastructure, other than the fledgling efforts with avian influenza, or targeted nontraditional avenues of surveillance and research. National institutions charged with strategic planning for emerging diseases or intentional releases of zoonotic agents have emphasized improving diagnostic capabilities for detecting human infections, modifying the immune status of human or domestic animals through vaccines, producing better antiviral or antibacterial drugs, and enhancing human-based surveillance as an early warning system. With the possible exception of extensive human vaccination, each of these approaches target post-spillover events and none of these avenues of research will have the slightest impact on reducing the risk of additional emergence of viruses or other pathogens from wildlife. Novel schemes of preventing spillover of human pathogens from animal H(R)s can only spring from improving our understanding of the ecological context and biological interactions of pathogen maintenance among H(R)s. Although the benefit derived from investments to improve surveillance and knowledge of zoonotic pathogens circulating among wildlife H(R) populations is uncertain, our experience with HIV and the looming threat of pandemic avian influenza A inform us of the outcomes we can expect by relying on detection of post-spillover events among sentinel humans.

Surveillance for Newly Emerging Viruses

Surveillance is a fundamental tool for public health, producing information to guide actions. Modern surveillance tends to follow health measures such as the incidence of a disease or syndrome or even the occurrence of health-related behaviors. There are many reasons for conducting surveillance, and the data collected and the approach taken to analyzing those data are both influenced by the overall goal of a surveillance system. Surveillance systems aims mainly at detection also provide information that may be useful for other purposes. The goal of detecting an outbreak of a newly emerging virus, places specific demands on the type of data collected and the types of analysis performed. All approaches to surveillance share some common principles. While some of the underlying methods used in public health surveillance have evolved considerably in recent years, the general approach to surveillance has remained relatively constant. At a fundamental level, surveillance aims to (1) identify individual cases, (2) detect population patterns in identified cases, and then (3) convey information to decision-makers about population health patterns.

Chemical-biological terrorism and its impact on children

Children remain potential victims of chemical or biological terrorism. In recent years, children have even been specific targets of terrorist acts. Consequently, it is necessary to address the needs that children would face after a terrorist incident. A broad range of public health initiatives have occurred since September 11, 2001. Although the needs of children have been addressed in many of them, in many cases, these initiatives have been inadequate in ensuring the protection of children. In addition, public health and health care system preparedness for terrorism has been broadened to the so-called all-hazards approach, in which response plans for terrorism are blended with plans for a public health or health care system response to unintentional disasters (eg, natural events such as earthquakes or pandemic flu or manmade catastrophes such as a hazardous-materials spill). In response to new principles and programs that have appeared over the last 5 years, this policy statement provides an update of the 2000 policy statement. The roles of both the pediatrician and public health agencies continue to be emphasized; only a coordinated effort by pediatricians and public health can ensure that the needs of children, including emergency protocols in schools or child care centers, decontamination protocols, and mental health interventions, will be successful.

Review of syndromic surveillance: implications for waterborne disease detection

Syndromic surveillance is the gathering of data for public health purposes before laboratory or clinically confirmed information is available. Interest in syndromic surveillance has increased because of concerns about bioterrorism. In addition to bioterrorism detection, syndromic surveillance may be suited to detecting waterborne disease outbreaks. Theoretical benefits of syndromic surveillance include potential timeliness, increased response capacity, ability to establish baseline disease burdens, and ability to delineate the geographical reach of an outbreak. This review summarises the evidence gathered from retrospective, prospective, and simulation studies to assess the efficacy of syndromic surveillance for waterborne disease detection. There is little evidence that syndromic surveillance mitigates the effects of disease outbreaks through earlier detection and response. Syndromic surveillance should not be implemented at the expense of traditional disease surveillance, and should not be relied upon as a principal outbreak detection tool. The utility of syndromic surveillance is dependent on alarm thresholds that can be evaluated in practice. Syndromic data sources such as over the counter drug sales for detection of waterborne outbreaks should be further evaluated.

Simulated anthrax attacks and syndromic surveillance

Bioterrorism surveillance systems can be assessed using modeling to simulate real-world attacks.

We measured sensitivity and timeliness of a syndromic surveillance system to detect bioterrorism events. A hypothetical anthrax release was modeled by using zip code population data, mall customer surveys, and membership information from HealthPartners Medical Group, which covers 9% of a metropolitan area population in Minnesota. For each infection level, 1,000 releases were simulated. Timing of increases in use of medical care was based on data from the Sverdlovsk, Russia, anthrax release. Cases from the simulated outbreak were added to actual respiratory visits recorded for those dates in HealthPartners Medical Group data. Analysis was done by using the space-time scan statistic. We evaluated the proportion of attacks detected at different attack rates and timeliness to detection. Timeliness and completeness of detection of events varied by rate of infection. First detection of events ranged from days 3 to 6. Similar modeling may be possible with other surveillance systems and should be a part of their evaluation.

Syndromic Surveillance: is it a useful tool for local outbreak detection?

New surveillance systems are required to meet the demands of a changing world.

Propagation of program control: a tool for distributed disease surveillance

Purpose

The purpose of the study was (1) to identify the requirements for syndromic, disease surveillance and epidemiology systems arising from events such as the SARS outbreak in March 2003, and the deliberate spread of Bacillus anthracis, or anthrax, in the US in 2001; and (2) to use these specifications as input to the construction of a system intended to meet these requirements. An important goal was to provide information about the diffusion of a communicable disease without being dependent on centralised storage of information about individual patients or revealing patient-identifiable information.

Methods

The method applied is rooted in the engineering paradigm involving phases of analysis, system specification, design, implementation, and testing. The requirements were established from earlier projects’ conclusions and analysis of disease outbreaks. The requirements were validated by a literature study of syndromic and disease surveillance systems. The system was tested on simulated EHR databases generated from microbiology laboratory data.

Results

A requirements list that a syndromic and disease surveillance system should meet, and an open source system, “The Snow Agent system”, has been developed. The Snow Agent system is a distributed system for monitoring the status of a population's health by distributing processes to, and extracting epidemiological data directly from, the electronic health records (EHR) system in a geographic area.

Conclusions

Syndromic and disease surveillance tools should be able to operate at all levels in the health systems and across national borders. Such systems should avoid transferring patient identifiable data, support two-way communications and be able to define and incorporate new and unknown diseases and syndrome definitions that should be reported by the system. The initial tests of the Snow Agent system shows that it will easily scale to national level in Norway.

Modelling the Utility of Body Temperature Readings From Primary Care Consults for SARS Surveillance in an Army Medical Centre

Introduction: There is interest in surveillance systems for outbreak detection at stages where clinical presentation would still be undifferentiated. Such systems focus on detecting clusters of syndromes in excess of baseline levels, which may indicate an outbreak. We model the detection limits of a potential system based on primary care consults for the detection of an outbreak of severe acute respiratory syndrome (SARS). Materials and Methods: Data from an averaged-sized medical centre were extracted from the Patient Care Enhancement System (PACES) [the electronic medical records system serving the Singapore Armed Forces (SAF)]. Thresholds were set to 3 or more cases presenting with particular syndromes and a temperature reading of ≥38o C (T ≥38). Monte Carlo simulation was used to insert simulated SARS outbreaks of various sizes onto the background incidence of febrile cases, accounting for distribution of SARS incubation period, delay from onset to first consult, and likelihood of presenting with T ≥38 to the SAF medical centre. Results: Valid temperature data was available for 2012 out of 2305 eligible syndromic consults (87.2%). T ≥38 was observed in 166 consults (8.3%). Simulated outbreaks would peak 7 days after exposure, but, on average, signals at their peak would consist of 10.9% of entire outbreak size. Under baseline assumptions, the system has a higher than 90% chance of detecting an outbreak only with 20 or more cases. Conclusions: Surveillance based on clusters of cases with T ≥38 helps reduce background noise in primary care data, but the major limitation of such systems is that they are still only able to confidently detect large outbreaks. Key words: Communicable diseases, Emerging, Epidemiology, Military medicine, Syndrome

Diarrheal illness detected through syndromic surveillance after a massive power outage: New York City, August 2003

OBJECTIVES

We investigated increases in diarrheal illness detected through syndromic surveillance after a power outage in New York City on August 14, 2003.

METHODS

The New York City Department of Health and Mental Hygiene uses emergency department, pharmacy, and absentee data to conduct syndromic surveillance for diarrhea. We conducted a case-control investigation among patients presenting during August 16 to 18, 2003, to emergency departments that participated in syndromic surveillance. We compared risk factors for diarrheal illness ascertained through structured telephone interviews for case patients presenting with diarrheal symptoms and control patients selected from a stratified random sample of nondiarrheal patients.

RESULTS

Increases in diarrhea were detected in all data streams. Of 758 patients selected for the investigation, 301 (40%) received the full interview. Among patients 13 years and older, consumption of meat (odds ratio [OR]=2.7, 95% confidence interval [CI]=1.2, 6.1) and seafood (OR=4.8; 95% CI=1.6, 14) between the power outage and symptom onset was associated with diarrheal illness.

CONCLUSIONS

Diarrhea may have resulted from consumption of meat or seafood that spoiled after the power outage. Syndromic surveillance enabled prompt detection and systematic investigation of citywide illness that would otherwise have gone undetected.

The initial hospital response to an epidemic

The last decades have been characterized by the appearance of a substantial number of newly recognized or novel infectious agents and by the re-emergence of infectious diseases with a global impact. The objective of this article is to briefly describe the model of hospital response for early diagnosis and prompt management of patients with highly contagious infectious diseases. We reviewed the main components of hospital preparedness in response to clustering of highly contagious diseases. A model for the initial hospital response to an epidemic in our referral Institute is discussed. Prompt recognition and identification is the initial and indispensable step in facing any communicable diseases, regardless of whether it is a prevalent, a newly emerging one or deliberately released. The importance of developing and implementing nontraditional methods of public health surveillance and a system that allows a wide and immediate dissemination of information and exchange of views on risk assessment and risk management are highlighted. Case identification and laboratory capabilities and isolation procedures are the essential components for an initial hospital response. The recent bioterrorist events and the worldwide outbreaks of highly contagious infectious diseases have evidenced the need for institutional preparedness at each hospital and for identification of referral centers for patient isolation and of laboratories with adequate capabilities. Moreover, hospitals should develop a plan for coordinating all hospital components to respond to critical situations deriving from the admission of patients with highly contagious infectious diseases.

Validation of syndromic surveillance for respiratory infections

Study objective

A key public health question is whether syndromic surveillance data provide early warning of infectious outbreaks. One cause for skepticism is that biological correlates of the administrative and clinical data used in these systems have not been rigorously assessed. This study measures the value of respiratory data currently used in syndromic surveillance systems to detect respiratory infections by comparing it against criterion standard viral testing within a pediatric population.

Methods

We conducted a longitudinal study with prospective validation in the emergency department (ED) of a tertiary care children’s hospital. Children aged 7 years or younger who presented with a respiratory syndrome or who were tested for respiratory syncytial virus (RSV), influenza virus, parainfluenza virus, adenovirus, or enterovirus between January 1993 and June 2004 were included. We assessed the predictive ability of the viral tests by fitting generalized linear models to respiratory syndrome counts.

Results

Of 582,635 patient visits, 89,432 (15.4%) were for respiratory syndromes, and of these, 7,206 (8.1%) patients were tested for the viruses of interest. RSV was significantly related to respiratory syndrome counts (adjusted rate ratio [RR] 1.33; 95% confidence interval [CI] 1.04 to 1.71). In multivariate models including all viruses tested, influenza virus was also a significant predictor of respiratory syndrome counts (RR 1.47; 95% CI 1.03 to 2.10). This model accounted for 81.6% of the observed variability in respiratory syndrome counts.

Conclusion

Respiratory syndromic surveillance data strongly correlate with virologic test results in a pediatric population, providing evidence of the biologic validity of such surveillance systems. Real-time outbreak detection systems relying on syndromic data may be an important adjunct to the current set of public health systems for the detection and surveillance of respiratory infections.

A review of strategies for enhancing the completeness of notifiable disease reporting

Notifiable disease surveillance systems provide essential data for infectious disease prevention and control programs at the local, state, and national levels. Given that reporting completeness is known to vary considerably, this review identifies methods that can reliably enhance completeness of reporting. These surveillance-related activities include initiating active surveillance when appropriate; implementing automated, electronic laboratory-based reporting; strengthening ties with clinicians and other key partners in notifiable disease reporting; and increasing the use of laboratory diagnostic tests in identifying new cases. Despite ample data in support of these strategies, notifiable disease surveillance continues to receive insufficient attention and resources. Recent attention to public health preparedness provides an opportunity to strengthen notifiable disease surveillance and enhance completeness of reporting.

A space-time permutation scan statistic for disease outbreak detection

BACKGROUND

The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant.

METHODS AND FINDINGS

We propose a prospective space-time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest.

CONCLUSION

If such results hold up over longer study times and in other locations, the space-time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems.

Advances in detecting and responding to threats from bioterrorism and emerging infectious disease

Much progress has been made in recent years to strengthen local, state, national and international capacities to detect and respond to bioterrorism events and naturally occurring outbreaks of disease. New tools and systems are available to estimate the potential impact of a biological event and predict resource needs for effective response, enable earlier detection of an attack or outbreak, enhance diagnostic capacity and facilitate rapid intervention to mitigate the impact of an event on a community. These advances have required new approaches to preparedness, planning and surveillance, as well as new partnerships and collaborations across a range of disciplines. We examine some of these developments, discuss potential uses and limitations of these approaches, and identify priorities for the future.