Evaluating detection and diagnostic decision support systems for bioterrorism response

Developing Public Health Emergency Response Leaders in Incident Management: A Scoping Review of Educational Interventions

During emergency responses, public health leaders frequently serve in incident management roles that differ from their routine job functions. Leaders' familiarity with incident management principles and functions can influence response outcomes. Therefore, training and exercises in incident management are often required for public health leaders. To describe existing methods of incident management training and exercises in the literature, we queried 6 English language databases and found 786 relevant articles. Five themes emerged: (1) experiential learning as an established approach to foster engaging and interactive learning environments and optimize training design; (2) technology-aided decision support tools are increasingly common for crisis decision-making; (3) integration of leadership training in the education continuum is needed for developing public health response leaders; (4) equal emphasis on competency and character is needed for developing capable and adaptable leaders; and (5) consistent evaluation methodologies and metrics are needed to assess the effectiveness of educational interventions.These findings offer important strategic and practical considerations for improving the design and delivery of educational interventions to develop public health emergency response leaders. This review and ongoing real-world events could facilitate further exploration of current practices, emerging trends, and challenges for continuous improvements in developing public health emergency response leaders.

Advance detection technologies for select biothreat agents

This chapter provides a comprehensive update about various technological developments in the field of biothreat agent detection. We have attempted to provide the reader with all the basic information starting with the culture considered as gold standard of diagnosis, immunological assays like the immunochromatographic test (ICT) which includes the lateral flow rapid strip test, flow through spot test and molecular assays, polymerase chain reaction (PCR), real-time RT-PCR, isothermal gene amplification assays, next-generation sequencing (NGS) technology, etc. One major highlight of the chapter includes bio-monitoring and aerosol generation technologies. We have also provided information about the sensor technologies nanomaterials biosensors, various analytical techniques besides various instrumental technologies like mass spectroscopy and Raman chemical imaging. The last part of the chapter is devoted to some newer technologies like biodetectors where we have given details about those that are currently commercially available.

Rapid Detection of Bacillus anthracis Spores Using Immunomagnetic Separation and Amperometry

Portable detection and quantitation methods for Bacillus anthracis (anthrax) spores in pure culture or in environmental samples are lacking. Here, an amperometric immunoassay has been developed utilizing immunomagnetic separation to capture the spores and remove potential interferents from test samples followed by amperometric measurement on a field-portable instrument. Antibody-conjugated magnetic beads and antibody-conjugated glucose oxidase were used in a sandwich format for the capture and detection of target spores. Glucose oxidase activity of spore pellets was measured indirectly via amperometry by applying a bias voltage after incubation with glucose, horseradish peroxidase, and the electron mediator 2,2′-azino-bis (3-ethylbenzthiazoline-6-sulphonic acid). Target capture was mediated by polyclonal antisera, whereas monoclonal antibodies were used for signal generation. This strategy maximized sensitivity (500 target spores, 5000 cfu/mL), while also providing a good specificity for Bacillus anthracis spores. Minimal signal deviation occurs in the presence of environmental interferents including soil and modified pH conditions, demonstrating the strengths of immunomagnetic separation. The simultaneous incubation of capture and detection antibodies and rapid substrate development (5 min) result in short sample-to-signal times (less than an hour). With attributes comparable or exceeding that of ELISA and LFDs, amperometry is a low-cost, low-weight, and practical method for detecting anthrax spores in the field.

Social media and internet-based data in global systems for public health surveillance: a systematic review

CONTEXT

The exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century.

METHODS

This article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We did an exhaustive review of published articles indexed in the databases PubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance.

FINDINGS

Our literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done to develop event-based surveillance, the existing systems have limitations. Accordingly, there is a need for further development of automated technologies that monitor health-related information on the Internet, especially to handle large amounts of data and to prevent information overload. The dissemination to health authorities of new information about health events is not always efficient and could be improved. No comprehensive evaluations show whether event-based surveillance systems have been integrated into actual epidemiological work during real-time health events.

CONCLUSIONS

The acceptability of data from the Internet and social media as a regular part of public health surveillance programs varies and is related to a circular challenge: the willingness to integrate is rooted in a lack of effectiveness studies, yet such effectiveness can be proved only through a structured evaluation of integrated systems. Issues related to changing technical and social paradigms in both individual perceptions of and interactions with personal health data, as well as social media and other data from the Internet, must be further addressed before such information can be integrated into official surveillance systems.

Disease prediction models and operational readiness

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.

Use of computerized decision support systems to improve antibiotic prescribing

This decade will see the emergence of the electronic medical record, electronic prescribing and computerized decision support in the hospital setting. Current opinion from key infectious diseases bodies supports the use of computerized decision support systems as potentially useful tools in antibiotic stewardship programs. However, although antibiotic decision support systems appear beneficial for improving the quality of prescribing and reducing the costs of antibiotic prescribing, their overall cost-effectiveness, impact on patient outcome and antimicrobial resistance is much less certain. This review describes computerized decision support systems used to assist with antibiotic prescribing, the evidence for their effectiveness and the current and future roles.

A Web-based multidrug-resistant organisms surveillance and outbreak detection system with rule-based classification and clustering

BACKGROUND

The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials.

OBJECTIVES

To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection.

METHODS

Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system's performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel.

RESULTS

The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001).

CONCLUSION

This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates.

Optimizing the response to surveillance alerts in automated surveillance systems

Although much research effort has been directed toward refining algorithms for disease outbreak alerting, considerably less attention has been given to the response to alerts generated from statistical detection algorithms. Given the inherent inaccuracy in alerting, it is imperative to develop methods that help public health personnel identify optimal policies in response to alerts. This study evaluates the application of dynamic decision making models to the problem of responding to outbreak detection methods, using anthrax surveillance as an example. Adaptive optimization through approximate dynamic programming is used to generate a policy for decision making following outbreak detection. We investigate the degree to which the model can tolerate noise theoretically, in order to keep near optimal behavior. We also evaluate the policy from our model empirically and compare it with current approaches in routine public health practice for investigating alerts. Timeliness of outbreak confirmation and total costs associated with the decisions made are used as performance measures. Using our approach, on average, 80 per cent of outbreaks were confirmed prior to the fifth day of post-attack with considerably less cost compared to response strategies currently in use. Experimental results are also provided to illustrate the robustness of the adaptive optimization approach and to show the realization of the derived error bounds in practice.

Using a relational database to index infectious disease information

Mapping medical knowledge into a relational database became possible with the availability of personal computers and user-friendly database software in the early 1990s. To create a database of medical knowledge, the domain expert works like a mapmaker to first outline the domain and then add the details, starting with the most prominent features. The resulting "intelligent database" can support the decisions of healthcare professionals. The intelligent database described in this article contains profiles of 275 infectious diseases. Users can query the database for all diseases matching one or more specific criteria (symptom, endemic region of the world, or epidemiological factor). Epidemiological factors include sources (patients, water, soil, or animals), routes of entry, and insect vectors. Medical and public health professionals could use such a database as a decision-support software tool.

The bioscience revolution & the biological weapons threat: levers & interventions

In December 2008, the US Commission on the Prevention of Weapons of Mass Destruction Proliferation and Terrorism, released a report, World At Risk. The Report points to the fact that, not only is the use of a weapon of mass destruction in a terrorist attack before the end of 2013, more likely than not, but also to the fact that terrorists are more likely to be able to obtain and use biological weapons than nuclear. This paper examines the recommendations of the report in the context of the historic and geopolitical changes, in particular globalization. The authors highlight the "dual-use" dilemma, as described in the report, as the paradoxical use of technology developed for the benefit of mankind being used for sinister purposes. The mitigation of such a threat lies in broad stakeholder involvement and cooperation, including non-state actors, governments and the bio-tech industry itself. The importance of vigilance measures within the life science community is emphasized and, the authors propose, could include a web-based didactic course in bioterrorism and weapons of mass destruction identification. The site could outline safety protocols, have detailed disaster management tutorials, and could be specifically tailored for different subsets of industry and health professionals. The paper concludes with an endorsement of a multi-pronged approach including strong international guidelines and intelligence cooperation and preparatory measures such as the wide-spread use of detection systems as well as diagnostic decision support systems for bioterrorism detection at the local level.

Towards bioinformatics assisted infectious disease control

BACKGROUND

This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining.

RESULTS

This framework combines the genetic and geographic data of a pathogen to reconstruct its history and to identify the migration routes through which the strains spread regionally and internationally. A pilot study of Salmonella typhimurium genotype clustering and temporospatial outbreak analysis demonstrated better discrimination power than traditional phage typing. Half of the outbreaks were detected in the first half of their duration.

CONCLUSION

The microbial profiling and biosurveillance focused text mining tools can enable integrated infectious disease outbreak detection and response environments based upon bioinformatics knowledge models and measured by outcomes including the accuracy and timeliness of outbreak detection.

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.

Update on public health surveillance in emergency departments

The development of public health surveillance systems based on ED visits, in conjunction with other health and non-health-related data, is an important step to better understanding the health needs of the US population. There are multiple steps to develop a functional organization, and these actions require the support and involvement of many different partners. The ability to (1) analyze data; (2) distribute results; and (3) influence policy, funding, and patients' behavior are important outgrowths of these activities. This chapter discusses the opportunities and obstacles related to public health surveillance systems based on ED data.

Bioterrorism: is it a real threat?

The Geneva Protocol of 1925 commits the signatory nations to refraining from the use of biological weapons. However, the terrorist assaults of September 2001 and, subsequently, the anthrax-containing letters are cause for great concerns: new threats to the security of nations are expected, as terrorist organizations seem to increasingly explore novel ways of spreading terror. In this context, naturally emerging diseases such as SARS, monkeypox or West Nile fever assume new importance because it is difficult to distinguish between natural epidemics and possible bioweapon assaults. Great efforts on the part of governments and public health authorities are necessary to counteract these threats.