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

Alcohol-Related Problems Among Black Adults: the Role of False Safety Behaviors

BACKGROUND

Black adults who consume alcohol experience negative alcohol-related outcomes, indicating a need for culturally sensitive research aimed at identifying malleable psychological factors that may play a role in drinking related problems to inform prevention and treatment. One such factor is false safety behavior (FSB), which reflects behaviors geared toward decreasing anxiety short term but that maintains or increases anxiety long term. Although emerging data indicate that FSBs are related to substance use in predominantly White samples, no known studies have tested whether these behaviors are related to drinking behaviors among Black individuals.

METHODS

Participants were 163 Black undergraduate who endorsed current (past-month) alcohol use and completed an online survey.

RESULTS

FSB use frequency was robustly positively related to alcohol-related problems, even after controlling for peak eBAC, anxiety, depression, and relevant demographic variables. Anxiety was indirectly related to alcohol-related problems via more frequent FSB use.

CONCLUSIONS

Nearly all Black individuals who consume alcohol report using FSB to manage anxiety. More frequent FSB use is robustly related to more alcohol-related problems and may play an important role in the relation of anxiety with alcohol-related problems among Black individuals who endorse current alcohol use.

Syndromic surveillance of respiratory infections during protracted conflict: experiences from northern Syria 2016-2021

OBJECTIVE

Northern Syria faces a large burden of influenza-like illness (ILI) and severe acute respiratory illness (SARI). This study aimed to investigate the trends of Early Warning and Response Network (EWARN) reported ILI and SARI in northern Syria between 2016 and 2021 and the potential impact of SARS-CoV-2.

METHODS

We extracted weekly EWARN data on ILI/ SARI and aggregated cases and consultations into 4-week intervals to calculate case positivity. We conducted a seasonal-trend decomposition to assess case trends in the presence of seasonal fluctuations.

RESULTS

It was observed that 4-week aggregates of ILI cases (n = 5,942,012), SARI cases (n = 114,939), ILI case positivity, and SARI case positivity exhibited seasonal fluctuations with peaks in the winter months. ILI and SARI cases in individuals aged ≥5 years surpassed those in individuals aged <5 years in late 2019. ILI cases clustered primarily in Aleppo and Idlib, whereas SARI cases clustered in Aleppo, Idlib, Deir Ezzor, and Hassakeh. SARI cases increased sharply in 2021, corresponding with a severe SARS-CoV-2 wave, compared with the steady increase in ILI cases over time.

CONCLUSION

Respiratory infections cause widespread morbidity and mortality throughout northern Syria, particularly with the emergence of SARS-CoV-2. Strengthened surveillance and access to testing and treatment are critical to manage outbreaks among conflict-affected populations.

Infectious diseases prevention and control using an integrated health big data system in China

BACKGROUND

The Yinzhou Center for Disease Prevention and Control (CDC) in China implemented an integrated health big data platform (IHBDP) that pooled health data from healthcare providers to combat the spread of infectious diseases, such as dengue fever and pulmonary tuberculosis (TB), and to identify gaps in vaccination uptake among migrant children.

METHODS

IHBDP is composed of medical data from clinics, electronic health records, residents' annual medical checkup and immunization records, as well as administrative data, such as student registries. We programmed IHBDP to automatically scan for and detect dengue and TB carriers, as well as identify migrant children with incomplete immunization according to a comprehensive set of screening criteria developed by public health and medical experts. We compared the effectiveness of the big data screening with existing traditional screening methods.

RESULTS

IHBDP successfully identified six cases of dengue out of a pool of 3972 suspected cases, whereas the traditional method only identified four cases (which were also detected by IHBDP). For TB, IHBDP identified 288 suspected cases from a total of 43,521 university students, in which three cases were eventually confirmed to be TB carriers through subsequent follow up CT or T-SPOT.TB tests. As for immunization screenings, IHBDP identified 240 migrant children with incomplete immunization, but the traditional door-to-door screening method only identified 20 ones.

CONCLUSIONS

Our study has demonstrated the effectiveness of using IHBDP to detect both acute and chronic infectious disease patients and identify children with incomplete immunization as compared to traditional screening methods.

Is clinical primary care surveillance for tularaemia a useful addition to laboratory surveillance? An analysis of notification data for Finland, 2013 to 2019

BackgroundIn Finland, surveillance of tularaemia relies on laboratory-confirmed case notifications to the National infectious Diseases Register (NIDR).AimThe aim of the study was to assess the suitability and usefulness of clinical surveillance as an addition to laboratory notification to improve tularaemia surveillance in Finland.MethodsWe retrieved NIDR tularaemia surveillance and primary healthcare data on clinically diagnosed tularaemia cases in Finland between 2013 and 2019. We compared incidences, demographic distributions and seasonal trends between the two data sources.ResultsThe median annual incidence was 0.6 (range: 0.1-12.7) and 0.8 (range: 0.6-7.2) per 100,000 for NIDR notifications and primary healthcare notifications, respectively. Cases reported to NIDR were slightly older than cases reported to primary healthcare (median: 53 years vs 50 years, p = 0.04), but had similar sex distribution. Seasonal peaks differed between systems, both in magnitude and in timing. On average, primary healthcare notifications peaked 3 weeks before NIDR. However, peaks in NIDR were more pronounced, for example in 2017, monthly incidence per 100,000 of NIDR notifications peaked at 12.7 cases in September, while primary healthcare notifications peaked at 7.2 (1.8 ratio) in August.ConclusionsClinically diagnosed cases provide a valuable additional data source for surveillance of tularaemia in Finland. A primary healthcare-based system would allow for earlier detection of increasing incidences and thereby for early warning of outbreaks. This is crucial in order to implement targeted control and prevention measures as early as possible.

OUP accepted manuscript

Objective

Supporting public health research and the public’s situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data.

Materials and Methods

The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework’s effectiveness in maintaining the PK11 threshold of 0.01.

Results

When sharing COVID-19 county-level case data across all US counties, the framework’s approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%.

Conclusion

Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.

Representativeness of the FluWatchers Participatory Disease Surveillance Program 2015-2016 to 2018-2019: How do participants compare with the Canadian population?

BACKGROUND

FluWatch is Canada's national surveillance system that monitors the spread of influenza. Its syndromic surveillance component monitors the spread of influenza-like illness (ILI) in near-real time for signals of unusual or increased activity. Syndromic surveillance data are collected from two main sources: the Sentinel Practitioner ILI Reporting System and FluWatchers.We evaluated the representativeness of the most recent participant population to understand changes in representativeness since 2015, to identify demographic and geographic gaps and correlates/determinants of participation to characterize a typical participant.

METHODS

In this serial cross-sectional study, characteristics of participants during four consecutive influenza seasons (2015-2016, 2016-2017, 2017-2018 and 2018-2019) were compared with the 2016 Canadian Census and the 2015-2016, 2016-2017, 2017-2018 and 2018-2019 National Seasonal Influenza Vaccination Coverage Surveys. Associations between demographic factors and the level of user participation were also analyzed among the 2018-2019 FluWatchers population.

RESULTS

Infants (0-4 years) and older adults (65 years and older) were under-represented in FluWatchers across all four influenza seasons. Female and urban participants were significantly over-represented. Vaccination coverage remained significantly higher among the FluWatchers populations from the past four influenza seasons across all age groups. Level of participation among FluWatchers was associated with age and vaccination status, but not with sex or geography. Over its four years of implementation, the FluWatchers participant population became more representative of the Canadian population with respect to age and geography (urban/rural and provincial/territorial).

CONCLUSION

FluWatchers participants under-represent the tails of Canada's age distribution and over-represent those who engage in health promoting behaviours as indicated by high influenza vaccine coverage, consistent with typical volunteer-based survey response biases. Representativeness would likely improve with targeted recruitment of under-represented groups, such as males, older adults and Canadians living in rural areas.

School-based surveillance of acute infectious disease in children: a systematic review

BACKGROUND

Syndromic surveillance systems are an essential component of public health surveillance and can provide timely detection of infectious disease cases and outbreaks. Whilst surveillance systems are generally embedded within healthcare, there is increasing interest in novel data sources for monitoring trends in illness, such as over-the-counter purchases, internet-based health searches and worker absenteeism. This systematic review considers the utility of school attendance registers in the surveillance of infectious disease outbreaks and occurrences amongst children.

METHODS

We searched eight databases using key words related to school absence, infectious disease and syndromic surveillance. Studies were limited to those published after 1st January 1995. Studies based in nursery schools or higher education settings were excluded. Article screening was undertaken by two independent reviewers using agreed eligibility criteria. Data extraction was performed using a standardised data extraction form. Outcomes included estimates of absenteeism, correlation with existing surveillance systems and associated lead or lag times.

RESULTS

Fifteen studies met the inclusion criteria, all of which were concerned with the surveillance of influenza. The specificity of absence data varied between all-cause absence, illness absence and syndrome-specific absence. Systems differed in terms of the frequency of data submissions from schools and the level of aggregation of the data. Baseline rates of illness absence varied between 2.3-3.7%, with peak absences ranging between 4.1-9.8%. Syndrome-specific absenteeism had the strongest correlation with other surveillance systems (r = 0.92), with illness absenteeism generating mixed results and all-cause absenteeism performing the least well. A similar pattern of results emerged in terms of lead and lag times, with influenza-like illness (ILI)-specific absence providing a 1-2 week lead time, compared to lag times reported for all-cause absence data and inconsistent results for illness absence data.

CONCLUSION

Syndrome-specific school absences have potential utility in the syndromic surveillance of influenza, demonstrating good correlation with healthcare surveillance data and a lead time of 1-2 weeks ahead of existing surveillance measures. Further research should consider the utility of school attendance registers for conditions other than influenza, to broaden our understanding of the potential application of this data for infectious disease surveillance in children.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO 2019 CRD42019119737.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

BACKGROUND

The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.

METHODS

We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.

RESULTS

Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.

CONCLUSIONS

Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Evidence-Based African Swine Fever Policies: Do We Address Virus and Host Adequately?

African swine fever (ASF) is one of the most threatening diseases for the pig farming sector worldwide. Prevention, control and eradication remain a challenge, especially in the absence of an effective vaccine or cure and despite the relatively low contagiousness of this pathogen in contrast to Classical Swine Fever or Foot and Mouth disease, for example. Usually lethal in pigs and wild boar, this viral transboundary animal disease has the potential to significantly disrupt global trade and threaten food security. This paper outlines the importance of a disease-specific legal framework, based on the latest scientific evidence in order to improve ASF control. It compares the legal basis for ASF control in a number of pig-producing regions globally, considering diverse production systems, taking into account current scientific evidence in relation to ASF spread and control. We argue that blanket policies that do not take into account disease-relevant characteristics of a biological agent, nor the specifics under which the host species are kept, can hamper disease control efforts and may prove disproportionate.

Estimating COVID-19 cases and outbreaks on-stream through phone calls

One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R ² > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

BACKGROUND

Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system.

OBJECTIVE

The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information.

METHODS

We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly).

RESULTS

Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively.

CONCLUSIONS

We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

Cluster Detection Mechanisms for Syndromic Surveillance Systems: Systematic Review and Framework Development

Background

The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms.

Objective

Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework.

Methods

We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review.

Results

The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms.

Conclusions

The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

Background

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

Objective

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

Methods

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Results

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

Conclusions

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Smoking and social anxiety: the role of false safety behaviors

Despite the negative health consequences associated with smoking, most smokers find it difficult to quit. This is especially true for smokers with elevated social anxiety. One factor that may play a role in maintaining smoking with elevated anxiety is false safety behavior (FSB), behaviors geared toward decreasing anxiety short-term but that maintain or increase anxiety long-term. The present study tested whether FSB explained the relation of social anxiety severity with smoking among 71 current smokers. Avoidance-related FSB was the only type of FSB related to cigarettes smoked per day (CPD) and it was robustly related to more CPD. Further, social anxiety was related to CPD indirectly via FSB-Avoidance. Findings suggest that more frequent use of avoidance behaviors to manage anxiety may maintain smoking and may partially explain the high rates of smoking among those with elevated social anxiety. Thus, FSB may be a promising target in smoking cessation interventions, especially among those with elevated social anxiety.

Public health delivery in the information age: the role of informatics and technology

AIM

Public health systems have embraced health informatics and information technology as a potential transformational tool to improve real-time surveillance systems, communication, and sharing of information among various agencies. Global pandemic outbreaks like Zika and Ebola were quickly controlled due to electronic surveillance systems enabling efficient information access and exchange. However, there is the need for a more robust technology to enhance adequate epidemic forecasting, data sharing, and effective communication. The purpose of this review was to examine the use of informatics and information technology tools and its impact on public health delivery.

METHOD

Investigators searched six electronic databases. These were MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete, Cochrane Database of Systematic Reviews, COMPENDEX, Scopus, and Academic Search Premier from January 2000 to 31 March 2016.

RESULTS

A total of 60 articles met the eligibility criteria for inclusion. These studies were organized into three areas as (1) definition of the term public health informatics; (2) type of public health surveillance systems and implications for public health; and (3) electronic surveillance systems functionality, capability, training, and challenges. Our analysis revealed that due to the growing expectations to provide real-time response and population-centered evidence-based public health in this information-driven age there has been a surge in informatics and information technology adoption. Education and training programs are now available to equip public health students and professionals with skills in public health informatics. However, obstacles including interoperability, data standardization, privacy, and technology transfer persist.

CONCLUSION

Re-engineering the delivery of public health is necessary to meet the demands of the 21st century and beyond. To meet this expectation, public health must invest in workforce development and capacity through education and training in informatics.

Evaluation of the early warning, alert and response system after Cyclone Winston, Fiji, 2016

Objective

To assess the performance of an early warning, alert and response system (EWARS) developed by the World Health Organization (WHO) - EWARS in a Box - that was used to detect and control disease outbreaks after Cyclone Winston caused destruction in Fiji on 20 February 2016.

Methods

Immediately after the cyclone, Fiji's Ministry of Health and Medical Services, supported by WHO, started to implement EWARS in a Box, which is a smartphone-based, automated, early warning surveillance system for rapid deployment during health emergencies. Both indicator-based and event-based surveillance were employed. The performance of the system between 7 March and 29 May 2016 was evaluated. Users' experience with the system was assessed in interviews using a semi-structured questionnaire and by a cross-sectional survey. The system's performance was assessed using data from the EWARS database.

Findings

Indicator-based surveillance recorded 34 113 cases of the nine syndromes under surveillance among 326 861 consultations. Three confirmed outbreaks were detected, and no large outbreak was missed. Users were satisfied with the performance of EWARS and judged it useful for timely monitoring of disease trends and outbreak detection. The system was simple, stable and flexible and could be rapidly deployed during a health emergency. The automated collation, analysis and dissemination of data reduced the burden on surveillance teams, saved human resources, minimized human error and ensured teams could focus on public health responses.

Conclusion

In Fiji, EWARS in a Box was effective in strengthening disease surveillance during a national emergency and was well regarded by users.

Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization

Syndromic Surveillance aims at analyzing medical data to detect clusters of illness or forecast disease outbreaks. Although the research in this field is flourishing in terms of publications, an insight of the global research output has been overlooked. This paper aims at analyzing the global scientific output of the research from 1993 to 2017. To this end, the paper uses bibliometric analysis and visualization to achieve its goal. Particularly, a data processing framework was proposed based on citation datasets collected from Scopus and Clarivate Analytics’ Web of Science Core Collection (WoSCC). The bibliometric method and Citespace were used to analyze the institutions, countries, and research areas as well as the current hotspots and trends. The preprocessed dataset includes 14,680 citation records. The analysis uncovered USA, England, Canada, France and Australia as the top five most productive countries publishing about Syndromic Surveillance. On the other hand, at the Pinnacle of academic institutions are the US Centers for Disease Control and Prevention (CDC). The reference co-citation analysis uncovered the common research venues and further analysis of the keyword cooccurrence revealed the most trending topics. The findings of this research will help in enriching the field with a comprehensive view of the status and future trends of the research on Syndromic Surveillance.

Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore

Background

Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms.

Objective

The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records.

Methods

CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration.

Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation.

Results

The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status.

Conclusions

We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches

BACKGROUND

Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact.

OBJECTIVE

Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions.

METHODS

We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You).

RESULTS

WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information.

CONCLUSIONS

While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.

Anxiety and cannabis-related problem severity among dually diagnosed outpatients: The impact of false safety behaviors

Cannabis use disorder (CUD) co-occurs with anxiety disorders at high rates. Little is known about the mechanisms linking CUD and anxiety disorders. One theoretically-driven perspective is that individuals with anxiety disorders may be more apt to use FSBs (i.e., behaviors that may be effective in decreasing anxiety in the short-term, but can maintain and even exacerbate anxiety in the long-term), which can perpetuate cannabis use despite cannabis-related problems. The present study tested whether FSB use explained the relation of anxiety symptom severity with cannabis quantity and related problems among 77 adults with CUD and comorbid anxiety disorders seeking outpatient CUD treatment. Results indicated that FSB frequency was significantly related to anxiety symptom severity and cannabis problem severity, but not cannabis quantity. Anxiety symptom severity was indirectly (via FSB frequency) related to cannabis problem severity, but not to cannabis quantity. These novel findings suggest that more frequent use of FSBs may play an important role in cannabis problem severity among individuals with CUD and anxiety disorders.

Effectiveness and Cost Efficiency of Different Surveillance Components for Proving Freedom and Early Detection of Disease: Bluetongue Serotype 8 in Cattle as Case Study for Belgium, France and the Netherlands

Quick detection and recovery of country's freedom status remain a constant challenge in animal health surveillance. The efficacy and cost efficiency of different surveillance components in proving the absence of infection or (early) detection of bluetongue serotype 8 in cattle populations within different countries (the Netherlands, France, Belgium) using surveillance data from years 2006 and 2007 were investigated using an adapted scenario tree model approach. First, surveillance components (sentinel, yearly cross-sectional and passive clinical reporting) within each country were evaluated in terms of efficacy for substantiating freedom of infection. Yearly cross-sectional survey and passive clinical reporting performed well within each country with sensitivity of detection values ranging around 0.99. The sentinel component had a sensitivity of detection around 0.7. Secondly, how effective the components were for (early) detection of bluetongue serotype 8 and whether syndromic surveillance on reproductive performance, milk production and mortality data available from the Netherlands and Belgium could be of added value were evaluated. Epidemic curves were used to estimate the timeliness of detection. Sensitivity analysis revealed that expected within-herd prevalence and number of herds processed were the most influential parameters for proving freedom and early detection. Looking at the assumed direct costs, although total costs were low for sentinel and passive clinical surveillance components, passive clinical surveillance together with syndromic surveillance (based on reproductive performance data) turned out most cost-efficient for the detection of bluetongue serotype 8. To conclude, for emerging or re-emerging vectorborne disease that behaves such as bluetongue serotype 8, it is recommended to use passive clinical and syndromic surveillance as early detection systems for maximum cost efficiency and sensitivity. Once an infection is detected and eradicated, cross-sectional screening for substantiating freedom of infection and sentinel for monitoring the disease evolution are recommended.

Acquired immunity and asymptomatic reservoir impact on frontline and airport ebola outbreak syndromic surveillance and response

The number of surveillance networks for infectious disease diagnosis and response has been growing. In 2000, the World Health Organization (WHO) established the Global Outbreak Alert and Response Network, which has been endorsed by each of the 46 WHO African members since then. Yet, taming the dynamics and plague of the vicious Ebola virus disease (EVD) in African countries has been patchy and erratic due to inadequate surveillance and contact tracing, community defiance and resistance, a lack of detection and response systems, meager/weak knowledge and information on the disease, inadequacies in protective materials protocols, contact tracing nightmare and differing priorities at various levels of the public health system. Despite the widespread acceptance of syndromic surveillance (SS) systems, their ability to provide early warning alerts and notifications of outbreaks is still unverified. Information is often too limited for any outbreak, or emerging or otherwise unexpected disease, to be recognized at either the community or the national level. Indeed, little is known about the role and the interactions between the Ebola infection and exposure to other syndemics and the development of acquired immunity, asymptomatic reservoir, and Ebola seroconversion. Can lessons be learnt from smallpox, polio, and influenza immunity, and can immunization against these serve as a guide? In most endemic countries, community health centers and disease control and prevention at airports solely relies on passive routine immunization control and reactive syndromic response. The frontline and airport Ebola SS systems in West Africa have shown deficiencies in terms of responding with an alarming number of case fatalities, and suggest that more detailed insights into Ebola, and proactive actions, are needed. The quest for effective early indicators (EEE) in shifting the public and global health paradigm requires the development and implementation of a comprehensive and effective community or regional integrated pandemic preparedness and surveillance response systems tailored to local contexts. These systems must have mechanisms for early identification, rapid contact tracing and tracking, confirmation, and communication with the local population and the global community, and must endeavor to respond in a timely manner.

Relationship between the population incidence of pertussis in children in New South Wales, Australia and emergency department visits with cough: a time series analysis

Background

Little is known about the potential of syndromic surveillance to provide early warning of pertussis outbreaks. We conducted a time series analysis to assess whether an emergency department (ED) cough syndrome would respond to changes in the incidence of pertussis in children aged under 10 years in New South Wales (NSW), Australia, and to evaluate the timing of any association. A further aim was to assess the lag between the onset of pertussis symptoms and case notification in the infectious diseases surveillance system in NSW.

Methods

Using routinely collected data, we prepared a daily count time series of visits to NSW EDs assigned a provisional diagnosis of cough. Separate daily series were prepared for three independent variables: notifications of cases of pertussis and influenza and ED visits with bronchiolitis (a proxy measure of respiratory syncytial virus (RSV) infection). The study period was 1/1/2007-31/12/2010. A negative binomial multivariate model was used to assess associations between the outcome and independent variables. We also evaluated the median delay in days between the estimated onset of a case of pertussis and the date the local public health authority was notified of that case.

Results

When notified pertussis increased by 10 cases in one day, ED visits with cough increased by 5.2% (95% confidence interval (CI): 0.5%-10.0%) seven days later. Daily increases in the other independent variables had a smaller impact on cough visits. When notified influenza increased by 10 cases in one day, ED visits with cough increased by 0.8% (95% CI: 0%-1.7%) seven days later. When ED visits with bronchiolitis increased by 10 visits in one day, ED visits with cough increased by 4.8% (95% CI: 1.2%-8.6%) one day earlier. The median interval between estimated onset of pertussis and case notification was seven days.

Conclusions

Pertussis appears to be an important driver of ED visits with cough in children aged under 10 years. However, the median delay in notification of cases of pertussis was similar to the lag in the pertussis-associated short-term increases in ED visits with cough. Elevations in RSV and influenza activity may also explain increases in the ED cough syndrome. Real time monitoring of ED visits with cough in children is therefore unlikely to consistently detect a potential outbreak of pertussis before passive surveillance.

Randomized controlled trial of false safety behavior elimination therapy: a unified cognitive behavioral treatment for anxiety psychopathology

We tested the efficacy of a unified cognitive-behavioral therapy protocol for anxiety disorders. This group treatment protocol, termed false safety behavior elimination therapy (F-SET), is a cognitive-behavioral approach designed for use across various anxiety disorders such as panic disorder (PD), social anxiety disorder (SAD), and generalized anxiety disorder (GAD). F-SET simplifies, as well as broadens, key therapeutic elements of empirically validated treatments for anxiety disorders to allow for easier delivery to heterogeneous groups of patients with anxiety psychopathology. Patients with a primary anxiety disorder diagnosis (N=96) were randomly assigned to F-SET or a wait-list control. Data indicate that F-SET shows good efficacy and durability when delivered to mixed groups of patients with anxieties (i.e., PD, SAD, GAD) by relatively inexperienced clinicians. Findings are discussed in the context of balancing treatment efficacy and clinical utility.

Enhanced health event detection and influenza surveillance using a joint Veterans Affairs and Department of Defense biosurveillance application

Background

The establishment of robust biosurveillance capabilities is an important component of the U.S. strategy for identifying disease outbreaks, environmental exposures and bioterrorism events. Currently, U.S. Departments of Defense (DoD) and Veterans Affairs (VA) perform biosurveillance independently. This article describes a joint VA/DoD biosurveillance project at North Chicago-VA Medical Center (NC-VAMC). The Naval Health Clinics-Great Lakes facility physically merged with NC-VAMC beginning in 2006 with the full merger completed in October 2010 at which time all DoD care and medical personnel had relocated to the expanded and remodeled NC-VAMC campus and the combined facility was renamed the Lovell Federal Health Care Center (FHCC). The goal of this study was to evaluate disease surveillance using a biosurveillance application which combined data from both populations.

Methods

A retrospective analysis of NC-VAMC/Lovell FHCC and other Chicago-area VAMC data was performed using the ESSENCE biosurveillance system, including one infectious disease outbreak (Salmonella/Taste of Chicago-July 2007) and one weather event (Heat Wave-July 2006). Influenza-like-illness (ILI) data from these same facilities was compared with CDC/Illinois Sentinel Provider and Cook County ESSENCE data for 2007-2008.

Results

Following consolidation of VA and DoD facilities in North Chicago, median number of visits more than doubled, median patient age dropped and proportion of females rose significantly in comparison with the pre-merger NC-VAMC facility. A high-level gastrointestinal alert was detected in July 2007, but only low-level alerts at other Chicago-area VAMCs. Heat-injury alerts were triggered for the merged facility in June 2006, but not at the other facilities. There was also limited evidence in these events that surveillance of the combined population provided utility above and beyond the VA-only and DoD-only components. Recorded ILI activity for NC-VAMC/Lovell FHCC was more pronounced in the DoD component, likely due to pediatric data in this population. NC-VAMC/Lovell FHCC had two weeks of ILI activity exceeding both the Illinois State and East North Central Regional baselines, whereas Hines VAMC had one and Jesse Brown VAMC had zero.

Conclusions

Biosurveillance in a joint VA/DoD facility showed potential utility as a tool to improve surveillance and situational awareness in an area with Veteran, active duty and beneficiary populations. Based in part on the results of this pilot demonstration, both agencies have agreed to support the creation of a combined VA/DoD ESSENCE biosurveillance system which is now under development.

Syndromic surveillance for influenza in two hospital emergency departments. Relationships between ICD-10 codes and notified cases, before and during a pandemic

BACKGROUND

Interest in the use of emergency department (ED) data by syndromic surveillance systems to detect influenza outbreaks has been growing. Evaluations of these systems generally focus on events during influenza seasons. The aims of this study were to identify which emergency department disease codes best correlated with confirmed influenza cases and to determine if these same codes would be useful in the non-influenza season. The 2009 influenza pandemic in Victoria, Australia, provided further opportunity to examine the performance of the syndromic surveillance system during this event.

METHODS

We undertook a retrospective analysis of data from the Victorian Department of Health's pilot syndromic surveillance programme, 'SynSurv'. SynSurv automatically captures patient information as it is entered by ED staff. This information includes patient demographics, their presenting symptoms and a preliminary diagnosis using ICD-10 coding. To determine which codes were best correlated with influenza notifications, weekly counts for each of the ICD-10 diagnosis codes ever used in the dataset were calculated and compared with the corresponding weekly count of confirmed influenza cases. Correlations between these codes and confirmed influenza cases in the non-influenza season were then undertaken. The data covered the period from July 2001 until August 2009 and included the 2009 influenza pandemic.

RESULTS

There was a marked increase in weekly counts of both laboratory-confirmed influenza cases and relevant ICD-10 codes during the influenza pandemic period. The increase in laboratory confirmed cases was more than four times greater than the previous highest number reported, in 2007, even though the influenza-like-illness activity in the community was considered comparable to 2003 and 2007. We found five ICD-10 codes to be moderately and significantly correlated with influenza cases. None of these codes was correlated with laboratory confirmed influenza notifications outside the influenza season, at least in part because of the small number of influenza cases notified during that period.

CONCLUSIONS

This study suggests that the choice of codes made by ED staff to record a case of influenza-like illness is influenced by their perceptions of how much influenza is circulating at the time. The ability of syndromic surveillance to detect outbreaks early may be impeded because case diagnosis is influenced by what ED staff believes to be occurring in the community.

Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data

Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space-time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza.

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

Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS) was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS) started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real-time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and transmit information either in real time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC) in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

Geographic boundary analysis in spatial and spatio-temporal epidemiology: perspective and prospects

Geographic boundary analysis is a relatively new approach that is just beginning to be applied in spatial and spatio-temporal epidemiology to quantify spatial variation in health outcomes, predictors and correlates; generate and test epidemiologic hypotheses; to evaluate health-environment relationships; and to guide sampling design. Geographic boundaries are zones of rapid change in the value of a spatially distributed variable, and mathematically may be defined as those locations with a large second derivative of the spatial response surface. Here we introduce a pattern analysis framework based on Value, Change and Association questions, and boundary analysis is shown to fit logically into Change and Association paradigms. This article addresses fundamental questions regarding what boundary analysis can tell us in public health and epidemiology. It explains why boundaries are of interest, illustrates analysis approaches and limitations, and concludes with prospects and future research directions.

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.

Identifying pneumonia outbreaks of public health importance: can emergency department data assist in earlier identification?

OBJECTIVE

To retrospectively review the performance of a near real-time Emergency Department (ED) Syndromic Surveillance System operating in New South Wales for identifying pneumonia outbreaks of public health importance.

METHODS

Retrospective data was obtained from the NSW Emergency Department data collection for a rural hospital that has experienced a cluster of pneumonia diagnoses among teenage males in August 2006. ED standard reports were examined for signals in the overall count for each respiratory syndrome, and for elevated counts in individual subgroups including; age, sex and admission to hospital status.

RESULTS

Using the current thresholds, the ED syndromic surveillance system would have trigged a signal for pneumonia syndrome in children aged 5-16 years four days earlier than the notification by a paediatrician and this signal was maintained for 14 days.

CONCLUSION

If the ED syndromic surveillance system had been operating it could have identified the outbreak earlier than the paediatrician's notification. This may have permitted an earlier public health response.

IMPLICATIONS

By understanding the behaviour of syndromes during outbreaks of public health importance, response protocols could be developed to facilitate earlier implementation of control measures.

An exploratory study of disease surveillance systems in Norway

We conducted a qualitative study of the system for contagious disease surveillance in Norway. Semi-structured interviews were held with five general practitioners (GPs), including one person responsible for informing GPs in their region about potentially serious disease outbreaks. The interviews suggested that the existing system had several limitations, making it of little relevance to local epidemics or daily medical practice. Specifically, it was difficult and time-consuming for physicians to locate relevant information, and there was a substantial delay between reported diagnoses and eventual feedback about outbreaks. This resulted in information that was too old to be of value. The interviews also investigated design matters related to future realtime disease surveillance systems. The GPs expressed interest in a distributed system for realtime extraction and presentation of data from electronic record systems. They required that any such system be customizable to the specific needs of the doctor in order to be relevant in day-to-day practice, and that correct interpretation of data would be possible in the minimum of time.

Value of syndromic surveillance within the Armed Forces for early warning during a dengue fever outbreak in French Guiana in 2006

Background

A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.

Methods

Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.

Results

It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.

Conclusion

Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.

Rapid polymerase chain reaction-based screening assay for bacterial biothreat agents

OBJECTIVES

To design and evaluate a rapid polymerase chain reaction (PCR)-based assay for detecting Eubacteria and performing early screening for selected Class A biothreat bacterial pathogens.

METHODS

The authors designed a two-step PCR-based algorithm consisting of an initial broad-based universal detection step, followed by specific pathogen identification targeted for identification of the Class A bacterial biothreat agents. A region in the bacterial 16S rRNA gene containing a highly variable sequence flanked by clusters of conserved sequences was chosen as the target for the PCR assay design. A previously described highly conserved region located within the 16S rRNA amplicon was selected as the universal probe (UniProbe, Integrated DNA Technology, Coralville, IA). Pathogen-specific TaqMan probes were designed for Bacillus anthracis, Yersinia pestis, and Francisella tularensis. Performance of the assay was assessed using genomic DNA extracted from the aforementioned biothreat-related organisms (inactivated or surrogate) and other common bacteria.

RESULTS

The UniProbe detected the presence of all tested Eubacteria (31/31) with high analytical sensitivity. The biothreat-specific probes accurately identified organisms down to the closely related species and genus level, but were unable to discriminate between very close surrogates, such as Yersinia philomiragia and Bacillus cereus.

CONCLUSIONS

A simple, two-step PCR-based assay proved capable of both universal bacterial detection and identification of select Class A bacterial biothreat and biothreat-related pathogens. Although this assay requires confirmatory testing for definitive species identification, the method has great potential for use in ED-based settings for rapid diagnosis in cases of suspected Category A bacterial biothreat agents.