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1.  Monitoring the Impact of Influenza by Age: Emergency Department Fever and Respiratory Complaint Surveillance in New York City 
PLoS Medicine  2007;4(8):e247.
Background
The importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus.
Methods and Findings
We analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001–2002 through 2005–2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5–17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003–2004 (9.2 excess visits per 1,000 population) and 2004–2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups.
Conclusions
Influenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.
Don Olson and colleagues report that influenza-related morbidity in NYC from 2001 to 2006 was highly age- and strain-specific and conclude that surveillance using electronic data can provide timely and representative information about the epidemiology of circulating influenza viruses.
Editors' Summary
Background.
Seasonal outbreaks (epidemics) of influenza (a viral infection of the nose, throat, and airways) send millions of people to their beds every winter. Most recover quickly, but flu epidemics often disrupt daily life and can cause many deaths. Seasonal epidemics occur because influenza viruses continually make small changes to the viral proteins (antigens) that the human immune system recognizes. Consequently, an immune response that combats influenza one year may provide partial or no protection the following year. Occasionally, an influenza virus with large antigenic changes emerges that triggers an influenza pandemic, or global epidemic. To help prepare for both seasonal epidemics and pandemics, public-health officials monitor influenza-related illness and death, investigate unusual outbreaks of respiratory diseases, and characterize circulating strains of the influenza virus. While traditional influenza-related illness surveillance systems rely on relatively slow voluntary clinician reporting of cases with influenza-like illness symptoms, some jurisdictions have also started to use “syndromic” surveillance systems. These use electronic health-related data rather than clinical impression to track illness in the community. For example, increased visits to emergency departments for fever or respiratory (breathing) problems can provide an early warning of an influenza outbreak.
Why Was This Study Done?
Rapid illness surveillance systems have been shown to detect flu outbreaks earlier than is possible through monitoring deaths from pneumonia or influenza. Increases in visits to emergency departments by children for fever or respiratory problems can provide an even earlier indicator. Researchers have not previously examined in detail how fever and respiratory problems by age group correlate with the predominant circulating respiratory viruses. Knowing details like this would help public-health officials detect and respond to influenza epidemics and pandemics. In this study, the researchers have used data collected between 2001 and 2006 in New York City emergency departments to investigate these aspects of syndromic surveillance for influenza.
What Did the Researchers Do and Find?
The researchers analyzed emergency department visits categorized broadly into a fever and respiratory syndrome (which provides an estimate of the total visits attributable to influenza) or more narrowly into an influenza-like illness syndrome (which specifically indicates fever with cough and/or sore throat) with laboratory-confirmed influenza surveillance data. They found that emergency department visits were highest during peak influenza periods, and that the affect on different age groups varied depending on the predominant circulating viruses. In early 2002, an epidemic reemergence of B/Victoria-lineage influenza viruses caused increased visits among school-aged children, while adult visits did not increase. By contrast, during the 2003–2004 season, when the predominant virus was an A/H3N2 Fujian-lineage influenza virus, excess visits occurred in all age groups, though the relative increase was greatest and earliest among school-aged children. During periods of documented respiratory syncytial virus (RSV) circulation, increases in fever and respiratory emergency department visits occurred in children under five years of age regardless of influenza circulation. Finally, the researchers found that excess visits to emergency departments for fever and respiratory symptoms preceded deaths from pneumonia or influenza by about two weeks.
What Do These Findings Mean?
These findings indicate that excess emergency department visits for fever and respiratory symptoms can provide a reliable and timely surrogate measure of illness due to influenza. They also provide new insights into how different influenza viruses affect people of different ages and how the timing and progression of each influenza season differs. These results, based on data collected over only five years in one city, might not be generalizable to other settings or years, warn the researchers. However, the present results strongly suggest that the routine monitoring of influenza might be improved by using electronic health-related data, such as emergency department visit data, and by examining it specifically by age group. Furthermore, by showing that school-aged children can be the first people to be affected by seasonal influenza, these results highlight the important role this age group plays in community-wide transmission of influenza, an observation that could influence the implementation of public-health strategies such as vaccination that aim to protect communities during influenza epidemics and pandemics.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040247.
• US Centers for Disease Control and Prevention provides information on influenza for patients and health professionals and on influenza surveillance in the US (in English, Spanish, and several other languages)
• World Health Organization has a fact sheet on influenza and on global surveillance for influenza (in English, Spanish, French, Russian, Arabic, and Chinese)
• The MedlinePlus encyclopedia contains a page on flu (in English and Spanish)
• US National Institute of Allergy and Infectious Diseases has a feature called “focus on flu”
• A detailed report from the US Centers for Disease Control and Prevention titled “Framework for Evaluating Public Health Surveillance Systems for Early Detection of Outbreaks” includes a simple description of syndromic surveillance
• The International Society for Disease Surveillance has a collaborative syndromic surveillance public wiki
• The Anthropology of the Contemporary Research Collaboratory includes working papers and discussions by cultural anthropologists studying modern vital systems security and syndromic surveillance
doi:10.1371/journal.pmed.0040247
PMCID: PMC1939858  PMID: 17683196
2.  SAGES Update: Electronic Disease Surveillance in Resource-Limited Settings 
Objective
The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, open-source software tools for electronic disease surveillance in resource-limited settings. This demonstration will illustrate several new innovations and update attendees on new users in Africa and Asia.
Introduction
The new 2005 International Health Regulations (IHR), a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance with particular emphasis on resource-limited settings. More than a decade ago, in collaboration with the US Department of Defense (DoD), the Johns Hopkins University Applied Physics Laboratory (JHU/APL) developed the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). ESSENCE collects, processes, and analyzes non-traditional data sources (i.e. chief complaints from hospital emergency departments, school absentee data, poison control center calls, over-the-counter pharmaceutical sales, etc.) to identify anomalous disease activity in a community. The data can be queried, analyzed, and visualized both temporally and spatially by the end user. The current SAGES initiative leverages the experience gained in the development of ESSENCE, and the analysis and visualization components of SAGES are built with the same features in mind.
Methods
SAGES tools are organized into four categories: 1) data collection, 2) analysis & visualization, 3) communications, and 4) modeling/simulation/evaluation. Within each category, SAGES offers a variety of tools compatible with surveillance needs and different types or levels of information technology infrastructure. SAGES tools are built in a modular nature, which allows for the user to select one or more tools to enhance an existing surveillance system or use the tools en masse for an end-to-end electronic disease surveillance capability. Thus, each locality can select tools from SAGES based upon their needs, capabilities, and existing systems to create a customized electronic disease surveillance system. New OpenESSENCE developments include improved data query ability, improved mapping functionality, and enhanced training materials. New cellular phone developments include the ability to concatenate single SMS messages sent by simple or Smart Android cell phones. This ‘multiple-SMS’ message ability allows use of SMS technology to send and receive health information exceeding normal SMS message length in a manner transparent to the users.
Conclusions
The SAGES project is intended to enhance electronic disease surveillance capacity in resource-limited settings around the world. We have combined electronic disease surveillance tools developed at JHU/APL with other freely-available, interoperable software tools to create SAGES. We believe this suite of tools will facilitate local and regional electronic disease surveillance, regional public health collaborations, and international disease reporting. SAGES development, funded by the US Armed Forces Health Surveillance Center, continues as we add new international collaborators. SAGES tools are currently deployed in locations in Africa, Asia and South America, and are offered to other interested countries around the world.
PMCID: PMC3692858
software; surveillance; electronic; open-source
3.  Syndromic Surveillance from a Local Perspective – A Review of the Literature 
Objective
Review of the origins and evolution of the field of syndromic surveillance. Compare the goals and objectives of public health surveillance and syndromic surveillance in particular. Assess the science and practice of syndromic surveillance in the context of public health and national security priorities. Evaluate syndromic surveillance in practice, using case studies from the perspective of a local public health department.
Introduction
Public health disease surveillance is defined as the ongoing systematic collection, analysis and interpretation of health data for use in the planning, implementation and evaluation of public health, with the overarching goal of providing information to government and the public to improve public health actions and guidance [1,2]. Since the 1950s, the goals and objectives of disease surveillance have remained consistent [1]. However, the systems and processes have changed dramatically due to advances in information and communication technology, and the availability of electronic health data [2,3]. At the intersection of public health, national security and health information technology emerged the practice of syndromic surveillance [3].
Methods
To better understand the current state of the field, a review of the literature on syndromic surveillance was conducted: topics and keywords searched through PubMed and Google Scholar included biosurveillance, bioterrorism detection, computerized surveillance, electronic disease surveillance, situational awareness and syndromic surveillance, covering the areas of practice, research, preparedness and policy. This literature was compared with literature on traditional epidemiologic and public health surveillance. Definitions, objectives, methods and evaluation findings presented in the literature were assessed with a focus on their relevance from a local perspective, particularly as related to syndromic surveillance systems and methods used by the New York City Department of Health and Mental Hygiene in the areas of development, implementation, evaluation, public health practice and epidemiological research.
Results
A decade ago, the objective of syndromic surveillance was focused on outbreak and bioterrorism early-event detection (EED). While there have been clear recommendations for evaluation of syndromic surveillance systems and methods, the original detection paradigm for syndromic surveillance has not been adequately evaluated in practice, nor tested by real world events (ie, the systems have largely not ‘detected’ events of public health concern). In the absence of rigorous evaluation, the rationale and objectives for syndromic surveillance have broadened from outbreak and bioterrorism EED, to include all causes and hazards, and to encompass all data and analyses needed to achieve “situational awareness”, not simply detection. To evaluate current practices and provide meaningful guidance for local syndromic surveillance efforts, it is important to understand the emergence of the field in the broader context of public health disease surveillance. And it is important to recognize how the original stated objectives of EED have shifted in relation to actual evaluation, recommendation, standardization and implementation of syndromic systems at the local level.
Conclusions
Since 2001, the field of syndromic surveillance has rapidly expanded, following the dual requirements of national security and public health practice. The original objective of early outbreak or bioterrorism event detection remains a core objective of syndromic surveillance, and systems need to be rigorously evaluated through comparison of consistent methods and metrics, and public health outcomes. The broadened mandate for all-cause situation awareness needs to be focused into measureable public health surveillance outcomes and objectives that are consistent with established public health surveillance objectives and relevant to the local practice of public health [2].
PMCID: PMC3692931
evaluation; biosurveillance; situational awareness; syndromic surveillance; local public health
4.  Recommendations for Syndromic Surveillance Using Inpatient and Ambulatory EHR Data 
Objective
To develop national Stage 2 Meaningful Use (MUse) recommendations for syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record (EHR) data.
Introduction
MUse will make EHR data increasingly available for public health surveillance. For Stage 2, the Centers for Medicare & Medicaid Services (CMS) regulations will require hospitals and offer an option for eligible professionals to provide electronic syndromic surveillance data to public health. Together, these data can strengthen public health surveillance capabilities and population health outcomes (Figure 1).
To facilitate the adoption and effective use of these data to advance population health, public health priorities and system capabilities must shape standards for data exchange. Input from all stakeholders is critical to ensure the feasibility, practicality, and, hence, adoption of any recommendations and data use guidelines.
Methods
ISDS, in collaboration with the Division of Informatics Solutions and Operations at the Centers for Disease Control and Prevention (CDC), and HLN Consulting, convened a multi-stakeholder Work-group of clinicians, technologists, epidemiologists, and public health officials with expertise in syndromic surveillance. Recommended MUse guidelines were developed by performing an environmental scan of current practice and by using an iterative, expert and community input-driven process. The Workgroup developed initial guidelines and then solicited and received feedback from the stakeholder community via interview, e-mail, and structured surveys. Stakeholder feedback was analyzed using quantitative and qualitative methods and used to revise the recommendations.
Results
The MUse Workgroup defined electronic syndromic surveillance (ESS) characteristics. Specifically, data are characterized by their timeliness, sensitivity rather than specificity, population focus, limited personally identifiable information, and inclusion of all patient encounters within a specific healthcare setting (e.g., emergency department, inpatient, outpatient). Based on stakeholder input (n=125) and Workgroup expertise, the guidelines identify priority syndromic surveillance uses that can assist with: Monitoring population health;Informing public health services; andInforming interventions, health education, and policy by characterizing the burden of chronic disease and health disparities.
Similarly, the Workgroup identified data elements to support these uses in the hospital inpatient setting and possibly in the ambulatory care setting. They were aligned to previously identified emergency department and urgent care center data elements and Stage 1–2 clinical MUse objectives. Core data elements (required for certification) cover treating facility; patient demographics; subjective and objective clinical findings, including chief complaint, body mass index, smoking history, diagnoses; and outcomes. Other data elements were designated as extended (not required for certification) or future (for future consideration). The data elements and their specifications are subject to change based on applicable state and local laws and practices.
Based on their findings and recommended guidelines detailed in the report, the Workgroup also identified community activities and additional investments that would best support public health agencies in using EHR technology with syndromic surveillance methodologies.
Conclusions
The widespread adoption of EHRs, catalyzed by MUse, has the potential to improve population health. By identifying and describing potential ESS uses of new sources of EHR data and associated data elements with the greatest utility for public health, the recommendations set forth by the ISDS MUse Workgroup will serve to facilitate the adoption of MUse policy by both healthcare and public health agencies.
PMCID: PMC3692899
EHR; syndromic surveillance; Meaningful Use; inpatient; ambulatory
5.  Estimating Infection Attack Rates and Severity in Real Time during an Influenza Pandemic: Analysis of Serial Cross-Sectional Serologic Surveillance Data 
PLoS Medicine  2011;8(10):e1001103.
This study reports that using serological data coupled with clinical surveillance data can provide real-time estimates of the infection attack rates and severity in an emerging influenza pandemic.
Background
In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity.
Methods and Findings
We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1–2 wk before, and 3 wk after epidemic peak for individuals aged 5–14 y, 15–29 y, and 30–59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5–14 y, 15–19 y, and 20–29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30–59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%–10%.
Conclusions
Serial cross-sectional serologic data together with clinical surveillance data can allow reliable real-time estimates of IAR and severity in an emerging pandemic. Sero-surveillance for pandemics should be considered.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Every winter, millions of people catch influenza—a viral infection of the airways—and about half a million die as a result. These seasonal epidemics occur because small but frequent changes in the influenza virus mean that the immune response produced by infection with one year's virus provides only partial protection against the next year's virus. Occasionally, however, a very different influenza virus emerges to which people have virtually no immunity. Such viruses can start global epidemics (pandemics) and kill millions of people. The most recent influenza pandemic began in March 2009 in Mexico, when the first case of influenza caused by a new virus called pandemic A/H1N1 2009 (pdmH1N1) occurred. The virus spread rapidly despite strenuous efforts by national and international public health agencies to contain it, and on 11 June 2009, the World Health Organization (WHO) declared that an influenza pandemic was underway. By the time WHO announced that the pandemic was over (10 August 2010), pdmH1N1 had killed more than 18,000 people.
Why Was This Study Done?
Early in the 2009 influenza pandemic, as in any emerging pandemic, reliable estimates of pdmH1N1's transmissibility (how easily it spreads between people) and severity (the proportion of infected people who needed hospital treatment) were urgently needed to help public health officials plan their response to the pandemic and advise the public about the threat to their health. Because infection with an influenza virus does not always make people ill, the only way to determine the true size and severity of an influenza outbreak is to monitor the occurrence of antibodies (proteins made by the immune system in response to infections) to the influenza virus in the population—so-called serologic surveillance. In this study, the researchers developed a method that uses serologic data to provide real-time estimates of the infection attack rate (IAR; the cumulative occurrence of new infections in a population) and the infection-hospitalization probability (IHP; the proportion of affected individuals that needs to be hospitalized) during an influenza pandemic.
What Did the Researchers Do and Find?
The researchers tested nearly 15,000 serum samples collected in Hong Kong during the first wave of the 2009 pandemic for antibodies to pdmH1N1 and then used a mathematical approach called convolution to estimate IAR and IHP from these serologic data and hospitalization data. They report that if the serological data had been available weekly in real time, they would have been able to obtain reliable estimates of IAR and IHP by one week after, one to two weeks before, and three weeks after the pandemic peak for 5–14 year olds, 15–29 year olds, and 30–59 year olds, respectively. If serologic surveillance had begun three weeks after confirmation of community transmission of pdmH1N1, sample sizes of 150, 350, and 500 specimens per week from 5–14 year olds, 15–19 year olds, and 20–29 year olds, respectively, would have been sufficient to obtain reliable IAR and IHP estimates four weeks before the pandemic peak. However, for 30–59 year olds, even 800 specimens per week would not have generated reliable estimates because of pre-existing antibodies to an H1N1 virus in this age group. Finally, computer simulations of future pandemics indicate that serologic surveillance with 300 serum specimens per week would yield reliable estimates of IAR and IHP as soon as the true IAR reached about 6%.
What Do These Findings Mean?
These findings suggest that serologic data together with clinical surveillance data could be used to provide reliable real-time estimates of IARs and severity in an emerging influenza pandemic. Although the number of samples needed to provide accurate estimates of IAR and IHP in real life may vary somewhat from those reported here because of limitations in the design of this study, these findings nevertheless suggest that the level of testing capacity needed to provide real-time estimates of IAR and IHP during an emerging influenza pandemic should be logistically feasible for most developed countries. Moreover, collection of serologic surveillance data from any major city affected early in an epidemic could potentially provide information of global relevance for public health. Thus, the researchers conclude, serologic monitoring should be included in future plans for influenza pandemic preparedness and response and in planning for other pandemics.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001103.
A recent PLoS Medicine Research Article by Riley et al. provides further information on patterns of infection with the pdmH1N1 virus
The Hong Kong Centre for Health Protection provides information on pandemic H1N1 influenza
The US Centers for Disease Control and Prevention provides information about influenza for patients and professionals, including specific information on H1N1 influenza
Flu.gov, a US government website, provides access to information on seasonal, pandemic, and H1N1 influenza
WHO provides information on seasonal influenza and has information on the global response to H1N1 influenza (in several languages)
The UK Health Protection Agency provides information on pandemic influenza and on H1N1 influenza
More information for patients about H1N1 influenza is available through Choices, an information resource provided by the UK National Health Service
doi:10.1371/journal.pmed.1001103
PMCID: PMC3186812  PMID: 21990967
6.  An Epidemiological Network Model for Disease Outbreak Detection 
PLoS Medicine  2007;4(6):e210.
Background
Advanced disease-surveillance systems have been deployed worldwide to provide early detection of infectious disease outbreaks and bioterrorist attacks. New methods that improve the overall detection capabilities of these systems can have a broad practical impact. Furthermore, most current generation surveillance systems are vulnerable to dramatic and unpredictable shifts in the health-care data that they monitor. These shifts can occur during major public events, such as the Olympics, as a result of population surges and public closures. Shifts can also occur during epidemics and pandemics as a result of quarantines, the worried-well flooding emergency departments or, conversely, the public staying away from hospitals for fear of nosocomial infection. Most surveillance systems are not robust to such shifts in health-care utilization, either because they do not adjust baselines and alert-thresholds to new utilization levels, or because the utilization shifts themselves may trigger an alarm. As a result, public-health crises and major public events threaten to undermine health-surveillance systems at the very times they are needed most.
Methods and Findings
To address this challenge, we introduce a class of epidemiological network models that monitor the relationships among different health-care data streams instead of monitoring the data streams themselves. By extracting the extra information present in the relationships between the data streams, these models have the potential to improve the detection capabilities of a system. Furthermore, the models' relational nature has the potential to increase a system's robustness to unpredictable baseline shifts. We implemented these models and evaluated their effectiveness using historical emergency department data from five hospitals in a single metropolitan area, recorded over a period of 4.5 y by the Automated Epidemiological Geotemporal Integrated Surveillance real-time public health–surveillance system, developed by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology on behalf of the Massachusetts Department of Public Health. We performed experiments with semi-synthetic outbreaks of different magnitudes and simulated baseline shifts of different types and magnitudes. The results show that the network models provide better detection of localized outbreaks, and greater robustness to unpredictable shifts than a reference time-series modeling approach.
Conclusions
The integrated network models of epidemiological data streams and their interrelationships have the potential to improve current surveillance efforts, providing better localized outbreak detection under normal circumstances, as well as more robust performance in the face of shifts in health-care utilization during epidemics and major public events.
Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts.
Editors' Summary
Background.
The main task of public-health officials is to promote health in communities around the world. To do this, they need to monitor human health continually, so that any outbreaks (epidemics) of infectious diseases (particularly global epidemics or pandemics) or any bioterrorist attacks can be detected and dealt with quickly. In recent years, advanced disease-surveillance systems have been introduced that analyze data on hospital visits, purchases of drugs, and the use of laboratory tests to look for tell-tale signs of disease outbreaks. These surveillance systems work by comparing current data on the use of health-care resources with historical data or by identifying sudden increases in the use of these resources. So, for example, more doctors asking for tests for salmonella than in the past might presage an outbreak of food poisoning, and a sudden rise in people buying over-the-counter flu remedies might indicate the start of an influenza pandemic.
Why Was This Study Done?
Existing disease-surveillance systems don't always detect disease outbreaks, particularly in situations where there are shifts in the baseline patterns of health-care use. For example, during an epidemic, people might stay away from hospitals because of the fear of becoming infected, whereas after a suspected bioterrorist attack with an infectious agent, hospitals might be flooded with “worried well” (healthy people who think they have been exposed to the agent). Baseline shifts like these might prevent the detection of increased illness caused by the epidemic or the bioterrorist attack. Localized population surges associated with major public events (for example, the Olympics) are also likely to reduce the ability of existing surveillance systems to detect infectious disease outbreaks. In this study, the researchers developed a new class of surveillance systems called “epidemiological network models.” These systems aim to improve the detection of disease outbreaks by monitoring fluctuations in the relationships between information detailing the use of various health-care resources over time (data streams).
What Did the Researchers Do and Find?
The researchers used data collected over a 3-y period from five Boston hospitals on visits for respiratory (breathing) problems and for gastrointestinal (stomach and gut) problems, and on total visits (15 data streams in total), to construct a network model that included all the possible pair-wise comparisons between the data streams. They tested this model by comparing its ability to detect simulated disease outbreaks implanted into data collected over an additional year with that of a reference model based on individual data streams. The network approach, they report, was better at detecting localized outbreaks of respiratory and gastrointestinal disease than the reference approach. To investigate how well the network model dealt with baseline shifts in the use of health-care resources, the researchers then added in a large population surge. The detection performance of the reference model decreased in this test, but the performance of the complete network model and of models that included relationships between only some of the data streams remained stable. Finally, the researchers tested what would happen in a situation where there were large numbers of “worried well.” Again, the network models detected disease outbreaks consistently better than the reference model.
What Do These Findings Mean?
These findings suggest that epidemiological network systems that monitor the relationships between health-care resource-utilization data streams might detect disease outbreaks better than current systems under normal conditions and might be less affected by unpredictable shifts in the baseline data. However, because the tests of the new class of surveillance system reported here used simulated infectious disease outbreaks and baseline shifts, the network models may behave differently in real-life situations or if built using data from other hospitals. Nevertheless, these findings strongly suggest that public-health officials, provided they have sufficient computer power at their disposal, might improve their ability to detect disease outbreaks by using epidemiological network systems alongside their current disease-surveillance systems.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040210.
Wikipedia pages on public health (note that Wikipedia is a free online encyclopedia that anyone can edit, and is available in several languages)
A brief description from the World Health Organization of public-health surveillance (in English, French, Spanish, Russian, Arabic, and Chinese)
A detailed report from the US Centers for Disease Control and Prevention called “Framework for Evaluating Public Health Surveillance Systems for the Early Detection of Outbreaks”
The International Society for Disease Surveillance Web site
doi:10.1371/journal.pmed.0040210
PMCID: PMC1896205  PMID: 17593895
7.  Enhanced Disease Surveillance during the 2012 Republican National Convention, Tampa, FL 
Objective
To describe disease and illness surveillance utilized during the 2012 Republican National Convention (RNC) held August 26–30, 2012 in Tampa, FL.
Introduction
While the Tampa Bay Area has previously hosted other high profile events that required heightened disease surveillance (e.g., two Super Bowls), the 2012 RNC marked the first national special security event (NSSE) held in Florida. The Hillsborough County Health Department (HCHD), in conjunction with the Pinellas County Health Department (PinCHD) coordinated disease surveillance activities during this time frame. This presentation will focus of the disease surveillance efforts of the Hillsborough County Health Department during the 2012 RNC.
In addition to the surveillance systems that are used routinely, the HCHD Epidemiology Program implemented additional systems designed to rapidly detect individual cases and outbreaks of public health importance. The short duration of RNC, coupled with the large number of visitors to our area, provided additional surveillance challenges.
Tropical Storm Isaac, which threatened Tampa in the days leading up to RNC, and an overwhelming law enforcement presence likely dissuaded many protestors from coming to Tampa. As a result, a tiny fraction of the number of protestors that were expected actually showed up.
Methods
Our normal daily analysis of the emergency department (ED) data using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) was expanded to look in detail at ED volumes and chief complaints of those patients who live outside of a 5-county Tampa Bay area. This analysis used patient zip code to determine place of residence. Additionally, ESSENCE queries were utilized to look for heat, tear gas, and RNC related exposures. The ESSENCE system also receives Poison Control data every 15 minutes. Expanded analyses of the Poison Control data were conducted as well. Two Disaster Medical Assistance Teams (DMATs) were deployed in Tampa during the RNC. Data was collected electronically and transmitted through ESSENCE as well.
The HCHD also asked infection preventionists, health care providers, hotels, labs, and Mosquito Control to lower their reporting threshold to us during the RNC period. We provided updates to all our partners with respect to diseases and outbreaks of public health importance occurring in our county.
Results
No epidemiologic events linked to the RNC were detected through the HCHD’s enhanced surveillance that was conducted. Decreased patient volumes were seen during the RNC at our EDs closest to the convention site. No significant increases in ED visits from outside of our 5-county area were noted during the RNC. Urgent care centers reported seeing patients associated with the RNC for a variety of reasons including respiratory and gastrointestinal illness. DMAT surveillance showed mainly routine visits but four secret service agents did seek care for respiratory illness during the convention.
Conclusions
Substantial time and resources were devoted to disease surveillance in the 6 months leading up to the RNC and during the event. While no epidemiologic events were detected, the public health surveillance infrastructure has clearly been strengthened in our county. We are receiving our ED syndromic data, from many of our hospitals, every two hours as opposed to every day. We have established relationships with our urgent case centers and hope to begin receiving urgent care center data on a daily basis in the near future. Receiving DMAT data through ESSENCE could prove very useful in the future, especially in Florida where hurricanes are always a threat. Lastly the improved relationships with our health care providers should be beneficial as we move forward.
PMCID: PMC3692915
mass gathering; national special security event; convention
8.  A Health Department’s Collaborative Model for Disease Surveillance Capacity Building 
Objective
Highlight one academic health department’s unique approach to optimizing collaborative opportunities for capacity development and document the implications for chronic disease surveillance and population health.
Introduction
Public Health departments are increasingly called upon to be innovative in quality service delivery under a dwindling resource climate as highlighted in several publications of the Institute of Medicine. Collaboration with other entities in the delivery of core public health services has emerged as a recurring theme. One model of this collaboration is an academic health department: a formal affiliation between a health professions school and a local health department. Initially targeted at workforce development, this model of collaboration has since yielded dividends in other core public health service areas including community assessment, program evaluation, community-based participatory research and data analysis.
The Duval County Health Department (DCHD), Florida, presents a unique community-centered model of the academic health department. Prominence in local informatics infrastructure capacity building and hosting a CDC-CSTE applied public health informatics fellowship (APHIF) in the Institute for Public Health Informatics and Research (IPHIR) in partnership with the Center for Health Equity Research, University of Florida & Shands medical center are direct dividends of this collaborative model.
Methods
We examined the collaborative efforts of the DCHD and present the unique advantages these have brought in the areas of entrenched data-driven public health service culture, community assessments, program evaluation, community-based participatory research and health informatics projects.
Results
Advantages of the model include a data-driven culture with the balanced scorecard model in leadership and sub-departmental emphases on quality assurance in public health services. Activities in IPHIR include data-driven approaches to program planning and grant developments, program evaluations, data analyses and impact assessments for the DCHD and other community health stakeholders.
Reports developed by IPHIR have impacted policy formulation by highlighting the need for sub county level data differentiation to address health disparities. Unique community-based mapping of Duval County into health zones based on health risk factors correlating with health outcome measures have been published. Other reports highlight chronic disease surveillance data and health scorecards in special populations.
Partnerships with regional higher institutions (University of Florida, University of North Florida and Florida A&M University) increased public health service delivery and yielded rich community-based participatory research opportunities.
Cutting edge participation in health IT policy implementation led to the hosting of the fledgling community HIE, the Jacksonville Health Information Network, as well as leadership in shaping the landscape of the state HIE. This has immense implications for public health surveillance activities as chronic disease surveillance and public health service research take center stage under new healthcare payment models amidst increasing calls for quality assurance in public health services.
DCHD is currently hosting a CDC-funded fellowship in applied public health informatics. Some of the projects materializing from the fellowship are the mapping of the current public health informatics profile of the DCHD, a community based diabetes disease registry to aid population-based management and surveillance of diabetes, development of a proposal for a combined primary care/general preventive medicine residency in UF-Shands Medical Center, Jacksonville and mobilization of DCHD healthcare providers for the roll-out of the state-built electronic medical records system (Florida HMS-EHR).
Conclusions
Academic health centers provide a model of collaboration that directly impacts on their success in delivering core public health services. Disease surveillance is positively affected by the diverse community affiliations of an academic health department. The academic health department, as epitomized by DCHD, is also better positioned to seize up-coming opportunities for local public health capacity building.
PMCID: PMC3692891
Academic Health Departments; collaborative model; health informatics projects
9.  Update from CDC’s Public Health Surveillance & Informatics Program Office (PHSIPO) 
Objective
To provide updates on current activities and future directions for the National Notifiable Diseases Surveillance System (NNDSS), BioSense 2.0, and the Behavioral Risk Factor Surveillance System (BRFSS) and on the role of PHSIPO as the “home” at CDC for addressing cross-cutting issues in surveillance and informatics practice.
Introduction
The practice of public health surveillance is evolving as electronic health records (EHRs) and automated laboratory information systems are increasing adopted, as new approaches for health information exchange are employed, and as new health information standards affect the entire cascade of surveillance information flow. These trends have been accelerated by the Federal program to promote the Meaningful Use of electronic health records, which includes explicit population health objectives. The growing use of Internet “cloud” technology provides new opportunities for improving information sharing and for reducing surveillance costs. Potential benefits include not only faster and more complete surveillance but also new opportunities for providing population health information back to clinicians.
For public health surveys, new Internet-based sampling and survey methods hold the promise of complementing existing telephone-based surveys, which have been plagued by declining response rates despite the addition of cell-phone sampling. While new technologies hold promise for improving surveillance practice, there are multiple challenges, including constraints on public health budgets and the workforce. This panel will explore how PHSIPO is addressing these opportunities and challenges.
Methods
Panelists will provide updates on 1) PHSIPO’s role in engaging health departments, the organizations that represent them, and CDC programs in shaping national policies for implementing the Meaningful Use program, 2) how the BioSense 2.0 program is supporting growth in syndromic surveillance capacity, including its partnership with ISDS in developing standards for syndromic surveillance as part of Meaningful Use, 3) improvements that are underway in strengthening the NNDSS, including efforts to improve CDC’s support for health department disease reporting systems and to develop a “shared services” approach that could provide a platform for streamlining the exchange of information between health departments and CDC, 4) pilot development of Internet-based panels of survey volunteers to supplement existing telephone-based sampling in the BRFSS and of approaches to extend BRFSS survey information through consent-based linkage of survey responses to selected measures recorded in respondents’ EHRs.
Results
Potential questions or discussion points that might arise include: What can or should be done to assure that the population health objectives of Meaningful Use are fulfilled? What are the lessons learned to date in leveraging investments in the Meaningful Use of EHRs to improve disease reporting and syndromic surveillance systems? What are the next steps in developing BioSense 2.0 to assure that it leads to strengthened surveillance capacity at both state/local and regional/national levels? How can insights from the BioSense redesign be applied to improve case reporting and other surveillance capacities? What is CDC doing to address states’ concerns about the growing number of CDC surveillance systems? How will national discussions about the future of public health affect the future surveillance practice? What can be done to assure the ongoing representativeness of population health surveys? Is it feasible to link BRFSS responses to information obtained from EHRs? How can data from surveillance become part of the real-time evidence base for clinical decision making?
Conclusions
The intended outcome of the panel is to foster a conversation between the panelists and the audience, to inform the audience about recent developments in PHSIPO, to obtain insights from the audience about innovations and ideas arising from their experience, and to generate new ideas for approaches to meeting the needs of public health for surveillance information.
PMCID: PMC3692948
Surveillance; BioSense 2.0; Notifiable Diseases; BRFSS—Behavioral Risk Factor Surveillance System
10.  Enhanced Surveillance during the Democratic National Convention, Charlotte, NC 
Objective
To describe how the existing state syndromic surveillance system (NC DETECT) was enhanced to facilitate surveillance conducted at the Democratic National Convention in Charlotte, North Carolina from August 31, 2012 to September 10, 2012.
Introduction
North Carolina hosted the 2012 Democratic National Convention, September 3–6, 2012. The NC Epidemiology and Surveillance Team was created to facilitate enhanced surveillance for injuries and illnesses, early detection of outbreaks during the DNC, assist local public health with epidemiologic investigations and response, and produce daily surveillance reports for internal and external stakeholders. Surveillane data were collected from several data sources, including North Carolina Electronic Disease Surveillance System (NC EDSS), triage stations, and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).
NC DETECT was created by the North Carolina Division of Public Health (NC DPH) in 2004 in collaboration with the Carolina Center for Health Informatics (CCHI) in the UNC Department of Emergency Medicine to address the need for early event detection and timely public health surveillance in North Carolina using a variety of secondary data sources. The data from emergency departments, the Carolinas Poison Center, the Pre-hospital Medical Information System (PreMIS) and selected Urgent Care Centers were available for monitoring by authorized users during the DNC.
Methods
Within NC DETECT, new dashboards were created that allowed epidemiologists to monitor ED visits and calls to the poison center in the Charlotte area, the greater Cities Readiness Initiative region and the entire state for infectious disease signs and symptoms, injuries and any mention of bioterrorism agents. The dashboards also included a section to view user comments on the information presented in NC DETECT. Data processing changes were also made to improve the timeliness of the EMS data received from PreMIS.
Results
The DNC dashboards added to NC DETECT streamlined the workflow by placing all syndromes and annotations of interest in one place, with the date ranges and locations already pre-selected. Graphs in the dashboards could be easily copied and pasted into situation reports. The prompt development of these user-friendly tools provided effective surveillance for this mass gathering and ensured timely control measures, if necessary.
Conclusions
Syndromic surveillance systems can be enhanced to provide detailed, specific surveillance during mass gathering events. Elements that facilitate this enhancement include strong communication between skilled users and the informatics team, in order to minimize the burden placed on the surveillance team system users, data sources and system developers during the event. The visualizations developed as part of these new dashboards will be leveraged to provide additional tools to other NC DETECT user groups, including hospital-based public health epidemiologists and local health department users.
PMCID: PMC3692843
dashboards; enhanced surveillance; Democratic National Convention
11.  The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview 
PLoS Medicine  2011;8(1):e1000387.
Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care.
Background
There is considerable international interest in exploiting the potential of digital solutions to enhance the quality and safety of health care. Implementations of transformative eHealth technologies are underway globally, often at very considerable cost. In order to assess the impact of eHealth solutions on the quality and safety of health care, and to inform policy decisions on eHealth deployments, we undertook a systematic review of systematic reviews assessing the effectiveness and consequences of various eHealth technologies on the quality and safety of care.
Methods and Findings
We developed novel search strategies, conceptual maps of health care quality, safety, and eHealth interventions, and then systematically identified, scrutinised, and synthesised the systematic review literature. Major biomedical databases were searched to identify systematic reviews published between 1997 and 2010. Related theoretical, methodological, and technical material was also reviewed. We identified 53 systematic reviews that focused on assessing the impact of eHealth interventions on the quality and/or safety of health care and 55 supplementary systematic reviews providing relevant supportive information. This systematic review literature was found to be generally of substandard quality with regards to methodology, reporting, and utility. We thematically categorised eHealth technologies into three main areas: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. We found that despite support from policymakers, there was relatively little empirical evidence to substantiate many of the claims made in relation to these technologies. Whether the success of those relatively few solutions identified to improve quality and safety would continue if these were deployed beyond the contexts in which they were originally developed, has yet to be established. Importantly, best practice guidelines in effective development and deployment strategies are lacking.
Conclusions
There is a large gap between the postulated and empirically demonstrated benefits of eHealth technologies. In addition, there is a lack of robust research on the risks of implementing these technologies and their cost-effectiveness has yet to be demonstrated, despite being frequently promoted by policymakers and “techno-enthusiasts” as if this was a given. In the light of the paucity of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, it is vital that future eHealth technologies are evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle. Such evaluation should be characterised by careful attention to socio-technical factors to maximise the likelihood of successful implementation and adoption.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
There is considerable international interest in exploiting the potential of digital health care solutions, often referred to as eHealth—the use of information and communication technologies—to enhance the quality and safety of health care. Often accompanied by large costs, any large-scale expenditure on eHealth—such as electronic health records, picture archiving and communication systems, ePrescribing, associated computerized provider order entry systems, and computerized decision support systems—has tended to be justified on the grounds that these are efficient and cost-effective means for improving health care. In 2005, the World Health Assembly passed an eHealth resolution (WHA 58.28) that acknowledged, “eHealth is the cost-effective and secure use of information and communications technologies in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research,” and urged member states to develop and implement eHealth technologies. Since then, implementing eHealth technologies has become a main priority for many countries. For example, England has invested at least £12.8 billion in a National Programme for Information Technology for the National Health Service, and the Obama administration in the United States has committed to a US$38 billion eHealth investment in health care.
Why Was This Study Done?
Despite the wide endorsement of and support for eHealth, the scientific basis of its benefits—which are repeatedly made and often uncritically accepted—remains to be firmly established. A robust evidence-based perspective on the advantages on eHealth could help to suggest priority areas that have the greatest potential for benefit to patients and also to inform international eHealth deliberations on costs. Therefore, in order to better inform the international community, the authors systematically reviewed the published systematic review literature on eHealth technologies and evaluated the impact of these technologies on the quality and safety of health care delivery.
What Did the Researchers Do and Find?
The researchers divided eHealth technologies into three main categories: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. Then, implementing methods based on those developed by the Cochrane Collaboration and the NHS Service Delivery and Organisation Programme, the researchers used detailed search strategies and maps of health care quality, safety, and eHealth interventions to identify relevant systematic reviews (and related theoretical, methodological, and technical material) published between 1997 and 2010. Using these techniques, the researchers retrieved a total of 46,349 references from which they identified 108 reviews. The 53 reviews that the researchers finally selected (and critically reviewed) provided the main evidence base for assessing the impact of eHealth technologies in the three categories selected.
In their systematic review of systematic reviews, the researchers included electronic health records and picture archiving communications systems in their evaluation of category 1, computerized provider (or physician) order entry and e-prescribing in category 2, and all clinical information systems that, when used in the context of eHealth technologies, integrate clinical and demographic patient information to support clinician decision making in category 3.
The researchers found that many of the clinical claims made about the most commonly used eHealth technologies were not substantiated by empirical evidence. The evidence base in support of eHealth technologies was weak and inconsistent and importantly, there was insubstantial evidence to support the cost-effectiveness of these technologies. For example, the researchers only found limited evidence that some of the many presumed benefits could be realized; importantly, they also found some evidence that introducing these new technologies may on occasions also generate new risks such as prescribers becoming over-reliant on clinical decision support for e-prescribing, or overestimate its functionality, resulting in decreased practitioner performance.
What Do These Findings Mean?
The researchers found that despite the wide support for eHealth technologies and the frequently made claims by policy makers when constructing business cases to raise funds for large-scale eHealth projects, there is as yet relatively little empirical evidence to substantiate many of the claims made about eHealth technologies. In addition, even for the eHealth technology tools that have proven to be successful, there is little evidence to show that such tools would continue to be successful beyond the contexts in which they were originally developed. Therefore, in light of the lack of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, the authors say that future eHealth technologies should be evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle, and include socio-technical factors to maximize the likelihood of successful implementation and adoption in a given context. Furthermore, it is equally important that eHealth projects that have already been commissioned are subject to rigorous, multidisciplinary, and independent evaluation.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000387.
The authors' broader study is: Car J, Black A, Anandan C, Cresswell K, Pagliari C, McKinstry B, et al. (2008) The Impact of eHealth on the Quality and Safety of Healthcare. Available at: http://www.haps.bham.ac.uk/publichealth/cfhep/001.shtml
More information is available on the World Health Assembly eHealth resolution
The World Health Organization provides information at the Global Observatory on eHealth, as well as a global insight into eHealth developments
The European Commission provides Information on eHealth in Europe and some examples of good eHealth practice
More information is provided on NHS Connecting for Health
doi:10.1371/journal.pmed.1000387
PMCID: PMC3022523  PMID: 21267058
12.  International Collaboration for Improved Public Health Emergency Preparedness and Response in India 
Objective
This project aimed to contribute to ongoing efforts to improve the capability and capacity to undertake disease surveillance and Emergency Preparedness and Response (EPR) activities in India. The main outcome measure was to empower a cadre of trainers through the inter-related streams of training & education to enhance knowledge and skills and the development of collaborative networks in the regions.
Introduction
The International Health Regulations (IHR) 2005, provides a framework that supports efforts to improve global health security and requires that, member states develop and strengthen systems and capacity for disease surveillance and detection and response to public health threats. To contribute to this global agenda, an international collaborative comprising of personnel from the Health Protection Agency, West Midlands, United Kingdom (HPA); the Indian Institute of Public Health (IIPH), Hyderabad, Andhra Pradesh (AP) state, India and the Department of Community Medicine, Rajarajeswari Medical College and Hospital (RRMCH), Bangalore, Karnataka state, India was established with funding from the HPA Global Health Fund to deliver the objectives stated above.
Methods
In 2010, the project partners jointly developed training materials on applied Epidemiology & Disease Surveillance and EPR using existing HPA material as the foundation. Over a 2 year period, a total of two training courses per year were planned for each of the two locations in India. Courses were designed to be delivered through didactic lectures, simulation exercises, workshops and group discussions at the two locations, namely Bangalore and Hyderabad. The target audience included senior state level programme officers, District Medical and Health Officers, postgraduate students, academic and research staff from Community Medicine departments and staff from the collaborating institutions.
Course modules were formally evaluated by participants using structured questionnaires and an external evaluator. Debrief sessions were also arranged after each course to review the key lessons and identify areas for improvement.
In addition, staff exchanges of up to six weeks duration were planned during which public health specialists from both countries would spend time observing health protection systems/processes in their host country.
Results
During January 2010 to December 2011, a total of seven (n=7) training courses were delivered in Bangalore and Hyderabad with approximately 231 public health personnel in attendance over the period. Participants comprised of 128 personnel representing 74 organisations in 41 districts (22 districts from AP) at the Hyderabad location and 103 personnel from 14 organisations (30 districts) at the Bangalore location.
Course participants evaluated the content of the courses favourably with the majority (92%) rating the course modules as excellent or good. External evaluation of the courses was also favourable with several aspects of the course rated as good or excellent. IIPH and RRMC continue to deliver the courses and in the state of Karnataka, some participants at the EPR course were chosen by the health ministry to be part of Rapid Response Teams at District levels.
Two public health specialists from each of the Indian organisations spent six (6) weeks in the United Kingdom as part of the planned staff exchanges. The exchanges were assessed to have been successful with important areas for future collaboration identified including proposals to jointly develop an Emergency Preparedness and Response Manual for the Indian Public Health audience.
Conclusions
The implementation and maintenance of effective and sustainable systems to ensure global health security relies on a well-trained public health workforce in member states. This innovative collaborative project has gone some way towards meeting its objective of establishing and supporting a cadre of trainers to ensure sustainable improvement in public health capacity and capability in India. After the initial (training) phase of the project that was completely funded by the HPA, the partner organisations in India have worked to sustain and further develop the core objectives of this project. As a further step, course materials developed as part of this project will be used to provide a framework upon which e-learning material and postgraduate modules will be developed in each of these institutions in India.
PMCID: PMC3692801
Surveillance; Training; EPR; IHR
13.  MoH+: A Global, Integrated, and Automated View of Official Outbreak Reporting 
Objective
To introduce MoH+, HealthMap’s (HM) real-time feed of official government sources, and demonstrate its utility in comparing the timeliness of outbreak reporting between official and unofficial sources.
Introduction
Previous studies have documented significant lags in official reporting of outbreaks compared to unofficial reporting (1,2). MoH+ provides an additional tool to analyze this issue, with the unique advantage of actively gathering a wide range of streamlined official communication, including formal publications, online press releases, and social media updates.
Methods
Outbreaks reported by official sources were identified through MoH+ (healthmap.org/mohplus), which collects surveillance data published globally by ministries of health (MoH), other related ministries, government portals, government-affiliated organizations, and international governing bodies (Fig. 1). Reporting of these outbreaks was also identified in unofficial sources using various HM feeds including Google News, ProMED, and participatory surveillance feeds.
Of the 109 outbreaks identified since May 2012, 65 were excluded as they started before data collection, 7 were excluded as they were not reported by unofficial sources, and 1 was excluded as it was a non-natural outbreak. For the remaining 36 outbreaks, the median difference in first date of report between official and unofficial sources was analyzed using a Wilcoxon sign rank test.
Results
Outbreak reporting in official sources lagged by a statistically significant median of 2 days (p=0.003). Among unofficial sources, online news most often (75%) was the fastest to report an outbreak, followed by ProMED (22%) and participatory surveillance (3%). Among official sources, national government affiliated institutes were most often (41%) the fastest, and repeatedly providing prompt outbreak reports were the US Centers for Disease Control and Prevention (CDC), Public Health Agency of Canada, Finnish Food Safety Authority, Health Protection Scotland, UK Health Protection Agency, and French Institute of Public Health Surveillance (FIPHS). Following such institutes were the European CDC (ECDC) with 22% of first reports of outbreaks; MoH’s (17%); and WHO (10%). There were 4 instances in which official sources reported before unofficial sources—3 by the ECDC and 1 by FIPHS.
Conclusions
Compared to the Chan study reporting a 16 day lag between first public communication and WHO Outbreak News (1) and the Mondor study reporting a 10 day lag between non-government and government sources (2), the present study shows a much condensed lag of 2 days between unofficial and official sources. Because the two earlier studies cover a much broader historical time frame, one explanation for the reduced lag time is increased adoption of online communication by official government agencies. However, despite such improvements in communication, the lag persists, pointing to the importance of using informal sources for outbreak surveillance.
The present study was limited by small sample size, as the study is in its early stages. We will continue to gather data and all numbers will be updated in time for the presentation to reflect the larger database. Future directions of this study include characterization of official and unofficial reporting by region, language, disease, and source.
PMCID: PMC3692909
disease surveillance; outbreak reporting; timeliness; MOH; official sources
14.  Advancing Surveillance Outside the USA: The Canadian Policy, Practice, and Research Context 
Objective
To explore how ISDS can better support researchers and public health practitioners working in the field of disease surveillance outside the United States; and
To identify current surveillance issues in the Canadian public health system where ISDS can support dialogue and action.
Introduction
The international Society for Disease Surveillance has success-fully brought together practitioners and researchers to share tools, ideas, and strategies to strengthen health surveillance systems. The Society has evolved from an initial focus on syndromic surveillance to a broader consideration of innovation in health surveillance. More recently, ISDS has also worked to support surveillance research and practice in International resource-constrained settings. Individuals who work in surveillance in developed countries outside the USA, however, have received little direct attention from ISDS. The policy and practice contexts in these countries are often quite different than the USA, so there is a need to support surveillance innovation in these countries in a manner that fits the context. Canadian surveillance practitioners and researchers comprise the largest International group of ISDS members, and these members have expressed an interest in working with ISDS to translate surveillance innovations into prac-tice in Canada, where a national surveillance network and forum is lacking. This Round Table will consider how ISDS can help to sup-port members in countries like Canada and will identify next steps for promoting the science and practice of disease surveillance in the Canadian context.
Methods
Individuals attending the ISDS 2012 Conference with an interest in public health surveillance in Canada or other similar countries out-side the USA will be invited to discuss how ISDS can better support their activities. The discussion will be structured around questions and results received for a survey circulated to Canadian ISDS mem-bers. The goal will be to discuss whether there is a specific formal role ISDS can play in helping members in Canada and other similar coun-tries working in public health surveillance.
Results
Discussion will be prompted through sharing results of a recent survey distributed to all Canadian ISDS members and affiliates aimed at gauging their interest in developing a Canadian focused group within ISDS, whether they believe there is a need, and how we might accomplish this. The survey questions, range of answers, and impli-cations to future actions suggested in survey responses would drive the discussion.
PMCID: PMC3692846
International surveillance; Canada; Surveillance network
15.  Disease Surveillance and Achieving Synergy In Public Health Quality Improvement 
Objective
To examine disease surveillance in the context of a new national framework for public health quality and to solicit input from practitioners, researchers, and other stakeholders to identify potential metrics, pivotal research questions, and actions for achieving synergy between surveillance practice and public health quality.
Introduction
National efforts to improve quality in public health are closely tied to advancing capabilities in disease surveillance. Measures of public health quality provide data to demonstrate how public health programs, services, policies, and research achieve desired health outcomes and impact population health. They also reveal opportunities for innovations and improvements. Similar quality improvement efforts in the health care system are beginning to bear fruit. There has been a need, however, for a framework for assessing public health quality that provides a standard, yet is flexible and relevant to agencies at all levels.
The U.S. Health and Human Services (HHS) Office of the Assistant Secretary for Health, working with stakeholders, recently developed and released a Consensus Statement on Quality in the Public Health System that introduces a novel evaluation framework. They identified nine aims that are fundamental to public health quality improvement efforts and six cross-cutting priority areas for improvement, including population health metrics and information technology; workforce development; and evidence-based practices (1).
Applying the HHS framework to surveillance expands measures for surveillance quality beyond typical variables (e.g., data quality and analytic capabilities) to desired characteristics of a quality public health system. The question becomes: How can disease surveillance help public health services to be more population centered, equitable, proactive, health-promoting, risk-reducing, vigilant, transparent, effective, and efficient—the desired features of a quality public health system?
Any agency with a public health mission, or even a partial public health mission (e.g., tax-exempt hospitals), can use these measures to develop strategies that improve both the quality of the surveillance enterprise and public health systems, overall. At this time, input from stakeholders is needed to identify valid and feasible ways to measure how surveillance systems and practices advance public health quality. What exists now and where are the gaps?
Methods
Improving public health by applying quality measures to disease surveillance will require innovation and collaboration among stakeholders. This roundtable will begin a community dialogue to spark this process. The first goal will be to achieve a common focus by defining the nine quality aims identified in the HHS Consensus Statement. Attendees will draw from their experience to discuss how surveillance practice advances the public health aims and improves public health. We will also identify key research questions needed to provide evidence to inform decision-making.
Results
The roundtable will discuss how the current state of surveillance practice addresses each of the aims described in the Consensus Statement to create a snapshot of how surveillance contributes to public health quality and begin to articulate practical measures for assessing quality improvements. Sample questions to catalyze discussion include: —How is surveillance used to identify and address health disparities and, thereby, make public health more equitable? What are the data sources? Are there targets? How can research and evaluation help to enhance this surveillance capability and direct action?—How do we identify and address factors that inhibit quality improvement in surveillance? What are the gaps in knowledge, skills, systems, and resources?—Where can standardization play a positive role in the evaluation of quality in public health surveillance?—How can we leverage resources by aligning national, state, and local goals? —What are the key research questions and the quality improvement projects that can be implemented using recognized models for improvement?—How can syndromic surveillance, specifically, advance the priority aims?
The roundtable will conclude with a list of next steps to develop metrics that resonate with the business practices of public health at all levels.
PMCID: PMC3692848
public health quality; metrics; framework
16.  Incorporation of School Absenteeism Data into the Maryland Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) 
Objective
The state of Maryland has incorporated 100% of its public school systems into a statewide disease surveillance system. This session will discuss the process, challenges, and best practices for expanding the ESSENCE system to include school absenteeism data as part of disease surveillance. It will also discuss the plans that Maryland has for using this new data source, as well as the potential for further expansion.
Introduction
Syndromic surveillance offers the potential for earlier detection of bioterrorism, outbreaks, and other public health emergencies than traditional disease surveillance. The Maryland Department of Health and Mental Hygiene (DHMH) Office of Preparedness and Response (OP&R) conducts syndromic surveillance using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE). Since its inception, ESSENCE has been a vital tool for DHMH, providing continuous situational awareness for public health policy decision makers. It has been established in the public health community that syndromic surveillance data, including school absenteeism data, has efficacy in monitoring disease, and specifically, influenza activity. Schools have the potential to play a major role in the spread of disease during an epidemic. Therefore, having school absenteeism data in ESSENCE would provide the opportunity to monitor schools throughout the school year and take appropriate actions to mitigate infections and the spread of disease.
Methods
DHMH partnered with the Maryland State Department of Education (MSDE), local health departments, and local school systems to incorporate school absenteeism data into the syndromic surveillance program. There are 24 local public school systems and 24 local health departments in the state of Maryland. OP&R contacted each local school superintendent and each local health officer to arrange a joint meeting to discuss the expansion of the ESSENCE program to include school absenteeism data. Once the meetings were arranged, OP&R epidemiologists traveled to each local jurisdiction and presented their plan for the ESSENCE expansion. At each meeting were representatives from the local health department, as well as school health, school attendance, and school IT staff. This allowed all questions and concerns to be addressed from both sides. In addition to the targeted meetings and presentations, the Secretary of Health issued an executive order which required all local school systems to sign a memorandum of understanding (MOU) with DHMH. This MOU detailed the data elements to be shared with the ESSENCE program and the process by which this would be shared. While this order made data contribution mandatory, the site visits by the OP&R staff created a working relationship and partnership with the local jurisdictions. Data was collected from all public schools in the state including elementary, middle, and high schools.
Results
As of June 30, 2012, Maryland became the first state in the United States to incorporate 100% of its public school systems (1,424 schools) into ESSENCE. Each school system reports absenteeism data daily via an automated secure FTP (sFTP) transfer to DHMH. Due to its unique properties, Johns Hopkins Applied Physics Laboratory (JHUAPL) designed a new detection algorithm in ESSENCE specifically for this data source. OP&R epidemiologist review and analyze this data for disease surveillance purposes in conjunction with other data sources in ESSENCE (emergency department chief complaints, poison control center data, thermometer sales data, and over-the-counter medication sales data). Integrating school absenteeism data will provide a more complete analysis of potential public health threats. The process by which Maryland incorporated their public school systems’ data could potentially be used as a best practice for other jurisdictions. Not only was DHMH able to obtain data from all public schools in the state, but the process also enhanced collaboration between local health departments and public school systems.
PMCID: PMC3692827
ESSENCE; Surveillance; Absenteeism
17.  Establishing a Federal and State Data Exchange Pilot for Public Health Situational Awareness 
Objective
U.S. Department of Health and Human Services (HHS) Office of the Assistant Secretary for Preparedness and Response (ASPR) partnered with the Florida Department of Health (FDOH), Bureau of Epidemiology, to implement a new process for the unidirectional exchange of electronic medical record (EMR) data when ASPR clinical assets are operational in the state following a disaster or other response event. The purpose of the current work was to automate the exchange of data from the ASPR electronic medical record system EMR-S into the FDOH Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) system during the 2012 Republican National Convention (RNC).
Introduction
ASPR deploys clinical assets, including an EMR system, to the ground per state requests during planned and no-notice events. The analysis of patient data collected by deployed federal personnel is an integral part of ASPR and FDOH’s surveillance efforts. However, this surveillance can be hampered by the logistical issues of field work in a post-disaster environment leading to delayed analysis and interpretation of these data to inform decision makers at the federal, state, and local levels. FDOH operates ESSENCE-FL, a multi-tiered, automated, and secure web-based application for analysis and visualization of clinical data. The system is accessible statewide by FDOH staff as well as by hospitals that participate in the system. To improve surveillance ASPR and FDOH engaged in a pilot project whereby EMR data from ASPR would be sent to FDOH in near real-time during the 2012 hurricane season and the 2012 RNC. This project is in direct support of Healthcare Preparedness Capability 6, Information Sharing, and Public Health Preparedness Capability 13, Public Health Surveillance and Epidemiological Investigation.
Methods
In 2011, FDOH approached ASPR about securely transmitting raw EMR data that could be ingested by ESSENCE-FL during ASPR deployments in the state. Upon conclusion of an agreement for a date exchange pilot, data elements of interest from the ASPR EMR were identified. Due to the modular design ESSENCE-FL Microsoft SQL databases were easily adapted by the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to add a new module to handle receipt of ASPR EMR data including code to process the files, remove duplicates and create associations with existing reference information, such as system-defined geographic regions and age groups. Scripts were developed to run on the ASPR server to create and send updated files via secure file transfer protocol (SFTP) every 15 minutes to ESSENCE-FL. Prior ASPR event deployment data was scrubbed and sent to ESSENCE-FL as a test dataset to ensure appropriate receipt and ingestion of the new data source.
Results
EMR data was transmitted through a central server at ASPR to ESSENCE-FL every 15 minutes during each day of the 2012 RNC (August 26–31). In ESSENCE-FL, configuration allowed the data to be queried, analyzed, and visualized similar to existing ESSENCE-FL data sources. In all, data from 11 patient encounters were successfully exchanged between the partners. The data were used by ASPR and FDOH to simultaneously monitor in near real-time onsite medical response activities during the convention.
Conclusions
Timely access to patient data can enhance situational awareness and disease surveillance efforts and provide decision makers with key information in an expedient manner during disaster response and mass gatherings such as the RNC. However, data are siloed within organizations. The collaboration between FDOH, ASPR and JHU/APL made EMR data sharing and analysis more expeditious and efficient and increased timely access to these data by local, state, and federal epidemiologists. The integration of these data into the ESSENCE-FL system created one location where users could go to access data and create epidemiologic reports for a given region in Florida, including the RNC. To achieve these successes with partners in the future, it will be necessary to develop partnerships well in advance of intended data exchange. Future recommendations include robust pre-event testing of the data exchange process and planning for a greater amount of lead-time between enacting the official agreement and beginning data exchange.
PMCID: PMC3692893
Syndromic surveillance; Public health informatics; Data exchange; Federal and state collaboration
18.  Sharing Public Health Information with Non-Public Health Partners 
Objective
The objective of this project is to provide a technical mechanism for information to be easily and securely shared between public health ESSENCE users and non-public health partners; specifically, emergency management, law enforcement, and the first responder community. This capability allows public health officials to analyze incoming data and create interpreted information to be shared with others. These interpretations are stored securely and can be viewed by approved users and captured by authorized software systems. This project provides tools that can enhance emergency management situational awareness of public health events. It also allows external partners a mechanism for providing feedback to support public health investigations.
Introduction
Automated Electronic Disease Surveillance has become a common tool for most public health practitioners. Users of these systems can analyze and visualize data coming from hospitals, schools, and a variety of sources to determine the health of their communities. The insights that users gain from these systems would be valuable information for emergency managers, law enforcement, and other non-public health officials. Disseminating this information, however, can be difficult due to lack of secure tools and guidance policies. This abstract describes the development of tools necessary to support information sharing between public health and partner organizations.
Methods
The project initially brought together public health and emergency management officials to determine current gaps in technology and policy that prevent sharing of information on a consistent basis. Officials from across the National Capital Region (NCR) in Maryland, Virginia, and the District of Columbia determined that a web portal in which public health information could be securely posted on and captured by non-public health users (humans and computer systems) would be best. The development team then found open source tools, such as the Pebble blogging system, that would allow information to be posted, tagged, and searched in an easily navigable site. The system also provided RSS feeds both on the site as whole and specific tags to support notification. The team made modifications to the system to incorporate spring security features to allow the site to be securely hosted requiring usernames and passwords for access. Once the Pebble system was completed and deployed, the NCR’s aggregated ESSENCE system was adapted to allow users to submit daily reports and post time series images to the new site. An additional feature was created to post visualizations every evening to the site summarizing the day’s reports.
Results
The system has been in testing since March of 2012 and users of the system have provided valuable feedback. Based on the success of the tests, public health users in the NCR have begun working on the policy component of the project to determine when and how it should be used. Modifications to the system since deployment have included a single sign on capability for ESSENCE users and the desire to allow other features of ESSENCE to be posted beyond time series graphs, such as GIS maps and statistical reports.
Conclusions
Having tools that can promote exchange of information between public health and non-public health partners such as emergency management, law enforcement, and first responders can greatly enhance the situational awareness and impact overall preparedness and response. By having tools embedded in ESSENCE, users are able to integrate the information sharing aspects into their daily routines with a small amount of effort. With the use of open source tools, the same type of capability can be easily replicated in other jurisdictions. This presentation will describe the lessons learned and potential improvements the project will incorporate in the future.
PMCID: PMC3692892
Open Source; Emergency Management; Information Sharing
19.  Comprehensive effective and efficient global public health surveillance 
BMC Public Health  2010;10(Suppl 1):S3.
At a crossroads, global public health surveillance exists in a fragmented state. Slow to detect, register, confirm, and analyze cases of public health significance, provide feedback, and communicate timely and useful information to stakeholders, global surveillance is neither maximally effective nor optimally efficient. Stakeholders lack a globa surveillance consensus policy and strategy; officials face inadequate training and scarce resources.
Three movements now set the stage for transformation of surveillance: 1) adoption by Member States of the World Health Organization (WHO) of the revised International Health Regulations (IHR[2005]); 2) maturation of information sciences and the penetration of information technologies to distal parts of the globe; and 3) consensus that the security and public health communities have overlapping interests and a mutual benefit in supporting public health functions. For these to enhance surveillance competencies, eight prerequisites should be in place: politics, policies, priorities, perspectives, procedures, practices, preparation, and payers.
To achieve comprehensive, global surveillance, disparities in technical, logistic, governance, and financial capacities must be addressed. Challenges to closing these gaps include the lack of trust and transparency; perceived benefit at various levels; global governance to address data power and control; and specified financial support from globa partners.
We propose an end-state perspective for comprehensive, effective and efficient global, multiple-hazard public health surveillance and describe a way forward to achieve it. This end-state is universal, global access to interoperable public health information when it’s needed, where it’s needed. This vision mitigates the tension between two fundamental human rights: first, the right to privacy, confidentiality, and security of personal health information combined with the right of sovereign, national entities to the ownership and stewardship of public health information; and second, the right of individuals to access real-time public health information that might impact their lives.
The vision can be accomplished through an interoperable, global public health grid. Adopting guiding principles, the global community should circumscribe the overlapping interest, shared vision, and mutual benefit between the security and public health communities and define the boundaries. A global forum needs to be established to guide the consensus governance required for public health information sharing in the 21st century.
doi:10.1186/1471-2458-10-S1-S3
PMCID: PMC3005575  PMID: 21143825
20.  Utility of Syndromic Surveillance Using Novel Clinical Data Sources 
Objective
To document the current evidence base for the use of electronic health record (EHR) data for syndromic surveillance using emergency department, urgent care clinic, hospital inpatient, and ambulatory clinical care data.
Introduction
Historically, syndromic surveillance has primarily involved the use of near real-time data sent from hospital emergency department (EDs) and urgent care (UC) clinics to public health agencies. The use of data from inpatient and ambulatory settings is now gaining interest and support throughout the United States, largely as a result of the Stage 2 and 3 Meaningful Use regulations [1]. Questions regarding the feasibility and utility of applying a syndromic approach to these data sources are hampering the development of systems to collect, analyze, and share this potentially valuable information. Solidifying the evidence base and communicating the results to the public health surveillance community may help to initiate and build support for using these data to advance surveillance functions.
Methods
We conducted a literature search in the published and grey literature that scanned for relevant articles in the Google Scholar, Pub Med, and EBSCO Information Services databases. Search terms included: “inpatient/ambulatory electronic health record”; “ambulatory/inpatient/hospital/outpatient/chronic disease syndromic surveillance”; and “EHR syndromic surveillance”. Information gleaned from each article included data use, data elements extracted, and data quality indicators. In addition, several stakeholders who provided input on the September 2012 ISDS Recommendations [2] also provided articles that were incorporated into the literature review.
ISDS also invited speakers from existing inpatient and ambulatory syndromic surveillance systems to give webinar presentations on how they are using data from these novel sources.
Results
The number of public health agencies (PHAs) routinely receiving ambulatory and inpatient syndromic surveillance data is substantially smaller than the number receiving ED and UC data. Some health departments, private medical organizations (including HMOs), and researchers are conducting syndromic surveillance and related research with health data captured in these clinical settings [2].
In inpatient settings, many of the necessary infrastructure and analytic tools are already in place. Syndromic surveillance with inpatient data has been used for a range of innovative uses, from monitoring trends in myocardial infarction in association with risk factors for cardiovascular disease [3] to tracking changes in incident-related hospitalizations following the 2011 Joplin, Missouri tornado [3].
In contrast, ambulatory systems face a need for new infrastructure, as well as pose a data volume challenge. The existing systems vary in how they address data volume and what types of encounters they capture. Ambulatory data has been used for a variety of uses, from monitoring gastrointestinal infectious disease [3], to monitoring behavioral health trends in a population, while protecting personal identities [4].
Conclusions
The existing syndromic surveillance systems and substantial research in the area indicate an interest in the public health community in using hospital inpatient and ambulatory clinical care data in new and innovative ways. However, before inpatient and ambulatory syndromic surveillance systems can be effectively utilized on a large scale, the gaps in knowledge and the barriers to system development must be addressed. Though the potential use cases are well documented, the generalizability to other settings requires additional research, workforce development, and investment.
PMCID: PMC3692877
Syndromic surveillance; EHR; Meaningful Use
21.  Adaptation of GUARDIAN for Syndromic Surveillance During the NATO Summit 
Objective
To develop and implement a framework for special event surveillance using GUARDIAN, as well as document lessons learned post-event regarding design challenges and usability.
Introduction
Special event driven syndromic surveillance is often initiated by public health departments with limited time for development of an automated surveillance framework, which can result in heavy reliance on frontline care providers and potentially miss early signs of emerging trends. To address timelines and reliability issues, automated surveillance system are required.
Methods
The North Atlantic Treaty Organization (NATO) summit was held in Chicago, IL, May 19–21, 2012. During the NATO summit, the Chicago Department of Public Health (CDPH) was charged with collecting and analyzing syndromic surveillance data from emergency department (ED) visits that may indicate a man-made or naturally occurring infectious disease threat.
Ten days prior to the NATO summit surveillance period, Rush University Medical Center (RUMC) received a guidance document from CDPH outlining the syndromes for systematic surveillance, specifically febrile rash illness, localized cutaneous lesion, acute febrile respiratory illness, gastrointestinal illness, botulism-like illness, hemorrhagic illness, along with unexplained deaths or severe illness potentially due to infectious disease and cases due to toxins or suspected poisoning. RUMC researchers collected relevant ICD-9 codes for each syndrome category.
GUARDIAN (1), an automated surveillance system, was programmed to scan patient charts and match free text using National Library of Medicine free-text term to unique medical concept, which were further mapped to relevant ICD-9 codes. The baselines were developed using ED patient data from 1/1/2010 to 12/31/2011. Statistical references were established for unsmoothed, 24 hour counts (Baseline = Average; Threshold = +2 standard deviations).
During the NATO surveillance timeframe (May 13–26, 2012) automated results with prior reporting period’s counts, reference statistics, and charts were electronically sent to CDPH. In addition, ED charge nurses made manual surveillance reports by telephone at least daily. Open lines of communication were maintained between RUMC and CDPH during the event to discuss potential positive cases. In addition, a post-event debriefing was conducted to document lessons learned.
Results
The automated GUARDIAN surveillance reports not only provided timely counts of potentially positive cases for each syndrome but also provided trend analysis with baseline measures. The GUARDIAN User Interface was used to explain what data points could trigger positive cases. The Epic system was used to review patient charts, if further explanation was necessary. The observed counts never exceeded +2 standard deviations during the NATO surveillance period for any of the syndromes.
Based on the debriefing meeting between RUMC and CDPH, the top three achievements and lessons learned were as follows: Quick turnaround time (∼ 10 days) from surveillance concept development to automated implementation using GUARDIANSurveillance data was timely and reliableAdditional statistical information was beneficial to put trends in contextSystem may be too sensitive resulting in false alarms and additional investigative burden on public health departmentsNeed for development of user-interfaces with drill down capabilities to patient level dataClinicians don’t necessarily utilize exact terminology used in ICD-9 codes which could result in undetected cases.
Conclusions
This exercise successfully highlights rapid development and implementation of special event driven automated surveillance as well as collaborative approach between front-line entities such as emergency departments, surveillance researchers, and the department of public health. In addition, valuable lessons learned with potential solutions are documented for further refinements of such surveillance activities.
PMCID: PMC3692845
Emergency department; NATO Summit; automated surveillance
22.  An ISDS-Based Initiative for Conventions for Biosurveillance Data Analysis Methods 
Objective
The panel will present the problem of standardizing analytic methods for public health disease surveillance, enumerate goals and constraints of various stakeholders, and present a straw-man framework for a conventions group.
Introduction
Twelve years into the 21st century, after publication of hundreds of articles and establishment of numerous biosurveillance systems worldwide, there is no agreement among the disease surveillance community on most effective technical methods for public health data monitoring. Potential utility of such methods includes timely anomaly detection, threat corroboration and characterization, follow-up analysis such as case linkage and contact tracing, and alternative uses such as providing supplementary information to clinicians and policy makers.
Several factors have impeded establishment of analytical conventions. As immediate owners of the surveillance problem, public health practitioners are overwhelmed and understaffed. Goals and resources differ widely among monitoring institutions, and they do not speak with a single voice. Limited funding opportunities have not been sufficient for cross-disciplinary collaboration driven by these practitioners. Most academics with the expertise and luxury of method development cannot access surveillance data. Lack of data access is a formidable obstacle to developers and has caused talented statisticians, data miners, and other analysts to abandon the field. The result is that older research is neglected and repeated, literature is flooded with papers of varying utility, and the decision-maker seeking realistic solutions without detailed technical knowledge faces a difficult task.
Regarding conventions, the disease surveillance community can learn from older, more established disciplines, but it also poses some unique challenges. The general problem is that disease surveillance lies on the fringe of disparate fields (biostatistics, statistical process control, data mining, and others), and poses problems that do not adequately fit conventional approaches in these disciplines.
In its eighth year, the International Society of Disease Surveillance is well positioned to address the standardization problem because its membership represents the involved stakeholders including progressive programs worldwide as well as resource-limited settings, and also because best practices in disease surveillance is fundamental to its mission. The proposed panel is intended to discuss how an effective, sustainable technical conventions group might be maintained and how it could support stakeholder institutions.
Methods
Members of a Technical Conventions Group would have experience and dedication to advancing the science of disease surveillance. Primary functions would include: Specify and communicate technical problems facing professionals involved in routine monitoring of population health. Alternative use applications would also be considered, such as the use of epidemiological findings to inform clinical diagnoses.Independently evaluate the utility of proposed analytical solutions to well-defined problems in public health surveillance and confer approval or certification, perhaps on several levels, such as whether results can be replicated with shareable data. Approved solutions might be restricted to commonly available software such as the R language or Microsoft EXCEL.Facilitate sharing of tools and methodologies to evaluate methods and to visualize their results
The framework to be discussed in the proposed panel would be a means of keeping open lines of collaboration and idea-sharing. Overcoming obstacles toward this goal is worthy of a conference panel discussion whether or not it concludes that a conventions group is a viable approach.
Results
Three 15-minute panelist talks are proposed: Background: in-depth description of the dimensions of the problem aboveConstraints facing public health practitioners and requirements for practical analytic toolsStrawman conventions group: role, logistics, inclusiveness, methods of communicating with stakeholders and related organizations and producing/disseminating output.
For the 45 minutes of discussion, the first 15–20 will invite reactions to the first two talks to sharpen the scope of the effort. The remainder of the session will cover the advisability, feasibility, and logistics of an ISDS-based conventions group, and modify the straw-man group concept.
PMCID: PMC3692949
Standards; Data Analysis; Statistical Algorithms; Certification
23.  The Arctic Human Health Initiative: a legacy of the International Polar Year 2007–2009 
International Journal of Circumpolar Health  2013;72:10.3402/ijch.v72i0.21655.
Background
The International Polar Year (IPY) 2007–2008 represented a unique opportunity to further stimulate cooperation and coordination on Arctic health research and increase the awareness and visibility of Arctic regions. The Arctic Human Health Initiative (AHHI) was a US-led Arctic Council IPY coordinating project that aimed to build and expand on existing International Union for Circumpolar Health (IUCH) and Arctic Council human health interests. The project aimed to link researchers with potential international collaborators and to serve as a focal point for human health research, education, outreach and communication activities during the IPY. The progress of projects conducted as part of this initiative up until the end of the Arctic Council Swedish chairmanship in May 2013 is summarized in this report.
Design
The overall goals of the AHHI was to increase awareness and visibility of human health concerns of Arctic peoples, foster human health research, and promote health strategies that will improve health and well-being of all Arctic residents. Proposed activities to be recognized through the initiative included: expanding research networks that will enhance surveillance and monitoring of health issues of concern to Arctic peoples, and increase collaboration and coordination of human health research; fostering research that will examine the health impact of anthropogenic pollution, rapid modernization and economic development, climate variability, infectious and chronic diseases, intentional and unintentional injuries, promoting education, outreach and communication that will focus public and political attention on Arctic health issues, using a variety of publications, printed and electronic reports from scientific conferences, symposia and workshops targeting researchers, students, communities and policy makers; promoting the translation of research into health policy and community action including implementation of prevention strategies and health promotion; and promoting synergy and strategic direction of Arctic human health research and health promotion.
Results
As of 31 March, 2009, the official end of the IPY, AHHI represented a total of 38 proposals, including 21 individual Expressions of Intent (EoI), and 9 full proposals (FP), submitted to the IPY Joint Committee for review and approval from lead investigators from the US, Canada, Greenland, Norway, Finland, Sweden and the Russian Federation. In addition, there were 10 National Initiatives (NI-projects undertaken during IPY beyond the IPY Joint Committee review process). Individual project details can be viewed at www.arctichealth.org. The AHHI currently monitors the progress of 28 individual active human health projects in the following thematic areas: health network expansion (5 projects), infectious disease research (7 projects), environmental health research (7 projects), behavioral and mental health research (4 projects), and outreach education and communication (5 projects).
Conclusions
While some projects have been completed, others will continue well beyond the IPY. The IPY 2007–2008 represented a unique opportunity to further stimulate cooperation and coordination on Arctic health research and increase the awareness and visibility of Arctic regions.
doi:10.3402/ijch.v72i0.21655
PMCID: PMC3749855  PMID: 23971017
International Polar Year; Arctic Health; research; education outreach communication; Arctic Council
24.  The use of syndromic surveillance for decision-making during the H1N1 pandemic: A qualitative study 
BMC Public Health  2012;12:929.
Background
Although an increasing number of studies are documenting uses of syndromic surveillance by front line public health, few detail the value added from linking syndromic data to public health decision-making. This study seeks to understand how syndromic data informed specific public health actions during the 2009 H1N1 pandemic.
Methods
Semi-structured telephone interviews were conducted with participants from Ontario’s public health departments, the provincial ministry of health and federal public health agency to gather information about syndromic surveillance systems used and the role of syndromic data in informing specific public health actions taken during the pandemic. Responses were compared with how the same decisions were made by non-syndromic surveillance users.
Results
Findings from 56 interviews (82% response) show that syndromic data were most used for monitoring virus activity, measuring impact on the health care system and informing the opening of influenza assessment centres in several jurisdictions, and supporting communications and messaging, rather than its intended purpose of early outbreak detection. Syndromic data had limited impact on decisions that involved the operation of immunization clinics, school closures, sending information letters home with school children or providing recommendations to health care providers. Both syndromic surveillance users and non-users reported that guidance from the provincial ministry of health, communications with stakeholders and vaccine availability were driving factors in these public health decisions.
Conclusions
Syndromic surveillance had limited use in decision-making during the 2009 H1N1 pandemic in Ontario. This study provides insights into the reasons why this occurred. Despite this, syndromic data were valued for providing situational awareness and confidence to support public communications and recommendations. Developing an understanding of how syndromic data are utilized during public health events provides valuable evidence to support future investments in public health surveillance.
doi:10.1186/1471-2458-12-929
PMCID: PMC3539916  PMID: 23110473
Decision making; Pandemic influenza; Public health; Surveillance; Syndromic surveillance
25.  The epidemiology and surveillance response to pandemic influenza A (H1N1) among local health departments in the San Francisco Bay Area 
BMC Public Health  2013;13:276.
Background
Public health surveillance and epidemiologic investigations are critical public health functions for identifying threats to the health of a community. Very little is known about how these functions are conducted at the local level. The purpose of the Epidemiology Networks in Action (EpiNet) Study was to describe the epidemiology and surveillance response to the 2009 pandemic influenza A (H1N1) by city and county health departments in the San Francisco Bay Area in California. The study also documented lessons learned from the response in order to strengthen future public health preparedness and response planning efforts in the region.
Methods
In order to characterize the epidemiology and surveillance response, we conducted key informant interviews with public health professionals from twelve local health departments in the San Francisco Bay Area. In order to contextualize aspects of organizational response and performance, we recruited two types of key informants: public health professionals who were involved with the epidemiology and surveillance response for each jurisdiction, as well as the health officer or his/her designee responsible for H1N1 response activities. Information about the organization, data sources for situation awareness, decision-making, and issues related to surge capacity, continuity of operations, and sustainability were collected during the key informant interviews. Content and interpretive analyses were conducted using ATLAS.ti software.
Results
The study found that disease investigations were important in the first months of the pandemic, often requiring additional staff support and sometimes forcing other public health activities to be put on hold. We also found that while the Incident Command System (ICS) was used by all participating agencies to manage the response, the manner in which it was implemented and utilized varied. Each local health department (LHD) in the study collected epidemiologic data from a variety of sources, but only case reports (including hospitalized and fatal cases) and laboratory testing data were used by all organizations. While almost every LHD attempted to collect school absenteeism data, many respondents reported problems in collecting and analyzing these data. Laboratory capacity to test influenza specimens often aided an LHD’s ability to conduct disease investigations and implement control measures, but the ability to test specimens varied across the region and even well-equipped laboratories exceeded their capacity. As a whole, the health jurisdictions in the region communicated regularly about key decision-making (continued on next page) (continued from previous page) related to the response, and prior regional collaboration on pandemic influenza planning helped to prepare the region for the novel H1N1 influenza pandemic. The study did find, however, that many respondents (including the majority of epidemiologists interviewed) desired an increase in regional communication about epidemiology and surveillance issues.
Conclusion
The study collected information about the epidemiology and surveillance response among LHDs in the San Francisco Bay Area that has implications for public health preparedness and emergency response training, public health best practices, regional public health collaboration, and a perceived need for information sharing.
doi:10.1186/1471-2458-13-276
PMCID: PMC3681650  PMID: 23530722
Influenza A (H1N1); Epidemiology; Surveillance; Public health preparedness; Public health emergency response

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