Surveillance Systems

It has been noted since the era of Hippocrates that weather conditions at a specific location can influence the incidence of various infectious and noninfectious diseases. It has also been implied that variations in weather conditions influence the number of cases of infectious respiratory conditions. Syndromic surveillance was introduced in Athens, Greece, for the first time in July 2002 in the framework of increased preparedness for the Olympic Games of 2004. Our experience showed that the incidence of some syndromes parallels that of diseases surveyed by the mandatory notification system of the Hellenic Center for Diseases Control and Prevention that are known to have a strong seasonal pattern in their incidence e.g. influenza. Influenza incidence peaks at the same time with the “respiratory infection with fever” syndrome during spring. This study aimed at investigating possible relationships between the incidence of the “respiratory infection with fever” syndrome and meteorological parameters.

Objective

This study explores the possible impact of meteorological conditions on the incidence of clinical syndromes with an interest for public health in the basin of Athens, Greece.

Timely outbreak detection, and monitoring of morbidity and mortality among Katrina evacuees, and needs assessment for better planning and response were urgent information intensive priorities during Katrina relief efforts at Houston, and called for immediate deployment of a real-time surveillance and needs assessment system ad hoc, in order to collect and analyze relevant data at the scene. Initial requirement analysis revealed the following capabilities as essential to sustain effective response within the shelters:

• The ability to securely collect and integrate data from evacuees seeking any form of health services from all care providers (academic, volunteers, federal, NGOs and international aid organizations, etc), including demographic information, vital signs, chief complaints, disabilities, chronic conditions, current and past medications, traumas and injuries, exposure to toxic materials, clinical laboratory results, past medical history, discharge notes and diagnoses, and ability to collect free text entries for any other information (similar to a full-blown electronic medical records system).

• Proactive survey of demographic profile, physical and mental health status, as well as special needs assessment (e.g., dialysis, medications, etc) from all evacuees.

• The ability to collect uniform information, using any network-enabled device available: PCs, tablets, and handheld devices. 

• The ability to classify observations by processing sign and symptom, chief complaint, medication, and other diagnostic data (including free text entries) through ad-hoc definition of concepts such as (Gastrointestinal, Respiratory, Fever and Rash, etc). 

Objective

This paper presents lessons learned from leveraging Internet-based technologies and Services Oriented Architecture in providing timely, novel, and customizable solutions, just in time and for preparedness against unprecedented events such as natural disasters (e.g., Katrina) or terrorism.

This paper describes the initiation, development and proof-of-concept phase of the ISDS DiSTRIBuTE influenza morbidity surveillance project [1].

Although many syndromic surveillance (SS) systems have been developed and implemented, few have included response protocols to guide local health jurisdictions when alerts occur [1,2]. SS was first implemented in GA during the 2004 G-8 Summit. Six EDs in the Coastal Public Health District (PHD), 1 of 18 GA PHDs (Figure 1), conducted SS during that “national security special event.” Since that time, EDs in other PHDs have been actively recruited to participate in GA’s SS system. In GA, the PHD has the responsibility for monitoring SS data. Likewise, the PHD responds to alerts and initiates public health investigations and interventions; the state Division of Public Health (DPH) assists, if requested. To address these responsibilities, the Coastal PHD informally developed their own response practices.

Objective

To develop a template protocol to guide local response to syndromic surveillance alerts generated through analyses of emergency department (ED) visit data.

To date, most syndromic surveillance systems rely heavily on complicated statistical algorithms to identify aberrations. The assumption is that when the statistics identify something unusual, follow-up should occur. However, with multiple strata analyzed, small numbers for some strata, and wide variances in daily counts, the statistical algorithms will generate flags too often. Experience has shown that these flags usually have little or no public health significance. As a result, syndromic surveillance systems suffer from the ‘boy who cried wolf’ syndrome. It is clear that the analyst’s ability to use professional judgment to sift through multitudes of flags is very important to the success of the system, which suggests that statistics alone cannot identify issues of public health importance from ED data.

Objective

This study's aim was to refine an automated biosurveillance system in order to better suit the daily monitoring capabilities and resources of a health department.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition [1]. SSS seek early detection by focusing on pre-diagnostic symptoms that by themselves may not alarm clinicians. We have previously determined the performance of various Case Detector (CD) algorithms at finding cases of influenza-like illness (ILI) recorded in the electronic medical record of the Veterans Administration (VA) health system. In this work, we measure the impact of using CDs of increasing sensitivity but decreasing specificity on the time it takes a VA-based SSS to identify a modeled community-wide influenza outbreak. Objective This work uses a mathematical model of a plausible influenza epidemic to test the influence of different case-detection algorithms on the performance of a real-world syndromic surveillance system (SSS).

Wetter and stormier weather is predicted in the UK as global temperatures rise. It is likely there will be increases in river and coastal flooding. The known short and medium term health effects of flooding are drowning, injury, acute asthma, skin rashes and outbreaks of gastrointestinal and respiratory disease. Longer term health effects of flooding are thought to be psychological stress and increased rates of mental illness. Reacher et al. conducted a retrospective study of illness in a population affected by flooding in Lewes, South-East England during 2000 [1]. They found a significant raised risk of earache (RR=2.2) and gastroenteritis (RR=1.7) for flooded households. More striking was the higher level of psychological distress experienced by these residents (RR=4.1), which may have also explained some of the excess physical illness.

Objective

This paper describes the results of prospective real time syndromic surveillance conducted during a national flooding incident during 2007 in the UK.

Prehospital  EMS  data  is  rarely  mentioned  in  discus-sions  surrounding  syndromic  surveillance  for  covert  bio-terrorism  attacks  or  for  the  monitoring  of  syn-dromic  illness  such  as  bird  flu.    However,  EMS  dis-patch data may serve as the very first marker in such an event.  EMS dispatch data has many useful advan-tages  in  syndromic  surveillance.    These  include  the  ability to monitor across wide areas of geography and a  single  data  collection  source.    Additionally,  EMS  dispatchers  are  a  medically  trained  core  group  of  in-dividuals that use a single standardized set of interro-gation  questions  and  methods  with  specific  dispatch  codes  regarding  patient  conditions.    This  data  would  arguably be a more reliable source of data than mul-tiple  different  inputs  from  multiple  individuals  at  various clinics and hospitals emergency departments.  EMS  data  is  also  able  to  look  at  a  much  broader  group  of  individuals  both  by  volume  of  calls  and  by  geography,  since  they  are  instantaneously  able  to  capture  the  location  of  the  callers  when  dialing  911. EMS  dispatch  is  also  able  to  monitor  patient  move-ment to different accepting facilities.

Objective

This paper describes how the surveillance of actual EMS real time events occurring during normal operations were analyzed using a syndromic surveillance system and how these events can be used as surrogate markers for how a bio-surveillance system would act if an actual covert or overt terrorist event or pandemic illness were to occur

Sixty-one percent of known disease-causing agents that infect humans can also infect animals [1]. While humans are the primary reservoir for only 3% of zoonoses, detection of zoonotic disease outbreaks remains mostly dependant on the identification of human cases [2]. Very few of the diseases that are a threat to humans are reportable in pets. Over onethird of American households include at least one pet [3]. Pets can present with clinical signs of disease earlier than people after becoming infected at the same time [4]. Pets can also become infected first and act as a source of infection for humans [5]. Detection of an outbreak in pets may then provide for warning of an outbreak that could affect humans.

Objective

This paper describes occurrences of possible co-morbidity in pets and humans discovered in a retrospective study of veterinary microbiology records and through the application of syndromic surveillance methods in a prospective outbreak detection system using veterinary laboratory orders.

Management of software development projects involves a collection of well understood issues which are not often found in other project management areas. Identifying and managing these issues primarily requires that the manager is aware of the potential problems which can arise while developing software and what are the appropriate measures to control such problems.

Objective

Interest in syndromic surveillance through automated software systems is becoming more common and with this interest is an increase in small to medium sized software development projects. This paper discusses some of the common project management problems which occur when developing software in a community which does not have a long history of working in this area.