ISDS Conference

The New York State (NYS) Medicaid Program provides healthcare for 34% of the population in New York City (NYC) and 4%-20% in each of the 57 county populations up-state. Prescription data are collected through the sub-mission of claims forms to the Medicaid Program and transmitted daily to the NYS Syndromic Surveillance Program as summary counts by drug category and patient’s ZIP Code, age category, and sex. One of the 18 drug categories is influenza agents, which in-cludes rimantadine, oseltamivir, and zanamivir.

For surveillance of influenza-like illness (ILI) activity, the NYS and NYC Sentinel Physician Influenza Surveillance Program collects from sentinel physicians weekly reports of the total number of patients seen and the number of patients presenting with ILI (defined as temperature > 100 degrees F, presence of cough or sore throat, and absence of other known cause of these symptoms). Not all counties in NYS have sentinel physicians: in the 2003-2004 flu surveillance season (Week 40, in early October, 2003, to Week 20, in late May, 2004), 37 of 57 upstate counties and all 5 counties of NYC had sentinel physicians.

Objective

To evaluate the usefulness of daily counts of prescriptions for influenza agents charged to Medicaid insurance, by county of residence of the recipient, for detection of elevated ILI in NYS, currently monitored through physicians participating in the CDC Influenza Surveillance Program.

A number of syndromic surveillance systems include tools that quickly identify potentially large disease outbreak events. However, the high falsepositive rate continues to be a problem in all of these systems. Our earlier work has showed that multi-source information fusion can improve specificity of the syndromic surveillance systems. However, an anomalous health event that presents as only a few cases may remain undetected because the chief complaint data does not contain enough details. New linked data sources need to be used to enhance detection capabilities. The focus of this project examining the incorporation of laboratory, prescription medications and radiology data linked to the patient encounter within syndromic surveillance systems. These data source linkings may enhance the sensitivity of syndromic surveillance.

Lymphatic filariasis is one of the most prevalent of the tropical diseases, but is also the most neglected.Though significant advances have been made in the understanding both the disease and its control, there is general lack of information about its socioeconomic effects, prevalence and distribution in most endemic societies. Presently, there is global effort towards the elimination of the disease by 2020. The success of this programme depends largely on the use of simple, non-invasive procedures to identify endemic communities. Limb elephantiasis is one of the chronic symptoms of lymphatic filariasis that could be easily diagnosed by persons with minimum training. Therefore, the prevalence of elephantiasis could serve as a useful tool to determine the occurrence and spread of lymphatic filariasis in endemic communities.

Objective

This paper describes how limb elephantiasis was used to determine the occurrence and spread of lymphatic filariasis in Kano state, Nigeria as well as the use of the results for further epidemiological studies.

The first prototype syndromic surveillance in Japan was used during the G8 summit meeting in 2000 with two local prefectures involved. The second trial syndromic surveillance and the first internet-based surveillance used in 2002 for the Japan-Korea 2002 World Cup soccer games. Since 2002, surveillances on over-the-counter medications, ambulance call, and outpatient visits were explored as syndromic approach candidates for early detection. Internet-based events and case reporting frame work has been reviewed for outpatient visits daily reporting concurrently. Limited spread of electrical patient record and vast range of commercialized medical record formats posed obstacles to nationwide syndromic surveillance implementation.

Recent threats from bioterrorism and influenza pandemic empowered Japanese government introducing surveillance of rapid detection mechanism. In line with the revision of the Infection Control Law took place in 2007 April, national syndromic surveillance system was implemented.

Objective

This paper describes recent establishment of national surveillance system for early detection of infectious diseases in Japan. With diagnostic data fed from existed routine surveillance, newly introduced system is expected to provide timely information for control response. We aim to facilitate cross-informative regional surveillance by sharing our experience and system frame work.

This paper describes the early warning that has been activated by the syndromic surveillance system installed within the Armed Forces in French Guiana and the consequences for Public Health decisions.

The aerosol release of a pathogen during a bioterrorist incident may not always be caught on environmental sensors - it may be too small, may consist of a preparation that is coarse and heavy (and consequently precipitates quickly) or may simply have occurred in an uninstrumented location. In such a case, the first intimation of an event is the first definitive diagnosis of a patient. Being able to infer the size of the attack, its time, and the dose received has important ramifications for planning a response. Estimates drawn from such a short observation period will have limited accuracy, and hence establishing confidence levels (i.e., error bounds) on these estimates is an major concern. These estimates of outbreak characteristics can be also be used as initial conditions for epidemic models to predict the evolution of disease (along with error bounds in the predictions), in particular, communicable diseases in which the contagious period starts soon after infection (e.g., plague).

In this paper, we will consider anthrax and smallpox as our model pathogens. Since the contagious period of smallpox usually starts after the long incubation period (7–17 days), and the early epoch will consist only of index cases, we will model it as a non-contagious disease. Inputs will be obtained from simulated outbreaks as well as from the Sverdlovsk anthrax outbreak of 1979.

Objective

This paper presents a method that infers the number of infected people, the time of infection and the dose received from an aerosol release of a pathogen during a bioterrorism incident. Inputs into the inference process are the number of new diagnosed patients showing symptoms each day as observed over a short duration (3–4 days) during the early epoch of the outbreak.

Objective

To enable the early detection of pandemic influenza, we have designed a system to differentiate between severe and mild influenza outbreaks. Historic information about previous pandemics suggested the evaluation of two specific discriminants: (1) the rapid development of disease to pneumonia within 1-2 days and (2) patient age distribution, as the virus usually targets specific age groups. The system is based on the hypothesis that an increased number of diagnosed pneumonia cases offers an early indication of severe influenza outbreaks. This approach is based on the fact that pneumonia cases will appear promptly in a severe influenza outbreak and can be diagnosed immediately in a physician office visit, while a confirmed influenza diagnosis requires a laboratory test. Furthermore, laboratory tests are unlikely to be ordered outside of the expected influenza season.

Although rare in the US, the CDC reports 13-14 drinking-water-related disease outbreaks per year, affecting an average of about 1000 people. The US EPA has determined that the distribution system is the most vulnerable component of a drinking water system. Recognizing this vulnerability, water utilities are increasingly measuring disinfectant levels and other parameters in their distribution systems. The US EPA is sponsoring an initiative to fuse this distribution system water quality data with health data to improve surveillance by providing an assessment of the likelihood of the occurrence of a waterborne disease outbreak. This fused analysis capability will be available via a prototype water security module within a population-based public health syndromic surveillance system.

Objective

The objective of this paper is to illustrate a technique for combining water quality and population-based health data to monitor for water-borne disease outbreaks.

Bioterrorism surveillance is an integral component of DCHD’s Comprehensive Emergency Management Plan. This study was a collaborative effort between Duval County Health Department, University of South Florida’s Center for Biological Defense (CBD), and DataSphere, LLC. DCHD’s role in the project was to identify surveillance sites, involve community partners, share data/info with surrounding agencies, counties and the state department of health, and secure funding for the system. CBD’s role in the project was facilitating the operational and technical implementation of the system and serving as a liaison between hospitals, health departments, and DataSphere, LLC. DataSphere, LLC owns and operates BioDefend and was responsible for the technical setup and maintenance of the system. The study addressed the feasibility of automated data collection by healthcare facilities and issues related to implementation of a syndromic surveillance system.

Objective

The purpose of this study was to evaluate the implementation of the BioDefend syndromic surveillance system for its use.

Recent health events in France, such as the dramatic excess of mortality occurred during the 2003 heat wave showed the need for a better provision of information to health authorities. A new syndromic surveillance system based on the recording of general practitioner’s visits by SOS Médecins has been developed by the Aquitaine Regional Epidemiology unit (Cire).

Objective

To describe the surveillance system based on SOS Medecins data, the first GP emergency and healthcare network in France and to show the utility and validity of this data source as a real-time syndromic surveillance system.