Surveillance Systems

Investigators have used the volume of internet search queries to model disease incidence, especially influenza and general consumer behavior [1]. Our group has used search volume to model interest in FDA safety alerts and adverse drug event incidence. We found evi- dence of changes in search behavior following warnings and the ex- pected relationship between search volume and adverse drug event incidence. Thus, search volume may help provide near real time sur- veillance of drug use patterns to help monitor and mitigate risk to the population from adverse drug events. However, the use of search query volume as a proxy for drug use has yet to be validated.

We attempt to validate search volume estimation of drug utilization in three ways: 1) explore seasonal variations in search volume and outpatient utilization, 2) monitor change between substitute drugs fol- lowing patent expirations and 3) use search volume estimation meth- ods to estimate TB incidence.

Objective

To validate search volume estimation for outpatient medication prescribing.

The National Notifiable Disease Surveillance System (NNDSS) comprises many activities including collaborations, processes, standards, and systems which support gathering data from US states and territories. As part of NNDSS, the National Electronic Disease Surveillance System (NEDSS) provides the standards, tools, and resources to support reporting public health jurisdictions (jurisdictions). The NEDSS Base System (NBS) is a CDC-developed, software application available to jurisdictions to collect, manage, analyze and report national notifiable disease (NND) data. An evaluation of NEDSS with the objective of identifying the functionalities of NC systems and the impact of these features on the user’s culture is underway.

Objective

The culture by which public health professionals work defines their organizational objectives, expectations, policies, and values. These aspects of culture are often intangible and difficult to qualify. The introduction of an information system could further complicate the culture of a jurisdiction if the intangibles of a culture are not clearly understood. This report describes how cultural modeling can be used to capture intangible elements or factors that may affect NEDSS-compatible (NC) system functionalities within the culture of public health jurisdictions.

A large part of the applied research on syndromic surveillance targets seasonal epidemics, e.g. influenza, winter vomiting disease, rotavirus and RSV, in particular when dealing with preclinical indicators, e.g. web traffic. The research on local outbreak surveillance is more limited. Two studies of teletriage data (NHS Direct) have shown positive and negative results respectively. Studies of OTC pharmacy sales have reported similar equivocal performance. As far as we know, no systematic comparison of data sources with respect to multiple point-source outbreaks has so far been published. In the current study, we evaluated the potential of three data sources for syndromic surveillance by analyzing the correspondence between signal properties and point-source outbreak characteristics.

Objective

For the purpose of developing a national system of outbreak surveillance, we compared local outbreak signals in three sources of syndromic data – telephone triage of acute gastroenteritis (Swedish Health Care Direct 1177), web queries about symptoms of gastrointestinal illness (Stockholm County’s website for healthcare information), and OTC pharmacy sales of anti-diarrhea medication.

The late health events such as the heat wave of 2003 showed the need to make public health surveillance evolve in France. Thus, the French Institute for Public Health Surveillance has developed syndromic surveillance systems based on several information sources such as emergency departments. In Reunion Island, the chikungunya outbreak of 2005-2006, then the influenza pandemic of 2009 contributed to the implementation and the development of this surveillance system. In the past years, this tool allowed to follow and measure the impact of seasonal epidemics. Nevertheless, its usefulness for the detection of minor unusual events had yet to be demonstrated.

Objective

To show with examples that syndromic surveillance system can be a reactive tool for public health surveillance.

The New York State Veterinary Diagnostic Laboratory (NYSVDL) receives more than 100,000 diagnostic submissions a year that are not currently used in any formal syndromic surveillance system. In 2009, a pilot study of syndrome classification schemes was undertaken and in 2011 a new general submission form was adopted, which includes a check list of syndromes, as part of the clinical history.

Monitoring submissions to a veterinary diagnostic laboratory for increases in certain test requests is an established method of syndromic surveillance. The new general submission form allows for clinician selected syndromes to be monitored in addition to test request.

Objective

To assess the use and utility of a syndrome check list on the general submission form of a high volume veterinary diagnostic laboratory, and compare to the results of a 2009 pilot study

The IBBS is part of the Indonesian MoH HIV Surveillance System, which include Serological Surveillance, Behavioral Surveillance, Reproductive Tract Infection Survey, and monthly HIV/AIDS facilitybased (hospitals, HCs, VCT Sites) monthly reports. The IBBS 2011 was conducted in 11 provinces (22 districts/municipalities) encompassing eight Most At Risk Populations (MARPs) – injection drug users, transsexuals, men who have sex with men, youths, inmates, mobile men, direct female sex workers (FSWs), and indirect FSWs. Data of 442 direct FSWs of the Jayapura Municipality and Jayawijaya District (Papua Province) showed that 406 (91.85%) have sex with partners who did not use condoms. Of these 406 FSWs 60 (14.78%) were HIV positive and 231 (56.89%) were STD positive.

Objective

To analyze the Integrated Behavioral & Biological Surveillance (IBBS) 2011 data for designing a condom utilization program.

Development of effective policy interventions to stem disease outbreaks requires knowledge of the current state of affairs, e.g. how many individuals are currently infected, a strain’s virulence, etc, as well as our uncertainty of these values. A Bayesian inferential approach provides this information, but at a computational expense. We develop a sequential Bayesian approach based on an epidemiological compartment model and noisy count observations of the transitions between compartments.

Objective:

Develop fast sequential Bayesian inference for disease outbreak counts.