Surveillance Systems

The estimated incidence of imported malaria in France is about 4,000 cases per year (1). The epidemiological surveillance of malaria in France is mainly based on a hospital laboratory surveillance network, which captures around 50% of cases. There is no comprehensive population surveillance. The SNIIRAM provides data about hospital stays and outpatient drug reimbursements, procedures, examinations and sickness leaves for almost the whole French population(2). We aimed to evaluate the usefulness of the SNIIRAM for implementing epidemiological surveillance of malaria.

Objective

Estimate the accuracy of the French ational ealth nsurance nformation ystem (SNIIRAM) as a support for a nationwide malaria surveillance

While results from syndromic surveillance systems are commonly presented in the literature, few systems appear to have been thoroughly evaluated to examine which events can and cannot be detected, the time to detection and the efficacy of different syndromic surveillance data streams. Such an evaluation framework is presented.

Objective

To devise a methodology for evaluating the effectiveness of syndromic surveillance systems

Flu Near You allows individuals to volunteer to be a sentinel node of the syndromic surveillance (SyS) network. The platform has the potential to provide insight into the spread of influenza-like illness (ILI). CDC’s ILINet is the gold standard for tracking ILI at the national level, but does not track into the local level. Local health departments (LHD) frequently express a need for granular data specific to their jurisdictions. FNY attempts to meet this need by collecting and sharing data at the zip code level. Knowing how well FNY data correlates to ILINet data will give local health departments an important tool to communicate the arrival of influenza to their jurisdiction. However, there is significant skepticism at the quality of FNY data as compared to validated datasets.

Objective

Our objective is to provide evidence for the data quality of Flu Near You (FNY) by evaluating the national and Houston datasets against CDC ILI data.

The Risk Identification Unit (RIU) of the US Dept. of Agriculture’s Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. In an initiative to increase the monitored species, health issues, and data sources, CEAH epidemiologists are building a surveillance system based on weekly counts of laboratory test orders along with Colorado State Univ. laboratorians and statistical analysts from the Johns Hopkins Univ. Applied Physics Lab. Initial efforts used 12 years of equine test records from 3 state labs covering most Colorado horse testing. Trial syndrome groups were formed based on RIU experience and published articles. Data analysis, stakeholder input, and discovery of laboratory workflow details were needed to modify these groups and filter test records to eliminate alerting bias. Customized statistical monitoring methods were sought based on specialized lab information characteristics and on likely presentation and health significance of syndrome-associated diseases.

New York State Department of Health (NYSDOH) implemented a Communicable Disease Electronic Surveillance System (CDESS), a single and secure application used by 57 local health departments (LHDs), hospital infection control programs and NYSDOH staff to collect, integrate, analyze, and report data for infectious disease surveillance. New York State Immunization Information System (NYSIIS) is a mandated application for providers to report all vaccinations of persons < 19 years old residing in New York State (excluding New York City). Currently, LHD staff must manually search NYSIIS for vaccine preventable disease case investigations and re-enter the immunization histories into CDESS. NYSIIS has built a HL7 query functionality which can be used to automate the data exchange between NYSIIS and CDESS.

Objective

To establish the infrastructure to provide a linkage between the immunization registry and disease surveillance system using standard for data exchange.

Evidence from over 100 years of epidemiological study demonstrates a consistent, negative association between health and economic prosperity. In many settings, it is clear that causal links exist between lower socioeconomic status and both reduced access to healthcare and increased disease burden. However, our study is the first to demonstrate that the increased disease burden in at-risk populations interacts with their reduced access to healthcare to hinder surveillance.

Objective

Improve situational awareness for influenza by combining multiple data sources to predict influenza outbreaks in at-risk populations.

Real-time syndromic surveillance requires daily surveillance of a range of health data sources. Most real-time data sources from health care systems exhibit large day of the week fluctuations as service provision and patient behaviour varies by day of the week. Regular day of the week effects are further complicated by the occurrence of public holidays (usually 8 per year in England), which can limit the availability of certain services and affect patient behaviour. Simple seven day moving averages fail to provide a smoothed trend around public holidays and can lead to false alarms or potentially delays in detection of outbreaks.

Objective

To develop smoothing techniques for daily syndromic surveillance data that allow for the easier identification of trends and unusual activity independent of day of the week and holiday effects.

The mission of the Maricopa County Department of Public Health (MCDPH; Arizona) is to protect and promote the health and well-being of its residents and visitors. Surveillance efforts allow epidemiologists to quantify and characterize public health threats, but traditional methods take time. In an effort to enhance situational awareness, the Office of Epidemiology dedicated resources to begin developing a robust syndromic surveillance program. This abstract outlines steps for enhancing syndromic surveillance at a local public health department.

Objective

To demonstrate how a local public health department used the Centers for Disease Control and Prevention (CDC) Framework for Program Evaluation and a logic model to enhance its syndromic surveillance program.

Since 2003 some Arizona counties have followed mosquito surveillance protocols to trap the West Nile Virus vector, Culex spp., using CO2 traps. Despite low sensitivity of these traps to detect Aedes spp., one out of seven CO2 traps deployed in Santa Cruz County detected Aedes aegypti in 2014. Enhancing surveillance for Aedes spp. in this region is critical, given that local transmission of dengue has occured across the border in Nogales, Sonora. Limited resources in Santa Cruz County have previously inhibited efforts to enhance mosquito surveillance . To broaden the reach of county surveillance, we implemented a community participatory project by engaging residents to conduct ovitrapping, a non-technical trap that attracts Aedes spp.

Objective

The objective of this work is to develop an efficient communitybased strategy to enhance mosquito surveillance for Aedes spp., vector for chikungunya and dengue viruses, in Santa Cruz County on the U.S.-Mexico border. We aim to determine vector presence, distribution, and seasonality by using ovitraps maintained by community members.

The National Biosurveillance Integration Center (NBIC) has the responsibility to integrate, analyze, and share the nation’s biosurveillance information provided from capabilities distributed across public and private sectors. The integration of information enables early warning and shared situational awareness of biological events to inform critical decisions directing response and recovery efforts. In addition to its interagency partners, NBIC supports the Office of Health Affairs and DHS components responsible for safeguarding U.S. ports of entry. More than 150 U.S. international airports process an estimated two billion passengers and 50 million metric tons of cargo arriving in the U.S. from more than 1,000 international airports located outside the U.S. Entry and customs screening are points where travelers from international destinations pass; a logical location for assessing health of incoming travelers in order to identify and control import of diseases of emerging diseases. NBIC examined peer-reviewed literature, region-specific disease spectrum/frequency, and air travel patterns to assess options for ports of entry health screening as well as the challenges and potential benefits for active screening programs.

Objective

NBIC analysts evaluated the options and effectiveness of airport symptom-based health screening programs available during emerging disease outbreaks occurring outside the U.S.