Buckeridge David

Many studies evaluate the timeliness and accuracy of outbreak detection algorithms used in syndromic surveillance. Of greater interest, however, is defining the outcome associated with improved detection. In case of a waterborne cryptosporidiosis outbreak, public health interventions are aimed exclusively at preventing new infections, and not at medical treatment of infected individuals. The effectiveness of these interventions in reducing morbidity and mortality will depend on their timeliness, the level of compliance, and the duration of exposure to pathogen. In this work, we use simulation modeling to examine several scenarios of issuing a boil-water advisory (BWA) as a response to outbreak detection through syndromic surveillance, and quantify the possible benefits of earlier interventions.

Objective

To quantitatively assess the benefit of issuing a boil-water advisory for preventing morbidity and mortality from a waterborne outbreak of cryptosporidiosis.

Mandatory notification to public health of priority communicable diseases (CDs) is a cornerstone of disease prevention and control programs. Increasingly, the addresses of CD cases are used for spatial monitoring and cluster detection and public health may direct interventions based on the results of routine spatial surveillance. There has been little assessment of the quality of addresses in surveillance data and the impact of address errors on public health practice.

We launched a pilot study at the Montreal Public Health Department, wherein our objective was to determine the prevalence of address errors in the CD surveillance data. We identified address errors in 25% of all reported cases of communicable diseases from 1995 to 2008. We also demonstrated that address errors could bias routine public health analyses by inappropriately flagging regions as having a high or low disease incidence, with the potential of triggering misguided outbreak investigations or interventions. The final step in our analysis was to determine the impact of address errors on the spatial associations of campylobacter cases in a simulated point source outbreak.

Objective

To examine, via a simulation study, the potential impact of residential address errors on the identification of a point source outbreak of campylobacter.

School closure has long been proposed as a non-pharmaceutical intervention in reducing the transmission of pandemic influenza. Children are thought to have high transmission potential because of their low immunity to circulating influenza viruses and high contact rates. In the wake of pandemic (H1N1) 2009, simulation studies suggest that early and sustained school closure might be effective at reducing community-wide transmission of influenza. Determining when to close schools once an outbreak occurs has been difficult. Influenza-related absentee data from Japan were previously used to develop an algorithm to predict an outbreak of influenza-related absenteeism. However, the cause of absenteeism is frequently unavailable, making application of this model difficult in certain settings. For this study, we aimed to evaluate the potential for adapting the Japanese algorithm for use with all-cause absenteeism, using data on the rate of influenza-related nurse visits in

corresponding schools to validate our findings.

Objective

To determine the optimal pattern in school-specific all-cause absenteeism for use in informing school closure related to pandemic influenza.

The BioSense program’s mission is to support and improve public health surveillance infrastructure and human capacity required to monitor (with minimal lag) critical population health indicators of the scope and severity of acute health threats to the public health; and support national, state, and local responses to those threats. This mission is consistent with the 2006 Pandemic All Hazards Preparedness Act, and 2007 Homeland Security Presidential Directive (HSPD-21), both of which call for regional and nationwide public health situational awareness, through an interoperable network of systems, built on existing state and local situational awareness capability.

Objective

The objective of this study is that the Centers for Disease Control and Prevention will update the International Society for Disease Surveillance community on the latest activities for the BioSense program redesign (Centers for Disease Control and Prevention, USA).

In Montreal, notifiable diseases are reported to the Public Health Department (PHD). Of 44, 250 disease notifications received in 2009, up to 25% had potential address errors. These can be introduced during transcription, handwriting interpretation and typing at various stages of the process, from patients, labs and/or physicians, and at the PHD. Reports received by the PHD are entered manually (initial entry) into a database. The archive personnel attempts to correct omissions by calling reporting laboratories or physicians. Investigators verify real addresses with patients or physicians for investigated episodes (40–60%). 

The Dracones qualite (DQ) address verification algorithm compares the number, street and postal code against the 2009 Canada Post database. If the reported address is not consistent with a valid address in the Canada Post database, DQ suggests a valid alternative address.

Objective

To (1) validate DQ developed to improve data quality for public health mapping and (2) identify the origin of address errors.

The Distribute project began in 2006 as a distributed, syndromic surveillance demonstration project that networked state and local health departments to share aggregate emergency department-based influenza-like illness (ILI) syndrome data. Preliminary work found that local systems often applied syndrome definitions specific to their regions; these definitions were sometimes trusted and understood better than standardized ones because they allowed for regional variations in idiom and coding and were tailored by departments for their own surveillance needs. Originally, sites were asked to send whatever syndrome definition they had found most useful for monitoring ILI. Places using multiple definitions were asked to send their broader, higher count syndrome. In 2008, sites were asked to send both a broad syndrome, and a narrow syndrome specific to ILI.

Objective

To describe the initial phase of the ISDS Distribute project ILI syndrome standardization pilot.

Seasonal influenza epidemics are responsible for over 200,000 hospitalizations in the United States per year, and 39,000 of them are in children. In the United States, the Advisory Committee on Immunization Practices guides immunization practices, including influenza vaccination, with recommendations revised on an annual basis. For the 2006–2007 flu season, the Advisory Committee on Immunization Practices recommendations for influenza vaccination began including healthy children aged 24–59 months (two to four years), a shift that added 10.6 million children to the target group.

Canada has a parallel federal organization, the National Advisory Committee on Immunization, which is responsible for guiding the use of vaccines. Recommendations made by the National Advisory Committee on Immunization and the Advisory Committee on Immunization Practices around seasonal influenza vaccination was concordant until the 2006–2007 season. Starting in the 2010–2011 season, the National Advisory Committee on Immunization has further expanded its recommendations to additional pediatric age groups by including two- to four-year-olds for targeted seasonal influenza vaccination.

We took advantage of this divergence in policy between two neighboring countries with similar annual seasonal influenza epidemics to try to understand the effects of the

policy change in the United States to expand influenza vaccination coverage to other pediatric populations.

Objective

The objective of this study is to estimate the effect of expanding recommendations for routine seasonal influenza vaccination to include 24–59-month-old children.

During the spring of 2009, a public health emergency was declared in response to the emergence of the 2009 Influenza A (H1N1) virus. Owing to the response, timely data were needed to improve situational awareness and to inform public health officials. Traditional influenza surveillance is time-consuming and resource intensive, and electronic data sources are often more timely and resource saving. Collaboration began between the Centers for Disease Control and Prevention (CDC), the International Society for Disease Surveillance, and the Public Health Informatics Institute to expand syndromic Emergency Department (ED) surveillance through the Distribute project.

Distribute collects aggregate, daily or weekly reports of influenza-like illness (ILI) and total patient visits to EDs from participating health jurisdictions, stratified by age group and other variables. Additional variables included the three digit zip code of the patient’s residence as well as the disposition and temperature, however not all jurisdictions collect these variables. Distribute data are typically extracted from ED-based electronic health data systems. The ILI definition is determined by the participating jurisdiction that can be a city, county, or state. At the time of analysis, the network consisted of 33 jurisdictions.

Because ILI data reported to Distribute had not been systematically compared with data reported through other surveillance systems, CDC planned an evaluation of the Distribute data, which included a comparison to the Influenza-like Illness Network (ILINet). 

ILINet is a collaborative effort between the CDC, local and state health departments and primary health care providers. The network currently consists of approximately 3000 healthcare providers in all 50 states, Chicago, the District of Columbia, New York City, and the US Virgin Islands. Enrolled providers send CDC weekly reports via internet or fax that consist of the total number of patients seen for any reason and the number of those patients with ILI by age group. ILI is defined as fever (temperature of X1001F (37.8 1C)) and a cough and/or sore throat in the absence of a known cause other than influenza.

Objective

To compare ILI data reported to the Distribute surveillance project to data from an existing influenza surveillance system, the US Outpatient ILINet.