OTC Data

The New York State Department of Health (NYSDOH) Syndromic Surveillance System consists of five components: 1. Emergency Department (ED) Phone Call System monitors unusual events or clusters of illnesses in the EDs of participating hospitals; 2. Electronic ED Surveillance System monitors ED chief complaint data; 3. Medicaid data system monitors Medicaid-paid over-the-counter and prescription medica-tions; 4. National Retail Data Monitor/Real-time Outbreak and Disease Surveillance System monitors OTC data; 5. CDC’s BioSense application monitors Department of Defense and Veterans Administration outpatient care clinical data (ICD-9-CM diag-noses and CPT procedure codes), and LabCorp test order data.

Objective

This poster presentation provides an overview of the NYSDOH Syndromic Surveillance System, including data sources, analytic algorithms, and resulting reports that are posted on the NYSDOH Secure Health Commerce System for access by state, regional, county, and hospital users.

The New York State Department of Health (NYSDOH) currently applies EARS’s CuSum analyses to Medicaid Over the Counter and Prescription Medications data obtained from the Office of Medicaid Management's data warehouse. Daily drug category counts are compared with counts for a 7-day baseline period to generate C1, C2, and C3 signals for 62 counties and 8 Syndromic Surveillance Regions. Summary tables and graphs are posted on the NYSDOH Secure Health Commerce Network for access by state, regional, and county users.

The 7-day baseline CuSum method of analysis of syndromic surveillance databases can result in the generation of a large number of signals. Many signals are generated for counts that, upon manual review of 30-day or long-term trend graphs, are clearly within the range of normal daily variation and would not require follow up by public health authorities.

In order to prevent user desensitization to generated signals and minimize NYSDOH Syndromic Surveillance System end-user burden, supplemental measures that would indicate a daily count is higher than expected are currently being investigated.

Objective

To supplement CuSum analyses of syndromic surveillance databases within NYSDOH's Electronic Syndromic Surveillance System with other measures that would indicate a daily count is higher than expected in order to minimize the end-user burden of following up generated signals.

T-Cube is especially useful for rapidly retrieving responses to ad-hoc queries against large datasets of additive time series labeled using a set of categorical attributes. It can be used as a general tool to support any task requiring access to such data. From the application’s perspective it is transparent: it acts just like the database itself, but an incredibly quickly responding one. The authors had a chance to put T-Cubes into practical use as an enabling technology in applications requiring massive screening of multidimensional temporal data. These applications include two systems to support monitoring of food and agriculture safety and predictive analytics developed at the US Department of Agriculture and the Food and Drug Administration, as well as a system to monitor and forecast health of a fleet of aircraft operated by the US Air Force.

Objective

T-Cube, a data structure designed to efficiently represent large collections of temporal data has been shown to benefit surveillance applications involving monitoring sales of over-the-counter medications and emergency department visits. In this paper we present efficiencies which can be realized in practical applications of T-Cube beyond its original areas of deployment, and we advocate a widespread use of it as a technology which makes manual ad-hoc lookups as well as many kinds of complex automated analyses feasible.

Infection Control Law in Japan has asked doctors to cooperate in syndromic surveillance for pandemic flu and smallpox since 2007. However, doctors have to report by typing the number of patients on the web site, or by sending a fax to local public health centers. It imposes the heavy burden of reporting, and thus it has not worked well yet. Therefore, we need an automatic system for routine syndromic surveillance.

Objective

We performed some syndromic surveillance system for the Hokkaido Toyako G8 summit meeting in July 2008 in Japan as a counter-measure to bioterrorism attack or other health emergency. This presentation shows the workable syndromic surveillance systems in Japan.

Syndromic surveillance had been implemented in Dongcheng District with a view to probing into the feasibility of establishing a syndromic surveillance system in major Chinese cities, sieving syndromic surveillance indicators applicable to the eruption of infectious respiratory tract and digestive tract diseases, and attempting the operating method of data collection in different locations such as hospital and drug stores in Dongcheng of Beijing China.

Objective

The project has fund donated by World Bank under joint management of WHO and Ministry of Health of P.R.China , The target was try to build up a syndromic surveillance system in Beijing.

Syndromic surveillance using over the counter (OTC) sales has been shown to provide earlier signals of diarrheal and respiratory disease outbreaks than hospital diagnoses. Under normal circumstances, sales patterns of OTC sales related to gastrointestinal illness (GI) are high in the winter and low in the summer. The Canadian laboratory-based surveillance system that provides weekly counts of reportable bacterial, parasitic and viral isolates by province, has shown that bacterial and parasitic infections tend to be higher in summer and early fall, whereas viral infections (particularly Norovirus and Rotavirus) appear to peak in winter and spring. This suggests that the OTC sales reflect underlying community viral infections rather than bacterial or parasitic infections. If OTC sales are to be considered for use in syndromic surveillance of community GI, the nature of this relationship needs to be clarified. The main objective of this study was to compare temporal distributions of GI-related OTC sales to laboratory-isolate patterns of bacterial, parasitic and viral cases of human GI infections.

Objective

To assess if OTC sales of GI related medications are associated with temporal trends of reportable community viral, bacterial and parasitic infections.

To evaluate the potential of using the sales of Over the Counter (OTC) medicines for early detection of infections of public health concern, retrospective analysis of the sales of OTC common cold medications used for influenza-like illness (ILI) has been carried out in Japan since 2003. This presentation assess correlations and predictability of OTC sales to ILI for 2004-05 influenza season and compares with the results from 2003-04 season to discuss on robustness and versatility of OTC sales surveillance.

Monitoring sales of over-the-counter products is becoming increasingly common for purposes of public health surveillance. Sales data for anti-diarrheal medications have been used to monitor outbreaks of waterborne Cryptosporidium outbreaks. An attractive feature of is its focus upon coupling predictions of sales for a given day (based upon times series methods) with control chart methods from the field of statistical process control.

Objective

This paper suggests and illustrates several approaches to surveillance when data are available for several regions.

Bio-surveillance systems monitor multiple data streams (over-the-counter (OTC) sales, Emergency Department visits, etc.) to detect both natural disease outbreaks (e.g. influenza) and bio-terrorist attacks (e.g. anthrax re-lease). Many detection algorithms show impressive results under simulated environments, but the complex behavior of real-world data and high costs associated with processing false positives make it difficult to develop practical bio-surveillance systems. We believe that using expert knowledge from public health officials will help us to better understand the real-world data, improving our ability to distinguish actual disease outbreaks from non-outbreak patterns.

Objective

This paper describes the evolution of a bio-surveillance system that incorporates user feedback to improve system utility and usability. The system monitors national-level OTC pharmacy sales on a daily basis. We use fast spatio-temporal scan statistics to detect disease outbreaks.

A significant research topic in biosurveillance is how to group individual events—such as single emergency department (ED) visits and sales of over-thecounter healthcare (OTC) products—into counts of “similar” events. For OTC products, the goal is to find categories of individual products that have superior outbreak detection performance relative to categories that biosurveillance systems currently monitor. We have described a method to identify OTC categories that correlate more highly with disease activity than existing categories.1 However, it is an open question whether a category that correlates more highly—or according to some other model has a higher ‘association’—with disease activity than an existing category necessarily has superior detection performance. Here, we evaluate whether a linear regression procedure that clusters OTC products based on how well they ‘explain’ ED visits for influenzalike illness (ILI) can find categories with superior outbreak-detection performance for influenza.

Objective

To develop a procedure that identifies product categories with superior outbreak detection performance.