A Probabilistic Case-finding Algorithm for Chronic Disease Surveillance

There is a clear need for improved surveillance of chronic diseases to guide public health practice and policy. Chronic disease surveillance has tended to use administrative data, due to the need to link encounters for an individual over time and to have complete capture of all encounters. Case-detection algorithms generally combine variables found in the data using Boolean operators (i.e., AND, OR, NOT). For example, a commonly used algorithm for DM surveillance requires a patient to have one hospitalization or two physician visits within two years with a diagnostic code for DM.

August 22, 2018

Modeling Baseline Shifts in Multivariate Disease Outbreak Detection

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.

Objective:

June 25, 2018

Time of Arrival Analysis in NC DETECT to Find Clusters of Interest from Unclassified Patient Visit Records

TOA identifies clusters of patients arriving to a hospital ED within a short temporal interval. Past implementations have been restricted to records of patients with a specific type of complaint. The Florida Department of Health uses TOA at the county level for multiple subsyndromes (1). In 2011, NC DPH, CCHI and CDC collaborated to enhance and evaluate this capability for NC DETECT, using NC DETECT data in BioSense 1.0 (2).

August 20, 2018

Using the Flow of People in Cluster Detection and Inference

The traditional SaTScan algorithm[1],[2] uses the euclidean dis- tance between centroids of the regions in a map to assemble a con- nected (in the sense that two connected regions share a physical border) sets of regions. According to the value of the respective log- arithm of the likelihood ratio (LLR) a connected set of regions can be classified as a statistically significant detected cluster.

July 17, 2018

Another Type of Cluster Monitoring: Detection of Groups of Anomalous Patient Residence Locations

The Veterans Health Administration (VHA) uses the Electronic Surveillance System for the Early Notification of Community-based Epidemics to detect disease outbreaks and other health-related events earlier than other forms of surveillance. Although Veterans may use any VHA facility in the world, the strongest predictor of which health care facility is accessed is geographic proximity to the patient's residence. A number of outbreaks have occurred in the Veteran population when geographically separate groups convened in a single location for professional or social events.

May 02, 2019

Automated detection of data entry errors in a real time surveillance system

Real-Time Biosurveillance Program (RTBP) introduces modern surveillance technology to health departments in Sri Lanka and Tamil Nadu, India. Triage data from each patient visit (basic demographics, signs, symptoms, preliminary diagnoses) is recorded on paper at health facilities. Case records are transmitted daily to a central database using the RTBP mobile phone application. It is done by medical professionals in India, but in Sri Lanka, due to staffing constraints, the same duty is performed by lower cost personnel with limited domain knowledge.

June 18, 2019

Using administrative databases to identify cases of chronic kidney disease: a systematic review

CKD is currently the ninth leading cause of death in the United States. The prevalence of end-stage renal disease, the most severe form of CKD, has doubled in the last decade.1 Early detection and treatment of CKD is critical to slowdown the progression of the disease and to decrease the risk of other chronic conditions, such as cardiovascular disease.2 One accessible and cost-effective method for health research activities involves use of medical administrative databases, such as insurance claims databases and institutional medical record systems.

June 25, 2019

Evaluating the performance of two alternative geographic surveillance schemes

Influenza-like illness (ILI) data is collected by an Influenza Sentinel Provider Surveillance Network at the state (Iowa, USA) level. Historically, the Iowa Department of Public Health has maintained 19 different influenza sentinel surveillance sites.

June 17, 2019

Assessing address data quality for public health surveillance in Montreal

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.

June 18, 2019

Free-Text Processing To Enhance Detection Of Acute Respiratory Infections

Objective

We asked to what extent computerized processing of the full free-text clinical documentation could enhance syndrome detection compared to the sole use of structured data elements from a comprehensive electronic medical record.

July 30, 2018

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