Case-detection

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. While this approach to defining case-detection algorithms is straightforward, it has limitations. For example, if more than simple combinations of one or two variables are used, then it becomes unwieldy to represent the algorithm and it can be difficult to identity how different variables in the definition contribute to detection accuracy. A multivariable probabilistic case-detection algorithm can address these problems and facilitate exploration of how the multiple variables available from different data sources might improve case-detection accuracy1. In this research, we develop an approach for probabilistic multivariable case-detection and apply the method to a cohort of older adults with known DM status to demonstrate and evaluate the method.

Objective

To develop and validate a multivariable probabilistic algorithm for detecting cases of diabetes mellitus (DM) using clinical and demographic data.

Syndromic surveillance can be a useful tool for the early recognition of outbreaks and trends in emergency department (ED) data. In addition, as a more timely data source than traditional disease reporting, syndromic data may also be leveraged to identify individual disease cases, increasing the utility for first time or redundant case recognition.

San Diego County (COSD) performs daily ED syndromic surveillance. In order to assess the utility for early identification of specific conditions of public health interest (e.g., salmonellosis, meningitis, hazardous exposures, heat-related illness), a novel process entitled Priority Infectious Conditions Capture, was developed.

Objective

This paper describes an assessment of an enhanced surveillance process used to identify reportable diseases and conditions of public health importance from ED chief complaint data in COSD.

Case detection from chief complaints suffers from low to moderate sensitivity. Emergency Department (ED) reports contain detailed clinical information that could improve case detection ability and enhance outbreak characterization. We developed a text processing system called Topaz that could be used to answer questions from ED reports, such as: How many new patients have come to the ED with acute lower respiratory symptoms? Of the respiratory patients, how many had a productive cough or wheezing? How many of the respiratory patients have a past history of asthma?

Objective

To evaluate how well a text processing system called Topaz can identify acute episodes of 55 clinical conditions described in ED notes.

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.

Communicable diseases are underreported by physicians, especially diseases without laboratory tests. The goals of our study were to determine reporting levels for clinical chickenpox, describe clinical data elements common to chickenpox, and assess ability of an electronic syndromic surveillance system, BioSense, to capture chickenpox cases.

Approximately one quarter of people treated for tuberculosis (TB) have no supporting microbiology, and thus are not detectable through laboratory reporting systems. Health departments depend upon clinicians to report these cases, but there is important underreporting. We previously described the performance of several algorithms for TB detection using electronic medical record (EMR) and claims data, and noted good sensitivity when screening for >2 anti-TB drugs; however, the positive predictive value was only 30%. We re-evaluated this and other algorithms in light of evolving TB treatment practices and enhanced ability to apply complex decision rules to EMR data in real time.

Objective

To develop algorithms for case detection of TB using EMR data to improve notifiable disease reporting.

Objective

We performed a gold-standard manual chart review for gastro-intestinal syndrome to evaluate automated detection models based on both structured and non-structured data extracted from the VA electronic medical record.