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.