A Probabilistic Case-finding Algorithm for Chronic Disease Surveillance
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How to Cite

Brien, S., Mondor, L., Mayo, N., & Buckeridge, D. (2014). A Probabilistic Case-finding Algorithm for Chronic Disease Surveillance. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5015

Abstract

We developed and validated a multivariable probabilistic case-detection model to detect known cases of diabetes mellitus (DM) using clinical and demographic data. We applied our method to a cohort of older adult residents of the region of Sherbrooke, Quebec. Predictors were added to a logistic regression model and internally validated using a 2:1 split sample approach. Models were compared using measures goodness of fit, discrimination and accuracy. The best model incorporated all predictors into the model: male sex, age, at least one hospitalization, physician visit and drug dispensed for diabetes.

https://doi.org/10.5210/ojphi.v6i1.5015
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