A Probabilistic Case-finding Algorithm for Chronic Disease Surveillance

Stephanie Brien, Luke Mondor, Nancy Mayo, David Buckeridge

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.


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



Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org