Screening for impaired fasting glucose and diabetes using available health plan data.

AIMS: To develop and validate prediction equations to identify individuals at high risk for type 2 diabetes using existing health plan data.
METHODS: Health plan data from 2005 to 2009 from 18,527 members of a Midwestern HMO without diabetes, 6% of whom had fasting plasma glucose (FPG) ≥110mg/dL, and health plan data from 2005 to 2006 from 368,025 members of a West Coast-integrated delivery system without diabetes, 13% of whom had FPG ≥110mg/dL were analyzed. Within each health plan, we used multiple logistic regression to develop equations to predict FPG ≥110mg/dL for half of the population and validated the equations using the other half. We then externally validated the equations in the other health plan.
RESULTS: Areas under the curve for the most parsimonious equations were 0.665 to 0.729 when validated internally. Positive predictive values were 14% to 32% when validated internally and 14% to 29% when validated externally.
CONCLUSION: Multivariate logistic regression equations can be applied to existing health plan data to efficiently identify persons at higher risk for dysglycemia who might benefit from definitive diagnostic testing and interventions to prevent or treat diabetes.