A New Public Health Tool for Risk Assessment of Abnormal Glucose Levels

INTRODUCTION: Self-reported prediabetes and diabetes rates underestimate true prevalence, but mass laboratory screening is generally impractical for risk assessment and surveillance. We developed the Abnormal Glucose Risk Assessment-6 (AGRA-6) tool to address this problem. METHODS: Self-report data were obtained from the 1,887 adults (18 years or older) in the National Health and Nutrition Examination Survey (NHANES) 2005-2006 with fasting plasma glucose and oral glucose tolerance tests. We created AGRA-6 models by using logistic regression. Performance was validated with NHANES 2005-2006 data by using leave-1-out cross-validation. Standard performance characteristics (sensitivity, specificity, predictive values, area under receiver-operating characteristic curves) were assessed, as was the potential efficiency of the models to reduce laboratory testing in screening efforts. RESULTS: Performance was good for all models under testing conditions. Use of the AGRA-6 in screening efforts could reduce laboratory testing by at least 30% when sensitivity is maximized and at least 52% when sensitivity and specificity are balanced. CONCLUSION: The AGRA-6 appears to be an effective, feasible tool that uses self-reported data compatible with the Behavioral Risk Factor Surveillance System to assess population-level prevalence, identify abnormal glucose levels, optimize screening efforts, and focus interventions to reduce the prevalence of abnormal glucose levels.