20158962

PMC2831788

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.

Preventing Chronic Disease
CDC Public Access

3U32DP922744-05W1/DP/NCCDPHP CDC HHS/United States
UL1 RR024131/RR/NCRR NIH HHS/United States