A New and Simple Questionnaire to Identify People at Increased Risk for Undiagnosed Diabetes

William H Herman; Philip J Smith; Theodore J Thompson; Michael M Engelgau; Ronald E Aubert

doi:10.2337/diacare.18.3.382

Original Articles

A New and Simple Questionnaire to Identify People at Increased Risk for Undiagnosed Diabetes

William H Herman, MD, MPH,
Philip J Smith, PHD,
Theodore J Thompson, MS,
Michael M Engelgau, MD and
Ronald E Aubert, PHD

Epidemiology and Statistics Branch, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention Atlanta, Georgia

Address correspondence and reprint requests to William H. Herman, MD, MPH, Epidemiology and Statistics Branch, Division of Diabetes Translation (K-10), National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy., NE, Atlanta, GA 30341-3724.

Diabetes Care 1995 Mar; 18(3): 382-387. https://doi.org/10.2337/diacare.18.3.382

Abstract

OBJECTIVE To develop a simple questionnaire to prospectively identify individuals at increased risk for undiagnosed diabetes.

RESEARCH DESIGN AND METHODS People with newly diagnosed diabetes (n = 164) identified in the Second National Health and Nutrition Examination Survey and those with neither newly diagnosed diabetes nor a history of physician-diagnosed diabetes (n = 3,220) were studied. Major historical risk factors for undiagnosed non-insulin-dependent diabetes were defined, and classification trees were developed to identify people at higher risk for previously undiagnosed diabetes. The sensitivity, specificity, and predictive value of the classification trees were described and compared with those of an existing questionnaire.

RESULTS The selected classification tree incorporated age, sex, history of delivery of a macrosomic infant, obesity, sedentary lifestyle, and family history of diabetes. In a representative sample of the U.S. population, the sensitivity of the tree was 79%, the specificity was 65%, and the predictive value positive was 10%.

CONCLUSIONS This classification tree performed significantly better than an existing questionnaire and should serve as a simple, noninvasive, and potentially cost-effective tool for diagnosing diabetes in the U.S.