Improvements in Ability to Detect Undiagnosed Diabetes by Using Information on Family History Among Adults in the United States

• Activity Number: 257
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Quanhe Yang*+ and Tiebin Liu and Ramal Mooneshinghe and Rodolfo Valdez and Muin Khoury
• Companies: CDC and CDC and CDC and CDC and CDC
• Keywords: National Health and Nutritional Examination Survey (NHANES) ; Risk prediction ; Odds ratio (OR) ; Population screening ; model fitting ; Confidence interval (CI)

Abstract

Using NHANES 1999-2004, we compared logistic regression models with established risk factors (model 1) with model 2 that also included family history (FH) of diabetes to examine improvements in detecting undiagnosed diabetes in population. Adjusted ORs for undiagnosed diabetes were 1.7 (95% CI: 1.2, 2.5) and 3.8 (95% CI: 2.2, 6.3) for those with moderate and high familial risk respectively. Model 2 was superior to model 1 in detecting undiagnosed diabetes, as reflected by several significant improvements, including c-statistics of 0.826 vs. 0.842 (p=0.001) and integrated discrimination improvement (IDI) of 0.012 (95% CI: 0.004, 0.030). Using a risk threshold of 7.3%, adding FH would identify additional 620,000 cases (95% CI: 221,100, 1,020,000). Our findings suggest that adding FH of diabetes can provide significant improvements in detecting undiagnosed diabetes in US population.