Implementation of a prediabetes identification algorithm for overweight and obese Veterans.

Journal Article

Type 2 diabetes prevention is an important national goal for the Veteran Health Administration (VHA): one in four Veterans has diabetes. We implemented a prediabetes identification algorithm to estimate prediabetes prevalence among overweight and obese Veterans at Department of Veterans Affairs (VA) medical centers (VAMCs) in preparation for the launch of a pragmatic study of Diabetes Prevention Program (DPP) delivery to Veterans with prediabetes. This project was embedded within the VA DPP Clinical Demonstration Project conducted in 2012 to 2015. Veterans who attended orientation sessions for an established VHA weight-loss program (MOVE!) were recruited from VAMCs with geographically and racially diverse populations using existing referral processes. Each site implemented and adapted the prediabetes identification algorithm to best fit their local clinical context. Sites relied on an existing referral process in which a prediabetes identification algorithm was implemented in parallel with existing clinical flow; this approach limited the number of overweight and obese Veterans who were assessed and screened. We evaluated 1,830 patients through chart reviews, interviews, and/or laboratory tests. In this cohort, our estimated prevalence rates for normal glycemic status, prediabetes, and diabetes were 29% (n = 530), 28% (n = 504), and 43% (n = 796), respectively. Implementation of targeted prediabetes identification programs requires careful consideration of how prediabetes assessment and screening will occur.

Full Text

Duke Authors

Cited Authors

  • Moin, T; Damschroder, LJ; Youles, B; Makki, F; Billington, C; Yancy, W; Maciejewski, ML; Kinsinger, LS; Weinreb, JE; Steinle, N; Richardson, C

Published Date

  • January 2016

Published In

Volume / Issue

  • 53 / 6

Start / End Page

  • 853 - 862

PubMed ID

  • 28273326

Electronic International Standard Serial Number (EISSN)

  • 1938-1352

International Standard Serial Number (ISSN)

  • 0748-7711

Digital Object Identifier (DOI)

  • 10.1682/jrrd.2015.06.0104

Language

  • eng