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Identifying Optimal Survey-Based Algorithms to Distinguish Diabetes Type Among Adults with Diabetes (Journal of Clinical and Translational Endocrinology)

Publication Topics

California Health Interview Survey; Diabetes; Health Status and Conditions; Chronic Condition Prevalence

Publication Type

CHIS Journal Article

Publication Date


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<a onclick="OpenPopUpPage('http:\u002f\\u002f_layouts\u002flistform.aspx?PageType=4\u0026ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}\u0026ID=1796\u0026RootFolder=*', RefreshPage); return false;" href=";ListId={7AAD61FA-4BCB-48C0-B0B7-87AFDC3673EF}&amp;ID=1796&amp;RootFolder=*">Jennifer G. Nooney</a>

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Summary: Surveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, authors linked survey data collected from adult patients with diabetes to a gold standard diabetes type.

Authors collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. Authors tested 34 survey classification algorithms utilizing information on respondents’ report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of electronic health records (EHR) data and endocrinologist review of selected cases.

Findings: Algorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as “other diabetes type” all respondents who reported “other,” and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations.

The major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.

This study cites the California Health Interview Survey.


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Journal Article: Identifying Optimal Survey-Based Algorithms to Distinguish Diabetes Type Among Adults with Diabetes

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Press Release

Related Link 1

California Health Interview Survey (CHIS)

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Version: 3.0
Created at 10/28/2020 2:12 PM by i:0#.f|uclachissqlmembershipprovider|celeste
Last modified at 11/3/2020 8:18 PM by i:0#.f|uclachissqlmembershipprovider|venetia