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A New Public Health Tool for Risk Assessment of Abnormal Glucose Levels

Supporting Files
File Language:
English


Details

  • Journal Article:
    Preventing Chronic Disease (PCD)
  • Personal Author:
  • Description:
    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.
  • Subjects:
  • Source:
    Prev Chronic Dis. 7(2).
  • ISSN:
    1545-1151
  • Pubmed ID:
    20158962
  • Pubmed Central ID:
    PMC2831788
  • Document Type:
  • Funding:
  • Volume:
    7
  • Issue:
    2
  • Download URL:
  • File Type:
    Filetype[PDF - 463.10 KB]
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:c64d46dc442f933557a05560989e7f39a218232f88277c4829ec3928561f30e7e3358c7b2ba8c81d63a38df95c84c37396b546877354f2e93a3a0a78ff05a57a
File Language:
English
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