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Related Experiment Video

Updated: Feb 11, 2026

In Vivo Protocol of Controlled Subconcussive Head Impacts for the Validation of Field Study Data
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Identifying diabetes cases from administrative data: a population-based validation study.

Lorraine L Lipscombe1,2,3,4, Jeremiah Hwee5,6, Lauren Webster5

  • 1Women's College Research Institute, Women's College Hospital, 76 Grenville Street, Toronto, ON, M5S 1B1, Canada. Lorraine.Lipscombe@wchospital.ca.

BMC Health Services Research
|May 4, 2018
PubMed
Summary
This summary is machine-generated.

Validated algorithms accurately identify diabetes cases in health care databases. These methods enhance the study of diabetes trends and outcomes using administrative and electronic medical record data.

Keywords:
administrative databasesdiabeteselectronic medical record datavalidation methods

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Area of Science:

  • Health informatics
  • Chronic disease surveillance
  • Epidemiology

Background:

  • Health care administrative databases are valuable for studying chronic diseases like diabetes.
  • Accurate identification of diabetes cases is crucial for reliable research and surveillance.

Purpose of the Study:

  • To identify and validate optimal algorithms for detecting diabetes cases in health care administrative databases.
  • To ensure algorithms are suitable for diverse research needs, populations, and data sources.

Main Methods:

  • Linked Ontario health care administrative databases with primary care electronic medical records (EMRs).
  • Evaluated multiple adult diabetes case definitions based on data sources and time windows.

Main Results:

  • The optimal algorithm combined hospitalization/physician claims for diabetes with anti-diabetic medication prescriptions or specific fee codes (Sensitivity: 84.2%, Specificity: 99.2%, PPV: 92.5%).
  • Physician claims alone also showed strong performance: three claims within a year (Sensitivity: 79.9%, Specificity: 99.1%, PPV: 91.4%) or one claim anytime (Sensitivity: 93.6%, Specificity: 91.9%, PPV: 58.5%).

Conclusions:

  • Validated algorithms enable robust identification of diabetes cases in administrative health data.
  • These findings support the use of routinely collected health care data for diabetes research, trend analysis, and outcome studies.