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Open Access Research article

Positive predictive value of a case definition for diabetes mellitus using automated administrative health data in children and youth exposed to antipsychotic drugs or control medications: a Tennessee Medicaid study

William V Bobo1*, William O Cooper2, C Michael Stein3, Mark Olfson4, Jackie Mounsey1, James Daugherty5 and Wayne A Ray56

Author Affiliations

1 Department of Psychiatry, Vanderbilt University School of Medicine, 1500 21st Ave South, Suite 2200 Village at Vanderbilt, Nashville, TN, 37212, USA

2 Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN, 37212, USA

3 Division of Clinical Pharmacology, Department of Internal Medicine, Vanderbilt University School of Medicine, Nashville, TN, 37212, USA

4 Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA

5 Division of Pharmacoepidemiology, Department of Preventive Medicine, Vanderbilt University School of Medicine, Nashville, TN, 37212, USA

6 Geriatric Research, Education and Clinical Center, Veterans Administration Tennessee Valley Health Care System, Nashville, TN, 37212, USA

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BMC Medical Research Methodology 2012, 12:128  doi:10.1186/1471-2288-12-128

Published: 24 August 2012

Abstract

Background

We developed and validated an automated database case definition for diabetes in children and youth to facilitate pharmacoepidemiologic investigations of medications and the risk of diabetes.

Methods

The present study was part of an in-progress retrospective cohort study of antipsychotics and diabetes in Tennessee Medicaid enrollees aged 6–24 years. Diabetes was identified from diabetes-related medical care encounters: hospitalizations, outpatient visits, and filled prescriptions. The definition required either a primary inpatient diagnosis or at least two other encounters of different types, most commonly an outpatient diagnosis with a prescription. Type 1 diabetes was defined by insulin prescriptions with at most one oral hypoglycemic prescription; other cases were considered type 2 diabetes. The definition was validated for cohort members in the 15 county region geographically proximate to the investigators. Medical records were reviewed and adjudicated for cases that met the automated database definition as well as for a sample of persons with other diabetes-related medical care encounters.

Results

The study included 64 cases that met the automated database definition. Records were adjudicated for 46 (71.9%), of which 41 (89.1%) met clinical criteria for newly diagnosed diabetes. The positive predictive value for type 1 diabetes was 80.0%. For type 2 and unspecified diabetes combined, the positive predictive value was 83.9%. The estimated sensitivity of the definition, based on adjudication for a sample of 30 cases not meeting the automated database definition, was 64.8%.

Conclusion

These results suggest that the automated database case definition for diabetes may be useful for pharmacoepidemiologic studies of medications and diabetes.

Keywords:
Type 2 diabetes; Computer case definition; Health administrative data; Validity; Positive predictive value