Validity of administrative data claim-based methods for identifying individuals with diabetes at a population level.

OBJECTIVES: This study assessed the validity of a widely-accepted administrative data surveillance methodology for identifying individuals with diabetes relative to three laboratory data reference standard definitions for diabetes.
METHODS: We used a combination of linked regional data (hospital discharge abstracts and physician data) and laboratory data to test the validity of administrative data surveillance definitions for diabetes relative to a laboratory data reference standard. The administrative discharge data methodology includes two definitions for diabetes: a strict administrative data definition of one hospitalization code or two physician claims indicating diabetes; and a more liberal definition of one hospitalization code or a single physician claim. The laboratory data, meanwhile, produced three reference standard definitions based on glucose levels +/- HbA1c levels.
RESULTS: Sensitivities ranged from 68.4% to 86.9% for the administrative data definitions tested relative to the three laboratory data reference standards. Sensitivities were higher for the more liberal administrative data definition. Positive predictive values (PPV), meanwhile, ranged from 53.0% to 88.3%, with the liberal administrative data definition producing lower PPVs.
CONCLUSIONS: These findings demonstrate the trade-offs of sensitivity and PPV for selecting diabetes surveillance definitions. Centralized laboratory data may be of value to future surveillance initiatives that use combined data sources to optimize case detection.