Assessing quality of diabetes care by measuring longitudinal changes in hemoglobin A1c in the Veterans Health Administration.

CONTEXT: A1c levels are widely used to assess quality of diabetes care provided by health care systems. Currently, cross-sectional measures are commonly used for such assessments. OBJECTIVE: To study within-patient longitudinal changes in A1c levels at Veterans Health Administration (VHA) facilities as an alternative to cross-sectional measures of quality of diabetes care. DESIGN: Longitudinal study using institutional data on individual patient A1c level over time (October 1, 1998-September 30, 2000) with time variant and invariant covariates. SETTING: One hundred and twenty-five VHA facilities nationwide, October 1, 1998-September 30, 2000. PATIENTS: Diabetic veteran users with A1c measurement performed using National Glycosylated Hemoglobin Standardization Project certified A1c lab assay methods. EXPOSURES: Characteristics unlikely to reflect quality of care, but known to influence A1c levels, demographics, and baseline illness severity. MAIN OUTCOME MEASURE: Monthly change in A1c for average patient cared for at each facility. RESULTS: The preponderance of facilities showed monthly declines in within-patient A1c over the study period (mean change of -0.0148 A1c units per month, range -0.074 to 0.042). Individual facilities varied in their monthly change, with 105 facilities showing monthly declines (70 significant at .05 level) and 20 showing monthly increases (5 significant at .05 level). Case-mix adjustment resulted in modest changes (mean change of -0.0131 case-mix adjusted A1c units per month, range -0.079 to 0.043). Facilities were ranked from worst to best, with attached 90 percent confidence intervals. Among the bottom 10 ranked facilities, four remained within the bottom decile with 90 percent confidence. CONCLUSIONS: There is substantial variation in facility-level longitudinal changes in A1c levels. We propose that evaluation of change in A1c levels over time can be used as a new measure to reflect quality of care provided to populations of individuals with chronic disease.

Mixed group ranks: preference and confidence in classifier combination.

Classifier combination holds the potential of improving performance by combining the results of multiple classifers. For domains with very large numbers of classes, such as biometrics, we present an axiomatic framework of desirable mathematical properties for combination functions of rank-based classifiers. This framework represents a continuum of combination rules, including the Borda Count, Logistic Regression, and Highest Rank combination methods as extreme cases [11], [23], [4], [13]. Intuitively, this framework captures how the two complementary concepts of general preference for specific classifiers and the confidence it has in any specific result (as indicated by ranks) can be balanced while maintaining consistent rank interpretation. Mixed Group Ranks (MGR) is a new combination function that balances preference and confidence by generalizing these other functions. We demonstrate that MGR is an effective combination approach by performing multiple experiments on data sets with large numbers of classes and classifiers from the FERET face recognition study.