OBJECTIVE: To develop a risk adjustment method for HbA1c, based solely on administrative data and to determine the extent to which risk-adjusted HbA1c changes the identification of high- or low-performing medical facilities. RESEARCH DESIGN AND METHODS: Through use of pharmacy records, 204,472 diabetic patients were identified for federal fiscal year 1996 (FY96). Complete information (HbA1c levels, demographic data, inpatient records, outpatient pharmacy utilization records) was available on 38,173 predominantly male patients from 48 Veterans Health Administration (VHA) medical facilities. Hierarchical mixed-effects models were used to estimate risk-adjusted unique facility-level HbA1c. RESULTS: Predicted HbA1c demonstrated expected patterns for major factors known to influence glycemic control. Poorer glycemic control was seen in minorities and patients with greater disease severity, longer duration of disease (using treatment type or presence of amputation as surrogates), and more extensive comorbidity (measured by an adapted Charlson index). Better glycemic control was seen in Caucasians, older diabetic patients, and patients with higher outpatient utilization. The number of performance outliers was reduced as a result of risk adjustment. For mean HbA1c levels, 7 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. For high-risk HbA1c (>9.5%) rates, 12 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. CONCLUSIONS: Risk adjustment using only administrative data resulted in substantial changes in identification of high or low performers compared with non-risk-adjusted HbA1c. Although our findings are exploratory, risk adjustment using administrative data may be a necessary and achievable step in quality assessment of diabetes care measured by rates of high-risk HbA1c (>9.5%).
OBJECTIVE: To develop a risk adjustment method for HbA1c, based solely on administrative data and to determine the extent to which risk-adjusted HbA1c changes the identification of high- or low-performing medical facilities. RESEARCH DESIGN AND METHODS: Through use of pharmacy records, 204,472 diabeticpatients were identified for federal fiscal year 1996 (FY96). Complete information (HbA1c levels, demographic data, inpatient records, outpatient pharmacy utilization records) was available on 38,173 predominantly male patients from 48 Veterans Health Administration (VHA) medical facilities. Hierarchical mixed-effects models were used to estimate risk-adjusted unique facility-level HbA1c. RESULTS: Predicted HbA1c demonstrated expected patterns for major factors known to influence glycemic control. Poorer glycemic control was seen in minorities and patients with greater disease severity, longer duration of disease (using treatment type or presence of amputation as surrogates), and more extensive comorbidity (measured by an adapted Charlson index). Better glycemic control was seen in Caucasians, older diabeticpatients, and patients with higher outpatient utilization. The number of performance outliers was reduced as a result of risk adjustment. For mean HbA1c levels, 7 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. For high-risk HbA1c (>9.5%) rates, 12 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. CONCLUSIONS: Risk adjustment using only administrative data resulted in substantial changes in identification of high or low performers compared with non-risk-adjusted HbA1c. Although our findings are exploratory, risk adjustment using administrative data may be a necessary and achievable step in quality assessment of diabetes care measured by rates of high-risk HbA1c (>9.5%).
Authors: Manel Pladevall; L Keoki Williams; Lisa Ann Potts; George Divine; Hugo Xi; Jennifer Elston Lafata Journal: Diabetes Care Date: 2004-12 Impact factor: 19.112
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Authors: Amanda H Salanitro; Monika M Safford; Thomas K Houston; Jessica H Williams; Fernando Ovalle; Pamela Payne-Foster; Jeroan J Allison; Carlos A Estrada Journal: J Natl Med Assoc Date: 2011-03 Impact factor: 1.798
Authors: Sara A Quandt; Ronny A Bell; Beverly M Snively; Shannon L Smith; Jeanette M Stafford; Lindsay K Wetmore; Thomas A Arcury Journal: Ethn Dis Date: 2005 Impact factor: 1.847
Authors: Dan R Berlowitz; Arlene S Ash; Mark Glickman; Robert H Friedman; Leonard M Pogach; Audrey L Nelson; Ashley T Wong Journal: Health Serv Res Date: 2005-12 Impact factor: 3.402