Performance status of health care facilities changes with risk adjustment of HbA1c.

Abstract

OBJECTIVE -To develop a risk adjustment method for HbA 1c based solely on administrative data and to determine the extent to which risk-adjusted HbA 1c 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 (HbA 1c 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 HbA 1c .RESULTS -Predicted HbA 1c 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 HbA 1c levels, 7 facilities that were initially identified as statistically significant outliers were no longer outliers after risk adjustment. For high-risk HbA 1c (Ͼ9.5%) 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 HbA 1c . 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 HbA 1c (Ͼ9.5%).

Assessing Quality of Diabetes Care by Measuring Longitudinal Changes in Hemoglobin A1c in the Veterans Health Administration

“…Additionally, if newer enrollees are healthier, cross-sectional measures can appear better for similar nonquality reasons. Although currently used crosssectional measures of quality of diabetes care have made major contributions and demonstrable progress toward improvement, they cannot evaluate continued health care given over time to individuals with chronic diseases (Zhang et al 2000;Weiner and Long 2004). The longitudinal approach presented here measures changes in A1c over time within individuals, effectively overcoming some of the cross-sectional problems and capturing a new domain in quality measurement.…”

“…This index has been adapted for use in administrative data (Deyo, Cherkin, and Ciol 1992), which we used to construct Charlson index scores to capture general illness burden. Charlson score was used as a time invariant measure of comorbidity, similar to other reports (Berlowitz et al 1998;Chin et al 2000;Zhang et al 2000;Safford et al 2003). …”

“…Age has been associated with A1c levels, with older individuals having lower values (Harris et al 1999;Zhang et al 2000). Therefore, we included age at onset of the study period as a baseline adjuster.…”

“…Race was categorized as white, African American, Hispanic, Native American, Asian, other, and unknown. Marital status (married or not married) was included because it has been associated with A1c levels in older men (Zhang et al 2000;Safford et al 2003) and may reflect social support.…”

“…This reflects in part the limitation of currently available treatments and in part other factors such as patient age or severity of diabetes. To account for this, A1c levels can be case-mix adjusted to result in more meaningful quality-of-care assessments (Iezzoni 1997;Zhang et al 2000). However, case-mix adjustment methodology is not yet well established for intermediate health outcomes such as A1c levels, and most attention has been focused on cross-sectional analyses.…”