Patient complexity in quality comparisons for glycemic control: An observational study

doi:10.1186/1748-5908-4-2

Implementation Science

Volume 4

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Safford MM
Brimacombe M
Zhang Q
Rajan M
Xie M
Thompson W
Kolassa J
Maney M
Pogach L

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Brimacombe M
Zhang Q
Rajan M
Xie M
Thompson W
Kolassa J
Maney M
Pogach L
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Research article

Patient complexity in quality comparisons for glycemic control: An observational study

Monika M Safford¹ , Michael Brimacombe²^,3 , Quanwu Zhang² , Mangala Rajan² , Minge Xie²^,4 , Wesley Thompson²^,4 , John Kolassa²^,4 , Miriam Maney² and Leonard Pogach²

¹Deep South Center on Effectiveness at Birmingham VA Medical Center and University of Alabama at Birmingham, Birmingham, AL, USA

²VA New Jersey Healthcare System, East Orange, NJ, USA

³University of Medicine and Dentistry of New Jersey-New Jersey Medical School, Newark, NJ, USA

⁴Rutgers University, Piscataway, NJ, USA

author email corresponding author email

Implementation Science 2009, 4:2doi:10.1186/1748-5908-4-2


Published:	6 January 2009

Abstract

Background

Patient complexity is not incorporated into quality of care comparisons for glycemic control. We developed a method to adjust hemoglobin A1c levels for patient characteristics that reflect complexity, and examined the effect of using adjusted A1c values on quality comparisons.

Methods

This cross-sectional observational study used 1999 national VA (US Department of Veterans Affairs) pharmacy, inpatient and outpatient utilization, and laboratory data on diabetic veterans. We adjusted individual A1c levels for available domains of complexity: age, social support (marital status), comorbid illnesses, and severity of disease (insulin use). We used adjusted A1c values to generate VA medical center level performance measures, and compared medical center ranks using adjusted versus unadjusted A1c levels across several thresholds of A1c (8.0%, 8.5%, 9.0%, and 9.5%).

Results

The adjustment model had R²= 8.3% with stable parameter estimates on thirty random 50% resamples. Adjustment for patient complexity resulted in the greatest rank differences in the best and worst performing deciles, with similar patterns across all tested thresholds.

Conclusion

Adjustment for complexity resulted in large differences in identified best and worst performers at all tested thresholds. Current performance measures of glycemic control may not be reliably identifying quality problems, and tying reimbursements to such measures may compromise the care of complex patients.