Adjusting Diabetes Care Quality Measures for Patient Complexity
Author Information
Author(s): Monika M Safford, Michael Brimacombe, Quanwu Zhang, Mangala Rajan, Minge Xie, Wesley Thompson, John Kolassa, Miriam Maney, Leonard Pogach
Primary Institution: Deep South Center on Effectiveness at Birmingham VA Medical Center and University of Alabama at Birmingham
Hypothesis
How does adjusting hemoglobin A1c levels for patient complexity affect quality comparisons in diabetes care?
Conclusion
Adjusting for patient complexity significantly alters the identification of best and worst performing medical centers in diabetes care.
Supporting Evidence
- Adjustment for complexity led to significant changes in the ranking of medical centers.
- The model explained 8.3% of the variance in A1c levels.
- Many medical centers shifted from the top to the bottom ranks and vice versa after adjustment.
Takeaway
When doctors look at how well they are treating diabetes, they should consider how complicated each patient's situation is, because it can change who is doing well and who isn't.
Methodology
This cross-sectional observational study analyzed national VA data on diabetic veterans, adjusting A1c levels for patient complexity factors like age, marital status, comorbidities, and insulin use.
Potential Biases
Potential biases may arise from the reliance on administrative data and the exclusion of certain patient characteristics.
Limitations
The study could not include all aspects of patient complexity, particularly behavioral, cultural, and environmental factors.
Participant Demographics
The study sample had a mean age of 64 years, with 63% married, and 98% male.
Statistical Information
P-Value
0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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