Patient Complexity and Diabetes Quality of Care in Rural Settings

Abstract

Purpose Even though pay-for-performance programs are being rapidly implemented, little is known about how patient complexity affects practice-level performance assessment in rural settings. We sought to determine the association between patient complexity and practice-level performance in the rural United States. Basic procedures Using baseline data from a trial aimed at improving diabetes care, we determined factors associated with a practice’s proportion of patients having controlled diabetes (hemoglobin A1c ≤7%): patient socioeconomic factors, clinical factors, difficulty with self-testing of blood glucose, and difficulty with keeping appointments. We used linear regression to adjust the practice-level proportion with A1c controlled for these factors. We compared practice rankings using observed and expected performance and classified practices into hypothetical pay-for-performance categories. Main Findings Rural primary care practices (n = 135) in 11 southeastern states provided information for 1641 patients with diabetes. For practices in the best quartile of observed control, 76.1% of patients had controlled diabetes vs 19.3% of patients in the worst quartile. After controlling for other variables, proportions of diabetes control were 10% lower in those practices whose patients had the greatest difficulty with either self testing or appointment keeping (p < .05 for both). Practice rankings based on observed and expected proportion of A1c control showed only moderate agreement in pay-for-performance categories (κ = 0.47; 95% confidence interval, 0.32–0.56; p < .001). Principal Conclusions Basing public reporting and resource allocation on quality assessment that does not account for patient characteristics may further harm this vulnerable group of patients and physicians.

Quality of Diabetes Mellitus Care by Rural Primary Care Physicians

“…Third, patient complexity may explain a portion of the variability of diabetes control. We have previously found that glucose control (A1c ≤ 7%) was lower in the worst quartile of practices as compared to the best quartile of practices (19%‐23% vs. 75%‐76%) 31,32 . Practices in the worst quartile of control had higher proportions of younger patients, African Americans, and patients on insulin.…”

“…Practices in the worst quartile of control had higher proportions of younger patients, African Americans, and patients on insulin. Furthermore, practices in the worst quartile of control had a higher proportion of patients experiencing difficulty with self‐testing and keeping appointments 31 . Other studies have found that African Americans have higher A1c as compared to their white counterparts 33 and less self‐monitoring 34 .…”

“…We specifically selected patients who sought care at rural practices of primary care physicians (the physicians themselves were enrolled in a clinical trial 19 ). Generally, patients enrolled in clinical trials are healthier than non‐enrolled patients; however, in our study we had a large number of patients with multiple comorbidities, as documented by the Charlson's comorbidity index, 35 and a large number of practices with patients with uncontrolled diabetes 31 …”

“…We used generalized linear latent and mixed models (GLLAMM) and adjusted for group, period, interactions (group by time; intervention and control by before and after, respectively), age over 65, African American race, Medicaid insurance, insulin use, glucose self‐monitoring, peripheral vascular disease, adherence to appointments, and clustering effects of patients within practices. The choice of covariates was grounded in results from our prior work 19,31 . We did not compare other main outcomes, as the results of the second step in the statistical analysis were not significant.…”

Integrating patient complexity into health policy: a conceptual framework

“…Some argue that this already occurs; in theory, yes, but in practice not necessarily in a uniform and consistent manner. 82,89,91,92 An existing challenge surrounding the commissioning services to support complexity is the dual challenge of patient complexity and system complexity. These are often perceived as antagonistic; we argue that that the paradigm shift is to view these in a synergistic way.…”