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Leveling the Field: Addressing Health Disparities Through Diabetes Disease Management
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Leveling the Field: Addressing Health Disparities Through Diabetes Disease Management

Richard O. White, MD; Darren A. DeWalt, MD, MPH; Robert M. Malone, PharmD; Chandra Y. Osborn, PhD, MPH; Michael P. Pignone, MD, MPH; and Russell L. Rothman, MD, MPP

A subanalysis of a successful algorithm-driven primary care–based diabetes disease management program examines the relationships among patient characteristics, labor inputs, and improvement in A1C level.

The etiology of racial/ethnic and other socioeconomic disparities in diabetes and other chronic illness care is likely multifactorial. Disparities in provider behavior may be an important component. Sequist and colleagues22 recently conducted a provocative study in which they reported on variations in diabetes-related outcomes at the level of individual physicians. They examined a 14-site primary care group practice that used an integrated electronic medical record system and found significant within-physician variation between the percentages of white and black patients who were able to achieve recommended levels of glycemic, blood pressure, and lipid control. Patient sociodemographic factors accounted for a relative range of 13% to 38% of the observed racial/ethnic differences, whereas within-physician effects (ie, racial/ethnic disparities observed within the same physician panel) accounted for 68% to 75% of the observed differences. A study by Schulman and colleagues23 that surveyed 720 physicians eliciting recommendations regarding cardiac catheterization found that sex and race/ethnicity independently influenced how physicians managed their patients, with many physicians surveyed being less likely to recommend needed catheterizations among women or nonwhite patients.

One reason for the success of our program in improving care regardless of socioeconomic status may be the ability of disease management to eliminate potential disparities in provider-level behavior. The nondiscriminatory nature of providing algorithm-based care may serve as a means by which health systems could ensure delivery of effective and unbiased care, while accounting for and addressing the frequent special needs that arise when caring for traditionally vulnerable patient populations. As we observed in our program, this could be achieved through management practices that allowed for patient-centered approaches to care as individualized needs become evident (described herein). The use of a team approach to care with midlevel providers and a care coordinator working together with primary care physicians may also help to prevent provider-level disparities.

Our disease management team spent slightly more time among patients with lower income or no private insurance. This additional time may have been related to addressing economic barriers to attaining medications and other clinical care. More research is needed to better understand the resources required to achieve successful outcomes across all priority groups. In the present analysis, we also found that women and participants with less education showed slightly greater benefit from the intervention. Greater improvement in glycemic control among patients with less education and among women is more likely to reflect differences by intervention status rather than by education or sex. This was supported by the absence of an interaction effect between these variables and patient status with A1C level and should be contrasted with the previously reported analysis showing that patients having lower literacy had significantly more improvement in glucose control compared with those having higher literacy.20 Significant improvement in glycemic control based on literacy level may have been related to the fact that our intervention included several approaches to address literacy, including the use of simplified communication, the “teach back technique” for confirming patient understanding, lowerliteracy materials, and a care coordinator who confronted many social barriers.20 An approach in which individualized physician care of patients with diabetes is supplemented with an adequately tailored and focused chronic DDMP may be an important approach in eliminating disparate trends.

Several limitations were inherent in our study. Our sample size was small, and we may have lacked adequate power to detect differences based on patient characteristics alone. In addition, our documented labor inputs characterize time spent with patients by the disease management team and do not include time spent by primary care providers or the patients or utilization of clinical services or other resources. Furthermore, the time spent by the management team included some actions that were considered “outside” of the algorithm, such as discussion of certain medication changes with providers. As previously reported,21 discussion with providers was required for 112 of 500 drug titrations and for 233 of 348 new drug additions. The time required by such actions would be difficult to predict if our program was repeated. The generalizability of our findings is also reduced by the fact that our study was conducted at a single-site academic institution and did not have adequate representation of several racial/ethnic minorities such as Latinos. Furthermore, our participants at entry had poorly controlled diabetes, and it is arguable that the magnitude of our findings would be lessened if repeated in a population with better baseline control. Finally, although we believe that the algorithm-based care was a major factor contributing to the balanced improvement in A1C level across the priority groups, it remains a possibility that our findings were influenced by regression toward the mean or by the fact that our study did not find many clinically significant racial/ethnic disparities on baseline evaluation of our study population. One may argue that, if such disparities were in fact observed, our results may have differed in their magnitude. There may be other factors associated with the intervention that could have contributed to the similar improvements. For example, we focused on patient self-management skills and provided assistance with obtaining medications. These efforts combined with aggressive treatment using algorithmbased care might have had synergistic effects among certain individuals.

We do not go so far as to suggest that our program has eliminated racial/ethnic disparities but are confident that none were created among participants who received our intervention. A disease management program integrated into primary care can be a successful model for improving care among vulnerable patients with significant barriers. Emphasis on algorithm-based treatment and on team approaches to care can result in improvements regardless of race/ethnicity, sex, education, or other characteristics and represents an important tool for addressing healthcare disparities. Future research about the role of disease management to address health disparities in diabetes and other chronic illnesses is clearly indicated.

Author Affiliations: From the Division of General Internal Medicine (ROW), Meharry Medical College, Nashville, TN; the Division of General Medicine and Epidemiology (DAD, RMM, MPP), and the Division of Pharmacy Practice and Experiential Education (RMM), University of North Carolina at Chapel Hill; and the Division of General Internal Medicine and Public Health (CYO, RLR), Vanderbilt University Medical Center, Nashville, TN.

Funding Source: This study was completed with support from the Robert Wood Johnson Clinical Scholars Program, the University of North Carolina (UNC) Program on Health Outcomes, the UNC Division of General Internal Medicine, the Vanderbilt Center for Health Services Research, and the Vanderbilt Diabetes Research and Training Center (P60 DK020593), by grant NCRR NIH 5R25RR017577 from Meharry CRECD program (Dr White), and by grant  R25RR017577-08S1 from ARA Supplement (Dr White).

Author Disclosure: Dr Rothman reports serving on the Clear Health Communication Fellowship Advisory Board for Pfizer, Inc. The other authors (ROW, DAD, RMM, CYO, MPP) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DAD, RMM, MPP, RLR); acquisition of data (DAD, RMM, MPP); analysis and interpretation of data (ROW, DAD, MPP, RLR); drafting of the manuscript (ROW, CYO, RLR); critical revision of the manuscript for important intellectual content (ROW, DAD, CYO, MPP, RLR); statistical analysis (ROW, RLR); provision of study materials or patients (RLR); obtaining funding (RLR); administrative, technical, or logistic support (RMM, MPP); and supervision (RLR).

Address correspondence to: Richard O. White, MD, Division of General Internal Medicine, Meharry Medical College, 1005 Dr DB Todd Jr Blvd, Nashville, TN 37208. E-mail: rwhite@mmc.edu.

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