The American Journal of Managed Care January 2010
Leveling the Field: Addressing Health Disparities Through Diabetes Disease Management
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.
Objectives: To examine the relationships among patient characteristics, labor inputs, and improvement in glycosylated hemoglobin (A1C) level in a successful primary care–based diabetes disease management program (DDMP).
Study Design: We performed subanalyses to examine the relationships among patient characteristics, labor inputs, and improvement in A1C level within a randomized controlled trial. Control patients received usual care, while intervention patients received usual care plus a comprehensive DDMP.
Methods: The primary outcome was improvement in A1C level over 12 months stratified by intervention status and patient characteristics. Process outcomes included the number of actions or contacts with patients, time spent with patients, and number of glucose medication titrations or additions.
Results: One hundred ninety-three of 217 enrolled patients (88.9%) had complete 12-month followup data. Patients in the intervention group had significantly greater improvement in A1C level than the control group (−2.1% vs −1.2%, P = .007). In multivariate analysis, no significant differences were observed in improvement in A1C level when stratified by age, race/ethnicity, income, or insurance status, and no interaction effect was observed between any covariate and intervention status. Among intervention patients, we observed similar labor inputs regardless of age, race/ethnicity, sex, education, or whether goal A1C level was achieved.
Conclusions: Among intervention patients in a successful DDMP, improvement in A1C level was achieved regardless of age, race/ethnicity, sex, income, education, or insurance status. Labor inputs were similar regardless of age, race/ethnicity, sex, or education and may reflect the nondiscriminatory nature of providing algorithm-based disease management care.
(Am J Manag Care. 2010;16(1):42-48)
Incorporation of a diabetes disease management program (DDMP) into the primary care setting can aid in addressing diabetes care disparities, reflecting the nondiscriminatory nature of algorithm-based care.
- Agency for Healthcare Research and Quality–defined priority populations experience worse quality and access to care than their respective reference groups.
- Little is known about the ability of DDMPs to deliver unbiased care.
- Our DDMP demonstrated significant reduction in glycosylated hemoglobin (A1C) level among participants regardless of race/ethnicity, sex, education, or insurance status.
- Labor inputs were similar regardless of age, race/ethnicity, sex, education, or whether goal A1C level was achieved.
The Agency for Healthcare Research and Quality (AHRQ) has identified several priority groups that serve as targets for health disparities research and quality improvement. These groups include racial/ethnic minorities, persons with low incomes, women, children, older adults, those with disabilities, and rural inhabitants.5,6 In general, members of these priority groups along with those who have less education and no private insurance are more likely to receive poorer quality of care and to experience greater barriers in terms of access to care compared with their respective counterparts.5 For example, compared with white patients,7 Hispanic and lower-income patients with diabetes mellitus are less likely to receive the recommended services for diabetes care such as timely measurement of lipid levels and glycosylated hemoglobin (A1C) levels, and black patients have higher rates of many of the complications of diabetes such as admissions for lower extremity amputations.8 Although studies performed by Trivedi et al9 and by Chin et al10 have demonstrated that quality improvement measures can lead to reduced disparities in some diabetes-related processes of care, it has been more difficult to influence outcome measures such as attainment of goal blood glucose control (as measured by A1C level).11 Findings from recent studies9,12 have also suggested that disparities in diabetes care may be related to patient characteristics, provider characteristics, or system-level factors.
Disease management programs have been shown to be successful in improving care for patients with diabetes,13-17 and there is growing interest in the usefulness of such models in addressing healthcare disparities.12,18,19 However, little is known about the specific effect of disease management programs on disparate trends and particularly on their ability to provide unbiased care and to improve patient outcomes equally across priority groups. Results of a successful primary care–delivered algorithm-based diabetes disease management program (DDMP) have been previously reported.16 Evaluation of this program within a randomized controlled trial demonstrated significant improvements in A1C level, blood pressure, aspirin use, patient satisfaction, and other measures.16 The program was also found to require modest labor inputs.16,20,21 In the present study, we performed a subanalysis of this program to examine the relationships among patient characteristics, labor inputs, and improvement in A1C level, with a particular focus on evaluating the potential usefulness of programs such as this to influence the care and outcomes for patients considered to be within an AHRQ-defined priority group and for patients with less education or no private insurance.
We performed a randomized controlled trial of a DDMP among 217 patients with type 2 diabetes mellitus and poor glucose control (A1C level ≥8%) in an academic primary care practice. Details of the trial have been previously published.16,20,21 Patients in the intervention group received care from their primary care provider supplemented by a DDMP. The program included intensive management from clinical pharmacists and from a diabetes care coordinator who provided diabetes education, applied algorithms for treating glucose level abnormalities and decreasing cardiovascular risk, used an electronic registry to proactively identify patients in need of additional care, and addressed barriers to care. Treatment algorithms provided guidance on when to contact patients and information about titration or addition of new medications for glucose control and cardiovascular risk reduction. Control patients received a one-time disease management session from pharmacists, followed by usual care from their primary care provider. The primary outcome was improvement in A1C level over 12 months. The study was performed and subsequently analyzed with approval from the University of North Carolina at Chapel Hill and Vanderbilt University institutional review boards. Written consent was obtained from all study participants.
To estimate resources required for the DDMP, the study team documented process measures for all intervention patients. These “labor inputs” included the following: (1) minutes spent by the disease management team in direct contact with a patient or in activities related to patient care, (2) number of contacts with a patient, and (3) number of patientspecific glucose medication titrations or additions. These measures captured the labor of the DDMP and did not include any actions taken by the patient’s primary care providers.
All analyses were performed using commercially available statistical software (Stata 8.0; StataCorp LP, College Station, TX), and a 2-sided significance level of 5% was used for statistical inference. Baseline differences between intervention and control groups were determined using Wilcoxon rank sum test for continuous outcome variables and X2 test for categorical outcome variables. We examined the relationship between patient characteristics and improvement in the median A1C level using multivariate quantile regression analysis. Quantile regression modeling was chosen for this subanalysis because we report the median changes for each outcome of interest. The median reporting was required because of additional data stratification and subsequent changes in sample sizes. This should be contrasted with how the program was evaluated in previous publications.16,21 For each patient characteristic (age, race/ethnicity, sex, education, income, and nsurance status), we performed a separate model that included the study status, the patient characteristic in question, and an interaction term to evaluate the potential modifying effect of intervention status and the patient characteristic in question on change in A1C level. Patient characteristics were dichotomized based on priority criteria of the 2007 National Healthcare Disparities Report5 (with age dichotomized at 65 years [older adults], race/ethnicity as white vs nonwhite, and household annual income at $10,000). Education was dichotomized at less than high school and insurance as private versus nonprivate. We used Wilcoxon rank sum test to examine the relationship between each of the labor inputs (median time spent with patients, median number of actions or contacts with patients, or median number of glucose medication additions or titrations) and each of the aforementioned patient characteristics. We also used Wilcoxon rank sum test to analyze the relationship between each of 3 labor inputs and attainment of goal A1C level (≤7%) at 12 months of follow-up. Finally, we examined the relationship between each of 3 labor inputs and improvement in the median A1C level using multivariate quantile regression models for each labor input, with each model adjusting for the aforementioned patient characteristics.
From February 2001 to April 2002, 217 patients were enrolled. Complete follow-up data were available for 193 patients (88.9%) at 12 months. Baseline demographics revealed diversity, with substantial sociodemographically vulnerable participants having poor diabetes control. Most nonwhite participants were African American. Intervention and control patients generally had similar characteristics (Table 1). The A1C levels improved significantly more for intervention than control patients during the trial (Figure). From baseline to 12 months, control patients had a median 1.2% decrease in A1C level, whereas intervention patients had a median 2.1% decrease (difference, 0.9%; 95% confidence interval, 0.6%-1.3%; P = .007).
There was no significant difference in improvement in the median A1C level when stratified by age, race/ethnicity, sex, income, or insurance status (Figure and Table 2). There was slightly greater improvement in the median A1C level among women compared with men (−1.2% vs −0.5%) and among those with less than a high school education compared with those with at least a high school education (−1.1% vs −0.8%). Results of multivariate quantile regression analysis did not reveal any significant interaction effect on improvement in A1C level between participants’ intervention status and each of the respective patient characteristics of interest.
Among intervention patients, there were no differences in labor inputs (median time spent with patients, median number of actions or contacts with patients, or median number of glucose medication titrations or additions) when stratified by age, race/ethnicity, sex, or education (Table 3). The disease management team had significantly more contacts and spent more time with patients having a household annual income less than $10,000 or no private insurance. There were no observed differences in each of 3 labor inputs when stratified by patients obtaining the goal A1C level of 7% or less at the end of the trial. In multivariate analysis, there was no significant relationship between each of 3 labor inputs and improvement in the median A1C level when adjusted for age, race/ethnicity, sex, education, income, and insurance status.
In this subanalysis of our DDMP, a successful reduction in blood glucose (A1C) level was achieved irrespective of race/ethnicity and other important sociodemographic factors. Our findings were not influenced by the observed differences at baseline in terms of age and insurance status between the control and intervention groups. Also, none of the priority sociodemographic factors analyzed had a significant modifying effect with DDMP participation on improvement in A1C level. The time spent by the disease management team for intervention patients was provided similarly regardless of age, race/ethnicity, sex, or education. Improvement in A1C level did not require additional labor inputs compared with patients who did not show significant improvement when adjusted for patient characteristics. Our findings suggest that DDMPs may serve as a tool for providing equal quality of care and for addressing the current disparate trends in care often experienced by many of the AHRQ priority groups with diabetes.