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The American Journal of Managed Care September 2015
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Do Patient or Provider Characteristics Impact Management of Diabetes?
Erin S. LeBlanc, MD, MPH; A. Gabriela Rosales, MS; Sumesh Kachroo, PhD; Jayanti Mukherjee, PhD; Kristine L. Funk, MS; and Gregory A. Nichols, PhD
Building Upon the Strong Foundation of National Healthcare Quality
Charles N. Kahn III, MPH, President and CEO, Federation of American Hospitals
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Howard Beckman, MD, FACP, FAACH; Patricia Healey, MPH; and Dana Gelb Safran, ScD
Innovative Approach to Patient-Centered Care Coordination in Primary Care Practices
Robin Clarke, MD, MSHS; Nazleen Bharmal, MD, PhD; Paul Di Capua, MD, MBA; Chi-Hong Tseng, PhD; Carol M. Mangione, MD, MSPH; Brian Mittman, PhD; and Samuel A. Skootsky, MD
Private Sector Risk-Sharing Agreements in the United States: Trends, Barriers, and Prospects
Louis P. Garrison, Jr, PhD; Josh J. Carlson, PhD; Preeti S. Bajaj, PhD; Adrian Towse, MA, MPhil; Peter J. Neumann, ScD; Sean D. Sullivan, PhD; Kimberly Westrich, MA; and Robert W. Dubois, MD, PhD
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Rajeev K. Sabharwal, MPH; Jennifer S. Graff, PharmD; Erin Holve, PhD, MPH, MPP; and Robert W. Dubois, MD, PhD
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Jessica M. Franklin, PhD; Alexis A. Krumme, MS; William H. Shrank, MD, MSHS; Olga S. Matlin, PhD; Troyen A. Brennan, MD, JD, MPH; and Niteesh K. Choudhry, MD, PhD
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Joseph Vogelgesang, BS; David Drozek, DO; Masato Nakazawa, PhD; Jay H. Shubrook, DO
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Targeting a High-Risk Group for Fall Prevention: Strategies for Health Plans
Lee A. Jennings, MD, MSHS; David B. Reuben, MD; Sung-Bou Kim, MPhil; Emmett Keeler, PhD; Carol P. Roth, RN, MPH; David S. Zingmond, MD, PhD; Neil S. Wenger, MD, MPH; and David A. Ganz, MD, PhD
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Do Patient or Provider Characteristics Impact Management of Diabetes?

Erin S. LeBlanc, MD, MPH; A. Gabriela Rosales, MS; Sumesh Kachroo, PhD; Jayanti Mukherjee, PhD; Kristine L. Funk, MS; and Gregory A. Nichols, PhD
Many more patient than provider characteristics are associated with optimal or poor glycemic control and treatment intensification when glycemic control is initially lost.
Objectives: Patients with diabetes often exceed desired glycated hemoglobin (A1C) levels for months prior to medication adjustments. To determine if provider and patient characteristics predict glycemic control and treatment intensification.

Study Design: Observational retrospective cohort study using electronic medical record data. 

Methods: We studied 149 Kaiser Permanente Northwest primary care providers of 14,430 patients with diabetes, of whom 5823 (40.4%) were in optimal control (all A1Cs <7%) and 2446 (17%) were in poor control (at least 1 A1C >9%) in 2011. We also identified a subset of 107 primary care providers of 912 patients with diabetes who were initially in optimal control (A1C <7%) but had a subsequent A1C >7.5% from 2010 to 2011. We used hierarchical linear modeling to assess both patient and provider characteristics as predictors of glycemic control and treatment intensification after incident hyperglycemia.

Results: Patient characteristics associated with optimal control included older age, lower baseline A1C, shorter diabetes duration, and not using insulin (P <.001 for all). The inverse of these variables predicted poor control. No provider characteristics were associated with glycemic control or intensification. Older patients with a greater change in A1C were more likely to have therapy intensified after loss of glycemic control.

Conclusions: Patient, but not provider characteristics, predicted glycemic control and therapy intensification. Improving systems of care such as disease management services may be a better use of resources than focusing on individual providers.

Am J Manag Care. 2015;21(9):597-606
Take-Away Points
  • In a single integrated health maintenance organization, individual provider characteristics did not predict glycemic control or likelihood of treatment intensification; however, there was little variability between providers. 
  • These data suggest that systems of care may contribute more to glycemic control among patient populations compared to individual provider characteristics. 
  • Improving systems of care, such as disease management services, may be a better use of resources than focusing on individual providers.
Despite recommendations from the American Diabetes Association,1 only 53% of persons with diabetes in the United States between 2007 and 2010 had glycated hemoglobin (A1C) levels of less than 7%.2 As a progressive disease, type 2 diabetes requires ongoing treatment intensification to achieve and maintain glycemic control.3 Although there are several therapeutic guidelines for managing hyperglycemia,4-6 previous studies suggest that approximately 50% to 60% of patients with A1Cs above goal are not being prescribed a change in therapy or are actually having their therapy decreased.7,8 One factor driving this problem is “clinical inertia”—defined as a clinician recognizing a problem but failing to act9—which occurs often in the management of diabetes.10

Identifying patient and provider characteristics associated with poor glycemic control and/or delayed therapeutic intensification is essential to improving the health of persons with diabetes. Delays in treatment intensification not only lead to long periods of hyperglycemia, ranging from 1 to 7 years, but also to less likelihood of therapeutic success once therapy is intensified.11-16 The causes of these long delays in therapy intensification during diabetes management are not clear. Current guidelines suggest that patient characteristics such as older age, frailty, or multiple comorbidities may be legitimate reasons for delaying therapy intensification,1,17,18 but previous models showing the influence of such patient characteristics on glycemic control have had limited predictive power.11 Moreover, although some studies have found that diabetes specialists may opt to intensify patients’ treatment sooner than primary care providers (PCPs),8,10 there has been little examination of specific provider characteristics that may contribute to delays in diabetes therapy intensification.

To further understand reasons for inaction in the context of elevated A1Cs, we examined the extent to which clinician and patient characteristics were associated with glycemic control and treatment intensification after loss of glycemic control.

The study was reviewed and approved by the institutional review board of Kaiser Permanente Northwest.


KPNW is a nonprofit, group-model health maintenance organization that provides integrated comprehensive medical care to about 490,000 individuals in a 75-mile radius around Portland, Oregon. PCPs include physicians, nurse practitioners, and physician assistants of Internal Medicine and Family Practice departments. Patients are assigned a personal PCP, though they also visit other primary care clinicians for short-notice appointments or when their personal clinician is not available. A group of patients assigned to a provider is called a “panel.” To assist providers in caring for their patients with diabetes, KPNW provides evidence-based guidelines based on American Diabetes Association recommendations.19

Identification and Follow-up of Patients With Diabetes

KPNW uses healthcare utilization data to track and facilitate operations. An electronic medical record—in use since 1996—allows providers to record International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes at each patient contact and to update an electronic problem list (which also utilizes ICD-9-CM codes). A single regional laboratory processes most KPNW outpatient laboratory tests, and the results are stored in a searchable database. A pharmacy is located in each medical office, and most members have a pharmacy benefit, ensuring almost complete capture of data describing pharmaceutical dispenses.

Subjects were members of the KPNW electronic diabetes registry, established in 1989.20 Patients are automatically added to the registry at the first indication of diabetes. Criteria include physician-entered inpatient or outpatient diagnoses of diabetes (ICD-9-CM code 250.xx), dispensing of an antihyperglycemia agent, an A1C ≥7% (lowered to 6.5% in 2010), or a fasting plasma glucose >125 mg/dL. Clinicians may remove patients from the registry if their inclusion is subsequently determined to be erroneous.

Selection of Participants

We identified 178 PCPs who were continuously employed during 2011 (Figure 1). Of these, 149 (83.7%) had at least 10 patients with diabetes in their panel of patients, and were thus caring for a total of 14,430 diabetics. The outcomes of interest were the probability that patients were in optimal glycemic control (outcome 1A: all A1C values <7%; N = 5823 [40.4%]) or poor control (outcome 1B: at least 1 A1C ≥9%; N = 2446 [17%]) in 2011. We chose a value of <7% based on the American Diabetes Association’s position statement that for many patients with diabetes, a reasonable glycemic goal is A1C <7%.19

We identified a subset of the above population consisting of 107 PCPs with at least 4 patients with diabetes on their panel who were initially in optimal control (<7%) but had a subsequent A1C ≥7.5% from 2010 to 2011 (912 patients). Patients were required to have 1 year of follow-up available after the elevated A1C. We assessed the probability of treatment intensification, defined as the addition of a new antihyperglycemic medication (oral agent or insulin) or an increase in dosage of a current oral medication, within 90 days of the elevated A1C (outcome 2). We were unable to capture increases in insulin dosage among those already on insulin.

Patient and Provider Characteristics

We examined the demographics of both patients and PCPs. For patients, we also examined time in the KPNW diabetes registry (which has been shown in previous analyses to be a proxy for duration of diabetes), the most recent A1C prior to the observation period, and insulin use. We determined whether patients had a visit to any PCP (not just their own PCP), an endocrinologist, or a specialist during the observation period, and we determined the Deyo-adaptation of the Charlson comorbidity index.21 PCP characteristics included duration of employment at KPNW, primary care training (internal medicine vs family practice), and degree (MD vs nurse practitioner/physician assistant). We also examined the total number of patients and percentage of patients with diabetes on their panels.

Statistical Analyses

We report summary statistics to describe the characteristics of patients and PCPs. Continuous variables were summarized by medians and interquartile ranges and categorical variables by numbers and percentages. We used 2-level hierarchical linear models to assess patient and provider characteristics as predictors of glycemic control and treatment intensification after incident hyperglycemia. The first and second levels of the hierarchical linear models included patient and provider characteristics, respectively. We performed the following process for each of the 3 outcomes: first, we assessed bivariate associations within each of the 2 levels of the model. Secondly, we put together the level-1 patient variables that were statistically significant at a P <.10 level and that had enough variability to explain the outcome across almost all (99%) providers in the model. Third, we added all the level-2 provider variables as predictors of the intercept coefficient and evaluated if the provider variables explained additional variance in the outcomes above and beyond the patient characteristics. This last assessment was based on a χ2 test of the deviance difference of the models with and without the provider variables. Laplace estimations were used in all models due to the binary outcomes. Analyses were conducted using SAS version 9.2 (SAS Institute, Cary, North Carolina) and HLM version 7 for Windows (Scientific Software International, Inc, Lincolnwood, IL).

Baseline Characteristics

Patients had a median age of 63 years for outcome 1 (prevalent optimal and poor glycemic control) and 62 years for outcome 2 (probability of treatment intensification), and slightly more than half were men (Table 1). Patients had been in the KPNW diabetes registry for an average of 5 years. Glycemic control was <7% for more than half of all patients; close to one-fourth were using insulin. Most saw a PCP, but very few (<2%) saw an endocrinologist.

About half of the PCPs for these patients with diabetes were men, with about an even split between internal medicine and family practice. The majority were MDs and they had worked at KPNW for a median of 6 to 8 years. They had a median of 1200 to 1300 patients on their panels, 8% to 9% of whom had diabetes.

Patient and PCP Characteristics Associated With Optimal Glycemic Control (all A1Cs <7%)

In bivariate analyses, patients who were older and who had at least 1 visit to a specialist other than an endocrinologist were more likely to be in optimal glycemic control (Table 2). On the other hand, male patients with higher A1Cs at baseline, longer duration of diabetes, and insulin use were less likely to have optimal control. Patients with at least 1 visit to an endocrinologist were less likely to be in control; however, only 101 of the 149 PCPs had a mixture of patients who did and did not see an endocrinologist. Having a visit to a specialist other than an endocrinologist was associated with increased odds of being in optimal control. No PCP characteristics were associated with optimal control in the bivariate models. When we examined the patient characteristics in a multivariable model, older patients were still more likely to be in optimal control while patients with higher A1Cs at baseline, longer duration of diabetes, and insulin use were less likely to be in optimal control. For example, each additional year of age increased the odds of being in optimal control by 2% (odds ratio [OR], 1.023; 95% CI, 1.017-1.03; P <.001). Having a baseline A1C ≥7% decreased the odds of being in optimal glycemic control by 93% (OR, 0.07; 95% CI, 0.06-0.08; P <.001), and for each additional year with diabetes, it decreased by 7% (OR, 0.93; 95% CI, 0.92-0.95; P <.001). Users of insulin were almost half as likely to be in optimal control as nonusers of insulin (OR, 0.49; 95% CI, 0.42-0.58; P <.001). Adding provider characteristics to the multivariable model did not improve the predictive power of the models.

Patient and PCP Characteristics Associated With Poor Glycemic Control (at least 1 A1C ≥9%)

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