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Racial Disparities in Lipid Control in Patients With Diabetes
Darcy Saffar, MPH; L. Keoki Williams, MD, MPH; Jennifer Elston Lafata, PhD; George Divine, PhD; and Manel Pladevall, MD, MS
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Racial Disparities in Lipid Control in Patients With Diabetes

Darcy Saffar, MPH; L. Keoki Williams, MD, MPH; Jennifer Elston Lafata, PhD; George Divine, PhD; and Manel Pladevall, MD, MS
African Americans with diabetes are less likely than whites to be treated with lipid-lowering agents, have their medication altered, or reach LDL-C goal.
Objectives: To describe lipid management over time in a cohort of insured patients with diabetes and evaluate differences between African American and white patients.


Study Design: Automated claims data were used to identify a cohort of 11,411 patients with diabetes in 1997 to 1998. Patients were followed through 2007.


Methods: Rates of hypercholesterolemia testing, treatment, and goal attainment were measured annually. Treatment was determined by a claim for lipid-lowering agents, and goal attainment was defined as a low-density lipoprotein cholesterol (LDL-C) level <100 mg/dL.


Results: During the study period, LDL-C testing increased from 48% to 70% among African American patients and from 61% to 77% among white patients. Treatment with lipid-lowering drugs increased from 23% to 56% among African American patients and 33% to 61% among white patients. The proportion at goal increased from 35% to 76% and from 24% to 59% among white and African American patients, respectively. African American patients were less likely to be tested for LDL-C (odds ratio [OR] 0.79; 95% confidence interval [CI] 0.73-0.86), treated with lipidlowering agents (OR 0.72; 95% CI 0.65-0.80), have their medication dosage altered (OR 0.65; 95% CI 0.59-0.73), or attain LDL-C goal (OR 0.59; 95% CI 0.56-0.63) compared with white patients.


Conclusions: Although rates of LDL-C testing, treatment, and goal attainment improved over time, racial disparities in dyslipidemia management continued to exist. Further studies to determine the causes of differences in management by race are warranted.


(Am J Manag Care. 2012;18(6):303-311)
  • Using claims data, we determined that insured African Americans with diabetes were less likely than whites to be treated with lipid-lowering agents, have their medication altered, or reach low-density lipoprotein cholesterol (LDL-C) goal.

  • Rates of LDL-C testing, treatment, and goal attainment significantly improved in both races over time.

  • Nonadherence to lipid-lowering drugs was higher among African Americans than among whites.
Patients diagnosed with diabetes mellitus (DM) are at higher risk for cardiovascular disease (CVD) events and mortality than patients with no history of DM.1-4 In an effort to reduce this risk, national guidelines recommend strict hypercholesterolemia management, among other measures, in patients with DM. Racial disparities have been observed not only in the prevalence of DM and its complications but also in the management of hypercholesterolemia (lipid testing, treatment, and control/goal attainment).5-7 In 1 published study, investigators found that even among patients treated for hypercholesterolemia, African American patients were less likely to reach their low-density lipoprotein cholesterol (LDL-C) goal compared with white patients.8 Several reports have shown that even among patients with coronary heart disease (CHD), DM, or hypertension, African Americans are less likely to receive 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (ie, statin therapy) for dyslipidemia and/or achieve LDL-C control compared with white patients.9-14

Disparities in access to healthcare and healthcare-seeking behavior may explain why lipid management impact is better among whites than among African Americans.9 These disparities have been attributed to difficulties in accessing healthcare among uninsured minorities, and lower socioeconomic status has been associated with an inferior quality of care received.15,16 Even among insured African Americans, quality of care, particularly lipid treatment and control, is inferior to that received by other racial groups.17-23 However, some findings suggest that patients of differing race and ethnic groups receive equal benefits when treated appropriately.9,24 Further complicating matters, previous studies have also shown racial differences in adherence to lipid-lowering medications among patients with diabetes which might contribute to ethnic and racial disparities.25-28 This paper builds on previous literature by including information on care processes, clinical outcomes, patient sociodemographic and clinical characteristics, office visit and prescription drug copayments, treatment intensification, and medication adherence in the same study. With its large sample size, high proportion of African Americans, and long observation period, this study strengthens and expands previous findings.

To more fully investigate the question of racial disparities in lipid control, we describe annual rates of testing, treatment, and LDL-C goal achievement over a 10-year period in a large cohort of insured patients with diabetes receiving care in an integrated healthcare delivery system. We also evaluate whether dyslipidemia management differed between African American and white patients after controlling for numerous patient clinical characteristics and sociodemographic factors. This includes controlling for economic barriers beyond the mere presence of health insurance with variables such as prescription drug and physician office visit copayments. Further, we explore whether racial differences in rates of LDL-C goal achievement could be explained by racial differences in treatment intensification of and adherence to lipid-lowering drugs.

METHODS

Study Population and Setting

All study patients received care through a large integrated health system serving southeastern Michigan. This health system includes a 900-member multispecialty salaried medical group that delivers care in Detroit and surrounding communities. We identified a retrospective cohort of patients from multiple sites who were managed by the medical group and had insurance coverage through an affiliated health maintenance organization. All patients had prescription coverage, with tiered copayments based on the covering entity’s formulary. We followed cohort members from baseline (January 1, 1997, to December 31, 1998) until the first of either death, health plan disenrollment, or the end of the study period (ie, December 31, 2007). The Institutional Review Board of the Henry Ford Health System approved the study as described.

Inclusion and Exclusion Criteria

We used the National Committee for Quality Assurance’s Health Plan Employer Data and Information Set (HEDIS) criteria to identify patients diagnosed with diabetes in the baseline period.29 The patient had to meet at least 1 of the following 3 diagnostic definitions for diabetes: (1) >1 hospitalization or >2 outpatient visits with a diagnosis of DM (ICD-9-CM 250.xx); (2) a dispensing for insulin or an oral hypoglycemic medication (therapeutic class codes C4G, C4K, C4L, C4M, and C4N); or (3) a mean glycated hemoglobin (A1C) level >7% or a mean fasting plasma glucose >126 mg/dL on 2 separate occasions with a mean A1C >6.5%. Patients had to be 18 years or older at baseline and be continuously enrolled in the health plan, with pharmacy benefit coverage, for the 2-year baseline period.

Data Sources

Information on patient characteristics, including age, sex, marital status, and race, was available from electronic data sources maintained by the health system. At the time of this data collection, race was usually based on self-report, but could have been assigned by administrative staff at the time of the initial clinical encounter. Medical claims and encounter data were used to identify and construct the following: clinical characteristics and diagnosis variables, the Deyo adaptation of the Charlson Comorbidity Index,30 and measures reflective of medical care use (ie, frequency of outpatient visits and cardiology visits). Measures of laboratory test receipt and test results were obtained from an automated clinical laboratory system. Prescription drug claims data were used to compile prescription drug use and adherence measures. Medical group and health plan databases were linked using patients’ unique medical record numbers. Use of automated data to identify patients with diabetes has been previously validated.31,32

Analytical Variables

To examine trends in lipid management, we created indicator variables for LDL-C testing, treatment with a lipidlowering agent (therapeutic class codes D7L, M4E, and M4F), and LDL-C goal attainment during baseline and in each year of follow-up. Patient LDL-C values were based on the average annual value for the 2-year baseline period and for each follow-up year. The clinical laboratory system was also used to derive variables reflective of baseline and annual mean A1C.

Comorbidity scores were calculated using the diagnostic data available during the baseline years to construct the Deyo adaptation of the Charlson Comorbidity Index.30 Diagnostic (>1 inpatient discharge diagnosis or >2 outpatient diagnoses) and procedural data were used to construct indicators for baseline cardiovascular risk factors and diseases. These included hypertension, left ventricular hypertrophy (LVH), peripheral vascular disease (PVD), cerebrovascular diseases (including stroke or transient ischemic attack), CHD (including unstable angina and myocardial infarction), retinopathy, non-traumatic lower extremity amputation, end-stage renal disease (ESRD), and smoking status. Smoking status was determined by the presence of at least 1 diagnosis of tobacco use (ICD-9-CM code 305.1) in the 1997-1998 baseline period.

Prescription claims data were used to compute the continuous measure of medication gaps (CMG), a measure of nonadherence in pharmacotherapy.33,34 CMG is the sum of treatment gaps in medication refills over the total number of days in the observation period. Since some subjects were taking more than 1 lipid-modifying medication, CMGs for all drugs within the lipid-lowering class were averaged to create a composite CMG. CMG was calculated for 4 classes of hyperlipidemic drugs: statins, fibrates, nicotinic acid, and bile acid sequestrants. Within each drug class, CMG indexes were computed for those patients who filled at least 1 prescription per year (N = 2553 for statins, N = 415 for fibrates, N = 11 for nicotinic acid, and N = 63 for bile acid sequestrants) in the period between the first prescription claim after January 1, 1997, and the last prescription available or December 31, 2007 (ie, if the last prescription extended past the last day of observation).

As we have done previously,35,36 medication nonadherence was measured as 1-CMG (ie, reverse-coded) and multiplied by 100 to provide a scale of 0 to 100. Therefore, higher scores reflected better medication adherence. Dichotomous versions of the reverse-coded CMG were created to categorize patients as either “adherent” or “nonadherent.” Patients whose reversecoded CMG was less than 80% (ie, a gap in therapy greater than 20%) were classified as nonadherent whereas patients with a reverse-coded CMG greater than or equal to 80% were classified as adherent. Patients with no prescribed treatment were considered to have adherence equal to zero in the multivariable models. A cutoff of 80% has been used historically to differentiate adherent from nonadherent behavior37-39 and it is associated with the likelihood of achieving LDL-C goal.39,40

Sociodemographic information included age, race, gender, and marital status. Medical claims and encounter data were used to determine clinic, copayment amounts, and number of outpatient and specialty care (cardiology) physician visits during the 2-year baseline period. Race was categorized as “white,” “African American,” or “other.” The “other” population was included in the analyses and is presented in the tables. However, interpretation of these data is difficult due to the heterogeneity of the population and its small sample size (n = 423; 3.7%); therefore, results specific to this subgroup are not described. Residential street address was used to estimate median household income and level of education (as represented by proportion of males and females with high school or lower level of education) using geographical information system technology which assigned values based on the census block-group of residence.41

Statistical Analyses

Rates of testing, treatment, and LDL-C goal attainment stratified by race were estimated and compared by chi-square tests for each follow-up year (1999-2007). Values were declared missing if the patient was not continuously enrolled for the entire year of interest. Multivariable logistic regression models (generalized estimating equation, or GEE)42 that account for repeated events (ie, testing, treatment, dose adjustment, and goal achievement during the subsequent follow-up years), while controlling for baseline factors, were used to estimate racial effects on testing, treatment, and goal attainment variables.

 
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