Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes
Medication Adherence Does Not Explain Black-White Differences in Cardiometabolic Risk Factor Control among Insured Patients with Diabetes
Jennifer Elston Lafata 1 3 11
Andrew J. Karter 0
Patrick J. O'Connor
Heather Morris 6
Julie A. Schmittdiel 0
Katherine M. Newton 5
Marsha A. Raebel 4 9
Ram D. Pathak
Abraham Thomas 2
Melissa G. Butler 12
Kristi Reynolds 13
Beth Waitzfelder 10
John F. Steiner 4
0 Division of Research, Kaiser Permanente Northern California , Oakland, CA , USA
1 Henry Ford Health System , Detroit, MI , USA
2 Lutheran HealthCare , Brooklyn, NY , USA
3 School of Medicine, Virginia Commonwealth University , Richmond, VA , USA
4 Kaiser Permanente Colorado Institute for Health Research , Denver, CO , USA
5 Group Health Research Institute , Seattle, WA , USA
6 University of Florida , Gainesville, FL , USA
7 HealthPartners Institute for Education and Research , Minneapolis, MN , USA
8 Marshfield Clinic , Marshfield, WI , USA
9 University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences , Aurora, CO , USA
10 Kaiser Permanente Hawaii, Center for Health Research - Hawaii , Honolulu, HI , USA
11 Department of Social and Behavioral Health, Virginia Commonwealth University , Richmond, VA , USA
12 Kaiser Permanente Georgia Center for Health Research- Southeast , Atlanta, GA , USA
13 Department of Research and Evaluation, Kaiser Permanente Southern California , Los Angeles, CA , USA
adherent (i.e., MRA <80 %) to HbA1c oral medication(s); 58.4 % of blacks and 46.7 % of whites were not highly adherent to lipid medication(s); and 33.4 % of blacks and 23.7 % of whites were not highly adherent to BP medication(s). Across all cardiometabolic risk factors, blacks were significantly less likely to achieve control (p<0.01): 41.5 % of blacks and 45.8 % of whites achieved HbA1c <8 %; 52.6 % of blacks and 60.8 % of whites achieved LDLC <100; and 45.7 % of blacks and 53.6 % of whites a c h i e v e d S B P < 1 3 0 . A d j u s t i n g f o r m e d i c a t i o n adherence/treatment intensification did not alter these patterns or model fit statistics. CONCLUSIONS: Medication adherence failed to explain observed racial differences in the achievement of HbA1c, LDL-C, and SBP control among insured patients with diabetes.
diabetes care; racial disparities; medication adherence; cardiometabolic risk factors; J Gen Intern Med 31(2); 188-95 DOI; 10; 1007/s11606-015-3486-0 © Society of General Internal Medicine 2015
BACKGROUND: Among patients with diabetes, racial
differences in cardiometabolic risk factor control are
common. The extent to which differences in medication
adherence contribute to such disparities is not known. We
examined whether medication adherence, controlling for
treatment intensification, could explain differences in risk
factor control between black and white patients with
METHODS: We identified three cohorts of black and white
patients treated with oral medications and who had poor
risk factor control at baseline (2009): those with glycated
hemoglobin (HbA1c) >8 % (n=37,873), low-density
lipoprotein cholesterol (LDL-C) >100 mg/dl (n=27,954), and
systolic blood pressure (SBP) >130 mm Hg (n=63,641).
Subjects included insured adults with diabetes who were
receiving care in one of nine U.S. integrated health
systems comprising the SUrveillance, PREvention, and
ManagEment of Diabetes Mellitus (SUPREME-DM)
consortium. Baseline and follow-up risk factor control,
sociodemographic, and clinical characteristics were
obtained from electronic health records.
Pharmacydispensing data were used to estimate medication
adherence (i.e., medication refill adherence [MRA]) and
treatment intensification (i.e., dose increase or addition of new
medication class) between baseline and follow-up.
County-level income and educational attainment were
estimated via geocoding. Logistic regression models were
used to test the association between race and follow-up
risk factor control. Models were specified with and
without medication adherence to evaluate its role as a
RESULTS: We observed poorer medication adherence
among black patients than white patients (p < 0.01):
50.6 % of blacks versus 39.7 % of whites were not highly
Poor cardiometabolic risk factor control substantially increases
the risk of diabetes-related morbidity and mortality.1,2 Despite
the availability of effective medications, almost half of all
Americans with diabetes fail to maintain evidence-based goals
for glycated hemoglobin (HbA1c), low-density lipoprotein
cholesterol (LDL-C), and systolic blood pressure (SBP).3–7
Furthermore, suboptimal control is often more prevalent
among African Americans.1,2,8–20
Prior research has consistently found racial differences in
medication adherence.21–24 The reasons underlying such
differences are poorly understood, but are often thought to stem
from differences in care access, care quality, and
selfmanagement behaviors.18 As medication non-adherence is
associated with poor cardiometabolic risk factor control
among patients with diabetes,8–14 it has been suggested that
differences in medication adherence may be responsible for
subgroup differences in risk factor control among patients with
diabetes.18 These conclusions, however, are drawn from
crosssectional studies and from analyses that fail to consider
Longitudinal studies that address both medication
adherence and treatment intensification are needed to fully
understand the contribution of differences in medication
adherence to racial differences in cardiometabolic risk
factor control among patients with diabetes. Using the
large patient populations and extensive clinical data
available within the SUrveillance, PREvention, and
ManagEment of Diabetes Mellitus (SUPREME-DM)
consortium,25 we describe the achievement of
cardiometabolic risk factor control among insured black and white
patients with diabetes and poor baseline risk factor
control. We then examine the extent to which
medication adherence, controlling for treatment intensification,
may explain black–white differences in the subsequent
achievement of cardiometabolic risk factor control
among these insured patients.
Sample and Setting
We identified insured black and white patients aged 18 years
and older with diabetes who were receiving care from one of
nine health care systems in the U.S. participating in the
SUPREME-DM consortium: Group Health (Washington),
Henry Ford Health System/Health Alliance Plan (Michigan),
Marshfield Clinic (Wisconsin), and Kaiser Permanente
regions in Colorado, Northern California, Southern California,
Hawaii, Georgia, and the Northwest (Oregon and
Washington). Patients were identified as having diabetes if they had
two outpatient visits or one inpatient discharge with an
associated diagnostic code for diabetes (i.e., ICD-9 250.xx) or they
had at least one dispensing of any glucose-lowering
medication (exclusive of metformin) within the two-year period
ending December 31, 2010.25 To ensure adequate capture of
pharmacy dispensing data, we included only those patients
with continuous insurance coverage with a drug benefit
between January 1, 2009, and December 31, 2010.
Patients with diabetes were retrospectively selected for
inclusion in up to three cohorts of patients with poorly
controlled baseline cardiometabolic risk factors: (
poorly controlled HbA1c (>8 %), (
) poorly controlled
LDLC (>100 mg/dL), and/or (
) poorly controlled SBP
(>130 mm Hg). For each of the cohorts, patients were
eligible for inclusion if they had a test result reflective of
poor baseline risk factor control and a follow-up test
result in the 12- to 18-month period following their
baseline test. They also needed to be an oral medication
user, with two or more dispensings for a corresponding
medication (i.e., any oral glucose-lowering agent in the
Hb1c cohort, any lipid-lowering agent in the LDL-C
cohort, and any antihypertensive agent in the SBP
cohort) in the period between their baseline and follow-up
tests. Patients with no follow-up test result or with fewer
than two relevant oral medication dispensings were
excluded. Cohort members therefore included insured
patients with a follow-up test in 2010 for whom we were
able to follow medication management between this test
and a baseline test 12–18 months earlier that reflected
poor control (Fig. 1). All study sites either ceded
oversight to the Kaiser Permanente Colorado Institutional
Review Board (IRB) or received approval from their
local IRB for the study.
Study data were compiled from the SUPREME-DM
DataLink,13,25 a distributed data source of patients with
diabetes derived from the HMO Research Network
Virtual Data Warehouse (VDW).26,27 Within each delivery
system, the VDW contains data from electronic health
records (EHRs) joined with administrative data in
identically formatted tables. Included in the VDW is
information extracted from automated administrative and
encounter files on patient sociodemographic characteristics
and insurance coverage, dates and results of vital signs and
laboratory testing, pharmacy dispensing, and dates and
diagnoses associated with ambulatory visits and inpatient stays.
While each delivery system maintains their own VDW
database, its use enables the distribution of standardized analyses
programs for site-level database construction. Uniform
sitelevel databases are transferred and joined for analyses.
The primary outcome of interest was whether the patient
achieved the guideline-defined threshold for control at
follow-up (i.e., Bgoal attainment^), consistent with practice
standards during the period of observation (i.e., HbA1c<8 %,
LDL-C<100 mg/dl, and SBP<130 mm Hg).28 As a secondary
outcome, we also evaluated whether the patient had achieved a
clinically substantive improvement in control at follow-up
(i.e., Bimprovement^). For the HbA1c cohort, this was defined
as a reduction in HbA1c ≥1 %.29 For the LDL cohort, we used
a reduction in LDL-C of ≥10 %,30 and for the SBP cohort we
used a reduction in SBP ≥5.5 mm Hg.31
Medication adherence was calculated for oral
glucoselowering medications, antihypertensives, and lipid-lowering
agents within the HbA1c, SBP, and LDL-C cohorts,
respectively, and was measured using dispensing data from the
12month period immediately preceding the patient’s follow-up
test date. We estimated a continuous multiple-interval measure
of medication refill adherence (MRA)32,33 as the days’ supply
dispensed during the 12-month period divided by the number
of days of observation with evidence of a dispensing. For
patients taking medications from more than one medication
class simultaneously for a given indication, we used the
average MRA across those classes to obtain an overall measure of
adherence to oral medications. Patients with MRA≥80 % were
classified as Badherent^.34 Among patients who were adherent
in the follow-up period, we controlled for whether their
treatment was intensified during follow-up. Treatment
intensification was defined as any dispensing of a new class of relevant
medications (including insulin) or a dose increase within the
same class of medications, using dispensing data from the
period between baseline and follow-up test dates.35
Inpatient and outpatient diagnostic codes from the same
period were used to construct the Deyo adaptation of the
Charlson Comorbidity Index.36 Similarly, diabetes-related
comorbidities and complications including cardiovascular
disease (CVD), end-stage renal disease (ESRD), retinopathy, and
amputation were identified based on ICD-9 diagnosis and
procedure codes. Also available from the EHR were age,
gender, race, and body mass index (BMI). Race, regardless
of health care system, is almost always based on self-reported
information, and was extracted from the EHR to the VDW
(and thus DataLink) in accordance with the categories
included in the National Institutes of Health policy and guidelines on
including women and minorities as subjects in clinical
research.37 Contextual socioeconomic status (SES) indicators
were obtained from geocoded information on residential street
address and 2000 U.S. Census-reported county-level median
household income and educational attainment.13
Within each cohort, we calculated the percentage of patients
who were non-adherent to their medications during the
followup period and the percentage of patients who were adherent
without a corresponding treatment intensification as well as
the percentage who were adherent with a treatment
intensification following their baseline test result. We calculated the
percentage of patients within each cohort who (
achieved risk factor control (i.e., Bgoal attainment^) at the
end of the follow-up period (as defined by the
guidelinedefined threshold value) and (
) had attained a clinically
substantive improvement in control by the end of the
followup period. Unadjusted differences in the proportion of patients
with goal attainment and improvement by race were assessed
with chi-square tests, as were differences in adherence/
intensification status by race. Adjusted differences in the
proportion of patients achieving control and attaining a
substantive improvement were evaluated using logistic regression.
Models were fitted in two steps. In the base model, the
dependent variable (i.e., control or improvement) was
modeled as a function of race, controlling for the patient’s
age, income, gender, insulin use, Charlson comorbidity score,
level of the corresponding risk factor at baseline, BMI,
frequency of visits to primary care, whether they were seen in
endocrinology, the presence/absence of CVD, ESRD,
retinopathy, or amputation, and a fixed effect for delivery system.
Then, to test our hypothesis that medication adherence,
controlling for treatment intensification, explains black–white
differences in risk factor outcomes, we added the hypothesized
mediator, i.e., patient’s medication adherence/intensification
status, to the base model. The sensitivity of model results to
the inclusion of a continuous measure of MRA was also
assessed. All analyses were conducted using SAS version
9.4 software (SAS Institute Inc., Cary, NC).
We identified insured adults who were eligible for
inclusion in the HbA1c (N = 37,873), LDL-C (N = 27,954), and
SBP (N = 63,641) risk cohorts. By cohort inclusion
criteria, at baseline, all individuals included in each
cohort were in poor control for the corresponding risk
factor. While almost half of the members of the SBP
cohort were over the age of 64, just under a third of
the LDL-C cohort and approximately a quarter of the
HbA1c cohort were over age 64 (Table 1).
Approximately 20 % of each cohort comprised blacks, and
approximately half of each were women, while approximately
two-thirds of each cohort resided in communities where
the median household income ranged from $30,000 to
$69,999. At baseline, the median HbA1c was 9.2 % for
black patients compared to 9.0 % for white patients in
the HbA1C cohort; the median LDL-C was 123 mg/dL
for black patients and 119 mg/dL for white patients in
the LDL-C cohort; and median SBP was 142 mm Hg for
black patients and 140 mm Hg for white patients in the
SBP cohort (Table 1).
HbA1c hemoglobin A1c, LDL-C low-density lipoprotein cholesterol, SBP systolic blood pressure, CVD cardiovascular disease, ESRD end-stage renal
disease, BMI body mass index, PCP primary care, IQR interquartile range
Statistical methods used to test sociodemographic and other differences by race within each cohort:
*Chi-square test p<0.01
**Chi-square test p<0.05
†Wilcoxon rank-sum test p<0.001
‡Student t test p<0.01
Medication Adherence During Follow-Up
During the follow-up period, 42.0 % of the HbA1c cohort and
49.1 % of the LDL-C cohort were non-adherent to their
medications, compared to 25.7 % of the SBP cohort (Table 2).
Among adherent patients, between 14.5 % (HbA1c cohort)
and 26.2 % (LDL-C cohort) received a treatment intensification
during the follow-up period, and between 24.7 % (LDL-C
cohort) and 50.4 % (SBP cohort) received no treatment
intensification. Regardless of cohort, black patients were always
significantly more likely than white patients to be non-adherent
during the follow-up period, and less likely to be adherent
without a treatment intensification during follow-up.
Follow-Up Cardiometabolic Risk Factor Status
At follow-up, 44.9 % of the HbA1c cohort had achieved
HbA1c control (i.e., HbA1c <8 %) and 49.3 % had a clinically
meaningful improvement (i.e., at least one-percentage-point
reduction). In the SBP cohort, 52.0 % achieved control (SBP
<130 mm Hg), while 68.7 % had a clinically meaningful
improvement (reduction of 5.5 mm Hg or more). In the
HbA1c hemoglobin A1c, LDL-C low-density lipoprotein cholesterol, SBP systolic blood pressure
*Chi-square test p<0.0001
LDL-C cohort, 59.1 % achieved LDL-C control (LDL-C
<100 mg/dL), and 70.0 % had a clinically meaningful
improvement (reduction of 10 % or more).
Before controlling for other factors, regardless of cohort,
black patients were significantly less likely than white patients
to achieve control (Table 3). This was true regardless of their
medication adherence status. Similarly, black patients were
significantly less likely than white patients to have a clinically
beneficial improvement in either LDL-C or SBP level,
regardless of their medication adherence status (Table 3). Differences
in clinically beneficial HbA1c improvements were similar,
albeit less pronounced.
Results from adjusted models confirmed these findings
(Table 4). Regardless of cohort, compared to white patients,
black patients in all models were significantly less likely to
achieve control or a clinically meaningful improvement. These
race differences persisted in models that controlled for the
patient’s medication adherence status, altering neither
observed relationship patterns nor model fit statistics
substantively (differences in c-statistics ranged from 0.003 to
0.015). Results and findings did not differ substantively
with the inclusion of a continuous measure of MRA
(results not shown).
Our findings highlight persistent black–white differences in
both medication adherence and the attainment of optimal
cardiometabolic risk factor control among patients with
diabetes. Among large populations of insured patients receiving
care from integrated delivery systems located across the
United States, we found that black patients experiencing
suboptimal risk factor control at baseline were less likely than
white patients to adhere to medications during follow-up, and
were subsequently less likely to achieve recommended goal
levels for HbA1c, LDL-C, or SBP, even when their medication
was intensified. While medication adherence status was
significantly associated with achieving recommended goal levels,
it did not fully explain observed racial disparities in the control
of cardiometabolic risk factors among patients with diabetes.
Both medication adherence and treatment intensification are
advocated for controlling cardiometabolic risk factors among
patients with diabetes.38 While findings continually point to
delays in treatment intensification,39–43 findings of differences
in delay length by race have been inconsistent.39–43 On the
other hand, studies of medication adherence have consistently
found black patients to be less adherent to prescribed
medications.22–24 Our understanding of the impact of medication
adherence and treatment intensification to observed racial
differences in risk factor control among patients with diabetes
remains limited. This is especially true as few prior studies
have considered the joint effects of medication adherence and
treatment intensification on risk factor control among patients
with diabetes. One study that has done so did not explore
differences by patient race.44
The complex mix of patient, physician, and system factors
associated with both medication adherence45–47 and treatment
intensification42,48–50 has been well documented, as have the
ongoing challenges in achieving clinical control even when
patients are adherent and treatment is intensified.44,51,52 It is
therefore encouraging to see that within a relatively short
follow-up period, a substantial percentage of patients who
were in poor control at baseline were able to achieve
Where HbA1c <8 %, LDL <100 mg/dL, and systolic BP <130 mm Hg defined as achieving control
Where HbA1c reduction ≥1 %, LDL-C reduction ≥10 %, and SBP reduction ≥5.5 mm Hg
HbA1c hemoglobin A1c, LDL-C low-density lipoprotein cholesterol, SBP systolic blood pressure
*Chi-square test p<0.01
**Chi-square test p<0.05
Base models control for age, gender, income, Charlson Comorbidity Index, insulin dispensing, cardiovascular disease, end-stage renal disease,
retinopathy, amputation, BMI, baseline risk factor level, number of visits to primary care during follow-up, whether the patient was seen in
endocrinology during follow-up, and health care system. Models with adherence control for these factors plus medication adherence status in three
categories: non-adherent; adherent with intensification; adherent without intensification
recommended control levels. This finding was consistent
across each of the cardiometabolic risk factors, albeit notably
less so for HbA1c goal attainment. Furthermore, we found that
even larger percentages of those in poor control at baseline
achieved clinically beneficial improvements in control, even if
these improvements did not always reach
guidelinerecommended thresholds. Such positive findings are
consistent with recent national and international trends in HbA1c,
SBP, and LDL-C control.53–55
Our finding that black patients are less likely to achieve
recommended risk factor control even after considering
medication adherence and treatment intensification emphasize the
difficulty of overcoming such disparities and the likely
complex pathways through which race may impact the
achievement of clinically advocated treatment goals. Our
understanding of these complex pathways remains incomplete. Some of
these pathways could include race differences in
individuallevel socioeconomic status, functional health literacy, and the
quality of patient–physician communication. They may also
be related to the overall cultural competency of clinicians and
staff in the environment in which care is received. Stress—be
it at an individual, family, or community level—may further
contribute to observed racial disparities in adherence and
clinical control. Interventions addressing a complex array of
biological, physiologic, and behavioral factors are likely
needed to effectively address disparities. Given that
previous studies have shown that racial disparities persist in
both care processes and risk factor control, even within an
environment where the overall quality of care is
improving,53 it is likely that interventions specifically tailored to
African Americans are needed to reduce disparities in
CVD risk factor control. When desired treatment goals in
patients with diabetes are not met, the American Diabetes
Association now advocates for the evaluation of a diverse
collection of patient barriers such as financial barriers,
health literacy, diabetes-related distress, or depression, and
provider barriers such as competing clinical demands.56 To
fully address racial disparities, additional potential barriers
at the health care system, community, and policy level also
Several limitations of our study should be noted. First, our
method of ascertaining adherence excludes patients who
obtained less than two dispensings of a medication; thus we are
unable to identify non-adherent patients in cases where
medications were prescribed but never dispensed or where a new
medication was only dispensed a single time. This could result
in biases if African American patients are relatively less likely
than white patients to fill only one prescription for a
medication. This also implies a slight under-ascertainment of
intensification, given that ~5 % of patients prescribed a new
medication (i.e., the provider’s plan was for intensification) are
never dispensed the newly prescribed medication.14,59,60
Second, we measured medication dispensing, not actual
consumption or prescribing. As such, our measure of adherence may be
inflated, and our measure of treatment intensification will miss
prescriptions written but never filled. Furthermore, our
measure of secondary medication adherence ignored adherence to
prescribed insulin and gaps or stockpiling across refill
intervals, and thus we assume that their impact is uniform across
groups. Similarly, measures of treatment intensification
derived from electronic dispensing data (such as the one used
here) have not been formally validated and may have
shortcomings. For example, alterations in insulin dosing or changes
in dose that are achieved via an increase in the quantity of pills
taken are not identifiable via dispensing data. Nor can the
rationale behind observed intensifications be ascertained.
Thus, changes due to insurance coverage or other
nonclinical factors are captured equally to those driven by clinical
factors. Our results may not generalize to other practice sites or
patient populations. In particular, while both privately and
publicly insured individuals were included, patients without
insurance were excluded. Furthermore, because analyses were
limited to data available via automated sources, we were
unable to consider other factors that may influence medication
management and the achievement of control, including
patient–provider relationship factors, patient health beliefs,
preferences, and individual level income/socioeconomic status, or
broad system-level initiatives to achieve quality targets for risk
factor control. Strengths of our study include the use of
longitudinal data from geographically diverse health systems,
findings regarding adherence and cardiometabolic control that are
consistent with previous studies, and the ability to jointly
control for medication adherence and intensification.
In summary, the results of this study highlight the need to
look at factors beyond the joint effects of medication
adherence and treatment intensification in order to understand the
root causes of observed black–white differences in HbA1c,
LDL-C, and SBP control among patients with diabetes. These
factors may range from biological mechanisms that alter drug
effectiveness, to physiologic factors such as genetics, known
race differences in the viability of risk factor control markers
(e.g., race differences in glycosylation of hemoglobin),61 to
other patient, provider, and health system factors that may
influence health behaviors, treatment decisions, and health
Acknowledgments: We gratefully acknowledge the work of the
project managers at KPCO, Andrea Paolino, MA, and Michael
Shainline, MS, MBA. We thank the programmers and site
investigators at Kaiser Permanente Colorado, Northwest, Northern California,
Southern California, Georgia and Hawaii, Geisinger Health System,
Marshfield Clinic Research Foundation, Henry Ford Health System,
and Group Health Research Institute for their efforts in developing
and refining the SUPREME-DM datasets that informed this work.
Funders: This project was supported by grant number R01HS019859
from the Agency for Healthcare Research and Quality.
Prior Presentations: EDEG, April 2015, Chantilly, France.
Conflict of interest: The authors declare that they do not have a
conflict of interest.
Corresponding Author: Jennifer Elston Lafata, PhD; Department
of Social and Behavioral HealthVirginia Commonwealth
University, PO Box 980149, Richmond, VA 23298, USA
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