UCSF
UC San Francisco Previously Published Works
Title
Trends in Drug Utilization, Glycemic Control, and Rates of Severe Hypoglycemia, 2006-
2013.
Permalink
https://escholarship.org/uc/item/63b7h0mj
Journal
Diabetes care, 40(4)
ISSN
0149-5992
Authors
Lipska, Kasia J
Yao, Xiaoxi
Herrin, Jeph
et al.
Publication Date
2017-04-01
DOI
10.2337/dc16-0985
Peer reviewed
eScholarship.org Powered by the California Digital Library
University of California
Trends in Drug Utilization,
Glycemic Control, and Rates of
Severe Hypoglycemia, 2006–2013
Diabetes Care 2017;40:468–475 | DOI: 10.2337/dc16-0985
OBJECTIVE
To examine temporal trends in utilization of glucose-lowering medications, gly-
cemic control, and rate of severe hypoglycemia among p atients with type 2 di-
abetes (T2DM).
RESEARCH DESIGN AND METHODS
Using claims data from 1.66 million privately insured and Medicare Advantage
patients with T2DM from 2006 to 2013, we estimated the annual 1)age-andsex-
standardized proportion of patients who filled each class of agents; 2) age-, sex-,
race-, and region-standardized p roportion with hemoglobin A
1c
(HbA
1c
) <6%,
6to<7%, 7 to <8%, 8 to <9%, ‡9%; and 3) age- and sex-standardized rate of severe
hypoglycemia among those usi ng medications. Proportions were calculated over-
all and stratified by age-group (18–44, 45–64, 65–74, and ‡75 years) and number
of chronic comorbidities (zer o, one , and two or more).
RESULTS
From 2006 to 2013, use increased for metformin (from 47.6 to 53.5%), dipeptidyl
peptidase 4 inhibitors (0.5 to 14.9%), and insulin (17.1 to 23.0%) but declined for
sulfonylureas (38.8 to 30.8%) and thiazolidinediones (28.5 to 5.6%; all P < 0.001).
The proportion of patients with HbA
1c
<7% declined (from 56.4 to 54.2%; P < 0.001)
andwithHbA
1c
‡9% increased (9.9 to 12.2%; P < 0.001). Glycemic control varied by age
and was poor among 23.3% of the youngest and 6.3% of the oldest patients in 2013.
The overall rate of severe hypoglycemia remained the same (1.3 per 100 person-years;
P = 0.72), declined modestly among the oldest patients (from 2.9 to 2.3; P < 0.001), and
remained high among those with two or more comorbidities (3.2 to 3.5; P = 0.36).
CONCLUSIONS
During the recent 8-year period, the use of glucose-lowering drugs has changed
dramatically among patients with T2DM. Overall glycemic control has not im-
proved and remains poor among nearly a quarte r of the youngest pati ents. The
overall rate of severe hypog lycemia remains largely unchanged.
Glycemic management of t ype 2 diabetes (T2DM) has changed considerably over
the past decade (1,2). Clinicians and patients can now choose from 12 different
classes of glucose-lowering medications to manage hyperglycemia. Unlike insulin or
sulfo nylurea drugs, most of the newer agents have not be en implicated as a cause of
hypoglycemia, which can be a serious complication of glucose-lowering therapy,
particularly in the elderly (3). However, utilization of t hese newer agents has sig-
nifica ntly contributed to inc reasing costs of diabetes management (4,5).
1
Section of Endocrinology and Metabolism, De-
partment of Internal Medicine, Yale School of
Medicine, New Haven, CT
2
Division of Health Care Policy and Research,
Mayo Clinic, Rochester, MN
3
Robert D. and Patr icia E. Kern Center for the
Science of Health Care Delivery, Mayo Clinic, Ro-
chester, MN
4
Section of Cardiovascular Medicine, Depart-
ment of Internal Medicine, Yale School of Medi-
cine, New Haven, CT
5
Division of Primary Care Internal Medicine, De-
partment of Medicine, Mayo Clinic, Rochester,
MN
6
Section of General Medicine, Department of
Internal Medicine, Yale School of Medicine,
New Haven, CT
7
Department of Health Policy and Management,
Yale School of Public Health and Center for Out-
comes Research and Evaluation, Yale New Haven
Hospital, New Haven, CT
8
Division of Geriatrics, Department of Medicine,
University of California, San Francisco, San Fran-
cisco VA Health Care System, San Francisco, CA
9
Section of Geriatrics, Department of Internal
Medicine, Yale School of Medicine, New Haven,
CT
10
OptumLabs, Cambridge, MA
Corresponding author: Kasia J. Lipska, kasia.
lipska@yale.edu.
Received 5 May 2016 and accepted 31 August
2016.
This article contains Supplementary Data online
at http://care.diabetesjournals.org/lookup/
suppl/doi:10.2337/dc16-0985/-/DC1.
This article is featured in a podcast available at
http://www.diabetesjournals.org/content/
diabetes-core-updat e-podcasts.
© 2017 by the American Diabetes Association.
Readers m ay use this article as long as th e work
is properly cited, the use is educational and not
for profit, and the work is not altered. More infor-
mation is available at http://www.diabetesjournals
.org/content/license.
See accompanying artic les, pp. 440,
444, 453, 461, 476, 48 5, 49 4, 502,
509, 518, and 526.
Kasia J. Lipska,
1
Xiaoxi Yao,
2,3
Jeph Her rin,
4
Rozalina G. McCoy,
2,3,5
Joseph S. Ross,
6,7
Michael A. Steinm an,
8
Silvio E. Inzucchi,
1
Thomas M. Gill,
9
Harlan M. Krumholz,
4,7
and Nilay D. Shah
2,3,10
468 Diabetes Care Volume 40, April 2017
DIABETES AND AGING
In addition to changes in drug ther-
apies to reduce glucose levels, the re-
commended target glucose levels have
changed as well. In 2009, based on new
evidence from randomized clinical trials
(6–8), the American Diabet es Association
(ADA) clinical practice guidelines specifi-
cally recommended individualization of
glycemic targets according to age and mul-
timorbidity (9). In 2012, the ADA and the
American Geriatrics Society developed a
consensus statement that recognized
that older adults with life-limiting comor-
bidities are less likely to benefitfromtight
glycemic control (i.e., reaching a hemoglo-
bin A
1c
[HbA
1c
] ,7%) and are more vul-
nerable to hypoglycemia compared with
younger, healthier patients (10). There-
fore, less stringent glycemic targets (i.e.,
HbA
1c
,8or,9%) may be appropriate in
older patients, particularly in those with
multiple chronic conditions and estab-
lished vascular complications (11).
Given these changes in management
approaches, it is impo rtant to exam ine
the temporal tren ds in glucose-lowering
medication use, as well as resultant gly-
cemic control and rates of severe hypo-
glycemia. These trends provide information
about care patterns in response to changes
in treatment options and treatment targets
and can identify gaps in diabetes care and
areas for potential improvement.
A number of studies have separately ex-
amined some of these trends (2,12–16).
Data from physician audits have shown
that patients with diabetes are being pre-
scribed an increasing number of glucose-
lowering medications in ambulatory care
settings (1,2). Separate analysis of data
from the National Health and Nutrition Ex-
amination Survey (NHANE S) s uggested
that glycemic control has improved, and
just over half of patients achieved an
HbA
1c
,7% in 2007–2010 (13). Several
other studies indicated that treatment-re-
lated hypoglycemia continues to pose a
significant health threat, particularly for
older adults and for those with multiple
chronic conditions (12,14,15,17,18).
However, studies looking at medication
prescribing patterns were conducted at
different times and in different patient
populations compared with the studies
looking at glycemic control, precluding po-
tential inferences about any association
between medication use, glycemic con-
trol, and hypoglycemia. Moreover, they
did not examine how these trends differ
across age and comorbidity subgroups.
Accordingly, we examined trends in
utilization of drug treatment for diabe-
tes, as well as concurrent trends in gly-
cemic contr ol and rate s of hypogl ycemia
among privately insured and Medicare
Advantage patients with T2DM from
geographically diverse regions across
the U .S. using OptumLabs Data Ware-
house (OLDW). We examined these
trends in the overa ll population and in
subgroups of younger and older patients
and among patients with a varying num-
ber of other serious chronic conditions.
RESEARCH DESIGN AND METHODS
Data Source
We conducted a retrospective analysis of
medical and pharmacy administrative
claims from a large database, OLDW, which
includes privately insured and Medicare
Advantage enrollees throughout the U.S.
(19). The database contains data on more
than 100 million enrollees from geograph-
ically diverse regions across the U.S., with
the greatest representation from the South
and Midwest (20). We included commer-
cial health plans in OLDW that provided full
coverage for inpatient, outpatient, and
pharmacy services. Medical claims from
inpatient and outpatient visits include
ICD-9-CM (ICD-9, clinical modification) di-
agnosis co des, ICD-9 procedure codes,
Current Procedural Terminology, version
4 procedure codes, Healthcare Common
Procedure Coding System procedure co-
des, site of service codes, and provider
specialty codes. Pharmacy claims include
information on medications dispensed
and size and dates of prescriptions. Study
data were accessed using techniques com-
pliant with the Health Insurance Portability
and Accountability Act of 1996. Because
this study involved an analysis of preexist-
ing, de-identified data, it was exempt from
institutional review board approval.
Study Population
All adults (age $18 years) with at least
1 year of continuous enrollment between
2006 and 2013 were included if they met
criteria for T2DM during at least 1 year of
the study period. The definition of diabe-
tes was based on Health Plan Employer
Data and Information Set criteria: two
face-to-face encounters with a diagnosis
of diabetes (ICD-9 codes 250.x, 357.2,
362.0, and 366.41 [21] in any position on
the claim [22–24]) on different dates of
service in an outpatient setting or non-
acute inpatient setting, or one face-to-face
encounter in an acute inpatient or emer-
gency department (ED) setting, or use of
insulin or oral antihyperglycemic agents
(except metformin monotherapy) based
on pharmacy data. We examined claims
for diabetes ICD-9 codes over a 2-year pe-
riod, which included the year of interest
and the preceding year. Once patients met
criteria for diabetes in 1 year, they were
considered to have the diagnosis in sub-
sequent years. We excluded adults with a
diagnosis of gestational diabetes (ICD-9
code 648.0) and those with codes for
T1DM (250.x1 and 250.x3). If patient s
had codes for both T1DM and T2DM, we
excluded patients who had no claims for
oral glucose-lowering medications.
Main Outcomes
In each year, we determined the propor-
tion of the study population with one or
more pharmacy fills for the different clas-
ses of glucose-lowering agents. Combina-
tion agents were counted as two different
agents filled on the same date. Because
patients could fill more than one class of
agent during each year, the proportion of
patients in each year could exceed 100%.
Because patterns of medication fills may
change over time, we also calculated the
mean number of days covered for each
oral glucose-lower ing class of medica -
tions in each year based on the date of
fill and days’ supply. In each year, we de-
termined glycemic control category
(HbA
1c
,6, 6 to ,7, 7 to ,8, 8 to ,9,
or $9%) based on the last HbA
1c
obtained
during that calendar year. Data for HbA
1c
were available for a subset of patients
based on specific lab vendor. In each
year, we also determined rates of severe
hypoglycemia requiring ED visit or hospi-
tal admission or observation stay based
on validated principal discharge diagnosis
ICD-9 codes (25) among people who filled
at least one glucose-lowering agent. These
values were reported as events per 100
person-years at risk. Person-years at risk
were calculated based on days the patient
was alive, enrolled in the health plan, and
not hospitalized. A separate analysis was
performed among people who filled insu-
lin or insulin secretagogues, other glucose-
lowering medications, and no diabetes
agents.
Independent Variables
Independent variables included baseline
age (18–44, 45–64, 65–74, and $75
years), sex, race (white, black, Hispanic,
Asian, or unk nown), region (Midwest,
care.d iabetesjournals.org Lipska and Associate s 469
Northeast, South, and West), and the
number of serious chronic conditions
(zero, one, and two or more). The serious
chronic conditions were based on ICD-9
codes and included chronic kidney dis-
ease, chronic lung disease, heart failure,
myocardial infarction or coronary artery
disease, dementia, depression, atrial fibril-
lation, and stroke (see Supplementary
Table 1 for definitions). Househ old in-
come was based on data from AmeriLINK
(obtained through KnowledgeBase Mar-
keting) and linked to claims data.
Statistical Analyses
We summariz ed the characteris tics o f
the cohort by year. For drug utilization,
glycemic control, and rates of severe
hypoglycemia, we calculated age- and
sex-standardized rates for each year us-
ing 2013 as the reference year. Because
HbA
1c
was available only for a subset of
the total sample, which varied nonran-
domly by year, we estimated the annual
proportions of patients in each glycemic
control category using multinomial logis-
tic regression with HbA
1c
category as the
outcome and age, sex, race, and region as
independent variables. We used this
model to estimate the HbA
1c
category dis-
tribution for each year. We used logistic
regression to assess annual trends in the
use of each diabetes medication class and
assess trends in each glycemic control cat-
egory from 2006 to 2013. The trend in the
number of hypoglycemia events was as-
sessed using linear regression.
Sensitivity Analyses
Some patients were captured in multiple
years of data, raising concern that in-
creasing disease severity over time could
contribute to secular trends. We per-
formed sensitivity analyses that included
only 1 year of data per patient, selected at
random from all available years for that
patient (2006–2013). Analyses were per-
formed using SAS 9.3 (SAS Institute Inc.,
Cary, NC) and Stata 14.1 (Stata Corp, Col-
lege Station, TX).
RESULTS
There were 1,657,610 unique patients
with T2DM who met the inclusion criteria
between 2006 and 2013. Among the study
cohort, 33.7% were included in 1 year
only, 25.1% were included in 2 years
only, and 41.2% were included in 3 or
more years.
From 2006 to 2013, as the OLDW ex-
panded, and as diabetes prevalence
increased, the number of patients with
T2DM who contributed data nearly dou-
bled, from ;385,000 in 2006 to ;700,000
in 2013 (Table 1). Most notably, the annual
proportion of patients aged 65–74 years
and 75 years or older increased over this
8-year period as OLDW captured an in-
creasing number of Medicare Advantage
enrollees. The burden of comorbidities
and the proportion of patients with two
or more serious chronic conditions also
increased during the observation period.
Drug Utilization
Standardized utilization trends are shown
in Fig. 1 and Supplementary Table 2. From
2006 to 2013, use increased for metfor-
min (from 47.6 to 53.5%), dipeptidyl pep-
tidase 4 (DPP-4) inhibitors (0.5 to 14.9%),
glucagon-like peptide 1 agonists (3.3 to
5.0%), and insulin (17.1 to 23.0%) but de-
clined for sulfonylureas (38.8 to 30.8%)
and thiazolidinediones (28.5 to 5.6%; all
P , 0.001). The increased utilization of
insulin was primarily driven by adoption
of basal insulin analogs (10.9 to 19.3%;
P , 0.001) and rapid-acting insulin ana-
logs (6.7 to 11.6%; P , 0.001). The use of
human insulin products actually declined
(11.6 to 5.6%; P , 0.001). The proportion
of patients with a diagnosis of diabetes
who did not fill any glucose-l owering
medications declined slightly (25.7 to
24.1%; P , 0.001). When considering
treatment complexity, the use of oral
monotherapy increased slightly (from
24.3 to 26.4%) and the use of multiple
(two or more) oral agents declined (from
33.0 to 26.5%), whereas the use of insulin
alone and in combination with oral agents
increased (from 6.0 to 8.5% and from
11.1 to 14.6%, respectively; all P values
,0.001).
The mean number of days covered for
each class of oral glucose-lowering medica-
tions per year increased over the study pe-
riod (Supplementary Table 3), even for drug
classes with reduced rates of utilization.
Drug utilization varied by age, but over-
all trends were qualitatively similar
(Supplementary Table 4). The use of met-
formin was most prevalent among the
youngest age-group (18–44 years). Among
the oldest patients, metformin surpassed
sulfonylureas as the most prevalent diabe-
tes drug class after 2010. DPP-4 inhibitor
use increased among all age subgroups,
with the highest rates of use among mid-
dle-age patients (45–64 years) in 2013.
Use of any insulin increased across all
age subgroups, most steeply among
middle-age and older patients.
Drug utilization also varied by comor-
bidity burden (Sup plementary Table 5).
Among patients with two or more seri -
ous comorbidities, metformin, insulin,
and sulfony lureas were all used to a sim-
ilar extent, with a slight increase in met-
formin and insulin use and slight decline
in sulfonylureas by 2013.
Glycemic Control
Laboratory testing for HbA
1c
was avail-
able on a subset of the total sample
(25.6%). There were regional differences
among patients with and without HbA
1c
available (Supplementary Table 6).
Standardized rates of glycemic control
are shown in Fig. 2. From 2006 to 2013,
there were increasing trends in the pro-
portion of patients with HbA
1c
$9% and
HbA
1c
8to,9% (from 9.9 to 12.2% and 9.9
to 10.6%, respectively; P , 0.001). The
trend in the proportion of patients with
HbA
1c
7to,8% was not significant
(from 23.8 to 23.0%; P = 0.31). In contrast,
there were decreasing trends in the pro-
portion with HbA
1c
6to,7% (41.1 to
43.1%) and HbA
1c
,6% (15.3 to 13.1%;
both P , 0.001). Of note, the overall slight
decreasing trend for HbA
1c
6to,7% oc-
curred despite a slightly higher proportion
of patients in this glycemic category in
2013. Finally, the overall proportion of pa-
tients with HbA
1c
,7% declined over this
8-year period: 56.4% in 2006 and 54.2% in
2013, P , 0.001.
Glycemic control varied by age (Sup-
plementary Fig. 1) and number of serious
comorbidities (Supplementary Fig. 2).
Poor glycemic control (HbA
1c
$9%) was
most common among the youngest pa-
tients but increased slightly over time
across all age subgro ups (among the
youngest: 21.1 to 23.3%, P = 0.001; and
among the oldest: 5.3 to 6.3%, P , 0.001).
Similarly, poor glycemic control was most
common among patients without comor-
bidities but increased slightly across all
comorbidity subgroups (no comorbid-
ities: 14.0 to 14.8%, one comorbidity:
12.0 to 12.5%, and two or more comor-
bidities: 10.6 to 11.8%; all P , 0.001).
Severe Hypoglycemia
The age- and sex-standardized rate of se-
vere hypoglycemia among people who
filled at least one prescription for diabe-
tes medications was 1.3 events per 100
person-years in both years 2006 and
2013 (P value for trend over time 0.72)
470 Trends in Drug Utilization, Gly cemic Control, and Hypoglycemia Diabetes Care Volume 40, April 2017
(Fig. 3A). The proportion of severe hy-
poglycemia that led to hospital admi s-
sion declined from 19.2 to 18.2% over
this time period. Rates of severe hypo-
glycemia among patients using insulin or
insulin secretagogues or other glucose-
lowering agents and among those who
filled no medications are shown in Fig. 3B.
Rates of severe hypoglycemia were high-
est among the oldest patients and those
with multiple comorbidities compared with
younger, healthier adults (Supplementary
Figs. 3 and 4). Rates of severe hypoglycemia
increased slightly amon g the youngest
(from 0.8 to 0.9 events per 100 person-
years; P = 0.025) and middle-age (0.6 to
0.9 events per 100 person-years; P ,
0.001) patients and decreased among the
older (1.4 to 1.3 events per 100 person-
years; P , 0.001) and oldest (2.9 to 2.3
events per 100 person-years; P , 0.001)
patients over the study period. Rates of
severe hypoglycemia were also highest
among patients with two or more comor-
bidities compared with those with one or
no comorbidities and did not significantly
change over time (3.2 to 3.5 events per
100 person-years; P =0.36).
Sensitivity Analyses
Analyses that included only 1 year of
data per patient showed qualitatively
similarresultsfortrendsinglycemic
control (Supplementary Table 7). How-
ever, in analyses of severe hypoglyce-
mia, the standardized rate decreased
from 1.5 to 1.2 events per 100 person-
years with a trend P value of ,0.01
(Supplementary Fig. 5).
CONCLUSIONS
The landscape of glucose-lowering med-
ications available for T2DM has changed
Table 1—Characteristics of privately insured patients with T2DM included in the study, 2006–2013
2006 2007 2008 2009 2010 2011 2012 20 13
Total, n 384,964 447,634 469,528 538,239 593,422 642,817 682,310 700,117
Age (years)
18–44 15.0 14.1 13.4 11.7 10.1 9.3 8.5 8.0
45–64 58.7 56.3 54.9 50.3 45.8 44.0 41.6 41.1
65–74 14.8 16.5 17.7 21.3 25.0 26.7 28.7 29.1
$75 11.5 13.1 14.0 16.7 19.0 20.0 21.2 21.7
Sex
Female 47.3 48.2 48.3 48.8 49.2 49.3 49.2 49.0
Male 52.7 51.8 51.7 51.2 50.8 50.7 50.8 51.0
Race
White 63.3 64.6 66.2 66.4 67.5 67.3 67.1 64.1
Black 9.6 10.5 11.2 13.1 13.6 14.0 14.1 15.9
Hispanic 10.4 10.9 11.0 10.7 9.8 9.8 9.7 10.8
Asian 4.2 4.5 4.7 4.5 4.3 4.3 4.3 4.5
Unknown 12.5 9.5 6.9 5.4 4.8 4.7 4.8 4.8
Region
Midwest 25.0 22.6 22.3 23.0 23.6 24.2 24.8 25.5
Northeast 19.1 19.5 20.0 18.8 17.9 19.7 20.5 20.3
South 40.1 42.3 43.0 44.4 45.7 44.7 43.5 43.0
West 14.9 14.2 13.1 12.6 12.0 10.8 10.6 10.6
Unknown 0.9 1.4 1.7 1.3 0.7 0.6 0.6 0.6
Household income ($)
,40K 14.5 18.5 22.5 26.1 28.5 28.6 28.7 28.4
40–49K 5.2 6.3 7.6 8.5 8.8 9.0 9.1 9.3
50–59K 5.0 5.9 7.2 7.9 8.1 8.2 8.3 8.4
60–74K 6.7 7.8 9.5 10.2 10.2 10.4 10.5 10.6
75–99K 8.9 10.5 12.6 13.2 12.9 13.1 13.0 13.1
$100K 17.2 19.7 23.3 23.6 22.3 22.2 22.0 21.8
Unknown 42.5 31.3 17.3 10.4 9.2 8.4 8.5 8.4
Comorbidities
CAD/MI 18.7 19.1 19.7 21.1 21.8 21.9 22.0 21.9
CHF 8.3 8.7 8.8 9.7 10.2 10.4 10.6 10.7
Lung disease 13.8 14.5 15.3 16.4 17.0 17.2 17.7 18.0
CKD 6.1 6.9 7.9 9.8 11.1 11.9 12.8 13.9
Depression 6.1 6.3 6.5 7.2 7.5 7.7 8.1 8.4
Dementia 2.4 2.8 3.0 3.4 3.6 3.8 4.3 4.5
Stroke 2.9 3.1 3.2 3.5 3.6 3.6 3.8 3.9
Atrial fibrillation 4.5 5.0 5.3 6.1 6.9 7.3 7.7 8.0
Number of chronic
conditions
0 61.9 60.6 59.0 55.8 54.0 53.1 52.1 51.3
1 23.2 23.5 24.4 25.3 25.8 26.0 26.1 26.2
$2 14.9 15.9 16.6 18.9 20.2 20.9 21.8 22.5
Data are presented as % unless otherwise indicated. CAD, coronary artery disease; CHF, congestive heart failure; CKD, chronic kidney disease; MI,
myocardial infarction.
care.d iabetesjournals.org Lipska and Associate s 471