OBJECTIVE

To examine the longitudinal effects of medication nonadherence (MNA) on key costs and estimate potential savings from increased adherence using a novel methodology that accounts for shared correlation among cost categories.

RESEARCH DESIGN AND METHODS

Veterans with type 2 diabetes (740,195) were followed from January 2002 until death, loss to follow-up, or December 2006. A novel multivariate, generalized, linear, mixed modeling approach was used to assess the differential effect of MNA, defined as medication possession ratio (MPR) ≥0.8 on healthcare costs. A sensitivity analysis was performed to assess potential cost savings at different MNA levels using the Consumer Price Index to adjust estimates to 2012 dollar value.

RESULTS

Mean MPR for the full sample over 5 years was 0.78, with a mean of 0.93 for the adherent group and 0.58 for the MNA group. In fully adjusted models, all annual cost categories increased ∼3% per year (P = 0.001) during the 5-year study time period. MNA was associated with a 37% lower pharmacy cost, 7% lower outpatient cost, and 41% higher inpatient cost. Based on sensitivity analyses, improving adherence in the MNA group would result in annual estimated cost savings ranging from ∼$661 million (MPR <0.6 vs. ≥0.6) to ∼$1.16 billion (MPR <1 vs. 1). Maximal incremental annual savings would occur by raising MPR from <0.8 to ≥0.8 ($204,530,778) among MNA subjects. CONCLUSIONS Aggressive strategies and policies are needed to achieve optimal medication adherence in diabetes. Such approaches may further the so-called “triple aim” of achieving better health, better quality care, and lower cost. Diabetes affects ∼25 million Americans, or 8.3% of the population, and it is a leading cause of heart disease, stroke, kidney failure, lower limb amputations, and blindness among U.S. adults (1). Estimated direct medical costs attributable to this disease were$116 billion in 2007, and the number of patients with diabetes will more than double by 2050 (2). Thus, diabetes is a highly prevalent disease that is important for both public health and public policy reasons.

Figure 1

Trajectory of mean costs from the unadjusted, shared, random intercept log-normal GLMM (in $1,000 units) for each cost type over time by medication adherence status (veterans with type 2 diabetes 2002–2006). (A high-quality color representation of this figure is available in the online issue.) Close modal ### Longitudinal cost associations with MNA status Table 2 highlights findings of significant (P < 0.001) cost associations with MNA status among veterans with type 2 diabetes between 2002 and 2006 after adjustment for demographics and comorbidities and accounting for the correlation of cost categories over time. Relative to the adherent group, MNA was associated with a 37% lower pharmacy cost, 7% lower outpatient cost, and 41% higher inpatient cost between 2002 and 2006. Table 2 Longitudinal estimates of association of MNA with pharmacy, inpatient, and outpatient costs using generalized linear mixed models ### Estimates of potential VHA savings from adherence at various MPR levels Estimates of potential VHA savings from adherence at various MPR levels based on 17 February 2012 value dollars are shown in Table 3. Over the 5-year period, estimated annual potential savings were$1,158,009,119 at MPR = 1, $1,133,510,744 at MPR ≥0.9,$993,679,348 at MPR ≥0.8, $789,148,570 at MPR ≥0.7, and$661,529,175 at MPR ≥0.6. Estimated annual incremental cost savings ranged between $127,619,395 (from MPR = 0.6 to 0.7) and$1,158,009,119 (from MPR = 0.9 to 1.0). Cost savings would be optimized at improvement from MPR = 0.7 to ≥0.8 (\$204,530,778), and adherence levels >0.8 would lead to diminishing returns in terms of cost savings.

Table 3

Estimated total potential VHA savings from adherence at various MPR levels in 2012 values*

This analysis is one of the first and the largest, to date, to document the longitudinal effects of MNA on different types of healthcare cost. Our analysis demonstrates that the costs of MNA among diabetic patients are quite large and that these costs are mostly driven by inpatient expenditures. The potential cost savings that might be achieved from improving medication adherence are also substantial. These findings are significant both for health services researchers as well as healthcare policy makers.

Although the overall literature on the cost effects of MNA in diabetes is mixed (810), our findings are consistent with most well-done cross-sectional studies to date that have measured MNA and costs. For example, Balkrishan et al. (10) previously found that each 10% increase in adherence was associated with an 8.6% decrease in total annual healthcare costs. Similarly, Shenolikar et al. (18) reported that a 10% increase in adherence was associated with a 2% reduction in total medical costs and 4% reduction in diabetes-related medical costs.

In order to realize potential health benefits and cost savings, successful interventions are needed that can improve adherence and self-care behaviors among diabetic patients. However, an earlier systematic review of clinical trials designed to improve medication adherence included only two small clinical trials in diabetes (19). In general, these investigators concluded that most successful methods for improving chronic medication adherence were complex, labor intensive, and not predictably effective (19). However, several ongoing trials are currently testing innovative strategies that include individually tailored behavior change interventions, peer health coaching, diabetes self-management website engagement, and technology-assisted case management (2023). Clearly, further research in this area is needed.

In addition to complex behavioral interventions and technology-based solutions, several healthcare policy changes may contribute to improvements in medication adherence. First, both VHA and non-VHA studies show that decreased cost sharing for those with pharmacy benefits improves medication adherence (24). In the current study, we observed that veterans with high degrees of service connectedness (and thus exempt from copayments) were more likely to be adherent (26.3 vs. 24.5%, P < 0.005) (Table 1). This is consistent with an earlier experience in the Rand health insurance experiment (25); although we should note that our study was not designed to specifically examine the effects of copayments on medication adherence. Future studies are needed to determine optimal strategies for modulating copayment levels. Second, expansion of pharmacy benefits to greater numbers of patients should improve both medication adherence and health outcomes. For instance, implementation of Medicare Part D coverage for older Americans was associated with significant improvements in medication use and adherence, with differential reductions in nondrug medical spending for Medicare beneficiaries with limited prior drug coverage (26). Third, medication review visits by clinical pharmacists have been shown to improve appropriate medication prescribing in the elderly, reduce polypharmacy, and reduce adverse drug events in a cost-effective way (2729). Currently, reimbursement for medication review is either very low or nonexistent by third party payers and thus should be a focus of policy change (30).

Our use of data from the Veterans Affairs Health System is notable for several reasons. First, drawing from the largest integrated health system in the U.S., serving >5.5 million enrollees, our veteran cohort contained comprehensive demographic, clinical, pharmacy, and cost data on a national scale that would have been infeasible to assemble elsewhere (31). Second, although our research group and others have previously demonstrated that racial/ethnic, geographic, and rural/urban disparities exist in diabetes medication adherence and health outcomes (32,33), there is evidence that quality and equity of care for diabetes is higher within the VHA system (34,35). Third, generous VHA pharmacy benefits may tend to minimize the effects of MNA due to the inability of patients to afford medications. Thus, analyses of VHA data reflect a comprehensive, nationally representative sample where the effects of access, racial/ethnic differences, and medication costs are relatively minimized. These results are relevant for other healthcare systems and payers providing comprehensive healthcare coverage including inpatient, outpatient, and pharmacy benefits.

This analysis is also strengthened by the use of a robust statistical methodology. Analyses of cost data must overcome several statistical problems, including data skewness and heteroscedasticity (11). Longitudinal analyses of cost data must also account for variations in and correlations among cost outlays over time (M.G., Y.A., C.E.D., R.N.A., K. Hunt., L.E.E., unpublished data). In addition, it is desirable to analyze the discrete costs among relevant cost categories (inpatient, outpatient, and pharmacy). Our novel mGLMM technique effectively deals with these issues using a joint modeling with shared random intercept approach. We expect that this type of statistical approach will prove valuable to other research groups analyzing longitudinal cost data in the future.

Nevertheless, our study must be interpreted in light of certain limitations. First, only 2.2% of our sample was female. However, our cohort had >16,000 women. Second, we were unable to control for additional potential cost predictors, such as diabetes self-care behaviors, diabetes disease knowledge, and health beliefs about diabetes, that were not available in our dataset. The collection of such information was not feasible for a cohort so large. Third, our dataset contained a significant proportion of patients who were missing information on race, a consistent problem with other studies in this area. In addition, we did not attempt to separate diabetes-related costs from nondiabetes costs in this analysis because overall cost estimates are of interest for clinicians and policy makers, although this should be the subject of future research. Our analysis is also limited by the absence of cost data from other payers, especially Medicare. Thus, if subjects tended to use non-VHA sources for a large proportion of their healthcare, our estimates of cost may be low. However, our selection criteria, which necessitate multiple VHA visits and prescriptions for diabetes medications, tend to select patients who use the VHA for the majority of their healthcare. Also, because VHA pharmacy copayments tend to be lower than Medicare and private insurance, many veterans fill their prescriptions exclusively at the VHA.

Although MNA has historically received little attention, it has been recognized that improving adherence rates could dramatically impact patient outcomes while reducing overall health care spending (36). Future research in this area is warranted, and it must address additional barriers to medication adherence, including regimen complexity, medication beliefs, and treatment of comorbid depression. However, if successful strategies for improving medication adherence among patients with diabetes can be found, based on our findings, such approaches could fulfill the so-called “triple aim” endorsed by Berwick et al. (37).

This article represents the views of the authors and not those of the VHA or HSR&D.

This study was supported by Grant IIR-06-219 funded by the VHA Health Services Research and Development (HSR&D) program.

The funding agency did not participate in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

L.E.E. and P.D.M. conceived and designed the study, acquired, analyzed, and interpreted data, and critically revised the manuscript for important intellectual content. M.G. analyzed and interpreted data and drafted and critically revised the manuscript for important intellectual content. C.E.D., C.P.L., and N.R.A. analyzed and interpreted data and drafted the manuscript. Y.Z. analyzed and interpreted data. All authors approved the final manuscript. L.E.E. and M.G. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Centers for Disease Control and Prevention. National Diabetes Fact Sheet: General Information and Estimates on Diabetes in the United States. Atlanta, GA, U.S. Department of Health and Human Services, 2011. Available from http://www.cdc.gov/diabetes/pubs/factsheet11.htm
2.
Narayan
KM
,
Boyle
JP
,
Geiss
LS
,
JB
,
Thompson
TJ
.
Impact of recent increase in incidence on future diabetes burden: U.S., 2005-2050
.
Diabetes Care
2006
;
29
:
2114
2116
[PubMed]
3.
Agency for Healthcare Quality and Research. 2010 National Health Disparities Report. Rockville, MD, U.S. Department of Health and Human Services, 2010 (Rep. no. 11-0005). Available from http://www.cbo.gov/publication/41656
4.
Nathan
DM
,
Buse
JB
,
Davidson
MB
, et al
American Diabetes Association
European Association for Study of Diabetes
.
Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes
.
Diabetes Care
2009
;
32
:
193
203
[PubMed]
5.
U.S. Department of Veterans Affairs. VA/DoD clinical practice guidleline for the management of diabetes [Internet], 2011. Available from http://www.healthquality.va.gov/diabetes/DM2010_FUL-v4e.pdf. Accessed 6 December 2011
6.
Cramer
JA
.
A systematic review of adherence with medications for diabetes
.
Diabetes Care
2004
;
27
:
1218
1224
[PubMed]
7.
Schectman
JM
,
MM
,
Voss
JD
.
The association between diabetes metabolic control and drug adherence in an indigent population
.
Diabetes Care
2002
;
25
:
1015
1021
[PubMed]
8.
Salas
M
,
Hughes
D
,
Zuluaga
A
,
Vardeva
K
,
Lebmeier
M
.
Costs of medication nonadherence in patients with diabetes mellitus: a systematic review and critical analysis of the literature
.
Value Health
2009
;
12
:
915
922
[PubMed]
9.
Lau
DT
,
Nau
DP
.
Oral antihyperglycemic medication nonadherence and subsequent hospitalization among individuals with type 2 diabetes
.
Diabetes Care
2004
;
27
:
2149
2153
[PubMed]
10.
Balkrishnan
R
,
Rajagopalan
R
,
Camacho
FT
,
Huston
SA
,
Murray
FT
,
Anderson
RT
.
Predictors of medication adherence and associated health care costs in an older population with type 2 diabetes mellitus: a longitudinal cohort study
.
Clin Ther
2003
;
25
:
2958
2971
[PubMed]
11.
Mihaylova
B
,
Briggs
A
,
O’Hagan
A
,
Thompson
SG
.
Review of statistical methods for analysing healthcare resources and costs
.
Health Econ
2011
;
20
:
897
916
[PubMed]
12.
Miller
DR
,
Safford
MM
,
Pogach
LM
.
Who has diabetes? Best estimates of diabetes prevalence in the Department of Veterans Affairs based on computerized patient data
.
Diabetes Care
2004
;
27
(
Suppl. 2
):
B10
B21
[PubMed]
13.
Cramer
JA
,
Pugh
MJ
.
The influence of insulin use on glycemic control: how well do adults follow prescriptions for insulin?
Diabetes Care
2005
;
28
:
78
83
[PubMed]
14.
West
AN
,
Lee
RE
,
Shambaugh-Miller
MD
, et al
.
Defining “rural” for veterans’ health care planning
.
J Rural Health
2010
;
26
:
301
309
[PubMed]
15.
U.S. Department of Veterans Affairs. The health care system for veterans: an interim report [Internet]. Available from http://www.cbo.gov/ftpdocs/88xx/doc8892/maintext.3.1.shtml. Accessed 6 December 2011
16.
Quan
H
,
Sundararajan
V
,
Halfon
P
, et al
.
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data
.
Med Care
2005
;
43
:
1130
1139
[PubMed]
17.
Fieuws
S
,
Verbeke
G
,
Molenberghs
G
.
Random-effects models for multivariate repeated measures
.
Stat Methods Med Res
2007
;
16
:
387
397
[PubMed]
18.
Shenolikar
RA
,
Balkrishnan
R
,
Camacho
FT
,
Whitmire
JT
,
Anderson
RT
.
Comparison of medication adherence and associated health care costs after introduction of pioglitazone treatment in African Americans versus all other races in patients with type 2 diabetes mellitus: a retrospective data analysis
.
Clin Ther
2006
;
28
:
1199
1207
[PubMed]
19.
McDonald
HP
,
Garg
AX
,
Haynes
RB
.
Interventions to enhance patient adherence to medication prescriptions: scientific review
.
JAMA
2002
;
288
:
2868
2879
[PubMed]
20.
Griffin
SJ
,
Simmons
RK
,
Williams
KM
, et al
.
Protocol for the ADDITION-Plus study: a randomised controlled trial of an individually tailored behaviour change intervention among people with recently diagnosed type 2 diabetes under intensive UK general practice care
.
BMC Public Health
2011
;
11
:
211
[PubMed]
21.
Ghorob
A
,
Vivas
MM
,
De Vore
D
, et al
.
The effectiveness of peer health coaching in improving glycemic control among low-income patients with diabetes: protocol for a randomized controlled trial
.
BMC Public Health
2011
;
11
:
208
[PubMed]
22.
Glasgow
RE
,
Christiansen
SM
,
Kurz
D
, et al
.
Engagement in a diabetes self-management website: usage patterns and generalizability of program use
.
J Med Internet Res
2011
;
13
:
e9
[PubMed]
23.
Egede
LE
,
Strom
JL
,
Fernandes
J
,
Knapp
RG
,
Rojugbokan
A
.
Effectiveness of technology-assisted case management in low income adults with type 2 diabetes (TACM-DM): study protocol for a randomized controlled trial
.
Trials
2011
;
12
:
231
[PubMed]
24.
Maciejewski
ML
,
Bryson
CL
,
Perkins
M
, et al
.
Increasing copayments and adherence to diabetes, hypertension, and hyperlipidemic medications
.
Am J Manag Care
2010
;
16
:
e20
e34
[PubMed]
25.
Brook
RH
,
Ware
JE
Jr
,
Rogers
WH
, et al
.
Does free care improve adults’ health? Results from a randomized controlled trial
.
N Engl J Med
1983
;
309
:
1426
1434
[PubMed]
26.
McWilliams
JM
,
Zaslavsky
AM
,
Huskamp
HA
.
Implementation of Medicare Part D and nondrug medical spending for elderly adults with limited prior drug coverage
.
JAMA
2011
;
306
:
402
409
[PubMed]
27.
Hanlon
JT
,
Weinberger
M
,
Samsa
GP
, et al
.
A randomized, controlled trial of a clinical pharmacist intervention to improve inappropriate prescribing in elderly outpatients with polypharmacy
.
Am J Med
1996
;
100
:
428
437
[PubMed]
28.
Cowper
PA
,
Weinberger
M
,
Hanlon
JT
, et al
.
The cost-effectiveness of a clinical pharmacist intervention among elderly outpatients
.
Pharmacotherapy
1998
;
18
:
327
332
[PubMed]
29.
Williams
ME
,
Pulliam
CC
,
Hunter
R
, et al
.
The short-term effect of interdisciplinary medication review on function and cost in ambulatory elderly people
.
J Am Geriatr Soc
2004
;
52
:
93
98
[PubMed]
30.
Barnett
MJ
,
Frank
J
,
Wehring
H
, et al
.
Analysis of pharmacist-provided medication therapy management (MTM) services in community pharmacies over 7 years
.
J Manag Care Pharm
2009
;
15
:
18
31
[PubMed]
31.
U.S. Department of Veterans Affairs. Facts about the Department of Veterans Affairs [Internet], 2008. Available from http://www1.va.gov/opa/fact/docs/vafacts.pdf. Accessed 11 June 2012
32.
Egede
LE
,
Mueller
M
,
Echols
CL
,
Gebregziabher
M
.
Longitudinal differences in glycemic control by race/ethnicity among veterans with type 2 diabetes
.
Med Care
2010
;
48
:
527
533
[PubMed]
33.
Lynch
CP
,
Strom
JL
,
Egede
LE
.
Disparities in diabetes self-management and quality of care in rural versus urban veterans
.
J Diabetes Complications
2011
;
25
:
387
392
[PubMed]
34.
Kerr
EA
,
Gerzoff
RB
,
Krein
SL
, et al
.
Diabetes care quality in the Veterans Affairs Health Care System and commercial managed care: the TRIAD study
.
Ann Intern Med
2004
;
141
:
272
281
[PubMed]
35.
Trivedi
AN
,
Grebla
RC
.
Quality and equity of care in the Veterans Affairs Health-Care System and in Medicare Advantage health plans
.
Med Care
2011
;
49
:
560
568
[PubMed]
36.
Mitka
M
.
Improving medication adherence promises great payback, but poses tough challenge
.
JAMA
2010
;
303
:
825
[PubMed]
37.
Berwick
DM
,
Nolan
TW
,
Whittington
J
.
The triple aim: care, health, and cost
.
Health Aff (Millwood)
2008
;
27
:
759
769
[PubMed]