IMPACT OF MEDICARE PART D COVERAGE GAP ON MEDICARE BENEFICIARIES WITH COPD: ADHERENCE, HEALTHCARE RESOURCE
USE, AND COST
by
YANNI FAN YU
LARRY R. HERALD, COMMITTEE CHAIR
MEREDITH L. KILGORE HAIYAN QU
MIDGE N. RAY
A DISSERTATION
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of Doctor of Science
BIRMINGHAM, ALABAMA
2015
Copyright by Yanni Fan Yu
2015
iii
IMPACT OF MEDICARE PART D COVERAGE GAP ON MEDICARE
BENEFICIARIES WITH COPD: ADHERENCE, HEALTHCARE RESOURCE USE,
AND COST
YANNI FAN YU
ADMINISTRATION – HEALTH SERVICES
ABSTRACT
Medicare Part D provides prescription drug coverage for beneficiaries to support
their pharmacological treatment; however, the complex deductible structure within
benefit plans creates a major coverage gap and unexpected consequences. Some evidence
has demonstrated reduced adherence resulting from the coverage gap; however, little
research has evaluated the effect on healthcare resource use (HRU) and cost, and no
studies have been conducted for beneficiaries with chronic obstructive pulmonary disease
(COPD). This study examined the impact of the coverage gap on medication adherence
as well as healthcare resource use and medical cost among beneficiaries with COPD.
Claims data based on a 5% random sample of Medicare beneficiaries were used in
this retrospective cohort study. For each year from 2007 to 2010, beneficiaries diagnosed
with COPD were assigned to either an exposure cohort if they were at risk of the
coverage gap or a control cohort if they were not. Exposure and control cohorts were
matched using a high-dimensional propensity scores. Adherence was defined as no less
iv
than 80% of the proportion of days covered (PDC) by long-acting bronchodilators
(LABDs). HRU included medical encounters occurring in all care settings. Cost included
non-drug cost paid by Medicare. All outcomes were estimated at the calendar year level.
Multivariable logistic and generalized linear model (GLM) regressions controlling for
unbalanced covariates post-matching were applied with generalized estimating equation
technique to correct for potential correlation between repeated observations of the same
beneficiary.
The final exposure and control cohorts each included 4,147 patient-year
observations. The results showed that the coverage gap was associated with lower
adherence. Both positive and negative associations with HRU were found, but no
significant difference in cost was observed between two cohorts.
This is the first study assessing the effect of the coverage gap on patients with
COPD. The findings provide support for phasing out the coverage gap by 2020. More
generally, the findings highlight opportunities to design benefit offerings that can
improve beneficiaries’ access to healthcare in ways that can impact healthcare quality and
utilization.
Keywords: Medicare Part D, coverage gap, medication adherence, healthcare resource use, cost, Chronic Obstructive Pulmonary Disease
v
ACKNOWLEDGMENTS
I would like to thank my committee chairman, Dr. Larry Hearld, and committee
members Dr. Meredith Kilgore, Dr. Haiyan Qu, and Dr. Midge Ray for their guidance
and comments provided for my dissertation. Larry has been greatly patient and helpful to
guide me through the dissertation journey, edit my dissertation drafts meticulously, and
offer his advice tirelessly. I am deeply grateful for his valuable mentorship. I am very
thankful to Dr. Kilgore for providing me with access to Medicare claims data and helping
facilitate the technical set-up on my computer. His in-depth knowledge of Medicare data
and Part D programs has been tremendously helpful in my dissertation research. I greatly
appreciate that Haiyan and Midge gave their time, thoughts, and assistance throughout
the process; their comments have greatly improved my dissertation. I truly hold precious
the time spent with my whole committee, and the inspiration and learning obtained from
all of you.
Dr. Kilgore also connected me to his colleagues in the School of Public Health --
Dr. Huifeng Yun and Dr. Haichang Xin to obtain information. I want to thank Hui and
Haichang for their assistance. I would also like to thank Robert, Matthew, and Marcie
Battles for their IT expertise and their help with all of the technical problems related to
remote access, software, and hardware of my computer.
I would like to acknowledge the leadership provided by Dr. Robert S. Hernandez
in leading the program, the excellent operation led by Ms. Leandra Y. Celaya, and the
vi
enormous support provided by Dr. M. Elizabeth Hendrix. Also, I want to thank my
classmates for their company, interaction, and friendship for the past years. Because of all
of you, this adventure has been more enjoyable and memorable.
My deepest gratitude to my family, without whom, none of this would have been
possible. I want to thank Andrew, my husband, and my adorable daughters Esther and
Enya for their love, support, understanding, and patience, which makes my endeavor
more meaningful. You are treasures in my life. I want to thank my parents for their
timeless love and selfless support. You keep encouraging me to go further and make me
always believe the power and the value of education. Also my thanks to my parents-in-
law for their immeasurable assistance that freed me up from housework and enabled me
to devote more time to my research. Thanks to all of you, I am really fortunate to be able
to pursue something I want to pursue, to taste the joy of achieving challenging goals, and
to carry over your endless love throughout my whole life.
vii
TABLE OF CONTENTS
ABSTRACT .............................................................................................................. iii
ACKNOWLEDGMENTS ..........................................................................................v
LIST OF TABLES .................................................................................................... ix
LIST OF FIGURES .................................................................................................. xi
LIST OF ABBREVIATIONS .................................................................................. xii
CHAPTER ONE: INTRODUCTION .........................................................................1
Background ......................................................................................................................1
Problem Statement ...........................................................................................................5
Research Questions ........................................................................................................11
CHAPTER TWO: LITERATURE REVIEW ...........................................................14
Impact of Medicare Part D .............................................................................................14
Impact of Medicare Part D Coverage Gap .....................................................................21
Theoretical Framework ..................................................................................................30
CHAPTER THREE: METHODS .............................................................................38
Study Design ..................................................................................................................38
Data Source ....................................................................................................................41
Sample Selection ............................................................................................................43
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Outcome Measures .........................................................................................................48
Variables.........................................................................................................................51
Statistical Analysis .........................................................................................................62
CHAPTER FOUR: RESULTS .................................................................................67
Sample Size ....................................................................................................................68
Demographic and Baseline Characteristics ....................................................................72
Descriptive Statistics of Outcome Variables ..................................................................81
Regression Analysis and Hypotheses Testing ................................................................88
CHAPTER FIVE: DISCUSSION ...........................................................................103
Review of Findings and Comparison with Existing Evidence .....................................103
Strengths and Limitations.............................................................................................109
Implications ..................................................................................................................114
Conclusion ....................................................................................................................121
REFERENCES .......................................................................................................122
APPENDIX .............................................................................................................133
INSTITUTIONAL REVIEW BOARD Documentation .............................................133
ix
LIST OF TABLES
Table 1. Maintenance medications used for COPD ..................................................45
Table 2: ICD-9-CM diagnosis codes for CCI conditions .........................................55
Table 3. ICD-9-CM codes for select relevant comorbidities ....................................56
Table 4. Procedure codes for supplemental oxygen therapy ....................................57
Table 5. Oral corticosteroid ......................................................................................57
Table 6. A list of variables for primary and subgroup analyses ...............................59
Table 7. Sample size of study cohorts and subgroups. .............................................72
Table 8. Patient demographic and baseline characteristics of study cohorts before
and after matching.....................................................................................................75
Table 9. Demographic and baseline characteristics of subgroups of the exposure
cohort before matching. ............................................................................................79
Table 10. Adherence to LABDs in the matched control and exposure cohorts ........81
Table 11. Annual HRU in the matched control and exposure cohorts. ....................82
Table 12. Annual all-cause medical cost for the matched control and exposure
cohorts .......................................................................................................................83
Table 13. Adherence to LABDs in the mid-gap and late-gap subgroups in the
matched exposure cohort ..........................................................................................84
x
Table 13a. Quarterly adherence for the late-gap subgroup. ......................................85
Table 14. Monthly HRU of the mid-gap and the late-gap subgroups in the matched
exposure cohort .........................................................................................................86
Table 15. Monthly all-cause medical cost for the mid-gap and the late-gap
subgroups in the matched exposure cohort ...............................................................87
Table 16. Conditional logistic regression on adherence to LABDs ..........................90
Table 16a. Unadjusted and adjusted adherence ........................................................90
Table 17. GLM regression on annual number of all-cause outpatient visits ...........93
Table 17a. Unadjusted and adjusted number of all-cause outpatient visits ..............94
Table 18. GLM regression on annual number of all-cause ER visits ......................95
Table 18a. Unadjusted and adjusted number of all-cause ER visits .........................96
Table 19. GLM regression on annual number of all-cause inpatient visits. .............97
Table 19a. Unadjusted and adjusted number of all-cause inpatient visits ................98
Table 20. GLM regression on annual all-cause medical cost .................................100
Table 20a. Unadjusted and adjusted annual all-cause medical cost .......................101
Table 21. Summary for regression analysis and hypotheses testing .......................102
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LIST OF FIGURES
Figure 1. Average annual prevalence of COPD among adults by age group and
gender in the United States (2007-2009). ...................................................................3
Figure 2. Rational choice theory model. ...................................................................33
Figure 3. Subgroups of the exposure cohort. ............................................................47
Figure 4. Patient selection flow chart.. .....................................................................69
xii
LIST OF ABBREVIATIONS
ACE angiotensin-converting-enzyme
AIDS Acquired immunodeficiency syndrome
AMI acute myocardial infarction
ARB angiotensin II receptor blocker
ATS American Thoracic Society
CBO Congressional Budget Office
CCI Charlson Comorbidity Index
CHF congestive heart failure
CI confidence interval
CMS Centers for Medicare & Medicaid Services
COPD Chronic obstructive pulmonary disease
CRN cost-related non-adherence
DA descriptive analysis
DDD defined daily dose
DME durable medical equipment
ER emergency room
ERS European Respiratory Society
ESRD end-stage-renal-diseases
FD&C Federal Food, Drug and Cosmetic
FDA Food and Drug Administration
xiii
FFS Fee for Service
GEE generalized estimating equation
GERD gastroesophageal reflux disease
GLM generalized linear model
GOLD Global initiative for chronic Obstructive Lung Disease
HbA1c hemoglobin A1c, i.e., glycated hemoglobin
HCPCS Healthcare Common Procedure Coding System
HDPS high-dimensional propensity score
HD-PSM high-dimensional propensity score matching
HF heart failure
HMO health maintenance organization
HRU healthcare resource use
ICD-9-CM International Classification of Diseases, Ninth Edition, Clinical
Modification
IRR incidence rate ratio
LABD long-acting bronchodilator
LIS low income subsidy
MA multivariable analysis
MA-PD Medicare Advantage plan with prescription drug coverage
MMA Medicare Modernization Act
MPR medication possession ratio
MRA Medication Refill Adherence
NDC national drug code
xiv
OOP out-of-pocket
OR odds ratio
PDC proportion of days covered
PDE Part D Event
PDP Part D plans
PPIs proton-pump inhibitors
PSM propensity score matching
RR relative ratio
SABD short-acting bronchodilator
SD standard deviation
SE standard error
TrOOP true out-of-pocket
US United States
WHO World Health Organization
1
CHAPTER ONE
INTRODUCTION
Background
Chronic Obstructive Pulmonary Disease
Chronic obstructive pulmonary disease (COPD) is a progressive lung
disease characterized by airway narrowing or airflow obstruction, resulting in
breathing difficulties, reduced exercise capacity and physical limitation (GOLD,
2014). COPD can deteriorate over time, leading to respiratory worsening and
serious disability that require hospitalization. Many people suffer for years and
die prematurely from COPD or its complications (GOLD, 2014). COPD has
become the fourth leading cause of death in the world (WHO, 2008). Chronic
lower respiratory disease, which primarily includes COPD has become the third
leading cause of death in the United States (Hoyert & Xu, 2012) .
There is no cure for COPD and no existing medication has been shown to
modify the long-term decline in lung function; however, treatments and lifestyle
changes may help to slow the progression of COPD and lessen symptoms
(GOLD, 2014). For example, smoking cessation has been found to be effective
in improving symptoms for patients who smoke (Anthonisen et al., 1994; Baillie,
Mattick, Hall, & Webster, 1994). Appropriate pharmacologic therapy can help to
relieve symptoms and reduce the frequency and severity of exacerbations;
pulmonary rehabilitation can be used for patients with shortness of breath while
walking, and oxygen therapy is often prescribed to patients with severe COPD
(GOLD, 2014; Stoller, Panos, Krachman, Doherty, & Make, 2010).
2
Although as many as 24 million Americans may have COPD, only 12-15
million have been diagnosed (American-Lung-Association, 2014; NIH, 2008),
accounting for about 5% of adults in the United States (Akinbami & Liu, 2011).
COPD occurs more often in older age groups and in females. According to the
CDC report, almost half of all COPD patients are aged 65 years or older (Centers
for Disease Control and Prevention, 2012). As shown in Figure 1, COPD
prevalence was highest among men aged 75–84 years (11.2%), followed by
women aged 65–74 years (10.4%), and women aged 75–84 years (9.7%). Access
to appropriate therapies to manage COPD, especially pharmacologic treatment,
used to be challenging due to the lack of coverage for prescription medications
among the elderly population. With the increasing size of the elderly population
and the rising prevalence of chronic diseases, it has become important for
healthcare policy development to improving access to health services and
assisting treatment needs for this population.
3
Figure 1. Average annual prevalence of COPD among adults by age group and gender in the United States (2007-2009).
Note: Graphed based on data from CDC/NCHS, Health Data Interactive
and National Health Interview Survey
Medicare and Medicare Part D
Medicare is a national social insurance program in the United States (US).
Established in 1965 and administered by the federal government, Medicare
provides access to health insurance for Americans aged 65 years or older who
have worked and paid taxes for a certain number of years and for younger people
with disabilities or certain illnesses such as end stage renal disease or
amyotrophic lateral sclerosis (Centers for Medicare & Medicaid, 2013). The
Medicare program is composed of four parts as described in detail below. Part A
helps cover inpatient care in hospitals, skilled nursing facilities (not custodial or
long-term care), hospice care, and some home health. Part B covers physician
2.0 2.0
3.9
6.4
8.3
11.2
7.2
3.0
4.1
7.5
8.7
10.49.7
7.8
0.0
2.0
4.0
6.0
8.0
10.0
12.0
18-24 25-44 45-54 55-64 65-74 75-84 >=85
Age groups in years
Male
Female
Percent
4
services and outpatient care as well as other medical services not covered by Part
A, such as occupational therapy.
For Part C, with the passage of the 1997 Balanced Budget Act, Medicare
beneficiaries have been given the option to enroll in capitated health insurance
(Part C plans) instead of through the original fee for service (FFS) Medicare
payment system (Centers for Medicare & Medicaid). Initially known as
“Medicare+Choice”, most of the Part C plans have been re-branded as
“Medicare Advantage” plans since the Medicare Modernization Act (MMA) of
2003.
Medicare Part D, also known as the Medicare prescription drug benefit,
went into effect on January 1, 2006 as a result of the passage of MMA. It is a
federal program to subsidize the cost of prescription drugs and prescription drug
insurance premiums for Medicare beneficiaries. Anyone with Medicare Part A or
B is eligible for Part D. If they do not have drug coverage under their retirement
insurance plan, beneficiaries must enroll in a stand-alone Prescription Drug Plan
(PDP) or Medicare Advantage plan with prescription drug coverage (MA-PD)
when they are first eligible, or pay a penalty if they choose to join later. These
plans are approved and regulated by the Medicare program but designed and
administered by private health insurance companies and pharmacy benefit
managers. Plans choose which drugs or drug classes to cover and decide how
much of the drug cost to reimburse. (Centers for Medicare & Medicaid, 2013).
The Medicare program provides different levels of coverage for elderly
patients with COPD depending on the type of care and treatment they need. For
5
example, Medicare Part A provides some coverage if patients were hospitalized
due to exacerbation; Part B helps pay for smoking cessation counseling or
pulmonary rehabilitation if COPD is moderate to severe, and oxygen therapy
(equipment, supplies, and oxygen) (Medicare-Made-Clear, 2014). Since 2006,
pharmacologic treatments such as bronchodilators and inhaled steroids are
covered by Medicare Part D, with patients responsible for deductibles and
copayments. However, as noted earlier, the amount of patients’ out-of-pocket
(OOP) expenses and the specific medications included in the coverage vary
across different Part D plans.
Problem Statement
Burden of COPD and Challenges in COPD Management
COPD has a significant health impact and may generate a cycle of
physical, social, and psychosocial consequences (GOLD, 2014). In the early
stages of COPD, dyspnea may occur during daily activities such as walking and
climbing stairs, forcing individuals to limit their daily tasks at home and avoid
social situations involving these activities. Increasing inactivity reduces patients’
fitness level and leads to worsening shortness of breath, further immobility and
lack of fitness. Thus, patients with COPD are significantly less active than
healthy individuals (Pitta et al., 2005), and they are 11 times more likely to
report fair or poor health, 10 times more likely to report depression, and 5.5
times more likely to report poor sleep (Eisner, Yelin, Trupin, & Blanc, 2002).
Comorbidities are common in patients with COPD. Significantly higher rates of
both respiratory and non-respiratory comorbidities were reported in individuals
with COPD when compared to those without COPD (Darkow, Kadlubek, Shah,
6
Phillips, & Marton, 2007). The common comorbid conditions include: asthma
and other lung disease, hypertension, cardiovascular conditions such as
myocardial infarction, angina and heart failure, diabetes, depression,
osteoporosis, respiratory infection, bone fractures, sleep disorders, excessive
weight loss and muscle wasting, and glaucoma (ATS/ERS, 2004; GOLD, 2014).
The economic burden associated with COPD is also substantial. The total
cost to the United States for COPD was projected to be close to $50 billion in
2010, including $29.5 billion in direct healthcare expenditures: $13.2 billion in
hospital care, $5.5 billion in physician services, and $5.8 billion in prescription
drugs (American-Lung-Association, 2014; NHLBI, 2009). Indirect costs account
for over $20 billion of the expenditures, including $8 billion in indirect
morbidity cost and $12.4 billion in indirect mortality cost (American-Lung-
Association, 2014). COPD expenditures exceed the costs associated with heart
failure and is over two-fold higher than the cost for asthma (NHLBI, 2007). In
terms of healthcare resource use, COPD is associated with 636,000 hospital
admissions and more than 15 million physician office visits each year in the
United States (NHLBI, 2007).
COPD is more prevalent in older adults, with approximately two-thirds
of COPD patients aged 50 years or older (American-Lung-Association, 2013)
and 50% aged 65 years or older (Centers for Disease Control and Prevention,
2012); therefore, management of COPD has become one of the priorities for
CMS. However, managing COPD in the Medicare population is exceptionally
challenging due to the following reasons:
7
• Complex clinical management of COPD. The diagnosis of
COPD is not straightforward due to the early physical
symptoms of COPD common to many conditions. Ongoing
monitoring and responsive disease management is required as
the disease progresses and needs to be patient-specific, guided
by severity of disease, risk of exacerbation, drug availability,
and patient’s response (GOLD, 2014).
• The presence of other comorbid conditions in elderly patients
with COPD. Co-existing conditions such as asthma,
pneumonia, or congestive heart failure add more challenges to
COPD management. Managing the care of patients with
comorbid conditions incurred about 1.4 to 2.1 times higher
expenditures when compared to managing the care of
similarly aged Medicare beneficiaries with COPD who did
not have comorbidities (Blanchette, Gutierrez, Ory, Chang, &
Akazawa, 2008; Grasso, Weller, Shaffer, Diette, & Anderson,
1998).
Furthermore, managing elderly COPD patients is expensive and requires
a large amount of healthcare resources. The estimated total 12-month
expenditures in Medicare FFS beneficiaries with COPD were $8,482 on average
in 1992, about 2.4 times higher than the mean expenditures in those without
COPD ($3,511) (Grasso et al., 1998). The average 12-month total medical
8
spending in community-dwelling Medicare beneficiaries with COPD was as
high as $21,488, 10 years later (B. C. Stuart et al., 2007).
Medication Adherence in COPD
COPD is a progressive illness with worsening symptoms, therefore
patients need to be prescribed appropriate therapies and have ongoing
assessment to manage their disease and improve their health status (Make, Dutro,
Paulose-Ram, Marton, & Mapel, 2012). Pharmacotherapy is a cornerstone of
COPD management, and maintenance medications are effective in controlling
symptoms, maintaining lung function, and preventing COPD exacerbations
(ATS/ERS, 2004; GOLD, 2014).
Despite the availability of evidence-based guidelines and advances in the
management of COPD, research suggests that about 66% of commercially
insured patients with COPD and 71% of Medicare beneficiaries with COPD
were not prescribed any maintenance therapy (Make et al., 2012). Although
COPD medication adherence approached 80% in clinical trials (Vestbo et al.,
2009), observed adherence levels in real-world settings ranged from 10% to 60%,
and only half of all patients continuously used their prescribed medications for
one year (Breekveldt-Postma, Koerselman, Erkens, Lammers, & Herings, 2007;
Charles et al., 2010; Rand, 2005; Restrepo et al., 2008; Simoni-Wastila et al.,
2012; Toy et al., 2011).
Research has revealed a series of factors associated with COPD
medication non-adherence, including age and disease duration (Osterberg &
Blaschke, 2005), patients’ belief in their health and medication effectiveness
(Khdour, Hawwa, Kidney, Smyth, & McElnay, 2012); severity of COPD
9
(Cecere et al., 2012; Khdour et al., 2012); smoking status (Cecere et al., 2012;
Khdour et al., 2012); presence of comorbidities, especially depression (Khdour
et al., 2012; Yohannes, Baldwin, & Connolly, 2006); medication regimen
complexity (Cecere et al., 2012); and perception of provider skill (Cecere et al.,
2012). In addition to clinical and psychosocial variables (Charles et al., 2010),
financial factors are also important to treatment decision making. Cost sharing,
copayment, availability of generic versions of medications, and level of
insurance benefit coverage are found to be independent risk factors for
medication non-adherence among patients with chronic conditions (Dusetzina,
Winn, Abel, Huskamp, & Keating, 2014; Gibson et al., 2006; Gu, Zeng, Patel, &
Tripoli, 2010; Ho, Bryson, & Rumsfeld, 2009; E. Kim et al., 2010; Shrank et al.,
2006; WHO, 2003).
Coverage Gap in Medicare Part D
Although Medicare Part D provides prescription drug coverage to
support elderly patients with COPD for their pharmacological treatment, the
deductible structure for Part D is a complex design with multiple payment tiers
for Medicare and beneficiaries that creates a coverage gap for beneficiaries. The
deductible structure in a standard Part D plan can be summarized as:
1) First, there is a deductible (e.g., $295 in 2009) that the
beneficiary pays out-of-pocket before an insurer makes any
payments;
2) Second, once the initial deductible is met, subsequent
payments are split between the beneficiary (25%) and the
10
Medicare Part D plan (75%) up to a threshold (e.g., $896.25
in 2009);
3) Third, once this threshold is reached, a second deductible (e.g.,
$896.25 to $4,350.25 in 2009) is required for and paid solely
by beneficiaries up to a second threshold (e.g., $4,350.25 in
2009);
4) Finally, once the second threshold is met (e.g., $4,350.25 in
2009), additional payments are once again split between the
beneficiary (5%) and Medicare (95%), which is known as
catastrophic coverage.
The second deductible incurred in the third payment tier is a coverage
gap known as a “donut hole” because beneficiaries have to pay full cost of their
prescription drugs before they enter the final payment tier (catastrophic
coverage). The deductible amount thresholds vary by year. The coverage gap
was included in the Part D benefit design because the cost of providing
continuous coverage for prescription drugs with no gap would have exceeded the
budgetary limit imposed by the MMA (Jack Hoadley et al., 2007).
About 1.5 million beneficiaries reached the coverage gap in 2006 when a
large number of Part D enrollees had less than full year benefit (IMS, 2007), and
over 3 million of the 24 million Part D enrollees were estimated to reach the gap
in 2007 when most enrollees had full-year coverage (Hoadley et al., 2007).
Many beneficiaries with moderate to high drug expenses, especially those with
chronic illness such as COPD, face breaks in coverage that result in high out-of-
11
pocket cost for prescription drugs. Under these circumstances, beneficiaries may
adjust their demand for drug therapies. For example, beneficiaries may
discontinue the effective brand-name drugs they were taking and switch to a
cheaper but less effective treatment after they hit the coverage gap.
Research Questions
The Part D coverage gap raises concerns that enrollees who reach the
threshold might forgo needed medication because they face the full cost of their
prescriptions they during the gap. Multiple studies have assessed the effect of the
coverage gap on medication adherence in elderly patients (Polinski et al., 2011)
and in those with different chronic diseases including cardiovascular disease
(Jung, Feldman, & McBean, 2014; Polinski et al., 2012), diabetes (Gu et al.,
2010; Zeng, Patel, & Brunetti, 2013), mental illness (Fung et al., 2013), heart
failure (HF) (Nair et al., 2011), hypertension (Nair et al., 2011), and acute
myocardial infarction (B. Stuart et al., 2013). Findings have shown that the use
of brand-name medications was significantly decreased for most conditions, but
adherence to antidepressants or HF drugs did not change or was only slightly
reduced. However, no published studies have been found in the existing
literature, which evaluate the effect of the Part D coverage gap on medication
utilization among Medicare beneficiaries with COPD. In addition, the research
related to the effect of Part D coverage gap on resource use and cost is very
limited.
This study assessed the impact of Part D coverage gap among
beneficiaries diagnosed with COPD on three types of outcomes: (1) adherence to
COPD maintenance medications, (2) healthcare resource use, and (3) medical
12
cost. The following research questions guided the study: compared to the
Medicare beneficiaries with COPD who were not exposed to the Part D coverage
gap,
1) Is medication adherence lower for Medicare beneficiaries
with COPD who reached the Part D coverage gap?
2) Is total healthcare resource use (HRU) higher for Medicare
beneficiaries with COPD who reached the Part D coverage
gap?
3) Is the total medical cost (non-drug) higher for Medicare
beneficiaries with COPD who reached the Part D coverage
gap?
The findings from this study contributed to the literature in several ways
and have a number of policy implications. By examining the impact of Part D
coverage gap on medication adherence, resource use, and medical cost among
patients with COPD, research on the effect of the coverage gap is expanded to
another prevalent and serious chronic condition and to other important outcomes
beyond adherence, which have not been widely studied in the current literature.
Information derived from this study can help researchers and policy makers
better understand issues related to Medicare Part D and its coverage gap so as to
(1) further address potential issues related to Medicare Part D benefit design; (2)
provide relevant evidence for policy makers to form legislation or make reforms
to support the potential phase-out of Part D coverage gap by 2020; and (3)
inform clinicians, insurers, and public health administrators regarding the
13
association between cost-sharing and patient behavior, especially medication
adherence. A good balance between cost and health outcomes should be taken
into account in future benefit design to maximize the treatment benefit among
patients with chronic diseases such as CODP.
14
CHAPTER TWO
LITERATURE REVIEW
This chapter reviews the extant literature on the impact of Medicare Part
D and the coverage gap on adherence, healthcare resource use, and cost,
followed by a description of the theoretical framework related to the research
questions, concluding with the development of research hypotheses based on the
relevant empirical and theoretical literature. In the literature review section,
research on the impact of Medicare Part D is reviewed first, followed by a
review of the research on the impact of Medicare Part D coverage gap. Both
sections are organized around the three types of outcomes: adherence, healthcare
resource use, and cost. For adherence, the evidence is summarized for the
general Medicare population and subgroups with different chronic conditions
separately, when such research was available. Similarly, research related to
healthcare resource use and cost is summarized separately for drug-related and
non-drug related (medical or total) services when such studies were available.
Impact of Medicare Part D
Since 2006, when Part D was added to Medicare program to provide
outpatient prescription drug coverage, Medicare Part D has been claimed as a
success story for both beneficiaries and tax payers. Below are some highlights
that have been discussed:
• Part D spending is about 45% lower than the original 2004-
2013 projections. The 10-year projected cost has been reduced
15
by $100 billion in each of the last three years (Congressional
Budget Office, 2011, 2012).
• Average beneficiary Part D premiums are only 50% of the
projected amount. The average monthly premium was $30 in
2013 versus the original forecast of $61 (Centers for Medicare
& Medicaid, 2011, 2012).
• Other medical spending declined significantly after Part D.
Research showed that the implementation of Part D was
associated with a $1,200 decrease per beneficiary in annual
non-drug medical cost in both 2006 and 2007 (McWilliams,
Zaslavsky, & Huskamp, 2011).
• With Part D expanded coverage for seniors, beneficiaries are
highly satisfied with the program. In 2011, 90% of
beneficiaries had comprehensive drug coverage, and most of
them said their coverage worked well. Medicare and Medicaid
dually-eligible beneficiaries exhibited the highest satisfaction
(KRCresearch, 2013).
A large amount of research has been conducted to evaluate the impact of
Medicare Part D after its implementation. This research has mostly been
concentrated in the following areas: drug utilization; cost, including out-of-
pocket (OOP) cost or non-drug/other medical cost; healthcare resource use such
as hospitalizations, disparity in healthcare access, healthcare practice in nursing
homes; and medication adherence by patients with various chronic diseases
16
including hypertension, depression, diabetes and osteoporosis. Considering the
large number of published studies and the relevance to the research questions
presented in this proposal, the literature review on this topic focused on
quantitative analysis of the impact of Medicare Part D on medication adherence,
healthcare resource use, and cost in Medicare population and is organized by
these three outcomes of interest.
Impact on Medication Adherence
Medication non-adherence has been an issue reported by older
Americans owing to unaffordable OOP drug cost (Piette, Heisler, & Wagner,
2004; Safran et al., 2005; Safran et al., 2002). The implementation of Medicare
Part D was intended to address medication non-adherence by increasing access
to medication. Several studies have found that self-reported cost-related non-
adherence (CRN) among Medicare beneficiaries significantly decreased after
Part D implementation and the reduction was even larger in 2007 than in 2006,
with odds ratios ranging from 0.58 to 0.85 (Kennedy, Maciejewski, Liu, &
Blodgett, 2011; Madden, Graves, Ross-Degnan, Briesacher, & Soumerai, 2009;
Madden et al., 2008). One exception to this finding was that the significant
change in CRN was not observed in beneficiaries with fair-to-poor health
(Kennedy et al., 2011; Madden et al., 2008). The effect was consistent among
subgroups defined by disability status and the number of morbidities.
Beneficiaries with Different Chronic Diseases
Diabetes, hyperlipidemia, and hypertension are common chronic
conditions occurring in elderly people. Zhang et al. (2010) evaluated adherence
over six months among beneficiaries with diabetes, hyperlipidemia and/or
17
hypertension by comparing their medication possession ratio (MPR) based on
the level of their prior drug coverage. The authors found that Part D improved
MPRs by about 13 percentage points for hyperlipidemia and hypertension and
18 percentage points for diabetes among those without prior prescription drug
coverage. Improvement was lower, however, in groups with limited prior
coverage. It was also noted that even with the Part D benefit, about 50-80% of
these beneficiaries still did not attain good adherence (i.e., MPR≥80%) (Zhang,
Lave, Donohue, et al., 2010).
Diabetes. Employer-based retiree drug benefits have long been viewed as
the gold standard of drug coverage for Medicare beneficiaries. One study
examined anti-diabetic agent adherence for Medicare members who enrolled in
Medicare PDP compared to adherence for those who enrolled in retiree plans to
assess the effect of Part D on adherence of anti-diabetics (B. Stuart et al., 2011).
Similar adherence was observed across the two groups of patients, suggesting
that Part D benefit helps Medicare beneficiaries with diabetes to be comparably
adherent with their therapies to those with retiree drug benefits.
Depression. The beneficial effect of Part D coverage on medication
adherence was not consistent for beneficiaries with depression. The self-reported
CRN was less affected or not found to decline among respondents with
depressive symptoms (Zivin, Madden, Graves, Zhang, & Soumerai, 2009) or
depression (Kennedy et al., 2011). However, one study found the CRN
significantly decreased for groups of beneficiaries with no coverage, coverage
from Medigap plans, or coverage from Medicare HMO (ORs between 0.4 and
18
0.6) (Safran et al., 2010). The effect of the Part D program on medication
adherence among beneficiaries with depression was further evaluated in another
investigation using Medicare Advantage claims data (Donohue et al., 2011). The
study measured adherence by using MPR over a six month period after an index
antidepressant prescription, stratified by levels of prior drug coverage, and
showed that the odds of being adherent (MPR>=80%) was significantly higher
after Part D among the groups whose coverage improved with Part D (OR=1.86
for no prior coverage group; OR=1.74 for prior $150 cap group; OR=1.19 for
prior $350 cap group).
In summary, Medicare Part D has shown a positive effect on improving
medication adherence in general and in various disease populations, though the
effect is not consistent in the depressive population.
Impact on Healthcare Resource Use
The literature related to the impact of Part D on healthcare resource use is
limited and has shown mixed results. Afendulis et al. (2011) used hospital data
from 2005 to 2007 and linked it with state-level drug coverage data for 23 states
to compare changes in the probability of hospitalization before and after Part D
implementation (Afendulis & Chernew, 2011). The authors reported that Part D
reduced the overall rate of hospitalization by 4.1% and about 42,000
hospitalizations were avoided annually for eight ambulatory care sensitive
conditions (i.e., short-term complication of diabetes, uncontrolled diabetes,
COPD, CHF, angina, asthma, stroke, and AMI). Their calculation was
extrapolated to all 50 states and the District of Columbia in the United States,
with the assumption that the same relationship between drug coverage rate
19
changes and the trend in the ACSC hospitalization rate found in the 23-state
sample also prevailed in other states. This calculation indicated that the annual
number of hospitalizations avoided would have been approximately 76,000 per
year (Afendulis, He, Zaslavsky, & Chernew, 2011). In contrast, Liu and
colleagues did not find a statistically significant decrease in emergency room use
and hospitalizations as a result of the Part D coverage during the first year of
implementation (Liu et al., 2011).
Impact on Cost
Out-of-Pocket (OOP) Cost for Drugs
One intention of implementing Part D is to increase beneficiaries’ access
to prescription drugs by decreasing their OOP cost. A number of studies have
investigated the effect of Part D on OOP spending using longitudinal claims data
or other applicable data sources. Multiple studies reported a range of 13%-18%
decrease in patients’ OOP cost ($143 to $148 reduction per year) (Briesacher et
al., 2011; Ketcham & Simon, 2008; Polinski, Kilabuk, Schneeweiss, Brennan, &
Shrank, 2010; Yin et al., 2008; Zhang, Lave, Newhouse, & Donohue, 2010).
One study estimated the OOP cost from 2003 to 2006 among non-
institutionalized Medicare beneficiaries who enrolled in Part D and stratified the
results by prior drug coverage status (no coverage, with coverage from Medicare
HMO, with coverage from Medigap, or employer-sponsored coverage). The
study found significantly lower odds of spending more than $100 or $300 per
month on prescription drugs after Part D in all groups except for the group with
prior employer-sponsored coverage (Safran et al., 2010). However, mixed
findings were reported for different subgroups of Medicare beneficiaries. For
20
example, the cost reduction was not significant for elderly dual-eligible
beneficiaries (Polinski et al., 2010).
Furthermore, Basu, Yin, and Alexander compared beneficiaries’ OOP
expense and total expenditures in the first 18 months after the Part D
implementation between a “treatment” group (65-78 years old patients with dual
eligibility on 1/1/2005) and a “control” group (60-63 years old patients with
Medicaid coverage on 1/1/2005). They found no significant changes in the dual-
eligibles’ OOP cost or total monthly expenditures (Basu, Yin, & Alexander,
2010).
Non-Drug Cost
The research related to non-drug cost for traditional fee-for-service
Medicare members produced inconsistent results (Ingber, Greenwald, Freeman,
& Healy, 2010). One study compared the non-drug medical cost in those with
limited prior coverage and those with generous prior coverage and found that
total non-drug medical cost decreased after 1/1/2006 by an average of $306 per
quarter for beneficiaries with limited prior drug coverage relative to those with
generous prior coverage, mostly attributable to changes in spending on inpatient
(-$204 per quarter) and skilled nursing facility care (-$586 per quarter)
(McWilliams et al., 2011). The findings suggested that Part D was associated
with significant reductions in non-drug medical cost for Medicare beneficiaries
with limited prior drug coverage. Another study estimated the savings from Part
D for beneficiaries with CHF and reported the non-drug cost were $1,827 lower
per beneficiary per year (Dall, Blanchard, Gallo, & Semilla, 2013). The
magnitude was even greater among the previously uninsured population ($2,050
21
per year), but smaller for patients with limited ($773) or moderate ($465)
coverage prior to Part D enrollment. The authors estimated a cost reduction of
$2.9 billion savings in medical expenses, or $2.6 billion if accounting for the
increase in medication spending (Dall et al., 2013).
The potential of Part D to reduce non-drug cost may be limited to certain
disease populations. For example, cost savings on non-drug services was not
observed for the Medicare population with arthritis, where a non-significant
decrease in medical cost was observed (Cheng & Rascati, 2012).
Impact of Medicare Part D Coverage Gap
Despite the beneficial impact of Medicare Part D discussed earlier, the
coverage gap in Medicare Part D is a realistic challenge faced by Medicare
beneficiaries. During the first year of Medicare Part D implementation, large
variations in the number of beneficiaries who fell in the coverage gap for that
year were reported, depending on which plan beneficiaries were enrolled in: 6%-
58.8% for MA-PD plan enrollees (Ettner et al., 2010; Karaca, Streeter, Barton, &
Nguyen, 2008; M. H. Kim, Lin, & Kreilick, 2009; Raebel, Delate, Ellis, &
Bayliss, 2008; Schmittdiel et al., 2009; Schneeweiss, Patrick, et al., 2009; Zhang,
Donohue, Newhouse, & Lave, 2009), 43% for non-Medicaid beneficiaries in
PDPs (Karaca et al., 2008), and 40% for beneficiaries with employer-sponsored
coverage (Zhang et al., 2009). In 2007, a more consistent range of beneficiaries
(18.5%-26%) was reported to reach the Part D coverage gap (J Hoadley,
Hargrave, Cubanski, & Neuman, 2008; Pedan, Lu, & Varasteh, 2009).
Although concerns about potential negative effects of the coverage gap
on Medicare beneficiaries’ adherence and health outcomes were brought up at
22
the beginning of the program, Medicare still incorporated the Part D coverage
gap because the budget to fund the Part D program would have been insufficient
otherwise and policy makers wanted to use this as a means to contain healthcare
expenditures and lower the financial burden of Medicare through cost sharing
with beneficiaries (Rosenthal, 2004).
Impact on Medication Adherence
Medicare Part D with drug coverage is intended to improve access to
prescription drugs by helping patients mitigate the effects of OOP drug cost.
However, the coverage gap can work at cross-purposes with this goal. The U.S.
Department of Health and Human Services estimates that over a quarter of Part
D participants stop filling their prescribed drugs when they hit the coverage gap
(Claffey, 2010).
Adherence to Different Drug Classes
A 2008 report published by the Kaiser Family Foundation was one of the
first studies to examine the impact of the coverage gap on drug use/adherence.
Using national patient-level retail pharmacy claims data for Part D enrollees
from IMS Health and focusing on Part D enrollees taking one or more drugs in
the eight drug classes for common chronic conditions including Alzheimer’s
disease, high cholesterol, depression, diabetes, gastroesophageal reflux disease,
heart failure, hypertension, and osteoporosis, the researchers found that 15%
stopped taking one or more medications, 5% switched to an alternative drug in
that class, and 1% reduced their medication use after reaching the coverage gap
(J Hoadley et al., 2008).
23
Later, using Defined Daily Dose (DDD), Schneeweiss and colleagues
(2009) studied adherence to drugs in four essential medication classes:
clopidogrel, drugs used to treat asymptomatic conditions (statins), drugs used to
treat symptomatic conditions [proton-pump inhibitors (PPIs)], and less
expensive drugs (warfarin) and reported significant decreases ranging from 4.8
percentage points for statins to 6.3 percentage points for warfarin among
beneficiaries reaching the coverage gap (Schneeweiss, Patrick, et al., 2009).
Raebel et al. (2008) compared medication adherence measured by
Medication Refill Adherence (MRA) for six drug classes (statins,
antidepressants, anti-diabetic agents, anti-hypertensives, beta-blockers, and
diuretics) between Medicare members who reached the coverage gap and age-
and gender-matched members who did not reach the coverage gap. The study
found that adherence to chronic medications declined over time from 2005 to
2006 in both groups, but the reduction was greater for beneficiaries who reached
the coverage gap (Raebel et al., 2008). For the group reaching the coverage gap,
the decline in adherence from 2005 to 2006 was statistically significant for all
drug classes except for anti-diabetic agents and beta-blockers, and the decline
was smallest for diabetes drugs (3.4%) and largest for diuretics (8.3%).
Polinski and colleagues (2011) studied community-dwelling FFS
Medicare beneficiaries with prescription drug coverage through PDPs or a
retiree drug plan in 2006 or 2007 regarding changes in their use of
cardiovascular and hypoglycemic drugs (Polinski et al., 2011). They established
an “early Part D” cohort (2005-2006) and an “Established Part D” cohort (2006-
24
2007). Within each cohort, the “exposed” group (coverage gap) was matched
with an “unexposed” group (no coverage gap). Compared to unexposed patients,
exposed patients in both “early Part D” and “established Part D” cohorts had
significantly higher likelihood to discontinue a drug.
Adherence in Beneficiaries with Diabetes
Reduction in adherence related to the coverage gap for beneficiaries with
diabetes has been reported in several studies. Compared to those without the
coverage gap, the odds of adherence among patients with the coverage gap
decreased from 17% to almost 40% (ORs between 0.62 and 0.83). Two
additional studies found that MPR for antidiabetics decreased by 10.3% (Zhang,
Baik, & Lave, 2013), or 2.9 - 3.3 percentage points among patients with the
coverage gap with the biggest impact on the differential rates of starting or
stopping for several expensive drug classes (Joyce, Zissimopoulos, & Goldman,
2013).
Adherence in Beneficiaries with Heart Failure
Compared to the diabetic population, a smaller scale of effect on
adherence was observed in patients with heart failure. After beneficiaries entered
the coverage gap, a reduction of 2.5%-3.6% in adherence was reported by two
studies based on the same database (Baik et al., 2012; Zhang et al., 2013).
Adherence in Beneficiaries with Other Cardiovascular Diseases
Based on a 5% random sample of Medicare claims data, Li and
colleagues (2012) used a quasi-experimental design to determine the magnitude
of the effect of coverage gap on the usage of antihypertensive and lipid lowering
medications among beneficiaries with hypertension and hyperlipidemia (Li,
25
McElligott, Bergquist, Schwartz, & Doshi, 2012). Comparing across four groups
[with low income subsidy (LIS), with generic drug coverage, with generic and
brand-name drug coverage, and no gap coverage], the authors found that the
limited coverage was associated with an increased risk for medication non-
adherence (measured by PDC; OR=1.60 for antihypertensive; OR=1.59 for lipid
lowering drugs) as well as non-persistence (indicated if no supply of medication
for ≥ 30 days; OR=1.38 for anti-hypertensives; OR=1.35 for lipid lowering
drugs). In addition, the generic drug coverage during the coverage period did not
mitigate these effects, and the gap effect did not occur to medications which treat
symptoms such as pain relievers.
Another study based on a 5% random sample of Medicare claims data
assessed the effect of Part D benefit phases on adherence (measured by PDC)
with evidence-based medications (statin, clopidogrel, beta-blocker, and ACE
inhibitor/ARBs) among Medicare beneficiaries following acute myocardial
infarction. Benefit phases were defined as initial coverage phase, coverage gap
phase, and catastrophic coverage phase. For non-LIS enrollees, entering the
coverage phase was associated with significant reductions in mean PDC for all
four drug classes (-7.8% for statins, -7.0% for clopidogrel, -5.9% for beta-
blockers, and -5.1% for ACE inhibitors/ARBs); however, no significant changes
in adherence were observed when beneficiaries transitioned from the coverage
gap to the catastrophic coverage gap (B. Stuart et al., 2013).
Adherence in Beneficiaries with Depression
In several observational studies using claims data, the reported effect of
Part D coverage gap on adherence in the population with depression was
26
insignificant, either no change (Baik et al., 2012) or with modest effect (6.9%-12%
reduction without significance compared to pre-gap) (Zhang, Baik, Zhou,
Reynolds, & Lave, 2012). However, findings from another study based on
qualitative and semi-structured interviews with non-dually eligible Medicare
beneficiaries who had a mental illness suggested that adherence was negatively
impacted by the benefit structure (Bakk, Woodward, & Dunkle, 2014).
Overall, Medicare Part D coverage gap was found to be associated with
reduced medication adherence, although the association in the population with
depression or mental disorder was not conclusive.
Impact on Resource Use
Only one study was identified that examined the impact of the coverage
gap on healthcare resource use. Raebel et al. (2008) found that, compared to age-
and gender-matched members who did not have the coverage gap, those who
reached the coverage gap had 85% higher risk of being hospitalized [incidence
rate ratio (IRR)=1.85] and 60% higher risk of using emergency room services
(IRR=1.60) (Raebel et al., 2008).
Impact on Cost
The effect of the coverage gap on cost has not been widely studied and
most of the published research has focused on OOP cost. Limited data have been
reported regarding the impact of the coverage gap on other types of cost such as
non-drug cost.
OOP Cost
Multiple studies have shown that beneficiaries with the coverage gap had
higher OOP cost than those without (Sun & Lee, 2007). However, when OOP
27
costs were compared between the pre- and post-gap periods, Sun and Lee
reported that costs fell 28% from $2,441 to $1,757 due to the reduced days of
therapy by 16% (Sun & Lee, 2007). Similarly, for Medicare beneficiaries with
diabetes or heart failure, an overall decrease in monthly medication spending
after they entered the gap was observed, primarily due to the decreased use of
brand-name drugs (Zhang et al., 2013). The group without gap coverage had
their monthly pharmacy spending reduced by $73.15, on average, of which
$66.65 was for brand-name drugs. Although the coverage gap does disrupt the
use of prescription drugs among seniors with diabetes, the declines in usage are
modest and concentrated on higher cost, brand-name medications.
Non-Drug Cost
Only one study was identified regarding the effect of the coverage gap on
non-drug cost. Based on a 5% random sample of Medicare data, Zhang et al.
(2012) found that there were no significant increases in non-drug medical cost
after beneficiaries entered the coverage gap among those who were diagnosed
with depression and continuously enrolled in stand-alone Part D plans in 2007
(Zhang et al., 2012). In sum, consumption of brand prescription drugs was
impacted more than generic drugs as a result of the coverage gap and the effect
on non-drug cost was not conclusive.
Brief Review of Research Design/Methods
All of the reviewed research consisted of observational studies.
Difference-in-Difference or pre-post comparison was the most common design
used to reduce the potential selection bias between beneficiaries with and
without the coverage gap. Adjustment for confounders were incorporated in
28
most of the studies and propensity score matching technique was often used
when an “exposure” group (having coverage gap) and a “control” group (no
coverage gap) were defined. Stratifications in the “exposure” group were often
based on benefit design related to the coverage gap, i.e., coverage gap without
any additional benefit, coverage gap with additional generic drug benefit, or
coverage gap with additional benefit for both generic and brand-name drugs.
Assessment of Literature
The preceding review of the literature on the effects of Medicare Part D
indicates that Part D is associated with lower OOP cost, especially for long-term
medications; however, differences across subgroups of beneficiaries have been
observed. Studies on non-drug medical cost and resource uses were limited and
mixed results were reported. Some recent studies demonstrated savings and
reduction in non-drug medical cost and services attributable to Part D, but also
showed differences across subgroups.
As shown by the review, the coverage gap has posed a significant
challenge to beneficiaries in terms of their medication adherence and cost-
sharing burden. Although some research reported an offset effect on the OOP
cost when utilization of brand-name drugs decreased as a result of the coverage
gap, suboptimal health outcomes in beneficiaries and unexpected economic
burden to Medicare may still result from the coverage gap.
The review also highlights several gaps in the extant literature, including:
1) The majority of the research was based on the enrollees in
MA-PDs. Limited evidence was available on the impact of
Part D coverage gap among PDP enrollees. The beneficiaries
29
in PDP may have different characteristics compared to the
beneficiaries in MA-PDs, and they can experience different
benefit designs. So they may respond to the Part D and the
Part D coverage gap quite differently. Investigating the effect
of Part D coverage gap on the impacted beneficiaries in
various health insurance systems will be helpful to gain better
understanding regarding the Part D program.
2) Research on the impact of Part D or Part D coverage gap has
been devoted to heterogeneous subgroups of Medicare
beneficiaries defined by chronic diseases. However, the
current research has been limited to subgroups with diabetes,
depression, heart failure, and myocardial infarction. Given the
debilitating disease progression and the substantial burden of
COPD, as well as the high prevalence of COPD in the
Medicare population, assessing the impact of Part D coverage
gap on the COPD population would be useful to future
management of COPD in Medicare.
3) Study outcomes in the assessment of the impact of Part D
coverage gap are typically medication utilization (e.g.,
percentage of usage), adherence (PDC, MPR, MRA), and
OOP cost. There is limited evidence regarding the impact on
resource use and non-drug cost or total cost. Assessing the
impact of Part D coverage gap on overall resource use or non-
30
drug cost beyond adherence may help to build a holistic
picture regarding the consequences of the Part D coverage gap
from both clinical and economic perspectives, and may
provide stronger evidentiary support for refining policy or
developing strategies to solve identified problems.
Theoretical Framework
Theory Overview
Medication adherence is basically a patient’s choice regarding whether
he/she will take medicine, and how frequently or for how long he/she will take
the medicine. A patient, as a self-interested individual, usually makes such a
choice based on his/her preferences to maximize gain and to minimize loss.
Similarly, when a patient makes a decision to go to a physician’s office or an
emergency room, or when a patient decides how to spend money on healthcare,
he/she makes a preferred choice and takes rational actions to optimize his/her
benefit. One of the theories describing this behavior is rational choice theory,
also known as choice theory or rational action theory. Rational choice theory
provides a framework for understanding and modelling social and economic
behavior (Blume & Easley, 2008; Sen, 2008).
Rational choice theory was pioneered by sociologist Goerge Homas and
was developed further by other theorists such as Blau, Coleman, and Cook
during the 1960s and 1970s (Scott, 2000). This theory evolved to a formal
mathematical model of rational choice, and even became a basis of a Marxist
theory of class and exploitation. The theory not only has a strong economic
orientation, such as quantitative measurement of cost or profit and utility
31
motivated analysis, but also has a robust linkage to social and sociological
factors (Calvert, 1994).
Rational choice theory provides an approach to assessing decision-
making based on empirical evidence, understanding choices, and rationalizing
the inferences and conclusions. It consists of systematic evaluation of choice
options through an analysis of the various consequences of the choices,
including validity, rationality, value, and risk. Intuitively speaking, as people are
often motivated by the potential of making a profit or impacted by financial
factors such as cost, they tend to calculate the possible cost and benefits of any
action before deciding what to do.
Rational choice theory is built upon one central assumption, which is that
all social phenomena can be explained in terms of the individual actions that lead
to the phenomena. Another key element is the belief that all actions are
fundamentally “rational” or rationally motivated, even though some may appear
to be irrational, such as discontinuing effective therapy and seeking care in an
emergency room when the disease relapses. However, rational choice theory has
been criticized for its overemphasis on individualistic actions and inadequate
explanations of collective actions and social norms such as altruism, reciprocity
and trust, and situations where collective and non-individual benefits are pursued
(e.g., not-for-profit charity organizations or groups of volunteers) (Scott, 2000).
The application of rational choice theory is inherently a multilevel
enterprise and the theory is often used as an assumption of the behavior of
individuals in microeconomic models of human decision-making (Coleman,
32
1990; Hechter & Kanazawa, 1997; Hedström & Stern, 2008; Lohmann, 2008).
As depicted by Figure 2, at the lower level (micro level), the rational choice
model contains assumptions about individual cognitive capacities and values as
indicated by a lower case of x, and actions or behaviors taken by individuals are
indicated by a lower case of y. At the higher level (macro level), the rational
choice model includes specifications of different scenarios, where the upper case
of X is the current scenario for individuals at Time 1 and the upper case of Y is
the new scenario resulting from the actions of individuals at Time 2. In this
model, relationship 1 indicates that individual cognitive capacity or values (x)
are shaped by current scenario (X); relationship 2 describes how a person subject
to a given scenario at Time 1 will behave at Time 2 (y) based on his or her
values or beliefs (x); and relationship 3 denotes the change of the scenario (Y) as
a result of these behaviors (y). For example, the belief in the importance of
taking medications (x) is associated with how much an individual needs to pay
for medication and other healthcare resources (X). An individual may switch
from taking medication to using physician services (y) if he or she thinks
physician services are more affordable (x). As a result of these behaviors (y),
medication adherence is reduced and physician office visits are increased (Y).
33
Figure 2. Rational choice theory model.
Note: Adapted based on Figure 1 from Hechter & Kanazawa, Annu Rev Sociol (23):191-214.
Application of the Rational Choice Theory
Patients usually have a choice of whether to follow, modify or reject a
prescribed treatment. The choice is influenced, however, by attributes of the
treatments and other contextual factors such as new health policy or changes in
existing policies. More specifically, non-adherence may be explained rationally
by the following reasons:
• A patient’s belief that the treatment is not working;
• Side effects that negatively affect patient’s quality of life;
• Practical barriers to the treatment such as high cost; and
• Patients wanting to check if the illness is still there when they
stop taking medications.
Time 1 Time 2
Cognitive capacities,
values, beliefs
Actions, behaviors, Choices
Current scenario New scenario
34
When rational choice theory is applied to assess the impact of Medicare
Part D coverage gap on medication adherence, the consumption of goods and
services (i.e., prescription drugs and healthcare services in this case) is largely
driven by cost (i.e., out-of-pocket cost in this case). Medicare patients are
“rationally” motivated by their health needs and goals when they decide to take
their prescription drugs. When patients are aware of the Part D coverage gap,
they may make a rough prediction of their healthcare needs based on their
current health status, calculate their drug expenses at the beginning of a year, and
take “rational” actions to adjust their medication-taking and care-seeking
behavior accordingly. If taking medications is more affordable than healthcare
received in other settings, beneficiaries are more likely to comply with their
therapy to maintain their health status instead of using other resources.
However, in the situation where taking medications becomes more
expensive than seeking care in a physician office, emergency room (ER), or
hospital, beneficiaries may skip or forgo their prescription drugs or switch to less
costly generic drugs. Alternatively, they may leverage their Part A or B benefit
to use alternative resources more frequently when they cannot control symptoms
or deteriorate after discontinuing medications. In the end, beneficiaries will most
likely make the choice that they think can give them the greatest value or best
outcome possible. This thought process may result in several different scenarios
related to the coverage gap, including:
1) If Medicare beneficiaries are relatively healthy, they do not
need to worry about the coverage gap because the probability
35
of reaching the coverage gap is very small. In this case, their
medication taking behavior is not expected to have a
noticeable change as a result of the introduction of the
coverage gap, and their overall healthcare resource use and
cost are not expected to fluctuate as well.
2) If beneficiaries are marginally sick, they may carefully
arrange or organize their medication needs so that they may
manage to enter the coverage gap later during the year. Their
consumption of medications or alternative healthcare
resources can be marginally impacted by the coverage gap,
for example, they may potentially skip one or two
prescriptions or stop medications for a short period of time,
and seek care from healthcare providers when needed.
3) If beneficiaries have suboptimal health status, they are more
likely to enter the coverage gap and enter the coverage gap
earlier during the year. Their medication-taking behavior is
expected to shift dramatically after they reach the coverage
gap in comparison to before reaching the coverage gap. They
may not refill their medications by schedule or even stop
taking their medication completely because they have to
assume full cost of the prescription drugs. In this case,
beneficiaries may greatly increase the use of other healthcare
resources to achieve their health goals.
36
4) If beneficiaries are very sick, they may quickly enter and exit
the coverage gap and then enter the catastrophic coverage. As
these patients may want to maximize their benefit from the
catastrophic coverage, the coverage gap may exert a very
different effect on their medication-taking and care-seeking
behavior. They may use medication as much as possible at the
beginning of year so that they can be covered by the
catastrophic benefit as soon as possible. Substitute usage of
other healthcare resources may be less aggressive among
these patients.
As summarized in the literature review section, the Part D coverage gap
has resulted in significant reduced medication adherence. Although direct
evidence of the effect on healthcare resource use and cost is limited, a
relationship can be conjectured based on past research that has demonstrated
non-adherence to prescribed medications to be associated with “poor health
outcomes for patients, missed opportunities for therapeutic gain, and increased
health care cost often associated with a worsening of the condition being treated”
(Clifford & Coyne, 2014, p. 650). Therefore, the following hypotheses were
tested in this study:
1) Among Medicare beneficiaries with COPD, the coverage gap
will be associated with lower medication adherence to COPD
long-term maintenance therapies.
37
2) Among Medicare beneficiaries with COPD, the coverage gap
will be associated with higher consumption of healthcare
resources (non-drug).
3) Among Medicare beneficiaries with COPD, the coverage gap
will be associated with higher all-cause medical cost (from
payer’s perspective).
38
CHAPTER THREE
METHODS
This chapter describes the methods used in the study. It consists of study
design, data sources, sample selection, outcome measures, and data analysis.
Study Design
This was a retrospective cohort study using longitudinal observational
data. Details on the study cohort definitions are specified in the Study Cohorts
section. The primary analysis was focused on adherence to COPD long-term
maintenance therapies, all-cause medical healthcare resource use (HRU), and all-
cause medical cost during a calendar year between 2007 and 2010 among the
Part D beneficiaries with COPD who were at risk of and reached the coverage
gap (“exposure” cohort) as compared to those with COPD who were not at risk
of the coverage gap. Those who were dually eligible or had other drug benefits
that covered both brand and generic drugs during the gap (“control” cohort) were
not subject to the coverage gap, and thus were not included in the study.
In addition to the primary analysis, subgroups were constructed within
the exposure cohort based on when they reached the coverage gap. Specifically,
beneficiaries were assigned to the late-gap group if they reached the coverage
gap in November or later, the mid-gap group if between March and November,
or the early-gap group if earlier than March (detailed definitions are described in
the Study Cohort section). Outcomes prior to the coverage gap (pre-gap) and
during the coverage gap (in-gap) were compared between the subgroups. As the
39
focus of this study is the impact of Part D coverage gap, the period after COPD
patients reached the catastrophic threshold is considered out of the study scope
and was not included in this study.
High-Dimensional Propensity Score Analysis and Matching
In an observational study, selection bias is an important issue when
comparing groups and exposure is not randomly assigned. Propensity score
matching (PSM) was used in this study to generate comparable exposure and
control cohorts with balanced demographic and clinical characteristics. A
propensity score for an individual is the conditional probability of receiving
exposure given the observed factors or characteristics prior to exposure (Little &
Rubin, 2000). Therefore, individuals with similar propensity scores will tend to
have similar levels of the covariates, thereby removing or minimizing the bias
due to the covariates (Little & Rubin, 2000). Generally, a logistic regression is
constructed to estimate the propensity of subjects getting treatment, with the
dependent variable being the treatment received (dichotomous variable with 1
and 0) (D'Agostino, 1998).
Following the calculation of propensity scores, selection bias can be
accounted for in multiple ways: stratification, adjustment in a regression analysis,
and matching (D'Agostino, 1998). Unlike regression adjustment that is made
during the calculation, matching removes or minimizes the bias before
estimating the effect. Generally, matching is employed when there is a relatively
large sample size and a sufficient number of confounders that are available or
can be created for matching. After matching the observations in the two groups
on their propensity scores, the significance of differences in outcomes between
40
the two matched groups will be analyzed using techniques for non-independent
samples or matched pairs.
In a traditional approach to generating propensity scores, a number of
relevant confounders or covariates are defined based on available data and then
specified in the logistic model. Model specification is primarily guided by
knowledge related to exposure and the study population characteristics. When
using longitudinal healthcare claims data, the covariates typically include
demographics (e.g., age, gender), history of major medical conditions, overall
comorbidity scores, prior medication use, or history of healthcare resource use
(e.g., ER, hospitalization, physician office visits) over a given period of time
before exposure initiation (Charlson, Pompei, Ales, & MacKenzie, 1987; Gagne,
Glynn, Avorn, Levin, & Schneeweiss, 2011; Romano, Roos, & Jollis, 1993;
Schneeweiss et al., 2001). However, these covariates are not always adequate to
specify the degree of certainty/uncertainty in the causality between exposure and
outcome.
Although claims data provide rich information about patients and health
services, other important attributes are unavailable (e.g., laboratory results,
functional status, smoking status, over-the-counter medication) (Brookhart,
Sturmer, Glynn, Rassen, & Schneeweiss, 2010). In addition, empirically
identifying appropriate proxies for patient health status out of a large number of
variables in claims data is a significant challenge. In this context, Schneeweiss
and colleagues developed an automated algorithm for healthcare claims data to
set up proxies by assessing diagnosis codes, procedure codes, and prescribed
41
medication codes (Schneeweiss, Rassen, et al., 2009). These empirically
identified variables can be used alone or in conjunction with investigator-
selected variables to estimate a propensity score. This method has been labeled
high-dimensional propensity score (HDPS) analysis.
In this study, the dependent variable for the logistic regression used in the
HDPS analysis was the membership of the exposure or control cohort. The
propensity score indicates the probability of being in the exposure cohort. The
initial exposure and control cohorts were matched 1:1 based on the propensity
score using Greedy matching method (Parsons, 2009).
Data Source
A random sample of 5% of Medicare beneficiaries was used for this
study. The Medicare administrative claims database is a comprehensive data
source covering all beneficiaries who were enrolled in Medicare, capturing
information on demographic characteristics, enrollment, prescription drug events
(PDE), medical encounters in inpatient and outpatient settings, and health
services incurred in other facilities such as hospice or skilled nursing home. The
5% random sample used for this study included data from the Beneficiary
Summary files from 2005 to 2010, Part A and Part B claims from 2005 to 2010,
Part D Event (PDE) Data, and the Plan Characteristics files from 2006 to 2010.
This study was approved by the UAB Institutional Review Board and by the
CMS Privacy Board.
The Beneficiary Summary File provides demographic and enrollment
information about beneficiaries. Starting in 2006, this file also includes Part D
enrollment information. The Part A data file includes information on inpatient
42
hospital stays, including length of stay, diagnosis-related group, department-
specific charges, and up to 10 individual diagnosis and procedure codes. Part B
claims data include claims submitted by physicians and other healthcare
providers and facilities for the services covered by Medicare Part B. Each claim
contains ICD-9-CM (International Classification of Disease, Ninth Edition,
Clinical Modification) diagnosis and procedure codes, date and place of services,
demographic information of beneficiaries, and a physician identification number.
Data from outpatient hospitals, skilled nursing homes, hospice care facilities, and
durable medical equipment (DME) are also included in Part B claims data. All
data files can be linked with the denominator file that provides demographic
information of all the beneficiaries entitled to Medicare, including state and
county codes, zip code, date of birth, date of death, gender, race, and age.
The Part D Event data and the Drug and Plan Characteristics files contain
elements that provide information on beneficiary demographics, plan
characteristics, prescription fill date, drug characteristics (e.g., national drug
code [NDC] number, days of supply, quantity supplied, and fill number), and
cost and payment information (e.g., dispensing fee, patient paid amount, Part D
paid amount). The drug characteristics file can be used to determine the generic
equivalence of different medications (i.e., brand-name, generic name, strength
and dosage form). The plan characteristics file can be used to determine: (1)
whether a particular plan was PDP or MA-PD, (2) whether it offered coverage
for some or all drugs during the coverage gap, and (3) type of cost-sharing
43
strategies (e.g., deductible) used before beneficiaries reach the coverage gap. All
data files can be linked via the unique de-identified ID number.
Sample Selection
General Inclusion and Exclusion Criteria
Because the coverage gap thresholds varied by calendar year, patient
selection and outcome measures were employed at a yearly level. Considering
that many Medicare beneficiaries did not have a full year benefit in 2006, data
files from 2007 to 2010 were used for this study. Beneficiaries who met all of
the following inclusion criteria were selected to form a general patient pool:
• Had “of age” listed as the reason for Medicare eligibility, i.e.,
age is greater than 65 years as of six months prior to January
1 of a calendar year;
• Had a full year eligibility during a respective calendar year
and six months of continuous eligibility prior to January 1 of
the respective calendar year (baseline period);
• Had at least two outpatient claims with a diagnosis of COPD
(ICD-9-CM diagnosis codes: 491.xx, 492.xx, 494.xx, 496.xx)
on different dates OR at least one emergency room (ER) or
inpatient claim with COPD as the primary diagnosis during a
respective calendar year;
• Had at least two prescriptions of long-term maintenance
therapy for COPD (long-acting bronchodilator, LABD) filled
on different dates during a respective calendar year (Table 1).
44
Exclusion criteria:
• Were enrolled with a Medicare Advantage plan in any month
during a respective calendar year;
• Had a diagnosis of asthma (ICD-9-CM diagnosis code:
493.xx) during a respective calendar year, because some of
the LABD medications are also indicated for asthma;
• Had a diagnoses of cancer (ICD-9-CM diagnosis codes:
140.xx-239.xx) during a respective calendar year, because
patients with cancer may have different medication utilization
and spending patterns compared to other Medicare
beneficiaries;
• Had a disability or end-stage-renal-disease (ESRD, ICD-9-
CM diagnosis codes: 585.5x, 585.6x) during a respective
calendar year, because their benefits can differ substantially
from other Medicare beneficiaries.
45
Table 1
Maintenance Medications Used for COPD
Class Medication Generic Names Medication Brand-names (Patent Expiration Time)
LABD Medications
• Arformoterol
• Formoterol
• Indacaterol
• Salmeterol
• Tiotropium
• Budesonide+formoterol
• Fluticasone+salmeterol
• Mometasone+formoterol
• Brovana
• Foradil
• Onbrez/Arcapta
• Serevent
• Spiriva
• Symbicort
• Advair
• Seretide
SABD Medications
• Albuterol+ Ipratropium
• Ipratropium
• Levalbuterol
• Metaproterenol
• Pirbuterol
• Albuterol or Salbutamol
• Combivent
• Atrovent
• Xopenes
• Alupent
• Maxair
• Accuneb, Ventolin, Preventil, Asthalin, Asthavent, ProAir, Airomir, AZMASOL, Ventosol, Asmol,Vospire
Note: LABD – long-acting bronchodilator, SABD – short-acting bronchodilator.
Study Cohorts
Beneficiaries who met the selection criteria were divided into two study
cohorts: a “control” cohort that included beneficiaries who were not subject to or
at risk of the coverage gap and an “exposure” cohort that included beneficiaries
who were subject to the coverage gap. The definitions of these two cohorts are as
follows:
46
Control cohort
If beneficiaries fell into one of the following categories, they were
assigned to the control cohort:
• Had Medicare-Medicaid dual eligibility for the whole year;
• Qualified for Part D low-income-subsidies (LIS), i.e.,
received LIS for at least one month before and after they
entered the coverage gap; or
• Had additional benefits covering brand and generic drugs
during the gap.
Because beneficiaries in the control cohort were not exposed to the
coverage gap, even when their pharmacy spending exceeded the coverage gap
threshold, their drug coverage remained intact throughout the whole calendar
year.
Exposure cohort and subgroups in exposure cohort
If beneficiaries did not have dual eligibility or low-income-subsidies or
full benefit to help with the coverage gap during a calendar year, they were
assigned to the exposure cohort. It should be noted that beneficiaries who were
exposed to but did not reach the coverage gap in a respective year were
identified as “no-reaching gap subgroup.” Though characteristics of this group
were described in subgroup analysis, this subgroup was not included in the main
analysis. This exclusion was based on the assumption that beneficiaries who are
relatively healthy were much less likely to reach the coverage gap; therefore,
their medication-taking behavior was not expected to noticeably change as a
result of presence of the coverage gap.
47
Three additional subgroups were identified for subgroup analysis.
Beneficiaries who reached the coverage gap between March 1 and October 31
were identified as the “mid-gap subgroup”. Beneficiaries who reached the
coverage gap on and after November 1 were identified as the “late-gap
subgroup”. Finally, patients who reached the coverage gap before March 1 were
identified as the “early-gap subgroup”. Similar to the “no-reaching gap
subgroup”, the early-gap subgroup was included in the subgroup analysis but not
in the main analysis. This exclusion was based on the assumption that
beneficiaries who are very sick may have reached the coverage gap early and
wanted to maximize their medication usage during the gap period to enter the
catastrophic phase sooner. This group of patients was anticipated to be small and
to respond to the coverage gap differently than other subgroups. Figure 3 depicts
the subgroup designation within the exposure cohort.
Figure 3. Subgroups of the exposure cohort.
For the subgroup analysis, the day when a beneficiary reached the
coverage gap (i.e., his/her total drug spending reached the coverage gap
threshold in a calendar year) was defined as the gap date. The period prior to the
Jan 1 Mar 1 Nov 1 Dec 31
Late-gap subgroup:
Reach the coverage gap after 10/31
Mar
Mid-gap subgroup: Reach the coverage gap on 3/1--10/31
Early-gap subgroup: Reach the coverage gap before 3/1
Exposure cohort
No-reaching gap subgroup Not reach the coverage gap
48
gap date in a calendar year was defined as a pre-gap period; the period from the
index date until the last day of the coverage gap when a beneficiary reached the
catastrophic coverage or death or the end of a respective calendar year,
whichever was earlier, was defined as the in-gap period.
Outcome Measures
Primary Analysis
Adherence
In past research, methods to evaluate medication adherence included pill
counting, patient diaries, self-reporting, and use of claims data (Hess, Raebel,
Conner, & Malone, 2006). In this study, prescription claims data were used and
medication adherence was assessed at a drug class level, i.e., medications in the
LABD category were treated as one drug class. With retrospective pharmacy
claims data, multiple measures have been used in the literature to measure
adherence, such as the proportion of days covered (PDC), medication possession
ratio (MPR), and gaps in filling prescriptions (Hess et al., 2006). In this study,
adherence was measured by PDC.
Yearly PDC was defined as the proportion of days covered by LABD
relative to the treatment period during a calendar year. The treatment period was
calculated as the duration from the fill date of the first LABD prescription until
the end of the year. The formula to calculate the yearly PDC is specified below:
Yearly PDC = 100 x
Treatment period in days
Number of days covered by LABD prescriptions within a calendar year
49
PDC equal to or greater than 80% was regarded as good adherence. A
dichotomous variable for adherence was constructed as 1 if PDC ≥ 80%, and 0
otherwise.
Healthcare resource use (HRU)
All-cause HRU was measured as the number of visits in different service
settings, including outpatient (including physician office and other outpatient
encounters), emergency room (ER), and inpatient hospitals.
These variables were constructed for each calendar year from 2007 to
2010. In addition, considering COPD patients may switch from LABD to SABD
because SABD cost is lower, the utilization of SABD was assessed as well.
Specifically, the analysis included the following HRU outcomes:
• Number of outpatient visits per year
• Number of ER visits per year
• Number of inpatient visits per year
• Number of days supplied for SABD prescriptions per year
Cost
All-cause medical cost was estimated from the payer’s perspective, so
beneficiaries’ copays and deductibles were not included in the calculation.
Specifically, all-cause medical cost was defined as the cost related to medical
services offered in all settings including outpatient, ER, and inpatient. Like HRU
outcome variables, the cost variable was also constructed separately for each
calendar year from 2007 to 2010.
50
Subgroup Analysis
Adherence
For the subgroup analysis, PDC was estimated for the pre-gap and the in-
gap periods separately. The pre-gap treatment period was calculated as the
number of days between the fill dates of the first LABD prescription to the gap
date. The in-gap treatment period was calculated as the number of days between
the fill dates of the first LABD prescription after reaching the gap until the end
of gap, or end of a calendar year, or death, whichever occurred earlier. The
formula to calculate the pre-gap and in-gap PDC is specified below:
Pre-Gap PDC = 100 x
In-Gap PDC = 100 x
Healthcare resource use (HRU)
The number of visits in outpatient, ER, and inpatient settings and the
number of days supplied for SABD prescriptions were calculated on a monthly
basis separately for the pre-gap and the in-gap periods. Specifically, the
following variables were included for the HRU assessment:
• Monthly number of outpatient visits
• Monthly number of ER visits
• Monthly number of inpatient visits
• Monthly number of days supplied for SABD prescriptions
Number of days covered by LABD prescriptions within the pre-gap period
Pre-gap treatment period in days
Number of days covered by LABD prescriptions within the in-gap period
In-gap treatment period in days
51
Cost
Finally, all-cause medical cost was calculated on a monthly basis
separately for the pre-gap and the in-gap periods.
Variables
The following variables were considered as independent variables for the
analysis.
Variables for Primary Analysis
For the primary analysis, the key independent variable of interest was a
dichotomous indicator of membership in the exposure or the control cohort
(1=exposure cohort, 0=control cohort).
For the PSM, 300 variables were generated as proxies based on ICD-9-
CM diagnosis codes and CPT procedure codes from different dimensions
(outpatient, ER, and inpatient) using the HDPS technique. In addition, the
following variables were also included in the PSM:
• Age: Age was calculated as of six months before January 1 of
each calendar year.
• Gender: Male or female (1=female, 0=male, with male as
reference group).
• Ethnicity: Based on the categories provided in the data,
ethnicity was defined as Caucasian or other (1=Caucasian,
0=other).
• Region: Geographic locations of patient residence were
determined from residence state documented in the eligibility
52
file and were grouped based on the U.S. census classification
(i.e., Northeast, Midwest, South, and West regions).
• Charlson Comorbidity Index (CCI) in the baseline period:
CCI was assessed using medical claims six months prior to
the beginning of a respective calendar year (baseline period)
based on the primary or secondary ICD-9-CM diagnosis
codes listed on claims. The CCI adapted by Deyo and
described in Table 2 is the most commonly used index in
health outcome studies (D'Hoore, Bouckaert, & Tilquin, 1996;
Deyo, Cherkin, & Ciol, 1992; Schneeweiss & Maclure, 2000).
To achieve an aggregate score, the CCI assigns a weight
ranging from 1 to 6 (higher indicating greater disease severity)
to separate conditions defined by ICD-9-CM codes that are
associated with medical services provided to a patient.
• Presence of select relevant comorbidities in the baseline
period: Presence of a comorbidity was defined as having or
not having the condition based on ICD-9-CM diagnosis code
(binary, 1=having the condition, 0=not having the condition).
These comorbid conditions include hyperlipidemia,
hypertensive disease, heart disease, osteoporosis, depression,
diseases of the musculoskeletal system and connective tissue.
Table 3 includes details on all of the conditions considered in
the analysis.
53
• Number of unique drug prescriptions filled in the baseline
period. The national drug code (NDC) is a unique 10-digit, 3-
segment numeric identifier assigned to each medication listed
with the Federal Food, Drug and Cosmetic (FD&C) Act
enforced by US Food and Drug Administration (FDA). The
CMS created an 11-digit NDC derivative which includes the
labeler, product or package code segments of NDC with
leading zeroes wherever they are needed to result in a fixed
length of 5-4-2 configuration. The first segment of a code
identifies the drug manufacturer, the second segment
identifies the specific product, and the third segment identifies
the package size. The first nine digits of the CMS NDC
derivative recorded in the Part D data file were used to
identify the unique drug dispensed to beneficiaries. Therefore,
the number of unique drugs filled for that year was defined as
the number of different unique 9-digit CMS NDC derivatives
recorded during that year.
• Number of all-cause ER visits in the baseline period
• Number of all-cause inpatient visits in the baseline period
• Any COPD diagnosis in the baseline period (1= at least one
ICD-9 code for COPD, 0=no ICD-9 code for COPD).
Beneficiaries with no COPD diagnosis in the baseline period
were considered as incident COPD patients.
54
• Any LABD prescription in the baseline period (1= at least
one LABD prescription, 0=no LABD prescription).
Beneficiaries without LABD prescription in the baseline
period were considered as LABD new users.
• Any supplemental oxygen therapy in the baseline period.
The consensus guidelines recommend adding supplemental
oxygen therapy for patients with very severe COPD (GOLD,
2014); therefore, it can serve as a proxy of the severity of
COPD. Supplemental oxygen therapy was identified by the
ICD-9 procedure codes or Healthcare Common Procedure
Coding System (HCPCS) codes listed in Table 4.
• Any use of oral corticosteroid in the baseline period (1= at
least one oral corticosteroid, 0=no oral corticosteroid). Oral
corticosteroids are recommended by consensus guidelines for
patients during exacerbations of COPD (GOLD, 2014);
therefore, it can also work as a proxy of the higher severity of
COPD if patients receive systemic corticosteroid (Table 5).
55
Table 2
ICD-9-CM Diagnosis Codes for CCI Conditions
Charlson Comorbid Conditions ICD-9-CM Diagnosis
Codes
Myocardial infarction 410.x, 412.x
Congestive heart failure 428.x
Peripheral vascular disease 443.9, 441.x, 785.4, V43.4
Cerebrovascular disease 430-438.x
Chronic pulmonary disease 490-496.x, 500-505.x, 506.4
Dementia 290.x
Paralysis 342.x, 344.1x
Diabetes 250.0x – 250.3x, 250.7x
Diabetes with sequela 250.4x – 250.6x, 250.8x, 250.9x
Moderate or severe renal disease 582.x, 583.x, 585.x, 586.x, 588.x
Mild liver disease/Various cirrhosis 571.x
Moderate or severe liver disease 572.x, 456.x
Ulcer disease 531-534.x
Rheumatologic disease 710.x, 714.x, 725.x
AIDS 042.x – 044.x
Any tumor 140-195.x
Metastatic solid tumor 196-199.x
Note: Created based on Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619
56
Table 3
ICD-9-CM Codes for Select Relevant Comorbidities
Chronic conditions or Disease classes ICD-9-CM Diagnosis
Codes
Asthma 493.xx
Diabetes 250.xx
Hypertensive disease 401.xx-405.xx
Heart disease 410.xx-429.xx
Cerebrovascular disease 430.xx-438.xx
Depressive disorder 296.2x, 296.3x, 300.4, 311
Anxiety 293.84, 300.0x, 300.21, 300.22 300.23, 300.29, 300.3x, 300.5x, 300.89, 300.9x, 308.xx 309.81, 313.0x, 313.1x, 313.21, 313.22, 313.3x, 313.82, 313.83
Diseases of the musculoskeletal system and connective tissue
710.xx-719.xx
Deficiency anemia 281.xx, 285.xx
Lipid disorder 272.0x, 272.1x, 272.2x, 272.3x, 272.4x
Osteoporosis 733.0x, V17.81
Osteoarthritis 715.xx V13.4 (arthritis)
GERD 530.81, 530.10, 530.11, 530.12, 530.19
Sleep apnea 780.51, 780.53, 780.57, 327.20, 327.21, 327.23, 327.27, 327.29
Obesity
278.xx, V77.8, V85.2-V85.5
57
Table 4
Procedure Codes for Supplemental Oxygen Therapy
Supplemental Oxygen Therapy Description
ICD-9 procedure code: V46.2 Machine dependent supplemental oxygen
HCPCS codes:
E0431 Compressed-oxygen systems
E1390, E1391 Oxygen concentrator
E1392 Portable oxygen concentrator
Table 5
Oral Corticosteroid
Brand-name Generic Name
Diprolene, Betaderm, Betnovate, Diprosone Betamethasone
Decadron, Maxidex, Ozurdex, Baycadron Dexamethasone
Cortenema, Solu-cortef, Cortef, Cortifoam Hydrocortisone
Medrol Dosepak, Solu-Medrol, Medrol, MethylPREDNISolone
Methylprednisolone
Deltasone, Sterapred, Rayos, Sterapred DS Prednisolone
Deltasone, Sterapred, Rayos, Sterapred DS Prednisone
Kenalog-40, Aristocort, Azmacort, Kenalog-10
Triamcinolone
Cortone Acetate Cortisone
Depo-Dilar Paramethasone
The distribution of these characteristics was presented before and after
the matching process. Variables with a statistically significant difference
between exposure and control cohorts after propensity score matching were
included in a separate multivariable model to further adjust for differences. This
multivariable model included the following covariates:
58
• Unbalanced covariates after the PSM
• Cohort membership, i.e., in the exposure cohort or the control
cohort
Variables for Subgroup Analysis
The same variables described in primary analysis were included in the
descriptive portion of the subgroup analysis. Table 6 below summarizes the
variables included in the primary analysis and subgroup analysis, respectively,
and the definition of each variable.
Table 6
A List of Variables for Primary and Subgroup Analyses
Variable Definition Variable Type
Primary Analysis
(PSM/DA/MA)
Subgroup Analysis
(DA only)
Outcome Variable
Proportion of days covered (PDC)
The proportion of days covered by LABD relative to the treatment period during a calendar year
Continuous, ranging between 0 and 1
DA, MA DA
Adherence PDC is equal to or greater than 80% Binary, 1 indicating adherent, and 0 otherwise
DA, MA DA
All-cause outpatient visits
Number of all-cause outpatient visits occurred in a calendar year
Count DA, MA DA
All-cause ER visits Number of all-cause ER visits occurred in a calendar year
Count DA, MA DA
All-cause inpatient visits Number of all-cause inpatient visits occurred in a calendar year
Count DA, MA DA
All-cause medical cost Cost related to medical services offered in all settings including outpatient, ER, and inpatient in a calendar year, paid by Medicare
Continuous DA, MA DA
Independent Variables
59
Variable Definition Variable Type
Primary Analysis
(PSM/DA/MA)
Subgroup Analysis
(DA only)
Age Age in years as of 6 months before January 1st of each calendar year
Continuous PSM, DA, MA DA
Gender Male or female Binary, 1 indicating female, and 0 male
PSM, DA, MA DA
Ethnicity Caucasian or other ethnicities Binary, 1 indicating Caucasian, and 0 otherwise
PSM, DA, MA DA
Region Grouped state codes based on the U.S. census classification: NorthEast, MidWest, South, or West
Categorical: NorthEast, MidWest, South, or West
PSM, DA, MA DA
Baseline CCI score An aggregate score based on a weight ranging from 1 to 6 assigned to 17 different conditions identified by ICD-9-CM codes
Continuous PSM, DA, MA DA
Presence of select comorbidities in the baseline period
Comorbidities considered relevant to COPD population and defined by ICD-9-CM diagnosis codes listed on medical claims
Binary, 1 indicating presence of the disease, and 0 otherwise
PSM, DA, MA DA
Number of unique drugs filled in the baseline period
The number of different unique 9-digit CMS NDC derivatives recorded during a calendar year
Continuous PSM, DA, MA DA
60
Variable Definition Variable Type
Primary Analysis
(PSM/DA/MA)
Subgroup Analysis
(DA only)
Any COPD diagnosis in the baseline period
Having medical claims with COPD diagnosis code in the baseline period.
Binary, 1 indicating prevalent COPD patients, and 0 incident COPD patients
PSM, DA, MA DA
Any LABD prescription in the baseline period
Receiving LABD prescription in the baseline period.
Binary, 1 indicating receiving LABD prescription before, and 0 LABD new users
PSM, DA, MA DA
Any supplemental oxygen therapy in the baseline period
Receiving supplemental oxygen therapy in the baseline period, identified by ICD-9-CM procedure code or Healthcare Common Procedure Coding System (HCPCS) codes
Binary, 1 indicating received oxygen therapy, and 0 otherwise
PSM, DA, MA DA
Any oral corticosteroid use in the baseline period
Receiving oral corticosteroid in the baseline period.
Binary, 1 indicating received oral corticosteroid, and 0 otherwise
PSM, DA, MA DA
Number of all-cause ER visits in the baseline period
Number of all-cause ER visits occurred in the baseline period
Count PSM, DA, MA DA
61
Variable Definition Variable Type
Primary Analysis
(PSM/DA/MA)
Subgroup Analysis
(DA only)
Number of all-cause inpatient visits in the baseline period
Number of all-cause inpatient visits occurred in the baseline period
Count PSM, DA, MA DA
Membership of the exposure or the control cohort
Patients categorized into the exposure (with the coverage gap) or the control cohort (without the coverage gap)
Binary, 1 indicating exposure cohort, and 0 control cohort
PSM, DA, MA --
Membership of the mid-gap or the late-gap subgroup
Patients who reached the Part D gap between 4/1 and 10/31 were assigned to the mid-gap subgroup; patients who reached the gap after 10/31 were assigned to the late-gap subgroup.
Categorical: mid-gap, and late-gap
MA DA
Note: PSM – propensity score matching, DA – descriptive analysis, MA – multivariable analysis
62
63
Statistical Analysis
The following sections describe the data analysis and statistical methods
applied in this study.
Descriptive Analysis
A descriptive analysis was conducted to summarize patient demographic
and clinical characteristics, HRU, and cost for study cohorts. Means and
standard deviations (SD) were reported for continuous variables and frequency
distributions with percentages reported for categorical variables.
Primary analysis
Patient demographic and clinical characteristics of the exposure and the
control cohorts were summarized before and after the PSM. Yearly adherence,
HRU, and cost were reported for the two matched cohorts after the PSM. Before
the PSM, the Student’s t-test was used to detect differences between the
exposure and the control cohorts for continuous variables (e.g., age, CCI score,
medical encounters, cost), and the Chi-square test for categorical variables,
including demographics (e.g., gender, ethnicity) and comorbidities (e.g., diabetes,
hypertensive disease). After the PSM, McNemar’s test was used for categorical
variables and the paired t-test for continuous variables.
Subgroup analysis
Patient demographic and clinical characteristics were summarized for all
subgroups of the exposure cohort. To assess differences between the subgroups,
the Student’s t-test was used for continuous variables (e.g., age, CCI score), and
the Chi-square test for categorical variables including demographics (e.g.,
gender, ethnicity) and comorbidities (e.g., diabetes, hypertensive disease). To
64
assess differences in monthly HRU and cost before and after reaching the
coverage gap for the mid-gap and the late-gap subgroups in the matched
exposure cohort, the McNemar’s test was used for categorical variables and the
paired t-test for continuous variables.
Multivariable Analysis
The following section describes the multivariable analysis performed for
the outcome variables of adherence, HRU, and cost in the primary analysis. No
multivariable analysis was conducted for the subgroup analysis.
Logistic Regression for HDPS Analysis and Matching
A logistic regression model was employed to generate the propensity
score with membership in the exposure cohort or the control cohort as the
dependent variable. The independent variables included the empirically
identified covariates based on HDPS analysis and other independent variables
specified in the variable section. In HDPS analysis, the diagnosis code and the
procedure codes in outpatient, ER, and inpatient settings were specified as data
dimensions (i.e., six dimensions) and the 200 most prevalent codes in each data
dimension were used in the analysis after code recurrence assessment. Within
each data dimension, the possible amount of confounding was calculated for
each variable based on a multiplicative model and all variables were sorted in
descending order (details specified in (Schneeweiss, Rassen, et al., 2009).
Next, the top 300 variables were selected, as research shows that the
HDPS algorithm might have reached or been very close to its full potential to
adjust for confounding effect with approximately 300 empirically selected
covariates (J. A. Rassen, Glynn, Brookhart, & Schneeweiss, 2011). A SAS
65
Macro for HDPS algorithm developed by Rassen and colleagues was adapted for
this analysis (J. Rassen, Doherty, Huang, & Schneeweiss, 2013).
After the propensity score was generated, matching was conducted at 1:1
between the exposure cohort and the control cohort using the Greedy 5�1 digit
technique (Parsons, 2009). With this technique, propensity scores were arranged
in descending order and then observations were attempted to be matched on the
first five digits of the score. If all cases were not matched, then a four digit
match was attempted. This process was repeated until matches were attempted
on the first digit of the propensity score. This process maximized the number of
matched pairs while minimizing errors. Observations that could not be matched
using this technique were excluded.
Generalized linear models (GLMs)
Because statistical significance still existed between cohorts for some
covariates after the PSM, multivariable regression analysis was performed to
adjust for potential residual confounding effects when comparing the matched
exposure cohort and the matched control cohort.
Adherence. A conditional logistic regression model was constructed with
adherence (1= PDC ≥80%), 0 = PDC<80%) as the dependent variable. The
independent variables included unbalanced covariates after the PSM and the
variable indicating membership in the exposure or the control cohort.
HRU. Three Generalized Linear Models (GLMs) with negative binomial
distribution and log link function were employed with the number of outpatient,
ER, or inpatient visits as the dependent variables. Unlike Poisson models,
66
negative binomial regression models do not require the assumption of equality
between the conditional mean and variance, and can particularly correct for
overdispersion, i.e., the variance is greater than the conditional mean (Hilbe,
2011; Osgood, 2000; Paternoster & Brame, 1997). The independent variables
included unbalanced covariates after the PSM and the variable indicating
membership in the exposure or the control cohort.
Cost. A GLM regression with gamma distribution and log link function
was employed with all-cause medical cost as the dependent variables. The
independent variables included unbalanced covariates after the PSM and the
variable indicating membership in the exposure or the control cohort.
Generalized Estimating Equation (GEE)
In this study, over 50% of patients had observations for two or more
years from 2007 to 2010. When patients have multiple observations over time,
there may be correlation between repeated measures. GEE technique was applied
in the multivariable models to correct for the correlation between repeated
observations of a patient. (Hardin & Hilbe, 2003; Liang & Zegger, 1986). GEE
is an extension of the quasi-likelihood approach used to analyze longitudinal and
other correlated data (Burton, Gurrin, & Sly, 1998; Diggle, Liang, & Zeger,
1994; Wedderburn, 1974).
All analyses were performed using SAS® 9.2 (SAS Institute Inc., Cary,
NC, USA). P-values less than 0.05 were considered to be statistically significant.
67
CHAPTER FOUR
RESULTS
This chapter presents the results from the analyses conducted to examine
the study hypotheses. The results are reported in four parts: (1) sample size of
study cohorts and subgroups after application of inclusion and exclusion criteria;
(2) demographic and baseline characteristics of the study sample before and after
PSM; (3) descriptive statistics of the outcome variables; and (4) the findings for
multivariable regression analysis and hypothesis testing associated with the
outcome variables.
As described in Chapter 3, two sets of analyses were conducted – a
primary analysis and a subgroup analysis. Considering that beneficiaries in the
exposure cohort could enter into the Part D coverage gap at different time points
during a calendar year, subgroups were created to explore if the timing of
reaching the Part D gap led to different outcomes. In the primary analysis, the
analyses were conducted at the cohort level for the exposure cohort and the
control cohort. In the subgroup analysis, the analyses were conducted at the
subgroup level defined within the exposure cohort, including no-reaching gap,
early-gap, mid-gap, and late-gap subgroups depending on whether and when
beneficiaries reached the Part D coverage gap during a calendar year. The
primary analysis included both descriptive and multivariable analyses; while the
subgroup analysis only included a descriptive analysis as the comparison
68
between the pre-gap and in-gap periods within the exposure cohort is not a main
objective of this research.
Sample Size
Application of the patient selection criteria resulted in 5,366; 5,650;
5,991; and 6,268 unique beneficiaries diagnosed with COPD and treated with
LABD for the years 2007-2010, respectively (Figure 4). Each year nearly 20%
of those beneficiaries enrolled with Part D benefit were not subject to the
coverage gap (i.e., assigned into the control cohort) and the remaining
beneficiaries were at risk of the coverage gap (i.e., assigned into the overall
exposure cohort).
69
Figure 4. Patient selection flow chart.
The overall exposure cohort was further categorized into different
subgroups based on whether and when they reached the Part D coverage gap
(Table 7). Between 33% and 44% of the exposure cohort (33% in 2007 and 44%
in 2010) did not reach the coverage gap and were not included in the final
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exposure cohort to implement matching. Among those who reached the coverage
gap, only 1.5% or fewer (n=118 for four years) reached the coverage gap earlier
than March 1 in the year (i.e., early-gap subgroup) and most of them (88%-95%)
entered the catastrophic phase. The early-gap subgroup was not included in the
final exposure cohort to implement matching as well considering that this group
of patients may have very different medication utilization patterns because they
were motivated to maximize their medication usage to enter the catastrophic
phase sooner after they reach the coverage gap. Over 80% of the beneficiaries
who reached the coverage gap reached the gap between March 1 and October 31
in the year (i.e., mid-gap subgroup), and over 20% of them entered the
catastrophic phase before the year end. About 17%-19% of the beneficiaries who
reached the Part D gap reached the gap on or after November 1 in the year (i.e.,
late-gap subgroup), and only one patient entered the catastrophic phase before
the year end.
As reported in Table 7, from 2007 to 2010, the final control cohort
contained 1,011; 1,012; 1,145; and 1,176 beneficiaries and the final exposure
cohort (the mid-gap + the late-gap subgroups) contained 2,786; 2,746; 2,721;
and 2,751 beneficiaries. For the final control cohort, over 7% of the patients had
observations across four years, about 16% across three years, and 33% across
two years; for the final exposure cohort, only about 1% of the patients had
observations across four years, 6% across three years, and 21% across two years.
Combined across all years, there were 4,344 patient-year observations in the
control cohort and 11,004 patient-year observations in the exposure cohort
71
before implementation of propensity score matching (PSM). After the 1:1
matching, both matched control and exposure cohorts included 4,147 patient-
year observations, which was the final study sample for hypothesis testing.
72
Table 7
Sample Size of Study Cohorts and Subgroups
Before matching
Year
2007
Year
2008
Year
2009
Year
2010 Total
Enrolled with Part D: Not exposed to
Part D coverage gap 1011 1012 1145 1176 4344
Enrolled with Part D: Exposed to
Part D coverage gap 4191 4487 4675 4920 18273
No-reaching gap subgroup 1372 1700 1930 2149 7151 Reached the gap 2819 2787 2745 2771 11122
1) Early-gap subgroup 33 41 24 20 118 Entered the catastrophic phase 29 39 22 19 109
2) Mid-gap subgroup 2305 2271 2203 2231 9010 Entered the catastrophic
phase 511 504 483 469 1967 3) Late-gap subgroup 481 475 518 520 1994
Entered the catastrophic phase 1 0 0 0 1 Final exposure cohort (mid-gap+late-gap) before matching 2786 2746 2721 2751 11004 Final control cohort before matching 1011 1012 1145 1176 4344
After matching
Year
2007
Year
2008
Year
2009
Year
2010 Total
Matched exposure cohort 987 970 1087 1103 4147
1) Mid-gap subgroup 821 804 896 912 3433 Entered the catastrophic phase 216 227 228 248 919
2) Late-gap subgroup 166 166 191 191 714 Entered the catastrophic phase 0 0 0 0 0
Matched control cohort 987 970 1087 1103 4147
Note: Early-gap subgroup: entering the coverage gap before March 1, mid-gap subgroup:entering the coverage gap between March 1 and October 31, late-gap subgroup: entering the coverage gap on and after November 1.
Demographic and Baseline Characteristics
The mean age of the patients was 77.4 years (SD=7.6), the majority were
female, and over 90% were Caucasians. The study sample was heavily
concentrated in the South, followed by the Midwest and Northeast. Beneficiaries
in the West were underrepresented in the study. The mean CCI score was about
73
2 and among the selected comorbidities, the most prevalent one in the baseline
period was hypertensive disease (over 65%), followed by heart disease (over
50%) and hyperlipidemia (over 45%). Over 80% of the study sample was
prevalent COPD patients, over 70% of the patients had used a LABD and a large
proportion of them received oxygen therapy or oral corticosteroids (about 30%)
in the baseline period. Beneficiaries had substantial medication burden in the
baseline period, with an average of over 10 different classes of medications, and
had 0.4 ER visits and 0.3 inpatient visits in the baseline six month period.
Detailed results are reported in Table 8.
Primary Analysis: Control vs. Exposure Cohort Before and After Matching
As shown in Table 8, before matching, the control cohort and the
exposure cohort were significantly different in almost all of the demographic and
baseline characteristics except for several baseline comorbidities. The control
cohort was a little older, included more female beneficiaries and fewer
Caucasians, contained more residents in the Midwest and fewer in the West, and
had a higher CCI score. Among selected baseline comorbidities, the control
cohort had higher prevalence than the exposure cohort for all diseases except for
hyperlipidemia and sleep disorder (lower prevalence), and asthma and
osteoporosis (similar prevalence).
A higher percentage of patients in the exposure cohort had a diagnosis of
COPD in the baseline period, used LABD or oxygen therapy but had fewer
classes of medication in the baseline period than the control cohort. In addition,
the exposure cohort used fewer ER and inpatient services in the baseline period.
Details are presented in Table 8.
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After implementing PSM, the control and the exposure cohorts were
generally balanced in demographic and baseline characteristics. Statistical
differences were only observed in the prevalence of several baseline
comorbidities including hyperlipidemia, anemia, depression, anxiety, GERD,
sleep disorder, disease of the musculoskeletal system and connective tissue, and
hypertensive disease. However, compared to results before matching, the
differences became smaller as indicated in Table 8. In addition, post-match
standardized difference was also calculated to assess whether the distribution of
the variables describing patients’ demographic and baseline characteristics was
similar between the matched cohorts, i.e., to diagnose the balance of matching
(Austin, 2009). The standardized difference was generally small with most of the
absolute values less than 10% except several baseline comorbidity variables,
indicative of acceptable balance between the matched cohorts (Normand et al.,
2001). Detailed results are presented in Table 8.
Table 8
Patient Demographic and Baseline Characteristics of Study Cohorts Before and After Matching
Before matching After matching
Control
(n=4344) Exposure (n=11004)
P-value Control
(n=4147) Exposure (n=4147)
P-value Std Diff ƚ
in %
Age (mean, SD) 77.41 (7.64) 76.59 (7.22) <.0001 77.38 (7.64) 77.31 (7.46) .9081 -0.93 Female (n, %) 3114 (71.69) 7187 (65.31) <.0001 2963 (71.45) 2933 (70.73) .4675 -1.60 Caucasian (n, %) 4008 (92.27) 10744 (97.64) <.0001 3925 (94.65) 3912 (94.33) .5316 -1.37 Region (n, %) <.0001 .1259
NorthEast 1121 (25.81) 2485 (22.58) 1064 (25.66) 1025 (24.72) -2.17 MidWest 1363 (31.38) 2583 (23.47) 1291 (31.13) 1311 (31.61) 1.04 West 226 (5.2) 1473 (13.39) 226 (5.45) 284 (6.85) 5.82 South 1632 (37.57) 4457 (40.5) 1564 (37.71) 1525 (36.77) -1.95
Deyo-Charlson comorbidity index in the baseline period* (mean, SD)
2.22 (1.69) 1.87 (1.44) <.0001 2.16 (1.63) 2.17 (1.65) .975 0.61
Other comorbidities in the baseline period (n, %)
Asthma 429 (9.88) 1079 (9.81) .8953 405 (9.77) 457 (11.02) .0613 4.11 Hyperlipidemia 1970 (45.35) 5223 (47.46) <.0001 1881 (45.36) 2294 (55.32) <.0001 20.02 Heart disease 2480 (57.09) 5633 (51.19) <.0001 2331 (56.21) 2353 (56.74) .6261 1.07 Deficiency anemia 1035 (23.83) 1793 (16.29) <.0001 952 (22.96) 828 (19.97) .0009 -7.29 Depression 768 (17.68) 1119 (10.17) <.0001 717 (17.29) 526 (12.68) < .0001 -12.93 Anxiety 462 (10.64) 770 (7) <.0001 443 (10.68) 353 (8.51) .0027 -7.37 Osteoporosis 584 (13.44) 1533 (13.93) .4302 552 (13.31) 608 (14.66) .0763 3.89 Osteoarthritis 998 (22.97) 2076 (18.87) <.0001 928 (22.38) 918 (22.14) .7918 -0.58 GERD 869 (20) 1554 (14.12) <.0001 819 (19.75) 683 (16.47) .0001 -8.52 Sleep disorder 236 (5.43) 881 (8.01) <.0001 217 (5.23) 367 (8.85) < .0001 14.17 Diseases of the musculoskeletal
system and connective tissue 1739 (40.03) 3570 (32.44) <.0001 1624 (39.16) 1532 (36.94) .0375 -4.57
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Hypertensive disease 2998 (69.01) 7170 (65.16) <.0001 2828 (68.19) 2921 (70.44) .0268 4.86 Obesity 182 (4.19) 378 (3.44) .0247 159 (3.83) 177 (4.27) .3161 2.20
Prevalent COPD diagnosis in the baseline period (n, %)
3625 (83.45) 9370 (85.15) .0084 3461 (83.46) 3491 (84.18) .3711 1.96
Prescribed with LABDs in the baseline period (n, %)
3204 (73.76) 8339 (75.78) .0089 3073 (74.1) 3023 (72.9) .2135 -2.73
Prescribed with oral corticosteroid in the baseline period (n, %)
1237 (28.48) 3037 (27.6) .2749 1175 (28.33) 1173 (28.29) .9611 -0.11
Order of oxygen therapy in the baseline period (n, %)
1273 (29.30) 3811 (34.63) <.0001 1245 (30.02) 1252 (30.19) .8669 0.37
Number of unique prescription drugs in the baseline period (mean, SD)
11.95 (7.25) 10.02 (5.63) <.0001 11.59 (6.85) 11.69 (6.35) .0933 1.51
Number of all-cause ER visits in the baseline period (mean, SD)
0.43 (0.87) 0.33 (0.78) <.0001 0.42 (0.84) 0.44 (0.98) .5431 2.19
Number of all-cause inpatient visits in the baseline period (mean, SD)
0.33 (0.72) 0.27 (0.66) <.0001 0.32 (0.71) 0.34 (0.76) .9636 2.72
Note: ƚ Std Diff=Standardized Difference.
*The baseline period was defined as six months prior to the start of a calendar year.
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77
Subgroup Analysis: Subgroups in Exposure Cohort Before Matching
Among the 18,273 patient-year observations from 2007 to 2010 in the
overall exposure cohort (before cohort matching), there were 7,151 observations
in the no-reaching gap subgroup, 118 observations in the early-gap subgroup,
9,010 observations in the mid-gap group, and 1,994 observations in the late-gap
group. Although the early-gap and the no-reaching gap subgroups were not
included in the final exposure cohort to implement matching, they were included
in the descriptive analysis.
As described in Table 9, among the four subgroups of the overall
exposure cohort (before cohort matching), the early-gap subgroup was, on
average, the youngest (mean age=74.4 years) and the mid-gap subgroup was, on
average, the oldest (mean age=76.7 years). The no-reaching subgroup had the
lowest percentage of female beneficiaries (about 60%). Across all subgroups,
over 60% of the beneficiaries were females and a majority of the subgroups
resided in the South. The early-gap subgroup appeared to have the worst
comorbidity burden with a mean CCI score of 2.8 (SD=2.1) while the no-
reaching gap subgroup had lowest mean CCI score of 1.5 (SD=1.3). Similarly,
the early-gap subgroup had the highest prevalence of baseline comorbidities
compared to other subgroups, while the no-reaching gap subgroup had the
lowest prevalence. Similar patterns were observed for medication use and ER or
inpatient services in the baseline period.
When comparing the mid-gap and the late-gap subgroups in the overall
exposure cohort (before cohort matching), the mid-gap subgroup was older, had
a higher mean CCI score (1.93 vs. 1.58, P< .0001), had a higher prevalence of
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comorbidities except anxiety and obesity, and was more likely to use LABD
(77.48% vs. 68.10%, P< .0001), oral corticosteroids (28.52% vs. 23.42%,
P< .0001), or oxygen therapy (35.66% vs. 29.99%, P< .0001) in the baseline
period (Table 9). The mid-gap subgroup also used more classes of medications
(10.54 vs. 7.64, P< .0001) and ER/inpatient services (0.34 vs. 0.28, P=.0004 for
ER; 0.28 vs. 0.21, P< .0001 for inpatient visits) in the baseline period than the
late-gap subgroup.
Table 9
Demographic and Baseline Characteristics of Subgroups of the Exposure Cohort Before Matching
Early-gap
subgroup (n=118) No reaching gap
subgroup (n=7151) Mid-gap subgroup
(n=9010) Late-gap subgroup
(n=1994)
P-value (mid- vs. late-gap)
Age (mean, SD) 74.42 (6.31) 75.86 (6.88) 76.75 (7.25) 75.86 (7.03) < .0001
Female (n, %) 77 (65.25) 4327 (60.51) 5912 (65.62) 1275 (63.94) .1553
Caucasian (n, %) 115 (97.46) 6892 (96.38) 8796 (97.62) 1948 (97.69) .8560
Region (n, %)
.0019
NorthEast 28 (23.73) 1263 (17.66) 2102 (23.33) 383 (19.21)
MidWest 23 (19.49) 1820 (25.45) 2060 (22.86) 523 (26.23)
West 19 (16.1) 1094 (15.3) 1198 (13.3) 275 (13.79)
South 48 (40.68) 2971 (41.55) 3645 (40.46) 812 (40.72)
Deyo-Charlson comorbidity index in the baseline period* (mean, SD)
2.75 (2.08) 1.48 (1.25) 1.93 (1.46) 1.58 (1.28) < .0001
Other comorbidities in the baseline period (n, %)
Asthma 12 (10.17) 552 (7.72) 895 (9.93) 184 (9.23) .3376 Hyperlipidemia 65 (55.08) 3157 (44.15) 4803 (53.31) 968 (48.55) .0001 Heart disease 77 (65.25) 2853 (39.9) 4786 (53.12) 847 (42.48) < .0001 Deficiency anemia 30 (25.42) 866 (12.11) 1543 (17.13) 250 (12.54) < .0001 Depression 32 (27.12) 449 (6.28) 952 (10.57) 167 (8.38) .0034 Anxiety 10 (8.47) 368 (5.15) 626 (6.95) 144 (7.22) .6645 Osteoporosis 19 (16.1) 797 (11.15) 1324 (14.69) 209 (10.48) < .0001 Osteoarthritis 32 (27.12) 1110 (15.52) 1755 (19.48) 321 (16.1) .0005 GERD 27 (22.88) 813 (11.37) 1325 (14.71) 229 (11.48) .0002
79
Sleep disorder 21 (17.8) 451 (6.31) 747 (8.29) 134 (6.72) .0194 Diseases of the musculoskeletal
system and connective tissue 55 (46.61) 1923 (26.89) 3018 (33.5) 552 (27.68) < .0001
Hypertensive disease 80 (67.8) 4048 (56.61) 6005 (66.65) 1165 (58.43) < .0001 Obesity 7 (5.93) 186 (2.6) 321 (3.56) 57 (2.86) .1183
Prevalent COPD diagnosis in the baseline period (n, %)
111 (94.07) 5493 (76.81) 7738 (85.88) 1632 (81.85) < .0001
Prescribed with LABDs in the baseline period (n, %)
94 (79.66) 4490 (62.79) 6981 (77.48) 1358 (68.10) < .0001
Prescribed with oral corticosteroid in the baseline period (n, %)
41 (34.75) 1478 (20.67) 2570 (28.52) 467 (23.42) < .0001
Order of oxygen therapy in the baseline period (n, %)
53 (44.92) 1950 (27.27) 3213 (35.66) 598 (29.99) < .0001
Number of unique prescription drugs in the baseline period (mean, SD)
17.97 (8.03) 6.75 (4.66) 10.54 (5.68) 7.64 (4.71) < .0001
Number of all-cause ER visits in the baseline period (mean, SD)
0.43 (0.83) 0.18 (0.52) 0.34 (0.83) 0.28 (0.77) .0004
Number of all-cause inpatient visits in the baseline period (mean, SD)
0.4 (0.85) 0.18 (0.52) 0.28 (0.68) 0.21 (0.56) < .0001
Note: *The baseline period was defined as six months prior to the start of a calendar year. SD=standard deviation.
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Descriptive Statistics of Outcome Variables
Primary Analysis: Matched Control and Exposure Cohorts
LABD Adherence
After cohort matching, the mean annual PDC in the matched control
cohort was 0.70 (SD=0.25), while the mean annual PDC in the matched
exposure cohort was 0.69 (SD=0.24; Table 10). Given that adherence is defined
as a PDC equal to or greater than 0.8, 45.7% of observations in the matched
control cohort showed as adherent, while 42% of the observations of the
matched exposure cohort reflected adherence.
Table 10
Adherence to LABDs in the Matched Control and Exposure Cohorts
Matched control cohort
(n=4147) Matched exposure cohort
(n=4147)
PDC (mean, SD) 0.70 (0.25) 0.69 (0.24) Adherent (n, %) 1894 (45.70%) 1742 (42.01%)
Note: PDC≥0.80 was defined as being adherent. SD=standard deviation.
HRU
As reported in Table 11, the mean number of annual outpatient visits was
higher in the matched control cohort than that in the matched exposure cohort
(27.19 vs. 26.31). On average, the matched control cohort was prescribed with
more days of SABD than the matched exposure cohort (89.27 vs. 71.33 days).
On the other hand, the matched control and exposure cohorts had similar
numbers of ER and inpatient visits.
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Table 11
Annual HRU in the Matched Control and Exposure Cohorts
Matched control cohort
(n=4147)
Matched exposure cohort
(n=4147)
Number of all-cause outpatient visits (mean, SD) 27.19 (21.98) 26.31 (18.39) Number of all-cause ER visits (mean, SD) 0.85 (1.39) 0.83 (1.47) Number of all-cause inpatient visits (mean, SD) 0.66 (1.18) 0.66 (1.19) Number of days supplied for SABD prescriptions (mean, SD)
89.27 (131.91) 71.33 (110.56)
Note: SD=standard deviation
All-Cause Medical Cost
Table 12 summarizes the mean annual all-cause medical cost for the
matched control and exposure cohorts. The matched control and the matched
exposure cohorts had a comparable mean all-cause medical cost. Specifically, on
average, Medicare paid about $13,871 and $13,396 per patient per year for the
medical services for the control and the exposure cohort beneficiaries,
respectively. Considering the high degree of skewness of the cost, the log-
transformed cost was also calculated for two cohorts. The average all-cause
medical cost was similar between the matched control and exposure cohorts,
8.61 and 8.66, respectively.
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Table 12
Annual All-Cause Medical Cost for the Matched Control and Exposure Cohorts
Matched control cohort
(n=4147) Matched exposure cohort
(n=4147)
Mean (SD) Median (q1, q3) Mean (SD) Median (q1, q3)
All-cause medical cost
13871.07 (20110.86)
5175.48 (2044.48, 17189.43)
13396.26 (19861.42)
5573.61 (2304.81, 15811.42)
Log (all-cause medical cost) 8.61 (1.49) 8.55 (7.62, 9.75) 8.66 (1.37) 8.63 (7.49, 9.67) Note: SD=standard deviation. q1=25%tile, q3=75%tile
Subgroup Analysis: Pre-Gap vs. In-Gap period for Mid-gap and Late-Gap
Subgroups in the Matched Exposure Cohort
LABD Adherence
The pre-gap treatment period was calculated as the duration from the fill
date of the first LABD prescription to the gap date; the in-gap treatment period
was calculated as the duration from the fill date of the first LABD prescription
after reaching the gap until the end of gap or calendar year or death, whichever
occurred first. In the matched exposure cohort, the mean pre-gap treatment
period was 153 and 253 days within a calendar year for the mid- and late-gap
subgroups, respectively. The mean in-gap treatment period was 134 and 32 days
within a calendar year for the mid-gap and the late-gap subgroups, respectively.
In the mid-gap subgroup, pre-gap PDC and in-gap PDC were 0.50 and 0.46,
respectively. Likewise, a higher adherence rate was observed in the pre-gap
period (about 20%) than in the in-gap period (about 16%). In the late-gap
subgroup, PDC and adherence rate during the in-gap period were both
84
significantly higher than the rates during the pre-gap period. However, this result
needs to be interpreted with caution as the exposure time of the late-gap
subgroup for receiving LABDs was much shorter than that of the mid-gap
groups due to the definition of the subgroups. That is, as patients who entered
the coverage gap on and after November 1 of the year were assigned to the late-
gap subgroup, the late-gap subgroup could only have a maximum of two months
of exposure time by the end of a calendar year. Table 13 presents detailed results.
Table 13
Adherence to LABDs in the Mid-Gap and Late-Gap Subgroups in the Matched
Exposure Cohort
Pre-gap period In-gap period
Mid-gap subgroup (n=3433)
Treatment duration in days (mean, SD) 152.60 (68.37) 134.30 (56.88) PDC (mean, SD) 0.49 (0.29) 0.46 (0.49) Adherent (n, %) 672 (19.57%) 555 (16.17%)
Late-gap subgroup (n=714)
Treatment duration in days (mean, SD) 252.68 (85.86) 31.62 (17.79) PDC (mean, SD) 0.29 (0.22) 0.88 (0.18) Adherent* (n, %) 32 (4.48%) 310 (43.42%)
Note: * Adherent was defined if PDC≥0.80. SD=standard deviation.
Additional analysis was conducted to assess adherence in different
quarters of the pre-gap period for the late-gap subgroup. It appeared that patients
were much less adherent earlier during the year, and became more adherent
when they got closer to the date when they hit the coverage gap. This trend was
quite consistent across the year from 2007 to 2010 (Table 13a).
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Table 13a
Quarterly Pre-Gap Adherence for the Late-Gap Subgroup
Quarter 1 prior to the coverage gap
start date
Quarter 2 prior to the coverage gap start
date
Quarter 3 prior to the coverage gap start
date
2007
% being adherent* 14.03% 5.37% 0.00% 2008
% being adherent 11.26% 2.51% 0.00% 2009 % being adherent 13.88% 7.51% 0.00% 2010 % being adherent 12.20% 5.66% 0.00%
Note: * PDC≥0.80 is defined as being adherent.
HRU
In the matched exposure cohort, the mean duration of the period from
January 1 until reaching the gap (pre-gap period) was 189 and 332 days within a
calendar year for the mid-gap and late-gap subgroups, respectively. The mean
duration of the in-gap period was 152 and 33 days within a calendar year for the
mid-gap or the late-gap subgroups, respectively. In the mid-gap subgroup,
patients had similar monthly HRU in all settings and similar length of SABD
therapy for the pre-gap and the in-gap periods. Higher average monthly SABD
days in the in-gap period as compared to the pre-gap period was observed,
although this result should be interpreted with caution for the reasons described
earlier – by definition, the duration of the in-gap period for the late-gap subgroup
was much shorter compared to the pre-gap period, so the length of exposure to
SABD therapy was shorter for the in-gap period than the pre-gap period
(denominator). In addition, almost 50% of SABDs prescriptions filled by
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beneficiaries had 25 or 30 days of supply, and about 25% ranged from 15 to 20
days or 90 days –15 days (6%), 20 days (5%) or 90 days (4%). Because of these
differences, beneficiaries in the in-gap period and pre-gap period could
potentially accumulate a similar number of days of supply (numerator) and the
in-gap period could end up with having a much higher number of monthly
SABD days than the pre-gap period. In terms of resource use, the late-gap
subgroup had higher monthly number of outpatient, ER, and inpatient visits in
the pre-gap period than in the in-gap period. In addition, rates of HRU of the
late-gap subgroup were lower than rates of the mid-gap subgroup for both pre-
gap and in-gap periods. Table 14 presents detailed results.
Table 14
Monthly HRU of the Mid-Gap and the Late-Gap Subgroups in the Matched
Exposure Cohort
Pre-gap period
(mean, SD) In-gap period (mean, SD)
Mid-gap subgroup (n=3433)
Length of period in days 189.19 (64) 151.86 (50.18) Monthly number of all-cause outpatient visits 2.26 (1.75) 2.20 (1.79) Monthly number of all-cause ER visits 0.17 (0.13) 0.16 (0.22) Monthly number of all-cause inpatient visits 0.16 (0.17) 0.14 (0.11) Monthly number of days supplied for SABD prescriptions 13.43 (11.27) 14.29 (11.35)
Late-gap subgroup (n=714)
Length of period in days 332.5 (18.15) 31.62 (17.79) Monthly number of all-cause outpatient visits 1.90 (1.42) 1.56 (2.35) Monthly number of all-cause ER visits 0.16 (0.13) 0.10 (0.35) Monthly number of all-cause inpatient visits 0.14 (0.11) 0.09 (0.29) Monthly number of days supplied for SABD prescriptions 9.51 (9.24) 50.53 (101.03)
All-Cause Medical Cost
In the matched exposure cohort, the mid-gap subgroup had higher
monthly expenses per patient for medical services in the pre-gap period
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compared to the in-gap period ($1,135 vs. $1,085, P=0.0236; respectively).
Similarly, the late-gap subgroup had higher monthly expenses per patient for
medical services in the pre-gap period compared to the in-gap period ($987.71 vs.
$338.70, respectively). In addition, the average medical cost of the late-gap
subgroup was lower than that of the mid-gap subgroup for both pre-gap and in-
gap periods. Table 15 presents detailed results.
Table 15
Monthly All-Cause Medical Cost for the Mid-Gap and the Late-Gap Subgroups
in the Matched Exposure Cohort
Pre-gap period In-gap period
Mean (SD) Median (q1, q3) Mean (SD) Median (q1, q3)
Mid-gap subgroup (n=3433)
Monthly all-cause medical cost
1135.23 (2089.17)
345.76 (141.90, 1106.62)
1085.87 (2185.31)
313.58 (146.23, 880.25)
Late-gap subgroup (n=714)
Monthly all-cause medical cost
987.71 (1673.54)
338.70 (135.27, 1094.99)
575.25 (2057.77)
112.44 (0.00, 331.90)
Note: SD=standard deviation.
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Regression Analysis and Hypotheses Testing
Below are the three hypotheses which were presented in Chapter 2:
4) Hypothesis 1: Among Medicare beneficiaries with COPD, the
coverage gap will be associated with lower medication
adherence to COPD long-term maintenance therapies.
5) Hypothesis 2: Among Medicare beneficiaries with COPD, the
coverage gap will be associated with higher consumption of
healthcare resources (non-drug).
6) Hypothesis 3: Among Medicare beneficiaries with COPD, the
coverage gap will be associated with higher medical cost
(from payer’s perspective).
Multiple regression models were constructed to test these hypotheses.
The outcome variables for the models included LABD adherence, annual
number of all-cause outpatient visits, annual number of all-cause ER visits,
annual number of all-cause inpatient visits, and annual all-cause medical cost. In
all models, age, gender, cohort membership (exposure vs. control), and the
remaining unbalanced variables after implementing PSM were included as
independent variables.
Regression Analyses for Hypothesis 1 (LABD Adherence)
The first hypothesis pertains to the association between reaching the
coverage gap and LABD adherence in patients with COPD. A conditional
logistic regression model was constructed to test this hypothesis, including a
binary variable with 1 indicating being adherent and 0 otherwise as the outcome
variable. The regression results are summarized in Table 16.
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Unadjusted results showed the matched exposure cohort had lower
adherence rate than the matched control cohort. After adjustment for sample
selection bias (via PSM) and controlling for age, gender and the unbalanced
covariates after the PSM in the regression model, beneficiaries who reached the
coverage gap were associated with lower odds of being adherent compared to
beneficiaries who did not reach the coverage gap. Specifically, beneficiaries in
the mid-gap subgroup (i.e., reached the coverage gap earlier than October) had
about 7% lower odds but without statistical significance (Odds ratio [OR]=0.931,
95% confidence interval [CI]: 0.846, 1.024); while beneficiaries in the late-gap
subgroup had nearly 40% lower odds (OR=0.603, 95% CI: 0.493, 0.738) to be
adherent. In addition, hyperlipidemia, depression, and disease of the
musculoskeletal system and connective tissues were found to be associated with
lower likelihood of being adherent (Table 16). The adjusted adherences were
estimated from the regression model and reported in Table 16a, and the results
were slightly higher than unadjusted estimations.
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Table 16
Conditional Logistic Regression on Adherence to LABDs
Variable OR 95% CI P-value
Mid-gap exposure subgroup vs. control cohort 0.931 0.846 1.024 .1415
Late-gap exposure subgroup vs. control cohort 0.603 0.493 0.738 < .0001 Age in years 1.005 0.998 1.013 .1809 Female (vs. Male) 1.104 0.967 1.261 .1425 Hyperlipidemia 0.869 0.774 0.975 .0172 Deficiency anemia 1.034 0.899 1.189 .6374 Depression 0.848 0.721 0.997 .0463 Anxiety 0.974 0.798 1.188 .7927 GERD 1.057 0.913 1.224 .4568 Sleep disorder 0.981 0.793 1.215 .8622 Diseases of the musculoskeletal system and connective tissue
0.809 0.719 0.911 .0004
Hypertensive disease 0.970 0.852 1.105 .6502
Note: OR= odds ratio, CI=confidence interval.
Table 16a
Unadjusted and Adjusted Adherence
Unadjusted Outcomes Adjusted Outcomes Matched control
Matched exposure
Matched control
95% CI Matched exposure
95% CI
Adherence* 45.70% 42.01% 48.6% 48.1%, 49.2% 43.4%
42.8%, 43.9%
Note: * Adherence is defined if PDC≥0.80. CI=confidence interval.
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Regression Analyses for Hypothesis 2 (All-Cause HRU)
The second hypothesis was related to the association between reaching
the Part D gap and all-cause HRU for patients with COPD. Three separate GLM
models with a negative binomial distribution and log link function were
constructed for the three HRU outcomes: all-cause outpatient visits, all-cause ER
visits, and all-cause inpatient visits. Because the study sample covered multiple
years and more than half of the patients had observations in multiple calendar
years, GEE technique was applied in the models to correct for the potential
correlation between repeated measures within one patient.
Annual Number of All-Cause Outpatient Visits
Unadjusted results showed the matched exposure cohort had lower mean
number of outpatient visits than the matched control cohort. After adjustment for
sample selection bias (via PSM) and controlling for age, gender, and the
unbalanced covariates after the PSM in the regression model, cohort membership
was shown as a significant predictor of the number of outpatient visits; further,
the timing of reaching the coverage gap had a different effect on the number of
outpatient visits. Specifically, beneficiaries reaching the gap earlier were
associated with almost 4% higher number of outpatient visits than the control
cohort (relative ratio [RR]=3.78%, β=0.0371, standard error [SE]=0.016,
P= .0201);while beneficiaries reaching the gap later in the year were associated
with over 5% lower number of outpatient visits than the control cohort with
marginal statistical significance (RR= -5.46%, β= -0.056, SE=0.029, P= .0545).
All other independent variables were significantly associated with the outcome
variable. Specifically, one additional year in age was associated with about 1.5%
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higher number of outpatient visits (relative ratio [RR]=1.48%, β=0.015, P< .001),
while female patients had over 5% fewer outpatient visits than male patients
(RR= -5.64%, β= -0.058, P= .0011). All of the unbalanced baseline
comorbidities were associated with a higher number of outpatient visits, with the
highest difference in diseases of the musculoskeletal system and connective
tissue (RR=34.04%, β=0.293, P< .0001) and the lowest difference in
hyperlipidemia (RR=6.82%, β=0.066, P< .0001). Detailed regression results are
presented in Table 17. The adjusted annual number of all-cause outpatient visits
were estimated from the regression model and reported in Table 17a, and the
results were similar to unadjusted estimations.
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Table 17
Negative Binomial Regression on Annual Number of All-Cause Outpatient Visits
Variable Coefficient (β) SE 95% CI
P-value RR
Intercept 1.765 0.082 1.604 1.926 < .0001 -- Exposure: mid-gap subgroup vs. control cohort
0.037 0.016 0.006 0.068 .0201 3.78%
Exposure: late-gap subgroup vs. control cohort
-0.056 0.029 -0.114 0.001 .0550 -5.46%
Age in years 0.015 0.001 0.013 0.017 < .0001 1.48% Female (vs. Male) -0.058 0.018 -0.093 -0.023 .0011 -5.64% Hyperlipidemia 0.066 0.016 0.034 0.098 < .0001 6.82% Deficiency anemia 0.280 0.018 0.244 0.315 < .0001 32.25% Depression 0.266 0.021 0.224 0.307 < .0001 30.41% Anxiety 0.074 0.026 0.022 0.125 .0051 7.66% GERD 0.140 0.019 0.102 0.178 < .0001 14.99% Sleep disorder 0.200 0.028 0.144 0.254 < .0001 22.01% Diseases of the musculoskeletal system and connective tissue
0.293 0.016 0.262 0.324 < .0001 34.04%
Hypertensive disease 0.110 0.018 0.073 0.146 < .0001 11.57%
Note: SE – standard error, CI – confidence interval, RR – relative ratio.
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Table 17a
Unadjusted and Adjusted Number of All-Cause Outpatient Visits
Unadjusted Outcomes Adjusted Outcomes Matched control
Matched exposure
Matched control
95% CI Matched exposure
95% CI
Number of all-cause outpatient visit per year
27.19 26.31 26.87 26.57, 27.17
26.80 26.52, 27.08
Note: CI – confidence interval.
Annual Number of All-Cause ER Visits
Unadjusted results showed the matched exposure cohort had a similar
mean number of ER visits as the matched control cohort. After adjusting for
sample selection bias (via PSM) and controlling for age, gender, and the
unbalanced covariates after the PSM, the mid-gap subgroup was not a significant
predictor of the number of ER visits (β=0.016, SE=0.039, P= .6728), and the
late-gap subgroup was associated with about 13% lower number of ER visits
(RR= -13.23%, β= -0.142, SE=0.072, P= .0496). One additional year in age was
associated with 1.33% higher number of ER visits (RR=1.33% β=0.013,
P< .0001), while gender was not a significant factor. Several baseline
comorbidities including deficiency anemia, depression, anxiety, GERD, disease
of musculoskeletal system and connective tissue, and hypertensive disease
showed a significant positive relationship with the number of ER visits, with the
highest difference for GERD (RR=32.47%, β=0.281, P< .0001) followed by
diseases of the musculoskeletal system and connective tissue (RR=32.35%,
β=0.280, P< .0001). Hyperlipidemia and sleep disorder were not significantly
associated with the number of ER visits. Detailed results are presented in Table
18. The adjusted annual number of all-cause ER visits was estimated from the
95
regression model and reported in Table 18a, and the results were similar to
unadjusted estimations.
Table 18
Negative Binomial Regression on Annual Number of All-Cause ER Visits
Variable Coefficient (β) SE 95% CI
P-value RR
Intercept -1.548 0.192 -1.925 -1.172 < .0001 -- Exposure: mid-gap subgroup vs. control cohort
0.017 0.039 -0.060 0.093 .6728 1.66%
Exposure: late-gap subgroup vs. control cohort
-0.142 0.072 -0.284 -0.0002 .0496 -13.23%
Age in years 0.013 0.003 0.008 0.018 < .0001 1.33% Female (vs. Male) -0.071 0.044 -0.158 0.015 .1073 -6.86% Hyperlipidemia 0.009 0.038 -0.066 0.084 .8133 0.90% Deficiency anemia 0.272 0.044 0.185 0.359 < .0001 31.21% Depression 0.164 0.050 0.066 0.262 .0010 17.79% Anxiety 0.144 0.061 0.024 0.264 .0189 15.45% GERD 0.281 0.051 0.182 0.380 < .0001 32.47% Sleep disorder 0.016 0.068 -0.118 0.150 .8156 1.61% Diseases of the musculoskeletal system and connective tissue
0.280 0.039 0.204 0.357 < .0001 32.35%
Hypertensive disease 0.136 0.043 0.052 0.219 .0014 14.53%
Note: SE – standard error, CI – confidence interval, RR – relative ratio.
Table 18a
Unadjusted and Adjusted Number of All-Cause ER Visits
Unadjusted Outcomes Adjusted Outcomes Matched control
Matched exposure
Matched control
95% CI
Matched exposure
95% CI
Number of all-cause ER visit per year
0.85 0.83 0.86 0.85, 0.87
0.82 0.81, 0.83
Note: CI – confidence interval.
Annual Number of All-Cause Inpatient Visits
Unadjusted results showed the matched exposure cohort had a similar
mean number of inpatient visits as the matched control cohort. After adjusting
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for sample selection bias (via PSM) and controlling for age, gender, and the
unbalanced covariates after the PSM, cohort membership and the time of
reaching the coverage gap were not significant predictors of the number of
inpatient visits (β=0.030, SE=0.042, P= .4683 for the mid-gap subgroup; β= -
0.081, SE=0.077, P= .0932 for the late-gap subgroup). One additional year in
age was associated with 1.12% higher number of inpatient visits (RR=1.12%,
β=0.011, P< .0001), and female patients were associated with over 14% fewer
inpatient visits (RR=14.68%, β= -0.159, P= .0004). Baseline comorbidities,
including deficiency anemia, depression, GERD, sleep disorder, disease of
musculoskeletal system and connective tissue, and hypertensive disease, were
significantly and positively associated with the number of inpatient visits, with
highest difference for deficiency anemia (RR=38.31%, β=0.324, P<0.0001),
followed by GERD (RR=29.24%, β=0.257, P< .0001). Hyperlipidemia and
anxiety were not significantly associated with number of inpatient visits.
Detailed regression results are presented in Table 19. The adjusted annual
number of all-cause inpatient visits were estimated from the regression model
and reported in Table 19a, and the results were similar to unadjusted estimations.
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Table 19
Negative Binomial Regression on Annual Number of All-Cause Inpatient Visits
Variable Coefficient (β) SE 95% CI
P-value RR
Intercept -1.528 0.199 -1.918 -1.139 < .0001 -- Exposure: mid-gap subgroup vs. control cohort
0.030 0.042 -0.051 0.111 .4683 3.06%
Exposure: late-gap subgroup vs. control cohort
-0.081 0.077 -0.231 0.070 .2932 -7.73%
Age in years 0.011 0.003 0.006 0.016 < .0001 1.12% Female (vs. Male) -0.159 0.045 -0.247 -0.071 .0004 -14.68% Hyperlipidemia -0.010 0.041 -0.091 0.071 .8104 -0.99% Deficiency anemia 0.324 0.045 0.236 0.412 < .0001 38.31% Depression 0.130 0.055 0.022 0.239 .0184 13.93% Anxiety 0.056 0.064 -0.070 0.182 .3847 5.75% GERD 0.257 0.048 0.163 0.350 < .0001 29.24% Sleep disorder 0.166 0.082 0.005 0.327 .0436 18.02% Diseases of the musculoskeletal system and connective tissue
0.240 0.041 0.160 0.320 < .0001 27.11%
Hypertensive disease 0.107 0.046 0.017 0.196 .0194 11.26%
Note: SE – standard error, CI – confidence interval, RR – relative ratio.
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Table 19a
Unadjusted and Adjusted Number of All-Cause Inpatient Visits
Unadjusted Outcomes Adjusted Outcomes Matched control
Matched exposure
Matched control
95% CI
Matched exposure
95% CI
Number of all-cause inpatient visit per year
0.66 0.66 0.67 0.66, 0.67
0.66 0.65, 0.66
Note: CI – confidence interval.
Regression Analyses for Hypothesis 3 (Annual All-Cause Medical Cost)
The third hypothesis was related to the association between reaching the
Part D gap and annual all-cause medical cost in beneficiaries with COPD. A
GLM model with a Gamma distribution and log link function was constructed
for the cost outcome - annual all-cause medical cost. Once again, given that the
study sample included data spanning multiple years and more than half of the
beneficiaries had observations in multiple calendar years, GEE technique was
applied in the models to correct for the potential correlation between repeated
measures within one beneficiary.
Unadjusted results showed the matched exposure cohort had similar
mean annual medical cost as the matched control cohort. After adjusting for
sample selection bias (via PSM) and controlling for age, gender, and the
unbalanced covariates after the PSM in the regression model, cohort membership
and the timing of reaching the coverage gap were not significantly associated
with medical cost (β=0.004, SE=0.034, P= .8974 for the mid-gap subgroup; β= -
0.069, SE=0.068, P= .3049 for the late-gap subgroup). Regarding other
covariates included in the model, each additional year in age was associated with
1.72% higher medical cost (RR=1.72%, β=0.017, P< .0001) and female patients
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were associated with nearly 13% lower medical cost than male patients (RR= -
12.47%, β= -0.133, P= .0003). All baseline comorbidities except hyperlipidemia
and anxiety were positively and significantly associated with medical cost, with
highest difference for deficiency anemia (RR=43.02%, β=0.358, P< .0001),
followed by disease of musculoskeletal system and connective tissue
(RR=35.59%, β=0.305, P< .0001). Detailed regression results are presented in
Table 20. The adjusted annual all-cause medical cost was estimated from the
regression model and reported in Table 20a, and the results were similar to
unadjusted estimations.
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Table 20
GLM Regression on Annual All-Cause Medical Cost
Variable Coefficient (β) SE 95% CI
P-value RR
Intercept 7.877 0.167 7.549 8.204 < .0001 -- Mid-gap exposure subgroup vs. control cohort
0.004 0.034 -0.061 0.070 .8974 0.43%
Late-gap exposure subgroup vs. control cohort
-0.069 0.068 -0.202 0.063 .3049 -6.70%
Age in years 0.017 0.002 0.013 0.021 < .0001 1.72% Female (vs. Male) -0.133 0.037 -0.206 -0.060 .0003 -12.47% Hyperlipidemia 0.008 0.033 -0.0570 0.074 .8020 0.83% Deficiency anemia 0.358 0.037 0.286 0.430 < .0001 43.02% Depression 0.221 0.044 0.136 0.306 < .0001 24.73% Anxiety 0.060 0.052 -0.042 0.160 .2504 6.11% GERD 0.217 0.040 0.138 0.295 < .0001 24.20% Sleep disorder 0.269 0.062 0.148 0.391 < .0001 30.92% Diseases of the musculoskeletal system and connective tissue
0.305 0.0326 0.241 0.368 < .0001 35.59%
Hypertensive disease 0.096 0.0380 0.021 0.170 .0117 10.03%
Note: SE – standard error, CI – confidence interval, RR – relative ratio.
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Table 20a
Unadjusted and Adjusted Annual All-Cause Medical Cost
Unadjusted Outcomes Adjusted Outcomes Matched control
Matched exposure
Matched control
95% CI Matched exposure
95% CI
Annual all-cause medical cost
13871.07 13396.26 13970.91
13798.01, 14143.81
13428.30 13269.54, 13587.06
Note: CI – confidence interval.
Summary of Findings for Regression Analysis and Hypothesis Testing
Table 21 summarizes the hypothesized relationships, as compared to the
observed relationship, between the outcome variables and exposure status (i.e.,
reaching the Part D coverage gap as well as the timing of reaching the gap). The
direction of the relationships is presented as positive (+) or negative (-). Positive
“+” indicates that reaching the Part D gap was associated with greater likelihood
of being adherent, higher HRU, or higher cost; while negative “-” indicates that
reaching the coverage gap was associated with lower likelihood of being
adherent, lower HRU, or lower cost. For the column of “Statistical significance”,
“x” indicates having statistical significance and “√” indicates not having
statistical significance. Generally, hypothesis 1 (LABD adherence) was
supported by the findings of this study, though statistical significance was not
found for the mid-gap subgroup (i.e., reaching the coverage gap earlier than
October). The evidence from this analysis did not support the hypothesized
positive relationship between reaching the coverage gap and HRU or medical
cost, except that a positive association was detected between reaching the
coverage gap and the number of outpatient visits if beneficiaries reached the
coverage gap earlier than November. On the other hand, if beneficiaries reached
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the coverage gap after October, they were observed to be associated with a lower
number of ER or inpatient visits. These results are discussed in detail in Chapter
5.
Table 21
Summary for Regression Analysis and Hypotheses Testing
Outcome variable
Hypothesized relationship with the Part
D gap vs. Control cohort Finding
s Statistical
significance
Hypothesis 1 LABD adherence Negative (-) Exposure cohort:
Mid-gap subgroup - x
Exposure cohort: Late-gap subgroup
- �
Hypothesis 2 All-cause outpatient visits
Positive (+) Exposure cohort: Mid-gap subgroup
+ �
Exposure cohort: Late-gap subgroup
- x
All-cause ER visits
Positive (+) Exposure cohort: Mid-gap subgroup
+ x
Exposure cohort: Late-gap subgroup
- �
All-cause inpatient visits
Positive (+) Exposure cohort: Mid-gap subgroup
+ x
Exposure cohort: Late-gap subgroup
- x
Hypothesis 3 All-cause medical cost
Positive (+) Exposure cohort: Mid-gap subgroup
+ x
Positive (+) Exposure cohort: Late-gap subgroup
- x
Note: x – without statistical significance, � – with statistical significance
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CHAPTER FIVE
DISCUSSION
The objective of this study was to assess the impact of the Part D
coverage gap on medication adherence, healthcare resource use, and total
medical cost among Medicare beneficiaries with COPD by addressing the
following research questions:
1) Is medication adherence to LABD lower for Medicare patients
with COPD who reached the Part D coverage gap?
2) Is total healthcare resource use (HRU) in the outpatient, ER and
inpatient setting higher for Medicare patients with COPD who
reached the Part D coverage gap?
3) Is total all-cause medical cost (non-drug) higher for Medicare
patients with COPD who reached the Part D coverage gap?
This chapter provides a summary and discussion of the key findings
related to these questions and presents their implications for future research,
management, and policy. First, the results related to each hypothesis are
summarized and explained. Then, the strengths and limitations of the study are
described. Finally, the implications of the study’s findings for researchers,
healthcare administrators, and policy makers are discussed.
Review of Findings and Comparison with Existing Evidence
Research Question/Hypothesis 1: LABD Adherence
Question 1: Is medication adherence to LABD lower for Medicare
patients with COPD who reached the Part D coverage gap?
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Prior to multivariable regression analysis, the matched exposure cohort
was shown to have statistically lower yearly PDC for LABD therapies than the
matched control cohort. Compared to the control cohort, 3.7% fewer (or relative
difference of 8%) of the patients in the exposure cohort were adherent. The
regression analysis that controlled for age, gender, cohort membership, and
unbalanced characteristics found that patients in the exposure cohort were
associated with lower odds of being adherent than patients in the control cohort.
Higher magnitude of effect was observed in beneficiaries who reached the
coverage gap earlier. Collectively, these findings provide support for the
hypothesis that reaching the Part D coverage gap negatively affects medication
adherence among Medicare patients with COPD.
One explanation for this finding is offered by rational choice theory,
which suggests that individuals are motivated to make “rational” choices in order
to maximize their benefits. When Medicare beneficiaries enter the coverage gap,
they bear a higher economic burden to obtain their medications. Under these
circumstances, when assessing the value and benefit of their medications, if they
believe that stopping or skipping brand drugs or using cheaper generic drugs will
offer more benefit than paying higher out-of-pocket cost, they are more likely to
choose non-adherence to more expensive brand-name drugs.
This finding is consistent with most of the existing evidence that shows
the Part D coverage gap is associated with reduced medication adherence. For
example, Fung et al. (2010) found that the odds of adherence among diabetic
patients with the Part D coverage gap decreased by 17% compared to those
105
patients without the Part D coverage gap (Fung et al., 2010). Likewise, Stuart
and colleagues (2013) found that the PDC was 7.8% lower for statins, 7.0%
lower for clopidogrel, or 5.9% lower for beta-blocker for beneficiaries exposed
to the coverage gap compared with those not exposed.
Additionally, in the subgroup analysis of the exposure cohort in this
study, the mid-gap subgroup (i.e., entering the coverage gap between March 1
and October 31) had a 6% lower mean PDC after reaching the coverage gap
compared to their adherence prior to reaching the gap. Comparatively, a
reduction of 2.5%-3.6% in MPR was reported in beneficiaries with heart failure
after they entered the coverage gap (Baik et al., 2012; Zhang et al., 2013).
Research Question/Hypothesis 2: All-Cause HRU
Question 2: Is total healthcare resource use (HRU) in the outpatient, ER,
and inpatient setting higher for Medicare patients with COPD who reached the
Part D coverage gap?
The hypothesis that Medicare beneficiaries who were exposed to the
coverage gap will experience higher consumption of healthcare resources (non-
drug) was partially supported by the analysis. The descriptive analysis showed
that the annual number of all-cause HRU was similar between the exposure and
the control cohorts, except for the number of outpatient visits and days supplied
for SABD (lower for the exposure cohort). After controlling for post-PSM
differences in the multivariable analysis, different directions of relationship
between cohorts and the annual number of all-cause HRU were detected based
on the timing of reaching the gap. Generally, a positive relationship was
observed in beneficiaries who reached the gap between March and October with
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significance found for the outpatient setting only, and a negative relationship
was observed in beneficiaries who reached the gap after October with
significance found in the ER and inpatient settings.
This hypothesis was based on a belief that beneficiaries may seek care at
a physician’s office, ER, or hospital more often after they were exposed to the
coverage gap because they would reduce their use of medications when their
out-of-pocket cost increased. If beneficiaries hit the coverage gap earlier in the
year (i.e., mid-gap subgroup), they were more likely to follow this pattern
because there were a couple of months or longer when they had to pay full price
of COPD prescription drugs before the start of next coverage cycle (i.e., a new
calendar year), especially in the outpatient setting where health services were
relatively inexpensive when they had other Medicare health benefit coverage;
but less dramatic shift was observed in the ER or inpatient settings for this mid-
gap subgroup. Also, this does not appear to be the case for the beneficiaries who
reached the coverage gap after October (late-gap subgroup), and in fact opposite
results were shown that this group of beneficiaries used less HRU especially in
the ER and the inpatient settings. There are several potential explanations for the
lack of a substitution effect between LABDs and medical services. First, COPD
is symptomatic; as the GOLD guidelines specify, maintenance therapy with
LABDs is usually needed to control symptoms and prevent exacerbation when
COPD is at the moderate level or higher (GOLD, 2014), and there were few
convenient alternatives (such as generic LABDs) available to help slow disease
progression for the period of 2007-2010. Second, the out-of-pocket (OOP) cost
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of medical services can be comparable to OOP cost of brand LABDs when
COPD is out of control, which may present an irrational choice for beneficiaries.
For example, it was estimated that healthcare expenses for seniors averaged
$2,714 per day for inpatient stays and $651 for ER visits (Machlin, 2009). Given
the OOP amount paid by Medicare beneficiaries accounts for approximately 15%
of the total expense (Machlin, 2009), beneficiaries pay about $407 per day for a
hospital stay and $98 for an ER visit. According to general Medicare benefit
policy, the hospital inpatient deductible for beneficiaries is $1,206 per benefit
period with additional coinsurance if the length of stay exceeds 60 days
(Medicare, 2015). In comparison, on average, the full price of 30-day supply of
brand LABDs ranges from about $150 to $350 (GoodRx, 2015). Thus, patients
might be less motivated to completely give up maintaining their lung function
and controlling their symptoms via medication as other options could be more
costly. Another possibility is that these patients did not have severe COPD and
had relatively stable health status, so they did not consume excess healthcare
resources over the year. In addition, there is possibly a lagged effect of coverage
gap exposure on utilization. That is, in this study only HRU in the current year
was estimated for each calendar year and it may take a longer time to see the
effect on health resource utilization.
Notably, the findings of this study are not consistent with the findings
reported by Raebel et al. (2008) who found that beneficiaries who reached the
coverage gap had 85% higher risk of being hospitalized and 60% higher risk of
using emergency room services as compared to those without the coverage gap
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(Raebel et al., 2008). One potential reason for the difference relates to the study
population. The study by Raebel et al. focused on Medicare beneficiaries
covered by a commercial integrated healthcare delivery network (Kaiser
Permanente Colorado, KPCO) without focusing on a specific chronic condition
in defining the study cohort. Also, their study examined these relationships
shortly after the Part D coverage gap was implemented (2005-2006), and our
study examined the associations for multiple years after the implementation from
2007 to 2010.
Research Question/Hypothesis 3: All-Cause Medical Cost
Question 3: Is total all-cause medical cost (non-drug) higher for
Medicare patients with COPD who reached the Part D coverage gap?
The study found that the mean annual all-cause medical cost was similar
between the exposure and the control cohorts. Thus, the hypothesis that exposure
to the Part D coverage gap would be associated with higher all-cause medical
cost was not supported. The rationale for this hypothesis was that cost, as a
monetary manifestation of HRU, would increase as beneficiaries increased other
types of utilization as a substitute for prescription drugs. However, as the
analysis indicated, HRU in the ER and inpatient settings, which usually was the
driver of overall medical cost, did not significantly increase following exposure
to the Part D coverage gap.
This finding is consistent with the study by Zhang and colleagues (2012)
who reported no significant increases in non-drug medical spending in
beneficiaries with depression who reached the coverage gap as compared to
those who were not exposed to the coverage gap (Zhang et al., 2012).
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Strengths and Limitations
Strengths
This is the first study to evaluate the impact of the Part D coverage gap
using the longitudinal national Medicare claims data for beneficiaries with
COPD. Other studies have analyzed the general Medicare population and sub-
populations with other conditions (e.g., diabetes, cardiovascular diseases, mental
illness). To date, however, no research has been conducted in COPD population
in regards to Medicare Part D coverage gap. Chronic lower respiratory disease,
which primarily includes COPD has become the third leading cause of death in
the United States (Hoyert & Xu, 2012). The total annual cost (direct and indirect)
of treating and managing COPD in the United States was estimated to be close to
$50 billon (American-Lung-Association, 2014; NHLBI, 2009). Approximately
12 to 15 million adults in the United States are diagnosed with COPD
(American-Lung-Association, 2014; NIH, 2008), and almost half of them were
aged 65 years or older (Centers for Disease Control and Prevention, 2012).
Consequently, effective management of COPD has become one of the priorities
for Medicare. Thus, this study provided information regarding the impact of the
Part D coverage gap in an important Medicare population. In addition, this study
added to the limited evidence base on the impact of the coverage gap on HRU
and cost.
This is the first study to explore the effect of timing of hitting the
coverage gap on outcomes. The published articles in the existing literature
usually assessed the impact of coverage gap from the perspective of with vs.
without or before vs. after; no other studies have investigated the difference
110
possibly related to when beneficiaries hit the gap. This study is also one of the
few studies evaluating outcomes related to the coverage gap that included
Medicare beneficiaries who were not exposed to the coverage gap as a control
group. Only two other studies used a similarly defined control group focused on
assessing the effect of reaching the coverage gap on drug utilization (Polinski et
al., 2010; Polinski et al., 2012). Instead, most published studies used pre-post
design and Difference-in-Difference methods where the beneficiaries who
reached the coverage gap served as a control group for themselves. The pre-post
design or Difference-in-Difference method does not require balanced baseline
characteristics between comparative groups because only one group is included
in the analysis, so selection bias is minimized by design. When chronic and
progressive diseases such as cardiovascular disease, diabetes, or COPD are
assessed in studies, a patient’s disease may change differently in the post-period
compared to the pre-period, especially if pre-period and post-period are not short.
Consequently, the effect of a policy on the outcomes may be contaminated by a
changing disease state. It can be challenging to adjust for this change in the pre-
post design. In contrast, in a matched design (such as the one used in this study)
the adjustment of baseline disease status is a part of the exposure-control design
to make the two groups more comparable and have similar disease states. As a
result, the effect of disease progression may be less of a concern
The methodologies used in the analysis also provide several strengths for
this study. First, the high-dimensional propensity score matching (HD-PSM) was
adopted to further mitigate possible selection biases and adjust for the observed
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confounding effect between the exposure and the control cohorts. In an
observational study, when the exposure is not randomly assigned, propensity
score matching (PSM) is one of the often-used approaches to help reduce
selection bias based on predefined demographics, comorbidities, historical
medication use or healthcare resource use before the exposure initiation. The
HD-PSM method extends beyond traditional PSM by maximizing the usage of
information provided by claims data. Matching two cohorts with the propensity
score generated in this way is able to estimate effect closer to randomized trials
compared to the traditional PSM with typical covariates only (Schneeweiss,
Rassen, et al., 2009). This is the first study applying HD-PSM method in the
assessment of the impact of Part D coverage gap. Second, four years of data
were used in this study and outcomes were defined at the year level to account
for different Part D thresholds for each year. Consequently, beneficiaries with
enrollment for multiple years can have repeated observations. GEE technique
was used in the assessment to correct for the potential correlation between the
repeated observations from the same beneficiary. Third, multivariable regression
models that controlled for unbalanced characteristics post-matching adjusted for
residual confounding effects.
Limitations
This study has several limitations. First, medical and pharmacy claims
data used in this analysis were primarily used for administrative purposes to
obtain reimbursement, therefore, there is potential for coding errors that may
cause diagnostic and procedural misclassification. At least two COPD diagnosis
codes in an outpatient setting and at least two LABD prescriptions were required
112
in the patient identification stage to help reduce potential misclassifications. In
addition, the pharmacy claims only provided information on whether
prescriptions were filled by patients and there was no confirmation on whether
patients actually took the medication. Therefore, adherence was measured based
on the assumption that patients took the drugs after they filled the prescriptions.
Second, the study is subject to the limitations of retrospective
observational studies. As it was not an experimental design and did not
randomly assign exposure, the findings can only be interpreted as association
and no causality can be concluded. Although multiple strategies were applied to
minimize selection bias, they could only account for observed covariates and
were unable to control for unobserved factors, for example, patient’s lung
function, beneficiaries’ functional status, or beneficiaries’ behavior (e.g., self-
selection of high premium plan to avoid or reduce the burden produced by the
coverage gap).
Third, the methods adopted in this study have their own limitations.
Though matching based on HD-PS method can improve performance due to its
ability to adjust for additional confounding effects omitted in the traditional
PSM, HD-PS analysis was still empirical in nature and heavily relied on how
data were collected and recorded in the database. For example, the performance
of HD-PS analysis using administrative claims data might be different from
using electronic medical records data. Furthermore, because HD-PS analysis is
capable of generating hundreds of covariates, selection and prioritization of
appropriate covariates are usually based on the magnitude of the association
113
between each covariate and the study outcome. When the study outcome is rare,
the prioritization rule may miss potentially important covariates. (Schneeweiss,
Rassen, et al., 2009).
Fourth, the GLM regression provides a broad statistical approach to
analyzing various outcomes or predictors with different distributions. It is more
generally applicable than the traditional Ordinary Least-Squares regression;
however, it is still based on the normality assumption. The cost outcome is
usually highly skewed and outliers are commonly observed. Although the log-
link function and gamma distribution were applied to address the skewness, it is
possible that the transformed distribution in the GLM was still not perfectly
normal.
Fifth, the study cohort was composed of beneficiaries from the Medicare
FFS program, so the results might not be generalizable to the Medicare
population enrolled with Medicare Advantage plans. Similarly, the study period
ended in 2010 due to data availability and it was not clear if the impact of the
coverage gap identified in this study remained after 2010. Studies in the
managed Medicare population or using more recent data may provide more
insight.
Finally, the cost estimation in this analysis was only focused on medical
cost. Medicare does not directly pay drug claims submitted by the Part D plans,
but rather pays the part D plans in the way of subsidized premiums for their
provided Part D coverage. Future studies utilizing data including drug cost
114
would help to determine if and how much the Part D coverage gap can produce
cost offsets between prescription drugs and medical services for Medicare.
Implications
This study assessed the impact of the Medicare Part D coverage gap on
adherence, healthcare resource use, and medical cost among the beneficiaries
with COPD and has important research, managerial, and policy implications.
The following section describes these implications in greater detail.
Implications for Future Research
Like most published studies that have examined the Part D coverage gap
and medication adherence, a patient’s adherence was defined at the drug class
level (e.g., LABDs for COPD in this study; antidiabetics, statin or beta-blockers
or ACE inhibitors for MI, antihypertensive drugs, or lipid-lowering drugs for
other studies). Discontinuation of a particular LABD or switch between different
LABDs were not defined and not regarded as non-adherence in this study. MPR
and PDC are the most commonly used measures, but they do not capture actual
medication taking behaviors. This might be one reason for less consistent
directional association with adherence observed for the population with mental
illness compared to those with other chronic conditions in the literature,
considering individuals with mental illness often have more irregular patterns in
medication utilization. More refined adherence measurements that capture more
detailed drug-taking behavior is an area for future research on the impact of Part
D coverage gap on adherence. This research could include topics such as
discontinuation and restart, brand switch due to different formulary status, drug
augmentation or titration, and delay in drug dispensing. A lagged effect of
115
adherence level in one calendar year on the adherence level in the following year
could be another area to explore.
In this study, longitudinal data across four years were used to assess the
overall impact of the Part D coverage gap. Several things, however, are worth
noting with respect to establishing the coverage gap. First, different coverage
gap thresholds are established each calendar year with an increasing trend.
Second, different Part D drug plans can implement their own threshold for the
coverage gap based on their respective benefit designs. Future studies may want
to explore whether the level of the threshold leads to different adherence or
health service utilization. Likewise, the elasticity of the threshold may be an
interesting topic for future research, i.e., evaluating how sensitive beneficiaries
are to the threshold (e.g., how much medication adherence or care seeking
behavior is changed in response to different threshold levels), or what level of
threshold will generate cost-offsets or even cost-savings for insurers.
This study focused on the comparison of beneficiaries who were exposed
to and reached the coverage gap with those who were not exposed to the gap
because of financial assistance or other provided benefit or coverage. The
subgroup in the exposure cohort that was exposed to but did not reach the gap
was excluded from the analysis because they were assumed to be relatively
healthy and their drug utilization may not be impacted by the existence of the
coverage gap. This subgroup accounted for 30-40% of the overall exposure
cohort. Further research can be conducted to investigate their pattern of drug use
and health service utilization and related cost to confirm this assumption.
116
Another subgroup that was exposed to and reached the gap prior to March 1 was
excluded as well based on the assumption that they may have maximized their
drug utilization after reaching the gap in order to enter the catastrophic coverage
sooner. This subgroup accounted for less than 1% of the overall exposure cohort,
but it would be interesting to further investigate their drug and resource
utilization and related cost to test this assumption.
The defined outcomes (adherence, resource use, and cost) were not
assessed for the catastrophic phase as it was beyond the study scope of this
research. In this analysis, about 10% of the overall exposure cohort entered the
catastrophic phase during a respective calendar year. Whether and how drug
utilization and resource use in the catastrophic phase differs from the in-gap and
pre-gap periods are interesting questions to be taken up in future research.
Given the observed different effects on outcomes in the mid-gap and the
late-gap subgroups as compared to the control cohort in this study, researchers
might consider further assessing the impact of timing of reaching the gap in
greater detail using more advanced methodology such as time-varying analysis
to ascertain its association with outcomes.
Although no consistent significant association was observed with HRU
or cost in this study, researchers might want to explore the impact of the Part D
coverage gap on other types of outcomes such as clinical outcomes (e.g., disease
exacerbation, adverse event or episode, blood pressure control, or lung function
maintenance) or patient-reported outcomes (e.g., quality of life, or patient
satisfaction) so that more information on the impact of the coverage gap can be
117
provided to healthcare administrators and policymakers to better inform their
decision making.
Robust analytic techniques have been applied to this study to mitigate the
potential selection bias and confounding effects. For future studies, different
design methods can be explored based on the selected data source to further
validate the findings of this analysis. Researchers may even consider a
randomized trial with a small to moderate scale to make causal inferences.
Implications for Management and Policy
The findings from this study also provide insights for decision-makers
and administrators in healthcare. Medicare Part D is intended to lower
medication expenditures for Medicare beneficiaries. However, its complex cost-
sharing design creates gaps in coverage. Previous research (Baik et al., 2012;
Fung et al., 2010; Gu et al., 2010; Joyce et al., 2013; Zhang et al., 2013) and this
analysis have shown the gap negatively impacts medication adherence. Decline
in adherence indicates a disruption in medication treatment, which suggests that
insurance benefit with a gap in coverage possibly brought about negative
unintended consequences while improving beneficiaries’ access to healthcare.
Cycling in and out of a coverage gap may be disruptive to beneficiaries because
they have to make changes to their care plans in order to lower their risk of
falling into the gap and minimize the potential consequences of entering the gap.
Beneficiaries would have to give different behavioral responses at different
phases related to the coverage gap in order to mitigate the potential short-term
health effect. For example, prior to entering the coverage gap, beneficiaries
exposed to the gap might be cautious of their drug utilization so they can delay
118
the entry into the gap. After entering the gap, beneficiaries may choose not to
spend or reduce the prescription drug expense if it is close to year end, but may
choose to maximize their drug utilization to enter the catastrophic coverage
sooner if it is still early in the year. If beneficiaries are in the catastrophic phase,
they may be less concerned due to the catastrophic coverage. As the coverage
gap is initiated for each calendar year, beneficiaries without financial assistance
or subsidies may adjust their behavior and drug usage in anticipation of the gap
based on the experience of the previous year. This type of behavioral adjustment
can be stressful to the elderly beneficiaries and bring about certain long-term
consequences on beneficiaries’ health.
The coverage gap is planned to close out by 2020. By 2020, beneficiaries
will pay 25% of the total cost for covered brand-name and generic drugs during
the gap. Although the cost of closing the coverage gap may present a serious
challenge to policy makers in the current fiscal climate, it is expected that the
coverage gap closure will benefit beneficiaries. However, one study suggested
that phasing out the coverage gap under healthcare reform may still increase
drug cost if the drug has low clinical value (Li et al., 2012). The question of how
to redesign the drug benefit post gap close-out is what policy-makers and benefit
managers need to think through. A stable benefit with higher coinsurance can
make the process less complicated and reduce the financial risk associated with
pharmaceutical expenditures for healthcare payers. Also, more nuanced cost-
sharing insurance policies such as value-based insurance design may be needed
to provide incentives to encourage beneficiaries to use high-value medications
119
and discourage them from overusing medications with low marginal benefit
(Fendrick, Smith, Chernew, & Shah, 2001). With that, policy makers can
consider Part D cost-sharing approaches and utilization management tools to
promote appropriate use of covered products and maximize the clinical and
economic value for beneficiaries as well as insurers.
On the other hand, other research showed that the elderly were likely to
reduce the use of essential medications due to drug copayment (Artz, Hadsall, &
Schondelmeyer, 2002; Ellis et al., 2004; Federman, Adams, Ross-Degnan,
Soumerai, & Ayanian, 2001; Piette, Wagner, Potter, & Schillinger, 2004;
Tamblyn et al., 2001). A study evaluated the “first-dollar coverage” (i.e., no cost
sharing) of ACE inhibitors for Medicare beneficiaries with diabetes and reported
that the program helped to extend patient life and reduce Medicare program cost,
suggesting that full Medicare coverage of essential medications or high-value
drugs can be a cost-effective option for Medicare (Rosen et al., 2005).
These discussions suggest that benefit redesign not be a “one-size-fits-all”
strategy. Healthcare administrators need to work with multi-disciplinary expert
teams covering medical, economics, policy, and other areas and take both
clinical and economic values into consideration to establish robust coverage
policies that can be customized for beneficiaries with different profiles. Such
efforts, however, present nontrivial challenges for administrators and policy
makers in the form of substantial time, resources, and administrative efforts
required to design “personalized” coverage for a diverse population.
120
Prior to the close-out of the Part D coverage gap, healthcare
administrators and health plans should make efforts to help beneficiaries
transition through the gap smoothly and minimize the risk of experiencing high
out-of-pocket cost and preventable adverse outcomes from medication non-
adherence. Health plans can take more proactive approaches to raise awareness
of the coverage gap among beneficiaries and physicians, such as mailing or
calling members to alert them of their proximity to the gap long before they
reach it. Health plans can also provide beneficiaries with personalized
information on cost-saving options that may help delay their entry into the gap.
Finally, health plans may consider educating beneficiaries of the importance of
adherence and develop strategies to help them remain compliant with their
medication regimens and ultimately reduce the use of expensive medical
encounters and decrease cost for both beneficiaries and the health plan.
Health plans may also want to consider working with healthcare
providers on strategies to improve medication adherence. Patients directly
interact with and usually trust healthcare providers for their health issues.
Therefore, patients receiving education from healthcare providers regarding the
importance of medication adherence and optimal treatment regimen may be
more receptive than the same information received from health plans.
Healthcare cost is increasing rapidly and the projected net expenditure
for the Part D program from 2009 to 2018 is estimated to be $727.3 billion
(Roumie, 2012). Healthcare payers and decision-makers as well as beneficiaries
may be more receptive to policy changes if they promote appropriate drug
121
utilization and achieve a consistent approach for Part D plan formularies, and
subsequently result in cost savings to Medicare, less cost-related non-adherence
and lower economic burden to beneficiaries, and ultimately improvement in the
health of Medicare beneficiaries.
Conclusion
The purpose of this study was to investigate association of the Medicare
Part D coverage gap relative to medication adherence, healthcare resource use,
and cost among Medicare beneficiaries with COPD. Findings from this analysis
revealed that reaching Part D coverage gap lowered the odds of medication
adherence in COPD. However, the association with healthcare resource use was
mixed and the association with cost was inconclusive.
There is little information available regarding the effect of Part D
coverage gap on health resource use or cost outcomes and no published studies
on the Part D coverage gap and beneficiaries with COPD. This study provides
insights to fill this evidence gap. Building on these findings, additional research
related to other important illness or other meaningful outcomes will be pertinent
for increasing the knowledge base in this area, improving current benefit design,
developing future benefit structures, and optimizing the quality of health policy
decisions in order to help Medicare provide the best healthcare for its members
with the most cost-effective outcomes.
122
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APPENDIX
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