- Email: [email protected]
Prescription drug spending and hospital use among Medicare beneficiaries with heart failure
Journal Pre-proof Prescription drug spending and hospital use among medicare beneficiaries with heart failure Blake Tyler McGee, Melinda K. Higgins, Victoria Phillips, Javed Butler PII:
S1551-7411(19)30918-0
DOI:
https://doi.org/10.1016/j.sapharm.2019.12.019
Reference:
RSAP 1421
To appear in:
Research in Social & Administrative Pharmacy
Received Date: 13 September 2019 Accepted Date: 20 December 2019
Please cite this article as: McGee BT, Higgins MK, Phillips V, Butler J, Prescription drug spending and hospital use among medicare beneficiaries with heart failure, Research in Social & Administrative Pharmacy (2020), doi: https://doi.org/10.1016/j.sapharm.2019.12.019. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Prescription Drug Spending and Hospital Use among Medicare Beneficiaries with Heart Failure Blake Tyler McGee, PhD, MPH, RNa,1, Melinda K. Higgins, PhDb, Victoria Phillips, DPhilc, and Javed Butler, MBBS, MBA, MPHd a
Laney Graduate School, Emory University 201 Dowman Dr. NW Atlanta, GA 30322 USA b
Nell Hodgson Woodruff School of Nursing, Emory University 1520 Clifton Rd. NW Atlanta, GA 30322 USA (404) 727-5180 [email protected]
c
Rollins School of Public Health, Emory University 1518 Clifton Rd. NW Atlanta, GA 30322 USA (404) 727-9974 [email protected]
d
Department of Medicine, University of Mississippi 2500 N State St. Jackson, MS 39216 USA (601) 984-5602 [email protected] 1
Present affiliation (for correspondence): Byrdine F. Lewis College of Nursing and Health Professions Georgia State University P.O. Box 4019 Atlanta, GA 30302 USA (404) 413-1180 [email protected] Disclosures: Dr. Butler has served as a paid consultant or advisor on unrelated projects for the following companies: Amgen, Array, Astra Zeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squib, CVRx, G3, Innolife, Janssen, Medtronic, Merck, Novartis, Relypsa, Stealth Peptide, SC Pharma, Vifor and ZS Pharma. The other authors have no potential conflicts of interest to declare.
1
Abstract
2
Background: Heart failure (HF) is a common cause of hospitalization in Medicare. Optimal
3
medication adherence lowers hospitalization risk in HF patients. Although out-of-pocket
4
spending can adversely affect adherence to HF medications, it is unknown whether medication
5
spending ultimately increases hospital use for Medicare beneficiaries with HF.
6
Objective: To examine the association between out-of-pocket medication payments and HF-
7
related hospital use among Medicare Part D subscribers.
8
Methods: Retrospective analysis of the 2010-12 Medicare Current Beneficiary Survey. The
9
sample comprised community-dwelling respondents with fee-for-service Medicare, continuous
10
Part D coverage, and self-reported HF (n=819 participant-year records). The effects of average
11
out-of-pocket payment for a 30-day HF-related prescription on odds and frequency of
12
hospitalization and total inpatient days attributable to HF were estimated. Design-adjusted
13
models adjusted for sociodemographic and health status variables, survey year and censoring,
14
and included a pre-specified interaction of out-of-pocket payment with Medicaid co-eligibility.
15
Results: The interaction term was statistically significant in all the models. For beneficiaries
16
without Medicaid, average out-of-pocket payment per prescription was not significantly
17
associated with odds of HF-related hospitalization (odds ratio = 1.01, 95% CI = 0.98–1.05, P =
18
.399). The association between out-of-pocket payment and hospitalization frequency was
19
statistically significant (incidence rate ratio [IRR] = 1.02, 95% CI = 1.00–1.05, P = .048), as was
20
the association between out-of-pocket payment and total inpatient days (IRR = 1.04, 95% CI =
21
1.00–1.08, P = .041). For Medicaid co-eligible beneficiaries, the validity of model estimates is
22
limited, because the range of actual out-of-pocket payments was negligible.
23
Conclusions: Fee-for-service Medicare beneficiaries with Part D, self-reported HF, and no
24
supplemental Medicaid tolerated out-of-pocket medication payments without elevated risk of
25
HF-related hospital use, but medication spending modestly increased hospital use intensity.
26
Therefore, Part D plans with higher out-of-pocket requirements may warrant additional scrutiny. 1
27 28
Keywords: heart failure; Medicare Part D; health expenditures; cost sharing; hospitalization
29
2
30
Prescription Drug Spending and Hospital Use among Medicare Beneficiaries with Heart Failure
31
Introduction
32
Heart failure (HF) is the most common reason for hospital admission in the Medicare
33
program and is the second leading cause of hospitalization among adults aged 65–84 in the
34
United States.1 HF places a substantial burden on the U.S. health care system: by 2020, it will
35
account for an estimated $50 billion in direct medical costs annually.2 High adherence to
36
prescription drug regimens reduces the risk of poor outcomes in HF patients, including
37
hospitalization, emergency care use, and mortality.3-5 Yet adherence to HF treatment, which
38
often consists of multi-drug regimens, may be negatively affected by the out-of-pocket payment
39
requirements in prescription drug plans.6-8
40
Health insurers, including Medicare Part D plan sponsors, require out-of-pocket
41
payments such as copays and deductibles to dissuade excess benefit use. However, studies in
42
patients with chronic diseases have shown an inverse relationship between prescription drug
43
spending and medication adherence: as out-of-pocket expenses in health plans rise, adherence
44
often declines.7,9-16 But whether and when copay-linked non-adherence contributes to adverse
45
health outcomes remains unclear. The RAND Health Insurance Experiment of the 1970s-80s
46
demonstrated negligible impact of less generous insurance plans on average health outcomes,
47
except among low-income participants with hypertension.17,18 But this finding has been
48
questioned due to unequal attrition across experimental conditions.19 Study results since then
49
have been mixed, though 76% of papers included in a 2012 review of out-of-pocket payments in
50
pharmacy plans showed evidence of an adverse effect on health outcomes.20
51
Even though prescription drug spending in chronic disorders has been studied
52
extensively, HF has received little attention in this area. Only one published study in the last 13
53
years has reported the effect of prescription drug payments on hospital use in HF, despite the
54
significance of HF as a cause of hospitalization and related expenses. Cole et al. estimated the
55
effect of medication copays on hospitalization and medical costs in a retrospective study of 3
56
United Healthcare claims from 2002. They found that a $10 rise in copay for angiotensin-
57
converting enzyme (ACE) inhibitors was associated with predicted 6.1% higher odds of HF-
58
related hospitalization, and that a $10 rise in β-blocker copay predicted 8.7% increased odds of
59
hospitalization for HF.6 Although this study included subscribers of Medicare supplemental
60
plans, it was not exclusive to Medicare beneficiaries, and it predated the launch of the Part D
61
program.
62
With no recent published studies of prescription drug spending and health outcomes in
63
HF, and none focused on the Medicare population, there is need for more evidence to
64
understand the consequences of out-of-pocket payment policies in Part D plans. Therefore, we
65
designed the present study to investigate the link between out-of-pocket drug spending and
66
hospital use among community-dwelling Part D subscribers with fee-for-service Medicare and
67
self-reported HF. Specifically, the purpose of this study was to determine the association
68
between average out-of-pocket payment per HF prescription and: (1) the odds of a HF-related
69
hospital admission, (2) the incidence of HF-related hospitalization, and (3) total inpatient days
70
related to HF. We hypothesized that higher drug spending would be associated with greater
71
hospital use. Methods
72 73 74
Sample selection This study was a retrospective secondary analysis of pooled data from the Medicare
75
Current Beneficiary Survey (MCBS) Cost and Use files for 2010-12, the three most recent years
76
available when the data use agreement was executed. The design of the MCBS has been
77
described extensively elsewhere.21-24 Briefly, it is a rotating panel survey of Medicare
78
beneficiaries that enrolls nearly 12,000 new participants every year and follows them for about
79
48 months. Extensive health and financial questionnaire data are matched when possible to
80
events in the Medicare claims records. Cost and use data are available for three calendar years
4
81
for each panel, and the sample is designed to be representative of the ever-enrolled Medicare
82
population in each year. The average response rate for 2010-12 was 62.3%.24
83
To address the study aims, we selected a subsample of MCBS participants. A
84
participant was included if he or she (1) replied “yes” to the survey item, “(In the past 12
85
months), has a doctor (ever) told you that you had congestive heart failure?”, (2) had continuous
86
Part D coverage in the year of observation, (3) did not live in a facility for any part of the
87
observation year, and (4) was not enrolled in a Medicare Advantage (MA) plan during the
88
observation year. Participants with no record of acquiring a HF-related prescription (see list
89
below) during the year of observation were excluded. We could not use diagnosis codes from
90
Medicare claims for sample selection, since claims were also the basis for the outcome
91
definitions. MA enrollees were excluded due to incomplete reporting of encounter data by plan
92
sponsors.22 Facility-dwelling respondents were excluded, because health care workers may
93
directly administer their medications.10 Part D coverage was required to minimize the number of
94
prescription records with incomplete or imputed transaction data.25 Moreover, the aim was to
95
evaluate out-of-pocket spending in prescription drug plans, not drug prices generally.
96
Study variables
97
A prescription was considered HF-related if the generic drug name could be identified as
98
an ACE inhibitor, an angiotensin receptor blocker (ARB), a non-ocular β blocker, a cardiac
99
glycoside, a diuretic, an aldosterone antagonist, or a vasodilator (hydralazine or isosorbide, if
100
both were filled). Direct renin inhibitors were not included unless combined with another relevant
101
agent in a single formulation. To estimate patient spending requirements for prescription drugs,
102
beneficiary (out-of-pocket) payments from Part D records were used, because MCBS does not
103
report the benefit structure of specific pharmacy plans. We standardized the out-of-pocket
104
payment for each HF-related prescription to a 30-day supply.6,7 For example, a $12 copay for a
105
90-day supply was treated as three $4 payments. We then averaged these payments across the
5
106
year of observation and converted the result to 2012 dollars using the Consumer Price Index for
107
All Urban Consumers.
108
HF-related hospitalization was identified by any inpatient Medicare claim with a principal
109
or secondary diagnosis of HF, based on International Classification of Diseases, Ninth Revision,
110
Clinical Modification (ICD-9-CM) codes 428.xx, 402.x1, 404.x1, or 404.x3.26 Total inpatient days
111
was defined by computing the length of each HF-related hospital stay from admission through
112
discharge dates, then summing all the lengths of stay during the observation year. Hospital
113
stays that straddled the calendar year were retained, since they represented less than 1% of
114
admissions. Emergency department (ED) visits were initially considered as an outcome of
115
interest, but Medicare records with ED flags represent only visits that do not result in inpatient
116
admission. Since just 2% of the sample had an ED visit for HF without a subsequent admission,
117
ED visits were not examined.
118
Covariate selection was based primarily on prior studies of HF medication adherence
119
and hospitalization.6,23 Sociodemographic covariates included sex, race/ethnicity, marital status,
120
annual income, educational attainment, Census region, urbanicity (living in a Metropolitan
121
Statistical Area or not), Medicaid co-eligibility, and age. Health-related covariates included self-
122
rated health status compared to others the same age and compared to one year prior, body
123
mass index (BMI), self-reported difficulty walking 2-3 blocks or ¼ mile, and basis for Medicare
124
entitlement (disability or age only). In addition, the Charlson Comorbidity Index (CCI), a
125
summary score of comorbidities weighted by severity, was computed from diagnosis codes on
126
Medicare claims and adjusted to exclude HF, giving a theoretical range of 0–30.27 Sex,
127
race/ethnicity, marital status, urbanicity, Medicaid co-eligibility, and disability were dichotomized.
128
Income and BMI were log-transformed due to right-skewed distributions. Education level was
129
roughly normally distributed after the categories between high school diploma and bachelor’s
130
degree were combined, so education was treated as a continuous variable. Self-rated health
6
131
status and difficulty walking each were reported on five-point scales, with higher scores
132
reflecting worse health, and were treated as continuous.
133
Analysis plan
134
Design-adjusted generalized linear models were built for multivariable analysis. We used
135
logistic regression to model the association between average spending per HF prescription and
136
the odds of HF-related hospital admission. We used a Poisson model to test the association
137
between medication spending and number of HF-related hospitalizations. We built a negative
138
binomial model for the association between drug spending and inpatient days because of
139
evidence of over-dispersion in the outcome variable.28 Since participants with no hospital
140
admission (83% of records) by definition could not have more than zero days in the hospital, we
141
also built a zero-inflated negative binomial model to examine inpatient days and compared the
142
results with the conventional model.28,29
143
Standard errors were computed by Taylor-series linearization to account for the design
144
effect of stratified, cluster sampling.30 All models contained a flag for death or lost to follow-up to
145
adjust for censoring, year of participation as a proxy for time effects, and a count of HF-related
146
drug classes used in the observation year as a proxy for disease severity, because the MCBS
147
lacks clinical data such as HF stage, and we reasoned that HF patients with more advanced
148
illness were likely prescribed a greater number of drug classes.23 The models also included a
149
pre-specified interaction term to determine if prescription drug spending effects were moderated
150
by Medicaid co-eligibility. Based on bivariate analysis, we hypothesized that Medicaid benefits
151
or factors related to Medicaid eligibility influenced the relationship between medication spending
152
and hospitalization. After reviewing the initial models, we removed participant age due to
153
evidence of collinearity and because the MCBS sample design already stratifies by age. The
154
alpha level was set at .05.
155 156
The first author had full data access under a data use agreement with the Centers for Medicare & Medicaid Services (CMS). CMS provided sampling weights for each participant 7
157
record to account for unequal probabilities of selection, post-stratification, and nonresponse.21
158
For participants with multiple years of data, a weighted average of their cross-sectional
159
sampling weights was applied, because exclusion of multi-year participants in pooled analysis of
160
MCBS data is not recommended.21 The multivariable models were fitted with Stata version 15.1.
161
The institutional review board of the university where study activities were conducted approved
162
this study. Results
163 164 165
Sample characteristics The final sample consisted of 819 participant-year records, derived from 550 unique
166
individuals. Accounting for sampling weights, the majority (58%) were female, and 23% were
167
non-white, multiracial, and/or Latino (Table 1). About 9% had completed a four-year degree or
168
higher, and 63% were unmarried at the time of survey. The mean age was 73.4 years, and
169
mean annual income was $23,630 (in 2012 dollars). Over half (58%) rated their health as fair or
170
poor compared to others the same age, 23% had a Medicare-qualifying disability or disease,
171
mean BMI was 30.2 (obese), and mean adjusted CCI was 3.7.
172
The average out-of-pocket payment for a 30-day HF prescription was $4.03, with a
173
range of $0.00 to $89.50, and 43% of the weighted sample were co-eligible for Medicaid
174
benefits. Seventeen percent were hospitalized with a primary or secondary diagnosis of HF
175
during the year of observation, while the mean number of HF-related hospital admissions was
176
0.3, and mean inpatient days attributable to HF was 1.5. Participants used an average of 2.4
177
distinct drug classes for HF treatment, and 4% of participants were lost to follow-up or died
178
during the year of observation.
179
Multivariable analysis
180
In the multivariable models, the interaction of average payment per prescription with
181
Medicaid eligibility was statistically significant (Table 2). Therefore, the interaction term was
182
retained, and out-of-pocket spending effect estimates are conditional on Medicaid status. For 8
183
the Medicaid non-eligible group, average out-of-pocket payment per HF prescription was not
184
significantly associated with odds of HF-related hospitalization: the odds ratio (OR) was 1.01
185
(95% confidence interval [CI] = 0.98–1.05), P = .399. In the Poisson model, the estimated effect
186
of average spending per prescription on HF-related hospitalization was statistically significant,
187
incidence rate ratio (IRR) = 1.02 (95% CI = 1.00–1.05), P = .048. In the conventional negative
188
binomial model, the estimated effect of spending per prescription on HF-related inpatient days,
189
IRR (95% CI) = 1.04 (1.00–1.08), also was significant, P = .041. The effect estimate for inpatient
190
days was smaller and non-significant in the zero-inflated model, IRR (95% CI) = 1.02 (1.00–
191
1.04), P = .080.
192
In the Medicaid-eligible group, average out-of-pocket payment appeared inversely
193
associated with odds of HF-related hospital admission, OR (95% CI) = 0.68 (0.47–1.00), P =
194
.048, incidence of HF-related admission, IRR (95% CI) = 0.70 (0.54–0.91), P = .008, and HF-
195
related inpatient days, IRR (95% CI) = 0.67 (0.48–0.94), P = .019. However, these associations
196
must be interpreted with caution given the limited range of mean out-of-pocket payments
197
observed in the Medicaid-eligible group ($0.00–6.94). Figure 1 displays the marginal effects of
198
average out-of-pocket drug payment on HF-related inpatient days, grouped by Medicaid status,
199
as predicted by the non-inflated negative binomial model.
200
Discussion
201
In this sample of community-dwelling, fee-for-service Medicare beneficiaries with Part D
202
coverage and self-reported HF, there was no evidence of an association between average out-
203
of-pocket payment per HF prescription and odds of HF-related hospitalization in the absence of
204
supplemental Medicaid. On the other hand, our data suggested that out-of-pocket prescription
205
spending was modestly associated with HF-related hospitalization incidence and inpatient days
206
for this population. We observed that an increase of $1 in the average prescription payment was
207
associated with a 2% rise in hospital admissions and a 2–4% rise in inpatient days attributable
9
208
to HF, depending on the model. Though small, these increases may be clinically and
209
economically meaningful, given the severity and costliness of HF.
210
Although this study did not measure adherence, one possible explanation for the mixed
211
findings could be the relative immediacy and severity of HF complications due to non-
212
adherence. Compared to other chronic diseases in which out-of-pocket medication spending
213
has been studied, HF is a severe condition with a poor prognosis, and low adherence is likely to
214
precipitate exacerbation requiring acute care.3-5,23 As a result, HF patients may be largely
215
insensitive to the price of medications that prevent exacerbations. A previous analysis of select
216
drug classes in these data showed that mean adherence was high: 88–90% for β blockers, ACE
217
inhibitors, and ARBs.8 In addition, average out-of-pocket drug spending was very low in this
218
study, with a weighted mean of about $4.00 per 30-day supply. With overall low costs and high
219
adherence, it is plausible that drug prices did not substantially affect hospital use.
220
The only other published study to investigate HF drug copays and hospital outcomes
221
found that risk of HF-related hospitalization rose with copay-attributable declines in medication
222
adherence: odds of hospital admission increased by a predicted 6% with each $10 rise in ACE
223
inhibitor copay, and by 9% per $10 rise in β-blocker copay.6 However, that study was conducted
224
in a commercially insured population, which may have been younger and healthier on
225
average—and therefore more price-sensitive—compared to fee-for-service Medicare
226
beneficiaries. In addition, $10 is more than double the average out-of-pocket payment per
227
prescription in our sample, limiting comparability between the two studies.
228
We did not directly examine the effect of adherence on outcomes because of difficulties
229
estimating adherence across multiple drug classes from claims data. Previous analysis of these
230
data showed that out-of-pocket spending on β blockers as a proportion of monthly income was
231
inversely associated with adherence.8 Yet the effect size was small, and that analysis found no
232
evidence of a spending effect on adherence to ACE inhibitors or ARBs. Other published studies
233
in age-diverse, commercially insured cohorts have reported lower adherence at higher out-of10
234
pocket spending levels in HF, but the effects were only significant at copays of at least $10 or
235
$20.6,7 With an average out-of-pocket payment of just $4 in this sample, adherence may not
236
have been strongly affected, which would have attenuated the pathway from prescription drug
237
spending to hospital use.
238
Notably, the number of HF-indicated drug classes used was a significantly positive
239
predictor of all three hospital use outcomes. This study analyzed the effect of average out-of-
240
pocket payment for a single 30-day supply, rather than total out-of-pocket pharmacy
241
expenditure, to avoid inflating the effect estimates for patients who were prescribed more drugs.
242
Number of drug classes was included in the models to adjust for disease severity, and it is likely
243
that its significant association with hospital use reflects sicker patients needing the hospital
244
more. However, it is possible that this effect also captured total drug spending burden, and its
245
inclusion in the models could have attenuated the average drug payment effects. Charlson
246
Comorbidity Index was also significantly associated with hospital use in all the models, further
247
suggesting a complex relationship between comorbidities, polypharmacy, and hospitalization.
248
In the Medicaid-eligible group, the effects of out-of-pocket drug spending on
249
hospitalization and inpatient days were statistically significant in the direction opposite of that
250
hypothesized. However, the range of observed average out-of-pocket payments was only $0 to
251
$6.94, with the majority of dual-eligible beneficiaries spending under $1 per prescription, so
252
extrapolation of this effect may not be meaningful. If anything, higher out-of-pocket payments
253
may reflect slightly higher incomes, because Medicaid drug copays are income-dependent in
254
some states.31,32 Modestly higher incomes may have enabled better access to preventive care,
255
or higher copays may have dissuaded hospital use if they also applied to inpatient services, but
256
we did not test these hypotheses.
257
To our knowledge, this is the first published study of out-of-pocket spending in pharmacy
258
plans and related hospital use to focus specifically on Medicare beneficiaries with HF, and the
259
first to use population-based survey data. Notwithstanding the small effect sizes, we found 11
260
statistically significant and potentially meaningful associations between out-of-pocket drug
261
spending and hospital use intensity despite very low out-of-pocket costs, infrequent hospital
262
use, and adjustment for many covariates. Moreover, our findings can be extrapolated to all
263
community-dwelling, fee-for-service Medicare beneficiaries with self-reported HF and
264
continuous Part D coverage, because the MCBS is representative of ever-enrolled Medicare
265
beneficiaries nationally.24 Future research should clarify the relationship between drug spending
266
and hospital use in HF with longitudinal methods and complete benefit design information,
267
especially given the recent availability of costly new medications for HF management.
268
Additionally, because non-white or Latino patients and patients with lower educational
269
attainment had lower adjusted odds and incidence of HF-related hospitalization in this study,
270
future research should address potential health equity implications.
271
Limitations
272
As with all retrospective database studies, measurement error was possible. Since we
273
could not observe the benefit structure of beneficiaries’ Part D plans, we estimated out-of-
274
pocket liability using average beneficiary payments. That approach was not sensitive to the
275
dynamic nature of Part D plans, which included yearly deductibles and coverage gaps.33 If
276
measurement error was random, it probably biased our results toward the null. Furthermore,
277
some prescriptions and outcomes may have been misclassified. A prescription was defined as
278
HF-related if it matched a pre-determined list of drug names, but some drugs could be used for
279
other conditions, and effects of non-HF drug prices may also be important. An outcome could
280
have been misclassified if HF was listed as the secondary diagnosis for an unrelated encounter.
281
However, using only the principal diagnosis would have excluded patients treated for conditions
282
which HF precipitated, such as pulmonary edema. Since the claims did not specify contributing
283
conditions for Diagnosis-Related Groups, we relied on principal and secondary ICD-9 codes, an
284
approach modeled on a prior study of patients with HF admissions in the MCBS.26
12
285
Another limitation of this study was its correlational design. Since prescription drug
286
spending was averaged over the year of observation, the outcomes analyzed could have
287
occurred before some of the out-of-pocket drug payments. This problem of temporality limits
288
any causal inference that can be made. Relying on questionnaire responses to identify HF
289
cases may have resulted in misclassification, because some patients may not understand or
290
remember their diagnosis. Self-reported HF in the Atherosclerosis Risk in Communities (ARIC)
291
Study had 61% agreement with a CMS claims-based definition, but that definition lacked
292
precision as it allowed HF diagnosis codes in any position on the claim and left out hypertensive
293
heart disease with heart failure (ICD-9-CM 402.x1, 404.x1 and 404.x3).34 In addition, no
294
disease-specific clinical data (e.g. HF stage or ejection fraction) were available in the MCBS, so
295
it was difficult to assess whether patients with more advanced illness were less price-sensitive.
296
As noted above, we used number of HF drug classes as a proxy for disease severity, but that
297
approach may have confounded our interpretation of price effects.
298
Pooling data from multiple survey years could have underestimated standard errors due
299
to autocorrelation, since observations from multi-year participants were not truly independent.
300
But the necessity of correcting for this additional clustering in complex samples is unclear, as
301
long as the primary sampling unit is specified correctly.21 Exclusion of survey-reported
302
prescriptions that did not match a Medicare record may have biased spending estimates.
303
However, only about 6% of unmatched survey prescriptions reflect true out-of-plan use for Part
304
D subscribers with common chronic diseases in MCBS data; many of the rest were probably
305
duplicates.25 And as with any observational study, findings may be spurious because of omitted
306
variables or sampling error.
307 308
Conclusions This study offers support for the notion that community-dwelling, fee-for-service
309
Medicare beneficiaries with HF can absorb modest prescription drug prices in Part D plans
310
without raising the risk of HF-related hospital admission. However, among beneficiaries with no 13
311
supplemental Medicaid (and greater average out-of-pocket exposure), there is evidence that
312
higher drug spending is modestly associated with hospitalization incidence and total days
313
hospitalized due to HF. Therefore, Part D plans with high out-of-pocket requirements for
314
essential HF medications may warrant further scrutiny, especially for beneficiaries with
315
polypharmacy and multiple comorbidities. These findings contribute useful information to
316
ongoing clinical and policy discussions about optimal prescription benefit design for older adults
317
with HF.
318 Acknowledgements
319 320
Rebecca A. Gary, PhD, RN, FAAN, FAHA, and David Howard, PhD, reviewed early
321
drafts of this paper and provided helpful feedback. Rishika Parikh, MPH, MA, assisted with
322
editing and submission.
323 Funding
324 325 326
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
327
14
References
328 329 330
1.
Pfuntner A, Wier LM, Stocks C. Most frequent conditions in US hospitals, 2011: H-CUP
331
Statistical Brief #162. Agency for Healthcare Research & Quality; 2013. Available at:
332
https://www.hcup-us.ahrq.gov/reports/statbriefs/sb162.pdf. Accessed Sep. 13, 2019.
333
2.
Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular
334
disease in the United States: a policy statement from the American Heart Association.
335
Circulation. 2011;123(8):933-944.
336
3.
Esposito D, Bagchi AD, Verdier JM, Bencio DS, Kim MS. Medicaid beneficiaries with
337
congestive heart failure: association of medication adherence with healthcare use and
338
costs. Am J Manag Care. 2009;15(7):437-445.
339
4.
Marti CN, Georgiopoulou VV, Giamouzis G, et al. Patient-reported selective adherence
340
to heart failure self-care recommendations, a prospective cohort study: the Atlanta
341
Cardiomyopathy Consortium. Congest Heart Fail. 2013;19(1):16-24.
342
5.
Wu JR, Lennie TA, Dekker RL, Biddle MJ, Moser DK. Medication adherence, depressive
343
symptoms, and cardiac event-free survival in patients with heart failure. J Card Fail.
344
2013;19(5):317-324.
345
6.
Cole JA, Norman H, Weatherby LB, Walker AM. Drug copayment and adherence in
346
chronic heart failure: effect on cost and outcomes. Pharmacotherapy. 2006;26(8):1157-
347
1164.
348
7.
Patterson ME, Blalock SJ, Smith AJ, Murray MD. Associations between prescription
349
copayment levels and beta-blocker medication adherence in commercially insured heart
350
failure patients 50 years and older. Clin Ther. 2011;33(5):608-616.
351
8.
McGee BT, Phillips V, Higgins MK, Butler J. Prescription drug spending and medication
352
adherence among Medicare beneficiaries with heart failure. J Manag Care Spec Pharm.
353
2019;25(6):705-713.
15
354
9.
Barron J, Wahl P, Fisher M, Plauschinat C. Effect of prescription copayments on
355
adherence and treatment failure with oral antidiabetic medications. P&T.
356
2008;33(9):532-553.
357
10.
Chen SY, Shah SN, Lee YC, Boulanger L, Mardekian J, Kuznik A. Moving branded
358
statins to lowest copay tier improves patient adherence. J Manag Care Pharm.
359
2014;20(1):34-42.
360
11.
Colombi AM, Yu-Isenberg K, Priest J. The effects of health plan copayments on
361
adherence to oral diabetes medication and health resource utilization. J Occup Environ
362
Med. 2008;50(5):535-541.
363
12.
Doshi JA, Zhu J, Lee BY, Kimmel SE, Volpp KG. Impact of a prescription copayment
364
increase on lipid-lowering medication adherence in veterans. Circulation.
365
2009;119(3):390-397.
366
13.
Kim YA, Rascati KL, Prasla K, Godley P, Goel N, Dunlop D. Retrospective evaluation of
367
the impact of copayment increases for specialty medications on adherence and
368
persistence in an integrated health maintenance organization system. Clin Ther.
369
2011;33(5):598-607.
370
14.
Li P, McElligott S, Bergquist H, Schwartz JS, Doshi JA. Effect of the Medicare Part D
371
coverage gap on medication use among patients with hypertension and hyperlipidemia.
372
Ann Intern Med. 2012;156(11):776-784.
373
15.
Thornton Snider J, Seabury S, Lopez J, McKenzie S, Goldman DP. Impact of type 2
374
diabetes medication cost sharing on patient outcomes and health plan costs. Am J
375
Manag Care. 2016;22(6):433-440.
376
16.
of patient cost sharing. J Manag Care Spec Pharm. 2018;24(9):847-855.
377 378 379
Henk HJ, Lopez JMS, Bookhart BK. Novel type 2 diabetes medication access and effect
17.
Brook RH, Ware JE, Jr., Rogers WH, et al. Does free care improve adults' health? Results from a randomized controlled trial. N Engl J Med. 1983;309(23):1426-1434. 16
380
18.
Experiment. Health Aff (Millwood). 2004;23(6):107-113.
381 382
19.
20.
Eaddy MT, Cook CL, O'Day K, Burch SP, Cantrell CR. How patient cost-sharing trends affect adherence and outcomes: a literature review. P&T. 2012;37(1):45-55.
385 386
Nyman JA. American health policy: Cracks in the foundation. J Health Polit Policy Law. 2007;32(5):759-783.
383 384
Newhouse JP. Consumer-directed health plans and the RAND Health Insurance
21.
Briesacher BA, Tjia J, Doubeni CA, Chen Y, Rao SR. Methodological issues in using
387
multiple years of the Medicare Current Beneficiary Survey. Medicare Medicaid Res Rev.
388
2012;2(1).
389
22.
Cubanski J, Swoope C, Damico A, Neuman T. How much is enough? Out-of-pocket
390
spending by Medicare beneficiaries: a chartbook. Kaiser Family Foundation; July 2014.
391
Available at: http://files.kff.org/attachment/how-much-is-enough-out-of-pocket-spending-
392
among-medicare-beneficiaries-a-chartbook-report. Accessed Sep. 13, 2019.
393
23.
Lopert R, Shoemaker JS, Davidoff A, et al. Medication adherence and Medicare
394
expenditure among beneficiaries with heart failure. Am J Manag Care. 2012;18(9):556-
395
563.
396
24.
Centers for Medicare & Medicaid Services. Medicare Current Beneficiary Survey: CY
397
2012 Cost and Use. February 2016. Available at: https://www.cms.gov/Research-
398
Statistics-Data-and-Systems/Research/MCBS/Codebooks-Items/2012CostAndUse.html.
399
Accessed Sep. 13, 2019.
400
25.
2014;20(9):743-748.
401 402 403
Roberto PN, Stuart B. Out-of-plan medication in Medicare Part D. Am J Manag Care.
26.
DiMartino LD, Shea AM, Hernandez AF, Curtis LH. Use of guideline-recommended therapies for heart failure in the Medicare population. Clin Cardiol. 2010;33(7):400-405.
17
404
27.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying
405
prognostic comorbidity in longitudinal studies: development and validation. J Chronic
406
Dis. 1987;40(5):373-383.
407
28.
215.
408 409
29.
30.
Heeringa SG, West BT, Berglund PA. Applied Survey Data Analysis. Boca Raton: CRC Press; 2010.
412 413
Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1-14.
410 411
Hayat MJ, Higgins M. Understanding Poisson regression. J Nurs Educ. 2014;53(4):207-
31.
Hartung DM, Carlson MJ, Kraemer DF, Haxby DG, Ketchum KL, Greenlick MR. Impact
414
of a Medicaid copayment policy on prescription drug and health services utilization in a
415
fee-for-service Medicaid population. Med Care. 2008;46(6):565-572.
416
32.
Medicaid. Med Care. 2017;55(5):520-527.
417 418
Kostova D, Fox J. Chronic health outcomes and prescription drug copayments in
33.
Hoadley J, Cubanski J, Neuman T. Medicare Part D in 2016 and trends over time.
419
Kaiser Family Foundation; 2016. Available at: http://kff.org/medicare/report/medicare-
420
part-d-in-2016-and-trends-over-time/. Accessed Sep. 13, 2019.
421
34.
Camplain R, Kucharska-Newton A, Loehr L, et al. Accuracy of self-reported heart failure:
422
the Atherosclerosis Risk in Communities (ARIC) Study. J Card Fail. 2017;23(11):802-
423
808.
424
18
425
Table 1
426
Sample Characteristics Count (unweighted)
Variable TOTAL
Percent (weighted)
95% CI for Percent (design-adjusted)
819
100.00
---
---
Male
345
41.6%
36.6%
46.6%
Female
474
58.4%
53.4%
63.4%
Non-white or Latino
199
23.3%
18.4%
28.2%
White, non-Latino
619
76.7%
71.8%
81.6%
Married
281
37.1%
32.7%
41.6%
Unmarried
537
62.9%
58.4%
67.3%
11
1.1%
0.1%
2.2%
143
16.5%
13.3%
19.6%
9 -12 grade
171
21.3%
17.9%
24.8%
High school diploma
233
28.3%
24.6%
32.1%
185
24.1%
20.2%
28.1%
Bachelor’s degree
43
5.0%
3.0%
7.0%
Post-graduate
31
3.7%
1.3%
6.0%
Eligible
354
43.2%
38.7%
47.6%
Ineligible
465
56.8%
52.4%
61.3%
Gender
Race/ethnicity a
Marital status
Education No schooling th
Nursery-8 grade th
th
Vocational/technical, some college, or associate’s
Medicaid coverage
19
Urbanicity Metropolitan area
489
61.6%
55.6%
67.5%
Non-metro. area
330
38.4%
32.5%
44.4%
Northeast
100
13.8%
10.3%
17.3%
Midwest
204
24.7%
19.1%
30.4%
West
101
12.3%
6.5%
18.0%
South or Puerto Rico
414
49.2%
42.8%
55.6%
Excellent
21
2.4%
1.4%
3.5%
Very good
107
11.9%
9.4%
14.5%
Good
236
27.6%
23.7%
31.5%
Fair
274
34.8%
30.9%
38.7%
Poor
177
23.3%
19.9%
26.6%
44
5.6%
3.7%
7.7%
Somewhat better
107
13.2%
10.1%
16.3%
About the same
339
40.1%
35.8%
44.4%
Somewhat worse
268
33.3%
29.5%
37.1%
59
7.7%
5.5%
9.9%
None
117
15.0%
11.4%
18.5%
A little
75
8.9%
7.0%
10.8%
Some
84
10.0%
8.0%
12.0%
144
17.3%
14.5%
20.1%
Region
Health vs. others the same age
Health now vs. one year ago Much better
Much worse Difficulty walking ¼ mile or 2-3 blocks
A lot
20
Unable
394
48.8%
44.1%
53.4%
Disability/ESRD
162
22.8%
18.5%
27.1%
Age only
657
77.2%
72.9%
81.5%
Died or LTFU
34
4.1%
2.4%
5.8%
Alive, retained
785
96.0%
94.2%
97.6%
2010
284
33.8%
30.7%
37.0%
2011
274
34.4%
31.8%
36.9%
2012
261
31.8%
28.7%
34.9%
Yes
138
17.0%
14.0%
19.9%
No
681
83.0%
80.1%
86.0%
Medicare entitlement
Censoring
Year of observation
HF-related hospitalization
Mean (weighted)
Variable
95% CI for Mean (design-adjusted)
Range (unweighted)
73.4
72.3
74.4
27.0
99.0
23.6
21.3
26.0
0.7
263.2
30.2
29.5
30.9
15.5
70.5
3.7
3.4
3.9
0
17.0
2.4
2.3
2.5
1.0
6.0
4.0
3.4
4.6
0
89.5
HF-related hospitalizations
0.3
0.2
0.3
0
8.0
HF-related hospitalized days
1.5
1.2
1.9
0
47.0
Age (in years) Annual income (000s)
b,c
Body mass index Charlson Comorbidity Index
d
HF-related drug classes used Mean out-of-pocket payment per 30-day HF Rx
b,e
427
Note. CI = confidence interval; ESRD = end-stage renal disease; HF = heart failure; LTFU = lost to follow-
428
up; Rx = prescription.
21
429
a
b
c
430
retirement account payments for participant and spouse. Modified to exclude heart failure. Standardized
431
to a 30-day supply.
Includes multiracial participants. Converted to 2012 dollars. Includes Social Security, pension, and d
e
22
432
Table 2
433
Estimated Associations with Hospital Use
Parameter
Any HF-related hospitalization OR
Mean out-of-pocket b payment per Rx
95% CI
No. of HF-related hospitalizations IRR
No. of HF-related a inpatient days
95% CI
IRR
95% CI
1.01
0.98-1.05
1.02c
1.00-1.05
1.04c
1.00-1.08
c
1.28-5.98
2.11
c
1.10-4.04
2.74
c
1.26-5.92
Eligible for Medicaid Yes
2.76
No
Reference
Reference
Reference
Rx payment*Medicaid interaction
0.67
c
0.46-0.99
0.68
c
0.52-0.89
0.65
Health vs. others the d same age
1.42
c
1.11-1.82
1.22
c
1.01-1.47
1.70
1.10
0.83-1.46
1.03
0.80-1.34
c
0.10-0.60
0.31
c
0.18-0.55
1.09
0.90-1.32
1.18
c
1.01-1.36
c
0.22-0.75
0.63
0.35-1.11
Health vs. a year agod Body mass index (log) Difficulty walking 1/4 d mile or 2-3 blocks
0.24
c
0.46-0.91
c
1.25-2.30
1.05
0.80-1.38
c
0.04-0.51
1.07
0.86-1.34
c
0.20-0.68
0.14
Medicare entitlement Disability/ESRD
0.41
Age only Charlson Comorbidity Index (modified)
Reference c
1.17-1.47
1.26
0.72-2.19
1.31
0.37
Reference
Reference
c
1.15-1.29
1.36
1.05
0.70-1.56
1.75
1.22
c
1.23-1.50
c
1.02-3.02
Gender Male Female
Reference
Reference
Reference
Race/ethnicity Non-white, mixed race, and/or Latino White, non-Latino
0.46
c
0.22-1.00 Reference
0.61
0.32-1.17 Reference
0.50
c
0.26-0.97 Reference
23
Presently married Yes
0.60
No
0.29-1.23
0.48c
Reference
0.30-0.77 Reference
c
1.03-1.47
1.20
c
1.04-1.38
Northeast
1.54
0.86-2.77
1.52
c
Midwest
1.25
0.76-2.07
West
0.80
0.33-1.94
Education level
1.23
0.59
0.30-1.14 Reference
c
1.09-1.59
1.07-2.17
1.40
0.68-2.88
1.39c
1.00-1.95
1.17
0.69-1.98
0.73
0.40-1.34
c
0.16-0.74
1.32
Census region
South or Puerto Rico
Reference
0.34
Reference
Reference
Urbanicity Metropolitan area
1.25
Non-metro. area Annual income (log)
0.88-1.79
0.96
Reference
0.73-1.27
c
1.95
Reference
1.28-2.97 Reference
0.88
0.52-1.49
0.87
0.57-1.32
0.71
0.49-1.01
0.76
0.28-2.10
0.92
0.46-1.85
0.72
0.25-2.08
Censoring Died or LTFU Alive and retained
Reference
Reference
Reference
Year of observation 2010
1.11
0.74-1.67
1.27
0.92-1.76
2011
0.75
0.44-1.28
0.95
0.63-1.45
2012 No. of HF drug classes
Reference 2.11
c
1.58-2.83
c
1.20-2.95
1.19
0.72-1.98
1.88
Reference 1.80
c
1.50-2.17
Reference c
1.91
1.51-2.42
434
Note. CI = confidence interval; ESRD = end-stage renal disease; HF = heart failure; IRR = incidence rate
435
ratio; LTFU = lost to follow-up; OR = odds ratio; Rx = prescription.
436 437 438 439 440
a
Estimates from conventional negative binomial model shown. Standardized to a 30-day supply. c Significant at the p < .05 level. d Five-point scale; higher score reflects worse health or function. b
24
441
442 443
Figure 1. Predicted number of total days hospitalized for heart failure (HF) by average out-of-
444
pocket (OOP) payment per 30-day HF prescription and Medicaid status. (Note. The majority of
445
observed mean OOP payments in the Medicaid-eligible group were < $1.00.)
446
25
Prescription Drug Spending and Hospital Use among Medicare Beneficiaries with Heart Failure
Highlights • • • • •
Out-of-pocket payments in Part D deter at least some appropriate medication use Whether drug payments increase hospital use in heart failure (HF) is unknown Mean payment per 30-day supply was not associated with hospitalization odds Mean payment was associated with small increases in hospital use intensity Evidence for a strong drug spending effect on hospital use in HF is lacking
S1551-7411(19)30918-0
DOI:
https://doi.org/10.1016/j.sapharm.2019.12.019
Reference:
RSAP 1421
To appear in:
Research in Social & Administrative Pharmacy
Received Date: 13 September 2019 Accepted Date: 20 December 2019
Please cite this article as: McGee BT, Higgins MK, Phillips V, Butler J, Prescription drug spending and hospital use among medicare beneficiaries with heart failure, Research in Social & Administrative Pharmacy (2020), doi: https://doi.org/10.1016/j.sapharm.2019.12.019. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Prescription Drug Spending and Hospital Use among Medicare Beneficiaries with Heart Failure Blake Tyler McGee, PhD, MPH, RNa,1
Laney Graduate School, Emory University 201 Dowman Dr. NW Atlanta, GA 30322 USA b
Nell Hodgson Woodruff School of Nursing, Emory University 1520 Clifton Rd. NW Atlanta, GA 30322 USA (404) 727-5180 [email protected]
c
Rollins School of Public Health, Emory University 1518 Clifton Rd. NW Atlanta, GA 30322 USA (404) 727-9974 [email protected]
d
Department of Medicine, University of Mississippi 2500 N State St. Jackson, MS 39216 USA (601) 984-5602 [email protected] 1
Present affiliation (for correspondence): Byrdine F. Lewis College of Nursing and Health Professions Georgia State University P.O. Box 4019 Atlanta, GA 30302 USA (404) 413-1180 [email protected] Disclosures: Dr. Butler has served as a paid consultant or advisor on unrelated projects for the following companies: Amgen, Array, Astra Zeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squib, CVRx, G3, Innolife, Janssen, Medtronic, Merck, Novartis, Relypsa, Stealth Peptide, SC Pharma, Vifor and ZS Pharma. The other authors have no potential conflicts of interest to declare.
1
Abstract
2
Background: Heart failure (HF) is a common cause of hospitalization in Medicare. Optimal
3
medication adherence lowers hospitalization risk in HF patients. Although out-of-pocket
4
spending can adversely affect adherence to HF medications, it is unknown whether medication
5
spending ultimately increases hospital use for Medicare beneficiaries with HF.
6
Objective: To examine the association between out-of-pocket medication payments and HF-
7
related hospital use among Medicare Part D subscribers.
8
Methods: Retrospective analysis of the 2010-12 Medicare Current Beneficiary Survey. The
9
sample comprised community-dwelling respondents with fee-for-service Medicare, continuous
10
Part D coverage, and self-reported HF (n=819 participant-year records). The effects of average
11
out-of-pocket payment for a 30-day HF-related prescription on odds and frequency of
12
hospitalization and total inpatient days attributable to HF were estimated. Design-adjusted
13
models adjusted for sociodemographic and health status variables, survey year and censoring,
14
and included a pre-specified interaction of out-of-pocket payment with Medicaid co-eligibility.
15
Results: The interaction term was statistically significant in all the models. For beneficiaries
16
without Medicaid, average out-of-pocket payment per prescription was not significantly
17
associated with odds of HF-related hospitalization (odds ratio = 1.01, 95% CI = 0.98–1.05, P =
18
.399). The association between out-of-pocket payment and hospitalization frequency was
19
statistically significant (incidence rate ratio [IRR] = 1.02, 95% CI = 1.00–1.05, P = .048), as was
20
the association between out-of-pocket payment and total inpatient days (IRR = 1.04, 95% CI =
21
1.00–1.08, P = .041). For Medicaid co-eligible beneficiaries, the validity of model estimates is
22
limited, because the range of actual out-of-pocket payments was negligible.
23
Conclusions: Fee-for-service Medicare beneficiaries with Part D, self-reported HF, and no
24
supplemental Medicaid tolerated out-of-pocket medication payments without elevated risk of
25
HF-related hospital use, but medication spending modestly increased hospital use intensity.
26
Therefore, Part D plans with higher out-of-pocket requirements may warrant additional scrutiny. 1
27 28
Keywords: heart failure; Medicare Part D; health expenditures; cost sharing; hospitalization
29
2
30
Prescription Drug Spending and Hospital Use among Medicare Beneficiaries with Heart Failure
31
Introduction
32
Heart failure (HF) is the most common reason for hospital admission in the Medicare
33
program and is the second leading cause of hospitalization among adults aged 65–84 in the
34
United States.1 HF places a substantial burden on the U.S. health care system: by 2020, it will
35
account for an estimated $50 billion in direct medical costs annually.2 High adherence to
36
prescription drug regimens reduces the risk of poor outcomes in HF patients, including
37
hospitalization, emergency care use, and mortality.3-5 Yet adherence to HF treatment, which
38
often consists of multi-drug regimens, may be negatively affected by the out-of-pocket payment
39
requirements in prescription drug plans.6-8
40
Health insurers, including Medicare Part D plan sponsors, require out-of-pocket
41
payments such as copays and deductibles to dissuade excess benefit use. However, studies in
42
patients with chronic diseases have shown an inverse relationship between prescription drug
43
spending and medication adherence: as out-of-pocket expenses in health plans rise, adherence
44
often declines.7,9-16 But whether and when copay-linked non-adherence contributes to adverse
45
health outcomes remains unclear. The RAND Health Insurance Experiment of the 1970s-80s
46
demonstrated negligible impact of less generous insurance plans on average health outcomes,
47
except among low-income participants with hypertension.17,18 But this finding has been
48
questioned due to unequal attrition across experimental conditions.19 Study results since then
49
have been mixed, though 76% of papers included in a 2012 review of out-of-pocket payments in
50
pharmacy plans showed evidence of an adverse effect on health outcomes.20
51
Even though prescription drug spending in chronic disorders has been studied
52
extensively, HF has received little attention in this area. Only one published study in the last 13
53
years has reported the effect of prescription drug payments on hospital use in HF, despite the
54
significance of HF as a cause of hospitalization and related expenses. Cole et al. estimated the
55
effect of medication copays on hospitalization and medical costs in a retrospective study of 3
56
United Healthcare claims from 2002. They found that a $10 rise in copay for angiotensin-
57
converting enzyme (ACE) inhibitors was associated with predicted 6.1% higher odds of HF-
58
related hospitalization, and that a $10 rise in β-blocker copay predicted 8.7% increased odds of
59
hospitalization for HF.6 Although this study included subscribers of Medicare supplemental
60
plans, it was not exclusive to Medicare beneficiaries, and it predated the launch of the Part D
61
program.
62
With no recent published studies of prescription drug spending and health outcomes in
63
HF, and none focused on the Medicare population, there is need for more evidence to
64
understand the consequences of out-of-pocket payment policies in Part D plans. Therefore, we
65
designed the present study to investigate the link between out-of-pocket drug spending and
66
hospital use among community-dwelling Part D subscribers with fee-for-service Medicare and
67
self-reported HF. Specifically, the purpose of this study was to determine the association
68
between average out-of-pocket payment per HF prescription and: (1) the odds of a HF-related
69
hospital admission, (2) the incidence of HF-related hospitalization, and (3) total inpatient days
70
related to HF. We hypothesized that higher drug spending would be associated with greater
71
hospital use. Methods
72 73 74
Sample selection This study was a retrospective secondary analysis of pooled data from the Medicare
75
Current Beneficiary Survey (MCBS) Cost and Use files for 2010-12, the three most recent years
76
available when the data use agreement was executed. The design of the MCBS has been
77
described extensively elsewhere.21-24 Briefly, it is a rotating panel survey of Medicare
78
beneficiaries that enrolls nearly 12,000 new participants every year and follows them for about
79
48 months. Extensive health and financial questionnaire data are matched when possible to
80
events in the Medicare claims records. Cost and use data are available for three calendar years
4
81
for each panel, and the sample is designed to be representative of the ever-enrolled Medicare
82
population in each year. The average response rate for 2010-12 was 62.3%.24
83
To address the study aims, we selected a subsample of MCBS participants. A
84
participant was included if he or she (1) replied “yes” to the survey item, “(In the past 12
85
months), has a doctor (ever) told you that you had congestive heart failure?”, (2) had continuous
86
Part D coverage in the year of observation, (3) did not live in a facility for any part of the
87
observation year, and (4) was not enrolled in a Medicare Advantage (MA) plan during the
88
observation year. Participants with no record of acquiring a HF-related prescription (see list
89
below) during the year of observation were excluded. We could not use diagnosis codes from
90
Medicare claims for sample selection, since claims were also the basis for the outcome
91
definitions. MA enrollees were excluded due to incomplete reporting of encounter data by plan
92
sponsors.22 Facility-dwelling respondents were excluded, because health care workers may
93
directly administer their medications.10 Part D coverage was required to minimize the number of
94
prescription records with incomplete or imputed transaction data.25 Moreover, the aim was to
95
evaluate out-of-pocket spending in prescription drug plans, not drug prices generally.
96
Study variables
97
A prescription was considered HF-related if the generic drug name could be identified as
98
an ACE inhibitor, an angiotensin receptor blocker (ARB), a non-ocular β blocker, a cardiac
99
glycoside, a diuretic, an aldosterone antagonist, or a vasodilator (hydralazine or isosorbide, if
100
both were filled). Direct renin inhibitors were not included unless combined with another relevant
101
agent in a single formulation. To estimate patient spending requirements for prescription drugs,
102
beneficiary (out-of-pocket) payments from Part D records were used, because MCBS does not
103
report the benefit structure of specific pharmacy plans. We standardized the out-of-pocket
104
payment for each HF-related prescription to a 30-day supply.6,7 For example, a $12 copay for a
105
90-day supply was treated as three $4 payments. We then averaged these payments across the
5
106
year of observation and converted the result to 2012 dollars using the Consumer Price Index for
107
All Urban Consumers.
108
HF-related hospitalization was identified by any inpatient Medicare claim with a principal
109
or secondary diagnosis of HF, based on International Classification of Diseases, Ninth Revision,
110
Clinical Modification (ICD-9-CM) codes 428.xx, 402.x1, 404.x1, or 404.x3.26 Total inpatient days
111
was defined by computing the length of each HF-related hospital stay from admission through
112
discharge dates, then summing all the lengths of stay during the observation year. Hospital
113
stays that straddled the calendar year were retained, since they represented less than 1% of
114
admissions. Emergency department (ED) visits were initially considered as an outcome of
115
interest, but Medicare records with ED flags represent only visits that do not result in inpatient
116
admission. Since just 2% of the sample had an ED visit for HF without a subsequent admission,
117
ED visits were not examined.
118
Covariate selection was based primarily on prior studies of HF medication adherence
119
and hospitalization.6,23 Sociodemographic covariates included sex, race/ethnicity, marital status,
120
annual income, educational attainment, Census region, urbanicity (living in a Metropolitan
121
Statistical Area or not), Medicaid co-eligibility, and age. Health-related covariates included self-
122
rated health status compared to others the same age and compared to one year prior, body
123
mass index (BMI), self-reported difficulty walking 2-3 blocks or ¼ mile, and basis for Medicare
124
entitlement (disability or age only). In addition, the Charlson Comorbidity Index (CCI), a
125
summary score of comorbidities weighted by severity, was computed from diagnosis codes on
126
Medicare claims and adjusted to exclude HF, giving a theoretical range of 0–30.27 Sex,
127
race/ethnicity, marital status, urbanicity, Medicaid co-eligibility, and disability were dichotomized.
128
Income and BMI were log-transformed due to right-skewed distributions. Education level was
129
roughly normally distributed after the categories between high school diploma and bachelor’s
130
degree were combined, so education was treated as a continuous variable. Self-rated health
6
131
status and difficulty walking each were reported on five-point scales, with higher scores
132
reflecting worse health, and were treated as continuous.
133
Analysis plan
134
Design-adjusted generalized linear models were built for multivariable analysis. We used
135
logistic regression to model the association between average spending per HF prescription and
136
the odds of HF-related hospital admission. We used a Poisson model to test the association
137
between medication spending and number of HF-related hospitalizations. We built a negative
138
binomial model for the association between drug spending and inpatient days because of
139
evidence of over-dispersion in the outcome variable.28 Since participants with no hospital
140
admission (83% of records) by definition could not have more than zero days in the hospital, we
141
also built a zero-inflated negative binomial model to examine inpatient days and compared the
142
results with the conventional model.28,29
143
Standard errors were computed by Taylor-series linearization to account for the design
144
effect of stratified, cluster sampling.30 All models contained a flag for death or lost to follow-up to
145
adjust for censoring, year of participation as a proxy for time effects, and a count of HF-related
146
drug classes used in the observation year as a proxy for disease severity, because the MCBS
147
lacks clinical data such as HF stage, and we reasoned that HF patients with more advanced
148
illness were likely prescribed a greater number of drug classes.23 The models also included a
149
pre-specified interaction term to determine if prescription drug spending effects were moderated
150
by Medicaid co-eligibility. Based on bivariate analysis, we hypothesized that Medicaid benefits
151
or factors related to Medicaid eligibility influenced the relationship between medication spending
152
and hospitalization. After reviewing the initial models, we removed participant age due to
153
evidence of collinearity and because the MCBS sample design already stratifies by age. The
154
alpha level was set at .05.
155 156
The first author had full data access under a data use agreement with the Centers for Medicare & Medicaid Services (CMS). CMS provided sampling weights for each participant 7
157
record to account for unequal probabilities of selection, post-stratification, and nonresponse.21
158
For participants with multiple years of data, a weighted average of their cross-sectional
159
sampling weights was applied, because exclusion of multi-year participants in pooled analysis of
160
MCBS data is not recommended.21 The multivariable models were fitted with Stata version 15.1.
161
The institutional review board of the university where study activities were conducted approved
162
this study. Results
163 164 165
Sample characteristics The final sample consisted of 819 participant-year records, derived from 550 unique
166
individuals. Accounting for sampling weights, the majority (58%) were female, and 23% were
167
non-white, multiracial, and/or Latino (Table 1). About 9% had completed a four-year degree or
168
higher, and 63% were unmarried at the time of survey. The mean age was 73.4 years, and
169
mean annual income was $23,630 (in 2012 dollars). Over half (58%) rated their health as fair or
170
poor compared to others the same age, 23% had a Medicare-qualifying disability or disease,
171
mean BMI was 30.2 (obese), and mean adjusted CCI was 3.7.
172
The average out-of-pocket payment for a 30-day HF prescription was $4.03, with a
173
range of $0.00 to $89.50, and 43% of the weighted sample were co-eligible for Medicaid
174
benefits. Seventeen percent were hospitalized with a primary or secondary diagnosis of HF
175
during the year of observation, while the mean number of HF-related hospital admissions was
176
0.3, and mean inpatient days attributable to HF was 1.5. Participants used an average of 2.4
177
distinct drug classes for HF treatment, and 4% of participants were lost to follow-up or died
178
during the year of observation.
179
Multivariable analysis
180
In the multivariable models, the interaction of average payment per prescription with
181
Medicaid eligibility was statistically significant (Table 2). Therefore, the interaction term was
182
retained, and out-of-pocket spending effect estimates are conditional on Medicaid status. For 8
183
the Medicaid non-eligible group, average out-of-pocket payment per HF prescription was not
184
significantly associated with odds of HF-related hospitalization: the odds ratio (OR) was 1.01
185
(95% confidence interval [CI] = 0.98–1.05), P = .399. In the Poisson model, the estimated effect
186
of average spending per prescription on HF-related hospitalization was statistically significant,
187
incidence rate ratio (IRR) = 1.02 (95% CI = 1.00–1.05), P = .048. In the conventional negative
188
binomial model, the estimated effect of spending per prescription on HF-related inpatient days,
189
IRR (95% CI) = 1.04 (1.00–1.08), also was significant, P = .041. The effect estimate for inpatient
190
days was smaller and non-significant in the zero-inflated model, IRR (95% CI) = 1.02 (1.00–
191
1.04), P = .080.
192
In the Medicaid-eligible group, average out-of-pocket payment appeared inversely
193
associated with odds of HF-related hospital admission, OR (95% CI) = 0.68 (0.47–1.00), P =
194
.048, incidence of HF-related admission, IRR (95% CI) = 0.70 (0.54–0.91), P = .008, and HF-
195
related inpatient days, IRR (95% CI) = 0.67 (0.48–0.94), P = .019. However, these associations
196
must be interpreted with caution given the limited range of mean out-of-pocket payments
197
observed in the Medicaid-eligible group ($0.00–6.94). Figure 1 displays the marginal effects of
198
average out-of-pocket drug payment on HF-related inpatient days, grouped by Medicaid status,
199
as predicted by the non-inflated negative binomial model.
200
Discussion
201
In this sample of community-dwelling, fee-for-service Medicare beneficiaries with Part D
202
coverage and self-reported HF, there was no evidence of an association between average out-
203
of-pocket payment per HF prescription and odds of HF-related hospitalization in the absence of
204
supplemental Medicaid. On the other hand, our data suggested that out-of-pocket prescription
205
spending was modestly associated with HF-related hospitalization incidence and inpatient days
206
for this population. We observed that an increase of $1 in the average prescription payment was
207
associated with a 2% rise in hospital admissions and a 2–4% rise in inpatient days attributable
9
208
to HF, depending on the model. Though small, these increases may be clinically and
209
economically meaningful, given the severity and costliness of HF.
210
Although this study did not measure adherence, one possible explanation for the mixed
211
findings could be the relative immediacy and severity of HF complications due to non-
212
adherence. Compared to other chronic diseases in which out-of-pocket medication spending
213
has been studied, HF is a severe condition with a poor prognosis, and low adherence is likely to
214
precipitate exacerbation requiring acute care.3-5,23 As a result, HF patients may be largely
215
insensitive to the price of medications that prevent exacerbations. A previous analysis of select
216
drug classes in these data showed that mean adherence was high: 88–90% for β blockers, ACE
217
inhibitors, and ARBs.8 In addition, average out-of-pocket drug spending was very low in this
218
study, with a weighted mean of about $4.00 per 30-day supply. With overall low costs and high
219
adherence, it is plausible that drug prices did not substantially affect hospital use.
220
The only other published study to investigate HF drug copays and hospital outcomes
221
found that risk of HF-related hospitalization rose with copay-attributable declines in medication
222
adherence: odds of hospital admission increased by a predicted 6% with each $10 rise in ACE
223
inhibitor copay, and by 9% per $10 rise in β-blocker copay.6 However, that study was conducted
224
in a commercially insured population, which may have been younger and healthier on
225
average—and therefore more price-sensitive—compared to fee-for-service Medicare
226
beneficiaries. In addition, $10 is more than double the average out-of-pocket payment per
227
prescription in our sample, limiting comparability between the two studies.
228
We did not directly examine the effect of adherence on outcomes because of difficulties
229
estimating adherence across multiple drug classes from claims data. Previous analysis of these
230
data showed that out-of-pocket spending on β blockers as a proportion of monthly income was
231
inversely associated with adherence.8 Yet the effect size was small, and that analysis found no
232
evidence of a spending effect on adherence to ACE inhibitors or ARBs. Other published studies
233
in age-diverse, commercially insured cohorts have reported lower adherence at higher out-of10
234
pocket spending levels in HF, but the effects were only significant at copays of at least $10 or
235
$20.6,7 With an average out-of-pocket payment of just $4 in this sample, adherence may not
236
have been strongly affected, which would have attenuated the pathway from prescription drug
237
spending to hospital use.
238
Notably, the number of HF-indicated drug classes used was a significantly positive
239
predictor of all three hospital use outcomes. This study analyzed the effect of average out-of-
240
pocket payment for a single 30-day supply, rather than total out-of-pocket pharmacy
241
expenditure, to avoid inflating the effect estimates for patients who were prescribed more drugs.
242
Number of drug classes was included in the models to adjust for disease severity, and it is likely
243
that its significant association with hospital use reflects sicker patients needing the hospital
244
more. However, it is possible that this effect also captured total drug spending burden, and its
245
inclusion in the models could have attenuated the average drug payment effects. Charlson
246
Comorbidity Index was also significantly associated with hospital use in all the models, further
247
suggesting a complex relationship between comorbidities, polypharmacy, and hospitalization.
248
In the Medicaid-eligible group, the effects of out-of-pocket drug spending on
249
hospitalization and inpatient days were statistically significant in the direction opposite of that
250
hypothesized. However, the range of observed average out-of-pocket payments was only $0 to
251
$6.94, with the majority of dual-eligible beneficiaries spending under $1 per prescription, so
252
extrapolation of this effect may not be meaningful. If anything, higher out-of-pocket payments
253
may reflect slightly higher incomes, because Medicaid drug copays are income-dependent in
254
some states.31,32 Modestly higher incomes may have enabled better access to preventive care,
255
or higher copays may have dissuaded hospital use if they also applied to inpatient services, but
256
we did not test these hypotheses.
257
To our knowledge, this is the first published study of out-of-pocket spending in pharmacy
258
plans and related hospital use to focus specifically on Medicare beneficiaries with HF, and the
259
first to use population-based survey data. Notwithstanding the small effect sizes, we found 11
260
statistically significant and potentially meaningful associations between out-of-pocket drug
261
spending and hospital use intensity despite very low out-of-pocket costs, infrequent hospital
262
use, and adjustment for many covariates. Moreover, our findings can be extrapolated to all
263
community-dwelling, fee-for-service Medicare beneficiaries with self-reported HF and
264
continuous Part D coverage, because the MCBS is representative of ever-enrolled Medicare
265
beneficiaries nationally.24 Future research should clarify the relationship between drug spending
266
and hospital use in HF with longitudinal methods and complete benefit design information,
267
especially given the recent availability of costly new medications for HF management.
268
Additionally, because non-white or Latino patients and patients with lower educational
269
attainment had lower adjusted odds and incidence of HF-related hospitalization in this study,
270
future research should address potential health equity implications.
271
Limitations
272
As with all retrospective database studies, measurement error was possible. Since we
273
could not observe the benefit structure of beneficiaries’ Part D plans, we estimated out-of-
274
pocket liability using average beneficiary payments. That approach was not sensitive to the
275
dynamic nature of Part D plans, which included yearly deductibles and coverage gaps.33 If
276
measurement error was random, it probably biased our results toward the null. Furthermore,
277
some prescriptions and outcomes may have been misclassified. A prescription was defined as
278
HF-related if it matched a pre-determined list of drug names, but some drugs could be used for
279
other conditions, and effects of non-HF drug prices may also be important. An outcome could
280
have been misclassified if HF was listed as the secondary diagnosis for an unrelated encounter.
281
However, using only the principal diagnosis would have excluded patients treated for conditions
282
which HF precipitated, such as pulmonary edema. Since the claims did not specify contributing
283
conditions for Diagnosis-Related Groups, we relied on principal and secondary ICD-9 codes, an
284
approach modeled on a prior study of patients with HF admissions in the MCBS.26
12
285
Another limitation of this study was its correlational design. Since prescription drug
286
spending was averaged over the year of observation, the outcomes analyzed could have
287
occurred before some of the out-of-pocket drug payments. This problem of temporality limits
288
any causal inference that can be made. Relying on questionnaire responses to identify HF
289
cases may have resulted in misclassification, because some patients may not understand or
290
remember their diagnosis. Self-reported HF in the Atherosclerosis Risk in Communities (ARIC)
291
Study had 61% agreement with a CMS claims-based definition, but that definition lacked
292
precision as it allowed HF diagnosis codes in any position on the claim and left out hypertensive
293
heart disease with heart failure (ICD-9-CM 402.x1, 404.x1 and 404.x3).34 In addition, no
294
disease-specific clinical data (e.g. HF stage or ejection fraction) were available in the MCBS, so
295
it was difficult to assess whether patients with more advanced illness were less price-sensitive.
296
As noted above, we used number of HF drug classes as a proxy for disease severity, but that
297
approach may have confounded our interpretation of price effects.
298
Pooling data from multiple survey years could have underestimated standard errors due
299
to autocorrelation, since observations from multi-year participants were not truly independent.
300
But the necessity of correcting for this additional clustering in complex samples is unclear, as
301
long as the primary sampling unit is specified correctly.21 Exclusion of survey-reported
302
prescriptions that did not match a Medicare record may have biased spending estimates.
303
However, only about 6% of unmatched survey prescriptions reflect true out-of-plan use for Part
304
D subscribers with common chronic diseases in MCBS data; many of the rest were probably
305
duplicates.25 And as with any observational study, findings may be spurious because of omitted
306
variables or sampling error.
307 308
Conclusions This study offers support for the notion that community-dwelling, fee-for-service
309
Medicare beneficiaries with HF can absorb modest prescription drug prices in Part D plans
310
without raising the risk of HF-related hospital admission. However, among beneficiaries with no 13
311
supplemental Medicaid (and greater average out-of-pocket exposure), there is evidence that
312
higher drug spending is modestly associated with hospitalization incidence and total days
313
hospitalized due to HF. Therefore, Part D plans with high out-of-pocket requirements for
314
essential HF medications may warrant further scrutiny, especially for beneficiaries with
315
polypharmacy and multiple comorbidities. These findings contribute useful information to
316
ongoing clinical and policy discussions about optimal prescription benefit design for older adults
317
with HF.
318 Acknowledgements
319 320
Rebecca A. Gary, PhD, RN, FAAN, FAHA, and David Howard, PhD, reviewed early
321
drafts of this paper and provided helpful feedback. Rishika Parikh, MPH, MA, assisted with
322
editing and submission.
323 Funding
324 325 326
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
327
14
References
328 329 330
1.
Pfuntner A, Wier LM, Stocks C. Most frequent conditions in US hospitals, 2011: H-CUP
331
Statistical Brief #162. Agency for Healthcare Research & Quality; 2013. Available at:
332
https://www.hcup-us.ahrq.gov/reports/statbriefs/sb162.pdf. Accessed Sep. 13, 2019.
333
2.
Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular
334
disease in the United States: a policy statement from the American Heart Association.
335
Circulation. 2011;123(8):933-944.
336
3.
Esposito D, Bagchi AD, Verdier JM, Bencio DS, Kim MS. Medicaid beneficiaries with
337
congestive heart failure: association of medication adherence with healthcare use and
338
costs. Am J Manag Care. 2009;15(7):437-445.
339
4.
Marti CN, Georgiopoulou VV, Giamouzis G, et al. Patient-reported selective adherence
340
to heart failure self-care recommendations, a prospective cohort study: the Atlanta
341
Cardiomyopathy Consortium. Congest Heart Fail. 2013;19(1):16-24.
342
5.
Wu JR, Lennie TA, Dekker RL, Biddle MJ, Moser DK. Medication adherence, depressive
343
symptoms, and cardiac event-free survival in patients with heart failure. J Card Fail.
344
2013;19(5):317-324.
345
6.
Cole JA, Norman H, Weatherby LB, Walker AM. Drug copayment and adherence in
346
chronic heart failure: effect on cost and outcomes. Pharmacotherapy. 2006;26(8):1157-
347
1164.
348
7.
Patterson ME, Blalock SJ, Smith AJ, Murray MD. Associations between prescription
349
copayment levels and beta-blocker medication adherence in commercially insured heart
350
failure patients 50 years and older. Clin Ther. 2011;33(5):608-616.
351
8.
McGee BT, Phillips V, Higgins MK, Butler J. Prescription drug spending and medication
352
adherence among Medicare beneficiaries with heart failure. J Manag Care Spec Pharm.
353
2019;25(6):705-713.
15
354
9.
Barron J, Wahl P, Fisher M, Plauschinat C. Effect of prescription copayments on
355
adherence and treatment failure with oral antidiabetic medications. P&T.
356
2008;33(9):532-553.
357
10.
Chen SY, Shah SN, Lee YC, Boulanger L, Mardekian J, Kuznik A. Moving branded
358
statins to lowest copay tier improves patient adherence. J Manag Care Pharm.
359
2014;20(1):34-42.
360
11.
Colombi AM, Yu-Isenberg K, Priest J. The effects of health plan copayments on
361
adherence to oral diabetes medication and health resource utilization. J Occup Environ
362
Med. 2008;50(5):535-541.
363
12.
Doshi JA, Zhu J, Lee BY, Kimmel SE, Volpp KG. Impact of a prescription copayment
364
increase on lipid-lowering medication adherence in veterans. Circulation.
365
2009;119(3):390-397.
366
13.
Kim YA, Rascati KL, Prasla K, Godley P, Goel N, Dunlop D. Retrospective evaluation of
367
the impact of copayment increases for specialty medications on adherence and
368
persistence in an integrated health maintenance organization system. Clin Ther.
369
2011;33(5):598-607.
370
14.
Li P, McElligott S, Bergquist H, Schwartz JS, Doshi JA. Effect of the Medicare Part D
371
coverage gap on medication use among patients with hypertension and hyperlipidemia.
372
Ann Intern Med. 2012;156(11):776-784.
373
15.
Thornton Snider J, Seabury S, Lopez J, McKenzie S, Goldman DP. Impact of type 2
374
diabetes medication cost sharing on patient outcomes and health plan costs. Am J
375
Manag Care. 2016;22(6):433-440.
376
16.
of patient cost sharing. J Manag Care Spec Pharm. 2018;24(9):847-855.
377 378 379
Henk HJ, Lopez JMS, Bookhart BK. Novel type 2 diabetes medication access and effect
17.
Brook RH, Ware JE, Jr., Rogers WH, et al. Does free care improve adults' health? Results from a randomized controlled trial. N Engl J Med. 1983;309(23):1426-1434. 16
380
18.
Experiment. Health Aff (Millwood). 2004;23(6):107-113.
381 382
19.
20.
Eaddy MT, Cook CL, O'Day K, Burch SP, Cantrell CR. How patient cost-sharing trends affect adherence and outcomes: a literature review. P&T. 2012;37(1):45-55.
385 386
Nyman JA. American health policy: Cracks in the foundation. J Health Polit Policy Law. 2007;32(5):759-783.
383 384
Newhouse JP. Consumer-directed health plans and the RAND Health Insurance
21.
Briesacher BA, Tjia J, Doubeni CA, Chen Y, Rao SR. Methodological issues in using
387
multiple years of the Medicare Current Beneficiary Survey. Medicare Medicaid Res Rev.
388
2012;2(1).
389
22.
Cubanski J, Swoope C, Damico A, Neuman T. How much is enough? Out-of-pocket
390
spending by Medicare beneficiaries: a chartbook. Kaiser Family Foundation; July 2014.
391
Available at: http://files.kff.org/attachment/how-much-is-enough-out-of-pocket-spending-
392
among-medicare-beneficiaries-a-chartbook-report. Accessed Sep. 13, 2019.
393
23.
Lopert R, Shoemaker JS, Davidoff A, et al. Medication adherence and Medicare
394
expenditure among beneficiaries with heart failure. Am J Manag Care. 2012;18(9):556-
395
563.
396
24.
Centers for Medicare & Medicaid Services. Medicare Current Beneficiary Survey: CY
397
2012 Cost and Use. February 2016. Available at: https://www.cms.gov/Research-
398
Statistics-Data-and-Systems/Research/MCBS/Codebooks-Items/2012CostAndUse.html.
399
Accessed Sep. 13, 2019.
400
25.
2014;20(9):743-748.
401 402 403
Roberto PN, Stuart B. Out-of-plan medication in Medicare Part D. Am J Manag Care.
26.
DiMartino LD, Shea AM, Hernandez AF, Curtis LH. Use of guideline-recommended therapies for heart failure in the Medicare population. Clin Cardiol. 2010;33(7):400-405.
17
404
27.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying
405
prognostic comorbidity in longitudinal studies: development and validation. J Chronic
406
Dis. 1987;40(5):373-383.
407
28.
215.
408 409
29.
30.
Heeringa SG, West BT, Berglund PA. Applied Survey Data Analysis. Boca Raton: CRC Press; 2010.
412 413
Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1-14.
410 411
Hayat MJ, Higgins M. Understanding Poisson regression. J Nurs Educ. 2014;53(4):207-
31.
Hartung DM, Carlson MJ, Kraemer DF, Haxby DG, Ketchum KL, Greenlick MR. Impact
414
of a Medicaid copayment policy on prescription drug and health services utilization in a
415
fee-for-service Medicaid population. Med Care. 2008;46(6):565-572.
416
32.
Medicaid. Med Care. 2017;55(5):520-527.
417 418
Kostova D, Fox J. Chronic health outcomes and prescription drug copayments in
33.
Hoadley J, Cubanski J, Neuman T. Medicare Part D in 2016 and trends over time.
419
Kaiser Family Foundation; 2016. Available at: http://kff.org/medicare/report/medicare-
420
part-d-in-2016-and-trends-over-time/. Accessed Sep. 13, 2019.
421
34.
Camplain R, Kucharska-Newton A, Loehr L, et al. Accuracy of self-reported heart failure:
422
the Atherosclerosis Risk in Communities (ARIC) Study. J Card Fail. 2017;23(11):802-
423
808.
424
18
425
Table 1
426
Sample Characteristics Count (unweighted)
Variable TOTAL
Percent (weighted)
95% CI for Percent (design-adjusted)
819
100.00
---
---
Male
345
41.6%
36.6%
46.6%
Female
474
58.4%
53.4%
63.4%
Non-white or Latino
199
23.3%
18.4%
28.2%
White, non-Latino
619
76.7%
71.8%
81.6%
Married
281
37.1%
32.7%
41.6%
Unmarried
537
62.9%
58.4%
67.3%
11
1.1%
0.1%
2.2%
143
16.5%
13.3%
19.6%
9 -12 grade
171
21.3%
17.9%
24.8%
High school diploma
233
28.3%
24.6%
32.1%
185
24.1%
20.2%
28.1%
Bachelor’s degree
43
5.0%
3.0%
7.0%
Post-graduate
31
3.7%
1.3%
6.0%
Eligible
354
43.2%
38.7%
47.6%
Ineligible
465
56.8%
52.4%
61.3%
Gender
Race/ethnicity a
Marital status
Education No schooling th
Nursery-8 grade th
th
Vocational/technical, some college, or associate’s
Medicaid coverage
19
Urbanicity Metropolitan area
489
61.6%
55.6%
67.5%
Non-metro. area
330
38.4%
32.5%
44.4%
Northeast
100
13.8%
10.3%
17.3%
Midwest
204
24.7%
19.1%
30.4%
West
101
12.3%
6.5%
18.0%
South or Puerto Rico
414
49.2%
42.8%
55.6%
Excellent
21
2.4%
1.4%
3.5%
Very good
107
11.9%
9.4%
14.5%
Good
236
27.6%
23.7%
31.5%
Fair
274
34.8%
30.9%
38.7%
Poor
177
23.3%
19.9%
26.6%
44
5.6%
3.7%
7.7%
Somewhat better
107
13.2%
10.1%
16.3%
About the same
339
40.1%
35.8%
44.4%
Somewhat worse
268
33.3%
29.5%
37.1%
59
7.7%
5.5%
9.9%
None
117
15.0%
11.4%
18.5%
A little
75
8.9%
7.0%
10.8%
Some
84
10.0%
8.0%
12.0%
144
17.3%
14.5%
20.1%
Region
Health vs. others the same age
Health now vs. one year ago Much better
Much worse Difficulty walking ¼ mile or 2-3 blocks
A lot
20
Unable
394
48.8%
44.1%
53.4%
Disability/ESRD
162
22.8%
18.5%
27.1%
Age only
657
77.2%
72.9%
81.5%
Died or LTFU
34
4.1%
2.4%
5.8%
Alive, retained
785
96.0%
94.2%
97.6%
2010
284
33.8%
30.7%
37.0%
2011
274
34.4%
31.8%
36.9%
2012
261
31.8%
28.7%
34.9%
Yes
138
17.0%
14.0%
19.9%
No
681
83.0%
80.1%
86.0%
Medicare entitlement
Censoring
Year of observation
HF-related hospitalization
Mean (weighted)
Variable
95% CI for Mean (design-adjusted)
Range (unweighted)
73.4
72.3
74.4
27.0
99.0
23.6
21.3
26.0
0.7
263.2
30.2
29.5
30.9
15.5
70.5
3.7
3.4
3.9
0
17.0
2.4
2.3
2.5
1.0
6.0
4.0
3.4
4.6
0
89.5
HF-related hospitalizations
0.3
0.2
0.3
0
8.0
HF-related hospitalized days
1.5
1.2
1.9
0
47.0
Age (in years) Annual income (000s)
b,c
Body mass index Charlson Comorbidity Index
d
HF-related drug classes used Mean out-of-pocket payment per 30-day HF Rx
b,e
427
Note. CI = confidence interval; ESRD = end-stage renal disease; HF = heart failure; LTFU = lost to follow-
428
up; Rx = prescription.
21
429
a
b
c
430
retirement account payments for participant and spouse. Modified to exclude heart failure. Standardized
431
to a 30-day supply.
Includes multiracial participants. Converted to 2012 dollars. Includes Social Security, pension, and d
e
22
432
Table 2
433
Estimated Associations with Hospital Use
Parameter
Any HF-related hospitalization OR
Mean out-of-pocket b payment per Rx
95% CI
No. of HF-related hospitalizations IRR
No. of HF-related a inpatient days
95% CI
IRR
95% CI
1.01
0.98-1.05
1.02c
1.00-1.05
1.04c
1.00-1.08
c
1.28-5.98
2.11
c
1.10-4.04
2.74
c
1.26-5.92
Eligible for Medicaid Yes
2.76
No
Reference
Reference
Reference
Rx payment*Medicaid interaction
0.67
c
0.46-0.99
0.68
c
0.52-0.89
0.65
Health vs. others the d same age
1.42
c
1.11-1.82
1.22
c
1.01-1.47
1.70
1.10
0.83-1.46
1.03
0.80-1.34
c
0.10-0.60
0.31
c
0.18-0.55
1.09
0.90-1.32
1.18
c
1.01-1.36
c
0.22-0.75
0.63
0.35-1.11
Health vs. a year agod Body mass index (log) Difficulty walking 1/4 d mile or 2-3 blocks
0.24
c
0.46-0.91
c
1.25-2.30
1.05
0.80-1.38
c
0.04-0.51
1.07
0.86-1.34
c
0.20-0.68
0.14
Medicare entitlement Disability/ESRD
0.41
Age only Charlson Comorbidity Index (modified)
Reference c
1.17-1.47
1.26
0.72-2.19
1.31
0.37
Reference
Reference
c
1.15-1.29
1.36
1.05
0.70-1.56
1.75
1.22
c
1.23-1.50
c
1.02-3.02
Gender Male Female
Reference
Reference
Reference
Race/ethnicity Non-white, mixed race, and/or Latino White, non-Latino
0.46
c
0.22-1.00 Reference
0.61
0.32-1.17 Reference
0.50
c
0.26-0.97 Reference
23
Presently married Yes
0.60
No
0.29-1.23
0.48c
Reference
0.30-0.77 Reference
c
1.03-1.47
1.20
c
1.04-1.38
Northeast
1.54
0.86-2.77
1.52
c
Midwest
1.25
0.76-2.07
West
0.80
0.33-1.94
Education level
1.23
0.59
0.30-1.14 Reference
c
1.09-1.59
1.07-2.17
1.40
0.68-2.88
1.39c
1.00-1.95
1.17
0.69-1.98
0.73
0.40-1.34
c
0.16-0.74
1.32
Census region
South or Puerto Rico
Reference
0.34
Reference
Reference
Urbanicity Metropolitan area
1.25
Non-metro. area Annual income (log)
0.88-1.79
0.96
Reference
0.73-1.27
c
1.95
Reference
1.28-2.97 Reference
0.88
0.52-1.49
0.87
0.57-1.32
0.71
0.49-1.01
0.76
0.28-2.10
0.92
0.46-1.85
0.72
0.25-2.08
Censoring Died or LTFU Alive and retained
Reference
Reference
Reference
Year of observation 2010
1.11
0.74-1.67
1.27
0.92-1.76
2011
0.75
0.44-1.28
0.95
0.63-1.45
2012 No. of HF drug classes
Reference 2.11
c
1.58-2.83
c
1.20-2.95
1.19
0.72-1.98
1.88
Reference 1.80
c
1.50-2.17
Reference c
1.91
1.51-2.42
434
Note. CI = confidence interval; ESRD = end-stage renal disease; HF = heart failure; IRR = incidence rate
435
ratio; LTFU = lost to follow-up; OR = odds ratio; Rx = prescription.
436 437 438 439 440
a
Estimates from conventional negative binomial model shown. Standardized to a 30-day supply. c Significant at the p < .05 level. d Five-point scale; higher score reflects worse health or function. b
24
441
442 443
Figure 1. Predicted number of total days hospitalized for heart failure (HF) by average out-of-
444
pocket (OOP) payment per 30-day HF prescription and Medicaid status. (Note. The majority of
445
observed mean OOP payments in the Medicaid-eligible group were < $1.00.)
446
25
Prescription Drug Spending and Hospital Use among Medicare Beneficiaries with Heart Failure
Highlights • • • • •
Out-of-pocket payments in Part D deter at least some appropriate medication use Whether drug payments increase hospital use in heart failure (HF) is unknown Mean payment per 30-day supply was not associated with hospitalization odds Mean payment was associated with small increases in hospital use intensity Evidence for a strong drug spending effect on hospital use in HF is lacking