Racial Disparities in Treatment Utilization for Multiple Myeloma
Mark A. Fiala, BS, CCRP
1,2
and Tanya M. Wildes, MD, MSCI
1
1
Division of Oncology, Department of Medicine, Washington University School of Medicine, St.
Louis MO
2
George Warren Brown School of Social Work, Washington University, St. Louis MO
Abstract
Background—Recent treatment advances have greatly improved the prognosis of patients with
multiple myeloma. However, some of these newer, more effective treatments are intensive and
expensive and their utilization is still low, particularly among black patients.
Methods—In this study, we reviewed the utilization patterns of stem cell transplantation and
bortezomib using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked
database.
Results—After controlling for overall health and potential access barriers, black patients were
37% (p < 0.0001) less likely to utilize stem cell transplantation, and 21% (p< 0.0001) less likely to
utilize bortezomib. Moreover, we found that the underutilization of these agents was the associated
with a 12% (
p
= 0.0007) increase in hazard ratio for death among black patients.
Conclusion—Eliminating health disparities, a current focus of U.S. public policy, is highly
complex, as illustrated by the results of this study. In patients with multiple myeloma, treatment
disparities are not completely explained by potential access barriers. Additional factors, such as
structural barriers in the health care system and individual decision-making among black and
white patients, must be explored to fully explain the disparity.
Keywords
Cancer disparities; race; access barriers; stem cell transplantation; SEER-Medicare; multiple
myeloma
Introduction
Treatment advances over the past 2 decades have greatly improved the survival of patients
with multiple myeloma (MM).
1
However, some of these newer, more effective treatments
are often much more intensive and expensive than other treatment options and their
utilization on the population level is low. For example, only approximately 13% of MM
patients undergo autologous stem cell transplantation (ASCT).
2
Corresponding author:
Mark A. Fiala, BS CCRP, 660 S. Euclid Avenue, Campus Box 8056, St. Louis MO 63110, USA, Phone:
314-454-8302, Fax: 314-273.0580, mfiala@dom.wustl.edu.
Disclosures: The authors have no conflicts of interest to disclose.
Author Contributions: Both authors contributed to all aspects of the research, analysis, and manuscript preparation.
HHS Public Access
Author manuscript
Cancer
. Author manuscript; available in PMC 2018 May 01.
Published in final edited form as:
Cancer
. 2017 May 01; 123(9): 1590–1596. doi:10.1002/cncr.30526.
Author Manuscript Author Manuscript Author Manuscript Author Manuscript
Utilization of ASCT also varies based on patient demographics. Older patients and racial/
ethnic minorities are less likely to undergo ASCT.
3,4
Older patients are more likely to have
comorbid conditions or other contraindications to the procedure and, until recently, the
procedure was largely reserved for patients under 78 years of age in part due to Medicare
coverage guidelines.
5
These factors could account for the age-related disparity. The cause of
the racial disparity is less clear. Researchers initially attributed it to access barriers such as
low socioeconomic status (SES) and/or inadequate health insurance;
4
both have been linked
to poorer survival in MM.
6
However, our group recently reported that black patients treated
at our institution were 54% less likely to undergo ASCT after controlling for potential access
barriers.
7
In this study, we aimed to confirm our previous finding on racial disparities in ASCT
utilization among patients with MM in a nationally representative sample. We also reviewed
the utilization patterns of bortezomib among white and black patients, a topic not reported to
date. We chose bortezomib as a comparator as it has largely become the standard of care in
recent years and, unlike ASCT, bortezomib is not exclusive to specialized centers; it can be
administered at any facility that administers chemotherapy. We then analyzed how the
utilization patterns of these two treatments impact outcome disparities in MM.
Methods
Data Source
The source of data for this study was the National Cancer Institute’s (NCI) Surveillance,
Epidemiology, and End Results (SEER)-Medicare linked database. SEER collects
demographics, tumor characteristics, and survival data from 18 population-based cancer
registries throughout the United States, covering approximately 26% of the U.S. Population
including 23% of African-Americans.
8
In the SEER-Medicare linked database, the SEER
registry data is linked to Medicare enrollment and claims data. Of all people 65 years of age
or older in the SEER registry, 93% have been matched to their corresponding Medicare
data.
8
The SEER-Medicare database has been described in detail elsewhere.
9
At the time
this study was conducted, the SEER-Medicare linkage included all Medicare-eligible
persons appearing in the SEER data through 2011 and their Medicare claims through 2013.
The following Medicare claims files were used for this analysis: Medicare Provider Analysis
and Review (MEDPAR), which includes all hospital short-stay, long-stay, and skilled
nursing facility bills [Medicare Part A]; National Claims History (NCH), which includes all
physician/supplier bills [Part B]; Outpatient, which includes all bills from institutional
outpatient providers [Part B]; Home Health Agency (HHA), which includes all claims for
home health services [Part B]; and Durable Medical Equipment (DME), which includes all
claims for durable medical equipment such as oxygen tanks [Part B].
Inclusion and Exclusion Criteria
In total, 46,328 patients diagnosed with MM between 2000 and 2011 were available in the
SEER-Medicare database. Identification of MM was made using the World Health
Organization (WHO) International Classification of Diseases for Oncology, 3rd Edition
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(ICD-O-3) histology codes 9731-9732, and 9734, which were all recoded to 34000 in the
SEER database.
10
We excluded cases with duplicate or incomplete records including death certificate or
autopsy cases, cases not enrolled in Medicare Part A and Part B at diagnosis, and managed
care (HMO) enrollees. We also excluded all cases that did not have ≥1 claim within one year
prior to diagnosis as this is another indicator of a possible incomplete record. We also
excluded cases where reported race was not white or black or where MM was diagnosed
prior to age 65. Medicare eligibility prior to age 65 is restricted to those with severe illness
or disability and thus these patients may not be generalizable. This left 20,916 patients for
the analyses. Supplementary Figure 1 details the screening flow chart.
The SCT utilization analysis was limited to patients who were < 78 years old at MM
diagnosis as coverage for older patients was previously restricted as described above. The
BTZ utilization analysis was limited to patients diagnosed after 2003 to coincide with its
FDA approval. The patients who were included in both the SCT and BTZ utilization
analyses were included in the survival analysis.
Variables
Treatment utilization was determined using the Medicare claims data (NCH and
OUTPATIENT) with the International Classification of Diseases (9th revision), Clinical
Modification (ICD-9-CM) diagnosis and procedure codes which are used to identify claims
for treatments or procedures.
11
The presence or absence of the corresponding procedure codes for ASCT (HCPCS 38241 or
ICD-9 codes 41.00, 41.01, 41.04, 41.07, or 41.09) and allogeneic stem cell transplantation
(HCPCS 38240 or 38242, or ICD-9 codes 41.02, 41.03, 41.05, 41.06 or 41.08) were used to
determine SCT utilization. Although allogeneic transplantation is uncommon among MM
patients who have not previously failed ASCT, it is sometimes administered to younger
patients, those with identical twins, or those who fail mobilization.
12
Therefore, we included
the codes for the procedure to prevent this from confounding the analyses. Bortezomib
utilization was determined by the presence or absence of the HCPCS code J9041.
Patients were coded as black or white and male or female based on SEER data. Age at MM
diagnosis and year of diagnosis were treated as continuous variables. We used
socioeconomic status (SES), Medicaid enrollment, and geography to control for potential
access barriers. Direct SES data at the patient level is not available through SEER-Medicare.
Instead, the database contains information from the 2000 Census reported at the tract level in
which the patient lived at diagnosis; the median household income (MHI), a continuous
variable, was used to approximate SES. Patients enrolled in Medicaid at any time during the
year of diagnosis were considered as enrolled. Geography was determined by the 2003
Rural/Urban Continuum Code from the Department of Agriculture’s Economic Research
Service for the county of home residence at diagnosis.
Performance status (PS) is not directly available in the database. In its absence, Medicare
claims were used to identify several claims-based indicators of PS, including the use of
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oxygen and related respiratory therapy supplies, wheelchairs and supplies, home health
agency use, and skilled nursing facility use within 12 months prior to MM diagnosis. PS was
then coded as normal (0 indicators) or poor (1 or more indicators) as previously described.
13
Comorbidities were not directly available in the database as well. Diagnosis and procedure
codes from the Medicare claims data (MEDPAPR, NCH, and OUTPATIENT) for the 12
months prior to MM diagnosis were used to calculate a Charlson Comorbidity Index (CCI)
score for each patient using established algorithms.
14,15
Overall survival was defined as the number of months from MM diagnosis to death from any
cause. Patients still living were censored at the time of the data cutoff. Full details on
variable coding can be found in Supplementary Table 1.
Statistical Analysis
Multivariate logistic regression models of ASCT and BTZ utilization were created in a step-
wise fashion to determine how each set of variables influenced utilization. Initially only
race, sex, and year of diagnosis were included in the model [Model 1]. Then overall health
measures (age at diagnosis, PS, and CCI) were added [Model 2]. Lastly, potential access
barriers were added (MHI, Medicaid, and geography) [Model 3].
Cox regression models of survival were then created in a step-wise fashion to determine how
each set of variables impacted overall survival. Initially only race, sex, and year of diagnosis
were included in the model [Model 1]. Then ASCT and BTZ utilization were added [Model
2]. Lastly overall health measures and potential access barriers were added [Model 3].
Ethical Considerations
This study was conducted as part of a research protocol approved by the Washington
University Institutional Review Board (IRB). This specific analysis was exempt from IRB
review as the data is not individually identifiable and, therefore, is not covered by 45 Code
of Federal Regulations part 46 “Protection of Human Subjects” per the guidelines set forth
by the Department of Health and Human Services.
16
Results
Demographics are summarized in Table 1. A total of 20,916 patients met the eligibility
criteria for analysis. Eighty-four percent (n = 17,574) were white and 16% (n = 3,342) were
black. Black patients were younger at diagnosis (75.9 years [
SD
=7.0] compared to 77.3
years [
SD
=7.0];
t
= 10.94,
p
< 0.0001), but were more likely to have indicators of poor PS
(27% compared to 19%;
x
2
= 92.29,
p
< 0.0001), and a CCI ≥ 1 (59% compared to 47%;
x
2
= 223.98,
p
< 0.001). Black patients also had lower MHI on average ($35,500 [SD=$17,300]
compared to $52,400 [SD=$24,200];
t
= 47.55,
p
< 0.0001) and were more likely to be
Medicaid beneficiaries (34% compared to 11%;
x
2
= 1120.89,
p
< 0.0001).
SCT Utilization
Fifty-four percent of patients (n = 11,269) were eligible for the SCT utilization analysis.
Overall SCT utilization was low; only 7% of patients underwent the procedure. Utilization
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was higher among whites than blacks (8% compared to 4%;
x
2
= 34.37,
p
< 0.0001). In the
initial regression model, blacks were 49% less likely to utilize SCT than whites (
p
<
0.0001). After controlling overall health, there was no change; blacks were 49% less likely
to utilize SCT (
p
< 0.0001). After additionally controlling for potential access barriers (MHI,
Medicaid, and urban/rural status), they were 37% less likely (
p
< 0.0001). The model was
significantly associated with SCT utilization (
x
2
= 508.02
(10)
,
p
< 0.0001). Results from the
regression models of SCT utilization are summarized in Table 2.
BTZ Utilization
Seventy-seven percent of the whole cohort (n = 16,037) were eligible for the BTZ utilization
analysis. Overall 36% of patients utilized BTZ. Utilization was higher among whites than
blacks (36% compared to 30%;
x
2
= 39.10, p < 0.0001). In the initial regression model,
blacks were 24% less likely to utilize BTZ than whites (
p
< 0.0001). After controlling
overall health, the disparity increased; blacks were 30% less likely (
p
< 0.0001). After
additionally controlling for potential access barriers, they were 21% less likely (
p
< 0.0001).
The model was significantly associated with BTZ utilization (
x
2
= 1733.10
(10)
,
p
< 0.0001).
Results from the regression models of BTZ utilization are summarized in Table 3.
Survival
The survival analysis included 8,625 patients who were eligible for both the SCT and BTZ
utilization analyses. At the time of data cutoff, 69% (n=6,013) of the patients had expired.
Overall, the hazard ratio of black patients was 12% higher than whites (
p
= 0.0007). After
controlling for SCT and BTZ utilization, there was no significant (
p
= 0.1124) difference in
hazard ratio between the two groups. When overall health and potential access barriers were
added the hazard ratio of black patients was 9% lower than whites (p = 0.0068). The model
was significantly associated with survival (
x
2
= 1026.66
(12)
, p < 0.0001). Results from the
Cox regression survival models of are summarized in Table 4.
Discussion
In this study, we found that disparities in treatment utilization for MM persist despite
controlling for potential access barriers and overall health status. In our previous study of
disparities in SCT utilization, blacks were 51% less likely to utilize SCT compared to 37%
in the current study.
7
The differences in results could be due to variations in methodology,
such as patient selection (the previous study included patients of all ages, the median age
was 59 years at diagnosis), or could represent differences in patients treated at a NCI-
designated Comprehensive Cancer Center versus the population in general.
The effects of overall health and potential access barriers on the disparities in SCT and BTZ
utilization were very similar. After controlling for overall health, there was no change in
underutilization of SCT among blacks, but there was an increase in the underutilization of
BTZ. Although, black patients in this study were more likely to have indicators of poor PS
and a higher CCI, they were also younger on average; therefore, differences in overall health
did not explain the disparity in treatment utilization. Potential access barriers explained only
a small portion of the underutilization of SCT and BTZ.
Fiala and Wildes
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