ORIGINAL ARTICLE

Use of and spending on supportive care medicationsamong Medicare beneficiaries with cancer

Ilene H. Zuckerman &Amy J. Davidoff &Mujde Z. Erten &

Bruce Stuart & Thomas Shaffer &

J. Samantha Dougherty & Candice Yong

Received: 4 December 2013 /Accepted: 2 March 2014# Springer-Verlag Berlin Heidelberg 2014

AbstractPurpose The study objective was to provide population-basedestimates of supportive care medication (SCM) use amongMedicare beneficiaries with cancer and determine factorsrelated to SCM receipt.Methods This retrospective cohort study of community-basedMedicare beneficiaries used theMedicare Current BeneficiarySurvey (1997–2007). Dependent variables comprised use andspending on SCMs for three medication classes: opioids,antidepressants/sedative/hypnotics (ASH), and antiemetics.Independent variables of interest were supplemental insurancecoverage, cancer site, and treatment. Multivariate modelsdetermined factors affecting receipt of, and spending on,SCMs. We also compared SCM use and spending amongbeneficiaries with and without cancer in order to understand

what portion of SCM use and spending could be attributed tocancer as opposed to other comorbid conditions.Results A total of 1,836 Medicare beneficiaries with cancerand 9,898 beneficiaries without cancer were eligible for thestudy. Beneficiaries with cancer were more likely to receiveopioids, ASH, and antiemetics compared to non-cancer ben-eficiaries. Adjusted annual payments for antiemetics were onaverage $637 higher in with cancer versus without cancer(p<0.01), while ASH payments were $184 lower (p<0.01).Opioid spending was similar among cancer and non-cancerusers. Relative to colon cancer, beneficiaries with prostatecancer were least likely to receive any of the three SCMclasses. Receipt of antineoplastic treatment increased theprobability of use of all three classes of SCMs. Insurancecoverage did not influence the use of or spending on opioidsor antiemetics, but was associated with both outcomes forASH. The use of all three SCM classes was significantly lowerduring years before Part D implementation of the newMedicare Part D prescription drug benefit and was higherafter implementation of Part D.Conclusion This study provides population-based informa-tion on SCM use among Medicare beneficiaries with cancer.Cancer site and treatment modality were important predictorsof SCM use.

Keywords Cancer . Medicare . Part D . Supportive care

Introduction

A diagnosis of cancer commonly triggers a cascade of staginginterventions, treatments, and sequelae and can create a sub-stantial health and financial shock to an older adult. There isan extensive body of literature examining predictors of pri-mary treatment for cancer, such as radiation, chemotherapy,and surgery and how insurance coverage and cost of

Electronic supplementary material The online version of this article(doi:10.1007/s00520-014-2187-2) contains supplementary material,which is available to authorized users.

I. H. Zuckerman :A. J. Davidoff : B. Stuart : T. Shaffer :J. S. Dougherty :C. YongPeter Lamy Center on Drug Therapy and Aging, PharmaceuticalHealth Services Research Department, University of MarylandSchool of Pharmacy, Baltimore, MD, USA

I. H. Zuckerman (*)IMPAQ International, LLC, 10420 Little Patuxent Parkway, Suite300, Columbia, MD 21044, USAe-mail: izuckerman@impaqint.com

A. J. DavidoffAgency for Healthcare Research and Quality, Rockville, MD, USA

M. Z. ErtenDepartment of Surgery, University of Vermont College of Medicine,Burlington, VT, USA

J. S. DoughertyPharmaceutical Research and Manufacturers of America,Washington, DC, USA

Support Care CancerDOI 10.1007/s00520-014-2187-2

treatments affect access [1–11]. However, along with anycurative, adjunctive, or palliative treatment for the malignan-cy, patients will require supportive care medications to man-age pain and symptoms resulting from both the treatments andany progression of the disease. To date, there are few empir-ical studies that describe use of supportive care medications(SCMs) among older adults with cancer and factors related touse; these studies focus either on only one malignancy [12, 13],are not population-based [14–18], or are not based in the USA[13, 19, 20] and therefore do not reflect the specific coverageenvironment faced by Medicare beneficiaries. Informationabout the use of SCMs is an important issue for the Medicareprogram, given the large proportion of cancer patients who areMedicare beneficiaries. Prior to implementation of theMedicare Part D prescription drug benefit in 2006, Medicaredid not provide prescription drug coverage, but manyMedicarebeneficiaries had drug coverage if they had insurance providedthrough their employer or Medicaid (the government-fundedprogram for the poor) or purchased it privately. Understandingthe role of supplemental medical and prescription drug cover-age, including Medicare Part D, is important for policy makersevaluating the impact of Part D in the context of other coverage.

This paper focuses on factors related to the use of SCMsthat are used for symptom management, specifically opioidsfor pain management, antidepressants, and sedative/hypnotics(used primarily to manage emotional distress), and anti-emetics used to manage nausea and vomiting. We focus onthese symptoms because they are common among cancerpatients [15, 21–23], and management has been reported tobe inadequate [13, 24–27]. The objectives of this study wereto provide population-based estimates of SCM use amongMedicare beneficiaries with cancer and to determine factorsrelated to receipt of SCMs, with special interest in the effect ofsupplemental insurance coverage. In addition, since thesemedications are commonly used in individuals without cancer,we compared the use of, and spending on, SCM amongMedicare beneficiaries with and without cancer in order tounderstand what portion of SCM use and spending could beattributed to cancer as opposed to other comorbid conditions.

Methods

Our data source for this study was the Medicare CurrentBeneficiary Survey (MCBS), a multipurpose rotating-panelsurvey sponsored by the Centers for Medicare and MedicaidServices (CMS). The MCBS provides comprehensive demo-graphic, health status and functioning, health care access, andutilization information from a nationally representative sam-ple of beneficiaries of the Medicare program—the US federalhealth insurance program for people who are aged 65 yearsand older, for certain younger people with disabilities, and forpatients with end-stage renal disease [28]. A new panel of

respondents, about 4,500 Medicare beneficiaries, is inductedinto the MCBS each year and interviewed up to 11 timesduring a 4-year period. Approximately 12,000 Medicare ben-eficiaries are interviewed annually. TheMCBS is supplement-ed with Medicare enrollment records and Part A, Part B, andPart D administrative claims records. An encrypted identifi-cation number is assigned to each participating Medicarebeneficiary in the MCBS and can be used to link survey datafiles to theMedicare administrative files. TheMedicare claimscontain beneficiary information and information about thebilled service, including the International Classification ofDiseases, Ninth Revision, Clinical Modification (ICD-9-CM)diagnosis codes, procedure codes, total amount paid, andbeneficiary liabilities.

We identified a cohort of newly diagnosed community-based beneficiaries with cancer between 1997 and 2007 usinga claims-based algorithm. Beneficiaries with at least one inpa-tient or two outpatient claims with a cancer diagnosis (ICD-9-CM codes 140-172, 174-208, 225, 227.3, or 227.4) after a 12-month observation “washout” period without any cancer diag-nosis were included in the cancer cohort. The two outpatientclaims had to be at least 30 days apart and within a 12-monthperiod. We assigned an index date using the date of the firstqualifying cancer claim. Only beneficiaries that were continu-ously enrolled in Parts A and B during the study period wereincluded. Medicare beneficiaries that were enrolled in aMedicare Advantage plan during the study period were exclud-ed, due to missing claims information. Finally, beneficiariesresiding in a long-term care facility or with incomplete surveysfor reasons other than death were excluded. Beneficiaries whootherwise met the study inclusion criteria but did not have anewly diagnosed or prevalent cancer comprised the non-cancercohort to allow comparison of the use of and spending on SCMamong cancer and non-cancer patients.

Measures

Our dependent variables comprised the use of and spendingon SCMs during the cancer index and subsequent year for thefollowing three medication classes: opioid analgesics,antidepressants/sedative/hypnotics (ASH), and antiemetics.We examined the MCBS Prescription Medication Events(PME) files to determine annual prevalence of each of themedication classes (PME records do not have dates of service,so we could only ascertain annual prevalence of medicationuse). The PME files are based on self-reports of prescriptionmedication use collected during the MCBS in-home survey.Survey respondents are instructed to gather their prescriptionvials and pharmacy receipts in preparation for the interviewer.The surveyor reviews each prescription with the respondent (orproxy) and records themedication name, strength, dosage form,and quantity. Spending was measured based on the total paidamount (beneficiary and supplemental insurance or Part D). All

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spending amounts were inflated to 2007 dollars using theUnited States Bureau of Labor Statistics Consumer PriceIndex (CPI). In addition to the PME, we also examined proce-dure codes on Part B records to categorize claims with codes forany of the medications that fell into the SCM classes of interest.For each SCM class, medication use was defined as at least onePME record or Part B claim.

Our independent variables of interest were supplementalinsurance coverage, cancer site, type of cancer treatment, andyear of diagnosis. Supplemental insurance coverage catego-ries included (a) employment-related medical and prescriptioncoverage, (b) other private medical and prescription coverage,(c) public (e.g., Medicaid) medical and prescription coverage,(d) medical (any type) without prescription coverage, and (e)no supplemental insurance coverage. Among beneficiarieswith cancer, we examined the cancer site (breast, lung, pros-tate, colon, or all other cancer sites and those without a knownprimary site) and type of cancer treatment (chemotherapy,cancer-related surgery, radiation therapy) to provide informa-tion regarding cancer beneficiaries who are likely to be usersof each SCM class. Cancer site was identified based on ICD-9-CM codes. Finally, we examined the index year of cancerdiagnosis to determine whether the use of SCMs changed afterimplementation of Part D in 2006.

In addition to the key independent variables, we also ex-amined other factors that may be associated with the use of,and spending on, SCMs among beneficiaries with cancer.These factors included sociodemographic factors (age, sex,race, marital status, supplemental security income status, ed-ucation, income, assets, urban status, geographic region),clinical factors (number of comorbid conditions as a countof the Hierarchical Chronic Conditions [29]; self-reportedfunctional status as a count of limitations in activities of dailyliving), responses to a survey question regarding attitudesabout care seeking, and whether the subject died during thestudy period. As there was individual variation in the monthsobserved overall and the date of the cancer diagnosis, wecontrolled for months observed in the analyses by poolingsurvivors with non-cancer controls and for months before andafter cancer diagnosis among survivors.

Statistical analysis

Comparison of cancer and non-cancer cohorts We first com-pared the unadjusted differences in beneficiary characteristicsand the proportion receiving each SCM class among cancerand non-cancer cohorts, using the Wald tests adjusted forsurvey data. We estimated the probability of receiving sup-portive care therapy for each medication class using probitregression models, estimated on the pooled cancer and non-cancer cohorts, with a binary indicator for cancer. The adjust-ed differences in supportive care use were estimated from themarginal probability associated with the cancer indicator.

Mean spending (dollars) and unadjusted and adjusted spend-ing differences between cancer and non-cancer cohorts weregenerated in a similar manner, using generalized linear modelswith a gamma distribution and a log link.

Use and spending among the cancer cohort We generatedmarginal probabilities from multivariate probit models esti-mating the probability of receiving SCM therapy for Medicarebeneficiaries with cancer for each of the three different med-ication classes. To estimate marginal effects of spending oneach of the supportive medication classes among Medicarebeneficiaries with cancer receiving the SCM class, we usedgeneralized linear models with a gamma distribution and a loglink. We included the same control variables as in the probitmodels. To determine the association of categorical variableswith use or spending, we conducted joint Wald tests.

Analyses were conducted to account for the MCBS com-plex survey design. All data manipulations were conductedusing SAS 9.2, and all bivariate and multivariate analyseswere conducted using Stata 12.

Results

We identified 1,836 Medicare beneficiaries with cancer whomet our study inclusion criteria. The mean (standard error) agewas 75.2 (0.2), and the majority was white (82.1 %) and male(51.6 %). When compared to 9,898 beneficiaries withoutcancer, the cancer cohort was significantly older, more likelyto be married, male, and have a higher prevalence of comor-bidities and functional limitations; the cancer cohort was lesslikely to have public insurance with prescription coverage andmore likely to lack any supplemental insurance. Among thecancer cohort, 36.6 % received cancer-related surgery, 31.0 %received antineoplastic agents, and 15.3 % received radiationtherapy (Table 1).

Use of and spending on supportive care: cancer versusnon-cancer beneficiaries

Beneficiaries with cancer were more likely to receive opioids,ASH, and antiemetics compared to their non-cancer counter-parts (Table 2). For example, half (50.7 %) of beneficiarieswith cancer, but only 30.7 % of those without cancer, receivedopioids, a 20.0 percentage point difference. The adjusteddifference was somewhat smaller in magnitude (17.2 points).Similar patterns held for both ASH and antiemetics, althoughthe magnitude of the difference for ASH was smaller (8.0percentage points unadjusted and 4.7 percentage points ad-justed). For each of these three classes of medications, bene-ficiaries with cancer were more likely to receive these medi-cations through bothMedicare Part B and non-Part B paymentsources (data not shown).

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Table 1 Characteristics of cancer and non-cancer cohorts, Medicare Current Beneficiary Survey 1997–2007

Characteristics Cancer cohort (N=1,836) Non-cancer cohort (N=9,898) p-value

Mean/% se Mean/% se

Age (years) 75.5 0.2 71.5 0.1 <0.001

Age≥65 years and former SocialSecurity Disability Insurance

13.2 0.8 22.9 0.5 <0.001

Female 48.4 1.2 56.2 0.5 <0.001

Race/ethnicity

White 82.1 1.1 80.2 0.8 0.085

Black 8.6 0.8 8.7 0.6 0.858

Hispanic 5.8 0.6 6.5 0.5 0.301

Other 3.4 0.5 4.5 0.3 0.042

Currently married 56.8 1.2 51.8 0.6 <0.001

Education

No high school 14.1 0.9 14.2 0.6 0.919

Some high school 14.8 1.0 16.1 0.5 0.168

High school grad 29.7 1.2 30.4 0.7 0.597

Some higher education 41.4 1.4 39.3 0.8 0.095

Income as % of FPL (in hundreds) 3.2 0.1 3.1 0.1 0.525

Assets (in thousands) 174.5 8.5 155.6 4.8 0.039

Location

Urban 72.5 1.7 70.6 1.4 0.086

Region

East 20.2 1.3 19.1 0.8 0.249

South 40.8 1.8 40.3 1.4 0.670

Midwest 24.9 1.4 24.9 1.1 0.972

West 14.0 1.4 15.6 1.2 0.036

Calendar year first observed

1997–1999 13.8 0.9 14.2 0.7 0.621

2000–2001 22.9 1.3 22.2 1.0 0.504

2002–2003 21.0 1.1 20.1 0.4 0.423

2004–2005 22.9 1.3 22.0 0.8 0.447

2006–2007 19.4 1.3 21.4 0.9 0.099

Insurance coverage categories

Employment-related medical and prescription 37.3 1.4 35.9 0.7 0.362

Other private medical and prescription 8.1 0.7 9.5 0.4 0.080

Public medical and prescription 12.8 0.8 17.9 0.6 <0.001

Medical without prescription 21.7 1.1 22.3 0.6 0.594

None 20.1 1.0 14.4 0.4 <0.001

Cancer site

Breast 16.9 0.9 n/a n/a n/a

Lung 11.6 0.8 n/a n/a n/a

Prostate 21.9 0.9 n/a n/a n/a

Colon 15.0 0.9 n/a n/a n/a

Others 34.6 1.1 n/a n/a n/a

HCC comorbid conditions 7.0 0.1 4.4 0.0 <0.001

Activities of daily living limitations

0–1 87.5 0.8 88.6 0.4 0.236

2–3 7.1 0.6 7.5 0.3 0.509

4 or more 5.4 0.5 4.0 0.2 0.010

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Among opioid users, the amount paid for these medicationswere similar for cancer and non-cancer beneficiaries (Table 2).Spending on ASH was significantly lower in cancer benefi-ciaries compared to those without [adjusted difference −$184

(p<0.05)]. Among antiemetic users, adjusted payments forthese medications among cancer beneficiaries were on aver-age $637 higher than those without cancer (p<0.01).

The Appendix lists the PME medications used and theirtotal expenditures. In both the cancer and non-cancer cohorts,hydrocodone-acetaminophen and propoxyphene napsylate-acetaminophen were the most prevalent opioids. The mostcommon ASH used in both cohorts included anxiolytics al-prazolam and lorazepam, zolpidem (Ambien®), a sedativehypnotic, and the antidepressant sertraline (Zoloft®). Themost commonly used antiemetic in the cancer cohort wasprochlorperazine, while meclizine was most prevalent in thenon-cancer cohort.

Factors associated with use of and spending on supportivecare medications among beneficiaries with cancer

When limiting to the cancer cohort, we found that cancer sitewas associated with the use of all three SCM classes (jointWald test p<0.05). Relative to colon cancer, those with pros-tate cancer were the least likely to receive any of the threeSCM classes. Lung cancer beneficiaries were most likely toreceive antiemetics (Table 3).

Receipt of antineoplastic treatment increased the probabil-ity of the use of all three classes of SCMs (Wald test p<0.05);the greatest effect was seen with antiemetics. Cancer-relatedsurgery or radiation therapy both were associated with in-creased likelihood of receipt of opioids and antiemetics, butnot with ASH (Table 3).

Insurance coverage did not influence the use of opioids orantiemetics, but was associated with use of ASH (joint Waldtest p<0.05). Beneficiaries with public medical and

Table 1 (continued)

Characteristics Cancer cohort (N=1,836) Non-cancer cohort (N=9,898) p-value

Mean/% se Mean/% se

Attitudes about care seeking

Usually go to doctor as soon as feeling bad 33.6 1.2 33.0 0.8 0.623

Missing 14.9 0.9 3.8 0.3 <0.001

Died during observation period 21.3 1.0 3.0 0.2 <0.001

Months in the study 17.8 0.2 19.4 0.1 <0.001

Months in the study post cancer 12.4 0.2 n/a n/a n/a

Months in the study pre cancer 5.5 0.1 n/a n/a n/a

Cancer treatment

Antineoplastic therapy 31.0 1.2 n/a n/a n/a

Cancer-related surgery 36.6 1.3 n/a n/a n/a

Radiation therapy 15.3 0.9 n/a n/a n/a

We presented standard errors above since we applied a survey design in obtaining percentage and mean values. p-values refer to t tests to comparesupportive care therapy modality of “cancer” to “non-cancer”

se standard error, FPL federal poverty level, n/a not applicable, HCC hierarchical condition categories

Table 2 Supportive care medication therapy overall and by presence ofcancer, 1997–2007 (N=11,734)

Opioids ASH Antiemetics

Proportion of Medicare beneficiaries receiving supportive caremedication therapy

Cancer (N=1,836) (%) 50.7 41.3 27.3

Non-cancer (N=9,898) (%) 30.7 33.3 11.4

Unadjusted difference (%) 20.0* 8.0* 15.9*

Adjusted difference (%) 17.2* 4.7* 13.4*

Spending on supportive care medication therapy

Cancer (N=1,836) ($) 186 485 651

Non-cancer (N=9,898) ($) 313 787 134

Unadjusted difference ($) −127* −302* 518*

Adjusted difference ($) −40 −184* 637*

The number of total users in the Proportion of Medicare beneficiariesreceiving supportive care medication therapy panel is defined by presenceof a HCPC code regardless of payment being zero; the number of totalusers in the Spending on supportive care medication therapy panel isdefined by the presence of a HCPC code and positive payments. Adjustedmodels were estimated by probit or GLM with gamma distribution andlog link controlling for cancer, age, SSIDI, sex, race, marital status,education, FPL, assets, urban status, region, HCC count, ADL, attitudesabout care seeking, died during study period, months in the study,supplemental insurance, and index year. Adjusted difference is the mar-ginal probability of cancer indicator

ASH antidepressants and sedative/hypnotics

*p<0.01 (significance levels of results from t-tests to compare supportivecare therapy modality of “cancer” to “non-cancer”)

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Table 3 Probit regression results from any use models for Medicare beneficiaries with cancer, 1997–2006 (N=1,836)

Opioids ASH Antiemetics

Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value

Cancer site (ref: colon)†,‡,§

Breast −0.009 0.046 0.838 −0.037 0.043 0.381 −0.060 0.036 0.099

Lung 0.029 0.050 0.564 0.094 0.053 0.076 0.124 0.047 0.009

Prostate −0.122 0.041 0.003 −0.134 0.041 0.001 −0.132 0.034 <0.001

Others 0.020 0.035 0.557 −0.050 0.037 0.178 0.023 0.034 0.502

Cancer treatment

Antineoplastic therapy 0.107 0.029 <0.001 0.078 0.027 0.004 0.347 0.025 <0.001

Cancer-related surgery 0.193 0.026 <0.001 0.017 0.029 0.551 0.066 0.026 0.011

Radiation therapy 0.148 0.037 <0.001 0.048 0.034 0.166 0.072 0.035 0.038

Insurance coverage categories (ref: none)‡

Employment-related medical and prescription −0.006 0.062 0.927 0.063 0.062 0.309 −0.042 0.055 0.448

Other private medical and prescription −0.032 0.075 0.670 0.079 0.078 0.310 −0.036 0.064 0.575

Public medical and prescription −0.052 0.070 0.453 −0.092 0.063 0.143 −0.031 0.055 0.570

Medical without prescription −0.064 0.058 0.271 0.014 0.065 0.825 −0.070 0.049 0.159

Calendar year CA first observed(ref: 2004–2005)†,‡,§

1997–1999 −0.190 0.044 <0.001 −0.127 0.040 0.001 −0.117 0.030 <0.001

2000–2001 −0.096 0.037 0.010 −0.022 0.034 0.521 −0.082 0.029 0.005

2002–2003 −0.051 0.039 0.199 −0.030 0.039 0.445 −0.009 0.032 0.793

2006–2007 0.085 0.044 0.052 0.028 0.045 0.532 0.039 0.038 0.305

Age −0.006 0.014 0.659 −0.007 0.011 0.502 −0.012 0.011 0.247

Age squared <0.001 <0.001 0.810 <0.001 <0.001 0.883 <0.001 <0.001 0.579

Age≥65 years and former SocialSecurity Disability Insurance

−0.015 0.050 0.760 0.101 0.051 0.048 −0.026 0.036 0.481

Female 0.089 0.031 0.004 0.103 0.033 0.002 0.059 0.029 0.045

Race/ethnicity (ref: white)

Black 0.013 0.053 0.804 −0.049 0.047 0.298 −0.047 0.044 0.280

Hispanic −0.144 0.057 0.011 −0.023 0.060 0.702 0.005 0.059 0.933

Other 0.009 0.079 0.906 0.114 0.073 0.119 −0.067 0.056 0.231

Currently married 0.053 0.030 0.083 0.008 0.030 0.779 −0.007 0.030 0.816

Education (ref: no high school)

Some high school −0.059 0.046 0.200 −0.039 0.051 0.444 −0.025 0.043 0.565

High school grad −0.020 0.043 0.636 −0.064 0.047 0.171 0.010 0.038 0.796

Some higher education −0.082 0.042 0.051 −0.038 0.049 0.437 0.039 0.039 0.324

Income as % of FPL (in hundreds) −0.006 0.005 0.274 −0.004 0.005 0.391 0.002 0.004 0.574

Assets (in thousands) <0.001 <0.001 0.397 <0.001 <0.001 0.622 <0.001 <0.001 0.691

Location

Urban −0.030 0.034 0.375 0.005 0.033 0.880 −0.043 0.026 0.094

Region (ref: south)†

East −0.121 0.039 0.002 −0.005 0.044 0.903 −0.048 0.029 0.096

Midwest 0.016 0.034 0.649 0.063 0.034 0.064 −0.041 0.024 0.092

West 0.067 0.038 0.076 0.036 0.050 0.478 −0.027 0.035 0.430

HCC count 0.013 0.004 <0.001 0.022 0.004 <0.001 0.009 0.003 0.003

Activities of daily livinglimitations (ref: 0–1)†,‡

2–3 0.091 0.044 0.040 0.169 0.047 <0.001 0.061 0.046 0.186

4 or more −0.039 0.061 0.523 0.130 0.054 0.017 −0.002 0.052 0.964

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prescription coverage were least likely to receive ASH, whilethose with other private medical and prescription coveragewere most likely to receive them (Table 3).

To test whether implementation of Part D in 2006 influ-enced the use of or spending on SCMs, we examined whetherthe index year of cancer diagnosis was associated with use orspending. The use of all three SCM classes was significantlylower during years before Part D implementation and washigher after Part D implementation (Table 3).

Table 4 provides regression results for spending modelsamong users of the specific SCM classes. Cancer site was notassociated with spending on any of the three SCM classes(joint Wald test p>0.05 for all three classes). Type of cancertreatment was associated with spending on antiemetics (jointWald test p<0.05); among users of antiemetics, cancer bene-ficiaries who received antineoplastic treatment spent $807more on antiemetics; those who underwent cancer-relatedsurgery spent $205 less on antiemetics. Based on the jointWald tests, supplemental insurance was associated withspending among users of ASH, but not opioids or antiemetics.Among medication users, there was no consistent relationshipbetween index year and spending. The index year was signif-icantly associated with spending on ASH, but there was not atemporal change that related to the year of Part Dimplementation.

Discussion

This analysis provides a comprehensive look at three classesof SCMs among a nationally representative sample ofMedicare beneficiaries with newly diagnosed cancer, com-pared to those without cancer. Cancer beneficiaries are morelikely to receive opioids, ASH, and antiemetics compared tonon-cancer beneficiaries. Among users, those with cancerspend significantly more on antiemetics, but spend less onASH. There was no significant difference in opioid spending

between cancer and non-cancer opioid users after adjusting forpotential confounders.

ASH prevalence among our cancer cohort was 41.3 %,which is higher than the previously reported prevalence ofantidepressant use in community-based cancer practices [14].The higher prevalence estimates in our study are likely due totwo differences in measurement, relative to the study de-scribed by Ashbury et al. [14]. In our analysis, we combinedantidepressants and sedative/hypnotics into one classification.Our rationale was based on the NCCNGuidelines for DistressManagement [30], which state that all cancer patients shouldbe screened to ascertain their levels of distress, and that if apharmacologic intervention is warranted, then antidepressantsand antianxiety medications have been shown to be beneficialin the treatment of distress in adult cancer patients.Additionally, our measure of ASH use included sedativesand antianxiety medications covered by Part B; these medi-cations may have been administered in conjunction with out-patient surgical procedures. When we limit ASH to medica-tions dispensed for home use (as reported through the PMEfiles), our estimate (32.9 %) is closer to other reported esti-mates [14]. Not surprisingly, beneficiaries who received anti-neoplastic treatment had a higher probably of antiemetic useand also spent more on antiemetics compared to those who didnot receive antineoplastics. Beneficiaries with lung cancerwere more likely to use antiemetics, controlling for the aver-age effect of antineoplastic therapy. This may be due tochemotherapy-induced nausea and vomiting that is commonwith specific treatments for lung cancer, particularly platinum-based regimens, or the common use of concurrent chemora-diation or palliative radiation therapy. Interestingly, beneficia-ries with lung cancer did not spend significantly more onantiemetics compared to other beneficiaries with cancer.

Among cancer patients, supplemental insurance coveragedid not appear to influence the use of opioids or antiemetics,but influenced the use of ASH. We found that, relative tobeneficiaries without supplemental insurance, beneficiaries

Table 3 (continued)

Opioids ASH Antiemetics

Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value

Attitudes about care seeking

Usually go to doctor as soon as feeling bad 0.047 0.030 0.119 0.008 0.027 0.779 0.023 0.027 0.396

Died during observation period 0.117 0.042 0.006 −0.030 0.044 0.499 0.191 0.045 <0.001

Months in the study post cancer 0.012 0.003 <0.001 0.006 0.002 0.011 0.008 0.002 <0.001

Months in the study pre cancer 0.013 0.005 0.006 0.006 0.004 0.212 0.006 0.004 0.113

Italicized values indicate p value <0.05 for marginal effect

ASH antidepressants and sedative/hypnotics, FPL federal poverty level, HCC hierarchical condition categories

†p<0.05, variable is jointly significant for opioids equation; ‡p<0.05, variable is jointly significant for ASH equation; §p<0.05, variable is jointlysignificant for antiemetics equation

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Table 4 GLM regression results from spending models for Medicare beneficiaries with cancer among users, 1997–2007

Opioids (N=797) ASH (N=631) Antiemetics (N=457)

Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value

Cancer site (ref: colon)

Breast 69.94 51.95 0.178 61.74 83.25 0.458 −84.81 77.17 0.272

Lung 120.40 63.01 0.056 105.71 86.51 0.222 −41.96 86.74 0.629

Prostate 65.22 44.57 0.143 78.24 94.50 0.408 −203.74 62.54 0.001

Others 58.93 30.13 0.050 46.83 66.06 0.478 −94.85 73.28 0.196

Cancer treatment

Antineoplastic therapy 4.69 20.48 0.819 −65.62 45.35 0.148 807.37 73.54 <0.001

Cancer-related surgery −25.33 17.93 0.158 −30.84 38.77 0.426 −204.51 51.10 <0.001

Radiation therapy 38.34 31.37 0.222 −32.78 58.45 0.575 11.97 71.58 0.867

Insurance coverage categories (ref: none)‡

Employment-related medical and prescription 65.98 50.66 0.193 64.87 110.66 0.558 −198.66 120.70 0.100

Other private medical and prescription 29.74 59.01 0.614 −43.05 100.80 0.669 −191.00 78.16 0.015

Public medical and prescription 64.96 62.52 0.299 218.89 164.28 0.183 −164.22 95.02 0.084

Medical without prescription 11.00 43.61 0.801 −92.39 96.64 0.339 −173.64 94.06 0.065

Calendar year CA first observed (ref: 2004–2005)‡

1997–1999 4.08 31.74 0.898 −95.64 72.14 0.185 −91.12 74.91 0.224

2000–2001 40.83 30.92 0.187 −161.63 49.72 0.001 −25.90 70.60 0.714

2002–2003 43.94 30.82 0.154 −1.79 65.74 0.978 70.38 79.71 0.377

2006–2007 81.76 42.48 0.054 −145.72 49.32 0.003 −7.41 70.63 0.916

Age −26.54 9.84 0.007 −36.36 17.61 0.039 −8.45 16.13 0.600

Age squared 0.16 0.07 0.017 0.22 0.12 0.077 −0.01 0.12 0.943

Age≥65 years and formerSocial Security Disability Insurance

75.17 38.57 0.051 35.90 65.53 0.584 −55.11 85.25 0.518

Female 78.17 25.91 0.003 −13.66 50.56 0.787 78.38 51.49 0.128

Race/ethnicity (ref: white)‡

Black −22.61 26.63 0.396 −232.34 46.62 <0.001 29.09 93.91 0.757

Hispanic 1.17 44.25 0.979 92.48 84.89 0.276 −93.64 65.93 0.156

Other −15.72 38.44 0.683 −68.56 85.27 0.421 −83.34 88.78 0.348

Currently married 4.23 21.95 0.847 −139.45 51.60 0.007 88.23 47.96 0.066

Education (ref: no high school)

Some high school 58.27 37.13 0.117 160.10 86.36 0.064 −124.45 64.45 0.053

High school grad 26.00 26.86 0.333 80.34 68.57 0.241 −87.25 67.39 0.195

Some higher education −3.13 26.77 0.907 144.68 61.47 0.019 −16.62 71.23 0.816

Income as % of FPL (in hundreds) 0.39 3.39 0.909 −9.71 9.93 0.328 −11.78 3.34 <0.001

Assets (in thousands) −0.03 0.03 0.375 0.09 0.09 0.291 −0.03 0.12 0.816

Location

Urban −63.51 28.70 0.027 −62.34 44.63 0.163 77.46 52.67 0.141

Region (ref: south)†

East −72.26 20.99 0.001 159.54 82.86 0.054 123.53 97.06 0.203

Midwest −43.85 23.07 0.057 82.57 63.50 0.193 35.66 54.44 0.512

West −18.07 24.68 0.464 42.97 69.74 0.538 −8.75 54.98 0.874

HCC comorbid conditions 4.02 2.62 0.125 17.14 5.82 0.003 5.36 7.05 0.447

Activities of daily living limitations (ref: 0–1)†,‡

2–3 123.42 53.85 0.022 166.30 67.81 0.014 135.58 137.40 0.324

4 or more 42.83 42.59 0.315 140.64 85.82 0.101 −46.87 73.75 0.525

Attitudes about care seeking

Usually go to doctor as soon as feeling bad −38.78 17.57 0.027 11.95 45.88 0.794 10.56 47.96 0.826

Support Care Cancer

with employer-sponsored or other private medical and pre-scription coverage were more likely, and those with publicmedical and prescription coverage were less likely to receivethese medications. Interestingly, although the type of insur-ance did not influence the use of antiemetics, we found thatusers of these medications with supplemental insurancetended to spend less than users without any supplementalinsurance. These patterns suggest that there may be formularyrestrictions or other access constraints onASH associatedwithsome types of supplemental insurance.While we failed to findan effect of supplemental insurance on the use of opioids andantiemetics, a prior research has identified an effect of sup-plemental insurance on receipt of cancer therapy [31]. In thecurrent study, we note the strong association between use ofcancer treatments and SCMs. It is possible that supplementalinsurance is highly relevant to supportive care use, but onlythrough the effect on cancer treatments. Sensitivity analysesthat dropped the cancer treatments from the models did notalter the impact of supplemental insurance, leading us toconclude that newly diagnosed cancer patients are not partic-ularly responsive to supplemental insurance when it comes tomanagement of uncomfortable symptoms. The use of all threeSCM classes increased over time, suggesting that implemen-tation of Part D may have had some influence on improvingaccess to these SCMs. However, the trends appear to havestarted before implementation of Part D, suggesting changesin practice patterns, or availability of newer agents over time.

Our study is subject to several limitations. While we areable to report on the use of SCMs, we cannot attribute them tomanagement of specific symptoms experienced by those withcancer and, thus, cannot draw any conclusions about appro-priateness of use or potential under-use of SCMs. To theextent that these symptoms may be reported in claims, thereis the potential for linking use to symptoms. However, withrelatively small sample sizes of each individual cancer andtreatment, we did not attempt to examine those relationships.Furthermore, claims-based diagnoses are not likely to besensitive to less severe symptoms of nausea, distress, or pain;thus, we would expect a large proportion of SCM use to lack

justification with a diagnosis. More general limitations relateto the use of survey and claims data. For example, our use ofICD-9-CM diagnosis codes on claims to both identify benefi-ciaries with cancer and to assign cancer site is subject to errorand may result in a downward bias in our estimates. The studyrelied upon self-reported data on prescription medication usethat may be subject to recall bias. The PME file, however,offers the advantage of providing information on prescriptiondrugs not paid for by insurance; thus, we can observe medi-cations even in beneficiaries without drug coverage. Furtherresearch using prescription drug claims will provide an im-portant opportunity to confirm the levels of use and spendingfor prescription SCMs.

It is worth noting that we used the overall CPI, rather thanthe prescription drug subcomponent, to inflate costs to 2007dollars. This analytic choice was made so that we could usethe dataset to examine multiple dimensions of health carespending for Medicare beneficiaries with cancer, includingout-of-pocket spending relative to income. Our estimates ofsupportive care drug spending may be conservative to theextent that the CPI changed less than the prescription drugsubcomponent over the span of our study (1997–2007). Sinceour focus was on comparing cancer to non-cancer, andlooking at determinants of use and spending among benefi-ciaries with cancer, this analytic choice did not impact ourresults. Finally, the MCBS files available for our study includ-ed only 2 years after the implementation of Medicare Part D,limiting our ability to draw conclusions about the effects onthe use of prescription SCMs as this new program started up.It will be important to update the analysis as additional yearsof MCBS data become available, in particular to assess therole of Part D on access and spending by Medicare beneficia-ries with cancer.

Conclusion

This study provides population-based descriptive informationon SCM use among Medicare beneficiaries with cancer.

Table 4 (continued)

Opioids (N=797) ASH (N=631) Antiemetics (N=457)

Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value Marginaleffect

Standarderror

p-value

Died during observation period 141.38 53.35 0.008 −73.89 58.28 0.205 85.28 66.78 0.202

Months in the study post cancer 6.89 1.94 <0.001 15.34 3.85 <0.001 12.91 4.29 0.003

Months in the study pre cancer 3.84 2.90 0.186 13.90 6.89 0.044 −3.53 7.40 0.634

Italicized values indicate p value <0.05 for marginal effect

GLM generalized linear models, ASH antidepressants and sedative/hypnotics FPL federal poverty level, HCC hierarchical condition categories

‡p<0.05, variable is jointly significant for ASH equation; †p<0.05, variable is jointly significant for opioids equation; §p<0.05, variable is jointlysignificant for antiemetics equation

Support Care Cancer

Management of symptoms associated with cancer, as well ascomorbid conditions, is clearly important to improving qualityof life for those with cancer. Anticipated access to SCMs isalso likely to have a positive effect on initiation of treatmentregimens such as chemotherapy, and realized access is likelyto increase the chances of therapy completion. Our resultsindicated that both cancer site and treatment modality wereimportant predictors of SCM use. However, we did not findevidence that socioeconomic status, including income, educa-tion, or even insurance, were important factors that influencedcancer patients’ decisions to seek relief from symptoms.Further research will be important to disentangle the role ofaccess to SCMs on receipt and completion of recommendedtherapies and how the Medicare Part D benefit may haveaffected access to these medications.

Acknowledgments Funding sources American Cancer Society RSGI-10-109-01-CPHPS and Supplemental Medical and Drug Insurance andCancer Related Spending are gratefully acknowledged by the authors.

Disclaimer This article was initiated while Dr. Davidoff was employedat the University of Maryland Baltimore. The opinions expressed in thisarticle are the author’s own and do not reflect the view of the Agency forHealthcare Research and Quality, the Department of Health and HumanServices, or the US government.

Presentations Portions of this paper were presented at Harry and ElsaJiler—American Cancer Society Professors Meeting, November 1, 2011.

Conflict of interest None of the authors have a personal financialrelationship with the organization that sponsored the research. The spon-sor (American Cancer Society) had no role in the design, methods, datacollection, analysis, or preparation of the paper.

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