Prospective, Observational Study of Pain and Analgesic Prescribing in Medical Oncology Outpatients With Breast, Colorectal, Lung, or Prostate Cancer

Article (PDF Available)inJournal of Clinical Oncology 30(16):1980-8 · April 2012with48 Reads
DOI: 10.1200/JCO.2011.39.2381 · Source: PubMed
Abstract
Pain is prevalent among patients with cancer, yet pain management patterns in outpatient oncology are poorly understood. A total of 3,123 ambulatory patients with invasive cancer of the breast, prostate, colon/rectum, or lung were enrolled onto this prospective study regardless of phase of care or stage of disease. At initial assessment and 4 to 5 weeks later, patients completed a 25-item measure of pain, functional interference, and other symptoms. Providers recorded analgesic prescribing. The pain management index was calculated to assess treatment adequacy. Of the 3,023 patients we identified to be at risk for pain, 2,026 (67%) reported having pain or requiring analgesics at initial assessment; of these 2,026 patients, 670 (33%) were receiving inadequate analgesic prescribing. We found no difference in treatment adequacy between the initial and follow-up visits. Multivariable analysis revealed that the odds of a non-Hispanic white patient having inadequate pain treatment were approximately half those of a minority patient after adjusting for other explanatory variables (odds ratio, 0.51; 95% CI, 0.37 to 0.70; P = .002). Other significant predictors of inadequate pain treatment were having a good performance status, being treated at a minority treatment site, and having nonadvanced disease without concurrent treatment. Most outpatients with common solid tumors must confront issues related to pain and the use of analgesics. There is significant disparity in pain treatment adequacy, with the odds of undertreatment twice as high for minority patients. These findings persist over 1 month of follow-up, highlighting the complexity of these problems.

Full-text (PDF)

Available from: Victor T Chang
Prospective, Observational Study of Pain and Analgesic
Prescribing in Medical Oncology Outpatients With Breast,
Colorectal, Lung, or Prostate Cancer
Michael J. Fisch, Ju-Whei Lee, Matthias Weiss, Lynne I. Wagner, Victor T. Chang, David Cella,
Judith B. Manola, Lori M. Minasian, Worta McCaskill-Stevens, Tito R. Mendoza, and Charles S. Cleeland
See accompanying editorial on page 1907 and article on page 1974; listen to the podcast by
Dr Von Roenn at www.jco.org/podcasts
Michael J. Fisch, Tito R. Mendoza, and
Charles S. Cleeland, The University of
Texas MD Anderson Cancer Center, Hous-
ton, TX; Ju-Whei Lee and Judith B.
Manola, Dana-Farber Cancer Institute,
Boston, MA; Matthias Weiss, Marshfield
Clinic, Marshfield, WI; Lynne I. Wagner and
David Cella, Northwestern University Fein-
berg School of Medicine, Chicago, IL;
Victor T. Chang, Department of Veterans
Affairs, New Jersey Health Care System,
East Orange, and University of Medicine
and Dentistry of New Jersey, Newark, NJ;
and Lori M. Minasian and Worta McCaskill-
Stevens, National Cancer Institute,
Bethesda, MD.
Submitted September 12, 2011; accepted
January 21, 2012; published online ahead
of print at www.jco.org on April 16, 2012.
Supported in part by Public Health Service
Grants No. CA37604, CA23318,
CA026582, and CA17145 and grants from
the National Cancer Institute, National Insti-
tutes of Health, and the Department of
Health and Human Services.
Presented in part at the 47th Annul Meet-
ing of the American Society of Clinical
Oncology, June 3-7, 2011, Chicago, IL.
The contents of this article are solely the
responsibility of the authors and do not
necessarily represent the official views of
the National Cancer Institute. M.J.F. had
full access to all of the data in the study
and made the final decision to submit
them for publication.
Authors’ disclosures of potential conflicts
of interest and author contributions are
found at the end of this article.
Clinical Trials repository link available on
JCO.org.
Corresponding author: Michael J. Fisch,
MD, MPH, The University of Texas MD
Anderson Cancer Center, Department of
General Oncology, Unit 410, 1515
Holcombe Blvd, Houston, TX 77030-4009;
e-mail: mfisch@mdanderson.org.
© 2012 by American Society of Clinical
Oncology
0732-183X/12/3016-1980/$20.00
DOI: 10.1200/JCO.2011.39.2381
ABSTRACT
Purpose
Pain is prevalent among patients with cancer, yet pain management patterns in outpatient
oncology are poorly understood.
Patients and Methods
A total of 3,123 ambulatory patients with invasive cancer of the breast, prostate, colon/rectum, or
lung were enrolled onto this prospective study regardless of phase of care or stage of disease. At
initial assessment and 4 to 5 weeks later, patients completed a 25-item measure of pain,
functional interference, and other symptoms. Providers recorded analgesic prescribing. The pain
management index was calculated to assess treatment adequacy.
Results
Of the 3,023 patients we identified to be at risk for pain, 2,026 (67%) reported having pain or
requiring analgesics at initial assessment; of these 2,026 patients, 670 (33%) were receiving
inadequate analgesic prescribing. We found no difference in treatment adequacy between the
initial and follow-up visits. Multivariable analysis revealed that the odds of a non-Hispanic white
patient having inadequate pain treatment were approximately half those of a minority patient
after adjusting for other explanatory variables (odds ratio, 0.51; 95% CI, 0.37 to 0.70; P.002).
Other significant predictors of inadequate pain treatment were having a good performance
status, being treated at a minority treatment site, and having nonadvanced disease without
concurrent treatment.
Conclusion
Most outpatients with common solid tumors must confront issues related to pain and the use of
analgesics. There is significant disparity in pain treatment adequacy, with the odds of undertreat-
ment twice as high for minority patients. These findings persist over 1 month of follow-up,
highlighting the complexity of these problems.
J Clin Oncol 30:1980-1988. © 2012 by American Society of Clinical Oncology
INTRODUCTION
Pain is one of the most devastating symptoms in
patients with cancer. A meta-analysis of more than
50 studies revealed that more than 50% of patients
with cancer in the United States experience pain and
that pain is most prevalent among patients with a
high disease burden.
1
Although pain is prevalent among patients
with cancer, many frequently receive inadequate
pain treatment despite established pain treatment
guidelines. More than 20 years ago, the Eastern Co-
operative Oncology Group (ECOG) conducted a
landmark study of pain needs in 1,308 patients in
outpatient oncology, finding that 42% had inade-
quate analgesic prescribing.
2
The findings of the
ECOG study and other studies led to an increased
emphasis on symptom management in cancer. Nev-
ertheless, reducing the burden of symptoms remains
challenging. In 2002, a National Institutes of Health
State-of-the-Science Panel noted that “Additional
research is needed on the definition, occurrence, the
treatment of pain, depression, and fatigue, alone and
in combination, in adequately funded prospective
studies,”
3
providing the impetus for this prospective
observational study in oncology outpatients.
The primary objective of the present report was
to assess the prevalence of pain and analgesic use in
JOURNAL OF CLINICAL ONCOLOGY ORIGINAL REPORT
VOLUME 30 NUMBER 16 JUNE 1 2012
1980 © 2012 by American Society of Clinical Oncology
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outpatient oncology practice. The secondary objectives were to iden-
tify the characteristics of patients with self-reported pain, the adequacy
of opioid prescribing, and the factors associated with the undertreat-
ment of pain.
PATIENTS AND METHODS
Study Design and Patients
From March 3, 2006, to May 19, 2008, we enrolled oncology outpatients
at any point in the trajectory of their care for invasive breast, lung, prostate, or
colorectal cancer. Patients were registered at 38 institutions, including six
academic sites and 32 community clinics. Registering institutions were classi-
fied as minority-based if they had 40% or greater minority participation in
previous clinical trials or in the current study; three academic sites and 10
community sites were coded as minority-based sites in our analysis. Patients
treated in academic centers were enrolled from disease site–specific clinics. In
contrast, patients treated in community clinics were enrolled from general
oncology clinics. To reduce selection bias, each site devised a recruitment
algorithm that was not biased toward symptom management issues and ap-
proved by the ECOG coordinating center.
In addition to a clinical diagnosis of invasive breast, lung, prostate, or
colorectal cancer, patients had to be at least 18 years of age, receiving care at an
ECOG-affiliated institution, willing to complete the follow-up survey, and
judged by the study screener to have cognitive function adequate for complet-
ing study surveys. The protocol was approved by the institutional review
boards at each registering institution. The anticipated accrual goal was 2,310
patients; however, because of the brisk accrual toward the end of the study,
final accrual was 3,123 to allow all consented patients to participate. All pa-
tients provided written informed consent. The protocol and case report forms
are accessible on the study web site.
4
Study Procedures
Patients were recruited when they checked in for their clinic appoint-
ments, and patients’ information was collected before their visit with a clini-
cian. Patients and their treating clinicians were surveyed at the initial visit and
at follow-up 28 to 35 days later.
Patients were asked to read the instructions at the beginning of each
questionnaire and complete all items in terms of their experience during the
preceding 24 hours. Reasons for incomplete forms were documented on the
compliance form. Patients who could not complete the follow-up question-
naire because of acute illness were given the option to mail the forms to the
treating clinic by day 42 after the initial visit.
Survey Measures
The initial survey was used to collect patients’ basic clinical and demo-
graphic information, including cancer treatment history and current thera-
pies. At the initial and follow-up visits, patients reported symptom intensity
and functional interference using a modification of the MD Anderson Symp-
tom Inventory (MDASI), a validated 19-item measure that is very similar to
the Brief Pain Inventory in terms of structure and patient burden.
5,6
Patients
used the MDASI to rate the symptoms and functional interference items that
they most frequently experienced in the previous 24 hours “at their worst” on
an 11-point Likert scale ranging from 0 (“not present”) to 10 (“as bad as you
can imagine”). Clinicians reported patients’ specific medications, including
those that were newly prescribed. A clinician-specific survey was used to
ascertain symptom prioritization and symptom attribution.
The pain management index (PMI) is a conservative and well-validated
instrument that is the most frequently used measure of pain treatment ade-
quacy for opioid prescribing.
7,8
The PMI is modeled on the WHO pain
treatment paradigm, which involves a three-step analgesic ladder that pro-
gresses from nonopioid analgesics to weak opioids and then strong opioids,
depending on the self-reported pain intensity.
6,9
The PMI was calculated by
subtracting the pain score from the analgesic score for each patient. Pain scores
ranged from 0 to 3 and were based on the pain ratings patients reported on the
modified MDASI. Pain scores of 0, 1, 2, and 3 corresponded to MDASI scores
indicating no pain (0), mild pain (1 to 4), moderate pain (5 or 6), and severe
pain (7 to 10), respectively. Analgesic scores ranged from 0 to 3 and were used
to gauge the potency of the prescribed medications indicated on the medica-
tion form at each visit. If multiple analgesics were prescribed, the most potent
medication was scored. The analgesic score was coded as 0 for no medications,
1 for nonopioids, 2 for weak opioids, and 3 for strong opioids. Morphine,
fentanyl, oxycodone, and methadone were considered strong opioids.
Statistical Analysis
Statistical analysis focused on the dichotomization of patients based on
the PMI value: undertreatment (PMI 0) versus acceptable treatment
(PMI 0). Patients who did not have pain and were not taking pain medica-
tions were not considered to be at risk for inadequate pain management and
were thus excluded from the statistical analysis. Descriptive statistics were used
to summarize the findings for patients at risk for inadequate pain manage-
ment. Data collected at the initial assessment and data collected at the
follow-up visits 4 to 5 weeks later were analyzed separately.
To identify predictors of pain treatment adequacy, we performed uni-
variable and multivariable logistic regression analyses of patients’ demo-
graphic and clinical data as well as clinician response data. The logistic
regression analysis modeled the probability of undertreatment. Because pa-
tients receiving care at the same clinic are likely to be treated more similarly
than patients receiving care at different clinics, we treated the data as clustered
and used generalized linear models with generalized estimating equations to
account for the intracluster correlation. In both the univariable and multivari-
able logistic regression models, patients for whom the values of any of the
variables were missing were excluded from data analysis. Explanatory variables
in the univariable analysis revealed to be significant (P.10) for the response
variable (ie, pain treatment adequacy) were included in the multivariable
analysis. Any nonsignificant predictors (P.10) were trimmed from the
multivariable model. This procedure was repeated until all predictors in the
model met the criteria of P.10. Finally, a model of interactions between any
of the main effects in the multivariable analysis was fitted. SAS statistical
software (version 9.2; SAS Institute, Cary, NC) was used for all data analyses.
RESULTS
Pain, Analgesic Prescribing, and Symptom Attributes
at Initial Assessment
We identified 3,023 patients at risk for pain; of these patients,
2,026 (67%) had pain or were receiving analgesics and were included
in the statistical analysis (Fig 1). Of these 2,026 patients, 1,356 (67%)
had adequate pain management. Patient demographics and disease
characteristics at the initial assessment of the 2,026 patients at risk for
pain are presented in Table 1. Most of these 2,026 patients had breast
cancer (1,009 patients; 50%); 427 patients (21%) had colorectal can-
cer, 385 patients (19%) had lung cancer, and 205 patients (10%) had
prostate cancer. At the initial assessment, 78% of these patients were
receiving cancer therapy. Patients’ median age was 60 years (range, 18
to 93 years), and 75% of the patients were between 45 and 75 years of
age. The median time from initial disease diagnosis to study registra-
tion was 16 months (range, 0 to 627 months). Most patients were
women (71%), white (86%), and had an ECOG performance status
level of 0 (50%). Approximately one fourth of the patients analyzed
were minority patients (9% Hispanic or Latino, 12% black, 1% Asian,
and 1% other minority).
The analgesics prescribed in relation to pain severity at initial
assessment and follow-up are summarized in Figure 2. At initial as-
sessment, 404 patients (13%) were being treated with strong opioids,
and 584 patients (19%) had moderate or severe pain. Of the patients
with moderate or severe pain, 241 (41%) were not receiving an opioid
analgesic. Twenty percent of the patients in severe pain were not
Pain and Analgesic Prescribing in Medical Oncology Patients
www.jco.org © 2012 by American Society of Clinical Oncology 1981
receiving any analgesic. The most commonly prescribed nonopioids
were acetaminophen (35%) and nonsteroidal anti-inflammatory
agents (19%). The most prevalent moderate or severe nonpain symp-
toms at initial assessment were fatigue (35%), sleep disturbance
(27%), and drowsiness (23%). More than one third of patients (40%)
reported having at least three moderate or severe symptoms. Clini-
cians attributed symptoms at least moderately to cancer or cancer
treatment in 51% of patients. One or more factors associated with an
increased risk of pain (neuropathic pain syndrome, incidental pain,
psychological distress, addiction behavior, or cognitive impair-
ment)
10
were noted in 49% of patients. On the basis of the clini-
cians’ reports, 3% of patients had a history of addiction behavior,
3% had partial cognitive impairment, 28% had psychological dis-
tress, and 31% had incidental pain. Clinicians judged pain to be the
top-ranked symptom in 22% of patients and one of the top three
symptoms in 35% of patients.
Follow-Up Assessment of Pain, Analgesic Prescribing,
and Pain Treatment Adequacy
Table 2 tabulates changes in pain and analgesia between initial
and follow-up assessments. Among the 1,457 patients who had both
PMI values available at initial and follow-up assessments, the McNe-
mar test revealed no significant changes in treatment adequacy be-
tween the two visits (across disease sites or by disease site). Of the 406
patients with undertreatment at initial assessment, only 31% received
acceptable pain treatment by the follow-up visit. Moreover, 10% of
the 1,051 patients with acceptable treatment at baseline became un-
dertreated by the time of follow-up.
Predictors of Pain Treatment Adequacy
Multivariable analysis of the initial and follow-up data revealed
that non-Hispanic white patients, patients treated at sites with mostly
non-Hispanic white patients, patients with poor performance status,
and patients with advanced cancer who were receiving cancer treat-
ment were least likely to receive inadequate pain treatment in terms of
opioid prescribing.
The odds ratios (ORs) for inadequate pain treatment from the
univariable and multivariable logistic regression analyses at initial and
follow-up assessments are summarized in Table 3.
Multivariable analysis revealed that across registering institutions
(minority-based or majority-based), the odds of a non-Hispanic white
patient having inadequate pain treatment at both the initial assess-
ment and follow-up were approximately half those of a minority
patient after adjusting for other explanatory variables (OR, 0.51; 95%
CI, 0.37 to 0.70; P.002). There were no significant pairwise interac-
tions in the final model.
Minority patients and patients with less care complexity (less
weight loss, analgesic use, pain severity, lower stage, and fewer meta-
static sites) were more likely than their counterparts to have incom-
plete follow-up data at the second time-point for the PMI (Appendix
Table A1, online only).
DISCUSSION
The present study shows that in the United States, pain is as prevalent
in ambulatory oncology patients with common solid tumors as it was
more than 20 years ago, despite the fact that opioid prescribing in the
United States has increased more than 10-fold since 1990.
11
It is
appropriate that a recent Institute of Medicine report has called for
coordinated, national efforts to create a cultural transformation in the
way the nation understands and approaches pain management and
prevention.
12
In the present study, two thirds of the patients who were
determined to be at risk of pain reported having pain or receiving
analgesic treatment, and one third of the patients who had pain or used
analgesics received inadequate treatment for their pain. Furthermore, we
Potentially analyzable patients
(n = 3,106)
Enrolled patients
(N = 3,123)
Missing pain or analgesic scores
Initial assessment (n = 83)
Follow-up assessment (n = 622)
Inadequate pain management
Initial assessment (n = 670; 33%)
Follow-up assessment (n = 586; 34%)
Acceptable pain management
Initial assessment (n = 1,356; 67%)
Follow-up assessment (n = 1,122; 66%)
At risk for pain
Initial assessment (n = 3,023)
Follow-up assessment (n = 2,484)
No pain and no analgesics
Initial assessment (n = 997; 33%)
Follow-up assessment (n = 776; 31%)
With pain or analgesics
Initial assessment (n = 2,026; 67%)
Follow-up assessment (n = 1,708; 69%)
In pilot portion of study (n = 10)
Never started study (n = 6)
Ineligible (n = 1)
Registry cancellation (n = 1)
Patient refusal (n = 1)
Other (n = 3)
Never sent in forms (n = 1)
Fig 1. Flow diagram of patient enrollment.
Fisch et al
1982 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
Table 1. Patient Demographics and Disease Characteristics at Baseline for All Potentially Analyzable Patients (N 3,106)
Characteristic
Potentially Analyzable Patients
Patients in PMI Analysis at Initial Assessment
Patients Not in
PMI Analysis
at Initial
Assessment
Patients in
PMI Analysis
at Initial
Assessment Breast
Disease Site Colorectal
Prostate Lung
No. % No. % No. % No. % No. % No. %
No. of patients 1,080 35 2,026 65 1,009 50 427 21 205 10 385 19
Time from diagnosis, months 1,069 1,987 985 422 198 382
Mean 35 35 44 23 59 15
Median 14 16 20 12 42 7
Range 0-308 0-627 0-627 0-197 0-269 0-149
Age, years 1,080 2,026 1,009 427 205 385
Mean 62 60 57 60 70 64
Median 63 60 57 59 71 64
Range 28-92 18-93 18-91 23-89 47-93 35-88
45 92 8 221 11 154 15 51 12 0 0 16 4
45-60 343 32 741 37 432 43 163 38 35 17 111 29
60-75 460 43 771 38 342 34 148 35 93 45 188 49
75 185 17 293 14 81 8 65 15 77 38 70 18
Sex
Male 340 31 596 29 3 0 215 50 205 100 173 45
Female 740 69 1,430 71 1,006 100 212 50 0 0 212 55
Race
White 926 87 1,722 86 862 86 357 84 171 86 332 87
Black 127 12 237 12 116 12 58 14 26 13 37 10
Asian 11 1 19 1 13 1 3 1 0 0 3 1
Native Hawaiian 0 0 2 0 1 0 1 0 0 0 0 0
Native American 3 0 14 1 5 1 3 1 0 0 6 2
Indian 0 0 1 0 1 0 0 0 0 0 0 0
Patient refusal 1 0 1 0 0 0 0 0 0 0 1 0
Multiracial 2 0 3 0 0 0 1 0 2 1 0 0
Unknown 10 27 — 11 4 — 6 6 —
Ethnicity
Hispanic or Latino 116 12 169 9 68 7 53 13 23 13 25 7
Non-Hispanic 879 88 1,693 91 858 93 356 87 157 87 322 92
Patient refusal 4 0 3 0 1 0 0 0 0 0 2 1
Institution refusal 1 0 3 0 1 0 1 0 0 0 1 0
Unknown 80 158 — 81 17 — 25 35 —
Race/ethnicity
Minority 248 25 434 23 197 21 117 29 51 27 69 20
White and non-Hispanic 753 75 1,440 77 735 79 292 71 135 73 278 80
Unknown 79 152 — 77 18 — 19 38 —
ECOG PS*
0 744 69 1,011 50 628 63 192 45 81 40 110 29
1 298 28 807 40 310 31 198 47 94 46 205 53
2 34 3 197 10 64 6 36 8 28 14 69 18
Unknown 4 11 — 7 1 — 2 1 —
Weight loss in previous 6 months
5% 967 91 1,664 83 879 89 320 75 176 87 289 75
5% 101 9 338 17 113 11 105 25 26 13 94 25
Unknown 12 24 — 17 2 — 3 2 —
Current status of disease
CR 512 48 645 32 471 47 118 27 21 10 35 9
PR 43 4 104 5 34 3 16 4 18 9 36 9
SD 406 38 930 46 371 37 216 51 111 54 232 61
PD 108 10 338 17 128 13 76 18 55 27 79 21
Unknown 11 9 — 5 1 — 0 3 —
(continued on following page)
Pain and Analgesic Prescribing in Medical Oncology Patients
www.jco.org © 2012 by American Society of Clinical Oncology 1983
found that the odds of inadequate analgesic prescribing are twice as high
for minority patients compared with non-Hispanic white patients.
Pain control remains a serious issue in patients with cancer through-
out the world, as rates of undertreatment have also been reported in
studies from industrialized nations such as Canada and some European
countries.
13-15
That the magnitude and scope of pain treatment inad-
equacy has not decreased substantially in the past two decades in the
United States despite a long-standing awareness of this problem is
surprising. In the early 1990s, nearly 900 ECOG clinicians were sur-
veyed about pain treatment barriers; approximately 50% of respon-
dents believed their patients had good pain control, and a number of
the surveyed clinicians cited concerns about pain assessment, opioid
adverse effects, and the reluctance of patients to report pain and take
medications.
16
Several observational and survey-based studies from
various oncology care settings in the United States and Europe
17-19
have since confirmed the results of the earlier ECOG survey, namely,
that pain is not a primary concern for many patients and that patients
and clinicians have misgivings about the adverse effects of pain medica-
tions; the costs associated with opioids and the medications used to miti-
gate their adverse effects; and the perceived dangers of driving, operating
machinery, or caring for children while using certain pain medications.
In contrast to the 1994 ECOG pain study,
2
the present study did
not reveal age and sex to be significant factors for pain treatment
adequacy. Our analysis also included individual race and ethnicity
variables, as well as follow-up data (data that were not collected in the
earlier ECOG pain study). The present study corroborates others’
Table 1. Patient Demographics and Disease Characteristics at Baseline for All Potentially Analyzable Patients (N 3106) (continued)
Characteristic
Potentially Analyzable Patients
Patients in PMI Analysis at Initial Assessment
Patients Not in
PMI Analysis
at Initial
Assessment
Patients in
PMI Analysis
at Initial
Assessment Breast
Disease Site Colorectal
Prostate Lung
No. % No. % No. % No. % No. % No. %
Current stage of disease
NED 578 54 754 37 532 53 138 33 36 18 48 13
Locoregional 221 20 368 18 160 16 51 12 29 14 128 33
Metastatic 233 22 726 36 275 27 197 46 114 56 140 36
Locoregional and metastatic 44 4 171 9 39 4 39 9 25 12 68 18
Unknown 4 7 — 3 2 — 1 1 —
Prior chemo/immuno/hormonal therapy
No 460 43 735 36 320 32 160 37 73 36 182 47
Yes 620 57 1,290 64 689 68 267 63 132 64 202 53
Unknown 0 1 — 0 0 — 0 1 —
Prior radiation therapy
No 661 62 1,121 56 511 51 314 74 86 42 210 55
Yes 411 38 886 44 489 49 109 26 117 58 171 45
Unknown 8 19 — 9 4 — 2 4 —
Currently receiving cancer treatment
No 359 33 448 22 194 19 106 25 49 24 99 26
Yes 721 67 1,578 78 815 81 321 75 156 76 286 74
Metastatic sites
No site 754 70 1,080 53 676 67 175 41 66 32 163 42
Single site 198 18 487 24 150 15 130 30 101 49 106 28
Multiple sites 128 12 459 23 183 18 122 29 38 19 116 30
Disease stage and current treatment
status
Advanced stage and currently
treated 243 23 800 40 297 30 210 50 124 61 169 44
Advanced stage and not currently
treated 34 3 97 5 17 2 26 6 15 7 39 10
Nonadvanced stage and currently
treated 477 44 774 38 516 51 111 26 31 15 116 30
Nonadvanced stage and not currently
treated 322 30 348 17 176 17 78 18 34 17 60 16
Unknown 4 7 — 3 2 — 1 1 —
Institution type
Academic 101 9 202 10 75 7 41 10 43 21 43 11
Community 979 91 1,824 90 934 93 386 90 162 79 342 89
Clinic practice type
Majority based 783 73 1,541 76 787 78 302 71 140 68 312 81
Minority based 297 27 485 24 222 22 125 29 65 32 73 19
Abbreviations: CR, complete response; ECOG PS, Eastern Cooperative Oncology Group performance status; NED, no evidence of disease; PD, progressive disease;
PMI, pain management index; PR, partial response.
*ECOG PS, where a level of 0 indicates full activity, without restriction. Higher levels indicate greater impairment in function.
Fisch et al
1984 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
findings about the inequality of pain treatment adequacy between
minority patients and non-Hispanic white patients and shows that
these findings persist at short-term follow-up. This finding of pain
treatment disparity has also been observed across a variety of noncan-
cer settings.
20,21
Minority patient factors, such as beliefs about the
value of stoicism, concerns about opioid addiction and adverse effects,
and reluctance to report pain or request analgesics putatively influenced
this disparity.
21-23
Some studies have suggested that communication dif-
ficulties between non-Hispanic white physicians and minority patients
are common and may lower mutual trust and thus quality of
care.
22,24-26
When communication and trust between minority pa-
tients and their physicians are problematic, concerns about opioid-
associated deaths, opioid diversion problems, and recreational opioid
use may exacerbate disparities in pain treatment adequacy. Of note,
the observation that white patients were also more likely to be under-
treated at minority sites suggests that system factors (eg, opioid avail-
ability) could also be contributing to the disparity.
The complexity of care and symptom burden that patients with
cancer experience throughout the trajectory of their care pose particular
concerns. In the present study, 40% of patients seen in outpatient oncol-
ogy settings at any point in their illness had at least three concurrent
moderate or severe symptoms. Cancer survivors, like patients actively
being treated for their disease, have complex and often unmet needs,
and pain assessment and treatment are poorly understood in this
population.
27
Patients with nonadvanced cancer who were not receiv-
ing cancer-directed treatment were especially likely to be undertreated
for pain. This disparity may be explained in part by the fact that nearly
50% of these patients experienced symptoms that oncologists believed
were not attributable to cancer or cancer therapy and thus were not
treated aggressively. This potential gap in pain treatment could be
bridged with improved coordination of care between oncologists and
nononcology providers. For example, Temel et al
28
described the
benefits of the early integration of palliative care specialists for patients
with lung cancer receiving initial chemotherapy, and the Indiana
Cancer Pain and Depression trial
29
demonstrated the value of symp-
tom management collaboration between oncologists and other pro-
viders. Finally, the availability of effective pain medication that is not
perceived to interfere with driving, work performance, social interac-
tions, or bowel habits could improve adherence to pain treatment.
This study is the largest prospective evaluation of pain and other
symptoms in outpatient oncology in the United States. The distribution of
Table 2. Tabulation of Pain and Analgesia at Initial and Follow-Up Assessments
Pain at Baseline
Pain at Follow-Up
Total
Missing No Pain Mild Pain Moderate Pain Severe Pain
No. % No. % No. % No. % No. %
No pain 152 9 1,048 65 319 20 56 3 40 3 1,615
Mild pain 81 9 211 25 416 48 89 10 66 8 863
Moderate pain 34 12 43 16 77 28 62 22 60 22 276
Severe pain 45 14 35 11 63 20 63 20 113 35 319
Analgesia at Baseline†
Analgesia at Follow-Up
Total
Missing No Analgesic Non-Opioid Weak Opioid Strong Opioid
No. % No. % No. % No. % No. %
No analgesic 240 16 1,159 77 64 4 24 2 23 1 1,510
Nonopioid 102 15 45 6 524 75 17 2 13 2 701
Weak opioid 40 9 34 8 25 6 312 72 23 5 434
Strong opioid 57 14 25 6 15 4 9 2 302 74 408
All potentially analyzable patients with pain score reported (regardless of the availability of the analgesic score) at initial assessment were analyzed.
†All potentially analyzable patients with analgesic score reported (regardless of the availability of the pain score) at initial assessment were analyzed.
131
Severe Pain
Moderate Pain
Mild Pain No Pain
Severe Pain
Moderate Pain
Mild Pain No Pain
57
69
316
776
41 43
240
270
60 67
118
89
103
71
123
41
63
71
361
997
56 51
207 383
68
75
154
133
128
72
73
No analgesic
Nonopioid
Weak opioid
Strong opioid
A
Patients (%)
Pain Score
70
50
60
40
20
10
30
0
No analgesic
Nonopioid
Weak opioid
Strong opioid
B
Patients (%)
Pain Score
70
50
60
40
20
10
30
0
Fig 2. Analgesic prescribing in relation to pain severity at (A) initial assessment
and (B) follow-up 28 to 35 days later. The numbers of patients are displayed
according to the WHO category of analgesic prescribing and their self-reported
level of pain intensity.
Pain and Analgesic Prescribing in Medical Oncology Patients
www.jco.org © 2012 by American Society of Clinical Oncology 1985
Table 3. Univariable and Multivariable Logistic Regression Analyses for Undertreatment of Pain at Initial Assessment and Follow-Up
Predictor and Level
Initial Assessment Follow-Up Assessment
%
Undertreatment
(No.)
Univariable
(n 1,874-2,026)
Multivariable
(n 1,836) %
Undertreatment
(No.)
Univariable
(n 1,565-1,708)
Multivariable
(n 1,461)
POR (95% CI) POR (95% CI) POR (95% CI) POR (95% CI)
Disease site
Colorectal 35 (149/427) .037 0.98 (0.74 to 1.30) 36 (135/373) .635 1.04 (0.79 to 1.38)
Prostate 32 (65/205) 0.85 (0.53 to 1.35) 30 (47/155) 0.80 (0.43 to 1.48)
Lung 26 (99/385) 0.63 (0.46 to 0.86) 32 (101/320) 0.85 (0.61 to 1.17)
Breast 35 (357/1009) 1.00 35 (303/860) 1.00
Age, years
45-60 33 (242/741) .537 0.89 (0.65 to 1.21) 34 (214/625) .802 0.86 (0.58 to 1.27)
60-75 32 (247/771) 0.86 (0.65 to 1.15) 34 (227/663) 0.86 (0.61 to 1.21)
75 35 (103/293) 0.99 (0.68 to 1.45) 32 (76/237) 0.78 (0.48 to 1.26)
<45 35 (78/221) 1.00 38 (69/183) 1.00
Sex
Female 33 (471/1430) .880 0.98 (0.75 to 1.28) 35 (419/1209) .704 1.05 (0.80 to 1.39)
Male 33 (199/596) 1.00 34 (167/499) 1.00
ECOG PS
1 33 (263/807) .001 0.86 (0.62 to 1.18) .012 0.93 (0.68 to 1.26) 36 (232/653) <.001 0.97 (0.69 to 1.36) .004 1.16 (0.83 to 1.62)
2 20 (40/197) 0.45 (0.31 to 0.67) 0.55 (0.36 to 0.83) 18 (28/158) 0.38 (0.25 to 0.57) 0.45 (0.27 to 0.73)
036 (364/1011) 1.00 1.00 36 (322/886) 1.00 1.00
Race/ethnicity
White and non-Hispanic 29 (411/1440) .010 0.38 (0.24 to 0.61) .002 0.51 (0.37 to 0.70) 30 (371/1229) .018 0.41 (0.25 to 0.68) .001 0.50 (0.35 to 0.70)
Minority 51 (221/434) 1.00 1.00 511 (180/352) 1.00 1.00
Disease stage and treatment
Advanced stage and currently
treated 24 (195/800) .001 0.39 (0.29 to 0.53) .002 0.40 (0.29 to 0.54) 26 (179/685) .002 0.41 (0.30 to 0.58) .005 0.41 (0.30 to 0.57)
Advanced stage and not
currently treated 309 (29/97) 0.52 (0.33 to 0.81) 0.55 (0.34 to 0.87) 30 (24/80) 0.50 (0.28 to 0.91) 0.60 (0.34 to 1.09)
Nonadvanced stage and
currently treated 37 (285/774) 0.71 (0.56 to 0.89) 0.69 (0.55 to 0.88) 38 (256/666) 0.73 (0.56 to 0.95) 0.68 (0.52 to 0.90)
Nonadvanced stage and
not currently treated 45 (157/348) 1.00 1.00 46 (124/269) 1.00 1.00
Type of site
Minority based 49 (235/485) .019 2.39 (1.41 to 4.04) .026 1.64 (1.10 to 2.44) 48 (196/412) .037 2.11 (1.22 to 3.64) .059 1.53 (1.00 to 2.33)
Majority based 28 (435/1541) 1.00 1.00 30 (390/1296) 1.00 1.00
rESS risk status†
Poor risk 32 (378/1183) .309 0.88 (0.69 to 1.12) 33 (297/900) .403 0.88 (0.66 to 1.18)
Good risk 35 (287/826) 1.00 36 (238/665) 1.00
Pain mechanism
Nociceptive 29 (249/857) .073 0.74 (0.53 to 1.01) 31 (214/690) .277 0.74 (0.53 to 1.05)
Neuropathic 35 (87/246) 0.98 (0.74 to 1.30) 33 (62/188) 0.81 (0.57 to 1.16)
Insufficient information to
classify 48 (14/29) 1.68 (0.71 to 3.97) 42 (8/19) 1.20 (0.40 to 3.61)
No pain syndrome 36 (314/878) 1.00 38 (252/668) 1.00
Incidental pain
Presence of incidental pain 32 (242/769) .579 0.89 (0.68 to 1.16) 33 (201/606) .641 0.94 (0.66 to 1.33)
Insufficient information to
classify 35 (24/68) 1.06 (0.55 to 2.04) 41 (17/42) 1.29 (0.71 to 2.32)
Absence of incidental pain 34 (400/1174) 1.00 35 (321/928) 1.00
Distress/addiction
Psych distress alone present 31 (193/618) .314 0.87 (0.67 to 1.13) 34 (149/442) .133 0.96 (0.71 to 1.29)
Addiction alone present 43 (9/21) 1.44 (0.65 to 3.20) 33 (5/15) 0.94 (0.30 to 2.92)
Both present 23 (10/43) 0.58 (0.29 to 1.16) 11 (3/28) 0.23 (0.07 to 0.74)
Insufficient information to
classify 32 (11/34) 0.92 (0.37 to 2.27) 45 (21/47) 1.52 (0.78 to 2.99)
Neither present 34 (445/1300) 1.00 35 (362/1045) 1.00
Cognitive function
Partial impairment 33 (28/84) .999 1.01 (0.57 to 1.81) 30 (24/80) .577 0.82 (0.52 to 1.30)
Insufficient information to
classify 33 (2/6) 1.01 (0.16 to 6.53) 43 (3/7) 1.43 (0.45 to 4.56)
No impairment 33 (638/1927) 1.00 34 (512/1490) 1.00
(continued on following page)
Fisch et al
1986 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
the four solid tumors is typical for outpatient cancer care, including the
low relative proportion of patients with prostate cancer. This study had
several potential limitations. First, these findings can be generalized to
patients with common solid tumors who receive care at sites associated
with a US clinical cooperative group, yet a significant number of
ambulatory patients with cancer have less common solid tumors or
hematologic malignancies and/or receive care at sites outside the co-
operative group system. In addition, we did not collect data on pa-
tients’ comorbidities, insurance status, or socioeconomic status or
clinicians’ attributes (eg, age, race, and sex); these factors may influ-
ence pain management practice. Also, 28% of the patients in the
present study did not have complete PMI data at follow-up for treat-
ment adequacy assessment. These data were not missing at random, as
these patients tended to be healthier and were likely to be minority
patients or patients enrolled at minority-based sites. This pattern of
missing data is itself a unique observation with potential utility for
planning future research. Yet the patterns of pain expression and
analgesic prescribing at the initial assessment and follow-up as re-
vealed by multivariable analysis were remarkably similar. Finally, al-
though the PMI is the best available and most widely used instrument
to measure pain treatment adequacy, it remains only a gross indicator
of pain treatment adequacy because it focuses on opioid analgesic
prescribing categories and does not reflect the dosing of opioids or use
of nonopioid pain interventions.
In conclusion, pain remains a significant concern in ambula-
tory oncology. Non-Hispanic white patients and patients with the
most obvious burden of illness are most likely to receive adequate
cancer pain management. Innovative pain treatments and refined
measures of pain management adequacy as well as the better inte-
gration of nononcology clinical resources into the oncology setting
all hold promise for improving outcomes in outpatient care of patients
with cancer. Improved communication between providers and all
patients about pain and pain treatment holds promise to help formu-
late the most appropriate patient-centered treatment goals and max-
imize health outcomes.
AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS
OF INTEREST
The author(s) indicated no potential conflicts of interest.
Table 3. Univariable and Multivariable Logistic Regression Analyses for Undertreatment of Pain at Initial Assessment and Follow-Up (continued)
Predictor and Level
Initial Assessment Follow-Up Assessment
%
Undertreatment
(No.)
Univariable
(n 1,874-2,026)
Multivariable
(n 1,836) %
Undertreatment
(No.)
Univariable
(n 1,565-1,708)
Multivariable
(n 1,461)
POR (95% CI) POR (95% CI) POR (95% CI) POR (95% CI)
Clinician-judged difficulties
attributed to cancer
The other choices 28 (242/853) .007 0.69 (0.53 to 0.89) 31 (180/587) .057 0.78 (0.61 to 1.00)
Not at all/a little bit 37 (424/1162) 1.00 36 (358/991) 1.00
Clinician-judged difficulties
attributed to cancer
treatment
The other choices 32 (288/900) .494 0.92 (0.72 to 1.17) 36 (244/678) .155 1.16 (0.95 to 1.42)
Not at all/a little bit 34 (377/1114) 1.00 33 (293/898) 1.00
Clinician-judged pain as top 1
area
Yes 29 (169/579) .041 0.78 (0.62 to 0.98) 31 (145/472) .119 0.80 (0.62 to 1.03)
No 35 (499/1440) 36 (394/1108)
Clinician-judged pain as top 3
areas
Yes 30 (284/943) .092 0.78 (0.58 to 1.03) 31 (239/778) .042 0.78 (0.58 to 1.03) .067 0.77 (0.59, 0.99)
No 36 (384/1076) 37 (300/802)
Clinician-judged financial
problems as top 1 area
Yes 40 (10/25) .498 1.35 (0.58 to 3.14) 42 (8/19) .546 1.41 (0.48 to 4.11)
No 33 (658/1994) 34 (531/1561)
Clinician-judged financial
problems as top 3 areas
Yes 36 (47/130) .387 1.16 (0.85 to 1.57) 40 (36/89) .201 1.33 (0.90 to 1.99)
No 33 (621/1889) 34 (503/1491)
Pain discrepancy‡
Yes 35 (270/780) .252 1.12 (0.93 to 1.35) 31 (188/599) .162 0.82 (0.63 to 1.07)
No 32 (398/1239) 36 (351/981)
NOTE. Undertreatment is being modeled in the analysis model. The level bolded in each predictor is the reference group in the logistic regression model.
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; OR, odds ratio; rESS, revised Edmonton Staging System.
ECOG PS, where a level of 0 indicates full activity, without restriction. Higher levels indicate greater impairment in function.
†rESS refers to the revised Edmonton Staging System for pain. Poor–risk patients have at least one high-risk indicator (neuropathic pain, incident pain, psychological
distress or addictive behavior, or cognitive impairment).
‡Pain discrepancy refers to mismatch between the clinician and patient regarding pain perception. If the patient assigned a score 5 (moderate/severe) to the
pain item at initial assessment and the clinician ranked pain as the top 3 areas or the patient assigned a score 5 (not present/mild) to the pain item and the clinician
did not rank pain as the top 3 areas, no discrepancy was noted.
Pain and Analgesic Prescribing in Medical Oncology Patients
www.jco.org © 2012 by American Society of Clinical Oncology 1987
AUTHOR CONTRIBUTIONS
Conception and design: Michael J. Fisch, Lynne I. Wagner, David Cella,
Judith B. Manola, Lori M. Minasian, Tito R. Mendoza, Charles S. Cleeland
Administrative support: Judith B. Manola
Collection and assembly of data: Matthias Weiss, Lynne I. Wagner
Data analysis and interpretation: Michael J. Fisch, Ju-Whei Lee,
Matthias Weiss, Lynne I. Wagner, Victor T. Chang, David Cella, Lori M.
Minasian, Worta McCaskill-Stevens
Manuscript writing: All authors
Final approval of manuscript: All authors
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■■■
Fisch et al
1988 © 2012 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
    • "Despite people living with cancer having frequent contact with a range of health providers (Momen, Hadfield, Harrison, & Barclay, 2013), their pain is frequently poorly controlled. It is currently estimated that 30—50% of people receiving cancer treatment and 70% of people with advanced cancer experience unrelieved pain, while pain persists for approximately 33% of cancer survivors (Deandrea, Montanari, Moja, & Apolone, 2008; Fisch, Lee, & Weiss, 2012; Foley, 2011; Stockler & Wilcken, 2012). Unrelieved cancer pain impacts adversely on the patient's everyday functioning, other symptoms, and their quality of life (Dalal & Bruera, 2013). "
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