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J Clin Oncol. Jun 1, 2012; 30(16): 1980–1988.
Published online Apr 16, 2012. doi:  10.1200/JCO.2011.39.2381
PMCID: PMC3383175
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
Michael J. Fisch, Tito R. Mendoza, and Charles S. Cleeland, The University of Texas MD Anderson Cancer Center, Houston, 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 Feinberg 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.
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/at/mdanderson.org.
Received September 12, 2011; Accepted January 21, 2012.
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 undertreatment twice as high for minority patients. These findings persist over 1 month of follow-up, highlighting the complexity of these problems.
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 Cooperative Oncology Group (ECOG) conducted a landmark study of pain needs in 1,308 patients in outpatient oncology, finding that 42% had inadequate analgesic prescribing.2 The findings of the ECOG study and other studies led to an increased emphasis on symptom management in cancer. Nevertheless, 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 outpatient oncology practice. The secondary objectives were to identify the characteristics of patients with self-reported pain, the adequacy of opioid prescribing, and the factors associated with the undertreatment of pain.
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 classified 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 approved 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 completing 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 patients 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 appointments, and patients' information was collected before their visit with a clinician. 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 questionnaire 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 demographic information, including cancer treatment history and current therapies. At the initial and follow-up visits, patients reported symptom intensity and functional interference using a modification of the MD Anderson Symptom 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 adequacy for opioid prescribing.7,8 The PMI is modeled on the WHO pain treatment paradigm, which involves a three-step analgesic ladder that progresses 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 medication 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 medications 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 management. 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 univariable and multivariable logistic regression analyses of patients' demographic and clinical data as well as clinician response data. The logistic regression analysis modeled the probability of undertreatment. Because patients 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 multivariable 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.
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 cancer, 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 registration 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).
Fig 1.
Fig 1.
Flow diagram of patient enrollment.
Table 1.
Table 1.
Patient Demographics and Disease Characteristics at Baseline for All Potentially Analyzable Patients (N = 3,106)
The analgesics prescribed in relation to pain severity at initial assessment and follow-up are summarized in Figure 2. At initial assessment, 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 receiving any analgesic. The most commonly prescribed nonopioids were acetaminophen (35%) and nonsteroidal anti-inflammatory agents (19%). The most prevalent moderate or severe nonpain symptoms 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. Clinicians 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 impairment)10 were noted in 49% of patients. On the basis of the clinicians' reports, 3% of patients had a history of addiction behavior, 3% had partial cognitive impairment, 28% had psychological distress, 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.
Fig 2.
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. (more ...)
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 McNemar test revealed no significant changes in treatment adequacy between 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 undertreated by the time of follow-up.
Table 2.
Table 2.
Tabulation of Pain and Analgesia at Initial and Follow-Up Assessments
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 treatment 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.
Table 3.
Table 3.
Univariable and Multivariable Logistic Regression Analyses for Undertreatment of Pain at Initial Assessment and Follow-Up
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 assessment 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 interactions in the final model.
Minority patients and patients with less care complexity (less weight loss, analgesic use, pain severity, lower stage, and fewer metastatic sites) were more likely than their counterparts to have incomplete follow-up data at the second time-point for the PMI (Appendix Table A1, online only).
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 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 throughout the world, as rates of undertreatment have also been reported in studies from industrialized nations such as Canada and some European countries.1315 That the magnitude and scope of pain treatment inadequacy 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 surveyed about pain treatment barriers; approximately 50% of respondents 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 Europe1719 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 medications; the costs associated with opioids and the medications used to mitigate 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' 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 noncancer 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.2123 Some studies have suggested that communication difficulties between non-Hispanic white physicians and minority patients are common and may lower mutual trust and thus quality of care.22,2426 When communication and trust between minority patients 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 undertreated at minority sites suggests that system factors (eg, opioid availability) 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 oncology 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 receiving 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 al28 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 trial29 demonstrated the value of symptom management collaboration between oncologists and other providers. Finally, the availability of effective pain medication that is not perceived to interfere with driving, work performance, social interactions, 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 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 cooperative group system. In addition, we did not collect data on patients' comorbidities, insurance status, or socioeconomic status or clinicians' attributes (eg, age, race, and sex); these factors may influence pain management practice. Also, 28% of the patients in the present study did not have complete PMI data at follow-up for treatment 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 revealed by multivariable analysis were remarkably similar. Finally, although 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 ambulatory 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 integration 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 formulate the most appropriate patient-centered treatment goals and maximize health outcomes.
Acknowledgment
We thank Joseph Munch, Kate Newberry, and Joann Aaron for editorial assistance.
Appendix
Lead physicians at participating sites: Joseph A. Sparano, MD, Albert Einstein College of Medicine; Bruce J. Averbrook, MD, FACS, Case Western-MetroHealth Medical Center; Al B. Benson III, MD, FACP, Northwestern University; James Lockhart, MD, St Francis Hospital: Oklahoma CCOP; Jim Cleary, MD, University of Wisconson; Matthias Weiss, MD, PhD, Marshfield Clinic; Paul Gilman, MD, Lankenau Hospital: Main Line Health CCOP; Eduardo Pajon, MD, West Michigan Cancer Center: Kalamazoo CCOP; Raymond Lord, MD, West Michigan Cancer Center: Kalamazoo CCOP; J. Philip Kuebler, MD, PhD, Columbus CCOP; Stephen Grubbs, MD, Christiana Care CCOP; James Wade, MD, Decatur Memorial Hospital: Central Illinois CCOP; Robert Veith, MD, Louisiana State University Medical Center MBCCOP; Jyotsna Fuloria, MD, Ochsner Clinic; Janice Dutcher, MD, OLM Cancer Center MBCCOP; Jose Bufill, MD, Memorial Hospital of South Bend: Northern Indiana CRC CCOP; Fa-Chyi Lee, MD, University of New Mexico MBCCOP; Gerald Bayer, MD, St Vincent Hosp Regional Cancer Center CCOP; Witold B. Rybka, MD, Penn State Cancer Institute; Thomas Lad, John H. Stroger Hospital of Cook County MBCCOP; Luis Baez-Diaz, MD, FACP, San Juan Minority Based CCOP; David L. Grinblat, MD, Evanston CCOP-NorthShore University; Martin Wiesenfeld, MD, Cedar Rapids Oncology Associates; Daniel Nikcevich, MD, PhD, Duluth Clinic; Robert Behrens, MD, Iowa Oncology Research Assoc.: Iowa CCOP; John Kugler, MD, Illinois Oncology Research Associates CCOP; Shaker Dakhil, MD, FACP, Wichita CCOP; Rex Mowat, MD, Toledo Community Hosp Oncology Program CCOP; Philip Stella, MD, Michigan Cancer Research Consortium CCOP; Gamini Soori, MD, MBA, FACP, FRCP, CPE, Missouri Valley Cancer Consortium; Miroslaw Mazurczak, MD, Sanford Cancer Center: Sioux Community CCOP; Steven N. Wolff, MD, Meharry Medical College; Anand Jillella, MD, Georgia Health Sciences University: Medical College of Georgia MBCCOP; David J. Peace, MD, University of Illinois at Chicago MBCCOP; Lucile Adams Campbell, MD, Howard University Cancer Center; Marianne N. Prout, MD, MPH, Boston University Schools of Medicine & Public Health; Carlos Vallejos, MD, Grupo de Estudios Clinical Oncology del Peru.
Table A1.
Patient Demographics and Disease Characteristics by PMI Data Completeness (N = 2,026)
CharacteristicPMI Data Completeness
Total
P*
No (n = 569)
Yes (n = 1,457)
No.%No.%No.%
Time from diagnosis, months
    Median161516
    Range0-3680-6270-627
Age, years
    Median616060
    Range24-9018-9318-93
    < 4563111581122111
    45-60211375303674137
    60-75202365693977138
    ≥ 7593162001429314
Disease site
    Breast27749732501,00950
    Colorectal116203112242721
    Prostate7113134920510
    Lung105182801938519
Sex
    Male174314222959629
    Female395691,035711,43071
Race/ethnicity.0225
    Minority142272922243423
    White and non-Hispanic390731,050781,44077
    Unknown37115152
ECOG PS
    027048741511,01150
    1247435603980740
    ≥ 25191461019710
    99911011
Weight loss.0011
    < 5%490881,174811,66483
    ≥ 5%70122681933817
    Unknown91524
Analgesic score at baseline< .0001
    No analgesic226402691949525
    Nonopioid167295303669734
    Weak opioid84153462443021
    Strong opioid92163122140420
Pain score at baseline.0126
    No pain147264423058929
    Mild pain (1-4)269475844085342
    Moderate pain (5-6)78141911326913
    Severe pain (7-10)75132401731516
Current stage of disease.0010
    No evidence of disease236415183675437
    Locoregional117212511736818
    Metastatic166295603972636
    Locoregional and metastatic49912281719
    Unknown167
Currently receiving cancer treatment< .0001
    No160282882044822
    Yes409721,169801,57878
Metastatic sites.0014
    No sites of metastatic disease33960741511,08053
    Single site124223632548724
    Multiple sites106183532445923
    Advanced stage and currently treated189336114280040
    Advanced stage and not currently treated265715975
    Nonadvanced stage and currently treated220395543877438
    Nonadvanced stage and not currently treated133232151534817
    Unknown167
Institution type< .0001
    Academic10218100720210
    Community467821,357931,82490
Clinic practice type.0393
    Majority based415731,126771,54176
    Minority based154273312348524
Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; PMI, pain management index.
*This P value reported was from a χ2 test, not adjusted for multiple comparisons.
n = 556 for patients without PMI complete data; n = 1,431 for patients with PMI complete data.
ECOG PS, where a level of 0 indicates full activity, without restriction. Higher levels indicate greater impairment in function.
Footnotes
See accompanying editorial on page 1907 and article on page 1974; listen to the podcast by Dr Von Roenn at www.jco.org/podcasts
Supported in part by Public Health Service Grants No. CA37604, CA23318, CA026582, and CA17145 and grants from the National Cancer Institute, National Institutes of Health, and the Department of Health and Human Services.
Presented in part at the 47th Annul Meeting 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 trial information can be found for the following: NCT00303914.
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The author(s) indicated no potential conflicts of interest.
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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