© 2004 by Oxford University Press
2004 © Oxford University Press
Article |
Descriptive Review of the Literature on Breast Cancer Outcomes: 1990 Through 2000
J. Mandelblatt, C. Armetta, W. Liang (Department of Oncology and Cancer Clinical and Economic Outcomes Core, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, D.C.); K. R. Yabroff (National Cancer Institute, Bethesda, MD and formerly Georgetown University Medical Center); W. Lawrence (Agency for Healthcare Research and Quality and formerly Georgetown University Medical Center)
Correspondence to: Jeanne Mandelblatt, M.D., M.P.H., Lombardi Cancer Center, Georgetown University Medical Center, 2233 Wisconsin Ave., Ste. 317, Washington, D.C. 20007 (e-mail: mandelbj{at}georgetown.edu).
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ABSTRACT |
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 TopNotes Abstract IntroductionMethodsResultsDiscussionReferences |
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Background: There is increasing interest in the incorporation of nonbiomedical outcomes into cancer research. Objective: Our goal was to review the use of nonbiomedical outcomes in research on breast cancer care. Data sources: We conducted a MEDLINE search of all studies on breast cancer quality of life, preferences, satisfaction, and economics that were published during the period from January 1, 1990, through December 31, 2000. We also searched bibliographies of published articles. Study selection: We included original primary research and excluded reviews, methods papers, studies conducted outside the United States and Western Europe, or studies with fewer than 100 subjects. Data abstraction: Data were abstracted by using a structured tool. Data synthesis: There were 1089 articles identified; 230 were included. The greatest proportion of research focused on survivorship followed by screening. The most frequently reported outcomes were health-related quality of life (54%) followed by economic analyses (38%) and patient satisfaction (14%); only 9% measured patient preferences. Few studies included more than 10% nonwhite populations or focused on the elderly. No single instrument was used in more than 10% of the studies, and many authors developed de novo tools for use in their study. Methodologic flaws were prevalent and included use of nonstandard economic methods, insufficient power, low or differential response rates, and lack of control for baseline status. Conclusion: At present, outcomes measurement is variable in its approach and application to breast cancer care. More research is needed to develop practical approaches that are reliable, valid, and feasible in routine practice, that are applicable to diverse populations, and that can inform interventions to improve the quality of care across the full spectrum of services.
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INTRODUCTION |
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The most general health outcome measure is survival. For breast and other cancers, 5-year survival or the interval of disease-free survival has customarily been used to evaluate the success of treatment. Clinical events, such as severity of illness, tumor response, or stage shifts, have served as intermediate measures of outcome, principally because they are believed to be associated with differences in survival. For many diseases, such as advanced breast cancer, curative treatment is not always possible. Still, the outcomes of noncurative care may be very different. For instance, therapeutic strategies may be associated with similar survival but different toxic effects; alternatively, one therapy may yield better survival but more severe side effects, while another may offer poorer survival but better quality of life (QOL) during the patient's remaining months or years. Thus, decisions about alternative therapies are often based on QOL considerations, in addition to the likelihood of survival (1,2). Even when cures are possible, the costs of treatment may exceed an individual's or society's willingness to pay.
Although QOL has been an implied medical outcome since the time of Hippocrates (3,4), the landmark paper by Karnofsky et al. (5) marked the first explicit effort of physicians to systematically assess the impact of cancer treatments on the patient's QOL and not quantity of life. The next major tools developed to assess the impact of cancer and breast cancer therapy on QOL were not developed until the early 1980s. In the prototype, the Spitzer Quality of Life Index, QOL continued to be physician rated (6). Thus, physician assessment of symptoms, toxic effects, and/or QOL remained the standard in breast cancer outcomes research for almost four decades.
Increasing consumerism and patient participation in health care decisions, occurring in parallel with the growth in interest in outcomes of care in the late 1970s and the 1980s, set the stage for the development of patient-based measures of general and breast cancer-related QOL (4,7). For example, in 1979, Ware et al. (8) presented results validating a patient's self-reported measure of general health status from the Rand Health Insurance Experiment. Preliminary incorporation of such measures into cancer cooperative group randomized controlled trials (RCTs) occurred in the late 1980s (4,9).
Despite the recent explosion of QOL measures, particularly for breast cancer, routine incorporation of QOL outcomes into cancer research has been slow, and when used, outcomes have often been poorly measured. For instance, in a review of QOL measurement across a variety of medical conditions, Gill and Feinstein (10) found that 159 different measures of QOL were used in 75 studies, but fewer than half defined the target domains, only 17% included a patient-rated QOL, and just 9% elicited patients' preferences for health outcomes. Thus, there is a clear need for expanded research on practice-based outcomes measures. For such measures to be clinically relevant and feasible to collect, it is key that the research and clinical communities work together to take an active leadership role in this process.
To set the stage for such activities, we review the current use of health outcomes across the spectrum of breast cancer care—from primary prevention and early detection through survivorship or death—that could be used as the basis of a research agenda for measuring the outcomes of breast cancer care. This review is intended to serve as a focal point for discussion and extension of existing efforts to improve the outcomes of breast cancer services.
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METHODS |
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TopNotesAbstractIntroductionMethodsResultsDiscussionReferences |
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We conducted a qualitative review of the literature on breast cancer health outcomes across all phases of breast cancer care, including primary prevention, early detection, diagnosis, local treatment, adjuvant treatment, treatment of metastatic disease, survivorship, and the dying experience (11).
Conceptualization and Definition of Health Outcomes
Outcomes research is the study of the net effects of the health care process on the health and well-being of individuals and populations. As such, it can encompass a wide breadth of issues, including research on satisfaction with care, effectiveness, costs of care, and measurement of patient preferences and QOL. The current construct of QOL draws on earlier use of social indicators, such as housing and employment status, to measure the well-being of populations (12-14). Breslow (15) expanded the measurement of population well-being to include the World Health Organization definition of health as "not only the absence of disease or of disability, but an overall state of physical, mental, and social well-being." de Haes (16) later defined QOL as "the subjective evaluation of life as a whole," and Cella and Cherin (17) expanded this definition to include patients' satisfaction with their level of functioning compared with an ideal level. Both of these latter definitions reflect the importance of the individual's subjective experience; the second definition highlights the role of personal values, or preferences, for health outcomes (3).
The current consensus among outcomes researchers is that QOL is a multidimensional concept (3,18-23). The term "health-related QOL" (HRQOL) is used to refer to overall QOL in a health context (24). Although there is variation in the number and types of HRQOL domains suggested by different researchers, there is substantial overlap. Most researchers would agree that the following domains should be considered when deciding on HRQOL measures: 1) somatic concerns, such as pain and symptoms; 2) functional ability; 3) family well-being; 4) emotional well-being; 5) spirituality; 6) treatment satisfaction, including financial impact of illness; 7) future orientation; 8) sexuality, intimacy, and body image; 9) social functioning; 10) occupational functioning; and 11) preferences (3,22,25-28). The precise number and types of domains should be sufficiently broad to capture the impact of the illness or treatment on the patient but not to impose an undue burden on the patient or researcher; if the impact is largely unknown, the largest number of dimensions should be included while maintaining parsimony. Any one domain may be an outcome in and of itself.
A 1991 National Cancer Institute-sponsored workshop on the measurement of QOL in cancer clinical trials (22) recommended that assessment be multidimensional, include general and cancer-specific tools, be patient self-reported, be measured at more than one point in time, and be evaluated after controlling for relevant medical and sociodemographic characteristics. These considerations guided our data abstraction.
For the purposes of this review, health outcomes were defined as HRQOL or any of its domains, preferences for outcomes, satisfaction with health care or treatment decisions, and economic outcomes, such as costs of care, cost-effectiveness analysis (CEA), decision analysis, and cost-benefit analysis.
Data Sources
Multiple sources were used to identify all potential research for inclusion in the analysis. A MEDLINE search was conducted for all English language articles published during the period from January 1, 1990, through December 31, 2000. The earlier date of 1990 was selected to include research that was representative of current health outcomes and QOL research methods. The following key words were used for the search: Breast Neoplasms and Quality of Life, Health Status, Economics, Health Services Research, Delivery of Health Care, or Quality of Health Care. To limit our search more narrowly to QOL, satisfaction with health care or treatment decisions, or economic outcomes, our search of Quality of Health Care and Delivery of Health Care was restricted to articles listing these key words as a major MeSH term. For the other key words, we selected articles using these terms as major or minor MeSH headings. Because this search did not identify any articles on outcomes associated with the dying experience of breast cancer patients (other than survival), we conducted a supplemental search on this topic using the following terms: Palliative Care or Death and Dying and Breast Neoplasms or Cancer.
Study Selection
The "Methods" and "Results" sections were reviewed for eligibility by two investigators using a standardized abstraction form (29). Study inclusion criteria consisted of being conducted in the United States and Western Europe, being published in a peer-reviewed journal, and presenting data on a health outcome. We excluded 859 articles, including phase I or II randomized trials (n = 7), studies with fewer than 100 subjects (n = 67) (since there would be insufficient power for the majority of measures), reviews (n = 180), comments/letters (n = 140), reports focusing on outcomes methodology (n = 68), studies conducted outside the United States or Western Europe (n = 45) (for comparability of health care systems, attitudes, and culture), studies that did not include breast cancer (n = 7), and studies that did not include a QOL outcome (n = 345). If outcomes data, or data subsets, had been published in more than one journal or time period, the largest dataset that contained the highest quality of information was selected for abstraction, although all of the measures were noted. The full listing of articles retrieved by the search is available from the authors.
Data Abstraction
For studies eligible for inclusion, the following data were abstracted: the phase of care, study design, population characteristics (e.g., age and race), country, number of subjects, outcomes included, tools or methods used to measure these outcomes, language of measurement, assessment of comorbid conditions, mode of administration of measures, and whether the authors reported any reliability testing for instrument performance in their study population or a statement about the power to detect clinically meaningful differences in outcomes between groups.
Data Analysis
Data were summarized by use of descriptive statistics, Student's t tests for differences in means, analysis of variance for differences in groups, and chi-square tests for differences in proportions and trends over time.
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RESULTS |
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There were 1089 original research articles identified by the search; 230 (21%) of these met the criteria for study inclusion (see Appendix Table 1) (30-264). These 230 articles reported on 155 distinct study samples. In the study period, there was a general increase in the number of articles that included breast cancer outcomes (Fig. 1; Table 1). This increase was largely due to increases in studies of QOL in the treatment and survivorship phases of care and economic studies of screening.
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Among the 230 articles, the most common outcome reported was HRQOL (54.4%) followed by costs/economic outcomes (38.3%), whereas there were statistically significantly fewer satisfaction (13.9%) or preference (9%) outcomes reported (P<.001). The proportion of these different types of outcomes varied across phase of care, with studies of survivorship having the greatest proportion of outcomes focused on QOL (69.1%) and studies of screening (9.7%) and diagnostic care (0%) having the lowest proportion of QOL outcomes (P<.001). These latter two phases had the highest proportion of economic studies compared with the other phases of care (P<.001) (data not shown). These findings are likely to be due to the nature of screening and diagnosis where the QOL outcomes are short term and difficult to capture, whereas the economic impact of differing strategies on survival may be large.
We did not identify any studies about the quality of the dying experience for breast cancer patients per se. Within the metastatic treatment phase, there were 10 studies about palliative care that met our inclusion criteria (52-61); most were cross-sectional in design. The studies measured "will to live" (55), pain and symptom control (52-54,56,60,61), satisfaction (58), psychologic distress (52,57), and insight into prognosis (56). One study measured outcomes of the patients' caregivers (59).
Among the study sample, the most prevalent type of study was an economic study (n = 91); 65.9% of these were CEAs and 24.2% were descriptions of the costs of care (Table 2). Among the 139 noneconomic studies, slightly more than one half (55.4%) were controlled studies (RCTs, longitudinal cohorts, or case-control studies), whereas almost one half were uncontrolled cross-sectional analyses. In a comparison of study design by phase of care, there were twice as many RCTs among studies of local and metastatic disease phases of care than among studies of other phases, whereas an uncontrolled, cross-sectional design was employed twice as often in studies of the survivorship phase of care as in studies of other phases (P<.01).
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The overwhelming majority of studies focused on white populations speaking
the dominant language; only 24
(62-85)
included more than 10% minority patients, and only three
(68,70,86)
reported more than 30% minority subjects. Five studies
(69,87-90)
noted use of translation of outcomes assessments into another language or
languages. Few studies [e.g.,
(76,91-93)]
concentrated on elderly women (women aged 
65 years).
There was a wide variety of instruments used in the study sample (Table 3) (94-195). The most commonly used tool was the Medical Outcomes Study SF-36 (8), but no single instrument was used in more than 10% of the studies (Table 4). Among the 139 noneconomic studies, 54.0% (n = 75) used previously validated tools, 20.1% (n = 28) relied solely on nonvalidated measurements developed de novo for the study, 19.4% (n = 27) used a combination of validated and de novo instruments, and 2.1% (n = 2) did not state what measures were used (see Appendix Tables 1, 2, 3, 4, 5, 6, 7). There were 127 validated tools represented in the 75 studies that used one or more validated measures. The average number of instruments used per noneconomic study was 2.8 (range, 1-10). Studies about survivorship or local therapy had the highest average number of measures per study (3.7 and 2.9, respectively), and studies of screening, metastatic disease, and primary prevention used the lowest number of measures per study (1.7, 2, and 2.1, respectively). With the exception of the studies of chemotherapy-induced nausea and vomiting-prevention interventions, all QOL studies included more than one domain of QOL.
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Although most of the noneconomic studies did use previously validated and reliable measures, only one quarter (n = 37 of 139; 26.6%) reported the reliability and validity of the measure in their patient population; none reported data on reliability in population subgroups (e.g., by age or race). Among the 91 economic studies, only one (196) met current standards for a good quality analysis (e.g., societal perspective, quality-adjusted using population values, discounted, including nonmedical and medical costs, and all relevant downstream costs and effects), although nonstandard approaches may be appropriate in certain circumstances or for specified purposes [e.g., evaluation within a health maintenance organization (197)].
In terms of power, only 21 studies (63,69,90,96,198-214) presented information on their power to detect clinically meaningful differences in noneconomic outcomes. Comorbidity is an important covariate that has the potential to confound outcomes results. However, comorbidity was only specifically measured in four studies—one CEA (76), two cross-sectional studies of breast cancer survivors (65,205), and one cross-sectional study of newly diagnosed breast cancer patients (92). However, results were not clearly controlled for this potential confounder in the cross-sectional studies. Similarly, a woman's baseline level of QOL is one of the strongest predictors of her long-term QOL. In an RCT, women with low and high levels of baseline QOL should be distributed randomly across study arms. In longitudinal follow-up studies, baseline QOL can be controlled for in analyses of outcomes. However, in the 46% of the noneconomic outcomes studies that employed a cross-sectional design, effects of variations in initial status were not accounted for in describing results.
Additional sources of bias in interpreting existing outcomes research
include low response rates. Low response rates (
50%) are threats to
internal validity
(70,73,91,215-218);
potential differential nonresponse, such as seen in many RCTs with QOL
components [e.g.,
(219)],
or differential losses to follow-up by treatment arm or health status [e.g.,
(220-222)]
or response rates by group [e.g.,
(70,201,223)]
are threats to internal validity of the results. In the metastatic disease
phase, there is the further challenge of poor response rates or losses to
follow-up because of severe illness or death; use of proxy respondents was not
addressed in any of these studies. Finally, none of the studies evaluated
whether floor or ceiling effects limited their ability to detect differences
in outcome or evaluated the amount of missing data and their impact on
results.
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DISCUSSION |
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TopNotesAbstractIntroductionMethodsResultsDiscussionReferences |
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At present, outcomes measurement is variable in its approach and application to the spectrum of breast cancer care. This enormous variability is evidenced by our finding that no one instrument was used in more than 10% of the studies. Although the past decade has witnessed a dramatic increase in the volume of research on breast health outcomes, large gaps exist in certain phases of care, types of outcomes, populations, and standards of measurement. For instance, only one study evaluated the quality of the dying experience for breast cancer patients, only 15.9% considered patient satisfaction, and only 4.1% included patient preferences. Only one half of the studies were controlled, and studies of survivors were the least likely to include a controlled design that accounted for baseline differences in health and functioning and other important covariates. At present, the overwhelming majority of research focuses on white, nonelderly populations.
In terms of the quality of the research, this overview highlights certain cross-cutting deficiencies—no studies commented on the potential for floor or ceiling effects to influence results, only one CEA would be considered to be adequate by current standards, and, among the noneconomic studies, only four measured comorbidity, 37 tested reliability in their own patient population, and 21 noted whether they had sufficient power to detect clinically meaningful differences in outcome. These types of limitations have been noted in other reviews as well (379). Thus, we recommend that a common set of standards be established for the design and conduct of outcomes studies (Table 5), including development of standard, practical tools to measure the burden of illness (including the number of illnesses, severity, and impact on function).
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Given the complexity of breast cancer care and the heterogeneity in patient populations, no one instrument is sufficiently comprehensive, is sensitive to clinically meaningful changes in outcomes across all phases of care, and has acceptable respondent and provider burden. However, it should be possible to develop a "core" set of measures for measuring breast cancer outcomes that can be supplemented by sensitive phase-specific tools. Other issues, such as standardizing the optimal timing of measurement within and across phases, were beyond the scope of this review and are important to address in future research. Finally, it will be important to examine whether conclusions about outcomes are affected by choice of instrument.
Our review also highlights the paucity of patient preference data. We suggest that additional research be conducted to develop practical methods to measure patient preferences and that these measures be transportable into economic analyses that calculate cost per quality-adjusted life-year gained. More work is also necessary to understand the quality of the dying experience from both the patient's and the caregiver's perspectives.
In summary, more research is needed to develop standard approaches that are 1) practical in a variety of health care settings, 2) reliable and valid, 3) applicable to broad age and race groups and culturally diverse populations, 4) useful to track changes in outcomes as individuals move through the phases of cancer care, and 5) informative for designing interventions to improve the quality of breast cancer services across the entire spectrum of care. Partnerships to facilitate translation of outcomes research from research centers to diverse community practices will be essential for reaching these goals.
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NOTES |
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Supported in part by Public Health Service (PHS) grants HS08395 from the Agency for Health Care Policy and Research, Office of the Assistant Secretary for Health, Department of Health and Human Services (DHHS) (J. Mandelblatt); by grant DAMD17-94-J-4212 from the Department of the Army, Breast Cancer Center Grant (J. Mandelblatt, W. Liang, and W. Lawrence); and by PHS grants R01CA72908 (J. Mandelblatt and K. R. Yabroff) and T332CA09314-19 (K. R. Yabroff) from the National Cancer Institute (NCI), National Institutes of Health, DHHS. Prepared for the NCI under contract 263-MQ-112555.
We acknowledge the critical reviews of earlier versions of the manuscript by Drs. Joseph Lipscomb and Molla Donaldson, NCI, National Institutes of Health, DHHS, Bethesda, MD, and the manuscript preparation by Trina McClendon, Michelle Rogers, and Nicole S. Lee, Georgetown University, Washington, D.C.
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