 To systematically assess clinical and economic evidence for oncology orphan drugs marketed in the United States and to highlight the challenges and opportunities for evidence development within this pharmaceutical category.

 We conducted systematic literature searches of the Medline and Embase databases for clinical and cost-effectiveness studies published before June 2010 for all oncology orphan drugs marketed in the United States. We used the Grading of Recommendations Assessment, Development and Evaluation method and the Quality of Health Economic Studies criteria to assess the quality of the selected studies.

We identified 60 randomized controlled trials and 21 cost-effectiveness analyses to support 47 oncology orphan drugs. A total of 21 drugs had moderate or high-quality bodies of clinical evidence, 11 had low-quality or very low quality clinical evidence, and 15 drugs could not be evaluated because we were unable to identify clinical evidence that met our inclusion criteria. The Spearman rank correlation coefficient for the level of evidence for oncology orphan drugs and disease prevalence was 0.3 (95% confidence interval, 0.0-0.5). The cost-effectiveness analyses received quality scores between 72 and 100 (range 0-100), with a mean score of 85.

The results of our study show that oncology orphan drugs marketed in the United States have varying levels and quality of clinical evidence and a paucity of evidence regarding economic value. Innovative analytic and policy approaches are needed to develop and implement a decision-making framework for this pharmaceutical category that is consistent with evidence-based medicine and comparative effectiveness research.

	We conducted a systematic literature review to critically assess the clinical and economic evidence supporting oncology orphan drugs marketed in the United States.

		Oncology orphan drugs marketed in the United States have varying levels and quality of clinical evidence, and a paucity of evidence demonstrating their economic value.

		The current levels of clinical and economic evidence present challenges for decision making about oncology orphan drug availability and accessibility.

		Innovative analytic and policy approaches are necessary to develop and implement a decision-making framework for oncology orphan drugs that is consistent with evidence-based medicine and comparative effectiveness research.

	Comparative effectiveness research (CER) can be described as the assessment of a medical intervention against alternative interventions with the intent of identifying treatment strategies that are likely to have preferable benefit-risk profiles or are considered cost-effective in real-world clinical settings. The purpose of CER is to assist healthcare providers, payers, patients, and decision makers in making informed healthcare decisions that will improve individual and population health.

	Definitions of a rare disease differ around the world and the prevalence threshold varies between countries.

 In the European Union, a disease is considered rare if it affects fewer than 215,000 individuals, while in the United States, a rare disease is described as a condition with prevalence of less than 200,000 or a disease with distinct subpopulations consisting of fewer than 200,000 individuals nationwide.

 As of 2010, 362 drugs indicated for rare diseases (orphan drugs) have received US Food and Drug Administration (FDA) market approval, and oncology therapies comprise the largest clinical subcategory.

 In general, orphan drugs have relatively higher costs than other drugs because manufacturers must rely on smaller patient populations to recoup development investments. In the United States, 15 orphan drugs were commercialized between 2006 and 2008; 6 of these cost more than $100,000 per patient per year.

	Previous studies have suggested that orphan drugs have less robust bodies of evidence because smaller patient populations and limited knowledge of rare conditions constrain the design, conduct, analysis, and interpretation of clinical trials.

 Due to the rapid rate of oncology orphan drug development and the significant financial burden associated with cancer treatments, there have been increasing pressures on drug manufacturers to demonstrate the value of their products. Less robust bodies of evidence will present particular challenges to CER and will make decision making about orphan drug accessibility difficult. The comparative effectiveness data supporting oncology orphan drugs marketed in the United States have not been well studied. The primary objective of this study was to systematically and critically assess the level and quality of clinical and economic evidence currently available for all oncology orphan drugs marketed in the United States. A secondary objective was to highlight the challenges and opportunities for evidence development within this pharmaceutical category.

	The Cumulative List of Designated Approved Orphan Products (www.fda.gov/orphan/designate/allap.rtf) describes drug products that have received orphan designation and have ever received marketing approval from the FDA.

	We used this list, updated May 5, 2009, by the FDA, to identify products indicated to treat rare cancers. Orphan products indicated for diagnosis, palliative care, or treatment of secondary conditions associated with cancer, such as neutropenia, were not included in this study. Any product withdrawn or discontinued from the US market as of June 2010 was also excluded.

	For each oncology orphan drug included in this study, we conducted a literature search in the Medline and Embase databases for randomized controlled trials (RCTs) and cost-effectiveness analyses published prior to June 2010, using search terms that included the drug’s US brand name, generic name, disease indication, and the terms “randomized,” “efficacy,” “cost-effectiveness,” and “economic.” Our priority was to identify RCTs, but we also used the literature search to identify observational studies (prospective or retrospective) or other studies that described the treatment effect of the drug. We also identified published articles through information provided on the FDA Web site for new drug approvals and manual searches of article references. We defined cost-effectiveness analysis using the definition established by the Panel on Cost-Effectiveness in Health and Medicine as “An analytic tool in which costs and effects of a program and at least one alternative are calculated and presented in a ratio of incremental cost to incremental effect.”

	For each literature search, 1 author (MMC) reviewed all of the titles and abstracts of studies likely to meet the following inclusion criteria: (1) original RCT or cost-effectiveness analysis evaluating the orphan drug used for its approved orphan indication, or observational study or published article describing the treatment effect of the orphan drug used for its approved orphan indication; (2) comparative RCTs in which the orphan drug is compared with placebo or a clinically relevant comparator; (3) if the orphan drug (eg, drug A) is used as part of a combination therapy regimen, the trial is designed so that the clinical effect of the orphan drug can be isolated and directly assessed (eg, drugs A, B, and C vs drugs B and C); (4) article is published in the English language; and (5) entire article is available for review. 

 presents a flow diagram that describes the literature search methods and the restrictions applied to our search.

	For each clinical study that met the inclusion criteria, we abstracted information about the comparator, patient characteristics, study design and treatment allocation, primary outcome measure, statistical analytic method, reporting of treatment effect with uncertainty, and study sponsor. For each cost-effectiveness analysis that met the inclusion criteria, we abstracted information about the comparator, study perspective, methods, data sources, primary outcome measure, base-case and sensitivity analysis results, and study sponsor (if reported).

	We used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method, an evaluation system created by a diverse group of international guideline developers, to assess the quality of clinical bodies of evidence. The GRADE system assesses the quality of a body of evidence by focusing on 4 main components: study design, quality, consistency of evidence, and directness of comparator, population, and intervention. The method also evaluates limitations, potential biases, and uncertainty to assign 1 of 4 possible grades: high, moderate, low, and very low.

 We adapted the grading system to our study by including a fifth grade category, “not able to assess,” to describe circumstances where we could not identify published studies that met our inclusion criteria. 

 details the GRADE methodology and how it was implemented in this study, and defines each quality grade.

	We used the Quality of Health Economic Studies (QHES) grading criteria, a validated quantitative instrument developed by health economists, to assess the quality of cost-effectiveness studies. The QHES grading system consists of 16 criteria that are described in 

 Each criterion is associated with a weighted score that was\ assigned in its entirety for each criterion perceived by the lead author to be satisfactory. Zero points were assigned to each criterion that was perceived to be unsatisfactory. Certain criteria, such as subgroup analysis, were not applicable for all studies. In these circumstances, we used both a best-case and worst-case scoring method where full points and zero points were assigned to each inapplicable criterion. The weighted scores were summed across all criteria for a total of 100 possible points for each study.

	We graphically explored whether disease prevalence was associated with the level and quality of evidence for oncology orphan drugs and calculated the Spearman rank correlation coefficient to estimate the strength of the relationship. We obtained estimates of disease prevalence from Orphanet, an electronic reference for rare disease information in Europe (www.orpha.net).

 This resource was the only one we identified that provided a collection of prevalence estimates for rare diseases. Although the data were derived in Europe, we believe the estimates are of similar magnitude in the United States, assuming similar risk factors for rare cancers. The assessment of correlation excluded 5 drugs: aldesleukin for metastatic melanoma, valrubicin for urinary bladder carcinoma, doxorubicin liposome for metastatic ovarian cancer, and toremifene citrate and exemestane for breast cancer. These cancers are not considered rare. The drugs are indicated for smaller subpopulations of patients with specific disease, physical, or genetic characteristics. Prevalence estimates for patients with these specific characteristics were not reported. We also explored whether the level and quality of evidence for oncology orphan drugs is associated with receipt of FDA accelerated approval.

	We initially identified 48 oncology orphan drugs for inclusion in this study from the Cumulative List of Designated Approved Orphan Products. Of these drugs, 3 (Idamycin, Vesanoid, and Mylotarg) have been discontinued or withdrawn from the US market, and 5 drugs (Gleevec, Sprycel, Tasigna, Velcade, and Temodar) were indicated to treat more than 1 orphan indication or patient population; each indication was independently evaluated for these drugs. We did not include expanded or supplemental approvals for nonorphan indications. In total, 47 orphan drugs and indications were included in this study and are listed in 

 by their brand and generic names. The majority of oncology orphan drugs are indicated for rare blood cancers (n = 30). Table 3 also describes the date of full and/or accelerated regulatory approval, and lists the number of RCTs identified in the published literature that met the inclusion criteria with citations of all published articles that were used to inform the body of evidence.

	We identified a total of 60 RCTs that met our inclusion criteria. The greatest number of RCTs were conducted for interferon alpha-2a indicated for chronic myelogenous leukemia (CML) (n = 7), aldesleukin indicated for metastatic renal cell carcinoma (n = 7), and rituximab indicated for non-Hodgkin B-cell lymphoma (n = 6). The majority of trials we identified comprised the evidence base used for regulatory approval.

 summarizes the clinical evidence grades assigned in this study. Twelve drugs had moderate-quality evidence, including cladribine for hairy cell leukemia and arsenic trioxide for acute promyelocytic leukemia. Although we did not identify any RCTs for these drugs that met our inclusion criteria, their clinical bodies of evidence consisted of observational studies that consistently reported similar treatment effects or a dose-response association with little threat to validity. Individual GRADE ratings assigned to each drug and their clinical bodies of evidence are listed in Table 3.

 shows that there are drugs indicated for cancers with lower prevalence that had high-quality evidence and drugs indicated for cancers with higher prevalence that had low-quality evidence or lacked a sufficient body of evidence for review. The Spearman rank correlation coefficient between disease prevalence and the level of evidence for oncology orphan drugs was 0.3 (95% confidence interval [CI], 0.0-0.5). This indicates that there may be a weak correlation between the prevalence of rare cancers and the number of RCTs conducted for drugs indicated to treat rare cancers. A total of 14 drugs in our study received accelerated approval for the orphan indication. Of these, zero had high-quality bodies of evidence and 5 had moderate-quality bodies of evidence. Although our sample size was small, these results provided some indication that the accelerated approval provision does not optimize high-quality evidence generation for oncology orphan drugs.

	We identified 21 cost-effectiveness studies that met our inclusion criteria. Of those studies, 10 evaluated the use of either interferon alpha or imatinib mesylate, or compared both for CML. 

 summarizes the cost-effectiveness studies included in this assessment and describes comparators, study perspectives, base-case incremental cost-effectiveness ratios, and results of sensitivity analyses.

	The cost-effectiveness analyses received QHES scores ranging from 72 to 100, with a mean score of 85. The clinical data used to inform the studies were largely obtained from either randomized trials or patient medical records. The majority of studies implemented modeling techniques to project longer-term health outcomes that could not be obtained from limited clinical data. Cost inputs were obtained from administrative databases and the published literature. The most common shortcomings were as follows: 11 studies used data inputs from sources that might potentially incorporate bias and did not provide explicit discussion about the direction and magnitude of potential biases; 6 studies did not specify the source of funding for the study, which is recommended to add transparency; and 5 studies either failed to specify the time horizon for analysis, conducted analysis using a short time horizon that is not expected to fully capture clinically significant costs and effects, or did not implement adequate discounting for both future costs and outcomes. Table 4 reports the quality scores assigned to each cost-effectiveness study using the best-case scoring method where full points were assigned to inapplicable criteria. Scores assigned using the worst-case approach, where zero points were assigned to inapplicable criteria, are reported in parentheses.

	We conducted a systematic literature review to identify and assess clinical and economic evidence for 47 oncology orphan drugs marketed in the United States and applied 2 independent grading frameworks to the selected studies to critically assess the quality of each body of evidence. The supporting bodies of evidence available for marketed oncology orphan drugs vary in quality, with limited evidence demonstrating their economic value.

	Although the GRADE methodology is relatively explicit, reviewer judgment is required. In particular, we found that it was necessary to grade certain criteria subjectively in order to better accommodate oncology orphan drug characteristics. For example, the majority of RCTs for oncology orphan drugs had relatively small sample sizes that could lead to substantial imprecision (eg, lack of power, wide confidence intervals). We assigned lower ratings for studies that did not meet predetermined enrollment criteria or appeared underpowered to detect differences in their primary end point, but we did not consider a smaller sample size in itself to be a limitation. We critically assessed each study and lowered ratings when we identified instances of bias that were not explicitly addressed. We also lowered ratings when studies were poorly reported, or when bodies of evidence consisted of studies that reported inconsistent treatment effects.

	The number of RCTs identified in our study is comparable to the number in a study conducted by Tsimberidou and colleagues that evaluated the long-term marketing outcome of oncology drugs approved by the FDA without a randomized trial.

 In their study, 68 approved oncology drugs were identified and almost half (n = 31) were approved without an RCT. Kesselheim and colleagues recently evaluated pivotal trial characteristics of orphan and nonorphan drugs approved between 2004 and 2010 to treat cancer and concluded that pivotal trials for approved oncology orphan drugs were more likely to be smaller, nonrandomized, and unblinded, and to use surrogate end points to assess efficacy.

 The study is limited in that it only evaluated pivotal trials and did not account for other available clinical information. Also, the study was restricted to recently approved oncology orphan drugs and did not review the entire pharmaceutical category. We observed the same characteristics in the clinical trials that were included in our study. We also observed that there is a lack of published post-marketing studies and information about longer-term safety and efficacy.

	We hypothesized that the level of evidence available for oncology orphan drugs would have a strong correlation with disease prevalence in that larger patient populations enable better evidence generation. However, our results suggested a potentially weak correlation between the prevalence of rare cancers and the level of evidence available for drugs indicated to treat rare cancers. A weak relationship suggests that evidence development for oncology orphan drugs may not depend as much on the size of a particular patient population. Instead, evidence development in this pharmaceutical category may depend more on other factors, possibly regulatory requirements or reimbursement considerations.

	The overall dearth of cost-effectiveness studies in this pharmaceutical category, and in oncology as a whole, may reflect evidence limitations or publication bias, where studies of drugs with higher costs, greater benefit-risk uncertainty, or lower effectiveness are not published. In assessing the economic challenges of orphan drugs, Drummond and colleagues stated: “In short, if standard health technology assessment (HTA) procedures were to be applied to orphan drugs, virtually none of them would be ‘cost-effective’.”

 This conclusion was largely based on 2 factors: (1) high incremental cost per quality-adjusted life-year and (2) insufficient breadth and quality of clinical evidence for orphan drugs compared with drugs for more common diseases.

 Our results demonstrated that, contrary to these prior suggestions, it is feasible for some oncology orphan drugs to be considered cost-effective in specific healthcare settings using standard methods of health technology assessment. The clinical and economic value of each orphan drug should be assessed individually, on a case-by-case basis. We observed that all of the drugs considered cost-effective in their respective studies had moderate-quality to high-quality bodies of clinical evidence. This finding suggests there may be a relationship between the level and quality of clinical evidence and the likelihood that an oncology orphan drug has published estimates of cost-effectiveness.

	Evidence development is costly and challenging for all healthcare interventions, but particularly for orphan drugs, due to smaller patient populations and limited clinical knowledge of rare conditions. The trade-offs to generating more robust bodies of evidence may include delayed product accessibility, higher costs, or reduced availability of therapies. For conditions that have therapeutic alternatives, these trade-offs may be acceptable. However, for life-threatening conditions with limited therapy options, these trade-offs may not be considered acceptable. For rare diseases with only a single treatment option, one may question whether economic analysis should apply. Cost-effectiveness analysis is useful in that it provides information about the value of a health intervention compared with an alternative, which may be best supportive care or no therapy. From an equity perspective, McCabe and colleagues argue that there is no sustainable reason why the cost-effectiveness of orphan drugs should be evaluated differently from other drugs.

 However, healthcare systems may wish to consider additional factors when making decisions about orphan drugs, such as budget impact, disease severity, availability of alternative therapies, or societal preferences toward patients with rare diseases.

	Our study highlights 3 important policy questions. First, what types of study designs, incentives, or methods can be used to encourage better evidence development for oncology orphan drugs? Second, how much evidence is necessary or considered sufficient to healthcare decision makers, and what types of evidence should be generated prior to and after marketing approval? Finally, what are the process and decision-making criteria for evaluating comparative effectiveness data for oncology orphan drugs? Emerging private and public initiatives are attempting to address these questions to some extent with evolving methods such as coverage with evidence development

 but additional innovative analytic methods and policy approaches are necessary.

	We recognize several limitations to this study. The most important limitation is that the GRADE assessment framework provides information about the amount of confidence that can be associated with a body of evidence and does not relay any information about the magnitude of clinical benefit or safety of a drug. Certain oncology orphan drugs with low-grade published evidence may yield significant clinical benefits, and conversely, certain drugs with high-grade evidence may yield very little benefit. Currently, there is no established framework for quantifying the magnitude of benefits or risks from health interventions. Garrison and colleagues suggest pairing CER and benefit-risk analysis into 1 framework and describe how cost-effectiveness analysis models could be adapted to conduct quantitative benefit-risk assessments.

 It is important for manufacturers to engage in continuous evidence generation, even after a product is commercialized, so that the effectiveness and safety of their products can be accurately assessed. Comparative effectiveness research methods that capture and incorporate nonpublished clinician or patient experiences could also be useful to help better identify the real-world value of marketed oncology orphan drugs.

	Given the large number of drugs included in this review and the numerous keywords we could have used to search for potential clinical and economic studies, this review may not have captured all relevant drugs or studies. We used broad search terms in 2 major databases and also conducted manual searches of reference citations; this methodology allowed us to screen as many studies as possible. Only 1 author (MMC) reviewed potential studies, abstracted information, critically assessed articles, and assigned quality ratings. Although attempts were made to be accurate and consistent, it is possible that unintentional errors and inconsistencies could have reduced or improved the quality rating for a body of evidence. The QHES is limited in its ability to identify poorly analyzed studies and does not have a benchmark for total scores, which limits its ability to quantitatively categorize and distinguish high-quality studies from low-quality studies.

	The results of our study show that oncology orphan drugs marketed in the United States have varying levels and quality of clinical evidence and a shortage of evidence demonstrating economic value. It is uncertain whether the current evidence levels for oncology orphan drugs marketed in the United States are sufficient to support decision-making practices consistent with principles of evidence-based medicine and CER. This issue remains an open policy question that requires additional evaluation.

Articles

POLICY

This study critically assessed the published clinical and economic evidence supporting oncology orphan drugs marketed in the United States.

Systematic Review of Comparative Effectiveness Data for Oncology Orphan Drugs

 Autologous peripheral stem cell transplantation (ASCT) with high-dose chemotherapy is a preferred treatment for relapsed non- Hodgkin lymphoma (NHL) patients. Estimated failure rates with current stem cell mobilization (SCM) regimens are 5% to 30%. Granulocyte colony-stimulating factor (G-CSF) with plerixafor (G P) is superior to G-CSF alone for SCM in heavily pretreated NHL patients.

 To conduct a cost-utility evaluation of G P versus G-CSF as a method for SCM in patients with diffuse large B-cell lymphoma (DLBCL), the most common subtype of NHL.

 A Markov model simulated the care process of DLBCL patients undergoing ASCT using data from the Washington University site of the plerixafor phase III study. Other data and utilities were taken from the literature. Costs were Medicare allowable. Using microsimulation we estimated the incremental cost-utility ratio (ICUR) over the patient’s remaining lifetime.

 The expected lifetime cost of providing care for DLBCL patients using G P was $25,567 more than G-CSF, but they accumulated 1.74 more quality-adjusted life-years (QALYs) for an ICUR of $14,735 per QALY. In sensitivity analyses this result was robust to clinically relevant changes in assumptions.

 Using G P for SCM in ASCT of patients with DLBCL meets accepted standards of cost-effectiveness, primarily because of its effectiveness in SCM.

	Plerixafor with granulocyte colony-stimulating factor (G-CSF) has been shown to be superior to G-CSF alone for stem cell mobilization in heavily pretreated patients with non-Hodgkin lymphoma.

		Use of plerixafor plus G-CSF for stem cell mobilization in autologous stem cell transplantation of patients with diffuse large B cell lymphoma has a lifetime cost of $25,576 more than G-CSF alone.

		Plerixafor plus G-CSF provides 1.74 more quality-adjusted life-years.

		It thus meets most accepted standards of cost-effectiveness.

	High-dose chemotherapy in conjunction with autologous peripheral stem cell transplantation (ASCT) has emerged as a preferred treatment modality for a variety of hematologic malignancies including multiple myeloma (MM), non-Hodgkin lymphoma (NHL), and Hodgkin lymphoma (HL).

 The 2 most commonly used mobilization regimens are the cytokine granulocyte colony-stimulating factor (G-CSF) and G-CSF plus chemotherapy.

 However, failure rates with current mobilization regimens are estimated to be between 5% and 30%.

 This failure adds substantially to the cost of this already costly procedure due to the need for multiple apheresis sessions, as well as the implementation of costly remobilization protocols.

 An additional economic burden of this reduced efficacy is increased morbidity and mortality, as transplantation with suboptimal stem cell numbers may lead to prolonged hospitalization and increased transfusion requirement as fewer patients proceed to engraftment.

	Plerixafor is a new agent for stem cell mobilization which is used in conjunction with G-CSF or on its own. Plerixafor is a direct inhibitor of the interaction between the stromal cell—derived factor 1, expressed on bone marrow stromal cells, and its receptor CXCR4, expressed on CD34 stem cells. Several studies have shown that plerixafor, when used with G-CSF, results in successful stem cell mobilization in heavily pre-treated patients with NHL, HL, and MM.

 Investigators in 2 phase III, multicenter, randomized, double-blind, placebo-controlled studies, who compared G-CSF plus plerixafor (G+P) with G-CSF alone for mobilization of stem cells in patients with MM and NHL, reported that patients mobilized with G+P were significantly more likely to achieve the target CD34+ cell count with fewer apheresis sessions and less need for a rescue remobilization.

 Thus, this increased efficacy reduces the required number of apheresis sessions, reduces the likelihood of remobilization, and increases the likelihood of successful engraftment. However, we are not aware of any previous group that has investigated this question using rigorous economic methods.

	We conducted an economic evaluation of the cost utility of the use of G+P as a first-line treatment for stem cell mobilization in patients with NHL versus use of G-CSF alone. Data for the model came from the Washington University School of Medicine, site of the plerixafor phase III study.

 We constructed a decision-analytic model to replicate the series of events experienced by a patient undergoing stem cell mobilization, apheresis, and ASCT. We conducted these analyses taking a societal perspective and using a microsimulation approach to model estimation.

	We constructed a Markov decision model using TreeAge software (TreeAge Software, Inc, Williamstown, Massachusetts) to compare the cost and effectiveness of using G+P for stem cell mobilization in patients undergoing hematopoietic stem cell transplantation for treatment of relapsed diffuse large B-cell lymphoma (DLBCL), the most common subtype of NHL, with those receiving only G-CSF for mobilization. Model estimation was performed using a microsimulation process.

 The Markov model is a mathematical representation of an iterative process with a “Markov cycle” representing the length of the iterative process.

 In this case, the iterative process describes the transplantation process from apheresis through transplant and beyond. Initially, in the first cycle, the individual enters the model at the apheresis arm and begins cell collection (see 

). Continuing the first cycle, if cell collection is successful, the individual then enters the transplant arm and undergoes stem cell transplantation, subsequently entering the posttransplant cycle. The individual completes the first year of the process in the posttransplant cycle. In this part of the model, the individual cycles through the posttransplant cycle 1 year at a time, each year facing a probability of cancer recurrence and death from natural causes. The cycling through the posttransplant arm ends when the individual has a recurrence of DLBCL or dies from other causes (see explanation below). Microsimulation refers to a process where only 1 individual is sent through the model at a time, facing various probabilities for occurrences as he proceeds. Each individual completes all cycles before another individual enters the model. The model described here has 3 elements, 2 of which have distinct iterative processes: (1) the collection of stem cells for transplant (Markov cycle = apheresis session); (2) the engraftment process (noniterative); and (3) the post-engraftment period (Markov cycle = 1 year).

	The data for the first part were taken from the participants randomized (N = 20) at Washington University School of Medicine as part of a phase III trial of plerixafor.

 In the phase III trial, patients preparing to undergo ASCT were given 10 μg/kg of G-CSF for up to 8 days. Beginning on day 4, they were given either 240 μg/kg of plerixafor or the placebo daily for up to 4 days while undergoing apheresis. Apheresis began in the morning of day 5 and continued daily for up to 4 days or until at least 5 X 10

 CD34+ cells per kilogram were collected. Those patients who failed to collect at least 0.8 X 10

 CD34+ cells per kilogram after 2 apheresis sessions or at least 2 X 10

 CD34+ cells per kilogram in 4 apheresis sessions were given the option of entering an open-label rescue procedure. After a minimum 7-day rest period, the rescue patients received a course of G+P (240 μg/kg) identical to the treatment plan described above and underwent up to 4 sessions of apheresis. Those who failed to collect sufficient cells in rescue either went to transplant with a suboptimal number of cells or did not receive transplantation.

	Transition probabilities for the Markov matrix were estimated as the probability that a person would require the next apheresis session and were calculated as the proportion of people in the current session who proceeded to the next apheresis session. This proportion was parameterized as a random variable using the mean and variance to estimate a beta distribution. Costs were estimated by applying Medicare-allowable charges to actual utilization of participants prior to undergoing the stem cell mobilization process. Anyone who did not proceed to the transplant arm (high-dose chemotherapy and engraftment after stem cell mobilization and apheresis) was considered to have died and not to have generated any additional costs or benefits for the model.

	In the model, participants “wait” for stem cell engraftment after transplantation. During that time they require transfusion support and remain at high risk for infections, likely requiring antibiotics. Costs during this period were based upon the resources used by patients enrolled in the clinical trial, and costs were those that were Medicare allowable in Eastern Missouri.

	After successful engraftment, “participants” in the model entered the post-ASCT segment of the model. For the first 3 years posttransplant, participants face a probability of recurrence of lymphoma, based on the report by Fenske and colleagues.

 This probability is 38% in the first year, with a total probability over 3 years of 50% (see 

	If a participant had a recurrence, we made the conservative assumption that it would be fatal. We also assumed that if the participant survived for 3 years posttransplant, her annual mortality rate reflected that of the general public. This assumption was tested in sensitivity analyses.

	Key model probabilities, costs, and utility values are summarized in 

	In valuing the outcomes of a medical intervention, quality of life is often a key outcome, assuming survival is achieved.

 In our investigation, we measured quality of life using “utility,” which, in contrast to clinical measures, is a preference-based measure that quantifies the person’s perception of the importance of functional limitation. Utility is measured on a of health comparable to death and 1.0 the value for perfect health. The methods by which utility is measured have been described in detail elsewhere.

	The utility measure is used to calculate a quality-adjusted life-year (QALY). The QALY is a measure of a person’s expected life span that is weighted by the quality of life enjoyed during those years. For example, assume that living with congestive heart failure is found to have an associated utility loss of 0.35 (or 35%), for a final utility of 0.65. Each year the person would experience a quality of life that is 0.65 of a year spent in perfect health, or a QALY of 0.65. If they live for 5 years they would accumulate 3.25 QALYs, compared with the 5.0 QALYs of someone living in perfect health. In the context of this study, the QALYs accumulated would represent the years of survival of those patients who undergo high-dose chemotherapy and engraftment, multiplied by the quality of life they experience during treatment and after engraftment. There have been a number of studies that examined the utilities for patients with NHL, but few have looked specifically at patients who underwent ASCT. Van Agthoven et al compared the utility for patients undergoing peripheral blood stem cell transplantation with that for patients undergoing autologous bone marrow] transplant.

 They estimated the utility for NHL patients the day before ASCT, 14 days post-ASCT, and 3 months post-ASCT to be 0.75, 0.53, and 0.78, respectively. We validated these estimates by comparing them with similar work by other investigators. Doorduijn et al examined patients undergoing cyclophosphamide, doxorubicin hydrochloride, vincristine sulfate, and prednisone (CHOP) chemotherapy for NHL.

 They estimated utility scores for NHL patients at the time treatment was initiated as 0.74 for patients with an International Prognostic Index score of 0 to 1 and 0.44 for an International Prognostic Index score of 2 to 3. Patient-reported utility returned to pretherapy scores at 3 months. Uyl-de Groot et al elicited utility scores for NHL patients in remission after undergoing autologous bone marrow transplant at 6 months, 12 months, and 24 months to be 0.63, 0.84, and 0.91, respectively.

 Therefore, in our model, patients were assigned a utility score of 0.75 while undergoing apheresis, 0.53 during high-dose chemotherapy and engraftment, and 0.78 postengraftment (see Table 1). We believe that estimation of an annual utility score of 0.78 for people who are asymptomatic following ASCT is very conservative since this is comparable to the utility reported by people with severe macular degeneration

 and reflects more utility loss than recent stroke victims (0.145).

 However, we believe that this conservative approach is appropriate given that the expected benefit of treatment with plerixafor is to increase the probability of progression to ASCT (and thus survival), and to overestimate the benefit of survival would be to favor treatment with plerixafor.

	Our time frame for this evaluation was the patient’s lifetime, the perspective of the analysis was societal, and all costs and utilities were discounted at 3%.

	Our primary outcome in estimation of this model was the incremental cost-utility ratio (ICUR):

	                         _________________

 denotes the average cost accrued (and QALYs gained) for someone who receives G+P as first-line treatment, and G denotes the cost and QALYs for those who received G-CSF alone as first-line treatment. The ICUR represents the cost of “buying” 1 additional year of perfect health. Costeffectiveness of an intervention is determined by comparing the ICUR with the policy maker’s willingness to pay (WTP) to gain a QALY.

	We estimated the model using a microsimulation approach.

 Microsimulation is a method of mathematical modeling in which all sources of uncertainty are recognized and incorporated into the estimation. Conceptually, individual participants proceeded through the model 1 at a time, accumulating costs and benefits (ie, QALYs) as they progress through the tree. Each time they approach a “fork” in the pathway (ie, a probability node), the path they choose from there is determined by a random drawing from the distribution at that node. For instance, in the model presented here, a participant who has completed engraftment must proceed to the probability node to determine whether he will have a relapse. At that node there is a 38% probability that he will have a relapse; therefore (again conceptually), the participant “flips a coin” to determine the direction in which he will proceed. After this participant has made his way through the model, the next one takes her turn. After all participants in the cohort have completed their journey, the ICUR is estimated by taking the average of the cost and benefits of these participants. Our primary estimation of the model was conducted using a Monte Carlo microsimulation with 500,000 trials (participants).

	The influence of uncertainty on model results was evaluated in 2 ways. First, we performed 1-way sensitivity analyses to determine for which parameters a change in the base value resulted in a change in the cost-effectiveness decision (ie, if plerixafor was found to be cost-effective in the base case, the change in the value would result in it not being cost-effective, or vice-versa).

 For this purpose we used WTPs of $50,000 and $100,000 per QALY as our thresholds for determination of cost-effectiveness. In order to evaluate the overall stability of the model, we conducted a probabilistic sensitivity analysis using a Monte Carlo simulation to estimate net-benefit acceptability curves.

 All variables in the model were parameterized as distributions for this purpose.

Summary of Results of Phase III Trial at Washington University School of Medicine

	Twenty participants with DLBCL were randomized at Washington University (N = 20). Ten were randomized to receive G+P as a first-line regimen for stem cell mobilization, and 10 were randomized to G-CSF + placebo (with plerixafor as part of the rescue protocol). In the G+P arm all patients (10/10) achieved the minimal goal of CD34 cells after initial mobilization and proceeded to ASCT, while in the G-CSF + placebo arm only 5 of 10 achieved the minimal goal and proceeded directly to ASCT. Of the 5 remaining patients in the G-CSF + placebo arm, 1 was considered to be a poor candidate for rescue and did not proceed to ASCT. Four patients underwent the rescue protocol with G+P, but only 2 of 4 achieved the minimal CD34+ cell count required and went to ASCT. Therefore, 3 of 10 patients (30%) from the original G-CSF + placebo arm failed the mobilization process and did not proceed to ASCT.

Summary of Cost-Utility and Cost-Effectiveness Analysis

	The results of our analysis are presented in 

. The expected cost of providing care to patients with relapsed DLBCL referred to autologous transplant over their lifetime was $93,180 when G+P was used as a first-line treatment for stem cell mobilization. This was $25,450 more than the expected cost of using G-CSF alone as the first-line treatment. People who received G+P as a first-line treatment had an expected benefit of 6.80 QALYs following treatment. That was 1.75 QALYs more than people who had G-CSF alone as their first-line treatment. This was largely the result of the improved likelihood of proceeding to ASCT following mobilization and apheresis. The ICUR for use of G+P as a firstline mobilization regimen was $14,574 per QALY.

	As this intervention primarily affected the stem cell mobilization process, we also evaluated the cost-effectiveness of having an additional patient proceed to ASCT as a secondary outcome (taking the payer’s perspective). We found that mobilization using G+P as a first-line treatment cost $18,113 more per patient than did G-CSF alone. As 100% of G+P participants progressed to ASCT versus 70% of those treated with G-CSF alone, the incremental effectiveness of plerixafor was 30%. This yielded an incremental cost-effectiveness ratio of $60,377. In other words, it cost an additional $60,377 for each incremental patient who progressed to ASCT using G+P versus those who progressed to ASCT relying on a stimulation protocol with G-CSF alone.

METHODS

Use of granulocyte colony-stimulating factor plus plerixafor for stem cell mobilization is cost-effective in pretreated patients with non-Hodgkin lymphoma.

Economic Evaluation of Plerixafor for Stem Cell Mobilization

To examine potential weight-related disparities in receipt of preventive screening exams and to compare several quality indicators and health behaviors among overweight/obese men and healthy-weight men enrolled in 2 large managed care plans.

 Cross-sectional analysis nested within a diverse cohort of men participating in the California Men’s Health Study (CMHS) (N = 80,771).

We extracted utilization of serum cholesterol, triglycerides, glucose, glycosylated hemoglobin, sigmoidoscopy exams, and prostatespecific antigen tests from health plan electronic sources. CMHS survey data provided information about diet and physical activity. Adjusted odds ratios and 95% confidence intervals were estimated to assess the association of screening exams and behaviors with categories of body mass index (BMI).

 Tests for cholesterol, glucose, and diabetes control increased across categories of BMI, while overweight and obese men were less likely to undergo screening exams for colorectal and prostate cancer. Smoking and alcohol consumption were less frequent among overweight/obese men; however, they reported diets higher in fat and lower in fruits and vegetables, and were much less likely to report moderate/vigorous activity and much more likely to be sedentary.

Managed care organizations might reduce weight-related health risks and disparities in care with targeted efforts to promote cancer screenings, healthy diets, and physical activity among overweight and obese patients.

	Concerns persist about quality of care and health behaviors that contribute to the elevated health risks of overweight/obese patients. Among men enrolled in managed care

		 Overweight/obese men were more likely to be screened for serum cholesterol, glucose, and among patients with diabetes, blood sugar control.

		 In contrast, overweight/obese men were less likely to be screened for colorectal and prostate cancer.

		 While overweight/obese men were less likely to consume alcohol or smoke, they reported less healthy diets and much less physical activity.

	The dramatic increase in the prevalence of overweight and obesity presents a serious threat to public health. Two-thirds of US adults are overweight (body mass index [BMI] 

), and approximately one-third are classified as obese (BMI 

 Overweight and obesity are associated with multiple serious and costly health outcomes, including coronary heart disease, high blood cholesterol, hypertension, stroke, type 2 diabetes, gallbladder disease, osteoarthritis, sleep apnea, and some forms of cancer such as kidney and colorectal.

 Adding to this substantial health burden are weightrelated social and psychological consequences that can adversely affect the quality of care received by overweight and obese patients, and promote behaviors that are harmful to their health.

	Negative attitudes and stereotypes about overweight and obesity are widespread and may be especially acute in the medical setting.

 Heavier patients report that physicians are one of the most common sources of weight bias.

 National surveys among large random samples of primary care physicians found more than 50% viewed obese patients as unattractive and noncompliant.

 Numerous studies find that health professionals describe overweight and obese patients as unmotivated and nonadherent to prevention and healthcare recommendations. 

 Consequently, physicians may have low expectations for heavier patients, find it less satisfying to treat them, and be less likely to promote preventive services or spend time on health education counseling.

 Weight stigma can further impact health through patients’ negative responses to perceived bias including delays in seeking care, missed appointments, unhealthy eating behaviors, and exercise avoidance.

	While the pervasiveness of weight-related stereotypes is well documented, it is unclear whether and how weight stigma translates into disparities in receipt of healthcare services. In response to persistent concerns about the quality of care received by overweight and obese patients, recent studies were conducted among Medicare recipients and US veterans.

 The purpose of this study conducted among socioeconomically diverse middle-aged and older men enrolled in 2 large managed care plans is 2-fold. First, we examined potential weightrelated disparities in receipt of preventive screening exams that are commonly included in quality performance measures. 

 Secondly, we compared health behaviors across categories of BMI to identify potential opportunities to reduce some of the increased risks of overweight and obesity.

	This study was conducted among subjects in the California Men’s Health Study (CMHS). Participants were recruited from the Kaiser Permanente Southern and Northern California health plans. Together, these nonprofit managed care organizations provide comprehensive healthcare to more than 6 million members. The study protocol was approved by the institutional review boards of both organizations.

	Eligible participants included all 850,000 male health plan enrollees between 45 and 69 years of age. Between 2002 and 2003, 84,170 men joined the cohort. Participants were comparable to similar-aged men enrolled in the health plans (and to men residing in California) with respect to sociodemographic characteristics and BMI.

Details of the cohort design and recruitment methods were described previously by Enger and colleagues.

	In all analyses, we excluded men for whom we could not calculate a valid BMI (n = 2863) or who reported a BMI of less than 18.5 kg/m

 (n = 536), resulting in a sample of 80,771. When examining cancer screenings, we excluded men younger than 50 years, those who had been a member of the health plans for fewer than 5 years, and those at aboveaverage risk for prostate or colorectal cancer (CRC).

	We extracted data on membership, primary care utilization, laboratory tests, and cancer screenings from health plan electronic databases. The CMHS baseline survey collected sociodemographic information (race/ethnicity, age, educational attainment, household income, marital status, country of birth), height and weight, personal and family histories of disease, and health behaviors.

	Body mass index was calculated as weight in kilograms divided by the square of height in meters (kg/m

) and classified according to guidelines of the National Heart, Lung, and Blood Institute.

 We defined underweight as a BMI of less than 18.5 kg/m

, healthy weight as a BMI of 18.5-24.9 kg/m

 or greater. We further categorized obesity into class I obesity (BMI 30.0-34.9 kg/m

) and class II/III obesity (BMI 35.0 kg/m

	We examined 6 quality performance measures for common preventive services related to cardiovascular disease, diabetes, and cancer screening. Recommendations for these tests are not weight related. Time intervals for the screening tests were based on the health plans’ clinical practice guidelines in place when the cohort was established. Utilization of laboratory tests was obtained for the 2 years prior to the baseline survey, including serum glucose, cholesterol, triglycerides, and among participants with diabetes, glycosylated hemoglobin (A1C). We assessed CRC screening with use of sigmoidoscopy, the first-line screening test for CRC recommended by the health plans in the 5 years preceding the baseline survey.

 To differentiate screening from diagnostic sigmoidoscopy we used 

Current Procedural Terminology, 4th Edition

 codes and a validated automated data algorithm

 and limited this assessment to men at average risk (ie, those with no personal history of CRC or colorectal polyps, and no family history of CRC). Serum prostate-specific antigen (PSA) testing was extracted from laboratory databases during the 5 years preceding the baseline survey. To differentiate screening from diagnostic tests, we excluded men whose PSA tests were ordered by a urologist, and men who reported they had completed a PSA test due to symptoms or as follow-up to previous abnormal results.

	Dietary factors were assessed with a detailed semiquantitative food-frequency questionnaire developed for the Women’s Health Initiative and other studies

 We calculated total energy intake, percentage of calories from fat, and daily servings of fruits and vegetables. Consumption of alcoholic beverages included beer, wine, and liquor.

	Physical activity was determined with questions adapted from the Coronary Artery Risk Development in Young Adults (CARDIA) Physical Activity History,

 which asked about the frequency and duration of participation in specific types of moderate and vigorous recreational, household, and work-related activities. The CARDIA Physical Activity History has indirect validity against aerobic capacity and percentage of fat, and a strong inverse relationship with most cardiovascular disease risk factors.

Regular moderate/vigorous physical activity was defined as 3.5 hours per week or more spent in activities of 3 or more metabolic equivalents. Participation in vigorous physical activity was defined as 3.5 hours per week or more spent in activities of 6 or more metabolic equivalents. We defined sedentary behavior outside of work with reports of the total time spent watching television, sitting at a computer, or reading 6 or more hours per day.

	We determined the frequencies of sociodemographic characteristics, preventive screening exams, and health behaviors to describe these factors across categories of BMI. Given the large sample size, and therefore the probability of detecting clinically irrelevant differences, analyses focused on the adjusted magnitude of the differences found for overweight/obese men compared with healthy-weight men. We fit multivariable logistic regression models to calculate the adjusted odds ratio (AOR) and 95% confidence interval (CI) estimating the effect of BMI categories on utilization of screening exams and health behaviors. Models were adjusted for race/ethnicity, age, education, income, marital status, country of birth, number of outpatient visits, department of primary care provider, and health plan. Models were run with BMI categorized as described above, as well as a continuous factor, to test for trends in utilization of screening exams and health behaviors with increasing BMI. Analyses were conducted using SAS version 9.1.3 (SAS Institute Inc, Cary, North Carolina).

	The majority of the subjects were white non-Latino, were less than 60 years old, had at least some college, had a household income of $60,000 or more, and were married or living with a partner. In the previous 5 years, a quarter had 31 or more outpatient visits (

). Almost two-thirds had a primary care physician in internal medicine, and approximately half were members of Kaiser Permanente Southern California. Most had been born in the United States (data not shown).

	A quarter of the men were in the healthy weight range (BMI 18.5-24.9 kg/m

), almost half were in the overweight range (BMI 25.0-29.9 kg/m

), a fifth were in the class I obesity range (BMI 30.0-34.9 kg/m

), and 8% were in the class II/III obesity category (BMI 35.0 kg/m

). Proportionately, fewer Latinos and African Americans and more Asians were in the healthy category compared with whites (16%, 18%, and 51% compared with 25%, respectively). Healthyweight men more often had a college education and were less likely to be born in the United States. As expected, healthyweight men had less outpatient utilization (Table 1).

describes utilization of preventive services. The overall frequency of testing for serum glucose, cholesterol, and triglycerides in the previous 2 years was 52%, 70%, and 63%, respectively. Among men with diabetes, 70% had an A1C test. Across all these tests, adjusted results found overweight and obese men were significantly more likely to have been tested. Less than one-third of men 50 years of age or older at average risk for CRC had undergone screening sigmoidoscopy in the preceding 5 years. In contrast to results found for laboratory tests, compared with healthy-weight men, CRC screening decreased significantly across categories of BMI (overweight men, AOR 0.93, 95% CI 0.88-0.99; class I obesity, AOR 0.84, 95% CI 0.78-0.90; class II/III obesity, AOR 0.72, 95% CI 0.65-0.80). Similarly, while almost three-fourths of men had a PSA test in the previous 5 years, those with class I obesity were 10% less likely and men with class II/ III obesity were 20% less likely than healthy-weight men to have been tested.

displays men’s reported health behaviors by BMI categories. As expected, total daily average calorie intake increased with BMI. Less than one-third of the men met national recommendations for dietary fat intake (

30% calories from fat), and the likelihood of meeting the recommendations decreased significantly with increasing BMI. Compared with healthy-weight men, obese men were less than half as likely to eat a lower fat diet. Similarly, fewer than one-third of all men met the national recommendation for consuming at least 5 daily servings of fruits and vegetables, with overweight and obese men about 10% less likely than healthy-weight men to meet this recommendation. Although almost two-thirds of the men reported participating in regular moderate/vigorous activity, overweight men were almost 20% less likely and class I and II/III obese men were 49% and 69%, respectively, less likely than were healthyweight men to be active. Similar results were found for vigorous activity. Conversely, the percentage of men reporting 6.5 or more sedentary hours each day increased significantly from the healthy to obese weight categories. On the positive side, heavier men were less likely to report drinking more than 5 alcoholic beverages per week or to be a current smoker, and were more likely to have never smoked. Of note, similar results were found when the analyses for preventive services and health behaviors were run with BMI as a continuous variable, and when men with class II/III obesity were included in or excluded from the models.

	In a diverse cohort of middle-aged and older men, we assessed utilization of preventive screening exams and health behaviors by BMI ranging from healthy weight to class II/III obesity in the managed care setting. We found no evidence for weight-related disparities in laboratory tests associated with cardiovascular disease and diabetes. However, despite equivalent access to care, overweight and obese men were less likely to be screened for CRC and prostate cancer. In addition, while heavier men reported less consumption of alcoholic beverages and were less likely to smoke, their diets and levels of physical activity more often failed to meet national recommendations for health.

	The majority of Americans are at elevated risk for numerous diseases as a result of their higher-than-recommended weight. The widespread and increasing prevalence of perceived weight discrimination in the United States

 raises concern about the healthcare that heavier patients receive. Unhealthy behaviors that add to the adverse effects of overweight/obesity are common and may be exacerbated in response to perceived weight bias. Our findings highlight factors that healthcare organizations could address to reduce the risk associated with overweight and obesity.

	Previously, the limited studies of weight-related disparities in care found mixed results, perhaps due to small sample size and reliance on self-report.

 In addition, those studies were mainly focused on cancer screening in women.

Recently, 2 large studies assessed quality indicators among overweight and obese patients during comparable years and with time intervals for screening exams similar to those in our study. Like us, Chang and colleagues

 found higher rates of A1C and lipid screening among overweight and obese male Medicare beneficiaries and US veterans. However, their examination of lipid screening was limited to patients with diabetes. They found no differences in receipt of CRC screening by weight category; however, their overall rates were higher (52% and 73% in the Medicare and veteran samples, respectively, compared with 32% in this study), possibly due to the older age of the subjects, inclusion of fecal occult blood testing as a CRC screening test, and reliance on self-reports from many subjects. Also in contrast to our study, Yancy and colleagues

 found higher rates of CRC and PSA testing among obese male veterans compared with those at healthy weights. Although they too included more screening tests for CRC in addition to sigmoidoscopy, overall rates were similar to those found in our analysis. The rate of PSA testing, on the other hand, was less than half of the rate observed among our health plan members, perhaps due to services received outside the US Department of Veterans Affairs system. Unlike previous studies, we attempted to exclude tests for CRC and prostate cancer that had a high probability of being ordered for diagnostic purposes instead of screening. This may account for differences in our findings. Overweight and obese men are at greater risk for colon cancer

 and possibly more aggressive forms of prostate cancer,

 and have higher prostate cancer mortality.

 Managed care organizations should monitor the use of cancer screening exams among heavier patients, raise provider awareness about potential disparities, and promote use among all patients. Future studies of weight-related disparities need to examine cancer screening among patients of similar age, avoid reliance on self-reports of health service utilization, and distinguish between screening and diagnostic exams.

, 2005 recommended a diet limiting fats to 20% to 35% of total calories and consumption of at least 5 servings of fruits and vegetables per day.

 While less than one-third of men met those dietary goals, overweight and obese men were significantly less likely to consume a diet that decreased their risk for cardiovascular disease, type 2 diabetes, and some cancers, as well as weight gain and obesity. Similarly, physical activity is associated with multiple health benefits

 and prevents weight gain and increases weight loss, especially when combined with reduced caloric intake. Of concern, this study found obese men were at least half as likely as healthyweight men to report recommended levels of moderate/vigorous physical activity. Despite long-standing calls for physicians to advise patients about the risks of excess weight,

 fewer than half of obese patients report receiving such advice.

 Yet when patients are advised by their physicians, they are more likely to try to lose weight, demonstrating once again the importance of physician recommendations.

We found overweight and obese men made substantially more primary care visits, thus providing clinicians added opportunities to counsel patients about health behaviors, as well as cancer screening.

	A recent review described the growing evidence for the impact of weight bias on healthcare utilization as a result of both physician and patient factors.

 Misconceptions about the causes of obesity and the motivation of heavier patients are commonly identified in surveys and experimental studies conducted among health  professionals.

On the other hand, overweight and obese patients may be reluctant to seek care due to embarrassment and perceived antifat bias.

Provider education can help reduce negative attitudes about obesity

and consequently might reduce weight-related disparities in care. Education also can help clinicians be sensitive to the stigmatizing experiences heavier patients deal with in the medical setting, and the importance of their attitude and choice of words when treating these patients.

Future studies need to identify effective interventions for counseling patients about weight-related health issues. Healthcare organizations must ensure providers have and spend sufficient time in counseling their overweight and obese patients.

	A strength of this study is the availability of health plan data, as patients who feel that physicians are negatively biased may also have a greater tendency to underreport care. Other strengths include the large, multiethnic cohort of men and the examination of healthcare utilization without the confounding effect of insurance coverage. In contrast to previous reports, we distinguished diagnostic from screening cancer exams and adjusted results for outpatient utilization.

	While responses to the CMHS survey are subject to the potential limitations of self-reported cross-sectional data, it is reassuring that the prevalences of overweight and obesity were similar to those in reports with men living in California

 in comparable years. In addition, we were able to validate the self-reported heights and weights with the electronic medical records of nearly 20,000 men.

 Overall reports of dietary data and levels of physical activity are consistent with national surveys.

 Other limitations include our inability to explain why overweight and obese men were less likely to be screened for CRC and prostate cancer. Perhaps urgent problems or management of comorbidities were the focus of physician concern during medical encounters. Tests might have been recommended, but patients might have refused or did not follow through with exams that were ordered. In addition, these results might not generalize to women or to younger adults, for whom the stereotypes and stigma associated with obesity may be more salient than in this older cohort.

 Future research into weight-related disparities in care and health behaviors should include reports from both patients and providers about delivery and adherence to health recommendations. Effective patient counseling and interventions that reduce barriers to care need to be developed and tested among overweight and obese patients.

Healthcare organizations may reduce weight-related health risks and disparities in care among overweight/obese patients through promoting cancer screening exams, healthier diets, and physical activity.

January 2012

Systematic Review of Comparative Effectiveness Data for Oncology Orphan Drugs

Economic Evaluation of Plerixafor for Stem Cell Mobilization

Preventive Care and Health Behaviors Among Overweight/Obese Men in HMOs

Hypertension Control in Ambulatory Care Patients With Diabetes

The Impact of Hospitalists on Length of Stay and Costs: Systematic Review and Meta-Analysis

Financial Incentives and Use of Cesarean Delivery: Taiwan Birth Data 2003 to 2007

Impact of Cost-Sharing on Treatment Augmentation in Patients With Depression

Implementation of Paperless Credentialing in a Multi-State Managed Care Organization

TSI Assay Utilization: Impact on Costs of Graves' Hyperthyroidism Diagnosis