Affiliations of authors: J. L. Wagner, Congressional Budget Office, Washington, DC; S. R. Alberts, J. A. Sloan, S. Cha, J. Killian, M. J. O'Connell, P. Van Grevenhof, C. G. Chute, Mayo Clinic, Rochester, MN; J. Lindman, Western Michigan University, Kalamazoo, MI.
Correspondence to: Steven R. Alberts, M.D., M.P.H., Division of Medical Oncology, Mayo Clinic, 200 First St., SW, Rochester, MN 55905.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Managed care administrators are understandably concerned that patient enrollment in cancer clinical trials increases medical care cost. Although this concern may be justified in certain well-publicized cases, such as very expensive new treatments for conditions with no currently available therapy, cancer clinical trials span a wide array of interventions and disease stages. Most cancer trials today involve the use of chemotherapy. Little is known at present whether the treatment regimens of cancer trials increase or decrease the costs of care over the remaining lifetimes of cancer patients.
Information on the incremental patient care costs (or cost savings) associated with cancer clinical trials can help put such concerns into proper perspective and, thereby, facilitate arrangements for patients insured by managed care organizations to participate in such studies. To our knowledge, no published study has evaluated the costs associated with participation in cancer trials. Estimates of differences in patient care costs between trial enrollees and equivalent patients receiving conventional cancer care across a wide spectrum of clinical studies can assist in fiscal planning, negotiations for sharing of patient care costs, and financial risk management.
For these reasons, we conducted a matched case-control comparison of the cumulative incremental patient care costs attributable to participation in phase II and phase III cancer treatment trials from the date of trial entry until either death or 60 months after trial entry.
![]() |
SUBJECTS AND METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
We identified all residents of Olmsted County, MN, who entered cancer clinical trials at the Mayo Clinic Cancer Center from January 1, 1988, through December 31, 1994. This sampling period permitted relatively complete enumeration of the 5-year history of medical services used by trial participants. The Rochester Epidemiology Project, a cooperative effort of the principal sources of medical care in Olmsted County, provides an umbrella for population-based research, including a comprehensive medical care utilization database (2).1 The year 1988 was chosen as the earliest date for inclusion in the study for the following two reasons: 1) Health care utilization and cost data are available in electronic form for 1987 and later, and 2) changes in medical technology or in the nature of clinical protocols could invalidate earlier data.
Identification of case patients began with an inventory of all clinical protocols at the Mayo Clinic Cancer Center that were accruing patients during the sampling period. All of the protocols were funded by the National Cancer Institute either through the North Central Cancer Treatment Group or directly to the Mayo Clinic Cancer Center, and all were chemotherapy trials. Selected data on each protocol and on each patient enrolled during the study period were obtained from electronic and paper files maintained at the Mayo Clinic Cancer Center.
All protocols were screened to eliminate nonclinical or ancillary studies, such as those involving only record reviews or secondary analyses of laboratory specimens. The remaining protocols fell into one of the following five trial types: 1) pilot trials, 2) phase I treatment trials, 3) phase II treatment trials, 4) phase III treatment trials, or 5) cancer control trials. We merged the lists of participants in each protocol into a master list of unique patients enrolled in one or more cancer clinical trials, and we further restricted the sample to those who had enrolled in at least one phase II or phase III study.
Many patients participated in more than one cancer trial. Although no patients participated simultaneously in more than one treatment trial, some entered two or more treatment trials sequentially during the study period or participated simultaneously in a treatment and a cancer control study. Approximately 10% of all case patients participated in more than one trial during the study period. We regard multiple trial enrollments partly as consequences of the familiarization of patients with the clinical research environment and the frequent contact between trial participants and clinical research teams. Thus, entering one trial may predispose individuals to enter other trials, with their accompanying cascade of cost impacts. Therefore, we did not exclude case patients from the sample if they were enrolled in more than one cancer trial over the study period, provided that the first trial entered was a qualified phase II or phase III treatment trial.
We excluded all trial participants who were not residents of Olmsted County on the date of trial enrollment. Of 2466 individuals enrolled at Mayo Clinic Cancer Center in phase II or phase III cancer treatment trials in the study period, 176 (7%) were Olmsted County residents on the date of trial enrollment.
Selection of Control Patients
The selection of control patients occurred in a two-stage process designed to maximize similarity between case patients and their matched control patients on demographic and clinical characteristics likely to affect both trial eligibility and prognosis independent of the trial. We balanced the goal of achieving demographic and clinical equivalence between case patients and control patients against the constraints on the number of available control patients.
In the first stage, we identified all potential control patients through a review of the Mayo Clinic Tumor Registry. We matched the characteristics of the 176 case patients with those of all cancer patients recorded in the registry. Potential control patients were Olmsted County residents who between 1988 and 1996 were classified as having malignant disease diagnosed before autopsy and as having a date and place of treatment recorded in the Mayo Clinic Tumor Registry. Registry data elements in the first-stage matching criteria included age, sex, site of the primary cancer, stage of cancer, and year of diagnosis. Year of diagnosis pertained either to the initial diagnosis of cancer or to the initial diagnosis of metastatic disease as discussed below. An age range of up to ±7 years was allowed in matching the control patient with a case patient. Patients were matched for the site of their primary tumor by use of the three-digit code as described in the International Classification of Diseases for Oncology (ICD-O) (3), with additional groupings to minimize the number of case patients for whom no match would be found.
We developed an algorithm to match the date of diagnosis of each potential control patient with that of the case patient. Treatment protocols were divided into those for metastatic and those for nonmetastatic disease. Using this separation, we matched potential control patients with nonmetastatic disease on their initial date of diagnosis of cancer. Potential control patients whose diagnosis date was within ±3 years of the case patient's diagnosis date were accepted, except for patients with colorectal cancer. Because surgical adjuvant therapy became standard medical practice in 1990 for treatment of colorectal cancer, case patients diagnosed in 1989 and earlier were matched only with potential control patients also diagnosed within 3 years of the case patient in 1989 or earlier. Case patients whose colorectal cancers were diagnosed in 1990 or later were matched only with potential control patients diagnosed in that later period. Case patients entered into protocols for treatment of metastatic disease were matched in the same way, except that the relevant diagnosis date was the date of diagnosis of metastatic disease as recorded in the Mayo Clinic Tumor Registry. Patients with colorectal cancer were again divided into those diagnosed before 1990 and those diagnosed in 1990 or later.
Through the above process, we identified 617 unique potential control patients for 133 case patients undergoing treatment on protocol. Thus, 43 (24%) of the 176 case patients could not be matched in the first stage.
In the second stage, the medical records of potential control patients identified in the first stage were reviewed to further ascertain their appropriateness as matches. Review of the medical records began with the potential control patients for those case patients with the fewest available potential control patients. Potential control patients for each case patient were randomly assigned a rank order for medical record review. If a potential control patient met the eligibility criteria for a case patient's clinical protocol, his or her record was selected and was ineligible for selection as a control patient for any other case patient. In the interests of time, we further elected to restrict the number of potential control patients for any case patient to no more than 10, when a case patient had more than 10 potential control patients.
The matching criteria used in the medical record review were the eligibility criteria specific to the relevant treatment protocol and an assessment of the patient's performance status. We considered performance status to be an important predictor of both longevity and ability to tolerate therapy. Trial eligibility criteria generally included type and stage of cancer, specific laboratory parameters, and performance status as measured by the criteria of the Eastern Cooperative Oncology Group (4). To be considered eligible for the trial, the potential control patient's medical record could have no mention of a condition or finding violating protocol eligibility at any time from diagnosis date to an assigned trial entry-equivalent date. The trial entry-equivalent date for the control patient was chosen so that the period between the date of diagnosis and the date of entry (or entry-equivalent date) in the trial would be the same for both patients in a matched case and control pair. (For example, if the case patient was diagnosed with cancer of the cervix on January 1, 1990, and entered a phase II or phase III trial for cervical cancer on January 1, 1991, then the matched control patient who was diagnosed with cervical cancer on January 1, 1992, would be assigned a trial entry-equivalent date of January 1, 1993.) The second stage yielded matches for 61 (46%) of the 133 case patients surviving the first-stage matching process.
Cost Measurement
The primary end point of the study was the cumulative 5-year incremental medical care cost. This cost was defined as the total excess cost for case patients compared with that of equivalent control patients incurred from trial entry date or trial entry-equivalent date until the date of death or the end of the 60th 30-day month, whichever came first. The follow-up period was limited to 5 years because too few observations would be available to provide stable cost estimates beyond this period. Secondary end points were the excess cost incurred by participants from the date of enrollment in the trial to the end of the 12th month and the average monthly cost incurred throughout the follow-up period.
The Olmsted County utilization database, an archived source of provider billing data for Olmsted County medical care providers, was the basis for cost estimation. This database is available in electronic format starting with 1987 data and presently containing data through the end of 1995. It captures 90%-95% of all physician and hospital services used by Olmsted County residents (2). The proportion may be even higher for cancer patients.
Although complete capture of all categories of health care costs was the goal, certain categories were excluded, notably outpatient prescription drugs, durable medical equipment, ambulance and other transportation services, outpatient services provided by allied health professionals (such as physical and occupational therapists or clinical psychologists), and nursing home care. The utilization database includes services in these categories provided by the medical facilities participating in the Rochester Epidemiology Project, but it does not include items provided by drugstores, dispensers, distributors, and independent allied health professionals. In the interests of consistency, therefore, we eliminated all such services from the cost estimates. We also did not capture services provided to study subjects outside Olmsted County, such as the Veterans Affairs Medical Center in Minneapolis or the University of Minnesota Hospital, because the utilization database does not include these institutions. Also excluded were the costs of experimental agents provided free of charge by trial sponsors or third parties such as drug companies. These items did not enter the billing systems of the institutions participating in the Rochester Epidemiology Project.
The utilization database contains detailed billing records for every medical encounter and service rendered by the participating providers. We used a costing system developed by researchers at the Mayo Clinic to assign a unit cost to each service. That system assigns a standardized inflation-adjusted unit cost to each service or procedure in 1995 U.S. dollars. Although the services provided represent the practice choices of Olmsted County providers, the value of each unit of service has been adjusted to national cost norms by use of widely accepted valuation techniques (5).2
The use of standardized unit costs is desirable because of the well-known discrepancies between billed charges, which are directly available in the utilization database, and "opportunity" costs in health care (5-8).3 These differences vary by type of service, among providers, and over time, so billed charges can give a distorted picture of cost differences between groups of patients treated with different services over various times. The unit costing system assigns 1995 Medicare fee-schedule rates to all physician and outpatient ancillary services provided from 1987 through 1995. Hospital charges are converted to costs by applying department-level cost-to-charge ratios reported by all hospitals to Medicare. Each unit cost is normalized to a national 1995 value by use of regional hospital market-basket indexes reported annually by the Prospective Payment Assessment Commission (9).
Lifetime (or 5-year) cost is most appropriately measured as the net present value of the stream of costs incurred over time from the trial entry date to the date of death or the end of the 5-year measurement period. The net present value of cumulative cost is the sum of costs incurred at each time point, weighted by a discount factor that reflects the decay in the value of money from trial entry to the time at which the cost is incurred. A commonly used annual discount rate for health care spending is 3% after adjustment for inflation (10). We estimated cumulative 5-year costs by using discount rates of 0% (i.e., no discounting) and 3%.
Although cost data are available at the level of the individual service and can be reported at any level of aggregation and by any unit of time, the small sample size precluded analysis of specific cost components (e.g., inpatient hospital, physician, and laboratory) or periods shorter than each 30-day interval after the trial entry or trial entry-equivalent date. Preliminary analysis of costs at a more disaggregated level showed no discernible patterns contradicting the findings for total medical costs.
Statistical Analysis
The primary analysis of cost differences was conducted on the total sample of 122 observations, containing 61 matched pairs of case and control patients. Paired comparison formed the primary basis of analysis involving intrapair differences in costs before adjustment for censored observations. Two-sample comparisons were also conducted of the Kaplan-Meier sample average cost, an estimate of mean cumulative (5-year) cost across a population in the presence of censored observations (11,12). The Kaplan-Meier sample average cost estimator has been shown to be an unbiased estimate of cumulative cost under conditions of independent censoring of observations, whereas cost analysis that is not adjusted for censored observations may be biased (12,13).
All comparison-wise type I error rates were set at 5%, and all testing procedures were two-sided. Paired ttests based on matched samples of 61 observations provide 80% power to detect differences of 0.37 standard deviation from zero, a moderate effect size according to Cohen's classification (14). The observed standard deviation of the differences in total cost was $74 354, so the 61 observations provided 80% power to declare an intrapair average difference of $27 510. Paired ttests on log-transformed costs led to no differences in inference and, therefore, are not reported. Power for the nonparametric procedures was of a comparable nature, given the assumptions of nonnormality. All P values are two-sided.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Table 1 shows the characteristics of case
patients and control patients who survived each step of the matching
process. The 133 case patients successfully matched in the first stage
were similar to the original sample, except that those case patients
for whom matches were found had poorer performance scores on average
(P<.001).
|
Many potential control patients identified in the first stage of matching were rejected in the
second stage of matching. Of the 133 case patients surviving the first stage, only 61 were
successfully matched in medical record review. These 61 case patients were enrolled in 36
different clinical protocols. The majority (54%) of excluded control patients were not
eligible for the trial or were not clinically equivalent to the case patient (Table 2). In 36% of the excluded records, however, discrepancies were found between
the medical record and other data sources, particularly the tumor registry.
|
Thirty-six subjects (30% of the 122 observations in the study) were censored at termination of cost measurement (December 1995). Of the 36 censored observations, the medical records of 35 subjects were active after the termination date. Thus, one study subject (a case patient) was potentially lost to follow-up before the cost measurement termination date.
About one half of the patients in the 61 matched pairs were drawn from the population of
patients with gastrointestinal or genitourinary cancers (Table 1), and
18% of the patients had breast cancer. All but 17% of the patients had late stage
tumors. The sexes were represented about equally. All but four of the case patients as well as five
of the control patients had an Eastern Cooperative Oncology Group performance status of either
0 or 1.
Cost Comparisons
Summary statistics for total costs before adjustment for censored
observations are given in Table 3. The mean 5-year
cost per patient was slightly more than $40 000 for both case
and control patients, but costs for case patients were approximately
5% higher than those for control patients, who did not participate
in trials. These results were not statistically significant, however,
and variability among the pairs was marked. Some case patients incurred
costs that were more than $200 000 greater than the costs
incurred by their matched control patients, whereas some control
patients incurred costs that were more than $200 000 greater than the
costs incurred by their matched case patients (Fig. 1).
|
|
In the first 30 days, patients enrolled in trials cost an average of $569 more than the control patients. Costs incurred during the first 90 days were almost identical between the two groups. By the end of the first year, however, the mean difference between case and control patients had risen to about $900, or about 4% of the mean cost for a patient not enrolled in a cancer trial. The difference in median cost at the end of the first year was statistically significant (P = .03), but the difference in means was not. Differences beyond the second year became more difficult to interpret because of the small number of patients surviving at that point. Overall, the average cost associated with being enrolled in a clinical trial was consistently 5%-11% higher than the average costs associated with not being enrolled in clinical trials.
For every 30-day month that a patient was alive and available to follow-up, the mean
difference between case and control patients was $247, and the median difference was $366
(Table 4). Although neither of these measures was statistically
significant,
the median difference did have a P value of .06. Thirty-nine (64%) of the pairs
involved case patients who incurred more expenses than the matched control patient. Table 4
also presents the maximum monthly cost incurred for each patient. This
analysis tests whether
patients who enter trials experience bolus amounts of treatment upon initial entry or cause the
system to incur greater catastrophic costs as a result of closer monitoring. Case patients had
slightly higher costs on average ($177 and $1342 difference in the mean and median,
respectively). However, in a substantial minority (25 pairs or 41%) of the 61 pairs, the
maximum cost for the control patient was higher than that for the case patient.
|
|
Kaplan-Meier survival analysis did not reveal a statistically significant difference in survival (logrank P = .06), but control patients in the sample survived longer than did case patients (median survival time = 724 days and 493 days, respectively). After 1 year, the adjusted survival rate in case patients was 63 survivors per 100 subjects, compared with 68 survivors per 100 subjects in control patients.
The cumulative 5-year Kaplan-Meier sample average costs for case and control patients
without discounting are shown in Fig. 2. The average cumulative
60-month cost after adjustment for censoring was $46 424 for the case patients and
$44 133 for the control patients, a difference of 5.2%. This difference was not
statistically significant (P = .833) based on an estimate of variance obtained by
the bootstrap method involving 10 000 simulated samples (15).
At the end of the first 12 months, the Kaplan-Meier sample average cumulative cost was
$24 645 for case patients versus $ 23 964 for
control patients, a difference of 2.8%. In 61% of the bootstrapped samples, case
patients had higher 5-year Kaplan-Meier sample average costs than control patients. Discounting
at a rate of 3% per year had minimal effect on the results. Thus, the estimated costs for
each group and cost differences between the two groups were essentially the same when
adjustments were made for censored observations as when they were not.
|
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Although several important categories of medical care costs went unmeasured, these were largely services that would be unlikely to differ systematically with trial enrollment. The most notable exception is outpatient prescription drugs. Experimental chemotherapeutic drugs are typically donated by the trial sponsor and would, therefore, not be part of the cost burden to patients or to insurers. However, other drugs, such as those for palliation of side effects or cancer symptoms, would add to patient care costs. If these outpatient prescription drug costs are higher under investigational protocols, their exclusion underestimates the incremental cost of clinical trials to patients and insurers. Also, to the extent that treatment trials compare an experimental drug donated by its sponsor with standard chemotherapy administered to hospital inpatients (whose costs were included in this study), the exclusion of experimental treatment costs underestimates the cost of cancer trials to society but not to insurers.
The longer survival of control patients in this sample affected the estimate of the per-month incremental costs of enrolling in a cancer trial. When total costs are divided by the number of months during which patients were available to follow-up, they were $247 per month higher for case patients than for control patients. However, over the full 5-year follow-up period, the Kaplan-Meier sample average monthly cost across the entire sample of case patients was only $38 higher than that for the control patients.
The high variation in 5-year costs within matched pairs underscores a major limitation of the study: its small sample size and the consequent limited statistical power to estimate true differences with much accuracy. High, unexplained variation in medical care expenditures is the rule rather than the exception throughout medical care. For example, in a study of non-elderly health maintenance organization enrollees in Minnesota, demographic and clinical predictors explained only 5%-10% of the variation in annual medical care costs (16). Our data do suggest that health plans may find it difficult to manage the costs of cancer patients in general unless they can spread the risks across a large population.
This study demonstrated the difficulty that can be encountered in trying to match case patients with eligible control patients by the use of multiple criteria. Our two-stage matching process demonstrated that reliance on data elements typically available in institutional tumor registries is inadequate to ensure equivalence between patient groups. Not only are the data items collected in registries insufficient to describe the clinical and prognostic attributes of patients, but also sometimes they may disagree with the medical record on which they are based. Ironically, the pool of eligible control patients also may have been limited by the strong commitment to clinical research on the part of both cancer clinicians and patients in Olmsted County.
Even with intensive efforts to find equivalent patients through detailed medical records review, the case-control methodology cannot fully rule out the possibility of unobserved selection biases in trial enrollment. Those who choose not to enroll may be predisposed to use medical care more or less intensively than those who do enroll in such studies. Clinicians might also encourage patients with more aggressive disease to enroll in clinical trials. Some control patients might have been improperly declared eligible because clinical findings bearing on eligibility were not recorded in the medical record. We know of no studies to suggest how such selection biases, if they exist, might be expected to affect treatment costs. Neither medical records nor clinical trial data systems routinely contain information on individuals who were judged eligible but refused enrollment. Systematic collection of such information as part of clinical trial designs would greatly facilitate the matching process in future research of this type.
That this study was conducted on cancer patients who were diagnosed at one institution and who resided in a single county with a population of approximately 110 000 raises questions about the generalizability of the findings across a broader spectrum of health care environments. Most importantly, patients who did not enroll in trials typically were served by the same clinicians and health care providers as those who enrolled. Thus, they were not subjected to different practice styles apart from the circumstances of the trial. In other communities, the probability of trial enrollment might be contingent on the practice styles and referral pathways of the primary care and cancer providers. Larger differences (of unpredictable direction) in medical costs might result.
All of the clinical trials investigated in this study evaluated chemotherapeutic agents. None compared a highly expensive new technology, such as bone marrow transplantation for late stage breast cancer, with much less expensive conventional management, yet managed care organizations clearly focus on such "outlier" trials when they express misgivings about funding clinical research (17). This study offers some reassurance that chemotherapeutic trials may not in and of themselves imply budget-breaking costs. Cancer itself is a high-cost illness. This study suggests that chemotherapy protocols may add relatively little to that cost. Replication of these results in other carefully designed studies across different care settings is needed before conclusive statements about relative costs can be made.
![]() |
NOTES |
---|
2 Detailed documentation of the unit costing methodology i
available from the authors upon request.
3 The logic behind the concept of opportunity cost is
described by Kahn (8) as follows: "The basic economic
problem,
in short, is the problem of choice. A decision to produce one good or
service is a decision to
produce less of all other goods and services taken as a bunch. It follows
that the cost to society of
producing anything consists, really, in the other things that must be
sacrificed in order to produce
it." (page 66).
The views expressed in this article are those of the author and do not necessarily represent the views of the Congressional Budget Office.
Supported by Public Health Service grant 3P30-15083-23S1 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services.
We thank Carol Frost for programming assistance, Annette Durhman and Joni Butler for technical assistance with the Mayo Clinic Tumor Registry, and Timothy McKeough for help in extracting data on Mayo Clinic Cancer Center trials.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
1
Mechanic R, Dobson A. The impact of managed care on clinical
research: a preliminary investigation. Health Affairs 1996;15:72-88.
2 Melton LJ 3rd. History of the Rochester Epidemiology Project. Mayo Clin Proc 1996;71:266-74.[Medline]
3 World Health Organization (WHO). International Classification of Diseases for Oncology. 2nd ed. Geneva (Switzerland): WHO; 1990.
4 Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982;5:649-55.[Medline]
5 Lipscomb J, Ancukiewicz M, Parmigiana G, Hasselblad V, Samsa G, Matchar DB. Predicting the cost of illness: a comparison of alternative models applied to stroke. Med Decis Making 1998;18(2 Suppl):S39-56.[Medline]
6 Finkler SA. The distinction between cost and charges. Ann Intern Med 1982;96:102-9.[Medline]
7 Finkler SA. The distinction between costs and charges. In: Finkler SA, editor. Issues in cost accounting for health care organizations. Gaithersburg (MD): Aspen Publishers; 1994. p. 81-93.
8 Kahn AE. The economics of regulation: volume 1, principles and institutions. New York (NY): John Wiley & Sons; 1970.
9 Prospective Payment Assessment Commission. Medicare and the American health care system: report to Congress. Washington (DC): Prospective Payment Assessment Commission; 1997.
10 Gold MR, Siegel J, Russell LB, Weinstein MC. Cost-effectiveness in health and medicine. New York (NY): Oxford University Press; 1996.
11 Etzioni R, Urban N, Baker M. Estimating the costs attributable to a disease with application to ovarian cancer. J Clin Epidemiol 1996;49:95-103.[Medline]
12 Lin DY, Feuer EJ, Etzioni R, Wax Y. Estimating medical costs from incomplete follow-up data. Biometrics 1997;53:419-34.[Medline]
13 Hallstrom AP, Sullivan SD. On estimating costs for economic evaluation in failure time studies. Med Care 1998;36:433-6.[Medline]
14 Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale (NJ): Laurence Erlbaum & Associates; 1988.
15 Efron B. The jackknife, the bootstrap, and other resampling plans. Philadelphia (PA): Society for Industrial and Applied Mathematics; 1982.
16 Fowles JB, Weiner JP, Knutson D, Fowler E, Tucker AM, Ireland M. Taking health status into account when setting capitation rates: a comparison of risk-adjustment methods. JAMA 1996;276:1316-21.[Abstract]
17 Daniels N, Sabin JE. Last chance therapies and managed care. Pluralism, fair procedures, and legitimacy. Hastings Cent Rep 1998;28:27-41.[Medline]
Manuscript received November 12, 1998; revised March 16, 1999; accepted March 18, 1999.
This article has been cited by other articles in HighWire Press-hosted journals:
![]() |
||||
|
Oxford University Press Privacy Policy and Legal Statement |