Affiliation of authors: Division of Research, Kaiser Permanente, Northern California, Oakland.
Correspondence to: Bruce H. Fireman, M.A., Division of Research, Kaiser Permanente, 3505 Broadway, Oakland, CA 94611 (e-mail: bhf{at}dor.kaiser.org).
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ABSTRACT |
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INTRODUCTION |
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We examined the cost of medical care received by cancer patients who entered clinical trials from 1994 through 1996 at Kaiser Permanente in Northern California, a large nonprofit health maintenance organization (HMO). We compared 135 patients enrolled in NCI-sponsored clinical trials with 135 matched control subjects, assessing the direct 1-year costs of medical care. Although trials open to Kaiser Permanente patients may not be representative of all trials and Kaiser Permanente patients in trials may not be representative of all patients in the same trials, analysis of the costs of care in trials at Kaiser Permanente may be useful beyond this HMO in evaluating ways to facilitate the conduct and financial support of cancer clinical trials in a managed care environment.
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SUBJECTS AND METHODS |
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Kaiser Permanente is a 50-year-old nonprofit HMO integrated with a multispecialty group
practice that provided comprehensive health care to approximately 2.4 million people at 17
hospitals and 31 clinics in Northern California during the 1994 through 1997 study period. The
Kaiser Permanente population is diverse with respect to race/ethnicity and socioeconomic status,
although the poor, the unemployed, the rich, and the aged are somewhat underrepresented (4). Approximately 100 patients per year enrolled in oncology clinical trials
at Kaiser Permanente, trials sponsored mainly by the NCI (through the National Surgical
Adjuvant Breast and Bowel Project [NSABP] and the Southwest Oncology Group
[SWOG]) but increasingly by pharmaceutical/biotech companies. Kaiser Permanente
oncologists (n 50, of whom five constitute a steering committee that coordinates trials)
open available trials to enrollment according to their perceptions of patients' needs and
interests, their own scientific interest in the research, the burdens of the research on physicians
and the health-care delivery system, and the adequacy of the resources provided. Enrollment in
randomized bone marrow transplantation (BMT) trials for breast cancer patients has been robust
(higher than most research centers). While it was assumed that medical care in BMT trials is
costly, it was decided that open access to well-designed BMT trials was the best approach to
dealing with the complex issues of BMT coverage in unproven situations.
For the study period, the Regional Cancer Registry at Kaiser Permanente records approximately 12 000 incident cases per year, including about 2000 incident cases per year of breast cancer, the cancer site of more than half of the Kaiser Permanente patients in clinical trials. The percentage of adult cancer patients eligible for a trial who enroll in a trial is modest (<10%) at Kaiser Permanente, as it is nationwide (perhaps 2%-3%).
Permission to conduct this research was obtained from the Institutional Review Board of the Kaiser Foundation Research Institute.
Study Subjects and Follow-up Time
There were 237 patients who enrolled in NCI-sponsored trials at Kaiser Permanente from 1994, when automated cost data were first available, through 1996, the last year of enrollment, permitting a full year of follow-up. We sought matched control subjects (comparison subjects) with cancer for all 203 enrollees (86%) who were Kaiser Permanente members and who were included in the NCI's Surveillance, Epidemiology and End Results (SEER)1 registry. For each enrollee, we identified as potential control subjects everyone in the SEER registry who met the following criteria: Kaiser Permanente membership with matching cancer site and stage at diagnosis, sex, year of birth (within 5 years), and date of diagnosis (within 1 year).
For each trial enrollee, the medical charts of potential control subjects were reviewed in random order until a control subject was identified who met the eligibility criteria for the enrollee's clinical trial (but never enrolled in a cancer trial). For example, eligibility for NSABP B-28 required completely resected breast cancer confined to one breast and ipsilateral lymph nodes. Patients had to have had a total mastectomy or lumpectomy and axillary lymph node dissection and histologic confirmation of invasive adenocarcinoma with at least one involved axillary lymph node. In the presence of bone pain, they must have had a bone scan and/or an x-ray negative for metastases. They could not have had contralateral breast cancer, ulceration, erythema, infiltration of skin or underlying chest wall, or peau d'orange. The potential participants must have been female between the ages of 18 and 78 years, with a life expectancy of at least 10 years. At the time of randomization, they had to have a white blood cell count of at least 4000/mm3 and a platelet count of at least 100 000/mm3. They had to have normal bilirubin and aspartate aminotransferase or alanine aminotransferase levels. Their creatinine level must have been normal. Potential participants with a lumpectomy were ineligible if the primary tumor was greater than 5 cm on physical examination or if they had any of the following: an invasive tumor or ductal carcinoma in situ in resection margins, diffuse tumors on mammogram (unless surgically amenable to lumpectomy), ipsilateral mass following lumpectomy (unless histologically benign), or breast irradiation before randomization. The estrogen and progesterone receptor status was required before randomization. Patients could not have had any prior therapy for breast cancer other than surgery. They could not have any contraindication to doxorubicin or paclitaxel therapy, including myocardial infarction, angina pectoris requiring medication, and history of documented congestive heart failure. They could not have any nonmalignant systemic disease that precluded treatment or follow-up, including any psychiatric or addictive disorder that precluded consent.
Matched control subjects were found for 135 (67%) of the 203 trial enrollees (291 patients in the SEER registry identified as potential control subjects were rejected after chart review because they did not fully meet the matching criteria). A "start date" was identified for each enrollee and each matched control subject, marking the beginning of the 12-month follow-up period for which costs were ascertained and compared. For enrollees, the start date is the date of enrollment in the trial. For control subjects, we sought dates in the course of their clinical care that were likely to be similar clinically to the enrollees' dates of enrollment. Thus, if the enrollee received chemotherapy in the trial and the control subject also received chemotherapy (while eligible for that trial), then we began follow-up for the control subject on a date before chemotherapy (that was matched to the enrollee for the number of days before the start of chemotherapy). If either the enrollee or the matched control subject did not receive chemotherapy, our algorithm for identifying the beginning of the control subject's follow-up then depended on whether or not the referent enrollee had metastatic disease when enrolled in the trial. If so, we counted the days from the enrollee's diagnosis of metastatic disease until enrollment; we then added this number of days to the date on which the control subject was diagnosed with metastatic disease to obtain the control subject's start date. Finally, if the enrollee did not have diagnosed metastatic disease on the date of enrollment, we counted the days from the enrollee's last hospital discharge date prior to enrollment (or cancer diagnosis date if this was later) until enrollment; we then obtained the control subject's start date by adding this number of days to the last hospital discharge date (or cancer diagnosis date) of the control subject prior to eligibility for the trial.
In four matched pairs, follow-up of either enrollee or control subject was shorter than 1 year because of dropout from the health plan. In these instances, follow-up of the other member of the pair was shortened so that the enrollee and the matched control were followed for the same number of days. However, if follow-up was shortened because of death, follow-up was continued for a full year from the start date for the other member of the pair. Death was ascertained from the SEER registry through 1997, mortality files of the State of California through 1997, and health plan clinical and administrative databases through 1998.
Ascertainment of Costs
We ascertained the direct costs of medical care that was provided (or paid for) by Kaiser Permanente over the 1-year follow-up period. Detailed data on each course of chemotherapy, including each drug name, dose, intravenous or oral administration, and outpatient or inpatient setting, were ascertained by chart review. All other data on the use and cost of medical care were obtained from linked automated clinical and administrative databases at Kaiser Permanente (5). The Kaiser Permanente Cost Management Information System (CMIS) was used to ascertain the costs of hospital services and outpatient clinic services that were provided by Kaiser Permanente, including pharmacy, laboratory, imaging, and home health services. CMIS integrates utilization data with the Kaiser Permanente general ledger. All costs in the ledger (with the exception of costs for insurance-related functions, such as marketing and membership accounting) are fully allocated to health care services. CMIS uses standard cost-accounting methods to allocate all building and administrative overhead. Similar cost-accounting methods were used to estimate costs for chemotherapy characterized by chart review. From the economist's perspective, we are examining "average" or "long-run" costs (rather than marginal costs), appropriate for evaluating the average or long-run medical costs of a program or policy that facilitates participation in clinical trials. For each unit of services, we used unit costs that reflect average annual costs throughout Kaiser Permanente in Northern California (rather than unit costs that are specific to the month and clinic of the utilization event), unadjusted for inflation and not discounted. Such adjustments would be of little consequence because there was little inflation at Kaiser Permanente from 1994 through 1997, follow-up lasted only 1 year, and cost differences between trial enrollees and matched control subjects would be inflated and discounted at the same rates.
For services that were provided by non-Kaiser Permanente providers, but paid for by Kaiser Permanente, we used the charges of the non-Kaiser Permanente providers as the costs to Kaiser Permanente of these "outside" services. The costs of donated drugs were omitted from our primary analyses but were included in additional analyses to assess the sensitivity of results to these costs.
Cost analysis is primarily from the HMO perspective. We report the direct costs of services covered by Kaiser Permanente. Out-of-pocket costs by patients to Kaiser Permanente (i.e., co-payments) are included, but costs for care obtained elsewhere and not covered by Kaiser Permanente, such as some alternative care or long-term care, are omitted. Building and administrative overhead supporting medical care are included. Research costs (recruiting patients, collecting and managing data, and development of research infrastructure) are omitted but will be examined in a separate analysis.
Statistical Analysis
The cost distributions of the trial enrollees and their matched control subjects, as well as the paired differences in cost, were examined. Means, standard deviations, and selected percentiles are reported for total medical care costs and for costs in selected categories, including chemotherapy and other outpatient and inpatient services.
The primary focus is a matched analysis of the paired cost differences between enrollees and control subjects. While the subjects' cost distributions are very skewed, the distributions of paired cost differences are more symmetric. The distributions of paired differences are flatter than the bell-shaped normal curve, and there are influential outliers, but log transformation would yield less interpretable results and would be especially problematic in cost categories, such as inpatient services, where some patients have no costs. Therefore, nonparametric Wilcoxon signed rank tests and corresponding confidence intervals (CIs) (6) were used for the primary assessment of the null hypothesis that clinical trials do not increase or decrease the cost of medical care. To permit consideration of the robustness of our findings, we also evaluated results obtained from paired t tests (and corresponding parametric estimates of CIs) using costs and also the log of costs.
Given the matched design, we relied mainly on close matching, rather than on regression models, to adjust for potential confounders. We supplemented the primary univariate analysis (of paired differences) with an ordinary least-squares regression model to adjust for differences in the Charlson Comorbidity Index (7,8) on the basis of hospital diagnoses (in addition to cancer) during the 5 years prior to the year under study. To evaluate differences among cancer clinical trials in their impact on costs, we added to this one-covariate regression model a set of trial-specific indicator variables for all enrollees in larger trials (more than two trial enrollees in our sample), with the enrollees in smaller trials (fewer than three enrollees) as the reference group. In this supplementary model, we focused on cost ratios rather than on cost differences, specifying the dependent variable as the paired difference in the log of costs (in part because this intertrial comparison examined only total costs rather than costs in categories of services that were not used by all patients).
We also expanded the univariate matched analyses of costs in selected service categories (e.g., BMT, other chemotherapy, other pharmacy, laboratory, and imaging) with univariate unmatched two-part analyses (akin to "two-equation models"), reporting 1) the proportion of enrollees and control subjects who had any costs in the service category and 2) the mean costs and enrollee/control cost ratios among patients with nonzero costs.
The variation in enrollees' costs was compared with the variation in control subjects' costs by use of the F test. Cox regression was used to compare mortality among trial enrollees with that among control subjects. All statistical tests are two-sided, with a .05 significance level.
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RESULTS |
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The mean of total medical care costs during the year after enrollment in a clinical trial was
$17 003, 10% more than the $15 516 mean cost for matched control
subjects during the comparable year (P = .011) (Table 1).
Among the trial enrollees, chemotherapy, including the costs of clinic visits for administering the
drugs as well as the cost of the drugs, accounted for 28% of all medical care costs. The
chemotherapy costs of trial enrollees were 40% higher than the chemotherapy costs of the
matched control subjects. Most of this difference is attributable to a higher number of
chemotherapy visits, although drug cost differences were attenuated because many trial enrollees
received donated drugs. The $1376 difference in chemotherapy costs between trial enrollees and
control subjects amounts to 93% of the $1487 difference in total costs. The mean
differences between trial enrollees and control subjects in the costs of hospital and clinic services
other than chemotherapy were smaller and unstable.
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The possibility that chance alone accounts for the paired cost differences is evaluated in Table
2. The null hypothesisthat clinical trials do not increase or
decrease the cost of careis rejected with respect to chemotherapy costs (P<.001), other outpatient costs (P = .049), and total costs (P =
.011) but not with respect to inpatient costs (P = .71). The 95% CI for the
impact of trials on chemotherapy costs extends from $776 to $2209. The 95% CI for the
impact of trials on total costs is wider: It extends from $564 to $4563. This upper bound for
trials' impact on total 1-year costs amounts to about 29% of the $15 516
mean for control subjects.
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Most chemotherapy costs were incurred during the initial 6 months of the study period:
94% of the chemotherapy costs for patients in trials and 83% for the matched
control subjects. The percent of other clinic costs incurred during the initial 6 months was
70% for trial enrollees and 62% for control subjects. In both groups, hospital costs
were similar during the first and second halves of the 1-year study period. During each half year,
control subjects' hospital costs were higher than those of the trial enrollees, but these
differences were not statistically significant. The higher total costs for trial enrollees shown in
Tables 1 and 2
are apparent only in the initial 6
months of follow-up and appear to derive primarily from chemotherapy.
BMT was received by four enrollees in trials (including one with BMT several months after a
non-BMT trial) and four control subjects (Table 3). These eight patients
with BMT include the four with the highest total 1-year costs among all 270 patients in the study
population. While 11 of the trial enrollees were in trials with a BMT arm, only three received
BMT. Another enrollee was randomly assigned to the BMT arm but never received the treatment;
the remaining seven were randomly assigned to receive other treatments. Nevertheless, 1-year
costs among these 11 patients were higher than 1-year costs among their matched control subjects
(Wilcoxon test; P = .054): roughly twice as high, exceeding the costs of control
subjects by about $20 000. All four of the control subjects who received BMT were
matched to enrollees in trials without any BMT arm. Patients in BMT trials received relatively
costly chemotherapy, even when they did not receive BMT. If we put aside the 11 matched pairs
in BMT trials to focus on the remaining 124 matched pairs, the $15 041 mean cost of
enrollees in trials were very similar to the $15 186 mean cost of their matched control
subjects. Among the 95 enrollees in non-BMT adjuvant breast cancer trials, mean 1-year costs
were $13 921, less than the $14 607 for their matched control subjects.
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Fewer than 10% of the patients in trials and control subjects used Kaiser Permanente
home health services, but these services were costly among those who used them, especially
among control subjects. Hospitalization was a little more common among control subjects, and
hospital costs, given hospitalization, were higher among the control subjects (Table 3). The somewhat higher hospital costs and home health costs of the control subjects
could be due to chance alone (P = .779 for hospital costs and P = .525 for home health costs).
Table 4 compares costs by clinical trial for the 10 clinical trials for
which we have costs for three or more patients. The differences among trials in mean cost are
substantial. The $40 633 mean 1-year cost for patients in SWOG 9061, a BMT trial, are
sevenfold higher than the $5608 mean cost in SWOG 9035, a melanoma vaccine trial.
Heterogeneity in the ratio of costs for trial enrollees to costs for control subjects is much less
substantial: These ratios range from 0.84 to 2.16. While it is suggestive that the highest of these
ratios is for a BMT trial, the numbers of patients per trial is modest, and we cannot reject the
global null hypothesis of no differences among these trials.
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DISCUSSION |
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Overall, similar results were obtained by use of parametric statistical methods that are more influenced by "outliers"patients with unusually high costs. For example, paired t tests done on log-transformed cost data yielded results similar to those obtained by use of the Wilcoxon test.
Two other recent studies have examined the direct medical care costs of patients in cancer clinical trials. Wagner et al. (10) compared the costs for 61 patients in cancer trials at the Mayo Clinic with those of matched control subjects, reporting mean 1-year costs of $24 645 in trial enrollees compared with $23 964 in control subjects (10). With data available on some patients for as long as 5 years, they found that trial enrollees cost as much as 10% more than control subjects over some follow-up periods.
At Group Health Cooperative (GHC), a nonprofit HMO in the Seattle area, Barlow and colleagues examined the costs for 40 patients in breast cancer trials and 28 patients in colon cancer trials (Barlow W, Taplin S, Beckord J, Ichikawa L: unpublished data), with adjusted comparisons to unmatched control subjects as well as matched analyses of the trial enrollees for whom well-matched (chart-confirmed) control subjects could be found. The 40 enrollees in the breast cancer trials had mean costs no higher than the 1100 unmatched control subjects during the 2 years following diagnosis, but the costs for trial enrollees were 26% higher than those for control subjects in the 26 available matched pairs (P = .04; Wilcoxon test). Patients in colon cancer trials at GHC cost slightly more than unmatched control subjects, but the difference was not statistically significant. Thus, these recent studies at the Mayo Clinic and GHC, like our study, did not find that participation in cancer trials is associated with large increases in the costs of medical care.
In the Kaiser Permanente setting, BMT trials have been the most costly, with trial participants (less than half of whom received BMT) about twice as costly as control subjects, who were themselves more costly than the control subjects for most other trials. Neither of the other published studies include patients from BMT trials. In any setting, the relative costs of participation in clinical trials may be influenced by the mix of the clinical trials that are offered and selected.
The relative costs of trials will also be influenced by the likelihood of receiving aggressive,
intensive care outside clinical trials. At Kaiser Permanente, usual care outside trials appears to be
quite variable in cost. The control subjects included the most expensive as well as the least
expensive patients. However, the cost distributions shown in Table 1
suggest that trials decrease the likelihood of low costs more than they increase the likelihood of
high costs. Trials typically focus attention on differences between an experimental treatment and a
standardized version of usual care. In trials, care is typically delivered by protocol and thereby
rendered unusually homogeneous within each treatment arm. Apparently, the variation in cost
between arms of the trial is often less than the variation within "usual care" outside
trials. Recently, there have been expanded efforts to measure costs within clinical trials, permitting
comparison of treatment arms with respect to cost and cost-effectiveness (11). It should be kept in mind that medical care outside clinical trials is likely to be more
heterogeneous in cost (and effectiveness) than medical care in a trial's
"control" arm.
Variation in "usual care" outside trials within Kaiser Permanente or any other setting renders problematic the selection of control subjects. If usual care varies according to physician and patient propensities that are difficult to measure, it is then a challenge to identify control subjects whose experience can inform us about what enrollees in trials would cost had they never been offered trials. How successfully did we meet this challenge and to what extent is problematic matching a source of bias in our results? No matched control subject was found for 68 of the enrollees (33%) in trials during the study period. The studies from the Mayo Clinic (10) and GHC (Barlow W, Taplin S, Beckord J, Ichikawa L: unpublished data) also report difficulty identifying closely matched control subjects (for whom there is evidence in the medical chart of eligibility for the clinical trial). We ascertained 1-year costs for 65 of the 68 unmatched trial enrollees by use of the same methods reported above. The mean of their 1-year costs was $25 957 compared with $17 003 for the 135 matched trial enrollees. A relatively high percentage of the unmatched enrollees had metastatic disease (25%) compared with the matched enrollees (18%), suggesting that they may have been relatively costly, regardless of enrollment in trials. Ten of the unmatched trial enrollees were in BMT trials. Mean 1-year costs were $49 008 for these 10, which was 25% higher than the mean costs for the 11 matched enrollees in BMT trials. (Three of the 10 unmatched trial enrollees received BMT compared with three of the 11 who were matched.) Another 31 of the unmatched trial enrollees had enrolled in other trials represented in our sample of 135 matched pairs. Mean 1-year costs were $15 822 among these 31 enrollees, only slightly above the $15 186 among their matched control subjects. Thus, the unmatched enrollees lend support to our findings that BMT trials are relatively costly, but matched enrollees in other trials at Kaiser Permanente have cost little more than they would have cost without trials.
Although the 135 control subjects were well matched by our criteria, they may differ from trial enrollees in unmeasured ways in the severity of their illness and in their propensity to use costly services. If our matched control subjects were more reluctant to undergo aggressive treatments, our results may then overstate the costs of trials. On the other hand, if our control subjects are sicker in unmeasured ways, they may be costlier than ideal control subjects, and our results may then understate the cost of trials. There were 22 trial enrollees (16%) with Charlson comorbidity scores unequal to those of their matched control subjects: eight enrollees with more comorbidity and 14 with less. Adjustment for comorbidity score would increase slightly from $1487 to $1531, our estimate of the additional cost of medical care associated with enrollment in clinical trials.
During the 1-year study period, there were 12 deaths among the control subjects compared with seven among the enrollees in trials. Extending follow-up through 1998, there were 33 deaths among control subjects compared with 23 among enrollees. Cox regression, stratified by trial, yielded an estimated relative risk of mortality of 0.60 for trial enrollees compared with control subjects (95% CI = 0.34-1.06; P = .08). The possibility of relatively favorable survival among enrollees in trials raises the possibility that they were less ill than their control subjects on the start date in unmeasured ways and/or that they received more effective medical care. While the survival benefits of experimental treatments in cancer trials have usually been modest or undetectable compared with control groups within trials, it is possible that trials tend to improve care in all arms by offering care that is more protocol guided, attentive, and/or aggressive. "Selection bias" is also possible; perhaps the physicians and patients who participate in trials are those whose interaction would result in more effective care inside or outside trials. Given that most of our trial enrollees had breast cancer, it is worth noting that survival with breast cancer has been reported to be more favorable at Kaiser Permanente in Northern California than in the surrounding fee-for-service population in a study of Medicare enrollees (12).
We focused on costs of care during the 1-year interval following enrollment in the trial. The modest differential in chemotherapy costs and total costs was entirely within the first 6 months. Among enrollees in trials, 94% of 1-year chemotherapy costs and 72% of 1-year total costs were incurred during the initial 6 months. Among control subjects, 83% of chemotherapy costs and 64% of 1-year total costs were in the initial 6 months. It seems likely that cost differentials during time periods beyond 1 year would be shaped primarily by recurrence and mortality. Any cost impact that is years downstream, and secondary to the impact of trials on disease progression and death, may be presumed remote from the cost concerns of managed care organizations facing policy decisions on patient access to clinical trials. If we do have evidence that clinical trials improve survival, then this would be the important finding. The downstream cost consequences of longer lives should not affect policy decisions on clinical trials.
The 1-year follow-up interval began at enrollment in the trial. Trials may incur costs before enrollment for tests done to ascertain eligibility, tests that otherwise might not be done. Costs for laboratory tests and imaging procedures during the 2 preceding weeks were $183 more per patient among enrollees than among their matched control subjects. Addition of the costs of these tests during the preceding 2 weeks, to the total of all medical costs during our 1-year follow-up period, raises by one percentage point (from 9.6% to 10.6%) our estimate of the percentage increase in medical care costs attributable to trials.
Cost differences between enrollees and control subjects are also somewhat higher than the
10% differential reported in Tables 2 and 4
,
if we add an estimate of the costs of donated drugs, as might be appropriate were we assessing
costs from the societal perspective rather than the HMO perspective (13).
The addition of imputed costs for donated drugs increased chemotherapy costs by $2629 per
enrollee and increased total costs by $2672. Thus, if Kaiser Permanente had purchased these
drugs, our estimate of the percentage increase in 1-year direct medical costs attributable to trials
would increase from 10% to 27%. From the societal perspective, however, it may be
more appropriate to use cost estimates for donated drugs that are much lower, based on what it
costs the drug company to manufacture and donate the drugs rather than what it would cost
Kaiser Permanente to buy them.
The enrollees in non-BMT trials in this study were treated by Kaiser Permanente physicians rather than referred to academic medical centers. How costs to an HMO may be associated with "losing control" of referred patients is beyond the scope of this report. A full accounting of the costs to Kaiser Permanente for participation in clinical trials would assess not only direct medical care costs but also the burden of recruiting patients, assuring that treatment protocols are followed, collecting and managing data, and supporting the infrastructure for research. Furthermore, trials may bring to the provider organization indirect benefits as well as costs. Participation in trials may enhance the appeal of an HMO to patients and physicians. Clinical trials are forces for technologic innovation in medicine. The clinical and scientific knowledge generated by trials is publicly available, regardless of participation in clinical trials. Nevertheless, participation in clinical trials by HMO physicians may position them to adopt new treatments sooner and otherwise influence how they deliver care outside clinical trials.
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CONCLUSION |
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NOTES |
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Supported by Public Health Service contract N01CN65107-01-1 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services and facilitated by the Northern California Cancer Center.
We thank Graciela Bonilla, Virginia Browning, and Tanya Rosen for their careful chart reviews; Martin Brown, Arnold Potosky, and Joe Selby for their thoughtful comments on the report; and the physicians, nurses, and data managers of Kaiser Permanente who entered and followed patients in oncology clinical trials.
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REFERENCES |
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1
Mechanic RE, Dobson A. The impact of managed care on clinical
research: a preliminary investigation. Health Aff (Millwood) 1996;15:72-88.
2
Brown ML. Cancer patient care in clinical trials sponsored by the
National Cancer Institute: What does it cost? J Natl Cancer Inst 1999;91:818-9.
3 Report of the National Cancer Institute Clinical Trials Program Review Group. August 26, 1997; p. 20. http://deainfo.nci.nih.gov/advisory/bsactprgmin.htm.
4 Krieger N. Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health 1992;82:703-10.[Abstract]
5
Selby JV. Linking automated databases for research in managed
care settings. Ann Intern Med 1997;127:719-24.
6 Conover WJ. Practical nonparametric statistics. New York (NY): John Wiley & Sons; 1980. p. 280-92, 460-1.
7 Charlson M, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373-83.[Medline]
8 Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613-9.[Medline]
9 Fireman BH, Quesenberry CP, Somkin CP, Jacobson AS, Baer D, West D, et al. Cost of care for cancer in a health maintenance organization. Health Care Financ Rev 1997;18:51-76.[Medline]
10
Wagner JL, Alberts SR, Soan JA, Cha S, Killian J,
O'Connell MJ, et al. Incremental costs of enrolling cancer patients in clinical trials: a
population-based study. J Natl Cancer Inst 1999;91:847-53.
11 Shulman KA, Boyko WL Jr. Evaluating cancer costs in NCI trials. Cancer Treat Res 1998;97:37-52.[Medline]
12
Potosky AL, Merrill RM, Riley GF, Taplin SH, Barlow W,
Fireman BH, et. al. Breast cancer survival and treatment in health maintenance organization
and fee-for-service settings. J Natl Cancer Inst 1997;89:1683-91.
13 Gold M, Siegel J, Russell L, Weinstein M. Cost-effectiveness in health and medicine. New York (NY): Oxford University Press; 1996.
14 American Joint Committee on Cancer Staging Manual, 5th ed. Baltimore (MD): Lippincott, Williams & Wilkins; 1996.
Manuscript received June 30, 1999; revised November 9, 1999; accepted November 16, 1999.
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