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--> 5 Health Care Delivery and Quality of Cancer Care The field of quality assessment in cancer care is relatively new, and investigators are just beginning to identify meaningful indicators of quality for many aspects of cancer care. Health services researchers have used established indicators to determine whether outcomes of care are affected by how care is delivered or who delivers care. Several aspects of the health care delivery system have the potential to affect quality: the resources or capacity of facilities (e.g., volume of services, scope of services, access to technology, nurse staffing levels, academic affiliation); characteristics of health care providers and systems (e.g., level of training, specialization, certification); and the way in which services are financed, organized, and delivered (e.g., managed care versus traditional fee-for-service [FFS] care; regionalization of services). This chapter summarizes the literature that examines the effects of these factors on the quality of cancer care. Whereas Chapter 4 was confined to a review of the literature on breast and prostate cancer, this summary focuses on the way attributes of the health care system affect quality more generally and thus includes studies of other cancers. Many studies, for example, address the relationship between professional or institutional experience, as measured by the number of operations performed, and outcomes for individuals with cancers for which high-risk surgery is indicated (e.g., pancreatic cancer). In only three areas was there a body of literature to examine: the effects of volume of cases handled by hospitals or individual physicians on outcome; the effects of specialization of facilities or physicians on outcome; and the effects of managed care versus fee-for-service care on process and outcome.
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--> A brief overview of the conduct of health services research, and certain cautions about inferences from such studies, are warranted before individual studies in these areas are reviewed. Evaluating the Strength of Evidence From Health Services Research Important questions about the way in which aspects of health care delivery affect outcomes usually cannot be answered with the most powerful research design, the randomized clinical trial. It would, for example, be unacceptable to most individuals recently diagnosed with cancer to be assigned randomly to an insurance plan, hospital, or doctor, although such studies are not impossible. For example, an experiment was conducted in the 1970s to assess how insurance plans and cost sharing affected health care and outcomes among the general population (New-house, 1998). Most of the time, however, considerations of cost and practicality lead health services researchers to conduct observational rather than experimental studies. Often cancer patients are identified retrospectively, through cancer registry data or hospital discharge records, and outcomes are compared across different health care settings or processes of care. Alternatively, individuals with cancer in different settings may be identified shortly after diagnosis and followed prospectively with systematic measurement tools designed to assess different outcomes (e.g., quality-of-life measures). Although they are generally less costly and easier to conduct, nonexperimental studies are subject to a number of potential biases that can make findings difficult to interpret. Any differences observed between study groups could be due to underlying differences in group membership rather than to the intervention or condition being evaluated. Individuals enrolled in health mainentance organizations (HMOs), for example, tend to be younger and healthier than those insured through FFS plans. Comparisons of groups that vary by insurance coverage must therefore control for differences in age and health status. However, information necessary to "adjust" the analysis to compare across groups of like individuals is not always available or may not capture all of the underlying differences. Some unique features of cancer and its diagnosis make these "case-mix" adjustments very important, but difficult (Dent, 1998). Differential use of screening and diagnostic tests, for example, can bias the results of comparative studies of cancer survival. In a classic study of survival following treatment for lung cancer, Feinstein and colleagues (Feinstein et al., 1985) found higher survival rates for individuals treated in 1977 than in the period from 1953 to 1964. Survival was better for the entire group and for subgroups in each of the three main TNM (tumor-node-metastasis) stages. The more recent cohort, however, had undergone many new diagnostic imaging procedures, which resulted in "stage migration." Many patients who previously would have been classified in a "good" stage were assigned to a "bad" stage. The use of new diagnostic techniques allows patients with unobserved metastases to "migrate'' from TNM stages with a better prognosis (e.g., Stage I or II) into those with a worse prognosis (Stages II and III). The migration would improve survival in the lower stages, because fewer patients with metastases are assigned to them. Migration would also improve survival in the higher stages, since the metastases in the newly added patients were silent rather than overt. This bias was called the Will Rogers phenomenon, after the humorist-philosopher who is said
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--> to have remarked, "When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states." This bias has been noted in studies of changes in survival over time and in comparisons of survival across geographic areas (Farrow et al., 1995) or by hospital type (Greenberg et al., 1991). In a study by Greenberg and colleagues of patients with non-small-cell lung cancer, the significantly better mortality observed at university cancer centers than at community hospitals disappeared when functional status, instead of stage, was used to adjust the analysis. The patients diagnosed in academic cancer centers underwent more staging procedures (e.g., bone and liver scans) and tended to be assigned to a higher stage than similar patients diagnosed in community hospitals (Greenberg et al., 1991). Another potential source of bias in observational studies is case selection. Findings from evaluations of the effect of managed care on cancer outcomes may not be generalizable if the study is limited to a convenience sample of a few plans. Techniques could be used to sample health plans, facilities within plans, and patients within facilities to obtain a nationally representative sample of patients. Another factor that makes it difficult to interpret the available health services research literature is the possibility of "publication bias," which means that studies showing the expected relationship are more likely to be published than those that find no relationship. Evidence of this sort of bias exists for clinical trials and other types of research such as observational studies. Underreporting of negative results appears to be related to a failure on the part of investigators to submit manuscripts for publication, not to selective rejection of negative results by journal editors (Dickersin, 1997). The next section reviews health services literature on hospital and provider characteristics and on managed care. The literature review is not exhaustive; only articles written in English were identified, and some studies of patients cared for before 1980 were excluded. Case Volume for Hospitals or Individual Physicians One structural measure that has been found to relate to outcomes for some conditions or procedures is volume, which refers to the number of times each year that a hospital (or clinician) performs a particular procedure or takes care of patients with a particular disease. Since the late 1970s, researchers have been studying this volume-outcome relationship. The area that has been studied most intensively is interventional cardiology, particularly coronary artery bypass graft (CABG) surgery (e.g., Hannan et al., 1995, 1997b) and percutaneous transluminal coronary angioplasty (PTCA, "angioplasty") (e.g., Ellis et al., 1997; Jollis et al., 1994, 1997). In all of these cases, a positive connection was found: the more procedures done per hospital (or where it was studied, per physician), the better are the outcomes, including fewer immediate deaths due to the procedures and lower complication rates. Similar findings have been reported for heart transplants (Hosenpud et al., 1994). Volume-outcome relationships have been reported for other procedures and services, including hip replacement (Kreder et al., 1997), abdominal aortic aneurysm surgery (Hannan et al., 1992; Kantonen et al., 1997), craniotomy for cerebral aneurysm (Solomon et al., 1996), hip and
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--> knee arthroplasty (Lavernia and Guzman, 1995), carotid endarterectomy (Ruby et al., 1996), thyroidectomy (Sosa et al., 1998), colorectal surgery (Rosen et al., 1996), and HIV/AIDS treatment (Kitahata et al., 1996). In these cases, the more procedures carried out, the better are the results, in at least some dimensions. Although most of the published studies of volume-outcome relationships have demonstrated better outcomes with higher volumes, this finding has not been universal. Conflicting results have been reported for trauma centers, for example, with at least two reports of better results with higher volumes (Konvolinka et al., 1995; Smith et al., 1990), one with poorer results with higher volumes (Tepas et al., 1998), and one study of trauma surgeon case volume showing no difference between high and low volumes (Richardson et al., 1998). A study of 28-day mortality rates for very low birth weight infants reported no difference associated with the number of such infants treated in the neonatal intensive care unit (Horbar et al., 1997). Within a hospital, processing a high volume of one type of service can lead to organizational efficiency, establishment of multidisciplinary teams, use of guidelines, and evaluation of outcomes. These aspects of specialization can all contribute to success. Alternatively, it may be that the experience gained by individual providers is the key to improving outcomes. Variations in mortality and complications are influenced more by patient variables than by organizational factors (e.g., volume, nursing surveillance, quality of interaction among professionals) according to a recent review of studies of the effects of these factors on patient outcomes (Mitchell et al., 1997). High-Risk Cancer Surgery The treatment of several cancers involves surgery that is complex and has high short-term risks for patients. One common cancer in this category is non-small-cell lung cancer (NSCLC). Three others that occur infrequently are pancreatic, esophageal, and gastric cancers. There is no effective screening procedure for NSCLC, and about one-half of NSCLC patients present with metastatic disease. About one-third of all NSCLC patients have their disease diagnosed at a stage where surgery is recommended as part of initial care. In a procedure called pulmonary resection, diseased portions of the lung are removed. Surgery is more commonly performed on younger patients and those with local or regional disease. Fewer than one in ten individuals with distant NSCLC receives surgery; these patients are more often treated with radiation (Table 5.1). The expected perioperative or 30-day mortality in university medical centers varies with the extent of the primary surgery, ranging from about 1 to 6 percent (Ginsberg et al., 1997). These absolute mortality risks are known to vary with patient characteristics (e.g., age, stage of disease, and comorbidity). A 30-day mortality rate of 17 percent after pneumonectomy (removal of part or all of the lung) was observed in an evaluation of a national sample of Medicare claims from the early 1980s (Whittle et al., 1991). Two studies have shown a relationship between high hospital volume and lower mortality for NSCLC. Romano and Mark (1992) used hospital discharge abstracts to assess the outcome of surgery for all adults (n = 12,439) who underwent pulmonary resections in 1983-1986 in 499 nonfederal California hospitals. Hospital volume was defined by the total number of resections for lung cancer per year and was divided into quartiles. In-hospital mortality was 3.8 percent after wedge resection, 3.7 percent after segmental resection, and 11.6 percent after
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--> pneumonectomy. The likelihood of an in-hospital death was 40 percent lower for high-volume hospitals (more than 24 procedures per year) than for low-volume hospitals (fewer than 9 procedures per year) for both lesser resections and pneumonectomy after controlling for patient demographic characteristics and clinical comorbidity (chronic obstructive pulmonary disease, coronary artery disease, and diabetes). The distribution of procedures by volume was as follows: 24 percent in low-; 50 percent in medium-; and 26 percent in high-volume hospitals (Table 5.2). Although volume had a significant effect, there was no difference in the risk of in-hospital death associated with teaching status. Hospitals were stratified into high, low, and non-teaching according to the number of residency programs at a facility. The effect of individual surgeon volume was not addressed. A limitation of this study is its reliance on hospital discharge data, which do not capture postdischarge events. The outcome is limited to in-hospital mortality, but this could be affected by hospital policies regarding length of stay (e.g., hospitals could have low in-hospital mortality but very high mortality following premature discharges). TABLE 5.1 Initial NSCLC Care in Virginia, 1989-1991 (percent) Total Local Regional Distant Initial Treatment Category Age < 64 Age > 65 Age < 64 Age > 65 Age < 64 Age > 65 Age < 64 Age > 65 (n = 336) (n = 1.132) (n = 71) (n = 331) (n = 105) (n = 331) (n = 160) (n = 468) Surgery only 23.6 21.2 74.6a 50.4a 23.1 18.8 1.3 3.1 Surgery plusb 13.1 7.8 5.6 4.1 28.8a 13.8a 5.6 6.1 Radiation 40.0 39.4 7.0 25.8 31.7 41.9 60.0 47.0 Radiation + chemotherapy 6.9 1.2 1.4 0.6 3.8 0.6 11.3 2.0 Chemotherapy 4.2 1.6 0.0 0.0 2.9 1.1 6.9 2.9 None 12.2 28.9 11.3 19.1 8.7 23.9 15.0a 38.9a Any surgery 36.7a 28.0a 80.2a 54.8a 51.9a 32.0a 6.9 9.1 Any radiation 59.1a 47.9a 14.0 30.5 65.4 56.0 76.2a 54.9a Any chemotherapy 11.9a 3.3a 1.4 0.8 8.6 2.0 18.8a 5.1a NOTE: Patients under age 65 were privately insured through Virginia Blue Cross/Blue Shield. Individuals age 65 and older were Medicare enrollees. Unstaged patients were excluded. a Differences between cohorts were statistically different at p < .00005. b "Surgery plus" is defined as surgery plus radiation, chemotherapy, or radiation and SOURCE: Hillner et al., 1998a.
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--> TABLE 5.2 California Postoperative In-Hospital Mortality with Lung Cancer Surgery, 1983-1986 Hospital Volume (no. per year) Lesser Resections Pneumonectomy Patients Adjusted Mortality (%) Adjusted Odds Ratio Patients Adjusted Mortality (%) Adjusted Odds Ratio <9 2,588 5.2 1.0 365 13.6 1.0 9-16 2,945 4.1 0.7 374 11.4 0.8 17-24 2,553 3.5 0.6 377 11.7 0.8 >24 2,822 3.4 0.6 413 9.7 0.6 SOURCE: Romano and Mark, 1992. A study of 30-day mortality examined a broader range of conditions. Begg and colleagues (1998) chose five procedures that involve preoperative judgment, diagnostic accuracy, meticulous surgical technique, and demanding postoperative care: pneumonectomy (removal of part or all of the lung), pancreatectomy (removal of part or all of the pancreas), esophagectomy (removal of part or all of the esophagus), hepatic resection (removal of part or all of the liver), and pelvic exenteration (removal of two or more pelvic organs in one operation). Medicare claims files were linked to Surveillance, Epidemiology, and End Results program (SEER) data for care provided to the elderly from 1984 to 1993. "Curative" surgery is rarely performed for these cancers in the elderly. Of all incident cases of these cancers over the 10-year period, the number of procedures within two months of diagnosis ranged from about 1 to 7 percent of all patients diagnosed (Table 5.3). TABLE 5.3 Medicare-SEER Patient Selection Statistics, 1984-1993 Procedure Primary Cancer Diagnosis Incident Cases Procedures Percentage Pancreatectomy Pancreas 19,205 742 3.9 Esophagectomy Esophagus 6,782 503 7.4 Pneumonectomy Lung-bronchus 103,425 1,375 1.3 Hepatic resection Colon-rectum 126,395 801 0.6 Pelvic exenteration Various 185,305 1,592 0.9 SOURCE: Begg et al., 1998. Within the small set of patients undergoing surgery, a trend of decreasing 30-day mortality with increasing volume was seen for all conditions except pneumonectomy. When the volume-mortality relationship was observed, the risk of death was at least double in low-, compared
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--> to high-volume hospitals. However, the confidence intervals surrounding these estimates are wide, and the only significant difference between low-and high-volume hospital mortality is for esophagectomy (Table 5.4). The p values for the effects of volume (measured continuously) on mortality for each site, after adjusting for comorbidity, stage, and age were as follows: • Esophagectomy p < .001 • Pancreatectomy p = .01 • Hepatic resection p = .05 • Pelvic exenteration p = .05 • Pneumonectomy p = .19 With a p value for statistical significance set at .05, only pneumonectomy fails to show a significant volume-outcome relationship. The share of high-risk procedures performed in low-volume facilities appears to be quite high, especially for procedures for which the volume-outcome relationship is the strongest (e.g., esophagectomy): • Esophagectomy 62% • Pancreatectomy 53% • Hepatic resection 60% • Pelvic exenteration 36% • Pneumonectomy 35% TABLE 5.4 30-Day Mortality (percent) for High-Risk Cancer Surgery Among Medicare Beneficiaries, by Hospital Volume, 1984-1993 Hospital Volumea Procedure Lowb (95% C.I.) Mediumc Highd (95% C.I.) Pancreatectomy 12.9 (9.7, 16.6) 7.7 5.8 (2.5, 11.0) Esophagectomy 17.3 (13.3, 22.0) 3.9 3.4 (0.7, 9.6) Pneumonectomy 13.8 ( 10.9, 17.2) 14.1 10.7 (8.0, 14.0) Hepatic resection 5.4 ( 3.6, 7.8) 3.5 1.7 (0.4, 5.0) Pelvic exenteration 3.7 ( 2.3, 5.5) 3.2 1.5 (0.7, 2.8) a Volume measured as total number of procedures performed between 1984 and 1993 for Medicare beneficiaries only. The volume measure underestimates total hospital volume because many procedures are also performed on younger patients. b 1-5 cases. c 6-10 cases. CIs not provided in publication. d >11 cases. SOURCE: Begg et al., 1998.
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--> TABLE 5.5 Relative Risk of In-Hospital Mortality by Procedure and Hospital Volume Tier, Pancreatic Cancer, 1990-1995 Hospital Volume Procedure Higha Mediumb Lowc Resections (n = 496) 1.0 8.0 (<0.01) 19.3 (<.001) Bypasses (n = 542) 1.0 1.9 (NS) 2.7 (<.05) Stents (n = 198) 1.0 4.8 (NS) 4.3 (NS) NOTE: NS = not significant. a Relative risk is 1.0 because the high-volume hospital (>20 cases) is the reference group. b 5-19 cases. c <5 cases. SOURCE: Sosa et al., 1998. A strength of this study is its use of the SEER-Medicare-linked database instead of hospital discharge data. Investigators were able to determine survival at a landmark point, 30 days after surgery, avoiding the potential bias associated with discharge data where only in-hospital mortality can be assessed. Potential limitations were an imprecise measure of hospital volume (only volume for patients over age 65 was known) and a lack of control for patient sociodemographic characteristics other than age (e.g., race, income). Five other studies provide evidence that high hospital case volume is predictive of better outcome for pancreatic surgery. Sosa et al. (1998) assessed the effect of hospital volume on in-hospital mortality for both palliative and curative surgical procedures for 1,236 patients with pancreatic cancer hospitalized in Maryland from 1990 to 1995. The relative risk of in-hospital mortality was significantly higher in low-compared to high-volume hospitals for resections and bypasses, but not for surgical insertion of stents (used for relief of obstruction) (Table 5.5). More than one-third (35 percent) of patients were cared for in low-, 22 percent in medium-, and 43 percent in high-volume hospitals. There was no effect of surgeon case volume on in-hospital death. Strengths of this study are its relatively large sample size, multivariate analytic techniques controlling for patient characteristics (age, gender, race, payer status, residence), comorbidity, urgency of admission, year of admission, and surgeon case volume. Stage information was not available. A potential limitation is that it relied on hospital discharge data. High-volume hospitals had shorter stays, and deaths could have occurred after discharge. Glasgow examined all discharge summaries for 1,705 patients undergoing pancreatic re-section in 298 California hospitals (those performing at least one pancreatic resection from 1990 to 1994) (Glasgow and Mulvihill, 1996). Low-volume hospitals had in-hospital death rates three times higher than high-volume centers (14.1 versus 3.5 percent) when adjustments were made for patient (age, sex, race, insurance status) and clinical characteristics (number of secondary diagnoses), admission type, and type of resection (Table 5.6). The vast majority of hospitals (88 percent) treated 10 or fewer patients per year, and more than half of the patients studied (53 percent) were treated at centers where 10 or fewer resections were performed in the five-year period.
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--> TABLE 5.6 California Hospital Pancreatectomy Volume and Outcomes, 1990-1994 Hospital Volume (no. of cases) No. of Hospitals Length of Hospital Stay (days) Total Charges ($) Patients Discharged to Home (%) Crude Mortality (%) Risk-Adjusted Mortality Rate (%) 1 to 5 210 22.7 87,857 74.3 14.1 14.1 6 to 10 53 22.7 76,593 80.0 10.4 9.6 11 to 20 20 22.9 78,003 81.8 8.9 8.7 21 to 30 9 20.2 70,959 92.1 5.7 6.9 31 to 50 4 23.9 111,497 87.1 8.2 8.3 >50 2 20.5 71,585 95.1 3.5 3.5 Mean 22.3 83,479 82.1 9.9 9.9 SOURCE: Glasgow and Mulvihill, 1996. TABLE 5.7 Volume-Outcome Effect for Pancreatectomy in New York State, 1984-1991 Hospital Volume (no. of surgeries over 7 years) No. of Hospitals Percentage of Total Patients Mean Length of Stay (days) Standardized Mortality (%) <10 124 24 35 18.9 10-50 57 54 32 11.8 51-80 1 3 22 12.9 >81 2 19 27 5.5 SOURCE: Lieberman et al., 1995. Employing a similar method, Lieberman et al. (1995) used hospital discharge abstracts to identify 1,972 patients having pancreatic resection in New York State between 1984 and 1991. Table 5.7 clearly shows the same higher-volume-better survival relationship, with mortality rates three times as high (18.9 versus 5.5 percent) in the low-(fewer than 10 cases) compared to the high-volume hospitals (more than 81 cases), after adjusting for patient characteristics (age, sex, race, number of secondary diagnoses), admission status, transfer status, year of surgery, and payer status. About one-quarter (24 percent) of patients were cared for in hospitals seeing fewer than 10 cases per year. The effect on outcomes of the volume of surgeries performed by physicians was also assessed and was not found to be a predictor of in-hospital mortality. The volume-outcome association is also evident in a National Cancer Data Base (NCDB) study of 8,917 cases of pancreatic cancer in 1983-1985, and 8,025 cases in 1990 from 978 hospitals (25 percent teaching institutions) (Janes et al., 1996). The 1990 unadjusted operative mortality among patients receiving potentially curative cancer surgery was 7.7 percent in hospitals where fewer than 5 patients were seen per year and 4.2 percent in hospitals where 20 or more patients per year were treated. The trend of better outcomes with higher volumes was evident at each stage of disease. Teaching and community comprehensive hospitals had lower operative mortality (4.7 and 4.6 percent, respectively), whereas community hospitals and hospitals without
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--> American College of Surgeons' Commission on Cancer approval had unadjusted mortality rates of 7.9 and 7.2 percent, respectively. A strength of this study is the large number of cases described and the extensive information gathered on each case (more than 160 data items for each patient). A large number of hospitals are represented in this study, and more than three-quarters of the hospitals invited to participate responded. Hospitals were asked to submit up to 25 consecutive patients treated during 1990. Results are not weighted to reflect the unequal probability of case selection (the chance of selection for a patient in a hospital with 25 or fewer cases is 100 percent, while the probability of selection for a patient in a hospital with 100 cases is 25 percent) making some findings difficult to interpret (e.g., the distribution of cases by hospital case volume). Results were not adjusted for patient or clinical characteristics. Wade et al. (1995) assessed 30-day mortality among 369 patients treated with Whipple resection (i.e., pancreatico-duodenectomy) for pancreatic or other periampullary adenocarcinomas from 1987 to 1991 in 78 Department of Veterans Affairs (VA) hospitals. Hospitals reporting more than two compared with fewer than one resection per year had the lowest operative mortality rates (4 versus 7 percent), but this difference was not significantly lower than the mortality rate in lower-volume hospitals. Results were not adjusted for patient or clinical characteristics. Prostate Cancer A new study by Lu-Yao and Yao (1998) looked at the relationship between outcomes and the number (or volume) of patients receiving surgical treatment for prostate cancer from a surgeon or facility. Using Medicare claims data from 1991 to 1994 for 101,604 men having radical prostatectomy, they found that high-volume facilities had significantly better surgical outcomes than facilities treating fewer patients. High-volume facilities had more favorable rates of survival, complications, and readmission following treatment by radical prostatectomy than lower-volume facilities. High-volume facilities also had shorter lengths of stay (Table 5.8). The results suggest a dose-response relationship, where facilities in the highest-volume quartile showed the best outcomes, followed by those in the third and lower quartiles. These analyses controlled for differences across facilities in patient age, race, year of surgery, surgeon specialty, and hospital teaching status. TABLE 5.8 Radical Prostatectomy Outcomes Among Medicare Beneficiaries, by Hospital Volume, 1991-1994 Odds Ratio Compared to High-Volume Hospitals Hospital Volume 30-Day Mortality Readmission Rate Surgical Complication Low 1.53 1.25 1.30 Medium-low 1.44 1.13 1.16 Medium-high 1.41 1.08 1.08 High 1.00 1.00 1.00 SOURCE: Lu-Yao and Yao, 1998.
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--> TABLE 5.9 Adjusted Risk Ratios for Death for Breast Cancer Patients Hospitalized in New York State, by Hospital Volume, 1984-1989 Hospital Volume (no. of surgeries per year) No. of Patients Percentage of Total Patients Risk Ratio 95% Confidence Interval <10 958 2.0 1.60 1.42, 1.81 11-50 14,440 30.2 1.30 1.22, 1.37 51-150 22,230 46.4 1.19 1.12, 1.25 > 151 10,262 21.4 1.00 — Total 47,890 100.0 — — SOURCE: Roohan et al., 1998. Breast Cancer Surgery Roohan and colleagues report on the effect of hospital volume of breast cancer surgical cases on the five-year survival of 47,890 women (white and black) treated for breast cancer in New York from 1984 to 1989, identified through hospital discharge data and linked to the New York State cancer registry (Roohan et al., 1998). At five years, patients from very low-volume hospitals (1-10 surgeries per year) had a 60 percent greater risk of all-cause mortality than patients from high-volume hospitals (150+ surgeries per year), after controlling for surgery type (mastectomy, limited surgery), patient age, cancer stage, comorbidity, race, socioeconomic status (census tract level), and distance to hospital. A gradient of risk was evident by volume category. Nearly one-third of patients (32 percent) were seen in the lower-volume hospitals (i.e., those with 50 or fewer surgical cases) (Table 5.9). The authors speculate that high-volume hospitals are more likely than others to provide effective postsurgical adjuvant treatments that have been shown to improve survival, perhaps because of better coordination of or access to these services (Roohan et al., 1998). Processes of breast cancer care were examined in a 1988 study of 5,766 newly diagnosed breast cancer patients in Illinois. Hospitals with low compared to high breast cancer case volumes were less likely to use indicated radiation therapy after partial mastectomy (for Stages I and II), but were not less likely to use hormone receptor tests (for Stages II through IV) (Hand et al., 1991). Evidence on The Volume-Outcome Relationship from Other Countries Studies from other countries support the finding that high volume leads to better outcomes. Scotland. Centralized treatment at high-volume centers improved outcomes for a type of testicular cancer (non-seminomatous germ cell tumors), even when controlling for participation in a protocol (although protocol treatment explained a large part of the variation) (Harding et al., 1993).
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--> risk for local recurrence was 2.5 (if not specialty trained) and 1.8 (if a low-volume surgeon). The relative risks against disease-free survival were 1.5 and 1.4 for non-specialty-trained or low-volume surgeons, respectively. In England, Sainsbury et al. (1995) assessed the effect of a surgeon's volume of cases on the five-year survival of 12,861 women with breast cancer treated with "curative" surgery between 1979 and 1988 in Yorkshire, England (population 3.6 million). There was no difference in survival between patients treated by surgeons seeing <10 and 10-29 cases per year, but if the surgeons saw >30 cases per year, the adjusted risk of death at five years was 0.85 (C.I. 0.77-0.93). About 50 percent of patients were seen by high-volume (>30 cases) surgeons. After controlling for case mix and clinical variables (e.g., axillary node status, histologic grade), variation among the consultants accounted for about an absolute 8 percent difference in survival. This benefit was principally associated with the greater use of chemotherapy. The evidence on the effects of specialization, either by hospitals or by physicians, does not present a consistent picture; most, but not all, studies show improved care with specialization. Findings from these observational studies must be interpreted with caution because they are highly subject to bias. Patients cared for by specialty providers and specialty centers differ from patients treated elsewhere, and analyses must account for these differences in case mix. Many studies do not appropriately control for important patient variables and clinical factors that likely vary by site of care. Specialty providers such as those in teaching hospitals differ from community-based providers in their use of staging procedures, which could contribute to a stage migration bias that favors specialists. Managed Care Versus Fee-For-Service Care There is a great deal of interest in the way patients with chronic illnesses such as cancer fare within managed care organizations (see definition and discussion of managed care in Chapter 2). Theoretically, quality of care could be compromised if individuals enrolled in managed care plans could not access needed cancer care specialists or services. On the other hand, care could be enhanced if managed care plans implemented effective early detection, clinical practice guidelines, or disease management programs to a greater extent than FFS plans. Although intriguing questions have been raised, there is little evidence on which to judge the impact of managed care on the quality of cancer care. Individuals enrolled in managed care plans are generally satisfied with the care that they receive, and in one study, Medicare beneficiaries in managed care did not have high rates of switching into FFS plans after a cancer diagnosis (Riley et al., 1996). This does not necessarily mean that beneficiaries were entirely satisfied with care, but that any dissatisfaction does not seem to lead to high levels of disenrollment. HMO enrollees as compared to those in FFS settings receive more cancer screening services (see Chapter 3). Only eight studies have looked directly at the effect of managed care on cancer care. Riley et al. (1999) recently examined treatment for early breast cancer between 1988 and 1993 among elderly women receiving care in HMO and FFS settings in 11 U.S. geographic areas. Use of breast conserving surgery was similar among women with early-stage disease enrolled in HMO and FFS plans (38 and 37 percent, respectively). Among women undergoing
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--> BCS, HMO enrollees were significantly more likely than those in FFS plans to receive radiation therapy (69 versus 64 percent). Analyses of treatment patterns were controlled for age, race, cancer history, year of diagnosis, stage at diagnosis, tumor size, county of residence, and education at the census tract level. Investigators found aggregate comparisons of the experiences of HMO and FFS populations to obscure important variation among HMO plans. Among BCS cases, for example, radiation therapy was more commonly received by HMO enrollees overall but the pattern varied by HMO. Enrollees in some HMOs were significantly more likely than those in FFS plans to have had radiation therapy, and in other HMOs, the opposite was true. The authors conclude that variation among HMOs is likely attributable to differences in both plan and market characteristics. Plans differ in their structure, organization, benefit packages, payment policies, practice protocols, and provider relationships. Market characteristics vary along many dimensions, such as degree of competition, managed care penetration, and availability of radiation facilities. These findings illustrate how difficult it is to generalize about managed care. In an earlier study, Potosky et al. (1997) compared HMO to FFS care among 13,358 Medicare beneficiaries diagnosed with breast cancer from 1985 to 1992 in the Seattle-Puget Sound and San Francisco Bay areas. Cancer registry data (i.e., SEER) were linked to Medicare administrative files to assess aspects of care and survival (Potosky et al., 1997). In San Francisco-Oakland, the 10-year adjusted risk of death due to breast cancer was 29 percent lower, and the overall adjusted risk of death 30 percent lower, among women belonging to an HMO (i.e., Kaiser Permanente of Northern California) compared to women insured by FFS plans (Table 5.13). A significant HMO mortality advantage was not found in the Seattle-Puget Sound area (i.e., Group Health of Puget Sound). Women enrolled in HMOs in both areas were more likely than those covered by FFS plans to have received breast conserving surgery (BCS) and, among those having BCS, were more likely to have had radiation therapy following surgery (Table 5.13). The authors conclude that long-term survival outcomes in the two prepaid group practice HMOs were at least equal to, and possibly better than, outcomes in the FFS system. In addition, the use of recommended therapy for early-stage breast cancer was more frequent in the two HMOs. Medicare patients with breast cancer in these two established nonprofit staff-and group-model HMOs appeared to receive better quality of care than Medicare enrollees in FFS. Strengths of this study were the large sample size; adjustments for sociodemographic (i.e., age, race, area-level educational status); and clinical factors (i.e., stage, whether the diagnosis was a single or first primary cancer, comorbidity); and the length of follow-up (10 years). The authors speculate that the observed HMO survival advantage is, in part, due to more frequent screening. HMO compared to FFS care was associated with earlier stage at diagnosis and within stage, with smaller tumors. Some of the HMO survival advantage could be artifactual if higher rates of screening within HMOs are identifying biologically less aggressive tumors, including those that would never have been detected via symptoms. The analysis controlled for stage, but not for the within-stage shift in tumor size. The findings from this study may not be generalizable to other areas or types of managed care plans (e.g., for profit, independent practice associations). The two HMOs included in the study embody core features of traditional managed care, which include an emphasis on creating long-standing relationships between patient and providers, preventive care, the practice of evidence-based medicine, less stringent utilization review with greater physician autonomy, and greater coordination of specialty care (Clancy and Brody, 1995).
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--> In contrast to the Potosky study, no difference in the use of breast conserving surgery was found among women with HMO versus FFS insurance in a recent study conducted in Massachusetts and Minnesota (Guadagnoli et al., 1998). Guadagnoli and colleagues examined the medical records of women diagnosed with breast cancer from 1993 to 1995 cared for in a random sample of hospitals in Massachusetts and a convenience sample of hospitals in Minnesota. Among 2,135 women eligible for breast conserving surgery, 74 percent of women in Massachusetts and 48 percent of women in Minnesota underwent BCS. Investigators examined correlates of BCS and mastectomy use including sociodemographic characteristics of women (age, education, household income, urban residence), insurance status (HMO or non-HMO), characteristics of the surgeon (gender, board certification, years since graduation), and hospital characteristics (teaching status, bed size, presence of American College of Surgeons-approved cancer program, presence of a radiation facility). According to multivariate analyses, women in Massachusetts cared for in a teaching hospital were twice as likely as other women to undergo BCS (odds ratio [OR] = 2.4; C.I. 1.3-4.6). In Minnesota, younger women and residents of urban areas were more likely than others to undergo BCS. The absence of an HMO effect in Massachusetts and Minnesota in this study, in contrast to the Potosky findings, could be due to a number of factors, including the use of more recent data (1993-1995 versus 1985-1992) or the difference in managed care providers in Massachusetts and Minnesota relative to California and Washington. Unadjusted HMO versus non-HMO BCS rates were not reported for Massachusetts and Minnesota, so one cannot tell if an HMO advantage is apparent when provider and hospital characteristics are left out of the model. The Potosky finding of an HMO advantage could hypothetically be related to hospital teaching status or to other hospital or provider characteristics that were not considered in their analyses. TABLE 5.13 Outcome and Process Odds Ratios for HMO versus FFS Care for Elderly Women with Breast Cancer (in situ, Stages I and II), by Location End Point San Francisco-Oakland Seattle-Puget Sound Outcome 10-year overall survivala 0.70 (0.62-0.79)b 0.86 (0.72-1.03) 10-year breast cancer survivala 0.71 (0.59-0.87) 1.01 (0.77-1.33) Process BCS 1.55 (1.35-1.77)c 3.39 (2.76-4.17) XRT post-BCSd 2.49 (1.95-3.19) 4.62 (3.20-6.66) NOTE: Reference group is FFS care. Outcome odds ratios less than 1 indicate greater survival, and process odds ratios greater than 1 indicate more frequent desired care. a Adjusted for age, race, census tract education and income, comorbidity, and stage. b Odds ratio of <l.0 means that women in HMOs were less likely to die than women in FFS. c Odds ratio of >l.0 means that women in HMOs were more likely to receive the treatment than women in FFS. d XRT post-BCS: x-ray therapy after breast conserving surgery. SOURCE: Potosky et al. (1997).
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--> Lee-Feldstein et al. (1994), using cancer registry data, found significantly worse five-year survival among women with localized breast cancer treated at HMO hospitals in Orange County, California, in 1984-1990, compared to women treated at teaching hospitals, small community hospitals, and large community hospitals. Patients treated at an HMO facility had a 63 percent increased risk of dying, compared with the reference group treated in small hospitals, when age, tumor size, number of positive lymph nodes, and type of treatment (e.g., breast conserving surgery with radiation versus no radiation) were controlled for. The excess deaths among HMO patients with localized disease were limited to 1984 through 1987. Only 380 HMO patients with localized disease were available for analysis, and the confidence interval around the point estimate (i.e., OR = 1.63) ranges from 1.16 to 2.30. No HMO mortality disadvantage for women with regional disease was found. The unexpected finding of a 45 percent increased risk of death for patients having a total mastectomy compared to those having BCS, when no difference is expected, has raised questions about the validity of this study (Hillner et al., 1998b). Furthermore, survival comparisons between HMO and non-HMO hospitals did not control for comorbidity, race, and socioeconomic status. Clinical and socioeconomic variables are likely to differ by type of hospital and are strongly related to survival of women with breast cancer (Charlson et al., 1987; Eley et al., 1994; Greenwald, 1992). Methodologic flaws of this study limit its interpretation. Retchin and Brown (1990) assessed the effect of being insured by an HMO in two different studies related to care for colorectal cancer in the elderly. The first study examined pre-and postoperative care processes for 330 patients diagnosed from 1983 to 1986 as part of an evaluation of the Medicare demonstrations in prepaid care. Some differences in use of diagnostics tests were observed, but findings are difficult to interpret because analyses were largely descriptive in nature, with few controls for clinical or sociodemographic characteristics. A more recent study of 813 patients diagnosed in 1989 compared perioperative care and outcomes within 19 geographically dispersed HMOs to FFS care (Retchin et al., 1997). There were some differences in processes of care; for example, compared to those covered by FFS plans, HMO enrollees had shorter lengths of stay and received fewer tests and services. There was no evidence that HMO members experienced different outcomes (e.g., hospital readmissions, in-hospital deaths, admissions within one year of discharge). A limitation of this study is the lack of control in some comparisons for sociodemographic characteristics, stage, and other clinical factors. Greenwald and Henke (1992) compared care and outcomes by HMO status among Medicare beneficiaries with prostate cancer diagnosed from 1980 to 1982 in the Seattle area. Patients in Group Health of Puget Sound (n = 131) relative to 1,032 FFS patients had less surgery, more radiation therapy, and—after adjustment for stage, urban location, and age—better survival. The relatively small sample from one HMO, the age of the data, and a lack of adjustment for clinical prognostic factors in the analysis limit the value of this study. Vernon and colleagues (1992) evaluated the effect of insurance status on the care of 330 patients with colorectal cancer diagnosed from 1984 to 1989 and seen by the same set of providers in one group practice in Houston, Texas. No systematic differences were found in the care offered to HMO and FFS patients (e.g., type of primary treatment). Limitations of this study include the small sample size and a lack of adjustments for differences in the HMO and FFS study populations (e.g., HMO members were younger). In summary, relatively few studies have compared cancer care under managed care and FFS financing and delivery arrangements. Most of these studies have involved comparisons of
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--> FFS care with care in a staff-or group-model HMO. These studies show processes and outcomes of care in these settings that are equal to or better than those in FFS settings. The findings of many of the studies of managed care can be challenged on methodological grounds—retrospective cross-sectional designs, small sample sizes, nonrandom potentially biased selection of cases, and inadequate use of control variables to adjust for underlying differences in patient populations served in HMOs and FFS. Furthermore, most of the studies have been limited to large staff-or group-model HMOs that now represent only about 15 percent of managed care enrollment (http://www.aahp.org). Five of the eight studies reviewed evaluated the care of patients diagnosed before 1990. Most of these studies included an analysis of mortality, so long follow-up times were necessary; however, the processes of care that may have contributed to differences in outcomes have in all likelihood changed. Recent evidence points to significant variation among HMOs in the quality of cancer treatment (Riley et al., 1999). Carefully designed, large studies are needed to assess how features of managed care plans and market areas affect the quality of cancer care. Key Findings Among the first questions many individuals ask after receiving a diagnosis of cancer care are, ''Where should I go for care?'' and "What kind of doctor should I see?" Health services research has not fully addressed these important questions. There is very limited evidence on the way structures and technical processes of care affect cancer care outcomes, and the strength of available evidence is weakened by methodological shortcomings of the research. Only a handful of studies were available for this review on the effects of managed care or on the effects of the volume and specialization of facilities or physicians on cancer care quality. Many of the available studies on these topics were done outside the United States, making inferences to care in the United States difficult. Most of the published literature includes mortality as the main outcome measure and has long periods of follow-up. Consequently, most of the studies apply to patients who were diagnosed with cancer in the early to late 1980s. A large body of evidence supports a relationship between high surgical case volume and better survival for several cancers for which high-risk surgery is indicated (e.g., pancreatic cancer, non-small-cell lung cancer). Several studies show very large effects, with low-volume hospitals having postsurgical mortality rates two to three times those of high-volume hospitals. A dose-response effect is also evident to support the finding that as volume increases, so do good outcomes. The observational studies described, however, must be interpreted cautiously because they are prone to biases that favor large centers (e.g., greater use of diagnostic tests can contribute to a stage migration bias; patients at high-volume centers tend to be healthier than at smaller hospitals). Studying the effect of institutional specialization on outcomes is difficult because specialization is often closely tied to the size of a facility and the volume of services. Nevertheless, a number of studies have attempted to identify differences in outcomes of facilities according to various measures of specialization—for example, whether they are cancer centers, university affiliated, or designated as research centers or have residency training programs. There does appear to be a consistent trend of improved outcomes associated with specialization, however defined.
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--> In most of these studies, however, facility size was not controlled for in the analysis, which makes it difficult to know whether improvement is truly an effect of specialization. Other limitations of the available research in this area are the small number of institutions included in the studies and a lack of information on whether underlying differences in patient populations across facilities are taken sufficiently into account. Furthermore, the potential for publication bias is significant in these kinds of studies (e.g., specialty centers would be likely to publish only positive results). At this time the evidence is insufficient, and well-designed studies are needed to understand the relationship between institutional specialization and outcomes. Very little can be said about the effects of physician specialization on outcomes of cancer care. Only one U.S. study was found that compared the outcomes of patients cared for by physicians with different levels of training. The one U.S. study and several studies conducted outside the United States appear consistently to show improved outcomes with specialization, but the definitions of specialization varied widely. For three of the studies, the outcomes of patients with ovarian cancer were compared according to whether their provider was a general surgeon or a gynecologist. Other studies used different definitions of specialization (e.g., training, interest, practices such as keeping separate records for cancer patients). Here again, the evidence is insufficient, and well-designed studies are needed to understand the importance of physician specialization. Few studies compared cancer care under managed care with fee-for-service care, and studies are usually limited to group-or staff-model HMOs that have a relatively small share of the total managed care enrollment. The limited body of evidence suggests that processes and outcomes of care in these managed care settings are equal to or better than those in fee-for-service settings. Recent evidence suggests that there is significant variation in quality of care among HMOs. There are a number of data systems in place that could substantially improve the quality of information available to provide additional insights into which structures and technical processes of care may lead to better patient outcomes. The National Cancer Data Base, for example, could be used more extensively to address quality issues if a nationally representative sample of facilities and providers was used. The SEER-Medicare-linked database appears to be an underutilized resource with which to evaluate aspects of care that affect outcomes for Medicare beneficiaries. Existing data systems, however, must be enhanced so questions about quality of care can be answered comprehensively on a national scale. An effective system would have to capture information about the person with cancer (e.g., age, race or ethnicity, socioeconomic status, insurance coverage); the condition (e.g., stage, grade, histological pattern, comorbid conditions); the treatment including significant outpatient treatments (e.g., adjuvant therapy); the providers (e.g., specialty training); where care was delivered (e.g., community hospital, cancer center); the type of delivery care system (e.g., managed care versus fee for service); and the outcomes (e.g., relapse, complications, death, satisfaction, quality of life). It may be costly and difficult to obtain all of the desirable data elements for all individuals in any one data system, so existing databases could be used effectively to identify a sample of patients for augmented data collection in targeted studies. Linking available databases is another
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--> option for expanding the set of variables for analysis. For example, linking information about hospitals or other facilities that provide care with patient-level databases would allow the analyses of important structural components of care. To improve the timeliness of data, data collection has to be standardized and automated to provide relatively quick turnaround of information. It is important to assess the following aspects of care, which could affect quality, but also will present analytic challenges: multidisciplinary care teams, referral patterns to specialists, second opinions, and the communication skills of health providers. References Aass N, Klepp O, Cavallin-Stahl E, et al. 1991. Prognostic factors in unselected patients with nonseminomatous metastatic testicular cancer: A multicenter experience. Journal of Clinical Oncology 9:818-826. Ballard-Barbash R, Potosky A, et al. 1996. Factors associated with surgical and radiation therapy for early stage breast cancer in older women. Journal of the National Cancer Institute 88(11):716-726. Basnett I, Gill M, Tobias JS. 1992. Variations in breast cancer management between a teaching and a non-teaching district. European Journal of Cancer 28A:1945-1950. Begg CB, Cramer LD, Hoskins WJ, Brennan MF. 1998. Impact of hospital volume on operative mortality for major cancer surgery. Journal of the American Medical Association 280(20):1747-1751. Charlson ME, Pomei P, Ales KL, MacKenzie CR. 1987. A new classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases 40:373-383. Clancy CM, Brody H. 1995. Managed care: Jekyll or Hyde? Journal of the American Medical Association 273:338-339. Clarke K, Howard GC, Elia MH, et al. 1995. Referral patterns within Scotland to specialist oncology centres for patients with testicular germ cell tumours. The Scottish Radiological Society and the Scottish Standing Committee of the Royal College of Radiologists. British Journal of Cancer 72:1300-1302. Davis S, Dahlberg S, Myers MH, et al. 1987. Hodgkin's disease in the United States: A comparison of patient characteristics and survival in the Centralized Cancer Patient Data System and the Surveillance, Epidemiology, and End Results Program. Journal of the National Cancer Institute 78:471-478. Dent DM. Cancer surgery: Why some survival benefits may be artefactual. British Journal of Surgery 85(4):433-434. Dickersin K. 1997. How important is publication bias? A synthesis of available data. AIDS Education and Prevention 9(Suppl. A):15-21. Eley JW, Hill HA, Chen VW, et al. 1994. Racial differences in survival from breast cancer. Results of the Cancer Institute Black/White Cancer Survival Study. Journal of the American Medical Association 272(12):947-954. Ellis SG, Weintraub, D Holmes, et al. 1997. Relation of operator volume and experience to procedural outcome or percutaneous coronary revascularization at hospitals with high interventional volumes . Circulation 95(11):2479-2484. Farrow D, Hunt W, Samet J. 1995. Biased comparisons of lung cancer survival across geographic areas: Effects of stage bias. Epidemiology 6(5):558-560.
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--> Hosenpud J, Breen T, Edwards E, et al. 1994. The effect of transplant center volume on cardiac transplant outcome: A report of the United Network for Organ Sharing scientific registry. Journal of the American Medical Association 271(23):1844-1849. Janes RH Jr., Niederhuber JE, Chmiel JS, et al. 1996. National patterns of care for pancreatic cancer. Results of a survey by the Commission on Cancer. Annals of Surgery 223:261-272. Johantgen ME, Coffey RM, Harris DR, et al.. 1995. Treating early-stage breast cancer: Hospital characteristics associated with breast-conserving surgery. American Journal of Public Health 85(10): 1432-1434. Jollis J, Peterson E, DeLong E, et al. 1994. The relation between the volume of coronary angioplasty procedures at hospitals treating Medicare beneficiaries and short-term mortality. New England Journal of Medicine 331(24):1625-1629. Jollis J, Peterson E, Nelson CL, et al. 1997. Relationship between physician and hospital coronary angioplasty volume and outcome in elderly patients. New England Journal of Medicine 95(11):2267-2270. Junor EJ, Hole DJ, Gillis CR. 1994. Management of ovarian cancer: Referral to a multidisciplinary team matters. British Journal of Cancer 70:363-370. Kantonen I, Lepantalo M, Salenius JP, et al. 1997. Mortality in abdominal aortic aneurysm surgery—The effect of hospital volume, patient mix, and surgeon's case load. European Journal of Vascular and Endovascular Surgery 14(5):375-379. Kehoe S, Powell J, Wilson S, Woodman C. 1994. The influence of the operating surgeon's specialisation on patient survival in ovarian carcinoma. British Journal of Cancer 70:1014-1017. Kingston RD, Walsh S, Jeacock J. 1991. Curative resection: the major determinant of survival in patients with large bowel cancer. Journal of the Royal College of Surgeons of Edinburgh 36:298-302. Kitahata NM, Koepsell TD, Deyo RA, et al. 1996. Physicians' experience with the acquired immunodeficiency syndrome as a factor in patients' survival. New England Journal of Medicine 334(11): 701-706. Kline RW, Smith AR, Coia LR, et al. 1997. Treatment planning for adenocarcinoma of the rectum and sigmoid: A Patterns of Care study. International Journal of Radiation Oncology, Biology, Physics 37:305-311. Konvolinka CW, Copes WS, Sacco WJ. 1995. Institution and per-surgeon volume versus survival out-come in Pennsylvania's trauma centers. American Journal of Surgery 170(4):333-340. Kreder HJ, Deyo RA, Koepsell T, et al. 1997. Relationship between the volume of total hip replacements performed by providers and the rates of postoperative complications in the State of Washington. American Journal of Bone and Joint Surgery 79(4):485-494. Lavernia CJ, Guzman JF. 1995. Relationship of surgical volume to short-term mortality, morbidity, and hospital charges in arthroplasty. Journal of Arthroplasty 10(2):133-140. Lee-Feldstein A, Anton-Culverm H, Feldstein P. 1994. Treatment differences and other prognostic factors related to breast cancer survival. Journal of the American Medical Association 271(15): 1163-1168. Lieberman MD, Kilburn H, Lindsey M, Brennan MF. 1995. Relation of perioperative deaths to hospital volume among patients undergoing pancreatic resection for malignancy. Annals of Surgery 222:638-645. Lu-Yao G, Yao S. 1998. Relationships between surgical volume, outcomes, and length of stay—A national study of patients undergoing radical prostatectomy. Abstract submitted to American Society of Clinical Oncology. McArdle CS, Hole D. 1991. Impact of variability among surgeons on postoperative morbidity and mortality and ultimate survival. British Medical Journal 302:1501-1505. Mella J, Biffin A, Radcliffe AG, et al. 1997. Population-based audit of colorectal cancer management in two UK health regions. British Journal of Surgery 84:1731-1736.
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