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Original Contribution |

Influence of Hospital Procedure Volume on Outcomes Following Surgery for Colon Cancer FREE

Deborah Schrag, MD; Laura D. Cramer, ScM; Peter B. Bach, MD; Alfred M. Cohen, MD; Joan L. Warren, PhD; Colin B. Begg, PhD
[+] Author Affiliations

Author Affiliations: Departments of Epidemiology and Biostatistics (Drs Schrag, Bach, and Begg and Ms Cramer), Medicine (Drs Schrag and Bach), and Surgery (Dr Cohen), Health Outcomes Research Group, Memorial Sloan-Kettering Cancer Center, New York, NY; and the Applied Research Branch, National Cancer Institute (Dr Warren).


JAMA. 2000;284(23):3028-3035. doi:10.1001/jama.284.23.3028.
Text Size: A A A
Published online

Context Survival following high-risk cancer surgery, such as pancreatectomy and esophagectomy, is superior at hospitals where high volumes of these procedures are performed. Conflicting evidence exists as to whether the association between hospital experience and favorable health outcomes also applies to more frequently performed operations, such as those for colon cancer.

Objective To determine whether hospital procedure volume predicts survival following colon cancer surgery.

Design, Setting, and Participants Retrospective cohort study of data from the Surveillance, Epidemiology and End Results–Medicare linked database on 27 986 colon cancer patients aged 65 years and older who had surgical resection for primary adenocarcinoma diagnosed between 1991 and 1996.

Main Outcome Measures Thirty-day postoperative mortality and overall and cancer-specific long-term survival, by hospital procedure volume.

Results We found small differences in 30-day postoperative mortality for patients treated at low- vs high-volume hospitals (3.5% at hospitals in the top-volume quartile vs 5.5% at hospitals in the bottom-volume quartile). However, the correlation was statistically significant and persisted after adjusting for age at diagnosis, sex, race, cancer stage, comorbid illness, socioeconomic status, and acuity of hospitalization (P<.001). The association was evident for subgroups with stage I, II, and III disease. Hospital volume directly correlated with survival beyond 30 days and also was not attributable to differences in case mix (P<.001). The association between hospital volume and long-term survival was concentrated among patients with stage II and III disease (P<.001 for both). Among stage III patients, variation in use of adjuvant chemotherapy did not explain this finding.

Conclusion Our data suggest that hospital procedure volume predicts clinical outcomes following surgery for colon cancer, although the absolute magnitudes of these differences are modest in comparison with the variation observed for higher-risk cancer surgeries.

Figures in this Article

The complexity of health care processes makes identification and measurement of the critical components of high-quality care especially challenging.1 Because hospital procedure volume is relatively easy to measure and is assumed to be a proxy for experience, it has long been examined as a predictor of clinical outcomes, and a volume-outcome relationship has been observed for a wide variety of surgeries.213 Concentration of surgeries in high-volume centers has been considered a strategy to improve the quality of care,2,1416 and in select instances, policies to achieve this goal have been implemented.17

For cancer patients, large population-based studies have demonstrated that hospital procedure volume can have a profound effect on outcomes following operations associated with high mortality, such as pancreatectomy.1820 Some prior studies have suggested that a volume-outcome effect may also exist for colon cancer surgery, which is performed more frequently but with less substantial morbidity and mortality.35,2126 However, these analyses have been limited by either sample size, lack of population-based case ascertainment, geographic diversity, insufficient clinical detail for risk adjustment, or incomplete mortality data. Recently, linkage of the Surveillance, Epidemiology, and End Results (SEER) registries to Medicare claims created a resource that combines the necessary ingredients for volume-outcome analyses for the US population aged 65 years and older.27

We used SEER-Medicare data to identify a population-based cohort of colon cancer patients to determine whether hospital procedure volume predicts short- and long-term survival following primary surgery. We hypothesized that hospital volume would predict both postoperative mortality and long-term survival, but anticipated that the absolute magnitude of these associations would be smaller than those observed for infrequently performed higher-risk operations, such as pancreatectomy and esophagectomy.

Data Sources

The SEER registries ascertain all incident cancer cases diagnosed in 5 states and 6 US metropolitan areas, representing approximately 14% of the US population.28 Information is collected on each incident cancer, including the primary site and histology classified according to the International Classification of Diseases for Oncology, Second Revision (ICD-O-2)29 schema, the tumor stage at diagnosis, and patient demographics.

The Medicare program provides health coverage for 97% of the US population aged 65 years and older. The Medicare Provider Analysis and Review files provide details of all hospitalizations for persons eligible for Medicare Part A. To receive payment, hospitals submit medical claims coding up to 10 diagnoses and 10 procedures using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) classification. For the 96% of Medicare beneficiaries who opt for Part B coverage, claims for care delivered in hospital outpatient departments and physicians' offices are also recorded. Medicare records document date of death based on information provided by the Social Security Administration. The SEER-Medicare data have been linked to facilitate population-based studies of the outcomes of cancer treatment. Ninety-four percent of patients in SEER aged 65 years and older have been successfully linked to their Medicare records.27

Cohort Definition

All Medicare-enrolled patients aged 65 years and older diagnosed as having primary colon cancer in a SEER area during the years 1991 to 1996 were potentially eligible for inclusion in our study. Colon cancers were defined using SEER codes for cancer sites 18.0 through 18.9, and 19.9; thus, tumors arising in the rectosigmoid were included. We restricted our cohort to patients with a histologic diagnosis of adenocarcinoma (SEER histology codes 8140, 8210, 8211, 8220, 8221, 8260, 8261, 8262, 8263, 8470, 8480, 8481, and 8490). Diagnoses noted exclusively on death certificates or at autopsy were excluded, as were those in which the month of diagnosis was unknown. Patients enrolled in a health maintenance organization were excluded from our cohort (16.5% of patients) because detailed claims are not submitted to Medicare by health maintenance organizations.

We searched Medicare claims for colon cancer surgeries performed within 6 months of primary diagnosis. Operations were defined according to the ICD-9-CM classification system (45.7x, 45.8, 48.4x, 48.5x, and 48.6x); thus, patients operated on exclusively for local resection or creation of an ostomy were excluded. Only patients undergoing surgery at hospitals located in 1 of the 9 states containing 1 of 11 SEER registries were included in the analysis because hospital volume could not be reliably measured at institutions outside SEER areas.

Outcomes of Colorectal Surgery

The 3 outcomes assessed were 30-day postoperative mortality, overall survival, and colon cancer-specific survival. Postoperative mortality was defined as death within 30 days of hospitalization for surgical resection. Date of hospitalization served as a proxy for date of surgery since it is more reliably coded in the Medicare database. Survival was defined by the interval from the date of hospitalization for resection until either death as reported to Medicare, or December 31, 1998, when censoring occurred. SEER reports cancer-specific mortality based on state death certificates but these vital statistics were current only through December 31, 1996. Therefore, colon cancer-specific survival was examined for the 98.5% of our cohort (27 561/27 986) who had available state death certificates or had not died prior to this date.

We hypothesized that greater use of adjuvant chemotherapy at high-volume hospitals might partially explain an observed volume-outcome relationship. We examined use of adjuvant chemotherapy among patients with stage III cancer who survived 3 months postoperatively, and were also enrolled in Medicare Part B, which is required for coverage of outpatient services. Those patients who had at least 1 claim for chemotherapy or its administration at any point during the 3-month postoperative period were considered recipients.

Hospital Procedure Volume

Hospitals were ranked by volume according to the number of operations performed between 1991 and 1996, an approach that has been previously validated.18 Examination of fluctuations of individual hospital volumes on a year-by-year basis demonstrated great stability. To avoid the possibility of selecting cut points with maximal P values, primary statistical analyses were performed using the Mantel-Haenszel test for trend without aggregation of the data into discrete volume categories. To facilitate display of our results and to adjust survival in a Cox proportional hazards model, we defined quartiles of hospital procedure volume (low, medium, high, and very high) based on the volume of operations performed on members of our cohort during the 6-year study period.

Potential Confounders

We used information on tumor size, nodal involvement, and spread coded in the SEER database to stage patients according to the American Joint Committee on Cancer schema. Patients with missing information about tumor extent, nodal involvement, or metastases were classified as unstaged. To adjust for potential confounding based on the severity of noncancer medical illness, we used Romano's30 modification of the comorbidity index originally developed by Charlson.31 We examined all available inpatient Medicare claims for the 12 months prior to the index surgical admission, as well as claims during the index admission, and assigned patients the maximal comorbidity observed. We used the median income in the census tract of residence to adjust for differences in patients' economic status.

Colon cancer resections performed on an emergency basis may be associated with high mortality. We used the Medicare claim code for emergent hospital admission and ICD-9 codes for emergent indications for surgery bowel obstruction (560.89, 560.90) and perforation (569.83) to permit an adjustment in multivariable analysis.

Statistical Analysis

The relationship between hospital procedure volume and postoperative mortality was examined using the Mantel-Haenszel test for trend. While we used the patient as the unit of analysis, we also performed a modified version of the Mantel-Haenszel test that adjusts for within-hospital correlations in the data.32 Multiple logistic regression, with vital status at 30 days as the outcome and hospital procedure volume as a continuous predictor, was used to adjust for potential confounding by sex, race, age at diagnosis, cancer stage, comorbidity, socioeconomic status, and the presence of emergent indications for surgery according to the categories shown in Table 1.

Table Graphic Jump LocationTable 1. Patients With Colon Cancer According to the Procedure Volume of the Hospital Where Surgery Was Performed*

The impact of hospital volume on survival is displayed using the Kaplan-Meier method. The Cox proportional-hazards method was used to examine the effects of potential confounders. The likelihood ratio test was used to compare a model that had all variables except procedure volume with a model that had all variables including procedure volume. All P values are 2-sided. When we developed our research protocol, we calculated the effect size that would allow for detection with a 2-sided significance level of .05 and 90% power for each planned analysis. With a sample size of 27 000 colon cancer patients and a 4% postoperative mortality rate, we had power to detect a 0.8% difference between low- and high-volume hospitals; for a subgroup of 7000 patients, we could detect a 1.5% difference; for a subgroup of 3500 patients, a 2.1% difference.

Characteristics of the Cohort

A total of 47 495 Medicare-eligible patients received an antemortem diagnosis of primary colon cancer at age 65 years and older during the years 1991-1996 in SEER areas. We sequentially excluded 2280 patients with histologies other than adenocarcinoma, 71 lacking a month of diagnosis, and 2934 with in situ tumors. Among the remaining 42 210 patients, 7999 were excluded because they were enrolled in a health maintenance organization or not enrolled in Medicare Part A at the time of diagnosis. From this group of 34 211, a total of 28 475 had surgical resection, and among these, 27 986 had surgery performed at a hospital located in a SEER area. The demographic and clinical characteristics of the 27 986 patients are displayed in Table 1.

Hospital Surgical Volume

In our cohort, colon cancer resections were performed at 611 different hospitals between 1991 and 1996. Hospital volume over the 6-year period ranged from 1 to 57 for the 440 low-volume hospitals (72%); 58 to 112 for the 89 medium-volume hospitals (15%); 113 to 165 for the 51 high-volume hospitals (8%); and 166 to 383 for the 31 very high-volume hospitals (5%). The numbers and characteristics of patients in each volume quartile category are shown in Table 1. The age, sex, and comorbidity of patients were similar across strata of hospital volume. Patients with race coded as other than white or black, those with unstaged tumors, and those with low socioeconomic status were more likely to undergo surgery at a low-volume hospital.

Postoperative Mortality

As shown in Table 2, the absolute magnitude of variation in 30-day postoperative mortality at hospitals with different procedure volumes was small. For example, the difference in 30-day mortality for patients operated on at the very high-volume compared with the low-volume hospitals was 2% (3.5% vs 5.5%). However, a consistent association between higher postoperative mortality and lower surgical procedure volume was evident (P<.001) and persisted after inclusion of potential confounders in multivariable logistic regression (P<.001). In subgroup analyses, we found that hospital volume was a significant predictor of mortality for patients with stage I, II, and III tumors, but not for patients with stage IV or unstaged disease, in which smaller sample sizes precluded our ability to detect effect sizes of less than 2%.

Table Graphic Jump LocationTable 2. Magnitude and Significance of the Association Between Hospital Procedure Volume and Mortality
Survival

The survival curves for patients treated at institutions in each volume quartile illustrate a clear association between procedure volume and overall survival (P<.001; Figure 1A). The difference in 5-year mortality for patients operated on at the very high- vs the low-volume hospitals was 4.4% (54.8%-50.4%; Table 2). Since a 2% absolute mortality difference is evident at 30 days, 45% (2/4.4) of the survival difference appears to be attributable to the immediate postoperative period and 55% to more distal events. Figure 1B demonstrates that similar results are obtained for colon cancer-specific survival (P<.001).

Figure 1. Postoperative Overall Survival (N = 27 986) and Colon Cancer-Specific Survival (n = 27 561) According to Hospital Procedure Volume
Graphic Jump Location

Adjusted risk ratios and confidence intervals for overall mortality for patients in each hospital volume category compared with patients operated on at the very high-volume institutions are shown in Table 2. The volume-outcome association remained highly significant for both overall mortality (P<.001) and colon cancer-specific mortality (P<.001) after adjusting for other variables.

We examined subgroups of patients with identical American Joint Committee on Cancer tumor stage (Figure 2). Whereas hospital volume was predictive of survival for patients with stage II (P<.001 adjusted) and stage III disease (P<.001 adjusted), it was not significant for patients with either stage I or stage IV disease.

Figure 2. Postoperative Stage-Specific Survival According to Hospital Procedure Volume
Graphic Jump Location
Other Factors

Our adjusted models included variables associated with either shorter colon cancer survival (advanced clinical stage, comorbidity, obstruction, perforation) or shorter life expectancy (male sex, older age, black race, and low socioeconomic status). Male sex, older age, black race, advanced clinical stage, high comorbidity, low income, obstruction or perforation, and emergent hospitalization were independent predictors of poor prognosis but adjusting for these variables did not change our results.

Variation in synchronous hepatic resection (ICD-9-CM codes 50.22, 50.3, 50.4) did not confound the volume-outcome association because it was performed at a similar low frequency (0.4%-0.6%) at hospitals in each volume quartile. We considered the possibility that regional differences in care might account for our results.33 Although high-volume hospitals were more highly concentrated in some SEER regions (Connecticut and Detroit, Mich) than in others (Iowa and Los Angeles, Calif), stratification by registry did not change our results because there was minimal geographic variation in mortality rates.

Postoperative Chemotherapy

The pronounced association between volume and long-term survival for patients with stage III disease (Figure 2) prompted us to examine patterns of postoperative chemotherapy use as a possible process measure that might account for this observation. Among the 6423 patients with stage III tumors who survived 3 months postoperatively and were enrolled in Medicare Part B, 3519 (54.8%) had at least 1 Medicare claim for chemotherapy within 3 months of surgery. Specifically, 51.2% of patients at low-volume; 56.6% at medium-volume; 55.6% at high-volume; and 55.5% at very high-volume hospitals received chemotherapy within 3 months of primary surgery. Although this trend was marginally significant (P = .02; and P = .07 when adjusted for within-hospital correlation), when we added chemotherapy use to our Cox model for stage III, hospital procedure volume remained a significant predictor of survival in adjusted analyses.

Among a population-based cohort of Medicare beneficiaries with primary colon cancer, we found that hospital procedure volume predicts both short- and long-term survival following surgical resection. We failed to find any evidence that underlying differences in the characteristics of patients accounted for our results. Although the association between postoperative mortality and hospital procedure volume is statistically significant, the absolute magnitude of the difference (2%) was more modest than the 7% to 15% differences observed for pancreatectomy and esophagectomy.18

Our results are consistent with those recently reported by Harmon et al,3 who analyzed the association between hospital volume and inhospital mortality for 9739 colorectal cancer patients treated at 50 hospitals in Maryland. In our cohort, the top 5% (31/611) of hospitals cared for 25% of patients; in their cohort, the top 12% (7/50) of hospitals cared for 32% of patients. Both studies show that in the United States, colon cancer surgery is currently performed at many hospitals with very low-case volumes, that there is a statistically significant correlation between high volume and favorable outcomes not attributable to differences in case mix, and that the order of magnitude of the absolute postoperative mortality difference is small (1.7%-2%).

Previous analyses demonstrating a relationship between hospital case volume and clinical outcomes have suggested that selected procedures should be regionalized and services restricted to centers performing a minimum number of cases.2,15 To illustrate the potential consequences of colon cancer surgery regionalization, we have calculated the number of hospitals that would have to discontinue colon cancer surgery and the number of patients who would need to be referred elsewhere if mandatory minimum volume thresholds were implemented (Table 3). Under the optimistic assumptions that rerouting patients to very high-volume hospitals would be feasible, cause no adverse consequences, and achieve surgical outcomes similar to those for patients in the top-volume quartile, our results show that regionalization would affect many institutions and require relocation of many patients to obtain modest, although appreciable, increments in survival. If our results are extrapolated to the approximately 70 000 colon cancer resections performed annually in the United States, in a best-case scenario, rerouting patients treated at hospitals in the low-volume quartile to the very high-volume quartile could potentially avert 350 postoperative deaths and a total of 770 deaths 5 years after colon cancer surgery. Whether regionalization of colon cancer surgery is warranted to achieve a benefit of this magnitude should be a matter of public policy debate.

Table Graphic Jump LocationTable 3. Potential Consequences of Hypothetical Policies Mandating Colon Cancer Surgery at Hospitals With Minimum Volume Thresholds*

In urban areas such as Detroit and Atlanta, Ga, hospitals with large caseloads are located in proximity to low-volume centers, but in areas such as Iowa and Utah, regionalization policies mandating care in high-volume institutions could require patients to travel long distances. As a result, the efficacy and expense of alternatives to regionalization, such as continuing education for surgical care teams at hospitals with low-case volumes, merit further study. In addition, we concur with Hillner et al2 who recently emphasized that identification of the mechanisms underlying variation in outcomes should facilitate initiatives tailored to address specific shortcomings and is therefore a research priority.

Several concerns regarding our analysis must be noted. First, the potential for inaccurate coding exists for any claims-based analysis.3436 However, the lack of ambiguity regarding the colon cancer diagnoses coupled with the fact that complete coding for major surgical procedures favorably affects hospital and physician reimbursement suggests that the claims-based approach we used should be more accurate than it may be for other conditions. Second, we determined surgical volume based only on the number of operations performed in the Medicare population. However, this method has been validated and Medicare case volume appears highly correlated with total volume.18

Although the SEER cohort is population-based, generalizability of our analysis may be limited by the restriction of our study cohort to the subset of Medicare-eligible patients older than 65 years who were not enrolled in a health maintenance organization at diagnosis.37 Nevertheless, because the median age of colon cancer diagnosis is 71 years,28 and less than 20% of patients were health maintenance organization enrollees, our cohort is representative of a substantial proportion of patients in the United States.

Why do high-volume hospitals achieve superior outcomes? Our analysis suggests that the answer is not attributable to differences in patient characteristics. The 30-day mortality differences suggest that either intraoperative and/or immediate postoperative care vary with institutional caseload. In part, the effect may result from the skill of the individual surgeon.38 However, other analyses have shown that hospital volume is a stronger determinant of outcomes than individual surgeon volume suggesting that access to an entire team of health care professionals (surgeons, anesthesiologists, nurses, radiologists) is important.3 It is unlikely that the association between hospital volume and both overall and colon cancer-specific survival is attributable to a single mechanism. Conceivably, surgeons at high-volume hospitals perform more meticulous dissections. Evidence that local recurrences were more common at low-volume hospitals would lend support to the interpretation that surgical expertise is the primary determinant of outcome, but this detail was not available from medical claims. Patients at high-volume hospitals may undergo more careful postoperative surveillance or receive more intensive subsequent treatment. However, no strong association between adjuvant chemotherapy treatment and hospital procedure volume was evident. Our claims-based approach did not permit us to examine cumulative dose or dose intensity of chemotherapy, and such differences might account for at least a small proportion of the volume-outcome effect.

Caution is warranted in interpreting our results and indeed those of all volume-outcome studies.16,39,40 Our analysis cannot demonstrate the direction of any causal relationship between volume and outcome. While we presume that high volumes contribute to good outcomes it is also plausible that good outcomes lead to high volumes. We emphasize that the implication of our analysis is not that colon cancer surgery should be limited to high-volume institutions. Rather, we intend that it provoke in-depth scrutiny of the processes of care at high-volume institutions that determine their success and those at low-volume institutions that may account for their relative shortcomings. Our results underscore the need for further research to identify those specific features and processes of care that underlie the volume-outcome relationship. This insight should help policymakers and should enable hospitals to design and implement strategies to improve the quality of care.

Chassin MR, Galvin RW. The urgent need to improve health care quality.  JAMA.1998;280:1000-1005.
Hillner BE, Smith TJ, Desch CE. Hospital and physician volume or specialization and outcomes in cancer treatment.  J Clin Oncol.2000;18:2327-2340.
Harmon JW, Tang DG, Gordon TA.  et al.  Hospital volume can serve as a surrogate for surgeon volume for achieving excellent outcomes in colorectal resection.  Ann Surg.1999;230:404-413.
Riley G, Lubitz J. Outcomes of surgery among the Medicare aged.  Health Care Financ Rev.1985;7:37-47.
Hannan EL, O'Donnell JF, Kilburn Jr H.  et al.  Investigation of the relationship between volume and mortality for surgical procedures performed in New York State hospitals.  JAMA.1989;262:503-510.
Hannan EL, Siu AL, Kumar D.  et al.  The decline in coronary artery bypass graft surgery mortality in New York State.  JAMA.1995;273:209-213.
Hannan EL, Racz M, Ryan TJ.  et al.  Coronary angioplasty volume-outcome relationships for hospitals and cardiologists.  JAMA.1997;277:892-898.
Hannan EL, Popp AJ, Tranmer B, Fuestel P, Waldman J, Shah D. Relationship between provider volume and mortality for carotid endarterectomies in New York state.  Stroke.1998;29:2292-2297.
Hughes RG, Hunt SS, Luft HS. Effects of surgeon volume and hospital volume on quality of care in hospitals.  Med Care.1987;25:489-503.
Hughes RG, Garnick DW, Luft HS.  et al.  Hospital volume and patient outcomes.  Med Care.1988;26:1057-1067.
Edwards EB, Roberts JP, McBride MA, Schulak JA, Hunsicker LG. The effect of the volume of procedures at transplantation centers on mortality after liver transplantation.  N Engl J Med.1999;341:2049-2053.
Roohan PJ, Bickell NA, Baptiste MS.  et al.  Hospital volume differences and five-year survival from breast cancer.  Am J Public Health.1998;88:454-457.
Yao SL, Lu-Yao G. Population-based study of relationships between hospital volume of prostatectomies, patient outcomes, and length of hospital stay.  J Natl Cancer Inst.1999;91:1950-1956.
Chassin MR, Hannan EL, DeBuono BA. Benefits and hazards of reporting medical outcomes publicly.  N Engl J Med.1996;334:394-398.
Dudley RA, Johansen KL, Brand R.  et al.  Selective referral to high-volume hospitals.  JAMA.2000;283:1159-1166.
Birkmeyer JD. High-risk surgery—follow the crowd.  JAMA.2000;283:1191-1193.
Gordon TA, Bowman HM, Tielsch JM, Bass EB, Burleyson GP, Cameron JL. Statewide regionalization of pancreaticoduodenectomy and its effect on in-hospital mortality.  Ann Surg.1998;228:71-78.
Begg CB, Cramer LD, Hoskins WJ, Brennan MF. Impact of hospital volume on operative mortality for major cancer surgery.  JAMA.1998;280:1747-1751.
Birkmeyer JD, Warshaw AL, Finlayson SR, Grove MR, Tosteson AN. Relationship between hospital volume and late survival after pancreaticoduodenectomy.  Surgery.1999;126:178-183.
Sosa JA, Bowman HM, Gordon TA.  et al.  Importance of hospital volume in the overall management of pancreatic cancer.  Ann Surg.1998;228:429-438.
Burns LR, Wholey DR. The effects of patient, hospital, and physician characteristics on length of stay and mortality.  Med Care.1991;29:251-271.
Simons AJ, Ker R, Groshen S.  et al.  Variations in treatment of rectal cancer.  Dis Colon Rectum.1997;40:641-646.
Parry JM, Collins S, Mathers J, Scott NA, Woodman CB. Influence of volume of work on the outcome of treatment for patients with colorectal cancer.  Br J Surg.1999;86:475-481.
Hermanek P, Hohenberger W. The importance of volume in colorectal cancer surgery.  Eur J Surg Oncol.1996;22:213-215.
Flood AB, Scott WR, Ewy W. Does practice make perfect? part I.  Med Care.1984;22:98-114.
Flood AB, Scott WR, Ewy W. Does practice make perfect? part II.  Med Care.1984;22:115-125.
Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry database.  Med Care.1993;31:732-748.
 National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) web site. Available at: http://www-seer.ims.nci.nih.gov. Accessed November 13, 2000.
Percy C, Van Holton V, Muir CE. International Classification of Diseases for Oncology, Second Edition. Geneva, Switzerland: World Health Organization; 1990.
Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data.  J Clin Epidemiol.1993;46:1075-1090.
Charlson ME, Pompei P, Ales KL.  et al.  A new method of classifying prognostic comorbidity in longitudinal studies.  J Chronic Dis.1987;40:373-383.
Begg MD. Analyzing k (2 × 2) tables under cluster sampling.  Biometrics.1999;55:302-307.
Nattinger AB, Gottlieb MS, Veum J.  et al.  Geographic variation in the use of breast-conserving treatment for breast cancer.  N Engl J Med.1992;326:1102-1107.
Hsia DC, Krushat WM, Fagan AB, Tebbutt JA, Kusserow RP. Accuracy of diagnostic coding for Medicare patients under the prospective-payment system.  N Engl J Med.1988;318:352-355.
Iezzoni LI. Assessing quality using administrative data.  Ann Intern Med.1997;127:666-674.
Lloyd SS, Rissing JP. Physician and coding errors in patient records.  JAMA.1985;254:1330-1336.
Merrill RM, Brown ML, Potosky AL.  et al.  Survival and treatment for colorectal cancer Medicare patients in two group/staff health maintenance organizations and the fee-for-service setting.  Med Care Res Rev.1999;56:177-196.
Porter GA, Soskolne CL, Yakimets WW, Newman SC. Surgeon-related factors and outcome in rectal cancer.  Ann Surg.1998;227:157-167.
Hannan EL. The relation between volume and outcome in health care.  N Engl J Med.1999;340:1677-1679.
Hillner BE, Smith TJ. Hospital volume and patient outcomes in major cancer surgery.  JAMA.1998;280:1783-1784.

Figures

Figure 1. Postoperative Overall Survival (N = 27 986) and Colon Cancer-Specific Survival (n = 27 561) According to Hospital Procedure Volume
Graphic Jump Location
Figure 2. Postoperative Stage-Specific Survival According to Hospital Procedure Volume
Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Patients With Colon Cancer According to the Procedure Volume of the Hospital Where Surgery Was Performed*
Table Graphic Jump LocationTable 2. Magnitude and Significance of the Association Between Hospital Procedure Volume and Mortality
Table Graphic Jump LocationTable 3. Potential Consequences of Hypothetical Policies Mandating Colon Cancer Surgery at Hospitals With Minimum Volume Thresholds*

References

Chassin MR, Galvin RW. The urgent need to improve health care quality.  JAMA.1998;280:1000-1005.
Hillner BE, Smith TJ, Desch CE. Hospital and physician volume or specialization and outcomes in cancer treatment.  J Clin Oncol.2000;18:2327-2340.
Harmon JW, Tang DG, Gordon TA.  et al.  Hospital volume can serve as a surrogate for surgeon volume for achieving excellent outcomes in colorectal resection.  Ann Surg.1999;230:404-413.
Riley G, Lubitz J. Outcomes of surgery among the Medicare aged.  Health Care Financ Rev.1985;7:37-47.
Hannan EL, O'Donnell JF, Kilburn Jr H.  et al.  Investigation of the relationship between volume and mortality for surgical procedures performed in New York State hospitals.  JAMA.1989;262:503-510.
Hannan EL, Siu AL, Kumar D.  et al.  The decline in coronary artery bypass graft surgery mortality in New York State.  JAMA.1995;273:209-213.
Hannan EL, Racz M, Ryan TJ.  et al.  Coronary angioplasty volume-outcome relationships for hospitals and cardiologists.  JAMA.1997;277:892-898.
Hannan EL, Popp AJ, Tranmer B, Fuestel P, Waldman J, Shah D. Relationship between provider volume and mortality for carotid endarterectomies in New York state.  Stroke.1998;29:2292-2297.
Hughes RG, Hunt SS, Luft HS. Effects of surgeon volume and hospital volume on quality of care in hospitals.  Med Care.1987;25:489-503.
Hughes RG, Garnick DW, Luft HS.  et al.  Hospital volume and patient outcomes.  Med Care.1988;26:1057-1067.
Edwards EB, Roberts JP, McBride MA, Schulak JA, Hunsicker LG. The effect of the volume of procedures at transplantation centers on mortality after liver transplantation.  N Engl J Med.1999;341:2049-2053.
Roohan PJ, Bickell NA, Baptiste MS.  et al.  Hospital volume differences and five-year survival from breast cancer.  Am J Public Health.1998;88:454-457.
Yao SL, Lu-Yao G. Population-based study of relationships between hospital volume of prostatectomies, patient outcomes, and length of hospital stay.  J Natl Cancer Inst.1999;91:1950-1956.
Chassin MR, Hannan EL, DeBuono BA. Benefits and hazards of reporting medical outcomes publicly.  N Engl J Med.1996;334:394-398.
Dudley RA, Johansen KL, Brand R.  et al.  Selective referral to high-volume hospitals.  JAMA.2000;283:1159-1166.
Birkmeyer JD. High-risk surgery—follow the crowd.  JAMA.2000;283:1191-1193.
Gordon TA, Bowman HM, Tielsch JM, Bass EB, Burleyson GP, Cameron JL. Statewide regionalization of pancreaticoduodenectomy and its effect on in-hospital mortality.  Ann Surg.1998;228:71-78.
Begg CB, Cramer LD, Hoskins WJ, Brennan MF. Impact of hospital volume on operative mortality for major cancer surgery.  JAMA.1998;280:1747-1751.
Birkmeyer JD, Warshaw AL, Finlayson SR, Grove MR, Tosteson AN. Relationship between hospital volume and late survival after pancreaticoduodenectomy.  Surgery.1999;126:178-183.
Sosa JA, Bowman HM, Gordon TA.  et al.  Importance of hospital volume in the overall management of pancreatic cancer.  Ann Surg.1998;228:429-438.
Burns LR, Wholey DR. The effects of patient, hospital, and physician characteristics on length of stay and mortality.  Med Care.1991;29:251-271.
Simons AJ, Ker R, Groshen S.  et al.  Variations in treatment of rectal cancer.  Dis Colon Rectum.1997;40:641-646.
Parry JM, Collins S, Mathers J, Scott NA, Woodman CB. Influence of volume of work on the outcome of treatment for patients with colorectal cancer.  Br J Surg.1999;86:475-481.
Hermanek P, Hohenberger W. The importance of volume in colorectal cancer surgery.  Eur J Surg Oncol.1996;22:213-215.
Flood AB, Scott WR, Ewy W. Does practice make perfect? part I.  Med Care.1984;22:98-114.
Flood AB, Scott WR, Ewy W. Does practice make perfect? part II.  Med Care.1984;22:115-125.
Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry database.  Med Care.1993;31:732-748.
 National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) web site. Available at: http://www-seer.ims.nci.nih.gov. Accessed November 13, 2000.
Percy C, Van Holton V, Muir CE. International Classification of Diseases for Oncology, Second Edition. Geneva, Switzerland: World Health Organization; 1990.
Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data.  J Clin Epidemiol.1993;46:1075-1090.
Charlson ME, Pompei P, Ales KL.  et al.  A new method of classifying prognostic comorbidity in longitudinal studies.  J Chronic Dis.1987;40:373-383.
Begg MD. Analyzing k (2 × 2) tables under cluster sampling.  Biometrics.1999;55:302-307.
Nattinger AB, Gottlieb MS, Veum J.  et al.  Geographic variation in the use of breast-conserving treatment for breast cancer.  N Engl J Med.1992;326:1102-1107.
Hsia DC, Krushat WM, Fagan AB, Tebbutt JA, Kusserow RP. Accuracy of diagnostic coding for Medicare patients under the prospective-payment system.  N Engl J Med.1988;318:352-355.
Iezzoni LI. Assessing quality using administrative data.  Ann Intern Med.1997;127:666-674.
Lloyd SS, Rissing JP. Physician and coding errors in patient records.  JAMA.1985;254:1330-1336.
Merrill RM, Brown ML, Potosky AL.  et al.  Survival and treatment for colorectal cancer Medicare patients in two group/staff health maintenance organizations and the fee-for-service setting.  Med Care Res Rev.1999;56:177-196.
Porter GA, Soskolne CL, Yakimets WW, Newman SC. Surgeon-related factors and outcome in rectal cancer.  Ann Surg.1998;227:157-167.
Hannan EL. The relation between volume and outcome in health care.  N Engl J Med.1999;340:1677-1679.
Hillner BE, Smith TJ. Hospital volume and patient outcomes in major cancer surgery.  JAMA.1998;280:1783-1784.
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