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Journal of Clinical Oncology, Vol 24, No 13 (May 1), 2006: pp. 2006-2012
© 2006 American Society of Clinical Oncology.
DOI: 10.1200/JCO.2005.04.2622

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Mortality After Major Surgery for Urologic Cancers in Specialized Urology Hospitals: Are They Any Better?

Badrinath R. Konety, Veerasathpurush Allareddy, Sanjukta Modak, Brian Smith

From the Departments of Urology, Epidemiology, Health Management and Policy, and Biostatistics, Carver College of Medicine and College of Public Health, University of Iowa, Iowa City, IA

Address reprint requests to Badrinath R. Konety, MD, MBA, Department of Urology, University of California San Francisco, 1600 Divisadero A624, San Francisco, CA 94143-1695; e-mail: bkonety{at}urology.ucsf.edu


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
PURPOSE: Specialty-specific hospitals and hospitals with a high volume of complex procedures have been shown to have better outcomes. We sought to determine whether a high volume of unrelated complex procedures or procedures in the same specialty area (urology) could translate into better outcomes after major urologic cancer surgery.

METHODS: We performed a cross-sectional analysis of administrative discharge abstract data from the Nationwide Inpatient Sample of the Health Care Utilization Project for years 1998 to 2002. Comparison of outcome after three major urologic cancer–related surgical procedures (radical cystectomy [RC], radical nephrectomy [RN], and radical prostatectomy [RP]) at hospitals by procedure-specific volume, specialized urology status, and Leapfrog criteria was obtained to determine in-hospital mortality after the procedure. All patients in the database with a diagnosis of bladder, kidney, or prostate cancer being admitted for RC, RN, or RP between 1998 and 2002 were included.

RESULTS: Neither specialized urology status nor meeting Leapfrog volume criteria for unrelated procedures was associated with lower odds of in-hospital mortality after any of the procedures examined. High-volume hospitals (for RC and RP) and moderate-volume hospitals (for RP) were associated with lower odds of mortality. None of the examined hospital volume–related factors was associated with lower odds of mortality after RN.

CONCLUSION: In-hospital mortality after two of three major urologic cancer procedures is affected only by procedure-specific volumes. Generalized process measures existing in hospitals performing a high volume of general urologic procedures or unrelated complex procedures may be less important determinants of procedure-specific outcomes in patients.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
High-volume hospitals and high-volume surgeons seem to have better outcomes in terms of mortality after major cancer surgery.1-4 This is also true of the following three most frequently performed major urologic cancer procedures: radical cystectomy (RC) for bladder cancer,2-5 radical nephrectomy (RN) for kidney cancer,3,6 and radical prostatectomy (RP) for prostate cancer.7-9

Donabedian,10 among others, originally advocated the structure, process, and outcomes framework to evaluate quality of health care. However, the impact of structure or process on outcomes from urologic cancer surgery has not been specifically addressed by prior studies.11 We postulated that the better outcomes and lower costs observed at high-volume centers after certain types of urologic cancer surgery12 are a result of improved structural components of the system such as nursing support, anesthesia, intensive care unit care, laboratory and radiology support, respiratory and physical therapy services, and so on. This postulation is supported by a recent study suggesting that volume of unrelated procedures that are comparable in complexity to the procedure of interest may be an even more powerful determinant of outcome than volume of the procedure of interest itself.13 Services rendered by key structural components of the hospital are generally applicable across disciplines, and it would be reasonable to expect that hospitals performing a high volume of procedures overall would have strong structural components. Further, if such structural components are key to determining postsurgical outcomes measured by variables such as in-hospital mortality, then hospitals with stronger structural components could be expected to have lower postoperative in-hospital mortality across the board after various types of unrelated surgery.

On the basis of this framework, we hypothesized that if common structural components of hospitals are important determinants of outcome after major urologic cancer surgery, then hospitals performing a large volume of noncancer urologic procedures or other complex surgical procedures should have better outcomes (as measured by in-hospital mortality) than hospitals performing lower volumes of these procedures.


    METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
Patient Selection
The National Inpatient Sample (NIS) of the Health Care Utilization Project from years 1998 to 2002 was used for the analysis. The NIS contains 5 to 8 million records from 1,000 hospitals in 35 states (approximating a 20% sample of all community hospitals in the United States). Patient records were selected for analysis from the NIS data set, as detailed below. For all analyses, the hospitals were ranked into terciles based on average number of admissions reported per year during the time period of study.

All patients with a principal diagnosis of bladder cancer, kidney cancer, or prostate cancer and discharged between 1998 and 2002 as listed in the Health Care Utilization Project NIS were identified using the appropriate Clinical Classifications Software Principal Diagnosis Codes. From this sample, patients with a principal procedure code (International Classification of Diseases, 9th revision, clinical modification [ICD-9-CM]) consistent with RC (5771), complete nephrectomy (555), nephroureterectomy (5551), nephrectomy of remaining kidney (5552), partial nephrectomy (554), and RP (605) were selected.

Definition of Specialized Urology Centers
Hospitals were categorized as specialized urology centers based on being in the top volume quartile by average annual volume for at least four of the seven most commonly performed noncancer-related urologic inpatient procedures per year. The seven most commonly performed noncancer-related inpatient urology procedures were identified by mapping current procedural technology codes to corresponding ICD-9-CM codes from Medicare data set year 2002. These procedures were performed on an inpatient basis between 63% and 99% of the time. The procedures were renal transplantation (5569), urinary diversion to intestine (5671), paraurethral suspension (5979, 596), transurethral prostatectomy (6021, 6029), suprapubic or retropubic prostatectomy (603, 604), suprapubic sling operations/levator muscle operation for urethrovesical suspension (594, 5971), and retropubic urethral suspension (595).

Leapfrog Criteria
We used the Leapfrog group’s evidence-based hospital referral volume thresholds for coronary artery bypass grafting (CABG; ≥ 450/year), percutaneous coronary intervention (PCI; ≥ 400/year), abdominal aortic aneurysm repair (AAA repair; ≥ 50/year), pancreatic resection (PAN; ≥ 11/year), and esophageal cancer surgery (ESO; ≥ 13/year) to categorize hospitals as either meeting or not meeting Leapfrog volume criteria.14 The 1,057 hospitals that performed RC, RN, or RP were categorized into groups based on whether they met none, one, or more than one Leapfrog volume standards (by average procedure volume over study period) for each of the previously mentioned procedures. Hospitals that met one or more Leapfrog volume standards and performed RC, RN, or RP were compared with hospitals that performed RC, RN, or RP but did not meet Leapfrog volume standards for the procedure or performed RC, RN, or RP but not any of the procedures included in the Leapfrog criteria.

Univariate Analysis
Univariate logistic regression models were built to assess the differences in odds of mortality after RC, RN, and RP between different hospital categories. Logistic regression models were fit separately to estimate unadjusted odds ratios (ORs) and 95% CIs for each of the procedures of interest for each of the hospital categories.

Multivariate Analysis
Multivariate logistic regression models were developed to examine the association between hospital categories and in-hospital mortality while controlling for other patient-related factors. Because some of the procedures included in the Leapfrog volume standards are related to the same organ system and hospitals meeting criteria for individual procedures were low (for PAN and ESO), we decided to collapse the Leapfrog procedures into three groups (CABG + PCI, AAA repair, and PAN + ESO). Regression analyses models were built using the collapsed Leapfrog procedure groups as well as each individual Leapfrog-specified procedure separately. Independent variables included age, sex (male is reference category), median length of stay by hospital, insurance status (insured v uninsured [reference category]), hospital volume (high v moderate v low [reference category]), specialized urology center status (nonspecialized is reference category), and Leapfrog criteria fulfillment (met standards for one Leapfrog-specified procedure v met standards for > one Leapfrog procedure, if applicable v did not meet any Leapfrog volume standards or did not perform any Leapfrog-specified procedure [reference category]). To correct for possible clustering of similar outcomes within hospitals, generalized estimating equation methods were used to fit the logistic regression models. An exchangeable correlation matrix was specified for the analysis to adjust for clustering. We also adjusted for the year effects by including indicator variables for each year. Controlling for comorbidities was performed using the method of Elixhauser et al15 based on ICD-9-CM diagnosis codes. The effect of each of the diagnoses on mortality for patients in this data set was analyzed using univariate logistic regression analysis. The disease conditions that were found significant at the 5% level were included as dichotomous variables (present v absent) in the final multivariate regression model.

We also conducted a trend test that modeled mortality as the outcome variable. An ordinal integer score was assigned based on meeting volume thresholds for each of the five Leapfrog-specified procedures (did not meet any threshold = 0, met one = 1, met two = 2, met three = 3, met four = 4, and met all five = 5). Similarly, a score was assigned based on hospital volume (low volume hospitals = 1, moderate volume = 2, and high volume = 3). Separate multivariate models were built for RC, RN, and RP. The independent variables were Leapfrog score and hospital volume scores. All other model specifications were similar to the prior multivariate analyses. Two-sided P values were calculated for all analyses. SAS version 9.0 (SAS Institute, Cary, NC) was used for all analyses.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
A total of 6,577 patients with a principal diagnosis of bladder cancer had undergone RC as the principal procedure at 1,057 hospitals. The total number of in-hospital deaths was 169 (2.6%). A total of 24,013 patients with a primary diagnosis of kidney cancer had undergone RN as the primary procedure at 1,604 hospitals. The in-hospital mortality rate after RN was 0.99% (237 deaths). A total of 61,039 patients with a primary diagnosis of prostate cancer had undergone RP as the principal procedure at 1,552 hospitals. The post-RP mortality rate was 0.11% (66 deaths). The distributions of RC, RN, and RP performed in the different hospital categories are listed in Table 1. The hospital volume cutoff values for these procedures are indicated in the footnotes to Table 1. The unadjusted odds of in-hospital mortality at hospitals with different volumes of individual procedures and in specialized urology centers compared with nonspecialized hospitals are listed in Table 2.


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Table 1. Hospital Distribution by Procedure Type, Volume Status, Specialized Status, and Leapfrog Criteria

 

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Table 2. Univariate Estimates of In-Hospital Mortality by Hospital Volume, Specialized Urology Centers Status, and Meeting Leapfrog Volume Thresholds

 
In univariate analyses, performance of RC at a high-volume hospital or a specialized urology center was associated with 47% or 35% lower odds of in-hospital mortality, respectively. Performance of RC at a hospital meeting Leapfrog volume thresholds for CABG, AAA repair, PAN, and ESO was associated with a 41%, 60%, 67%, and 69% lower odds of in-hospital mortality, respectively. There was a 34% lower odds of mortality when RC was performed in a hospital that also performed PAN but did not meet the Leapfrog volume criteria for PAN. There were no statistically significant differences in odds of in-hospital mortality after RN between the hospital volume categories or levels of Leapfrog volume threshold fulfillment. Performance of RP at high- and medium-volume hospitals was associated with 78% and 73% lower odds of mortality, respectively, when compared with performing RP in lower volume hospitals. Performance of RP in hospitals that met Leapfrog volume criteria for AAA repair and PAN was associated with 62% and 87% lower odds of in-hospital mortality, respectively. The odds of in-hospital mortality were reduced by 54% when RP was performed in hospitals that also performed PCI but did not meet the Leapfrog volume criteria for PCI.

The results of the multivariate logistic regression analyses for RC are listed in Table 3. Data in Tables 3 to 5 represent the results of five separate logistic regression models, each of which controls for the performance of either one (AAA) or a group of two Leapfrog-specified noncancer-related procedures.


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Table 3. Multivariate Estimates of Odds of In-Hospital Mortality After Radical Cystectomy Based on Hospital Volume, Specialized Urology Center Status, and Meeting Leapfrog Volume Thresholds

 

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Table 5. Multivariate Estimates of Odds of In-Hospital Mortality After Radical Prostatectomy by Hospital Volume, Specialized Urology Center Status, and Meeting Leapfrog Volume Thresholds

 
High-volume hospitals were associated with significantly reduced odds (41% lower) of in-hospital mortality after RC in hospitals also performing CABG and/or PCI. The results of the multivariate logistic regression analyses for RN are listed in Table 4. Neither specialized urology centers nor hospitals meeting Leapfrog volume thresholds were associated with odds of mortality after RN. The results of the multivariate logistic regression analyses for RP are listed in Table 5. Hospital volume is a significant predictor of risk of in-hospital mortality after RP, with high- and moderate-volume hospitals being associated with lower odds of mortality in all analyses. Meeting Leapfrog volume standards or designation as a specialized urology center did not affect in-hospital mortality after any of the three procedures. In analyzing the association between each individual Leapfrog-specified volume threshold and in-hospital mortality, results remained unchanged for RC and RN, but hospitals meeting Leapfrog volume standards for PAN had lower odds of mortality after RP (OR = 0.22; 95% CI, 0.05 to 0.99); however, this involved an extremely small proportion of cases of RP.


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Table 4. Multivariate Estimates of Odds of In-Hospital Mortality After Radical Nephrectomy by Hospital Volume, Specialized Urology Center Status, and Meeting Leapfrog Volume Thresholds

 
No significant trends were observed in number of Leapfrog volume thresholds met and odds of mortality after any of the procedures. Similarly, no significant trends were detected in hospital volume and odds of mortality for two of the three procedures (RC and RP). For RN, a linear relationship was observed between hospital volume and odds of mortality, which approached statistical significance (OR = 1.33; 95% CI, 1.00 to 1.78; P = .05).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
These results suggest that performance of a high volume of noncancer-related urologic procedures or other complex procedures does not guarantee a better outcome after major urologic cancer procedures. High procedure-specific volume seems to be the most significant predictor of in-hospital mortality after RC and RP, even after controlling for Leapfrog-specified procedures (Tables 3 and 5). The unanswered question is whether hospitals that simultaneously meet all five Leapfrog criteria, as opposed to one or two of the criteria, have better outcomes after urologic cancer procedures. However, the number of hospitals meeting all five standards in our data set was low (n = 37), and using only admissions to these hospitals would have severely limited the analysis from being meaningful as a result of the small sample size. To further ensure the robustness of our analysis, we created regression models using different reference categories for the Leapfrog variables (not performed or unmet), and the results were almost identical. We also examined the data using the propensity scoring approach, which also yielded almost identical results except for changes in point estimates (data not shown).

Our results are contradictory to those reported by Urbach and Baxter,13 who compared 30-day mortality between hospitals that performed a high or low volume (as defined by Leapfrog volume standards) of PAN, ESO, AAA repair, CABG, and PCI. They determined that the volume of an unrelated procedure (one of the other of these five procedures), particularly if it met the appropriate Leapfrog volume threshold, was a more powerful predictor of mortality after a particular procedure than volume of the particular procedure of interest. They did not analyze the impact of meeting Leapfrog standards on outcomes from non–Leapfrog-specified procedures.

It is interesting to note that there were no associations between PAN and ESO (frequently performed for resection of cancer) and odds of mortality in any of our analyses. They are abdominal procedures, and the commonalities of experience regarding the usual postoperative course and care of such patients could be expected to render a broad advantage in managing patients undergoing RC or RN. The explanation for the lack of a demonstrable mortality advantage for high-volume hospitals or hospitals meeting Leapfrog volume thresholds in terms of RN could be explained by variations in procedural complexity. The degree of surgical complexity and difficulty in an RN varies by stage of disease, with some patients even requiring concomitant coronary bypass to facilitate removal of tumor thrombus extending from the kidney to the right heart. The degree of surgical complexity is not captured in our administrative data set, and the complexity range is probably the greatest for RN among the three procedures studied. This is evident in the more broad range of ICD-9-CM codes used to select the nephrectomies as opposed to the single codes used to identify the RC or RP. Unfortunately, the coding system as it currently stands does not allow for more precise selection of a specific type of RN, and hence, these results must be viewed with caution.

Recent studies have pointed to a regionalization of some high-risk urologic procedures, such as RC, to high-volume centers,16 which is supported by prior studies highlighting the benefits of such regionalization.17 The Department of Health in the United Kingdom updated a directive in 2003 recommending that complex urologic cancer surgery be performed only in centers with a catchment population of 1 million and by surgeons who perform at least 10 such procedures a year.18,19 Further regionalization of care would have to be considered within the context of health care cost constraints because it would result in lost revenue and may jeopardize the existence of some of the smaller hospitals as well as place a significant burden on the patients who have to travel long distances, particularly in rural states, to obtain such care.20

Our results suggest that procedure-specific volume is a significant predictor of postoperative mortality after RC and RP. It is possible that process measures rather than structural components influence outcome after these procedures. However, structural components may affect frequency of postoperative complications, which do not always result in mortality. We need to further examine the association between various process measures on postoperative outcome such as mortality.

One limitation of our study is the lack of data on longer term mortality (30 days, 90 days, or 1 year), which is more commonly used as an outcome measure. We intentionally chose to use a short-term measure, such as in-hospital mortality, because structural components of care are most likely to directly affect the in-hospital course of the patient. Many other factors could be influential in determining patient recovery after discharge, such as availability and quality of postdischarge care. These effects would be captured by using a longer term outcome variable, such as 30- or 90-day mortality. However, one would assume that the type, quality, and range of postdischarge care available to patients being treated at large, urban hospitals or hospitals performing and meeting volume standards specified by Leapfrog criteria would have similar facilities available to them. The inability to link the NIS data to the national death index also precludes us from analyzing effects on longer term mortality. Lack of staging information is a concern because it is possible that more high-volume or referral centers are more likely to perform procedures in patients with advanced disease as part of clinical trials or use experimental therapeutic approaches that may not be routinely practiced at lower volume centers. Lack of data regarding neoadjuvant or adjuvant therapies that may impact a patient’s condition and in-hospital course could be an unmeasured confounding variable. We have not incorporated surgeon volume effects in our analysis because of missing surgeon identifiers in a large number of cases. Previous studies have indicated that surgeon volume effects can account for up to 39% of the hospital volume-based improvements in mortality in patients undergoing RC.4 Finally, Leapfrog criteria for evidence-based hospital referral were modified in 2003 to specify certain process and outcome measures to be included in the assessment mainly for AAA repair, CABG, and PCI. The NIS data are insufficient to assess whether hospitals met any of the process or outcome measures included in the new criteria. Because none of the Leapfrog-specified procedures had a bearing on outcomes, the lack of data regarding the process and outcome measures for these hospitals is unlikely to have affected the findings. This study focuses only on the volume criteria component of the Leapfrog evidence-based hospital referral.

Odds of in-hospital mortality after major urologic cancer procedures, such as RC and RP, are predominantly affected by procedure-specific volumes. Designation of a specialized urology center does not confer a mortality advantage with regards to RC, RN, or RP. Performance of a high volume of unrelated complex procedures is not associated with lower odds of in-hospital mortality, suggesting that structural components of care may be less important than appropriate process measures in determining the likelihood of serious adverse events, such as death, after major urologic cancer surgery.


    Authors’ Disclosures of Potential Conflicts of Interest
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
The authors indicated no potential conflicts of interest.


    Author Contributions
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 

Conception and design: Badrinath R. Konety

Provision of study materials or patients: Badrinath R. Konety

Collection and assembly of data: Badrinath R. Konety, Veerasathpurush Allareddy, Sanjukta Modak, Brian Smith

Data analysis and interpretation: Badrinath R. Konety, Veerasathpurush Allareddy, Sanjukta Modak, Brian Smith

Manuscript writing: Badrinath R. Konety, Veerasathpurush Allareddy, Sanjukta Modak

Final approval of manuscript: Badrinath R. Konety

 


    ACKNOWLEDGMENTS
 
We thank Hal Luft, PhD (Institute for Health Care Policy, University of California San Francisco, San Francisco, CA), for reviewing the manuscript.


    NOTES
 
Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 Authors' Disclosures of...
 Author Contributions
 REFERENCES
 
1. Halm EA, Lee C, Chassin MR: Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Ann Intern Med 137:511-520, 2002[Abstract/Free Full Text]

2. Begg CB, Cramer LD, Hoskins WJ, et al: Impact of hospital volume on operative mortality for major cancer surgery. JAMA 280:1747-1751, 1998[Abstract/Free Full Text]

3. Birkmeyer JD, Siewers AE, Finlayson EV, et al: Hospital volume and surgical mortality in the United States. N Engl J Med 346:1128-1137, 2002[Abstract/Free Full Text]

4. Birkmeyer JD, Stukel TA, Siewers AE, et al: Surgeon volume and operative mortality in the United States. N Engl J Med 349:2117-2127, 2003[Abstract/Free Full Text]

5. Konety BR, Dhawan V, Allareddy V, et al: Impact of hospital and surgeon volume on in-hospital mortality from radical cystectomy: Data from the Health Care Utilization Project. J Urol 173:1695-1700, 2005[CrossRef][Medline]

6. Taub DA, Miller DC, Cowan JA, et al: Impact of surgical volume on mortality and length of stay after nephrectomy. Urology 63:862-867, 2004[CrossRef][Medline]

7. 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 91:1950-1956, 1999[Abstract/Free Full Text]

8. Ellison LM, Heaney JA, Birkmeyer JD: The effect of hospital volume on mortality and resource use after radical prostatectomy. J Urol 163:867-869, 2000[CrossRef][Medline]

9. Hu JC, Gold KF, Pashos CL, et al: Role of surgeon volume in radical prostatectomy outcomes. J Clin Oncol 21:401-405, 2003[Abstract/Free Full Text]

10. Donabedian A: The quality of care: How can it be assessed? JAMA 260:1743-1748, 1988[Abstract/Free Full Text]

11. Montie JE: Quality of care in urology. BJU Int 94:755-756, 2004[Medline]

12. Konety BR, Allareddy V, Dhawan V, et al: Association between volume and charges for most frequently performed ambulatory and non-ambulatory surgery for bladder cancer: Is more cheaper? J Urol 172:1056-1061, 2004[Medline]

13. Urbach DR, Baxter NN: Does it matter what a hospital is "high volume" for? Specificity of hospital volume-outcome associations for surgical procedures: Analysis of administrative data. BMJ 328:737-740, 2004[Abstract/Free Full Text]

14. The Leapfrog Group: Purchasing principles. http://www.leapfroggroup.org/media/file/Leapfrog-Evidence-Based_Hospital_Referral_Fact_Sheet.pdf

15. Elixhauser A, Steiner C, Harris DR, et al: Comorbidity measures for use with administrative data. Med Care 36:8-27, 1998[CrossRef][Medline]

16. Hollenbeck BK, Taub DA, Miller DC, et al: Regionalization of radical cystectomy. J Urol 171:36, 2004 (suppl 4, abstr)[CrossRef]

17. Finlayson SR, Birkmeyer JD Tosteson AN, et al: Patient preferences for location of care: Implications for regionalization. Med Care 37:204-209, 1999[CrossRef][Medline]

18. National Institute of Clinical Excellence: Guidance on Cancer Services: Improving Outcomes in Urologic Cancers. London, United Kingdom, National Institute of Clinical Excellence, 2001

19. National Institute of Clinical Excellence: Draft, Urological Cancer Standards: Manual of Cancer Services Standards. London, United Kingdom, National Institute of Clinical Excellence, 2003

20. Ward MM, Jaana M, Wakefield DS, et al: What would be the effect of referral to high volume hospitals in a largely rural state? J Rural Health 20:344-354, 2004[CrossRef][Medline]

Submitted September 15, 2005; accepted February 22, 2006.


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