The regionalization of radical cystectomy to specific medical centers

J Urol. 2005 Oct;174(4 Pt 1):1385-9; discussion 1389. doi: 10.1097/01.ju.0000173632.58991.a7.

Abstract

Purpose: Regionalization of high risk surgical procedures to larger teaching hospitals has been suggested as a means to improve the quality of care. We established a novel framework for characterizing regionalization, implemented it to determine the extent to which regionalization of radical cystectomy has occurred and delineated whether specific patient characteristics are associated with this phenomenon.

Materials and methods: We used the Nationwide Inpatient Sample to identify 22,088 patients who underwent radical cystectomy for bladder cancer from 1988 to 2000. Regionalization was assessed using 5 structural hospital measures, including teaching status, urban location, discharge volume, cystectomy volume and bed capacity. Adjusted models were developed to identify the significance of temporal trends and assess the association of demographic factors with structural qualities.

Results: Compared with 1988 to 1990 subjects were more likely to undergo cystectomy at teaching hospitals (OR 1.8), high cystectomy volume hospitals (OR 1.2), high discharge volume hospitals (OR 1.7) and large bed capacity medical centers (OR 1.4) in 1998 to 2000. The concentration of cystectomy to urban medical centers during the study years was 90% to 92%. The proportion of subjects undergoing partial cystectomy decreased from 23.9% to 16.6% as regionalization occurred. Older subjects were less likely to be treated at these regionalized centers.

Conclusions: Without broad legislation from health care payers radical cystectomy has increasingly regionalized to specific medical centers. Despite this regionalization disparities in its use exist among specific, vulnerable patients. Addressing this may facilitate further concentration of this procedure.

MeSH terms

  • Aged
  • Cystectomy / statistics & numerical data*
  • Female
  • Health Services Accessibility
  • Hospitals / statistics & numerical data
  • Humans
  • Middle Aged
  • Models, Theoretical
  • Quality of Health Care
  • United States