title: Predictors of length of stay for transurethral prostatectomy in Victoria.
abstract: BACKGROUND
  Transurethral resection of prostate (TURP) is among the top 10 surgical conditions that account for hospital admission in Victoria. Bed utilization for TURP is an increasing concern in current times. This paper describes trends in length of stay (LOS) and identifies predictors of LOS for TURP in Victoria.
  
  
  METHODS
  Trends in TURP were studied using ICD-9-CM coded Victorian hospital morbidity data from public hospitals from 1987/88 to 1994/95. Detailed morbidity data from the same source for the financial year 1995/96 were used to study predictors of LOS by logistic regression.
  
  
  RESULTS
  Length of stay decreased significantly between 1987 and 1995 from 10.6 to 6.1 days. The strongest predictor of increased LOS was admission through the emergency room (odds ratio (OR) 14.7; 95% confidence interval (CI) 11.8-18.3). Other significant predictors were older age, lower socio-economic status, presence of comorbid conditions, occurrence of procedural morbidity, and hospital type and location.
  
  
  CONCLUSIONS
  The trend in decreasing LOS may be explained by increasingly efficient bed management in hospitals who are faced with an increasing need for cost control. Advances in surgical techniques and peri-operative care have also contributed to the decrease in LOS. Other factors that influence LOS can be divided into three categories: intrinsic patient factors, such as co-morbid conditions; procedure-specific factors such as peri-operative morbidity; and intrinsic hospital factors relating to capacity and resources. Such determinants of LOS may be of value to policy makers when considering the effective application of newer methods for treatment of benign prostatic hyperplasia.
authors.affiliation: Epidemiology Unit, Health Care Evaluation, Department of Human Services, Melbourne, Victoria, Australia. zahid.ansari@dhs.vic.gov.au
authors.forename: M Z
authors.initials: MZ
authors.lastname: Ansari
authors.forename: C R
authors.initials: CR
authors.lastname: MacIntyre
authors.forename: M J
authors.initials: MJ
authors.lastname: Ackland
authors.forename: E
authors.initials: E
authors.lastname: Chandraraj
authors.forename: D
authors.initials: D
authors.lastname: Hailey
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001509:Bed Occupancy; D015897:Comorbidity; D016001:Confidence Intervals; D003363:Cost Control; D004636:Emergency Service, Hospital; D005544:Forecasting; D006295:Health Resources; D006742:Hospital Bed Capacity; D017721:Hospital Costs; D006779:Hospitals, Public; D006801:Humans; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D010343:Patient Admission; D011050:Policy Making; D011468:Prostatectomy / Q000009:adverse effects / Q000379:methods; D011470:Prostatic Hyperplasia / Q000601:surgery; D012923:Social Class; D014739:Victoria