Modeling the volume-effectiveness relationship in the case of hip fracture treatment in Finland

BMC Health Serv Res. 2010 Aug 13:10:238. doi: 10.1186/1472-6963-10-238.

Abstract

Background: A common argument in the recent health policy debate is that treatment is more effective among care providers with large volumes. It is challenging, however, to examine the volume-effectiveness relationship empirically. Several suggestions have recently been made for methodological improvements in the examination of the volume-effectiveness relationship. The aim of this study is to develop an extended methodology for examining the volume-effectiveness relationship and demonstrate it for the case of hip fracture treatment.

Methods: Data consisting of 22,857 hip fracture patients from 52 hospitals in Finland in 1998-2001 were extracted from the administrative registers. The relationship between hospital and rehabilitation unit volumes and effectiveness was examined using a statistical model that allowed risk adjustments and hierarchical modeling of volume trends, developed for the purposes of this study. Four-month mortality and the alternative register-based measure of maintainability were used as effectiveness indicators.

Results: No clear relationship was found between hospital volume and the effectiveness of hip fracture treatment, but a novel result showing an association between the rehabilitation unit volume and effectiveness was detected. The face validity of the maintainability indicator seemed to be acceptable.

Conclusions: The methodological ideas presented allow for improved examination of the volume-effectiveness relationship. There are no indications that patients with hip fractures should only be treated in high-volume hospitals, though it may be beneficial to centralize the rehabilitation of hip fracture patients to specialized units.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Female
  • Finland
  • Hip Fractures / rehabilitation*
  • Hip Fractures / surgery*
  • Hospital Units / statistics & numerical data*
  • Hospitals / statistics & numerical data
  • Humans
  • Logistic Models
  • Male
  • Registries
  • Risk Adjustment
  • Treatment Outcome*