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Health Services and Outcomes Research Methodology

, Volume 4, Issue 3, pp 169–186 | Cite as

Hierarchical Generalised Linear Models with Time-Dependent Clustering: Assessing the Effect of Health Sector Reform on Patient Outcomes in New Zealand

  • Md. Monir HossainEmail author
  • Patrick Graham
  • Suzanne Gower
  • Peter Davis
Article

Abstract

New Zealand has one of the most reformed health systems in the world. This paper is primarily concerned with modelling the impact on hospital outcomes of the reforms of the early 1990s, when as part of a major, health sector wide reform process, the administration of public hospitals passed from elected Area Health Boards (AHBs) to Crown Health Enterprises (CHEs) operating under a competitive model of health care provision dominated by the funder/purchaser/provider split. The impact of reform processes on public hospitals is of particular interest since they consume 40%–50% of public expenditure on health, and have been repeatedly restructured in an attempt to contain the ever-expanding cost of health care. There is concern among both health professionals and the general public that these restructurings are reducing the quality of hospital services, and therefore negatively effecting patient outcomes. Using data from a study of 34 New Zealand public hospitals, we discuss the application of Bayesian hierarchical generalised linear models to the analysis of trends in patient outcomes over the period 1988–2001. The time-varying nature of the grouping of hospitals within larger health authorities complicates the application of HGLMs because the cluster structure of the data changes over the study period. An approach to dealing with such ‘time-dependent clustering’ by introducing period-specific authority level effects is developed. The analysis does not support the proposition that higher level authorities had an effect on outcome trends, or that the administrative changeover from AHBs to CHEs impacted on 60-day post-admission mortality.

hierarchical generalised linear models Bayesian inference hospital restructuring trends time-dependent clustering 

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Md. Monir Hossain
    • 1
    Email author
  • Patrick Graham
    • 2
  • Suzanne Gower
    • 2
  • Peter Davis
    • 2
  1. 1.Department of Epidemiology and BiostatisticsUniversity of South CarolinaColumbiaUSA
  2. 2.Department of Public Health and General Practice, Christchurch School of Medicine & Health SciencesUniversity of OtagoChristchurchNew Zealand

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