From numbers to understanding: the impact of demographic factors on seclusion rates

Int J Ment Health Nurs. 2010 Jun;19(3):169-76. doi: 10.1111/j.1447-0349.2010.00670.x.

Abstract

The reduction and, where possible, elimination of seclusion has been recognized as a national safety priority for mental health services in Australia, with significant attention devoted to strategies to achieve this goal. The aim of this study was to compare specific demographic characteristics between consumers who have been secluded to those who have not. Patient data (n = 3244) collected by 11 mental health services across Australia for six months over a 12 month period were analysed using demographic statistics. A comparison was undertaken between those who were secluded one or more times (n = 271) and those who were not secluded (n = 2973). Differences were measured with the use of independent samples t-tests and chi-square statistics. Age, gender, diagnosis, indigenous status and Health of the National Outcomes Scores (HoNOS) were found to be significant factors in relation to seclusion. Men, younger people, and indigenous people were found to be more likely to be secluded. In addition, consumers who scored higher on the behaviour,impairment and social subscales of HoNOS were more likely to be secluded. Comparative analysis of demographic characteristics of secluded and non-secluded patients can provide vital information for consideration when planning and evaluating seclusion reduction strategies.

MeSH terms

  • Adolescent
  • Adult
  • Age Distribution
  • Age Factors
  • Aged
  • Australia / epidemiology
  • Case-Control Studies
  • Chi-Square Distribution
  • Cross-Sectional Studies
  • Female
  • Humans
  • Male
  • Mental Disorders / epidemiology
  • Mental Disorders / nursing*
  • Middle Aged
  • Native Hawaiian or Other Pacific Islander / statistics & numerical data
  • Nursing Evaluation Research
  • Patient Isolation / statistics & numerical data*
  • Patient Selection*
  • Psychiatric Nursing / methods
  • Psychiatric Nursing / statistics & numerical data
  • Regression Analysis
  • Risk Factors
  • Sex Distribution
  • Sex Factors