The utility of estimating population-level trajectories of terminal wellbeing decline within a growth mixture modelling framework

R.A. Burns, J. Byles, D.J. Magliano, P. Mitchell, K.J. Anstey

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

Purpose: Mortality-related decline has been identified across multiple domains of human functioning, including mental health and wellbeing. The current study utilised a growth mixture modelling framework to establish whether a single population-level trajectory best describes mortality-related changes in both wellbeing and mental health, or whether subpopulations report quite different mortality-related changes. Methods: Participants were older-aged (M = 69.59 years; SD = 8.08 years) deceased females (N = 1,862) from the dynamic analyses to optimise ageing (DYNOPTA) project. Growth mixture models analysed participants’ responses on measures of mental health and wellbeing for up to 16 years from death. Results: Multi-level models confirmed overall terminal decline and terminal drop in both mental health and wellbeing. However, modelling data from the same participants within a latent class growth mixture framework indicated that most participants reported stability in mental health (90.3 %) and wellbeing (89.0 %) in the years preceding death. Conclusions: Whilst confirming other population-level analyses which support terminal decline and drop hypotheses in both mental health and wellbeing, we subsequently identified that most of this effect is driven by a small, but significant minority of the population. Instead, most individuals report stable levels of mental health and wellbeing in the years preceding death.

Original languageEnglish
Pages (from-to)479-487
Number of pages9
JournalSocial Psychiatry and Psychiatric Epidemiology
Volume50
Issue number3
DOIs
Publication statusPublished - Mar 2015
Externally publishedYes

Keywords

  • Epidemiology
  • Mental health
  • Mixture modelling
  • Mortality
  • Well-being

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