- Title
- The utility of estimating population-level trajectories of terminal wellbeing decline within a growth mixture modelling framework
- Creator
- Burns, R. A.; Byles, J.; Magliano, D. J.; Mitchell, P.; Anstey, K. J.
- Relation
- NHMRC.410215, ARC.CE110001029 & NHMRC.1002560 http://purl.org/au-research/grants/nhmrc/1002560
- Relation
- Social Psychiatry and Psychiatric Epidemiology Vol. 50, Issue 3, p. 479-487
- Publisher Link
- http://dx.doi.org/10.1007/s00127-014-0948-3
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2015
- Description
- 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.
- Subject
- well-being; mental health; mortality; epidemiology; mixture modelling
- Identifier
- http://hdl.handle.net/1959.13/1331954
- Identifier
- uon:26740
- Identifier
- ISSN:0933-7954
- Language
- eng
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