Social Psychiatry and Psychiatric Epidemiology

, Volume 50, Issue 3, pp 479–487

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
Original Paper



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.


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.


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.


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.


Well-being Mental health Mortality Epidemiology Mixture modelling 


  1. 1.
    Batterham PJ et al (2011) Comparison of age and time-to-death in the dedifferentiation of late-life cognitive abilities. Psychol Aging 26(4):844–851PubMedCrossRefGoogle Scholar
  2. 2.
    Bäckman L et al (2006) Death and cognition: synthesis and outlook. Eur Psychol 11(3):224–235CrossRefGoogle Scholar
  3. 3.
    Bosworth HB et al (2002) Terminal change in cognitive function: an updated review of longitudinal studies. Exp Aging Res 28(3):299–315PubMedCrossRefGoogle Scholar
  4. 4.
    Sliwinski MJ et al (2003) Modeling memory decline in older adults: the importance of preclinical dementia, attrition, and chronological age. Psychol Aging 18(4):658–671PubMedCrossRefGoogle Scholar
  5. 5.
    Sliwinski MJ et al (2006) On the importance of distinguishing pre-terminal and terminal cognitive decline. Eur Psychol 11:172–181CrossRefGoogle Scholar
  6. 6.
    Piccinin AM et al (2011) Terminal decline from within- and between-person perspectives, accounting for incident dementia. J Gerontol B Psychol Sci Soc Sci 66(4):391–401PubMedCrossRefGoogle Scholar
  7. 7.
    Gerstorf D et al (2008) Life satisfaction shows terminal decline in old age: longitudinal evidence from the German socio-economic panel study (SOEP). Dev Psychol 44(4):1148–1159PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Gerstorf D et al (2008) Decline in life satisfaction in old age: longitudinal evidence for links to distance-to-death. Psychol Aging 23(1):154–168PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Gerstorf D et al (2010) Late-life decline in well-being across adulthood in Germany, the United Kingdom, and the United States: something is seriously wrong at the end of life. Psychol Aging 25(2):477–485PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Vogel N et al (2013) Time-to-death-related change in positive and negative affect among older adults approaching the end of life. Psychol Aging 28(1):128–141PubMedCrossRefGoogle Scholar
  11. 11.
    Burns RA et al (2014) Trajectories of terminal decline in the wellbeing of older women: the DYNOPTA project. Psychol Aging 29(1):44–56PubMedCrossRefGoogle Scholar
  12. 12.
    Huppert FA et al (2009) Measuring Well-being Across Europe: description of the ESS Well-being module and preliminary findings. Soc Indic Res 91(3):301–315CrossRefGoogle Scholar
  13. 13.
    Kravitz RL et al (1992) Differences in the mix of patients among medical specialties and systems of care. Results from the medical outcomes study. J Amer Med Assoc 267(12):1617–1623CrossRefGoogle Scholar
  14. 14.
    Muthen B et al (2006) Item response mixture modeling: application to tobacco dependence criteria. Addict Behav 31(6):1050–1066PubMedCrossRefGoogle Scholar
  15. 15.
    Burns RA et al (2009) Investigating the structural validity of Ryff’s psychological well-being scales across two samples. Soc Indic Res 93(2):359–375CrossRefGoogle Scholar
  16. 16.
    Gallagher MW et al (2009) The hierarchical structure of well-being. J Pers Soc Psychol 77(4):1025–1050Google Scholar
  17. 17.
    Ryan RM et al (1997) On energy, personality, and health: subjective vitality as a dynamic reflection of well-being. J Pers Soc Psychol 65(3):529–565Google Scholar
  18. 18.
    Kasser T et al (1996) Further examining the American dream: differential correlates of intrinsic and extrinsic goals. Pers Soc Psychol B 22(3):280–287CrossRefGoogle Scholar
  19. 19.
    Nix GA et al (1999) Revitalization through self-regulation: the effects of autonomous and controlled motivation on happiness and vitality. J Exp Soc Psychol 35(3):266–284CrossRefGoogle Scholar
  20. 20.
    Bjorner JB et al (2007) Interpreting score differences in the SF-36 Vitality scale: using clinical conditions and functional outcomes to define the minimally important difference. Curr Med Res Opin 23(4):731–739PubMedCrossRefGoogle Scholar
  21. 21.
    Croog SH et al (1986) The effects of antihypertensive therapy on the quality of life. N Engl J Med 314(26):1657–1664PubMedCrossRefGoogle Scholar
  22. 22.
    Fowler FJ Jr et al (1988) Symptom status and quality of life following prostatectomy. J Amer Med Assoc 259(20):3018–3022CrossRefGoogle Scholar
  23. 23.
    Burns RA et al (2012) Positive components of mental health provide significant protection against likelihood of falling in older women over a 13-year period. Int Psychogeriatr 24(9):1419–1428PubMedCrossRefGoogle Scholar
  24. 24.
    Anstey KJ et al (2010) Cohort profile: the dynamic analyses to optimize ageing (DYNOPTA) project. Int J Epidemiol 39(1):44–51PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Noale M et al (2005) Predictors of mortality: an international comparison of socio-demographic and health characteristics from six longitudinal studies on aging: the CLESA project. Exp Gerontol 40:89–99PubMedCrossRefGoogle Scholar
  26. 26.
    Piccinin A et al (2008) Integrative analysis of longitudinal studies on aging: collaborative research networks, meta-analysis, and optimizing future studies. In: Hofer S et al (eds) Handbook on cognitive aging: interdisciplinary perspectives. Sage Publications, Thousand Oaks, pp 446–476Google Scholar
  27. 27.
    Burns RA et al (2013) Gender differences in the trajectories of late-life depressive symptomology and probable depression in the years prior to death. Int Psychogeriatr 25(11):1765–1773PubMedCrossRefGoogle Scholar
  28. 28.
    Ware JE Jr et al (1998) The factor structure of the SF-36 health survey in 10 countries: results from the IQOLA project. International quality of life assessment. J Clin Epidemiol 51(11):1159–1165PubMedCrossRefGoogle Scholar
  29. 29.
    Rumpf HJ et al (2001) Screening for mental health: validity of the MHI-5 using DSM-IV Axis I psychiatric disorders as gold standard. Psychiatry Res 105(3):243–253PubMedCrossRefGoogle Scholar
  30. 30.
    Skapinakis P et al (2005) Mental health inequalities in Wales, UK: multi-level investigation of the effect of area deprivation. Br J Psychiatry 186:417–422PubMedCrossRefGoogle Scholar
  31. 31.
    Gill SC et al (2006) Mental health and the timing of men’s retirement. Soc Psychiatry Psychiatr Epidemiol 41(7):515–522PubMedCrossRefGoogle Scholar
  32. 32.
    Bartsch LJ et al (2011) Examining the SF-36 in an older population: analysis of data and presentation of Australian adult reference scores from the dynamic analyses to optimise ageing (DYNOPTA) project. Qual Life Res 20(8):1227–1236PubMedCrossRefGoogle Scholar
  33. 33.
    Zhang JP et al (2010) Association of SF-36 with coronary artery disease risk factors and mortality: a PreCIS study. Prev Cardiol 13(3):122–129PubMedGoogle Scholar
  34. 34.
    Davidson MB (2005) SF-36 and diabetes outcome measures. Diabetes Care 28(6):1536–1537PubMedCrossRefGoogle Scholar
  35. 35.
    Kappelle LJ et al (1994) Prognosis of young adults with ischemic stroke. A long-term follow-up study assessing recurrent vascular events and functional outcome in the Iowa Registry of Stroke in Young Adults. Stroke 25(7):1360–1365PubMedCrossRefGoogle Scholar
  36. 36.
    Garratt AM et al (1993) The SF36 health survey questionnaire: an outcome measure suitable for routine use within the NHS? BMJ 306(6890):1440–1444PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Viramontes JL et al (1994) Relationship between symptoms and health-related quality of life in chronic lung disease. J Gen Intern Med 9(1):46–48PubMedCrossRefGoogle Scholar
  38. 38.
    Osthus TB et al (2012) Mortality and health-related quality of life in prevalent dialysis patients: comparison between 12-items and 36-items short-form health survey. Health Qual Life Outcomes 10:46PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Little RJA et al (1983) On Jointly Estimating Parameters and Missing Data by Maximizing the Complete-Data Likelihood. Am Stat 37(3):218–220Google Scholar
  40. 40.
    Kreuter F et al (2007) Longitudinal modeling of population heterogeneity: Methodological challenges to the analysis of empirically derived criminal trajectory profiles. In: Hancock GR, Samuelsen KM (eds) Advances in latent variable mixture models. Information Age Publishing Inc, Charlotte, NC, pp 53–75Google Scholar
  41. 41.
    Lo YT et al (2001) Testing the number of components in a normal mixture. Biometrika 88(3):767–778CrossRefGoogle Scholar
  42. 42.
    Nylund KL et al (2007) Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling-a Multidisciplinary Journal 14(4):535–569CrossRefGoogle Scholar
  43. 43.
    Bauer DJ et al (2003) Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychol Methods 8(3):338–363PubMedCrossRefGoogle Scholar
  44. 44.
    Muthen B (2003) Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003). Psychol Methods 8(3):369–377 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • R. A. Burns
    • 1
  • J. Byles
    • 2
  • D. J. Magliano
    • 3
  • P. Mitchell
    • 4
  • K. J. Anstey
    • 1
  1. 1.Centre for Research on Ageing, Health and WellbeingThe Australian National UniversityCanberraAustralia
  2. 2.Research Centre for Gender, Health and AgeingUniversity of NewcastleNewcastleAustralia
  3. 3.BakerIDI Heart and Diabetes InstituteMelbourneAustralia
  4. 4.Centre for Vision Research, Westmead Millennium Institute and Department of Ophthalmologythe University of SydneySydneyAustralia

Personalised recommendations