Depression trajectories among older community dwelling adults: Results from the Irish Longitudinal Study on Ageing (TILDA).

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Abstract

Abstract

Background: This study investigated the role of a large range psychological, attitudinal and health related variables as predictors of depression trajectories amongst older adults over a 4-year time period.

Methods: Data from three consecutive waves of the TILDA survey of older community dwelling adults aged 50+ in Ireland were combined for analysis. Depression symptom scores were assessed using the Center for Epidemiological Studies– Depression scale (CES-D). Changes in depression scores over the three time points were modelled as distinct trajectory classes using group-based trajectory modelling, whilst simultaneously controlling for demographic, attitudinal and health related predictors of these trajectory classes using multinomial regression.

Results:
Four distinct depression trajectories were identified as (1) a stable low symptom level group (79%), (2) a moderate but deteriorating symptoms group (7.6%), (3) a moderate but improving group (10.1%) and (4) a vulnerable group with consistently high symptoms (3.1%). Multinomial logistic regression indicated that limiting pain, mobility impairments, perceived stress and loneliness predicted membership of the moderate and higher depressive symptom classes. Retirement status and higher reported levels of worry were associated with a greater likelihood of membership of the moderate symptom classes only.

Limitations: Use of the CES-D is open to bias due to subjective nature of respondent reporting.

Conclusions: Results concur with previous studies on the development of depression among older people and highlight the key health related and psychological variables that may relate to interventions aimed at mitigating risks of developing depression among older adults.
Original languageEnglish
JournalJournal of Affective Disorders
Early online date29 Oct 2021
DOIs
Publication statusE-pub ahead of print - 29 Oct 2021

Keywords

  • Depression
  • Trajectory Class Modelling
  • Older Adults

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