Department of Economics and Business Economics

Assessing the relative importance of correlates of loneliness in later life: Gaining insight using recursive partitioning

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  • Linda Ejlskov
  • Jesper Wulff
  • Henrik Bøggild, Aalborg University, Denmark
  • Diana Kuh, University College London, United Kingdom
  • Mai Stafford, University College London, United Kingdom

Objectives: Improving the design and targeting of interventions is important for alleviating loneliness among older adults. This requires identifying which correlates are the most important predictors of loneliness. This study demonstrates the use of recursive partitioning in exploring the characteristics and assessing the relative importance of correlates of loneliness in older adults. Method: Using exploratory regression trees and random forests, we examined combinations and the relative importance of 42 correlates in relation to loneliness at age 68 among 2453 participants from the birth cohort study the MRC National Survey of Health and Development. Results: Positive mental well-being, personal mastery, identifying the spouse as the closest confidant, being extrovert and informal social contact were the most important correlates of lower loneliness levels. Participation in organised groups and demographic correlates were poor identifiers of loneliness. The regression tree suggested that loneliness was not raised among those with poor mental wellbeing if they identified their partner as closest confidante and had frequent social contact. Conclusion: Recursive partitioning can identify which combinations of experiences and circumstances characterise high-risk groups. Poor mental wellbeing and sparse social contact emerged as especially important and classical demographic factors as insufficient in identifying high loneliness levels among older adults.

Original languageEnglish
JournalAging & Mental Health
Pages (from-to)1486-1493
Number of pages8
Publication statusPublished - 2018

    Research areas

  • Loneliness, psycho-social interventions, random forest, recursive partitioning, regression trees

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