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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.

Background: The concept of social capital has received increasing attention as a determinant of population survival, but its significance is uncertain. We examined the importance of social capital on survival in a population study while focusing on gender differences. Methods: We used data from a Danish regional health survey with a five-year follow-up period, 2007–2012 (n = 9288, 53.5% men, 46.5% women). We investigated the association between social capital and all-cause mortality, performing separate analyses on a composite measure as well as four specific dimensions of social capital while controlling for covariates. Analyses were performed with Cox proportional hazard models by which hazard ratios and 95% confidence intervals were calculated. Results: For women, higher levels of social capital were associated with lower all-cause mortality regardless of age, socioeconomic status, health, and health behaviour (HR = 0.586, 95% CI = 0.421-0.816) while no such association was found for men (HR = 0.949, 95% CI = 0.816-1.104). Analysing the specific dimensions of social capital, higher levels of trust and social network were significantly associated with lower all-cause mortality in women (HR = 0.827, 95% CI = 0.750-0.913 and HR = 0.832, 95% CI = 0.729-0.949, respectively). For men, strong social networks were associated with a higher risk of all-cause mortality (HR = 1.132, 95% CI = 1.017-1.260). Civic engagement had a similar effect for both men (HR = 0.848, 95% CI = 0.722-0.997) and women (HR = 0.848, 95% CI = 0.630-1.140). Conclusions: We found differential effects of social capital in men compared to women. The predictive effects on all-cause mortality of four specific dimensions of social capital varied. Gender stratified analysis and the use of multiple indicators to measure social capital are thus warranted in future research. [ABSTRACT FROM AUTHOR]