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Meta-Analysis
. 2018 Jan 4;13(1):e0190033.
doi: 10.1371/journal.pone.0190033. eCollection 2018.

Association of loneliness with all-cause mortality: A meta-analysis

Laura Alejandra Rico-Uribe  1   2   3 , Francisco Félix Caballero  1   2   3 , Natalia Martín-María  1   2   3 , María Cabello  1   2   3 , José Luis Ayuso-Mateos  1   2   3 , Marta Miret  1   2   3
Affiliations
Meta-Analysis

Association of loneliness with all-cause mortality: A meta-analysis

Laura Alejandra Rico-Uribe et al. PLoS One. .

Abstract

Introduction: Loneliness has social and health implications. The aim of this article is to evaluate the association of loneliness with all-cause mortality.

Methods: Pubmed, PsycINFO, CINAHL and Scopus databases were searched through June 2016 for published articles that measured loneliness and mortality. The main characteristics and the effect size values of each article were extracted. Moreover, an evaluation of the quality of the articles included was also carried out. A meta-analysis was performed firstly with all the included articles and secondly separating by gender, using a random effects model.

Results: A total of 35 articles involving 77220 participants were included in the systematic review. Loneliness is a risk factor for all-cause mortality [pooled HR = 1.22, 95% CI = (1.10, 1.35), p < 0.001] for both genders together, and for women [pooled HR = 1.26, 95% CI = (1.07, 1.48); p = 0.005] and men [pooled HR = 1.44; 95% CI = (1.19, 1.76); p < 0.001] separately.

Conclusions: Loneliness shows a harmful effect for all-cause mortality and this effect is slightly stronger in men than in women. Moreover, the impact of loneliness was independent from the quality evaluation of each article and the effect of depression.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Preferred Reporting Items for Systematic Reviews (PRISMA) flow diagram.
Fig 2
Fig 2. Forest plot of the studies included in the meta-analysis.
Note: Forest plot displaying an inverse-variance weighted random-effect meta-analysis. The codes of this figure correspond to the codes of Table 1.
Fig 3
Fig 3. Forest plot of the studies included in the meta-analysis by gender.
Note: Forest plot displaying an inverse-variance weighted random-effect meta-analysis. The codes of this figure correspond to the codes of Table 1.
Fig 4
Fig 4. Forest plot of the studies included according to the risk of bias.
Note: Forest plot displaying an inverse-variance weighted random-effect meta-analysis. The codes of this figure correspond to the codes of Table 1.
Fig 5
Fig 5. Funnel plot depicting the relationship between effect size and standard error of effect for the studies in the meta-analysis.
See this image and copyright information in PMC

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